Method to Evaluate Plants and Soils to Optimize Conditions for Phytoremediation of Copper

A Thesis

Entitled

Catherine Lynn Buchanan

Submitted as partial fulfillment of the requirements for

The Master of Science

Degree in Biology

______Dr. Johan F. Gottgens Thesis Advisor

______Dr. Jiquan Chen Thesis Committee Member

______Dr. James A. Harrell Thesis Committee Member

______College of Graduate Studies

University of Toledo May 2010

This document is copyrighted material. Under copyright law, no parts of this document may be

reproduced without the expressed permission of the author.

An Abstract of

Method to Evaluate Plants and Soils to Optimize Conditions for Phytoremediation of Copper

Catherine Lynn Buchanan

Submitted as partial fulfillment of the requirements for The Master of Science Degree in Biology

The University of Toledo May 2010

Contaminated soil sites pose an immense problem globally. There is generally no affordable method to remove contaminants from soil apart from phytoremediation, a technology that works on removing contaminants from surface soils. Tests were conducted to evaluate the mechanisms that control adsorption of copper (Cu), as an example of a common contaminant in northwest Ohio, by soil particles in order to optimize the phytoremediation method. The major soil parameters that control adsorption of Cu include organic matter content, clay mineral content, pH, alkalinity and hardness. Particle size analysis was substituted for clay mineral content because of the close correlation between the two parameters. To characterize soil conditions, I used: 1) loss on ignition to estimate organic matter content; 2) inductively coupled plasma optical emission spectrometry to analyze plants and soils for Cu content; 3) laser diffraction for particle size analysis; 4) batch adsorption experiments to quantify Cu adsorption onto soils; 5) indicator paper to determine the pH of the adsorption solutions and on-site soils; and 6) titrations to determine alkalinity and hardness. Soils and plants were evaluated from three sites in northwest Ohio

(Treasure Island Dump, Bassett Street Warehouse, and Tiffin Landfill). Soil from a fourth site

(Emmajean) was analyzed to conduct a comparative analysis of soil conditions that pertain to

iii

adsorptive capacity and plant growth, and to evaluate whether it could be used as a soil amendment where plant growth is limited. Batch adsorption experiments indicated that alkalinity controls Cu adsorption to the soils. The ratio of alkalinity to hardness determined the buffering capacity with Treasure Island > Bassett Street > Tiffin Landfill > Emmajean listed in order of decreasing capacity. Below pH 4, Cu desorbed from the soil. A bio-concentration factor (BCF, i.e., the ratio of the plant and soil concentrations of Cu) was used to compare and contrast phytoremediation potential for different plants at different sites. A higher BCF implies a better ability to concentrate Cu. However, a low BCF might be misleading if the Cu concentration in the soil is extremely high. Therefore, to determine which plants performed the best, the Cu concentration of each individual plant was reviewed in conjunction with the BCF. The low alkalinity of the Emmajean soil confirmed its potential use as a soil amendment without increasing Cu adsorption. The Emmajean soil also had a higher organic matter and sand content, essential for nutrients and root support. For this study, three assumptions were made regarding the plants‟ ability to remove Cu. First, the weather conditions were the same in all the study locations.

Second, the plants‟ physiological ability to remove Cu is the same, no matter where they are rooted. And third, the soil properties that control adsorptive capacity and plant growth were the determining factors in the plants‟ ability to remove Cu. Physiological and age differences of the plants were not considered in this study. Cu concentrations among annuals, perennials and trees/shrubs were not significantly different (p=0.71). Cu levels in plant tissue and their corresponding soils from all sites combined were not correlated (r=0.154, df=90) highlighting the importance of other variables in predicting plant Cu concentrations under various levels of soil contamination. Using my results as a guideline to manipulate soil conditions, potted plant experiments can be designed, to find optimal conditions for plant growth and removal of Cu.

I dedicate this thesis to my mom,

Madeline Michele Buchanan,

and my dad,

Joseph York Buchanan,

who taught me to never give up.

The truth will always prevail.

Acknowledgements

Dr. Johan Gottgens for his never ending support in the abyss of insanity.

Drs. Johan Gottgens, Jiquan Chen and James Harrell, for their unwavering belief in me.

Dr. Robert Gearheart for his never ending long distance support and unwavering belief in me.

Jonathon Frantz and Doug Sturtz of the USDA Lab for their use of the ICP.

My very good friend, Yinka Oyeumi for his smiles and laughter.

My very good friend, Paul Stoltz, for the donation of a laptop, which has allowed me to continue my work while living in Florida. Love you Paul!

Chris Gail for his timeliness in solving computer problems.

Table of Contents

Abstract iii List of Tables viii List of Figures x List of Appendices x I. Introduction 1 1.1 Organic Matter 6 1.2 Clay Minerals and Zeolites 8 1.3 Adsorption properties 10 1.4 Cation Exchange Capacity 13 1.5 Soil pH 14 1.6 Alkalinity/Hardness 15 1.7 Objective 16 II. Methods and Materials 18 2.1 Site Selection 18 2.1.1 Treasure Island Dump 19 2.1.2 Bassett Street Warehouse 20 2.1.3 Tiffin Landfill 21 2.1.4 Emmajean Site 22 2.2 Plant and Soil Analyses 23 2.3 Total Organic Matter 28 2.4 Particle Size Analysis 28 2.5 Batch Adsorption Experiments 30

2.6 Partition Coefficient (Kd Value) 34 2.7 Visual MINTEQ 35 2.8 pH, Alkalinity/ Hardness 36

vii

III. Results and Discussion 39 3.1 Plant and Soil Copper Concentrations and Their Associated BCFs 39 3.2 Organic Matter and Particle Size 49 3.3 Copper Adsorption 50 3.4 pH, Alkalinity/Hardness 57 3.5 Visual MINTEQ Results 59 IV. Summary and Conclusions 61 4.1 Formulating Phytoremediation Designs Based on Analytical Results 61 4.2 Lessons Learned 64 4.3 Future Studies 66 References 67

Appendix A: Soil Adsorption Tables A1 Appendix B: Tables for Grain Size, Alkalinity, Hardness and Organic Matter Measurements B1

Appendix C: Results of Single Factor ANOVA C1

viii

List of Tables

Table 1: Summary of plant and tree seedling species listed by scientific name and common name. 24

Table 2: Visual MINTEQ input of the copper and nitrate concentrations used to check for complexation between the copper and nitrate ions at various pH levels. 35

Table 3: Summary of soil copper concentrations for sites. 41

Table 4: List of species that have an average BCF greater than 0.5. 42

Table 5 a, b, c: Total plant (roots, stems and leaves) copper concentrations of all species and sorted into annual, perennial, tree/shrub groups. 43, 44, 45

Table 6: Copper concentrations in plants common to two or three sites. 48

Table 7: Average soil particle size distributions. 49

Table 8: Average copper mass adsorbed and average Kd values for soils, and pH of the copper solutions. 53

Table 9: Measured alkalinity, hardness, pH and the associated hardness:alkalinity ratio for soil samples. 58

Table 10: Copper, nitrate and pH parameters used for the Visual MINTEQ experiments. 60

List of Figures

Figure 1 – Map of the study sites. 5

Figure 2 – Mean copper concentrations of annual, perennial and tree/shrub groups with error bars. 43

Figure 3 – Average plant copper concentration versus average soil copper concentration. 46

Figure 4 – Average percent copper mass adsorbed onto the Treasure Island, Bassett Street, Tiffin Landfill and Emmajean soils across various copper concentrations ranging from 1 to 200 ppm. 51

Figure 5 – Average percent copper mass adsorbed on soil versus pH. 52

Figure 6 – Changes in pH for copper standard solutions at concentrations between 1 and 200 ppm. 56

Chapter One

Introduction

Globally, there are many locations that have been extensively contaminated by human actions. These sites can pose a risk to human health and ecosystem integrity. They are often found in economically struggling countries that cannot afford to allocate funds to conduct massive clean ups. However, all of these communities have two things in common: plants and soils.

Therefore, the purpose of this study is to analyze plants and soils in contaminated regions to establish a protocol for the use of phytoremediation as the method of clean up.

In the state of Ohio, many soils are contaminated with heavy metals due to industrial dumping of toxic waste directly onto land or into water resources, prior to the adoption of the Clean

Water Act of 1972. Some of the most common heavy metals targeted for clean up in the state of

Ohio are copper, mercury, cadmium, chromium and lead (Sayre, 2001). “Ohio ranks in the top ten states in the nation for unacceptable levels of heavy metals. Ohio is third among all 50 states in mercury levels and second in copper release, with 152,625 kg released into the environment every year” (Sayre, 2001). Metals present a unique problem because they cannot be degraded as organic pollutants can, but must be either physically removed or immobilized (Meagher, 2000).

Contaminated sites are commonly remediated using conventional, engineering-based technologies, which aim to either, isolate and contain the pollutant, or decontaminate the sites to reduce the actual amount of pollution (Cunningham and Berti, 1993).

Conventional methods for remediation of contaminated soils include acid leaching, excavation and storage, physical separation of the pollutants, and electrochemical processes

(Brooks, 1998). Some on-site treatments are dilution of the contaminated soil with clean topsoil, or immobilization of the contaminants by use of complexing agents or increasing the soil pH by liming

(Khan et al., 2000). Some of the remediation methods listed do not address the removal of the contaminant from the soil, but merely shift the contaminant from one location to another, requiring further treatment for the actual removal of the contaminant. Nor do conventional methods address the issue of creating more hazardous materials as by-products of the removal process, which have to be disposed of as hazardous waste (Dijkstra et al., 2004). Other problems associated with these treatments are the potential to compromise the soil‟s physical structure, the reduction of microbial activity within the soil, and the destruction of a favorable environment for plant growth, resulting in barren land (Khan et al., 2000).

An economical alternative to conventional methods is phytoremediation, the use of plants to physically remove contaminants from the soil. Phytoremediation is a rapidly growing technology that is being studied on a worldwide basis due to its economical and non-destructive nature (Khan et al., 2000). With respect to heavy metal contamination, phytoremediation is also being studied for its potential to become a major mining industry known as phytoextraction or phytomining

(Brooks et al., 1998). Phytoremediation is a technology that works on removing contaminants from the surface layers of soil.

The advantages of utilizing plants for remediation of contaminated soils are becoming recognized. For a community wishing to save financial resources, phytoremediation is less costly 2

than conventional methods due to its low installation and maintenance costs (Rock, 1996).

Phytoremediation may also establish wildlife habitat and can add to the aesthetics and recreational benefits of the community (Rock, 1996). Additional advantages of phytoremediation are that 1) plants can stabilize and/or remove contaminants, 2) contaminants can be transferred to a treatment or disposal site with relative ease, and 3) diversity and productivity of the soil ecosystem may be maintained (Khan et al., 2005). Potential drawbacks of phytoremediation could be 1) the creation of an attractive nuisance (animals grazing on the plants), 2) the removal of the contaminant could take decades, and 3) the disposal of the harvested plant material.

The success of phytoremediation for removal of contaminants is dependent of several physiological characteristics of the plant. These characteristics include the ability to 1) hyperaccumulate the contaminant within its tissues, 2) produce high biomass, 3) adapt to metalliferous soils, 4) propagate easily, and 5) survive varying climatic conditions (Deram et al.,

2000). Efficient removal of the contaminant is possible through the continuous growth and harvesting of a high biomass–producing, hyperaccumulator species (Raskin et al., 1997). A hyperaccumulator is ambiguously defined as “a metallophyte that accumulates an exceptionally high level of a metal to a specified concentration or to a specified multiple of the concentration found in nonaccumulators” (USEPA, 1998). An example of a hyperaccumulator‟s uptake ability with regards to nickel is “a plant in which a nickel concentration of at least 1000 micrograms (μg) g−1 has been recorded in the dry matter of any above-ground tissue in at least one specimen growing in its natural habitat” (Proctor, 1999, p. 335). Some plants naturally uptake high concentrations of specific contaminants, while other plants can be induced to increase their uptake through the use of chelating agents such as ethylenediaminetetraacetic acid (EDTA) (Brooks et al.,

1998).

Although phytoremediation has been used for the removal of various contaminants, such as heavy metals, other studies have been conducted to expand the field of phytoremediation applications. Some of these studies include manipulating genes to increase plant uptake (Rugh et al., 1996), remediation of organic compounds through breakdown of toxic chemicals into non-toxic compounds by Lemna gibba (Ensley et al., 1997) and Cannabis sativa (Campbell et al., 2002), understanding the relationship between specific functional genotypes and the changes in microbial communities due to contamination of petrochemicals (Siciliano et al., 2003), studying the relationship between the microbial community and the plant for the detoxification of contaminants

(Hannink et al., 2001), assessing the health of fungal communities in root systems of Solidago gigantean in contaminated soils (Vallino et al., 2006), using aquatic plants for the removal of heavy metals (Salt et al., 1995) and small-scale oil spills in marsh environments (Dowty et al., 2001), and adding chelating agents (EDTA) to the soil to increase bioavailability of heavy metals (Jiang and

Yang, 2004).

The common focus of these studies is to enhance the remediation process of the plant for a target contaminant. However, a few questions need to be asked before a project is implemented.

For a community that is economically challenged, what is the feasibility of implementing these methods when funding is severely limited? What is the possibility of creating invasive species using gene manipulation? When using chelating agents, will the chelating agent also remove the nutrients from the soil? Although these questions are not directly related to the present study, they are pertinent in how to approach the clean-up of a site. Working with the resources that are available to the community is the basis for putting together a simple protocol to analyze the plants and soils, and determine if the natural conditions can be optimized for removal of a contaminant.

The four sites for the present study are located in the State of Ohio in the United States and are the Treasure Island Dump, Bassett Street Warehouse, Tiffin Landfill and Emmajean sites 4

(Figure 1). The heavy metal chosen for this study is copper because Ohio is second among all 50 states in the release of copper to the environment.

Latitude: N 41.6836 deg Longitude: W 83.4972 deg

Latitude: N 41.6808 deg Longitude: W 83.5054 deg

Latitude: N 41.0479 deg Longitude: W 83.2067 deg Latitude: N 41.6582 deg Longitude: W 83.6335 deg

Figure 1: Map of the study sites. Treasure Island Dump, Bassett Street and Emmajean Road are located in Toledo, Ohio. The Tiffin Landfill is located in Tiffin, Ohio.

Although copper functions as an essential nutrient to plants and animals, it has a Threshold Effect

Level (TEL) of only 36 mg/kg (MacDonald et al., 2000), nearly identical to that of lead. The TEL represents the sediment concentration below which adverse effects are expected to occur only rarely. The Probable Effect Level (PEL), the concentration above which adverse effects are expected to occur frequently, has been reported at 197 mg/kg for copper (ibid.). Within this context, adverse effects are defined as those associated with toxicity for benthic invertebrates such as the amphipod Hyalella azteca and the midge Chironomus riparius (cf. Ingersoll et al., 1996). 5

A plant‟s ability to take up contaminants is directly related to the bioavailability of the contaminant such as a heavy metal (Rieuwerts et al., 1998) which, in turn, is influenced by the adsorptive capacity of the soil (Selim and Iskandar, 1999). The adsorptive capacity determines how well the substrate is able to adsorb heavy metal ions and is influenced by the organic matter content of the soil, the quantity of various clay minerals, adsorption properties, cation exchange capacity, soil pH, alkalinity and hardness (Raikhy and Takkar, 1981). Each of these factors is briefly reviewed in terms of their role in contaminant uptake.

For this study, three assumptions were made regarding the plants‟ ability to remove copper. First, the climatic conditions are the same in all the study locations. Second, the plants‟ physiological ability to remove copper is the same no matter where they are rooted. Third, the soil properties that control adsorptive capacity and plant growth are the controlling factors in the plants‟ ability to remove copper, the basis for this study. Plant size and age were not considered in this study because the focus of the study was to analyze the soil parameters that control the bioavailability of copper.

1.1 Organic Matter

The origin and composition of the organic matter have a direct impact on the adsorptive capacity of the soil (Lair et al., 2006). Organic matter typically increases the complexation capacity of the soil, which is the maximum concentration of a given metal that can be tightly bonded per gram of substrate (Selim and Iskandar, 1999). Much of the organic matter found in soils consists of humic acids (HA), which are long carbon chain compounds, with high molecular weight, brown to black in color and composed of decayed plant material. Humic acids are soluble in alkali but insoluble in acid, and contain many charged sites where adsorption can occur (Weber, 2000). The

HA molecule is naturally oxidized, giving specific sites a negative charge, which results in excellent 6

metal complexation and influences the magnitude at which cations are able to adsorb (Casagrande et al., 2004).

Organic matter can affect adsorption in two ways that are opposite from each other.

First, the heavy metal ion may form a complex with the soluble fraction of the humic substance.

When a decrease in pH occurs, the soluble humic substance becomes mobile and serves as a transport mechanism for the heavy metal. Second, the heavy metal ion may form a complex with the solid portion of the humic substance. When an increase in pH occurs, the heavy metal ion stays bonded to the solid particle and is immobilized in the soil (Selim and Iskandar, 1999).

In a previous study (Arias et al., 2002), humic acids were found to enhance heavy metal adsorption (in particular copper) to clay mineral surfaces due to the number of available sites located on the carbon chain, and to assist in the formation of ionic bonds resulting in binding tightly the heavy metal ion to the clay mineral surface. With the addition of HA, the results of this study suggested that copper has the ability to form chelates and the ease of their formation increased with increasing concentration of HA.

