978-1-57278-201-3

Handbook on Sediment Quality

A Special Publication

Prepared by A Treatise on Sediment Quality Task Force of the Water Environment Federation

Raymond C. Whittemore, Chair

Alok Bhandari Edward R. Long Washington Braida G. Fred Lee Jennifer Byrnes Brower Drew McAvoy Jerome M. Diamond Allen J. Medine Robert M. Engler Robin Landeck Miller Mark W. Fitch George D. Nichol Upal Ghosh Tina Reese Cesar Gomez Joseph E. Rathbun Ferdinand Hellweger Tamara L. Sorell James E. Kilduff Paul Kevin Sibley Peter F. Landrum John R. Wolfe Eugene Joseph LeBoeuf

Under the Direction of the Water Quality and Ecology Subcommittee of the Technical Practice Committee

2002 Water Environment Federation 601 Wythe Street Alexandria, VA 22314-1994 USA http://www.wef.org IMPORTANT NOTICE The contents of this publication are not intended to be a standard of the Water Environment Federation (WEF) and are not intended for use as a reference in purchase specifications, contracts, regulations, statutes, or any other legal document. No reference made in this publication to any specific method, product, process, or service constitutes or implies an endorsement, recommendation, or warranty thereof by WEF. WEF makes no representation or warranty of any kind, whether expressed or implied, concerning the accuracy, product, or process discussed in this publication and assumes no liability. Anyone using this information assumes all liability arising from such use, including but not limited to infringement of any patent or patents.

Library of Congress Cataloging-in-Publication Data

Handbook on sediment quality / prepared by a Treatise on Sediment Quality Task Force of the Water Environment Federation under the direction of the Water Quality and Ecology Subcommittee of the Technical Practice Committee. p. cm.—(A special publication) Includes bibliographical references and index. ISBN 1-57278-201-3 (pbk.) 1. Sediments (Geology)—Analysis I. Water Environment Federation. Treatise on Sediment Quality Task Force. II. Water Environment Federation. Water Quality and Ecology Subcommittee. III. Special publication (Water Environment Federation) QE471.2 .H33 2002 628.1'61—dc21 2002011318

Printed in the USA 2002 Water Environment Federation

Founded in 1928, the Water Environment Federation (WEF) is a not-for-profit technical and educational organization with members from varied disciplines who work toward the WEF vision of preservation and enhancement of the global water environment. The WEF network includes more than 100,000 water quality professionals from 77 Member Associations in 31 countries.

For information on membership, publications, and conferences, contact

Water Environment Federation 601 Wythe Street Alexandria, VA 22314-1994 USA 703-684-2400 http://www.wef.org

iii Special Publications of the Water Environment Federation

The WEF Technical Practice Committee (formerly the Committee on Sewage and Industrial Wastes Practice of the Federation of Sewage and Industrial Wastes Associations) was created by the Federation Board of Control on October 11, 1941. The primary function of the Committee is to originate and produce, through appropriate subcommittees, special publications dealing with technical aspects of the broad interests of the Federation. These publications are intended to provide background information through a review of technical practices and detailed procedures that research and experience have shown to be functional and practical.

Water Environment Federation Technical Practice Committee Control Group

T. L. Krause, Chair G. T. Daigger, Vice-Chair G. Abbott B. G. Jones M. D. Nelson A. B. Pincince T. E. Sadick

iv Contents

Chapter Page 1. An Introduction to Sediments 1

2. Sorption of Organic Compounds by Soils and Sediments: Equilibrium and Rate Processes 7 Introduction 8 Sorption Mechanisms 10 Intermolecular Forces 12 Partitioning 12 Ion Exchange 12 Covalent Bonding 13 Modeling Contaminant-Phase Distribution Equilibria 14 Partitioning Theory 15 Estimation of Activity Coefficients 17 Flory–Huggins Theory 19

Correlations of Kd to Molecular Properties 21 Correlations with the Octanol–Water Partition Coefficient 22 Isotherm Models 24 The Generalized Langmuir Isotherm and Its Simplifications 26 Potential Theory 30 Combined Models 31 Materials and Methodologies for Collecting Sorption Data 34 Batch and Column Equilibration Techniques 34 Batch Equilibration Techniques 34 Fixed-Bed (Column) Reactor Techniques 37 Obtaining Representative Samples 38 Composition of the Solution Phase 39 Criteria for Achieving Equilibrium 40 Loss Mechanisms and Controls 41 Reactors and Reactor Components 43 Phase Separation 43 Desorption 44 Sorption Phenomena 45 Isotherm Linearity 45 Linear Partitioning to Isolated Mineral Surfaces 45 Linear Partitioning to Soils and Sediments 46 Nonlinear Sorption of Neutral Hydrophobic Organics 49 Nonlinear Sorption of Ionizable Organics 59 Competitive Effects 61 Competitive Adsorption Models 61 Ideal Adsorbed Solution Theory 62 Polanyi-Based Models 63

v Chapter Page Evidence for Sorbate Competition 64 Desorption Hysteresis and Reversibility 65 Effects of Particle Size 68 Effects of Organic Matter Composition and Structure 68 Chemical Characterization: Polarity and Aromaticity 68 Rubbery/Glassy Models 69 Microporosity 69 Crystallinity 70 Solids-Concentration Effect 72 Sorption Rate Processes 75 Local Equilibrium 76 Langmuir Kinetics 77 Empirical Rate Models 77 Mass-Transfer Controlled Kinetics 79 External Mass Transfer 79 Homogenous Solid-Phase (Surface) Diffusion 81 Pore and Combined Pore-Surface Diffusion 82 Diffusion in Macromolecules 83 Case I Transport 84 Case II Transport 84 Anomalous or Non-Fickian Transport 85 Macromolecular Diffusion Behavior in Natural Systems 86 Summary and Conclusions 86 References 87

3. Bioavailability in Sediments 99 Introduction 100 Current Research on Bioavailability in Sediments 102 Measuring Bioavailability 102 Alternative Extraction Methods 102 Membrane Analogs 103 Importance of Sediment Characteristics in Determining Bioavailability 104 Pore Water Concentrations Determine Bioavailability 104 Bioavailability of Sediment-Associated Contaminants 105 Remediation Using Bioavailability Reduction 106 Modeling 106 Field Studies 108 Bays 108 Lakes 109 Wetlands 109 Factors-Controlling Bioavailability 109 Acid-Volatile Sulfide 109 Organic Carbon 111

vi Chapter Page Processes Affecting Bioavailability in Sediments 112 Aging 112 Sorption 112 Seasonality 114 Bioturbation 114 Measuring Bioavailability 115 Acid-Volatile Sulfides 116 Equilibrium Partitioning 118 Toxicity Tests With Algae, Bacteria, or Representative Species 120 Current Regulatory Directions 121 Toxicity Calculations 122 References 123

4. Methods for Collecting, Storing, and Manipulating Sediments and Interstitial Water Samples for Chemical and Toxicological Analyses 139 Introduction 140 Potential Interferences and Artifacts in Sediment and Interstitial Water Collection, Handling, and Manipulation 141 Noncontaminant Factors 141 Changes in Bioavailability 142 Presence of Indigenous Organisms 143 Safety Concerns 143 Field Operation 144 Laboratory Operations 144 Special Considerations for Explosive Contaminated Sediments 144 Disposal of Sediments and Pore-Water Samples 144 Facilities, Equipment, and Supplies 144 Field Facilities 144 Laboratory Facilities 145 Equipment and Supplies 146 Equipment Cleaning and Decontamination 147 Procedures for Sediment and Interstitial Water Collection 148 Sediment Collection 148 Grab Samplers 150 Core Samplers 153 In Situ Interstitial Water Collection 158 Peeper Methods 160 Suction Methods 161 Retrieval of Interstitial Water Samples 161 Sample Transport and Storage 162 Sample Holding Times 163 Subsampling, Homogenizing, and Compositing Samples 164 General Information 164 Subsampling 165 Homogenization 168

vii Chapter Page Compositing 169 Sediment Sample Manipulations 170 Sieving 170 Recommended Sieves 171 Press Sieving 171 Wet Sieving 172 Alternatives to Sieving 172 Spiking Sediments 172 Preparation for Spiking 172 Spiking Methods 173 Verifying Homogeneity 173 Equilibration Times 174 Formulated Sediments and Organic Carbon Modification 175 Sediment Dilutions 176 Sediment Elutriates 176 Isolation of Interstitial Water 177 Centrifugation 178 Sediment Squeezing 179 Pressurized Devices 180 Quality Assurance and Quality Control 180 General Considerations 180 Quality Assurance and Quality Control Procedures for Sediment Collection and Manipulation 181 The Quality Assurance Project Plan 182 Data Quality Objectives 182 Project Organization 183 Standard Operating Procedures 183 Sediment Sample Documentation 183 Sample-Tracking Documentation 184 Recordkeeping 185 Quality Assurance Audits 185 Corrective Action (Management of Nonconformance Events) 185 Data Reporting 186 References 186

5. Testing for Toxicity and Bioaccumulation in Freshwater Sediments 199 Introduction 200 Selection of Test Protocols 201 Selection of Test Species 203 Assessing Species Sensitivity 205 Selecting Measurement Endpoints 208 Selection of Control Sediments 211 Selection of Sediment Phases for Use in Toxicity Testing 212 Introduction 212 Whole Sediments 212 Pore-Water Phase 215 viii Chapter Page Elutriate Phase 216 Suspended Phase 216 Comparative Toxicity of Whole Sediments, Elutriates, and Pore Water 218 Overview of Freshwater Species Used in Sediment-Toxicity Testing 219 Vascular Plants 219 Oligochaetes and Worms 221 Amphipods 221 Mayflies 222 Midges 222 Cladocerans 223 Fish 224 Amphibians 224 Miscellaneous Species 225 Methods to Assess Bioaccumulation in Freshwater Sediments 226 Invertebrate Bioaccumulation Tests 227 Fish Bioaccumulation Tests 232 Field Methods for Assessing Sediment Bioaccumulation 232 Field-Collected Samples 233 Transplantation and In Situ Exposures 234 Future Research Directions 235 Development of Bioassays and Endpoints 236 Issues Related to Exposure 237 In Situ Studies 237 Development of Sediment Quality Criteria and Bioassessment Approaches 238 Bioaccumulation and Risk Assessment 238 Concluding Remarks 239 References 239

6. Toxicity Tests of Marine and Estuarine Sediment Quality: Applications in Regional Assessments and Uses of the Data 259 Introduction and Background 260 Results from Surveys Performed Throughout North America 262 The Types of Toxicity Tests Commonly Used in Regional Monitoring 262 Spatial Gradients (or Patterns) in Toxicity 266 Incidence of Toxicity 279 Spatial (or Surficial) Extent of Toxicity 282 Predicting Toxicity with Numerical Sediment-Quality Guidelines 285 Ecological Relevance of Toxicity Tests 299 Conclusions and Outlook 303 References 307

ix Chapter Page 7. Empirical and Theoretical Shortcomings of Sediment- Quality Guidelines 317 Introduction 318 Co-occurrence Sediment-Quality Guidelines 318 Equilibrium Partitioning Sediment-Quality Guidelines 320 Empirical Test of Sediment-Quality Guidelines 320 Theoretical Limitations to Chemical Predictors 323 Conclusions 324 References 324

8. Sediment-Quality Modeling 327 Introduction 328 Model Framework 329 Basic Processes Affecting Sediment-Quality 329 Transport Processes 330 Water Column Transport 330 Settling 331 Resuspension 331 Sedimentation 331 Sediment–Water Diffusion 332 Mixing Within Bed Sediments 332 Reaction Processes 332 Sorption 332 Volatilization 333 Degradation–Decay 334 Advanced Model Features 334 Multiple Sorbent Classes 334 Water Column Solids 335 Non-Equilibrium Partitioning 336 Sediment Resuspension 336 Cohesive Sediments 337 Noncohesive Sediments 338 Behavior of Metals 338 Spatial Resolution 339 Commonly Used Model Frameworks 340 Hydrodynamic Models 341 UNET 342 DYNHYD5 342 RMA-2V 342 Princeton Ocean Model 343 Sediment-Transport Models 343 HEC-6 343 SED2D 344 SEDZL 344

x Chapter Page Chemical Fate and Transport Models 344 SMPTOX 344 HSCTM-2D 344 WASP5/IPX 344 EFDC 345 Model Calibration 345 Calibration Sequence 346 Short-Term Versus Long-Term Calibration 347 Alternate Observations 347 Calibration Sufficiency 348 Model Application 348 Remediation 348 Future Enviornmental Conditions 349 Natural Attenuation 350 Dredging 350 Capping 351 Case Study 351 Prevention of Sediment Contamination 353 Limitation and Proper Use 354 Bayou D’Inde Case Study 354 Outstanding Issues 357 Artifical Bed Mixing 357 Data Sufficiency to Validate Resuspension Rates 359 Bioturbation and Enhanced Sediment Diffusion 360 Model Representation of Active Management Scenarios 360 References 360

Index 367

List of Tables

Table Page 2.1 The GL isotherm and its simplifications. 27 2.2 Studies finding linear partitioning to sediments and other sorbents. 47 2.3 Studies finding nonlinear partitioning to sediments and other sorbents. 52 2.4 Ideal adsorbed solution theory model formulation. 63 4.1 Recommended sampling containers, holding times, and storage conditions for common types of sediment analyses. 147 4.2 Typical sediment volume requirements for various analyses. 149 4.3 Advantages and limitations of commonly used grab samplers. 151 4.4 Advantages and limitations of commonly used core samplers. 154 4.5 Optimal interstitial water collection methods. 159 5.1 Common organisms used in toxicity testing. 202 5.2 Comparison of the relative sensitivities of several freshwater species used in bulk sediment toxicity testing. 207 5.3 Some advantages and limitations of using different sediment phases in toxicity testing. 213 5.4 Compilation of laboratory tests incorporating standard test invertebrates in assessments of bioaccumulation of organic and metal contaminants from freshwater sediments. 229 6.1 Sediment toxicity tests and other analyses selected for use in 35 large-scale environmental assessments. 263 6.2 Incidence of toxicity in amphipod survival tests performed in studies conducted by NOAA and EMAP in U.S. estuaries. Tests were performed with A. abdita. 280 6.3 Frequency distribution of results of six toxicity tests performed on marine sediments. 281 6.4 Percent incidence of sediment samples classified as toxic or highly toxic in six toxicity tests. 282 6.5 Spatial extent of toxicity in amphipod survival tests performed with solid-phase sediments from 27 U.S. bays and estuaries. 283 6.6 Spatial extent of toxicity in sea urchin fertilization tests performed with 100% sediment pore waters from 22 U.S. bays and estuaries. 285 6.7 Incidence of toxicity in either amphipod tests alone or any of the two to four tests performed among samples in which individual ERMs were exceeded. 288 6.8 Incidence of toxicity in either amphipod tests alone or any of the two to four tests performed among samples in which individual PELs were exceeded. 289

xiii Table Page 6.9 Percent incidence of highly toxic samples and average percent amphipod survival in marine sediment samples classified according to numerical sediment quality guidelines. 290 6.10 Relationships between incidence of degraded benthic populations and mean SQG quotients with data summarized for Carolinian, Virginian, and Louisianian provinces. 292 6.11 Macrobenthic trophic structure in estuarine sediments of the northern Gulf of Mexico in randomly selected samples versus those in which chemical concentrations were nearly equal to or greater than the ERL values. 293 6.12 Relationships between total numbers of macrobenthic species and mean SQG quotients in northern Puget Sound. 294 6.13 Percentages of field-collected samples that were correctly predicted to be toxic in amphipod tests in relation to predicted sum of PAH toxic units in samples in which PAHs were principal contaminants of concern and samples in which other chemicals were present. 294 6.14 Incidence of toxicity in amphipod survival tests in samples within four ranges in total PAH concentrations defined by consensus-based, sediment-effect concentrations. 295 6.15 Incidence of toxicity in amphipod tests in samples in which ERM or PEL values were exceeded for sums of low- molecular-weight (LMW) PAHs, high-molecular-weight (HMW) PAHs, or 13 PAHs. 296 6.16 Incidence of toxicity and average percent survival in amphipod tests within five ranges in total PCB concentrations defined by consensus SECs. 297 6.17 Percentages of samples that were toxic in amphipod survival tests when predicted to be either toxic or nontoxic with different sets of sediment quality guidelines for trace metals. 298 6.18 Toxicity thresholds for total DDT in sediments derived from field-collected samples. 299 6.19 Incidence of toxicity in amphipod tests and average percent survival within 10 ranges in mean SQG quotients. 300 6.20 Percentages of samples estimated with logistic regression models to be toxic in amphipod survival tests with chemical concentrations equivalent to several SQG concentrations. 300 7.1 The ERLs and ERMs from Long et al. 319 7.2 Sediment quality guidelines based on equilibrium partitioning of neutral organic compounds or acid-volatile sulfide. 321 7.3 Frequencies of 10-day amphipod toxicity in samples with chemical concentrations in excess of a SQG. 322

xiv List of Figures

Figure Page 2.1 Schematic depiction of sediment heterogeneity (containing mineral and organic surfaces) and sorption mechanisms (partitioning, electrostatic bonding, ion-exchange, and covalent bonding). 11 2.2 A comparison of several different isotherm models over a wide range of concentrations. 29 2.3 An example of a distributed reactivity isotherm. 32 2.4 An example of a dual-mode isotherm. 33 2.5 A comparison of predictions made using the dual-mode (or dual-reactive domain) model. 35 2.6 An example of how sorbent heterogeneity, as exemplified by several distinct Langmuir sites, can result in nonlinear, Freundlich-type sorption behavior. 51 2.7 Hypothesized sequence of sorption by exposed mineral surfaces (domain I), amorphous organic matter (domain II) and condensed organic matter (domain III). 54 2.8 The effect of co-solute sorption observed by Chiou and Kile. 66 2.9 Folded-chain model for crystallinity (after Rosen, 1993). 71 2.10 Hopfenberg–Frisch chart of anomalous transport phenomena (after Vieth, 1991, from Hopfenberg and Frisch, 1969). 84 3.1 The effect of fractional approach to equilibrium, f, on the relationship between the apparent partition coefficient (Kd) and the sorbent concentration (s). 107 2– 3.2 Typical profiles of sulfate (SO4 ), total sulfide [S(II)], and oxygen (O2) in the overlying water and pore water of aquatic sediments. 110 4.1 Alternatives for subsampling and compositing sediment grab samples. 166 4.2 Alternatives for sampling and compositing sediment core samples. 167 6.1 Distribution of toxicity in Charleston Harbor as determined with Microtox tests. 268 6.2 Distribution of toxicity in Charleston Harbor as determined with urchin fertilization tests. 269 6.3 Distribution of toxicity in Sabine Lake as determined with urchin fertilization tests. 270 6.4 Distribution of toxicity in Boston Harbor as determined with amphipod survival tests. 271 6.5 Distribution of toxicity in Pensacola Bay as determined with urchin fertilization tests. 272 6.6 Distribution of toxicity in central Biscayne Bay as determined with amphipod survival tests. 273

xv Figure Page 6.7 Distribution of toxicity in southern Biscayne Bay as determined with urchin fertilization tests and amphipod survival tests. 274 6.8 Distribution of toxicity in Port Gardner Bay of northern Puget Sound as determined with urchin fertilization tests. 275 6.9 Distribution of toxicity in central Puget Sound as determined in cytochrome P-450 RGS assays. 276 6.10 Distribution of toxicity in Newark Bay as determined with amphipod survival tests. 277 6.11 Distribution of toxicity in San Diego Bay as determined with amphipod survival tests. 278 6.12 Average abundance of benthic amphipods in sediment samples within eleven ranges in percent amphipod survival. 302 6.13 Generalized relationship between increasing chemical contamination of sediments and measures of adverse biological effects. 305 8.1 Schematic of major contaminated sediment model properties. 330 8.2 Model framework considering sulfide precipitation of metals. 340 8.3 Example of two dimensionality of predicted velocity for Hudson River. 341 8.4 Demonstration of types of model output generated for remediation assessment. 349 8.5 Hudson River site map. 352 8.6 Example of calibration plot for the Hudson River. 353 8.7 Typical model-predicted sediment concentration comparison for prevention analysis showing long-term insensitivity to time. 354 8.8 Bayou d’Inde site map. 355 8.9 Example of Bayou d’Inde model results of HCB in the water column and sediments (1 mile × 1.609 = km). 356 8.10 Model abstraction of actual vertical sediment concentration distribution into a series of idealized layers. 357 8.11 Model abstraction of sediment bed immediately after a resuspension event. 358 8.12 Predicted efficiency of dredging with different modeling assumptions. 361

xvi Preface

Dr. Ray C. Whittemore, NCASI

HISTORY. Sediments, or the sand, dirt, and land runoff litter that lie on the bottom of every waterbody, are part of the aquatic system’s ecology because of the habitat they provide for plants and animals and their role in bearing the legacy of historical effluent treatment management practices. Most importantly, sediments provide shelter, food, and rearing grounds for bottom- dwelling organisms that are eaten by fish and larger animals in the traditional aquatic food web. Some fish species such as salmon, in fact, use gravel bottom substrates as a hatching medium. In recent years, attention to fish and shellfish consumption by humans and wildlife has been increasingly of public concern and gives rise to issues relating to bioaccumulation and biomagnifica- tions. Toxic compounds can bind to the organic carbon in fine sediment particles and gradually become integrated to the biomass of the sediment itself. The importance of sediments in the United States was first noted in the late 19th Century following public health concerns in cities along the Mississippi River. Although these early concerns were ultimately linked to bacterial contamination in raw untreated wastewater, they heightened scientific attention to this medium. Over the past few decades it has become widely understood that many different organic and inorganic chemicals have significantly contaminated the sediments of rivers, lakes, and estuaries in some locations. When these compounds are persistent, sediments contain a long- term record of past inputs to the aquatic system from atmospheric deposition to nonpoint and point sources. A number of processes, such as sorption and complexation, tend to immobilize this record and their aquatic effects, whereas others, such as bioturbation and other forms of sediment, scour, disturb, and disperse the record of inputs. Under certain conditions, contaminants can be released to overlying waters, either slowly by diffusion or current processes or in bulk during storm-induced wave actions. Thus, the sediments may become an important source of chemicals to surface water in which direct point source input, littoral, and atmospheric sources have been reduced or eliminated.

REGULATORY EVOLUTION. The study of the role of sediments in the fate and transport of contaminants is a relatively new phenomenon, although many would argue that the role of sediments as an ultimate sink of chemicals has long been known or, at a minimum, predicted. The regulation of sediment contamination is also an emerging process in the United States with the evolution of new regulatory policies and programs that began in the 1980s. Research and development of sediment-quality criteria (SQC) and sediment- quality guidelines methodologies and supporting toxicity test developments began in the late 1980s and results began to appear in the early 1990s. Widespread acceptance of any regulatory approach has not occurred and is

xvii hotly debated in some circles. Most significant of these is the apparent rift between the U.S. Army Corps of Engineers and the U.S. Environmental Protection Agency (U.S. EPA) over the significance of the terminology that includes “criteria”, which has legal connotations that would undermine historical U.S. Army Corps of Engineers mandates. Another relates to controversy surrounding the use of empirically derived sediment-quality measures (co-occurrence). Several significant technical and policy pieces, however, are still evolving within the U.S. EPA and serve as the regulatory justification of this book. States are being forced now to deal with contaminated sediment issues through total maximum daily load (TMDL) and other initiatives.

CONTAMINATED SEDIMENT MANAGEMENT STRATEGY. While independent academic research into issues related to sediment contamination has been ongoing for some time (Baker, 1980), more formal U.S. EPA attention emerged in the mid-1980s with the formation of an internal agency- wide steering committee to examine programmatic issues related to sediment contamination. This Technical Advisory Committee, formed in 1986 by then Administrator Reilly, reinforced the need for a more consistent U.S. EPA plan and started to examine possible approaches for deriving SQC. In 1988, the U.S. EPA formed two oversight committees to take a comprehensive look at the whole range of contaminated sediment issues—the Sediment Oversight Committee and the Sediment Oversight Technical Committee. While both have responsibilities in forming a cohesive contaminated sediment management strategy, the latter committee is dedicated to scientific and implementation issues. Representation includes U.S. EPA Headquarters, Office of Research and Development (ORD), and regional staff, with significant reliance on input from numerous public and academic consultants. The coordination of sediment management issues is necessary because the U.S. EPA has the authority under numerous statutes to address contaminated sediments as further described in Contaminated Sediments—Relevant Statutes and EPA Program Programs (U.S. EPA, 1990).

STRATEGY PROVISIONS. The U.S. EPA’s contaminated sediment management strategy describes actions that the agency will take to accomplish four strategic goals: (1) prevention of further sediment contamination that may cause unacceptable ecological or human health risks; (2) establish when practical, clean-up of existing sediment contamination that adversely affects the nation’s waterbodies or their uses, or that cause other significant effects on human health or the environment is warranted; (3) ensure that sediment dredging and dredged material disposal continue to be managed in an environmentally sound manner; and (4) ensure development and application of consistent methodologies for analyzing contaminated sediments. The strategy is composed of six component sections: assessment, prevention, remediation, dredged material management, research, and public outreach. In each section, the agency describes actions it may take to accomplish the four broad goals. Each will be separately discussed in the following paragraphs.

xviii Assessment. In the assessment section, the U.S. EPA proposes that many agency program offices use standard toxicity test methodologies and chemical-specific sediment-quality criteria to determine when sediments are contaminated. These same program offices will use sediment-quality criteria, when they are promulgated, to assess contaminated sediment sites in combination with toxicity testing and use these criteria to interpret sediment chemistry data. Upon promulgation, states may adopt and use these as water quality standards in their application to establish National Pollutant Discharge Elimination System (NPDES) permit limits. These criteria could also be used with other appropriate information to make site-specific decisions concerning corrective action at hazardous waste facilities and to assess Superfund sites. The agency has not yet determined how sediment-quality criteria will be used in dredged material testing. An additional element of the assessment section is the National Sediment Inventory (NSI) that will be used by U.S. EPA program offices as an assessment tool (U.S. EPA, 1994a). It will be used to identify contaminated sediment sites for consideration for remedial action; target facilities for possible injunctive relief or supplemental enforcement projects; identify problem pesticides and toxic substances that may require further regulation or be targeted for enforcement action; identify impaired waters for National Water Quality Inventory reports or development of TMDLs; target watersheds for nonpoint source management practices; and help select industries for further effluent guidelines development.

Prevention. To regulate the use of pesticides that may accumulate to toxic levels in sediments, the U.S. EPA intends to propose that acute sediment- toxicity tests be included in procedures required in support registration, deregistration, and special review of pesticides likely to sorb to sediments. To prevent other toxic substances from accumulating in sediments, the U.S. EPA will also propose incorporating acute sediment-toxicity tests and sediment- bioaccumulation tests to routine chemical review processes required under the Toxic Substances Control Act. In addition, the U.S. EPA intends to call for the development of guidelines for design of new chemicals to reduce bioavailability and partitioning of toxic chemicals to sediments. The agency’s Office of Enforcement and Compliance Assurance will take action to prevent sediment contamination by negotiating, in cases of noncom- pliance with permits, enforceable settlement agreements to require recycling and source reduction activities. The Office of Enforcement will also monitor the progress of federal facilities toxic emissions in reducing and will monitor the reporting of toxic releases to the public. The agency’s Office of Water and other program offices will work with nongovernmental organizations and the states to prevent point and nonpoint source contaminants from accumulating in sediments. The agency will (1) promulgate new and revised best-available treatment effluent guidelines for industries that discharge sediment contaminants; (2) encourage states to use biological sediment test methods to interpret water quality standards and adopt SQC as water quality standards; (3) encourage states to develop

xix TMDLs for impaired watersheds specifying point and nonpoint source load reductions necessary to protect sediment quality; (4) use the NSI to target active point sources of sediment contaminants for permit-compliance tracking; (5) ensure that discharges from Comprehensive Environmental Response, Compensation, and Liability Act sites and Resource Conservation and Recovery Act facilities subject to NPDES permits comply with permit requirements that protect sediment quality; and (6) use the NSI to target watersheds where technical assistance and grants would effectively be used to reduce nonpoint source loads of sediment contaminants.

Remediation. Several U.S. EPA program offices, including the Office of Water, Office of Emergency and Remedial Response, Office of Solid Waste, and Office of Enforcement will use the NSI to help target sites for enforcement action requiring contaminated sediment remediation. The Agency’s standard sediment toxicity and bioaccumulation tests (U.S. EPA, 1989 and 1994b) will be used to identify sites for remediation, assist in determining clean-up goals for contaminated sites, and monitor the effectiveness of remedial actions.

Dredged Material Management. The U.S. Army Corps of Engineers estimates that a small percentage of the total volume of sediment dredged for navigational channel maintenance requires handling due to the presence of toxics. The NSI inventory will be used to identify sites where dredged materials are contaminated. The agency’s standard sediment toxicity and bioaccumulation tests are now used in dredged material testing (U.S. EPA, 1994a).

Research. The agency’s ORD, through its Environmental Monitoring and Assessment Program, will continue to collect new chemical and biological data on sediment quality. These data will be included in the Agency’s NSI. The ORD will also develop new biological methods to assess ecological and human health effects of sediment contaminants, chemical-specific sediment- quality criteria, methods to conduct sediment toxicity evaluations, dredged material disposal fate and transport models, sediment wasteload allocation models, and technologies for remediation of contaminated sediment.

Outreach. The Agency will undertake a program of outreach and technology transfer to educate target audiences about contaminated sediment risk management. These target audiences include other federal agencies, state and local agencies, the regulated community, the scientific community, environmental advocacy groups, the news media, and the general public. Technical and nontechnical information will be provided to these audiences by developing a range of outreach products. The National Contaminated Sediment Task Force will monitor implementation of the U.S. EPA’s contaminated management strategy and subsequent development of a federal strategy.

BOOK ORGANIZATION. This book organizes material from respected academic researchers and consultants with interrelated experiences and xx viewpoints. In addition, significant viewpoints from federal agency personnel are included that are not intended to be the viewpoints of the U.S. EPA. We collectively acknowledge that sediment science remains an evolving area and that much more needs to be known before the management of contaminated sediments becomes routine and universally accepted. This book begins to address this need. The book is not without controversy, however, as Chapters 6 and 7 seemingly clash with respect to the interpretation of the validity of empirical sediment-effects data and what constitutes cause and effects proof. We decided that juxtaposition of these two viewpoints was proper because many who must use sediment-effects data in regulatory decision-making do not have expertise in weight-of-evidence determinations and might benefit from both viewpoints. I deeply thank this group for their dedication, hard work, and patience during the writing, review, and publication process. This effort was conceived and supported by the Water Environment Federation’s Toxic Substances Committee.

REFERENCES Baker, R. A. (Ed.) (1980) Contaminants and Sediments: Fate and Transport, Case Studies, Modeling, Toxicity. Vol. 1, Butterworth-Heinemann. U.S. Environmental Protection Agency (1989) Short-Term Methods for Estimating the Chronic Toxicity of Effluents and Receiving Waters to Freshwater Organisms. 2nd ed., EPA-600/4-89-001. U.S. Environmental Protection Agency (1990) Contaminated Sediments— Relevant Statutes and EPA Program Programs. U.S. Environmental Protection Agency (1994a) Framework for Development of the National Sediment Inventory. EPA-823/R0003, Washington, D.C. U.S. Environmental Protection Agency (1994b) Methods for Measuring the Toxicity and Bioaccumulation of Sediment-Associated Contaminants with Freshwater Invertebrates. EPA-600/R-94-024, Office of Research and Development, Duluth, Minnesota.

ACKNOWLEDGMENTS. This publication was produced under the direction of Raymond C. Whittemore, Chair. Principal authors and chapters for which they were responsible are

Jennifer Byrnes Brower (3) Jerome M. Diamond (4) David Dilks (8) Dr. Robert M. Engler (1) James E. Kilduff (2) Edward R. Long (6)

xxi Thomas P. O’Connor (7) Stan J. Pauwels (5)

Additional content and review was provided by

Allen Burton (4) Gary Cecchine (3) Rick Haley Kay T. Ho Eugene Joseph LeBoeuf (2) Marianne Nyman (2) Jerry Schnoor John Scott (4) Paul Sibley (5)

Authors’ and reviewers’ efforts were supported by the following organizations:

Abt Associates, Inc., Cambridge, Massachusetts Camp, Dresser & McKee, Detroit, Michigan Connecticut Agricultural Experiment Station, New Haven, Connecticut G. Fred Lee & Associates, El Macero, California Great Lakes Environmental Research, Ann Arbor, Michigan HydroQual, Inc., Mahwah, New Jersey IT Corporation, Mahwah, New Jersey Kansas State University, Manhattan, Kansas Limno Tech, Inc., Ann Arbor, Michigan Medine Environmental Engineering, Boulder, Colorado National Council for Air and Stream Improvement, Lowell, Massachusetts National Oceanic and Atmospheric Administration, Seattle, Washington, and Silver Spring, Maryland Proctor & Gamble, Cincinnati, Ohio RAND, Arlington, Virginia Rensselaer Polytechnic Institute, Troy, New York SAIC, Narragansett, Rhode Island Stanford University, Stanford, California Tetra Tech, Inc., Owings Mills, Maryland Triad Engineering, Inc., Milwaukee, Wisconsin U.S. Army Engineer Waterways Experiment Station, Vicksburg, Mississippi U.S. Environmental Protection Agency, Narragansett, Rhode Island University of Guelph, Ontario University of Iowa, Iowa City, Iowa University of Missouri, Rolla, Missouri Vanderbilt University, Nashville, Tennessee Wright State University, Dayton, Ohio

xxii Chapter 1 An Introduction to Sediments

Robert M. Engler, U.S. Army Engineer Waterways Experiment Station, Vicksburg, MS

Sediments in the aquatic ecosystem are the analogue to soil in the terrestrial ecosystem, as they are the source of substrate nutrients and micro- and macroflora and fauna that are the basis of support to living aquatic resources. Sediments are the key drivers of environmental food cycles and the dynamics of water quality. Aquatic sediments are derived and composed of natural physical, chemical, and biological components as generally related to their watersheds. Sediments range in particle distribution from micron-sized clay particles through silt, sand, gravel, rock, and boulders. Sediments originate from bed-load transport, beach and bank erosion, and land runoff, and are naturally sorted by size through prevalent hydrodynamic conditions. In general, fast-moving water will host coarse-grained sediments and quiescent water will host fine-grained sediments. Mineralogical characteristics of sediments vary widely and reflect watershed characteristics. Organic material in sediments is derived from tissues of plants, animals, aquatic and terrestrial sources, and various point and nonpoint wastewater discharges, and increase in concentration proportional to decreasing sediment particle size. Dissolved chemicals in the overlying and interstitial waters are a product of the inorganic and organic sedimentary materials as well as from runoff and groundwater and range from fresh to marine in salinity. This sediment/water mileau varies significantly over space and time, and its characteristics are driven by complex biogeochemical interactions among the inorganic and living and nonliving organic components. The sediment biotic community includes micro-, meso-, and macrofauna and flora that are interdependent among each other and their host sediment’s biogeochemical characteristics.

1 To appreciate the effect that sediments have on water quality and aquatic organisms, one must understand how chemical constituents, which may have various effects on aquatic organisms, are associated with aquatic sediments. Sediments may be separated into several components or phases that are classified by their composition and mode of transport to their aquatic location. Among them are detrital and authigenic phases. Detrital components are those that have been transported to a particular area, typically by water. Detrital materials are derived from soils of the surrounding watershed and from within the aquatic system, and can include (1) mineral grains and rock fragments (soil particles) as well as stable aggregates, (2) associated organic material, and (3) culturally contributed components derived from agricultural runoff and industrial and municipal waste discharges. Authigenic components are those that are formed in place or have not undergone appreciable transport. These materials are typically the result of aquatic organisms and, as an example, may include shell material (calcium chloride, CaCO3), diatom frustules (silicon dioxide), some organic compounds, and products of anaerobic or aerobic transformations. Human (anthropogenic) physical and chemical activities have drastically, if not radically, modified natural sediment systems. Physical modifications range from enhanced sedimentation caused by various land-use practices to sediment deprivation caused by stream alteration (e.g., dams, levees, and channelization). These modifications range from positive to negative influences on the overall sedimentary system. The most serious anthropogenic activities, however, are the introduction of synthetic chemicals to waterways by point and nonpoint sources as a result of agricultural, urban, and industrial activities; “natural” chemicals through mining, agricultural, and urban land practices; and pathogenic microbes from human and animal waste. Radiological contamination in sediments is another, but somewhat limited, concern from atmospheric nuclear testing, nuclear power and processing plants, and waste streams from hospitals and other sources. Contamination of the water invariably results in contamination of their underlying sediments. Significant sediment contamination is most often associated with the high surface area of inorganic and organic small silt- and clay-sized particles and organic coatings on silt and clay particles. For the better part of the industrial and agricultural revolution, there were few if any controls on point or nonpoint sources of contaminants. The growth of large urban areas did not necessarily include the treatment of domestic waste other than aquatic discharge. It was generally thought that sediments, especially fine-grained sediments, were efficient sinks for contaminants entering the system. This belief also applied to previously contaminated soil particulates entering the system. In general, contaminated sediments under appropriate geochemical conditions and with moderate organic matter content and a stable hydrodynamic environment can be an effective sink for metals and nonpolar organic contaminants. However, a better understanding of the short- and long-term toxicological effects of contaminated sediment has shown that some contaminated sediments can be a significant and long-term source of contaminants, and represent an unacceptable ecological and human

2 Handbook on Sediment Quality health risk. Sediment contamination ranges in severity from Superfund sites to insignificant contamination in nonindustrial locations. With regard to the ecological effect of sediments to water quality and aquatic organisms, one must understand how chemical constituents, which may have various toxicological effects on aquatic organisms, are associated with sediments. The following discussion is simply a broad overview of the complexity of chemical constituent distribution and interaction within and among sediments. Detailed discussions of sediment geochemistry, toxicology, and water quality interrelations are presented in the following chapters. A general examination of the in situ association of trace elements and compounds in sediments with various sediment phases requires that the water contained in interparticle voids or interstices be considered. This is termed interstitial water (IW), which is also called pore water. In relation to the overlying water, chemical constituents frequently may be enriched in the IW by several exchange mechanisms/locations, including exchange site of the silicate phase, and those that are associated with organic matter or trace elements complexed with the organic phase. Synthetic nonpolar organics such as polychlorinated biphenyls may be associated with active silicate as well as with the natural organic fraction or other organic fractions such as oil. Consequently, only a small amount of these low-solubility or slightly soluble constituents is found dissolved in the IW and, in fact, may be strongly resistant to desorption from the particulate fraction. An element or molecule can be present (partitioned) in a sediment in one or more sediment fractions (locations) with varying degrees of resistance to desorption. Possible locations include the lattice of crystalline minerals, the interlayer positions of polysilicate (clay) minerals, adsorption on mineral surfaces, association with hydrous iron and manganese oxides that exist as surface coatings on discrete particles, absorption or adsorption to sediment organic matter, and partitioned within the organic matrix. These locations represent a wide range of potential mobility and bioavailability. They range from stable components in the mineral lattices, which are essentially insoluble, to soluble components in the IW, which are readily mobile. Elements or molecules are also incorporated to living terrestrial and aquatic organisms and are relatively stable; however, they may be released during decomposition or defecation at the sediment water interface or within the sediment. Numerous sediment characterization procedures to elucidate the phase distribution of contaminants in dredged material have been applied to many types of marine and freshwater sediments, both aerobic and anaerobic. Physicochemical (Eh, pH) changes that occur after anaerobic bottom sediment is disturbed and resuspended may result in either solution or precipitation of many elemental species. Furthermore, disturbance of samples to be used for sediment characterization must be minimal because drying, grinding, and contact with atmospheric oxygen are undesirable. The general sediment phases are presented in a simplified fashion in the following list in their relative order of mobility and bioavailability. Interstitial water is the most mobile and, consequently, the most available. When contaminants enter a body of water and subsequently enter sediment particulate matter, they typically enter two or three phases in various concentrations

An Introduction to Sediments 3 that cannot be readily distinguished from levels of concern or bioavailability by bulk or total analysis.

• Interstitial water—an integral part of sediment, is in dynamic equilibrium with the silicate and organic-exchange phases of the sediment as well as with the easily decomposable organic phase. • Mineral-exchange phase—the portion of the chemical constituent (ion) that can be removed from cation-exchange sites of the sediment. In addition to cation exchange, ions can become associated with the surfaces of mineral sediments such as clays at nonexchange sites. Ionic organic contaminants may sorb to this phase in a manner that may be highly resistant to desorption. • Reducible phase—this phase is composed of hydrous oxides of iron and manganese as well as hydroxides of iron and manganese, which are relatively stable under reducing (anaerobic) conditions. Of particular importance are the toxic metals (arsenic, copper, cadmium, nickel, cobalt, and mercury) that may be associated with these discrete phases. • Organic phase—this phase or partition of the chemical constituents is considered to be solubilized after destruction of the organic matter or through harsh solvent extraction and contains very tightly bound constituents as well as those loosely complexed by organic molecules. • Residual phase—the residual phase contains primary minerals and particles as well as secondary weathered minerals and recalcitrant organics that are, for the most part, a stable portion of the sediments.

The toxicology and biogeochemical characteristics of sediments are much too site specific to review on a national or even regional basis. Effects are dominated by local pollution control measures, or the lack thereof, local watershed characteristics, geomorphology, physicochemistry, geochemistry, and more. Although a total chemical characterization may be useful in determining which contaminants are present, it rarely can be used to estimate or predict potential biological effects. A preferred approach would be based on a weight of evidence developed from various physical, chemical, and ecotoxicological measures. These are discussed generally below and in detail throughout the remainder of this report. Biological effects are a dose response, which is contingent on bioavailability of the contaminants, duration of exposure, life history of the organism, and a multitude of other environmental variables. For example, the bioavailability of nonpolar organics may be a function of the total organic carbon and even the forms of organic carbon in the sediment or of sulfides that may control the bioavailability of heavy metals. Because of the biogeochemical complexity of sediments, it is unlikely that a clear relationship between sediment chemistry and subsequent environmental effects such as biological availability will be developed in the foreseeable future. Slight changes in pH can significantly alter biological availability and mobility of metals. A change in the composition of organic carbon (plant- derived organic carbon versus soot) can significantly alter the sorption/desorption kinetics and subsequent biological availability of toxic organic chemicals.

4 Handbook on Sediment Quality Various toxicological assessment techniques have been developed to estimate the risks associated with contaminated sediments and are presented in detail in the following chapters. These evaluations range from simple water extracts to biologically derived water quality criteria or multiorganism chronic and acute benthic bioassays. These effects-based approaches recognize that aquatic sediments, in contrast to most industrial and domestic waste, are a complex mixture of natural and anthropogenic components whose potential environmental risks must be evaluated on a case-by-case basis using a weight of evidence approach. This introductory chapter has presented a rather simplistic overview of aquatic sediments. The following chapters of this book will thoroughly address these complex interactions and propose solutions to this complexity. The reader will find an examination and discussion of sorption processes, bioavailability, sediment collection and assessment techniques, toxicity testing, advantages and disadvantages of chemical sediment-quality guidelines, and sediment-quality modeling. These timely discussions will help the reader better comprehend the dilemma facing those who have to identify, assess, and manage contaminated sediments.

An Introduction to Sediments 5

Chapter 2 Sorption of Organic Compounds by Soils and Sediments: Equilibrium and Rate Processes

James Kilduff, Rensselaer Polytechnic Institute, Troy, NY Eugene LeBoeuf, Vanderbilt University, Nashville, TN Marianne Nyman, Rensselaer Polytechnic Institute, Troy, NY

8 Introduction 22 Correlations with the 10 Sorption Mechanisms Octanol–Water Partition 12 Intermolecular Forces Coefficient 12 Partitioning 24 Isotherm Models 12 Ion Exchange 26 The Generalized Langmuir 13 Covalent Bonding Isotherm and Its 14 Modeling Contaminant-Phase Simplifications Distribution Equilibria 30 Potential Theory 15 Partitioning Theory 31 Combined Models 17 Estimation of Activity 34 Materials and Methodologies for Coefficients Collecting Sorption Data 19 Flory–Huggins Theory 34 Batch and Column

21 Correlations of Kd to Molecular Equilibration Techniques Properties

7 34 Batch Equilibration 65 Desorption Hysteresis and Techniques Reversibility 37 Fixed-Bed (Column) Reactor 68 Effects of Particle Size Techniques 68 Effects of Organic Matter 38 Obtaining Representative Composition and Structure Samples 68 Chemical Characterization: 39 Composition of the Solution Polarity and Aromaticity Phase 69 Rubbery/Glassy Models 40 Criteria for Achieving 69 Microporosity Equilibrium 70 Crystallinity 41 Loss Mechanisms and Controls 72 Solids-Concentration Effect 43 Reactors and Reactor 75 Sorption Rate Processes Components 76 Local Equilibrium 43 Phase Separation 77 Langmuir Kinetics 44 Desorption 77 Empirical Rate Models 45 Sorption Phenomena 79 Mass-Transfer Controlled 45 Isotherm Linearity Kinetics 45 Linear Partitioning to 79 External Mass Transfer Isolated Mineral Surfaces 81 Homogenous Solid-Phase 46 Linear Partitioning to Soils (Surface) Diffusion and Sediments 82 Pore and Combined Pore- 49 Nonlinear Sorption of Surface Diffusion Neutral Hydrophobic 83 Diffusion in Macromolecules Organics 84 Case I Transport 59 Nonlinear Sorption of 84 Case II Transport Ionizable Organics 85 Anomalous or Non-Fickian 61 Competitive Effects Transport 61 Competitive Adsorption 86 Macromolecular Diffusion Models Behavior in Natural 62 Ideal Adsorbed Solution Systems Theory 86 Summary and Conclusions 63 Polanyi-Based Models 87 References 64 Evidence for Sorbate Competition

INTRODUCTION The fate of contaminants in sediment–water systems is, in large part, governed by sorptive interactions with sediment solids, including both mineral and organic constituents, and with dissolved and colloidal natural organic matter present in the solution phase. Contaminant sequestration by sediments may include such mechanisms as adsorption, ion exchange, partitioning, chemical bond formation, and diffusion-limited mass transport (Luthy et al., 1997, and Ononye and Graveel, 1994). Sorption processes may include adsorption, or accumulation on a two-dimensional surface, and absorption, or accumulation in a three-dimensional phase. These processes profoundly influence the behavior of chemicals in the environment. Sorption influences contaminant availability to the aqueous phase and, therefore, its mobility,

8 Handbook on Sediment Quality toxicity, and potential for phase transfer across the air–water interface. Sorbed species may exhibit different reactivity from those in free solution. For example, they may be less available to microorganisms and shielded from radiation and thus participate to a lesser extent in photochemical reactions. An understanding of contaminant sequestration is necessary to improve the prediction of organic compound fate, availability, and sediment toxicity. This, in turn, provides a basis for assessing exposure, evaluating risk, and defining remediation endpoints. We have restricted our focus to hydrophobic organic contaminants, an important class of pollutants including petroleum-derived aromatics, chlorinated solvents, pesticides, and such compounds as polychlorinated biphenyls (PCBs) that are no longer manufactured but exhibit long- term persistence in the environment. In this chapter, the underlying chemical and physical phenomena that contribute to organic contaminant sequestration by sediments are reviewed. In addition, we consider the many models available for describing sorption equilibria and kinetics and methodologies for collecting sorption data. Our primary concern is understanding sorption processes in sediments. However, many of the factors affecting sorption processes in sediments are analogous to those affecting uptake by soils and other “geosorbents.” In fact, most researchers do not restrict their research exclusively to either soils or sediments. A review of the literature will reveal many papers containing data for both soil and sediment; in part, this is because sorption mechanisms are similar for the two geosorbents. In addition, it is often necessary (or desirable) to study a wide range of sorbents, even including synthetic polymers, to fully understand sorption mechanisms. Therefore, we do not attempt to restrict our scope to sediments exclusively. In the Sorption Mechanisms section, fundamental intermolecular forces and mostly descriptive discussions of partitioning, ion exchange, and covalent bonding are reviewed. In the Modeling Contaminant-Phase Distribution Equilibria section, quantitative approaches to modeling sorption equilibria in natural systems are explored. This section begins with a detailed treatment of partitioning theory, based on the idea that sediment organic matter provides a phase into which organic chemicals can absorb or partition in a manner analogous to octanol–water partitioning. This analogy is pursued further in the section where correlations between partition coefficients and such chemical properties as solubility and octanol–water partition coefficient are developed. Models to describe adsorption equilibrium are discussed still later in the section using the Langmuir model as a starting point for more complex models such as the generalized Langmuir (GL) mode and its simplifications, Polanyi potential theory, and composite models that combine different isotherm models to represent physically realistic mechanisms. The next section discusses methodologies for collecting sorption data. Whereas such experiments are conceptually straightforward, there are many potential pitfalls, and we attempt to point out the significant ones. The next section reviews sorption phenomena and current theories in the context of current literature. The material presented in this section builds on all previous sections. The issue of isotherm linearity is investigated in detail, because isotherm shape can have a significant effect on pollutant transport. In addi-

Sorption of Organic Compounds 9 tion, the mathematics of pollutant transport becomes more complex when the sorption isotherm is nonlinear. Competitive effects are also treated in detail because they too can be important in assessing organic compound fate and such effects provide valuable insight to sorption mechanisms. Desorption hysteresis and reversibility are discussed later in the section as these phenomena may govern contaminant availability and sediment toxicity. The effects of particle size are reviewed, as is the influence of organic matter composition and structure, including effects of aromaticity, crystallinity, and microporosity. These concepts are used to help interpret sorption mechanisms. Finally, the last section addresses sorption rate processes. These mathematical relationships are important elements of the pollutant material balance, which form the basis for simulation of contaminant fate and transport. The section begins with the assumption of local equilibrium and its implications. This assumption can dramatically simplify the solution to material balance equations. In later sections, rate expressions based on the Langmuir model as well as other models that are often empirically based are considered. The influence of mass transfer on sorption kinetics and the models used to incorporate such effects are also discussed. Finally, the special features of diffusion in macromolecules are presented. There is mounting evidence that sorption by natural organic matter displays some characteristics of sorption by polymers, including diffusion-controlled sorption rates.

SORPTION MECHANISMS Sorption mechanisms governing equilibrium partitioning of organic compounds depend on the physical and chemical properties of both the organic compound and the sorbent phase (e.g., Hassett et al., 1980; Karickhoff, 1981; and Zierath et al., 1980). Organic pollutants of interest include hydrophobic organic compounds (HOCs) such as PCBs, polyaromatic hydrocarbons, dioxins and furans, substituted aromatics, chlorinated benzenes, chlorinated phenols, triazine herbicides, carbamate pesticides, and organophosphate pesticides, among others. The polarity of such compounds depends on halogen-substitution patterns, the presence of oxygen- and nitrogen-containing moieties, and the presence of ionizable functional groups. Many such compounds do not contain ionizable functional groups, but others do, and such moieties can significantly influence chemical properties and sorption behavior. Natural sediments are heterogeneous, and this heterogeneity is likely to occur on multiple scales. This is depicted schematically in Figure 2.1. Individual particles may have different origins and composition (e.g., shale and quartz), and each particle may be composed of different materials (e.g., mineral surfaces coated with organic matter). Mineral surfaces contain oxide functional groups that give the surface a pH-dependent charge and that can be involved in electrostatic and ligand-exchange reactions. The mineral surfaces may contain pores, both mesoporous (having widths between 2 and 50 nm)

10 Handbook on Sediment Quality Figure 2.1 Schematic depiction of sediment heterogeneity (containing mineral and organic surfaces) and sorption mechanisms (partitioning, electrostatic bonding, ion-exchange, and covalent bonding).

and micropores (having widths smaller than 2 nm). In many cases, the organic constituents of the sediment phase dominate sorption behavior, which can include both partitioning (absorption) and adsorption mechanisms. The dominant mechanism appears to depend on the source, composition, and age of the organic matter (e.g., Binkley, 1993; Hassett et al., 1980; Karickhoff, 1981; McGinley and Weber, 1993; Sikka et al., 1978; Wu and Gschwend, 1986; Zachara et al., 1984; Zachara et al., 1986; and Zhang et al., 1993). Soil and sediment organic matter is a heterogeneous mixture of partially or completely degraded molecules of plant and animal origin. The organic constituents may exist as fulvic and humic acids, humin, kerogen, or some combination. These classes of organic compounds, generally defined operationally, can be viewed as lying on a spectrum of diagenetic alteration. Some investigators have made a distinction between younger, amorphous organic matter, and older, denser, more crystalline forms. The latter may include shales and combustion residues such as soot, which can exhibit high sorption capacities. Ion exchange and adsorption to mineral surfaces may contribute significantly to the sorption of compounds having ionizable functional groups; in such cases, geosorbent cation-exchange capacity and solute speciation as a function of pH are important (Ainsworth et al., 1987). Some hydrophobic organic compounds have been shown to form covalent bonds with sediment humic substances, mineral surfaces, or both. All sorption processes are driven, in part, by some combination of intermolecular forces.

Sorption of Organic Compounds 11 INTERMOLECULAR FORCES. Sorption mechanisms include specific interactions between dissolved solutes and solid surfaces (or surface coatings) and thermodynamic gradients (solution entropy), important for solvophobic compounds. Specific interactions are generally categorized as chemical, electrostatic, and physical. Each classification has a characteristic energy of interaction and interaction range (Atkins, 1994). Short-range chemical forces leading to hydrogen bonding or covalent bonding (chemisorption) are characterized by large heats of adsorption, on the order of 20 kJ/mol for hydrogen bonds and higher for covalent bonds. Electrostatic ion–ion interactions can arise between ionizable solutes and surface moieties and are characterized by large interaction energies on the order of 250 kJ/mol and vary inversely to their separation distance, or as 1/r. Ion–dipole interactions exhibit much lower interaction energies, on the order of 15 kJ/mol, and vary as 1/r 2. Physical sorption is caused by net attractive interactions between partial charges of molecules (van der Waals forces). These include dipole–dipole interactions between polar molecules, dipole-induced dipole interactions between polar and ionizable molecules, and induced-dipole− induced-dipole interactions (London dispersion forces) between nonpolar molecules. Dipole–dipole interactions vary as 1/r 3 for fixed orientations and 1/r 6 for freely rotating orientations; other van der Waals interactions also vary as 1/r 6. Physical adsorption bonding forces are relatively weak, on the order of only a few kJ/mol. However, these may be amplified by a favorable thermodynamic gradient in solution entropy, effectively driving hydrophobic molecules out of solution (Voice and Weber, 1983).

PARTITIONING. An important mechanism of organic compound uptake by soils and sediments is partitioning into one or more organic phases associated with the mineral fraction. This process is conceptualized as contaminant distribution between two immiscible (Schwarzenbach et al., 1993) or partially miscible (Chiou et al., 1983) solutions. The contaminant is essentially considered dissolved or absorbed into a three-dimensional organic matrix consisting of a porous structure of macromolecule chains. Xing and Pignatello (1997) point out that this dissolution domain is composed of thermodynamically dynamic sites, whose energies average out as in a liquid. Thus, this process is analogous to the partitioning of a solute between water and an organic solvent phase (Chiou et al., 1983). In general, the extent of partitioning is governed by the difference between the partial molar excess free energy of the solute in the aqueous (w) and the sediment organic matter (om) phases.

ION EXCHANGE. Ion exchange is defined as the replacement of one adsorbed ion with another (Sposito, 1989). We restrict this to the sorption of fully solvated ionic species and exclude specific adsorption processes that involve the formation of an inner-sphere surface complex. Ion-exchange sites may exist on mineral surfaces and as part of the sediment-dissolved organic matter structure. Ion-exchange reactions are driven primarily (but not exclusively) by electrostatic (coulombic) interactions between exchange sites and dissolved ions in solution. In addition, van der Waals forces between

12 Handbook on Sediment Quality hydrophobic moieties on an organic ion and nonpolar regions of the solid phase can enhance sorption. The ion-exchange reaction is expressed generi- cally by the following reactions (Sposito, 1994):

b+ a+ bAXa(s) + aB (aq) ⇔ aBXb(s) + bA (aq) (2.1)

d– c– dCYc(s) + cD (aq) ⇔ cDYd(s) + dC (aq) (2.2)

Where a, b, c, and d = stoichiometric coefficients, A, B, C, and D = ions, X = 1 mol negative charge, and Y = 1 mol positive charge carried by the solid surface.

Cation exchange has been shown to be the predominant mechanism for sorption of organic bases (Karickhoff and Brown, 1979; Lee et al., 1997; and Nicholls and Evans, 1991), even at pH values 2 to 3 log units greater than the

solute pKa (Ainsworth et al., 1987; Bellin, 1993; and Zachara et al., 1986). The cation-exchange capacity (CEC) is used as a measure of a soil’s capacity to sorb cations; it is defined as the total number of exchangeable cations associated with negatively charge sites. For example, the following reaction represents the exchange of an inorganic cation (e.g., K+), initially present on the solid-phase surface, for a monovalent organic cation (BH+):

+ + KX(s) + BH (aq) ⇔ BXb(s) + K (aq) (2.3)

The preference for certain ions is related first to ionic charge and second to solvation energy, as manifested in the hydrated radius. The method for analyzing CEC assumes that exchange sites are primarily occupied by exchangeable cations (e.g., Na+,K+,Ca2+, and Mg2+) and that cation displacement is entirely attributable to the ammonium ion (Thomas, 1982). Sorption of aromatic bases is often positively correlated with both the CEC and the organic carbon content of the solid phase, and is influenced by solution phase

speciation (i.e., solution pH and solute pKa).

COVALENT BONDING. Some hydrophobic organic compounds (e.g., aromatic amines, phenolic compounds, and 2,4,6-trinitrotoluene) have been shown to undergo covalent bonding with natural organic matter (e.g., humic substances) from sediments, soils, and natural water. Such reactions lead to slow disappearance from the solution phase and possible immobilization (Achtnich et al., 1999; Burgos et al., 1996; and Ononye et al., 1989). The composition of natural organic matter as well as functional groups on synthetic organic sorbates have been recently studied to understand both reversible and irreversible covalent bonding of such compounds to soil or sediment organic matter (Achtnich et al., 1999, and Dec and Bollag, 1997). Organic bases, such as aromatic amines, are capable of forming covalent bonds with soil and sediment particles. Zachara et al. (1984) observed that

Sorption of Organic Compounds 13 sorption of aniline was partially irreversible for a relatively high carbon content soil, whereas sorption appeared to be reversible upon removal of the organic matter from the soil. Graveel et al. (1985) concluded that sorption of benzidine, α-naphthylamine, and p-toluidine involved a reaction with the phenolic components of humus-type material. Later, Ononye and Graveel (1994) studied reactions between α-naphthylamine and 4-methylaniline (as model solutes) and quinones (as representative of humic acid moieties). They found that imine formation was fast and reversible, whereas 1,4-nucleophilic addition of the amino group to the quinone ring was slower and irreversible. Parris (1980) showed that imine formation for aniline is also reversible. In contrast, both the imine and 1,4-nucleophilic additions were irreversible for benzidine (1,1'-biphenyl-4,4'-diamine) (Ononye et al., 1989). Weber et al. (1996) investigated the covalent bonding pathway for reactions of aromatic amines with dissolved organic matter. Like Ononye et al. (1989), they observed biphasic kinetics, which they attributed to rapid and slow sorption steps. These researchers concluded that dissolved organic matter was not a significant sink for aromatic amines in most aquatic environments. Instead, they suspected that the aromatic amines are more likely to be removed by covalent bonding to natural organic matter associated with bottom sediments. In a subsequent paper, Thorn et al. (1996) studied the nucleophilic addition reactions of aniline to humic substances. The study suggested that the formation of anilinohydroquinone nitrogen is a reversible step and that the anilinohydroquinone nitrogen represents a fraction of the covalently bound aniline. Additionally, their study suggested that the anilide nitrogens are also subject to exchange by other amines. Natural soils and sediments have also been shown to be oxidation catalysts (Bollag, 1992; Burgos et al., 1996; and Park et al., 1999). Phenolic compounds can undergo oxidative coupling reactions catalyzed by carbon surfaces, enzymes, and metal oxides (Bollag, 1992; Grant and King, 1990; and Park et al., 1999). Oxidative coupling is a surface reaction in which phenol multimers are formed through ether linkages, which are promoted by high pH, temperature, and the presence of dissolved oxygen. The lack of reversibility of phenol sorption by natural geosorbents (Bhandari et al., 1996, and Burgos et al., 1996) and model adsorbents (Grant and King, 1990, and Kilduff and King, 1997) has been shown to decrease as a result of oxidative coupling. It is possible that this reaction results in chemisorption or the formation of strongly physisorbed reaction products.

MODELING CONTAMINANT - PHASE DISTRIBUTION EQUILIBRIA

Of fundamental importance to both fate and transport predictions and risk assessment is the equilibrium distribution of a contaminant species (sorbate) between the aqueous and sediment phases (sorbent). The distribution coeffi-

14 Handbook on Sediment Quality cient is defined as the ratio of the solute concentration in the sorbed phase, qe, to the concentration in the solution phase, Ce (mol/L), at equilibrium

qe Kd = – (2.4) Ce

Typically, sorbed-phase concentrations have units of moles (or mass) per unit mass (or surface area) of sorbent. In general, the distribution coefficient is a function of temperature and aqueous-phase concentration of the sorbate of interest as well as the aqueous-phase concentrations of all solutes that may compete for sorption sites or change sorbate activity (in solution or in the sorbed phase)

Kd,i = Kd,i [T, xi, Σ xj] (2.5) j=1 Where

xi = the mole fraction of the contaminant of interest, xj = the mole fraction of species that may compete for sorption sites or otherwise affect sorption uptake, and T = temperature.

The relationship between qe and Ce at constant temperature is the sorption isotherm. The simplest possible isotherm is the linear isotherm, which results

when Kd is constant. This is rigorously true for adsorption only when surface coverage is low and the surface is energetically uniform. A constant Kd is typical of partitioning processes for compounds of environmental interest over

concentration ranges of practical importance. Often, a constant Kd is taken as evidence of a linear partitioning process. When a partitioning mechanism is either demonstrated or assumed, the constant distribution coefficient is often

referred to as Kd. From a practical perspective, Kd is approximately constant when the concentration range of interest is sufficiently small to guarantee that

the change in Kd is negligible or within limits of experimental error.

PARTITIONING THEORY. At least to some extent, nearly all soil and sediment organic matter provides an environment composed of a porous, three-dimensional organic matrix of macromolecule chains into which organic compounds can partition. The tendency of an organic compound to move from the aqueous phase to the sediment organic-matter phase and the resulting equilibrium distribution is governed by the difference between the partial molar excess free energy of the solute in each phase. Therefore, at least in part, uptake or organic compounds by soils and sediments is due to partitioning into one or more organic phases associated with the mineral fraction. This process is analogous to the partitioning of a (hydrophobic organic) solute between water and an organic solvent phase (Chiou et al., 1983). Note that from a mechanistic point of view partitioning occurs into organic matter; however, sediment organic matter is generally measured in terms of organic carbon (oc). Most natural organic matter has a carbon content of approxi-

Sorption of Organic Compounds 15 mately 50%; therefore, the mass fraction of organic matter, fom, is approximately twice the mass fraction of organic carbon, foc. At equilibrium, chemical potentials in the organic-matter-saturated aqueous and water-saturated organic-matter phases must be equal. Using subscripts to designate components, superscripts to designate phases, and designating the organic solute as component 1, chemical potentials are written

µµwo=+ γ womwom,, 11RTln 11 x (2.6)

µµom=+ o γ om,, w om w 11RTln 11 x (2.7)

Where µ = the chemical potential (kJ/mol); µo = the standard chemical potential (kJ/mol); R = gas constant; T = temperature;

γ1 = the organic solute activity coefficient; x1 = solute mole fraction; om,w = the water-saturated organic-matter phase; and w,om = the organic-matter-saturated aqueous phase (Chiou et al., 1983).

An equivalent criterion for equilibrium is that fugacities (i.e., ideal pressures that characterize the escaping tendency from a phase) in each phase are equal

ˆˆwom,,,,,,=== womγγ wom omw omw omw fx11111111 ffx f (2.8)

Where ˆ w,om ˆ om,w f 1 and f 1 = the fugacities of the organic solute in aqueous solution and water-saturated organic matter, respectively, and

f1 = a reference-state fugacity, typically taken as the pure component (or subcooled liquid) fugacity.

Using the same standard state for both phases

γ wom, x om, w ln1 = ln 1 γ om, w wom, (2.9) 1 x1

In the nomenclature of eq 2.4, we convert mole fraction units into concentration units more typically used for Ce (mol/L) and qe (mol/kg sediment)

x wom, fxom, w C ==1 and q om 1 (2.10) e V wom, e V om,, wρ om w

Where V w,om and V om,w = the molar volumes of the aqueous-solution phase and the water-saturated sediment organic phase, respectively (L/mol);

16 Handbook on Sediment Quality ρom,w = the sediment organic matter density (kg/L); and

fom = the mass fraction of organic matter in the sediment.

Chiou et al. (1983) used a value of 1.2 for the organic matter specific gravity based on a comparison of similar polymeric materials. It is generally valid to assume that pure water and water in equilibrium with organic matter have the same molar volume (V w,om = V w). Combining eq 2.9 and 2.10 yields the following relationship for the distribution coefficient

om,, wρ om w =−−γγwom,, omw V lnKd ln11 ln ln w (2.11) Vfom

The distribution coefficient is often normalized to the mass fraction of total

organic carbon, foc, which is typically measured by oxidation (combustion) and detection of evolved carbon dioxide. The organic carbon–normalized distribution coefficient is

om,, wρ om w =−−γγwom,, omw V − lnKoc ln11 ln lnw ln foc (2.12) Vfom

Equations 2.11 and 2.12 contain several parameters that may be expected to remain constant with organic contaminant concentration over the range expected for hydrophobic organic solutes. These include properties of the organic mater (molar volume, density, and mass fraction of the solid sorbent) and properties of the solution phase (molar volume). Therefore, it is clear

from these relationships (eqs 2.11 and 2.12) that Kd and Koc will remain constant as long as the activity coefficients in the aqueous and organic phases are constant. The following analysis will show that this is likely to be the case for sparingly soluble hydrophobic organic contaminants.

Estimation of Activity Coefficients. The aqueous-phase activity coefficient, w γ 1, is rigorously a function of mixture composition. It can be correlated using a model of excess Gibbs free energy. One such model is the van Laar equation (Smith et al., 1996, and Tsonopoulos and Prausnitz, 1971)

∞ −2 ∞ ⎡ x ln γ ⎤ lnγγw =+ ln ⎢1 11⎥ (2.13) 11 − γ ∞ ⎣ ()ln1 x12⎦

Where 1 = the infinite dilution activity coefficient of organic solute in water, and 2 = the infinite dilution activity coefficient of water in organic solute.

These can be estimated from correlations (Lyman et al., 1990, and Tsonopoulos and Prausnitz, 1971) or from mutual solubility data (Carlson and Colburn, 1942). Equation 2.13 can be used to demonstrate that the expected range of aqueous activity coefficients for most hydrophobic organic solutes is small.

Sorption of Organic Compounds 17 Values for all necessary parameters are available for benzene, which is relatively soluble in comparison to many contaminant classes of interest, including hydrocarbons, substituted benzenes, polycylic aromatic hydrocarbons (PAHs), phthalates, PCBs, and pesticides (Schwarzenbach et al., 1993). w,sat !4 For the benzene/water system, 1 = 2400, 2 = 430, and x1 = 4.2 × 10 . w,sat Using these values in eq 2.13 yields a value of 2378 for 1 , the activity coefficient in water saturated with organic solute, differing from 1 by less than 1%. For less soluble contaminants, the difference will be even smaller. For contaminants that are solids at environmental temperatures and pressures, an expression for the solution-phase activity coefficient at saturation can be developed by considering equilibrium between the aqueous phase (w) and the pure-solid phase (s), as described by

ˆˆw=== w,, satγγ w sat l s s s s fx11111111 ffxf (2.14)

Where w,sat x1 = the mole fraction solubility, w,sat 1 = the activity coefficient in water saturated with solid organic solute, ˆs f 1 = the fugacity of the pure solid, and l f1 = the fugacity of the pure subcooled liquid (found by extrapolating the vapor pressure curve from the triple point to the temperature of the solution).

Other terms have been defined previously. Assuming negligible solubility of water in the solid-organic phase, it is valid to assume that the mole fraction and activity coefficient of the organic species in the solid phase is unity: s s x1 1 =1; therefore ⎛ f s ⎞ x w,, satγ w sat = ⎜ 1 ⎟ (2.15) 11 ⎝ f l ⎠ 1 pure organic

The fugacity ratio was shown by Prausnitz (1969) to be

⎛ f s ⎞ ∆H f ⎛ T ⎞ ∆∆c ⎛ T ⎞ c T ln⎜ 1 ⎟ =−t −11+−p t − p ln t (2.16) ⎝ l ⎠ ⎝ ⎠ ⎝ ⎠ f1 RTt T R T R T Where H f = the enthalpy of fusion,

Tt = the triple point temperature, and cp = equal to the difference between the liquid- and solid-phase heat

capacities, (cp,liquid – cp,solid).

This expression essentially accounts for the energy required to overcome intermolecular forces in the crystalline organic solid. Application of this equation to phenanthrene was demonstrated by Huang and Weber (1997) and Tsonopoulos and Prausnitz (1971). The activity coefficient at saturation was

18 Handbook on Sediment Quality extrapolated to infinite dilution by Tsonopoulos and Prausnitz (1971) using a simplified two-suffix Margules equation

w, sat ∞ ln γ ln γ = 1 1 − 2 (2.17) ()1 x1

–7 For phenanthrene, having a mole fraction solubility of x1 = 1.31 × 10 , it is clear that there is negligible difference between infinite dilution activity coefficients and those at saturation.

FLORY–HUGGINS THEORY. The Flory–Huggins theory has been used to quantify the activity coefficient of the organic contaminant in the sediment om,w organic matter phase, 1 . The sediment organic matter phase is treated as an amorphous polymer, which is thought to be a reasonable representation. Chin and Weber (1989), Chiou et al. (1983), LeBoeuf and Weber (1999), Spurlock and Biggar (1994a, 1994b, 1994c) and have invoked this theory, which relates solute activity to a solute–organic matter interaction parameter

V1 ln(aom,w) = ln(xom,w γ om,w) = ln φ + φ 1 – –– + χφ 2 (2.18) 1 1 1 1 om ΂ Vom,w ΃ om Where

1 = the solute volume fraction in the organic matter, om = the volume fraction of organic matter, V 1 = the molar volume of organic solute, and χ = the Flory–Huggins interaction parameter.

The interaction parameter is composed of entropic and enthalpic contributions

(χ = χS + χH) and should remain relatively constant for dilute solutions (Hildebrand et al., 1970). Equation 2.18 can be simplified by expressing the volume fractions in terms of mole numbers and molar volumes and making the assumptions that (Chiou et al., 1983) (1) the volume of organic solute is 1 om small compared with the volume of sediment organic matter (n1V << nomV and om ≅ 1), (2) the number of moles of organic solute is small compared

with the moles of organic matter (n1 << nom), and (3) the organic solute molar volume is small compared with the molar volume of sediment organic matter (V1/V om << 1)

V1 lnγχom, w =++ ln (1 ) (2.19) 1 V om, w

Substitution into eq 2.11 yields

ρom, w =−−+−γχw 1 lnKVd ln1 ln (1 ) ln w (2.20) Vfom

For a hydrophobic organic to be miscible with a sediment organic-matter phase, the interaction parameter must be below a critical value of approxi-

Sorption of Organic Compounds 19 mately 0.5 (Blanks and Prausnitz, 1964). The entropic contribution to the solubility parameter is theoretically related to the polymer lattice coordination number but is found empirically to have a value between 0.3 and 0.4, with a mean value of 0.34, calculated by Blanks and Prausnitz (1964). They determined the enthalpic contribution from a modification of the Hildebrand– Scatchard theory of solutions

V1 χχ=+ A (2.21) S RT 12

where A12 = an interchange energy density. The interchange energy density characterizes solute–solute, polymer– polymer, and solute–polymer intermolecular forces. The solute–solute and polymer–polymer intermolecular forces are related to the energy of vaporization of the pure substances and are expressed in terms of their solubility parameters. The solute–polymer intermolecular forces are represented by an empirical term. For a nonpolar organic solute and a polar polymer (sediment organic matter), the interchange energy density is given by

=−λλ2 +− τ2 ψ A12() 1 2 2 2 (2.22)

Where

λ1 = the solubility parameter for the hydrophobic, nonpolar organic solute;

λ2 and τ2 = the nonpolar and polar solubility parameters of the polar sediment organic matter, respectively; and ψ = an empirical parameter that accounts for inductive dipole– induced dipole interactions between the organic solute and the sediment organic-matter phase.

The value of ψ varies from 0 to approximately 20 cal/cm3 and can be determined from a correlation with the product of λ1 and τ2 (Blanks and Prausnitz, 1964). A simplified form of eq 2.22 (corresponding to regular solution theory) has been applied by neglecting the polar solubility parameter of the sediment organic matter and the induction energy term, ψ. Using the simpler model,

Chiou et al. (1983) estimated a fitted value of λ2 = 13 based on partitioning of substituted benzenes to a silt loam soil. Curtis et al. (1986) found that a value of λ2 = 11.5 provided the best fit to an experimental data correlation, but a value of 10.5 provided a better overall correlation. LeBoeuf and Weber (1999) found that the solubility parameters for eight natural organic-matter samples ranged from 11.8 (relatively polar cellulose) to 10.5 (relatively nonpolar Illinois No. 6 coal), with decreasing values as the degree of diagenetic alteration increased and polarity decreased. It is difficult to accurately determine the solubility parameters for natural organic matter because of its heterogeneity. This has led to attempts to identify suitable surrogate materials. Chin and Weber (1989) applied the full model (eq 2.22) to the estimation of hydrophobic organic pollutant binding to dispersed (solution-phase) humic

20 Handbook on Sediment Quality substances. They used organic matter structural information to identify likely surrogate materials for which polar and nonpolar solubility parameter data are available. Using the concept that the solubility parameter of a polymer is similar to its primary subunit (Hildebrand et al., 1970), methyl salicylate, maleic anhydride (a subunit of polymaleic acid, a fulvic acid surrogate), and lignin were chosen as surrogate compounds. Chin and Weber (1989) found that the model was not sensitive to the choice of the entropic interaction parameter but was sensitive to the compound selected as a surrogate. Furthermore, the best surrogate depended on the polarity of the organic matter. Binding to commercial humic substances was well predicted using methyl salicylate as a surrogate; however, the model using lignin as a surrogate overpredicted binding, whereas the model based on maleic anhydride showed the opposite trend. Binding to the more polar natural fulvic acids, however, was best predicted using maleic anhydride as a surrogate compound. These findings suggest that sediment organic matter properties, as reflected in their polar and nonpolar solubility parameters, can have a significant effect on the extent of hydrophobic contaminant binding. LeBoeuf and Weber (1999) evaluated the applicability of the Flory– Huggins model for sorption of phenanthrene by 13 natural and synthetic organic macromolecules. Their study suggested that the model predicts relatively concentration-independent χ values for more fluidlike, rubbery macromolecules. In contrast, concentration-dependent χ values were found for more condensed, glassy macromolecules, and highly concentration- dependent, largely negative interaction parameters characterized sorption of phenanthrene onto Green River kerogen, Ohio Shale II kerogen, Illinois No. 6 and Wyoming coals, and poly(phenyl methacrylate). These greatly negative enthalpic contributions to the interaction parameter are only to be expected where specific sorbate–sorbent interactions exist, such as in polar sorbate– polar sorbent systems. The failure of the nonlinear partitioning model is explained by LeBoeuf and Weber (1999) in terms of its inability to predict these increased interactions of sorbing solutes within microvoids present within glassy matrices.

CORRELATIONS OF Kd TO MOLECULAR PROPERTIES. The relationships developed from partitioning theory provide a fundamental basis

for establishing the structure of correlations between Kd values and such molecular properties as solubility and octanol–water partition coefficient. An expression for solubility is obtained by considering a binary liquid organic–water system governed by eq 2.9. Under the assumptions that the solubility of water in the organic phase is small and the activity coefficient and mole fraction of organic in the organic-rich phase are unity, the following expression is valid:

1 S = γ ww,,11 (2.23) 1 V

where V w,1 = the molar volume of the organic saturated aqueous phase.

Sorption of Organic Compounds 21 Because the aqueous solubilities of most organic solutes of interest are small and do not change the molar volume of pure water to a significant extent, it is generally valid to assume that V w,1 = V w. Substituting for V w in eq 2.11, f γ wom, lnKS=− ln − lnγ om, w + lnom + ln 1 d 1 ρom,, w om w γ w,1 (2.24) V 1

In terms of the Flory–Huggins interaction parameter, eq 2.24 can be expressed as ργom wom, lnKSV=− ln − ln1 − (1 +χ ) − ln + ln 1 d γ w,1 (2.25) fom 1

By eq 2.25, the distribution coefficient is a function of molecular parameters (solubility and molar volume), a solute–organic matter interaction parameter, and properties of the sediment (organic matter density and mass fraction). The last term in eq 2.25 can be neglected when the presence of organic matter in w,om w,1 the aqueous phase does not significantly influence its activity (1 = 1 ). This assumption was made by Chiou et al. (1983).

Correlations with the Octanol–Water Partition Coefficient. The octanol–water partition coefficient quantifies the partitioning of an organic compound between two immiscible liquids. The magnitude of this coefficient has been shown to be a useful first-order predictor of the tendency of an organic compound to partition between water and soil and sediment organic matter. This is because, at least in part, the same molecular factors govern partitioning in the two systems (Schwarzenbach et al., 1993). The octanol–water partition coefficient, Kow, is defined as

oct, w = C1 Kow w, oct (2.26) C1

oct,w w,oct where C1 and C1 = the concentration of the organic compound in the water-saturated octanol phase and the octanol-saturated water phase, respectively.

Following Chiou and Schmedding (1982), we will first show that Kow is related to the organic compound solubility and then use that as a route to relate Kow with Kd. Equating the organic compound fugacity in each phase and using the equality xi = Ci V given in eq 2.10, eq 2.8 applied to this system provides the following relationship at equilibrium:

ˆˆw,,,,,,,, oct=== w oct w octγγ w oct oct w oct w oct w oct w fCVffCVf11 111111(2.27)

Grouping the concentration terms provides an expression for the octanol– water partition coefficient in terms of activity coefficients and phase molar volumes

22 Handbook on Sediment Quality Coct, w V w,, octγ w oct K ==1 1 ow w, oct oct,, wγ oct w (2.28) C1 V 1

Under the assumption that the molar volume of water is unaffected by the presence of small amounts of either organic compound or octanol, V w,1 = V w,oct = V w, eq 2.23 can be solved for the molar volume of water and substituted into eq 2.28

Coct, w 1 γ w, oct K ==1 1 ow w,, octγ w 1 oct,, wγ oct w (2.29) CSV11 1

Equation 2.29 can be written in logarithmic form

γ w, oct lnKS=− ln − lnγ oct,, w − ln V oct w + ln 1 ow 1 γ w,1 (2.30) 1

Under the assumption that the activity coefficient of the organic compound in the aqueous phase does not depend on concentration and is not affected by the w,1 w,oct presence of a small amount of octanol, then 1 is equal to 1 , and the last term in eq 2.30 drops out. Schwarzenbach et al. (1993) plot log Kow versus log S data for a wide range of organic compounds and demonstrate that, for most compounds having solubilities greater than approximately 10–6 mol/L, oct,w calculated values of 1 are between 1 and 10. Larger, less soluble organics (e.g., PCBs, 1,1,1-trichloro-2, 2-bis (p-chlorophenyl)ethane, PAHs) have either higher octanol-phase activity coefficients, enhanced solubilities in octanol-saturated water, or both.

The expression for ln Kd, from either eq 2.24 or 2.25, can be combined with eq 2.30 to yield a relationship between Kow and Kd. Solving eq 2.30 for –ln S and substituting the result into eq 2.24, provides the desired result

f γ wom, lnKK=+ ln lnγγoct,,, w + ln V oct w − ln om w + lnom + ln 1 d ow 11ρom,, w om w γ w,1 (2.31) V 1

As before, the last two terms in eq 2.31 generally can be neglected. In

practice, correlations developed between Kow and Kd (or Koc ) are expressed in a simpler fashion

ln Kd = a ln Kow + b (2.32)

where a and b = empirical constants. As is evident by inspection of eq 2.31, the empirical constants that appear in such correlations should depend, to some extent, on the class of compound evaluated (due to variations in activity coefficients in the octanol and organic- matter phases) and the properties of the organic matter investigated. There are

many correlations available in the literature to estimate Kd or Koc from Kow. When chemical class-specific and geosorbent domain (e.g., sediment versus aquifer soil) specific correlations are available, they are preferred. Examples

Sorption of Organic Compounds 23 are a correlation developed for chloro- and methyl-benzenes (Schwarzenbach and Westall, 1981; N = 13, r 2 = 0.95)

3 log Koc (cm /g oc) = 0.72 log Kow + 0.49 (2.33)

And a correlation developed for benzene and PAHs (Karickhoff 1981; N = 10, r 2 = 1.00)

log Koc (cm3/g o.c.) = 1.00 log Kow – 0.21 (2.34)

Other class-specific correlations have been tabulated by Schwarzenbach et al. (1993). In the absence of such correlations, a non-class-specific correlation such as the one developed by Baker et al. (1997) can be used. Their equation is based on 72 different chemicals from 11 classes of organic contaminants.

The correlation is valid for log Kow values ranging from 1.7 to 7.0. The lower limit of log Kow was chosen to ensure that partitioning to organic matter is a dominant mechanism of sorption, whereas the upper limit was chosen because analytical error creates high uncertainty for more hydrophobic compounds. The use as a non-class-specific descriptor was shown to be superior to correlations based on molecular connectivity indices and linear solvation energy relationships. The correlation is written (N = 72, r 2 = 0.91)

log Koc (cm3/g o.c.) = 0.903 log Kow + 0.094 (2.35) where the 95% confidence interval on the estimate of Koc is given by

12/ ⎛138+−(logK 3 . 92 )2 ⎞ ±066. ⎜ ow ⎟ (2.36) ⎝ 136 ⎠

Correlations between Koc and the octanol–water partition coefficient, Kow,are useful for making first-order estimates of partitioning behavior, but these should be used with caution. If the isotherm displays some nonlinearity at low solution-phase concentrations, the Kow -derived Koc value can be significantly underestimated at low concentrations, in some cases by a factor of 3 to 5. In addition, there is evidence that significant differences between Koc values (e.g., factor of 3) can occur for different soils from different origins (Garbarini and Lion, 1985, and Rutherford et al., 1992). Kile et al. (1995) and

Chiou et al. (1998) found that Koc values for sediments were approximately 2 times higher than those found for soils although, for the sorbents studied, there was consistency within a geosorbent class (i.e., either soil or sediment). Finally, if the soil or sediment phase of interest has been previously contaminated, it may display a high Koc that will not be accurately predicted by correlations developed for uncontaminated sediments (Kile et al., 1995).

ISOTHERM MODELS. Sorption isotherms are mathematical functions used to describe the equilibrium partitioning of a chemical (sorbate) between the

24 Handbook on Sediment Quality solution phase and a sorbent phase, at constant temperature (eq 2.5). The simplest possible sorption isotherm is the linear isotherm, which results when

Kd is constant. In many systems, it has been observed that the ratio of the sorbed and aqueous-phase concentrations (i.e., Kd) is not constant, and a nonlinear model is required to describe the isotherm relationship. One of the simplest adsorption isotherms is the Langmuir model, originally developed to describe the adsorption of gases by solids (Langmuir, 1918). Under the assumptions of a homogeneous surface, negligible interactions between adsorbed molecules, and single-layer adsorption, the functional form is typically written as QKCo q = e e + (2.37) 1 KCe Where Qo = the adsorbent capacity (mol/kg), and K = an equilibrium constant (L/mol), related to the energy of adsorption, and other terms have been defined previously in this section.

The Langmuir isotherm is based on an adsorption reaction in which a dissolved sorbate molecule A and a vacant surface site S form a sorbed “species” A ≡ S A + S → A ≡ S (2.38)

The solute concentration in the sorbed phase (A ≡ S, mol/L2) can be expressed 2 1 as qe by multiplying by the specific area, as (m /M sorbent). The solute concentration in the sorbed phase can be written in terms of the reaction equilibrium constant, K [A ≡ S] = K[A][S] (2.39)

where [A] = the solution-phase equilibrium concentration of species A (mol/L3),

which corresponds to Ce in eq 2.37. The functional form of eq 2.37 is obtained by solving for [A ≡ S] after expressing the concentration of vacant surface sites, [S], in terms of the total

concentration of surface sites, [ST]

[A ≡ S]= K[A]{[ST] − [A ≡ S ]} (2.40)

Sorbed-phase concentrations [A ≡ S] and [ST] can be expressed on a sorbent 0 mass basis (i.e., expressed as qe and Q ) by multiplying by the specific surface 2 area, as (m /kg sorbent). The rate of Langmuir adsorption, ra, is second order, ra = ka [A][S] while the desorption rate, rd, follows first-order kinetics, rd = kd [A ≡ S]. At equilibrium, the rate of adsorption equals the rate of desorption

Ka[A][S] = kd[A ≡ S] (2.41) Where

ka = the adsorption rate constant, and kb = the desorption rate constant.

Sorption of Organic Compounds 25 This yields a kinetic interpretation for the equilibrium constant, K, as the ratio of the adsorption and desorption rate constants

[]AS≡ k K = = a (2.42) [][]AS kd

The equilibrium constant K is often written as an “energy parameter”, b in eq 2.37. The Langmuir model was extended to include multilayer adsorption by Brunauer et al. (1938); this isotherm is typically referred to as the BET model, and is widely used to correlate gas adsorption data and determine the surface area of porous solids using such data. However, the BET model is not commonly used to describe uptake of organic compounds by soils and sediments. Many geosorbent surfaces are not homogeneous, and isotherms based on such assumptions typically do not describe uptake by soils and sediments accurately over large ranges of solution-phase concentration. There have been several approaches to incorporating the effects of heterogeneous surfaces on the adsorption isotherm. Freundlich (1926) found that a power law function could fit a wide variety of gas adsorption data, and his was an early attempt to model uptake by heterogeneous surfaces empirically. Like the Langmuir isotherm model, the Freundlich model is also a two-parameter function

n qe = KFC e (2.43)

Where the preexponential factor, KF, is known as a capacity parameter because it equals the uptake when the solution concentration is unity and the exponent, n, is known as an affinity parameter and is related to the spread of the site energy distribution, as will be discussed below.

The Generalized Langmuir Isotherm and Its Simplifications. Theoretically, if the local adsorption isotherm and the site-energy distribution function are known, the integral representation of the overall isotherm may be solved to yield a corresponding form for the overall adsorption isotherm; this approach was taken by Halsey and Taylor (1947) to derive the Freundlich isotherm based on a Langmuir local isotherm and an exponential site-energy distribution. Conversely, the mathematical forms of the local and overall isotherm models may be selected, and the integral equation may be solved to derive the corresponding site-energy distribution; this approach has been taken by Misra (1970), Sips (1948 and 1950), and Tóth et al. (1974). It has been shown that the isotherms derived by both of these analytical approaches represent simplifications of a more general isotherm equation, the Generalized Langmuir (GL) isotherm, which can be written as follows (Jaroniec, 1983)

n ⎡ ()bC m ⎤ m qQ= o ⎢ e ⎥ (2.44) e + m ⎣1 ()bCe ⎦

26 Handbook on Sediment Quality Where Qo = the adsorbent capacity; n and m = site-energy heterogeneity parameters (each taking values from 0 to 1); and b = a Langmuir-type adsorption energy parameter that incorporates a characteristic site energy Eo,

(Eo /RT) b = boe (2.45)

The constant bo is essentially a frequency factor, and Eo is the energy corresponding to the maximum site frequency, which determines the position of the energy distribution function on the energy axis. For a symmetric, quasi- Gaussian distribution, Eo represents the mean site energy whereas, for an exponential distribution, Eo represents the minimum site energy. The parameters m and n are heterogeneity parameters: the parameter m characterizes the shape of the site-energy distribution in the direction of lower values of E, whereas the parameter n characterizes the shape of the site-energy distribution in the direction of higher values of E (Derylo-Marczewska et al., 1984). For various combinations of limiting values of m and n, the Langmuir (L), Langmuir–Freundlich (LF), Toth (T), and Generalized Freundlich (GF) isotherms can be derived. The various isotherm forms are shown in Table 2.1. For completeness, the Redlich–Peterson equation is included because it is

Table 2.1 The GL isotherm and its simplifications.

Low- High- Isotherm concentration concentration No.a Code equation mn behavior behavior

m (n/m) o (bCe) o n o 1GL qe = Q ΄΅–––m (0,1) (0,1) Q (bCe) Q 1 + (bCe) o n Q (bCe) o n o 2LF qe = –––n m = n (0,1) Q (bCe) Q 1 + (bCe)

n o bCe 3GFqe = Q–––΄΅1 o n o 1 + bCe (0,1)Q (bCe) Q

o Q bCe o o 4L qe = ––– 1 1 Q bCe Q 1 + bCe o Q bCe o o 5T qe = ––––m (1/m) (0,1) 1 Q bCe Q (1 + (bCe) )

o n o 6F qe = Q (bCe) Q (bCen) ϱ

o Q bCe o ϱ 7RP qe = –––m Q bCe 1 + (bCe) a Notes: Isotherm equations 2 through 5 are derived by substituting the appropriate m and n values into GL isotherm (No. 1). The Freundlich isotherm (No. 6) is the low-concentration limit of isotherms 1 through 3. The Redlich–Peterson isotherm equation (No. 7) is obtained from a

Sorption of Organic Compounds 27 similar in form and can be derived from the GL equation by expanding the m m denominator with a Taylor series with respect to Ce about the point Ce = 0 (Derylo-Marczewska et al., 1984). Kinniburgh (1986) has discussed these isotherms and strategies for estimating their parameters using nonlinear regression. The values of m and n listed in Table 2.1 designate the values or range of values of these parameters that, when substituted into the GL model, result in the isotherm equation shown in the corresponding row. The column labeled high-concentration behavior shows the isotherm form that results when bCe >> 1, and the column labeled low-concentration behavior shows the isotherm form that results when bCe << 1. All of the isotherms in Table 2.1 satisfy the condition qe(Ce = 0,T) = 0, and all of the direct simplifications of the GL equation (LF, GF, and T isotherms) satisfy the condition o qe(Ce = ∞, T)=Q . The GL model reduces to the common Langmuir model when n = m = 1, corresponding to a uniform surface energy. At low surface loading, corresponding to low solute concentration (i.e., bCe << 1), the Langmuir isotherm o exhibits a linear region at low concentration (Henry’s region), with Kd =Qb. As solute concentration increases, asymptoticality approaches the sorbed- phase capacity, Qo. The behavior of the three-parameter isotherms differs significantly from the local Langmuir isotherm. The GL model reduces to the Toth isotherm when n = 1, and the F model when m = 1. Both of these models are asymptotic to the sorbed-phase capacity, but only the Toth isotherm predicts linear partitioning at low concentration (Henry’s region). The Redlich–Peterson isotherm also predicts a Henry’s region, but exhibits an infinite adsorption capacity at high concentrations, behavior that is physically unrealistic. The GL, LF, and GF isotherms all exhibit a limiting capacity, but none predicts a Henry’s region. Instead, these models reduce to the common Freundlich model in the n low (bCe << 1) solution-phase concentration region

(/nm ) ⎡ ()bC m ⎤ qQ= o ⎢ e ⎥ ≅==QbCo[( )mnm ](/ ) QbC on ( )n KCn (2.46) e + m e e Fe ⎣1 ()bCe ⎦

The classical Freundlich equation exhibits neither a linear region at low concentration nor a limiting capacity; nevertheless, it has proven successful in describing adsorption data in natural (soils and sediments) and engineered (e.g., activated carbon) systems over ranges of concentration of interest in environmental applications. In many such applications, concentrations may range from low :g/L to several orders of magnitude higher. In this range, adsorption data for many compounds are linear on log–log coordinates and exhibit neither a Henry’s region nor saturation behavior. The Freundlich isotherm is less successful, however, when the concentration range is broad- ened significantly. The behavior of the various isotherm equations relates, in part, to their underlying site-energy distributions. The Toth and LF equations both have quasi-Gaussian distributions. The LF distribution is symmetrical, whereas the

28 Handbook on Sediment Quality Figure 2.2 A comparison of several different isotherm models over a wide range of concentrations. All isotherms were fit to the same synthetic data that was linear on log–log coordinates over a concentration range of 1 to 1000 µg/L. The Freundlich isotherm shows an extrapolation of this synthetic data.

Toth distribution is widened toward lower energy values. The GF, Freundlich, and Redlich–Peterson isotherm equations have exponential distributions. When both heterogeneity parameters are equal to unity, all of the isotherms in Table 2.1 (except Freundlich) reduce to the Langmuir equation. When the Freundlich n value is equal to unity, the resulting isotherm corresponds to the linear (Henry) region of the Langmuir isotherm. In many cases, several of the isotherm equations shown in Table 2.1 will describe experimental data equally well, even though the shapes of the site- energy distributions are different. This is because over the concentration ranges commonly examined in engineering applications, the distribution functions and, therefore, the isotherm equations, are similar. This is one reason why the Freundlich isotherm has such widespread application for describing adsorption data. However, when experimental data are collected over a wider range of concentrations, the choice of isotherm equation becomes more critical, and a more detailed evaluation must be performed. These ideas are illustrated in Figure 2.2. The Langmuir, LF, GF, Toth, and Dubinin (discussed further in the next section) isotherms were fitted to synthetic data that were linear on log–log coordinates, described by the 0.558 Freundlich isotherm qe = 0.55Ce (with units of qe in µg/mg and Ce in µg/L). The fitted data ranged over 3 orders of magnitude, from 1 to 1000 µg/L,

Sorption of Organic Compounds 29 but the fitted isotherms were plotted over a wider range to illustrate their differences. All isotherms except the Langmuir model coincide closely over the fitted range but exhibit differences in behavior at (very) high and (very) low ranges of the data. Collecting data over these wide ranges is difficult because of analytical constraints at low concentrations and possible solubility limitations at high concentrations.

Potential Theory. The potential theory, as developed by Polanyi (1916) and later widely used in a treatment by Dubinin (Dubinin and Astakhov, 1971), postulates an adsorption space in the vicinity of the adsorbent surface and an adsorption potential, ε, equal to the decrease in potential energy of the adsorbate, relative to the bulk-phase chemical species. The adsorption space is like the atmosphere of a planet, with the adsorbate density increasing with proximity to the surface (Adamson and Gast, 1997). Therefore, adsorption results in enrichment of adsorbate molecules in the adsorption space. As the surface is approached, the potential may exceed that required for a phase change

Csat εnet = RT ln – (2.47) cbulk

In liquid systems, the net adsorption potential accounts for the displacement of solvent from the adsorption space. For nonpolar carbon surfaces, this is often taken as a constant (Xia and Ball, 1999), so that adsorption potential and net adsorption potential are proportional. It is typically assumed that the volume adsorbed can be attributed entirely to the volume of this condensed sorbate phase that forms in the volume contained between the solid surface and the equipotential surface (located at some distance above the solid surface), where the potential is just sufficient to cause phase change (Tien, 1994). Solutes that are liquids at the temperature of the sorption experiment are considered to be liquidlike in the sorbed phase, whereas those that are solids are expected to be solidlike in the sorbed phase. There is evidence that the liquidlike sorbed phase has a density nearly equal to the saturated liquid. However, it appears that, in many systems, the solidlike sorbed phase has a density lower than the pure compound because molecules do not “pack” as efficiently in adsorbent pores due to structural incompatibility between the crystal structure of the solid phase and the pore dimensions. The utility of the potential theory results, in part, from the fact that systems at the same value of ε are in corresponding states (Adamson and Gast, 1997). Therefore, by plotting the volume adsorbed versus the adsorption potential, a temperature-independent “characteristic curve” is obtained. This curve is determined by the structure of the adsorbent, assuming that sorbates do not interact specifically. Typically, the adsorption potential is normalized by a physicochemical property that reflects the adsorption mechanism (e.g., molar volume of the saturated liquid) so that the characteristic curves of different compounds may coincide. Xia and Ball (1999) found excellent correlation curves for benzene and several chlorinated benzenes (all liquids at the temperature of the sorption experiment) sorbed by a Delaware coastal plain

30 Handbook on Sediment Quality aquitard soil. In contrast, the data for 1,2,4,5-tetrachlorobenzene and several PAHs were not correlated as well. It was hypothesized that these sorbates, all solids at the temperature of the sorption experiment, did not pack as efficiently due to structural incompatibility between the crystal structure of the solid phase and the adsorbent pore dimensions. The Polanyi theory is essentially thermodynamic; no functional form

relating volume adsorbed and adsorption potential, Va = f(ε), was proposed. Dubinin proposed the following semiempirical relationship for microporous adsorbents:

o 2 Va = Va exp(–bε ) (2.48)

o where both Va, taken to be the micropore volume, and b are empirical parameters. Often, the exponent on the adsorption potential (here equal to 2) is also taken to be an adjustible parameter (e.g., Dubinin and Astakhov, 1971). Multiplication of the volume adsorbed by the liquid density yields an o isotherm in terms of the usual quantities, qe and Q . LeBoeuf and Weber (1996) and Xing and Pignatello (1997) found good fits of this equation to gas-

phase carbon dioxide (CO2) sorption by soil humic acids.

Combined Models. When the solid phase is heterogeneous and multiple sorption mechanisms may be operative, it is often desirable or necessary to combine several of the isotherm models described above to effectively capture these mechanisms and accurately describe uptake over wide ranges in solution-phase concentration, especially those less than about 10 to 20% of solubility. Weber et al. (1992) proposed the distributed reactivity model to relate the reactivity of natural soils and sediments to the particle-scale heterogeneity in their composition. Heterogeneity refers to the origin, type, geologic history, amount, and distribution of organic matter at the particle scale. When the solid phase is composed of regions having different reactivi- ties, sorption energy is not uniform, and the total uptake of solute is the sum of contributions from different regions of the solid phase. These regions may be characterized by the same sorption mechanism (e.g., partitioning) or by different mechanisms (e.g., partitioning and adsorption). Weber et al. (1992)

expressed the total uptake, qe,T, as the sum of linear (absorption) and nonlinear (adsorption components)

=+ qxqxqeT, ∑∑il, e, linearinl, e, nonlinear (2.49) i i

The linear uptake was modeled by distribution coefficients, whereas the nonlinear adsorption was modeled by the Freundlich isotherm

n m Σ Σ n qe,T = xi foci Koci Ce + xiKFi C ei (2.50) i i

Sorption of Organic Compounds 31 Figure 2.3 An example of a distributed reactivity isotherm. The heavy solid line represents a summation of the linear partitioning (dashed line) and Freundlich isotherms (light solid line) shown.

where xi = the mass fraction of a relatively (energetically) uniform component of the total sediment organic matter phase. Note that the linear part of the distributed reactivity model is typically expressed as a single mass-averaged distribution coefficient. Considering that it may be necessary to estimate model coefficients from a single adsorption isotherm, from a practical perspective, M is typically limited to 1 or 2. An example of a distributed reactivity isotherm is shown in Figure 2.3. Xing et al. (1996) proposed the application of the dual-mode model to organic compound uptake by geosorbents based on sorption in glassy polymers. In this model, the particle-scale heterogeneity is caused by two sorption domains, a linear dissolution and nonlinear “hole” or “microvoid” domain; sorption occurs simultaneously in these domains. The microvoids (or local inhomogeneities) constitute sites where specific adsorption occurs according to the Langmuir isotherm. The summation in the Langmuir term recognizes that the microvoids may have a distribution of energies

n o Qi bi Cei qeT = KpCe + Σ –– (2.51) i 1 + bi Cei

An example of a dual-mode isotherm is shown in Figure 2.4 for two Langmuir adsorption terms.

