United States Office of Water EPA-822-R-08-001

Environmental Protection Office of Science and Technology June 2008

Agency 4304 

Methodology for Deriving Ambient 

Water Quality Criteria for the Protection 

of Human Health (2000) 

Draft Technical Support Document 

Volume 3: Development of Site-Specific 

Bioaccumulation Factors 
EPA-822-B-08-001

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 

Draft 

Office of Science and Technology

Office of Water

U.S. Environmental Protection Agency

Washington, DC 20460
i 

NOTICE

The policies and procedures set forth in this document are intended solely to describe 

EPA methods and guidance for developing 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 quality criteria. This guidance 

does not substitute for the Clean Water Act or EPA’s regulations, nor is it a regulation itself. 

Thus, it does not impose legally binding requirements 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 accordance 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 intentionally left blank for the Foreword.
iii 

ACKNOWLEDGMENTS

Project Leader

Tala R. Henry U.S. EPA Office of Science and Technology 

Heidi Bethel U.S. EPA Office of Science and Technology 

Coauthors

Lawrence P. Burkhard U.S. EPA National Health and Environmental Effects Research 

Laboratory 

Philip M. Cook U.S. EPA National Health and Environmental Effects Research 

Laboratory 

Douglas D. Endicott Great Lakes Environmental Center, Inc. 

Keith G. Sappington U.S. EPA National Center for Environmental Assessment 

Erik L. Winchester U.S. EPA Office 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 Connecticut
iv 

TABLE OF CONTENTS 

1. INTRODUCTION ................................................................................................................. 1-1 

1.1 SCOPE OF DOCUMENT ........................................................................................ 1-3 

1.2 SITE-SPECIFIC BIOACCUMULATION FACTORS (SS BAFs)........................... 1-4 

1.3 GLOSSARY .............................................................................................................. 1-7 

REFERENCES .................................................................................................................... 1-12 

2. HOW TO SELECT AN APPROACH FOR DERIVING SITE-SPECIFIC BAFS............... 2-1 

2.1 BAF DERIVATION PROCEDURES FOR INORGANIC AND 

ORGANOMETALLIC CHEMICALS ...................................................................... 2-4 

2.2 BAF DERIVATION PROCEDURES FOR 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 DERIVING SITE-SPECIFIC BAFS?.......... 2-10 

 2.5.1 Site-specific Field-Measured BAFs............................................................. 2-11 

2.5.2 Site-specific BAFs Predicted from Measured Biota Sediment 

 Accumulation Factors (BSAFs)................................................................... 2-11 

2.5.3 Site-specific BAFs Predicted 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 Food Chain Multipliers ......................................... 2-12 

2.5.5 Site-specific BAFs Predicted from N-Octanol-Water Partition 

 Coefficient (Kow) Combined with a Food Chain Multipliers ...................... 2-13 

2.5.6 Site-specific BAFs Recalculated from National or Baseline BAFs............. 2-13 

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 .................................................................................................................... 2-21 

3. MEASURING SITE-SPECIFIC BIOACCUMULATION FACTORS................................. 3-1 

3.1 KEY STUDY DESIGN QUESTIONS FOR 

 DETERMINING SITE-SPECIFIC BIOACCUMULATION FACTORS............... 3-12 

3.2 HOW TO DESIGN A SAMPLING PLAN TO MEASURE BAFS........................ 3-18 

3.2.1 Determining the Number of Samples to Collect.......................................... 3-20 

3.2.1.1 Bootstrap BAF Resampling ...................................................... 3-21 

3.2.1.2 Monte Carlo BAF Analysis ....................................................... 3-22 

3.2.2 Modeling Simulation of BAF Sampling Designs ........................................ 3-23 

3.2.2.1 Using Model Simulations to Develop Field-Sampling Designs 3-24 

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

 Sampling Plan? ............................................................................................ 3-27 
v 

TABLE OF CONTENTS (CONTINUED)

3.3 MEASURING CHEMICAL CONCENTRATIONS IN BIOTA ............................ 3-30 

3.3.1 Target Analytes and Analytical Methods .................................................... 3-32 

3.3.2 Target Species and Size Class Selection...................................................... 3-33 

3.3.3 Sampling Locations .................................................................................... 3-37 

3.3.4 Sampling Times ........................................................................................... 3-42 

3.3.5 Sample Type ................................................................................................ 3-43 

3.3.6 Replicate Samples........................................................................................ 3-48 

3.3.7 Sample Collection Methods......................................................................... 3-49 

3.4 MEASURING CHEMICAL CONCENTRATIONS IN WATER .......................... 3-49 

3.4.1 Target Analytes and Analytical Methods .................................................... 3-50 

3.4.2 Phase Separation .......................................................................................... 3-51 

3.4.3 Sampling Locations ..................................................................................... 3-52 

3.4.4 Sampling Times ........................................................................................... 3-53 

3.4.5 Sample Type ................................................................................................ 3-54 

3.4.6 Replicate Samples........................................................................................ 3-55 

3.4.7 Sample Collection Methods......................................................................... 3-55 

REFERENCES .................................................................................................................... 3-57 

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 BIOACCUMULATION FACTORS ......................................................... 4-1 

4.1 DESCRIPTION OF METHOD 2 .............................................................................. 4-2 

4.2 KEY STUDY DESIGN QUESTIONS FOR DETERMINING BIOTASEDIMENT ACCUMULATION FACTORS......................................................... 4-11 

4.3 HOW CAN THE SEDIMENT/WATER COLUMN CHEMICAL 

CONCENTRATION QUOTIENT (IISOCW) BE DETERMINED? ......................... 4-18 

4.3.1 Measuring Site-specific Reference Chemical Concentrations in Water 

and Sediment................................................................................................ 4-20 

4.3.2 Estimating IISOCW /KOW ~ fPOC,water/fSOC,sediment 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 PLAN TO MEASURE BSAFs ...................... 4-27 

4.4.1 How Can Modeling Simulations Guide the Sampling Design Process? ..... 4-28 

4.4.2 Using Model Simulations to Develop Field-Sampling Designs.................. 4-29
vi 

TABLE OF CONTENTS (CONTINUED) 

4.4.3 Using Monte Carlo Simulation to Determine the Number of Samples to 

Collect and Analyze..................................................................................... 4-33 

4.4.4 How Can These Methods be Used to Help Design a Sampling Plan?......... 4-34 

4.5 MEASURING CHEMICAL CONCENTRATIONS IN SEDIMENT .................... 4-36 

4.5.1 Target Analytes and Analytical Methods .................................................... 4-37 

4.5.2 Sampling Locations and Depths .................................................................. 4-38 

4.5.3 Sample Type and Collection Method .......................................................... 4-43 

4.5.4 Replicate and Composite Samples............................................................... 4-44 

4.6 SCIENTIFIC ISSUES ASSOCIATED WITH METHOD 2 AND THE USE 

OF BSAFS TO PREDICT CHEMICAL BIOACCUMULATION......................... 4-46 

4.6.1 Evaluation of Method 2 Predictions of Site-specific BAFs......................... 4-46 

4.6.2 Is Chemical Equilibrium Assumed in the Calculation of a BSAF?............. 4-51 

4.6.3 Review of Existing Data for IISOCW ............................................................. 4-52 

4.6.4 How does IISOCW Reflect Steady State Conditions at a Site? ...................... 4-62 

4.6.5 Assumptions and Limitations Associated with Method 2 Predictions ........ 4-63 

4.6.6 How Reliable are Method 2 Predictions if the Sediment Organic 

Carbon Equilibrium Partitioning Assumption is in Error? .......................... 4-65 

REFERENCES .................................................................................................................... 4-69 

Appendix 4A - Modeling Simulation of BSAF Sampling Designs.................................... 4-75 

Appendix 4B - Determining the Number of Samples to Collect for a 

BSAF Measurement: Monte Carlo Analysis ............................................. 4-78 

Appendix 4C - Green Bay Mass Balance 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 extrapolating BSAFs (Method 3a) ........ 5-13 

5.1.2 Estimating Site-specific BAFs by extrapolating BEFs (Method 3b)........... 5-24 

5.2 PREDICTING SITE-SPECIFIC BAFS USING BCFS AND FOOD CHAIN 

MULTIPLIERS (FCMs).......................................................................................... 5-35 

5.2.1 Predicting Site-specific Baseline 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 ................................................................. 5-43 

5.2.2.1 Measuring Site-specific FCMs .................................................. 5-47 

5.2.2.2 Predicting FCMs Using A Food Chain Model .......................... 5-50 

5.2.2.3 Site-specific Adjustment of Food Chain Model Parameters...... 5-58 

5.2.2.4 Selection of a Food Web Structure ............................................ 5-59 

5.2.2.5 Alternative Food Chain Models................................................. 5-60
vii 

TABLE OF CONTENTS (CONTINUED) 

5.2.3 Predicting Site-specific Baseline BAFs using KOW and Food Chain 

 Multipliers (Method 4b)............................................................................... 5-60 

5.2.3.1 Validation of Method 4b............................................................ 5-66 

5.2.3.2 Selection of Appropriate KOWS for Partitioning 

(Bioavailability) Predictions ...................................................... 5-70 

5.3 Recalculating Site-specific BAFs from Baseline or National BAFs ....................... 5-71 

5.3.1 Assumptions and Limitations ...................................................................... 5-73 

5.3.2 Validation of Method 5................................................................................ 5-77 

5.3.3 How Can the Lipid Contents of Aquatic Organisms be Determined? ........ 5-82 

5.3.3.1 Assessing Site-specific Fish Consumption ................................ 5-83 

5.3.3.2 Measuring Lipid in Fish............................................................. 5-83 

5.3.3.3 Determining Site-specific Fish Lipid Using a Literature or 

Database Search......................................................................... 5-85 

5.3.3.4 How Should Lipid Data be Evaluated?...................................... 5-86 

5.3.3.5 Determining Fish Lipid using the National Default Lipid 

Data Base ................................................................................... 5-87 

5.3.4 How Can Site-specific Organic Carbon Concentrations be Determined?... 5-89 

5.3.4.1 Overview of Freely Dissolved Normalization Process.............. 5-90 

5.3.4.2 Measuring DOC and POC ......................................................... 5-91 

5.3.4.3 Determining DOC and POC Using a Literature or Database 

Search......................................................................................... 5-93 

5.3.4.4 How Should Organic Carbon Data Be Evaluated?.................... 5-94 

5.3.4.5 Determining Organic Carbon Concentrations using the 

National DOC/POC Database.................................................... 5-96 

REFERENCES .................................................................................................................... 5-99 

Appendix 5A - Internet-Accessible Databases Containing Lipid Content Data................ 5-104 

Appendix 5B - Internet-Accessible Databases Containing Organic Carbon Data ............ 5-110 

Appendix 5C - BSAFs for PCB congeners in Green Bay and Upper Hudson River ........ 5-114 

Appendix 5D - Lipid Content of Aquatic Organisms........................................................ 5-119 
viii 

TABLES 

Table 2-1. Strengths and Limitations of the Methods for Deriving Site-specific BAFs 

(SS BAFs) ............................................................................................................ 2-16 

Table 3-1. Illustrative Bioaccumulation Factor Sampling Design Structures. 

Uncertainties Associated With Design Structures Are Ecosystem Specific........ 3-28 

Table 3-2. Waterbody Type as Indicator of Temporal Concentration Variability................ 3-30 

Table 3A-1. Bootstrap Results for PCB 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 .................................................................................... 3-73 

Table 3A-2. Taylor Series Approximation of Confidence Limits for Baseline 

BAFs in Green Bay Zone 3.................................................................................. 3-78 

Tables 3C-1. 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 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 Variability in Chemical Concentrations 

in Fish and Water: Chemical Concentrations 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 Variability in Chemical Concentrations 

in Fish and Water: Chemical Concentrations 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 Variability in Chemical Concentrations 

in Fish and Water: Chemical Concentrations 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 Variability in Chemical Concentrations 

in Fish and Water: Chemical Concentrations Measured in 4 Fish and 2 

Water Samples ..................................................................................................... 3-90 

Table 4-1. Some Illustrative Biota–Sediment Accumulation Factor Sampling Design 

Structures. ............................................................................................................ 4-32 
ix 

TABLES (CONTINUED) 

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

 of Sediment .......................................................................................................... 4-46 

Table 4-3. Validation Statistics for Method 2: Ratio of Baseline BAF predicted/Baseline 

 BAF measured for all (combined) sampled fish species ........................................... 4-50 

Table 4-4. Geometric Mean Regression Equations (log IISOCW = A log KOW+ B) for 

Polychlorinated Biphenyls (PCBs) and Chlorinated Pesticides........................... 4-57 

Table 4-5. Average IISOCW / KOW Ratios for Three Different Ecosystems ........................... 4-61 

Table 4B-1. 90% Confidence Interval Ratios for BSAF as Function of the Variability in 

Chemical Concentrations in Sediment................................................................. 4-82 

A. Chemical Concentrations Measured in 2 Fish Samples............................... 4-82 

B. Chemical Concentrations Measured in 4 Fish Samples............................... 4-82 

C. Chemical Concentrations Measured in 6 Fish Samples............................... 4-82 

D. Chemical Concentrations Measured in 10 Fish Samples............................. 4-83 

E. Chemical Concentrations Measured in 30 Fish Samples............................. 4-83 

Table 4B-2. 90% Confidence Interval Ratios for JSocw as Function of the Variability in 

Chemical Concentrations in Sediment................................................................. 4-85 

A. Chemical Concentrations Measured in 2 Water Samples............................ 4-85 

B. Chemical Concentrations Measured in 4 Water Samples............................ 4-85 

C. Chemical Concentrations Measured in 6 Water Samples............................ 4-85 

D. Chemical Concentrations Measured in 10 Water Samples.......................... 4-86 

E. Chemical Concentrations Measured in 30 Water Samples.......................... 4-86 

Table 4B-3. 90% Confidence Interval Ratios for Method 2 BAF Predictions for PCB Congener 

149 as a Function of the Variability in Chemical Concentrations in Sediment... 4-91 

A. Chemical Concentrations Measured in 2 Fish and 2 Water Samples .......... 4-91 

B. Chemical Concentrations Measured in 4 Fish and 4 Water Samples .......... 4-91 

C. Chemical Concentrations Measured in 6 Fish and 6 Water Samples .......... 4-91 

D. Chemical Concentrations Measured in 10 Fish and 10 Water Samples ...... 4-92 

E. Chemical Concentrations Measured in 30 Fish and 30 Water Samples ...... 4-92 

Table 5-1. Method 3a BSAF Extrapolation Example Using PCB Data From Lake Michigan, 

Green Bay and the Hudson River. .....................................................................................5-20 

A. Extrapolating Lake Michigan Lake Trout (LM LT6) BSAFs to 3 Year 

Old Brown Trout in Zone 4 of Green Bay (GB BT3).................................. 5-20 
x 

TABLES (CONTINUED) 

B. Extrapolating Lake Michigan Lake Trout (LM LT6) BSAFs to 4 Year 

Old Walleye in Zone 4 of Green Bay (GB W4) .......................................... 5-20 

C. Extrapolating Lake Michigan Lake Trout (LM LT6) BSAFs to 4 Year 

Old Walleye in Zone 1 of Green Bay (GB W4) .......................................... 5-21 

D. Extrapolating Lake Michigan Lake Trout (LM LT6) BSAFs to 

Largemouth Bass at Hudson River mile 189 (RM 189 LMB)..................... 5-21 

E. Extrapolating Lake Michigan Lake Trout (LM LT6) BSAFs to 

Largemouth Bass at Hudson River mile 194 (RM 194 LMB)..................... 5-22 

Table 5-2. TCDD Bioaccumulation Equivalency 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. ............................................................................................. 5-29 

 

Table 5-3. Method 3b BEF Extrapolation Example Using PCB Data From Lake 

Michigan, Green Bay and the Hudson River..................................................................5-31 

A. Extrapolating Lake Michigan Lake Trout (LM LT6) BEFs to 3 Year 

Old Brown Trout in Zone 4 of Green Bay (GB BT3).................................. 5-31 

B. Extrapolating Lake Michigan Lake Trout (LM LT6) BEFs to 4 Year 

Old Walleye in Zone 4 of Green Bay (GB W4) .......................................... 5-31 

C. Extrapolating Lake Michigan Lake Trout (LM LT6) BEFs to 4 Year 

Old Walleye in Zone 1 of Green Bay (GB W4) .......................................... 5-32 

D. Extrapolating Lake Michigan Lake Trout (LM LT6) BEFs to 

Largemouth Bass at Hudson River mile 189 (RM 189 LMB)..................... 5-32 

E. Extrapolating Lake Michigan Lake Trout (LM LT6) BEFs to 

Largemouth Bass at Hudson River mile 194 (RM 194 LMB)..................... 5-33 

Table 5-4. Food Web Structure for National BAF Methodology (Flint, 1986; Gobas, 

1993) .................................................................................................................... 5-54 

Table 5-5. Environmental Parameters and Conditions Used for Determining FCMs for 

the National BAF Methodology........................................................................... 5-55 

Table 5-6. Food-Chain Multipliers for Trophic Levels (TLs) 2, 3, and 4 (Mixed Pelagic 

and Benthic Food Web Structure and IISOCW /KOW = 23).................................... 5-56 

Table 5-7. Validation Statistics for Method 4b: Ratio Between Predicted and Measured 

Baseline BAFs (Baseline BAFpredicted/Baseline BAFmeasured) based on PCB 

concentration data from Green Bay, Lake Michigan and the Hudson River....... 5-67 
xi 

TABLES (CONTINUED) 

Table 5-8. Summary Statistics: 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 KOWs Exceeding 4 ................ 5-68 

Table 5-9. BAFs and Baseline BAFs Confidence Limit Ratios (CLRs) for Adult 

alewife, Age 4 walleye, and Age 10 carp in Green Bay (All Zones 

Combined)............................................................................................................ 5-79 

Table 5-10. Evaluation Criteria for Lipid Data Sources ........................................................ 5-87 

Table 5-11. Evaluation Criteria for Organic Carbon Data Sources ........................................ 5-95 

Table 5-12. National Default Values for POC and DOC in U.S. Fresh and Estuarine 

Surface Waters..................................................................................................... 5-98 

Table 5C-1. BSAFs for PCB congeners based on measurements made in 

 Green Bay, Lake Michigan................................................................................ 5-114 

Table 5C-2. BSAFs for PCB congeners based on measurements made in the 

 Hudson River. .................................................................................................... 5-116 

Table 5D-1. Lipid Content of Aquatic Organisms Used to Derive National 

 Default Values of Lipid Fraction (fl)................................................................. 5-119 
xii 

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 forage fish and water concentrations, and BAFs 

 based on resampling 2 fish and 20 water 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............................................................................................ 3-72 

 

Figure 3A-4. Bootstrap resampling results for PCB congener 149 in Green Bay Zone 3 

 forage fish: 90% confidence interval ratios for BAF as a function of numbers 

 of fish and water samples................................................................................... 3-74 

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............................................................................ 3-75 

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....................................................................................... 3-76 

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.............................................................. 3-85 

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 congener 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.................................................................................... 3-87
xiii 

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 KOWs of 2, 3, 4, 

and 9................................................................................................................... 3-94 

Figure 3D-2. Ratio of the 10th to 90th percentile bioaccumulation factors (BAFs) for fieldsampling designs................................................................................................ 3-96 

Figure 4-1. The sediment-water concentration quotient (IISOCW) 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........................... 4-49 

Figure 4-3. Sediment-water column concentration coefficient (IISOCW) for PCBs in five 

different geographical zones in Green Bay, Lake Michigan ............................. 4-55 

Figure 4-4. Average sediment-water column concentration coefficients (IISOCW) for individual 

PCB congeners across the five different geographical zones in Green Bay, Lake 

Michigan ............................................................................................................ 4-56 

Figure 4-5. Sediment-water column concentration coefficients (IISOCW) for PCBs at river 

miles 189 and 194 .............................................................................................. 4-58 

Figure 4A-1. Ratio of the 10th to 90th percentile biota–sediment accumulation factor 

(BSAF) for field-sampling designs.................................................................... 4-76 

Figure 4B-1. 90% confidence interval ratio for BSAF as function of number of sediment 

samples............................................................................................................... 4-81 

Figure 4B-2. 90% confidence interval ratio for IISOCW as function of number of sediment 

samples............................................................................................................... 4-84 

Figure 4B-3. 90% confidence interval ratio for PCB-180 Method 2 BAFs 

as a function of the number of sediment samples.............................................. 4-87 

Figure 4B-4. 90% confidence interval ratio for four PCB congener Method 2 BAFs as a 

function of the number of sediment samples. .................................................... 4-88 

Figure 4B-5. 90% confidence interval ratio for PCB-149 Method 2 BAFs as a function of the 

number of sediment samples and variability of chemical concentrations in 

sediment. ............................................................................................................ 4-90 
xiv 

FIGURES (CONTINUED) 

Figure 4B-6. 90% confidence interval ratio for PCB-52 Method 2 BAFs as a function of 

concentration correlations.................................................................................. 4-94 

Figure 5-1. Decision framework for selecting a site-specific BAF derivation method based on 

BSAFs................................................................................................................. 5-5 

Figure 5-2. White sucker BSAFs (kg organic carbon/kg lipid) from six ecosystems plotted 

against BSAFs (kg organic carbon/kg lipid) for white sucker from sampling 

station 01208869................................................................................................. 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 lake trout from 

Lake Michigan ................................................................................................... 5-10 

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 organic 

carbon/kg lipid) for 6 year old lake trout from Lake Michigan........................ 5-11 

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. 2003) plotted against BSAFs (kg 

organic carbon/kg lipid) for 6 year old lake trout from Lake Michigan........... 5-12 

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 Method 3a. ........................................... 5-23 

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 Method 3b. ................................. 5-34 

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............................................................................. 5-57 

Figure 5-10. t BAFi,T s 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. ......................................................................................................... 5-78 

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.................................................... 5-81 
xv 

FIGURES (CONTINUED) 

Figure 5-12. Illustration of how the freely dissolved fraction calculated using equation 

3-12 varies as a function of KOW, for varying concentrations of DOC 

and POC............................................................................................................. 5-91 
xvi 

LIST OF ACRONYMS, SYMBOLS AND NOTATIONS 

AWQC Ambient water quality criteria 

BAF Bioaccumulation factor 

BAFfd

L Baseline BAF, in lipid normalized and based on freely dissolved chemical in 

water 

BCF Bioconcentration factor 

BCFfd

L Baseline bioconcentration factor 

t BCFT Bioconcentration factor based on total concentrations in tissue and water 

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 C C of total chemical in water w

fd Cw C of chemical freely dissolved in water 

Cs C of chemical in sediment 

Csoc C of chemical in sediment organic carbon 

Cl C of chemical in lipid 

Ct 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-bis(p-chlorophenyl)ethylene 

DDD 1,1-dichloro-2,2-bis(p-chlorophenyl)ethane 

DI Drinking water intake 

DOC Dissolved organic carbon 

EPA Environmental Protection Agency 

ffd Fraction freely dissolved 

fl Fraction lipid 

fsoc Fraction organic carbon in sediment 

FCM Food chain multiplier 

FIi Fish intake at trophic level i

GLI Great Lakes Water Quality Initiative 

IARC International Agency for Research on Cancer 

IRIS Integration Risk Information System 
xvii 

LIST OF ACRONYMS, SYMBOLS AND NOTATIONS (CONTINUED) 

kg Kilogram 

Kow n-Octanol-Water Partition Coefficient 

L Liter 

MCL Maximum Contaminant Level 

mg Milligrams 

ml Milliliters 

NFCS Nationwide Food Consumption Survey 

NOEL No Observed Effect Level 

NPDES National Pollutant Discharge Elimination System 

PAH Polycyclic Aromatic Hydrocarbon 

PCB Polychlorinated Biphenyls 

POC Particulate organic carbon 

RDA Recommended Daily Allowance 

RfD Reference dose 

RSC Relative source contribution to account for nonwater sources of exposure 

SAB Science Advisory Board 

Jsocw Sediment-water concentration quotient 

Dk/r Ratio between values of Jsocw 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 Department of Agriculture 

USEPA United States Environmental Protection Agency 

WQBEL Water Quality-Based Effluent Limits
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1. INTRODUCTION

 The Methodology for Deriving Ambient 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 under 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 bioaccumulate, 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 general population. The 2000 Human 

Health Methodology included guidance on chemical risk assessment, exposure, and 

bioaccumulation. To supplement the 2000 Human 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 focused on the technical components of the 

2000 Human Health Methodology that pertain to the development of national bioaccumulation 

factors for use in deriving national recommended ambient water quality criteria for protecting 

human health. A national bioaccumulation factor (National BAFi)

1

 is a mean BAF, based on 

concentrations of total chemical in wet tissue and water, for a specific trophic level "i". It is 

adjusted for the consumption-weighted average lipid content of commonly consumed aquatic 

organisms in that trophic level and the nationwide 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 for each of these trophic levels. 

 

1

 In TSD Volume 2, a slightly different symbol (National BAFTL n ) was used for national bioaccumulation factors. 

The two symbols are equivalent.
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 For those unfamiliar with EPA’s methodology for assessing chemical bioaccumulation 

(USEPA, 2003), it is useful to review how BAFs are factored into the calculation of 

recommended national AWQCs for the protection of human health. Equation 1-1 (below) is the 

generalized AWQC formula for noncancer effects. In this equation, trophic-level specific BAFis 

are in the denominator, along with information on the amount of fish of each trophic level (i) 

consumed on a daily basis (FIi), to estimate human exposure to contaminants through the aquatic 

food web (USEPA, 2000). 

( )























⋅+

⋅⋅=

∑=

4

i 2

BAFFIDI ii

BW AWQC RSCRfD (Equation 1-1) 

where: 

 AWQC = ambient water quality criterion (mg chemical/L water) 

 RfD = reference dose for noncancer effects (mg/kg/day) 

 RSC = relative source contribution to account for nonwater sources of exposure 

 BW = human body weight (kg) 

 DI = drinking water intake (L/day) 

 FIi = fish intake (kg/day) at trophic level i (i = 2, 3, 4) 

BAFi = 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 BAFi values of 103

 to 107 for aquatic ecosystems. For these 

chemicals, inspection of Equation 1-1 reveals that 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 chemical accumulation from all potential exposure 

routes (e.g., food, sediment, and water) that may be important in determining the chemical 

accumulation in the organism’s body. 
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1.1 SCOPE OF DOCUMENT 

EPA’s approach for deriving national BAFs includes separate procedures according to 

the type of chemical (e.g., nonionic organic, ionic organic, inorganic, and organometallic). For 

the purposes of the 2000 Human Health Methodology, nonionic organic chemicals are defined as 

organic compounds that do not ionize substantially in natural bodies of water. These chemicals 

are also referred to as “neutral” or “nonpolar” organics in the scientific literature. Ionic organic 

chemicals are considered to include those chemicals that contain functional groups with 

exchangeable protons, such as hydroxyl, carboxylic, and sulfonic and nitrogen (pyridine) groups, 

or bases that ionize by proton uptake. Ionic organic chemicals undergo ionization in water, the 

extent of which depends on the pH of the water and the pKa of the chemical. Ionic chemicals are 

considered separately when deriving national BAFs because the behavior of the anionic or 

cationic species of these chemicals in aquatic systems is much different from those of their 

neutral (un-ionized) counterparts. Inorganic and organometallic chemicals include: 

• inorganic minerals, 

• other inorganic compounds and elements, 

• metals, 

• metalloids, and 

• organometallic compounds. 

TSD Volume 2 focuses primarily on the procedures for determining BAFs for nonionic organic 

chemicals that bioaccumulate. The procedures for estimating bioaccumulation of nonionic 

organic chemicals are generally better developed than those for ionic chemicals. For the same 

reason, this document (TSD Volume 3) also focuses primarily on the procedures for determining 

site-specific BAFs for nonionic organic chemicals that bioaccumulate. Therefore, both the 

conditions under which these procedures can be applied and the limitations associated with their 

application must be understood for their proper application, and will be discussed further in 

Section 2. 
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1.2 SITE-SPECIFIC BIOACCUMULATION FACTORS (SS BAFs) 

 In developing TSD Volume 2 (USEPA, 2003), EPA envisioned the development of an 

additional Technical Support Document to provide guidance on how to modify the national 

bioaccumulation factors to derive BAFs that are more representative of the bioaccumulation 

potential at a given location (i.e., site-specific BAFs). This TSD (Volume 3) provides guidance 

on different approaches that investigators can take to develop site-specific BAFs, and the factors 

that should be considered when selecting an approach for a given situation. Neither of the 

bioaccumulation TSDs should be used alone to derive BAFs, but rather should be used in 

conjunction with the 2000 Human Health Methodology. The intended audience for this 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 

developing site-specific BAFs, and other users interested in site-specific bioaccumulation issues 

for other applications. 

 The bioaccumulation methodology used in the 2000 Human Health Methodology 

encourages developing site-specific BAFs because EPA recognizes that BAFs vary not only 

between chemicals and trophic levels, but also among different ecosystems, waterbodies; that is, 

among sites. National average BAF value for a given chemical and trophic level may not provide 

the most accurate estimate of bioaccumulation for certain water bodies 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. The bioaccumulation potential of 

a chemical can be affected by various site-specific physical, biological, 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. 
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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 absence of site-specific data, EPA believes that 

national BAFs are broadly applicable to sites throughout 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 methodology intended to produce the most accurate 

national average values for BAFs at each trophic 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 example): fish consumption 

patterns that are substantially different than national averages; species of aquatic organisms that 

have not been previously sampled or for which trophic level or feeding preference is unknown; 

and sediment-water chemical distribution, tissue lipid content or DOC concentration 

significantly different than the values assumed in the national methodology. In cases such as 

these, the derivation of site-specific BAFs would likely improve the accuracy of bioaccumulation 

estimates and, ultimately, the AWQC for the chemical 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 greater detail in TSD Volume 2 (USEPA, 2003). 

 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 organic chemicals (and ionic organic chemicals that 

behave similarly), the investigator can also predict site-specific BAFs from BSAFs. BSAFs are 

similar to field-measured BAFs because the concentration of a chemical in biota is calculated 

from the results of the analysis of samples of tissue and sediment collected at the site. BSAFs 
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also reflect an organism's exposure through all relevant exposure routes. EPA prefers 

field-measured BAFs and BSAFs over other methods of determining site-specific BAFs because 

they inherently account for all biotic and abiotic factors that affect bioaccumulation in a water 

body. EPA encourages the States, Territories 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 baseline3 BAFs, 

• extrapolating site-specific BAFs from BSAFs, or 

• predicting BAFs using laboratory-measured bioconcentration factors (BCFs) or 

octanol-water partition coefficients (Kows) coupled with food chain multipliers. 

Although these methods are the same as those described in TSD Volume 2, EPA expects 

that variations of these methods, described in this TSD, may be used to derive site-specific 

BAFs. EPA encourages those deriving site-specific BAFs to use as many of these methods as 

possible and then compare the results, applying judgment to select the best estimate. EPA 

recognizes that additional guidance is necessary to ensure that site-specific bioaccumulation 

factors are accurate and defensible, whether they are determined 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 different methods that 

the investigator can use to derive site-specific BAFs. 

• Section 3 discusses the derivation of field-measured site-specific BAFs, from data 

obtained on samples of tissue and water collected at the site. This section also 

provides guidance to the investigator for planning a field study to measure chemical 

concentrations in water and fish tissue. 

 

3 For nonionic organic chemicals (and certain ionic organic 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 concentration of chemical freely dissolved in water.
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• Section 4 presents the derivation of site-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 for deriving site-specific BAFs based upon 

applying one of the other methods described in TSD Volume 2 (USEPA, 2003). This 

section also discusses the adjustment of lipid and organic carbon values used in 

estimating the site-specific BAFs, and the alternatives that the investigator can use to 

determine site-specific values of lipid and organic carbon. 

1.3 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 sources (water, sediment and food). 

Bioaccumulation factor (BAFi ). 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 

BAFi is trophic level specific; this subscript was not used in TSD Volume 2. Several forms of the 

BAFi are used in this document: 

Total bioaccumulation factor ( t BAFi,T ). A BAF based on the total concentration of chemical in

the organism and the water. The total concentration of the chemical in the organism includes that 

in either a specific tissue (e.g., fillet) or the whole organism and is based on wet tissue weight. 

The total concentration of the chemical in water includes the chemical associated with particulate 

organic carbon, chemical associated with dissolved organic carbon, and chemical freely 

dissolved in the water. The t BAFi,T is expressed in liters per kilogram, and is trophic level 

specific. The subscript i was not used in TSD Volume 2. 

Baseline bioaccumulation factor (Baseline BAFi or BAF, 

fd

i L ) . For nonionic organic chemicals

(and certain ionic organic chemicals to which similar lipid and organic carbon partitioning 
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behavior applies4), a BAF that is based on the concentration of the chemical in the lipid fraction 

of tissue within an organism and the concentration of chemical freely dissolved in water. The 

baseline BAFi is trophic level specific, although the subscript i was not used in TSD Volume 2.

The baseline BAFi is expressed in liters per kilogram of lipid. 

Field-measured bioaccumulation factor. A t BAFi,T derived from analysis of tissue and water 

samples collected from the field. For moderately to highly hydrophobic chemicals, (log Kow > 4) 

it is usually preferable to measure a BAF,

fd

i l instead. 

Lipid-normalized and freely dissolved-based bioaccumulation factor (BAF,

fd

i l ). For nonionic 

organic chemicals (and ionic organic chemicals with similar lipid and organic carbon partitioning 

behavior3

), 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 BAF,

fd

i l is 

expressed in liters per kilogram of lipid. The subscript i was not used in TSD Volume 2. 

National trophic-level specific bioaccumulation factor (National BAFi). A BAF based on 

nationwide average lipid content for trophic level i and nationwide average organic carbon in 

ambient waters. The national BAFi is expressed in liters per kilogram wet tissue. In TSD Volume 

2, the symbol National BAFTL 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 the ratio does not change substantially over time. 

Total bioconcentration factor ( t BCFT ). A BCF based on the total concentration of chemical in 

 

4

 As discussed in TSD Volume 2, baseline and lipid-normalized BAFs for certain ionic 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 organic 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 essentially as a nonionic chemical for the purposes of determining site-specific 

BAFs. 
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the organism and the water. The total concentration of the chemical in the organism includes that 

in either a specific tissue or the whole organism and is based on wet tissue weight. The total 

concentration of the chemical in water includes the chemical associated with particulate organic 

carbon, the chemical associated with dissolved organic carbon, and 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 BCF is expressed in liters per kilogram. T

Baseline bioconcentration factor (Baseline BCF or BCFfd

L ). For nonionic organic chemicals 

(and certain ionic organic chemicals to which similar lipid and organic carbon partitioning 

behavior applies), a BCF that is based on the concentration of chemical freely dissolved in water 

and the concentration of the chemical 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 BCFl ). 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 situations where both the organism is exposed through 

water only and the ratio does not change substantially over time. The fd BCFl is expressed in 

liters per kilogram of lipid. 

Biomagnification. The increase in concentration of a chemical in the tissue of organisms along a 

series of predator-prey associations, primarily 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 (BMFi). The ratio (unitless) of the concentration of a chemical in a 

predator organism at trophic level i to the concentration of the chemical in the tissue of its prey 

organism at the next lowest trophic level for a given water body and chemical exposure. In TSD 

Volume 2, the symbol BMFTL n was used for this term. 

Biota-sediment accumulation factor (BSAFi). For nonionic organic chemicals (and certain 

ionic organic chemicals to which similar lipid and organic carbon partitioning behavior applies), 

the BSAFi is the ratio of the lipid normalized concentration of a chemical in tissue of an aquatic 

organism to its organic carbon normalized concentration in surface sediment. BSAFis are only 

 
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predictive of bioaccumulation for moderately to highly hydrophobic nonionic organic chemicals 

when: (1) the ratio does not change substantially over time; (2) both the organism and its food 

are exposed; and (3) the surface sediment is representative of average surface sediment in the 

vicinity of the organism. BSAFis are expressed in kilograms of sediment organic carbon per 

kilogram of lipid. The subscript i was not used in TSD Volume 2. 

Bioaccumulation Equivalency Factor (BEFk/r). For nonionic organic chemicals (and certain 

ionic organic chemicals to which similar lipid and organic carbon partitioning behavior applies), 

the BEFk/r is the ratio between the BSAF for a chemical k and the BSAF for another chemical r, 

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 chemical’s activity, or fugacity, is 

equal among all phases composing the system of interest. In systems at equilibrium, chemical 

concentrations in all phases will remain unchanged over time. 

Food-chain multiplier (FCMi). For nonionic organic chemicals (and certain ionic organic 

chemicals to which similar lipid and organic carbon partitioning behavior applies), the FCMi 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 individual organism normally feeds. 

Freely dissolved concentration ( fd C ). w For nonionic organic chemicals, the concentration of the 

chemical that is dissolved in ambient water, excluding the portions sorbed 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 best predicts bioaccumulation. 

Home range. The area to which an individual organism restricts most of its normal activities. 

Hydrophilic. Chemicals having a great affinity to water. Hydrophilic 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 highly hydrophobic organic chemicals (log Kow > 4) have a 

greater tendency to partition into nonpolar phases (e.g., lipid, organic carbon) than do 
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hydrophilic chemicals. 

Lipid-normalized concentration (CR). The total concentration of a chemical in a tissue or whole 

organism divided by the fraction of that tissue or whole organism that is lipid. 

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

n-octanol phase to its concentration in the aqueous phase in an equilibrated two-phase system of 

n-octanol and water. This is usually expressed as log Kow, the base 10 logarithm of the n-octanolwater partition coefficient. 

Sediment organic carbon-normalized concentration (Csoc ). For sediments, the total 

concentration of a contaminant in sediment divided by the fraction of organic carbon in 

sediment. 

Sediment-water column concentration quotient (Jsocw). The ratio of the concentration of 

chemical in the sediment, on an organic carbon basis, to that in the water column, on a freely 

dissolved basis. Jsocw when divided by the Kow 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 concentration 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 between phases and reactions within phases are 

balanced, so that concentrations of the chemical in the phases of the system are unchanged over 

time. A system at steady state is not necessarily 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 far particular organisms are along the chain from the primary 

producers (e.g., phytoplankton) at level 1, to herbivores (zooplankton; level 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.
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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 Document Volume 2: Development of National 

Bioaccumulation Factors. EPA-822-B-03-030. Office of Water, Washington, D.C. 
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2. HOW TO SELECT AN APPROACH FOR DERIVING SITE-SPECIFIC BAFs

 This section provides guidance on selecting an approach (or approaches) for deriving 

site-specific BAFs from the alternatives recommended by EPA in this document. The guidance is 

intended to apply to all sites in the United States, and to each of the parties (States, Territories or 

authorized Tribes and other stakeholders) 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 addition to other factors such as resource and schedule constraints, 

in conjunction with scientific preference when selecting an approach for deriving site-specific 

BAFs. 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 based 

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., nonionic 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 relative strengths and limitations of the BAF method, and the level of 

uncertainty associated with the bioaccumulation 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 decision 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 framework 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 suitable for the class and properties of chemicals 

to which the procedure applies. The investigator should use the same framework to select 

appropriate methods for deriving site-specific BAFs. 