Adding dissolved organic carbon (DOC) to the soil resulted in an increase in copper desorption in both acidic and alkaline soils, with the acidic soils desorbing more than the alkaline soils (Mesquita et al., 2004). At lower pH values, copper adsorption is insignificant due to the competition of the H+ ion. At pH values greater than 9, copper adsorption decreases because of the formation of dissolved organic-metal complexes, metal carbonate and hydroxide complexes

(Grassi et al., 2000). In a study conducted using the liquid fraction of animal manure and copper, there was a significant relationship between the solubility of the copper and the DOC concentration in solution (Selim and Iskandar, 1999). In addition to pH being a primary factor in the mobility of heavy metals, metal complexation with high molecular weight organic matter was the main component in increasing the solubility of the heavy metal (Selim and Iskandar, 1999). Increasing 7

the solubility of a heavy metal ion gives the plants a greater chance at being able to remove the heavy metal from the soil. However, increased solubility of the heavy metal does not necessarily mean the heavy metal is bio-available. The heavy metal ion and/or the organic molecule could be too large for the plant to uptake. The heavy metal ion also might not be within the root zone of the plant. A migration study of lead and copper found the metals to be in lower concentrations in plants where surface deposition had occurred, but double the concentrations in plants where the copper and lead had migrated into the root zone along geological fault lines (Farago et al., 1992).

Furthermore, the permeability of the soil may be too high resulting in leaching of the heavy metal right past the root zone of the plants.

Overall, organic matter and heavy metal interactions produce three interrelated groups of species that influence metal bioavailability to plants (Selim and Iskandar, 1999). First, the solid portion of organic material serves as a substrate that has the ability to tightly bind heavy metal ions. The tightly bound ions are removed from the soil pore water and become sequestered in the sediments, decreasing their bioavailability to plants (Selim and Iskandar, 1999). Second, dissolved organic matter can bind to heavy metals and form soluble heavy metal complexes that can be transported by groundwater, potentially becoming bio-available to plants (Selim and Iskandar,

1999). And, third, the most bio-available heavy metal ions are those that are free or weakly bonded to organic matter (outer-sphere complexes) and are easily transported to the plant by water (Bradl, 2004).

1.2 Clay Minerals and Zeolites

The clay mineral content of the soil influences the adsorption of heavy metals due to the properties of ionic and/or covalent bonds (Gong and Donahoe, 1997). For example, sandy soils do not have a high affinity for adsorbance of heavy metals due to the inert properties of sand (Zhang

et al., 2006). Clay minerals are primarily fine-grained inorganic, crystalline materials that are responsible for some of the cation exchange in soils (Hausenbuiller, 1978) and may lead to the adsorption of copper ions to the clay particles. The finer particles in the soil have a larger surface area per unit weight, upon which positive and negative charges attract charged ions and water.

The internal surfaces of fine clay particles increase the active surface area tremendously, particularly in the montmorillonite and hydrous mica clays, yet it remains unknown as to how great of an increase the internal surfaces contribute to the overall surface area (Allrichs, 1972). The finer clay particle is where a greater rate of adsorption takes place due to the availability of increased molar free energy. The particle, striving for a state of equilibrium to reduce the free energy, adsorbs more ions per unit area onto their surfaces (Zhang et al., 1999).

The internal interface of each clay particle is comprised of sheet like molecules, or units that may be held loosely together. As conditions change, the units may become disassociated from each other, and when brought closely together again, re-associated (Burden and Sims, 1999), which impacts the ability of the clay to adsorb ions. If the units are too close together, water has a difficult time passing through the interface thereby limiting contact of the heavy metal ion to the interface. If the units are too far apart, the water will carry them right through the interfaces, also limiting contact of the heavy metal to the interface (Burden and Sims, 1999).

The structure of the clay and zeolite particles also has an impact on the adsorptive capacity of the soil. For example, the clinoptilolite zeolite has sheet-like structures, connected by few bonds that are relatively widely separated, containing open rings of alternating eight and ten sides. The formation of the sheets form channels throughout the crystal structure that allows for the passing of ions, acting as a chemical sieve, allowing the passage of some and blocking the passage of others (Amethyst Galleries, 1999; Bektaş and Kara, 2003 ). A clay mineral that has a favorable structure for adsorption is sepiolite. Sepiolite occurs in fibrous chain-structures that vary 9

in length, but generally less than 5 mm in commercial samples. The channel-like structure of sepiolite provides freedom of movement of water within the structure, creating favorable conditions for ion exchange between the sepiolite and heavy metal contaminated water (Bektaş et al., 2004).

Although clinoptilolite and sepiolite are not found in Ohio, this serves as an example of understanding the role of clays and zeolites in a phytoremediation project.

To achieve ideal conditions for phytoremediation, adsorption of ions on clay minerals should be loose enough to allow plant uptake but tight enough to prevent migration of the ion to the groundwater. Therefore, the presence of clay material with a great adsorbing capacity is not a desirable characteristic for phytoremediation.

1.3 Adsorption properties

Adsorption of the copper ion uses the ionic properties of soil particles to create a covalent bond, an ionic bond, or chelation between the soil particle and the copper ion. The soil components that have demonstrated favorable adsorptive behaviors are silicate clay minerals and the humic acid (HA) fraction of organic matter (Weber, 2000). Therefore, the fraction of clay and the fraction of HA of the total soil sample are important to discern adsorption characteristics within the study soils.

The possibility of chemical reactions occurring that may interfere with phytoremediation must also be considered when planning remediation activities. Compounds (including organic matter) present in solution may compete with the copper ion for adsorption sites on the soil particles (Selim and Iskandar, 1999; van der Zee et al., 2004). Cations with a higher affinity than copper will out-compete the copper ion for a surface charge site (Hausenbuiller, 1978). Anions

may form precipitates with the copper ion reducing the bioavailability of the copper ion for uptake by the plant (Hausenbuiller, 1978), which is contrary to the desired goal for phytoremediation.

Model simulation programs, such as Visual MINTEQ (released by the USEPA in 1999) may be used, for example, to check if the copper ion is complexing with nitrate at various pH levels. Visual

MINTEQ has many functions. It can be used to determine ion speciation, solubility equilibrium, complexation of ions, adsorbed compounds, etc., in a solution of mixed components. MINTEQ has been used to study the anion effect of copper adsorption to various substrates, such as NH2-MCM-

41 (mobile crystalline material) (Lam et al., 2008) and variable charge soils (Yu et al., 2005). In both studies, the effects of the nitrate ion caused interference of copper adsorption by changing surface properties at the adsorption sites of the substrates and/or forming copper/anion complexes.

MINTEQ has also been used to support results of a pH-dependent test to characterize the leaching behavior of municipal solid waste incineration fly ash (Zhang et al., 2008). The results of the pH- dependent test demonstrated that pH played an important role in the leaching of heavy metals from fly ash.

Organic matter has the ability to form tight ionic bonds with the copper ion and the clay mineral particle. However, if soil conditions change, the organic matter may be released from the soil particle, taking with it the copper ion (Selim and Iskandar, 1999). As such, the solubility of the copper ion may be increased, possibly enhancing the bioavailability of that ion to the plants.

The adsorption mechanisms responsible for the copper ion going from a solution to a solid phase consist of three processes: adsorption, surface precipitation and fixation (Apak, 2002).

The adsorption of heavy metals is considered a two-dimensional process at the solid/water interface (Sposito, 1984) and is often characterized as either specific adsorption or non-specific adsorption (McBride, 1994). Specific adsorption forms inner-sphere complexes between the heavy metal ion, the clay particle and/or organic matter. It results in a strong, irreversible binding (Reed 11

and Cline, 1994). Non-specific adsorption is accomplished through cation exchange, forming weak outer-sphere complexes, using the electrostatic charge on the surfaces of the metals and the soil particles (McBride, 1994). Outer-sphere complexation is a reversible reaction that occurs fairly rapidly due to the electrostatic nature of the bond (Reed and Cline, 1994).

Surface precipitation is considered to be a three-dimensional “growth phenomenon” that occurs on the surface of the soil particles usually in saturated or supersaturated conditions (Selim and Iskandar, 1999). The factors controlling surface precipitation are the pH and relative concentrations of the cations and anions present (Reed and Matsumoto, 1993). Surface precipitation is commonly classified in one of three methods: 1) the formation of polymeric metal complexes; 2) a coprecipitate that is formed through a reaction with the ions from the sorbent; or 3) a homogeneous precipitate formed through the reactions of the ions within the solution, or their hydrolysis products (Selim and Iskandar, 1999).

Fixation is also three dimensional in nature and occurs by diffusion of an aqueous metal solution into the lattice network (pore spaces) of the clay minerals forming a solid particle (Sposito,

1986). Diffusion occurs when the system is at equilibrium, the lowest energy state possible (Selim and Iskandar, 1999).

Due to the influence that pH has on the mobility of the ions (Janssen et al., 1997), an optimal pH level needs to be maintained in order to achieve optimal adsorption of the copper ion

(Atanassova and Okazaki, 1997; Zhang et al., 2006). As stated previously, the soil components that demonstrate favorable conditions for adsorption are the clay minerals and the HA. However, the characteristic that is common to both clay minerals and HA that creates favorable conditions for adsorption is the same characteristic that has the ability to influence the pH. This characteristic is the large charged surface area. The charged surface area has the ability to pull cations and anions away from the hydrogen atom, or vice versa, affecting the pH of the soil (Hausenbuiller, 1978). 12

1.4 Cation Exchange Capacity

Cation exchange capacity (CEC) is a measured value depicting the soil‟s capacity to adsorb cations. It is determined by the amount of clay and/or humus present in the soil (Anderson et al., 1982). The value of the CEC also determines the rate at which water is transported between the clay particles (Brady and Weil, 1999). The greater the CEC, the greater the soil‟s potential to exchange cations, which is reflective of the soil‟s ability to buffer acidic impulses (Burden and Sims,

1999). The buffer capacity is a calculated proportion of acids to bases, known as the percent base saturation, directly influencing the pH, alkalinity and hardness in the soils (Burden and Sims, 1999).

One of the soil characteristics that influences the CEC is particle size. The smaller the particle, the greater the surface area and the amount of free energy available to bond ions. The large surface area results in nearly a zero surface strain between the ions and the soil particle

(Zhang et al., 1999). Clays are also classified according to particle size. Therefore, the CEC of a known soil type can be estimated based on the percentages of clay and organic matter present.

The CEC is measured in milliequivalents (me) per 100 grams of substrate, and ranges from 49 me/100 g to as little as 2 me/100 g. The higher CEC is usually associated with high fractions of expanding clay and organic matter. The lower CEC is usually associated with sandy soils and very little organic matter (Hausenbuiler, 1978).

When analyzing the soil that has been targeted for remediation activities, the CEC characteristic of the soil may change with depth due to the migration of organic matter and/or fine clay minerals, or a change in the soil strata (Wilcke, 2000). Change in the soil strata, for example, could occur on a site that is composed of fill material.

1.5 Soil pH

In a majority of adsorption studies, the primary controlling factor for adsorption of heavy metals is pH (Zhang et al., 2006). In studies where the pH was decreased, copper and zinc concentrations in the column leachate increased (Gong and Donahoe, 1997; Zhang et al., 2006).

Alkaline soils had a higher adsorption rate of copper compared to non-alkaline soils (Raikhy and

Takkar, 1981; Choudhury and Khanif, 2000).

Soil pH has a great effect on the plant‟s ability to take up heavy metal ions (Bradl, 2004). If the results of a site investigation indicate that a soil amendment is needed to encourage plant growth or to mobilize the target contaminant, the pH of the amendment must also be determined.

This would avoid overloading the soil system resulting in the loss of its buffering capacity (Paschke et al., 1999). When using phytoremediation, the heavy metal should be mobile enough for the plant to uptake it but not too mobile so that the metal migrates past the root system of the plant.

The ion must also not be so insoluble that uptake cannot take place. Otherwise it will accumulate within the soil profile.

The role of organic matter in soil acidification is not well understood. Studies have yielded various results, demonstrating different mechanisms that cause a decrease in pH. One mechanism is the accumulation of organic matter (Williams, 1980). The humic acid chains in organic matter have the capability to affect soil pH through the exchange of cations and anions from the many charged sites on these chains. The ion exchange activity influences the formation of base ions and acid cations (McCauley et al., 2003). A second mechanism is the natural occurrence of the nitrogen cycle within the soil profile, which causes a fluctuation in pH (Heylar,

1976). The process of nitrification produces acid, thereby lowering the pH. The process of denitrification creates alkaline conditions and counters the acid production from nitrification (APHA,

1992). And a third mechanism is the removal of inorganic cations at greater concentrations than 14

anions in plant products (Riley and Barber, 1969). The removal of inorganic cations (i.e. Mg++,

Ca++) affects the buffering capacity of the soil. If an acidic pulse were introduced, the soil would not be able to absorb the acid, causing a decrease in pH. However, a study conducted at the School of Agriculture at the University of Western Australia indicated that the addition of plant material either increased the pH and buffering capacity of the soils or left them unchanged, and that the accumulation of plant material did not necessarily decrease soil pH (Ritchie and Dolling, 1985).

In other adsorption studies, results indicated that soil pH was the primary cause controlling the relationship between the metals and the soils (Lair et al., 2006). As soil pH rises, the solubility of soil organic matter also increases. This increases the mobility and possibly the bioavailability of heavy metals by one of two methods. First, soluble organic matter binds the heavy metal ion forming an organic-metal complex. Or second, the soluble organic matter competes with the heavy metal ion for sites on the soil particle (Temminghoff et al., 1997; Lair et al., 2006). In the same study, the dissolved organic matter competed with the copper ion for charged sites, resulting in maximum adsorption of the dissolved organic matter onto soil solids at pH 4-5 (Lair et al., 2006).

Temminghoff et al. (1997) found decreased copper adsorption with decreasing pH, resulting in

30% copper adsorption onto dissolved organic matter at pH 3.9 and 99% of copper adsorption onto dissolved organic matter at pH 6.6. Thus, the combination of an increase in soil pH and amount of soil organic matter leads to higher adsorption of the copper ion (Lair et al., 2006).

1.6 Alkalinity/Hardness

The alkalinity and hardness within a soil system is attributed to the mineral content of the soil when moisture is added. Alkalinity and hardness control the pH of the soil system. In effect, they control many reactions within the soil. Hardness generally represents the presence of polyvalent cations, in particular calcium and magnesium (APHA, 1992).

Alkalinity is a measurement of a system‟s buffering capacity, the ability to resist a change in pH. It is measured as the sum of all titratable bases. Therefore, the higher the alkalinity, the greater the system‟s ability to absorb a change in pH. The buffering mechanisms are primarily bases such as bicarbonate and carbonate. Alkalinity is reported as CaCO3 mg/L because the carbonate ion is the primary base. Other bases include hydroxide, borates, silicates, phosphates, ammonium, sulfides, and organic ligands. Typically, a good buffer system has an alkalinity level between 100 and 200 CaCO3 mg/L (APHA, 1992). Hardness is also reported as CaCO3 mg/L because calcium carbonate is more common to cause hardness.

When hardness equals alkalinity, the significant cations present are calcium and magnesium. When hardness is greater than alkalinity, there may be significant amounts of other cations present, such as iron (Fe2+) and manganese (Mn2+) (APHA, 1992).

1.7 Objective

The objective of this study is to analyze a set of soil parameters that control soil adsorption

(organic matter content, clay mineral content [particle size as a proxy], pH, alkalinity and hardness) and to find the key parameters that dominate bioavailability of copper. Once the key parameters are found, phytoremediation projects may be designed to manipulate soil conditions to help optimize the remediation process.

In order to compare the adsorptive capacity of the soils, a bioconcentration factor (BCF) may be used. The BCF is the ratio of the copper concentration in the plant and the copper concentration in the surrounding soil; the higher the ratio, the greater the potential of the plant to remove copper from the soil. The BCF can be useful when analyzing the copper concentrations of the same species of plant located in different areas of the same site and also at multiple sites to compare plant performance.

To meet the objective, this study will test copper adsorption in four different sites and vary the pH of their soils in the laboratory. It is expected that at low pH levels and high soil copper concentrations, more copper will remain in solution and that the exact opposite will occur at high pH levels and low soil copper concentrations. If more copper remains in solution, the copper may become bioavailable to the plant, the desired conditions for phytoremediation. This relationship will be tested in the batch adsorption analysis. The four soils, three of which originate from existing brownfield sites in NW Ohio, will also be analyzed for total organic matter content, particle size, alkalinity and hardness to compare the physical and chemical characteristics that are important for both plant growth and adsorptive capacity of the soil. In addition, this study will link soil and plant copper levels to calculate a BCF ratio to evaluate which plants at the brownfield sites (Treasure

Island, Bassett Street and Tiffin Landfill) could be most suitable for phytoremediation. This study will discuss how the data may be used to amend soil properties in order to optimize the copper uptake ability of the plant.

Chapter Two

Methods and Materials

2.1 Site Selection

Joseph Hickey, a fellow graduate student at the University of Toledo, had chosen the sites for this study as part of his MS thesis research beginning with lists of brownfield sites from the City of Toledo, Ohio, Division of Environmental Services, and Hull & Associates, an environmental engineering firm in Toledo. From the lists, sites were chosen according to the anticipated concentrations of heavy metals. The list of sites was further narrowed down based on the presence of copper concentrations and permission to access the properties for collection of plant and soil samples. Two of the contaminated sites selected, Treasure Island and Bassett Street, are located in Toledo, Ohio and the third contaminated site, Tiffin Landfill, is located in Tiffin, Ohio

(Figure 1). The Emmajean site, also located in Toledo but not contaminated with copper, is dominated by soils in the Del Rey loam series, which is very common in the state of Ohio (USDA,

1980). It was chosen in order to conduct a comparative analysis of soil conditions that pertain to soil adsorptive capacity and plant growth, and to evaluate whether it could be used as a soil

amendment where plant growth is limited or for remediation at other locations like the three with copper contamination. The Emmajean site does not appear directly impacted by industrial dumping and has not been disturbed by human activities for the past few decades.