32 Handbook on Sediment Quality Figure 2.4 An example of a dual-mode isotherm. Two Langmuir isotherms o o 3 o o are used: Q1 = 4 mg/kg, b1 = 2 m /mg; Q1 = 4 mg/kg, b1 = 3 3 1 m /mg; Kp = 1 m /kg. Note that the nonlinear sorption capacity can be estimated by extrapolating the high-concentra-

tion linear region to the qe axis (after Chiou and Kile, 1998).

Xing et al. (1996) note that homogeneous polymers are generally modeled as having microvoids of a single energy (N = 1). The dual-mode sorption model was employed by LeBoeuf and Weber (1997 and 2000b), with N = 1, to describe the sorption of phenanthrene to eight natural organic matter samples and five synthetic polymers and by Huang et al. (1997), with N = 1 and N = 2, to describe the sorption of phenanthrene by several different natural geosorbents. These workers refer to the dual-mode model as the “dual reactive domain” model because it can be thought of as a simplification of the distributed reactivity model: the nonlinear (Freundlich) sorption term, representing uptake by a heterogeneous sorbent, is replaced by a Langmuir sorption, representing uptake by sites having a single sorption energy. Indeed, as will be shown in the next section, it is possible for the dual-mode model to exhibit behavior that closely resembles the Freundlich isotherm. It only takes a relatively few different terms in the Langmuir summation (i.e., different microvoid energies) for the behavior of the dual-mode model to approach quite closely that of the distributed reactivity model (with its Freundlich isotherm). Whereas Huang et al. (1997) found that the dual reactive domain model with two Langmuir terms fit the data better than a model with one Langmuir term, they note that the results are not readily interpretable unless the sediment organic matter is characterized in sufficient detail. Furthermore,

Sorption of Organic Compounds 33 as the number of model parameters increases, it can be difficult to find unique (singular) parameter sets using statistical search procedures. Xing and Pignatello (1997) note that some sorption data fit the dual-mode model and the Freundlich model equally well and that the Freundlich isotherm can be used as a less mechanistic but more convenient (because it has fewer parameters) surrogate for the dual-mode model. As Xia and Ball (1999) point out, when the number of sorption sites incorporated to the dual-mode model is limited to one or two, the shape of the resulting isotherm is distinct from that of the distributed reactivity model with its Freundlich isotherm. Evidence of the unique curvature of the dual-mode model, especially apparent on log qe –log Ce cordinates, as depicted schematically in Figure 2.5, is suggested by the data of Huang et al. (1997), LeBoeuf and Weber (1997 and 2000b), and Xia and Ball (1999). Xia and Ball (1999) compared the dual-mode model with a combined model based on the Polanyi–Manes approach (eq 2.48). The combined model took the following form

⎡ ε ⎤b a⎢ ⎥ V (2.52) =+o ⎣ s ⎦ qKCQepe 10

Where Qo = the sorption capacity,

Vs = the solute molar volume, and a and b = empirical coefficients.

Both the dual-mode and Polanyi–Manes models were able to fit the measured sorption data well and had comparable mean-weighted squared-error values. This was attributed, in part, to the similar shape of the Langmuir and Polanyi models, in the sense that each exhibits a plateau at high solution-phase concentrations.

MATERIALS AND METHODOLOGIES FOR COLLECTING SORPTION DATA

Equilibrium-phase distribution (sorption isotherm) data are typically obtained using one of two methods. The most commonly used is the batch-equilibration method. Fixed-bed (column) equilibration techniques are also used and offer the advantage of simultaneously providing data that can be analyzed to extract rate information. These two general approaches will be briefly introduced and followed by a discussion of experimental methodologies and aspects of sorption equilibria experiment design.

BATCH AND COLUMN EQUILIBRATION TECHNIQUES. Batch Equilibration Techniques. In the batch equilibration method, known amounts of the solid-phase sorbent and solution-phase contaminant are added

34 Handbook on Sediment Quality (a)

(b)

Figure 2.5 A comparison of predictions made using the dual-mode (or dual-reactive domain) model having a (a) Langmuir isotherm adsorption component with the distributed reactivity model having a (b) Freundlich isotherm adsorption component.

Sorption of Organic Compounds 35 to a batch reactor, which is kept completely mixed for a predetermined equilibration period. Batch reactors are typically glass vials, bottles, centrifuge tubes, or ampoules. Once the system has reached equilibrium (whether defined as a rigorous thermodynamic state or defined operationally), the solution and solid phases are separated, typically by centrifugation or filtration (this is discussed in more detail later in this section). Most commonly, only the equilibrium solution-phase concentration, Ce, is measured, and the solid-phase uptake, normalized by the dry mass of solid, qe, is calculated by

VC()− C = aq o e qe (2.53) Ms Where

Vaq = the aqueous solution volume, assumed to remain constant during the experiment;

Co = the initial concentration of contaminant; and Ms = the mass of sorbent in the reactor.

Each batch reactor represents one point on the sorption isotherm. Several reactors are used to yield a complete isotherm, each having different initial solute concentrations, solid-phase concentrations, or both. A possible disadvantage of this approach is that solid-phase uptake is quantified indirectly and incorporates errors associated with the measurement of two solution-phase concentrations (Co and Ce). To overcome this disadvantage, some researchers prefer to measure the solid phase directly. Measurement of solid-phase concentrations typically involves a Soxhlet-extraction step to recover the sorbed contaminant. A possible disadvantage of this approach is that if the sorbate has a tendency to sorb irreversibly (e.g., phenols, anilines, hydroxy- lated PAHs), uptake may be underestimated. A batch equilibration technique to measure sorption by colloidal material (often a solution of dissolved and colloidal organic matter) uses a dialysis bag that contains the sorbent phase and separates it from a surrounding aqueous solution; both the bag and the surrounding solution are contained within a single-batch reactor (Carter and Suffet, 1982; Chin and Weber, 1989; and Schlebaum et al., 1998). As with other batch techniques, the total amount of sorbate in the system is known. The properties of the dialysis bag are chosen so that it provides a containment barrier to the sorbent but allows the organic contaminant to move freely between the surrounding solution and the bag interior. Dialysis bags having molecular-weight cutoffs of approximately 500 to 1000 Da are typically used. As with other batch equilibration techniques, the batch reactors are sealed, kept well mixed, and allowed to reach equilibrium. Once equilibrium is reached, the concentration of the organic compound is measured inside and outside the bag. Solvent extraction of the contents of the dialysis bag, which contains the colloidal sorbent phase, yields the total concentration of compound, CT, which includes both free and bound organic matter. The concentration in the solution surrounding the dialysis bag yields the equilibrium concentration, Ce. Therefore, the mass of organic

36 Handbook on Sediment Quality compound that is taken up by the colloidal sorbent, qeMcolloid, is equal to Vb(CT − Ce), where Vb is the volume of solution contained within the dialysis bag. The colloid-phase uptake, qe, is thus calculated by

− − − = VCbT()()() C e= VCbT C e= CCTe qe (2.54) Ms CVcolloidb C colloid

where Ccolloid = the dissolved colloid concentration, typically expressed in terms of organic carbon. Initial experiments are needed to determine the required time to reach constant concentrations inside and outside the dialysis bag; periods of 3 to 6 days have been used (Carter and Suffet, 1982; Chin and Weber, 1989; and Schlebaum et al., 1998). In addition, control experiments are necessary to evaluate sorbate uptake by the dialysis bag. A final batch equilibration approach, used to measure partitioning to dissolved and colloidal material, is the solubility enhancement technique (Chiou et al., 1986). In this method, the organic compound of interest is added to a batch reactor in an amount well above its solubility. This is followed by the addition of aqueous solution containing a known concentra-

tion of dissolved and colloidal material, Ccolloid. The compound solubility, S,is measured in the absence of colloidal material, and the apparent solubility, S*, is measured in the presence of the colloidal material. The apparent solubility is equal to the actual solubility plus the amount of contaminant bound to the colloidal material

S* = S + qe Ccolloid = S + Kp SCcolloid = S(1 + Kp SCcolloid) (2.55)

The experiment is repeated using several different Ccolloid values, and Kp is determined from a plot of S*/S versus Ccolloid. This plot will be a straight line with an intercept of unity if Kp is constant.

Fixed-Bed (Column) Reactor Techniques. The fixed-bed reactor technique for measuring isotherm (and rate) data involves packing a column with a known mass of adsorbent; feeding the column at a known flowrate, Q, with a solution containing the contaminant of interest; and recording the effluent concentration from the fixed bed as a function of time. Note that the adsorbent bed is often shorter than the overall column and is held in place with glass beads or glass wool. Before introducing the feed solution, the columns are typically flushed with water containing the desired background matrix (e.g., groundwater, rainwater, etc.) until completely saturated; typically 50 to 150 pore volumes are sufficient (McBride et al., 1992). Once saturated, feed solution is introduced to the column until the adsorbent bed is exhausted, at

which time tx, the effluent concentration, equals the feed concentration. The uptake of contaminant by the solid phase can be calculated from a mass balance on the reactor. The difference between the mass fed to the reactor,

QCfeed tx, and the mass eluted from the reactor from t = 0 to t = tx, is equal to the mass taken up by the solid phase, plus the mass remaining in the column

Sorption of Organic Compounds 37 pore space at exhaustion. Therefore, uptake, qe, is calculated from the breakthrough curve by

t x ͵ feed Q (Cfeed – Ceffluent)dt – VporeC qe = –––––––o (2.56) Ms where Vpore = the entire pore space in the column, which can be determined from a conservative tracer experiment.

The mass of adsorbent in the reactor, Ms, is equal to ρp (1 – ε) AL, where ρp is the particle density, ε is the bed porosity, A is the column cross-sectional area, and L is the length of the packed bed within the reactor. Column experiments are often performed at pore water velocities of approximately 40 cm/h (Piatt and Brusseau, 1998). Each point on the isotherm requires a separate breakthrough curve; several breakthrough curves can be obtained from the same column setup by stepping the feed concentration to a higher value after the adsorbent bed has been exhausted at a lower one. If it is known, or can be reliably assumed that the uptake is linear over the concentration range of interest, the distribution coefficient, Kd, can be determined from one column experiment by fitting the breakthrough curve with an appropriate solution of the advection–dispersion equation. This is discussed in more detail in the Sorption Rate Processes section. Under the assumptions of linear partitioning and local equilibrium (see eq 2.75), analytical solutions to the advection–dispersion equation are available (e.g., Van Genuchten and Parker, 1984). The measurement of sorption isotherms using either the batch or column technique is conceptually straightforward; however, there are numerous details that must be addressed to ensure high-quality data, avoid erroneous results, and eliminate artifacts. It is important to (1) obtain a representative sample of sorbent; (2) use a solution-phase composition that is representative of the system being modeled; (3) select solid-to-solution ratios to achieve a sufficient range of equilibrium concentrations; (4) design the sorption reactor (including selection of reactor materials) to minimize losses; (5) determine the equilibration time; (6) select a phase-separation technique to achieve adequate separation while minimizing losses; and (7) determine whether it is necessary or desirable to dry the soil or sediments and, if so, determine any effect on sorption uptake.

OBTAINING REPRESENTATIVE SAMPLES. The need to obtain representative sorbent samples applies to both batch and column techniques. Sediment samples to be evaluated for their sorptive uptake should be collected in a quantity sufficient for all foreseen experiments. Sediment samples are typically air dried, then sieved to remove large particles and agglomerates and mixed well to produce as homogeneous a sample as possible. Representative samples can then be obtained using a variety of techniques, including coning and quartering and using commercially available sample reducers and riffle- type splitters. Because uptake is typically normalized by the dry weight of

38 Handbook on Sediment Quality adsorbent, representative samples should be oven dried to determine the moisture content of air-dried samples used in isotherm experiments.

COMPOSITION OF THE SOLUTION PHASE. Sorption experiments should be designed to achieve equilibrium solution-phase concentrations that span several orders of magnitude. Using the batch equilibration technique, this is done by selecting the appropriate initial solute and sorbent concentrations. Using the column technique, the feed concentration must be varied over the desired range because, at exhaustion, the feed and equilibrium concentrations are the same. Spanning a wide range of equilibrium concentrations is especially important when the investigator plans to extract mechanistic information from the isotherm data. For example, low concentrations (on the order of low parts per billion) may be necessary to identify nonlinearity and quantify the capacity of the nonlinear compartment. If isotherm data are needed to provide input to fate and transport models applicable to a highly contaminated site, it may be desirable to extend the upper limit of the

concentration range to near saturation (fractional solubility, Ce /S, approaching unity). Kinniburgh (1986) recommends spacing datapoints uniformly along an arithmetic concentration scale when errors are independent of concentration and uniformly along a logarithmic scale when errors increase with concentration. As the latter case is likely in many systems (e.g., losses typically increase with concentration), and because isotherm data should span several orders of magnitude in concentration, uniform spacing on a logarithmic scale is generally recommended. The mass of a compound that can be introduced to a batch reactor in the solution phase is limited by its aqueous solubility. To increase the mass of a low-solubility hydrophobic compound introduced to a batch reactor, a plating method can be employed (Karickhoff and Brown, 1979). First, the organic chemical is dissolved in acetone (or other suitable volatile solvent) and added to empty batch reactors. Then, the solvent is allowed to evaporate before adding the sorbent and solution phases. The concentration of the organic compound in the organic solvent should be high enough to ensure that the volume of the spike will be small, thus minimizing the required evaporation time. The equivalent (hypothetical) initial concentration for a plated compound can be calculated from the mass plated and the total solution volume added to the reactor. A significant challenge that must be addressed when

measuring isotherms for low-solubility (high Kow) compounds is the analytical requirement to measure low solution-phase concentrations. This is especially true considering that equilibrium solution-phase concentrations should span several orders of magnitude. Hydrophobic chemicals are often delivered to the sorption reactor (using the batch equilibration method) or to the feed solution (using the column technique) as a small aliquot of a concentrated stock solution made up in a solvent such as methanol. Therefore, the solution-phase composition includes a small amount of this solvent. It has been shown that the effects of cosolvent are negligibly small when the concentration of methanol does not exceed approximately 10–3 mole fraction (Ball and Roberts, 1991, and McGinley et

Sorption of Organic Compounds 39 al., 1993) or approximately 0.2% volume fraction (Nkedi-Kizza et al., 1985). However, the cited studies examined only a limited number of compounds, and it is considered good practice to investigate the effects of cosolvent on sorbate uptake in preliminary experiments. As discussed above, the plating method can be used to eliminate the presence of cosolvents in the sorption reactor. In addition to the concentration of the sorbate of interest, the solution-phase composition includes the background organic matter and inorganic-ion matrix and any chemicals added to inhibit microbial activity. If sorption data are required to make assessments of the behavior of a contaminant at a particular site, the water composition used in the sorption experiments should represent site conditions as closely as possible. It is perhaps most desirable to use water sampled directly from the site itself, although special precautions against the influence of microbial activity may be required. If sorption is being studied outside the context of a specific site, the background matrix should be chosen to be representative of a particular geochemical domain and minimize potential artifacts (unless such “artifacts” are effects that are characteristic of a particular domain, e.g., high ionic strength found in estuaries). Background matrices that are representative of many groundwater conditions include calcium chloride; typical concentrations used range from 0.005 to 0.01 M (Xia and Ball, 1999; Xing et al., 1996; and Young and Weber, 1995). Some investigators also provide buffering capacity, often using carbonate (e.g., McGinley et al., 1993), phosphate, or acetate buffers, depending on the desired pH range of the isotherm.

CRITERIA FOR ACHIEVING EQUILIBRIUM. When the batch equilibration technique is used, preliminary rate studies should be performed to estimate the time required for equilibration. Recent studies have shown that the time required to reach equilibrium may depend on sediment organic matter composition as well as concentration (Huang et al., 1998). For example, at a solution-phase equilibrium concentration of 500 µg/L, two sediment samples reached equilibrium in a matter of hours, whereas 90 days were required when the solution-phase equilibrium concentration was only 5 µg/L. A Lachine shale sample (180-µm particle size) did not reach equilibrium even after 368 days, whereas a kerogen sample extracted from the same shale reached equilibrium in approximately 90 days. Nyman et al. (1997) suggested that equilibrium was achieved within 5 days for sandy Lake Macatawa (Holland, Michigan) sediments and within 6 to 8 days for silty-clay Lake Macatawa sediments. Common criteria for equilibrium are based either on the time required for the solid-phase loading to reach a given percentage of qe (e.g., q(t)/qe = 0.99) or the time required for the solution-phase concentration C(t) to decrease to within some small percentage of Ce (e.g., C(t) = 1.01 Ce). These two criteria are not equivalent and are not equally appropriate for all experimental conditions (Randtke and Snoeyink, 1983). When the sorption experiment is designed such that removal of solute from solution at equilibrium is small

(i.e., low sorbent amounts leading to Ce /Co > 0.9), the solution phase will

40 Handbook on Sediment Quality appear to reach equilibrium quickly, but the solid-phase criterion will not be met. Randtke and Snoeyink (1983) present modeling results to confirm this

point. For example, when removal from solution was small (Ce /Co equal to 0.9), the solution-phase equilibrium criterion was met (i.e., C[t] = 1.001 Ce ) although the solid phase did not reach equilibrium (i.e., q[t]/qe was only equal to 0.91). Conversely, when removal from solution was large (Ce /Co equal to 0.1), the solid phase reached equilibrium (i.e., q[t]/qe = 0.999), but the solution phase did not (i.e., C[t] = 1.09 Ce ). Therefore, it is important to match the appropriate criterion for equilibrium with the experimental conditions. Furthermore, as this analysis points out, extremes in the designed

values of Ce /Co should be avoided. It is recommended that solute removal from solution be at least approximately 20% (Ce /Co < 0.80) but not much more than approximately 85% (Ce /Co > 0.15). It should be noted that, when Ce /Co = 0.5, both solution- and solid-phase equilibrium criteria are met simultaneously.

LOSS MECHANISMS AND CONTROLS. For both batch equilibration and column techniques, preliminary studies should be conducted to assess whether any changes in the solution-phase concentration occur as a result of loss mechanisms. Hydrophobic chemicals may adsorb to reactor walls and volatile compounds may slowly release from sorption reactors. In addition, many compounds can diffuse into the poly(tetrafluoroethylene) (Teflon) reactor components, for example, liners and septa used to seal screw caps and other vials (Huang et al., 1998, and Lion et al., 1990). Several different control reactors, containing solute but no sorbent and prepared the same way as sorbent-containing reactors, should be evaluated (e.g., Deitsch et al., 2000; LeBoeuf and Weber, 1997; and Nyman et al., 1997). Several different initial solute concentrations, spanning the range of equilibrium concentrations expected in isotherm experiments, should be evaluated to determine if losses depend on solute concentration. These experiments should be conducted for an equilibration time equal to that of the isotherm experiments. Losses of less than 5% in control reactors are generally considered small enough to neglect in further analysis; if larger losses are found, measured uptake values should be corrected to reflect their magnitude. In all cases, losses should be minimized and the investigator should attempt to identify an experimental system that keeps losses below 5%. One potentially important loss mechanism is microbial degradation. Many investigators use one or more approaches to minimize microbial activity during the sorption experiment. These include autoclaving, irradiation, the addition of chemicals toxic to microorganisms (e.g., mercuric chloride

[HgCl2] and sodium azide [NaN3]), and the addition of chemooxidants (e.g., ethylene or propylene oxide). Dao et al. (1982) evaluated the effects of autoclaving, cobalt-60 irradiation, oven-drying at 90 °C, and the addition of propylene oxide on the adsorption of aniline and diuron by soil. All techniques modified soil characteristics (such as organic carbon content and pH) and affected the adsorption of aniline and diuron. In general, propylene oxide and oven-drying increased diuron adsorption. Aniline-hydrochloric acid (HCl)

Sorption of Organic Compounds 41 adsorption decreased (in general) using three soils and six different treatments, except for oven-drying, which increased aniline sorption significantly. A study comparing the effects of autoclaving (121 °C for 1 hour) and gamma-irradiation (3.0-megarad dose) on the effects of pure culture growth of both Arthrobacter sp. and Pseudomonas sp. was performed by Salonius et al. (1967). They measured effects of sterilization on soil properties but did not study subsequent effects on sorption. As a result of sterilization, they found changes in aggregate stability, specific conductance, cation content ([Mg],

[Ca], [Na], [K], [B], [Mn], and [Cu]), pH, nitrate (NO3-N), ammonia (NH4-N), and soluble carbohydrate. The effects of both treatments were greater when moist soil was treated. They found that irradiation induced less change than autoclaving and recommended this approach. Wolf et al. (1989) demonstrated the influence of sterilization methods on selected microbiological, physical, and chemical properties of soil but did not study subsequent effects on sorption. These researchers used cobalt-60 irradiation, propylene oxide, mercuric chloride, sodium azide, and autoclaving (2× or 3×) as methods to reduce microbial activity in soils. They demonstrated that none of the above techniques completely eliminated the microbial population. They also found that none of these techniques influenced the soil CEC values, but propylene oxide significantly reduced the apparent surface area. The soil pH was found to increase by treatment with propylene oxide and sodium azide. Extractable manganese was increased upon autoclaving, oven-drying, cobalt-60 irradiation, and treatment with sodium azide. Of the various treatments tested, all but mercuric chloride resulted in substantial physical or chemical changes in the soils studied. In contrast, McLaren et al. (1962), in a study of enzyme activity (phosphatase, urease) in the presence of suitable nutrients after soil sterilization, found no evidence of changes in soil properties when cobalt-60 irradiation was used. Lotrario et al. (1995) studied several techniques to reduce biological activity such as gamma-irradiation, autoclaving, and amendment with either mercuric chloride, sodium azide, or ethylene oxide. Gamma-irradiation and autoclaving had a measurable effect on changes in the grain size distribution, possibly as a result of aggregation of soil particles. However, other treatments had little effect on measured soil properties. Only autoclaving was evaluated for its effect on the sorption of 1,1,2-trichloroethylene using the batch equilibration method; no significant effect was observed. Many workers use either sodium azide or mercuric chloride to inhibit biological activity. Sodium azide is typically added to sorption reactors at concentrations of 100 to 200 mg/L (Xia and Ball, 1999; Xing et al., 1996; and Young and Weber, 1995). Xing and Pignatello (1997) found that addition of mercuric chloride at a concentration of 200 mg/L had no effect on the uptake (1-day isotherm) of the pesticide metolachlor by a Florida peat soil. Huang et al. (1998) plated mixtures of sediment and phenanthrene solution after equilibration in the presence of 100 mg/L sodium azide for a period of 368 days. Plates containing growth medium, nutrients, and phenanthrene as the sole carbon source were inoculated at 32 °C for 2 days. No biological activity

42 Handbook on Sediment Quality was observed, and it was concluded that the azide was effective in inhibiting biodegradation of phenanthrene during the 368-day sorption experiment.

REACTORS AND REACTOR COMPONENTS. The most common reactors used in the batch equilibration method are borosilicate glass vials, bottles, centrifuge tubes, or flame-sealed glass ampoules (e.g., Huang et al., 1997, and Johnson et al., 1999). Stainless steel centrifuge tubes have been used (Means et al., 1980) and offer the advantage of being able to withstand high g forces during centrifugation to facilitate solid–solution phase separation. Often, reactors are sealed with Teflon-lined septa and, in some cases, aluminum or lead foil is added as a barrier between the Teflon lining and the solution phase (Huang et al., 1997; Leboeuf and Weber, 1997; and Xia and Ball, 1999). The foil minimizes the diffusion of hydrophobic contaminants into the Teflon, preventing a loss mechanism that can be important during long-term experiments. The batch reactor volumes are selected to provide a desired liquid-to- solid ratio; often, the reactors are filled with no headspace to minimize any volatilization losses. Reactors should be equilibrated under dark conditions to prevent possible photolysis reactions; for example, some workers wrap the batch reactors in aluminum foil. When the column reactor technique is used, columns, fittings, and tubing are typically made of stainless steel or glass (McBride et al., 1992, and Piatt and Brusseau, 1998).

PHASE SEPARATION. After equilibration in a batch reactor, phase separation is required; it may also be necessary in some studies involving a column reactor system if it is desired to extract the solid phase. If adequate phase separation is not achieved, measurements of solution-phase concentrations will include contaminant bound by nonsettling particles and colloids and

will thus be erroneously high. As a result, the distribution coefficient, Kd, will be erroneously low. This is one explanation for the solids-concentration effect,

where the measured Kd decreases as the solid-to-solution ratio increases. As this ratio increases, the solution-phase concentration of nonsettling particles and colloids also increases (Gschwend and Wu, 1985). The most common method used for phase separation is centrifugation. Typical centrifugal forces used range from approximately 550 to 1400 g for periods ranging from 15 to 30 minutes; the longer time periods correlate with the lower g forces (Pignatello and Huang, 1991; Xia and Ball, 1999; and Xing et al., 1996). Gschwend and Wu (1985) have calculated that 760 g for 20 minutes or 1700 g for 60 minutes is sufficient to achieve complete settling of particles having a density of 1.2 g/cm3 and diameter of 1.0 and 0.40 µm, respectively. However, even the most rigorous centrifugation condition (1700 g for 60 minutes) did not remove nonsettling particles and colloids

completely, and their effect was seen as a decrease in the Kp of a heptachloro- biphenyl when the solids concentration increased above approximately 2400 mg/L. Such effects should be smaller when lower solids concentrations are used, and when the sorbate is less hydrophobic. The effects of nonsettling particles and colloids were greatly reduced by prewashing the soil before conducting sorption experiments. Either prewashing the solids or explicitly

Sorption of Organic Compounds 43 measuring and accounting for the binding to nonsettling particles and colloids was recommended. This latter approach was taken by White and Pignatello (1999) using the solubility-enhancement technique. They created a solution of nonsettling particles using a solid-to-solution ratio 10 times greater than those used for all other experiments. This solution was centrifuged, and a series of dilutions was made to vary the colloid concentration. The ability of these solutions to enhance the solubility of phenanthrene (log Kow = 4.6) was evaluated, and it was concluded that the effect of nonsettling colloids could be neglected. They further cite the results of Villholth (1999), who found that partitioning of PAH compounds to groundwater colloids was negligible when the log Kow of the compound was less than 5.7.

DESORPTION. Desorption experiments can be conducted using both the batch equilibration method and column techniques. As with sorption experiments, the batch desorption approach is the more common. Once the contents of the batch reactor have reached equilibrium, the desorption experiment is initiated by removing a portion of the solution phase. The mass of solute remaining in the batch reactor is the mass taken up during the adsorption step, s qe Ms, and the mass present in the volume of residual solution remaining, s rs Ce V . The sum of these is the mass initially present before desorption. A known volume of solute-free aqueous regenerant solution, V r, is then added to the reactor, and the system is allowed to reequilibrate. Regenerant solution is often prepared in a large reactor containing solids at the same solid-to- solution ratio as the sorption experiments, but containing no sorbate. After desorption and reequilibration, the mass initially present is redistributed d between the mass sorbed to the solid phase, q e Ms, and the mass in the d rs r solution phase, Ce (V + V ). The total mass before and after desorption must be equal, therefore

s +−rs s drsr + d = qMe s V Ce Ce () V V qe (2.57) Ms

s s where = qe Ms = Vaq (Co – Ce). Several different batch desorption experimental protocols have been used, including the method of consecutive desorption (CD) (Davison and McDougal, 1973, and Swanson and Dutt, 1973) and the method of dilution desorption (DD) (Rao et al., 1978). The desorption of imidacloprid and triadimefon studied by Celis et al. (1999), and Cox et al. (1997), respectively, are examples of the CD method. In these studies, desorption studies were conducted immediately after sorption experiments. After centrifugation, the desorption experiment was initiated by removing 5 mL of solution for subsequent analysis and replacing it with 5 mL of solute-free solution. This mixture was mixed and shaken another 24 hours, and the desorption cycle was repeated four times. The method of dilution desorption uses a dilution factor in the desorption of organics from soil or sediment. In contrast to the CD method, the sorbate-to-solution ratio varies in the DD method. Altfelder et al. (2000) applied this method to desorption of chlortoluron from soil.

44 Handbook on Sediment Quality SORPTION PHENOMENA ISOTHERM LINEARITY. As discussed above, the distribution coefficient for a linear partitioning mechanism is expected to be constant as long as the solution- and solid-phase activity coefficients do not vary significantly and as long as the sorbed concentration is not high enough to significantly change the solid-phase properties (e.g., density, molar volume). In such cases, partitioning or absorption into “dissolution” domain will exhibit a linear isotherm. Isotherm linearity can also be expected in an adsorption domain (e.g., one described by a Langmuir isotherm) when the product of the affinity

parameter and the solution-phase concentration is small (e.g., bCe << 1). The literature is replete with examples of linear partitioning; however, as pointed out by Pignatello (1998), isotherm nonlinearity may be obscured because (1) data are collected over too narrow a concentration range (i.e., fewer than 2 orders of magnitude) and thus only appear linear; (2) data are too few in number or too scattered to assess linearity in a statistically meaningful way; and (3) data are fitted to a linear model based on a presumption that this model is correct, even though the data are actually nonlinear.

Linear Partitioning to Isolated Mineral Surfaces. Sorption of neutral organics to mineral surfaces is expected to be a minor contributor to total uptake in systems containing an appreciable organic carbon content; however, uptake by such surfaces may be dominant (although still small in magnitude)

in systems containing little organic carbon (e.g., fom < 0.002 [Schwarzenbach et al., 1993]). Specific interactions between hydrophobic organic compounds and mineral surfaces are not expected; rather, uptake is driven primarily by the compound’s fugacity in aqueous solution, as characterized by its activity coefficient. Mineral surfaces typically contain electron-rich ligands (oxygen, carboxyl) that make the surface hydrophilic; therefore, bonding of water to the surface is favored over hydrophobic organic molecules. Ionizable species, including natural organic matter, may exhibit specific interactions with surface ligands and may compete for sorption sites. Schwarzenbach et al. (1993) have tabulated data from several studies that have investigated sorption of organic compounds to silica, alumina, kaolinite, and montmorillonite. They propose a linear free energy relationship of the form

2 log Kmineral (L/m ) = a log γw + b (2.58)

where a = 1.37, b = –11.5 for silica, and b = –11.0 for kaolinite. Huang et al. (1996) measured uptake of phenanthrene by α-aluminum

oxide (Al2O3), quartz, kaolinite, amorphous silicon dioxide (SiO2), three porous silica gels, and bentonite, a swelling clay. The bentonite and porous silica gel isotherms were equilibrated for 21 days, whereas 14 days was used for the other, nonporous sorbents. Solution-phase concentrations ranged from

approximately 3 to 1000 µg/L (0.0005 < Ce /S < 0.16). With the exception of bentonite, rapid, linear sorption was observed. The agreement between

Sorption of Organic Compounds 45 experimentally measured log Kmineral values and those calculated using eq 2.58 was reasonable. The rapid uptake rates for the porous silica gels was taken as evidence that intraparticle diffusion did not occur and that phenanthrene did not access internal pore surfaces. This was consistent with the lower Kd values found for the silica gels, which were roughly in proportion to their smaller external surface areas (i.e., the gel bead sizes were approximately 2 orders of magnitude larger than nonporous adsorbents). For nonporous sorbents, a significant effect of solution pH was observed, suggesting that mineral surface charge density can influence sorption.

Linear Partitioning to Soils and Sediments. Many studies have found linear partitioning to soils and sediments. It should be noted, however, that most (but not all) such studies have used experimental methods that include one or more of the following features: (1) relatively high solution-phase concentrations

(minimum Ce /S values on the order of a few percent); (2) relatively narrow concentration ranges (fewer than two decades); and (3) relatively short equilibration times (typically on the order of 1 to 2 days). This is important because, more recently, several investigators have observed nonlinear sorption to soils and sediments. Some of these more recent studies used the same sorbents and solutes as those used in previous research that reported findings of linear uptake. In some cases, the different findings likely resulted from differences in the experimental methods used, in one or more of the above respects. Studies reporting linear sorption by sediments and other sorbents (soils and model sorbents), including important features regarding experimental conditions, are tabulated in Table 2.2. In two early studies, Karickhoff et al. (1979) observed linear, reversible adsorption and no hysteretic effects in a study of the uptake of several biphenyls and polyaromatic hydrocarbons by pond and river sediments, and Chiou et al. (1979) reported linear uptake of several chlorinated ethanes, ethenes, and propanes and benzenes by a Willamette silt loam. Excellent correlations between log Kp and log S were found. Means et al. (1980) found linear sorption of pyrene, 7,12-dimethylbenz[a]- anthracene, 3-methylcholanthrene, and dibenzanthracene by several soils and river sediments. Values for Kd were correlated with percent organic carbon but not with other sediment properties such as CEC, total nitrogen, or grain-size distribution. Chiou et al. (1983) found linear sorption of benzene, several substituted benzenes, and several PCBs by a Woodburn silt-loam soil after 24 hours of equilibration. This observation, and the fact that 1,3-dichlorobenzene and 1,2,4-trichlorobenzene did not exhibit competitive interactions, was taken as confirmation that the sorption mechanism was a partitioning process. The 2 correlation log(Koc) = 0.904 log(Kow) – 0.543 (2.11 < log(Kow) < 5.62; r = 0.996) was found (as reported later by Chiou et al., 1998); good correlations were also found with the product of molar solubility and molar volume and with molar solubility alone. Rutherford et al. (1992) reported linear uptake of carbon tetrachloride and benzene by high organic carbon content International Humic Substances Society (IHSS) Everglades peat, Houghton muck, and cellulose. It was found

46 Handbook on Sediment Quality Table 2.2 Studies finding linear partitioning to sediments and other sorbents.

foc Cp/S Contact Reference Compounds Sorbent (% w/w) range time (d)

Karickhoff and Brown PCB, PAHsa Sedimentb 0.086–3.29 0.1–0.5 24 (1979) Chiou et al. (1979) Ethanes, Silt loamd 1.6 0.04–0.95 NR ethenes, DCBc Means et al. (1980) PAHse Sedimentsf 0.11–2.38 0.0004– 24 0.037g Chiou et al. (1983) Benzenes, Silt loamd 1.9 0.001–0.90 24 PAHsh Garbarini and TCB HA, 12–64 NR 8–24 Lion (1986) cellulosei

Rutherford et al. CCl4, Peat, 44.4–57.1 0.10–0.82 24 (1992) benzene sedimentj k Kile et al. (1995) CCl4, 1,2-DCB Soil, 0.11–6.09 0.04–1 48–72 sedimentl Xing et al. (1996) Triazines, TCEm Chitin, 46–85 <0.30 48 polymer n LeBouf and Weber Phenanthrene HA, 26.0–65.7 0.0002– >336 (1997) polymer o,p 0.16 Chiou et al. (1998) PAHsq Soil, 0.40–5.2 0.005–0.30 48–72 sediment r Mackay and BTXs Woodt 0.5 0.01–0.25u 120–504 Gschwend (2000) a Pyrene, methoxychlor, tetracene, anthracene, 9-methyleanthracene, phenanthrene, 2,4,6,2,4,6- hexachlorobiphenyl, naphthalene, and 2-methylnaphthalene. b Pond and river sediment. c 1,2-dichloroethane, 1,2-dibromoethane, 1,1,1, trichloroethane, 1,1,2,2-tetrachloroethane, tetrachloroethene, 1,2, dichloropropane, and 1,2-dichlorobenzene. d Woodburn silt loam. e Pyrene, 7,12-dimethylbenz[a]anthracene, 3-methylcholoanthrene, and dibenzanthrene. f Ohio, Wabash, Illinois, and Mississippi river sediment. g Individual isotherms spanned less than one decade. h Benzene, anisole, chlorobenzene, ethylebenzene, 1,2-, 1,3-, and 1,4-dichlorobenzene, 1,2,4- trichlorobenzene, 2-chlorinated biphenyl, 2,2'-, 2,4', and 2,4', 4'-polychlorinated biphenyl. i Soil HA and FA, tanic acid, lignin, zein, cellulose, and Aldrich HA. j Houghton muck, Everglades peak, extracted peat, and cellulose. k Carbon tetrachloride and 1,2-dichlorobenzene. l Thirty-two soils and 26 lake, river, and marine sediments from U.S. and China. m Atrazine, prometen, cyanazine, 2-chloro-4,6-dimethoxy-s-triazine, 5-chloro-1,3-dimethexyben- zene, and trichloroethylene. n Cellulose, chitin, and polyethylene. o Aldrich humic acid, cellulose, poly(isobutyl methacrylate). p Isotherms were linear at temperatures above glass transition temperatures. q Paphthalene, phenanthrene, and pyrene. r Five soils, seven lake, river, and marine sediments. s Benzene, toluene, and o-xylene. t Lignin, cellulose, and hemicellulose. u Individual isotherms spanned less than one decade.

Sorption of Organic Compounds 47 that the value of Kom decreased significantly with increasing “polar/nonpolar” balance, (O + N)/C, quantified by elemental analysis. Kile et al. (1995) studied the sorption of carbon tetrachloride and 1,2,- dichlorobenzene to 32 soils and 36 sediment samples from the United States and China. Among soils alone, Koc or among sediments alone, values ranged within a factor of two. This invariance was attributed, in part, to the fact that the solutes were low polarity and present in relatively high concentrations. Under these conditions, sorption by soil organic matter is expected to dominate over sorption by mineral components. The average Koc values for sediments were about twice those of soils. This was attributed to organic matter fractionation and preservation of less polar constituents in the sediment phase and organic matter aging. Xing et al. (1996) measured the sorption of atrazine, several s-triazine analogs, and trichloroethylene by a mineral soil, an IHSS peat soil, soil humic acid particles, and several model sorbents chosen to test whether specific sorption occurs at internal sites within the sorbent matrix, analogous to sorption by glassy polymers. Single solute uptakes of all solutes by rubbery model sorbents (chitin, polyethylene, and cellulose) were linear. Rubbery polymers have an expanded, flexible structure; polymer chains have freedom of movement, and interactions with them are fleeting (i.e., liquidlike). However, with the exception of the uptake of trichloroethylene and 5-chloro- 1,3-dimethoxybenzene by the Cheshire soil, which exhibited Freundlich n values not significantly different from 1, uptake of all other solutes by all other sorbents, including the high organic-content peat soil, humic acid, and glassy polymers, was nonlinear. Based on competitive effects (discussed below) and model fits to the dual-mode model, the authors conclude that soil and sediment organic matter is a dual-mode sorbent with both linear partitioning and nonlinear (“hole-filling”) domains. In a similar study, LeBoeuf and Weber (1997) measured the sorption of phenethrene by several sorbents chosen to test the influence of solid-phase structure in terms of its “rubbery” and “glassy” nature, again by analogy with polymers. Uptake of phenanthrene by poly(isobutyl methacrylate), Aldrich humic acid, and cellulose at 45 °C was nearly linear, with Freundlich n values near unity. At this temperature, the sorbents were near or above their glass transition temperature, thus in a rubbery state. At lower temperatures, below their respective glass transitions, uptake by poly(isobutyl methacrylate) and Aldrich humic acid was nonlinear. Chiou et al. (1998) observed linear sorption of naphthalene, phenanthrene, and pyrene by several different soils and sediments. Whereas Koc values within a geosorbent class were relatively invariant, it was found that Koc values for sediments were approximately 1.6 times higher than those for soils. This was attributed to the higher polarity (assessed using 13C-nuclear magnetic resonance [NMR] spectra) of the soil organic matter. Mackay and Gschwend (2000) observed linear sorption of benzene, toluene, and o-xylene by presaturated Douglas fir sticks and Ponderosa pine chips. Linearity was assessed by Freundlich exponential coefficients

(n values) that averaged 0.96 ± 0.12. However, partition coefficients (Kwood) could not be predicted accurately using Koc – Kow correlations. This was

48 Handbook on Sediment Quality attributed to the fact that cellulose components in wood are measured as part

of the foc but do not exhibit high uptake, based on studies of isolated wood components (Garbarini and Lion, 1986). Values for Kd predicted using lignin partition coefficients and the lignin content of the wood was consistent with measured values (within a factor of 2), suggesting that lignin is the domi- nantly reactive component of wood and that it acts as a partitioning medium. It appears that sorption to some natural sorbents may be truly linear, suggesting a partition mechanism. As will be discussed in more detail in the next section, this may be restricted to organic matrices that exist above their glass transition temperature in a wetted state under ambient temperatures. In general, conclusions made about isotherm linearity should be viewed with caution, especially when relatively high solution-phase concentrations are used (potentially masking nonlinear regions), relatively narrow concentration ranges (fewer than two decades) are investigated, and short contact times (1 day or less) are used. In the next section, considerable evidence for nonlinear sorption will be presented. Theoretical reasons for expecting such behavior and experimental confirmation will be discussed.

Nonlinear Sorption of Neutral Hydrophobic Organics. This section summarizes the results of several representative studies that have identified nonlinear sorption by soils and sediment materials. In the following section, the nonlinear sorption of ionizable organic chemicals will be briefly discussed. Knowledge of whether equilibrium sorption is linear or follows a nonlinear isotherm is important because the shape of the isotherm can have a significant effect on the transport characteristics of a chemical in the environment. This is true even when other processes, such as diffusion-limited mass transfer, control the rate of sorption. In addition, the linearity of the isotherm can significantly affect the mathematical complexity of making transport predictions. Nonlinear differential equations are more difficult to solve and require numerical approaches. In contrast, when sorption is linear, it is often possible to apply a closed-form analytical solution to the transport problem. An approach commonly used to assess isotherm linearity is to determine the slope of isotherm data plotted on log–log coordinates, in essence, testing the empirical fit of the Freundlich model (Mackay and Gschwend, 2000). Nonlinear sorption behavior is indicated by either a nonconstant slope on log–log coordinates or a constant slope less than unity. The slope is typically accompanied by a confidence interval or some other statistical measure to

quantify a deviation from unity. When sorption is nonlinear, Kd values are not constant, and Kd values calculated from experimental data typically are not accurately predicted by Koc–Kow correlations. Several investigators show that, even though the isotherm may be nonlinear, the linear sorption model can be used to describe data reasonably well over limited concentration ranges. Furthermore, in some cases, nonlinear behavior can be obscured at high solution-phase concentrations. Therefore, isotherm data starting at a low concentration and spanning several orders of magnitude are necessary to clearly identify nonlinearity. Some soils contain diagenetically altered “hard” carbon, present as distinct particles such as shale. It is possible to physically separate such particles and

Sorption of Organic Compounds 49 evaluate their sorption characteristics; nonlinear sorption by these high- surface-area materials has been observed (e.g., Weber et al., 1992). However, nonlinear sorption is also observed even when such distinct particles are absent. Several investigators (Xing et al., 1996, and Young and Weber, 1995) have proposed that older, more diagenetically altered sediment organic carbon contains “condensed” glassy or crystalline regions analogous to those found in polymers. These regions have been hypothesized to account for nonlinear sorption behavior. One explanation for the nonlinear sorption behavior is adsorption by microvoids in the polymer, consistent with the dual-mode theory. Some researchers have concluded that sediment organic matter is a dual-mode sorbent with both linear partitioning and nonlinear (“hole-filling”) domains (Xing et al., 1996). Sorption experiments conducted at different temperatures have been used to provide evidence for sediment organic matter having a “rubbery” or “soft” partitioning domain and a more condensed, rigid, cross-linked “glassy” domain. As discussed in the previous section, LeBoeuf and Weber (1997) measured linear uptake of phenanthrene by several sorbents when they were in their “rubbery” state (at 45 °C). In this state (above the glass transition temperature), sorption is linear because polymer chains have freedom of movement and interactions among them are liquidlike. In contrast, uptake below the glass transition temperature was nonlinear. There is some evidence, based on kinetic studies, to suggest that sediments are coated with two “layers” of organic matter: an outer layer that serves as a partitioning phase and an inner, more condensed organic matter domain. Several investigators (Chiou et al., 2000, and Xia and Ball, 1999) have rationalized the nonlinear sorption of nonpolar solutes as uptake by high- surface-area carbonaceous material (HSACM) such as charcoal or soot. They assert that nonlinear capacities observed experimentally are within the monolayer adsorption capacity of the soil based on BET (N2) surface areas. In addition, the presence of high-surface-area carbonaceous material may provide a pore structure for adsorption by a pore-filling mechanism, which seems consistent with experimental findings. All of the adsorption isotherms presented in the Isotherm Models section exhibit nonlinear sorption behavior; therefore, if adsorption sites contribute to the sorptive capacity of the solid phase, at least some region of the isotherm can be expected to be nonlinear. Isotherm nonlinearity in the case of a homogeneous (Langmuir) adsorption domain will manifest when the product of the affinity parameter and the solution-phase concentration is large; that is, as the homogeneous sorption sites become occupied and qe approaches the solid-phase capacity, Q o. Isotherm nonlinearity may reflect heterogeneity in site energy or sorption potential, for example, as described by the Freundlich and Dubinin isotherms. Isotherm nonlinearity may also reflect heterogeneity in the sorption mechanism. For example, a “composite” isotherm, calculated by adding the contributions of different Langmuir isotherms (representing distinct site energies), could be fitted with a single Freundlich isotherm (Kilduff et al., 1998, and Weber et al., 1992). A similar analysis is reproduced in Figure 2.6. A relatively small number of Langmuir isotherms can combine to produce

50 Handbook on Sediment Quality Figure 2.6 An example of how sorbent heterogeneity, as exemplified by several distinct Langmuir sites, can result in nonlinear, Freundlich-type sorption behavior. The heavy line labeled “Composite” represents a summation of the four Langmuir isotherms shown.

Freundlich behavior over several orders of magnitude in solution-phase concentration. Note that the relatively high uptake in the low-concentration region of the Freundlich isotherm is caused by a relatively small number of high-energy sites; the majority of sites are represented by lower-energy but higher-capacity Langmuir isotherms. All combined sorption models predict nonlinearity because they contain adsorption terms. The distributed reactivity model has a Freundlich term; the dual-mode model has one or more Langmuir terms, and the Polanyi-based sorption model has a Dubinin-type term. However, if the contribution of adsorption sites is relatively small and the equilibrium concentrations for which isotherm data are collected are high enough (i.e., >20% of solubility) to ensure that adsorption sites are saturated even for the datapoints having the

lowest values of qe, the nonlinear portion of the isotherm can be difficult to discern. The remainder of this section discusses the results of several different studies that have identified nonlinear sorption behavior. Details concerning the solutes and sorbents studied and the experimental conditions used are tabulated in Table 2.3. Weber et al. (1992) reported uptake of tetrachloroethyl-

ene, dichlorobenzene, and trichlorobenzene by six soils having foc values ranging from 0.03 to 2.49 based on high-temperature oxidation with pure oxygen. The lowest concentrations measured were lower than those reported

Sorption of Organic Compounds 51 Table 2.3 Studies finding nonlinear partitioning to sediments and other sorbents.

foc Cp /S Contact Reference Compounds Sorbent (% w/w) range time (d)

Weber et al. (1992) PCB, DCB, TCBa Soilsb 0.03–2.49 0.001–0.2c 7–14 Young and Weber Phenanthrene Soilsd 0.67–6.41e 0.000 03– 7 (1995) 0.10f Weber and Huang Phenanthrene Sediments, 0.97–2.35 0.0003– 14 (1996) soilg 0.15h Huang et al. (1996) Phenanthrene Bentonite 0.001 0.0005–0.16l 21 Xing et al. (1996) Triazines, TCEm Soil, peat, 0–73 <0.30o 48 HAn LeBoeuf and Weber Phenanthrene HA, 26–65.7 0.0005– 14–28 (1997) polymer p 0.16q Xing and Pignatello DCB r Peat soil, 44.6–53.3 0.0002–0.5 1–30 (1997) HAs Haung et al. (1997) Phenanthrene Soil, 0.12–46 0.0003–0.15h 14–28 sedimentt Haung et al. (1998) Phenanthrene Soil, 0.45–2.57 0.0003–0.15h 21 sedimentu Chiou and Kile (1998) Phenol, TCE, Soil, 1.26–49.3 0.001–0.50 2–4 pesticidesv peatw Chiou et al. (2000) DCP, diuron, Peat, HA, 47.3–49.3 0.003–0.60 2–3 EDBx hunin y Xing and Pignatello DCB, DCP z Peat, loam 1.4–44.6 NR 2 (1998) soils Xia and Ball Benzenes, PAH Dover silt 1.49 0.001–50 7 (1999) loam a Telrachloroethylene, dichlorobenzene, and trichlorobenzene. b Spinks, Ottokee, Brookston, Miami, and Wasepi soils. c Concentration range 100–7000 µg/L. d Webster silt loam soil, Chelsea topsoil, and Ohio shale. e The range of foc measured by 121 °C persulfate oxidation was 0.67–2.38 while the range measured by 1020 °C combustion was 2.46–6.41. f Concentration range 0.20–600 µg/L. g U.S. EPA sediments 20, 22, 23, and U.S. EPA soil 15. h Concentration range 2–900 µg/L. l Concentration range 3–1000 µg/L. m Atrazine, prometon, cyanazine, 2-chloro-4,6-dimethoxy-s-triazine, 5-chloro-1,3-dimethoxybenzene, and trichloroethylene. n Cheshire sandy loam, Pahokee peat soil, soil humic acid particles, mesoporous silice, and Tenax. o Concentration range 2.5 to 3 decades. p Aldrich humic acid and poly(isobutyl methacrylate) (isotherms were nonlinear at temperatures below the glass transition temperatures) and Illinois coal. q Concentration range 3–1000 µg/L. s Pahokee peat soil and fractions (humic acid, humin), Alberta HA, and Windsor HA. t Nine soils, 10 U.S. EPA river sediments, and 1 U.S. EPA lake sediment. u U.S. EPA soils, 14, 20 and U.S. EPA sediments 15, 22, and 23. v Dichlorophenol, diuton; 1,1-dimethyl, 3-(3,4 dichlorophenyl)urca, monuron: 1,1-dimethyl-3-(p- chlorophenyl)urea, trichloroethylene, ethylene dibromide, and lindane: 1α,2α,3β,4α,5α,6β hexachlorocyclohexanes. w THSS Everglades peat soil and Woodburn soil. x Dichlorophenol, diuron (1,1-dimethyl-3-(3,4-dichlorophenyl)urea), and nonpolar ethylene dibromide. z 1,3-dichlorobenzene and 2,4-dichlorophenol; Cheshire sandy loam, Pahokee peat soil.

52 Handbook on Sediment Quality in most of the previous studies that reported linear sorption, and the concentration range was also greater, spanning more than 2 orders of magnitude. It is also noted that, whereas some isotherms contained fewer than 10 datapoints, the average number of datapoints for 18 isotherms was 29. Freundlich n values ranged from 0.68 to 1.0; for a given soil, the nonlinearity increased (n

value decreased) with increasing adsorbate Kow. The extent to which isotherms were nonlinear was evident when both the Freundlich and linear isotherms were plotted on the same log–log coordinates. The linear model fell out of the 95% confidence limits for the Freundlich model at concentrations below approximately 500 µg/L, even for isotherms having Freundlich n values as high as 0.85. Differences between measured data and the linear isotherm were more dramatic as the n value decreased and were more pronounced at low solute concentrations. It should be noted that the linear model did describe the data well over limited concentration ranges. Furthermore, in some cases, nonlinear behavior was obscured at high concentrations. This emphasizes the need to collect isotherm data starting at a low concentration and spanning several orders of magnitude to clearly identify nonlinearity. Some of the soils studied by Weber et al. (1992) contained a high-surface- area shale fraction, identified by visual observation and separated manually.

The foc measured for these soils depended on whether high-temperature or persulfate oxidation was used; the shale fraction was not oxidized by persulfate and was identified as “hard” carbon. Whereas uptake by this reactive fraction was significantly higher than the whole soil, it was not the source of nonlinearity for all soils that contained it because Freundlich n values for isolated reactive fractions were the same or higher than the whole soils in some cases. Furthermore, significantly nonlinear sorption was observed for soils that did not contain any oxidation-resistant organic matter fractions; that is, those that contained “soft” carbon organic matter that could be oxidized by persulfate. Young and Weber (1995) measured the uptake of phenanthrene by a Webster silt-loam soil, a high-organic-content Chelsea topsoil, and Ohio shale. Isotherms for all sorbents were nonlinear (Freundlich n < 0.80 in all cases), with the shale sample exhibiting the greatest extent of nonlinearity (Freundlich

n = 0.52). Freundlich KF values decreased and n values increased with increasing temperature (10 to 40 °C), but the changes were not statistically significant. Isosteric heats of adsorption became less exothermic with increasing temperature, consistent with a surface adsorption process, but absolute values were not significantly different from zero, in contrast to what would be expected for such a process. Their findings led these authors to propose that soil and sediment organic matter can be modeled by analogy with polymers having amorphous rubbery, amorphous glassy, and/or crystalline regions. The older, more diagenetically altered “condensed” glassy and crystalline regions were hypothesized to account for nonlinear sorption behavior. Weber and Huang (1996) studied the sorption of phenanthrene by three U.S. Environmental Protection Agency (U.S. EPA) sediments and one U.S. EPA soil. Isotherms were all nonlinear, with Freundlich n values ranging from

0.73 to 0.89. Values for Kd were calculated from the experimental data as a

Sorption of Organic Compounds 53 function of concentration and compared with predictions based on the Koc – Kow correlation proposed by Karickhoff (1981). The predicted Kd values were within a factor of 1.5 for experimental data corresponding to solution-phase concentrations greater than 100 µg/L but were low by a factor of 2.6 to 7 for data corresponding to solution-phase concentrations less than 10 µg/L. Uptake was measured as a function of both time and solution-phase concentration; the q − C relationship corresponding to non-equilibrium conditions was referred to as a “phase distribution relationship.” These were also fitted with the Freundlich isotherm; it was found that, over time, uptake (and the

Freundlich KF) increased and the phase-distribution relationship (PDR) became more nonlinear (the Freundlich n decreased). These trends in the PDR coefficients are depicted schematically in Figure 2.7. Significant nonlinearity was observed after a few hours. Pedit and Miller (1996) showed that similar trends are predicted when a pore-diffusion model is used to describe sorption rates; however, the model did not predict two features observed in one or more of the experimental

Figure 2.7 Hypothesized sequence of sorption by exposed mineral surfaces (domain I), amorphous organic matter (domain II), and condensed organic matter (domain III) (after Weber and Huang, 1996). Sorption by condensed organic matter is expected to be nonlinear, whereas linear sorption is expected by other domains. As a result of the sorption sequence, PDR coefficients change as a function of time.

54 Handbook on Sediment Quality datasets. First, the data were initially more linear (i.e., had higher Freundlich n values) than predicted by the model, which assumes a homogeneous sorbent having a specified equilibrium n value. Second, as shown in Figure 2.7, in

some datasets, the n value became constant before the KF value did. In contrast, the pore-diffusion model predicts that KF and n reach their equilibrium values at the same time. These findings led Weber and Huang (1996) to hypothesize a sequence of sorption by exposed mineral, amorphous organic matter, and condensed organic matter domains. This sequence of sorption is shown schematically in Figure 2.7. Sorption by exposed mineral surfaces and amorphous organic matter coatings is hypothesized to occur during an initiation stage, followed by sorption by a more condensed organic matter domain. Linear sorption by exposed mineral surfaces or amorphous organic matter forming an outer coating would explain the high n values observed at early times, with the more condensed organic-matter phase contributing to the nonlinear sorption after some initiation stage. Huang et al. (1996) studied the uptake of phenanthrene by bentonite. Equilibrium isotherms were significantly nonlinear, with Freundlich n values of 0.63 ± 0.03. Non-equilibrium PDRs were also measured and near-linear, non-equilibrium PDRs were observed for the early-time uptake, followed by

subsequent increases in Freundlich KF values and decreases in n values over time. The near-linear early-time phase distribution is not predicted by the

pore-diffusion model; however, the Freundlich KF and n values reached their equilibrium values at the same time, which is consistent with the model. It should be noted that the bentonite did not contain any measurable organic

fraction (foc < 0.01% w/w); therefore, the presence of organic carbon is not required for a sorbent to exhibit near-linear early-time partitioning (that cannot be captured by the pore-diffusion model). These changes in n values that are larger than those predicted by a pore-diffusion model could result from adsorbent heterogeneity, however. Huang et al. (1996) cited pore-scale heterogeneity as a factor contributing to the observed nonlinear equilibrium behavior. Solid-phase loading that was approaching the sorbent’s capacity was cited as another potential factor. As introduced in the previous section, Xing et al. (1996) studied the sorption of atrazine, several s-triazine analogs, and trichloroethylene by Cheshire sandy loam, Pahokee peat soil, soil humic acid particles, mesoporous silica, and poly 2,6-diphenyl-p-phenylene oxide (Tenax), a glassy polymer. Sorption isotherms were found to be nonlinear. Freundlich n values were significantly less than unity (with the exception of the uptake of trichloroethylene and 5-chloro-1,3-dimethoxybenzene by the Cheshire soil, which exhibited Freundlich n values not significantly different from 1). Sorption to Tenax was highly nonlinear, with Freundlich n values ranging from 0.52 to 0.72, which was attributed to a combination of linear partitioning and adsorption by microvoids in the polymer, that is, dual-mode sorption. Based on competitive effects (discussed below), the microvoids exhibit a degree of selectivity based on steric effects, polar effects, or both. Because sorbents containing soil organic matter exhibited trends similar to those displayed by glassy polymers and because model fits to the dual-mode isotherm produced estimates of sorption by microvoids on the order of 37 to

Sorption of Organic Compounds 55 54% of total uptake, the authors conclude that soil and sediment organic matter is a dual-mode sorbent with both linear partitioning and nonlinear (“hole-filling”) domains. Sorption to surfaces external to the soil organic matter matrix was discounted using a kinetic argument. It was found in related research that isotherms became more nonlinear over time, suggesting that nonlinear sorption sites accessible to bulk solution are less abundant than those found internal to the organic matter matrix. This is also consistent with slow diffusion through glassy regions that contain microvoids. As discussed in the previous section, LeBoeuf and Weber (1997) measured linear uptake of phenanthrene by several sorbents when they were in their “rubbery” state (at 45 °C), in which polymer chains have freedom of movement and interactions among them are liquidlike. In contrast, uptake of phenanthrene by poly(isobutyl methacrylate) and Aldrich humic acid at 5 °C, below their glass transitions, was nonlinear, with Freundlich n values below 0.80 in both cases. Illinios coal, having a glass transition temperature higher than 45 °C (and thus in a glassy state), exhibited nonlinear uptake at both temperatures. The authors assert that the data support the concept of soil and sediment organic matter as having a “rubbery” or “soft” partitioning domain and a more condensed, rigid, cross-linked “glassy” domain. By analogy with polymers in the glassy domain, polymer chains have restricted movement and a structure characterized by fixed free-volume microvoids that can serve as adsorption sites, thus leading to nonlinear sorption behavior. Xing and Pignatello (1997) found nonlinear uptake of 1,3-dichlorobenzene by Pahokee peat soil, and an acid-treated sample to remove ash content, with Freundlich n values of 0.839 and 0.856, respectively. The similar n value found for the de-ashed sample confirms that nonlinear behavior was not caused by mineral surfaces. Isotherms were equilibrated for 1 day, but it was shown that uptake by the acid-treated sample increased and the n value decreased to 0.818 upon additional equilibration time of 30 days. The degree of nonlinearity was the same as that found for uptake by polyvinyl chloride, a glassy polymer (n = 0.879). Fits to the dual-mode model showed that significant uptake (45 to 68%, increasing with decreasing solution-phase concentration) could be attributed to the Langmuir (hole-filing) component of the isotherm. Uptake of 1,2-dichlorbenzene and 1,3-dichlorbenzene by whole peat soil and its humic acid and humin fractions was compared by Xing and Pignatello (1997). Isotherm nonlinearity increased (Freundlich n decreased) as the degree of sorbent condensation increased (humic acid > whole soil > humin) as expected based on the dual-mode hypothesis. The uptake of 1,3- dichlorobenzene by polyvinyl chloride and peat soil and the uptake of metolachlor by peat soil became more linear with increasing temperature, up to 90 °C. In addition, the uptake of 1,3-dichlorobenzene and dichlorophenol by peat soil became more linear in the presence of a cosolvent (either methanol or dimethyl sulfoxide). Both effects can be explained by a reduction in the glassy nature of the solid-phase structure. Finally, the linearity of 1,2- and 1,3-dichlorobenzene by peat soil and three humic acids correlated (increased) with decreasing micropore volume, as determined from

Dubinin–Radushkevich plots of CO2 gas sorption. This was taken as evidence

56 Handbook on Sediment Quality of microvoids within the organic matter structure acting as adsorption sites, with total micropore volume acting as a measure of the hole-filling capacity available for sorption. Huang et al. (1997) and Huang et al. (1998) present data for uptake of phenanthrene by nine different soils (including muck soils, topsoils, and aquifer sands), 11 different sediments (including U.S. EPA lake and river sediments), and several base-extracted samples. Isotherms were equilibrated from between 14 and 28 days, and solution-phase concentrations spanned more than 2 orders of magnitude, from less than 10 to nearly 1000 µg/L

(0.015 < Ce /S < 0.15). For all samples, including those that were base- extracted, isotherms were nonlinear, with Freundlich n values ranging from

0.60 to 0.92. Even a high organic carbon content muck soil (foc = 46% w/w) exhibited an n value of 0.75. The reported results conflict with previous research using the same U.S. EPA samples and similar compounds (Karickhoff, 1981, and Means et al., 1980), which is attributed to the smaller concentration ranges examined in the earlier work. Both the Freundlich and the dual-mode models fit the data well, and neither was superior in all cases. It was found that both models were superior to the multiparameter models

tabulated in Table 2.1. Values for Koc did not exhibit any general trend with foc for the samples studied; however, because the isotherms were nonlinear,

computed Koc values did depend strongly on the solution-phase concentration. Values for Koc predicted using several different Koc – Kow correlations agreed with the Koc values computed from the data at solution-phase concentrations of approximately 1 mg/L; however, predicted values were significantly smaller

than Koc values computed from the data at lower solution-phase concentrations. Chiou and Kile (1998) studied the sorption of a range of polar and nonpolar organic compounds by two soils, an IHSS Everglades peat soil and a Woodburn soil. Both polar and nonpolar solutes all exhibited nonlinear

sorption at low Ce /S values. However, above Ce /S = 0.015 for nonpolar solutes and Ce /S = 0.13 for polar solutes, isotherms were nearly linear. Nonlinear sorption capacities were defined by extrapolating the linear region

of the isotherm back to the qe axis. Such an extrapolation implies that more than one mechanism operates over the entire concentration range, which is consistent with a dual-mode sorption mechanism. As discussed above, the total uptake is represented by the summation of a low-capacity (but relatively high-energy) Langmuir isotherm(s) and a linear isotherm, as shown schematically in Figure 2.5. Extrapolation of the linear portion yields the total o Langmuir capacity, ΣQi . The nonlinear sorption capacities of the polar solutes were higher than those of the nonpolar solutes and were greater for

the peat soil (high foc). The nonlinear portion of the sorption isotherms could be suppressed by the presence of a competing cosolute, making the isotherms more linear, as will be discussed in more detail in the next section. Chiou and Kile (1998) rationalized the nonlinear sorption of nonpolar solutes as uptake by HSACM such as charcoal or soot because the nonlinear capacities were well within the monolayer adsorption capacity of the soil

based on BET (N2) surface areas. However, the high nonlinear capacities of the polar solutes exceeded the amount that could be accounted for by the soil surface area, assuming monolayer sorption. A second nonlinear mechanism,

Sorption of Organic Compounds 57 attributed to specific interactions between polar solutes and active sites within the sediment organic matter structure, was proposed to account for this. Experiments done at low pH showed that these active sites were not confined to ionizable groups. The combined mechanisms of sorption to HSACM and specific interaction with active organic matter groups were referred to as the HSACM–SI model. The HSACM–SI model was explored further by Chiou et al. (2000). The uptake of polar dichlorophenol and diuron [1,1-dimethyl-3-(3,4- dichlorophenyl)urea] and nonpolar ethylene dibromide was investigated. Sorbents included the IHSS Everglades peat soil used in earlier work, its density- fractionated humic acid fraction, its base-insoluble humin fraction, and a humic acid isolated from a muck soil. Uptake of nonpolar ethylene dibromide by the peat and muck humic acids was linear, but uptake by the whole peat soil and its humin fraction were nonlinear (Freundlich n values ranged from 0.88 to 0.91). These results are consistent with the ideas of linear uptake by geologically younger organic matter and nonlinear uptake by more diagenetically altered material. However, while the dual-mode model would invoke glassy or condensed regions (by analogy with polymers) to explain the nonlinear uptake, the HSACM–SI model invokes a surface-adsorption mechanism because N2–BET surface areas are sufficient to account for the graphically determined nonlinear capacities. The uptake of polar dichlorophenol and diuron was nonlinear on all sorbents, including humic acids. Freundlich n values were lowest on the humin material but were still below 0.72 for humic acids (both compounds). For diuron uptake by humin and dichlorophenol uptake by all sorbents, the nonlinear capacity was greater than could be accounted for by sorbent surface area; this is explained by a specific interaction with the organic matter. The authors point out that for the distributed reactivity or dual-mode models to explain the data, humic acid would have to contain glassy regions or microvoid sorption sites that were solute specific. Distributed energy sorption sites able to discriminate on the basis of molecular structure were indeed invoked by Xing et al. (1996) to explain competitive sorption data. It should be noted that the nonpolar ethylene dibromide (log Kow = 1.99) is less hydrophobic than the polar diuron (log Kow = 2.68) and dichlorophenol (log Kow = 3.23); it is not clear whether the solute-specific behavior is more related to solute polarity or hydrophobicity. Xing and Pignatello (1998) measured the uptake of 1,3-dichlorobenzene, 2,4-dichlorophenol and several different aromatic acids by Pahokee peat soil and Cheshire fine sandy loam. Isotherms were equilibrated for 48 hours but, in selected experiments, it was shown that the isotherm nonlinearity increased when the equilibration time was extended. Uptake of all solutes was nonlinear, with Freundlich n values ranging from 0.51 to 0.86. These authors concluded that sorption was predominantly to the soil organic matter fraction and that this fraction was responsible for the observed nonlinearity. The possibility of phenol and aromatic acid hydrogen bonding to mineral surfaces, undergoing ion-exchange reactions, or coordination reaction with surface groups was noted; but in part because uptake by the low mineral content peat soil was similar to uptake by the Cheshire sandy loam, the role of mineral surfaces was seen as subordinate to the role of organic matter.

58 Handbook on Sediment Quality Xia and Ball (1999) reported the uptake of benzene, several chlorinated benzenes, and PAHs by a Dover silt loam aquitard soil. Equilibration times of 7 days were used; it was found that additional uptake over a 70-day period was small. The sorption data were fitted with both the dual-mode and Freundlich models; Freundlich n values ranged from a low of 0.71 (for pyrene) to a high of 1.0 (for benzene). It was not possible to rate one model as superior based on how well the criteria fit; however, the dual-mode model did appear to capture subtle curvature in the data that the Freundlich model

could not. The linear partition coefficient, Kd , and the initial isotherm slope o (Q b) were directly correlated with Kow, whereas the Freundlich n value was inversely correlated, demonstrating that adsorption is more important as solute hydrophobicity becomes greater. The authors note that this further implies that adsorption surfaces (sites) have low affinity for water. The data of Xia and Ball (1999) were also fitted to the Polanyi model; data for sorbates that are liquids at room temperature fell on a single characteristic curve, with a common value of the adsorption capacity of 0.141 cm3/kg soil. This value is roughly consistent with Qo, the values determined from the dual- mode analysis, which ranged from 0.076 to 0.137 cm3/kg soil for liquid sorbates. In contrast, the adsorption capacities for solid sorbates were lower than those for liquids. The effect was attributed to a pore-filling mechanism by a condensed-sorbate phase (either liquid or solid), with the solid sorbates exhibiting a significantly lower packing efficiency and hence lower capacity. Size effects were discounted because a liquid sorbate such as 1,2-dichlorobenzene, which has a larger molar volume than naphthalene, also had a higher adsorption capacity. The authors cite the possibility that HSACM may provide

a pore structure for adsorption by this mechanism. Above Ce /S values of approximately 0.10, the fractional contribution of adsorption was less than 40% for all chemicals studied.

Nonlinear Sorption of Ionizable Organics. There are many hydrophobic ionizable organic compounds listed as priority pollutants by the U.S. EPA (Keith and Telliard, 1979). Many of these compounds have one or more functional groups that can ionize at pH values typical of environmental

systems. These include carboxylic (–COOH), phenolic (–OH), amine (–NH2), and sulfonate (–SO3) groups. Such ionizable functional groups may interact with surfaces in ways not possible for neutral organic species. Electrostatic interactions and ligand-exchange reactions may significantly influence the extent and reversibility of sorption of such compounds as aromatic amines, phenols, and acidic herbicides (Deschauer and Kögel-Knabner, 1990; Jafvert, 1990; Lee et al., 1997; and Schwarzenbach et al., 1993). In contrast to the sorption of neutral organic molecules, it is generally accepted that ionizable organic compounds will exhibit nonlinear sorption behavior (Schwarzenbach et al., 1993). Specific interactions with surface species, ion exchange, and competition from other ions in solution can all lead to nonlinear sorption. Because solution pH can influence surface charge and the degree of ionization, this parameter can exert a strong influence on sorption of ionizable organics. In addition, because ions can shield surface charge and charged functional groups on the organic chemical, solution ionic strength can also

Sorption of Organic Compounds 59 influence sorption. By virtue of its lower solubility, it is generally expected that the sorption of the neutral form of an ionizable species will be greater than the ionized form. The fraction of the compound present in the neutral form, αN,is Organic acid α = 1 (2.59) N K 1+ a []H+

Organic base α = 1 + (2.60) N []H 1+ Ka It should be noted that, in some cases, the uptake of ionized species, while less than that of the neutral form, could still be significant. For example, Gunderson et al. (1997) found that sorption of ionized pentachloro-o- methoxyphenol by estuarine sediments was greater than the neutral form of less chlorinated o-methoxyphenols. The sorption of methylacridinium by 10 different soils and sediments was investigated by Brown and Combs (1985). They found nonlinear sorption described by a Langmuir-type isotherm. To develop a predictive isotherm model based on the Langmuir isotherm, Brown and Combs (1985) replaced the Langmuir capacity term with the soil cation-exchange capacity (CEC), which normalized sediment characteristics. Lee et al. (1997) studied the sorption of aniline, α-naphthylamine, and benzidine to three silty-clay loams with varying pH (4.4 to 7.2), CEC (10 to 30 cmolc /kg), and foc (1 to 3%). Under neutral pH conditions, when the aromatic base exists in its neutral form, partitioning to nonspecific sites was an important sorption mechanism

(i.e., Kp = foc Koc). In contrast, under acidic conditions, greater sorption was observed, with cation exchange being the primary sorption mechanism (Fabrega et al., 1998; Lee et al., 1997; and Schwarzenbach et al., 1993). As a result, competition from inorganic ions (e.g., calcium) and other organic amines can become significant (Lee et al., 1997) under low-pH conditions. Zhu et al. (2000) studied the sorption of phenol, p-nitrophenol, and aniline to dual-cation organobentonites from water. These researchers found that the sorption properties were affected by pH, equilibration time, sorbent inner- layer spacing, sorbent organic carbon content, and solute Kow. Sorption isotherms were dominated by strong nonlinear adsorption at lower concentrations and weaker linear partitioning at high concentration. Sorption of ionizable organic compounds by natural zeolite modified with a cationic surfactant (e.g., hexadecyl tri-methylammonium) was studied by Li et al. (2000). Isotherms for benzene, phenol, and aniline were linear over narrow concentration ranges, but a strong effect of pH for phenol and aniline was observed. Distribution coefficients were linearly dependent on the surface

60 Handbook on Sediment Quality coverage of surfactant, reaching a plateau at a coverage of approximately 100 mol/kg. Organic anion sorption has been studied by many researchers (Jafvert, 1990; Lee et al., 1991; Li and Sengupta, 1998; and Schellenberg et al., 1984). Sorption of chlorinated phenols and phenols can occur for both nondissoci- ated phenols and their conjugate bases (phenolates). Jafvert (1990) demonstrated that properties of the sediment affected the sorption of organic acids. Gunderson et al. (1997) found that sediment organic carbon and humic acid content were both important factors controlling sorption of chlorinated guaiacols (o-methoxy phenols).

COMPETITIVE EFFECTS. When sorption is governed by a linear partitioning mechanism, competition among different sorbates present as a mixture is not expected. Linear partitioning in a dissolution or absorption domain involves interactions between solutes and thermally dynamic sites that are constantly forming and disappearing, so that the interaction energy averages out, as in a liquid (Xing and Pignatello, 1997). Such interactions are fleeting and do not involve discreet sites, so competition among different solutes is not possible. Linear partitioning behavior in an adsorption domain implies low solid-phase loading, uniform sorption energy, high sorbent capacity, or some combination of these. At low solid-phase loadings, competition is not expected because different sorbates have equal access to sites on (or in) the solid phase. When the sorption capacity is high and the sorption energy is uniform, competition is not expected to occur unless the aqueous-phase concentration of one or more solutes is sufficiently high to affect aqueous- phase activities. If specific sites in a solid-phase sorbent exist and the loading is not too low (or the solid-phase capacity too high), competition for these sites among different sorbates may occur. For competition to occur, different sorbates must all have more or less equal access to these sites; for example, if a sorbate is sterically excluded from accessing adsorption sites, competition cannot occur. Likewise, different sorbates must have appreciable affinity for the same sorption sites, or significant competition will not occur. Finally, competing sorbates must be present in sufficient concentrations to manifest competitive interactions. Even if one sorbate has an affinity for the same site as another competing sorbate, it may not be able to compete effectively if it is present at low concentrations.

Competitive Adsorption Models. The simplest competitive adsorption model is based on the Langmuir isotherm discussed in the Isotherm Models section. The competitive Langmuir isotherm is based on two parallel adsorption reactions in which dissolved sorbate molecules A and B react with vacant surface sites S to form sorbed “species” A ≡ S and B ≡ S. Each sorption

reaction has its own equilibrium constant, Ki (e.g., [A ≡ S] = KA[A][S]). For two competing solutes, the adsorbent site balance yields [ST] = [A ≡ S] + [B ≡ S] + [S]. Using the site balance equation to eliminate [S] from the mass

Sorption of Organic Compounds 61 law relationship and expressing one surface concentration in favor of the other, yields the following result:

QKCo q = i iei, ei, + (2.61) 1 ∑ KCiei, i

o where Qi and Ki = single-solute isotherm parameters. In the development of this model, it is assumed that all competing solutes have equal access to the adsorbent surface area; the different Qo values reflect only the size difference of each sorbate. Jain and Snoeyink (1975) proposed a model that is applicable to systems in which part of the adsorbing surface is not available to all competing species. It is assumed that this noncompetitive surface area is reflected in different single solute capacities, Qo, for each sorbate. For example, in a bisolute system, if species A can access surface area inaccessible to species B, then o o this noncompetitive surface area will be proportional to QA – QB, and the o competitive surface area will be proportional to QB. The uptake of species A will include a competitive Langmuir term plus an additional term reflecting uptake by noncompettive surface area; the uptake of species B will include only a competitive Langmuir term

()QQKCo − o QKCo QKCo q = A B AeA, + B AeA, ; q = B BeB, eA, + + eB, + (2.62) 1 KCAeA, 11∑∑KCiei, bCiei, i i

IDEAL ADSORBED SOLUTION THEORY. A thermodynamically based model that is widely used to describe and predict competitive adsorption is based on the ideal adsorbed solution theory (Radke and Prausnitz, 1972). Application of this theory to bisolute uptake of organic compounds by soils has been described by McGinley et al. (1993). The Ideal Adsorbed Solution Theory (IAST) provides for the prediction of adsorption from mixtures based on the single-solute isotherms of each mixture component. An advantage of the IAST approach is its flexibility in allowing alternative isotherm forms, which allows for the best possible description of single-solute uptake for each component. As an example, the IAST equations for a bisolute system using the Freundlich isotherm for one component and the LF isotherm for the second component are summarized in Table 2.4 (where species A = component 1 and species B = component 2). This approach yields eight independent equations (Table 2.4,

Eqs i, ii, v to x). If (1) the single-solute isotherm parameters (KF and n1, and o Q , b, and n2); and (2) the initial concentrations (Co,1 and Co,2) are known, these equations can be solved simultaneously to determine the loading and equilibrium concentrations q1,C1,q2, and C2 for the mixture. The IAST was developed to account for the effects of adsorptive site competition, assuming that all sites are available to both components in the mixture. However, it is possible that some adsorption sites are not involved in competition. This may occur when some adsorption sites are specific to only

62 Handbook on Sediment Quality Table 2.4 Ideal adsorbed solution theory model formulation.

Mathematical Equation forma Description Single-solution systems

o n2 Q (bC2) i q2 = ––– Langmuir–Freundlich single-solute (bC )n2 1 + 2 isotherm for component 1

n1 ii q1 = KpC1 Freundlich single-solution isotherm for component 2 Bi-solution systems o C 1 o RT n dC RT n iii π = – ͵ K C o 1 = ᎏ1 – K C o 1 Spreading pressure for component 1 1 A f 1 C o An f 1 0 1 i A = specific adsorbent surface area o C 2 o o n2 o RT Q (bC2 ) dC2 iv– ᎏᎏπ = ᎏ͵ o 2 o n2 o RTQ A (bC2 ) C2 o n2 1+ 0 –– ln΄1+(bC2 ) ΅ An2

Spreading pressure for component 2

o 1 o n1 Q o n2 v – KF C1 = – ln[1 + (bC2 ) Equivalence of spreading pressures in n1 n2 the mixture

q vi C C o ––1 Rauolt’s law expression for the 1 = 1 ΂΃q + q 1 2 liquid-phase concentration of component 1 in the mixture q vii C C o ––2 Rauolt’s law expression for the 2 = 2 ΂΃q + q 1 2 liquid-phase concentration of component 2 in the mixture q q viii–1 + –2 = 1 Equivalence of adsorbed molecular q o q o 1 2 area per mole in single and bisolute systems

ix Co,1 = q1Do + C1 Mass balance, component 1

x Co,2 = q2Do + C2 Mass balance, component 2 a o o o o Notes: C1,C2,q1, and q2, are liquid- and solid-phase equilibrium concentrations in hypothetical single-solute systems at a spreading pressure identical to that of mixture. one component in a mixture or when molecules have sufficiently different sizes and the access of larger molecules to surfaces located in small pores is restricted (Jain and Snoeyink, 1975.

POLANYI-BASED MODELS. Competitive uptake of chlorinated benzenes and PAHs by a Dover silt loam was modeled using a Polanyi-based approach (Xia and Ball, 2000). The Polanyi-based competitive adsorption model predicted competition between di-chlorobenzene and tri-chlorobenzene well and validated the assumption that liquid sorbates form an ideal sorbed-phase liquid

Sorption of Organic Compounds 63 solution. The model also predicted competitive uptake of (solid) phenanthrene in the presence of other (solid) PAHs and (solid) tetrachlorobenzene. This appeared to validate the assumption that each component in the sorbed phase retained its single component sorbed-phase density and packing efficiency.

Evidence for Sorbate Competition. McGinley et al. (1993) studied the bisolute sorption of tetrachloroethene and either dichlorobenzene or trichlorobenzene by several different soils. Tetrachloroethene uptake by a given soil was reduced in the presence of a constant concentration of trichlorobenzene when the single-solute tetrachloroethene uptake by that soil was nonlinear (i.e., exhibited a Freundlich n value less than 1). In addition, mutual competition between tetrachloroethene and dichlorobenzene was found. However, if the single-solute tetrachloroethene isotherm was linear, no competitive effects were observed. The IAST model was able to successfully predict the observed competitive adsorption behavior. McGinley et al. (1993) observed that the extent of reduction (competition) was lower as the equilibrium solution-phase concentration of tetrachloroethene (and total solute concentration) increased, in opposition to what would be expected if competitive sorption occurred through partitioning reactions. Xing et al. (1996) observed that single-solute atrazine uptake by a mineral soil, Cheshire sandy loam, was nonlinear, and bisolute competitive interactions between atrazine and several s-triazine analogs, 5-chloro-1,3-dimethoxybenzene, and trichloroethylene were observed. In contrast, no competitive interactions were observed when the sorbent was a rubbery polymer, for which atrazine uptake was linear. When the sorbent was Pahokee peat soil, humic acid, or Tenax, significant competition between atrazine and prometon was observed in bisolute systems. The similarity between the behavior of peat soil, humic acid, and the glassy polymer is taken as evidence that the sorption behavior of mineral-free soil organic matter is consistent with dual-mode sorption. The similarity between the behavior of Cheshire soil, peat soil, and humic acid was taken as evidence that Cheshire soil behavior can be attributed to its organic constituents. Weak or no competitive interactions between atrazine and trichloroethylene were observed in uptake by the Pahokee peat soil, humic acid, or Tenax. This was taken as evidence of sorption site selectivity, attributed to nonoverlapping Langmuir sorption (hole-filling) domains. This was further supported by the fact that the IAST predicts the competition between atrazine and prometon, but overestimates the competitive effects of trichloroethylene. Xing and Pignatello (1997) observed that the presence of chlorobenzene reduced the uptake of 1,3-dichlorobenzene by Pahokee peat soil and glassy polyvinyl chloride. In these experiments, the initial concentration of 1,3- dichlorobenzene was held constant at 2 mg/L and chlorobenzene was varied

(up to 400 mg/L). The reduction in Kd was significant; changes were largest at low cosolute concentrations and leveled off at high chlorobenzene concentrations. Therefore, competition was not an effect of the cosolute on aqueous- phase activity coefficients or on solid-phase properties. The patterns of reduction in Kd were similar for the peat soil and Tenax and are consistent with the dual-mode model. Uptake of 1,2-dichlorobenzene by peat soil, its

64 Handbook on Sediment Quality humic acid, and its humin fraction was measured in the presence of various concentrations of 1,3-dichlorobenzene. Competitive effects were greatest for those sorbents on which single-solute uptake was most nonlinear; reductions

in Kd occurred in the order humin > peat soil > humic acid. Several workers have observed that competitive interactions between solutes can suppress the nonlinear uptake of one or both. One explanation of this phenomenon is that a sorbate has reduced access to higher-energy sorption sites in the presence of a competitor. Soil and sediment phases may have a range of specific sorption sites that vary in steric and electronic characteristics, and thus represent regions of different reactivity or sorption energy (Xing and Pignatello, 1997). Each type of site may be limited in number; therefore, different site populations will likely have different capacities. The result is a site distribution for the solid phase. In general, unless access is kinetically limited, high-energy sites will be filled first because this lowers the Gibb’s energy of the system to the greatest extent. As the capacity of higher-energy regions is approached and lower energy regions begin to be accessed, solutes may compete for available high-energy sites. The effect of competition is to make the isotherm of the primary adsorbate more linear. As a result, those single-solute isotherms capable of description by the Freundlich isotherm have n values shifted toward unity in the presence of a cosolute. This was seen in the uptake of di-chlorobenzene, tri-chlorobenzene, and phenanthrene by a Dover silt loam (Xia and Ball, 2000). However, the suppression of nonlinearity for a liquid sorbate is greater when the cosolute is also a liquid, compared with when the cosolute is a solid. In accordance with the Polanyi theory, Xia and Ball (2000) assume that the sorbed phase of a solid sorbate is a solidlike phase that may contain voids accessible to liquid sorbates; therefore, the linearity of a liquid sorbate is suppressed less by a solid cosolute. The nonlinear portion of the sorption isotherms measured by Chiou and Kile (1998) could be suppressed by the presence of a competing cosolute, making the isotherms more linear. The linear slopes of measured isotherms in the presence of cosolutes were similar to those of the single-solute isotherms. This finding is consistent with a dual-mode sorption mechanism and the subsequent loss of nonlinear capacity because of the presence of a cosolute. This phenomenon is shown schematically in Figure 2.8. Both polar and nonpolar cosolutes were effective in reducing the small nonlinear portion of the nonpolar solute isotherms. The same was true of the polar solute isotherms; however, competition from polar cosolutes more effectively reduced the nonlinearity and more variability was seen in different polar solute/cosolute systems.

DESORPTION HYSTERESIS AND REVERSIBILITY. Hysteresis refers to nonsingular or path-dependent sorption and desorption, such that the

relationship between qe and Ce followed during desorption is not the same as that which occurs during sorption. The occurrence of hysteresis is accompanied by a greater apparent affinity in the desorption direction and is generally d acknowledged when, for a given Ce, the Kd in the desorption direction, Kd ,is s greater than the Kd in the sorption direction, Kd (Huang et al., 1998).

Sorption of Organic Compounds 65 Figure 2.8 The effect of cosolute sorption observed by Chiou and Kile (1998). The isotherm becomes more linear in the presence of cosolute, which is consistent with a dual-mode sorption mechanism with subsequent loss of nonlinear capacity as a result of competition by the cosolute.

Hysteresis is frequently observed in sorption of organic compounds to soils and sediments (e.g., Altfelder et al., 2000; Clay et al., 1988; Di Toro and Horzempa., 1982; Huang and Weber, 1997; Huang et al., 1998; Kan et al., 1998; LeBoeuf and Weber, 2000b; Miller and Pedit, 1992; Vaccari and Kaouris, 1988; and Xue and Selim, 1995). Furthermore, the desorption equilibrium relationship may depend upon the maximum solid-phase concentration achieved during the sorption process (Miller and Pedit, 1992). There are several possible explanations for desorption hysteresis, including (1) losses of solute to reactor surfaces during sorption and desorption; (2) slow rates of sorption and insufficient time allowed for either sorption or desorption equilibrium; (3) sorption to nonsettling dissolved or colloidal organic matter; (4) competition from other (possibly natural) compounds present in the sediment; (5) biodegradation; (6) chemical reaction and the formation of site-specific, solute–sorbent bonds; and (7) change of sorbent characteristics over time, so that the molecular environment into which a solute sorbs is different from that which it desorbs (Miller and Pedit ,1992). One source of solute loss during sorption and desorption is diffusion of organic compounds into the Teflon often used to line septa closures (Huang et al., 1998, and Lion et al., 1990). However, even when the Teflon septa was covered with silver foil, Huang et al. (1998) found that losses were greater than those observed when flame-sealed ampoules were used as batch reactors. A protocol using the latter reactor type was recommended.

66 Handbook on Sediment Quality s If equilibrium is not reached in the adsorption step, the value of Kd will be underestimated and sorption may continue to occur during the desorption d cycle. A significantly higher Kd may result, even if equilibrium is reached during the desorption step. If equilibrium is reached during the sorption step but is not reached during the desorption step because of slow desorption d kinetics, the solid-phase loading and Kd will again be overestimated. Huang et al. (1998) defined a hysteresis index that is essentially the percentage increase in uptake observed during desorption, evaluated at the same solution-phase equilibrium concentration

d s ᎏqe – qe Hystersis index = ΄΅s (2.63) qe T,Ce

They found that the hysteresis index increased by a factor of 2 when the equilibration time for the sorption step was decreased from 14 days (at which time apparent equilibrium was observed) to 1 day. Sorption to nonsettling dissolved or colloidal organic matter and competition from other compounds present in the sediment are two hypotheses for a

“solids concentration effect,” so called because lower apparent Kd values are observed with increasing solids concentration. In some cases, covalent bond formation can manifest as sorption hysteresis; Ononye et al. (1989) showed that aromatic amines can undergo covalent bonding with humic substances from sediments, soils, and natural water, leading to irreversible sorption and immobilization. DiToro and Horzempa (1982) studied the reversibility of PCB binding to sediments. The authors determined partition coefficients for desorption isotherms and concluded that the partition coefficients for desorption isotherms were greater than for the sorption ones. Their results demonstrated that the isotherm was divided into reversible and resistant components. The reversible process did not exhibit hysteritic behavior. However, the isotherm does not appear to be defined by a single reversible isotherm. Huang and Weber (1997) quantified the apparent sorption–desorption hysteresis observed for several different sediment and soil samples. These researchers also characterized the soil organic matter associated with the phenomenon, correlating the oxygen-to-carbon ratios between sorption and desorption behaviors as a practical tool for assessing sorption–desorption hysteresis, sorption affinity, and isotherm nonlinearity associated with sediments and soils. LeBoeuf and Weber (2000b) evaluated phenanthrene sorption by and desorption from 13 natural and model organic macromolecules. For sorption equilibration periods of 42 to 365 days and desorption periods of 42 days, the authors found significant desorption hysteresis with more rigid, condensed, glassy macromolecules and relatively little to no hysteresis for rubbery macromolecules. They attributed the observed hysteresis to both a sequester- ing of phenanthrene within microvoids of the glassy sorbents as well as a possible failure to reach true desorption equilibrium, even after 42 days.

Sorption of Organic Compounds 67 EFFECTS OF PARTICLE SIZE. Karickhoff et al. (1979) fractionated pond and river sediments into five size fractions. Distribution coefficients correlated well with the fraction of organic carbon for particles smaller than 50 µm, including clays (<2 µm) and silts (2 to 50 µm). Sands (>50 µm) showed significantly different behavior and had much lower Kd and organic carbon- normalized (Koc) values. The individual Kd values for a given sediment fraction were added to obtain the distribution coefficient for the whole sediment

= KxKfd ∑ i oc,, i oc i (2.64) i

Where

xi = the mass fraction, and foc = the fraction of organic carbon in a given particle size range.

Weber et al. (1983) found that the <75-µm size fraction of a Saginaw River sediment sample exhibited significantly greater uptake of Aroclor 1254 (a commercial mixture of PCBs). They attributed the observation to a greater carbon content of the smaller size fraction and note that effects of particle size likely reflect the nonhomogeneous physicochemical nature of natural sediments rather than particle size per se.

EFFECTS OF ORGANIC MATTER COMPOSITION AND STRUCTURE. Chemical Characterization: Polarity and Aromaticity. Uhle et al. (1999) compared binding of PCB congeners to dissolved organic carbon (DOC) isolated from two geochemical “end-member” environments: an Antarctic lake with DOC derived solely from phytoplankton and bacteria and the Great Dismal Swamp (Virginia), with DOC derived from lignin precursors (decayed higher plant debris). The Antarctic lake DOC had a higher percentage of aliphatic carbon and the Great Dismal Swamp DOC had a higher percentage of aromatic moieties, as determined by 13C solid-state cross polarization magic-angle spin (CPMAS) NMR. The authors hypothesized that the aliphatic DOC would be less polarizable than the aromatic DOC and used this to explain the higher observed binding to the Great Dismal Swamp DOC. The authors assert that polarity (as often expressed in terms of elemental oxygen content) alone may not be a good predictor of binding properties. As evidence, they cite the fact that the Antarctic lake and Great Dismal Swamp DOCs were both isolated using preparative LC and, therefore, are likely to have similar polarity but still have significantly different structure and PCB- binding properties. Elemental analyses have been used to assess changes in polarity of organic matter samples; these changes are usually attributed to diagenetic alteration of carbohydrates and cellulose to less polar aliphatic and lignin components (Kile et al., 1999). Kile et al. (1999) studied the effects of soil and sediment organic matter polarity using solid-state 13C CPMAS NMR. Organic carbon associated with polar functional groups was estimated by combining carbohy-

68 Handbook on Sediment Quality drate and carboxyl–amide–ester carbons. Aqueous partition coefficients for

soils and sediments were measured and it was found that the Koc for soils was approximately 60% that of sediments and clustered into two distinct popula-

tions. When Koc was plotted as a function of percent polar carbon, data for both soils and sediments taken as a whole exhibited an inverse correlation

between Koc and percent polar carbon; however, within a geosorbent type, trends were not significant. When Koc was plotted as a function of aromaticity, no significant trends were observed for either the whole dataset or for the soil or sediment subsets.

Rubbery/Glassy Models. Weber et al. (1992) confirmed that organic matter associated with soils and sediments could vary significantly and that different types of organic matter showed different abilities to sorb organic contaminants. Whereas 1,2,4-trichlorobenzene (TCB ) uptake by six different soils increased with increasing fraction organic carbon, normalizing the uptake by

foc,i did not normalize the isotherms. The determination of organic carbon content was shown to depend on the temperature used during the oxidation step. The difference between organic carbon measured by low-temperature persulfate oxidation (“soft” carbon) and high-temperature oxidation with pure oxygen (“hard” carbon) was shown to vary among soil samples. Isotherm nonlinearity and uptake increased with increasing “hard-carbon” content and solute hydrophobicity. Particles of shale, likely entrained to soils by glacial activity, were thought to represent diagenetically altered “hard” carbon. This explanation was consistent with the finding that base extraction did not reduce the uptake of TCB by whole soils. Isolated shale particles had surface areas much higher than whole soils (13 vs 2 to 4 m2/g), and oxygen-to-carbon (0.47 to 0.60) and hydrogen-to-carbon (1.2 to 1.4) ratios intermediate between cellulose and humic acids. Even among these isolated particles, however, differences in composition were apparent. The first direct evidence in support of the rubbery/glassy model for natural organic matter derives from the thermal analysis work of LeBoeuf and coworkers (LeBoeuf and Weber, 1997, 2000b, and Young and LeBoeuf, 2000). Through use of standard differential scanning calorimetry (DSC), LeBoeuf and Weber (1997, 2000a and 2000b) detected glass transitions of

Aldrich humic acid (Tg = 62 °C), Leonardite humic acid (Tg = 72 °C), and a lignin (Tg = 70 °C). Evaluation of Aldrich humic acid and other synthetic polymers with known glass transitions under water-saturated conditions suggested a correlation of the reduction of the transition temperature to the amount of water sorbed within the macromolecule, where more polar macromolecules (as defined by larger Hildebrand solubility parameters) sorbed

more water and thus had the greatest reduction in their Tg. Evaluation of differential heating rates during differential scanning calorimetry (DSC)

showed a shift in Tg characteristic of glass transition behavior according to Gibbs-DiMarzio theory. Use of more sensitive instruments such as thermal mechanical analysis and temperature-modulated DSC revealed additional

glass transition behavior in a peat humic acid (Tg = 62 °C) and a Suwannee River fulvic acid (Tg = 36 °C) (Young and LeBoeuf, 2000).

Sorption of Organic Compounds 69 Microporosity. As noted in a recent comparison of N2 to CO2 sorption on natural materials by LeBoeuf and Weber (1996) and De Jonge and

Mittelmeijer-Hazeleger (1996), CO2 measurements indicate a larger surface area of the organic matrix compared with nitrogen; a finding quite similar to that observed for coals (Vorres, 1993), activated carbons (Garrido et al., 1987), and synthetic organic polymers (LeBoeuf and Weber, 1996). Because the molecular dimensions of N2 and CO2 are similar (hard sphere diameter: N2 = 0.386 nm, CO2 = 0.360 nm; molecular cross-sectional area: N2 = 2 2 0.162 nm ,CO2 = 0.170 nm ), the difference in sorption capacity is explained by De Jonge and Mittelmeijer-Hazeleger (1996) as a function of activated diffusion. Assuming an Arrhenius-type relationship, it is expected that the diffusion coefficient at liquid nitrogen temperatures will be significantly smaller than its value under the ice water conditions used for CO2 sorption. Hence, the N2 isotherm does not represent equilibrium sorbed-phase concentrations, whereas the CO2 presumably has reached equilibrium. A more detailed analysis of this phenomenon is provided by Gregg and Sing (1982). Gregg and Sing (1982), in interpretation of the hypothesis of Maggs (1953) and Zwietering and van Krevelen (1954), note that if the width of a micropore is close to the diameter of a sorbate molecule, the molecule will encounter an energy barrier to its passage through the narrow constriction. This will result in a positive correlation between temperature and the rate of sorbate molecule entry to the micropore. In other words, at sufficiently high temperatures, the rate of sorbate movement into the micropore will be fast enough to attain equilibrium within the course of the experiment. A second explanation for the observed increase in measured surface area with CO2 relative to N2 may lie with the possibility of chemisorption and the presence of a strong quadrapole moment (Gregg and Sing, 1982) within the

CO2 molecule. Although one may envision chemisorption of CO2 within an organic matrix, evaluation of CO2 desorption from several natural sorbents by De Jonge and Mittelmeijer-Hazeleger (1996) revealed no evidence of chemisorption. The presence of a quadrapole moment likely means that the adsorption isotherm of CO2 may be highly sensitive to the presence of ions or polar groups on the surface of the sorbent (Gregg and Sing, 1982). Whereas this reasoning may explain some of the increase in CO2-sorption capacity compared with N2 as observed by LeBoeuf and Weber (1996) for more polar natural organic materials such as Aldrich and peat humic acids, it does not explain the large increase in CO2-sorption capacity compared to N2 sorption for relatively nonpolar Illinois No. 6 and Wyoming coals, and especially does not explain the increase in CO2 uptake by nonpolar synthetic polymers used in their study.

Crystallinity. Crystallinity refers to a regular, ordered, three-dimensional crystal lattice portion of a macromolecule (Kroschwitz, 1990), where the macromolecule chains align themselves in perfect parallel array. There are no purely crystalline macromolecules; even so-called crystalline polymers have some amorphous content. Nonetheless, crystalline regions of macromolecules can form up to 98% of the polymer structure (Rosen, 1993) and have conse- quent large effects on mechanical behavior.

70 Handbook on Sediment Quality Figure 2.9 Folded-chain model for crystallinity (after Rosen, 1993).

Crystalline regions are formed by noncovalent bonds between polymer chains of similar structure that are strong enough to overcome the disordering effects of thermal energy. Hence, macromolecules with a high crystalline

melting point (Tm) (i.e., that temperature at which the crystalline regions begin to come apart) typically are bound together by stronger interactions such as

hydrogen or dipole bonds than those polymers with a lower Tm. Variances in the degree of crystallinity also are obtained from differential thermal histories of samples. For example, a normally semicrystalline polymer can be formed in a totally amorphous state by rapid quenching of the sample from a temper-

ature above the Tm. This rapid cooling causes the polymer chains to “freeze” in an expanded, amorphous position by not providing sufficient time or thermal energy at the lower temperature to allow polymer chain reallignment into crystalline regions. Macromolecules with relatively homogeneous chain compositions are also more likely to form crystalline regions than polymers with irregular chain structures that may have a number of protruding side functional groups (Rosen, 1993). Given the relative heterogeneity of soil or sediment organic macromolecules, one would thus expect the occurrence of crystallinity in these natural systems to be rare, with only small or so-called microcrystalline regions present. However, for more homogeneous biopolymers or largely diagenetically altered natural organic matter (e.g., coals), one may expect to find significantly larger regions of crystallinity. The physical properties of semicrystalline polymers have been described using a number of models, including the fringed micelle model (Elias, 1977), and more recently with the folded-chain model represented in Figure 2.9 and reptation theory.

