United States Office of Water EPA-822-R-09-008 Environmental Protection Office of Science and Technology September 2009 Agency 4304 

# Methodology for Deriving Ambient Water Quality Criteria for the Protection of Human Health (2000)

Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors

### EPA-822-R-09-008



# Methodology for Deriving Ambient Water Quality Criteria for the Protection of Human Health (2000)

Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors

Office of Science and Technology Office of Water U.S. Environmental Protection Agency Washington, DC 20460i NOTICE
The policies and procedures set forth in this document are intended solely to describe EPA methods and guidance for de veloping or revising ambient water quality criteria to protect human health, pursuant to Section 304(a) of the Clean Water Act, and to serve as guidance to States and authorized Tribes for developing their own water qu ality criteria. This guidance does not substitute for the Clean Water Act or EP A’s regulations, nor is it a regulation itself. Thus, it does not impose legally binding requirement s on EPA, States, Tribes, or the regulated community, and may not apply to a particular situation depending on the circumstances.

This document has been reviewed in acco rdance with U.S. EPA policy and approved for publication. Mention of trade names or commercial products does not constitute an endorsement or recommendation for use.ii This page left blank intentionally.iii FOREWORD

This Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors (Site-Specific TSD) provides technical details on how state and tribal water quality staff scientists or risk assesso rs ("investigators"), who are responsib le for deriving stat e or tribal water quality standards, may develop site-specific bi oaccumulation factors (BAFs) for use in deriving ambient water quality criteria for protecting human health. Guidance on different approaches that investigators can take and the factors that should be considered when selecting an approach for a given situation is provided. This information allows states and tribes to derive BAFs that are more representative of the bioaccumulatio n potential at a given location.

The Site-Specific TSD was developed as a supplemental document to the Methodology for Deriving Ambient Water Quality Criteria fo r the Protection of Human Health (2000) (Human Health Methodology) that update d and revised the existing 19 80 Guidelines and Methodology. The Human Health Methodology in cludes guidance on chemical risk assessment, exposure and bioaccumulation.

As part of the Human Health Methodology, EPA de veloped detailed procedures and guidelines for estimating bioaccumulation factor (BAF) values for use in deriving or revising ambient water quality criteria. The Technical Support Document Volume 2: Development of National Bioaccumulation Factors (EPA-822-R-03-030) (National TSD) di scusses the technical basis for developing BAFs, the underlying assumptions and uncertainties inherent to the approach, and applying the bioaccumulation component of th e Human Health Methodology. The National and Site- Specific TSDs should be used in conjunc tion with the 2000 Huma n Health Methodology to develop BAFs for use in calculating ambient water quality criteria.iv This page left blank intentionally.v A CKNOWLEDGMENTS

Project Leader
Tala R. Henry U.S. EPA Offi ce of Science and Technology Heidi Bethel U.S. EPA Office of Science and Technology

Coauthors

Lawrence P. Burkhard U.S. EPA National H ealth and Environmental Effects Research Laboratory Philip M. Cook U.S. EPA National Health and Environmental Effects Research Laboratory Douglas D. Endicott Great Lake s Environmental Center, Inc. Keith G. Sappington U.S. EPA National Center for Environmental Assessment Erik L. Winchester U.S. EPA Of fice of Science and Technology

U.S. EPA Technical Reviewers

Ann Johnson U.S. EPA Office of Policy, Economics and Innovation Elsie Sunderland U.S. EPA Office of the Science Advisor

External Peer Reviewers
Brendan Hickie Trent University, Ontario, Canada Donald Mackay University of Toronto & Trent University, Ontario, Canada Robert Mason University of Connecticutvi This page left blank intentionally.vii TABLE OF CONTENTS

1. INTRODUCTION ............................................................................................................... .. 1-1 1.1 BACKGROUND ....................................................................................................... 1-3 1.2 SCOPE OF DOCUMENT ......................................................................................... 1-5 1.3 SITE-SPECIFIC BIOACCUMULA TION FACTORS (SS BAFs) .......................... 1-7 1.4 GLOSSARY .............................................................................................................. 1-9 REFERENCES .................................................................................................................... 1 -13

2. HOW TO SELECT AN APPROACH FOR DERIVING SITE-SPE CIFIC BAFS............... 2-1 2.1 BAF DERIVATION PROCEDURES FOR INORGANIC AND ORGANOMETALLIC CHEMICALS ...................................................................... 2-4 2.2 BAF DERIVATION PROCEDURES FO R IONIC ORGANIC CHEMICALS....... 2-5 2.3 BAF DERIVATION ASSUMPTIONS ..................................................................... 2-6 2.4 WHAT IS THE DEFINITION OF A SITE? ............................................................. 2-7 2.5 WHAT ARE THE METHODS FOR DE RIVING SITE-SPECIFIC BAFS?.......... 2-10 2.5.1 Site-specific Field-Measured BAFs............................................................. 2-11 2.5.2 Site-specific BAFs Predicte d from Measured Biota Sediment Accumulation Factors (BSAFs)................................................................... 2-11 2.5.3 Site-specific BAFs Predicte d from Extrapolated BSAFs or BEFs Measured at a Reference Site............................................................. 2-12 2.5.4 Site-specific BAFs Predicted from Laboratory Measured BCFs Combined with a F ood Chain Multipliers ......................................... 2-12 2.5.5 Site-specific BAFs Predicted from N -Octanol-Water Partition Coefficient (K ow ) Combined with a Food Chain Multipliers ...................... 2-13 2.5.6 Site-specific BAFs Recalculate d from National or Baseline BAFs............. 2-14 2.5.7 Advantages and Limitations of Site-specific BAF Approaches .................. 2-14 2.5.8 Weight-of-Evidence Approach to Selecting a Site-specific BAF................ 2-18 REFERENCES .................................................................................................................... 2 -20

3. MEASURING SITE-SPECIFIC BIOACCUMULATION FACTORS................................. 3-1 3.1 KEY STUDY DESIGN QUESTIONS FOR DETERMINING SITE-SPECIFIC BI OACCUMULATION FACTORS............... 3-11 3.2 HOW TO DESIGN A SAMPLING PLAN TO MEASURE BAFS........................ 3-17 3.2.1 Determining the Number of Samples to Collect.......................................... 3-19 3.2.1.1 Bootstrap BAF Resampling ...................................................... 3-20 3.2.1.2 Monte Carlo BAF Analysis ....................................................... 3-21 3.2.2 Modeling Simulation of BAF Sampling Designs ........................................ 3-22 3.2.2.1 Using Model Simulations to Develop Field-Sampling Designs 3-23 3.2.3 How Can These Methods be used to Help Design a BAF Sampling Plan? ............................................................................................ 3-26viii TABLE OF CONTENTS (CONTINUED)

3.3 MEASURING CHEMICAL CONCENTRATIONS IN BIOTA ............................ 3-28 3.3.1 Target Analytes and Analytical Methods .................................................... 3-30 3.3.2 Target Species and Size Class Selection...................................................... 3-31 3.3.3 Sampling Locations .................................................................................... 3-35 3.3.4 Sampling Times ........................................................................................... 3-40 3.3.5 Sample Type ................................................................................................ 3-41 3.3.6 Replicate Samples........................................................................................ 3-46 3.3.7 Sample Collection Methods......................................................................... 3-47 3.4 MEASURING CHEMICAL CONCEN TRATIONS IN WATER .......................... 3-47 3.4.1 Target Analytes and Analytical Methods .................................................... 3-48 3.4.2 Phase Separation .......................................................................................... 3-49 3.4.3 Sampling Locations ..................................................................................... 3-50 3.4.4 Sampling Times ........................................................................................... 3-51 3.4.5 Sample Type ................................................................................................ 3-52 3.4.6 Replicate Samples........................................................................................ 3-52 3.4.7 Sample Collection Methods......................................................................... 3-53 REFERENCES .................................................................................................................... 3 -55
Appendix 3A: Determining the Number of Samples to Collect for a BAF Measurement: Bootstrap Analysis.................................................................................... 3-65 Appendix 3B: PCB Congener Concentrations Measured by 79 Green Bay Mass Balance Study................................................................. 3-79 Appendix 3C: Determining the Number of Samples to Collect for a BAF Measurement: Monte Carlo Analysis ................................................................................ 3-83 Appendix 3D: Modeling Simulation of BAF Sampling Designs ...................................... 3-91

4. MEASURING BIOTA-SEDIMENT ACCUMULATION FACTORS TO PREDICT SITE-SPECIFIC BIOACCUMU LATION FACTORS ......................................................... 4-1 4.1 DESCRIPTION OF METHOD 2 .............................................................................. 4-2 4.2 KEY STUDY DESIGN QUESTI ONS FOR DETERMINING BIOTASEDIMENT ACCUMULATION FACTORS......................................................... 4-11 4.3 HOW CAN THE SEDIMENT/W ATER COLUMN CHEMICAL CONCENTRATION QUOTIENT (II SOCW ) BE DETERMINED? ......................... 4-18 4.3.1 Measuring Site-specific Reference Chemical Concentrations in Water and Sediment................................................................................................ 4-20 4.3.2 Estimating J socw / K ow ~ f OC

/f

SOC by Assuming Steady State ........................ 4-22 4.3.3 Using Transport and Fate Models to Determine the Fugacity Gradient Ratio............................................................................................................. 4-23 4.4 HOW TO DESIGN A SAMPLING PL AN TO MEASURE BSAFs ...................... 4-27 4.4.1 How Can Modeling Simulations Guid e the Sampling Design Process? ..... 4-27 4.4.2 Using Model Simulations to De velop Field-Sampling Designs.................. 4-29ix TABLE OF CONTENTS (CONTINUED)
4.4.3 Using Monte Carlo Simulation to De termine the Number of Samples to Collect and Analyze..................................................................................... 4-31 4.4.4 How Can These Methods be Used to Help Design a Sampling Plan?......... 4-33 4.5 MEASURING CHEMICAL CONCENTRATIONS IN SEDIMENT .................... 4-35 4.5.1 Target Analytes and Analytical Methods .................................................... 4-36 4.5.2 Sampling Locations and Depths .................................................................. 4-37 4.5.3 Sample Type and Collection Method .......................................................... 4-42 4.5.4 Replicate and Composite Samples............................................................... 4-43 4.6 SCIENTIFIC ISSUES ASSOCIAT ED WITH METHOD 2 AND THE USE OF BSAFS TO PREDICT CHEM ICAL BIOACCUMULATION......................... 4-45 4.6.1 Evaluation of Method 2 Predictions of Site-specific BAFs......................... 4-45 4.6.2 Is Chemical Equilibrium Assumed in the Calculation of a BSAF?............. 4-50 4.6.3 Review of Existing Data for J socw .............................................................. 4-51 4.6.4 How does II SOCW Reflect Steady State Cond itions at a Site? ...................... 4-60 4.6.5 Assumptions and Limitations Associ ated with Method 2 Predictions ........ 4-61 4.6.6 How Reliable are Method 2 Predictions if the Sediment Organic Carbon Equilibrium Partitioning Assumption is in Error? .......................... 4-63 REFERENCES .................................................................................................................... 4 -66
Appendix 4A - Modeling Simulati on of BSAF Sampling Designs.................................... 4-73 Appendix 4B - Determining the Number of Samples to Collect for a BSAF Measurement: Monte Carlo Analysis ............................................. 4-77 Appendix 4C - Green Bay Mass Bala nce PCB Congener Concentrations: Organic Carbon-Normalized Surficial (0-1 cm) Sediment........................ 4-95

5. ESTIMATING SITE-SPECIFIC BAFs BY EXTRAPOLATION, PREDICTION OR RECALCULATION.............................................................................................................. 5-1 5.1 ESTIMATING SITE-SPECIFIC BAFS BY EXTRAPOLATING BSAFS OR BEFS (METHODS 3A AND 3B).............................................................................. 5-3 5.1.1 Estimating Site-specific BAFs by ex trapolating BSAFs (Method 3a) ........ 5-12 5.1.2 Estimating Site-specific BAFs by ex trapolating BEFs (Method 3b)........... 5-23 5.1.3 How Can a Reference Site be Chosen?........................................................ 5-33 5.2 PREDICTING SITE-SPECIFIC BA FS USING BCFS AND FOOD CHAIN MULTIPLIERS (FCMs).......................................................................................... 5-35 5.2.1 Predicting Site-specific Baselin e BAFs using Laboratory-Measured BCFs and FCMs (Method 4a)...................................................................... 5-36 5.2.1.1 Validation of Method 4a ............................................................ 5-42 5.2.2 Determining Site-specific FCMs ................................................................. 5-44 5.2.2.1 Measuring Site-specific FCMs .................................................. 5-46 5.2.2.2 Predicting FCMs Using a Food Chain Model............................ 5-49 5.2.2.3 Site-specific Adjustment of Food Chain Model Parameters...... 5-57 5.2.2.4 Selection of a Food Web Structure ............................................ 5-58 5.2.2.5 Alternative Food Chain Models................................................. 5-59x TABLE OF CONTENTS (CONTINUED)
5.2.3 Predicting Site-specific Baseline BAFs using K OW and Food Chain Multipliers (Method 4b)............................................................................... 5-59 5.2.3.1 Validation of Method 4b............................................................ 5-65 5.2.3.2 Selection of Appropriate K OWS for Partitioning (Bioavailability) Predictions ...................................................... 5-69 5.3 Recalculating Site-specific BAFs from Baseline or National BAFs ....................... 5-70 5.3.1 Assumptions and Limitations ...................................................................... 5-72 5.3.2 Validation of Method 5................................................................................ 5-76 5.3.3 How Can the Lipid Contents of Aqua tic Organisms be Determined? ........ 5-81 5.3.3.1 Assessing Site-specific Fish Consumption ................................ 5-82 5.3.3.2 Measuring Lipid in Fish............................................................. 5-82 5.3.3.3 Determining Site-specific Fish Lipid Using a Literature or Database Search......................................................................... 5-84 5.3.3.4 How Should Lipid Data be Evaluated?...................................... 5-85 5.3.3.5 Determining Fish Lipid usi ng the National Default Lipid Data Base ................................................................................... 5-86 5.3.4 How Can Site-specific Organic Carbon Concentrations be Determined?... 5-87 5.3.4.1 Overview of Freely Dissolved Normalization Process.............. 5-89 5.3.4.2 Measuring DOC and POC ......................................................... 5-90 5.3.4.3 Determining DOC and POC Using a Literature or Database Search......................................................................................... 5-92 5.3.4.4 How Should Organic Carbon Data Be Evaluated?.................... 5-93 5.3.4.5 Determining Organic Carbon Concentrations using the National DOC/POC Database.................................................... 5-95 REFERENCES .................................................................................................................... 5 -98
Appendix 5A - Internet-Acce ssible Databases Containing Lipid Content Data................ 5-103 Appendix 5B - Internet-Acce ssible Databases Containing Organic Carbon Data ............ 5-109 Appendix 5C - BSAFs for PCB congeners in Green Bay and Upper Hudson River ........ 5-113 Appendix 5D - Lipid Content of Aquatic Organisms........................................................ 5-119xi TABLES
Table 2-1. Strengths and Limitations of th e Methods for Deriving Site-specific BAFs (SS BAFs) ............................................................................................................ 2-16

Table 3-1. Illustrative Bioaccumulati on Factor Sampling Design Structures. Uncertainties Associated W ith Design Structures Ar e Ecosystem Specific........ 3-26

Table 3-2. Waterbody Type as Indicator of Tem poral Concentration Variability................ 3-28
Table 3A-1. Bootstrap Results for P CB Congener 149 Forage Fish BAF: 90% Confidence Limit Ratio (Upper Confidence Limit/Lower Confidence Limit) as a Function of the Number of Fish and Water Samples. Smaller Ratios Indicate Less Uncertainty .................................................................................... 3-72
Table 3A-2. Taylor Seri es Approximation of Confidence Limits for Baseline BAFs in Green Bay Zone 3.................................................................................. 3-77
Tables 3C-1. 90% Confidence Limit Ratios (Upp er Confidence Limit/Lower Confidence Limit) for BAF as Functions of the Variability in Chemical Concentrations in Fish and Water: Chemical Concentrations Measured in 2 Fish and 2 Water Samples. ..... 3-88

Tables 3C-2. 90% Confidence Limit Ratios (Upper Confidence Limit/Lower Confidence Limit) for BAF as Functions of the Va riability in Chemical Concentrations in Fish and Water: Chemical Concen trations Measured in 4 Fish and 4 Water Samples. .................................................................................................... 3-89

Tables 3C-3. 90% Confidence Limit Ratios (Upper Confidence Limit/Lower Confidence Limit) for BAF as Functions of the Va riability in Chemical Concentrations in Fish and Water: Chemical Concen trations Measured in 6 Fish and 6 Water Samples. .................................................................................................... 3-89

Table 3C-4. 90% Confidence Limit Ratios (Upper Confidence Limit/Lower Confidence Limit) for BAF as Functions of the Va riability in Chemical Concentrations in Fish and Water: Chemical Concen trations Measured in 2 Fish and 4 Water Samples. .................................................................................................... 3-90

Table 3C-5. 90% Confidence Limit Ratios (Upper Confidence Limit/Lower Confidence Limit) for BAF as Functions of the Va riability in Chemical Concentrations in Fish and Water: Chemical Concen trations Measured in 4 Fish and 2 Water Samples ..................................................................................................... 3-90

Table 4-1. Some Illustrativ e Biota–Sediment Accumulati on Factor Sampling Design Structures. ............................................................................................................ 4-31xii TABLES (CONTINUED)

Table 4-2. Number of 3-inch Diameter Cores Required to Composite 500 mL of Sediment .......................................................................................................... 4-45

Table 4-3. Validation Statistics fo r Method 2: Ratio of Baseline BAF predicted /Baseline BAF measured for all (combined) sampled fish species ........................................... 4-49
Table 4-4. Geometric Mean Regression Equations (log J socw = A log K OW + B) for Polychlorinated Biphenyls (PCBs) and Chlorinated Pesticides........................... 4-53
Table 4-5. Average J socw / K ow Ratios for Three Different Ecosystems............................... 4-59

Table 4B-1. 90% Confidence Interval Ratios fo r BSAF as Function of the Variability in Chemical Concentrations in Sediment................................................................. 4-81
A. Chemical Concentrations Measured in 2 Fish Samples............................... 4-81 B. Chemical Concentrations Measured in 4 Fish Samples............................... 4-81 C. Chemical Concentrations Measured in 6 Fish Samples............................... 4-81 D. Chemical Concentrations Measured in 10 Fish Samples............................. 4-82 E. Chemical Concentrations Measured in 30 Fish Samples............................. 4-82

Table 4B-2. 90% Confidence Interval Ratios for J socw as Function of the Variability in Chemical Concentrations in Sediment................................................................. 4-84
A. Chemical Concentrations Measured in 2 Water Samples............................ 4-84 B. Chemical Concentrations Measured in 4 Water Samples............................ 4-84 C. Chemical Concentrations Measured in 6 Water Samples............................ 4-84 D. Chemical Concentrations Measured in 10 Water Samples.......................... 4-85 E. Chemical Concentrations Measured in 30 Water Samples.......................... 4-85

Table 4B-3. 90% Confidence Interval Ratios for Method 2 BAF Predictions for PCB Congener 149 as a Function of the Variability in Ch emical Concentrations in Sediment... 4-90

A. Chemical Concentrations Measured in 2 Fish and 2 Water Samples .......... 4-90 B. Chemical Concentrations Measured in 4 Fish and 4 Water Samples .......... 4-90 C. Chemical Concentrations Measured in 6 Fish and 6 Water Samples .......... 4-90 D. Chemical Concentrations Measured in 10 Fish and 10 Water Samples ...... 4-91 E. Chemical Concentrations Measured in 30 Fish and 30 Water Samples ...... 4-91

Table 5-1. Method 3a BSAF Ex trapolation Example Using PCB Data From Lake Michigan, Green Bay and the Hudson River

. ..................................................................................... 5-19
A. Extrapolating Lake Michigan Lake Trout (LM LT6) BSAFs to 3 Year Old Brown Trout in Zone 4 of Green Bay (GB BT3).................................. 5-19xiii TABLES (CONTINUED)
B. Extrapolating Lake Michigan Lake Trout (LM LT6) BSAFs to 4 Year Old Walleye in Zone 4 of Green Bay (GB W4) .......................................... 5-19 C. Extrapolating Lake Michigan Lake Trout (LM LT6) BSAFs to 4 Year Old Walleye in Zone 1 of Green Bay (GB W4) .......................................... 5-20 D. Extrapolating Lake Michigan Lake Trout (LM LT6) BSAFs to Largemouth Bass at Hudson River mile 189 (RM 189 LMB)..................... 5-20 E. Extrapolating Lake Michigan Lake Trout (LM LT6) BSAFs to Largemouth Bass at Hudson River mile 194 (RM 194 LMB)..................... 5-21
Table 5-2. TCDD Bioaccumulation Equi valency Factors (BEFs) Derived For Toxicologically Important PCDDs A nd PCDFs From Lakewide Averages Of Concentrations In Lake Ontario Lake Trout And Surface Sediment In Depositional Areas. ............................................................................................. 5-28
Table 5-3. Method 3b BEF Extrapolation Example Using PCB Data From Lake Michigan, Green Bay and the Hudson River

. ................................................................. 5-30
A. Extrapolating Lake Michigan Lake Trout (LM LT6) BEFs to 3 Year Old Brown Trout in Zone 4 of Green Bay (GB BT3).................................. 5-30 B. Extrapolating Lake Michigan Lake Trout (LM LT6) BEFs to 4 Year Old Walleye in Zone 4 of Green Bay (GB W4) .......................................... 5-30 C. Extrapolating Lake Michigan Lake Trout (LM LT6) BEFs to 4 Year Old Walleye in Zone 1 of Green Bay (GB W4) .......................................... 5-31 D. Extrapolating Lake Michigan Lake Trout (LM LT6) BEFs to Largemouth Bass at Hudson River mile 189 (RM 189 LMB)..................... 5-31 E. Extrapolating Lake Michigan Lake Trout (LM LT6) BEFs to Largemouth Bass at Hudson River mile 194 (RM 194 LMB)..................... 5-32

Table 5-4. Food Web Structure for Nati onal BAF Methodology (Flint, 1986; Gobas, 1993) .................................................................................................................... 5-54

Table 5-5. Environmental Parameters a nd Conditions Used for Determining FCMs for the National BAF Methodology........................................................................... 5-54
Table 5-6. Food-Chain Multipliers for Trophic Levels (TLs) 2, 3, and 4 (Mixed Pelagic and Benthic Food Web Structure and J socw / K ow = 23)...................................... 5-55

Table 5-7. Validation Statistics for Method 4b: Ratio Between Predicted and Measured Baseline BAFs (Baseline BAF predicted /Baseline BAF measured ) based on PCB concentration data from Green Bay, Lake Michigan and the Hudson River....... 5-66xiv TABLES (CONTINUED)
Table 5-8. Summary Sta tistics: Differences Between Log Baseline BAFs Predicted with Method 4b and Log Baseline BAFs Measured from Lake Ontario (Oliver and Niimi, 1988) for Chemicals with Log K ow Exceeding 4................... 5-67

Table 5-9. BAFs and Baseline BAFs C onfidence Limit Ratios (CLRs) for Adult alewife, Age 4 walleye, and Age 10 carp in Green Bay (All Zones Combined)............................................................................................................ 5-78

Table 5-10. Evaluation Criteria for Lipid Data Sources ........................................................ 5-86

Table 5-11. Evaluation Criteria for Organic Carbon Data Sources ........................................ 5-94

Table 5-12. National Default Values for PO C and DOC in U.S. Fresh and Estuarine Surface Waters..................................................................................................... 5-97
Table 5C-1. BSAFs for PCB congeners based on measurements made in Green Bay, Lake Michigan................................................................................ 5-113
Table 5C-2. BSAFs for PCB congeners based on measurements made in the Hudson River. .................................................................................................... 5-115
Table 5D-1. Lipid Content of Aquatic Organisms Used to Derive National Default Values of Lipid Fraction (f l ).................................................................. 5-119xv FIGURES
Figure 2-1 Framework for selection of methods for deriving national BAFs. ...................... 2-2
Figure 3A-1. Bootstrap distributions of forage fish and water concentrations, and BAFs based on resampling 20 fish and 20 water concentrations from Green Bay Zone 3 PCB 18 data ....................................................................................................... 3-70
Figure 3A -2. Bootstrap distributions of fora ge fish and water concentrations, and BAFs based on resampling 2 fish and 20 wate r concentrations from Green Bay zone 3 PCB 18 data............................................................................................ 3-71
Figure 3A-3. Bootstrap distributions of forage fish and water concentrations, and BAFs based on resampling 2 fish and 2 water concentrations from Green Bay zone 3 PCB 18 data............................................................................................ 3-71
Figure 3A-4. Bootstrap resampling results fo r PCB congener 149 in Green Bay Zone 3 forage fish: 90% confidence interval ra tios for BAF as a function of numbers of fish and water samples................................................................................... 3-73
Figure 3A-5. Bootstrap resampling results for PCB congeners 18, 52, 149 and 180 in Green Bay Zone 3 forage fish: comparison of mean percent bias of BAF as a function of the numbers of water samples............................................................................ 3-74
Figure 3A-6. Bootstrap resampling results for PCB congener 149 in Green Bay Zone 3 forage fish: Root mean square error (RMSE) for BAF as a function of numbers of fish and water samples....................................................................................... 3-75
Figure 3C-1. Comparison of Monte Carl o and Bootstrap results: Ratio of 90% confidence limits for BAF as a function of numbers of fi sh and water samples for PCB congener 149 in Green Bay Zone 3 predator fish.............................................................. 3-84
Figure 3C-2. Comparison of Monte Carlo and Bootstrap results: Ratio of 90% confidence limits for BAF as a function of numbers of fish and water samples for PCB congener 149 in Green Bay Zone 3 forage fish. ................................................ 3-86
Figure 3C-3. Monte Carlo results for PCB conge ner 149 in Green Bay Zone 3 predator fish: Ratio of 90% confidence limits for BAF as a function of correlation between biota and water concentrations.................................................................................... 3-87xvi FIGURES (CONTINUED)
Figure 3D-1. (A) Daily chemical concentrations in the model river segment. (B) Daily chemical concentrations in piscivorous fish for chemicals with log K OW s of 2, 3, 4, and 9................................................................................................................... 3-94
Figure 3D-2. Ratio of the 10 th to 90 th percentile bioaccumulation factors (BAFs) for fieldsampling designs................................................................................................ 3-96
Figure 4-1. The sediment-water concentration quotient ( J socw ) for three different chemical loading scenarios............................................................................................... 4-26
Figure 4-2. Predicted BAFs using Method 2 and 4b plotted against measured baseline BAFs for different sampling locations and fish species for the Green Bay (species and zone) and Hudson River (species and river mile) ecosystems........................... 4-48
Figure 4-3. Sediment-water colu mn concentration coefficient ( J socw ) for PCBs in five different geographical zones in Green Bay, Lake Michigan ............................. 4-54
Figure 4-4. Average sediment-water co lumn concentration coefficients ( J socw ) for individual PCB congeners across the five different geographical zones in Green Bay, Lake Michigan ............................................................................................................ 4-55
Figure 4-5. Sediment-water column concentration coefficients ( J socw ) for PCBs at river miles 189 and 194........................................................................................................ 4-56
Figure 4A-1. Ratio of the 10 th to 90 th percentile biota–sediment accumulation factor (BSAF) for field-sampling designs.................................................................... 4-74
Figure 4B-1. 90% confidence inte rval ratio for BSAF as func tion of number of sediment samples............................................................................................................... 4-80
Figure 4B-2. 90% confidence interval ratio for J socw as function of number of sediment samples............................................................................................................... 4-83
Figure 4B-3. 90% confidence interval ratio for PCB-180 Method 2 BAFs as a function of the number of sediment samples.............................................. 4-86
Figure 4B-4. 90% confidence interval ratio for four PCB congener Method 2 BAFs as a function of the number of sediment samples. .................................................... 4-87
Figure 4B-5. 90% confidence in terval ratio for PCB-149 Method 2 BAFs as a function of the number of sediment samples and variab ility of chemical concentrations in sediment. ............................................................................................................ 4-89xvii FIGURES (CONTINUED)
Figure 4B-6. 90% confidence interval ratio for PCB-52 Method 2 BAFs as a function of concentration correlations.................................................................................. 4-93
Figure 5-1. Decision framewor k for selecting a site-specifi c BAF derivation method based on BSAFs................................................................................................................. 5-4
Figure 5-2. White sucker BSAFs (kg organi c carbon/kg lipid) from si x ecosystems plotted against BSAFs (kg organic carbon/kg lip id) for white sucker from sampling station 01208869................................................................................................. 5-8
Figure 5-3. BSAFs (kg or ganic carbon/kg lipid) for PC DD/Fs with nonzero mammalian toxicity equivalence factors (TEFs) from four ecosystems plotted against BSAFs (kg organic carbon/kg lipid ) for 6 year old lake trout from Lake Michigan ..................................................................................................... 5-9
Figure 5-4. BSAFs (kg organi c carbon/kg lipid) for PCBs fr om Green Bay, Hudson River, and Detroit River (Leadley et al. 1998) plotted against BSAFs (kg organic carbon/kg lipid) for 6 year old lake trout from Lake Michigan........................ 5-10
Figure 5-5. BSAFs (kg organi c carbon/kg lipid) for PCBs and PCDD/Fs from Lake Ontario (USEPA, 1995) and Tokyo Bay (Naito et al. 2003) plotted against BSAFs (kg organic carbon/kg lipid) for 6 year old lake trout from Lake Michigan........... 5-11
Figure 5-6. BSAFs (kg organi c carbon/kg lipid) for PCBs fr om Green Bay and the Hudson River plotted against BSAFs (kg organic carbon/kg lipid) extrapolated from Lake Michigan (6 year old lake trout) using Method 3a. ........................................... 5-22
Figure 5-7. BSAFs (kg organi c carbon/kg lipid) for PCBs fr om Green Bay and the Hudson River plotted against BSAFs (kg organi c carbon/kg lipid) esti mated from Lake Michigan (6 year old lake trou t) BEFs using Method 3b. ................................. 5-33
Figure 5-8. Relationship between baseline BAFs measured at Bayou d’ Indie and BAFs predicted using Method 4a................................................................................. 5-43
Figure 5-9. The FCMs determined for the national BAF methodology for trophic levels 2 through 4............................................................................. 5-56
Figure 5-10. t ,T BAF i s and Baseline BAFs for PCB congener 149 (2,2',3,4',5',6- hexachlorobiphenyl) (±1 sd) for adult al ewife for different spatial zones in Green Bay. ......................................................................................................... 5-77
Figure 5-11. Box plots compar ing baseline (TL 3 or 4 B) and field-measured (TL 3 or 4 F) BAFs for six PCB congeners obtained from Green Bay, Lake Ontario, and Hudson River ecosystems for 13 fish species.................................................... 5-80xviii FIGURES (CONTINUED)
Figure 5-12. Illustration of how the freely dissolved fraction calculated using equation 3-12 varies as a function of K ow , for varying concentrations of DOC and POC............................................................................................................. 5-90xix LIST OF ACRONYMS, SYMBOLS AND NOTATIONS AWQC Ambient water quality criteria BAF Bioaccumulation factor

t T BAF Bioaccumulation factor based on total concentrations in tissue and water
fd l BAF BAF, lipid normalized and based on fr eely dissolved chemical in water
fd L BAF Baseline BAF, lipid normalized and based on freely dissolved chemical in water BCF Bioconcentration factor fd l BCF BCF, lipid normalized and based on fr eely dissolved chemical in water
fd L BCF Baseline BCF, lipid normalized and based on freely dissolved chemical in water

t T BCF Bioconcentration factor based on total concentrations in tissue and water BEF Bioaccumulation equivalency factor BMF Biomagnification factor BSAF Biota-sediment accumulation factors BW Human body weight C Concentration superscript fd Freely dissolved chemical superscript t Total chemical subscript w In water subscript soc In sediment organic carbon subscript t In tissue subscript l In lipid subscript r Reference chemical subscript k Individual chemical of interest subscript i In organism at trophic level i

t

w C C of total chemical in water

fd

w C C of chemical freely dissolved in water

s C C of chemical in sediment C soc C of chemical in sediment organic carbon C l C of chemical in lipid C t C of chemical in the specified wet tissue CDC U.S. centers for disease control and prevention CSFII Continuing survey of food intake by individuals CWA Clean Water Act DDT 1,1,1-trichloro-2,2- bis(p-chlorophenyl)ethane DDE 1,1-dichloro-2,2-b is(p-chlorophenyl)ethylene DDD 1,1-dichloro-2,2-b is(p-chlorophenyl)ethane DI Drinking water intake DOC Dissolved organic carbon EPA Environmental Protection Agency f fd Fraction freely dissolved f l Fraction lipid f
oc Fraction organic carbon in suspended solids f
soc Fraction organic carbon in sedimentxx LIST OF ACRONYMS, SYMBOL S AND NOTATIONS (CONTINUED)
FCM Food chain multiplier FI i Fish intake at trophic level i GLI Great Lakes Water Quality Initiative IARC International Agency for Research on Cancer IRIS Integration Risk Information System kg Kilogram K ow n-Octanol-Water Partition Coefficient L Liter MCL Maximum Contaminant Level mg Milligrams ml Milliliters NFCS Nationwide F ood Consumption Survey NOEL No Observed Effect Level NPDES National Pollutant Di scharge Elimination System PAH Polycyclic Aromatic Hydrocarbon PCB Polychlorinated Biphenyl POC Particulate organic carbon RDA Recommended Daily Allowance R

f

D Reference dose RSC Relative source contribution to ac count for nonwater sources of exposure SAB Science Advisory Board J socw Sediment-water con centration quotient D k/r Ratio between values of J socw for reference chemical and chemical of interest k SDWA Safe Drinking Water Act STORET Storage Retrieval TMDL Total Maximum Daily Load TSD Technical Support Document USDA United States De partment of Agriculture USEPA United States Envir onmental Protection Agency WQBEL Water Quality-Based Effluent Limits1-1 1. INTRODUCTION

This Technical Support Document (TSD) provi des guidance on different approaches that investigators can take to develop site-speci fic bioaccumulation factors (BAFs) that are representative of the bioaccumulation potential at a given location, and the factors that should be considered when selecting an approach for deve loping a site-specific BAF in a given situation.
This TSD should not be used alone to derive BAFs, but rather s hould be used in conjunction with
the Methodology for Deriving Ambient Water Qua lity Criteria for the Protection of Human Health (2000) (USEPA, 2000) and TSD Volume 2: Development of National Bioaccumulation
Factors (USEPA, 2003). The intended audience for th is TSD includes State and Tribal water quality staff scientists or risk assessors (“investigators”) who are responsible for deriving State or Tribal water quality standards, stakeholders interested in de veloping site-specific BAFs, and other users interested in site-specific bioaccu mulation issues for other applications. The 2000 Human Health Methodology for derivi ng ambient Water Quality Criteria will provide more flexibility for decision-making at th e state, tribal and EPA regional levels. It is most likely that the methodology will result in more stringent wa ter quality criteria for chemicals that bioaccumulate and generally similar values for chemicals that do not.

The bioaccumulation methodology used in the 2000 Human Health Methodology encourages developing site-specific BAFs becau se EPA recognizes that BAFs vary not only between chemicals and trophic levels, but also among different ecosystems and waterbodies; that is, among sites. The bioaccumulation potential of a chemical can be affected by various site-specific physical, biologica l, and chemical factors:

• water temperature and dissolved oxygen concentration;

• sediment-water disequilibria;

• organism health, physiology and growth rate;

• food chain structure;

• food quality; and

• organic carbon composition.1-2 National average BAF value for a given chemi cal and trophic level may not provide the
most accurate estimate of bioaccumulation for ce rtain waterbodies in the United States. At a given location, the BAF for a chemical may be higher or lower than the national BAF, depending on the nature and extent of site-specific influences. In addition, the fish consumption habits of the local human population will guide the selection of the target species for which the investigator develops site-specific BAFs.

The goal in deriving site-specific BAFs is to determine the most accurate estimates of
bioaccumulation feasible for each site. In the ab sence of site-specific data, EPA believes that national BAFs are broadly applicable to sites th roughout the United States and can be applied to achieve an acceptable degree of accuracy when estimating bioaccumulation potential at most sites. National BAFs are derived using a me thodology intended to produce the most accurate national average values for BAFs at each troph ic level. The investigator should view the derivation of site-specific BAFs as a process to improve upon the accuracy of the national BAFs for a particular site. EPA expects that in most instances, the derivation of site-specific BAFs will be motivated by some knowledge or expectation that unique site-specific factors may cause BAFs to diverge from the national values. These factors include (for exam ple): fish consumption patterns that are substantially di fferent than national averages; sp ecies of aquatic organisms that have not been previously sampled or for whic h trophic level or feedi ng preference is unknown;
and sediment-water chemical distribution, tissue lipid c ontent or DOC concentration significantly different th an the values assumed in the national methodology. In cases such as these, the derivation of site-spe cific BAFs would likely improve th e accuracy of bioaccumulation estimates and, ultimately, the AWQC for the chemi cal of concern at that site. The issue of what range of sites the national BAFs are intended to represent, and the potential variation in BAF values between sites, is considered in gr eater detail in TSD Volume 2 (USEPA, 2003).1-3 1.1 BACKGROUND

The Methodology for Deriving Am bient Water Quality Criteria for the Protection of Human Health (2000) (USEPA, 2000) presented technical guidance and the steps that EPA
follows to derive new and revised national recommended ambient water quality criteria (AWQCs) for the protection of human health und er Section 304(a) of the Clean Water Act. Water quality criteria define the maximum levels of a pollutant necessary to protect designated
uses in ambient waters. For chemicals that bioaccum ulate, water quality criteria also describe the
maximum advisable concentration of a chemical in freshwater and estuarine fish and shellfish tissue to protect consumers of fish and shellfish among the ge neral population. The 2000 Human Health Methodology included guidance on chem ical risk assessment, exposure, and bioaccumulation. To supplement the 2000 Huma n Health Methodology, EPA is developing a series of Technical Support Documents (TSDs) on Risk Assessment, Exposure Assessment, and Bioaccumulation. The first volume, Volume 1: Risk Assessment (EPA-822-B-00-005), was published with the 2000 Human Health Methodology in October of 2000.

In 2003, the EPA published a second technical support document, Volume 2: Development of National Bioaccumulation Factors (EPA-822-R-03-030), to accompany the 2000 Human Health Methodology. That document fo cused on the technical components of the 2000 Human Health Methodology that pertain to the developmen t of national bioaccumulation factors for use in deriving national recommende d ambient water quality criteria for protecting
human health. A national bioaccum ulation factor (National BAF i ) 1 is a mean BAF, based on
concentrations of total chemical in wet ti ssue and water, for a sp ecific trophic level " i ". It is adjusted for the consumption-weighted averag e lipid content of comm only consumed aquatic organisms in that trophic level and the nationw ide average organic carbon concentration in
ambient waters. In this document we refer to national BAFs as plural, because the human population usually consumes aquatic organisms from more than one trophic level and, therefore, EPA develops a national BAF fo r each of these trophic levels.

1 In TSD Volume 2, a slightly different symbol (National BAF TL n ) was used for national bioaccumulation factors. The two symbols are equivalent.1-4 For those unfamiliar with EPA’s methodol ogy for assessing chemical bioaccumulation (USEPA, 2003), it is useful to review how BAFs are factor ed into the calculation of recommended national AWQCs for the protection of human health. Equation 1-1 (below) is the generalized AWQC formula for non cancer effects. In this equation, trophic-level specific BAF i s are in the denominator, along with information on the amount of fish of each trophic level ( i ) consumed on a daily basis (FI i ), to estimate human exposure to contaminants through the aquatic food web (USEPA, 2000).



## ( )    

 

   

 

⋅ + ⋅ ⋅ = 

## ∑

= 4

2 i i i BAF FI DI BW RSC RfD AWQC (Equation 1-1)

where: AWQC = ambient water quality criterion (mg chemical/L water)
R f

D = reference dose for noncancer effects (mg/kg/day)
RSC = relative source cont ribution to account for nonwat er sources of exposure BW = human body weight (kg) DI = drinking water intake (L/day)
FI i = fish intake (kg/day) at tr ophic level i (i = 2, 3, 4)
BAF i = bioaccumulation factor (L/kg) at trophic level i ( i = 2, 3, 4) based on
concentrations of total chemical in wet tissue and water

For contaminants that bioaccumulate extensively, such as hydrophobic nonionic organic chemicals, researchers report BAF i values of 10 3 to 10 7 for aquatic ecosystems. For these chemicals, inspection of Equation 1-1 reveals th at the AWQC will be inversely proportional to the BAF. The EPA’s approach to estimating uptake into fish and shellfish emphasizes the use of bioaccumulation factors, which account for chemi cal accumulation from all potential exposure routes (e.g., food, sediment, a nd water) that may be important in determining the chemical accumulation in the organism’s body. As noted in Section 1.2 of the 2000 Human Health Methodology, however, EPA and State/Tribal de cision-makers retain the discretion to use different, scientifically defensible, methodologie s to develop human heal th criteria on a case-bycase basis that differ from this Methodology (i.e ., the use of BAFs and Equation 1-1) where1-5 appropriate. For example, in January 2001, EP A published ambient water quality criteria (AWQC) recommendations for methylmercury for the protection of people who eat fish and shellfish. This criterion, 0.3 mg methylmercury/kg fish tissue wet weight, marked EPA's first issuance of a water quality criteri on expressed as a fish and shellf ish tissue value rather than as an ambient water column value (USEPA, 2009).

1.2 SCOPE OF DOCUMENT

EPA’s approach for deriving national BAFs in cludes separate procedures for different types of chemicals (e.g., nonionic organic, ioni c organic, inorganic, and organometallic). For the purposes of the 2000 Human Health Methodol ogy, nonionic organic chemicals are defined as organic compounds that do not ioni ze substantially in natural bodies of wate r. These chemicals
are also referred to as “neutral ” or “nonpolar” organics in the sc ientific literature. TSD Volumes 2 and 3 focus primarily on calculation proce dures for determining BAFs for nonionic organic
chemicals that bioaccumulate in th e lipids of fish and partition to organic carbon in water due to the hydrophobicity of the chemical . BAF calculation procedures a nd theories presented in both documents are based on this partitioning beha vior which does not a pply to ionic organic chemicals, in their ionized form, or inorgani c and organometallic compounds. Therefore, the BAF calculation procedures presented in this document are applicable mainly to nonionic organic chemicals. The procedures for esti mating the bioaccumulation of nonionic organic chemicals are generally better deve loped than those for other types of chemicals. The conditions under which these procedures can be applied and th e limitations associated with their application must be understood for their proper application, and will be discussed further in Section 2.

Ionic chemicals are considered separately when deriving national BAFs because the environmental partitioning behavior of the ani onic or cationic species of these chemicals in aquatic systems is much different from those of their neutral (un-ioni zed) counterparts. Ionic organic chemicals are considered to include those chemicals that contain functional groups which can either readily donate protons (e.g ., organic acids with hydroxyl, carboxylic, and sulfonic groups) or readily accept protons (e.g ., organic bases with amino and aromatic1-6 heterocyclic nitrogen groups). The neutral species of ionic or ganic chemicals are thought to behave in a similar manner as nonionic orga nic compounds (e.g., partit ioning to lipids and organic carbon as a function of hydrophobicity). However, ionic orga nic chemicals undergo ionization in ambient water, the extent of whic h depends on the pH of the water and the pKa of the chemical (see Section 5.5 of the 2000 Human Health Methodology for guidance). The ionized chemical species exhibit a considerably more complex partitioning behavior than nonionic organic chemicals, involving multiple partitioning mechanisms (e.g., ion exchange, electrostatic, and hydrophobi c interactions (Jafvert et al., 1990; Jafvert 1990; Schwarzenbach, et al., 1993). As discussed in Section 5.5 of the 2000 Human Health Methodology, procedures for deriving BAFs for these chemicals differ dependi ng on the extent to whic h the fraction of the total chemical is likely to be represented by th e ionized (cationic, anio nic) species in surface

waters.

Inorganic and organometallic chemicals are also considered separately from nonionic organic chemicals, due to several important factors. These chemical groups include:

• inorganic minerals, • other inorganic compounds and elements, • metals, • metalloids, and • organometallic compounds.

As discussed in Section 5.6 of the 2000 Hu man Health Methodology, the derivation of BAFs for inorganic and organometallic chemicals differs in several ways from procedures for nonionic organic chemicals. First, these chemicals do not partition to lipids and organic carbon in ambient waters as do nonionic organic chemicals. Second, the bioavailabili ty of inorganics and organometallics in water tends to be chemical-specific. Third, at the present time there are no general bioaccumulation models that can be used to predict BAFs for inorganic and organometallic chemicals as a whole, unlike the hydrophobicity-based, lipid and organic carbon partitioning models that are ava ilable for nonionic organic chemicals. The procedures presented in this TSD (Volume 3), which are based on the partitioning behavior of nonionic organic1-7 chemicals to the lipids in fish and organic ca rbon in water, are not a pplicable for calculating BAFs for ionic organic chemicals (when ionized) and inorganic or organometallic chemicals.

1.3 SITE-SPECIFIC BIOACCUMUL ATION FACTORS (SS BAFs)

There are two general approaches for deriving site-specific BAFs. The preferred approach is to calculate site-specific BAFs or biota-sediment accumulation factors (BSAFs) from data gathered in the site(s) of interest. BAFs derived from data obtained from samples of tissue
and water collected at the site - referred to as "field-measured BAFs" - are the most direct measures of bioaccumulation. For nonionic organi c chemicals (and ionic organic chemicals that behave similarly), the investigat or can also predict site-specifi c BAFs from BSAFs. BSAFs are
similar to field-measured BAFs because the concen tration of a chemical in biota is calculated from the results of the analysis of samples of tissue and sediment collected at the site. BSAFs also reflect an organism's exposure through all relevant exposure routes. EPA prefers
field-measured BAFs and BSAFs over other met hods of determining site-specific BAFs because they inherently account for all biotic and abiotic factors that affect bioaccumulation in a waterbody. EPA encourages the States, Territo ries and authorized Tribes to develop field-measured BAFs and BSAFs whenever possible.

The second general approach is to estimate site-specific BAFs indirectly using one of the
other methods described in TSD Volume 2 (USEPA, 2003). These methods include:

• recalculating site-specific BAFs from baseline 3 BAFs,

• extrapolating site-specific BAFs from BSAFs, or

• predicting BAFs using laboratory-measured bioconcentration factors (BCFs) or octanol-water partition coefficients (K ow s) coupled with food chain multipliers.

3 For nonionic organic chemicals (and certain ionic orga nic chemicals to which similar lipid and organic carbon partitioning behavior applies), the baseline BAF is the ratio between the chemical concentration in the lipid fraction of tissue and the concentra tion of chemical freely dissolved in water.1-8 Although these methods are the same as those described in TSD Volume 2, EPA expects
that variations of thes e methods, described in this TSD, ma y be used to derive site-specific BAFs. EPA encourages those deri ving site-specific BAFs to use as many of these methods as possible and then compare the results, applying judgment to select the best estimate. The guidance discusses benefits and limitations of each approach (see Table 2-1) and EPA
recommends consideration of these when choos ing method(s). EPA believe s that the additional guidance provided in this TSD will help to ensure that site-specific bioaccumulation factors are accurate and defensible, whether they are determin ed directly by field measurement or indirectly by estimation methods.

The remainder of this document is organized into four sections:

• Section 2 discusses the definition of a site , and introduces the di fferent methods that the investigator can use to derive site-specific BAFs.

• Section 3 discusses the deriva tion of field-measured site-specific BAFs, from data obtained on samples of tissue and water coll ected at the site. This section also provides guidance to the investigator for pl anning a field study to measure chemical concentrations in water and fish tissue.

• Section 4 presents the derivation of si te-specific BAFs predicted from BSAFs measured at the site, and also provides guidance related to measuring chemical concentrations in sediment.

• Section 5 presents the other methods fo r deriving site-specific BAFs based upon applying one of the other methods descri bed in TSD Volume 2 (USEPA, 2003). This section also discusses the adjustment of lipid and organic carb on values used in estimating the site-specific BAFs, and the alte rnatives that the investigator can use to determine site-specific values of lipid and organic carbon.1-9 1.4 GLOSSARY

The following terms and their definitions are used throughout this document, and were based upon the glossary provided in TSD Volume 2 (USEPA, 2003) . Differences between this
TSD and Volume 2 are noted below.

Bioaccumulation. The net accumulation of a chemical by an aquatic organism as a result of uptake from all environmental sour ces (water, sediment and food). Bioaccumulation factor ( BAF i ). The ratio of the concentration of a chemical in the tissue of an aquatic organism to its concentration in water, in situations where both the organism and its food are exposed and the ratio does not change substantially over time. The subscript i indicates that a
BAF i is trophic level specific; this subscript was not used in TSD Vo lume 2. Several forms of the
BAF i are used in this document:

Total bioaccumulation factor ( t l i , BAF ). A BAF based on the total concentration of chemical in the organism and the water. The total concentrati on of the chemical in the organism includes that in either a specific tissue (e.g., fillet) or the whole organism and is ba sed on wet tissue weight.
The total concentration of the chem ical in water includes the chemi cal associated with particulate organic carbon, chemical asso ciated with dissolved organi c carbon, and chemical freely
dissolved in the water. The t l i , BAF is expressed in liters per ki logram, and is trophic level specific. The subscript i was not used in TSD Volume 2.
Baseline bioaccumulation factor ( Baseline BAF i or

, BAF f d i L )

. For nonionic organic chemicals
(and certain ionic organic chem icals to which similar lipid and organic carbon partitioning behavior applies 4 ), a BAF that is based on the concentrati on of the chemical in the lipid fraction

4 As discussed in TSD Volume 2, baseline and lipid-nor malized BAFs for certain ioni c organic chemicals can be derived using methods developed for nonionic organic chemicals, which rely on lipid and organic carbon partitioning theory. In these cases, similar lipid and or ganic carbon partitioning behavior should be known or inferred (i.e., based on negligible ionization) for the ionic chemical in question. If the relative extent of ionization that is likely to occur at pH ranges that are typical of U. S. surface waters is negligible (see the 2000 Human Health Methodology for guidelines on this determination), and if the un-ionized form of the ionic chemical behaves like a nonionic organic chemical, in which lipid and organic carbon partitioning controls the behavior of the chemical, then the chemical can be treated essen tially as a nonionic chemical for the purposes of determining site-specific BAFs.1-10 of tissue within an organism and the concentrat ion of chemical freely dissolved in water. The
baseline BAF i is trophic level specifi c, although the subscript i was not used in TSD Volume 2.
The baseline BAF i is expressed in liters per kilogram of lipid.
Field-measured bioaccumulation factor . A t T i , BAF derived from analysis of tissue and water samples collected from the field. For mode rately to highly hydrophobic chemicals, (log K ow > 4) it is usually preferable to measure a fd l i , BAF instead.
Lipid-normalized and freely dissolved-based bioaccumulation factor ( fd l i , BAF ). For nonionic organic chemicals (and ionic orga nic chemicals with similar lipid and organic carbon partitioning behavior), a BAF that is based on the lipid-normalized concentration of a chemical in tissue of an organism and the concentration of the chemical freely dissolved in water. The fd l i , BAF is expressed in liters per kilogr am of lipid. The subscript i was not used in TSD Volume 2. National trophic-level specifi c bioaccumulation factor ( National BAF i ). A BAF based on nationwide average lipid content for trophic level i and nationwide average organic carbon in
ambient waters. The national BAF i is expressed in liter s per kilogram wet tissue. In TSD Volume 2, the symbol National BAF TL n was used for this term.
Bioconcentration. The net accumulation of a chemical by an aquatic organism as a result of uptake directly from the ambient water only, through gill membranes or other external body
surfaces. Bioconcentration factor ( BCF ). The ratio of the concentration of a chemical in the tissue of an aquatic organism to its concentration in water, in situations where the organism is exposed through the water only and th e ratio does not change s ubstantially over time. Total bioconcentration factor ( t T BCF ). A BCF based on the total concentration of chemical in the organism and the water. The total concentrati on of the chemical in the organism includes that in either a specific tissue or the whole organi sm and is based on wet tissue weight. The total
concentration of the chemical in water includes th e chemical associated with particulate organic carbon, the chemical associated w ith dissolved organic carbon, a nd the chemical freely dissolved
in the water. A BCF is often referred to as a “laboratory-measured BCF” because it can be measured only in the laboratory. A BCF reflects only the accumulation of a chemical through the organism’s exposure to water. The t T BCF is expressed in liters per kilogram.1-11 Baseline bioconcentration factor ( Baseline BCF or fd L BCF ). For nonionic organic chemicals (and certain ionic organic chem icals to which similar lipid and organic carbon partitioning behavior applies), a BCF that is based on the con centration of chemical fr eely dissolved in water
and the concentration of the ch emical in the lipid fraction of tissue. The baseline BCF is expressed in liters per kilogram of lipid. Lipid-normalized and freely dissolved-based bioconcentration factor ( fd l BCF ). The ratio of the lipid-normalized concentration of a chemical in tissue of an organism to the concentration of the chemical freely dissolved in water, in situ ations where both the organism is exposed through water only and the ratio does not ch ange substantially over time. The fd l BCF is expressed in liters per kilogram of lipid. Biomagnification. The increase in concentration of a chem ical in the tissue of organisms along a series of predator-prey associations, primar ily through the mechanism of dietary accumulation. Biomagnification occurs across trophic (food chain) levels as opposed to bioaccumulation, which occurs within a trophic level. Biomagnification factor ( BMF i ). The ratio (unitless) of the con centration of a chemical in a predator organism at trophic level i to the concentration of the chem ical in the tissue of its prey organism at the next lowest tr ophic level for a given waterbody and chemical exposure. In TSD Volume 2, the symbol BMF TL n was used for this term. Biota-sediment accumulation factor ( BSAF i ). For nonionic organic chemicals (and certain ionic organic chemicals to which similar lipid a nd organic carbon partiti oning behavior applies),
the BSAF i is the ratio of the lipid normalized concentr ation of a chemical in tissue of an aquatic organism to its organic carbon normalized concentration in surface sediment. BSAF i is only predictive of bioaccumulation for moderately to highly hydrophobic nonionic organic chemicals when: (1) the ratio does not change substantiall y over time; (2) both the organism and its food are exposed; and (3) the surface sediment is repr esentative of average surface sediment in the vicinity of the organism. BSAF i is expressed in kilograms of sediment organic carbon per kilogram of lipid. The subscript i was not used in TSD Volume 2. Bioaccumulation Equivalency Factor (BEF k/r ). For nonionic organic chemicals (and certain ionic organic chemicals to which similar lipid a nd organic carbon partiti oning behavior applies),
the BEF k/r is the ratio between the BSAF for a chemical k and the BSAF for another chemical r ,1-12 when both BSAFs are measured in the same ecosystem. Depuration. Loss of a chemical from an organism as a result of any active or passive physiological process. Equilibrium. A thermodynamic condition under which a ch emical’s activity, or fugacity, is equal among all phases composing the system of in terest. In systems at equilibrium, chemical concentrations in all phases will remain unchanged over time. Food-chain multiplier ( FCM i ). For nonionic organic chemicals (and certain ionic organic chemicals to which similar lipid and organic carbon partitioning behavior applies), the FCM i is
the unitless ratio of a baseline BAF for an organism at trophic level i to the baseline BCF (usually determined for organisms in trophic level one). The subscript i was not used in TSD
Volume 2. Foraging range. The area in which an indivi dual organism normally feeds. Freely dissolved concentration ( fd

w C ). For nonionic organic chemicals, the concentration of the chemical that is dissolved in ambient water, excluding the portions sorb ed onto particulate and dissolved organic carbon (POC and DOC). The freely dissolved chemical concentration is considered to represent the most bioavailable form of an organic chemical in water and, therefore, is the form that be st predicts bioaccumulation. Home range. The area to which an individual organism restricts most of its normal activities. Hydrophilic. Chemicals having a great af finity to water. Hydrophi lic chemicals are usually charged or have polar side groups to their structure that will attract water. Hydrophobic. Lacking affinity for water; the extent to which a chemical avoids partitioning into the water phase. Moderately to hi ghly hydrophobic organic chemicals (log K ow > 4) have a greater tendency to partiti on into nonpolar phases (e.g., li pid, organic carbon) than do hydrophilic chemicals. Lipid-normalized concentration ( C l ). The total concentration of a ch emical in a tissue or whole organism divided by the fraction of that ti ssue or whole organism that is lipid.

n -Octanol-water partition coefficient ( K ow ). The ratio of the concentration of a chemical in the

n -octanol phase to its concentration in the aqueou s phase in an equilibrat ed two-phase system of

n -octanol and water. This is usually expressed as log K ow , the base 10 logarithm of the n -octanolwater partition coefficient. Sediment organic carbon-normalized concentration ( C SOC ). For sediments, the total1-13 concentration of a contaminant in sediment divided by the fraction of organic carbon in
sediment. Sediment-water column concentration quotient ( J socw ). The ratio of the concentration of chemical in the sediment, on an organic carbon ba sis, to that in the water column, on a freely
dissolved basis. J socw when divided by the K ow of the chemical provides a measure of the chemical’s thermodynamic gradient between the sediment and the water column, for a given ecosystem. The sediment-water column concentrati on quotient is expressed in liters per kilogram of organic carbon. Steady state. A condition reached by a system (e.g., an ecosystem composed of water, biota and sediment) when rates of chemical movement be tween phases and reactions within phases are balanced, so that concentrations of the chemi cal in the phases of the system are unchanged over time. A system at steady state is not necessari ly at equilibrium; steady-state conditions often exist when some or all of the phases of the system have different activities or fugacities for the
chemical. Trophic Level. A trophic level of an organism is its position in a food chain. Levels are numbered according to how fa r particular organisms are along the chain from the primary producers (e.g., phytoplankton) at level 1, to he rbivores (zooplankton; le vel 2), to predators (forage fish; level 3), to carnivores or top predators (level 4). Uptake. Movement of chemical from the environment into an organism as the result of any active or passive process.

REFERENCES
USEPA, 2000. Methodology for Deriving Ambient Water Quality Criteria for the Protection of Human Health (2000). Office of Water, Washington, DC.
USEPA, 2000 (b). Methodology for Deriving Ambient Water Quality Criteria for the Protection of Human Health (2000)

. Technical Support Document Volume 1: Risk Assessment. Office of Science and Technology, Office of Water. Washington, DC. EPA-822-B-00-005. August.

USEPA. 2003. Methodology for Deriving Ambient Water Quality Criteria for the Protection of Human Health (2000). Technical Support Docume nt Volume 2: Development of National Bioaccumulation Factors. EPA-822-B-03-030. Office of Wa ter, Washington, D.C.

USEPA. 2009. Guidance for Implementing the Januar y 2001 Methylmercury Water Quality1-14 Criterion. Office of Science and Technology, Office of Water. Washington, D.C. EPA-823-R09-002. January.2-1 2. HOW TO SELECT AN APPROACH FO R DERIVING SITE-SPECIFIC BAFs

This section provides guidan ce on selecting an approach (or approaches) for deriving site-specific BAFs from the alternatives recomm ended by EPA in this document. The guidance is intended to apply to all sites in the United States, a nd to each of the parties (States, Territories or
authorized Tribes and other stak eholders) that may be interested in deriving site-specific BAFs. EPA recognizes that these parties may derive site-specific BAFs for different purposes, and may
also have different views as to what constitutes a "site". The investigator should consider these institutional perspectives, in a ddition to other factors such as resource and schedule constraints, in conjunction with scientific preference when se lecting an approach fo r deriving site-specific
BAFs. State and Tribal decision makers also reta in the discretion to use EPA’s national BAFs, or scientifically defensible methodol ogies, including those discussed in this guidance and others, to develop site-specific BAFs, as appropriate. As a result, there is not a single approach that is preferable, or even applicable, for all sites. In each case the investigator should determine the hierarchy of preferred approaches ba sed upon all of these considerations.

The methodology EPA uses to derive national BAFs for setting AWQCs for the protection of human health depends on the type of chemical (i.e., non ionic organic, ionic organic, inorganic, and organometallic). For a given chemical, the choice of a method for deriving a national BAF depends on several factors. These factors include the properties of the chemical of interest, the relativ e strengths and limitations of th e BAF method, and the level of uncertainty associated with the bioaccumula tion or bioconcentration measurements. Because selecting the most appropriate BAF method(s) for a given chemical and data set involves multiple evaluation steps, EPA developed a d ecision framework for deriving national BAFs (Figure 2-1). This framework illustrates the major steps and decisions that will ultimately lead to calculating a national BAF. Use of this framewor k leads to selection of one of six possible procedures (shown at the bottom of Figure 2-1) for deriving national BAFs. Each procedure
includes those BAF derivation methods that are su itable for the class and properties of chemicals to which the procedure applies. The investigat or should use the same framework to select appropriate methods for de riving site-specific BAFs.2-2 Figure 2-1. Framework for selection of met hods for deriving national BAFs.2-3 The first step in the national BAF derivati on framework involves pr ecisely defining the chemical of concern. The purpose of this step is to ensure consistency between the form(s) of
chemical used to derive national BAFs and the fo rm(s) used as the basis of the health assessment (e.g., the reference dose or point of departure/uncertainty factor). Although this step is usually unambiguous for single chemicals th at are stable in th e environment, comp lications can arise when assessing chemicals that occur as mixtur es or undergo complex tr ansformations in the environment. The second step of the framewor k consists of collecting and reviewing data on bioaccumulation and bioconcentration. The third step involves classifying the chemical into one of three broadly defined categ ories: nonionic organic, ioni c organic, and inorganic/ organometallic. This step is important because the BAF derivation methods presented in this document, and summarized in Section 2.5, are generally only applicab le to nonionic organic chemicals and other chemicals with similar pa rtitioning and bioaccumulation behavior. For the purposes of the 2000 Human Health Methodology, nonionic organic chemi cals are defined as organic compounds that do not ioni ze substantially in natural bodies of wate r. These chemicals
are also referred to as “neu tral” or “nonpolar” or ganics in the scientific literature (Schwartzenbach et al., 1993; Mackay, 2001). Due to their neutrality, nonionic organic
chemicals tend to associate with other neutral (or near neutral) compartments in aquatic ecosystems (e.g., lipid, organic carbon). Exampl es of nonionic organic chemicals which have been widely studied in terms of their bioaccumulation include pol ychlorinated biphenyls (PCBs), polychlorinated dibenzo-p-dioxi ns and furans, many chlorinated pesticides, and polycyclic aromatic hydrocarbons (PAHs).

Ionic organic chemicals are considered to in clude those chemicals that contain functional groups with exchangeable protons, such as : hydroxyl, carboxylic, su lfonic, and nitrogen (pyridine) groups and functional gr oups that readily ac cept protons such as amino and aromatic heterocyclic nitrogen (p yridine) groups. Ionic organic chemi cals undergo ionization in water, the extent of which depends on pH and the pKa of th e chemical. Because the i onized species of these chemicals behave differently from the neutral sp ecies, separate guidance is provided for deriving BAFs for ionic organic chemicals. Procedures for deriving national BAFs for ionic organic chemicals are provided in Section 5.5 of the 2000 Human Health Methodology.2-4 Inorganic and organometallic chemicals include:

• inorganic minerals,

• other inorganic compounds and elements,

• metals (e.g., copper, cad mium, chromium, zinc),

• metalloids (selenium, arsenic) and

• organometallic compounds (e.g., methylmercury, tributyltin, tetraalkyllead).

As discussed in Section 5.6 of the 2000 Hu man Health Methodology, the derivation of BAFs for inorganic and organometallic chemicals differs in several ways from procedures for nonionic organic chemicals. EPA does not consid er the BAF derivation methods presented in this document (TSD Volume 3) to apply to mo st inorganic and organometallic chemicals.

Additional guidance on the first three steps of the framework is found in Section 5.3 of the 2000 Human Health Methodology. Once the chemi cal is classified into one of the three chemical categories, additional evaluation steps are necessary to determine which of the BAF procedures should be used to derive a national BA F. Again, the investigator should use the same framework to select appropriate met hods for deriving site-specific BAFs.

2.1 BAF DERIVATION PROCEDURES FOR INORGANIC
AND ORGANOMETALLIC CHEMICALS

For inorganic and organometallic chemicals, the primary factor to be evaluated is the
likelihood that the chemical wi ll undergo biomagnification in the food web. At present, evaluating the biomagnification po tential for this group of chem icals is almost exclusively limited to analyzing empirical da ta on the importance of the a quatic food web (dietary) exposure and biomagnification in determin ing chemical concentrations in aquatic species. For example,
available data indicate that methylmercury biomagnifies in aquatic food webs, whereas other chemicals in the inorganic and organometallic category do not routinely biomagnify (e.g., copper, zinc, lead). If biomagnifi cation is considered to be likely, then field-measured BAFs are the preferred BAF method, followed by laboratory- measured BCF adjusted with an FCM. If biomagnification is determined to be unlikely, field-measured BAFs and laboratory-measured2-5 BCF are considered to be of equal utility fo r deriving national BAFs, all other factors being equal. Additional guidance on determining nati onal BAFs for inorganic and organometallic chemicals is provided in Secti on 5.6 of the 2000 Human Health Me thodology. It should be noted
that metal bioaccumulation can va ry substantially across organism s due to a number of factors including physiological differen ces and variation in mechanisms by which organisms take up, distribute, detoxify, store, and eliminate metals from their tissues.

EPA’s Framework for Metals Risk Assessment (USEPA, 2007) outlines key principles about metals and describes how they should be considered in conducting human health and ecological risk assessments. Issu es involving the bioavailability and bioaccumulation of metals in aquatic ecosystems are discussed in Chapter 5.2.5 of the Framework , while bioaccumulation and trophic transfer of metals are discussed in Chapter 5.2.5.4. Due to these issues, EPA does not
consider the BAF derivation met hods presented in this document (TSD Volume 3) to apply to
most metals.

2.2 BAF DERIVATION PROCEDURES FOR IONIC ORGANIC CHEMICALS

For chemicals classified as ionic organic chem icals, the primary evaluation step involves estimating the relative extent of ionization and evaluating their partitioning behavior with lipids and organic carbon. This evaluati on should include determining the relative extent of ionization that is likely to occur at pH ranges that are typical of the si te water (see the 2000 Human Health Methodology for guidelines on this determination) . If the relative extent of ionization is negligible, and if the unionized form of the ionic chemical behaves like a nonionic organic chemical (i.e., lipid and organic carbon partitioning controls the behavior of the chemical), then the chemical can be treated essentially as a noni onic chemical for the purposes of deriving sitespecific BAFs. If ionization is c onsidered potentially important, or if non-lipid and non-organic
carbon mechanisms control the behavior of the chemi cal, then the ionic chemical is treated in the same way as inorganic and organometallic ch emicals for deriving national BAFs. Additional guidance for deriving national BAFs for ionic organic chemicals is provided in Section 5.5 of the 2000 Human Health Methodology. Perfluorinated al kyl acids are an exam ple of ionic organic
chemicals. Some of these chemicals bioconcentr ate and biomagnify in food webs via non-lipid2-6 mediated mechanisms; i.e., lipid and organi c carbon partitioning behavior observed for nonionic organic chemicals does not apply. For the perflu orinated alkyl acids, Pr ocedure 6 (Figure 2-1)
would be used to derive national default BAFs.

2.3 BAF DERIVATION ASSUMPTIONS

The methods for deriving national and site-s pecific BAFs share a number of fundamental assumptions. First, EPA assumes that properly derived BAFs can provide a best estimate of chemical bioaccumulation under steady state (i.e ., long-term) conditions that exist in the ecosystem.

The second major assumption associated with the use of BAFs for nonionic chemicals is that adjusting the BAF for the organism's lipid content and the chemical concentration that is freely dissolved removes much of the variability in BAFs across different species (within a trophic level) and across sites. This is the rationale for calcu lating baseline BAFs for nonionic organic chemicals. Section 4 of TSD Volume 2 (USEPA, 2003) provides th e scientific basis for this assumption and a detailed discussion of ba seline BAFs. EPA presumes that the residual
variation in BAFs across differe nt species and sites reflects other factors that influence
bioaccumulation. These include:

• differences in chemical loading histor ies (i.e., sediment-w ater disequilibria);

• food web structure;

• organism health and physiology;

• water quality factors such as temperature; and

• food quality.

Each of these factors may vary across eco systems and sites within an ecosystem.

A third major assumption with the use of any BAF is that the steady-state bioaccumulation of a chemical can be accurately predicted from a constant ratio of tissue to water concentration (i.e., the BAF is indepe ndent of exposure concentration). For nonionic2-7 organic chemicals, this assumption is generally supported by empirical and mechanistic evidence (i.e., uptake via passive di ffusion; Kelly et al. 2004).

2.4 WHAT IS THE DEFINITION OF A SITE?

Investigators typically determine a site-s pecific BAF for a specific chemical, target species, and site. Each of these factors may infl uence the value of the site-specific BAF. The "site" refers to a spatial scale of interest smaller than the National level. Obviously, this definition encompasses a great range of spatial scales and differen t aggregations of waterbodies. A site can be a State, Territory or authorized Tr ibe; all surface waterbodi es of particular type (e.g., lakes, rivers, ponds, stream s, wetlands) in a State; a waters hed; an individual waterbody; or, a segment of a waterbody. A site may also include intermittent and ephemeral waters that are by nature highly variable on seasonal, annual, an d inter-annual time-scales. The nature of this variability will in large part, determine the type s of fish that may be encountered in these waterbody types. The appropriateness of a BAF developed for fish that may inhabit these waters is a very site-specific decision, and some aspects of this guidance may not be applicable to such waters. As such, EPA recommends that a state or tribe consult with EPA prior to beginning development of a site-specific BAF for these type s of waters. In genera l, a site is defined according to the interest or need of the agency or interest group, or can be based on the extent of contamination of a waterbody by a bioaccumulative chemical. For example, many site-specific
BAFs will be determined at the State level, to support fish consumption advisories issued by the States. Another example would include site-speci fic BAFs for watersheds in the Total Maximum Daily Load (TMDL) process. Site-specific BAFs may also be determined for waterbodies and waterbody segments receiving point source discharges such as industrial or municipal effluents, combined sewer overflows (CSOs) and stormw ater outfalls. Other sites could include depositional areas where contaminated sediment s accumulate and bioaccumulation potential is enhanced (i.e., areas where water velocity slows and organic-rich sediments are deposited), or
areas where contaminated sediments ar e disturbed by dredging activities.2-8 The spatial scale of both BAFs and BS AFs should also be related to the home 5 range of the aquatic organism of interest. With the notable exception of migratory sp ecies such as striped bass and some species of eels and salmon, this range will typically be confined to a single waterbody. Even at this scale, however, measur ing BAFs (or alternatively BSAFs) may not be the preferred method of determin ing site-specific BAFs. For exam ple, the difficulty or expense of measuring the concentration of some chemicals in water may be prohibitive. In these situations, it may be desirable to extrapolate a site-specific BAF using a high-quality baseline BAF or BSAF from a comparable site, or fr om a national BAF based upon a substantial number of measurements. In other cases, a site-specific BAF predicted from the product of a BCF or an octanol-water partition coefficient (K ow ) and a food chain multiplier may be preferred. This could be the case when site-specific data are very limited.

For sites larger than a single waterbody, the methods preferred for determining site-specific BAFs may be different than those preferred for a single waterbody. In particular, developing either a field-measur ed BAF or BSAF (site-specif ic BAF methods 1 and 2) can become impractical because each waterbody must be sampled, and the necessary sampling effort
increases as the number of waterbodies increases . On the other hand, for the other methods of determining site-specific BAFs the sampling effort increases marginally (methods 3 and 4) or not at all (methods 5 and 6) as the number of waterbodies increases.

The investigator should carefully consider trade-offs between the management objective or need (e.g., state, waterbody, area of concern, Superfund site) versus the spatial heterogeneity in bioaccumulation within that site. For large sites, the site-specific BAF must necessarily represent the BAFs for all waterbod ies or ecosystems within the si te. The within-site variation in BAFs among waterbodies should be minimal to estimate an accurate s ite-specific BAF for a large site. This requirement can only be met if the waterbodies in the site are comparable in terms of the ecosystem factors known to influe nce bioaccumulation potential (e.g., chemical loadings histories [sediment-water disequilibriu m]; food web structure; organism health and

5 Depending upon the characteristics of the site, chemical of interest, and target species, as well as the predominant bioaccumulation exposure pathway(s), it may be more appr opriate to relate spatial scale of the site to the foraging range instead of the home range . Although we refer to home range throughout this document, the investigator should understand that foraging range may be more appropriate depending upon these site-specific factors.2-9 physiology; water quality factors such as temperature; and food quality). The issue is not simply one of size, but rather the lik elihood that the variability in bioaccumulation (and the underlying factors such as sediment-water disequilibria, bi oavailability and biomagnification) will increase with the size of the site, and information on thes e factors should be considered when defining the site. One approach that may improve the comparab ility of these ecosystem factors for large-scale
sites is to derive s ite-specific BAFs for each type of waterbody (e.g., lakes, rivers, ponds, streams, estuaries, wetlands) within a State, Territory or other region. Even if this approach is

used, it is still important for the investigator to evaluate the comparability of the chemical bioaccumulation potential for the waterbodies within the site.

