Storm Water Runoff First Flush Modeling and Treatment

Home , Effluent, First flush

1 STORM WATER RUNOFF FIRST FLUSH MODELING AND TREATMENT

WITH A HYDRODYNAMIC DEVICE

A dissertation presented to

the faculty of

the Russ College of Engineering and Technology of Ohio University

In partial fulfillment

of the requirement for the degree

Doctor of Philosophy

Yuming Su

March 2007 2 This dissertation entitled

STORM WATER RUNOFF FIRST FLUSH MODELING AND TREATMENT

WITH A HYDRODYNAMIC DEVICE

YUMING SU

has been approved for

the Department of Civil Engineering

and the Russ College of Engineering and Technology by

Gayle F. Mitchell

Neil D. Thomas Professor of Civil Engineering

Dennis Irwin

Dean, Russ College of Engineering and Technology 3 ABST RACT SU, YUMING, Ph.D., March 2007, Integrated Engineering

STORM WATER RUNOFF FIRST FLUSH MODELING AND TREATMENT WITH A

HYDRODYNAMIC DEVICE (233 pp.)

Director of Dissertation: Gayle F. Mitchell

A new hydrodynamic treatment device is developed and tested in this study. The device contains two vertical concentric cylinders designed to intercept and retain sediments, oil and grease, and floatables from storm water runoff. This dissertation presents the findings on the performance of the device using simulated storm water.

Removal characteristics are analyzed. This design provides good removal performance for suspended solids and oil and grease. Other advantages of this device are its compactness and flexibility for configuring in different modes.

Analyzing cost-benefits can provide a reference for engineers and researchers to estimate performance of best management practices (BMPs), conduct storm water management plans, and develop discharge regulations, etc. Two popular mathematical expressions are formulated and then utilized to illustrate the cost-benefit relationships.

Cost-benefit relations for storm water BMPs are conceptually illustrated in a graph and discussed. Suspended solids removals via an ideal settling tank and via the hydrodynamic treatment device are used to illustrate the relationship and effectiveness calculation. Calculated effectiveness coefficients are higher for the hydrodynamic separator compared to an ideal settling tank.

A first order wash-off model is used to evaluate the significance of first flush.

Assuming an exponentially decreasing concentration during a storm event, concentration is modeled as a function of a first flush coefficient (Cff), cumulative runoff depth, and 4 initial pollutant concentration. Pollutant mitigation data from a wetland receiving runoff from a highway section in Ohio were obtained for the first flush analysis. It shows that the first order model and Cff provide a means to quantitatively evaluate the significance of first flush. Moreover, element models are established to analyze the first flush characteristics under complicated runoff situations, such as runoff from a large area, via a detention/retention device and when a constant pollutant source is present. Visual Basic programs are created to conduct the numerical analysis, and it is observed that the significance of first flush tends to be reduced under complicated conditions. This result matches well with the field data and some previous postulations.

Approved:

Gayle F. Mitchell

Neil D. Thomas Professor of Civil Engineering 5 Preface

This quotation is from a famous Chinese book, Caigen Tan, written by an ancient intellectual in the Ming Dynasty, Hong Yingming (1560~1615). This book is a combination of Chinese philosophy of Buddhism, Daoism and Confucianism.

While accurately translating the above saying is not feasible, a straightforward translation by the author of this dissertation is included below:

“Only the one, who can appreciate the nature and understands its philosophy, can pursue the nature’s scheme without disturbing its consistency;

Only the one, who can follow the nature’s rhythm and obey the rules of the nature, can accomplish a harmonious and successful life.”

-Hong Yingming, 1560~1615, Caigen Tan (in Chinese)

The philosophy of this quote is that a human being is only a trivial creature of this universe. We start from nothing by borrowing our physical entity from Mother Nature, and end by returning everything back to her. The art of living should be a combination of following the rhythm of nature, caring the harmony, understanding nature, leaving everything intact unless necessary, fixing all the disturbance, and returning all what you borrowed.

All of these, I found, is not only the art of living, but also the base of environmental engineering – eternal synchronization between human and nature. 6 Acknowledgments

This dissertation and the work are dedicated to my father, Kaizi Su, and my mother, Guifen Li, for their loyal support, guidance and love. The author would also like to acknowledge Liangliang Zhou, for her love and sincere encouragement.

Thank you to Dr. Gayle F. Mitchell for guiding me and for the great deal of effort and support she gave to this project as a great advisor.

Special thanks go to the rest of my graduate committee Dr. Ben J. Stuart, Dr.

Dusan Sormaz, Dr. Shad M. Sargand, Dr. Teruhisa Masada, and Dr. Tingyue Gu for their technical review of this dissertation and their thoughtful insights.

Table of Contents

Page

Abstract ……………………………………………………………………………………………….…...3

Preface...... 5

Acknowledgments...... 6

List of Tables ...... 12

List of Figures...... 13

CHAPTER 1 Introduction...... 19 1.1 Project Introduction ...... 19 1.2 Outline of Work Performed ...... 20 1.3 Objectives of the Study...... 21 1.4 Outline of Dissertation...... 21 1.5 Copyright Information ...... 22

CHAPTER 2 Review of Storm Water Literature...... 24 2.1 Scope of Storm Water Runoff Issues...... 24 2.1.1 Impacts of Land Development...... 24 2.1.2 Pollutants in Storm Water...... 25 2.2 Storm Water Runoff from Highways and Bridges ...... 27 2.2.1 Highway Runoff...... 27 2.2.2 Bridge Deck Runoff...... 27 2.3 Storm Water Runoff Treatment and Best Management Practices ...... 28 2.3.1 Storm Water Regulations...... 28 2.3.2 Definition of Best Management Practices ...... 30 2.3.3 Structural Best Management Practices ...... 30 2.3.4 BMP Performance Evaluation ...... 31 2.4 Hydrocyclone and Applications...... 35 2.4.1 Hydrocyclone...... 35 2.4.2 Hydrocyclone Applications and Manufacturing...... 36 2.4.3 Hydrocyclone Separation Modeling ...... 38 2.5 First Flush Definition and Characteristics ...... 38 2.5.1 General Definitions...... 38 2.5.2 Existence of First Flush ...... 38 8

2.5.3 First Flush Variations...... 39 2.5.4 Evaluation of Significance of First Flushes...... 40 2.5.5 Storm Water Management Model...... 42 2.5.6 Existing Issues ...... 45

CHAPTER 3 Overview of Hydrodynamic Structural BMP Designs ...... 47 3.1 Existing Hydrodynamic Devices ...... 47 3.1.1 Downstream Defender by Hydro International...... 47 3.1.2 CDS Storm Water Treatment Unit...... 48 3.1.3 Hydroworks...... 50 3.1.4 VortSentry by Stormwater 360 ...... 52 3.2 New Hydrodynamic Treatment Device ...... 53 3.3 Summarization of Hydrodynamic Treatment Device Designs ...... 54

CHAPTER 4 Hydrodynamic Device Design Theory ...... 55 4.1 Description of the Device ...... 55 4.1.1 Basic Design Configuration...... 55 4.1.2 Internal Components...... 55 4.1.3 Installation Configuration Overview ...... 60 4.1.4 Pipe Connections ...... 62 4.2 Flow Regime Features ...... 63 4.3 Design Advantages and Disadvantages ...... 64 4.3.1 Advantages of New Design ...... 64 4.3.2 Disadvantages of New Design...... 65

CHAPTER 5 Storm Water Runoff Test Methodologies...... 67 5.1 Methodologies of Laboratory Tests...... 67 5.1.1 Description of Laboratory Scale Model...... 67 5.1.2 Laboratory Apparatus Set Up ...... 69 5.1.3 Descriptions of Simulated Storm Water Constituents ...... 70 5.1.4 Laboratory Tests ...... 73 5.1.5 Analysis Methods...... 76 5.2 Methodologies of Field Tests...... 77 5.2.1 Site Description...... 77 5.2.2 Sample Analysis Methods Used from Bridge Runoff Analysis ...... 81

CHAPTER 6 Laboratory Test Results and Discussions ...... 84 6.1 Rotation Flow Velocity Measurements ...... 84 6.2 Suspended Solids Tests...... 85 6.2.1 Sediment Capacity Determination ...... 85 9

6.2.2 Scouring Tests...... 87 6.2.3 Suspended Solids Removal...... 90 6.2.4 Suspended Solids Removal versus Particle Sizes...... 92 6.3 Oil Removal Tests...... 96 6.4 Oil Adsorption Tests...... 97

CHAPTER 7 Field Site Description and Installation Plan...... 101 7.1 Storm Water Runoff Characteristics...... 101 7.2 Description of Installation Plan ...... 102 7.3 Further Performance Evaluation Studies ...... 106

CHAPTER 8 BMP Cost-Benefit Analysis and Certainty Principle...... 107 8.1 Storm Water Cost-Benefit Concepts...... 107 8.1.1 Introduction...... 107 8.1.2 BMP Costs ...... 107 8.1.3 BMP Benefits...... 107 8.2 Methodologies...... 108 8.2.1 Simplification of the Problem...... 108 8.2.2 Conceptual SS Removal Mechanisms ...... 108 8.2.3 SS Separation via Ideal Setting Tank...... 111 8.2.4 SS Separation via Hydrodynamic Device...... 112 8.3 Cost-Benefit Relations and Certainty Principle...... 113 8.3.1 BMP Certainty Principle...... 113 8.3.2 BMP Cost-Benefit Relations...... 114 8.3.3 Cost-Benefit Relation Expressions ...... 116 8.4 BMP Removal - Surface Area Analysis ...... 119 8.4.1 Removal - Surface Area Analysis Example...... 119 8.4.2 Removal - Surface Area for Sizing of BMPs...... 122

CHAPTER 9 First Order Modeling of Runoff First Flushes ...... 124 9.1 Structure of the Modeling ...... 124 9.2 First Order Wash-off Model Application and Validation...... 125 9.3 Characteristics of First Flush with Continuous Sources...... 128 9.3.1 Modeling Methodology ...... 130 9.3.2 Characteristics of First Flush Effects with Continuous Sources...... 132 9.4 Characteristics of First Flush for a Large Drainage Area...... 133 9.4.1 Modeling Methodology ...... 135 9.4.2 Characteristics of First Flush Effects from a Large Drainage Area...... 140 9.4.3 Area Runoff Model Application Example...... 147 10

9.5 Characteristics of First Flush via a Retention/Detention Device...... 150 9.5.1 Modeling Methodology ...... 151 9.5.2 Conceptual Simulation via a CSTR or PFR...... 153 9.5.3 R-D Model Validation: First Flush Reduction via a Wetland ...... 163 9.6 First Flush Characteristics...... 166 9.6.1 Summarization of First Flush Effects via Different Influences...... 166 9.6.2 General First Flush Characteristics...... 167

CHAPTER 10 Conclusions and Recommendations ...... 169 10.1 Conclusions...... 169 10.1.1 Applicability of the Treatment System...... 169 10.1.2 Cost Benefit Analysis ...... 170 10.1.3 First Flush Modeling and Characterization...... 170 10.2 Recommendations...... 172 10.2.1 Hydrodynamic Treatment Device...... 172 10.2.2 Cost-Benefit Analysis ...... 172 10.2.3 First Flush Modeling...... 172

REFERENCES ...... 174

APPENDIXES ...... 184

APPENDIX 1 Precipitations Frequency And Site Plan...... 184

APPENDIX 2 Sediment Storage Capacity Calculation...... 188

APPENDIX 3 Field Device Fabrication Drawings (Patent Pending)...... 191

APPENDIX 4 Modified First Order Wash-off Model and Build-up Model ...... 207 A4. 1 First Order Wash-off Model ...... 207 A4.1.1 Concentration versus Runoff Volume ...... 208 A4.1.2 Normalized Cumulative Mass versus Normalized Cumulative Volume 211 A4.1.3 Finding the Cff and Kw ...... 213 A4.1.4 First Flush Point (FFP)...... 217 A4.1.5 Summarization of First Flush Parameters...... 219 A4. 2 First Order Build-up Model ...... 221 A4. 3 Correlation of Drainage Mass and Runoff Concentration ...... 224

APPENDIX 5 Mathematical Derivations ...... 225 Appendix 5.1 CSTR Outlet Concentration - Analytical Solution...... 225 Appendix 5.2 CSTR Peak Concentration Calculation...... 228 11

Appendix 5.3 Derivation of S from NCM-NCV ...... 230 Appendix 5.4 First Flush Coefficients vs. b ...... 231 Appendix 5.5 Derivation of First Flush Point (FFP) ...... 232

List of Tables Page

Table 2-1 Summary of Urban Stormwater Pollutants (AMEC Earth and Environmental et

al., 2001) ...... 26

Table 3-1 Comparisons of Hydrodynamic Treatment Devices ...... 54

Table 5-1 Major Dimensions of the Lab Test Model ...... 69

Table 5-2 Soil Properties ...... 72

Table 6-1 Comparison of Scouring Resistance Characteristics...... 89

Table 7-1 Bridge Runoff Constituent Concentrations ...... 101

Table 7-2 Components of the Field Treatment Device...... 106

Table 8-1 Summarization of Effectiveness Coefficients ...... 122

Table 9-1 Nomenclatures and Abbreviations in Section 9.3 ...... 128

Table 9-2 Nomenclatures and Abbreviations in Section 9.4 ...... 134

Table 9-3 Calculation Example, Major Parameters in the Calculation ...... 148

Table 9-4 Nomenclature and Abbreviations in Section 9.5...... 150

Table A4-1 List of Nomenclatures and Abbreviations in Appendix 4 ...... 208

Table A4-2 Summarization of Runoff Properties with Different Cff Values...... 220

List of Figures Page

Figure 2-1 Typical Hydrocyclone Separation (FloTrend Systems, Inc. 2006)...... 35

Figure 3-1 Downstream Defender (Hydro International, 2006) ...... 48

Figure 3-2 CDS Storm Water Unit (CDS, 2006)...... 49

Figure 3-3 Hydroworks (Hydroworks Inc, 2006)...... 51

Figure 3-4 VortSentry by Vortechnics (Vortechnics, 2006)...... 52

Figure 3-5 Conceptual Description of the Device (Su and Mitchell, 2005) ...... 53

Figure 4-1 Sediment Baffle Plate Design (Su and Mitchell, 2005) ...... 56

Figure 4-2 Illustration of Inlet Baffle Plate Design (Su and Mitchell, 2005)...... 57

Figure 4-3 Integrated Bypass Design...... 58

Figure 4-4 Screening and Metal Adsorption Design ...... 60

Figure 4-5 Schematic Example - Series Installation...... 61

Figure 4-6 Schematic Example - Parallel Installation ...... 61

Figure 4-7 Schematic Example of Pretreatment/Bypass Application...... 62

Figure 4-8 Schematic Example of Offline Application ...... 62

Figure 4-9 Schematic Illustration of Connection Configurations...... 63

Figure 5-1 Lab Test Model (Left: Plan View; Right: Profile View) ...... 67

Figure 5-2 Dimensions of Laboratory Test Model ...... 68

Figure 5-3 Laboratory Apparatus Set Up...... 70

Figure 5-4 Particle Size Distributions of Soil Samples (by Volume)...... 71

Figure 5-5 Soil Classification Using USDA Diagram...... 71 14

Figure 5-6 Simulated Dynamic Flow for Oil Adsorption...... 76

Figure 5-7 USGS Satellite Map of Bridge Runoff Site ...... 79

Figure 5-8 Drainage Surface and Existing Runoff Pattern ...... 79

Figure 5-9 Illustration of Highway Runoff Site (Su and Mitchell, 2006b) ...... 80

Figure 6-1 Angular Speed versus Flow Rates...... 84

Figure 6-2 Sediment Accumulation, Profile View ...... 86

Figure 6-3 Sediment Topography ...... 87

Figure 6-4 Scouring Test Results...... 88

Figure 6-5 Scouring and Sediment Resuspension at 63 L/min (Right: No Baffle Plate;

Left: With Baffle Plate) ...... 90

Figure 6-6 Steady State SS Removal versus Flow Rates, Soil #1 ...... 91

Figure 6-7 Steady State SS Removal versus Flow Rates, Soil #3 ...... 91

Figure 6-8 Steady State SS Removal versus Flow Rates, Soil #4 ...... 92

Figure 6-9 Removal vs. Particle Sizes ...... 94

Figure 6-10 VortSentry Removal vs. Particle Size Distribution (Allen, 2004)...... 95

Figure 6-11 Forces on Oil Droplet and Soil Particle ...... 97

Figure 6-12 Oil Removal Jar Test...... 98

Figure 6-13 Oil Leaching Observation ...... 99

Figure 6-14 Absorbency under Dynamic Flow Conditions...... 100

Figure 7-1 Particle Size Distribution of Runoff Samples (by Volume)...... 102

Figure 7-2 Conceptual Illustration of Installation...... 103

Figure 7-3 Schematic Overview of Manhole and Treatment Device ...... 103 15

Figure 7-4 Overview of Manhole Unit ...... 104

Figure 7-5 Conceptual Overview of the Treatment Unit...... 105

Figure 8-1 Illustration of Limitations of Separation Methods...... 109

Figure 8-2 SS Removal via an Ideal Settling Tank...... 111

Figure 8-3 Conceptual BMP Cost-Benefit Analyses...... 115

Figure 8-4 Removal vs. Normalized Surface Area- Bridge Site PSD ...... 120

Figure 8-5 Removal vs. Normalized Surface Area - PSD from National Urban Runoff

Program Study ...... 120

Figure 8-6 Determination of Effectiveness Coefficient k Using PSD from Bridge Deck

Runoff ...... 121

Figure 8-7 Determination of Effectiveness Coefficient k’ Using PSD from National

Urban Runoff Program Study ...... 121

Figure 8-8 Flow Diagram of BMP Sizing...... 123

Figure 9-1 Cff Variations for Various Constituents...... 126

Figure 9-2 Average Cff and Kw for Various Constituents ...... 126

Figure 9-3 Normalized Cumulative Mass versus Normalized Cumulative Depth Using

Mean Cff Values ...... 127

Figure 9-4 First Flush with Continuous Source...... 129

Figure 9-5 First Order Wash-off Model Approach...... 130

Figure 9-6 First Flush Coefficient vs. Continuous Source Ratio...... 132

Figure 9-7 Kc vs. Continuous Source Ratio ...... 133

Figure 9-8 Element Mesh for Slim Area Model ...... 135 16

Figure 9-9 Element Mesh for Round Area Model...... 136

Figure 9-10 FirstFlush Area 1.0 User Interface ...... 139

Figure 9-11 Normalized Flow Rates versus Normalized Time ...... 140

Figure 9-12 Characteristics of Concentration Changes (Slim Area) - Normalized

Concentration versus Normalized Cumulative Volume ...... 142

Figure 9-13 Characteristics of First Flushes (Slim Area) - Normalized Cumulative Mass

versus Normalized Cumulative Volume...... 142

Figure 9-14 Ka versus ra for a Slim Area...... 145

Figure 9-15 Ka versus ra for a Slim Area (Known Cff-area to Find Unknown Ka and Cff-

element)...... 145

Figure 9-16 Ka versus Collection Time Ratio for a Round Area (Known Cff-element to Find

Unknown Ka and Cff-area)...... 146

Figure 9-17 Ka versus Collection Time Ratio for a Round Area (Known Cff-area to Find

Unknown Ka and Cff-element) ...... 146

Figure 9-18 Calculation Example for Dissolved Solids (Data Source from March 23,

1981) ...... 149

Figure 9-19 Calculation Example for Zinc (Data Source from March 23, 1981)...... 149

Figure 9-20 Illustration of CSTR and PFR Applications in First Flush Analysis ...... 151

Figure 9-21 FirstFlush RD VB Program Interface ...... 153

Figure 9-22 Left: Normalized Concentration vs. Normalized Cumulative Runoff Volume;

Right: Normalized Cumulative Mass vs. Normalized Cumulative Runoff Volume,

(rv =0.1; Rf =50%; Cff-inlet =1.0; Cinitial/C0=0.1) ...... 155 17

Figure 9-23 First Flush Reduction via a PFR ...... 157

Figure 9-24 First Flush Reduction via a CSTR ...... 158

Figure 9-25 Kv vs. rv for PFR...... 159

Figure 9-26 Kv vs. rv for CSTR ...... 159

Figure 9-27 Illustration of CSTRs in Series in FF Analysis...... 161

Figure 9-28 First Flush Reduction via Two CSTRs in Series ...... 162

Figure 9-29 Kv vs. rv for 2-CSTR in Series ...... 162

Figure 9-30 Comparison of CSTR, PFR and 2-CSTR in Series...... 163

Figure 9-31 Average First Flush Coefficients at the Wetland Inlet and Outlet...... 164

Figure 9-32 Distribution of Cff at the Wetland Inlet and Outlet ...... 165

Figure 9-33 Comparison of Cff at the Wetland Inlet and Outlet ...... 166

Figure 9-34 Summarization of First Flush Influencing Factors...... 167

Figure A1-1 Location of the Proposed Site for Precipitation ...... 184

Figure A1-2 Site Location from U.S. Census Bureau Maps and Cartographic Resources,

2006...... 185

Figure A1-3 Water Shed Information from U.S. Environmental Protection Agency

(USEPA, 2006a) ...... 186

Figure A1-4 West Union Bridge Existing Site Plan (Burgess & Niple Engineers

Architects, 1999)...... 187

Figure A4-1 Normalized Concentration versus Normalized Cumulative Depth...... 210

Figure A4-2 NCM-NCV Plot with Different Cff Values...... 212

Figure A4-3 Determination of Cff from Concentration Data ...... 214 18

Figure A4-4 Fe Concentration vs. Normalized Cumulative Depth ...... 215

Figure A4-5 Fe Normalized Cumulative Mass vs. Normalized Cumulative Depth...... 215

Figure A4-6 FFP Determination Example (Cff = 2.0) ...... 218

Figure A4-7 Summarization of Creating the First Order Wash-off Model ...... 220

Figure A4-8 Illustration of Equivalent Antecedent Dry Days and Overall Antecedent Dry

Days ...... 222

Figure A4-9 Mass/Maximum Mass vs. Total Antecedent Dry Days...... 223

CHAPTER 1 INTRODUCTION

1.1 Project Introduction

As a result of increased urbanization, adverse impacts associated with storm water runoff to receiving water bodies have become apparent. Best Management Practices

(BMPs) have been studied by many researchers and companies to fulfill the requirements of storm water regulations such as the U.S. EPA Phase I rules in 1990 and Phase II rules in 1999 (USEPA, 1996; and USEPA, 2000). Properly designed BMP devices can effectively reduce the pollutant concentration and minimize environmental impacts via pollutant removal principles such as filtration, sedimentation and adsorption, etc. With increasing governmental regulations, devices that can mitigate pollutants such as suspended solids and oil and grease are in demand.

A structural unit employed as a BMP for mitigation of storm water runoff can be considered as a unit for intercepting and separating pollutants. The receiving environment benefits from pollutant reductions, while costs are unavoidable for the design, fabrication, installation, and maintenance of the unit. Evaluation and comparison of the overall cost-benefits of various structural BMPs would be helpful to regulators, designers and BMP selectors.

Understanding runoff quantity, especially the property of the peak pollutant concentration, is a necessary component in storm water management. Typically, the peak concentrations of contaminants in storm water runoff occur during the initial stage, known as the “first flush”, which contains a large percentage of total pollution in a 20

relatively small percentage of runoff volume. To provide useful statistical analysis, a meaningful model and quantitative definition of “first flush” is needed. Moreover, analysis of first flush and its influencing factors are also important steps to the storm water modeling and treatment designs.

1.2 Outline of Work Performed

This dissertation presents information on developing the hydrodynamic device for treatment of storm water runoff. In this study, a new hydrodynamic flow-through device was developed and tested. The device contains two vertical concentric cylinders designed to intercept and retain sediments, oil and grease, and floatables from storm water runoff (Su and Mitchell, 2005). The performance of a laboratory scale device was evaluated using simulated storm water. And, the initial design of a full scale field treatment device is proposed for a bridge runoff site.

This dissertation presents information on conceptual cost and benefit of BMP application for storm water runoff treatment. Exponential and power expressions were formulated and then utilized to illustrate the cost-benefit relationships. Suspended solids removals via an ideal settling tank and the hydrodynamic treatment device were modeled to illustrate the relationships and their effectiveness (Su and Mitchell, 2006a).

A storm water runoff first flush model is further modified to quantitatively analyze first flush effects and their influencing factors. Pollutant mitigation data from a wetland receiving runoff from a highway section in previous study were obtained for the first flush analysis. Then, based on the first order wash-off model, element models were established to analyze the first flush characteristics under complicated runoff situations, 21

such as runoff from a large area, via a detention/retention device and when a constant pollutant source is present (Su 2002; Su and Mitchell, 2004; and Su and Mitchell, 2006b).

1.3 Objectives of the Study

Specific objectives of the hydrodynamic device design are as follows: 1) investigate the efficiency of a laboratory scale model in removing suspended solids and oil from simulated storm water; 2) investigate oil removal with adsorbent materials; 3) recommend design modifications to enhance the efficiency of the unit; and 4) based on laboratory results and modeling, conduct initial design of a field unit.

Specific objectives of cost-benefit analysis are as follows: 1) categorize and compare conceptual costs and benefits of treatment mechanisms for prevalent structural

BMPs; 2) plot and discuss typical conceptual cost-benefit curves; 3) simplify the cost- benefit analysis using suspended solids (SS) removal versus normalized surface area; and

4) develop mathematical expressions to illustrate the conceptual cost-benefit relationships.

The specific objectives of first flush modeling include: 1) investigate and model the constituent wash-off and build-up processes; 2) analyze first flush characteristics based on first order modeling; and 3) analyze first flush reduction under complicated conditions, such as runoff from a large area, via a detention/ retention device and when a constant pollutant source is present.