 
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Figure 2-1. Framework for selection of methods for deriving national BAFs.
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The first step in the national BAF derivation framework involves precisely 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 form(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 that are stable in the environment, complications can arise 

when assessing chemicals that occur as mixtures or undergo complex transformations in the 

environment. The second step of the framework consists of collecting and reviewing data on 

bioaccumulation and bioconcentration. The third step involves classifying the chemical into one 

of three broadly defined categories: nonionic organic, ionic organic, and inorganic/ 

organometallic. This step is important because some of the four BAF methods summarized in 

Section 2.5 are specific to certain chemical groups (e.g., the BSAF method for nonionic organic 

chemicals). For the purposes of the 2000 Human Health Methodology, nonionic organic 

chemicals are defined as organic compounds that do not ionize substantially in natural bodies of 

water. These chemicals are also referred to as “neutral” or “nonpolar” organics 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). Examples of nonionic organic chemicals which have 

been widely studied in terms of their bioaccumulation include polychlorinated biphenyls (PCBs), 

polychlorinated dibenzo-p-dioxins and furans, many chlorinated pesticides, and polycyclic 

aromatic hydrocarbons (PAHs). 

Ionic organic chemicals are considered to include those chemicals that contain functional 

groups with exchangeable protons, such as: hydroxyl, carboxylic, sulfonic, and nitrogen 

(pyridine) groups and functional groups that readily accept protons such as amino and aromatic 

heterocyclic nitrogen (pyridine) groups. Ionic organic chemicals undergo ionization in water, the 

extent of which depends on pH and the pKa of the chemical. Because the ionized species 

of these chemicals behave differently from the neutral species, 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. 
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Inorganic and organometallic chemicals include: 

• inorganic minerals, 

• other inorganic compounds and elements, 

• metals (e.g., copper, cadmium, chromium, zinc), 

• metalloids (selenium, arsenic) and 

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

Procedures for deriving BAFs for inorganic and organometallic chemicals are provided in 

Section 5.6 of the 2000 Human Health Methodology. 

Additional guidance on the first three steps of the framework is found in Section 5.3 of 

the 2000 Human Health Methodology. Once the chemical 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 BAF. Again, the investigator should use the same 

framework to select appropriate methods 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 will undergo biomagnification in the food web. At present, 

evaluating the biomagnification potential for this group of chemicals is almost exclusively 

limited to analyzing empirical data on the importance of the aquatic food web (dietary) exposure 

and biomagnification in determining 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 biomagnification 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-measured 
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BCF are considered to be of equal utility for deriving national BAFs, all other factors being 

equal. Additional guidance on determining national BAFs for inorganic and organometallic 

chemicals is provided in Section 5.6 of the 2000 Human Health Methodology. It should be noted 

that metal bioaccumulation can vary substantially across organisms due to a number of factors 

including physiological differences 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. Issues 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. 

2.2 BAF DERIVATION PROCEDURES FOR IONIC ORGANIC CHEMICALS

For chemicals classified as ionic organic chemicals, the primary evaluation step involves 

estimating the relative extent of ionization and evaluating their partitioning behavior with lipids 

and organic carbon. This evaluation should include determining the relative extent of ionization 

that is likely to occur at pH ranges that are typical of the site 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 nonionic chemical for the purposes of deriving sitespecific BAFs. If ionization is considered potentially important, or if non-lipid and non-organic 

carbon mechanisms control the behavior of the chemical, then the ionic chemical is treated in the 

same way as inorganic and organometallic chemicals 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 alkyl acids are an example of ionic organic 

chemicals. Some of these chemicals bioconcentrate and biomagnify in food webs via non-lipid 

mediated mechanisms; i.e., lipid and organic carbon partitioning behavior observed for nonionic 
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organic chemicals does not apply. For the perfluorinated alkyl acids, Procedure 6 (Figure 2-1) 

would be used to derive national default BAFs. 

2.3 BAF DERIVATION ASSUMPTIONS 

 The methods for deriving national and site-specific BAFs share a number of fundamental 

assumptions. First, EPA assumes that properly derived BAFs can provide a reasonable 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 calculating baseline BAFs for nonionic 

organic chemicals. Section 4 of TSD Volume 2 (USEPA, 2003) provides the scientific basis for 

this assumption and a detailed discussion of baseline BAFs. EPA presumes that the residual 

variation in BAFs across different species and sites reflects other factors that influence 

bioaccumulation. These include: 

• differences in chemical loading histories (i.e., sediment-water disequilibria); 

• food web structure;

• organism health and physiology;

• water quality factors such as temperature; and 

• food quality.

Each of these factors may vary across ecosystems 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 independent of exposure concentration). For nonionic 

organic chemicals, this assumption is generally supported by empirical and mechanistic evidence 
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(i.e., uptake via passive diffusion; Kelly et al. 2004). 

2.4 WHAT IS THE DEFINITION OF A SITE? 

 Investigators typically determine a site-specific BAF for a specific chemical, target 

species, and site. Each of these factors may influence 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 different aggregations of water bodies. 

A site can be a State, Territory or authorized Tribe; all surface water bodies of particular type 

(e.g., lakes, rivers, ponds, streams, wetlands) in a State; a watershed; an individual water body; 

or, a segment of a water body. In general, 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 water body 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-specific BAFs for watersheds in the Total Maximum Daily Load (TMDL) process. 

Site-specific BAFs may also be determined for water bodies and water body segments receiving 

point source discharges such as industrial or municipal effluents, combined sewer overflows 

(CSOs) and stormwater outfalls. Other sites could include depositional areas where contaminated 

sediments accumulate and bioaccumulation potential is enhanced (i.e., areas where water 

velocity slows and organic-rich sediments are deposited), or areas where contaminated sediments 

are disturbed by dredging activities. 

 The spatial scale of both BAFs and BSAFs should also be related to the home5

 range of 

the aquatic organism of interest. With the notable exception of migratory species such as striped 

bass and some species of eels and salmon, this range will typically be confined to a single water 

body. Even at this scale, however, measuring BAFs (or alternatively BSAFs) may not be the 

preferred method of determining site-specific BAFs. For example, the difficulty or expense of 

measuring the concentration of some chemicals in water may be prohibitive. In these situations, 

 

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 appropriate 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. 
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it may be desirable to extrapolate a site-specific BAF using a high-quality baseline BAF or 

BSAF from a comparable site, or from 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 (Kow) 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 water body, the methods preferred for determining 

site-specific BAFs may be different than those preferred for a single water body. In particular, 

developing either a field-measured BAF or BSAF (site-specific BAF methods 1 and 2) can 

become impractical because each water body must be sampled, and the necessary sampling effort 

increases as the number of water bodies 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 water bodies increases. 

 The investigator should carefully consider trade-offs between the management objective 

or need (e.g., state, water body, 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 water bodies or ecosystems within the site. The within-site variation in 

BAFs among water bodies should be minimal to estimate an accurate site-specific BAF for a 

large site. This requirement can only be met if the water bodies in the site are comparable in 

terms of the ecosystem factors known to influence bioaccumulation potential (e.g., chemical 

loadings histories [sediment-water disequilibrium]; food web structure; organism health and 

physiology; water quality factors such as temperature; and food quality). The issue is not simply 

one of size, but rather the likelihood that the variability in bioaccumulation (and the underlying 

factors such as sediment-water disequilibria, bioavailability and biomagnification) will increase 

with the size of the site, and information on these factors should be considered when defining the 

site. One approach that may improve the comparability of these ecosystem factors for large-scale 

sites is to derive site-specific BAFs for each type of water body (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 water bodies within the site. 
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 For large-scale sites, EPA recommends that States, Territories and authorized Tribes 

consider using the national BAFs, or the baseline BAFs for individual species, to determine the 

site-specific BAFs. National BAFs, if available, are based upon the highest-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 bioaccumulation potential at larger geographic scales. For 

these reasons, considerable information would be lost if a site-specific BAF were developed 

without incorporating the national BAF values or the baseline BAFs for individual species that 

are referenced in the Water Quality Criteria documents for specific chemicals. At large-scale 

sites, careful determination and justification will be needed as to why bioaccumulation data6

 used 

for deriving EPA national bioaccumulation factors are not considered applicable to the site. 

 

It is important to identify the fish consumption habits of local populations because the 

commonly-consumed fish serve as the dietary exposure pathway for bioaccumulative chemicals. 

EPA encourages States, Territories and authorized Tribes to use local or regional fish 

consumption data when developing and adopting criteria for their water quality standards, 

because local or regional fish and shellfish consumption patterns can differ substantially from 

national consumption patterns. BAFs vary between aquatic species due to several factors, 

including trophic level, benthic versus pelagic feeding preferences and habitat preferences, 

growth rate and migration. Even more variation 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 should 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. 

 

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 Or, an appropriate subset of the bioaccumulation data used to calculate the national BAFs. 
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2.5 WHAT ARE THE METHODS FOR DERIVING SITE-SPECIFIC BAFS? 

This section provides an overview of the methods 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 (Kow), combined with a site-specific 

food-chain multiplier; 

5. Site-specific BAFs recalculated from national 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 BAFs 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-specific BAFs based on adjustment of existing 

national or baseline BAFs. In most situations, the first approach (measure new baseline BAF 

values) is preferable. We summarize each of the site-specific BAF methods below, and relate 

each to the corresponding method in the national BAF methodology. As noted in Section 1, the 

methods for deriving site-specific BAFs are closely related to the methods presented in TSD 

Volume 2 for calculating national BAFs. In Sections 3 through 5 of this TSD, we describe each 

of the recommended methods in greater detail, emphasizing the scientific basis for each method 

and technical issues associated with implementing each approach. 
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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 from the site of interest, referred to here as a 

"site-specific field-measured BAF." Because the data are collected from a natural aquatic 

ecosystem, a field-measured BAF reflects an organism'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. Therefore, 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 Predicted from Measured Biota-sediment 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 sediment 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 

distribution 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 national bioaccumulation methodology. This method is 

appropriate for moderate to highly hydrophobic nonionic organic chemicals, and certain 

ionic organic chemicals that exhibit lipid and organic carbon partitioning behavior similar 

to that of nonionic organic chemicals.
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2.5.3 Site-specific BAFs Predicted from Extrapolated BSAFs or BEFs Measured at a 

Reference Site 

 The investigator may extrapolate site-specific 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 available for the chemical of 

concern. Alternatively, if a high-quality BSAF for a reference chemical is available for the site of 

interest, then the investigator can use a bioaccumulation equivalence factor (BEF, defined as the 

ratio between BSAFs for the chemical of concern and the reference chemical) measured at a 

reference site to extrapolate a BSAF. Since these are actually two related methods, we refer to 

BSAF extrapolation as method 3a and BEF extrapolation as method 3b. For either method, 

conversion of the BSAF into a site-specific BAF is accomplished 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 BEFs extrapolation methods. 

2.5.4 Site-specific BAFs Predicted from Laboratory-Measured BCFs Combined with a 

Food Chain Multipliers 

 The investigators can predict 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 hydrophobic nonionic organic chemicals. For such chemicals, a 

food-chain multiplier (FCM) should be used to adjust the value of a laboratory-measured BCF to 

better account for chemical accumulation through the food web as a result of dietary exposures. 

The investigator should measure, estimate (from existing data), or predict (using food chain 

models) the FCM to reflect biomagnification of the chemical for a particular trophic level under 

site-specific conditions. 
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 A laboratory-measured BCF often reflects the chemical metabolism that occurs in an 

organism of interest during the BCF measurement. However, a BCF experiment will not account 

for metabolism of a chemical that occurs at lower 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 method with caution to chemicals which metabolize 

in biota, because the method may overpredict BAFs for such chemicals. This is site-specific BAF 

derivation method 3a, 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 (Kow) 

Combined with a Food Chain Multipliers

 The investigators can also predict a site-specific BAF for nonionic organic chemicals by 

using the product of the chemical's Kow and a FCM for a particular trophic level under sitespecific conditions. The Kow is strongly correlated with the BCF for this class of chemicals, 

particularly for those chemicals that are poorly metabolized by aquatic organisms. For these 

chemicals, the investigator can substitute the measured or predicted Kow for the BCF when 

predicting a site-specific BAF. The investigator must also adjust the Kow 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 3b, and it corresponds to Method 4 of EPA's bioaccumulation methodology. This method 

is appropriate for non- or poorly-metabolized nonionic organic chemicals, but can also be 

applied to certain ionic chemicals having similar partitioning behavior. This approach may 

overpredict BAFs for chemicals that are metabolized by aquatic organisms, because metabolism 

is not incorporated in either the Kow or the FCM. 

2.5.6 Site-specific BAFs Recalculated from National or Baseline BAFs 

 The investigators can recalculate a site-specific BAF from baseline or national BAFs for 

a chemical by modifying the default values for the aquatic organism lipid content and/or the 
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dissolved organic carbon (DOC) concentration. The 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 baseline 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 deriving site-specific BAFs using the methods summarized above. 

These strengths and limitations, as summarized in Table 2-1, form the basis for selecting 

approach(es) to derive site-specific BAFs. In general, measuring new baseline BAF values 

(methods 1 and 2) is preferable to extrapolating or adjusting existing baseline BAFs (methods 3 

through 5). For example, the field-measured BAF method is advantageous because it applies to 

all chemical types, and because it accounts for site-specific factors that affect bioavailability, 

biomagnification, and metabolism. Nevertheless, field-measured BAFs cannot be readily 

determined 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 

offer a number of the same strengths as field-measured BAFs (e.g., they account for 

biomagnification, 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 currently limited to nonionic organic chemicals of moderate to high 

hydrophobicity. Burkhard et al. (2003a) discuss the relative merits of site-specific BAF versus 
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BSAF measurements for different classes of bioaccumulative chemicals. In general, BSAF 

approach (Method 2) will be preferable for moderately to highly-hydrophobic organic chemicals, 

while 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 site-specific BAFs, these methods also increase the 

available bioaccumulation dataset. As noted in Table 2-1, these methods may not be preferred for 

determining BAFs for large-scale sites (e.g., sites that encompass multiple water bodies or 

ecosystems), because the level of effort associated with sampling increases with the number of 

water bodies. Further guidance regarding the relative level of confidence associated with 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 support 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 with each method’s assumptions, as demonstrated by Arnot and Gobas (2004) for 

bioaccumulation predictions made with alternative models. 
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Table 2-1. Strengths and Limitations of the Methods 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 

water bodies 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 Kow ≥ 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 

water bodies 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 Kow ≥ 4 

• 3b: Accuracy depends on 

representativeness and quality of 

the estimate of chemical 

distribution between sediment 

and water 
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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 water bodies 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 Kow and FCM 

• Readily applied with minimal 

input data 

• Level of effort does not increase 

with spatial scale, number and 

type of water bodies within site 

• Limited to nonionic organic 

chemicals 

• Chemical metabolism, when 

present, not accounted for 

• Accuracy depends on accuracy 

of Kow 

• 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 National 

BAFs 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 BAFs from national or baseline BAFs, are methods that the 

investigator should consider if high quality data are available 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 relatively 
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few chemicals and sites. The issue of what constitutes "high quality" data for BAFs and BSAFs 

are discussed in Sections 3 and 4. 

 Site-specific BAFs predicted using a BCF or Kow and food chain multiplier have the 

advantage of requiring limited site-specific data, and can be readily applied to many sites, or sites 

that encompass many water bodies. BAFs predicted from a laboratory-measured BCF and a FCM 

can be applied to all chemical types, and data for BCFs are generally more plentiful than data for 

field-measured BAFs. However, acceptable BCFs for highly hydrophobic chemicals (i.e., those 

with a log Kow > 6) appear to be very limited, often because of lack of ancillary data that affect 

bioavailability (e.g., dissolved organic carbon). Deriving site-specific BAFs using Kow and FCMs 

(where appropriate) offers a distinct advantage in that no laboratory data (besides a Kow) or field 

data are needed to derive a BAF. However, this 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 accuracy of the FCM may be questionable unless the 

prediction is confirmed by data. Burkhard et al. (2003b) compared the performance of 

predictions made using national bioaccumulation methodologies 2 and 4, and found that method 

4 was more sensitive to ecosystem conditions, particularly the temporal dynamics of several 

important factors (lipid, foodweb structure, and exposure concentrations). TSD Volume 2 

(USEPA, 2003) and a number of other publications (Burkhard et al. 2003a and 2003b) provide 

further discussions of the advantages and limitations of the site-specific BAF approaches, and the 

possible trade-offs between 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 individual BAFs by using a weightof-evidence approach that takes into account the uncertainty in the individual BAFs and the data 

preference hierarchy (i.e., field-measured BAFs are preferred over BAFs derived using the other 

methods). Investigators are encouraged to determine site specific BAFs using all of the possible 

methods available. As noted in the previous sections, selecting the most appropriate derivation 

procedure depends greatly on chemical properties. Section 5.4.2 of the 2000 Human Health 

Methodology provides a guide for selecting the most appropriate final BAF when the uncertainty 
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Section 2 EPA Review Draft September 4, 2007

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is similar between two individual baseline BAFs calculated using different methods. Section 6.1 

of TSD Volume 2 and Section 5.4.3.2 of the 2000 Human Health Methodology provide more 

detailed discussions of this step. 

All BAF values should be reviewed carefully to assess their sufficiency, quality, 

variability, and overall uncertainty. Large differences in individual site-specific BAFs for a given 

species or trophic level (e.g., greater than a factor of 10) should be investigated further. As a 

result, some or all of the site-specific BAFs for a given trophic level might not be used. 

Procedural and quality assurance guidelines are described in Sections 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 relative 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 higher tier method. In general, when site-specific 

BAFs are available for more than one BAF method within a given trophic level, the final sitespecific BAF for each trophic level should be selected 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 greater 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 for that trophic level should be selected from 

the lower tier method. 

When the weight of evidence among the various BAF methods is being considered, 

greater confidence in a site-specific BAF is generally assumed when the BAFs are in agreement 

across a greater number of methods within a given trophic level. However, lack of agreement 

among site-specific BAFs derived from different methods does not necessarily indicate less 

confidence, if such disagreements can be adequately explained. For example, if the chemical of 
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concern is metabolized by aquatic organisms represented by a baseline BAF value, one would 

expect disagreement between a baseline BAF derived from a field-measured BAF (the highest 

priority data) and a baseline BAF predicted from a Kow 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 BAF data from a “more preferred” method may be 

offset by the greater quantity and diversity of data that are available from an otherwise “less 

preferred” method for a given data preference hierarchy. 
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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. Mount. 2003(a). The relationship of bioaccumulative 

chemicals in water and sediment to residues 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 prediction 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 Sediments 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., Scholfield, C.L., Bowie, G.L. and D.B. Porcella. 1990. 

Factors affecting mercury accumulation in fish in the Upper Michigan Peninsula. Environ. 

Toxicol. Chem. 9, 919-930. 

Kelly, B.C., Gobas, F.A.P.C. and M.S. McLachlan. 2004. Intestinal absorption and 

biomagnification of organic contaminants 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. and J. Van Sickle. 2007. Mercury Concentration in 

Fish from Streams and Rivers Throughout the Western United States. Environ. Sci. Technol.

41(1); 58-65. 

Phillips, G.R.and D.R. Buhler. 1978. 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 Fish 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. 
Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors 

Section 2 EPA Review Draft September 4, 2007

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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 Protection 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 Document Volume 2: Development of National 

Bioaccumulation Factors. Office of Water, U.S. Environmental Protection Agency, Washington, 

D.C. EPA-822-B-03-030. 

USEPA. 2006. Draft Guidance for Implementing the January 2001 Methylmercury Water 

Quality Criterion. EPA 823-B-04-001. U.S. Environmental 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. 
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3. MEASURING SITE-SPECIFIC BIOACCUMULATION FACTORS

 Bioaccumulation factors are used to relate chemical concentrations in aquatic organisms 

to concentrations in the ambient media (e.g., water and sediment) of aquatic ecosystems. The 

most direct measure of site-specific bioaccumulation is a BAF derived from data obtained from 

samples of tissue and water collected from the site 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., water, sediment, diet), because 

the data are collected from a natural aquatic ecosystem. A field-measured BAF also reflects 

factors that influence the bioavailability, biomagnification 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 these 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 BAFi,T , also referred to as the “fieldmeasured” BAF. The t BAFi,T is calculated from the total concentration of chemical in the 

appropriate wet tissue of the aquatic organism sampled at trophic level i, and the total 

concentration of the chemical in the ambient water at the sampling site: 

t Total BAF = BAF,T

t

i

w

C

C= (Equation 3-1) 

where: 

 Ct = total concentration of the chemical in tissue 

 Cw = total concentration of chemical in water 

Average or mean chemical concentrations are used 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 total BAF is presented in the 

following example. 
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Calculation of site-specific Total BAFs from measurements at the 

site (Method 1)

A hypothetical lake is contaminated by chemical x. Data obtained from 

field studies in the lake indicate that the mean concentration of the 

chemical in the water column, 49.5 μg/L, reflects adequate temporal and 

spatial averaging, based on the Kow of this chemical. Consumption 

surveys of the local population 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. population 

belong to trophic level 2. Based on the field studies, the average 

chemical 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 BAFis calculated using equation 3-

1: 

t BAF,T

t

i

w

C

C = (Equation 3-1)

t

2,T

2.4 mg L 1000 g BAF = 48.5 L/kg kg 49.5 g

t

w

C

C mg

μ

μ = ⋅⋅ =

The site-specific total BAF for chemical x in crayfish is 48.5 L/kg. 

Generally, site-specific total BAFs would also be determined for 

commonly consumed organisms from trophic levels 3 and 4. 
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 The second form of bioaccumulation factor is the baseline BAF, which is applied 

specifically to nonionic organic chemicals. The baseline BAF (BAF,

fd

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

i iL fd

w

= =− l

l

 (Equation 3-2) 

where: 

 Cl = lipid-normalized concentration of the chemical in tissue 

Cfd

w = concentration of chemical that is freely dissolved in water 

 fl = mass fraction of wet tissue that is lipid 

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 lipid-normalized and freely 

dissolved-based bioaccumulation factor (BAF,

fd

i l ): 

,

1 Baseline BAF BAF

f

fd

i i = −l

l (Equation 3-3)

The derivation of the baseline BAF and it’s relationship to BAF,

fd

i l (Equation 3-3) is discussed in 

TSD Volume 2 (USEPA, 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 level 4 BAF using Method 1 for a 

nonionic, hydrophobic organic chemical (chemical x). Because this is an organic chemical, the sitespecific BAF should be calculated as a baseline BAF from measurements of lipid-normalized chemical 

concentrations in consumed tissue and freely-dissolved 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 baseline 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 river to measure concentrations of chemical x in the aquatic 

food chain and the water column, to support the development 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 
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Calculation of site-specific Baseline BAFs from measurements at the site (continued)

during sampling in 1993, and three composite samples were analyzed. Twelve water samples were 

also collected on a near-monthly basis in 1993. As recommended in the Section 3 guidance, lipid 

contents were measured in all fish composite samples, and dissolved and particulate organic carbon 

(DOC and POC) concentrations were measured in all water samples. The following data were 

obtained from the study:

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 concentration of the chemical in the water column 

reflects adequate temporal and spatial averaging, based on the hydrophobicity of this chemical 

(logKow = 5.84), and was representative of the average exposure of chemical x to the target fish. 
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Calculation of site-specific Baseline BAFs from measurements at the site (continued)

Lipid and Freely-dissolved Normalization of Concentration Data

Chemical concentrations in fish (Ct) were normalized by the lipid content (fl) of each sample:

Cl = Ct / fl

The lipid-normalized chemical concentration (Cl )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 largemouth 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 (ffd) was also calculated for each 

sample, using equation 3-6: 

 

fd = 1 / (1 + POC w DOC w f ο ο ⋅ 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

1.49 5.18 1 10 0.08 10

10 10

fd f mg POC L kg mg DOC L kg

L kg mg L kg mg

= − − + ⋅ ⋅ +⋅ ⋅ ⋅

 = 0.431
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Calculation of site-specific Baseline BAFs from measurements at the site (continued)

The freely dissolved chemical concentration ( fd Cw ) is calculated as: 

fd C fC w fd w = ⋅

The freely dissolved fraction and freely dissolved chemical concentration is calculated for each 

sample as tabulated below: 

 

Sampling Date ffd

Freely-dissolved 

Concentration (ng/L)

January-93 0.431 1.76 

February-93 0.421 1.70 

March-93 0.374 2.14 

April-93 0.669 6.25 

May-93 0.666 5.97 

May-93 0.436 4.31 

June-93 0.496 6.59 

June-93 0.422 6.31 

July-93 0.345 5.51 

August-93 0.656 7.66 

August-93 0.438 5.07 

September-93 0.449 4.06 

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

fd Cw = 4.78 ng/L. 
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Calculation of site-specific Baseline BAFs from measurements at the site (continued)

Calculating a Site-specific Baseline BAF

The site-specific baseline BAF was then calculated using the average Cl ( 66.4 μg/g-lipid), fl (1.24%) 

and fd Cw (4.78 ng/L) as shown below:

C 1 Baseline BAF

C f i fd

w

= − l

l

 (Equation 3-2) 

66.4 1000 1000 1 7 Baseline BAF = 4 4.78 1 0.0124 1.39 10 / g n L g g

g lipid ng g kg L kg lipid μ

− μ

⋅ ⋅ ⋅− =× −

 

The site-specific baseline BAF for chemical x in largemouth bass is 1.39 ×107

 L/kg-lipid. 

Calculating a Site-specific Total BAF 

In order to determine a water quality standard for chemical x at the unnamed river site, the sitespecific baseline BAF must be converted to a total BAF. Recalling the relationship between the 

baseline BAF and the total BAF ( ,

t BAFi T ): 

t Site Specific BAF ( Baseline BAF +1) i T, i fd =⋅ ⋅ f f l (rearranged Equation 3-4) 

Using averages of measured values for lipid content (1.24%) and calculated freely dissolved fractions 

(0.484), the site-specific total BAF can be calculated:

( ) 7 4 Site-Specific 0.0124 1.39 10 1 0.484 8.34 10 4,

t L L BAF T kg l − kg = ⋅ × +⋅ = ×

The site-specific total BAF for chemical x in largemouth bass is 8.34 × 104

 L/kg. 
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 There are important advantages to calculating 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 chemical pressures) that 

govern the bioavailability and bioaccumulation of these chemicals and facilitate comparisons 

across ecosystems (Mackay, 2001). The lipid and freely-dissolved normalizing of concentrations 

also reduces the variance in BAFs among sites and trophic levels for these chemicals. Lipid 

normalization is useful for hydrophobic nonionic organic chemicals, because these 

chemicals partition extensively into the lipid fraction of tissues. For other classes of

chemicals, lipid partitioning is usually much more limited, and lipid normalization is not 

appropriate. Likewise, normalizing the concentrations of hydrophobic nonionic organic 

chemicals in water by the freely-dissolved fraction is helpful in reducing the variability of BAFs, 

since only the freely-dissolved phase of the chemical is considered to be bioavailable 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 carbon (i.e., commonly measured as 

dissolved organic carbon) and particulate organic carbon. The freely dissolved chemical is 

generally only a fraction of the analytically determined concentration, particularly for highly 

hydrophobic chemicals (log Kow >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. 

 The baseline BAF can be calculated from a BAFT

t

as shown in Equation 3-4 by using 

information on the lipid fraction (fl) of the tissue of concern for the study organism and the 

fraction of the total chemical that is freely dissolved in the ambient water (ffd):

BAF, 1 Baseline BAF 1

f f

t

i T

i

fd

 

= −⋅       l

 (Equation 3-4) 

where: 

BAF,

t

i T = Total BAF 

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

 

 
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TSD Volume 2 (USEPA, 2003) provides more detailed information on derivation of the 

baseline BAF equation. An alternative formula for the relationship between the total BAF and 

the baseline BAF is offered by Arnot and Gobas (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 determination of site-specific BAFs for nonionic 

organic chemicals, which generally follow a hydrophobic organic chemical paradigm (i.e., 

chemicals that preferentially partition into the lipid and organic carbon phases). The investigator 

should also be aware that not all classes of organic chemicals necessarily follow this paradigm. 

Examples of “other” classes of organic chemicals, for which this TSD may not apply, include: 

• Perfluorinated substances, especially polyfluorinated 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 Kow can not be measured. 

• Organosilicon compounds (Allen et al., 1997; Fackler et al., 1995). These substances 

can be sparingly soluble in water and highly volatile thus bioaccumulation testing is 

difficult. 

• Methylmercury, the highly bioaccumulative form of mercury, is an ionic organic 

chemical. 

Several of these are ionic organic chemicals; derivation of BAFs for these chemicals is discussed 

in Section 2.2. BAFs for inorganic/organometallic chemicals is discussed in Section 2.1. 

 The investigator should be careful to use sensitive 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 below the minimum detection 

limit (MDL) for the analytical method, the uncensored value should be used in the calculation of 
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 3-10 

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 the investigator apply statistical approaches for 

averaging with censored data. These include Helsel (2005), Helsel and Hirsch (2005), ElShaarawi and Dolan (1989), Newman et al. (1989) and Newman (1995). These approaches can 

be used with normally and log-normally distributed data. Berthouex and Brown (1994) 

recommend that unbiased means should only be calculated from concentration data if fewer than 

20% of the reported values are nondetects. 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 chemical concentrations in water can help the 

investigator determine whether to include or exclude data for concentrations less than the MDL 

and/or not detected at all. 

 Ideally, data obtained from the open literature (e.g., peer-reviewed journals, scientific 

reports, professional society proceedings) can be used to calculate site-specific BAFs, provided 

that the appropriate measurements have been made and information is available indicating the 

quality and usability of the data. A number of bioaccumulation databases compile such 

information. 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 according to 6 criteria and rates each BAF measurement with 

an overall confidence level. This data quality assessment is valuable as guidance for 

investigators, who should ensure that high quality 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/KIZON_start_hazkizon.html). 

 

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_er4-06 
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 Additional bioconcentration and bioaccumulation databases are under development; for 

example the European Chemical Industry Council (CEFIC) Long Range Research Initiative for 

Predicting the Environmental Fate of Chemicals.. Once published, these and other databases may 

also become valuable resources to the investigator. Given sufficient supporting information, the 

investigator could calculate reliable site-specific 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. Therefore, it will generally be 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 when developing water quality standards for the 

protection of human health (USEPA, 2000a). However, protocols for measuring site-specific 

BAFs have not previously been available. Although 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 sampling effort. This Section provides 

guidance to the investigator considering this method of deriving site-specific BAFs and 

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 questions 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-specific BAF prior to sampling and to develop a field 

study design based upon this variability. Following that are sections that address sampling design 

considerations necessary to measure site-specific BAFs specific to biota (Section 3.3) and water 

(Section 3.4). 
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3.1 KEY STUDY DESIGN QUESTIONS FOR DETERMINING 

SITE-SPECIFIC BIOACCUMULATION FACTORS

 Although a field-measured BAF is a direct measure 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 for determining site-specific BAFs, collecting field 

data at the site of interest is probably the most difficult approach. The text box below highlights 

several key factors for the investigator to consider for a field 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 term average concentrations for the chemical in 

biota and exposure media. The home range of the target species will dictate the spatial scale of 

the sampling effort. Chemical temporal and spatial distributions, organism life history, and 

duration of exposure, among other factors, all contribute to BAF uncertainty and should be 

addressed by the field sampling plan. 

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, ionic 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 water bodies 

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

 biota and water) 

• Ecosystem type 

 
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Sampling requirements for biota and water will largely be controlled by the variability of 

chemical concentrations at the field site. Chemical 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 chemical 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 these 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, and applying BAFs is very important. 

The investigator faced with designing a field study to determine a site-specific BAF 

should consider the following series of key questions, intended to identify factors of the problem 

to be addressed by the sampling plan. 

Key Study Design Questions 

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

• The home range of the target species will dictate 

the spatial scale of the sampling effort. 

• Size of site / number of water bodies 

• Sampling characteristics (temporal and spatial 

variability of chemical concentrations in 

biota and water) 

• Ecosystem type 
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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 within a factor of 2, 5 or 10)? How do I determine the level of 

effort (i.e., the number and type of biota and water samples) associated with different levels of 

accuracy? 

4. Biota. Which species should I sample?

• 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 modeling 

• 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 
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5. Temporal Variability (i.e., Sampling Event Frequency). How many times do I need to 

sample biota and water at the site? 

Biota Sampling Considerations 

• Consider chemical properties (hydrophobicity and metabolism) 

• Consider biota characteristics (migration, reproduction, availability, other 

seasonal characteristics based on climate, etc.) 

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

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

• Lessons learned from bootstrap/simulation examples 

Water Sampling Considerations

• Consider chemical properties (hydrophobicity) 

• Consider ecosystem conditions (e.g., variability due to hydrodynamics) 

and temporal aspects of chemical loadings 

• Lessons learned from bootstrap/simulation examples 
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6. Spatial Variability (i.e., Number of Stations). How many locations should be sampled? 

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 water body and target 

organisms 

• Consider evidence of spatial gradients in exposure concentrations as well 

as the presence of sources 

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

• Consumption characteristics (harvesting areas) 

• Ecosystem properties (size of site, spatial differences in hydrodynamics, 

etc.) 

• Consider spatial design options (e.g., random, stratified, systematic, 

judgement) 
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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 and biota parameters should I 

consider measuring? 

Of course, the bioaccumulation field data should 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 water body or ecosystem within the site for 

which site-specific BAFs are to be derived. Sampling in a subset of water bodies is a valid 

• Appropriate methods depend on contaminant and water body 

• 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 and metabolism) 

• Consider ecosystem conditions (variability 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 particulate 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 measurements for ionic organic chemicals 

• Location information, such as GPS coordinates 
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approach only if it can be demonstrated that the resulting BAFs are representative and can be 

extrapolated to other locations in the site where the criteria and values will be applied. In 

practice, this may be difficult unless considerable information is available for the water bodies 

that comprise the site. 

3.2 HOW TO DESIGN A SAMPLING PLAN TO MEASURE BAFS 

 In designing a field study to measure BAFs, 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 and spatial variability of the chemical in the ecosystem and on 

the dynamics of the chemical in the biota. A successful sampling design procedure for any field 

study should consider the following ecosystem conditions and chemical properties: 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 hydrophobicity; and metabolism of the chemical in all 

organisms composing the food web. The investigator defines the frequency of sample collection 

(i.e., the number and spacing of sampling events in time), the spatial distribution of sample 

collection locations, and the total number of samples to be collected. Having the appropriate 

sampling frequency and spatial distribution (the sampling design structure) enables the 

determination of BAFs that are representative of the long-term average 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 process, precision of the 

measurements is balanced against the costs associated with sample collection 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. 

 Three sampling design approaches will be demonstrated in the following subsections 

(Sections 3.2.1 thru 3.2.3). These approaches are complementary, and the investigator can apply 
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them individually or together to design a sampling plan appropriate for measuring a site-specific 

BAF. 

Section 3.2.1 presents and demonstrates a statistical method (Bootstrap resampling) to 

determine the required numbers of samples of biota 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 collected 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 field season because of the labor required for their 

collection. EPA is aware of relatively few field data sets that are adequate for this type of 

evaluation (see Section 5.1.1 of TSD Volume 2 [USEPA, 2003] for discussion). These include 

PCB congener data from the Hudson River (TAMS, 1998), the Green Bay Mass Balance Study 

(USEPA, 1989b), and the Lake Michigan Mass Balance (USEPA, 2004). 

 A second approach modifies the Bootstrap method by substituting simulated data 

generated using Monte Carlo methods for measurements of chemical concentrations in biota and 

water. The Monte Carlo approach 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 concentration measurements in biota and water. The 

Monte Carlo approach is presented in Section 3.2.1.2. Monte Carlo results (BAF confidence 

limits) were compared to results from the Bootstrap, and were also repeated using different 

variances in chemical concentrations and different 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 obtaining representative samples for the BAF determinations. With 

model simulations, biota and water data can be created on a day-to-day basis assuming different 

ecosystem conditions and chemical properties (e.g., temporal and spatial concentration profiles, 

life history scenarios, metabolism rate, and hydrophobicity). For these simulations to be 
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meaningful, the model constructs should provide reasonable representations of ecosystem 

conditions and chemical properties. Model simulations are used to evaluate how the following 

factors influence the spatial distribution of samples, and the number and temporal spacing of 

sampling events, required to accurately determine BAFs: 

• how the temporal and spatial variability in chemical concentrations, 

 

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

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

• the disequilibrium between chemical concentrations in the sediment and water 

column. 

Section 3.2.2 discusses the sampling design approach using model simulations. 

3.2.1 Determining the Number of Samples to Collect 

 As discussed in the previous section, the precision of a BAF measurement is defined 

largely by the total number of samples that are collected and analyzed. The investigator 

designing the sampling plan should determine the necessary numbers of samples based upon the 

expected variability in chemical concentrations and the goal for precision of the BAF 

measurement. The investigator should address two problems in order to determine this 

relationship: 

• The variability of the chemical concentration 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 variables, no formulas are available for their 

exact sampling variances (CDM, 2002). It is fairly unusual to collect environmental 

data specifically for the purpose of calculating a concentration ratio, such as a BAF. 

As a consequence, relatively little attention has been paid to the relationship between 

the accuracy of a ratio of two random variables and the sample sizes2

. 

 

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 relationship between sample size and the resulting variances. For 

example, the DQO-PRO software program (http://www.instantref.com/download-dqo-pro.html) calculates numbers 
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3.2.1.1 Bootstrap BAF Resampling 

The precision of BAF measurements 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 examples using Green Bay Mass Balance Study PCB 

congener data in Appendix 3A. The ratio of the 90th to the 10th percentile values of the 

distribution of BAFs in each bootstrap resampling calculation was found 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 indicates 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 combinations of the number of fish and water 

concentrations can yield comparable BAF precision. For example, the same confidence limit 

ratio for PCB congener 149 forage fish BAF was obtained by resampling 10 fish and 6 water 

concentrations or 4 fish and 10 water concentrations. 

 If site-specific data for concentrations of the target chemical in biota and water are 

available, then Bootstrap resampling is probably the best way to determine the number of 

samples to collect and analyze in order to determine 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, required to improve the precision of 

the BAF derived from these measurements. Bootstrap 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 ecosystem comparable to their site, 

 

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.
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then bootstrap resampling could be conducted with that data, and sample sizes selected 

accordingly. 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 variables (Metropolis and Ulam, 1949; Robert and Casella, 2004). The 

investigator can use Monte Carlo to simulate chemical concentrations for biota and water when 

sufficient data are not available for the site of interest. To do so, 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 (or 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 measurements. 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 could be directly compared. 

In general, the Monte Carlo analysis of BAF precision as a function of sample sizes 

produced results comparable to those obtained via Bootstrap resampling. For moderately variable 

chemical concentrations, the ratio of the 90th to the 10th 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, although 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 appears to be warranted only for sites having very high variability in chemical concentrations 

in fish and/or water. It is most effective for the investigator to apply more sampling effort to the 

chemical concentration (biota vs. water) that is more variable. Correlations between chemical 

concentrations in biota and water 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 were more beneficial in terms of reducing BAF 
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uncertainty for smaller sample sizes. Mis-specifying the concentration 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 Carlo analysis. However, the investigator should 

always use averaging methods appropriate to the 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 highly 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 accuracy of the measurement at all. 

 

3.2.2 Modeling Simulation of BAF Sampling Designs 

 Once the appropriate number of biota and water samples to collect has been determined, 

the investigator should allocate the samples both spatially and temporally, in order to complete 

the sampling design. Burkhard (2003) performed model simulations to explore how the 

variabilities in water and sediment chemical concentrations 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 models were used, so that the 

connections between input and resulting variabilities in BAF designs were 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 developed by Thomann et al. (1997). Details of the 

modeling approach, and presentation and discussion of the results, are presented in Appendix 

3D. 