2.1.1 Treasure Island Dump

Treasure Island Dump, a municipal and industrial waste dump, has a footprint of 5.3 hectares and lies adjacent to the 9.3 hectare Manhattan Dump. Treasure Island stopped receiving waste in 1968 (Mannik & Smith, 2006) and is currently listed as a Super Fund Site (USEPA, 2000).

In 1981, Owens-Illinois, Inc. and Libbey Plant 27, a glass manufacturing plant, submitted a

CERCLA Notification of Hazardous Waste Site (103[c]) form listing unknown quantities of arsenic and heavy metals at the site. In 1993, a screening site inspection was conducted by PRC

Environmental Management, Inc. Groundwater, surface water and sediment samples were tested to determine the presence and concentrations of contaminants. Semi-volatile organic compounds, pesticides and heavy metals were confirmed at the site (Mannik & Smith, 2006).

The City of Toledo acquired Treasure Island in the mid 1990‟s and had placed a 0.15 to

0.30 meters thick soil and clay layer to cap the dump (Mannik & Smith, 2006). Today, approximately half of the western side of Treasure Island Dump has been regraded with fill-dirt and a new recreational park is being built. The eastern half of the landfill has dense plant communities with large stands of older trees, suggesting limited disturbance. The site has several ponds with a combination of trees, shrubs and plants growing on the banks. The remainder of the site, located away from the banks, is sparsely dotted with vegetation, mostly grasses and small forbs. There are a few unpaved dirt roads that run through the site to access the ponds. A playground and picnic area have also been developed on the site. The topography is primarily flat with a few large mounds of soil pushed up by earth moving equipment.

2.1.2 Bassett Street Warehouse

The Bassett Street Warehouse site (Figure 1), a former manufacturing and hazardous waste storage facility, is 1.54 hectares in size and listed as a brownfield with the City of Toledo.

The property had been used for a variety of heavy industrial and commercial purposes since the mid-1890s. Many of the businesses handled toxic chemicals, such as solvents, numerous petrochemicals, and chemicals for photographic development and dry cleaning. Other businesses, such as an automotive repair facility, required constant vehicle traffic over the property (Mannik &

Smith, 2004). Bassett Street Warehouse is located south of Manhattan Marsh. In 1992, approximately 350 drums containing various hazardous wastes were found inside the warehouse and the USEPA completed emergency removal and destruction of the warehouse. No further remedial action has been taken. The Bassett Street Warehouse is currently listed as a Super Fund

Site (USEPA, 2000).

In 2000, a Phase II Environmental Site Assessment was conducted by Midwest

Environmental Consultants (MEC), Inc., a member of The Mannik and Smith Group, Inc. The

Phase II was conducted for the City of Toledo to assess the potential environmental liabilities prior to the acquisition of the property. The site contains the remnants of the warehouse (concrete foundation, brick, wood, and metal), which was destroyed by arson in 1993. Upon inspection of the site by MEC, there were several noticeable areas where opportunistic dumping (construction and demolition debris) had taken place. MEC had estimated that approximately 4,673 to 9,345 cubic meters of construction and demolition debris had been dumped at the site. MEC also estimated that there were approximately 300 to 400 large truck tires dumped at the site.

The Phase II included the analysis of ten soil borings to test for contaminants (MEC,

2000). The results of the analytical soils tests detected no presence of polychlorinated biphenyls 20

(PCBs). Several volatile (VOC) and semi-volatile organic compounds (SVOC) were detected but all were found to be below Ohio‟s Voluntary Action Program Single Parameter Residential or

Commercial Standards (VAP standards). Contrary to my findings (see Section 3.6), heavy metals were detected at the site but arsenic was the only metal above the VAP standards.

Methylene chloride was detected in a soil pile at a concentration of 84 parts per billion

(ppb). The presence of barium, chromium and lead were also detected but all were reported below the VAP standards. Found near the northeastern edge of the site, at Soil Boring No. 10, a thick sand sequence was uncovered, believed to be fill material from foundry sand.

In 2004, The Mannik & Smith Group conducted a Phase II Property Assessment of the

Bassett Street Warehouse site for the City of Toledo. The results of the Phase II confirmed the presence of VOCs, SVOCs and RCRA metals. None of the VOCs present were above the VAP standards. The following SVOCs were detected to be above the VAP standards: benzidine, solvent, plastics hardener, benzo(a)anthracene, benzo(a)pyrene, benzo(b)fluoroanthene, and dibenzo(a,h)anthracene. The RCRA metal reported to be above the VAP standards was arsenic

(37 ppm). Even though lead was reported to be below the VAP standards, the lead concentration was high at 1,200 ppm.

The land surrounding the former location of the warehouse is covered with vegetation, mostly grasses and forbs. The terrain is flat and the site has been used as a construction dumpsite, with piles of broken concrete scattered throughout. A large concrete pad exists on the site. There are still small areas of bare soil.

2.1.3 Tiffin Landfill

The Tiffin Landfill, located in Tiffin, Ohio (Figure 1), has a footprint of 16.2 hectares, of which 8.1 hectares was used for municipal and industrial landfill operations. The Tiffin Landfill, an 21

unlined facility, received municipal and industrial waste from 1956 to 1972 (ATSDR, 2001). The landfill cap is sloped towards the ditch line surrounding the landfill where the water is transported to the area‟s stormwater system to direct runoff away from the landfill. The top of the landfill cap is well vegetated with tall grasses, forbs and small stands of trees. A passive gas system was installed to vent methane that is created from the decomposing organic matter. The Tiffin Landfill did not have a soil analysis of contaminants conducted since landfill operations ceased prior to the passage of the Clean Water Act. However, Joe Hickey‟s laboratory analyses confirmed the presence of high copper concentrations (see Section 3.6). The topography of the landfill cover is uneven but relatively flat.

2.1.4 Emmajean Site

As stated previously, the Emmajean site was chosen in order to conduct a comparative analysis of soil conditions that pertain to adsorptive capacity and plant growth, and to evaluate whether it could be used as a soil amendment where plant growth is limited. The site was chosen due to the soil type, the location and the condition of the land, and the absence of direct industrial impact or disturbance for the past few decades. The soil type is the Del Rey series, a very common soil in the state of Ohio (USDA, 1980). If results indicate the Del Rey series has favorable properties for plant growth and phytoremediation, then costs can be greatly reduced in terms of transport of materials and the purchasing of soil amendments needed to manipulate the pH. Plants were not collected at the Emmajean site to test for copper concentration because the site is not a brownfield and contamination was not a concern. The Emmajean site is located at the end of

Emmajean Road in a residential area in Toledo, Ohio, and is the property of one of the homeowners. The site consists of a small stand of densely packed small trees and a few shrubs.

2.2 Plant and Soil Analyses

Joseph Hickey collected plants and soils from the above mentioned sites (excluding

Emmajean) and arranged for the ICP analyses of copper concentrations. The plants chosen for copper analysis were based on their abundance and the depth of their root system. The depth of the root system is important because removal of the contaminant is accomplished in the root zone.

The average root zone depth where treatment of the contaminant would take place is generally up to 0.5 meters. Ideally the root zone would be very fibrous, with strands extending in every direction, achieving a greater root surface area available for removal of the contaminant. Table 1 lists the plants that were sampled. The Treasure Island and Bassett Street sites were divided into four sections with two samples taken from each section, and the Tiffin Landfill site was divided into five sections with two samples taken from each section (Table 3). The plant and soil sampling locations are labeled first by site name followed by a number (i.e., Tiffin1), denoting the section from where the sample was taken.

Table 1: Summary of plant and tree seedling species listed by scientific name and common name. The plant location code is the site name plus site section from where the plant was sampled. The number of plant specimens collected per species in each site section is given by „n‟. Plant identification and sampling done by Joseph Hickey.

Species Common Name Location n Ambrosia trifida great ragweed Treasure2 3 Asclepias incarnata milkweed Tiffin2 2 Asclepias incarnata milkweed Tiffin4 3 Asclepias sp. milkweed Tiffin2 4 Asclepias sp. milkweed Tiffin3 2 Asclepias sp. milkweed Tiffin3 3 Asclepias syriaca milkweed Bassett3 1 Brassica Nigra black mustard Treasure2 3 Brassica Nigra black mustard Bassett3 2 Carya sp. hickory nut Tiffin1 1 Carya sp. hickory nut Tiffin2 1 Catalpa speciosa catalpa Treasure2 3 Centaurea stoebe spotted knapweed Bassett1 2 Centaurea stoebe spotted knapweed Bassett2 2 Centaurea stoebe spotted knapweed Bassett4 2 Cercus canadensis eastern rebud Tiffin1 1 Chenopodium album lambsquarters Treasure4 1 Chenopodium album lambsquarters Tiffin1 3 Cichorium intybus chicory Treasure4 3 Cichorium intybus chicory Bassett2 2 Cichorium intybus chicory Bassett3 2 Cichorium intybus chicory Bassett4 3 Cichorium intybus chicory Tiffin2 3 Cirsium arvense Canada thistle Treasure1 3 Cirsium arvense Canada thistle Treasure2 3 Cirsium arvense Canada thistle Tiffin5 3 Cirsium vulgare bull thistle Tiffin5 3 Cirsium vulgare bull thistle Treasure4 3 Cornus drummondii roughleaf dogweed Treasure4 3 Cornus sp. roughleaf dogweed Treasure4 3 Cornus sp. roughleaf dogweed Tiffin3 3 Crataegus L. hawthorn Treasure4 2 Crataegus L. hawthorn Treasure4 3 Daucus carota queen anne's lace/wild carrot Bassett2 3 Daucus carota queen anne's lace/wild carrot Bassett4 1 Daucus carota queen anne's lace/wild carrot Tiffin2 3 Daucus carota queen anne's lace/wild carrot Tiffin3 3 Dipsacus sylvestris Fuller's teasel Bassett3 1 Dipsacus sylvestris Fuller's teasel Bassett4 2 Dipsacus sylvestris Fuller's teasel Tiffin3 4 Erigeron annuus eastern daisy fleabane Tiffin3 3 Eupatoriadelphus purpureus sweetscented joe pye weed Treasure1 2 Fragaria Virginiana Virginia strawberry Treasure1 2 Fragaria Virginiana Virginia strawberry Bassett1 2 24

Table 1 continued

Species Common Name Location n Geum canadense white avens Treasure1 2 Geum canadense white avens Tiffin1 4 Heuchera americana American alumroot Tiffin1 3 Lonicera tatarica tatarian honeysuckle Treasure2 2 Lonicera tatarica tatarian honeysuckle Tiffin1 1 Lythrum salicaria purple loosestrife Treasure2 1 Melilotus officinalis yellow sweetclover Treasure4 2 Melilotus officinalis yellow sweetclover Treasure4 3 Mimulus ringens Allegheny monkeyflower Tiffin4 3 Nepeta cataria catnip Treasure1 1 Oxalis stricta common yellow oxalis Tiffin1 1 Parthenocissus quinquefolia Virginia creeper Treasure2 2 Parthenocissus quinquefolia Virginia creeper Tiffin1 2 phragmites australis common reed Treasure1 2 phragmites australis common reed Bassett2 4 Phragmites australis common reed Tiffin4 3 Physocarpus opulifolius common ninebark Tiffin1 2 Populus deltoides eastern cottonwood Treasure1 3 Populus deltoides eastern cottonwood Bassett1 1 Populus deltoides eastern cottonwood Bassett2 2 Populus deltoides eastern cottonwood Bassett4 3 Populus deltoides eastern cottonwood Tiffin2 3 Populus deltoides eastern cottonwood Tiffin4 3 Populus deltoides eastern cottonwood Tiffin5 1 Prunella vulgaris common selfheal Tiffin2 3 Rheum sp. rhubarb Treasure2 3 Rhus glabra smooth sumac Treasure3 3 Rhus glabra smooth sumac Tiffin2 4 Robinia pseudoacacia black locust Treasure3 2 Rubus occidentalis black raspberry Tiffin1 3 Rumex crispus curly dock Treasure2 1 Rumex crispus curly dock Bassett1 3 Salix sp. willow Tiffin4 3 Salix sp. willow Tiffin5 2 Solidago sp. goldenrod Treasure1 2 Solidago sp. goldenrod Bassett1 3 Solidago sp. goldenrod Bassett2 1 Solidago sp. goldenrod Tiffin1 1 Solidago sp. goldenrod Tiffin2 3 Solidago sp. goldenrod Tiffin3 3 Teucrium canadense Canada germander Treasure4 3 Teucrium canadense Canada germander Bassett1 2 Viola canadensis Canadian white violet Treasure2 3 Vitis aestivalis summer grape Bassett1 4 Vitis aestivalis summer grape Tiffin1 2

The site copper concentrations of the plants and soils were determined to obtain their

BCFs. The plants were collected by grabbing onto the stalk, as close to the soil as possible, and pulling them out of the ground. The plants were placed between sheets of newspaper and stored until they were analyzed for copper. The soils of the Treasure Island and Tiffin Landfill sites were composed of fill material, and the Bassett Street site has the original heterogeneous soil. The soil samples were collected by Joseph Hickey with a shovel, digging a hole up to 20 cm in depth into the root zones of the plants. The soil samples were placed in plastic zip-loc bags and stored in a freezer until they were analyzed for copper adsorption.

I collected a second set of soil samples, one for each of the four sites, for the alkalinity and hardness tests, and another from Emmajean for the batch adsorption experiments. Those for the alkalinity and hardness tests were collected using a 30.5 cm cylindrical polyethylene liner. The places I chose to collect the soil samples were locations on the sites where plants were growing in abundance. Finding clumps of plants, I placed the bottom of the liner on top of the soil next to the plant stalks. Holding a block of wood on top of the liner and using a rubber mallet, I pounded the liner into the ground until its top was even with the ground surface. Using a shovel, I dug around the liner to remove it from the ground and capped it on both ends. The Emmajean sample was collected in the approximate center of the site, where most of the plants were growing, with a shovel following the procedure used by Joseph Hickey as described above. All the soil samples were then returned to the lab and placed in the freezer.

All plant parts (roots, stems and leaves) for each species were washed in 0.1 N HCl for 30 seconds to separate adsorbed (external) from absorbed (internal) copper, re-rinsed in deionized water, and then dried at 70°C for 48 hours before measuring the weight of the dry biomass. The dry biomass was ground in a stainless steel Wiley mill to pass 1 mm screen (20-mesh).

Prior to analysis, the frozen soil samples for the adsorption analysis were thawed and air dried for 24 hours. Each sample was carefully mixed and ground using a mortar and pestle to separate the soil particles. The soil was not sieved, but gravel was removed by hand due its inert properties in the adsorption process. The pieces of decaying plant material were also removed in order to maximize the inorganic soil characteristics that could possibly affect the adsorption process.

Both the plant and soil samples collected by Joseph Hickey were digested in an HNO3 acid solution in a microwave digester (MARS; CEM Corp, Matthews, N.C.) using 1.000 gram (dry- weight) samples and USEPA method 3052 with an additional peroxide step. Copper concentrations of the digested solutions were determined with Inductively Coupled Plasma Optical

Emission Spectroscopy (ICP-OES; Model IRIS Intrepid II, Thermo Corp., Waltham, Mass.) using the 224.7 nm wavelength for copper. All ICP analyses were run by Doug Sturtz of the USDA

Agricultural Research Service, Greenhouse Production Research Group at the University of

Toledo, Toledo, Ohio. The samples were carried by argon gas into the plasma stream. A peach leaf standard reference material with a known copper concentration was used to check the accuracy of the ICP every 20 samples. For quality control, the 100 ppm Cu standard was analyzed every 10 samples to check the accuracy of the instrument. The ICP‟s detection limit was calculated to be 0.002 ppm using the equation of Thomson et al. (2003).

X dl X mb KX sd

Where: Xdl = smallest amount that can be measured with reasonable certainty

Xmb= mean of the measured instrument blanks K = a numerical factor chosen for desired confidence level (3 was used for a 90 percentile confidence level based on the

Standard deviation of Xsd)

Xsd= standard deviation of the measured instrument blanks = 0.0176

2.3 Total Organic Matter

Total organic matter was measured using the loss on ignition method (de Vos et al., 2005).

The soils used for the this test were collected by Joseph Hickey and only one sample per site was analyzed due to the destructive nature of the test and the difficulty associated with gaining access to the sites to collect more soil. I conducted the analysis at University of Toledo‟s Lake Erie Center in Oregon, OH. The mass of the empty foil tins was weighed and recorded. Using a teaspoon, approximately 25 to 30 g of soil sample was added to each foil tin and dried at 105°C for 24 hours, cooled, weighed and recorded. The mass of the foil tin was subtracted from the total (soil plus tin) to obtain the „initial‟ soil mass. The samples were then placed in a muffle furnace for 5 hours at

450°C. The ignited samples were re-hydrated and dried at 105°C for 24 hours, cooled, reweighed and recorded. The mass of the tin was again subtracted from the total mass to obtain the „final‟ soil mass. The final mass subtracted from the initial mass yields an estimate of the mass of total organic matter. The total organic matter content, divided by the initial mass of the soil yields the percent total organic matter. These results are reported in Table B3 of Appendix B.

2.4 Particle Size Analysis

The analyses for clay content by mineral type can be expensive (approximately $500 per sample) when dealing with heterogeneous soils if the samples need to be sent to an independent laboratory. This expense is related to how many soil samples are needed to accurately characterize the location and type of clay minerals. To estimate the total quantity of clay minerals, a particle size analysis may be conducted because clays are also classified according to particle size with the clay fraction consisting of particles no greater than 5 microns (μm) in diameter in accordance with ASTM standards. The overwhelming majority of the material in the clay size fraction is probably clay minerals with quartz (SiO2 or silica) making up most of the rest. 28

I conducted the particle size analysis in Dr. Timothy Fisher‟s lab at the University of

Toledo. Approximately 5 g of well-mixed soil was placed in a Petri dish and enough glacial acetic acid was added to cover the sample and remove carbonates. Enough water was then added to prevent complete evaporation of the liquid while sitting under the fume hood for a period of 24 hours. A volume of 15 ml of 5% hydrogen peroxide was added to the Petri dish to remove organics and this was allowed to sit for an additional 24 hours. Excess fluid was removed using a pipette.