For large-scale sites, EPA recommends that States, Territories and authorized Tribes consider using the national BAFs, or the baseli ne BAFs for individual species, to determine the site-specific BAFs. National BAFs, if available, are based upon the highe st-quality data for bioaccumulation potential, a careful evaluation of the assumptions made in predictions or estimates, and use a weight-of-evidence determination approach. As a result, the national BAFs
are considered reliable estimates of bioaccumu lation potential at larger geographic scales. For these reasons, considerable information would be lost if a site-speci fic BAF were developed without incorporati ng the national BAF values or the baseli ne BAFs for individual species that are referenced in the Water Quality Criteria doc uments for specific chemicals. At large-scale sites, careful determination and justification will be needed as to why bioaccumulation data 6 used for deriving EPA national bioaccum ulation factors are not consider ed applicable to the site.

It is important to identify the fish consum ption habits of local populations because the commonly-consumed fish serve as the dietary exposure pathway for bioaccumulative chemicals. EPA encourages States, Territories and author ized Tribes to use local or regional fish consumption data when developing and adopting criteria for their water quality standards, because local or regional fish a nd shellfish consumption patterns can differ substantially from national consumption patterns. BAFs vary betw een aquatic species due to several factors, including trophic level, benthic versus pelagic feeding prefer ences and habitat preferences,

6 Or, an appropriate subset of the bioaccumulation data used to calculate the national BAFs.2-10 growth rate and migration. Even more variati on is possible when one considers the different types of tissues that individuals may consume. Thus, the preferred approach for determining BAFs, as well as many of the details associated with data collection efforts to support their derivation, will depend upon identifying the fish species and tissue types commonly consumed by the local populations. In all cases, the primary selection criterion shoul d be that the target species is among the species commonly consumed in the study area, and that the species is of recreational or sustenance fishing value.

2.5 WHAT ARE THE METHODS FOR DE RIVING SITE-SPECIFIC BAFS?

This section provides an overview of the me thods for deriving site-specific BAFs. These
include:
1. Site-specific BAFs calculated from field data obtained from the site of interest (i.e., “field measured” BAFs);
2. Site-specific BAFs predicted from biota-sediment accumulation factors (BSAFs) calculated from field data obtained from the site of interest;
3. Site-specific BAFs predicted from (3a) extrapolated BSAFs or (3b) bioaccumulation equivalence factors (BEFs) measured at a reference site;

4. Site-specific BAFs predicted from (4a) laboratory-measured BCFs or (4b) the chemical's n -octanol-water partition coefficient (K ow ), combined with a site-specific food-chain multiplier;
5. Site-specific BAFs recalculated from na tional or baseline BAFs by adjusting the tissue lipid content and/or organic carbon concentration to reflect site-specific conditions.

The approach to deriving site-specific BA Fs using methods 1 and 2 involves measuring
new baseline BAF values. Method 3 involves extrapolating measured values from other sites. Methods 4 and 5 involve derivation of site-spe cific BAFs based on adjustment of existing
national or baseline BAFs. In most situations , the first approach (measure new baseline BAF values) is preferable, when resources and data availability permit. We summarize each of the site-specific BAF methods below, and relate each to the co rresponding method in the national BAF methodology. As noted in Section 1, the methods for deriving site-specific BAFs are2-11 closely related to the methods presented in TSD Volume 2 for calculating national BAFs. In Sections 3 through 5 of this TSD, we descri be each of the recommended methods in greater detail, emphasizing the scientific basis for each method and technical issues associated with implementing each approach.

2.5.1 Site-specific Field-Measured BAFs

The most direct measure of site-specific bioaccumulation is a BAF derived from data obtained from samples of tissue and water collected fr om the site of interest, referred to here as a "site-specific field-measured BAF." Because th e data are collected from a natural aquatic ecosystem, a field-measured BAF reflects an or ganism's exposure to a chemical through all relevant exposure routes (e.g., water, sediment, diet). A field-measured BAF also reflects factors that influence the bioavailability, biomagnification and metabolism of a chemical in the aquatic organism or its food web. Theref ore, field-measured BAFs are appropriate for all chemicals, regardless of the extent of chemical metabolism in biota from a site. This is site-specific BAF derivation method 1, and it corresponds to Method 1 of EPA's national bioaccumulation methodology.

2.5.2 Site-specific BAFs Pred icted from Measured Biota-sedi ment Accumulation Factors (BSAFs)

The investigator can predict a site-specific BAF from a BSAF that is calculated from the
concentrations of a chemical in tissue and sedi ment samples from the site of interest. The sediment sample must be representative of the surficial sediment within the home range of the organism. A BSAF is similar to a field-measured BAF in that the concentration of a chemical in a biota sample reflects an organism's exposure through all relevant routes. A BSAF also accounts for bioavailability and chemical metabolism in the aquatic organism or its food web. A BSAF may be converted to a BAF based upon the distri bution of the chemical between sediment and water, which can be either estimated or measured for a reference chemical . This is site-specific BAF derivation method 2, and it corresponds to Method 2 of EPA's na tional bioaccumulation methodology. This method is appropriate for mo derate to highly hydrophobic nonionic organic2-12 chemicals, and certain ionic organic chemicals th at exhibit lipid and or ganic carbon partitioning
behavior similar to that of nonionic organic chemicals.

2.5.3 Site-specific BAFs Pred icted from Extrapolated BSAFs or BEFs Measured at a
Reference Site

The investigator may extrapolate site-speci fic BAFs from BSAFs measured at another (reference) site using two approaches. The first approach is to directly extrapolate a high-quality BSAF (as discussed in Section 4) to the site of interest, if one is availa ble for the chemical of concern. Alternatively, if a high- quality BSAF for a reference chemi cal is available for the site of interest, then the investigator can use a bioaccumulation equivale nce factor (BEF, defined as the
ratio between BSAFs for the chemical of concer n and the reference chemical) measured at a reference site to extrapolate a BSAF. Since thes e are actually two relate d methods, we refer to BSAF extrapolation as method 3a and BEF extr apolation as method 3b. For either method,
conversion of the BSAF into a site-specific BAF is accompli shed using Method 2 of EPA's national bioaccumulation methodology. Methods 3a and 3b are appropriate for moderate to highly hydrophobic nonionic organic chemicals, and to certain ionic organic chemicals for which similar lipid and organic carbon partitioning behavior applies. Section 5 of this document provides a full description of the BSAFs and BE Fs extrapolation methods, and Section 5.1.3
addresses how to choos e a reference site.

2.5.4 Site-specific BAFs Pred icted from Laboratory-Measured BCFs Combined with a Food Chain Multipliers

The investigators can predic t site-specific BAFs as the product of laboratory-measured BCF values and a food chain multiplier (FCM). A laboratory-measured BCF typically reflects only the accumulation of a chemical through the organisms' exposure to water. The BCF will likely underpredict BAFs for chemicals for which accumulation from sediment or dietary sources is important, including hydr ophobic nonionic organic chemicals. For such chemicals, a food-chain multiplier (FCM) should be used to adju st the value of a laboratory-measured BCF to better account for chemical accumulation through th e food web as a result of dietary exposures. The investigator should measure, estimate (f rom existing data), or predict (using food chain2-13 models) the FCM to reflect biomagnification of the chemical for a particular trophic level under site-specific conditions.

A laboratory-measured BCF often reflects the chemical metabolism that occurs in an organism of interest during the BCF measuremen t. However, a BCF experiment will not account
for metabolism of a chemical that occurs at lo wer trophic levels in the food web because the experiment excludes chemical accumulation from dietary sources. Estimating site-specific BAFs using laboratory-measured BCFs and a food chain multiplier is appropriate for all chemicals, although the investigator should apply this met hod with caution to chemicals which metabolize in biota, because the method may overpredict BAFs for such chemicals. This is site-specific BAF derivation method 4a, and it corresponds to Method 3 of EPA's national bioaccumulation methodology.

2.5.5 Site-specific BAFs Predicted from N -octanol Water Partition Coefficient (K ow ) Combined with a Food Chain Multipliers

The investigators can also predict a site-specific BAF fo r nonionic organic chemicals by using the product of the chemical's K ow and a FCM for a particular trophic level under sitespecific conditions. The K ow is strongly correlated with the BCF for this class of chemicals, particularly for those chemical s that are poorly metabolized by aquatic organisms. For these chemicals, the investigator can substitute the measured or predicted K ow for the BCF when predicting a site-specific BAF. The in vestigator must also adjust the K ow with a FCM to account for chemical accumulation through the food web as a result of dietary exposures, for nonionic organic chemicals where food web exposure is important. This is site-specific BAF derivation method 4b, and it corresponds to Method 4 of EPA's bioaccumu lation methodology. This method is appropriate for non- or poorly-metabolized nonionic organic chemicals, but can also be applied to certain ionic chemicals having sim ilar partitioning behavior. This approach may overpredict BAFs for chemicals that are metabol ized by aquatic organisms, because metabolism is not incorporated in either the K ow or the FCM.2-14 2.5.6 Site-specific BAFs Recalculated from National or Baseline BAFs

The investigators can recalcul ate a site-specific BAF from baseline or national BAFs for a chemical by modifying the default values for the aquatic organism lip id content and/or the dissolved organic carbon (DOC) concentration. Th e investigator can modify these parameters in the national BAF calculation by:

1. Conducting site-specific field studies to generate representative data,

2. Conducting a literature search to obtain data more representative of local conditions, and/or

3. Selecting an appropriate subset of the national database that EPA used to derive the default values.

Site-specific BAFs recalculated from baseli ne or national BAFs are appropriate for all chemicals, regardless of the extent of chemical metabolism in biota. This is site-specific BAF derivation method 5, and is an extension of Method 1 of EPA's bioaccumulation methodology.

2.5.7 Advantages and Limitations of Site-specific BAF Approaches

There are method-specific strengths and limitations which the investigator should
consider and balance when de riving site-specific BAFs usi ng the methods summarized above. These strengths and limitations, as summarized in Table 2-1, form the basis for selecting approaches to derive site-specific BAFs. Resour ce limitations, institutional context, and the use to which the BAFs will be put may also be importan t selection factors. In general, all else equal, measuring new technical baseline BAF values (methods 1 and 2) is preferable to extrapolating or adjusting existing baseline BAFs (methods 3 th rough 5). For example, the field-measured BAF method is advantageous because it applies to al l chemical types, and because it accounts for site-specific factors that affect bioavailability, biomagnification, and metabolism. Nevertheless, field-measured BAFs cannot be readily determin ed for chemicals that are very difficult to accurately measure at low concentrations in the water column (e.g., 2,3,7,8-TCDD). Site-specific
BAFs derived from field-measured BSAFs of fer a number of the same strengths as2-15 field-measured BAFs (e.g., they account for bi omagnification, metabolism, and site-specific factors affecting bioavailability). In addition, the BSAF approach is the only field-based method that the investigator can use for chemicals such as 2,3,7,8-TCDD that are difficult to measure in ambient water. However, application of the BSAF method is curren tly limited to nonionic organic chemicals of moderate to high hydrophobi city. Burkhard et al . (2003a) discuss the relative merits of site-specific BAF versus BSAF measurements for different classes of bioaccumulative chemicals. In general, BSAF approach (Method 2) will be preferable for moderately to highly-hydr ophobic organic chemicals, wh ile for less hydrophobic organic chemicals, ionic organic chemicals and inorganic and organometallic chemicals, field-measured BAFs (Method 1) will be the preferred approach. Aside from producing the highest-quality sitespecific BAFs, these methods also increase the av ailable bioaccumulation dataset. As noted in Table 2-1, these methods may not be preferred fo r determining BAFs for large-scale sites (e.g., sites that encompass multiple waterbodies or ecosystems), because the level of effort associated with sampling increases with the number of wate rbodies. Further guidance regarding the relative
level of confidence associated wi th each approach is offered in Sections 4.6.1 and 5.2.3.1 and Burkhard et al. (2003b). As more data become available to su pport derivation of site-specific BAFs by the different methods, it may be possible to generalize the ranges of relative errors or changes in the confidence intervals associated w ith each method’s assumptions, as demonstrated by Arnot and Gobas (2004) for bioaccumulation pr edictions made with alternative models.2-16 Table 2-1. Strengths and Limitations of the Me thods for Deriving Site-specific BAFs (SS BAFs)

SS BAF Derivation Approach SS BAF Method Strengths Limitations

Derive new baseline BAF values 1. Field measured SS BAF • Preferred method applicable to all chemical types
• Incorporates chemical biomagnification and metabolism
• Reflects site-specific attributes that affect bioavailability and dietary exposure • Representative chemical concentration in water may be difficult to quantify

• Level of effort increases with spatial scale, number and type of waterbodies within site

2. SS BAF predicted from measured BSAF • Preferred method for highly hydrophobic chemicals
• Incorporates chemical biomagnification and metabolism
• Reflects site-specific attributes that affect bioavailability and dietary exposure

• Useful for chemicals that are difficult to analyze in water

• Use of chemical concentrations in sediment reduces temporal variability • Limited to nonionic organic chemicals with log K ow ≥ 4
• Accuracy depends on representativeness and quality of the estimate of chemical distribution between sediment and water
• Locating representative sediment sampling locations may be difficult

• Level of effort increases with spatial scale, number and type of waterbodies within site

Extrapolate measured values from other sites 3. SS BAF extrapolated from BSAF (3a) or BEF (3b) • Incorporates chemical biomagnification and metabolism
• Quality of BSAFs or BEFs measured at another site may be superior to site-specific measurements • High-quality data currently limited to few sites and chemicals
• 3b: Limited to nonionic organic chemicals with log K ow ≥ 4
• 3b: Accuracy depends on representativeness and quality of the estimate of chemical distribution between sediment and water2-17 Table 2-1. (continued) Strengths and Limitations of the Methods for Deriving Site-specific BAFs (SS BAFs)

SS BAF Derivation Approach SS BAF Method Strengths Limitations

4a. SS BAF predicted from BCF and FCM • Applicable to all chemical types (although FCMs have only been developed for nonionic organic chemicals)

• Level of effort does not increase with spatial scale, number and type of waterbodies within site
• BCF may account for chemical metabolism in target organisms
• Large BCF database available

• Standardized test methods • May not account for chemical metabolism in food web
• High-quality data currently limited for highly hydrophobic chemicals
• FCM predicted using food chain model is uncertain unless confirmed with site-specific data

4b. SS BAF predicted from K ow and FCM • Readily applied with minimal input data

• Level of effort does not increase with spatial scale, number and type of waterbodies within site • Limited to nonionic organic chemicals
• Chemical metabolism, when present, not accounted for
• Accuracy depends on accuracy of K ow • FCM predicted using food chain model is uncertain unless confirmed with site-specific data Adjust existing national or baseline BAFs

5. SS BAF recalculated from baseline BAF • Quality of baseline or NationalBAFs may be superior to sitespecific measurements • High-quality data currently limited to few sites and chemicals
• Depending on method used to derive national BAF, may or may not incorporate chemical biomagnification and metabolism

Extrapolating site-specific BAFs from BSAFs or BEFs measured at a reference site, or recalculating site-specific BA Fs from national or baseline BAFs, are methods that the investigator should consider if high quality data are av ailable for the chemical of concern. In such cases, extrapolating or recalculating BAFs may be the most effective way to quantify site-specific bioaccumulation. Unfortunately, high quality data are currently limited to relatively2-18 few chemicals and sites. The issue of what cons titutes "high quality" da ta for BAFs and BSAFs
are discussed in Sections 3 and 4.

Site-specific BAFs predicted using a BCF or K ow and food chain multiplier have the advantage of requiring limited site-specific data, a nd can be readily applied to many sites, or sites that encompass many waterbodies. BAFs predic ted from a laboratory-measured BCF and a FCM can be applied to all chemical types, and data fo r BCFs are generally more plentiful than data for field-measured BAFs. However, acceptable BCFs for highly hydrophobic chemicals (i.e., those with a log K ow > 6) appear to be very limited, often becau se of lack of ancillary data that affect bioavailability (e.g., dissol ved organic carbon). Deriving site-specific BAFs using K ow and FCMs (where appropriate) offers a di stinct advantage in that no laboratory data (besides a K ow ) or field data are needed to derive a BAF. However, th is method is limited to nonionic organic chemicals that are non- or poorly-metabolized . Finally, if the FCMs used in either of these approaches is predicted with a food chain model, then the accu racy of the FCM may be questionable unless the prediction is confirmed by da ta. Burkhard et al. (2003b) co mpared the performance of predictions made using national bioaccumulatio n methodologies 2 and 4, and found that method 4 was more sensitive to ecosystem conditions, pa rticularly the temporal dynamics of several important factors (lipid, foodw eb structure, and exposure co ncentrations). TSD Volume 2 (USEPA, 2003) and a number of other publicat ions (Burkhard et al . 2003a and 2003b) provide
further discussions of the advant ages and limitations of the site-specific BAF approaches, and the possible trade-offs betw een different methods.

2.5.8 Weight-of-Evidence Approach to Selecting a Site-specific BAF

The final site-specific BAF must be selected from the indi vidual BAFs by using a weightof-evidence approach that takes into account the uncertainty in th e individual BAFs and the data preference hierarchy (i.e., field-measured BAFs are preferred over BAFs derived using the other methods). Investigators are encour aged to determine site specific BAFs using all of the possible methods available. As noted in the previous se ctions, selecting the most appropriate derivation procedure depends greatly on chemical proper ties. Section 5.4.2 of the 2000 Human Health Methodology provides a guide for se lecting the most appropriate fi nal BAF when the uncertainty2-19 is similar between two individua l baseline BAFs calculated using different methods. Section 6.1
of TSD Volume 2 and Section 5.4.3.2 of th e 2000 Human Health Methodology provide more detailed discussions of this step.

All BAF values should be reviewed carefu lly to assess their sufficiency, quality, variability, and overall uncertainty. Large differen ces in individual site-sp ecific BAFs for a given species or trophic level (e.g., greater than a factor of 10) should be inves tigated further. As a result, some or all of the s ite-specific BAFs for a given tr ophic level might not be used. Procedural and quality assurance guidelines are described in S ections 3 and 4, and should be used to evaluate the quality, variability, and uncertainty of site-specific BAFs.

The data preference hierarchy for each BAF derivation procedure (Figure 2-1 and further detailed in Table 2-1) is based on the relativ e strengths and limitations of each BAF method and reflects the general preference of field-measured data over laboratory- or model-based estimates of bioaccumulation. Importantly, this hierarchy is intended for use as a guide for selecting the
final baseline BAF rather than as a steadfast rule . Departures from this data preference hierarchy are entirely appropriate when considerations of uncertainty and weight of evidence indicate that a lower tier method would be preferred over a highe r tier method. In general, when site-specific
BAFs are available for more than one BAF methodwithin a given trophic level, the final sitespecific BAF for each trophic level should be select ed from the most preferred BAF method. If uncertainty in a trophic level–mean baseline BAF based on a higher tier (more preferred) method is judged to be substantially gr eater than one from a lower tier method, and the weight of evidence from the various methods suggests that a BAF value from a lower tier method is likely to be more accurate, then the final baseline BAF fo r that trophic level should be selected from
the lower tier method.

When the weight of evidence among the va rious BAF methods is being considered, greater confidence in a site-specific BAF is gene rally assumed when the BAFs are in agreement across a greater number of methods within a give n trophic level. However, lack of agreement among site-specific BAFs derived from differe nt methods does not necessarily indicate less confidence, if such disagreements can be adequate ly explained. For exampl e, if the chemical of2-20 concern is metabolized by aquatic organisms re presented by a baseline BAF value, one would expect disagreement between a baseline BAF deri ved from a field-measur ed BAF (the highest priority data) and a baseline BAF predicted from a K ow and model-derived FCM. In addition, consideration should also be given to the quantity and diversity of bioaccumulation
measurements that underlie the calculation of a trophic level–mean baseline BAF. In some cases, the uncertainty associated with very limited BA F data from a “more preferred” method may be offset by the greater quantity and diversity of da ta that are available from an otherwise “less preferred” method for a given data preference hierarchy.

REFERENCES

Arnot, J.A. and F.A.P.C. Gobas. 2004. A Food Web Bioaccumulation Model for Organic Chemicals in Aquatic Ecosystems. Environ. Toxicol. Chem. 23(10): 2343-2355.

Burkhard, L. P., Cook, P. M. and D. R. M ount. 2003(a). The relationship of bioaccumulative chemicals in water and sediment to resi dues in fish: A visualization approach. Environ. Toxicol. Chem. 22 (11), 2822-2830.
Burkhard, L. P., Endicott, D. D., Cook, P. M ., Sappington, K. G. and E. L. Winchester. 2003(b). Evaluation of two methods for predic tion of bioaccumulation factors. Environ. Sci. Technol. 37 (20), 4626-4634.
Fujiki, M., Hirota, R. and S. Yamaguchi. 1977. The mechanism of methylmercury accumulation in fish. In Management of Bottom Sediment s Containing Toxic Substances ; Peterson, S. A., Randolph, K. K., Eds. U. S. Environmental Protection Agency: Corvallis, OR, pp 89-95. EPA600/3-77-083.
Grieb, T.M., Driscoll, C.T., Gloss, S.P., Scholfi eld, C.L., Bowie, G.L. and D.B. Porcella. 1990. Factors affecting mercury accumulation in fi sh in the Upper Michigan Peninsula. Environ. Toxicol. Chem . 9, 919-930.
Kelly, B.C., Gobas, F.A.P.C. and M.S. Mc Lachlan. 2004. Intes tinal absorption and biomagnification of organic contaminan ts in fish, wildlife, and humans Environ. Toxicol. Chem. 23 (10): 2324-2336.
Mackay, D. 2001. Multimedia Environmental Models: The Fugacity Approach, Second Edition. CRC Press, Boca Raton, Florida.
Peterson, S.A., Herlihy, A.T., Hughes, R.M. a nd J. Van Sickle. 2007. Mercury Concentration in Fish from Streams and Rivers Thro ughout the Western United States. Environ. Sci. Technol.2-21 41(1); 58-65.
Phillips, G.R.and D.R. Buhler. 197 8. The relative contributions of methylmercury from food or water to rainbow trout ( Salmo gairdneri ) in a controlled laboratory environment. Trans. Am. Fish. Soc., 107 , 853-861.
Roelke, M.E., Schultz, D.P., Facemire, Sundlof, S.F. and H.E. Royals. 1991. Mercury contamination in Florida panthers. Report to the Florida Panther Interagency Committee

. Florida Game and Fresh Water Fi sh Commission. Gainsville, FL.
Schwarzenbach, R.P., Gschwend, P.M. and D.M. Imboden. 1993. Environmental Organic Chemistry (second ed.) . John Wiley & Sons, Hoboken, New Jersey.

USEPA. 2001a. Mercury Update: Impact on Fish Advisories (Fact Sheet). Office of Water, U.S. Environmental Protection Agency, Washington, D.C. EPA-823-F-01-011.

USEPA. 2001b. Water Quality Criterion for the Protecti on of Human Health: Methylmercury. Office of Science and Technology, Office of Water, U.S. Environmental Protection Agency, Washington, D.C. EPA-823-R-01-001.

USEPA. 2003. Methodology for Deriving Ambient Water Quality Criteria for the Protection of Human Health (2000). Technical Support Docume nt Volume 2: Development of National Bioaccumulation Factors. Office of Water, U.S. Environmen tal Protection Agency, Washington, D.C. EPA-822-B-03-030.

USEPA. 2006. Draft Guidance for Impl ementing the January 2001 Methylmercury Water Quality Criterion . EPA 823-B-04-001. U.S. Environmen tal Protection Agency, Office of Water, Washington, DC. August, 2006.

USEPA. 2007. Framework for Metals Risk Assessment . EPA 120/R-07/001. U.S. Environmental Protection Agency, Office of the Science Advisor, Washington, DC. March, 2007.2-22 This page is intentionally blank.3-1 3. MEASURING SITE-SPECIFIC BIOACCUMULATION FACTORS

Bioaccumulation factors are used to relate ch emical concentrations in aquatic organisms
to concentrations in the ambi ent media (e.g., water and sediment ) of aquatic ecosystems. The
most direct measure of site-s pecific bioaccumulation is a BAF derived from data obtained from samples of tissue and water collected from the s ite of interest. These data are then used to calculate a site-specific, field-measured BAF. A field-measured BAF reflects an organism's exposure to a chemical through all relevant exposure routes (e.g., wa ter, sediment, diet), because the data are collected from a natural aquatic ecosystem. A field-measured BAF also reflects factors that influence the bioavailability, bioma gnification and metabolism of a chemical in the aquatic organism and/or its food web. Therefore, field-measured BAFs are appropriate for all chemicals, regardless of the extent to which thes e factors influence bioaccumulation at the site.

Two forms of the BAF are used by EPA in the 2000 Human Health Methodology (USEPA, 2003). The first is the total BAF, denoted t ,T 

### BAF i , also referred to as the “fieldmeasured” BAF. The t ,T 

### BAF i is calculated from the total c oncentration of chemical in the appropriate wet tissue of the aquatic organism sampled at trophic level i, and the total
concentration of the chemical in th e ambient water at the sampling site:



### C Total BAF = BAF C t t i,T

w 

### = (Equation 3-1)

where:

C t = tota l concentration of the chemical in tissue C w = total concentration of ch emical in water
Average or mean chemical concentrations are us ed for each phase in the calculation of the total BAF (Equation 1), since multiple samples of biota and water should be collected to characterize chemical concentrations at a site. Calculating a to tal BAF is presented in the following example.3-2 The second form of bioaccumulation factor is the baseline BAF, which is applied specifically to nonionic organic ch emicals. The baseline BAF (

, 

### BAF f d i L ) is calculated using the lipid-normalized concentration in tissue and the freely dissolved chemical concentration in the

water:

, 

### C 1 Baseline BAF BAF C f fd l i iL fd w l 

### = =− (Equation 3-2)

where:

C l = lipid-normalized concentration of the chemical in tissue 

### C f d

w = concentration of chemical that is freely dissolved in water f l = mass fraction of wet tissue that is lipid Calculation of site-specific Total BAFs from measurements at the site (Method 1)

A hypothetical lake is contaminated by chemical x . Data obtained from fiel d studies in the lake indicate that the mean concen tration of the chemical in th e water column, 49.5 μg/L, reflects adequate temporal and spat ial averaging, based on the K ow of this chemical. Consumption surveys of the local populati on indicate that crayfish ( Orconectes sp. ) is a commonly consumed organism, and was selected as a target organism for sampling and BAF determination. Review of the trophic level assignment of aquatic species corresponding to CSFII consumption categories (Table 6-4 in TSD Volume 2 [USEPA, 2003]) indicates that crayfish that are commonly consumed by the general U.S. popula tion belong to trophic level 2. Based on the field studies, the average chemi cal concentration in crayfish is 2.4 mg/kg. Data obtained from field studies also indicates that the mean concentration of the chemical in the water column is representative of the average exposure of chemical x to the crayfish. The total BAF is calculated using equation 3-1:



### C BAF C t t i,T

w 

### = (Equation 3-1)



### C 2.4 mg L 1000 g BAF = 48.5 L/kg C kg 49.5 g t t 2,T

w 

### mg μ μ = ⋅⋅ =

The site-specific total BAF for chemical x in crayfish is 48.5 L/kg. Ge nerally, site-specific total BAFs would also be determined for commonly consumed organisms from trophic levels 3 and 4.3-3 Again, average or mean chemical concentrations are used for each phase in Equation 3-2.

The baseline BAF is also related to (but not the same as) the lipi d-normalized and freely dissolved-based bioaccumulation factor ( 

### BAF f d i,l ):

, 

### 1 Baseline BAF BAF f fd i il l



### = − (Equation 3-3)

The derivation of the baseline BAF and its relationship to 

### BAF f d i,l (Equation 3-3) is discussed in TSD Volume 2 (U SEPA, 2003) and Arnot and Gobas (2004). Calculating a baseline BAFs is presented in the following example.

Calculation of site-specific Baseline BAFs from measurements at the site (Method 1)
This example illustrates the development of a site specific, trophic le vel 4 BAF using Method 1 for a nonionic, hydrophobic orga nic chemical (chemical x ). Because this is an organic chemical, the site-specific BAF should be calculated as a baseline BAF from measurements of lipidnormalized chemical concentrations in consumed tissue and freely-dissolv ed concentrations in ambient water at the site. Calculating a baseline BAF facilitates comparison to other BAF values and may reduce the variance of the BAF. The base line BAF can be converted to a total BAF for calculation of a water quality standard for the site.

Site-Specific Data
A field study was conducted in an unnamed rive r to measure concentrations of chemical x in the aquatic food chain and the water column, to support the deve lopment of site-specific BAFs. A review of the dietary preferences of the sport fish caught and consumed by the local population indicated that largemouth bass was a preferred species at trophic level 4. Therefore, this fish was targeted for collection during sampling in 1993, and three composite samples were analyzed. Twelve water samples were also collected on a near-monthly basis in 1993. As reco mmended in the Section 3 guidance, lipid contents were measured in all fish composite samples, a nd dissolved and particulate organic carbon (DOC and POC) concentrations were measured in all water samples. The following data were obtained from the study:3-4 Calculation of site-specific Baseline BAFs from measurements at the site (continued)

LARGEMOUTH BASS

Date Chemical x Concentration (μg/g) % Lipid
August-93 0.797 1.16
August-93 1.040 1.45
August-93 0.646 1.10

WATER COLUMN

Date Chemical x Concentration (ng/L) DOC Concentration (mg/L) POC Concentration (mg/L) January-93 1.02 5.18 1.49
February-93 1.01 5.47 1.55

March-93 1.43 5.53 1.98
April-93 2.34 4.39 0.36
May-93 2.24 4.41 0.37
May-93 2.47 5.12 1.46

June-93 3.32 4.84 1.08

June-93 3.74 5.32 1.55
July-93 4.00 5.53 2.31
August-93 2.92 4.83 0.37
August-93 2.89 4.95 1.46
September-93 2.26 4.90 1.38

Review of these data indicated that the mean concentr ation of the chemical in the water column reflects adequate temporal and spatial averaging, based on the hydrophobicity of this chemical (logK ow = 5.84), and was representative of the average exposure of chemical x to the target fish.
Lipid and Freely-dissolved Normalization of Concentration Data
Chemical concentrations in fish ( C t ) were normalized by the lipid content ( f

l ) of each sample: C

l = C t / f l3-5 Calculation of site-specific Baseline BAFs from measurements at the site (continued)

The lipid-normalized chemical concentration ( C l ) in each sample is tabulated:

Sampling Date Lipid-normalized Concentration (μg/g-lipid) August-93 (1) 68.7
August-93 (2) 71.7
August-93 (3) 58.7
August sample average 66.4

The lipid-normalized chemical concentrations in la rgemouth bass were then averaged, to determine a mean value of 66.4 μg/g-lipid.
The freely dissolved fraction of chemical in the water column (f fd

) was also calculated for each sample, using equation 3-6:

ο w ο w 

### f = 1 / (1 + POC DOC

fd 

### ⋅ Κ + 0.08 ⋅ ⋅ Κ ) (Equation 3-6)

For example, the freely dissolved chemical fraction of the January water sample is:

5.84 5.84 6 6 1 f 1.49 5.18 1 10 0.08 10 10 10 fd m g POC L k g m g DOC L k g L k g m g L k g m g =

− − + ⋅ ⋅ +⋅ ⋅ ⋅

= 0.431

The freely dissolved chemical concentration ( 

### C f d

w ) is calculated as:



### C fC

fd w fd w 

### = ⋅

The freely dissolved fraction and freely dissolved chem ical concentration is calculated for each sample as tabulated below:3-6 Calculation of site-specific Baseline BAFs from measurements at the site (continued)

Sampling Date f fd Freely-dissolved Concentration (ng/L) January-93 0.431 0.440
February-93 0.421 0.425

March-93 0.374 0.534
April-93 0.670 1.57
May-93 0.667 1.49
May-93 0.436 1.08

June-93 0.496 1.65

June-93 0.423 1.58
July-93 0.344 1.38
August-93 0.656 1.92
August-93 0.438 1.27
September-93 0.449 1.02

The freely-dissolved chemical concentrations were then averaged, to determine a mean value of 

### C f d

w = 1.20 ng/L.

Calculating a Site-specific Baseline BAF
The site-specific baseline BAF was th en calculated using the average C l ( 66.4 μg/g-lipid ), f l (1.24%) and


### C f d

w (1.20 ng/L) as shown below:



### C 1 Baseline BAF C f l i fd w l 

### = − (Equation 3-2)

66.4 1000 1000 7 1 4 1.20 1 0.0124 

### Baseline BAF = 5.55 10 / g ng g L g lipid ng g kg 

### L kg lipid μ μ

− 

### ⋅⋅ ⋅− =× −

The site-specific baseline BAF for chemical x in largemouth bass is 5.55 ×10 7 L/kg-lipid.3-7 There are important advantages to calcu lating bioaccumulation factors for hydrophobic nonionic organic chemicals as baseline BAFs because these expressions acknowledge the thermodynamic relationships (or fugacities, that can be thought of as chem ical pressures) that govern the bioavailability and bioaccumulation of these chemicals and facilitate comparisons across ecosystems (Mackay, 2001). The lipid and fr eely-dissolved normalizing of concentrations also reduces the variance in BAFs among sites and trophic levels for these chemicals. Lipid normalization is useful for hydr ophobic nonionic organic chemical s, because these chemicals partition extensively into the lipid fraction of ti ssues. For other classes of chemicals, lipid partitioning is usually much more limited, and li pid normalization is not appropriate. Likewise, normalizing the concentrations of hydrophobic nonionic organic chemicals in water by the freely-dissolved fraction is help ful in reducing the variability of BAFs, since only the freelydissolved phase of the chemical is considered to be bioava ilable in water. Hydrophobic nonionic organic chemicals in water are present in the freely dissolved form as well as in association with dissolved or colloidal organic ca rbon (i.e., commonly measured as dissolved organic carbon) and particulate organic carbon. The fr eely dissolved chemical is gene rally only a fraction of the analytically determined con centration, particularly for highly hydrophobic chemicals (log K ow
> 5.5). Determining the freely dissolved fraction of a nonionic organic chemical in water, by measurement or calculation, is discussed in Section 3.4.2. Calculation of site-specific Baseline BAFs from measurements at the site (continued)

Calculating a Site-specific Total BAF
In order to determine a water quality standard for chemical x at the unnamed river site, the site-specific baseline BAF must be converted to a total BAF. R ecalling the relationship between the baseline BAF and the total BAF (

, 

### BAF t i T ): 

### Site Specific BAF (f Baseline BAF +1) f t i,T l i fd 

### =⋅ ⋅ (rearranged Equation 3-4)

Using averages of measured values for lipid conten t (1.24%) and calculated freely dissolved fractions (0.484), the site-specific total BAF can be calculated: 

# ( ) 7 5 4, Site-Specific BAF 0.0124 5.55 10 1 0.484 3.32 10 t L L

T kg l kg

− = ⋅ × +⋅ = ×

The site-specific total BAF for chemical x in largemouth bass is 3.32 × 10 5 L/kg.3-8 The baseline BAF can be calculated from a 

### BAF T t as shown in Equation 3-4 by using
information on the lipid fraction (f l ) of the tissue of concern fo r the study organism and the fraction of the total chemical that is freely dissolved in the ambient water (f fd

):

, BAF 1 Baseline BAF 1 f f t i T i fd l  

= −⋅

      (Equation 3-4)

where:

, 

### BAF t i T = Total BAF f fd

= fraction of the total concentration of chemical in water that is freely dissolved

TSD Volume 2 (USEPA, 2003) provides more detailed information on derivation of the baseline BAF equation. An alternative formula for the relationship betw een the total BAF and the baseline BAF is offered by Arnot and Goba s (2004). The latter may be advantageous for calculating baseline BAFs for less hydrophobic organic chemicals that have total BAFs approaching 1.0.

This TSD specifically addresses the determ ination of site-specific BAFs for nonionic organic chemicals, which generally follow a hydrophobic organic chemical paradigm (i.e., chemicals that preferentially pa rtition into the lipid and organic carbon phases). The investigator
should also be aware that not all classes of or ganic chemicals necessarily follow this paradigm. Examples of “other” classes of organic chemi cals, for which this TSD may not apply, include:

• Perfluorinated substances, especially polyf luorinated octyl carboxylic acid (PFOA) and sulfonic acid (PFOS) (Scott et al., 2006; Moody et al., 2002; Giesy and Kannan, 2001),

• Surfactants (Tolls and Sijm, 2000; Tolls et al., 1994);

• Synthetic Dyes & Pigments (Lynch, 2000). Most pigments and many dyes are so sparingly soluble in water that K ow can not be measured.

• Organosilicon compounds (Allen et al., 1997; Fackler et al., 1995). These substances can be sparingly soluble in water and highl y volatile thus bioaccumulation testing is difficult.3-9 • Methylmercury, the highly bioaccumulative form of mercury, is an ionic organic chemical. The methods described in this TSD for determining site-specific BAFs do not apply to methylmercury.

Several of these are ionic organic chemicals; derivation of BAFs for these chemicals is
discussed in Section 2.2. BAFs fo r inorganic/organometallic chem icals is discussed in Section
2.1.

The investigator should be careful to use sensi tive analytical methods and appropriate
statistical treatment of low-end censored data . Concentrations of bioaccumulative chemicals (especially dissolved concentrations in water) are frequently near or below the analytical detection limit. Where the chemical is present at concentrations belo w the minimum detection limit (MDL) for the analytical method, the uncensore d value should be used in the calculation of
the mean concentration. When the chemical is not detected at all (i.e., no response above instrumental noise), 1⁄2 of the MDL is commonly used as a replacement value (e.g., in USEPA’s Superfund program). However, calculation of BAFs using half of the MDL for concentrations in water can result in spurious and non-predictive BAFs. When chemical concentrations are not detected in some samples, EPA recommends that th e investigator apply statistical approaches for averaging with censored data. These include Helsel (2005), He lsel and Hirsch (2005), ElShaarawi and Dolan (1989), Newman et al. (198 9) and Newman (1995). These approaches can be used with normally and log-normally di stributed data. Berthouex and Brown (1994) recommend that unbiased means only be calculated from concentration data if fewer than 20% of the reported values are nondetect s. The investigator should be aware that even if the methods mentioned above are used, working with low-end censored data introduces greater uncertainty in
values both of mean chemical concentrations and of BAFs. Graphical analysis of chemical
concentrations in biota and BAFs versus chem ical concentrations in water can help the investigator determine whether to include or ex clude data for concentrat ions less than the MDL
and/or not detected at all.

Ideally, data obtained from the open literatu re (e.g., peer-reviewed journals, scientific reports, professional society proceedings) can be used to calculate site -specific BAFs, provided3-10 that the appropriate measurements have been ma de and information is available indicating the quality and usability of the data. A number of bioaccumulation databases compile such information. Weisbrod et al. (2007) provide a re view of databases containing information for BCFs, BAFs and BSAFs. At the present time, the BAF database of Arnot and Gobas (2006) 1 is recommended as a resource to investigators. This database contains 1,656 BAF values measured for 842 organic chemicals in 219 aquatic species. What makes this database especially useful is that it includes a data quality assessment accord ing to 6 criteria and rates each BAF measurement with an overall confidence level. This data qua lity assessment is valuable as guidance for investigators, who should ensure that high qua lity data are being used to derive BAFs. Another database is the Japanese National Institute of Technology and Evaluation (NITE) Biodegradation
and Bioconcentration Database of Existing Chemical Substances: (http://www.safe.nite .go.jp/english/kizon/KI ZON_start_hazkizon.html ).

Additional bioconcentration and bioaccumu lation databases are under development; for example the European Chemical Industry Counc il (CEFIC) Long Range Re search Initiative for Predicting the Environmental Fate of Chemicals.

.. Once published, these and other databases may also become valuable resources to the i nvestigator. Given suffici ent supporting information, the investigator could calculate reliable site-s pecific BAFs and make some assessment of the overall uncertainty in the BAF values. Unfortunately, relatively few high-quality bioaccumulation datasets are available, and those that do exist are limited in terms of number of chemicals, sites and species of interest. Ther efore, it will generallybe necessary for the investigator to generate the data required to derive the BAF by sampling at the specific site.

EPA prefers to use field-measured BAFs wh en developing water quality standards for the protection of human health (USEPA, 2000a). Howe ver, protocols for measuring site-specific BAFs have not previously been available. Althou gh a field-measured BAF is a direct measure of bioaccumulation at a site, the BAF will only have predictive power if a number of important factors are properly addressed in the design of the field sampli ng effort. This Section provides guidance to the investigator considering this method of de riving site-specific BAFs and

1 The Arnot and Gobas BCF/BAF database is available at http://pubs.nrc-cnrc.gc.ca/cgi-bin/rp/rp2_supp_e?er_a06-005_er4-063-11 specifically addresses the design of plans for the collection of biota and water samples necessary
to determine accurate BAF values.

In the next section (3.1), a series of key que stions are presented to the investigator faced with designing a field study to determine a site -specific BAF. Section 3.2 illustrates several methods to assess the variability of site-specifi c BAF prior to sampling and to develop a field study design based upon this variab ility. Following that are secti ons that address sampling design considerations necessary to measure site-speci fic BAFs specific to biota (Section 3.3) and water (Section 3.4).

3.1 KEY STUDY DESIGN QUES TIONS FOR DETERMINING
SITE-SPECIFIC BIOACCUMULATION FACTORS

Although a field-measured BAF is a direct meas ure of bioaccumulation at a site, this does not mean that it is simple to collect the data necessary to determine an accurate BAF value. In fact, of all the methods presented in this TSD fo r determining site-specifi c BAFs, collecting field data at the site of interest is probably the mo st difficult approach. Th e text box below highlights several key factors for th e investigator to consider for a fiel d study. The most important aspect of conducting a successful field study to measure site -specific BAFs is collecting representative samples of the biota and water. In general, samples will be most representative when the measured concentrations are reflective of long te rm average concentrations for the chemical in biota and exposure media. The home range of the ta rget species will dictate the spatial scale of the sampling effort. Chemical temporal and sp atial distributions, orga nism life history, and duration of exposure, among other factors, all contribu te to BAF uncertainty and should be addressed by the field sampling plan. Investigat ors are encouraged to report the uncertainty associated with measured site-specific BAFs.3-12 Key Factors to Consider When Designing a Field Study to Determine a Site-specific BAF Chemical of Concern:

• Type of chemical (i.e., nonionic organic, i onic organic, inorganic, and organometallic)

• Hydrophobicity

• Metabolism

• Distribution between sediment and water

• Availability of accepted analytical methods

• Sensitivity of analytical method (especially in water) Target Biota Species:

• Consumption by human population

• Size of consumed organisms

• Trophic level and prey items

• Lipid content

• Migration and movement The Site:

• Size of site / number of waterbodies

• Sampling characteristics (temporal and spatial variability of chemical concentrations in biota and water)

• Ecosystem type

Sampling requirements for biota and water will largely be controlled by the variability of
chemical concentrations at the field site. Chemi cal concentrations in biota and water vary, both in space and time, in different ways and due to a variety of factors, as discussed in Sections 3.3 and 3.4. As will be seen, the properties of the chem ical itself play an important role in defining this variability. Separate field designs and approaches should be considered for sampling water and biota, due to differences inherent in thes e media. Concurrent sampling of biota and water may not be the optimum field sampling design for many bioaccumulative chemicals. The investigator must take all of these factors into account when specifying a sampling design to determine a field-measured BAF. Additionally, the application of data quality assurance procedures when measuring, estimating, a nd applying BAFs is very important.

The investigator faced with designing a fi eld study to determine a site-specific BAF should consider the following seri es of key questions, intended to identify factors of the problem to be addressed by the sampling plan.3-13 Key Study Design Questions

1. Site Definition. Have I adequately defined my site of in terest in terms of spatial extent?

2. Study Feasibility. Can I adequately detect the chemical in water with available analytical methods (e.g., with a detection frequency > 80%)?

3. Precision Goal. What is the minimum level of accuracy in BAF measurements I am willing to accept (i.e., confidence limits with in a factor of 2, 5 or 10)? Ho w do I determine the level of effort (i.e., the number and type of biota and wate r samples) associated with different levels of accuracy? • The home range of the target species will dictate the spatial scale of the sampling effort.

• Size of site / number of waterbodies

• Sampling characteristics (temporal and spatial variability of chemical concentrations in biota and water)

• Ecosystem type

• Investigate detection limits of available analytical methods

• Compare to expected chemical concentrations

• Data Quality Objectives (DQO) process (USEPA, 2000c)

• Bootstrap or Monte Carlo simulations

• Bioaccumulation modeling3-14 4. Biota. Which species should I sample?

5. Temporal Variability (i.e., Sampling Event Frequency). How many times do I need to sample biota and water at the site?

6. Spatial Variability (i.e ., Number of Stations). How many locations should be sampled? • Consider consumption patterns of human population

• Availability of species of appropriate size at the site

• Diversity of exposure pathways (i.e., benthic & pelagic)

• Dietary composition/trophic status

Biota Sampling Considerations

• Consider chemical properties (hydrophobicity an d metabolism)

• Consider biota characteristics (migra tion, reproduction, av ailability, other seasonal characteristics based on climate, etc.)

• Consider consumption pattern (e.g., ti mes of year they are harvested)

• Time of year and temperature, as rela ted to the dynamics of lipid content

• Lessons learned from boot strap/simulation examples

Water Sampling Considerations

• Consider chemical pr operties (hydrophobicity)

• Consider ecosystem conditions (e.g., variability due to hydrodynamics) and temporal aspects of chemical loadings

• Lessons learned from boot strap/simulation examples

• Consider evidence of spatia l gradients in exposure concentrations as well as the presence of sources

• Biota characteristics (mobility/hom e range, habitat preference, etc.)

• Consumption characteris tics (harvesting areas)

• Ecosystem properties (size of site, sp atial differences in hydrodynamics, etc.)

• Consider spatial sampling design opti ons (e.g., random, stratified, systematic, judgment)3-15 7. Biota Sample Type. What types of biota samples should I collect (i.e., age/size, tissue, quantity, etc)? What ages/sizes of these species are consumed?

8. Water Sample Type. What types of water samples should I collect?

9. Chemical Analytical Methods. Which analytical methods should I use?

10. Biota Sampling Methods

. How should biota be sampled? • Which tissues are most commonly consumed and how are they prepared?

• Does this vary with organism size?

• Composite vs. individual samples

• Chemical analysis requirements

• Individual grab samples vs. composites?

• Temporal averaging

• Depth integration

• Composite vs. individual samples

• Chemical analysis requirements

• Must be specific for the individual chemical(s) of

concern

• Appropriate methods depend on waterbody and target organisms3-16 11. Water Sampling Methods. How should water be sampled?

12. Water/Biota Sampling Correspondence. How should I coordinate biota and water sampling (e.g., concurrent vs. staggered sampling)?

13. Ancillary Measurements

. What other chemical, water a nd biota parameters should I consider measuring?

Of course, the bioaccumulation field data shou ld be collected at the specific site for which criteria are to be applied and with the target species of concern. For large-scale sites, EPA recommends that samples be collected from each waterbody or ecosystem within the site for which site-specific BAFs are to be derived. Sa mpling in a subset of waterbodies is a valid • Appropriate methods depend on contaminant and waterbody

• Measure total or filtered chemical concentrations?

• “Clean” sampling techniques for different types of chemicals must be used

• Collection and analysis of blank samples

• Consider chemical properties (hydrophobicity an d metabolism)

• Consider ecosystem conditions (v ariability due to hydrodynamics) and temporal aspects of chemical loadings

• Lessons learned from bioaccumulation model simulations

• For organic chemicals, lipid and dissolved organic carbon are important ancillary measurements

• Useful ancillary measurements for biota include age, sex, trophic status and tagging information

• Total suspended solids (TSS) and pa rticulate organic carbon (POC) are useful ancillary measurements for water

• Chemical concentrations in sediment and sediment organic carbon content are important data

• pH and alkalinity are important meas urements for ionic organic chemicals

• Location information, such as GPS coordinates3-17 approach only if it can be demonstrated that th e resulting BAFs are representative and can be extrapolated to other locations in the site wher e the criteria and values will be applied. In practice, this may be difficult unless considerable information is available for the waterbodies that comprise the site.

3.2 HOW TO DESIGN A SAMPLI NG PLAN TO MEASURE BAFS

In designing a field study to measure BA Fs, the investigator should determine the appropriate number of biota and water samples to collect as well as their spatial and temporal allocation. Straightforward guidance on this issue is not readily available because study designs vary depending on the temporal a nd spatial variability of the ch emical in the ecosystem and on the dynamics of the chemical in the biota. A su ccessful sampling design procedure for any field study should consider the following ecosystem cond itions and chemical pr operties: spatial and temporal gradients in chemical concentrations; chemical distribution between the sediment and the water column; life history patterns of the organism (e.g., migration, diet, and food web structure and composition); the chemical’s hydrophobi city; and metabolism of the chemical in all organisms composing the food web. The investigator defines the fr equency of sample collection (i.e., the number and spacing of sampling events in time), the spatial di stribution of sample collection locations, and the total number of sa mples to be collected. Having the appropriate sampling frequency and spatial distribution (t he sampling design structure) enables the determination of BAFs that are representativ e of the long-term aver age conditions in an ecosystem and provides BAFs with low bias and good accuracy. Lack of bias in the BAF determination depends upon the representativeness of samples collected and analyzed, while precision depends upon the numbers of samples. In the optimization proce ss, precision of the measurements is balanced against the costs asso ciated with sample colle ction and analysis, and in many cases, compositing of samples is required to limit costs associated with the chemical analyses. These sampling design issues are addressed in this section.3-18 Three sampling design approaches will be demonstrated in the following subsections (Sections 3.2.1 thru 3.2.3). These approaches are complementary, a nd the investigator can apply them individually or together to design a sampli ng plan appropriate for measuring a site-specific
BAF.

Section 3.2.1 presents and demonstrates a st atistical method (Bootst rap resampling) to determine the required numbers of samples of bi ota and water necessary to achieve a desired precision for the BAF measurement. This approach uses field data to estimate the relationship between BAF precision and the number of samples co llected for chemical analysis in biota and water. Adequate field data sets for this type of evaluation are limited because this analysis requires coordinated fish, water, and sediment data over time. In most field studies, fish and sediment samples might be collected once in a fi eld season because of the labor required for their
collection. EPA is aware of relati vely few field data sets that are adequate for this type of evaluation (see Section 5.1.1 of TSD Volume 2 [U SEPA, 2003] for discussion). These include PCB congener data from the Hudson River (T AMS, 1998), the Green Bay Mass Balance Study (USEPA, 1989b), and the Lake Mich igan Mass Balance (USEPA, 2004).

A second approach modifies the Bootstrap method by substituting simulated data generated using Monte Carlo methods for measuremen ts of chemical concentrations in biota and water. The Monte Carlo appro ach requires less site-specific data than does the Bootstrap procedure, but still gives the investigator a way to estimate to what extent the precision of the BAF depends upon the number of chemical concentr ation measurements in biota and water. The Monte Carlo approach is pres ented in Section 3.2.1.2. Monte Ca rlo results (BAF confidence limits) were compared to results from the Boot strap, and were also re peated using different
variances in chemical concentrations and diffe rent degrees of correlation between biota and
water chemical concentrations.

A third approach to sampling study design emphasizes the sampling structure (number of sampling events over time and space), using model simulations to evaluate the relative influences of the underlying factors in obtai ning representative samples for the BAF determinations. With model simulations, biota and water data can be created on a day-to-day basis assuming different3-19 ecosystem conditions and chemical properties (e. g., temporal and spatial concentration profiles, life history scenarios, metabolism rate, and hydrophobicity). For thes e simulations to be meaningful, the model constructs should provi de reasonable representations of ecosystem conditions and chemical properties. Model simula tions are used to evaluate how the following factors influence the spatial di stribution of samples, and the number and temporal spacing of sampling events, required to accurately determine BAFs:

• how the temporal and spatial variab ility in chemical concentrations,

• the chemical’s hydrophobicity and metabolism rate in fish,

• the structure of the aquatic food web (b enthic vs. pelagic components), and

• the disequilibrium between chemical con centrations in the sediment and water column.

Section 3.2.2 discusses the sampling desi gn approach using model simulations.

3.2.1 Determining the Number of Samples to Collect

As discussed in the previous section, th e precision of a BAF measurement is defined largely by the total number of samples that ar e collected and analyzed. The investigator designing the sampling plan should determine the necessary numbers of samples based upon the expected variability in chemical concentra tions and the goal for precision of the BAF measurement. The investigator should addre ss two problems in order to determine this relationship:

• The variability of the chemical concentra tion in biota and water should be known or estimated. This is a problem due to the scarcity of high-quality bioaccumulation datasets, and because prior knowledge of chemical concentrations at the site of interest is unlikely.

• Because BAFs are ratios of random variable s, no formulas are available for their exact sampling variances (CDM, 2002). It is fairly unusual to collect environmental data specifically for the pur pose of calculating a concentration ratio, such as a BAF.3-20 As a consequence, relatively little attenti on has been paid to the relationship between the accuracy of a ratio of two random variables and the sample sizes 2

.

3.2.1.1 Bootstrap BAF Resampling The precision of BAF mea surements calculated from different numbers of chemical concentration measurements can be estimated by Bootstrap resampling, if data are available. This numerical simulation method can be used by the investigator to determine the required numbers of biota and water samples, once the goal for BAF precision has been established. The Bootstrap method is demonstrated by exampl es using Green Bay Mass Balance Study PCB congener data in Appendix 3A. The ratio of the 90 th to the 10 th percentile values of the
distribution of BAFs in each boot strap resampling calculation was f ound to be a useful measure of variability. This ratio (the confidence limit ratio or CLR) is a measure of the range or width of the BAF confidence interval; a smaller CLR indicate s less uncertainty (and greater precision) in the BAF calculated from a given sampling design. The Bootstrap resampling examples show that the precision (as well as the bias) of BAF estimates are sensitive to the sample sizes of chemical concentrations in both fish and water, especially for sample sizes smaller than 6. These examples also indicate that resampling different comb inations of the number of fish and water concentrations can yield comparable BAF pr ecision. For example, the same confidence limit ratio for PCB congener 149 forage fish BAF wa s obtained by resampling 10 fish and 6 water
concentrations or 4 fish a nd 10 water concentrations.

If site-specific data for concentrations of the target chemical in biota and water are available, then Bootstrap resa mpling is probably the best way to determine the number of samples to collect and analyze in order to de termine a BAF of the desired precision. Bootstrap resampling can also be useful if a site-specific BAF is measured and the uncertainty is found to be unacceptably large. In this case, the bootstrap can be used to estimate the additional sampling effort, in terms of numbers of biota and/or water samples, requi red to improve the precision of

2 It should be noted that for individual random variables, such as chemical concentrations in biota or water, there is considerable guidance available regarding the relations hip between sample size and the resulting variances. For example, the DQO-PRO software program ( http://www.instantref.com/download-dqo-pro.html ) calculates numbers of samples relative to uncertainties in environmental data. EPA also distributes the program ProUCL ( www.epa.gov/nerlesd1/tsc/software.htm ) which calculates upper bound confidence limits for data using a number of different parametric and nonparametric statistical methods.3-21 the BAF derived from these measurements. Bootstra p resampling is a much less useful tool when site-specific data for concentrations of the target chemical in biota and water are not available. If the investigator can find chemical concentration data for an ecos ystem comparable to their site, then bootstrap resampling could be conducted w ith that data, and sample sizes selectedaccordingly. However, there are other approaches that should also be considered when sitespecific data are limited.

3.2.1.2 Monte Carlo BAF Analysis

Monte Carlo methods are used to simulate data using assumptions about the probability distribution(s) of random variab les (Metropolis and Ulam, 1949; Robert and Casella, 2004). The investigator can use Monte Carlo to simulate ch emical concentrations for biota and water when
sufficient data are not available fo r the site of interest. To do s o, the investigator should fit an appropriate distribution (i .e., normal, lognormal, uniform) to the available data for the chemical concentrations in biota and water. Simulated (o r synthetic) data can then be generated and
substituted for the chemical concentration measurements. The same Bootstrap resampling procedure (Section 3.3.1) can then be applied to estimate how the precision of the BAF depends upon the number of chemical concentration measur ements. Monte Carlo analysis is demonstrated by examples in Appendix 3C, based on the same data used for the Bootstrap resampling method (Green Bay Mass Balance data for PCB congeners 18, 52, 149 and 180 in zone 3) so that the
results of the two methods c ould be directly compared.

In general, the Monte Carlo analysis of BAF precision as a function of sample sizes produced results comparable to those obtained vi a Bootstrap resampling. For moderately variable chemical concentrations, the ratio of the 90 th to the 10 th percentile values of the distribution of BAFs (confidence limit ratios, CLRs) were mostly 5 or less. BAF confidence limit ratios decline predictably as the number of water and/or fish samples increase, alt hough once the number of samples exceeds about 6, the reductions in BAF confidence limit ratios become incrementally much smaller. Depending upon the requirements fo r BAF accuracy, exceeding sample sizes of 10 appears to be warranted only for sites having ve ry high variability in ch emical concentrations
in fish and/or water. It is most effective for th e investigator to apply mo re sampling effort to the chemical concentration (biota vs. water) that is more variable. Correlations between chemical3-22 concentrations in biota and wa ter were found to be beneficial in terms of reducing the BAF confidence limit ratios, percent bias and root mean square errors (RMSE). The benefits increased with the magnitude of the correlation, and we re more beneficial in terms of reducing BAF uncertainty for smaller sample sizes. Mis-specifying the concentr ation distribution (i.e., specifying a lognormal concentration distribution when the actual distribution was normal) had
little or no effect on the outcome of the Monte Ca rlo analysis. However, the investigator should always use averaging methods appropriate to th e concentration data, for example applying the lognormal transformation only when justified.

Both Bootstrap resampling and Monte Carlo analysis demonstrate that compositing of water and/or fish samples for analysis is high ly effective for reducing the cost of chemical analyses, assuming that composite samples are formed appropriately (see Sections 3.3.5 and 3.4.5). A single measured concentration from a well-formed composite is equivalent to the arithmetic average of concentrations measured in samples used to form the composite. In fact, the numerical analyses demonstrate that, from the standpoint of measuring a BAF, sample compositing does not reduce the accur acy of the measurement at all.

3.2.2 Modeling Simulation of BAF Sampling Designs

Once the appropriate number of biota and wa ter samples to collect has been determined, the investigator should al locate the samples both spatially and temporally, in order to complete the sampling design. Burkhard (2003) perfor med model simulations to explore how the
variabilities in water and sediment chemical c oncentrations translate into the variabilities associated with BAFs (and BSAFs, which will be discussed separately in Section 4) based upon different sampling designs. Rather generic conditions and simple m odels were used, so that the connections between input and resulting variabil ities in BAF designs we re straightforward and apparent. The approach used for the model (i.e ., a river segment with a food web consisting of four trophic levels) is a variant of that deve loped by Thomann et al. (1997). Details of the modeling approach, and presentati on and discussion of the resu lts, are presented in Appendix
3D.3-23 3.2.2.1 Using Model Simulations to Develop Field-Sampling Designs
The kinetics of chemical uptake and loss by th e fish (or other aquatic organism) controls
the chemical residue that reside s in the organism. These kinetic processes are directly dependent upon the chemical’s hydrophobicity and metabolism ra te in the fish. Successful field-sampling designs should account for the chemical uptake a nd loss kinetics, and for the changes in chemical concentrations occurring in the fish’s envi ronment. The modeling simulations strongly
demonstrate that smaller uncertainties can be obtained by using properly developed sampling design structures. The haphazard collection of sa mples for the measurement of BAFs can, and most often will, result in highly uncertain BAF values. Consequently, such measured values will have poor predictive power.

Burkhard’s (2003) simulations provide subs tantial insight into what an appropriate sampling design structure might be for BAF measurements:

• For chemicals with log K ow of 4 and less with an y rate of metabolism , the concurrent collection of fish and water samples over time provides the smallest degree of uncertainty for BAF measurements; the sp acing of sampling events does not seem critical.

• For nonmetabolizable and slowly metabolizable chemicals with log K ow of 5 and greater , practically identical BAF uncer tainties are obtained using sa mpling designs consisting of the collection of a series of water samples over time, and eith er the concurrent collection of fish samples with each water sample or th e collection of fish samples with the last water sample. From a field-sampling perspec tive, the one-time collection of fish is appealing because of the logistics of assembling field-sampling crews.

• In contrast to chemicals with lower K ow , nonmetabolizable chemicals with log K ow of 6 and greater require numerous water samples spaced widely apart over time to obtain lower uncertainty in the BAF measurement. The spacing and timing of fish collection is again relatively unimportant.

• With increasing chemical metabolism rate , appropriate sampling design structures transition from the numerous water samples sp aced widely over time (with concurrent fish collection with the last or all water sa mples) to the designs appropriate for lower K ow chemicals; that is, concurrent collection of water and fish samples over time with sample spacing not being very critical.3-24 This transition in appropriate sampling desi gn structures suggests that the concurrent sampling design is a more robust or universally applicable design because it can be used for chemicals of all hydrophobicities and all metabolism rates. This advantage for the concurrent sampling design will be especially useful when information is lacking on the chemical’s metabolism rate in the fish, a s ituation that exists for nearly all nonionic organic chemicals.

For sampling to determine BAFs, chemicals with large K ow s will generally require that numerous water samples be averaged over ti me to establish the long-term chemical concentrations in the water. In contrast, for chemicals with low K ow s, because the concentrations in the fish mimic those in water, the time scale for establishing the chemical concentrations in the water shrinks to concurrent sampling of both fish and water. For less hydrophobic chemicals,
current chemical concentrations in the wa ter provide a good predic tor of the chemical
concentration in the fish.

Chemicals with intermediate metabolis m rates and mid-rang e hydrophobicities (log K ow s of 4 to 6) present one of the more difficult chal lenges in selecting an ap propriate sampling design structure for measuring BAFs. This range of hydrophobicities lies within the transition zone between the much more obvious design stru ctures appropriate for low and high K ow chemicals.

The process for developing successful field- sampling structures for BAF measurements can primarily focus upon three parameters: temporal variability, metabolism, and K ow . These three parameters can range widely, and depe nding upon their values, require dramatically different field designs. The greatest number of samples would be required for high-K ow chemicals in aquatic ecosystems subjected to ex tremely high temporal variability in chemical concentrations in water. Although spatial variability ha s been discounted as a dominant factor in sampling design, knowing or understanding the immedi ate home range of the sampled organisms is required. Without this information, one cannot ascertain whether the collected water samples are reflective of the actual water exposure hist ory for the sampled organisms. Poor spatial coordination of fish samples w ith their actual water exposur es will yield BAFs with poor accuracy and large biases.3-25 The sampling structure (i.e., number of sa mpling events over time and space) can be developed for the chemical and ecosystem of interest based upon the modeling simulations. By using the modeling results from Appendix 3D as a guide, some illustrative BAF sampling structures have been developed (Table 3-1). Th ese illustrative designs provide a sense of how sampling design structures might be influenced by differences in temporal variabilities, metabolism rates, and K ow s. Alternative sampling designs can provide similar uncertainties. The illustrative designs incorporate some considerations from a field implementation perspective (for example, collection of water samples once a week as opposed to every 3 or 5 days) and are based upon a continuum or gradient of modeling respon ses. As indicated above, the total number of samples required for a successful measurement is dependent upon the desired precision, and, thus, the illustrative samp ling structures suggest the number and spacing of sampling events for a field study, and not the total number of samples needed for the study.

The effects and importance of the immediate ho me range of the fish are not included in the illustrative sampling structur es in Table 3-1. Alt hough spatial variability of the chemical in the ecosystem is not directly included in the illustrative sampling structures, sample collection for each sampling event should span the home range of the organisms in the ecosystem. Home range depends upon the species , although larger fishes tend to have larger home ranges (see Section 3.3.2). Information about the home range of the fish leads to an assessment of where the fish resides relative to the spatial variability in chemical concentrations. By collecting samples across the organism’s home range, a truer pictur e of the average chemical exposures to the organisms of interest will be obtained. The ideal situation for measuring a BAF is when there are minimal concentration gradients across the organi sm’s home range. Howeve r, spatial variability in the concentrations of the chemical does not add large uncertainties into the measured BAF beyond those caused by temporal variability of the ch emical concentrations in the water. Further, bioaccumulation simulations for migrating fish su ggest that BAFs can be measured with low uncertainty even when extreme spatial concentr ations exist at the fi eld site, provided the measurements are performed in more contamin ated locations of the site for more hydrophobic chemicals, i.e., log K ow >5 (Burkhard, 2003).3-26 In addition to the home range issue above , the illustrative BAF sampling structures (Table 3-1) do not include the effects of co llecting composite water samples over time. For higher K ow chemicals, compositing reduces the uncerta inty in the BAF measurement, whereas the uncertainty in the BAF measurement is increased by compositing for lower K OW chemicals.

3.2.3 How Can These Methods be used to Help Design a BAF Sampling Plan?

As indicated in Section 3 .2.1, the total number of samples required for a successful BAF measurement is dependent upon the precision desire d by the investigator. On the other hand, the illustrative sampling structures in Table 3-1 suggest the number and spacing of sampling events for a field study, but not the necessa ry number of samples. The results of bootstrap resampling or Monte Carlo analysis can be used together with modeling simulations as the basis for a rational sampling design process. The design process is outlined below.

Table 3-1. Illustrative Bioaccu mulation Factor Sampling Desi gn Structures. Uncertainties Associated With Design Stru ctures Are Ecosystem Specific

Sampling Events log K ow Metabolism Rate Sampling Design a Minimum number (depends on temporal variability) Minimum spacing between sampling events (d) ≤ 3 low 2nd series 1 7 4 low 2nd series 2 7 5 low 1st or 2nd series 1, 3, 6 b 7 ≥ 6 low 1st or 2nd series 1, 5, 8 b 30 ≤ 4 medium 2nd series 1 7 5 medium 1st or 2nd series 1, 2, 3 b 7 ≥ 6 medium 1st or 2nd series 1, 4, 6 b 30 all high 2nd series 1 7

a 1st series = collection of a series of water samples with the collection of fish samples concurrently with the last water sample; 2nd series = co llection of paired fish and water samples with each sampling event. b Values are ordered according to low, medium, and high temp oral variability, respectively.

1. The investigator determines the goal fo r accuracy of the BAF measurement, and expresses this goal as the rati o of 90% confidence limits. In the examples presented in Appendices A and C, the BAF confidence li mit ratios typically ranged from about 2
to 12.3-27 2. The investigator determines the number of biota and water concentration samples to collect. Suggestions are offered in Appendi ces A and C for determining the numbers of samples to collect.

a) If site-specific data or data representativ e of the site and chemical are available, bootstrap resampling can help determine sample numbers;

b) If more representative data are not available, Monte Carlo analysis as demonstrated by example in Appendix C may be useful.

c) The investigator should a void collecting fewer than 6 concentration samples in biota and (especially) water, unless significant uncertainty in the BAF is acceptable.

3. The investigator selects an appropriate sampling design structure. Suggestions are
offered in Section 3.2.2.1 based on:

a) Chemical factors: hydrophobicity (log K ow ) and rate of metabolism; and

b) Temporal variability of water concentr ations, based upon factors of the waterbody
at the site.

Table 3-2 illustrates the relationship betw een categories of waterbodies (lakes and reservoirs, estuaries and tidal rivers, rivers and streams) and the degree of temporal variability in concentrations observed fo r various chemicals. The coefficient of variation (CV) for the chemical concentra tions generally increas e as one moves from quiescent waterbodies towards those that ar e more advective (flowing) with shorter hydraulic residence times. Therefore, th e investigator can use the waterbody categories in Table 3-2 to estimate the tem poral variability of water concentrations.3-28 4. The investigator allocates the number of samples (based upon guidance from Step 2) evenly among sampling events (determined in Step 3).

Table 3-2. Waterbody Type as Indicator of Temporal Concentration Variability a

low variability < - - - - - - - - - - - - - - - - - > high variability Lakes and Reservoirs Estuaries and Tidal Rivers Rivers and Streams Waterbody CV b Waterbody CV Waterbody CV Lake Michigan (dissolved

PCBs) 0.37 Hudson River (Aroclor 1254) 0.60 Mississippi River, MS (chloroform) 0.97

James River estuary (Kepone) 0.60 Naugatuck River, CT (dissolved copper) 0.60

Green Bay zone 3 (PCBs) 0.6 - 0.8 lower Fox River, WI (dissolved PCBs) congeners 28+31

congener 149
congener 180 0.54 – 0.57

0.37 – 1.11

0.55 - 2.19 Lake Michigan

Tributaries (PCBs) 0.19 – 1.50

a Variability of chemical concentrations may be higher at concentrations approaching the limit of detection. b CV = coefficient of variation.

3.3 MEASURING CHEMICAL CONCENTRATIONS IN BIOTA

This section describes the development of a field plan for sampling biota to support the measurement of a site-specific BAF. This is based upon a number of documents, including USEPA (2000b), USEPA (1997a), Versar (1982) and USEPA (1997b; Section 4.2). These documents provide more detailed guidance on the sampling design of field studies and recommend field procedures for collecting, pr eserving, and shipping samples to a processing laboratory for target analyte anal ysis. Planning and documentation of all field procedures ensures
that collection activities are cost -effective and that sample integrity is preserved during all field activities. EPA’s systematic planning tool is the Data Quality Objectives (DQO) process. The3-29 elements of systematic planning are stated in Chapter 3 of the EPA Manual 5360 - EPA Quality Manual for Environmental Programs (USEPA, 2000d) and include:

• Identification and involvement of the proj ect manager, sponsoring organization and responsible official, project personnel, st akeholders, and experts, etc. (e.g., all customers and suppliers);

• Description of the project goals , objectives, and questions an d issues to be addressed;

• Identification of project sc hedule, resources (including budget), milestones, and any applicable requirements (e.g. regulatory re quirements, contract ual requirements);

• Identification of the type of data needed a nd how the data will be used to support the project's objectives;

• Determination of the quantity of data need ed and specification of performance criteria for measuring quality;

• The data will be obtained (including ex isting data) and identification of any constraints on data collection;

• Specification of QA and QC activities to assess the quality performance criteria;

• Description of how the acquired data will be analyzed, evaluated and assessed against its intended use and the quali ty performance criteria.
The investigator and field sampling staff s hould develop a detailed sampling plan prior to initiating a field study. As described by EPA (USE PA, 2000b), there are seven major parameters to be specified prior to the initiation of any field biota sample collection activities:

• Target analytes and analytical methods

• Target species (and size classes)

• Sampling locations

• Sampling times

• Sample type (whole organism or edible portion)

• Replicate samples

• Sample collection methods3-30 The role of each of these parameters in developi ng an appropriate field plan for biota sampling is
discussed below.

3.3.1 Target Analytes and Analytical Methods
Site-specific BAFs are measured in order to support the derivation of AWQC for specific contaminants. Knowing the chemical of concer n, the investigator should make a number of decisions regarding the analysis of tissue samples. This includes considering whether analytical alternatives exist for measuring the chemical of concern, and if so, which method is most appropriate. In addition, it may be necessary to specify which form of the chemical should be measured. Furthermore, it is important to an alyze tissue samples using methods that are chemical-specific, sensitive, accurate and precise.

Bioaccumulation factors should only be determ ined for individual chemicals. In cases
where the chemical of interest is a mixture (e.g., PCBs, chlordane), the study design will require that individual chemicals compos ing the mixture of interest be quantified individually. This will result in BAFs for individual components of the chemical mixture. This requirement is necessary
because individual chemicals in a mixture usually behave differently in the environment (i.e., different portions of the indivi dual components of the mixture will be present in different amounts among the sediment, water and biota). The i nvestigator must select analytical methods that are specific for the individual chemical (or chem icals, in the case of a mixture) of interest.

In addition, the analytical method should be sensitive enough to quantify the chemical concentrations in both biota and water. Examples of highly-bioaccumulative chemicals that are difficult to quantify in water include a number of toxicologically-signi ficant polychlorinated dibenzodioxin (PCDD) congeners. Unless the in vestigator is confident that chemical concentrations can be quantifie d in the majority (preferably > 80%) of both biota and water samples, a different approach of de termining BAFs should be considered.

It is also important to measure other parameters th at are significant to the bioaccumulation process. For nonionic organic chem icals, lipid content of the target species
should be measured in the same tissue in whic h the contaminant was measured to permit lipid3-31 normalization. This will usually be the fillet for finfish and the edible tissue for shellfish (see subsection 3.3.2).

3.3.2 Target Species and Size Class Selection
The investigator should specify the aquatic or ganisms to be targeted for sampling, as well as the size ranges to collect. The target spec ies and sizes selected for sampling should be commonly consumed locally and of harvestable size. State fishing regulations may be a source of useful information and may be especially impor tant to consider when compositing fish. Other aspects of the field sampling plan will follow fr om identifying the target species. For example, the home range of the target species will dict ate the spatial scale of the sampling effort.