Sorption of Organic Compounds 71 In the folded-chain model, the crystalline regions (or so-called crystallites) are represented by portions of macromolecule chains tightly folded onto themselves in a parallel array of nanometer dimensions. Because the macromolecule chains are on the order of micrometers in length, they tend to extend from one crystalline region to another through less dense amorphous regions. The crystalline regions of macromolecules are similar to crosslinks in that the macromolecule chains are interconnected by the crystallites, but differ in that exposure to increased thermal energy or a “good” solvent will tend to separate and dissolve the polymer chains contained in crystallites, whereas the macromolecule itself will degrade at the higher temperatures required for disintegration of covalently bonded crosslinks. Reptation theory description of macromolecule structure is analogous to a bowl of live snakes (Teraoka et al., 1992). In this “bowl” reside a mesh of entangled, linear, flexible macromolecule chains that continue to wriggle within a minimal range, effectively forming a tubelike structure. It is within this tube that the polymer chains move back and forth and, over sufficient periods of time, the polymer chain can actually move along the tube to new interaction sites with fellow macromolecule chains or other media. As one might imagine, an increasing presence of crystallinity imparts a rigidity or stiffness to a macromolecular structure. In semicrystalline macromolecules significantly below their melting point, crystallinity imparts an inaccessibility to a portion of the macromolecule (Barton, 1990). Considering the polymer as a whole, this results in a lower average solute concentration at equilibrium compared with the actual solute concentration in the amorphous regions and can have similarly profound influences on the rate of uptake of solutes within macromolecular matrices. The degree or amount of crystallinity is determined by a number of different methods, including small-angle, X-ray diffraction. In this technique, the scattering of X-rays in the small angle (so-called Bragg angle) region by crystalline sections is differentiated from the broad diffraction pattern characteristic of amorphous regions (Kroschwitz, 1990).

SOLIDS-CONCENTRATION EFFECT. The solids-concentration effect refers to the apparent decrease in the distribution coefficient with increasing solids concentration. This effect has been observed when isotherms are conducted using a variable-dose methodology and is most pronounced for compounds having large partition coefficients (Kd > 1000 mL/g) (Van Hoof and Andren, 1991). Voice et al. (1983) observed that the apparent Kd for chlorobenzene; naphthalene; 2,5,2'-trichlorobiphenyl; and 2,4,5,2',4',5'- hexachlorobiphenyl sorbed by three Lake Michigan sediment samples decreased significantly with increasing solids-concentration, mass/volume

(M/V) (or dose, Do). Trends were nearly linear on log Kd versus log Do coordinates, with slopes ranging from !0.14 to !0.86. Weber et al. (1983) observed a solids-concentration effect for the uptake of Aroclor 1254 (commercial PCB mixture) by algae, riverine suspended solids, riverine sediments, and montmorillonite clay. Trends between log Kd and log Do were similar to those found by Voice et al. (1983). These authors proposed that the effect was

72 Handbook on Sediment Quality caused by the transfer of material, capable of binding the solute, from the solid phase to the solution phase during the course of the sorption experiment. The material could be dissolved, colloidal, or particulate but, in any case, is not removed in the solid–solution separation step (typically centrifugation). In experiments without organic solute, it was found that both turbidity and total organic carbon in the centrifuged solution phase correlated with solids concentration. Because the organic carbon concentration was fairly low (typically <1 mg/L), Voice et al. (1983) theorized that the solid phase was releasing “microparticles” capable of binding hydrophobic organic solutes. Van Hoof and Andren (1991) classify these particles as ranging from 0.001 to 1 µm in size. Curl and Keoleian (1984) have proposed an “implicit adsorbate” model to explain such anomalies. The model is a competitive adsorption model that postulates the presence of a competing sorbate initially present on the soil or sediment, and thus subsequently present in solution in proportion to the solids concentration in the isotherm experiment. Curl and Keoleian (1984) do not identify these “implicit” adsorbates. However, both natural and anthropogenic compounds may compete with contaminant compounds. For example, more recently, Xing and Pignatello (1998) have shown that simple, naturally occurring aromatic acids (e.g., p-hydroxybenzoic and vannilic acids) com- peted effectively with 1,3-dichlorobenzene and 2,4-dichlorophenol for sorption by Cheshire and Pahokee soils. The effect of an implicit adsorbate was shown by Curl and Keoleian (1984) using a two-component competitive Langmuir model QbCo QbCo q = A AA ; q = B BB (2.65) eA,,++ eB ++ 11bCAA bC BB bCAA bC BB

The concentration of component B in the system will depend on the solids

concentration, CB = f(M/V). Therefore, the apparent distribution coefficient, * KA = qe, A/CA, will also depend on the solids concentration

o ∗ Qb K = A A (2.66) A ++ 1()bCAA b B fM/V

As the solids concentration increases, CB increases, and the apparent distribution coefficient for species A decreases concomitantly. Di Toro (1985) presented a “particle interaction model” to describe the effects of solids concentration on apparent partition coefficients. Di Toro (1985) assumed that the sorbed phase is comprised of two components, a reversible (labile) and a desorption−resistant component. The particle interaction model applies to the reversible component of the sorbed phase. The model was developed in a manner similar to the development of the Langmuir model, by considering sorption and desorption reactions. If the sorbent loading is low, the number of available sites is approximately equal to the total site concentra-

tion; therefore, the Langmuir adsorption rate, ra, can be written

ra = ka[A][S] ≈ ka [A][ST] (2.67)

Sorption of Organic Compounds 73 The rate of desorption, rd, includes a first-order desorption reaction as used in the Langmuir model, and an additional second-order desorption term that represents particle interactions

ra = kd [A S] + kdp [A S]Do (2.68)

Where

kd = the first-order desorption rate constant [A ≡ S] = the concentration of sorbed species,

kdp = the particle-interaction desorption rate constant, and

Do = the particle concentration.

The resulting isotherm is

KS[] A KS [] A KS [] A []AS≡= T = T = T k k KD + dp + dp 1+ o (2.69) 11Do KDo ν kd ka

Where K= the Langmuir equilibrium constant equal to the ratio of the first-order

adsorption and first-order desorption rate constants (ka /kd), and ν = the ratio of the first-order adsorption and particle-interaction desorp-

tion rate constant (ka /kdp).

The product KST can be thought of as the Langmuir low-coverage partition coefficient (or Henry’s constant). The observed-partition coefficient is equal to the Langmuir-partition coefficient divided by the particle interaction term

(1 + KDo /ν). Therefore, the magnitude of the observed-partition coefficient decreases with increasing particle concentration. When the particle concentration is small, or the particle-interaction desorption rate constant is small compared with ka, there is no effect of particle-induced desorption, and the observed-partition coefficient is equal to KST. Van Hoof and Andren (1991) investigated a kinetic explanation of the solids-concentration effect in the uptake of 4-monochlorobiphenyl by monodisperse polystyrene spheres. A solids-concentration effect was observed in constant initial concentration, variable-dose experiments because reactors having higher solids concentrations approached equilibrium more slowly, and hence exhibited lower Kd values. Particle aggregation, which could result in lower surface area and longer diffusion path lengths, was ruled out in this system. In addition, it was shown that solution-phase mass transfer to the particle surface was not limiting. A dual mode of sorption by glassy polystyrene was invoked as a possible explanation for the observed kinetic effects. The dual-mode theory postulates the existence of heterogeneous sites in addition to a dissolution domain; these sites are micropores that can bind solutes strongly and hinder diffusion. When the solids concentration is high, the solution-phase concentration and solid-phase loading are low, and a greater proportion of the sorbed mass may be located in the slower-diffusing micropore domain.

74 Handbook on Sediment Quality SORPTION RATE PROCESSES It has been shown in both field and laboratory experiments that the sorption of organic compounds by soils and sediments cannot always be described as an equilibrium process (e.g., Piatt and Brusseau, 1998). Therefore, an understanding of sorption rate processes is necessary for modeling solute transport in environmental systems and interpreting data collected in the laboratory. Applications for solute-transport modeling include assessing contamination and developing remediation strategies. In addition, a knowledge of sorption kinetics can provide insight to sorption mechanisms. The objective of this section is to present various formulations for expressing sorption rate processes and discuss their applications. Because we are often interested in how solution phase concentrations vary in time or space, sorption rate processes will first be discussed more generally in the context of solution-phase mass-balance equations. This will be followed by a more detailed discussion of different ways to formulate mass-balance equations for the solid phase. In some special circumstances, the solution-phase and solid phase, mass-balance equations do not need to be solved simultaneously; these situations will be described. The simplest reactor type is the constant-volume, well-mixed batch reactor. If the mixing intensity is high, such that transport of solute to the surface of the solid phase does not limit sorption rate, the rate of change of mass in the solution phase must equal the rate of change of mass in the solid phase

∂C ∂q V =−DV (2.70) ∂t o ∂t Where V = the solution volume, and 3 Do = the solids concentration (kg solids/m solution).

Therefore the product VDo is the mass of solids in the reactor. The minus sign is needed because if the solid-phase concentration is increasing, ∂q/∂t is a positive quantity that is a sink for the solution phase. If an environmental system can be modeled as a completely mixed flow reactor, accumulation of mass in the solution phase is governed by transport of mass by the advective (bulk) flow, Q, and uptake by the solid phase. Under the assumption that uptake by the solid phase is not limited by the rate of mass transport to the solid surface, the solution-phase, mass-balance equation is

∂C ∂q V =−−QC QC D V (2.71) ∂t IN o ∂t

Finally, mass transport in plug-flow reactors, including transport in porous media, is often modeled with the advection–dispersion equation. Including a

Sorption of Organic Compounds 75 term for uptake by the solid phase, this is written for one-dimensional transport as follows:

∂C ∂C ∂2C ∂q =−v + D − D (2.72) ∂t ∂x h ∂x 2 o ∂t Where v = the solution-phase velocity, and

Dh = the coefficient of hydrodynamic dispersion.

Note that, in a porous medium,

1− ε D = ρ (2.73) osε

Where

ρs = the solid-phase particle density, and ε = the porosity (Vvoid /Vtotal).

The term (1–ε)/ε is the solid-phase volume per volume of liquid in the porous medium. The majority of the remainder of this section will focus on the various ways to represent sorption rate phenomena, that is, present the various mathematical expressions developed to represent the ∂q/∂t term. Before doing so, however, a special condition, referred to as local equilibrium, will be introduced. When the local equilibrium assumption is appropriate, solution of the solid-phase mass balance can be avoided.

LOCAL EQUILIBRIUM. When the rate of sorption is fast compared with other processes governing solute transport, uptake by the solid phase may be essentially instantaneous, and a “local equilibrium” condition obtains. This means that the time scales for mass transfer (1) to solid-phase particle surfaces and (2) within solid-phase pores or matrices are small compared with the time scales of macroscopic processes of fluid transport (Weber et al., 1991). This condition will manifest when solution velocities are slow and particle sizes are small; such conditions often characterize those used in liquid chromatography. When the solution and solid phases are in local equilibrium, the dissolved and sorbed solute concentrations are related by the isotherm relationship, and solution of a solid-phase mass balance is unnecessary. Under local equilibrium conditions, the rate of uptake can be directly related to the rate of change of solution-phase concentration

⎡∂ ⎤ ∂ ∂ q = q C ⎢ ∂ ⎥ ∂ ∂ (2.74) ⎣ t ⎦local equilibrium C t

where ∂q/∂C = the slope of the isotherm relationship (e.g., ∂q/∂C = Kd for a (n–1) linear isotherm and nKF Ce ) for the Freundlich isotherm. The local equilibrium assumption makes solute-transport modeling straightforward because only one (i.e., the liquid phase) mass-balance equation needs

76 Handbook on Sediment Quality to be solved. Solving the liquid-phase mass-balance equation is facilitated further when the isotherm expression is linear. In this case,

− ε ∂ ∂ ∂2 ⎡ + ρ 1 ⎤ C =− C + C ⎢1 Kd s ⎥ v Dh 2 (2.75) ⎣ ε ⎦ ∂t ∂x ∂x

where the term in brackets is known as the retardation factor, R. Dividing through by the retardation factor, it is evident that the resulting equation has the same mathematical form as the advection–dispersion equation without a sorption term, the difference being that the solution-phase velocity and coefficient of hydrodynamic dispersion are replaced with their effective or * retarded values, v* = v/R and Dh = Dh/R. Therefore, the advective– dispersion equation with linear, local equilibrium sorption can be solved analytically. Whereas this is convenient and useful for making first-order assessments of contaminant transport, it should be used with caution, as its underlying assumptions may not be met in many environmental systems.

LANGMUIR KINETICS. One approach to developing an expression for sorption kinetics is based on the kinetic argument used to develop the Langmuir isotherm. It is assumed that the rate of adsorption is second order in terms of solution concentration and the concentration of vacant sorption sites, whereas the rate of desorption is first order in the sorbed-phase concentration. In the absence of other (i.e., mass transfer to the particle surface) rate limitations, incorporating these assumptions yields the following rate expression

dq =−kCQ[]o qt()− kqt () (2.76) dt a d Where 3 ka = the second-order adsorption rate constant (L /M solute/t), and –1 kd = the first-order desorption rate consant (t ).

Schlebaum et al. (1998) used the Langmuir rate model to describe the desorption of pentachlorobenzene from an IHSS peat humic acid. The model fit early-time data but did not represent the longer-time data well; this was attributed to a nonlabile fraction that desorbs slowly. In addition, the Langmuir model shows a dependence of initial sorbed-phase concentration, which was not observed in the experimental rate data.

EMPIRICAL RATE MODELS. Empirical sorption-rate models, written by analogy with homogeneous solution-phase kinetics, generally lack physical significance but can be useful for simulating observed rate behavior. In addition, these rate laws are simpler to apply than diffusion models because they do not require information about particle size and shape, pore geometry, or diffusion coefficients. The simplest is the reversible first-order model, which postulates uptake in proportion to the solution-phase concentration and desorption in proportion to the sorbed-phase concentration. This model can be

Sorption of Organic Compounds 77 derived from the Langmuir model under the assumption that uptake, q(t), remains small compared with capacity

dq =−=kQCo kqt() kC′ − kqt () (2.77) dt a d a d

Where –1 –1 k'a = a pseudo-first-order sorption rate constant (L mol s ), and −1 kd = first-order desorption rate constant (t ).

(Note that the product of k'a and the adsorbent dose yields a quantity having units of [t–1]). The ability of the first-order rate model to describe the desorption of pentachlorobenzene from an IHSS peat humic acid was compared with the Langmuir rate model by Schlebaum et al. (1998). As for the Langmuir model, the first-order model fit early-time data but did not represent the longer-time data well. However, the first-order model did not show a dependence of initial sorbed-phase concentration, which was consistent with experimental observations. Another form of a first-order sorption-rate model is based on a “linear solid-phase driving force.” The linear driving-force rate law is an exact result when the intraparticle transport mechanism is homogeneous solid diffusion (see the Evidence for Sorbate Competition section) and when the concentration profile within the solid phase is assumed to be parabolic. Assuming an equilibrium condition between the bulk solution and the particle surface, the solid-phase flux is written as follows

=− FkDqqso[] e (2.78) where ks = a solid-phase, mass-transfer coefficient (m/s). The rate of uptake by the solid phase is the flux multiplied by as,the interfacial area per unit mass of sorbent (i.e., the specific surface area)

dq =−kaD[] q q (2.79) dt ss o e where the product ks as Do can be taken as an empirical first-order rate con- –1 stant, k's (t ). This rate constant can be interpreted in terms of an intraparticle diffusion coefficient; this idea will be developed further later in the section. The linear solid-phase driving force model was evaluated by Rijnaarts et al.

(1990), who used a linear isotherm (i.e., qe = kd C). The model fit some desorption data well, comparable to a radial diffusion model (see below), but could not capture the effects of different mixing conditions. It should be noted that the linear solid-phase driving force model could be combined with a film mass-transfer rate process, which allows for explicit incorporation of a mass- transfer coefficient that could account for different mixing conditions. Piatt and Brusseau (1998) used a two-domain model incorporating an instantaneous (local equilibrium) sorption domain and a rate-limited domain described by the solid-phase driving force rate law. The model was used to

78 Handbook on Sediment Quality describe breakthrough curves for several PAHs, substituted benzenes, and chlorinated ethenes. Based on the hypothesis that natural geosorbents are heterogeneous and may contain high-affinity sites in addition to partitioning domains, Schlebaum et al. (1999) combined the reversible first-order kinetic model, representing desorption from labile sites, with a nonlinear nth-order model to represent desorption from slowly desorbing high-affinity sites. The nonlinear model is written as follows: n dq =−C ka kqd (2.80) dt Do

(n–1) where ka is an nth-order rate constant (Vsolution /Msolute) (1/t). This model was capable of describing and predicting independent datasets over a time period of up to 150 hours. Another approach used to account for sorbent heterogeneity is the two-site model. The solid phase is modeled as having rapidly sorbing sites that are in local equilibrium and slow, kinetically limited sites (Cameron and Klute,

1977, and Travis and Etnier, 1981). The total sorption, qT, is the sum of

uptake by these two sites, qT = q1 + q2. If the rapidly sorbing sites (q1)are modeled with (for example) a linear isotherm and the kinetically limited sites

(q2) are modeled (for example) with the reversible first-order model, the resulting two-site model is

dq dq dq ∂C T =+=12K +[]kC′ − kq (2.81) dt dt dt d ∂t a d

MASS-TRANSFER CONTROLLED KINETICS. External Mass Transfer. In many systems, there exists a solution-phase sorption rate limitation that can be conceptualized as diffusion across a thin, quiescent liquid film (or boundary layer) that exists at the solid-phase particle surface. Diffusion across this film is assumed to occur without reaction and under a pseudo-steady-state condition; that is, the concentration profile across the film is linear. While the slope of the profile may change in response to changes in the solution- and sorbed-phase concentrations, these changes are assumed to occur sufficiently slowly so that profile remains linear. Under these assumptions, the flux across the liquid film is given by

=− FkCCf ()s (2.82)

where kf = the film mass-transfer coefficient (L/t), which is equal to the free liquid diffusivity, Dl, divided by the film thickness, δ. As the film thickness is typically unknown, the mass-transfer coefficient is treated as an empirical parameter. Because the film is assumed to be at steady state and no mass can accumulate in it, the rate of mass transfer across the film must equal the rate of sorption to the solid phase dq =−ak() C C (2.83) dt s f s

Sorption of Organic Compounds 79 where as is the solid-phase external surface area per unit mass (specific surface area).

The external “film” mass-transfer coefficient, kf , can be measured in both batch and column systems by statistical search procedures (curve fitting) or using specially designed experiments or experimental setups. The rate of sorption is most sensitive to film mass-transfer resistance at early times soon after sorption commences. This fact can be exploited in the design of column tests to measure kf by making the column short enough to ensure that breakthrough is immediate. Under these conditions, the early-time breakthrough curve can be analyzed to evaluate external mass-transfer resistance (Cooney, 1991, and Weber and Liu, 1980). If experimental data are lacking, the value of the film mass-transfer coefficient can be estimated using one of a number of correlations.

Wakao and Funazkri (1978) proposed a correlation for estimating kf that is valid at low Reynolds numbers, NRe. Most such correlations have a similar form

kf Dp 0.60 1/3 NSh = – = 2.0 + 1.1N Re N Sc ; 3 < NRe < 10 000 (2.84) Dl Where

NSh = the Sherwood number, Dp = the particle diameter, and Dl = the liquid diffusivity.

The Reynolds number, NRe, is given by Dp εvρ/µ, where εv is the approach velocity; the Schmidt number, NSc, is given by µ/Dl ρ, where µ/ρ = ν≈1.12 × 10!6 m2/s (15 °C). External mass transfer can be combined with other rate mechanisms to construct a “resistance-in-series” model. For example, the solid-phase driving force rate law can be written as follows:

dq =−KaD[] q q (2.85) dt ss o e

Where Ks is an overall mass-transfer coefficient incorporating both diffusion through the liquid film at the particle surface and the solid-phase (intraparticle), mass-transfer resistance

11m =+ (2.86) ′ Ks kkf s

Here, m is the isotherm slope, equal to Kd for a linear isotherm, and approximated by the isotherm slope between the points q, C* and q*, and C, where C* is the liquid-phase concentration in equilibrium with the actual solid-phase loading, q; and q* is the solid-phase loading in equilibrium with the actual liquid-phase concentration, C (Cooney, 1999).

80 Handbook on Sediment Quality Homogeneous Solid-Phase (Surface) Diffusion. The physical basis for applying diffusion models to natural geosorbents is the fact that soils and sediment particles are at least partly porous as a result of their aggregated nature because of fractures in individual grains and due to the existence of penetrable organic matter phases (Pignatello and Xing, 1996, and Wu and Gschwend, 1986). The simplest diffusion model treats the solid phase as an amorphous and homogeneous sphere (Cooney, 1999). The rate expression is basically the diffusion equation written in spherical coordinates and in terms of the sorbed-phase concentration

∂q 1 ∂ ⎛ ∂q⎞ = ⎜rD2 ⎟ (2.87) ∂trr2 ∂ ⎝ s ∂r ⎠

where Ds = the solid (intraparticle) diffusion coefficient. The intraparticle diffusion coefficient is expected to show a concentration dependence, but it is often treated as a constant and is moved outside of the derivative. This rate law is also consistent with surface diffusion and has been applied to both uniformly microporous solid phases such as zeolites (Ruthven, 1984) and heterogeneous solid phases such as activated carbons. An expression for solute uptake from systems in which the external mass transfer is small, the uptake is small compared with the total quantity of solute in the system (so that the concentration at the particle surface is constant), and the particle size distribution is narrow is given by Crank (1975):

∞ qt() 61⎛ −Dn22π t⎞ =−1 ∑ exp⎜ s ⎟ (2.88) qnπ22⎝ R2 ⎠ e n=1 p Where

Rp = the particle radius, and q(t) = the average solid-phase loading, integrated over the particle radius.

Rp 3 ͵ 2 q(t)= –3 q(r)r dr (2.89) Rp 0

Cooney (1999) shows, based on the work of Glueckauf (1955) that, if the solid-phase concentration profile is approximated by a parabola, the homogeneous solid (surface) diffusion rate expression simplifies to the linear solid- phase driving force model

dq 15D =−s [] 2 qqe (2.90) dt Rp

where again qe is the loading at equilibrium with the solution-phase concentration at the particle surface. Comparing eq 2.79 and 2.90, we see that the

first-order rate constant, k's = ks as Do, can be interpreted in terms of the solid-

Sorption of Organic Compounds 81 phase diffusion coefficient. This approach was used by Piatt and Brusseau (1998), who used a more general expression

aD k′ = s (2.91) s l2 where a is a geometric shape factor and l is taken as a characteristic diffusion length.

Pore and Combined Pore-Surface Diffusion. It has been proposed (e.g., Wu and Gschwend, 1986) that the physical mechanism controlling uptake of organic solutes by soils and sediments is radial diffusion in liquid-filled pore fluids held in interstices of aggregates or organic matter itself. A mass balance on a spherical shell yields the following rate equation:

∂q ∂C 1 ∂ ⎡ ∂C ⎤ ρε+ p = εD ⎢r2 p ⎥ (2.92) pp∂t ∂t pprr2 ∂ ⎣ ∂r ⎦ Where

ρp and εp = the particle density and porosity, respectively; Dp = the pore-diffusion coefficient; and Cp = the dissolved solute concentration in the pore-solution phase.

The pore-diffusion coefficient is equal to the free-liquid diffusivity divided by an effective tortuosity, τ; this parameter accounts for the fact that diffusion paths are not straight and may include tortuous and dead-end pores. In addition, the effective tortuosity accounts for pore constriction effects that may become important in pores having small diameters. Note that, in contrast to previous rate expressions, the left-hand side of the pore-diffusion equation is in terms of solute uptake per unit volume of sorbent. Local equilibrium between solute dissolved in the pore fluid and sorbed by the solid phase is generally assumed. Therefore,

∂C ε D 11∂ ⎡ ∂C ⎤ D ∂ ⎡ ∂C ⎤ ppp= ⎢r2 pp⎥ = ⎢r2 p⎥ (2.93) ∂ ρε∂∂+ 2 ∂ ∂ 2 ∂ ∂ t pp(/qC ) r r⎣ r ⎦ Rrint r⎣ r ⎦ where Rint is the intraparticle (internal) retardation factor that causes an apparently slower diffusion process. Note that, for nonlinear sorption isotherms, the slope decreases as the concentration increases; therefore, the intraparticle retardation term becomes smaller and the effective diffusivity,

Dp/Rint, will increase with concentration. Note that, for a linear isotherm and under the local equilibrium assumption, the mathematical forms of the homogeneous solid (surface) diffusion and pore diffusion models are identical because ∂ ∂ ⎡ Cp ⎤ 11∂q ⎡ Cp ⎤ ∂q ⎢ ⎥ = and ⎢ ⎥ = (2.94) ∂tK∂t ∂rK∂r ⎣ ⎦local equilibriumd ⎣ ⎦ local equilibrium d

82 Handbook on Sediment Quality The only difference lies in the physical interpretation and numerical value of the respective diffusion coefficients. Crittenden et al. (1986) used a rate law that combines the rate processes of pore and surface diffusion

∂q ∂C 1 ∂ ⎡ ∂C ∂q ⎤ ρε+ p = ⎢ εDr22p + ρDr ⎥ (2.95) pp∂t ∂trr2 ∂ ⎣ pp ∂r ps ∂r ⎦

where all terms have been defined previously. Under the local equilibrium assumption, in addition to the relationship defined by eq 2.65,

⎡∂ ⎤ ∂ ∂C q = q p ⎢ ∂ ⎥ ∂ ∂ (2.96) ⎣ r ⎦local equilibrium C r

With this substitution, the combined pore-surface diffusion model can be written more simply as

∂C 1 ∂ ⎡ ∂C ⎤ pp= D ⎢r2 ⎥ (2.97) ∂t eff rr2 ∂ ⎣ ∂r ⎦

with an effective intraparticle diffusion coefficient defined by

ερDDqC+∂∂(/ ) D = pp ps eff ρε∂∂+ (2.98) pp(/qC )

where again, ∂q/∂C = Kd for a linear isotherm.

Diffusion in Macromolecules. Diffusion within organic macromolecular matrices depends on a number of variables, including solute size and its ability to form interactions with the sorbent. If the solute has a molecular size much smaller than the monomer unit of the sorbent and little intermolecular interaction is expected, rotation of only one or two monomer units is required to provide sufficient cross-sectional area for the solute to “jump” from one position to another. This description typifies diffusion of simple gases such as nitrogen and carbon dioxide in several polymers (Fujita, 1968). For larger solute molecules such as HOCs, which are comparable or larger than the monomer unit of the sorbent, diffusion requires larger chain rotations. Thus, diffusion of the solute also depends on the size of the penetrant and the

relaxation rate of the polymer chains, which is related to the Tg of the sorbent as well as the concentration of the solute in the polymer. Consequently, the degree of crosslinking can impart solute immobility through sieve effects. The higher degree of crosslinking, the more difficult it is for polymer chains to move to accommodate solute molecules. At extremely high crosslinking densities or in crystalline regions, total solute rejection is expected. Given the relatively complex dependence of diffusion on polymer characteristics, observed differences are categorized according to their respective behavior to include case I (Fickian), case II, super case II, and anomalous or non-Fickian diffusion and are illustrated in Figure 2.10.

Sorption of Organic Compounds 83 Figure 2.10 Hopfenberg–Frisch chart of anomalous transport phenomena (after Vieth, 1991, from Hopfenberg and Frisch, 1969).

CASE I TRANSPORT. Case I, or Fickian diffusion, references rates of diffusion that are orders of magnitude faster than the relaxation times of the polymer and obeys Fick’s first and second laws. For rubbery polymers well above their Tg, the motion of polymer molecules is sufficient, even at low solute concentrations, that the polymer is able to accommodate the solute molecule almost instantaneously (Fujita, 1968). Thus, no internal stress within the polymer is built up, resulting in no time dependence of the diffusion coefficient. Fickian diffusion in glassy polymers has also been observed at low solute concentrations. This behavior is attributed to the activation energy required to move polymer segments. At low solute concentration, there is not enough energy to “push” through the polymer chains. Hence, only sorption to the surface and near-surface regions occur and no relaxational dependence is observed.

CASE II TRANSPORT. Case II transport manifests from relaxation times much slower than the diffusion rate of the solute. This causes the development of a sharp sorption front of solute moving at a constant velocity from a swollen (rubberlike) state to an inner (glasslike) state. As the temperature or concentration of the sorbing solute is lowered, the velocity of the advancing sorption front quickly drops. This is interpreted as the point where the glass transition temperature (Tg) of the polymer (a sorbing solute tends to lower the

84 Handbook on Sediment Quality Tg) is reached. Hence, case II behavior only occurs below a polymer Tg, and above certain concentrations of solute. Examples of case II transport have been observed in sorption of n-hexane by polystyrene (Enscore et al., 1977), organic vapors by ethyl cellulose (Barrer and Barrie, 1957), and organic vapors and liquids in polyvinyl chloride (Berens, 1989). Super case II transport obtains as an extreme example of case II transport. Super case II only occurs under high solute concentrations such that the sorption front is so abrupt that it causes crazing of the polymer surface (Hopfenberg and Stannett, 1973).

ANOMALOUS OR NON-FICKIAN TRANSPORT. Anomalous or non-Fickian diffusion obtains when relaxation rates and diffusion are comparable and can be considered intermediate to case I and case II behavior. Anomalous diffusion has been reported by a number of investigators for a wide variety of solutes and sorbents, for example, sorption of ethylbenzene by poly(ethyl methacrylate) (Vrentas et al., 1984); organic vapors by ethyl cellulose (Barrer and Barrie, 1957); and organic vapors and liquids in polyvinyl chloride (Berens, 1989). Bagley and Long (1955) performed a series of interval sorptions of acetone on cellulose acetate. Their results showed that the sorption process was described by two stages. The first stage showed a relatively rapid initial increase in sorbed-phase concentration to an initial quasi-equilibrium that closely followed Fickian-type behavior, followed by a second stage of slowly increasing sorbed-phase concentration until a true equilibrium value was achieved. The first stage is explained in terms of low solute concentration, where sorption to the surface and near-surface regions occurs and little relaxational dependence is observed. The second stage is explained by the energy associated with swelling a polymer matrix. Park (1968) notes that the glassy polymer undergoes an elastic expansion upon absorption of the solute until the elastic forces increase the chemical potential of the sorbed solute to equal that in bulk solution such that no further sorption occurs. However, upon standing, the stressed polymer chain slowly relaxes, causing a decrease in the sorbed solute chemical potential, thus allowing additional solute sorption to reestablish equilibrium. This diffusion–relaxation model has been used to explain a number of anomalies, including sorption curves involving an initial maximum uptake, followed by temporary desorption and subsequent resorption (Berens and Hopfenberg, 1978, and Peppas and Urdahl, 1986). Given the above discussion, it is important to note several additional effects on diffusion rates. First, non-Fickian diffusion and case II transport is related to dual-mode sorption. Frisch (1980) ascribes this behavior in terms of the evidenced partially immobile solute sorbed to relatively fixed microvoids within the polymer. Thus, for polymers evidencing specific interactions with the solute (e.g., hydrogen bonding), diffusion is expected to be even slower. Second, Berens (1989) notes that the difference in diffusion coefficients for simple gas molecules in polymers in the rubbery state are approximately 4 times the values observed in the glassy state. However, for solvent molecules on the order of 0.5 to 0.6 nm in diameter, the ratio of rubbery-state diffusion coefficient to glassy-state diffusion coefficient may raise to 106 or 108 and can

Sorption of Organic Compounds 85 reach as high as 1012 for plasticizers 0.8 to 1 nm in diameter. Finally, desorption behavior for glassy polymers consistently shows a marked hysteresis from the sorption curve. Park (1968) explains that this hysteresis occurs because a number of interchain associations of the polymer are not reformed on desorbing from an initial saturated equilibrium state. Hence, the polymer on the desorption limb of a hysteresis loop is in a different configuration than the original polymer in the saturation limb. This is confirmed by integral sorption and desorption, where the second cycle sorption curve will lie close to the first cycle desorption curve.

MACROMOLECULAR DIFFUSION BEHAVIOR IN NATURAL SYSTEMS. Considering the above descriptions, it is possible that soil organic matter may display all modes of diffusion, depending on the local conditions (although case I and anomalous diffusion are most likely in normal environmental conditions). For instance, at experimental temperatures significantly above ambient, a highly swollen humic acid in aqueous solution at neutral pH may be expected to behave in similar fashion to a lightly cross-linked rubbery polymer. Hence, if the presence of cross-links and specific interactions between solute and sorbent are discounted, diffusing solutes would likely display Fickian-type diffusion. If, however, the experimental temperature is dropped considerably, it is possible that the humic polymer may revert to a more (but not entirely) glassy behavior, resulting in non-Fickian diffusion

(Pignatello, 1998). If the sorbent is coal with a known Tg of approximately 350 °C (e.g., Illinois No. 6 coal), case II transport may be obtained, as evidenced by sorption studies of pyridine on coal by Ritger (1985) and Barr- Howell et al. (1986). Finally, for conditions related to concentrated solvent spills in the environment, it is possible that a super case II condition may arise, causing crazing and cracking of glassy organic matrices in the presence of pure solute (e.g., non-aqueous-phase liquids).

SUMMARY AND CONCLUSIONS In this chapter, the underlying chemical and physical phenomena that contribute to organic contaminant sequestration by sediments were reviewed. In addition, how these phenomena can be described mathematically to provide a basis for developing descriptive and predictive models has been considered. The theory of linear partitioning and its mathematical implications for predicting transport were discussed in detail. This approach has seen widespread practical application. However, more recent investigation has identified a range of nonlinear sorption phenomena. The distinction between linear and nonlinear sorption is important in the context of contaminant transport, availability, and desorption rate. Nonlinear sorption may be caused by diagenetically altered carbonaceous components of sediments (e.g., shales), natural sediment organic matter that behaves in ways similar to glassy or crystalline polymers, and microporous black carbon such as soot and other

86 Handbook on Sediment Quality byproducts of combustion processes. Models to describe nonlinear sorption equilibria were discussed in detail, and examples from the literature were chosen to illustrate the efficacy of describing sorption equilibria with such models. These include isotherms developed for heterogeneous surfaces (e.g., Freundlich), isotherms developed for microporous sorbents (e.g., Polanyi potential theory), and composite models that combine isotherm equations to represent sorption by polymer-like organic matter (e.g., the dual-mode model combining Langmuir and linear components). Sorption phenomena and mechanisms of uptake were discussed in the context of sediment organic matter composition and structure, including aromaticity, crystallinity, and microporosity. Current research continues to evaluate how sediment properties influencing sorption can be best characterized and how these properties can be used to help predict organic contaminant sorption, reversibility, and availability in the environment.

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90 Handbook on Sediment Quality Gunderson, J.L.; Macintyre, W.G.; and Hale, R.C. (1997) pH-Dependent Sorption of Chlorinated Guaiacols on Estuarine Sediments: The Effects of Humic Acids and TOC. Environ. Sci. Technol., 31, 188. Halsey, G., and Taylor, H.S. (1947) The Adsorption of Hydrogen on Tungsten Powders. J. Chem. Phys., 15, 624. Hassett, J.J.; Means, J.C.; Banwart, W.L.; and Wood, S.G. (1980) Sorption Properties of Sediments and Energy-Related Pollutants. EPA-600/3-80-04, U.S. Environmental Protection Agency, Environ. Res. Lab., Athens, Ga. Hildebrand, J.H.; Prausnitz, J.M.; and Scott, R.L. (1970) Regular and Related Solutions. Van Nostrand Reinhold, New York. Hopfenberg, H.B., and Frisch, H.L. (1969) Transport of Organic Micromole- cules in Amorphous Polymers. J. Polymer Sci., Part B, 7, 405. Hopfenberg, H.B., and Stannett, V.T. (1973) The Diffusion and Sorption of Gases and Vapors in Glassy Polymers. The Physics of Glassy Polymers, R.N. Haward, (Ed.), Wiley & Sons, New York, 504. Huang, W., and Weber, W.J., Jr. (1997) A Distributed Reactivity Model for Sorption by Soils and Sediments. 10. Relationships Between Desorption, Hysteresis, and the Chemical Characteristics of Organic Domains. Environ. Sci. Technol., 31, 2562. Huang, W.; Schlautman, M.A.; and Weber, W.J., Jr. (1996) A Distributed Reactivity Model for Sorption by Soils and Sediments. 5. The Influence of Near-Surface Characteristics in Mineral Domains. Environ. Sci. Technol., 30, 2993. Huang, W.; Young, T.M.; Schlautman, M.A.; Yu, H.; and Weber, W.J., Jr. (1997) A Distributed Reactivity Model for Sorption by Soils and Sedi- ments. 9. General Isotherm Nonlinearity and Applicability of the Dual Reactive Domain Model. Environ. Sci. Technol., 31, 1703. Huang, W.; Yu, H.; and Weber, W.J., Jr. (1998) Hysteresis in the Sorption and Desorption of Hydrophobic Organic Contaminants by Soils and Sediments. 1. A Comparative Analysis of Experimental Protocols. J. Contam. Hydrol., 31, 129. Jafvert, C.T. (1990) Sorption of Organic Acid Compounds to Sediments: Initial Model Development. Environ. Toxicol. Chem., 9, 1259. Jain, J.S., and Snoeyink, V.L. (1975) Adsorption from Bisolute Systems on Active Carbon. J. Water Pollut. Control Fed., 45, 2463. Jaroniec, M. (1983) A New Isotherm Equation for Single-Solute Adsorption from Dilute Soutions on Energetically Heterogeneous Solids. Monatsh. Chem., 114, 711. Johnson, M.D.; Huang, W.; Dang, Z.; and Weber, W.J., Jr. (1999) A Distrib- uted Reactivity Model for Sorption by Soils and Sediments. 12. Effects of Subcritical Water Extraction and Alternations of Soil Organic Matter on Sorption Equilibria. Environ. Sci. Technol., 33, 1657. Kan, A.T.; Fu, G.; Hunter, M.; Chen, W.; Ward, C.H.; and Tomson, M.B. (1998) Irreversible Sorption of Neutral Hydrocarbons to Sediments: Experimental Observations and Model Predictions. Environ. Sci. Technol., 32, 892. Karickhoff, S.W. (1981) Semi-Empirical Estimation of Sorption of Hydropho- bic Pollutants on Natural Sediments and Soil. Chemosphere, 10, 833.

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98 Handbook on Sediment Quality Chapter 3 Bioavailability in Sediments

Jennifer Brower, RAND, Arlington, VA Gary Cecchine, RAND, Arlington, VA

100 Introduction 109 Factors Controlling 102 Current Research on Bioavailability Bioavailability in Sediments 109 Acid-Volatile Sulfide 102 Measuring Bioavailability 111 Organic Carbon 102 Alternative Extraction Methods 112 Processes Affecting Bioavailability 103 Membrane Analogs in Sediments 104 Importance of Sediment 112 Aging Characteristics in 112 Sorption Determining Bioavailability 114 Seasonality 104 Pore-Water Concentrations 114 Bioturbation Determine Bioavailability 115 Measuring Bioavailability 105 Bioavailability of Sediment- 116 Acid-Volatile Sulfides Associated Contaminants 118 Equilibrium Partitioning 106 Remediation Using 120 Toxicity Tests With Algae, Bioavailability Reduction Bacteria, or Representative 106 Modeling Species 108 Field Studies 121 Current Regulatory Directions 108 Bays 122 Toxicity Calculations 109 Lakes 123 References 109 Wetlands

99 INTRODUCTION The U.S. Environmental Protection Agency’s (U.S. EPA’s) report to Congress on contaminated sediment in January 1998 (U.S. EPA, 1997) identified areas in the continental United States where sediment may be contaminated at levels that may adversely affect aquatic life and human health. The report found that 26% of the surveyed watersheds are sufficiently contaminated with toxic pollutants to pose potential risks to people who eat fish from them and to fish and wildlife. The U.S. EPA’s evaluation of the data shows that sediment contamination exists in every region and state of the country, and there are 96 areas (watersheds) of probable concern (APCs). The APCs represent approximately 10% of the sediment underlying U.S. surface waters. More than two-thirds of these watersheds already have an active fish consumption advisory in place. Water bodies affected include streams, lakes, harbors, near shore areas, and oceans. At the tier 1 level, where associated adverse effects are probable, polychlorinated biphenyls (PCBs), mercury, organochlorine pesticides, and polycyclic aromatic hydrocarbons (PAHs) are the most frequent chemical indicators of sediment contamination. Because of the widespread and diverse nature of the sediment contamination in the United States and worldwide, understanding the long-term fate of sediment-associated contaminants is important. In particular, it is important to understand the interaction between the measured chemical concentrations in sediment and interstitial (pore) water and the associated effect on biota. There have been many empirical and theoretical attempts to correlate toxicity with measured chemical concentrations in the environment. In most cases, tests that measure toxicity do not also measure the internal dose of toxicant to the organisms tested. The correlation between measured environmental chemical concentrations and toxicity estimates, therefore, are often uncertain, depending on an unknown or estimated amount of toxicant available for uptake from the environment by the test organism. Similarly, “The presence of a contaminant does not itself indicate the potential for adverse effects. A contaminant can have toxic effects only if it occurs in a bioavailable form.” (Suter et al., 2000). In this chapter, the concept of bioavailability is considered. Bioavailability can be defined as the fraction of the chemical concentration in the environment that is available to interact with living organisms. Whereas this definition may be short and simple, it is actually difficult to predict and measure the bioavailability of compounds to one organism and even more difficult when considering communities. This simple definition of bioavailability is useful because, “Uptake factors developed for soil and sediment are highly variable because of the large variance in the properties controlling bioavailability in those media. Other measures of soil or sediment concentration such as concentrations in pore water or aqueous extracts may be more useful for deriving uptake factors and other models, but are seldom used.” (Suter et al., 2000). There are many confounding factors in determining bioavailability, including the complicated geochemistry of the sediment, the salinity of the water,

100 Handbook on Sediment Quality the influence of organisms on their environment, and the wide variety of potential biochemical reactions that may occur once an organism has come into contact with a compound (Forbes et al., 1998). Bioavailability may also be a function of the exposure route: organisms that ingest sediment may receive a different dose than those with dermal contact only. Each of these categories is confounded by countless variables that further increase the difficulty in predicting bioavailability. For example, bioavailability in sediment may also change on biologically relevant temporal scales, especially in lotic (e.g., river) environments where the characteristics of the overlying water can be highly variable temporally. Also, each of these three factors is often difficult (or impossible) to replicate under experimental conditions. The total amount of a compound that can be extracted from sediment using commonly harsh techniques does not usually equal the exposure or bioavailable dose to an organism and, therefore, does not reflect the risk. Therefore, bioavailability in some form should be considered for a complete environmental risk assessment (Utvik and Johnsen, 1999). Bioavailability has been recognized as important not only for risk assessment and remediation but also for conservation of ecosystems (Yu et al., 2000). Bioavailability is important both for assessing the relative risk associated with a particular site and for assessing the potential utility of bioremediation (Cornelissen, Rigterink, et al., 1998; Heitzer et al., 1992; and Reid et al., 2000). Compounds that are not bioavailable and do not bioaccumulate may not be readily available to microorganisms for bioremediation. Further, attempts to alter the chemistry of these compounds in situ to promote bioavailability and remediation could result in an adverse effect on the ecosystem to be remediated. In sediments, many factors control the bioavailability of the accumulated contaminants, including chemical type, pH, organic matter quality, content, and type; the concentration of acid-volatile sulfides; and oxygen concentration. In addition, both chemical–physical and biological processes such as sorption and biodegradation or biotransformation can affect bioavailability. Because of factors such as the complex interactions among sediment and overlying water, temporal variations in temperature and redox potential, and variability among organisms, it is difficult to use models to predict toxicity or bioremediation potential. This is especially true when one considers that target organisms may also vary in sensitivity because of the same factors. However, many researchers are working to design representative bioavailability tests. In a practical sense, determining the contribution of bioavailability at a particular site might be performed either from models based on empirical evidence about the relationship between chemical concentrations in sediments and bioaccumulation in resident organisms, or bioavailability might be determined by empirical measurements in situ. As is reviewed in this chapter, there are many variables that affect bioavailability and, in many cases, there are insufficient empirical data to quantify the relationship mentioned above. In this case, site-specific measurements may be warranted. Perhaps by analyzing large data sets, some actual relationships may be discerned between chemical concentrations in sediments and resident organisms; however, it could be the

Bioavailability in Sediments 101 case that such relationships are elusive or not discernable through current analysis methods. In any event, the concept of bioavailability is an important one in determining the effects and risks of contaminants in sediments. Because contaminants that are more bioavailable would pose greater risk to human health and the environment than similarly potentially toxic but less available contaminants, it is necessary to better understand these factors and processes and integrate that understanding into the regulatory framework. To understand bioavailability and its effect on humans and ecosystems, a variety of basic research and field studies have been undertaken. Methods used for measuring bioavailability will also be reviewed. Although the definition of bioavailability used in this chapter is similar to that used by the U.S. EPA, “the presence of a substance in a form that organisms can take up” (U.S. EPA, 1994), bioassimilation, bioaccumulation, and toxicity are also discussed when related to demonstrating the amount of a substance that is bioavailable.

CURRENT RESEARCH ON BIOAVAILABILITY IN SEDIMENTS

MEASURING BIOAVAILABILITY. Many methods are under investigation for measuring bioavailable compounds. Bioavailability not only depends on the environment (temperature, organic carbon content, etc.), but also on the target species and the compound of interest. Representative research on current measurement methods such as in vitro digestive fluid extraction, synthetic biological membrane analogs, and sorption to resins is discussed below. There are many examples of similar methods in the literature, each tuned to a specific environment or target species; however, this chapter is meant to be a descriptive but not comprehensive review of the subject.

ALTERNATIVE EXTRACTION METHODS. Researchers at the University of California at Berkeley have used in vitro digestive fluid extraction to measure sediment-bound contaminant bioavailability (Mayer et al., 1996, and Weston and Mayer, 1998a, 1998b). When a deposit-feeding organism ingests sediment, the gut chemistry determines if the contaminants can be desorbed from the particles and are, therefore, available for absorption. The researchers mimic this process in vitro by incubating contaminated sediments in digestive fluid and expressing bioavailability as the percentage of contaminant solubilized in those fluids. Weston and Mayer chose the polychaete Arenicola brasiliensis because of its large size and the amount of digestive fluid that can be recovered. Using the polychaete’s digestive fluid, they examined California sediments contaminated with PAHs, PCBs, or trace metals. Because invertebrate gut fluid pH is near neutral, the results under- scored doubts about the biological relevance of traditional strong acid

102 Handbook on Sediment Quality extractions. Digestive fluid extraction yielded only 12 to 50% of the PAH in spiked sediment, similar to the amount solubilized in vivo. Assessment based on total PAHs would have overestimated the risk by a factor of 2 to 8. The extractability of PAH in digestive fluid depended on the organic carbon content of the sediment, and extraction efficiency correlated directly with contaminant concentration. The researchers also showed that holding time affects extraction efficiency. When sediment was spiked with PAHs and immediately extracted by digestive fluid, 70% of the PAHs were solubilized. Storage of the sediment for 3 weeks reduced the extraction efficiency to 35%. Sediment aging has been shown to decrease bioavailability in other bioaccumulation and microbial degradation studies as well. Whether this observation is an artifact or reflects an expectation of the natural environment is a question worthy of further research and is addressed later in this chapter. Lawrence et al. (1999) also examined bioavailability using digestive fluid. In vitro tests to evaluate the bioavailability of inorganic mercury and monomethylmercury with the digestive fluid of the deposit-feeding lugworm Arenicola marina found that digestive fluid solubilized these compounds to a greater extent than seawater. Monomethylmercury was more readily solubilized from sediment than mercury. The dependence of solubilization on organic carbon content was also confirmed in this study. Methylmercury solubilization was inversely proportional to sediment organic matter content, but the results for mercury were not definitive. Kelsey and Alexander (1997) compared extraction of atrazine and phenanthrene using a variety of organic solvents and solvent combinations to mineralization by Pseudomonas. Mineralization, the complete degradation of an organic compound to carbon dioxide and water, was used as a surrogate for bioavailability. Atrazine bioavailability to bacteria was best predicted by extraction using a 1:1 methanol–water mixture, whereas phenanthrene bioavailability was best predicted using n-butanol. This study demonstrated that a relative decrease in bioavailability was related to a relative decrease in extractability (Reid et al., 2000).

MEMBRANE ANALOGS. Macrae and Hall (1998) assessed three methods for determining availability of PAHs in marine sediments. The methods used semipermeable membrane devices (SPMDs), Tenax TA, or polyethylene tube dialysis (PTD). The SPMDs are made from nonporous polyethylene tubing coated with a thin layer of triolein. Hydrophobic compounds become concentrated in the triolein relative to the water phase, similar to what might occur in animal tissues. The PTD method is similar to the SPMD method except that the sediment–water slurry is sealed inside polyethylene tubing and dialyzed against the solvent. In the third method, the resin, Tenax TA, was used to concentrate contaminants desorbed from sediment particles. A sufficient amount of Tenax TA was incubated in the glass bottles to prevent saturation. The SPMDs, Tenax TA, and PTD extracted similar amounts of PAHs, with the PTD extracting the highest percentage. All methods assume that a contaminant must be in the water phase to be bioavailable. For PAHs, absorption by the biological membrane simulants increased with octanol–water partition coeffi-

Bioavailability in Sediments 103 cient for two to four aromatic rings but declined due to the lower permeability of PAHs with more than four rings. Because the membranes were damaged when in contact with sediment for more than 24 hours, the method only reflects short-term exposure of biota. In the Tenax TA method, particles are separated from the sediment after incubation, so sediment-associated PAH is not reflected in the numbers; however, there is no membrane in this method to limit diffusion, so it may represent total dissolved PAH rather than the bioavailable fraction. As organic matter increased, the amount of PAH desorbed decreased with the SPMD method, especially with higher-molecular- weight compounds. Utvik and Johnsen (1999) explored the use of SPMDs and blue mussels (Mytilus edulis) to determine the bioavailability of PAHs from oil field- produced water in the North Sea. The SPMDs reflected the water-soluble fraction of the PAHs, which the researchers hypothesized represented the exposure route for organisms at lower trophic levels. The mussel residues represented both the water-soluble fraction and the bioavailable particle- bound fraction. Lake et al. (1996) utilized C-18-coated silica particles as a surrogate for benthic uptake of hydrophobic compounds from sediment. Gustafson and Dickhut (1997) also used resins to sorb compounds from the water column as a surrogate for the available fraction.

IMPORTANCE OF SEDIMENT CHARACTERISTICS IN DETERMIN- ING BIOAVAILABILITY. The effect of particle surface characteristics on bioavailability and bioaccumulation of the pesticides 2,4-dichlorophenol and pentachlorophenol in benthic organisms was studied by Davies et al. (1999).

Bioconcentration exhibited a parabolic curve as a function of Kd, the distribution coefficient (mol/kg solid)(mol/kg solution), in both Lumbriculus variegatus (an oligochaete worm) and Chironomus riparius (midge) larvae; however, the method does not translate for natural sediments because they are a mixture of a variety of components and, therefore, the Kd concept is not applicable (Davies et al., 1999). Using well-characterized sediments, the researchers were able to determine that bioavailability increased significantly with an increasing octanol–water partition coefficient (Kow, an increasing value indicates a higher propensity for a substance to partition to organic phases).

PORE-WATER CONCENTRATIONS DETERMINE BIOAVAIL- ABILITY. It is generally assumed that the amount of toxicant freely dissolved is the bioavailable fraction in aquatic environments. This assumption can be only partially applied to sediments, however. Much empirical evidence suggests that the toxicity of sediments can be estimated from pore water (interstitial water) concentrations (NOAA, 1995). Contaminants may partition between pore water and sediment particles, and the former generally correlates with a higher bioavailability (although sediment ingesters may extract particle-bound toxicant in the gut). This partitioning was described by Landrum and Robbins (1990) as a multikinetic process involving both a reversible portion and a resistant portion.

104 Handbook on Sediment Quality As a demonstration that interstitial water correlated to bioavailability, Kosnian et al. (1999) determined that Ambersorb® 1500, a carbonaceous resin, reduced the bioavailability of fluoranthene in sediment by reducing pore water concentrations. Proportional reductions in whole-body fluoranthene concentrations and reduced sensitivity to UV light were observed in 10- day toxicity tests with the oligochaete L. variegates. Hellou et al. (1999) studied the effect of raw wastewater on sediment quality and its effect on winter flounder (Pseudopleurinectes americanus). The sludge contained PCBs, dichloro–diphenyltrichloroethane and metabolites, dioxins and furans, PAHs, and sulfur heterocycles. Winter flounder were exposed in tanks for 4 months while they were dormant. Contaminants were measured before and after incubation with harsh extractions, and bioavailability was examined via tissue burden on contaminants after 4 months. Only the PAHs exhibited degradation. A dose-response was observed for PCB con- gener 153 and unresolved congeners 138/163/164 in muscle. Of the dioxins and furans investigated, only 2,3,7,8-tetrachlorofuran was detected in muscle. It was detected at the same level in all fish regardless of the amount of sludge they had been exposed to, indicating similar bioavailability possibly due to limited water solubility. In other words, only the dissolved fraction was bioavailable. Zwiernik et al. (1999) found that bioavailability of PCB to degradative bacteria correlated with PCB water concentrations using regression. The researchers also found that the presence of PAHs reduced the water concentration of PCBs, reducing their bioavailability and rate of dechlorination. Knezovich and Inouye (1993) found that, while tadpoles exposed to contaminated sediment did accumulate the quaternary ammonium surfactant hexadecylpyridinium bromide (HPB) in the gastrointestinal tract, a higher percentage of the contaminant was found in the gills, the primary site of toxic action. The accumulation of sublethal concentrations of HPB was reduced significantly by the presence of clay or sediment compared with free aqueous concentrations of HPB in all tissues. This result was most pronounced in the gills of the tadpoles, clams, and minnows tested (Knezovich and Inouye, 1993). Carvalho et al. (1999) compared the bioavailability of free and colloidally bound trace metals, silver, cadmium, barium, iron, tin, zinc, cobalt, mercury, and manganese. In general, uptake kinetics were similar for both forms, although barium, tin, and zinc free ions accumulated to a greater extent than colloidally bound metals.

BIOAVAILABILITY OF SEDIMENT-ASSOCIATED CONTAMINANTS. Forbes and Forbes (1997) found that Potamopyrgus antipodarum, a deposit- feeding gastropod, assimilated fluoranthene with a 22.4 to 46% efficiency, suggesting that sediment ingestion and not pore-water concentration might be the primary route of uptake. For hydrophobic organic contaminants with octanol–water partition coefficients greater than 5.5, sediment ingestion has been shown to be a significant or primary exposure route for deposit-feeding organisms (Forbes et al., 1998, and Lamoreaux and Brownawell, 1999).

Bioavailability in Sediments 105 Forbes et al. (1998) found that fluoranthene-contaminated sediment ingestion was the dominant uptake mechanism for deposit-feeding organisms in sediment.

REMEDIATION USING BIOAVAILABILITY REDUCTION. A strategy that reduces contaminant bioavailability to target species can be used to remediate contaminated sites without sediment or contaminant removal. This process has been cited as especially useful for heavy metals (Traina and Laperche, 1999). For instance, lead-contaminated soils and sediments may contain anglesite (PbSO4), cerussite (PbCO3), and various lead oxides (e.g., litharge, PbO) as well as Pb2+ adsorbed to iron and manganese hydroxides. These lead oxides, sulfates, and carbonates are highly soluble in acidic to near neutral environments and can pose a significant environmental risk. However, lead phosphates [e.g., pyromorphite, Pb5(PO4)3Cl] are less soluble and more geochemically stable over a wide pH range, therefore posing less risk. Application of soluble or solid-phase phosphates (i.e., apatites) to contaminated soils and sediments encourages the formation of pyromorphites, reducing chemical lability and bioavailability without its removal from the contaminated media (Traina and Laperche, 1999). Leonard et al. (1999) examined the potential of increasing acid-volatile sulfides (AVS) as a remediation solution for sites contaminated with toxic metals. Iron sulfide was barely effective in increasing AVS; however, spiking with sodium sulfide or sodium sulfide combined with iron sulfate increased AVS and reduced simultaneously extracted metal (SEM) in cadmium-, zinc-, and nickel-spiked sediment and a field sediment contaminated with copper.

MODELING. Whereas assessing the risk of contaminated sediments will always be, at some level, dependent on site-specific characteristics, it would be desirable to use models that can predict bioavailability with a defined certainty. Much has been written on the bioavailability of metals; however, there is still a debate about whether total metal activity or free-ion activity determines bioavailability (Van Leeuwen, 1999). In the free-ion activity model, it is assumed that the transport of the metal in solution is fast compared with uptake by the organism. However, in many cases, the organism may be driving dissolution of the metal complexes, as recognized by Bosma et al. (1997) and others. Shrestha and Orlob (1996) developed a model to characterize the concentrations and speciation of heavy metals in estuarine systems to assess potential adverse effects and guide remediation. The model, a two-dimensional, vertically averaged finite element model, incorporated deposition and erosion, heavy metal sorption, and governing hydromechanical transport mechanisms. The model was able to represent phenomena governing the fate of nickel- contaminated sediments in South San Francisco Bay. Valsaraj and Thibodeaux (1999) examined both the linear driving force (LDF) and particle diffusion (PD) models, both commonly used in the chemical engineering field to describe mass-transfer resistances for chemical uptake by aqueous suspended particles. The LDF model assumes that the

106 Handbook on Sediment Quality Figure 3.1 The effect of fractional approach to equilibrium, f, on the relationship between the apparent partition coefficient

(Kd) and the sorbent concentration (s) (from Valsaraj and Thibodeaux, 1999): (1) the turbulent region in the bulk fluid with no concentration gradient and transport is rapid; (2) the thin boundary layer around the particle, which provides significant mass-transfer resistance; (3) the transport within the particle is controlled by intraparticle diffusion processes; and (4) the sorption characterized by the equilibrium partition constant, K*d.

sorption rate is proportional to the difference between the solute concentration in the fluid and the equilibrium concentration (visualized in Figure 3.1), whereas the PD model only describes mass-transfer resistance on the particle side. Numerous investigators have used the PD model to characterize adsorption kinetics in sediments (Thibodeaux et al., 1993, and Wu and Gschwend, 1986). The kinetic models described by Valsaraj and Thibodeaux (1999) provide criteria for distinguishing water-side versus particle-side limitations to sorption. The uptake kinetics demonstrate that the time to approach thermodynamic equilibrium may be long and increases with particle size and compound hydrophobicity. Park and Erstfeld (1999) developed a dynamic model incorporating adsorption–desorption, metabolism, volatilization, and biochemical degradation to examine the effect of sediment organic carbon content on bioavailability. The authors validated the model using the organochlorine pesticide chlordane and two types of sediment with varying organic matter content. They found that sediment organic carbon significantly affects bioavailability of hydrophobic compounds, especially when the organic carbon content is low and small changes produce large variations in bioaccumulation.

Bioavailability in Sediments 107 FIELD STUDIES In addition to laboratory and theoretical studies of bioavailability, many field studies of toxicity include basic research that increases the fundamental understanding of bioavailability. In this section, large-scale studies that contain basic research are included. Many sediment studies now include measures of bioavailability in addition to traditional measures such as bulk-contaminant concentration, and a few illustrative examples are discussed below.

BAYS. Sediment studies have been underway in San Francisco Bay since the 1970s. Currently, the studies are concentrating on the temporal changes in sediment-metal concentrations caused by geologic inputs, anthropogenic pollution, riverine inputs, and geochemical processes such as sorption (Luoma et al., 1997). Khim et al. (1999) have been examining the character and distribution of organic contaminants in Masan Bay (Korea) sediments. Sediments of this semiclosed bay are potentially contaminated by industrial and municipal wastewater from two Korean cities. Twenty-eight samples were analyzed for total organic carbon, nonylphenol (NP), octylphenol, bisphenol A (BPA), organochlorine pesticides, PCB congeners, and PAHs. Concentrations of the chemicals were not related to total organic carbon (TOC) concentration. Sediment fractions were fractionated into 3 hexane- dichloromethane-eluted fractions to perform in vitro bioassays; however, bioavailable quantities (dissolved-water concentrations) of the compounds present were not measured directly to provide toxicity information independent of organism but predicted from sediment concentrations and Koc values. Predicted values were less than those reported to elicit toxic responses in aquatic organisms for NP and BPA but exceeded sediment quality guidelines at some sites for PCBs and PAHs. Metals biomonitoring using mollusks (Turbo coronatus, Acanthopleura haddoni, Ostrea cucullata, and Pitar sp.) was evaluated in the Gulf of Aden, Yemen (Szefer et al., 1999) by comparing tissue concentrations in the mollusks to bioavailable concentrations of metals in sediments. Sediments were digested using a harsh acid mixture of concentrated HNO3, HClO4, and HF and a less harsh procedure using 1 M hydrochloric acid (HCl) originally described by Luoma and Bryan (1981) to compare total with bioavailable metal concentrations. The average bioavailable percent of total average lead, zinc, chromium, cadmium, and iron in the study area were, respectively, 63, 11, 2, 30, and 0.3. Total iron concentrations were at least 2 orders of magnitude higher than the other metals. The researchers found significant interspecies variation of tissue-metal concentrations from the same sampling site. Mason and Lawrence (1999) found that the distribution and bioavailability of mercury and monomethylmercury to benthic organisms in Baltimore Harbor and the Chesapeake Bay were correlated to sediment organic matter content. Sediment organic content was a better predictor of monomethylmercury than iron content or AVS. The authors also found that sediment organic content best explained the variation in the bioaccumulation factor.

108 Handbook on Sediment Quality Importantly, the authors concluded that because total mercury, methylmercury concentration, and bioavailability were decoupled, remediation based on total mercury would not predict improvements in toxicity.

LAKES. Arsenic, manganese, and nickel sediment contamination in Outer Malletts Bay, Lake Champlain, Vermont, was assessed for toxicity to the cladoceran Ceriodaphnia dubia using pore-water concentrations. This study has merit because Boucher and Watzin (1999) adjusted the test water to match the pH, hardness, alkalinity, conductivity, and dissolved organic carbon of the in situ water to ensure that environmental conditions were not modifying toxicity attributable to the contamination. Researchers used a toxicity identification evaluation (TIE), which uses several sediment extraction and washing techniques to selectively remove toxic fractions to determine toxic agents in complex effluents. In studies of the Kenwick Reservoir, California, low concentrations of AVS and high concentrations of SEM predicted significant bioavailability of the contaminants of concern: copper, cadmium, and zinc (Finlayson et al., 2000). These predictions were confirmed with toxicity tests using rainbow trout (Oncorhynchus mykiss), the amphipod Hyalella azteca, and Ceriodaphnia dubia. Low pH and alkalinity may have also contributed to toxicity.

WETLANDS. Yu et al. (2000) studied the behavior of trace metals in the Mai Po and Inner Deep Bay Ramsar site of Hong Kong, an intertidal mudflat of a tropical wetland. Metals sorbed onto the iron and manganese oxides in the sediments were most likely to be remobilized. The authors found that the relative diffusive fluxes of trace metals represented the relative availability to the benthic infauna of the site.

FACTORS CONTROLLING BIOAVAILABILITY

Current research suggests that the availability of sediment-held contaminants to living organisms that do not ingest sediments is controlled by the concentrations that dissolve in the sediment pore waters (Boucher and Watzin, 1999; and Kosnian et al., 1999). Tomson and Pignatello (1999) suggest that pollutant mass transfer regulates bioavailability. However, others are testing these theories.

ACID-VOLATILE SULFIDE. Acid-volatile sulfide has been extensively studied and widely incorporated to models for assessing sediment contamination (Ankley et al., 1996; Berry et al., 1996; Carlson et al., 1991; Di Toro et al., 1990; Di Toro et al., 1992; and Wang and Chapman, 1999). In marine waters where the concentration of sulfate is approximately 28 mM (Stumm

Bioavailability in Sediments 109 and Morgan, 1996), sulfate-reducing bacteria (e.g., Desulfovibrio and Desulfotomaculum) reduce sulfate to sulfide during the decomposition of organic matter (Wang and Chapman, 1999). A typical sulfide profile is shown in Figure 3.2. Although concentrations of sulfate are much lower in freshwater (typically 0.12 mM [Stumm and Morgan, 1996] in rivers) under conditions of large amounts of organic sedimentary matter, measurable amounts of sulfide may be present (Wang and Chapman, 1999). When sulfide is present, it chelates metals, for instance, forming iron and manganese sulfide solids, including amorphous iron sulfide, mackinawite, greigite, pyrrhotitie, troilite, pyrite (Davison, 1991, and Morse et al., 1987), and pink and green manganese sulfides (Emerson et al., 1983) through reactions with Fe2+ and Mn2+ (Wang and Chapman, 1999). These sulfide complexes render the metals largely unavailable, and many investigators have shown that when the ratio of AVS to SEM exceeds 1, there is no toxicity (see the Measuring Bioavailability section). All of the above-mentioned complexes, with the exception of pyrite, are easily dissolved in acid (thus, the term AVS) and can be displaced by other metals (contaminants) to form more insoluble metal sulfides. Monomethylmercury is the most bioaccumulative form of mercury (Mason and Lawrence, 1999). Researchers have observed an inverse relationship between the dissolved sulfide concentration and methylmercury production and concentration in a variety of sediments (Benoit et al., 1999, and Sferra et al., 1999). In the presence of sulfide, mercury’s bioavailability to methylating bacteria is reduced in sediments (Benoit et al., 1999).

2– Figure 3.2 Typical profiles of sulfate (SO4 ), total sulfide [S(II)], and

oxygen (O2) in the overlying water and pore water of aquatic sediments (from Wang and Chapman, 1999).

110 Handbook on Sediment Quality ORGANIC CARBON. As noted above, many researchers have correlated increasing sediment organic carbon with decreased bioavailability (Kelsey and Alexander, 1997; Lawrence et al., 1999; and Macrae and Hall, 1998). Organic carbon is widely recognized as an important determinant of bioavailability. Weston and Mayer (1998a, 1998b) found that the extractability of PAH in digestive fluid in vitro depends on the sediment’s organic carbon content. Baptista Neto et al. (2000) found high correlations between sediment organic carbon and iron, manganese, nickel, lead, zinc, and chromium concentrations. Davies et al. (1998) developed an assay for copper availability in fresh water. Using this assay, a linear correlation (r = 0.93, p = 0.005) was observed between bioavailability and the aqueous copper complexation capacity, supporting the role of natural organic matter in reducing copper toxicity. This study provides additional evidence that the bioavailability of copper in freshwater is significantly reduced in the presence of natural organic matter. Standley (1997) found that sediment organic matter reduced bioavailability of the organochlorine pesticide dieldrin to the oligochaete L. variegatus. Baptista Neto et al. (2000) also found that sediment size (in addition to organic carbon) controls the abundance and distribution of metals in surface sediments. In addition to sediment organic matter, dissolved organic carbon (DOC) affects contaminant concentration through sorption, reducing bioavailability (Breault et al., 1996; Landrum et al., 1996; Lawrence et al., 1999; Mason and Lawrence, 1999; Servos et al., 1989; and Schubauer-Berigan and Ankley, 1991). Breault et al. (1996) found that between 84 and 99% of copper present was bound to dissolved fulvic acid and ethylenediaminetetraacetic acid (EDTA) in seven stream samples measured by potentiometric titration. The fraction of copper bound to DOC decreased with increasing water hardness. Both the quantity and quality of organic matter have been shown to influence bioavailability (Karickhoff et al., 1979). Sediment organic carbon provides a primary food source for benthic invertebrates. DeWitt et al. (1992) found that sediment pore water partitioning and toxicity of fluoranthene to the infaunal amphipod Rheopyxinius abronius were affected by sediment organic matter quality. Gunnarsson, Hollertz, and Rosenberg (1999) found that the polychaete Neries diversicolor preferentially fed on enriched sediment organic matter that also contained higher concentrations of the contaminant 3,3'4,4'-14C-tetrachlorobiphenyl (TCB). Hunt et al. (2000) found that N/C provides a reasonable indicator of bacterial bioavailability for complex, recalcitrant carbon moieties typical of many aquatic systems. Neither O/C nor H/C was predictive of bacterial concentration. Gunnarsson, Granberg, et al. (1999) examined the effect of sediment organic carbon quality on contaminant uptake and benthic invertebrate growth. Reactivity of the organic carbon was measured by respiration and dissolved inorganic nitrogen flux. Growth rates of the brittle star, TCB uptake rates and steady-state TCB concentrations differed significantly between treatments and was correlated to the quality of the sediment organic carbon as measured by amino acid, lipid, C, N, and polyphenolic compounds (Gunnarsson, Granberg, et al., 1999). Gunnarsson, Granberg, et al. (1999) suggested that organic matter quality should be studied and considered when developing sediment quality criteria. DeWitt et

Bioavailability in Sediments 111 al. (1992) suggested that such a consideration is of limited utility because, although they did find that organic matter quality affected partitioning and toxicity, the absolute range of toxicities was small. It has also been noted that silver and cadmium attached to labile polymeric organic carbon were significantly more bioavailable for digestive uptake by Leptocheirus plumulosus (an infaunal, estuarine amphipod that is commonly used in sediment toxicity tests) than metal complexed with recalcitrant organic carbon, mineralogical features such as iron oxides, or phytoplankton.

PROCESSES AFFECTING BIOAVAILABILITY IN SEDIMENTS

AGING. Although bioavailability depends on the target organism, there are several trends or governing factors. For instance, it has been shown that bioavailability decreases with increasing sediment-pollutant contact time for many organisms (Belfroid et al., 1996; Chen et al., 1999; Landrum, 1989; Loonen et al., 1997; and Reid et al., 2000). Aging appears to make the contaminant less available (Macrae and Hall, 1998). As compounds interact with sediment over a period of time, a process termed aging, it is generally accepted that there are (at least) two fractions that exist: a rapidly desorbed fraction and a fraction that is more slowly desorbed (and potentially not desorbed at all, depending on environmental conditions) (Cornelissen, Van Noort, and Govers, 1997, and Reid et al., 2000). These fractions have been more widely described for soil (Hatzinger and Alexander, 1997; Mader et al., 1997; Piatt and Brusseau, 1998; Pignatello and Xing, 1996; Schlebaum et al., 1998; and Xing and Pignatello, 1997) and sediment (Carroll et al., 1994).

SORPTION. For contaminants in the environment, that portion of the contaminant in the vapor or solution phase primarily determines bioavailability. Therefore, it is important to understand not only the thermodynamic forces that govern equilibrium distribution, but also the rates of exchange of contaminants between the fluid and solid phases (Tomson and Pignatello, 1999). A more detailed discussion of sorption processes in sediments is presented in Chapter 2. Sorption of contaminants to sediments reduces bioavailability (Chen et al., 1999, and Tomson and Pignatello, 1999). Sorption is influenced by both sediment and contaminant characteristics, most notably, hydrophobicity for organic contaminants (as measured by Kow). Many researchers have impli- cated organic matter as the primary sediment characteristic governing bioavailability (Cornelissen, Van Noort, and Govers, 1997, 1998). “Equilibrium sorption and desorption processes at solid–liquid interfaces in subsurface saturated systems are referred to generally as either absorption or adsorption processes. Absorption relates to the partitioning of a contaminant (sorbate) into the natural organic matter or mineral components of sediments

112 Handbook on Sediment Quality or soils (sorbents). Adsorption refers to the physical or chemical binding of the sorbate at surfaces or interfaces between a solution and a sorbent. Sorption of hydrophobic organic contaminants (HOCs) from aqueous solutions by soil or sediment organic matrices (SOMs) has been described in terms of linear models (Chiou et al., 1979; Karickoff et al., 1979; Schwarzenbach and Westall, 1981; Chiou et al., 1983; John et al., 2000) and nonlinear models (Miller and Weber, 1986; Weber et al., 1992; Spurlock and Biggar, 1994a, 1994b; Young and Weber, 1995; Xing et al., 1996; LeBoeuf and Weber, 1997; Huang et al., 1997; Jepsen and Lick, 1999; Koelmans et al., 2000).…” Sorption of HOCs from aqueous solution by chemically homogeneous synthetic polymers has also been observed to be nonlinear by Xing et al. (1996 and Xing and Pig- natello (1996) and LeBoeuf and Weber (1997) (quotation from LeBoeuf and Weber, 1999), indicating that nonlinear sorption is prevalent in both chemically heterogeneous natural macromolecular systems as well as in homogeneous synthetic polymers. The sorption of HOCs from aqueous solution into natural and synthetic organic matrices can be linear or nonlinear. As discussed above, it is generally agreed that there are two distinct kinetic phases of desorption of chemicals from sediments. Cornelissen, Van Noort, and Govers (1998), and Cornelissen, Rigterink, et al. (1998) indicated that the fast desorption represented the fraction of a compound that is microbially bioavailable (Reid et al., 2000). Generally, the fast-desorbed fraction underestimates the bioavailable fraction and could, therefore, be considered a conservative estimator of biodegradation potential. Cornelissen, Van Noort, and Govers (1998) and Cornelissen, Rigterink, et al. (1998) showed that the amount of PAH sorbed to sediment that was degraded in a bioreactor exhibited a 1:1 relationship with the amount of PAH that could be desorbed in an aqueous solution with Tenax TA. Using kinetic studies, they were able to show that it was the quickly desorbing fraction that was biodegraded, supporting the theory that bioavailability is limited by mass-transfer kinetics (Bosma et al., 1997; Cornelissen, Van Noort, and Govers, 1998; and Cornelissen, Rigterink, et al., 1998). Cornelissen, Van Noort, and Govers (1997) also demonstrated that the slowly desorbing fraction increased with both increasing solute hydrophobicity and increasing equilibration time for PAHs, PCBs, and chlorobenzenes. Kan et al. (1998) proposed an isotherm consisting of two terms, a linear term to represent reversible sorption and a Langmuir term to represent irreversible sorption as follows:

tot irr irr q = Koc × OC × C + (K oc × OC × q max × f × C)/ irr irr (q max × f + K oc × OC × C) (3.1)

Where q = solid-phase contaminant concentration (mg/g sediment); irr q max = maximum capacity of irreversible fraction (mg/g sediment); C = aqueous-phase concentration (mg/mL); OC = fractional soil–sediment organic carbon content (g/g); and irr f = fraction of q max that is filled during adsorption.

Bioavailability in Sediments 113 The irreversible sorption constant is independent of the physical–chemical properties of the contaminant. Chen et al. (1999) showed that using a linear isotherm (standard equilibrium partitioning) and the National Sediment

Inventory (NSI) sediment quality advisory level (SQALoc) for 1,4- dichlorobenzene of 35 µg/g OC, results in a protective aqueous concentration of 0.023 mg/mL. If this protective aqueous concentration were used in eq. 3.1, the resulting protective-sediment concentration would be 144 µg/g sediment or 3512 µg/g OC (in their example). This is 2 orders of magnitude higher than the SQALoc, demonstrating that the standard equilibrium partitioning model severely overpredicts toxicity and may lead to unnecessarily strict guidelines or regulations. Bjorkland et al. (1999) showed that the fraction of PCBs associated with rapidly or slowly desorbing sites on historically contaminated sediments and soil was inconsistent among samples, indicating that the sample matrix and not the specific PCB controlled desorption behavior. Particle size, water content, organic content, and PCB concentration did not control desorption, and the controlling factor was not identified. It should be noted that desorption from the irreversible phase may occur (Chen et al., 1999); however, the apparent equilibrium constant is very large and the resulting aqueous-phase concentration is low. If this level is low enough to be considered safe, then the contaminated sediment can remain in place; however, if the concentration is too high, the contaminated sediment must be removed because extremely large pore volumes would be required to flush the system (Chen et al., 1999) and the irreversible portion is not easily accessible for biodegradation. Fu et al. (1994) used modeling to demonstrate that it would take 22 pore volumes to remove naphthalene- and dichlorobenzene-like compounds, assuming reversible adsorption but would take 3330 pore volumes if most of the compound were in the irreversible fraction. The effect of surfactants or cosolvents was not examined (Fu et al., 1994). Biological effect through physical or biochemical processes on the irreversible fraction also needs to be examined before using these new results for risk estimation (Chen et al., 1999).

SEASONALITY. Researchers have found that bioavailability changes seasonally (Balras, 1999, and Mendoza et al., 1996). In a study of lead, cadmium, copper, cobalt, nickel, and manganese concentrations in water, sediment, and fish (Cyprinus carpio and Barbus plebejus) in the upper Sakarya River Basin, average lead, cadmium, copper, nickel, and manganese concentrations varied seasonally, with lead, cadmium, and cobalt concentrations increasing in sediment samples in August and October (Balras, 1999). Mendoza et al. (1996) found that bioavailable cadmium and lead concentrations peaked in July, whereas cobalt peaked in early spring in a study in rural district 63 in Mexico. Chromium varied throughout the year. One possible reason for increased metal bioavailability during summer is a concurrent decrease in AVS levels during that season.

BIOTURBATION. It has been demonstrated that water movement through bioturbation, irrigation, wave action, and resuspension can increase bioavail-

114 Handbook on Sediment Quality ability (Aller and Aller, 1992; Caffrey et al., 1996; and Santschi et al., 1990) and enhance mixing and redistribution of sediment-associated contaminants (Karickhoff and Morris, 1985). Bioturbation is the mixing of sediment particles, redistribution within the sediment column, and sediment resuspension in overlying water as a consequence of burrowing, feeding, defecation, and tube-building activities by benthic organisms (Ciarelli et al., 1999). Such redistributions could prevent or delay the establishment of a sediment–water equilibrium of contaminants such as PAHs, significantly increasing the potential for their accumulation by food or suspended sediment (McElroy et al., 1990, and Meador et al., 1995). For example, the burrowing oligochaete Lumbriculus variegatus was reported to increase the concentration of cadmium in interstitial (pore) water and its uptake (Peterson et al., 1996). Ciarelli et al. (1999) found that bioturbation by a marine amphipod significantly increased the concentration of total suspended solids in the overlying water, which resulted in a related increase in the total aqueous concentration of sediment-bound contaminants. Rasmussen et al. (2000) found that the effect of bioturbation on mobilization of contaminant by the lugworm depended on the distribution of cadmium in the sediment. Gunnarsson, Granberg, et al. (1999) found that carbon, but not TCB, was transported in the sediment by the brittle star. In other research, Gunnarsson, Hollertz, and Rosenberg (1999) found that bioturbation increased TCB release from the sediment to the water column. Bioturbation plus improved organic matter quality enhanced transport the most. Whereas it seems logical that bioturbation could increase the bioavailability of contaminants by several mechanisms, including the resuspension of contaminant sediment particles, the role of bioturbation in determining bioavailability and the transfer of contaminants through the food web remains unclear. For example, Wall et al. (1996) studied the effect of benthic carp bioturbation on the uptake of cadmium by Daphnia magna and reported no significant effects because of resuspension of contaminated sediment. Whereas more research may be needed to better predict the effect of bioturbation on bioavailability in contaminated sediment, it may also be appropriate to investigate microhabitats of concern to determine the role of bioturbation in changes of contaminant flux, which could theoretically result in significantly differing levels of bioavailability on short temporal scales.

MEASURING BIOAVAILABILITY Typically, strong acids or organic solvents are used in a chemical analysis to recover the total, rather than bioavailable, contaminant (Reid et al., 2000). Common exhaustive extraction procedures include the Soxhlet extraction (Brilis and Marsden, 1990) and the Tessier extraction procedure (Tessier et al., 1979). However, harsh extraction methods do not always correlate with the amount of a compound that is available to a living organism (Bosma et al., 1997) or the associated risk (Reid et al., 2000). Some proposed selective extractions (for example, a weak acid extraction for trace metals) claim to

Bioavailability in Sediments 115 quantify the bioavailable fraction, but none has been generally accepted or broadly adopted (Suter et al., 2000). Cornelissen, Van Noort, and Govers (1997), Kelsey and Alexander (1997), and Reid et al. (1999) have attempted to use more representative measures for bioavailable compounds. However, until the complex relationships are understood that control exposure, uptake, and effect, no chemical method will accurately predict bioavailability in the environment (Reid et al., 2000). The bulk-sediment concentration can be used to estimate bioavailability for non-ionic organic compounds and metals using an equilibrium partitioning approach. The free pore-water concentration of non-ionic compounds, most often considered the bioavailable fraction, can be estimated by normalizing the bulk concentration to organic carbon content. This method is preferred by the U.S. EPA (1993). The DOC concentration and the partition coefficient must be known because a high proportion of the chemical can be complexed to dissolved organic matter. For metals, one can measure the fraction of the bulk concentration that is bound to sulfide. This method is considered valid for five divalent metals: cadmium, copper, lead, nickel, and zinc (Suter et al., 2000). Extracted pore water has been shown to give a conservative estimate of sediment toxicity for metals (Ankley et al., 1991, and Boucher and Watzin, 1999). Researchers have demonstrated that toxicity in pore water does not necessarily indicate toxicity in overlying waters (Ankley et al., 1991, and Burgess et al., 1993). Biological methods measuring toxicity or bioaccumulation are currently used widely to indicate bioavailability, yet interpretation of the results can be confounded by factors unrelated to bioavailability. For example, estimates of toxicity can also be modified by the organism’s prior acclimation or adaptation. Bioaccumulation as a measure of bioavailability is confounded by behaviors affecting exposure (such as feeding and respiration rates) as well as metabolism of the contaminant of interest (U.S. EPA, 1998b). In virtually all laboratory experiments with field-contaminated or spiked sediments, the samples are stored for a period of time. Cole et al. (2000) examined the effect of storage on the toxicity of fluoranthene-spiked sediments to Rhepoxynius abronius. Sediment storage time has been shown to affect toxicity in field-contaminated marine and freshwater samples as well as metal-spiked sediments (Becker and Ginn, 1995; Carr and Chapman, 1995; Dave, 1992; DeFoe and Ankley, 1998; Dillon et al., 1994; Malueg et al., 1986; Moore et al., 1995; Othoudt et al., 1991; Robinson et al., 1988; and Stemmer et al., 1990). It is reasonable to assume that decreases in toxicity are caused by decreasing bioavailability in spiked samples as contaminants age.

ACID-VOLATILE SULFIDES. Di Toro et al. (1990 and 1992) describe AVS as a reactive pool of solid-phase sulfide available to bind with metals and reduce free-metal ion concentrations. They proposed a model where AVS and metal are extracted from sediment with 1N hydrochloric acid (HCl). The metal concentration that is also extracted with the HCl is termed the SEM. Released sulfide can be trapped using various absorbents, including silver nitrate (Di Toro et al., 1990); sulfide antioxidant buffer (Cornwell and Morse, 1987); and liquid nitrogen cold-trapping (Casas and Crecelius, 1994). The

116 Handbook on Sediment Quality AVS concentration can then be measured by colorimetry (Allen et al., 1993, and Cornwell and Morse, 1987); gravimetric analysis (Di Toro et al., 1990, and Leonard et al., 1993); sulfide ion-specific electrode (Pesch et al., 1995); or gas chromatography with photoionization detection (Casas and Crecelius, 1994). It is difficult to measure the toxicity of sulfide in the laboratory because hydrogen sulfide is volatile and HS2– oxidizes readily (Millero et al., 1987). The model predicts that acute toxicity is unlikely when the SEM/AVS molar ratio is less than 1. The available metal is likely to become toxic when the molar ratio is greater than 1. According to Hare et al. (1994), the SEM concentration in excess of the AVS concentration is a better measure of toxicity than the molar ratio because two sediments can have the same molar ratios but have widely different SEM concentrations. Although this method has been validated for five divalent metals: cadmium, copper, lead, nickel, and zinc (Ankley et al., 1996; Suter et al., 2000; and Wang and Chapman, 1999), there are several limitations (NOAA, 1995)

• It does not predict bioavailability because the SEM in excess of AVS can be bound to something else. • SEM must be measured for all five metals mentioned above simultaneously. • It is currently used for acute toxicity. • It does not necessarily provide information for bioaccumulation. • It is inaccurate for low-AVS sediments. • Acid volatile sulfides and SEM vary widely in space and time (Ankley et al., 1996; Besser et al., 1996; Hare et al., 1994; Howard and Evans 1993; Leonard et al., 1993; Mackey and Mackay, 1996; and Van den Berg et al., 1998)

Besser et al. (1996) found that using the SEM/AVS ratio as a measure of bioavailability accurately predicted the bioaccumulation of Cu and Zn, but not growth, in the midge larvae. Recently, the AVS method has been verified for silver as well (Berry et al., 1999). Berry et al. (1999) found that sediments with an excess of AVS relative to SEM were generally not toxic. Sediments with an SEM/AVS ratio greater than 1 and no measurable AVS were generally toxic; however, sediments with measurable AVS were not toxic. When Long et al. (1998) compared the abilities of sediment concentrations of SEM–AVS and dry-weight-normalized trace metals to correctly predict both toxicity and absence of toxicity compared by analyzing 77 field- collected samples, sediment guidelines based on dry-weight-normalized concentrations were equally or slightly more accurate in predicting both nontoxic and toxic results in laboratory tests. The bioavailability of other metals, such as cobalt, mercury, and arsenic, that form metal sulfides less soluble than iron and manganese monosulfides can be reduced by AVS in sediments, and their toxicity is likely to be reduced as well (Wang and Chapman, 1999). Although many studies have shown that the presence of AVS in excess of SEM reduces bioavailability and/or toxicity, thermodynamic formation

Bioavailability in Sediments 117 constants and bioavailability of specific metal sulfide compounds are not well known, so the details of sulfides’ role in controlling metal toxicity is still difficult to predict (Wang and Chapman, 1999). Researchers have validated the use of AVS in field experiments. Yu et al. (2000) found that significant amounts of trace metals in the suboxic layer of the Mai Po mudflats were bound to manganese oxides and that reduction of manganese oxides contributed significantly to remobilization. The AVS in the anoxic layer was the primary site of sequestration of the trace metals. Yu et al. (2000) focused on reduced sediments; it should be noted that oxic conditions could result in an opposite situation, where oxides sequester trace metals and reduction causes mobilization. Berry et al. (1996) defined the interstitial toxic water unit (IWTU) as, “the measured interstitial water concentration divided by the water-only lethal concentration of 50% (LC50) for that particular compound for the test organism.” For example, a sediment with an IW concentration equal to the water- only of LC50 concentration for the test organism would have 1.0 IWTU. When more than one toxic metal is present, IWTUs were calculated as the sum of the toxic units of the individual metals, for example,

IWTU Cd + Ni = (IW concentration Cd/LC50Cd) + (IW concentration Ni/LC50Ni) (3.2)

Thus, if pore water is the principal source of metal toxicity, and availability of metals is the same in water of water-only tests and IW in sediment tests, 50% mortality would be expected with sediments having IWTUs of 1.0. However, as discussed earlier, availability of metals in interstitial water can be modified by several factors alone or in combination. Further, the assumption of an additive toxic effect for multiple metals suggests that the IWTU should be used with caution when assessing risk. Because a variety of studies have shown that dry-weight concentrations of metals in sediments do not predict toxicity across sediments and several studies with marine and estuarine sediments have shown that pore water or AVS concentrations can predict toxicity in sediments contaminated with cadmium, copper, nickel, lead, or zinc across a wide range of sediment types, Berry et al. (1996) conducted six separate experiments where the amphipod Ampelisca abdita was exposed to sediments of varying AVS and SEM concentrations in 10-day toxicity tests. Amphipod mortality was dependent on the amount of incubated sediment when plotted against dry-weight metals concentration in the sediment but was independent of sediment concentration when plotted against SEM–AVS or IWTUs. Sediments with SEM–AVS < 1.0 or IWTU < 0.5 were toxic less than 3% of the time, whereas sediments with SEM/AVS > 1.0 or IWTU > 0.5 were toxic at least 80% of the time, demonstrating that understanding the basic chemical reactions that control the availability of cadmium, copper, lead, nickel, and zinc in sediments may allow explanation of biological responses.

EQUILIBRIUM PARTITIONING. Equilibrium partitioning predicts the biological effects of hydrophobic compounds on the basis of their organic-

118 Handbook on Sediment Quality carbon-normalized concentration in sediment and estimated pore-water concentrations (Kane Driscoll and Landrum, 2000). The equilibrium partitioning approach uses existing water quality benchmarks to define sediment quality benchmarks based on available data that indicate benthic and water column organisms demonstrate similar sensitivities to toxic chemicals (Di Toro et al., 1991). This has been validated with toxicity bioassays using benthic organisms (Hoke et al., 1994; Mason and Lawrence, 1999; and Swartz et al., 1990). In other cases, however, organisms have been more or less sensitive than predicted using equilibrium partitioning (Boese et al., 1990; Hoke et al., 1995; Kane Driscoll et al., 1997; Landrum et al., 1991; and Landrum et al., 1994). As discussed earlier in the chapter, the equilibrium partitioning model may significantly overpredict toxicity. As described by Di Toro et al. (1991), “The equilibrium partitioning

approach uses the mass fraction of organic carbon in sediment (foc) and the chemical-specific partition coefficient between water and organic carbon (Koc) to calculate sediment quality benchmarks” as follows (Fuchsman et al., 1999):

Sediment quality benchmark = foc × Koc × Water quality benchmark (3.3)

This equation assumes the following: (1) the water and the sediment have reached equilibrium with respect to the given nonpolar organic chemical (2) the bioavailable fraction is equal to the fraction dissolved in the pore water, and (3) the dissolved fraction is determined by sorption to the organic carbon fraction of the sediment. Assumptions 2 and 3 have been supported by research with chlorinated benzenes and PAHs (Connell et al., 1988; DeWitt et al., 1992; Knezovich and Harrison, 1988; Kosnian et al., 1999; and Swartz et al., 1995). However, more recent studies measuring the partitioning of chlorobenzenes in sediment indicate that interstitial concentrations may be overestimated by 1 to 2 orders of magnitude, possibly because not all chemical is available for partitioning (Cornelissen, Rigterink, et al., 1997; Kan et al., 1998; McGroddy et al., 1996; and Ten Hulscher et al., 1997), and that equilibrium may not be a reasonable assumption (Pignatello and Xing, 1996). When a chemical has not reached equilibrium, higher concentrations may be found in pore water than expected at equilibrium. According to Forbes et al. (1998), “In contrast to most current regulatory practice a number of biotic factors strongly suggest that thermodynamic equilibrium may be a rare occurrence in natural sediments at scales relevant to bioavailability and it may be unwise to uncritically assume that pore-water contaminant is the most available.” This may be true in environments where contamination is recent, ongoing, or persistently modified by processes such as bioturbation; however, it may be reasonable to assume that, in environments where contaminant aging has occurred, the pore water assumption may be reasonable. Field and laboratory studies have shown that bioaccumulated concentrations may depart significantly from predicted values assuming equilibrium conditions. Paine et al. (1996) found PAHs at levels lower than predicted by equilibrium, whereas Lake et al. (1990) found that PCBs are often accumulated at higher levels by deposit than predicted. Further, because sedimentary

Bioavailability in Sediments 119 organic carbon particles are preferential sorption sites for hydrophobic organic contaminants, Boese et al. (1990) suggested that benthic organisms may feed preferentially on these organic-carbon-rich particles and, therefore, be exposed to greater concentrations of contaminants and greater bioaccumulation than what the equilibrium partitioning approach would suggest. Kane Driscoll and Landrum (1997) found that the critical body residue (CBR) approach complemented the equilibrium partitioning method for predicting and assessing the toxicity of non-ionic organic contaminants in sediments. The CBR measures body burdens of a compound in relation to toxic effects (McCarty and MacKay, 1993). This approach predicts that, for narcotic chemicals, the potency will be constant for similar organisms measured at the site of toxic action (Fay et al., 2000; Kane Driscoll and Landrum, 1997; and McCarty and MacKay, 1993). If critical body residues could be established for many compounds and organisms and correlated to equilibrium partitioning results, then technically simpler equilibrium partitioning could be used alone in the future. The correlation between metal species pore-water concentrations and trace metal toxicity has also been established (Green et al., 1993; Hoke et al., 1990; Kemp and Swartz, 1988; Schubauer-Berigan and Ankley, 1991; Swartz et al., 1989; and Yu et al., 2000). Yu et al. (2000) found that the concentration rank of trace metals varied from the pore water to the anoxic sediment because of speciation.

TOXICITY TESTS WITH ALGAE, BACTERIA, OR REPRESENTATIVE SPECIES. According to the U.S. EPA (1994), toxicity tests expose test organisms to a medium and evaluate the effect on one or more attributes of the organism to determine whether the chemical concentration is high enough to cause adverse effects. Toxicity tests contribute to ecological risk assessments in many ways, including demonstrating the bioavailability of contaminants. Both acute and chronic toxicity tests are used in ecological risk assessment. Factors such as the physical and chemical parameters of the medium (pH, organic carbon, etc.) as well as the test organism and the test method affect the bioavailability or are differentially affected by the bioavailable contaminant. “Sediment toxicity tests are in a less advanced stage of development than are aqueous toxicity tests” (Office of Emergency and Remedial Response, 1994, in Suter et al., 2000). A small number of protocols have been standardized and even fewer have been validated against biosurvey data. However, acute toxicity tests using marine and estuarine amphipods and oligochaetes are widely used to assess the toxicity of sediments (Harkey et al., 1995; Long et al., 1995; MacDonald et al., 1996). There are many standard microbial tests to determine bioavailability, including mineralization or degradation of radiolabeled substrates, determination of biological oxygen demand, and removal of dissolved organic carbon (Reid et al., 2000). Relatively recently, researchers have begun to use bioluminescence assays where the lux gene is inserted to the electron transport chain of the microorganism to demonstrate bioavailability (among others: Applegate et al., 1998; King et al., 1990; Layton et al., 1998; Paton et al., 1995; Selifonova et al., 1993; Sticher et al., 1997; and Unge et al. 1999) and toxicity

120 Handbook on Sediment Quality (Ben-Israel et al., 1998; Boyd et al., 1997; and Hollis et al., 2000). Many of these assays have been used primarily to assess bioavailability in water and soil but, depending on the sample, separation can also be applied to sediments. These bioavailability and toxicity assays are faster and less expensive than comparable assays using more complex organisms; however, differences in toxicity estimates must be expected (Reid et al., 1998). The assays work well for aqueous media; however, assessment of bioavailability in sediments (and soils—if one assumes that some of the bound chemical will eventually desorb into the aqueous phase and become bioavailable or that a portion of sediment-bound chemicals are bioavailable to sediment ingesters) is complicated by the use of organic extractants that may affect the biosensor and change the bioavailability of sorbed compounds (Bundy et al., 1997, and Reid et al., 2000). Davies et al. (1998) developed a bacterial bioavailability freshwater assay for copper. Copper-sensitive bacteria were isolated from a tropical river water sample. The 48-hour bioassay involved addition of copper-sensitive bacteria to filtered water samples amended with growth-stimulating nutrients. The

endpoint is growth, and the effects concentration of 50% (EC50) for inorganic copper has been reported as 7.1 g/L. Matrix-matched control solutions were preseparated by adding the metal complexing agent EDTA to a portion of each sample tested. To optimize sensitivity, the nutrient medium stimulated bacterial growth but did not significantly alter copper speciation. Using this

assay, bacterial 48-hour EC15 values ranged from 5.0 to 25.0 g Cu/L, 2 to 10 times higher than the observed EC15 value of 2.8 g/L for inorganic copper in deionized water (no organic matter present).

CURRENT REGULATORY DIRECTIONS

The NSI focused on risk to benthic organisms exposed directly to contaminated sediments and human consumers of exposed organisms (U.S. EPA, 1997). The U.S. EPA evaluated sediment chemical-characteristic data, chemical residue levels in edible tissues of organic organisms, and sediment toxicity data from the same sampling site when available. The sediment chemical-characteristic data were compared with (1) sediment screening levels, including draft SQCs, SQALs, effects range-median and effects range- low values, probable effects levels and threshold effects levels, and apparent effects thresholds (AETs); (2) AVS–SEM; and (3) lethality based on sediment toxicity data. Although, the U.S. EPA did not explicitly mention bioavailability when collecting data, some of the measures mentioned above incorporate concepts of bioavailability, such as draft SQCs and AVS/SEM. Using this approach, 26% of the 21 000 sampling stations were classified as tier 1 (associated adverse effects are probable), 49% were classified as tier 2 (associated adverse effects are possible but expected infrequently), and 25% were classified as tier 3 (no indication of associated adverse effects).

Bioavailability in Sediments 121 The primary recommendation of the U.S. EPA was to encourage additional examination of contaminated sediments to gather additional sediment chemical-characteristic data and related biological data to understand human health and ecological risk. One other significant recommendation related specifically to bioavailability was to “incorporate a weight-of-evidence approach and measures of chemical bioavailability into sediment monitoring programs.” To do this, the U.S. EPA recognized the value of the AVS–SEM and equilibrium partitioning approach for organic chemicals by specifying collection of AVS and SEM in areas of metal contamination and measurement of TOC when sites are contaminated with organic chemicals. The U.S. EPA also recommended matching sediment chemical-characteristic data and toxicity tests, linked through TIE approaches to identify the chemicals that are causing toxic effects. The U.S. EPA program offices that intend to use standard sediment testing methods to determine sediment contamination include the Office of Water, the Office of Pesticide Programs, the Office of Pollution Prevention and Toxics, the Office of Emergency and Remedial Response, and the Office of Solid Waste. When appropriate, U.S. EPA will use SQCs to interpret sediment chemical-characteristic data in the assessment of contaminated sites (U.S. EPA, 1998a). In addition, the U.S. Geological Survey is attempting to address bioavailability using a kinetic model of bioaccumulation that incorporates site-specific field data and species-specific bioaccumulation processes determined in the laboratory (Luoma et al., 1997).