1.4 Outline of Dissertation

Chapter 1 of this dissertation introduces the runoff problem and objectives for this study. Chapter 2 is the summarization of literature of previous research and findings on 22

storm water runoff relative to this study. Chapter 3 introduces and provides discussions of several commercially available treatment facilities. Chapter 4 provides a detailed discussion of the hydrodynamic device development and design. Chapter 5 includes the methodologies for the laboratory and field research. Chapter 6 presents and discusses the results of tests on a laboratory scale hydrodynamic treatment device. Chapter 7 presents site information and details of a proposed field scale system which is to be installed at a bridge site. Chapter 8 introduces a conceptual analysis of cost-benefit relationships for storm water runoff treatment. Chapter 9 presents runoff quality modeling and the analysis of the first flush reduction under different conditions. Conclusions and recommendations appear in Chapter 10.

1.5 Copyright Information

The hydrodynamic treatment device presented in this study is U.S. utility patent pending (July 18, 2005 ~) and is under the protection of patent laws. For further patent information, please contact the inventors or patent office:

1) Yuming Su, P.E., Email: [email protected], Homepages: http://yumingsu.com and http://StormBMP.com, Phone: (770) 653- 4005;

2) Gayle F. Mitchell, Ph.D., P.E., Email: [email protected], Phone:

(740) 593-1470; and/or

3) Ohio University Technology Transfer Office: Robert S. Malott, [email protected], (740) 593-4167. 23

Programs created in this dissertation (First Flush Area 1.0 - see Section 9.4 and

First Flush RD 2.0 - see Section 9.5) can be either downloaded from the following website or by contacting the author:

1) Web URL: http://yumingsu.com/;

2) Email: Yuming Su .

CHAPTER 2 REVIEW OF STORM WATER LITERATURE

2.1 Scope of Storm Water Runoff Issues

Great efforts were taken in the U.S. to reduce the point-source pollutants in the municipal and industrial wastewaters since the Clean Water Act Amendments in 1972.

Storm water concerns were mainly on storm water drainage issues before the 1960s’

(USEPA, 1983). Besides the runoff quantity concerns, storm water runoff may wash off various pollutants such as debris, chemicals, soil, and other pollutants that accumulated on surfaces and from the surrounding atmosphere. When the pollutants enter the receiving water body, adverse environmental effects may occur. In management of storm water increased emphasis is now being placed on water quality in addition to water quantity (USEPA, 1983; and Hunt et al., 2002a).

2.1.1 Impacts of Land Development

Land development activities have profound impacts to the natural storm water cycle. Those impacts, such as reduced vegetation, increased impervious areas, and reduced natural land depressions and water storages, can result in increased runoff volume, lessened runoff infiltration and increased runoff flow velocity across the land.

Common runoff quantity related land development impacts include flooding issues and reduced surface water recharge to the aquifer (USEPA, 2005).

Moreover, land development also has significant impacts on runoff quality.

When runoff runs across the land surface, it washes off and carries away various natural and human-made pollutants. Raised magnitude of pollution (oil and grease, metals, 25

pesticides, etc.) and reduced natural baffle measures (such as losing of vegetation and natural wetlands) resulted in more serious storm water related environmental issues in the recent decades (USEPA, 2005; and AMEC Earth and Environmental, et al. 2001, Center for Watershed Protection, 1998).

Numerous studies have shown that urban runoff, as the major non-point source, accounts for a significant percentage of various pollutants (USEPA, 1984 and Walker,

1999). Currently, the non-point source is the largest contribution to the nation’s water quality problems. It is estimated that nationwide over half the pollutants in streams and lakes are transported by storm water runoffs (USEPA, 1984).

2.1.2 Pollutants in Storm Water

Major pollutants found in urban runoffs include but are not limited to: sediment, oxygen-demanding substances, nutrients, hydrocarbons, toxic substances (metals and some chemical compounds), bacteria and floatable debris (Dennison, 1996; and Barret et al., 1996). Table 2-1 summarizes the major stormwater pollutants and their effects. 26

Table 2-1 Summary of Urban Stormwater Pollutants (AMEC Earth and Environmental et al., 2001)

Among all the pollutants, suspended solids are considered one of the major pollutants in many studies. Soil particles may settle out of natural water bodies onto aquatic plants, thus devastating the aquatic environment. Also, excessive suspended solids in the water may prevent sunlight from reaching aquatic plants, choke fish and other organisms, etc. (USEPA, 1995).

2.2 Storm Water Runoff from Highways and Bridges

2.2.1 Highway Runoff

Federal Highway Administration (1984) and Stahre and Urbonas (1993) studied the primary pollutants and pollutant sources in the highway storm water runoff. Some common highway runoff constituents and their main sources are: particulates from atmosphere deposited on the pavement; nitrogen and phosphorus from roadside fertilizations; heavy metals from vehicle wear; sodium and chloride from deicing activities; oil and grease from spills and leak of lubricants; pathogens from soil litter and bird dropping; rubber from tire wear. Similar pollutants and associated sources were also identified in the study by Barret et al. in 1993.

The magnitudes of constituents, however, are highly site-specific because of the variable traffic characteristics, climatic conditions, maintenance policies, surrounding land use, percent pervious and impervious areas, age and condition of vehicular operation, vegetation types and accidental spills, etc. (Yu, 1993). Since there are so many factors influencing the runoff quality, Hamilton and Harrison (1991) indicated that highway runoff quality was very difficult to predict.

2.2.2 Bridge Deck Runoff

Storm water/snowmelt runoff from bridges contains constituents such as suspended solids, oil and grease, metals, deicers, paint debris, and other material.

Scupper drains can keep excess runoff off the bridge, but several studies have shown that direct scupper drainage to some receiving waters (e.g., small lakes) can lead to localized increases in certain pollutant concentrations (Marsalek et al., 1997; and McClure, 2003). 28

The U.S. EPA has recommended management practices that restrict the use of scupper drains on bridges less than 400 ft in length, and on bridges crossing very sensitive ecosystems (USEPA, 1993).

Basically, bridge runoff treatment can be categorized into off-bridge or on-bridge treatment. In the off-bridge method, “Runoff is directed off the end of the bridges so overland flow can remove some pollutants, and there is an opportunity to contain the runoff of any accidental spills” (Minnesota DOT, 2002). Drainage to land allows some form of passive or active (via a broad range of Best Management Practices) improvement of the storm water.

When the topography, length, and slope at some bridge locations preclude design or retrofit for gravity drainage back to land, or when sensitive ecosystems need protection, on-bridge treatment becomes important (Dupuis, 2002). Some potential locations to install on-bridge treatment facilities are beside the bridge, under the bridge, or on the bridge post or other structural members. In some cases, more compact and light weight treatment units should be considered to accommodate bridge site conditions (Su and Mitchell, 2004).

2.3 Storm Water Runoff Treatment and Best Management Practices

2.3.1 Storm Water Regulations

In 1972, U.S. Congress amended the Federal Water Pollution Control Act (also referred to as the Clean Water Act). All point source discharges to waters of the United

State are required to have authorization from the National Pollution Discharge

Elimination System (NPDES). Great efforts have been taken on reducing point source 29

discharges and pollutant load to waters under the NPDES program have been controlled.

However, past efforts were mainly limited to industrial process wastewater and municipal sewer treatment plants. Storm water discharges, although contributing significant amount of total pollutant loads, were little regulated (USEPA, 1996; and USEPA, 2000).

In response to this need, U.S. Congress required the U.S. Environmental

Protection Agency (USEPA) to establish phased NPDES for storm water discharges in

1987. And, regulations such as the USEPA Phase I and Phase II rules have been established to reduce adverse environmental impacts by instituting controls on storm water discharges (USEPA, 1996; and USEPA, 2000).

The EPA Phase I rule (55 FR 47990), published on November 16, 1990, requires permit applications for storm water discharge associated with 1) eleven categories of industrial activity which includes construction activity that disturbs five or more acres of land, and 2) storm water discharge from municipal separate storm sewer systems (MS4) severing population 100,000 and more (USEPA, 1996). Moreover, on August 7, 1995,

USEPA promulgated Phase II rule (60 FR 40229, also referred as Phase II Final Rule) further addressing the permit coverage to 1) construction activity disturbing between one and five acres of land (i.e., small construction activities), and 2) certain regulated small municipal separate storm sewer systems serving population less than 100,000 (USEPA,

2000). With increasing storm water regulations, treatment devices that can mitigate pollutants are in demand.

2.3.2 Definition of Best Management Practices

The primary method to control storm water discharges is through the applications of Best Management Practices (USEPA, 2002). Best Management Practices (BMPs) in storm water treatment are defined as “Activities or structural improvements that help reduce the quantity and improve the quality of stormwater runoff” (Forester, 2006).

D'Arcy and Frost (2001) defined BMPs as “measures intended to provide an on-the- ground practical solution to diffuse pollution problems from all sources and sectors”.

USEPA identified six major BMP measures under the National Pollution

Discharge Elimination System program in the National Menu of Stormwater Best

Management Practices: 1) Public Education and Outreach; 2) Public

Participation/Involvements; 3) Illicit Discharge Detection/Elimination; 4) Construction

Site Runoff Control; 5) Post-Construction Runoff Control; and 6) Pollution

Prevention/Good Housekeeping (USEPA, 2002). Besides non-structural management activities, most structural BMP measures fall in category four and five. In an effort to minimize storm water runoff pollution, conventional water/waste water treatment processes and operations have been applied, and many proprietary treatment devices have been introduced.

2.3.3 Structural Best Management Practices

Typical structural practices include detention, dry, extended dry and wet ponds; infiltration trenches and basins; porous pavement; vegetative filter strips/grassed swales; wetlands; and various fabricated devices/processes (USEPA, 1999). 31

USEPA conducted a study on Preliminary Data Summary of Urban Storm Water

Best Management Practices in 1999, in which structural BMPs were categorized as: 1) infiltration systems such as infiltration basins and porous pavement; 2) detention systems such as basins and underground vaults; 3) retention systems such as wet ponds and constructed wetland systems; 4) filtration systems such as media filters and bioretention systems; 5) vegetated systems such as grass filter strips and vegetated swales; 6) minimizing directly-connected impervious surfaces; and 7) miscellaneous and vendor- supplied systems such as oil/water separators and hydrodynamic devices. USEPA also noted in the study that some BMPs are more frequently used, while others are new and less widely used (USEPA 1999).

Although a wide variety of BMPs are available to address storm water runoff treatment, selecting structural BMPs is highly site-specific to factors such as cost, pollutant characteristics, performance, suitability to site, and etc. EPA also concluded that realizable performance data and widely accepted data interpretation methods are needed in the storm water runoff studies (USEPA 1999).

2.3.4 BMP Performance Evaluation

Throughout the storm water BMP literature, a reliable, independent, credible and commonly accepted method is needed to evaluate the performances of BMPs to make informed technology decisions. One approach is the Environmental Technology

Verification (ETV) Program through NSF International (NSF) and EPA started in 1995.

The ETV Program develops testing protocols and verifies the performance of innovative technologies through performance verification and information dissemination (USEPA 32

2006b; USEPA 2006a; and NSF International 2006). The main elements of ETV storm water BMP protocol includes: 1) minimum 15 qualified sampling event; 2) minimum 5 subsamples via automatic composite sampling; and 3) pollutant list based on vendor claims, which typically may include solids, nutrients, heavy metals, petroleum/ hydrocarbons and microbiological/ bacteria. NSF and EPA continuously improved the protocols and the latest Draft 4.1, evolved from several earlier versions of the original protocol, was released in March, 2002 (USEPA and NSF International, 2002). Major modifications in the latest version includes: 1) adding a requirement of suspended sediment concentration as a measure of solids load; 2) including sand/silt split; 3) provision of additional guidelines on proper use of automated samplers and sample splitting; 4) permitting the analysis of captured sediment/pollutants; and 5) improving guidance on sampling and lab Quality Assurance (USEPA and NSF International, 2002; and Hackett et al. 2003).

The ETV program creates one good protocol and guidance for BMP performance evaluation to minimize the variations and errors during sampling and sample analysis.

The ETV program has been used in the performance evaluation studies in both conventional BMPs such as detention ponds, grassy waterways, and vendor-supplied systems (Conlon, 2006). However, improvements to the protocol are still needed. For example, although sand/silt split was emphasized in the updated version, the full particle size distribution analysis is not required. Moreover, since BMP performance is highly site specific (such as rainfall pattern, rainfall duration, antecedent dry days, etc.) further studies on a credible method to compare different ETV results at different sites are 33

needed. The program is time consuming, and only a portion of structural BMP manufacturers have conducted the ETV program. The ETV only provides an approach for field evaluation of BMPs, however, no laboratory test standard was provided.

Besides the ETV program, several researchers and research institutes conducted more comprehensive third-party BMP performance evaluations and verifications protocols. In some studies, side-by-side comparison was conducted to eliminate the discrepancy brought by site condition variation (Nnadi et al. 2005; and Roseen et al.,

2006). Conventional BMPs (sand filter, retention pond, bioretention unit, etc.) and proprietary BMPs (Contech Stormwater Solutions, ADS, CDS, Environment 21,

AquaShield, etc.) were installed at the storm drain of a 9.0 acre commuter parking lot site at the University of New Hampshire. Effluents from the site were evenly distributed and converted into various BMPs to create identical test conditions. Automatic samples were taken at both the inlet and outlet of various BMPs and side-by-side BMP performance data were recorded to identify runoff and pollutant removal characteristics (Rossen et al.,

2005). In this side- study, seasonal variations were observed for most conventional and proprietary BMPs. Although various performances were observed, infiltration and filtration systems generally have the highest removal efficiency (Rossen et al., 2005).

University of Central Florida also conducted a side-by-side study to determine the relative pollutant removal effectiveness of three proprietary BMPs (Baysaver, CDS, and

Stormceptor) (Nnadi et al. 2005). A controlled laboratory condition was used and the setup of the facility has the ability to pump required flows and split identically to three

BMPs to replicate runoff events. Various flow rates, constituents, particle sizes, and 34

sediment concentrations were simulated by mixing constituents and water prior to pumping to the treatment systems. Samples were taken at the inlet and outlet of the BMPs for sediments (total suspended solids, total dissolved solids, and bed load), nutrients

(nitrogen and phosphorous), metals (Fe, Cu, Zn, Cr, Ni, and Cd), and litters. Nnadi et al.

(2005) discovered that all the units had good performance in removing the coarse sediment particles but the overall removal of solids is low. In this study, CDS and

Stormceptor units exhibited higher removal efficiencies than Baysaver unit.

Besides ETV protocol and side-by-side BMP studies, many proprietary BMP vendors and research institutes also conducted performance evaluations using various approaches. Moll conducted performance variations on Crystal Stream Treatment

Devices (CST) (Moll 2006; and Moll 2005) by monitoring sediment retained in the CST units from various sites; Tonto (2005) and Lee et al. (2005) studied the performances of

Stormceptors units by laboratory tests on full scale models under various controlled flow and mass load conditions. More BMP performance evaluation studies were conducted via field installations and investigations, such as CDS Treatment Device performance investigation on a bridge runoff site (Pathapathi and Sansalone, 2005); Contech media filters field evaluations on a roadway site (Ridder, 2006); and ADS Water Quality Unit field study on a parking lot runoff site (Su, 2002).

Through all the BMP performance evaluation literatures, the experimental set-ups, site selections, runoff characteristics, sampling methodologies, sample analysis and data interpretations vary from one to another. Comparing BMP performances between different studies is very difficult. 35

Moreover, even fewer standards and protocols are available for laboratory BMP evaluations. After reviewing a wide range of performance standards developed across the country, Mailloux (2006) concluded that laboratory-testing protocols, such as those developed by ASTM for other industries, are incomplete for storm water applications.

2.4 Hydrocyclone and Applications

2.4.1 Hydrocyclone

Hydrocyclones use centrifugal force and gravity to separate heavier particles from the fluid. Different from cyclones, the hydrocyclone applies to liquid instead of a gas

(Heiskanen, 1993). A typical hydrocyclone configuration is shown in Figure 2-1

(FloTrend Systems, Inc. 2006).

Figure 2-1 Typical Hydrocyclone Separation (FloTrend Systems, Inc. 2006)

A typical hydrocyclone usually consists of a cylindrical body as the major separation zone. A central overflow tube connected to the top portion of the cylinder serves as the exit for the treated fluid (overflow). An inlet tube is attached to the upper section of the cylinder at the point of entry, and the untreated fluid is injected tangentially into the device. The flow regime causes fluid swirling within the device and generates centrifugal force. Another exit on the bottom of the hydrocyclone (underflow) is for the discharge of condensed fluid (Matvienko et al. 2000).

2.4.2 Hydrocyclone Applications and Manufacturing

Hydrocyclones were originally designed for use in the pulp and paper industry for treatment of the paper stock to remove debris with high density (Peel, 1999; Bradley,

1965; and Bliss, 1992). Nowadays, hydrocyclones are used extensively in mineral, powder processing, pulp and paper manufacturing, environmental and chemical engineering for particle separation and particle classifications (Svarovsky, 1984).

Although most applications involve solid-liquid separations, there are also reported hydrocyclone applications for liquid-liquid separations, such as on oil/water separations (Bradley, 1965; Young et al, 1994; and Beeby and Nicols, 1993).

Hydrocyclones are also used as separators in water/waste water treatment processes.

Examples include applications of sandy particle removal from wastewater and grit separation for primary sludge (USfilter, 2005; and Columbia Regional Waste Water

Treatment Plant, 2006). Another hydrocyclone application example includes the Actiflo process (also named advanced high rate flocculated settling process) in the tertiary waste water treatment. Hydrocyclones are used in the Actiflo process to separate microsands 37

and particles from the clarifier and to recycle the heavier materials to the flocculation process (City of Melbourne, 2006; Rundle, 2006; and Onondaga County Department of

Water and Environment Protection, 2006). Advanced hydrocyclones can be combined with air injection in the Air Sparged Hydrocyclone (ASH) wastewater treatment system.

The mechanism of ASH is a combination of centrifugal separation with air flotation to remove contaminants (Heiskanen, 1993).

Hydrocyclones are commercially available by a number of manufacturers, including Yardney Water Management Systems, Inc. (2006); Sanborn Technologies

(2006); Deister Concentrator Co., L.L.C. (2006); Krebs Engineers (2006); NATCO

Group (2006); and Vortex Ventures Inc. (2006).

Although several hydrodynamic BMPs have similar appearance as a hydrocyclone (see detailed illustrations of hydrodynamic separators in Section 3 of this dissertation), no reported hydrocyclone application as a flow-through BMP was found through the literature review (Vortechnics, 2006; CDS Technologies, 2006; Hydro

International, 2006; and Hydroworks Inc., 2006). Although the vertical configuration, vortex flow pattern and inlet/outlet positions of some hydrodynamic BMPs are similar to those of a hydrocyclone, the separation mechanism and operation conditions vary substantially. For example, all reported hydrocyclones operate under pressure, which requires maintaining a certain feeding velocity to achieve the designed performances.

The feeding velocity in a hydrocyclone is achieved by a pump or pressure vessel, instead of gravitational flow. Usually a hydrocyclone operates continuously with certain overflow/underflow rates. 38

2.4.3 Hydrocyclone Separation Modeling

The flow regime in a hydrocyclone is highly complex as a result of the swirl motion as well as the details of the hydrocyclone geometry. Accurately simulating the fluid flow is difficult; and modeling mass transport in a hydrocyclone is even harder

(Ansys Inc, 2006). Continuous Fluid Dynamic (CFD) models divide the spatial domain into small cells to form a volume mesh or grid, and then apply a suitable algorithm to solve the equations of motion. Some CFD software includes Fluent (Fluent Inc. 2006),

CFX (Ansys Inc, 2006) and Fluid3D (Flow Science Inc. 2006).

2.5 First Flush Definition and Characteristics

2.5.1 General Definitions

Typically, the peak concentrations of constituents in storm water runoff occur at the initial stage of the event, which is defined as the “first flush”. (Australia EPA, 2006;

Gupta and Saul, 1996; and Hager, 2001). The term first flush has been used to refer to the initial stage of a storm event which contains a large percentage of total pollution in a relatively small percentage of runoff volume (Maestri et al, 1988; Gupta and Saul, 1996; and Hager, 2001). On the other hand, the term, “last flush”, is noted in some literature

(Hager, 2001) as an antagonistic term to first flush, which is defined as the high pollutant load in the later portion of an event.

2.5.2 Existence of First Flush

Several researchers have reported higher concentrations of constituents at the initial portion of the rainfall-runoff event, for example for suspended solids, COD, and 39

metals (Chui, 1997; Krebs et al., 1999; Sansalone and Buchberger, 1996; Larsen et al.,

1998; and Stenstrom et al., 2000). The nature of first flush is build-ups of mass on drainage surfaces and intermittent wash-offs of mass. As a result, normally the early portion of the runoff washes off through an area that has more abundant constituent mass.

Then, when mass washed off by the runoff is positively related to the amount of mass on the drainage area, first flush occurs.

The magnitude of first flush is critical in the design of stormwater pollution controls. Characterization and treatment of the first flush runoff could minimize adverse water quality effects, while requiring less volume to be treated (Sansalone and Cristina

2004; Su and Mitchell, 2003; and Australia EPA 2006).

2.5.3 First Flush Variations

However, the non existence of the first flush is also prevalent among some reported studies. For example, a section in the Australia EPA’s web site (2006) illustrated some observations (http://www.epa.nsw.gov.au/mao/stormwater.htm):

“Intensive monitoring of stormwater runoff from some (usually larger) catchments has failed to observe this phenomenon”.

“While the theory of first flush is straightforward, first flush may not be observed for one or more of the following reasons:

The drainage characteristics of the catchment may prevent it. Particularly in large catchments, initial runoff from the most distant parts of the catchment may not reach the catchment outlet for some time after a storm starts. This time lag is rarely an issue for smaller, individual premises.” (Australia EPA, 2006). 40

“Pollutant sources that are effectively continuous may exist within the catchment.

First flush is generally seen only where the supply of pollutants is limited. Sediment washing off from soil erosion, for example, will not give a first flush because the supply of soil particles is (for all practical purposes) unlimited.” (Australia EPA, 2006).

Sansalone (2005) studied runoff characteristics from a highway bridge site and observed that rainfall intensity and runoff flow rate significantly interfered with the runoff constituent concentrations and concluded that first flush may be driven by both mass on drainage surfaces and intensity of the runoff. In an urban runoff study conducted by Ellis and Sutherland (1979), storm water runoff and water quality data from twelve sites were analyzed throughout the states of Ohio, Wisconsin, Florida, North Carolina,

Indiana, Michigan and Virginia. Ellis and Sutherland further described the constituent concentration in the runoff into different categories. In a total of 72 storm events analyzed, 42% of the events were advanced (first flush dominates), 29% of the events were mixed (first and last flush both exist), 19% of the events were simultaneous (no significant first nor last flush), and the remaining 10% of the event were lagging (last flush dominates).

2.5.4 Evaluation of Significance of First Flushes

Throughout historic storm water literature, a well-defined “first flush” is needed for statistical analysis of this phenomenon (Sansalone and Cristina 2004; Hunt et al.,

2002b; and Su and Mitchell, 2003). The definition of the first flush utilized in data analysis and comparison vary significantly. For example, in Florida multiple study sites have shown that the first 2.5 cm of runoff, defined as the first flush, carried 90% of the 41

pollutant load from the entire storm event in urban drainage areas (Schiffer, 1989).

Bertrand-Krajewski et al. (1998) defined first flush as an event in which 80% of the total pollutant load was discharged during 30% (80:30) of the runoff volume. Utilizing this definition, they did not consistently find a pronounced first flush when studying urban separate and combined sewer system discharges. The Bertrand-Krajewski group investigated other definitions and indicated that Stahre and Urbonas (1990) utilized an

80% loading in 20% (80:20) flow definition, and Wanielista and Yousef (1993) proposed a 50% loading in 25% (50:25) flow. All these numerical definitions are based on a major portion of the mass discharge occurring within a small portion of the total runoff volume, but the actual numbers used in definitions vary significantly, providing difficulties in statistical analysis and comparison among different definitions (Su and Mitchell, 2003;

Su 2002).

To logically analyze storm water runoff data, natural logarithmic transformation of the normalized cumulative mass vs. normalized cumulative volume plots was used by several researchers to allow for linear regression of the previously non-linear correlations

(Hunt et al, 2002b; Bertrand-Krajewski et al., 1998; and Philippe and Ranchet, 1987).

These researchers indicated the power function relationship can simplify first flush determinations as in Equations 1-1 and 1-2.

F(x) = xb Equation 1-1

ln(F(x)) = b (ln(x)) Equation 1-2 where: x = normalized cumulative volume;

F(x) = normalized cumulative mass; and 42

b = power function coefficient.

The power function coefficient can be determined by linear regression of the logarithmic transformed data. The prevalence of first flush for various parameters over various storms can be determined utilizing statistical analysis with the power coefficients. For analytical data comparison, b values for 80:20, 80:30, and 50:25 defined first flushes (see definitions in the first paragraph of Section 2.5.4) have been estimated as 0.138, 0.185, and 0.5, respectively (Hunt et al., 2002b). However, this power function model does not have physical meaning. The power function coefficient b ranges from 0 to1 for first flush but from 1 to infinity for last flush. This is inconvenient because simply averaging the b values for events may exaggerate the last flush influence since it may add much more weight to the average b value (Su, 2002).

2.5.5 Storm Water Management Model

The USEPA’s Storm Water Management Model (SWMM) is a large, relatively complex dynamic rainfall-runoff simulation software package, which originally was developed by the USEPA’s National Risk Management Research Laboratory in a joint development effort with CDM, Inc., in 1971 (Rossman 2005). In the last several decades, the SWMM model has been improved and new versions/upgrades have been released with improved functions (Rossman 2005). SWMM is capable of simulating the transformation of precipitation to urban runoff, routing of the runoff from the drainage surface through drainage networks as well as simulating runoff storage and some BMP simulations. Tsihrintzis and Hamid (1998) studied runoff from four small sites in Florida.

A total of 58 storm events were first studied to calibrate the SWMM input parameters. 43

Subsequently, 16 individual rainfall events were used for the model verification for constituents including biological oxygen demand (BOD), total suspended solids (TSS), nitrogen (TKN) and lead. The calibrated SWMM model exhibited a good simulation with the observed runoff data for both hydrographs and pollutant loadings.