Several biological attributes of the target species should also be considered when sampling target species for BAF determinations used in deriving human health criteria. For example, the size/age of the organism can affect the extent of bioaccumulation in the organism. Young fish can exhibit lower accumulation of some contaminants due to growth dilution. In addition, the reproductive status (e.g., pre/pos t spawning) can alte r the body burden of contaminants, with significant contaminant loss observed due to maturation and release of sperm or eggs. The investigator should consider determin ing the age of sampled fish because older fish tend to accumulate higher c oncentrations for many chemicals, and changes in behavior (movement, migration, diet) are often related to organism age. The size of the target species should be representative of the size being consumed by the target human population. If this size range is broad, stratifying sampling strategies by size class is necessary, pa rticularly when taking composite samples. The timing of sampling s hould include the period of most frequent harvesting of the species.

In freshwater ecosystems, one bottom-feedi ng and one predator fish species should be collected. In estuarine/marine ecosystems, either one bivalve species and one finfish species or two finfish species should be collected. Second- and third-choice target species should be
selected in the event that the re commended target species cannot be successfully collected at the
site. The same criteria used to select the recomm ended target species should be used to select alternate target species.3-32 The correlation between increasing size (a ge) and contaminant tissue concentration
observed for some freshwater finf ish species (Voiland et al. 1991) may be much less evident in estuarine/marine finfish species (Pollock, 1993). The movement of estuarine and marine species from one niche to another as they mature may ch ange their exposure at a contaminated site. The size of estuarine/marine target sp ecies collected should st ill be representative of the size being consumed by the target human population.

The trophic level of the fish species sampled should be determined by taking into account the life stage(s) consumed and food web structure at the location(s) of inte rest. Site-specific data, such as gut contents or stable isotope analyses, are preferre d for determining trophic levels. Jardine et al. (2006) provide gui dance on the application and inte rpretation of stable isotope ratios to measure trophic levels. The investigator should also consult with fisheries experts and refer to life history literatu re for the species of interest when making trophic level
determinations. Most often the field studies used w ill be those that include fish at or near the top of the aquatic food chain (i.e., in trophic levels 3 and/or 4). In situati ons where consumption of lower trophic level organisms re presents an important human e xposure route, such as certain types of shellfish at trophic leve l 2, the field study should also in clude appropriate target species at this trophic level.

Behavior and life cycle aspects are importa nt because they can significantly affect exposure and overall bioaccumulation. For each target species, the investigator should address the following questions.

• Do the species of interest migrate? If the answer is yes, the investigat or should know the approximate arrival and departure dates fo r the organisms of interest for your site.

• Are there multiple populations of the organisms of interest? If the answer is yes, which population does the investigator wish to sample? Populations may exhibit
different behavior characte ristics (e.g., migration, ra nge, habitat, and feeding preference), which can lead to differences in chemical exposure and bioaccumulation.3-33 For example, two populations of a fish speci es may both inhabit a tributary - one that migrates seasonally between the tributary a nd the adjacent embayment, and a resident population that does not migrate. If the tributary is highly contaminated by a
bioaccumulative chemical relative to the embayment, chemical concentrations will probably be higher in the reside nt fish. In this example, th e investigator should target the collection of fish from the resident popul ation in order to measure the site-specific BAF for the tributary. This could be a ccomplished by sampling when the migratory population is absent. The field plan should ta ke the behavioral differences of multiple populations into account, in order to samp le organisms from a specific population. Otherwise, it may be difficult or impossi ble to distinguish which population(s) are represented by the sampled organisms.

• What is the home range size for th e target species at your site? Depending upon the site, the degree of difficulty in defining the immediate home range of the organism can vary widely. In situations where the movement of the organisms is confined by the geography of the site (e.g., dams or fa lls) the home range of the organisms can probably be defined fairly easily. In estuarie s, the home range of some fish species are constrained by salinity gradients. Home ranges can be determined by tagging/recapture, radio-telemetry, and/or ultr asonic telemetry studi es at the site of
interest. Valuable information can be obtained from fisheries biologists or
recreational fishermen that are familiar w ith the waterbody and fish species. Home ranges for freshwater fishes can also be esti mated using the allometric relationships of Minns (1995):

H = exp [-2.91 + 3.14 HAB + 1.65 ln (L)]

⋅ ⋅ (Equation 3-5a)

or H = exp [3.33 +2.98 HAB + 0.58 ln (W)]

⋅ ⋅ (Equation 3-5b)

where: H = home range size (m 2 ) HAB = 0 for rivers and 1 for lakes L = body length (mm), and W = organism body weight (g).3-34 An example of applying equation 3-5 is presented below. For freshwater invertebrates, and for marine and estuarin e ecosystems, allometric relationships for home range have not been reported.

Having a good understanding of the immediat e home range of the species is important. Organisms with smaller home ranges will be more representative of the study site than those with large home range s which extend beyond the study site. Just because an organism is caught at a samp ling location, one can not infer that the
chemical residue in the fish are due to the chemicals residing at the study site. Knowledge of the organism’s home range is the only way that the investigator can Estimating the home range for freshwater fish
The bluegill sunfish is a common inhab itant of small lakes and creeks. A representative length for an adult bluegi ll is 180 mm (7 inches). Using equation 3- 5a, we can estimate the home range for a bluegill sunfish in a lake:
H = exp [-2.91 + 3.14 HAB + 1.65 ln (L)]

⋅ ⋅
= exp [-2.91 + 3.14 1.0 + 1.65 ln (180 mm)]

⋅ ⋅

2 

### = exp (8.80) = 6,624 m

According to equation 3-5a, the estimated home range of a bluegill sunfish in a lake is 6,624 m 2 (1.6 acres).
To estimate the home range in a river, a HAB of 0 would be substituted for the value of 1.0 used above in equation 3-5a:

2 

### H = exp [-2.91 + 3.14 0 + 1.65 ln (180 mm)] = exp (5.66) = 287 m

⋅ ⋅
According to Carlander (1969), the home ra nge for this species is considerably smaller than the estimates calculated above: typically less than 0.25 acres (~1,000

m 2 ) in lakes and not exceeding 30 meters in streams.3-35 establish the connection of the fish (or other aquatic organism) to the sampling
location.

It is very useful to consult with loca l fisheries experts du ring the sampling design phase of the field study to help in determ ining the immediate home range, diet and trophic level of the organisms at the site. Although the above allometric relationships are available for estimating home ranges, the investigator should not necessarily
assume that the “calculated” and “actual ” immediate home ranges for the organisms are the same. The investigator will still need to determine, to the extent possible, the immediate home ranges for the organisms at the site.

• Do the organisms of interest exhibit diel (day/n ight) behavior in habitat at your site? If the answer is yes, the sampling plan should re flect the typical behavi or for the species of
interest.

Behavioral information for the species of inte rest will be used to specify the location and timing of field sampling for biota, as di scussed in the following 2 subsections.

3.3.3 Sampling Locations

Selection of biota sampling locations may be quite straightforward where the source of pollutant introduction is highly lo calized, or if site-specific h ydrologic features create a sink where chemically contaminated sediments accu mulate and the bioaccumulation potential might be enhanced. Upstream and downstream water quality and sediment monitoring stations/locations bracketing point source discharges, outfalls, a nd regulated disposal sites can often be used to characterize the geographic exte nt of the contaminated area. Within coves or small embayments where streams enter large la kes or estuaries, the geographic extent of contamination may also be characterized via mu ltilocational sampling to bracket the areas of concern. Such sampling designs are clearly most e ffective where the target species are sedentary or of limited mobility (Gilbert, 1987) . In addition, the existence of barriers to migration, such as dams, should be taken into consideration. Select ion of sampling sites should also consider temporal and spatial variations in food web structure that may occu r across the study area.3-36 Site selection consid erations should also include the number of samples necessary to characterize different waterbody type s (lakes, rivers, estuaries, a nd coastal marine waters) based on hydrodynamics (including waterbody size). Typical ly, as the size of a waterbody increases (from small lakes to larger lakes to Great Lakes; or from streams, to rivers, to estuaries, to coastal marine waters), more samples are need ed to achieve a specific precision. For example, fish inhabiting relatively small la kes are likely to be exposed to a relatively homogeneous aquatic
environment of contaminant concentrations. In a riverine, estuarine, or coastal situations, however, the hydrodynamics of the ecosystem can greatly affect the magnitude and nature of chemical exposure in the water. Thus, the amount of time that any fish spends exposed to the contamination may be highly variable as compar ed to the relatively homogeneous exposures that might occur in smaller, less hydrologically dyna mic lake ecosystems. Large sites with strong spatial gradients may require spatially-stratifi ed sampling designs. Guidance on optimizing data collection options, via different probability or authoritative sampling designs, is offered in USEPA (2002a).

The investigator should also consider the inherent migratory nature of the species when determining the number of samples to collect. Sm allmouth bass, a riverine species, have a home range of 500 to 4,500 m 2 , but typically migrate up to 45 km (28 miles) (Reid and Rabeni, 1989). In contrast, many Great Lakes fish species, as well as riverine, estuarine, and marine species, migrate considerable distances during spawning periods. Several Great Lakes species also move upstream considerable distances into tributary ri vers to spawn. Lake tr out in the Great Lakes have been found to migrate up to 300 km (200 mile s) with larger fish migrating 300 miles (480 km) (Daly et al. 1962). For many marine species, es tuaries are the spawning areas for the adults and nursery areas for the devel oping juveniles, who eventually travel offshore as adults and return again to the estuaries to spawn. For th ese species, migratory or seasonal movements can take place both from inshore to offshore areas and along the coasts. Obviously, the number of samples needed to calculate an accurate aver age chemical concentration for bluegill sunfish inhabiting a relatively homogeneous environment with respect to contaminant concentrations is quite different from that required for the more mobile species like the smallmouth bass and lake

trout.3-37 Home range should also be considered for sh ellfish. Bivalve molluscs, like the oyster or mussel, cement themselves to hard substrates as young spat and are then unable to move. Although clams and scallop species are slightly mo re mobile, they also typically stay in the general area in which they first settled out of the wate r column. For crustaceans like the blue crab and lobsters, however, movements both into and out of estuaries as well as into deeper water offshore are possible. As the complexity of the hydrodynamics of an ecosystem increases and the mobility of the target species increases, so too do the number of samples and the number of sampling stations required to accura tely determine the average chemi cal concentration at the site.

Biota sampling should be conducted in freque ntly fished areas, possibly including the following locations.

Areas near point source discharges such as

• industrial or municipal discharges,

• combined sewer overflows (CSOs),

• urban storm drains, and

• urbanized embayments or tributaries in large estuary and lake systems.

Areas near nonpoint source inputs such as

• landfills, Resource Conservation and Rec overy Act (RCRA) sites, or Superfund Comprehensive Environmental Respons e, Compensation, and Liability Act (CERCLA) sites,

• areas of intensive agricultural, silvicultural, or resource extraction activities,

• areas of urban/suburban land development,

• areas subject to significant shoreline or bank erosion and/or interactions between a river and adjacent floodplains, and

• areas receiving inputs through multimedi a mechanisms such as hydrogeologic connections or atmospheric deposition.3-38 Areas acting as potential pollutant sinks where contaminated sedi ments accumulate and bioaccumulation potential might be enhanced, such as

• areas where water velocity slows and or ganic-rich sediments are deposited, such as near the inside bank of stream or river bends, deep pools, and impoundments above dams,

• areas of ponds and lakes where water depths exceed the wave “base” that limits sediment resuspension due to the oscillat ory shear stresses from wind waves, and

• the convergence zone between fresh and saltwater in estuaries.

Identifying sampling locations near signi ficant point source disc harges are usually straightforward. It is often more difficult, however, for the investig ator to identify clearly defined sampling locations in areas affected by polluta nts from nonpoint sources. For these sites,
assessment information summarized in state Sec tion 305(b) reports should be reviewed before locations are selected. State 305(b) reports ar e submitted to the EPA Assessment and Watershed Protection Division biennially and provide an in ventory of the water quality in each state. The 305(b) reports often contain S ection 319 nonpoint source assessmen t information that may be useful in identifying major sources of nonpoint s ource pollution to state waters. States may also use a method for targeting pesticide hotspots in es tuarine watersheds that employs pesticide use
estimates from NOAA's National Coastal Polluta nt Discharge Inventor y (Farrow et al. 1989).

In addition to the intensity of subsistence, sport, or commercial fishing, factors for evaluation when selecting fish and shellf ish sampling sites include (Versar, 1982):

• proximity to water and sediment sampling sites

• availability of data on fish or shellfish community structure

• bottom condition

• type of sampling equipment

• accessibility of the site3-39 The most important benefit of locating fish or shellfish sampling sites near sites selected for water and sediment sampling is the possibility of correlating contaminant concentrations in different environmental compartments (water, se diment, and fish). Selecting sampling sites in proximity to one another is also more cost-eff ective because it provides opportunities to combine sampling trips for different matrices.

The availability of data on the indige nous fish and shellfish communities is a consideration when selecting sites. Information on preferred feeding areas and migration patterns is valuable in locating populati ons of the target species (Versar, 1982). Knowledge of habitat preference provided by fisheries biologists or co mmercial fishermen may significantly reduce the time required to locate a suita ble population of the target species at a given site.

One additional consideration associated w ith sample site selection is whether the sampling area includes waters inhabited by thre atened or endangered species. If such waterbodies are to be monitored, the investigator should obtain a permit from the U.S. Fish and Wildlife Service (USFWS) if their sampling effort could potentially impact a freshwater species
or from the National Marine Fish eries Service (NMFS) if their sa mpling effort could potentially impact any marine or anadromous species c overed under the Endangered Species Act (ESA) of 1973. A species is listed under one of two categorie s, endangered or thr eatened, depending on its status and the degree of threat it faces. An e ndangered species is one that is in danger of extinction throughout all or a significant portion of its range. A threatened sp ecies is one that is likely to become endangered in the foreseeable fu ture. The USFWS maintains a list of all plant and animal species native to the United States that are candidates or proposed for possible addition to the Federal List. A complete listing of the current status of all threatened and endangered species as well as information abou t each USFWS region is available on-line on the USFWS website at http://enda ngered.fws.gov/wildlife.html. Additional information can be gleaned by contacting the specific USFWS regional office that has primary responsibility for an endangered species.3-40 3.3.4 Sampling Times

Selection of the most appropriate times for sa mpling is very important, particularly when target species will be sampled on only a few occasions. Sampling should be conducted during the period when the target species is most frequently harvested (USEPA, 1989a; Versar, 1982) but should also consider the reproduc tive cycle and lipid dynamics of the target species, which may be related to temperature as well as season. In fresh waters the most desirable sampling period may be from late summer to early fall (i.e ., August to October) (Phillips, 1980; Versar, 1982). The lipid content of many species is generally highest at this time. Also, water levels are typically lower during this time, thus simplifyi ng collection procedures. The late summer-early fall sampling period may not be appropriate, howeve r, if it does not coincide with the legal harvest season of the target sp ecies, and/or the target spec ies spawns during this period 3 . A third exception to late summer-early fall sampli ng concerns monitoring for the organophosphate pesticides. Sampling for these compounds is best if conducted during late spring or early summer within 1 to 2 months following pesticide applic ation because these compounds are degraded and metabolized relatively rapidly compared to orga nochlorine pesticides. However, the target species should be sampled during the spring only if the species can be legally harvested at this time. It should also be noted that sampling c onsiderations for aquati c and aquatic-dependent wildlife could be different from that for the protection of human health.

In estuarine and coastal waters, the most appropriate sampling time is during the period when most fish are caught and consumed (usu ally summer for recreational and subsistence fishers). For estuarine/marine shellfish (bival ve molluscs and crustaceans), two situations may exist. The legal harvesting season may be strictly controlled for fisheries resource management purposes, or harvesting may be open year round. In the first situation, shellfish contaminant monitoring should be conducted during the lega l harvest period. In the second situation, monitoring should correspond with the period when the majority of harvesting is conducted during the legal season. The invest igator should consider differe nt sampling times for target shellfish species if differences exist between th e commercial and recreatio nal harvesting periods.

3 If the target species can be legally harvested during its spawning period, then sampling to determine contaminant concentrations can be conducted during this time.3-41 Ideally, the investigator selects a sampling pe riod that avoids the sp awning period of the target species, including the period one month before and after spawning, because many aquatic species are subject to stress during spawning. Tissue samples co llected during this period may not be representative of the normal population. For example, feeding habits, body fat (lipid) content, and respiration rates may change dur ing spawning and may influence pollutant uptake and clearance. The collection of samples may also a dversely affect some spec ies, such as trout or bass, by damaging the spawning grounds. Most fish ing regulations protec t spawning periods to enhance propagation of important fishery spec ies. Species-specific information on spawning periods and other life history f actors is available in numerous sources (e.g., Carlander, 1969; Emmett et al. 1991; Pflieger, 1975; Phillips, 1980). In addition, digi tized life history information is available in many states through the Multista te Fish and Wildlife Information Systems (1990) on the web at http://fwie.fw.vt.edu.

Sample timing for species that migrate into and out of the site is a particular concern. EPA’s preferred method of accounting for organism migration is to collect aquatic organisms just before they migrate back out of the site. This approach maximizes the amount of time the organism spends at the site of interest, and pr ovides the best estimate of the residue in the organism based upon the organism’s exposure at th e site. If the organisms spend a very short time at the site (e.g., the fish migr ate through the site in a few days to a week), the determination of a BAF for that species is not useful, even if the BAF can be measured. A site-specific BAF measured in this way may be biased because th e chemical concentrations measured in water
from the site would not be re flective of the organism’s recen t exposure history. The degree of bias will be related to the uptake and elimina tion kinetics (related to hydrophobicity) and the
metabolism rate of the chemical , as discussed in Section 3.2.2.

3.3.5 Sample Type

EPA has recommended composite samples of fish fillets or of the edible portions of shellfish for analysis of chem icals of concern in bioaccumula tion studies (USEPA 1987; 1989a). Composite samples are homogeneous mixtures of samples from two or more individual3-42 organisms of the same species collected at a par ticular site and analyzed as a single sample. Because the costs of preparing and analyzing i ndividual samples are highe r than the costs of preparing and analyzing a composite sample, the la tter sample type is the most cost-effective for estimating average tissue concentrations in ta rget species populati ons. For a well-formed composite, a single measured concentration should be similar to the arithmetic average of concentrations for the individual organisms within the composite. Compositing may be necessary
in order to collect sufficient tissue for chemical analysis of smaller organisms. Even for larger organisms such as predator fish, however, co mpositing offers advantages over sampling and analyzing individual organisms. Most impor tantly, composites formed from individual organisms collected at a specific time and place will avoid sampling the variability in chemical
concentrations between indivi duals, which can be a signific ant component of the overall variability. Since BAFs measured for use in deriving national recommended ambient water quality criteria for protecting human health ar e intended to represent average, long-term chemical concentrations, it is neither necessary no r desirable to sample variability in chemical concentrations among individual organisms. Although composite sampling will not reveal
extreme contaminant concentration values in indivi dual organisms, this is not considered a major disadvantage given the goal of measuring av erage chemical concentrations for BAF
determination.

Fish samples should reflect how the targ et population commonly prepares fish for consumption. The investigator should select a composite sample type for chemical analysis based on the tissue types and prep aration methods of fish consum ed by the target population of concern. For example, few consumers in the general population eat the skin of the fish, which may justify its removal for analysis. Analysis of sk inless fillets may also be more appropriate for some target species such as catfish and other sc aleless finfish species. In contrast, using whole fish with skin-on as the sample type for a ssessing PCBs, dioxins/fur ans, or organochlorine pesticide exposures in populations of Nativ e Americans, Asian Americans, CaribbeanAmericans, or other ethnic gr oups that consume whole fish in a stew or soup is warranted because these contaminants accumulate in fatty tissues of the fish. Cooking the whole fish to make a stew or soup releases the PCBs, dioxins/f urans, or organochlorine contaminants into the broth; thus, analysis of whol e fish should mirror the way the consumer prepares the fish.3-43 Similarly, the investigator should consider wh ether using skin-on fillets (with belly-flap included) is appropriate for the general fishing population, since this is a standard filleting method among recreational fishers. This method also allows for the inclusion of the fatty belly flap tissue and skin in which hydrophobic, noni onic organic chemicals such as PCBs and
dioxins/furans concentrate. It also takes into account the fact that some consumers may not neatly trim the more highly contaminated fatty tissue from the edible muscle fillet tissue. Additional guidance regarding site -specific fish consumption is provided in Section 4.3.3.1 of the 2000 Human Health Methodology.

In any study design, it is important that biota samples be collected and composited in size/age classes. For fish, dietary composition changes substantially w ith size/age, and these changes can result in differences in BAFs am ong age classes. For forage fish, common classes are young-of-the-year (YOY), juvenile s, and adults. For piscivorous fishes, common classes are year classes (e.g., 2, 3, 6, and 10 years old). Th e investigator should c onsider the following guidelines for the compositing of biota samples.

• Composited organisms must all be of the same species: Individual organisms combined to form composite samples must be of the same species because bioaccumulation potential can vary among different species. Accurate taxonomic iden tification prevents the mixing of closely related species with th e target species. Indi viduals from different species should not be combined to form a composite sample (USEPA, 1989a, 1990).

• The composited organisms should all satisfy an y legal requirements of harvestable size or weight, including “slo t-limit” restrictions 4 : Alternatively, they should at least be of consumable size if no legal harv est requirements are in effect.

• Composite samples should be comprised of equal weights of ground tissue from each organism: Samples comprised of equal tissue weights from each organism will provide

4 A slot limit is a protected size range (e.g., lengths between 15” and 24”) requiring the release of fish within the specific range. Fish smaller or larger than the “slot” may be harvested.3-44 the least-biased average of concentrations for the indivi dual organisms within the composite.

• Composited organisms should be of similar size: Individual organisms used in composite samples should be of similar size (WDNR, 1988). For fish or shellfish, the total length (or size) of the smallest individua l in any composite sample shoul d be no less than 75 percent of the total length (or size) of the largest individual in the composite sample (USEPA, 1990). For example, if the largest fish is 200 mm , then the smallest individual included in the composite sample should be at least 150 mm. In the California Mussel Watch Program, a predetermined size range (55 to 65 mm) for the target bivalves ( Mytilus californianus and M. edulis ) is used as a sample selection criterion at all sampling sites to reduce size-related variability (Phillips, 1988). Similarly, the Texas Water Commission (1990) specifies the target size range for each of the recommended target fish species
collected in the state's fish contaminant monitoring program.

For persistent chlorinated organic compounds (e.g., DDT, diox in, PCBs, and toxaphene) the larger (older) individual s within a population are genera lly the most contaminated (Phillips, 1980; Voiland et al. 1991). As noted earlier, this correlation between increasing size and increasing contaminant concentration is most striking in freshwater finfish species and is less evident in estuarine and ma rine species. Size is us ed as a surrogate for age, which provides some estimate of the tota l time the individual organism has been at risk of exposure. Therefore, the primary targ et size range ideally s hould include the larger individuals harvested at each sampling site.

• Composited organisms should be collected at the same time: Individual organisms used in a composite sample ideally should be collect ed at the same time (i.e., collected as close to the same time as possible but no more than 1 week apart). This is done to minimize temporal changes in contaminant concentra tions (e.g., associated with the reproduction cycle of the target species). If organisms used in the sa me composite are collected on different days (no more than 1 week apart) because a sampling crew was unable to collect all fish needed to prepare the composite sample on the same day, the individual fish3-45 should be processed within 24 hours. The fish may have to be filleted and frozen until all the fish to be included in the composite are delivered to the laboratory. At that time, the composite homogenate sample may be prepared.

• Organisms of the target species should be co llected in sufficient numbers to provide sufficient mass of composite homogenate sample of edible tissue for analysis of target analytes: Composite sample should contain adequate tissue mass so that sufficient material will be available for the analysis of all target analytes. Th e investigator should determine the required tissue homogenate mass based upon the lowest expected chemical concentration of the target analyte(s) and th e sensitivity of the analytical method. Sample mass requirements can vary considerably de pending upon the chemical(s) and analytical method. Analyses of PCBs and TCDD/TCDFs in aquatic biota typi cally requires about 10 to 20 grams (wet weight) of tissue. Up to 200 grams of tissue may be required for multiple analytes (e.g., numerous priority po llutants including PCBs, dioxins, pesticides and metals).

Given the variability in size among target species, only approximate ranges can be suggested for the number of individual organism s to collect to achieve adequate sample mass (USEPA, 1989a; Versar, 1982). For fish, 3 to 10 individuals should be collected for a composite sample for each target species; for shellfish, 3 to 50 individuals should be collected for a composite sample. In some cases, however, more than 50 small shellfish (e.g., mussels, shrimp, crayfish) may be need ed to obtain the recommended 200-g sample
mass. The same number of individuals should be used in each composite sample for a given target species at each sampling site.

Deviations from the recommended study desi gn have implications that may make the statistical analyses more comp licated. The statistical methods for analyzing composite samples are made tractable and easier-to-use by simplif ying the study design. Using equal numbers of organisms in replicate composite sample s is one way to do this (USEPA, 2002a).3-46 For shellfish samples, the recommended composite sample type for chemical analysis should also consider the tissue type consumed by the target populati on. The specific tissues considered to be edible will vary among target shellfish species based on local consumer preference. For example, several states (Mai ne, Maryland, Massachusetts, New Hampshire, New Jersey and New York) have issued advisori es for a variety of contaminants (PCBs, dioxins/furans, or cadmium) in specific glands or tissues of crustaceans such as lobsters and crabs. Some consumers of lobsters, Homarus americanus, enjoy eating the tomalley (digestive gland of the lobster), which has been shown to contain higher concentrations of chemical contaminants than the claw, leg, or tail meat typically consumed by members of the general population. Similarly, for the blue crab, Callinectes sapidus, as well as other crab species, the hepatopancreas (digestive gland) is consumed by some individuals ; this has also been found to contain higher concentrations of contaminants than claw, leg, or body muscle tissue. A precise description of the sample type (i ncluding the number and size of the individual crustaceans in the composite) should be documented in the program record for each target species. A similar situation exists with respect to selection of th e appropriate sample type for bivalve molluscs.

For freshwater turtles, the study objectives and sample type consumed by the target population at risk should be of primary consideration. However, EPA recommends use of individual turtle samples rather than co mposite samples for evaluating turtle tissue contamination. As with shellfish, the tissues of fr eshwater turtles consider ed to be edible vary based on the dietary and culinary practices of local populations.

3.3.6 Replicate Samples

EPA recommends that replicate composite samp les of each target spec ies be collected at each sampling site. Field replicates are distinct composite samples comprised of tissue from different individuals of the same species. They should be collected at a minimum of 10 percent of the sampling sites (USEPA, 2000b). The collecti on and storage of replicate samples, even if not analyzed at the time due to inadequate res ources, allow for follow up QC checks. These sites should be identified during the planning phase.3-47 3.3.7 Sample Collection Methods

The selection of equipment and methods for sampling fish and shellfish is a topic beyond the scope of this document. In re sponse to the variations in en vironmental conditions and target species of interest, fisheries biologists have had to devise sampling methods that are intrinsically selective for certain species and sizes of fish a nd shellfish (Versar, 1982). This selectivity is a great advantage for sampling biota for the purpo se of measuring a BAF, because minimizing
factors such as differences in taxa and size improves the accuracy of the measurement.

Sample collection activities can be initiated in the field only after an approved sampling plan has been developed and all permits fo r collection are in hand. Recommended sampling equipment and its use, considerations for ensuri ng preservation of sample integrity, and field record keeping and chain-of-custody proce dures associated with sample processing, preservation, and shipping are di scussed in USEPA (2000b; 1997a).

3.4 MEASURING CHEMICAL CONCENTRATIONS IN WATER

This section provides guidance on the developm ent of a field plan for sampling water in support of the measurement of a site-specific BAF. For water sampling, there are seven major parameters to be specified prior to the initiati on of any field water sample collection activities:

• Target analytes and analytical methods

• Phase separation

• Sampling locations

• Sampling times

• Sample type

• Replicate samples

• Sample collection methods

The role of each of these in developing an appropriate field plan for water sampling is discussed below. As was the case for biota samp ling, the investigator and field sampling staff3-48 should develop a detailed sampling plan prior to initiating the field st udy. In addition to the documents specifically referenced below, EPA publishes a number of guidance documents for environmental sampling design. These include the Superfund Program Representative Sampling Guidance, Volume 5: Water and Sediment, Part I (USEPA, 1995) and the EPA Quality System Documents, located at: http://www.epa.gov/quality/qa_docs.html (e.g., Guidance for Choosing a Sampling Design for Environmenta l Data Collection, EPA QA/G-5S ; USEPA. 2002a).

3.4.1 Target Analytes and Analytical Methods

The method used to analyze water samples fo r concentrations of chemicals of concern must be compatible and consistent with the method selected for analysis of biota samples (Section 3.3.1). Bioaccumulation factors are only dete rmined for individual chemicals. In cases
where the chemical of interest is a mixtur e (e.g., PCBs, chlordane), individual chemicals composing the mixture should be quantified individua lly. This will result in BAFs for individual components of the chemical mixture. Where appr opriate, BAFs should be expressed for specific
forms of the chemical of concern.

The investigator should also ensure that the method chos en to analyze chemicals of concern in water is sufficiently sensitive to measure ambient concentrations. For highlybioaccumulative chemicals, sensitivity of the anal ytical method is commonly more critical for water than it is for biota; for these chemicals the water concentration may be vanishingly small even though the concentrati ons in tissue are high enough to cause concern. Unless the investigator is confident that chemical con centrations can be measured in the majority (preferably > 80%) of both biota and water sample s, a different approach of determining BAFs should be considered. This is why the BSAF approach is preferred for highly-hydrophobic
nonionic chemicals that are difficult or impossible to analyze in water.

Contamination of water samples is also an issue of concern, especially when ambient concentrations are very low. The field sampling plan should incorporate adequate blank samples to detect contamination from sampling equipment and containers, as well as contamination that3-49 can occur during sample transport, handling and analysis. Guidance regarding the use of blanks and other quality control sample s is provided in USEPA (2002b).

For organic chemicals with log K ow >4, POC and DOC conc entrations should be measured in all water samples along with the chemicals of concern. Measuring POC and DOC will allow the investigator to estimate the freely dissolved chemical fraction using Equation 3-6. Other parameters such as temperature, pH , dissolved oxygen, conductiv ity/salinity, chlorophyll a and total suspended solids can also be measured, as they provide information to help interpret the bioavailability and subsequent bi oaccumulation of contaminants by aquatic organisms. pH is an especially important parameter for ioni zing organic chemicals (see Section 2.2).

3.4.2 Phase Separation

Calculating the total BAF (Equa tion 3-1) requires the inves tigator to measure the total chemical concentration in water. For hydr ophobic chemicals, including nonionic organic chemicals, a baseline BAF can be calculate d instead (Equation 3-2). The advantage of determining a baseline BAF is that the variab ility of the BAF will us ually be reduced when compared to a total BAF determined at the sa me site (Burkhard et al. 2003; USEPA, 2003). Normalizing the total BAF also reduces the vari ance between sites and trophic levels for organic chemicals. However, the investigator needs the freely dissolved chemical concentration in water
in order to calculate the baseline BAF. Th e freely dissolved (or bioavailable) chemical
concentration can be calculated from the total chemical concentration measured in water, by calculating the freely dissolved chemical fraction:

ο w ow 

### f = 1 / (1 + POC DOC

fd 

### ⋅ Κ + 0.08 ⋅ ⋅ Κ ) (Equation 3-6)

where:
DOC = the average dissolved organic ca rbon concentration in the water column (kg of organic carbon/L of water) and
POC = the average particulate organic car bon concentration in the water column (kg of organic carbon/kg of particulate matter).3-50 K ow is used to estimate the partition coe fficient between POC and freely dissolved chemical, and 0.08 A K ow is used to estimate partition coefficient between DOC and freely dissolved chemical (Burkhard, 2000) . The applicability of Equation 3-6 has been demonstrated for many nonionic organic chemi cals and in numerous aquatic ecosystems (see Section 4.2 of TSD Volume 2; USEPA, 2003).

Alternatively, dissolved concentrations of hydrophobic chemicals can be measured directly by separating the water sample into di fferent fractions. There are a number of methods of achieving chemical phase separation that ha ve been developed for water sampling including filtration, semi-permeable membrane devices [SPMD], solvent-filled dialysis bags, centrifugation and solid phase extraction. Depend ing on the method, the investigator may still need to apply Equation 3-6 to determine dissolved bioavailable concentr ations, because not all methods will effectively separate DOC-bound chem ical from the truly dissolved fraction. The preferred option for calculating the freely dissolved chemical concentration will often be apparent once the investigator c onsiders the feasibility and logistics associated with collecting the necessary volumes of total vers us phase-separated water samples.

3.4.3 Sampling Locations

Probably the most important f actor in measuring a BAF with predictive power is that the water sampling locations be reflective of the immediate home range of the target organism (Section 3.3.2 and 3.3.3). The importance of collecti ng water samples which are reflective of the organism’s home range can not be overstated. Spending time and re sources to better define this relationship will greatly decrease the uncerta inty associated with the resulting BAFs.

Once the home range of the targ et organism has been defined for the site of interest, the investigator can select water sampling locations that best reflect the organism’s chemical exposure. In this regard, much of Section 3.3.3 fo r selection of biota samp ling locations is also appropriate for water sampling. Information about the preferences of the target organism - in terms of environmental factors (e.g., temperatur e, transparency, light penetration, depth, water velocity, substrate type, vegetati on cover or debris) - can be valu able in terms of sampling the3-51 water at the most frequently-inhabited locations. If information is available about concentration gradients in the waterbody, samples should be collect ed along this gradient. If such data are not available, the investigator can solicit expert opinion as to wh ere concentration gradients should be expected. References such as Chapra and Reckhow (1983) and Thomann and Meuller (1987), that deal with expected chemical concentra tion gradients for different waterbody types and appropriate sampling designs, can be espe cially valuable in this regard.

3.4.4 Sampling Times

Temporal as well as spatial variability can be high for water concentrations of certain chemicals. As was shown in Section 3.3.3, con centrations of many hydrophobic chemicals in water are expected to be much more variable th an concentrations in aquatic organisms. Thus, individual water samples taken at one point in tim e will likely not be adequate to reflect average exposure to the target species. Water samples shoul d be collected and analyzed so that chemical concentrations can be averaged over the approxim ate time it takes for the target species to reach steady state, which varies depending on the hydr ophobicity and rate of metabolism of the chemical within the target organism. For example, chemicals with high K ow values are expected to reach steady-state in top trophi c level organisms much more sl owly than chemicals with low
K ow values; thus, they require gr eater temporal averaging of wa ter column concentrations for estimating BAFs. For large fish, a year or longer may be required for concentrations of highly hydrophobic organic chemicals to reac h steady state. Other factors to consider when determining the frequency of sampling include target specie s migration and other as pects of life history.

The investigator should also consider how concentrations of the chemical of concern respond to the various sources, tr ansport and fate pathways at the site. Generally, temporal
variations in chemical concentration are rela ted to factors such as seasonality, flow rate, stratification and external loading and fluxes. Wa ter quality studies and models can be extremely valuable guides for predicting how bioavailable ch emical concentrations will vary with time. The investigator can check for their av ailability at the site of intere st or at comparable sites via federal or state programmatic activities (e.g., TMDL, Superfund) . Water quality models (site-3-52 specific or generic) can also be used to test alternative sampling designs, as was demonstrated in
Section 3.3.3.

3.4.5 Sample Type

Water samples collected to support the m easurement of a site-specific BAF may be analyzed for target chemical concentrations individually or as composites. The objective of measuring the average chemical exposure to the target species can be accomplished by either type of sampling. Compositing may be a better water sampling strategy for highly-hydrophobic chemicals ( log K ow >4) than for less hydrophobic chemicals, as discussed in Section 3.3.3.

Compositing can be used to reduce the number of analyses required to determine the average chemical concentration in water. Depending on the specific method, reducing the number of analyses may substantially lower th e cost. Composites may be formed from water samples either temporally (i.e., samples collected at different times at one location) or spatially (samples collected at one time at different lo cations). For temporal composite samples, the analytical method-specific holding time should no t be exceeded for any portion of the sample. The investigator should consider whether there is enough informa tion to form a composite that will result in a measured concentration comparab le to the average exposur e to the organism of interest. If not, the composite sample may produ ce a biased mean concen tration. In this case, individual sample analysis may result in a more accurate average water concentration and BAF.

3.4.6 Replicate Samples

Samples should be replicated in order to evaluate precision of sample collection and analytical methods, regardless of whether individual samples or composites are collected. EPA recommends that replicate water samples be co llected for a minimum of 10 percent of water samples, although replication rates of 30% or higher are often used for water sampling in field studies (TAMS, 1998; USEPA, 19 89b; USEPA, 2004) . Compositing replicate samples collected at one location and time is an effective way to improve precision.3-53 3.4.7 Sample Collection Methods

Many different methods have been develope d for water sampling, so it is difficult to provide generalized guidance beyond using methods that are appropriate for the chemical of concern. Sampling techniques and equipment tend to be specific to classes of chemicals, often due to unique concerns. A good example is the “clean-technique” protocol developed for lowlevel trace metal sampling, for which avoiding samp le contamination is an overriding objective. For hydrophobic organic chemicals, minimizing the contact of water samples with surfaces and sorptive materials prior to extraction is a prior ity. For volatile chemicals, eliminating contact with the atmosphere and containe r head spaces are priorities.

Similarly, water sampling techniques and the associated equipment are often somewhat specialized for different waterbody types. Samp ling water from a deep lake calls for methods different from those appropriate for sampling a shallow fast-flowing stream.

There are numerous sources of information that can be used by the i nvestigator to select sample collection methods appropriate for the site and the chemical of concern. Standard Methods for the Examinati on of Water and Wastewater (APHA, 2004) is a reference that covers many aspects of water monitori ng, including sample collection. The American Society for Testing and Materials (ASTM) also publishes references that address methods for collecting water samples.

Another valuable resource for information a bout water sample collection methods is the National Field Manual for the Collec tion of Water-Quality Data (USGS, 1998). This manual is a collection of handbooks devoted to subjects including:

• Preparations for Water Sampling

• Selection of Equipment for Water Sampling

• Cleaning of Equipment for Water Sampling

• Collection of Water Samples

• Processing of Water Samples3-54 The National Field Manual addresses sampling of novel chemical contaminants such as wastewater, pharmaceutical, and antibiotic comp ounds; arsenic species; and low-level mercury.

Another useful source of information fo r water sampling methods are EPA documents such as Standard Operating Procedures (SOP s) developed for specific field studies. These
documents often reflect the state of the art in terms of sampling and analytical methods, which may be more current than the reference materials listed previously. For example, the Lake Michigan Mass Balance Methods Compendium (USEPA, 1997) describes the sampling and analytical methods used in that study. The Methods Compendium describes SOPs for water sample collection specifically for atrazine and atrazine metabolites, nonionic hydrophobic organic chemicals (PCB congeners and trans-nonachlor), mercury, and particulate and dissolved organic carbon.3-55 REFERENCES

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## APPENDICES3-64 This page left blank intentionally3-65 

## Appendix 3A

Determining the Number of Samples to Collect for a BAF Measurement: Bootstrap Analysis

The accuracy of BAF measurements calculated from different numbers of chemical concentration measurements can be estimated by Bootstrap resampling. This common numerical simulation method can be used by the investigator to determine the required numbers of biota and water samples. This method will be dem onstrated by examples using Green Bay Mass Balance Study PCB congener data.

Bootstrap sampling method
Bootstrap estimation is a computer intens ive resampling method for estimating sampling
distributions and confidence limits of statistics for which the theoretical sampling distribution is not known (Efron, 1982). To estimate 90% confidence limits for the mean of n samples, one would repeatedly (i.e., thous ands of times) select n values with replacement from the original data and calculate the mean of each bootstrap sample. The 95th and 5th percentiles of the distribution of bootstrap means are estimates of the 90% confidence limits. The bootstrap algorithm can be implemented on a personal comput er in a number of ways (Simon, 1997). The advantage of the bootstrap is that the investig ator is not required to make any assumptions regarding the distributio n of data (Simon, 1969), thereby avoidi ng potential errors in statistical analysis if these assumptions are violated.

In this case, the investigator is interested in estimating the accuracy 5 of BAFs in terms of precision 6 and bias for alternative numbers of biota ( n b ) and water ( n w ) samples. The bootstrap is applied to resample n b biota and n w water concentrations from a high quality data set. For each resample, mean biota and water c oncentrations are calculated, and then the BAF is calculated as
the ratio of the mean concentra tions. This procedure is repeat ed many times, until a stable
distribution of BAF values is generated. The bootstrap distri bution of BAFs provide the

5 Accuracy is the degree of conformity of a measured quantity to its actual (true) value. 6 Accuracy is the degree of veracity while precision is the degree of reproducibility.3-66 investigator with estimates of the variance as well as the bias 7 . For example, the 90% confidence limits are estimated by the 95th and 5th percenti les of the bootstrap distribution of BAFs. By repeating this procedure using different numbers of biota ( n b ) and water ( n w ) samples and comparing the BAF dispersion results, the inves tigator can determine a sampling design that meets their requirements for BAF accuracy.

Bootstrap estimation does require the investigator to ha ve data, which can be a problem. As mentioned previously, EPA is aware of relatively few field data sets that are adequate for evaluating BAF sampling designs. For the presen t exercise, PCB congene r concentration data
were combined from zones 3a and 3b of the Gr een Bay Mass Balance dataset (see Burkhard et al. 2003 and Endicott, 2001 for descriptions of th is data). This combination produced a dataset containing measurements of P CB congener concentrations for 93 dissolved water samples, 66 forage fish (alewife and rainbow smelt) samples and 42 predator fish (brown trout and walleye) samples. Concentrations of 4 congeners ( BZ 8 18, 52, 149 and 180), which span the range of hydrophobicity for bioaccumulative PCBs, were selected for BAF calculations. Hawker and Connell (1988) reported the following K ow values for these congeners:

PCB congener log K ow 18 5.24 52 5.84 149 6.67 180 7.36

None of these congeners are significantly metabolized by aquatic organisms. The PCB concentrations were lipid-normalized and adjusted for bioavailability (i.e., filtered water concentrations were converted to freely dissolv ed concentrations using equation 3-6) prior to analysis. The concentration data are presented in Appendix 3B.

7 Bias is the discrepancy of the estimate from the “true” BA F value. In this case, the latter is based on the averages of biota and water concentration data using all available samples. In reality, the sample average is still just an estimate of the true value.

8 PCB congeners can be identified according to a nu mbering scheme published by Balschmiter and Zell (1980), commonly referred to as “BZ” numbers.3-67 Forage and predator fish BAFs were cal culated for each of the four PCB congeners. 10,000 bootstrap resamples were used for each calc ulation. In addition, e ach calculation was repeated 100 times, to ensure the results were independent of small random fluctuations which
were observed between calculations. For each BA F calculation, the following results were saved for analysis:

• The mean BAF and standard deviation;

• the 90 % confidence limits of the BAF distribution;

• the ratio of the 90% BAF confidence limits (upper confidence limit/lower confidence limit) or confidence limit ratio (CLR), which is also proportional to the BAF variance,

• the mean bias, defined below:


## ( )

1 

### mean bias = BAF BAF / b n

observed n b

n 

### n

= 

###  

−    



# ∑

where:

BAF observed = the BAF calculated using all of the observed biota and water concentrations,

BAF n = the BAF calculated using the biota and water concentrations sampled in bootstrap resample n, and

n b = the number of bootstrap resamples used in each calculation (10,000)

and

• the root mean square error (RMSE), defined as:



## ( ) 2

1 

### RMSE = BAF BAF / b n

observed n b

n 

### n

= 

###  

−    



# ∑

The bootstrap procedure was repeated for sample si zes ranging from 2 to 60 fish, and from 2 to 90 water samples.3-68 Taylor series approximation
If the investigator makes the assump tion that the sampling distributions are approximately normal, then first order Taylor series approximation can also be applied to estimate the variance of the BAF. This provides the investigator with a way to confirm (test) the results of Bootstrap resampling. The Taylor seri es approximations are developed below for both
the total BAF and the baseline BAF. The total BA F is calculated from two measured variables (see equation 3-1): the total concentration of chemi cal in the appropriate wet tissue of the aquatic organism; and the total concentrat ion of the chemical in the ambient water. The variance of the
calculated BAF includes variances each of these variables. The Taylor series approximation for
the standard deviation of the total BAF is (Mood et al. 1974): 

# ( ) ( ) 

# ( ) 1/2 2 2 2
C C CC BAF 1 BAF 2 BAF C t t w tw T t t T tw T

w s s s rs s   = + ⋅ −⋅ ⋅ ⋅ ⋅    

where s C t and s C w are the standard deviations for C t and C w , respectively; and r tw is the
correlation coefficient 9 between C t and C w . The baseline BAF is calcula ted from four measured variables (see equation 3-2): the concentration of the chemical in the organism on a wet weight basis; the lipid content of the wet tissue; the to tal concentration of the chemical in the ambient water; and the fraction of the water concentratio n that is freely dissolve d. The variance of the
calculated baseline BAF includes variances each of the four measured variables. For the baseline BAF, the Taylor series approximati on for the standard deviation is: 

# ( ) ( ) 

# ( ) 1/2 2 2 2
C C C C BAF 1 BAF 2 BAF C fd fd fd l l w w l fd fd l ld l fd

w s s s rs s   = + ⋅ −⋅ ⋅ ⋅ ⋅    
where: 

# () () 1/2 2 2 2 CC C 1 C2 C f l t l tl l f lt f l l s s s rs s   = + ⋅ −⋅ ⋅ ⋅ ⋅    

and

9 We assumed that the PCB concentrations in fish and water were uncorrelated within each of the spatial zones sampled in Green Bay.3-69 

# ( ) ( ) ( ) 1/ 2 2 2 2
C C C 1 C2 C f fd w w w fd fd w fd wd fd w d s s s rss   = + ⋅ −⋅ ⋅ ⋅ ⋅    

Where s , s C l , and are the standard deviations for the BAF, C l , and 

### C
w fd , respectively;

and r l d is the correlation coefficient 4 between C l and 

### C
w fd

, r lt is the correlation coefficient

between C t and C l , and r wd

is the correlation coefficient between C w and f d .

For sufficiently large sample sizes, the approximate (1- " /2)·100 percent confidence limits for the BAF (total or baseline) are given by:

/ 2 

### BAF BAF 

### z s

α 

### ± ⋅

where " is a probability of exceedance and z " /2 is the value of the st andard normal distribution. Generally, the degree of confidence will only be accu rate if the sample size is greater than 30. Taylor series approximation was used to estima te confidence limits for the BAFs in Green Bay. These estimates were then compared with the confidence limits determ ined using the bootstrap, in order to check and confirm the accuracy of the latter.

Bootstrap BAF results
The distribution of 10,000 chemical concentratio ns in forage fish and water generated by bootstrap resampling of the Green Bay congener 18 data, as well as the BAFs calculated from
the ratio of these concentrations , are displayed as histograms in Figure 3A-1. This particular example was based upon resampling 20 fish and 20 water concentrations. The concentration distributions are symmetrical and approximately normal, as is the distribution of BAFs. If bootstrap resampling is repeated using fewer fish and water concentrations, a number of changes occur in the output distributions of both the chemical concentra tions and the BAFs. Figure 3A-2 displays histograms for 2 fish and 20 water con centrations resampled from the same data. The output distribution of fish concentrations is more dispersed (wider) and as ymmetrical (the right
tail of the distribution is ex tended) by comparison to Figur e 3A-1. This difference is a consequence of resampling fewer (2 as opposed to 20) fish concentrations. As a result, the BAF distribution in Figure 3A-2 is al so more dispersed and asymmetrical. A more extreme example of3-70 limiting the sample sizes is displayed in Figure 3A-3, which shows histograms for 2 fish and 2 water concentrations resampled from the same da ta. The output distribu tions of both fish and
water concentrations are now more dispersed and asymmetrical in comparison to Figure 3A-1, although the effect of reducing the number of concentrations resampled from the data is more pronounced for water than for fish. The BAF distri bution in Figure 3A-3 is also more dispersed and asymmetrical than the distribu tions from the previous examples (Figures 3A-1 and 3A-2). In comparison to Figure 3A-2, the BAF distribution in Figure 3A-3 is notably more asymmetrical.
This was an effect on BAF distri butions that was observed in all of the bootstrap trials that involved few (less than 6) resa mples of water concentrations.

F igure 3A-1. Bootstrap distributions of forage fish and water concentrations, and BAFs based on resampling 20 fish and 20 wate r concentrations from Green Bay Zone 3 PCB 18 data3-71 The 90% confidence limit ratio (CLR) was used as a measure of the precision for the
distribution of BAFs in each bootstrap resampling calculation. The CLRs for BAFs determined by resampling varying numbers of fish and water c oncentrations are tabulated in Table 3A-1 for PCB congener 149 forage fish. The precision of BAF estimates was sensitive to the sample sizes
of chemical concentrations in both fish and wate r, especially for sample sizes smaller than 6. These results also indicate that resampling diffe rent combinations of the number of fish and water concentrations can yield comparable BA F precision. For example, the same CLR for PCB Figure 3A-2. Bootstrap distributions of forage fish and water concentrations, and BAFs based on resampling 2 fish and 20 water concentrations from Green Bay zone 3 PCB 18 data.

F igure 3A-3. Bootstrap distributions of forage fi sh and water concentrations, and BAFs based on resampling 2 fish and 2 water concentrations from Green Bay zone 3 PCB 18 data.3-72 congener 149 forage fish BAF (Table 3A-1) is obtained by resampling 10 fish and 6 water
concentrations or 4 fish and 10 water concentrations. The BAF confidence limit ratios are also plotted as functions of resample size in Figure 3A-4. Similar results were obtained for the other congeners (not shown). Predator fish BAF results for each of the congeners were very similar to
the results for the co rresponding forage fish.

Table 3A-1. Bootstrap Result s for PCB Congener 149 Forage Fish BAF: 90% Confidence Limit Ratio (Upper Confidence Limit/Lower Co nfidence Limit) as a Function of the Number of Fish and Water Samples. Smal ler Ratios Indicate Less Uncertainty.

Number of Water Samples Number of Fish Samples
2 4 6 8 10 20 30 60 90 2 6.65 4.95 4.37 4.04 3.82 3.31 3.13 2.94 2.87 4 5.75 4.10 3.55 3.25 3.06 2.59 2.41 2.23 2.16 6 5.46 3.81 3.29 3.01 2.81 2.35 2.18 1.99 1.92 8 5.32 3.65 3.16 2.87 2.69 2.24 2.06 1.86 1.79 10 5.23 3.55 3.07 2.80 2.61 2.17 1.98 1.78 1.71 20 5.02 3.34 2.91 2.64 2.46 2.02 1.84 1.62 1.55 30 4.95 3.25 2.86 2.59 2.41 1.98 1.79 1.57 1.49 60 4.89 3.17 2.82 2.54 2.35 1.93 1.73 1.51 1.423-73 Figure 3A-4. Bootstrap resampling results for PCB congener 149 in Green Bay Zone 3 forage fish: 90 % confidence li mit ratios for BAF as a f unction of the numbers of fish and water samples.

Precision is an important component of BAF accuracy, but the investigator should also be
concerned with the bias of the BAF measur ement. The bootstrap resampling results also
demonstrate how bias of the BAF measurement is influenced by resampli ng different numbers of fish and water concentrations. While the precis ion of BAFs depended on the sample sizes of both fish and water, the results reveal that the m ean bias is only sensitiv e to the number of water concentrations resampled. This result was somewh at surprising, and appears to be related to the strong influence of variability in the denominator of a ratio. Figure 3A-5 plots the mean percent bias for each of the PCB congener forage fish BA Fs as functions of resample size. For each congener, the curves for different numbers of fish concentrati on resamples fall on top of each other, demonstrating that the mean bias of BA Fs is only sensitive to the number of water concentrations resampled. BAF biases for each c ongener in Figure 3A-5 show the same pattern, depending solely on the sample size of chemical concentrations in water. The bias in BAFs 0 1 2 3 4 5 6 7

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 number of water samplesBAF 90% confidence limit ratio

2 4 6 8 10 20 30 60 number of fish samples:3-74 appears to be correlated to the hydrophobicity of the chemical, as the bias increases slightly for the more hydrophobic PCB congeners.

Figure 3A-5. Bootstrap resampling results for PCB c ongeners 18, 52, 149 and 180 in Green Bay Zone 3 forage fish: Comparison of mean percent bias of BAF as a function of the numbers of water samples.

The root mean square error (RMSE) of the BAFs was also calculated for each bootstrap resample. RMSE is an aggregate measure of accuracy, incorporating both precision and bias. Figure 3A-6 plots the forage fish BAF RMSEs fo r congener 149 as functi ons of resample size. The curves plotted in this figure are almost iden tical in shape to the ratios of BAF confidence limits plotted in Figure 3A-4 . The similarity between the plots of RMSEs and ratios of BAF confidence limits demonstrates that precision is the major component of BAF accuracy in the bootstrap resamples. Therefore, it is reasonable for the investigat or to select the appropriate number of biota and water samples based upon th e ratios of BAF confidence limits, although the negative mean percent bias associated with sa mpling fewer than 6 water concentrations should
also be considered. -40

-35

-30

-25

-20

-15

-10

-5

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 number of water samplesBAF percent bias

PCB 18

PCB 52

PCB 149

PCB 180 congener:3-75 Figure 3A-6. Bootstrap resampling results for PCB conge ner 149 in Green Bay Zone 3 forage fish: Root mean square error (RMSE) for BAF as a function of the numbers of fish and water samples.

Taylor estimates confirm Bootstrap results
Table 3A-2 presents the concentration statisti cs (average and standard deviation) for each PCB congener, the statistics for the predator a nd forage fish BAFs, and the confidence limits and confidence limit ratios calculated using Taylor series approximation. In all cases, the Taylor series confidence limits are practically identical to the confidence limits obtained by bootstrap resampling a large number of fish ( n b ) and water ( n w ) concentrations. This agreement confirms that the Bootstrap algor ithm was performing properly. 0 10 20 30 40 50 60 70

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 number of water samplesRMSE % of mean BAF

2 4 6 8 10 20 30 60 number of fish samples:3-76 How does compositing affect the relations hip between number of samples and the accuracy of the BAF?

Compositing of fish and/or water samples for analysis is recommended as a highly effective option in Sections 3.3.5 and 3.4.5. A single measured concentration from a well-formed composite should be equivalent to the average or mean of concentrations measured in samples used to form the composite. A number of the Bootstrap resampling tests were modified to simulate sample compositing, and the results (e. g., fish and water concentration means, BAFs and 90% CLRs) were compared to the original (unmodified) tests. In all cases, we found no difference between the tests simulating sample compositing versus th ose that did not. The simulation results indicate that the accuracy of a BAF depends on the number of biota and water samples that are collected, and not on whether the samples are analyzed individually or as composites.

How can the Bootstrap help determine the num ber of samples to collect and analyze?
If site-specific data for concentrations of the target chemical in biota and water are available, then Bootstrap resa mpling is probably the best way to determine the number of samples to collect and analyze in order to de termine a BAF of the desired accuracy. Bootstrap resampling can also be useful if a site-specific BAF is measured and the uncertainty is found to be unacceptably large. In this case, the bootstrap can be used to estimate the additional sampling effort, in terms of numbers of biota and/or water samples, requi red to improve the accuracy of
the BAF derived from these measurements. Bootstra p resampling is a much less useful tool when site-specific data for concentrations of the target chemical in biota and water are not available. If the investigator can find chemical concentration data for an ecos ystem comparable to their site, then bootstrap resampling could be conducted w ith that data, and sample sizes selectedaccordingly. However, there are other approaches that should also be considered when sitespecific data are limited.3-77Table 3A-2. Taylor Series Approximationof Confidence Limits for Baseline BAFis in Green Bay Zone 3CONCENTRATIONPREDATOR FISHFORAGE FISHCHEMICALANDSTATISTICPredatorFish Cl(ng/g-l)ForageFish Cl(ng/g-l)WaterCfdw(ng/L)BaselineBAF490%LCLa90%UCLb90%CLRcBaselineBAF390%LCL90%UCL90%CLRPCB 18:Average183 84.0 0.0601 3,051 1,970 4,132 2.10 1,399 1,1511,6471.43St. deviation243 57.5 0.0388St. error657 151PCB 52:Average1401 649 0.0590 23,760 18,928 28,592 1.51 11,0099,317 12,700 1.36St. deviation931 331 0.0393St. error2,938 1,028PCB 149:Average537 240 0.00659 81,502 63,924 99,081 1.55 36,349 30,053 42,645 1.42St. deviation329 103 0.00579St. error10,686 3,827PCB 180:Average544 211 0.00102 534,098 412,216 655,979 1.59 207,284 168,325 246,242 1.46St. deviation350 104 0.000949St. error74,092 23,683aLCL: Lower confidence limit;bUCL: Upper confidence limit;cCLR: Confidence limit ratio.3-78 This page left blank intentionally3-79 

## Appendix 3B

PCB Congener Concentratio ns Measured by Green Bay Mass Balance Study

1. Green Bay Mass Balance PCB Congener C oncentrations: Dissolved Water Column

Date Zone Station Layer a PCB 149 (ng/L) PCB 18 (ng/L) PCB 180 (ng/L) PCB 52 (ng/L) 5/1/1989 GB0Z3A GB0019 0.0038 0.0165 0.00045 0.0233 5/2/1989 GB0Z3B GB0018 0.0097 0.0487 0.00070 0.0416 5/3/1989 GB0Z3A GB0013 0.0047 0.0190 0.00098 0.0346 5/3/1989 GB0Z3A GB0013 0.0131 0.0578 0.00599 0.0935 5/3/1989 GB0Z3A GB0014 0.0067 0.0551 0.00088 0.0617 5/3/1989 GB0Z3B GB0015 0.0037 0.0897 0.00080 0.0817 5/3/1989 GB0Z3A GB0016 0.0108 0.0294 0.00332 0.0418 5/3/1989 GB0Z3B GB0017 0.0074 0.1026 0.00089 0.0979 5/4/1989 GB0Z3A GB0008 0.0061 0.0797 0.00074 0.0716 5/4/1989 GB0Z3A GB0009 0.0065 0.0472 0.00065 0.0426 5/4/1989 GB0Z3A GB0010 0.0046 0.0764 0.00051 0.0719 5/4/1989 GB0Z3B GB0011 0.0052 0.1074 0.00049 0.0941 6/11/1989 GB0Z3A GB0016 0.0075 0.0217 0.00094 0.0191 6/11/1989 GB0Z3B GB0017 0.0047 0.0828 0.00059 0.0708 6/11/1989 GB0Z3B GB0018 0.0046 0.0277 0.00047 0.0244 6/11/1989 GB0Z3A GB0019 0.0047 0.0204 0.00041 0.0140 6/11/1989 GB0Z3B GB0020 0.0035 0.0233 0.00052 0.0185 6/12/1989 GB0Z3A GB0008 0.0057 0.0709 0.00068 0.0674 6/12/1989 GB0Z3A GB0009 0.0044 0.0459 0.00059 0.0486 6/12/1989 GB0Z3A GB0010 0.0058 0.0795 0.00088 0.0767 6/12/1989 GB0Z3B GB0011 0.0078 0.1040 0.00102 0.0974 6/12/1989 GB0Z3A GB0013 0.0062 0.0374 0.00079 0.0415 6/12/1989 GB0Z3A GB0013 0.0061 0.0376 0.00080 0.0399 6/12/1989 GB0Z3A GB0014 0.0041 0.0369 0.00048 0.0374 6/12/1989 GB0Z3B GB0015 0.0044 0.1018 0.00066 0.0901 7/30/1989 GB0Z3A GB0016 0.0046 0.0248 0.00074 0.0251 7/30/1989 GB0Z3B GB0017 0.0038 0.0470 0.00069 0.0499 7/30/1989 GB0Z3B GB0018 EPI 0.0037 0.0239 0.00025 0.0197 7/30/1989 GB0Z3B GB0018 HYP 0.0050 0.0282 0.00104 0.0384 7/30/1989 GB0Z3A GB0019 0.0050 0.0244 0.00060 0.0216 7/30/1989 GB0Z3B GB0020 0.0037 0.0290 0.00055 0.0309 7/30/1989 GB0Z3B GB0020 0.0033 0.0240 0.00050 0.0291 7/31/1989 GB0Z3A GB0008 0.0054 0.1248 0.00081 0.1055 7/31/1989 GB0Z3A GB0008 0.0072 0.1811 0.00089 0.1817 7/31/1989 GB0Z3A GB0009 0.0048 0.0536 0.00048 0.0614 7/31/1989 GB0Z3A GB0010 0.0064 0.1477 0.00072 0.1468 7/31/1989 GB0Z3A GB0010 0.0061 0.1442 0.00081 0.1455 7/31/1989 GB0Z3B GB0011 0.0067 0.1395 0.00090 0.1505 7/31/1989 GB0Z3A GB0013 0.0040 0.0360 0.00056 0.0597 7/31/1989 GB0Z3A GB0014 0.0035 0.0478 0.00063 0.0528 7/31/1989 GB0Z3B GB0015 0.0056 0.0333 0.00072 0.0352 9/15/1989 GB0Z3B GB0018 EPI 0.0030 0.0188 0.00006 0.0145 9/15/1989 GB0Z3B GB0018 HYP 0.0071 0.0337 0.00107 0.0263 9/15/1989 GB0Z3A GB0019 EPI 0.0023 0.0204 0.00033 0.0135 9/15/1989 GB0Z3A GB0019 HYP 0.0039 0.0243 0.00052 0.0278 9/15/1989 GB0Z3B GB0020 0.0035 0.0211 0.00071 0.0158 9/16/1989 GB0Z3A GB0009 0.0087 0.1206 0.00094 0.0840 9/16/1989 GB0Z3A GB0013 0.0053 0.0573 0.00074 0.03923-80 Date Zone Station Layer a PCB 149 (ng/L) PCB 18 (ng/L) PCB 180 (ng/L) PCB 52 (ng/L)

9/16/1989 GB0Z3A GB0013 0.0058 0.0627 0.00117 0.0414 9/16/1989 GB0Z3A GB0014 0.0055 0.0507 0.00078 0.0387 9/16/1989 GB0Z3B GB0015 EPI 0.0039 0.0336 0.00091 0.0260 9/16/1989 GB0Z3B GB0015 HYP 0.0044 0.0432 0.00108 0.0366 9/16/1989 GB0Z3A GB0016 EPI 0.0040 0.0293 0.00066 0.0215 9/16/1989 GB0Z3A GB0016 HYP 0.0057 0.0412 0.00108 0.0313 9/16/1989 GB0Z3B GB0017 EPI 0.0035 0.0267 0.00063 0.0220 9/16/1989 GB0Z3B GB0017 HYP 0.0038 0.0263 0.00072 0.0219 9/17/1989 GB0Z3A GB0008 0.0064 0.1256 0.00091 0.1105 9/17/1989 GB0Z3A GB0010 0.0053 0.0910 0.00079 0.0704 9/17/1989 GB0Z3B GB0011 0.0061 0.1034 0.00084 0.0932 10/22/1989 GB0Z3A GB0016 0.0072 0.0386 0.00132 0.0360 10/22/1989 GB0Z3B GB0018 0.0041 0.0503 0.00112 0.0485 10/22/1989 GB0Z3A GB0019 0.0061 0.0402 0.00163 0.0380 10/22/1989 GB0Z3B GB0020 0.0057 0.0297 0.00133 0.0238 10/23/1989 GB0Z3A GB0009 0.0029 0.0321 0.00092 0.0436 10/23/1989 GB0Z3A GB0010 0.0046 0.1067 0.00111 0.1312 10/23/1989 GB0Z3B GB0011 0.0047 0.1153 0.00125 0.1246 10/23/1989 GB0Z3A GB0013 0.0044 0.0452 0.00143 0.0518 10/23/1989 GB0Z3A GB0013 0.0095 0.0506 0.00123 0.0548 10/23/1989 GB0Z3A GB0014 0.0065 0.1133 0.00126 0.1132 10/23/1989 GB0Z3B GB0015 0.0043 0.0927 0.00099 0.0961 10/23/1989 GB0Z3B GB0017 0.0038 0.0429 0.00100 0.0459 10/24/1989 GB0Z3A GB0008 0.0052 0.1508 0.00085 0.1543

2/8/1990 GB0Z3B GB0015 0.0229 0.0673 0.00257 0.0948 2/9/1990 GB0Z3A GB0013 0.0266 0.0512 0.00395 0.0725 2/9/1990 GB0Z3A GB0014 0.0274 0.0713 0.00340 0.0994 2/10/1990 GB0Z3A GB0009 0.0267 0.0785 0.00379 0.0891 2/10/1990 GB0Z3A GB0010 0.0256 0.1139 0.00378 0.1360 2/10/1990 GB0Z3B GB0011 0.0307 0.1613 0.00322 0.1400 2/11/1990 GB0Z3A GB0008 0.0179 0.1048 0.00233 0.1148 4/28/1990 GB0Z3A GB0016 0.0034 0.0321 0.00039 0.0280 4/28/1990 GB0Z3B GB0017 0.0044 0.0590 0.00044 0.0493 4/28/1990 GB0Z3B GB0018 0.0027 0.0233 0.00039 0.0216 4/28/1990 GB0Z3A GB0019 0.0045 0.0205 0.00089 0.0175 4/28/1990 GB0Z3B GB0020 0.0051 0.0247 0.00006 0.0201 4/29/1990 GB0Z3A GB0008 0.0033 0.0733 0.00077 0.0618 4/29/1990 GB0Z3A GB0009 0.0033 0.0415 0.00037 0.0330 4/29/1990 GB0Z3A GB0010 0.0037 0.0331 0.00040 0.0293 4/29/1990 GB0Z3A GB0010 0.0025 0.0339 0.00064 0.0241 4/29/1990 GB0Z3B GB0011 0.0025 0.0479 0.00023 0.0412 4/29/1990 GB0Z3A GB0013 0.0029 0.0389 0.00036 0.0326 4/29/1990 GB0Z3A GB0014 0.0031 0.0369 0.00028 0.0340 4/29/1990 GB0Z3A GB0014 0.0029 0.0355 0.00028 0.0344 4/29/1990 GB0Z3B GB0015 0.0038 0.0699 0.00063 0.0581

a Layer refers to epilimnion (EPI) and hypolimnion ( HYP) sampled separately when the water column was thermally stratified; otherwise, a single mid-depth sample was collected.3-81 2. Green Bay Mass Balance PCB Congener Concen trations: Lipid-Normalized Forage Fish

Common Name Life Stage b Zone Date PCB 149 (ng/g-l) PCB 18 (ng/g-l) PCB 180 (ng/g-l) PCB 52 (ng/g-l)
ALEWIFE A GB0Z3A 6/10/1989 288 13 240 367 ALEWIFE A GB0Z3A 6/10/1989 454 91 522 295 ALEWIFE A GB0Z3A 6/10/1989 513 37 587 611 ALEWIFE A GB0Z3A 10/3/1989 197 132 184 724 ALEWIFE A GB0Z3A 6/10/1989 352 110 288 1072 ALEWIFE A GB0Z3A 8/8/1989 402 44 366 512 ALEWIFE A GB0Z3A 10/3/1989 197 68 197 518 ALEWIFE A GB0Z3A 10/3/1989 258 53 258 549 ALEWIFE A GB0Z3A 8/8/1989 292 36 292 551 ALEWIFE A GB0Z3A 6/10/1989 326 33 274 223 ALEWIFE A GB0Z3B 6/5/1989 326 42 357 403 ALEWIFE A GB0Z3B 10/4/1989 206 266 188 786 ALEWIFE A GB0Z3B 10/4/1989 229 165 182 882 ALEWIFE A GB0Z3B 10/4/1989 240 146 218 874 ALEWIFE A GB0Z3B 8/22/1989 465 323 374 1200 ALEWIFE A GB0Z3B 9/13/1989 157 99 157 619 ALEWIFE A GB0Z3B 9/13/1989 188 139 172 694 ALEWIFE Y GB0Z3A 6/10/1989 311 54 199 466 ALEWIFE Y GB0Z3A 6/16/1989 275 25 213 296 ALEWIFE Y GB0Z3A 10/3/1989 88 29 64 201 ALEWIFE Y GB0Z3A 6/20/1989 195 47 129 570 ALEWIFE Y GB0Z3A 6/16/1989 286 49 245 495 ALEWIFE Y GB0Z3A 9/12/1989 58 36 43 228 ALEWIFE Y GB0Z3A 9/12/1989 137 232 87 847 ALEWIFE Y GB0Z3A 9/12/1989 40 22 32 134 RAINBOW SMELT A GB0Z3A 10/3/1989 232 79 232 732 RAINBOW SMELT A GB0Z3A 6/16/1989 152 23 159 184 RAINBOW SMELT A GB0Z3A 5/17/1989 479 115 479 1310 RAINBOW SMELT A GB0Z3A 5/1/1989 235 54 220 568 RAINBOW SMELT A GB0Z3A 6/16/1989 171 63 133 255 RAINBOW SMELT A GB0Z3A 8/9/1989 192 73 167 627 RAINBOW SMELT A GB0Z3A 8/9/1989 223 117 223 881 RAINBOW SMELT A GB0Z3A 8/9/1989 229 69 229 758 RAINBOW SMELT A GB0Z3A 10/3/1989 298 141 215 1242 RAINBOW SMELT A GB0Z3A 10/3/1989 204 80 172 768 RAINBOW SMELT A GB0Z3A 5/17/1989 376 97 279 940 RAINBOW SMELT A GB0Z3B 5/1/1989 304 103 234 1125 RAINBOW SMELT A GB0Z3B 5/1/1989 325 172 240 1266 RAINBOW SMELT A GB0Z3B 10/4/1989 184 64 184 485 RAINBOW SMELT A GB0Z3B 5/1/1989 440 101 377 1289 RAINBOW SMELT A GB0Z3B 5/1/1989 387 113 327 1488 RAINBOW SMELT A GB0Z3B 5/1/1989 427 140 305 1463 RAINBOW SMELT A GB0Z3B 8/9/1989 235 112 199 866 RAINBOW SMELT A GB0Z3B 10/4/1989 234 79 249 685 RAINBOW SMELT A GB0Z3B 8/9/1989 231 121 190 883 RAINBOW SMELT A GB0Z3B 10/4/1989 267 103 200 867 RAINBOW SMELT A GB0Z3B 8/9/1989 195 97 154 832 RAINBOW SMELT Y GB0Z3A 6/16/1989 135 30 111 175 RAINBOW SMELT Y GB0Z3A 6/20/1989 100 21 84 197 RAINBOW SMELT Y GB0Z3A 6/16/1989 103 11 79 165 RAINBOW SMELT Y GB0Z3A 6/20/1989 125 24 89 204 RAINBOW SMELT Y GB0Z3A 9/14/1989 187 43 199 474 RAINBOW SMELT Y GB0Z3A 8/9/1989 162 48 157 392 RAINBOW SMELT Y GB0Z3A 10/3/1989 239 59 256 657 RAINBOW SMELT Y GB0Z3A 9/14/1989 198 23 198 455 RAINBOW SMELT Y GB0Z3A 10/3/1989 160 45 135 4513-82 Common Name Life Stage b Zone Date PCB 149 (ng/g-l) PCB 18 (ng/g-l) PCB 180 (ng/g-l) PCB 52 (ng/g-l)
RAINBOW SMELT Y GB0Z3A 10/3/1989 155 83 179 361 RAINBOW SMELT Y GB0Z3B 5/1/1989 161 109 100 697 RAINBOW SMELT Y GB0Z3B 5/1/1989 169 113 96 733 RAINBOW SMELT Y GB0Z3B 5/1/1989 143 77 101 568 RAINBOW SMELT Y GB0Z3B 11/2/1989 156 78 147 459 RAINBOW SMELT Y GB0Z3B 9/5/1989 235 67 216 603 RAINBOW SMELT Y GB0Z3B 8/9/1989 198 66 144 519 RAINBOW SMELT Y GB0Z3B 11/2/1989 193 66 162 761 RAINBOW SMELT Y GB0Z3B 8/9/1989 272 91 211 603 RAINBOW SMELT Y GB0Z3B 11/2/1989 225 87 225 734 b Forage fish were composited as adult (A) and young-of-year (Y). 3. Green Bay Mass Balance PCB Congener Concentr ations: Lipid-Normalized Predator Fish

Common Name Age Zone Date PCB 149 (ng/g-l) PCB 18 (ng/g-l) PCB 180 (ng/g-l) PCB 52 (ng/g-l) BROWN TROUT 2 GB0Z3A 9/26/1989 380 101 397 930 BROWN TROUT 2 GB0Z3A 6/9/1989 251 120 215 902 BROWN TROUT 2 GB0Z3A 6/9/1989 269 72 239 595 BROWN TROUT 2 GB0Z3A 9/11/1989 523 133 413 1253 BROWN TROUT 2 GB0Z3A 7/30/1989 298 169 264 1220 BROWN TROUT 2 GB0Z3A 7/22/1989 531 72 493 830 BROWN TROUT 2 GB0Z3A 9/26/1989 120 391 80 879 BROWN TROUT 2 GB0Z3B 10/10/1989 618 163 618 1410 BROWN TROUT 2 GB0Z3B 6/17/1989 252 35 229 387 BROWN TROUT 2 GB0Z3B 8/22/1989 730 1265 652 3891 BROWN TROUT 2 GB0Z3B 10/10/1989 775 164 760 1639 BROWN TROUT 2 GB0Z3B 9/9/1989 412 79 461 889 BROWN TROUT 2 GB0Z3B 10/10/1989 657 144 657 1540 BROWN TROUT 3 GB0Z3A 9/26/1989 541 123 484 1233 BROWN TROUT 3 GB0Z3A 6/6/1989 531 116 558 1157 BROWN TROUT 3 GB0Z3A 9/11/1989 561 89 507 1095 BROWN TROUT 3 GB0Z3A 7/22/1989 363 85 357 907 BROWN TROUT 3 GB0Z3A 9/26/1989 884 198 976 2375 BROWN TROUT 3 GB0Z3A 9/11/1989 386 95 332 813 BROWN TROUT 3 GB0Z3B 10/10/1989 467 71 484 710 BROWN TROUT 3 GB0Z3B 10/18/1989 480 69 516 720 BROWN TROUT 3 GB0Z3B 10/18/1989 667 91 694 1129 BROWN TROUT 3 GB0Z3B 5/17/1989 369 141 336 1142 BROWN TROUT 3 GB0Z3B 9/12/1989 579 113 632 1129 BROWN TROUT 3 GB0Z3B 9/8/1989 466 76 519 916 WALLEYE 3 GB0Z3B 10/22/1989 115 278 431 2011 WALLEYE 3 GB0Z3B 5/11/1989 444 102 389 1019 WALLEYE 3 GB0Z3B 10/21/1989 603 86 619 1253 WALLEYE 3 GB0Z3B 5/2/1989 587 147 587 1602 WALLEYE 3 GB0Z3B 7/12/1989 544 97 575 1109 WALLEYE 3 GB0Z3B 8/22/1989 545 79 529 1184 WALLEYE 4 GB0Z3A 6/19/1989 563 188 563 1578 WALLEYE 4 GB0Z3A 11/9/1989 141 66 562 1171 WALLEYE 4 GB0Z3A 9/11/1989 333 81 360 752 WALLEYE 4 GB0Z3A 11/9/1989 620 1089 544 3183 WALLEYE 4 GB0Z3A 8/31/1989 439 69 439 986 WALLEYE 4 GB0Z3B 5/2/1989 560 93 412 994 WALLEYE 4 GB0Z3B 10/21/1989 738 114 631 1342 WALLEYE 4 GB0Z3B 7/12/1989 701 116 701 1590 WALLEYE 4 GB0Z3B 7/12/1989 660 156 546 1921 WALLEYE 4 GB0Z3B 10/22/1989 2292 542 2500 5625 WALLEYE 4 GB0Z3B 5/2/1989 568 220 568 18333-83 

## Appendix 3C

Determining the Number of Samples to Collect for a BAF Measurement: Monte Carlo Analysis

To demonstrate the Monte Carlo method, a Latin Hypercube Monte Carlo generator program (Wyss and Jorgensen, 1998) was used to si mulate 300 chemical concentrations in biota and water. Based upon previous statistical an alysis of the Green Bay PCB congener data (Endicott, 2001), we assumed that chemical con centrations were lognormally distributed. Means and variances were calculated from the log-tran sformed concentration data. The Latin Hypercube method guarantees that the simulated probability di stributions of the conc entrations are exact, which is advantageous because it reduces the number of realizations necessary for the results to converge (McKay et al. 1979). The generator pr ogram also applied Iman and Conover’s (1982) algorithm for inducing the specified degree of rank correlation between ch emical concentrations in biota and water, without alte ring their statistical distributions . Statistical properties of all parameter distributions output by the Latin H ypercube generator were confirmed against the specified variance-covariance structure in each test.