To encourage separation of the soil particles, 15 ml of 40% sodium hexametaphosphate was added and let sit for a final 24 hours. The sample was then re-suspended in Nanopure water. The particle size was determined by laser diffraction using the MasterSizer 2000 (Malvern Instruments,

Malvern, Worcestershire, United Kingdom). Particle size distribution is obtained through measurements of scattering intensity as a function of the scattering angle, and the wavelength and polarization of laser light based on the applicable P scattering model (Coulter, 2006). The mixer of the MasterSizer 2000 was first filled with Nanopure water and the sample was then introduced using a medicine dropper. The instrument was programmed to read the sample three times and average the results, and each sample was run three times for extra precision. Using ASTM standards, clay size was classified to be less than 5.0 microns (µm), silt between 5 and 74 µm, and sand was greater than 74 µm. It‟s assumed for purposes of this study, that most of the clay size fraction consists of clay minerals. These results are reported in Table B1 of Appendix B.

2.5 Batch Adsorption Experiments

Only one of the soil samples collected by Joseph Hickey from each of the contaminated sites (Treasure Island, Bassett Street and Tiffin Landfill) plus another one from the Emmajean site were used for the copper adsorption experiments, but all these samples were analyzed in triplicate.

The copper masses adsorbed by the soil samples were measured using the batch equilibrium method of the USEPA (1999). In this method, a soil sample is placed in a solution with a known copper concentration. The amount of copper removed from the solution after a set period of time is assumed to have been adsorbed by the soil. In the explanation that follows, the lettered columns referred to are for tables A1 to A4 in Appendix A. A key for the column labels is also provided in

Appendix A.

Triplicate soil samples of 1±0.002 g (SM, Column M) were immersed in 25±0.02 ml aqueous solutions consisting of copper concentrations of approximately 1, 5, 10, 25, 50, 100, 150 and 200 ppm (C0,ICP, Column C) in Nalgene centrifuge tubes. Analysis blanks were run using

1±0.002 g of soil sample in 25±0.02 ml of Nanopure water.

Prior to the addition of the soil samples, the actual copper concentrations in the solutions were measured by ICP spectrometry (ICP-OES; Model IRIS Intrepid II, Thermo Corp., Waltham,

Mass.) using the 224.700 nm wavelength for copper (C0,ICP, Column C). All ICP analyses were run by Doug Sturtz of the USDA Agricultural Research Service, Greenhouse Production Research

Group at the University of Toledo, Toledo, Ohio. The samples were carried by argon gas into the plasma stream. Standards of 0, 50, 100, 150, 200 and 250 ppm were run every 20 samples to continuously check the accuracy of the instrument. For quality control, the 100 ppm standard was analyzed every 10 samples to check the accuracy of the instrument. The ICP‟s detection limit was calculated as noted in Section 2.2 and found to be 0.002 ppm. 30

After addition of the soil samples, the solutions were hand shaken in order to suspend the soil particles, and the centrifuge tubes were then placed on a shaker table and continuously agitated for a period of 24 hours at a temperature of 20±2°C. Previous research (Hartley, 2004) indicated that a period of 24 hours was sufficient time for adsorption equilibrium to be reached.

Without disturbing or removing the settled soil particles, I extracted as much solution as possible, from each centrifuge tube using a Pipetteman auto-pipette. The quantity of fine soil particles suspended in the centrifuge tube determined how far the pipette tip could be extended into the solution with the result the aliquot volumes were 10, 15 or 20 ml (VA, Column E). In the case of the

Tiffin Landfill soils, the aliquots were diluted by a factor of 5 (DF, Column F) by adding a volume of

Nanopure water equal to four times the aliquot volume. To make the correct spreadsheet calculation, 0.20 was used as the correction factor because the actual volume of the aliquot was

1/5th the total volume or 20%. The dilution step was necessary because the copper concentration in the Tiffin Landfill samples was too high to be measured by the ICP instrument. For the other soils, no dilution was needed and so the „dilution factor‟ is equal to 1 in the tables in Appendix A.

The aliquots were filtered with a #41 Whatman filter paper into 25 ml glass vials to catch soil particles that could damage the ICP. A 9.7 ml volume of each filtered aliquot was transferred to a

10 ml ICP tube and treated with concentrated (commercial grade, 15.8 Molar) nitric acid in the amount of three percent by volume of the ICP tube (0.3 ml) for a total solution volume of 10 ml.

This step is required for the batch adsorption media analyzed by the ICP instrument to ensure the same pH levels of all samples. To account for the 3% dilution effect of the added acid over the total volume of 10 ml, I used three percent (0.03) by volume as a correction factor in the spreadsheet calculation. It was thus a 10 ml solution that was analyzed by the ICP for all soil samples.

From the above analytical results, the amount of copper adsorbed by the soil samples was calculated as follows.

STEP 1: Find the mass of copper in the original solution (M0 in mg, Column D).

1mg 0.001l M C V l 0 0,ICP 0 1ml 1ppm

Where: C0,ICP is the copper concentration (in ppm) measured by ICP in the original solution (Column C; note that 1 ppm = 1 mg/l) V0 is the volume of the original solution, which is 25±0.02ml (note that 1 ml = 0.001 l). For the calculations, 0.025 l was used.

STEP 2: Apply the corrections for acid content and, where necessary, dilution factor to the copper concentration measured by ICP in the aliquot (CA,COR in ppm; Column I).

1 C C C A A,COR DF A,ICP A,ICP

Where: CA,ICP is the copper concentration (in ppm) measured in the aliquot by ICP (Column H). DF is the dilution factor (dimensionless), which is always 0.2 for Tiffin Landfill and 1 for the other sites (Column F) A is the percent by volume of acid, which is always 0.03 (Column G)

Note that if there was no dilution of the original solution, the first term in the above equation drops out.

STEP3: Find the mass of copper in the aliquot (MA in mg, Column J)

1mg 0.001l M C V l A A,COR A 1ml 1ppm

Where: CA,COR is from Step 2 (note that 1 ppm = 1 mg/l) VA is the aliquot volume (in ml, Column E; note that 1 ml = 0.001 l)

STEP 4: Calculate the mass of copper in the original 25 ml solution after 24 hours (MT in mg, Column K) based on MA from Step 3 and assuming that the copper ions were uniformly distributed throughout the original solution.

V0 M T M A VA

Where: V0 is from Step 1 MA and VA are from Step 3

STEP 5: Find the mass of copper remaining in the original solution after removal of the aliquot (MR in mg, Column L)

MR M T M A

Where: MA is from Step 3 MT is from Step 4

STEP 6: Find the mass of copper adsorbed by the soil sample (MS in mg, Column M)

MS M 0 M A MR

Where: M0 is from Step 1 MA is from Step 3 MR is from Step 5

Note that when using contaminated soils, negative values will occur when the contaminant is desorbing from the soil particle and going into solution. Such soils are ideal candidates for phytoremediation.

STEP 7: Find the mass of copper adsorbed by the soil sample in units of mg/kg (MS‟ Column O)

M 1000 g M ' S S SM 1kg

Where: MS is from Step 6 SM is the mass of soil (g) originally placed in the centrifuge tube (Column N)

The pH values in Column T of Tables A1 to A4 are the measured pH values (using

Whatman, Type CS pH indicator papers) of the copper solutions after a 24 hour contact time with the batch adsorption soils. Figure 3 displays both the pH values of the copper solutions prior to and after contact with the batch adsorption soils.

2.6 Partition Coefficient (Kd Value)

The adsorptive capacity of a soil is quantitatively represented by the partition coefficient, also known as the Kd value (see Appendix A, tables A1 to A4). The Kd value is a ratio that describes the relationship between the solid and aqueous phases of a contaminant. Specifically, it is the ratio of the quantity of adsorbed copper per mass of substrate to the amount of adsorbate remaining in solution at equilibrium (USEPA, 1999). To calculate the Kd value, an empirical model was employed because of the textural heterogeneity of the Ohio soils used for the batch method

(USEPA, 1999). The Freundlich and Langmuir isotherms are inappropriate for this method because they are used only for homogenous substrates (USEPA, 1999). The soils used in this study are composed of fill material, excluding the Emmajean soil (Del Rey Series), which is also a heterogeneous substrate.

The following empirical model is based on the premise that the adsorption reaction is independent of contaminant concentration in the aqueous phase and that, in striving for equilibrium, desorption is equal to adsorption (reversible process). This follows from the assumption that all adsorption sites on the soil particle are created equal and there exists only one dissolved aqueous contaminant, giving all ions in solution the same probability of being bound to a site. The reaction is also assumed to take place at fixed pH and temperature (USEPA, 1999). To calculate the Kd value, the following equation was used (USEPA, 1999):

MS ' K d C A,COR

Where: Kd = partition coefficient (l/kg) MS‟= copper mass adsorbed on the substrate (mg/kg), from Step 7 in Section 2.5 CA,COR = copper mass not adsorbed in aqueous solution (mg/l), from Step 2 in Section 2.5

In Tables A1 to A4, Column I (CA,COR) has zero values at the copper concentrations from 1 to 10 ppm, excluding the Emmajean site, which has zero values from 1 to 5 ppm. To avoid a zero value for copper concentration (CA,COR) in the denominator, I substituted 0.0001 for this parameter.

2.7 Visual MINTEQ

As discussed in Section 1.3, anions may form precipitates with the copper ion. The consequent increase in size of the molecule may reduce the bioavailability of the copper ion.

Although nitrate was not added to the copper solutions, the copper standard used to make the copper concentrations (Table 2) was preserved with 2% (by volume) nitric acid in order to prevent precipitation of the copper ions. Therefore, the possibility of interference from nitrate does exist, which is the reason for including nitrate in the MINTEQ analysis. Each concentration was run, varying the pH from 4.0 to 7.5 in 0.5 increments, which is the range of pH of particular interest in these soils.

Table 2: Visual MINTEQ input of the copper and nitrate concentrations used to check for complexation between the copper and nitrate ions at various pH levels.

Copper (ppm) 1 5 10 25 50 100 150 200

Nitrate (ppm) 0.02 0.1 0.2 0.5 1 2 3 4

2.8 pH, Alkalinity/ Hardness pH

The pH of the aqueous copper solutions was determined using pH indicator papers

(Whatman, Type CS). The first indicator paper range is from pH = 3.8 to 5.5, the second indicator paper range is from pH = 6.0 to 8.1, with increments of 0.2. The copper solutions were tested for pH differences before and after contact with the soil. These results are reported in Column T of

Tables A1 to A4 in Appendix A. The pH of the soil samples taken for the alkalinity and hardness analyses were tested at the site using the same indicator papers (pH = 6.0 to 8.1) and a mixture of approximately 2 g of soil to 5 ml of Nanopure water. The soil was taken from a depth of approximately 20 cm.

Alkalinity/Hardness

To ensure good contact of the water molecules to the various soil components within each individual sample taken from the sites, each soil sample was removed from its liner and thoroughly mixed. I conducted the analyses at the University of Toledo‟s Lake Erie Center in Oregon, Ohio.

Approximately 100 g of soil was mixed with approximately 500 ml of reverse osmosis water. For the water to develop good contact with the soil particles, the samples were incubated for a period of 24 hours in ambient light conditions and at a room temperature of 22°C. The samples were filtered with a Buchner funnel and #41 Whatman filter paper. The following methods were used to determine the alkalinity and hardness of the filtrate (i.e., the liquid passing through the filter). At 24 hours, the pH of the filtrates (pHFILTRATE in Table 9) was recorded prior to beginning the alkalinity/hardness tests. The pH was measured using a VWR SP301 pH meter and a Thermo

Scientific Orion 092518 pH probe, a sealed reference with a non-refillable Ag/AgCl internal reference system. The probe was calibrated using 4.0 and 10.0 pH standard buffer solutions. 36

To test for alkalinity (measured in units of milligrams of CaCO3 per liter of filtrate), the initial pH of the filtrates was taken to determine which indicator to use. If the pH was below 8.3, bromocresol green was used, and if above 8.3, then phenothalein was used. The pH of all the filtrates was below 8.3. Using a volumetric pipette, one hundred milliliters of filtrate was measured into a 250 ml beaker and the beaker was placed on a stir plate. After placing a stir magnet into the filtrate and turning on the stir plate, several drops of bromocresol green indicator were added to the beaker to give the filtrate a light blue color. If too much indicator is added, the bromocresol green will mask the endpoint, which is a color change from blue to straw yellow. The initial volume (Vi) reading of 0.02 N H2SO4 in the buret was recorded. The filtrate was titrated with 0.02 N H2SO4 acid until the pH endpoint was reached. The final volume (Vf) reading of the buret was recorded.

The initial reading is subtracted from the final to get the total volume of acid used, which is denoted by the letter A in the following equation. The alkalinity data are in Table B2 of Appendix B. Total alkalinity was calculated using the equation provided by Standard Methods for the Examination of

Water and Wastewater (APHA, 1992):

mgCaCO A N Conv Alkalinity 3 l Sample

Where: A = ml of sulfuric acid used (Vf – Vi) N = Normality of sulfuric acid = 0.02 N (equivalents/l ) Sample = 100 ml of filtrate 50 mg 1,000 ml Conv = me l me = milliequivalent

To test for hardness (measured in units of milligrams of CaCO3 per liter of filtrate), using a volumetric pipette, fifty milliliters of filtrate was measured into a 250 ml beaker and the beaker was placed on a stir plate. After placing a stir magnet into the filtrate and turning on the stir plate, 2 drops of potassium hydroxide were added to elevate the pH to approximately 10. Approximately

0.05 g of dry eriochrome black indicator was added to give the filtrate a noticeable red color. If too much indicator is added, the eriochrome black will mask the endpoint, which is a color change from red to blue. The initial volume (Vi) reading of 0.01 M ethylenediaminetetraacetic acid (EDTA) in the buret was recorded. The sample was slowly titrated with 0.01 M (moles/liter) EDTA until the end point was reached. The final volume (Vf) reading of the buret was recorded. The initial reading is subtracted from the final reading to get the total volume of EDTA used, which is denoted by the letter A in the following equation. The hardness data are in Table B2 of Appendix B. Hardness was calculated using the equation provided by Standard Methods for the Examination of Water and

Wastewater (APHA, 1992):

mg CaCO A B 1,000 ml Hardness 3 l ml Sample l

Where: A = ml of EDTA used (Vf – Vi) B = mg CaCO3 equivalent of EDTA Sample = 50 ml of filtrate (For 0.01 M EDTA solution, B = 1 mg CaCO3)

Chapter Three

Results and Discussion

3.1 Plant and Soil Copper Concentrations and Their Associated BCFs

When designing a phytoremediation project, several issues are relevant with respect to plants that are targeted for the project. First, on a contaminated site, the abundance of species that grow demonstrates their ability to produce biomass under those conditions. Second, the bioconcentration factor (BCF; i.e., the copper concentration in plants divided by the copper concentration in the soil surrounding the plants‟ roots) of an individual species will demonstrate how well the plant is able to concentrate the contaminant from the soil. Third, if there is a species that is common to more than one site but has differing BCFs, analyzing the soils at these sites for possible amendments may clarify differences in the uptake ability of that species. Fourth, if the

BCF varies at different locations within the same site, comparing soil characteristics for the same plant species might be useful. This applies especially if these soils are fill material that can come from numerous locations. And fifth, the species of plant needs to be considered to avoid the possible introduction of invasive species at the site.

Table 3 lists the results of the soil analyses for copper concentrations at the sites. The areas with the highest amounts of copper were most likely areas where industrial dumping took place. To calculate the BCF (Table 4), the soil copper concentrations (Table 3) were averaged for each section. For example, the average copper concentration for section 1 of Treasure Island is

18.46 ppm. The plant copper concentrations for each species taken from Treasure Island 1 was divided by 18.46 ppm. To calculate the BCF for the Cirsium arvense, the copper concentration

19.42 ppm was divided by 18.46 ppm, yielding a BCF of 1.05. Table 4 lists the species that have a

BCF greater than 0.5 and the number of plant specimens (n) taken from the site. Tables 5a, b, c provide average copper concentrations for the plant species (separated by growthform and duration) collected in the site sections. The number of specimens collected at each site varies according to the abundance of the species. Obviously, the best situation for effective phytoremediation would be to have the plant with the highest BCF as the dominant species.

However, some of the species with the highest BCF are trees (Carya sp.). Large trees may not be the most desirable kind of plant to have on a phytoremediation site due to the large root system.

The roots can create pathways for water infiltration into the subsoil, especially if the soil experiences swelling and shrinking according to weather conditions. This would lead to possible leaching of the contaminant beyond the soil layer with active plant root uptake. However, planting saplings and maintaining their small size through frequent harvesting, would prevent the sapling from developing a large root system. The soil samples are labeled by site name followed by a section number with an appended alphanumeric code, denoting the locations within a section.

Table 3: Summary of soil copper concentrations for sites. The soil samples are listed by site name and section number with the appended A or B (sometimes followed by a number) representing multiple sub-samples from a section. The copper concentrations reported are for the individual samples and the average for a site section.