SWMM accounts for various hydrologic and hydrology processes to model the generation and transport of runoff flows. SWMM is capable of estimating the pollutant loads associated with storm water runoff, which can be considered as a simulation of the first flush generation.

2.5.5.1 SWMM Pollutant Build-up Model

Several mass build-up equations are available in the most- updated SWMM model: the linear build-up model, the power function model, the exponential model and the Michaelis- Menton Model (a non-linear empirical model). The SWMM exponential model assumes that mass build-up follows an exponential growth curve that approaches a maximum limit asymptotically. The exponential accumulation equation has physical meanings and several street constituent accumulation datasets have been analyzed with the exponential model to derive the best fit equation parameters (Sutherland et al. 1998).

However, the SWMM’s exponential model has several drawbacks. The accumulation equation does not account for the initial mass left on drainage surfaces after the previous storm event or street sweeping. It was assumed that any rain or any sweeping would remove all of the accumulated pollutants from the street. The method to adjust the model to simulate the mass remaining on the drainage surfaces is not addressed in the SWMM. 44

2.5.5.2 SWMM Pollutant Wash-off Model

SWMM also supports different pollutant wash-off models to simulate the wash- off of pollutants from the drainage surface (Huber and Dickerson, 1988). One model in the SWMM is referred to as exponential wash-off equation, in which it is assumed that the mass of constituents washed off the surface in any time interval is proportional to the mass remaining on the drainage surface. The SWMM 4.0 and later versions further introduce modified exponential wash-off equations by making wash-off rate at each time step proportional to runoff rate raised to the power of a coefficient (WASHPO), as shown in Equation 1-3 (Rossman 2005).

-P (t) = d(PSHED)/dt = -RCOEFX * rWASHPO * PSHED Equation 1-3 where P = pollutant mass washed off at time t;

r = runoff rate;

PSHED = pollutant mass available for wash-off at time t; and

RCOEFX, WASHPO = wash-off coefficients.

Introducing the second wash-off coefficient in the SWMM increased the calibration flexibility of the model. However, there is no apparent way to distinguish the influences of the two factors, especially when concentration changes dramatically during a storm event. Although the SWMM build-up and wash-off model provides a logical and meaningful approach to simulate storm water constituent concentration, which also explains the formation of first flush conditions, SWMM is intended to simulate single event or long-term (continuous) runoff quantity and quality rather than for first flush analysis. SWMM does not provide further derivations from the exponential wash-off 45

model, such as derivations of equations on a normalized cumulative mass versus normalized cumulative runoff chart. Using SWMM to analyze first flush is not addressed and can not be easily accomplished by the software package itself.

2.5.6 Existing Issues

The existence of first flush is still a controversial issue by some researchers.

Factors leading to first flush reduction are emerging; however, the explanation and magnitude of those effects are not thoroughly studied. Moreover, limitation on first flush analysis and lack of standard analysis methods also worsen the debate on the existence of first flushes.

A study of first flush requires a large amount of sampling and accurate sampling operations. First flush can be influenced by many parameters and maybe masked if the sampling is inaccurate or the sampling information is limited. Most manual sampling, composite sampling, sampling part of the event, sampling without rainfall/runoff data, or inadequate number of samples in an event, and inadequate number of sampled events do not provide sufficient quality for first flush analysis.

The definition of first flush is not clear. It is explained in different ways by different researchers. Some refer to first flush as a phenomenon, while others refer to it as a certain portion of the initial rainfall event. Moreover, methods of processing and evaluating the first flush vary much among researchers. Lack of quantitative definition or standard analysis procedures restrains comparisons among different first flush approaches.

Although the latest versions of USEPA Stormwater Management Model contain mass transfer models to logically simulate the constituent build-ups and wash-offs in the 46

storm water runoff; and part of the model can be used to logically explain and simulate first flush formation, very little model derivations were conducted and a first flush analytical method is needed to process the runoff monitoring data for first flushes. 47

CHAPTER 3 OVERVIEW OF HYDRODYNAMIC STRUCTURAL BMP DESIGNS

3.1 Existing Hydrodynamic Devices

Among dozens of structural storm water treatment devices, several typical hydrodynamic treatment devices are introduced in this chapter.

3.1.1 Downstream Defender by Hydro International

Downstream Defender by Hydro International is a vertically oriented separator which is designed to separate floatables and sediment from storm water (see Figure 3-1).

Storm water is introduced tangentially into the side of the outer cylinder, initially spiraling around between the walls of the inner and outer cylinders. Floatables and oil rise to the surface and are trapped between cylinders. Main flow travels downward and leaves the system via outlet pipe from the inner cylinder. This flow regime enables sediment separation at the bottom portion of the device. Downstream Defender also includes a baffle plate at the lower portion of the device to protect the sediment from scouring (Hydro International, 2006).

Access for Sediment Removal

Baffle Plate

Sediment Chamber

Figure 3-1 Downstream Defender (Hydro International, 2006)

Hydro International has conducted both laboratory tests and Computational Fluid

Dynamic (CFD) modeling to optimize the configurations (Phipps et al. 2004; Faram and

Harwood, 2002). The patented sediment baffle plate also is reported to have good performance on reducing scouring (Hydro International, 2006).

Based on the author’s interpretation of the vendor’s literature information, the sediment baffle plate configuration causes sediment to accumulate mainly in the center portion of the storage chamber, while the perimeter storage space is not fully functional.

The central column of the device in the inner cylinder is designed for maintenance operation but creates a dead end zone during treatment.

3.1.2 CDS Storm Water Treatment Unit

The CDS unit is designed mainly based on hydrodynamic screening separation

(see Figure 3-2). Storm water enters the inner cylinder through a diversion deflector.

The rotational flow is introduced in the inner side of the cylindrical screen. Larger 49

particles are intercepted by the screen and drop to the bottom of the unit. Flow passes through the screen and exits the device via the outlet.

Screen Sediment Chamber

Figure 3-2 CDS Storm Water Unit (CDS, 2006)

Based on the author’s interpretation of the vendor’s literature information, CDS has conducted intensive studies on the screening technology to reduce clogging issues.

Screen configurations are adjustable to accommodate various site conditions. The flow regime in a CDS unit is smooth with no dead end zone or shortcut in the design.

However, a completely non-blocking and self-cleaning screen is very hard to achieve, especially when high organic pollutants are present in the runoff. No sediment baffle plate was designed in the CDS unit to prevent the pollutants captured from scouring. 50

3.1.3 Hydroworks

A Hydroworks separator (see Figure 3-3) is designed to remove suspended solids, oil, and trash from storm water runoff. Untreated storm water is conveyed into the inner cylinder of the Hydroworks treatment device, which is offset to one side of the unit. The water strikes the inner wall of the inner cylinder tangentially and thus forms a vortex flow regime in the inner cylinder. Then, the vortex flow exits from the bottom opening of the inner chamber and enters the outer cylinder. Treated runoff passes under the outlet baffle and exits the unit through the outlet.

Based on the author’s interpretation of the vendor’s literature information,

Hydroworks does not require many modifications on the existing manhole or piping system by adding an inserted inner cylinder in the treatment system. Fabrication of

Hydroworks is reportedly easy, and it can be fit in most existing manhole structures.

However, the flow regime is not smooth which creates turbulences and shortcut paths and reduces the separation efficiency. No sediment baffle plate is designed in Hydroworks to protect the captured sediments.

Figure 3-3 Hydroworks (Hydroworks Inc, 2006) 52

3.1.4 VortSentry by Stormwater 360

VortSentry is a hydrodynamic treatment device with a small footprint (see Figure

3-4). A deflector is installed at the inlet where water from the inlet is conveyed into vortex flow. Then, the flow spirals down in the main treatment chamber and exits the treatment device between the deflector and the outlet.

Inlet Baffle Plate

Sedimentation Zone

Figure 3-4 VortSentry by Vortechnics (Vortechnics, 2006)

Based on the author’s interpretation of the vendor’s literature information,

VortSentry treatment system is easy to install, especially when the site is congested. The internal structure of the VortSentry is simple with one integrated insert. However, the design does not have sediment baffle plates or other structures to protect the retained 53

sediment from scouring. VortSentry also has limited capacity to remove floatables and is designed to removal coarse particles.

3.2 New Hydrodynamic Treatment Device

A new hydrodynamic treatment device is developed (hereafter referred to the

“device”) to treat storm water runoff. The device contains two vertical concentric cylinders. Storm water runoff enters the inner cylinder via a tangential inlet and is directed around the inside wall of the inner cylinder. This rotational flow allows settleable solids to settle down to the bottom of the device, while the oil and floatables are retained on the water surface in the inner cylinder. The treated water is passed to the outlet between the walls of the two cylinders. The general overview of the device is shown in Figure 3-5 Detailed internal structures are further described in Chapter 4, such as the inlet baffle plate, sediment baffle plate, oil adsorbent materials, metal adsorbent materials, and integrated bypass (Su and Mitchell 2005).

Figure 3-5 Conceptual Description of the Device (Su and Mitchell, 2005)

3.3 Summarization of Hydrodynamic Treatment Device Designs

Since most of the devices do not have sufficient and reliable field performance data, comparison of removal efficiency among devices was not presented. Table 3-1 summarizes a comparison of other information of the hydrodynamic treatment devices.

Information presented is based on literature supplied by the manufacturer and interpretation by the author.

Table 3-1 Comparisons of Hydrodynamic Treatment Devices

l l

b n s e a v t o

t e

i o s l a a t e l s o t o a m p a l a h c

e f l i p r

n r

, l p l y a l

s f b a b a f e d

a t v m l i a f

f d e l

o f b f f e

o t o a m t m s o

b r n e l r

d d r e e t a g e e i e b e l r v m d t l i e n m n n t o d I u I e a s e s p N S i s M u D S

Downstream Defender by Hydro yes no no yes no Concrete/Fiberglass 2 International CDS yes no yes no yes Concrete/Metal Screen 1 Hydroworks yes no no no yes Concrete/Thermoplastics 1 VortSentry yes no yes no yes Concrete/Steel 1 New Hydrodynamic yes optional yes yes yes Thermoplastics 1 Treatment Device

Detailed design discussions and performance tests on the new hydrodynamic device are presented in the following sections of this dissertation. 55

CHAPTER 4 HYDRODYNAMIC DEVICE DESIGN THEORY

4.1 Description of the Device

4.1.1 Basic Design Configuration

The new treatment device is a hydrodynamic flow-through separator that is designed to remove settleable solids, oil and grease, and floatables from stormwater runoff. Adsorbent materials can be added in the inner cylinder and the space between cylinders to enhance the separation of metal and oil/grease from the runoff. Maintenance is needed periodically to remove and dispose of the retained pollutants from the device through the inner cylinder (Su and Mitchell, 2005). Proposed construction material of the device is thermoplastic materials, such as high density polyethylene (HDPE) or polyvinyl chloride (PVC).

4.1.2 Internal Components

Other internal components of the device such as the sediment baffle plate, inlet

baffle plate, bypass and adsorption materials are introduced in this section. The internal

components are designed to either improve the pollutant removals or prevent scouring of

the stored pollutants.

4.1.2.1 Sediment Baffle Plate

For a treatment device to maintain good overall removal performance during the service period, it should not only remove pollutants, but also retain pollutants (Phipps et al. 2004).

A sediment baffle plate is designed to be installed below the inner cylinder but above the floor of the outer cylinder to improve sediment retention. One design configuration tested in the laboratory is shown in Figure 4-1.

Ring structure enables access to the sediment during maintenance operations

Hsb

Dsb

Figure 4-1 Sediment Baffle Plate Design (Su and Mitchell, 2005)

The purpose of the sediment baffle plate is to separate the vortex flow region and the sediment storage region. The blades of the baffle plate reduce and dispense flow 57

energy while the central ring of the baffle plate provides access to the sediment region during maintenance operations.

4.1.2.2 Inlet Baffle Plate and Oil Adsorption

Inlet turbulence at the oil retention zone in the device can cause oil emulsification thus decreasing the oil removal efficiency. An inlet baffle plate was designed as shown conceptually in Figure 4-2. The plate consists of a 270 degree cylinder and a plate connecting it to the inner cylinder wall. The baffle plate is designed to trap and shelter retained oils and floatables to its central portion. For better oil removal, adsorbent materials can be installed in the center of the structure. The inlet baffle plate was designed not only to physically protect the adsorbent materials from direct wash-offs but also concentrate oil for better adsorption.

Baffle plate Adsorbent (Diameter = Dib-r) Material

Oil retention zone

Baffle plate

Inner Static water cylinder level

Outer cylinder

Figure 4-2 Illustration of Inlet Baffle Plate Design (Su and Mitchell, 2005) 58

4.1.2.3 Integrated Bypass

The integrated bypass structure includes a bypass aperture and an optional bypass outlet (an application of using the device as a bypass unit is illustrated in Figure 4-7).

Under high flow rates, excessive flow may pass through the bypass structure as shown in

Figure 4-3.

Bypass outlet Outlet (optional)

Inlet Bypass aperture on inner cylinder

Bypass Water Level Static water level

Bypass outlet (optional)

Figure 4-3 Integrated Bypass Design

The bypass aperture on the inner cylinder is designed as a shortcut path for the bypass flow. At low flow rates, the water level in the inner cylinder is low and the entire runoff goes through the treatment device and the treatment volume can be maximized. 59

While at high flow conditions, the water level increases, and some runoff may pass through the aperture to the outlet. The flow in the treatment chamber can remain low even at high flow rates and the previously captured pollutants can be retained. Thus, with proper designed aperture, this by-pass mechanism may increase the long term performance of the device by protecting the sediment from scouring. An optional bypass outlet can be used if separation of treatment flow and bypass flow is needed for subsequent treatment.

The positions of the aperture and the bypass should be customized based on device treatment capacity and the water head losses of the system. An optimized bypass ratio can be set by maximizing the pollutant mass reduction. Correlations of the mass removal and water level should be studied to optimize the system for highest overall removals. One design example of flow bypass is the Flow Splitter Design by Contech

Storm Water Solutions (2006). Further studies are needed to customize the bypass design for each field installation.

4.1.2.4 Adsorption of Metals

Metal adsorbent materials such as Type-M sponge (Dynaphore Inc. 2006) or granulated activated carbon (GAC) can be added between the two cylinders as illustrated in Figure 4-4. Upper and lower screens are needed to retain the adsorbent materials within the device. Properly designed supporting structure should be used to hold the screens in place. The spiral flow regime below the lower screen can reduce the possibility of clogging and the upper flow regime encourages the contact of water and adsorbent materials, thus enhancing the removal efficiency. 60

Upper screen

Adsorbent materials

Lower screen Inner cylinder

Outer cylinder

Figure 4-4 Screening and Metal Adsorption Design

The configurations of the screen and adsorbent materials may vary. As one example, a PVC screen with 0.25’’ openings and 0.5’’ Type-M adsorbent sponges by

Dynaphore, Inc. (Dynaphore Inc. 2006) was chosen for the proposed field device application.

4.1.3 Installation Configuration Overview

Besides the individual treatment configuration of applying the device alone, various other configurations could be utilized. Treatment in series (See Figure 4-5) can be utilized to enhance the treatment quality and may potentially be used in some environmentally sensitive applications. Treatment in parallel (See Figure 4-6) can be considered to increase the overall treatment capacity. The device can also be used as a pretreatment and bypass unit with other treatment devices (See Figure 4-7). Besides 61 inline treatment configurations, offline treatment configuration can also be used (See

Figure 4-8) (Su and Mitchell, 2005).

Figure 4-5 Schematic Example - Series Installation

Figure 4-6 Schematic Example - Parallel Installation 62

Figure 4-7 Schematic Example of Pretreatment/Bypass Application

Figure 4-8 Schematic Example of Offline Application

4.1.4 Pipe Connections

Compared to a horizontal three chamber device tested in a previous study (Su

2002), the vertical design configuration is more compact and requires less occupation of land for installation. Moreover, as shown in Figure 4-9, the inlet, outlet and bypass can be designed in various directions to accommodate the geometry of the site and 63

connecting pipe. The inlet pipe will have to follow the tangential line of the inner cylinder, but either clockwise or anticlockwise configuration could be selected.

Basic Configuration

1 Configuration Variations

1 2

Figure 4-9 Schematic Illustration of Connection Configurations

4.2 Flow Regime Features

Although the configuration of the device and the flow pattern are similar to those of hydrocyclones, this device is mainly designed and operated for gravitational separation by detention. Unlike a typical hydrocyclone, the treatment device works under low surface load, low velocity gravitational flow conditions. 64

However, the spiral flow regime in the design is proposed to create some unique features which may enhance the performance for pollutant removal: 1) A smooth flow regime is created in the device which can reduce the possibilities of flow short-cut and dead ends, thus better utilizing the entire detention volume for enhanced pollutant removal; 2) The hydrodynamic flow shapes the retained sediment into a cone on the bottom of the device. With the help from the sediment baffle plate installation, possibility of sediment scouring can be reduced (See Section 4.1.2.1 for inlet baffle plate design and Section 5.1.4.2 for laboratory observations; 3) With the installation of an inlet baffle plate and oil adsorption materials, spiral flow pushes and traps retained oil to the center of the device for better adsorption (See Section 4.1.2.2 for inlet baffle plate design and Section 5.1.4.4 for laboratory observations); 4) The spiral flow regime is perpendicular to the screen openings, which creates shear forces to screens and thus help reduce the risk of clogging (See Section 4.1.2.4 for screen configurations); and 5) Spiral flow regime has less restrictions on the direction of the inlet and outlet alignments, which provides flexibility for connections. (See Section 4.1.4 for pipe connection configurations). The following section summarizes the pros and cons of the design.

4.3 Design Advantages and Disadvantages

4.3.1 Advantages of New Design

• Compact and space saving

The vertical design configuration is a compact design and provides a space saving

solution. Comparison of the device with an ideal settling tank with the same

footprint is further discussed in Chapter 8 to illustrate the space saving features. 65

• Single point access during maintenance

Retained pollutants could be accessed from the central cylinder of the device

during the maintenance process, which is less than what is required by the

Downstream Defender (two access manholes) (Hydro International, 2006) and the

horizontal treatment device (three manholes) (Mitchell and Su, 2002; Su, 2002).

• Various application configurations

The device can be applied in various application configurations, such as inline or

offline, in series or in parallel, individual or applied with other devices.

• Smooth flow regime

No obstacle structure exists in the device thus minimizing the possibility of

turbulence and related water head loss.

• Integrated bypass structure

The bypass design is integrated with the device. Bypass flow is adjustable by

controlling the elevation and size of the aperture.

• Flexible and easy accommodation to existing pipes

The connecting pipe alignment of the device is flexible to accommodate different

application environments.

4.3.2 Disadvantages of New Design

• The hydrodynamic device contains more components (one outer cylinder, one

inner cylinder, one inlet baffle plate, one sediment baffle plate, and two screens) 66

than the horizontal device tested previously (two plate weirs and one screen) (Su,

2002).

• Some parts of the design (such as the inlet baffle plate design and tangential inlet

design) are complicated and may require special care and equipments during the

fabrication.

• The hydrodynamic design may require more vertical space than some horizontal

treatment designs. The height of the hydrodynamic device tested in the laboratory

is nearly twice the diameter of the outer cylinder pipe, while the depth of the

horizontal treatment device equals the diameter of the pipe (Su, 2002).

• The maximum capacity of the device is limited to the diameter of pipe the

manufacturer can fabricate. 67

CHAPTER 5 STORM WATER RUNOFF TEST METHODOLOGIES

5.1 Methodologies of Laboratory Tests

5.1.1 Description of Laboratory Scale Model

A laboratory test model was fabricated as shown in Figure 5-1. Cast acrylic materials were used to build the model for a clear observation of the flow and pollutant removal process.

Bypass Bypass

Inlet

Outlet Inlet

Figure 5-1 Lab Test Model (Left: Plan View; Right: Profile View)

Major dimensions of the laboratory model are shown in Figure 5-2. 68

Doc=12’’

Dic = 8’’ Outlet

8’’

2.5’’

Inlet Hic=18’’

Hoc= 24’’

Inner cylinder

Outer cylinder

Din= 3’’

Dout= 3’’

D = 3’’ All by dimensions are in inches

Figure 5-2 Dimensions of Laboratory Test Model

Basic device dimensions and configurations are shown in Table 5-1. 69

Table 5-1 Major Dimensions of the Lab Test Model

Dimensions/Descriptions Total Height 24’’ 0.6096 m of Outer Cylinder (Hoc) Height of Inner Cylinder (Hic) 18’’ 0.4572 m Static Water Height 14.6875’’ 0.3731 m Height of Sediment Storage Space 6’’ 0.1524 m 12’’ OD; 11.5’’ID; 0.3048 m OD; 0.2921 m ID; Dia. of Outer Cylinder (D ) oc ¼’’WT 0.00635m WT 8’’ OD; 7.5’’ID; 0.2032 m OD; 0.1905 m ID; Dia. of Inner Cylinder(D ) ic ¼’’ WT 0.00635m WT Dia of Connecting Pipes: Inlet, Outlet 3'' OD; 2.625’’ ID; 0.0762 m OD; 0.06668m ID; and Bypass (Din, Dout and Dby) 3/16 WT 0.004763m WT Cross Sectional Area of the Inner 44.18 inch2 0.02850 m2 Cylinder Cross Sectional Area of the Outer 103.87 inch2 0.06701m2 Cylinder Cross Sectional of the Area between 53.60 inch2 0.03458 m2 Cylinders Static Water Volume 1472.7 inch3 24.13 L Maximum Water Volume Retained 1717.1inch3 28.14 L (before Bypass) Maximum Sediment Storage Volume 623.3 inch3 10.21 L Static Water Weight (1g/cm3) 53.2 lb. 24.13 kg Maximum Sediment Weight 30.0 lb. 13.5 kg (2.65g/cm3 with 50% void space) Top View Flow Direction - () Clockwise (OD: outer diameter; ID: inner diameter; Dia: diameter; WT: wall thickness)

5.1.2 Laboratory Apparatus Set Up

Models were set up and tested in the laboratory conditions using simulated storm water. The apparatus setup is shown conceptually in Figure 5-3. 70

1: hose, connected to tap water source 2: stirrer 3: mixing cup with stirrer 4: 20-gallon (80 L) container 5: treatment unit

1 2 3 4 5

Figure 5-3 Laboratory Apparatus Set Up

A hose connects to a tap water source, allowing flow into the mixing cup. Soil sample premixed with water or oil sample is added directly to the mixing cup then passes into the testing device.

5.1.3 Descriptions of Simulated Storm Water Constituents

5.1.3.1 Soil Descriptions

Four different soil samples were used to investigate suspended solids (SS) removal. Particle size distributions (PSD) measured by standard sieve analysis and/or a

Beckman Coulter LS-230 (see Section 5.1.5.3) are shown in Figure 5-4.

Figure 5-4 Particle Size Distributions of Soil Samples (by Volume)

USDA’s National Soil Survey Laboratory criteria (USDA, 1999) were used to assign soil textures. Figure 5-5 shows the USDA Soil Classification Diagram for the tested soil samples and Table 5-2 summarizes the soil classification.

Soil #1

Soil #2 Soil #4

Soil #3

Figure 5-5 Soil Classification Using USDA Diagram 72

Table 5-2 Soil Properties Particle Category and Size Soil #1 Soil #2 Soil #3 Soil #4 % of Gravel > 2000 0% 19% 0% 0% (micron) % of Sand 50- 56% 70% 100% 12% 2000(micron) % of Silt 2-50(micron) 36% 6% 0% 73% % of Clay 0 -2(micron) 8% 4% 0% 15% USDA Particle Size Sandy Loamy Medium Silt Loam Classifications Loam Sand with Sand Some Gravels D50 100 700 380 micron 12 micron micron micron

Particle size distributions (PSD) of Soil #1 (Sandy Loam) and Soil #4 (Silt Loam) are similar to the SS PSD presented in the Nationwide Urban Runoff Program study

(USEPA 1983), and thus should be representative of SS in the runoffs. Samples of Soil

#2 and #3, much coarser than typical SS in the runoffs, were also used in the laboratory.

Those samples can provide simulation on runoff with coarser SS PSD (such as erosion from a sandy construction site).

Soil was mixed with water at least 24 hours prior to testing to ensure complete soil particle separation. Inlet SS concentration was calculated from known soil added and measured flow rates. The glass fiber filtration method, APHA Standard Method #2540

(Clesceri, et al, 1998) was used to determine SS concentration at the outlet. Particle size distributions of the soil sample at both the inlet and outlet were analyzed using standard sieves and a LS-230 laser diffusion particle size analyzer (Beckman Coulter Inc., 1994).

5.1.3.2 Oil Description

To minimize pollution in the laboratory test condition, vegetable oil was used in the lab test system to observe the floating phase. 10w-30 motor oil was used in oil removal tests and some bench scale oil adsorption tests.

5.1.4 Laboratory Tests

5.1.4.1 Sediment Capacity Tests

The treatment device is designed to be cleaned before reaching its sediment storage capacity to avoid excessive scouring. To determine the maximum amount of sediment that could be contained in the device, soil was continuously added in the inlet under moderate flow rate. The formation of sediment was observed until the storage capacity was reached.

5.1.4.2 Scouring Tests

Scouring tests were conducted by testing the SS concentration at the outlet of the device when tap water flowed through the device with moderate amount of sediment in it.

5.1.4.3 SS Removal Tests

Inlet SS concentration was calculated from known soil added and measured flow rates using Equation 5-1.

W C = Equation 5-1 (1+θ)Q where: C = inlet SS concentration (mg/L);

W = weight of soil sample added per unit time (mg/min); 74

θ = gravimetric soil moisture content; and

Q = water flow rate (L/min).

Removal of SS was calculated for both the overall removal of each soil sample and removal in each particle size range (116 series between 0.04 ~2000 microns) by using Equation 5-2 (Su and Mitchell, 2004).