The Monte Carlo analysis was based on the sa me data used in the Bootstrap resampling method (Green Bay Mass Balance data for PCB c ongeners 18, 52, 149 and 180 in zone 3) so that
the results of the two methods coul d be directly compared. In pract ice, the investigator would be faced with the problem of estimating appropriate concentration distributions without site-specific data. When such prior informati on about chemical concentrations is lacking, the investigator has
a limited number of choices:

• Conduct a pilot study - a small number of sa mples may provide suitable preliminary estimate of the concentration distribution;

• Use data from a study of a similar site; or

• Use a crude approximation of standard deviation adopted from USEPA (1989a): Given a range of concentrations (bas ed on judgment or expert opinion), an approximate value of standard deviat ion may be computed by dividing the concentration range by 6.3-84 A variety of tests were also run to see how sensitive the results of Monte Carlo analysis were to the following factors:

• Different degrees of correlation between biota and water chemical concentrations,

• Different variances in chem ical concentrations, and

• Inappropriate choice of concentration distributions.

How do the results of Monte Carlo and Bootstrap analyses compare?
In general, the analysis of BAF accuracy as a function of sample sizes using the Monte Carlo method produced results comparable to those obtained via Bootstrap resampling. The
Monte Carlo results for BAF confidence limit ratios were less sensitive to sample sizes than the Bootstrap results, as shown in Figure 3C-1. The M onte Carlo results also tended to underestimate the bias in BAF ratios. The two methods produced quite similar values of RMSE, except for large sample numbers, when the Monte Carlo RMSE estimates were smaller. Overall, the Monte
Carlo BAF statistics are consistent with the Boot strap results. This outco me is expected, given
that the concentration distributi ons used in the Monte Carlo met hod were based on the same data used in the Bootstrap resampli ng. For this case, when the information regarding chemical concentrations are input consis tently, both methods function prope rly and generate comparable
results.

The Monte Carlo and Bootstrap predictions of BAF confidence limit ratios for congener 149 forage fish are compared in Figure 3C-2. As was the case for predator fish, the two methods produce generally comparable results for this conge ner, as well as the others (not shown). The Monte Carlo results were again less sensitive to sample sizes than the Bootstrap results. The concentration data for forage fish were nor mally distributed, although a lognormal distribution
was assumed in the Monte Carlo simulation. A pparently, mis-specifying the concentration
distribution had little or no eff ect on the outcome of the Monte Carlo analysis. This result is consistent with the guidance offered by Bert houex and Brown (1994), that making the lognormal assumption was usually beneficial or (at worst) harmless.3-85 Figure 3C-1. Comparison of Monte Carlo and Bootstrap results: Ratio of 90% confidence limits for BAF as a function of numbers of fish and water samples for PCB congener 149 in Green Bay Zone 3 predator fish. Number of wate r samples are varied across x-axis; number of fish samples plotte d as separate curves. Bootstrap resampling results are plotted as solid lines; Monte Carlo results as dashed lines. 0 1 2 3 4 5 6 7 8

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 number of water samplesBAF 90% confidence limit ratio

2B 4B 8B 30B 2MC 4MC 8MC 30MC number of fish samples/analysis method:3-86 Figure 3C-2. Comparison of Monte Carlo and Bootstrap results: Ratio of 90% confidence limits for BAF as a function of numbers of fish and water samples for PCB congener 149 in Green Bay Zone 3 forage fish. Number of water samples ar e varied across x-axis; number of fish samples plotted as separate curves. Bootstrap resampling results are plotted as solid lines; Monte Carlo results as dashed lines.

What is the effect of correlation betw een fish and water concentrations on BAF estimates?

The Monte Carlo BAF analyses were repe ated, using varying degrees of correlation
between chemical concentrations in biota and water. Correlat ion coefficients ranged from 0.001 (no correlation) to 0.9 (high positiv e correlation). It is reasonable to expect the concentrations of
bioaccumulative chemicals to be correlated in biota and water, although from the standpoint of statistical analysis it is more convenient to assume the conc entrations are uncorrelated and independent.

Correlations between chemical concentra tions in biota and wa ter reduced the BAF confidence limit ratios, percent bias and RMSE fo r all congeners. The benefits increased with the 0 1 2 3 4 5 6 7

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 number of water samplesBAF 90% confidence limit ratio

2B 4B 8B 30B 2MC 4MC 8MC 30MC number of fish samples/analysis method:3-87 magnitude of the correlation, and were more be neficial for smaller sample sizes. This is illustrated in Figure 3C-3, which plots the predat or fish BAF confidence limit ratios for congener
149 as a function of the concentr ation correlation coefficient fo r sample sizes of 2/2, 10/10 and 30/30 ( n b / n w ). These results show that assuming chemical concentrations in biota and water to be uncorrelated (regardless of whether they actually are) is a conservative approach to selecting numbers of samples for the purpose of determining site-specific BAFs.

Figure 3C-3. Monte Carlo results for PCB congener 149 in Green Bay Zone 3 predator fish: Ratio of 90% confidence limits for BA F as a function of correlation between biota and water concentrations. The 3 curves are for simulations using different numbers of fish and water samples. Monte Carlo results for PCB congener 149 in Green Bay zone 3 forage fish: ratio of 90% exceedence limits for BAF as a function of correlation coefficient between biota and water concentrations

0 1 2 3 4 5 6 7 8

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 concentration correlation coefficientBAF ratio (96% / 5%) 2 fish/2 water

10 fish/10 water

30 fish/30 water number of concentrations sampled:BAF 90% confidence limit ratio

Concentration correlation coefficient3-88 How do Monte Carlo BAF results change if different variances for chemical concentrations in fish and water are assumed?

The Monte Carlo method described above wa s repeated using different degrees of variability in chemical concentrations in both fish and water. This was done to provide the investigator with general guida nce for selecting sample sizes that would be applicable for different sites. Specifically, lognormal chemical concentration distributions, having coefficients
of variation 10 (CV) ranging from 0.4 to 0.8, were specified independently for fish and water. For each case, the Monte Carlo method was used to calculate the 90% confidence limit ratio in BAFs. Selected results from this analysis are pr esented in Tables 3C-1 through 5. Each sub-table presents the 90% BAF confidence limit ratios as a f unction of the coefficient of variation (CV) of
chemical concentration measurements in fish and water, for a specific number of fish and water concentrations. For example, Table 3C-1 is a tabu lation of results for 2 fish and 2 water samples. For this case, highly-variable chemical concentra tions result in 90% BAF confidence limit ratios that exceed 10. For moderately variable chemical concentrations, the BAF confidence limit ratios are mostly 5 or less.

Table 3C-1. 90% Confidence Limit Ratios (Upper Confidence Limit/Lower Confidence Limit) for BAF as Functions of the Variabilit y in Chemical Concentrations in Fish and Water: Chemical Concentrations Measu red in 2 Fish and 2 Water Samples

Water concentration coefficient of variation (CV) Fish concentration CV 0.4 0.50 0.60 0.70 0.80 0.4 3.54 4.10 4.90 5.62 6.66 0.5 4.21 4.74 5.56 6.33 7.41 0.6 4.86 5.41 6.40 7.26 8.34 0.7 5.74 6.38 7.37 8.34 9.38 0.8 6.68 7.35 8.51 9.40 10.67

Table 3C-2 is a tabulation of results for 4 fi sh and 4 water samples. For this case, highlyvariable chemical concentrations result in 90% BAF confidence limit ratios that exceed 5. For moderately variable chemical c oncentrations, the BAF confidence li mit ratios are in the range of 3 to 4. For low-variability chemical concentratio ns, the BAF confidence limit ratios are mostly

10 The coefficient of variation is the ratio of the standard deviation to the mean.3-89 smaller than 3. Results for 6 fish and 6 water samp les are presented in Table 3C-3. In this case, highly-variable chemical concentrations result in 90% BAF confidence limit ratios that are in the range of 3 to 4, while BAF confidence limit ratios are less than 3 for low to moderately variable
chemical concentrations. Once the number of samples exceeds about 6, the reductions in BAF confidence limit ratios become incrementally much smaller. Depending upon the requirements for BAF accuracy, exceeding sample sizes of 10 a ppears to be warranted only for sites having very high variability in chemical con centrations in fish and/or water.

Table 3C-2. 90% Confidence Limit Ratios (Upper Confidence Limit/Lower Confidence Limit) for BAF as Functions of the Variabilit y in Chemical Concentrations in Fish and Water: Chemical Concentrations Measu red in 4 Fish and 4 Water Samples

Water concentration coefficient of variation (CV) Fish Concentration CV 0.4 0.50 0.60 0.70 0.80 0.4 2.47 2.77 3.07 3.49 3.89 0.5 2.74 3.06 3.41 3.80 4.23 0.6 3.11 3.39 3.75 4.20 4.65 0.7 3.49 3.79 4.18 4.59 5.02 0.8 3.92 4.33 4.64 5.12 5.65

Table 3C-3. 90% Confidence Limit Ratios (Upper Confidence Limit/Lower Confidence Limit) for BAF as Functions of the Variabilit y in Chemical Concentrations in Fish and Water: Chemical Concentrations Measu red in 6 Fish and 6 Water Samples

Water concentration coefficient of variation (CV) Fish Concentration CV 0.4 0.50 0.60 0.70 0.80 0.4 2.08 2.29 2.54 2.80 3.08 0.5 2.29 2.49 2.71 3.00 3.25 0.6 2.50 2.73 2.96 3.21 3.55 0.7 2.78 3.00 3.21 3.52 3.81 0.8 3.11 3.33 3.56 3.82 4.103-90 The tabulations for unbalanced sample numbers (i.e., n b ... n w ) also demonstrate that it is most effective for the investigator to apply more sampling effort to the chemical concentration (biota vs. water) that is more variable. For example, compare Table 3C-4 (2 fish, 4 water concentrations) and 3C-5 (4 fish, 2 water concen trations). In the former case, highly-variable
water concentrations result in lower BAF confid ence limit ratios than do highly-variable fish
concentrations. In the latter case the situation is reversed. In other wo rds, additional sampling counteracts the tendency for highly- variable fish or water concen trations to inflate the BAF
confidence interval ratio.

Table 3C-4. 90% Confidence Limit Ratios (Upper Confidence Limit/Lower Confidence Limit) for BAF as Functions of the Variabilit y in Chemical Concentrations in Fish and Water: Chemical Concentrations Measu red in 2 Fish and 4 Water Samples

Water concentration coefficient of variation (CV) Fish Concentration CV 0.4 0.50 0.60 0.70 0.80 0.4 3.01 3.32 3.64 4.09 4.53 0.5 3.57 3.87 4.22 4.71 5.16 0.6 4.32 4.60 4.99 5.40 5.94 0.7 5.05 5.49 5.79 6.30 6.95 0.8 6.06 6.31 6.87 7.36 7.88

Table 3C-5. 90% Confidence Limit Ratios (Upper Confidence Limit/Lower Confidence Limit) for BAF as Functions of the Variability in Chemical Concentrations in Fish and Water: Chemical Concentrations Measu red in 4 Fish and 2 Water Samples

Water concentration coefficient of variation (CV) Fish Concentration CV 0.4 0.50 0.60 0.70 0.80 0.4 2.99 3.58 4.32 5.11 6.16 0.5 3.31 3.93 4.61 5.49 6.44 0.6 3.67 4.25 4.97 5.80 6.66 0.7 4.04 4.68 5.45 6.35 7.33 0.8 4.48 5.20 5.94 6.82 7.803-91 

## Appendix 3D

Modeling Simulation of BAF Sampling Designs

Burkhard (2003) performed model simulations to understand how the variabilities in
water and sediment chemical concentrations transl ate into the variabilities associated with BAFs (and BSAFs, which will be discussed separately in Section 4) base d upon different sampling designs. Different models were constructed to evaluate temporal and spatial variability in chemical concentrations. As noted by Burkhard (2003), for these simulations to be meaningful the model constructs should provide reasonable representations of ecosystem conditions and chemical properties. Because the m odels are generic (i.e., not calibra ted to site-specific data), the results are intended to compare different samp ling designs in terms of the resulting BAF precision, and do not offer definitive pr edictions with known certainty.

Model for evaluating temporal variability
A model river segment with a point sour ce discharge was constructed, and the total
chemical load was assumed to be released from the point discharge. Daily instream chemical concentrations were calculated by using a simple dilution model based on the daily stream flow, the discharge flow, and the chemical concentrat ion in the discharge. The model river segment was assumed to contain a food web consisting of z ooplankton, benthic invert ebrates, forage fish, and piscivorous fish. Three food web structures we re considered: pelagic, where forage fish eat only zooplankton; benthic, where forage fish eat only benthic invertebrates; and mixed, where the diet of forage fish consists of 50% zooplan kton and 50% benthic invert ebrates. Time-variable chemical concentrations in forage and pisc ivorous fish were modeled by integrating the differential equations for the chemical mass balance within each organism (Gobas, 1993). Average chemical concentrations in the forage and piscivorous fishes were calculated each day, using the exposure concentrations from the dilution model. Chemical concentrations in the lower food web organisms (zooplankton and benthic invert ebrates) were calculated using the modeling assumptions of Gobas (1993), which were e quilibrium conditions w ith their respective environments (i.e., water column and sediment, respectively). The lower trophic levels serve as3-92 important “entry points” for hydrophobic organic chemicals into the aquatic food web. Details of the modeling approach can be found in Bur khard (2003). Although simplistic, this model’s predictions of chemical concentr ations in fish and how they vary in response to temporal variation in exposure concentratio ns is consistent with more contemporary and/or site-specific
calibrated models.

Model for evaluating spatial variability
To evaluate the effect of spatial as well as temporal variability, the above river segment was divided into six subsegments with one subs egment receiving the point source discharge. Daily chemical concentrations in that subs egment were determined by using the method described previously for the model for eval uating temporal variability. Daily chemical concentrations in the other subsegments were dete rmined as ratios of the concentration predicted in the modeled subsegment. Sediment chemical con centrations were set and held constant for the entire simulation by using the average water colu mn chemical concentration in each subsegment (for the entire flow data set), and J socw /K ow = 1.

Simulations were performed by randomly moving 100 piscivorous fishes up and down the river through the river subsegments. In sampling a specific subsegment, the chemical concentration in fish was obtained by averaging the chemical concentrations in all piscivorous fishes present in the subsegment at the samp ling event. When calculating BAFs and BSAFs for sampling designs with multiple water, fish, and sediment samples, average chemical concentrations were used for each phase in the above equations.

Modeling results
Examples of the predictions made by the te mporal variability model are shown in Figure 3D-1. Daily chemical concentrations in the ri ver segment were calcula ted by using Mississippi River flow data for the 1995 calendar year (Figur e 3D-1.a). A substantia l change in chemical concentrations is observed around day 75, due to a ra pid increase in daily flow rates, which were sustained through the first half of 1995. Chemical concentrations in piscivorous fish were calculated from the daily chemical concentratio ns in the river for chemicals with log K ow s ranging from 2 to 9 (Figure 3D-1b). Comparison of the chemical concentrations in the river to3-93 the predicted concentrations in piscivorous fish re veals that concentrations in the fish will change (relative to the chemical concentrations in the ambient water) at rates th at are dependent upon the hydrophobicity of the chemical. In all time-variable bioaccumulation models, the rate of change
in chemical concentrations in biota is controlle d by the overall rate of chemical loss from the organism. This rate is the sum of elimination ra tes via gills and gut, the organism growth rate,
and the rate of chemical metabolism or biotrans formation. The rate of change decreases with increasing K ow because the elimination rates are mode led as inverse functions of hydrophobicity.
For chemicals with low K ow s (log K ow <3), the rate of change is so fast that chemical concentrations in the fish will mimic the trends of the chemical concentrations in water, for example, compare the scaled concentrations for log K ow s of 2 and 3 (Figure 3D-1b) to the daily chemical concentrations (Figure 3D-1.a). For chemicals with large K ow s, chemical concentrations in the fish change slowly relative to changes in the chemical concentrations in the ambient water and will follow the long-term trends for the chem ical concentrations in the river. For highlyhydrophobic chemicals, the organism growth rate becomes an important factor in determining
the overall rate of chemical loss. From a field study design perspective, the rate at which the chemical concentrations in the fish change re lative to the rate of change of the chemical
concentration in river will str ongly influence the design for the field study, for example, number of samples collected over what time period.

With the simulated daily chemical concentra tions in fish and water and the chemical concentration in the sediment, hypothetical fiel d-sampling designs were evaluated by sampling
the simulated data as if one we re actually performing a field st udy. Consider the simplest field design possible, the collection of fish, water, and sediment on one day. With the 1995 data, this field design can be performed 365 times, once for each day of the year. Computationally, when performed for all possible dates, this field desi gn results in 365 BAFs. Consider another field design consisting of two sample collections space d two weeks apart; this field-sampling design can be performed successfully 351 times with the 1995 data. When performed for all possible dates, this field design results in 351 BAFs. The distribution of these BAF values provides an estimate of the uncertainty of the BAF “measured ” in the field sampling design being simulated.3-94 Figure 3D-1. (A) Daily chemical concentrations in the model river segment. (B) Daily chemical concentrations in piscivorous fish for chemicals with log K ow s of 2, 3, 4, . . . , and 9. The daily chemical concentrations in piscivorous fish have been scaled to the largest value for each K ow . The daily chemical concentrations for log K ow s of 2 and 3, after scaling, are practically identical; the log K ow data are plotted in gray.3-95 The ratio of the 90th to 10th percentile valu es of the distribution of BAF predictions was found to be a useful measure of variability. This ratio defines the range or width of the data, and
smaller ratios result in smaller uncertaintie s for a given sampling design. For the example presented above, ratios of the 90th to 10th pe rcentile BAFs of 8.63 and 5.94 were obtained for the daily and two-week sampling designs, respec tively. These ratios easily demonstrate which design provides the lower uncerta inties, on average, in the measured BAF. To summarize the overall process for evaluating field sampling de signs, the following steps are performed: (1) determine the daily concentrations of chemical in the river; (2) compute the daily chemical concentration in the fish; (3) with the field desi gns of interest, sample the data for all possible dates, and calculate BAFs for all possible date s; (4) determine the ratio of the 90th to 10th percentile values; and (5) compare the ratios of the 90th to 10th percentil e values to determine which designs resulted in the smallest uncertaint y. Steps 3 and 4 were performed for a number of field designs.

Temporal variability in ch emical concentrations
Sampling designs consisting of 1 to 14 samp ling events to collect grab water samples with uniform spacing between water sample coll ection dates ranging from 1 to 60 days were evaluated by using the model river segment with Mississippi River flow data for the years 1955 through 1995, J socw /K ow = 1, and the mixed benthic– pelagic f ood web. The results, in terms of the ratios of the 90th to 10th pe rcentile BAF values, are plotte d in Figure 3D-2. Two different sampling designs were considered for the collection of piscivorous fish. In the first series, piscivorous fish were collected once and their co llection coincided with the date of the last collected water sample. This design is commonl y used in many field studies because of the logistics of assembling a field crew for sampling fish and sediment. The second series consisted of the collection of piscivorous fish concurrently with each water sample.3-96 Figure 3D-2. Ratio of the 90th to 10th percentile bioaccumulation factors (BAFs) for fieldsampling designs consisting of daily water samples spaced 1 (

— ), 3 ( • ), 5 ( ), 7 ( ), 10 (line), 14 (•), 30 (

 ), and 60 (

– ) d apart, with fish samples collected concurrently with the last water sample (first series) and with fish samples co llected with each water sample (second series), when using Mississippi River (USA) flow data for years 1955 to 1995.

For chemicals with log K ow s greater than 5, the ratios of 90th to 10th percentile BAFs for the first and second series were pr actically identical; ther efore, only the results for the first series are reported in Figure 3D-2. For chemicals with log K ow s greater than 5, increasing the number of water samples as well as the spacing between water sample collection dates reduces the uncertainty of the measured BAF. In contrast, for chemicals with log K ow s of 4 or less, the second sampling design had smaller uncertainties.

If the above sampling designs are modified by the collection of composite water samples over time rather than using grab water samples, the most dramatic effect is observed when3-97 limited numbers of samples are used; for example, four and fewer. For these designs, chemicals with log K ow s of 5 and greater have smaller uncertaintie s in the measured BAFs in comparison to those determined using the grab water sample s. However, as the number of water samples collected increases, the uncertainties associated with the grab water sampling designs approach those of the composite sample designs. For chemicals with higher K ow s, only 1 or 2 additional samples are necessary to obtain the same uncer tainty in BAFs as would be obtained by composite sampling of water. In other words, a sampling design using 7 or 8 water grab samples results in about the same BAF uncertainty as a sampling design using 6 water composite samples. For log K ow s of 2 and 3, exactly the opposite beha vior was observed between the grab and composite water sampling designs: compositi ng caused larger uncertainties in the measured BAFs for both series of sampling designs. For log K ow of 4, the compositing sample designs provided lower uncertainties for the second design series, whereas rather mixed results were observed for the first design series. These results suggest that compositing sample designs are most useful for chemicals with log K ow > 4.

The chemical concentrations used in th e temporal variability simulations had a coefficient of variation of 118%. Burkhard ( 2003) found qualitatively similar results to those presented above were obtained when lower tempor al variabilities were us ed. There may also be situations where the temporal variability in chemi cal concentrations is higher than 118%, and in this case the design of an appropriate field sampling plan will be more challenging. Such situations may include ecosystems subject to fre quent or periodic storm ev ents or tidal action, or systems with unusual sediment transport dynamics . As the temporal variability in chemical concentrations increases, so will the required number of water samples in order to accurately measure the average concentration. In ecosystems where high temporal variability in chemical concentrations is expected, the investigator should consider whether another method of determining the site-specific BAF, such as meas uring a BSAF (method 2), is more appropriate in terms of accuracy and cost than measuring the BAF directly.

It is interesting to compare the modeling- based results of BAF uncertainty shown in Figure 3D-2 to the bootstrap resampling results from Section 3.2.1.3. For example, the ratio of3-98 90% exceedance limits estimated by bootstrap for PCB congener 149 (Figure 3B-4) can be compared to the modeling results for log
K ow = 7 (the 10 th panel in Figure 3D-2). Even though th e exceedance limits are not the same (90% for bootstrap BAFs vs. 80% for modeled BA Fs), the BAF ratios are quite comparable. For 2 widely-spaced water samples and a chemical in the range of 6 < log K ow < 7, the simulated
BAF confidence interval ratio is 6 to 7 versus the Bootstrap resampling ratio of 6.7 for PCB 149 (log K ow = 6.67). In addition, both methods produce th e same trends in BAF ratio as a function of sample size. This is somewhat remarkable gi ven that the two methods arrive at estimates of BAF uncertainty via completely different appro aches, procedures, information and assumptions. The consistency of results lends credibility to both approaches.

Metabolism
When metabolism of a chemical occurs, measured BAFs will be smaller than those
measured in the absence of me tabolism (with the same hydrophobi city) because of the increase
in the overall elimination or transformation rate of the chemical. Arnot and Gobas (2003) suggest
that chemicals with metabolic biotransformation ra tes greater than 0.1 to 0.2 /day in fish do not appear to biomagnify in aquatic food webs. Ex amination of the result s of the simulations suggests that when metabolism does occur, the a ppropriate sample design for a chemical of a given K ow would be best described by the sample design for a chemical with a smaller K ow (with no metabolism) and the degree of smallness is de pendent upon the metabolism rate. In situations where metabolism rates cannot be reliably determin ed, the use of the second series of sampling designs would provide lower uncer tainties for BAF measurements.

Food web structure and sediment–water chemical concentra tion relationship
The magnitude of a BAF is dependent upon a number of ecosystem and environmental parameters and conditions, notably food web length, food web composition, and the sedimentwater column chemical concentration relationship. However, it is common to assume that these parameters and conditions are essentially fixe d in a given ecosystem, in which case their influences upon the variability observed in a measur ed BAF are expected to be small. If any of these factors vary, the extent of bioaccumulati on may change. However, we also expect that water concentrations usually ch ange more rapidly than these other factors. Burkhard (2003)3-99 conducted sensitivity analyses which demonstrated that food web structure and sediment-water chemical concentration relationships are usually not important considerations that need to be factored into a sampling design.

Spatial variability in chemical concentrations
In the results discussed above, no spatial va riability in chemical concentrations was considered. In most ecosystems, however, concentrations of chemi cal contaminants in sediments and water do vary spatially. To provide some insi ght into the importance of spatial variability, a
series of random walk 11 simulations was performed with the model river segment, which was now divided into six subsegments. In these simulations, chemical concentration gradients spanned up to two orders of magnitude (Burkhard , 2003). Examination of th e results (not shown) suggests that chemical concentra tion gradients do not add large uncertainties into the measured BAFs, beyond those caused by te mporal variability alone.

Additional random walk simulations were perf ormed to further evaluate the effects of spatial variability. These results suggested that BA Fs can be measured with low uncertainty even when extreme spatial concentration gradients exist at the field site. However, these simulations also suggest that measurements for BAFs probably should be designed around the more contaminated reaches of the sites. When organi sms are collected from the least contaminated sites, uncertainties of the BAF measurements can become very large for chemicals with log K ow s between 3 and 5.

11 Random walk is the idea of taking successive steps, each in a random direction. Trea ting motion (in this case, the movement of fish) as a random walk is a simulation tool used to simplify the complex ways objects (e.g., fish) move in nature.3-100 This page left blank intentionally4-1 4. MEASURING BIOTA-SEDIMENT ACCUMULATION FACTORS TO PREDICT SITE-SPECIFIC BIOACCUMULATION FACTORS

Biota-sediment accumulation factors (BSAFs) ar e calculated from the concentrations of a chemical in tissue and sediment samples from the site of interest. BSAFs are expressions of net bioaccumulation by an organism as a result of uptake from all environmental sources and processes. A BSAF is similar to a field-measured BAF in that the chemi cal concentration in the biota sample reflects the organism's exposure thro ugh all routes. Also like a field-measured BAF,
a BSAF accounts for bioavailabilit y, because the sediment concentr ations are normalized to the organic carbon content. Similarl y, both field-measured BAFs and BSAFs account for chemical metabolism in the aquatic organism or its food we b. Because of these similarities, a site-specific baseline BAF can be predicted from a BSAF, prov ided there is data on the distribution of the
chemical between sediment and water at the site . This approach is Meth od 2 of EPA's national bioaccumulation methodology.

The purpose of Method 2 is to convert the bioaccumulation informa tion contained in a measured BSAF to the corresponding baseline BA F value for a chemical. Prediction of a sitespecific baseline BAF from a BSAF requires data for concentrations for multiple chemicals
measured in ambient water and sediment from the site, preferably from a common sedimentwater-biota data set. This method is useful when the concentration of th e chemical of interest cannot be measured in the ambient water, and in some circumstances when it can. Method 2 is appropriate for moderate to highly hydrophobic nonionic organic chemicals, and certain ionic organic chemicals that exhibit lip id and organic carbon partitioning behavior similar to that of nonionic organic chemicals. Since this method speci fically applies to orga nic chemicals, we will refer to site-specific baseline BAFs as “site-sp ecific BAFs” in this se ction, although the reader should recognize that we are referr ing to baseline values throughout.4-2 4.1 DESCRIPTION OF METHOD 2

The BSAF is defined as the ratio of the lip id normalized concentration of a chemical in an organism sampled at trophic level i to the organic carbon norma lized concentration of the
chemical in surficial sedi ment (Ankley et al., 1992):

C /f C BSAF C /f C tl l i

s soc soc

= = Equation 4-1

where:
BSAF i = Biota-sediment accumulation factor for aquatic organism at trophic level i , C t = Chemical concentration in the organism [M chem /M tissue ],
f l = Lipid fraction of the organism,
C s = Chemical concentration in surficial sediment [M chem /M sediment ], and
f soc = Organic carbon fraction of the sediment.

In addition:

C l

= Lipid-normalized concentration of the chemical in whole fish [M chem /M lipid ],
and
C soc = Organic carbon-normalized concentra tion of the chemical in the surface sediment [M chemical /M organic carbon ] There are Three Steps Involved in Applying Method 2 to Determine a Site-Specific Bioaccumulation Factor:

1. Calculate a BSAF from the average concen trations of the chemical of interest in tissue and sediment samples from the site
2. Determine the distribution of concentra tions between sediment and water at
the site for one or more reference chemicals
3. Predict the site-specific BAF using the Method 2 equation (Equation 4-2)4-3 The BSAF has units of kg of organic car bon/kg of lipid. The use of lipid and organic
carbon normalized concentrations makes the BSAF an approximate fugacity 1 ratio (Burkhard et al. 2003a).

BSAFs for fish and other organisms not in intimate contact with the sediments can only be determined using field data. Meaningful BSAFs, i.e., values which enable accurate prediction of chemical residues in fish, re quire that the sediment samples be reflective of the organism = s recent exposure history. In general, BSAFs should be determined from spatially and temporally coordinated fish and surficial sediment samples under conditions in which recent loadings of the chemicals to ecosystem are relatively unchange d (Burkhard et al. 2003a ). Average chemical
concentrations are used in the calculation of the BSAF as they are for BAFs, since multiple samples should be collected to properly character ize chemical concentrat ions at a site. The appropriate averaging method (e .g., arithmetic or geometric m ean) depends upon the distribution of the concentration data, and should be selected following data review.

Both BSAFs and baseline BAFs can provide good measures of the relative bioaccumulation potential of hydrophobic organic ch emicals if based on accurate measurements of concentrations in appropriate samples of biota, sediment, and water. When calculated from a common organism-sediment-water sample set, chemical-specific differences in BSAFs or
baseline BAFs reflect the net effect of biomagnification, me tabolism, bioenergetics, and bioavailability factors on each chemical’s bioaccumulation.

Method 2 predicts the site-specific BAF from the measured BSAF for the chemical ofinterest, using the sediment-water concentration quotient determined for one or more similarlybehaving reference chemicals. Specifically, this method uses sediment-water concentration quotients ( J socw s) for reference chemicals to estimate values of C f d

w that cannot be measured for

the chemical of interest. Each chemical’s K ow should also be known, because the ratio of J socw to K ow provides the basis for relating reference ch emicals to the chemical of interest. The

1 Fugacity expresses chemical concentrations as a partial pressure, which indicates the tendency of a chemical to “prefer” one phase (e.g., lipid, organic car bon, dissolved) over another (Mackay, 1979).4-4 following equation is used to predict the site-s pecific baseline bioaccumulation factor for the
chemical of interest from a BSAF measured at the site:

/ , ow,

ow, D K 1 Site-Specific Baseline BAF BSAF K f 

### Π k r socw r k i i r l  

= −     Equation 4-2

The subscripts k and r refer to the chemical of interest and a reference chemical, respectively.
Also:

J socw = Sediment-water c oncentration quotient [L 3 /M]
D k/r = Ratio of the fugacity gradients (modeled as J socw / K ow ) between sediment and
water for chemical k in comparison to that of a reference chemical r

The sediment-water concentration quotient , determined for one or more reference chemicals as the ratio of measuring chemical con centrations in sediment and dissolved in water at the site, is a critical parameter in predicting th e baseline BAF from a BSAF. It is calculated as:

,

,

, C C 

### Π s oc r
socw r f d

w r = Equation 4-3

where:

C soc,r = concentration of a reference chem ical in dry sediment, normalized to sediment organic carbon

, C f d

w r = concentration of the reference chemical that is freely dissolved in water

Again, average chemical concentrations should be used in the calculation of the sediment-water concentration quotient, and appropriate averagin g methods should be selected following review
of the concentration data.4-5 The reference chemicals should have a hydrophobicity and organic carbon partitioning behavior similar to the chemical of interest, and the dissolved concentration of the reference chemicals in water must be quantifiable at th e site. Octanol-water pa rtition coefficients (K ow s) are used to adjust for any differences in hydrophobic ity between the chemical of interest and the reference chemicals. In an ecosystem at equi librium, fugacity theory predicts that the J socw should equal K ow . Therefore, J socw / K ow is called the fugacity gradient. In many cases, the fugacity gradients between sediments and water fo r both reference chemicals and the chemical of interest are arguably similar. In fa ct, this similarity provides a usef ul criterion for the selection of reference chemicals. In cases where site-specific evidence suggests or demons trates that fugacity gradients are not the same, the explicit differen ce may be represented by the fugacity gradient ratio, D k/r :

, ow, /
, ow, / K D / K 

### Π Π s ocw k k k r

s ocw r r = Equation 4-4

D k/r is an additional parameter that can be used to improve the accuracy of the BAF prediction by Method 2, if the necessary data are available for the chemical of interest and the reference chemicals. In practice, D k/r is often assumed to be 1.0.

In some situations, it may be possible to estimate or predict J socw for the chemical of interest directly. This alternative is discussed in Section 4.4. If a reasona bly certain estimate of J socw is available for the chemical, the baseline BAF can be predicted directly from the BSAF using this simplification of the Method 2 equation:

1 Baseline BAF =BSAF 

### Π i i socw l f − Equation 4-5

This seems like an attractive alternative, because data for reference chemicals has been eliminated from the equation. However, without measurements of J socw for reference chemicals, it may be difficult or impossible to reliably estimate J socw for the chemical of interest at the site. EPA prefers equation 4-2 and the use of reference chemical data for J socw as being a more robust4-6 method for predicting site-specific BAFs. Calcul ating a site-specific BAF using Method 2 is presented in the following example.

Prediction of a site-specific BAF from BSAFs determined by measurements at the site (Method 2)
This example illustrates the developmen t of a site-specific trophic level 4 BAF using Method 2. This method involves pr edicting a site-specific baseline BAF using a BSAF measured at the site for th e chemical of interest, as well as the sediment-water concentrati on quotient for one or more reference chemicals. In Section 4.6, it is suggested that multiple re ference chemicals be used to predict a site-specific baseline BAF with Method 2, because this improves the accuracy of the result.
In this example, data from Lake Onta rio are used to derive a baseline BAF from a BSAF for PCB congener 126, which cannot be readily detected in water (USEPA, 1995; Cook and Burkhard, 1998). To simplify this example, a site-specific BAF is derived for only one trophic level 4 organism; in this case, age 5-7 lake trout. A review of the diet ary preferences of the larger sizes of lake trout that are commonly consumed confirms that these are trophic level 4 organisms. Previously, the PCB congen ers 52, 105, and 118 have been used as the reference chemicals for calculati ng baseline BAFs for PCB 126 (USEPA, 1995; Cook and Burkhard, 1995). These three congeners were selected because (1) they have similar physicoc hemical properties and are all from a single chemical class, (2) they are well quantified in sediment and biota, and (3) available data indicate they have loading histories similar to PCB 126 and thus the fugacity gradients ( J socw /K ow ) should be similar. In this example, the detailed, step-by-step calculations for each component of the equation are shown only for reference PCB congener 118. In practice, the same steps are performed for all reference congeners, but for this example, only the final sitespecific baseline BAFs are shown for PCBs 52 and 105.
chemical Log K ow C l C soc C f iltered

w PCB 126 6.9 12.3 ng/g- l 3.83 ng/g-SOC PCB 118 6.7 555 μg/g-SOC 34 pg/L4-7 Prediction of a site-specific BAF from BSAFs determined by measurements at the site (Method 2 , continued)
Determining a BSAF for the Chemical of Interest from Measurements at the Site

The BSAF for PCB 126 is determinedas the ratio between the lipidnormalized concentrations of the chemi cal in 5 to 7-year-old lake trout (C l ) and the average organic carbon-normalized concentration of the chemical in surface sediment (C soc ) using equation 4-1. On the ba sis of data collected from Lake Ontario, the average C lof PCB 126 in age 5-7 lake trout is 12.3 ng/glipid, and the average C soc of PCB 126 in the sediment is 3.83 ng/g-organic carbon (actual calculations for these normalized values are not shown here). Therefore:

C 12.3 4, 126 C 3.83 

### BSAF 3.21 l

soc ng g oc g oc PCB g lipid ng g-lipid − −

− 

### == ⋅ = Equation 4-1

The trophic level 4 BSAF for PCB 126 in 5 to 7-year-old lake trout is 3.21.
Determining a Sediment-Water Concentration Quotient ( J socw ) for a Reference Chemical

Sediment-water concentrat ion quotients for the reference chemicals can be determined from site-specific measurements by using equation 4-3. This calculation will be shown for one of th e three reference chemicals, PCB 118. To calculate J socw , the concentration of reference chemical that is freely dissolved in water ( C f d

w ) is needed. This concentrat ion can be calculated from the freely dissolved chemical fraction (f fd ) (using equation 3-6) and the chemical concentration measured in the water column. The measured DOC concentration is 2.0 mg/L and the K ow for PCB 118 = 5.01×10 6 (log K ow = 6.7). Using equation 3-6, the freely di ssolved fraction of PCB 118 in Lake Ontario water is calculated as follows:

ο w ο w f = 1 / (1 + POC DOC

fd ⋅ Κ + 0.08 ⋅ ⋅ Κ ) Equation 3-6

In this example, the chemical concentr ation was measured in a filtered sample, so POC is set equal to zero (assumi ng all particulates were removed by filtration):

6 6 1 f 0.56 2.0 1 0.08 5.01 10 10 fd mg DOC L kg L kg mg = =

− + ⋅ ⋅× ⋅4-8 Prediction of a site-specific BAF from BSAFs determined by measurements at the site (Method 2, continued)

The concentration of PCB 118 measured in filtered Lake Ontario water is 34 pg/L. Thus, ( C f d

w ) 118 = 0.56×34 pg/L = 19 pg/L or 1.9×10 -5 F g/L.

The average (C soc ) 118 = 555 F g/g-SOC (sediment organic carbon). By substituting these valu es into equation 4-3, J socw for the reference chemical (PCB 118) is calculated as:

,

,

, C C

s oc r
socw r f d

w r = 

### ∏ Equation 4-3

,118 5 , 118 C 555 7 ,118 1.9 10 C 

### 2.9 10 /

soc fd

w g L
socw kg SOC g 

### L kg SOC μ μ −
− × 

### = = ⋅ =× − 

### Π

Calculating a Site-Specific Baseline BAF
A site-specific baseline BAF may be pr edicted from the field-measured BSAF for the chemical of interest (PCB 126) and J socw /K ow for each reference chemical using equation 4-2:

126/ , ow,126 4,126 4,126
ow, D K 1 Site-Specific Baseline BAF K f r socw r

r l BSAF = − 

### Π

Since the loading histories and fugacity ratios of the chemical of interest (PCB 126) and the reference chemicals (PCBs 52, 105 and 118) are assumed to be similar, D k/r ~ 1 in equation 4-2. To complete the calculation to predict the sitespecific baseline BAF for PCB 126 using reference chemical PCB 118, the appropriate K ow values for PCB 126 (7.9 × 10 6 or log K ow = 6.9) and the fraction of lipid for lake trout (20% or 0.20) are entered into equation 4-2, along with the other terms which ha ve been previously calculated: 

## ( ) 

# ( ) ( ) ( )

7 6

4,126 6 

### 1 2.9 10 7.9 10 1 Site-Specific Baseline BAF 3.21 0.20 5.01 10 kg L kg SOC L kg L − 

### × ×

=⋅ − × =1.5 × 10 8 L/Kg-lipid4-9 Prediction of a site-specific BAF from BSAFs determined by measurements at the site (Method 2, continued)
The site-specific baseline BAFs usi ng reference PCB congeners 52 and 105 are derived in the same manner as fo r PCB 118. The predicted site-specific baseline BAFs that result are 3.7×10 8 using congener 52 and 1.6×10 8 using congener 105. Once all the site-specific baseline BAFs have been predicted, the final site-specific baseline BAF is derived by calculating the geometric mean of the three baseline BAFs, which in this case is 2.1×10 8 L/kg.
Calculating a Site-specific Total BAF
In order to determine a water quality standard for PCB 126 in Lake Ontario, the site-specific baseline BAF must be c onverted to a site-specific total BAF. Recalling the relationship between the baseline BAF and the total BAF ( BAF T t ):

T

, 

### Site Specific BAF (f Baseline BAF +1) f i t l i fd 

### =⋅ ⋅ (rearranged Equation 3-4)

For PCB 126, the site-specific base line BAF at trophic level 4 was calculated to be 2.1×10 8 L/kg-lipid. The freely disso lved fraction of PCB 126 in the Lake Ontario water colum n, which contains an average POC concentration of 0.075 mg/L, can be calculated using equation 3-6:

0.075 2.0 6 6 6 6 10 10 1 ,126 1 7.94 10 0.08 7.94 10 

### f 0.35 mg POC kg mg DOC kg L L L kg L kg mg mg fd

− − + ⋅× ⋅ +⋅ ⋅× ⋅ 

### = =

The average lipid content for lake trout was 20% or 0.20. With thisinformation, the site-specific total BAF can be recalculated from the sitespecific baseline BAF: 

# ( ) 8 7 4, Site-Specific BAF 0.20 2.1 10 1 0.35 1.4 10 / t L

T kg l L kg

− = ⋅ × +⋅ = ×

The site-specific total BAF for PCB 126 in Lake Ontario lake trout is 1.4×10 7 L/kg. This BAF for PCB 126 relates the total concentration of chemical in water to the total concentration of chemical in tissue of trophic level 4 organisms.4-10 Ecosystems are rarely at thermodynamic equ ilibrium, so a BSAF inherently includes a measure of the “disequilibrium” associated with th e distribution of a chemi cal in the ecosystem. The deviation from the expected equilibrium va lue of approximately 1-2 (McFarland and Clarke, 1986) is determined by the net effect of all fact ors that contribute to th e disequilibrium between sediment and aquatic organisms. A BSAF value greater than 1-2 can occur through biomagnification or when chemical concentratio ns in surface sediment have not reached steady
state with those in water. A BSAF value of less than 1-2 can occur from diagenesis of organic carbon in sediments, kinetic limitations for chemical transfer from sediment to water or water to the food web, and biological processes (such as growth or metabolism/biotransformation of the chemical in biota or its food web). The influenc e of these ecosystem factors on the value of a
BSAF is discussed in Section 4.6.2.

For high K ow chemicals (e.g. log K ow > 6), there are some distinct advantages of measuring BSAFs and using Method 2 to predict site-specific BAFs, over directly measuring site-specific BAFs. BSAFs are a more robust assessment tool for high K ow chemicals, particularly when there is any meaningful benthic connectio n in the food chain. Another important advantage to emphasizing BSAFs when assessing bioaccumulation of high K ow chemicals at a site is the ease and reliability of the measurements. Meas urement of concentrations of most highly hydrophobic nonionic organic chemicals in sedi ment can be performed fairly easily. Consequently, with an appropriate BSAF, chemi cal residues in fish can be readily predicted using the concentration of the chem ical of interest measured in se diment at the site. In contrast,
measurements in water can be difficult due to temporal fluctuations of the chemical
concentrations which are also often below method detection limits. Because concentrations of highly hydrophobic nonionic organic ch emicals are temporally more stable in both fish and sediments, BSAFs better integrate fluctuating exposure conditions than do BAFs (Burkhard, 2003). For high K ow chemicals, the relative concentration of chemical in the sediment is usually much larger than in the water. Hence, the an alytical difficulties in accurately determining chemical concentrations in water are much great er than for sediment. In addition, the importance of chemical binding to particulate, colloidal, and dissolved organic carbon in water becomes much greater at high K ow , making it more difficult to accurately determine the proportion that is freely dissolved

.4-11 Method 2 appears to be part icularly beneficial for pred icting site-specific BAFs for chemicals such as polychlorinated dibenzo-p-di oxins (PCDDs) and dibenzofurans (PCDFs), and certain polychlorinated biphenyl (PCB) congeners. These chemicals are often detectable in fish
tissues and sediments but are difficult to measur e in the water column and/or are subject to metabolism (biotransformation). Because BSAFs are based on field data and incorporate the effects of metabolism, biomagnifi cation, growth, and other factors, site-specific BAFs estimated
from BSAFs will reflect the net effect of all these factors.

Section 4 is organized in the following manner: Section 4.2 iden tifies key study design questions the investigator s hould address when planning a field study to measure BSAFs

. Section
4.3 addresses how to determine J socw , either by measuring this concentration ratio for one or more reference chemicals, or by approximation or model prediction. Section 4.4 provides the investigator with two complimen tary methods that can be used to design a sampling plan to
measure BSAFs and J socw . Section 4.5 contains specific guidance for sediment sampling. Finally, a number of scientific issues associated with the use of BSAFs and the Method 2 prediction of BAFs are discussed in Section 4.6.

4.2 KEY STUDY DESIGN QUESTIONS FOR DETERMINING BIOTA-SEDIMENT ACCUMULATION FACTORS

The most important aspect of conducting a successful field study to measure BSAFs is collecting representative samples of the biota and sediment. The sediment samples should be representative of the surfici al sediment within the home 2 range of the organism. Because of this, the home range of the target species will dictate the spatial scale of the sampling effort. Again, samples will be most representative when the me asured concentrations of the chemical in biota and sediment are reflective of long term av erage concentrations. Temporal and spatial distributions of chemical concentrations, orga nism life history, and duration of exposure among

2 As noted in Section 2.4, it may be more appropriate to sample sediment within the foraging range of the target organism.4-12 other factors all contribute to BSAF uncertainty and should be addresse d by the field sampling plan.

Average chemical concentrations are used in the calculation of the BSAF, since multiple samples should be collected to properly characteri ze chemical concentratio ns at a site. Sampling requirements for biota and sediment will largely be controlled by the variability of chemical
concentrations at the field site. As discussed in Section 3.3.4, chemical concentrations in biota vary both in space and time. In contrast, chemical concentrations in sediment generally exhibit significant spatial variability but little temporal va riability, the latter ofte n exhibited as a slow rate of change due to sediment erosion and deposition processes. Again, the properties of the chemical itself play an important ro le in defining this variability.

To predict a site-specific BA F from a BSAF determined at a site, the investigator should also accurately determine the other paramete rs required for Method 2 (Equation 4-2). These
include the sediment-water co ncentration quotient for a reference chemical, the octanol-water partition coefficients for the chem ical of interest and the reference chemical, and the fugacity gradient ratio. The sediment-water concentratio n quotient is discussed in the next section.

The investigator faced with designing a fiel d study to determine a BSAF should consider the following series of key questi ons, intended to identify factors of the problem that should be addressed by the sampling plan. The application of data quality assurance procedures when measuring and applying BSAFs is very important.4-13 Key Factors to Consider When Designing a Field Study to Determine a BSAF and Pred ict a Site-Specific BAF

Characteristics of the Chemical of Interest

• Type of chemical – Method 2 is applicable to nonionic organic chemicals and similarly-beha ving ionic organic chemicals

• Hydrophobicity

• Metabolism
What is an Appropriate Reference Chemical?

• Type of chemical: similar to chemical of interest

• Hydrophobicity: K ow similar to chemical of interest

• Sensitivity of analytical methods (should be detectable in water and sediment)

• Should reflect steady-state conditions
What is an Appropriate Target Biota Species?

• Consumed by human population

• Size of consumed organisms

• Trophic level and prey items

• Relationship to sediment (i.e., benthic, epibenthic, pelagic)

• Lipid content

• Migration and movement (defines home range)

Characteristics of the Site

• Size of site / number of waterbodies

• Sampling characteristics (temporal and spatial variability of chemical concentrations)

• Ecosystem type

• Organic carbon (SOC, POC and DOC) concentrations

• Sediment deposition environment

• Spatial patterns of sediment t ype and chemical concentration distributions

• History and duration of chemical lo ading (chemical of interest and reference chemical)
Sediment Sampling

• Samples should be representative of target biota’s recent chemical exposure (i.e., within home range)

• Sampling the surficial layer of sediment (upper 1-2 cm)4-14 Key Study Design Questions

Reference Chemicals. Which reference chemicals are most appropriate to select for application of Method 2?

Study Feasibility. Can I adequately detect the chemical of interest in bi ota and sediment, and the reference chemicals in sedi ment and water, with available analytical methods (e.g., with a detection frequency > 80)?

1. Precision Goal. For an acceptable level of uncertainty in the site-specific BAF (e.g., within a factor of 10, a factor of 3, ± 100 %, ...) , how do I determine the necessary level of effort (in terms of the number of samp les to collect and analyze)? • Similar bioaccumulation and partitioning behavior

• Similar hydrophobicity and fugacity (i.e., J socw /K ow )

• Structural similarity

• Similar loading history and duration is preferred

• Investigate detection limits of available analytical methods

• Compare to expected chemical concentrations

• DQO process (USEPA, 2000c)

• Monte Carlo simulations

• Bioaccumulation modeling4-15 4. Biota. Which species should I sample?

5. Site Definition. Have I adequately defined my site of in terest in terms of spatial extent?

•

The field data should be collected at the specific site for which the BSAF will be used to predict a site-specific BAF, and with the target sp ecies of concern. For large-scale sites, EPA recommends that biota and sediment samples be collected from each waterbody or ecosystem
within the site for which BSAFs are to be derived.

6. Temporal Variability (i.e., Sampling Event Frequency). How many times do I need to sample biota and sediment at the site? • Consider consumption pattern s of the human population

• Availability of species at the site

• Diversity of exposure pathways (i.e., benthic & pelagic)

• Dietary composition/trophic status

• Ease of collection

Biota Sampling Considerations

• Consider chemical properties (hydrophobicity an d metabolism)

• Consider biota characteristics (migra tion, reproduction, availability, etc)

• Consider consumption pattern (e.g., ti mes of year they are harvested)

• Lessons learned from bioaccumulation modeling
Sediment Sampling Considerations

• Normally sampled once

• Guidance on sampling events provided in discussion of Bioaccumulation Modeling (Section 4.4.1)

• Spatial extent defined by ho me range of target species4-16 7. Spatial Variability (i.e., Number of Stations). How many locations should be sampled?

Once the number of sampling events is determ ined, the number of sampling stations/locations can be based on the number of samples required to obtain the desired precision (see Section 4.4.4).

8. Biota Sample Type. What types of biota samples should I collect (i.e., age/size, tissue, quantity, etc)?

9. Sediment Sample Type. What types of sediment samples should I collect?

10. Chemical Analytical Methods. Which analytical methods should I use? • Consider evidence of spatial gradients in chemical concentrations due to pollutant sources and transport processes

• Biota characteristics (mobility/hom e range, habitat preference, etc.)

• Consumption characteris tics (harvesting areas)

• Ecosystem properties (size of site, sp atial differences in hydrodynamics, etc.)

• Consider spatial design op tions (e.g., random, stratified, systematic, judgment)

• What ages/sizes of these species are consumed?

• Which tissues are most commonly cons umed and how are they prepared?

• Does this vary with organism size?

• Composite vs. individual samples

• Chemical analysis requirements

• See Section 3.3 for discussion of biota sampling

• Define depth of surficial sediment to sample

• Individual grab samples vs. composites?

• Chemical analysis requirements

• See Section 4.6 for discussion of sediment sampling

• Must be specific for the individual chemical(s) of

concern
• Must be able to measure and quantify ambient chemical concentrations (> 80% detection rate)4-17 11. Biota Sampling Methods

. How should biota be sampled?

12. Sediment Sampling Methods. How should sediment be sampled?

13. Biota/Sediment Sampling Correspondence. How should I coordinate biota and sediment sampling (e.g., concurrent vs. staggered sampling)? • Appropriate methods depend on contaminant and waterbody

• Surficial samples vs. ve rtically-resolved cores

• Select proper sampling device

• Appropriate methods depend on waterbody and organisms

• Consider chemical properties (hydrophobicity an d metabolism)

• Consider ecosystem conditions (v ariability due to hydrodynamics) and temporal aspects of chemical loadings

• Lessons learned from bioaccumulation model simulations (Section 4.4.1)4-18 14. Ancillary Measurements

. What other parameters do I need to measure?

Method 2 also requires measurements of refere nce chemical concentrat ions in water. The investigator should refer to Secti on 3.4 for guidance on water sampling.

4.3 HOW CAN THE SEDIMENT/WATER COLUMN CHEMICAL CONCENTRATION QUOTIENT ( J socw ) BE DETERMINED?

In Section 4.1, the sediment-w ater concentration quotient ( J socw ) was introduced as a critical parameter in predicting the site-specifi c BAF from a BSAF using Method 2. The data that are generated as a result of a BSAF study reflect how the chem ical of interest is distributed between biota, sediment and water by partitioning and fate mechanisms, in addition to bioaccumulation. In Method 2, the partitioning and fate factors are addressed by determining J socw , and using this parameter in the prediction of the site-specific BAF. In this Section, the investigator will find guidance rega rding three methods to determine J socw : measuring
concentrations of one or more reference chemical s in water and sediment at the site; estimating J socw /K ow as approximately the ratio of the fr action of organic carbon in water column particulates (f oc ) to that in surficial sediment (f soc ) by assuming steady state conditions; and predicting J socw for a chemical of interest using a pr operly calibrated and confirmed fate and transport model. • Lipid content in biota, organic carb on in sediment, and POC and DOC in water are required ancillary measurements

• Useful ancillary measurements for bi ota include age, sex, trophic status, stomach contents and tagging (stocking) information

• Total suspended solids (TSS) is a usef ul ancillary measurements for the water column

• Grain size, bulk density, and radioisotope concentr ations (in core layers) are useful ancillary measurements for sediment

• Home/foraging range of target species4-19 The distribution of a chemical between the sediment and overlying water at a site is described by the sediment-wat er concentration quotient ( J socw ), which was defined in Equation 4-3. By expressing the concentration of chemical in sediment on an organic carbon normalized
basis and the concentration of chemical in water on a freely dissolved basis, J socw is a measure of the degree to which the chemical’s distri bution between the surface sediment and the water column approaches or deviates from a c ondition of thermodynamic equilibrium for the waterbody. The degree of disequilibrium (departu re from equilibrium) is proportional to the degree to which the fugacity gradient ( J socw /K ow ) for the chemical diverges from a value of 1.0 ( J socw = K ow ). This assumes that (1) the or ganic carbon partition coefficient (K oc ) is the same in both the water column and the sediment and (2) K oc is also equal to the K ow . These assumptions and the empirical data upon which they are ba sed, are discussed in TSD Volume 2, Sections
4.2.4 and 4.3.

In the aquatic environment, three factors are primarily responsible for causing J socw to differ among ecosystems. First, the concentratio n distribution of nonionic organic chemicals in
the water column and sediment are the result of well-known fate and transpor t processes, such as particle sedimentation and resuspension, chemi cal sorption to and desorption from suspended and bed sediments, volatization, biological/chemical transformation, and water column transport. These processes vary among ecosystems (O’Con nor, 1988a-c). Second, the chemical loading history to the ecosystem play s an important role in its J socw . For example, increasing the loading
of a chemical to the water column causes an imme diate rise in the concentration of the chemical in the water, and over time, the concentration of the chemical in the sediment will gradually increase through sedimentation processes. If the loading of a chemical to the water column is decreased, the concentration of the chemical in the water column drops quickly, whereas the Alternatives for determining J socw :

• Measuring site-specific chemical concentratio ns in water and sediment for one or more
reference chemicals

• Estimating J socw /K ow as (f oc /f soc ) by assuming steady state

• Predicting J socw using transport and fate models4-20 concentration of the chemical in the sediments decreases slowly through burial of older and more contaminated sediments by newer and less cont aminated sediments (Endicott and Cook, 1994; USEPA, 2003). Third, differences in organic carbon content in wa ter column particulates (or suspended solids) and surface sediment vary among ecosystems (Gobas and MacLean, 2003; Burkhard, 2003a). The ratio of organic carbon co ntents (water column to surface sediment) approximates the steady-state value of J socw /K ow for the ecosystem due to diagenesis processes on the newly deposited surface sediments.

4.3.1 Measuring Site-Specific Reference Chemical Concentrations in Water and Sediment

The most direct and accurate way to determine J socw is based on measurements for appropriate reference chemicals at the site. As described above, the factors that are primarily responsible for causing J socw /K ow to vary tend to affect rela ted chemicals in similar ways.
Reference chemicals with J socw /K ow similar to that of the chemical of interest are preferred for
Method 2 and often are available. Theoretica lly, the difference between sediment-to-water fugacity ratios for two chemicals, “ k ” and “ r ” (D k/r ), can be used when reliable reference chemicals that meet the fugac ity equivalence condition (i.e., J socw, k /K ow, k ≈ J socw, r /K ow, r ) are not available. Related nonionic organic chemicals, approximately at stea dy state, should have similar, if not equal, values of J socw /K ow that are related to the f poc /f soc ratio. When steady-state conditions are not present, as is often the case, J socw /K ow values for related chemicals may be similar. The similarity of J socw /K ow for two chemicals can be i ndicated by similarities in molecular structure, which imply similar physical-chemical beha vior in water (e.g., hydrophobicity, persistence, and volatility), similar mass load ing histories, and similar concentration profiles in sediment cores.

The investigator should consider the following factors when selecting reference chemicals for measuring J socw :

1. The reference chemicals and the chemical of interest should have similar physicochemical properties, as well as simila r persistence in wate r and sediment. In addition, the reference chemicals and the chem ical of interest should have similar4-21 chemical structures (i.e., the investigator should not select an alkane as a reference chemical for a polycyclic aromatic hydrocarbon).

2. Obtaining J socw , r data for several referen ce chemicals with similar K ow s (log K ow ± 0.5) from the same water and sediment samp les is preferable and will ensure that predictions are more robust than those that would be obtained w ith only one reference
chemical.

3. Data for several reference chemicals and the chemical of interest should come from a common organism-water-sediment data set for a particular site. Preferably, C soc , r and
C soc , i should be measured from the same sediment samples, because this eliminates uncertainty attributable to spatial heterogeneity of C soc .

4. The K ow value for the target and reference chem icals should be selected as described
in Section 4.2.5 of TSD Volume 2 (USEPA, 2003).

5. Whenever possible, the loading histories fo r the reference chemicals and the chemical of interest should be similar, such that their sediment-water disequilibrium ratios ( J socw /K ow ) would not be expected to be substantially different (i.e., D k/r ~ 1). For example, a contaminant produced by com bustion processes over hundreds of years (e.g., pyrene) should not be used as a refe rence chemical for a recently-introduced contaminant (e.g., brominated diphenyl ether).

6. Guidelines for sampling and measurement of J socw are identical to those for sampling
and measurement of C f d

w under BAF Method 1, as described in Section 3.4, and C soc under BAF Method 2, as described in Section 4.5. Because concentrations of
bioaccumulative chemicals in surficial sedime nts are relatively constant on an annual basis in most carbonaceous, fine-sediment depositional areas, determination of an appropriate average C f d

w in systems with temporal fluctuations is the greatest challenge in measuring J socw .4-22 7. POC and DOC should be measured in the same samples used to measure chemical concentrations in water, and organic carbon should be measured in the samples used
to measure chemical concentrations in sediment.

Depending on the chemical of interest, it may be challenging to find suitable reference chemicals. In some cases, this may lead the inve stigator to use a different method to determine a site-specific BAF.

4.3.2 Estimating J socw / K

ow ~ f oc /f soc by Assuming Steady State

Over the long term, the asymptotic (steady state) value for J socw / K ow can be approximated as the ratio of the fraction of organic carbon in water column pa rticulates to that in surficial sediment (Burkhard et al., 2003a). As these authors point out, J socw / K ow = 1 at equilibrium. However, in natural systems the se diments and water column are almost never at equilibrium and deviations of J socw / K ow from 1 can be used to desc ribe this disequilibrium (or nonequilibrium) condition. When the dise quilibrium is less than one (i.e., J socw / K ow < 1) the
chemical concentration in the wate r column is enriched relative to that in the sediment. When the disequilibrium is great er than one (i.e., J socw / K ow > 1) the chemical concentration in the
sediment is enriched relative to that in the water column.

Water column particulates (i.e., suspended solids) in most ecosystems have organic carbon contents that are higher than the orga nic carbon contents of their corresponding sediments. This is because, as particles settle and become incorporated into the sediments, the more labile portions of the organic carbon (e .g., carbohydrates and lipids ) are converted to CO 2 by microbial and other processes a ssociated with diagenesis. The loss of organic matter without concomitant chemical loss effectively increases C soc in the sediment so that ecosystems tend to exceed equilibrium between surficial sediments and the water column; i.e., J socw / K ow > 1 (Gobas and MacLean, 2003). Such disequilibrium between sediment and water creates a thermodynamic gradient for chemical to move back into the water column, but chemical exchange between sediments and overlying water is slow, so dise quilibrium is maintained. The magnitude of this natural dise quilibrium for lakes appears to increase with increasing water4-23 depth due to increased organic carbon mineralization (Gobas and McLean, 2003). For ecosystems at steady-state, this disequilibrium approximates the ratio of the greater organic carbon content of the suspended solids to the less er organic carbon content of the sediments. Therefore, the expected steady-state for an ecosystem is not thermodynamic equilibrium ( J socw /
K ow = 1), but rather a J socw / K ow of approximately the ratio of organic carbon content in suspended and sediment particles.

The magnitude of the differences in sedi ment and water column particulate organic carbon contents in aquatic ecosystems is st rongly influenced by the hydrodynamics of the ecosystem, because particle sedimentati on, resuspension, and burial are directly controlled/influenced by the hydrodynamics of the ecosystem. Ecosystems with high resuspension rates (e.g., rivers) would most lik ely have smaller differences in organic carbon contents than ecosystems with lower resuspension rates (e.g., large lakes and reservoirs). Based upon typical organic carbon contents in aquati c environments (1–15% in water column particulate and 0.5–4% in sediment: Thurman, 1 985; Ittekkot, 1988;Wong et al. 2001; Reschke et al. 2002), steady-state J socw / K ow values are expected to range from approximately 2 to 10. The lesser value of 2 arises due to minimum exp ected differences in organic carbon content of particulate matter in the water column and sedime nts. The greater value of 10 allows for effects of chemical gradients and greater relativ e organic carbon amounts in the water column.

4.3.3 Using Transport and Fate Models to Determine the Fugacity Gradient Ratio

The third major factor influencing sediment–w ater disequilibrium is loading history. The previous discussion was based upon steady-state conditions, but because the mass transfer rate of
chemical between sediment and water column can be slow, steady-state conditions may not be achieved quickly. If chemical concen trations in sediment at the si te are far from steady state due to recent changes in loading, then J socw / K ow cannot be approximated as ~ f oc /f soc . Furthermore, differences in loading history between the chemical of interest and available reference chemicals can also complicate the use of Method 2 to predic t a site-specific BAF, because the investigator must then determine the appropriate va lue for the fugacity gradient ratio, D k/r . In these cases, a transport and fate model can be very help ful in evaluating nonst eady-state conditions.4-24 Transport and fate models are tools that are useful for simulating chemical concentrations in water and sediment, and their rates of change in response to changes in chemical loading (Chapra, 1997; Thomann and Meuller, 1987). C onsequently, these models can be used to simulate and predict J socw in response to changing chemical loading. For example, Burkhard et al. (2003a) used a relatively simple transport and fate model to predict time-dependent chemical distributions between overlying wa ter and surface sediments (i.e., J socw ) in a lake as a function of chemical loading rates. Th e transport and fate model wa s a two-compartment dynamic mass balance model consisting of a completely mi xed water column and an underlying surficial sediment layer. The model assumed complete mixing in the water column transport and accounted for inflow and outflow, solids settling, sediment resuspension and burial, diffusive exchange between the sediment and water colu mn, chemical volatilizat ion and photolysis, and time-variable chemical loading rates. Ecosyste m parameters and conditions representative of
Lake Ontario were taken from Endicott et al. (1990). This model and similar models (Thomann and DiToro, 1983; Mackay, 1989; Gobas et al. 1995; DePinto et al. 1998) have been calibrated and confirmed in the Lake Ontario ecosystem for a number of organic chemicals, including chlorinated pesticides, PCBs, PCDDs and PCDFs. Equilibrium partitioning of chemical in the sediment and water column between the par ticulate, dissolved or ganic carbon, and freely dissolved compartments was assumed, and part iculate and dissolved organic carbon partition coefficients were estimated using the rela tionships described in the 2000 Human Health Methodology (USEPA, 2000). The model was used to predict chemical concentrations in sediment and the water column for a given time and loading rate.

The importance of chemical loading on J socw is illustrated in Figure 4-1 for three different loading scenarios: (a) constant loadi ng of a chemical to the ecosystem over time, (b) constant loading of chemical to the ecosystem with a doubling of loading at year 50, and (c) constant loading of chemical to the ecosystem with an 80% reduction in loading at year 50. These figures were based on predictions made for a nonmetabolizable chemical with a log K ow of 6, using the model described above. In all three loading scenarios, the concentration of the chemical in the water column responds quickly to the change in loading, in contrast to the relatively slow response of the concentration of chemical in sediment. In these scenarios, sediment and water column particulates ha d organic carbon contents of 3% and 15%,4-25 respectively (f oc /f soc = 5). In all three scenarios, J socw / K ow reaches a plateau at a value of 4.91, nearly equal to the f oc /f soc ratio.

Scenario (c) is applicable to many chem icals such as PCBs and DDTs which are no longer manufactured or used, but are often found to be present in sediments at concentrations that exceed thermodynamic equilibrium with the wa ter column. The latter portion of scenario (c)
illustrates how J socw changes over time. Differences in ecosystem parameters and conditions, such as hydraulic retention rates, sedimenta tion and resuspension rate s, water column and surficial sediment layer volumes, and chemical loading rates between ecosystems, affect the specific time scales and slopes of the changes in C f d

w , C soc , and J socw associated with changes in chemical loading over time.

Transport and fate models have been app lied in many waterbodies, including Great Lakes Areas of Concern (AOCs), Superfund sites, a nd impaired waterbodies identified under Section 303(d) of the Clean Water Act. These models may be based on widely-available computer programs such as WASP7 (Wool et al. 2001), EFDC (Hamrick, 1996), and AQUATOX (Park et al. 2004), or proprietary modeling programs. Considerable effort and expertise is required to develop credible and reliable models of chemi cal fate and transport. Such models must be properly calibrated and confirmed to site-specific data in order for the investigator to have
confidence in the results. Gu idance on model calibration and confirmation is currently under development by EPA’s Council for Regulat ory Modeling (http: //cfpub.epa.gov/crem/).4-26

C fd (pg/L)020406080100120 C soc (ug/g of oc)0.00.10.20.30.40.50.6Water, freely dissolvedSedimentElapsed Time (years)0 20 40 60 80 100

Π socw01e+62e+63e+64e+65e+66e+67e+6 Π socw / K ow01234567a C fd (pg/L)050100150200 C soc (ug/g of oc)0.00.20.40.60.81.01.2Water, freely dissolvedSedimentElapsed Time (years)0 20 40 60 80 100

Π socw01e+62e+63e+64e+65e+66e+67e+6 Π socw / K ow01234567b C fd (pg/L)020406080100120 C soc (ug/g of oc)0.00.10.20.30.40.50.6Water, freely dissolvedSedimentElapsed Time (years)0 20 40 60 80 100

Π socw0.02.0e+64.0e+66.0e+68.0e+61.0e+71.2e+71.4e+7 Π socw / K ow02468101214c

w w

wFigure 4-1.The sediment-water concentration quotient (Jsocw) for three different chemical loadingscenarios: (a) constant loading of achemical to the ecosystem overtime, (b) aconstant loading of chemicalto the ecosystem with a doubling of loading at year 50,and (c)a constant loading of chemical to the ecosystem with an 80% reduction in loading atyear 50. Simulations performed for a chemicalwith a log Kowof 6 using Lake Ontario conditions and parameters.4-27 4.4 HOW TO DESIGN A SAMPLI NG PLAN TO MEASURE BSAFs

To design a field study to measure BSAFs at a site, the investigator should determine the appropriate number of biota, se diment, and water samples to colle ct as well as how to distribute them in time and space. The investigator should define the frequency of sample collection (i.e., the number and spacing of sampling events in ti me), and the spatial distribution of sample collection locations. Having the appropriate samp ling frequency and spatial distribution (the sampling design structure) is critic al for the determination of a BSAF that is representative of the long-term average conditions in an ecosystem. Th e collection and analysis of representative samples is the key to determining an accurate and unbiased BSAF, while the precision of a BSAF depends upon the number of samples. In th e optimization process, the precision of the chemical concentration averages are balanced agai nst the costs associated with sample collection and analysis. In many cases, compositing of samples is required to limit costs associated with the chemical analyses.

4.4.1 How Can Modeling Simulations Guid e the Sampling Design Process?

The following approach to designing a fi eld sampling program to measure BSAFs has been described by Burkhard (2003). First, the investigator should determine or reliably estimate the temporal variability of chem ical concentrations at the site , as well as the chemical’s K ow and rate of metabolism. Second, an assessment of the immediate home range of the biota is required along with an assessment of spatial variability in chemical concentr ations in sediment across this range. Third, the investigator should define th e required precision for the BSAF measurement. With this information, the investigator can de termine an appropriate sampling design structure (i.e., number of sampling events over time and space), for the chemical and site of interest. With the sampling structure delineated, the total numbers of samples can then be determined based upon the desired precision required for the BSAF measurement. Steps 1 through 4 of this approach are discussed in this section; step 5 is discussed in Section 4.4.4.4-28 The BSAF field sampling design approach is based on model simulations which
demonstrate how variabilities in chemical c oncentrations translate into the uncertainties associated with BSAFs in different sampling desi gns. In the model simulations, fish, water, and sediment concentrations were predicted on a day-to-day basis with different ecosystem conditions and chemical properties. In these evaluations, model simulations were used to evaluate how temporal and spatial variability in chemical concentrations, the chemical’s hydrophobicity, the chemical’s metabolism rate in fish, the structure of the aquatic food web (benthic vs. pelagic component s), and the disequilibrium between the sediment and water
column for the chemical influence the number a nd timing of sampling events and their spatial distribution required to accura tely and representatively de termine BSAFs. The modeling approaches used by Burkhard (2003) to evaluate temporal and spatial variability in chemical
concentrations were described in Section 3.2.2 and Appendix 3D, and the investigator is referred
to that Section for details. In the simulations us ed to evaluate BSAF sampling designs, chemical
concentrations in sediment were calculated as the product of the J socw and the average chemical concentration in water for the time period (i.e., sampling interval) of interest. Presentation and discussion of the results, as they pertain to BSAF sampling designs, are presented in Appendix
4A. BSAF Field Sampling Design Approach:

1. Determine the temporal variability of chemical concentrations at the site; 2. Assess the immediate home range of the biota and the spatial variability in chemical concentratio ns in sediment within this range; 3. Define the required precisi on for the BSAF measurement; 4. Select the appropriate samp ling design structure; and 5. Calculate total numbers of biota a nd sediment samples, and allocate them among sampling events4-29 4.4.2 Using Model Simulations to Develop Field-Sampling Designs

Burkhard’s (2003) simulations provide subs tantial insight into appropriate sampling design structures for BSAF meas urements, which are generally opposite of those for measuring
BAFs:

• For chemicals with log K ow of 5 and less, with any rate of metabolism , appropriate BSAF sampling design structures would cons ist of numerous concurrent sets of sediment and fish samples spaced widely over time.