Section Location Cu (ppm) avg soil Cu (ppm) Treasure Island 1A 15.73 18.46 Treasure Island 1B 21.19 Treasure Island 2A1 91.55 57.69 Treasure Island 2A2 45.32 Treasure Island 2B2 36.19 Treasure Island 3A 29.49 29.30 Treasure Island 3B 29.10 Treasure Island 4B 22.09 22.09 Bassett Street 1A 73.02 40.87 Bassett Street 1B 8.72 Bassett Street 2A 36.13 186.89 Bassett Street 2B 337.66 Bassett Street 3A 27.71 18.48 Bassett Street 3B 9.25 Bassett Street 4A 33.65 33.65 Bassett Street 4B 0.00 Tiffin Landfill 1A 8.41 7.41 Tiffin Landfill 1B 6.41 Tiffin Landfill 2A 17.13 238.87 Tiffin Landfill 2B 460.61 Tiffin Landfill 3A 38.15 33.76 Tiffin Landfill 3B 29.37 Tiffin Landfill 4A 20.51 22.43 Tiffin Landfill 4B 24.36 Tiffin Landfill 5A 28.38 33.25 Tiffin Landfill 5B 38.12

A few species with high BCF values are Vitis aestivalis, Rubus occidentalis, Oxalis stricta and Carya sp. These plants are perennials, native to Ohio and widespread. Vitis aestivalis is classified as a prohibited noxious weed. Oxalis stricta is also classified as a forb. Rubus occidentalis is a subshrub and the Carya sp. is a tree.

Table 4: List of species that have an average BCF greater than 0.5*.

Species Common Name Location n BCF Ambrosia trifida great ragweed Treasure2 3 0.52 Cichorium intybus chicory Bassett3 2 0.52 Dipsacus sylvestris Fuller's teasel Bassett3 1 0.55 Geum canadense white avens Treasure1 2 0.57 Asclepias sp. milkweed Tiffin3 2 0.62 Nepeta cataria catnip Treasure1 1 0.64 Salix sp. willow Tiffin4 3 0.72 Parthenocissus quinquefolia Virginia creeper Tiffin1 3 0.72 Cirsium vulgare bull thistle Treasure4 3 0.83 Melilotus officinalis yellow sweetclover Treasure4 2 0.83 Chenopodium album lambsquarters Treasure4 1 0.84 Cercis canadensis eastern rebud Tiffin1 1 0.97 Cirsium arvense canada thistle Treasure1 3 1.05 Eupatoriadelphus purpureus sweetscented joe pye weed Treasure1 2 1.06 Physocarpus opulifolius common ninebark Tiffin1 2 1.20 Lonicera tatarica tatarian honeysuckle Tiffin1 2 1.24 Solidago sp. goldenrod Tiffin1 2 1.72 Geum canadense white avens Tiffin1 4 1.92 Chenopodium album lambsquarters Tiffin1 3 1.93 Rubus occidentalis black raspberry Tiffin1 3 2.30 Heuchera americana american alumroot Tiffin1 3 2.47 Vitis aestivalis summer grape Tiffin1 1 2.87 Oxalis stricta common yellow oxalis Tiffin1 2 2.90 Carya sp. hickory nut Tiffin1 1 5.92 *n = number of samples collected The Tiffin Landfill has the plants with the greatest BCF values. Table 5 also shows that the

Treasure Island and Tiffin Landfill plants have the greatest potential to concentrate copper with consistently the highest copper concentrations in the plant tissue. Table 5 groups the plants according to annual, perennial, trees/shrubs to illustrate if one group of plants has a greater ability

Table 5 a, b, c: Total plant (roots, stems and leaves) copper concentrations of all species and sorted into annual, perennial, tree/shrub groups. Copper concentrations listed in ascending order. The copper concentration was measured using the dry weight of the plants.

Species Common Name Type Location n Avg Cu ppm Brassica Nigra black mustard annual Treasure2 3 1.11 Brassica Nigra black mustard annual Bassett3 2 1.79 Melilotus officinalis yellow sweetclover annual Treasure4 3 7.66 Erigeron annuus eastern daisy fleabane annual Tiffin3 3 13.20 Chenopodium album lambsquarters annual Treasure4 1 14.31 Melilotus officinalis yellow sweetclover annual Treasure4 2 18.35 Chenopodium album lambsquarters annual Tiffin1 3 18.56 Ambrosia trifida great ragweed annual Treasure2 3 30.06

to accumulate copper in its tissues. Although age was not considered in this project, grouping the plants by duration and in ascending order of copper concentration (Table 5 a, b, c) is a good place to begin a study focusing on varying copper concentrations according to growthform differences.

0 average average copper concentration (ppm)

tree/shrub perennial annual

Figure 2 – Mean copper concentrations of annual, perennial and tree/shrub groups with error bars of one standard deviation.

Table 5b. Continued

Species Common Name Type Location n Avg Cu ppm Rumex crispus curly dock perennial Treasure2 1 2.40 Teucrium canadense Canada germander perennial Treasure4 3 3.77 Cirsium arvense Canada thistle perennial Treasure1 3 3.86 Teucrium canadense Canada germander perennial Bassett1 2 3.90 Daucus carota queen anne's lace/wild carrot perennial Bassett2 3 4.03 Dipsacus sylvestris Fuller's teasel perennial Bassett3 1 4.19 Centaurea stoebe spotted knapweed perennial Bassett1 2 4.20 Daucus carota queen anne's lace/wild carrot perennial Bassett4 1 4.38 Fragaria Virginiana Virginia strawberry perennial Treasure1 2 4.47 Fragaria Virginiana Virginia strawberry perennial Bassett1 2 4.47 Dipsacus sylvestris Fuller's teasel perennial Bassett4 2 4.60 Rumex crispus curly dock perennial Bassett1 3 5.00 Daucus carota queen anne's lace/wild carrot perennial Tiffin2 3 5.13 Parthenocissus quinquefolia Virginia creeper perennial Treasure2 2 5.36 Asclepias syriaca milkweed perennial Bassett3 1 5.60 Centaurea stoebe spotted knapweed perennial Bassett2 2 5.66 Centaurea stoebe spotted knapweed perennial Bassett4 2 6.48 Asclepias incarnata milkweed perennial Tiffin2 2 7.09 Cichorium intybus chicory perennial Treasure4 3 7.40 Cichorium intybus chicory perennial Bassett2 2 7.70 Asclepias sp. milkweed perennial Tiffin2 4 7.75 Cichorium intybus chicory perennial Bassett3 2 9.34 Cichorium intybus chicory perennial Bassett4 3 9.58 Vitis aestivalis summer grape perennial Bassett1 4 10.04 Cirsium vulgare bull thistle perennial Tiffin5 3 10.05 Dipsacus sylvestris Fuller's teasel perennial Tiffin3 4 10.12 Cichorium intybus chicory perennial Tiffin2 3 10.48 Geum canadense white avens perennial Treasure1 2 10.59 Mimulus ringens Allegheny monkeyflower perennial Tiffin4 3 10.95 Cirsium arvense Canada thistle perennial Treasure2 3 11.54 Catalpa speciosa catalpa perennial Treasure2 3 11.56 Asclepias sp. milkweed perennial Tiffin3 2 11.58 Nepeta cataria catnip perennial Treasure1 1 11.86 Daucus carota queen anne's lace/wild carrot perennial Tiffin3 3 11.91 Geum canadense white avens perennial Tiffin1 4 14.25 Asclepias incarnata milkweed perennial Tiffin4 3 16.51 Viola canadensis Canadian white violet perennial Treasure2 3 17.37 Parthenocissus quinquefolia Virginia creeper perennial Tiffin1 2 17.51 Prunella vulgaris common selfheal perennial Tiffin2 3 17.68 Heuchera americana American alumroot perennial Tiffin1 3 18.27 Cirsium vulgare bull thistle perennial Treasure4 3 18.35 Cirsium arvense Canada thistle perennial Tiffin5 3 19.42 Eupatoriadelphus purpureus sweetscented joe pye weed perennial Treasure1 2 19.66 Asclepias sp. milkweed perennial Tiffin3 3 21.01 Vitis aestivalis summer grape perennial Tiffin1 2 21.24 Oxalis stricta common yellow oxalis perennial Tiffin1 1 21.51 Rheum sp. rhubarb perennial Treasure2 3 23.38

Figure 2 displays the mean copper concentrations between the three groups with error bars of one standard deviation. A single factor analysis of variance (ANOVA) was calculated resulting in a p value of 0.711 indicating there is no significant difference between the groups in copper concentration. The result of the ANOVA is located in Appendix C.

Table 5c. Continued

Species Common Name Type Location n Avg Cu ppm Populus deltoides eastern cottonwood tree/shrub Treasure1 3 0.38 Cornus sp. roughleaf dogweed tree/shrub Treasure4 3 2.96 Cornus sp. roughleaf dogweed tree/shrub Tiffin3 3 3.28 Rhus glabra smooth sumac tree/shrub Treasure3 3 4.13 Cornus drummondii roughleaf dogweed tree/shrub Treasure4 3 5.37 Crataegus L. hawthorn tree/shrub Treasure4 2 5.55 Lonicera tatarica tatarian honeysuckle tree/shrub Treasure2 2 5.56 phragmites australis common reed tree/shrub Treasure1 2 5.63 phragmites australis common reed tree/shrub Bassett2 4 5.84 Solidago sp. goldenrod tree/shrub Treasure1 2 6.30 Populus deltoides eastern cottonwood tree/shrub Bassett1 1 6.95 Cercis canadensis eastern rebud tree/shrub Tiffin1 1 7.21 Salix sp. willow tree/shrub Tiffin4 3 7.39 Solidago sp. goldenrod tree/shrub Bassett1 3 7.44 Populus deltoides eastern cottonwood tree/shrub Bassett2 2 7.61 Populus deltoides eastern cottonwood tree/shrub Bassett4 3 7.74 Phragmites australis common reed tree/shrub Tiffin4 3 8.44 Physocarpus opulifolius common ninebark tree/shrub Tiffin1 2 8.87 Populus deltoides eastern cottonwood tree/shrub Tiffin2 3 9.03 Solidago sp. goldenrod tree/shrub Bassett2 1 9.16 Lonicera tatarica tatarian honeysuckle tree/shrub Tiffin1 1 9.20 Crataegus L. hawthorn tree/shrub Treasure4 3 9.90 Robinia pseudoacacia black locust tree/shrub Treasure3 2 10.33 Solidago sp. goldenrod tree/shrub Tiffin1 1 11.32 Lythrum salicaria purple loosestrife tree/shrub Treasure2 1 11.48 Solidago sp. goldenrod tree/shrub Tiffin2 3 12.77 Rhus glabra smooth sumac tree/shrub Tiffin2 4 14.16 Populus deltoides eastern cottonwood tree/shrub Tiffin4 3 14.18 Solidago sp. goldenrod tree/shrub Tiffin3 3 14.24 Populus deltoides eastern cottonwood tree/shrub Tiffin5 1 15.66 Salix sp. willow tree/shrub Tiffin5 2 16.07 Rubus occidentalis black raspberry tree/shrub Tiffin1 3 17.01 Carya sp. hickory nut tree/shrub Tiffin1 1 43.84 Carya sp. hickory nut tree/shrub Tiffin2 1 61.81

As a predictor of plant copper concentration, the BCF results can be misleading. A plant with a low BCF could result from a high concentration of copper in the soil but the plant could have highest copper content in its tissues. Note that the highest copper levels are encountered in a species of tree. The plants that are common to two and three sites are listed in Table 6. Some of the plants perform about the same among the sites (Cichorium intybus, Chenopodium album,

Phragmites australis) whereas other plants vary widely in their uptake ability (Cirsium arvense,

Solidago sp., Parthenocissus quinquefolia, Rhus glabra).

average plant copper concentration (ppm) 0 0 50 100 150 200 250 300

average soil copper concentration (ppm)

Figure 3 – Average plant copper concentration versus average soil copper concentration.

The average plant and soil copper concentrations were correlated (Figure 3) to determine if a relationship exists between the two variables. Using p=0.05 to determine the critical r value for df=90 (r=0.205), the CORREL function in Excel yielded a value of r = 0.154. The low value indicated the two variables are not related, which is supported by R2 = 0.0236 from the linear regression. Apparently, soil copper levels do not correlate with plant copper levels.

This suggests that different conditions exist at each location that influences the plants‟ ability to remove copper. Bassett 2 and Tiffin 2 have the highest soil copper concentrations with averages of 186.89 and 238.87 ppm, respectively. The plants in Bassett 2, however, consistently have the lowest copper concentrations in their tissues. Alternatively, the age of the plants might play an important role. Physiological and age differences were not considered because the focus of the study was to analyze the soil parameters that control the bioavailability of copper. Evidently, copper levels in plant species were not different among plant duration and growthform (e.g., annual, perennial, tree/shrub) as shown in Figure 2.

Bassett Street consistently appeared to have the lowest biomass of plants growing at a site and all its plants have a copper concentration of around 10 ppm. There are two possible explanations for why the Bassett Street plants are having difficulty growing and removing copper from the soil. First, there may be competition with the high levels of arsenic and lead, as found in the Phase II Property Assessment conducted by Mannik & Smith (2004). The concentration of lead was determined to be as high as 1,200 ppm (Mannik & Smith, 2004). Second, the heavy industrial use that the site has experienced over the past 100 years may play a role. At present, there is still active dumping of construction material at the site. The constant driving over the land with heavy machinery results in compaction of the soil, inhibiting plant growth. In addition, the heavy machinery could be constantly crushing the plants and prevent the plants from reaching maturity.

Table 6: Copper concentrations in plants common to two or three sites. Plant Common Name Location Cu (ppm) Populus deltoides eastern cottonwood Bassett1 0.38 Cirsium arvense Canada thistle Tiffin5 3.86 Daucus carota queen anne's lace/wild carrot Tiffin3 4.03 Rhus glabra smooth sumac Treasure3 4.13 Daucus carota queen anne's lace/wild carrot Bassett2 4.38 Daucus carota queen anne's lace/wild carrot Bassett4 5.13 Parthenocissus quinquefolia Virginia creeper Tiffin1 5.36 Lonicera tatarica tartarian honeysuckle Treasure2 5.56 Phragmites australis common reed Treasure1 5.63 Phragmites australis common reed Tiffin4 5.84 Solidago sp. goldenrod Bassett2 6.30 Populus deltoides eastern cottonwood Bassett4 6.95 Cichorium intybus chicory Treasure4 7.40 Solidago sp. goldenrod Bassett1 7.44 Populus deltoides eastern cottonwood Tiffin4 7.61 Cichorium intybus chicory Bassett2 7.70 Populus deltoides eastern cottonwood Bassett2 7.74 Phragmites australis common reed Bassett2 8.44 Populus deltoides eastern cottonwood Treasure1 9.03 Solidago sp. goldenrod Treasure1 9.16 Lonicera tatarica tartarian honeysuckle Tiffin1 9.20 Cichorium intybus chicory Tiffin2 9.34 Cichorium intybus chicory Bassett2 9.58 Vitis aestivalis summer grape Bassett1 10.04 Cirsium vulgare bull thistle Tiffin5 10.05 Cichorium intybus chicory Bassett4 10.48 Geum canadense white avens Treasure1 10.59 Solidago sp. goldenrod Tiffin3 11.32 Cirsium arvense Canada thistle Treasure2 11.54 Daucus carota queen anne's lace/wild carrot Tiffin2 11.91 Solidago sp. goldenrod Tiffin1 12.77 Rhus glabra smooth sumac Tiffin2 14.16 Populus deltoides eastern cottonwood Tiffin5 14.18 Solidago sp. goldenrod Tiffin2 14.24 Geum canadense white avens Tiffin1 14.25 Chenopodium album lambsquarters Tiffin1 14.31 Populus deltoides eastern cottonwood Tiffin2 15.66 Parthenocissus quinquefolia Virginia creeper Treasure2 17.51 Cirsium vulgare bull thistle Treasure4 18.35 Chenopodium album lambsquarters Treasure4 18.56 Cirsium arvense Canada thistle Treasure1 19.42 Vitis aestivalis summer grape Tiffin1 21.24

3.2 Organic Matter and Particle Size

Table 7 displays the particle size distribution and percent organic matter for the single composite soil samples from each site. The soil fractions that are primarily responsible for adsorption are the clay particles in the clay size fraction and organic matter. However, organic matter is also responsible for increasing the mobility of copper ions at low pH. Organic matter contains multiple charged sites to which the copper ions can be bonded. When the pH decreases, the organic matter dissolves and becomes mobile in the soil profile. The copper ion, bound to the organic matter, travels with the dissolved organic matter. The silt and sand size fractions probably consist almost entirely of inert silicate minerals like quartz, feldspar and others. The soils with the most favorable characteristics for adsorption are the Treasure Island and Tiffin Landfill soils, which have the highest amounts of clay and silt. Even though none of the soils can be classified as organic, the relatively high amount of organic matter (8.0%) in the Emmajean soil may be favorable for adsorption and phytoremediation. The higher amount of organic matter also contributes to favorable growing conditions for vegetation. The vegetation at the Emmajean site consisted primarily of a dense stand of trees and shrubs. The other sites have similar organic matter content, ranging from 2.5% to 3.7%, which may be enough to provide conditions that are favorable for adsorption and phytoremediation of the copper ion even with a high fraction of sand content, due to the natural properties inherent in organic matter.

Table 7: Average soil particle size distributions. The results are classified in accordance with ASTM standards according to diameter (d) of the particle size: clay < 5 microns (μm); 5 μm < silt > 74 μm; and 74 μm > sand, with their standard deviations in parentheses. The results of percent organic matter were calculated using loss on ignition method. One of Joseph Hickey‟s soil samples from each site was analyzed for both particle size and organic matter with the particle size analysis results provided in Table B1 of Appendix B.

%Clay <5 µm %Silt 5 µm- %Sand >74 µm %Organic Sample Name (sd) 74 µm (sd) (sd) Matter Treasure Island 15 (1) 48 (2) 37(3) 3.4 Bassett Street 15 (2) 42 (5) 43 (7) 3.7 Tiffin Landfill 20 (2) 52 (7) 28 (10) 2.5 Emmajean 3 (0.2) 32 (1) 65 (1) 8.0 49

3.3 Copper Adsorption

Figure 4 displays the variation in copper adsorption for each soil. The initial copper concentration (ppm) of the standard solution is graphed on the abscissa and the average percent copper mass adsorbed on soil is graphed on the ordinate. The graph illustrates the percent copper mass adsorbed for each initial copper concentration after a period of 24 hours of contact with the soils. Because some of the soils used for this study are contaminated with copper (excluding

Emmajean), there are negative numbers, indicating that copper is desorbing from the soil particles.