 
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3.2.2.1 Using Model Simulations to Develop Field-Sampling Designs 

 The kinetics of chemical uptake and loss by the fish (or other aquatic organism) controls 

the chemical residue that resides in the organism. These kinetic processes are directly dependent 

upon the chemical’s hydrophobicity and metabolism rate in the fish. Successful field-sampling 

designs should account for the chemical uptake and loss kinetics, and for the changes in chemical 

concentrations occurring in the fish’s environment. The modeling simulations strongly 

demonstrate that smaller uncertainties can be obtained by using properly developed sampling 

design structures. The haphazard collection of samples 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 substantial insight into what an appropriate 

sampling design structure might be for BAF measurements: 

• For chemicals with log Kows of 4 and less with any rate of metabolism, the concurrent 

collection of fish and water samples over time provides the smallest degree of uncertainty 

for BAF measurements; the spacing of sampling events does not seem critical. 

• For nonmetabolizable and slowly metabolizable chemicals with log Kows of 5 and greater, 

practically identical BAF uncertainties are obtained using sampling designs consisting of 

the collection of a series of water samples over time, and either the concurrent collection 

of fish samples with each water sample or the collection of fish samples with the last 

water sample. From a field-sampling perspective, the one-time collection of fish is 

appealing because of the logistics of assembling field-sampling crews. 

• In contrast to chemicals with lower Kows, nonmetabolizable chemicals with log Kows 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 spaced widely over time (with concurrent 

fish collection with the last or all water samples) to the designs appropriate for lower Kow

chemicals; that is, concurrent collection of water and fish samples over time with sample 

spacing not being very critical. 
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 This transition in appropriate sampling design 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 situation that exists for nearly all nonionic organic chemicals. 

 For sampling to determine BAFs, chemicals with large Kows will generally require that 

numerous water samples be averaged over time to establish the long-term chemical 

concentrations in the water. In contrast, for chemicals with low Kows, 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 water provide a good predictor of the chemical 

concentration in the fish. 

 Chemicals with intermediate metabolism rates and mid-range hydrophobicities (log Kows 

of 4 to 6) present one of the more difficult challenges in selecting an appropriate sampling design 

structure for measuring BAFs. This range of hydrophobicities lies within the transition zone 

between the much more obvious design structures appropriate for low and high Kow chemicals. 

 

 The process for developing successful field-sampling structures for BAF measurements 

can primarily focus upon three parameters: temporal variability, metabolism, and Kow. These 

three parameters can range widely, and depending upon their values, require dramatically 

different field designs. The greatest number of samples would be required for high-Kow

chemicals in aquatic ecosystems subjected to extremely high temporal variability in chemical 

concentrations in water. Although spatial variability has been discounted as a dominant factor in 

sampling design, knowing or understanding the immediate 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 history for the sampled organisms. Poor spatial 

coordination of fish samples with their actual water exposures will yield BAFs with poor 

accuracy and large biases. 
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 The sampling structure (i.e., number of sampling 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). These illustrative designs provide a sense of how 

sampling design structures might be influenced by differences in temporal variabilities, 

metabolism rates, and Kows. 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 responses. As indicated above, the total number of 

samples required for a successful measurement is dependent upon the desired precision, and, 

thus, the illustrative sampling 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 home range of the fish are not included in 

the illustrative sampling structures in Table 3-1. 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. 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 picture 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 organism’s home range. However, 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 chemical concentrations in the water. Further, 

bioaccumulation simulations for migrating fish suggest that BAFs can be measured with low 

uncertainty even when extreme spatial concentrations exist at the field site, provided the 

measurements are performed in more contaminated locations of the site for more hydrophobic 

chemicals, i.e., log Kow>5 (Burkhard, 2003). 
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 In addition to the home range issue above, the illustrative BAF sampling structures 

(Table 3-1) do not include the effects of collecting composite water samples over time. For 

higher Kow chemicals, compositing reduces the uncertainty in the BAF measurement, whereas 

the uncertainty in the BAF measurement is increased by compositing for lower KOW 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 desired 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 necessary 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. 
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Table 3-1. Illustrative Bioaccumulation Factor Sampling Design Structures. Uncertainties 

Associated With Design Structures Are Ecosystem Specific 

Sampling Events 

log Kow

Metabolism 

Rate

Sampling 

Designa 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, 6b 7

≥ 6 low 1st or 2nd series 1, 5, 8b 30

≤ 4 medium 2nd series 1 7

5 medium 1st or 2nd series 1, 2, 3b 7

≥ 6 medium 1st or 2nd series 1, 4, 6b 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 = collection of paired fish and water samples with each sampling event. 

b Values are ordered according to low, medium, and high temporal variability, respectively.

1. The investigator determines the goal for accuracy of the BAF measurement, and 

expresses this goal as the ratio of 90% confidence limits. In the examples presented in 

Appendices A and C, the BAF confidence limit ratios typically ranged from about 2 

to 12. 

2. The investigator determines the number of biota and water concentration samples to 

collect. Suggestions are offered in Appendices A and C for determining the numbers 

of samples to collect. 

a) If site-specific data or data representative 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. 
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c) The investigator should avoid 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 Kow) and rate of metabolism; and 

b) Temporal variability of water concentrations, based upon factors of the water 

body at the site. 

Table 3-2 illustrates the relationship between categories of water bodies (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 water bodies towards those that are more advective (flowing) with shorter 

hydraulic residence times. Therefore, the investigator can use the water body 

categories in Table 3-2 to estimate the temporal variability of water concentrations. 

4. The investigator allocates the number of samples (based upon guidance from Step 2) 

evenly among sampling events (determined in Step 3). 
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Table 3-2. Waterbody Type as Indicator of Temporal Concentration Variabilitya

 low variability < - - - - - - - - - - - - - - - - - > high variability

Lakes and Reservoirs Estuaries and Tidal Rivers Rivers and Streams

Waterbody CVb 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, preserving, and shipping samples to a processing 

laboratory for target analyte analysis. 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. The 
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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 project manager, sponsoring organization and 

responsible official, project personnel, stakeholders, and experts, etc. (e.g., all 

customers and suppliers); 

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

• Identification of project schedule, resources (including budget), milestones, and any 

applicable requirements (e.g. regulatory requirements, contractual requirements); 

• Identification of the type of data needed and how the data will be used to support the 

project's objectives; 

• Determination of the quantity of data needed and specification of performance criteria 

for measuring quality; 

• The data will be obtained (including existing 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 quality performance criteria. 

 The investigator and field sampling staff should develop a detailed sampling plan prior to 

initiating a field study. As described by EPA (USEPA, 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 methods 
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The role of each of these parameters in developing 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 concern, 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 analyze tissue samples using methods that are 

chemical-specific, sensitive, accurate and precise. 

 Bioaccumulation factors should only be determined 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 composing 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 individual components of the mixture will be present in different 

amounts among the sediment, water and biota). The investigator must select analytical methods 

that are specific for the individual chemical (or chemicals, 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-significant polychlorinated 

dibenzodioxin (PCDD) congeners. Unless the investigator is confident that chemical 

concentrations can be quantified in the majority (preferably > 80%) of both biota and water 

samples, a different approach of determining BAFs should be considered. 

 It is also important to measure other parameters that are significant to the 

bioaccumulation process. For nonionic organic chemicals, lipid content of the target species 

should be measured in the same tissue in which the contaminant was measured to permit lipid 
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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 organisms to be targeted for sampling, as well 

as the size ranges to collect. The target species 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 important to consider when compositing fish. Other 

aspects of the field sampling plan will follow from identifying the target species. For example, 

the home range of the target species will dictate 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/post spawning) can alter the body burden of 

contaminants, with significant contaminant loss observed due to maturation and release of sperm 

or eggs. The investigator should consider determining the age of sampled fish because older fish 

tend to accumulate higher concentrations 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, particularly when taking 

composite samples. The timing of sampling should include the period of most frequent 

harvesting of the species. 

 In freshwater ecosystems, one bottom-feeding 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 recommended target species cannot be successfully collected at the 

site. The same criteria used to select the recommended target species should be used to select 

alternate target species. 
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 The correlation between increasing size (age) and contaminant tissue concentration 

observed for some freshwater finfish 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 change their exposure at a contaminated site. The 

size of estuarine/marine target species collected should still 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 interest. Site-specific data, 

such as gut contents or stable isotope analyses, are preferred for determining trophic levels. 

Jardine et al. (2006) provide guidance on the application and interpretation of stable isotope 

ratios to measure trophic levels. The investigator should also consult with fisheries experts and 

refer to life history literature for the species of interest when making trophic level 

determinations. Most often the field studies used will 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 situations where consumption of 

lower trophic level organisms represents an important human exposure route, such as certain 

types of shellfish at trophic level 2, the field study should also include appropriate target species 

at this trophic level. 

 Behavior and life cycle aspects are important 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 investigator should know 

the approximate arrival and departure dates for 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 characteristics (e.g., migration, range, habitat, and feeding 

preference), which can lead to differences in chemical exposure and bioaccumulation. 
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For example, two populations of a fish species may both inhabit a tributary - one that 

migrates seasonally between the tributary and 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 resident fish. In this example, the investigator should target 

the collection of fish from the resident population in order to measure the site-specific 

BAF for the tributary. This could be accomplished by sampling when the migratory 

population is absent. The field plan should take the behavioral differences of multiple 

populations into account, in order to sample organisms from a specific population. 

Otherwise, it may be difficult or impossible to distinguish which population(s) are 

represented by the sampled organisms. 

• What is the home range size for the 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 falls) the home range of the organisms can 

probably be defined fairly easily. In estuaries, the home range of some fish species 

are constrained by salinity gradients. Home ranges can be determined by 

tagging/recapture, radio-telemetry, and/or ultrasonic telemetry studies at the site of 

interest. Valuable information can be obtained from fisheries biologists or 

recreational fishermen that are familiar with the waterbody and fish species. Home 

ranges for freshwater fishes can also be estimated 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 (m2

) 

 HAB = 0 for rivers and 1 for lakes 

 L = body length (mm), and 

 W = organism body weight (g). 

 
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An example of applying equation 3-5 is presented below. For freshwater 

invertebrates, and for marine and estuarine ecosystems, allometric relationships for 

home range have not been reported. 

Having a good understanding of the immediate 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 ranges which extend beyond the study site. Just 

because an organism is caught at a sampling 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 

establish the connection of the fish (or other aquatic organism) to the sampling 

location. 

 

It is very useful to consult with local fisheries experts during the sampling design 

phase of the field study to help in determining 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. 

Estimating the home range for freshwater fish

The bluegill sunfish is a common inhabitant of small lakes and creeks. A 

representative length for an adult bluegill 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 m2

 (1.6 acres). 
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• Do the organisms of interest exhibit diel (day/night) behavior in habitat at your site? If 

the answer is yes, the sampling plan should reflect the typical behavior for the species of 

interest. 

Behavioral information for the species of interest will be used to specify the location and timing 

of field sampling for biota, as discussed 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 localized, or if site-specific hydrologic features create a sink 

where chemically contaminated sediments accumulate and the bioaccumulation potential might 

be enhanced. Upstream and downstream water quality and sediment monitoring 

stations/locations bracketing point source discharges, outfalls, and regulated disposal sites can 

often be used to characterize the geographic extent of the contaminated area. Within coves or 

small embayments where streams enter large lakes or estuaries, the geographic extent of 

contamination may also be characterized via multilocational sampling to bracket the areas of 

concern. Such sampling designs are clearly most effective 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. Selection of sampling sites should also consider 

temporal and spatial variations in food web structure that may occur across the study area. 

Estimating the home range for freshwater fish (continued)

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 range 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. 
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 Site selection considerations should also include the number of samples necessary to 

characterize different waterbody types (lakes, rivers, estuaries, and coastal marine waters) based 

on hydrodynamics (including waterbody size). Typically, 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 needed to achieve a specific precision. For example, 

fish inhabiting relatively small lakes 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 compared to the relatively homogeneous exposures that 

might occur in smaller, less hydrologically dynamic lake ecosystems. Large sites with strong 

spatial gradients may require spatially-stratified 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. Smallmouth bass, a riverine species, have a home 

range of 500 to 4,500 m2

, 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 rivers to spawn. Lake trout in the Great Lakes 

have been found to migrate up to 300 km (200 miles) with larger fish migrating 300 miles (480 

km) (Daly et al. 1962). For many marine species, estuaries are the spawning areas for the adults 

and nursery areas for the developing juveniles, who eventually travel offshore as adults and 

return again to the estuaries to spawn. For these 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 average 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. 
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 Home range should also be considered for shellfish. Bivalve molluscs, like the oyster or 

mussel, cement themselves to hard substrates as young spat and are then unable to move away 

from pollution effects. Although clams and scallop species are slightly more mobile, they also 

typically stay in the general area in which they first settled out of the water 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 accurately determine the average 

chemical concentration at the site. 

 Biota sampling should be conducted in frequently fished areas near chemical contaminant 

sources, possibly including the following locations. 

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. 

 Nonpoint source inputs such as 

• landfills, Resource Conservation and Recovery Act (RCRA) sites, or Superfund 

Comprehensive Environmental Response, 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 multimedia mechanisms such as hydrogeologic 

connections or atmospheric deposition. 
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 Areas acting as potential pollutant sinks where contaminated sediments accumulate and 

bioaccumulation potential might be enhanced, such as 

• areas where water velocity slows and organic-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 oscillatory shear stresses from wind waves, and 

• the convergence zone between fresh and saltwater in estuaries. 

 Identifying sampling locations near significant point source discharges are usually 

straightforward. It is often more difficult, however, for the investigator to identify clearly defined 

sampling locations in areas affected by pollutants from nonpoint sources. For these sites, 

assessment information summarized in state Section 305(b) reports should be reviewed before 

locations are selected. State 305(b) reports are submitted to the EPA Assessment and Watershed 

Protection Division biennially and provide an inventory of the water quality in each state. The 

305(b) reports often contain Section 319 nonpoint source assessment information that may be 

useful in identifying major sources of nonpoint source pollution to state waters. States may also 

use a method for targeting pesticide hotspots in estuarine watersheds that employs pesticide use 

estimates from NOAA's National Coastal Pollutant Discharge Inventory (Farrow et al. 1989). 

 In addition to the intensity of subsistence, sport, or commercial fishing, factors for 

evaluation when selecting fish and shellfish 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 site 
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 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, sediment, and fish). Selecting sampling sites in 

proximity to one another is also more cost-effective because it provides opportunities to combine 

sampling trips for different matrices. 

 The availability of data on the indigenous fish and shellfish communities is a 

consideration when selecting sites. Information on preferred feeding areas and migration patterns 

is valuable in locating populations of the target species (Versar, 1982). Knowledge of habitat 

preference provided by fisheries biologists or commercial fishermen may significantly reduce the 

time required to locate a suitable population of the target species at a given site. 

 One additional consideration associated with sample site selection is whether the 

sampling area includes waters inhabited by threatened 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 Fisheries Service (NMFS) if their sampling effort could potentially 

impact any marine or anadromous species covered under the Endangered Species Act (ESA) of 

1973. A species is listed under one of two categories, endangered or threatened, depending on its 

status and the degree of threat it faces. An endangered species is one that is in danger of 

extinction throughout all or a significant portion of its range. A threatened species is one that is 

likely to become endangered in the foreseeable future. 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 about each USFWS region is available on-line on the 

USFWS website at http://endangered.fws.gov/wildlife.html. Additional information can be 

gleaned by contacting the specific USFWS regional office that has primary responsibility for an 

endangered species.
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3.3.4 Sampling Times

Selection of the most appropriate times for sampling 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 reproductive 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 simplifying collection procedures. The late summer-early 

fall sampling period may not be appropriate, however, if it does not coincide with the legal 

harvest season of the target species, and/or the target species spawns during this period3

. A third 

exception to late summer-early fall sampling 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 application because these compounds are degraded and 

metabolized relatively rapidly compared to organochlorine 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 considerations for aquatic 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 (usually summer for recreational and subsistence 

fishers). For estuarine/marine shellfish (bivalve 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 legal harvest period. In the second situation, 

monitoring should correspond with the period when the majority of harvesting is conducted 

during the legal season. The investigator should consider different sampling times for target 

shellfish species if differences exist between the commercial and recreational 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. 
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 Ideally, the investigator selects a sampling period that avoids the spawning 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 collected during this period may 

not be representative of the normal population. For example, feeding habits, body fat (lipid) 

content, and respiration rates may change during spawning and may influence pollutant uptake 

and clearance. The collection of samples may also adversely affect some species, such as trout or 

bass, by damaging the spawning grounds. Most fishing regulations protect spawning periods to 

enhance propagation of important fishery species. Species-specific information on spawning 

periods and other life history factors is available in numerous sources (e.g., Carlander, 1969; 

Emmett et al. 1991; Pflieger, 1975; Phillips, 1980). In addition, digitized life history information 

is available in many states through the Multistate 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 provides the best estimate of the residue in the 

organism based upon the organism’s exposure at the site. If the organisms spend a very short 

time at the site (e.g., the fish migrate 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 the chemical concentrations measured in water 

from the site would not be reflective of the organism’s recent exposure history. The degree of 

bias will be related to the uptake and elimination 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 chemicals of concern in bioaccumulation studies (USEPA 1987; 1989a). 
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Composite samples are homogeneous mixtures of samples from two or more individual 

organisms of the same species collected at a particular site and analyzed as a single sample. 

Because the costs of preparing and analyzing individual samples are higher than the costs of 

preparing and analyzing a composite sample, the latter sample type is the most cost-effective for 

estimating average tissue concentrations in target species populations. 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, compositing offers advantages over sampling 

individual organisms. Most importantly, composites formed from individual organisms collected 

at a specific time and place will avoid sampling the variability in chemical concentrations 

between individuals, which can be a significant component of the overall variability. Since BAFs 

measured for use in deriving national recommended ambient water quality criteria for protecting 

human health are intended to represent average, long-term chemical concentrations, it is neither 

necessary nor desirable to sample variability in chemical concentrations among individual 

organisms. Although composite sampling will not reveal extreme contaminant concentration 

values in individual organisms, this is not considered a major disadvantage given the goal of 

measuring average chemical concentrations for BAF determination. 

 The investigator should select a composite sample type for chemical analysis based on 

the tissue types consumed 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, 

particularly when measuring a BAF for mercury. Analysis of skinless fillets may also be more 

appropriate for some target species such as catfish and other scaleless finfish species. In contrast, 

using whole fish with skin-on as the sample type for assessing PCBs, dioxins/furans, or 

organochlorine pesticide exposures in populations of Native Americans, Asian Americans, 

Caribbean-Americans, or other ethnic groups 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/furans, or organochlorine contaminants 

into the broth; thus, analysis of whole fish should mirror the way the consumer prepares the fish. 

Similarly, the investigator should consider whether using skin-on fillets (with belly-flap 
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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, nonionic 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. 

 In any study design, it is important that biota samples be collected and composited in 

size/age classes. For fish, dietary composition changes substantially with size/age, and these 

changes can result in differences in BAFs among age classes. For forage fish, common classes 

are young-of-the-year (YOY), juveniles, and adults. For piscivorous fishes, common classes are 

year classes (e.g., 2, 3, 6, and 10 years old). The investigator should consider 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 identification prevents 

the mixing of closely related species with the target species. Individuals from different 

species should not be combined to form a composite sample (USEPA, 1989a, 1990). 

• The composited organisms should all satisfy any legal requirements of harvestable size 

or weight, including “slot-limit” restrictions4

: Alternatively, they should at least be of 

consumable size if no legal harvest 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 

the least-biased average of concentrations for the individual 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 
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size) of the smallest individual in any composite sample should 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, dioxin, PCBs, and toxaphene) 

and methylmercury, the larger (older) individuals within a population are generally 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 marine species. Size is used 

as a surrogate for age, which provides some estimate of the total time the individual 

organism has been at risk of exposure. Therefore, the primary target size range ideally 

should 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 collected 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 concentrations (e.g., associated with the reproduction 

cycle of the target species). If organisms used in the same 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 fish 

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. 

 

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. 
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• Organisms of the target species should be collected 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. The investigator should 

determine the required tissue homogenate mass based upon the lowest expected chemical 

concentration of the target analyte(s) and the sensitivity of the analytical method. Sample 

mass requirements can vary considerably depending upon the chemical(s) and analytical 

method. Analysis of mercury in aquatic biota typically requires about 1 gram (wet 

weight) of tissue. PCB and TCDD/TCDF analyses requires 10 to 20 grams of tissue. Up 

to 200 grams of tissue may be required for multiple analytes (e.g., numerous priority 

pollutants 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 organisms 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 needed 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 design have implications that may make the 

statistical analyses more complicated. The statistical methods for analyzing composite samples 

are made tractable and easier-to-use by simplifying the study design. Using equal numbers of 

organisms in replicate composite samples is one way to do this (USEPA, 2002a). 

 For shellfish samples, the recommended composite sample type for chemical analysis 

should also consider the tissue type consumed by the target population. The specific tissues 

considered to be edible will vary among target shellfish species based on local consumer 

preference. For example, several states (Maine, Maryland, Massachusetts, New Hampshire, New 
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Jersey and New York) have issued advisories 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 (including 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 the 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 composite samples for evaluating turtle tissue 

contamination. As with shellfish, the tissues of freshwater turtles considered to be edible vary 

based on the dietary and culinary practices of local populations. 

3.3.6 Replicate Samples

 EPA recommends that replicate composite samples of each target species be collected at 

each sampling site. Field replicates (i.e., duplicates) 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 collection and storage of replicate 

samples, even if not analyzed at the time due to inadequate resources, allow for follow up QC 

checks. These sites should be identified during the planning phase. 
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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 response to the variations in environmental 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 and shellfish (Versar, 1982). This selectivity is a 

great advantage for sampling biota for the purpose 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 for collection are in hand. Recommended sampling 

equipment and its use, considerations for ensuring preservation of sample integrity, and field 

record keeping and chain-of-custody procedures associated with sample processing, 

preservation, and shipping are discussed in USEPA (2000b; 1997a). 

3.4 MEASURING CHEMICAL CONCENTRATIONS IN WATER

 This section provides guidance on the development 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 initiation 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 
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 The role of each of these in developing an appropriate field plan for water sampling is 

discussed below. As was the case for biota sampling, the investigator and field sampling staff 

should develop a detailed sampling plan prior to initiating the field study. 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 Environmental Data Collection, EPA QA/G-5S; USEPA. 2002a). 

3.4.1 Target Analytes and Analytical Methods 

 The method used to analyze water samples for 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 determined for individual chemicals. In cases 

where the chemical of interest is a mixture (e.g., PCBs, chlordane), individual chemicals 

composing the mixture should be quantified individually. This will result in BAFs for individual 

components of the chemical mixture. Where appropriate, BAFs should be expressed for specific 

forms of the chemical of concern. 

 The investigator should also ensure that the method chosen to analyze chemicals of 

concern in water is sufficiently sensitive to detect ambient concentrations. For highlybioaccumulative chemicals, sensitivity of the analytical 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 concentrations in tissue are high enough to cause concern. Unless the 

investigator is confident that chemical concentrations can be quantified in the majority 

(preferably > 80%) of both biota and water samples, 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. 
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 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 that 

can occur during sample transport, handling and analysis. Guidance regarding the use of blanks 

and other quality control samples is provided in USEPA (2002b). 

 For organic chemicals with log Kow >4, POC and DOC concentrations 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, conductivity/salinity, chlorophyll a

and total suspended solids can also be measured, as they provide information to help interpret the 

bioavailability and subsequent bioaccumulation of contaminants by aquatic organisms. pH is an 

especially important parameter for ionizing organic chemicals (see Section 2.2). 

 

3.4.2 Phase Separation 

 Calculating the total BAF (Equation 3-1) requires the investigator to measure the total 

chemical concentration in water. For hydrophobic chemicals, including nonionic organic 

chemicals, a baseline BAF can be calculated instead (Equation 3-2). The advantage of 

determining a baseline BAF is that the variability of the BAF will usually be reduced when 

compared to a total BAF determined at the same site (Burkhard et al. 2003; USEPA, 2003). 

Normalizing the total BAF also reduces the variance 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. The freely dissolved (or bioavailable) chemical 

concentration can be calculated from the total chemical concentration measured in water, by 

calculating the freely dissolved chemical fraction: 
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df οw ow f = 1 / (1 + POC ⋅ Κ + 0.08 ⋅ ⋅ Κ ) DOC (Equation 3-6) 

where: 

 

 DOC = the average dissolved organic carbon concentration in the water column 

(kg of organic carbon/L of water) and 

 POC = the average particulate organic carbon concentration in the water column 

(kg of organic carbon/kg of particulate matter). 

Kow is used to estimate the partition coefficient between POC and freely dissolved chemical, and 

0.08&Kow 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 chemicals 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 different fractions. There are a number of methods 

of achieving chemical phase separation that have been developed for water sampling including 

filtration, semi-permeable membrane devices [SPMD], solvent-filled dialysis bags, 

centrifugation and solid phase extraction. Depending on the method, the investigator may still 

need to apply Equation 3-6 to determine dissolved bioavailable concentrations, because not all 

methods will effectively separate DOC-bound chemical from the truly dissolved fraction. The 

preferred option for calculating the freely dissolved chemical concentration will often be 

apparent once the investigator considers the feasibility and logistics associated with collecting 

the necessary volumes of total versus phase-separated water samples. 

3.4.3 Sampling Locations 

 Probably the most important factor 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 collecting water samples which are reflective of the 
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organism’s home range can not be overstated. Spending time and resources to better define this 

relationship will greatly decrease the uncertainty associated with the resulting BAFs. 

 Once the home range of the target 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 for selection of biota sampling locations is also 

appropriate for water sampling. Information about the preferences of the target organism - in 

terms of environmental factors (e.g., temperature, transparency, light penetration, depth, water 

velocity, substrate type, vegetation cover or debris) - can be valuable in terms of sampling the 

water at the most frequently-inhabited locations. If information is available about concentration 

gradients in the water body, samples should be collected along this gradient. If such data are not 

available, the investigator can solicit expert opinion as to where concentration gradients should 

be expected. References such as Chapra and Reckhow (1983) and Thomann and Meuller (1987), 

that deal with expected chemical concentration gradients for different water body types and 

appropriate sampling designs, can be especially valuable in this regard. Another option for 

estimating the chemical concentration gradient is to conduct a dye study, if the source of the 

contaminant is known. 

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, concentrations of many hydrophobic chemicals in 

water are expected to be much more variable than concentrations in aquatic organisms. Thus, 

individual water samples taken at one point in time will likely not be adequate to reflect average 

exposure to the target species. Water samples should be collected and analyzed so that chemical 

concentrations can be averaged over the approximate time it takes for the target species to reach 

steady state, which varies depending on the hydrophobicity and rate of metabolism of the 

chemical within the target organism. For example, chemicals with high Kow values are expected 

to reach steady-state in top trophic level organisms much more slowly than chemicals with low 

Kow values; thus, they require greater temporal averaging of water column concentrations for 

estimating BAFs. For large fish, a year or longer may be required for concentrations of highly 
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hydrophobic organic chemicals to reach steady state. Other factors to consider when determining 

the frequency of sampling include target species migration and other aspects of life history. 

 The investigator should also consider how concentrations of the chemical of concern 

respond to the various sources, transport and fate pathways at the site. Generally, temporal 

variations in chemical concentration are related to factors such as seasonality, flow rate, 

stratification and external loading and fluxes. Water quality studies and models can be extremely 

valuable guides for predicting how bioavailable chemical concentrations will vary with time. The 

investigator can check for their availability at the site of interest or at comparable sites via 

federal or state programmatic activities (e.g., TMDL, Superfund). Water quality models (sitespecific 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 measurement 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 Kow>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 the 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 locations). For temporal composite samples, the 

analytical method-specific holding time should not be exceeded for any portion of the sample. 

The investigator should consider whether there is enough information to form a composite that 

will result in a measured concentration comparable to the average exposure to the organism of 

interest. If not, the composite sample may produce a biased mean concentration. In this case, 

individual sample analysis may result in a more accurate average water concentration and BAF. 
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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 (i.e., duplicate) water samples be collected 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, 1989b; USEPA, 2004) . Compositing replicate 

samples collected at one location and time is an effective way to improve precision. 

3.4.7 Sample Collection Methods 

 Many different methods have been developed 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 sample 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 priority. For volatile chemicals, eliminating contact 

with the atmosphere and container head spaces are priorities. 

 Similarly, water sampling techniques and the associated equipment are often somewhat 

specialized for different water body types. Sampling 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 investigator to select 

sample collection methods appropriate for the site and the chemical of concern. Standard 

Methods for the Examination of Water and Wastewater (APHA, 2004) is a reference that covers 

many aspects of water monitoring, including sample collection. The American Society for 

Testing and Materials (ASTM) also publishes references that address methods for collecting 

water samples. 
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Another valuable resource for information about water sample collection methods is the National 

Field Manual for the Collection 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 Samples 

The National Field Manual addresses sampling of novel chemical contaminants such as 

wastewater, pharmaceutical, and antibiotic compounds; arsenic species; and low-level mercury. 

 Another useful source of information for water sampling methods are EPA documents 

such as Standard Operating Procedures (SOPs) 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. 
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USEPA, 2000b. Guidance for Assessing Chemical Contaminant Data for Use in Fish Advisories. 

Volume 1, Fish Sampling and Analysis, Third Edition. Office of Water, Washington, DC. EPA 

823-B-00-007. 

USEPA, 2000c. Guidance for the Data Quality Objectives Process/G4. Quality Staff, Office of 

Environmental Information, Washington, DC. EPA 600-R-96-055. 

USEPA, 2000d. EPA Quality Manual for Environmental Programs, 5360 A1. Quality Staff, 

Office of Environmental Information, Washington, DC. 

http://www.epa.gov/quality/qs-docs/5360.pdf

USEPA. 2002a. Guidance for Choosing a Sampling Design for Environmental Data Collection, 

EPA QA/G-5S. Quality Staff, Office of Environmental Information, United States 

Environmental Protection Agency, Washington, D.C. December. 

http://www.epa.gov/quality/qs-docs/g5s-final.pdf

USEPA. 2002b. Guidance for Quality Assurance Project Plans, EPA QA/G-5. Quality Staff, 

Office of Environmental Information, United States Environmental Protection Agency, 

Washington, D.C. December. http://www.epa.gov/quality/qs-docs/g5-final.pdf

USEPA, 2003. Methodology for Deriving Ambient Water Quality Criteria for the Protection 

of Human Health (2000), Technical Support Document Volume 2: Development of National 

Bioaccumulation Factors. United States Environmental Protection Agency, Office of Water, 

Office of Science and Technology. EPA-822-R-03-030

USEPA. 2004. Results of the Lake Michigan Mass Balance Study: polychlorinated biphenyls 

and trans-nonachlor data report. U.S. Environmental Protection Agency, Great Lakes National 

Program Office. Chicago, Illinois. EPA-905/R-01-011. 

USGS. 1998 (and other dates). National field manual for the collection of water-quality data: 

U.S. Geological Survey Techniques of Water-Resources Investigations, book 9, chaps. A1-A9. 

http://pubs.water.usgs.gov/twri9A.

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.

Voiland, M.P., K.L. Gall, D.J. Lisk, and D.B. MacNeill. 1991. Effectiveness of recommended 

fat-trimming procedures on the reduction of PCB and Mirex levels in Brown trout (Salmo trutta) 

from Lake Ontario. J. Great Lakes Res. 17(4):454-460.

WDNR (Wisconsin Department of Natural Resources). 1988. Fish Contaminant Monitoring 

Program-Field and Laboratory Guidelines. Report No. 1005.1. Madison, WI. 
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Wyss, G.D., and K.H. Jorgensen. 1998. A User’s Guide to LHS: Sandia’s Latin Hypercube 

Sampling Software. Risk Assessment and Systems Modeling Department, Sandia 

National Laboratories, Albuquerque, New Mexico. SAND98-0210.
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APPENDICES 
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 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 demonstrated by examples using Green Bay Mass 

Balance Study PCB congener data. 

Bootstrap sampling method 

 Bootstrap estimation is a computer intensive 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., thousands 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 computer in a number of ways (Simon, 1997). The 

advantage of the bootstrap is that the investigator is not required to make any assumptions 

regarding the distribution of data (Simon, 1969), thereby avoiding potential errors in statistical 

analysis if these assumptions are violated. 

 In this case, the investigator is interested in estimating the accuracy5

 of BAFs in terms of 

precision6

 and bias for alternative numbers of biota (nb) and water (nw) samples. The bootstrap is 

applied to resample nb biota and nw water concentrations from a high quality data set. For each 

resample, mean biota and water concentrations are calculated, and then the BAF is calculated as 

the ratio of the mean concentrations. This procedure is repeated many times, until a stable 

distribution of BAF values is generated. The bootstrap distribution 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. 
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investigator with estimates of the variance as well as the bias7. For example, the 90% confidence 

limits are estimated by the 95th and 5th percentiles of the bootstrap distribution of BAFs. By 

repeating this procedure using different numbers of biota (nb) and water (nw) samples and 

comparing the BAF dispersion results, the investigator can determine a sampling design that 

meets their requirements for BAF accuracy. 

 Bootstrap estimation does require the investigator to have 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 present exercise, PCB congener concentration data 

were combined from zones 3a and 3b of the Green Bay Mass Balance dataset (see Burkhard et 

al. 2003 and Endicott, 2001 for descriptions of this data). This combination produced a dataset 

containing measurements of PCB 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 ( BZ8

 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 Kow values for these congeners: 

PCB congener log Kow

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 dissolved 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” BAF 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.
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 Forage and predator fish BAFs were calculated for each of the four PCB congeners. 

10,000 bootstrap resamples were used for each calculation. In addition, each calculation was 

repeated 100 times, to ensure the results were independent of small random fluctuations which 

were observed between calculations. For each BAF 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: 

mean bias = ( ) BAF BAF n observed n

n

n

b

b

− 



 



 =

∑

1

/ 

where: 

 BAFobserved = the BAF calculated using all of the observed biota and water 

concentrations, 

 BAFn = the BAF calculated using the biota and water concentrations 

sampled in bootstrap resample n, and 

 nb = the number of bootstrap resamples used in each 

calculation (10,000) 

and 

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

RMSE = ( ) BAF BAF n observed n

n

n

b

b

− 



 



 =

∑ 2

1

/ 

 

8

 PCB congeners can be identified according to a numbering scheme published by Balschmiter and Zell (1980), 

commonly referred to as “BZ” numbers. 
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The bootstrap procedure was repeated for sample sizes ranging from 2 to 60 fish, and from 2 to 

90 water samples. 

Taylor series approximation 

 If the investigator makes the assumption 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 series approximations are developed below for both 

the total BAF and the baseline BAF. The total BAF is calculated from two measured variables 

(see equation 3-1): the total concentration of chemical in the appropriate wet tissue of the aquatic 

organism; and the total concentration 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): 

s ( ) ( ) ( ) C s BAF s r s s BAF BAF w

C T

t

C TwC C T

t

T

t t wt w = + ⋅ −⋅ ⋅ ⋅ ⋅ 

 



1

2

2 2 2 1 2/

 

where sCt and sCw are the standard deviations for Ct and Cw, respectively; and rTw is the 

correlation coefficient9

 between Ct and Cw. The baseline BAF is calculated 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 total concentration of the chemical in the ambient 

water; and the fraction of the water concentration that is freely dissolved. The variance of the 

calculated baseline BAF includes variances each of the four measured variables. For the baseline 

BAF, the Taylor series approximation for the standard deviation is: 

s ( ) ( ) ( ) C s BAF s r s s BAF

BAF w

fd C l

fd

C ld C C l

fd

l

fd l w

fd l w

= + ⋅− fd ⋅⋅ ⋅ ⋅ 



 





1

2

2 2 2 1 2/

 

where:

s () () f C s C s rs s C

l

C l f lt C f l lt l t l = + ⋅ −⋅ ⋅ ⋅ ⋅ 

 

1

2

2

2

2 1 2/

 

 

9

 We assumed that the PCB concentrations in fish and water were uncorrelated within each of the spatial zones 

sampled in Green Bay. 
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and 

s ( ) ( ) ( ) f s C s r s sC C

d

C w

fd

fd Wd C fd w

fd

w

fd w w = + ⋅ −⋅ ⋅ ⋅ ⋅ 

 

1

2

2 2 2 1 2/

 

where sBAF, sCl, and sCw

fd are the standard deviations for the BAF, CL, and Cw

fd , respectively; and 

rld is the correlation coefficient4

 between Cl and Cw

fd , rlt is the correlation coefficient between Ct

and Cl, and rwd is the correlation coefficient between Cw and fd. 

 For sufficiently large sample sizes, the approximate (1-"/2)·100 percent confidence limits 

for the BAF (total or baseline) are given by: 

BAF z sBAF ± ⋅ α /2 

where " is a probability of exceedance and z"/2 is the value of the standard normal distribution. 

Generally, the degree of confidence will only be accurate if the sample size is greater than 30. 

Taylor series approximation was used to estimate confidence limits for the BAFs in Green Bay. 

These estimates were then compared with the confidence limits determined using the bootstrap, 

in order to check and confirm the accuracy of the latter. 

Bootstrap BAF results 

 The distribution of 10,000 chemical concentrations 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 concentrations and the BAFs. Figure 3A-2 

displays histograms for 2 fish and 20 water concentrations resampled from the same data. The 

output distribution of fish concentrations is more dispersed (wider) and asymmetrical (the right 

tail of the distribution is extended) by comparison to Figure 3A-1. This difference is a 

consequence of resampling fewer (2 as opposed to 20) fish concentrations. As a result, the BAF 
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distribution in Figure 3A-2 is also more dispersed and asymmetrical. A more extreme example of 

limiting the sample sizes is displayed in Figure 3A-3, which shows histograms for 2 fish and 2 

water concentrations resampled from the same data. The output distributions 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 distribution in Figure 3A-3 is also more dispersed 

and asymmetrical than the distributions 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 distributions that was observed in all of the bootstrap trials that 

involved few (less than 6) resamples of water concentrations. 

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
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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.
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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.
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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 concentrations 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 water, especially for sample sizes smaller than 6. 

These results also indicate that resampling different combinations of the number of fish and 

water concentrations can yield comparable BAF precision. For example, the same CLR for PCB 

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 corresponding forage fish. 

Table 3A-1. Bootstrap Results for PCB 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. 

Number of Number of Water Samples

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 1.42 
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Figure 3A-4. Bootstrap resampling results for PCB congener 149 in Green Bay Zone 3 

forage fish: 90 % confidence limit ratios for BAF as a function 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 measurement. The bootstrap resampling results also 

demonstrate how bias of the BAF measurement is influenced by resampling different numbers of 

fish and water concentrations. While the precision of BAFs depended on the sample sizes of 

both fish and water, the results reveal that the mean bias is only sensitive to the number of water 

concentrations resampled. This result was somewhat 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 BAFs as functions of resample size. For each 

congener, the curves for different numbers of fish concentration resamples fall on top of each 

other, demonstrating that the mean bias of BAFs is only sensitive to the number of water 

concentrations resampled. BAF biases for each congener 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 samples

BAF 90% confidence limit ratio

2

4

6

8

10

20

30

60

number of fish 

samples:
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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 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. 

 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 for congener 149 as functions of resample size. 

The curves plotted in this figure are almost identical 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 investigator to select the appropriate 

number of biota and water samples based upon the ratios of BAF confidence limits, although the 

negative mean percent bias associated with sampling 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 samples

BAF percent bias

PCB 18

PCB 52

PCB 149

PCB 180

congener:
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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 the numbers of fish and water 

samples. 