TOXICITY CALCULATIONS. In 1994 (U.S. EPA, 1994), the U.S. EPA stated that toxicity tests of sediments are in the “early stages of development.” The U.S. federal government and state governments use a variety of sediment quality guidelines. The U.S. EPA has endorsed equilibrium partitioning as the best available methodology for estimating the toxicity of hydrophobic organic chemicals in sediment (Fuchsman et al., 1999, and U.S. EPA, 1992). Equilibrium partitioning attempts to take bioavailability into account by assuming that only the fraction in the pore water is available for uptake. However, equilibrium-partitioning-based criteria do not take into account the two-phase desorption model that is becoming widely accepted (Chen et al., 1999; LeBoeuf and Weber, 1999; and Tomson and Pignatello, 1999). After contaminant aging and remediation, there is often a residual amount of contaminant in the sediment or soil that often poses little or no toxicological risk (Chen et al., 1999). “The inability to estimate the long-term contaminant fate greatly increases the uncertainty of any cleanup or waste management plan and therefore forces all the interested parties to take a worst-case, and typically the most expensive, option to be safe. On the basis of the information reported in a recent (NRC) [National Research Council] report and others, overengineering can be estimated to lead to inefficiencies by a factor on the order of 3 to 10” (NRC, 1997, in Chen et al., 1999). Washington State uses the AET to measure sediment quality. The AET evaluates paired chemical analyses and toxicity test results and benthic community data from filed samples and represents the highest compound concentration at which no toxic effects were observed (Fuchsman et al.,

122 Handbook on Sediment Quality 1999). These thresholds are generally conservative because they do not differentiate contributions to toxicity among chemicals in the data set. The U.S. EPA and New York State derive sediment quality criteria using the equilibrium partitioning approach discussed above, and differences between the U.S. EPA’s guidelines and New York’s guidelines reflect differences in water quality benchmarks (Fuchsman et al., 1999). The U.S. EPA’s guidelines use the lowest available (most conservative) toxicity test result and divide by a safety factor, so the criteria depend on the methodology used to study toxicity. Therefore, criteria are not equally conservative (Fuchsman et al., 1999). The U.S. EPA estimates that equilibrium partitioning can overesti- mate toxics thresholds by four- to fivefold (U.S. EPA, 1993). Fuchsman et al. (1999) present a probabilistic model to assess the toxicity of chlorobenzenes in sediment that is based on equilibrium partitioning but also incorporates all available and relevant toxicity data and allows assessment of toxicity with mixtures of chlorobenzenes. The U.S. EPA developed the TIE (discussed above) method to ascribe toxicity in mixed effluents. This procedure is now applied to ambient water and sediments as well (Boucher and Watzin, 1999; Hoke et al., 1992; Lebo et al., 1999; Lebo et al. 2000; and Norberg-King et al., 1991). In addition, the U.S. EPA’s research is directed at a better understanding of the fate of chemicals in the environment and the associated risk. “The new strategic plan for Office of Research and Development (ORD) is based on the risk para- digm” (Veith, 1998). Veith specifically identified bioavailability, tissue-based effects models, and pharmacokinetics as areas for increased research. It is clear that a standardized set of approaches is required to assess bioavailability that takes into account the target organism, site-specific environmental parameters, and contaminants of concern. Continuing research correlating results from AVS results, equilibrium partitioning, and other relatively simple chemical characterizations with toxicity to a number of organisms may increase confidence in bioavailability as a guiding factor for ecological risk assessment.

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Bioavailability in Sediments 125 Chiou, C.T.; Porter, P.E.; and Schmedding, D.W. (1983) Partition Equilibria of Nonionic Organic Compounds Between Soil Organic Matter and Water. Environ. Sci. Technol., 17, 227. Ciarelli, S.; van Straalen, N.M.; Klap, V.A.; and van Wezel, A.P. (1999) Effects of Sediment Bioturbation by the Estuarine Amphipod Corophium volutator on Fluoranthene Resuspension and Transfer into the Mussel (Mytilus edulis). Environ. Toxicol. Chem., 18, 318. Cole, F.A.; Boese, B.L.; Swartz, R.C.; Lamberson, J.O.; and DeWitt, T.H. (2000) Effects of Storage on the Toxicity of Sediments Spiked With Fluoranthene to the Amphipod Rhepoxynius abronius. Environ. Toxicol. Chem., 19L, 744. Connell, D.W.; Bowman, M.; and Hawker, D.W. (1988) Bioconcentration of Chlorinated Hydrocarbons from Sediments by Oligochaetes. Ecotoxicol. Environ. Saf., 16, 293. Cornelissen, G.; Rigterink, H.; Ferdinandy, M.M.A.; and Van Noort, P.C.M. (1998) Rapidly Desorbing Fractions of PAHs in Contaminated Sediments as a Predictor of the Extent of Bioremediation. Environ. Sci. Technol., 32, 966. Cornelissen, G.; Rigterink, H.; Vrind, B.A.; ten Hulscher, D. Th.E.M.; Ferdinandy, M.M.A; and van Noort, P.C.M. (1997) Two-Stage Desorption Kinetics and In Situ Partitioning of Hexachlorobenzene and Dichloroben- zenes in a Contaminated Sediment. Chemo, Sphere, 35, 2405. Cornelissen, G.; Van Noort, P.C.M.; and Govers, H.A.J. (1997) Desorption Kinetics of Chlorobenzenes, Polycyclic Aromatic Hydrocarbons, and Polychlorinated Biphenyls: Sediment Extraction with Tenax and Effects of Contact Time and Solute Hydrophobicity. Environ. Toxicol. Chem., 16, 1351. Cornelissen, G.; Van Noort, P.C.M.; and Govers, H.A.J. (1998) Mechanisms of Slow Desorption of Organic Compound From Sediments: A Study Using Model Sorbents. Environ. Sci. Technol., 32, 3124. Cornwell, J.C., and Morse, J.W. (1987) The Characterization of Iron Sulfide Minerals in Anoxic Marine Sediments. Mar. Chem., 22, 193. Dave, G. (1992) Sediment Toxicity and Heavy Metals in Eleven Lime Reference Lakes of Sweden. Water, Air, Soil Pollut., 63, 187. Davies, C.M.; Apte, S.C.; and Johnstone, A.L. (1998) A Bacterial Bioassay for the Assessment of Copper Bioavailability in Freshwaters. Environ. Toxicol. Water Qual., 13, 263. Davies, N.A.; Edwards, P.A.; Lawrence, M.A.M.; Taylor, M.G.; and Simkiss, K. (1999) Influence of Particle Surfaces on the Bioavailability to Different Species of 2,4-Dichlorophenol and Pentachlorophenol. Environ. Sci. Technol., 33, 2465. Davison, W. (1991) The Solubility of Iron Sulfides in Synthetic and Natural Waters at Ambient Temperature. Aquat. Sci., 53/54, 309. DeFoe, D.L., and Ankley, G.T. (1998) Influence of Storage Time on Toxicity of Freshwater Sediments to Benthic Macroinvertebrates. Environ. Pollut., 99, 123. DeWitt, T.H.; Ozretich, R.J.; Swartz, R.C.; Lamberson, J.O.; Schults, D.W.; Ditsworth, G.R.; Jones, J.K.P.; Hoselton, L.; and Smith, L.M. (1992) The

126 Handbook on Sediment Quality Effects of Organic Matter Quality on the Toxicity and Partitioning of Sediment-Associated Fluoranthene to the Infaunal Amphipod, Rhepoxynius abronius. Environ. Toxicol. Chem., 11, 197. DeWitt, T.H.; Hickey, C.W.; Morrisey, D.J.; Nipper, M.G.; Roper, D.S.; Williamson, R.B.; van Dam, L.; and Williams, E.K. Do Amphipods Have the Same Concentration-Response to Contaminated Sediment In Situ as In Vitro? Environ. Toxicol. Chem., 18, 1026. Dillon, T.M.; Moore, D.W.; and Jarvis, A.S. (1994) The Effects of Storage Temperature and Time on Sediment Toxicity. Arch. Environ. Contam. Toxicol., 27, 51. Di Toro, D.M.; Mahoney, J.D.; Hansen, D.J.; and Berry, W.J. (1990) Toxicity of Cadmium in Sediments: The Role of Acid Volatile Sulfide. Environ. Toxicol. Chem., 9, 1487. Di Toro, D.M.; Zarba, C.S.; Hansen, D.J.; Berry, W.J.; Swartz, R.C.; Cowan, C.E.; Pavlou, S.P.; Allen, H.E.; Thomas, N.A.; and Paquin, P.R. (1991) Technical Basis for Establishing Sediment Quality Criteria for Nonionic Organic Chemicals Using Equilibrium Partitioning. Environ. Toxicol. Chem., 10, 15412. Di Toro, D.M.; Mahoney, J.D.; Hansen, D.J.; Scott, K.J.; Carlson, A.R.; and Ankley, G.T. (1992) Acid Volatile Sulfide Predicts the Acute Toxicity of Cadmium and Nickel in Sediments. Environ. Sci. Technol., 26, 96. Emerson, S.; Jacobs, L.; and Tebo, B. (1983) The Behavior of Trace Metals in Marine Anoxic Waters: Solubilities at the Oxygen–Hydrogen Sulfide Interface. In Trace Metals in Sea Water. C.S. Wong, E. Boyle, K.W. Bruland, J.D. Burton, and E.D. Goldberg (Eds.), Plenum, New York, 579. Fay, A.A.; Brownawell, B.J.; McElroy, A.A.; and Elskus, A.A. (2000) Critical Body Residues in the Marine Amphipod Ampelisca abdita: Sediment Exposures with Nonionic Organic Contaminants. Environ. Toxicol. Chem., 19, 1028. Finlayson, B.; Fujimura R.; and Huang, Z.-Z. (2000) Toxicity of Metal- Contaminated Sediments from Keswick Reservoir, California, USA. Environ. Toxicol. Chem., 19, 485. Forbes, V.E., and Forbes, T.L. (1997) Dietary Absorption of Sediment-Bound Fluoranthene by a Deposit-Feeding Gastropod Using the 14C:51Cr Dual- Labeling Method. Environ. Toxicol. Chem., 16, 1002. Forbes, T.L.; Forbes, V.E.; Giessing, A.; Hansen, R.; and Kure, L.K. (1998) Relative Role of Pore Water Versus Ingested Sediment in Bioavailability of Organic Contaminants in Marine Sediments. Environ. Toxicol. Chem., 17, 2453. Fu, G.; Kan, A.T.; and Tomson, M.B. (1994) Adsorption and Desorption Hysteresis of Polycyclic Aromatic Hydrocarbons in Surface Sediment. Environ. Toxicol. Chem., 13, 1559. Fuchsman, P.C.; Duda, D.J.; and Barber, T.R. (1999) A Model to Predict Threshold Concentrations for Toxic Effects of Chlorinated Benzenes in Sediment. Environ. Toxicol. Chem., 18, 2100. Green, A.S.; Chandler, G.T; and Blood, E.R. (1993) Aqueous, Pore-Water and Sediment-Phase Cadmium: Toxicity Relationship for a Meiobenthic Copepod. Environ. Toxicol. Chem., 12, 1497.

Bioavailability in Sediments 127 Gunnarsson, J.S.; Granberg, M.E.; Nielsson, H.C.; Rosenberg, R.; and Hellman, B. (1999) Influence of Sediment-Organic Matter Quality on Growth and Polychlorobiphenyl Bioavailability in Echinodermata (Amphiura filiformis). Environ. Toxicol. Chem., 18, 1534. Gunnarsson, J.S.; Hollertz, K.; and Rosenberg, R. (1999) Effects of Organic Enrichment and Burrowing Activity of the Polychaete Neries diversicolor on the Fate of Tetrachlorobiphenyl in Marine Sediments. Environ. Toxicol. Chem., 18, 1149. Gustafson, K.E., and Dickhut, R.M. (1997) Distribution of Polycyclic Aromatic Hydrocarbons in Southern Chesapeake Bay Surface Water: Evaluation of Three Methods for Determining Freely Dissolved Water Concentrations. Environ. Toxicol. Chem., 16, 452. Hare, L.; Carignan R.; and Huerta-Diaz, M.A. (1994) A Field Study of Metal Toxicity and Accumulation by Benthic Invertebrates: Implications for the Acid Volatile Sulfide (AVS) Model. Limnol. Oceanog., 39, 1653. Harkey, G.A.; Van Hoof, P.L.; and Landrum, P.F. (1995) Bioavailability of Polycyclic Aromatic Hydrocarbons from a Historically Contaminated Sediment Core. Environ. Toxicol. Chem., 14, 1551. Hatzinger, P.B., and Alexander, M. (1997) Biodegradation of Organic Com- pounds Sequestered in Organic Solids or in Nanopores Within Silica Particles. Environ. Toxicol. Chem., 2215. Heitzer, A.; Webb, O.F.; Thonnard, J.E.; and Sayler, G.S. (1992) Specific and Quantitative Assessment of Naphthalene and Salicylate Bioavailability by Using a Catabolic Reporter Bacterium. Appl. Environ. Microbiol., 58, 1839. Hellou, J.; Mackay, D.; and Banoub, J. (1999) Levels, Persistence and Bioavailability of Organic Contaminants Present in Marine Harbor Sedi- ments Impacted by Raw Sewage. Chemosphere, 38, 457. Hoke, R.A.; Ankley, G.T.; Cotter, A.M.; Goldenstein, T.; Kosnian, P.A.; Phipps, G.L.; and Van der Meiden, F.M. (1994) Evaluation of Equilibrium Partitioning Theory for Predicting Acute Toxicity of Field-Collected Sediments Contaminated with DDT, DDE, and DDD to the Amphipod Hyalella azteca. Environ. Toxicol. Chem., 13, 157. Hoke, R.A.; Giesy, J.P.; Ankley, G.T.; Newsted, J.I.; and Adams, J.R. (1990) Toxicity of Sediments from Western Lake Erie and Maumee River at Toledo, Ohio, 1987: Implication for Current Dredged Material Disposal Practices. J. Great Lakes Res., 16, 457. Hoke, R.A.; Giesy, J.P.; and Kreis, R.G. (1992) Sediment Pore Water Toxicity Identification in the Lower Fox River and Green Bay, Wisconsin, Using the Microtox Assay. Ecotoxicol. Environ. Saf., 23, 343. Hoke, R.A.; Kosian, P.A.; Ankley, G.T.; Cotter, A.M.; Van der meiden, F.M.; Phipps, G.L.; and Durhan, E.J. (1995) Check Studies With Hyalella azteca and Chironomous tentans in Support of the Development of a Sediment Quality Criterion for Dieldrin. Environ. Toxicol. Chem., 13, 435. Hollis, R.P.; Killham, K.; and Glover, L.A. (2000) Design and Application of a Biosensor for Monitoring Toxicity of Compounds to Eukaryotes. Appl. Envion. Microbiol., 66, 1676.

128 Handbook on Sediment Quality Howard, D.E., and Evans, R.D. (1993) Acid-Volatile Sulfide (AVS) in a Seasonally Anoxic Mesotrophic Lake: Seasonal and Spatial Changes in Sediment AVS. Environ. Toxicol. Chem., 12, 1051. Huang, W.; Schlautman, M.A.; and Weber, W.J., Jr. (1997) A Distributed Reactivity Model for Sorption by Soil and Sediments. 10. Relationships Between Desorption Hysteresis and the Chemical Characteristics of Organic Domains. Environ. Sci. Technol., 31, 2562. Hunt, A.P.; Parry, J.D.; and Hamilton-Taylor, J. (2000) Further Evidence of Elemental Composition as an Indicator of the Bioavailability of Humic Substances to Bacteria. Limnol. Oceanogr., 45, 237. Jepsen, R., and Lick, W. (1999) Nonlinear and Interactive Effects in the Sorption of Hydrophobic Organic Chemicals by Sediments. Environ. Toxicol. Chem., 18, 1627. John, D.M.; House, W.A.; and White, G.F. (2000) Environmental Fate of Nonylphenol Ethoxylates: Differential Adsorption of Homologs to Compo- nents of River Sediment. Environ. Toxicol. Chem., 19, 293. Kan, A.T.; Fu, G.; Hunter, M.; Chen, W.; Ward, C.H.; and Tomson, M.B. (1998) Irreversible Sorption of Neutral Hydrocarbons to Sediments: Observations and Model Predictions. Environ. Sci. Technol., 32, 892. Kane Driscoll, S.B., and Landrum, P.F. (2000) A Comparison of Equilibrium Partitioning and Critical Body Residue Approaches for Predicting Toxicity of Sediment-Associated Fluoranthene to Freshwater Amphipods. Environ. Toxicol. Chem., 16, 2179. Kane Driscoll, S.B.; Harkey, G.; and Landrum, P.F. (1997) Accumulation and Toxicokinetics of Fluoranthene in Sediment Bioassays with Freshwater Amphipods. Environ. Toxicol. Chem., 16, 742. Karickhoff, S.W.; Brown, D.S.; and Scott, T.A. (1979) Sorption of Hydropho- bic Pollutants on Natural Sediments. Water Res., 13, 241. Karickhoff, S.W., and Morris, K.R. (1985) Impact of Tubificid Oligochaetes on Pollutant Transport in Bottom Sediments. Environ. Sci. Technol., 19, 51. Kelsey, J.W., and Alexander, M. (1997) Declining Bioavailability and Inap- propriate Estimation of Risk of Persistent Compounds. Environ. Toxicol. Chem., 16, 582. Kemp, P.F., and Swartz, R.C. (1988) Acute Toxicity of Interstitial and Particle-Bound Cadmium to a Marine Infaunal Amphipod. Mar. Environ. Res., 26, 135. Khim, J.S.; Kurunthchalam, K.; Villeneuve, D.L.; Koh, C.H.; and Giesy, J.P. (1999) Characterization Distribution of Trace Organic Contaminants in Sediment from Masan Bay, Korea. 1. Instrumental Analysis. Environ. Sci. Technol., 33, 499. Khim, J.S.; Villeneuve, D.L.; Kurunthchalam, K.; Lee, K.T.; Snyder, S.A.; Koh, C.H.; and Giesy, J.P. (1999) Alkylphenols, Polycyclic Aromatics, Hydrocarbons, and Organochlorines in Sediments from Lake Shihwa. Korea: Instrumental and Bioanalytical Characterization. Environ. Toxicol. Chem., 18, 2424. King, J.M.H.; Digrazia, P.M.; Applegate, B.; Burlage, R.; Sanseverino, J.; Dunbar, P.; Larimer F.; and Sayler, G.S. (1990) Rapid, Sensitive Biolumi-

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Bioavailability in Sediments 135 U.S. Environmental Protection Agency (1997) Incidence and Severity of Sediment Contamination in Surface Waters of the United States: Volume 1: National Sediment Quality Survey. EPA-823/R-97-006, Science and Technology, Washington, D.C. U.S. Environmental Protection Agency (1998a) U.S. EPA’s Contaminated Sediment Management Strategy: Reinventing Government to Streamline Decision-Making. Office of Water, http://www.U.S.EPA.gov/ostwater/cs/ manage/execsum.html (accessed May 2000). U.S. Environmental Protection Agency (1998b) UC Berkeley Researchers Use In Vitro Technique to Measure Bioavailability of Sediment-Associated Contaminants. Contaminated Sediments News Issue 22, Fall 1998,EPA- 823/N-98-007, http://www.U.S.EPA.gov/OST/pc/csnews/csnews22.html#4 (accessed April 2000). Utvik, T.I.R., and Johnsen, S. (1999) Bioavailability of Polycyclic Aromatic Hydrocarbons in the North Sea. Environ. Sci. Technol., 33, 1963. Valsaraj, K.T., and Thibodeaux, L.J. (1999) On the Linear Driving Force Model for Sorption Kinetics of Organic Compounds on Suspended Sedi- ment Particles. Environ. Toxicol. Chem., 18, 1679. Van den Berg, G.A.; Loch, J.P.G.; van den Heijdt, L.M.; and Zwolsman, J.J.G. (1998) Vertical Distribution of Acid-Volatile Sulfide and Simultane- ously Extracted Metals in a Recent Sedimentation Area of the River Meuse in the Netherlands. Environ. Toxicol. Chem., 17, 758. Van Leeuwen, H.P. (1999) Metal Speciation Dynamics and Bioavailability: Inert and Labile Complexes. Environ. Sci. Technol., 33, 3743. Veith, G.D. (1998) Contaminated Sediments: State of the Science and Future Research Directions. National Sediment Bioaccumulation Conference, Bethesda, Md., http://www.U.S. EPA.gov/OST/cs/veith.pdf (accessed May 2000). Wall, S.B.; Isely, J.J.; and LaPoint, T.W. (1996) Fish Bioturbation of Cadmium-Contaminated Sediments: Factors Affecting Cd Availability to Daphnia magna. Environ. Toxicol. Chem., 15, 294. Wang, F., and Chapman, P.M. (1999) Biological Implications of Sulfide in Sediment: A Review Focusing on Sediment Toxicity. Environ. Toxicol. Chem., 16, 2526. Weber, W.J., Jr.; McGinley, P.M.; and Katz, L.E. (1992) A Distributed Reactivity Model for Sorption by Soils and Sediments. 1. Conceptual Basis and Equilibrium Assessments. Environ. Sci. Technol., 26, 1955. Weston, D.P., and Mayer, L.M. (1998a) Comparison of In Vitro Digestive Fluid Extraction and Traditional In Vivo Approaches as Measures of Polycyclic Aromatic Hydrocarbon Bioavailability from Sediments. Environ. Toxicol. Chem., 17, 830. Weston, D.P., and Mayer, L.M. (1998b) In Vitro Digestive Fluid Extraction as a Measure of the Bioavailability of Sediment-Associated Polycyclic Aromatic Hydrocarbons: Sources of Variation and Implications for Parti- tioning Models. Environ. Toxicol. Chem., 17, 820. Wu, S.-C., and Gschwend, P.M. (1986) Sorption Kinetics of Hydrophobic Organic Compounds to Natural Sediments and Soils. Environ. Sci. Technol., 20, 717.

136 Handbook on Sediment Quality Xing, B.; Pignatello, J.J.; and Gigliotti, B. (1996) Competitive Sorption Between Atrazine and Other Organic Compounds in Soils and Model Sorbents. Environ. Sci. Technol., 30, 2432. Xing, B., and Pignatello, J.J. (1996) Time Dependent Isotherm Shape of Organic Compounds in Soil Organic Matter: Implications for Sorption Mechanisms. Environ. Toxicol. Chem., 15, 1282. Xing, B., and Pignatello, J.J. (1997) Dual-Model Sorption of Low-Polarity Compounds in Glassy Poly(Vinyl Chloride) and Soil Organic Matter. Environ. Sci. Technol., 31, 792. Young, T.M., and Weber, W.J., Jr. (1995) A Distributed Reactivity Model for Sorption by Soil and Sediments. 3. Effects of Diagenetic Processes on Sorption Energetics. Environ. Sci. Technol., 27, 92. Yu, K-.T.; Lam, M.H.-W.; Yen, Y.-F.; and Leung, A.P.K. (2000) Behavior of Trace Metals in the Sediment Pore Waters of Intertidal Mudflats of a Tropical Wetland. Environ. Toxicol. Chem., 19, 535. Zwiernik, M.J.; Quensen, J.F., III; and Boyd, S.A. (1999) Residual Petroleum in Sediments Reduces the Bioavailability and Rate of Reductive Dechlori- nation of Aroclor 1242. Environ. Sci. Technol., 33, 3574.

Bioavailability in Sediments 137

Chapter 4 Methods for Collecting, Storing, and Manipulating Sediments and Interstitial Water Samples for Chemical and Toxicological Analyses

Jerome Diamond, Tetra Tech, Inc., Owings Mills, MD Allen Burton, Wright State University, Dayton, OH John Scott, SAIC, Narragansett, RI

140 Introduction 144 Special Considerations for 141 Potential Interferences and Explosive Contaminated Artifacts in Sediment and Sediments Interstitial Water Collection, 144 Disposal of Sediments and Handling, and Manipulation Pore-Water Samples 141 Noncontaminant Factors 144 Facilities, Equipment, and 142 Changes in Bioavailability Supplies 143 Presence of Indigenous 144 Field Facilities Organisms 145 Laboratory Facilities 143 Safety Concerns 146 Equipment and Supplies 144 Field Operation 147 Equipment Cleaning and 144 Laboratory Operations Decontamination

139 148 Procedures for Sediment and 176 Sediment Dilutions Interstitial Water Collection 176 Sediment Elutriates 148 Sediment Collection 177 Isolation of Interstitial Water 150 Grab Samplers 178 Centrifugation 153 Core Samplers 179 Sediment Squeezing 158 In Situ Interstitial Water 180 Pressurized Devices Collection 180 Quality Assurance and Quality 160 Peeper Methods Control 161 Suction Methods 180 General Considerations 161 Retrieval of Interstitial 181 Quality Assurance and Water Samples Quality Control Procedures 162 Sample Transport and Storage for Sediment Collection and 163 Sample Holding Times Manipulation 164 Subsampling, Homogenizing, and 182 The Quality Assurance Compositing Samples Project Plan 164 General Information 182 Data Quality Objectives 165 Subsampling 183 Project Organization 168 Homogenization 183 Standard Operating 169 Compositing Procedures 170 Sediment Sample Manipulations 183 Sediment Sample 170 Sieving Documentation 171 Recommended Sieves 184 Sample-Tracking 171 Press Sieving Documentation 172 Wet Sieving 185 Recordkeeping 172 Alternatives to Sieving 185 Quality Assurance Audits 172 Spiking Sediments 185 Corrective Action 172 Preparation for Spiking (Management of 173 Spiking Methods Nonconformance Events) 173 Verifying Homogeneity 186 Data Reporting 174 Equilibration Times 186 References 175 Formulated Sediments and Organic Carbon Modification

INTRODUCTION Assessments of sediment quality commonly include analyses of anthropogenic contaminants, benthic community structure, physicochemical characteristics, and toxicity to aquatic organisms. The accuracy and representativeness of these data will depend in part on the degree to which the sediment samples reflect the in situ physicochemical environment from which they were collected (ASTM, 1994; Environment Canada, 1994; and U.S. EPA 1994, 1999). The manner in which sediments are collected, stored, and manipulated can greatly influence the results of any sediment-quality evaluation. Addressing these variables in an appropriate and systematic manner can help in interpreting sediment-quality data and facilitate comparisons among studies designed to characterize the sediment environment.

140 Handbook on Sediment Quality Maintaining sample integrity is a critical consideration in selecting appropriate sampling and handling methods for sediment or interstitial water. The ability of a method to meet study objectives is an important selection criterion; different objectives require different sampling devices and methods. In addition, the analysis objectives of the study could also be a factor determining appropriate sampling and handling procedures. Sample collection, storage, and handling methods discussed in this chapter are designed to minimize physicochemical artifacts and ensure that appropriate samples are used for testing and analysis.

POTENTIAL INTERFERENCES AND ARTIFACTS IN SEDIMENT AND INTERSTITIAL WATER COLLECTION, HANDLING, AND MANIPULATION

The term interference in this chapter refers to any sediment or interstitial water collection, storage, or manipulation technique that, when introduced, can potentially affect chemical measurements, test organism responses, and contaminant bioaccumulation unrelated to in situ sediment characteristics. Interferences can potentially confound interpretation of analytical or test results in two ways: (1) introduction of a contaminant to the sample, either through inappropriate or improperly cleaned sampling equipment, or through improper storage or manipulation procedures; and (2) changes in chemical bioavailability or physical conditions of the sample, resulting in altered chemical equilibria and toxicity. The sediment environment is actually composed of a complex set of microenvironments; chemical gradients; and other interacting physical, chemical, and biological processes. Many of these characteristics influence contaminant bioavailability, toxicity to benthic and planktonic organisms, microbial degradation, and chemical sorption. Any disruption of, or interference with, the natural sediment environment complicates interpretations of treatment effects, causative factors, and in situ comparisons. We focus our discussion on three overall categories of interfering factors: (1) noncontaminant factors such as alterations in sediment or interstitial water characteristics that are independent of chemical concentration, (2) changes in chemical bioavailability (due to sediment or interstitial water collection, manipulation, and storage), and (3) the presence of indigenous organisms.

NONCONTAMINANT FACTORS. Several aspects of sample collection, storage, and manipulation procedures result in physicochemical interferences

Methods for Collecting, Storing, and Manipulating 141 that, in turn, can alter contaminant concentration and bioavailability. For example, photoinduced toxicity caused by UV light may be important for some compounds associated with sediment (e.g., polycyclic aromatic hydrocarbons [PAHs]) (Ankley et al., 1994, and Davenport and Spacie, 1991). Therefore, if samples are to be analyzed for PAHs, or if a particular sampling area is thought to contain PAHs, sampling and sample storage methods should reduce or eliminate additional UV exposure (e.g., store samples in the dark; avoid prolonged exposure of the sample to sunlight if samples are homogenized in the field). Another example is laboratory extraction of interstitial water from sediment. Recent information suggests that centrifugation can alter or increase chemical concentrations in the interstitial water (Sarda and Burton, 1995). Thus, centrifugation can, under certain circumstances, introduce noncontaminant interferences to interstitial water chemistry and contaminant bioavailability. Natural geomorphological and physicochemical characteristics (such as sediment texture) may influence the response of test organisms (DeWitt et al., 1988, and Suedel and Rodgers, 1994). Thus, the sampling device should also yield samples that truly reflect in situ physical conditions as closely as possible. Dredge-type samplers are discouraged for this reason because these samplers disturb the physical integrity of the sediment more than most other types of sampling devices. Additionally, devices that rely on a closing mechanism (e.g., Ponar or Eckman samplers) are unlikely to yield representative samples in sediments with a high percentage of gravel because the sampler may not close properly. Sediment samples collected under these conditions may not reflect the particle-size distribution at the intended location.

CHANGES IN BIOAVAILABILITY. Sediment collection, handling, testing, and storage may alter contaminant bioavailability and concentration by changing the physical, chemical, or biological characteristics of the sediment. Proper storage of samples can minimize interferences resulting from alteration in contaminant bioavailability. For example, plastic bags and containers may allow sediment oxidation to occur, as oxygen can permeate many plastics. Introduced sediment oxidation can alter the bioavailability of some metals (Ankley et al., 1996) as well as alter the abundance of other compounds (such as sulfides and several volatile chemicals). The result may be altered toxicity of the sample and altered interstitial water concentrations of certain contaminants. The type of sampling device can also introduce interferences in sample chemical bioavailability if the characteristics of the device are not carefully aligned with study objectives. For example, metal sampling devices or containers that are not stainless steel can introduce unwanted metal contamination to samples. The use of appropriate sampling devices and sample storage containers for different analyses is important to minimize interferences from contamination, cross-contamination among samples, or unin- tended alteration of chemical bioavailability. Manipulation procedures (e.g., homogenization, compositing, sieving) are generally thought to increase availability of organic compounds because they

142 Handbook on Sediment Quality disrupt the equilibrium of organic carbon in the pore water–particle system. Similarly, oxidation of anaerobic sediments, through compositing and homogenization, may increase the availability of certain metals (Di Toro et al., 1990). Sieving may increase contaminant bioavailability through oxidation and alteration of the physical sample integrity (ASTM, 1994, and U.S. EPA, 2000). Storing and manipulating sediment or interstitial samples at temperatures different than those in the field might also affect contaminant solubility, partitioning coefficients, or other physicochemical characteristics. Interaction between sediment and overlying water in the sample container may also influence bioavailability (Stemmer et al., 1990b).

PRESENCE OF INDIGENOUS ORGANISMS. Some sediments may contain indigenous organisms that can interfere with subsequent toxicity testing (U.S. EPA, 2000) or confound interpretations of sediment-associated chemical concentrations. Indigenous organisms such as oligochaetes readily sorb nonpolar organics and can alter sediment concentrations. Unfortunately, the processes required to remove indigenous organisms require further sediment processing, which may lead to altered bioavailability of any sediment-associated contaminants. If it is necessary to remove indigenous species, it is preferable to handpick the organisms with forceps. If there are too many organisms or they are too difficult to find, pressure sieving may be required.

SAFETY CONCERNS Collection and processing of sediments may involve substantial risks to personal safety and health. Contaminants in field-collected sediments may include carcinogens, mutagens, human pathogens (e.g., Cryptosporidium cysts), and toxic compounds. Toxins may also originate from water or sediment-born microorganisms such as Pfisteria piscidia. Because sediment collection often occurs without complete knowledge of the source or degree of hazard, contact with sediment needs to be minimized by (1) using gloves; laboratory coats, safety glasses, face shields, and respirators, as appropriate, and (2) manipulating sediments in open air, under a ventilated hood, or in an enclosed glove box. Personnel collecting sediment samples and handling sediments should take all safety precautions necessary for the prevention of bodily injury and illness that might result from ingestion or invasion of infectious agents, inhalation or absorption of corrosive or toxic substances through skin contact, and asphyxi- ation because of lack of oxygen or presence of noxious gases. Work with some sediments may require compliance with rules pertaining to the handling of hazardous materials. In cases where highly contaminated sediments are known or likely to occur, sampling personnel should complete the Occupational Safety and Health Administration (OSHA) 40-hour training for handling hazardous and toxic waste. In addition, a site-specific health and safety plan should be prepared for field operations if hazardous substances are

Methods for Collecting, Storing, and Manipulating 143 thought to be present. Personnel in contact with sediments should not work alone.

FIELD OPERATION. Field sampling is inherently dangerous and generally requires specialized training. Operation of boats and sampling equipment, even under ideal weather and hydrological conditions, carries a certain degree of risk. This danger is greatly compounded in bad weather. Safety protocols will be project specific, but they should directly address concerns for the water body(ies), sampling equipment, and staff that handle sediments for each project.

LABORATORY OPERATIONS. Laboratory personnel should be trained in proper practices for handling contaminated sediments and using chemical reagents. Routinely encountered chemicals include acids and organic solvents. Special handling and precautionary guidance in material safety data sheets provided by the supplier should be followed for reagents and other purchased chemicals. All containers should be adequately labeled to indicate their contents. Strong acids and volatile organic solvents should be used in a fume hood or under an exhaust canopy over the work area.

SPECIAL CONSIDERATIONS FOR EXPLOSIVE CONTAMINATED SEDIMENTS. The presence of surficial or buried unexploded ordnance (UXO) represents a serious safety issue that must be addressed before sediment collection activities take place. Where there is some evidence that explosive materials could be found at a sampling location, plans for sediment collection must be submitted to the Department of Defense Explosive Safety Board for concurrence. If it is suspected that UXO has been inadvertently collected, immediately contact the local fire department.

DISPOSAL OF SEDIMENTS AND PORE-WATER SAMPLES. Disposal of sediments and chemicals should be in accordance with federal, state, and local laws and regulations. Wastes are considered hazardous if they have the characteristics of being ignitable, corrosive, reactive, or toxic, or are listed as hazardous waste. Such wastes should be entered into a hazardous waste log maintained by the facility and an investigative derived waste plan should be developed and used by the laboratory for the disposal of such wastes.

FACILITIES, EQUIPMENT, AND SUPPLIES

FIELD FACILITIES. Methods for evaluating sediment quality are highly dependent on the choice and availability of appropriate facilities, equipment, and supplies for both field and laboratory operations. Sediment samples may be obtained using hand-held devices or from a sampling platform (a vessel,

144 Handbook on Sediment Quality ice, a plane, or a helicopter) using a sediment collection device such as a grab sampler. Divers (in some instances, certified divers) and diving equipment may be required in some cases. The type of waterbody and the selected sediment sampling gear will dictate what kind of vessel is necessary to collect sediment. A small vessel with little onboard space and equipment can be adequate to obtain small sediment samples from shallow water sites. In contrast, obtaining large samples or samples from deep sites or cohesive (compacted) sediments may require a vessel with much more deck space and lifting capacity. If samples are to be processed on board, the vessel should provide facilities that are equivalent to those required in a land-based facility. Sediment collection requires ample deck space to handle cores, grabs, and winches. The strength capacity of all lines and cables should be evaluated before sampling to ensure that there is sufficient lifting capacity for sampling operations. The vessel should be equipped with sufficient winch power to handle the weight of the sampling devices, taking into account the additional suction pressure associated with extraction of cohesive sediments. The weight of both the sampling equipment and the sediment should be considered in determining the required strength capacity of all lines and cables on the vessel. Vessel requirements for sample handling are dependent on the type of samples collected and the study objectives. The limiting factor is generally the space that may be required for manipulation of the sediments (e.g., homogenization and compositing) and the specific storage needs required for the samples and supplies. Ice chests or refrigeration may be required, depending on the duration of the operation and the particular analyses desired. Space for storage of decontamination materials, including solvents and holding containers, needs to be available, as well as areas for storing clean sampling gear and containers to avoid contamination risks. Space for personal safety equipment is also a necessity.

LABORATORY FACILITIES. In the laboratory, supply and equipment needs and facility requirements are based on the type of sample processing and testing or analyses that are to be performed. Laboratory issues that need to be addressed include sample storage; sample characterization (e.g., ammonia, total organic carbon, particle-size distribution, acid-volatile sulfide, or metals) and associated analytical methods, sediment, and interstitial water manipulation (e.g., homogenization, sediment spiking, sieving, pore-water extraction); and project quality assurance. Many of these issues are addressed through the preparation of a laboratory quality assurance plan describing all procedures and management strategies to ensure accurate analysis and reporting. Laboratories that have such a plan in place and that are certified by the appropriate agency for the analyses of concern (where this is available) should be used. For sample-processing activities, the laboratory should be temperature and light controlled (ASTM, 1994, and U.S. EPA, 1991, 2000). The conditions in the laboratory should comply with OSHA requirements and other safety and legal requirements. To minimize sample contamination and protect the safety of laboratory personnel, the laboratory should be kept free from contamina-

Methods for Collecting, Storing, and Manipulating 145 tion and be well ventilated, free of dust and drafts, and not susceptible to extreme temperature changes. Hoods should be used to minimize potential hazardous exposures of personnel to volatile organic contaminants and prevent cross-contamination of sediment samples. Bench tops should be constructed of inert, corrosion-resistant materials. It is important to maintain high standards of cleanliness in the laboratory and work areas and to frequently monitor these areas for contamination. Walls should be covered with waterproof paint that provides a smooth surface that can be easily cleaned or disinfected. Floors should be covered with material that can be maintained by wet-mopping and should not be swept or dry- mopped. Sinks should not be used for the disposal of reagents or samples that are regulated wastes according to the Resource Conservation and Recovery Act. The laboratory must have refrigerated storage and freezer space sufficient for storing samples. Each storage facility should be equipped with a certified thermometer. Sediment samples should be stored at 4 °C (ASTM, 1994; Environment Canada, 1994; and U.S. EPA, 2002), unless otherwise specified. Freezer space is generally required to maintain ice packs for transport of samples and storage of samples for some types of analyses.

EQUIPMENT AND SUPPLIES. Equipment and supplies that contact stock solutions, sediments, or overlying water should not contain substances that can be leached or dissolved in amounts that would interfere with chemical or toxicity analyses. Such substances could include zinc from glassware, glass- fiber filters, or metal devices and certain organic compounds from plasticware. Use of appropriate equipment-cleaning procedures will minimize such interferences. In addition, equipment and supplies that contact sediment or water should be chosen to minimize sorption of test materials from water. Glass, type-316 stainless steel, high-density polyethylene (HDPE), polycarbonate, and fluorocarbon plastics should be used whenever possible to minimize leaching, dissolution, and sorption (ASTM, 1994, and U.S. EPA, 2002). High- density plastic containers are recommended for organism holding, acclimation, and culture chambers (U.S. EPA, 1991, 2002). These materials should be washed in detergent, acid rinsed, and soaked in flowing water for 1 week or more before use. Copper, brass, lead, galvanized metal, and natural or neo- prene rubber should not be in contact with sediment, overlying water, or stock solutions. New plastic products should be tested for toxicity before general use by exposing organisms to them under ordinary test conditions. Table 4.1 summarizes the appropriate types of sampling containers and preservation requirements for various types of contaminants associated with sediments. In general, sediments and pore waters with multiple or unknown chemical types should be stored in containers made from high-density polyethylene or polytetrafluoroethylene (PTFE). Samples for organic contaminant analysis should be stored in brown borosilicate glass containers with PTFE lid liners, whereas plastic containers are recommended when the chemicals of concern are heavy metals. Amber-colored glass containers or clear containers wrapped tightly with an opaque material (e.g., clean aluminum foil) are used and thereby reduce photoinduced transformations of chemicals such as PAHs.

146 Handbook on Sediment Quality Table 4.1 Recommended sampling containers, holding times, and storage conditions for common types of sediment analyses (ASTM 1994).

Holding Storage Contaminanta Containerb time condition Ammonia P, G 28 days Sulfate P, G 28 days Sulfide P, G 28 days R or NaOH; pH >9 Oil and grease G 28 days HCl, pH <2

Mercury P, G 6 weeks H2SO4, pH <2; R

Metals (except Cr or Hg) P, G 6 months NHO3, pH <2; F Extractable organics (including G, PTFE- 7 days (until R; F phthalates, airosamines, lined cap extraction) organochlorine pesticides, 30 days (after PCB aromatics, isophorone, extraction) PAHs, haloethers, chlorinated hydrocarbons, and TCDD) Purgables (halocarbons and G, PTFE- 14 days R; F aromatics lined septum Pesticides G, PTFE- 7 days (until R; F lined cap extraction 30 days (after extraction) Sediment toxicity (acute and P, PTFE 2 weeksc R, dark chronic Bioaccumulation testing P, PTFE 2 weeksc R, dark a Polychlorinated biphenyl and Tetrachlorodibenzo-p-dioxin. b P = plastic; G = glass, PTFE = polytetrafluoroethylene; R = refrigerate; and F = freeze. c Holding time may be longer depending on the magnitude and type of contaminants present.

Equipment Cleaning and Decontamination. Equipment used to collect and store sediment samples must be cleaned before use and should be decontami- nated during sampling and other activities that require the handling of multiple samples, particularly heavily contaminated samples. Failure to perform appropriate cleaning and decontamination in these instances could significantly reduce the value of a sediment characterization study. An approach recommended by ASTM (1994) for field samples of unknown composition includes (1) soap and water wash, (2) distilled water rinse, (3) methanol rinse, (4) methylene chloride rinse, and (5) site water rinse. Solvent decontamination may be necessary in situations where organic chemical contamination is possible. A nitric acid rinse may be appropriate in surveys where metal contamination is likely. If sediment can be collected from the interior of the sampling device and away from potentially contaminated surfaces of the sampler, it may be adequate to rinse with site water between sites. See ASTM (1994) and the U.S. Environmental Protection Agency (U.S. EPA, 2002) for detailed recommendations.

Methods for Collecting, Storing, and Manipulating 147 PROCEDURES FOR SEDIMENT AND INTERSTITIAL WATER COLLECTION

SEDIMENT COLLECTION. The appropriate sediment-sampling method will depend on the study objectives, the sampling platform (e.g., vessel, on foot, ice), ease of access to the sampling site, physical characteristic of the sediments, number of sites to be sampled, amount of sediment required for each sample, and budget. Before sampling, a positioning system with an appropriate degree of resolution should be used to identify the location of sampling sites. There are a variety of navigation and position-fixing systems available, including optical or line-of-sight techniques, electronic-positioning systems, and satellite-positioning systems (Environment Canada, 1994). For studies of large areas (e.g., large lakes or offshore marine environments), where an accuracy of ±100 m typically is sufficient, either the long range–navigation system or global-positioning system (GPS) is generally suitable. Differential GPS is recommended where high positioning accuracy is required. For nearshore areas, where suitable permanent targets are available, radar can be used if the desired position accuracy is between 10 and 100 m. Differential GPS or a microwave system should be used if position accuracy needs to be <10 m. For small areas where sampling sites are numerous or spaced relatively close together, it is important to use a high-accuracy microwave system or differential GPS. There are two basic approaches for collecting bottom sediment samples: sampling from a platform and sampling by hand. Sampling platforms include vessels, ice, and airborne vehicles. Vessels used in sediment sampling range from small powerboats to oceangoing research vessels more than 30.5 m (100 ft) long. Sampling by hand may involve the use of a coring device and may be accomplished either by wading in shallow waters, diving, or from surface ice. Diving is typically more costly and difficult than sampling from a platform but often yields better quality samples, particularly sediment cores. In areas with sufficient ice cover, sediment samples can be obtained by sampling through a hole in the ice. The type of sediment-particle size is one factor that will dictate the appropriate type of sampling device. Currently, there is no one sampler that satisfies all considerations. Many samplers are capable of recovering a relatively undisturbed sample in soft, fine-grained sediments, but fewer are suitable for sampling harder sediments containing significant quantities of sand, gravel, firm clay, or till (Mudroch and Azcue, 1995). As discussed later in this section, certain grab samplers or corers are particularly adapted for sampling more difficult sediments. The quantity of sediment to be collected at each sampling site is another important consideration in the selection of a sampling device. The required

148 Handbook on Sediment Quality quantity of sediment typically depends on the number and type of physicochemical or biological analyses desired. Typical volume requirements for common types of sediment analyses are summarized in Table 4.2. These requirements could vary depending on specific study objectives. “Mini” grab samplers, for example, such as the petite Ponar, are relatively easy to use and require less lifting capacity than larger grab samplers, but they also collect much less sample. This could necessitate many grabs at a single location to obtain enough sample for analyses. Another important consideration in choosing a collection device is the sediment depth that needs to be sampled. Often, the desired depth of sampling is intimately related to the overall study objectives. Generally, grab samplers are recommended for the collection of surface sediments, whereas corers are recommended for obtaining sediment depth profiles. Another sampling method for surficial sediments includes the use of divers who scrape off surficial sediment directly into containers (Domagalski, 2001). This method can ensure that sediment associated with epibenthos is sampled only and not deeper layers. Surficial sediment sampling provides information on the horizontal distribution of properties of interest for the most recently deposited material. Information obtained from analysis of surface sediments can be used, for example, to map the distribution of organisms or a chemical contaminant on the sediment across a body of water. A sediment depth profile, on the other hand, includes both the surficial sediment layer and the sediment underneath this layer. Sediment depth profiles are collected to study historical changes in parameters of interest as revealed through changes in their (depth) vertical distribution. Core devices are especially recommended for projects in which it is critical to maintain the integrity of the sediment profile because they are considered to be the least disruptive. Furthermore, core samplers should be used where it is important to maintain an oxygen-free environment (e.g., if one is examining metal bioavailability or is sampling sites in which metals could be toxic) because they limit oxygen exchange with the air more effectively than grab samplers. When a study calls for large volumes of intact sediment, box corers are appropriate. A fourth type of sediment-sampling device, a dredge sampler, is dragged along the bottom for the purpose of collecting large benthos. Dredge

Table 4.2 Typical sediment volume requirements for various analyses.

Sediment analysis Volume Inorganic chemicals 90 mL Nonpetroleum organic chemicals 230 mL Total organic carbon, moisture 300 mL Particle size 230 mL Petroleum hydrocarbons 250–1000 mL Acute toxicity tests 1–3 L Bioaccumulation tests 3 L Pore water extraction 2 L Elutriate preparation 1 L

Methods for Collecting, Storing, and Manipulating 149 samplers cause disruption of sediment and pore-water integrity, as well as loss of fine-grained sediments. For these reasons, only grab and core samplers are recommended for sediment collection. For more detailed descriptions of the various sediment-sampling devices and in-depth discussion of their merits and limitations, several comprehensive reviews are recommended (ASTM, 1994; Baudo, 1990; Blomqvist, 1990; Burton, 1992; Environment Canada, 1994; Golterman et al., 1983; Mudroch and Azcue, 1995; Mudroch and MacKnight, 1994; Plumb, 1981; and U.S. EPA, 2002). Field notes should be recorded for each sample that is collected. Notes may include measurement of the penetration depth and descriptions of the physical characteristics and vertical structure of the sediment (e.g., texture, color, biological structures, debris, presence of oily sheen, changes in sediment characteristics with depth, and presence and depth of the redox potential discontinuity (layer). Observations regarding site characteristics and conditions at the time of sampling are also recommended. Sediment odor evaluation is potentially dangerous depending on the chemicals present in the sediment (ASTM, 1994) and is, therefore, discouraged as a general practice. All field sediment handling and evaluation procedures are potentially dangerous and subject to personnel safety precautions.

Grab Samplers. Grab samplers consist either of a set of jaws that shut when lowered into the surface of the bottom sediment or a bucket that rotates into the sediment when it reaches the bottom. These samplers have the advantage of being relatively easy to handle and operate, readily available, moderately priced, and versatile in terms of the range of substrate types they can effectively sample. Grab samplers can collect either small or large volumes of sediment, depending on the size of the sampler. Careful use of grab samplers is required to avoid problems such as lack of penetration of sediment, loss of sediment from tilting or washout upon ascent, mixing of sediment layers at impact, and loss of fine-grained surface sediments from the bow wave during descent (ASTM, 1994; Environment Canada, 1994; and U.S. EPA, 2002). Advantages and disadvantages of the various grab collection devices are summarized in Table 4.3. The Van Veen, Ponar, and Petersen samplers are generally considered to be excellent sampling devices based on their ability to collect most types of surface sediments, including both hard and soft substrates, in a variety of environments (e.g., lakes, rivers, estuaries, and marine waters). However, the Petersen grab does not have a hinged lid to permit subsampling of the collected sediment as do the Ponar and Van Veen. A modified Van Veen sampler is used in several national and regional estuarine monitoring programs, including the National Oceanic and Atmospheric Administration National Status and Trends Program, the U.S. EPA Environmental Monitoring and Assessment Program, and the U.S. EPA National Estuary Program. Small or lightweight samplers, such as the Birge–Ekman, petite Ponar, or mini Shipek, may sometimes be advantageous because of easy handling, particularly from a small vessel or using only a hand line. However, these samplers are not recommended for use in areas with strong currents or high waves or during poor weather conditions and intense vessel motion. This is

150 Handbook on Sediment Quality e of jaws may contaminate sample may contaminate incomplete closur aller volume does not minimize aller volume Loss of fine-grained Loss of fine-grained current due to light Restricted to low Subsampling may be restricted by size • Metal frame w water usew water activation weight and messenger intact, • Possible permitting subsamplingsedimentsAdequate for most substrates that are • Sm results in sample loss frame construction silt • Handles easily without winch or crane • • bottom Good for coarse and firm • Possible contamination from metal • subsampling Allows • subsampling Allows of top flaps ) Advantages Disadvantages 3 3400 • 300 13 • use water Can be adapted for shallow • current conditions Restricted to low ≤ ≤ estuaries; useful on sand, silt, and clay not compacted disturbance to sample estuaries; useful on sand, silt, and clay • Adequate on most substrates • sample obtained Large sediment soft sediments, silt,and sand soft sediments, silt,and sand • Can be adapted for shallo • Good for soft sediments, sand, and • Good for soft sediments, sand, and • penetration depth target exceed May • by Penetration depth exceeded silt weight of sampler Device/ Depth of of Volume dimensions Use sample (cm) sample (cm MacIntyre estuaries 20 000 substrates sediment • may require winch Heavy; Ponar, petite Deep lakes, and rivers 0–10 1000 • Birge–Ekman, large and marine areas; Lakes 0–30 Ponar, standard Deep lakes, and rivers 0–20 7250 • grab sampler Most universal • Descent may disturb fine-grained Table 4.3Table Canada, Environment from and limitations of commonly used grab samplers (modified Advantages 1994). Orange Peel, Smith– Deep lakes, and rivers 0–30 10 000 to • Designed for sampling hard • Birge–Ekman, small and marine areas; Lakes 0–10

Methods for Collecting, Storing, and Manipulating 151 lid cover to permit subsampling lid cover Metal frame may contaminate sample Metal frame may contaminate sample Ekman and Ponar • close prematurely May • of Shares all other disadvantages • sediment lose fine-grained May bow wave effects wave bow • helps reduce cover Screened bucket ) Advantages Disadvantages 3 18 000 • Fluorocarbon plastic liner can help • Requires winch ≤ ters and large inlandters and large subsampling • may require winch Heavy; estuaries; useful on sand, silt, or clay; ef- in marine envi-fective ronments in deep waterand strong currents 75 000; 18 to 75 L are not compacted sediment • May not close completely • close prematurely in rough May waters • estuaries; useful on most • Penetrates most substrates • Lacks substrates lakes and reservoirs; not and reservoirs; lakes useful for compacted sandyclay or till substrates substrates that are soft • sediments Retains fine-grained effectively crane from most platforms • Samples small volume Device/ Depth of of Volume dimensions Use sample (cm) sample (cm (e.g., grab”) “Young metal contamination avoid • expensive Relatively standard wa Table 4.3Table Canada, Environment from and limitations of commonly used grab samplers (modified Advantages 1994). (continued) Veen Van Deep lakes, and rivers 0–30 18 000 toAdequate on most substrates that • • Descent may disturb fine-grained Modified Van Veen Van Modified and marine areas Lakes 0–30 Petersen Deep lakes, and rivers 0–30 9450 • sample large Provides • may require winch Heavy; Mini Shipek Lakes, useful for most 0–3 500 • Handles easily without winch or • penetration Requires vertical Shipek sampler, Used primarily in marine 0–10 3000 • opens to permit Sample bucket •

152 Handbook on Sediment Quality particularly true for the Birge–Ekman sampler, which requires relatively calm, low-current velocity conditions for proper performance of its activating messenger. Lightweight samplers generally have the disadvantage of being less stable during sediment penetration, tending to fall to one side as a result of inadequate or incomplete penetration, resulting in inadequate samples. Another key factor in choosing a grab sampler is how well it protects the sample from disturbance and washout. Both the Ponar and Van Veen samplers have mesh screens and rubber flaps to cover the jaws. Water can pass through these samplers during descent, reducing disturbance from shock waves at the sediment–water interface. The rubber flaps also serve to protect the sediment sample from washout during ascent. Although the Shipek is also recommended for sampling soft substrates, it can result in the loss of the topmost 2 to 3 cm of very fine, unconsolidated sediment (Mudroch and MacKnight, 1994). The Shipek is not recommended for use with compact sediments because the low volume of material collected by this device results in excess headspace, which permits the sample to be tilted or disturbed (Mudroch and Azcue, 1995). In addition to the Van Veen, Ponar, and Petersen grabs, the Smith–McIntyre or Orange Peel samplers are effective for sampling hard substrates in deep lakes, rivers, or estuaries. However, these latter two devices have the disadvantage of being substantially heavier than the other samplers, requiring the use of a heavy-duty winch and appropriate vessel. When retrieving a grab sample, the following acceptability criteria should be met to ensure that a satisfactory sample was collected (Environment Canada, 1994; U.S. EPA, 1986, 2002):

• The sampler is not overfilled so that the sediment surface is pressed against the top of the sampler; • Overlying water is present (indicates minimal leakage); • The overlying water is clear or not excessively turbid; • The sediment–water interface is intact and relatively flat, with no sign of channeling or sample washout; • The desired depth of penetration has been achieved; and • There is no evidence of incomplete closure of the sampler or that it penetrated at an angle or was tilted upon retrieval (i.e., loss of sediment).

Core Samplers. Although core samplers have the advantage of collecting minimally disturbed, intact sediment samples from both surficial sediments (upper 15 to 30 cm) and deeper sediments (>30 cm deep), there are few corer devices that function efficiently in substrates with significant proportions of sand, gravel, clay, or till. Another significant limitation of core samples is that the volume of any given depth horizon within the profile sample is relatively small. Thus, depending on the number and type of analyses needed, repetitive sampling at a site may be required to obtain the desired quantity of material from a given depth. Coring devices used to obtain sediment profiles (see Table 4.4) include hand-corers (50- to 120-cm core tubes), single gravity corers for sediment samples that are 0 to 50 cm deep (e.g., Kajak–Brinkhurst, Phleger) or 0 to 200 cm deep (e.g., Benthos and Alpine), multiple corers (0 to 50 cm deep,

Methods for Collecting, Storing, and Manipulating 153 Requires careful handling to avoid contaminate sample of consuming operation and removal liners due to small sample • Barrel and core cutter metal may • and time- Requires repetitive ter • Requires careful handling to prevent ates substrate with grea for laboratory shipment Subsampling intact sample is • Rapid; samples immediately ready ) Advantages Disadvantages 3 30 000 • Collects large, undisturbed sample •481 machinery Requires heavy • Reduces risk of sample contamination • 1374 • than the Collects greater volume • corer Same as Phleger ≤ ≤ ≤ Use sample (cm) sample (cm sediment must optimized solidated be at least 1m depth of the uncon- • semiconsolidated • Penetrates with sharp cutting edge sediment spillage 120 cm soft or semi- available; deposition 70 cm sediments ≤ a ≤ 120 cm long) 50 cm long) sediments ≤ ≤ Device/ Depth of of Volume dimensions or glass tube (3.5–7.5 cm i.d.; if SCUBA or deep waters long) consolidated deposits historical study of sediment sampling • risk of contamination Minimal movable fluorocar-movable bon plastic or glass more consolidated liners (3.5–7.5 cm sediments can be i.d.; obtained ease through use of handles • of fluorocarbon All advantages • of liners before Requires removal spillage plastic or glass tube apply sampling repetitive Brinkhurst corer(5 cm i.d.; long) soft fine-grained corer Phleger • alter the sediment profile May Phleger corer (3.5 cm Phleger i.d.; Table 4.4Table Canada, Environment from and limitations of commonly used grab samplers (modified Advantages 1994). Fluorocarbon plastic waters wadeable Shallow 0–10 96 to 442 • layering and permits Preserves • Small sample size requires repetitive Hand corer with re- except Same as above 0–10 96 to 442 • Penetr Gravity corer,Gravity and rivers; Deep lakes 0–50 Box corer the but Same as above 0–50 corer,Gravity Kajak– and rivers; Deep lakes 0–70

154 Handbook on Sediment Quality incompletely integrity penetration may fail metal contamination ing the sediment profile • and penetrate nonvertically May • Requires a lifting capacity of 2000 kg • Disturbs sediment stratas and • Compacts sediment sample • Piston and piston positioning at • (0 to 0.5 m) layer Disturbs surface • penetration Fins promote vertical • Compacts sediment sample, alter- ) Advantages Disadvantages 3 10 263 • Retains complete sample from tube • Requires weights for deep penetra- 1924 • penetration depths Samples different • for vertical Lacks stabilizing fins ≤ ≤ 2 m 2 m • for greater sample Piston provides • onsite to Cores must be extruded 0–3 m ≤ > 2 m deep when Use sample (cm) sample (cm ≤ lakes; most substrateslakes; turbed sediment core in deep waters >2000 kg soft to semiconsolidateddeposits • Metal barrels introduce risk of Soft, fine-grained a 70 cm long) core liner is 750 to 1000 kg ≤ Device/ Depth of of Volume dimensions corer (6.6, 7.1 cmi.d.; sediments to the is fitted because the core valve tion so the required lifting capacity (3.5 cm i.d.) consolidated substrates with interchangeable steel barrel penetration corer rod; used with extension retention other containers Table 4.4Table Canada, Environment from and limitations of commonly used grab samplers (modified Advantages 1994). (continued) Benthos gravity Alpine gravity corerAlpine gravity Soft, fine-grained, semi- Piston corers deep Ocean floor and large 3–20 m • undis- a relatively recovers Typically • Requires lifting capacity of BMH-53 Piston of Waters

Methods for Collecting, Storing, and Manipulating 155 • Loses 10 to 20% of sample (e.g. 10 m long) ) Advantages Disadvantages 3 Use sample (cm) sample (cm sand, silty, sand sand substratesgravelly • Can be operated from small vessels • (0–0.5 m) layer Disturbs surface minimum disturbance require divers a Device/ Depth of of Volume dimensions SCUBA = self-contained underwater breathing apparatus. = self-contained underwater SCUBA (6.7 cm i.d.) 9000 m deep) can be used ment so small vessels • for recovery Requires calm water (5.0 to 7.5 cm i.d.) oceans, lakes; large 13 253 samples sandy substrates with • Assembly and disassembly might i.d. = inner diameter. Table 4.4Table Canada, Environment from and limitations of commonly used grab samplers (modified Advantages 1994). (continued) Boomerang corer Ocean floor (up to 1 m 3525a b • Requires minimal shipboard equip- • Only penetrates 1.2 m Vibratory corer Vibratory Continential shelf of 3–6 m 5890– • it effectively deep profiles For • intensive Labor

156 Handbook on Sediment Quality 2 to 4 barrels), box corers (0 to 50 cm deep), and piston corers for sediment samples deeper than 200 cm. Small-diameter and gravity corers can be unreliable in obtaining unaltered sediment profiles. Piston corers are recommended if there is interest in sediment stratigraphy. In addition to gravity corers, there are several other devices available from commercial suppliers. Boomerang corers (0 to 1.2 m deep), for example, are available for collecting samples from the seafloor, and percussion corers or vibrating corers with a stationary piston (0 to 13 m deep) are used to sample a variety of sediments (Environment Canada, 1994, and Mudroch and MacKnight, 1994) and are used extensively in monitoring programs throughout the U.S. A portable vibrating corer is available for the collection of sediments from water depths up to 18 m (Lanesky et al., 1979). There are only a few corers that can be operated without a mechanical winch. The more common of these include the standard Kajak–Brinkhurst corer, suitable for sampling soft, fine-grained sediments, and the Phleger corer, suitable for a wider variety of sediment types ranging from soft to sandy, semicompacted material, as well as peat and plant roots in shallow lakes or marshes (Mudroch and Azcue, 1995). The 5-cm inner diameter (i.d.) core tube used with the Kajak–Brinkhurst corer recovers a greater quantity of sediment than the 3.5-cm i.d. core tube used with the Phleger corer. Whereas grab sampling of surface sediments from a small vessel can be a single-person operation, at least two people are required for coring operations that include positioning of the sampling vessel, lowering the core overboard, and capping the bottom of the core upon retrieval. The Benthos and Alpine gravity corers also require the use of a winch or crane for deployment. The Benthos gravity corer is recommended for recovering up to 3-m-long cores from soft, fine-grained sediments. Recent models include stabilizing fins on the upper part of the corer to promote vertical penetration to the sediment, and weights can be mounted externally to enhance penetration (Mudroch and Azcue, 1995). The Alpine gravity corer is finless and has an interchangeable steel barrel (4.1 cm i.d.) in lengths of 0.6, 1.2, and 1.8 m. The lack of fins can make it difficult to obtain vertical penetration consistently with this device; extreme care must be taken during deployment to avoid sheared strata and disturbed surfaces (Mudroch and Azcue, 1995). Multiple corers, consisting of several core barrels on a single fin and weight system, have been developed for special applications such as multiple sampling at one site, comparative studies, and determination of sediment heterogeneity over a small area (Brinkhurst et al., 1969; Hamilton et al., 1970; Jones and Watson-Russell, 1984; and Kemp et al., 1971). Box corers are gravity corers designed for collecting large rectangular sediment cores at various water depths, penetration rates, and different sediment types. The basic box corer consists of a stainless steel box equipped with a frame to add stability and facilitate vertical penetration on low slopes. Several types of box corers are commercially available. Examples include the Gray–O’Hara box corer, the Reineck box corer, and several Ekman-type devices. When deployed properly, box corers can obtain undisturbed sediment samples of excellent quality. They are recommended particularly for studies of the sediment–water interface or when there is a need to collect larger

Methods for Collecting, Storing, and Manipulating 157 volumes of sediment from the depth profile. Because of the heavy weight and large size of almost all box corers, they can be operated only from a vessel with a large lifting capacity and sufficient deck space. Sediment inside a box corer can be subsampled by inserting narrow core tubes to the sediment. Carlton and Wetzel (1985) describe a box corer that permits sediment and overlying water to be held intact as a laboratory microcosm under either the original in situ conditions or other laboratory-controlled conditions. Recently, a box corer was developed that enables horizontal subsampling of the entire sediment volume recovered by the device (Mudroch and Azcue, 1995).

IN SITU INTERSTITIAL WATER COLLECTION. Sediment interstitial water (pore water), defined as the water occupying the spaces between sediment or soil particles, is often isolated to provide either a matrix for toxicity testing or to provide an indication of the concentration and partitioning of contaminants within the sediment matrix. Interstitial water sampling may be useful for determining chemical contamination or toxicity, depending on the study objectives and nature of the sediments at the study site (Carr and Nipper, 2001). Sediments with relatively coarse particle size (such as gravel or cobble) or hard, compacted clays are not likely to contain interstitial water that is significantly contaminated. Therefore, studies that require interstitial water sampling will include sediment types ranging from sandy to noncom- pacted silt-clays. In most studies, interstitial water is collected from depositional zones that contain finer (silt-clays) and potentially contaminated sediments. In situ methods are generally superior for collecting interstitial water than laboratory methods (e.g., centrifugation), especially when it is essential to maintain the chemical integrity of the sample. In situ interstitial water collection has been used to sample dissolved gases (Sarda and Burton, 1995), volatile organic compounds (Knezovich and Harrison, 1987), and metals (Di Toro et al., 1990). However, in situ methods often isolate relatively small volumes of interstitial water and, in nonwadeable waters, are limited to depths in which divers can operate. Pressure filtration has also been used to collect interstitial water in situ, but this method is not recommended if air is used because this will aerate the sediment, resulting in probable changes in contaminant bioavailability. The principal methods recommended for in situ collection of interstitial water involve either deployed “peepers” (Adams, 1991; Bottomly and Bayly, 1984; Brumbaugh et al., 1994; Bufflap and Allen, 1995; Carignan and Lean, 1991; and Carignan et al., 1985) or suction techniques (Howes et al., 1985; Knezovich and Harrison, 1987). Both methods have a high likelihood of maintaining in situ conditions (Burgess and McKinney, 1997). In cases where in situ deployment is impractical, peepers or suction devices may be placed in relatively undisturbed sediments collected by box core or grab samplers. A summary of various methods for in situ collection and characterization of pore water is provided in Table 4.5. Because different applications (e.g., toxicity testing, chemical analyses) have different sample volume requirements (Table 4.2), there is no clearly superior method for isolating interstitial water. The two primary issues of concern regarding interstitial water sample

158 Handbook on Sediment Quality gradient resolution. genate chamber and materials to prevent oxidation genate chamber and materials to prevent small sample some chambers only allow effects; care must be used on collection to volumes; sample oxidation. prevent a possible. gradient resolution. vertical used. with sandy sediments. centrifuge; difficult processing; relatively free of temperature,processing; relatively oxidation, and easy to con- inexpensive and pressure effects; possible on nature of samplestruct; some selectivity hours to days for allow in >0.6 m depth waters; membranes,via specific wide range of membrane/mesh pore sizes, equilibration, and/or internal solutes or substrates. with site and which will vary chamber; methods not standardized and used infrequently; some membranes such as dialysis/ cellulose are subject to biofouling; must deoxy- in deep(<0.6 m) core method may not require diving water, collection, rapid no laboratory processing;closed system, limited to softer sediments; core small volumes; contamination; methods which prevents include airstone, syringes, probes, and cores. methods used in- in deep waters; for deployment airstone method ease; may require diving frequently and by limited number of laboratories. ) Advantages Disadvantages 3 dependent possible with some sediments. volumes loss of increased loss of metals and organics; dependent possible with some sediments; closed system volumes loss of increased loss of metals and organics; dependent commonly volumes; anoxic/cold processing; large centrifugation conditions; requires high-speed Sediment Volume Device depth (cm) (cm Incorporation of filtration into any of the collection methods may result in loss of metal and organic compounds. of the collection methods may result in loss metal and organic into any Incorporation of filtration Squeezing Sampler large Use with all sediment types; may process in field; Alteration of chemical characteristics may occur; Suction Sampler large Use with all sediment types; may process in field; Alteration of chemical characteristics may occur; Table 4.5Table Optimal interstitial water collection methods. Peeper 0.2–10 1–500 Most accurate method, artifacts, reduced no laboratory by hand, Requires deployment thus requiring diving a In situ suction 0.2–30 1–250Centrifugation Reduced artifacts, water shallow gradient definition; Sampler Requires custom, nonstandard collection devices; Most accurate of laboratory processing methods; allows Some chemical loss/alteration; results depend on

Methods for Collecting, Storing, and Manipulating 159 integrity are (1) the ability of the sampling device to maintain chemical concentrations in the natural state by minimizing adsorbion of chemicals to the device or leaching from it, and (2) the ability to maintain the sample in the redox state existing at the site.

Peeper Methods. Peepers are small chambers with membrane or mesh walls containing clean water of either the appropriate salinity or hardness or distilled water that are buried in sediments and, into which, surrounding interstitial waters infiltrate. Dissolved solutes diffuse through the porous wall into the peeper, and the contained water will reach equilibrium with the ambient pore water. The design concept for sediment peepers originated as modifications of the dialysis bag technique used by Mayer (1976) and Hesslein (1976). The designs consisted of either a flat base plate or a cylindri- cal dialysis probe (Bottomly and Bayly, 1984) with compartments covered by dialysis membranes and a cover for collection of multiple samples at various depths in the sediment profile. Further modifications to these designs have incorporated sampling ports, large sample compartments, and various types of membranes with different pore sizes. These modifications are usually required based on specific project objectives regarding sample volumes and contaminants of interest. A simplified design using a 1-µm polycarbamate membrane over the opening of a polyethylene vial has been shown to be effective in capturing elevated levels of copper and zinc (Brumbaugh et al., 1994). Larger pore-sized mesh has been used, which allows faster equilibration time (Sarda and Burton, 1995). Another design that shows promise is a semipermeable membrane device filled with a nonpolar sorbent (Macrae and Hall, 1998). This design has been used effectively to collect nonpolar organic compounds in overlying water (Huckins et al., 1990) and PAHs in interstitial water (Axelman et al., 1999). Peepers are generally constructed from acrylic material or another material that has low reactivity with potential chemicals of concern. Some materials such as polymers may be inappropriate for studies of certain nonpolar compounds. Cellulose membranes are also unsuitable, as they decompose too quickly. Before deployment, the peepers should be soaked in acid for at least 2 weeks. In preparation for pore-water collection, peeper chambers should be filled with deoxygenated water, which can be prepared by nitrogen purging for 24 hours before insertion. The peepers may need to be transported to the deployment site in a sealed oxygen-free water bath to avoid potential changes to the sediment–water equilibrium caused by dissolved oxygen interactions. Plastic samplers can contaminate anoxic sediments with diffusible oxygen (Carignan et al., 1994). In addition, when samples are collected and processed, exposure to oxygen should be minimized. Samples should be processed quickly for use in toxicity tests or may be frozen for later chemical analyses. After deployment, the equilibration time for peepers may range from hours to a month, but a deployment period of 1 to 2 weeks is most often used (Adams, 1991). The optimal equilibration time is a function of sediment type,

160 Handbook on Sediment Quality study objectives, contaminants of concern, and temperature (e.g., Carr et al., 1989; Howes et al., 1985; Mayer, 1976; Simon et al., 1985; and Skalski and Burton, 1991). There are several potential artifacts associated with peepers that use dialysis membranes (Carignan et al., 1985). Organic carbon, produced biogenically in situ, may be elevated in peepers (4- to 8-µm mesh size). Depending on the mesh size used, colloids may also be present, but these are typically lower in concentration than in laboratory-centrifuged samples (Chin and Gschwend, 1991). Introduced organisms have been exposed to aerobic sediments using peepers with a pore size of 149 µm (Burton, 1993, and Skalski and Burton, 1991). With this type of peeper, equilibration of conductivity was observed within hours of peeper insertion to the sediment. Sediments that were high in clay and silt fractions usually were anoxic and were not amenable to in situ organism exposure. In several studies, analysis of interstitial water from replicate peepers has demonstrated extreme heterogeneity in water quality characteristics (Frazier et al., 1996, and Sarda and Burton, 1995). The potential for high variability in interstitial water chemical characteristics should be taken into account when using these sampling devices.

Suction Methods. An alternative interstitial water sampling technique involves direct suction and filtration. There are a variety of suction devices, most of which consist of a syringe or tube of varying length with a port(s) positioned at the appropriate position. The device is inserted to the sediment to the desired depth and a manual, spring-operated, or vacuum-gas suction is applied to directly retrieve the water sample. A variation on this approach uses a peeperlike porous cup or perforated tube with filters that are incubated in the sediment for a period of time allowing interstitial water to infiltrate the chamber. The samples are then retrieved as before by suction, or manual, vacuum, or peristaltic pumping.

Retrieval of Interstitial Water Samples. Samples from in situ pore water devices should be retrieved in a way that minimizes disturbance or contamination of the sample. Two problems that may be encountered with various in situ devices are lack of precision in penetration depth and disturbance during sampling or upon retrieval (Sayles et al., 1973). Generally, collection of the sampling device involves wading or diving, and it is critical that there be minimum suspension of surrounding sediment. Pore water may be removed in situ using a syringe, but the volume of the collection device should be much larger than the sample volume needed to minimize dilution with non- equilibrated water. From the syringe, the sample may be injected to small vials (10 to 40 mL) with Teflon-lined septum caps (Uchrin and Ahlert, 1985). Alternatively, for retrieval of an intact collection device, a liner can be driven into the sediment around the sampler so that an external core filled with sediment is retrieved (Hoppner, 1981). Care should be taken to avoid exposure to oxygen if pore waters are anoxic. Inert gas such as nitrogen may be applied to displace oxygen, both during sample retrieval and transfer, to any secondary storage container.

Methods for Collecting, Storing, and Manipulating 161 In cases where immediate sampling is required, a 13-guage needle syringe (with or without a device to exclude contamination from overlying water) may be inserted directly to the sediment (ASTM, 1994, and Mudroch and MacKnight, 1994). Long needles may be marked to ensure penetration to the desired depth. After sample retrieval, pore water usually needs to be recovered and stabilized quickly (e.g., within 5 minutes) to prevent oxidative changes or volatilization. Procedures for stabilization are dependent on the analyses to be performed. When nonvolatile compounds are the target analytes, acidification is often stipulated, whereas organic carbon and methane may be stabilized with saturated mercuric chloride (Mudroch and MacKnight, 1994). Samples are typically cooled to 4 °C as soon as possible for transport to the laboratory. U.S. EPA methods for toxicity testing of surface waters and effluents (U.S. EPA, 1991) do not allow samples to be frozen in storage or transport. It is generally believed that pore water should also not be frozen before toxicity testing (ASTM, 1994, and Environment Canada, 1994). However, work by Ho et al. (1997) suggests that, at least in certain cases, freezing does not alter toxicity of interstitial water. It may be prudent to provide such a demonstration for the sites of interest before proceeding to freeze pore-water samples before biological testing.

SAMPLE TRANSPORT AND STORAGE

Transport and storage methods need to be used that maintain structural and chemical qualities of sediment and pore-water samples. In most studies, grab samples are placed in containers that may or may not serve as the ultimate storage container. The containers might be stored temporarily in the field or transported immediately to a laboratory for storage. If sediment-core samples are not sectioned or subsampled in the field, they should be stored upright, in the core liner, for intact transportation to the laboratory. If sectioning or subsampling takes place in the field, then the subsamples may also be transferred to sample containers and stored temporarily. The sample containers with the field-collected sediments are then placed into a transport container and shipped to the laboratory. In the field, samples can be stored in refrigerated units on board the sampling vessel or in insulated containers containing ice or frozen ice packs. Dry ice can be used to freeze samples for temporary storage and transport for certain chemical analyses (U.S. EPA, 2002). It is always important to know chilling capacities and efficiencies to ensure that provisions for temperature regulation are adequate. Care should be taken to prevent refrigerated samples from freezing and keep frozen samples from thawing. In some cases, it is more efficient to transfer samples to a local storage facility where they can be either frozen or refrigerated. If a freight carrier is used to transport samples, the user must be aware of any potentially limiting regulations (e.g., regarding

162 Handbook on Sediment Quality the use of ice or dry ice). Samples that have a recommended storage temperature should be cooled to that temperature before placement in the transport container. Light should be excluded from the transport container. Freezing changes the sediment volume depending on the water content and permanently changes the structure of the sediment. Many studies elect to freeze samples for metal and organic chemical analysis based on findings that suggest minimal effects on measured concentrations (Table 4.1). If toxicity tests are to be conducted, it may be acceptable to split the sample, freezing subsamples for chemical analysis, but subsamples for biological testing are typically not frozen (ASTM, 1994, and U.S. EPA, 2002). For core samples, the entire space over the sediment in the core liner should be filled with site water, and both ends of the core liner should be completely sealed to prevent mixing of the sediment inside before transport. The cores should be maintained in an upright position and secured into either a transport container (e.g., cooler or insulated box) with ice or ice packs or into a refrigerated unit that can maintain a temperature near 4 °C (Environment Canada, 1994). If the transport container cannot accommodate long core samples such as from piston corers (core liners >1 m), then the core samples can be cut into shorter lengths before transporting and the ends securely capped such that no air is trapped inside the liners. The method and equipment used to cut or section the core should cause minimal disruption of the integrity of the sediment within the section, should not contaminate the sample, and should result in no loss of the sediment column. Impregnating unconsolidated sediment cores with epoxy or polyester resins will preserve sediment structure and texture (Crevello et al., 1981, and Ginsburg et al., 1966) but not sediment-chemical characteristics. Therefore, this procedure is not recommended for transporting or storing sediment samples for chemical characterization or biological testing (Environment Canada, 1994).

SAMPLE HOLDING TIMES. Holding times are governed by sediment type and contaminant characteristics (ASTM, 1994, and U.S. EPA, 2002). Because these qualities are not always known, a general recommendation is to store sediments and pore water in the dark at 4 °C (U.S. EPA, 2000, 2002). Recommended storage times for several types of commonly required analyses are summarized in Table 4.1. Samples collected for toxicity tests should be used as quickly as possible, although recommended maximum holding times range from 2 weeks (ASTM, 1994) to 8 weeks (U.S. EPA and U.S. Army Corps, 1998). Preferred sample storage times reported for toxicity tests have varied substantially (Becker and Ginn, 1990; Carr and Chapman, 1992; Defoe and Ankley, 1998; Dillon et al., 1994; Moore et al., 1996; Sarda and Burton, 1995; and Sijm et al., 1997), and differences appear to depend primarily on the type or class of contaminants present. Extended storage of sediments that contain high concentrations of labile contaminants (e.g., ammonia, volatile organics) may lead to loss of these contaminants and a corresponding reduction in toxicity. Under these circumstances, the sediment should be tested as soon as possible after collection, but

Methods for Collecting, Storing, and Manipulating 163 not later than 2 weeks (Sarda and Burton, 1995, and U.S. EPA, 2000). Sediments that exhibit low level to moderate toxicity may exhibit higher variability in toxicity when tested after storage of short duration (e.g., 2 weeks). Testing could actually be more reliable after longer storage for these types of samples if the longer storage reduces potential interference associated with indigenous predators (DeFoe and Ankley, 1998). Sediments contaminated with relatively stable compounds (e.g., high-molecular-weight compounds such as polychlorinated biphenyls (PCBs) or those that exhibit moderate to high toxicity) do not seem to vary appreciably in toxicity with increased storage time (DeFoe and Ankley, 1998, and Moore et al., 1996). Longer-term storage may be acceptable in such cases. Periodic measurements of contaminants of concern provide a useful context for interpretation of toxicity test results when sediments or pore waters are stored for extended periods of time, but this is rarely cost-effective. It may be more efficient to conduct pore-water toxicity tests within 2 weeks of sediment collection, corresponding with the start of sediment tests (Ingersoll, 1995). Sediment cores collected for stratigraphical or geological studies can be stored at 4 °C in a humidity-controlled room for several months without any substantial changes in sediment properties (Mudrock and Azcue, 1995).

SUBSAMPLING, HOMOGENIZING, AND COMPOSITING SAMPLES

GENERAL INFORMATION. The key concerns in subsampling, homogenizing, and compositing samples are to avoid contamination of personnel and samples and maximize the representativeness of the sediment materials collected within the practical constraints of the study. The decision to subsample and composite sediment samples within or among stations depends on the purpose and the objectives of the study, the nature and heterogeneity of the sediments, the volume of sediment required for analytical or toxicity assessment, the degree of statistical resolution that is acceptable, and the cost/benefit of not performing these procedures (U.S. EPA, 2002). Subsam- pling, homogenization, and compositing may be accomplished in the field if facilities, space, and equipment are available. If volatile contaminants are parameters of interest, more controlled field conditions (e.g., glove box with nitrogen) or laboratory conditions may be preferred. If space is limited in the field, samples should be homogenized and composited in the laboratory. Subsampling is often used to isolate sediments from a particular depth within a core sample. The depth horizons sampled will depend on the study objectives as well as the nature of the substrate, the biologically active depth, and sedimentation rates at the site. Common subsampling depth intervals include the 0- to 2-cm layer (for recent deposition), the 0- to 5-cm or 0- to

164 Handbook on Sediment Quality 15-cm layers (for biological activity, depending on resident organisms), and project-specific depths corresponding to study requirements, such as dredging depths for navigation or remediation (Environment Canada, 1994, and U.S. EPA and U.S. Army Corps, 1998). Subsampling is also done to obtain split samples for different analyses or replicates for the same analysis. Finally, subsampling is often done for the express purpose of creating a composite sample at a site (see below). Homogenization is used to (1) reduce within-site chemical or toxicological variability, (2) obtain sufficient sediment volume required for analysis, and (3) accommodate financial and technical constraints on sampling effort and analytical measurements. In some cases, maintenance and evaluation of heterogeneity may be integral aspects of the study, requiring a greater need to retain sediment integrity. Compositing refers to combining subsamples from two or more samples and analyzing the resulting pooled sample (Keith, 1993). Compositing is often necessary when a relatively large amount of sediment must be obtained at each sampling site (for instance, to conduct one or more toxicity tests or several different physical, chemical, or biological analyses). However, creating a composite sample also yields “average” chemical and toxicological conditions for a given site that may provide sufficient information at relatively lower cost for some project objectives. It is important to note that all field handling of sediment grab, core, and interstitial water samples carries the risk of sample contamination. Therefore, such handling should be kept to a minimum. There are several occasions during sample handling when preventing contamination deserves special attention, such as

• Making field measurements of pH and redox potential, • Parameters of interest are present at relatively low concentrations, • Contaminated and uncontaminated sediment samples are collected during one trip, and • The parameter of interest is easily volatilized.

Field personnel should be aware of and take measures to avoid common sources of sample contamination on board vessels, such as exhaust fumes, lubricants, and rust. The quality assurance project plan (QAPP) should include the use of field and trip blanks to document potential errors resulting from such extraneous sources of contamination. All utensils (e.g., spoons, scoops, spatulas) that come in direct contact with sediment samples during handling should be made of noncontaminating materials (e.g., glass, high- quality stainless steel, or Teflon). Such utensils, as well as the sampling equipment itself, should be carefully cleaned between samples and sampling stations to avoid cross-contamination.

SUBSAMPLING. Before opening a grab-sampling device, its outside should be carefully rinsed with water from the sampling site and the overlying water should be removed by slow siphoning using a clean tube near one side of the sampler (PSEP, 1995). Decanting the water or opening the jaws slightly to let

Methods for Collecting, Storing, and Manipulating 165 the water run out are not recommended, as these methods may result in unacceptable disturbance or loss of fine-grained sediment and organic matter (U.S. EPA, 2002). If the overlying water in a sediment sampler is turbid, it should be allowed to settle if possible (Environment Canada, 1994). Subsampling should occur directly after overlying water is removed to minimize sediment oxidation. If a sediment grab sample is to be subsampled in the laboratory, it should be released carefully and directly to a labeled container made of a chemically inert material such as PTFE or perhaps HDPE. The container needs to be large enough to accommodate the sediment sample and should be tightly sealed with the air excluded (Environment Canada, 1994). If the retrieved grab sample is to be subsampled in the field, then access to the surface of the sample without loss of water or fine-grained sediment is a prerequisite for sampler selection. This typically dictates the use of a grab sampler with bucket covers that are either removable or hinged to allow open access to the surface of the sediment sample (U.S. EPA, 2002). The general subsampling and compositing process for grab samples is illustrated in Figure 4.1 (U.S. EPA, 2002). Removal of a portion of the collected sediment from the grab sampler (i.e., subsampling) can be performed using a spoon or scoop made of inert, noncontaminating material (e.g., Teflon, titanium, or high-quality stainless steel). It is recommended when subsampling to exclude sediment that is in direct contact with the sides of the grab sampler as a general precaution against potential contamination from the device. Each subsample may be placed into a separate clean, prelabeled container. As a general rule, each labeled sample container should be tightly sealed and the air excluded. However, if the sample is to be frozen, it is advisable to leave a small amount of headspace in the container to

Figure 4.1 Alternatives for subsampling and compositing sediment grab samples.

166 Handbook on Sediment Quality Figure 4.2 Alternatives for sampling and compositing sediment core samples.

accommodate expansion and avoid breakage. The presence of oxygen during sediment handling has been reported to be both relatively unimportant (Brumbaugh et al., 1994) or very important (Besser et al., 1995), depending on the sediment characteristics, the contaminants of concern, and the study objectives. In studies involving metal contamination or volatile contaminants, it may be necessary to subsample a grab sample under oxygen-free conditions to minimize oxidative changes. In these cases, it may be preferable to subsample the grab sampler using a hand-coring device (Environment Canada, 1994, and U.S. EPA, 2002). The core should be inserted immediately upon retrieval of the grab sampler, then removed and placed into a glove box or bag that is flushed with a constant, controlled volume of inert gas (e.g., nitrogen). The sediment within the core can then be extruded under oxygen-free conditions to deaerated containers. Like grab samples, core samples may be composited in the field or the laboratory. Although there might be occasions when it is desirable to composite incremental core depths, it is recommended that only horizons of similar stratigraphy be composited (Figure 4.2) (U.S. EPA, 2002). Depending on the study objectives and desired sampling resolution, individual horizons within a single core can be homogenized to create one or more “depth composites” for that core or corresponding horizons from two or more cores might be composited (Figure 4.2). Thorough homogenization of the composite sample, by

Methods for Collecting, Storing, and Manipulating 167 hand or with a mechanical mixer, is recommended before analysis or testing. Whenever subsampling of sediment cores is required, particularly for determination of contaminants, the sediment should be extruded from the core liners and subsampled within 24 hours of collection (Environment Canada, 1994). This can be accomplished in the field if appropriate facilities and equipment are available or in the laboratory after transport. Methods for subsampling sediment cores include gradual extrusion, dissection of a mounted core using a jigsaw, a segmented gravity corer, a hand corer, or scoops and spoons. Cutting devices range from stainless steel knives to Teflon or nylon string. A piston-type extruder that applies upward pressure on the sediment is an instrument commonly used to gradually expose a core for sectioning (Kemp et al., 1971). The capped core liner containing the sediment and overlying water is uncapped at the lower end and placed vertically on top of the piston. The top cap is removed and the water is siphoned off to avoid disturbance of the sediment–water interface. The core liner is then pushed slowly down until the surface of the sediment is at the upper end of the liner. Sediment sections are collected by pushing the liner down and cutting the exposed sediment into sections of the desired thickness using a stainless steel or Teflon cutter (Environment Canada, 1994, and Mudroch and Azcue, 1995). The 1- to 2-mm outer layer of sediment that has been in contact with the plastic or metal liner should be discarded from each sediment section to avoid contamination. If it is desirable to maintain an oxygen-free environment during subsampling (e.g., sulfide oxidation and increased metal bioavailability), then all handling or manipulations should take place in a glove box or bag filled with an inert gas and modified to accommodate the core liner through an opening (Environment Canada, 1994, and Mudroch and MacKnight, 1994). Cores of more-consolidated material can be mounted onto a horizontal U- shaped rail and the liner cut using a saw mounted on a depth-controlling jig (Mudroch and Azcue, 1995). Another alternative for sectioning and subsampling is a segmented gravity corer, which consists of a series of rings placed on top of one another. Subsampling is carried out by rotating the rings around its other axis so that it cuts sediment layers of similar thickness. This segmented core tube is suitable for sampling fine-grained sediments and allows one person in the field to subsample the core into 1-cm sections (Mudroch and Azcue, 1995). Sediment from box-core samples can be effectively subsampled with a small hand corer after the overlying water has been carefully siphoned off and discarded. Hand corers with small inner diameters less than 3 cm tend to compact sediments, so they must be used with care. Spoons or scoops have also been used to subsample surface sediments from a box corer (Environment Canada, 1994).

HOMOGENIZATION. Before homogenization, unrepresentative materials (e.g., twigs, shells, leaves, stones, wood chips, and seagrass) are often removed and documented in an appropriate field log. Sediment may be transferred to a clean glass or stainless steel (nonreactive) bowl and thoroughly mixed with clean stainless steel spoons or spatulas (nonreactive) until

168 Handbook on Sediment Quality textural, color, and moisture homogeneity are achieved (Burgess and McKinney, 1997; Environment Canada, 1994; PSEP, 1995; and U.S. EPA, 2002). Intensive manual mixing in a suitably large container is typically sufficient to homogenize many wet sediment samples (U.S. EPA, 2002). Hand mixing has also been performed by rolling the sediment out flat on a sheet of plastic or precombusted foil and tumbling the sediment by alternately raising each corner of the sheet (Mudroch and MacKnight, 1994). Mechanical mixers that have been used include portable cement mixers (bare metal and Teflon- lined) and portable drills fitted with a variety of stainless steel paddles (Call et al., 1999; Ditsworth et al., 1990; and Stemmer et al., 1990a). Regardless of the mixing method, the effectiveness of the method should be demonstrated using a homogenate replicate. Homogenate replicates consist of two or more subsamples taken from different locations within a mixed sample. Homogenization can introduce potential sources of contamination or chemical alteration that may affect results in subsequent toxicological testing or chemical analysis (Environment Canada, 1994). For example, mixing introduces oxidation, which could result in loss of acid-volatile sulfide and changes in metal bioavailability (Ankley et al., 1996, and Di Toro et al., 1990). In core samples, homogenizing different sediment depths is often associated with loss of sediment integrity and changes in chemical speciation through alterations in the natural redox gradient, volatilization, sorption, desorption, or biological activity (ASTM, 1994). Therefore, the quickest, most efficient mixing method should be used to homogenize sediment samples. Laboratory processing before storage should be performed on samples collected within 72 hours and preferably within 24 hours of collection (Environment Canada, 1994, and U.S. EPA, 2002). After the field-collected sediment has been homogenized, it is generally partitioned among sample containers. Partitioning sediments for chemical or toxicity analyses may be accomplished using various methods. In one method, a number of small portions are removed from random locations in the mixing container and distributed randomly in all sample jars until the appropriate volume of sediment is contained in each sample jar for each analysis. During distribution, the sediment is periodically mixed using a glass rod or porcelain spatula to minimize stratification effects caused by differential settling, especially if the sediment is prone to rapid settling (ASTM, 1994). An alternative is to use a splitter box designed to contain and then divide the homogenated sediment. When homogenization has been accomplished by rolling, partitioning using the coning and caking technique is often efficient. In this method, the material is formed into a cone shape, placed in the center of a sheet, spread into a circular cake, and divided into pie shapes. Once the sediment is divided, opposite slices are removed and combined. The split samples are then remixed to achieve the desired volume (Mudroch and MacKnight, 1994).

COMPOSITING. Compositing typically involves the combination of three to five individual grab samples (ASTM, 1994). To prepare a composite sample, each individual sample should be completely homogenized before an aliquot is taken for the composite sample. An equal volume of sediment should be taken from each individual sample to make up the composite sample. The

Methods for Collecting, Storing, and Manipulating 169 sample identifiers and volume of sediment from each of the individual samples that contributed to the composite should be documented. The composite sample should be homogenized before it is used in a toxicity test or for chemical analysis (Environment Canada, 1994, and U.S. EPA, 2002). There are two alternatives for compositing sediment samples from grab samplers (see Figure 4.1): (1) compositing and homogenizing (mixing) in the field, and (2) compositing in the field and homogenizing in the laboratory. The first alternative involves the following steps: (1) placing subsamples from multiple grabs at a site in a clean container; (2) mixing the subsamples to form a homogenous composite sample; (3) placing the composite sample in one or more containers, depending on the number of analyses to be performed; and (4) transporting the composite sample to a laboratory (or laboratories) for testing. The second alternative involves (1) placing subsamples from individual grab samples in a clean container to form a composite sample, (2) transporting the composite to a laboratory, and (3) homogenizing the sample at the laboratory to prepare it for testing.

SEDIMENT SAMPLE MANIPULATIONS

SIEVING. Sieves are often used to mechanically separate sediment components into various particle-size classes. Sediment samples are sieved to (1) remove unrepresentative material, such as shells, stones, and twigs; (2) increase homogeneity and replicability of samples; (3) remove indigenous organisms before toxicity testing; (4) facilitate organism counting, sediment handling, and subsampling; and (5) examine the effects of particle size on toxicity, bioavailability, or contaminant partitioning (ASTM, 1994). In many cases, sieving is probably not appropriate because it can substantially change the physicochemical characteristics of the sediment sample. For certain toxicity tests, however, it may be necessary to remove potential competitors or predators that are naturally present in the sample (U.S. EPA, 1994, 2000). Sieving is a relatively severe manipulation that can significantly alter in situ sediment characteristics by selectively removing certain fractions and contaminants from the sediment sample (U.S. EPA, 2002). For example, wet sieving of sediment through fine mesh (<500-µm openings) has been shown to result in decreased percent total organic carbon and decreased concentration of total PCBs, which may have been associated with fine suspended organic matter lost during the sieving process (Day et al., 1995). In addition, as with other sample-manipulation techniques, sieving can disrupt the natural chemical equilibrium by homogenizing or otherwise changing the biological activity within the sediment (Environment Canada, 1994). If sieving is performed, it should be done for all samples to be tested, including control and reference sediments (ASTM, 1994). It may be desirable, in these cases, to measure certain key chemical parameters (e.g., dissolved organic carbon [DOC], total organic carbon [TOC], acid volatile sulfide

170 Handbook on Sediment Quality [AVS], and simultaneously extracted metals [SEM]) before and after sieving to document any changes that may have occurred (U.S. EPA, 2000). In addition, it may be useful to document the effect of sieving on the sediment sample by conducting comparative toxicity tests using sieved and unsieved sediment (Environment Canada, 1994).

Recommended Sieves. The mesh type and size should be chosen based on the following considerations: (1) the type of toxicity test and test organisms to be used; (2) potential predators or competitors present in the sample; (3) potential adsorption or contamination of the chemical of interest due to sieving; and (4) the nature of the sample, including its particle-size distribution, volume, and size of debris. For instance, marine toxicity test procedures recommend press-sieving sediments using a 2-mm mesh screen unless indigenous test organisms are visible, in which case, a 1-mm mesh screen is recommended (U.S. EPA, 1994). Stainless steel, brass, or plastic woven-polymer sieves (e.g., polyethylene, polypropylene, nylon, and Teflon) with mesh sizes that vary from 0.24 to 2.0 mm have been used to sieve sediment for toxicity tests (Giesy et al., 1990; Johns et al., 1991; Keilty et al., 1988a, 1988b, 1988c; Landrum and Faust, 1991; Pastorok and Becker, 1990; and Stemmer et al., 1990a, 1990b). Nylon or Nitex-type plastic sieves are recommended when inorganic constituents are of concern or are to be analyzed (ASTM, 1994, and PSEP, 1995). Stainless steel or brass sieves are recommended when organic substances are of concern or are to be analyzed (ASTM, 1994). Generally, sieving through a 10-mesh (2-mm openings) sieve is acceptable as a basis to discriminate between sediment and other materials (ASTM, 1994). For toxicity testing, the most frequently used mesh size is 1.0 mm (Environment Canada, 1994). While a 1.0-mm mesh will remove most adult amphipods, a mesh of 0.25 mm may be needed to remove immature amphipods and most macrofauna (Day et al., 1995; Landrum et al., 1992; and U.S. EPA, 2000). In marine sediments, sieves with a mesh size of 0.5 mm are effective in removing most of the immature Rhepoxynius abronius and Corophium spinicorna (Swartz et al., 1985). The PSEP (1995) recommends a 0.5-mm sieve to remove Ampelisca abdita from sediment.

Press Sieving. Press sieving is generally regarded as the preferred method if sieving is performed at all. Sediment particles are generally hand pressed through a sieve with a specified mesh size using chemically inert paddles (Giesy et al., 1990, and Johns et al., 1991). Matter retained by the screen, such as organisms, shell fragments, gravel, and debris, should be recorded in a logbook and discarded (U.S. EPA and U.S. Army Corps, 1991). No additional water is added to sediment when press sieving because the addition of water could result in changes in contaminant concentration and bioavailability. Pressure sieving sediments with debris, vegetation, or a high clay content through a single mesh size smaller than 1 mm can be difficult, and a series of sieves might be required (Environment Canada, 1994). Sediment samples that are going to be used for both chemical analysis and toxicity tests should be sieved together, homogenized, and then split for their respective analyses.

Methods for Collecting, Storing, and Manipulating 171 Wet Sieving. If sediments cannot be dry sieved by pressure, wet sieving may be required. Wet sieving involves swirling sediment particles within a sieve using water to facilitate the mechanical separation of smaller from larger particles. Additionally, mechanical shakers or stirring with a nylon brush can be useful (Mudroch and MacKnight, 1994). A slurry, made with water that has separated from the sediment during storage or transport, may be sufficient to wash particles smaller than the selected mesh size through the sieve. Wet samples that may have settled during transit should be stirred to incorporate as much field water as possible. In some cases, addition of a small volume of running water may be required (ASTM, 1994).

Alternatives to Sieving. Unwanted materials from the sediment sample can be hand picked rather than sieved before processing. Soon after collection, large particles and indigenous organisms may be carefully removed from sediment displayed on a sorting tray made of cleaned, chemically inert material. The sediment should be hand picked with forceps. A stereomicroscope or magnifying lens may facilitate the process or may be used to determine if sieving is necessary. Before physicochemical analyses, removal of large debris can also be done by hand. However, if the volume of sediment is too large and hand sorting is not practical, then sieving is the next best alternative for selectively removing unwanted materials. Methods other than sieving to inhibit endemic biological activity in field- collected sediments include autoclaving, freezing, and gamma irradiation of sediments. These are not generally recommended procedures and each has unique effects on the physicochemical and biological characteristics of the sediment.

SPIKING SEDIMENTS. Whole sediments may be spiked with individual chemicals or chemical mixtures. Spiked sediments are used in toxicity tests to determine the effects of the chemical of interest on different species. Spiking may also provide information concerning chemical interactions and transformation rates. The design of spiking experiments and interpretation of results should always consider the complexing capacity of the sediment, recognizing that this governs many chemical and biological processes (ASTM, 1994; Environment Canada, 1995; O’Donnel et al., 1985; and Stemmer et al., 1990a, 1990b). In preparation for toxicity and bioaccumulation tests, recommendations regarding the choice of test concentrations should be consulted (ASTM, 1994, and U.S. EPA, 1994, 2000). These tests may involve serial dilutions of spiked sediments.

Preparation for Spiking. Sediments may be stored in sealed containers at 4 °C until they are to be spiked. Debris and organisms should be removed as soon as possible before sample storage to reduce deterioration of sediment quality due to decomposition of dying infauna and organic debris. It is recommended that a subsample of the sediment be analyzed following methods for at least moisture content, pH, ammonia, TOC, AVS, particle-size distribution, and background levels of the chemicals to be spiked (U.S. EPA, 2002). It is particularly important to determine the TOC concentration if the

172 Handbook on Sediment Quality sediment is to be spiked with a non-ionic organic compound. Similarly, the AVS concentration should be measured after spiking if the sediment is to be spiked with a divalent metal such as copper, cadmium, or zinc (Ankley et al., 1996). The moisture content of a sediment should be determined before spiking to standardize the amount of chemical spiked on a dry-weight basis. Generally, the moisture content should be determined on triplicates for each sample by measuring the weight lost after 24 hours of oven drying at 105 °C. After drying, the samples should be cooled to room temperature in a desiccator before taking dry-weight measurements (Yee et al., 1992). The mean wet density, expressed as milligrams of water per cubic centimeters, is measured using the same drying method, performed on known volumes. This allows spiking to be normalized from a volume basis to an equivalent dry-weight basis.