Cout− p Cout * Pout Rp = 1− = 1− Equation 5-2 Cin− p Cin * Pin where: Rp = removal for particles in a certain size range;

Cin-p and Cout-p = inlet and outlet concentrations respectively for particles in a

certain range (mg/L);

Cin and Cout = inlet and outlet SS concentrations (mg/L) respectively; and

Pin and Pout = fractions of particles in a certain range (by weight) at the inlet and

outlet, respectively.

5.1.4.4 Oil Removal and Oil Adsorption Tests

Bench scale tests were conducted to find a proper adsorbent material to capture oil and grease from the oil retention zone. Adsorbent materials tested included popcorn shaped Smart Sponge by AbTech, Inc. (AbTech industries, 2006), Oil-Only adsorbent by

PIG Inc. (New PIG Cooperation. 2006), and Type-O Sponge by Forager Ltd. (Dynaphore,

2006). The oil sample used in this study was 10w-30 motor oil.

• Oil removal test: Adsorption tests were conducted in a series of identical 1L

glass jars. The procedure was as follows: for each of the jars, add 600mL tap 75

water; add 5~7mL motor oil; add fresh adsorbent material; stop the adsorption by

separating the adsorbent from the mixture; gently wash the adsorbent for 10~20

seconds in tap water; allow the adsorbent to dry at 60ºC over night; determine the

mass of oil adsorbed in the adsorbent by either measuring the oil concentration

remaining in the jar or measuring the weight difference of the adsorbent before

and after the adsorption (Su and Mitchell, 2004).

• Oil retaining capacity test: To test the adsorbency of the materials in a simulated

dynamic flow condition in the device, saturated materials (submerged in 50-50

water-oil mixture for 24 hours) were added into 1-liter beakers. Tap water at 0.5

L/min was introduced spirally into the beaker which caused rotational flow and

washed unstable oil from the adsorbent material. Adsorbent material was dried

and weighed to examine the oil remaining from the wash-offs (Su and Mitchell,

2005). Figure 5-6 illustrates the test apparatus set up for oil retaining capacity

tests.

Tap water

1L beaker

Adsorption materials

Figure 5-6 Simulated Dynamic Flow for Oil Adsorption

5.1.5 Analysis Methods

5.1.5.1 Flow Rate Measurements

In the laboratory tests, flow rates were measured using a stopwatch and a volumetric cylinder. Volume of water obtained at the outlet was measured and was divided by time to yield the flow rate. Flow rate was the average of at least three measurements after steady state flow was achieved. Most measurements were within the deviation of 5%.

5.1.5.2 SS Concentration

The inlet SS concentration in the laboratory tests was calculated from the measured flow rates and soil added. APHA Standard Method #2540 was used to 77

determine SS concentration in the outlet (Clesceri, 1998). For SS removal tests, at least four samples were taken. Typical volume of the sample was between 100 to 200 mL.

Samples were filtered through a weighed standard glass-fiber filter, and the residue retained on the filter was dried to constant weight at 105oC. The increase in weight is divided by the volume of sample to achieve SS concentrations. Standard deviation of difference of 2.8 mg/L was reported in a single-laboratory study by analyzing 50 water and wastewater samples in duplicate (Clesceri, 1998).

5.1.5.3 Particle Size Distribution

Particle size distributions of the soil sample at both the inlet and outlet were analyzed mainly using Beckman Coulter LS-230 laser diffusion particle size analyzer

(Beckman Coulter Inc., 1994). Standard sieve analysis was used to determine the particle size distribution of soil sample 2.

5.2 Methodologies of Field Tests

5.2.1 Site Description

To further evaluate the performance of the device, a bridge site was identified and a full size HDPE treatment device was proposed to treat bridge deck runoffs. Technical discussions were conducted with engineers from Advanced Drainage System Inc. (ADS) and Athens County - Street and Road Department for fabrication and installations of the device. The fabrication of the device is undergoing and the installation is planned tentatively in the spring of 2007. Thus, at the time this dissertation is presented, large scale sampling and performance evaluation of the field unit are not available. However, 78 some grab samples were taken from the bridge site and analyzed for initial design purposes as well as the cost-benefit analysis of the device.

Large scale storm water runoff sampling from a 30,000 average daily traffic highway section in Lancaster, Ohio, was conducted in a previous study of runoff quality analysis and wetland performance evaluations (Hunt, 1997; Hunt et al., 2002b; Su and

Mitchell, 2006b). For this study, the Lancaster runoff quality data were used for the first flush modeling analysis.

Detailed illustrations of the two runoff sites studied are described below.

5.2.1.1 Bridge Runoff Site

The bridge site is located in Athens City, Ohio, near the junction of Route 56 and

Route 682, as shown in Figure 5-7 (USGS, 1994). Runoff from the 0.4 acre of concrete bridge deck flows to the south side of the bridge, where it is collected by a concrete open drain along the edge of the bridge and directed to the southeast corner of the site. A concrete channel collects the bridge deck runoff at the southeast corner of the bridge and runoff discharges directly into the Hocking River. An illustration of the surface flow pattern is shown in Figure 5-8. Site location maps from U.S. Census Bureau Maps and

Cartographic Resources (2006) and water shed information for the receiving water bodies

(USEPA, 2006a) are presented in Appendix 1.

West Union St.

West Union Bridge Site To Athens Downtown and Ohio University, 2 miles

Hocking River

Route 56 Route 682

Figure 5-7 USGS Satellite Map of Bridge Runoff Site

Figure 5-8 Drainage Surface and Existing Runoff Pattern 80

5.2.1.2 Highway Runoff Site

The highway runoff site is located on U.S Route 33 in Fairfield County, north of

Lancaster, Ohio, Ohio Department of Transportation (ODOT) District 6. The drainage area as shown in Figure 5-9, consisted of the two inner most lanes of the four-lane highway composed of 1,189 m2 of asphalt pavement, draining to 279 m2 of vegetated median. Runoff flows into a median drain and thence via conduit to the wetland inlet

(Hunt 1997).

Figure 5-9 Illustration of Highway Runoff Site (Su and Mitchell, 2006b)

For completeness, in-situ sampling and sample analysis methods for the Lancaster highway site are presented. Two ISCO 6700 automatic samplers were used by Hunt

(1997) for continuous sampling at the site. During the storm events, series samples were 81

collected at both wetland inlet and outlet locations. Peripheral devices (including two rain gauges, one water quality probe and one flow area/velocity probe), were used to record the rainfall, flow, dissolved oxygen concentration (DO), pH, temperature, conductivity, and sampling event data to ISCO internal memory (Hunt, 1997). Water samples taken by the samplers were then transported to an environmental laboratory for constituent concentration analysis (TSS, COD, dissolved and total metals, etc.). Raw water quality and runoff data collected by Hunt (1997) were obtained as Excel spreadsheets and were further analyzed by the author for the first flush characterization

(Su and Mitchell, 2006b). Composite distributed runoff from the pavement and rainfall were analyzed periodically (Hunt 1997). Because composite samples do not reproduce sufficient information on water quality change during storm events, first flush analysis on these two sources was not conducted in this study.

5.2.2 Sample Analysis Methods Used from Bridge Runoff Analysis

Grab samples from the bridge runoff site were collected and transported to an environmental laboratory at Ohio University. The following methods were used to analyze the constituent concentrations in the bridge runoff water samples.

5.2.2.1 Metals

American Public Health Association’s Standard Method for Examination of

Water and Waste Water (noted as Standard Method hereafter) #3111 (Metals by Flame

Atomic Absorption Spectrometer) was followed for metal analysis (Clesceri, 1998).

Total metals including sodium, cadmium, chromium, iron, manganese, nickel, lead, and 82

zinc were analyzed using Perkin-Elmer Atomic Absorption 300 (AA) spectrophotometer.

Calibration, quality assurance and quality control and cleaning procedures were followed from manufacturer recommendations.

5.2.2.2 Chemical Oxygen Demand (COD)

COD of samples was analyzed using Reactor Digestion and Colorimetric Method with the HACH DR/2000 Direct Reading Spectrophotometer, Program 430 (HACH

Company, 1996). The 2.00mL samples were placed into the tubes with COD digestion solutions. After completely mixing, the solutions were placed in a heated reactor for two hours. Once completed, the tube was cooled down to 120oC in the reactor. Then the

HACH DR/2000 was used to colorimetrically determine the COD of the water sample.

5.2.2.3 Suspended Solids

Standard Method #2540 was followed to determine the suspended solids (SS) concentration (Clesceri, 1998). The typical amount of sample used was 25 to 50 mL.

Description of the analysis method and the precision can be found in Section 5.1.5.2.

5.2.2.4 Particle Size Distribution

Particle size distributions of bridge runoff samples were analyzed using a

Beckman Coulter LS-230 laser diffraction particle size analyzer (Beckman Coulter Inc.,

1994). In laser diffraction particle size analysis, water samples containing particles pass through a beam of laser light. Scattered light passes through a series of lens and reaches the laser diffraction detector array. The particle size distribution (by volume) is inferred 83

from the collected diffracted laser data using built-in inversion software (Beckman

Coulter Inc., 1994). 84

CHAPTER 6 LABORATORY TEST RESULTS AND DISCUSSIONS

6.1 Rotation Flow Velocity Measurements

Water tracer (dye study) was added at the inlet of the laboratory device and the average angular speed of the flow in the inner cylinder was measured under different flow rates. As shown in Figure 6-1, higher flow rates resulted in higher rotational flow velocity, and this may also result in high flow velocity on the surface of the settled sediments.

1.0 0.9 c e

S 0.8 / e l

c 0.7 y C

0.6 , d

e 0.5 e p 0.4 S

r a

l 0.3 u

g 0.2 n A 0.1 0.0 0 10 20 30 40 50 60 70 Flow Rate (L/min)

Figure 6-1 Angular Speed versus Flow Rates

To achieve a good overall removal, scouring of retained pollutants should be avoided. Some typical measures include: 1) restrict treatment flow and bypass a portion of the flow around the treatment facility; and 2) apply baffle plate structures. Both measures are used in this design as described in Sections 4.1.2.1 and 4.1.2.2, respectively.

Further field tests are needed to evaluate the design under field application conditions. 85

6.2 Suspended Solids Tests

6.2.1 Sediment Capacity Determination

The accumulation of sediment in the sediment chamber was observed under moderate flow conditions (approximately 14 L/min) without the baffle plate installed. To determine the maximum amount of sediment that could be contained in the device, suspended solids (SS) concentrations above 9 g/L using Soil #3 (medium sand) were added in the inlet and the sedimentation process was recorded. Soil #3 was chosen for this experiment because it is largely made up of medium sandy particles, which can be easily tracked visually in the device. Test was ended after 120 minutes, when approximately 90% of the space below the inner cylinder was filled with sediment. It was observed that sediment started to build up between the cylinders and block the flow paths. One conservative approach for the sediment capacity design is attached in

Appendix 2. Recommended sediment capacity for this design is 64% of the entire sediment storage volume.

The profile view of sediment accumulation is illustrated in Figure 6-2.

Inner Cylinder 8

7 )

h 6 c n i ( 5 h t p e

D 4

t 90% Full n e 3 m 70% Full d e

S 2 30% Full

1 1% Full

0 -6 -5 -4 -3 -2Botto-m1 of th0e Dev1ice 2 3 4 5 6

Figure 6-2 Sediment Accumulation, Profile View

Figure 6-3 shows the topography of captured sediment with approximately 135g of sediment in the chamber. Similar sediment shapes were also observed in the

Downstream Defender (Hydro International, 2006). It was observed that generally speaking, the movements of the fine particles near the bottom of the device were spirally towards the center of the device. This sediment movement direction is different from to the main direction of the flow, which is generally from inner cylinder to the outer cylinder. Thus, this flow regime may potentially prevent the settled sediment from escaping out of the device.

Higher Outlet

Sediment depth

Inlet

Lower

Figure 6-3 Sediment Topography

6.2.2 Scouring Tests

Tap water scouring test was conducted when 90% of the sediment chamber was filled with Soil #1 (approximately 9.0 liters of sediment). Tests were conducted from the low flow condition (14.5 L/min) to high flow conditions (up to 63 L/min). Outlet suspended solids (SS) concentrations were measured and plotted in Figure 6-4. The relative mass of sediment scored from the device through the entire scouring test was a trivial portion to the entire volume of the sediment in the chamber.

Scouring Flow Rates (L/min)

Figure 6-4 Scouring Test Results

During the scouring test, a peak concentration appeared after 0.3 ~ 1.5 minutes of the test. After the peak, the outlet SS concentrations decreased to a relatively steady state after 2~3 minutes.

When comparing the scouring test results of the device with those of the ADS multi-chamber model (Su, 2002), the device exhibited high resistance to the scouring

(average outlet SS concentration from the new device was approximately 1% of the outlet

SS concentration from the ADS horizontal design).

The key reason causing the difference is that the invert of the sediment storage zone in the vertical design is lower than that in the horizontal design. Moreover, the vortex flow traps the sediments to the center of the device, which prevent sediments from escaping to the perimeter outlets. Table 6-1 compares the parameters of the two devices. 89

Table 6-1 Comparison of Scouring Resistance Characteristics

Distance between Volume of the outlet invert Static Water Tendency of sediment water retained to the surface of Depth (inch) movements (L) the sediment (inch) Hydrodynamic Moving towards the 24 14.7 8.7 device center of the device ADS Model Moving towards the 90 8 2 ~ 6 (Su, 2002) outlet

To further protect the settled sediment from scouring, a sediment baffle plate

(Figure 4-1) was designed and installed in the laboratory scale model. At the start of the test, there were approximately 3 kg Soil #1 in the device. Visual observation was conducted to compare the sediment resuspension under various scouring flow conditions with or without the baffle plate. It was observed that the sediment baffle plate can significantly reduce the flow velocity above the sediment under high flow conditions

(above 30 L/min and up to 63 L/min monitored), thus reducing the resuspension of particles. However, the difference was not significant when flow rates were low (less than 20 L/min). Figure 6-5 contains photos showing the sediment resuspension before and after a baffle plate was installed (flow rate was 63 L/min). The sediment baffle not only reduced sediment resuspension, but also reduced the slopes of the retained sediments.

Baffle Plate Resuspension Zone Resuspension Zone

Settled Settled Sediment Sediment

Figure 6-5 Scouring and Sediment Resuspension at 63 L/min (Right: No Baffle Plate; Left: With Baffle Plate)

Further quantitative performance evaluations of the baffle plates on sediment protection and SS removal are needed in the future studies.

6.2.3 Suspended Solids Removal

Suspended solids removal tests were conducted with the lab model at different flow rates. The initial condition of the model was approximately 6 kg Soil #1 or Soil #4 which were added into the model with no baffle plate installed. The inlet SS concentration was about 1.4~2.7 g/L. Lab test flow rates were between 12 L/min and

65.3 L/min. Tests conducted on Soil #2 resulted in above 99% removal at all flow rates tested. Steady state removal of SS versus flow rates was plotted in Figure 6-6, Figure 6-7 and Figure 6-8 for Soil #1, Soil #2 and Soil #4, respectively. Removal of the coarser soil

(Soil #3) remained above 99.7 % over the applied flow rates. The removal of the finer soil (Soil #1) declined to between 60 to 70 % above 30 L/min flow. Removal of the finest soil sample (Soil #4) was lower than other soil samples and only 20% to 40% removal at flow above 30 L/min. 91

100%

90% SS Removal Test, Soil #1, Fine Soil 80% l a v 70% o m e 60% y = -0.1063Ln(x) + 1.0657 R

2 S 50% R = 0.9263 S 40% 30%

20% 0 10 20 30 40 50 60 70 Flow Rates (L/min)

Figure 6-6 Steady State SS Removal versus Flow Rates, Soil #1

100.00%

99.95% SS Removal Test, Soil #3, Sand

l 99.90% a v

o 99.85% m e R

99.80% S S 99.75% y = -0.0012Ln(x) + 1.0025 R2 = 0.8302 99.70%

99.65% 0 10 20 30 40 50 60 70 Flow Rates (L/min)

Figure 6-7 Steady State SS Removal versus Flow Rates, Soil #3

100% 90% SS Removal Test, Soil #4, Finest Soil 80%

l 70% a v

o 60% m

e 50% R

S 40% S 30% y = -0.1739Ln(x) + 0.9787 20% R2 = 0.9999 10% 0% 0 10 20 30 40 50 60 70 Flow Rates (L/min)

Figure 6-8 Steady State SS Removal versus Flow Rates, Soil #4

As shown in Figure 6-6, Figure 6-7 and Figure 6-8, SS removal versus flow rates with the new device is in general accordance with logarithmic decreasing function. Both the SS particle size distributions and flow rates have major effects on the removal.

Similar results were also found in other researches such as ADS treatment unit removal tests (Su, 2002) and Hydroworks hydrodynamic device removal (Hydroworks, 2005).

6.2.4 Suspended Solids Removal versus Particle Sizes

Although SS removals versus flow rates can provide useful information on understanding the removal under various flow conditions, using this removal data directly in a design is not recommended. This is because particle size distributions at each runoff site is unique and may not match any of the distributions tested in the laboratory. For design and sizing purposes, information on SS removal in each specific particle size 93

range is needed. In this study, removal versus particle sizes is achieved by conducting individual mass balance calculations (See Equation 5-2) in each particle range.

SS concentration and particle size distribution data for Soil #1 and #4 were used to calculate the removal for different particle size ranges. With known inlet and outlet SS concentrations, flow rates, and inlet and outlet particle size distributions, Equation 5-2 was applied for mass balance calculations in each particle size range. Then, SS removals for particles in every size ranges were plotted in a graph. Figure 6-9 shows the SS removal characteristics of different particles under various flow rates. As shown in

Figure 6-9, all particles above 180 microns were captured by the device at all flow rates tested. Higher flow rates resulted in lower removal for particles in all smaller ranges. As with all devices of this type, larger particles are more easily removed. Test results show that some removal was achieved even for very fine particles.

100%

90% 5 L/min (Surface 80% Load = 0.075 m/min) 10 L/min (Surface 70% Load = 0.149 m/min) 20 L/min (Surface s

e 60%

t Load = 0.299 m/min) a R

l 30 L/min (Surface

a 50%

v Load = 0.448 m/min) o

m 40 L/min (Surface e 40% R Load = 0.597 m/min) 30% 50 L/min (Surface Load = 0.746 m/min) 20% 60 L/min (Surface Load = 0.896 m/min ) 10% 65 L/min (Surface Load = 0.970 m/min) 0% 0 20 40 60 80 100 120 140 160 180 200 Partical Size (micron)

Figure 6-9 Removal vs. Particle Sizes

Allen (2004) conducted various SS removal tests on a full size four-foot diameter

VortSentry VS-40 Model (See Figure 3-4) using fine silica sand. SS removals versus particle sizes were analyzed at 0.5 cfs flow rate and Figure 6-10 was plotted in Allen’s study. The surface load applied to the VortSentry model was

Flow 0.5cfs = = 0.0398 ft / s = 0.73m / min, which is close to the high flow SurfaceArea π ⋅ 2 ft ⋅ 2 ft rate condition tested in this study (~ 48 L/min). Comparing the 50 L/min plots in Figure

6-9 via the hydrodynamic device to Figure 6-10, the SS removal versus particle sizes achieved in this study are similar to those conducted by Allen (2004). Both units exhibits good performance in removing coarse particles (i.e. >200 microns) while limited 95

performance in removing finer particles. Under the same surface load condition, the four-foot VortSentry V-40 exhibited higher removal on particles between 30 and 150 microns than the laboratory unit. However, it should be noted that the size of the V-40 device studied by Allen is about 60 times bigger than the laboratory unit tested in this study. Further studies on removal via a full size hydrodynamic device are needed (such as the 36’’ field device as presented in Appendix 3) and will provide more meaningful comparisons.

Figure 6-10 VortSentry Removal vs. Particle Size Distribution (Allen, 2004)

It was also noted that under high flow rates conditions (above 60 L/min), negative removals for some SS particles (57µm ~ 88µm) were observed. This implies that concentrations of those particles presented at the outlet were higher than those at the inlet. 96

This may potentially be caused by scouring of pre-settled sediments, or could be the system errors during sampling and/or sample analysis.

6.3 Oil Removal Tests

Observations for removal of oil were conducted under various flow conditions between 5 L/min to 65 L/min by adding oil into the inlet of the device. Little oil was observed in the outlet. Oil droplets mainly were retained on the surface of the inner cylinder. It was also observed that due to the turbulence on the surface of the inner cylinder, oil emulsification of the retained oil occur when the flow rate is high or the flow duration is long.

Oil removal tests conducted on the device was mainly based on visual observations by adding oil into the inlet of the device; further quantitative studies on oil particle size distributions and removal rates are needed.

The physical process of separation of oil from water by buoyancy is similar to the separation of SS from water by gravity. Stokes' law can be applied to calculate the flotation velocity (Tchobanoglous and Burton, 1991). However, gravitational separation of oil from storm water is more difficult for several reasons: 1) Separation driving force on a typical oil droplet is only 12% of a typical solid particle of the same size (See Figure

6-11 for a graphical illustration of separation forces). 2) Oil droplets may be attached to soil particles and make the mixture less floatable. A previous study showed that when the volumetric soil to oil ratio is above 1:8.3, the mixed oil-soil unit has specific weight greater than 1 thus no longer floatable (Su, 2002); and 3) turbulence in the flow may break oil into finer droplets or even emulsify the oil. 97

Figure 6-11 Forces on Oil Droplet and Soil Particle

To enhance oil removal, reducing the flow turbulences and adding adsorbent materials are considered. An inlet baffle structure as shown in (Figure 4-2) was installed in the laboratory model with Smart Sponge adsorbents (Ab tech Inc., 2006) added at the center of the inlet baffle. Tests were mainly based on visual observations with water tracers (dye study). It was observed that the inlet baffle plate reduced the turbulence on the surface of the device and shielded the adsorbent materials from scouring, while the prevalent spiral flow in the inner cylinder remained intact. Further studies on quantitative performance evaluation of the inlet baffle plate design are needed in further studies.

6.4 Oil Adsorption Tests

Results of the oil removal tests for three adsorbent materials (Type-O Sponge,

Smart Sponge, and Oil-Only Adsorbent) are shown in Figure 6-12. All adsorbent 98

materials exhibited fast adsorption in the first 15 minutes. Highest removals were achieved by Oil-Only adsorbent. Smart Sponge’s adsorption rate was slow at the beginning, but exceeded the Type-O sponge after 12 hours by about 5%.

Figure 6-12 Oil Removal Jar Test

Adsorbent materials were placed on tissue paper after the oil removal test (Figure

6-13). Significant leaching of previously adsorbed oil was observed from Type-O (10-15

% of the oil adsorbed), and some leaching was observed from Oil-Only material (1.5-2 % of the total oil adsorbed). No leaching was observed from Smart Sponge, indicating stronger binding of oil by this material.

Oil-only

Type M

Smart Sponge

Figure 6-13 Oil Leaching Observation

To further test the oil adsorption in a hydrodynamic environment, saturated Oil-

Only material and Smart Sponge were tested under simulated dynamic flow conditions as illustrated in Section 5.1.4.4 and Figure 5-6. Test results are shown in Figure 6-14. Smart

Sponge exhibited strong binding with the oil adsorbed. Only 15% oil was released from

Smart Sponge in a 48 hour period, compared to about 55% oil released from Oil-Only.

This indicates that the Smart Sponge has very good binding on oil adsorption compared with the Oil-Only adsorbent.

100

Figure 6-14 Absorbency under Dynamic Flow Conditions

Oil-Only is lower in cost (approximately $20 per cu. ft compared to $160 per cu. ft for Smart Sponge and $300 per cu. ft for Type-O) and exhibited fast adsorption. Smart

Sponge exhibited strong binding to the oil adsorbed. It is recommended that a combination of these two be considered in a future field application.

Besides oil absorption tests, further tests on metal removal using adsorbents (such as Type-M sponge) are needed to in the field investigation phase, which is beyond the scope of the dissertation.

101

CHAPTER 7 FIELD SITE DESCRIPTION AND INSTALLATION PLAN

7.1 Storm Water Runoff Characteristics

Grab samples were obtained at the drain receiving runoff from the bridge deck in

Athens, Ohio, during the initial portion of one moderate storm event on November 4th,

2005. Constituent concentrations are presented in Table 7-1 in comparison with those in the runoffs from a parking lot site at Ohio University, Athens, Ohio (Su, 2002). The parking lot runoff characteristics shown in Table 7-1 are the event mean averages of 13 storm events, while the bridge runoff is a grab sample from one event. Further monitoring data are needed for further studies after the device installation.

Table 7-1 Bridge Runoff Constituent Concentrations

Constituents Bridge Runoff Parking Lot Runoff Concentration (mg/l) Concentration (mg/L) (Su, 2002) Fe (total) 9.254 1.24 Pb (total) 0.013 0.146 Ni (total) 0.006 Not detected in most events Na (total) 8.257 86.3 Zn (total) 1.193 0.0365 Mn (total) 0.14 0.36 Cd (total) Not detected Not detected in most events Cr(total) Not detected Not detected in most events Oil and Grease Not tested 12 Suspended Solids (SS) 467.5 200 COD 167.5 112

High Suspended Solids (SS), Chemical Oxygen Demand (COD), zinc and iron concentrations from the bridge runoff site were observed while concentrations of Pb, Ni,

Mn and Na were lower. 102

Particle size distribution (PSD) of the sample is shown in Figure 7-1, in comparison with an average PSD from various urban sites studied in the National Urban

Runoff Program (USEPA, 1983).

Figure 7-1 Particle Size Distribution of Runoff Samples (by Volume)

The particle sizes in the runoff from the site were coarser than those from the

National Urban Runoff Program (USEPA, 1983). The bridge runoff particle sizes (D50

=16 micron) are similar to Soil #1 (D50 = 100 micron) and Soil #4 (D50 = 12 micron) used in the laboratory (see Table 5-2). Further study is needed to conduct flow-weighted composite sample analysis of the entire runoff event.