• For nonmetabolizable ch emicals with log K ow greater than 5 , the collection of one concurrent set of sediment and fish samples would, in all likelihood, be an appropriate BSAF sampli ng design structure.

• With increasing chemical metabolism rate , appropriate BSAF sampling design structure transitions from the single conc urrent collection of sediment and fish samples to designs appropriate for lower K ow chemicals, that is , the collection of numerous concurrent sets of sediment a nd fish samples spaced widely over time.

Chemicals with intermediate metabolism rates (0.01 d -1 to 0.001 d -1 , corresponding to metabolic half-lives of 50 to 500 days) a nd/or moderate hydrophobicities (4 < log K ow of < 6) present difficult challenges when selecting an appropriate sampli ng design structure for measuring BSAFs, as they do for BAFs. This ra nge of hydrophobicities lies within the transition zone between the much more obvious design structures appropriat e for low and high K ow chemicals.

The process for developing successful field- sampling structures for BSAF measurements can primarily focus upon three parameters: temporal variability, metabolism, and K ow , as was the case for measuring BAFs. These three paramete rs can range widely, and depending upon their values, dramatically different field designs w ould result. Although spat ial variability is not usually a predominant factor in sampling de sign, knowing or understanding the immediate home range of the sampled organisms is required. Wit hout this information, the investigator cannot ascertain whether sediment samples have been coll ected that are reflective of the actual chemical exposure history for the sampled organisms. Poor spatial coordination of fish and sediment4-30 samples will likely yield BSAFs with poor accura cy and large biases. In addition, the samples collected to measure BSAFs should be designed around the more contaminated regions within the site. Burkhard (2003) noted that BSAFs can be measured w ith low uncertainty even when spatial concentration gradients at the site are extreme, if these gui delines are followed carefully.

The sampling design structure (i.e., number of sampling events over time and space) can be developed for the chemical and ecosystem of interest based upon Burkhard’s (2003) modeling simulations. Using the modeling results as a gui de, some illustrative BSAF sampling structures have been developed (Table 4-1). These illust rative designs provide a sense of how sampling design structures might be influenced by differe nces in temporal con centration variabilities, metabolism rates, and K ow s. The modeling results do not refl ect the total uncertainty for the illustrative designs, because biases and errors in sampling, compositing, and chemical analysis were not included. Furthermore, temporal va riabilities typical of other ecosystems (e.g., estuaries, reservoirs, lakes, and small stream s) have not been evaluated. Additionally, the simulations were made using data for the entire calendar year, and fiel d sampling is typically performed during warmer weather or better weather conditions. Alt hough the illustrative sampling structures suggest the number and spaci ng of sampling events for a field study, they do not prescribe the total number of samples required for a successful BSAF field study.

The effects and importance of the immediate ho me range of the fish are also not included in the illustrative sampling structures (Table 4-1) . Although spatial variability of the chemical in the ecosystem is not directly included in the illustrative sampling structures, sample collection for each sampling event should span the home range of the organisms in the ecosystem. Depending upon species, the home ranges are different; larger fishes tend to have larger home ranges (as discussed in Section 3.3.2). By colle cting samples across the organism’s home range, a truer picture of the average chemical exposures to the organisms of interest will be obtained. The ideal situation for determining a BSAF is when there are minimal concentration gradients across the organism’s home range at the site. Ho wever, random walk (migration) simulations suggest that BSAFs can be measured with low uncertainty even when extreme spatial concentrations exist at the field site, provi ded the measurements are performed in more
contaminated locations of the site.4-31 Table 4-1. Some Illustrative Biota–Sedime nt Accumulation Factor Sampling Design Structures. All Sampling Even ts are Assumed to be Widely Spaced in Time (e.g., 60, 90, 120 or 180 Days).

Metabolism rate Temporal variability log K ow Minimum number of sampling events a
low high ≤ 3, 4 , 5, ≥ 6 8, 5, 2, 1
low medium ≤ 4, ≥ 5 2, 1
low low ≤ 3, ≥ 4 2, 1
medium high ≤ 5, 6, ≥ 7 8, 4, 1
medium medium ≤ 5, ≥ 6 4, 1
medium low ≤ 3, ≥ 4 2, 1

high high all K ow s 9 high medium all K ow s 4 high low all K ow s 2

a The first value corresponds to the first value in the log K ow column, the second value corresponds to the second value in the log K ow column, etc.

4.4.3 Using Monte Carlo Simulation to Determine the Number of Samples to Collect and Analyze

In Section 3.3.1, the use of the Bootstrap and Monte Carlo simulations was demonstrated to estimate the number of biota and water sample s required to determine a site-specific BAF of a known precision. The investigator ca n also use these methods to estimate how the precision of the BSAF depends upon the number of biota and se diment samples, and how the precision of the
sediment-water con centration quotient ( J socw ) depends upon the number of sediment and water samples. Furthermore, if chemical concentrations in biota (for the chemical of interest and the reference chemical), sediment (chemical of in terest) and water (refere nce chemical) are all simulated using Monte Carlo, the investigator can use the results to de termine how the precision of site-specific BAF predictions made usi ng Method 2 depend upon the number of samples
collected from each medium at the site.4-32 Monte Carlo simulations of BSAF precision are demonstrat ed by examples in Appendix 4B, based again on Green Bay Mass Balan ce data for PCB congeners 18, 52, 149 and 180.
Monte Carlo simulations of PCB congener concentrations were made using lognormal distribution moments (mean and CV) as measured in Green Bay zone 3 for dissolved water, lipid-normalized predator fish, and organi c-carbon normalized su rficial sediment.

The uncertainty of BSAFs and J socw calculated in the Monte Carlo simulations were sensitive to the number of sediment samples, and th is sensitivity increased with the variability of the sediment chemical concentrations. The variability of chemical concentrations in sediment affected the uncertainty of BSAFs and J socw , particularly for small sediment sample sizes ( n s # 6). For highly variable chemical concentrations in sediment, increasing th e number of sediment samples used to calculate the mean concentr ation had a significant impact on reducing the uncertainty of BSAFs, up to a sample size of about 6. Collecting additional sediment samples (i.e., greater than 10) had little e ffect on the precision of BSAFs.

For BAFs derived using Method 2, the resu lts were similar to those for BSAF and J socw , although the confidence limit ratios (CLRs) used as measures of precision were much larger. As
was the case for BSAFs and J socw s, only small reductions in th e uncertainty of Method 2 BAF predictions were gained using se diment sample sizes larger than about 6. Once the number of samples exceeded about 6, the reductions in BA F prediction CLRs become incrementally much smaller. This was the case even when the variability of chemical concentrations in sediment was large. Depending upon the requirements for pred ictive BAF uncertainty, exceeding sample sizes of 10 appears to be warranted only for sites having very high variability in chemical
concentrations in sediment.

Within-chemical correlations were found to be mildly helpful in terms of reducing the uncertainty of Method 2 BAFs; on average, a ra nk correlation coefficient of 0.5 reduced the CLRs by 21%. Within-media correlation, especially the correlation between chemical concentrations in sediment, significantly redu ced the uncertainty of Method 2 BAF predictions when few sediment samples are collected. When onl y two sediment samples are used to calculate
the BSAF and J socw , a rank correlation coefficient of 0.5 reduced the CLR by 50% in4-33 comparison to the uncorrelated simulation. Overall, concentration correlati ons were found to be helpful in terms of improving the precision of Me thod 2 BAF predictions; this was especially the case when relatively few samples were drawn from sediment concentrations that were correlated
between chemicals.

4.4.4 How Can These Methods Be Used to Help Design a Sampling Plan?

The investigator can combine the proceedi ng two approaches to design a sampling plan to collect the data necessary to predict a BAF of defined accuracy and precision using Method 2. The illustrative sampling structures from S ection 4.4.2 (Table 4-1) suggest the number and spacing of sampling events for a field study necessa ry to determine an unbiased BAF, but not the necessary number of samples to collect. On the other hand, the total number of samples that the investigator should collect in or der to obtain a desired BAF can be estimated using the results of the Monte Carlo simulations presented in Sect ion 4.4.3. EPA recommends that the results of modeling simulations be used together with statis tical methods such as M onte Carlo analysis as4-34 the basis for a rational sampling design proce ss. The design process is outlined below:

1. The investigator determines the goal for precision of th e BAF prediction, and expresses this goal as the 90% CLR.

2. The investigator selects an appropriate sampling design structure. Guidance is offered in
Section 4.5.1 based on:

a. Chemical factors: hydrophobicity (log K ow ) and rate of metabolism; and

b. Temporal variability of water con centrations, based upon factors of the waterbody
at the site.

Table 3-2 illustrates the relationship betw een categories of waterbodies (lakes and reservoirs, estuaries and tidal rivers, rivers and streams) and the degree of temporal variability in concentrations observed for various chemicals. The coefficient of variation (CV) for the chemical concentrations generally increase as one moves from quiescent waterbodies towa rds those that are more advective (flowing) with shorter hydraulic residence times. Therefore, if site-specific data are not available, the inves tigator can use the waterbody categories in Table 3-2 to estimate the temporal variability of water concentrations.

3. The investigator determines the number of bi ota, sediment and water samples to collect. Guidance is offered in Sections 4.4.3 for required sample sizes:

a. If site-specific data or data representa tive of the site and chemical are available, the investigator should consider con ducting Monte Carlo simulations, using
concentration moments for the site , to determine sample numbers.

b. Unless reduced precision and increase d bias are acceptable, the investigator should avoid collecting fewer than 6 sa mples of each medium. These samples4-35 may be composited within each medium, without reducing the precision of the results (see guidance offered regarding sample compositing in Sections 3.3.5, 3.4.5 and 4.6.4).

4. The investigator allocates the number of samples (based upon guidance from Step 3) evenly among sampling events (determined in Step 2).

4.5 MEASURING CHEMICAL CO NCENTRATIONS IN SEDIMENT

This section provides guidance on the developm ent of a field plan for sampling sediment to support the determination of a BSAF for a site. This gui dance is based upon a number of documents, including WDNR (1998), USEPA (2001) , USEPA (1995) and Versar (1982). These documents provide more detailed guidance on the sampling design of field studies, and recommend field procedures for collecting, pr eserving, and shipping sediment samples to a processing laboratory for chemical analysis. Plan ning and documentation of all field procedures should be emphasized to ensure that collection activities are cost-effect ive and that sample integrity is preserved during al l field activities. The investig ator should follow EPA’s Data Quality Objectives (DQO) process as a recomme nded systematic planning tool. The information compiled in the DQO process is then used to develop a project-specific Quality Assurance Project Plan (QAPP) which should be used to plan the sediment sampling plan.

The investigator and field sampling staff s hould develop a detailed sampling plan prior to initiating a field study. For sediment sampling, ther e are four major parame ters that should be specified prior to the initiation of any field sampling activities:

• Target analytes and analytical methods (including ancillary measurements)

• Sampling locations and depth

• Sample type and collection method

• Replicate and composite samples4-36 The role of each of these parameters in deve loping an appropriate field plan for sediment sampling is discussed below.

Unlike biota and water sampling, the timing of collection is usua lly not a significant factor for sediment sampling. When properly sa mpled, sediments provide time-stable measures of concentrations of persiste nt bioaccumulative chemicals in aquatic systems. Therefore, sediment sampling can be conducted at a time that is convenient to the field study. The important exception is that sediment sampling should not be conducted immediately after a major disruption in the ecosystem, (e.g., severe fl ooding, chemical spills, dam removal, lock replacements or dredging operations). At such times, the chemical concen trations in surficial sediment may not be representative of the sedime nts to which the resident organisms have been exposed. Ecosystems adjust fairly quickly to sedi ment disruptions, and a ye ar or two is generally
sufficient time to allow chemical concentrations in the ecosystem to adjust to the new conditions.

4.5.1 Target Analytes and Analytical Methods

Analytical method(s) used to measure chem ical concentrations in sediment must be compatible and consistent with the methods selected for analys is of biota (Section 3.3.1) and water (Section 3.4.1) samples. BSAFs and J socw s should only be determined for individual
chemicals. In cases where the chemical of inte rest is a mixture (e.g., PCBs, chlordane), the study design must require that individual chemicals co mposing the mixture be quantified individually.

The investigator should ensure that the methods chosen to analyze chemicals in sediment is sufficiently sensitive to detect ambient co ncentrations at the si te. Based on the methods chosen, the investigator should then determine the minimum sediment mass and volume required for each sample. This requirement is usually stated explicitly in the description of methods used to analyze chemical concentrations in sediment. Since Method 2 calls for the measurement of multiple chemicals (chemical of interest, plus one or more reference chemicals) in sediment samples, the required sample mass and/or volume ma y be 2 or 3 times larger than required for the analysis of an individual chemical.4-37 Organic carbon content should be determined in all sediment samples analyzed for the
chemical of interest and refe rence chemicals. The investigat or should also consider other ancillary measurements, which may be helpful in in terpreting variations in sediment chemistry in terms of physicochemical, biological, and transp ort processes. These include grain size, bulk density, percent moisture, sediment oxygen dema nd (SOD), acid volatile sulfide (AVS), and oil and grease. If sediment cores are collected, radi oisotope (i.e., Cs-137 and Pb-210) measurements may be useful in determining the depo sition age of individual core sections.

4.5.2 Sampling Locations and Depths

The spatial distribution of ch emical concentrations in sedi ment are often highly variable. Therefore, the investigator should carefully cons ider and select the appropriate locations for sediment sampling. A key consideration for the inve stigator faced with determining a BSAF at a site, is that the sediment samples should be re flective of the target or ganism’s recent exposure history. In practice, this means collecting sedime nt samples within an area defined by the home range of the organism. Depending upon the specie s, the home ranges are different, and larger fishes tend to have larger home ranges. With in formation about the home range of the fish, an
assessment of where the fish resides relative to th e spatial variability in chemical concentrations can be performed. Clearly, where large concentrati on gradients exist at the field site, extra care should be taken in selecting sediment sampling locations to collect representative samples. Geostatistical methods may be used to help identify optimal sampling locations (Leadon, 2000), if some data are available for the spatial distribution of the chemical in the sediment at the site.

The issue of identifying the home range of an organism is discussed in Section 3.3.2. Once the home range is defined, the investigator should next select a spatial sampling design. Guidance is available from several sources (U SEPA, 2002; USEPA, 2001[Table 2-1] ) regarding sampling design alternatives that may be a ppropriate for estimating the mean chemical concentration required for the BSAF. Sampli ng designs are frequently based upon collecting sediment samples at the same locations where biot a are collected. This is not necessarily the best4-38 approach to selecting sediment sampling locatio ns, because these samples may not reflect the biota’s average chemical exposure over the home range.

To measure chemical concentrations that ar e reflective of the target organism’s recent exposure history, it is important to sample the surficial layer of sediment. The thickness of the surficial sediment layer is defined by the rate of sediment deposition, bioturbation or physical mixing processes, and other factors responsible for vertical distribution of sediments and
associated chemical contaminants within the bed. If this is done succ essfully, the sediment samples will be “connected” to the bi ota in terms of chemical exposure.

EPA recommends that samples of surface sedi ments should be collected from locations in which carbonaceous sediment, containing the chemical of interest and the re ference chemicals, is regularly deposited and is representative of av erage surface sediment in the vicinity of the organism. When selecting sediment sampling loca tions, it is important to consider sediment deposition and erosion zones, since grain size an d related characteristics (including conventional parameters such as sediment organic carbon an d acid volatile sulfide, as well as chemical concentrations) are likely to vary between these two sediment envi ronments. In fluvial (flowing) waterbodies, sediments tend to deposit and accumula te in locations where current velocities are lower (e.g., inside stream bends, in deep pools, ab ove dams or other obstructions). In lacustrine waterbodies, sediments usually accumulate in deep er water. Depositional zones typically contain fine-grained (silt and clay) sediment deposits wh ich tend to have higher organic carbon content. Higher concentrations of hydrophobic chemicals ar e usually associated with fine-grained sediments. Coarser particle type s (e.g., sand and gravel) are usua lly found in erosional zones. Depending on the target organism, one or the other sediment zone may be favored as a substrate or habitat. However, sediment sampling in erosional zones is not recommended because
chemical concentrations measured in the sediments typically found in such locations tend to be unrepresentative of the chemical exposure for most aquatic organisms.

Determining the appropriate depth of sediment to collect during sampling is as important a consideration as properly locating the samples. For the investigator determining a BSAF, it is best to sample the upper-most surficial sediment layer, because this sediment contains the4-39 chemical concentrations to which fish are expos ed, as well as the benthic food web for the fish. Generally, the most recently deposited sediments and most epifaunal and infaunal organisms are found in this surficial layer. The goal for the investigator is to sa mple a thin layer of surficial sediment. EPA considers the top 1 cm of sedime nt to be ideal (Burkha rd et al. 2004), although for many sites this may be overly conservative as well as impractical in terms of sampling methods and analytical volume/mass requirements. Depending on site-specific factors, it may be acceptable to increase this depth to the upper 2 or 4 cm of sediment. When benthic organisms, especially oligochaetes (small earthworm-like or ganisms) are abundant, the surficial sediment horizon is often vertically well-mixed by biotur bation to a depth up to about 10 cm (Boudreaux, 1994; Thibodeaux and Bierman, 2003). However, samp les containing sediment from deeper in the bed may tend to bias the measurement of chemical concentrations, because the concentrations in deeper sediment intervals are often different than in the surficial sediment. Additionally, if loading histories for some but not all of the chemicals have changed, sediment samples extending to deeper levels might provide skewed representations of the distribution of chemicals. Sediment samples extending from the se diment surface to much deeper levels in the sediments, e.g., 0-20 cm (a common sediment samp ling protocol) will in many cases be too deep to be acceptable, and could represent time periods extending to decades or more depending upon sedimentation rates. The investig ator should review any available data for the site that can be used to determine the appropriate depth of surf icial sediment for sampling. These data include sediment core profiles of chemical concentr ations, physical propertie s or counts of benthic organism abundance. If such data cannot be found, the most conservative approach for the investigator would be to sample th e top 1-2 cm of surficial sediment.

A review of existing background information fr om all reasonably available sources for a site or study area should be the first step in collecting data for a sediment quality assessment. The
information obtained in a review of a site's hi storical (industrial and other uses) and existing sediment data costs relatively little and can provide informati on about the likelihood and types of contamination that may be present. Historical information can help guide study plans and may reduce the amount of field work and analysis needed to accomplish information goals. Various types of information may be available for a s ite background review, and the investigator should pursue this data at the project planning stage:4-40 Historical Information - Historical information is us eful in trying to find out what chemical contaminants may have been intr oduced to the waterbody historically, and can indicate specific contaminants that may be targeted as reference chemicals. Historical
information includes:

• Land use - agricultural, industrial, residential, recreational;

• Water usage - industrial, municipal wast ewater treatment plants, power plants, municipal water intakes, shipping;

• Dredging activity; and

• River, lake, estuary or har bor morphology and bathymetry.

Recent information - Additional information, generated w ithin the past 10 years, should also be sought, such as:

• Precise description of designated uses;

• Quantity and quality of potential and known inputs;

• Point sources - locations of outfalls from industrial discharges, storm sewers, etc.;

• Non-point sources of sediment and chemical contamination;

• Any previous sampling and chemical analysis data;

• Sediment (bathymetric) maps - Many harbor s have up-to-date bathymetric maps of the harbor area. The local harbor authorit y, U.S. Army Corps of Engineers (USACE), U.S. Coast Guard, or National Oceanic a nd Atmospheric Association (NOAA) should be able to provide that information.

From this historical information, the i nvestigator may be able to develop an understanding of the following factors affecti ng contaminant source pathways: bathymetry, water current patterns, tribut ary flows, watershed hydrology a nd land uses, sediment and soil types, and sediment deposition rates. Assembling this information can be helpful in evaluating4-41 sampling locations and sample designs (i.e., the c hoice of sampling on a regular grid or stratified random sampling). The following are suggested sources of information relevant to sediments and
chemical contamination:

• STORET (http://www.epa.gov/storpubl/) - A database maintained by EPA to store and make available data on many water qua lity parameters, including contaminant concentrations in sediment and fish.

• NWISWeb (http://waterdata.usgs.gov/nwis) - Database maintained by US Geological Survey, also including contaminant concentr ations in water and sediment for major tributaries.

• Sediment and Fish Contaminant Databases, e.g.:

• USEPA Ecotox,

• USEPA Fish Residue,

• USEPA PCB Residue,

• USACE Residue and other studies of sediment pollution and sediments.

• Published scientific research - A search of the published literature (mostly journals) should be conducted for any research that has been conducted at the study site.

• Published and unpublished reports - Studies ma y have been carried out by states and reported, but never formally published. These reports may contain valuable information about sediment sites.

• Case files - Many states maintain files c ontaining information and reports on previous and ongoing remediation projects. EPA studi es and information about Superfund and RCRA sites, Remedial Action Projects (R APs), basin plans, Total Maximum Daily Load (TMDL), and NPDES permit records may also be contained in the State case files.

• Local government or academic related rese arch - Local health agencies, Fish and Wildlife Service, and colleges and universities (natural resources, environmental chemistry and environmental science and engineering programs ) may be excellent resources and sources of information.

• Selected chemical spill reporting system (E PA) - Information is available from the states and directly from the EPA.4-42 • Pesticide spill reporting system (EPA).

• Reports of pollution-caused fish kills.

• Pollution incident reporting sy stem (U.S. Coast Guard).

• Identification of In-Place Pollutants and Priorities for removal (EPA).

• Hazardous waste sites and Manageme nt facilities reports (EPA).

• U.S. Army Corps of Engineers (ACE) studi es of sediment pollution and sediments.

4.5.3 Sample Type and Collection Method

Sediment samples are most commonly collected using a coring device or a dredge or grab sampler. The type of collecting equipment chosen will depend on sediment texture, site location (depth and current velocity), analyses to be performed, and study goals. Guidance in selecting appropriate sediment sampling equipment is provided in Chapter 3 of USEPA (2001). The
technical manual includes flowcharts for selectin g appropriate core and grab samplers based on site-specific factors.

A piston corer allows excellent quantitativ e and qualitative sampling to a specified sediment depth with little disturbance of the se diment-water interface. Samples can be separated or stratified by depth or color/texture to analyze distin ct layers of sediment, although the sediment along the side of the core may smear as the core penetrates, slightly distorting the stratification of the sediment. A corer may not be able to penetrate or retain very sandy substrates. Coring in high clay-content sediments where grab samplers won't work is possible if the water is not too deep, but may be difficult with a push corer and may require the use of a slide hammer or vibrating corer. A hand-operated, 3 inch diameter core sampler with an optional piston and extensions for deeper water can be effectively used in soft sediments with some silt/clay content in water up to ~30 ft deep. A large bore corer will provide a larger volume of sediment per attempt. This is important if discre te sample replicates are desired. Even with the large bore core tube, samples may need to be composited to obtain enough sediment volume for the required chemical analyses.4-43 Grab samplers rely on their own weight and gravity to penetrate the sediment as well as the leverage from the closing of the jaws. For this reason, they are not as efficient in water flowing with a velocity over one meter per seco nd. They normally take a discreet "bite" of sediment to a fairly consistent and measur able depth. Grabs often cause a shock wave upon descent which may disturb very fine sediment at the sediment-water interface. Common grab samplers include the petite Ponar and Ekman dr edges, both of which can be hand operated from
a small flat-bottomed boat. The Ponar is bette r suited to sampling hard or sandy sediments because of the greater ability to penetrate, whil e the Ekman is more suited to sampling in soft sediments in low flow waters. Neither grab sample r will effectively sample hard clays; a coring device or shovel such as a sharpshooter sp ade should be used at these sites.

4.5.4 Replicate and Composite Samples

As discussed in Section 4.4, the number of sa mples directly affects the representativeness and completeness of the sediment data for estim ating the mean chemical concentration. The number of samples collected and analyzed will always be a compromise between the desire of obtaining high quality data and th e constraints imposed by analyti cal costs, sampling effort and logistics. The investigator can use two strategies to find an appropriate balance between
confidence in the data and cost of collect ing it: replication an d sample compositing.

Sample replication is used to assess measur ement precision, and can be used to determine the variability in data due to analytical errors and sampling reproducibility, factors which can be significant in comparison to the spatial variance in chemical concentratio ns. Different kinds of replicates can be collected, depending on the ty pe of precision desired by the investigator. Analytical replicates are used to assess analytic al data quality. Field replicates can be used to provide useful information on the heterogeneity of chemical concentrations within sediment, for either the site or for locations (stations) within the site. Results of field replicate analysis yield the overall (combined) variability or precision of both the field and laboratory operations. This variability is an important f actor in estimating the minimum number of sediment samples necessary to determine a BSAF or Π socw of known precision, as discus sed in Section 4.4.3. When collecting replicate samples to statistically comp are sediment deposits, sample sites within each4-44 deposit should be randomly located for statistical comparisons to be valid. Each replicate sample
should be taken from an area of sedi ment undisturbed by previous samples.

A composite sample is formed by combining material from more than one sample or subsample. Because a composite sample is a combin ation of individual aliquots, it represents an average of the characteristics making up the sample. Although compositing results in a less detailed description of the variability in chemi cal concentrations, it is generally considered an excellent way to average the naturally heterogeneous physical and chemical conditions in sediment that often exist at a site, even within a relatively small area. Compositing is also a practical way to control analyt ical costs while still providing a reliable mean chemical concentration based upon samples from a large number of locations. Compositing of sediment samples is not recommended where combini ng samples could serve to dilute a highlyconcentrated but localized sediment “hot spot”. Also, sediment samples from locations with very different grain size characteristic s or different stratigraphic layers of core samples should not be composited. Multiple grabs or cores for a composite sample should be taken from a relatively homogeneous sediment deposit (i.e., all grab s should be of similar sand/silt content).

In some cases, composite samples are needed to generate sufficient sample volume for all analyses. This is particularly true when sampling a relatively th in layer of surficial sediment. Table 4-2 shows the number of 3-inch core sample s that must be composited to generate 500 mL of sediment, a common volume requirement for analysis of multiple organic chemicals. As this table illustrates, an increasi ng number of samples must be co mposited to obtain a required sediment volume, as the thickness of the su rficial sediment being sampled decreases.4-45 Table 4-2. Number of 3-inch Diameter Cores Required to Composite 500 mL of Sediment

surficial sediment sample thickness (cm) volume per core (cm 3 ) number of cores required to obtain

500mL volume
1 45.6 11
2 91.2 6
4 182 3
10 456 1

4.6 SCIENTIFIC ISSUES ASSOCI ATED WITH METHOD 2 AND THE USE OF BSAFs TO PREDIC T CHEMICAL BIOACCUMULATION

EPA’s Method 2 bioaccumulation methodology ha s not received widespread attention in either the scientific or regulatory communities. Nor does EPA have much experience in the application of Method 2 to pred ict site-specific BAFs. Consequently, there are many potential issues to address dealing with the reliabil ity of Method 2 predictions and the underlying assumptions upon which it is based. This section discusses a number of significant scientific
issues related to the application of Method 2.

4.6.1 Evaluation of Method 2 Predic tions of Site-Specific BAFs?

A number of studies have been conducted to evaluate the prediction of BAFs by Method 2, using BSAFs measured at a site. Evaluation e fforts have been conducted with data collected from three aquatic ecosystems in the United St ates: Lake Ontario; Green Bay/Fox River, Wisconsin; and the Hudson River, New York. EPA previously published information on evaluation of the Method 2 approach by using da ta on PCBs, chlorinated benzenes, pesticides, and 2,3,7,8-tetrachlorodibenzo- p -dioxin (TCDD) collected from Lake Ontario and the mid-bay region of Green Bay (USEPA, 1995c). Baseline BA Fs for PCBs, chlorinated benzenes, and some4-46 pesticides were predicted from BSAFs for Lake Ontario salmonids and compared with measured baseline BAFs from the same system. The base line BAFs predicted from BSAFs were within a
factor of 4 of the measured baseline BAFs. Fu rthermore, when predicted baseline BAFs for TCDD and PCBs from Green Bay brown trout and Lake Ontario salmonids were compared, the baseline BAFs predicted from BSAFs were gene rally within a factor of 2 of the measured baseline BAFs. Although there were a few outliers in the observed trends, the results of this evaluation effort showed Method 2 generally work s well, not only for pred icting baseline BAFs with data from the same ecosystem (Lake Onta rio), but also for predicting baseline BAFs between systems (Green Bay vs. Lake Ontario).

Burkhard et al. (2003b) exte nded the previous evaluations for Method 2 by comparing
results of field-measured baseline BAFs w ith baseline BAFs predicted from BSAFs using additional PCB data collected from Green Bay/F ox River and the Hudson River. The data sets
for this latest evaluation effort were se lected from the 1989–1990 Green Bay Mass Balance Study (http://www.epa.gov/grtlakes/gbdata) and th e Hudson River PCBs Reassessment Remedial Investigation/Feas ibility Study (USEPA, 1998). The former study included data from the lower Fox River and the inner, middle, and outer zo nes of Green Bay. The Hudson River data were collected over several years by a number of federal and state ag encies and private groups and were assembled into a single database (USEPA, 1998) from which data were selected for this analysis. The reference PCB congeners used in this evaluation effo rt included three of those used in the previous validations (PCB 52, 105, 118) (USEPA, 1995b) as well as PCBs 18, 28, 149, 174, and 180. This evaluation was performed using the geometric mean of the baseline BAFs predicted by using as many reference chemicals as possible from the eight PCB congeners listed
above. EPA recommends that several reference chemicals be used with Method 2 and that K ow s be matched as closely as possible, because slightly smaller predictive errors were observed in the evaluation study when the chemicals of interest and the reference chemicals had more closely
matched K ow s (Burkhard et al. 2003b). The evaluation e ffort by Burkhard et al. (2003b) included baseline BAFs for several fish species in addition to salmonids (e.g., carp, walleye, shad, alewife, yellow perch, white perch, pumpkinseed , red-breasted sunfish, and largemouth bass), some of which spanned several age classes.4-47 A summary of the evaluation exercise is pr esented here and a detailed discussion is provided by Burkhard et al. ( 2003b). Baseline BAFs predicted with Method 2 were plotted against field-measured baseline BAFs, to visually demonstrate the accuracy and precision of the predictions. The agreement betw een measured and predicted

, BAF f d i L s using Method 2 is illustrated in Figure 4-2 for Green Bay and the Hudson River, for a variety of the fish species. The ratio of predicted-to-measured congener-specific baseline BAFs (BAF predicted /BAF measured ) was used to evaluate the agreement between Method 2-predicted ba seline BAFs and fieldmeasured baseline BAFs. Table 4-3 presents z one (Green Bay data) and location-specific (Hudson River data) statistics for the BAF predicted /BAF measured ratio. Table 4-3 also presents the percentage of BAF predicted /BAF measured ratios that fall within specifie d ranges of the distribution. In general, the agreement between Method 2-predicted baseline BAF and field-measured baseline BAF values is very good, with a ma jority of predicted BAF values fa lling within a factor of 2 of the field-measured BAF values. In addition, >9 0% of Method 2-predicted BAFs (94.5% from Green Bay and 90.7% from Hudson River) are within a factor of 5 of the field-measured baseline BAFs. For most zones in Green Bay, the 95% exceedance levels (i.e., 95% of the
BAF predicted /BAF measured values) fall within the range of 0.2 (one-fifth of the predicted baseline BAF) to 5.0 (five times the predicted baseline BAF). Results for the Hudson River indicated generally similar agreement between Method 2- predicted baseline BAFs and field-measured baseline BAFs. Overall, these an alyses strongly support the use ofMethod 2 to estimate sitespecific BAFs from field-measured BSAFs.4-48 Figure 4-2. Predicted baseline BAFs using method 2 (blu e triangle) and 4b (p ink circle) plotted against measured baseline BAFs for different sa mpling locations and fish species for the Green Bay (species and zone) and Huds on River (species and river mile) ecosystems. The solid lines are perfect (1:1) agreement, the dot-long dash lines are 2x from pe rfect agreement, and the dotted lines are 5x from perfect agreement.4-49 Table 4-3. Validation Statistics fo r Method 2: Ratio of Baseline BAF predicted /Baseline BAF measured for all (combined) sampled fish species

Method 2: Exceedance Levels and Comparison Statistics Location 95% Mean Median 5% % within 2x % within 5x Green Bay

Zone 1 0.39 0.88 0.88 1.66 87.6 100
Zone 2a 0.25 1.27 0.89 3.47 69.8 92.8
Zone 3a 0.21 1.25 0.73 3.78 51.5 94.1
Zone 3b 0.16 1.08 0.69 2.71 53 91.4
Zone 4 0.31 3.33 1.07 3.79 31.9 97.4
All zones 0.22 1.53 0.81 3.29 55.7 94.5
Hudson River

RM 194 0.46 1.12 0.99 2.12 81.9 95.2
RM 189 0.33 1.00 1.03 1.55 87.5 100
RM 169 0.11 2.01 0.59 9.91 19.0 68.3
RM 144 0.67 1.19 0.97 2.14 92.3 100
RM 122 0.70 2.43 2.16 4.81 45.8 95.8
RM 114 1.20 3.86 3.78 6.91 16.7 83.3
All stations 0.13 1.50 1.10 4.42 64.9 90.7
RM = river mile.

The agreement between Method 2 BAF predictions and measurements was less satisfactory in Green Bay zone 4 and at H udson River RM-114. In both ecosystems, these locations are relatively distant from the majo r known sources of chemical contamination. As PCBs are transported greater distances, they are increasingly subject to va rious transport and fate processes which can alter their concentrations and concentration ratios (i.e., the “weathering” process: Burkhard et al., 1985; Mackay et al., 1992; Manchester , 1993). PCB concentrations are
lower at these “distant” locations than in othe r zones/river stations closer to the major known sources. Lower chemical concentrations are genera lly less accurate than higher concentrations,4-50 which may lead to greater errors in BAF predic tions. In addition, other sources (e.g., atmospheric deposition) may become more signi ficant contributors of PCBs at these distant locations. Each of these factors may play some role in the poor er fit of the Method 2 BAF predictions to
measurements made at distant locations.

4.6.2 Is Chemical Equilibrium Assumed in the Calculation of a BSAF?

The BSAF definition (equation 4-1) does not invoke or include the assumption of equilibrium conditions for the chemical between the organism and sedime nt (Ankley et al., 1992; Thomann et al., 1992). As shown by Thomann et al. (1992), BSAFs are appropriate for describing bioaccumulation of sediment contaminan ts in aquatic food webs with non-equilibrium
conditions between both the sediment and fi sh, and sediment and its overlying water. Equilibrium is regarded as a reference condi tion for describing degrees of disequilibrium between chemical concentrations in biota, sedime nt and water. Therefore, chemical equilibrium is not a requirement for measurement, prediction, or appl ication of BSAFs.

When calculating BSAFs for benthic inverteb rates, numerous investigators (Lake et al. 1984; McElroy and Means, 1988; Bierman, 1990; La ke et al. 1990; Ferraro et al. 1990) have invoked two assumptions: (1) equilibrium conditi ons and (2) no metabolism of the chemical. These assumptions, when combined with EqP (equilibrium partitioning) theory (DiToro et al. 1991), lead to the conclusion that the BSAF, fo r these specific conditions, is equal to the partitioning relationship of the chemical between or ganic carbon in the sediment and lipids of the organism. Depending upon the affinities of the nonpol ar organic chemical for lipid and sediment organic carbon, the BSAF, under these specific c onditions, should be in the range of 1 to 2 (McFarland and Clarke, 1986.). For aquatic organisms tightly conn ected to the sediments like oligochaetes and other benthic invertebrates, experimental m easurements (Lake et al. 1990; Tracy and Hansen, 1996) are generally consistent with the theoretical value, i.e., in the range of 1
to 2.

These data show that chemical equilibr ium is a sound fundamental theory for nonionic organic chemicals when appropriately a pplied to conditions near equilibrium.4-51 However, there are solid mechanistic reasons why fish should not be in equilibrium with sediments within their home range (Thomann et al. 1992). For fish, BSAFs incorporate wide ranges of influences including: biomagnification due to the trophi c level of the fish, sedimentwater column chemical disequilibrium, the diet of the fish and its underlying food web, the fish’s home range, and chemical metabolism within the fish and its food web (Burkhard et al. 2003a). Suggestions that BSAFs for fish should be in th e range of 1 to 2 by combining the definition of the BSAF with the assumptions of equilibrium conditions and no metabolism are incorrect (Wong et al. 2001). Due to these factors, measured BSAFs with values above or below 1 to 2 are entirely reasonable for fish (Burkhard et al. 2003 a). BSAFs outside of this range for fish do not violate the general definition of BSAFs nor i nvalidate the usefulness of BSAFs in predicting
chemical residues in fish for sediment contaminants.

For BSAFs to have predictive power in term s of determining BAFs (i.e., Equation 4-2),
the ratio of chemical concentrations between bi ota and sediment should not change substantially over time. This implies that the site is at or near steady state conditions for the chemical of interest and the reference chemicals. The parameter D k/r offers the investigator some ability to
correct for differences in sedime nt-water concentration quotients ( Π socw ) that may reflect mild departures from this condition. To reiterate, steady state conditions do not require chemical equilibrium.

4.6.3 Review of Existing Data for J socw

Reliable measurements of J socw s are rather limited because of a number of factors. These
include :

• the difficulties in measuring the concentr ations of hydrophobic organic chemicals in natural waters because they occur at very lo w concentrations, that is, less than 1 ng/L;

• the lack of data for sediment and water sa mples that are temporally and/or spatially coordinated;4-52 • the lack of data for sediment samples coll ected from the uppermost 1 or 2 cm of the sediments;

• the lack of measurements of POC and DOC for the water samples analyzed for the hydrophobic organic chemicals;

• the lack of determinations of the sediment organic carbon content; and

• the fact that studies designe d specifically for determining J socw are not usually performed.

In addition, combining sediment measurements from one study with water measurements from another study can resu lt in large biases in J socw s due to differences in analytical methodologies (e.g., different surrogates for reco very corrections, different standards) and sample designs.

Review of a number of differe nt data sets, as described in Burkhard (1998), revealed three data sets of suitable quality for which J socw s could be determined. These data sets were from Lake Ontario (Oliver and Niimi, 1988), Hudson River (USEPA, 19 97; USEPA, 1998), and Green Bay in the Lake Michigan ecosystem (w ww.epa.gov/grtlakes/gbdata/). The Green Bay and
Hudson River data sets containe d data for PCBs only, and the La ke Ontario data set contained data for chlorinated pesticides, PCBs, and a few chlorinated benzenes, toluene, and butadiene. The data for the chlorinated benzenes, toluene, a nd butadiene in the Lake Ontario data set were not used in this analysis because these chemical s volatilize to the atmosphe re relatively easily in comparison with the higher molecular weight PCBs and chlorinated pesticides.

Figure 4-3 shows the J socw s for selected PCB congeners in five different zones of Green Bay. For the individual PCB congeners, the geomet ric mean regressions were performed on data for the five different zones in the Green Bay sy stem because both variables were measured with error (Ricker, 1973). Th e slopes of the log J socw -log K ow regressions from the different zones were not significantly different among th e five zones (comparison of slope test, " = 5%). Therefore, average J socw s were determined for each PCB congener with data from all zones (Figure 4-4). The geometric mean regression statistics are reported in Table 4-4 for each zone and for the average of all zones. Examination of Figures 4-3 and 4-4 and Table 4-4 reveals that4-53 for PCBs, J socw is strongly dependent on the K ow , and slopes of slightly less than 1 were
obtained. Examination of J socw s for Lake Ontario and Hudson River reveals trends similar to those in Green Bay; a strong dependence of J socw on K ow for the PCBs and chlorinated pesticides (Figure 4-5 and Tabl e 4-4), and slopes of 1 and slig htly less than 1 were obtained.

Table 4-4. Geometric Mean Re gression Equations (log J socw = A C log K ow + B) for Polychlorinated Biphenyls (PCBs) and Chlorinated Pesticides

Ecosystem Slope (±sd) Intercept (±sd) n r s xy Green Bay (PCBs) Zone 1 0.95 (±0.04) 1.21 (±0.22) 46 0.97 0.17 Zone 2a 0.92 (±0.09) 1.13 (±0.61) 31 0.82 0.34 Zone 3a 0.87 (±0.06) 1.61 (±0.36) 63 0.86 0.37 Zone 3b 0.83 (±0.06) a 1.88 (±0.36) 60 0.85 0.33 Zone 4 0.86 (±0.08) 1.31 (±0.53) 46 0.76 0.46 All zones, congener averages 0.92 (±0.06) 1.20 (±0.38) 77 0.82 0.43 Hudson River (PCBs) RM 189 0.87 (±0.08) 1.81 (±0.45) 32 0.86 0.13 RM 194 0.72 (±0.08) a 3.16 (±0.42) 27 0.84 0.16
Lake Ontario (PCBs and chlorinated pesticides) 1.05 (±0.08) 0.83 (±0.49) 55 0.84 0.46 n = number of data points r = correlation coefficient
sd = standard deviation

s xy = standard error of estimate

a slope significantly different from 1.0, " = 1%.4-54 Green Bay Zone 1LogΠsocw

4 5 6 7 8 9 10

Green Bay Zone 2aLogΠsocw

4

5 6 7 8 9

Green Bay Zone 3aLogΠsocw

4 5 6 7 8 9

Green Bay Zone 3bLogΠsocw

4 5 6 7 8 9

Green Bay Zone 4

Log K ow 456789LogΠsocw

4 5 6 7 8 9 Figure 4-3. Sediment-water column concentration coefficient ( J socw ) for PCBs in five different geographical zones in Green Bay, Lake Michigan. The circled data points are the PCB congeners numbers (log K ow ) 18 (5.24), 28 + 31 (5.67), 52 (5.84), 101 (6.38), 118 (6.74), 149 (6.67), 174 (7.11), and 180 (7.36). The geometric mean regression and their 95% confidence limits are plotted.4-55 

## All Zones, Congener Averages



## Log K ow 

### 456789LogΠsocw



### 4 5 6 7 8 9 10

Figure 4-4. Average sediment-water column concentration coefficients ( J socw ) for individual PCB congeners across the five different geograp hical zones in Green Bay, Lake Michigan. The circled data points are the PCB congeners numbers (log K ow ) 18 (5.24), 28 + 31 (5.67), 52 (5.84), 101 (6.38), 118 (6.74), 149 (6.67), 174 (7.11), and 180 (7.36). The geometric mean regression and their 95% confidence limits are plotted.4-56 

### Hudson River Mile 189LogΠsocw



### 5 6 7 8 9 10



### Hudson River Mile 194



## Log K ow 

### 45678LogΠsocw



### 5 6 7 8 9

Figure 4-5 . Sediment-water column concentration coefficients ( J socw ) for PCBs at river miles 189 and 194. The circled data points ar e the PCB congeners numbers (log K ow ) 18 (5.24), 28 + 31 (5.67), 52 (5.84), 101 (6.38), 118 (6.74), 149 (6.67), 174 (7.11), and 180 (7.36). The geometric mean regression and their 95% confidence limits are plotted.4-57 In the Green Bay ecosystem, chemical concentrations in both sediments and the water column decrease with increasing zone number. Z one 1 is at the mouth of the Fox River, the source of PCBs to the bay, and zone 4 connects th e bay to Lake Michigan. Zone 1, the region of highest chemical concentrations, has mu ch less variability in the measured J socw s and the largest slope for the log J socw –log K ow relationship among all sa mpling zones in Green Bay. Comparison of the variability existing in zone s 1 through 4, as illustrated by the 95% confidence intervals in Figure 4-3, suggests that variability increases with increasing distance from the source of the PCBs (Table 4-4), and this trend pa rallels the concentration gradient in Green Bay. The consistency and slope of the J socw –K ow relationships observed in zone 1 data might be more illustrative of the underlying J socw –K ow relationships than those of the other zones because of lower uncertainties associated with the analytical measurements, the chemical concentrations are high (i.e., concentrations well a bove the quantitation limits or high signal-to-noise ratio). This can be explored by data visual ization methods (e.g., plotting BAF and/or BSAF variability against water, sediment and biot a concentrations). Alternatively, the difference may reflect the greater role that various transport and fate pr ocesses (which depend on chemical-specific factors of solubility, volatility and resist ance to biochemical degradation) play in outer regions of Green Bay as opposed to their much more limited role n earer the sources of contamination in the Fox
River.

From a theoretical standpoint, log J socw –log K ow relationships will have a slope of 1 if the ecosystem is at equilibrium. In addition, EPA believes that ecosystems at steady state or with conditions that approximate the longer term average conditions will also have slopes nearly equal to 1. A number of factors could cause the slope to be less th an 1; these include
volatilization losses (assuming net water-to-air flux for all chemicals) although this requires information about the relative volatility of th e different chemicals (Mackay et al., 1992),
inaccuracies in the calculation of the concentration of chemical that is freely dissolved in the water column (the denominator in the J socw term), and measurement error in determining the
concentrations of chemical in the se diments and/or water column. The log J socw –log K ow relationships for the Hudson River, Lake Ontari o, and Green Bay ecosystems have slopes that are 1 or slightly less than 1 for PCBs and chlorina ted pesticides (Table 4-4). The smallest slopes were observed with the Hudson River ecosystem data. The Hudson River ecosystem is much4-58 more dynamic and possibly further from steady-st ate conditions than are the Lake Ontario and Green Bay ecosystems, because of changing flows over time and recent changes in PCB loadings. Given the similarity in slopes among a ll three ecosystems, the conditions in the Hudson River do not appear to be greatly differen t from those in the other two ecosystems.

Given that the slopes for the log J socw –log K ow relationships in Green Bay, the Hudson River, and Lake Ontario are close to 1, and the fact that ecosystems tend to move toward the theoretical slope of 1 over time, EPA assumes a sl ope of 1 for this relationship. Data analyses and averaging performed for the three ecosystems yielded average J socw /K ow ratios of 7.21 for Green Bay, 14.3 and 48.4 for Hudson River, and 23.4 for Lake Ontario (Table 4-5). The large differences in average J socw /K ow ratios between the two Hudson River sampling stations suggest distinctly different behaviors in the two sampling stations, and, th erefore, an overall ratio was not computed for the Hudson River. The EPA believes that the differences in average J socw /K ow ratios among the three ecosystems evaluated here illustrate the range of variability that occurs among ecosystems across the nation. Because J socw s are a function of both current and past chemical loadings to the ecosystems, J socw /K ow ratios both larger a nd smaller than those observed probably exist in the nation. For highl y contaminated sites (e.g., Superfund sites with large concentrations of chemicals in the sediments), J socw /K ow ratios could become very large. For new chemicals that are just being introdu ced or discharged into the environment, J socw /K ow ratios will be small because very little of the chemical is present in the sediment. Degradation processes such as hydrolysis, photolysis, and metabolism can also strongly influence the J socw /K ow ratio, depending on where these processes o ccur (i.e., the sediment and/or the water column).4-59 Table 4-5. Average J socw /K ow Ratios for Three Different Ecosystems

Percentile Ecosystem Average Ratio (±sd) 5% 10% 90% 95%
Green Bay (PCBs) Zone 1 9.15 (±4.97) 4.34 5.55 13.8 17.3 Zone 2a 6.35 (±6.73) 1.24 1.37 13.1 21.0 Zone 3a 10.3 (±13.3) 1.27 1.88 21.7 25.6 Zone 3b 9.48 (±10.6) 1.68 2.00 20.1 29.9 Zone 4 4.49 (±6.68) 0.60 0.75 6.95 8.10 All zones, congener averages 7.21 (±6.68) 1.01 1.76 13.3 16.5

Hudson River (PCBs) RM 189 14.3 (±8.98) 6.03 7.36 23.4 34.7 RM 194 48.4 (±47.6) 18.9 22.6 69.5 83.6

Lake Ontario (PCBs and chlorinated pesticides) 23.4 (±25.1) 2.96 3.57 52.6 82.4

Overall average J socw /K ow 23.3 (±18.0)

sd = standard deviation

Because the degradation rates for the observe d PCBs and chlorinated pesticides in the environment are extremely slow, the average J socw /K ow ratio of 23.3 for the three ecosystems is representative of chemicals that are very slowly degraded (o r have long half-lives in the environment). Chemicals with higher degrad ation rates will, in all likelihood, have J socw /K ow ratios that are different from those for the P CBs and chlorinated pesticides, and EPA believes
that the J socw /K ow ratios will be smaller for such chemical s, on average, than those for the PCBs and chlorinated pesticides reported here.4-60 4.6.4 How does J socw Reflect Steady State Conditions at a Site?

Bioaccumulation of hydrophobic nonionic organic chemicals in aquatic organisms is dependent on a number of ecosystem conditions including food chain length (Rasmussen et al. 1990), food web composition (Vander Zanden an d Rasmussen, 1996; Burkhard, 1998), and the
chemical distribution between sediments and wa ter (Thomann et al., 1992; Endicott and Cook, 1994). The impacts of food web composition and ch emical distribution between sediments and

water are interrelated because sediments and wate r are the primary exposure media for the benthic and pelagic component s, respectively, of the food web (Burkhard, 1998). Chemical
concentrations in benthic inverteb rates at the base of the benthic food web ar e directly controlled by the concentrations of chemicals in the sediment s. Chemical concentrations at the base of the pelagic food web (e.g., phytoplankton) are directly controlled by the concentration of chemicals in the water. Therefore, differen ces in distribution of chemical between sediment and water, as well as differences in benthic versus pe lagic food web composition, will affect the bioaccumulation of nonionic organic chemi cals in forage and piscivorous fish.

Ecosystems at thermodynamic equilibrium, a c ondition that rarely exists in nature, should theoretically have J socw s equal to the chemical’s K ow . Consequently, ecosystem models typically
characterize J socw by using its ratio to K ow as a measure of the degree to which the ecosystem is in disequilibrium (Thomann et al ., 1992), or, alternatively, as a measure of the fugacity ratio (Campfens and Mackay, 1997). A J socw /K ow ratio of 1 is equivalent to equilibrium conditions
between the sediments and the water column. A ratio of 25, which has been typical of Lake
Ontario conditions for PCBs and DDTs since th e 1970s, is a disequilibrium condition in which
the chemical is enriched in the sediments re lative to the water column because of greater loadings of the chemical to the ecosystem in the past. For ratios less than 1, the chemical is
enriched in the water column relative to the sedi ments; in this situation, the aquatic ecosystem is being loaded with the chemical, but sediments have not reached steady state with the water ( J socw constant). With continued loading, sediment contamination increase s until a steady-state condition is reached ( J socw constant) and the J socw /K ow ratio is in the 2–10 range. The lower
bound of 2 arises from minimum expected differences in th e organic carbon content of particulate matter in the water column and se diments. The upper bound of 10 allows for the4-61 effects of chemical gradients and greater rela tive organic carbon amounts in the water column. Green Bay, a fairly shallow and vertically well-mixed ecosystem receiving a continuous load of PCBs from the contaminated Fox River in Wisconsin, has a J socw /K ow ratio of approximately 5. This ratio indicates that the system is close to steady state and that most or all of the disequilibrium is attributable to differences in organic carbon in the water and sediments.

On the basis of monitoring reports and hist orical loading data, EPA expects that most persistent nonionic organic chemicals will have J socw /K ow ratios in the range of 2-40. This expectation does not apply when such chemicals have not been present in an ecosystem long enough to approach expected steady-state concentra tions in surficial sedi ments. In this case, J socw /K ow will be substantially lower than 2, in dicating low exposure potential through the
benthic food web.

4.6.5 Assumptions and Limitations Asso ciated with Method 2 Predictions

EPA is currently restricting the applicati on of Method 2 for determining site-specific BAFs to nonionic organic chemicals with a log K ow of $ 4. This restriction primarily reflects the
lack of validation of this met hod as applied to less hydrophobic ch emicals. In addition, the need for this method is greater for chemicals with higher log K ow s because of the difficulties associated with detecting and measuring such chemicals in ambient water. Method 2 has not been validated for superhydrophobic (log K ow > 8) chemicals either. Future development and evaluation of this method may lead to its ap plication to a broader range of chemicals.

The primary assumptions and limitations for Method 1 also apply to Method 2. The primary limitation associated with Method 2 for predicting site-specific BAFs - namely, the variability of C f d

w - is common to both methods. Temporal changes in C f d

w are responsible for most deviations from steady state between biota, water, and sediments. The magnitude of errors
associated with fluctuations in C f d

w will be the same for Method 2 as for Method 1. Therefore, it may be appropriate to compare the precision of these two methods in situations where the
chemical of interest can be measured in water. In the M onte Carlo analysis of Green Bay PCB data for Methods 1 (Appendix 3C) and 2 (Appendi x 4B), BAFs determined by Method 1 were4-62 consistently more precise than by Method 2 wh en each was based on a comparable number of samples.

In deriving Equation 4-2, the assumption is made that J socw values for reference
chemicals are chosen from the same sediment data set used to calculate the BSAFs for the
chemical of interest. If this cannot be done (e.g., a common data set is not available for the chemical of interest and reference chemicals) an d sediment concentrations from different data sets are used instead, an error will be introduced in the Method 2 BAF prediction. This error will be proportional to any inequality in sediment concentrations between the data sets. Therefore, if
the BSAF and J socw values are not based on the same sedi ment data set, the investigator is cautioned to be particularly c oncerned with the consistency in sampling and analyses between
data sets.

Although EPA recommends that C soc values represent spatially averaged surface sediment contamination levels in the region aff ecting the organism’s exposure, Method 2 should
be accurate even when the C soc value used for the BSAF and J socw does not accura tely represent spatially-averaged conditions. This is because the C soc need only reflect the relative level of
contamination of sediments over time.

Inaccuracies associated with the use of J socw /K ow from reference chemicals to estimate

C f d

w s for chemicals of interest under Method 2 have a linear impact on the accuracy of baseline BAFs. For example, if J socw /K ow is 10 but the estimate used is 20, the calculated baseline BAF will be greater than the true value by a factor of 2. The measurements of J socw /K ow to date indicate an expected range of 5–40 for most c ontamination scenarios. If the data quality considerations for choosing J socw /K ow for the chemical of interest are followed, the magnitude of
the errors associated with the choice of J socw /K ow should be no greater than twofold.

The strength of Method 2 is that it utilizes measurements of relative (not absolute)
differences in bioaccumulation between chemical s with structural similarity. When properly sampled, sediments provide time-stable measures of concentrations of pers istent bioaccumulative chemicals in aquatic systems. Method 2 is cu rrently the only viable method for estimating4-63 baseline BAFs for nonionic organi c chemicals with (1) a log K ow of $ 4, (2) concentrations in water that are often undetectable, and (3) significant rates of ch emical metabolism by organisms. Important examples of chemicals with these characteristics are PCDDs, PCDFs, and non-ortho
PCBs.

4.6.6 How Reliable Are Method 2 Prediction s if the Sediment Organic Carbon Equilibrium Partitioning A ssumption is in Error?

Equilibrium partitioning (EqP) of organic chem icals between dissolved concentrations in sediment pore water and sediment organic matte r is a fundamental assu mption in the Method 2 methodology for predicting BAFs. The EqP assumpti on is made both explicitly, in the use of J socw /K ow in equation 4-2, as well as implicitly in the use of BSAFs as tools for predicting bioaccumulation. Although equilibrium partitioning has proven to be a very powerful tool for simplifying the sorption behavior of organic chemicals in the e nvironment, the EqP assumption has been repeatedly challenged by findings such as sorption nonlinearity (Chiou and Kile, 1998), multiphase and retarded sorption and desorption kinetics (Karickhoff and Morris, 1985), field observations of elevated partiti on coefficients in suspended se diment (Lohmann et al. 2005), and heterogeneous sorption properties of different classes of organic carbon (Young and Weber, 1995). Many of these factors appear to c ontribute relatively lit tle variability to J socw /K ow and BSAFs, based on site-specific measurements. The justification for the use of the equilibrium partitioning assumption and the 3-phase partitioning model for organic chemicals is presented by EPA in Section 4.2.3 of TSD Volume 2 (USEPA, 2003).

Research demonstrating that specific organic chemicals (e.g., PAHs, planar PCBs) have a great affinity for part icular kinds of organic matter (e.g., coal, kerogen, coke and soot, collectively known as black carbon) is a particular concern, es pecially given publications that specifically relate this phenome non to variability in BSAFs. Fo r example, Cornelissen et al. (2005) state that the observed di fference in field-measured BSAFs between (planar) PAHs and (mainly nonplanar) PCBs may be explained by sorp tion to “carbonaceous geosorbents” or black carbon. This implies BSAF variability due to both chemical- and site- (i.e., sediment) specific factors, which are not accounted for in the Method 2 pr ediction methodology.4-64 At the present time it is difficult for EPA to ev aluate the possibility th at differences in the bioavailability of certain chemi cals may be associated with the affinity of those chemicals for different types of organic car bon, or predict how this might affect BSAFs or Method 2 predictions. For example, the depression of BSAF s for certain planar P CB congeners is difficult to assess because of the paucity of black carbon (BC) measurements in sediments and suspended
solids and the lack of measured BC-water partition coefficients. EPA has begun examining the effect of BC on chemical-specific differences in BSAFs. Burkhard et al. (2004) considered the possibility that the high sorption affinity of pl anar (non-ortho) PCBs for BC could explain the variability in BSAFs measured for lake trout in Lake Michig an. Kukkonen et al. (2003) has reported a BC content of 0.03% (dw) for La ke Michigan. Based upon this measurement, approximately 1% of the total organic carbon in th e sediment of Lake Michigan is BC. Burkhard et al. (2004) estimate that concentrations of non-ortho PCBs (i.e., PCBs 77, 81, 126, and 169) would be lower by factors of 11x, 40x, 30x, and 235x, respectively, in pore water when BC is present as compared to when no BC is present. For the ortho substituted PCBs, concentrations in pore water would only decrease by approximately 1.5 fold. Therefore, the depression of the
BSAFs for the non-ortho substituted PCBs relative to those for the ortho substituted PCBs might be attributable, in part, to reduced bioavailabil ity. In contrast, all ortho substituted PCBs were predicted to have approximately the same reduc tions in bioavailability. Thus, bioavailability considerations do not appear to be the cause of the depressi on of BSAFs observed for only some of the ortho substituted PCBs in Lake Mich igan. Additionally, given the relatively large differences in predicted pore water factors be tween the non-ortho and ortho substituted PCBs,
non-ortho PCBs should have much lower BSAF s than were measured, especially PCB 169. Overall, the differences in bioavailability (m easured as BSAFs) between PCB congeners could not be explained satisfactorily by the a ffinity of specific chemicals for BC.

The bioavailability reduction estimates made by Burkhard et al. (2004) were based on a calculation using the two phase equation of Accardi-Dey and Gschwe nd (2003), and BC-water
distribution coefficients for ort ho and non-ortho substituted PCBs (Barring et al. 2002). Burkhard et al. (2004) point out that in making their estimates of bioavailability reduction, they are extrapolating chemical concentrations in pore wa ter by 6 to 9 orders of magnitude lower than
those used for determination of the Freundlich parameters. These calculations, which imply a4-65 reduced bioavailability due to BC, involve extr apolations to environmental conditions which have not been tested, and ar e therefore highly uncertain.

Furthermore, native compounds or organic matter may compete with specific chemicals for sorption to BC (Cornelissen et al. 2005). This may attenuate or counteract the enhanced sorption of these chemicals, and limit the e rror made by assuming equilibrium partitioning. Further work is required to confirm the extent of enhanced sorption to BC in aquatic systems.

Other complications in the application of Method 2 may arise from enhanced partitioning
of certain chemicals to BC. For example, the steady-state J socw /K ow approximated by the ratio of organic carbon contents (f oc /f soc ) assumes that the makeup of or ganic carbon in the water column
and sediment are similar. Gschwend and othe rs (Gustafson et al. 1997; Accardi-Dey and Gschwend, 2002; Bushel and Gustafsson, 2000) ha ve reported that organic carbon from anthropogenic sources, e.g., BC, have sorptive cap acities different from naturally derived organic carbon. Thus, for ecosystems such as harbors and Superfund sites where a relatively large portion of the organic carbon in the sediment might arise fr om anthropogenic sources, the steady-state J socw /K ow may differ from the above ratio due to differences in the organic carbon composition between the water and sediments.

Additional guidance regarding th e application of Method 2 may be necessary as scientific understanding of the extent and magnitude of the chemical-specific differences in partitioning behavior of different types of organic carbon improves. In the mean time, a number of precautions should be taken by the investigator to limit errors in Method 2 predictions due to this
factor:

• Select reference chemicals w ith partitioning behavior si milar to the chemical of concern. Chemicals reported to have enhan ced affinity for BC include PAHs; planar (non-ortho and mono-ortho substituted) PCBs; and planar chlorobenzenes, PCDD/PCDFs and PBDEs.

• At sites where sediments c ontain significant amounts of BC, the three-phase model could be modified to include a fourth phase consisting of BC. Gustafsson et al. (1997) describe a methodology for estimating th e partition coefficients for BC.4-66 • Ensure that the methods to calculate dissolved chemical fractions are used consistently throughout the application of Me thod 2. For example, if an adjustment to chemical bioavailability due to BC is made in sediment, a corresponding bioavailability adjustment should also be made for suspended solids in the water column.

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Versar, Inc.1982. Sampling Protocols for Collecting Surface Water, Bed Sediment, Bivalves and Fish for Priority Pollutant Analysis-Final Draft Report. EPA Contract 68-01-6195. Prepared for Office of Water Regulations and Standards, U.S. Environmental Protection Agency. Springfield, VA.

WDNR. 1998. Field Procedures Manual, Sedi ment Sampling Guidelines. Wisconsin Department of Natural Resources. September, 1998. Version IV - Intranet Edition Available from http://www.dnr.state.wi.us/org /water/wm/wqs/sediment/sampling/table.htm

. Wong, C, S., Capel, P. D. and Nowell, L. H. 2001. National-scale, field- based evaluation of the biota-sediment accumulation factor model. Environ. Sci. Technol. 35 (9), 1709-1715.
Wool, T.A, Ambrose, R.B., Mar tin, J.L. and E.A. Comer. 2001. Water Quality Analysis Simulation Program (WASP). Draft: User’s Manual. US Environmental Protection Agency, Region 4, Atlanta, Georgia and Environmenta l Research Laboratory, Athens, Georgia.
Young, T.M. and W.J. Weber, Jr. 1995. A Distribut ed reactivity model for Sorption by Soils and Sediments. 3. Effects of Diagenei c Processes on Sorption Energetics. Environ. Sci. Technol. 29, 92.4-72 This page left blank intentionally.4-73 

## Appendix 4A

Modeling Simulation of BSAF Sampling Designs

Burkhard (2003) performed model simulations to understand how the variabilities in
water and sediment chemical con centrations translate into the variabilities associated with BSAFs as well as BAFs based upon different sampling designs. Different models were constructed to evaluate temporal and spatial variability in chem ical concentrations. As noted by Burkhard (2003), for these simulations to be m eaningful the model constructs should provide reasonable representations of ecosystem conditions and chemical properties. Because the models are generic (i.e., not calibrated to site-specific data), the results are intended to compare different sampling designs in terms of the precision, and do not offer definitive predictions with known certainty. The investigator should also realize that models, includi ng those used to perform these simulations, are being continuously updated as ne w data become available for testing and as scientific understanding e volves. Appendix 3D (Modeling Simulation of BAF Sampling Designs) presents the models used to evaluate temporal and spatial variability in chemical
concentrations.

A number of BSAF sampling designs were evaluated by modeling chemical bioaccumulation in a river segment, assuming a mixed benthic/pelagic food web and 

### A socw /K ow

= 1, for chemicals with log K ow s ranging from 2 to 9. In these designs, fish and sediment were collected on the same day and sampling was repeated at fixed intervals with spacings of 1, 7, 14, 30, and 120 d (Figure 4A-1). In this figure, the uncertainty is presented as the ratio of 90 th to the
10 th percentile confidence limits of the BSAFs. For chemicals with log K ow s greater than 5, the five sampling designs provided practically iden tical uncertainties for the measured BSAFs and the uncertainty was essentially independent of th e number of samples coll ected. The collection of one set of samples (i.e., one day of sampling) pr ovided BSAF confidence limit ratios of less than 3 for all five sampling designs. In contrast, larg er uncertainties in the measured BSAFs were observed for chemicals with log K ow s less than 5. For the less- hydrophobic chemicals, increasing both the number of sampling even ts and their spacing in time re duced the uncertainties in the
measured BSAFs.4-74 Figure 4A-1. Ratio of the 10 th to 90th percentile biota–sedime nt accumulation factor (BSAF) for field-sampling designs consisting of concurrent fish and sediment sample collections spaced 1 (

— ), 7 ( ), 14 ( ), 30 ( ), and 120 ( • ) d apart, when using Mississi ppi River (USA) flow data for years 1955 to 1995. Results are based on mode ling assumptions including (a) mixed benthicpelagic food web and (b) J socw /K ow = 1.

The simulations presented above were made assuming that the chemical was not metabolized by the fish. When metabolism does occur, the appropriate sample design for a chemical of a given K ow would be best described by the sa mple design for a chemical with a
smaller K ow (with no metabolism). In this case, the effective reduction of K ow due to metabolism would be proportional to the rate of metabolism relative to the overall de puration rate, which is the sum of elimination rates via gill and gut, the or ganism growth rate, and the rate of chemical
metabolism.

The kinetics of chemical uptake and loss by th e fish (or other aquatic organism) controls
the chemical residue that reside s in the organism. These kinetic processes are directly dependent upon the chemical’s hydrophobicity and metabolism ra te in the fish. Successful field-sampling designs should account for the chemical uptake a nd loss kinetics, and for the changes in chemical concentrations occurring in the fish’s envi ronment. The modeling simulations strongly4-75 demonstrate that lower uncertainties can be obtained by using properly developed sampling design structures. The haphazard collection of samples for the measurement of chemical concentrations in biota and sediment can, and most often will, result in BSAF values with poor accuracy and large biases. Consequently, the measured values will have poor predictive power.

The modeling simulations suggest that food web structure and sedi ment–water chemical concentration quotients are not usually important c onsiderations to be f actored into a sampling design. Chemical concentration gradients do not add large uncertaintie s into the measured BSAFs beyond those caused by temporal variabil ity alone. BSAFs can be measured with low uncertainty even when extreme spatial concentra tion gradients exist at the field site. However, these simulations also suggest that measur ements for BSAFs probably should be designed
around the more contaminated reaches of the site.4-76 This page left blank intentionally.4-77 

## Appendix 4B

Determining the Number of Samples to Collect for a BSAF Measurement: Monte Carlo Analysis

Monte Carlo simulation can be used to estimate how the pr ecision of the BSAF depends upon the number of biota and sediment samples, and how the precision of the sediment-water concentration quotient ( J socw ) depends upon the number of sediment and water samples. The
Monte Carlo method can also be us ed to simulate chemical concentrations in biota (for the
chemical of interest and the re ference chemical), sediment (c hemical of interest) and water (reference chemical) simultaneously, so the investig ator can determine how the precision of sitespecific BAF predictions made using Method 2 depend upon the number of samples collected
from each medium at the site.

The Latin Hypercube Monte Carlo generator program was used to simulate 300 chemical concentrations in biota, sediment and water. M eans and variances that were calculated from logtransformed concentration data measured for PCB congeners in Green Bay (Lake Michigan) zone 3 forage and predator fish, surficial sediment and dissolved wa ter were used as inputs to the Monte Carlo generator. Data from this zone were selected becaus e a relatively large number of concentration measurements (93 in water, 66 in forage fish, 42 in predator fish, and 39 in sediment) were available. The sediment concen tration data are presen ted in Appendix 4C; the
data for PCB concentrations in fish and wa ter were presented in Appendix 3B. Chemical
concentrations were simulated for each of 4 congeners (PCB-18, 52, 149 and 180) in all three media (biota, sediment and water). From these simulated data, alternative numbers of biota ( n b ), sediment ( n s ) and water ( n w ) concentrations were sampled and averaged, to simultaneously compute BSAFs, J socw s and predicted BAFs. This procedure was repeated many times, until stable distributions of BSAF, J socw and BAF values were generated. As we will demonstrate, these distributions can be used by the investigator as estimates of the unc ertainty of the BSAF, J socw , or the BAF predicted using Method 2. For example, the 90% confidence limits are estimated by the 95th and 5th pe rcentiles of the BAF distribution. By repeating this procedure using different n b , n s and n w and comparing the results, the investigator can determine a sampling4-78 design that meets their requirements for BSAF and/or BAF precision. Uncer tainty in chemical
K ow s were not included in these computations, and th e fugacity gradient ratio was assumed to be
1.

Monte Carlo simulations of PCB congener concentrations were made using lognormal distribution moments (mean and CV) as measuredin Green Bay for dissolved water, lipidnormalized predator fish, and organic-carbon norma lized surficial sediment. Additional sediment concentrations were simulated using a range of different lognormal CVs: 0.6, 0.9, 1.2, and 1.5. The variability of chemical concen trations in sediment at many site s will fall within this range of values. For example, the lognormal CVs for PCB congener concentrations in Green Bay sediment ranged from 0.71 to 1.3. The impact of different levels of variability in water concentrations was discussed in Section 3.2.1.2 and Appendix 3C.

Unless stated otherwise, concentrations in these simulations were assumed to be uncorrelated between media and between chemicals. As was the case for site-specific BAFs in Section 3.3.2, the ratios of 90% c onfidence limits (upper CL/lower CL ) were used as measures of the uncertainty of the distributions of BSAFs, J socw s and BAFs in each simulation.

How is the Uncertainty of BSAF and J socw Affected by the Number of Sediment Samples and Different Chemical Concentration Variances?

The uncertainty of BSAFs and J socw calculated in the Monte Carlo simulations were sensitive to the number of sediment samples, and th is sensitivity increased with the variability of
the sediment chemical concentrations. For exam ple, Figure 4B-1 shows how the 90% confidence limit ratio (CLR) for the BSAF varies based upon (1) the number of samples used to calculate the mean sediment concentration, and (2) the co efficient of variation (CV) of the underlying population of chemical concentrations. The BSAF CL Rs in Figure 4B-1 are averages for the four PCB congeners, and the BSAFs were calculated usi ng mean chemical concentrations calculated from 6 biota samples. These results are also shown in Table 4B-1, which includes CLRs for BSAFs calculated using mean chemical concentra tions from different numbers of sediment and fish samples. For highly variable chemical concen trations in sediment, in creasing the number of sediment samples used to calculate the mean co ncentration has a significant impact on reducing4-79 the uncertainty of BSAFs, up to a sample size of about 6. Collecting additional sediment samples (i.e., greater than 10) has little effect on the precision of BSAFs, as illustrated by the CLRs in Figure 4B-1.