Taylor estimates confirm Bootstrap results 

 Table 3A-2 presents the concentration statistics (average and standard deviation) for each 

PCB congener, the statistics for the predator and 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 (nb) and water (nw) concentrations. This agreement confirms 

that the Bootstrap algorithm 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 samples

RMSE % of mean BAF

2

4

6

8

10

20

30

60

number of fish 

samples:
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How does compositing affect the relationship 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 those 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 number of samples to collect and analyze? 

 If site-specific data for concentrations of the target chemical in biota and water are 

available, then Bootstrap resampling is probably the best way to determine the number of 

samples to collect and analyze in order to determine 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, required to improve the accuracy of 

the BAF derived from these measurements. Bootstrap 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 ecosystem comparable to their site, 

then bootstrap resampling could be conducted with that data, and sample sizes selected 

accordingly. However, there are other approaches that should also be considered when sitespecific data are limited. 
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Table 3A-2. Taylor Series Approximation of Confidence Limits for Baseline BAFis in Green Ba

CHEMICAL CONCENTRATION PREDATOR FISH FOR

AND 

STATISTIC

Predator 

Fish Cl

(ng/g-l)

Forage 

Fish Cl

(ng/g-l)

Water 

Cfdw

(ng/L)

Baseline 

BAF4

90% 

LCLa

90% 

UCLb

90% 

CLRc

Baseline 

BAF3

90% 

LCL

PCB 18: 

Average 183 84.0 0.0601 3,051 1,970 4,132 2.10 1,399 1,151 

St. deviation 243 57.5 0.0388 

St. error 657 151 

PCB 52: 

Average 1401 649 0.0590 23,760 18,928 28,592 1.51 11,009 9,317 

St. deviation 931 331 0.0393 

St. error 2,938 1,028 

PCB 149: 

Average 537 240 0.00659 81,502 63,924 99,081 1.55 36,349 30,053 

St. deviation 329 103 0.00579 

St. error 10,686 3,827 

PCB 180: 

Average 544 211 0.00102 534,098 412,216 655,979 1.59 207,284 168,32

St. deviation 350 104 0.000949 

St. error 74,092 23,683 

a LCL: Lower confidence limit; b UCL: Upper confidence limit; c CLR: Confidence limit ratio.
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Appendix 3B 

 

PCB Congener Concentrations Measured by Green Bay Mass Balance Study 

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

Date Zone Station Layera 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 0.0392 
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Date Zone Station Layera 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.
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2. Green Bay Mass Balance PCB Congener Concentrations: Lipid-Normalized Forage Fish 

Common Name Life 

Stageb 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 45 135 451 
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Common Name Life 

Stageb 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 Concentrations: 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 1833 
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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 simulate 300 chemical concentrations in biota 

and water. Based upon previous statistical analysis of the Green Bay PCB congener data 

(Endicott, 2001), we assumed that chemical concentrations were lognormally distributed. Means 

and variances were calculated from the log-transformed concentration data. The Latin Hypercube 

method guarantees that the simulated probability distributions of the concentrations are exact, 

which is advantageous because it reduces the number of realizations necessary for the results to 

converge (McKay et al. 1979). The generator program also applied Iman and Conover’s (1982) 

algorithm for inducing the specified degree of rank correlation between chemical concentrations 

in biota and water, without altering their statistical distributions. Statistical properties of all 

parameter distributions output by the Latin Hypercube generator were confirmed against the 

specified variance-covariance structure in each test. 

 The Monte Carlo analysis was based on the same data used in 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 could be directly compared. In practice, the investigator would be 

faced with the problem of estimating appropriate concentration distributions without site-specific 

data. When such prior information about chemical concentrations is lacking, the investigator has 

a limited number of choices: 

• Conduct a pilot study - a small number of samples 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 (based on judgment or expert opinion), an 

approximate value of standard deviation may be computed by dividing the 

concentration range by 6. 
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 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 chemical 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 Monte 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 Bootstrap results. This outcome is expected, given 

that the concentration distributions used in the Monte Carlo method were based on the same data 

used in the Bootstrap resampling. For this case, when the information regarding chemical 

concentrations are input consistently, both methods function properly 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 congener, 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 normally distributed, although a lognormal distribution 

was assumed in the Monte Carlo simulation. Apparently, mis-specifying the concentration 

distribution had little or no effect on the outcome of the Monte Carlo analysis. This result is 

consistent with the guidance offered by Berthouex and Brown (1994), that making the lognormal 

assumption was usually beneficial or (at worst) harmless. 
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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 water samples are 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. 

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 samples

BAF 90% confidence limit ratio

2B

4B

8B

30B

2MC

4MC

8MC

30MC

number of fish 

samples/analysis 

method:
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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 are 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 between fish and water concentrations on BAF 

estimates? 

 The Monte Carlo BAF analyses were repeated, using varying degrees of correlation 

between chemical concentrations in biota and water. Correlation coefficients ranged from 0.001 

(no correlation) to 0.9 (high positive 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 concentrations are uncorrelated and 

independent. 

 Correlations between chemical concentrations in biota and water reduced the BAF 

confidence limit ratios, percent bias and RMSE for 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 samples

BAF 90% confidence limit ratio

2B

4B

8B

30B

2MC

4MC

8MC

30MC

number of fish 

samples/analysis 

method:
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magnitude of the correlation, and were more beneficial for smaller sample sizes. This is 

illustrated in Figure 3C-3, which plots the predator fish BAF confidence limit ratios for congener 

149 as a function of the concentration correlation coefficient for sample sizes of 2/2, 10/10 and 

30/30 (nb / nw). 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 BAF 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 coefficient

BAF 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 coefficient
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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 was repeated using different degrees of 

variability in chemical concentrations in both fish and water. This was done to provide the 

investigator with general guidance for selecting sample sizes that would be applicable for 

different sites. Specifically, lognormal chemical concentration distributions, having coefficients 

of variation10 (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 presented in Tables 3C-1 through 5. Each sub-table 

presents the 90% BAF confidence limit ratios as a function 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 tabulation of results for 2 fish and 2 water samples. 

For this case, highly-variable chemical concentrations 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 Variability in Chemical Concentrations in Fish and 

Water: Chemical Concentrations Measured 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 fish and 4 water samples. For this case, highlyvariable chemical concentrations result in 90% BAF confidence limit ratios that exceed 5. For 

moderately variable chemical concentrations, the BAF confidence limit ratios are in the range of 

3 to 4. For low-variability chemical concentrations, the BAF confidence limit ratios are mostly 
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smaller than 3. Results for 6 fish and 6 water samples 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 appears to be warranted only for sites having 

very high variability in chemical concentrations in fish and/or water. 

Table 3C-2. 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 Measured 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 Variability in Chemical Concentrations in Fish and 

Water: Chemical Concentrations Measured in 6 Fish and 6 Water Samples 

Fish 

Concentration 

CV

Water concentration coefficient of variation (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 4.10 

 

10 The coefficient of variation is the ratio of the standard deviation to the mean. 
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The tabulations for unbalanced sample numbers (i.e., nb g nw) 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 concentrations). In the former case, highly-variable 

water concentrations result in lower BAF confidence limit ratios than do highly-variable fish 

concentrations. In the latter case the situation is reversed. In other words, additional sampling 

counteracts the tendency for highly-variable fish or water concentrations 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 Variability in Chemical Concentrations in Fish and 

Water: Chemical Concentrations Measured 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 Measured 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 7.80 
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Appendix 3D 

 

Modeling Simulation of BAF Sampling Designs 

 Burkhard (2003) performed model simulations to understand how the variabilities in 

water and sediment chemical concentrations translate into the variabilities associated with BAFs 

(and BSAFs, which will be discussed separately in Section 4) based 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 models are generic (i.e., not calibrated to site-specific data), the 

results are intended to compare different sampling designs in terms of the resulting BAF 

precision, and do not offer definitive predictions with known certainty. 

Model for evaluating temporal variability 

 A model river segment with a point source 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 concentration in the discharge. The model river segment 

was assumed to contain a food web consisting of zooplankton, benthic invertebrates, forage fish, 

and piscivorous fish. Three food web structures were 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% zooplankton and 50% benthic invertebrates. Time-variable 

chemical concentrations in forage and piscivorous 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 invertebrates) were calculated using the modeling 

assumptions of Gobas (1993), which were equilibrium conditions with their respective 

environments (i.e., water column and sediment, respectively). The lower trophic levels serve as 
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important “entry points” for hydrophobic organic chemicals into the aquatic food web. Details 

of the modeling approach can be found in Burkhard (2003). Although simplistic, this model’s 

predictions of chemical concentrations in fish and how they vary in response to temporal 

variation in exposure concentrations 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 subsegment receiving the point source discharge. 

Daily chemical concentrations in that subsegment were determined by using the method 

described previously for the model for evaluating temporal variability. Daily chemical 

concentrations in the other subsegments were determined as ratios of the concentration predicted 

in the modeled subsegment. Sediment chemical concentrations were set and held constant for the 

entire simulation by using the average water column chemical concentration in each subsegment 

(for the entire flow data set), and Asocw/Kow =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 sampling 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 temporal variability model are shown in Figure 

3D-1. Daily chemical concentrations in the river segment were calculated by using Mississippi 

River flow data for the 1995 calendar year (Figure 3D-1.a). A substantial change in chemical 

concentrations is observed around day 75, due to a rapid 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 concentrations in the river for chemicals with log Kows 

ranging from 2 to 9 (Figure 3D-1b). Comparison of the chemical concentrations in the river to 
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the predicted concentrations in piscivorous fish reveals that concentrations in the fish will change 

(relative to the chemical concentrations in the ambient water) at rates that are dependent upon the 

hydrophobicity of the chemical. In all time-variable bioaccumulation models, the rate of change 

in chemical concentrations in biota is controlled by the overall rate of chemical loss from the 

organism. This rate is the sum of elimination rates via gills and gut, the organism growth rate, 

and the rate of chemical metabolism or biotransformation. The rate of change decreases with 

increasing Kow because the elimination rates are modeled as inverse functions of hydrophobicity. 

For chemicals with low Kows (log Kow <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 Kows of 2 and 3 (Figure 3D-1b) to the daily 

chemical concentrations (Figure 3D-1.a). For chemicals with large Kows, 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 chemical 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 relative to the rate of change of the chemical 

concentration in river will strongly influence the design for the field study, for example, number 

of samples collected over what time period. 

With the simulated daily chemical concentrations in fish and water and the chemical 

concentration in the sediment, hypothetical field-sampling designs were evaluated by sampling 

the simulated data as if one were actually performing a field study. 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 design results in 365 BAFs. Consider another field 

design consisting of two sample collections spaced 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. 
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Figure 3D-1. (A) Daily chemical concentrations in the model river segment. (B) Daily chemical 

concentrations in piscivorous fish for chemicals with log KOWs of 2, 3, 4, . . . , and 9. The daily 

chemical concentrations in piscivorous fish have been scaled to the largest value for each KOW. 

The daily chemical concentrations for log KOWs of 2 and 3, after scaling, are practically 

identical; the log KOW 3 data are plotted in gray. 
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 The ratio of the 90th to 10th percentile values 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 uncertainties for a given sampling design. For the example 

presented above, ratios of the 90th to 10th percentile BAFs of 8.63 and 5.94 were obtained for 

the daily and two-week sampling designs, respectively. These ratios easily demonstrate which 

design provides the lower uncertainties, on average, in the measured BAF. To summarize the 

overall process for evaluating field sampling designs, 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 designs of interest, sample the data for all possible 

dates, and calculate BAFs for all possible dates; (4) determine the ratio of the 90th to 10th 

percentile values; and (5) compare the ratios of the 90th to 10th percentile values to determine 

which designs resulted in the smallest uncertainty. Steps 3 and 4 were performed for a number of 

field designs. 

Temporal variability in chemical concentrations 

 Sampling designs consisting of 1 to 14 sampling events to collect grab water samples 

with uniform spacing between water sample collection 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, Asocw/Kow =1, and the mixed benthic– pelagic food web. The results, in terms of 

the ratios of the 90th to 10th percentile BAF values, are plotted 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 collection coincided with the date of the last 

collected water sample. This design is commonly 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. 
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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 collected with each water sample (second series), 

when using Mississippi River (USA) flow data for years 1955 to 1995. 

 

 For chemicals with log Kows greater than 5, the ratios of 90th to 10th percentile BAFs for 

the first and second series were practically identical; therefore, only the results for the first series 

are reported in Figure 3D-2. For chemicals with log KOWs 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 KOWs 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 when 
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limited numbers of samples are used; for example, four and fewer. For these designs, chemicals 

with log KOWs of 5 and greater have smaller uncertainties in the measured BAFs in comparison 

to those determined using the grab water samples. 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 Kows, only 1 or 2 additional 

samples are necessary to obtain the same uncertainty 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 KOWs of 2 and 3, exactly the opposite behavior was observed between the grab 

and composite water sampling designs: compositing caused larger uncertainties in the measured 

BAFs for both series of sampling designs. For log KOW 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 KOW > 4. 

The chemical concentrations used in the temporal variability simulations had a 

coefficient of variation of 118%. Burkhard (2003) found qualitatively similar results to those 

presented above were obtained when lower temporal variabilities were used. There may also be 

situations where the temporal variability in chemical 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 frequent or periodic storm events 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 measuring 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 of 

90% exceedance limits estimated by bootstrap for PCB congener 149 (Figure 3B-4) can be 
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compared to the modeling results for logKow = 7 (the 10th panel in Figure 3D-2). Even though the 

exceedance limits are not the same (90% for bootstrap BAFs vs. 80% for modeled BAFs), the 

BAF ratios are quite comparable. For 2 widely-spaced water samples and a chemical in the range 

of 6 < log Kow < 7, the simulated BAF confidence interval ratio is 6 to 7 versus the Bootstrap 

resampling ratio of 6.7 for PCB 149 (log Kow = 6.67). In addition, both methods produce the 

same trends in BAF ratio as a function of sample size. This is somewhat remarkable given that 

the two methods arrive at estimates of BAF uncertainty via completely different approaches, 

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 metabolism (with the same hydrophobicity) because of the increase 

in the overall elimination or transformation rate of the chemical. Arnot and Gobas (2003) suggest 

that chemicals with metabolic biotransformation rates greater than 0.1 to 0.2 /day in fish do not 

appear to biomagnify in aquatic food webs. Examination of the results of the simulations 

suggests that when metabolism does occur, the appropriate sample design for a chemical of a 

given Kow would be best described by the sample design for a chemical with a smaller Kow (with 

no metabolism) and the degree of smallness is dependent upon the metabolism rate. In situations 

where metabolism rates cannot be reliably determined, the use of the second series of sampling 

designs would provide lower uncertainties for BAF measurements. 

Food web structure and sediment–water chemical concentration 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 fixed in a given ecosystem, in which case their 

influences upon the variability observed in a measured BAF are expected to be small. If any of 

these factors vary, the extent of bioaccumulation may change. However, we also expect that 

water concentrations usually change more rapidly than these other factors. Burkhard (2003) 

conducted sensitivity analyses which demonstrated that food web structure and sediment-water 
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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 variability in chemical concentrations was 

considered. In most ecosystems, however, concentrations of chemical contaminants in sediments 

and water do vary spatially. To provide some insight into the importance of spatial variability, a 

series of random walk11 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 the results (not shown) 

suggests that chemical concentration gradients do not add large uncertainties into the measured 

BAFs, beyond those caused by temporal variability alone. 

 Additional random walk simulations were performed to further evaluate the effects of 

spatial variability. These results suggested that BAFs 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 organisms are collected from the least contaminated 

sites, uncertainties of the BAF measurements can become very large for chemicals with log Kows 

between 3 and 5. 

 

11 Random walk is the idea of taking successive steps, each in a random direction. Treating 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. 
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4. MEASURING BIOTA-SEDIMENT ACCUMULATION FACTORS 

TO PREDICT SITE-SPECIFIC BIOACCUMULATION FACTORS 

 Biota-sediment accumulation factors (BSAFs) are 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 chemical concentration in the 

biota sample reflects the organism's exposure through all routes. Also like a field-measured BAF, 

a BSAF accounts for bioavailability, because the sediment concentrations are normalized to the 

organic carbon content. Similarly, both field-measured BAFs and BSAFs account for chemical 

metabolism in the aquatic organism or its food web. Because of these similarities, a site-specific 

baseline BAF can be predicted from a BSAF, provided there is data on the distribution of the 

chemical between sediment and water at the site. This approach is Method 2 of EPA's national 

bioaccumulation methodology. 

The purpose of Method 2 is to convert the bioaccumulation information contained in a 

measured BSAF to the corresponding baseline BAF 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 the 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 lipid and organic carbon partitioning behavior similar to that of 

nonionic organic chemicals. Since this method specifically applies to organic chemicals, we will 

refer to site-specific baseline BAFs as “site-specific BAFs” in this section, although the reader 

should recognize that we are referring to baseline values throughout. 
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4.1 DESCRIPTION OF METHOD 2 

 The BSAF is defined as the ratio of the lipid normalized concentration of a chemical in 

an organism sampled at trophic level i to the organic carbon normalized concentration of the 

chemical in surficial sediment (Ankley et al., 1992): 

/

/

t

i

s soc soc

C f C BSAF

Cf C = = l l Equation 4-1 

where: 

BSAFi = Biota-sediment accumulation factor for aquatic organism at trophic level i, 

Ct = Chemical concentration in the organism [Mchem/Mtissue], 

fR = Lipid fraction of the organism, 

Cs = Chemical concentration in surficial sediment [Mchem/Msediment], and 

fsoc = Organic carbon fraction of the sediment. 

In addition: 

CR = Lipid-normalized concentration of the chemical in whole fish [Mchem/Mlipid], 

and 

There are Three Steps Involved in Applying Method 2 to Determine 

a Site-Specific Bioaccumulation Factor:

1. Calculate a BSAF from the average concentrations of the chemical of interest 

in tissue and sediment samples from the site 

2. Determine the distribution of concentrations 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) 
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Csoc = Organic carbon-normalized concentration of the chemical in the surface 

sediment [Mchemical/Morganic carbon] 

The BSAF has units of kg of organic carbon/kg of lipid. The use of lipid and organic carbon 

normalized concentrations makes the BSAF an approximate fugacity1

 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, require 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 unchanged (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 characterize chemical concentrations at a site. The 

appropriate averaging method (e.g., arithmetic or geometric mean) 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 chemicals 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, metabolism, bioenergetics, and 

bioavailability factors on each chemical’s bioaccumulation. 

 Method 2 predicts the site-specific BAF from the measured BSAF for the chemical of 

interest, using the sediment-water concentration quotient determined for one or more similarlybehaving reference chemicals. Specifically, this method uses sediment-water concentration 

quotients (Jsocws) for reference chemicals to estimate values of Cfd

w that cannot be measured for 

 

1

 Fugacity expresses chemical concentrations as a partial pressure, which indicates the tendency of a chemical to 

“prefer” one phase (e.g., lipid, organic carbon, dissolved) over another (Mackay, 1979). 
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the chemical of interest. Each chemical’s Kow should also be known, because the ratio of Jsocw to 

Kow provides the basis for relating reference chemicals to the chemical of interest. The following 

equation is used to predict the site-specific baseline bioaccumulation factor for the chemical of 

interest from a BSAF measured at the site: 

/ , ,

,

 1 Site-Specific Baseline BAF f

k r socw r ow k

i i

ow r

D K

BSAF

K

  Π = −  

  l

Equation 4-2

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

Also: 

Jsocw = Sediment-water concentration quotient [L3/M] 

Dk/r = Ratio of the fugacity gradients (modeled as Jsocw/Kow) 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 concentrations in sediment and dissolved in water 

at the site, is a critical parameter in predicting the baseline BAF from a BSAF. It is calculated as: 

,

, 

,

soc r

socw r fd

w r

C

C

Π =

 Equation 4-3

where: 

 Csoc,r = concentration of a reference chemical in dry sediment, normalized to 

sediment organic carbon 

C ,

fd

w r = concentration of the reference chemical that is freely dissolved in water 
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Again, average chemical concentrations should be used in the calculation of the sediment-water 

concentration quotient, and appropriate averaging methods should be selected following review 

of the concentration data. 

 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 the site. Octanol-water partition coefficients (Kows) 

are used to adjust for any differences in hydrophobicity between the chemical of interest and the 

reference chemicals. In an ecosystem at equilibrium, fugacity theory predicts that the Jsocw

should equal Kow. Therefore, Jsocw/Kow is called the fugacity gradient. In many cases, the 

fugacity gradients between sediments and water for both reference chemicals and the chemical of 

interest are arguably similar. In fact, this similarity provides a useful criterion for the selection of 

reference chemicals. In cases where site-specific evidence suggests or demonstrates that fugacity 

gradients are not the same, the explicit difference may be represented by the fugacity gradient 

ratio, Dk/r: 

, ,

/

, ,

/

/

socw k ow k

k r

socw r ow r

K

D

K

Π= Π

 Equation 4-4

Dk/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, Dk/r is often assumed to be 1.0. 

 In some situations, it may be possible to estimate or predict Jsocw for the chemical of 

interest directly. This alternative is discussed in Section 4.4. If a reasonably certain estimate of 

Jsocw 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 i isocw f

Π −

l

 Equation 4-5 
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 This seems like an attractive alternative, because data for reference chemicals has been 

eliminated from the equation. However, without measurements of Jsocw for reference chemicals, 

it may be difficult or impossible to reliably estimate Jsocw for the chemical of interest at the site. 

EPA prefers equation 4-2 and the use of reference chemical data for Jsocw as being a more robust 

method for predicting site-specific BAFs. Calculating 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 development of a site-specific trophic level 4 BAF 

using Method 2. This method involves predicting a site-specific baseline BAF 

using a BSAF measured at the site for the chemical of interest, as well as the 

sediment-water concentration quotient for one or more reference chemicals. In 

Section 4.6, it is suggested that multiple reference 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 Ontario 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 dietary preferences of the larger sizes of 

lake trout that are commonly consumed confirms that these are trophic level 4 

organisms. Previously, the PCB congeners 52, 105, and 118 have been used as 

the reference chemicals for calculating baseline BAFs for PCB 126 (USEPA, 

1995; Cook and Burkhard, 1995). These three congeners were selected 

because (1) they have similar physicochemical 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 ( Jsocw/Kow) 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 Kow CR Csoc Cfd

w

PCB 126 6.9 12.3 ng/g-R 3.83 ng/g-soc 

PCB 118 6.7 555 μg/g-soc 34 pg/L 
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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 determined as the ratio between the lipidnormalized concentrations of the chemical in 5 to 7-year-old lake trout (CR) 

and the average organic carbon-normalized concentration of the chemical in 

surface sediment (Csoc) using equation 4-1. On the basis of data collected from 

Lake Ontario, the average CR of PCB 126 in age 5-7 lake trout is 12.3 ng/glipid, and the average Csoc of PCB 126 in the sediment is 3.83 ng/g-organic 

carbon (actual calculations for these normalized values are not shown here). 

Therefore: 

12.3

4, 126 3.83 3.2 soc

C ng g oc g oc BSAF PCB C g lipid ng g-lipid

− −

− == ⋅ = l

 Equation 4-1 

The trophic level 4 BSAF for PCB 126 in 5 to 7-year-old lake trout is 3.2. 

Determining a Sediment-Water Concentration Quotient (Jsocw) for a 

Reference Chemical

Sediment-water concentration 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 the three reference chemicals, PCB 118. 

To calculate Jsocw , the concentration of reference chemical that is freely 

dissolved in water (Cfd

w ) is needed. This concentration can be calculated from 

the freely dissolved chemical fraction (ffd) (using equation 3-6) and the 

chemical concentration measured in the water column. The measured DOC 

concentration is 2.0 mg/L and the Kow for PCB 118 = 5.5×106

 (log Kow = 6.7). 

Using equation 3-6, the freely dissolved fraction of PCB 118 in Lake Ontario 

water is calculated as follows: 

fd = 1 / (1 + POC w DOC w f ο ο ⋅ Κ + 0.08 ⋅ ⋅ Κ ) Equation 3-6 

In this example, the chemical concentration was measured in a filtered sample, 

so POC is set equal to zero (assuming all particulates were removed by 

filtration): 

6

6

1 0.53 2.0 1 0.08 5.5 10

10

fd f mg DOC L kg

L kg mg

= = − + ⋅ ⋅× ⋅
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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, (Cfd

w )118 = 0.53×34 pg/L = 18 pg/L or 1.8×10-5 )g/L. 

The average (Csoc)118 = 555 )g/g-SOC (sediment organic carbon). By 

substituting these values into equation 4-3, Jsocw for the reference chemical 

(PCB 118) is calculated as: 

,

, 

,

soc r

socw r fd

w r

C

C

Π = Equation 4-3

,118

5

, 118

555 7

,118 1.8 10

3.1 10 / soc

fd

w

C g L

socw C kg SOC g

L kg SOC μ

μ − − × Π = = ⋅ =× −

Calculating a Site-Specific Baseline BAF

A site-specific baseline BAF may be predicted from the field-measured BSAF 

for the chemical of interest (PCB 126) and Jsocw/Kow for each reference 

chemical using equation 4-2:

126/ , ,126

4,126 4,126

,

1 Site-Specific Baseline BAF f

r socw r ow

ow r

D K

BSAF

K

Π = −

l

 

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, Dk/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 Kow values for PCB 126 (7.8 × 106

 or log Kow = 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 have been previously calculated: 

( )( )( )

( )

7 6

4,126 6

1 3.1 10 7.8 10 1 Site-Specific Baseline BAF 3.2 5.5 10 0.20

L kg

kg SOC L

kg

L

− × × = ⋅ − ×

 =1.4 × 108 L/Kg-lipid 
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Prediction of a site-specific BAF from BSAFs determined by 

measurements at the site (Method 2, continued)

The site-specific baseline BAFs using reference PCB congeners 52 and 105 

are derived in the same manner as for PCB 118. The predicted site-specific 

baseline BAFs that result are 3.7×108

 using congener 52 and 1.6×108 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 2.0×108

 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 converted to a site-specific 

total BAF. Recalling the relationship between the baseline BAF and the total 

BAF ( BAFt

T ): 

T Site Specific BAF ( Baseline BAF +1) i t, i fd =⋅ ⋅ f f l (rearranged Equation 3-4)

For PCB 126, the site-specific baseline BAF at trophic level 4 was 

calculated to be 2.0×108

 L/kg-lipid. The freely dissolved fraction of PCB 126 

in the Lake Ontario water column, which contains an average POC 

concentration of 0.075 mg/L, can be calculated using equation 3-6: 

0.075 6 6 2.0

6 6 10 10

1

,126 1 7.8 10 0.08 7.8 10 mg POC kg mg DOC kg 0.35 L L L kg Lkg mg mg

fd f − − + ⋅× ⋅ + ⋅ ⋅× ⋅ = =

The average lipid content for lake trout was 20% or 0.20. With this 

information, the site-specific total BAF can be recalculated from the sitespecific baseline BAF: 

( ) 8 7 Site-Specific 0.20 2.0 10 1 0.35 1.4 10 / 4,

t L BAF T kg l L kg − = ⋅ × +⋅ = ×

The site-specific total BAF for PCB 126 in Lake Ontario lake trout is 1.4×107

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. 
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Ecosystems are rarely at thermodynamic equilibrium, so a BSAF inherently includes a 

measure of the “disequilibrium” associated with the distribution of a chemical in the ecosystem. 

The deviation from the expected equilibrium value of approximately 1-2 (McFarland and Clarke, 

1986) is determined by the net effect of all factors that contribute to the disequilibrium between 

sediment and aquatic organisms. A BSAF value greater than 1-2 can occur through 

biomagnification or when chemical concentrations 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 influence of these ecosystem factors on the value of a 

BSAF is discussed in Section 4.6.2. 

 For high Kow chemicals (e.g. log Kow > 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 Kow chemicals, 

particularly when there is any meaningful benthic connection in the food chain. Another 

important advantage to emphasizing BSAFs when assessing bioaccumulation of high Kow

chemicals at a site is the ease and reliability of the measurements. Measurement of 

concentrations of most highly hydrophobic nonionic organic chemicals in sediment can be 

performed fairly easily. Consequently, with an appropriate BSAF, chemical residues in fish can 

be readily predicted using the concentration of the chemical of interest measured in sediment 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 chemicals are temporally more stable in 

both fish and sediments, BSAFs better integrate fluctuating exposure conditions than do BAFs 

(Burkhard, 2003). For high Kow chemicals, the relative concentration of chemical in the sediment 

is usually much larger than in the water. Hence, the analytical difficulties in accurately 

determining chemical concentrations in water are much greater than for sediment. In addition, 

the importance of chemical binding to particulate, colloidal, and dissolved organic carbon in 

water becomes much greater at high Kow, making it more difficult to accurately determine the 

proportion that is freely dissolved.
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 Method 2 appears to be particularly beneficial for predicting site-specific BAFs for 

chemicals such as polychlorinated dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs), and 

certain polychlorinated biphenyl (PCB) congeners. These chemicals are often detectable in fish 

tissues and sediments but are difficult to measure in the water column and/or are subject to 

metabolism (biotransformation). Because BSAFs are based on field data and incorporate the 

effects of metabolism, biomagnification, 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 identifies key study design 

questions the investigator should address when planning a field study to measure BSAFs. Section 

4.3 addresses how to determine Jsocw, 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 complimentary methods that can be used to design a sampling plan to 

measure BSAFs and Jsocw. 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 surficial sediment within the home2

 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 measured concentrations of the chemical in biota 

and sediment are reflective of long term average concentrations. Temporal and spatial 

distributions of chemical concentrations, organism 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. 
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other factors all contribute to BSAF uncertainty and should be addressed by the field sampling 

plan. 

 

 Average chemical concentrations are used in the calculation of the BSAF, since multiple 

samples should be collected to properly characterize chemical concentrations 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 variability, the latter often exhibited as a slow 

rate of change due to sediment erosion and deposition processes. Again, the properties of the 

chemical itself play an important role in defining this variability. 

 To predict a site-specific BAF from a BSAF determined at a site, the investigator should 

also accurately determine the other parameters required for Method 2 (Equation 4-2). These 

include the sediment-water concentration quotient for a reference chemical, the octanol-water 

partition coefficients for the chemical of interest and the reference chemical, and the fugacity 

gradient ratio. The sediment-water concentration quotient is discussed in the next section. 

 The investigator faced with designing a field study to determine a BSAF should consider 

the following series of key questions, 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. 
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Key Factors to Consider When Designing a Field Study to Determine 

a BSAF and Predict a Site-Specific BAF

The characteristics of the chemical of interest

• Type of chemical - Method 2 is applicable to nonionic organic chemicals and similar-behaving 

ionic organic chemicals 

• Hydrophobicity 

• Metabolism 

What is an appropriate reference chemical?

• Type of chemical - Similarity to chemical of concern 

• Hydrophobicity - Kow similar to chemical of concern 

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

• Should reflect steady-state conditions

What is an appropriate target biota species?

• Consumption 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 water bodies 

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

• Ecosystem type 

• SOC, POC and DOC concentrations 

• Sediment deposition environment 

• Spatial patterns of sediment type and chemical concentration distributions 

• History and duration of chemical loading (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) is preferred 
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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 biota and sediment, and 

the reference chemicals in sediment 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 samples to collect and analyze)?

• Similar bioaccumulation and partitioning 

behavior 

• Similar hydrophobicity (Kow ± 0.5) 

• 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 modeling 
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4. Biota. Which species should I sample?

5. Site Definition. Have I adequately defined my site of interest 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 species of concern. For large-scale sites, EPA 

recommends that biota and sediment samples be collected from each water body 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 patterns 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 and metabolism) 

• Consider biota characteristics (migration, reproduction, availability, etc) 

• Consider consumption pattern (e.g., times 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 home range of target species 
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7. Spatial Variability (i.e., Number of Stations). How many locations should be sampled?

Once the number of sampling events is determined, 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/home range, habitat preference, etc.) 

• Consumption characteristics (harvesting areas) 

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

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

• What ages/sizes of these species are consumed? 

• Which tissues are most commonly consumed 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) 
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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 water 

body 

• Surficial samples vs. vertically-resolved cores 

• Select proper sampling device 

• Consider chemical properties (hydrophobicity and metabolism) 

• Consider ecosystem conditions (variability due to hydrodynamics) and 

temporal aspects of chemical loadings 

• Lessons learned from bioaccumulation model simulations (Section 4.4.1) 

• Appropriate methods depend on water body and organisms 
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14. Ancillary Measurements. What other parameters do I need to measure?

Method 2 also requires measurements of reference chemical concentrations in water. The 

investigator should refer to Section 3.4 for guidance on water sampling. 

4.3 HOW CAN THE SEDIMENT/WATER COLUMN CHEMICAL CONCENTRATION 

QUOTIENT (Jsocw) BE DETERMINED? 

 In Section 4.1, the sediment-water concentration quotient (Jsocw) was introduced as a 

critical parameter in predicting the site-specific BAF from a BSAF using Method 2. The data that 

are generated as a result of a BSAF study reflect how the chemical 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 

Jsocw, and using this parameter in the prediction of the site-specific BAF. In this Section, the 

investigator will find guidance regarding three methods to determine Jsocw : measuring 

concentrations of one or more reference chemicals in water and sediment at the site; estimating 

Jsocw /Kow as approximately the ratio of the fraction of organic carbon in water column 

particulates (fpoc) to that in surficial sediment (fsoc) by assuming steady state conditions; and 

predicting Jsocw for a chemical of interest using a properly calibrated and confirmed fate and 

transport model. 

• Lipid content in biota, organic carbon in sediment, and POC and DOC in 

water are required ancillary measurements 

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

stomach contents and tagging (stocking) information 

• Total suspended solids (TSS) is a useful ancillary measurements for the 

water column 

• Grain size, bulk density, and radioisotope concentrations (in core layers) 

are useful ancillary measurements for sediment 

• Home/foraging range of target species 
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 The distribution of a chemical between the sediment and overlying water at a site is 

described by the sediment-water concentration quotient (Jsocw), 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, Jsocw is a measure 

of the degree to which the chemical’s distribution between the surface sediment and the water 

column approaches or deviates from a condition of thermodynamic equilibrium for the water 

body. The degree of disequilibrium (departure from equilibrium) is proportional to the degree to 

which the fugacity gradient (Jsocw/Kow) for the chemical diverges from a value of 1.0 (Jsocw= 

Kow). This assumes that (1) the organic carbon partition coefficient (Koc) is the same in both the 

water column and the sediment and (2) Koc is also equal to the Kow. These assumptions and the 

empirical data upon which they are based, are discussed in TSD Volume 2, Sections 4.2.4 and 

4.3. 

 In the aquatic environment, three factors are primarily responsible for causing Jsocw to 

differ among ecosystems. First, the concentration distribution of nonionic organic chemicals in 

the water column and sediment are the result of well-known fate and transport processes, such as 

particle sedimentation and resuspension, chemical sorption to and desorption from suspended 

and bed sediments, volatization, biological/chemical transformation, and water column transport. 

These processes vary among ecosystems (O’Connor, 1988a-c). Second, the chemical loading 

history to the ecosystem plays an important role in its Jsocw. For example, increasing the loading 

of a chemical to the water column causes an immediate 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 

Alternatives for determining Jsocw:

• Measuring site-specific chemical concentrations in water and sediment for one or more 

reference chemicals 

• Estimating Jsocw /Kow as (fpoc/fsoc) by assuming steady state 

• Predicting Jsocw using transport and fate models 
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decreased, the concentration of the chemical in the water column drops quickly, whereas the 

concentration of the chemical in the sediments decreases slowly through burial of older and more 

contaminated sediments by newer and less contaminated sediments (Endicott and Cook, 1994; 

USEPA, 2003). Third, differences in organic carbon content in water column particulates (or 

suspended solids) and surface sediment vary among ecosystems (Gobas and MacLean, 2003; 

Burkhard, 2003a). The ratio of organic carbon contents (water column to surface sediment) 

approximates the steady-state value of Jsocw/Kow 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 Jsocw is based on measurements for 

appropriate reference chemicals at the site. As described above, the factors that are primarily 

responsible for causing Jsocw/Kow to vary tend to affect related chemicals in similar ways. 

Reference chemicals with Jsocw/Kow similar to that of the chemical of interest are preferred for 

Method 2 and often are available. Theoretically, the difference between sediment-to-water 

fugacity ratios for two chemicals, “k” and “r” (Dk/r), can be used when reliable reference 

chemicals that meet the fugacity equivalence condition (i.e., Jsocw,k/Kow,k ≈ Jsocw,r/Kow,r ) are not 

available. Related nonionic organic chemicals, approximately at steady state, should have 

similar, if not equal, values of Jsocw/Kow that are related to the fpoc/fsoc ratio. When steady-state 

conditions are not present, as is often the case, Jsocw/Kow values for related chemicals may be 

similar. The similarity of Jsocw/Kow for two chemicals can be indicated by similarities in 

molecular structure, which imply similar physical-chemical behavior in water (e.g., 

hydrophobicity, persistence, and volatility), similar mass loading histories, and similar 

concentration profiles in sediment cores. 

 The investigator should consider the following factors when selecting reference 

chemicals for measuring Jsocw: 

1. The reference chemicals and the chemical of interest should have similar 

physicochemical properties, as well as similar persistence in water and sediment. In 
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addition, the reference chemicals and the chemical of interest should have similar 

chemical structures (i.e., the investigator should not select an alkane as a reference 

chemical for a polycyclic aromatic hydrocarbon). 

2. Obtaining (Jsocw)r data for several reference chemicals with similar Kows (log Kow ± 

0.5) from the same water and sediment samples is preferable and will ensure that 

predictions are more robust than those that would be obtained with 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, (Csoc)r and 

(Csoc)i should be measured from the same sediment samples, because this eliminates 

uncertainty attributable to spatial heterogeneity of Csoc. 

4. The Kow value for the target and reference chemicals should be selected as described 

in Section 4.2.5 of TSD Volume 2 (USEPA, 2003). 

5. Whenever possible, the loading histories for the reference chemicals and the chemical 

of interest should be similar, such that their sediment-water disequilibrium ratios 

(Jsocw/Kow) would not be expected to be substantially different (i.e., Dk/r ~ 1). For 

example, a contaminant produced by combustion processes over hundreds of years 

(e.g., pyrene) should not be used as a reference chemical for a recently-introduced 

contaminant (e.g., brominated diphenyl ether). 

6. Guidelines for sampling and measurement of Jsocw are identical to those for sampling 

and measurement of Cfd

w under BAF Method 1, as described in Section 3.4, and Csoc

under BAF Method 2, as described in Section 4.5. Because concentrations of 

bioaccumulative chemicals in surficial sediments are relatively constant on an annual 

basis in most carbonaceous, fine-sediment depositional areas, determination of an 
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appropriate average Cfd

w in systems with temporal fluctuations is the greatest 

challenge in measuring Jsocw. 

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 investigator to use a different method to determine a 

site-specific BAF. 