7.2 Description of Installation Plan

Proposed treatment system includes a collection manhole to collect runoff at the existing down drain and a treatment device, as illustrated in Figure 7-2 and Figure 7-3. 103

Samplers

Collection Manhole

Treatment Device

Figure 7-2 Conceptual Illustration of Installation

Figure 7-3 Schematic Overview of Manhole and Treatment Device 104

Figure 7-4 shows the proposed collection manhole structure design. The purpose of the manhole design is to convert the open channel from the existing structure to the treatment unit. For monitoring purposes, the bottom slope of the manhole was designed to ensure no sedimentation in the manhole.

42’’ I.D. HDPE

Existing concrete drain

21’’

12’’

36’’ A 5% concrete slope on the bottom of the unit to reduce sediment accumulation

6’’ I.D.

Figure 7-4 Overview of Manhole Unit

105

Figure 7-5 is an illustration of the components of the treatment structure.

Fabrication plans were designed and drafted by the author and plotted by Advanced

Drainage Systems (ADS) and is attached in Appendix 3. Fabrication of the device was till in progress when this dissertation was published.

#2.1 #2 #2.2

#2.3

#1.2 #1.4

#1.3

#1.1

#1.5

#1.6

#1.7

Figure 7-5 Conceptual Overview of the Treatment Unit 106

Table 7-2 provides a summarization of components of the treatment device.

Table 7-2 Components of the Field Treatment Device

Components * Descriptions General dimensions Part #1 Part #1.1 Inner cylinder 18’’ ID * 33’’ Part #1.2 Outer cylinder 36’’ ID* 60’’ Part #1.3 Inlet pipe 6’’ ID *30’’ Part #1.4 Outlet pipe 6’’ ID * 12’’ Part #1.5 Screens 36’’ Dia Part #1.6 Sediment baffle 15’’ ring + 8*10.5’’ blades; 3’’ high plate Part #1.7 Bottom plate 41.7’’ dia, water tight Part #2 Inlet baffle plate Not attached to part #1. Part #2.1 Plate 21.2’’ dia Part #2.2 270 degree cut 12’’ ID * 20’’; 2/4 of a pipe Part #2.3 Plate 6’’*20’’ Part #3 Plate lid 41.7’’ dia, not attached to part #1. * Note: see Appendix 3 for details of the components

7.3 Further Performance Evaluation Studies

Large scale sampling using automatic sampling at both the inlet and outlet of the treatment device is needed to evaluate the performance of the device in receiving pollutants from bridge runoff. However, field investigation of this design is beyond the scope of this dissertation.

107

CHAPTER 8 BMP COST-BENEFIT ANALYSIS AND CERTAINTY PRINCIPLE

Part of the contents presented in Chapter 8 was published in the proceedings of the 2006 American Surface Water Quality Conference and Exposition July 2006 (Su and

Mitchell, 2006a).

8.1 Storm Water Cost-Benefit Concepts

8.1.1 Introduction

A structural unit employed as a Best Management Practice (noted as BMP hereafter) for mitigation of storm water runoff can be considered as a unit intercepting and separating pollutants. The receiving environment benefits from pollutant reductions, while costs are unavoidable for the design, fabrication, installation, and maintenance of the unit. Evaluation and comparison of the overall cost-benefits of various structural

BMPs would be helpful to regulators, designers and others.

8.1.2 BMP Costs

The BMP cost is defined as a combination of all the costs and drawbacks of applying the BMP, such as costs for design, materials, fabrication, installation, maintenance, longevity, land occupation, water head loss and chemicals (Echols, 2002).

8.1.3 BMP Benefits

Benefits are defined as all the constructive benefits of utilizing the BMPs, which are mainly pollutant removal and flood control (Echols, 2002). The benefit commonly 108

has a maximum value at which pollutant reduction and other positive BMP functions are implemented to the maximum.

8.2 Methodologies

8.2.1 Simplification of the Problem

In this section, suspended solids (SS) removals via prevalent mechanisms are discussed to describe the cost-benefit relationship in storm water treatment. Although the mechanism of removal would vary, the general analytical processes and principles are applicable to other pollutants too with proper modifications.

8.2.2 Conceptual SS Removal Mechanisms

In a typical storm water treatment system, runoff is treated via physical and/or chemical processes in which some solids are intercepted and retained in the system, and then the treated runoff is discharged to the receiving environment. Major separation mechanisms are categorized into 1) gravity; 2) screening and filtration; 3) hydrodynamic; and 4) absorption, adsorption, coagulation and flocculation.

Directly related BMP cost and benefit of the above removal mechanisms are discussed below. Figure 8-1 schematically illustrates the removal mechanisms. 109

A Discrete System

(a): Gravity Separation: Consume time (∆t)

(b): Screening/ (c): Hydrodynamic (d): Adsorption/absorption Filtration/ Infiltration: Separation: /Coagulation/Flocculation: Consume Water Head Consume momentum Consume chemicals (∆M) (∆h) (∆p)

Figure 8-1 Illustration of Limitations of Separation Methods

1) Gravitational Separation: In a gravitational separation unit, such as a settling tank, settleable particles are removed by settling into the sediment storage zone, which is highly controlled by properties of the SS particles and water (Newton’s Law). To achieve a desired SS removal, sufficient settling time (∆t) is required, which is directly proportional to the surface area/volume ratio of the device. Thus, to increase removal, a larger unit generally is needed which directly relates to higher costs. 110

Gravitational separation is a simple process, accomplished with an uncomplicated structure, and requires almost no energy input, but requires sufficient area/volume.

Maintenance primarily involves sediment removal.

2) Screening and Filtration: Separation enhanced by a screen or filtration media reduces the requirement of surface area/volume. However, higher water head loss (∆h) is directly associated with these processes. To further increase SS removal, finer screens or finer filtration media are needed which result in higher water head loss. On the other hand, to reduce the water head loss, increasing the screening/filtration area and reducing the filtration speed are most common solutions which all require larger devices. Thus, reduced volume is balanced by increased water head loss. Frequent maintenance may also be required.

3) Hydrodynamic Separation: Hydrodynamic separators need to consume water momentum (∆p) to create desired rotary motion to enhance separation. While a power supply is not provided for runoff treatment, this momentum is usually created by consuming water head as well. Maintenance is similar to that required for gravity separation.

4) Absorption, Adsorption, Coagulation and Flocculation: Flow through absorbers/adsorbers can enhance pollutant removal, particularly dissolved material, but screening or filtration is needed to retain/maintain the absorbent/adsorbent within the treatment system, which increases the water head loss. In some cases, passive absorption/adsorption requires little water head but the removal efficiency is limited.

Coagulation and flocculation mechanisms can form larger particles, thus reducing the 111 volume needed for settling. However, chemical and/or material costs (∆M) are incurred, as well as probably greater maintenance costs.

8.2.3 SS Separation via Ideal Setting Tank

The settling tank is one common and simple design for SS separation. An ideal settling tank under laminar flow conditions was considered as the benchmark BMP in this study. Modeling of an ideal settling tank is shown in Figure 8-2.

V0 : Cut-off Velocity, V0= Q/A

Q: Flow A: Surface Area Rate

Vs: Settling Velocity Escaping Zone

Figure 8-2 SS Removal via an Ideal Settling Tank

It was assumed that the removal (R) of SS is based on Equation 8-1 and Equation

8-2 (Tchobanoglous, 1991).

R = 100%, when Vs > V0 Equation 8-1

R = Vs/V0, when Vs< V0; Equation 8-2 112

where: V0 = Cutoff Settling Velocity (m/s); and V0=Q/A;

(ρ − ρ )D 2 g Vs = Particle Settling Velocity (m/s); and V = ss water (Stokes’ law); s 18µ

Q = Flow Rate (m3/s);

A = Tank Surface Area (m2);

3 ss = Particle Density (kg/m );

3 water = Water Density (kg/m );

D = Particle Diameter (m);

g = Acceleration of Gravity (m/s2);

R = Removal of Particles with Settling Velocity = Vs; and

= Water Viscosity (N·s/m2).

8.2.4 SS Separation via Hydrodynamic Device

It was assumed that suspended solids (SS) removal via the hydrodynamic device is a function of particle size and surface load. SS removal on each particle size range (Rp) was calculated from laboratory test results in Figure 6-9. The overall removal (R) is calculated from total mass balance calculation of all particle size ranges, as shown in

Equation 8-3.

ƒ(Cin ⋅V ⋅ R p ⋅ Pp ) Massremoved ranges R = = = ƒ(Pp * R p ) Equation 8-3 Mass in V ⋅ Cin ranges

Where: R = Overall SS removal for certain particle size distribution;

Massin = Inlet SS mass (g);

Massremoved = Removed SS mass (g); 113

V = Treatment flow volume (L);

Cin = Inlet SS concentration (g/L);

Rp = Removal of particles in a certain size range;

Pp = Fraction of particles in a certain size range (untreated) (g/g);

8.3 Cost-Benefit Relations and Certainty Principle

8.3.1 BMP Certainty Principle

From the analysis of the prevalent BMP separation mechanisms, reducing the area/volume related costs (by reducing detention time ∆t) is commonly achieved by extra consumption of water head ∆h, water momentum ∆p, or chemicals ∆M. The total cost is balanced between consuming time and consuming energy/chemicals. Although it is possible to achieve a device several times more efficient than a settling tank (such as an inclined plate/tube settler), extremely effective BMPs with very low costs and very high benefits are not feasible.

The higher the BMP benefits, the higher the BMP costs, and vice versa; achieving a low cost - high benefit BMP is difficult/impossible. In this study, this is

referred to as the Certainty Principle.

The Certainty Principle illustrates a natural rule of separation. To separate a mixture (from an unorganized, high entropy phase to an organized, lower entropy phase), consumption of time (∆t), energy (∆E; ∆p and ∆h), and/or chemicals (∆M) is unavoidable.

In real world applications, consumption of time usually requires larger BMPs,

which results in an increase in related land occupational costs and unit costs. Since most

BMPs depend on non-mechanized energy sources, high energy losses can reduce 114

treatment capacity, which can be compensated by larger units but at additional cost, and can increase potential of flooding and/or require by passing of a portion of the flow, impairing the benefit and increasing the cost. Consumption of chemicals is directly related to ongoing treatment costs.

8.3.2 BMP Cost-Benefit Relations

A typical cost-benefit analysis includes estimates and totals up the equivalent money value of the benefits and costs of projects, and to establish whether they are worthwhile. Cost- benefit analysis is a very common method for decision makers to maximize efficiency and net benefits.

Figure 8-3 conceptually illustrates the relations between BMP costs and BMP benefits. In the figure, the cost-benefits of a settling tank are used as a benchmark device

(one of the simplest BMP) while cost-benefit of an Ultimate BMP (defined as the most

efficient BMP achievable) is also plotted. It was also assumed that an ultimate BMP is

more efficient than the simple BMP (such as declined plate settler or other advanced

treatment devices).

Several assumptions and engineering judgments were made when plotting the

conceptual BMP cost-benefit relationships for the Ultimate BMP and the Simple BMP. It was assumed that no benefit is achievable when no BMP is conducted; increasing costs

(such as bigger treatment device) can increase the benefits (such as higher removal); and the maximum benefit has a certain limit (when there is no contamination to the environment) which is associated with very high costs. 115

A 45 degree standard line was also plotted in the cost benefit chart, which represents a situation when cost equals to benefit. When the cost-benefit curve is above the standard line, positive net benefit is achievable.

*1) Maximum Benefit

*4) BMP *2) Ultimate BMP Benefits *5) *8) *3) Simple BMP - ideal settling tank

*6)

* 7) Maximum BMP net benefit

BMP Costs

Figure 8-3 Conceptual BMP Cost-Benefit Analyses

As illustrated in the figure, several zones and spots are identified as noted: 1) represents the existence of a maximum benefit value. However, the cost to achieve the maximum benefit value is extremely high; 2) illustrates the cost -benefit for some ultimate achievable BMP; 3) shows the results for a settling tank; 4) represents some

“ super” BMP, which is not realistic since it is not possible to achieve a significant low cost- high benefit result, no matter what BMP or BMPs are used; 5) denotes a well designed and applied BMP; 6) illustrates a bad BMP either from poor design, improper 116

application of a good design or other conditions; 7) is the maximum achievable net benefit, which is measured by the maximum distance between the cost-benefit curve and the 45 degree standard base line; and 8) represents a level which should be considered as the basis for decision making for storm water regulators; treatment regulations at this level may promote applications with the maximized net benefit.

With sufficient environmental assessment studies and extensive BMP performance analysis, approximations of a cost-benefit as illustrated in Figure 8-3 are theoretically feasible. However, significant efforts are required and are beyond the scope of this study.

8.3.3 Cost-Benefit Relation Expressions

Due to the complexity of cost-benefit measurements, it is impossible to use a

simple equation to precisely illustrate the cost benefit relations. However, it is feasible to use some mathematical expressions to illustrate the principle conceptually. A mathematical expression should at least match the following properties of the cost-benefit relations and the Certainty Principle: 1) Cost0, benefit 0; 2) Cost∞; benefit

dBenefit maximum benefit; 3) Cost, benefit or ( > 0 ); and 4) Crest curve or dCost

d 2 Benefit ( < 0 ). Any equation with the above properties may potentially be used to dCost 2 illustrate the cost-benefit relations and the Certainty Principle.

In this study, two popular mathematical expressions are utilized, the exponential expression and the power expression. The equations are as follows: 117

8.3.3.1 Cost-Benefit Relationship: Exponential Expression

1 (1− e(−K⋅C) ) × ( ) = 1 Equation 8-4 B

or linear correlation expression:

ln(1− B) = −K ⋅ C Equation 8-5 where: C = BMP Cost;

B = Normalized Benefit and B= Benefit/Maximum Benefit (unitless);

K= Cost Effectiveness Coefficient (unitless, >0);

8.3.3.2 Cost-Benefit Relationship: Power Expression

(1− B) × (C +1) K '= 1 Equation 8-6

or linear correlation expression:

ln(1− B) = −K'⋅ln(C +1) Equation 8-7

where: K’= Cost Effectiveness Coefficient (unitless, >0).

As can be seen from Equation 8-4 and Equation 8-6, the higher the K or K’ values,

the higher the benefits achievable at the same costs. It is also noted that more

complicated expressions and/or more appropriate parameters besides K and K’ may be

considered to fit the relationship better. However, accuracy and calibration of the

conceptual equations are beyond the scope of this study, but hopefully will be

quantifiable when sufficient cost-benefit information is available.

118

8.3.3.3 Removal - Normalized Surface Area: Exponential and Power Expressions

Although limited by the availability of cost-benefit studies and calculating the K and K’ is beyond this study, it is still valuable to use these expressions to analyze major

components of the cost and benefit. For example, in the following equations, normalized

benefit (B) was replaced by removal (R), and BMP cost (C) was replaced by normalized

BMP surface area (A/Ad).

A (−k⋅ ) 1 (1− e Ad ) × ( ) = 1; Equation 8-8 R

A and ln(1− R) = −k ⋅ Equation 8-9 Ad

A (1− R)× ( +1) k '= 1; Equation 8-10 Ad

A and ln(1− R) = −k'⋅ln( +1) Equation 8-11 Ad

where: A = BMP Surface Area (ft2);

2 Ad = Drainage Area (ft ); and

k and k’ = Effectiveness Coefficients (unitless, >0);

Equation 8-8 to Equation 8-11 simplify the cost-benefit relationship by considering the surface area (a major factor of cost) and removal (a major factor of benefit) only, and k and k’ are used to illustrate the effectiveness. Higher k and k’ indicate more effective treatment performance per normalized BMP surface area.

The cost-benefit relationship and removal-normalized surface area relationship (K,

K’, k and k’) can provide the basic information for BMP selection and BMP performance 119

evaluation. In the following sections, an example is provided to illustrate the effectiveness of SS removal via an ideal settling tank and a hydrodynamic device.

8.4 BMP Removal - Surface Area Analysis

8.4.1 Removal - Surface Area Analysis Example

Runoff from a 100% impervious drainage surface with 1.0 in/hour rainfall intensity was assumed for the model analysis, which is benchmarked as a unit runoff condition for the analysis. For runoff from various conditions, new diagrams can be simply derived by adjusted sizing factors. Both particle size distributions (PSD) from the bridge site and the National Urban Runoff Program (NURP) (USEPA, 1983) were used to evaluate the SS removal (See Figure 7-1).

SS removal calculations via an ideal settling tank and the hydrodynamic device with various surface areas were conducted. SS removal via the ideal settling tank was calculated based on Stokes' Law as described in Section 8.2.3. SS removal via the hydrodynamic device was calculated based on laboratory test results on SS removal versus various particle size ranges as discussed in Section 8.2.4 and Figure 6-9.

Removal versus normalized BMP surface area data using particle size distribution data from the bridge site study and the National Urban Runoff Program (USEPA, 1983) were plotted, as shown in Figure 8-4 and Figure 8-5, respectively. Comparison of the hydrodynamic device and settling tank for the same surface area shows that the hydrodynamic device achieves a higher predicted removal.

120

Figure 8-4 Removal vs. Normalized Surface Area- Bridge Site PSD

Figure 8-5 Removal vs. Normalized Surface Area - PSD from National Urban Runoff Program Study

Determination of the Effectiveness Coefficients (k and k’ ) is derived from

Equation 8-9 and Equation 8-11. Sample calculations of k and k’ using the bridge runoff

PSD are presented in Figure 8-6 and Figure 8-7 respectively. 121

A/Ad 0 0.00 0.02 0.04 0.06 -0.5 y = -35.46x

) R2 = 0.7509

R -1 - 1 (

n l -1.5 y = -86.32x R2 = 0.8650 -2 Ideal Settling Tank -2.5 Hydrodynamic Device Linear (Ideal Settling Tank) Linear (Hydrodynamic Device)

Figure 8-6 Determination of Effectiveness Coefficient k Using PSD from Bridge Deck Runoff

ln [A/Ad+1] 0 0 0.01 0.02 0.03 0.04 0.05 0.06 -0.5 y = -36.28x ) R2 = 0.7583

R -1 - 1 (

n -1.5 l y = -87.19x R2 = 0.8684 -2 Ideal Settling Tank -2.5 Hydrodynamic Device Linear (Ideal Settling Tank) Linear (Hydrodynamic Device)

Figure 8-7 Determination of Effectiveness Coefficient k’ Using PSD from National Urban Runoff Program Study

122

Effectiveness coefficients of the hydrodynamic device and the ideal settling tank are summarized in Table 8-1.

Table 8-1 Summarization of Effectiveness Coefficients Effectiveness Hydrodynamic Device Ideal Settling Tank Coefficients Bridge Site NURP Bridge NURP k 86.32 87.41 35.46 19.41 k’ 87.19 88.30 36.28 19.86

The hydrodynamic device exhibits higher effectiveness coefficients than that of the ideal settling tank. This suggests that for the devices with the same surface area/load, the hydrodynamic device may achieve higher removals than the ideal settling tank.

8.4.2 Removal - Surface Area for Sizing of BMPs

As can be seen from Figure 8-4 and Figure 8-5, the procedure of plotting removal versus normalized surface area can also be used as a fundamental procedure for sizing a

BMP. A design flow chart of this procedure is shown in Figure 8-8.

123

Drainage Area (A) Design Rainfall Intensity (I) Runoff Coefficient (C)

Design Runoff Quantity (Q=CIA)

Surface Load (Q/A) Runoff PSD

BMP Surface

Area (A) 100% 90% 5 L/min (Surface 80% Load = 0.075 m/min) 10 L/min (Surface Removal vs. 70% Load = 0.149 m/min) 20 L/min (Surface s

e 60% t Load = 0.299 m/min) a R

l 30 L/min (Surface

a 50% v Load = 0.448 m/min) o m

e 40% 40 L/min (Surface R Load = 0.597 m/min) 30% 50 L/min (Surface Surface Load and Load = 0.746 m/min) 20% 60 L/min (Surface Load = 0.896 m/min ) 10% 65 L/min (Surface Load = 0.970 m/min) 0% 0 20 40 60 80 100 120 140 160 180 200 Particle sizes Partical Size (micron)

Predicted SS removal (R)

Figure 8-8 Flow Diagram of BMP Sizing

When the design treatment flow rate (Q), BMP removal characteristics (R vs. surface load at various PSD), and design PSD is determined, a predicted removal versus BMP surface area can be plotted. The predicted removal versus BMP surface area can help the designer determine the desired surface area of the BMP and then further design the dimensions of the device.

124

CHAPTER 9 FIRST ORDER MODELING OF RUNOFF FIRST FLUSHES

9.1 Structure of the Modeling

A basic first flush wash-off model and a build-up model were first addressed in a previous study by the author (Su, 2002, Su and Mitchell, 2003). In this dissertation, both models were justified and developed further to better illustrate the significance of the first flush and to match consistency of further model developments.

Modifications have been made to the previous model (introducing the concepts of first flush coefficient, extra model validations etc.) and a standardized and quantified first flush analysis method is created. In order to keep the constancy of the entire modeling approach, modified first flush wash-off and build-up models from previous works are summarized and presented in Appendix 4 of this dissertation.

Then, further modeling studies and model validations were conducted in this research. Validation and application of the basic model using field data are presented in

Section 9.2. Based on the basic first flush analysis method, runoff characteristics from more complicated situations were also analyzed in this study, such as runoff with continuous source contributions (Section 9.3), runoff from large areas (Section 9.4), and runoff via detention-retention systems (Section 9.5). First flush properties and principles based on modeling are summarized in Section 9.6. The author also wants to address than part of the research in Section 9.4 has been published in 2004 (Su and Mitchell, 2004), and Section 9.5 has been published in another paper in 2006 (Su and Mitchell, 2006b).

125

9.2 First Order Wash-off Model Application and Validation

Basic theory of the modeling approach for the wash-off is addressed in Appendix

4. However, due to the limitation of the previous work, little model validation study was conducted. Only one storm event data from a parking lot site was used, which is not sufficient to illustrate the model. Further validation study was recommended in the pervious work (Su, 2002).

In this dissertation, a total of thirteen storm events at a highway section of US-33 were analyzed for first flush (FF) model evaluation and first flush analysis. An individual Cff (first flush coefficient) value was calculated for each constituent in each storm event. Ninety-seven Cff values were obtained from total suspended solids (TSS),

COD, and metals’ data. The gross Cff data exhibited a normal distribution with a mean of

-1 0.65 and a standard deviation of 1.3. The Kw (wash-off coefficient, depth ) data also

exhibited a normal distribution with a mean of 9.26 in-1 and a standard deviation of 17.2

-1 in . Variations of Cff and Kw are high, which is similar to variations of b values using the power function method (Hunt, 2002b). Seventy four percent of the Cff and Kw were

positive, indicating the existence of first flush in most rainfall-runoff events at the highway site.

Figure 9-1 graphically illustrates the Cff variations for each constituent, and Figure

9-2 shows the mean values of Cff and Kw for each constituent. TSS (total suspended

solids) showed stronger first flush effects than COD and metals. Mean Cff and Kw values,

all of which were positive, indicate first flush effects occurred for these constituents.

126

Max

Median

Min

Figure 9-1 Cff Variations for Various Constituents

1.60 40 1.40 Cff Mean 35 1.20 Kw Mean 30 ) 1

1.00 25 - h f c f n

0.80 20 i C (

0.60 15 w K 0.40 10 0.20 5 0.00 0 Ca Fe Mg Ni Pb Zn COD TSS Constituents

Figure 9-2 Average Cff and Kw for Various Constituents

127

Using mean Cff values obtained from Figure 9-2, Figure 9-3 was plotted to illustrate the normalized cumulative mass versus normalized cumulative depth for various constituents from this site.

To illustrate the use of Figure 9-3, from the curve of TSS, 67% of the TSS on average will be found in the first 50% of the runoff from this site. Data from this site indicate a very mild first flush effect, much less significant than the 80:20, 80:30, and

50:25 findings. (Stahre and Urbonas, 1990; Bertrand-Krajewski et al. 1998 and

Wanielista and Yousef 1993).

standard line Ca standard line Fe Mg COD Ni Pb TSS 1 Zn 1 0.9 0.9 s s

s 0.8 s a

a 0.8 M M

0.7 e e 0.7 v v i i t t a a

l 0.6 l 0.6 u u m m u 0.5 u 0.5 C C

d d 0.4

e 0.4 e z z i i l l a 0.3 a 0.3 m m r r o o 0.2 N 0.2 N 0.1 0.1 0 0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Normalized Cumulative Depth Normalized Cumulative Depth

Figure 9-3 Normalized Cumulative Mass versus Normalized Cumulative Depth Using Mean Cff Values

128

9.3 Characteristics of First Flush with Continuous Sources

Nomenclature and abbreviations used in Section 9.3 are shown in Table 9-1.

Table 9-1 Nomenclatures and Abbreviations in Section 9.3

Symbols Description Units

Cp partial concentration from first order portion Mass/Volume

Cp0 Initial partial concentration from first order portion Mass/Volume

Cc Partial concentration from the continuous source Mass/Volume

Ct Overall concentration Mass/Volume

Ct0 Overall initial concentration Mass/Volume

Cff-p First flush coefficient of the first order portion -

Cff-total Overall first flush coefficient -

rc Continuous source ratio -

Vc Continuous source runoff volume Volume

Vp First order portion runoff volume Volume

Kc Continuous source influence factor -

It is common to consider that some constant pollutant source may exist during the storm event (Australia EPA, 2006). For example, runoff from a highway section may contain some constituents accumulated on the drainage area before the event and some continuously entering the system from moving vehicles or constant soil erosions during the event. The former can be considered as a first order source and the latter is a continuous source. A conceptual illustration of combining the two effects is shown in

Figure 9-4. 129

e C z p −C ff − p ⋅x i l = e

a . c m C p0 r n o o N C time

First Order Source e z i l

a . c m r n o o N C Continuous Source time

C −C ⋅x d t ff −total

e ≈ e z i l Ct0

a C

. c c m r n o o N C time

Figure 9-4 First Flush with Continuous Source

The effects of adding a continuous source to the significance of first flush are discussed in this chapter. An example is shown in Figure 9-5. One curve is a combination of 30% continuous source and 70% first flush source with Cff = 1.0; while

the other curve is achieved by plotting 0% continuous source and 100% first flush with

Cff = 0.63. From Figure 9-5, it is seen that using the first order wash-off model with adjusted coefficients can approximate the concentrations with continuous sources. This provides a solution to measure the first flush reduction when a continuous source joins the first flush source. Hence, the objective is to use a first order model to analyze the first flush characteristics when a continuous source is present in the runoff.