Spiking Methods. Spiking of both wet and dry sediments is common, but wet spiking is recommended because air drying may reduce the representativeness of the sample by changing its physicochemical characteristics (ASTM, 1994, and Northcott and Jones, 2000). Important considerations are that spiked sediments should be stored for a sufficient time to approach chemical equilibrium between the sediment and pore water. U.S. EPA (2002) recommends a 1-month equilibration time although, in some instances, 2 months or more may be needed (Driscoll and Landrum, 1997, and Landrum et al., 1992). Temperature and mixing also should be carefully controlled to minimize alterations in sediment characteristics (ASTM, 1994). The three most common techniques for wet-spiked sediments are (1) jar rolling (Cole et al., 2000, and Ditsworth et al., 1990), (2) additions to sediment suspension (Stemmer et al., 1990a), and (3) the slurry technique (Birge et al., 1987). Generally speaking, the jar rolling method is more suitable for spiking larger batches of sediment that will be divided into replicate subsamples, whereas the slurry and suspension techniques are better for spiking the proper amount of sediment in individual replicates in a test. Thorough mixing of spiked sediments has also been accomplished using Eberbach and gyro- rotary shakers (Stemmer et al., 1990b). Less commonly, chemicals are added to the water overlying the sediment and allowed to sorb with no mixing (Gerould and Gloss, 1986; O’Neill et al., 1985; Pritchard et al., 1986; Stephenson and Kane, 1984; and Tsushimoto et al., 1982). Shell coating techniques that introduce dry chemicals to wet sediment have also been developed, principally to eliminate the potential disadvantages of solvent carriers. The chemical may be either coated on the inside walls of the container (Ditsworth et al., 1990) or coated onto silica sand (Kosian et al., 1999). In each shell coating method, the chemical is dissolved in solvent, placed in a glass spiking container (with or without sand), and the solvent is slowly evaporated. Wet sediment then sorbs the chemical from the dry surfaces.

Verifying Homogeneity. One of the most important criteria for both choice of mixing methodology and the form of the chemical used in the preparation of

Methods for Collecting, Storing, and Manipulating 173 a spiked sediment is that homogeneous mixing occurs within the sample (U.S. EPA, 2002). Therefore, regardless of the spiking technique used, care should be taken to ensure complete and homogeneous mixing. One useful approach is to conduct chemical analyses on subsamples to verify that concentrations of the spiked element or compounds are uniform throughout the mixed material. Three or more subsamples of the spiked sediment should be randomly sampled to determine the concentration of the substance being tested. A coefficient of variation of <10% has been achieved with cadmium-spiked substrates prepared using the rolling technique (Ditsworth et al., 1990). It is not possible to make firm recommendations concerning mixing times required to achieve homogenous mixtures in different situations. Mixing time after spiking should be limited to a few minutes or hours (1 to 24), and temperatures should be kept to a minimum (e.g., 4 °C) due to rapid physicochemical and microbiological alterations that may occur in the sediment that, in turn, may alter bioavailability and toxicity (ASTM, 1994; Environment Canada, 1994; and U.S. EPA, 2002). Mixing time might be extended for more stable organic compounds (e.g., PCBs) and for some metals (e.g., cadmium and copper) without adverse effects. Tests have shown, for example, that bioaccumulation of a PCB was not affected by an extended mixing period (McElroy and Means, 1988).

Equilibration Times. In spiked sediment experiments, equilibration of the toxicant with the sediment matrix may be as critical to the outcome of the study as the spiking and mixing methods used. Standardization is difficult, however, because equilibration times differ widely for compounds and sediments with differing characteristics. Consequently, equilibration times and storage procedures for spiked sediments vary widely among studies (Burton, 1991). It is important to achieve compound equilibration when applying equilibrium-partitioning theory to predict toxicity (Di Toro et al., 1991). For metals, equilibration times could be as short as 24 hours (Cole et al., 2000; Jenne and Zachara, 1984; and Nebecker et al., 1986), but 1 to 2 weeks is generally sufficient (Environment Canada, 1995, and Northcott and Jones,

2000). For organic compounds with low partition coefficients (Kow), equilibration times as short as 24 hours have been used (DeWitt et al., 1989), but for chemicals with a high partition coefficient, 2 or more months may be necessary to establish equilibrium (Landrum et al., 1992). The duration of contact between the toxicant and sediment particles can affect both the partitioning and bioavailability of the contaminant (Landrum, 1989; Landrum and Faust, 1991; Landrum et al., 1992; and Nkedi-Kizza et al., 1985). For example, Landrum et al. (1992) found that the partitioning of pyrene and phenanthrene between sediment particles and interstitial water increased significantly with time, whereas the uptake-rate coefficients for the amphipod, Diporeia sp., decreased significantly for both chemicals as contact time increased. This effect apparently occurs because of initial rapid labile sorption followed by movement of the toxicant to resistant sorption sites (Di Toro et al., 1991, and Karickhoff and Morris, 1985a and 1985b). Boundaries for the sorption time can be estimated from the partition coefficient for the sediment following the calculations in Karickhoff and Morris (1985a, 1985b). It is important to

174 Handbook on Sediment Quality recognize that the quantity of toxicant spiked might exceed the complexation capacity of the test sediment system, prohibiting equilibrium. Unless definitive information is available regarding equilibration time for a given sediment, holding samples for at least 2 weeks is advised (ASTM, 1994, and U.S. EPA, 2002), and periodic monitoring to empirically establish stability of pore-water concentrations is highly recommended (U.S. EPA, 2002).

FORMULATED SEDIMENTS AND ORGANIC CARBON MODIFICA- TION. Formulated sediments (also called reconstituted, artificial, or synthetic sediments) are standardized mixtures of natural materials that mimic certain types of natural sediments (U.S. EPA, 2002). Similar to studies using reconstituted waters or soils, formulated sediments may have several advantages in the assessment of sediment contamination. A primary use of formulated sediments is as a control sediment in toxicity testing. Unlike natural sediments, which are typically heterogeneous over space and time and may have unknown physicochemical characteristics, physical, chemical, and biological components of formulated sediments are generally consistent and defined, which is conducive for experimentation under highly controlled conditions. Furthermore, formulated sediments can provide a basis for method standardization for conducting sediment research and can provide a consistent testing media for evaluating performance-based criteria necessary for test acceptability. A variety of methods describe procedures for making formulated sediments (Harrahy and Clements, 1997; Kemble et al., 1999; Naylor, 1993; Suedel and Rodgers, 1994; U.S. EPA, 2000; and Walsh et al., 1992). Suppliers of various components can be found in U.S. EPA (2000). Regardless of the artificial sediment recipe used, the sediment’s characteristics should be thoroughly documented. Most of these procedures have not been subjected to standardization and consensus approval or round-robin (ring) testing. The formulated sediment reported by Kemble et al. (1999) performed adequately in a U.S. EPA interlaboratory comparison study (U.S. EPA, 2000). A critical component of formulated sediments is the source of organic carbon. Many procedures have used peat as the source of organic carbon. Other sources of organic carbon include humus, potting soil, maple leaves, composted cow manure, rabbit chow, cereal leaves, chlorella, trout chow, Tetramin, and Tetrafin. Alpha cellulose has also been recently used successfully as a source of organic carbon in spiking sediment toxicity studies (Kemble et al., 1999, and Ribeiro et al., 1994). It is not clear that any one source of organic carbon is routinely superior to another source. An important consideration may be the ratio of carbon to nitrogen to phosphorus. Carbon can range from 30 to 47%, nitrogen from 0.7 to 45 mg/g, and phosphorus from below detection limits to 11 mg/g for several different carbon sources (U.S. EPA, 2000). Thus, sources of organic carbon can provide very different chemical characteristics that could affect the performance and reliability of the formulated sediment. Conditioning may be necessary to produce a more realistic formulated sediment environment that is free from artifactual stressors. However, sediment changes resulting from conditioning may be difficult to reproduce accurately. The need for conditioning will depend on the organic carbon

Methods for Collecting, Storing, and Manipulating 175 source and the contaminant being spiked. Several investigators have observed improved results, such as better water quality and greater similarity to natural sediments after conditioning of formulated sediment for 2 to 7 days (Burgess et al. 1993, and U.S. EPA, 2000).

SEDIMENT DILUTIONS. Studies with whole sediment dilutions of spiked or field-contaminated sediments provide information about the fate and effects of contaminants with respect to change in concentration. Such information can be used to derive the concentrations of sediment that exceed toxicity thresholds under various conditions. This can help select and priori- tize sites for remedial action. Sediment dilution studies may also help estimate the time necessary to eliminate sediment toxicity given a specific rate of degradation or burial (Giesy and Hoke, 1989). Sediment dilution studies can also be used to demonstrate effects of complex chemical mixtures in sediments on biota, especially where mortality is high (Nelson et al., 1993, and Swartz et al., 1995). To obtain concentration-effect information in solid-phase sediment toxicity evaluations, test sediment can be diluted using a “clean” noncontaminated sediment. The diluent sediment should have physicochemical characteristics similar to the test sediment, including organic carbon content and particle size, but should not contain concentrations of toxicants above normal background levels (ASTM, 1994). For dilutions, it is ideal to use sediment similar in composition and particle size to the test sediments from the same system (Nelson et al., 1993). Sediment dilutions should be homogenized using techniques recommended previously in this chapter and equilibrated, considering the factors identified under spiking methods. The clean sediment should be combined with the test sediment in ratios determined on a dry-weight basis to achieve the desired nominal dilution series. Volume-to-volume dilutions have also been reported (e.g., Johns et al., 1985, and Schlekat et al., 1995) and are acceptable if tight control is not needed. Weight-to-weight dilutions (e.g., Giesy et al., 1990, and Johns et al., 1991) enable a more straightforward calculation of dose-response curves. Sediment dilution can change organic matter concentration, particle composition, and particle-size distribution. Therefore, results from such experiments should be interpreted with care (ASTM, 1994). The choice of diluent sediment is critical. For example, in a study with Hyalella azteca,a fine silt-clay control soil used as a diluent was more effective at reducing toxicity than a silica-sand diluent (Nelson et al., 1993). Nelson et al. (1993) speculated that the higher concentration of organic carbon (14.3% organic carbon in the silica-clay relative to 0.26% in the sand) may have provided more sorptive surfaces and more effectively limited metal bioavailability. In addition, the response pattern was not consistent between diluted sediments. The diluting sediment may also oxygenate the test sediment, thereby reducing the AVS concentration (or other complexing agents) or change the test sediment porosity, thus altering availability and sorption kinetics.

SEDIMENT ELUTRIATES. Many studies of sediment toxicity have evaluated aqueous extractions of suspended sediment called elutriates. The

176 Handbook on Sediment Quality elutriate method was initially developed to assess the effects of dredging operations on water quality (Lee and Jones, 1992, 2001, and U.S. Army Corps, 1976), but it is applicable to any situation where the resuspension of sediment-bound toxicants is of concern. The elutriate method has been adapted to evaluate the effects of common natural events, such as bioturbation and storms, that might disturb sediments and affect water quality (Ankley et al., 1991, and U.S. EPA and U.S. Army Corps, 1991). The U.S. EPA (2000) lists 18 aquatic organisms as candidates for elutriate toxicity testing. The list includes both freshwater and saltwater species of crustaceans, fish, bivalves, and echinoderms. Standard effluent toxicity test procedures are also appropriate for elutriates, including tests with various vascular and nonvascular plant species (Ingersoll, 1995). Whereas there are several procedural variations, the basic method for elutriate preparation involves combining various mixtures of water and sediment (usually in the ratio of 4 parts water to 1 part sediment, by volume) and shaking, bubbling, or stirring the mixture for 1 hour (Ankley et al., 1991; Burgess et al., 1993; Daniels et al., 1989; and Ross and Henebry, 1989). The water phase is then separated from the sediment by settling or centrifugation. Once an elutriate has been prepared, it should be analyzed or used in biological tests immediately or as soon as possible. It should not be stored for longer than 24 hours at 4 °C unless the test method dictates otherwise (Environment Canada, 1994, and U.S. EPA and U.S. Army Corps, 1998). Elutriate tests are not designed to measure the toxicity of interstitial waters or bedded sediments. Sediment elutriates have been found to be both more toxic (Hoke et al., 1990), equally as toxic (Flegel et al., 1994), and less toxic (Ankley et al., 1991) than interstitial water, primarily because of differences in toxicant bioavailability in the two types of media (Harkey et al., 1994). In general, elutriates have been found to be less toxic than bulk sediments (Ankley et al., 1991, and Burgess et al., 1993). Because aqueous extracts of whole sediment might not accurately represent the exposure observed in whole sediments (Harkey et al., 1994), sediment elutriates are not universally accepted as an appropriate test fraction with which to assess the toxicity of bulk sediments. Filtering the elutriate is generally discouraged, but it may be necessary for some toxicity tests, such as the algal growth assay with Selenastrum capricor- nutum. Filtration can reduce the toxicity of sediment elutriates due to sorption of dissolved chemicals on the filtration membrane and retention of colloids. If an elutriate must be filtered, only pretreated filters should be used, discarding the first 10 to 15 mL of elutriate passing through the filter (Environment Canada, 1994). Testing with a filtered elutriate should include an assessment to determine the extent of analyte adsorption or desorption to or from the filter.

ISOLATION OF INTERSTITIAL WATER. When relatively large volumes of interstitial water are required (such as for toxicity testing), only grab or core sampling with subsequent centrifugation (Ankley et al., 1990; Ankley et al., 1991; Burgess et al., 1993; Carr and Nipper, 2001; Giesy et al., 1990; Jahnke, 1988; and Landrum et al., 1987) or sediment squeezing (Carr et al.,

Methods for Collecting, Storing, and Manipulating 177 1989; Carr and Chapman, 1992; and Long et al., 1990) are typically effective. Most sediment collection and processing methods have been shown to alter interstitial water chemistry (e.g., Bufflap and Allen, 1995; Burgess and McKinney, 1997; Carr and Nipper, 2001; Sarda and Burton, 1995; and Schults et al., 1992) and, therefore, potentially alter contaminant bioavailability and toxicity. Some important interstitial water constituents, including DOC, dimethylsulfide, ammonia, major cations, and trace metals, can be significantly altered by the collection method (Bischoff et al., 1970; Bufflap and Allen, 1995; Howes et al., 1985; Lyons et al., 1979; Sarda and Burton, 1995; and Sayles et al., 1973). Increased sample handling associated with centrifugation, suction, or squeezing may cause significant increases in key constituents such as ammonia, sulfide, and DOC compared with those collected via in situ peepers or core-port suction. Other constituents, such as salinity, dissolved inorganic carbon, sulfide, and sulfate, might not be affected by collection, providing oxidation is prevented. Immediate collection of interstitial water is recommended because significant chemical changes might occur even when sediments are stored for periods as short as 24 hours (Hulbert and Brindel, 1975; Kemble et al., 1994; and Sarda and Burton, 1995. Optimal collection and handling methods will depend on the intended use of the sample, characteristics of the sediment, and the contaminants of concern (e.g., acidification is appropriate for metal analysis but not for toxicity testing). Sediments that are contaminated with strongly nonpolar organics (such as PCBs) are not likely to change in concentration or toxicity over short time periods (Defoe and Ankley, 1998, and Moore et al., 1996). Filtration is not generally recommended, as it may result in loss of some dissolved metals and organics through sorption to glass fiber or polycarbonate membranes (Schults et al., 1992, and Word et al., 1987). Further information on the effects of centrifugation speed, filtration, and aerobic conditions on some chemicals in interstitial waters can be obtained (Adams, 1991; Ankley and Schubauer-Berigan, 1994; Bray et al., 1973; Bufflap and Allen, 1995; Carr and Chapman, 1992; Klinkhammer, 1980; Schults et al., 1992; and Simon et al., 1985).

Centrifugation. For toxicity testing, interstitial waters have been isolated over a range of centrifugal forces and temperatures (Ankley et al., 1991; Burgess et al., 1993; Giesy et al., 1990; Landrum et al., 1987; and Schults et al., 1992) with centrifuge bottles of various compositions. For routine toxicity testing of interstitial waters, sediments are generally centrifuged at 10 000g for a 30-minute period (ASTM, 1994; Environment Canada, 1994; and U.S. EPA, 2002). High-speed centrifugation followed by filtration with 0.2-µm membrane filters produced results that were comparable to in situ dialysis for metals and organic carbon (Carignan et al., 1985; Jenne and Zachara, 1984; and Kemble et al., 1994). Centrifugation at low (e.g., 2500g) speeds or use of a larger pore- size filtration membrane (e.g., 45-µm mesh) resulted in retention of both colloidal materials and aquatic bacteria as well as dissolved contaminants in the interstitial water sample (Carr and Chapman, 1992, and Jenne and Zachara,

178 Handbook on Sediment Quality 1984). High-speed centrifugation (e.g., 10 000g) or repeated centrifugation is necessary to remove colloids and dispersible clays (Adams, 1991; Ankley and Schubauer-Berigan, 1994; Brownawell and Farrington, 1986; Carr and Nipper, 2001; and Chin and Gschwend, 1991), both of which may introduce undesirable interferences to analytical or toxicological analysis. Typically, toxicity is reduced with high-speed centrifugation or filtration due to the removal of particle-associated contaminants (Ankley and Schubauer-Berigan, 1994; Bufflap and Allen, 1995; Sasson-Brickson and Burton 1991; and Schults et al., 1992). ASTM (1994) recommends that the temperature for centrifugation should reflect the ambient temperature of collection to ensure that the equilibrium between particles and interstitial water is not altered. Alternatively, a temperature of 4 °C may be preferred to minimize temperature-mediated chemical and biological processes (Environment Canada, 1994). It is difficult to centrifuge interstitial water from sediments that are pre- dominately coarse sand (U.S. EPA and U.S. Army Corps, 1998). A modified centrifuge bottle has been developed for coarse sand with an internal filter that can recover 75% of the interstitial water compared with 25 to 30% from squeezing (Saager et al., 1990). Polytetrafluoroethylene bottles collapse at 3000g but have been used successfully up to 2500g when filled to 80% of capacity (Burgess et al., 1993). At low centrifugation speeds without filtration, removal of colloids may not be possible. The influence of dissolved and colloidal organic carbon may be estimated by measuring the organic carbon content. If small volumes of water are required for testing, higher-speed centrifugation can be performed with glass tubes (up to 10 000g) (Word et al., 1987). If metal toxicity is not a concern, then high-speed centrifugation in stainless steel centrifuge tubes is suitable. If test samples are contaminated with photoreactive compounds such as PAHs, exposure of the sample to light should be minimized to limit degradation or alteration of potentially toxic compounds. This can be accomplished by using amber bottles and reduced lighting.

Sediment Squeezing. Isolation of interstitial water by squeezing has been performed using a variety of procedures and devices (Adams, 1991; Carr et al., 1989; Jahnke, 1988; Long et al., 1990; and Watson and Frickers, 1990). Inexpensive low-pressure mechanical squeezers can be constructed and may provide specialized capacities such as collection of pore-water profiles from core samples (Bender et al., 1987). In all cases, the interstitial water is passed through a filter that is a part of the apparatus. As explained above, filters can reduce toxicity and contaminant concentrations by retaining contaminant- associated particles and also by contaminant sorption onto the filter matrix (Bray et al., 1973; Sasson-Brickson and Burton, 1991; Schults et al., 1992; and Troup et al., 1974). The characteristics of filters and the filtering apparatus should be carefully considered. Squeezing has been shown to produce a number of artifacts due to shifts in equilibrium from pressure, temperature, and gradient changes (e.g., Bischoff et al., 1970; Mangelsdorf and Wilson, 1969; Sayles et al., 1973; Schults et al., 1992; and Troup et al., 1974). Squeezing can affect the electrolyte concentra-

Methods for Collecting, Storing, and Manipulating 179 tion in the interstitial water, particularly with a drop near the end of the squeezing process. Therefore, if squeezing is used, moderate pressures should be applied along with electrolyte (conductivity) monitoring during extraction (Kriukov and Manheim, 1982). Significant alterations to interstitial water composition can occur when squeezing is conducted at temperatures different from ambient (e.g., Bischoff et al., 1970; Mangelsdorf and Wilson, 1969; and Sayles et al., 1973). Other sources of alteration of interstitial water when using the squeezing method are contamination from overlying water (Bender et al., 1987; Bollinger et al., 1992; and Santschi et al., 1997).

Pressurized Devices. Small volumes of interstitial water can be isolated for chemical analysis by vacuum filtration (Jenne and Zachara, 1984, and Knezovich and Harrison, 1987), gas pressurization (Long et al., 1990), or displacement after removing the sediment from the aquatic environment (Adams, 1991). When preparing sediments for interstitial-water metals analysis, care must be taken to maintain the anoxic conditions of deeper sediments by performing the procedures under an inert atmosphere (Adams, 1991). Suction using an aquarium air stone recovered up to 1500 mL from 4 L of sediment suctioned in an anoxic environment (Santschi et al., 1997). Using a hand vacuum with an aquarium stone has been shown to be an effective method of collecting interstitial water (Sarda and Burton, 1995). The air stone is attached to a 50-mL syringe via plastic tubing. The stone is inserted in the sediment to the desired depth and then suction applied. Problems common to laboratory suction methods are loss of equilibration between the interstitial water and the solids, filter clogging, and oxidation (Sarda and Burton, 1995). Ammonia concentrations in water obtained by this system were similar to those collected with in situ peepers (Sarda and Burton, 1995).

QUALITY ASSURANCE AND QUALITY CONTROL

GENERAL CONSIDERATIONS. Quality assurance activities provide a formalized system for evaluating the technical adequacy of sample collection and laboratory analysis activities. These quality assurance (QA) activities begin before samples are collected and continue after laboratory analyses are completed, requiring ongoing coordination and oversight. The QA program should integrate management and technical practices to a single system to provide data that are sufficient, appropriate, and of known and documented quality. Developing and maintaining a QA program requires an ongoing commit- ment by project management and also includes the following: (1) appointment of a QA officer with the responsibility and authority to develop and maintain a QA program; (2) preparation of a QAPP with data quality objectives; (3) preparation of written descriptions of standard operating procedures (SOPs) for sediment sampling and manipulations, instrument calibration, sample chain of custody, and laboratory sample tracking system; and (4) provision of

180 Handbook on Sediment Quality adequate, qualified technical staff and suitable space and equipment to ensure reliable data. Further guidance for developing a QA program can be found in publications from U.S. EPA (1994, 1995, 1999). Quality assurance practices within a laboratory should address all activities that affect the quality of the final data such as (1) sediment sampling and handling, (2) condition and operation of equipment, (3) instrument calibration, (4) replication, (5) use of standards, (6) recordkeeping, and (7) data evaluation. Quality control (QC) practices consist of more focused, routine, day-to-day activities carried out within the scope of the overall QA program. Quality control is the routine application of procedures for obtaining data that are accurate (precise and unbiased), representative, comparable, and complete. Quality control procedures include activities such as identification of sampling and analytical methods, calibration and standardization, and sample custody and recordkeeping. Audits, reviews, and complete and thorough documentation are used to verify compliance with predefined QC procedures. Project-specific QA plans (QAPP) provide a detailed plan for activities performed at each stage of the study and outline the data quality objectives that should be achieved. Through periodic reporting, QA activities provide a means for management to track progress and milestones, performance of measurement systems, and data quality. A complete project-specific QA–QC effort has two primary components: a QA program implemented by the responsible agency (i.e., the data user) and QC programs implemented by the parties responsible for collection and analyses (i.e., the data generators).

QUALITY ASSURANCE AND QUALITY CONTROL PROCEDURES FOR SEDIMENT COLLECTION AND MANIPULATION. Given that sediments are spatially and temporally variable (Stemmer et al., 1990a) and that sampling may cause loss of sediment integrity or changes in chemical equilibrium (ASTM, 1994, and U.S. EPA, 2002), the QA–QC procedures for sample collection should include the following principal elements: (1) implementing a sound sampling approach based on the intended use of the data; (2) using sampling methodologies that allow the collection of representative samples based on data needs; (3) using sampling devices that minimize the disturbance or alteration to the media’s chemical composition; (4) using decontamination procedures that reduce cross-contamination potential between sampling points; and (5) using proper sample containers and preservation techniques that maximize the integrity of samples. To ensure the appropriateness of the sample collection protocol for sample integrity and data of suitable quality, a program of scheduled field QC samples, such as field replicates (duplicates, splits, field spikes), field blanks (rinsate equipment), bottle, trip, and background (upgradient) samples is critical. All field QC samples should be handled exactly as the sediment samples and should be treated as blind samples to minimize bias in the analysis. A random portion of the samples should also be analyzed by a third party to evaluate the primary laboratory’s performance. Quality control replicates (duplicates, splits) should be collected for analysis by the primary laboratory to determine analytical variability (U.S. EPA, 1995).

Methods for Collecting, Storing, and Manipulating 181 The procedures for sediment manipulations described should maintain the sample in a chemical condition as similar as possible to that at the time of collection. Quality assurance procedures are established to assure that SOPs are followed and that contamination is neither introduced to nor lost from the manipulated sample. For example, samples to be analyzed for trace metals should not come in contact with metal surfaces (except stainless steel). Sample tracking sheets should document date, time, and investigator related to removal and replacement of samples from storage. Specific manipulation procedures should follow established SOPs that minimize chemical alteration of the sample (excepting chemical spiking), maintain sediment physical properties, and include replication and blank samples.

THE QUALITY ASSURANCE PROJECT PLAN. Quality assurance project plans vary in content depending on program needs but should address the following elements:

• A description of the project organization and responsibilities; • Definition of data quality objectives; • Sampling, analysis, and measurement procedures; • Instrument calibration procedures; • Procedures for recording, reducing, validating, and reporting data; • Procedures for performing QA verification and internal QC checks; • Preventive maintenance schedules; • Specific routine procedures to evaluate precision, accuracy, and completeness; • Definitions of deviations and appropriate corrective actions; and • Information on appropriate indoctrination and training.

Data Quality Objectives. Data quality objectives (DQOs) are qualitative and quantitative statements of the overall uncertainty that a decision-maker is willing to accept in results or decisions derived from data. The DQOs provide the framework for planning environmental data operations consistent with the data user’s needs. They also define performance-based goals for accuracy (precision and bias), representativeness, comparability, and completeness, as well as the required sensitivity of measurements (e.g., target detection limits). Accuracy is defined in terms of bias (how close the measurement is to the true value) and precision (how variable the measurements are when repeated). In general, as the sensitivity and accuracy of a technique increases, so does the cost. The DQOs are based on the intended use of the data, technical feasibility, and consideration of cost. Thus, DQOs define the type of sampling design, sampling protocols, and sediment manipulations that will be required in a given project. If, for example, a project requires measuring historic as well as current sediment-quality conditions, some form of core sampler would probably be necessary as opposed to a grab (e.g., Ponar sampler) sampler. Similarly, if a project required keeping samples as close as possible to the in situ condition before analysis, then manipulations such as sieving would probably not be advisable.

182 Handbook on Sediment Quality The DQOs for precision and bias established for each measurement parameter should be based on prior knowledge of the measurement system used, method validation studies, and the requirements of the study. Precision of approximately 30 to 50% relative percent difference between measurements (the random error of measurement) and bias of no more than 50% of the true value (the systematic error of measurement) are typically adequate. The following categories of observations require DQOs, given the testing objectives described and the statistical power required:

• Water quality (temperature, salinity, hardness, dissolved oxygen, alkalinity, ammonia, etc.), • Minimum response in negative performance control sample, • Sensitivity of test organisms (reference toxicant effects) in toxicological analyses, • Inter- and intralaboratory performance standards, • Frequency of observations and measurements, and • Number of replicates.

Project Organization. A table or chart should be provided illustrating project organization and lines of authority. The organizational chart should also include all subcontractors and their key points of contact. The organizational chart should identify QA managers, including subcontractors, and should illustrate their relationship to other project personnel. The QA managers should be organizationally independent of the project management so that the risk of conflict of interest is minimized. The frequency and mechanisms of communications among the project team members should also be identified. Schedules for sampling and manipulation events, progress reports, site visits, and teleconferences should be provided as well as descriptions of special occurrences that would trigger additional communication.

Standard Operating Procedures. The SOPs are written descriptions of routine methods and should be provided for all methods used. A large number of field and laboratory operations can be standardized and presented as SOPs. General types of procedures that benefit from SOPs include field measurements ancillary to sample collection (e.g., water quality measurements or mixing model input measurements); chain of custody, sample handling, and shipment; and routine analytical methods for chemical analyses and toxicological analyses. The SOPs ensure that all persons conducting work are following the same procedures and that the procedures do not change over time. All personnel should be thoroughly familiar with the SOPs before work is initiated. Deviations from SOPs may affect data quality and integrity. If it is necessary to deviate from approved SOPs, these deviations must be documented and approved through an appropriate chain of command. Personnel responsible for ensuring the SOPs are adhered to must be identified in the QA plan.

SEDIMENT SAMPLE DOCUMENTATION. Bound field logbooks should be used for the maintenance of field records. All entries should be dated and

Methods for Collecting, Storing, and Manipulating 183 time of entry recorded. All aspects of sample collection and handling as well as visual observations should be documented in field logbooks. Documentation should be recorded in prenumbered bound notebooks using indelible ink pens in sufficient detail so that decision logic may be traced back once reviewed. Documentation should include

• Project name, • Sampling locations, • Dates and times, • Sampling personnel present, • Level of personal protective equipment worn, • Weather or any environmental condition that may affect samples, • Equipment used to collect samples, • Calibration data, • Deviations to approved work plans or SOPs, • Sketch of sampling area, • Notation of the system identifying and tracking samples, • Notation of any visitors to the site, • Initials and date on each page, and • Lining out of any remaining blank portions or pages.

Proper field sheet, sample labeling, chain of custody, and sample-tracking documentation should be maintained as appropriate. Specific details concerning sample documentation and sample management should be included in planning documents and reviewed by the sampling team before initializing the sampling program.

Sample-Tracking Documentation. Samples delivered to the laboratory should be accompanied by a chain-of-custody record that includes the name of the study, location of collection, date and time of collection, type of sample, sample name or number, number of containers, analysis required, and the collectors’ signatures. When turning over possession of samples, the relinquisher and the receiver sign, date, and record the time on the record sheet. The record sheet allows the transfer of a group of samples at one time. When the laboratory takes possession of the samples, each should be assigned a unique laboratory identification designation. This ensures a consistent system for tracking within the laboratory. If the samples arrive at the laboratory when designated personnel are not there to receive them, the samples are put in a secure location and the transfer is conducted when the appropriate personnel are present. Upon arrival at the laboratory, samples are inspected for condition and temperature, and sample container labels are verified against the chain-of- custody record or sample tracking form. Sample information is entered on laboratory log-in datasheets used to maintain information regarding sample (receipt, shipping, collection date, and storage). To allow for accurate identification of samples, information contained on sample tracking forms must match identically with information contained on the sample container labels. The tracking form lists both the collectors’ and the laboratorys’ identification

184 Handbook on Sediment Quality designations. Verified tracking forms are signed by the laboratory personnel with date and time in ink. Missing or compromised samples (e.g., inappropriate preservation to maintain integrity, inappropriate containers, and unlabeled or mislabeled containers) are documented on the tracking forms. When samples are removed from storage, the sample-tracking form accompanies it and documents data, time, and investigator associated with any manipulations. The manipulation type is noted on the form in detail or by reference to an approved laboratory SOP. Any deviations to the SOP are also noted. Should the sample be modified in such a way that additional subsamples are created, additional tracking forms must also be created.

Recordkeeping. Proper recordkeeping is essential to the scientific defensibil- ity of a sediment sampling and manipulation program. A separate file should be maintained for each sampling and manipulation event or closely related events. This file should contain field logs, chain-of-custody forms, sample- tracking forms, storage records, and any QA–QC documentation and records. Original documentation should be signed and dated by the originator.

Quality Assurance Audits. In addition to the QA–QC procedures conducted on a routine basis, quality audits (i.e., performance and quality systems audits) may be conducted. Performance audits refer to independent checks to evaluate the quality of data produced during testing. There are three types of performance audits: sampling, test, and data processing. These audits are independent of normal QC checks performed by the operator. Performance auditing procedures are

1. Sample auditing: the auditor uses a separate set of calibrated standards to check the sample collection system; 2. Test auditing: the auditor is provided with set of a duplicate sample or split portion; and 3. Data-processing audit: the auditor spot checks calculations or a dummy set of raw data is inserted followed by review of validated data.

A systems audit is an on-site inspection and review of the QA system. The systems audit is performed to verify that the organization is following the policies and procedures described in its QA–QC plan and in appropriate SOPs. Systems audits are performed by an auditor typically from an accrediting body.

CORRECTIVE ACTION (MANAGEMENT OF NONCONFORMANCE EVENTS). The QA officer and the responsible manager are responsible for reviewing the circumstances of all instances of occurrence of nonconformities to determine whether corrective action should be taken. The manager is responsible for determining if new samples are required, if the customer should be notified, if additional testing is necessary, or whether the results should be confirmed. Corrective action may take two forms: that of addressing technical problems associated with project activities and that of addressing QA–QC infractions based on performance. Technical problems in meeting project objectives

Methods for Collecting, Storing, and Manipulating 185 may range in magnitude from failure to meet minor procedural requirements to major problems associated with inappropriate methods or data loss. Established procedures for corrective action of minor technical problems are often included in the SOPs for cases where performance limits or acceptance criteria have been exceeded. On-the-spot corrective actions are noted on datasheets. Significant or recurrent QA–QC problems that require long-term corrective action, such as modification of SOPs, are reported. Depending on the nature and severity of the problem, an approach may be developed. Any corrective action is documented by management. Infractions of QA–QC policies by staff are identified and addressed by the management. Minor infractions are corrected through additional training or closer supervision. Significant or recurrent infractions are corrected through reassignment of technical personnel. Corrective actions relative to sample collection and manipulation may include, but are not limited to, review of the data and calculations, flagging and qualification of suspect data, or possible resampling. A review that provides a preliminary check of all “out-of-limit” events is performed as soon as the data for a given parameter or test is tabulated and verified for accuracy. Out-of-limit events are flagged to determine whether new samples are required.

Data Reporting. In addition to reporting the raw data from a given sediment- quality study or analysis, the data report should include additional QA information such as

• A copy of the sample chain-of-custody record, including documentation of sample collection date and time; • Documentation of the laboratory certification number; • Documentation of the analysis method used; • Documentation of analysis date and time (or testing period in the case of toxicity tests); • Documentation that data for spikes, duplicates, standards, and other information meet laboratory QA–QC requirements for chemical analytes; • Documentation that reference toxicant test data meets laboratory QA–QC requirements for toxicity tests; and • Documentation of any deviations in sample preparation or analysis protocols.

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192 Handbook on Sediment Quality hoyi by Dissolved Organic Matter of Sediment Interstitial Waters. Environ. Toxicol. Chem., 6, 11. Landrum, P.F.; Eadie, B.J.; and Faust, W.R. (1992) Variation in the Bioavail- ability of Polycyclic Aromatic Hydrocarbons to the Amphipod Diporeia (spp.) with Sediment Aging. Environ. Toxicol. Chem., 11, 1197. Lanesky, D.E.; Logan, B.W.; Brown, R.G.; and Hine, A.C. (1979) A New Approach to Portable Vibracoring Underwater and on Land. J. Sediment Petrol., 49, 654. Lee, G.F., and Jones, R.A. (1992) Water Quality Aspects of Dredging and Dredged Sediment Disposal. In Handbook of Dredging Engineering. McGraw-Hill, New York, 9–23. Lee, G.F., and Jones, R.A. (2001) Water Quality Aspects of Dredging and Dredged Sediment Disposal. 2nd Ed., McGraw-Hill, New York. Long, E.R.; Buchman, M.F.; Bay, S.M.; Breteler, R.J.; Carr, R.S.; Chapman, P.M.; Hose, J.E.; Lissner, A.L.; Scott, J.; and Wolfe, D.A. (1990) Compara- tive Evaluation of Five Toxicity Tests with Sediments from San Francisco Bay and Tomales Bay, California. Environ. Toxicol. Chem., 9, 1193. Lyons, W.B.; Gaudette, J.; and Smith, G. (1979) Pore Water Sampling in Anoxic Carbonate Sediments: Oxidation Artifacts. Nature, 277, 48. Macrae, J.D., and Hall, K.J. (1998) Comparison of Methods Used to Deter- mine the Availability of Polycyclic Aromatic Hydrocarbons in Marine Sediments. Environ. Sci. Technol., 32, 3809. Mangelsdorf, P.C., and Wilson, T.R.S. (1969) Potassium Enrichments in Interstitial Waters of Recent Marine Sediments. Science, 165, 171. Mayer, L.M. (1976) Chemical Water Sampling in Lakes and Sediments with Dialysis Bags. Limnol. Oceanogr., 21, 909. McElroy, A.E., and Means, J.C. (1988) Factors Affecting the Bioavailability of Hexachlorobiphenyls to Benthic Organisms. In Aquatic Toxicology and Hazard Assessment. 10th Vol., W.J. Adams, G.A. Chapman, and W.G. Landis (Eds.), ASTM-STP 971, ASTM, Philadelphia, Pa., 149. Moore, D.W.; Dillon, T.M.; and Gamble, E.W. (1996) Long-Term Storage of Sediments: Implications for Sediment Toxicity Testing. Environ. Pollut., 89, 341. Mudroch, A., and Azcue, J.M. (1995) Manual of Aquatic Sediment Sampling. CRC/Lewis, Boca Raton, Fla. Mudroch, A., and MacKnight, S.D. (1994) CRC Handbook of Techniques for Aquatic Sediment Sampling. 2nd Ed., CRC Press, Boca Raton, Fla., 210. Naylor, C. (1993) Guide to the Preparation of Artificial Sediment for Use in Tests with Chironomus riparius. Standard Operating Procedure. Dep. Animal Plant Sci., Univ. Sheffield, Sheffield, U.K. Nebecker, A.V.; Onjukka, S.T.; Cairns, M.A.; and Kraweztk, D.F. (1986) Survival of Daphinia magna and Hyalella azteca in Cadmium Spiked Water. Environ. Toxicol. Chem., 5, 933. Nelson, M.K.; Landrum, P.F.; Burton, G.A., Jr.; Klaine, S.J.; Crecelius, E.A.; Byl, T.D.; Gossiaux, D.C.; Tsymbal, V.N.; Cleveland, L.; Ingersoll, C.G.; and Sasson-Brickson, G. (1993) Toxicity of Contaminated Sediments in Dilution Series with Control Sediments. Chemosphere, 27, 1789.

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194 Handbook on Sediment Quality Sayles, F.L.; Wilson, T.R.S.; Hume, D.N.; and Mangelsdorf, P.C., Jr. (1973) In Situ Sampler for Marine Sedimentary Pore Waters: Evidence for Potassium Depletion and Calcium Enrichment. Science, 180, 154. Schlekat, C.E.; Scott, K.J.; Swartz, R.C.; Albrecht, B.; Anrim, L.; Doe, K.; Douglas, S.; Ferretti, J.A.; Hansen, D.J.; Moore, D.W.; Mueller, C.; and Tang, A. (1995) Interlaboratory Comparison of a 10-Day Sediment Toxicity Test Method Using Ampelisca abdita, Eohaustorius estuarius, and Lep- tocheirus plumulosus. Environ. Toxicol. Chem., 14, 2163. Schults, D.W.; Ferraro, S.P.; Smith, L.M.; Roberts, F.A.; and Poindexter, C.K. (1992) A Comparison of Methods for Collecting Interstitial Water for Trace Organic Compounds and Metals Analyses. Water Res., 26, 989. Sijm, R.T.H.; Haller, M.; and Schrap, S.M. (1997) Influence of Storage on Sediment Characteristics and Drying Sediment on Sorption Coefficients of Organic Contaminants. Bull. Environ. Contam. Toxicol., 58, 961. Simon, N.S.; Kennedy, M.M.; and Massoni, C.S. (1985) Evaluation and Use of a Diffusion Controlled Sampler for Determining Chemical and Dis- solved Oxygen Gradients at the Sediment–Water Interface. Hydrobiologia, 126, 135. Skalski, C., and Burton, G.A. (1991) Laboratory and In Situ Sediment Toxicity Evaluations Using Early Life Stages of Pimephales promelas. M.S. thesis, Wright State Univ., Dayton, Ohio. Stemmer, B.L.; Burton, G.A., Jr.; and Leibfritz-Frederick, S. (1990a) Effect of Sediment Spatial Variance and Collection Method on Cladoceran Toxicity and Indigenous Microbial Activity Determinations. Environ. Toxicol. Chem., 9, 1035. Stemmer, B.L.; Burton, G.A., Jr.; and Leibfritz-Frederick, S. (1990b) Effects of Sediment Test Variables on Selenium Toxicity to Daphnia magna. Environ. Toxicol. Chem., 9, 381. Stephenson, R.R., and Kane, D.F. (1984) Persistence and Effects of Chemi- cals in Small Enclosures in Ponds. Arch. Environ. Contam. Toxicol., 13, 313. Suedel, B.C., and Rodgers, J.H., Jr. (1994) Development of Formulated Reference Sediments for Freshwater and Estuarine Sediment Testing. Environ. Toxicol. Chem., 13, 1163. Swartz, R.C.; DeBen, W.A.; Jones, J.K.; Lamberson, J.O.; and Cole, F.A. (1985) Phoxocephalid Amphipod Bioassay for Marine Sediment Toxicity. Aquat. Toxicol. Hazard Assessment, Seventh Symposium, ASTM STP 854, Philadelphia, Pa., 284. Swartz, R.S.; Schults, D.W.; Ozretich, R.O.; Lamberson, J.O.; Cole, F.A.; and DeWitt, T.H. (1995) A Model to Predict the Toxicity of Polynuclear Aromatic Hydrocarbon Mixtures in Field Collected Sediments. Environ. Toxicol. Chem., 14, 1977. Troup, B.N.; Bricker, O.P.; and Bray, J.T. (1974) Oxidation Effect on the Analysis of Iron in the Interstitial Water of Recent Anoxic Sediments. Nature, 249, 237.

Methods for Collecting, Storing, and Manipulating 195 Tsushimoto, G.; Matsumura, F.; and Sago, R. (1982) Fate of 2,3,7,8-Tetra- chlorodebenzo-p-Dioxin (TCDD) in an Outdoor Pond and in Model Aquatic Ecosystems. Environ. Toxicol. Chem., 1,61 Uchrin, C.G., and Ahlert, W.K. (1985) In Situ Sediment Oxygen Demand Determinations in the Passaic River (NJ) During the Late Summer/Early Fall 1983. Water Res., 19, 1141. U.S. Army Corps of Engineers (1976) Ecological Evaluation of Proposed Discharge of Dredged or Fill Material into Navigable Waters. Miscella- neous Paper D-76-17, Waterways Experiment Station, Vicksburg, Miss. U.S. Environmental Protection Agency (1986) Test Methods for Evaluating Solid Waste (SW-846): Physical/Chemical Methods. U.S. EPA, Office of Solid Waste, Washington, D.C. U.S. Environmental Protection Agency (1991) Methods for Measuring the Acute Toxicity of Effluents and Receiving Waters to Freshwater and Marine Organisms. 4th Ed., EPA-600/4-90-027F, Cincinnati, Ohio. U.S. Environmental Protection Agency (1994) Methods for Measuring the Toxicity of Sediment-Associated Contaminants with Estuarine and Marine Amphipods. EPA-600/R-94-025, Narragansett, R.I. U.S. Environmental Protection Agency (1995) QA/QC Guidance for Sampling and Analysis of Sediments, Water, and Tissues for Dredged Material Evaluations (Chemical Evaluations). EPA-832/B-95-002, Office of Water, Washington, D.C. U.S. Environmental Protection Agency (1999) Use in the Development of a Quality Assurance Project Plan. EPA-QA/G-5S, Office of Environmental Information, Washington, D.C. U.S. Environmental Protection Agency (2000) Methods for Measuring the Toxicity and Bioaccumulation of Sediment-Associated Contaminants With Freshwater Invertebrates. 2nd Ed., EPA-600/R-99-064, Duluth, Minn. U.S. Environmental Protection Agency (2002) Methods for Collection, Storage, and Manipulation of Sediments for Chemical and Toxicological Analyses: Technical Manual. EPA-823/B-01-002. Office of Water, Wash- ington, D.C. U.S. Environmental Protection Agency and U.S. Army Corps of Engineers (1991) Evaluation of Dredged Material Proposed for Ocean Disposal: Testing Manual. EPA-503/8-91-001, Office of Water, Waterways Experi- ment Station, Vicksburg, Miss. U.S. Environmental Protection Agency and U.S. Army Corps of Engineers (1998) Evaluation of Dredged Material Proposed for Discharge in Waters of the U.S.: Testing Manual. EPA-823/B-98-004, Washington, D.C. Walsh, G.E.; Weber, D.E.; Esry, L.K.; Nguyen, M.T.; Noles, J.; and Albrecht, B. (1992) Synthetic Substrata for Propagation and Testing of Soil and Sediment Organisms. Pedobiologia, 36,1. Watson, P.G., and Frickers, T.E. (1990) A Multilevel, In Situ Pore-Water Sampler for Use in Intertidal Sediments and Laboratory Microcosms. Limnol. Oceanogr., 35, 1381.

196 Handbook on Sediment Quality Word, J.Q.; Ward, J.A.; Frankli n, L.M.; Cullinan, V.I.; and Kiesser, S.L. (1987) Evaluation of the Equilibrium Partitioning Theory for Estimating the Toxicity of the Nonpolar Organic Compound DDT to the Sediment Dwelling Amphipod Rhepoxynius abronius. Battelle/Marine Research Laboratory Report, Task 1, WA56, Sequim, Wash., 60. Yee, S.; Van Rikxoort, M.; and McLeay, D. (1992) The Effect of Holding Time on Eohaustorius washingtonianus During Ten-Day Sediment Bioas- says and Reference Toxicant Tests. Report prepared for Environment Canada and the Inter-Governmental Aquatic Toxicity Group, North Vancouver, B.C., 53.

Methods for Collecting, Storing, and Manipulating 197

Chapter 5 Testing for Toxicity and Bioaccumulation in Freshwater Sediments

Stan J. Pauwels, Abt Associates, Inc., Cambridge, MA Paul Sibley, University of Guelph, Guelph, ONT

200 Introduction 221 Oligochaetes and Worms 201 Selection of Test Protocols 221 Amphipods 203 Selection of Test Species 222 Mayflies 205 Assessing Species Sensitivity 222 Midges 208 Selecting Measurement Endpoints 223 Cladocerans 211 Selection of Control Sediments 224 Fish 212 Selection of Sediment Phases for 224 Amphibians Use in Toxicity Testing 225 Miscellaneous Species 212 Introduction 226 Methods to Assess 212 Whole Sediments Bioaccumulation in 215 Pore-Water Phase Freshwater Sediments 216 Elutriate Phase 227 Invertebrate Bioaccumulation 216 Suspended Phase Tests 218 Comparative Toxicity of Whole 232 Fish Bioaccumulation Tests Sediments, Elutriates, and 232 Field Methods for Assessing Pore Water Sediment Bioaccumulation 219 Overview of Freshwater Species 233 Field-Collected Samples Used in Sediment-Toxicity Testing 234 Transplantation and In Situ 219 Vascular Plants Exposures

199 235 Future Research Directions 238 Bioaccumulation and Risk 236 Development of Assessment Bioassays and Endpoints 239 Concluding Remarks 237 Issues Related to Exposure 239 References 237 In Situ Studies 238 Development of Sediment- Quality Criteria and Bioassessment Approaches

INTRODUCTION Contaminated sediments are an important environmental issue with national implications. Even though the surface waters in the United States are cleaner now than in the past, some of their sediments contain high levels of pollutants (U.S. EPA, 1997). Because of increased regulatory concern about the long- term ecological effects of such pollutants, both freshwater and marine sediments have undergone extensive testing. It is a challenge to predict the biological effects of contaminated sediments using analytical data alone. The reasons are that sediments can be highly heterogeneous and contain complex mixtures of organics and inorganics. Bioassays, therefore, are an important tool to assess effects by measuring toxic responses and bioaccumulation in test organisms. All bioassays attempt to answer the same basic question: What is the level and intensity of response that results from exposure to contaminants found in sediments? In this chapter, we outline some of the decisions that must be made on which sediment fraction to test, what species and life stage to select, how long to expose organisms, what endpoints to measure, and what measurements to take. The choices are many and can result in different answers. Freshwater sediments can be tested as a solid phase, elutriate phase, pore-water phase, or suspended phase. Test species include microorganisms, plants, invertebrates, or fish. Life stages tested range from embryos to adults. Exposures last from minutes to weeks or months. Finally, the endpoints measured range from subcellular responses to effects at the individual or population level. The reasons for using bioassays are as numerous as the number of papers published on the topic. These reasons range from the applied, such as assessing the spatial extent of toxicity at contaminated sites or developing data for ecological risk assessment, to the theoretical, such as calculating sediment criteria or assessing bioavailability theories based on equilibrium partitioning. Given these wide interests, it is no surprise that a large number of bioassays have been developed over the years. This chapter reviews the application of bioassays in sediment toxicity and bioaccumulation assessments. The emphasis is on freshwater species and tests. The literature search focused mostly on the period from 1990 to early 1999. Whereas the search was not exhaustive, we are confident that it covers significant developments over the last 5 to 10 years in this rapidly maturing field.

200 Handbook on Sediment Quality SELECTION OF TEST PROTOCOLS

Over the last decade, agencies and organizations in the United States have developed or adapted various freshwater sediment testing protocols and guidelines (e.g., ASTM, 1997c, 2000c, and U.S. EPA, 1994a, 1994b, 1994c, 2000). These help ensure that results are comparable and of good quality. Testing protocols have also been developed by Environment Canada (1997a, 1997b), several European countries (Hill et al., 1993), the Organization for Economic Cooperation and Development, and others. Whereas the purpose of our review was not to analyze these protocols in detail, some general observations follow:

• Most standard protocols address acute effects with exposures lasting up to 10 days. Such effects may be useful for regulatory testing but may be insufficient to identify sublethal, chronic responses, which can have longer-term population effects. • Most standard protocols assess survival as the endpoint of concern. Other endpoints include growth (mayflies, midges, freshwater amphipods) or behavior (freshwater oligochaetes). More recently, standard protocols to assess reproduction in Chironomus tentans and Hyalella azteca (see Table 5.1 for common names of species typically used in toxicity testing) have been introduced (U.S. EPA, 2000). • Most standard protocols use early life stages because they are more sensitive than older life stages. • A limited number of standard protocols are available to test true benthic species (such as mayflies, midges, amphipods, or worms). More protocols address water column species.

Researchers have also developed new protocols or have modified existing ones to use on new species or life stages. In general, these have focused on longer-term, subchronic or behavioral effects not covered by existing standard freshwater protocols. Examples include a seed germination, seedling survival, and growth test with three species of wetland plants (Walsh, Weber, Simon, Brashers, and Moore, 1991); a 28-day survival and reproduction test with the oligochaete worm Tubifex tubifex (Reynoldson et al., 1991); a 14- to 28-day survival, growth, and reproduction test with the oligochaete worm Lumbriculus variegatus (Dermott and Munawar, 1992, and Phipps et al., 1993); a 3- to 6-day sediment avoidance test with L. variegatus (West and Ankley, 1998); a 30-day emergence test with Chironomus riparius (Watts and Pascoe, 1996); a 10- to 30-day survival, growth, and reproduction test with the estuarine amphipod Leptocheirus plumulosus (McGee et al., 1993); and a 10-day survival test with the euryhaline amphipod Corophium sp. (Hyne and Everett, 1998). These test methods provide additional sensitive endpoints to measure chronic effects of contaminated sediments on aquatic biota.

Testing for Toxicity and Bioaccumulation in Freshwater Sediments 201 Table 5.1 Common organisms used in toxicity testing.

Scientific Common Testing name name mode Amphiascus tenuiremis marine copepod benthic Brachionus sp. water flea (cladoceran) water column Branchiura sowerbyi freshwater oligochaete worm benthic Caenorhabditis elegans freshwater nematode benthic Campeloma decisum freshwater snail benthic Ceriodaphnia dubia waterflea (cladoceran) water column Chironomus decorus midge benthic Chironomus riparius midge benthic Chironomus tentans midge benthic Corbicula fiuminea freshwater clam benthic Corophium sp. euryhaline amphipod benthic Corophium spinicorne marine amphipod benthic Crassostrea gigas oyster water column Daphnia magna waterflea (cladoceran) water column Daphnia pulex waterflea (cladoceran) water column Diporeia sp. freshwater amphipod benthic Diporeia hoyi freshwater amphipod benthic Dorosoma cepedianum gizzard shad water column Echinochloa crusgalli crusgalli freshwater marsh grass benthica Echinochloa crusgalli zelayensis freshwater marsh grass benthica Elodea densa freshwater plant benthica Gammarus lacustris freshwater amphipod benthic Gammarus pseudolimnaeus freshwater amphipod benthic Gasterosteus aculeatus threespine stickleback water column Gobionellus beleosoma marine goby (fish) water column Helisoma sp. freshwater snail N/Ab Hexagenia bilineata mayfly benthic Hexagenia limbata mayfly benthic Hexagenia rigida mayfly benthic Hyalella azteca freshwater amphipod benthic Hydrilla verticullata freshwater plant benthica Hygrophyla onogaria freshwater plant benthica Ichtnybotus hudsoni mayfly benthic Ictalurus punctatus channel catfish water column Leiostomus xanthurus spot (euryhaline fish) water column Lemna gibba duckweed (freshwater plant) water column Lemna minor duckweed (freshwater plant) water column Leptocheirus plumulosus estuarine amphipod benthic Limnodrilus hoffmeisteri freshwater oligochaete worm benthic Ludwigia natans freshwater plant benthica Lumbriculus terrestris freshwater oligochaete worm benthic Lumbriculus variegatus freshwater oligochaete worm benthic Lysimachia nummularia freshwater plant benthica Lytechinus pictus sea urchin N/Ab Macomona liliana marine clam benthic Myriophyllum sibiricum watermilfoil (freshwater plant) benthic Mysidopsis bahia mysid shrimp water column

202 Handbook on Sediment Quality Table 5.1 Common organisms used in toxicity testing. (continued)

Scientific Common Testing name name mode Mysis relicta opossum shrimp water column Mytilus edulis marine mussel water column Mytilus galloprovincialis marine mussel water column Oncorhynchus mykiss rainbow trout water column Panagrellus redivivus freshwater nematode water column Paratya compressa improvisa freshwater shrimp water column Photobacterium phosphoreum marine bacterium (Microtox) water column Pimephales promelas fathead minnow water column Rhepoxynius abronius marine amphipod benthic Simocephalus vetulus waterflea (cladoceran) water column Spartina alterniflora salt marsh plant benthica Sphaerium novaezelandiae freshwater clam benthic Streblospio benedicti euryhaline polychaete worm benthic Stylodrillus heringianus freshwater oligochaete worm benthic Tubifex tubifex freshwater oligochaete worm benthic Utterbackia imbecillis freshwater mussel benthic Vallisneria americana freshwater plant benthica Xenopus laevis African clawed frog water column a For the purpose of this table, rooted plants tested in sediments are considered “benthic.” b This species is typically found on top of the sediment or attached to hard surfaces.

SELECTION OF TEST SPECIES Selecting appropriate test species is an early decision in sediment-toxicity testing. Test species can be divided into two broad ecological categories. The first category includes water column species (such as fish, mysids, cladocerans, etc.), for which many test protocols exist. These species have been used in traditional hazard assessments of chemicals or in support of regulatory effluent toxicity testing, and many have been adapted for sediments. The second category includes true benthic species, which spend at least part of their life cycle in or on sediment. Both groups have different modes of exposure. Water column species do not bury into sediments. They are either exposed directly to overlying water or via pore water diffusing into the overlying water. Benthic species are exposed by direct contact with pore water and ingestion of contaminated sediment. Differences in the lifestyles of benthic species, however, could also have an effect on toxicity (Landrum and Robbins, 1990). Swartz et al. (1990) speculated that different routes of exposures were responsible for different responses by the tube-dwelling amphipod Corophium spinicorne (less sensitive) and the free burrowing amphipod Rhepoxynius abronius (more sensitive) exposed to fluoranthene-spiked marine sediments. Tay et al. (1992)

Testing for Toxicity and Bioaccumulation in Freshwater Sediments 203 noted a similar effect with the same two species exposed to contaminated harbor sediments. Carlson et al. (1991) speculated that lower toxicity in the freshwater snail Helisoma sp. compared with the oligochaete worm L. variegatus exposed to the same metal-spiked sediment may have reflected different life history strategies: the snails moved over the sediment surface and on the beaker walls, whereas the worms were usually half buried in the sediment. Finally, Ingersoll et al. (2000) measured toxic responses in the freshwater amphipod H. azteca exposed for 10 days to contaminated whole sediment and overlying water. The authors reported no toxic effects in organisms exposed only to the overlying water; in contrast, significant mortality was observed in organisms exposed directly to the sediment. Some species, such as C. tentans, mayflies, or the marine amphipod C. spinicorne, build tubes or borrows that may limit direct contact with the sediments. They may get more exposure from overlying water pumped through the tube than from direct contact with sediment (Tay et al., 1992). Other benthic species, such as freshwater and marine worms, and the marine amphipod R. abronius, are free burrowers, which move on or through the sediment and receive maximum exposure. Criteria are available to select the “right” test species. These include (1) ecological relevance, (2) sensitivity, (3) discriminatory power, (4) tolerance to a range of sediment characteristics, (5) direct contact with sediment, (6) feeding habits, (7) geographic distribution, (8) easy culturing in the laboratory or ready availability from the field, and (9) high survival under control conditions (ASTM, 1997c; Burton, Ingersoll et al., 1996; DeWitt et al., 1989; Dillon, 1993; Hoke et al., 1993; Ingersoll et al., 1995; and Tay et al., 1992). Most published papers on sediment-toxicity testing, however, do not justify the species selection process. It is likely that species are chosen on a more pragmatic basis, including (1) regulatory acceptability, (2) cost associated with testing, (3) recognized test protocols or guidelines, (4) technical resources and equipment requirements, (5) convenient, year-round availability, or (6) previous experience of the laboratory with the test species. Because no one species fits all criteria, researchers have suggested a battery approach based on selecting two or more species to cover different exposure pathways, trophic levels, and lifestyles (Burton, 1992; Burton, Ingersoll, et al., 1996; Cheung et al., 1997; and Hoke et al., 1993). Burton, Ingersoll, et al. (1996) tested freshwater whole sediment and elutriates on 24 species and measured 41 endpoints. The authors concluded that groups of assays had similar responses to a given exposure. They also identified a few species and endpoints that represented a larger set of species. Burton, Ingersoll, et al. (1996) concluded that a test battery based on two or three species should suffice to detect most sediment toxicity in freshwater. Their proposed species are the cladocerans Ceriodaphnia dubia and Daphnia magna, the midges Chironomus riparius and C. tentans, the amphipods H. azteca and Diporeia sp., the fathead minnow Pimephales promelas, the mayfly Hexagenia bilineata, and the vascular plants Hydrilla verticillata and Lemna minor. Giesy et al. (1990) compared the sensitivity of D. magna, H. limbata, and Hyalella azteca exposed to pore water and whole sediment and concluded that all three could be useful in identifying sediment toxicity.

204 Handbook on Sediment Quality Day, Dutka, et al. (1995) tested 46 sediment samples and correlated the results from microbial assays with those on whole sediment-toxicity tests on macroinvertebrates and with in situ benthic community structure. Strong correlations existed among the various bioassays and endpoints and benthic community structure; this suggested that a small set of sensitive bioassays could be selected to cover a wide range of responses. Even though battery testing is strongly recommended, this approach can be quite costly if the study involves many samples or species. One way to narrow down the choices consists of preselecting sensitive species or endpoints and eliminating nontoxic sediment samples from further testing. This can be accomplished by using screening tests (which use only undiluted sediments) or range-finding tests (which use a few dilutions over a wider range, such as 100, 10, and 1%). These types of bioassays are conducted with fewer replicates and organisms, fewer dilutions, and shorter exposure durations (e.g., Douglas et al., 1993; McGee et al., 1993; Strawbridge et al., 1992; and Suedel et al., 1993). Preselection helps identify nontoxic sediments early on and eliminates species that are insensitive or respond the same as other species; this approach can limit toxicity testing, focus the assessment, and save resources. Species selection also depends on the purpose of the study. For example, toxicity identification evaluations (TIEs) can help identify possible sources of toxicity in sediment samples. Testing is typically conducted using small volumes of pore water. As a result, TIEs rely on species that need only small exposure volumes, such as Microtox, C. dubia, or fathead minnow larvae. On the other hand, if the reason for testing is to perform a more comprehensive aquatic ecological risk assessment, both benthic and water column species should be used.

ASSESSING SPECIES SENSITIVITY One reason to choose a battery approach is that a species’ response may vary with the endpoints assessed, the physical or chemical nature of the sediment, and the contaminant mixture (Ingersoll and Nelson, 1990, and Suedel et al., 1993). Burton (1991) observed that no single test species is always the most sensitive. It is a challenge to use the sediment toxicity literature to compare species sensitivity. The fact is that most studies expose different species and life stages to different sediment fractions (e.g., H. azteca to whole sediment and P. promelas to an elutriate). This complicates separating fraction toxicity from species or life-stage sensitivity. To make valid comparisons, the species should be tested at least to the same sediment fraction. Even so, sensitivity also varies by endpoint and exposure duration. For example, Burton et al. (1989) reported that H. azteca detected toxicity in whole sediments after 10 or more days but not after 48 hours. Cladocerans exposed to the same sediment measured toxicity after 48 hours.

Testing for Toxicity and Bioaccumulation in Freshwater Sediments 205 With these caveats in mind, the papers included in this review were evaluated to assess species sensitivity. A study was retained only if it had data on at least three different species tested on the same sediment fraction. Species were ranked from “1” (most sensitive) to “n” (least sensitive) by how each response compared with the others within each study. Species with similar sensitivities received the same rank. Endpoints were consistent within studies but varied among studies. The endpoints included a lethal concentration of 50% (LC50), the contaminant concentration at which 50% of the test organisms are killed at the end of the exposure period; effects concentration of 50% (EC50), the contaminant concentration at which 50% of the test organisms exhibit a sublethal response at the end of the exposure period; no observed effect concentration, the highest contaminant concentration at which no significant differences in target endpoints are observed in the test organisms compared with controls at the end of the exposure period; and percent survival and growth. To facilitate interpretation, an average sensitivity rank was calculated for each species and sediment fraction. Species with fewer than three rank values were excluded. Because this calculation resulted in the most sensitive species having the smallest average rank, the values were inverted (1/X) to obtain the relative rank. Table 5.2 shows the data for freshwater whole sediment only. Insufficient species were tested three or more times on elutriate or pore water to make a valid comparison for these fractions. The information in Table 5.2 should be interpreted with caution because some species (such as H. azteca and C. tentans) were tested more often than others (such as Hexagenia sp. and C. riparius). Nonetheless, a pattern is suggested. Six of the seven species retained are benthic invertebrates. Daphnia magna was the only water column species included in this analysis. Average sensitivities ranked as follows (from most sensitive to least sensitive): H. azteca > C. tentans > Hexagenia sp. > D. magna > T. tubifex > L. variegatus = C. riparius. The high sensitivity of H. azteca is consistent with the fact that it is by far the most common benthic species used in freshwater sediment- toxicity testing. The sensitivity of C. riparius and L. variegatus ranked lowest; this was not unexpected due to the ability of these two species to withstand degraded sediments. It is also noteworthy that species sensitivity can vary greatly between studies. For example, Day et al. (1995) reported that T. tubifex was the most sensitive species when compared with Hexagenia sp., C. riparius, and H. azteca, whereas Reynoldson et al. (1995) showed that it was the least sensitive when compared with the same set of species. Such differences probably reflect the combination of species and endpoints tested within each study, the nature of the contamination in the sediments, or the test conditions. In conclusion, data on the sensitivity of freshwater species used in sediment testing are limited (at least for elutriate and pore water) and somewhat contradictory when available. This reinforces the idea that a battery approach should be used to cover multiple exposure pathways, trophic levels, life stages, and endpoints (Burton, 1991).

206 Handbook on Sediment Quality b References Relative rank Relative aa = the number of ranks for a species. n = 3) n = 5) ( n = 13) ( 61.9 28.6 37.5 n a ranks = the sum of for a species and Σ = 5) ( n = 6) ( 100; where n × tentans riparius hoyi azteca variegatus tubifex promelas mykiss )] n = 4) ( ranks/ sp. n Σ = 5) ( 41.7 57.1 60.0 28.6 n ( < 3; insufficient data for use in the analysis. < 3; insufficient a 1 2 — — — — 3 — — — — Burton et al., 1989 C. D. Hexagenia C. C. D. H. L. T. P. Oncorhynchus ——— —— —— 4 —— — —— —— — — — — — 2 2— — — — —— — 2— — — — — 1— 1 2 2 2— — — 1 — — 2 — — 3 — 1 — — 3 2 3 — — 1 3 — 1 — 1 1 3 — — — — — — 1 — — — — 1 — 1 — — 1 1 4 2 — 3 — 3 3 — 2 — — — — 2 — 1 — — — — — 3 3 3 4 — — 3 — — 2 3 Ankley, Schubauer-Berigan, and Dierkes, — 1991 — — et al., West — 1993 — — — — et al., Kemble — — 1994 Day, Kirby, — 1 and Reynoldson, 1995 Krantzberg, — 1994 — — — et al., West — 1993 — et al., Reynoldson 1995 — et al., Whiteman 1996 — — — Dermott and Munawar, 1992 Suedel et al., — 1993 — Day, Dutka, et al., 1995 et al., Sibley 1997 Suedel and Rodgers, 1996 Relative rank = [1/ ( Relative n dubia magna Table 5.2Table sediment toxicity testing. species used in bulk freshwater of several sensitivities Comparison of the relative a b

Testing for Toxicity and Bioaccumulation in Freshwater Sediments 207 SELECTING MEASUREMENT ENDPOINTS

In an ecological risk assessment, two types of endpoints are identified. Assessment endpoints define a valuable environmental resource to be protected (e.g., a sensitive benthic community or a local sports fishery). In most cases, such endpoints cannot be quantified directly but have to be inferred based on surrogate measures called measurement endpoints. For this review, a measurement endpoint is a quantifiable response by test organisms after exposure to contaminated whole sediments, pore water, elutriates, or suspensions. Many endpoints have been reported in the literature. Most fall into two broad categories: short-term tests, which use lethality as the primary endpoint, and long-term tests, including partial or whole life cycle tests, which include growth, emergence, or reproduction as primary endpoints. Reproductive endpoints have received less attention because fewer standardized test protocols are available to assess them. Growth is a highly sensitive endpoint that correlates closely with reproductive success in benthic and water column invertebrates (Burgess et al., 1993; McGee et al., 1993; Sibley et al., 1996; and Sibley, Benoit, and Ankley, 1997). Others have shown that certain reproductive endpoints are more sensitive than growth (Dermott and Munawar, 1992, and Pesch et al., 1991) or vice versa (Burgess et al., 1993, and Phipps et al., 1993). McGee et al. (1995), however, reported that H. azteca exposed for 28 days to harbor sediments grew larger than the controls. They speculated that this response might have been caused by hormesis (i.e., the stimulation of physiological processes due to low levels of exposures to toxicants). Another reason may be that partial mortalities in low to moderately contaminated sediments result in lower densities of test organisms and hence more space and food resources for the survivors (Call et al., 1999). Growth is an appropriate endpoint for assessing longer-term, population-type effects in the field when sediments are not acutely toxic and reproductive endpoints cannot be measured directly (Sibley, Benoit, and Ankley, 1997). Even though there are relatively few standard protocols, many species used in sediment testing can be assessed for reproductive effects. For freshwater sediments, these include the cladocerans Simocephalus vetulus, Daphnia pulex, and C. dubia (Bridges et al., 1996; Burton, Ingersoll, et al., 1996; Nelson et al., 1993; Othoudt et al., 1991; Sloterdijk et al., 1989; and Suedel and Rodgers, 1994), the amphipod H. azteca (Ingersoll et al., 1998), the midge C. tentans (Benoit et al., 1997, and Sibley et al., 1996), and the oligochaete worms Branchiura sowerbyi (Casellato et al., 1992), L. hoffmeisteri (Lotufo and Fleeger, 1996), L. variegatus (Dermott and Munawar, 1992; Phipps et al., 1993; and West et al., 1993), and T. tubifex (Bailey et al., 1995; Day, Dutka, et al., 1995; Day, Kirby, and Reynoldson, 1995; Reynoldson, 1994; Reynoldson et al., 1991; Reynoldson et al., 1995; and Sibley, Legler, et al., 1997).

208 Handbook on Sediment Quality Overall, though, the studies presented in the previous paragraph are only a small part of all the studies obtained for this review. Reproductive endpoints may not be favored because the tests can last longer and hence typically cost more than the short-term or subchronic exposures on the same species (for example, compare the 10-day lethality test versus 28-day reproduction test with T. tubifex). In addition, fewer standardized test protocols are available to measure reproduction. Researchers instead have focused on other sublethal endpoints, such as growth in amphipods or delay in emergence of adult midges. Several water column invertebrates (including C. dubia, D. magna, Mysidopsis bahia, and various copepod species) reproduce within short time intervals. One alternative to estimate reproductive effects of sediments on benthic species might be to expose water column species with rapid life cycles directly to pore water. Pore water is a primary exposure route for nonpolar organic compounds in sediments (Adams, 1987). If we assume that these water column species have similar sensitivities as benthic invertebrates, they could serve as potential surrogates to estimate reproductive effects. This relationship should be carefully tested to determine if it is valid and useful in estimating benthic reproductive effects. One confounding factor is that under certain circumstances, ingestion can be a significant exposure route, often as important or more important than pore water (Forbes et al., 1998, and Leppanen and Kukkonen, 1998). Early life stages can be used to increase the sensitivity of a test because these life stages are more sensitive than the adult stage. For example, fathead minnows are typically tested using recently hatched larvae (≤24 hours old), cladocerans are tested using neonates (≤24 hours old), midges and freshwater amphipods are tested using early instars, and marine amphipods may be tested using juveniles and subadults. In fact, McGee et al. (1993) showed that, in 10-day exposures with juvenile (1 to 2 weeks old) and adult Leptocheirus plumulosus, juveniles were more sensitive than adults. Green et al. (1996) exposed three life stages of the marine copepod Amphiascus tenuiremis to a pesticide spiked in sediment and reported that the earliest life stage was more sensitive than the two older ones. Behavioral endpoints have received limited attention. Smith and Logan (1993) reviewed the use of invertebrate behavior in aquatic toxicology. In the field, water column and some benthic species might avoid or move away from contaminated sediments (Lenihan et al., 1995). Behavior could limit or prevent exposure to contaminated sediments in laboratory settings; in the field, it could increase the chances of mortality from predation if benthic invertebrates are forced to move out of contaminated sediments. McCloskey and Newman (1995) tested the preference of the Asiatic clam (Corbicula fluminea) and a snail (Campeloma decisum) for three freshwater sediments with different levels of metals. The sediments were paired and placed next to each other in an aquarium. The test organisms were inserted along the interface between the two sediments. The location and burial status of each was noted for 2 weeks. The results showed that snail distribution was unaffected by any sediment, whereas the clams preferred clean sediment. West and Ankley (1998) developed an avoidance bioassay with the oligochaete L. variegatus using an exposure chamber specifically designed to

Testing for Toxicity and Bioaccumulation in Freshwater Sediments 209 measure such a response. The test starts when equal numbers of worms are placed in each of three beakers housed within a larger beaker; one beaker contains the test sediment whereas the other two contain glass beads, a neutral substrate into which the worms can burrow. Sediment avoidance may be indicated if less than 33% of the worms are found in the test sediment at the end of the test. Roper and Hickey (1994) compared lethality with avoidance and burial and morbidity in the marine clam Macomona liliana exposed to sediments spiked with copper and chlordane. Endpoint sensitivity (from most to least) was ranked as avoidance > burial and morbidity > mortality. With copper, the response threshold for avoidance was 6 times lower than for mortality. With chlordane, an avoidance response was observed at a concentration approximately 20 times lower than the threshold for burial and morbidity. Keilty et al. (1988b) compared acute lethality and sediment avoidance endpoints in two species of freshwater oligochaetes exposed to endrin-contaminated sediments.

They reported that the 96-hour EC50 for burrowing avoidance in the two species was approximately 46 and 150 times lower than their corresponding

96-hour LC50. Lenihan et al. (1995) performed extensive laboratory and field studies to assess the behavioral responses of several Antarctic benthic species to gradients of petroleum hydrocarbons in marine sediments. In almost every laboratory and field experiment, the test organisms either failed to burrow or, when given a choice, actively avoided the contaminated sediments. Thompson et al. (1991) measured sediment avoidance in the sea urchin Lytechinus pictus exposed to sediments spiked with hydrogen sulfide. At higher concentrations, the urchins avoided sediment by remaining attached to the sides of the aquaria. Chandler and Scott (1991) randomly placed small amounts of pesticide-containing sediment in wells of four-row by four-column tissue culture plates. Each plate was surrounded by glass and covered with 500 mL of seawater. Larvae of the polychaete Streblospio benedicti were added to the overlying water in each plate and allowed to settle. After 7 days, a strong pattern was noted between number of larvae settled and pesticide concentration. Assessing precopulatory guarding behavior in amphipods could become a potentially useful behavioral endpoint (Blockwell et al., 1998, 1999). Even though it has not been used with contaminated sediments, it is a relatively sensitive endpoint to contaminants in general. With increasing interest in endocrine disrupters that can affect reproductive processes at very low concentrations, this type of behavioral endpoint is likely to receive more scrutiny in the future. Finally, various authors have reported unusual behaviors but did not quantify them (Cleveland et al., 1997; Drenner et al., 1993; Kukkonen and Landrum, 1994; Landrum et al., 1991; Lotufo and Fleeger, 1996; Nipper et al., 1998; Sibley, Legler, et al., 1997; and Whiteman et al., 1996). Several times, avoidance behavior seems to have limited the exposure of test organisms to sediments and either lowered toxicity values (Kukkonen and Landrum, 1994, and Whiteman et al., 1996) or resulted in unusual dose- response curves (Nelson et al., 1993). Clearly, behavioral endpoints are sensitive and may not have received the full attention they deserve.

210 Handbook on Sediment Quality SELECTION OF CONTROL SEDIMENTS

Choosing a good control sediment is a critical step in developing quality toxicity data. A control sediment (1) establishes that the test organisms are healthy and the test conditions do not result in unusual mortality or sublethal responses, and (2) is used as a diluent or as a clean sediment for spiking studies. Control sediments should result in predictable and acceptable survival by the test species (Schlekat et al., 1995). Ideally, control sediments come from a clean area in the vicinity of where the contaminated sediments are collected. They then represent true reference sediments. Some researchers have used a standard control sediment from one or more uncontaminated sites for all their toxicity testing, irrespective of where the contaminated sediments originate. These standard sediments may be used for culturing and are known to be nontoxic to the test species. Others have tested formulated sediments (Suedel and Rodgers, 1994) or simply used clean sand as a substrate. Selecting the appropriate control sediment requires caution and planning. Research has shown that test species exposed to clean control sediments can experience unusual mortalities (Kemble et al., 1994; Leonard et al., 1995; Nipper et al., 1989; and West et al., 1993). If survival is poor in such sediments, then one cannot determine if the responses in the contaminated sediments are because of xenobiotics or the physicochemical nature of the sediment itself. The choice of a control sediment as a diluent can also affect toxicity.

Nelson et al. (1993) calculated the LC50 for contaminated freshwater harbor sediment diluted with two types of control sediments. They showed that, depending on the species tested, origin of the contaminated sediment, and

type of control sediment used, the LC50 was either the same, different, or could not be calculated due to U-shaped dose-response curves. Weber et al. (1995) grew two species of vascular aquatic plants in natural sediments and laboratory-prepared artificial sediments, with and without the addition of contaminants. The origin and characteristics of the control sediments resulted in different response patterns by the plant species. Walsh, Weber, Simon, and Brashers (1991) exposed three species of marsh plants to natural and synthetic sediments spiked with herbicides. They reported that growth inhibition in two species was similar in both types of sediments. Growth inhibition in the third species, however, was greater in the synthetic sediment than in the natural sediment. Watts and Pascoe (1996) measured weight changes in larval C. riparius exposed for 10 days to a clean, natural sediment and two formulated sediments. All three sediments were also spiked with similar amounts of copper. The authors reported no weight loss in the natural sediment but significant weight loss in both formulated sediments even though the copper levels in the three sediments were the same. Bailey et al. (1995) investigated the macroin-

Testing for Toxicity and Bioaccumulation in Freshwater Sediments 211 vertebrate community structure and sediment toxicity in 50 nearshore reference sites in the Great Lakes. Even though all reference sites were in clean areas, there was much variation in all the endpoints measured. They concluded that care is needed when choosing control sites to compare with affected areas. The importance (and difficulty) of selecting good reference sites is the primary reason behind the notion of a “reference envelope” or “reference condition” (Bailey et al., 1998, and Reynoldson et al., 1997). Finally, total organic carbon (TOC) and acid volatile sulfide (AVS) regulate the bioavailability of organic compounds and metals in sediments (see Chapter 3 for additional details). If used as a diluent, control sediments with TOC or AVS levels significantly different from the test sediments can affect the toxicity of sediment samples (Nelson et al., 1993). These results show that the physicochemical nature of sediments can affect toxicity by affecting the bioavailability of xenobiotics or by affecting the test organisms directly. Choosing a control sediment is one of the many important decisions required to produce a quality database.

SELECTION OF SEDIMENT PHASES FOR USE IN TOXICITY TESTING

INTRODUCTION. Four sediment fractions can be tested to assess sediment toxicity: whole sediment, elutriates, pore water, and suspended sediments. Table 5.3 describes the main advantages and limitations of each fraction (the sediment fraction associated with organic solvent extracts are not discussed in this section; see Chapter 6 for additional details on this topic). Much effort has focused on whole sediment. However, which fraction to test depends on the goals of the study. For example, the elutriate phase could be tested if sediment resuspension, such as at a dredge material disposal site, is an issue. Whole sediment could be tested to support a benthic survey at a contaminated site. Pore water could be tested as part of a TIE study to identify contaminants responsible for toxicity. Regardless of which fraction is used, all sediment handling can affect toxicity (see Chapter 4 for additional details on sediment handling and preparation). Sampling, transporting, storing, and preparing sediments can alter their physical and chemical characteristics. Handling sediment may include sieving, vigorous mixing, dilution with reference water to generate an elutriate, centrifugation and pressure filtration to produce pore water, and other steps. These manipulations can change the chemical characteristics of the sediment samples. Little is known about what effect this has on the original toxicity because it is so dependent on the type of sediment, contaminant concentrations, test species used, and endpoints measured.

WHOLE SEDIMENTS. Whole sediment is the fraction most often used in sediment testing. The primary advantage is that it best represents field

212 Handbook on Sediment Quality y suspensions over time suspensions over Exposure is limited to contaminants dissolved in the aqueous phase Exposure is limited to contaminants dissolved Exposure is limited to contaminants dissolved in the aqueous phase Exposure is limited to contaminants dissolved sediment or pore water • to generate sediment suspensions No standardized approach exists •TIE Cannot be used to perform evaluations sediments • Special equipment required to generate and maintain true test species sedimentsreference sediments • it is a function of the ratio whole of toxicity is relative; Level • relationship between measured toxicity in elutriate vs solid The dissolved phasedissolved control or reference sediments for use in toxicity testing as compared with elutriate or pore- phaseswater time increase toxicity over • for testing is rather limited Number of benthic species available • of suspended effects when assessing field Most representative • Limited applications • Useful to assess “benthic species” column” using “water effects • Represents a primary source of toxicity for aquatic species• criteria water data can be compared with surface Toxicity • standard, Many for testing nonbenthic species are available •TIEs Useful to perform sediment •• can be routinely produced in the small volumes Only relatively problems associated with solid- or elutriate-phase dilutions Avoids • laboratory • sample procedures can result in siginficant extraction Pore-water disturbance • of test material can be produced quickly and easily volumes Large • On average, elutriates are less toxic compared with whole • Most suitable to detect toxicity from dredged or disposed• issues tied to solid-phase dilutions using control or Avoids •TIE evaluations Can be used in • sample disturbance Elutriate production leads to significant • standard, Many for testing nonbenthic species are available phase is uncertain • volume water sediment to extraction • exposures of field Most representative • Most often used in sediment toxicity evaluations• via ingestion, Benthic species are exposed dermal contact, and• required to prepare whole sediment samples less effort Relatively • Conditions may be changed when diluting test sediments with • In static tests, desorption of contaminents from sediments ma test water cal characteristics of the overlying • in the sediment can change physicochemi- Microbial activity Sediment phase Advantages Limitations Suspended phase Pore-water phase Pore-water Table 5.3Table sediment phases in toxicity testing. and limitations of using different Some advantages Elutriate phase Whole sediment

Testing for Toxicity and Bioaccumulation in Freshwater Sediments 213 exposures for benthic invertebrates. However, the number of benthic species available for whole sediment testing is limited. In addition, there are uncertainties with diluting field-collected sediments with control sediments. The details of the test methods depend on the species used and the purpose of the study. Such details include the size of the test vessel, volume of sediment and water used, number of organisms per vessel, number of replicates per concentration, feeding and light regime, the duration of the test, the endpoints measured, the sediment and water quality variables monitored, use of reference toxicant testing, and other quality assurance–quality control issues. Whereas a comprehensive outline of specific test details is beyond the scope of this section, most whole sediment tests have a number of basic elements in common. Sediment samples are collected in the field and brought back to the laboratory for preparation and testing. In some cases, clean control or formulated sediments are spiked with one or more contaminants. Field samples may need to be passed through a 1- to 2-mm sieve to remove coarse debris or macrobenthos and to enhance homogenization. Individual samples may also be mixed together to form a composite sample. Either way, a subsample should be obtained and stored for future physical and chemical analyses. The test organisms are either cultured in the laboratory or collected in the field. All field-collected organisms should first undergo a period of acclimation to ensure that they are in good health. This period can last from days to weeks. Ideally, both cultured and field-collected organisms should undergo periodic reference toxicant testing to ensure that their sensitivity falls within an acceptable range. Recent insights to the role and importance of reference testing have been addressed by McNulty et al. (1999). These authors suggest that reference tests are useful and should be continued, but at a lower frequency than previously suggested. They also indicate that the use of minimum condition attributes (e.g., minimum survival, size, weight, or reproduction) is more appropriate to assess the condition of organisms used to initiate a test. The size of the test vessel, amount of whole sediment used, and volume of water added all vary by test protocol. In all cases, a given volume of sediment is placed in each test vessel. Contaminant-free water of acceptable quality to the organisms is then carefully poured over the sediments. If the vessel is a 1000-mL glass beaker (which is typical for benthic invertebrates tested on whole sediments), the volumes of sediment and water are approximately 200 and 800 mL, respectively. The material is allowed to equilibrate for up to 24 hours before the organisms are placed at random into the vessels. At a minimum, water quality is checked in each vessel at the beginning and end of a test. Individual vessels may also be monitored during the test. Water quality parameters may include pH, water hardness, salinity, alkalinity, conductivity, temperature, and dissolved oxygen. Other variables, including ammonia (NH3), hydrogen sulfide (H2S), TOC, grain size, AVS-simultaneously extracted metal (SEM), cation-exchange capacity, and others, may also need monitoring on a case-by-case basis. The test vessels are kept at proper temperature by placing them in a temperature-controlled water bath or chamber. Gentle air bubbling may also be needed, as some sediments have high biochemical oxygen demand. The

214 Handbook on Sediment Quality tests are typically carried out under a regulated photoperiod, such as 16 hours of light to 8 hours of darkness. Finally, the need for feeding depends on the species and test duration. These and other requirements are outlined in the standard protocols and should be closely followed. Any protocol deviations should be noted. The organisms should be observed throughout the test. Any unusual behavior should be noted, as it may provide information on test conditions. For instance, several researchers have reported that benthic species may fail to burrow in contaminated sediments. Such behaviors may reduce exposure and toxicity. At the end of a test, the contents of the vessels are carefully removed and the surviving organisms collected. Depending on the species and endpoint of concern, the investigator may count surviving organisms to calculate lethality or reproductive success, quantify growth, assess developmental effects, and measure bioaccumulation or ability to rebury in clean sediments.

PORE-WATER PHASE. Pore water is the aqueous phase present in the interstitial spaces between sediment particles. Because of its close contact with the solid phase, pore water may contain dissolved fractions of contaminants that have dissociated from the contaminant-binding phases (such as organic material for organic compounds and AVS or cation-exchange surfaces on clay particles for metals). As such, it represents a sediment phase in which toxicants are highly bioavailable. The main advantage of using pore water is that it represents a primary route of exposure to aquatic biota. Also, data from tests with pore water can be compared directly with ambient water quality criteria. A major disadvantage of testing pore water is that exposure is limited to the contaminants dissolved in the aqueous phase (i.e., toxicity resulting from ingestion is not assessed). Also, only smaller volumes of pore water are routinely available for testing unless significant amounts of sediments are processed. Several authors reported that the toxicity of pore water in water column species was similar to whole sediment toxicity in benthic species (see below). Some have suggested that the best way to use water column species to assess sediment toxicity may be via exposure to pore water (Giesy et al., 1990) and that water column species should not be used in bulk sediment tests when evaluating in situ toxicity to benthic invertebrates (Ankley, Schubauer- Berigan, and Dierkes et al., 1991). The validity of this model will also depend in part on the physical and chemical characteristics of the sediments, the species used and endpoints measured, and the nature of the contaminants (Giesy et al., 1990; Harkey, Landrum, and Klaine, 1994). Several techniques exist to prepare samples of pore water (Ankley and Schubauer-Berigan, 1994, and Winger et al., 1998; see also Chapter 4 for additional details on this subject). These include (1) centrifugation of wet sediments, (2) compression using a sediment press, (3) syringe extraction, (4) dialysis, and (5) vacuum suction. The centrifugation method is by far the most commonly used technique (e.g., Ankley, Lodge, et al., 1992; Green et al., 1993; Hoke et al., 1993; Kemble et al., 1994; and others) and is the one discussed here.

Testing for Toxicity and Bioaccumulation in Freshwater Sediments 215 Collecting pore water using centrifugation is fairly straightforward. Sediment subsamples are placed in appropriate receptacles and centrifuged at high speed for up to 1 hour. The supernatant is carefully decanted or siphoned off and used immediately in toxicity testing or stored at 4 °C for later use. Some researchers have suggested filtering the pore water after centrifugation. Others, however, have reported that filtration reduces the toxicity of pore water (and elutriate), apparently due to the adsorption of contaminants onto the filter or sediment particles retained on the filter (Ankley and Schubauer- Berigan, 1994; Sasson-Brickson and Burton, 1991; and Winger et al., 1998). It has been recommended that aqueous sediment extracts not be filtered before they are used in toxicity testing. The toxicity of pore water is assessed in the same way as whole sediment, except that no solid phase is present in the test vessel. If benthic organisms are used with pore water, it may be necessary (depending on the test species selected) to provide an artificial substrate such as clean sand or glass beads to reduce the potential for stress-related mortality due to the inability to burrow.

ELUTRIATE PHASE. Elutriates are designed to evaluate toxicity of sediments after disturbances by ship propellers, wave action, currents, flooding, or human activities such as dredging or dredge disposal. The primary difference between an elutriate and a suspension (see next section) is that all sediment particles have been removed by centrifugation. Significant advantages of using elutriate fractions for sediment-toxicity testing are that they can be produced in large volumes, do not involve mixing different types of whole sediment samples, and can be tested on water column species. The limitations are that elutriates tend to be less toxic than whole sediment or pore water, and toxicity is operationally defined by the volumes of water used in the extraction process. Most elutriates are prepared using a variation on the method described by Ankley, Schubauer-Berigan, and Dierkes (1991). Briefly, this approach consists of mixing the sediment with dilution water, usually on a 1:4 (volume- per-volume) basis. The mixture is placed on a laboratory rotator or similar device and shaken for a predetermined amount of time, typically 30 to 60 minutes. This may be followed by a settling period before the aqueous phase is decanted and centrifuged for up to 1 hour. Alternatively, the mixture may be immediately centrifuged without removing the supernatant first. After centrifugation, the elutriate may undergo a filtration step. It is then used immediately in testing or stored at 4 °C for later use. Several exceptions to this general method have been reported in the literature (e.g., Dawson et al., 1988, and Long et al., 1990). Elutriate toxicity is tested in the same way as whole sediment, except that no solid phase is present in the test vessel. If benthic organisms are used with elutriates, it may be necessary (depending on the test species selected) to provide an artificial substrate such as clean sand or glass beads to reduce the potential for stress-induced mortality due to the inability to burrow.

SUSPENDED PHASE. Suspended sediments have not been widely tested but could represent a significant route of exposure if sediments are dumped or

216 Handbook on Sediment Quality entrained in the water column. Suspensions differ from elutriates in that sediment particles are not removed before testing begins. They are prepared by mechanical mixing or vigorous aeration. Quasi-suspensions have also been prepared by vigorous mixing of a sediment–water slurry, and testing the organisms directly on a settled suspension without further agitation. There are currently no standardized methods for testing the toxicity of sediment suspensions. Several approaches have been used, however, to generate suspensions. Some of these approaches are discussed below. Gregg et al. (1997) created turbid water for use in feeding experiments with the marine goby, Gobionellus beleosoma, by blending polycyclic aromatic hydrocarbon (PAH)-spiked sediment with 150 mL of water and pouring the slurry into individual crystallizing dishes. The sediments settled rapidly because no attempt was made to keep them suspended over the 24-hour exposure period. The overlaying water remained turbid for the first several hours of the experiment. A similar approach was reported by Dave (1992). The author used D. magna to test whole sediment toxicity by adding sediment to water (1:3) and stirring the mixture with a magnetic stirrer. Part of the suspension was removed, placed in a small Petri dish, and further diluted with water. After settling for 1 hour, the test organisms were introduced to the test vessels and exposed for 48 hours. Sved and Roberts (1995) used a modified continuous-flow serial diluter to test the acute toxicity of a creosote-contaminated sediment suspension to the fish Leiostomus xanthurus. Samples were first sieved to retain the silt and clay fractions. Suspensions were prepared daily by adding preweighed amounts of sediment to flasks containing reference water and stirring continuously with small propellers. Creosote was added to one of the bottles to generate a high PAH-suspended sediment stock solution used in the serial diluter. Drenner et al. (1993) added insecticide-spiked sediments to mesocosms and aerated the tanks to maintain a suspension over an 8-day exposure period. Schmidt-Dallmier et al. (1992) described a sediment-suspension system for use with small aquatic organisms. It consists of a 4-L beaker with a large inverted glass funnel fitted snugly inside the bottom of the beaker. A propeller blade and shaft were placed inside the funnel. Water and suspended sediments flowed continuously through a stainless steel mesh screen at the top of the funnel, which also prevented entrainment. This system was tested on young fathead minnows with clean sediment to check for mortality over a 7-day period. Because less than 5% of the fish died, the authors suggested that this system might be appropriate to test the effects of suspended fractions on larval and juvenile fish and invertebrates. Bonnett et al. (2000) used flow-through microcosms to compare the toxicity of suspended and nonsuspended contaminated harbor sediments. The authors created a suspension by vigorously mixing the sediments for 15 minutes. The sediment particles were allowed to settle down before the solid phase, and overlying water was tested for toxicity over a period of 25 days. These examples demonstrate the main limitation of testing suspended sediments: without a clearly defined protocol, suspensions are generated in

Testing for Toxicity and Bioaccumulation in Freshwater Sediments 217 different nonstandardized ways. Another concern relates to the physical (abrasive) effects of suspended particles, which may be present in the test chambers. As a result, it could be difficult to compare toxicity data across studies. Suspended sediments should only be used if there is a compelling reason for testing this fraction. Otherwise, elutriates should be selected because they can be prepared more consistently based on standard protocols.

COMPARATIVE TOXICITY OF WHOLE SEDIMENTS, ELUTRIATES, AND PORE WATER. Interpreting toxicity data will vary depending on which sediment fraction is tested. Direct comparisons can be difficult if different species, exposure durations, or endpoints are used for each phase. Studies that have tested the toxicity of two or more sediment fractions with the same species can give us information on comparative toxicity. Burton et al. (1989) exposed H. azteca, D. magna, and C. dubia for 48 hours to whole sediment and their elutriates. They reported that whole sediment was in general more toxic than elutriates. Giesy et al. (1990) sampled contaminated river sediments and exposed C. tentans and Hexagenia limbata to whole sediment and pore water. They reported that with H. limbata the solid phase was nontoxic, whereas the pore water was highly toxic. Chironomus tentans, on the other hand, measured significant toxicity both in the bulk phase and pore water. Ankley, Schubauer-Berigan, and Dierkes (1991) exposed P. promelas, C. dubia, H. azteca, and L. variegatus for 48 to 96 hours to bulk phase, pore water, and elutriate samples of sediments collected at seven sites. They concluded that pore water was at least as toxic as elutriates to all four species. Pore water (but not elutriate) was also a good predictor of whole sediment toxicity. This is consistent with the fact that the response pattern of benthic organisms exposed to whole sediments correlates best with contaminant levels in pore water. Sasson-Brickson and Burton (1991) used C. dubia to test river sediments. They reported that elutriates were more toxic than pore water or whole sediment. However, pore water was always as toxic or more toxic than whole sediment. Schubauer-Berigan and Ankley (1991) tested river sediments with fathead minnow, an amphipod, and a worm. They reported the highest toxicity in whole sediment. Pore water was intermediate and the elutriate phase was essentially nontoxic. Kemble et al. (1994) assessed the pore-water and solid-phase toxicity of sediments contaminated by metals. They reported that rainbow trout was relatively insensitive to the solid phase. The fish, however, had toxic responses to pore water from 5 of the 13 sediment samples. Winger and Lasier (1995) collected harbor sediments and exposed H. azteca for 10 days to the solid phase and pore water. Toxicity in pore water was always higher than in whole sediment. Sibley, Legler, et al. (1997) measured toxicity of solid phase, elutriates, and pore water from sediments collected downstream of an industrial outfall. For all species, pore water was more toxic than whole sediment or elutriates. Except for one species (H. azteca), elutriates were less toxic than whole sediment.

218 Handbook on Sediment Quality Green et al. (1993) used the marine copepod Amphiascus tenuiremis to test the toxicity of cadmium in pore-water and solid-phase sediments spiked with the metal. They reported that cadmium was more toxic in pore water than in the solid phase. Beiras and His (1995) obtained hydrocarbon-contaminated sediments and tested the elutriate phase (passed through a 0.45-µm filter) and suspended phase (passed through a 32-µm filter) for toxicity using the oyster Crassostrea gigas embryogenesis assay. They found no statistically significant differences between the two fractions. Several observations can be made from these studies. With some notable exceptions, elutriates tend to be least toxic and pore water most toxic. Whole sediments fall in between the two. Pore water is the most concentrated (aqueous) fraction of a sediment sample that can be tested (Ankley, Schubauer-Berigan, and Dierkes, 1991). In a whole sediment test, the bedded sediment is overlain by two to four volumes of clean test water. Undoubtedly, pore water diffuses into the water column and clean water enters the sediment, thereby diluting the remaining pore water. Clark et al. (1989) exposed three marine water column species to sediments spiked with a pesticide. Toxicity occurred only when concentrations of the pesticide in the overlaying water exceeded a critical threshold. Elutriates are usually prepared by mixing one volume of whole sediment with four volumes of water and separating the supernatant by filtration or centrifugation. This too results in significant dilution and reductions in toxicity (Ankley, Schubauer-Berigan, and Dierkes, 1991, and Sibley, Legler, et al., 1997). Di Toro et al. (1990) also suggested that elutriate preparation could result in higher toxicities because of the oxidation of metal sulfides and the release of solubilized metals into the water column.

OVERVIEW OF FRESHWATER SPECIES USED IN SEDIMENT- TOXICITY TESTING

Burton (1991, 1992), Burton et al. (1992), Giesy and Hoke (1990), Ingersoll et al. (1995), Keddy et al. (1995), and Traunspurger and Drews (1996) have provided excellent reviews on the use of bioassays and the selection of test species for assessing sediment contamination in freshwater environments. Our purpose here is to briefly review species and endpoints that are commonly used in sediment toxicity testing.

VASCULAR PLANTS. Relatively few studies have used aquatic vascular plants on contaminated sediments. Lemna gibba (duckweed) and Myriophyllum sibiricum (watermilfoil) are examples of aquatic vascular plants with standardized toxicity test protocols (ASTM, 1997a, 2000a). In the test with L. gibba, the plants float freely on the water surface with their roots

Testing for Toxicity and Bioaccumulation in Freshwater Sediments 219 exposed to the water column; the plants, therefore, would not directly touch sediments if used with whole sediments. For example, Jenner and Janssen- Mommen (1993) exposed duckweed for 14 days to aqueous leachates from coal ash and contaminated sediments. The endpoints measured included number of fronds, dry weight, and percent of leaf surface area covering the test vessel. Rooted plants, on the other hand, are exposed to contaminants both in sediments (via the roots) and in the water column (via stems, leaves, buds, or adventive roots in some cases) (Ribeyre and Boudou, 1994, and Weber et al., 1995). Biernacki et al. (1997) used two varieties of the submersed macrophyte Vallisneria americana (wild celery) to test the phytotoxicity of contaminated river sediment. Individual ramets were inserted to the sediments and exposed for 1 week. The endpoints were the number, growth, and surface area of leaves and roots. The study showed good responses for most of the endpoints. Nelson et al. (1993) exposed the plant Hydrilla verticillata (hydrilla) for 14 days to contaminated river and harbor sediments. The roots were inserted to the sediments at the start of the experiment. Endpoints measured included root and shoot length, dehydrogenase activity, chlorophyll a concentration, and peroxidase activity. The results were inconclusive because no concentration-response relationships could be established. Walsh, Weber, Simon, and Brashers (1991) tested germination and seedling growth and survival in two freshwater marsh grasses (Echinochloa crusgalli crusgalli and Echinochloa crusgalli zelayensis) and a salt marsh plant (Spartina alterniflora) exposed to pesticides spiked in natural and artificial sediments. The germination and early growth test lasted for 10 days; the seedling growth and survival test lasted from 2 to 4 weeks. The authors concluded that the plant species used in the study could be useful to estimate the effects of xenobiotics in sediments. Walsh, Weber, Simon, Brashers, and Moore (1991) used two varieties of the common freshwater marsh grass E. crusgalli to test the toxicity of six different types of effluents mixed into reference sediments. Seeds were first germinated for 4 days in clean water before they were transferred to the sediment–effluent mixtures and exposed for 12 days. The authors measured survival and growth. None of the effluent-spiked sediments had an effect on seedling survival but three resulted in reduced growth. Ribeyre and Boudou (1994) exposed four species of rooted macrophytes (Elodea densa, Ludwigia natans, Lysimachia nummularia, and Hygrophyla onogaria) for 28 days to different combinations of clean sediments and test water to measure growth effects. The authors reported differences in growth rates depending on sediment and water type. The authors concluded that the link between sediment–test water characteristics and plant growth is complex and not well understood. Finally, Burton, Ingersoll, et al. (1996) tested many species and endpoints on contaminated sediments to assess sensitivity, discriminatory power, and redundancy. The plants Lemna minor (growth, chlorophyll a and biomass) and H. verticillata (chlorophyll a, dehydrogenase activity, shoot and root length, peroxidase activity) were tested on whole sediments collected at three Great Lakes contaminated sites. A statistical analysis showed that neither

220 Handbook on Sediment Quality plant species contributed more information than that provided by more common animal species and endpoints. The authors suggested, however, that one or both macrophytes be included in a test battery due to their unique level of biological organization and role in ecosystem functioning. Clearly, the development of plant tests for assessing sediment toxicity is an area open for more research. The paucity of plant uses in sediment testing may be due to several reasons, including plants’ relatively large size, slow growth rates, and long growth cycles. In addition, there is a paucity of standard protocols to test toxicity, assess endpoints, and measure contaminant uptake (Biernacki et al., 1997). It probably also reflects insufficient regulatory interest in this area.