4.3.2 Estimating Jsocw /Kow ~ fpoc/fsoc by Assuming Steady State 

 Over the long term, the asymptotic (steady state) value for Jsocw /Kow can be 

approximated as the ratio of the fraction of organic carbon in water column particulates to that in 

surficial sediment (Burkhard et al., 2003a). As these authors point out, Jsocw /Kow = 1 at 

equilibrium. However, in natural systems the sediments and water column are almost never at 

equilibrium and deviations of Jsocw /Kow from 1 can be used to describe this disequilibrium (or 

nonequilibrium) condition. When the disequilibrium is less than one (i.e., Jsocw /Kow < 1) the 

chemical concentration in the water column is enriched relative to that in the sediment. When the 

disequilibrium is greater than one (i.e., Jsocw /Kow > 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 organic 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 CO2

by microbial and other processes associated with diagenesis. The loss of organic matter without 

concomitant chemical loss effectively increases Csoc in the sediment so that ecosystems tend to 

exceed equilibrium between surficial sediments and the water column; i.e., Jsocw /Kow > 1 
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(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 disequilibrium is maintained. The 

magnitude of this natural disequilibrium for lakes appears to increase with increasing water 

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 lesser organic carbon content of the sediments. 

Therefore, the expected steady-state for an ecosystem is not thermodynamic equilibrium (Jsocw

/Kow= 1), but rather a Jsocw /Kow of approximately the ratio of organic carbon content in 

suspended and sediment particles. 

 

 The magnitude of the differences in sediment and water column particulate organic 

carbon contents in aquatic ecosystems is strongly influenced by the hydrodynamics of the 

ecosystem, because particle sedimentation, resuspension, and burial are directly 

controlled/influenced by the hydrodynamics of the ecosystem. Ecosystems with high 

resuspension rates (e.g., rivers) would most likely 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 aquatic environments (1–15% in water column 

particulate and 0.5–4% in sediment: Thurman, 1985; Ittekkot, 1988;Wong et al. 2001; Reschke 

et al. 2002), steady-state Jsocw /Kow values are expected to range from approximately 2 to 10. 

The lesser value of 2 arises due to minimum expected differences in organic carbon content of 

particulate matter in the water column and sediments. The greater value of 10 allows for effects 

of chemical gradients and greater relative 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–water 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 concentrations in sediment at the site are far from steady state due 

to recent changes in loading, then Jsocw /Kow cannot be approximated as ~ fpoc/fsoc. Furthermore, 
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differences in loading history between the chemical of interest and available reference chemicals 

can also complicate the use of Method 2 to predict a site-specific BAF, because the investigator 

must then determine the appropriate value for the fugacity gradient ratio, Dk/r. In these cases, a 

transport and fate model can be very helpful in evaluating nonsteady-state conditions. 

 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). Consequently, these models can be used to 

simulate and predict Jsocw 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 water and surface sediments (i.e., Jsocw) in a lake as a function 

of chemical loading rates. The transport and fate model was a two-compartment dynamic mass 

balance model consisting of a completely mixed 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 column, chemical volatilization and photolysis, and 

time-variable chemical loading rates. Ecosystem 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 particulate, dissolved organic carbon, and freely 

dissolved compartments was assumed, and particulate and dissolved organic carbon partition 

coefficients were estimated using the relationships 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 Jsocw is illustrated in Figure 4-1 for three 

different loading scenarios: (a) constant loading 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. 
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These figures were based on predictions made for a nonmetabolizable chemical with a log Kow 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 had organic carbon contents of 3% and 15%, 

respectively (fpoc/fsoc = 5). In all three scenarios, Jsocw/Kow reaches a plateau at a value of 4.91, 

nearly equal to the fpoc/fsoc ratio.
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Cfd (pg/L)

0

20406080

100120

Csoc (ug/g of oc)

0.00.10.20.30.40.50.6

Water, freely dissolvedSediment

Elapsed Time (years)

0 20 40 60 80 100

Πsocw

01e+62e+63e+64e+65e+66e+67e+6

Πsocw / Kow

01234567

a

Cfd (pg/L)

0

50

100150200

Csoc (ug/g of oc)

0.00.20.40.60.81.01.2

Water, freely dissolvedSediment

Elapsed Time (years)

0 20 40 60 80 100

Πsocw

01e+62e+63e+64e+65e+66e+67e+6

Πsocw / Kow

01234567

b

Cfd (pg/L)

0

20406080

100120

0 

Πsocw

0.02.0e+64.0e+66.0e+68.0e+61.0e+71.2e+71.4e+7

c

www

Figure 4-1. The sediment-water concentration quotient (Jsocw) for three different chemical loading scenarios: (a) 

chemical to the ecosystem overtime, (b) a constant loading of chemical to the ecosystem with a doubling of loadia constant loading of chemical to the ecosystem with an 80% reduction in loading at year 50. Simulations perform

with a log Kow of 6 using Lake Ontario conditions and parameters.
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 Scenario (c) is applicable to many chemicals 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 water column. The latter portion of scenario (c) 

illustrates how Jsocw changes over time. Differences in ecosystem parameters and conditions, 

such as hydraulic retention rates, sedimentation and resuspension rates, water column and 

surficial sediment layer volumes, and chemical loading rates between ecosystems, affect the 

specific time scales and slopes of the changes in Cfd

w , Csoc, and Jsocw associated with changes in 

chemical loading over time. 

 Transport and fate models have been applied in many water bodies, including Great 

Lakes Areas of Concern (AOCs), Superfund sites, and impaired water bodies 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 chemical 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. Guidance on model calibration and confirmation is 

currently under development by EPA’s Council for Regulatory Modeling 

(http://cfpub.epa.gov/crem/).

4.4 HOW TO DESIGN A SAMPLING PLAN TO MEASURE BSAFs 

 To design a field study to measure BSAFs at a site, the investigator should determine the 

appropriate number of biota, sediment, and water samples to collect 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 time), and the spatial distribution of sample 

collection locations. Having the appropriate sampling frequency and spatial distribution (the 

sampling design structure) is critical for the determination of a BSAF that is representative of the 

long-term average conditions in an ecosystem. The collection and analysis of representative 
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samples is the key to determining an accurate and unbiased BSAF, while the precision of a 

BSAF depends upon the number of samples. In the optimization process, the precision of the 

chemical concentration averages are balanced against 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 Guide the Sampling Design Process? 

 The following approach to designing a field sampling program to measure BSAFs has 

been described by Burkhard (2003). First, the investigator should determine or reliably estimate 

the temporal variability of chemical concentrations at the site, as well as the chemical’s Kow 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 concentrations in sediment across this 

range. Third, the investigator should define the required precision for the BSAF measurement. 

With this information, the investigator can determine 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. 

 

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 concentrations in sediment within this range; 

3. Define the required precision for the BSAF measurement; 

4. Select the appropriate sampling design structure; and 

5. Calculate total numbers of biota and sediment samples, and allocate 

them among sampling events 
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 The BSAF field sampling design approach is based on model simulations which 

demonstrate how variabilities in chemical concentrations translate into the uncertainties 

associated with BSAFs in different sampling designs. 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 components), and the disequilibrium between the sediment and water 

column for the chemical influence the number and timing of sampling events and their spatial 

distribution required to accurately and representatively determine 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 used to evaluate BSAF sampling designs, chemical 

concentrations in sediment were calculated as the product of the Jsocw 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. 

4.4.2 Using Model Simulations to Develop Field-Sampling Designs 

 Burkhard’s (2003) simulations provide substantial insight into appropriate sampling 

design structures for BSAF measurements, which are generally opposite of those for measuring 

BAFs: 

 

• For chemicals with log Kow s of 5 and less, with any rate of metabolism, appropriate 

BSAF sampling design structures would consist of numerous concurrent sets of 

sediment and fish samples spaced widely over time. 

• For nonmetabolizable chemicals with log Kow s greater than 5, the collection of one 

concurrent set of sediment and fish samples would, in all likelihood, be an 

appropriate BSAF sampling design structure. 
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• With increasing chemical metabolism rate, appropriate BSAF sampling design 

structure transitions from the single concurrent collection of sediment and fish 

samples to designs appropriate for lower Kow chemicals, that is, the collection of 

numerous concurrent sets of sediment and 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) and/or moderate hydrophobicities (4 < log Kow s of < 6) 

present difficult challenges when selecting an appropriate sampling design structure for 

measuring BSAFs, as they do for BAFs. This range of hydrophobicities lies within the transition 

zone between the much more obvious design structures appropriate for low and high Kow

chemicals. 

 The process for developing successful field-sampling structures for BSAF measurements 

can primarily focus upon three parameters: temporal variability, metabolism, and Kow, as was the 

case for measuring BAFs. These three parameters can range widely, and depending upon their 

values, dramatically different field designs would result. Although spatial variability is not 

usually a predominant factor in sampling design, knowing or understanding the immediate home 

range of the sampled organisms is required. Without this information, the investigator cannot 

ascertain whether sediment samples have been collected that are reflective of the actual chemical 

exposure history for the sampled organisms. Poor spatial coordination of fish and sediment 

samples will likely yield BSAFs with poor accuracy 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 with low uncertainty even when 

spatial concentration gradients at the site are extreme, if these guidelines 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 guide, some illustrative BSAF sampling structures 

have been developed (Table 4-1). These illustrative designs provide a sense of how sampling 

design structures might be influenced by differences in temporal concentration variabilities, 

metabolism rates, and Kow s. The modeling results do not reflect the total uncertainty for the 

illustrative designs, because biases and errors in sampling, compositing, and chemical analysis 
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were not included. Furthermore, temporal variabilities typical of other ecosystems (e.g., 

estuaries, reservoirs, lakes, and small streams) have not been evaluated. Additionally, the 

simulations were made using data for the entire calendar year, and field sampling is typically 

performed during warmer weather or better weather conditions. Although the illustrative 

sampling structures suggest the number and spacing of sampling events for a field study, they do 

not prescribe the total number of samples required for a successful BSAF field study. 
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Table 4-1. Some Illustrative Biota–Sediment Accumulation Factor Sampling Design 

Structures. All Sampling Events are Assumed to be Widely Spaced in Time (e.g., 60, 90, 

120 or 180 Days).

Metabolism rate Temporal 

variability log Kow Minimum number of sampling eventsa

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 Kows 9

high medium all Kows 4

high low all Kows 2

a

 The first value corresponds to the first value in the log Kow column, the second value 

corresponds to the second value in the log Kow column, etc. 

 The effects and importance of the immediate home 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 collecting 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. However, random walk (migration) simulations 

suggest that BSAFs can be measured with low uncertainty even when extreme spatial 

concentrations exist at the field site, provided the measurements are performed in more 

contaminated locations of the site. 
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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 samples required to determine a site-specific BAF of a 

known precision. The investigator can also use these methods to estimate how the precision of 

the BSAF depends upon the number of biota and sediment samples, and how the precision of the 

sediment-water concentration quotient (Jsocw) 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 interest) and water (reference chemical) are all 

simulated using Monte Carlo, the investigator can use the results to determine how the precision 

of site-specific BAF predictions made using Method 2 depend upon the number of samples 

collected from each medium at the site. 

 Monte Carlo simulations of BSAF precision are demonstrated by examples in Appendix 

4B, based again on Green Bay Mass Balance 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 organic-carbon normalized surficial sediment. 

 The uncertainty of BSAFs and Jsocw calculated in the Monte Carlo simulations were 

sensitive to the number of sediment samples, and this sensitivity increased with the variability of 

the sediment chemical concentrations. The variability of chemical concentrations in sediment 

affected the uncertainty of BSAFs and Jsocw, particularly for small sediment sample sizes (ns6). 

For highly variable chemical concentrations in sediment, increasing the number of sediment 

samples used to calculate the mean concentration 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 effect on the precision of BSAFs. 

 For BAFs derived using Method 2, the results were similar to those for BSAF and Jsocw, 

although the confidence limit ratios (CLRs) used as measures of precision were much larger. As 
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was the case for BSAFs and Jsocws, only small reductions in the uncertainty of Method 2 BAF 

predictions were gained using sediment sample sizes larger than about 6. Once the number of 

samples exceeded about 6, the reductions in BAF 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 predictive 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 rank correlation coefficient of 0.5 reduced the 

CLRs by 21%. Within-media correlation, especially the correlation between chemical 

concentrations in sediment, significantly reduced the uncertainty of Method 2 BAF predictions 

when few sediment samples are collected. When only two sediment samples are used to calculate 

the BSAF and Jsocw, a rank correlation coefficient of 0.5 reduced the CLR by 50% in 

comparison to the uncorrelated simulation. Overall, concentration correlations were found to be 

helpful 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 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 proceeding 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 Section 4.4.2 (Table 4-1) suggest the number and 

spacing of sampling events for a field study necessary 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 order to obtain a desired BAF can be estimated using the results of 

the Monte Carlo simulations presented in Section 4.4.3. EPA recommends that the results of 

modeling simulations be used together with statistical methods such as Monte Carlo analysis as 
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the basis for a rational sampling design process. The design process is outlined below: 

 

 1. The investigator determines the goal for precision of the 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 Kow) and rate of metabolism; and 

 b. Temporal variability of water concentrations, based upon factors of the water 

body at the site. 

Table 3-2 illustrates the relationship between categories of water bodies (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 water bodies towards those that are more advective 

(flowing) with shorter hydraulic residence times. Therefore, if site-specific data 

are not available, the investigator can use the water body categories in Table 3-2 

to estimate the temporal variability of water concentrations. 

 3. The investigator determines the number of biota, 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 representative of the site and chemical are available, 

the investigator should consider conducting Monte Carlo simulations, using 

concentration moments for the site, to determine sample numbers. 

b. Unless reduced precision and increased bias are acceptable, the investigator 

should avoid collecting fewer than 6 samples of each medium. These samples 
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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 CONCENTRATIONS IN SEDIMENT

 This section provides guidance on the development of a field plan for sampling sediment 

to support the determination of a BSAF for a site. This guidance 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, preserving, and shipping sediment samples to a 

processing laboratory for chemical analysis. Planning and documentation of all field procedures 

should be emphasized to ensure that collection activities are cost-effective and that sample 

integrity is preserved during all field activities. The investigator should follow EPA’s Data 

Quality Objectives (DQO) process as a recommended 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 should develop a detailed sampling plan prior to 

initiating a field study. For sediment sampling, there are four major parameters 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 samples 
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The role of each of these parameters in developing an appropriate field plan for sediment 

sampling is discussed below. 

 Unlike biota and water sampling, the timing of collection is usually not a significant 

factor for sediment sampling. When properly sampled, sediments provide time-stable measures 

of concentrations of persistent 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 flooding, chemical spills, dam removal, lock 

replacements or dredging operations). At such times, the chemical concentrations in surficial 

sediment may not be representative of the sediments to which the resident organisms have been 

exposed. Ecosystems adjust fairly quickly to sediment disruptions, and a year 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 chemical concentrations in sediment must be 

compatible and consistent with the methods selected for analysis of biota (Section 3.3.1) and 

water (Section 3.4.1) samples. BSAFs and Jsocws should only be determined for individual 

chemicals. In cases where the chemical of interest is a mixture (e.g., PCBs, chlordane), the study 

design must require that individual chemicals composing the mixture be quantified individually. 

 

 The investigator should ensure that the methods chosen to analyze chemicals in sediment 

is sufficiently sensitive to detect ambient concentrations at the site. 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 may be 2 or 3 times larger than required for 

the analysis of an individual chemical. 
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 Organic carbon content should be determined in all sediment samples analyzed for the 

chemical of interest and reference chemicals. The investigator should also consider other 

ancillary measurements, which may be helpful in interpreting variations in sediment chemistry in 

terms of physicochemical, biological, and transport processes. These include grain size, bulk 

density, percent moisture, sediment oxygen demand (SOD), acid volatile sulfide (AVS), and oil 

and grease. If sediment cores are collected, radioisotope (i.e., Cs-137 and Pb-210) measurements 

may be useful in determining the deposition age of individual core sections. 

4.5.2 Sampling Locations and Depths 

 The spatial distribution of chemical concentrations in sediment are often highly variable. 

Therefore, the investigator should carefully consider and select the appropriate locations for 

sediment sampling. A key consideration for the investigator faced with determining a BSAF at a 

site, is that the sediment samples should be reflective of the target organism’s recent exposure 

history. In practice, this means collecting sediment samples within an area defined by the home 

range of the organism. Depending upon the species, the home ranges are different, and larger 

fishes tend to have larger home ranges. With information about the home range of the fish, an 

assessment of where the fish resides relative to the spatial variability in chemical concentrations 

can be performed. Clearly, where large concentration 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 (USEPA, 2002; USEPA, 2001[Table 2-1] ) regarding 

sampling design alternatives that may be appropriate for estimating the mean chemical 

concentration required for the BSAF. Sampling designs are frequently based upon collecting 

sediment samples at the same locations where biota are collected. This is not necessarily the best 
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approach to selecting sediment sampling locations, because these samples may not reflect the 

biota’s average chemical exposure over the home range. 

 To measure chemical concentrations that are 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 successfully, the sediment 

samples will be “connected” to the biota in terms of chemical exposure. 

 EPA recommends that samples of surface sediments should be collected from locations in 

which carbonaceous sediment, containing the chemical of interest and the reference chemicals, is 

regularly deposited and is representative of average surface sediment in the vicinity of the 

organism. When selecting sediment sampling locations, it is important to consider sediment 

deposition and erosion zones, since grain size and related characteristics (including conventional 

parameters such as sediment organic carbon and acid volatile sulfide, as well as chemical 

concentrations) are likely to vary between these two sediment environments. In fluvial (flowing) 

water bodies, sediments tend to deposit and accumulate in locations where current velocities are 

lower (e.g., inside stream bends, in deep pools, above dams or other obstructions). In lacustrine 

water bodies, sediments usually accumulate in deeper water. Depositional zones typically 

contain fine-grained (silt and clay) sediment deposits which tend to have higher organic carbon 

content. Higher concentrations of hydrophobic chemicals are usually associated with finegrained sediments. Coarser particle types (e.g., sand and gravel) are usually 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 the 
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chemical concentrations to which fish are exposed, 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 sample a thin layer of surficial 

sediment. EPA considers the top 1 cm of sediment to be ideal (Burkhard 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 organisms) are abundant, the surficial sediment 

horizon is often vertically well-mixed by bioturbation to a depth up to about 10 cm (Boudreaux, 

1994; Thibodeaux and Bierman, 2003). However, samples 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 sediment surface to much deeper levels in the 

sediments, e.g., 0-20 cm (a common sediment sampling 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 investigator should review any available data for the site that can be 

used to determine the appropriate depth of surficial sediment for sampling. These data include 

sediment core profiles of chemical concentrations, physical properties or counts of benthic 

organism abundance. If such data cannot be found, the most conservative approach for the 

investigator would be to sample the top 1-2 cm of surficial sediment. 

 A review of existing background information from 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 historical (industrial and other uses) and existing 

sediment data costs relatively little and can provide information 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 site background review, and the investigator should 

pursue this data at the project planning stage: 
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Historical Information - Historical information is useful in trying to find out what 

chemical contaminants may have been introduced to the water body 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 wastewater treatment plants, power plants, 

municipal water intakes, shipping; 

• Dredging activity; and 

• River, lake, estuary or harbor morphology and bathymetry. 

Recent information - Additional information, generated within 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 harbors have up-to-date bathymetric maps of 

the harbor area. The local harbor authority, U.S. Army Corps of Engineers (USACE), 

U.S. Coast Guard, or National Oceanic and Atmospheric Association (NOAA) should 

be able to provide that information. 

 From this historical information, the investigator may be able to develop an 

understanding of the following factors affecting contaminant source pathways: bathymetry, 

water current patterns, tributary flows, watershed hydrology and land uses, sediment and soil 

types, and sediment deposition rates. Assembling this information can be helpful in evaluating 

sampling locations and sample designs (i.e., the choice of sampling on a regular grid or stratified 
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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 quality parameters, including contaminant 

concentrations in sediment and fish. 

• NWISWeb (http://waterdata.usgs.gov/nwis) - Database maintained by US Geological 

Survey, also including contaminant concentrations 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 may 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 containing information and reports on previous 

and ongoing remediation projects. EPA studies and information about Superfund and 

RCRA sites, Remedial Action Projects (RAPs), 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 research - 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 (EPA) - Information is available from the 

states and directly from the EPA. 

• Pesticide spill reporting system (EPA). 
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• Reports of pollution-caused fish kills. 

• Pollution incident reporting system (U.S. Coast Guard). 

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

• Hazardous waste sites and Management facilities reports (EPA). 

• U.S. Army Corps of Engineers (ACE) studies 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 selecting appropriate core and grab samplers based on 

site-specific factors. 

 A piston corer allows excellent quantitative and qualitative sampling to a specified 

sediment depth with little disturbance of the sediment-water interface. Samples can be separated 

or stratified by depth or color/texture to analyze distinct 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 discrete 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. 
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 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 second. They normally take a discreet "bite" of 

sediment to a fairly consistent and measurable 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 dredges, both of which can be hand operated from 

a small flat-bottomed boat. The Ponar is better suited to sampling hard or sandy sediments 

because of the greater ability to penetrate, while the Ekman is more suited to sampling in soft 

sediments in low flow waters. Neither grab sampler will effectively sample hard clays; a coring 

device or shovel such as a sharpshooter spade should be used at these sites. 

4.5.4 Replicate and Composite Samples 

 As discussed in Section 4.4, the number of samples directly affects the representativeness 

and completeness of the sediment data for estimating 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 the constraints imposed by analytical costs, sampling effort and 

logistics. The investigator can use two strategies to find an appropriate balance between 

confidence in the data and cost of collecting it: replication and sample compositing. 

 

 Sample replication is used to assess measurement 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 concentrations. Different kinds of 

replicates can be collected, depending on the type of precision desired by the investigator. 

Analytical replicates are used to assess analytical 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 factor in estimating the minimum number of sediment samples 

necessary to determine a BSAF or Πsocw of known precision, as discussed in Section 4.4.3. When 

collecting replicate samples to statistically compare sediment deposits, sample sites within each 
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deposit should be randomly located for statistical comparisons to be valid. Each replicate sample 

should be taken from an area of sediment 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 combination 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 chemical 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 analytical 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 combining samples could serve to dilute a highlyconcentrated but localized sediment “hot spot”. Also, sediment samples from locations with very 

different grain size characteristics 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 grabs 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 thin layer of surficial sediment. 

Table 4-2 shows the number of 3-inch core samples 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 increasing number of samples must be composited to obtain a required 

sediment volume, as the thickness of the surficial sediment being sampled decreases. 
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Table 4-2. Number of 3-inch Diameter Cores Required to Composite 500 mL of Sediment 

surficial sediment 

sample thickness 

(cm) 

volume per 

core (cm3) 

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 ASSOCIATED WITH METHOD 2 AND 

THE USE OF BSAFs TO PREDICT CHEMICAL BIOACCUMULATION

 

 EPA’s Method 2 bioaccumulation methodology has not received widespread attention in 

either the scientific or regulatory communities. Nor does EPA have much experience in the 

application of Method 2 to predict site-specific BAFs. Consequently, there are many potential 

issues to address dealing with the reliability 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 Predictions 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 efforts have been conducted with data collected 

from three aquatic ecosystems in the United States: 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 data 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 BAFs for PCBs, chlorinated benzenes, and some 
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pesticides were predicted from BSAFs for Lake Ontario salmonids and compared with measured 

baseline BAFs from the same system. The baseline BAFs predicted from BSAFs were within a 

factor of 4 of the measured baseline BAFs. Furthermore, 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 generally 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 works well, not only for predicting baseline BAFs 

with data from the same ecosystem (Lake Ontario), but also for predicting baseline BAFs 

between systems (Green Bay vs. Lake Ontario). 

 Burkhard et al. (2003b) extended the previous evaluations for Method 2 by comparing 

results of field-measured baseline BAFs with baseline BAFs predicted from BSAFs using 

additional PCB data collected from Green Bay/Fox River and the Hudson River. The data sets 

for this latest evaluation effort were selected from the 1989–1990 Green Bay Mass Balance 

Study (http://www.epa.gov/grtlakes/gbdata) and the Hudson River PCBs Reassessment Remedial 

Investigation/Feasibility Study (USEPA, 1998). The former study included data from the lower 

Fox River and the inner, middle, and outer zones of Green Bay. The Hudson River data were 

collected over several years by a number of federal and state agencies 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 effort 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 Kows 

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 Kows (Burkhard et al. 2003b). The evaluation effort 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. 
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 A summary of the evaluation exercise is presented 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 between measured and predicted BAF, 

fd

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 (BAFpredicted/BAFmeasured) 

was used to evaluate the agreement between Method 2-predicted baseline BAFs and fieldmeasured baseline BAFs. Table 4-3 presents zone (Green Bay data) and location-specific 

(Hudson River data) statistics for the BAFpredicted/BAFmeasured ratio. Table 4-3 also presents the 

percentage of BAFpredicted/BAFmeasured ratios that fall within specified 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 majority of predicted BAF values falling within a factor of 2 of 

the field-measured BAF values. In addition, >90% 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 

BAFpredicted/BAFmeasured 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 analyses strongly support the use of Method 2 to estimate sitespecific BAFs from field-measured BSAFs. 
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Figure 4-2. Predicted baseline BAFs using method 2 (blue triangle) and 4b (pink circle) 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. The solid lines 

are perfect (1:1) agreement, the dot-long dash lines are 2x from perfect agreement, and the dotted 

lines are 5x from perfect agreement.
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Table 4-3. Validation Statistics for Method 2: Ratio of Baseline BAFpredicted/Baseline 

BAFmeasured 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 Hudson River RM-114. In both ecosystems, these 

locations are relatively distant from the major known sources of chemical contamination. As 

PCBs are transported greater distances, they are increasingly subject to various 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 other zones/river stations closer to the major known 

sources. Lower chemical concentrations are generally less accurate than higher concentrations, 
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which may lead to greater errors in BAF predictions. In addition, other sources (e.g., atmospheric 

deposition) may become more significant contributors of PCBs at these distant locations. Each of 

these factors may play some role in the poorer 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 sediment (Ankley et al., 1992; 

Thomann et al., 1992). As shown by Thomann et al. (1992), BSAFs are appropriate for 

describing bioaccumulation of sediment contaminants in aquatic food webs with non-equilibrium 

conditions between both the sediment and fish, and sediment and its overlying water. 

Equilibrium is regarded as a reference condition for describing degrees of disequilibrium 

between chemical concentrations in biota, sediment and water. Therefore, chemical equilibrium 

is not a requirement for measurement, prediction, or application of BSAFs. 

 When calculating BSAFs for benthic invertebrates, numerous investigators (Lake et al. 

1984; McElroy and Means, 1988; Bierman, 1990; Lake et al. 1990; Ferraro et al. 1990) have 

invoked two assumptions: (1) equilibrium conditions 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, for these specific conditions, is equal to the 

partitioning relationship of the chemical between organic carbon in the sediment and lipids of the 

organism. Depending upon the affinities of the nonpolar organic chemical for lipid and sediment 

organic carbon, the BSAF, under these specific conditions, should be in the range of 1 to 2 

(McFarland and Clarke, 1986.). For aquatic organisms tightly connected to the sediments like 

oligochaetes and other benthic invertebrates, experimental measurements (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 equilibrium is a sound fundamental theory for nonionic organic 

chemicals when appropriately applied to conditions near equilibrium. 
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 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 trophic 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 the 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. 2003a). BSAFs outside of this range for fish do not 

violate the general definition of BSAFs nor invalidate the usefulness of BSAFs in predicting 

chemical residues in fish for sediment contaminants. 

 For BSAFs to have predictive power in terms of determining BAFs (i.e., Equation 4-2), 

the ratio of chemical concentrations between biota 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 Dk/r offers the investigator some ability to 

correct for differences in sediment-water concentration quotients (Πsocw) 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 Jsocw

 Reliable measurements of Jsocws are rather limited because of a number of factors. These 

include : 

• the difficulties in measuring the concentrations of hydrophobic organic chemicals in 

natural waters because they occur at very low concentrations, that is, less than 1 ng/L; 

• the lack of data for sediment and water samples that are temporally and/or spatially 

coordinated; 
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• the lack of data for sediment samples collected 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 designed specifically for determining Jsocw are not usually 

performed. 

In addition, combining sediment measurements from one study with water measurements from 

another study can result in large biases in Jsocws due to differences in analytical methodologies 

(e.g., different surrogates for recovery corrections, different standards) and sample designs. 

 Review of a number of different data sets, as described in Burkhard (1998), revealed 

three data sets of suitable quality for which Jsocws could be determined. These data sets were 

from Lake Ontario (Oliver and Niimi, 1988), Hudson River (USEPA, 1997; USEPA, 1998), and 

Green Bay in the Lake Michigan ecosystem (www.epa.gov/grtlakes/gbdata/). The Green Bay and 

Hudson River data sets contained data for PCBs only, and the Lake Ontario data set contained 

data for chlorinated pesticides, PCBs, and a few chlorinated benzenes, toluene, and butadiene. 

The data for the chlorinated benzenes, toluene, and butadiene in the Lake Ontario data set were 

not used in this analysis because these chemicals volatilize to the atmosphere relatively easily in 

comparison with the higher molecular weight PCBs and chlorinated pesticides. 

 Figure 4-3 shows the Jsocws for selected PCB congeners in five different zones of Green 

Bay. For the individual PCB congeners, the geometric mean regressions were performed on data 

for the five different zones in the Green Bay system because both variables were measured with 

error (Ricker, 1973). The slopes of the log Jsocw-log Kow regressions from the different zones 

were not significantly different among the five zones (comparison of slope test, " = 5%). 

Therefore, average Jsocws 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 that 

for PCBs, Jsocw is strongly dependent on the Kow, and slopes of slightly less than 1 were 
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obtained. Examination of Jsocws for Lake Ontario and Hudson River reveals trends similar to 

those in Green Bay; a strong dependence of Jsocw on Kow for the PCBs and chlorinated 

pesticides (Figure 4-5 and Table 4-4), and slopes of 1 and slightly less than 1 were obtained. 
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Green Bay Zone 1

Log 

Πsocw

4

5

6

7

8

9

10

Green Bay Zone 2a

Log 

Πsocw

4

5

6

7

8

9

Green Bay Zone 3a

Log 

Πsocw

4

5

6

7

8

9

Green Bay Zone 3b

Log 

Πsocw

4

5

6

7

8

9

Green Bay Zone 4

Log Kow

456789

Log 

Πsocw

4

5

6

7

8

9

Figure 4-3. Sediment-water column 

concentration coefficient (Jsocw) for 

PCBs in five different geographical 

zones in Green Bay, Lake Michigan. 

The circled data points are the PCB 

congeners numbers (log Kow) 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. 
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All Zones,

 Congener Averages

Log Kow

456789

Log 

Πsocw

4

5

6

7

8

9

10

Figure 4-4. Average sediment-water column concentration coefficients (Jsocw) for individual 

PCB congeners across the five different geographical zones in Green Bay, Lake Michigan. The 

circled data points are the PCB congeners numbers (log Kow) 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.
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Table 4-4. Geometric Mean Regression Equations (log Jsocw = A & log Kow+ B) for 

Polychlorinated Biphenyls (PCBs) and Chlorinated Pesticides

Ecosystem Slope (±sd) Intercept (±sd) n r sxy

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 

 sxy = standard error of estimate 

 a slope significantly different from 1.0, " = 1%.
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Hudson River Mile 189

Log 

Πsocw

5

6

7

8

9

10

Hudson River Mile 194

Log Kow

45678

Log 

Πsocw

5

6

7

8

9

Figure 4-5. Sediment-water column concentration coefficients (Jsocw) for PCBs at river miles 

189 and 194. The circled data points are the PCB congeners numbers (log Kow) 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. 
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 In the Green Bay ecosystem, chemical concentrations in both sediments and the water 

column decrease with increasing zone number. Zone 1 is at the mouth of the Fox River, the 

source of PCBs to the bay, and zone 4 connects the bay to Lake Michigan. Zone 1, the region of 

highest chemical concentrations, has much less variability in the measured Jsocws and the largest 

slope for the log Jsocw–log Kow relationship among all sampling zones in Green Bay. 

Comparison of the variability existing in zones 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 parallels the concentration gradient in Green Bay. 

The consistency and slope of the Jsocw–Kow relationships observed in zone 1 data might be more 

illustrative of the underlying Jsocw–Kow 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 above the quantitation limits or high signal-to-noise ratio). This 

can be explored by data visualization methods (e.g., plotting BAF and/or BSAF variability 

against water, sediment and biota concentrations). Alternatively, the difference may reflect the 

greater role that various transport and fate processes (which depend on chemical-specific factors 

of solubility, volatility and resistance to biochemical degradation) play in outer regions of Green 

Bay as opposed to their much more limited role nearer the sources of contamination in the Fox 

River. 

 From a theoretical standpoint, log Jsocw–log Kow 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 than 1; these include 

volatilization losses (assuming net water-to-air flux for all chemicals) although this requires 

information about the relative volatility of the 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 Jsocw term), and measurement error in determining the 

concentrations of chemical in the sediments and/or water column. The log Jsocw–log Kow

relationships for the Hudson River, Lake Ontario, and Green Bay ecosystems have slopes that 

are 1 or slightly less than 1 for PCBs and chlorinated pesticides (Table 4-4). The smallest slopes 

were observed with the Hudson River ecosystem data. The Hudson River ecosystem is much 
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more dynamic and possibly further from steady-state 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 all three ecosystems, the conditions in the Hudson 

River do not appear to be greatly different from those in the other two ecosystems. 

 Given that the slopes for the log Jsocw–log Kow 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 slope of 1 for this relationship. Data analyses 

and averaging performed for the three ecosystems yielded average Jsocw/Kow 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 Jsocw/Kow ratios between the two Hudson River sampling stations suggest 

distinctly different behaviors in the two sampling stations, and, therefore, an overall ratio was not 

computed for the Hudson River. The EPA believes that the differences in average Jsocw/Kow

ratios among the three ecosystems evaluated here illustrate the range of variability that occurs 

among ecosystems across the nation. Because Jsocws are a function of both current and past 

chemical loadings to the ecosystems, Jsocw/Kow ratios both larger and smaller than those 

observed probably exist in the nation. For highly contaminated sites (e.g., Superfund sites with 

large concentrations of chemicals in the sediments), Jsocw/Kow ratios could become very large. 

For new chemicals that are just being introduced or discharged into the environment, Jsocw/Kow

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 

Jsocw/Kow ratio, depending on where these processes occur (i.e., the sediment and/or the water 

column). 
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Table 4-5. Average Jsocw/Kow 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 Jsocw/Kow 23.3 (±18.0) 

sd = standard deviation

 Because the degradation rates for the observed PCBs and chlorinated pesticides in the 

environment are extremely slow, the average Jsocw/Kow ratio of 23.3 for the three ecosystems is 

representative of chemicals that are very slowly degraded (or have long half-lives in the 

environment). Chemicals with higher degradation rates will, in all likelihood, have Jsocw/Kow

ratios that are different from those for the PCBs and chlorinated pesticides, and EPA believes 

that the Jsocw/Kow ratios will be smaller for such chemicals, on average, than those for the PCBs 

and chlorinated pesticides reported here.
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4.6.4 How does Jsocw 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 and Rasmussen, 1996; Burkhard, 1998), and the 

chemical distribution between sediments and water (Thomann et al., 1992; Endicott and Cook, 

1994). The impacts of food web composition and chemical distribution between sediments and 

water are interrelated because sediments and water are the primary exposure media for the 

benthic and pelagic components, respectively, of the food web (Burkhard, 1998). Chemical 

concentrations in benthic invertebrates at the base of the benthic food web are directly controlled 

by the concentrations of chemicals in the sediments. 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, differences in distribution of chemical between sediment and water, as 

well as differences in benthic versus pelagic food web composition, will affect the 

bioaccumulation of nonionic organic chemicals in forage and piscivorous fish. 

 Ecosystems at thermodynamic equilibrium, a condition that rarely exists in nature, should 

theoretically have Jsocws equal to the chemical’s Kow. Consequently, ecosystem models typically 

characterize Jsocw by using its ratio to Kow 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 Jsocw/Kow 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 the 1970s, is a disequilibrium condition in which 

the chemical is enriched in the sediments relative 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 sediments; in this situation, the aquatic ecosystem is 

being loaded with the chemical, but sediments have not reached steady state with the water 

(Jsocw constant). With continued loading, sediment contamination increases until a steady-state 

condition is reached (Jsocw constant) and the Jsocw/Kow ratio is in the 2–10 range. The lower 

bound of 2 arises from minimum expected differences in the organic carbon content of 

particulate matter in the water column and sediments. The upper bound of 10 allows for the 
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effects of chemical gradients and greater relative 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 Jsocw/Kow 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 historical loading data, EPA expects that most 

persistent nonionic organic chemicals will have Jsocw/Kow 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 concentrations in surficial sediments. In this case, 

Jsocw/Kow will be substantially lower than 2, indicating low exposure potential through the 

benthic food web.

4.6.5 Assumptions and Limitations Associated with Method 2 Predictions 

 EPA is currently restricting the application of Method 2 for determining site-specific 

BAFs to nonionic organic chemicals with a log Kow of 4. This restriction primarily reflects the 

lack of validation of this method as applied to less hydrophobic chemicals. In addition, the need 

for this method is greater for chemicals with higher log Kows because of the difficulties 

associated with detecting and measuring such chemicals in ambient water. Method 2 has not 

been validated for superhydrophobic (log Kow > 8) chemicals either. Future development and 

evaluation of this method may lead to its application 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 Cfd

w - is common to both methods. Temporal changes in Cfdw are responsible for 

most deviations from steady state between biota, water, and sediments. The magnitude of errors 

associated with fluctuations in Cfd

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 Monte Carlo analysis of Green Bay PCB 
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data for Methods 1 (Appendix 3C) and 2 (Appendix 4B), BAFs determined by Method 1 were 

consistently more precise than by Method 2 when each was based on a comparable number of 

samples. 

 In deriving Equation 4-2, the assumption is made that Jsocw 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) and 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 Jsocw values are not based on the same sediment data set, the investigator is 

cautioned to be particularly concerned with the consistency in sampling and analyses between 

data sets. 

 Although EPA recommends that Csoc values represent spatially averaged surface 

sediment contamination levels in the region affecting the organism’s exposure, Method 2 should 

be accurate even when the Csoc value used for the BSAF and Jsocw does not accurately represent 

spatially-averaged conditions. This is because the Csoc need only reflect the relative level of 

contamination of sediments over time. 

 Inaccuracies associated with the use of Jsocw/Kow from reference chemicals to estimate 

Cfd

w s for chemicals of interest under Method 2 have a linear impact on the accuracy of baseline 

BAFs. For example, if Jsocw/Kow 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 Jsocw/Kow to date 

indicate an expected range of 5–40 for most contamination scenarios. If the data quality 

considerations for choosing Jsocw/Kow for the chemical of interest are followed, the magnitude of 

the errors associated with the choice of Jsocw/Kow should be no greater than twofold. 

 The strength of Method 2 is that it utilizes measurements of relative (not absolute) 

differences in bioaccumulation between chemicals with structural similarity. When properly 
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sampled, sediments provide time-stable measures of concentrations of persistent bioaccumulative 

chemicals in aquatic systems. Method 2 is currently the only viable method for estimating 

baseline BAFs for nonionic organic chemicals with (1) a log Kow of 4, (2) concentrations in 

water that are often undetectable, and (3) significant rates of chemical 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 Predictions if the Sediment Organic Carbon 

Equilibrium Partitioning Assumption is in Error? 