For example, for Treasure Island at 200 ppm, the average percent copper mass adsorbed is less than 10 percent, which implies, at high copper concentrations, less mass is adsorbing to the soil particle.

The Kd value (Table 8) is a ratio of the concentration of contaminant in the solid phase to the concentration of contaminant in the liquid phase (Kd = Ai/Ci). Therefore, the greatest ratio of solid phase to liquid phase will yield the highest Kd value. In other words, if all of the contaminant

(copper ion) is adsorbed onto the soil particle, the concentration of the liquid phase will be zero.

The interpretation of such a value is that the soil particles have adsorbed 100% or close to 100% of the contaminant and the adsorbate (copper) is very tightly bound and sequestered to the soil particle.

100

0 25 50 75 100 125 150 175 200 average average percent Cu mass adsorbed on soil

-20

-40

initial copper concentration of solution (ppm) -60

treasure bassett tiffin emmajean

Figure 4 – Average percent copper mass adsorbed onto the Treasure Island, Bassett Street, Tiffin Landfill and Emmajean soils across various copper concentrations ranging from 1 to 200 ppm. Each sample consisted of 25 ml of copper standard and 1 g soil. All samples were run in triplicate and the averages are plotted. Each site symbol is accompanied by a two-standard deviation error bar that is based on the triplicate analysis. Negative values imply copper desorption.

100

0 3 4 5 6 7 8

average percentaverage Cu mass adsorbed onsoil -20

-40 pH

treasure bassett tiffin emmajean

Figure 5 – Average percent copper mass adsorbed on soil versus pH. The negative values indicate copper is desorbing from the soils.

Figure 4 displays that at copper concentrations of 1 to 10 ppm, 100% adsorption occurs for

Treasure Island, Bassett Street and Tiffin Landfill soils. At concentrations greater than 10 ppm, less mass is adsorbed and more mass remains in solution. Figure 5 displays the relationship between the average percent of copper mass adsorbed onto soil versus pH levels. At pH levels less than 6, little copper mass is adsorbed, versus at pH levels greater than 6.5 when almost 100% of the copper mass is adsorbed. At pH values less than four, negative values indicate that copper is desorbing from the soils.

Table 8 summarizes the copper mass adsorbed at each concentration, the respective Kd

values and the pH of the copper solution. With the exception of the Emmajean site, all soils adsorb

100% of the copper mass at 1 ppm in the pH range from 6.3 to 7.5. The Treasure Island, Bassett

Street and Tiffin Landfill soils are able to adsorb nearly 100% of the copper mass up to and

including 10 ppm in the pH range from 6.3 to 6.9. The Emmajean soil begins to lose its adsorptive

capacity at 5 ppm at pH 6.3, which suggests the soil is limited in its ability to adsorb the copper ion.

At 10 and 25 ppm copper in solution, the Emmajean soil/solution mix has a pH of 4.6 and

the copper mass adsorbed is much lower than for the other soils. At 25 ppm, the Treasure Island

and Bassett Street soils have a pH of 6.0 and the Tiffin Landfill soil‟s pH has decreased to 4.4. The

Tiffin Landfill soil is only able to adsorb approximately 60% of the mass that the Treasure Island

and the Bassett Street soils adsorb at that exposure.

Table 8: Average copper mass adsorbed and average Kd values for soils, and pH of the copper solutions. All samples were run in triplicate with the results provided in Tables A1 to A4 in Appendix A. The mass adsorbed was calculated from the copper concentration results from the ICP analysis. The Kd value is a ratio of the quantity of the adsorbate adsorbed per mass of solid to the amount of the adsorbate remaining in solution at equilibrium.

Avg Treasure Avg Avg Bassett Copper Mass Adsorbed Avg Treasure Treasure Mass Adsorbed Avg Bassett Avg Bassett

Concentration (mg) Kd (l/kg) pH (mg) Kd (l/kg) pH 1ppm 0.02 196430.04 7.50 0.02 196138.78 6.30 5ppm 0.12 1226948.83 6.60 0.12 1229529.82 6.30 10ppm 0.25 843258.46 6.30 0.25 5579.39 6.30 25ppm 0.52 167.23 6.00 0.49 87.18 6.00 50ppm 0.83 48.20 5.50 0.49 15.87 5.50 100ppm 0.66 8.75 3.80 0.38 4.39 3.80 150ppm 0.35 2.69 3.80 0.21 1.55 3.80 200ppm 0.35 2.00 3.80 0.24 1.34 3.80 Avg Emmajean Avg Copper Avg Tiffin Mass Avg Tiffin Kd Avg Tiffin Mass Adsorbed Emmajean Avg

Concentration Adsorbed (mg) (l/kg) pH (mg) Kd (l/kg) Emmajean pH 1ppm 0.03 272661.79 6.90 0.02 196874.60 6.30 5ppm 0.13 1357568.93 6.90 0.09 65.75 6.30 10ppm 0.26 2607540.22 6.60 0.10 18.07 4.60 25ppm 0.31 21.31 4.40 0.12 5.94 4.60 50ppm 0.19 4.02 3.80 0.12 2.64 3.80 100ppm -0.01 -0.06 3.80 0.15 1.62 3.80 150ppm -0.17 -1.06 3.80 0.01 0.08 3.80 200ppm -1.17 -4.13 3.80 0.01 0.04 3.80 53

At levels of 50 ppm copper in solution, the pH drops to 3.8 for both the Emmajean and

Tiffin Landfill soil, and the amount of copper the Tiffin Landfill soil is able to adsorb has dramatically decreased. Although the pH for Treasure Island and Bassett Street is 5.5, the Treasure Island soil is still able to adsorb the most copper. The Bassett Street soil maintains the same copper mass adsorbed at both 25 and 50 ppm, which are at pH‟s 6.0 and 5.5, respectively. The pH of the solutions decreases due to the stock solution being preserved with 2 volume percent nitric acid, meaning that in 500 ml of copper stock solution, 10 ml is nitric acid. Therefore, to make a copper standard using 1 ml of copper solution in 1 liter of water, 0.02 ml is nitric acid. To make a copper standard using 50 ml of copper solution in 1 liter of water, 1 ml is nitric acid. The more copper in the stock solution used to make a copper standard, the more acidic the standard becomes.

At 100 ppm copper in solution, the Treasure Island and Bassett Street soils are now experiencing a decrease in the amount of copper they are able to adsorb. The Tiffin Landfill soil is now desorbing copper, and this is represented by the negative values for the mass and Kd variables. The Emmajean soil has maintained roughly the same adsorptive capacity from 5 ppm to

100 ppm.

At 150 and 200 ppm copper in solution, the copper mass adsorbed by the Treasure Island and Bassett Street soils is approximately the same as at lower concentrations. The Tiffin Landfill soil is beginning to desorb a greater quantity of copper, increasing desorption at 200 ppm. The

Emmajean soil is not able to adsorb copper at 150 ppm and begins to desorb copper at 200 ppm

(Figure 4), as depicted by the negative values.

Excluding the Emmajean soil, at low concentrations of copper (1 ppm to 10 ppm) and at neutral or close to neutral pH, the soil could be sequestering the copper. Although the Kd values suggest that sequestration is occurring, a simple leach test could be conducted to actually determine if this is the case. If sequestration is occurring in the 1 ppm to 10 ppm concentrations,

then there would not be a need to remove the copper at the site. At the higher concentrations of copper solution and low pH, the opposite occurs. The Kd ratio is decreased meaning there is more adsorbate in solution than adsorbed to the soil particles. Therefore, the copper ion, remaining in solution, has the potential to be bioavailable to the plants for uptake.

The batch adsorption copper solutions were tested for pH both before and after contact with the soil. Figure 6 shows that the Treasure Island and Bassett Street soils also have the greatest capability to buffer an acidic input. The Tiffin Landfill soil begins to lose its buffering capability before both the Treasure Island and Bassett Street soils. The low-alkalinity Emmajean soil has even less buffering capability. The pH levels of 100, 150 and 200 ppm are most likely less than 3.8 but could not be accurately measured because the lowest value on the pH paper was 3.8.

The results in Table 8 and Figure 6 suggest that a pH of 6 to 6.3 would be a good place to begin potted plant experiments as this acidity is where all soils sharply decrease in their ability to adsorb copper. For the experiments, I would maintain the pH range and vary the copper concentration in accordance to the needs of the design, beginning with a concentration of 25 ppm.

I would choose the plants that yielded the greatest tissue copper concentrations from this study.

Even though the pH levels lower than 6 have a greater mobile copper concentration, plants do not grow well in such highly acidic soils.

8 7 6 5

4 pH 3 2 1 0 1 ppm 5 ppm 10 ppm 25 ppm 50 ppm 100 ppm 150 ppm 200 ppm copper concentrations (ppm)

original copper soln treasure bassett tiffin emmajean

Figure 6 – Changes in pH for copper standard solutions at concentrations between 1 and 200 ppm. Prior to contact with the soils, the pH of the copper solutions was measured using Whatman, Type CS pH indicator papers. The batch adsorption samples were run in triplicate. After 24 hour contact with the soil, the pH was measured again. The bar graph illustrates the pH of the original copper solutions (grid) is much lower prior to contact with the Treasure Island (dots), Basset Street (horizontal hatch), Tiffin Landfill (vertical hatch) and Emmajean (diamonds) soils. The pH of the 100, 150 and 200 ppm solutions is recorded to be 3.8, the lowest value on the pH paper.

3.4 pH, Alkalinity/Hardness

The soil pH results (Table 9) indicate that the soils maintain a pH that is close to or at neutral. pHfiltrate is the pH of the mixture of reverse osmosis (RO) water and soil after 24 hours.

The results indicate the Treasure Island, Bassett Street and Tiffin Landfill soils are buffering the pH of the RO water, which has a pH of 6.9. The results of the Emmajean filtrate demonstrate that the soil has low alkalinity, meaning the conjugate bases that are able to resist a change in pH are lower in concentration and would not be able to buffer an acidic pulse.

Alkalinity and hardness tests were run on the soils because of the control that these parameters have on pH. The results of the alkalinity test (Table 9) indicate that the Treasure Island and the Tiffin Landfill soils have good buffer systems with alkalinity values of 102 and 122 mg

CaCO3/l, respectively. The Bassett soil has an alkalinity value of 69 mg CaCO3/l, which is indicative of some buffering capacity. The Emmajean soil has little buffering capability with an alkalinity value of 20 mg CaCO3/l. The alkalinity (Alk) and hardness (Har) results have a strong nonlinear relationship (R2=0.97) for the second degree polynomial regression model:

Alk = -345.147 + [15.1188 x Har] – [0.111057 x Har2].

The Treasure Island, Bassett Street and Emmajean soils have a hardness of 42, 39 and

31 mg CaCO3/l, respectively, placing the soils in the “soft” category (APHA, 1992). The Tiffin

Landfill soil has a hardness of 89 mg CaCO3/l, placing the soil in the “moderately hard” category.

Remembering the relationship between alkalinity and hardness, a ratio (hardness/alkalinity) can be calculated to describe the buffering capabilities of a soil. The lower the value of that ratio, the higher the buffering capability of the soil. A value greater than one is indicative of the presence of significant amounts of other cations.

Table 9: Measured alkalinity, hardness, pH and the associated hardness:alkalinity ratio for soil samples. Soil pH was analyzed on-site and for only one sample per site.

Sample Alkalinity Hardness Hardness (mg CaCo3/l) (mg CaCo3/l) Alkalinity pHFILTRATE pHSOIL Treasure Island 102 42 0.41 7.09 6.6 Bassett Street 69 39 0.57 7.87 7.2 Tiffin Landfill 122 89 0.73 7.55 6.9 Emmajean 20 31 1.55 6.47 6.6

The Bassett Street site has unfavorable soil conditions for plant growth due to excessive

soil compaction, but a good buffering capability (hardness to alkalinity ratio of 0.57). The

combination of unfavorable soil conditions for plant growth and the good buffering capability of the

soil would result in poor copper uptake by the plants. The results of my copper adsorption tests

(Table 8) suggest that this soil is able to buffer an acidic pulse and adsorb the copper ion relatively

well down to a pH of 5.5. This, in turn, causes the copper ion to be unavailable to the plant for

uptake.

The Tiffin Landfill soil has a higher hardness to alkalinity ratio (0.73) and is not able to

buffer acidic pulses as well as either the Treasure Island or Bassett Street soils. The Tiffin Landfill

soil samples begin to lose their buffering capability at 25 ppm (Table 8), which is noted by a drastic

decrease in pH to 4.4. The Treasure Island and Bassett Street soils maintain a pH of 6.0 at 25

ppm, and a pH of 5.5 at 50 ppm. With a slight acidic pulse, the pH is lowered and the copper

becomes mobile, possibly making the copper ion bioavailable to the plants.

The Treasure Island plants perform better than the Bassett Street plants, even though the

hardness to alkalinity ratio (0.41) is much lower (Table 9). Upon acquisition of the dump, the City

of Toledo placed a 0.15 to 0.30 meters thick soil and clay cap over the waste at Treasure Island.

The newly applied soil provided favorable growing conditions for plants.

The results of the pH, alkalinity and hardness analyses indicate that alkalinity is a controlling factor for copper mobility. The alkalinity of the soil system must be overburdened by an acidic or basic pulse in order for a change in pH to occur. In the batch adsorption lab analyses, nitric acid was introduced at varying concentrations. When the soils began to lose their buffering capacity, the pH decreased resulting in greater mobility of copper in the soil.

3.5 Visual MINTEQ Results

No research has been published on complexation between copper and nitrate over a range of copper concentrations and pH levels. The results of Visual MINTEQ (Table 10) across the pH range of 4 to 7.5, and copper concentrations of 1 ppm to 200 ppm, show that the copper and nitrate ions do not complex. The nitrate remains in solution at greater than 99% and the concentration of copper nitrate remains at levels of less than 1%. Visual MINTEQ supports the results of the batch adsorption experiment, which has shown that a higher fraction of the copper ion remains in solution at a pH less than 6.3.

Table 8 illustrates the leaching behavior of copper at a pH of less than 4 for the Tiffin

Landfill soils as depicted by the negative values. This is consistent with the study conducted by

Zhang et al., (2008), where the leaching of heavy metals from MSWI fly ash was shown to be pH dependent and supported by Visual MINTEQ.

Table 10: Copper, nitrate and pH parameters used for the Visual MINTEQ experiments. The results are given as percent in solution.

Inputs Component pH 4 pH 4.5 pH 5 pH 5.5 pH 6 pH 6.5 pH 7 pH 7.5 -1 Cu = 1 ppm %NO3 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 + NO3 = 0.02 ppm %CuNO3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 %Cu+1 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Inputs Component pH 4 pH 4.5 pH 5 pH 5.5 pH 6 pH 6.5 pH 7 pH 7.5 -1 Cu = 5 ppm %NO3 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 + NO3 = 0.1 ppm %CuNO3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 %Cu+1 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Inputs Component pH 4 pH 4.5 pH 5 pH 5.5 pH 6 pH 6.5 pH 7 pH 7.5 -1 Cu = 10 ppm %NO3 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 + NO3 = 0.2 ppm %CuNO3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 %Cu+1 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Inputs Component pH 4 pH 4.5 pH 5 pH 5.5 pH 6 pH 6.5 pH 7 pH 7.5 -1 Cu = 25 ppm %NO3 99.9 99.9 99.9 99.9 99.9 99.9 100.0 100.0 + NO3 = 0.5 ppm %CuNO3 0.1 0.1 0.1 0.1 0.1 0.1 0.0 0.0 %Cu+1 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Inputs Component pH 4 pH 4.5 pH 5 pH 5.5 pH 6 pH 6.5 pH 7 pH 7.5 -1 Cu = 50 ppm %NO3 99.9 99.8 99.8 99.8 99.8 99.9 99.9 100.0 + NO3 = 1 ppm %CuNO3 0.1 0.2 0.2 0.2 0.2 0.1 0.1 0.0 %Cu+1 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Inputs Component pH 4 pH 4.5 pH 5 pH 5.5 pH 6 pH 6.5 pH 7 pH 7.5 -1 Cu = 100 ppm %NO3 99.6 99.6 99.6 99.6 99.7 99.8 99.9 100.0 + NO3 = 2 ppm %CuNO3 0.4 0.4 0.4 0.4 0.3 0.2 0.1 0.0 %Cu+1 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Inputs Component pH 4 pH 4.5 pH 5 pH 5.5 pH 6 pH 6.5 pH 7 pH 7.5 -1 Cu = 150 ppm %NO3 99.5 99.5 99.5 99.5 99.5 99.7 99.9 100.0 + NO3 = 3 ppm %CuNO3 0.5 0.5 0.5 0.5 0.5 0.3 0.1 0.0 %Cu+1 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Inputs Component pH 4 pH 4.5 pH 5 pH 5.5 pH 6 pH 6.5 pH 7 pH 7.5 -1 Cu = 200 ppm %NO3 99.3 99.3 99.3 99.3 99.4 99.7 99.9 100.0 + NO3 = 4 ppm %CuNO3 0.7 0.7 0.7 0.7 0.6 0.3 0.1 0.0 +1 %Cu 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Chapter Four

Summary and Conclusions

4.1 Formulating Phytoremediation Designs Based on Analytical Results

Conducting soil physical and chemical analyses is a practical and useful process to quantify the soil conditions that control the uptake ability of copper by plants. The soil parameters that control adsorption of copper include organic matter content, clay mineral content, pH, alkalinity and hardness. These are the same soil parameters that contribute to favorable or unfavorable conditions for plant growth.