OLIGOCHAETES AND WORMS. A number of aquatic oligochaete species have been successfully used to test contaminated sediments. Because of their burrowing behavior, most studies have been on whole sediments. However, they have also been tested on pore water and elutriates (Ankley, Schubauer-Berigan, and Dierkes, 1991; Harkey, Landrum, and Klaine, 1994; Schubauer-Berigan and Ankley, 1991; and Sibley et al., 1997). The more common worms (Limnodrilus hoffmeisteri, L. variegatus, and T. tubifex) are cultured in the laboratory and are available year-round. Because of their larger size, relative lack of sensitivity to contaminants, and consumption of (and high exposure to) sediment-organic materials, these organisms, especially L. variegatus, have been used in sediment-bioaccumulation studies (Ankley, Schubauer-Berigan, and Dierkes, 1991; Harkey, Lydy, et al., 1994; Ingersoll et al., 1995; Kukkonen and Landrum, 1994; and Mac et al., 1990). Adult Tubifex have also been used in longer-term exposures ($28 days) to assess reproductive endpoints, in particular, cocoon production and number of young (Bailey et al., 1995; Day, Dutka, et al., 1995; Day, Kirby, and Reynoldson, 1995; Reynoldson, 1994; Reynoldson et al., 1991; Reynoldson et al., 1995; and Sibley, Legler, et al., 1997). With few exceptions (e.g., Day, Dutka, et al., 1995, and Dermott and Munawar, 1992), oligochaete species may not always be the most appropriate organisms to measure toxicity in sediment because of their relatively lower sensitivities to contaminants.

AMPHIPODS. Amphipods are important benthic fauna in freshwater environments. They are detritivores that can burrow into surface sediments to feed and hide. As a result, they contact sediments directly. The amphipod species that have been used the most in freshwater sediment studies are Diporeia sp. and H. azteca, although Gammarus pseudolimnaeus and Gammarus lacustris have also been used (Lynch and Johnson, 1982, and Schuytema et al., 1990). Among amphipod species, H. azteca has been used most extensively, largely because Diporeia sp. cannot be cultured (U.S. EPA, 2000). In addition, of all the species used in freshwater sediment testing, H. azteca was selected most frequently. H. azteca has been used in a variety of studies and is often a key component of the battery of test species used in assessing the ecological risks of contaminated sediments (Canfield et al., 1996; Pastorok et al., 1994; Reynoldson et al., 1995; and Schlekat et al., 1994).

Testing for Toxicity and Bioaccumulation in Freshwater Sediments 221 Hyalella azteca typically is exposed for up to 28 to 30 days. The primary endpoints measured are survival and growth. Studies have also examined other endpoints, such as bioaccumulation, sexual maturation, and the consumption rate of leaf discs (Borgmann et al., 1991; Burton et al., 1996; Ingersoll et al., 1995; Winger and Lasier, 1995; and Winger et al., 1993). Recently, Ingersoll et al. (1998) have introduced a generation test with H. azteca in which survival, growth (weight or length), and reproduction are measured in 42-day exposures.

MAYFLIES. Several mayfly species have been used in freshwater toxicity testing, including Hexagenia limbata, Hexagenia bilineata, Hexagenia rigida, and Ichtnybotus hudsoni. By far the most common species is H. limbata. The early life stages of mayfly species develop in surficial sediments. They construct burrows and undergo several stages of development, after which they leave the sediments and emerge from the water as adults. Mayflies have been exposed as juveniles predominantly to whole sediments. However, a few researchers have also used Hexagenia in pore water or elutriates exposures (Burton, Ingersoll, et al., 1996; Giesy et al., 1990; and Sibley, Legler, et al., 1997). Short-term exposures (4 to 10 days) have been used to measure survival and molting frequency (Burton, Ingersoll, et al., 1996; Giesy et al., 1990; Hickey and Martin, 1995; and Sibley, Legler, et al., 1997). Mayflies have also been exposed for 15 to 30 days and been assessed for survival, growth, and bioaccumulation (Ankley, Lodge, et al., 1992; Bailey et al., 1995; Day, Dutka, et al., 1995; Krantzberg, 1994; Krantzberg and Boyd, 1992; Odin et al., 1996; and Reynoldson et al., 1995). Mayflies have been used mostly as part of larger batteries of test species to compare interspecies sensitivities or spatial variations in sediment toxicity.

MIDGES. Midges are insects that are widely distributed and often dominant in freshwater environments. During the larval stage of their development, many midge species burrow and construct tubes in surface sediments. Hence, they are an integral part of the benthic community. Because of their burrowing activity, relative ease of culturing, rapid life cycle, and sensitivity, some midges have been heavily used in sediment-toxicity testing. The two midge species used most often are C. riparius and C. tentans. Most studies exposed midge larvae (first to fourth instar, <24 hours to 20 days old) for up to 10 days (occasionally up to 2 weeks), which is the standard duration specified by the test protocols. In these tests, endpoints measured were lethality and growth as well as bioavailability, hatching success, and head capsule width. A few studies looked at partial life cycle effects. Pittinger et al. (1989) exposed C. riparius larvae to sediments spiked with surfactants. The test was continued for more than 3 weeks until all live midges emerged as winged adults. Benoit et al. (1997) have introduced a full life cycle test with C. tentans. This test is initiated with <24-hour-old larvae and is conducted through one complete generation. The endpoints assessed include survival (larvae and adults), growth, emergence, and reproduction. Sibley et al. (1996) applied this test to assess AVS as a predictor of chronic toxicity of metal-spiked sediments.

222 Handbook on Sediment Quality Most studies have exposed midges to whole sediments. This is not surpris- ing given the benthic lifestyle of the test species. However, both midge species are quite flexible and have been tested on all three of the sediment phases discussed above. For example, Harkey, Landrum, and Klaine (1994) exposed C. riparius to whole sediment, elutriates, and pore water to measure the bioavailability of spiked organic compounds in these three phases. Sibley, Legler, et al. (1997) assessed the acute toxicity to C. riparius of the same three sediment fractions contaminated with pulp-mill effluent. Schubauer- Berigan and Ankley (1991) exposed C. tentans to sediment pore water and elutriate to evaluate the suitability of either fraction for use in sediment TIE analysis.

CLADOCERANS. Cladocerans are an important part of the zooplankton in fresh waters, with a long history as aquatic toxicity test species. Daphnia magna and C. dubia are used in regulatory effluent testing. Because the organisms are easily maintained and cultured, many commercial laboratories can test cladocerans on contaminated sediments. Cladocerans do not bury in sediments but can scour the sediment surface for food (Burton et al., 1989). They are, therefore, exposed not only to dissolved compounds in the water column but also potentially to contaminated particles via ingestion. Because cladocerans are an important link in the aquatic food web, they are well suited for use in sediment-toxicity testing as representative water column species. Most sediment-toxicity studies with cladocerans have used D. magna and C. dubia. Both species have often been shown to be quite sensitive. Brachionus sp., Simocephalus vetulus, and Daphnia pulex are three other cladocerans that have been used to compare species sensitivity with sediment elutriates (Burton, Ingersoll, et al., 1996, and Sloterdijk et al., 1989). In most cases, cladocerans are part of a larger battery of test species to assess the toxicity of whole sediments, elutriates, and pore water (Ankley, Schubauer- Berigan, and Dierkes, 1991; Burton et al., 1989; Hoke et al., 1990; Hoke et al., 1993; Kemble et al., 1994; Sibley, Legler, et al., 1997; and Suedel and Rodgers, 1996). Both D. magna and C. dubia have been used to assess acute (≤96-hour exposures) and chronic (≥7-day exposures) toxicity in whole sediment, elutriates, and/or pore water. With few exceptions, the test organisms used are neonates (≤48 hours old). Because of its small volume requirements (10 to 20 mL) and high sensitivity, C. dubia is commonly used in TIE studies (Ankley and Schubauer-Berigan, 1994; Ankley et al., 1996; Ankley, Schubauer-Berigan, and Dierkes, 1991; Gupta and Karuppiah, 1996; Schubauer-Berigan and Ankley, 1991; and Schubauer-Berigan et al., 1993). Daphnia magna is not reported to have been used in such studies. Even though cladocerans are not true benthic species, they have been used in a large number of sediment studies because of their sensitivity, ready availability, and relatively short life cycle. Using cladocerans in stand-alone sediment bioassays could be problematic because of the negative influence of suspended sediments on survival. The rapid life cycle of C. dubia allows testing of chronic, reproductive endpoints. Ankley, Schubauer-Berigan, and Dierkes, (1991) reported having problems using C. dubia in whole sediment

Testing for Toxicity and Bioaccumulation in Freshwater Sediments 223 assays, however, because of the poor recovery of these tiny organisms from test and control sediments

FISH. A few freshwater fish species have been used in sediment toxicity testing. Fish have been tested on whole sediments, pore water, and elutriates. Exposures to whole sediment, however, are mostly indirect via dissolved contaminants in the test water. Because of the long maturation period of fish, no study assessed reproductive effects. Instead, most have focused on shorter-term subchronic endpoints, including lethality, growth, and bioaccumulation. Otto et al. (1994) exposed immature rainbow trout (Oncorhynchus mykiss) for 21 days to sediments collected at a site receiving pulp-mill effluents. They measured Cytochrome p450-dependent enzymes and oxidant-mediated responses, and concluded that changes in monooxygenase activities are a sensitive tool to monitor sediment toxicity in fish. Munkittrick et al. (1995) used mixed function oxygenase (MFO) induction as the primary endpoint in a sediment bioassay using rainbow trout. These authors found that induction was apparent within 4 days of exposure and that identification of sediment toxicity with this bioassay compared favorably with tests using H. azteca and D. magna. Kemble et al. (1994) used newly hatched rainbow trout on whole sediments (21- or 28-day exposures) and pore water (96-hour exposures) contaminated with heavy metals. Trout were relatively insensitive to whole sediments but identified significant toxicity in several of the pore-water samples. Drenner et al. (1993) examined the effects of suspended, insecticide-spiked sediments on mortality and behavior in gizzard shad (Dorosoma cepedianum) after 8 days of exposure in outdoor tank mesocosms. Gregg et al. (1997) observed feeding behavior in the goby (G. beleosoma) exposed to suspended sediments spiked with diesel fuel. Sved and Roberts (1995) tested the acute toxicity of creosote-contaminated suspended sediments to the spot (Leiostomus xanthurus). The fathead minnow (P. promelas) is by far the most popular fish species used in sediment testing. This fish is a standard species for many toxicity studies and is also widely used in regulatory effluent testing programs. As a result, a large toxicity database exists for this fish. Laboratory cultures are easy to maintain, and test organisms are available year-round. The fathead minnow has been used to test whole sediments, elutriates, and pore water. The species is particularly well suited for use with pore water because embryos require only small volumes of water (10 to 20 mL). They are, therefore, commonly used in TIEs. Recently hatched embryos (<24 hours old) are the most common life stage tested because of their high sensitivity. Short- term exposures (≤96 hours) assess survival, whereas longer-term exposures (≥7 days) also include growth and bioaccumulation as measurement endpoints. In addition, several studies have used developmental endpoints to assess toxicity (Burton, Ingersoll, et al., 1996, and Dawson et al., 1988).

AMPHIBIANS. Few studies have used amphibians to measure sediment toxicity. This probably reflects the limited number of amphibian test species

224 Handbook on Sediment Quality and standardized protocols for use in aquatic toxicity testing. Two studies (described below) used the African clawed frog (Xenopus laevis) embryo–larval assay. The test procedures for this species are standardized (ASTM, 1997b). Both studies assessed only short-term developmental effects. Dawson et al. (1988) used aqueous extracts from metal-contaminated sediments to assess developmental toxicity using the frog embryo teratogene- sis assay–Xenopus (FETAX) assay. This test measures survival, growth, and embryo malformation after a 96-hour exposure. A comparison of these three endpoints with similar fathead minnow embryo responses indicated that the

fish LC50 and EC50 were 2 to 3 times lower than the frog. Hutchins et al. (1998) used the FETAX assay to measure the effect of nitrate-based bioremediation on the toxicity of petroleum-contaminated sediments in a column study. Column leachates were tested with Xenopus at time intervals of 1, 4, and 7 months after initiation of the study. Mortality and developmental toxicity declined significantly over time. Development of amphibian bioassays for sediment-toxicity testing is an area where additional research is needed. This may be particularly important considering the sensitivity of amphibians to environmental contaminants and recent concerns regarding the suspected role of various stressors (UV light, endocrine disrupters) in causing declines/malformations in amphibian populations and species (Ankley et al., 1998, and Ankley, Tiegte, et al., 1998).

MISCELLANEOUS SPECIES. Several miscellaneous species have been used to assess toxicity of contaminated freshwater sediments. For example, Hatakeyama and Shiraishi (1994) assessed mortality and growth in a freshwater shrimp (Paratya compressa improvisa) exposed for 9 days to sediments spiked with a pesticide. Mac et al. (1990) measured bioaccumulation and survival in the Asiatic clam (Corbicula fluminea) exposed for 10 days to contaminated sediments. McCloskey and Newman (1995) used C. fluminea and the snail Campeloma decisum in a 14-day avoidance–preference test to assess the effects of metal-contaminated sediments on the organism’s behavior. Hickey and Martin (1995) exposed the clam Sphaerium novaezelandiae together with four other benthic test species to sediments contaminated by industrial effluent for 10 days to assess their relative sensitivities. Carlson et al. (1991) exposed the snail Helisoma sp. for 10 days to cadmium-spiked sediments with different levels of AVS to determine cadmium toxicity. Sloterdijk et al. (1989) exposed the nematode Panagrellus redivivus to elutriates from contaminated river sediments to assess sensitivity compared with several other species. Traunspurger et al. (1997) developed a bioassay with the nematode Caenorhabditis elegans for testing the toxicity of whole sediments. These and other papers indicate that many species are used to assess the toxicity of contaminated freshwater sediment fractions. However, a few species clearly predominate. These include the fathead minnow and the cladocerans D. magna and C. dubia, together with the midges C. riparius and C. tentans, the amphipod H. azteca, the mayfly Hexagenia sp., and the oligochaetes L. variegatus and T. tubifex. These species are generally quite sensitive, easy to culture and handle in the laboratory, available year-round,

Testing for Toxicity and Bioaccumulation in Freshwater Sediments 225 representative of different trophic levels and exposure pathways, testable using standardized protocols, and recognized–recommended by regulatory agencies in North America. This group represents only a handful of species, however. If necessary, for more site-specific sediment-toxicity assessments, a wider array of species is available. Numerous standardized aquatic toxicity-testing protocols exist to test additional fish and invertebrates. These may need only small modifications for use with sediments. Also, additional research is needed to expand testing options using aquatic macrophytes, amphibians, and fish.

METHODS TO ASSESS BIOACCUMULATION IN FRESHWATER SEDIMENTS

Most hydrophobic contaminants and heavy metals in aquatic environments become entrained in sediments. Their availability to biota depends on the relationship between organic and inorganic binding phases, interstitial water, and feeding ecology. There are two primary routes for contaminants in sediments to accumulate in benthic organisms: (1) uptake of the dissolved fraction of a chemical from interstitial (pore) water across cutaneous or gill surfaces, and (2) ingestion of sediments. Uptake from interstitial water is believed to be the most important route (Fisher, 1995). This view has led to the development of empirical models, such as equilibrium partitioning, to predict bioaccumulation of organic contaminants based on carbon-normalized chemical concentrations in sediments (Di Toro et al., 1991, and U.S. EPA, 1990). Accumulation may also occur via ingestion of sediment: contaminants desorb from the sediment in the gut of benthic organisms, pass across the intestinal wall, and accumulate in organs. Whereas the relative contribution of each source depends on factors such as physicochemical properties of the compounds (e.g., Kow) and biological determinants (e.g., life history and feeding ecology); in most cases, both mechanisms will contribute to bioaccumulation. Bioaccumulation tests were first developed to assess the bioavailability of contaminants in dredged sediments (U.S. EPA, 1977). Since then, numerous methods, using a range of organisms, have become available; many have been developed into standardized protocols (ASTM, 2000b; U.S. EPA, 2000; and U.S. EPA and U.S. Army Corps, 1991, 1998). Bioaccumulation tests are often conducted in conjunction with toxicity tests or may constitute part of a tiered approach in sediment ecological risk assessments (e.g., U.S. EPA and U.S. Army Corps, 1998). Ingersoll et al. (1997) identified four general approaches to measure bioaccumulation of sediment-associated contaminants: (1) laboratory tests where organisms are exposed to sediment for a defined period of time; (2) field, or in situ, assessments where organisms are either collected from the contaminated site or transplanted to the study site from a reference site or laboratory; (3) assessment of food web transfer; and (4) models to

226 Handbook on Sediment Quality predict bioaccumulation processes (e.g., equilibrium partitioning model, bioenergetics-based toxicokinetic models, etc.). We discuss only the first two approaches here because a detailed review of each is beyond the scope of this chapter. For the latter two approaches, the reader is referred to several reviews and perspectives (Ankley, 1996; Burkhard, 1998; Di Toro et al., 1991; Landrum et al., 1992; McKim and Nicholls, 1994; and Walker, 1990).

INVERTEBRATE BIOACCUMULATION TESTS. Benthic macroinvertebrates are typically used to assess bioaccumulation in sediments (Johnson et al., 1993, and Mac and Schmitt, 1992). Many of the species discussed earlier in relation to sediment-toxicity testing can also be used to assess bioaccumulation in the laboratory and field (Table 5.3). In freshwater environments, these include: mayflies (e.g., Hexagenia: Nebeker et al., 1984, and Odin et al., 1994), worms (e.g., L. variegatus: Phipps et al., 1993), cladocerans (D. magna: Nebeker et al., 1984), midges (C. tentans and C. riparius: Harrahy and Clements, 1997; Muir et al., 1982; and Muir et al., 1983), amphipods (e.g., H. azteca and Diporiea spp.: Harkey et al., 1997, and Landrum and Nalepa, 1999), and opossum shrimp (Mysis relicta: Lester and McIntosh, 1994). Although standardized bioaccumulation tests have been used to assess bioaccumulation in dredged marine sediments for many years (U.S. EPA and U.S. Army Corps, 1991, 1998), a standardized bioaccumulation test for assessing freshwater bulk sediments has only recently been introduced using L. variegatus (U.S. EPA, 1994c, 2000). The failure to develop standardized methods for other freshwater species is due to one or more of the following factors: (1) difficulty in culturing; (2) life cycles that are either too short or too long; (3) insufficient biomass for residue analysis due to small organism size; (4) high sensitivity to contaminants; or (5) poor ecological relevance (Ingersoll et al., 1995). The successful application of L. variegatus in bioaccumulation tests reflects a number of factors, including an infaunal life history, feeding by whole- sediment ingestion, ease of culturing and handling, presence of sufficient tissue mass for chemical analysis, and ability to tolerate high contaminant levels (Ingersoll et al., 1995; Mac et al., 1990; Phipps et al., 1993; and Traunspurger and Drews, 1996). The L. variegatus bioaccumulation test starts when adults are selected according to length and placed in the contaminated sediment (1 to 5 g per replicate) in a standard exposure system (Benoit et al., 1993, and Zumwalt et al., 1994). The organisms are exposed for 28 days and are not fed. At the end of the test, the worms are sieved from the sediment and placed in a separate vessel with clean water to purge gut contents. A 6- to 10-hour purging period appears to suffice to remove >90% of gut contents (Kukkonen and Landrum, 1995a, and Mount et al., 1999). The worms are then preserved in a suitable fashion (freezing, solvent) until tissue residues can be analyzed. Phipps et al. (1993) and Ingersoll et al. (1995) provide additional details and applications of the L. variegatus bioaccumulation bioassay. This assay has been used extensively to assess bioaccumulation of both metals and organic contaminants (Kukkonen and Landrum, 1994, 1995b, and Loonen et al., 1997) (Table 5.3).

Testing for Toxicity and Bioaccumulation in Freshwater Sediments 227 Other oligochaetes have also been used to assess toxicity and bioaccumulation in freshwater sediments (Table 5.4). They may also be useful to develop standardized bioaccumulation tests. Such species include L. hoffmeisteri (Egeler et al., 1997; Lotufo and Fleeger, 1996; and Wiederholm et al., 1987), T. tubifex (Egeler et al., 1997), Branchiurus sowerbyi (Casellato et al., 1992), S. heringianus (Keilty et al., 1988a, 1998b), and Lumbriculus terrestris (Mac et al., 1990). T. tubifex, in particular, has been used in long-term sediment- toxicity assessments (Reynoldson, 1994; Reynoldson et al., 1991; and Sibley, Legler, et al., 1997) and may be well suited for use in a standardized bioaccumulation test. Standardized tests with T. tubifex and L. hoffmeisteri may be quite relevant from an ecotoxicological perspective because bioaccumulation could be assessed together with and interpreted in relation to biological endpoints such as growth and reproduction. Such an approach could provide a more comprehensive assessment of sediment toxicity, thereby reducing uncertainty in risk assessments of contaminated sediments. Recent advances in test methods with other freshwater invertebrate species may renew interest in their use for assessing bioaccumulation. In particular, Ingersoll et al. (1998) and Benoit et al. (1997) developed life cycle tests with the amphipod H. azteca and the midge C. tentans, respectively. The life cycle test with H. azteca lasts for 42 days, during which time survival, growth, and reproduction are assessed. The life cycle test with C. tentans lasts between 40 and 60 days; endpoints of interests include survival, growth, emergence, and reproduction. The impetus to develop integrated bioaccumulation tests with H. azteca and C. tentans stems from the potential to undertake comprehensive assessments of sediment toxicity, similar to that discussed in relation to T. tubifex and L. hoffmeisteri. In both life cycle tests, exposure duration during the juvenile (H. azteca) or larval (C. tentans) periods is well suited to assess bioaccumulation. For Hyalella azteca, the prereproductive period lasts 28 to 35 days; this is consistent with the exposure duration of 28 days recommended to measure the bioaccumulation of organic contaminants using L. variegatus (U.S. EPA, 2000). Hyalella azteca, on the other hand, do not tolerate as wide a range of sediment contamination as L. variegatus and other oligochaetes. Thus, H. azteca may be best suited to assess bioaccumulation in sediments of low to moderate contamination in which survival is likely to be high. Harkey et al. (1997) used H. azteca in a 30-day bioaccumulation test of a fluoranthene- spiked sediment, and Ingersoll et al. (1994) used this organism in a 28-day bioaccumulation assessment of metal-contaminated sediments (see Table 5.4). From a practical standpoint, the small size of these amphipods may require testing numerous organisms to ensure that enough tissue is available to measure residues. Unlike L. variegatus and C. tentans (see below), however, the gut of H. azteca may not have to be purged at the end of a bioaccumulation test because gut contents contribute little to the weight of this organism (Mount et al., 1999). In bioaccumulation tests with C. tentans, exposures would last approximately 20 days, which coincides approximately with the maximum developmental period of the larval stage before pupation. This test has the advantage that transfer of contaminants between larval, pupal, and adult stages could be

228 Handbook on Sediment Quality Laboratory Egeler et al. (1997) Laboratory Egeler et al. (1997) c c a Test (days) source Organisms Reference b Chemical(s) duration Sediment pyrene Natural (spiked) Laboratory and Landrum (1994) Kukkonen EndrinManyPCBsManyLindaneManyManyPCBs 40–55PAHs 4–79Endrin 15 33–110 12 Natural (spiked) 33–110(1984) Natural (spiked) 4–79 Natural 15 Field Natural (spiked) 0.5–16 (OECD) Artificial Natural Laboratory 40–55 Field Natural (spiked) Field Oliver Natural (spiked) et al. (1988a) Keilty Natural (spiked) Natural (spiked) Laboratory Field (1987) Oliver Field Field Klump et al. (1987) Field (1984) Oliver (1987) Oliver Frank et al. (1986) Klump et al. (1987) et al. (1988 a, Keilty b) PyrenePolychlorinated alkanesTCDD 28Benzo(a)pyrene;Cadmium/nickelPCB 7–168 hCadmium 28 Natural (spiked)Cadmium/nickel 10Hexachlorobenzene 28Hexachlorobenzene (spiked) Natural Hexachlorobenzene Laboratory 28 10Hexachlorobenzene Spiked Not available 10 Laboratory 44 Fisk et al. (1998) Not available 30 (spiked) Natural 12 Natural (spiked) and Landrum (1995a) Kukkonen Natural (spiked) (spiked) Natural Laboratory Laboratory Laboratory Field Natural (spiked) Natural Laboratory (OECD) Artificial Laboratory Phipps et al. (1993) Laboratory Loonen et al. (1997) et al. (1989) Nebeker (1998) Laboratory and Kukkonen Leppanen et al. (1990) Schuytema Carlson et al. (1991) et al. (1988) Schuytema Laboratory Laboratory et al. (1991) et al. (1992) Ankley Ankley and metal contaminants from freshwater sediments. freshwater and metal contaminants from Organism tested T. tubifex T. Stylodrilus heringianus L. hoffmeisteri L. variegatus Oligochaeta Table 5.4Table in assessments of bioaccumulation organic standard test invertebrates Compilation of laboratory tests incorporating

Testing for Toxicity and Bioaccumulation in Freshwater Sediments 229 (continued) a Test (days) source Organisms Reference b Chemical(s) duration Sediment hexachlorobiphenyl FluoranthenePAHs/PCBsBenzo(a)pyrene; 30 1–14 4–28 Natural (spiked) Natural (spiked) Natural (spiked) Field Laboratory Field et al. (1997) Driscoll Harkey, Lydy, et al. (1994) Landrum et al. (1997) Benzo(a)pyrenePhthalate estersPAHsSeveralPCB/DDE 5Metals 28CopperHexachlorobiphenylPyrethroidsMany 3–8 h 72 hSeveral (spiked) Natural 24 h 24 h (spiked) Natural HerbicidesChlorobenzenes 10 10 Laboratory 3–48 h (spiked) Natural Fluoranthene Laboratory (spiked) Natural (spiked) Natural (spiked) Natural FluorantheneMetals 48 h Borchart et al. (1997) 15Hexachlorobenzene et al. (1996) Brown 96 h Laboratory 24–96 h (spiked) Natural Artifical LaboratoryMany Laboratory Laboratory Natural (spiked)Anthracene 30 28Applehans (1987) Swindoll and 30 (spiked) Natural (1994) (spiked) Natural Clements and Kiffney Laboratory Fry and Fisher (1990) et al. (1992) Lydy (spiked) Natural Laboratory (spiked) Natural 28 Muir et al. (1985) Laboratory Laboratory Laboratory Natural 0.5–8 h Besser et al. (1995) Laboratory Natural (spiked) Natural (spiked) Laboratory 28 Muir et al. (1983) Harrahy and Clements (1997) and Harrison (1988) Knezovich Muir et al. (1982) Natural Laboratory et al. (1984) Nebeker Natural Laboratory Laboratory (spiked) Natural et al. (1990) Schuytema et al. (1997) Driscoll et al. (1997) Harkey Laboratory Laboratory Laboratory et al. (1984) Nebeker (1983) Landrum and Scavia Ingersoll et al. (1994) and metal contaminants from freshwater sediments. freshwater and metal contaminants from spp. Benzo(a)pyrene 28 Natural (spiked) Laboratory and Landrum (1998) Kukkonen Organism tested C. tentans C. decorus Diporiea C. riparius H. azteca Table 5.4Table in assessments of bioaccumulation organic standard test invertebrates Compilation of laboratory tests incorporating Chironomidae Amphipoda

230 Handbook on Sediment Quality es, and use of standard or common test species. -dioxin p (continued) a Test (days) source Organisms Reference b Chemical(s) duration Sediment hexachlorobiphenyl pyrene Benzo(a)pyreneHexachlorobiphenyl PAHs Several Hexachlorobiphenyl 30Hexachlorobenzene 7General 1–18 30MethylmercuryMethylmercury (spiked) Natural MethylmercuryChlorophenols (spiked) Natural Natural (spiked) 28PCBs 28 Natural (spiked) 15 Field 15 Field Field 10 Laboratory Natural Natural/spiked Landrum (1989) Natural (spiked) and Landrum (1993) and Johnson (1982) Lydy et al. (1990) Schuytema Lynch Natural (spiked) 24 (field) Natural Laboratory Laboratory Laboratory Laboratory Odin et al. (1994) Field et al. (1984) Nebeker Odin et al. (1995) Field sediment Saouter et al. (1991) Field Metcalfe and Hayton (1989) Lester and McIntosh (1994) Hexachlorbiphenyl 13 Field sediment Field Klump et al. (1991) Tetrachlorobiphenyl/Pyrene/phenanthrene 6–28Benzo(a)pyrene 1–32 Natural (spiked) 7 Natural (spiked) Field Field (spiked) Natural (1994) Landrum and Faust Field Landrum et al. (1994) and Landrum (1993) Lydy and metal contaminants from freshwater sediments. freshwater and metal contaminants from sp. General 28 Natural/spiked Laboratory et al. (1984) Nebeker (continued) Benzo(a)pyrene; 10 Natural (spiked) Field and Landrum (1995b) Kukkonen Organism tested Gammarus pseudolimnaeus Gammarus lacustris H. limbata Hexagenia Mysis relicta Hexagenia rigida Hexagenia H. azteca DDE = dichloro-bis (chlorophenyl) ethylene; PCB = Polychlorinated biphenyl; TCDD = Tetrachlorodebenzo- TCDD = ethylene; PCB = Polychlorinated biphenyl; DDE = dichloro-bis (chlorophenyl) OECD = Organization for Economic Cooperation and Development OECD = Organization The tests in this table were selected based on the following criteria:The tests in this table were selected based on the following sediment exposures, whole or spiked measured tissue residu Other taxa Table 5.4Table in assessments of bioaccumulation organic standard test invertebrates Compilation of laboratory tests incorporating a b c Amphipoda (continued)

Testing for Toxicity and Bioaccumulation in Freshwater Sediments 231 determined. However, unlike H. azteca, bioaccumulation tests with C. tentans may require a purging period due to potentially high contribution of gut contents to body mass in this organism (Sibley, Monson, and Ankley, 1997). This could result in a loss of some of the accumulated contaminants and an underestimate of body burden; similar concerns have been raised in relation to purging in L. variegatus (Mount et al., 1999). Whereas C. riparius could also be used to measure bioaccumulation, emergence in this midge typically begins sooner than in C. tentans (Watts and Pascoe, 1998) such that the uptake period would be shorter.

FISH BIOACCUMULATION TESTS. Fish have been widely used in the laboratory to assess contaminant bioaccumulation from water and food. Fish are rarely used, however, to assess bioaccumulation from contaminated sediments. There are currently no standardized fish bioaccumulation tests. To date, only one study describes a protocol using fish to assess bioaccumulation in freshwater sediments (Mac et al., 1990, and Mac and Schmitt, 1992). This test uses fathead minnow (P. promelas) in 10-day exposures to sediment under flow-through conditions. Ankley, Cook, et al. (1992) modified this test to measure bioaccumulation in polychlorinated biphenyl (PCB)-contaminated sediment. These authors found that PCB accumulation in P. promelas was consistently lower in fish than in any of the benthic organisms that they tested, including L. variegatus. They suggested that using P. promelas in laboratory tests could greatly under-predict exposure to sediment-associated compounds that bioaccumulate. Few other sediment bioaccumulation tests have been conducted with fish. Dabrowska and Fisher (1993) used channel catfish (Ictalurus punctatus) to assess the accumulation of hexachlorobiphenyl from spiked sediments. In that study, hatchery-reared I. punctatus (7 to 11 g) were exposed to the spiked sediments for 10 days. Schuytema et al. (1990) exposed fathead minnows to hexachlorobenzene in water-only and sediment-spiked exposures. These authors found that bioconcentration factors were lower in the sediment exposures. Van Wezel and Jonker (1998) measured lethal body burden in sticklebacks (Gasterosteus aculeatus) to assess sediments contaminated with a mixture of lipophilic compounds. Munkittrick et al. (1995) described a protocol for assessing bioavailability of sediment-associated PAHs to fish by measuring the induction of MFO in rainbow trout (O. mykiss) after 4- to 7-day exposures to the sediment. Potentially, this test could be modified to include analysis of tissue residues or application to other fish.

FIELD METHODS FOR ASSESSING SEDIMENT BIOACCUMULA- TION. Measuring contaminant levels in tissue from field-collected benthic organisms is perhaps the best approach to assess bioaccumulation in contaminated sediments (Lee, 1992). The advantage is that it provides good evidence of exposure. Field-based estimates of body burdens also provide ecological realism because they reflect integrated exposure over the entire life of the organism, thereby incorporating the influence of important life-history characteristics (e.g., molting, pupation) and environmental influences. Field bioaccumulation reflects in situ sediment conditions and is thus less influ-

232 Handbook on Sediment Quality enced by sediment handling issues common with laboratory tests. These attributes provide a more realistic estimate of exposure and bioaccumulation in aquatic organisms than is possible in laboratory tests. Characterization of exposure is an important and uncertain variable in the risk assessment process (Chapman et al., 1997b). Hence, it is no surprise that toxicity and bioaccumulation assessments under field conditions have received more attention in recent years (Burton, Hickey, et al., 1996; Dewitt et al., 1996; and Salazar and Salazar, 1998). There are two ways to assess field bioaccumulation: (1) indigenous organisms are collected for analysis from sediments at the contaminated site, or (2) organisms are transplanted from a control area to the contaminated site and recollected for tissue analysis after a period of exposure. The second approach includes using laboratory-cultured organisms exposed in situ in special exposure chambers (Chappie and Burton, 1997; Crane et al., 2000; Sibley et al., 1999; and Tucker and Burton, 1999). These approaches are considered in greater detail below.

FIELD-COLLECTED SAMPLES. The most common method to measure bioaccumulation in the field is to collect and analyze organisms from the contaminated site. The organisms are typically collected with a grab, corer, or dredge and brought to the laboratory to be removed from the sediment samples. The organisms are either placed in a holding vessel to purge their intestinal tract or they are preserved for later analysis. Several publications provide guidance and rationale for gut purging (Brooke et al., 1996; Chapman, 1985; Hare et al., 1989; Mac et al., 1990; Mount et al., 1999; and Sibley, Monson, and Ankley, 1997). Contaminant levels measured in tissue from field-collected organisms can be compared wih those measured in organisms collected from a reference site (Green et al., 1989); where possible, the reference site should have hydrodynamic and geophysical conditions similar to the study site. Tissue residues can also be compared with sediment concentrations of the contaminants of concern, normalized to the appropriate binding phase (e.g., organic carbon for organic contaminants, AVS for metals). This provides a measure of bioavailability, the ratio of which yields an estimate of the normalized bioaccumulation factor. Whereas field-collected organisms provide an ecologically realistic measure of exposure, this approach is limited to sediments with low to moderate toxicity: many organisms cannot survive in highly contaminated sediments or insufficient tissue samples may be available when organisms do occur at a site (Lee, 1992). Issues related to sample size can be addressed by choosing the proper target species. For example, oligochaetes are often used because they are common at contaminated sites and survive in highly contaminated sediment. Mussels are also well suited for in situ bioaccumulation assessments (Giesy and Hoke, 1989). When available, these organisms have been used for this purpose (Doherty, 1990; Metcalfe and Charlton, 1990; Metcalfe and Hayton, 1989; and Metcalf-Smith, 1994), often as part of routine biomonitoring programs (e.g., Mussel Watch) and assessment of industrial effluents. However, at low to moderately contaminated sites, the heterogeneous nature of benthic invertebrate distributions may mean that not

Testing for Toxicity and Bioaccumulation in Freshwater Sediments 233 all sites or stations have the organisms of interest (Salazar and Salazar, 1998). If they are present, such organisms may not yield enough biomass for data comparison. Seasonal differences in species distribution and abundance can make this problem worse. These drawbacks can be a problem when bioaccumulation is a part of biomonitoring programs. They could be overcome by using transplanted, in situ exposures, as discussed in the next section.

TRANSPLANTATION AND IN SITU EXPOSURES. In transplantation studies, organisms either collected at clean field sites or cultured in the laboratory are transferred to a contaminated site. Mussel transplantation has been used most extensively to assess bioaccumulation. In marine environments, members of the genus Mytilus (e.g., M. edulis or M. galloprovincialis) are most widely used (Nelson, 1990, and Salazar and Salazar, 1998); in freshwater environments, mussels from the genera Elliptio, Lampsilis, Anodonta, or Corbicula are commonly used (Doherty, 1990). Mussels are ideal organisms for biomonitoring and field assessments of bioaccumulation because they are ubiquitous, sedentary, easy to cage and collect, and provide ample tissue mass for chemical analyses (Salazar and Salazar, 1998). Most bioaccumulation studies with transplanted mussels have monitored waterborne contaminants, particularly those associated with industrial effluents (see review by Doherty, 1990). Because many mussel species are filter feeders, they are used less often to assess bioaccumulation in contaminated sediments (Traunspurger and Drews, 1996). Even though contaminants may be taken up during filter feeding, the primary source of these contaminants is from organic particles (including food) suspended in the water and not from the sediment itself. This can lead to underestimates of exposure and clearly suggests that caution should be exercised in the choice of bivalve species used to assess bioaccumulation. In marine environments, deposit- feeding mussels have been used successfully to assess bioaccumulation in contaminated sediments (Boese et al., 1995, and Lee et al., 1990), thereby avoiding the issue of filter feeding. Deposit-feeding mussels are less common in freshwater environments and thus have not been used to study bioaccumulation. Of the few freshwater bivalve groups that do feed on sediments (e.g., sphaeriids), these are generally too small to be of practical use in bioaccumulation studies. Despite these limitations, several studies have used mussels to assess bioaccumulation from contaminated freshwater sediments. These studies found that accumulation is generally less variable for organic compounds than for metals (Baumard et al., 1998; Elder and Mattraw, 1984; Mac et al., 1984; Pereira et al., 1996; and Tatum, 1986). Warren et al. (1995) showed that juveniles of the freshwater mussel Utterbackia imbecillis had higher mortality when exposed to sediments versus water-only exposures, suggesting that the response of this mussel partially reflected integration of sediment-associated contaminants. No standardized approach exists to do transplantation studies despite the wide use of mussels in transplant assessments of sediment toxicity and the availability of many methods. However, a field bioaccumulation test with mussels in marine environments has recently been accepted as an ASTM standard. Many in situ tests involve an enclosed exposure container such as a

234 Handbook on Sediment Quality meshed bag or cage (Salazar and Salazar, 1998). For sediment exposures, sediment from the contaminated site may be added to the cage, which is then suspended in the water column (Hayer and Pihan, 1996), or the test chamber may be inserted directly on or in the sediment or substratum (Salazar and Salazar, 1998, and Warren et al., 1995). The advantage of using transplanted organisms is that enough biomass is typically available for chemical analysis. This depends to some degree on the type of organisms used and the site under investigation. For example, moving test organisms into new areas can result in stress due to a lack of adequate food or unsuitable environmental conditions, even after acclimation (Salazar and Salazar, 1998). Ingersoll et al. (1997) have also suggested that the accuracy of the results may be affected by the duration of exposure relative to the life cycle of a test species and uncertainties over the kinetics of bioaccumulation in transplanted organisms. An emerging area of interest in transplant studies is the development of in situ toxicity test methods using standard test organisms cultured in the laboratory. In this approach, test organisms are transferred from the laboratory to the field and exposed to the contaminated sediments in specially designed exposure containers (Chappie and Burton, 1997; Crane et al., 2000; Ireland et al., 1996; Sasson-Brickson and Burton, 1991; Sibley et al., 1999; and Tucker and Burton, 1999). Exposure periods can range from 4 to 14 days, after which the organisms are removed from the sediment, counted, and weighed. This approach has been successfully applied with H. azteca and D. magna (Chappie and Burton, 1997; Ireland et al., 1996; and Rowland and Burton, 2000), C. tentans and L. variegatus (Rowland and Burton, 2000; Sibley et al., 1999; and Tucker and Burton, 1999), and C. riparius (Crane et al., 2000). Whereas these tests rely on laboratory-cultured organisms, they could also use organisms from a clean reference site; however, the life stage and history of such organisms could be difficult to ascertain. To date, these tests have been used predominantly to assess sediment toxicity, but they could easily be modified to measure bioaccumulation, particularly for L. variegatus (Rowland and Burton, 2000, and Sibley et al., 1999).

FUTURE RESEARCH DIRECTIONS

Sediment toxicology is one of the youngest branches of environmental toxicology, yet great strides have been made to develop standardized test methods. In both marine and freshwater sediments, several standardized protocols are available for assessing sediment toxicity and bioaccumulation (ASTM, 2000c; Environment Canada, 1997a, 1997b; and U.S. EPA, 2000). Moreover, current protocols will be updated as new tests are developed and old tests refined. For example, the first edition of the U.S. Environmental Protection Agency sediment test manual contained only standardized 10-day tests to assess freshwater sediment toxicity with H. azteca and C. tentans

Testing for Toxicity and Bioaccumulation in Freshwater Sediments 235 (U.S. EPA, 1994c). The most recent edition includes protocols for long-term, life cycle exposures with these organisms (U.S. EPA, 2000). Efforts are ongoing to develop comparable long-term tests to assess contaminated sediments in marine environments. In this section, we discuss several generic areas of research that need to be addressed to advance the field of sediment-toxicity testing forward. These include (1) continued development of test methods and identification of sensitive endpoints, particularly those associated with reproduction; (2) identification and quantification of routes of exposure and the relationships between exposure and larval feeding and behavioral ecology; (3) development and evaluation of in situ test methods; (4) continued development and validation of sediment-quality criteria; and (5) inclusion of bioaccumulation in the risk assessment process. It is prudent to consider future advances within each of the above research areas in the context of risk assessment, which has become an integral component of sediment toxicology. The basis for such an approach has been emphasized in a recent publication on the risk assessment of contaminated sediments (Ingersoll et al., 1997). Finally, we have not considered the use of models to predict bioaccumulation processes (e.g., equilibrium partitioning model, bioenergetics-based toxicokinetic models, etc.); however, we expect that these will continue to be developed and to serve an important role in the risk assessments of contaminated sediments (Ingersoll et al., 1997, and Luoma and Fisher, 1997).

DEVELOPMENT OF BIOASSAYS AND ENDPOINTS. There may be less need today for new species to conduct sediment-toxicity testing compared with a decade ago. New bioassays may still be required, however, to address emerging issues such endocrine disruption. These chemicals can affect receptors at environmentally relevant concentrations by causing subtle changes in development, behavior, or reproduction (Ankley et al., 1998, and Ankley, Tiegte, et al., 1998). Although some existing sediment bioassays may be able to quantify endocrine-related responses, many were not developed to detect such subtle effects and are not sensitive enough to be useful. Thus, there has been considerable interest in developing new assays, or modifying existing ones (e.g., Benoit et al., 1997, and Ingersoll et al., 1998), to detect the subtle effects caused by these (and other) compounds. For example, the life cycle test for C. tentans discussed above was recently used to assess the toxicity of nonylphenol, a known endocrine disrupting compound (Kahl et al., 1998). In addition to assessing growth and reproduction, this test, along with the H. azteca life cycle test (Ingersoll et al., 1998), could also be adapted to assess endocrine-mediated behavioral effects. Tissue cell lines have also been used to assess sediment toxicity and may represent a useful alternative to whole organism tests in certain applications (Huuskonen et al., 1998). Within the context of new and existing toxicity tests, focused research is required to assess relative endpoint sensitivity. Indeed, our literature review showed that, despite much progress in developing sediment toxicity tests, we have a surprisingly poor understanding of the relative sensitivity of the endpoints used in traditional toxicity tests. This is an important area for future

236 Handbook on Sediment Quality research, particularly for compounds such as endocrine disrupters, whose effects may appear at concentrations too low to be detected using the traditional endpoints of sediment-toxicity tests. Some recent studies suggest that this area is beginning to receive the quantitative attention it deserves (Gray et al., 1998; Ingersoll et al., 1998; Kemble et al., 2000; Sibley, Benoit, and Ankley, 1997; and Sibley et al., 2001).

ISSUES RELATED TO EXPOSURE. Research is needed to identify and quantitatively differentiate between routes of exposure and the uptake of sediment-associated contaminants by benthic organisms. Our present understanding of modes of exposure for even key benthic species is weak (Luoma and Fisher, 1997). For example, traditional thinking has held that interstitial (pore) water is the primary route of exposure for sediment-associated contaminants, but recent work has shown that uptake via ingestion may be an equally if not more important route for many organisms (Fisher, 1995; Landrum et al., 1989; and Leppanen and Kukkonen, 1998). In this context, studies should focus not only on toxicological aspects of contaminant bioavailability (e.g., partitioning, kinetics, etc.), but also on ecological aspects. In the latter case, exposure to and uptake of contaminants by benthic organisms should be assessed in terms of their relationship to feeding ecology (e.g., deposit versus suspension feeding) and larval behavior, sediment nutritional value, and organic composition (Dauwe et al., 1999, and Watling, 1991), and microscale geophysical and chemical environmental gradients. These studies should be conducted at a scale that is relevant both to the organism (e.g., sediment– organism interactions) and in terms of establishing cause-and- effect linkages with higher levels of biological organization.

IN SITU STUDIES. At present, sediment-toxicity data used in risk assessments are predominantly laboratory based. Such tests can provide a basis to examine contaminant exposure and to establish cause-and-effect relationships; they do not, however, provide a strong basis for quantitative prediction of effects under field conditions (Ankley, 1997, and Clements and Kiffney, 1994). This lack of ecological realism reflects two important factors. First, receptors in laboratory tests do not measure or integrate exposure in the same manner as do receptors in the field. Second, there is a poor understanding of important processes that control population and community dynamics, relative to the response of individual organisms (Ankley, 1997). Because these factors create uncertainty in the risk assessment process, there has been much interest to develop methods for assessing in situ sediment toxicity (Burton, Hickey, et al., 1996, and Dewitt et al., 1996). Methodologically, research should focus on developing tests that best capture the range of environmental exposure and ecological conditions typically experienced by benthic organisms in contaminated sediments under field conditions. In freshwater sediments, considerable work has already occurred in this context (Chappie and Burton, 1997; Sasson-Brickson and Burton, 1991; and Sibley et al., 1999). One of the key advantages of in situ tests is that they provide an opportunity to evaluate the ecological relevance

Testing for Toxicity and Bioaccumulation in Freshwater Sediments 237 of endpoints tested in the laboratory under field conditions. Effort in this regard should not be directed toward validation of laboratory tests per se, but rather on understanding the myriad sources of variation that affect exposure and response in situ tests with the view of minimizing the uncertainty inherent in laboratory-to-field extrapolations. Recent research indicates that some effort in this direction is beginning to take place (Burton et al., 2000; Dewitt et al., 1999; Greenberg et al., 2000; and Sibley et al., 1999).

DEVELOPMENT OF SEDIMENT-QUALITY CRITERIA AND BIOASSESSMENT APPROACHES. From a regulatory perspective, an important use of sediment-toxicity test data is to derive sediment-quality criteria to protect, assess, and monitor biological resources. Over the past decade, several approaches have been proposed for this purpose, including chemically based methods such as equilibrium partitioning (Di Toro et al., 1991, and U.S. EPA, 1990) and numerically derived guidelines (Long et al., 1998; MacDonald et al., 1996; and MacDonald et al., 2000), biologically based approaches such as bioassessment protocols that include multimetric or multivariate (reference condition) procedures (Bailey et al., 1998, and Reynoldson et al., 1997), and integrative approaches such as the sediment quality triad (SQT) (Chapman et al., 1997a) or benthic assessment of sediment (BEAST) (Reynoldson et al., 1995) among others (Day et al., 1997). The advantages and disadvantages of each approach have been debated at length, and little consensus has been achieved to date. Integrative approaches, such as the SQT, BEAST, and reference condition, are well suited to the risk assessment process, as they offer a weight-of-evidence perspective; however, such approaches can also be expensive to implement and are generally not intended as a basis upon which to render regulatory decisions. Future research concerning the development of sediment-quality criteria must continue to evaluate, compare, and refine, where possible, each of the above approaches to better define the inherent limitations of each and thereby minimize the uncertainty in the risk assessment process. A key objective in this regard is the need to evaluate sediment quality criteria using field studies, preferably conducted in various geographical locations (Ingersoll et al., 1997).

BIOACCUMULATION AND RISK ASSESSMENT. Bioaccumulation has become an important part of the risk assessment process, as proposed by Franke et al. (1994) and Feijtel et al. (1997). Bioaccumulation is not an effect per se; the primary role of bioaccumulation testing is to estimate the exposure experienced by organisms in contaminated sediments (Feijtel et al., 1997, and Ingersoll et al., 1997). Hence, it should be included in the exposure characterization of the risk assessment process (Feijtel et al., 1997). Including bioaccumulation in risk assessment gives a basis on which to incorporate tissue residues and body burdens for estimating risk, rather than the traditional approach of using external surrogates of dose (e.g., sediment and water concentrations). Internal estimates of exposure are considered to give a more realistic indication of the true exposure experienced by the organism because it is closer to the actual target receptors (McCarty and Mackay, 1993).

238 Handbook on Sediment Quality CONCLUDING REMARKS This paper reviewed the recent literature on the use of bioassays to measure toxicity and bioaccumulation in contaminated sediments. Whereas it is relatively easy to collect sediments in the field, it is a real challenge to generate useful data in the laboratory. The reason is that numerous physical, chemical, and biological variables can affect the outcome of a test. These variables include species and life stage sensitivity, the type of sediment matrix tested, the type of endpoints measured, the duration of exposure, the presence of unrelated toxicants such as ammonia or hydrogen sulfide, and many others. Without a thorough understanding and appreciation of how each variable can affect a test, it is easy to generate and misinterpret data that do not reflect actual conditions. By discussing these pitfalls on the basis of published literature, it is hoped that the reader has obtained a better appreciation of the complexities that surround sediment-toxicity testing.

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248 Handbook on Sediment Quality Exposures with the Amphipod Hyalella azteca and the Midge Chironomus tentans. Paper presented at 21st Annu. Meeting Soc. Environ. Contam. Toxicol., Nashville, Tenn. Klump, J.V.; Krezoski, J.R.; Smith, M.E.; and Kaster, J.L. (1987) Dual Tracer Studies of the Assimilation of an Organic Contaminant from Sediments by Deposit Feeding Oligochaetes. Can. J. Fish. Aquat. Sci., 44, 1574. Klump, J.V.; Kaster, J.L.; and Sierszen, M.E. (1991) Mysis relicta Assimila- tion of Hexachlorobiphenyl from Sediments. Can. J. Fish. Aquat. Sci., 48, 284. Knezovich, J.P., and Harrison, F.L. (1988) The Bioavailability of Sediment- Sorbed Chlorobenzenes to Larvae of the Midge, Chironomus decorus. Ecotoxicol. Environ. Saf., 15, 226. Krantzberg, G. (1994) Spatial and Temporal Variability in Metal Bioavailabil- ity and Toxicity of Sediment from Hamilton Harbor, Lake Ontario. Environ. Toxicol. Chem., 13, 1685. Krantzberg, G., and Boyd, D. (1992) The Biological Significance of Contami- nants in Hamilton Harbor Sediment. Environ. Toxicol. Chem., 11, 1527. Kukkonen, J., and Landrum, P.F. (1994) Toxicokinetics and Toxicity of Sediment Bound Pyrene in Lumbriculus variegatus (Oligochaeta). Environ. Toxicol. Chem., 14, 523. Kukkonen, J., and Landrum, P.F. (1995a) Effects of Sediment-Bound Polydi- methylsiloxane on the Bioavailability and Distribution of Benzo(a)pyrene in Lake Sediment to Lumbriculus variegatus. Environ. Toxicol. Chem., 14, 523. Kukkonen, J., and Landrum, P.F. (1995b) Measuring Assimilation Efficiencies for Sediment-Bound PAH and PCB Congeners by Benthic Organisms. Aquat. Toxicol., 32, 75. Kukkonen, J.V.K., and Landrum, P.F. (1998) Effect of Particle-Xenobiotic Contact Time on Bioavailability of Sediment-Associated Benzo(a)pyrene to Benthic Amphipod, Diporiea spp. Aquat. Toxicol., 42, 229. Landrum, P.F. (1989) Bioavailability and Toxicokinetics of Polycyclic Aromatic Hydrocarbons Sorbed to Sediments for the Amphipod Ponto- poriea hoyi. Environ. Sci. Technol., 23, 588. Landrum, P.F., and Faust, W.R. (1994) The Role of Sediment Composition on the Bioavailability of Laboratory-Dosed Sediment-Associated Organic Contaminants to the Amphipod, Diporiea spp. Chem. Spec. Bioavail., 6, 85. Landrum, P.F., and Nalepa, T. (1999) Review of the Factors Affecting the Ecotoxicology of Diporiea spp. J. Great Lakes Res., 24, 889. Landrum, P.F., and Robbins, J.A. (1990) Bioavailability of Sediment-Associ- ated Contaminants to Benthic Invertebrates. In Sediments: Chemistry and Toxicity of In-Place Pollutants. R. Baudo, J.P. Giesy, and H. Muntau (Eds.), Lewis Publishers, Inc., Chelsea, Mich., 237. Landrum, P.F., and Scavia, D. (1983) Influence of Sediment on Anthracene Uptake, Depuration, and Biotransformation by the Amphipod Hyalella azteca. Can. J. Fish. Aquat. Sci., 40, 298. Landrum, P.F.; Dupuis, W.S.; and Kukkonen, J. (1994) Toxicokinetics and Toxicity of Sediment-Associated Pyrene and Phenanthrene in Diporiea

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252 Handbook on Sediment Quality Nipper, M.G.; Greenstein, D.J.; and Bay, S.M. (1989) Short- and Long-Term Sediment Toxicity Test Methods with the Amphipod Grandidierella japonica. Environ. Toxicol. Chem., 8, 1191. Nipper, M.G.; Roper, D.S.; Williams, E.K.; Martin, M.L.; van Dam, L.F.; and Mills, G.N. (1998) Sediment Toxicity and Benthic Communities in Mildly Contaminated Mudflats. Environ. Toxicol. Chem., 17, 502. Odin, M.; Feurtet-Mazel, A.; Ribyere, F.; and Boudou, A. (1994) Actions and Interactions of Temperature, pH and Photoperiod on Mercury Bioaccumu- lation by Nymphs of the Burrowing Mayfly Hexagenia rigida, from the Sediment Contamination Source. Environ. Toxicol. Chem., 13, 1291. Odin, M.; Ribeyre, F.; and Boudou, A. (1995) Cadmium and Methylmercury Bioaccumulation by Nymphs of the Burrowing Mayfly Hexagenia rigida from the Water Column and Sediment. Environ. Sci. Pollut. Res., 2, 145. Odin, M.; Ribeyre, F.; and Boudou, A. (1996) Temperature and pH Effects on Cadmium and Methylmercury Bioaccumulation by Nymphs of the Burrow- ing Mayfly Hexagenia rigida, from Water Column or Sediment Source. Arch. Environ. Contam. Toxicol., 31, 339. Oliver, B.G. (1984) Uptake of Chlorinated Organics from Anthropogenically Contaminated Sediments by Oligochaete Worms. Can. J. Fish. Aquat. Sci., 41, 878. Oliver, B.G. (1987) Biouptake of Chlorinated Hydrocarbons From Labora- tory-Spiked and Field Sediments by Oligochaete Worms. Environ. Sci. Technol., 21, 785. Othoudt, R.A.; Giesy, J.P.; Grzyb, K.R.; Verbrugge, D.A.; Hoke, R.A.; Drake, J.B.; and Anderson, D. (1991) Evaluation of the Effects of Storage Time on the Toxicity of Sediments. Chemosphere, 22, 801. Otto, D.M.; Lindstrom-Seppa, P.; and Sen, C.K. (1994) Cytochrome P450- Dependent Enzymes and Oxidant-Mediated Responses in Rainbow Trout Exposed to Contaminated Sediments. Ecotoxicol. Environ. Saf., 27, 265. Pastorok, R.A.; Peek, D.C.; Sampson, J.R.; and Jacobson, M.A. (1994) Ecological Risk Assessment for River Sediments Contaminated by Cre- osote. Environ. Toxicol. Chem., 13, 1929. Pereira, W.E.; Domagalski, J.L.; and Hostettler, F.D. (1996) Occurrence and Accumulation of Pesticides and Organic Contaminants in River Sediment, Water, and Clam Tissues from the San Joaquin River and Tributaries, California. Environ. Toxicol. Chem., 15, 172. Pesch, C.E.; Munns, W.R., Jr.; and Gutjahr-Gobell, R. (1991) Effects of a Contaminated Sediment on Life History Traits and Population Growth Rate of Neanthes arenaceodentata (Polychaeta: Nereidae) in the Laboratory. Environ. Toxicol. Chem., 10, 805. Phipps, G.L.; Ankley, G.T.; Benoit, D.A.; and Mattson, V.R. (1993) Use of the Aquatic Oligochaete Lumbriculus variegatus for Assessing the Toxicity and Bioaccumulation of Sediment-Associated Contaminants. Environ. Toxicol. Chem., 12, 269. Pittinger, C.A.; Woltering, D.M.; and Masters, J.A. (1989) Bioavailability of Sediment-Sorbed and Aqueous Surfactants to Chironomus riparius (Midge). Environ. Toxicol. Chem., 8, 1023.

Testing for Toxicity and Bioaccumulation in Freshwater Sediments 253 Reynoldson, T.B. (1994) A Field Test of Sediment Bioassay with the Oligochaete Worm Tubifex tubifex (Muller, 1774). Hydrobiologia, 278, 223. Reynoldson, T.B.; Bailey, R.C.; Day, K.E.; and Norris, R.H. (1995) Biological Guidelines for Freshwater Sediment Based on Benthic Assessment of SedimenT (the BEAST) Using a Multivariate Approach for Predicting Biological State. Aust. J. Ecol., 20, 198. Reynoldson, T.B.; Norris, R.H.; Resh, V.H.; Day, K.E.; and Rosenberg, D.M. (1997) The Reference Condition: A Comparison of Multiple and Multivari- ate Approaches to Assess Water-Quality Impairment Using Benthic Macro Invertebrates. J. N. Am. Benthol. Soc., 16, 833. Reynoldson, T.B.; Thompson, S.P.; and Bamsey, J.L. (1991) A Sediment Bioassay Using the Tubificid Oligochaete Worm T. tubifex. Environ. Toxicol. Chem., 10, 1061. Ribeyre, F., and Boudou, A. (1994) Experimental Study of Inorganic and Methylmercury Accumulation by Four Species of Freshwater Rooted Macrophytes from Water and Sediment Contamination Sources. Ecotoxicol. Environ. Health, 28, 270. Roper, D., and Hickey, C. (1994) Behavioral Responses of the Marine Bivalve Macomona liliana Exposed to Copper- and Chlordane-Dosed Sediments. Mar. Biol., 118, 673. Rowland, C.D., and Burton, G.A., Jr. (2000) In Situ Bioaccumulation of Sediment-Associated PAH’s and PCB’s in the Freshwater Oligochaete Lumbriculus variegatus and Amphipod Hyalella azteca. Paper presented at 21st Annu. Meeting Soc. Environ. Contam. Toxicol., Nashville, Tenn. Salazar, M.H., Applied Biomonitoring (1999) Kirkland, Wash. Personal communication. Salazar, M.H., and Salazar, S.M. (1998) Using Caged Bivalves as Part of an Exposure-Dose-Response Triad to Support an Integrated Risk Assessment Strategy. In Ecological Risk Assessment: A Meeting of Policy and Science. A. de Peyster and K. Day (Eds.), SETAC Press, Pensacola, Fla., 167. Saouter, E.; Ribeyre, F.; Boudou, A.; and Maury-Brachert, R. (1991) Hexage- nia rigida (Ephemeroptera) as a Biological Model in Aquatic Ecotoxicol- ogy: Experimental Studies on Mercury Transfers from Sediment. Environ. Pollut., 69, 51. Sasson-Brickson, G., and Burton, G.A., Jr. (1991) In-Situ and Laboratory Sediment Toxicity Testing with Ceriodaphnia dubia. Environ. Toxicol. Chem., 10, 201. Schlekat, C.E.; McGee, B.L.; Boward, D.M.; Reinharz, E.; Velinsky, D.J.; and Wade, T.L. (1994) Tidal River Sediments in the Washington, DC, Area: III. Biological Effects Associated with Sediment Contamination. Estuaries, 17, 334. Schlekat, C.E.; Scott, J.K.; Swartz, R.C.; Albrecht, B.; Antrim, L.; Doe, K.; Douglas, S.; Ferretti, J.E.; Hansen, D.J.; Moore, D.W.; Mueller, C.; and Tang, A. (1995) Interlaboratory Comparison of a 10-Day Sediment Toxicity Test Method Using Ampelisca abdita, Eohaustorius setuarius and Lep- tocheirus plumulosus. Environ. Toxicol. Chem., 14, 2163.

254 Handbook on Sediment Quality Schmidt-Dallmier, M.J.; Atchison, G.J.; Steingraeber, M.T.; and Knights, B.C. (1992) A Sediment Suspension System for Bioassays with Small Aquatic Organisms. Hydrobiologia, 245, 157. Schubauer-Berigan, M.K., and Ankley, G.T. (1991) The Contribution of Ammonia, Metals and Non-Polar Organic Compounds to the Toxicity of Interstitial Water from an Illinois River Tributary. Environ. Toxicol. Chem., 10, 925. Schubauer-Berigan, M.K.; Amoto, J.R.; Ankley, G.T.; Baker, S.E.; Burkhard, L.P.; Dierkes, J.R.; Jenson, J.J.; Lukasewycz, M.T.; and Norberg-King, T.A. (1993) The Behavior and Identification of Toxic Metals in Complex Mixtures: Examples from Effluent and Sediment Pore Water Toxicity Identification Evaluations. Arch. Environ. Contam. Toxicol., 24, 298. Schuytema, G.S.; Krawczyk, D.F.; Griffis, W.L.; Nebeker, A.V.; and Robideaux, M.L. (1990) Hexachlorobenzene Uptake by Fathead Minnows and Macro Invertebrates in Recirculating Sediment/Water Systems. Arch. Environ. Contam. Toxicol., 19,1. Schuytema, G.A.; Krawczyk, D.F.; Griffis, W.L.; Nebeker, A.V.; Robideaux, M.L.; Brownawell, B.J.; and Westall, J.C. (1988) Comparative Uptake of Hexachlorobenzene by Fathead Minnows, Amphipods, and Oligochaete Worms from Water and Sediments. Environ. Toxicol. Chem., 7, 1035. Sibley, P.K.; Ankley, G.T.; and Benoit, D.A. (2001) Factors Affecting Repro- duction and the Importance of Adult Size on Reproductive Output in Chironomus tentans. Environ. Toxicol. Chem., 20, 1296. Sibley, P.K.; Ankley, G.T.; Cotter, A.M.; and Leonard, E.N. (1996) Predicting Chronic Toxicity of Sediments Spiked with Zinc: An Evaluation of the Acid-Volatile Sulfide Model Using a Life-Cycle Test with the Midge Chironomus tentans. Environ. Toxicol. Chem., 15, 2102. Sibley, P.K.; Benoit, D.A.; and Ankley, G.T. (1997) The Significance of Growth in Chironomus tentans Sediment Toxicity Tests: Relationship to Reproduction and Demographic Endpoints. Environ. Toxicol. Chem., 16, 336. Sibley, P.K.; Benoit, D.A.; Ankley, G.T.; Balcer, M.K.; West, C.; Phipps, G.L.; and Hoke, R.A. (1999) An Exposure Apparatus for the In Situ Assessment of Sediment Toxicity and Bioaccumulation. Environ. Toxicol. Chem., 18, 2325. Sibley, P.K.; Legler, J.; Dixon, D.G.; and Barton, D.R. (1997) Environmental Health Assessment of the Benthic Habitat Adjacent to a Pulp Mill Dis- charge. I. Acute and Chronic Toxicity of Sediments to Benthic Macro Invertebrates. Arch. Environ. Contam. Toxicol., 32, 274. Sibley P.K.; Monson, P.D.; and Ankley, G.T. (1997) The Effect of Gut Contents on Dry Weight Estimates of Chironomus tentans Larvae: Implica- tions for Interpreting Toxicity in Freshwater Sediment Toxicity T. Environ. Toxicol. Chem., 16, 1356. Sloterdijk, H.; Champoux, L.; Jarry, V.; Couillard,Y.; and Ross, P. (1989) Bioassay Responses of Micro-Organisms to Sediment Elutriates from the St. Lawrence River (Lake St. Louis). Hydrobiologia, 188/189, 317.

Testing for Toxicity and Bioaccumulation in Freshwater Sediments 255 Smith, E.H., and Logan, D.T. (1993) Invertebrate Behavior as an Indicator of Contaminated Water and S. In Environmental Toxicology and Risk Assess- ment. 2nd Vol., G.W. Gorsuch, F.J. Dwyer, C.G. Ingersoll, and T.W. La point (Eds.), ASTM STP 1216, ASTM, Philadelphia, Pa., 48. Strawbridge, S.; Coull, B.C.; and Chandler, G.T. (1992) Reproductive Output of a Meiobenthic Copepod Exposed to Sediment Associated Fenvalerate. Arch. Environ. Contam. Toxicol., 23, 295. Suedel, B.C., and Rodgers, J.H., Jr. (1994) Development of Formulated Reference Sediments for Freshwater and Estuarine Sediment Testing. Environ. Toxicol. Chem., 13, 1163. Suedel, B.C., and Rodgers, J.H., Jr. (1996) Toxicity of Fluoranthene to Daphnia magna, Hyalella azteca, Chironomus tentans, and Stylaria lacustris in Water-Only and Whole Sediment Exposures. Bull. Environ. Contam. Toxicol., 57, 132. Suedel, B.C.; Rodgers, J.H., Jr.; and Clifford, P.A. (1993) Bioavailability of Fluoranthene in Freshwater Sediment Toxicity Tests. Environ. Toxicol. Chem., 12, 155. Sved, D.W., and Roberts, M.H. (1995) A Novel Use of the Continuous-Flow Serial Diluter: Aquatic Toxicity Testing of Contaminated Sediments in Suspension. Water Res., 29, 1169. Swartz, R.C.; Schults, D.W.; Dewitt, T.H.; Ditsworth, G.R.; and Lamberson, J.O. (1990) Toxicity of Fluoranthene in Sediment to Marine Amphipods: A Test of the Equilibrium Partitioning Approach to Sediment Quality Criteria. Environ. Toxicol. Chem., 9, 1071. Swindoll, C.M., and Applehans, F.M. (1987) Factors Influencing the Accumu- lation of Sediment-Sorbed Hexachlorobiphenyl by Midge Larvae. Bull. Environ. Contam. Toxicol., 39, 1055. Tatum, H.E. (1986) Bioaccumulation of Polychlorinated Biphenyls and Metals from Contaminated Sediment by Freshwater Prawns, Macro- brachium rosenbergii and Clams, Corbicula fluminea. Arch. Environ. Contam. Toxicol., 15, 171. Tay, K.L.; Doe, K.G.; Wade, S.J.; Vaughan, D.A.; Berrigan, R.E.; and Moore, M.J. (1992) Sediment Bioassessment in Halifax Harbor. Environ. Toxicol. Chem., 11, 1567. Thompson, B.T.; Bay, S.; Greenstein, D; and Laughlin, J. (1991) Sublethal Effects of Hydrogen Sulfide in Sediments on the Urchin Lytechinus pictus. Mar. Environ. Res., 31, 309. Traunspurger, W., and Drews, C. (1996) Toxicity Analysis of Freshwater and Marine Sediments with Meio- and Macrobenthic Organisms: A Review. Hydrobiologia, 328, 215. Traunspurger, W.; Haitzer, M.; Hoss, S.; Beier, S.; Ahlf, W.; and Steinberg, C. (1997) Ecotoxicological Assessment of Aquatic Sediments with Caenorhabditis elegans (Nematoda): A Method for Testing Liquid Medium and Whole-Sediment Samples. Environ. Toxicol. Chem., 16, 245. Tucker, K.A., and Burton, G.A., Jr. (1999) Assessment of Nonpoint-Source Runoff in a Stream Using In Situ and Laboratory Approaches. Environ. Toxicol. Chem., 18, 2797.

256 Handbook on Sediment Quality U.S. Environmental Protection Agency (1977) Ecological Evaluation of Proposed Discharge of Dredged Material Into Ocean Waters: Implementa- tion Manual for Section 103 of PL 92-532. Environmental Effects Labora- tory, U.S. Army Engineer Waterways Experiment Station, Vicksburg, Miss. U.S. Environmental Protection Agency (1990) Evaluation of the Equilibrium Partitioning Approach for Assessing Sediment Quality Criteria. Report of the Sediment Criteria Subcommittee, SAB-EETFC-89-027, Washington, D.C. U.S. Environmental Protection Agency (1994a) Evaluation of Dredged Material Proposed for Discharge in Waters of the U.S.: Testing Manual. EPA-823/B-94-002, Office of Water, Washington, D.C. U.S. Environmental Protection Agency (1994b) Evaluation of Dredged Material Proposed for Discharge in Waters of the US: Testing Manual (Draft). Inland Testing Manual. EPA-823/B-94-002, Washington, D.C. U.S. Environmental Protection Agency (1994c) Methods for Measuring the Toxicity and Bioaccumulation of Sediment-Associated Contaminants With Freshwater Invertebrates. EPA-600/R-94-024, Duluth, Minn. U.S. Environmental Protection Agency (1997) The Incidence and Severity of Sediment Contamination in Surface Waters of the United States, Vol. 1: National Sediment Quality Survey. EPA-823/R-97-006, Washington, D.C. U.S. Environmental Protection Agency (2000) Methods for Measuring the Toxicity and Bioaccumulation of Sediment-Associated Contaminants with Freshwater Invertebrates. 2nd Ed., EPA- 600/R-99-064, Washington, D.C. U.S. Environmental Protection Agency and U.S. Army Corp of Engineers (1991) Evaluation of Dredged Material Proposed for Ocean Disposal: Testing Manual. Office of Water (WH-556F), EPA-503/8-91-001, Washing- ton, D.C. U.S. Environmental Protection Agency and U.S. Army Corp of Engineers (1998) Evaluation of Dredged Material Proposed for Ocean Disposal: Testing Manual. Office of Water, EPA-823/B-98-004, Washington, D.C. Van Wezel, A.P., and Jonker, M.T. (1998) Use of Lethal Body Burden in the Risk Quantification of Field Sediments: Influence of Temperature and Salinity. Aquat. Toxicol., 42, 287. Walker, C.H. (1990) Kinetic Models to Predict Bioaccumulation of Pollutants. Functional Ecol., 4, 295. Walsh, G.E.; Weber, D.E.; Simon, T.L.; and Brashers, L.K. (1991) Toxicity Tests of Effluents with Marsh Plants in Water and Sediment. Environ. Toxicol. Chem., 10, 517. Walsh, G.E.; Weber, D.E.; Simon, T.L.; Brashers, L.K.; and Moore, J.C. (1991) Use of Marsh Plants for Toxicity Testing of Water and Sediment. In Second Symposium on Use of Plants for Toxicity Assessment. 2nd Vol., J. Gorsuch and W. Wang (Eds.), ASTM, Philadelphia, Pa., 341. Warren, L.W.; Klaine, S.J.; and Finley, M.T. (1995) Development of a Field Bioassay with Juvenile Mussels. J. N. Am. Benthol. Soc., 14, 341. Watling, L. (1991) The Sedimentary Milieu and Its Consequences for Resi- dent Organisms. Am. Zool., 31, 789.

Testing for Toxicity and Bioaccumulation in Freshwater Sediments 257 Watts, M.M., and Pascoe, D. (1996) Use of Freshwater Macroinvertebrate Chironomus riparius (Diptera: Chironomidae) in the Assessment of Sediment Toxicity. Water Sci. Technol., 34, 7–8, 101. Watts, M.M., and Pascoe, D. (1998) Selection of an Appropriate Life-Cycle Stage of Chironomus riparius Meigen for Use in Chronic Sediment Toxicity Testing. Chemosphere, 36, 1405. Weber, D.E.; Walsh, G.E.; and MacGregor, M.A. (1995) Use of Vascular Aquatic Plants in Phytotoxicity Studies with Sediments. In Environmental Toxicology and Risk Assessment B. 3rd Vol., J.S. Hughes, G.R. Biddinger, and E. Mones (Eds.), ASTM STP 1218, ASTM, Philadelphia, Pa., 187. West, C.W., and Ankley, G.T. (1998) A Laboratory Assay to Assess Avoidance of Contaminated Sediments by the Freshwater Oligochaete Lumbriculus variegatus. Arch. Environ. Contam. Toxicol., 35, 20. West, C.W.; Mattson, V.R.; Leonard, E.N.; Phipps, G.L.; and Ankley, G.T. (1993) Comparison of the Relative Sensitivity of Three Benthic Inverte- brates to Copper-Contaminated Sediments from the Keweenaw Waterway. Hydrobiologia, 262, 57. Whiteman, F.W.; Ankley, G.T.; Kahl, M.D.; Rau, D.M.; and Balcer, M.D. (1996) Evaluation of Interstitial Water as a Route of Exposure for Ammonia in Sediment Tests with Benthic Macro Invertebrates. Environ. Toxicol. Chem., 15, 794. Wiederholm, T.; Wiederholm A.M.; and Milbrink, G. (1987) Bulk Sediment Bioassays with Five Species of Fresh-Water Oligochaetes. Water, Air, Soil Pollut., 36, 131. Winger, P.V., and Lasier, P.J. (1995) Sediment Toxicity in Savannah Harbor. Arch. Environ. Contam. Toxicol., 28, 357. Winger, P.V.; Lasier, P.J.; and Geitner, H. (1993) Toxicity of Sediments and Pore Water from Brunswick Estuary, Georgia. Arch. Environ. Contam. Toxicol., 25, 371. Winger, P.V.; Lasier, P.J.; and Jackson, B.P. (1998) The Influence of Extrac- tion Procedure on Ion Concentrations in Sediment Pore Waters. Arch. Environ. Contam. Toxicol., 35,8. Zumwalt, D.C.; Dwyer, F.J.; Greer, I.E.; and Ingersoll, C.G. (1994) A Water- Renewal System that Accurately Delivers Small Volumes of Water to Exposures. Environ. Toxicol. Chem., 13, 1311.

258 Handbook on Sediment Quality Chapter 6 Toxicity Tests of Marine and Estuarine Sediment Quality: Applications in Regional Assessments and Uses of the Data

Edward R. Long, ERL Environmental, Salem, OR

260 Introduction and Background 282 Spatial (or Surficial) Extent 262 Results from Surveys Performed of Toxicity Throughout North America 285 Predicting Toxicity with 262 The Types of Toxicity Tests Numerical Sediment-Quality Commonly Used in Regional Guidelines Monitoring 299 Ecological Relevance of 266 Spatial Gradients (or Patterns) Toxicity Tests in Toxicity 303 Conclusions and Outlook 279 Incidence of Toxicity 307 References

259 INTRODUCTION AND BACKGROUND

After their initial use in the 1970s, primarily in evaluations of prospective dredge materials, toxicity tests of sediments have proliferated in number and diversity, and their applications have expanded to include many other purposes (Melzian, 1990, and Swartz, 1989). Toxicity tests of sediments have become incorporated to many regional and national monitoring programs to develop information on toxicologically significant chemical contamination. The use of toxicity tests has become more common because they provide information on the bioavailability and toxicological significance of sediment- associated toxicants. The tests are relatively easy to perform. Widely accepted, standardized protocols for these tests are available, thereby ensuring that data will be comparable among studies and laboratories. Data from the tests are easily understood and interpreted, and can be generated relatively quickly and inexpensively (Lamberson et al., 1992). Data from some tests frequently have shown strong correlations with significant alterations to resident benthic populations at sampling sites. Unlike analyses of the structure of benthic communities, laboratory tests of toxicity are relatively immune to the confounding effects of natural factors such as water depths, sediment texture, and salinity. Currently, sediment-toxicity tests are used in many regional, environmental monitoring programs in North America to obtain information on the presence, severity, spatial patterns, and surficial extent of toxicity. In some studies (e.g., Swartz et al., 1986), data have been collected over a time series to determine if sediment quality is improving or degrading. They are used in field studies of ecological risks at hazardous waste sites in concert with chemical analyses of both sediments and tissues of invertebrates and fish (MacDonald et al., 1992). They have been used in field validations of numerical guidelines derived for sediment quality (this chapter). Wastewater districts have used them to determine the quality of sediments within zones of influence of wastewater discharges (Suer, 1990). A number of reviews of the types of saltwater-toxicity tests for sediments, their endpoints, and most common applications have been published previously (Chapman, 1988; Lamberson et al., 1992; and Swartz, 1989). These publications included lists and descriptions of a wide variety of tests that had been used in field surveys, laboratory spiked-sediment bioassays, toxicity and chemistry research, numerical guidelines development, and for regulatory purposes. Toxicological endpoints ranged from enzyme activity to whole- animal mortality and included measures of reproductive success. Burton et al. (1992) provided a similar review for freshwater tests of sediments. Additional recent information on sediment toxicity tests is summarized in Chapter 5 of this book. Helpful guidance for collecting, storing, and manipulating sediment samples is provided in Chapter 4.

260 Handbook on Sediment Quality Toxicity tests are often coupled with data from chemical analyses and benthic community analyses to complete a triad of measures (Long and Chapman, 1985). Assessments of sediment quality are most meaningful when conducted with all three components of the sediment-quality triad (SQT), which consists of chemical analyses, toxicological tests, and metrics of benthic community composition. The SQT approach, first applied in Puget Sound (Long and Chapman, 1985) and further verified in San Francisco Bay (Chapman et al., 1987), was intended to provide analysts with a weight of evidence to use in judging the relative quality of sediments. To aid ecologists in interpretation of the data from SQT studies, Chapman (1996) provided examples of graphic presentations, tabular decision matrices, and summary indices that encompass results of chemical, toxicological, and benthic analyses. In the examples given, toxicity tests provided information on whether chemical contaminants were bioavailable and toxicologically significant. As a component of a triad study, chemical analyses of sediments can provide information on the presence and concentrations of mixtures of potentially toxic substances in sediment samples. The probability of toxicity can be estimated by comparing the chemical data with effects-based, numerical standards or guidelines. However, information gained from these analyses alone does not provide a direct measure of the toxicological significance of the chemicals. Data from toxicity tests can be used to classify and map sediments based on biological activity to define the bioavailability of potentially toxic substances and estimate spatial and temporal trends in sediment quality (Hall et al., 1991; Hill et al., 1993; and Swartz, 1989). The results of laboratory tests can stand alone as indicators of degraded conditions and do not require either field validation or concordance with sediment chemistry (Chapman, 1995). However, measures of the structure and function of benthic populations and communities can provide important indicators of in situ adverse effects of toxicity among local resident animals (Canfield et al., 1994). The purpose of this chapter is to provide a review of the applications of toxicity tests in regional studies of saltwater sediments and to summarize some of the recent analyses of data available from such studies. Specific objectives are (1) to review the types of tests most commonly used in regional surveys of saltwater sediments; (2) illustrate the types of spatial patterns (or gradients) observed in toxicity within different marine bays and estuaries; (3) quantify the percent incidence (or frequency) of “toxic” responses among types of toxicity tests in U.S. estuaries; (4) compare the spatial (or surficial) extent of toxicity among different toxicity tests and survey areas; (5) summarize the accuracy with which numerical sediment-quality guidelines correctly classify samples as either toxic or nontoxic in sediments; and (6) summarize information on the ecological relevance of toxicity tests in benthic infaunal populations. Data from surveys conducted by the National Oceanic and Atmospheric Administration (NOAA) and the U.S. Environmental Protection Agency (U.S. EPA) were used in the analyses.

Toxicity Tests of Marine and Estuarine Sediment Quality 261 RESULTS FROM SURVEYS PERFORMED THROUGHOUT NORTH AMERICA

THE TYPES OF TOXICITY TESTS COMMONLY USED IN REGIONAL MONITORING. In their review of the status of sediment- toxicity tests, Lamberson et al. (1992) concluded that, “The assessment of the toxicological effects of sediment-associated chemicals can be made with a variety of relatively simple bioassays. These tests directly measure the toxicological effects of the bioavailable fractions of the contaminants under controlled conditions.” They provided a lengthy list of saltwater test species and endpoints that had been developed and used during the 1980s. However, they provided very little perspective as to which tests and endpoints were most frequently used in estuarine and marine surveys, which had been abandoned or infrequently used, and which tests might become more popular in the future. The purpose of this chapter is not to evaluate or critique the various attributes and weaknesses of the difference types of toxicity tests. Results of such analyses can be found in excellent previous reviews (Ingersoll et al., 1997, and Lamberson et al., 1992) and in Chapter 5. Regional surveys or assessments were conducted for many of the major urbanized and industrialized bays and estuaries of the Atlantic, Gulf, and Pacific coasts during the 1980s and 1990s (Table 6.1) Surveys conducted by NOAA’s National Status and Trends Program have been completed in 25 selected bays and estuaries along all three coastlines (Long, 2000a, 2000b; Long, Robertson, et al., 1996; and Turgeon et al., 1998). These surveys used several sediment-toxicity tests as measures of toxicant-related effects after an objective evaluation of the performance of various candidate tests (Long et al., 1990). Sediment-toxicity studies also were conducted in the Virginian, Louisianian, Carolinian, and Californian biogeographic provinces as a part of the Environmental Monitoring and Assessment Program (EMAP)—Estuaries studies of U.S. EPA (Paul et al., 1992). Smaller-scale, regional EMAP surveys have been conducted in several specific bays and estuaries, notably New York and New Jersey Harbor (Adams et al., 1998) and the Southern California Bight (Bay, 1996). Three tests were used most frequently: (1) survival of adult amphipods exposed to solid-phase (i.e., whole) sediments, (2) fertilization success of echinoderm eggs after exposures of sperm to pore waters and impaired development of embryos exposed to pore waters, and (3) microbial bioluminescence (i.e., Microtox) tests in exposures to solvent extracts of sediments. Amphipod survival tests of solid-phase sediments were performed in all except one of the 35 studies for which data were assembled (Table 6.1). In most cases, either Ampelisca abdita or Rhepoxynius abronius were used as

262 Handbook on Sediment Quality Reference 1996 and 1998 1992 et al., 1995 et al., 1995 Strobel et al., 1995 Summers et al., 1993 Hyland, et al. b bahia mercenaria Mysidopsis M. bahia M. bahia Mercenaria Mercenaria X Long and Markel, X Long, Wolfe, X Dioxin Long, Wolfe, X Wolfe, et al., 1994 X et al., Sapudar 1994 X RGS et al., Fairey 1996 X Costa and Sauer, 1993 XX XX X X SFEI, 1997 tests analyses analyses tests centrotus centrotus purpuratus lateralis Strongylo- Mulinia M. lateralis a Solid X X Solvent Solvent Solvent X X Long, et al., 1994 punctulata purpuratus Amphipod Pore Other abdita abronius rufescens estuarius edulis Ampelisca A. abdita A. abdita A. verrilli A. abdita A. abdita A. abdita A. abdita Arbacia Rhepoxynius Haliotis R. abronius Shaustorius Ehaustorius Mytilus A. abdita area tests tests tests Study survival water Microtox Elutriate Benthic Chemical toxicity Bay, CA estuary, NY Sound, NY/CT Bay, CA Bay, CA Bay, CA Table 6.1Table assessments. environmental use in 35 large-scale toxicity tests and other analyses selected for Sediment province Virginian Louisianian province Carolinian province San Francisco Solvent Hudson–Raritan Newark Bay,Newark NJ Long Island Tampa Bay,Tampa FL San Pedro San Diego San Diego San Francisco Delaware Bay,Delaware DE

Toxicity Tests of Marine and Estuarine Sediment Quality 263 Reference 1995 1991 et al., 1990 et al., 1996 1998 Harmon, et al., 1999 Robertson, et al., 1999 SCCWRP, 1995 b copepod 1999 pictus Lytechinus X Kinnetics Laboratories, X Cunningham X X X Hyland and Costa, X X Carr, 1993 X X Striplin et al., 1993 tests analyses analyses tests a Solvent X Long, Sloane, Solvent +Solvent X Long, et al., 1997 Solvent +Solvent X RGS, Long, Scott, et al., Solvent X Long, Scott, et al., Solvent X X RGS, Long, Sloane, et al., Solvent X X RGS Long, Hameedi, Solvent X X RGS Long, Hameedi, Saline X X Tetra-Tech, 1989 Amphipod Pore Other japonica gigas A. abdita A. punctulata Gandidierella Crassostrea G. japonica A. punctulata A. abdita A. abdita A. punctulata A. abdita A. punctulata A. abdita A. punctulata A. abdita A. punctulata A. abdita A. punctulata A. abdita A. punctulata A. abdita S. purpuratus R. abronius S. purpuratus R. abronius R. abronius area tests tests tests Study survival water Microtox Elutriate Benthic Chemical toxicity Bay, MA Angeles, CA River, LA bays, FL Mutatox Harbor, SC Mutatox copepod 1998 Sound, WA shelf, CA Table 6.1Table assessments. (continued) environmental use in 35 large-scale Sediment toxicity tests and other analyses selected for Masschusetts Port of Los Galveston Bay,Galveston TX Lower Calcasieu Lower Boston Harbor, MA Western Florida Western Charleston Savannah River,Savannah SC Biscayne Bay, FL Sabine Lake, TX No. Puget Palos Verdes Verdes Palos Puget Sound, WA Puget Sound, WA

264 Handbook on Sediment Quality Reference data Services, 1990 1999 Boyd, 1989 1998 1997 data Hall et al., 1991 sp. and Goyette b benedicti, pugio Streblospio Streblospio Palaemonetes Palaemonetes Macoma X PTI Environmental X X X McGee et al., XX X X Bay, 1996 X X RGS Anderson et al., tests analyses analyses tests a Solvent X X Adams et al., Solvent X X RGS NOAA, unpublished Solvent X X RGS NOAA, unpublished Amphipod Pore Other dytiscus plumulosus R. abronius R. abronius Lepidactylus Leptocheirus Leptocheirus R. abronius R. abronius S. purpuratus A. abdita A. abdita S. purpuratus A. abdita S. punctulata A. abdita S. punctulata area tests tests tests Study survival water Microtox Elutriate Benthic Chemical toxicity Sound, WA Bay, MD/VA Bay, MD/VA BC shelf, CA Harbor, NY and NJ bays, CA RGS = cytochrome P-450 RGS assay and dioxin = dioxin equivalency bioassay. P-450 RGS assay and dioxin = equivalency RGS = cytochrome Solid = solid phase, extract, solvent = organic solvent saline = aqueous extract, and Mutatox = assay. Table 6.1Table assessments. (continued) environmental use in 35 large-scale Sediment toxicity tests and other analyses selected for So. Puget a b Chesapeake Chesapeake Chesapeake Chesapeake Vancouver Harbor, Vancouver So. California New York/New Jersey York/New New Southern California Delaware Bay,Delaware DE Galveston Bay,Galveston TX

Toxicity Tests of Marine and Estuarine Sediment Quality 265 the test species. Generally, A. abdita was used in surveys of the Atlantic and Gulf coasts and R. abronius was used along the Pacific coast. However, Grandidierella japonica was used in tests performed with Galveston Bay and Port of Los Angeles sediments, Lepidactylus dytiscus and Leptocheirus plumulosus were used in two surveys of Chesapeake Bay, and Eohaustorius estuarius was used in San Francisco Bay. In most studies, the amphipod tests were accompanied by one or two other tests (Table 6.1). Sediment pore waters were tested in many studies, often with gametes or embryos of the echinoderms Arbacia punctulata or Strongylocentrotus purpuratus. Endpoints of fertilization success of eggs and morphological development of embryos were determined in most cases. Tests of the toxicity of sediment extracts to the microbial bioluminescent activity of cultured bacteria (the Microtox test) were used frequently, often with an organic solvent extract of the sediments. However, in some Puget Sound surveys, saline extracts were used in the tests. In several of the NOAA surveys (western Florida, Charleston Harbor, Sabine Lake) the Mutatox variant of this test was performed on the solvent extracts (Johnson and Long, 1998). In the Carolinian province EMAP surveys, Microtox tests were performed on solid-phase samples. In addition to the three most frequently used tests, a variety of others have been performed in some surveys (Table 6.1). Tests of embryological development were performed in several regions with the embryos of either echinoderms or mollusks that were exposed to sediment elutriates. Acute survival tests were performed with mysids, adult clams, polychaetes, and shrimp. On the Palos Verdes shelf, acute survival tests were performed with adult urchins. Life cycle tests with the meiobenthic copepod Amphiascus tenuiremis (following methods of Green et al., 1993) were conducted in Charleston Harbor and Biscayne Bay to identify the incidence of chronic effects with several reproductive endpoints. In Newark Bay, a dioxin equivalency bioassay (following protocols of Tillitt et al., 1991) with a cultured cell line was performed on sediment extracts and fractions thereof in an area in which high concentrations of dioxins and furans were expected. In San Diego Bay, Charleston Harbor, Biscayne Bay, Sabine Lake, Galveston Bay, Delaware Bay, and northern Puget Sound, cytochrome P-450 RGS assays (Anderson et al., 1995) were performed on sediment extracts. Enzymatic responses in these tests are induced by the presence of mixed-function oxidase inducers, such as dioxins and furans, certain polychlorinated biphenyls (PCBs), and many polynuclear aromatic hydrocarbons (PAHs). In all of the surveys (Table 6.1), chemical analyses of aliquots of the samples were performed to identify the associations, if any, between measures of toxicity and the concentrations of substances that may have contributed to toxicity. In 19 of the 35 studies, the toxicity tests were accompanied by analyses of resident benthic populations to determine if infaunal populations were altered at sampling locations in which toxicity was observed.

SPATIAL GRADIENTS (OR PATTERNS) IN TOXICITY. The degree (severity) and magnitude (size) of toxicity problems in estuaries and marine bays are functions of the characteristics of the sources of toxicants, the

266 Handbook on Sediment Quality physical distribution and sedimentation of the toxicants in the receiving system, and their bioavailability. Whereas the problem of poor sediment quality has been recognized for many years (Bolton et al., 1985; NRC, 1989; and Power and Chapman, 1992), information on the environmental persistence, bioavailability, and toxicological significance of sediment contamination has been poorly characterized (Power and Chapman, 1992). Data compiled from surveys conducted in urbanized areas such as Tampa Bay (Carr et al., 1996), Puget Sound (Ginn and Pastorok, 1992), and Elizabeth River (Huggett et al., 1992) have shown that severely degraded conditions often occur in the most urbanized and industrialized reaches of these estuarine systems. However, they also suggested that the spatial magnitudes in degraded conditions differ among areas (Long, 1998, 2000a, and Turgeon et al., 1998). Therefore, without actually mapping the spatial patterns in measures of environmental degradation such as toxicity, it is difficult to understand the magnitude of the problem. The objectives of most regional surveys and monitoring programs included in Table 6.1 were similar. Usually, they were designed to determine the presence and severity of toxicity; spatial gradients and scales of toxicity; and associations among measures of toxicity, chemical contamination, and benthic community structure. Standardized methods (many of those described in Chapter 4) were used in these surveys. Often, data were illustrated on base maps of the study areas to depict the spatial patterns or gradients, if any, in the results. Based on reviews of the data from many of the surveys listed in Table 6.1, four general types of patterns in toxicity have emerged. The four types of patterns are (1) a general lack of acute toxicity accompanied by significant results in sublethal tests in widely scattered sediments with no obvious spatial pattern or gradient in toxicological responses, (2) acute toxicity in many samples also without obvious spatial patterns in results (i.e., patchy), (3) toxicity restricted to relatively small waterways or harbors that adjoin the main basin of the estuary or bay, or (4) widespread toxicity in acute tests distributed throughout the majority of the estuary. In the first case, toxicity in the amphipod survival tests was either not observed or occurred infrequently, and only a small percentage of widely scattered samples were toxic in any of the tests. Examples of areas in which this general lack of toxicity occurred included San Francisco Bay (Long and Markel, 1992), Charleston Harbor (Long, Scott, et al., 1998), Galveston Bay (Carr, 1993), Carolinian province (Hyland et al., 1996, 1998), Sabine Lake (Long, Hameedi, Harmon, et al., 1999), and offshore Southern California Bight (Bay, 1996). Significant results in the amphipod tests were rarely observed in these areas, and a small minority of samples was toxic in any of the sublethal tests that were performed. In the survey of Charleston Harbor (SC), sediments were collected from 61 locations in the Wando, Cooper, and Ashley Rivers and in the lower Charleston harbor at the confluence of the three rivers (Long, Scott, et al., 1998). None of the samples was significantly toxic in the amphipod tests done with A. abdita. However, results were significant in about one-quarter of the samples in Microtox tests and in one-half of the samples in urchin fertiliza-

Toxicity Tests of Marine and Estuarine Sediment Quality 267 Figure 6.1 Distribution of toxicity in Charleston Harbor as determined with Microtox tests (p = probability).

tion tests. Data from the Microtox tests of solvent extracts were evaluated with a three-tiered statistical procedure that involved increasing degrees of conservatism (Mann–Whitney, Dunnett’s, and distribution-free) to identify spatial gradients in results relative to negative controls. Data indicated that samples intermediate in toxicity (results were significant with Mann–Whitney alone or both Mann–Whitney and Dunnett’s) were scattered throughout the area in no readily apparent gradient (Figure 6.1). A slightly larger proportion of samples with significant results occurred in the Cooper River than in the Ashley and Wando Rivers, and no significant results were observed in samples from the lower Charleston Harbor. Hyland et al. (1995, 1996) reported that most samples from the Carolinian province, which included Charleston Harbor, were not toxic in amphipod tests, but toxicity was observed in some samples in one or more sublethal tests. Sea urchin fertilization tests also were performed with the Charleston Harbor samples, using A. punctulata in three pore-water concentrations (100%, 50%, and 25% pore water). The responses in these tests were distributed somewhat differently as compared with the data from the Microtox tests (Figure 6.2). In this test, higher percentages of the samples from the Ashley River and Shipyard Creek were toxic in all three pore-water concentrations relative to those from the Cooper and Wando Rivers, and some samples from

268 Handbook on Sediment Quality Figure 6.2 Distribution of toxicity in Charleston Harbor as determined with urchin fertilization tests (pw = pore water).

the lower harbor were highly toxic. In both the Microtox and urchin fertilization tests, areas in which toxicity was observed were interspersed with areas in which no significant toxicity was observed and the two tests did not show the same spatial patterns in results. In a survey of Sabine Lake and vicinity (Long, Hameedi, Harmon, et al., 1999), 66 samples were collected in Sabine Lake (an estuarine lagoon on the Texas/Louisiana border) and the adjoining Neches River, Sabine River, and Intracoastal Waterway (Figure 6.3). There were many petrochemical facilities that bordered the waterways in this area; therefore, toxicity was expected, especially in samples collected near Port Neches and Port Arthur. The data from the sea urchin fertilization tests (A. punctulata) exemplified the lack of distinct spatial patterns in toxicity in this area. Samples that were toxic in both the amphipod and urchin tests were scattered throughout the study area. Toxicity in the urchin tests was apparent in a few samples from the Neches River, one sample from the Sabine River, and a few samples from the Intracoastal Waterway near Port Arthur and Sabine Pass. Curiously, several samples collected in and seaward of the entrance channel, farthest from the industrial facilities, were toxic in the urchin tests. In the second type of spatial pattern, samples determined to be toxic in acute amphipod survival tests were widely scattered throughout the study area

Toxicity Tests of Marine and Estuarine Sediment Quality 269 Figure 6.3 Distribution of toxicity in Sabine Lake as determined with urchin fertilization tests (pw = pore water).

interspersed with nontoxic samples; therefore, data did not show clear spatial gradients. Examples of this pattern included Massachusetts Bay (Hyland and Costa, 1995), Boston Harbor (Long, Sloane, et al., 1996), and the Hudson–Raritan Estuary (Long, Wolfe, et al., 1995). In each case, there appeared to be multiple sources of toxicants or multiple contaminant deposition zones, probably resulting in the scattered pattern in toxicity. Results from tests of 55 samples in a survey of Boston Harbor (MA) exemplify this pattern (Long, Scott, et al., 1996). Significant toxicity in amphipod tests (A. abdita) was apparent in 12 of the samples collected from stations scattered throughout the harbor (Figure 6.4). Four of the six samples

270 Handbook on Sediment Quality Figure 6.4 Distribution of toxicity in Boston Harbor as determined with amphipod survival tests (p = probability).

from the Chelsea River were toxic. Otherwise, roughly equal proportions of samples were toxic and nontoxic in the Inner Harbor, Northwest Harbor, Central Harbor, and Southeast Harbor regions. One sample collected outside the harbor was toxic in an area expected to represent reference conditions. Collectively, data indicated a lack of any clear spatial gradients in toxicity. In the third type of spatial pattern, toxicity was observed only in relatively small, industrialized waterways, bayous, and harbors adjacent to the main basins of the survey areas, but not in the main basins themselves. This type of spatial pattern was observed in many of the NOAA surveys of estuaries and marine bays. In Long Island Sound (NY/CT), toxicity occurred in many small

Toxicity Tests of Marine and Estuarine Sediment Quality 271 Figure 6.5 Distribution of toxicity in Pensacola Bay as determined with urchin fertilization tests (pw = pore water).

inlets and bays adjoining the sound (Wolfe et al., 1994). In Biscayne Bay (FL), severe toxicity in amphipod tests was restricted mainly to the lower Miami River canal, several other canals, and a few other small areas (Long, Sloane, et al, 1999). In the survey of Pensacola Bay (FL), severe toxicity occurred mainly in Bayou Chico, a highly industrialized harbor adjoining the bay near the city of Pensacola (Long et al., 1997). In the lower Savannah River (GA), acute toxicity occurred only in a few samples collected near the city of Savannah (Long, Scott, et al., 1998). In St. Simons Sound (GA), toxicity was restricted to the very small Brunswick Harbor and Terry Creek (Long, Scott, et al., 1998). In northern Puget Sound (WA), toxicity observed in sublethal tests was primarily observed in samples from Everett Harbor (Long, Hameedi, Robertson, et al., 1999). Data from NOAA’s survey of Pensacola Bay also provided an example of the third kind of pattern (Long et al., 1997). In that survey, none of the 40 samples was toxic in amphipod survival tests. However, the samples indicated a wide range in response in tests of morphological development of sea urchin embryos (A. punctulata) exposed to pore waters (Figure 6.5). Most of the samples from the open waters of the Pensacola Bay and adjoining East Bay and Escambia Bay were not toxic in these tests. Toxicity was restricted mainly to samples from Pensacola harbor and nearby Bayou Chico, the most industrialized portions of this region. Overall, 49 of 226 samples (22%) were significantly toxic in amphipod tests (A. abdita) in the survey of Biscayne Bay (Long, Sloane, et al., 1999). As a part of this survey, 58 samples were collected in the lower Miami River and Port of Miami and tested for toxicity (Figure 6.6). Amphipod survival

272 Handbook on Sediment Quality Figure 6.6 Distribution of toxicity in central Biscayne Bay as determined with amphipod survival tests (p = probability).

was significantly depressed in 20 of 21 samples (95%) from the lower Miami River canal, indicative of the unusually toxic conditions in that area. However, toxicity diminished quickly seaward of the mouth of the river, where only 8 of the 37 samples (22%) collected were toxic. A few samples scattered throughout the bay and port indicated toxic conditions (including several collected near marinas); however, the most severe toxicity in the amphipod tests was largely restricted to the Miami River and its tributaries. In the southern reaches of Biscayne Bay, toxicity was reported in amphipod survival tests (A. abdita) or sea urchin fertilization tests (A. punctulata) in samples collected in several canals that enter the bay (Long, Sloane, et al., 1999). Toxic conditions were apparent in most of the samples from four of the five canals that were sampled (Figure 6.7). Unlike the conditions in many other survey areas, however, toxic conditions in one or the other of these tests continued eastward into the main basin of the bay and formed what appeared to be a plume seaward of the mouths of the canals. Two samples collected near the eastern boundary of the bay and nearly 8 km (5 miles) from the canals were highly toxic in all tests performed. Therefore, in this area, toxicity was observed in the small, peripheral canals but, unlike the situations in most other areas, toxicity continued seaward of the canals in at least one of the tests. During 1997, 1998, and 1999, NOAA and the Washington State Department of Ecology collected 300 sediment samples from the northern, central, and southern regions, respectively, of Puget Sound. Each sample was tested for chemical concentrations, toxicity, and benthic community structure.