 Equilibrium partitioning (EqP) of organic chemicals between dissolved concentrations in 

sediment pore water and sediment organic matter is a fundamental assumption in the Method 2 

methodology for predicting BAFs. The EqP assumption is made both explicitly, in the use of 

Jsocw/Kow 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 environment, 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 partition coefficients in suspended sediment (Lohmann et al. 2005), and 

heterogeneous sorption properties of different classes of organic carbon (Young and Weber, 

1995). Many of these factors appear to contribute relatively little variability to Jsocw/Kow 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 particular kinds of organic matter (e.g., coal, kerogen, coke and soot, 

collectively known as black carbon) is a particular concern, especially given publications that 

specifically relate this phenomenon to variability in BSAFs. For example, Cornelissen et al. 

(2005) state that the observed difference in field-measured BSAFs between (planar) PAHs and 

(mainly nonplanar) PCBs may be explained by sorption to “carbonaceous geosorbents” or black 
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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 prediction methodology. 

 At the present time it is difficult for EPA to evaluate the possibility that differences in the 

bioavailability of certain chemicals may be associated with the affinity of those chemicals for 

different types of organic carbon, or predict how this might affect BSAFs or Method 2 

predictions. For example, the depression of BSAFs for certain planar PCB 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 planar (non-ortho) PCBs for BC could explain the 

variability in BSAFs measured for lake trout in Lake Michigan. Kukkonen et al. (2003) has 

reported a BC content of 0.03% (dw) for Lake Michigan. Based upon this measurement, 

approximately 1% of the total organic carbon in the 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 bioavailability. In contrast, all ortho substituted PCBs were 

predicted to have approximately the same reductions in bioavailability. Thus, bioavailability 

considerations do not appear to be the cause of the depression of BSAFs observed for only some

of the ortho substituted PCBs in Lake Michigan. Additionally, given the relatively large 

differences in predicted pore water factors between the non-ortho and ortho substituted PCBs, 

non-ortho PCBs should have much lower BSAFs than were measured, especially PCB 169. 

Overall, the differences in bioavailability (measured as BSAFs) between PCB congeners could 

not be explained satisfactorily by the affinity 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 Gschwend (2003), and BC-water 

distribution coefficients for ortho and non-ortho substituted PCBs (Barring et al. 2002). Burkhard 
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et al. (2004) point out that in making their estimates of bioavailability reduction, they are 

extrapolating chemical concentrations in pore water by 6 to 9 orders of magnitude lower than 

those used for determination of the Freundlich parameters. These calculations, which imply a 

reduced bioavailability due to BC, involve extrapolations to environmental conditions which 

have not been tested, and are 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 error 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 Jsocw/Kow approximated by the ratio of 

organic carbon contents (foc,water/foc,sediment) assumes that the makeup of organic carbon in the 

water column and sediment are similar. Gschwend and others (Gustafson et al. 1997; AccardiDey and Gschwend, 2002; Bushel and Gustafsson, 2000) have reported that organic carbon from 

anthropogenic sources, e.g., BC, have sorptive capacities 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 from anthropogenic sources, the 

steady-state Jsocw/Kow may differ from the above ratio due to differences in the organic carbon 

composition between the water and sediments.

Additional guidance regarding the 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 with partitioning behavior similar to the chemical of 

concern. Chemicals reported to have enhanced affinity for BC include PAHs; planar 

(non-ortho and mono-ortho substituted) PCBs; and planar chlorobenzenes, 

PCDD/PCDFs and PBDEs. 
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• At sites where sediments contain 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 the partition coefficients for BC. 

• Ensure that the methods to calculate dissolved chemical fractions are used 

consistently throughout the application of Method 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, Sediment 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., Martin, 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 Environmental Research Laboratory, Athens, Georgia.

Young, T.M. and W.J. Weber, Jr. 1995. A Distributed reactivity model for Sorption by Soils and 

Sediments. 3. Effects of Diageneic Processes on Sorption Energetics. Environ. Sci. Technol. 29, 

92. 
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Appendix 4A 

 

Modeling Simulation of BSAF Sampling Designs 

 Burkhard (2003) performed model simulations to understand how the variabilities in 

water and sediment chemical concentrations 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 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 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, including those used to perform these 

simulations, are being continuously updated as new data become available for testing and as 

scientific understanding evolves. 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 Asocw/Kow

=1, for chemicals with log Kows 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 90th to the 

10th percentile confidence limits of the BSAFs. For chemicals with log Kows greater than 5, the 

five sampling designs provided practically identical uncertainties for the measured BSAFs and 

the uncertainty was essentially independent of the number of samples collected. The collection of 

one set of samples (i.e., one day of sampling) provided BSAF confidence limit ratios of less than 

3 for all five sampling designs. In contrast, larger uncertainties in the measured BSAFs were 

observed for chemicals with log Kows less than 5. For the less-hydrophobic chemicals, increasing 
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both the number of sampling events and their spacing in time reduced the uncertainties in the 

measured BSAFs. 

Figure 4A-1. Ratio of the 10th to 90th percentile biota–sediment accumulation factor (BSAF) for 

field-sampling designs consisting of concurrent fish and sediment sample collections spaced 1 

(—), 7 ( ), 14 ( ), 30 (r), and 120 (•) d apart, when using Mississippi River (USA) flow data 

for years 1955 to 1995. Results are based on modeling assumptions including (a) mixed benthicpelagic food web and (b) Jsocw/Kow = 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 Kow would be best described by the sample design for a chemical with a 

smaller Kow (with no metabolism). In this case, the effective reduction of Kow due to metabolism 

would be proportional to the rate of metabolism relative to the overall depuration rate, which is 

the sum of elimination rates via gill and gut, the organism growth rate, and the rate of chemical 

metabolism. 

 

 The kinetics of chemical uptake and loss by the fish (or other aquatic organism) controls 

the chemical residue that resides in the organism. These kinetic processes are directly dependent 
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upon the chemical’s hydrophobicity and metabolism rate in the fish. Successful field-sampling 

designs should account for the chemical uptake and loss kinetics, and for the changes in chemical 

concentrations occurring in the fish’s environment. The modeling simulations strongly 

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 sediment–water chemical 

concentration quotients are not usually important considerations to be factored into a sampling 

design. Chemical concentration gradients do not add large uncertainties into the measured 

BSAFs beyond those caused by temporal variability alone. BSAFs 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 BSAFs probably should be designed 

around the more contaminated reaches of the site. 
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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 precision of the BSAF depends 

upon the number of biota and sediment samples, and how the precision of the sediment-water 

concentration quotient (Jsocw) depends upon the number of sediment and water samples. The 

Monte Carlo method can also be used to simulate chemical concentrations in biota (for the 

chemical of interest and the reference chemical), sediment (chemical of interest) and water 

(reference chemical) simultaneously, so the investigator 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. Means 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 water were used as inputs to the 

Monte Carlo generator. Data from this zone were selected because 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 concentration data are presented in Appendix 4C; the 

data for PCB concentrations in fish and water 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 (nb), 

sediment (ns) and water (nw) concentrations were sampled and averaged, to simultaneously 

compute BSAFs, Jsocws and predicted BAFs. This procedure was repeated many times, until 

stable distributions of BSAF, Jsocw and BAF values were generated. As we will demonstrate, 

these distributions can be used by the investigator as estimates of the uncertainty of the BSAF, 

Jsocw, or the BAF predicted using Method 2. For example, the 90% confidence limits are 

estimated by the 95th and 5th percentiles of the BAF distribution. By repeating this procedure 

using different nb, ns and nw and comparing the results, the investigator can determine a sampling 
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design that meets their requirements for BSAF and/or BAF precision. Uncertainty in chemical 

Kows were not included in these computations, and the 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 measured in Green Bay for dissolved water, lipidnormalized predator fish, and organic-carbon normalized 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 concentrations in sediment at many sites 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% confidence limits (upper CL/lower CL) were used as measures of 

the uncertainty of the distributions of BSAFs, Jsocws and BAFs in each simulation. 

How is the Uncertainty of BSAF and Jsocw Affected by the Number of Sediment Samples 

and Different Chemical Concentration Variances? 

 The uncertainty of BSAFs and Jsocw calculated in the Monte Carlo simulations were 

sensitive to the number of sediment samples, and this sensitivity increased with the variability of 

the sediment chemical concentrations. For example, 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 coefficient of variation (CV) of the underlying 

population of chemical concentrations. The BSAF CLRs in Figure 4B-1 are averages for the four 

PCB congeners, and the BSAFs were calculated using 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 concentrations from different numbers of sediment and 

fish samples. For highly variable chemical concentrations in sediment, increasing the number of 
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sediment samples used to calculate the mean concentration has 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) 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 Jsocw. 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 Jsocw calculated using mean chemical concentrations 

from different numbers of sediment and water samples. As was the case for the BSAF, 

increasing the number of samples used to calculate the mean sediment concentration has a 

significant impact on reducing the uncertainty of Jsocw , up to a sample size of 6. Collecting 

additional sediment samples (i.e., greater than 10) has little effect on the uncertainty of Jsocw s. 
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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 samples

BSAF ratio (95% / 5%)

CV = 0.6

CV = 0.9

CV = 1.2

CV = 1.5

Figure 4B-1. 90% confidence interval ratio 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 samples 
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Table 4B-1. 90% Confidence Interval Ratios for 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 3.01
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Table 4B-1 (Continued). 90% Confidence Interval 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 2.33
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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 samples

Pi ratio (95% / 5%)

CV = 0.6

CV = 0.9

CV = 1.2

CV = 1.5

Figure 4B-2. 90% confidence interval ratio for Jsocw as function of number of sediment 

samples. (Average results for 4 PCB congeners are plotted; Jsocws were calculated using 6 

water samples drawn from Green Bay data). Sediment-water concentration quotient 90% confidence limit ratio 

Number of sediment samples
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Table 4B-2. 90% Confidence Interval Ratios for JSocw 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 3.28
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Table 4B-2 (continued). 90% Confidence Interval Ratios for JSocw 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 declining CLRs (and lower uncertainty) as a function 

of the number of sediment samples is consistent for BAFs calculated using different reference 

chemicals, some differences are also apparent. For example, sediment concentrations for PCB-52 
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are significantly more variable (CV=1.29) than for the other chemicals, and this is reflected in 

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 Figure 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

5 10 15 20 25 30 35

BAF 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 (results shown for 10 biota and 10 water samples). In each curve 

plotted in this figure, the BAF 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 samples

BAF 90% confidence limit ratio 
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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 35

BAF ratio ( 95% / 5% )

pcb149

pcb18

pcb180

pcb52

Figure 4B-4. 90% confidence interval ratio for four PCB congener Method 2 BAFs as a 

function of the number of sediment samples (results shown for 10 biota and 10 water 

samples). In this Figure, BAF confidence interval ratios are plotted as separate curves for 

each PCB congener. 

 0 5 10 15 20 25 30 35 

number of sediment samples

BAF 90% confidence limit ratio 
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How Does the Variability of Chemical Concentrations in Sediment Affect the 

Precision of Method 2 BAF Predictions? 

 Additional Monte Carlo simulations were run and analyzed, in order to develop 

generalized guidance for selecting sample sizes that would be applicable for different sites. To 

do so, the simulations were repeated using different CVs for chemical concentrations in 

sediment. As discussed above, the variability of chemical concentrations in sediment affected the 

uncertainty of BSAFs and Jsocw, particularly for small sediment sample sizes (ns6). For Method 

2 BAFs, the results are similar to those for BSAF and Jsocw, although the CLRs are much larger. 

This is shown in Figure 4B-5, which plots the 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, Jsocw and BAF. The results shown in Figure 4B-5 are for Method 2 

BAFs calculated with mean chemical concentrations 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 sample sizes. Each sub-table presents the 90% BAF 

CLR as a function of the number of sediment samples and the underlying variability 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 sediment. If 6 sediment samples are used to make 

the Method 2 prediction, the resulting 90% BAF CLRs are much lower, 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 and water samples (Table 4B-3.d), and 30 fish and 

water samples (Table 4B-3.e). 

 As was the case for BSAFs and Jsocws, 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 number 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 sediment is large. Depending upon the requirements for 
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predictive BAF uncertainty, exceeding sample sizes of 10 appears to be warranted only for sites 

having very high variability in chemical concentrations in sediment. 

Figure 4B-5. 90% confidence interval ratio for PCB-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 35

BAF ratio ( 95% / 5% )

CV=0.6

CV=0.9

CV=1.2

CV=1.5

Number of sediment samples

BAF 90% confidence limit ratio 
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Table 4B-3. 90% Confidence Interval Ratios 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 5.18
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Table 4B-3 (continued). 90% Confidence Interval Ratios for Method 2 BAF Predictions for 

PCB Congener 149 as a Function of the Variability 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 Concentration Correlations on Method 2 BAF 

Predictions? 

 The BAFs predicted above using Method 2 were based upon simulated chemical 

concentrations in biota, sediment and water that were uncorrelated and independent. In a real 

ecosystem, however, the concentrations of bioaccumulative chemicals are likely be correlated in 

biota, sediment and water. Two different kinds of correlation are possible: within-chemical

correlation of the concentrations of a specific chemical between different 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 processes to differ between chemicals due to their 
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physicochemical properties. Within-media correlation of chemical concentrations is also 

expected and could result, for example, if the concentrations of multiple chemicals were higher 

in one or a subset of sediment samples. Since Method 2 BAF predictions 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 concentrations were repeated to evaluate how 

within-chemical and within-media correlation would alter the estimates of BAF uncertainty 

presented in Section 4.4.5. To simulate within-chemical correlation, rank correlation coefficients 

of 0.5 were specified for each PCB congener between fish, sediment and water concentrations. 

The resulting BAF CLRs are shown as a function of sediment sample 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 correlations 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 simulate within-media correlation of chemical 

concentrations. Rank correlation coefficients of 0.5 were specified in each sample medium 

(biota, sediment and water) between PCB congener concentrations. The resulting BAF CLRs are 

again shown as a function of sediment sample size in Figure 4B-6. Within-media correlation, 

especially the correlation between chemical concentrations in sediment, significantly reduced 

the uncertainty of Method 2 BAF predictions when few sediment samples are collected. When 

only two sediment samples are used to calculate the BSAF and Jsocw, the 0.5 correlation reduced 

the CLR by 50% in comparison to the uncorrelated simulation. For 4 sediment samples, the 

reduction in the CLR was 40%, and for 6 samples the CLR reduction was 30%. Overall, 

concentration correlations were found to be helpful 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 that 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 illustrate that a conservative number of samples 

will be specified if chemical concentrations are assumed to be uncorrelated. In other words, the 
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Section 4 EPA Review Draft September 4, 2007

4-94 

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 indicated 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 35

BAF ratio ( 95% / 5% )

uncorrelated concentrations

within-chemical correlation

within-media correlation

Figure 4B-6. 90% confidence interval ratio for PCB-52 Method 2 BAFs as a function of 

concentration correlations (results shown for 10 biota and 10 water samples). In each curve 

plotted in this figure, the BAF confidence interval ratios were calculated from simulated fish, 

sediment and water concentrations incorporating different assumptions regarding correlations 

between the data. 

Number of sediment samples

BAF 90% confidence limit ratio 
4-95 

Appendix 4C 

Green Bay Mass Balance PCB Congener Concentrations: 

Organic Carbon-Normalized Surficial (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). 
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5-1 

 

5. ESTIMATING SITE-SPECIFIC BAFs BY EXTRAPOLATION, 

PREDICTION OR RECALCULATION 

 The previous 2 sections of this TSD have described EPA’s preferred methods of 

determining site specific BAFs: determining a BAF directly based on site-specific measurements, 

or determining a BSAF based on site-specific measurements and then predicting a BAF from the 

BSAF. Another approach to determine site-specific BAFs is to estimate a site-specific BAF 

indirectly using one of the other methods described 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 

(Kows) coupled with food chain multipliers, or recalculating site-specific 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 Kows 

• Recalculating site-specific BAFs from baseline BAFs (Method 5, 

with 2 options: 

5a. Adjustment for site-specific lipid content, and/or 

5b. Adjustment for site-specific DOC 

 EPA considers the BAF estimation methods described in this section to be less preferred 

than the direct determination of the BAF or BSAF based on measurements made at the site, 
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Section 5 EPA Review Draft September 4, 2007

5-2 

because these estimation methods may not capture all of the site-, chemical- and/or speciesspecific factors that influence chemical bioaccumulation. In addition, the estimation methods rely 

upon assumptions that may be difficult to confirm. As a result, the site specific BAFs estimated 

by these methods are uncertain due to factors beyond 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 specific BAF. Rather, it is meant to caution the investigator to 

carefully consider whether a particular BAF 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 national BAFs are broadly applicable to sites 

throughout the United States and achieve an acceptable 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 conditions, 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 concentrations, trophic-level specific lipid contents, 

trophic structures, Jsocw/Kow, and other parameters (USEPA, 2003). 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 example): fish 

consumption patterns that are substantially different than national averages; species of aquatic 

organisms that have not been previously sampled or for which trophic level or feeding preference 

is unknown; and sediment-water chemical distribution, tissue lipid content, POC and/or DOC 

concentration significantly different than the values assumed in the national methodology. In 

cases such as these, the derivation of site-specific BAFs would likely improve the accuracy of 

bioaccumulation estimates and, ultimately, the AWQC for the chemical of concern at that site. 
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Section 5 EPA Review Draft September 4, 2007

5-3 

 The three alternatives for estimating site specific BAFs are presented and discussed in the 

following sections. Section 5.1 addresses Methods 3a and 3b, estimating site-specific BAFs by 

extrapolating BSAFs or BEFs. Predicting site-specific BAFs using BCFs and food chain 

multipliers (Methods 4a and 4b) is covered in Section 5.2. Method 5, recalculating 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 extrapolate 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 bioaccumulation equivalence factor (BEF, defined as 

the ratio between BSAFs for the chemical of interest and the reference chemical) to extrapolate a 

BSAF. Since these are actually two related methods, we refer to BSAF extrapolation as Method 

3a and extrapolation of a BEF as Method 3b. Figure 5-1 presents a decision framework flowchart 

for selecting an applicable BAF derivation method, 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 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. 

Methods 3a and 3b have the greatest potential for highly hydrophobic nonionized organic 

chemicals that may be metabolized in the food chain and are difficult and/or expensive to measure 

as freely dissolved concentrations 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. 
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Section 5 EPA Review Draft September 4, 2007

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 

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

Section 5 EPA Review Draft September 4, 2007

5-5 

Figure 5-1. Decision framework for selecting a site-specific BAF derivation method based on 

BSAFs 

 BSAFs have been used by EPA for predicting chemical residues in aquatic organisms 

from contaminated sediments, especially for Superfund sites (USEPA, 1999). A site-specific 

BSAF (i.e., a BSAF determined at the site) is clearly most desirable when making predictions, 

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 apply

Method3b; or, select a 

different method 

yes 

yes 

no 

yes no 

yes 

no

no 

no
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Section 5 EPA Review Draft September 4, 2007

5-6 

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 measurements in other ecosystems may be used in 

developing the site-specific BAF for the site of interest. This is Method 3a of the site-specific 

BAF methodology: direct extrapolation of a BSAF determined for the chemical of interest at a 

reference site to a site of interest. BSAF extrapolation has received increasing 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 concentrations in water, (2) they remove the site-specific variability in 

BAFs due to differences in the sediment-water concentration quotient, Jsocw, and (3) they remove 

the site-specific variability due to differences in organic carbon and lipid contents. 

BSAFs can be adjusted, using ratios predicted by food chain bioaccumulation models, to 

account for differences between sites 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 loading histories (Burkhard et al., 2006). This approach, called hybrid 

bioaccumulation modeling, uses mechanistic bioaccumulation models to assist in extrapolating 

field-measured BSAFs by explicitly accounting for the differences between ecosystems. Although 

additional work is required to fully evaluate and develop the hybrid extrapolation approach for 

routine application, it appears promising as a method for improving the accuracy of BSAF 

extrapolation. 

 When BSAFs from one ecosystem are directly 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 often not appreciated by users of BSAF data. As 
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Section 5 EPA Review Draft September 4, 2007

5-7 

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 chain/web to water and sediment, 

3. the length of the food web (or trophic level 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 differences between sites are largely 

accommodated by expressing BSAFs and BAFs in terms of concentrations 

normalized to organic carbon, lipid, and freely dissolved fraction in water), and 

5. metabolic transformation rates of the chemical within the food web. 

The first four factors can vary widely among ecosystems. 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 this section to demonstrate the 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 variability 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 congener-
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Section 5 EPA Review Draft September 4, 2007

5-8 

specific BSAFs determined in Green Bay and the Hudson River are reported in Appendix 5C. In 

Green Bay, the variability in BSAFs between spatial zones for a particular congener and fish 

species was found to be comparable to the variability of baseline BAFs. 

 Sets of BSAFs across ecosystems have consistent, if not identical, scaling, ranking, or 

ordering of the individual chemicals (Burkhard et al., 2005). When BSAF values are plotted for 

one ecosystem against another, chlorinated 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. 
Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors 

Section 5 EPA Review Draft September 4, 2007

5-9 

BSAFs for white suckers at various sites 

White Sucker

from 402108076363701

BSAFs

0.1

1

White Sucker

from 413345072531001

BSAFs

0.1

1

10

White Sucker

from 01573560

BSAFs for white suckers 

from sampling station 01208869

0.1 1 10

BSAFs

0.1

1

10

White Sucker

from 01171500

BSAFs

0.1

1

10

White Sucker

from 01191000

BSAFs for white suckers 

from sampling station 01208869

0.1 1

BSAFs

0.1

1

White Sucker 

from 400037076423701

BSAFs

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 carbon/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 (orange 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 (') and significance level ("), and 1:1 line (dotted) are provided. Note: the y-axes 

have different scales in some of the subgraphs.
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Section 5 EPA Review Draft September 4, 2007

5-10 

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 lake 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-HxCDD (yellow diamond); 1,2,3,6,7,8-HxCDD 

(pink up triangle); 1,2,3,7,8,9-HxCDD (purple 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 data points) for geometric mean regression 

line (solid), Spearman’s coefficient of rank correlation (') 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 1

BSAFs

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 0.1

BSAFs

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 & 5

BSAFs

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 = <0.0001
Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors 

Section 5 EPA Review Draft September 4, 2007

5-11 

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 organic carbon/kg lipid) for 6 year 

old lake trout from Lake Michigan. The symbol-color combination represents the same chemical 

in all six subgraphs, i.e., PCB-18 (blue diamond), 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 (orange square), PCB-85 (yellow circle), 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), Spearman’s coefficient of rank correlation (') 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 10

BSAFs

0.1

1

10

Hudson River (6)

River Mile 194

Largemouth Bass

BSAFs

0.1

1

10

100

Hudson River (6)

River Mile 122

Yellow Perch

BSAFs

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 10 100

BSAFs

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 = 0.06
Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors 

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5-12 

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., 2003) 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 descriptions 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 prediction limits for the regression, Spearman’s 

coefficient of rank correlation (') 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 (orange), 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 10 100

BSAFs

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 10 100

BSAFs

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 = <0.0001
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5-13 

 The highly significant relative ranking phenomenon 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 substantially lower rates of metabolism in 

fish, i.e., PCBs. The demonstration of consistent scaling/ranking of individual BSAFs across 

ecosystems is quite remarkable. It creates opportunities for improving our understanding of 

bioaccumulation processes in aquatic ecosystems, and for improving the accuracy of site-specific 

bioaccumulation factors that are estimated by extrapolating 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 incorporating the BSAF ranking behavior 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 estimate for the site-specific BAF can be obtained. 

5.1.1 Estimating Site-Specific BAFs by Extrapolating BSAFs (Method 3a)

 Method 3a estimates the site-specific baseline BAF by extrapolating the BSAF determined 

for the chemical of interest by measurements in another ecosystem, which is then multiplied by 

the sediment-water concentration quotient (Jsocw) for the site to obtain a trophic level-specific 

baseline BAF: 

,

1 Baseline BAF = BSAF i i j socw f

Π −

l

 (Equation 5-1) 

The terms in this equation are defined as follows: 

 Baseline BAFi = Baseline BAF estimated for the site and the chemical of 

interest, for organism at trophic level i (defined in Equation 

3-2); 
Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors 

Section 5 EPA Review Draft September 4, 2007

5-14 

 BSAFi,j = BSAF for chemical of interest for organism at trophic level i 

extrapolated from ecosystem j (defined in Equation 4-1); 

Πsocw = Sediment-water concentration quotient for the site and the 

chemical of interest (defined in Equation 4-3); 

fl = Lipid content (fraction) of the target species or tissue. 

 In most cases, Jsocw 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-water concentration quotient for a 

reference chemical is usually substituted in equation 5-1, similar to the way Jsocw for reference 

chemicals are used in Method 2. Equation 5-2 is used to calculate the site-specific baseline BAF 

for chemical k, when Jsocw is based on measurements for reference chemicals r: 

/ ,,

, ,

,

 1 Baseline BAF =BSAF k r socw r ow k

ik i j

ow r

D K

K f

Π −

l

 (Equation 5-2) 

where: 

 Dk/r = Ratio of the fugacity gradient (modeled as Jsocw/Kow) between sediment 

 and water for chemical of interest k in comparison to that of a reference 

 chemical r 

 Each of the parameters in equations 5-1 and 5-2 (BAF, BSAF and Jsocw) is calculated 

using chemical concentrations normalized for lipid (in biota) and/or organic carbon (sediment) 

contents and adjusted for the dissolved fraction of the chemical in water. Method 3a is 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. Since a BSAF 

is based on lipid and organic carbon normalized chemical concentrations (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 specific trophic level. Method 3a does not address site-specific 
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Section 5 EPA Review Draft September 4, 2007

5-15 

variability in BSAFs. The only site-specific adjustment made by the investigator using Method 3a 

involves selecting a value for Jsocw, based upon measurements, estimates or predictions, as 

discussed in Section 4.3. Calculating a site-specific BAF using Method 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. 

The reference BSAFs used in this example was obtained from data published by 

Burkhard et al. (2004) for PCBs, 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 representative sampling: southern Lake Michigan is well-mixed; fish 

samples consisted 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 data 

confirmed that the concentration measurements were both consistent and 

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. 
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5-16 

Extrapolating a Site-Specific BAF From BSAFs 

Determined at Another Site (Method 3a, continued)

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 consumed 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 9.53 

It is assumed that no sediment-water concentration quotient is available for the 

chemical of interest at the site, a common situation. Instead, Jsocw measured at 

the site for reference chemicals will be used with Method 3a to estimate the 

site-specific baseline BAF. PCB congeners 52 and 105 have been used as 

reference chemicals for calculating baseline BAFs for other PCBs (USEPA, 

1995a; Cook and Burkhard, 1995). These congeners serve as appropriate 

reference chemicals because (1) they have similar physicochemical properties, 

(2) they are well quantified in sediment and biota, and (3) available data 

indicate they have loading histories similar to PCB 101 and thus their fugacity 

ratio (Jsocw,r/Kow,r) values should be similar. The sediment-water 

concentration quotient measured for PCBs 52 and 105 in Green Bay zone 3a 

are tabulated below. 

PCB congener Jsocw log Kow

52 4.24×106 5.84 

105 2.37×107 6.65 
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5-17 

Extrapolating a Site-Specific BAF From BSAFs 

Determined at Another Site (Method 3a, continued)

Estimating the site-specific baseline BAF using method 3a

Equation 5-2 is used to calculate the site-specific baseline BAF for chemical i, 

when Jsocw is based on measurements for reference chemicals r: 

/ ,,

,

 

, ,

1 Baseline BAF =BSAF k r socw r ow k

ow r ik i j

D K

K f

Π −

l

The Kow of the chemical of interest, PCB 101, is 2.40×106

; 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, Dk/r

~ 1. Therefore, a site-specific baseline BAF for PCB 101 is calculated using 

equation 5-2 with reference chemical PCB 52: 

( ) ( )( )( )

( )

101/52 ,52 ,101

4,101 4,52

,52

6 6

8

5

1 Baseline BAF

1 4.24 10 2.40 10 1 7.71 1.13 10 /

6.92 10 0.11

SOCW OW

OW

D K

BSAF

K

L kg l

f

Π = −

× × = − = × − ×

l

Likewise, a site-specific baseline BAF for PCB 101 can also be calculated 

with reference chemical PCB 105: 

( ) ( )( )( )

( )

101/105 ,105 ,101

4,101 4,105

,105

7 6

7

6

1 Baseline BAF

1 2.37 10 2.40 10 1 7.71 8.36 10 /

5.25 10 0.11

SOCW OW

OW

D K

BSAF

K

L kg l

f

Π = −

× × = − = × − ×

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 using Method 3a is therefore 

9.73×107 L/kg-lipid. 
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5-18 

 Dk/r, the ratio of the fugacity gradient (Jsocw/Kow) for chemical of interest k in comparison 

to reference chemical r, is an especially important parameter in equation 5-2. Unfortunately, high 

quality datasets for Jsocw, from which Dk/r can be calculated, are very limited. Selecting 

appropriate reference chemicals, and accurate values of Dk/r is important, because significant 

reproducible differences in Jsocw 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 ecosystem, and Hudson River is a relatively fast 

moving river ecosystem. Additionally, the aquatic food webs in the ecosystems are different and 

have different top predatory 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, the lower Fox River entering into Green Bay, and 

Zone 4, the deeper outer portion of the bay. These 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 hydrodynamic 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 applying measurements made in one ecosystem to 

the site of interest, as demonstrated in the 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 
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5-19 

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 River miles 189 and 194. In each sub-table, the BSAFs 

for 9 representative PCB congeners measured in Lake Michigan lake trout are extrapolated to the 

fish species indicated, and are used to estimate the site-specific BAF via Method 3a (equation 5-

1). The Lake Michigan lake trout BSAFs are also compared to independently-determined BSAFs 

for each fish species at each site, and the error involved in BSAF extrapolation is presented in 

Table 5-1. 

 Comparison of the BSAF extrapolation errors in Table 5-1 suggest that Method 3a 

estimates tend to be consistently biased at each site. This is most apparent for the BSAFs 

extrapolated to Green Bay sites (Tables 5-1, A through C). BSAFs extrapolated to fish in zone 4 

of Green Bay are negatively biased for all of the congeners included in the 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 extrapolated Lake Michigan BSAFs to estimate site-specific 

BAFs for walleye in Green Bay Zone 4, the predicted BAFs would be too small by a factor of 

about 2.4. On the other hand, if the investigator used the Lake Michigan BSAFs to estimate sitespecific BAFs for walleye in Zone 1 of Green Bay, 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 extrapolation 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 also be recognized that using PCB 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 can span up to several orders of magnitude. 
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5-20 

Table 5-1. Method 3a BSAF Extrapolation Example 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 

Kow

LM LT6 

BSAF 

GB BT3 

BSAF 

BSAF 

Extrapolation

Error 

Jsocw 

Measured in 

Green Bay 

Zone 4 

Predicted 

Site-specific 

Baseline BAF 

Site-specific 

Log Baseline 

BAF 

18 5.24 1.38 3.29 -58% 1.11x106 1.53 x106 6.18 

28/31 5.67 1.50 4.15 -64% 3.89 x106 5.83 x106 6.77 

52 5.84 5.16 23.2 -78% 2.00 x106 1.03 x107 7.01 

110 6.48 4.75 13.4 -65% 7.33 x106 3.48 x107 7.54 

118 6.74 5.57 11.0 -49% 2.99 x107 1.66 x108 8.22 

149 6.67 9.05 23.5 -61% 3.10 x106 2.81 x107 7.45 

180 7.36 11.8 13.9 -15% 1.32 x107 1.56 x108 8.19 

174 7.11 8.30 14.2 -42% 9.64 x106 8.00 x107 7.90 

196/203 7.65 9.15 16.4 -44% 8.12 x107 7.43 x108 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 

Kow

LM LT6 

BSAF 

GB W4 

BSAF 

BSAF 

Extrapolation

Error 

Jsocw 

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 x106 1.53 x106 6.18 

28/31 5.67 1.50 2.99 -50% 3.89 x106 5.83 x106 6.77 

52 5.84 5.16 26.5 -81% 2.00 x106 1.03 x107 7.01 

110 6.48 4.75 16.1 -70% 7.33 x106 3.48 x107 7.54 

118 6.74 5.57 14.3 -61% 2.99 x107 1.66 x108 8.22 

149 6.67 9.05 22.2 -59% 3.10 x106 2.81 x107 7.45 

180 7.36 11.8 39.2 -70% 1.32 x107 1.56 x108 8.19 

174 7.11 8.30 18.7 -56% 9.64 x106 8.00 x107 7.90 

196/203 7.65 9.15 20.3 -55% 8.12 x107 7.43 x108 8.87 
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Section 5 EPA Review Draft September 4, 2007

5-21 

Table 5-1 (Continued). Method 3a BSAF Extrapolation 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 

Kow

LM LT6 

BSAF 

GB W4 

BSAF 

BSAF 

Extrapolation

Error 

Jsocw 

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 x106 2.16 x106 6.34 

28/31 5.67 1.5 0.47 216% 5.64 x106 8.47 x106 6.93 

52 5.84 5.16 2.07 149% 3.98 x106 2.06 x107 7.31 

110 6.48 4.75 2.05 131% 1.89 x107 8.98 x107 7.95 

118 6.74 5.57 3.15 77% 4.87 x107 2.71 x108 8.43 

149 6.67 9.05 3.06 196% 2.51 x107 2.27 x108 8.36 

180 7.36 11.8 2.92 304% 1.68 x108 1.99 x109 9.30 

174 7.11 8.3 3.70 124% 5.36 x107 4.45 x108 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 

Kow

LM LT6 

BSAF 

RM 189 

LMB BSAF

BSAF 

Extrapolation

Error 

Jsocw 

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 x106 4.23 x106 6.63 

28/31 5.67 1.50 2.46 -39% 6.18 x106 9.27 x106 6.97 

52 5.84 5.16 6.56 -21% 5.24 x106 2.70 x107 7.43 

110 6.48 4.75 11.5 -59% 1.85 x107 8.77 x107 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% ND 
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5-22 

Table 5-1 (Continued). Method 3a BSAF Extrapolation 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 

Kow

LM LT6 

BSAF 

RM 194 

LMB BSAF

BSAF 

Extrapolation

Error 

Jsocw 

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 x107 1.43 x107 7.16 

28/31 5.67 1.50 0.921 63% 1.60 x107 2.40 x107 7.38 

52 5.84 5.16 2.82 83% 1.60 x107 8.25 x107 7.92 

110 6.48 4.75 4.11 15% 5.32 x107 2.52 x108 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% ND 
Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors 

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5-23 

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 Method 3a. The symbol-color combinations 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 = largemouth 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 BSAF

extrapolated BSAF

GB z4 BT3

GB z4 W4

GB z1 W4

HR RM189 LMB

HR RM194 LMB

5x

2x

1:1 line

1/2

1/5
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Section 5 EPA Review Draft September 4, 2007

5-24 

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 for the chemical of interest and a reference 

chemical in another ecosystem. The BEF is multiplied by a BSAF measured at the site for the 

reference chemical and the Jsocw for the chemical of interest k at the site: 

, k/r , ,

1 Baseline BAF = BSAF BEF

j i i r socw k f

Π −

l

 (Equation 5-3) 

 As was the case for Method 3a, Jsocw 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 Jsocw for reference chemicals are used in Method 

2. Equation 5-4 is used to calculate the site-specific baseline BAF for the chemical of interest k, 

when Jsocw is based on measurements for reference chemicals r: 

/ ,,

,

 

, ,/

1 Baseline BAF = BSAF BEF k r socw r ow k

ow r i i r j k r

D K

K f

Π −

l

 (Equation 5-4) 

As was the case for Method 3a (equation 5-2), the parameter Dk/r plays an important role in 

equation 5-4. 

The bioaccumulation equivalency factor BEFj,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: 

 BSAFj,k = BSAF for chemical of interest k determined by measurements at 

another site j, and 
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5-25 

 BSAFj,r = BSAF for reference chemical r determined by measurements at 

another site j. 

Although the terms in this equation have been previously defined, the investigator should note the 

use and meaning of the various subscripts associated with each term in equations 5-4 and 5-5: 

 BSAFi,r = BSAF for reference chemical determined by measurements of 

organism at trophic level i at the site (equation 4-1); 

Asocw,k = Sediment-water concentration quotient 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 Jsocw) is calculated using chemical concentrations normalized for lipid (in biota) 

and/or organic carbon (sediment) contents and adjusted for the dissolved fraction of the chemical 

in water. Method 3b is 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. Site-specific variability in BSAFs is addressed in Method 3b by 

incorporating a BSAF for a reference chemical determined by measurements at the site, as well as 

a site-specific value for Jsocw, based upon measurements, estimates or predictions (as discussed in 

Section 4.3). Equations 5-3 and 5-4 should only be applied to fish or other aquatic biota within a 

specific trophic level. Calculating 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 previous example, it is assumed that no 

sediment-water concentration quotient (Jsocw ) is available for the chemical of 

interest at the site. PCB 52 was chosen as a reference chemical for this example; 

both Jsocw (4.24×106 ) 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. 
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5-26 

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 bioaccumulation 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 individual lake trout composites; for 

example, the BEF based on PCB concentrations in 6 year old lake trout is: 

( ) ( )

( ) BEF

BSAF

BSAF 89/52

89

52

LM

LM yr

LM yr

= = = ,

,

.

. . 6

6

4 80

5 16 0 929

Likewise, the BEFs for 8 and 9 year old lake trout are 0.860 and 0.890, 

respectively, as tabulated below. The 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 0.929 

8 year old 0.860 

9 year old 0.890 
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5-27 

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 site-specific baseline BAF for chemical k, 

when Jsocw is based on measurements for reference chemical r and the BEFj,k/r

is extrapolated from another site: 

/ ,,

, ,/

,

1

Baseline BAF k r SOCW r OW k

i ir jk r

OW r

D K

BSAF BEF

K f

Π = −

l

The Kow of the chemical of interest, PCB 89, is 1.17×106

; 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, Dk/r

~ 1. A site-specific baseline BAF for PCB 89 is calculated using equation 5-4 

with reference chemical PCB 52: 

( )( ) ( )( )( )

( )

89 / 52 ,52 ,89

4 4,52 ,89 / 52

,52

6 6

7

5

 1

Baseline BAF

1 4.24 10 1.17 10 1 5.67 0.896 3.66 10 /

6.92 10 0.11

SOCW OW

LM

OW

D K

BSAF BEF K f

L kg l

Π = −

× × = − = × − ×

l

Since only one reference chemical was used in this example, 3.66×107 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 multiple reference chemicals, and then 

averaging the results. 
Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors 

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5-28 

 Method 3b offers the investigator two significant advantages in comparison to Method 3a. 