130

1.00 ) 0 C /

C 0.80 (

n o i t a

r 0.60 t n e c n

o Constant Source (30%) 0.40 C + FF with Cff=1.0 (70%) d e z i l FF with Cff=0.63 a 0.20 m r o N 0.00 0 0.2 0.4 0.6 0.8 1 Normalized Cumulative Runoff Volume (x)

Figure 9-5 First Order Wash-off Model Approach

9.3.1 Modeling Methodology

It is assumed that the constituents in the runoff are from two parts. The first portion is the constant concentration source Cc during the event. And, the second portion

is the first order wash-off source described in Equation 9-1.

C −C ⋅x ( p ) = e ff − p Equation 9-1 C p0

where: Cff-p = first flush coefficient for the first order wash-off source (unitless);

x = normalized cumulative runoff volume or depth (unitless);

Cp =constituent concentration (Mass/Volume); and

Cp0 = initial constituent concentration (Mass/Volume).

The weight of the continuous source to the total sources is defined as Continuous

Source Ratio (rc), as shown in Equation 9-2. 131

Cc ⋅Vc rc = Equation 9-2 Cc ⋅Vc + C p0 ⋅V p0

where: rc = continuous source ratio (unitless);

Cc = continuous source concentration (mass/volume);

Cp0 = first flush source initial concentration (mass/volume);

Vc = continuous source runoff volume (volume); and

Vp0 = first flush source runoff volume (volume).

Then, the normalized constituent concentration of the total drainage area can be calculated from Equation 9-3

−C ff − p ⋅x Ct = rc ⋅ Cc + (1− rc ) ⋅ C p0 ⋅ e Equation 9-3

Where Ct: overall constituent concentration (mass/volume);

Finally, with known overall constituent runoff concentration, the overall first flush coefficient is estimated from Equation 9-4.

C −C ⋅x t ≈ e ff −total Equation 9-4 Ct 0

where: Cff-total = overall first flush coefficient (unitless); and

Ct0 = initial overall constituent concentration, (mass/volume).

Different Cff-p and rc values were input into an Excel spread sheet and the excel

Goal-Seek function was used to find the closet match of Cff-total to illustrate the FF when

continuous source exists. Cff-total is compared with Cff-p to analyze the first flush reduction

due to the existence of continuous sources. 132

9.3.2 Characteristics of First Flush Effects with Continuous Sources

Figure 9-6 presents the values of Cff-total under different rc and Cff-p conditions with

the higher rc producing lower Cff-total. When rc = 1 (100% continuous source), Cff-total =0.

3.0 Cff-p=0.6 2.4 Cff-p=1.2 Cff-p=1.8 l a

t 1.8 Cff-p=2.4 o t

- Cff-p=3.0 f f 1.2 C

0.6

0.0 0 0.2 0.4 0.6 0.8 1 rc, Continous Source Ratio

Figure 9-6 First Flush Coefficient vs. Continuous Source Ratio

To quantitatively analyze the first flush change due to the presence of the continuous sources, a new term, the Continuous Source Influence Factor, Kc, is defined

in Equation 9-5.

C ff −t Kc = Equation 9-5 C ff − p

Kc values versus Continuous Source Ratio (rc) were calculated from Figure 9-6 and

plotted in Figure 9-7. 133

1.0 Cff-p=0.6 0.8 Cff-p=1.2 Cff-p=1.8

l Cff-p=2.4 a 0.6 t o

t Cff-p=3.0 - f f

C 0.4

0.2

0.0 0 0.2 0.4 0.6 0.8 1

rc, Continous Source Ratio

Figure 9-7 Kc vs. Continuous Source Ratio

It is shown in Figure 9-6 and Figure 9-7 that existence of the continuous source results in reduced first flush effects (Kc<1). The magnitude of first flush reduction is mainly influenced by rc. Cff-p also has some influence on Kc.

9.4 Characteristics of First Flush for a Large Drainage Area

Nomenclature and abbreviations used in Section 9.4 are shown in Table 9-2. 134

Table 9-2 Nomenclatures and Abbreviations in Section 9.4

Symbols Description Units n Total number of elements - i One element number (0<=i<=n) -

Celement Constituent concentration from one element Mass/Volume

Celement0 Initial constituent concentration from one element Mass/Volume

Carea Constituent concentration from the drainage area Mass/Volume

Cff-element Element first flush coefficient -

Cff-area Area first flush coefficient - V Runoff volume Volume/area M Mass Mass/area Q Flow rate from the area Volume/time

Qmax Peak flow rate from the area Volume/time

Tc Maximum collection time Time

Trf Rainfall duration Time

ra Collection time ratio -

Ka Area influence factor -

As discussed in Section 2.5.3, one first flush phenomenon identified by previous researchers is that first flush can hardly be observed in large catchments (Australia EPA

2006). However, neither the reason behind this phenomenon nor the significance of first flash reduction has been studied.

The objective of this investigation was to use a first order model and an element model to analyze the first flush characteristics from a large drainage area. Two conceptual drainage shapes, the slim and round area model were studied. The slim area model can be used to characterize first flush from areas such as a highway section or a bridge; the round area model can be used to characterize first flush from a watershed 135

approximately round or fan shaped. Runoff properties from other shapes can be analyzed with some adjustments to the procedure and program used in this study.

9.4.1 Modeling Methodology

9.4.1.1 Slim Area Element Methods

A one dimensional element model was used to numerically analyze the runoff from a slim area such as runoff collected from a bridge deck. The drainage area was meshed into uniform elements, as shown schematically in Figure 9-8 (Su and Mitchell,

2004).

n … 2 1 No. n No. n-1 Total Number of … No. i+1 No. i Elements: n No. i-1 … … No. 3 No. 2 Element No. 1

…

1 dV = TotalVolume ⋅ Collection Point i n

i CollectionTime = MaximumCollectionTime ⋅ i n

Figure 9-8 Element Mesh for Slim Area Model

136

9.4.1.2 Round Drainage Area Element Methods

A two dimensional round area model can be simplified into a one dimensional model shown schematically in Figure 9-9. Each element has different drainage size, which is proportional to the distance from the collection point.

No. n

No. n-1

No. i+1 1 No. i 2 n … No. i-1 Total Number of Elements: n … No. 3 No. 2 Element No. 1

2 ⋅π ⋅ i dV = TotalVolume ⋅ Collection Point i π ⋅ n2

i CollectionTime = MaximumCollectionTime ⋅ i n

Figure 9-9 Element Mesh for Round Area Model

137

9.4.1.3 First Flush from One Element

Constituent concentration change in the runoff from each element is based on a first-order wash-off model (Su and Mitchell, 2003). It is assumed that the constituent concentration follows an exponentially decreasing function as described in Equation

A4-5, or more specifically, Equation 9-6.

C −C ⋅x element = e ff −element Equation 9-6 Celement −0

where: Cff-element = first flush coefficient of each element.

9.4.1.4 Summing Element Effects

It is assumed that the runoff from one element is drained from the drainage area and does not influence the wash-offs of the following elements. It is also assumed that constituents are conservative and flows from elements to the collecting point follow plug- flow. Thus, the characteristics of the runoff from the whole area can be obtained by summing up the effects of elements contributing to the runoff at any time, as described in

Equation 9-7.

ƒ(dV *Celement ) dM total contributing _ elements Carea = = Equation 9-7 dVtotal ƒ(dV ) contributing _ elements

where Carea = constituent concentration in the area runoff at any time (mass/volume);

dMtotal = total mass in the area runoff over any period (mass);

dVtotal = total area runoff volume over any time period (volume); 138

dV = volume of runoff from an element that contributes to the area runoff over

any period (volume); and

Celement= the constituent concentration in the runoff from an element

(mass/volume).

With sufficient number of elements and sufficient number of time periods, a numerical simulation of the continuous runoff model can be obtained. Tests were conducted to ensure sufficient number of meshes used to achieve necessary accuracy.

Each drainage area was meshed into 500 elements, and each rainfall period was meshed into 500 stages which ensure sufficient accuracy.

9.4.1.5 Finding the Cff from Concentration Changes

The constituent concentrations at the collection point from a large drainage area were calculated and plotted in a Normalized Cumulative Mass vs. Normalized

Cumulative Volume (NCM-NCV) chart. The region enclosed between the plots and the standard line was measured and used to find the corresponding Cff using Equation A4-10.

9.4.1.6 Programming and Model Implementation

To implement the calculations, a computer program, FirstFlush Area 1.0, (see

Figure 9-10, the screen shot of FirstFlush Area 1.0 user interface) using Visual Basic 6.0 was created for the implementation of the modeling and calculation of area runoffs.

139

Figure 9-10 FirstFlush Area 1.0 User Interface

With FirstFlush Area 1.0, the user can select the number of elements of the area and time. After the user inputs the Cff value of the elements (Cff-element) and the collection

time ratio (defined as the maximum runoff collection time or time of concentration / rainfall duration) of the area, FirstFlush Area 1.0 runs through the first flush and the element model, then plots the numerical solutions to the flow and constituent concentrations for the area runoff.

140

9.4.2 Characteristics of First Flush Effects from a Large Drainage Area

9.4.2.1 Characteristics of the Flow Rate

Runoff flow rates of the slim area are normalized by dividing them by the total flow rate Q, which is defined in Equation 9-8.

Q = Cr ⋅ I ⋅ A Equation 9-8

where: Cr = runoff coefficient (unitless);

I = rainfall intensity (Distance/Time); and

A = drainage area (Distance2).

Shown in Figure 9-11 is the characteristics of the normalized flow rates over a slim area, assuming uniform rainfall intensity.

1.1

1 Runoff Collection Tim e Ratio =0 e t 0.9 Runoff Collection Tim e Ratio =0.5 a ) R 0.8 Runoff Collection Tim e Ratio =1.0

Q / w 0.7 Runoff Collection Tim e Ratio =1.5 e o t l 0.6 Runoff Collection Tim e Ratio =2.0 a F

R d

0.5 e w

z 0.4 i o l l a

F 0.3 ( m

r 0.2 o 0.1 N 0 0 0.5 1 1.5 2 2.5 3 3.5 4 Norm alized Tim e (Tim e/Rainfall Duration)

Figure 9-11 Normalized Flow Rates versus Normalized Time

Define ra as Collection Time Ratio as shown in Equation 9-9.

ra = Tc /Trf Equation 9-9 141

Where Tc= maximum runoff collection time (time);

Trf = rainfall duration (time); and

ra = collection Time Ratio (unitless).

Then Equation 9-10 gives the values of the peak flow rates (Q peak) for a slim area. Qpeak, is essential to treatment system designs.

When ra <= 1; Q peak= Q

When ra > 1; Q peak= (Trf /Tc ) ⋅ Q Equation 9-10

Runoff flow rates from a round area have the same peak values as shown in

Equation 9-10. However, a sag curve (beginning stage before Q reaches the Qpeak) and a crest curve (end stage when Q starts to drop from Qpeak) replace the straight lines in

Figure 9-11.

9.4.2.2 Concentration Change

A first flush condition with element Cff = 3.0 is used to illustrate runoff quality for

both the entire slim area and an element (ra = Tc/Trf =0). Concentrations with different

collection time ratios (ra or Tc/Trf) are plotted in Figure 9-12 and Figure 9-13. 142

1 Collection Tim e Ratio = 0 0.9 Collection Tim e Ratio = 0.5

n Collection Tim e Ratio = 1.0 o i t 0.8 Collection Tim e Ratio =1.5 a r t Collection Tim e Ratio =2.0 n

e 0.7 c n o 0.6 C

t n e

u 0.5 t i t s

n 0.4 o C

e 0.3 z i l a 0.2 m r o

N 0.1

0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Norm alized Cum ulative Rainfall Volum e

Figure 9-12 Characteristics of Concentration Changes (Slim Area) - Normalized Concentration versus Normalized Cumulative Volume

0.9

0.8 s s

a 0.7 M

e v i

t 0.6 a l u

m 0.5 u C

d 0.4 e z i l

a 0.3 m

r Collection Tim e Ratio =0 o Collection Tim e Ratio =0.5 N 0.2 Collection Tim e Ratio =1.0 Collection Tim e Ratio =1.5 0.1 Collection Tim e Ratio =2.0 Standard Line 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Norm alized Cum ulative Rainfall Volum e

Figure 9-13 Characteristics of First Flushes (Slim Area) - Normalized Cumulative Mass versus Normalized Cumulative Volume

143

As shown in Figure 9-12, as the ra increases, a more rapid decrease occurs in the

constituent concentration for the initial portion of the runoff, followed by a more gradual

decline. For events with a collection time ratio greater than 1 (Tc>Trf), a constant concentration period exists during the event. This phenomenon is a result of the plug flow

assumption. During this period, runoff collected is contributed from an area with size

(A‡Trf /Tc), from which the element runoffs evenly mix at different concentrations. The

concentration equals to the flow weighted event mean concentration in the runoff from an

element (or the area). Derived from Equation A4-15, the concentration equals

1− e (−C ff ) to ( ⋅C0 ) . The constant concentration period starts at Trf and ends at the C ff maximum collection time (Tc). The normalized rainfall volumes at this stage are between

[(Trf/Tc)/2] and [1-(Trf/Tc)/2].

Figure 9-13 illustrates the significance of first flush effects from a slim area under

different collection time ratios. Curves with higher collection time ratios are closer to the

standard line than those with lower collection time ratios, which indicate less significant

first flush effects.

Concentration change and NCM-NCV from a round area are similar to those in

Figure 9-12 and Figure 9-13. Round area concentrations also exhibit a more rapid

decrease in the constituent concentration for the initial portion of the runoff, followed by

a more gradual decline. Runoff from a round area does not have a constant concentration

stage. Round area runoff also exhibits less significant first flush effects. Curves with

higher collection time ratios are closer to the standard line for the round area model. 144

9.4.2.3 First Flush Reduction

As the collection time ratio increases, the curve approaches the standard line (see

Figure 9-13), indicating less prevalent first flush effects. To quantify the reduction of the

first flush by the collection time ratio, S, the area enclosed between the curve and the

standard line on normalized cumulative mass versus normalized cumulative volume

charts was measured, and Cff was calculated from Equation A4-10. To quantify the

change of the first flush effects due to the collection time ratio, a new term, the Area

Influence Factor, Ka, is defined in Equation 9-11.

C ff −area K a = Equation 9-11 C ff −element where: Cff-element = the first flush coefficient of each element; and

Cff-area = the first flush coefficient of the whole area.

Different Cff-element values and collection time ratios were input into the FirstFlush

1.0 program. Then the Cff-area values were calculated and used to find Ka values. Ka values versus collection time ratios under different Cff-element conditions are plotted in

Figure 9-14 (slim area) and Figure 9-16 (round area). In a similar manner, Figure 9-15

and Figure 9-17 are plotted as Ka versus collection time ratios under different Cff-area conditions. Ka values could help the designer estimate Cff-area from Cff-element or estimate

Cff-element from Cff-area with known collection time ratios. 145

1.0

0.9 ) t n e 0.8 Elem ent Cff = 0.1 m

e Elem ent Cff = 1.0 l E 0.7 Elem ent Cff = 2.0 e n

o Elem ent Cff = 3.0

f 0.6 o f f C

0.5 / a e r 0.4 A e h t 0.3 f o f f 0.2 C (

, a

K 0.1

0.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Collection Tim e Ratio (Maxm um Collection Tim e / Rainfall Duration)

Figure 9-14 Ka versus ra for a Slim Area (Known Cff-element to Find Unknown Ka and Cff-area)

1.0

) 0.9 t Area Cff= 0.1 n e Area Cff= 0.5 m 0.8 e l Area Cff= 1.0 E

e 0.7

n Area Cff= 2.0 o f

o 0.6 Area Cff= 3.0 f f C

0.5 a e r

A 0.4 e h t

f 0.3 o f f C

( 0.2

, a

K 0.1

0.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Collection Time Ratio (Maxmum Collection Time / Rainfall Duration)

Figure 9-15 Ka versus ra for a Slim Area (Known Cff-area to Find Unknown Ka and Cff-element)

146

1.0

0.9

) Element Cff = 0.1 t n

e 0.8 Element Cff = 1.0 m e l Element Cff = 2.0 E

0.7 e

n Element Cff = 3.0 o

f 0.6 o

f f C

0.5 a e r A

0.4 e h t

f o

0.3 f f C (

, 0.2 a K 0.1

0.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Collection Time Ratio (Maxmum Collection Time / Rainfall Duration)

Figure 9-16 Ka versus Collection Time Ratio for a Round Area (Known Cff- element to Find Unknown Ka and Cff-area)

1.0

) 0.9 t Area Cff= 0.1 n e Area Cff= 0.5

m 0.8 e l Area Cff= 1.0 E

e 0.7

n Area Cff= 2.0 o f

o 0.6 Area Cff= 3.0 f f C

0.5 a e r

A 0.4 e h t

f 0.3 o f f C

( 0.2

, a

K 0.1

0.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Collection Time Ratio (Maxmum Collection Time / Rainfall Duration)

Figure 9-17 Ka versus Collection Time Ratio for a Round Area (Known Cff- area to Find Unknown Ka and Cff-element)

147

As shown from Figure 9-14 to Figure 9-17, Ka values are all less than 1, indicating that the runoff from a large area has less significant first flush effects than that

from its elements (Cff-area < Cff-element). Areas with higher collection time ratios tend to

have less significant first flush. This concurs with other studies in large catchments that

first flush may not be observed because “ initial runoff from the most distant parts of the

catchment may not reach the catchment outlet for some time after a storm starts”

(Australia EPA, 2006). In another storm water first flush document, NC Department of

Environment and Nature Resources (2006) also stated that “ first flush systems play an

important role in the control of stormwater pollution, particularly if the drainage area is

small and large parts of the catchment are impervious” .

As indicated in Figure 9-14, the collection time ratio (ra = Tc/Trf) is the predominant factor influencing Ka. Ka varies only slightly for Cff-element from 0.1 to 3.0.

Ka does decrease more rapidly for large Cff-element. (The dashed lines in Figure 9-15

indicate the region of the curve which produces impractical results).

Ka of a slim area is smaller than Ka of a round area, but the difference is not significant. This indicates that the shape of the drainage area may have less significant influence on the first flush reductions.

9.4.3 Area Runoff Model Application Example

Runoff data from research conducted by McKenzie and Irwin (1983) is used to

illustrate the calculation and model application. The runoff was from a 1,048-foot long,

62,415 ft2 section of a heavily used urban highway bridge in central Miami. Three

asphalt traffic lanes and one emergency lane sloped longitudinally downward to the 148 monitored collection site near the end of the bridge. Concentrations of dissolved solids and zinc during one storm event in March 23, 1981, were used to measure the Cff-area values and then calculate the Cff-element values. Major parameters and calculation results are listed in Table 9-3.

Table 9-3 Calculation Example, Major Parameters in the Calculation

a S

t m

s e K 0 , t o r a

o i ) m

a 1

r o t c - n f - l f D

f o n f a f

T r s 4 P w ( o o f d

C e R

l o

t A r t n e

5 e a

d e e m n n n a u u e n i

1 d e n r r i o e ) m - t g e w K a F f m R r i o a i u -

r u i

a 9 f i f t o , t f

t T F m T i ) D T b

n m f e / m u t a

C u r c r l n d

s o c u e s u O n K l T l r u

T e r

o d n s q , ( a o f t i ( a f g m

u i n f o t r a i i , A m t E s t d l a C c n

T x C c

n F o i a e e / e , e g r a

e l l c a e l f a m l n R p

l e e T i R , l o r M m

s o e

a m - a d , - f C f f C a f U n S K S C a C Dissolved Figure 18 min 9 min 0.50 0.160 2.03 0.88 2.30 Solids 9-18 Figure Zinc 18 min 9 min 0.50 0.098 1.20 0.89 1.35 9-19

As shown in Table 1, the Cff-element and Cff-area of dissolved solids are 2.30 and

2.03, and those of zinc are 1.35 and 1.20, respectively. Figure 9-18 and Figure 9-19 and are plotted for comparison of the model results and field data. First flush effects of an element are only slightly more significant than those of an area in this particular example.

Obviously, for validation of the model a much larger data base is needed that encompasses many rainfall-runoff events with preferably data obtained from elemental runoff as well as the entire slim area. 149

1.0 s

s 0.8 a M

e v i t a

l 0.6 u m u C

e 0.4

z Dissolved Solids Field i l

a Data (Area Runoff)

m Washoff Model- Area r o Cff=2.03

N 0.2 Washoff Model - Element Cff=2.30 Standard Line 0.0 0 0.2 0.4 0.6 0.8 1 Normalized Cumulative Runoff Volume

Figure 9-18 Calculation Example for Dissolved Solids (Data Source from March 23, 1981)

1.0 s

s 0.8 a M

e v i t a

l 0.6 u m u C

e 0.4

z Dissolved Zinc Field i l

a Data (Area Runoff) m

r Washoff Model- Area o Cff=1.20

N 0.2 Washoff Model - Element Cff=1.35 Standard Line 0.0 0 0.2 0.4 0.6 0.8 1 Normalized Cumulative Runoff Volume

Figure 9-19 Calculation Example for Zinc (Data Source from March 23, 1981) 150

9.5 Characteristics of First Flush via a Retention/Detention Device

Nomenclature and abbreviations used in Section 9.5 are shown in Table 9-4.

Table 9-4 Nomenclature and Abbreviations in Section 9.5

Symbols Description Units PFR Plug Flow Reactor - CSTR Continuous Stirred Tank Reactor - RD Retention-detention device -

Cin Constituent concentration at inlet Mass/Volume

C0 Initial constituent concentration at inlet Mass/Volume

Cinitial Constituent concentration in the reactor before the event Mass/Volume

Cff-inlet Inlet first flush coefficient -

Cff-outlet Outlet first flush coefficient -

Rs Steady state removal -

Rf Flow through removal - R Overall removal - V Detention volume Volume

Vrf Runoff volume Volume V1 Detention volume of CSTR-1 Volume V2 Detention volume of CSTR-2 Volume

rv Volume factor -

Kv Volume influence factor - Normalized runoff volume where peak outlet concentration x - peak occurred

The objectives were to: 1) use a first order model to analyze the first flush characteristics at the outlet of a conceptual retention-detention (RD) system and 2) compare the significance of first flush at the inlet and outlet for a wetland receiving highway runoff.

151

9.5.1 Modeling Methodology

A retention-detention (RD) unit/device was simplified and represented as a Plug

Flow Reactor (PFR) or a Continuous Stirred Tank Reactor (CSTR). Such units/devices are usually characterized by large storage volume (V). Overall removal (R) of constituents/ pollutants introduced to the unit is achieved by Flow through Removal

(Rf) during a rainfall/snowfall- runoff event or Static Removal (Rs) during dry days. An

illustration of an ideal RD device treating stormwater runoff is shown in Figure 9-20.

PFR/CSTR C inlet x

CSTR PFR

or Diffusion = ∞ Diffusion = 0

CSTR PFR outlet outlet

Figure 9-20 Illustration of CSTR and PFR Applications in First Flush Analysis

152

It is assumed that before the event, constituent concentration in the reactors is

Cinitial, a constant value. During the event, the inlet constituent concentration (Cin) is assumed as a first order decreasing function, as in Equation 9-12:

C −C ⋅x in = e ff −inlet Equation 9-12 C0 where: Cff-inlet = first flush coefficient at the inlet (unitless); and

Cin = inlet constituent concentration (mass/volume).

Extreme diffusion in the CSTR and zero diffusion in the PFR were assumed. It

was also assumed that the volume of the reactors remain the same through the event.

A Visual Basic program FirstFlush RD 2.0 was created to simulate the process of mass reduction and first flush reduction via the RD system. FirstFlush RD 2.0 is a computer program based on simple continuous fluid dynamic calculations. Figure 9-21

shows the interface of the FirstFlush RD 2.0. Numerical solution of outlet concentrations

was simulated with various initial conditions by changing Cff-inlet, V, Rf and Rs, etc. The significance of first flushes at the outlet was calculated using Equation A4-10 from

plotting NCM-NCV and was used for first flush reduction analysis.

153

Figure 9-21 FirstFlush RD VB Program Interface

Analytical solutions for constituent concentrations are also presented in this

section. Minimum number of elements for numerical calculations was determined by

comparing the difference between the analytical and numerical solutions prior to using

the program for FF analysis. Nine hundred elements were used in FirstFlush RD 2.0 to ensure accuracy above 99.5%.

9.5.2 Conceptual Simulation via a CSTR or PFR

9.5.2.1 Illustration of Effluent Concentrations

The analytical solutions of the effluent concentration via a PFR and CSTR are

listed below. 154

Define rv=V/Vrf as Volume Ratio, as shown in Equation 9-13.

V rv = Equation 9-13 Vrf

PFR outlet expressions are shown in Equation 9-14 and Equation 9-15.

When x < rv , Cout = Cinitial Equation 9-14

−C ff −inlet ⋅( x−rv ) When x ≥ rv , Cout = (1− R f ) ⋅C0 ⋅ e Equation 9-15 where V = volume of the reactor (volume)

Vrf = total runoff volume of an event (volume);

Cout = outlet constituent concentration (mass/volume).