The copper concentrations of plants sampled at three locations (Table 5) indicate that the plants located at the Treasure Island and Tiffin Landfill sites are most efficient at absorbing copper in the tissue. When comparing the copper concentrations of plants species that are common to two or three sites (Table 6), it seemed that the same plants do not always perform the same for different sites. Cichorium intybus (7 to 10.5 ppm), Chenopodium album (14 to 18.6 ppm) and

Phragmites australis (5.6 to 8.4 ppm) absorb copper with approximately the same ability among all sites. However, other species have a wider range of copper concentrations. These include

Populus deltoides (0.4 to 15.7 ppm), Solidago spp. (6.3 to 14.2 ppm), Parthenocissus quinquefolia

(5.4 to 17.5 ppm) and Rhus glabra (4.1 to 14.2 ppm).

Assuming environmental conditions are the same at all sites and that the plants‟ physiological ability to uptake copper and the plants‟ age are the same no matter where they are rooted, different soil conditions will most likely influence the plants‟ ability to absorb copper. The adsorptive capacity of the soil and the soil contaminants and conditions vary by site. Adding a soil amendment to improve the soil chemistry is one approach to improve copper uptake by plants.

Manipulating the microbial communities around the root zones may also be effective but is likely more complex to accomplish. Improving oxygen content in the soils through plowing is another option, keeping in mind that increased oxygen content levels may affect pH levels and copper mobility.

Table 8 displays that the maximum Kd value does not necessarily occur at the maximum copper mass adsorbed. The prediction that at low pH levels and high copper concentrations, more copper will remain in solution, and vice versa, is supported by the data from the batch adsorption analyses. Using the results of the adsorption study, percent organic matter, grain size analysis, pH, alkalinity and hardness tests, the Treasure Island and Tiffin Landfill soils may be augmented to optimize the plant uptake of copper. For example, the alkalinity could be reduced by adding peat moss, leaf mold, and well-composted sawdust. Plant growth might be increased by mixing in

Emmajean Del Rey soil, which has a higher fraction of sand (which enables the roots to spread out more) and organic matter (which provides the nutrients needed for plant growth).

According to its history, Bassett Street has numerous contaminants at the site, all of them below VAP standards. It also has poor soil conditions from heavy industrial use for over 100 years.

The most probable reason for the sparse vegetation at the site is the compaction of the soils from the constant vehicle traffic required for trash disposal and storage of hazardous wastes. There

also continues to be dumping of construction material at the site. The Bassett Street soil also has the most alkaline pH at 7.2. Copper sorption of soils was greatest at the higher pH values, which would inhibit the copper uptake ability of the plants. To improve soil conditions, I would first clear the site of the construction material. Then, to give plants the opportunity to spread their roots, I would turn the soil to at least 0.30 meters in depth and mix in an additional 0.15 meter layer of topsoil. Also, to bring the pH down, a soil amendment could be added, such as peat moss, leaf mold, and well-composted sawdust. Although the cost of hauling soil may be expensive, this method does not require the cost of digging up the soil and hauling it as a hazardous waste to a combustion facility. This will create a hazardous by-product that will need to be disposed after which clean soil has to be hauled back to the site.

Another method would be to seed the area with rapid growing vegetation that is known to remove copper, such as various Solidago species native to Ohio. The use of non-native plants may result in the introduction of an invasive species.

The Emmajean soil has little ability to buffer an acidic pulse, as indicated by the low alkalinity and hardness values. The soil has also little silt and clay, and the highest percent of sand and organic matter. Even though the Emmajean Kd value is 65.75 l/kg, the maximum adsorptive capacity occurred at 5 ppm, indicating that the Emmajean soil has relatively low adsorptive properties. The Emmajean soil could be used as a soil amendment, such as in Bassett Street, to help plant growth without risk of creating conditions that would increase the adsorptive capacity of the soil.

In order to estimate the amount of copper being absorbed by the plants, a mass balance may be calculated. Using a plot of a given size, one can determine the plant biomass in that plot and analyze that biomass for copper content. Determining the copper concentration in the soil from where the biomass was collected will allow the calculation of the fraction of copper from the

copper reservoir in the soil that can be removed if that biomass is harvested. Multiplying the result of that particular plot by the area of the site will yield an estimate of copper that can be removed by the plants at that point in time. In order to calculate copper removed by the plants for a longer period of time, sampling should be done on a regular basis with the frequency of sampling determined by how quickly the plants produce biomass.

The objective of this study was to analyze a set of soil parameters that impact soil adsorption (organic matter content, clay mineral content [particle size as a proxy], pH, alkalinity and hardness) to find the key parameter(s) controlling bioavailability of copper to the plant. The key parameter that controlled adsorption of copper in this study was alkalinity. Using the results of this study as a guide, phytoremediation projects can be designed to manipulate the alkalinity to find conditions that will enable the plants to increase the absorption of copper in their tissues.

4.2 Lessons Learned

When I received the plant and soil copper concentration data, it was in an excel file without information of where the samples were taken. If I could do this over again, I would have participated in the plant and soil sample collection. As such, I could have created a sample database of where, when and how the samples were taken. I also could have made sure to collect extra soil in order to have enough soil for all of the analyses needed. In addition, that would have enabled me to more accurately characterize the soils at the sites, especially for the alkalinity/hardness, total organic matter and particle size tests. Also, I could have helped to develop a random soil sampling protocol.

Developing a map of the soil sample locations would have been helpful to collect more samples. This is particularly true because, during the soil analyses, I was running out of sample.

Due to the destructive nature of the tests, I only ran one soil sample per site for alkalinity/hardness,

total organic matter and particle size analyses. This was not enough to fully characterize soil conditions over a large area and I should have collected additional soil samples. Also, I was unaware if any soil sampling was done in areas without plants. It would have been interesting to see if a difference in contaminant levels existed between locations with and without plants.

I should have conducted more pH sampling of the sites. The pH samples should have been taken at the same places where the soil samples were taken, as well as at locations where no plants were growing. pH sampling in the root zone would most likely yield skewed results due to the impact of roots on the conditions in the soil. Roots, for example, release oxygen into the soil and foster microbial populations that, in turn, manipulate soil conditions.

Ideally, one soil sample should have been analyzed for every plant that was collected and the soil should have come from directly under the root zone. The soil copper concentration data consisted of only two soil samples per section in each site. In essence, I used 25 soil copper concentrations for more than 200 plant samples. For many plant species, more than 2 samples were collected. In addition to analyzing the soil samples from under the plants, random soil sampling would have allowed a comparative analysis between non-random and random sampling.

More samples would improve statistical correlations between plant and soil copper concentrations.

The nitric acid in the stock copper standard solution used for the batch adsorption experiments had an important impact on the direction of the study, particularly because it resulted in copper solutions with different pH levels. It would have been better to study the impact of pH and varying copper levels on adsorption separately.

4.3 Future Studies

This study is a start to expand phytoremediation of copper and find native plants that have the greatest potential at accumulating copper in their tissues. I am stressing the use of native plants because of the numerous problems that have been created from using non-native species.

A new study should begin with the plants that have the greatest copper accumulation potential in their tissues and growing them from seedlings in lysimeters. The lysimeter design could be a column with a closed cone at the bottom that is filled with sand. A non-woven geotextile covering the sand would prevent the top soil layer from migrating into the sand. Depending on how many lysimeters are used, the top layers could be varied among fill types, such as loam, clay loam, sandy loam, etc. Water that does migrate into the sand layer should be pumped out by installing a tube with a suction device along the side of the lysimeter that extends into the cone. Also, moisture monitoring probes could be used to keep track of fluctuations in the moisture content within the layers. Such confined lysimeters can use contaminated soil and water without fear of the contaminant migrating into the groundwater. Therefore, the soils used in these experiments could be spiked with various copper concentrations and the soil conditions could be manipulated to test plants‟ contaminant uptake dynamics.

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Appendix A

Soil Adsorption Tables

Key to Tables A1 to A4

1. Column A: Sample Name

2. Column B: ICP Name

3. Column C: C0,ICP (ppm); Initial copper concentration from ICP analysis

4. Column D: M0 (mg); Initial copper mass calculated from C0,ICP

5. Column E: VA (ml); Aliquot Volume

6. Column F: DF; Dilution Factor

7. Column G: A (% by volume); Acid added to the ICP tube

8. Column H: CA, ICP (ppm); Copper concentration in aliquot

9. Column I: CA, COR (ppm); Corrected copper concentration of the aliquot

10. Column J: MA (mg); Mass of copper in aliquot

11. Column K: MT (mg); Total copper mass in 25 ml solution after 24 hours

12. Column L: MR (mg); Copper mass remaining after removal of aliquot

13. Column M: MS (mg); Copper mass adsorbed by soil

14. Column N: SM (g); Soil mass

15. Column O: MS,mg/kg; Copper mass adsorbed to soil in units of mg/kg

16. Column P: Sample name

17. Column Q: Average MS (mg); Average copper mass adsorbed by soil

18. Column R: Kd (l/kg); Partition coefficient

19. Column S: Average Kd (l/kg); Average partition coefficient

20. Column T: pH; pH value of copper solution after contact with soil

21. Column U: % Cu Mass Adsorbed

Table A1: Treasure Island Dump Adsorption Results

A B C D E F G H I J K L M N O

Sample Name ICP Name C0,ICP (ppm) M0 (mg) VA (ml) DF A (% by volume) CA,ICP (ppm) CA,COR (ppm) MA (mg) MT(mg) MR (mg) MS (mg) SM (g) MS' (mg/kg) treasure1-1PPM 63 0.78 0.02 15 1 0.03 -0.20 0.00 0.00 0.00 0.00 0.02 1.0009 19.37 treasure2-1PPM 64 0.79 0.02 20 1 0.03 -0.20 0.00 0.00 0.00 0.00 0.02 1.0029 19.60 treasure3-1PPM 65 0.80 0.02 20 1 0.03 -0.19 0.00 0.00 0.00 0.00 0.02 1.0004 19.96 treasure1-5PPM 66 4.95 0.12 20 1 0.03 -0.13 0.00 0.00 0.00 0.00 0.12 1.0054 123.01 treasure2-5PPM 67 4.93 0.12 20 1 0.03 -0.14 0.00 0.00 0.00 0.00 0.12 1.0017 122.98 treasure3-5PPM 68 4.89 0.12 20 1 0.03 -0.13 0.00 0.00 0.00 0.00 0.12 1.0018 122.09 treasure1-10PPM 69 10.05 0.25 20 1 0.03 -0.03 0.00 0.00 0.00 0.00 0.25 1.0004 251.22 treasure2-10PPM 70 10.01 0.25 20 1 0.03 0.10 0.10 0.00 0.00 0.00 0.25 1.0003 247.76 treasure3-10PPM 71 10.00 0.25 20 1 0.03 0.02 0.02 0.00 0.00 0.00 0.25 1.0024 249.02 treasure1-25PPM 72 25.34 0.63 20 1 0.03 7.58 7.80 0.16 0.20 0.04 0.44 1.0013 437.77 treasure2-25PPM 73 25.22 0.63 20 1 0.03 2.51 2.58 0.05 0.06 0.01 0.57 1.0028 564.46 treasure3-25PPM 74 25.13 0.63 20 1 0.03 2.42 2.49 0.05 0.06 0.01 0.57 1.0008 565.58 treasure1-50PPM 75 50.96 1.27 20 1 0.03 17.53 18.06 0.36 0.45 0.09 0.82 1.0009 821.97 treasure2-50PPM 76 50.69 1.27 20 1 0.03 17.23 17.75 0.35 0.44 0.09 0.82 0.9994 823.98 treasure3-50PPM 77 50.56 1.26 20 1 0.03 15.81 16.28 0.33 0.41 0.08 0.86 0.9994 857.47 treasure1-100PPM 78 102.74 2.57 20 1 0.03 73.93 76.15 1.52 1.90 0.38 0.66 1.0019 663.60 treasure2-100PPM 79 101.58 2.54 20 1 0.03 71.63 73.78 1.48 1.84 0.37 0.69 1.0041 692.15 treasure3-100PPM 80 101.53 2.54 20 1 0.03 74.28 76.51 1.53 1.91 0.38 0.63 1.0017 624.41 treasure1-150PPM 81 145.85 3.65 20 1 0.03 129.50 133.39 2.67 3.33 0.67 0.31 1.0006 311.39 treasure2-150PPM 82 145.33 3.63 20 1 0.03 127.20 131.02 2.62 3.28 0.66 0.36 1.0005 357.75 treasure3-150PPM 83 144.72 3.62 20 1 0.03 125.40 129.16 2.58 3.23 0.65 0.39 1.0004 388.67 treasure1-200PPM 84 191.27 4.78 20 1 0.03 170.30 175.41 3.51 4.39 0.88 0.40 1.0031 395.32 treasure2-200PPM 85 189.93 4.75 20 1 0.03 169.40 174.48 3.49 4.36 0.87 0.39 1.0011 385.83 treasure3-200PPM 86 190.04 4.75 20 1 0.03 173.90 179.12 3.58 4.48 0.90 0.27 1.0012 272.62

Copper analyses for blanks and standards using Nanopure water. Replicate 1 Replicate 2 Replicate 3 Standard (ppm) (ppm) (ppm) blank 0.05 -0.01 -0.05 std 1, 50 ppm 52.18 51.63 52.14 std 2, 100 ppm 103.60 103.50 102.50 std 3, 150 ppm 153.30 152.40 151.40 std 4, 200 ppm 199.60 199.00 197.50 std 5, 250 ppm 244.70 243.50 242.90

Table A1 continued

P Q R S T U Average MS Average Kd % Cu Mass

Sample Name (mg) Kd (l/kg) (l/kg) pH Adsorbed treasure1-1PPM 193671.70 treasure2-1PPM 196032.76 treasure3-1PPM 0.02 199585.67 196430.04 7.5 100 treasure1-5PPM 1230129.80 treasure2-5PPM 1229789.36 treasure3-5PPM 0.12 1220927.33 1226948.83 6.6 100 treasure1-10PPM 2512195.12 treasure2-10PPM 2469.64 treasure3-10PPM 0.25 15110.61 843258.46 6.3 100 treasure1-25PPM 56.09 treasure2-25PPM 218.51 treasure3-25PPM 0.52 227.09 167.23 6 83 treasure1-50PPM 45.52 treasure2-50PPM 46.43 treasure3-50PPM 0.83 52.66 48.20 5.5 66 treasure1-100PPM 8.71 treasure2-100PPM 9.38 treasure3-100PPM 0.66 8.16 8.75 3.8 26 treasure1-150PPM 2.33 treasure2-150PPM 2.73 treasure3-150PPM 0.35 3.01 2.69 3.8 10 treasure1-200PPM 2.25 treasure2-200PPM 2.21 treasure3-200PPM 0.35 1.52 2.00 3.8 7

Table A2: Bassett Street Warehouse Adsorption Results

A B C D E F G H I J K L M N O

A (% by

Sample Name ICP Name C0,ICP (ppm) M0 (mg) VA (ml) DF volume) CA,ICP (ppm) CA,COR (ppm) MA (mg) MT(mg) MR (mg) MS (mg) SM (g) MS' (mg/kg) basett1-1PPM 1 0.78 0.02 20 1 0.03 -0.04 0.00 0.00 0.00 0.00 0.02 1.001 19.36 basett2-1PPM 2 0.79 0.02 20 1 0.03 -0.06 0.00 0.00 0.00 0.00 0.02 1.002 19.61 basett3-1PPM 3 0.80 0.02 20 1 0.03 -0.06 0.00 0.00 0.00 0.00 0.02 1.005 19.87 bassett1-5PPM 4 4.95 0.12 20 1 0.03 -0.02 0.00 0.00 0.00 0.00 0.12 1.001 123.60 bassett2-5PPM 5 4.93 0.12 20 1 0.03 -0.03 0.00 0.00 0.00 0.00 0.12 1.000 123.14 bassett3-5PPM 6 4.89 0.12 20 1 0.03 -0.03 0.00 0.00 0.00 0.00 0.12 1.002 122.12 bassett1-10PPM 7 10.05 0.25 20 1 0.03 0.03 0.03 0.00 0.00 0.00 0.25 1.001 250.43 bassett2-10PPM 8 10.01 0.25 20 1 0.03 0.09 0.09 0.00 0.00 0.00 0.25 1.002 247.69 bassett3-10PPM 9 10.00 0.25 20 1 0.03 0.05 0.05 0.00 0.00 0.00 0.25 1.002 248.24 bassett1-25PPM 10 25.34 0.63 20 1 0.03 5.13 5.28 0.11 0.13 0.03 0.50 1.003 500.16 bassett2-25PPM 11 25.22 0.63 20 1 0.03 6.34 6.53 0.13 0.16 0.03 0.47 1.002 466.55 bassett3-25PPM 12 25.13 0.63 20 1 0.03 5.06 5.22 0.10 0.13 0.03 0.50 1.001 497.26 bassett1-50PPM 13 50.96 1.27 20 1 0.03 29.70 30.59 0.61 0.76 0.15 0.51 1.001 508.88 bassett2-50PPM 14 50.69 1.27 20 1 0.03 30.01 30.91 0.62 0.77 0.15 0.49 1.003 492.77 bassett3-50PPM 15 50.56 1.26 20 1 0.03 30.69 31.61 0.63 0.79 0.16 0.47 0.997 475.04 bassett1-100PPM 16 102.74 2.57 20 1 0.03 84.18 86.71 1.73 2.17 0.43 0.40 1.004 399.33 bassett2-100PPM 17 101.58 2.54 20 1 0.03 83.94 86.46 1.73 2.16 0.43 0.38 1.001 377.78 bassett3-100PPM 18 101.53 2.54 20 1 0.03 84.40 86.93 1.74 2.17 0.43 0.36 1.002 364.26 bassett1-150PPM 19 145.85 3.65 20 1 0.03 133.20 137.20 2.74 3.43 0.69 0.22 1.001 216.17 bassett2-150PPM 20 145.33 3.63 20 1 0.03 131.90 135.86 2.72 3.40 0.68 0.24 1.001 236.62 bassett3-150PPM 21 144.72 3.62 20 1 0.03 133.40 137.40 2.75 3.44 0.69 0.18 1.002 182.42 bassett1-200PPM 22 191.27 4.78 20 1 0.03 176.30 181.59 3.63 4.54 0.91 0.24 1.002 241.66 bassett2-200PPM 23 189.93 4.75 20 1 0.03 177.10 182.41 3.65 4.56 0.91 0.19 1.002 187.56 bassett3-200PPM 24 190.04 4.75 20 1 0.03 173.00 178.19 3.56 4.45 0.89 0.30 1.000 296.10