First, by extrapolating a BEF instead of a BSAF, Method 3b takes advantage the “relative 

scaling” phenomenon evident in BSAFs for multiple chemicals between sites, as discussed in 

Section 5.1. The relative ranking of BSAFs 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-quality datasets (e.g., Lake Michigan, Hudson River 

and Green Bay) as have been described in this 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 practical considerations such as analytical 

detectability. In many cases, PCBs will make good choices 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 Burkhard, 1995; USEPA, 1995a) for use in estimating 

BAFs for PCDDs and PCDFs. Lake Ontario sediment and fish residue data (Lodge et al. 1994) 

provided the basis for calculating BEFs in the GLI. Table 5-2 provides estimated BEFs calculated 

from lake-wide average concentrations 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. Comparisons to BEFs calculated from data obtained for other ecosystems 

confirms these bioaccumulation 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 detectable 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. 
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Section 5 EPA Review Draft September 4, 2007

5-29 

Table 5-2. TCDD Bioaccumulation Equivalency 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 Kowa 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 7.0b 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 chlorination (Burkhard and Kuehl, 1987). EPA does not 

 recommend the use of these log Kow values for use in deriving bioaccumulation factors. 
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5-30 

 To further illustrate the estimation of BAFs using Method 3b, Lake Michigan lake trout 

BEFs for PCB congeners were extrapolated to brown trout and walleye 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 reference 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 calculation 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 Michigan lake trout are extrapolated to the fish 

species indicated, and are used to estimate the site-specific BAF via Method 3b (equation 5-3). In 

Table 5-3A and B, the Lake Michigan BEFs are extrapolated to 3 year old brown trout and 4 year 

old walleye in Zone 4 of Green Bay, and in Table 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-specific BSAFs for each fish species at each site 

in Table 5-3, and the error involved in BSAF 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 for Method 3a (Table 5-1 and Figure 5-6) demonstrate that, although 

the Method 3b estimates are not perfect, they are in much better agreement with the measured 

BSAF than the BSAFs which are directly extrapolated from one ecosystem to another (i.e., 

Method 3a). The average bias in Method 3b estimates ranges from a low of -8% for brown trout 

in Green Bay zone 4, to a high of 82% for largemouth bass at Hudson River mile 194. On an 

individual congener basis, variability 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 the case for Method 3a, the majority of the errors in 

BSAFs extrapolation by Method 3b were smaller than 100%, with the errors in BSAF 

extrapolation falling within the ± factor of 5 range (for this example). 
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5-31 

Table 5-3. Method 3b BEF Extrapolation Example 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 

Kow

LM 

LT6 

BSAF

GB BT3 

BSAF

BEF 

(PCB 

118)

Method 3b 

BSAF 

Prediction

Measured 

GB BT3 

BSAF

Method 3b

BSAF 

Error

Jsocw 

Measured 

in Green 

Bay Zone 

4

Predicted 

Site-specific

 Log Baseline 

BAF

18 5.24 1.38 0.248 2.73 3.29 -17% 1.11 x106 6.48 

28/31 5.67 1.50 0.269 2.96 4.15 -29% 3.89 x106 7.06 

52 5.84 5.16 0.926 10.2 23 -56% 2.00 x106 7.31 

110 6.48 4.75 0.853 9.38 13 -30% 7.33 x106 7.84 

118 6.74 5.57 11.0 1.00 2.99 x107

149 6.67 9.05 1.62 17.9 24 -24% 3.10 x106 7.74 

180 7.36 11.8 2.12 23.3 14 68% 1.32 x107 8.49 

174 7.11 8.30 1.49 16.4 14 15% 9.64 x106 8.20 

196/203 7.65 9.15 1.64 18.1 16 10% 8.12 x107 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 

Kow

LM 

LT6 

BSAF

GB W4 

BSAF

BEF 

(PCB 

118)

Method 3b 

BSAF 

Prediction

Measured 

GB W4 

BSAF

Method 3b

BSAF 

Error

Jsocw 

Measured 

in Green 

Bay Zone 

4

Predicted 

Site-specific

 Log Baseline 

BAF

18 5.24 1.38 0.248 3.54 2.67 33% 1.11 x106 6.59 

28/31 5.67 1.50 0.269 3.85 2.99 29% 3.89 x106 7.18 

52 5.84 5.16 0.926 13.2 26.5 -50% 2.00 x106 7.42 

110 6.48 4.75 0.853 12.2 16 -24% 7.33 x106 7.95 

118 6.74 5.57 14.3 1.00 2.99 x107

149 6.67 9.05 1.62 23.2 22 5% 3.10 x106 7.86 

180 7.36 11.8 2.12 30.3 39 -23% 1.32 x107 8.60 

174 7.11 8.30 1.49 21.3 19 14% 9.64 x106 8.31 

196/203 7.65 9.15 1.64 23.5 20 16% 8.12 x107 9.28 
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Section 5 EPA Review Draft September 4, 2007

5-32 

Table 5-3 (Continued). Method 3b BEF Extrapolation 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 

Kow

LM 

LT6 

BSAF

GB W4 

BSAF

BEF 

(PCB 

118)

Method 3b 

BSAF 

Prediction

Measured 

GB W4 

BSAF

Method 3b

BSAF 

Error

Jsocw 

Measured 

in Green 

Bay Zone 

1

Predicted 

Site-specific

 Log 

Baseline 

BAF

18 5.24 1.38 0.248 0.78 0.575 36% 1.11 x106 5.94 

28/31 5.67 1.50 0.269 0.85 0.474 79% 3.89 x106 6.52 

52 5.84 5.16 0.926 2.92 2.07 41% 2.00 x106 6.77 

110 6.48 4.75 0.853 2.69 2.05 31% 7.33 x106 7.29 

118 6.74 5.57 3.15 1.00 2.99 x107

149 6.67 9.05 1.62 5.13 3.06 68% 3.10 x106 7.20 

180 7.36 11.8 2.12 6.68 2.92 129% 1.32 x107 7.95 

174 7.11 8.30 1.49 4.70 3.70 27% 9.64 x106 7.66 

196/203 7.65 9.15 1.64 5.18 ND 8.12 x107 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 

Kow

LM 

LT6 

BSAF

RM 189 

LMB 

BSAF

BEF 

(PCB 

118)

Method 3b 

BSAF 

Prediction

Measured 

RM 189 

LMB 

BSAF

Method 3b

BSAF 

Error

Jsocw 

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 x106 7.11 

28/31 5.67 1.50 0.269 4.59 2.46 86% 6.18 x106 7.45 

52 5.84 5.16 0.926 15.8 6.56 141% 5.24 x106 7.92 

110 6.48 4.75 0.853 14.5 11.5 26% 1.85 x107 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% ND 
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Section 5 EPA Review Draft September 4, 2007

5-33 

Table 5-3 (Continued). Method 3b BEF Extrapolation 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 

Kow

LM 

LT6 

BSAF

RM 194 

LMB 

BSAF

BEF 

(PCB 

118)

Method 3b 

BSAF 

Prediction

Measured 

RM 194 

LMB 

BSAF

Method 3b

BSAF 

Error

Jsocw 

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 x107 7.17 

28/31 5.67 1.50 0.269 1.55 0.921 69% 1.60 x107 7.39 

52 5.84 5.16 0.926 5.34 2.82 89% 1.60 x107 7.93 

110 6.48 4.75 0.853 4.92 4.11 20% 5.32 x107 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% ND 
Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors 

Section 5 EPA Review Draft September 4, 2007

5-34 

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 Method 3b. The symbol-color combinations 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 = largemouth 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 BSAF

BSAF 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

1/5
Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors 

Section 5 EPA Review Draft September 4, 2007

5-35 

5.2 PREDICTING SITE-SPECIFIC BAFS USING BCFS AND 

FOOD CHAIN MULTIPLIERS (FCMs)

 A site-specific BAF can be predicted as the product of a BCF coupled with a food chain 

multiplier (FCM). In effect, this method uses a simple bioaccumulation model to predict a sitespecific BAF. The investigator has numerous options for predicting a site-specific BAF using 

this method. The BCF can be either a value based on laboratory experiments for the chemical of 

concern (Method 4a), or can be estimated using the Kow 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 Kows 

 In addition, the investigator has the option to measure, estimate (from existing data), or 

predict (using food chain models) the FCM to reflect biomagnification of the chemical for a 

particular trophic level under site-specific conditions. Because food chain multipliers are trophic 

level-specific, the investigator should determine 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 underpredict BAFs for chemicals for which accumulation 

from sediment or dietary sources is important, including hydrophobic nonionic organic 

chemicals. Therefore, a FCM is used to adjust the value of a BCF to better account for chemical 

accumulation through the food web as a result of dietary exposures. For nonionic organic 

chemicals (and certain ionic organic chemicals to which similar lipid and organic carbon 

partitioning behavior applies), the food-chain multiplier is defined as the ratio of a baseline BAF 
Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors 

Section 5 EPA Review Draft September 4, 2007

5-36 

for an organism of a particular trophic level to the lipid-normalized BCF (usually determined for 

organisms in trophic level one). 

5.2.1 Predicting Site-Specific Baseline BAFs using laboratory-measured BCFs and FCMs 

(Method 4a) 

 For Method 4a, a laboratory-measured BCF (BCF) and FCM are used to predict a sitespecific baseline BAF. This method is applicable to nonionic organic chemicals that have 

moderate-to-high hydrophobicity (log Kow 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 equation to calculate the baseline BAF for a site:

t

T

fd

BCF 1 Baseline BAF = FCM 1

f i i f

  ⋅ −⋅     l

 (Equation 5-6)

where: 

t BCF = Total BCF (BCF = C T t/Cw) 

 ffd = fraction of the total concentration of chemical in BCF test water that is 

freely dissolved 

 fR = fraction of the tissue that is lipid in the test organism 

FCMi = 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 provided in Appendix A of TSD Volume 2 (USEPA, 2003). Guidance 

on selecting appropriate BCFs and FCMs, and the 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. 
Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors 

Section 5 EPA Review Draft September 4, 2007

5-37 

Site-Specific BAF Predicted Using the Product of a

Laboratory-Measured BCF and a FCM (Method 4a)

This example illustrates the prediction 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 Method 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 concentration of chemical k in fish tissue 

(0.325 )g/kg) and the laboratory test water (1.6 ng/L) in a water-only exposure 

test. The laboratory-measured BCF calculated for chemical k is 203 L/kg, as 

shown below: 

0.325 1000

1.6 203 / t

w

t C g n L g BCFT C kg ng g L kg μ

μ == ⋅ ⋅ =

 Determining a FCM Based on Measurements

As discussed in Section 5.2.2, site-specific FCMs can be determined by 

measurements or food chain model predictions. In this example, the FCM for 

trophic level 3 will be calculated from concentrations of chemical k measured 

in the food chain. The following data were obtained from field studies at the 

site: 

Sample Trophic 

level

Concentration of 

chemical k ()g/kg)

Lipid or organic 

carbon (%)

Sediment 1 1.95 7.4

Phytoplankton 1 1.2 1.2

Zebra mussels 2 1.3 1.3

Crayfish 3 1.7 1.7
Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors 

Section 5 EPA Review Draft September 4, 2007

5-38 

Site-Specific BAF Predicted 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 (66% of diet by weight) and phytoplankton (34% 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 = (CR, TL2) / (CR , TL1) (Equation 5-11) 

 BMF TL3 = (CR , TL3) / (CR, TL2) (Equation 5-12) 

Since zebra mussels consume both phytoplankton and sediment, the BMF 

must be calculated using a weighted average of chemical concentrations in 

their diet items, sediment and phytoplankton: 

( ) 16.1

012.0

35.0 75.0

074.0

95.1 25.0

013.0kg

g0.431

ZMTL2 BMF =

















− ⋅⋅+















− ⋅⋅

− ⋅

=

lipidkg

kg

kg

g

SOCkg

kg

kg

g

lipidkg

kg

μ μ

μ

Likewise, the BMF for crayfish must be calculated using the weighted average 

of chemical concentrations in their diet items, phytoplankton and zebra 

mussels:

( )

( ) ( )

725.0

013.0

431.0 3/2

012.0

35.0 3/1

017.0kg

g0.392

cfTL3 BMF =

















− ⋅⋅+















− ⋅⋅

− ⋅

=

lipidkg

kg

kg

g

lipidkg

kg

kg

g

lipidkg

kg

μ μ

μ

FCMs can be calculated for trophic levels 2 and 3 using equations 5-8 and 5-9:

 

 FCM 2 = BMF2 = 1.16 

 FCM 3 = BMF3 # BMF2 = 0.725 · 1.16 = 0.844 
Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors 

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Site-Specific BAF Predicted Using the Product of a

Laboratory-Measured BCF and a FCM (Method 4a, continued)

Predicting a site-specific baseline BAF 

The t BCF is converted to a baseline BA T F for a specific trophic level by 

incorporating information on the fraction of the chemical that is freely 

dissolved in the bioconcentration test water (ffd), the fraction of tissue or 

aquatic organism tested that is lipid (fR), 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: 

t

T

i i

fd

BCF 1 Baseline BAF = FCM 1

f l f

  ⋅ − ⋅    

 

Determining the fraction of chemical k that is freely dissolved in the 

bioconcentration test water (ffd) requires information on the POC and DOC 

concentrations in the test water and the Kow of chemical k. 

For this example, the median POC concentration in the test water is 0.5 mg/L 

(5.0×10-7 kg/L) and the median DOC concentration is 10 mg/L (1.0×10-5

kg/L). It is important that the POC and DOC concentrations used in calculating 

the freely dissolved fraction for baseline BAFs be determined from the water 

used in the BCF study. It is not appropriate to use site-specific POC and DOC 

concentrations to derive baseline BAFs from t BCF s T . 

The Kow for chemical k is 2×104

, or a log Kow of 4.3. Based on these data, the 

fraction of chemical k that is freely dissolved is 0.975, calculated using 

equation 3-12: 

( ) 4 4

6 6

1 0.975 0.5 10 1 2 10 0.08 2 10

10 10

fd chemical i f mg POC L kg mg DOC L kg

L kg mg L kg mg

= = − − + ⋅ × ⋅ +⋅ ⋅ × ⋅

The fR of the fish species sampled in the laboratory in this example was 2% 

(0.02). Using this fR, the FCM measured at the site, and the t BCFT and ffd

calculated above, a site-specific baseline BAF of 8.8×103

 L/kg-lipid is 
Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors 

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5-40 

Site-Specific BAF Predicted Using the Product of a

Laboratory-Measured BCF and a FCM (Method 4a, continued)

calculated as follows: 

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 standard 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 measured 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, which contains an average POC concentration of 0.54 

mg/L, can be calculated using equation 3-12: 

4 4

6 6

1 0.984 0.55 3.5 1 2 10 0.08 2 10

10 10

fd f mg POC L kg mg DOC L kg

L kg mgLkg mg

= = − − + ⋅ × ⋅ +⋅ ⋅ × ⋅

The lipid content for crayfish of harvestable size at the site is 1.7%. The sitespecific total BAF can then be recalculated from the site-specific baseline 

BAF: 

Site -Specific BAFt ( ) L kg T L = ⋅ × +⋅ = 0 017 88 10 1 0 984 147 kg l −

3 .. . /

The site-specific total BAF for chemical k in crayfish is 147 L/kg. 
Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors 

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5-41 

 Method 4a is based on the following assumptions: (1) a high-quality BCF is a reliable 

measure of the bioconcentration potential of a chemical in a particular species 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 effects 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 concentration of chemical that is accumulated in 

the organism. However, if induction of metabolic 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 may not be captured in the BCF measurement. 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 Kow (i.e., Method 4b) because 

of the potential of the BCF to include the effects of metabolic processes. Furthermore, BCFs can 

be measured or obtained for specific species of interest. 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 assumption 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 BCF for chemicals that are metabolized will be 

smaller than those predicted from measured BCFs for chemicals of equal hydrophobicity but 

which are not metabolized. 

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

Section 5 EPA Review Draft September 4, 2007

5-42 

due principally to the difficulties associated with performing BCF measurements for highly 

hydrophobic chemicals. Conditions appropriate for performing these measurements are described 

in Section 5.3.1 of TSD Volume 2. When evaluating BCF data in the literature, one often finds 

measurements performed with (1) conditions that do not meet current standards, for example, a 

solvent carrier such as acetone is used to introduce 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 parameters, for example, no lipid data, no POC and 

DOC data, and/or an inability to determine whether 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 chemicals 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, EPA invested considerable effort 

in examining the scientific literature for measured BCF s for PCB congeners and was not able to 

find BCF s of appropriate quality. 

 Burkhard et al. (1997) evaluated Method 4a by using field data for chlorinated benzenes, 

butadienes, and hexachloroethane from Bayou d’Inde, Lake Charles, Louisiana. The results of 

this evaluation showed that field-measured baseline BAFs were within a factor of 3 for 88% and 

a factor of 5 for 94% of the baseline BAFs predicted 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 baseline BAFs were less than the measured baseline 

BAFs (53%, n = 32). The chemicals whose field-measured baseline BAFs were in least 

agreement with the predicted baseline BAFs were hexachloroethane, Z-pentachlorobutadiene, 

and hexachlorobutadiene for Callinectes sapidus (blue crab). Metabolism of these chemicals by 
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C. sapidus is suggested as the cause of the poor agreement 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 (a), and 

Brevoortia patronus (c).

5.2.2 Determining Site-Specific FCMs 

 FCMs are used in both Methods 4a and 4b to calculate the dietary transfer of a chemical. 

They represent a measure of the chemical’s tendency 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 food web. Because FCMs can vary due to sitespecific factors, the investigator should consider determining a FCM value that is most 

appropriate for use in estimating a baseline BAF using Method 4a or 4b. FCMs for a particular 

Predicted Log Baseline BAF

234567

Measured Log Baseline BAF

2

3

4

5

6

7
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chemical, organism, and site can be determined using field data and/or a food web model. By 

definition, a FCM is: 

t

T

fd

Baseline BAF Baseline BAF FCM

BCF 1 Baseline BCF

1

f

i i

i

f

= =   − ⋅     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. 
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Calculation of food chain multipliers

This example illustrates the calculation of food chain multipliers for 

hexachlorobenzene (HCB) in trophic level 3 and 4 fish using 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 values is 415,000 L/Kg-lipid. 

Baseline BAF values for HCB can be calculated for fish in the Lake Ontario 

ecosystem based on available data (Oliver 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 (Cl) were normalized by the lipid content (fl) of each 

sample: Cl = Ct / fl

The lipid-normalized chemical concentration 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 (ffd) can be calculated using equation 3-6: 

 

fd = 1 / (1 + POC w DOC w f ο ο ⋅ Κ + 0.08 ⋅ ⋅ Κ ) 

The log Kow for HCB is 5.73 (Arnot and Gobas, 2006), so Kow = 5.37x105

. The 

water samples were centrifuged to remove particulates prior to extraction, so the POC 

concentration is (presumably) zero:

 

5

6

1

2 1 0.08 5.37 10

10

fd f mg DOC L kg

L kg mg

= − +⋅ ⋅× ⋅

 = 0.92
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Calculation of food chain multipliers (continued)

The freely dissolved chemical concentration ( fd Cw ) is calculated as: 

 = 0.92 150 pg/L = 138 pg/L

fd C fC w fd w = ⋅

⋅

Baseline BAFs were then calculated for alewife and lake trout, using Equation 3-2:

C 1 Baseline BAF

C f i fd

w

= − l

l

 

 

286 1000 1000 1 6

3 138 1 0.07

517 1000 1000 1 6

4 138 1 0.174

Baseline BAF = 2.07 10 /

Baseline BAF = 3.74 10 /

ng L pg g

g lipid pg ng kg

ng L pg g

g lipid pg ng kg

L kg lipid

L kg lipid

−

−

⋅ ⋅ ⋅ −= × −

⋅⋅ ⋅−= × −

Equation 5-7 can then be used to calculate 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. 
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 In effect, equation 5-7 says that the FCM is the ratio between the chemical accumulation 

via all relevant routes (aqueous, dietary, and sediment) and the chemical bioconcentrated via 

aqueous exposure only. When the BCF is lipid normalized 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 exposure for the organisms. When organisms occupy 

higher trophic levels in food webs, concentrations 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 chemical generally becomes important when the 

chemical’s hydrophobicity exceeds a log Kow 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 incorporate the conditions existing at the site where the measurements 

are performed. This includes the existing disequilibrium, chemical metabolism, and influences 

due to the structure of the food web (i.e., predator-prey relationships and benthic-pelagic 

components). FCMs derived from field measurements also account for any metabolism of the 

pollutant of concern by the aquatic organisms used to calculate the FCM. 

 Specifically, FCMs can be derived from chemical 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-normalized concentrations of the nonionic organic 

chemical measured at the site, in appropriate predator and prey species, using the following 

equations: 

 FCM 2 = BMF2 (Equation 5-8) 

 FCM 3 = BMF3 # BMF2 (Equation 5-9) 

 FCM 4 = BMF4 # BMF3 # BMF2 (Equation 5-10) 
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where: 

 FCMi = food chain multiplier for trophic level i and 

 BMFi = 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 next lowest trophic level. For nonionic organic 

chemicals, BMFs can be calculated from lipid-normalized concentrations of chemical in tissues 

of biota at a site according to the following equations: 

BMF2 = CR, 2 / CR , 1 (Equation 5-11) 

 BMF3 = CR , 3 / CR, 2 (Equation 5-12) 

 BMF4 = CR , 4 / CR , 3 (Equation 5-13) 

where: 

CR,i = lipid-normalized concentration of chemical in tissue or whole 

organism at a specified trophic level (i = 2, 3, or 4). 

Examples of applying equations 5-8 through 5-13 were presented in the Method 4A example. 

 In addition to the guidance offered in Section 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 identify the appropriate trophic levels for the 

aquatic organisms and appropriate predator-prey 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, because predator-prey relationships for some 
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species can vary widely over space and time. When a predator species consumes 

multiple prey species at a particular trophic level, chemical concentrations in prey 

species should be appropriately weighted (if the data are 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 
13C, 
15N, and 
34S, respectively; 

Peterson and Fry, 1987; Jardine et al., 2006) and analysis of gut contents. Ratios of 

stable isotopes can change between diet and consumer due to differential digestion or 

fractionation during assimilation and metabolic processes. Metabolic fractionation 

also may cause isotope ratios of different tissues to vary substantially within 

individual consumers (McCutchan et al. 2003). General information on determining 

trophic levels of aquatic organisms 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 human exposure via consumption of aquatic 

organisms. For higher trophic levels (e.g., 3 and 4), aquatic species used to calculate 

FCMs should be those that are commonly consumed by humans. The species sampled 

should also reflect size and age ranges that are typical of human consumption patterns 

at the site. 

3. The study from which the FCMs are derived should contain enough supporting 

information to determine that tissue samples 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 target species to the chemical of interest; 

longer averaging periods are generally necessary for chemicals with greater 

hydrophobicity. 
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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 selected 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 used 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. 

 In deriving FCMs using a food web model, the investigator assumes that (1) the model is 

valid for the particular chemical, aquatic organism, food web and site, and (2) appropriate values 

are selected for all necessary model inputs. In other 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 Methodology. Also described are the parameters 

used with the model: the food web structure, Jsocw (or, equivalently, Cfd

w and Csoc), and the 

chemical metabolism rate in the various organisms 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 (1993) model, for example, allows the user to 

input a metabolic transformation rate constant. Because all food web models require the above 

input parameters, these inputs are not unique to the food web model selected by EPA. 

 For a food web model to provide useful predictions, it should have the following general 

characteristics and qualities. First, the model should provide a full and complete description of 

the bioaccumulation process. Specifically: 
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• All biotic components of the food web must be represented: plankton, benthic 

invertebrates, forage fish, and piscivorous fish. 

• It should account for chemical uptake and 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 predicting 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 average 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 the food chain model, (3) the model code does not 

require substantial validation each time it is used, and (4) the model parameters and other inputs 

can be readily measured or estimated. 

 Although these attributes can make a food chain model relatively easy to use, the 

accuracy or uncertainty of model predictions depends largely upon how they are applied. Food 

chain model predictions may be highly uncertain unless they are confirmed by data (i.e., 

chemical concentrations in the modeled organisms). Burkhard (1998) determined that the 

uncertainty of food chain model predictions due to parameter variability 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 90th to 10th percentiles of the model 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 the various aspects of the bioaccumulation 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 
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must have an adequate understanding of the model and the science upon which it is based. This 

understanding may be gained from training courses [such as those offered by the EPA Center for 

Exposure Assessment Modeling (CEAM), the Manhattan College Summer Institute for Water 

Pollution Control, or short courses offered in conjunction with scientific conferences such as the 

annual meetings of the Society of Environmental Toxicology and Chemistry (SETAC)] or 

“User’s Guide” documentation, although these sources 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 documenting prior applications of the model. The modeler must then 

acquire the site-specific data or validated estimates for all model inputs, run the model, and 

verify and confirm the predictions (USEPA, 2003b). The goal of confirmation is to determine 

and quantify the agreement between model predictions and observations. In the case of food web 

bioaccumulation models, comparisons between predictions and observations should be made for 

chemical concentrations in the organism or tissue1

 of interest, as well as chemical concentrations 

in organisms at lower trophic levels in the food 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 the performance of a model and the robustness of its 

predictions include peer reviews, numbers of past applications and their successes, and similarity 

of other applications to the chemical, food web, and site of interest. The following references are 

offered as resources to investigators 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 Meuller (1987); Thomann and Connolly (1984); and, 

Connolly et al. (1992). The investigator should also be aware that EPA has developed quality 

assurance guidance applicable to model applications, which outlines the elements of a QAPP for 

modeling (USEPA, 2002). 

 

1

The Gobas, Thomann, and most other food chain models predict chemical concentrations on a whole 

organism basis. Chemical concentrations in specific tissues can be recomputed from concentrations 

predicted in the whole organism using (1) measured ratios of chemical concentrations between tissue and 

whole organisms (e.g., Niimi and Oliver, 1989) or (2) the ratio between tissue and whole organism lipid 

content. 
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 Food chain models with the characteristics and desirable qualities 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 extensive 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 assessments of (1) the accuracy and precision of the 

models, (2) the sensitivity of the predicted concentrations to changes in input parameters, and (3) 

the uncertainty associated with the concentrations 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 Kows ranging from 3 to 8. 

 EPA used the Gobas food chain model2

 to predict FCMs in the National BAF 

Methodology. The rationale for this choice was discussed in USEPA (2003). In part, EPA 

selected the Gobas model because the computer program was widely available on the Internet 

(http://www.rem.sfu.ca/toxicology/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 for 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 different 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,Cfd

w . To compensate for this discrepancy in the methods of 

accounting for bioavailability, EPA sets the concentration of the TOC in the Gobas model to an 

infinitesimally small value (i.e., 1x10-30). By doing so, the total concentration of the chemical 

 

2

 The Gobas food chain model is also known as Aquaweb; the current version is Aquaweb v1.2. 
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input to the model becomes essentially equal to the Cfd

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 Kow 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: 

Baseline BAF FCM i

i Kow

= (Equation 5-14) 

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 
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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

Jsocw/Kow 23 

 Using Equation 5-14, FCMs were calculated for each trophic level in the Lake Ontario 

food web. Table 5-6 lists the FCMs for trophic level 2 (zooplankton), 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 log Kow of the 

chemical in Figure 5-9. As shown by the relationships between FCMs and logKows in Table 5-6 

and Figure 5-9, significant biomagnification at trophic levels 3 and 4 occurs for nonmetabolized 

organic chemicals with logKows between about 5 and 8.5. The highest FCMs (13.3 for TL3 and 

24.7 for TL 4) were determined for nonmetabolized organic chemicals with logKows in the range 

of 6.7 to 7.0. Constant FCMs of 1 (no biomagnification) were determined for trophic level 2. 
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Table 5-6. Food-Chain Multipliers for Trophic Levels (TLs) 2, 3, and 4 (Mixed Pelagic and 

Benthic Food Web Structure andJsocw/Kow = 23) 

Log Kow TL 2 TL 3a TL 4 Log Kow TL 2 TL 3a 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 geometric mean of the FCMs for sculpin and alewife.
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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 Kow

food chain multiplier (FCM)

trophic level 2

trophic level 3

trophic level 4
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 5.2.2.3 Site-specific adjustment of food chain model parameters 

 As noted previously, conditions and parameters of relevance to a food chain model at a 

particular site may be different from the representative 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 input to the food chain model. A number of food chain model 

parameters can be adjusted to improve predictions of biomagnification at the site of interest. The 

most important of these are related to factors which primarily determine bioaccumulation of 

nonionic organic chemicals by fish (Burkhard et al. 2003a). These include: 

• chemical disequilibrium between sediment and water (i.e., Jsocw/Kow and sediment 

organic carbon content); 

• the relative benthic/pelagic connectivity of the food web; 

• the length of the food chain (i.e., the trophic level of the organism); and 

• species-specific parameters for organisms in the food chain/web (lipid content and 

weight), as well as bioenergetic parameters (e.g., growth, respiration, consumption) 

which are computed as allometric functions of organism weight and water 

temperature in the Gobas and Thomann models. 

 The sensitivity of model-predicted FCMs to these factors is discussed in Burkhard (1998) 

and Burkhard et al. ( 2003b). In all cases, parameter adjustment should be limited to values 

determined to be representative and unbiased based 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 hydrophobicity of the chemical (Kow) and the rate of chemical metabolism 

in the food chain - to be appropriate since these parameters are properties of the chemical and (in 

the case of metabolism rate) the food chain organisms. 

 
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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 structures vary across ecosystems and for different 

organisms within the ecosystem, and these differences also influence bioaccumulation (Burkhard 

et al. 2003(b)). For highly-hydrophobic chemicals (logKows of 6 to 7), the food web structure 

becomes a very significant factor in bioaccumulation predictions. The information necessary to 

construct a food web includes the diet of the individual organisms composing the food web and 

their weights and lipid contents. Based upon Burkhard’s (1998) sensitivity analysis, model 

predictions made by the Gobas model were relatively insensitive to organism weights and 

feeding preferences of piscivorous fish for all Kows. For chemicals with higher log Kows, the 

predictions were more sensitive for Jsocw, feeding preferences of forage fish upon benthic 

invertebrates, and lipid contents. The most sensitive input parameter was the feeding preferences 

of forage fish, that is, the percentage of zooplankton (pelagic component) and benthic 

invertebrates (benthic component) in their diet. The benthic/pelagic 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 different age classes of species within an ecosystem. Of all the 

ecosystem types, the purely pelagic food webs might be the least 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 lakes 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 purely 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 River (Iannuzzi et al. 1996). Mixed food webs are 

common in all ecosystems and, EPA believes, far outnumber the purely pelagic and benthic food 

webs. There are numerous examples of mixed benthic/pelagic food webs, such as the food webs 

for lake trout in the Great Lakes (Flint, 1986; Morrison et al. 1997), lobster in the New Bedford 
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harbor (Connolly, 1991), whitefish and rainbow trout in the Fraser River (Gobas et al. 1998), 

white perch in the Chesapeake Bay (Baird and Ulanowicz, 1989), and perch, bass, and crappie in 

Little Rock Lake (Martinez, 1991). Purely pelagic and/or benthic species can exist in ecosystems 

containing species with a mixed benthic/pelagic food web, for example, flounder and lobster in 

New Bedford harbor (Connolly, 1991).

5.2.2.5 Alternative food chain models 

 Food chain models of chemical bioaccumulation 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 FCMs. 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) accuracy and precision of the model predictions, (2) input parameter 

sensitivities, and (3) uncertainties associated with the model predictions. 

5.2.3 Predicting Site-Specific Baseline BAFs using Kow and Food Chain Multipliers 

 (Method 4b) 

A site-specific baseline BAF for nonionic organic chemicals can also be predicted using 

the product of the chemical's Kow and a FCM for a particular trophic level under site-specific 

conditions. Method 4 uses the following baseline BAF equation: 

Baseline BAF FCM i ow i = ⋅ K (Equation 5-15) 

where: 

 FCMi = the food-chain multiplier for trophic level i, determined from appropriate 

field data or predicted for site-specific conditions 

 Kow = n-octanol-water partition coefficient 

 The Kow can be substituted for the BCF when predicting a site-specific baseline BAF for 

hydrophobic organic chemicals, particularly for those chemicals that are poorly metabolized by 
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aquatic organisms, because the Kow is strongly correlated with the BCF for these chemicals. As 

with Method 4a, the Kow 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 Kows greater than or equal to 4 and 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 Kow or the FCM. Because the Kow is 

assumed to be equal to the baseline BCF, the organic carbon and lipid normalization procedures 

used in Method 4a (equation 5-7) are not needed here. The determination of appropriate FCMs 

and selection of Kow values are discussed below; further details on these topics can be found in 

Section 4.4 and Appendix B of TSD Volume 2 (USEPA, 2003). 

 

Calculating a site-specific BAF using Method 4b is presented in the following example. 
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Site-Specific BAF Predicted Using the Product of

the Chemical Kow 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 Kow for the chemical 

and a FCM, which must be determined for the chemical, site, and trophic level 

of the target organism. In this method, Kow is assumed to be equal to the 

baseline BCF, as discussed in Section 5.2.3. Method 4 requires selection of an 

appropriate Kow 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 structure presented in the previous 

example, based on the following data from the site: 

 

MEASUREMENT AVERAGE VALUE

Chemical k Filtered Water 

Concentration 0.16 ng/L 

Chemical k Sediment Concentration 1.95 )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% 
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Site-Specific BAF Predicted Using the Product of

the Chemical Kow and a FCM (Method 4b continued)

Determining the chemical Kow

Guidance for selecting an appropriate Kow for the chemical is provided in 

Section 5.2.3.2 and TSD Volume 2 (USEPA, 2003). For the purposes of this 

example, a Kow value of 2×104

 (log Kow= 4.3) will again be used for chemical 

k. 

Calculating the Freely Dissolved Chemical Concentration and SedimentWater Fugacity Gradient 

The freely dissolved chemical concentration (Cfd

w ) and the sediment-water 

fugacity gradient (Jsocw/Kow) should be calculated from the site data. The 

freely dissolved chemical concentration can be calculated from the filtered 

concentration and the freely dissolved fraction calculated using equation 3-6 

(assuming that POC is removed from the samples by filtration): 

( ) 4 4

6 6

1 0.994 0 3.5 1 2.0 10 0.08 2.0 10

10 10

fd i f mg POC L kg mg DOC L kg

L kg mgL kg mg

= = − − + ⋅× ⋅ + ⋅ ⋅× ⋅

0.994 0.16 / 0.159 ( ) fd C f C w fd w =⋅= ⋅ = ng L ng/L

The sediment-water fugacity gradient can then be calculated using equation 4-

3: 

( ) ( )

( ) SOCkgL

g

ng

Lng

SOCkg

kg

kg

g

C

C

fd

w

SOC

SOCW ×=⋅ −

















− ⋅

==∏ /1065.1 1000

/159.0

074.0

95.1

5

μ

μ

Π SOCW

Kow

L

kg SOC

kg

L = ⋅= ×

− ×

1 65 10

2 0 10

5

4 8 25 .

. .

 
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Site-Specific BAF Predicted Using the Product of

the Chemical Kow and a FCM (Method 4b continued)

Predicting a FCM using the Gobas Aquaweb model

In this example, the investigator applies the Gobas Aquaweb food chain 

model to predict a FCM for chemical k in crayfish, using the same food chain 

structure presented in the previous example. Model prediction of FCMs may 

be preferred to determining FCMs from data in a number of circumstances, 

for example when the necessary data are unavailable, or when such 

measurements may not reflect steady state conditions at the site. In this case, 

it is appropriate to run the model with site-specific conditions, because (1) the 

sediment-water fugacity gradient ( Jsocw/Kow) of 8.3 at this site is 

substantially smaller than the National methodology default value of 23, and 

(2) the lipid contents of the food chain organisms are considerably smaller 

than the values used for trophic levels 2 and 3 in the National BAF 

methodology predictions. 

The Gobas Aquaweb model can be downloaded from 

http://www.rem.sfu.ca/toxicology/models/AQUAWEBv1.2_BIOv1.2.xls as 

an Excel spreadsheet, which is simple to apply. Once the tabulated site data 

from above and the feeding preferences are input, the model predicts a 

concentration of chemical k in crayfish of 0.0557 ng/g-wet. The predicted 

concentration can be converted to a baseline BAF of 5.01×103 L/kg-lipid: 

 

Baseline BAF L kg l ng

g wet

g wet

g lipid

L

ng

g lipid = ⋅ ⋅ ⋅ =× − − kg lipid

−

−

−

−

0 0557

0 017 0 159

1000

1

3 501 10 .

. . . /

and a FCM of 0.25 (using equation 5-14): 

FCM Baseline BAF

K

L

kg l

kg l

ow L = = ⋅= ×

−

−

×

 5 01 10

2 0 10

3

4 0 25 .

. .

Predicting a site-specific baseline BAF 

The calculation of a site-specific baseline BAF for chemical k in crayfish, 

using the selected Kow and FCM calculated from the Gobas model prediction 

is straightforward, as shown below: 

 Site-Specific Baseline BAF = Kow × FCM (Equation 5-15) 

 = (2.0×104

 L/kg-lipid) × 0.25 

 = 5.0×103

 L/kg-lipid 

The site-specific baseline BAF for chemical k in crayfish is predicted by 

Method 4b to be 5.0×103

 L/kg-lipid. 
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 A number of assumptions are associated with predicting site-specific baseline BAFs by 

Method 4b. First, it is assumed that the Kow 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 assumptions incorporated into 

the FCMs (whether measured or modeled) are directly incorporated into the predictions made 

with Method 4b. For detailed information on the assumptions incorporated into the FCMs, refer 

to Section 5.2.2. 

 Method 4b assumes that the Kow is equal to the chemical’s baseline BCF. This 

assumption is supported by equilibrium partitioning theory. This theory assumes that (1) the 

bioconcentration process can be viewed as a partitioning of a chemical between the lipid of 

aquatic organisms and water and the Kow is a useful surrogate for this partitioning process, and 

(2) a linear relationship exists between the Kow and the BCF. Mackay (1982) demonstrated the 

usefulness of Kow as a surrogate for this partitioning process by presenting a thermodynamic 

basis for the partitioning process for bioconcentration. 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 Kow. 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 Kow for organic 

chemicals and the log BCF measured for fish and other aquatic organisms exposed to those 

chemicals. In addition, when the regression equations are constructed with BCFs reported on a 

lipid-normalized basis, the slopes and intercepts are not significantly different from 1 and 0, 

respectively. For example, de Wolf et al. (1992) adjusted a relationship reported by Mackay 

(1982) to a lipid-normalized basis and obtained the following relationship: 

 log BCF = 1.00 log Kow + 0.08 (Equation 5-16) 

 For highly-hydrophobic chemicals (log Kow >6.0), reported BCFs are often not equal to 

the Kow even for nonmetabolized chemicals, because the measurements were not performed 

and/or reported with appropriate experimental conditions. BCFs for nonmetabolized chemicals 

are equal to the Kow when the BCF values meet the following quality assurance criteria: 
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• reported on a lipid-normalized basis, 

 

• determined using the concentration 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 (k1) and elimination (k2) rate constants, and 

• determined with no solvent carriers 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 using data collected from a number of diverse 

aquatic ecosystems: Lake Ontario, Green Bay/Fox 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 between 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 Burkhard et al. (2003b). Figure 4-2 compares Method 2 and Method 4b 

predictions to the baseline BAFs measured in the Green Bay and Hudson River ecosystems. 
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Table 5-7. Validation Statistics for Method 4b: Ratio Between Predicted and Measured 

Baseline BAFs (Baseline BAFpredicted/Baseline BAFmeasured) 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 ratios 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 baseline BAFs (>75% within a factor of 2, and 

100% within a factor of 5; Table 5-7). However, 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 underprediction of measured baseline BAFs with Method 4b. On the 

other hand, for the Hudson River data set, the variability associated with baseline BAFs 
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predicted using Method 4b was generally smaller than that associated with Method 2. Burkhard 

et al. (2003b) discuss several factors that might be involved with the underprediction of the 

baseline BAFs for river miles 169, 189, and 194 using 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 field samples that were not temporally 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 Measured from Lake Ontario (Oliver and 

Niimi, 1988) for Chemicals with Log Kows 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, and hexachloroethane. Bayou d’Inde is a lowland 

channel that meanders through a brackish-freshwater marsh that is influenced by tide. This 

ecosystem is very different from either the Great Lakes or the Hudson River and provides a 

useful demonstration of the applicability of Method 4b across different ecosystems. Because this 
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evaluation of Method 4b was conducted before the development of the final National BAF 

Methodology, it was performed with FCMs and default values for POC and DOC that are 

marginally different from those that are used in the National BAF Methodology (USEPA, 2003). 