CSTR outlet expressions are derived by solving the mass balance (see Equation 9-16) and

boundary conditions (Equation 9-17):

−C ff −inlet ⋅x V ⋅ dCout = (1− R f ) ⋅ (Vrf ⋅C0 ⋅ e ⋅ dx) −Vrf ⋅ Cout ⋅ dx Equation 9-16

When x=0, Cout = Cinitial Equation 9-17

Solving the differential Equation 9-16, Equation 9-18 is obtained for the CSTR model

(see Appendix 5.1 CSTR Outlet Concentration - Analytical Solution).

x 1 − » ( −C ff −inlet )⋅x ÿ Cout rv 1 rv Cinitial = (1− R f ) ⋅ e ⋅ … ⋅ (e −1) + Ÿ C0 …1− rv ⋅ C ff −inlet C0 (1− R f )⁄Ÿ

Equation 9-18

Illustrations of the effluent characteristics for the PFR and CSTR are shown in

Figure 9-22. The left Figure 9-22 illustrates the normalized concentration (C/C0) versus normalized runoff volume(x). In general, the RD device delays the occurrence of the peak

concentration and also reduces the peak concentration. As shown in Figure 9-22 (right) 155

for normalized cumulative mass versus normalized cumulative runoff volume, the outlet

curves are closer to the standard line, indicating reduced first flush effects.

Figure 9-22 Left: Normalized Concentration vs. Normalized Cumulative Runoff Volume; Right: Normalized Cumulative Mass vs. Normalized Cumulative Runoff Volume, (rv =0.1; Rf =50%; Cff-inlet =1.0; Cinitial/C0=0.1)

9.5.2.2 Influencing Factors

It is observed from Figure 9-22 and Equation 9-14 to Equation 9-18, first flush

effects at the outlet depend on several influencing factors. 1) Inlet Runoff

Characteristics and Cff-inlet: inlet runoff characteristics have a major influence on the

shape of the curve, especially the latter portion of the curve. 2) Volume Ratio, rv: rv

controls the constituent break-through volume and the time where the peak concentration

occurs. 3) Rf: Rf has major influence on outlet concentrations, especially on the

concentration after the break-through. 4) Cinitial and Rs: Cinitial controls the outlet 156

concentration at the beginning of the event, which is related to Rs. Diffusion Factor:

Diffusion usually delays the occurrence of the peak concentration, as well as reducing the

peak concentration value. CSTR and PFR are the two extreme cases with large or no

diffusion, respectively. Other RD systems would generally have features between these

two cases. One example of analysis of a RD system with two CSTRs in series is

discussed in Section 9.5.2.5.

9.5.2.3 Peak Concentration

Peak outlet concentration causes the most serious effects to the receiving water

body. For a PFR, the peak concentration at the outlet occurs when xpeak = rv. The value of the peak constituent concentration is (1-Rf)*C0.

The occurrence of peak concentration at the CSTR outlet can be obtained by

solving the x in Equation 9-18 with dCout / dt = 0 condition. Equation 9-19 is obtained

(Appendix 5.2 CSTR Peak Concentration Calculation).

Cinitial ⋅ (r − C ff ) ln(r − ) − ln(C ff ) C0 ⋅ (1− R f ) x peak = Equation 9-19 (r − C ff )

Then the value of the peak concentration can be obtained from Equation 9-18 using the

xpeak value. Generally speaking, for a CSTR, the peak concentration at the outlet occurs

when x is greater than rv. The concentration is lower than (1-Rf)*C0 as a result of diffusion.

157

9.5.2.4 First Flush Analysis for PFR and CSTR

The effluent runoff concentration was analyzed using FirstFlush RD 2.0 and the numerical solution of the outlet first flush coefficient (Cff-outlet) was calculated. Figure

9-23 and Figure 9-24 show the Cff-outlet of a PFR and CSTR, respectively. Two trials were

applied. The “ trial #1” is an ideal condition, where Cinitial = 0 (or Rs =100%) was assumed,

−C ff −inlet while “ trial #2” is a conservative condition, where Rs = Rf = 0% and Cinitial = C0 ⋅ e were assumed.

Inlet Cff:

Figure 9-23 First Flush Reduction via a PFR -Cff-inlet (Trial #1: Rs=100%, Cinitial=0; Trial #2: Rs=Rf=0%; Cinitial = C0*e )

158

Inlet Cff:

Figure 9-24 First Flush Reduction via a CSTR -Cff-inlet (Trial #1: Rs=100%, Cinitial=0; Trial #2: Rs=Rf=0%; Cinitial=C0*e )

To quantify the change of the first flush effects due to the collection time ratio, a

new term, the Volume Influence Factor, Kv, is introduced and defined in Equation 9-20.

C ff −inlet K v = C ff −outlet Equation 9-20 where: Cff-inlet = first flush coefficient at the entrance of the RD reactor (mass/volume);

Cff-outlet = first flush coefficient at the outlet of the RD reactor (mass/volume).

Calculating Kv values from the Figure 9-23 and Figure 9-24 achieves Figure 9-25

(PFR results) and Figure 9-26 (CSTR results), respectively. 159

1 0.0 trial #1 0.0 trial #2 0.6 trial #1 0.6 trial #2 1.2 trial #1 1.2 trial #2 1.8 trial #1 1.8 trial #2 0.5 2.4 trial #1 2.4 trial #2 3.0 trial #1 3.0 trial #2 0 R F P

-0.5 , v K -1

-1.5

-2 0 0.1 0.2 0.3 0.4 0.5

V/Vrf, PFR

Figure 9-25 Kv vs. rv for PFR -Cff-inlet (Trial #1: Rs =100%, Cinitial=0; Trial #2: Rs = Rf = 0%; Cinitial=C0*e )

0.5

0 R T S

C -0.5

, v K -1 0.0 trial #1 0.0 trial #2 0.6 trial #1 0.6 trial #2 1.2 trial #1 1.2 trial #2 -1.5 1.8 trial #1 1.8 trial #2 2.4 trial#1 2.4 trial #2 3.0 trial #1 3.0 trial #2 -2 0 0.1 0.2 0.3 0.4 0.5

V/Vrf, CSTR

Figure 9-26 Kv vs. rv for CSTR -Cff-inlet (Trial #1: Rs =100%, Cinitial=0; Trial #2: Rs=Rf=0%; Cinitial = C0*e ) 160

From Figure 9-23 to Figure 9-26, Cff-outlet is always smaller than Cff-inlet and Kv is always smaller than 1.0. This indicates that first flush can be reduced via the PFR and

CSTR, respectively. Larger RD systems (higher rv) have greater effects on first flush reduction. In some cases, negative Cff at the outlet may occur if the RD volume is large and inlet Cff is low, which implies higher concentration at the latter portion of an event.

Comparing PFT and CSTR, a PFR has more significant effects on first flush reduction.

9.5.2.5 First Flush Analysis via Two -CSTRs in Series

Section 9.5.2.4 quantitatively analyzed the first flush reduction via an ideal PFR

or CSTR. For a reactor under non-ideal conditions, typically either a PFR with diffusion

factor or CSTRs in series can be used for the simulation. Discussed in this section is an

analysis example of first flush reduction via two CSTRs in series. It was assumed that

the two CSTRs are identical (V1=V2) and are connected in series. An illustration of the

system treating storm water first flushes is shown in Figure 9-27. 161

2-CSTRs inlet C

CSTR- 1 CSTR-2

V1 V2

Total V=V1+V2 2-CSTRs outlet

Figure 9-27 Illustration of CSTRs in Series in FF Analysis

Advanced functions in FirstFlush RD 2.0 were used to simulate the two-CSTR in series condition. Conditions and analysis procedures stated in Section 9.5.2.4 were

repeated in this analysis. Numerical solution of outlet constituent concentrations was

calculated with various initial conditions (Cff-inlet, V and trial #1 or #2) and then later evaluated for first flush reductions.

Cff-outlet versus Volume Ratios (V/Vrf) are plotted in Figure 9-28 and the calculated

Volume Influence Factors (Kv = Cff-outlet/Cff-inlet) are plotted in Figure 9-29.

162

3 0.0 trial #1 0.0 trial #2 0.6 trial #1 0.6 trial #2 1.2 trial #1 1.2 trial #2 1.8 trial #1 1.8 trial #2 2 2.4 trial#1 2.4 trial #2 3.0 trial #1 3.0 trial #2

1 t e l t u o

- 0 f f C

-1

-2

-3 0 0.1 0.2 0.3 0.4 0.5

(V1+V2)/Vrf (2 CSTRs in Series)

Figure 9-28 First Flush Reduction via Two CSTRs in Series -Cff-inlet (Trial #1: Rs=100%, Cinitial=0; Trial #2: Rs=Rf=0%; Cinitial=C0*e )

0.5 s e i r e

S 0

n i

R -0.5 T S C - -1 0.0 trial #1 0.0 trial #2 2

, 0.6 trial #1 0.6 trial #2 v

K 1.2 trial #1 1.2 trial #2 -1.5 1.8 trial #1 1.8 trial #2 2.4 trial#1 2.4 trial #2 -2 3.0 trial #1 3.0 trial #2 0 0.1 0.2 0.3 0.4 0.5

(V1+V2)/Vrf, 2-CSTRs in Series

Figure 9-29 Kv vs. rv for 2-CSTR in Series -Cff-inlet (Trial #1: Rs=100%, Cinitial=0; Trial #2: Rs=Rf=0%; Cinitial=C0*e )

163

Comparing Figure 9-28 to Figure 9-23 and Figure 9-24, and comparing Figure

9-29 to Figure 9-25 and Figure 9-26, it was observed that the significance of first flush reduction via two-CSTRs in series is less than that of a PFR and greater than that of a

CSTR. An example of this comparison between a CSTR, PFR and two-CSTRs in series is illustrated in Figure 9-30 (V/Vrf = 0.2).

1.5

0.5 t e l t u o

- 0 f f C CSTR, trial #2 -0.5 2 CSTR in Series, trial #2 PFR, trial #2 -1 CSTR trial#1 2 CSTR in series, trial #1 PFR, trial #1 -1.5 0 0.5 1 1.5 2 2.5 3

Cf f -inlet

Figure 9-30 Comparison of CSTR, PFR and 2-CSTR in Series -Cff-inlet (V /Vrf = 0.2; Trial #1: Rs=100%, Cinitial=0; Trial #2: Rs=Rf=0%; Cinitial=C0*e )

9.5.3 R-D Model Validation: First Flush Reduction via a Wetland

Thirteen storm events were analyzed for model evaluation and first flush analysis.

An individual Cff value was calculated for each constituent in each storm event. Ninety- seven Cff values were obtained from TSS, COD and metals’ data. 164

9.5.3.1 First Flush Reduction for Each Constituent

Figure 9-31 shows the average Cff values for each constituent at the wetland inlet and outlet. Most metals, COD and TSS exhibited reduced first flush effects at the outlet.

The range of Cff reduction was from 14% (Ni) to 84% (Ca). However, Fe exhibited an

increased first flush coefficient at the outlet. High variations of Fe concentrations were

observed at the inlet, and in 30% of the events, Fe exhibited negative Cff values at the inlet.

Figure 9-31 Average First Flush Coefficients at the Wetland Inlet and Outlet

9.5.3.2 Distribution of First Flush Reduction

Cff distribution at the wetland inlet and outlet were statistically analyzed and

plotted in Figure 9-32. Average Cff at the outlet was 0.46 ± 0.92 compared to 0.75 ± 1.30

at the inlet. The reduction of first flush coefficient was 38%, indicating statistically less 165

significant first flush occurred at the wetland outlet. It was also found that the standard

deviation of Cff at the outlet is less than that at the inlet.

Figure 9-32 Distribution of Cff at the Wetland Inlet and Outlet

Figure 9-33 shows the comparison of Cff at the wetland inlet and outlet for each positive first flush event for all constituents.

166

5.0

4.0 t

n 3.0 e c i f f e

o 2.0 C

h s u

l 1.0 F

t s r i 0.0 F

e 0.0 1.0 2.0 3.0 4.0 5.0 l t

u -1.0 O

-2.0

-3.0 Inlet First Flush Coefficient

Figure 9-33 Comparison of Cff at the Wetland Inlet and Outlet

As shown in Figure 9-33, most of the data points are below the 45 degree line,

indicating first flush effects were reduced via the wetland. High scattering of data

occurred. This is due to the variation of rainfall intensity, rainfall duration, runoff

volume, antecedent dry days, and removal.

9.6 First Flush Characteristics

9.6.1 Summarization of First Flush Effects via Different Influences

Figure 9-34 summarizes the first flush influence factors. All three influencing factors have a tendency to reduce first flush. Thus, when a first flush runs through a more complicated environmental system, either with a continuous source, in a large area, and/or an RD system, a reduced first flush effect is expected.

167

C ff −end ≈ C ff −initial ⋅ K c ⋅ K a ⋅ K v ⋅ K else

Continuous Area Influence Volume Other Factors, Source Influence Factor, 0

Continuous Collection Time Volume Ratio, rv Source Ratio, rc Ratio, ra Cin, Type of the Cin Cin, Shape of the reactor, Rs and Rf drainage area Concentrations See Section 9.3 See Section 9.4 See Section 9.5

Figure 9-34 Summarization of First Flush Influencing Factors

Besides Kv, Kc and Ka, other factors (Kelse) exist and can be modeled via similar methods

and evaluated. For example, the Volume Reduction Factor, Kd, may exist due to that certain first portion of runoff can be trapped in dead ends of the drainage system and can

not reach the collection point. In addition, the Differential Removal Factor, Kr, could represent the fact that removal of constituents via treatment devices may not be unvarying under different concentration conditions (higher concentration may be

associated with higher removal). Kr and Kd may also be smaller than 1 and result in reducing the magnitude of first flushes. Further analysis of these factors is needed in

further studies.

9.6.2 General First Flush Characteristics

General speaking, first flush does exist, originally. However, first flush is a subtle

phenomenon, and it is driven and biased by many other factors, such as runoff flow rate, 168 runoff duration and volume, land activities and runoff pattern, etc. It could be easily masked during some individual events. Moreover, first flush is an unstable, low-entropy phenomenon. Almost all natural forces have a tendency of reducing the magnitude of first flushes- such as continuous source loading, time of concentration, retention and detention, etc.

169

CHAPTER 10 CONCLUSIONS AND RECOMMENDATIONS

10.1 Conclusions

10.1.1 Applicability of the Treatment System

This study provided information on the applicability of mitigating storm water

runoff pollutants, specifically, suspended solids and oil and grease via a new

hydrodynamic device. The device contains two vertical concentric cylinders. Storm

water runoff passes into the inner cylinder from a tangential inlet and is directed

downwards around the inside wall of the inner cylinder. This rotational flow allows

settleable solids to settle down to the bottom of the device, while the oil and floatables

are retained on the water surface in the inner cylinder. Tests were conducted on a 12’ ’

diameter laboratory scale model. This design provided good removal performance for SS

and oil and grease. Up to a flow rate of 65 L/min, above 99% removal of two sandy soils

was obtained, and over 60% SS removal of a clayey soil was achieved. Other advantages

of this device are its compactness and flexibility for configuring in different modes.

Three adsorbent materials were investigated for uptake of oil and grease after

capture in the device. Although one of the three, the Smart Sponge, showed a slower rate

of adsorption, it exhibited strong binding to the oil in a dynamic flow environment.

Based on initial tests, Oil-Only material showed faster adsorption. Although some oil

may be released in a dynamic flow condition from Oil-Only, a combination of Oil-Only

with Smart Sponge maybe more effective, although tests are needed in the future.

170

10.1.2 Cost Benefit Analysis

Cost-benefit relations for storm water BMPs were conceptually illustrated in a

graph and discussed. Two popular mathematical expressions were formulated and then

utilized to illustrate the cost-benefit relationships. SS removal via an ideal settling tank

and hydrodynamic separator was used to illustrate the relationship and effectiveness

calculation. Calculated effectiveness coefficients were higher for the hydrodynamic

separator compared to an ideal settling tank.

As indicated in the discussion, achieving a low cost - high benefit BMP is

difficult/impossible. Utilization of reasonable cost-benefit expressions may lead to a

simple expression of the cost- benefit for a BMP, which is useful for BMP evaluation,

comparison and selection.

10.1.3 First Flush Modeling and Characterization

By introducing the first order modeling method, concentration variation during a

storm event was simplified. Cff, a first flush coefficient, and Kw, a wash-off coefficient, were used for quantitative analysis of first flush effects. Statistical analysis of the runoff

data from a highway site indicates that a moderate first flush is occurring from a low

-1 volume highway. The significance of first flushes of the TSS (Cff = 1.44, Kw = 23.7 in )

-1 at this site is higher than metals and COD (Cff = 0.48, Kw = 6.07 in ). For metals, mean

Cff from high to low are: Mg (1.14), Ca(0.77), Zn (0.76), Pb (0.55), Fe(0.45), and Ni

-1 -1 -1 -1 (0.27) and mean Kw are: Zn (12.2 in ), Fe(8.55 in ), Mg (7.96 in ), Pb (7.10 in ),

-1 -1 Ca(3.56 in ), and Ni (2.96 in ). High variations of Cff and Kw were found in this study for different storm events. 171

Based on the results from this initial modeling effort of one drainage site, the

utilization of a wash-off model appears to have good viability for analysis of first flush

effects for drainage areas. The resulting Cff and Kw values provide a measure to analyze

first flush effects, relate and compare results for various storm water constituents and

potentially lead to a better methodology for designing treatment facilities for

management of storm water. Further validation and calibration of the model using

several additional data sets are needed.

One dimensional element models were established for analyzing runoff from slim

areas, which could be used for design of treatment devices for runoff from bridges. Less

significant first flush effects are expected from the entire area than that from an element

(Cff-area < Cff-element). In this model the collection time ratio (Tc/Trf) and first flush coefficient of an element (Cff-element) predominate in influencing the first flush coefficient

of the area (Cff-area). An area influence factor, defined as Ka= Cff-area / Cff-element, was introduced and obtained for various collection time ratios. Similar results were found for

analyzing runoff from a round area.

This conceptual model well explains the postulation that less first flush effects are

expected for large drainage areas due to the mixing of runoffs from different collection

time regions.

Retention-detention devices (either PFR or CSTR) can reduce the significance of

first flushes. Reduction via a PFR is more significant than a CSTR. Outlet peak

concentration via CSTR is lower than that via PFR. The volume of the reactor has major 172

influence on the reduction. Other factors include inlet first flush coefficient (Cff), static- state removal (Rs) and flow-through removal (Rf) etc.

Analysis of the constituent data at a wetland inlet and outlet receiving highway

runoff showed that the wetland reduced first flush effects from a highway runoff site.

First flush reduction was observed for TSS, COD and most metals analyzed. Average Cff was reduced from 0.75 to 0.46 or 38%.

10.2 Recommendations

10.2.1 Hydrodynamic Treatment Device

Field installation and testing of a full size treatment device are needed for in-situ

performance evaluation of this design. Further studies are needed to size the device,

refine it for field application and evaluate the long term performance of the device. The

maintenance and waste disposal of materials collected by the device is also needed.

10.2.2 Cost-Benefit Analysis

In this study, only a conceptual cost benefit analysis for suspended solids removal

was conducted. Further analysis is needed on other BMPs to verify the cost-benefit

concepts and expressions proposed in this study.

10.2.3 First Flush Modeling

First flush analysis needs long-term, large scale, and sophisticated sampling and

sample analysis efforts, which is not available in most studies. Although proved to be a

useful method to analyze the first flushes from the site investigated, further validations of 173 the model under various conditions are needed in order to gage its utility for design of treatment devices.

174

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APPENDIXES

APPENDIX 1 PRECIPITATIONS FREQUENCY AND SITE PLAN

Following information is from "Precipitation-Frequency Atlas of the United

States". NOAA Atlas 14, Volume 2, Version 2. (NOAA, 2006).

Ohio 39.318 N 82.117 W

Figure A1-1 Location of the Proposed Site for Precipitation 185

Figure A1-2 Site Location from U.S. Census Bureau Maps and Cartographic Resources, 2006 186

Figure A1-3 Water Shed Information from U.S. Environmental Protection Agency (USEPA, 2006a) 187

Figure A1-4 West Union Bridge Existing Site Plan (Burgess & Niple Engineers Architects, 1999)

188

APPENDIX 2 SEDIMENT STORAGE CAPACITY CALCULATION

Outer Cylinder Inner Cylinder

Water Flow

Sediment Movements

Resettling Massive Dynamic Zone Scouring Settling Zone Zone

Outer cylinder Inner cylinder

0.5*D2

0.5*D1

h H

Sediment

Figure A2-1 Illustration of Maximum Sediment Condition 189

• Area between the cylinders

π * D * D π * D * D π A = 1 1 − 2 2 = (D 2 − D 2 ) 1 4 4 4 1 2 where: D1 = I.D of the outer cylinder (inch); and

D2 = O.D. of the inner cylinder (inch);

• Area between the inner cylinder and the sediment

A2 = π * D2 * h where: h = the minimum distance from the bottom of the inner cylinder to the sediment

(inch);

• The following assumption is made for maximum sediment volume calculation:

A2 > A1 * f where: f = the safety factor, f should be >=1 in order to maintain the flow velocity at the

scouring zone slower than the flow velocity between the cylinders.

π π * D * h > (D 2 − D 2 )* f 2 4 1 2

f 2 2 h > (D1 − D2 ) 4D2

• The total sediment volume is

π * D 2 V= 1 *(H − h) 4 where: H = distance between the cylinder bottoms (inch); and

V = volume of the sediment (inch3). 190

For conservative calculations, flat sediment surface is assumed.

2 π * D1 f 2 2 V< * (H − (D1 − D2 )) 4 4D2

• Calculation example for the lab test model

H=6’’; D1=11.5’’; D2=8’’; and use f = 1.0;

π * D 2 1 V< 1 *(6''− (11.5''2 −8''2 )) 4 4 ⋅8''

V< 401 inch3 (or 64% of the total sediment chamber volume)

191

APPENDIX 3 FIELD DEVICE FABRICATION DRAWINGS (PATENT PENDING)

192

193

194

195

196 197

198

199

200

201

202

203 204

205

206

207

APPENDIX 4 MODIFIED FIRST ORDER WASH-OFF MODEL AND BUILD-UP MODEL

The following section is a summarization of a modified modeling mainly based

on author’ s previous study (Su, 2002). The first flush model from a simple, uniform and

small area was studied in the thesis research. However, several critical modifications

were made, including adding the first flush coefficient concept (Cff) as well as several minor changes. This part of the model and the first flush coefficient concept are the core

part of the entire modeling study. Thus, those related modeling work after modifications

is summarized in this Appendix and further developments of the model under various

conditions are presented in Chapter 9 of the dissertation.

A4. 1 First Order Wash-off Model

Firstly, analysis of first flushes modeling from a simple, uniform, and small

drainage area is discussed this section. Table A4-1 shows the list of symbols used in this

section. 208

Table A4-1 List of Nomenclatures and Abbreviations in Appendix 4

Symbols Description Units B Power function coefficient in the power function model - C Constituent concentration in the runoff Mass/Volume

C0 Constituent concentration at the beginning of the runoff Mass/Volume

Cavg Flow weighted mean constituent concentration of the runoff event Mass/Volume

Cff First flush coefficient Mass/Volume

Cr Runoff coefficient - D Cumulative runoff depth during a storm event Depth

Dtotal Total runoff depth of the event Depth

Kb exponential mass build-up factor Time-1 Kw First order wash-off coefficient, exponential wash-off factor Depth-1 M Cumulated mass in the runoff during a storm event per area Mass/area

Mtotal Total mass in the runoff of a storm event per area Mass/area S Area enclosed between NCM-NCV curve and standard line - t Antecedent dry days Time

teq Equivalent antecedent dry days Time

ttotal Total antecedent dry days Time V Cumulative runoff volume Volume

Vtotal Total runoff volume of the event Volume W Mass on the drainage surface Mass/Area

Wt Mass at the beginning of the rainfall event Mass/Area

W0 Initial mass at the end of the previous rainfall event Mass/Area

Wmax Maximum mass that could be accumulated on drainage surface Mass/Area x Normalized cumulative runoff volume or depth Depth/Depth Normalized cumulative runoff volume or depth at the first flush x Depth/Depth ff point y Normalized cumulative mass Mass/Mass

yff Normalized cumulative mass at the first flush point Mass/Mass

A4.1.1 Concentration versus Runoff Volume

A first order wash-off model was established to illustrate the concentration change during a storm event. The concentration is described as an exponential 209

decreasing function approaching zero, as shown in Equation A4-1. The physical meaning

of this model is that the amount of mass washed off from the drainage surface by the

runoff is proportional to the amount of mass remaining on the surface.

−Kw ⋅D C = C0 ⋅ e Equation A4-1

-1 where: Kw = wash-off factor (depth );

C = constituent concentration (mass/volume);

C0 = initial concentration at the beginning of an event (mass/volume); and

D = cumulative depth of runoff (depth).

Dividing each side of Equation A4-1 by C0, and introducing total runoff depth,

yield the normalized concentration expression as shown in Equation A4-2.

D C −Kw ⋅Dtotal ⋅( ) ( ) = e Dtotal Equation A4-2 C0 where: (C/C0)=normalized constituent concentration; and

Dtotal = total cumulative runoff depth of the event (depth).

To further simplify this equation, new parameters Cff (First Flush Coefficient), and x (normalized cumulative runoff volume or depth) are defined in Equation A4-3 and

Equation A4-4.

C ff = K w ⋅ Dtotal Equation A4-3

D V x = = Equation A4-4 Dtotal Vtotal where: Cff =first flush coefficient (unitless);

x = normalized cumulative runoff volume or depth (unitless); 210

V =cumulative runoff volume (volume); and

Vtotal =total runoff volume of the event (volume).

Applying Cff and x into Equation A4-2, a simplified Equation A4-5 can be achieved.

C −C ⋅x ( ) = e ff Equation A4-5 C0

Figure A4-1 illustrates the graphical correlation of concentration versus runoff

depth for various Cff values.

1.0 0.9

n o

i 0.8 t Cff: a r t 0.7 0.2 n e

c 0.6 0.5 n

o 0.5 1.0 C

d 0.4 2.0 e z

i 4.0 l 0.3 a 8.0 m 0.2 r o

N 0.1

Hsb 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Normalized Rainfall Depth

Figure A4-1 Normalized Concentration versus Normalized Cumulative Depth

As from Equation A4-5 and Figure A4-1, Cff is the one and only factor illustrating the significance of the first flush effect in a storm event. Cff>0 indicates first flush effect exists, and a larger Cff indicates a more significant first flush effect; Cff <0 indicates “ last 211

flush” (where latter portion of the runoff contains more constituents than those in the

former portion or the runoff); Cff =0 indicates constant constituent concentration with no

first flush effects.