Copper analyses for blanks and standards using Nanopure water. Replicate 1 Standard (ppm) Replicate 2 (ppm) Replicate 3 (ppm) blank -0.05 0.00 0.04 std 1, 50 ppm 52.11 52.24 52.19 std 2, 100 ppm 103.20 102.40 102.60 std 3, 150 ppm 152.10 151.70 151.70 std 4, 200 ppm 199.10 199.10 201.10 std 5, 250 ppm 243.30 243.60 243.50

Table A2 continued

P Q R S T U

Average Kd % Cu Mass

Sample Name Average MS (mg) Kd (l/kg) (l/kg) pH Adsorbed basett1-1PPM 193613.66 basett2-1PPM 196130.54 basett3-1PPM 0.02 198672.14 196138.78 6.3 100 bassett1-5PPM 1236030.88 bassett2-5PPM 1231387.45 bassett3-5PPM 0.12 1221171.13 1229529.82 6.3 100 bassett1-10PPM 8745.86 bassett2-10PPM 2809.31 bassett3-10PPM 0.25 5182.99 5579.39 6.3 100 bassett1-25PPM 94.73 bassett2-25PPM 71.48 bassett3-25PPM 0.49 95.33 87.18 6 78 bassett1-50PPM 16.63 bassett2-50PPM 15.94 bassett3-50PPM 0.49 15.03 15.87 5.5 39 bassett1-100PPM 4.61 bassett2-100PPM 4.37 bassett3-100PPM 0.38 4.19 4.39 3.8 15 bassett1-150PPM 1.58 bassett2-150PPM 1.74 bassett3-150PPM 0.21 1.33 1.55 3.8 6 bassett1-200PPM 1.33 bassett2-200PPM 1.03 bassett3-200PPM 0.24 1.66 1.34 3.8 5

Table A3: Tiffin Landfill Adsorption Results

A B C D E F G H I J K L M N O

Sample Name ICP Name C0,ICP (ppm) M0 (mg) VA (ml) DF A (% by volume) CA,ICP (ppm) CA,COR (ppm) MA (mg) MT (mg) MR (mg) MS (mg) SM (g) MS' (mg/kg) tiffin1-1PPM 1 1.09 0.03 20 0.2 0.03 -0.09 0.00 0.00 0.00 0.00 0.03 1.006 26.38 tiffin2-1PPM 2 1.10 0.03 10 0.2 0.03 -0.08 0.00 0.00 0.00 0.00 0.03 1.000 26.55 tiffin3-1PPM 3 1.09 0.03 10 0.2 0.03 -0.09 0.00 0.00 0.00 0.00 0.03 1.003 26.48 tiffin1-5PPM 4 5.42 0.14 10 0.2 0.03 -0.08 0.00 0.00 0.00 0.00 0.13 1.002 131.88 tiffin2-5PPM 5 5.44 0.14 10 0.2 0.03 -0.08 0.00 0.00 0.00 0.00 0.13 1.001 131.92 tiffin3-5PPM 6 5.47 0.14 10 0.2 0.03 -0.08 0.00 0.00 0.00 0.00 0.13 1.003 131.64 tiffin1-10PPM 7 10.57 0.26 10 0.2 0.03 0.04 0.18 0.00 0.00 0.00 0.26 1.003 258.89 tiffin2-10PPM 8 10.53 0.26 10 0.2 0.03 0.00 0.00 0.00 0.00 0.00 0.26 1.000 263.17 tiffin3-10PPM 9 10.60 0.26 10 0.2 0.03 0.03 0.15 0.00 0.00 0.00 0.26 1.004 260.21 tiffin1-25PPM 10 26.62 0.67 10 0.2 0.03 2.88 14.47 0.14 0.36 0.22 0.30 1.002 303.05 tiffin2-25PPM 11 26.74 0.67 10 0.2 0.03 2.83 14.24 0.14 0.36 0.21 0.31 1.002 311.82 tiffin3-25PPM 12 26.73 0.67 10 0.2 0.03 2.88 14.48 0.14 0.36 0.22 0.31 1.003 305.26 tiffin1-50PPM 13 54.60 1.37 10 0.2 0.03 9.36 47.10 0.47 1.18 0.71 0.19 1.001 187.37 tiffin2-50PPM 14 54.44 1.36 10 0.2 0.03 9.39 47.21 0.47 1.18 0.71 0.18 1.002 180.45 tiffin3-50PPM 15 54.69 1.37 10 0.2 0.03 9.29 46.71 0.47 1.17 0.70 0.20 1.001 199.29 tiffin1-100PPM 16 102.52 2.56 10 0.2 0.03 19.92 100.20 1.00 2.50 1.50 0.06 1.004 57.76 tiffin2-100PPM 17 102.62 2.57 10 0.2 0.03 21.06 105.93 1.06 2.65 1.59 -0.08 1.005 -82.39 tiffin3-100PPM 18 102.69 2.57 10 0.2 0.03 20.41 102.66 1.03 2.57 1.54 0.00 1.000 0.72 tiffin1-150PPM 19 146.16 3.65 10 0.2 0.03 29.72 149.49 1.49 3.74 2.24 -0.08 1.004 -83.04 tiffin2-150PPM 20 146.67 3.67 10 0.2 0.03 32.00 160.96 1.61 4.02 2.41 -0.36 1.005 -355.60 tiffin3-150PPM 21 147.39 3.68 10 0.2 0.03 29.78 149.79 1.50 3.74 2.25 -0.06 1.000 -60.04 tiffin1-200PPM 22 192.40 4.81 10 0.2 0.03 40.01 201.25 2.01 5.03 3.02 -0.22 1.001 -220.91 tiffin2-200PPM 23 194.16 4.85 10 0.2 0.03 42.14 211.96 2.12 5.30 3.18 -0.45 1.001 -445.01 tiffin3-200PPM 24 194.77 4.87 10 0.2 0.03 61.28 308.24 3.08 7.71 4.62 -2.84 1.001 -2832.67

Copper analyses for blanks and standards using Nanopure water. Replicate 1 Replicate 2 Replicate 3 Standard (ppm) (ppm) (ppm) blank -0.04 0.02 0.01 std 1, 50 ppm 52.55 52.41 51.99 std 2, 100 ppm 105.20 104.70 105.00 std 3, 150 ppm 150.10 149.50 150.00 std 4, 200 ppm 198.30 198.60 198.40 std 5, 250 ppm 244.20 245.30 243.70

Table A3 continued

P Q R S T U Average MS Average Kd % Cu Mass

Sample Name (mg) Kd (l/kg) (l/kg) pH Adsorbed tiffin1-1PPM 269961.74 tiffin2-1PPM 275497.45 tiffin3-1PPM 0.03 272526.18 272661.79 6.9 100 tiffin1-5PPM 1352164.25 tiffin2-5PPM 1358484.82 tiffin3-5PPM 0.13 1362057.71 1357568.93 6.9 100 tiffin1-10PPM 2588891.16 tiffin2-10PPM 2631650.00 tiffin3-10PPM 0.26 2602079.51 2607540.22 6.6 99 tiffin1-25PPM 20.94 tiffin2-25PPM 21.90 tiffin3-25PPM 0.31 21.08 21.31 4.4 46 tiffin1-50PPM 3.98 tiffin2-50PPM 3.82 tiffin3-50PPM 0.19 4.27 4.02 3.8 14 tiffin1-100PPM 0.58 tiffin2-100PPM -0.78 tiffin3-100PPM -0.01 0.01 -0.06 3.8 0 tiffin1-150PPM -0.56 tiffin2-150PPM -2.21 tiffin3-150PPM -0.17 -0.40 -1.06 3.8 -5 tiffin1-200PPM -1.10 tiffin2-200PPM -2.10 tiffin3-200PPM -1.17 -9.19 -4.13 3.8 -24

Table A4 Emmajean Adsorption Results

A B C D E F G H I J K L M N O

Sample Name ICP Name C0,ICP (ppm) M0 (mg) VA (ml) DF A (% by volume) CA,ICP (ppm) CA,COR (ppm) MA (mg) MT(mg) MR (mg) MS (mg) SM (g) MS' (mg/kg) emmajean1-1PPM 15 0.78 0.02 15 1 0.03 -0.14 0.00 0.00 0.00 0.00 0.02 1.000 19.38 emmajean2-1PPM 16 0.79 0.02 15 1 0.03 -0.15 0.00 0.00 0.00 0.00 0.02 0.996 19.74 emmajean3-1PPM 17 0.80 0.02 15 1 0.03 -0.15 0.00 0.00 0.00 0.00 0.02 1.001 19.94 emmajean1-5PPM 18 4.95 0.12 15 1 0.03 1.47 1.52 0.02 0.04 0.02 0.09 1.001 85.64 emmajean2-5PPM 19 4.93 0.12 15 1 0.03 1.19 1.22 0.02 0.03 0.01 0.09 0.994 93.10 emmajean3-5PPM 20 4.89 0.12 15 1 0.03 1.32 1.36 0.02 0.03 0.01 0.09 0.999 88.35 emmajean1-10PPM 21 10.05 0.25 15 1 0.03 5.50 5.67 0.09 0.14 0.06 0.11 0.995 110.14 emmajean2-10PPM 22 10.01 0.25 15 1 0.03 5.82 5.99 0.09 0.15 0.06 0.10 0.998 100.73 emmajean3-10PPM 23 10.00 0.25 15 1 0.03 5.64 5.81 0.09 0.15 0.06 0.10 1.003 104.43 emmajean1-25PPM 24 25.34 0.63 15 1 0.03 19.24 19.82 0.30 0.50 0.20 0.14 0.996 138.55 emmajean2-25PPM 25 25.22 0.63 15 1 0.03 20.02 20.62 0.31 0.52 0.21 0.12 1.000 115.16 emmajean3-25PPM 26 25.13 0.63 15 1 0.03 20.18 20.79 0.31 0.52 0.21 0.11 0.999 108.74 emmajean1-50PPM 27 50.96 1.27 15 1 0.03 44.24 45.57 0.68 1.14 0.46 0.13 1.004 134.37 emmajean2-50PPM 28 50.69 1.27 15 1 0.03 44.69 46.03 0.69 1.15 0.46 0.12 0.999 116.46 emmajean3-50PPM 29 50.56 1.26 15 1 0.03 44.74 46.08 0.69 1.15 0.46 0.11 0.998 112.27 emmajean1-100PPM 30 102.74 2.57 15 1 0.03 95.07 97.92 1.47 2.45 0.98 0.12 1.002 120.23 emmajean2-100PPM 31 101.58 2.54 15 1 0.03 91.64 94.39 1.42 2.36 0.94 0.18 0.999 179.91 emmajean3-100PPM 32 101.53 2.54 15 1 0.03 92.21 94.98 1.42 2.37 0.95 0.16 1.000 163.75 emmajean1-150PPM 33 145.85 3.65 15 1 0.03 139.00 143.17 2.15 3.58 1.43 0.07 0.997 67.17 emmajean2-150PPM 34 145.33 3.63 15 1 0.03 141.20 145.44 2.18 3.64 1.45 0.00 0.996 -2.58 emmajean3-150PPM 35 144.72 3.62 15 1 0.03 141.70 145.95 2.19 3.65 1.46 -0.03 0.994 -31.09 emmajean1-200PPM 36 191.27 4.78 15 1 0.03 186.70 192.30 2.88 4.81 1.92 -0.03 0.994 -25.90 emmajean2-200PPM 37 189.93 4.75 15 1 0.03 181.60 187.05 2.81 4.68 1.87 0.07 1.000 72.07 emmajean3-200PPM 38 190.04 4.75 15 1 0.03 185.40 190.96 2.86 4.77 1.91 -0.02 1.003 -23.10

Table A4 Emmajean Adsorption Results

A10

Table A4 continued

P Q R S T U Average MS Average Kd % Cu Mass

Sample Name (mg) Kd (l/kg) (l/kg) pH Adsorbed emmajean1-1PPM 193826.62 emmajean2-1PPM 197371.00 emmajean3-1PPM 0.02 199426.19 196874.60 6.3 100 emmajean1-5PPM 56.44 emmajean2-5PPM 76.02 emmajean3-5PPM 0.09 64.79 65.75 6.3 73 emmajean1-10PPM 19.43 emmajean2-10PPM 16.81 emmajean3-10PPM 0.10 17.97 18.07 4.6 42 emmajean1-25PPM 6.99 emmajean2-25PPM 5.58 emmajean3-25PPM 0.12 5.23 5.94 4.6 19 emmajean1-50PPM 2.95 emmajean2-50PPM 2.53 emmajean3-50PPM 0.12 2.44 2.64 3.8 10 emmajean1-100PPM 1.23 emmajean2-100PPM 1.91 emmajean3-100PPM 0.15 1.72 1.62 3.8 6 emmajean1-150PPM 0.47 emmajean2-150PPM -0.02 emmajean3-150PPM 0.01 -0.21 0.08 3.8 0 emmajean1-200PPM -0.13 emmajean2-200PPM 0.39 emmajean3-200PPM 0.01 -0.12 0.04 3.8 0

A11

Appendix B

Tables for Grain Size, Alkalinity, Hardness and Organic Matter Measurements

B1 [Type a quote from the document or the summary of an interesting point. You can position the text box anywhere in the document. Use the Text Box Tools tab to change the formatting of the pull quote text box.]

Table B1: Soil Grain Size Analysis Results

% d Between % d Below 5.000 μm & % d Above SD d < 5.000 Sd d 5.000 μm - Sample Name1 5.000 μm2 74.000 μm 74.000 μm d (0.5) μ μm 74.000 μm SD > 74.000 μm SD d (0.5) μm Treasure Island A 16.287 47.966 35.747 30.433 Treasure Island B 14.434 45.861 39.705 37.91 Treasure Island C 15.482 50.517 34.001 30.379 Treasure Island Average 15.402 48.117 36.482 32.563 0.929 2.332 2.923 4.333 Bassett Street A 17.192 45.904 36.905 32.655 Bassett Street B 13.752 36.352 49.896 73.62 Bassett Street C 13.778 42.829 43.394 58.304 Bassett Street Average 14.901 41.685 43.414 52.614 1.979 4.876 6.496 20.699 Tiffin Landfill A 18.506 51.682 29.812 32.374 Tiffin Landfill B 17.52 45.244 37.236 39.152 Tiffin Landfill C 21.637 60.108 18.255 22.107 Tiffin Landfill Average 19.224 52.348 28.428 29.052 2.150 7.454 9.565 8.582 Emmajean A 2.404 33.864 63.732 98.485 Emmajean B 2.872 31.706 65.422 103.133 Emmajean C 2.591 32.855 64.554 102.576 Emmajean Average 2.623 32.806 64.571 101.364 0.236 1.080 0.845 2.538

1The lettered results A – C represent triplicate analyses of a single sample. 2d = diameter of soil grain, SD = standard deviation of diameter

Table B2: Soil Alkalinity and Hardness Measurements1

Alkalinity Hardness

Sample2 Vi (ml) Vf (ml) Alkalnity Vi (ml) Vf (ml) Hardness Treasure Island 14 24.2 102 17.3 19.4 42 24.2 34.4 102 19.4 21.55 43 34.5 44.7 102 21.6 23.7 42 102 42.33 Bassett 9.7 16.6 69 3.3 5.2 38 16.6 23.5 69 5.2 7.2 40 23.5 30.4 69 7.2 9.15 39 69 39 Tiffin 15.1 27.3 122 24.6 29.1 90 27.3 39.5 122 29.1 33.5 88 39.5 51.7 122 33.5 37.9 88 122 88.67 Emmajean 3 5 20 0 1.5 30 5 7 20 1.5 3.1 32 7 9 20 3.1 4.7 32 20 31.33

1 Vi = initial volume reading, Vf = final volume reading 2The three sets of results for each site represent triplicate analysis of a single sample.

Table B3: Percent Organic Matter in Soil1

m2 - pan Organic Matter % Organic Sample Name Foil Pan (gm) m1 + Pan (gm) m2 (gm) (gm) m1- pan (gm) (gm) Matter Treasure Island 1.675 23.578 22.836 21.161 21.903 0.742 3.507 Bassett Street 1.695 25.060 24.191 22.496 23.365 0.870 3.866 Tiffin Landfill 1.684 29.033 28.347 26.663 27.349 0.686 2.573 Emmajean 1.699 23.222 21.509 19.810 21.523 1.713 8.646 1Obtained through loss on ignition.

Appendix C

Results of Single Factor ANOVA

Anova: Single Factor

SUMMARY Groups Count Sum Average Variance Perennial 47 493.2089 10.49381 36.03388 Annual 8 105.0523 13.13154 92.55126 Tree/shrub 34 386.7952 11.37633 131.1391

ANOVA Source of Variation SS df MS F P-value F crit Between Groups 52.79356 2 26.39678 0.342246 0.711136 3.102552 Within Groups 6633.008 86 77.128

Total 6685.801 88