Figure 4B-2 is a similar plot of the 90% confidence limit ratio (CLR) for J socw . The CLRs in Figure 4B-2 are averages for the four PCB congeners, and were again calculated using
mean chemical concentrations calculated from 6 water samples. These results are also shown in Table 4B-2, which includes CLRs for J socw calculated using mean chemical concentrations
from different numbers of sediment and wate r samples. As was the case for the BSAF, increasing the number of samples used to calc ulate the mean sediment concentration has a significant impact on reducing the uncertainty of J socw , up to a sample size of 6. Collecting additional sediment samples (i.e., greater than 10) has little effect on the uncertainty of J socw s.4-80 Ratio of Confidence Limits for BSAF: average results for 4 PCB congeners (6 biota samples)

0 2 4 6 8 10 12 14 16 18

0 5 10 15 20 25 30 35 number of sediment samplesBSAF ratio (95% / 5%) CV = 0.6
CV = 0.9
CV = 1.2
CV = 1.5

Figure 4B-1. 90% confidence interval ra tio for BSAF as function of number of sediment samples (Average results for 4 PCB congeners are plotted; BSAFs were calculated using 6 biota samples drawn from Green Bay predator fish data).BSAF 90% confidence limit ratio

Number of sediment samples4-81 Table 4B-1. 90% Confidence Interval Ratios fo r BSAF as Function of the Variability in Chemical Concentrations in Sediment

A. Chemical Concentrations Measured in 2 Fish Samples

Number Of Sediment Samples CV=0.6 CV=0.9 CV=1.2 CV=1.5

2 6.43 9.62 14.2 19.5

4 5.12 6.53 8.68 10.8

6 4.68 5.60 6.98 8.34

10 4.34 4.88 5.68 6.48

30 4.00 4.17 4.43 4.71

B. Chemical Concentrations Measured in 4 Fish Samples

Number Of Sediment Samples CV=0.6 CV=0.9 CV=1.2 CV=1.5

2 5.05 7.99 12.0 16.7

4 3.82 5.11 7.00 8.91

6 3.42 4.26 5.49 6.74

10 3.10 3.59 4.32 5.06

30 2.77 2.93 3.18 3.42

C. Chemical Concentrations Measured in 6 Fish Samples

Number Of Sediment Samples CV=0.6 CV=0.9 CV=1.2 CV=1.5

2 4.59 7.44 11.4 16.1

4 3.40 4.68 6.47 8.43

6 3.01 3.83 5.00 6.26

10 2.69 3.17 3.87 4.58

30 2.36 2.52 2.77 3.014-82 Table 4B-1 (Continued). 90% Confidence Inter val Ratios for BSAF as Function of the Variability in Chemical Concentrations in Sediment

D. Chemical Concentrations Measured in 10 Fish Samples

Number Of Sediment Samples CV=0.6 CV=0.9 CV=1.2 CV=1.5

2 4.24 7.02 10.8 15.3

4 3.06 4.34 6.04 7.97

6 2.67 3.50 4.63 5.84

10 2.35 2.84 3.53 4.20

30 2.02 2.19 2.43 2.67

E. Chemical Concentrations Measured in 30 Fish Samples

Number Of Sediment Samples CV=0.6 CV=0.9 CV=1.2 CV=1.5

2 3.89 6.59 10.3 15.0

4 2.72 4.01 5.65 7.47

6 2.33 3.19 4.27 5.48

10 2.00 2.53 3.18 3.87

30 1.64 1.83 2.09 2.334-83 Ratio of Confidence Limits for Pi: average results for 4 PCB congeners (6 water samples)

0 2 4 6 8 10 12 14 16 18

0 5 10 15 20 25 30 35 number of sediment samplesPi ratio (95% / 5%) CV = 0.6
CV = 0.9
CV = 1.2
CV = 1.5

Figure 4B-2. 90% confidence interval ratio for J socw as function of number of sediment samples. (Average results for 4 PCB congeners are plotted; J socw s were calculated using 6 water samples drawn from Green Bay data).Sediment-water concentration quotient90% confidence limit ratio

Number of sediment samples4-84 Table 4B-2. 90% Confidence Interval Ratios for J Socw as Function of the Variability in Chemical Concentrations in Sediment

A. Chemical Concentrations Measured in 2 Water Samples

Number Of Sediment Samples CV=0.6 CV=0.9 CV=1.2 CV=1.5

2 7.46 11.2 15.7 21.9

4 6.04 7.76 9.70 12.2

6 5.55 6.68 7.89 9.51

10 5.18 5.86 6.52 7.49

30 4.82 5.04 5.24 5.56

B. Chemical Concentrations Measured in 4 Water Samples

Number Of Sediment Samples CV=0.6 CV=0.9 CV=1.2 CV=1.5

2 5.52 8.58 12.6 17.9

4 4.25 5.72 7.39 9.71

6 3.83 4.79 5.85 7.27

10 3.50 4.07 4.70 5.53

30 3.18 3.37 3.56 3.85

C. Chemical Concentrations Measured in 6 Water Samples

Number Of Sediment Samples CV=0.6 CV=0.9 CV=1.2 CV=1.5

2 4.91 7.81 11.7 16.8

4 3.69 5.06 6.71 8.99

6 3.28 4.19 5.21 6.67

10 2.96 3.51 4.10 4.92

30 2.63 2.82 3.02 3.284-85 Table 4B-2 (continued). 90% Co nfidence Interval Ratios for J Socw as Function of the Variability in Chemical Concentrations in Sediment

D. Chemical Concentrations Measured in 10 Water Samples

Number Of Sediment Samples CV=0.6 CV=0.9 CV=1.2 CV=1.5

2 4.43 7.23 11.0 15.8

4 3.23 4.56 6.17 8.25

6 2.84 3.72 4.73 6.10

10 2.52 3.06 3.63 4.42

30 2.19 2.38 2.58 2.86

E. Chemical Concentrations Measured in 30 Water Samples

Number Of Sediment Samples CV=0.6 CV=0.9 CV=1.2 CV=1.5

2 3.95 6.66 10.3 15.1

4 2.78 4.08 5.69 7.60

6 2.39 3.26 4.30 5.52

10 2.06 2.60 3.21 3.95

30 1.71 1.92 2.12 2.41

What are the Confidence Interval Ratios for Method 2 BAFs?

Because concentrations in biota, sediment and water were simulated for 4 chemicals simultaneously, it was possible to calculate 3 BAFs for each chemical using Method 2 (one
chemical as the “chemical of interest” and 3 as “reference chemicals”). CLRs for Method 2 BAF predictions for PCB-180 are shown as a function of sediment sample numbers in Figure 4B-3. In this figure, the CLRs are plotted separately for BAF predictions made using the different reference chemicals. Although the trend of declin ing CLRs (and lower uncertainty) as a function of the number of sediment samples is consistent for BAFs calculated us ing different reference chemicals, some differences are also apparent. Fo r example, sediment concentrations for PCB-52 are significantly more variable (CV=1.29) than for the other chemi cals, and this is reflected in4-86 higher CLRs for Method 2 BAF predictions when this congener is used as the reference
chemical.

The CLRs for Method 2 BAF predictions were found to be fairly consistent for different chemicals of interest. This is shown in Figur e 4B-4, which plots CLRs for Method 2 BAFs for each congener as the chemical of interest, again as a function of sediment sample size. The CLRs in this figure were averaged across BAFs calculated using the three reference chemicals.

Method 2 Prediction of BAF for PCB congener 180 uncorrelated concentrations; GBMB concentration data moments

0 5 10 15 20 25

0 5 10 15 20 25 30 35BAF ratio ( 95% / 5% ) pcb 18 pcb 52 pcb 149

Figure 4B-3. 90% confidence interval ratio for PCB- 180 Method 2 BAFs as a function of the number of sediment samples (r esults shown for 10 biota and 10 water samples). In each curve plotted in this figure, the BA F confidence interval ratios were calculated by Method 2 using a different PCB congener as a reference chemical. 0 5 10 15 20 25 30 35 number of sediment samplesBAF 90% confidence limit ratio4-87 Method 2 Prediction of BAF for 4 PCB congeners uncorrelated concentrations; GBMB concentration data moments

0 5 10 15 20 25

0 5 10 15 20 25 30 35BAF ratio ( 95% / 5% ) pcb149 pcb18 pcb180 pcb52

Figure 4B-4. 90% confidence interval ratio for f our PCB congener Method 2 BAFs as a function of the number of sediment sample s (results shown for 10 biota and 10 water samples). In this Figure, BAF confidence interv al ratios are plotted as separate curves for each PCB congener. 0 5 10 15 20 25 30 35 number of sediment samplesBAF 90% confidence limit ratio4-88 How Does the Variability of Chemical Concentrations in Sediment Affect the Precision of Method 2 BAF Predictions?

Additional Monte Carlo simu lations were run and analy zed, in order to develop generalized guidance for selecting sample sizes that would be appl icable for different sites. To do so, the simulations were repeated using di fferent CVs for chemical concentrations in sediment. As discussed above, the variability of ch emical concentrations in sediment affected the uncertainty of BSAFs and J socw , particularly for small sediment sample sizes ( n s # 6). For Method 2 BAFs, the results are sim ilar to those for BSAF and J socw , although the CLRs are much larger. This is shown in Figure 4B-5, which plots th e Method 2 BAFs predicted for PCB-149 as a
function of the chemical concentration variance in sediment and the number of sediment samples used to calculate the BSAF, J socw and BAF. The results shown in Figure 4B-5 are for Method 2
BAFs calculated with mean chemical concentrat ions based on 10 biota and 10 water samples. The results for PCB-149 are also presented in Table 4B-3, along with CLRs for other combinations of biota, sediment and water samp le sizes. Each sub-table presents the 90% BAF
CLR as a function of the number of sediment sa mples and the underlying va riability of chemical concentrations in sediment, for a specific number of fish and water concentrations. For example,
Table 4B-3.a is a tabulation of results for 2 fish and 2 water samples. For this case, if the BAF is predicted using 2 sediment samples, the 90% BAF CLRs vary from 16 to 75 depending upon the variability of chemical concentrations in the se diment. If 6 sediment samples are used to make the Method 2 prediction, the resu lting 90% BAF CLRs are much lo wer, varying from 10 to 23. If 30 sediment samples are used, the 90% BAF CLRs are further reduced to 8 to 10. The other subtables in Table 4B-3 present 90% BAF CLRs for 4 fish and water samples (Table 4B-3.b), 6 fish and water samples (Table 4B-3.c), 10 fish a nd water samples (Table 4B-3.d), and 30 fish and water samples (Table 4B-3.e).

As was the case for BSAFs and J socw s, the results in Table 4B-3 demonstrate that only small reductions in the uncertainty of Method 2 BAF predictions are gained using sediment sample sizes larger than about 6. Once the numbe r of samples exceeds about 6, the reductions in BAF prediction CLRs become incrementally much smaller. This is the case even when the variability of chemical concentrations in sedime nt is large. Depending upon the requirements for4-89 predictive BAF uncertainty, exceeding sample sizes of 10 appears to be warranted only for sites having very high variability in chem ical concentrations in sediment.

Figure 4B-5. 90% confidence interval ratio for P CB-149 Method 2 BAFs as a function of the number of sediment samples and variability of chemical concentrations in sediment (results shown for 10 biota and 10 water samples). In each curve plotted in this figure, the BAF confidence interval ratios were calculated by using a different coefficient of variation (CV) for sediment concentrations. Ratio of Confidence Limits for Method 2 BAF Predictions for PCB congener 149 average BAF for 3 reference chemicals & varying sediment concentration CVs

0 10 20 30 40 50 60

0 5 10 15 20 25 30 35BAF ratio ( 95% / 5% ) CV=0.6
CV=0.9
CV=1.2
CV=1.5

Number of sediment sam p lesBAF 90% confidence limit ratio4-90 Table 4B-3. 90% Confidence Interval Rati os for Method 2 BAF Predictions for PCB Congener 149 as a Function of the Variability in Chemical Concentrations in Sediment

A. Chemical Concentrations Measured in 2 Fish and 2 Water Samples

Number Of Sediment Samples CV=0.6 CV=0.9 CV=1.2 CV=1.5

2 15.5 27.6 47.2 75.3

4 11.3 16.0 22.9 33.7

6 9.96 12.8 16.9 23.2

10 8.96 10.5 12.7 16.1

30 8.01 8.36 9.11 10.0

B. Chemical Concentrations Measured in 4 Fish and 4 Water Samples

Number Of Sediment Samples CV=0.6 CV=0.9 CV=1.2 CV=1.5

2 10.5 20.0 35.9 60.9

4 7.18 11.0 16.4 25.4

6 6.17 8.47 11.7 16.8

10 5.41 6.64 8.4 11.1

30 4.65 4.96 5.55 6.29

C. Chemical Concentrations Measured in 6 Fish and 6 Water Samples

Number Of Sediment Samples CV=0.6 CV=0.9 CV=1.2 CV=1.5

2 9.05 17.9 32.6 56.3

4 5.98 9.46 14.6 22.8

6 5.06 7.2 10.1 14.9

10 4.34 5.5 7.1 9.61

30 3.64 3.96 4.48 5.184-91 Table 4B-3 (continued). 90% Confidence Interval Ratios for Method 2 BAF Predictions for PCB Congener 149 as a Function of the Vari ability in Chemical Concentrations in Sediment
D. Chemical Concentrations Measured in 10 Fish and 10 Water Samples

Number Of Sediment Samples CV=0.6 CV=0.9 CV=1.2 CV=1.5

2 7.95 16.2 30.0 53.1

4 5.07 8.33 13.1 20.7

6 4.2 6.2 8.97 13.3

10 3.53 4.65 6.12 8.49

30 2.87 3.19 3.67 4.33

E. Chemical Concentrations Measured in 30 Fish and 30 Water Samples

Number Of Sediment Samples CV=0.6 CV=0.9 CV=1.2 CV=1.5

2 6.87 14.7 27.6 49.6

4 4.2 7.25 11.7 18.8

6 3.39 5.25 7.87 11.8

10 2.74 3.82 5.22 7.29

30 2.08 2.44 2.87 3.52

What are the Effects of Chemical Con centration Correlations on Method 2 BAF Predictions?

The BAFs predicted above using Met hod 2 were based upon simulated chemical concentrations in biota, sediment and water th at were uncorrelated and independent. In a real ecosystem, however, the concentrations of bioaccu mulative chemicals are likely be correlated in biota, sediment and water. Two differe nt kinds of correlation are possible: within-chemical correlation of the concentrations of a specific chemical between differe nt sampled media, and
within-media correlation between different chemicals. Within-chemical correlation is generally expected, due to factors such as the magnitude of chemical loading; in contrast, we expect the transport, partitioning and bioaccumulation proce sses to differ between chemicals due to their4-92 physicochemical properties. Within-media correla tion of chemical concentrations is also expected and could result, for example, if the co ncentrations of multiple chemicals were higher in one or a subset of sediment samples. Since Method 2 BAF pred ictions are based on concentrations of at least 2 chemicals (chemical of interest and one or more reference chemicals) in all three media, either kind of correlation may affect the uncertainty of the BAF prediction.

The Monte Carlo simulations of chemical con centrations were repeated to evaluate how
within-chemical and within-media correlation w ould alter the estimates of BAF uncertainty presented in Section 4.4.5. To simulate within-c hemical correlation, rank correlation coefficients of 0.5 were specified for each PCB congener betw een fish, sediment and water concentrations. The resulting BAF CLRs are show n as a function of sediment sa mple size in Figure 4B-6, along
with the uncorrelated simulation results for PCB- 52. Results were similar for the other congeners (not shown). Within-chemical corre lations were found to be mildly helpful in terms of reducing the uncertainty of Method 2 BAFs; on average, the 0.5 correlation reduced the CLRs by 21%.

The same approach was used to simula te within-media correlation of chemical
concentrations. Rank correlation coefficients of 0.5 were specified in each sample medium (biota, sediment and water) between PCB congene r concentrations. The resulting BAF CLRs are again shown as a function of sediment sample size in Figure 4B-6. W ithin-media correlation, especially the correlatio n between chemical concentrations in sediment, significantly reduced the uncertainty of Method 2 BAF predictions wh en few sediment samples are collected. When only two sediment samples are us ed to calculate the BSAF and J socw , the 0.5 correlation reduced the CLR by 50% in comparison to the uncorrela ted simulation. For 4 sediment samples, the reduction in the CLR was 40%, and for 6 sa mples the CLR reduction was 30%. Overall,
concentration correlations were found to be he lpful in terms of improving the precision of Method 2 BAF predictions; this was especially the case when relatively few samples were drawn
from sediment concentrations th at were correlated between chemicals. Such correlations are reasonable to expect in sediment data from a specific site, and help to explain why Method 2 BAF predictions are so robust. In many cases, the investigator will not know a priori whether chemical correlations exist; these simulations ill ustrate that a conserva tive number of samples will be specified if chemical concentrations are assumed to be uncorrelated. In other words, the4-93 investigator who determines the number of samples assuming uncorrelated chemical concentrations (i.e., the sample size guidance in Tables 4B-1 through 4B-3) can expect to
determine a BAF no more uncertain than indicate d in these tabulations.

Method 2 Prediction of BAF for PCB congener 52 influence of correlation between concentrations

0 5 10 15 20 25

0 5 10 15 20 25 30 35BAF ratio ( 95% / 5% ) uncorrelated concentrations
within-chemical correlation
within-media correlation

Figure 4B-6 . 90% confidence interval ratio for P CB-52 Method 2 BAFs as a function of concentration correlations (results shown for 10 biota and 10 wa ter samples). In each curve plotted in this figure, the BAF confidence interval ratios were calculated from simulated fish, sediment and water concentrati ons incorporating different assu mptions regarding correlations between the data. Number of sediment sam p lesBAF 90% confidence limit ratio4-94 This page left blank intentionally.4-95 

### Appendix 4C Green Bay Mass Balance PCB Congener Concentrations: Organic Carbon-Normalized Sur ficial (0-1 cm) Sediment

year zone sediment station percent organic carbon PCB 18 (ng/g-SOC) PCB 52 (ng/gSOC) PCB 149 (ng/gSOC) PCB 180 (ng/gSOC) 1989 GB0Z3B 58 5.09 41 61 21 12 1989 GB0Z3A 54 5.82 33 48 20 14 1990 GB0Z3B 52 4.78 37 54 18 14 1988 GB0Z3B 48 9.53 40 59 25 28 1987 GB0Z3B 47 NA 1988 GB0Z3B 44 7.76 22 27 8.6 11 1988 GB0Z3B 43 9.28 76 117 45 47 1988 GB0Z3B 43 9.28 113 147 51 31* 1988 GB0Z3B 42 8.49 61 108 41 43 1989 GB0Z3B 40 3.83 40 57 18 14 1988 GB0Z3B 39 9.03 110 155 50 31 1987 GB0Z3B 39 9.03 132 155 47 49 1990 GB0Z3B 38 8.27 75 134 34 6.2 1988 GB0Z3A 33 8.23 66 115 43 17 1988 GB0Z3B 32 8.08 69 90 31 17 1987 GB0Z3B 32A 7.39 55 105 29 9.1 1987 GB0Z3B 32A 7.39 134 229 31 15* 1987 GB0Z3B 31 7.41 198 284 86 72 1987 GB0Z3B 30 7.89 169 223 62 7.6 1989 GB0Z3A 27 7.95 171 223 71 38 1987 GB0Z3A 27A 6.99 150 275 67 33 1989 GB0Z3B 26 8.01 228 6.4* 107 32 1987 GB0Z3B 24 6.39 240 289 67 31 1987 GB0Z3A 22 6.52 238 367 78 26* 1987 GB0Z3A 22 6.52 173 336 55 87 1987 GB0Z3A 22 6.52 304 414 134 58* 1987 GB0Z3B 21 7.82 237 314 83 77 1988 GB0Z3B 20 6.83 296 339 71 97 1988 GB0Z3B 20 6.83 243 199 38 19 1988 GB0Z3A 18 7.51 85 141 40 38 1989 GB0Z3A 17 7.54 139 207 49 38 1987 GB0Z3A 17 7.54 271 357 84 72 1988 GB0Z3B 16 6.91 416 628 123 97 1989 GB0Z3A 13 4.12 177 183 38 28 1987 GB0Z3B 12 5.56 545 755 143 139 1989 GB0Z3A 11 0.17 155 28* 94 73* 1988 GB0Z3A 10 1.51 262 344 80 69 1987 GB0Z3A 10A 3.84 326 381 84 109 1988 GB0Z3B 9 4.43 315 483 95 104 1988 GB0Z3B 8 5.45 505 702 126 104 Note: * Denotes sediment PCB concentration below limit of quantification (LOQ); replacement value estimated using the Maximum Likelihood method of El-Shaarawi and Dolan (1989).4-96 This page left blank intentionally.5-1 5. ESTIMATING SITE-SPECIFIC BAFs BY EXTRAPOLATION, PREDICTION OR RECALCULATION

The previous 2 sections of this TSD ha ve described EPA’s preferred methods of determining site specific BAFs: determining a BA F directly based on site -specific measurements, or determining a BSAF based on site-specific m easurements and then predicting a BAF from the BSAF. Another approach to determine site-speci fic BAFs is to estimate a site-specific BAF indirectly using one of the other methods de scribed in TSD Volume 2 (USEPA, 2003). These methods include extrapolating site-specific BAFs from BSAFs, predicting BAFs using laboratory-measured bioconcentration factors ( BCFs) or octanol-water partition coefficients (K ow s) coupled with food chain multipliers, or re calculating site-specifi c BAFs from baseline BAFs. EPA expects that variations of these methods, as described in this section, may be used to derive site-specific BAFs.

ALTERNATIVES FOR ESTIMATING SITE-SPECIFIC BAFs:

• Extrapolating site-specific BAFs from BSAFs measured at another site (Method 3, with 2 options) 3a. BSAF extrapolation 3b. BEF extrapolation

• Predicting BAFs using a BCF coupled with food chain multipliers (Method 4, with 2 options): 4a. Laboratory-measured BCFs 4b. BCFs estimated using K ow s

• Recalculating site-specific BAFs from baseline BAFs (Method 5, with 2 options: 5a. Adjustment for site-speci fic lipid content, and/or 5b. Adjustment for site-specific DOC

EPA considers the BAF estimation methods desc ribed in this section to be less preferred
than the direct determination of the BAF or BSAF based on measurements made at the site, because these estimation methods may not capture all of the site-, chem ical- and/or species-5-2 specific factors that influence chemical bioaccu mulation. In addition, the estimation methods rely upon assumptions that may be diffic ult to confirm. As a result, th e site specific BAFs estimated by these methods are uncertain due to factors bey ond those discussed in previous sections (i.e., sampling bias, measurement errors, etc.). This does not mean that these estimation methods cannot produce a good site specifi c BAF. Rather, it is meant to caution the investigator to carefully consider whether a particular BA F estimation method is appropriate given the characteristics of the chemical, organism and site.

The investigator should also consider whether the application of these methods to estimate site-specific BAFs will improve upon the accuracy of the national BAFs for a particular site. As discussed in Section 2, EPA believes that nati onal BAFs are broadly applicable to sites throughout the United States and achieve an acce ptable degree of accuracy; because national BAFs are derived using a methodology intended to produce national average values for BAFs at each trophic level. EPA also recognizes that cond itions, parameters, etc. at a site could be different from the representative values used in the National methodology calculations. In the national methodology, default values were used for many important ecosystem and food web parameters. These include POC and DOC concentr ations, trophic-level sp ecific lipid contents, trophic structures, J socw /K ow , and other parameters (USEPA, 2003) . The investigator should view the derivation of site-specific BAFs as a pr ocess to improve upon the accuracy of the national BAFs for a particular site. EPA expects that in most instances, the derivation of site-specific BAFs will be motivated by some knowledge or e xpectation that unique site-specific factors may cause BAFs to diverge from the national values . These factors include (for example): fish consumption patterns that are subs tantially different than national averages; species of aquatic organisms that have not been previously sampled or for which trophic leve l or feeding preference is unknown; and sediment-water chemical distri bution, tissue lipid cont ent, POC and/or DOC concentration significantly different than the values assumed in the national methodology. In cases such as these, the derivation of site-spe cific BAFs would likely improve the accuracy of bioaccumulation estimates and, ultimately, the AWQC for the chemical of concern at that site.

The three alternatives for estimating site sp ecific BAFs are presented and discussed in the following sections. Section 5.1 addresses Method s 3a and 3b, estimating site-specific BAFs by5-3 extrapolating BSAFs or BEFs. Predicting s ite-specific BAFs using BCFs and food chain multipliers (Methods 4a and 4b) is covered in Section 5.2. Method 5, recalc ulating site-specific
BAFs from baseline or national BAFs, is addressed in Section 5.3.

5.1 ESTIMATING SITE-SPECIFIC BAFs BY EXTRAPOLATING BSAFs OR BEFs (METHODS 3A AND 3B)

One alternative for estimating site specific BAFs is based upon extrapolating BSAFs from
a reference site to the site of interest. The investigator may extrapol ate trophic level-specific
BSAFs measured at a reference site using one of two approaches. The first approach is to directly extrapolate a high-quality BSAF to the site, when one has been determined for the chemical of interest at another site. Alternatively, if a high- quality BSAF for a reference chemical is available for the site, and BSAFs for that reference chemical and the chemical of interest are available at another site, then the investigator can use a bio accumulation equivalence f actor (BEF, defined as
the ratio between BSAFs for the chemical of intere st and the reference chemical) to extrapolate a BSAF. Since these are actually two related met hods, we refer to BSAF extrapolation as Method 3a and extrapolation of a BEF as Method 3b. Figur e 5-1 presents a decision framework flowchart for selecting an applicable BAF derivation met hod, based upon the BSAFs that may be available to the investigator. For either method, conversion of the BSAF into a site-specific baseline BAF is accomplished using Method 2 of EPA's bioaccu mulation methodology. Methods 3a and 3b are appropriate for moderate to hi ghly hydrophobic nonionic organic chem icals, and to certain ionic organic chemicals for which similar lipid and organic carbon partitioning behavior applies.
Methods 3a and 3b have the greatest potential for highly hydrophobic nonionized organic chemicals that may be metabolized in the food ch ain and are difficult and/or expensive to measure as freely dissolved concentrati ons in water. Since a BSAF is based on lipid and organic carbon normalized chemical concentrations (Equation 4- 1), no other adjustment for these factors is

necessary.5-4 Site-Specific BAF Method 3

• Extrapolating site-specific BAFs from BSAFs measured at another site, with 2 options: 3a. BSAF extrapolation 3b. BEF extrapolation

Figure 5-1. Decision framework for selecting a site -specific BAF derivation method based on BSAFs

Has a BSAF been determined for the chemical of interest (COI) at the site?

Method 2 Has a BSAF been determined for a reference chemical (RC) at the site?

Has a BEF been determined for the RC and COI at a reference site? Has a BSAF been determined for the COI at a reference site?

Method 3a Collect additional data (e.g., determine a BSAF for the COI at the site); or, select a different method Method 3b Determine a BEF for the RC and COI at a reference site, and appl y Method3b; or, select a different method yes

yes

no yes no yes no

no

no5-5 BSAFs have been used by EPA for predicti ng chemical residues in aquatic organisms from contaminated sediments, especially fo r Superfund sites (USEPA, 1999). A site-specific BSAF (i.e., a BSAF determined at the site) is clearly most desirable when making predictions, because this BSAF incorporates all processes and conditions influencing bioaccumulation at the site. When a BSAF is determined by measurements at the site, Method 2 is used to predict the site-specific BAF, as described in Section 4. However, BSAFs are unavailable for many sites, and high-quality BSAFs determined from measuremen ts in other ecosystems may be used in developing the site-specific BAF fo r the site of interest. This is Method 3a of the site-specific BAF methodology: direct extrapolat ion of a BSAF determined for th e chemical of interest at a
reference site to a site of interest. BSAF extr apolation has received in creasing attention as a method for predicting site-specific BAFs (Wong et al. 2001), as BSAFs become more widely available. As discussed in Section 4, BSAFs are useful measures of bioaccumulation for organic chemicals because: (1) they do not require difficult (and potentially highly variable)
measurements of chemical concentr ations in water, (2) they remove the site-specific variability in
BAFs due to differences in the se diment-water concentration quotient, J socw , and (3) they remove the site-specific variability due to differe nces in organic carbo n and lipid contents.

BSAFs can be adjusted, using ratios predicted by food chai n bioaccumulation models, to
account for differences between site s in the degree of connection of fish to benthic/pelagic food chains, as well as differences between sites in the sediment-water disequilibrium due to
differences in chemical loadi ng histories (Burkhard et al., 200 6). This approach, called hybrid bioaccumulation modeling, uses mechanistic bioaccu mulation models to assist in extrapolating field-measured BSAFs by explicitly accounting for the differences between ecosystems. Although additional work is required to fully evaluate a nd develop the hybrid extr apolation approach for routine application, it appears promising as a method for improving the accuracy of BSAF extrapolation.

When BSAFs from one ecosystem are direct ly applied to another ecosystem (e.g., Method 3a), the investigator is assuming that the underlying conditions and parameters affecting
bioaccumulation are the same between the site of interest and the reference site where the BSAFs were determined. This implicit assumption is ofte n not appreciated by users of BSAF data. As5-6 discussed by Burkhard et al. (2005), the major conditions and parameters incorporated into a
measured BSAF are:

1. the distribution of the chemical between the sediment and water column,
2. the relationship of the food ch ain/web to water and sediment,
3. the length of the food web (or trophic le vel of the organism – although this is normally accommodated in the trophic- level specific BAF calculation),
4. bioavailability of the chemical due to amounts and types of organic carbon in the ecosystem (although bioavailability diffe rences between sites are largely accommodated by expressing BSAFs and BAFs in terms of concentrations normalized to organic carbon, lipid, and fr eely dissolved fraction in water), and

5. metabolic transformation rates of the chemical within the food web.

The first four factors can vary widely among ecosy stems. In contrast, the fifth factor will, in all likelihood, vary much less among ecosystems. Significant unexplained variability can also arise from sampling and analytical factors. This unfortunately complicates, to an unknown degree, examples provided later in th is section to demonstrate th e methods with actual data.

The validity of BSAF extrapolation can be directly evaluated by comparing BSAFs
determined at sites that differ in terms of the conditions, parameters, and connections that affect chemical bioaccumulation. Wong et al. (2001) measured BSAFs for p , p’ -DDE in white suckers that ranged from 1.7 to 27 (with a median value of 8.8) across 36 different riverine ecosystems. These authors concluded that BSAF extrapolation was a useful tool for estimating bioaccumulation in rivers, but cautioned that va riability in BSAF values between sites (and between different kinds of sites, e.g. rivers and lakes) might limit the accuracy and utility of this approach. Burkhard et al. (2003b) measured very similar BSAFs for 93 PCB congeners in 6 fish species across 6 spatial zones in Green Bay, Lake Michigan (average BSAF = 7.8; average congener-specific minimum and maximum values ranging from 1.3 to 25), and for 125 PCB congeners in 6 fish species at 6 locations in the Hudson River (average BSAF = 7.7; average congener-specific minimum and maximum values ranging from 2.5 to 11). Average congenerspecific BSAFs determined in Green Bay and the Hudson River are reported in Appendix 5C. In5-7 Green Bay, the variability in BSAFs between spa tial zones for a particul ar congener and fish species was found to be comparable to the variability of baseline BAFs.

Sets of BSAFs across ecosystems have consis tent, if not identical, scaling, ranking, or ordering of the individual chemicals (Burkhard et al., 2005). When BSAF va lues are plotted for one ecosystem against another, ch lorinated pesticides (Figure 5- 2), PCDD/Fs (Figure 5-3), PCBs (Figure 5-4), and PCBs together with PCDD/Fs (Figure 5-5) fall on a line with slopes close to 1.0 and have Spearman's coefficient of rank correlation that are also close to 1.0.

The highly significant relative ranking phe nomenon appears to occur in ecosystems despite their differences, or errors or biases in the measurements used to determine the BSAFs.
This behavior holds for chemicals metabolized by fish, i.e., PCDD/Fs (Kleeman et al. 1988; Opperhuizen et al. 1990), as well as for chemicals with substantia lly lower rates of metabolism in fish, i.e., PCBs. The demonstration of consiste nt scaling/ranking of individual BSAFs across ecosystems is quite remarkable. It creates opportunities for improvi ng our understanding of bioaccumulation processes in aquatic ecosystems, and for improving the accuracy of site-specific bioaccumulation factors that are estimated by ex trapolating BSAFs (Burkhard et al. 2005). In particular, extrapolating a ratio of BSAFs (a bioaccumulation equivalency factor or BEF) from another ecosystem and adjusting it using a BSAF for a reference chemical at the site, improves the Method 3 extrapolation by in corporating the BSAF ranking be havior in the methodology. This is the basis for Method 3b. By incorporating more information from other ecosystems (e.g., a BEF instead of a BSAF) and adjusting this information to reflect conditions at the site (via measuring BSAFs for reference chemicals), an improved es timate for the site-specific BAF can be obtained.5-8 BSAFs for white suckers at various sites

White Sucker from 402108076363701BSAFs

0.1 1 White Sucker from 413345072531001BSAFs

0.1 1 10

White Sucker from 01573560

BSAFs for white suckers from sampling station 01208869 0.1 1 10BSAFs

0.1 1 10 White Sucker from 01171500BSAFs

0.1 1 10

White Sucker from 01191000

BSAFs for white suckers from sampling station 01208869 0.1 1BSAFs

0.1 1 White Sucker from 400037076423701BSAFs

0.01 0.1 1 10 100

r = 92% slope = 1.58 " 0.21 (8) ρ = 0.90 α = 0.0020 r = 95% slope = 0.64 " 0.08 (6) ρ = 0.99 α = 0.0003

r = 74% slope = 0.78 " 0.18 (8) ρ = 0.67 α = 0.071

r = 93% slope = 0.57 " 0.09 (6) ρ = 0.94 α = 0.0048 r = 95% slope = 0.70 " 0.09 (6) ρ = 0.83 α = 0.042

r = 27% slope = 0.96 " 0.35 (7) ρ = 0.29 α = 0.53 A

B

C D

E

F

FIGURE 5-2. White sucker BSAFs (kg organic carbon/ kg lipid) from six ecosystems plotted against BSAFs (kg organic car bon/kg lipid) for white sucker from sampling station 01208869 for p,p ’-DDD (turquoise diamond symbols), p,p ’-DDT (brown diamond), p,p ’-DDE (green diamond), cis-chlordane (blue circle), trans-chlordane (o range circle), trans-nonachlor (purple square), dieldrin (blue triangle), and cis-nonachlor (blue square) ( 4 ). The sampling locations are those reported by Wong et al. (2001). The correlation coefficient ( r ), slope (standard deviation, number of data points) for geometric mean regression line (solid), Spearman’s coefficient of rank correlation ( D ) and significance level ( " ), and 1:1 line (dotted) are provided. Note: the y-axes have different scales in some of the subgraphs.5-9 FIGURE 5-3. BSAFs (kg organic carbon/kg lipid) for PCDD/Fs with nonzero mammalian toxicity equivalence factors (TEFs) from four ecosystems plotted against BSAFs (kg organic carbon/kg lipid) for 6 year old la ke trout from Lake Michigan. The symbol-color combination represents the same chemical in all four subgraphs, i.e., 2,3,7,8-TeCDD (green diamond); 1,2,3,7,8-PeCDD (orange diamond); 1,2,3,4,7,8-HxCD D (yellow diamond); 1,2,3,6,7,8-HxCDD (pink up triangle); 1,2,3,7,8,9-HxCDD (purpl e up triangle); 1,2,3,4,6,7,8-HpCDD (blue up triangle), OCDD (turquoise up triangle); 2,3,7,8-TeCDF (green up triangle); 1,2,3,7,8/1,2,3,4,8-PeCDF (orange up triangle); 2,3,4,7,8-PeCDF (yellow up triangle); 1,2,3,4,7,8/1,2,3,4,7,9-HxCDF (pink down triangle); 1,2,3,6,7, 8-HxCDF (purple down triangle); 2,3,4,6,7,8-HxCDF (blue down triangle); 1,2,3,4,6,7,8- HpCDF (green down triangle); 1,2,3,4,7,8,9-HpCDF (orange down triangle); and OCDF (yellow down triangle). The correlation coefficient ( r ), slope (standard deviation, number of da ta points) for geometric mean regression line (solid), Spearman’s coe fficient of rank correlation ( D ) and significance level ( " ), and 1:1 line (dotted) are provided. 95% confidence limits on the Lake Michigan BSAFs are provided. Lake Kasumigaura Carp ( 10 )

BSAFs for 6 year old lake trout

southern Lake Michigan ( 8 ) 0.0001 0.001 0.01 0.1 1BSAFs

0.00001 0.0001 0.001 0.01 0.1 1 Lake Shinji Sea Bass ( 11 )

BSAFs for 6 year old lake trout

southern Lake Michigan ( 8 ) 0.0001 0.001 0.01 0.1BSAFs

0.00001 0.0001 0.001 0.01 0.1

1 Tokyo Bay Sea Bass ( 12 )BSAFs

0.00001 0.0001 0.001 0.01 0.1

1 10 Ya-Er Lake ( 13-15 ) Carp, ponds 3, 4 & 5BSAFs

0.00001 0.0001 0.001 0.01 0.1 1 10

r = 93% slope = 1.05 " 0.095 (16) ρ = 0.94 α = <0.0001 r = 83% slope = 0.580 " 0.083 (15) ρ = 0.80 α = 0.0003

r = 92% slope = 1.05 " 0.099 (16) ρ = 0.93 α = <0.0001 r = 96% slope = 1.20 " 0.089 (16) ρ = 0.96 α = <0.00015-10 FIGURE 5-4. BSAFs (kg organic carbon/kg lipid) for PCBs from Green Bay, Hudson River, and Detroit River (Leadley et al., 1998) plotted against BSAFs (kg orga nic carbon/kg lipid) for 6 year old lake trout from Lake Michigan. The symbol-c olor combination represents the same chemical in all six subgraphs, i.e., PCB-18 (blue diam ond), PCB-22 (turquoise diamond), PCB-26 (yellow square), PCB-28/31 (purple diamond), PCB-49 ( purple square), PCB-52 (green square), PCB56/60 (pink diamond), PCB-66 (orang e square), PCB-85 (yellow circ le), PCB-87 (orange circle), PCB-91 (pink square), PCB-97 (turquoise square), PCB-99 (blue circle), PCB-118 (blue square), PCB-141 (turquoise circle), PCB-146 (pink circle), PCB-149 (green circle), and PCB-180 (purple circle). The correlation coefficient ( r ), slope (standard deviation, number of data points) for geometric mean regression line (solid), Sp earman’s coefficient of rank correlation ( D ) and significance level ( " ), and 1:1 line (dotted) are provided. 95% confidence limits are provided for each BSAF when available. Green Bay, Zone 1 Adult Alewife ( 6 )BSAFs

0.1 1 10 100

Green Bay, Zone 1 Walleye, 4 yrs ( 6 )BSAFs

0.1 1 10

Green Bay, Zone 3b Walleye, 4 yrs ( 6 )

BSAFs for 6 year old lake trout

southern Lake Michigan ( 8 ) 0.1 1 10BSAFs

0.1 1 10 Hudson River ( 6 ) River Mile 194 Largemouth BassBSAFs

0.1 1 10 100

Hudson River ( 6 ) River Mile 122 Yellow PerchBSAFs

0.1 1 10

Detroit River ( 5 ) Trenton Channel Brown Bullhead

BSAFs for 6 year old lake trout

southern Lake Michigan ( 8 ) 0.1 1 10 100BSAFs

1 10 100 r = 82% slope = 0.97 " 0.13 (18) ρ = 0.76 α = 0.0002 r = 91% slope = 1.09 " 0.10 (18) ρ = 0.95 α = <0.0001

r = 80% slope = 1.04 " 0.15 (18) ρ = 0.65 α = 0.0033 r = 96% slope = 1.02 " 0.073 (17) ρ = 0.94 α = <0.0001

r = 92% slope = 1.29 " 0.13 (16) ρ = 0.84 α = <0.0001 r = 52% slope = 0.72 " 0.19 (10) ρ = 0.61 α = 0.065-11 FIGURE 5-5. BSAFs (kg organic carbon/kg lipid) for PCBs and PCDD/Fs from Lake Ontario (USEPA, 1995) and Tokyo Bay (Naito et al., 200 3) plotted against BSAFs (kg organic carbon/kg lipid) for 6 year old lake trout from Lake Michigan. The symbol-color combination represents the same chemical in both subgraphs, and their desc riptions are listed in Figures 5-2 and 5-3. The correlation coefficient ( r ), slope (standard deviation, number of data points) for geometric mean regression line (solid), 95% confidence and pr ediction limits for the regression, Spearman’s coefficient of rank correlation ( D ) and significance level ( " ), and 1:1 line ( dotted) are provided. 95% confidence limits are provided for each BSAF when available. The Tokyo Bay data set had only one PCB in common with the PCBs used in Figure 5-3. The other nine PCBs with nonzero TEFs were plotted against BSAFs for Lake Michigan 6 year old lake trout, i.e., all dotted circles: PCB-77 (brown), PCB-81 (yellow), PCB-105 (o range), PCB-114 (blue), PCB-123 (pink), PCB126 (purple), PCB-156 (turquoise), PCB-167 (red), and PCB 169 (green). Lake Ontario Lake Trout ( 7 )

BSAFs for 6 year old lake trout

southern Lake Michigan ( 8 ) 0.0001 0.001 0.01 0.1 1 10 100BSAFs

0.0001 0.001

0.01

0.1

1 10 100 Tokyo Bay Sea Bass ( 9 )

BSAFs for 6 year old lake trout

southern Lake Michigan ( 8 ) 0.001 0.01 0.1 1 10 100BSAFs

0.001 0.01 0.1 1 10 100 1000

r = 98% slope = 1.13 " 0.045 (26) ρ = 0.98 α = <0.0001 r = 96% slope = 0.79 " 0.041 (32) ρ = 0.96 α = <0.00015-12 5.1.1 Estimating Site-Specific BAFs by Extrapolating BSAFs (Method 3a)

Method 3a estimates the site-specific baseli ne BAF by extrapolating the BSAF determined for the chemical of interest by measurements in another ecosystem, which is then multiplied by
the sediment-water conc entration quotient ( J socw ) for the site to obtain a trophic level-specific
baseline BAF:

, socw 1 Baseline BAF = BSAF f 

### Π i ij l − (Equation 5-1)

The terms in this equation are defined as follows:

Baseline BAF i = Baseline BAF estimated for the site and the chemical of interest, for organism at trophic level i (defined in Equation 3-2);
BSAF i,j = BSAF for chemical of intere st for organism at trophic level i extrapolated from ecosystem j (defined in Equation 4-1);
Π socw = Sediment-water concentrat ion quotient for the site and the
chemical of interest (defined in Equation 4-3);
f l = Lipid content (fraction) of the target species or tissue.

In most cases, J socw will be unknown for the chemical of interest, because this coefficient
is based on the chemical concentration measured in water, which is usually undetectable when
Method 3 is selected to estimate BAFs. Therefore, the sediment-wat er concentration quotient for a reference chemical is usually substitu ted in equation 5-1, similar to the way J socw for reference5-13 chemicals are used in Method 2. Equation 5-2 is us ed to calculate the s ite-specific baseline BAF
for chemical k , when J socw is based on measurements for reference chemicals r :

/ socw, ow,

, , ow, D K 1 Baseline BAF =BSAF K f 

### Π kr r k ik i j

r l − (Equation 5-2)

where:

D k/r = Ratio of the fugacity gradient (modeled as J socw /K ow ) between sediment
and water for chemical of interest k in comparison to that of a reference
chemical r

Each of the parameters in equa tions 5-1 and 5-2 (BAF, BSAF and J socw ) is calculated using chemical concentrations no rmalized for lipid (in biota) a nd/or organic carbon (sediment) contents and adjusted for the dissolved fraction of the chemical in water. Method 3a is appropriate for moderate to highly hydr ophobic nonionic organic chemicals, and to certain ionic organic chemicals for which similar lipid and organic car bon partitioning behavior applies. Since a BSAF is based on lipid and organic ca rbon normalized chemical concentr ations (Equation 4-1), no other adjustment for these factors is necessary. Equations 5-1 and 5-2 should only be applied to fish or other aquatic biota within a sp ecific trophic level. Method 3a does not address site-specific variability in BSAFs. The only site-specific adjustment made by the investigator using Method 3a involves selecting a value for J socw , based upon measurements, es timates or predictions

, as discussed in Section 4.3. Calcula ting a site-specific BAF using Me thod 3a is presented in the following example.

Extrapolating a Site-Specific BAF From BSAFs Determined at Another Site (Method 3a )
In this example, high-quality BSAF measurements from Lake Michigan are used to derive site-specific BAFs for a number of PCB congeners in adult (4 year old) walleye in Green Bay. The site is defined to be the middle portion of Green Bay, corresponding to sampling zone 3a from the Green Bay Mass Balance Study. Walleye is a popular sport fish, commonly caught and consumed by the local community. The dietary preference of adult walleye, based upon gut content analyses, places this species in trophic level 4.5-14 Extrapolating a Site-Specific BAF From BSAFs Determined at Another Site (Method 3a, continued )

The reference BSAFs used in this exam ple was obtained from data published by Burkhard et al. (2004) for P CBs, PCDDs, and PCDFs from a study conducted in southern Lake Michigan that was specifically designed to determine BSAFs in multiple age classes (2, 3, 5-6 and 8-9 year old) of lake trout. The data were based upon highly re presentative sampling: southern Lake Michigan is well-mixed; fish samples c onsisted of 5 fish per composite sample and multiple composites per age class were analyzed; and sediments were sampled from five depositional areas surrounding the location where the fish were collected. Analyses of these da ta confirmed that the concentration measurements were both consistent a nd representative. The consistency in chemical-specific BSAFs determined in the Burkhard et al. (2004) study demonstrated that highly-reproducible BSAFs could be obtained from a site when appropriately sampled.
The chemical of interest is PCB 101, for which no BAF value has been determined at the site for the adult walleye target species. BSAFs for this PCB were determined in various age classes of lake trout in Lake Michigan by Burkhard et al. (2004). A review of the dietary preferences of the larger sizes of lake trout that are commonly c onsumed by the general U.S. population confirms that these organisms belong to trophic level 4. The BSAFs measured for PCB 101 in composite samples of large Lake Michigan lake trout are tabulated below; the geometric mean of the BSAFs is 7.71.

Lake Trout Composite Sample (age) BSAF for PCB 101

6 year old 7.53 8 year old 6.40 9 year old 9.535-15 Extrapolating a Site-Specific BAF From BSAFs Determined at Another Site (Method 3a, continued)

It is assumed that no sediment-water c oncentration quotient is available for the chemical of interest at the site, a common situation. Instead, J socw measured at the site for reference chemicals will be used with Method 3a to estimate the site-specific baseline BAF. PCB cong eners 52 and 105 have been used as reference chemicals for calculating base line BAFs for other PCBs (USEPA, 1995a; Cook and Burkhard, 1995). These congeners serve as appropriate reference chemicals because (1) they ha ve similar physicochemical properties, (2) they are well quantified in sediment and biota, and (3) available data indicate they have loadi ng histories similar to PCB 101 and thus their fugacity ratio ( J socw,r /K ow , r ) values should be similar. The sediment-water concentration quotient measured for P CBs 52 and 105 in Green Bay zone 3a are tabulated below.

PCB congener J socw log K ow
52 4.24×10 6 5.84
105 2.37×10 7 6.65

Estimating the site-specific baseline BAF using method 3a
Equation 5-2 is used to calculate the site -specific baseline BA F for chemical i, when J socw is based on measurements for reference chemicals r:

/ socw , ow ,

ow , , , 1 D K Baseline BAF =BSAF K f 

### Π kr r k

r ik i j l −5-16 Extrapolating a Site-Specific BAF From BSAFs Determined at Another Site (Method 3a, continued)

The K ow of the chemical of interest, PCB 101, is 2.40×10 6 ; the lipid content of the target species, adult walleye, is 11 %. Since the fugacity ratios of the chemical of interest and the reference chemicals are assumed to be similar, D k/r ~ 1. Therefore, a site-specific baselin e BAF for PCB 101 is calculated using equation 5-2 with reference chemical PCB 52:



### ( ) ( ) 

## ( )( ) ( ) 101/52 SOCW,52 OW,101 4,101 4,52 OW,52

6 6 8 5 D K 1 Baseline BAF K
1 4.24 10 2.40 10 1 7.71 1.13 10 / 0.11 6.92 10 

### Π 

## f l BSAF

L kg l = −

× × = − =× − ×

Likewise, a site-specific baseline BAF for PCB 101 can also be calculated with reference chemical PCB 105:



## ( ) ( ) 

# ( )( ) ( ) 101/105 SOCW,105 OW,101 4,101 4,105 OW,105

7 6 7 7 D K 1 Baseline BAF K
1 2.37 10 2.40 10 1 7.71 9.82 10 / 0.11 4.47 10 

### Π 

## f l BSAF

L kg l = −

× × = − =× − ×
The final site-specific baseline BAF for the chemical of interest should be calculated as the geometric mean of the individual site-specific baseline BAFs calculated using the different reference chemicals. In this example, the final site-specific baseline BAF for PCB 101 estimated usi ng Method 3a is therefore 1.06×10 8 L/kg-lipid.5-17 D k/r , the ratio of the fugacity gradient ( J socw /K ow ) for chemical of interest k in comparison
to reference chemical r , is an especially important parame ter in equation 5-2. Unfortunately, high quality datasets for J socw , from which D k/r can be calculated, are very limited. Selecting appropriate reference chemical s, and accurate values of D k/r is important, because significant reproducible differences in J socw values between individual PCB, PCDD, PCDF, and PAH congeners are greater than previously recognized.

High-quality PCB congener data from southern Lake Michigan (Burkhard et al. 2004), 2 locations in Green Bay (Burkhard et al. 2003b), and 2 locations in the Hudson River (Burkhard et al. 2003b) will be used to further illustrate the Method 3a BSAF extrapolation methodology. These ecosystems are substantially different: Lake Michigan is a cold deepwater oligotrophic ecosystem, Green Bay is a shallow eutrophic ecos ystem, and Hudson River is a relatively fast moving river ecosystem. Additionall y, the aquatic food webs in the ecosystems are different and have different top predat ory species: lake trout ( Salvelinus namaycush ) in Lake Michigan, brown trout ( Salmo trutta ) and walleye ( Stizostedion vitreum ) in Green Bay, and largemouth bass ( Micropterus salmoides ) and yellow perch ( Perca flavescens ) in Hudson River. For the Green Bay ecosystem, data used were from Zone 1, th e lower Fox River enteri ng into Green Bay, and Zone 4, the deeper outer portion of the bay. Thes e are the most distinctly different two zones across the bay in terms of conditions and parameters, with Zone 1 having the highest concentrations of PCBs, and hydr odynamic and sediment transport dynamics characteristic of an urban river. In the Hudson River, two locations in Thompson Island Pool, river miles (RMs) 189 and 194, were used.

BSAF extrapolation involves directly appl ying measurements made in one ecosystem to the site of interest, as demonstrated in th e following examples. In Table 5-1, PCB congener BSAFs measured in southern Lake Michigan for 6 year old trout were extrapolated to predator fish at multiple locations in the Green Bay and Hudson River ecosystems. In Table 5-1A and B, the Lake Michigan BSAFs are extrapolated to 3 year old brown trout and 4 year old walleye in Zone 4 of Green Bay, and in Table 5-1C the Lake Michigan BSAFs are extrapolated to 4 year old walleye in Zone 1 of Green Bay. In Tables 5-1 D and E, the Lake Michigan BSAFs are extrapolated to largemouth bass at Hudson Rive r miles 189 and 194. In each sub-table, the BSAFs5-18 for 9 representative PCB congeners measured in La ke Michigan lake trout are extrapolated to the fish species indicated, and are us ed to estimate the site-specifi c BAF via Method 3a (equation 5- 1). The Lake Michigan lake trout BSAFs are al so compared to independently-determined BSAFs for each fish species at each si te, and the error involved in BSAF extrapolation is presented in
Table 5-1.

Comparison of the BSAF extrapolation erro rs in Table 5-1 suggest that Method 3a estimates tend to be consistently biased at e ach site. This is most apparent for the BSAFs extrapolated to Green Bay sites (Tables 5-1, A th rough C). BSAFs extrapolated to fish in zone 4 of Green Bay are negatively biased for all of th e congeners included in th e tabulation; the average
bias is -53% for brown trout and -61 for walleye. BSAFs extrapolated to fish in zone 1 of Green Bay are positively biased for all of the congeners, with an average bias of +167% for walleye. To put this in context, if an investigator extrapol ated Lake Michigan BSAFs to estimate site-specific BAFs for walleye in Green Bay Zone 4, the pred icted BAFs would be too small by a factor of about 2.4. On the other hand, if the investigator us ed the Lake Michigan BSAFs to estimate sitespecific BAFs for walleye in Zone 1 of Green Ba y, the BAFs would be too large, by a factor of about 1.7. The errors in BSAFs extrapolated to Hudson River sites (Tables 5-1, D and E) appear to be more random, except for congener 18, which is highly biased at river mile 194. On an individual congener basis, the largest BSAF ex trapolation errors were 304% for PCB 180 in Green Bay zone 1 walleye, and 275% for PCB 18 in largemouth bass at Hudson River mile 194. However, the majority of BSAF extrapolation errors were smaller than 100%. A graphical comparison between the measured and extrapolated BSAFs is presented in Figure 5-6. This figure
demonstrates that the errors in BSAF extrapolation fall within the ± factor of 5 range (for this example) but that Method 3a extrapolation does not account for much of the site-specific variability in BSAFs. It should al so be recognized that using P CB data does not fully demonstrate the benefits of Method 3a for the greater range of BSAF values for other potential chemicals of concern (e.g., TCDDs, TCDFs, PAHs), which ca n span up to several orders of magnitude.5-19 Table 5-1. Method 3a BSAF Extrapolation Exam ple Using PCB Data From Lake Michigan, Green Bay and the Hudson River.

A. Extrapolating Lake Michigan Lake Trout (LM LT6) BSAFs to 3 Year Old Brown Trout in Zone 4 of Green Bay (GB BT3)

PCB Congener Log K ow LM LT6 BSAF GB BT3 BSAF BSAF Extrapolation Error J socw Measured in

Green Bay

Zone 4 Predicted Site-specific Baseline BAF Site-specific Log Baseline BAF

18 5.24 1.38 3.29 -58% 1.11x10 6 1.53 x10 6 6.18 28/31 5.67 1.50 4.15 -64% 3.89 x10 6 5.83 x10 6 6.77

52 5.84 5.16 23.2 -78% 2.00 x10 6 1.03 x10 7 7.01 110 6.48 4.75 13.4 -65% 7.33 x10 6 3.48 x10 7 7.54 118 6.74 5.57 11.0 -49% 2.99 x10 7 1.66 x10 8 8.22 149 6.67 9.05 23.5 -61% 3.10 x10 6 2.81 x10 7 7.45 180 7.36 11.8 13.9 -15% 1.32 x10 7 1.56 x10 8 8.19 174 7.11 8.30 14.2 -42% 9.64 x10 6 8.00 x10 7 7.90 196/203 7.65 9.15 16.4 -44% 8.12 x10 7 7.43 x10 8 8.87

B. Extrapolating Lake Michigan Lake Trout (LM LT6) BSAFs to 4 Year Old Walleye in Zone 4 of Green Bay (GB W4)

PCB Congener Log K ow LM LT6 BSAF GB W4 BSAF BSAF Extrapolation Error J socw Measured in

Green Bay

Zone 4 Predicted Site-specific Baseline BAF Site-specific Log Baseline BAF

18 5.24 1.38 2.67 -48% 1.11 x10 6 1.53 x10 6 6.18 28/31 5.67 1.50 2.99 -50% 3.89 x10 6 5.83 x10 6 6.77

52 5.84 5.16 26.5 -81% 2.00 x10 6 1.03 x10 7 7.01 110 6.48 4.75 16.1 -70% 7.33 x10 6 3.48 x10 7 7.54 118 6.74 5.57 14.3 -61% 2.99 x10 7 1.66 x10 8 8.22 149 6.67 9.05 22.2 -59% 3.10 x10 6 2.81 x10 7 7.45 180 7.36 11.8 39.2 -70% 1.32 x10 7 1.56 x10 8 8.19 174 7.11 8.30 18.7 -56% 9.64 x10 6 8.00 x10 7 7.90 196/203 7.65 9.15 20.3 -55% 8.12 x10 7 7.43 x10 8 8.875-20 Table 5-1 (Continued). Method 3a BSAF Extr apolation Example Using PCB Data From Lake Michigan, Green Bay and the Hudson River.

C. Extrapolating Lake Michigan Lake Trout (LM LT6) BSAFs to 4 Year Old Walleye in Zone 1 of Green Bay (GB W4)

PCB Congener Log K ow LM LT6 BSAF GB W4 BSAF BSAF Extrapolation Error J socw Measured in

Green Bay

Zone 1 Predicted Site-specific Baseline BAF Site-specific Log Baseline BAF

18 5.24 1.38 0.58 140% 1.57 x10 6 2.16 x10 6 6.34 28/31 5.67 1.5 0.47 216% 5.64 x10 6 8.47 x10 6 6.93

52 5.84 5.16 2.07 149% 3.98 x10 6 2.06 x10 7 7.31 110 6.48 4.75 2.05 131% 1.89 x10 7 8.98 x10 7 7.95 118 6.74 5.57 3.15 77% 4.87 x10 7 2.71 x10 8 8.43 149 6.67 9.05 3.06 196% 2.51 x10 7 2.27 x10 8 8.36 180 7.36 11.8 2.92 304% 1.68 x10 8 1.99 x10 9 9.30 174 7.11 8.3 3.70 124% 5.36 x10 7 4.45 x10 8 8.65 196/203 7.65 9.15 ND ND

D. Extrapolating Lake Michigan Lake Trout (LM LT6) BSAFs to Largemouth Bass at Hudson River mile 189 (RM 189 LMB)

PCB Congener Log K ow LM LT6 BSAF RM 189 LMB BSAF BSAF Extrapolation Error J socw Measured at Hudson River Mile 189 Predicted Site-specific Baseline BAF Site-specific Log Baseline BAF

18 5.24 1.38 1.29 7% 3.07 x10 6 4.23 x10 6 6.63 28/31 5.67 1.50 2.46 -39% 6.18 x10 6 9.27 x10 6 6.97

52 5.84 5.16 6.56 -21% 5.24 x10 6 2.70 x10 7 7.43 110 6.48 4.75 11.5 -59% 1.85 x10 7 8.77 x10 7 7.94 118 6.74 5.57 17.1 -67% ND 149 6.67 9.05 18.4 -51% ND 180 7.36 11.8 24.1 -51% ND 174 7.11 8.30 20.0 -59% ND 196/203* 7.65 9.15 30.4 -70% ND5-21 Table 5-1 (Continued). Method 3a BSAF Extr apolation Example Using PCB Data From Lake Michigan, Green Bay and the Hudson River.

E. Extrapolating Lake Michigan Lake Trout (LM LT6) BSAFs to Largemouth Bass at Hudson River mile 194 (RM 194 LMB)

PCB Congener Log K ow LM LT6 BSAF RM 194 LMB BSAF BSAF Extrapolation Error J socw Measured at Hudson River Mile 194 Predicted Site-specific Baseline BAF Site-specific Log Baseline BAF

18 5.24 1.38 0.368 275% 1.04 x10 7 1.43 x10 7 7.16 28/31 5.67 1.50 0.921 63% 1.60 x10 7 2.40 x10 7 7.38

52 5.84 5.16 2.82 83% 1.60 x10 7 8.25 x10 7 7.92 110 6.48 4.75 4.11 15% 5.32 x10 7 2.52 x10 8 8.40 118 6.74 5.57 5.77 -3% ND 149 6.67 9.05 6.36 42% ND 180 7.36 11.8 7.28 62% ND 174 7.11 8.30 5.23 59% ND 196/203* 7.65 9.15 8.92 3% ND5-22 FIGURE 5-6. BSAFs (kg organic carbon/kg lipid) for PCBs from Green Bay and the Hudson River plotted against BSAFs (kg organic carbon/kg lipid) extrapolated from Lake Michigan (6 year old lake trout) using Met hod 3a. The symbol-color combinati ons represent particular fish species and ecosystem locations. GB = Green Bay; z1 = zone 1; z4 = zone 4; RM 189 = Hudson River mile 189; RM 194 = Hudson River mile 194; BT3 = 3-year old brown trout; W4 = 4-year old walleye; CP1 = 1-year old carp; LMB = larg emouth bass; YP = yellow perch. The 1:1 line (solid), as well as ±2x (short dashed) and ± 5x (long-short dashed) lines are also provided. 0.1

1 10 100

0.1 1 10 100 measured BSAFextrapolated BSAF GB z4 BT3 GB z4 W4 GB z1 W4 HR RM189 LMB HR RM194 LMB 5x 2x 1:1 line 1/2 1/55-23 5.1.2 Estimating Site-Specific BAFs by Extrapolating BEFs (Method 3b)

Method 3b estimates the site-specific baseline BAF by extrapolating a high quality BEF (a ratio of BSAFs) determined by measurements fo r the chemical of interest and a reference chemical in another ecosystem. The BEF is multi plied by a BSAF measured at the site for the
reference chemical and the J socw for the chemical of interest k at the site:

, k/r , socw, 1 Baseline BAF = BSAF BEF f 

### Π
j i ir k l − (Equation 5-3)

As was the case for Method 3a, J socw will usually be unknown for the chemical of interest. Therefore, the sediment -water concentration quotient for a reference chemical is usually substituted in equation 5-3, similar to the way J socw for reference chemicals are used in Method 2. Equation 5-4 is used to calcula te the site-specific baseline BA F for the chemical of interest k , when J socw is based on measurements for reference chemicals r :

/ socw , ow,

ow , , ,/ 1 K Baseline BAF = BSAF BEF K f 

### Π kr r k

r i ir jk r l D

− (Equation 5-4)

As was the case for Method 3a (equation 5-2), the parameter D k/r plays an important role in equation 5-4.

The bioaccumulation equivalency factor BEF j , k/r between the chemical of interest k and the
reference chemical r at another site (j) is:

, , /

, BSAF BEF BSAF j k jk r j r = (Equation 5-5)

where:

BSAF j,k

= BSAF for chemical of interest k determined by measurements at
another site j , and
BSAF j,r

= BSAF for reference chemical r determined by measurements at
another site j.5-24 Although the terms in this equation have been prev iously defined, the investigator should note the use and meaning of the various subscripts associat ed with each term in equations 5-4 and 5-5:

BSAF i,r = BSAF for reference chemical determined by measurements of organism at trophic level i at the site (equation 4-1);
A socw ,k = Sediment-water concentration quot ient for the site and the chemical of interest, i (defined in Equation 4-3).

As was the case for Method 3a, each of the parameters in equations 5-4 and 5-5 (BAF, BEF, BSAF and J socw ) is calculated using chemical concentra tions normalized for lipid (in biota) and/or organic carbon (sediment) contents and adju sted for the dissolved fraction of the chemical in water. Method 3b is appropriate for m oderate to highly hydrophobic nonionic organic chemicals, and to certain ionic organic chemi cals for which similar lipid and organic carbon partitioning behavior applies. Site-specific vari ability in BSAFs is addressed in Method 3b by incorporating a BSAF for a reference chemical dete rmined by measurements at the site, as well as a site-specific value for J socw , based upon measurements, estimates or predictions (as discussed in Section 4.3). Equations 5-3 and 5-4 should only be a pplied to fish or other aquatic biota within a specific trophic level. Calcula ting a site-specific BAF using Method 3b is presented in the following example.

Extrapolating a Site-Specific BAF From BEFs Determined at Another Site (Method 3b )
In this example, Method 3b is used to extrapolate a BEF, or ratio of BSAFs, from Lake Michigan to the Green Bay site. In this case, the chemical of interest is PCB 89, for which no BAF value has been determined at the site for the adult walleye target species. As in the pr evious example, it is assumed that no sediment-water con centration quotient ( J socw ) is available for the chemical of interest at the site. PCB 52 was chosen as a reference chemical for this example (K ow =6.92×10 5 ). Both J socw (4.24×10 6 ) and a BSAF (5.67) have been determined for this PCB in Green Bay zone 3b. BSAFs for both of these PCBs were determined in various age classes of lake trout in Lake Michigan by Burkhard et al. (2004), as tabulated below.5-25 Extrapolating a Site-Specific BAF From BEFs Determined at Another Site (Method 3b, continued)

Lake Trout Composite Sample (age) BSAF for PCB 89 BSAF for PCB 52 6 year old 4.80 5.16 8 year old 4.59 5.34 9 year old 6.21 6.90

Determining the bioaccumulatio n equivalency factor (BEF)
Method 3b extrapolates one or more bioaccumulation equivalency factors (BEFs) from another site j. The BEF is calculated using BSAFs for chemical of interest k and reference chemical r as : 

# ( ) ( ) ( ) k k/r

r BSAF BEF BSAF j j j = (Equation 5-5)

BEFs were calculated with BSAFs for i ndividual lake trout composites; for example, the BEF based on PCB concentra tions in 6 year old lake trout is:


## ( ) 

# ( ) ( ) ,6 89 4.80 5.16 89/52 ,6 52 BSAF BEF 0.930 BSAF LM yr LM LM yr = ==

Likewise, the BEFs for 8 and 9 y ear old lake trout are 0.860 and 0.900, respectively, as tabulated below. Th e geometric mean of the three BEFs, 0.896, was the value extrapolated to Green Bay.

LAKE TROUT COMPOSITE SAMPLE (AGE) BIOACCUMULATION EQUIVALENCY FACTOR (BEF) 6 year old 0.930 8 year old 0.860 9 year old 0.9005-26 Extrapolating a Site-Specific BAF From BEFs Determined at Another Site (Method 3b, continued)
Estimating the site-specific baseline BAF using method 3b
Equation 5-4 is used to calculate the s ite-specific baseline BAF for chemical k, when J socw is based on measurements for reference chemical r and the BEF j,k/r is extrapolated from another site:

/ SOCW, OW, , ,/ OW, 

### D K 1 BSAF BEF Baseline BAF K f kr r k i ir jk r l r 

### = − 

## Π

The K ow of the chemical of interest, PCB 89, is 1.17×10 6 ; the lipid content of the target species, adult walleye, is 11 %. Since the fugacity ratios of the chemical of interest and the reference chemicals are assumed to be similar, D k/r ~ 1. A site-specific baseline BAF for PCB 89 is calculated using equation 5-4 with reference chemical PCB 52:



### ( )( ) ( ) 

## ( )( ) ( ) 89 / 52 SOCW,52 OW,89 ,89 / 52 4 4,52
OW,52
6 6 7 5 D K 1 Baseline BAF BSAF BEF K f
1 4.24 10 1.17 10 1 5.67 0.896 3.64 10 / 0.11 6.92 10 Π

LM l

L kg l = −

× × = − =× − ×
Since only one reference chemical was used in this example, 3.64×10 7 L/kglipid is the final site-specific baseline BAF for PCB 89 estimated using Method 3b. As discussed in Section 5.1.3, EPA recommends repeating the Method 3a and 3b calculations using multip le reference chemicals, and then averaging the results.5-27 Method 3b offers the investigator two signifi cant advantages in comparison to Method 3a. First, by extrapolating a BEF instead of a BSAF, Method 3b ta kes advantage the “relative scaling” phenomenon evident in BSAFs for multiple chemicals between sites, as discussed in Section 5.1. The relative ranking of BS AFs has been demonstrated to be more consistent than the BSAFs values themselves. Therefore, BEFs, which turn BSAF ranking into ratios, should also be highly consistent between sites. Secondly, there are practical advantages to Method 3b in terms of the BSAFs that are required to make the extrapolation (Equation 5-3). For many organic chemicals, BEFs will be available from high-qua lity datasets (e.g., Lake Michigan, Hudson River and Green Bay) as have been described in th is and previous sections. Method 3b requires the investigator to determine the BSAF for a reference chemical at the site; however, the investigator can select the reference chemical based upon pr actical considerations such as analytical detectability. In many cases, PCBs will make good c hoices as references chemicals, because they can be readily quantified by available methods.

BEFs were introduced by EPA in the Great Lakes Water Quality Initiative (GLI) Technical Support Document (Cook and Burkhar d, 1995; USEPA, 1995a) for use in estimating
BAFs for PCDDs and PCDFs. Lake Ontario sedime nt and fish residue da ta (Lodge et al. 1994) provided the basis for calculating BEFs in th e GLI. For example, table 5-2 illustrates the calculation of BEFs from lake-wide average con centrations of toxicologically important PCDDs
and PCDFs in surface sediment and lake trout samples collected in 1987 for the EPA Region II Lake Ontario TCDD Bioaccumulation Study. Comparis ons to BEFs calculated from data obtained for other ecosystems confirms these bioaccumulati on potential differences and suggests that this BEF set would be predictive of bioaccumulation differences for PCDDs and PCDFs for fish in ecosystems outside the Great Lakes. This is important because very few PCDDs and PCDFs
measured as sediment contaminants are also det ectable in fish tissue. Based on the between-site comparisons of BSAFs presented in Section 5.1 (Figures 5-2 through 5- 5), other persistent bioaccumulative organic chemicals such as PCBs and chlorinated pesticides also exhibit this
behavior.5-28 Table 5-2. TCDD Bioaccumulation Equi valency Factors (BEFs) Derived For Toxicologically Important PCDDs And PCDFs From Lakewide Averages Of Concentrations In Lake Ontario Lake Trout And Surface Sediment In Depositional Areas.

Congener Log K ow a,c BSAF TCDD BEF
2,3,7,8-TCDD 7.02 0.059 1.0 1,2,3,7,8-PeCDD 7.50 0.054 0.92 1,2,3,4,7,8-HxCDD 7.80 0.018 0.31 1,2,3,6,7,8-HxCDD 7.80 0.0073 0.12 1,2,3,7,8,9-HxCDD 7.80 0.0081 0.14 1,2,3,4,6,7,8-HpCDD 8.20 0.0031 0.051
OCDD 8.60 0.00074 0.012 2,3,7,8-TCDF 6.5 b 0.047 0.80 1,2,3,7,8-PeCDF 7.0 b 0.013 0.22 2,3,4,7,8-PeCDF 7.0 b 0.095 1.6 1,2,3,4,7,8-HxCDF 7.5 b 0.0045 0.076 1,2,3,6,7,8-HxCDF 7.5 b 0.011 0.19 2,3,4,6,7,8-HxCDF 7.5 b 0.040 0.67 1,2,3,7,8,9-HxCDF 7.5 b 0.037 0.63 1,2,3,4,6,7,8-HpCDF 8.0 b 0.00065 0.011 1,2,3,4,7,8,9-HpCDF 8.0 b 0.023 0.39

OCDF 8.80 0.001 0.016

a Burkhard and Kuehl, 1987. b Estimated based on degree of chlori nation (Burkhard and Kuehl, 1987).

c EPA neither approves nor reco mmends the use of these log K ow values, which were measured and/or estimated over 20 years ago, fo r use in deriving bioa ccumulation factors. See Section 5.2.3.2 for guidance on selection of appropriate K ow values.5-29 To further illustrate the estimation of BAFs using Method 3b, Lake Michigan lake trout BEFs for PCB congeners were extr apolated to brown trout and walle ye in Zones 1 and 4 of Green Bay and to largemouth bass at RMs 189 and 194 of the Hudson River (Table 5-3). For these calculations, we chose to use PCB-118 as the refe rence chemical, so the BSAF for this congener was used to scale the Lake Michigan BEFs for each ecosystem. The product of the PCB-118 BSAF and the Lake Michigan BEFs provide the predicted BAFs. The choice of reference congener will affect the BAF estimate made using Method 3b; generally, more robust BAF estimates will be obtained by repeating the calc ulation using multiple reference chemicals and then averaging the results (Burkhard et al . 2003b). In each sub-table, the BSAFs for 9 representative PCB congeners measured in Lake Mi chigan lake trout are extrapolated to the fish species indicated, and are used to estimate the site-specific BAF via Me thod 3b (equation 5-3). In Table 5-3A and B, the Lake Michigan BEFs are ex trapolated to 3 year old brown trout and 4 year old walleye in Zone 4 of Green Bay, and in Tabl e 5-3C they are extrapolated to 4 year old walleye in Zone 1 of Green Bay. In Tables 5-3D and E, the Lake Michigan BEFs are extrapolated to largemouth bass at Hudson River miles 189 and 194. The Lake Michigan lake trout BSAFs are also compared to independently-determined site-sp ecific BSAFs for each fish species at each site in Table 5-3, and the error involved in BS AF extrapolation is presented as well.

A graphical comparison between the measured and estimated BSAFs extrapolated from BEFs is presented in Figure 5-7. Comparison of the BSAF estimation errors for Method 3b (Table 5-3 and Figure 5-7) to those fo r Method 3a (Table 5-1 and Figure 5-6) demonstrate that, although the Method 3b estimates are not pe rfect, they are in much better agreement with the measured BSAF than the BSAFs which are directly extrap olated from one ecosystem to another (i.e., Method 3a). The average bias in Method 3b estimat es ranges from a low of -8% for brown trout in Green Bay zone 4, to a high of 82% for la rgemouth bass at Hudson River mile 194. On an individual congener basis, variab ility in BEF extrapolation errors is again greater for the Hudson River sites than for those in Green Bay. In particular, large positive errors were calculated for PCB-18 for all species and locations on the Hudson River. It is not obvious why the extrapolation errors were so large for this congener. As was th e case for Method 3a, the majo rity of the errors in BSAFs extrapolation by Method 3b were sma ller than 100%, with the errors in BSAF extrapolation falling within the ± factor of 5 range (f or this example).5-30 Table 5-3. Method 3b BEF Extrapolation Exam ple Using PCB Data From Lake Michigan, Green Bay and the Hudson River.