Burkhard et al. (1997) found good agreement between the predicted and measured baseline 

BAFs for both the fish and invertebrates sampled. 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 differences between predicted and measured BAFs) were evenly 

distributed, except for the large smelt. The trophic level for the large 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 level 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 predicted with Method 4b were in excellent agreement 

with the measured BAFs: More than 90% of the predicted baseline BAFs were within a factor of 

5 of the measured baseline BAFs. In the Hudson 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 within a factor of 5 of the measured 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 provides excellent predictions for ecosystems that 

have not recently experienced a major change or disruption in chemical loadings or flows. Of all 
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the ecosystems examined, the extreme temporal dynamics observed for several important factors 

(e.g., fish lipid content, food web structure, exposure concentrations) 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 quality considerations specified in Section 3.4 and 

3.5 for deriving baseline BAFs from field data. Nonetheless, EPA believes that the application of 

the BAF methods to this location was a useful exercise and illustrates that useful predictions are 

possible using Method 4b in ecosystems with extreme temporal dynamics.

5.2.3.2 Selection of appropriate Kows for partitioning (bioavailability) predictions 

 The Kow of the chemical of interest is used in several components of the BAF 

methodology, for example, to estimate BAFs, hydrophobicity, and partitioning in water; as well 

as in the prediction of baseline BAFs from BSAFs (Method 2) and in the use of the food chain 

model to predict FCMs for Method 4. Each of these procedures is highly sensitive to the value 

selected for Kow. Thus, it is important for the investigator to select the most accurate and 

appropriate Kow for a given chemical. Although a variety of methods are available to measure or 

estimate3

 Kow values, the reliability of these methods varies according to the Kow of the 

chemical. In addition, many unreliable or erroneous Kow values can be found in the literature or 

in chemical property databases (Linkov et al. 2005; Pontolillo and Eganhouse, 2002). 

 A detailed approach for selecting reliable Kow values was published in Appendix B of 

TSD Volume 2 (USEPA, 2003). EPA’s methodology for selecting Kow values divides the range 

of Kows into three groups (logKow < 6, 6 < logKow < 8, and logKow > 8) to reflect the differences 

in chemical properties and behaviors due to differing hydrophobicities. In general, “high quality” 

measured values (i.e., data judged to be reliable based on EPA guidelines) are preferred over 

estimates. Kows measured by the slow stir method are considered reliable up to a value of 108

. 

Shake flask Kow measurements are reliable up to 106

 as long as sufficient attention is given to 

 

3

 For example, the EPI (Estimation Programs Interface) suite of physical/chemical property 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/episuitedl.htm) includes KOWWIN. This model estimates the 

log Kow of chemicals using an atom/fragment contribution method. 
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micro emulsion effects; for classes of chemicals that are not highly sensitive to emulsion effects 

(i.e., polycyclic aromatic hydrocarbons) this range may extend to 106.5. For chemicals with log 

Kow > 5, it is highly unlikely to find multiple “high quality” measurements. Therefore, assigning 

Kow’s from estimation techniques may be necessary. When multiple Kow values are found, 

evaluating the quality of the data (measured or estimated) should include checking the 

consistency between the values. What is considered reasonable agreement in log Kow data 

depends primarily on the magnitude of the log Kow value. Therefore, EPA has established the 

following ranges of acceptable variation for this exercise: 

• 0.5 for log Kow > 7, 

• 0.4 for 6 log Kow 7 , and 

• 0.3 for log Kow < 6.

 

 Statistical methods should be applied to Kow data as appropriate. However, the 

investigator should recognize that robust estimates are generally difficult to obtain due the 

paucity of data and the determinate/methodic 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 chemical can be recalculated from a baseline 

or national BAF by using values for the aquatic organism lipid content and/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 methodology. The investigator can modify one or both of these 

parameters in the site-specific recalculation of the BAF by: 

• conducting site-specific field studies to generate representative data, 

• conducting a literature or database search to obtain data more representative of local 

conditions, or 
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• selecting an appropriate subset of the national database that EPA used to derive the 

default values. 

Method 5 is applicable to nonionic organic chemicals, and similarly-behaving ionic organic 

chemicals. The formula for recalculating a site-specific BAF from the baseline BAF is: 

T Site Specific BAF ( Baseline BAF +1) i,t i fd =⋅ ⋅ f f l (Equation 5-17) 

where: 

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

water column at the site 

 fR = fraction of the organism tissue that is lipid 

 Equation 5-17 is a rearrangement of the equation relating the total and the baseline BAF 

for nonionic organic chemicals (equation 3-3). In other words, recalculating a site-specific BAF 

means converting a baseline BAF into a total BAF 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-specific 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 encouraged 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 standards. The use of such locally or regionally derived (i.e., site-
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specific) data is encouraged over national-scale data because local or regional consumption 

patterns of fish and shellfish (and thus the amount of lipid consumed from aquatic organisms) 

can differ from national consumption patterns, and 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 adopting criteria into their own water quality 

standards. EPA encourages the use of appropriate locally or regionally derived values of DOC or 

POC over nationally derived values because local or regional conditions that affect DOC and 

POC concentrations can differ substantially from those represented by nationally 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 extrapolation between species 

and sites, this practice nevertheless involves making a series of assumptions 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 deriving national values for nonionic organic chemicals 

in the 2000 Human Health methodology and TSD Volume 2 (USEPA, 2003). The investigator 

should refer to these documents for further details regarding the scientific 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 condition, 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. 
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 The first assumption is generally supported by the empirical evidence and underlying 

theory that supports many widely used bioaccumulation models. This assumption is also 

supported by the findings that for organic chemicals that are not metabolized, BCF is strongly 

correlated with Kow. (e.g., Veith et al. 1979b; Isnard and Lambert, 1988; de Wolf et al. 1992). In 

determining Kows, n-octanol is considered to be a surrogate for lipid. Chiou (1985) used triolein 

(glyceryl trioleate) as a surrogate for lipid and also found 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 basis of the total fraction of tissue that is lipid 

assumes that lipids are a single, uniform compartment. In reality, total lipid content in fish 

includes different lipid classes, including relatively polar phospholipids, which are common in 

cell membranes, and generally nonpolar triacylglycerols, which are common in storage lipids 

(Henderson and Tocher, 1987). The variation in lipid-partitioning behavior of nonionic organic 

chemicals is thought to be a function of differences in polarity of lipid classes, as fewer 

chemicals become associated with the more polar “membrane-bound” lipids than storage lipids 

(Ewald and Larsson, 1994; van Wezel and Opperhuizen, 1995; Randall et al. 1998). 

 In practical terms, the potential impact that 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 suggestion is based on observations 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 Bergen et al. (2001) reported 

significantly higher fractions of polar lipids in leaner populations compared with fatter 

populations. Because of the greater polarity of their lipids, very lean tissues are likely to exhibit 

different chemical/lipid-partitioning behavior than fatty tissues. Bergen et al. (2001) reported 

stronger correlations between chemical concentrations and mussels with higher total (and 

nonpolar) lipid content, which led to their suggestion that lipid normalization may work best 

above some threshold of lipid content. However, the narrow range of lipid content evaluated in 
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their study (about a factor of two) and the reliance on total PCB measurements (as opposed to 

individual congeners) might have limited their ability to identify meaningful 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 determine lipid content. Specifically, different 

solvents have been used to extract lipids, which leads to different quantities (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 varied twofold or less 

among two of the more common extraction 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, differences among lipid-normalized 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 solvent but also the lipid content 

of the tissue. Because lean tissues contain proportionally 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 extracted using strictly nonpolar solvent systems. 

The assumptions associated with adjustment of BAFs by dissolved chemical fraction 

include: 

1. Hydrophobic organic chemicals exist 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 organic matter. This assumption is supported by 

a wealth of experimental evidence (Hassett and Anderson, 1979; Carter and Suffet, 

1982; Landrum et al. 1984; Gschwend and Wu, 1985; McCarthy and Jimenez, 1985a; 

Eadie et al. 1990, 1992). The total concentration of the chemical in water is the sum 

of the concentrations of the freely dissolved chemical and the sorbed chemical 

(Gschwend and Wu, 1985; USEPA, 1993). 

2. Chemicals in the freely dissolved phase of the water are in equilibrium with chemical 

associated with the DOC and POC (including plankton) phases of the water column. 

The relationship used by EPA to relate the freely dissolved chemical concentration to 

the concentrations of chemical associated with DOC and POC (equation 3-12) 

assumes equilibrium among these phases. For a given ecosystem, DOC and POC 
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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 freely dissolved is the best measure of the 

fraction of nonionic organic chemical available for uptake by aquatic organisms, both 

in sediment porewaters and ambient surface waters (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 Cfd

w , EPA does not ignore the chemical associated with 

dissolved organic carbon (DOC) and particulate 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 equilibrium conditions among these three phases, 

the chemical concentration in the water column expressed using any of the three phases, 

individually or in combination, is indicative of the chemical concentrations 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 Jimenez, 1985b; McCarthy et al. 1985; Carlberg et al. 

1986; Black and McCarthy., 1988; Servos and Muir, 1989; Kukkonen et al. 1989). For example, 

it has been reported that the percentage reduction in gill uptake efficiency of benzo[a]pyrene and 

2,2',5,5'-tetrachlorobiphenyl in rainbow trout is equal to the percentage reduction in freely 

dissolved chemical concentration in the presence of DOC (Black and McCarthy, 1988). The 

authors of this study concluded that only the chemical that was freely 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 concentration of chemical that is freely dissolved in the water column 

decreases in proportion to the amount of DOC present in the water. These studies clearly support 

EPA’s assumption that chemical bioavailability of nonionic organic chemicals to aquatic 
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organisms is reduced in the presence of DOC and POC. Excellent reviews 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 the 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 comparing 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 BAFs across species and ecosystems. In each 

comparison, baseline adjustment of BAFs was found 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 zones. 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 nearly 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 increasing BAFs across zones due to increasing 

bioavailability of dissolved PCBs, caused by declining particulate and dissolved organic carbon 

across zones, appeared to be removed 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 would have larger variances and poor predictive 
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power. The above comparisons were all within the Green Bay ecosystem, and they demonstrate 

that corrections for lipid content and freely dissolved chemical concentrations in water reduce 

variability associated with the baseline BAFs. 

FIGURE 5-10. BAFt

Ts (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 variances 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 each of the geographical zones. The variances of 

the baywide BAFs and baseline BAFs were calculated as described in detail in Burkhard et al. 

(2003a). The results of these calculations are summarized, by species, using the ratio of 90th to 

10th and 95th to 5th percentile exceedance limits in Table 5-9. Overall, the baseline BAFs had 

smaller ratios than the BAFs and the adjustment/conversion of BAFs to baseline 

BAFs resulted in an approximately twofold decrease in variability (Burkhard et al. 2003ab). 
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Table 5-9. BAFs and Baseline BAFs Confidence 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, baseline 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 species (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 average trophic level of 2.5) to be lumped with 

species with slightly higher trophic levels (e.g., adult alewife with average trophic level of 3.5) at 

the nominal trophic levels. The baseline BAFs had substantially lower variability in comparison 
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to the total BAFs for trophic levels 3 and 4 fishes, i.e., an average 2.3-fold decrease in the 

coefficients of variation (Figure 5-11). Additionally, the 75th/25th and 90th/10th percentile ranges 

were smaller by 2x and 5x, respectively, for the baseline BAFs for both trophic levels. These 

results demonstrate that the corrections for lipids and freely dissolved chemical concentrations 

reduce variability when extrapolating BAFs across ecosystems and across species of similar 

trophic levels. The variability not due to lipid content and freely dissolved chemical 

concentrations could include: differences 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. 
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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 segregated 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 line inside the box, the 25th and 75th percentiles 

are the ends of the box, the 10th and 90th percentiles are the T-lines, and outliers, points beyond 

the 10th and 90th percentiles, are the dots ( ).
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5.3.3 How Can the Lipid Contents of Aquatic Organisms be Determined? 

 Lipid content is used to adjust BAFs for nonionic organic chemicals because it has been 

shown to influence the magnitude of bioaccumulation in aquatic organisms (Mackay, 1982; 

Connolly and Pederson, 1988; Thomann, 1989). Therefore, lipid content in consumed aquatic 

organisms is an important factor for characterizing potential human exposure to nonionic organic 

chemicals. Since baseline BAFs are lipid 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 discusses 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 BAF from a baseline BAF (Method 5). 

 EPA recommends using local or regional data on the consumption rates and lipid content 

of consumed aquatic species when recalculating baseline BAFs. The use of such locally or 

regionally derived (i.e., site-specific) data is encouraged over national-scale data because local or 

regional consumption patterns of fish and shellfish (and thus the amount of lipid consumed from 

aquatic organisms) can differ from national consumption 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 variability in lipid content of aquatic organisms, 

principally differences in physiology, metabolism, organism health or condition, and feeding 

ecology among and within species. These factors and, consequently, the lipid content in a 

particular tissue can vary as a function of season, temperature, reproductive status, migratory 

patterns, sampling location (both within and across water bodies), 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 estimated 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 variability in lipid content within a single trophic level (both are 

piscivorous, trophic level-4 fish and are frequently consumed by local populations). Wide 
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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 addition, the distribution of lipids in a particular aquatic organism is 

not uniform across all tissue types, resulting in differences in lipid fraction depending on the 

tissue sampled (e.g., fillet, whole body, muscle). Finally, differences among analytical methods 

used to extract and measure lipids and associated analytical error can contribute 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 consumption 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 determining lipid content(s) for the fish species and 

tissue types that are commonly consumed by the local populations. In all cases, the primary 

selection criterion should be that the target species is/are among the species commonly 

consumed in the study area, and that the species is of recreational or sustenance fishing value. 

5.3.3.2 Measuring Lipid in Fish 

 If site-specific lipid data are not otherwise available, the 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 investigator should ensure that an appropriate method is used for 

measuring lipid content. Most methods involve determining lipid content gravimetrically (i.e., by 

weight), following solvent extraction of the lipid 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 in 
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lipid-normalized concentrations and, consequently, in the site-specific BAF recalculation 

because of the solvent system used in lipid extraction. 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 polar/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 various 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 chemical and tissue types. Different tissues have 

different lipid compositions that, in turn, may alter the chemical/lipid partitioning process. From 

a toxicological perspective, the science is presently not clear on which classes of lipids (e.g., 

phospholipids, free fatty acids, mono-, di-, and triglycerides) are most relevant with respect to 

different organic chemicals. For example, DDT has been reported to bind to relatively polar 

membrane-bound lipids, which suggests these membrane 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 chlorobiphenyls 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 the Bligh and Dyer (1959) chloroform/methanol 

extraction method in combination with gravimetric 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 types of lipids extracted. The Bligh-Dyer method also extracts both 

polar and nonpolar lipids. Based on these and other considerations, Randall et al. (1998) also 
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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. Randall 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 Lipid Using a Literature or Database Search 

 Scientific publications, reports, and online databases also contain data for organism and 

tissue lipid contents measured in many ecosystems. Determining lipid content in this manner 

may be an economical and expedient alternative to field measurement, if the appropriate data can 

be found. The investigator faces two main challenges in doing so, however: 

• How to expediently find and acquire lipid data for 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 retrieval of specific data from these databases is 

generally straightforward. 

 The following is an inventory and descriptions of Internet-accessible databases 

containing lipid content data for many ecosystems, water bodies 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) 
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• U.S. Army Corps of Engineers 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 databases described above, or other sources, should 

be evaluated by the investigator in terms of their 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 three top entries in Table 5-10 (species of interest, consumed tissue types 

and method of lipid analysis) are considered to be essential information. Without these three 

criteria, lipid content data is essentially unusable because site-specific BAFs are defined in terms 

of the consumption of specific organisms and/or tissues, and because the extraction method is a 

critical factor in lipid determination. Care should be taken to review the differences in the 

extraction method used to measure the lipid content of a given species across studies. As 

discussed in Section 5.3.2.2, differences 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 data for which differences in lipid contents are believed to be 

largely due to differences in extraction methods. 
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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 influencing 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 criteria in Table 5-10 (QA statistics, collection location/time, and sample 

factors influencing variability) are important because they provide information the investigator 

can use to understand the sources of variability in lipid data for the target species/tissues. 

Although the databases often contain 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 evaluating the representativeness of lipid 

content data. 

5.3.3.5 Determining fish lipid using the national default lipid data base 

 The investigator may also obtain lipid content data by selecting values from the database 

for fish lipid developed by EPA during the development of the National BAF TSD Volume 2 
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(USEPA; 2003). Information on the lipid fraction of aquatic organisms was obtained for the 

national database from a variety of primary and 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 (USDA) Nutrient Data Bank (Exler, 1987) 

• A review from National Marine Fisheries Service of the National Oceanic and 

Atmospheric Administration (NOAA) (Sidwell, 1981) 

• Two California databases (California Toxic 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 from primary literature were used. The resulting 

National lipid database is tabulated in Appendix 5D. The lipid data in this tabulation were 

carefully screened and reviewed to correct or remove erroneous entries, extreme values of lipid 

content, and duplicate records. 
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5.3.4 How Can Site-Specific Organic Carbon Concentrations be Determined?

 The concentrations of dissolved and particulate 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 dissolved is the best measure of the bioavailable 

concentration of nonionic organic chemicals available for uptake by aquatic organisms. As 

discussed in Section 3.5.1, sorption of the chemical to DOC and POC reduces chemical 

bioavailability to aquatic organisms. Bioavailability and the freely dissolved fraction are reduced 

in waters containing higher concentrations of organic carbon. Therefore, DOC and POC 

concentrations are important factors for characterizing potential human exposure to nonionic 

organic chemicals. Baseline BAFs are adjusted for the freely dissolved fraction of chemical in 

water according to the national BAF methodology, and recalculating a site-specific BAF 

(Method 5, equation 5-17) involves multiplying the baseline BAF by the freely dissolved 

fraction. This section discusses how the investigator can determine appropriate concentration of 

DOC and POC in the water column from a site. This can then be used to calculate the freely 

dissolved fraction, which is used in turn to recalculate 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 baseline BAFs. The use of such locally or regionally 

derived (i.e., site-specific) data is encouraged over nationally representative concentrations 

because local or regional conditions that affect DOC and POC concentrations can differ 

substantially from those represented by nationally derived values. There is substantial variability 

in the median values of DOC and POC concentrations in U.S. surface waters USEPA, 2003a). 

This variability is believed to result from naturally occurring conditions and processes that 

contribute to spatial and temporal variability in the delivery and biogeochemical cycling of 

organic carbon in surface waters. Some of these factors include climatology (e.g., arid, arctic, 

alpine, and tropical zonal differences) and trophic status (e.g., oligotrophic, mesotrophic, and 

distrophic lakes), discharge volume 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 
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analytical error can contribute to variability in reported values of lipid fraction. To address 

uncertainty in site-specific BAFs resulting from this natural variability in DOC and POC 

concentrations, EPA encourages States and authorized Tribes to use appropriate local or regional 

data on the organic carbon content of applicable waters when adopting 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 guidance to the investigator regarding options for determining 

appropriate organic carbon (DOC and POC) concentrations for site-specific BAF recalculation. 

 

5.3.4.1 Overview of freely dissolved normalization process 

 The freely dissolved fraction of nonionic organic chemicals is calculated using equation 

3-12 of the 2000 Human Health Methodology (USEPA, 2000a), which requires DOC and POC 

concentrations appropriate for the site and the octanol-water partition coefficient (Kow) for the 

chemical of concern: 

fd f = 1 / (1 + POC ⋅ Κ + 0.08 ⋅ ⋅ Κ ) ο ο w w DOC (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 Kow. As this figure illustrates, 

the calculated freely dissolved fraction is sensitive to organic carbon concentrations for a wide 

range of chemical hydrophobicities (i.e., logKow from 4 to 8.5). For chemicals with logKow > 6.6, 

the freely dissolved fraction varies by more than an order of magnitude for organic carbon levels 

within the 10th to 90th percentile range. The site-specific BAF recalculated by Method 5 

(equation 5-15) will change in proportion to the variation in the freely dissolved fraction. 

Therefore, in order to recalculate an accurate site-specific BAF, it is important for the 

investigator to determine organic carbon concentrations that are representative of the site. 
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FIGURE 5-12. Illustration of how the freely dissolved fraction calculated using equation 3-12 

varies as a function of Kow, for varying concentrations of DOC and POC. Organic carbon 

concentrations were based on nationally 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 were 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 concentrations in the water 

column at the site. This would be the preferred approach if site-specific DOC and POC data were 

not otherwise available. In general, 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 concentrations. 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 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

3456789

log Kow

freely dissolved chemical fraction (ffd)

national median POC and DOC

10% organic carbon

90% organic carbon
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sampling of DOC and POC concentrations over space and time is needed to achieve 

representative estimates for calculating freely dissolved chemical fractions . The sampling and 

averaging of DOC and POC concentrations should follow the guidance for field studies to 

measure chemical concentrations in water (Section 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 especially important for highly hydrophobic chemicals because the 

impact of DOC and POC on the site-specific BAF recalculation is greatest with these chemicals. 

The guidance offered in this section specifically relates to the separation of organic carbon into 

DOC and POC fractions and the selection of analytical methods for measuring 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 depending upon membrane selection 

and hardware. For example, a membrane with a 0.45-)m cutoff may be used in one study, 

whereas centrifugation that retains all particles with a size of 1.0 )m or greater may be used in 

another study. Typically, the particle size cutoff between POC and DOC fractions is 0.1-1 )m. 

DOC is principally composed of carbohydrates, carboxylic acids, amino acids, hydrocarbons, 

hydrophilic acids, and humic and fulvic acids. POC is principally composed of some larger 

humic acids, microbes, small plankton, plant litter, and ligneous matter (Suffet et al. 1994; 

Thurman, 1985). The material retained by filtration 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 measure dissolved and particulate organic carbon 

concentrations. The first (and preferred) method 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 concentrations 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 performed on the TOC (i.e., whole water) and DOC fractions of the 

samples. In this method, POC concentrations are determined by the difference between TOC and 
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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-ultraviolet oxidation method (Standard Methods #5310C) is generally preferred to 

the wet oxidation method (Standard Methods #5310D), because the former method is more 

sensitive and has a significantly 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 uncertainty in DOC and POC data 

(USEPA, 2003b). 

5.3.4.3 Determining DOC and POC using a literature or database search 

 

 Scientific publications, reports, and online databases also contain 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 accessed 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 for many aquatic organisms and ecosystems, but 

search and retrieval of specific data from these databases is generally straightforward. 

 The following is an inventory and descriptions of Internet-accessible databases 

containing organic carbon data for many ecosystems, water bodies and locations in the United 

States. The databases include: 

• Environmental Monitoring and Assessment Program (EMAP) 

• STORET (STOrage and RETrieval) and Legacy Data Center (LDC) and 

• National Water Information System (NWIS) 
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Descriptions of each database, and how DOC and 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 databases 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 dissolved chemical fractions and subsequently 

recomputing site-specific BAFs. Table 5-11 provides a list of evaluation criteria for organic 

carbon data sources. Because a “site” may be variously defined as a particular water body or 

segment, a type of waterbody within a state or region, or even all waterbodies within a state or 

region, this guidance should be flexible in order to properly address each situation. The order of 

the criteria generally corresponds to their importance in the evaluation process. The three top 

entries in Table 5-11 (waterbody types of interest, 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 order to be usable. QA/QC and sampling data should 

also be available for the investigator to evaluate the validity and representativeness of the data. 
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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 whether POC was determined by difference; 

i.e., TOC - DOC) 

• detection limits 

• % recovery 

• relative standard deviation (RSD) 

Sample factors influencing 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 restricted to “ambient” sampling stations only, to 

exclude so-called specialty stations (i.e., those stations designated for special purposes such as 

storm water runoff and biological and sediment monitoring). When POC and/or DOC 

concentrations are reported to be below analytical detection levels, it may be appropriate to 

estimate the concentration value as half of the reported detection level. However, the investigator 

should consider discarding censored data with “high” detection levels (i.e., >1.0 mg/L for DOC 

and >0.2 mg/L for POC) because of the greater 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 calculated as the difference from two other measurements (i.e., POC = 

TOC – DOC; DOC = TOC – POC), the calculation should only be performed using data from 
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the same sample to avoid introducing error, and the results should be screened to remove 

negative organic carbon concentrations. 

 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 eliminate 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 present-day conditions that can affect organic carbon 

concentrations in surface waters. 

5.3.4.5 Determining organic carbon concentrations 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 carbon developed by EPA during the development of 

the National BAF TSD Volume 2 (USEPA; 2003). Information on organic carbon concentrations 

representative of different types of surface waters 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 values; restriction to 

samples collected in the following water body types: estuaries, lakes, reservoirs, and streams 

(including rivers); elimination of pre-1980 data ; and, removal of extreme values based on the 

criteria of Thurman (1985). 
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 Table 5-12 shows descriptive statistics surrounding the median values for DOC and POC, 

in addition to values for specific water body 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, the coefficient of variations around the means are all above 100% and approach or 

equal 200% in some cases. Ratios of the 95th to the 5th percentiles range from a factor of 5 to 30, 

depending on water body type and parameter. This variation is not unexpected, given the high 

degree of temporal and spatial heterogeneity represented in the database. It is also apparent that 

the type of water body (lake, stream, estuary) has some impact on the DOC and POC 

distributions. For example, median values of DOC and POC from samples designated as 

“stream/river” are nearly twice those designated as “lakes.” This difference is probably related to 

the differing hydrologic, biogeochemical, and watershed characteristics of streams and lakes. 

Given the relatively high degree of variation that is evident in DOC and POC concentrations in 

surface waters across the United States, EPA recommends that States and Tribes 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 regional values cannot be 

derived, then it may be appropriate to use conservative (i.e., 90 or 95th percentile) values for 

DOC and POC concentrations listed in Table 5-12, when calculating the freely dissolved 

chemical fraction for site-specific BAF recalculation.
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Table 5-12. National Default Values for POC 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 

5th 0.8 0.7 1.0 1.7 0a 0a 0.08 0.1 

10th 1.2 1.0 1.4 2.0 0 0a 0.1 0.3 

25th 2.0 2.1 1.8 2.3 0.2 0.2 0.2 0.5 

75th 5.4 6.9 2.6 3.2 1.1 1.4 0.5 1.4 

90th 9.7 11.6 5.0 5.0 2.3 3.1 0.8 2.2 

95th 14 16.5 7.8 9 3.9 5 1.3 3 

95th/5th 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 databases. Data retrieval: January 2000 
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Appendix 5A 

Internet-Accessible Databases Containing Lipid Content Data 

 EMAP 

 EMAP is an EPA research program to develop 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 dynamics, and demonstrate multi-agency monitoring 

through large regional projects. EMAP data are organized according 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 LIPID 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 accessed at this stage lists “% LIPIDS” as a 

keyword, making this selection returns lipid data from only one project, the 1993-1994 REMAP 

Region 1 Fish Tissue Organic Concentrations 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 there are any lipid data available. This can be done 

by searching for the “LIPID” parameter in the “Variables” list that appears at the top of each 

dataset output. This output is formatted as comma-separated value (CSV) text, which can be 

saved from a web browser and then opened in a spreadsheet. At the time of this report, there 

seems to be no other good, direct way to search EMAP for lipid content data with any reliability 

or certainty of completeness. This is unfortunate, because EMAP data sets have the most 

complete supporting information of any of the online databases. Many of the EMAP data sets 
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have an accompanying documentation and references that provide information about the study 

design, sampling procedures, analytical 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 geographic area you wish to search (state, hydrologic region (HUC) or 

latitude-longitude box) 

4. Select “Sample medium type” and “Parameter 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 collected by the USGS. The 

data can be searched by geographic area and by parameter group (lipids 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. 
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 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 documented 

in numerous reports and technical memoranda. These range from project-specific reports to 

national protocols. Specific analytic procedures or codes are not identified for the lipid content 

data in the NWISWeb database. The lack of supporting information, combined with the difficulty 

of searching for lipid content as a specific parameter (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 operational system actively being populated with water quality data. 

The LDC contains historical water quality data dating back to the early part of the 20th century 

and collected up to the end of 1998. STORET contains data collected beginning in 1999, along 

with older data that has been properly documented and migrated from the LDC. Both systems 

contain raw biological, chemical, and physical data on surface and ground water collected by 

federal, state and local agencies, Indian Tribes, volunteer groups, academics, and others. All 50 

States, territories, and jurisdictions 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 collected, 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 Internet 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 desired geographic location by Latitude/Longitude, 
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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” 

box): 49289 for lipids 

7. Click the “Done” button 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 the data is emailed in approximately 24 hours 

(extremely large files may take slightly longer). 

To access lipid data from STORET, the investigator can follow these instructions: 

ACCESSING STORET FOR LIPID CONTENT DATA 

1. Select “Browse” or “Download Modernized STORET Data” 

2. Under “STORET Biological Results,” select the link for “Biological 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” search 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 the number of data that were found 

10. To download the results, press “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 (such 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 
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headings may be slightly off and some “chunks” of data may be displaced, so review 

and editing of the data may be necessary. 

 There are a number of differences between 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. However, the quality of some of this information is suspect and the 

supporting information may be incomplete or missing. 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 reports, which describe the standards, methods, 

practices, and other metadata supplied by data owners to document 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 the U.S. Army Corps of Engineers. This database contains both 

BSAF and organism lipid content data, primarily obtained 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 error statistics. The BSAF database 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 National Lake Fish Tissue Study 

 The National Study of Chemical Residues in Lake Fish Tissue was a screening-level study 

designed to estimate the national distribution of selected persistent, bioaccumulative, and toxic 

(PBT) chemicals in fish tissue from lakes and reservoirs of the continental United States. The 

study involves the collection of predator and bottom-dwelling fish from 500 randomly selected 
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lakes and reservoirs of the continental 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 fish 

species followed EPA guidance (USEPA, 1995b). Samples were edible tissue (skin-on fillet) 

composites of targeted predator species and total body tissue (whole fish) composites of targeted 

bottom-dwelling species. 

 Lipid contents were determined for all samples 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 procedures were followed throughout the study, and are well 

documented. Although EPA maintains a Web site for this project 

(http://www.epa.gov/waterscience/fishstudy/), the data are currently being 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.
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Appendix 5B 

Internet-Accessible Databases Containing Organic Carbon Data 

 EMAP 

 EMAP data are accessible via the EPA 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 that 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 concentrations in the EMAP project datasets. 

 STORET and LDC 

 Both the LDC and STORET are accessible 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: 
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ACCESSING THE LDC FOR ORGANIC 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 organization or the desired geographic location by 

Latitude/Longitude, State/County (only one county 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” button under “Submit Form” 

8. Select a detailed data report in HTML format and press “Continue” 

 The LDC contains a large quantity of data reported prior to 1999, however the quality of 

some of this information is suspect, and the 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, the investigator can follow these instructions:
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ACCESSING STORET FOR ORGANIC CARBON DATA 

1. Select “Browse” or “Download Modernized STORET Data” 

2. Under “STORET Regular Results,” select the link for “Regular 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 “Water” under the “Activity Medium” box 

6. In the “Characteristic” search box, enter “Carbon” and click the “Search” button 

7. In the pop-up window which appears, click 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 the number of data that were found 

10. To download the results, press “Continue” and after the search engine has completed the 

search (which may take a 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 (such 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 “chunks” 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 documented 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 standards, 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 the analytical remark codes found in the “Analytical Proc. ID” output can be 

found at http://www.epa.gov/storet/modern/doc/FieldLabAnltPrcdAndEqpDetail.pdf . 
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NWIS 

 NWIS also contains a large amount of organic carbon data for surface waters in the United 

States. The NWISWeb is accessed at http://waterdata.usgs.gov/nwis/qw. 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 geographic area you wish to search (state, hydrologic region (HUC), 

or latitude-longitude box) 

4. Select “Sample Medium Type” and “Parameter Groupings” under “Data Attribute” 

5. Select “Geographic Region” desired in first list 

6. Select [9] “Surface water” in second list 

7. Select “Major Inorganics” 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 (percent recovery) and precision (relative standard 

deviation) are available for the analysis of TOC and POC. In general, however, the lack of 

supporting information, combined with the difficulty of searching for organic carbon as a specific 

parameter (versus manually searching within the data for the entire “major inorganics” parameter 

group) limits the utility of this database. 
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Appendix 5C 

BSAFs for PCB congeners in Green Bay and Upper Hudson River 

Table 5C-1. BSAFs for PCB congeners 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 classes and fish species are tabulated.

PCB CONGENER LOG 

KOW

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 2.61 
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Table 5C-1 (continued). BSAFs for PCB congeners 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 classes and fish species are tabulated. 

PCB CONGENER LOG 

KOW

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 25.4 
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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 classes and fish species are tabulated. 

PCB CONGENER LOG 

KOW

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 15.6 
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Table 5C-2 (continued). 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 classes and fish species are tabulated. 

PCB CONGENER LOG 

KOW

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 13.2 
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Table 5C-2 (continued). 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 classes and fish species are tabulated. 

PCB CONGENER LOG 

KOW

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 15.4 
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Appendix 5D 

Lipid Content of Aquatic Organisms 

Table 5D-1. Lipid Content of Aquatic Organisms Used to Derive National Default Values of Lipid Fraction 

CSFII Consumption 

Category (Habitat)a Common Name Scientific Name

Species Mean 

Lipid Content 

(%)

CVb No. Obs. 

S

Anchovy (estuarine) Striped anchovy Anchoa hepsetus 2.8 NR 23 

European anchovy Engraulis encrasicholus 4.8 0.34 26 Northern anchovy Engraulis mordax 10.7 NR 16 

Carp (freshwater) Common carp Cyprinus carpio 5.4 0.86 2,792 Catfish (freshwater) White catfish Ameiurus catus 4.3 0.58 204 3

Black bullhead Ameiurus melas 1.1 0.70 113 3Yellow bullhead Ameiurus natalis 1.4 0.99 95 Brown bullhead Ameiurus nebulosus 2.6 0.72 988 3Channel catfish Ictalurus punctatus 5.3 0.71 1,427 3

Catfish (estuarine) White catfish Ameiurus catus 4.3 0.58 204 3

Brown bullhead Ameiurus nebulosus 2.6 0.72 988 3Channel catfish Ictalurus punctatus 5.3 0.71 1,427 3

Cisco (freshwater) Cisco Coregonus Artedii 1.9 0.65 69 Clam (estuarine) Hard shell clam Mercenaria mercenaria 0.7 NR 47 

Soft shell clam Mya arenaria 1.2 NR 3 Venus clam (Littleneck Japanese) Tapes (venerupis) decussatus 1.2 NR 15 Venus clam (Shortneck) Tapes japonica 1.8 NR 3 Venus clam (Asari) Tapes philippinarum 2.6 3 
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CSFII Consumption 

Category (Habitat)a Common Name Scientific Name

Species Mean 

Lipid Content 

(%)

CVb No. Obs. 

S

Venus clam Venus gallina 0.9 NR 29 Venus clam (hard) Venus lusoria 0.6 NR 5 

Crab (estuarine) Blue crab Callinectes sapidus 1.3 1.19 101 

Dungeness crab Cancer magister 1.0 0.26 24 Queen crab Chionoectes opilio 1.2 0.30 6 

Crayfish (freshwater) Crayfish (mixed sp.) Astacus and Orconectes 1.1 NR 5 Croaker (estuarine) White croaker Genyonemus lineatus 4.2 0.88 37 4, 

Atlantic croaker Micropogonias undulatus 3.2 0.47 8 Yellowfin croaker Umbrina roncador 1.8 0.70 3 

Eel (estuarine) Eel, mixed species Anguilla spp. 11.7 0.28 14 Flatfish (estuarine) Sole and flounder Bothidae and Pleuronectidae 1.2 0.80 596 Flounder (estuarine) Sole and flounder Bothidae and Pleuronectidae 1.2 0.80 596 Herring (estuarine) Blueback herring Alosa aestivalis 7.2 0.45 92 

Atlantic herring Clupea harengus 9.0 0.51 2,524 Pacific herring Clupea pallasi 13.9 0.39 128 

Mullet (freshwater) Striped mullet Mugil cephalus 3.8 0.62 43 Oyster (estuarine) Pacific oyster Crassostrea gigas 2.3 0.33 13 

Eastern oyster Crassostrea virginica 2.5 0.56 193 

Perch (estuarine and  freshwater) White perch Morone americana 3.5 0.72 682 

Yellow perch Perca flavescens 1.0 0.79 841 

Pike (freshwater) Northern pike Esox lucius 0.6 1.01 904 

Muskellunge Esox Masquinongy 1.1 0.87 35 
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CSFII Consumption 

Category (Habitat)a Common Name Scientific Name

Species Mean 

Lipid Content 

(%)

CVb No. Obs. 

S

Chain pickerel Esox niger 0.4 0.74 72 

Rockfish (estuarine) Striped bass Morone saxatilis 5.3 0.59 7,657 3, 

Rockfish Sebastes spp. 1.6 NR 81 

Salmon (estuarine & freshwater) Pink salmon Oncorhynchus gorbuscha 3.5 0.49 144 

Chum salmon Oncorhynchus keta 3.8 0.62 13 Coho salmon Oncorhynchus kistuch 2.9 0.75 617 Sockeye salmon Oncorhynchus nerka 8.6 0.32 48 Chinook salmon Oncorhynchus tshawytscha 3.4 0.93 873 Atlantic salmon Salmo salar 6.3 0.74 7 

Scallop (estuarine) Scallop, mixed species Pectinidae 0.8 0.35 114 Shrimp (estuarine) Shrimp, mixed species Panaeidae and Pandalidae 1.7 0.39 100 Smelt, rainbow (estuarine & freshwater) Rainbow smelt Osmerus mordax 4.1 0.46 130 Snails (freshwater) Snails, mixed species Vivaparadidae, Helixidaed 1.4 0.75 11 Sturgeon (estuarine & freshwater) Lake sturgeon Acipenser fulvescens 9.4 0.63 51 

White sturgeon Acipenser transmontanus 1.3 0.67 7 4

Trout, mixed spp. (freshwater) Rainbow trout Oncorhynchus mykiss 5.1 0.67 556 3

Cutthroat trout Salmo clarki 1.2 0.79 15 Brown trout Salmo trutta 7.4 0.73 615 3Brook trout Salvelinus fontinalis 4.0 0.56 96 3
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CSFII Consumption 

Category (Habitat)a Common Name Scientific Name

Species Mean 

Lipid Content 

(%)

CVb No. Obs. 

S

Lake trout Salvelinus namaycush 12.3 0.62 910 3

Trout, mixed spp. (estuarine) Rainbow trout Oncorhynchus mykiss 5.1 0.67 556 3

Cutthroat trout Salmo clarki 1.2 0.79 15 

Trout, (freshwater)e Rainbow trout Oncorhynchus mykiss 5.1 0.67 556 3Whitefish Whitefish, mixed spp. Coregonus spp. 5.9 0.64 68 

a Habitat designation (freshwater, estuarine) assigned to the CSFII consumption categories. See the Exposure Assessment volume of TS

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 literatu

GBMB. See Section 6.2.2 for a description of data sources. 

d In addition to these two families, specific genera represented include Ampullaria, Vivaparus, Achatina, Murex, Thais, Nassa, and Apo

e Information from the CSFII survey indicates that rainbow trout is appropriate for the “trout, freshwater” category.