Toxicity Tests of Marine and Estuarine Sediment Quality 273 Figure 6.7 Distribution of toxicity in southern Biscayne Bay as determined with urchin fertilization tests and amphipod survival tests (pw = pore water and p = probability).

The samples from northern Puget Sound that were most toxic in tests of sea urchin fertilization (S. purpuratus) were those collected in Everett Harbor, a small harbor that adjoins Port Gardner Bay (Long, Hameedi, Robertson, et al., 1999) (Figure 6.8). All nine of the samples from Everett Harbor were toxic in at least the tests of 100% pore waters, and four were toxic in tests of all three pore-water concentrations. In contrast, none of the samples from the adjoining Port Gardner Bay was toxic in any pore-water concentrations. One of the other tests performed in Puget Sound was the cytochrome P-450 Reporter Gene System (RGS) assay (Long, Hameedi, Robertson, et al., 1999), a test of enzyme induction attributable to the presence of dioxins and furans, certain coplanar PCBs, and many PAHs (Anderson et al., 1995). Based on statistical analyses of the national database for the test, it was determined that responses of less than 11.1 mg benzo[a]pyrene equivalents per gram of sediment (mg/g) could be viewed as background levels (Long, Hameedi, Robertson, et al., 1999). Also, based on the same analyses, responses, greater than 37.1 mg/g were accepted as representative of elevated induction levels. The RGS responses for 100 samples collected in central Puget Sound (Long et al., 1999) are shown in Figure 6.9. The highest enzyme induction responses occurred in samples collected from the urbanized embayments.

274 Handbook on Sediment Quality Figure 6.8 Distribution of toxicity in Port Gardner Bay of northern Puget Sound as determined with urchin fertilization tests (pw = pore water).

Samples from Elliott Bay and the lower Duwamish River waterway near Seattle gave the strongest responses, followed by several samples from Sinclair Inlet near Bremerton and two samples from Eagle Harbor on Bainbridge Island. Most of the samples from Dyes Inlet and Liberty Bay, both adjacent to mostly residential areas, gave intermediate responses. Some samples from the Puget Sound main basin between Seattle and Bainbridge Island also showed intermediate responses. All of the samples collected in the main basin north of Seattle indicated the weakest enzyme induction levels. Therefore, the data showed consistent spatial trends of decreasing toxic response with increasing distance from the urban–industrial areas. In the fourth case, toxicity was relatively severe and pervasive throughout the majority of the study area. Examples of this pattern included Newark Bay (Long, Wolfe, et al., 1995), San Diego Bay (Fairey et al., 1996), and the lower Calcasieu River (Cunningham et al., 1990). Among the 57 samples collected in Newark Bay and vicinity, all except 9 were at least significantly toxic in amphipod survival tests performed with A. abdita (Long, Wolfe, et al., 1995).

Toxicity Tests of Marine and Estuarine Sediment Quality 275 Figure 6.9 Distribution of toxicity in central Puget Sound as determined in cytochrome P-450 RGS assays.

Among the 48 samples in which percent survival was significantly different from controls, mean survival in 38 samples was less than 80% of controls. None of the amphipods survived in tests of two of the samples. Toxicity was pervasive throughout the lower reaches of the Passaic and Hackensack rivers, Newark Bay, the marine terminals of Newark Bay, and along much of Arthur Kill (Figure 6.10).

276 Handbook on Sediment Quality Figure 6.10 Distribution of toxicity in Newark Bay as determined with amphipod survival tests (p = probability).

In a survey of San Diego Bay and vicinity, the California State Water Resources Control Board and NOAA collected 105 samples for toxicity tests performed with the amphipod R. abronius (Fairey et al., 1996). Conditions in San Diego Bay were nearly equivalent to those in Newark Bay; that is, toxicity in this test was pervasive throughout much of the bay (Figure 6.11). Many of the samples from South Bay, the main basin of the bay, the commercial and pleasure boat basins of the bay, and the northern and western portions of the bay were toxic. Overall, amphipod survival was significantly reduced in 68 of the 105 samples collected in San Diego Bay. Some samples collected in sandy shoals near the mouth of the bay were not toxic. In contrast to the conditions in San Diego Bay, toxicity was reported for only one sample collected in nearby Mission Bay, none from the San Diego River, and four of the six samples tested from the Tijuana Slough estuary (Figure 6.11). Based on the data available from large-scale surveys, it was apparent that acute toxicity as measured in the amphipod survival tests frequently was

Toxicity Tests of Marine and Estuarine Sediment Quality 277 Figure 6.11 Distribution of toxicity in San Diego Bay as determined with amphipod survival tests (p = probability).

restricted to the most urbanized or industrialized reaches of bays and estuaries (Long, 1997). Often, the samples that were most toxic had been collected in areas such as the Chelsea River (Boston Harbor); Miami River (Biscayne Bay); Newark Bay and its tributaries (Hudson–Raritan estuary); and among the boat basins, shipyards, and maritime channels of San Diego Bay. In Tampa Bay, acute toxicity was observed mainly in Ybor Channel and adjacent maritime channels (Carr et al., 1996). In northern Chesapeake Bay, the most toxic samples were collected in Baltimore Harbor and immediate vicinity (McGee et al., 1999). In some cases, the toxicity observed in these restricted areas continued seaward into the main basins of the system in a pattern of decreasing severity (central Puget Sound) or without any apparent pattern (southern Biscayne Bay). In many areas surveyed thus far (e.g., Charleston Harbor, northern Puget Sound, Mission Bay), acute toxicity as determined with the amphipod tests was not apparent or very infrequent and significant results were observed only in other (sublethal) tests. Often, the spatial patterns in the results of sublethal tests in these areas were similar to those for

278 Handbook on Sediment Quality acute tests in other areas, that is, the most severe responses in urban harbors, decreasing seaward toward the mouth of the bay or estuary. Data from some surveys have shown strong concordance between measures of sediment toxicity and measures of other types of biological effects in the benthos, resident bivalves, or demersal fish (Long, 1997).

INCIDENCE OF TOXICITY. Questions often arise in sediment-quality assessments as to what consitutes a “toxic” sediment sample. Thus far, lacking ecologically relevant thresholds in toxicity results, most programs have used pair-wise, statistical approaches to define significant differences between sample means and negative control means (Lamberson et al., 1992). Thursby et al. (1997) reevaluated this approach and proposed using minimum significant differences as criteria for classifying samples as toxic or not. They observed that approximately 19% of samples in their database (n = 767) would be so classified in tests with A. abdita. When amphipod survival or some other toxicological endpoint is very low (say, less than 50% of the control) or very high (say, greater than 90% of the control), the classification of samples is relatively straightforward. However, lacking clearcut criteria, controversies can arise when results are equivocal and professional judgments (opinions) are needed. In their analysis of the uncertainties associated with risk assessments for sediments, Ingersoll et al. (1997) outlined a number of evaluation criteria for judging the suitability of potential toxicity tests. These criteria included measures of precision (replicability) of the test, sensitivity of the test to chemical gradients (or dose), and ability of the test to discriminate between toxic and nontoxic conditions. They concluded that these criteria were test specific and that chronic test endpoints were more likely to classify samples as toxic than acute endpoints. The amphipod survival tests, the most commonly used acute toxicity tests in North America (Table 6.1), have been performed on numerous samples in large-scale and regional environmental assessments by NOAA and the U.S. EPA. In these programs, samples were collected at locations determined with a random process within numerous estuaries and bays along each of the three U.S. coastlines. Sampling was not focused on known toxic hotspots or toxicant sources. Therefore, the data from both programs represent conditions throughout major regions of each survey area, including locations near and far from toxicant sources. An expanded version of the database used by Thursby et al. (1997) and appended with data from other tests was examined to describe the actual distribution of results in samples tested throughout U.S. estuaries and marine bays to provide a basis for defining the frequency of “toxic” conditions. Data from a total of 1167 samples were generated in NOAA’s surveys of Hudson–Raritan Estuary, Newark Bay, Long Island Sound, Biscayne Bay, Boston Harbor, northern Puget Sound, six estuaries of South Carolina and Georgia, Galveston Bay, Sabine Lake, western Florida, and Tampa Bay. Data were generated by the U.S. EPA for the Louisianian Province in the Gulf of Mexico (n = 786) and the Virginian Province along the Atlantic coast (n = 677). Thus, data from the amphipod tests provide the most consistent and

Toxicity Tests of Marine and Estuarine Sediment Quality 279 Table 6.2 Incidence of toxicity in amphipod survival tests performed in studies conducted by the NOAA and U.S. EPA/EMAP in U.S. estuaries. Tests were performed with A. abdita. Percent control- EMAP— EMAP— adjusted NOAA surveys Lousianian Virginian Total amphipod ______(n = 1167)______(n = 786)______(n = 667)______(n = 2630) survival Number Percent Number Percent Number Percent Number Percent ≥100 358 30.7 185 23.5 191 28.2 734 27.9 90–99.9 512 43.9 431 54.8 294 43.4 1237 47.0 80–89.9 130 11.1 97 12.3 103 15.2 330 12.5 70–79.9 42 3.6 36 4.6 34 5.0 112 4.3 60–69.9 22 1.9 13 1.6 20 3.0 55 2.1 50–59.9 17 1.5 6 0.7 7 1.0 30 1.1 40–49.9 13 1.1 4 0.5 7 1.0 24 0.9 30–39.9 21 1.8 1 0.1 5 0.7 27 1.0 20–29.9 12 1.0 3 0.4 4 0.6 19 0.7 10–19.9 16 1.4 6 0.7 3 0.4 25 1.0 0.0–9.9 22 1.9 4 0.5 9 1.3 35 1.3 sum <80% 165 14.1 73 9.3 89 13.1 327 12.4

widely applied procedure for evaluating the toxicological properties of saltwater sediments nationwide. The frequency distributions of control-adjusted percent survival for tests performed with A. abdita are listed in Table 6.2 for the NOAA and U.S. EPA programs. Overall, the data represent results from 2630 discrete samples. Using statistical criteria for classifying samples as toxic proposed by Thursby et al. (1997), a small minority of samples were toxic (i.e., control-adjusted survival was less than 80%). Mean survival was less than 80% in 14, 9, and 13% of the samples in the three sets of surveys, respectively, or 12% (327 of 2630) of samples overall. Therefore, a large majority of the samples (88%) were nontoxic, using the classification methods of Thursby et al. (1997). The frequency distributions of results from tests with two amphipod survival tests (A. abdita and R. abronius), two tests of sea urchin fertilization (A. punctulata and S. purpuratus), and two tests of sea urchin embryological development (A. punctulata and S. purpuratus) are compared in Table 6.3. Data from NOAA and EMAP studies along the Atlantic and Gulf of Mexico coasts were assembled for A. abdita and compared with those from studies performed by NOAA and the states of California and Washington along the Pacific coast with R. abronius. Results of tests performed by the U.S. Geological Survey for NOAA in surveys of Atlantic coast and Gulf of Mexico estuaries (A. punctulata) are compared with those generated by NOAA and the states of California and Washington (with S. purpuratus). These data indicate that the distributions of results for both amphipod species were somewhat different (Table 6.3). There were more samples with low survival (<80% of controls) in the tests with R. abronius (42%) than in the tests with A. abdita (13%). Survival ranged from 60 to 80% of negative

280 Handbook on Sediment Quality Table 6.3 Frequency distribution of results of six toxicity tests performed on marine sediments.

Percent of samples within each Numbers ______range in control-adjusted results Test type of samples 0–20 20–40 40–60 60–80 ≥80 Amphipod survival (A. abdita) 3186 2.4 1.7 2.1 6.4 87.4 Amphipod survival (R. abronius) 1668 2.8 4.1 9.2 25.4 58.3 Urchin fertilization (A. punctulata) 640 15.2 4.2 5.6 8.4 65.9 Urchin fertilization (S. purpuratus) 354 35.3 10.5 9.0 10.7 32.5 Urchin development (A. punctulata) 500 53.8 2.4 3.2 4.4 35.4 Urchin development (S. purpuratus) 463 51.2 4.8 4.8 4.3 35.0

controls in 25% of the samples tested with R. abronius, whereas only 6% of samples had equivalent results in tests with A. abdita. In tests with R. abronius, 9% indicated survival from 40 to 60% compared with 2% of the samples tested with A. abdita. In both tests, however, there was a steady and gradual decrease in the proportion of samples in each classification as survival decreased. Similar frequency distributions were not apparent in the tests performed with the sea urchins. In the tests of both fertilization success and embryological development, there appeared to be bimodal distributions in the results. Most samples indicated percent fertilization and percent normal development either greater than 80% or 0 to 20%. The distribution of the fertilization success data differed between the two species, whereas they were similar for results of embryological development. The percentages of samples classified as either toxic (i.e., survival less than controls with p < 0.05) or highly toxic (i.e., survival less than controls with p < 0.05 and mean survival <80% of controls) in the tests with A. abdita were considerably lower than in the other tests (Table 6.4). Whereas approximately 12% of samples were highly toxic in tests with A. abdita, approximately 40% were highly toxic in tests with R. abronius, and 38 to 65% were toxic in the four kinds of sea urchin tests with 100% pore water. These data (Tables 6.2 to 6.4) suggest that highly significant, acute toxicity as measured in tests performed with A. abdita was an unusual outcome (12% incidence of toxic samples) in U.S. estuaries and marine bays. Toxicity in sediment, as measured with this test, was widely scattered among marine bays and estuaries and occurred relatively infrequently. The areas studied in the NOAA and EMAP surveys overlapped but were different in both size and geographic location. Nevertheless, the distributions of the data were remarkably consistent among the three data sets despite the differences in the areas

Toxicity Tests of Marine and Estuarine Sediment Quality 281 Table 6.4 Percent incidence of sediment samples classified as toxic (i.e., mean survival < controls, p < 0.05) or highly toxic (p < 0.05 and mean survival < 80% of controls) in six toxicity tests.

Number Percent Percent Test type of samples toxic highly toxic Amphipod survival (A. abdita) 2060 23.2 12.3 Amphipod survival (R. abronius) 1668 63.4 40.1 Urchin fertilization (A. punctulata) 640 39.8 38.0 Urchin fertilization (S. purpuratus) 354 65.5 64.7 Urchin development (A. punctulata) 500 63.4 63.2 Urchin development (S. purpuratus) 463 62.0 60.5

that were surveyed. Samples generally were classified as toxic more frequently in the chronic sea urchin tests of pore waters than in the acute amphipod tests of solid-phase sediments.

SPATIAL (OR SURFICIAL) EXTENT OF TOXICITY. The problem of sediment contamination and toxicity has been recognized on a national scale for many years (Bolton et al., 1985, and NRC, 1989); however, quantification of the actual degree or extent of the problem has been precluded by a lack of sufficient data. The percentages of samples in which toxicity occurs could be compared among survey regions as a means of ranking relative sediment quality. However, such comparisons are subject to possible biases as a result of differences among regions in sampling designs, sample distribution, and intensity. More equitable comparisons among regions should be based on a probabalistic sampling design and weighting of the data to the sizes of the areas sampled and the numbers of samples tested within each area. In the EMAP and NOAA studies of sediment quality, the results of toxicity tests were weighted to the surficial areas sampled within a priori defined sampling strata (Long, Robertson, et al., 1996, and Paul et al., 1992). Results of NOAA surveys conducted through 1995 were reported and compared among survey areas (Long, Robertson, et al., 1996, and Turgeon et al., 1998). Data from additional surveys in Galveston Bay, Biscayne Bay, northern Puget Sound, and Delaware Bay were described and compared with data from the EMAP surveys of estuarine provinces (Long, 2000a, 2000b). In both programs, samples were classified as toxic when the mean responses for each sample (amphipod survival or urchin fertilization) were less than 80% of controls. Tests of amphipod survival in solid-phase sediments and urchin fertilization (or equivalent endpoints) in pore waters were performed in the NOAA surveys on portions of the same samples. Estimates of the spatial (or surficial) extent of toxicity differed widely among survey areas and between the two tests (Tables 6.5 and 6.6). The amphipod tests were conducted with relatively unaltered bulk sediments using juvenile (not larval) forms of adult animals that are important components of infaunal assemblages, following widely accepted and standardized protocols. In these tests, A. abdita was used in all surveys except those conducted in

282 Handbook on Sediment Quality Table 6.5 Spatial extent of toxicity (square kilometers and percentages of total area) in amphipod survival testsa performed with solid-phase sediments from 27 U.S. bays and estuaries.

______Amphipod survival Number Total Toxic Percent Year of area area of total Survey areas sampled samples (km2) (km2)area Newark Bay 93 57 13.0 10.8 85.0 San Diego Bay 93 117 40.2 26.3 65.8 California coastal lagoons 94 30 5.0 2.9 57.9 Tijuana River 93 6 0.3 0.2 56.2 Long Island Sound 91 60 71.9 36.3 50.5 Hudson–Raritan Estuary 91 117 350.0 133.3 38.1 San Pedro Bay 92 105 53.8 7.8 14.5 Biscayne Bay 95–96 226 484.2 62.3 12.9 Boston Harbor 93 55 56.1 5.7 10.0 Delaware Bay 97 73 2346.8 145.4 6.2 Savannah River 94 60 13.1 0.2 1.2 St. Simons Sound 94 20 24.6 0.1 0.4 Tampa Bay 92–93 165 550.0 0.5 0.1 Central Puget Sound 98 100 737.4 1.0 0.1 Northern Chesapeake Bay 98 53 2265.0 0.0 0.0 Pensacola Bay 93 40 273.0 0.04 0.0 Galveston Bay 96 75 1351.1 0.0 0.0 Northern Puget Sound 97 100 773.9 0.0 0.0 Choctawhatchee Bay 94 37 254.5 0.0 0.0 Sabine Lake 95 66 245.9 0.0 0.0 Apalachicola Bay 94 9 187.6 0.0 0.0 St. Andrew Bay 93 31 127.2 0.0 0.0 Charleston Harbor 93 63 41.1 0.0 0.0 Winyah Bay 93 9 7.3 0.0 0.0 Mission Bay 93 11 6.1 0.0 0.0 Leadenwah Creek 93 9 1.7 0.0 0.0 San Diego River 93 2 0.5 0.0 0.0 National estuarine average 1998 1696 10 281.2 432.8 4.2 a Test animal was A. abdita, except in California, where R. abronius was used.

California, where R. abronius was used in the tests. Tests were performed on nearly 1700 samples collected through the 1998 field season. The spatial extent of toxicity, expressed as the percentage of the total survey area, ranged from 85% in Newark Bay to 0% in 13 areas (Table 6.5). Survey areas in which toxicity was most pervasive (>50% of the study area) included Newark Bay, San Diego Bay, southern California coastal lagoons, Tijuana River estuary, and Long Island Sound bays. However, the largest area in which toxicity was observed in the amphipod tests was the Hudson–Raritan estuary, in which toxicity was estimated to occur throughout an area of 133 km2. Also, the spatial extent of toxicity in Biscayne Bay was relatively large (62 km2),

Toxicity Tests of Marine and Estuarine Sediment Quality 283 representing approximately 13% of that region. The magnitude of toxicity was greatest in the survey areas of the northeast and Southern California and smallest in the estuaries and bays of the southeast and northwest (Puget Sound). None of the samples from northern Chesapeake Bay and northern Puget Sound was toxic, and only one sample from central Puget Sound was toxic. Based on the NOAA data compiled from surveys completed through 1998, an overall average of 4.2% of the estuarine surficial area sampled was toxic in the amphipod tests. The spatial extent of toxicity in amphipod tests conducted with A. abdita in the Louisianian, Virginian, Carolinian, and Californian biogeographic provinces was reported in EMAP studies (Summers et al., 1993; Strobel et al., 1995; Hyland et al., 1996; and Bay, 1996, respectively). In these large estuarine areas, the surficial extent of toxicity was 8.4, 10, 2, and 0%, respectively, for a combined average of 7.3%, similar to the scope of toxicity observed in the combined NOAA data. All of these data together suggest that toxicity in this acute 10-day test is not pervasive; rather, it is confined to relatively small portions of the areas surveyed. As shown in the maps for areas such as Pensacola Bay and Charleston Harbor, acute toxicity was most often observed in highly industrialized waterways, harbors, bayous, and maritime basins. The sea urchin fertilization tests combine the attributes of testing the pore waters extracted from the sediments (instead of the bulk sediments) and using a sublethal endpoint measured with the gametes of the organisms, instead of acute mortality tests of the adult life stage forms used in the amphipod tests. Toxicants in the pore waters are expected to be highly bioavailable (i.e., not bound to the sediment particles). Sublethal bioassays often are more sensitive to toxicants than acute tests, and early life stages (including gametes) often are more sensitive than their respective adults. These tests were performed with the urchin A. punctulata in most areas. However, the purple sea urchin S. purpuratus was used in studies of Puget Sound and most California surveys. The red abalone Haliotis rufescens was used in the survey of San Pedro Bay, with the endpoint of percent normal morphological development of the embryos. Because of the nature of the urchin tests, toxicity was much more widespread in this test than in the amphipod survival tests (Table 6.6). The spatial extent of toxicity in tests of 100% pore water ranged from 98% in San Pedro Bay (with H. rufescens embryo development) to 0% in Leadenwah Creek, SC. Along with San Pedro Bay, the survey areas in Tampa Bay, San Diego Bay, Mission Bay, and San Diego River ranked among the highest in spatial extent of toxicity. However, the survey areas in Mission Bay, San Diego River, and Tijuana River were very small. The largest areas of toxicity (229 to 777 km2) were observed in northern Chesapeake Bay, Delaware Bay, Tampa Bay, Biscayne Bay, and Galveston Bay. Survey areas in which toxicity was least widespread (<10% of the area) included Boston Harbor, Pensacola Bay, and northern and central Puget Sound. Based on the data compiled through 1998, an average of approximately 26% of the overall estuarine area sampled was toxic.

284 Handbook on Sediment Quality Table 6.6 Spatial extent of toxicity (square kilometers and percentages of total area) in sea urchin fertilization tests performed with 100% sediment pore waters from 22 U.S. bays and estuaries. Unless specified differently, tests were performed with A. punctulata.

Urchin fertilization ______in 100% pore waters Number Total Toxic Percent Year of area area of total Survey areas sampled samples (km2) (km2)area San Pedro Baya 92 105 53.8 52.6 97.7 Tampa Bay 92–93 165 550 463.6 84.3 San Diego Bayb 93 117 40.2 25.6 76.0 Mission Bay 93 11 6.1 4.0 65.9 Tijuana River 93 6 0.3 0.2 56.2 San Diego River 93 2 0.5 0.3 52.0 Biscayne Bay 95–96 226 484.2 229.5 47.4 Choctawhatchee Bay 94 37 254.5 113.1 44.4 California coastal lagoons 94 30 5 2.1 42.7 Winyah Bay 93 9 7.3 3.1 42.2 Apalachicola Bay 94 9 187.6 63.6 33.9 Galveston Bay 96 75 1351.1 432.0 32.0 Charleston Harbor 93 63 41.1 12.5 30.4 Savannah River 94 60 13.1 2.42 18.4 Delaware Bay 97 73 2346.8 247.5 10.5 Boston Harbor 93 55 56.1 3.8 6.6 Sabine Lake 95 66 245.9 14.0 5.7 Pensacola Bay 93 40 273 14.4 5.3 Northern Puget Soundb 97 100 773.9 40.6 5.2 St. Simons Sound 94 20 24.6 0.7 2.6 St. Andrew Bay 93 31 127.2 2.3 1.8 Leadenwah Creek 93 9 1.7 0 0.0 National estuarine average 1995 940 2082.6 886.3 42.6 National estuarine average 1996 1136 3723.3 1439.8 38.7 National estuarine average 1997 1309 6837.8 1728.0 25.3 a Embryological development tests of pore waters were performed with Haliotis rufescens. b Fertilization tests of pore waters were performed with S. purpuratus.

PREDICTING TOXICITY WITH NUMERICAL SEDIMENT QUALITY GUIDELINES. Numerical sediment quality guidelines have been derived with either equilibrium-partitioning models or one of several empirical approaches (U.S. EPA, 1992). Guidelines developed thus far for saltwater have been intended primarily for informal (nonregulatory) use in the interpretation of chemical data from sediment analyses. The state of Washington, on the other hand, adopted sets of numerical values calculated with the apparent effects threshold (AET) approach as state saltwater standards (WDOE, 1995). Because

Toxicity Tests of Marine and Estuarine Sediment Quality 285 toxic chemicals often occur in complex mixtures in marine and estuarine sediments, it is anticipated that sediment quality guidelines (SQGs) frequently would be used in situations in which it would be difficult to determine which chemicals caused or contributed to toxicity. Some have speculated that currently available SQGs are not predictive of contaminant-induced toxicity (O’Connor and Paul, 2000, and Spies, 1989), despite considerable evidence to the contrary (Chapman et al., 1991, and Long and MacDonald, 1998). To quantify and demonstrate their relative effectiveness in the classification of samples, several studies have been conducted to estimate the protectiveness and predictive abilities of SQGs. None of the studies summarized in this chapter was implemented with data collected for the sole purpose of quantifying the effectiveness of SQGs; rather, they were based on evaluations of data gathered in field studies and assessments commisioned for other purposes. One set of marine SQGs was derived by evaluating matching chemical- and biological-effects data compiled from many studies performed throughout North America. Effects range—low (ERL) and effects range—median (ERM) values were calculated as the 10th percentile and 50th percentile (median), respectively, of the database in which adverse biological effects were observed. Only the chemical concentrations associated with observed adverse effects were used in the derivation of these SQGs. The ERL and ERM values were derived for 25 chemicals and three classes of substances (Long, MacDonald, et al., 1995). The ERL values represent concentrations below which adverse effects rarely occurred and above which effects might begin. As median values, the ERM concentrations represent midrange levels above which effects were observed much more frequently, but not necessarily in all cases. Using a similar database, but considering both the “effects” data and the “no-effects” data, MacDonald et al. (1996) calculated threshold effects level (TEL) and probable effects level (PEL) values for 31 chemicals. The ERLs and TELs were functionally equivalent, as were the ERMs and PELs. Equivalent ERL–ERM and TEL–PEL values have been derived for freshwater sediments using similar methods (Ingersoll et al., 1996, and Smith et al., 1996). In addition, toxicity thresholds were determined for sediment- associated selenium in freshwater (Van Derveer and Canton, 1997). By compiling and examining effects-based guidelines for total PCBs, MacDonald et al. (2000) reported that both saltwater and freshwater values were similar and, therefore, justified merging the two databases for development of consensus-based effects concentrations. Following this same approach, MacDonald et al. developed consensus-based sediment guidelines for many other substances in freshwater. The predictive abilities of the consensus-based freshwater guidelines were subsequently analyzed with an independent database following many of the procedures described in this chapter for evaluation of marine SQGs (Ingersoll et al., 2001). The family of empirical approaches to SQG development contrast with the assumptions and methods used to derive guidelines with theoretical (i.e., equilibrium partitioning) approaches. In the theoretical approaches, it is assumed that the toxicity of a sediment-associated toxicant is a function of its bioavailability and bioavailability, in turn, is a function of the concentrations of primarily total organic carbon and acid-volatile sulfides in the sediments

286 Handbook on Sediment Quality (Ankley et al., 1996; Di Toro and McGrath, 2000; and MacDonald and Salazar [Eds.], 1995). In these approaches, toxicity thresholds in sediments are estimated by applying partitioning coefficients to results of acute and chronic tests of individual substances or classes of substances determined in water-only bioassays. It is assumed in this approach that toxicological thresholds determined for laboratory test waters would be equivalent to those for interstitial waters of sediments. To quantify the actual predictive abilities of these SQGs, Long, Field, and MacDonald (1998) assembled data from 1068 samples collected nationwide by the U.S. EPA and NOAA in which results were reported from chemical analyses of sediments and matching toxicity tests with amphipods and other organisms. These data were not used in the derivation of the guidelines. Among the 1068 samples, there were 329 in which none of the chemical concentrations equalled or exceeded the ERL values. Because the ERLs were calculated as the 10th percentiles of the effects database, it was expected that approximately 10% of samples would be toxic when all chemical concentrations were lower than these values. Examination of the independent database showed that 11% of the 329 samples were highly toxic (mean survival significantly different from controls and less than 80% of controls) in amphipod tests. In the same analysis, 9% of samples were highly toxic when none of the chemical concentrations equaled or exceeded TELs from MacDonald et al. (1996). Using a similar database consisting of NOAA and U.S. EPA survey data, O’Connor et al. (1998) reported that only 5% of 481 samples were toxic when all chemical concentrations were lower than the ERLs. Thus, the differences between the predicted outcomes and actual observations in these three analyses were approximately 1, 1, and 6%, respectively. The predictive abilities of individual ERM values were determined in the same study (Long, Field, and MacDonald, 1998). The incidence of significant toxicity in amphipod tests alone and in any of two or three tests performed was determined for each chemical or chemical class (Table 6.7). Analyses of the independent database showed that, in most cases, the incidence of toxicity was greater than 50% when individual ERM values were exceeded, ranging from 51 to 100% (Table 6.7). When results from other toxicity tests (n = 437)— usually sublethal assays—were considered, the incidence of toxicity generally increased by factors of approximately 20 to 30%. The predictive abilities of individual PEL guidelines were similar to those for the ERMs (Table 6.8). In most cases, 60% or more of the samples were toxic in amphipod tests when the individual PELs were equaled or exceeded. Generally, the incidence of toxicity rose to more than 90% when data from other tests were considered. Because the ERM and PEL guidelines were derived as midrange values, approximately 50% of samples were expected to be toxic when chemical concentrations equaled or exceeded them. However, the outcomes of these analyses (incidence of toxicity typically >60% in amphipod tests) often exceeded the predictions by more than 10%. In some cases, the observed incidences of toxicity exceeded the predicted rates by 40 to 50%. During the analyses of the predictive ability of the SQGs, Long, Field, and MacDonald (1998) observed that the incidence of toxicity increased with

Toxicity Tests of Marine and Estuarine Sediment Quality 287 Table 6.7 Incidence of toxicity in either amphipod tests alone or any of the two to four tests performed among samples in which individual ERMs were exceeded (from Long, Field, and MacDonald, 1998).

Amphipod tests Any test performed ______(n = 1068)______(n = 437) Percent Percent significantly significantly Chemicala Number toxic Number toxic Metals Cadmium 2 100 0 na Chromium 5 100 2 100 Copper 25 52 22 82 Lead 35 83 20 95 Mercury 126 66 81 90 Nickel 21 76 5 100 Silver 38 65 22 100 Zinc 56 66 32 88 PAHs 2-Methylnaphthalene 6 100 4 100 Dibenz(a,h)anthracene 43 72 31 81 Acenaphthene 13 77 7 100 Acenaphthylene 7 100 6 100 Anthracene 25 76 19 89 Benz(a)anthracene 47 77 30 90 Benzo(a)pyrene 63 64 46 83 Chrysene 38 68 26 92 Fluoranthene 32 72 21 95 Fluorene 17 71 10 90 Naphthalene 4 75 4 100 Phenanthrene 40 75 25 96 Pyrene 66 67 46 89 Sum LPAHs 48 80 31 96 Sum HPAHs 76 60 56 84 Sum total PAHs 11 91 6 100 Chlorinated hydrocarbons p,p'-DDE 89 55 70 92 Total DDTs 107 61 82 96 Total PCBs 194 51 162 84 a LPAH = low-molecular-weight polynuclear aromatic hydrocarbons; HPAH = high-molecular- weight polynuclear aromatic hydrocarbons; and PCBs = polychlorinated biphenyls.

increasing numbers of chemicals that exceeded the midrange guidelines. To further clarify this relationship, Long and MacDonald (1998) demonstrated that the incidence of toxicity in amphipod tests increased from 24 to 32% when 1 to 5 ERMs or PELs were exceeded to 85 to 88% when more than 10 ERMs or more than 20 PELs were exceeded (Table 6.9). The incidence of toxicity increased steadily from approximately 10 to 75% over four ranges in

288 Handbook on Sediment Quality Table 6.8 Incidence of toxicity in either amphipod tests alone or any of the two to four tests performed among samples in which individual PELs were exceeded (from Long, Field, and MacDonald, 1998).

Amphipod tests Any test performed ______(n = 1068)______(n = 437) Percent Percent significantly significantly Chemicala Number toxic Number toxic Metals Cadmium 21 81 6 100 Chromium 41 66 24 92 Copper 179 59 146 87 Lead 122 63 85 92 Mercury 127 66 82 89 Nickel 74 66 37 94 Silver 109 59 82 88 Zinc 126 62 87 86 PAHs 2-Methylnaphthalene 47 73 22 95 Dibenz(a,h)anthracene 80 64 65 85 Acenaphthene 84 62 56 95 Acenaphthylene 47 77 40 98 Anthracene 131 56 100 89 Benz(a)anthracene 116 61 93 88 Benzo(a)pyrene 126 59 100 88 Chrysene 116 56 93 88 Fluoranthene 103 59 80 88 Fluorene 74 70 51 94 Naphthalene 38 74 23 100 Phenanthrene 106 60 77 92 Pyrene 117 60 94 89 Sum LPAHs 117 64 79 92 Sum HPAHs 114 58 90 87 Sum total PAHs 56 68 38 89 Chlorinated hydrocarbons p,p'-DDE 3 33 3 67 p,p'-DDD 144 65 115 92 p,p'-DDT 97 67 68 94 Total DDTs 101 64 78 95 Total PCBs 191 49 159 83 Dieldrin 41 80 25 96 Lindane 54 19 50 86 a DDT = trichloro-bis(chlorophenyl)ethane; DDD = dichloro-bis(chlorophenyl)ethane; DDE = dichloro-bis(chlorophenyl)ethylene; LPAH = low-molecular-weight polynuclear aromatic hydrocarbons; and HPAH = high-molecular-weight polynuclear aromatic hydrocarbons.

Toxicity Tests of Marine and Estuarine Sediment Quality 289 = 1513) n = 219) ( n = 226) ( n Biscayne Pearl Combined b = 1068) ( n 76738083 43 47 41 38 31 31 31 10 44 56 56 45 41 46 41 37 = 1513) ( n c c d d samples Average, control-adjusted a = 219) ( n 80% of control) or highly toxic. ≥ 80% of control) or highly toxic. ≥ = 226) ( n 80% of controls. Biscayne Pearl Combined National < in amphipod survival testsin amphipod survival amphipod survival Percent highly toxic Percent b 0.05, mean survival < 0.05, mean survival p < p = 1068) ( n National ______0.1 11 3 41.5 0 75 75 93 100 95 94 93 0.1 10 3 6 8 93 95 92 93 2.3 77 75 33 < > < > according to numerical sediment quality guidelines (from Long et al.,according to numerical sediment quality guidelines (from 2000). Chemical characteristics database Bay Harbor summary database Bay Harbor summary Mean ERM quotients Mean PEL quotients No ERLs exceededNo TELs exceededMean ERM quotients 0.11–0.5Mean PEL quotients 0.11–1.51–5 ERMs exceeded1–5 PELs exceeded 30 11 25Mean ERM quotients 0.51–1.5 9Mean PEL quotients 1.51–2.36–10 ERMs exceeded 156–20 PELs exceeded 46 32 23 5 50 24Mean ERM 6 quotients 5 73 46 12 14 52 67 37 47 0 21 61 24 21 67 9 33 59 2 8 49 32 71 49 48 18 81 84 92 57 92 48 85 74 79 81 94 66 83 93 97 48 93 66 63 93 46 71 70 102 86 68 86 86 47 92 76 99 57 92 70 79 66 68 73 88 59 70 >20 PELs exceeded 88 75 50 Mean PEL quotients >10 ERMs exceeded 85 75 33 Data from Long, Field, and MacDonald, 1998. toxic ( 100% (three to three) either marginally Mean survival significantly different from controls and different significantly Mean survival toxic ( either marginally to two) 100% (two relative to sediment guidelinesrelative ( Table 6.9Table in marine sediment samples classified amphipod survival percent incidence of highly toxic samples and average Percent a b c d Category 1 Category Category 2 Category 3 Category 4 Category

290 Handbook on Sediment Quality mean guideline quotients (Long and MacDonald, 1998) (Table 6.9). Data from these analyses provided the basis for recommendations on using SQGs to estimate the probabilities of acute toxicity in sediments. Reanalysis of the data set used by Long and MacDonald (1998) showed that the average, control-adjusted survival of the amphipods decreased from >90% to <50% in the four ranges in chemical concentrations (Table 6.9). Despite the fact that these analyses were performed on data from 1068 samples collected along portions of all three U.S. coastlines, concerns remained that the guidelines may not be applicable over a wide variety of geochemical and estuarine conditions. Table 6.9, prepared by Long et al. (2000), includes comparable data from a NOAA survey conducted in Biscayne Bay (FL), in which many of the sediments were carbonate sands (Long et al., 1999). Considerable amounts of shell and coral debris occurred in many samples from Biscayne Bay, contributing to the unusual geochemistry. Some of the canals and waterways adjoining Biscayne Bay were sampled in which hyposaline conditions and organically enriched muds were encountered. Another data set (n = 218) was obtained from a study conducted in 1997 by the U.S. Navy (Ogden Environmental, in preparation). This survey of Pearl Harbor (HI) reported fine-grained sediments in most samples, resulting from erosion of upland soils and lava. In both of the new studies, methods for chemical analyses of surficial sediments and amphipod toxicity tests with A. abdita were comparable with those previously reported (Long and MacDonald, 1998). The data from Biscayne Bay and Pearl Harbor generally indicated patterns of increasing toxicity in association with increasing chemical concentrations similar to those observed in the national database (Table 6.9). Addition of the data from these two areas to the combined database had little affect on either the incidence of toxicity or average survival. The probabilities of toxicity remained low (<10%) and average survival very high (92 to 93%) for samples in which all chemical concentrations were less than the guidelines or mean guideline quotients were less than 0.1. The probabilities of toxicity increased steadily and average survival decreased incrementally as chemical concentrations increased relative to the guidelines. In the category with highest chemical concentrations, the probabilities of toxicity reached maxima of 73 to 83% and average survival dropped to 37 to 46%. In their analyses of sediment contaminant and toxicity data collected throughout the San Francisco Bay estuary, Thompson et al. (1999) showed a very strong statistical correlation (r = –0.681, p = 0.0001, n = 142) between mean ERM quotient values and percent survival in tests with E. estuarius. None of the toxicity tests was significant in samples with mean ERM quotients <0.105. The incidence of toxicity increased to approximately 50% with values of 0.114 to 0.182, increased to 89% with values above 0.185, and peaked at 100% with values >0.219. In contrast, the correlations between results of bivalve embryo tests of sediment elutriates and mean ERM quotients were not significant. The composition of the mixtures of chemicals that were correlated with amphipod survival often differed among the regions of the bay. The authors concluded that there was evidence that the additive

Toxicity Tests of Marine and Estuarine Sediment Quality 291 Table 6.10 Relationships between incidence of degraded benthic populations and mean SQG quotients with data summarized for Carolinian, Virginian, and Louisianian provinces (Hyland, Van Dolah, and Snoots, 1999, and Hyland, Van Dolah, Paul, et al., 1999a, 1999b).

Guideline Range in mean Number Percent with type Percentile SQG quotients of stations degraded benthos ERM 5th ≤0.008 194 15 5th–50th 0.008–0.044 696 37 50th–95th 0.044–0.407 422 62 95th >0.407 37 78 PEL 5th ≤0.012 173 17 5th–50th 0.012–0.077 720 36 50th–95th 0.077–0.812 419 61 95th >0.812 37 78

effects of numerous substances in the sediments contributed to toxicity in the amphipod survival tests. Reliance on data from laboratory toxicity tests to evaluate the predictive abilities of sediment guidelines has been criticized (O’Connor and Paul, 2000). Using a benthic index of biotic integrity (B-IBI) developed for the EMAP estuaries program, Hyland, Van Dolah, and Snoots (1999) determined the degree to which estuarine benthic populations of the Carolinian province were degraded over ranges in mean SQG quotients. They reported that the incidence of degraded benthos was 5% in samples with mean ERM quotients ≤2900.02 or with mean PEL quotients ≤0.035. The incidence of degraded benthic composition increased to 78% and 73% in samples with mean ERM quotients ≥0.058 or mean PEL quotients ≥0.096, respectively. The calculations of the benthic index accounted for possible effects of differences in salinity and dissolved oxygen among the samples, primarily by excluding samples with low values. Nevertheless, the benthic index values were determined to be correlated with these and other natural environmental variables in degraded sediments. In subsequent analyses of other datasets, Hyland and colleagues have determined that the relationships observed in the Carolinian province also occurred elsewhere. Based on the previous statistical methods applied to data from the Carolinian province (Hyland, Van Dolah, and Snoots, 1999), additional analyses were performed with data merged together from the Carolinian, Virginian, and Louisianian provinces (Hyland, Van Dolah, Paul, et al., 1999a, 1999b) (Table 6.10). The categories in mean SQG quotients were determined as the 5th, 50th, and 95th percentiles of the quotients from the frequency distributions of the data. The percentages of samples with benthos classified as degraded, based on the B-IBI, are compared within four ranges in mean SQG quotients. The incidence of benthic effects was 15 to 17% in samples with the lowest guideline quotients (<0.008 and <0.012), increased to 36 to 37%, 61 to 62%, and 78% with increasing chemical concentrations. The data

292 Handbook on Sediment Quality Table 6.11 Macrobenthic trophic structure in estuarine sediments of the northern Gulf of Mexico in randomly selected samples versus those in which chemical concentrations were nearly equal to or greater than the ERL values (from Brown et al., 2000).

______Mean percent of total abundance ______Random Metals Metals PAHs DDT Trophic group sites >ERLs ∼ERLs >ERLs >ERLs Surface deposit 29 15 17 12 14 Subsurface deposit 28 46 56 42 64 Filter 25 27 13 40 15 Carnivore 12 10 11 3 4 Omnivore 2 1 <111 Others 4 2 2 2 3

combined from the three provinces indicated that the relationships between the benthos and guideline quotients reported for the Carolinian province alone were robust. In a very interesting study of the benthos in Gulf of Mexico estuaries, Brown et al. (2000) showed that the trophic structure of infauna in samples in which ERL values were exceeded differed from that in randomly selected sites (Table 6.11). The proportion of the infauna composed of surface deposit feeders was considerably lower (12 ± 3 to 17 ± 4%) in samples with chemical concentrations greater than the ERLs than in samples chosen randomly (29 ± 6%). Subsurface deposit feeders, primarily oligochaetes, showed the opposite pattern with higher proportionate abundance (42 ± 8 to 64 ± 10%) in sediments with chemical concentrations equal to or greater than the ERLs compared with randomly selected sites (28 ± 6%). The abundance of the latter trophic group was positively correlated with the concentrations of some toxicants. These samples had chemical concentrations that approximated or exceeded the ERL values for metals, PAHs, or trichloro-bis(chlorophenyl) ethane (DDT), but not the ERM values. They were not toxic in laboratory tests performed with amphipods. The investigators, nevertheless, demonstrated that the trophic structure shifted in samples with this slight degree of contamination that was insufficient to cause acute toxicity. Therefore, their results corroborated the observations of Hyland, Van Dolah and Snoots (1999) and Hyland, Van Dolah, Paul, et al. (1999a, 1999b), indicating that shifts in macrobenthic trophic structure could be detected in samples with slight degrees of contamination when acute toxicity was not apparent. Based on data gathered during the survey of northern Puget Sound (Long, Hameedi, Robertson, et al., 1999), adverse changes in the macrobenthos were apparent in samples that were not toxic in the acute amphipod tests. In this region, the average numbers of taxa in samples decreased with increasing guideline quotients. There was an average of approximately 60 benthic species in samples from northern Puget Sound when mean ERM quotients were the lowest (<0.1) (Table 6.12). Average numbers of taxa diminished

Toxicity Tests of Marine and Estuarine Sediment Quality 293 Table 6.12 Relationships between total numbers of macrobenthic species and mean SQG quotients in northern Puget Sound.

Range in mean Number Mean numbers ERM quotients of samples of species ≤0.1 27 60.2 0.11–0.2 51 45.1 0.21–0.3 13 35.4 0.31–0.4 2 24.0 0.41–0.5 4 33.5 >0.5 3 30.7

rapidly to approximately 25 to 35 species as the quotients increased to 0.2 or higher. These data suggested that, as observed in the estuaries of the Atlantic and Gulf of Mexico coasts, changes in the benthic communities occurred in association with chemical concentrations below those that caused significant amphipod mortality. Swartz et al. (1995) derived guidelines for concentrations of total PAHs with a theoretical approach involving use of equilibrium partitioning models. The incidence of acute toxicity was determined in field-collected samples over six ranges in PAH toxic units (Table 6.13). At relatively low chemical concentrations, the results differed between the dataset in which PAHs were of primary concern and the dataset in which other mixtures of contaminants were present. The correct predictions of mortality ranged from 72 to 100% in samples known to have high PAH concentrations and from 44 to 100% in

Table 6.13 Percentages of field-collected samples that were correctly predicted to be toxic in amphipod tests in relation to predicted sum of PAH toxic units in samples in which PAHs were principal contaminants of concern and samples in which other chemicals were present (from Swartz et al., 1995).

Correct predictions Correct predictions ______where PAHs were of concern where______PAHs were not of concern Sum of PAH Number of Percent Number of Percent toxic units samplesa of total samplesa of total ≤ 0.1 34.2 95.0 185.0 57.8 0.10–0.24 25.1 93.3 21.6 44.1 0.25–0.49 18.7 71.9 7.2 55.4 0.50–0.99 8.5 77.3 6.0 75.0 1.00–4.99 6.2 77.5 3.4 85.0 >5.00 6.0 100 1.0 100 Total correct predictions 86.6% 56.8% a Sum of the lower number of samples observed or predicted to be in each of three mortality classes (<13, 13–24, and >24% mortality).

294 Handbook on Sediment Quality Table 6.14 Incidence of toxicity in amphipod survival tests in samples within four ranges in total PAH concentrations defined by consensus-based, sediment-effect concentrations (from Swartz, 1999).

______Total PAH concentrations > TEC > MEC < TEC < MEC < EEC > EEC PAH-contaminated sites Number of samples 36 60 24 12 Percent toxic 6% 43% 50% 100% Mean percent mortality 8% 34% 38% 97% EMAP sites Number of samples 633 43 2 No data Percent toxic 13% 44% 100% Mean percent mortality 11% 28% 40%

samples known to have high concentrations of other chemicals as well as PAHs. Total correct predictions of mortality were 87 and 57% in the two datasets. Furthermore, Swartz et al. (1995) reported that the probabilities of toxicity ranged from 60 to 100% for several different SQGs derived to be predictive of effects and ranged from 5 to 6% for several SQGs derived to be threshold values (i.e., predictive of nontoxicity). In the calculation of consensus-based guidelines for total PAHs, Swartz (1999) reported that many of the guidelines derived with different empirical and theoretical approaches resulted in very similar concentrations and that this similarity probably was not coincidental. To illustrate this point, he calculated a threshold effects concentration (TEC) of 290 µg/goc, a median effects concentration (MEC) of 1800 µg/goc, and an extreme effects concentration (EEC) of 10 000 µg/goc as the means of clusters of individual guidelines derived for the same narrative purpose. Field verifications of the predictive abilities of the three concentrations were performed with data from EMAP surveys and a compilation of datasets from surveys of PAH-contaminated sites in the U.S. and Canada. In the PAH-contaminated sites, the incidence of toxicity increased steadily from 5.6 to 43 to 50% and to 100% in the four concentration ranges defined by the three guidelines (Table 6.14). In these same samples, average percent mortality increased from 8 to 34 to 38% and to 97% within the four ranges. Swartz (1999) also reported that the numbers of species of crustaceans and mollusks in samples from San Diego Bay and Elliott Bay declined as PAH concentrations increased across the four ranges. The ERM value (Long, MacDonald, et al., 1995) and the PEL value (MacDonald et al., 1996) for saltwater sediments were included in the calculations of the MEC by Swartz (1999) for total PAHs. Swartz (1999) showed that 50% of samples were toxic when the MEC was exceeded. As a basis for comparisons between the predictive abilities of the MEC and the

Toxicity Tests of Marine and Estuarine Sediment Quality 295 Table 6.15 Incidence of toxicity in amphipod survival tests in samples in which ERM or PEL values were exceeded for sums of low- molecular-weight (LMW) PAHs, high-molecular-weight (HMW) PAHs, or 13 PAHs (from Long, Field, and MacDonald, 1998).

______[n] Percent significantly toxic Chemical concentrations >ERM >PEL Sum 7 LMW PAHs [48] 80 [117] 64 Sum 6 HMW PAHs [76] 60 [114] 58 Sum total (13) PAHs [11] 91 [56] 68

ERM and PEL values, results from Long, Field, and MacDonald (1998) comparable with those of Swartz (1999) are summarized in Table 6.15. In this analysis, 91 and 68% of samples were toxic in amphipod tests when the ERM or PEL for total PAHs was exceeded. The incidence of toxicity was somewhat lower when ERM or PEL values were considered for sums of low- or high- molecular-weight PAHs. Nevertheless, the incidence of toxicity exceeded 50% in all cases, somewhat better than the results reported by Swartz (1999). The ERL guideline for total PAHs and the lower theshold value derived with equilibrium partitioning (EqP) theory were equally accurate in predicting nontoxicity. In two separate studies, 89% of samples were correctly classified as nontoxic when no ERLs were exceeded (Long, Field, and MacDonald, 1998) and when the PAH concentrations were less than the lower toxicity threshold (Di Toro and McGrath, 2000). The presence of toxicity was correctly predicted in 89% of samples with the upper EqP-derived value and in 91% of samples with the empirically derived ERM (Table 6.15). Using the approach of Swartz (1999), many different SQGs derived with either theoretical or empirical approaches for freshwater or saltwater were assembled to calculate “consensus-based” sediment effect concentrations (SEC) for total PCBs (MacDonald et al., 2000). In this evaluation, some SQGs were assembled that had been derived for the purpose of identifying total PCB concentrations below which adverse effects were rare. The geometric mean of these SQGs (the TEC) was calculated to be 0.04 mg/kg, dry weight (dw). The geometric mean of SQGs that were derived as midrange values above which effects were expected more frequently was calculated to be 0.4 mg/kg dw (the MEC). Finally, the geometric mean of SQGs that had been derived to be highly predictive of toxicity was determined to be 1.7 mg/kg (the EEC). In their evaluation of the performance of the SEC guidelines for total PCBs, MacDonald et al. (2000) assembled datasets from surveys in which PCB concentrations indicated a wide range in concentrations. They reported that the incidence of toxicity in amphipod survival tests increased from 12% with PCB concentrations

296 Handbook on Sediment Quality Table 6.16 Incidence of toxicity and average percent survival in amphipod tests within five ranges in total PCB concentrations defined by consensus SECs (from MacDonald et al., 2000).

Range in Average total PCB Number Percent Percent SEC concentration of samples toxic survival 0.04–0.4 mg/kg, dw 391 32.7 75.6 >MEC–EEC >0.4–1.7 mg/kg, dw 133 49.6 65.8 >MEC >0.4 mg/kg, dw 161 55.9 58.3 >EEC >1.7 mg/kg, dw 28 85.7 37.7

at concentrations >EEC (Table 6.16). Average amphipod survival in these categories was 90, 76, 66, and 38%. Therefore, the predictive ability (i.e., as measured by percent incidence of toxicity and average percent survival) of the consensus-based SEC for total PCBs was very similar to that observed in Table 6.9 for all the effects-range and effects-level SQGs. Also, these data indicated an association between PCB concentrations and toxicity almost identical to that reported by Swartz (1999) for the SECs for total PAHs. Some investigators have concluded that dry-weight concentrations of metals are not predictive of toxicity (see Chapters 3 and 5). To objectively compare the predictive abilities of guidelines for trace metals derived with empirical approaches and theoretical approaches, Long, MacDonald, et al. (1998) analyzed a data set (n = 77) assembled by Hansen et al. (1996). These data were compiled from various locations with matching metals concentrations and amphipod survival data. The metals guidelines derived with empirical approaches included the AET values (WDOE, 1995) and the ERL–ERM values (Long, MacDonald, et al., 1995), and expressed in units of dry weight. The guidelines derived with theoretical methods were the simultaneously extracted metals-to-acid volatile sulfides ratios (SEM/AVS) or differences (Hansen et al., 1996). Among the 52 samples in which SEM/AVS ratios were <1.0 (i.e., predicting nontoxicity), 81% were not toxic (error rate of 19%). In comparison, 97 and 100% of samples were not toxic when bulk metals concentrations were less than all AETs or ERLs, respectively, giving error rates of 3% and 0% (Table 6.17). When SEM–AVS ratios were >1.0, 28% of samples were toxic (error rate of 22%, assuming at least 50% would be toxic). When one or more metals concentrations exceeded an AET or ERM, 35% were toxic in these samples (error rate of 15% for both sets of SQGs). Therefore, the empirically derived SQGs were somewhat better predictors of nontoxic conditions than the guidelines derived with the theoretical approach, and both sets of values were roughly equivalent in predicting the presence of toxicity. The AET values for Puget Sound were evaluated in assessments of their “sensitivity,” defined as the proportion of toxic samples that were predicted to be toxic. Also, they were evaluated for “efficiency,” that is, the proportion of

Toxicity Tests of Marine and Estuarine Sediment Quality 297 Table 6.17 Percentages of samples that were toxic in amphipod survival tests when predicted to be either toxic or nontoxic with different sets of sediment quality guidelines for trace metals (data from Long, MacDonald, et al., (1998).

Chemical Percent Percent characteristics Number correct Number correct relative to of classification of classification sediment guidelines samples as toxic samples as nontoxic SEM/AVS ratios ≤1.00 52 81 SEM/AVS differences <0.00 52 81 All metals concentrations 1.00 25 28 SEM/AVS differences ≥0.00 25 28 SEM/AVS ratios >5.00 8 50 One or more metals concentrations >AET 46 35 One or more metals concentrations >ERL 49 35 Mean ERM quotients for metals ≥1.0 37 38

stations designated as affected that were correctly predicted. These evaluations were conducted with data from several toxicity tests and macroinvertebrate infaunal analyses (Ginn and Pastorok, 1992). Evaluations were conducted for the guidelines established for each of 47 chemicals or chemical groups. Sensitivity ranged from 58 to 93% and efficiency ranged from 37 to 72%, depending on the biological test. The AET values were adopted as state standards (WDOE, 1995) and have become incorporated to the monitoring, regulatory, and permitting programs for Puget Sound. In their study of the toxicity thresholds of total DDT (sum of six isomers), Swartz et al. (1994) showed that adverse effects in the benthos occurred at concentrations below those that caused 50% mortality in laboratory tests

(LC50) (Table 6.18). The study focused on DDT-contaminated sediments in an industrialized channel of San Francisco Bay. In tests of Lauritzen Canal sediments performed with E. estuarius, the LC50 for total DDT was determined to be 2500 µg/g organic carbon. This result compared favorably with the LC50s for a site in Hunstville, AL (2580 µg/gOC), and off Palos Verdes, CA (1040 µg/gOC). In the Lauritzen Canal samples, toxicity began at concentrations greater than 300 µg/gOC, roughly equivalent to the concentrations associated with significant losses of resident amphipods in the benthos. The mean guideline quotients derived for evaluation in Table 6.9 were based on the normalization of chemical concentrations with all of the avail-

298 Handbook on Sediment Quality Table 6.18 Toxicity thresholds for total DDT in sediments derived from field-collected samples (from Swartz et al., 1994)a.

Biological test Total DDT

Field-derived 10-day LC50 Lauritzen Canal, CA (E. estuarius) 2500 Huntsville, AL (H. azteca) 2580 Palos Verdes, CA (R. abronius) 1040 10-day sediment toxicity threshold R. abronius >300 Benthic amphipods missing or rare Lauritzen Canal, CA >100 Palos Verdes, CA >200

a OC = organic carbon and LC50 = lethal concentration for 50% of test animals.

able guidelines (e.g., all ERMs and all PELs for individual chemicals). It is reasonable to assume that some chemicals were less important contributors to toxicity than others and, as a result, would tend to confuse or mask chemistry–toxicity relationships. If this were true, the agreement between the predicted toxicity and observed toxicity should improve if the data from the nonconcordant chemicals were ignored when calculating the mean guideline quotients (Fairey et al., 2001). By iteratively eliminating chemicals from consideration and merging guidelines from several approaches, the best agreement between predicted and observed toxicity was attained (Table 6.19). With mean guideline quotients derived with data from nine chemicals (five trace metals, four organics), toxicity occurred in only 6% of samples (n = 804) when the quotients were less than 0.1. Average amphipod survival in these samples was 95%. These are slight improvements over the results shown in Table 6.9. The incidence of toxicity increased steadily to 91%, and average survival decreased steadily to 31% over 10 ranges in the guideline quotients, again representing an improvement in the chemistry–toxicity relationship shown in Table 6.9. The relationships between chemical concentrations in sediments and toxicity were further explored with a logistic regression model (Field et al., 1999). In addition, the authors estimated the predictive abilities of three sets of SQGs (ERLs, ERMs, AETs) for six chemicals with the model (Table 6.20). With this approach, the model predicted that 15 to 31% of samples would be toxic when concentrations were less than the ERLs, 38 to 53% would be toxic when concentrations exceeded the ERMs, and 68 to 94% would be toxic when concentrations exceeded the AETs. These results agree relatively well with the intended purposes of the sets of guidelines and substantiate the results of other studies of predictive ability.

ECOLOGICAL RELEVANCE OF TOXICITY TESTS. Amphipods are the most commonly used taxa in toxicity tests of sediment in North America (Ingersoll et al., 1997; Lamberson et al., 1992; and Swartz, 1989). However, despite their increasing importance in regulatory and scientific issues, the

Toxicity Tests of Marine and Estuarine Sediment Quality 299 Table 6.19 Incidence of toxicity in amphipod tests and average percent survival within 10 ranges in mean SQG quotients (from Fairey et al., 2001).

Ranges in mean Number Percent Average SQGa quotients of samples significantly toxic percent survival 0–0.1 804 6 95 0.1–0.25 490 19 85 0.25–0.5 304 32 77 0.5–0.75 139 32 77 0.75–1.0 90 47 74 1.0–1.25 58 59 63 1.25–1.5 26 77 60 1.5–2.0 23 70 62 2.0–2.5 20 80 54 >2.5 33 91 31 a Quotients based on the following chemical guidelines: Cadmium, 4.21 µg/g dw (PEL) (MacDonald et al., 1996). Copper, 270 µg/g dw (ERM) (Long, MacDonald, Smith, and Calder, 1995). Silver, 1.77 µg/g dw (ERM) (Long, MacDonald, Smith, and Calder, 1995). Lead, 112.18 µg/g dw (PEL) (MacDonald et al., 1996). Zinc, 410 µg/g dw (ERM) (Long, MacDonald, Smith, and Calder, 1995). Total chlordane, 6 ng/g dw (ERM) (Long and Morgan, 1992). Dieldrin, 4.3 ng/g dw (PEL) (MacDonald et al., 1996). Total PAH, 1800 µg/gOC (concensus) (Swartz, 1999). Total PCB, 400 ng/g dw (concensus) (MacDonald, et al., 2000). ecological relevance of such toxicity tests has not been determined empirically in controlled, cause–effect experiments (Ingersoll et al., 1997). Therefore, it is uncertain whether results of acute tests, such as those used in dredged material assessments, protect against adverse population-level effects (Voorhees et al., 1998). Lamberson et al. (1992) concluded that there were no theoretical ecological models with which to predict ecological effects with toxicological data, and as a result, “Ultimately, sediment toxicity test end-

Table 6.20 Percentages of samples estimated with logistic regression models to be toxic in amphipod survival tests with chemical concentrations equivalent to several SQG concentrations (from Field et al., 1999).

______Guidelines values ___Estimated______percentage_____ toxic____ Chemical Units ERL ERM AET ERL ERM AET Lead mg/kg, dw 46.7 218 1 200 0.24 0.53 0.82 Mercury mg/kg, dw 0.15 0.7 2.3 0.15 0.41 0.66 Zinc mg/kg, dw 150 410 3 800 0.24 0.51 0.94 Fluoranthene ng/g, dw 600 5100 30 000 0.31 0.52 0.69 Phenanthrene ng/g, dw 240 1500 21 000 0.26 0.51 0.83 Total PCBs ng/g, dw 22.7 180 3 100 0.20 0.38 0.68

300 Handbook on Sediment Quality points must be shown to be predictive of community- and ecosystem-level responses.” Users of the dredged material testing manuals are cautioned that, “Mortality of a certain percent of the organisms of a particular species in a laboratory test does not imply that the population of that species around the disposal site would decline by the same percent if the proposed disposal takes place” (U.S. EPA and U.S. Army Corps, 1991). Anecdotal evidence from a few field studies has shown that amphipods are among the first taxa to disappear from benthic communities when exposed to pollution (ASTM, 1993). In a study conducted off southern California, Swartz et. al. (1986) described good correspondence between chemical contamination of sediments, decreased survival of amphipods in laboratory tests, and decreased amphipod abundance in the benthic infauna. Similar results were reported for studies in Elliott Bay (WA) (Ferraro and Cole, 1997), San Francisco Bay (CA) (Swartz et al., 1994), Commencement Bay (WA) (Swartz et al., 1982), and Baltimore Harbor (MD) (McGee et al., 1999). Invariably, in these studies, the abundance of sensitive taxa (notably amphipods and other crustaceans) decreased as amphipod survival in the laboratory tests decreased (Long et al., 2000). Results of attempts to relate population-level responses to measures of toxicity in laboratory tests or other measures of stress with population models have appeared recently (Kuhn et al., 2000; Snell and Serra, 2000; and Tanaka and Nakanishi, 2000). However, these studies were not performed with data from solid-phase amphipod tests. Biological endpoints were predicted reductions in population growth rates or population extinction times. Using a different approach (Long et al., 2001), the relationships between percent mortality in laboratory tests and the abundance and diversity of resident infauna were described for 14 survey areas in U.S. estuaries. Matching toxicity and benthic data collected with similar methods from a total of 1463 samples were reviewed. The data sets ranged in size from nine samples to 634 samples. The data from each study were summarized separately and then combined together. There were 231 samples classified as “toxic”, that is, in which control- adjusted survival was less than 80% (Long et al., 2001). In 92% of these samples, at least one measure of diversity or abundance was <50% of the respective, average reference value. In 67% of these samples, at least one measure of benthic infauna abundance or diversity was <10% of the reference average when the samples were classified as toxic. Frequently, the abundance of arthropods, crustaceans, or amphipods appeared to be the most sensitive measure of infaunal structure. In all samples from Puget Sound, Palos Verdes, Chesapeake Bay, Biscayne Bay, and Delaware Bay, at least one of the benthic measures was <50% of the respective reference averages when survival was <80%. In addition, all of the samples from Puget Sound, Palos Verdes, and Biscayne Bay had benthic measurements less than 10% of reference in the toxic samples. In many datasets, at least one measure of infauna diversity or abundance decreased as amphipod survival decreased in the laboratory tests. However, there were many other datasets in which there was no apparent concordance between metrics of benthic diversity and abundance and results of the amphipod survival tests.

Toxicity Tests of Marine and Estuarine Sediment Quality 301 Figure 6.12 Average abundance of benthic amphipods in sediment samples within eleven ranges in percent amphipod survival.

In many of the datasets compiled for review, the average abundance of amphipods was substantially lower in toxic samples than in nontoxic samples (Long et al., 2001). Based on the data compiled from 11 studies, amphipods were absent in 39% of the toxic samples (n = 222) and in 28% of the nontoxic samples (n = 552). These data indicated that the absence of amphipods was not a particularly discriminatory benthic assessment endpoint. Although a greater proportion of samples that were toxic were devoid of these animals, amphipods also were absent from many of the nontoxic samples, probably because of habitat-related factors other than toxicity. To determine if average abundances of amphipods per sample decreased as survival decreased, the data from many surveys were combined together and compared among 11 ranges in amphipod survival (Figure 6.12). In the combined database, there was an average of 332 amphipods in samples when survival was greater than the controls. Average amphipod abundance appeared to initially increase as amphipod survival decreased, reaching averages of 800 to 900 animals per sample when control-adjusted survival was 50 to 70%. Then, average abundance decreased to 4 to 33 animals per samples as survival decreased below 50%. This pattern was driven in large part by the large EMAP datasets, but was apparent in other individual datasets as well. Considerable variability in the data was noted within each category of survival.

302 Handbook on Sediment Quality CONCLUSIONS AND OUTLOOK Data were assembled for this chapter from many regional marine and estuarine surveys to illustrate some applications of sediment-toxicity tests in forming a weight-of-evidence regarding the relative quality of sediments. Clearly, the amphipod survival tests of solid-phase sediments have been most frequently used in North America. Often, they have been accompanied by tests of pore waters and solvent extracts. As frequently recommended in testing protocols and textbooks, most surveys and assessments of sediment quality have relied on data from multiple tests. Toxicity data have been used to identify spatial patterns or gradients in toxicity portrayed in easily understood maps. Depending on a variety of local conditions and factors, toxicity can be very predictable and show clear gradients relative to known sources. In other areas, toxicity may be highly restricted in scope or very heterogeneous and patchy. In a few bays and estuaries, acute toxicity seemed to be pervasive and severe. Data compiled from many U.S. estuaries indicate that acute toxicity in the amphipod survival tests is a relatively rare observation. Only a small minority of samples from regional estuarine surveys was classified as “toxic”, however, the incidence of toxicity was much higher in other more sensitive tests. The frequency distribution of results of toxicity tests differed among species and types of tests, indicating that different tests rarely show duplicative results. The spatial scales (or extent) of toxicity in U.S. estuaries differed among tests and among survey areas. Based on results of the amphipod survival tests, the spatial extent of toxicity ranged from 0 to 85% and the combined estuarine average was approximately 4 to 7%, depending on the database. Based on results of toxicity tests of pore waters, however, the spatial scales ranged from 0 to 98% and the combined estuarine average was approximately 26%, indicating that toxicity in more sensitive tests was much more widespread. Toxicity as measured in pore-water tests occurred more frequently than in amphipod tests, encompassing on average approximately 26% of the surficial area that was tested. Data from regional surveys have been compiled by several investigators to quantify the abilities of SQGs to correctly predict both the presence and the absence of toxicity in sediments. Despite the many issues involving bioavailability of sediment-sorbed toxicants (see Chapter 3), guidelines prepared with empirical approaches and theoretical approaches appear to be similar in predictive ability. Often, the discrepancies between predicted frequencies of toxicity and observed outcomes were less than 10%, sometimes as little as 1%. In some cases, the predictive abilities exceeded expectations. In all cases, there were consistent patterns of increasing toxicity associated with increasing chemical conentrations. The incidence of toxicity was remarkably similar whether the guidelines were derived with an empirical approach or a theoretical approach. The significance of these exercises is that the various sets of guidelines can be used with confidence in estimating the likelihood of toxicity. Spies (1989) speculated that the application of SQGs, such as the

Toxicity Tests of Marine and Estuarine Sediment Quality 303 AETs for Puget Sound, would lead to high incidences of false positives. Objective analyses of matching chemical and toxicity data suggest that less than 10% of samples were toxic when toxicity was not predicted with the guidelines. Thus, the weight-of-evidence suggests that natural sedimentologi- cal factors, unmeasured chemicals in sediments, or toxic chemicals for which there are no SQGs seem to be minor problems in the applications of the SQGs. Also, these data suggest that potentially toxic chemicals frequently covary with each other and, therefore, act as suitable surrogates for unmeasured substances. Data emerging from several studies indicate that adverse changes in the composition of benthic communities occur at chemical concentrations approximately 1 order of magnitude below those that trigger toxicity in laboratory tests. The associations between chemical contamination as gauged by the guidelines and measures of benthic community composition first reported by Hyland, Van Dolah, and Snoots, (1999) subsequently have been observed in Puget Sound and San Francisco Bay by other investigators. The ecological relevance of laboratory tests has been debated for many years but rarely quantified with actual data. Data (n > 1400) reviewed from many surveys along all three U.S. coastlines suggest that benthic infaunal populations were, in general, less abundant and diverse where toxicity was observed than in nontoxic samples. Abundance of amphipods and other crustaceans often were the most sensitive benthic endpoints, diminishing remarkably when amphipod survival dropped below 50% in the laboratory tests. The relationships between organic enrichment of soft sediments and the abundance, biomass, and species richness of the benthos were evaluated by Pearson and Rosenberg (1978). Based on their analyses of many datasets from Scandinavian fjords, they prepared a generalized diagram of these relationships. However, they did not offer an equivalent model for the effects of contamination of sediments by chemical toxicants. The Pearson and Rosenberg model showed the positive response over time of the benthos to increasing inputs of organic matter to the sediments, followed by the severe negative effects of overenrichment. Their model was based on numerous individual studies, but the principles and relationships they observed were widely applicable. An equivalent conceptual model or diagram involving the effects of toxic chemicals could combine elements of the SQT. Therefore, it could include observations of increasing chemical concentrations, decreasing survival in laboratory tests, and decreasing abundance and diversity in the benthos. Such a diagram should be based on a summary of numerous empirical observations, as was the approach used for organic enrichment by Pearson and Rosenberg (1978). A generalized SQT diagram is proposed in Figure 6.13. The scale of increasing chemical concentrations is based on the mean ERM quotients as previously reported (Long and MacDonald, 1998). The pattern of decreasing percent amphipod survival is based on the data assembled by Long et al. (2000) and Fairey et al. (2001) shown in Tables 6.9 and 6.19, respectively. The trend showing a decrease in the biological index of benthic integrity is

304 Handbook on Sediment Quality Figure 6.13 Generalized relationship between increasing chemical contamination of sediments and measures of adverse biological effects.

based on the analyses summarized by Hyland, Van Dolah, Paul, et al. (1999a, 1999b) for ranges in mean ERM quotients (shown in Table 6.10). Average amphipod abundance is a diagrammatic plot of the histogram shown in Figure 6.12. The generalized diagram suggests that the benthic index, a multiparameter index based on numerous individual metrics of the abundance and diversity of macroinvertebrate infaunal assemblages, declined quickly at relatively low chemical concentrations. The marked decrease in percent amphipod survival (data for A. abdita and R. abronius combined) occurred at chemical concentrations approximately 1 order of magnitude higher than observed with the benthic index and at a much slower rate. Hyland, Van Dolah, and Snoots (1999) speculated that acute toxicity tests may not be as sensitive to the presence of sediment-sorbed toxicants as the resident benthos. On the other hand, some of the changes in benthic community metrics may have been, in part, attributable to natural environmental factors in the estuarine samples, such as differences in dissolved oxygen and salinity among polluted stations that covaried with the toxicant concentrations. In many case studies and with data from many studies combined, average abundance of resident amphipods initially showed considerable variability among samples in which survival was relatively high. Such variability was probably a function of the many natural environmental factors that would be expected to cause differences in abundance among both toxic and nontoxic samples. Samples with relatively low amphipod abundance probably were collected in sandy habitats, which would not be expected to accumulate toxic

Toxicity Tests of Marine and Estuarine Sediment Quality 305 levels of contaminants. Sandy habitats would provide less food and soft substrates for burrowing macroinvertebrates than fine-grained sediments. Average amphipod abundance peaked when amphipod survival dropped to approximately 50 to 70%, probably reflecting the presence of more fine- grained particles that would tend to accumulate higher toxicant concentrations than sandy materials. Such sediments also would tend to accumulate sufficient food material to support amphipods able to adapt to the presence of toxicants. Amphipod abundance then decreased sharply as amphipod survival continued to decline, probably as a result of the toxicity of increasing chemical concentrations or declining oxygen concentrations that often covary with high chemical contamination in industrialized harbors. A similar pattern of increasing benthic abundance and diversity followed by sharp decreases over spatial and temporal scales in inputs of organic matter to sediments was reported in studies of Scandinavian fjords (Pearson and Rosenberg, 1978). The use of sediment-toxicity tests in saltwater continues to increase in both regulatory and nonregulatory programs in North America. Most of the focus in recent years has been on the amphipod survival tests in regulatory decisions; however, other tests involving chronic, life-cycle exposures have been undergoing development. Between-site differences in toxicity are easier to detect in toxicity tests than in benthic community analyses because measures of benthic structure are susceptible to the effects of many natural factors, such as depth, salinity, sediment texture, predation, and recruitment. As summarized in the first sections of this chapter, toxicity tests are being used in a number of monitoring programs as a means of identifying regional patterns or gradients in toxicologically significant contamination and for determining the severity and scope of toxicant-induced effects. As opposed to evaluations of the spatial patterns in the concentrations of numerous chemical substances, spatial gradients in toxicity can be illustrated with one or two maps. The statistical significance of results can be expressed in illustrations and graphs and numerical results shown as histograms. As in the EMAP and NOAA studies, the surficial extent of toxic conditions can be expressed in easily understood units of area and percentages of the survey areas. Protocols and methods for many tests with multiple toxicological endpoints performed on different phases of sediments provide scientists and managers a “menu” of options and the opportunity to tailor the types of data generated to the objectives of the study (Ingersoll et al., 1997). With the development of life-cycle exposures (Chandler, 1990), data that are very relevant to biological populations can be expected in future research. With the development of tests specific to certain toxicants in sediments, such as the cytochrome P-450 HRGS assay (Anderson et al., 1995), it may become possible to tailor the battery of tests in sediment-quality assessments to the types of chemical substances expected in the sediments and identify which chemicals are of concern before chemical analyses are completed. Sediment-toxicity tests are not without weaknesses and criticism. Some tests can be criticized for not being representative of in situ conditions and for being responsive to factors other than chemical toxicants (Spies, 1989). Furthermore, they can be criticized for providing measurement endpoints that managers have difficulty understanding, especially when tests are conducted

306 Handbook on Sediment Quality on extracts of sediments or with nonindigenous species or with sublethal endpoints (Lamberson et al., 1992). The lack of chemical specificity of most tests, although often viewed as a virtue among toxicologists, can be interpreted as a weakness and a failing of sediment-toxicity tests. It is not unusual for analysts to encounter contaminated sediments that are not toxic or vice versa. Data from toxicity tests of samples containing complex mixtures of substances cannot be used to identify the causative agents. Toxicity identification evaluations are needed to empirically determine causality and, therefore, attribution to sources. When results of toxicity tests and chemical analyses do not correspond well with each other, interpretations of the data are open to debate. Thus far, although considerable amounts of data from field studies are available to describe the correspondence between measures of toxicity and measures of benthic community alterations at sampling sites, no studies specifically designed and commisioned to address the predictive ability of toxicity tests have been conducted. In addition, there are very little empirical data with which to equate toxicity in bed sediment samples to adverse biological effects predicted at prospective dredged material disposal sites. Despite these weaknesses, whether perceived or real, sediment-toxicity tests have many strong virtues (Ingersoll et al., 1997). Because of their attractive features, sediment-toxicity tests have been used very successfully in many diverse programs. They serve an important role in the management of contaminated sediments and the protection of important biological resources. They are best applied in a weight-of-evidence approach to assess potential environmental risks, as suggested in the introduction to this volume. However, many difficult issues still remain regarding sensitivity, chemical specificity, precision, discriminatory power, causal relationships, and ecological relevance of such tests (Ingersoll et al., 1997). As more experience is gained in additional uses of them, it may become possible to further clarify these issues and more clearly describe the relationships between chemical contamination, toxicity, and in situ benthic population effects in sediments.

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Toxicity Tests of Marine and Estuarine Sediment Quality 311 Program. NOAA Technical Memorandum NOS OMA52. National Oceanic and Atmospheric Administration, Seattle, Wash. Long, E.R.; Field, L.J.; and MacDonald, D.D. (1998) Predicting Toxicity in Marine Sediments with Numerical Sediment Quality Guidelines. Environ. Toxicol. Chem., 17, 714. Long, E.R.; Hameedi, M.J.; Harmon, M.; Sloane, G.M.; Carr, R.S.; Beiden- bach, J.; Johnson, T.; Scott, K.J.; Mueller, C.; Anderson, J.W.; Wade, T.L.; and Presley, B.J. (1999) Survey of Sediment Quality in Sabine Lake, Texas and Vicinity. NOAA Tech. Memorandum NOS CCMA 137, National Oceanic and Atmospheric Administration, Silver Spring, Md., 27. Long, E.R.; Hameedi, J.; Robertson, A.; Dutch, M.; Aasen, S.; Ricci, C.; Welch, K.; Kammin, W.; Carr, R.S.; Johnson, T.; Biedenbach, J.; Scott, K.J.; Mueller, C.; and Anderson, J. (1999) Sediment Quality in Puget Sound. Year 1 Report: Northern Puget Sound. NOAA Tech. Memorandum NOS NCCOS CCMA No. 139 and Washington State Department of Ecology Publication No. 99-347, Washington State Department of Ecology, Olympia, Wash. Long, E.R.; Hong, C.; and Severn. C. (2001) Relationships Between Acute Sediment Toxicity in Laboratory Tests and the Abundance and Diversity of Benthic Infauna in Marine Sediments: A Review. Environ. Toxicol. Chem., 20, 46. Long, E.R.; MacDonald, D.D.; Cubbage, J.C.; and Ingersoll, C.G. (1998) Predicting the Toxicity of Sediment-Associated Trace Metals with Simulta- neously-Extracted Trace Metal:Acid-Volatile Sulfide Concentrations and Dry Weight-Normalized Concentrations: A Critical Comparison. Environ. Toxicol. Chem., 17, 972. Long, E.R.; MacDonald, D.D.; Severn, C.G.; and Hong, C.B. (2000) Classify- ing the Probabilities of Acute Toxicity in Marine Sediments with Empiri- cally-Derived Sediment Quality Guidelines. Environ. Toxicol. Chem., 19, 2598. Long, E.R.; MacDonald, D.D.; Smith, S.L.; and Calder, F.D. (1995) Incidence of Adverse Biological Effects within Ranges of Chemical Concentrations in Marine and Estuarine Sediments. Environ. Manage., 19, 81. Long, E.R.; Robertson, A.; Wolfe, D.A.; Hameedi, J.; and Sloane, G.M. (1996) Estimates of the Spatial Extent of Sediment Toxicity in Major U.S. Estuaries. Environ. Sci. Technol., 30, 3585. Long, E.R.; Scott, G.I.; Kucklick, J.; Fulton, M.; Thompson, B.; Carr, R.S.; Biedenbach, J.; Scott, K.J.; Thursby, G.B.; Chandler, G.T.; Anderson, J.W.; and Sloane, G.M. (1998) Magnitude and Extent of Sediment Toxicity in Selected Estuaries of South Carolina and Georgia. NOAA Tech. Memoran- dum NOS ORCA 128, National Oceanic and Atmospheric Administration, Silver Spring, Md. Long, E.R.; Sloane, G.M.; Carr, R.S.; Johnson, T.; Biedenbach, J.; Scott, K.J.; Thursby, G.B.; Crecelius, E.; Peven, C.; Windom, H.L.; Smith, R.D.; and Loganathon, B. (1997) Magnitude and Extent of Sediment Toxicity in Four Bays of the Florida Panhandle: Pensacola, Choctawhatchee, St. Andrew, and Apalachicola. NOAA Tech. Memorandum NOS ORCA 117, National Oceanic and Atmospheric Administration, Silver Spring, Md.

312 Handbook on Sediment Quality Long, E.R.; Sloane, G.M.; Carr, R.S.; Scott, K.J.; Thursby, G.B.; and Wade, T.L. (1996) Sediment Toxicity in Boston Harbor: Magnitude, Extent, and Relationships with Chemical Toxicants. NOAA Technical Memorandum NOS ORCA 96, National Oceanic and Atmospheric Administration, Silver Spring, Md. Long, E.R.; Sloane, G.M.; Scott, G.I.; Thompson, B.; Carr, R.S.; Biedenbach, J.; Wade, T.L.; Presley, R.J.; Scott, K.J.; Mueller, C.; Brecken-Fols, G.; Albrecht, B.; Anderson, J.W.; and Chandler, G.T. (1999) Magnitude and Extent of Chemical Contamination and Toxicity in Sediments of Biscayne Bay and Vicinity. NOAA Tech. Memorandum NOS ORCA 141, National Oceanic and Atmospheric Administration, Silver Spring, Md. Long, E.R.; Wolfe, D.A.; Carr, R.S.; Scott, K.J.; Thursby, G.B.; Windom, H.L.; Lee, R.; Calder, F.D.; Sloane, G.M.; and Seal, T. (1994) Magnitude and Extent of Sediment Toxicity in Tampa Bay, Florida. NOAA Tech Memo. NOS ORCA 78, National Oceanic and Atmospheric Administra- tion, Silver Spring, Md., 138. Long, E.R.; Wolfe, D.A.; Scott, K.J.; Thursby, G.B.; Stern, E.A.; Peven, C.; and Schwartz, T. (1995) Magnitude and Extent of Sediment Toxicity in the Hudson–Raritan Estuary. NOAA Tech. Memo NOS ORCA 88, National Oceanic and Atmospheric Administration, Silver Spring, Md., 230. Long, E.R., Buchman, M.F.; Bay, S.M.; Breteler, R.J.; Carr, R.S.; Chapman, P.M.; Huse, J.E.; Lissner, A.L.; Scott. J.; and Wolfe, D.A. (1990) Compara- tive Evaluation of Five Toxicity Tests with Sediments from San Francisco Bay and Tomales Bay, California. Environ. Toxicol. Chem., 9, 1193. MacDonald, D.A., and Salazar, S.M. (Eds.) (1995) The Utility of AVS/EqP in Hazardous Waste Site Evaluations. NOAA Tech. Memorandum NOS ORCA 87, National Oceanic and Atmospheric Administration, Seattle, Wash. MacDonald, D.A.; Matta, M.B.; Field, L.J.; Cairncross, C.; and Munn, M.D. (1992) The Coastal Resource Coordinator’s Bioassessment Manual. NOAA Rep. No. HAZMAT 93-1, National Oceanic and Atmospheric Administra- tion, Seattle, Wash. MacDonald, D.D.; Carr, R.S.; Calder, F.D.; Long, E.R.; and Ingersoll, C.G. (1996) Development and Evaluation of Sediment Quality Guidelines for Florida Coastal Waters. Ecotoxicology, 5, 253. MacDonald, D.D.; DiPinto, L.M.; Field, L.J.; Ingersoll, C.G.; Long, E.R.; and Swartz, R.C. (2000) Development and Evaluation of Consensus-Based Sediment Effect Concentrations for Polychlorinated Biphenyls. Environ. Toxicol. Chem., 19, 1403. MacDonald, D.D.; Ingersoll, C.G.; and Berger, T.A. (2000) Development and Evaluation of Consensus-Based Sediment Quality Guidelines for Freshwa- ter Ecosystems. Arch. Environ. Contam. Toxicol., 39, 20. McGee, B.L.; Fisher, D.J.; and Turley, S. (1999) Assessment of Sediment Contamination, Acute Toxicity, and Population Viability of the Estuarine Amphipod Leptocheirus plumulosus in Baltimore Harbor, Maryland, USA. Environ. Toxicol. Chem., 18, 2151. Melzian, B.D. (1990) Toxicity Assessment of Dredged Materials: Acute and Chronic Toxicity as Determined by Bioassays and Bioaccumulation Tests.

Toxicity Tests of Marine and Estuarine Sediment Quality 313 Pp 49-64 In Proceedings, International Seminar on the Environmental Aspects of Dredging Activities, Secretariat d’Etat Apres du Premier Min- istre, Nantes, France. National Oceanic and Atmospheric Administration/Office of Resources Conservation and Assessment, Seattle, Wash. Unpublished data. National Research Council (1989) Contaminated Marine Sediments: Assess- ment and Remediation. National Academy Press, Washington, D.C. O’Connor, T.P.; Daskalakis, K.D.; Hyland, J.L.; Paul, J.F.; and Summers, J.K. (1998) Comparisons of Sediment Toxicity with Predictions Based on Chemical Guidelines. Environ. Toxicol. Chem., 17, 468. O’Connor, T.P., and Paul, J.F. (2000) Misfit Between Sediment Toxicity and Chemistry. Mar. Pollut. Bull., 40, 59. Ogden Environmental and Energy Services, Inc. (In preparation) Pearl Harbor Remedial Investigation/Feasibility Study (RI/FS). Pacific Division, U.S. Naval Engineering Command (PACNAVFACENGCOM), Pearl Harbor, Haw. Paul, J.F.; Scott, K.J.; Holland, A.F.; Weisberg, S.B.; Summers, J.K.; and Robertson, A. (1992) The Estuarine Component of the US EPA’s Environ- mental Monitoring and Assessment Program. Chem. Ecol., 7, 93. Pearson, T.H., and Rosenberg, R. (1978) Macrobenthic Succession in Relation to Organic Enrichment and Pollution of the Marine Environment. Oceanogr. Mar. Biol. Annu. Rev., 16, 229. Power, E.A., and Chapman, P.M. (1992) Assessing Sediment Quality. In Sediment Toxicity Assessment. G.A. Burton, Jr. (Ed.), Lewis Publishers, Inc., Boca Raton, Fla., 1. PTI Environmental Services, Inc. (1990) Technical Report. Reconnaissance Survey of Chemical Contamination and Biological Effects in Southern Puget Sound. Prepared for U.S. EPA Region 10, Seattle, Wash., 56. San Francisco Estuary Institute (1997) San Francisco Estuary Regional Monitoring Program for Trace Substances. 1995 Annu. Rep., San Francisco Estuary Institute, Richmond, Calif. Sapudar, R.A.; Wilson, C.J.; Reid, M.L.; Long, E.R.; Stephenson, M.; Puckett, M.; Fairey, R.; Hunt, J.; Anderson, B.; Holstad, D.; Newman, J.; Birosik, S.; and Smythe, H. (1994) Sediment Chemistry and Toxicity in the Vicinity of the Los Angeles and Long Beach Harbors. California State Water Resources Control Board and National Oceanic and Atmospheric Administration, Sacramento, Calif., 81. Southern California Coastal Water Research Project (1995) Toxicity of Sediments on the Palos Verdes Shelf. In Southern California Coastal Water Research Project. Annual report 1993–94., Westminster, Calif., 109. Smith, S.L.; MacDonald, D.D.; Keenleyside, K.A.; Ingersoll, C.G.; and Field, L.J. (1996) A Preliminary Evaluation of Sediment Quality Assessment Values for Freshwater Ecosystems. J. Great Lakes Res., 22, 624. Snell, T.W., and Serra, M. (2000) Using Probability of Extinction to Evaluate the Ecological Significance of Toxicant Effects. Environ. Toxicol. Chem., 19, 2357. Spies, R.B. (1989) Sediment Bioassays, Chemical Contaminants and Benthic Ecology: New Insights or Just Muddy Water? Mar. Environ. Res., 27, 73.

314 Handbook on Sediment Quality Striplin, P.L.; Sparks-McConkey, P.J.; Dietrich, H.; and Svendsen, F.A. (1993) Puget Sound Ambient Monitoring Program Marine Sediment Monitoring Program. Annual Rep. 1991, Washington Department of Ecology, Olympia, Wash., 115. Strobel, C.J.; Buffum, H.W.; Benyi, S.J.; Petrocelli, E.A.; Reifsteck, D.R.; and Keith. D.J. (1995) Statistical Summary EMAP—Estuaries Virginian Province: 1990 to 1993. EPA-620/R-94-026, U.S. Environmental Protection Agency, Narragansett, R.I, 72. Suer, L. (1990) Personal communication. Oakland Regional Water Quality Control Board, Oakland, California Summers, J.K.; Macauley, J.M.; Heitmuller, P.T.; Engle, V.D.; Adams, A.M.; and Brooks, G.T. (1993) Statistical Summary: EMAP—Estuaries Louisian- ian Province: 1991. EPA-600/R-93-001, U.S. Environmental Protection Agency, Gulf Breeze, Fla., 101. Swartz, R.C. (1989) Marine Sediment Toxicity Tests. In Proc. Nat. Res. Council Symp. Contam. Sed. K. Kamlet (Ed.), National Academy Press, Washington, D.C, 115. Swartz, R.C. (1999) Consensus Sediment Quality Guidelines for Polycyclic Aromatic Hydrocarbon Mixtures. Environ. Toxicol. Chem., 18, 780. Swartz, R.C.; DeBen, W.A.; Sercu, K.A.; and Lamberson, J.O. (1982) Sediment Toxicity and the Distribution of Amphipods in Commencement Bay, Washington, USA. Mar. Pollut. Bull., 13, 359. Swartz, R.C.; Cole, F.A.; Schults, D.W.; and DeBen, W.A. (1986) Ecological Changes on the Palos Verdes Shelf Near a Large Sewage Outfall: 1980–1983. Mar. Ecol. Prog. Ser., 31,1. Swartz, R.C.; Cole, F.A.; Lambertson, J.O.; Ferraro, S.P.; Schults, D.W.; DeBen, W.A.; Lee, H., II; and Ozretich, R.J. (1994) Sediment Toxicity, Contamination and Amphipod Abundance at a DDT- and Aieldrin-Contami- nated Site in San Francisco Bay. Environ. Toxicol. Chem., 13, 949. Swartz, R.C.; Schults, D.W.; Ozretich, R.J.; Lamberson, J.O.; Cole, F.A.; DeWitt, T.H.; Redmond, M.S.; and Ferraro, S.P. (1995) Sigma PAH: A Model to Predict the Toxicity of Polynuclear Aromatic Hydrocarbon Mixtures in Field-Collected Sediments. Environ. Toxicol. Chem., 14, 1977. Tanaka, Y., and Nakanishi, J. (2000) Mean Extinction Time of Populations Under Toxicant Stress and Ecological Risk Assessment. Environ. Toxicol. Chem., 19, 2856. Tetra-Tech, Inc. (1989) Technical Report. Puget Sound Ambient Monitoring Program 1989. Marine Sediment Monitoring. Prepared for Washington Department of Ecology, Olympia, Wash., 203. Thompson, B.; Anderson, B.; Hunt, J.; Taberski, K.; and Phillips, B. (1999) Relationships between Sediment Contamination and Toxicity in San Francisco Bay. Mar. Environ. Res., 48, 285. Thursby, G.B.; Heltshe, J.; and Scott, K.J. (1997) Revised Approach to Toxicity Test Acceptability Criteria Using a Statistical Performance Assessment. Environ. Toxicol. Chem., 16, 1322. Tillitt, D.E.; Giesy, J.P.; and Ankley, G.T. (1991) Characterization of the H4IIE Rat Hepatoma Cell Bioassay as a Tool for Assessing Toxic Potency

Toxicity Tests of Marine and Estuarine Sediment Quality 315 of Planar Halogenated Hydrocarbons (PHHs) in Environmental Samples. Environ. Sci. Technol., 25, 87. Turgeon, D.D.; Hameedi, J.; Harmon, M.R.; Long, E.R.; McMahon, K.D.; and White, H.H. (1998) Sediment Toxicity in U.S. Coastal Waters. NOAA Special Rep. National Oceanic and Atmospheric Administration, Silver Spring, Md. U.S. Environmental Protection Agency (1992) Sediment Classification Methods Compendium. EPA-823/R-92-006, Office of Water, Washington, D.C. U.S. Environmental Protection Agency and U.S. Army Corps of Engineers (1991) Evaluation of Dredged Material Proposed for Ocean Disposal. Testing Manual. EPA-503/8-91, 001, U.S. Environmental Protection Agency, Washington, D.C. Van Derveer, W.D., and Canton, S.P. (1997) Selenium Sediment Toxicity Thresholds and Derivation of Water Quality Criteria for Freshwater Biota of Western Streams. Environ. Toxicol. Chem., 16, 1260. Voorhees, D.J.; Kane-Driscoll, S.B.; and Bridges, T.S. (1998) Improving Dredged Material Management Decisions with Uncertainty Analysis. Tech. Rep. DOER-3, U.S. Army Corps of Engineers, Vicksburg, Miss. Washington Department of Ecology (1995) Sediment Management Standards, Chapter 173-204 WAC. Publication No. 96-252, Olympia, Wash. Wolfe, D.A.; Bricker, S.B.; Long, E.R.; Scott K.J.; and Thursby, G.B. (1994) Biological Effects of Toxic Contaminants in Sediments from Long Island Sound and Environs. NOAA Tech. Memo. NOS ORCA 80, National Oceanic and Atmospheric Administration, Silver Spring, Md., 113.

316 Handbook on Sediment Quality Chapter 7 Empirical and Theoretical Shortcomings of Sediment-Quality Guidelines

Thomas P. O’Connor National Oceanic and Atmospheric Administration Silver Spring, MD

318 Introduction 323 Theoretical Limitations to 318 Co-occurrence Sediment- Chemical Predictors Quality Guidelines 324 Conclusions 320 Equilibrium Partitioning 324 References Sediment-Quality Guidelines 320 Empirical Test of Sediment- Quality Guidelines

317 INTRODUCTION In the United States, the U.S. Environmental Protection Agency (U.S. EPA), Environmental Monitoring and Assessment Program (EMAP)-Estuaries Program and the National Oceanic and Atmospheric Administration (NOAA) bioeffects surveys provide large data sets with which to test proposed relationships between sediment chemistry and toxicity. Whereas there is an increased likelihood that sediment will be toxic as its concentration of chemical contaminants increases, the connection is too weak to allow chemical data to be used as a sole basis for management decisions. Additionally, guidelines based on equilibrium partitioning have two questionable assumptions: (1) that thermodynamic equilibrium exists, and (2) pore water (also called interstitial water) is a medium of exposure. Sediment quality guidelines (SQGs), proposed methods for attaching biological significance to chemical data, fall into two categories: those basing estimates of toxicity on water quality criteria and assumptions about equilibrium and pore water and those based on compilations of co-occurrences between biological characteristics and bulk chemical concentrations. Using a large dataset compiled from NOAA and U.S. EPA surveys that simultaneously measured sediment chemistry and toxicity data, O’Connor and Paul (2000) showed that none of the SQGs is a sufficiently reliable predictor of toxicity to be used for sediment management. That analysis is repeated here and expanded to include recent modifications of the co-occurrence methods and a newly developed equilibrium-based SQG for polycylic aromatic hydrocarbons (PAHs).

CO-OCCURRENCE SEDIMENT- QUALITY GUIDELINES

Long and Morgan (1990) introduced SQGs based on exhaustive compilations of data from reports where bulk chemical concentrations in sediment were quantified with a measure of biological response. The responses could be from bioassays of any kind or field evidence that benthic communities were, to some extent, abnormal. Using only cases with a biological effect, each chemical was ranked by concentration. The 10th percentile among those concentrations was designated the effects range low (ERL) and the 50th percentile deemed the effects range median (ERM). The SQG then rested on a claim that sediments with bulk concentrations below the ERL were not likely to be toxic, whereas bulk concentrations above the ERM signaled a likelihood of toxicity. It was never demonstrated that any particular chemical out of many measured in any particular sample was responsible for the biological effect and that sample. By 1995, Long et al. (1995) had published a list of ERLs and ERMs for nine trace elements, 12 PAHs, p,p’dichlorodiphenyltrichloroethylene

318 Handbook on Sediment Quality Table 7.1 The ERLs and ERMs from Long et al. (1995) (ERLs are bulk sediment concentrations (dry-weight) below which sediment is unlikely to be toxic and ERMs are concentrations above which toxicity is probable).

Chemical ERL ERM Ag 1 ppm 3.7 ppm As 8.2 70 Cd 1.2 9.6 Cr 81 370 Cu 34 270 Hg 0.15 0.71 Ni 21 52 Pb 47 220 Zn 150 410 p,p'-DDE 2.2 ppb 27 ppb Total DDTa 1.6 46 Total PCB 23 180 LMWPAHa 550 3 160 HWWPAHa 1700 9 600 Total PAHa 4000 45 000 Acenapthene 16 500 Acenaphthylene 44 640 Anthracene 85 1 100 Flourene 19 540 2-Methylnaphthalene 70 670 Naphthalene 160 2 100 Phenanthrene 240 1 500 Benzo(a)pyrene 261 1 600 Chrysene 384 2 800 Dibenzo(a,h)anthracene 63 260 Fluoranthene 600 5 100 Pyrene 670 2 600 a DDE is part of the total DDT and individual PAH compounds are part of the total PAH, low- molecular-weight PAH (LMWPAH) (2- and 3-ring compounds), and high-molecular-weight PAH (HMWPAH) (more than 3-ring compounds). However, double and triple counting is of no consequence when we are interested in knowing only whether there is at least one occurrence of the ERL or ERM having been exceeded.

(DDE), and for the groups: total dichlorodiphenyltrichloroethanes (DDTs), total polychlorinated biphenyls (PCBs), low-molecular-weight PAHs, high- molecular-weight PAHs, and total PAHs (Table 7.1). A similar set of SQGs based on co-occurrence has been developed by MacDonald et al. (1996). In their case, the SQGs are threshold effect-levels (TELs) and probable effect levels (PELs). The differences between TELs and ERLs and between PELs and ERMs are small, so the validity of SQGs based on co-occurrence is tested here only with ERLs and ERMs. Originally, an occurrence of any one ERM being exceeded was considered a likely indication that a sample was toxic. However, upon examination of

Empirical and Theoretical Shortcomings 319 large data sets, Long and MacDonald (1998) categorized the likelihood of toxicity as low, medium, or high if 1 to 5, 6 to 10, or more than 10 chemical concentrations, respectively, exceeded an ERM. As an alternative, they introduced the ERM quotient (ERMQ), which is the mean among ratios of chemical concentrations to ERMs. An ERMQ >1 would imply a medium likelihood of toxicity.

EQUILIBRIUM PARTITIONING SEDIMENT-QUALITY GUIDELINES The co-occurrence methods have no theoretical underpinning and lack any basis for attaching observed effects with particular chemicals. Alternative SQGs, on the other hand, are based on the assumption that pore water is the medium of exposure and that chemical quality of pore water can be calculated from bulk chemical characteristics by assuming thermodynamic equilibrium between sediment and pore water (Table 7.2). The SQG is then the long- established water quality criterion applied to calculated pore-water quality. There are SQGs resting on these assumptions for neutral organic compounds (U.S. EPA, 1993) and for trace metals with less soluble sulfides than ferrous sulfide (Di Toro et al., 1992). In the first instance, pore-water concentrations are calculated on the basis of organic carbon–normalized bulk concentrations; in the second, metals cannot reach toxic levels in pore water if bulk sediment contains sufficient acid-volatile sulfide (AVS) to theoretically precipitate all metals as sulfides. Two expansions of equilibrium partitioning theory for neutral organics have been derived for PAHs in which pore water can be deemed toxic if the incremental toxicities of individual compounds add up to a total toxicity greater than 1. The first of these (Swartz et al., 1995) is based on measured or estimated lethal concentrations to 50% of test organisms (LC50) for 13 PAH compounds. The second (Di Toro et al., 2000) assumes that toxicity is a narcotic effect so that all PAHs are equally toxic on a molar basis, and the critical concentration is that of sum of concentrations of 34 individual compounds.