A. Extrapolating Lake Michigan Lake Trout (LM LT6) BEFs to 3 Year Old Brown Trout in Zone 4 of Green Bay (GB BT3)

PCB

Congener Log K ow LM LT6 BSAF GB BT3 BSAF BEF (PCB 118) Method 3b BSAF Prediction Measured GB BT3

BSAF Method 3b BSAF Error J socw Measured in Green Ba y Zone 4 Predicted Site-specific Log Baseline BAF

18 5.24 1.38 0.248 2.73 3.29 -17% 1.11 x10 6 6.48 28/31 5.67 1.50 0.269 2.96 4.15 -29% 3.89 x10 6 7.06

52 5.84 5.16 0.926 10.2 23 -56% 2.00 x10 6 7.31 110 6.48 4.75 0.853 9.38 13 -30% 7.33 x10 6 7.84 118 6.74 5.57 11.0 1.00 2.99 x10 7 149 6.67 9.05 1.62 17.9 24 -24% 3.10 x10 6 7.74 180 7.36 11.8 2.12 23.3 14 68% 1.32 x10 7 8.49 174 7.11 8.30 1.49 16.4 14 15% 9.64 x10 6 8.20 196/203 7.65 9.15 1.64 18.1 16 10% 8.12 x10 7 9.17

B. Extrapolating Lake Michigan Lake Trout (LM LT6) BEFs to 4 Year Old Walleye in Zone 4 of Green Bay (GB W4)

PCB Congener Log K ow LM LT6 BSAF GB W4 BSAF BEF (PCB 118) Method 3b BSAF Prediction Measured GB W4 BSAF Method 3b BSAF Error J socw Measured in Green Ba y Zone 4 Predicted Site-specific Log Baseline BAF

18 5.24 1.38 0.248 3.54 2.67 33% 1.11 x10 6 6.59 28/31 5.67 1.50 0.269 3.85 2.99 29% 3.89 x10 6 7.18

52 5.84 5.16 0.926 13.2 26.5 -50% 2.00 x10 6 7.42 110 6.48 4.75 0.853 12.2 16 -24% 7.33 x10 6 7.95 118 6.74 5.57 14.3 1.00 2.99 x10 7 149 6.67 9.05 1.62 23.2 22 5% 3.10 x10 6 7.86 180 7.36 11.8 2.12 30.3 39 -23% 1.32 x10 7 8.60 174 7.11 8.30 1.49 21.3 19 14% 9.64 x10 6 8.31 196/203 7.65 9.15 1.64 23.5 20 16% 8.12 x10 7 9.285-31 Table 5-3 (Continued). Method 3b BEF Extrapol ation Example Using PCB Data From Lake Michigan, Green Bay and the Hudson River.

C. Extrapolating Lake Michigan Lake Trout (LM LT6) BEFs to 4 Year Old Walleye in Zone 1 of Green Bay (GB W4)

PCB Congener Log K ow LM LT6 BSAF GB W4 BSAF BEF (PCB 118) Method 3b BSAF Prediction Measured GB W4 BSAF Method 3b BSAF Error J socw Measured in Green Ba y Zone 1 Predicted Site-specific Log Baseline

BAF 18 5.24 1.38 0.248 0.78 0.575 36% 1.11 x10 6 5.94 28/31 5.67 1.50 0.269 0.85 0.474 79% 3.89 x10 6 6.52

52 5.84 5.16 0.926 2.92 2.07 41% 2.00 x10 6 6.77 110 6.48 4.75 0.853 2.69 2.05 31% 7.33 x10 6 7.29 118 6.74 5.57 3.15 1.00 2.99 x10 7 149 6.67 9.05 1.62 5.13 3.06 68% 3.10 x10 6 7.20 180 7.36 11.8 2.12 6.68 2.92 129% 1.32 x10 7 7.95 174 7.11 8.30 1.49 4.70 3.70 27% 9.64 x10 6 7.66 196/203 7.65 9.15 1.64 5.18 ND 8.12 x10 7 8.62

D. Extrapolating Lake Michigan Lake Trout (LM LT6) BEFs to Largemouth Bass at Hudson River mile 189 (RM 189 LMB)

PCB Congener Log K ow LM LT6 BSAF RM 189 LMB BSAF BEF (PCB 118) Method 3b BSAF Prediction Measured RM 189

LMB BSAF Method 3b BSAF Error J socw Measured at Hudson River Mile 189 Predicted Site-specific Log Baseline BAF

18 5.24 1.38 0.248 4.22 1.29 228% 3.07 x10 6 7.11 28/31 5.67 1.50 0.269 4.59 2.46 86% 6.18 x10 6 7.45

52 5.84 5.16 0.926 15.8 6.56 141% 5.24 x10 6 7.92 110 6.48 4.75 0.853 14.5 11.5 26% 1.85 x10 7 8.43 118 6.74 5.57 17.1 1.00 ND 149 6.67 9.05 1.62 27.7 18.4 50% ND 180 7.36 11.8 2.12 36.1 24.1 50% ND 174 7.11 8.30 1.49 25.4 20.0 27% ND 196/203 7.65 9.15 1.64 28.0 30.4 -8% ND5-32 Table 5-3 (Continued). Method 3b BEF Extr apolation Example Using PCB Data From Lake Michigan, Green Bay and the Hudson River.

E. Extrapolating Lake Michigan Lake Trout (LM LT6) BEFs to Largemouth Bass at Hudson River mile 194 (RM 194 LMB)

PCB Congener Log K ow LM LT6 BSAF RM 194 LMB BSAF BEF (PCB 118) Method 3b BSAF Prediction Measured RM 194

LMB BSAF Method 3b BSAF Error J socw Measured at Hudson River Mile 194 Predicted Site-specific Log Baseline

BAF 18 5.24 1.38 0.248 1.43 0.368 289% 1.04 x10 7 7.17 28/31 5.67 1.50 0.269 1.55 0.921 69% 1.60 x10 7 7.39

52 5.84 5.16 0.926 5.34 2.82 89% 1.60 x10 7 7.93 110 6.48 4.75 0.853 4.92 4.11 20% 5.32 x10 7 8.42 118 6.74 5.57 5.77 1.00 ND 149 6.67 9.05 1.62 9.37 6.36 47% ND 180 7.36 11.8 2.12 12.2 7.28 68% ND 174 7.11 8.30 1.49 8.60 5.23 64% ND 196/203 7.65 9.15 1.64 9.48 8.92 6% ND5-33 FIGURE 5-7. BSAFs (kg organic carbon/kg lipid) for PCBs from Green Bay and the Hudson River plotted against BSAFs (kg organic carbon/kg lipid) estimated from Lake Michigan (6 year old lake trout) BEFs using Met hod 3b. The symbol-color combinati ons represent particular fish species and ecosystem locations. GB = Green Bay; z1 = zone 1; z4 = zone 4; RM 189 = Hudson River mile 189; RM 194 = Hudson River mile 194; BT3 = 3-year old brown trout; W4 = 4-year old walleye; CP1 = 1-year old carp; LMB = larg emouth bass; YP = yellow perch. The 1:1 line (solid), as well as ±2x (short dashed) and ± 5x (long-short dashed) lines are also provided.

5.1.3 How Can a Reference Site be Chosen?

To estimate a site-specific BAF by extrapolating a BSAF or BEF, BSAF measurements from an appropriate reference site ar e required. A good reference site is one where as many of the ecosystem criteria known to affect BSAFs (listed on page 5-7) as possible are similar to the site of interest. Therefore, the investigator should search for a reference site where the sedimentwater column chemical distribution, degree of benthic food chain linka ge, length of the food web, and bioavailability due to amounts and types of organic carbon are as similar as possible to the site of interest. Beyond th ese general considerations, there is relatively little guidance 0.1

1 10 100

0.1 1 10 100 measured BSAFBSAF from extrapolated BEF GB z4 BT3 GB z4 W4 GB z1 W4 HR RM189 LMB HR RM194 LMB 5x 2x 1:1 line 1/2 1/55-34 available regarding how best to choose a refere nce site with the goal of maximizing accuracy in extrapolation between sites. Some studies have de monstrated that a wide range of reference sites may be suitable. Sites where BSAFs were compar ed by Burkhard et al. (2005) and found to be generally compatible included large and small lakes, embayments, rivers and connecting
channels.

Beyond ecosystem characteristics, better sites for BSAF or BEF extrapolation generally have higher numbers of samples for both biota and sediment, and sampling that provides for good spatial and temporal averaging. Ecosystems th at are well-mixed in terms of contaminant
concentrations will be easier to sample and should produce data that are simpler to average. Other aspects of quality assurance, such as good analytical techniques and sediment samples that are reflective of the actual expos ure environment for the fishes are important, and have already
been discussed in Sections 3.3 and 4.4.

If the investigator is consider ing BEF extrapolation (Method 3b), then the availability of a
suitable reference chemical at the reference site should also be cons idered. Similarity of chemical properties (hydrophobicity, metabolism, persistence, etc.) between th e chemical of interest and
the reference chemical can affect the sensitivit y of BEF extrapolation across species and sites. Selection of a suitable reference chemical (o r chemicals) based upon the similarity of their properties to the chemical of interest is discussed in Sec tion 4 (pages 4-5, 13 an 14).

If sufficient BSAF data are availa ble for the site of interest an d a potential reference site, the investigator can assess the compatibility be tween the sites by compar ing the rank order of
BSAFs for different chemicals at each site . As discussed in Section 5.1, consistent ordering/ranking of BSAFs across eco systems may be a useful as an indicator of the validity of BSAF or BEF extrapolation.5-35 5.2 PREDICTING SITE-SPECIFIC BAFS USING BCFS AND FOOD CHAIN MULTIPLIERS (FCMs)

A site-specific BAF can be predicted as th e product of a BCF coupl ed with a food chain multiplier (FCM). In effect, this method uses a simple bioaccumulation model to predict a sitespecific BAF. The investigator has numerous opt ions for predicting a site-specific BAF using
this method. The BCF can be either a value base d on laboratory experiments for the chemical of concern (Method 4a), or can be estimated using the K ow of the chemical (Method 4b).

Site-Specific BAF Method 4

• Predicting BAFs using a BCF coupled with food chain multipliers, with 2 options: 4a. Laboratory-measured BCFs 4b. BCFs estimated using K ow s

In addition, the investigator has the option to measure, estim ate (from existing data), or predict (using food chain models) the FCM to re flect biomagnification of the chemical for a particular trophic level under site-specific cond itions. Because food chain multipliers are trophic level-specific, the investigator should determ ine a FCM for each trophic level for which sitespecific BAFs are being predicted using Method 4.

By definition, a BCF reflects only the accumulation of a chemical through the organisms' exposure to water. The BCF will likely underpredi ct BAFs for chemicals for which accumulation from sediment or dietary sources is important, includi ng hydrophobic nonionic organic chemicals. Therefore, a FCM is used to adjust the value of a BCF to be tter account for chemical accumulation through the food web as a result of dietary exposures. For nonionic organic chemicals (and certain ionic organic chemical s to which similar lipid and organic carbon partitioning behavior applies), the food-chain multip lier is defined as the ratio of a baseline BAF for an organism of a particular trophic level to the lipid-normalized BCF (u sually determined for organisms in trophic level one).5-36 5.2.1 Predicting Site-Specific Baseline BAFs using laboratory-measured BCFs and FCMs (Method 4a)

For Method 4a, a laboratory-measured BCF (B CF) and FCM are used to predict a sitespecific baseline BAF. This method is applicab le to nonionic organic chemicals that have moderate-to-high hydrophobicity (log K ow $ 4) and low potential for being metabolized, and other chemicals that biomagnify. The BCF must be used in conjunction with an FCM because nonaqueous routes of exposure and subsequent biomagnification are of concern for these types of chemicals. Method 4a uses the following equa tion to calculate the ba seline BAF for a site:

BCF 1 Baseline BAF = FCM 1 f f t T i i fd l  

⋅ −⋅

      (Equation 5-6)

where:

= Total BCF (BCF = C t /C w )
f fd

= fraction of the total concentration of chemical in BCF test water that is freely dissolved
f l = fraction of the tissue that is lipid in the test organism
FCM i = the food-chain multiplier for trophic level i , determined from appropriate field data or predicted for site-specific conditions

The baseline BAF and FCM in equation 5-6 are both trophic level-specific. The technical basis for Equation 5-6 is provi ded in Appendix A of TSD Volu me 2 (USEPA, 2003). Guidance on selecting appropriate BCFs and FCMs, and th e derivation of FCMs using food web models and field data, are provided below and discussed in greater detail in Section 5-3 of TSD Volume 2. Calculating a site-specific BAF using Method 4a is presented in the following example.5-37 Site-Specific BAF Predicte d Using the Product of a Laboratory-Measured BCF and a FCM (Method 4a)
This example illustrates the predictio n of a site-specific BAF for a trophic level 3 fish using Methods 4a for a nonionic organic chemical (chemical k). Calculating a site-specific BAF using Me thod 4a requires the investigator to use a laboratory-measured total BCF and a FCM.
Calculating a laboratory-measured BCF
Determination of a BCF requires information on the total concentration of chemical k in fish tissue and the total concentration of chemical k in the laboratory test water. Experimental data are available from an aquatic toxicology laboratory for the total concentrati on of chemical k in fish tissue (0.325 F g/kg) and the laboratory test water (1.6 ng/L) in a water-only exposure test. The laboratory-measured BCF calcu lated for chemical k is 203 L/kg, as shown below:

C 0.325 1000 C 1.6 

### BCF 203 / t

w g ng t L T kg ng g 

### L kg μ μ 

### == ⋅ ⋅ =

Determining a FCM Based on Measurements

As discussed in Section 5.2.2, site-sp ecific FCMs can be determined by measurements or food chain model predic tions. In this example, the FCM for trophic level 3 will be calculated from c oncentrations of chemical k measured in the food chain. The following data were obtained from fiel d studies at the site:

Sample Trophic level Concentration of chemical k ( F g/kg) Lipid or organic carbon (%)
Sediment 1 1.95 7.4 Phytoplankton 1 0.35 1.2

Zebra mussels 2 0.431 1.3 Crayfish 3 0.392 1.75-38 Site-Specific BAF Predicte d Using the Product of a Laboratory-Measured BCF and a FCM (Method 4a, continued)

Crayfish are frequently consumed by the local population, and were determined to be a preferred species at trophic level 3. At this site, crayfish consume zebra mussels (2/3 of diet by weight) and phytoplankton (1/3 of diet); zebra mussels consume phytoplankton (75%) and sediment (25%). Biomagnification factors (BMFs) can be calculated for trophic levels 2 and 3 using equations 5-11 and 5-12 and lipid- normalized chemical concentrations (the use of these equations is discussed in Section 5.2.2.1:

BMF TL2 = (C  , TL2 ) / (C l , TL1 ) (Equation 5-11)
BMF TL3 = (C l , TL3 ) / (C l , TL2 ) (Equation 5-12)

Since zebra mussels consume both phytoplankton and sediment, the BMF must be calculated using a weighted av erage of chemical concentrations in their diet items, sediment and phytoplankton:

( ) 16

. 1

012

. 0 35

. 0 75

. 0 074

. 0 95

. 1 25

. 0 013

. 0 kg g 0.431

BMF ZM TL2 =        

− ⋅ ⋅ +        

− ⋅ ⋅ − ⋅

= lipid kg kg kg g SOC kg kg kg g lipid kg kg

μ μ μ

Likewise, the BMF for crayfish must be calculated using the weighted average of chemical concentrations in thei r diet items, phytoplankton and zebra mussels:

( ) 

### ( ) ( ) 725

. 0

013

. 0 431

. 0 3 / 2 012

. 0 35

. 0 3 / 1 017

. 0 kg g 0.392

BMF cf TL3 =        

− ⋅ ⋅ +        

− ⋅ ⋅ − ⋅

= lipid kg kg kg g lipid kg kg kg g lipid kg kg

μ μ μ

FCMs can be calculated for trophic levels 2 and 3 using equations 5-8 and 5-9:

FCM 2 = BMF 2 = 1.16
FCM 3 = BMF 3 @ BMF 2 = 0.725 · 1.16 = 0.8445-39 Site-Specific BAF Predicte d Using the Product of a Laboratory-Measured BCF and a FCM (Method 4a, continued)

Predicting a site-specific baseline BAF

The t T 

### BCF is converted to a baseline BA F for a specific trophic level by incorporating information on the fracti on of the chemical that is freely dissolved in the bioconcentration test water (f

fd

), the fraction of tissue or aquatic organism tested that is lipid (f l ), and the site- and trophic level-specific FCM for the chemical. The site-specific baseline BAF is calculated from the BCF using equation 5-4:

i i BCF 1 Baseline BAF = FCM 1 f f t T
fd l  

⋅ − ⋅

     

Determining the fraction of chemical k that is freely dissolved in the bioconcentration test water (f fd

) requires information on the POC and DOC concentrations in the test water and the K ow of chemical k.
For this example, the median POC concen tration in the test water is 0.5 mg/L and the median DOC concentration is 10 mg/L. It is important that the POC and DOC concentrations used in calcul ating the freely dissolved fraction for baseline BAFs be determined from the water used in the BCF study. It is not appropriate to use site-s pecific POC and DOC concentrations to derive baseline BAFs from s.

The K ow for chemical k is 2×10 4 , or a log K ow of 4.3. Based on these data, the fraction of chemical k that is free ly dissolved is 0.975, calculated using equation 3-12: 

### ( )

4 4 6 6 1 f 0.975 0.5 10 1 2 10 0.08 2 10 10 10 fd chemical i mg POC L kg mg DOC L kg L kg mg L kg mg = =

− − + ⋅× ⋅ + ⋅ ⋅× ⋅

The f  of the fish species sampled in the laboratory in this example was 2% (0.02). Using this f l , the FCM measured at the site, and the and f fd calculated above, a site-sp ecific baseline BAF of 8.8×10 3 L/kg-lipid is calculated as follows (Equation 5-4):5-40 Site-Specific BAF Predicte d Using the Product of a Laboratory-Measured BCF and a FCM (Method 4a, continued)

L kg 3 3 

### 203 1 Baseline BAF = 0.844 1 8.8 10 / 0.975 0.02 L kg lipid  
⋅ −⋅ = × −

   

Calculating a site-specific total BAF
In order to determine a water quality st andard for chemical k at the example site, the site-specific baseline BAF must be converted to a site-specific total BAF. The average POC concentration m easured at the site is 0.54 mg/L, and the average DOC is 3.5 mg/L. The freely dissolved fraction of chemical k in the site water column can be calculated using equation 3-12:

4 4 6 6 1 f 0.984 0.54 3.5 1 2 10 0.08 2 10 10 10 fd mg POC L kg mg DOC L kg L kg mg L kg mg = =

− − + ⋅× ⋅ + ⋅ ⋅× ⋅

The lipid content for crayfish of harves table size at the site is 1.7%. The sitespecific total BAF can then be recalcul ated from the site-specific baseline BAF: 

# ( ) 3 Site-Specific BAF 0.017 8.8 10 1 0.984 147 / T L t kg l L kg

− = ⋅ × +⋅ =

The site-specific total BAF for ch emical k in crayfish is 147 L/kg.5-41 Method 4a is based on the following assump tions: (1) a high-qual ity BCF is a reliable measure of the bioconcentration potential of a chemi cal in a particular speci es or trophic level of aquatic organism, (2) the measured BCF and the baseline BAF predicted with Method 4a are independent of chemical concentration in the water, and (3) FCMs account for biomagnification processes caused by the consumption of contaminated food in aquatic food webs.

Method 4a predictions address the e ffects of chemical metabolization on bioaccumulation, although BAFs predicted for metabolizing chemicals by this method may be inaccurate for a number of reasons. BCF for chemicals that are metabolized by the test organisms incorporate the effects of the metabolism on the con centration of chemical th at is accumulated in the organism. However, if induction of meta bolic systems is required, or co-occurring contaminants (i.e., that exist in the environment) are required for the metabolism to take place,
then the effect of metabolism ma y not be captured in the BCF meas urement. Therefore, the range of effects of metabolism on BCF will be chemical specific. Nevertheless, EPA believes that high-quality BCFs may provide a better measure of bioconcentration potential for chemicals than assuming that the lipid-normalized BCF is equal to the chemical’s K ow (i.e., Method 4b) because of the potential of the BCF to include the effect s of metabolic processes. Furthermore, BCFs can be measured or obtained for specific species of in terest. This specificity may reduce uncertainties associated with extrapolating bioaccumulation factors among species with known or suspected differences in metabolic pathways or capacity.

The baseline BAFs derived with Method 4a for chemicals that are metabolized will not include the effects of all metabolic processes because of the assumpti on of no metabolism used in deriving the FCMs (Table 5-6). However, the method will incorporate those metabolic processes or effects that are captured in the BCF measurement, and in field derived FCMs, when used. Baseline BAFs predicted from measured BC F for chemicals that are metabolized will be smaller than those predicted from measured BCFs for chemicals of equal hydrophobicity but
which are not metabolized.5-42 A major limitation associated with Method 4a is the current lack of high-quality measured BCF data for highly hydrophobic chemicals in any organism class. This lack of data is due principally to the difficulties associated with performing BCF m easurements for highly hydrophobic chemicals. Conditions appropriate for performing these measurements are described
in Section 5.3.1 of TSD Volume 2. When evaluati ng BCF data in the lite rature, one often finds measurements performed with (1) conditions that do not meet curre nt standards, for example, a
solvent carrier such as acetone is used to intr oduce the chemical into the aqueous phase or the
concentration in water exceeded the chemical’s solubility, and (2) poor and/or incomplete reporting of measurement conditions and parame ters, for example, no lipid data, no POC and DOC data, and/or an inability to determine whet her steady-state conditions were obtained in the experiment. In addition, some BCFs were measured with chemical mixtures, such as Aroclors, and resolving the effects of co-occurring chemical s on micelle formation is often intractable. As BCF data become available for highly hydrophobic chemicals in the future, the impact of this limitation will lessen. Specific guidance for conducting BCF experiments or for reviewing studies for appropriate values to use in this method is provided in Section 5.3.1 of TSD Volume 2 (USEPA, 2003).

5.2.1.1 Validation of Method 4a

To date, EPA has performed only a limited number of evaluations of Method 4a because
of a lack of BCF data of the appropriate quality. For example, EP A invested considerable effort in examining the scientific literature for measur ed BCF s for PCB congeners and was not able to find BCF s of appropriate quality.

Burkhard et al. (1997) evalua ted Method 4a by using field data for chlorinated benzenes, butadienes, and hexachloroethane from Bayou d’I nde, Lake Charles, Louisiana. The results of
this evaluation showed that field-measured baseli ne BAFs were within a factor of 3 for 88% and a factor of 5 for 94% of the baseline BAFs pred icted using Method 4a (n = 32) (Figure 5-8). The
median of the ratios of the field-measured baseline BAFs to predicted baseline BAFs was 1.03, and approximately one-half of the predicted base line BAFs were less than the measured baseline BAFs (53%, n = 32). The chemicals whose fiel d-measured baseline BAFs were in least5-43 agreement with the predicted baseline BAFs were hexachloroethane, Z-pentachlorobutadiene,
and hexachlorobutadiene for Callinectes sapidus (blue crab). Metabolism of these chemicals by C. sapidus is suggested as the cause of the poor agr eement between the field-measured BAFs and the baseline BAFs predicted using this method (Burkhard et al. 1997).

FIGURE 5-8. Relationship between baseline BAFs measured at Bayou d’Indie and BAFs predicted using Method 4a. The dotted and dashed lines represent a factor of 3 and 5 difference between the measured and predicted baseline BAFs, respectively. Baseline BAFs measured using Callinectes sapidus ( ), Micropoganias undulatus ( • ), Fundulus heteroclitus (

~ ), and Brevoortia patronus ( ). Predicted Log Baseline BAF 234567Measured Log Baseline BAF

2 3 4 5 6 75-44 5.2.2 Determining Site-Specific FCMs

FCMs are used in both Methods 4a and 4b to ca lculate the dietary transfer of a chemical. They represent a measure of the chemical’s tend ency to biomagnify in aquatic food webs. FCM values can range from # 1 (no biomagnification) up to about 25 (significant biomagnification), depending upon the chemical, organism, and foodweb. Because FCMs can vary due to sitespecific factors, the investigator should cons ider determining a FCM value that is most appropriate for use in estimating a baseline BA F using Method 4a or 4b. FCMs for a particular chemical, organism, and site can be determined using field data and/or a food web model. By definition, a FCM is:

Baseline BAF Baseline BAF FCM Baseline BCF BCF 1 1 f f i i i t T
fd l = =  

− ⋅       (Equation 5-7)

Equation 5-7 is simply a rearrangement of Equation 5-6. Calculating a food chain multiplier using Equation 5-7 is presented in the following example.

Calculation of food chain multipliers
This example illustrates the calcula tion of food chain multipliers for hexachlorobenzene (HCB) in trophic le vel 3 and 4 fish us ing equation 5-7. According to the Arnot and Gobas (2006) BCF/BAF database, several “acceptable” baseline BCF values have been measured for HCB. The geometric mean of these va lues is 415,000 L/Kg-lipid.

Baseline BAF values for HCB can be calcu lated for fish in the Lake Ontario ecosystem based on available data (Oliv er and Niimi, 1988; Niimi and Oliver, 1989):
Fish Trophic level HCB concentration (ng/g) Lipid content (%) Alewife 3 20 7.0 Lake trout 4 90 17.4
Chemical concentrations in fish ( C l ) were normalized by the lipid content (f l ) of each sample: C l = C t / f l5-45 Calculation of food chain multipliers
The lipid-normalized chemical concentr ation in each sample is tabulated:
fish HCB Concentration (ng/g-lipid) Alewife 286
Lake Trout 517

These authors reported concentrations of HCB in Lake Ontario water to be 150 pg/L, with a DOC concentration of 2 mg/L. The freely dissolved fraction of chemical in the water column (f fd

) can be calculated using equation 3-6:

ο w ο w 

### f = 1 / (1 + POC DOC

fd 

### ⋅ Κ + 0.08 ⋅ ⋅ Κ )

The log K ow for HCB is 5.73 ( Arnot and Gobas, 2006), so K ow = 5.37x10 5

. The water samples were centrifuged to remove par ticulates prior to extraction, so the POC concentration is (presumably) zero:

5 6 1 f 0.92 2 1 0.08 5.37 10 10 fd mg DOC L kg L kg mg = =

− +⋅ ⋅× ⋅

The freely dissolved chemical concentration ( f d

w 

### C ) is calculated as: = 0.92 150 pg/L = 138 pg/L fd w fd w C fC =⋅ ⋅

Baseline BAFs were then calculated for al ewife and lake trout, using Equation 3-2:

C 1 Baseline BAF C f i fd

w = − l

l

286 1000 1000 6 1 3 138 1 0.07
517 1000 1000 6 1 4 138 1 0.174 Baseline BAF = 2.07 10 /
Baseline BAF = 3.74 10 / ng pg g L g lipid pg ng kg

ng pg g L g lipid pg ng kg L kg lipid L kg lipid −

− ⋅ ⋅ ⋅ −= × −

⋅⋅ ⋅−= × −5-46 In effect, equation 5-7 says that the FCM is the ratio between the chemical accumulation via all relevant routes (aqueous, dietary, and se diment) and the chemical bioconcentrated via aqueous exposure only. When the BCF is lipid no rmalized and corrected for growth dilution and bioavailability considerations (i.e., a baseline BCF), then the FCM will be relatively constant under steady-state conditions. Because a BCF is determined by using a water-only exposure to the chemical, it represents a trophic level 1 e xposure for the organisms. When organisms occupy higher trophic levels in food webs, concentra tions of many hydrophobic organic chemicals in
their tissues will exceed those that are due to water exposure only, because of dietary uptake of the chemical. The FCM for the organism’s trophic level accounts for the influences of dietary uptake by the organism. Dietary uptake of the ch emical generally becomes important when the chemical’s hydrophobicity exceeds a log K ow of 4 and the rate of chemical metabolism by the organism is small.

5.2.2.1 Measuring Site-Specific FCMs

Field data can be used to derive FCMs for nonionic organic chemicals. FCMs derived from field measurements incorpor ate the conditions existing at th e site where the measurements are performed. This includes the existing disequi librium, chemical metabolism, and influences Calculation of food chain multipliers (continued)
Equation 5-7 can then be used to calc ulate FCMs for alewife and lake trout:

6 5 3
6 5 4 

### Baseline BAF FCM Baseline BCF FCM = 2.07 10 / 4.15 10 = 5.0
FCM = 3.74 10 / 4.15 10 = 9.0 i i 

### =

× ×

× ×
Based on these data for baseline BCF and baseline BAFs, the trophic level 3 food chain multiplier for HCB is 5.0 and the trophic level 4 FCM is 9.0.5-47 due to the structure of the f ood web (i.e., predator-prey rela tionships and benthic-pelagic components). FCMs derived from field measurem ents also account for any metabolism of the pollutant of concern by the aquatic or ganisms used to calculate the FCM.

Specifically, FCMs can be derived from chemi cal concentrations measured in the target organism and in organisms at each lower trophic level in the organism’s food web. Field-derived FCMs should be calculated with lipid-normali zed concentrations of the nonionic organic chemical measured at the site, in appropriate predator and prey species, using the following equations:

FCM 2 = BMF 2 (Equation 5-8)
FCM 3 = BMF 3 @ BMF 2 (Equation 5-9)
FCM 4 = BMF 4 @ BMF 3 @ BMF 2 (Equation 5-10)

where:
FCM i = food chain multiplier for trophic level i and
BMF i = Biomagnification factor for trophic level i

.

The basic difference between FCMs and BMFs is that FCMs relate back to trophic level 1, whereas BMFs always relate back to the ne xt lowest trophic level. For nonionic organic chemicals, BMFs can be calculated from lipid-nor malized concentrations of chemical in tissues of biota at a site according to the following equations:

BMF 2 = C  , 2 / C l , 1 (Equation 5-11)
BMF 3 = C l , 3 / C l , 2 (Equation 5-12)
BMF 4 = C l , 4 / C l , 3 (Equation 5-13)
where:

C l,i = lipid-normalized concentration of chemical in tissue or whole organism at a specified trophic level ( i = 2, 3, or 4).5-48 Examples of applying equations 5-8 through 5-13 were presented in the Method 4A example.

In addition to the guidance offered in S ection 3.3 for determining baseline BAFs based on measurements made at the site, the following procedural and quality assurance guidelines apply to field-measured FCMs.

1. Information should be available to identif y the appropriate trophic levels for the aquatic organisms and appropriate predator-p rey relationships for the site for which FCMs are being determined. Information about trophic status is most accurate when obtained from the site(s) of interest, becau se predator-prey relationships for some species can vary widely ove r space and time. When a predator species consumes multiple prey species at a particular trophic level, chemical concentrations in prey species should be appropriat ely weighted (if the data ar e available) when used to
calculate field-based FCMs. A number of approaches are commonly applied to determine trophic levels of aquatic organisms, including shifts in stable isotope ratios (e.g., ratios for C, N, and S are reported as * 13 C, * 15 N, and * 34 S, respectively; Peterson and Fry, 1987; Jardine et al., 2006) a nd analysis of gut contents. Ratios of stable isotopes can change between diet a nd consumer due to differential digestion or fractionation during assimilation and metabo lic processes. Metabolic fractionation also may cause isotope ratios of different tissues to vary substantially within individual consumers (M cCutchan et al. 2003). General information on determining trophic levels of aquatic organism s can be found in USEPA 2000 a-c.

2. The aquatic organisms sampled from each trophic level should reflect the most important exposure pathways leading to hum an exposure via consumption of aquatic organisms. For higher trophic levels (e.g., 3 a nd 4), aquatic species used to calculate FCMs should be those that are commonly c onsumed by humans. The species sampled should also reflect size and age ranges that are typical of human consumption patterns
at the site.5-49 3. The study from which the FCMs are de rived should contain enough supporting information to determine that tissue sample s were collected and analyzed according to appropriate, sensitive, accurate, and precise methods.

4. The percent of tissue that is lipid should be either measured or reliably estimated for the tissue(s) used to determine the FCM.

5. The chemical concentrations in the tissues /organisms used to calculate FCMs should reflect long-term average exposures of the ta rget species to the ch emical of interest; longer averaging periods are generally necessary for chemi cals with greater hydrophobicity.

5.2.2.2 Predicting FCMs using a food chain model

Food chain model predictions can also be used to derive FCMs for nonionic organic chemicals. EPA applied the Gobas food chain model (Gobas, 1993) to predict FCMs as a function of trophic level and chemical hydrophobicity for the National BAF Methodology (USEPA, 2003). For that application, EPA select ed and applied representative values for the various input parameters to that model and calculated FCMs for trophic levels 2, 3 and 4 for a mixed benthic-pelagic food web. EPA recognized that the food chain modeling approach could also be used to predict FCMs for conditions and parameters at a particular site, which could be different from the representative values us ed in the national methodology calculations. FCMs predicted using site-specific conditions and parameters will likely differ from the FCMs predicted in the National BAF Methodology to the extent that site conditions and parameters differ from the nationally-representative conditions.5-50 In deriving FCMs using a food web model, the investigator assumes that (1) the model is valid for the particular chemical, aquatic organi sm, food web and site, and (2) appropriate values are selected for all necessary model inputs. In ot her words, the investigator is responsible for selecting both a model and its input parameters. This section discusses how EPA selected a food
web model for use in the 2000 Human Health Me thodology. Also described are the parameters used with the model: the food web structure, J socw (or, equivalently, 

### C f d

w and C soc ), and the

chemical metabolism rate in the various or ganisms of the food web. Although data on the metabolism of most chemicals is currently quite limited, when available this information should be considered and potentially used. The Gobas (1 993) model, for example, allows the user to input a metabolic transformation rate constant. Because all food web models require the above input parameters, these input s are not unique to the food web model selected by EPA.

For a food web model to provide useful pred ictions, it should have the following general characteristics and qualities. Firs t, the model should provide a fu ll and complete description of the bioaccumulation process. Specifically:

• All biotic components of the food web must be repr esented: plankton, benthic invertebrates, forage fish, and piscivorous fish.

• It should account for chemical uptake a nd loss from both food and water for all organisms.

• It should include chemical concentrations in sediment and the water column, because these environmental compartments are the primary exposure media for benthic invertebrates and phytoplankton, respectively, and these organisms reside at the base of the benthic and pelagic food web.

In addition, steady-state solutions for pred icting bioaccumulation in the food chain model are preferred over time-variant dynamic solutions for the food chain model, because AWQCs for the protection of human health are designed for long-term averag e conditions in ambient waters. Other desirable qualities include (1) the model is easy to run by the average user, (2) the model does not mix fate and transport models with th e food chain model, (3) the model code does not require substantial valida tion each time it is used, and (4) th e model parameters and other inputs can be readily measured or estimated.5-51 Although these attributes can make a food chain model relatively easy to use, the accuracy or uncertainty of mode l predictions depends largely upon how they are applied. Food chain model predictions may be highly uncerta in unless they are c onfirmed by data (i.e., chemical concentrations in the modeled organisms). Burkhard (1998) determined that the uncertainty of food chain model pr edictions due to parameter vari ability and error, for a very well-studied ecosystem (i.e., PCBs in the Lake Ontario salmonid food web), was on the order of a factor of 5 to 9 (i.e., the ratio of 90 th to 10 th percentiles of the m odel predictions for PCB concentrations in piscivorous fish). Uncertainty arises from a number of causes, but especially because models are only simplified approximations of the ecosystem. In the case of food chain models, the descriptions of th e various aspects of the bioaccu mulation process also rely upon many empirical relationships and correlations , all of which contain potential errors.

Applying a food chain model correctly (that is to say, making reliable predictions that are free of preventable errors) is an involved process. To correctly apply a model, the investigator must have an adequate understanding of the model and the science upon which it is based. This understanding may be gained from training course s [such as those offered by the EPA Center for Exposure Assessment Modeling (CEAM), the Manha ttan College Summer Institute for Water Pollution Control, or short courses offered in conj unction with scientific conferences such as the annual meetings of the Society of Environm ental Toxicology and Chemistry (SETAC)] or “User’s Guide” documentation, although these sour ces may assume the modeler has a fairly advanced background. The modeler should become thoroughly familiar with the data requirements, assumptions and limitations of a model. Much can be learned by reviewing publications and reports documenti ng prior applications of the model. The modeler must then acquire the site-specific data or validated estimates for all m odel inputs, run the model, and verify and confirm the predictions (USEPA, 20 03b). The goal of confirmation is to determine and quantify the agreement between model predicti ons and observations. In the case of food web bioaccumulation models, comparisons between pred ictions and observations should be made for chemical concentrations in the organism or tissue 1 of interest, as well as chemical concentrations

1 The Gobas, Thomann, and most other food chain m odels predict chemical c oncentrations on a whole organism basis. Chemical concentrations in speci fic tissues can be recomputed from concentrations predicted in the whole organism using (1) measured ra tios of chemical concentrations between tissue and5-52 in organisms at lower trophic levels in the f ood web (if available). If data for chemical concentrations is available for multiple trophic levels in the food web at the site, it may be appropriate to confirm the model predictions in terms of the BMFs that can be calculated from these data. Other ways to evaluate or test th e performance of a model a nd the robustness of its predictions include peer reviews, numbers of past applications a nd their successes, and similarity of other applications to the chemical, food web, a nd site of interest. The following references are offered as resources to investig ators considering the use of food chain models to predict BMFs: Gobas (1993); Gobas et. al. (1998); Arnot and Gobas (2004); Thomann (1989); Chapra (1997); Campfens and Mackay (1997); Morrison et al. (1996); Thomann et al. (1992); Connolly, (1991); Barber et al. (1991); Thomann and Meulle r (1987); Thomann and Connolly (1984); and, Connolly et al. (1992). The investigator should also be aw are that EPA has developed quality assurance guidance applicable to model applicat ions, which outlines the elements of a QAPP for modeling (USEPA, 2002).

Food chain models with the characteristics and desirable qualiti es summarized above include the models of Gobas ( 1993) and Thomann et al . (1992). These two models are widely accepted in the scientific community and are being used in a number of scientific and regulatory applications. Many other models are available, as discussed in USEPA (2003). Since some of
these latter models have extens ive input data requirements, and are designed for temporally and spatially variable solutions for the food web, they were not considered to be appropriate for this application. Burkhard (1998) performed a thorough evaluation of the Gobas (1993) and Thomann et al. (1992) steady-state food web models for predicting chemical concentrations in aquatic food webs. This evaluation included assessm ents of (1) the accuracy and precision of the models, (2) the sensitivit y of the predicted concen trations to changes in input parameters, and (3) the uncertainty associated with the concentrat ions predicted by the models. Burkhard’s (1998) evaluation using field data from Lake Ontario (Oliver and Niimi, 1988) demonstrated that the Gobas and Thomann models have similar predictive abilities for fish species at each trophic level and for chemicals with log K ow s ranging from 3 to 8.

whole organisms (e.g., Niimi and Oliv er, 1989) or (2) the ratio between tissue and whole organism lipid content.5-53 EPA used the Gobas food chain model 2 to predict FCMs in the National BAF Methodology. The rationale for this choice was discussed in USEPA (2 003). In part, EPA selected the Gobas model because the computer program was widely available on the Internet (http://www.rem.sfu.ca/toxico logy/models/AQUAWEBv1.2_BIOv1.2.xls ). In applying the Gobas model, however, EPA did not use the model’s method of accounting for chemical bioavailability. Gobas’s method fo r determining the freely dissolved (bioavailable) concentration
of the chemical in water makes no distinction between POC and DOC phases, but rather treats these two phases as one. This is significantly di fferent than the procedure used by EPA in the 2000 Human Health Methodology for determining the concentration of chemical that is freely dissolved in the ambient water, 

### C f d

w . To compensate for this discrepancy in the methods of accounting for bioavailability, EPA sets the concentrati on of the TOC in the Gobas model to an infinitesimally small value (i.e., 1 x 10 -30 ). By doing so, the total con centration of the chemical input to the model becomes essentially equal to the 

### C f d

w , due to the negligibly small bioavailability correction.

For the National BAF Methodology, FCMs were determined with the Gobas model using the Lake Ontario food web structure presented in Table 5-4 and the environmental parameters
and conditions listed in Table 5-5. For each value of K ow inputted to the Gobas model, predicted baseline BAFs were reported by the model for each organism in the food web. FCMs were calculated from the predicted BAFs using the following equation:

ow 

### Baseline BAF FCM K i i 

### = (Equation 5-14)

2 The Gobas food chain model is also known as Aquaweb; the current version is Aquaweb v1.2.5-54 Table 5-4. Food Web Structure for National BAF Methodology (Flint, 1986; Gobas, 1993)

Species Trophic Level Lipid Content Weight Diet
Phytoplankton 1 0.5% Zooplankton (mysids [ Mysis relicta] ) 2 5.0% 100 mg Phytoplankton Benthic Invertebrates ( Diporeia ) 2 3.0% 12 mg Sediment/Detritus Sculpin ( Cottus cognatus ) 3 8.0% 5.4 g 18% zooplankton, 82% Diporeia Alewife ( Alosa pseudoharengus ) 3 7.0% 32 g 60% zooplankton, 40% Diporeia
Smelt ( Osmerus mordax ) 3-4 4.0% 16 g 54% zooplankton, 21% Diporeia , 25% sculpin Salmonids ( Salvelinus namaycush, Oncorhynchus mykiss, Oncorhynchus velinus namaycush ) 4 11% 2,410 g 10% sculpin, 50% alewife, 40% smelt

Table 5-5. Environmental Parameters and Conditions Used for Determining FCMs for the National BAF Methodology

Parameter Value
Mean water temperature 8

 C
Organic carbon content of the sediment 2.7%

Metabolic transformation rate constants (all organisms) 0.0 d -1



### J socw /K ow 23

Using Equation 5-14, FCMs were calculated fo r each trophic level in the Lake Ontario
food web. Table 5-6 lists the FC Ms for trophic level 2 (zooplankt on), trophic level 3 (forage fish), and trophic level 4 (piscivorous fish). The FCMs determined for the national BAF methodology for trophic levels 2 through 4 are also plotted as a function of the logK ow of the5-55 chemical in Figure 5-9. As shown by th e relationships between FCMs and logK ow s in Table 5-6 and Figure 5-9, significant bioma gnification at trophic levels 3 a nd 4 occurs for nonmetabolized organic chemicals with logK ow s between about 5 and 8.5. The highest FCMs (13.3 for TL3 and 24.7 for TL 4) were determined for nonmet abolized organic chemicals with logK ow s in the range of 6.7 to 7.0. Constant FCMs of 1 (no biomagnifi cation) were determined for trophic level 2.

Table 5-6. Food-Chain Multipliers for Trophic Levels (TLs) 2, 3, and 4 (Mixed Pelagic and Benthic Food Web Structure and J socw /K ow = 23)

Log K ow TL 2 TL 3 a TL 4 Log K ow TL 2 TL 3 a TL 4

4.0 1.00 1.23 1.07 6.6 1.00 12.9 23.8 4.1 1.00 1.29 1.09 6.7 1.00 13.2 24.4 4.2 1.00 1.36 1.13 6.8 1.00 13.3 24.7 4.3 1.00 1.45 1.17 6.9 1.00 13.3 24.7 4.4 1.00 1.56 1.23 7.0 1.00 13.2 24.3 4.5 1.00 1.70 1.32 7.1 1.00 13.1 23.6 4.6 1.00 1.87 1.44 7.2 1.00 12.8 22.5 4.7 1.00 2.08 1.60 7.3 1.00 12.5 21.2 4.8 1.00 2.33 1.82 7.4 1.00 12.0 19.5 4.9 1.00 2.64 2.12 7.5 1.00 11.5 17.6 5.0 1.00 3.00 2.51 7.6 1.00 10.8 15.5 5.1 1.00 3.43 3.02 7.7 1.00 10.1 13.3 5.2 1.00 3.93 3.68 7.8 1.00 9.31 11.2 5.3 1.00 4.50 4.49 7.9 1.00 8.46 9.11 5.4 1.00 5.14 5.48 8.0 1.00 7.60 7.23 5.5 1.00 5.85 6.65 8.1 1.00 6.73 5.58 5.6 1.00 6.60 8.01 8.2 1.00 5.88 4.19 5.7 1.00 7.40 9.54 8.3 1.00 5.07 3.07 5.8 1.00 8.21 11.2 8.4 1.00 4.33 2.20 5.9 1.00 9.01 13.0 8.5 1.00 3.65 1.54 6.0 1.00 9.79 14.9 8.6 1.00 3.05 1.06 6.1 1.00 10.5 16.7 8.7 1.00 2.52 0.721 6.2 1.00 11.2 18.5 8.8 1.00 2.08 0.483 6.3 1.00 11.7 20.1 8.9 1.00 1.70 0.320 6.4 1.00 12.2 21.6 9.0 1.00 1.38 0.210 6.5 1.00 12.6 22.8

a The FCMs for trophic level 3 are the geometri c mean of the FCMs for sculpin and alewife.5-56 FIGURE 5-9. The FCMs determined for the national BAF methodology for trophic levels 2 through 4. 0 5 10 15 20 25 30

3456789 log Kowfood chain multiplier (FCM) trophic level 2 trophic level 3 trophic level 45-57 5.2.2.3 Site-specific adjustme nt of food chain model parameters

As noted previously, conditions and paramete rs of relevance to a food chain model at a particular site may be different from the repr esentative values used in the national methodology calculations. If the investigator determines that site conditions and parameters differ significantly from the nationally-representative conditions, it may be appropriate to recompute FCMs using site-specific conditions as i nput to the food chain model. A number of food chain model parameters can be adjusted to improve predictions of biomagnification at th e site of interest. The most important of these are related to factor s which primarily determine bioaccumulation of nonionic organic chemicals by fish (B urkhard et al. 2003a). These include:

• chemical disequilibrium between sediment and water (i.e., J socw /K ow and sediment organic carbon content);

• the relative benthic/pelagic connectivity of the food web;

• the length of the food chain (i.e., th e trophic level of the organism); and

• species-specific parameters for organisms in the food chain/web (lipid content and weight), as well as bioenergetic paramete rs (e.g., growth, resp iration, consumption) which are computed as allometric func tions of organism weight and water temperature in the Gobas and Thomann models.

The sensitivity of model-predicted FCMs to th ese factors is discusse d in Burkhard (1998) and Burkhard et al. ( 2003b). In all cases, pa rameter adjustment should be limited to values determined to be representative and unbiased ba sed upon data for the site, species, and chemical
of interest. EPA does not consider site-specific adjustment of parameters associated with the other two factors - the hydr ophobicity of the chemical (K ow ) and the rate of chemical metabolism in the food chain - to be appropria te since these parameters are pr operties of the chemical and (in the case of metabolism rate) the food chain organisms.5-58 5.2.2.4 Selection of a food web structure

To determine FCMs with a food web model such as the Gobas model, the food web
structure must be defined. Food web structur es vary across ecosystems and for different organisms within the ecosystem, and these differe nces also influence bioaccumulation (Burkhard et al. 2003(b)). For highly -hydrophobic chemicals (logK ow s of 6 to 7), the food web structure becomes a very significant factor in bioaccumu lation predictions. The information necessary to
construct a food web includes the diet of the i ndividual organisms composing the food web and their weights and lipid contents. Based upon Bur khard’s (1998) sensi tivity analysis, model predictions made by the Gobas model were rela tively insensitive to organism weights and feeding preferences of piscivorous fish for all K ow s. For chemicals with higher log K ow s, the predictions were more sensitive for J socw , feeding preferences of forage fish upon benthic invertebrates, and lipid contents . The most sensitive input parame ter was the feeding preferences of forage fish, that is, the percentage of zooplankton (pel agic component) and benthic invertebrates (benthic component ) in their diet. The benthic/pe lagic composition of the food web is, EPA believes, the most important characteristic for defining the structure of the food web for piscivorous fish because transfer of chemicals from the sediment to piscivorous fish occurs almost exclusively via their diet.

Food webs differ widely in their benthic/ pelagic compositions among ecosystems, among individual species, and among differ ent age classes of species with in an ecosystem. Of all the ecosystem types, the purely pe lagic food webs might be the l east common for piscivorous fish. However, purely pelagic food webs have been found in remote Ontario lakes for lake trout (Rasmussen et al. 1990) and in Adirondack lake s for brook trout and yellow perch (Havens, 1992). Purely benthic food webs are more common than purely pelagic food webs, but are still rather limited in nature. Some examples of pur ely benthic food webs can be found in tidal and estuarine ecosystems, such as the food webs for flounder in New Bedford harbor (Connolly, 1991) and striped bass in the tidal Passaic Ri ver (Iannuzzi et al. 1996). Mixed food webs are common in all ecosystems and, EPA believes, fa r outnumber the purely pela gic and benthic food webs. There are numerous examples of mixed bent hic/pelagic food webs, such as the food webs
for lake trout in the Great Lake s (Flint, 1986; Morrison et al. 1997), lobster in the New Bedford5-59 harbor (Connolly, 1991), whitefish and rainbow tr out in the Fraser Ri ver (Gobas et al. 1998), white perch in the Chesapeake Bay (Baird and Ul anowicz, 1989), and perch, bass, and crappie in Little Rock Lake (Martinez, 1991). Purely pelagi c and/or benthic species can exist in ecosystems containing species with a mixed benthic/pelagic food web, for ex ample, flounder and lobster in New Bedford harbor (Connolly, 1991).

5.2.2.5 Alternative food chain models

Food chain models of chemical bioaccumu lation are continually being developed and refined, so in the future, EPA may consider the use of other appropriately validated food web
models for the derivation of FC Ms. Any model considered should have the characteristics and qualities outlined in Section 5.2.2.2. and would have to be subjected to a validation process to address the issues of (1) accura cy and precision of the model predictions, (2) input parameter sensitivities, and (3) uncertainties as sociated with the model predictions.

5.2.3 Predicting Site-Specific Baseline BAFs using K ow and Food Chain Multipliers (Method 4b)

A site-specific baseline BAF for nonionic organi c chemicals can also be predicted using the product of the chemical's K ow and a FCM for a particular trophic level under site-specific
conditions. Method 4 uses the fo llowing baseline BAF equation:

ow Baseline BAF K FCM i i

= ⋅ (Equation 5-15)

where:

FCM i = the food-chain multiplier for trophic level i , determined from appropriate field data or predicted fo r site-specific conditions
K ow = n -octanol-water partition coefficient

The K ow can be substituted for the BCF when predicting a site-specific baseline BAF for hydrophobic organic chemicals, partic ularly for those chemicals th at are poorly metabolized by aquatic organisms, because the K ow is strongly correlated with th e BCF for these chemicals. As5-60 with Method 4a, the K ow must be adjusted with a FCM to account for chemical biomagnification through the food web as a result of dietary exposures. Method 4b is appropriate for non- or poorly-metabolized nonionic organic chemicals, but can also be applied to certain ionic chemicals having similar partitioning behavior . Method 4b is most appropriate for nonionic organic chemicals with log K ow s greater than or equal to 4 a nd low rates of metabolism. This approach may overpredict BAFs for chemicals that are metabolized by aquatic organisms, because metabolism is not incorporated in either the K ow or the FCM. Because the K ow is assumed to be equal to the baseline BCF, the or ganic carbon and lipid normalization procedures used in Method 4a (equation 5-7) are not need ed here. The determination of appropriate FCMs
and selection of K ow values are discussed below; further de tails on these topics can be found in Section 4.4 and Appendix B of TSD Volume 2 (USEPA, 2003).

Calculating a site-specific BA F using Method 4b is presented in the following example.5-61 Site-Specific BAF Predic ted Using the Product of the Chemical K ow and a FCM (Method 4b)
This example illustrates the prediction of a site-specific BAF using Method 4b, again using hydrophobic nonionic chemical k. To predict a site-specific BAF using Method 4b, the investigator uses the product of the K ow for the chemical and a FCM, which must be determined fo r the chemical, site, and trophic level of the target organism. In this method, K ow is assumed to be equal to the baseline BCF, as discussed in Sectio n 5.2.3. Method 4 requires selection of an appropriate K ow for the chemical, which is multiplied by an appropriate FCM to account for biomagnification.
For this example, Method 4b will be used to predict a site-specific BAF for crayfish using the same food chain stru cture presented in the Method 4a (page 5-37) example, based on the following data from the site:

MEASUREMENT AVERAGE VALUE

Chemical k Total Water Concentration 0.20 ng/L

Chemical k Sediment Concentration 1.95 F g/kg

Water Column POC 0.54 mg/L

Water Column DOC 3.5 mg/L

Sediment Organic Carbon 7.4%

Phytoplankton Lipid 1.2%

Zebra Mussel Lipid 1.3%

Crayfish Lipid 1.7%5-62 Site-Specific BAF Predic ted Using the Product of the Chemical K ow and a FCM (Method 4b continued)

Determining the chemical K ow Guidance for selecting an appropriate K ow for the chemical is provided in Section 5.2.3.2 and TSD Volume 2 (USEPA , 2003). For the purposes of this example, a K ow value of 2×10 4 (log K ow = 4.3) will again be used for chemical k.
Calculating the Freely Dissolved Che mical Concentration and SedimentWater Fugacity Gradient
The freely dissolved chemical concentration ( 

### C f d

w ) and the sediment-water fugacity gradient ( J socw /K ow ) should be calculated from the site data. The freely dissolved chemical concentration can be calculated from the total chemical concentration and the freely dissolved fraction calculated using equation 3-6:

984

. 0

10 10 0

. 2 5

. 3 08

. 0 10 10 0

. 2 54

. 0 1 1

6 4 6 4 =

⋅ × ⋅ −

⋅ + ⋅ × ⋅ − + =

mg kg kg L L DOC mg mg kg kg L L POC mg f

fd


## ( ) C f C 0.984 0.20 / 0.197

fd w fd w 

### ng L ng/L =⋅= ⋅ =

The sediment-water fugacity gradient can then be calcula ted using equation 4- 3:

SO C kg L g

ng L ng SOC kg kg kg g

SOCW fd

w SOC

− × = ⋅      

− ⋅

= = 

## ∏ / 10 34

. 1 1000 / 197

. 0 074

. 0 95

. 1

C C 5 μ μ

5 SOCW 4

ow 1.34 10 2.0 10 Π 6.71 K kg L kg SOC L

×

− × = ⋅=5-63 Site-Specific BAF Predic ted Using the Product of the Chemical K ow and a FCM (Method 4b continued)
The Gobas Aquaweb model can be downloaded from http://www.rem.sfu.ca/toxicol ogy/models/AQUAWEBv1.2_BIOv1.2.xls as an Excel spreadsheet, which is simple to apply. For th is example, several i nputs to the spreadsheet must be modified. On the EP (environmental parameters) sheet, the organic content of water must be changed to 1.0×10 -30 kg/L (this was discussed on page 5-52) and the organic carbon content of sediment must be changed to 7.4%, the site-specific value. On the CP (chemical specific parameters) sheet, data for chemical k must be entered. These include the log K ow of 4.30, the total water concentratio n of 0.197 ng/L (the freely dissolved concentration), and the sediment concentration of 1.95 ng/g.
Several changes are also required on the BP (biological parameters) sheet, although the Aquaweb model includes each of the foodweb sp ecies of interest for this site. Zebra mussels are the first invertebrate species in the model. The lipid content of zebra mussels should be 1.3%, and the feedi ng preferences (fraction of di et) should be changed to 0.75 phytoplankton and 0.25 sediment/detritus. Crayfish are the fifth inverteb rate species in the model. The lipid content of crayfish should be 1.7%, and the feeding preferences (fraction of diet) should be changed to 1/3 phytoplankt on and 2/3 invertebrate 1 (zebra mussels). For both invertebrate spesies, we will accept th e default 5% pore water ventilation fraction, although the spreadsheet notes that site-s pecific data should be considered.
Once these data are input, the model predicts a concentration of chemi cal k in crayfish of 0.199 ng/g-wet. The site-specific total BAF for chemical k in crayfish can be calculated from Equation 3-1:

t ,T C 0.199 / 1000 Total BAF = BAF = = = 995 / C 0.20 /

t i

w ng g g L kg ng L kg ⋅

The baseline BAF for chemical k in crayfish can be calculated from the total BAF using Equation 3-4, recalling that the freely dissolved fraction of chemical k in the site water column was 0.984:

, 4 BAF 1 995 / Baseline BAF 1 = 1 = 5.94 10 / f f 0.984 0.017 t i T i fd l L kg g wet L kg l g lipid  

−   = −⋅ −⋅ × −

   

−      

The FCM for chemical k in crayfish at this site can also be calculate d, using equation 5-14:

4
4 ow Baseline BAF 5.94 10 K 2.0 10 FCM= 2.98 kg l L kg l L − ×

− × = ⋅=5-64 A number of assumptions are associated w ith predicting site-specific baseline BAFs by Method 4b. First, it is assumed that the K ow is equal to the chemical’s baseline BCF, an assumption that is only valid for non-metabolized chemicals. Second, it is assumed that there is
no metabolism of the chemical in the food web. Third, the other a ssumptions incorporated into the FCMs (whether measured or modeled) are dire ctly incorporated into the predictions made
with Method 4b. For detailed information on the a ssumptions incorporated into the FCMs, refer
to Section 5.2.2.

Method 4b assumes that the K ow is equal to the chemical’s baseline BCF. This assumption is supported by equilibr ium partitioning theory. This theory assumes that (1) the bioconcentration process can be viewed as a pa rtitioning of a chemical between the lipid of aquatic organisms and water and the K ow is a useful surrogate for th is partitioning process, and (2) a linear relationship exists between the K ow and the BCF. Mackay (1982) demonstrated the
usefulness of K ow as a surrogate for this partitioning process by presenting a thermodynamic basis for the partitioning process for bioconcentra tion. In theory, it follows that the baseline BCF (i.e., BCF based on the concentration of chemical in lipid of organisms and freely dissolved in water) for organic chemicals should be similar, if not equal to, the K ow . This theory is supported by a considerable body of empirical data. As summarized by Isnard and Lambert (1988),
numerous studies have demonstrated a linear relationship between the log K ow for organic chemicals and the log BCF measured for fish and other aquatic organisms exposed to those chemicals. In addition, when the regression eq uations are constructed with BCFs reported on a lipid-normalized basis, the slopes and intercepts are not significantly di fferent from 1 and 0, respectively. For example, de Wolf et al. ( 1992) adjusted a relationship reported by Mackay (1982) to a lipid-normalized basis an d obtained the following relationship:

log BCF = 1.00 log K ow + 0.08 (Equation 5-16)

For highly-hydrophobic chemicals (log K ow >6.0), reported BCFs are often not equal to
the K ow even for nonmetabolized chemicals, becaus e the measurements were not performed and/or reported with appropriate experimental conditions. BCFs for nonmetabolized chemicals

are equal to the K ow when the BCF values meet the following quality assurance criteria:5-65 • reported on a lipid- normalized basis,

• determined using the concentr ation of the chemical that is freely dissolved in the exposure water,

• corrected for growth dilution,

• determined under steady-state conditions or from accurate measurements of the chemical’s uptake (k 1 ) and elimination (k 2 ) rate constants, and

• determined with no solvent ca rriers in the exposure water.

5.2.3.1 Validation of Method 4b

As noted in Section 4.6.1, Burkhard et al . (2003b) have validated and compared the predictive powers of Methods 2 (i.e., baseline BAFs predicted from field-measured BSAFs) and 4b. The validation exercises were performed usi ng data collected from a number of diverse aquatic ecosystems: Lake Ontario, Green Bay/Fo x River, the Hudson River, and Bayou d’Inde, Louisiana. With these data sets, baseline BAFs predicted using Method 4b were plotted against field-measured baseline BAF (i .e., Method 1) values. The agreement between baseline BAFs predicted using Method 4b and Method 1 baseline BAF values is generally good for Green Bay, although not as good as the agreement betw een Method 2 and Method 1 baseline BAFs (Burkhard et al. 2003b). In Green Bay, 59% of the baseline BAFs predicted using Method 4b were within a factor of 2, and 93% were within a factor of 5, of the measured baseline BAFs (Table 5-7). The validation exercises using the Green Bay/Fox River and Hudson River data are
described in detail in Burkha rd et al. (2003b). Figure 4-2 compares Method 2 and Method 4b predictions to the baseline BA Fs measured in the Green Ba y and Hudson River ecosystems.5-66 Table 5-7. Validation Statistics for Metho d 4b: Ratio Between Predicted and Measured Baseline BAFs (Baseline BAF predicted /Baseline BAF measured ) based on PCB concentration data from Green Bay, Lake Michigan and the Hudson River.

Method 4b: Exceedance Levels and Comparison Statistics Location 95% Mean Median 5% % within 2x % within 5x
Green Bay
Zone 1 0.32 1.17 0.89 2.75 69.8 98.1 Zone 2a 0.17 1.17 0.74 3.40 54.6 91 Zone 2b 0.23 1.18 0.83 3.01 61.0 94.9 Zone 3a 0.33 1.58 1.05 4.71 64.0 94.7 Zone 3b 0.23 1.35 0.90 4.15 60.5 94 Zone 4 0.15 1.43 0.61 5.28 40.5 82.2 All Zones 0.21 1.30 0.84 3.90 58.6 92.7 Hudson River
RM 194 0.06 0.16 0.11 0.38 3.6 25.3 RM 189 0.12 0.26 0.20 0.55 9.0 55 RM 169 0.10 0.95 0.41 1.89 35.3 76.5 RM 144 0.42 0.72 0.67 1.14 76.5 100 RM 122 0.40 0.70 0.67 1.27 80.0 100 RM 114 0.4 0.78 0.73 1.29 76.9 100 All Stations 0.08 0.50 0.24 1.07 26.3 60.7

RM = river mile

The accuracy of baseline BAFs predicted with Method 4b in the Hudson River varied among sites. Generally, the predicted baseline BAFs are biased low; this is evident in Table 5-8, where the mean and median predicted/measured rati os are less than 1 for all locations. At three
of the six stations in the Hudson River (river miles (RM) 114, 122, and 144), there was good agreement between predicted and measured base line BAFs (>75% within a factor of 2, and 100% within a factor of 5; Table 5-7). Howeve r, for river mile 169, agreement was not as good (35% within a factor of 2; 76% within a factor of 5). Finally, at two sites (river miles 189 and 194), there was substantial underp rediction of measur ed baseline BAFs with Method 4b. On the other hand, for the Hudson River data set, the variability associated with baseline BAFs5-67 predicted using Method 4b was generally smaller th an that associated with Method 2. Burkhard et al. (2003b) discuss several fa ctors that might be involved wi th the underprediction of the baseline BAFs for river miles 169, 189, and 194 us ing Method 4b. These include (1) the use of FCMs (Table 5-6) derived using conditions and parameters for the nation instead of for the Hudson River, (2) the use of fiel d samples that were not temporal ly and/or spatially coordinated and/or representative of the ecosystem, and (3 ) the sampling of an ecosystem with rapidly changing conditions in recent history due to unusual conditions in the river.

Table 5-8. Summary Statistics: Differences Between Log Baseline BAFs Predicted with Method 4b and Log Baseline BAFs Measu red from Lake Ontario (Oliver and Niimi, 1988) for Chemicals with Log K ow Exceeding 4

Organism Statistic Sculpin Alewife Small Smelt Large Smelt Piscivorous Fish Average 0.01 0.04 0.09 0.28 0.08 Standard Deviation 0.35 0.36 0.37 0.35 0.36

Count 51 49 46 47 57 Median 0.02 0.06 0.14 0.30 0.08 Within 2x 63% 59% 61% 47% 58% Within 5x 94% 94% 94% 92% 96% Negative Residual 53% 53% 59% 72% 56%
Positive Residual 47% 47% 41% 28% 44%

Burkhard et al. (1997) also evaluated the predictiveness of Method 4b against fieldmeasured baseline BAFs for trophic level 3 fish sampled from the Bayou d’Inde for selected chlorinated benzenes, chlorinated butadienes, an d hexachloroethane. Bay ou d’Inde is a lowland channel that meanders through a brackish-freshwat er marsh that is influenced by tide. This ecosystem is very different from either the Great Lakes or the Huds on River and provides a useful demonstration of the applicability of Method 4b across different ecosystems. Because this
evaluation of Method 4b was conducted before the development of the final National BAF5-68 Methodology, it was performed with FCMs and de fault values for POC and DOC that are marginally different from those that are us ed in the National BAF Methodology (USEPA, 2003). Burkhard et al. (1997) found good agreement be tween the predicted and measured baseline BAFs for both the fish and invertebrates samp led. Overall, approximately 90% of the Method 4b-predicted baseline BAFs were within a factor of 5 of the measured baseline BAFs, and the median ratio of the predicted baseline BAFs to the measured baseline BAFs was 1.64.

The EPA also compared the baseline BAFs predicted with Method 4b to measured BAFs for the Lake Ontario ecosystem (Table 5-8). The average differences between measured and predicted baseline BAFs were small for both forage and piscivorous fish, and more than 90% of the baseline BAFs predicted with Method 4b were within a factor of 5 of the measured BAFs. The residuals (i.e., the differe nces between predicted and measured BAFs) were evenly distributed, except for the large sm elt. The trophic level for the la rge smelt is estimated to be

. 3.5, owing to its consumption of smaller forage fish, and consequently, it was anticipated that the predicted baseline BAFs with trophic le vel 3 FCMs would be slightly lower than the measured BAFs for this species.

As summarized above, the predictive accuracy of Method 4b has been evaluated with
field data from four different ecosystems. For the Lake Ontario, Green Bay/Fox River, and Bayou d’Inde ecosystems, baseline BAFs predicte d with Method 4b were in excellent agreement with the measured BAFs: More than 90% of the pr edicted baseline BAFs were within a factor of
5 of the measured baseline BAFs. In the Huds on River, for three of the sampling stations, baseline BAFs predicted with Method 4b were in excellent agreement with measured BAFs: 100% of the predictions were with in a factor of 5 of the measur ed baseline BAFs. For the other three sampling stations in the Hudson River, baseline BAFs predicted with Method 4b were
much smaller than the measured BAFs, but the predictions were consistent with those based on a complex site-specific, time-dependent food web bioaccumulation model (QEA, 1999).

Overall, EPA believes that Method 4b provide s excellent predictions for ecosystems that have not recently experienced a major change or di sruption in chemical loadings or flows. Of all the ecosystems examined, the extreme temporal dynamics observed for several important factors5-69 (e.g., fish lipid content, food web structure, exposure concentrat ions) in the Hudson River makes
this site a severe test of all the BAF methodologies. In fact , the Hudson River data set may arguably fail to meet the sampling and data qualit y considerations specif ied in Section 3.4 and 3.5 for deriving baseline BAFs from field data. None theless, EPA believes th at the application of
the BAF methods to this location wa s a useful exercise and illustra tes that useful predictions are possible using Method 4b in ecosystems with extreme temporal dynamics.

5.2.3.2 Selection of appropriate K ow s for partitioning (bioavailability) predictions

The K ow of the chemical of interest is us ed in several components of the BAF methodology, for example, to estimate BAFs, hydrophobi city, and partitioning in water; as well as in the prediction of baseline BAFs from BS AFs (Method 2) and in the use of the food chain model to predict FCMs for Method 4. Each of thes e procedures is highly sensitive to the value
selected for K ow . Thus, it is important for the investigator to search the literature and chemical property databases for all available data for K ow of the chemical of inte rest, and then select the most accurate and appropriate value of K ow for the chemical. Although a variety of methods are
available to measure or estimate 3 K ow values, the reliability of these methods varies according to
the K ow of the chemical. In addition, many unreliable or erroneous K ow values can be found in
the literature or in chemical property databases (Linkov et al . 2005; Pontolillo and Eganhouse, 2002).

A detailed approach fo r selecting reliable K ow values was published in Appendix B of TSD Volume 2 (USEPA, 2003). EPA’ s methodology for selecting K ow values divides the range
of K ow s into three groups (logK ow < 6, 6 < logK ow < 8, and logK ow > 8) to reflect the differences in chemical properties and behavi ors due to differing hydrophobicities. In general, “high quality” measured values (i.e., data judged to be reli able based on EPA guidelines) are preferred over
estimates. K ow s measured by the slow stir method are c onsidered reliable up to a value of 10 8

. Shake flask K ow measurements are reliable up to 10 6 as long as sufficient attention is given to

3 For example, the EPI (Estimation Programs Interface) su ite of physical/chemical proper ty and environmental fate estimation models developed by the EPA’s Office of Pollution Prevention Toxics and Syracuse Research Corporation (http://www.epa.gov/oppt/exposure/pubs/episuite dl.htm) includes KOWWIN. This model estimates the log K ow of chemicals using an atom/fragment contribution method.5-70 micro emulsion effects; for classes of chemicals that are not highly sensitive to emulsion effects (i.e., polycyclic aromatic hydrocar bons) this range may extend to 10 6.5 . For chemicals with log
K ow > 5, it is highly unlikely to find multiple “hi gh quality” measurements. Therefore, assigning
K ow ’ s from estimation techniques may be necessary. When multiple K ow values are found, evaluating the quality of the data (measured or estimated) should include checking the consistency between the values. What is c onsidered reasonable agreement in log K ow data depends primarily on the magnitude of the log K ow value. Therefore, EPA has established the following ranges of acceptable va riation for this exercise:

• 0.5 for log K ow > 7,

• 0.4 for 6 # log K ow # 7 , and

• 0.3 for log K ow < 6.

Statistical methods should be applied to K ow data as appropriate. However, the investigator should recognize that robust estimates are generally difficult to obtain due the paucity of data and the determinate/met hodic nature of most measurement error(s).

5.3 RECALCULATING SITE-SPECIFIC BAFS FROM BASELINE
OR NATIONAL BAFS

A site-specific BAF for a nonionic organic ch emical can be recalculated from a baseline or national BAF by using values for the aquatic or ganism lipid content an d/or the organic carbon (DOC and POC) concentrations that are representative of conditions at the site. This is Method 5 of EPA’s site-specific BAF me thodology. The investigator can modify one or both of these parameters in the site-specifi c recalculation of the BAF by:

• conducting site-specific fi eld studies to generate representative data,

• conducting a literature or database search to obtain data more repr esentative of local conditions, or5-71 • selecting an appropriate subset of the national database that EPA used to derive the default values.

Method 5 is applicable to noni onic organic chemicals, and si milarly-behaving ionic organic chemicals. The formula for recalculating a site-specific BAF from the baseline BAF is:



### Site Specific BAF (f Baseline BAF +1) f T i,t l i fd



### =⋅ ⋅ (Equation 5-17)

where:

f fd

= fraction of the total concentration of chemical that is freely dissolved in the
water column at the site
f  = fraction of the organism tissue that is lipid

Equation 5-17 is a rearrangement of the equa tion relating the total and the baseline BAF for nonionic organic chemicals (e quation 3-3). In other words, recalculating a site-specific BAF means converting a baseline BAF into a total BA F using values for the tissue lipid fraction
and/or dissolved chemical fraction in water that are most appropriate for the organism and the
site.

Site-Specific BAF Method 5

• Recalculating site-specific BAFs from baseline BAFs, with 2 options: 5a. Adjustment for site-speci fic lipid content, and/or 5b. Adjustment for site-specific DOC

Although EPA uses national default values of lipid fraction to derive national human health AWQC, States and authorized Tribes are en couraged to use local or regional data on the lipid content and consumption rates of consumed aquatic species when adopting criteria into their own water quality standard s. The use of such locally orregionally derived (i.e., sitespecific) data is encouraged over national-scal e data because local or regional consumption5-72 patterns of fish and shellfish (a nd thus the amount of lipid cons umed from aquatic organisms) can differ from national consumption patterns, a nd because lipid contents of specific organisms at a site can vary from nationally derived values due to factors and conditions of the ecosystem. Likewise, EPA encourages States and authorized Tribes to use site-specific data on the organic carbon content of applicable waters when a dopting criteria into their own water quality standards. EPA encourages the use of appropriate locally or regionally deri ved values of DOC or POC over nationally derived values because loca l or regional conditions that affect DOC and POC concentrations can differ substantially from those represented by na tionally derived values.

5.3.1 Assumptions and Limitations

Although both theory and empirical evidence support the concept of adjusting BAFs for lipid content and dissolved chemical fractions to facilitate their extra polation between species and sites, this practice neverthe less involves making a series of a ssumptions that deserve to be explicitly stated and evaluated. The same assumptions (and justifications) were made by EPA to support the use of baseline BAFs when derivi ng national values for nonionic organic chemicals in the 2000 Human Health methodology and TSD Vo lume 2 (USEPA, 2003). The investigator
should refer to these documents fo r further details regarding the sc ientific basis for the use of
baseline BAFs.

The assumptions associated with adjustment of BAFs by lipid normalization can be stated

as:

1. For a given species and exposure conditi on, the total concentration of a nonionic organic chemical in the tissue of an organism at or near steady state varies in direct proportion to the lipid content in the tissue of interest.

2. The degree of proportionality of chemical concentration with lipid content does not depend on the composition of lipids present in tissue.5-73 The first assumption is generally supporte d by the empirical evid ence and underlying theory that supports many widely used bio accumulation models. This assumption is also supported by the findings that for organic chemi cals that are not metabol ized, BCF is strongly
correlated with K ow. (e.g., Veith et al. 1979b; Isnard and Lamb ert, 1988; de Wolf et al. 1992). In determining K ow s, n -octanol is considered to be a surroga te for lipid. Chiou (1985) used triolein (glyceryl trioleate) as a su rrogate for lipid and also f ound good agreement between BCFs and triolein/water partition coefficients.