A4.1.2 Normalized Cumulative Mass versus Normalized Cumulative Volume

To better illustrate the first flush effects, the normalized cumulative mass versus

normalized cumulative runoff volume (NCM-NCV) charts have been used by many

researchers. The accumulated mass in the runoff (M) can be derived from Equation A4-6

while the total accumulated mass in an entire storm event (Mtotal) is derived from

Equation A4-7.

V D x −K w D −C ff x M = — C ⋅ dV = Cr — C0 ⋅ e dD = Cr — C0 ⋅ e dx 0 0 0

C0 −C ff ⋅x = Cr (1− e ) Equation A4-6 K w

Vtotal Dtotal 1 − K w D −C ff x M total = — C ⋅ dV = C r — C 0 ⋅ e dD = C r — C 0 ⋅ e dx 0 0 0

C 0 −C ff = C r (1 − e ) Equation A4-7 K w

where: M = cumulative mass per unit area (mass/area);

Mtotal = total mass in the runoff per unit drainage area (mass/area); and

Cr= runoff coefficient (unitless);

The normalized cumulative mass is achieved by dividing M by Mtotal as shown in

Equation A4-8, 212

M 1− e−C ff ⋅x y = = Equation A4-8 −C ff M total 1− e where: y = normalized cumulative mass.

Equation A4-8 can be used to develop the normalized cumulative mass versus

normalized runoff volume chart (NCM-NCV) as shown in Figure A4-2. NCM-NCV is a

very popular way to identify the existence of first flush. Plots above the standard line (45

degree line, indicating constant constituent concentration during the storm event) indicate

the existence of the first flush, while under indicates the “ last flush” .

1.0 0.9 0.8

s s

a 0.7

M Cff:

v 0.6

i 0.2 t a l

u 0.5 0.5 m

u 1.0

C 0.4

e 2.0

z 0.3 i l

a 4.0

m 0.2 r

o 8.0 N 0.1 0.0 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Normalized Cumulative Rainfall Depth

Figure A4-2 NCM-NCV Plot with Different Cff Values

As can be seen from Figure A4-2 and Equation A4-8, the only factor influencing

the shape of the curve is Cff. First flush events with positive Cff values have the plots 213

above the standard line. A larger positive Cff value results in a curve further above the

standard line. Last flush events have negative Cff values and plots are below the standard

line.

The Cff value is easy to be used in statistical analysis when more than one storm

event are considered. Same magnitude of first and last flushes (defined as symmetric to

standard line on NCM-NCV) has the same absolute values of Cff, which means that they take the same weight of influence when averaging. This feature of Cff is better than the power-function method, in which last flushes (b=1~∞) take more weight then first flushes

(b=0~1) when averaging the b-coefficients.

A4.1.3 Finding the Cff and Kw

A4.1.3.1 Conversion between Cff and Kw

From known Cff and known rainfall/runoff depth, Kw can be calculated using

Equation A4-3, or vice versa. Kw is a property of the wash-off process, which is a factor

of drainage conditions, pollutant type and condition, weather conditions, and rainfall

intensity. Cff is proportional to Kw and is inversely proportional to cumulative rainfall

depth. Thus, according to this model, heavier rainfall events tend to have more

significant first flush effects.

A4.1.3.2 From Known Concentrations

When the concentration C during an event is known, Cff can be determined by linear regression of the logarithmic transformed concentration (ln C) versus normalized runoff volume or depth (x), as shown in Equation A4-9, which is derived from Equation

A4-5. 214

ln(C) = −C ff ⋅ x + ln(C0 ) Equation A4-9

When plotting the logarithmic concentration (ln C) versus normalized cumulative runoff

depth (x), Cff equals the negative value of the slope of the trend line. On the other hand,

the intercept of the tendency line on the y axis gives the value of (ln C0).

Data of iron concentration in a storm water runoff event from a highway section

was plotted in Figure A4-3 (storm event #30, conduit flow from highway US-33), as an

example of determination of Cff and C0 based on the first order model.

2.0

1.5 y = -1.95x + 1.46 ) 2 n

o R = 0.86 i t 1.0 a r t n

e 0.5 c n o C

( 0.0 n L -0.5

-1.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Normalized Cumulative Depth

Figure A4-3 Determination of Cff from Concentration Data

From the trend line in Figure A4-3 (slope = -1.95; intercept= 1.46), Cff = 1.95 and C0 = e1.46 = 4.4 (mg/l) can be calculated. If the total rainfall depth of the event is given, (in this

-1 example Dtotal = 0.49 inches), Kw = Cff/Dtotal = 1.95/0.49 inch = 3.99 inch . Comparison of the model and field data is shown in Figure A4-4 and Figure A4-5.

215

Fe, Field 5 Fe, First ) l /

g 4 Order Model m (

n o i t

a 3 r t

n -1.95x

e y = 4.4e c n 2 o C

0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Normalized Cumulative Depth

Figure A4-4 Fe Concentration vs. Normalized Cumulative Depth

1.0 0.9 s

s 0.8 a M

e 0.7 v i t a

l 0.6 u m

u 0.5 C

d 0.4 e z

i Fe Field l

a 0.3 Data m r

o 0.2

N First Order 0.1 Model 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Normalized Cumulative Depth

Figure A4-5 Fe Normalized Cumulative Mass vs. Normalized Cumulative Depth

A4.1.3.3 Enclosed Area S in NCM-NCV

When the NCM-NCV data are available, Cff and Kw can be calculated from the

NCM-NCV as well. When the first flush effect exists, Cff is greater than zero, and the 216

plots of the NCM-NCV are above the standard line. The area encircled between the plots

and the standard line can be used as a parameter to evaluate the extent of first flush

effects. Deriving the area S equations provides a way of estimating Cff. The area is derived and presented in Equation A4-10 (see Appendix 5.3 Derivation of S from NCM-

NCV), which could be used to estimate Cff value.

1 1 1 S = − − Equation A4-10 −C ff 1− e C ff 2

where: S = area encircled by standard line and below the NCM-NCV plots.

Under extremely intense first flush conditions, Cff approaches infinity, and S

approaches ½. Under no first flush conditions, Cff approaches zero, and S approaches 0.

S values are always between 0 and 0.5 for first flushes. For statistic analysis, S is defined

with negative values when the standard line is above the plots on the NCM-NCV chart.

A4.1.3.4 From y:x Definitions

The conventional y:x definition can be interpreted as the (x,y) point on NCM-

NCV chart. Given the y:x definition, the Cff can be obtained by solving Equation A4-8 with known x and y values.

A4.1.3.5 From Power Function Coefficients

Obviously, the power function model results in a different curve on the NCM-

NCV chart from first order model. Thus, only an approximated correlation between b and Cff is possible. The method used in this study is to find the correlation of b and Cff

based on the same enclosed area S on the NCM-NCV chart (see Appendix 5.4 First Flush 217

Coefficients vs. b). Equation A4-11 was derived to describe the correlation between b and Cff.

−C ff −C ff e + C ff e −1 b = − Equation A4-11 −C ff e + C ff −1

where: b = the power function coefficient in the power function model.

Equation A4-12 is an experiential solution which performs the same functions as

Equation A4-11.

2 ln(b) = 0.0122 ⋅ C ff − 0.3488⋅ C ff Equation A4-12

A4.1.4 First Flush Point (FFP)

A4.1.4.1 Definition of FFP

The first flush definition approach in this research was achieved by defining the

First Flush Point (FFP). On the cumulative mass versus cumulative volume chart, the

FFP is defined as the contact point of the 45 degree tangential in the NCM-NCV chart

(the point with the maximum distance to the standard line).

An example of the FFP when Cff equals 2.0 is shown in Figure A4-6. 218 s

1.0 s

Cff=2.0 M 1.0

v n i FFP: (0.419, 0.657) t o a i 0.8 l t u

a 0.8 r m t u n C e 0.6 FFP: (0.419, 0.435) c d n

e 0.6 o z i l C a

d m

0.4 r e

Cavg/C0=0.435 o 0.4 z i l N Cff=2.0 a m

r 0.2

o 0.2 N

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 Normalized Cumulative Volume Normalized Cumulative volume

Figure A4-6 FFP Determination Example (Cff = 2.0)

Coordinates of the FFP is provided in Equation A4-13 (see Appendix 5.5

Derivation of First Flush Point (FFP)).

1− e(−C ff ) ln( ) −C ff C ff C ff + e −1 (x , y ) = ( , ) Equation A4-13 ff ff −C ff − C ff C ff − C ff e

where: xff = normalized cumulative depth at the FFP; and

yff = normalized cumulative mass at the FFP.

FFP is a critical point on a runoff event. The constituent concentration at the FFP equals to the event mean concentration. The FFP on the normalized concentration versus normalized cumulative volume chart is shown in Equation A4-14. The correlation of event mean concentration and C0 is shown in Equation A4-15. 219

1− e(−C ff ) ln( ) (−C ff ) Cavg C ff 1− e (x ff , ) = ( , ) Equation A4-14 C0 − C ff C ff

(−C ff ) Cavg 1− e = Equation A4-15 C0 C ff

where: Cavg= event mean constituent concentration (mass/volume).

A4.1.4.2 Properties of FFP

As from Equation A4-13 and Equation A4-14, FFP is related to Cff. The FFP is

important due to some of its characteristics. Understanding the FFP is necessary for

storm treatment designs and environmental impact studies.

On the NCM-NCV chart, FFP is the point with the maximum deviance to the standard line. Practically, the FFP may be considered as the most critical point of the event for a treatment device or to the receiving water body. Constituent concentration at the FFP equals the average concentration of the whole event (event mean concentration).

Before the FFP of a storm event, the constituent has higher than average concentration; after this point, concentrations are lower than the event average.

A4.1.5 Summarization of First Flush Parameters

Figure A4-7 shows the different ways to determine Cff from available runoff data

or from other first flush definitions/models. 220

Known Other Definitions or Known Runoff Data

Known Conservative Known b in Power Known C vs. x Known NCM- Definitions Function Model NCV

Plot ln(C) vs. x Find Enclosed Known Area S Y:X

Equation A4-8 Equation A4-11 Equation A4-9 Equation A4-10 ->Cff ->Cff ->Cff ->Cff

Kw can be calculated from Cff

Figure A4-7 Summarization of Creating the First Order Wash-off Model

Table A4-2 summarizes parameters derivable with known Cff. This table can also be used to calculate Cff from other known parameters.

Table A4-2 Summarization of Runoff Properties with Different Cff Values

Cff xff yff Cavg/C0 S B y:20 y:30 y:50 Equation Equation Equation Equation Equation Equation Equation Equation A4-13 A4-13 A4-14 A4-10 A4-11 A4-8 A4-8 A4-8 0.0 0.500 0.500 1.000 0.000 1.000 0.200 0.300 0.500 0.2 0.492 0.517 0.906 0.017 0.936 0.216 0.321 0.525 0.4 0.483 0.533 0.824 0.033 0.875 0.233 0.343 0.550 0.6 0.475 0.550 0.752 0.050 0.819 0.251 0.365 0.574 0.8 0.467 0.566 0.688 0.066 0.767 0.269 0.387 0.599 1.0 0.459 0.582 0.632 0.082 0.718 0.287 0.410 0.622 2.0 0.419 0.657 0.432 0.157 0.523 0.381 0.522 0.731 4.0 0.351 0.769 0.245 0.269 0.301 0.561 0.712 0.881 6.0 0.299 0.836 0.166 0.336 0.196 0.701 0.837 0.953 221

A4. 2 First Order Build-up Model

Besides wash-off, another part of the first flush formation is the constituents’ accumulation on the drainage surface, which usually increases between storm events.

Thus, understanding the constituent build-up is also necessary to model the constituent concentration.

In this study, mass build-up on the drainage surface is assumed to be exponentially increasing toward a certain maximum mass load. It is assumed that there is no additional build-up beyond the maximum mass loads on the drainage surface, since mass build-up balanced with wind re-suspension and other forces that drive constituents off the surface. Equation A4-16 shows the mass build up as a function of build-up factor, antecedent dry days and initial mass load.

− Kb ⋅t W = W0 + (Wmax −W0 ) ⋅ (1− e ) Equation A4-16

where: t = antecedent dry days (time, usually days);

W = mass on the drainage surface (mass/area);

W0 = initial mass at the end of the previous rainfall event (mass/area);

Wmax= maximum mass can be accumulated on the surface (mass/area); and

-1 -1 Kb = first order mass build-up coefficient (time , usually day ).

As in Equation A4-16, the mass left from a previous runoff event also influences

the total amount of mass. To simplify Equation A4-16, a new parameter, equivalent initial antecedent dry days (teq), is introduced. The physical meaning of Equivalent

Initial Antecedent Dry Days is the number of days needed to build-up to W0 from zero

mass. The definition is expressed in Equation A4-17. 222

W ln(1− 0 ) Wmax teq = Equation A4-17 − K b

where: teq = equivalent initial antecedent dry days (time, usually day).

Then, a newly defined Overall Antecedent Dry Days (ttotal) is used as the sum of equivalent initial antecedent dry days (teq) and antecedent day days (t), as shown in

Equation A4-18.

ttotal = teq + t Equation A4-18

Where ttotal = total antecedent dry days (time, usually day).

A conceptual illustration of mass over time and total antecedent dry days is shown

in Figure A4-8.

Rainfall Event, Dry days Rainfall event, Wash-off Model Build-up model Wash-off Model

Wmax a e r a

g Wt a n i a

r W d

n o

s s W0 a M

t eq t Time

t total

Figure A4-8 Illustration of Equivalent Antecedent Dry Days and Overall Antecedent Dry Days 223

By defining the overall antecedent dry days, mass build-up equations could be simplified as shown in Equation A4-19. Figure A4-9 shows the correlation between mass on the surface and total antecedent day days using Equation A4-19.

W = (1− e −Kb ⋅ttotal ) Equation A4-19 Wmax

x 1 a m

/ 0.9

s Kb=0.1 (day-1)

s 0.8 a M (

0.7 Kb=0.2 (day-1) d a

) 0.6 o l s

s s 0.5 Kb=0.4 (day-1) a s a M 0.4 m

d 0.3 Kb=0.6 (day-1) e z i l

a 0.2 Kb=0.8 (day-1) m

r 0.1 o

N 0 Kb=1.0 (day-1) 0 2 4 6 8 10 Total antecedent dry days (day)

Figure A4-9 Mass/Maximum Mass vs. Total Antecedent Dry Days

Besides to simplify the mass build-up model, another reason of using total antecedent dry days is to define a more meaningful parameter to replace the conventional antecedent dry days previously used by other researchers. Although it is commonly believed that longer antecedent dry days may result in higher pollutant concentrations, some researchers did not find good correlation between pollutant concentration/mass and antecedent dry days (Kim et al. 2004; Tsihrintzis and Hamid 1998; Hunt, 1997; and Su, 224

2002). This may possibly be because the initial mass (W0) was not considered. By introducing the total antecedent dry days, a better correlation is expected.

A4. 3 Correlation of Drainage Mass and Runoff Concentration

The constituent concentration in the runoff of a storm event could be calculated

by dividing the washed off mass by the runoff volume. Equation A4-20 is derived from

the wash-off model.

1 W = lim C ⋅ e −K w ⋅Dtotal ⋅x dx Dtotal →∞ — 0 Equation A4-20 0

Solving the above equation, Equation A4-21 is derived:

C0 = W * K w Equation A4-21

Then, combining Equation A4-19 and Equation A4-21 yields Equation A4-22.

−Kbttotal C0 = Wmax ⋅ (1− e ) ⋅ K w Equation A4-22

Then, combining Equation A4-5 and Equation A4-22 achieves Equation A4-23.

−Kbttotal −C ff ⋅x C = Wmax ⋅ (1− e ) ⋅ K w ⋅ e Equation A4-23

Equation A4-22 and Equation A4-23 provide a connection between the first order

wash-off model and the first order build-up model. This provides a possible way to

estimate the mass load on the drainage area by analyzing the constituent concentrations in

the runoffs. Equation A4-23 also can be used to estimate the Wmax and Kb. However, similar to the difficulties of finding the teq, further study is needed to find a simplified statistical method of calculating Wmax and Kb. 225

APPENDIX 5 MATHEMATICAL DERIVATIONS

Appendix 5.1 CSTR Outlet Concentration - Analytical Solution

V = Volume of the CSTR Vrf = Volume of the runoff during an event Cff, Cff-inlet = First flush coefficient at the inlet x = Normalized runoff volume, x∈[0,1] Cinitial = Constituent concentration in the CSTR when x = 0 C0 = Initial inlet constituent concentration C, C(x); Cout = Constituent concentration in the CSTR or at the outlet r = Vrf/V , a constituent Rf = Flow through removal

Step 1: Mass Balance Expressions

Constituent Mass stored: V ⋅ dC

−C ff ⋅x Constituent Mass in: Vrf ⋅C0 ⋅ e ⋅ (1− R f ) ⋅ dx

Constituent Mass out: Vrf ⋅C ⋅ dx

Step 2: Boundary Conditions C = Cinitial when x=0

Step 3: Solving the Differential Equations

Constituent Mass Stored in the CSTR = Constituent Mass in- Constituent Mass

out

−C ff ⋅x V ⋅ dC = Vrf ⋅C0 ⋅ e ⋅ (1− R f )dx - Vrf ⋅C ⋅ dx

dC Vrf −C ⋅x Vrf = C ⋅ e ff (1− R ) − C dx V 0 f V

dC −C ⋅x + r ⋅ c = r ⋅ C ⋅ e ff (1− R ) dx 0 f 226

dy This is a first order differential equation + p(x)y = q(x) , where dx

−C ff ⋅x p(x) = r and q(x) = r ⋅C0 ⋅ e (1− R f ) . General solution is

−— p(x)dx » — p(x)dx ÿ y = e q(x)e dx + Const …— ⁄Ÿ

−— r⋅dx » — r⋅dx ÿ C = e r ⋅ C ⋅ (1− R ) ⋅ e dx + Const …— 0 f ⁄Ÿ

−r⋅x −C ff ⋅x r⋅x C = e [r ⋅C0 ⋅ (1− R f ) ⋅ — e ⋅ e dx + Const]

−r⋅x (r−C ff )⋅x C = e [r ⋅C0 ⋅ (1− R f ) ⋅ — e dx + Const]

» (r−C ff )⋅x ÿ −r⋅x e C = e …r ⋅C0 ⋅ (1− R f ) ⋅ + ConstŸ … (r − C ff ) ⁄Ÿ

Step 4: Apply boundary conditions:

» (r−C ff )⋅x ÿ −r⋅x e e …r ⋅ C0 ⋅ (1− R f ) ⋅ + ConstŸ = Cinitial ; x = 0 … (r − C ff ) ⁄Ÿ

» (r−C ff )⋅0 ÿ −r⋅0 e e …r ⋅C0 ⋅ (1− R f ) ⋅ + ConstŸ = Cinitial … (r − C ff ) ⁄Ÿ

» 1 ÿ …r ⋅C0 ⋅ (1− R f ) ⋅ + ConstŸ = Cinitial … (r − C ff ) ⁄Ÿ

» 1 ÿ => Const = Cinitial − …r ⋅ C0 ⋅ (1− R f ) ⋅ Ÿ … (r − C ff )⁄Ÿ

Step 5: Analytical Solution:

» (r−C ff )⋅x ÿ −r⋅x e C = e …r ⋅C0 ⋅ (1− R f ) ⋅ + ConstŸ … (r − C ff ) ⁄Ÿ 227

» (r−C ff )⋅x » ÿ ÿ −r⋅x e 1 C = e …r ⋅C0 ⋅ (1− R f ) ⋅ − …r ⋅C0 ⋅ (1− R f ) ⋅ Ÿ + Cinitial Ÿ … (r − C ff ) … (r − C ff )⁄Ÿ ⁄Ÿ

» (r−C ff )⋅x » ÿ ÿ C −r⋅x e 1 Cinitial = (1− R f ) ⋅ e …r ⋅ − …r ⋅ Ÿ + Ÿ C0 … (r − C ff ) … (r − C ff )⁄Ÿ C0 ⋅ (1− R f )⁄Ÿ

SinceCout = C ;C ff −inlet = C ff ;

» ÿ Cout −r⋅x r (r−C ff −inlet )⋅x Cinitial = (1− R f ) ⋅ e ⋅ … ⋅ (e −1) + Ÿ C0 …r − C ff −inlet C0 (1− R f )⁄Ÿ

Or in another expression is

C r −C ⋅x r C = e ff − e −r⋅x + initial ⋅ e −r⋅x C0 ⋅ (1− R f ) (r − C ff ) (r − C ff ) C0 ⋅ (1− R f ) 228

Appendix 5.2 CSTR Peak Concentration Calculation

Peak constituent ion concentration occurred when:

dC = 0 => dx

Thus,

r −C ⋅x r C d( e ff − e −r⋅x + initial e −r⋅x ) (r − C ) (r − C ) C ⋅ (1− R ) ff ff 0 f = 0 dx

2 r ⋅C ff −C ⋅x − r − r ⋅C - e ff − e −r⋅x + initial e −r⋅x = 0 (r − C ff ) (r − C ff ) C0 ⋅ (1− R f )

C ff −C ⋅x − r − C − e ff − e −r⋅x + initial e −r⋅x = 0 (r − C ff ) (r − C ff ) C0 ⋅ (1− R f )

C ff −C ⋅x r C − e ff + e −r⋅x − initial e −r⋅x = 0 (r − C ff ) (r − C ff ) C0 ⋅ (1− R f )

C ≈ C ’ ff −C ff ⋅x ∆ − r initial ÷ −r⋅x − e + ∆ + ÷ ⋅ e = 0 (r − C ff ) « (r − C ff ) C0 ⋅ (1− R f ) ◊

C ≈ C ’ ff −C ff ⋅x ∆ − r initial ÷ −r⋅x e = ∆ + ÷ ⋅ e (r − C ff ) « (r − C ff ) C0 ⋅ (1− R f ) ◊

≈ C ’ ≈ r − C ’ (r−C ff )⋅x ∆ − r initial ÷ ∆ ff ÷ e = ∆ + ÷ ⋅∆ ÷ « (r − C ff ) C0 ⋅ (1− R f ) ◊ « − C ff ◊

≈ C ⋅ (r − C ) ’ (r−C ff )⋅x ∆ r initial ff ÷ e = ∆ − ÷ « C ff C ff ⋅C0 ⋅ (1− R f ) ◊ 229

≈ C ’ ∆ r − initial ⋅ (r − C ) ÷ C (1 R ) ff (r−C ff )⋅x ∆ 0 ⋅ − f ÷ e = ∆ ÷ ∆ C ff ÷ ∆ ÷ « ◊

Thus the peak concentration occurs when:

Cinitial ⋅ (r − C ff ) ln(r − ) − ln(C ff ) C0 ⋅ (1− R f ) =>Peak concentration xpeak = ; (r − C ff ) 230

Appendix 5.3 Derivation of S from NCM-NCV

1 1 (1− e −C ff ⋅x ) S = — (F(x) − S(x) )dx = — ( C − x)dx F(x) (1 e− ff ) 0 0 − M C

where: N S(x) F(x) = runoff concentration curve on normalized cumulative mass versus volume chart ; and NCV S(x) = standard line on normalized cumulative mass versus volume chart S(x)=x (45 degree line)

1 1− e −Cff ⋅x 1 S = — ( −Cff )dx − — xdx 0 1− e 0

1 1 −Cff *x 1 2 1 S = −Cff — (1− e )dx − (x ) 0 1− e 0 2

1 1 1 −Cff ⋅x 1 S = −Cff (—1dx − — e dx) − 1− e 0 0 2

1 1 1 −Cff ⋅x 1 S = −Cff (1− — e d(−Cff ⋅ x)) − 1− e − Cff 0 2

1 1 1 S = (1− (e−Cff −1)) − 1− e−Cff − Cff 2

1 1 1 1 S = − (e−Cff −1)) − 1− e−Cff 1− e−Cff − Cff 2

1 1 1 S = − − ( Equation A4-10) −C ff 1− e C ff 2

231

Appendix 5.4 First Flush Coefficients vs. b

1 1 1 First order model: S = − − ( Equation A4-10) −C ff 1− e C ff 2

1 1 Power Function Model: S '= — x b dx − 0 2

1 1 S '= ⋅ x (b+1) 1 − b +1 0 2

1 1 S '= − b +1 2

When S=S’

1 1 1 1 1 − − = − −C ff 1− e C ff 2 b +1 2

1 1 1 − = −C ff 1− e C ff b +1

−C ff C ff − (1− e ) 1 = −C ff (1− e ) ⋅ C ff b +1

C − C e −C ff b +1 = ff ff −C ff C ff + e −1

C − C e −C ff C + e −C ff −1 b = ff ff − ff −C ff −C ff C ff + e −1 C ff + e −1

e −C ff + C e −C ff −1 b = − ff (Equation A4-11) −C ff e + C ff −1 232

Appendix 5.5 Derivation of First Flush Point (FFP)

1− e −C ff ( x) (y) = Equation A4-8, 1− e −Cff

The equation of tangential line is y’

1− e −Cff ⋅x) (y)'= ( )' 1− e −Cff

(e −C ff (x) )' (y)'= 1− e −C ff

−Cff ⋅(x) (e ) *(−C ff ) (y)'= 1− e −C ff

−C ff (x) (e ) * (−C ff ) (y)'= 1− e −C ff

When (y)’ =1, 45 degree tangential line, 233

(e −C ff (x) ) * (−C ) 1 = ff 1− e −C ff

−C ff −C ff (x) 1− e = (e ) *(−C ff )

1− e −C ff = (e −Cff (x) ) − C ff

1− e −C ff ln( ) = −C ff (x) − C ff

1− e −C ff (x) = ln( ) /(−C ff ) − C ff

Thus, the normalized volume at the FFP (x) is:

1− e(−C ff ) ln( ) C ff x ff = − C ff

And yff can be easily calculated from Equation A4-13