EMPIRICAL TEST OF SEDIMENT- QUALITY GUIDELINES

The 2475-sample dataset (O’Connor and Paul, 2000, and references therein) allows an empirical test of how well the proposed SQGs conform to sediment toxicity or, in the case of the ERLs and AVS methods, to nontoxicity. Samples were judged toxic if less than 80% of test amphipods did not survive a 10-day exposure. This test was introduced by Swartz et al. (1979) because surveys

320 Handbook on Sediment Quality Table 7.2 Sediment quality guidelines based on equilibrium partitioning of neutral organic compounds or acid-volatile sulfide (SQGs for neutral organics do not apply if total organic carbon (TOC) content of sediment is less than 0.2%, dry weight).

______Individual chemicals (U.S. EPA 1993) a ______Chemical ______SQG Dieldrin 20 000 ng/g TOC Endrin 760 Acenaphthene 230 000 Fluoranthene 660 000 Phenanthrene 240 000

______Sum of 13 PAHs (Swartz et al., 1995)

Σ (PAHiw/LC50) > 1

Where PAHiw is the interstitial water concentration calculated on the basis of the equilibrium partitioning assumptions and LC50 is the 10-day lethal concentration to amphipods.

______Narcosis due to combined 34 PAHs (Di Toro and McGrath, 2000) a Σ a Σ ______Biological basis ______SQG ( 34PAH) ______SQG ( 23PAH)

FAV (CL = 19.3) 29 µM/g TOC 18 µM/g TOC

LC50 (CL = 12.2) 18 11

FCV (CL = 8.79) 5.7 3.5

Where CL is body burden on a lipid basis of PAHs that corresponds to the final acute values (FAV) and final chronic values (FCV) for water quality criteria and for the 10- day LC50 for an amphipod species. The SQG’s are calculated on the basis of the equilibrium partitioning assumptions. The SQGs are based on the sum of concentrations of 34 PAH compounds. Only 23 PAH compounds were measured (as in most of the samples in the 2475-sample dataset used here). Di Toro et al. (2000) calculated the median ratio of 1.64 between Σ34PAH and Σ23PAH.

______Acid-Volatile Sulfide (AVS) (Di Toro et al., 1992)

This SQG applies only to metals whose solubilities are less than that of ferrous sulfide (FeS). If on, a mole basis, the sum of concentrations of such metals (i.e., ΣZn, Cu, Ni, Pb, Cd, Hg, and Ag) is less than the mole concentration of sulfide that can be volatized with 1 M hydrochloric acid (HCI) (i.e., AVS), the concentrations of all of those metals in interstitial water is below water quality criteria and that sediment cannot exert toxicity due to those metals. a Based on final chronic value of the water quality criteria and the equilibrium partitioning assumptions. from a variety of locations showed that amphipod species decreased in abundance near sources of contamination. Through wide application, it has become the de facto standard of sediment toxicity. The AVS guideline weighs AVS against the sum of concentrations of metals that are simultaneously extracted (SEM) with AVS in 1 M hydrochloric acid (HCl). The metal concentrations in our large dataset are based on total extractions (TotM) in

Empirical and Theoretical Shortcomings 321 Table 7.3 Frequencies of 10-day amphipod toxicity in samples with chemical concentrations in excess of a SQG (or nontoxicity in samples with a SQG for nontoxicity).

All samplesa Total Toxic 2475 388 (16%) Sample with one chemical concentration > ERM 453 186 (41%) Sample with six or more chemical concentrations > ERM 46 29 (63%) Samples with ERMQ (1/n Σ(Conc/ERM) > 1 59 37 (63%) Samples with one neutral organic concentration > SQG 31 13 (42%)

Samples with Σ(PAHiw/LC50) > 1 17 9 (53%)

Samples with Σ23PAH > 18 µM/g TOC 12 7 (58%)

Samples with Σ23PAH > 11 µM/g TOC 25 12 (48%)

Samples with Σ23PAH > 3.5 µM/g TOC 123 54 (44%)

Total Nontoxic 2475 2087 (84%) Samples with no chemical concentration >ERL 730 697 (95%) Samples with ΣTotMb < AVS 1256 1117 (89%) a LC = lethal concentration and TOC = total organic carbon. b ΣTotM = sum of total Zn, Cu, Ni, Pb, Cd, Hg, and Ag extracted with HF. concentrated hydrofluoric acid (HF). Because TotM must equal or (usually) exceed SEM, any sample in which AVS exceeds TotM is also one in which AVS exceeds SEM. The results, shown in Table 7.3, reveal that, generally, less than one-half of the samples with chemical concentrations greater than any of the SQGs were actually toxic. A few did better than 50%, but at the price of excluding many samples. For example, SQGs requiring six or more ERM exceedances, or an ERMQ >1, were approximately 60% accurate in identifying toxic samples, but that increase in accuracy caused the SQG to apply to 10 times fewer samples than the simple single-ERM exceedance SQG. Similarly, the SQG based on calculated pore-water toxicity of 13 PAHs was 53% accurate but applied only to 17 samples, and the SQG based on acute toxicity from 34 PAHs was 58% accurate but applied to only 12 samples. The two SQGs that predict nontoxicity appear to have worked well; only 5 and 11% of the samples without an ERL exceedance or where AVS exceeded metal concentrations, respectively, were toxic. This success, however, may be an artifact because fully 84% of the samples were nontoxic. A rigorous test of these SQGs would examine their ability to identify nontoxic samples from a set of primarily toxic samples. Also, it is worth noting that the median TotM concentrations among samples with and without excess AVS were 1.63 and 1.65 µM/g, respectively, or essentially identical. The corresponding median AVS concentrations were very different, 6.44 and 0.43 µM/g, respectively. So, despite the fact that the objective of SQGs is to estimate toxicity from chemical contamination, this guideline is very sensitive to AVS and has little to do with metal contamination.

322 Handbook on Sediment Quality In the entire 2475-sample dataset, only 388 (16%) were toxic. So, whereas the SQGs that predict toxicity did so with generally less than 50% accuracy, they did much better than a random choice would have done. This fact favors the use of SQGs as guidelines to identify samples requiring further examination, such as performing toxicity tests or quantifying the indigenous benthic community. The demonstrated 50% accuracy also signals, however, that SQGs, by themselves, should not be used by environmental managers as a basis for any decisions.

THEORETICAL LIMITATIONS TO CHEMICAL PREDICTORS

The SQGs based on equilibrium partitioning theory rest on two questionable assumptions: (1) that pore water is in equilibrium with sediment, and (2) benthic organisms expose themselves to pore water. Equilibrium has been demonstrated (McGroody and Farrington, 1995) not to apply to PAHs that are in sediments as particles of soot, and Di Toro et al. (2000) acknowledge that SQGs cannot be applied to sediments with that component, especially common in sediments with high PAH concentrations. Even for other neutral organic compounds, there are no examples from the field where concentrations in the bulk sediment and pore-water phases are in equilibrium. The only actual measurements of pore-water concentrations brought to bear by the U.S. EPA (1993) on the issue of demonstrating equilibrium are laboratory data with spiked systems. Systems in thermodynamic equilibrium are not only at steady state (no temporal gradients) but also have no spatial gradients. There are countless examples in the literature of sharp vertical gradients in chemical concentrations in sediments. In cases where sediment cores can be dated, these vertical gradients provide chronologies of changes in chemical flux to the seafloor. Though measured much less frequently, there are also gradients in pore-water concentrations. For example, Luther et al. (1998) have measured sharp concentration differences in pore water over a distance of a few millimeters. Even more to the point, however, are the facts that Luther et al. (1998) and Zeibis et al. (1996) have managed to measure oxygen concentrations within the burrows of benthic organisms. Infaunal benthic organisms demonstrably ventilate their burrows. Whereas surrounding sediments and pore waters are anoxic, organisms ensure that the water they live in is aerobic. So, benthic organisms are not living in pore water. Even if pore water is in thermodynamic equilibrium with bulk sediment, it is not a medium of exposure (or toxicity) to benthic life.

Empirical and Theoretical Shortcomings 323 CONCLUSIONS The connection between sediment chemistry and toxicity is not strong enough to be predictive. There is no obvious way to improve the situation. Whereas the chemical data are fixed, the definition of toxicity is somewhat fluid. Requiring a more than 20% loss of amphipods, or using a less sensitive assay, or requiring a “hit” in more than one assay would all result in fewer samples being declared toxic. Then, all of the various SQG exceedances would still apply and their corresponding accuracies would be even lower. Using a more sensitive test would find more toxic samples and seem to increase accuracy but also increase the percentage of samples that are toxic but not predicted to be so by any SQG (already 202, or 52%, of the 388 toxic samples in Table 7.3 do not exceed any chemical SQG). In the extreme, one could use a series of tests and declare a sample toxic on the basis of a “hit” in any one. Long and MacDonald (1998), for example, did that with a set of samples on which three tests were run. This did result in 78% of the samples with a single ERM exceedance also being toxic, but with 70% of all the samples (regardless of chemistry) being in the toxic category. In effect, a low threshold for declaring toxicity makes predictions based on chemistry or any other basis irrelevant. Nonetheless, the SQGs are better than a random choice for finding toxic sediments and they can serve as guidelines to identify samples requiring further examination.

REFERENCES Di Toro, D.M.; Mahoney, J.D.; Hansen, D.J.; Scott, K.J.; Carlson, A.R.; and Ankley, G.T. (1992) Acid-Volatile Sulfide Predicts the Acute Toxicity of Cadmium and Nickel in Sediments. Environ. Sci. Technol., 26, 96. Di Toro, D.M., and McGrath, J.A. (2000) Technical Basis for Narcotic Chemicals and Polycyclic Aromatic Hydrocarbon Criteria. II. Mixtures and Sediments. Environ. Toxicol. Chem., 19, 1971. Long, E.R., and MacDonald, D.D. (1998) Recommended Uses of Empirically Derived, Sediment Quality Guidelines for Marine and Estuarine Ecosystems. Human Ecol. Risk Assess., 4, 1019. Long, E.R., and Morgan, L.G. (1990) The Potential for Biological Effects of Sediment Sorbed Contaminants Tested in the National Status and Trends Program. NOAA Tech. Memorandum NOMA 52, National Oceanic and Atmospheric Administration, Seattle, Wash. Long, E.R.; MacDonald, D.D.; Smith, S.L.; and Calder, F.D. (1995) Incidence of Adverse Biological Effects within Ranges of Chemical Concentrations in Marine and Estuarine Sediments. Environ. Manage., 19, 81.

324 Handbook on Sediment Quality Luther, G.W., III; Brendel, P.J.; Lewis, B.L.; Sundby, B.; Lefrancois, L.;

Silverberg, N.; and Nuzzioi, D.B. (1998) Simultaneous Measurement of O2, Mn, Fe, I–, and S(–II) in Marine Pore Waters with a Solid-State Voltammetric Microelectrode. Limnol. Oceanogr., 43, 325. McGroody, S.S., and Farrington, J.W. (1995) Sediment Pore Water Partitioning of Polycyclic Aromatic Hydrocarbons in Three Cores from Boston Harbor, Massachusetts. Environ. Sci. Technol., 29, 1542. MacDonald, D.D.; Carr, R.S.; Calder, F.D.; Long, E.R.; and Ingersoll, C.R. (1996) Development and Evaluation of Sediment Quality Guidelines for Florida Coastal Waters. Exotoxicology, 5, 253. O’Connor, T.P., and Paul, J.F. (2000) Misfit between Sediment Toxicity and Chemistry. Mar. Pollut. Bull., 40, 59. Swartz, R.C.; DeBen, W.A.; and Cole. F.A. (1979) A Bioassay for the Toxicity of Sediment to Marine Macrobenthos. J. Water Pollut. Control Fed., 51, 944. Swartz R.C.; Schults D.W.; Ozretich R.J.; Lambertson J.O.; Cole F.A.; DeWitt T.H.; Redmond M.S.; and Ferraro, S.P. (1995) ΣPAH: A Model to Predict the Toxicity of Polynuclear Aromatic Hydrocarbon Mixtures in Field-Collected Samples. Environ. Sci. Technol., 14, 1977. U.S. Environmental Protection Agency (1993) Technical Basis for Sediment Quality Criteria for Nonionic Contaminants for the Protection of Benthic Organisms by Using Equilibrium Partitioning. EPA-822/R-93-011, Office of Science and Technology, Health and Ecological Criteria Division, Washington, D.C. Zeibis, W.; Forster, M.; Huettel, M.; and Jorgensen, B.B. (1996) Complex Burrows of the Mud Shrimp Callianassa truncata and Their Geochemical Impact in the Sea Bed. Nature, 382, 619.

Empirical and Theoretical Shortcomings 325

Chapter 8 Sediment-Quality Modeling

David Dilks, Limno-Tech, Inc. Ann Arbor, MI

328 Introduction 341 Hydrodynamic Models 329 Model Framework 342 UNET 329 Basic Processes Affecting 342 DYNHYD5 Sediment Quality 342 RMA-2V 330 Transport Processes 343 Princeton Ocean Model 330 Water Column Transport 343 Sediment-Transport Models 331 Settling 343 HEC-6 331 Resuspension 344 SED2D 331 Sedimentation 344 SEDZL 332 Sediment–Water Diffusion 344 Chemical Fate and Transport 332 Mixing Within Bed Sediments Models 332 Reaction Processes 344 SMPTOX 332 Sorption 344 HSCTM-2D 333 Volatilization 344 WASP5/IPX 334 Degradation–Decay 345 EFDC 334 Advanced Model Features 345 Model Calibration 334 Multiple Sorbent Classes 346 Calibration Sequence 335 Water Column Solids 347 Short-Term Versus Long-Term 336 Non-Equilibrium Partitioning Calibration 336 Sediment Resuspension 347 Alternate Observations 337 Cohesive Sediments 348 Calibration Sufficiency 338 Noncohesive Sediments 348 Model Application 338 Behavior of Metals 348 Remediation 339 Spatial Resolution 349 Future Environmental 340 Commonly Used Model Conditions Frameworks 350 Natural Attenuation

327 350 Dredging 357 Artificial Bed Mixing 351 Capping 359 Data Sufficiency to Validate 351 Case Study Resuspension Rates 353 Prevention of Sediment 360 Bioturbation and Enhanced Contamination Sediment Diffusion 354 Limitation and Proper Use 360 Model Representation of Active 354 Bayou D’Inde Case Study Management Scenarios 357 Outstanding Issues 362 References

INTRODUCTION Mathematical models can play an important role in the management of sediment quality. Mathematical models can be used to help prevent contamination of sediments by defining maximum allowable contaminant loads to the water column; more commonly, they have been used to evaluate alternative management strategies to remediate sediment contamination. The key outputs from these models are predictions of pollutant concentrations over space and time. These predicted concentrations can be used to drive exposure and risk assessments, determine pollutant export to other environmental compartments, and be directly compared with sediment-quality criteria. Mathematical frameworks to assess sediment contamination were first widely developed in the 1980s and have evolved significantly over time to include more detailed spatial, temporal, and process resolution. Despite the advances, several challenges remain in the proper application of these models. These include representation of settling and resuspension processes, computational efficiency, mathematical representation of vertical gradients in bed sediment quality, data constraints encountered when increasing spatial and temporal resolution, description of diffusion processes across the sediment–water interface, and representation of active management scenarios. It is important to recognize at the outset that sediment contamination models are merely mathematical simplifications of the real world. They are generally driven by incomplete datasets and can have extremely limited accuracy in predicting specific future concentrations. Nonetheless, they are often the best option available for trying to quantify important cause–effect relationships or define the relative benefits associated with various sediment- management alternatives. This chapter is divided into four sections. The first section is entitled Model Framework, and describes the processes that are contained in sediment-quality models as well as the most commonly used sediment-modeling frameworks. The second section is Model Calibration, which discusses how a general model framework is adapted to describe conditions at a specific site. The third section describes Model Application and the role that mathematical modeling plays in two aspects of sediment-quality management: remediation and prevention. The final section, Issues, describes the challenges that face the application of sediment models in the present and near future.

328 Handbook on Sediment Quality MODEL FRAMEWORK Mathematical models are designed to consider the most important processes affecting the system under study and the behavior of those processes mathematically. The mathematical model frameworks that were initially developed considered these processes on a relatively broad scale. Model frameworks have rapidly evolved to include much greater spatial, temporal, and process detail. In response to this increase in detail, specialized submodels are often applied to specifically address fine-scale hydrodynamics and sediment- transport processes. This section begins with a discussion of the primary processes that are described in sediment-quality models and describes recent advancements in how these processes have been described in mathematical models. It con- cludes with a discussion of the most commonly used public domain sediment- modeling frameworks.

BASIC PROCESSES AFFECTING SEDIMENT QUALITY. Many processes are known to affect sediment quality. These processes occur both in the overlying water column and the sediments themselves and include transport as well as reaction processes. This section begins with a basic description of the important processes described in most sediment-quality models and then proceeds to outline how these processes have been described in greater detail in more complex models. The basic framework for models describing sediment quality is best described in O’Connor (1988b, 1988c, 1988d) and Thomann (1989). A schematic of the basic sediment-quality model framework is shown in Figure 8.1. The framework describes both the overlying water column as well as the sediments because processes that occur in the water column can have a direct effect on the sediments. Frameworks of this type were typically solved for steady-state solutions over one spatial dimension (for rivers) or zero dimensions (for lakes assumed to be well mixed). The processes described in the basic model framework can be summarized as

• Transport processes — Water column transport, — Settling, — Resuspension, — Sedimentation, — Sediment–water diffusion, and — Bed-sediment mixing. • Reaction processes — Sorption, — Volatilization, and — Loss–degradation processes.

Sediment Quality Modeling 329 Figure 8.1 Schematic of major contaminated sediment model properties.

A brief discussion of each process is provided immediately below, with a more detailed discussion of the advancement in model description of selected processes provided later in the section.

TRANSPORT PROCESSES. Many processes affect sediment quality strictly due to the transport of dissolved and particle-bound contaminant from one location to another. The transport processes typically considered in a sediment contamination model include water column transport, settling, resuspension, sedimentation, sediment–water diffusion, and mixing within the bed sediments.

Water Column Transport. Water column transport affects sediment contamination in many ways. The original source of sediment contamination typically comes via discharge of the contaminant to the water column. As indicated in Figure 8.1, this transport can be advective (e.g., current-driven) or dispersive (e.g., driven by tidal mixing) in nature. Water column transport will initially dictate the movement of both dissolved and particle-sorbed contaminant until the time when particle-sorbed contaminants settle out. Transport via the water column dictates where in the sediments a discharged contaminant may settle; it also dictates the ultimate fate of a contaminant that is remobilized from the sediment. Transport throughout the water column behaves quite differently across systems depending on the nature of the water body. Transport behaves differently in rivers than in estuaries or lakes, with the importance of advection and dispersion varying across systems. Separate (i.e., independent of the sediment-quality model) hydrodynamic models are often used to describe water column transport. The need for these detailed models is not necessarily

330 Handbook on Sediment Quality driven by the need to more accurately describe water column transport itself; they are typically required to describe fine-scale hydrodynamic shear stresses that will drive settling and resuspension. Nonetheless, the results of these hydrodynamic models are also used to define water column transport through advective and dispersive transport.

Settling. The transport of contaminants sorbed to particulate matter is dictated by the movement of the sorbent particles. The settling of particulate matter out of the water column is the primary mechanism in which water column contamination is delivered to the sediments. The rate at which particles settle out of the water column is dictated by the nature of the particle and the turbulence of the system. The settling velocity of a particle in a quiescent system is dictated by the particle’s size and density, as described by Stokes’ law. The effective settling velocity in a natural system is also affected by the local turbulence at the sediment–water interface, which dictates the probability that a solids particle will “stick” to the sediments on contact as opposed to being immediately reentrained to the water column. Earlier contaminated sediment model frameworks used a single-state variable to characterize the particles in a natural system. The importance of particle behavior on the ultimate fate and transport of contaminants has led to more detailed descriptions of settling processes, including development of independent sediment transport models to describe particle behavior for multiple particle sizes and types. These will be discussed later in this chapter.

Resuspension. As the converse of settling, contaminants sorbed to bedded sediments are reintroduced to the water column when the sediments are resuspended in the water column. The modeling of resuspension is a critical component of any contaminated sediment modeling effort, as the rate of resuspension in response to extreme events will determine the likelihood that deeply buried contamination will become actively available in the environment. Resuspension occurs when excess shear stress is applied to the sediment–water interface, typically caused by high-flow events in rivers or wind-driven currents in shallow lentic systems. The rate of sediment resuspension depends on many factors in the water column and sediments. The primary water column factor is the shear stress that occurs at the sediment–water interface. The erodibility of sediments can depend on particle size, cohesiveness of the sediments, and degree of sediment compaction. The importance of particle behavior on the ultimate fate and transport of contaminants has led to more detailed descriptions of resuspension processes, including development of independent sediment- transport models. The same sediment-transport models mentioned above to describe settling can also be used to describe resuspension. These will be discussed later in this chapter.

Sedimentation. Sedimentation, or net burial, occurs when the long-term mass solids flux due to settling exceeds the resuspension solids flux. In these conditions, there is a net gain of solids in the sediment bed. Contaminants that were formerly near the surface are “transported” deeper into the sediment

Sediment Quality Modeling 331 bed. This is actually a passive transport process because there is no net movement of contamination; the contamination appears to be moving “deeper” (i.e., further away from the water surface) due to the increase in sediment depth. Sedimentation can be an important process determining long-term contaminant concentrations, as it can sequester historical contamination and provide a barrier to retard movement to the sediment–water interface. Sedimentation is also one of the more easily calibrated model processes, as it can be directly estimated from observations of changes in sediment depth or from sediment cores documenting the historical burial of trace materials.

Sediment–Water Diffusion. A flux of dissolved-phase contaminant will occur when there is a concentration gradient between dissolved chemical in the sediment pore water and dissolved chemical in the water column. The direction of this exchange is almost always from the sediments to the water column, as sediment pore-water concentrations typically exceed water column concentrations. The rate of chemical flux will be dictated by the concentration gradient, as well as the effective diffusion coefficient between pore water and sediments. Characterization of this diffusion coefficient, especially as it is used to represent other sediment–water transfer processes such as bioturbation, is discussed in more detail in the Issues section at the end of this chapter.

Mixing Within Bed Sediments. Physical mixing of the bed sediments (and sediment-bound contaminants) can also affect pollutant distribution within the sediments. This mixing is generally caused by benthic fauna moving within the sediments and disturbing the vertical structure of the sediments. This mixing generally occurs in the upper few centimeters of the sediment bed, corresponding to the depth of penetration of the benthic fauna. The rate of mixing tends to be site specific and can vary seasonally due to temperature influences on biological activity. This process has been traditionally represented in sediment-quality models by treating the most surficial sediment layer as being vertically well mixed, with the depth of this layer representing the depth of mixing. As mentioned in the previous paragraph and discussed later, this bioturbation also plays a role is mediating the exchange of solids and contaminants between the sediments and water column.

REACTION PROCESSES. Several other physical or chemical processes beyond direct transport affect sediment contamination. Loosely defined here as “reaction processes”, they include sorption, volatilization, and other degradation–decay processes.

Sorption. Sorption of chemical between a dissolved and particulate phase is a primary determinant of chemical fate in a contaminated sediment system. The transport of contaminants sorbed to particulate matter is dictated by the behavior of the particle, whereas transport of dissolved pollutant is dictated by the movement of water. Sorption is important for a wide range of sediment contaminants (organics and metals), but the controlling processes have been best defined for hydrophobic organic chemicals.

332 Handbook on Sediment Quality The degree of sorption that occurs between a chemical and particulate matter depends both on the nature of the chemical and the nature of the particle. Karickhoff (1984) and Karickhoff et al. (1979) have shown that organic carbon is the principal sorbent compartment for hydrophobic organic chemicals in aquatic systems. The partitioning potential of a chemical is characterized by its organic carbon partition coefficient. The sorption of nonpolar, hydrophobic, organic compounds correlates well with the organic carbon content of the sediment. Rao and Davidson (1980) and Karickhoff et al. (1979) developed empirical expressions relating equilibrium coefficients to laboratory measurements, leading to reliable means of estimating appropriate values. At equilibrium, the fraction of chemical in the dissolved phase is calculated within a basic contaminated sediment model framwework as

fd = Θ/(Θ + Koc POC) (8.1)

fp = KocPOC /(Θ + Koc POC) (8.2)

Where

fd = fraction of chemical in the dissolved phase (dimensionless); Θ = porosity of sediments (dimensionless);

fp = fraction of chemical in the particulate (i.e., sorbed) phase (dimensionless); 3 Koc = chemical-specific organic carbon partition coefficient (L water/M organic carbon); and POC = particulate organic carbon content (M organic carbon/L3 water).

Laboratory-determined values for the organic carbon partition coefficient, Koc, have been shown to be approximately linearly related to the octanol–water

partition coefficient, Kow, and it is common to assume that Koc = Kow. As will be discussed later, this basic representation can be expanded to consider multiple sorbent classes as well as non-equilibrium conditions.

Volatilization. Volatilization is the exchange of a chemical across the air–water interface, as dissolved chemical attempts to equilibrate with the gas- phase concentration. Equilibrium occurs when the water column dissolved concentration equals the atmospheric partial pressure divided by Henry’s law constant. This partial pressure is determined by both chemical properties (molecular weight and Henry’s law constant) and environmental conditions (temperature). Volatilization is typically described using a two-layer resistance model (Whitman, 1923). This model assumes that two “stagnant films” exist at the air–water interface, bounded by well-mixed compartments on either side. The volatilization rate is controlled by the combined effect of liquid- and gas- phase resistance. The chemical-specific Henry’s law constant determines the driving force for volatilization, whereas resistance depends on chemical- specific diffusivities in water and air and the thickness of the stagnant films at the air–water interface. The volatilization theory predicts a transfer velocity of

Sediment Quality Modeling 333 chemical across the air–water interface, which is typically converted to a first- order loss rate for modeling purposes,

–1 –1 –1 (vV) = Kl + (RTa/HeKg) kV = vV /h (8.3)

Where

vV = volatilization transfer velocity (L/T); Kl = transfer velocity through the liquid phase (L/T); R = gas law constant;

Ta = temperature; He = Henry’s law constant; Kg = transfer velocity through the gas phase (L/T); kV = volatilization loss rate coefficient (1/T); and h = water depth (L).

Degradation–Decay. Many organic chemicals can have their concentrations reduced due to chemical and biochemical reactions such as hydrolysis and biodegradation. Because of potentially long residence times of chemicals in bed sediments, even small rates of decay can have a significant effect on long- term concentrations. The magnitude of the decay processes varies significantly across the spectrum of sediment contaminants. Chemical-specific rate coefficients have been tabulated for most sediment contaminants of concern (e.g., Howard et al., 1991, and Schnoor et al., 1987). A priori determination of site-specific values for decay is difficult, as these processes are affected by environmental conditions. Site-specific values of degradation rates should be determined in cases where long-term measurements of parent and daughter compounds are available. Degradation–decay is typically treated as a first- order loss process, and the rate of decay is specified through a simple rate coefficient.

ADVANCED MODEL FEATURES. The first-generation contaminated sediment models consisted essentially of the framework shown in Figure 8.1. Model evolution has led to enhanced resolution with respect to

• Multiple sorbent (including particle size) classes, • Water column solids, • Non-equilibrium partitioning, • Sediment resuspension, • Behavior of metals, and • Spatial resolution (horizontal and vertical).

MULTIPLE SORBENT CLASSES. One enhancement to sediment contamination models has been an increase in the resolution of sorbent classes. Where originally a single solids class was simulated, recent modeling efforts have considered dissolved organic carbon as a sorbent class as well as multiple solids size classes.

334 Handbook on Sediment Quality Multiple solids size classes are of interest because the settling and resuspension properties of sediments vary directly as a function of their size. Furthermore, different solids size classes have different organic carbon concentrations, as smaller size classes (e.g., silts, clays) tend to be richer in organic material than larger size classes (e.g., sand). Differences in specific surface area and surface charges can also be important across different solids classes. The partitioning of pollutant between phases in a multiple sorbent class model can be described as

fd = Θ/(Θ + ΣKoc,i OCi) (8.4)

fp,i = KocOCi /(Θ + ΣKoc,i OCi) (8.5)

Where

fd = fraction of chemical in the dissolved phase (dimensionless); Θ = porosity of sediments (dimensionless);

fp,i = fraction of chemical sorbed to phase i (dimensionless);

Koc,i = chemical- and phase-specific organic carbon partition coefficient (L3 water/M organic carbon); and

OCi = organic carbon concentration in phase i (M organic carbon/ L3 water).

The organic carbon partition coefficient is generally held constant across all

particulate sorbents (and is termed Kpoc). Because particulate sorbent phases are typically selected by size class, the phase-specific organic carbon concentration can be determined by conducting measurements after samples have been fractionated into the appropriate size classes. In addition to organic carbon in particulate form, dissolved organic carbon (DOC) can also be an important sorption compartment in determining contaminant fate (Bierman et al., 1992, and Eadie et al., 1990). Note that the organic carbon partition coefficient is listed as phase specific in eqs 8.3 and 8.4. Whereas the organic carbon partition coefficient is generally treated as constant across particulate organic carbon components, dissolved chemical partitioning to DOC has been demonstrated to be lower than predicted by use

of an octanol–water partition coefficient. The DOC partition coefficient, Kdoc, is typically estimated as

Kdoc = Kpoc × (binding efficiency) (8.6)

where the binding efficiency (ranging from 0.01 to 0.1; e.g., Bierman et al., 1992) is based on analysis of field data measurements of each chemical phase.

WATER COLUMN SOLIDS. Advanced sediment-contamination models often consider more than one particle type, concurrent with the use of multiple sorbent classes to describe partitioning. This enhanced description

Sediment Quality Modeling 335 covers both settling and resuspension processes. With respect to settling processes, use of multiple solids classes allows differential settling rates to be used across size classes, reflecting differences in particle diameter and density. Flocculation of smaller size class particles can also be described and is proving to be important in some systems. Theoretical relationships (e.g., Stoke’s law) can be used to define the relative differences in settling velocity for each size class, but site-specific calibration data across size classes are necessary to accurately define the respective velocities. In systems where primary production plays an important role in the overall solids balance, separate eutrophication models are often applied to describe the organic carbon cycle and the internal production of particulate organic carbon. The Green Bay Mass Balance Study (Bierman et al., 1992) is a good example where a concerted eutrophication modeling effort was an integral component of the overall toxics modeling framework. Application of a site- specific eutrophication model can provide many ancillary benefits to a contaminated-sediment modeling study, such as independent estimates of solids fluxes and diffusive exchange rates across the sediment–water interface.

NON-EQUILIBRIUM PARTITIONING. The equations described above are based on the assumption that phase partitioning can be described as an equilibrium process. In reality, sorption and desorption are both kinetic processes with inherently “faster” and “slower” reaction phases (Pignatello and Xing, 1996) and some components that are essentially irreversible. Whereas non-equilibrium partitioning has been modeled, the time scales of concern at most contaminated sediment sites are long enough (relative to sorption–desorption reaction rates) that most sediment-quality models are based on the assumption of equilibrium partitioning.

SEDIMENT RESUSPENSION. The description of sediment resuspension in contaminated sediment models is typically divided into two categories, based on the nature of the bedded sediments

• Noncohesive sediments, primarily composed of sands and larger size classes, and • Cohesive sediments, primarily composed of silt and clay, that exhibit interparticle reactions.

The main parameters affecting the entrainment of noncohesive sediments include grain size and shape (and their distributions), the shear stress applied at the sediment–water interface, bed roughness, and specific weight of the sediments. Bed sediments that are primarily fine grained or possess a high clay content exhibit interparticle effects that are cohesive in nature. The resulting entrainment properties for these cohesive sediments are different from noncohesive sediments. Because most contaminants of interest are associated preferentially with fine-grained sediments, this distinction is of considerable importance. Separate submodels are, therefore, used to describe the resuspension for cohesive versus noncohesive sediments.

336 Handbook on Sediment Quality Cohesive Sediments. The interparticle electrochemical influences of cohesive sediments have a significantly greater influence on their entrainment characteristics than does particle diameter. Relatively small amounts of clay in the sediment–water mixture can result in critical shear stresses larger than those in noncohesive materials of similar size distribution (Raudkivi, 1990). Previous studies on the entrainment of cohesive sediments hypothesize that the scour magnitude is primarily influenced by the excess applied shear stress and the state of consolidation (or age after deposition) of the bed sediments (Mehta et al., 1989; Partheniades, 1965; and Xu, 1991). The mass of material resuspended can be expressed in the following functional form:

M = f(τ – τc; age, other sediment properties)

Where M = the mass of material resuspended, τ = the applied shear stress, and

τc = the bed critical shear stress.

The function f has been expressed in a variety of different forms ranging from linear (e.g., Partheniades, 1965), exponential, (e.g., Parchure and Mehta, 1985), and the power relationship developed by Lick et al. (1995) and Gailani et al. (1991). The applied shear stress is a function of water velocity and roughness at the sediment–water interface, as is predicted by hydrodynamic models. Lick et al. (1995), based on statistical analysis of laboratory and field data, proposed an erosion equation of the following form: τ τ m ε ao × – c = –n ΂΃–– (8.7) td τc Where ε = the net total amount of material resuspended (g/cm2); τ = the applied shear stress; 2 τc = the bed critical shear stress (dynes/cm ); td = the time after deposition; and a0, n, and m = empirical constants.

The depth of scour can then be calculated as ε zscour = – (8.8) Cbulk Where

zscour = the depth of scour (cm), and 3 Cbulk = the dry bulk-sediment density (g/cm ).

These equations have been applied and results validated to several rivers (e.g., Fox, Detroit, and Buffalo rivers [McNeil, 1994]).

Sediment Quality Modeling 337 Noncohesive Sediments. Noncohesive sediments are primarily composed of sands and larger size classes and do not exhibit the interparticle reactions associated with cohesive sediments. Erosion of noncohesive sediments occurs when the sediment-transport capacity of the overlying water flow exceeds the actual sediment burden being carried by the flow. A flow will have transport capacity for a particular particle diameter (size class) when the shear stress applied to those particles as a result of the flow exceeds the critical shear stress of the particle size class. When the flow continuously has excess transport capacity, the bed is scoured as transportable particles are entrained when exposed at the bed surface. Because the transport capacity of the flow is inversely related to the particle size, differential scouring takes place with the smaller particles being removed in greater proportion than the larger particles. The particle size distribution of the bed surface shifts progressively towards larger particles. If sufficient large particles are present that cannot be transported under flow conditions, the bed surface will come to consist primarily of the larger particles, with smaller particles sheltered underneath them. This layer of coarse particles, called the armor layer, will persist unless higher flows and their associated shear stresses erode it, causing further coarsening and the establishment of a new armor layer. Borah (1989) gives equations for the depth of scour that will occur before the establishment of an armor layer, assuming a well-mixed surface layer with constant particle specific gravity and that a monolayer of the smallest non- transportable particle present in the bed material will be created. The formulation is conservative in that the potential for finer particles to be trapped (hiding) in the armor layer is ignored. An active layer thickness is defined as

D T = ––a (8.9) (1 – φ)Pa Where T = the thickness of the active layer (cm);

Da = the smallest armor size (cm); φ = the porosity of the bed material; and

Pa = the fraction of all the armor sizes present in the bed material.

Da is computed using a modified version of the Shields Curve (Shields, 1936, and van den Berg and van Gelder, 1993). The scour depth is then computed as

E = T – Da where E is the scour depth. These equations have been applied and the results validated for laboratory (Little and Mayer, 1972) and field (Karim and Kennedy, 1982) data.

BEHAVIOR OF METALS. The modeling of metals behavior in sediments is complicated by the large number of potentially controlling physical–chemical factors. Tessier and Campbell (1987) have indicated that trace metals can be associated with the following phases in natural sediments: (1) adsorbed at

338 Handbook on Sediment Quality particle surfaces (e.g., clays, humic acids, metal oxyhydroxides); (2) carbonate-bound (e.g., discrete carbonate minerals co-precipitated with significant carbonate phases); (3) occluded in iron or manganese oxyhydroxides (e.g., discrete nodules, cement between particles, coatings on particles); (4) bound up with organic matter in either living or detrital form; (5) sulfide-bound (e.g., amorphous sulfides formed in situ or more crystalline forms); and (6) matrix- bound (e.g., bound in lattice positions in aluminosilicates) in resistant oxides or sulfides. Models have been developed to describe in detail the speciation and partioning of metals in the aquatic environment. These models have extensive data requirements and are restricted to describing equilibrium conditions such that they have limited use in contaminated sediment assessment. Simpler, management-oriented model frameworks have been developed that limit data requirements by restricting the number of processes described. Several researchers (e.g., Davis and Leckie, 1978; Hart, 1982; Jenne and Zachara, 1987; and Tessier et al., 1993) have demonstrated the importance of organic material in serving as a primary component in driving the partitioning of metals in sediments. Research on the availability of metals in sediments (Allen et al., 1993, and Di Toro et al., 1990) demonstrated that acid-volatile sulfide controlled the availability of simultaneously extracted metals in anoxic sediments. Sulfide reacts with the divalent ions of many transition metals (e.g., cadmium, copper, mercury, nickel, lead, and zinc) to form insoluble precipitates. Because of the insolubility of metal sulfides, pore-water concentrations of these metal ions can be greatly reduced. The basic sediment–water quality model framework discussed previously can be adapted to consider sulfide precipitation as well as partitioning to organic material. Figure 8.2 shows the model framework for a single chemical. Because of the competition among metals for available sulfides, the model must concur- rently consider concentrations of Cd, Pb, Zn, Ni, and Cu to determine the extent to which each metal precipitates with sulfide (Dilks et al., 1995).

SPATIAL RESOLUTION. The importance of refined spatial resolution has become more pronounced for sediment modeling assessment. This is due both to increased data collection at contaminated sediment sites, indicating high degrees of spatial heterogeneity, and the localization of some remediation alternatives to focus specifically on sediment “hot spots”. Increased spatial resolution has been introduced to contaminated sediment models in the horizontal and vertical dimensions. Earlier contaminated sediment models of rivers typically had a single longitudinal dimension, representing distance downstream from the source. As more spatially resolute data become available, the importance of lateral gradients in most contaminated river systems has become clear. Stream morphology causes lateral and longitudinal gradients in current velocities. These variable velocities lead to differential settling and resuspension of various particle types across the streambed. This can lead to strong spatial heterogeneity in sediment contaminant concentrations because the affinity of contaminants to sorb to particles depends on the type of particle. For example, areas of a river with lower velocities will tend to

Sediment Quality Modeling 339 Figure 8.2 Model framework considering sulfide precipitation of metals. accumulate smaller-diameter sediments with higher organic carbon content (and, therefore, higher contamination) than an area with faster current velocities. The resuspension of sediments has been shown to be a highly nonlinear relationship with respect to current velocity. For this reason, use of spatially averaged river properties will provide a less accurate description of the fate and transport of sediment contamination than will use of a more spatially resolute model. Figure 8.3 shows an example of the strong two- dimensional pattern of predicted velocity during the 100-year flood for the Hudson River in Upstate New York. Increases in spatial resolution can also be important in the vertical plane. This is especially the case when historical contamination has led to the occurrence of peak contamination at some depth in the sediments, overlain by relatively cleaner sediments reflective of more recent conditions. Basic modeling frameworks that consider only a single active sediment layer are not well suited to describe these conditions. Instead, contaminated sediment models have evolved to allow consideration of any number of vertical sediment layers.

COMMONLY USED MODEL FRAMEWORKS

Several public domain model frameworks have been applied to assess sediment contamination issues. The importance of small-scale hydrodynamics on particle behavior, coupled with the importance of particle behavior on

340 Handbook on Sediment Quality Figure 8.3 Example of two dimensionality of predicted velocity for Hudson River.

contaminant transport, has led to the emergence of specialized submodels devoted to these topics. The results of these submodels can be sequentially linked to provide a complete sediment assessment, that is

Hydrodynamic model → Sediment transport model → Chemical fate and transport model

The submodel linkage described above can be accomplished by running each submodel independently of the others; some model frameworks link multiple submodels together as part of an integrated modeling package. Proprietary modeling packages exist that provide capabilities equivalent to the frameworks described in this section. These models are not discussed in this section because their proprietary nature limits their widespread applicability.

HYDRODYNAMIC MODELS. Hydrodynamic model frameworks have been widely used historically for purposes other than contaminated sediment assessment, although their results are often directly translatable for use in contaminated sediment assessment. A variety of models are potentially suitable to describe hydrodynamic transport in support of contaminated sediment assessment. The four most applicable public domain models are as follows:

Sediment Quality Modeling 341 • UNET, • DYNHYD5, • RMA-2V, and • Princeton Ocean Model (POM).

UNET. UNET (HEC, 1997) simulates one-dimensional unsteady flow through a full network of open channels. It is suitable for simulating dendritic and split flows (such as flows around islands) and levee failures. It is supported by the U.S. Army Corps of Engineers Hydrologic Engineering Center (HEC). In addition to solving the network system, UNET provides the user with the ability to apply several external and internal boundary conditions, including flow and stage hydrographs, rating curves, gated and uncontrolled spillways, pumping stations, bridges, culverts, and levee systems. Being one dimensional, UNET assumes no lateral or vertical gradients in any hydrodynamic aspects of the system. The UNET code is also contained in the new HEC-River Analysis System (RAS), part of HEC’s “Next Generation Software” that is ultimately designed to replace UNET and other HEC models. The primary advantage of UNET for contaminated sediment application is that it is a numerically efficient, dynamic model. Its primary disadvantage is that its results are not automatically linked to any water quality model, such that external linkages must be developed for its use in a water quality modeling context.

DYNHYD5. DYNHYD5 is a one-dimensional model that is based on the relatively simple link-node concept to represent a water body (Ambrose et al., 1993). It is supported by the U.S. Environmental Protection Agency (U.S. EPA) Center for Exposure Assessment Modeling (CEAM) in Athens, Georgia. The model solves the one-dimensional equations of continuity and momentum describing the movement of a long wave in a shallow water system. The model is distributed as a companion model to the WASP5 water quality model and is typically applied externally to provide hydrodynamic inputs to WASP5. The numerical solution technique in DYNHYD5 requires small time steps to ensure mathematical stability; however, DYNHYD5 results may be stored as longer user-specified averages for input to the water quality model. For example, numerical stability considerations may require that DYNHYD5 be run with a 1-minute time step, whereas the WASP5 model may be run using a 30-minute time step. In these cases, it is possible to force DYNHYD5 to generate hydrodynamic results that contain the average of 30 times steps for input to WASP5. The primary advantages of DYNHYD5 for contaminated sediment modeling is that its results are automatically linked to the WASP5 water quality model, such that no external linkages need to be developed. Its primary disadvantages are its crude link-node approach and numerical solution technique.

RMA-2V. RMA-2V is a two-dimensional finite element hydrodynamic analysis model simulating lateral and longitudinal variability and assuming

342 Handbook on Sediment Quality that no vertical hydrodynamic gradients exist. The governing equations provide for the conservation of mass and conservation of momentum in both the x and y directions. It also computes the dynamic boundary between wet and dry regions in the model. Flow separations and eddy currents are accurately modeled. Its output can be directly used by the sediment transport model SED-2D to simulate sediment transport as well as the water quality model RMA4 to model contaminant migration. The primary advantage of RMA-2V for contaminated sediment assessment is the ability to simulate lateral variations in hydrodynamics and link its results to certain other water quality models. Its primary disadvantage is that a linkage to a complete sediment-transport–water quality modeling package is not available. The SED-2D model does not consider contaminants and the RMA4 water quality model can only simulate simple decaying constituents and is not applicable for the simulation of sediment contamination. Linkages between RMA-2V and the water quality model WASP5 have been developed for site-specific applications but have required extensive programming efforts that are ill-suited for widespread application to other sites.

Princeton Ocean Model. The POM, developed by Blumberg and Mellor (1980), is a three-dimensional hydrodynamic model. POM is a sigma coordi- nate, free-surface, primitive equation ocean model, which includes a turbulence submodel. It has been used for hydrodynamic modeling of estuaries, coastal regions, and open oceans. It has been applied at nearly 100 sites around the world. The primary advantage of POM for contaminated sediment assessment is its ability to simulate lateral, longitudinal, and vertical variations in water movement. Hydrodynamic results of POM have been linked to various sediment-contamination models, although no direct linkage to any public domain sediment-contamination model is currently available.

SEDIMENT-TRANSPORT MODELS. Another category of models are available that simulate strictly the fate and transport of sediment material. Results from these models can be used to provide forcing functions for contaminant fate and transport models. These models include HEC-6, SED2D, and SEDZL.

HEC-6. The HEC-6 (U.S. Army Corps, 1976, 1990) program computes sediment transport for one-dimensional systems. Being one dimensional, HEC-6 assumes no lateral or vertical gradients in any hydrodynamic aspects of the system. It is supported by the U.S. Army Corps of Engineers HEC. HEC-6 results are not directly linked to any water quality models; however, results from HEC-6 can be transferred to other water quality models. The HEC-6 code is also contained in the new HEC-RAS, part of HEC’s “Next Generation Software” that is ultimately designed to replace HEC-6 and other HEC models. The advantage of this model for contaminated sediment application is that it can provide information on the settling and resuspension of solids for use as input to water quality models. Its primary disadvantage is that it is only one dimensional.

Sediment Quality Modeling 343 SED2D. SED2D (U.S. Army Corps, 1996) is a two-dimensional sediment- transport model contained in the TABS modeling framework developed by the U.S. Army Corps of Engineers. It is a modification of an earlier sediment transport model called STUDH. The model is capable of simulating both cohesive and noncohesive sediment, although separate model simulations are required for each solids size class.

SEDZL. SEDZL is a sediment-transport model originally developed for the U.S. EPA by Ziegler and Lick (1986). SEDZL simulates the transport, deposition, and resuspension of cohesive and noncohesive sediments. The model predicts temporal and spatial variations in multiple size classes and bed elevations and estimates the particle size distribution within the bedded sediments (Ziegler, 1999). SEDZL is available in a public domain version as well as a proprietary one. It is a powerful tool for sediment-transport assessment; however, the full power of the tool requires extensive site-specific data.

CHEMICAL FATE AND TRANSPORT MODELS. A series of public domain models are available that consider information on hydrodynamic, sediment transport, and kinetic processes and provide simulation of sediment quality. These models include SMPTOX, the Hydrodynamic, Sediment, and Contaminant Transport Model (HSCTM-2D), WASP5/In-Place Pollutant eXport Model (IPX), and EFDC.

SMPTOX. SMPTOX (LTI, 1995) is a simple, one-dimensional steady-state model capable of describing hydrophobic organic chemicals and metals in the water column and surficial sediments. SMPTOX contains no separate hydrodynamic component, as steady flow conditions are specified by the user. Sediment transport is described through the use of spatially variable settling and resuspension velocities for a single-solids class. SMPTOX is of limited value in describing situations where time variability is of concern because of its steady-state framework. As discussed below, however, it can be applied to address prevention of future sediment contamination.

HSCTM-2D. HSCTM2D is a finite element modeling system for simulating two-dimensional, vertically integrated, surface water flow (typically riverine or estuarine hydrodynamics), sediment transport, and contaminant transport (Hayter et al., 1995). The model is supported by the U.S. EPA CEAM. The HSCTM-2D modeling system consists of two modules, one for hydrodynamic modeling and the other for sediment- and contaminant-transport modeling. These modules can be run in coupled or uncoupled modes. The hydrodynamic component of HSCTM-2D is based on the RMA-2V model described above and has similar capabilities. The sediment- and contaminant-transport module simulates aggregation, erosion, deposition, adsorption, and desorption.

WASP5/IPX. WASP5 (Ambrose et al., 1993) is the U.S. EPA’s general- purpose surface water quality modeling system. The WASP5 model is supported by the U.S. EPA CEAM. The model can be applied in one, two, or

344 Handbook on Sediment Quality three dimensions and is designed for linkage with the hydrodynamic model DYNHYD5. WASP5 has also been successfully linked with other one-, two-, and three-dimensional hydrodynamic models such as RIVMOD, RMA-2V, and EFDC. WASP5 can also accept user-specified advective and dispersive flows. WASP5 provides separate submodels for conventional and toxic pollutants. The TOXI5 submodel simulates the transformation of up to three different chemicals and three different solids classes. The primary advantage of using WASP5 is that it provides the flexibility to describe almost any water quality constituent of concern, and its widespread use and acceptance. WASP5 has two primary disadvantages. First, WASP5 is designed to read hydrodynamic results only from the one-dimensional DYNHYD5 model. The coupling of WASP5 with multidimensional hydrodynamic model results requires extensive site-specific linkage efforts. Second, WASP5 contains fairly rudimentary sediment-transport routines, although results from sediment-transport models can be specified to WASP5 as forcing functions. IPX (Velleux et al., 1994) is an enhancement of the WASP model framework specifically designed to address contaminated sediments. Enhancements over WASP of the IPX framework include process description of sediment aging, decreased sediment resuspendability with increasing age, and resuspension of freshly deposited sediments as a function of shear stress at the sediment–water interface.

EFDC. EFDC (Hamrick and Wu, 1997) is a linked three-dimensional, finite difference hydrodynamic and water quality model developed at the Virginia Institute of Marine Sciences in Gloucester, Virginia. The primary advantage of EFDC for contaminated sediment assessment is that it is the sole public domain model to provide detailed simulation of three-dimensional hydrodynamics, sediment transport, and chemical fate. Its primary disadvantage is the amount of resources necessary to successfully apply it. Because of extensive data requirements and the recent development of this model, there is not a large body of experience in applying it to contaminated sediment systems.

MODEL CALIBRATION Before applying a site-specific contaminated sediment model to evaluate management alternatives, it is necessary to first calibrate the model to existing field observations. Model calibration involves adjusting a process model’s coefficients within an acceptable range of values (this acceptable range generally depends on experience with other similar systems and reported literature values) until the model captures the observed spatial and temporal behavior of state variables and processes in the system. Deterministic, process-oriented mass-balance models are a simplified representation of the full complexity of the actual system. In that regard, models of this type are designed to describe the behavior of those processes and state variables that

Sediment Quality Modeling 345 are important to the problem under consideration. If such a site-specific model can be formulated and parameterized (i.e., calibrated) to simulate the system observations of interest for a period approaching that of the desired prediction period, then the model is considered usable for making predictions of the system’s behavior in response to external perturbations. This section discusses the calibration of contaminated sediment models. It is divided into subsections of

• Calibration sequence, • Short-term versus long-term calibration, • Alternate observations, and • Calibration sufficiency.

CALIBRATION SEQUENCE. The typical calibration strategy for contaminated sediment models proceeds sequentially as (1) hydrodynamics, (2) solids, and (3) contaminant. This sequence minimizes the number of free parameters to be considered at any point during the calibration process because the hydrodynamic calibration can be conducted independently of the solids and contaminant calibration and the solids calibration can be initiated independently of the contaminant calibration. The hydrodynamic calibration first considers water stage and current velocity, with the bottom roughness (i.e., Manning’s) coefficient serving as the primary calibration parameter. Transport processes for dissolved material are typically calibrated through comparison to a conservative tracer (e.g., salinity) through adjustment of diffusion–turbulence coefficients in the model. The primary calibration comparison for the sediment is via comparison with observed suspended solids concentrations. Depending on the nature of the sediment-transport model being used, calibration may also consider comparison of simulated versus observed particle-size distributions. The contaminant calibration generally commences after the hydrodynamic calibration is completed and the sediment calibration initiated. The chemicals that accumulate at levels of concern in sediments tend to be fairly nonreactive, and the primary reaction process (i.e., sorption, volatilization) coefficients can be independently defined. For this reason, the adjustment of the remaining independent kinetic coefficients often does not have a large effect on predicted concentration. The calibration approach described above may not always be conducted in a purely sequential manner. Some process coefficients are common across multiple models, and calibration to state variables for latter models in the sequence may conflict with initial parameter estimates. Because it is desirable to obtain a scientifically credible and internally consistent calibration across all models, feedback between the hydrodynamic, solids, and contaminant steps of the calibration often requires some iteration. For example, contaminants can serve as a “tracer” of solids, because under many conditions, solids and contaminants are mutually constrained.

346 Handbook on Sediment Quality SHORT-TERM VERSUS LONG-TERM CALIBRATION. The ideal dataset for calibration of a contaminated sediment model would consist of long-term observations of all model-forcing functions and response variables. The long time scale is desired because model projections often cover a decadal time frame, and it is desirable to demonstrate the validity of model predictions over an equally long time period. The data typically available for model calibration at a contaminated sediment site typically fall into two categories

1. Short-term (i.e., a few years) intensive data collection designed specifically to support a modeling effort, and 2. Long-term (many years to decades) less intensive data collected for purposes other than to support a modeling study.

Both types of data can be used in concert to support the calibration process. If used judiciously, high-resolution, short-term datasets can provide useful information to help constrain parameters that control the long-term behavior of a system. Slight biases that are not detectable in short-term simulations can manifest themselves when simulations are carried out over long time periods, which is where long-term datasets are of value. Calibration to long-term historical data (often called “hindcasting”) faces the challenge that many of the required model forcing functions (e.g., external loading of solids or contaminant) are not measured for long portions of the simulation period. Nonetheless, the conduct of these long-term hindcasts, substituting reasonable assumptions for the missing data, can often identify long-term instabilities in a model that would not be manifested during a short-term calibration. Because of the long time frames involved in sediment-contamination processes, the typical modeling approach of “calibration to one dataset, verification–validation to an independent dataset” is generally not applicable for sediment-contamination models. The ability to simulate both long- and short-term datasets using consistent parameter values is roughly analogous to calibration and verification–validation as applied to models that are applied only to (relatively) short-term datasets.

ALTERNATE OBSERVATIONS. The traditional approach to model calibration is to compare predicted with observed values for model state variables (i.e., suspended solids, contaminant concentrations). Often it is possible to gain a reasonable calibration to concentration data while still having relatively poor model performance in comparison with mass budgets, especially over long-term simulations. Therefore, in conducting the calibration, cumulative mass fluxes and changes in sediment-mass reservoirs should be included as important metrics of model calibration. Other important alternate observations that can constrain a model calibration are changes in bed elevation and trends in fish tissue contaminant levels. Historical bed elevation data can provide important information regarding whether an area is “net erosional” or “net depositional” over time as well as insight to the rate of change. Trends in observed fish tissue data can provide an additional constraint to the contaminant calibration, especially when

Sediment Quality Modeling 347 sediment–water quality model results are linked to some type of bioaccumulation or food web model.

CALIBRATION SUFFICIENCY. One more reality of model calibration should be recognized. Virtually all process-oriented water quality models are underdetermined in that they have more calibration coefficients (degrees of freedom) than state variables. Furthermore, monitoring data are rarely sufficient to define the “true” state of the system. Often, there is not a unique set of model coefficients that will give a best fit to the observed data. This raises the question of, “When is a calibration sufficient?” As of now, there are no universally accepted criteria that define the suitability of a model calibration. Statistical analysis of the comparison between model results and observed data should be conducted to evaluate goodness of fit and identify any potential biases. Model sensitivity analyses or model uncertainty analyses should be conducted to estimate the uncertainty associated with a given model calibration. This uncertainty should be considered as part of the management decisions regarding how to use the model results.

MODEL APPLICATION Sediment-contamination models are typically applied to address two management issues: remediation and prevention. Remediation is presently the most common application. It is of concern when dealing with existing sediment contamination and is designed to assess the risks associated with natural remediation and alternative engineering solutions. Prevention is of concern when managing areas that are currently not contaminated and is designed to address the question, “How much of a constituent can be discharged to a water body while still maintaining acceptable sediment quality?” As sediment quality criteria (SQC) are established, it is expected that National Pollutant Discharge Elimination System permits will be written to ensure compliance with SQC. Sediment-quality models will be integral to this permit development.

REMEDIATION. The most common management application of contaminated sediment models is for assessment of sites with existing contamination. Depending on the history of the source, the bulk of the contamination may be located in surficial sediments or buried at depth and covered with relatively clean sediments. The primary role of models in remediation assessment is to define the future risks associated with alternative remediation scenarios. Figure 8.4 illustrates the type of information generated by models for remediation. Figure 8.4 compares model predictions for natural remediation to predictions for an engineered alternative and shows how each alternative will affect future concentrations. These results can be used to provide two types of information related to risk assessment: (1) the length of time required for each alternative to achieve risk-based target concentrations, and (2) exposure

348 Handbook on Sediment Quality Figure 8.4 Demonstration of types of model output generated for remediation assessment.

assessment information to allow calculation of the cumulative environmental risk posed by each alternative over time. The specification of the future environmental conditions is a necessary step in simulating the effect of remediation alternatives with the model. This section first describes selection of future environmental conditions and then describes how models are used to represent the three most common remediation alternatives: natural attenuation, dredging, and capping.

FUTURE ENVIRONMENTAL CONDITIONS. The response of a contaminated sediment site to remediation alternatives can be greatly affected by the environmental conditions that occur independently of the management scenarios of interest. These environmental conditions can include river flow, winds, external solids loads, and contaminant loads from sources not being considered for management (e.g., upstream or atmospheric sources). Future environmental conditions can be specified by directly repeating a period of observed historical conditions or by synthetically generating a set of environmental conditions that mimic historical variability. Appropriate selection of these conditions is crucial because environmental events such as floods or hurricanes play such important roles in transporting sediments and their associated contaminants. Therefore, it is essential that selected environmental conditions contain a sufficient frequency of these extreme events. Model predictions are sensitive not just to the presence of extreme events, they can also be extremely sensitive to the timing of when these events occur. The results for a future simulation that has an extreme event occur in the first 5 years will be vastly different than the results for the same simulation that had the extreme event occur in the last 5 years. Because the timing of future environmental conditions cannot be specified, there is no way to specify the “correct” future environmental conditions. The most rigorous way to account

Sediment Quality Modeling 349 for this is to conduct a probabilistic exposure assessment, where the variability in future concentrations is determined as a function of the expected variability in environmental conditions. The results of the probabilistic exposure assessment can then be evaluated to provide a better understanding of the risks associated with each alternative. Because events are so important to the fate and transport of deeply buried contaminants, more detailed focus can also be given to the simulation of individual extreme events independent of long-term simulations. These critical condition scenarios can provide insight to the percentage of sediment contamination that will be remobilized.

NATURAL ATTENUATION. Natural attenuation refers to a “no-intervention” management scenario where the natural processes are sufficient to reduce sediment contamination to target levels over an acceptable time period. The primary advantages to natural attenuation are that it is the lowest cost- management alternative and that it does not produce the negative secondary ramifications (e.g., habitat disturbance) associated with other alternatives. Many processes can contribute to natural attenuation; these include (Peterson et al., 1999)

• Mixing with cleaner sediments via suspension and redeposition, • In-place burial (covering) by deposition of progressively cleaner sediments, • Chemical oxidation and reduction, • Aerobic and anaerobic degradation, and • Complexation of metals by sulfate-reducing bacteria.

Peterson et al. (1999) also point out some basic hindrances to natural remediation

• Ongoing contamination sources, • Lack of clean sediment deposition, • Lack of sufficient organic carbon or sulfide to bind the contaminants of concern, and • Contaminants that are resistant to degradation.

With respect to modeling, natural attenuation is the most straightforward management scenario to address. This is because existing measurements of sediment concentrations, as well as all of the model process coefficients determined during the calibration process, can be directly used for model forecast. This is in contrast to the other alternatives discussed below that require assumptions regarding future sediment-quality distributions (i.e., dredging) or future sediment behavior (i.e., capping). Whereas natural attenuation involves a “no-intervention” scenario, it does require a long-term monitoring program to verify its effectiveness.

DREDGING. Dredging consists of the physical removal of contaminated sediments from some or all of the area of concern. The primary advantage to

350 Handbook on Sediment Quality dredging is that it can remove large quantities of contamination from the site, guaranteeing that they will not lead to future environmental exposure. The physical act of dredging can also cause numerous negative secondary consequences. These include remobilization of some of the contaminants that were buried at depth, alteration–destruction of the benthic community, and short- term elevation of surface concentrations at sites where the contamination was buried at depth. Dredging can be a preferred remediation alternative when it is required for other purposes (e.g., navigation), when the contamination is confined to a small and easily accessible area, and when a suitable means of disposal is available.

CAPPING. Capping consists of the placement of an isolating physical barrier at the sediment surface to prevent the remobilization of sediment contamination and isolate the contaminants from contact with the benthic community. Caps usually consist of natural materials, such as sand or armor stone, although geosynthetic materials and have also been used. Capping is conducted without relocating or causing a significant disruption to the original bed (NRC, 1997). The advantages to capping are that it can be conducted without causing significant disruption to the original sediment bed and that it promotes natural anaerobic degradation while isolating the contaminant from the environmental receptors of concern. Disadvantages include potential long-term erosion of the cap and alteration of the benthic community.

CASE STUDY. The Hudson River PCB Superfund site encompasses the Hudson River from Hudson Falls to the Battery in New York Harbor, a distance of 318 km (198 river miles). Over a 30-year period ending in 1977, several hundred thousand pounds of polychlorinated biphenyls (PCBs) were discharged from two facilities used in the manufacture of electrical capacitors. A significant portion of the PCBs discharged to the river adhered to suspended particulates and subsequently accumulated downstream in bottom sediments as they settled in the impounded pool behind the former Fort Edward Dam (Figure 8.5). A modeling investigation was conducted to reassess the 1984 “No Action” decision of U.S. EPA concerning sediments contaminated with PCBs in the Upper Hudson River (LTI et al., 2000). A linked hydrodynamic–contaminant fate and transport model was developed and calibrated to the Hudson River site. The hydrodynamic model was based on the RMA-2V modeling framework. A finite element grid was constructed for the Thompson Island Pool section of the river and floodplain. Upstream boundary flows and downstream water elevations were specified as boundary conditions to RMA-2V, and the bottom roughness and turbulent exchange coefficients were calibrated to best match observed velocity, stage, and rating curve information for the study area. The contaminant fate and transport model used, HUDTOX, was a modification of the WASP5 model framework discussed previously. The HUDTOX model provides three-dimensional, time-variable mass-balance calculations for water, solids, and PCBs. HUDTOX model segmentation was determined

Sediment Quality Modeling 351 WARREN Glens Hudson Hadley Falls Falls R M 210 Bakers Falls

RM 200 Fort Edward RM 220 Thompson Island Lock Post No. 7 Kill Dead Cr. oses RM 190 M Snook Kill Thompson Is. Dam Fort Muller Dam Lock No. 6 WASHINGTON

Batten Kill Lock No. 5 Schuyincville RM 180 SARATOGA Fish Cr. Covsville

Hudson R. Sulhwater RM 170 Lock No. 4 Lock No. 3 Mechanicville Hoosic R. Lock No. 2 Mohawk R. RENSSELAER RM 160 Lock No. 1

Waterford SCHENECTADY Cohoes Great Federal Dam ALBANY Island Troy

Figure 8.5 Hudson River site map. by the management scales of interest and available data and was significantly coarser than the hydrodynamic grid. Hydrodynamic model results from RMA- 2V were spatially and temporally processed to transform predicted water velocities into flows that were routed through the HUDTOX segments. Sediment scour was computed independently of the HUDTOX framework and specified to the model as a flow-dependent forcing function. The HUDTOX model was applied in a structured sequence as follows:

• Historical calibration for the sum of the trichloro- through decachloro- homologue groups for a 21-year period from 1977 to 1997; • Hindcast application for total PCB and five congeners for the period 1991 to 1997; • Independent model validation for 1998; • 70-year model forecast from 1998 to 2067; and • Sensitivity analyses for the historical calibration and the forecast simulation periods.

The historical calibration was the principal development vehicle for the model, which was focused on representing long-term PCB trends in the water and sediments. Figure 8.6 shows an example comparison between predicted and observed PCB concentrations over time. The 7-year hindcast provided a separate test of the model calibration.

352 Handbook on Sediment Quality Figure 8.6 Example of calibration plot for the Hudson River.

The calibrated model was subjected to validation using an independent set of water column PCB data for 1998. After successful validation of the model, 70-year forecast simulations are now being conducted to assess the long-term system response to various remediation alternatives. Predicted sediment and water column PCB concentration histories for each of these simulations are being used as input to a health risk assessment, which will be used to guide management actions for the site. Sensitivity analyses were conducted for both the historical calibration and forecast simulation periods to provide an indication of model performance.

PREVENTION OF SEDIMENT CONTAMINATION. Prevention of future sediment contamination is listed as a key component of the U.S. EPA’s Contaminated Sediment Strategy (U.S. EPA, 1998). Models can play a key component in a sediment-contamination prevention strategy, by defining maximum allowable contaminant loading rates that will achieve long-term compliance with sediment-quality criteria. The use of models to address the prevention of contamination provides some simplification and some additional challenges compared with model assessment of remediation alternatives. The primary simplification is in terms of time scale. The use of models to address remediation focuses on defining the length of time until sediment-quality objectives can be attained and requires a time variable modeling assessment and associated data needs. The use of models to address prevention focuses on maximum allowable long- term loads and can often be assessed using a steady-state modeling framework. The prevention question is also simplified in terms of necessary vertical resolution. The detailed assessment of contamination at depth in the sedi-

Sediment Quality Modeling 353 Figure 8.7 Typical model-predicted sediment concentration comparison for prevention analysis showing long-term insensitivity to time. ments, which often drives remediation assessments, is not of concern for modeling studies of prevention. Figure 8.7 shows how model results can be used to assess the prevention of sediment contamination by comparing predicted concentrations for two alternative levels of loading. In these cases, sediment concentrations approach an asymptotic level as time progresses, such that model predictions can be evaluated independently of time response. The key question for this case is no longer, “How long (will it take for the system to recover)?”, as it was for remediation in Figure 8.5, but instead is, “How much (loading can the system assimilate)?”

LIMITATIONS AND PROPER USE. The primary limitation of modeling sediment prevention is a lack of the necessary data to adequately support model application. The cost of sediment remediation alternatives is high and the number of sites currently being studied is relatively low, which results in the collection of large quantities of data to support remediation modeling assessment. The resource situation is reversed for prevention modeling. The number of sites potentially affected is enormous, and requirements for costly controls are not imminent. For this reason, there is only a small fraction of the data necessary to support model application. The potential applicability of steady-state models to assess prevention reduces data needs substantially compared with remediation modeling; nonetheless, the information required to fully support even steady-state model application is rarely available. Models can be applied in the face of limited data, as long as their uncertainty is quantified and taken into consideration when using the results to support sediment management decisions.

BAYOU D’INDE CASE STUDY. The Bayou d’Inde case study involved a chemical plant that discharged hexachlorobenzene (HCB), hexachloroethane,

354 Handbook on Sediment Quality Figure 8.8 Bayou d’Inde site map.

hexachlorobutadiene, and 1,2,4 trichlorobenzene through an effluent–cooling water canal at an industrial site in the Calcacieu River Estuary near Lake Charles, Louisiana (Figure 8.8). The model’s spatial domain included the discharge canal as well as a 3.9-km (2.4-mile) portion of the Bayou d’Inde above and below the discharge canal. Ambient concentration data in the water column, sediments, and biota were available from monitoring data collected during two surveys in 1990, although the objectives of this monitoring did not include support of a sediment-quality model. This site was selected to be representative of future sites that might require model assessment of prevention needs with expected data constraints. Insufficient data were available to support a rigorous model calibration, yet sufficient data were available on model state variables to determine how well an “uncalibrated” model could describe observed sediment-contamination levels (Dilks et al., 1993). The mathematical model framework used was the one-dimensional steady- state SMPTOX model described previously, which included mass-balance calculation of dissolved and organic carbon-sorbed chemical in the water column and sediments. Hydraulic transport was manually specified to represent freshwater advection and tidal-average dispersion. Insufficient suspended solids data were available to allow calibration of settling velocities, although radioactive coring data at the site provided an indication of long-term sedimentation rates. A gross settling velocity for water column solids was selected from the literature, and average resuspension rates were determined that would result in a steady-state vertical solids balance for each segment. Volatilization rates for each chemical were calculated from measurements of overlying wind speed, water temperature and depth, and chemical-specific molecular weights and Henry’s law constants. Chemical degradation rates in the water column and sediment were specified as the mean of the values obtained from the scientific literature.

Sediment Quality Modeling 355 Figure 8.9 Example of Bayou d’Inde model results of HCB in the water column and sediments (1 mile × 1.609 = km).

Sample results are shown in Figure 8.9 for HCB in the water column and sediments. The model reflects the increase in concentrations observed at River Mile 1.1, where the PPG canal enters the Bayou. Whereas the model was generally quite consistent with the observed water column values, the comparison with observed sediment data was inconclusive due to the high variability in and limited number of observed sediment values. Whereas predicted sediment concentrations in Bayou d’Inde were generally consistent with observed values for all chemicals, model predictions in the PPG canal were consistently lower than observed concentrations. Anecdotal evidence indicated that historical wastewater loads to the canal were orders of magnitude higher than their current values, meaning that existing sediment concentrations had not yet reached a steady-state balance with existing loads. Whereas large decreases in historical loads were known to have occurred, insufficient time-series loading data were available to support application of a time-variable model. This discrepancy did not exist in the Bayou d’Inde, where routine navigational dredging had removed the historical sediment

356 Handbook on Sediment Quality (a) (b) Figure 8.10 Model abstraction of (a) actual vertical sediment concentration distribution into (b) a series of idealized layers.

contamination and allowed existing sediment concentrations to more closely reflect steady-state conditions. This case study demonstrated that steady-state models have the potential to describe observed sediment concentrations, although the patchiness of the data and non-steady-state conditions in the canal precluded a more rigorous assessment.

OUTSTANDING ISSUES The science of sediment-quality modeling has advanced greatly in recent years because the costs involved in remediation at several high-profile sites has led to an increased investment in data collection and model application. Despite these advances, several issues–problems remain. Four prevalent problems are artificial bed mixing, data sufficiency, bioturbation and sediment-diffusion issues, and model representation of active management scenarios.

ARTIFICIAL BED MIXING. Models represent simple abstractions of real- world systems, and one such abstraction is the division of sediments into a series of vertical layers. Figure 8.10 shows a hypothetical vertical profile of a site with sediment contamination buried at depth. The left half (a) of Figure 8.10 shows the actual concentration distribution, with the dark horizontal lines representing the boundary between sediment layers in the model. The right

Sediment Quality Modeling 357 Figure 8.11 Model abstraction of sediment bed immediately after a resuspension event. half (b) of Figure 8.10 shows the concentration distribution represented in the model, using the average of the observed vertical profile in each layer. When an erosion event removes sediment from the top layer of the sediment bed, the size of the first layer is decreased, as indicated in the top left side of Figure 8.11. Models such as WASP and IPX attempt to maintain constant depths for each sediment layer and readjust the definition of sediment in each layer periodically. In response to the scour shown in the left of

358 Handbook on Sediment Quality Figure 8.11, the model shifts the definition of layers to maintain the originally specified depths, as shown in the top right-hand side of the figure. This shifting of layers occurs in the model throughout the sediment column. Because the model assumes that each layer is completely mixed, concentrations must be recalculated in each sediment layer in response to the shifting definition of layers. This recalculation causes the uppermost layer to mix with the second layer, which is appropriate, but also causes the (inappropriate) mixing of the deeper layers, as shown in the bottom of the figure. Each individual sediment-mixing step introduces a small amount of artificial mixing, and the long-term effect of performing this step introduces a large amount of mixing. Over the course of a model simulation that contains small amounts of erosion and deposition, the constant redefinition of sediment boundaries artificially mixes the contamination from the deeper layers towards the surface. This artificial mixing can lead to significant overpredic- tion of the bioavailability of contamination buried at depth. Adaptations have been made to the WASP model framework to restrict this mixing to the two uppermost sediment layers. This adaptation also reduces the amount of computational time required for the sediments and allows for use of thinner sediment layers, which further reduces the artificial mixing. Whereas these adaptations have been made for site-specific applications of the WASP model, they are not available in the publicly distributed version.

DATA SUFFICIENCY TO VALIDATE RESUSPENSION RATES. Despite the large quantity of resources typically devoted to modeling at contaminated sediment sites, data sufficiency remain a common problem, especially with respect to validating resuspension rates. This problem is caused by the high degree of spatial variability exhibited by surficial sediments as well as the incomplete nature of data typically available for hindcast simulations. Validation of model resuspension rates is further confounded by lack of historical data on solids loadings. Theoretically, net sedimentation/erosion over a stream segment could be empirically determined from observed suspended solids data by comparing the instream solids load at the upstream end of a segment to the instream solids load at the downstream end of the segment. Unfortunately, the full mass balance equation is

Net resuspension load over river segment = Load observed at downstream segment – Load observed at upstream segment – External solids loads – Internal solids production (8.10)

The high stream flow events of most interest with respect to resuspension are typically associated with wet weather periods that generate tributary solids loading of similar magnitude to resuspension. Historical monitoring programs that provide long-term datasets of instream solids concentrations rarely provide sufficient data on external loads to allow eq 8.6 to be used to accurately define resuspension rates.

Sediment Quality Modeling 359 BIOTURBATION AND ENHANCED SEDIMENT DIFFUSION. A present concern being faced by many contaminated sediment modeling efforts is proper characterization of contaminant transfer across the sediment–water interface during non-eroding periods. Benthic biological activity can mix, transport, and alter the composition of surficial sediments and pore water through a process called bioturbation. Bioturbation is not a single process. Instead, it is the net effect of benthic biological activities such as feeding, burrowing, locomotion, and tube building (Bosworth and Thibodeaux, 1990, and Thibodeaux, 1996). Benthic organisms also enhance pore-water diffusion (i.e., bioirrigation) with burrowing and feeding activities; bioirrigation may be significant for solute transport across the sediment–water interface (Van Rees et al., 1996). Moreover, benthic biological activities may either stabilize or destabilize sediments (Davis, 1993, and Graf and Rosenberg, 1997), thus influencing the likelihood of sediment (contaminant) transport. Mathematical models have been used to describe bioturbation and bioirrigation by a number of researchers. The majority of existing models are either too site specific or require an inordinate amount of site-specific data to allow their widespread integration to existing sediment–contamination models. Traditional modeling approaches have consisted of increasing the sediment–water diffusion coefficient to represent bioturbation. Research in bioturbation modeling is ongoing, however, and this situation may change in the future.

MODEL REPRESENTATION OF ACTIVE MANAGEMENT SCENAR- IOS. Sediment-quality models for remediation are typically developed and applied before the initiation of any remediation activities. This makes the models better suited for addressing natural remediation than active management scenarios. Representation of active management scenarios within a model requires numerous assumptions to be made. For dredging scenarios, four categories of assumptions must be made regarding (1) sediment concentration distribution after dredging; (2) dredging start time and duration; (3) amount of contaminant released during the dredging operations; and (4) changes in bathymetry, hydrodynamics, and transport processes. Assumptions for these categories are especially important to large-scale sediment sites (382 3000 m3 [>500 000 cubic yards]) for estimating the relative benefits of dredging over other alternatives in terms of risk reduction. Whereas it is possible to mitigate releases (via contained dredging) and minimize postremediation surface sediment concentrations (via placement of clean material over dredged areas), the mitigation measures will increase the costs and duration of the operation, which need to be accounted for in the overall feasibility analysis. As illustrated in Figure 8.12, differing assumptions for dredging efficiency and duration can potentially result in dramatically different conclusions regarding the benefits of dredging over other alternatives. Figure 8.12 shows the projected surface sediment concentrations over time for two dredging scenarios and natural attenuation. The first dredging scenario includes optimistic assumptions that dredging can be readily implemented in 5 years, will require 3 years to complete, and residual surface concentrations will be effectively reduced by placement of clean material over dredged areas. The

360 Handbook on Sediment Quality Figure 8.12 Predicted efficiency of dredging with different modeling assumptions.

second dredging scenario includes less optimistic assumptions, that due to implementation difficulties (landfill siting issues, access and dewatering constraints, subsurface obstructions, lower dredge production rates, etc.), dredging will not be implemented for 8 years and requires 10 years to implement and will have higher surface sediment concentrations when completed. As can be seen, it could be argued that the first dredging scenario provides some short-term benefit over natural attenuation, whereas the benefits of the second scenario are questionable at best. Releases during dredging can increase risks during the dredging operation. Whereas these releases may be insignificant for small sites with a short duration of remediation, for large sites with potential implementation duration of decades, the releases may significantly increase risks for a relatively long period and can lead to potential recontamination of dredged areas. Changes in the bathymetry and hydrodynamics caused by dredging can change deposition and resuspension patterns in the system. For example, deepening a reach of a system may increase postremediation deposition and increase the rate of recovery after remediation for that reach. For capping scenarios, similar assumptions must be made regarding postcapping surface sediment concentration distributions, duration of implementation, releases during capping, and changes in bathymetry. In addition, the long-term behavior of the cap must be represented in the model. Considerations in the development of the assumptions include implementation constraints as well as geotechnical properties of the sediment, cap thickness, and the properties of the cap materials. Important issues that affect the ultimate effectiveness of the cap include cap stability, degree of mixing between the cap and sediment, dissolved transport of contaminants through the cap, and winnowing of fines from the cap. Assumptions for dredging and capping scenarios can be developed and supported by (1) evaluating quantitative information from previous experience at other similar sites; (2) conducting site-specific, field-scale pilot studies; and

Sediment Quality Modeling 361 (3) consulting experienced contractors. Models can then be used to evaluate the relative reductions in risks achieved by specific dredging operations. Important feasibility study questions that can be quantitatively assessed through application of the model include

• When will risks be reduced to acceptable levels? • What factors may impede achieving acceptable levels (residual concentrations, other sources)? • Will dredging or capping significantly reduce the time to achieve acceptable levels? • Will there be a need to place clean fill over dredged areas to achieve acceptable levels? • If present, where are the priority areas of the system? (Where could we get the biggest bang for the buck in terms of risk reduction?) • How important is it to mitigate releases during dredging and capping?

REFERENCES Allen, H.E.; Fu, G.; and Deng, B. (1993) Analysis of Acid-Volatile Sulfide (AVS) and Simultaneously Extracted Metals (SEM) for the Estimation of Potential Toxicity in Aquatic Sediments. Environ. Toxicol. Chem., 12, 1441. Ambrose, R.B., Jr.; Wool, T.A.; and Martin, J.L. (1993) The Water Quality Analysis Simulation Program, WASP5-Part A: Model Documentation. U.S. Environmental Protection Agency, Environmental Research Laboratory, Office of Research and Development, Athens, Ga. Bierman, V.J., Jr.; DePinto, J.V.; Young, T.C.; Rodgers, P.W.; Martin, S.C.; and Raghunathan, R. (1992) Development and Validation of an Integrated Exposure Model for Toxic Chemicals in Green Bay, Lake Michigan. Large Lakes and Rivers Research Branch Environmental Research Laboratory. U.S. Environmental Protection Agency, Duluth, Mich. Blumberg, A.F., and Mellor, G. (1980) A Coastal Ocean Numerical Model. In Mathematical Modeling of Estuarine Physics. J. Sundermann and K.-P Holz (Eds.), Proc. Int. Symposium, Hamburg, Germany, August 24–26, 1978, Springer-Verlag, Berlin, 203. Borah, D.K. (1989) Scour Depth Prediction Under Armoring Conditions, J. Hydraul. Eng., 115, 1421. Bosworth, W.S., and Thibodeaux, L.J. (1990) Bioturbation: A Facilitator of Contaminant Transport in Bed Sediments. Environ. Prog., 9, 211. Davis, W.R. (1993) The Role of Bioturbation in Sediment Resuspension and Its Interaction with Physical Shearing. J. Exp. Mar. Biol. Ecol., 171, 187. Davis, J.A., and Leckie, J.O. (1978) Effect of Adsorbed Complexing Ligands on Trace Metal Uptake by Hydrous Oxides. Environ. Sci. Technol., 12, 1309. Dilks, D.W.; Helfand, J.S.; and Bierman, V.J., Jr. (1995) Development and Application of Models to Determine Sediment Quality Criteria-Driven

362 Handbook on Sediment Quality Permit Limits for Metals. Proceedings of Toxics Substances in Water Environments: Assessment and Control. Water Environment Federation, Alexandria, Va. Dilks, D.W.; Helfand, J.S.; Bierman, V.J., Jr.; and Burkhard, L. (1993) Field Application of a Steady-State Mass Balance Model for Hydrophobic Organic Chemicals in an Estuarine System. Water Sci. Technol., 28, 8–9, 263. Di Toro, D.M.; Mahony, J.D.; Hansen, D.J.; Scott, K.J.; Hicjs, M.B.; Mayr, S.M.; and Redmond, M.S. (1990) Toxicity of Cadmium in Sediments: The Role of Acid Volatile Sulfide. Environ. Toxicol. Chem., 9, 1487. Eadie, B.J.; Moorehead, N.R.; and Landrum, P.F. (1990) Three-Phase Parti- tioning of Hydrophobic Organic Compounds in Great Lakes Waters. Chemosphere, 20, 161. Gailani, J.; Ziegler, C.K.; and Lick, W. (1991) Transport of Suspended Solids in the Lower Fox River. J. Great Lakes Res., 17, 479. Graf, G., and Rosenberg, R. (1997) Bioresuspension and Biodeposition: A Review. J. Mar. Syst., 11, 269. Hamrick, J.M., and Wu, T.S. (1997) Computational Design and Optimization of the EFDC/HEM3D Surface Water Hydrodynamic and Eutrophication Models. In Next Generation Environmental Models and Computational Methods. G. Delic and M. Wheeler (Eds.), SIAM, Philadelphia, Pa. Hart, B.T. (1982) Uptake of Trace Metals by Sediments and Particulates: A Review. Hydrobiologia, 91, 299. Hayter, E.J.; Bergs, M.A.; Gu, R.; McCutcheon, S.C.; Smith, S.J.; and Whiteley, H.J. (1995) HSCTM-2D, A Finite Element Model for Depth- Averaged Hydrodynamics, Sediment and Contaminant Transport. National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Athens, Ga. Hydrologic Engineering Center (1997) UNET. One-Dimensional Unsteady Flow through a Full Network of Open Channels. U.S. Army Corps of Engineers, Davis, Calif. Howard P.H.; Boethling, R.S.; Jarvis, W.F.; Meylan, W.M.; and Michalenko, E.M. (1991) Handbook of Environmental Degradation Rates. Lewis Publishers, Inc., Chelsea, Mich. Jenne, E.A., and Zachara, J.M. (1987) Factors Influencing the Sorption of Metals. In Fate and Effects of Sediment-Bound Chemicals in Aquatic Systems. K.L. Dickson, A.W. Maki, and W.A. Brungs (Eds.), Pergamon Press, New York, 83. Karickhoff, S.W.; Brown, D.S.; and Scott, T.A. (1979) Sorption of Hydropho- bic Pollutants on Natural Sediments. Water Res., 13, 241. Karickhoff, W.W. (1984) Organic Pollutant Sorption in Aquatic Systems. J. Hydraul. Eng., 110, 707. Karim, M.F., and Kennedy, J.F. (1982) IALLUVIAL: A Computer-Based Flow and Sediment Routing for Alluvial Streams and Application to the Missouri River. Rep. No. 250, Iowa Institute of Hydraulic Research, Iowa City, Iowa.

Sediment Quality Modeling 363 Lick, W.; McNeil, J.; Xu, Y.; and Taylor, C. (1995) Resuspension Properties of Sediments from the Fox, Saginaw, and Buffalo Rivers. J. Great Lakes Res., 21, 257. Little, W.C., and Mayer, P.G. (1972) The Role of Sediment Gradation on Channel Armoring. Ga. Inst. Technol., School Civ. Eng., Atlanta, Ga. LTI (1995) SMPTOX Version 4.0 (SMPTOX4) User’s Manual. Prepared for U.S. Environmental Protection Agency, Office of Science and Technology, Washington, D.C. LTI; Menzie Cura & Associates, Inc.; and Tetra-Tech, Inc. (2000) Phase 2 Report. Further Site Characterization and Analysis. Volume 2D-Revised Baseline Modeling Report. Hudson River PCBs Reassessment RI/FS. Prepared for U.S. Environmental Protection Agency Region II and U.S. Army Corps of Engineers Kansas City District. McNeil, J.D. (1994) Measurements of the Resuspension and Erosion of Sediments in Rivers. Ph.D. dissertation, Univ. Calif., Santa Barbara, Calif. Mehta, A.J.; Hayter, E.J.; Parker, W.R.; Krone, R.B.; and Teeter, A.M. (1989) Cohesive Sediment Transport. 2. Application. J. Hydraul. Eng., 115, 1094. NRC (1997) Contaminated Sediments in Ports and Waterways: Cleanup Strategies and Technologies. Committee on Contaminated Marine Sedi- ments, Marine Board. National Academy Press, Washington, D.C. O’Connor, D.J. (1988a) Models of Sorptive Toxic Substances in Freshwater Systems. I: Basic Equations. J. Environ. Eng., 114, 507. O’Connor, D.J. (1988b) Models of Sorptive Toxic Substances in Freshwater Systems. II: Basic Equations. J. Environ. Eng., 114, 533. O’Connor, D.J. (1988c) Models of Sorptive Toxic Substances in Freshwater Systems. II: Lakes and Reservoirs. J. Environ. Eng., 114, 507. O’Connor, D.J. (1988d) Models of Sorptive Toxic Substances in Freshwater Systems. I: Streams and Rivers. J. Environ. Eng., 114, 552. Parchure, T.M., and Mehta, A.J. (1985) Erosion of Soft Cohesive Sediment Deposits. J. Hydraul. Eng., 111, 1308. Partheniades, E. (1965) Erosion and Deposition of Cohesive Soils. J. Hydraul. Div., 91, 105. Peterson, G.; Wolfe, J.; and DePinto, J. (1999) Decision Tree for Sediment Management. Prepared for the Sediment Management Work Group, Detroit, Mich. Pignatello, J.J., and Xing, B. (1996) Mechanisms of Slow Sorption of Organic Chemicals to Natural Particles. Environ. Sci. Technol., 30,1. Rao, P.S., and Davidson, J.M. (1980) Estimation of Pesticide Retention and Transformation Parameters Required for Nonpoint Source Pollution Models. In Environmental Impact of Nonpoint Source Pollution. Ann Arbor Sciences, Ann Arbor, Mich., 23. Raudkivi, A.J. (1990) Loose Boundary Hydraulics. 3rd Ed., Pergamon Press. New York. Schnoor, J.L.; Sato, C.; McKechnie, D.; and Sahoo, D. (1987) Processes, Coefficients, and Models for Simulating Toxic Organics and Heavy Metals in Surface Waters. EPA-600/3-87-015, U.S. Environmental Protection Agency, Office of Research and Development, Athens, Ga.

364 Handbook on Sediment Quality Shields, A. (1936) Anwendung der Ahnlichkeitsmechanik und der Turbulenz- forschung auf die Geschiebebewegung. Mitteilungen der Preussischen Versuchsanstalt fur Wasserbau und Schiffbau, No. 26, Berlin, Germany. Tessier, A.Y., and Campbell, P.G.C. (1987) Partitioning of Trace Metals in Sediments: Relationships with Bioavailability. Hydrobiologia, 149, 43. Tessier, A.Y.; Couillard,Y.; Campbell, P.G.C.; and Auclair, J.C. (1993) Modeling Cd Partitioning in Oxic Lake Sediments and Cd Concentrations in the Freshwater Bivalve Anodonta grandis. Limnol. Oceanogr., 38,1. Thibodeaux, L.J. (1996) Environmental Chemodynamics: Movement of Chemicals in Air, Water, and Soil. Wiley & Sons, New York. Thomann, R.V. (1989) Deterministic and Statistical Models of Chemical Fate in Aquatic Systems. In Springer Advanced Text in Life Sciences. Levin et al. (Eds.), Springer-Verlag, New York. U.S. Army Corps of Engineers (1976) HEC-6 Scour and Deposition in Rivers and Reservoirs. Hydrologic Engineering Center, Davis, Calif. U.S. Army Corps of Engineers (1990) Status and Capabilities of Computer Program HEC-6: Scour and Deposition in Rivers and Reservoirs. Hydro- logic Engineering Center, Davis, Calif. U.S. Environmental Protection Agency (1998) Contaminated Sediment Management Strategy. EPA-823/R-98-001, Washington, D.C. van den Berg, J., and van Gelder, A. (1993) Prediction of Suspended Bed Material Transport in Flows over Silt and Very Fine Sand. Water Resour. Res., 29, 1393. Van Rees, K.C.J.; Reddy, K.R.; and Rao, P.S.C. (1996) Influence of Benthic Organisms on Solute Transport in Lake Sediments. Hydrobiologia, 317, 31. Velleux, M.; Gailani, J.; and Endicott, D. (1994) A User’s Guide to IPX, the In-Place Pollutant Export Water Quality Modeling Framework. U.S. Environmental Protection Agency, Large Lakes Research Station, Grosse Ile, Mich. Whitman, W.G. (1923) The Two-Film Theory of Gas Adsorption. Chem. Met. Eng., 29, 147. Xu, Y. (1991) Transport Properties of Fine-Grained Sediments. Ph.D. dissertation, Univ. Calif., Santa Barbara, Calif. Ziegler, C.K. (1999) Sediment Stability at Contaminated Sediment Sites. Prepared for the Sediment Management Work Group, Detroit, Mich. Ziegler, C.K., and Lick, W. (1986) A Numerical Model of the Resuspension, Deposition, and Transport of Fine-Grained Sediments in Shallow Water. Rep. ME-86-3, University of California, Santa Barbara, Calif.

Sediment Quality Modeling 365

Index

field methods, 232 in situ exposures, 234 A in situ studies, 237 research trends, 235 Acid-volatile sulfide, 109, 116 risk assessment, 238 Active management scenarios, 360 transplantation, 234 Activity coefficients, 17 Bioassays, 236 Adsorption models, 61 Bioavailability, 102–143 Advanced model features, 334 aging, 112 Alternative extraction methods, 102 bioturbation, 114 Alternatives to sieving, 172 changes in, 142 Amphibians, 224 factors controlling, 109 Amphipod survival tests, 283 field studies, 108 Amphipods, 221 measuring, 102, 115 Aniline, 14 seasonality, 114 Anomalous transport, 85 sorption, 112 Apparent effects thresholds, 285 Bioavailability reduction, 106 Aromatic amines, 13 Bioturbation, 360 Aromaticity, 68 Biscayne Bay, 273, 291 Artificial bed mixing, 357 Boston Harbor, 271 B C Batch equilibration, 34 Calibration sequence, 346 Bayou D’Inde, 354 Calibration sufficiency, 348 Behavior of metals, 338 Capping, 351 Benthic amphipods, abundance, 302 Case I transport, 84 BET model, 26 Case II transport, 84 Bioaccumulation tests, 227 Cation-exchange capacity, 13 Bioaccumulation, 226–238 Charleston Harbor, 267 field-collected samples, 233

367 Chemical fate and transport Dual-mode isotherm, 32 models, 344 Dual-reactive domain, 33 Chemical predictors, 323 DYNHYD5, 342 Crystallinity, 70 Cladocerans, 223 Cohesive sediments, 337 Collecting sorption data, 34 E Colloid-phase uptake, 37 Column equilibration, 34 Ecological relevance of toxicity Combined models, 31 tests, 299 Commonly used model EFDC, 345 frameworks, 340 Effects range, 286 Competitive adsorption models, 61 Elutriate phase, 216 Competitive effects, 61 Empirical rate models, 77 Compositing, 169 Endpoints, 208 Consensus-bases, 295 Energy of vaporization, 20 Contaminant sequestration, 8 Enhanced sediment diffusion, 360 Contaminant-phase distribution Enthalpic contributions, 19 equilibria, 14 Entropic contributions, 19 Contaminated sediments, Equilibration techniques, 34 explosive, 144 Equilibrium, achieving, 40 Control sediments, 211 Equilibrium partitioning, 118, 320 Core samplers, 153 External mass transfer, 79 Covalent bonding, 13 D F Fickian transport, 84 DDT, toxicity thresholds, 299 Field operation, 144 Decontamination, 147 Field studies, 108-109 Degradation–decay, 334 bays, 108 Degrees of conservatism, 268 bioavailability, 108 Desorption, 44 lakes, 109 Desorption hysteresis, 65 wetlands, 109 Diffusion, 81–86 Fish, 224, 227 combined pore-surface, 82 Fixed-bed reactor, 37 homogenous solid-phase, 81 Flory–Huggins theory, 19 in macromolecules, 83 Formulated sediments, 175 pore, 82 Freshwater sediment testing, 200 Disposal, pore-water samples, 144 Freshwater sediment testing, Disposal, sediments, 144 bioaccumulation, 226 Dredging, 350 Freshwater test protocols, 201 Fugacity ratio, 18

368 Handbook on Sediment Quality Interstitial water collection, 148 in situ, 158 G Interstitial water samples, retrieval, 161 Geosorbents, 9 Invertebrates, 227 Grab samplers, 150 Ion exchange, 11, 12 Gulf of Mexico, 293 Ionizable organics, 59 Isotherm linearity, 45 H Isotherm models, 24 HEC-6, 343 Henry’s constant, 74 L Henry’s region, 28 Laboratory operations, 144 Hildebrand–Scatchard theory, 20 Langmuir isotherm, 25 Homogeneity, verifying, 173 Langmuir kinetics, 77 Homogenization, 168 Linear partitioning, 45, 46 Hopfenberg–Frisch chart, 84 Local equilibrium, 76 HSACM-SI model, 58 Logistic regression models, 300 HSCTM-2D, 344 London dispersion forces, 12 Hydrodynamic models, 341 Loss controls, 41 Hydrophobic organic compounds, 10 Loss mechanisms and controls, 41 Hydrophobic organic contaminants, 9 Hydrophobic organic solutes, 17 Hydrophobic organics, 49 Hysteresis index, 67 M Macromolecular diffusion, 86 Margules equation, 19 I Mass transfer, 79 Mayflies, 222 Ideal adsorbed solution theory, 62 Membrane analogs, 103 Imine formation, 14 Miami River, 273 Implicit adsorbate, 73 Microporosity, 70 Incidence of toxicity, 279 Midges, 222 Indigenous organisms, 143 Mixing within bed sediments, 332 Infinite dilution activity Model application, 348 coefficients, 19 Model calibration, 345 Intermolecular forces, 12 Modeling framework, 329 Interstitial water, 148–180 Modeling, alternate observations, 347 centrifugation, 178 Modeling, bioavailability, 106 isolation of, 177 Modeling, contaminant-phase pressurized devices, 180 distribution equilibria, 14 sediment squeezing, 179 Multiple sorbent classes, 334

Index 369 Pore-water samples, disposal, 144 Port Gardner Bay, 275 N Potential interferences, 141 Potential theory, 30 Natural attenuation, 350 Predicting toxicity, 285 Newark Bay, 276 Press sieving, 171 Noncohesive sediments, 338 Princeton ocean model, 343 Noncontaminant factors, 141 Probable effects level, 286, 289, 296 Non-equilibrium partitioning, 336 Puget Sound, 273, 293 Non-Fickian transport, 85 Nonlinear sorption, 49, 59 Numerical sediment-quality guidelines, 285 Q Quality assurance, 182 Quality assurance audits, 185 O Quality assurance project plan, 182 Octanol–water partition coefficient, 22 Oligochaetes, 221 Organic carbon modification, 175 R Organic carbon, 111 Reaction processes, 332 Oxidation catalysts, 14 Reactor components, 43 Recommended sieves, 171 Redlich–Peterson equation, 27 Regional assessments, 260 P Regional monitoring, 262 Regulatory trends, 121 Particle interaction model, 73 Remediation, 106, 348 Particle size, 68 Retrieving interstitial water Partitioning, 12 samples, 161 Partitioning theory, 15 Reversibility, 67 PCB concentration ranges, 297 Reynolds number, 80 Pearl Harbor, 291 RMA-2V, 342 Peeper methods, 160 Rubbery/glassy models, 69 Pensacola Bay, 272 Phase separation, 43 Phase distribution relationship, 54 Polanyi-based models, 63 Polanyi–Manes approach, 34 S Polarity, 68 Sabine Lake, 269 Polynuclear aromatic hydrocarbons, Safety concerns, 144–147 266, 294, 296 equipment cleaning, 147 Pore-water concentrations, 104 field facilities, 144 Pore-water phase, 215 laboratory facilities, 144

370 Handbook on Sediment Quality Sample holding times, 163 Selection of test species, 203 Sample storage, 162 Short-term versus long-term Sample transport, 162 calibration, 347 Sample-tracking documentation, 184 Sieving, 170 San Diego Bay, 277 Simultaneously extracted metals-to- San Francisco Bay, 291, 298 acid volatile sulfides ratios, 297 Sea urchin fertilization tests, 284 SMPTOX, 344 SED2D, 344 Solid-phase sediments, 283 Sediment characteristics, 104 Solids-concentration effect, 72 Sediment collection, 148–186 Solution phase, 39 corrective action, 185 Solution-phase speciation, 13 data quality objectives, 182 Sorbate competition, 64 data reporting, 186 Sorption, 332 project organization, 183 Sorption isotherm, 15 quality control, 181 Sorption mechanisms, 10 recordkeeping, 185 Sorption phenomena, 45 standard operating procedures, 183 Sorption rate processes, 75 Sediment dilutions, 176 Spatial extent of toxicity, 282 Sediment elutriates, 176 Spatial gradients in toxicity, 266 Sediment phases, 212–219 Spatial resolution, 339 comparative, 218 Species sensitivity, 199 elutriate, 216 Spiking methods, 173 suspended, 216 Spiking sediments, 172 whole sediments, 212 Spiking, preparation, 172 Sediment-quality criteria, 238 Subsampling, 164 Sediment-quality guidelines, 318–323 Suction methods, 161 co-occurrence, 318 empirical test, 320 equilibrium partitioning, 320 shortcomings of, 318 T Sediment-quality modeling, 328 Sediment-quality, processes Threshold effects level, 286 affecting, 329 Toth isotherm, 27 Sediment resuspension, 336 Toxicity calculations, 122 Sediment sample documentation, 183 Toxicity patterns, 267 Sediment sample manipulations, 170 Toxicity testing, 120 Sediment sample, equilibration Toxicity testing, species, 219 times, 174 Toxicity tests in regional Sediment testing, freshwater, 200 monitoring, 262 Sediment-associated Toxicity tests, ecological contaminants, 105 relevance, 299 Sediment-effect concentrations, 295 Transport processes, 330–332 Sediments, safety concerns, 143 resuspension, 331 Sediment-transport models, 343 sedimentation, 331 Sediment–water diffusion, 332 sediment–water diffusion, 332 SEDZL, 344 settling, 331

Index 371 U W UNET, 342 WASP5/IPX, 344 Water column solids, 335 Water column transport, 330 Wet sieving, 172 V Worms, 221 Van der Waals forces, 12 Van Laar equation, 17 Vascular plants, 219 Volatilization, 333

372 Handbook on Sediment Quality