The second assumption pertains to the utility of the total lipid content as a normalizing factor for species and tissues with widely varying lipid fractions and lipid compositions. The process of normalizing BAFs and BCFs on the basi s of the total fraction of tissue that is lipid assumes that lipids are a single, uniform compartment. In realit y, total lipid content in fish includes different lipid classes, including relatively polar phos pholipids, which are common in cell membranes, and generally nonpolar triacylg lycerols, which are common in storage lipids (Henderson and Tocher, 1987). The variation in li pid-partitioning behavi or of nonionic organic chemicals is thought to be a function of differe nces in polarity of lipid classes, as fewer chemicals become associated with the more pol ar “membrane-bound” lipids than storage lipids (Ewald and Larsson, 1994; van Wezel a nd Opperhuizen, 1995; Randall et al. 1998).

In practical terms, the potential impact th at differences in lipid composition might have on chemical partitioning and lipid normalization seems to be most relevant for very lean tissues (e.g., those less than 1%–2% total lipids). This s uggestion is based on obse rvations that lean tissues of some fish species contain a much greater proportion of polar phospholipids (24%– 65%) than do “fatty” tissues (1.5%–8.7%; Ewald and Larsson, 1994). Similar observations have been made with populations of ribbed mussels, for which Be rgen et al. (2001) reported significantly higher fractions of polar lipids in leaner populations compared with fatter populations. Because of the greater po larity of their lipids, very l ean tissues are likely to exhibit different chemical/lipid-partitioning behavior than fatty tissues. Bergen et al. (2001) reported stronger correlations between chemical concen trations and mussels with higher total (and nonpolar) lipid content, which led to their suggestion that lipi d normalization may work best above some threshold of lipid content. However, the narrow range of lipid content evaluated in5-74 their study (about a factor of tw o) and the reliance on total PCB measurements (as opposed to individual congeners) might have limited their ability to identify mean ingful trends between chemical concentrations and lipid content.

Differences in lipid composition in tissues of aquatic organisms also relate to a complication associated with methods used to de termine lipid content. Specifically, different solvents have been used to extract lipids, which leads to different quantit ies (and types) of lipid being extracted from the same tissue of aquatic organisms. In a study by Randall et al. (1991), lipid fraction varied by nearly fourfold among four extraction methods but va ried twofold or less among two of the more common ex traction methods (chloroform-methanol and acetone-hexane). Following up on their previous work, Randall et al . (1998) report that if different solvents are used to extract lipids and PCB congeners, diffe rences among lipid-normali zed concentrations can vary more than fivefold, depending on the solvent combination. The relative difference among lipid extraction methods depends not only on the polarity of the solv ent but also the lipid content of the tissue. Because lean tissues contain propor tionally more polar lipids than fatty tissues, differences in the lipid extraction efficiency for different solvents tend to be greatest for lean tissues (de Boer, 1988; Ewald et al. 1998). This finding led these authors to caution the use of lipid data from lean tissues that have been ex tracted using strictly nonpolar solvent systems.

The assumptions associated with adjustment of BAFs by dissolved chemical fraction
include:

1. Hydrophobic organic chemicals exis t in water in three phases : (1) the freely dissolved phase, (2) sorbed to the organic fraction of suspended solids (i.e., particulate organic carbon), and (3) sorbed to dissolved organi c matter. This assumption is supported by a wealth of experimental evidence (Ha ssett and Anderson, 1979; Carter and Suffet, 1982; Landrum et al. 1984; Gschwend a nd Wu, 1985; McCarthy and Jimenez, 1985a; Eadie et al. 1990, 1992). The tota l concentration of the chem ical in water is the sum of the concentrations of the freely diss olved chemical and the sorbed chemical (Gschwend and Wu, 1985; USEPA, 1993).

2. Chemicals in the freely dissolved phase of th e water are in equilibrium with chemical associated with the DOC and POC (includi ng plankton) phases of the water column. The relationship used by EPA to relate the freely dissolved chemical concentration to the concentrations of chemical associ ated with DOC and POC (equation 3-12) assumes equilibrium among these phases. For a given ecosystem, DOC and POC5-75 define the partitioning of the chemical among the three phases. Section 4.2.1 of TSD volume 2 provides background information regarding the derivation of this relationship.

3. The concentration of chemical that is fr eely dissolved is the best measure of the fraction of nonionic organic chemical avai lable for uptake by aquatic organisms, both in sediment porewaters and ambient surface wa ters (Suffet et al. 1994; DiToro et al. 1991). Sorption of the chemical to DOC and POC reduces chemical bioavailability to aquatic organisms.

By basing the baseline BAFs on 

### C f d

w , EPA does not ignore the chemical associated with dissolved organic carbon (DOC) and particulat e organic carbon (POC) in the water column. As stated above, the chemical associated with DOC and POC in the water column is assumed to be in equilibrium with the chemical freely dissolved in the water column. Therefore, any additions or removal of chemical from any of the three phases (i.e., freely dissolved chemical, chemical associated with DOC, and chemical associated with POC) will cause a re-equilibration of the chemical among the three phases. Due to the e quilibrium conditions among these three phases,
the chemical concentration in the water colu mn expressed using any of the three phases, individually or in combination, is indicative of the chemical c oncentrations in the other water column phases for a given set of ecosystem conditions.

Reduced chemical uptake by aquatic organisms in the presence of DOC has been extensively reported for both ambient waters and waters containing added DOC (Leversee et al. 1983; Landrum et al. 1985; McCarthy and Jimen ez, 1985b; McCarthy et al. 1985; Carlberg et al. 1986; Black and McCarthy., 1988; Servos and Mu ir, 1989; Kukkonen et al. 1989). For example, it has been reported that the per centage reduction in gill uptake e fficiency of benzo[a]pyrene and 2,2',5,5'-tetrachlorobiphenyl in rainbow trout is equal to the percenta ge reduction in freely dissolved chemical concentration in the pr esence of DOC (Black and McCarthy, 1988). The authors of this study concluded th at only the chemical that was fr eely dissolved in the water was available for uptake by the fish. Similarly, Landrum et al. (1985), McCarthy et al. (1985), and Servos and Muir (1989) reported that chemical uptake rates were reduced when DOC was present and that the concentrati on of chemical that is freely dissolved in the water column decreases in proportion to the amount of DOC pres ent in the water. These studies clearly support EPA’s assumption that chemical bioavailabil ity of nonionic organic chemicals to aquatic5-76 organisms is reduced in the presence of DOC and POC. Excellent revi ews on the science of bioavailability are provided by Hamelink et al. (1994) and Kukkonen (1995).

5.3.2 Validation of Method 5

Baseline BAFs are based upon the freely dissolved chemical concentrations in ambient
water and the chemical concentrations in th e lipid fraction of the organisms, and these adjustments allow for better extrapolation of BAFs across species and locations by reducing variability in the extrapolation. To evaluate the merits of this approach, two different comparisons were made. The first involved co mparing baseline BAFs and BAFs within and across the different sampling zones of Green Bay for individual species. The second evaluation involved the comparison of baseline BAFs and BA Fs across species and ecosystems. In each comparison, baseline adjustment of BAFs was f ound to significantly reduce the variability in BAFs between zones, species and ecosystems.

Bay-wide baseline BAFs and BAFs were calculated using a sample-size weighted average of the BAFs from each of the sampling z ones. The variances of the bay wide baseline BAFs and BAFs were calculated (in log-space ) by summing the variances of the chemical
concentrations in fish and water across all zones and correcting for the covariance of the
chemical concentrations in fish and water within zones. Overall, the baseline BAFs had smaller variances than BAFs, and the baseline adjustments of the BAFs reduced their standard deviations by half (Figure 5-10). This figure also shows that the baseline BAFs varied less across the zones than the BAFs, i.e., the baseline BAFs were near ly constant across zones compared to the BAFs.
The BAFs tended to increase from zone 1 to 4 (Figure 5-10), and this difference was more pronounced for the more hydrophobic congeners 149 and 180, consistent with equilibrium partitioning theory. The observed trend of incr easing BAFs across zones due to increasing bioavailability of dissolved PCBs, caused by d eclining particulate and dissolved organic carbon across zones, appeared to be re moved by baseline adjustment. The decrease in variances for the baseline BAFs (in comparison with those for BAFs) will result in lower variances for site specific BAFs derived using Method 5. Without baseline adjustment , direct extrapolation of the BAFs from Green Bay to another ecosystem wo uld have larger variances and poor predictive5-77 power. The above comparisons were all within the Green Bay ecosystem, and they demonstrate that corrections for lipid content and freely disso lved chemical concentrations in water reduce variability associated with the baseline BAFs.

FIGURE 5-10. s ( z ) and Baseline BAFs (  ) for PCB congener 149 (2,2',3,4',5',6- hexachlorobiphenyl) (±1 sd) for adult alewife for different spatial zones in Green Bay.

To further evaluate the relative variance s associated with BAFs and baseline BAFs, baywide BAFs were compared. Bay-wide BAFs and baseline BAFs were calculated using a sample size weighted average of the BAFs for ea ch of the geographical zones. The variances of the baywide BAFs and baseline BAFs were calculat ed as described in detail in Burkhard et al. (2003a). The results of these calculations ar e summarized, by species, using the ratio of 90 th to
10 th and 95 th to 5 th percentile exceedance limits in Table 5-9. Overall, the baseline BAFs had
smaller ratios than the BAFs and the ad justment/conversion of BAFs to baseline BAFs resulted in an approximately twofold d ecrease in variability (Burkhard et al. 2003ab).5-78 Table 5-9. BAFs and Baseline BAFs Confiden ce Limit Ratios (CLRs) for Adult alewife, Age 4 walleye, and Age 10 carp in Green Bay (All Zones Combined)

90th to 10th Percentile CLR 95th to 5th Percentile CLR PCB Congener BAF Baseline BAF BAF Baseline BAF

Adult alewife

18 4.98 3.11 7.86 4.3

52 5.48 2.85 8.90 3.84

149 3.33 1.88 4.70 2.26

180 4.08 2.20 6.10 2.76 Age 4 walleye

18 3.57 3.50 5.14 5.00

52 4.04 2.74 6.01 3.65

149 3.11 2.12 4.30 2.62

180 3.96 2.12 5.87 2.63 Age 10 carp

18 4.87 4.23 7.65 6.39

52 6.75 3.49 11.6 4.99

149 5.96 1.87 9.91 2.24

180 7.09 2.17 12.4 2.71

To assess across-ecosystem variabilities, ba seline and total BAFs for six PCB congeners (PCBs 22, 52, 85, 118, 146, and 149) were assembled from the Green Bay, Lake Ontario, and Hudson River ecosystems for thirteen fish speci es (Figure 5-11). When possible, age-class specific BAFs were assembled, and trophic levels for the different species were assigned using nominal/rounded trophic levels. These assignments caused species with slightly lower trophic level positions (e.g., adult gizzard shad with aver age trophic level of 2.5) to be lumped with species with slightly higher troph ic levels (e.g., adult alewife with average trophic level of 3.5) at the nominal trophic levels. The baseline BAFs had substantially lower vari ability in comparison5-79 to the total BAFs for trophic levels 3 and 4 fish es, i.e., an average 2.3-fold decrease in the coefficients of variation (Figure 5-11). Additionally, the 75 th /25 th and 90 th /10 th percentile ranges were smaller by

. 2x and

. 5x, respectively, for the baseline BAFs for both trophic levels. These results demonstrate that the corrections for lipid s and freely dissolved chemical concentrations reduce variability when extrapolating BAFs acr oss ecosystems and across species of similar trophic levels. The variability not due to li pid content and freely dissolved chemical
concentrations could include: diffe rences in nominal vs. actual trophic level assignments for the individual species; differences in disequilibrium of the ecosystem; analytical and sampling errors and biases; and differences in age, size, growth rate, and/or reproductive status of the individual organisms.5-80 FIGURE 5-11. Box plots comparing baseline (TL 3 or 4 B) and field-measured (TL 3 or 4 F) BAFs for six PCB congeners obtained from Green Bay, Lake Ontario, and Hudson River ecosystems for 13 fish species with samples segr egated according to year classes and sampling location, e.g., 4-year-old walleye from zone 4 in Green Bay and adult perch from RM 194 in the Hudson River. For box plots, the median is the li ne inside the box, the 25th and 75th percentiles are the ends of the box, the 10th and 90th percentiles are the T-lin es, and outliers, points beyond the 10th and 90th percen tiles, are the dots ( ).5-81 5.3.3 How Can the Lipid Contents of Aquatic Organisms be Determined?

Lipid content is used to adjust BAFs fo r nonionic organic chemicals because it has been shown to influence the magnitude of bioaccu mulation in aquatic organisms (Mackay, 1982; Connolly and Pederson, 1988; Thomann, 1989). Theref ore, lipid content in consumed aquatic organisms is an important factor for characteri zing potential human expos ure to nonionic organic chemicals. Since baseline BAFs are lipi d normalized according to the national BAF methodology, recalculating a site-specific BAF (Method 5, equation 5-17) involves multiplying the baseline BAF by the appropriate lipid content. This section disc usses how an investigator can determine lipid contents for aquatic organisms and/or tissues consumed from a site, to be used in recalculating a site-specific BA F from a baseline BAF (Method 5).

EPA recommends using local or regional data on the consumption ra tes and lipid content of consumed aquatic species when recalculating baseline BAFs. The use of such locally or regionally derived (i.e., site-specifi c) data is encouraged over nati onal-scale data because local or regional consumption patterns of fish and shellfis h (and thus the amount of lipid consumed from aquatic organisms) can differ from national cons umption patterns. Lipid contents of specific organisms at a site can also vary from nationally derived values due to factors and conditions of the specific ecosystem.

A number of factors can lead to variabili ty in lipid content of aquatic organisms, principally differences in physiology, metabolis m, organism health or condition, and feeding ecology among and within species. These factor s and, consequently, the lipid content in a particular tissue can vary as a function of season, temperatur e, reproductive status, migratory patterns, sampling location (both within and ac ross waterbodies), age, size, life stage, the availability of prey, and other factors. For example, the mean percent lipid in fillets of lake trout, Salvelinus namaycush , a notoriously “fatty” species, is estim ated to be about 12%. This value is about 18 times the mean percent lipid found in fillets of northern pike, Esox lucius (0.7%), which illustrates the potentially large va riability in lipid content within a single trophic level (both are piscivorous, trophic level-4 fish and are frequently consumed by local populations). Wide5-82 variation in lipid content can also occur within a species. The coefficient of variation of percent lipid can approach or, in some cases, exceed 100% within a species, even when data are limited to specific tissue types. In addi tion, the distribution of lipids in a particular aquatic organism is not uniform across all tissue type s, resulting in differences in lipid fraction depending on the tissue sampled (e.g., fillet, whole body, muscle). Finally, differences amo ng analytical methods used to extract and measure lipids and associated analytical error can cont ribute to variability in reported values of lipid fraction.

The following sections offer guidance to the investigator regarding options for determining appropriate lipid contents for site-specific BAF recalculation.

5.3.3.1 Assessing Site-specific Fish Consumption

It is important to identify the fish consum ption habits of local populations because the commonly-consumed fish serve as the dietary exposure pathway for bioaccumulative chemicals. The investigator should base their efforts on dete rmining lipid content(s) for the fish species and tissue types that are commonly consumed by th e local populations. In all cases, the primary
selection criterion should be that the target species is/a re among the species commonly consumed in the study area, and that the species is of recreational or su stenance fishing value.

5.3.3.2 Measuring Lipid in Fish

If site-specific lipid data are not otherwise available, th e investigator may choose to measure actual lipid contents of the target species and/or tissues. In general, guidance offered in Section 3.4 ( Measuring Chemical Concentrations in Biota ) is applicable for designing a field study (e.g., sample numbers, frequency of collection, location, etc.) to measure lipid content. In this case, however, the inves tigator should ensure that an appropriate method is used for measuring lipid content. Most me thods involve determining lipid content gravimetrically (i.e., by weight), following solvent extraction of the lip id from whole organism or tissue samples. Differences in the polarity of the solvents used to extract lipids from tissue can result in the extraction of different amounts of lipid (Honeycutt et al. 1995). This can lead to variability in5-83 lipid-normalized concentrations and, consequen tly, in the site-specific BAF recalculation because of the solvent system used in lipid extr action. Of particular concern are differences in the solvent extraction efficiencies of lipid and chemicals in extremely lean tissues (e.g., <1%-2% lipid). In such tissues, more polar (or mixed pol ar/nonpolar) solvent systems tend to extract more lipids than do nonpolar solvent systems. This phenomenon is believed to result from the proportionately greater fraction of polar lipids in lean tissues as compared with fatty tissues.

A variety of solvent systems that extract va rious lipid classes have been proposed for use in normalizing tissue chemical concentrations by lipid content. However, a clear consensus has not emerged on which method is most appropriate for all tissues, species, and nonionic organic chemicals. Although it is desirable to have one standardized lipid extraction method for normalizing concentrations of nonionic organic chemicals, it seems possible that no single method would be equally appropriate for all chem ical and tissue types. Different tissues have different lipid compositions that, in turn, may a lter the chemical/lipid partitioning process. From a toxicological perspective, th e science is presently not clear on which classes of lipids (e.g., phospholipids, free fatty acids, mono-, di-, and trig lycerides) are most rele vant with respect to different organic chemicals. For example, DDT ha s been reported to bind to relatively polar membrane-bound lipids, which suggests these me mbrane lipids might be relevant to DDT toxicity (Chefurka and Gnidec, 1987). Randall et al. (1998) reported that 27% of extractable PCBs were associated with the more polar, membrane–bound lipid pool (i .e., extractable with chloroform/methanol), whereas 73% were associated with the neutral lipid pool (i.e., extractable with hexane). Similarly, de Boer (1988) reported that chlorobiphe nyls were associated with both bound (membrane) and unbound (storage) lipid pools in fish. These findings further suggest that membrane-bound lipids should not be ignored when selecting lipid extraction methods.

To promote consistency with other data, including field studies measuring BAFs and BSAFs, EPA recommends the continued use of th e Bligh and Dyer (1959) chloroform/methanol extraction method in combination with gravim etric measurement of lipid. The Bligh-Dyer method is recommended because it is widely used for lipid measurements and has been well
characterized in terms of the t ypes of lipids extracted. The Bligh- Dyer method also extracts both polar and nonpolar lipids. Based on these and other considerations , Randall et al. (1998) also5-84 recommend the Bligh-Dyer method as a standard technique for total lipid extraction pending more research to identify the complex neutral chemical/lipid relationships and subsequent development of a definitive standard method. Rand all et al. (1998) also recommend that if other lipid extraction methods are used, results should be compared to results obtained using the BlighDyer method to allow conversion of the results to Bligh-Dyer equivalents.

5.3.3.3 Determining Site-Specific Fish Li pid Using a Literature or Database Search

Scientific publications, repor ts, and online databases also co ntain data for organism and tissue lipid contents measured in many ecosystem s. Determining lipid content in this manner may be an economical and expedien t alternative to field measuremen t, if the appropriate data can be found. The investigator faces two ma in challenges in doing so, however:

• How to expediently find and acquire lipid data fo r the species, tissue, region, waterbody type, etc. of interest, and

• How to evaluate the quality of the lipid content data.

EPA, other federal and state environmental agencies, and other organizations maintain large electronic databases of aquatic chemistry and ecosystem data that can be accessed via the Internet. These databases are probably the best available resource for lipid content data. Not only are they used as repositories and clearinghouses for aquatic environmental data for many aquatic organisms and ecosystems, but search and retrie val of specific data from these databases is generally straightforward.

The following is an inventory and descri ptions of Internet- accessible databases containing lipid content data for many ecosyst ems, waterbodies and locations in the United
States. The databases include:

• Environmental Monitoring and Assessment Program (EMAP)

• National Water Information System (NWIS)

• STORET (STOrage and RETrieval) and Legacy Data Center (LDC)5-85 • U.S. Army Corps of Engineer s BSAF and Lipid Database

• National Study of Chemical Residues in Lake Fish Tissue

Descriptions of each database, and how lipid content data can be accessed from each, are provided in Appendix 5A.

5.3.3.4 How Should Lipid Data be Evaluated?

Lipid content data acquired from the database s described above, or other sources, should be evaluated by the investigator in terms of thei r usability for establishing lipid contents to be used for recomputing site-specific BAFs. Table 5-10 provides a list of evaluation criteria for lipid data sources. The order of the criteria generally corresponds to their importance in the evaluation process. The thr ee top entries in Table 5-10 (species of interest, consumed tissue types and method of lipid analysis ) are considered to be essentia l information. Without these three criteria, lipid content data is essentially unusable because site-specific BA Fs are defined in terms of the consumption of specific organisms and/or tissues, and because the extraction method is a critical factor in lipid determ ination. Care should be taken to review the differences in the extraction method used to measure the lipid c ontent of a given species across studies. As discussed in Section 5.3.2.2, differen ces in the polarity of solvents used to extract lipids from
tissue can result in the extraction of different amounts of lipid. This can lead to variation in lipid contents and, consequently, in recomputed BAFs because of the solvent system used. It may be appropriate to exclude certain da ta for which differences in lipid contents are believed to be largely due to differences in extraction methods.5-86 Table 5-10. Evaluation Criteria for Lipid Data Sources

Evaluation Criteria for Lipid Data Sources: Species of interest Consumed tissue types for species of interest Method used for lipid analysis including tissue solvent QA (% recovery and relative standard deviation) Collection information (location and time) Sample factors influe ncing variability:

• Age

• Size (length and/or weight)

• Sex

• Compositing Presence of under-represented species (e.g., marine fish) Data quantity
Occurrence of extreme values

The next three cr iteria in Table 5-10 (QA statistics, collection location/time, and sample factors influencing variability ) are important because they provi de information the investigator
can use to understand the sources of variability in lipid data for the target species/tissues. Although the databases often c ontain considerable data for species of importance for commercial, sport, or sustenance fishing, the presence of under-represented species is included in the table because they contain few if any measurements for species that are not. Data quantity
and occurrence of extreme values are useful criteria for evaluati ng the representati veness of lipid
content data.

5.3.3.5 Determining fish lipid usin g the national default lipid data base

The investigator may also obtain lipid conten t data by selecting values from the database for fish lipid developed by EPA during the de velopment of the National BAF TSD Volume 2 (USEPA; 2003). Information on the lipid fracti on of aquatic organisms was obtained for the5-87 national database from a variety of primary a nd secondary sources. The following major sources of lipid data were used in the derivation of national default values of lipid fraction:

• EPA’s National Sediment Quality Survey database (USEPA, 2001a)

• EPA’s National Study of Chemical Residues in Fish (USEPA, 1992a)

• EPA’s Green Bay Mass Balance Study (USEPA, 1992b, 1995c),

• U.S. Department of Agriculture’s (US DA) Nutrient Data Bank (Exler, 1987)

• A review from National Marine Fisherie s Service of the National Oceanic and Atmospheric Administrati on (NOAA) (Sidwell, 1981)

• Two California databases (California Toxi c Substances Monitoring Program and Bay Protection and Toxic Cleanup Program)

When insufficient data were available from the above sources for certain species, targeted
literature searches were conducted and data fr om primary literature were used. The resulting National lipid database is tabul ated in Appendix 5D. The lipid data in this tabulation were carefully screened and reviewed to correct or remove erroneous en tries, extreme values of lipid content, and duplicate records.

5.3.4 How Can Site-Specific Organic Carb on Concentrations be Determined?

The concentrations of dissolved and partic ulate organic carbon (DOC and POC) are used to calculate the freely dissolved fraction of nonionic organic chemicals in water. The concentration of chemical that is freely disso lved is the best measure of the bioavailable concentration of nonionic organi c chemicals available for uptake by aquatic organisms. As discussed in Section 3.5.1, sorption of the ch emical to DOC and POC reduces chemical bioavailability to aquatic organisms. Bioavailabil ity and the freely dissolv ed fraction are reduced in waters containing higher concentrations of organic ca rbon. Therefore, DOC and POC concentrations are important factors for char acterizing potential human exposure to nonionic organic chemicals. Baseline BAFs are adjusted fo r the freely dissolved fraction of chemical in water according to the national BAF methodol ogy, and recalculating a site-specific BAF5-88 (Method 5, equation 5-17) involves multiplying the baseline BAF by the freely dissolved fraction. This section discusses how the investigat or can determine appropr iate concentration of
DOC and POC in the water column from a site. Th is can then be used to calculate the freely dissolved fraction, which is used in turn to re calculate a site-specific BAF from a baseline BAF (Method 5).

EPA recommends using local or regional data on the concentrations of DOC and POC in the site water column when recalculating baselin e BAFs. The use of such locally or regionally derived (i.e., site-specific) da ta is encouraged over nationally representative concentrations because local or regional conditions that aff ect DOC and POC concentrations can differ substantially from those represen ted by nationally derived values. Th ere is substantial variability
in the median values of DOC and POC concentr ations in U.S. surface waters USEPA, 2003a). This variability is believed to result from na turally occurring conditi ons and processes that contribute to spatial and temporal variability in the delivery and bioge ochemical cycling of organic carbon in surface waters . Some of these factors includ e climatology (e.g., arid, arctic, alpine, and tropical zonal differences) and troph ic status (e.g., oligotrophic, mesotrophic, and distrophic lakes), discharge vol ume and source (for streams and rivers), watershed size and landscape characteristics, season, and the extent of tidal influence (for estuaries). In addition, differences among analytical methods used to extract and measure lipids and associated analytical error can contribute to variability in reported values of lipid fraction. To address uncertainty in site-specific BAFs resulting fr om this natural variability in DOC and POC concentrations, EPA encourages States and authori zed Tribes to use approp riate local or regional data on the organic carbon content of applicable waters when a dopting criteria into their own water quality standards.

The following sections review the process for calculating the fraction of chemical that is freely dissolved, and offer guida nce to the investigator rega rding options for determining appropriate organic carbon (DOC and POC) concentrations for site-specific BAF recalculation.5-89 5.3.4.1 Overview of freely di ssolved normalization process

The freely dissolved fraction of nonionic orga nic chemicals is calcu lated using equation 3-12 of the 2000 Human Health Methodology (USE PA, 2000a), which requires DOC and POC concentrations appropriate for the site a nd the octanol-water pa rtition coefficient (K ow ) for the
chemical of concern:

ο w ο w 

### f = 1 / (1 + POC DOC

fd 

### ⋅ Κ + 0.08 ⋅ ⋅ Κ ) (Equation 3-6)

Figure 5-12 illustrates the effect of varying concentrations of DOC and POC on the freely dissolved fraction calculated using equation 3-6 as a function of K ow . As this figure illustrates, the calculated freely dissolved fr action is sensitive to organic carbon concentrations for a wide range of chemical hydrophobicities (i.e., logK ow from 4 to 8.5). For chemicals with logK ow > 6.6, the freely dissolved fraction varies by more than an order of magnitude for organic carbon levels
within the 10 th to 90 th percentile range. The site-sp ecific BAF recalculated by Method 5 (equation 5-15) will change in proportion to th e variation in the fr eely dissolved fraction. Therefore, in order to recalcu late an accurate site-specific BAF, it is important for the investigator to determine organic carbon concentr ations that are repres entative of the site.5-90 FIGURE 5-12 . Illustration of how the freely dissolve d fraction calculated using equation 3-12 varies as a function of K ow , for varying concentrations of DOC and POC. Organic carbon concentrations were based on na tionally representative data for all waterbody types summarized in Table 5-16. National median values were 2.9 mg/L DOC and 0.5 mg/L POC; 10% values were 1.2 mg/L DOC and 0 POC; 90% values we re 9.7 mg/L DOC and 2.3 mg/L POC.

5.3.4.2 Measuring DOC and POC

The investigator may choose to measure organic carbon concentra tions in the water column at the site. This would be the preferred approach if site-specifi c DOC and POC data were not otherwise available. In genera l, guidance offered in Section 3.5 ( Measuring Chemical
Concentrations in Water ) is applicable for designing a field study (e.g., sample numbers, frequency of collection, location, etc.) to measure organic carbon c oncentrations. Concentrations of DOC and POC in a body of water are expected to vary over time as a function of precipitation events, season, hydrodynamics, and numerous other attributes of a watershed. Thus, sufficient sampling of DOC and POC concentrations over space and time is needed to achieve 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

3456789 log Kowfreely dissolved chemical fraction (ffd) national median POC and DOC

10% organic carbon

90% organic carbon5-91 representative estimates for calculating freely di ssolved chemical fractions . The sampling and averaging of DOC and POC concentrations shou ld follow the guidance for field studies to measure chemical concentrations in water (S ection 3.5 ) as well as the guidance in Section 3.3.3.4 regarding how optimization of water sampling depends on the target chemical’s hydrophobicity. This is especia lly important for highly hydro phobic chemicals because the impact of DOC and POC on the site-specific BAF r ecalculation is greatest with these chemicals. The guidance offered in this sect ion specifically relates to the se paration of organic carbon into
DOC and POC fractions and the selection of an alytical methods for m easuring organic carbon.

The separation of POC from DOC in water samples is operationally defined by filtering or centrifugation. With both techniques, the distinction between POC and DOC is usually defined in terms of a particle size cutoff, which can differ de pending upon membrane selection and hardware. For example, a membrane with a 0.45- F m cutoff may be used in one study, whereas centrifugation that retains all particles with a size of 1.0 F m or greater may be used in another study. Typically, the pa rticle size cutoff between POC and DOC fractions is 0.1-1 F m. DOC is principally composed of carbohydrates , carboxylic acids, amino acids, hydrocarbons, hydrophilic acids, and humic and fulvic acids. PO C is principally composed of some larger humic acids, microbes, small pla nkton, plant litter, and ligneou s matter (Suffet et al. 1994; Thurman, 1985). The material retained by filtrat ion or centrifugation is the POC fraction. The
DOC fraction is defined as the ambient water remaining after filtration or centrifugation is performed. Total organic carbon (TOC) is the sum of the POC and DOC fractions.

Two methods are commonly used to measur e dissolved and particulate organic carbon concentrations. The first (and preferred) met hod is to perform organic carbon analyses on the DOC and POC fractions of the same samples. This method should be chosen unless the concentration of organic carbon is too low to quantify in one of the fractions (typically this would be the POC fraction, because POC concentra tions are usually lower than DOC in surface waters). If organic carbon cannot be quantified in the POC fraction, the investigator can have the organic carbon analysis performe d on the TOC (i.e., whole water) and DOC fractions of the samples. In this method, POC concentrations ar e determined by the difference between TOC and5-92 DOC (i.e., POC = TOC - DOC). The disadvantage of calculating POC by difference is that the result will be less precise than the analytical methods.

Several different methods are available for analyzing organic carbon in water samples. The persulfate-ultravio let oxidation method ( Standard Methods #5310C) is generall y preferred to the wet oxidation method ( Standard Methods #5310D), because the fo rmer method is more sensitive and has a significan tly lower detection limit. EPA’ s approved method for organic carbon analysis in water samples (EPA SW-846 Method 9060A) allows for either of these
methods to be used. The errors associated with different analytical methods for organic carbon appear to be small, relative to other sources of variability and uncertain ty in DOC and POC data (USEPA, 2003b).

5.3.4.3 Determining DOC and POC usin g a literature or database search

Scientific publications, repor ts, and online databases also co ntain data for organic carbon concentrations measured in many ecosystems. As was the case for lipid content, determining
DOC and POC concentrations in this manner may be an economical and expedient alternative to field measurement, if the appropriate data can be found. Large electronic databases of aquatic chemistry and ecosystem data that can be acc essed via the Internet are probably the best available resource for organic carbon data. Not only are they used as repositories and clearinghouses for aquatic environmental data fo r many aquatic organisms and ecosystems, but search and retrieval of specific data from th ese databases is generally straightforward.

The following is an inventory and descri ptions of Internet- accessible databases containing organic carbon data for many ecosystems, waterbodies and locations in the United
States. The databases include:

• Environmental Monitoring and Assessment Program (EMAP)

• STORET (STOrage and RETrieval) a nd Legacy Data Center (LDC) and

• National Water Information System (NWIS)5-93 Descriptions of each database, and how DOC a nd POC data can be accessed from each, are provided in Appendix 5B.

5.3.4.4 How should organic carbon data be evaluated?

Organic carbon data acquired from the data bases described above, or other sources, should be evaluated by the investigator in terms of their usability for establishing DOC and POC values to be used for calculating freely disso lved chemical fractions and subsequently recomputing site-specific BAFs. Table 5-11 provide s a list of evaluation criteria for organic carbon data sources. Because a “site” may be vari ously defined as a particular waterbody or segment, a type of waterbody within a state or re gion, or even all waterbodies within a state or region, this guidance should be flex ible in order to properly addre ss each situati on. The order of the criteria generally corresponds to their importance in the eval uation process. The three top
entries in Table 5-11 (waterbody types of inte rest, QA/QC information, and sample factors influencing variability ) are considered to be essential information. Organic carbon data must be available for the site waterbody type(s) in orde r to be usable. QA/QC and sampling data should also be available for the investigator to evalua te the validity and representativeness of the data.5-94 Table 5-11. Evaluation Criteria for Organic Carbon Data Sources

Evaluation Criteria for Organic Carbon Data Sources:
Waterbody type(s) of interest
QA/QC information:

• analytical methods (including whethe r POC was determined by difference; i.e., TOC - DOC)

• detection limits

• % recovery

• relative standard deviation (RSD)
Sample factors influe ncing variability:

• watershed ecoregion, size and land use

• waterbody type and trophic status

• spatial/temporal representativeness and bias

• station type (“ambient” only)

• hydrograph

• tidal influence Data quantity Sampling period (collected since 1980)
Occurrence of extreme values

For example, station types should be restri cted to “ambient” sampling stations only, to exclude so-called specialty stations (i.e., those st ations designated for sp ecial purposes such as storm water runoff and biological and sedime nt monitoring). Wh en POC and/or DOC concentrations are reported to be below analytic al detection levels, it may be appropriate to
estimate the concentration value as half of the re ported detection level. However, the investigator should consider discarding censored data with “h igh” detection levels (i.e., >1.0 mg/L for DOC and >0.2 mg/L for POC) because of the greate r uncertainty involved in estimating definitive
values of DOC and POC in these situations. Finally, in cases where the parameter of interest (POC or DOC) must be calculat ed as the difference from two other measurements (i.e., POC = TOC – DOC; DOC = TOC – POC), the calculation should only be performed using data from the same sample to avoid introducing error, and the results should be screened to remove negative organic car bon concentrations.5-95 Data quantity and occurrence of extreme values are useful criteria in terms of evaluating the representativeness of organic carbon data. Sampling period is also included as a factor in Table 5-11, as it may be appropriate to eliminat e data collected prior to implementation of secondary wastewater treatment (e.g. 1980) because of the greater uncertainty in using presecondary treatment era data to represent presen t-day conditions that can affect organic carbon
concentrations in surface waters.

5.3.4.5 Determining organic carbon concen trations using the National DOC/POC Database

The investigator may also determine DOC and POC concentrations by selecting values from the database for surface water organic car bon developed by EPA during the development of
the National BAF TSD Volume 2 (USEPA; 2003). Information on or ganic carbon concentrations representative of different types of surface wate rs was obtained for the national database from a variety of primary and secondary sources. Data on the concentrations of DOC and POC in U.S.
surface waters were obtained from two databases:

• The U.S. Geological Survey’s (USGS) WATSTORE database

• EPA’s historical STORET database (recently renamed the Legacy Data Center [LDC] database)

Numerous steps were then taken to process and screen the DOC and POC data so that only the most appropriate data would be retained for calculating the national default values (USEPA, 2003). The screening steps included: deletion of suspect or uncertain va lues; restriction to samples collected in the following waterbody types: estuaries, lakes, re servoirs, and streams (including rivers); elimination of pre-1980 data ; and, removal of extreme values based on the criteria of Thurman (1985).

Table 5-12 shows descriptive statistics su rrounding the median values for DOC and POC, in addition to values for specific waterbody types. It is evident from Table 5-12 that the variation in DOC and POC concentrations is relatively large. For example, with the exception of estuaries,5-96 the coefficient of variations around the means are all above 100% and approach or equal 200% in
some cases. Ratios of the 95 th to the 5 th percentiles range from a f actor of 5 to 30, depending on waterbody type and parameter. This variation is not unexpected, given the high degree of temporal and spatial heterogeneity represented in the database. It is also appa rent that the type of waterbody (lake, stream, estuary) has some impact on the DOC and POC distributions. For example, median values of DOC and POC from sa mples designated as “stream/river” are nearly twice those designated as “lakes.” This differenc e is probably related to the differing hydrologic, biogeochemical, and watershed characteristics of streams and lakes. Given the relatively high degree of variation that is evid ent in DOC and POC concentrati ons in surface waters across the United States, EPA recommends that States and Trib es consider deriving appropriate values of DOC and POC by using local or regional data (as described in the previous two sections) when sufficient data are available. If local or regi onal values cannot be derived, then it may be appropriate to use cons ervative (i.e., 90 or 95 th percentile) values for DOC and POC concentrations listed in Table 5-12, when calcula ting the freely dissolved chemical fraction for site-specific BAF recalculation.5-97 Table 5-12. National Default Values for PO C and DOC in U.S. Fresh and Estuarine Surface Waters

DOC (mg/L) POC (mg/L) Statistic All Types Stream/ River Lake/ Reservoir Estuary All Types Stream/ River Lake/ Reservoir Estuary

Median 2.9 3.8 2.1 2.7 0.5 0.6 0.3 0.9

Mean 4.6 5.6 2.9 3.4 1.0 1.3 0.5 1.2

Std. 5.1 5.9 3.0 2.6 2.0 2.5 1.0 1.8
CV 111% 105% 103% 76% 200% 192% 200% 150%

n 111,059 69,589 25,704 15,766 86,540 48,238 23,483 14,819
5 th 0.8 0.7 1.0 1.7 0a 0a 0.08 0.1
10 th 1.2 1.0 1.4 2.0 0 0a 0.1 0.3
25 th 2.0 2.1 1.8 2.3 0.2 0.2 0.2 0.5
75 th 5.4 6.9 2.6 3.2 1.1 1.4 0.5 1.4
90 th 9.7 11.6 5.0 5.0 2.3 3.1 0.8 2.2
95 th 14 16.5 7.8 9 3.9 5 1.3 3
95 th /5 th 17.5 23.6 7.8 5.3 — — 16.3 30.0

a Values calculated to be less than zero because of measurement error Source: U.S. EPA LDC and USGS WATSTORE da tabases. Data retrieval: January 20005-98 REFERENCES

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Linkov, I., Ames, M.R., Crouch, E.A.C. and F. Satterstrom. 2005. Uncertainty in Octanol-Water Partition Coefficient: Implications for Risk Assessment and Remedial Costs . Environ. Sci. Technol., 39 (18), 6917-6922.
Lodge, K., P.M. Cook, D.R. Marklund, S.W. Kohlbry, J. Libal, C. Harper, B.C. Butterworth and A.G. Kizlauskas. 1994. Accumulation of polyc hlorinated dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs) in sediments and fi shes of Lake Ontario. In preparation.
Mackay, D. 1982. Correlation of bioconcentration factors. Environ Sci Technol 16:274-278.
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.
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## Appendix 5A
Internet-Accessible Databases Co ntaining Lipid Content Data

EMAP

EMAP is an EPA research program to devel op tools to monitor and assess the status and trends of national ecological resources. EMAP aims to advance the science of ecological monitoring and ecological risk assessment, guide national monitoring with improved scientific understanding of ecosystem integrity and dynami cs, and demonstrate multi-agency monitoring through large regional projects. EMAP data are organized accord ing to these projects, which include 5 major surface water projects and a number of smaller regional EMAP (REMAP) projects. EMAP data are accessible via the EPA web site, at http://www.epa.gov/emap.

ACCESSING EMAP LI PID CONTENT DATA 1. Access the EMAP web site on the internet at http://www.epa.gov/emap/ 2. Click on DATA 3. Click on the EMAP data directory (http://osapub.epa.gov/emap/emap.search )

Although the Data Set Search Engine accesse d at this stage lists “% LIPIDS” as a keyword, making this selection returns lip id data from only one project, the 1993-1994 REMAP Region 1 Fish Tissue Organic Concen trations by Composite data set . A better approach is to first select a regional project, select view data for the appropriate dataset of that project [e.g., “Tissue Data (Organics)”], and then browse to see if ther e are any lipid data ava ilable. This can be done by searching for the “LIPID” parameter in the “V ariables” list that appears at the top of each dataset output. This output is formatted as co mma-separated value (CSV) text, which can be saved from a web browser and then opened in a sp readsheet. At the time of this report, there seems to be no other good, direct way to search EM AP for lipid content data with any reliability or certainty of completeness. This is unfort unate, because EMAP data sets have the most complete supporting information of any of the on line databases. Many of the EMAP data sets5-104 have an accompanying documentation and referen ces that provide information about the study design, sampling procedures, analy tical methods, and QA procedures.

NWIS

NWIS is a database system (48 separate NWIS databases nationwide) maintained by the US Geological Survey to provide access to current and historical water-resources data collected by the Survey at approximately 1.5 million sites in all 50 States, the District of Columbia, and Puerto Rico. The NWISWeb web site can be used to search for and retrieve NWIS data by category (e.g., surface water, ground water, or water quality) and by geographic area. The NWISWeb is accessed at http: //waterdata.usgs.gov/nwis/qw

.

ACCESSING NWISWEB LLIPID CONTENT DATA 1. Access NWISWeb on the internet at http://waterdata.usgs.gov/nwis/qw 2. Click on “Samples” 3. Select the type of geographi c area you wish to search (s tate, hydrologic region (HUC) or latitude-longitude box) 4. Select “Sample medium type” and “Param eter groupings” under “Data Attribute” 5. Select “Geographic region” desired in first list 6. Select [C] “Animal tissue” in second list 7. Select “Organics” in third list 8. Under “Output Format,” choose a Tab-separated data file 9. Press “Submit” 10. Save output as a text file and open as a tab-separated text file in spreadsheet 11. Sort the data by lipid code (49289: % lipids in whole organism, or 63595: % lipids in tissue)

NWISWeb contains a significant amount of lipid content data collect ed by the USGS. The data can be searched by geogra phic area and by parameter group (lip ids are found in the organics parameter group), but not by a specific parameter. Once the search is performed as described in the text box above, all the available lipid content data for the particular geographic area will be
obtained.

There is limited supporting data available online in the NWISWeb database. Field and laboratory protocols, changes in those protocols, and other QA /QC information are documented5-105 in numerous reports and technical memoranda. Th ese range from project-specific reports to national protocols. Specific anal ytic procedures or codes are not identified for the lipid content data in the NWISWeb database. The lack of sup porting information, combin ed with the difficulty of searching for lipid content as a specific para meter (versus manually searching within the data for the entire organics parameter group) limits the utility of this database.

STORET and LDC

EPA maintains two data management systems containing water quality information for the nation's waters: the Legacy Data Center (LDC), and STORET. The LDC is a static, archived database and STORET is an ope rational system actively being popul ated with water quality data. The LDC contains historical water quality data da ting back to the early pa rt of the 20th century and collected up to the end of 1998. STORET c ontains data collected beginning in 1999, along
with older data that has been properly documented and migrated from the LDC. Both systems contain raw biological, chemi cal, and physical data on surf ace and ground water collected by federal, state and local agencies, Indian Tribes , volunteer groups, academics, and others. All 50 States, territories, and jurisd ictions of the U.S. are represented in these systems.

Each sampling result in the LDC and in STORET is accompanied by information about where the sample was taken (latitude, longitude, state, county, Hydrologic Unit Code and a brief site identification), when the sample was colle cted, the medium sampled (e.g., water, sediment, fish tissue), the monitoring organization, and the sampling and analytical methods used. Both the
LDC and STORET are accessible via the Intern et at http://www.epa.gov/storet/dbtop.html

. The following are detailed instructions for accessing lipid data from the LDC:

ACCESSING THE LDC FOR LIPID CONTENT DATA
1. Select “Browse” or “Download Legacy STORET Data” 2. Select the link for the “Advanced Query Form” 3. Under “Station type,” check the “Surface water” box 4. Select either the organization or the de sired geographic location by Latitude/Longitude, State/County (only one county can be selected at a time), or HUC. 5. Enter sampling data range, if desired (optional) 6. Enter the desired parameter codes (these can be looked up using the “Search by name”5-106 box): 49289 for lipids 7. Click the “Done” butt on under “Submit Form” 8. Select a detailed report in HTML format and press “Continue”

The investigator can also make advanced searches of geographic locations as well as specific parameters in the LDC. If data from more than 50 stations are retrieved from within the search results, the information is emailed to the investigator and cannot be viewed immediately. In this case, a link that can be accessed to download th e data is emailed in approximately 24 hours (extremely large files may take slightly longer).

To access lipid data from STORET, the inve stigator can follow these instructions:

ACCESSING STORET FOR LIPID CONTENT DATA 1. Select “Browse” or “Downloa d Modernized STORET Data” 2. Under “STORET Biological Results,” sele ct the link for “Bio logical results by geographic location” 3. Select the desired geographic location by State and County, Latitude/Longitude box, HUC, etc; (the entire data set can also be selected) 4. Select a sampling date range , if desired (optional) 5. Select “Fish/Nekton” under the “Community Sampled” box 6. In the “Characteristic” s earch box, enter “Lipid” and click the search button 7. In the pop-up window which appears, click on “Lipid (unspecified mix)” and then click the “Select” button 8. This selection will be placed into the “Characteristic name” box on the search page; press “Continue” 9. The “Results Search Summary” shows th e number of data that were found 10. To download the results, pre ss “Continue” and after the search engine has completed the search (which may take as number of minutes for large data sets), click the appropriate link to download the data 11. After the download is completed, the data will be displayed in a text format 12. Save the file as a “Text File” 13. Open the file in a spreadsheet program (suc h as Microsoft Excel) as a “Text File;” you will need to indicate that this is a file delimited using the “~” character (in Excel, click on the box for “Other” under delimiters and type the ~ symbol,, which is found on the keyboard over the accent symbol, next to number 1, then click “Finish”) 14. If the retrieval succeeded, a new spreadsheet should be opened with your data. Some headings may be slightly off and some “c hunks” of data may be displaced, so review and editing of the data may be necessary.5-107 There are a number of differences be tween the LDC and STORET that should be considered by the investigator searching for lipid content data. LDC contains a huge quantity of data reported prior to 1999. Howeve r, the quality of some of this information is suspect and the supporting information may be incomplete or missi ng. STORET is a superior database in terms of data access, quality, and completeness of supporting information. STORET provides documentation of data quality, in the form of re ports, which describe the standards, methods, practices, and other metadata s upplied by data owners to documen t the quality of the monitoring results found in STORET. At this time, it holds significantly less data (as of January 2006, the lipid retrieval from STORET documented above returned 959 results), reported since 1999.

U.S. Army Corps of Engineers BSAF and Lipid Database

The Biota-Sediment Accumulation Factor Database is an internet-accessible database (http://el.erdc.usace.army.mil/bsaf/BSAF.html) maintained by the Engineer Research and Development Center (ERDC) of th e U.S. Army Corps of Engineers. This database contains both BSAF and organism lipid content data, primarily obt ained from peer reviewed journal articles. All data are documented to the original reference, and include information about the tissue sampled, the number of measurements, and any available erro r statistics. The BSAF da tabase contains lipid data for over 300 aquatic species and other groups, which can be selected via pull-down menus
and/or viewed in tables. The BSAF database web site also provides simple instructions for downloading and transferring data in to a spreadsheet program.

EPA's Office of Water Nati onal Lake Fish Tissue Study

The National Study of Chemical Re sidues in Lake Fish Tissue was a screening-level study designed to estimate the national di stribution of selected persiste nt, bioaccumulative, and toxic (PBT) chemicals in fish tissue from lakes and re servoirs of the continental United States. The study involves the collection of predator and bo ttom-dwelling fish from 500 randomly selected
lakes and reservoirs of the con tinental United States (excluding the Great Lakes) over a period of four years (~125 lakes per year), commencing in 1999 and 2000. The selection of target fish5-108 species followed EPA guidance (USEPA, 1995b). Samples were edible tissue (skin-on fillet) composites of targeted predator species and to tal body tissue (whole fish) composites of targeted bottom-dwelling species

.

Lipid contents were determined for all sa mples using the procedure described in EPA Methods 1613B and 1668A, the same procedure used in EPA’s National Dioxin Study. Consistent field and laboratory QA proce dures were followed througho ut the study, and are well documented. Although EPA maintains a Web site for this project (http://www.epa.gov/wate rscience/fishstudy/), the data are currently be ing released on CDs that contain the results of quality-assured raw data in large spreadsheet files. The CDs can be ordered by contacting the National Lake Fish Tissue Study Manager , whose contact information is provided on the study web site.5-109 

## Appendix 5B Internet-Accessible Databases Containing Organic Carbon Data

EMAP

EMAP data are accessible via the EP A web site, at http://www.epa.gov/emap . As discussed in Appendix 5A, the preferred approach to access this database is to first select a regional project, select view data for the appropriate dataset of th at project (i.e., Water Chemistry Data”), and then browse to see if there are any organic carbon data available. This can be done by searching for the “ORGANIC CARBON” parameter in the “Variables” list that appears at the top of each dataset output. Following this approach, DOC data were found in the following regional projects and datasets:

• 1991-1994 Northeast Lakes, Water Chemistry Data Summarized by Lake

• 1993-1994 Region 1, Lake Dissolved Organic Carbon Data Set

• 1993-1996 Mid-Atlantic Streams, Water Chemistry

• 1994-1995 Region 10, Validated Water Chemistry Data

• 1997-1998 MAIA Streams, Validated Water Chemistry

No data were found for POC or TOC concen trations in the EMAP project datasets.

STORET and LDC

Both the LDC and STORET are acc essible via the Internet at http://www.epa.gov/storet/dbtop.html . To access DOC and POC data from the LDC, the investigator should follow the following instructions:5-110 ACCESSING THE LDC FOR ORG ANIC CARBON CONTENT DATA 1. Select “Browse” or “Download Legacy STORET Data” 2. Select the link for the “Advanced Query Form” 3. Under “Station type,” check the “Surface Water” box 4. Select either the orga nization or the desired geographic location by Latitude/Longitude, State/County (only one coun ty can be selected at as time), or HUC 5. Enter sampling date range, if desired (optional) 6. Enter the desired parameter codes (these can be looked up using the “Search by name” box): 00680 and 00690 for TOC, 00681 and 00684 for DOC, 00689 and 80102 for POC 7. Click the “Done” butt on under “Submit Form” 8. Select a detailed data report in HTML format and press “Continue”

The LDC contains a large quant ity of data reported prior to 1999, however the quality of some of this information is suspect, and th e supporting information may be incomplete or missing. For the LDC data, the analytical methods used to determine DOC and POC concentrations were not reported in the database. As EPA notes on the STORET web site, all data owned by Agency "112WRD" (USGS) have been removed from the LDC. This resolves a problem of duplicate data appearing in both EPA (LDC) and USGS (NWIS) databases that previously confronted users of both systems.

To access organic carbon data from STORET, th e investigator can follow these instructions:5-111 ACCESSING STORET FOR ORGANIC CARBON DATA 1. Select “Browse” or “Downloa d Modernized STORET Data” 2. Under “STORET Regular Results,” select th e link for “Regular re sults by geographic location” 3. Select the desired geographic location by State and County, Latitude/Longitude box, HUC, etc; (the entire data set can also be selected) 4. Select a sampling date range , if desired (optional) 5. Select “Water” under th e “Activity Medium” box 6. In the “Characteris tic” search box, enter “Carbon” and click the “Search” button 7. In the pop-up window which appears, clic k on “Carbon, organic” and then click the “Select” button 8. This selection will be placed into the “Characteristic” name box on the search page; press “Continue” 9. The “Results Search Summary” shows th e number of data that were found 10. To download the results, pre ss “Continue” and after the search engine has completed the search (which may take a numb er of minutes for large data sets), click the appropriate link to download the data 11. After the download is completed, the data will be displayed in a “Text” format 12. Save the file as a “Text” file 13. Open the file in a spreadsheet program (suc h as Microsoft Excel) as a “Text” file; you will need to indicate that this is a file delimited using the “~” character (in Excel, click on the box for “Other” under delimiters and type the “~” symbol, which is found on the keyboard over the accent symbol, next to number 1, then click “Finish”) 14. If the retrieval succeeded, a new spreadsheet should be opened with your data. Some headings may be slightly off and some “c hunks” of data may be displaced, so review and editing of the data may be necessary.

STORET also holds a significant amount of organic carbon data. As of January 2006, the organic carbon retrieval from STORET docum ented above returned 44,962 results for data reported since 1999. As previously noted, STORET provides documentation of data quality, in the form of reports which document the standa rds, methods, practices, and other metadata supplied by data owners to document the quality of the monitoring results found in STORET. A
PDF file that details th e analytical remark codes found in the “Analytical Proc. ID” output can be found at http://www.epa.gov/storet/modern /doc/FieldLabAnltPrcdAndEqpDetail.pdf

.5-112 NWIS

NWIS also contains a large amount of organi c carbon data for surface waters in the United
States. The NWISWeb is accessed at http://waterdata.usgs.gov/nwis/q w. Details regarding access of NWIS to obtain organic carbon data are provided below:

ACCESSING NWIS WEB ORGANIC CARBON DATA
1. Access NWISWeb on the internet at: http://waterdata.usgs.gov/nwis/qw 2. Click on “Samples” 3. Select the type of geographi c area you wish to search (s tate, hydrologic region (HUC), or latitude-longitude box) 4. Select “Sample Medium Type” and “Param eter Groupings” under “Data Attribute” 5. Select “Geographic Region” desired in first list 6. Select [9] “Surface water” in second list 7. Select “Major Inorga nics” in third list 8. Under “Output Format,” choose a tab-separated data file 9. Press “Submit” 10. Save output as a “Text” file and open as a tab-separated text file in spreadsheet 11. Sort the data by “Organic Carbon” codes:

• 00689 – Organic carbon, suspended sediment, total, milligrams per liter

• 00681 – Organic carbon, water, filtered, milligrams per liter

• 00680 – Organic carbon, water, unfiltered, milligrams per liter

For NWIS data, estimates of accuracy (perce nt recovery) and precision (relative standard deviation) are available for the analysis of TO C and POC. In general, however, the lack of supporting information, combined with the difficulty of searching for organi c carbon as a specific parameter (versus manually searching within the da ta for the entire “major inorganics” parameter group) limits the utility of this database.5-113 

## Appendix 5C BSAFs for PCB congeners in Gree n Bay and Upper Hudson River
Table 5C-1. BSAFs for PCB congeners based on me asurements made in Green Bay, Lake Michigan. Average, coefficient of variation (CV), minimum and maximum BSAFs for each congener across all zones, age cla sses and fish species are tabulated.

PCB CONGENER LOG K OW AVERAGE BSAF BSAF CV MINIMUM BSAF MAXIMUM BSAF PCB 5+8 5.02 0.264 1.16 0.0536 1.17

PCB 6 5.06 0.405 0.489 0.140 0.728 PCB 7 5.07 0.362 0.294 0.231 0.500 PCB 16+32 5.30 0.767 0.587 0.189 1.98

PCB 17 5.25 3.11 1.55 0.274 16.0 PCB 18 5.24 1.91 1.07 0.171 8.41 PCB 19 5.02 0.66 0.347 0.303 1.06 PCB 22 5.58 1.04 1.23 0.148 4.66 PCB 24+27 5.40 2.34 1.13 0.362 9.79

PCB 25 5.67 1.54 0.949 0.179 5.59 PCB 26 5.66 1.78 1.01 0.204 7.40 PCB 28+31 5.67 1.56 0.705 0.229 4.15

PCB 29 5.60 0.224 0.000 0.224 0.224 PCB 33 5.60 0.686 0.937 0.108 2.39 PCB 37+42 5.80 0.876 0.487 0.318 1.44

PCB 40 5.66 1.66 0.598 0.618 4.05 PCB 41+64+71 5.87 1.90 0.585 0.541 4.32 PCB 43 5.75 7.25 0.508 2.84 15.9 PCB 45 5.53 2.84 0.655 0.597 7.47 PCB 46 5.53 1.03 0.614 0.398 3.05 PCB 47+48 5.82 10.3 1.08 0.544 36.0

PCB 49 5.85 5.60 1.17 0.777 27.9 PCB 52 5.84 7.65 1.12 1.02 34.53 PCB 53 5.62 2.39 0.404 0.976 4.18 PCB 56+60 6.11 2.00 0.867 0.288 8.62

PCB 63 6.17 4.12 0.614 1.09 9.10 PCB 66+95 6.17 4.72 0.719 0.469 16.3 PCB 70+76 6.17 2.05 1.04 0.257 10.7

PCB 74 6.20 3.63 0.816 0.614 14.1 PCB 77+110 6.42 5.05 0.889 0.740 21.6 PCB 81 6.36 11.4 0.474 3.68 28.3 PCB 82 6.20 6.09 0.987 0.806 25.5 PCB 83 6.26 8.98 0.647 2.47 28.3 PCB 84+92 6.20 4.77 0.898 1.02 16.5

PCB 85 6.30 7.38 0.710 1.03 25.8 PCB 87 6.29 7.62 0.885 0.907 30.0 PCB 89 6.07 4.79 0.560 1.97 9.09 PCB 91 6.13 6.78 0.747 1.16 19.9 PCB 97 6.29 7.20 0.760 0.966 24.2 PCB 99 6.39 6.12 0.623 1.45 18.1 PCB 100 6.23 1.58 0.487 0.563 2.615-114 Table 5C-1 (continued). BSAFs for PCB congen ers based on measurements made in Green Bay, Lake Michigan. Average, coefficient of variation (CV), minimum and maximum BSAFs for each congener across all zones, age cl asses and fish species are tabulated.

PCB CONGENER LOG K OW AVERAGE BSAF BSAF CV MINIMUM BSAF MAXIMUM BSAF PCB 101 6.38 8.39 0.623 1.18 23.0 PCB 105+132+153 6.72 6.06 0.828 0.913 25.3 PCB 107 6.71 7.12 0.783 1.62 25.6 PCB 114+134 6.60 156 1.10 7.94 497 PCB 118 6.74 5.74 0.808 0.772 20.0 PCB 119 6.58 2.89 0.546 0.398 7.17 PCB 124+135+144+147 6.67 6.04 0.833 1.14 17.8 PCB 128 6.74 9.29 0.546 2.92 22.2 PCB 129+178 6.94 5.56 0.742 0.970 18.9 PCB 130 6.80 11.2 0.750 1.62 31.3 PCB 131 6.58 1.63 0.366 0.970 2.53 PCB 136 6.22 6.65 1.63 0.199 19.1 PCB 138+158+163 6.95 11.07 0.765 1.19 40.84

PCB 141 (+179) 6.78 12.19 0.860 1.13 42.94 PCB 146 6.89 8.76 1.13 0.236 32.59 PCB 149 6.67 8.68 0.971 0.889 38.15 PCB 151 6.64 7.11 0.596 2.25 18.13 PCB 156+171+202 7.18 26.34 0.501 9.94 48.12 PCB 167 7.27 13.41 0.697 3.06 40.58 PCB 170+190 7.37 5.78 1.03 0.95 22.41 PCB 172+197 7.32 23.03 0.671 4.861 64.51 PCB 174 7.11 6.30 0.867 1.09 25.9 PCB 175 7.17 6.06 0.652 1.91 18.7 PCB 177 7.08 9.05 0.864 1.50 26.7 PCB 180 7.36 11.85 1.05 0.722 50.0 PCB 182+187 7.19 7.36 0.931 0.635 27.5 PCB 183 7.20 7.22 0.812 1.56 25.1 PCB 189 7.71 8.45 0.280 5.66 14.2 PCB 191 7.55 34.93 0.953 5.23 127 PCB 193 7.52 13.98 0.928 1.83 49.2 PCB 194 7.80 2.60 0.620 0.428 6.97 PCB 195+208 7.64 1.01 0.991 0.0626 3.86 PCB 196+203 7.65 8.70 0.981 1.65 33.2 PCB 198 7.62 0.35 0.676 0.196 0.876 PCB 201 7.62 6.05 0.651 2.21 14.8 PCB 200 7.27 6.50 0.937 1.15 25.0 PCB 206 8.09 1.44 1.08 0.178 5.98 PCB 207 7.74 0.68 0.672 0.117 1.70 PCB 209 8.18 0.31 1.10 0.0788 1.25 AVERAGE (ALL

CONGENERS) 7.85 0.790 1.28 25.45-115 Table 5C-2. BSAFs for PCB congeners based on measurements made in the Hudson River. Average, coefficient of variation (CV), minimum and maximum BSAFs for each congener across all zones, age cl asses and fish species are tabulated.

PCB CONGENER LOG K OW AVERAGE BSAF BSAF CV MINIMUM BSAF MAXIMUM BSAF PCB 1 4.46 0.149 0.774 0.0594 0.406 PCB 3 4.69 0.0340 0 0.0340 0.0340 PCB 4 4.65 0.407 0.933 0.0324 1.19 PCB 6 5.06 0.144 0.977 0.00668 0.360 PCB 8 5.07 0.250 1.02 0.00851 0.733 PCB 9 5.06 0.121 1.02 0.0173 0.296 PCB 10 4.84 0.655 1.19 0.0471 3.15 PCB 15 5.30 0.0882 0.956 0.00314 0.220 PCB 16 5.16 0.570 0.734 0.0769 1.30 PCB 17 5.25 1.85 0.274 1.47 2.57 PCB 18 5.24 1.19 0.876 0.123 3.75 PCB 19 5.02 0.877 0.810 0.0480 2.14 PCB 20 5.57 3.61 0.177 3.09 4.32 PCB 22 5.58 1.53 0.552 0.496 3.12 PCB 25 5.67 1.08 0.733 0.0952 2.73 PCB 26 5.66 1.62 0.713 0.124 4.10 PCB 27 5.44 1.58 0.420 0.924 2.16 PCB 28 5.67 2.34 0.586 0.918 5.81 PCB 31 5.67 1.71 0.729 0.171 4.34 PCB 32 5.44 1.43 0.963 0.0409 5.38 PCB 33 5.60 2.46 0.254 1.91 3.15 PCB 34 5.66 1.17 1.03 0.0706 4.21 PCB 37 5.83 0.469 0.867 0.0870 1.31 PCB 40 5.66 1.82 0.623 0.172 3.60 PCB 41 5.69 1.40 1.01 0.247 2.98 PCB 42 5.76 5.01 0.198 4.12 6.19 PCB 44 5.75 4.63 0.521 1.75 11.5 PCB 45 5.53 3.07 0.139 2.59 3.50 PCB 47 5.85 3.99 0.778 0.157 12.1 PCB 48 5.78 4.05 0.448 1.86 6.65 PCB 49 5.85 5.30 0.747 0.361 16.0 PCB 51 5.63 2.95 0.942 0.0635 10.7 PCB 52 5.84 5.24 0.725 0.468 15.2 PCB 53 5.62 1.82 0.492 0.370 2.88 PCB 56 6.11 4.09 0.574 1.24 9.93 PCB 60 6.11 4.99 0.689 1.30 13.2 PCB 63 6.17 6.01 0.356 3.08 8.95 PCB 64 5.95 5.83 0.884 0.835 21.5 PCB 66 6.20 6.67 0.508 2.67 14.9 PCB 67 6.20 63.6 0.584 12.4 119 PCB 70 6.20 6.54 0.564 2.53 15.4 PCB 74 6.20 7.78 0.167 6.33 9.48 PCB 77 6.36 1.92 0.767 0.295 4.71 PCB 82 6.20 7.58 0.731 3.67 15.65-116 Table 5C-2 (continued). BSAFs for PCB congeners based on measurements made in the Hudson River. Average, coefficient of var iation (CV), minimum and maximum BSAFs for each congener across all zones, age cl asses and fish species are tabulated.

PCB CONGENER LOG K OW AVERAGE BSAF BSAF CV MINIMUM BSAF MAXIMUM BSAF PCB 83 6.26 6.53 0.444 3.38 12.6 PCB 84 6.04 3.98 0.835 0.180 12.8 PCB 85 6.30 11.1 0.525 5.24 26.3 PCB 87 6.29 7.82 0.332 4.03 12.3 PCB 101 + 90 6.37 9.01 0.496 2.65 21.3 PCB 91 6.13 6.03 0.725 0.285 13.4 PCB 92 6.35 8.40 0.758 0.981 24.2 PCB 95 6.13 5.23 0.805 0.271 15.0 PCB 96 5.71 3.62 0.104 3.19 3.90 PCB 97 6.29 8.81 0.418 4.98 16.7 PCB 99 6.39 11.1 0.526 3.11 27.5 PCB 105 6.65 10.5 0.322 7.83 15.3 PCB 107 6.71 13.6 0.414 7.34 25.2 PCB 110 6.48 9.04 0.200 7.38 11.5 PCB 114 6.65 20.9 0.459 6.68 38.5 PCB 118 6.74 10.9 0.454 4.14 18.8 PCB 119 6.58 14.7 0.397 9.37 22.3 PCB 122 6.64 15.5 0.447 10.6 20.5 PCB 123 6.74 5.90 0.105 5.46 6.34 PCB 128 6.74 11.0 0.552 4.87 23.0 PCB 129 6.73 7.41 0.123 6.37 8.10 PCB 135 6.64 19.1 0.266 12.8 25.0 PCB 136 6.22 8.48 0.485 3.51 15.0 PCB 137 6.83 13.5 0.523 8.25 21.5 PCB 138 6.83 9.96 0.575 2.13 23.3 PCB 141 6.82 10.8 0.516 5.83 24.9 PCB 144 6.67 24.5 0.681 4.46 59.4 PCB 146 6.89 15.1 0.635 6.63 38.5 PCB 149 6.67 8.69 0.717 0.689 22.4 PCB 151 6.64 8.55 0.136 7.73 9.37 PCB 153 6.92 13.6 0.529 7.33 30.3 PCB 156 7.18 13.6 0.402 8.52 21.3 PCB 158 7.02 13.1 0.492 8.21 27.5 PCB 167 7.27 13.6 0.493 8.60 28.6 PCB 170 7.27 12.4 0.644 6.14 30.1 PCB 172 7.33 11.7 0.524 6.84 23.5 PCB 174 7.11 13.0 0.396 7.77 20.0 PCB 177 7.08 10.3 0.565 4.28 22.4 PCB 178 7.14 1.85 0.274 1.47 2.57 PCB 180 7.36 12.5 0.608 6.48 28.4 PCB 183 7.20 13.0 0.0276 12.6 13.25-117 Table 5C-2 (continued). BSAFs for PCB congeners based on measurements made in the Hudson River. Average, coefficient of var iation (CV), minimum and maximum BSAFs for each congener across all zones, age cl asses and fish species are tabulated.

PCB CONGENER LOG K OW AVERAGE BSAF BSAF CV MINIMUM BSAF MAXIMUM BSAF PCB 187 7.17 16.9 0.727 7.18 49.4 PCB 201 7.62 14.7 0.531 7.80 30.4 PCB 202 7.24 11.0 0.356 8.20 13.7 PCB 203 7.65 11.5 0.691 5.21 31.4 AVERAGE (ALL

CONGENERS) 7.66 0.565 3.52 15.45-118 This page left blank intentionally.5-119Appendix 5DLipid Content of Aquatic OrganismsTable 5D-1. Lipid Content of AquaticOrganisms Used to Derive NationalDefault Values of Lipid Fraction (fR)CSFII ConsumptionCategory (Habitat)aCommon NameScientific NameSpecies MeanLipid Content(%)CVbNo. Obs.DataSourcecCSFII MeanLipid (%)Anchovy (estuarine)Striped anchovyAnchoa hepsetus2.8NR2316.1European anchovyEngraulis encrasicholus4.80.34262Northern anchovyEngraulis mordax10.7NR161Carp (freshwater)Common carpCyprinus carpio5.40.862,79235.4Catfish (freshwater)White catfishAmeiurus catus4.30.582043, 4, 52.9Black bullheadAmeiurus melas1.10.701133, 4, 5Yellow bullheadAmeiurus natalis1.40.99953, 5Brown bullheadAmeiurus nebulosus2.60.729883, 4, 5Channel catfishIctalurus punctatus5.30.711,4273, 4, 5Catfish (estuarine)White catfishAmeiurus catus4.30.582043, 4, 54.0Brown bullheadAmeiurus nebulosus2.60.729883, 4, 5Channel catfishIctalurus punctatus5.30.711,4273, 4, 5Cisco (freshwater)CiscoCoregonus Artedii1.90.656921.9Clam (estuarine)Hard shell clamMercenaria mercenaria0.7NR471, 61.3Soft shell clamMya arenaria1.2NR31Venus clam (LittleneckJapanese)Tapes (venerupis) decussatus1.2NR151Venus clam (Shortneck)Tapes japonica1.8NR31Venus clam (Asari)Tapes philippinarum2.6315-120CSFII ConsumptionCategory (Habitat)aCommon NameScientific NameSpecies MeanLipid Content(%)CVbNo. Obs.DataSourcecCSFII MeanLipid (%)Venus clamVenus gallina0.9NR291Venus clam (hard)Venus lusoria0.6NR51Crab (estuarine)Blue crabCallinectes sapidus1.31.1910131.1Dungeness crabCancer magister1.00.26242Queen crabChionoectes opilio1.20.3062Crayfish (freshwater)Crayfish (mixed sp.)Astacus and Orconectes1.1NR521.1Croaker (estuarine)White croakerGenyonemus lineatus4.20.88374, 5, 6, 73.0Atlantic croakerMicropogonias undulatus3.20.4782Yellowfin croakerUmbrina roncador1.80.7035Eel (estuarine)Eel, mixed speciesAnguilla spp.11.70.2814211.7Flatfish (estuarine)Sole and flounderBothidae and Pleuronectidae1.20.8059621.2Flounder (estuarine)Sole and flounderBothidae and Pleuronectidae1.20.8059621.2Herring (estuarine)Blueback herringAlosa aestivalis7.20.4592310.0Atlantic herringClupea harengus9.00.512,5242Pacific herringClupea pallasi13.90.391282Mullet (freshwater)Striped mulletMugil cephalus3.80.624323.8Oyster (estuarine)Pacific oysterCrassostrea gigas2.30.331322.4Eastern oysterCrassostrea virginica2.50.561932Perch (estuarine andfreshwater)White perchMorone americana3.50.726823, 42.3Yellow perchPerca flavescens1.00.798413, 5Pike (freshwater)Northern pikeEsox lucius0.61.019043, 40.7MuskellungeEsox Masquinongy1.10.87353Chain pickerelEsox niger0.40.74723, 45-121CSFII ConsumptionCategory (Habitat)aCommon NameScientific NameSpecies MeanLipid Content(%)CVbNo. Obs.DataSourcecCSFII MeanLipid (%)Rockfish (estuarine)Striped bassMorone saxatilis5.30.597,6573, 4, 5, 73.5RockfishSebastes spp.1.6NR812Salmon(estuarine & freshwater)Pink salmonOncorhynchus gorbuscha3.50.4914424.7Chum salmonOncorhynchus keta3.80.62132Coho salmonOncorhynchus kistuch2.90.756173Sockeye salmonOncorhynchus nerka8.60.32482Chinook salmonOncorhynchus tshawytscha3.40.938733Atlantic salmonSalmo salar6.30.7472Scallop (estuarine)Scallop, mixed speciesPectinidae0.80.3511420.8Shrimp (estuarine)Shrimp, mixed speciesPanaeidae and Pandalidae1.70.3910021.7Smelt, rainbow(estuarine & freshwater)Rainbow smeltOsmerus mordax4.10.461303, 84.1Snails (freshwater)Snails, mixed speciesVivaparadidae, Helixidaed1.40.751111.4Sturgeon(estuarine & freshwater)Lake sturgeonAcipenser fulvescens9.40.635135.4White sturgeonAcipenser transmontanus1.30.6774, 5, 7Trout, mixed spp.(freshwater)Rainbow troutOncorhynchus mykiss5.10.675563, 4, 56.0Cutthroat troutSalmo clarki1.20.79153Brown troutSalmo trutta7.40.736153, 4, 5Brook troutSalvelinus fontinalis4.00.56963, 4, 5Lake troutSalvelinus namaycush12.30.629103, 4, 5Trout, mixed spp.(estuarine)Rainbow troutOncorhynchus mykiss5.10.675563, 4, 53.25-122CSFII ConsumptionCategory (Habitat)aCommon NameScientific NameSpecies MeanLipid Content(%)CVbNo. Obs.DataSourcecCSFII MeanLipid (%)Cutthroat troutSalmo clarki1.20.79153Trout, (freshwater)eRainbow troutOncorhynchus mykiss5.10.675563, 4, 55.1WhitefishWhitefish, mixed spp.Coregonus spp.5.90.646825.9a Habitat designation (freshwater, estuarine) assigned to the CSFII consumption categories. See the Exposure Assessment volumeof TSD Volume 2 (USEPA,2003) for details.b Coefficient of variation.c Data sources: 1 = Sidwell (1981), 2 = Exler (1987), 3 = NSI (USEPA, 2001a), 4 = USEPA (1992a), 5 = CATSMP, 6 = primary literature, 7 = BPTCP, 8 =GBMB. See Section 6.2.2 for a description of data sources.d In addition to these two families, specific genera represented includeAmpullaria, Vivaparus, Achatina, Murex, Thais, Nassa,andAporrhais.e Information from the CSFII survey indicates that rainbow trout is appropriate for the “trout, freshwater” category.