United States
Environmental Protection
Agency
Office of Water
Office of Science and Technology
4304
EPA-822-R-08-001
June 2008
oEPA 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
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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
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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.
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This page intentionally left blank for the Foreword.
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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
in
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TABLE OF CONTENTS
1. INTRODUCTION 1-1
1.1 SCOPE OF DOCUMENT 1-3
1.2 SITE-SPECIFIC BIO ACCUMULATION 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 7V-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 PL AN 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
IV
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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 BIO ACCUMULATION FACTORS 4-1
4.1 DESCRIPTION OF METHOD 2 4-2
4.2 KEY STUDY DESIGN QUESTIONS FOR DETERMINING BIOTA-
SEDIMENT 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 /K0w ~ 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
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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 BIO AC CUMULATION 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 aBSAF? 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 Select on of a Food Web Structure 5-59
5.2.2.5 Alternative Food Chain Models 5-60
VI
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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 K0ws 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
DataBase 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 5 A - 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
Vll
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TABLES
Table 2-1. Strengths and Limitations of the Methods for Deriving Site-specific BAFs
(SSBAFs) 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-l. 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 3 A-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
Vlll
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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 K0w+ B) for
Polychlorinated Biphenyls (PCBs) and Chlorinated Pesticides 4-57
Table 4-5. Average IIsocw / K0w 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 • gocw 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
IX
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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 189LMB) 5-21
E. Extrapolating Lake Michigan Lake Trout (LM LT6) BSAFs to
Largemouth Bass at Hudson River mile 194 (RM 194LMB) 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 189LMB) 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
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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 K0ws 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 (f«) 5-119
XI
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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 3 A -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 3 A-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
offish 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 3 A-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 offish 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 offish 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
Xll
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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 K0wS of 2, 3, 4,
and 9 3-94
Figure 3D-2. Ratio of the 10th to 90th percentile bioaccumulation factors (BAFs) for field-
sampling 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
(BSAP) for field-sampling designs 4-76
Figure 4B-1. 90% confidence interval ratio for B SAP 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
Xlll
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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. BAF^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
XIV
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FIGURES (CONTINUED)
Figure 5-12. Illustration of how the freely dissolved fraction calculated using equation
3-12 varies as a function of K0w, for varying concentrations of DOC
andPOC 5-91
XV
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LIST OF ACRONYMS, SYMBOLS AND NOTATIONS
AWQC
BAF
BAFf
BCF
BCF
BMF
BSAF
BW
C
superscript ^tt?
superscript t
subscripts
subscript soc
subscript t
subscript^
subscript r
subscript k
subscript /'
ce
Ct
CDC
CSFII
CWA
DDT
DDE
ODD
DI
DOC
EPA
L
£
f«
FCM
FI,-
GLI
IARC
IRIS
Ambient water quality criteria
Bioaccumulation factor
Baseline BAF, in lipid normalized and based on freely dissolved chemical in
water
Bioconcentration factor
Baseline bioconcentration factor
Bioconcentration factor based on total concentrations in tissue and water
Biomagnification factor
Biota-sediment accumulation factors
Human body weight
Concentration
Freely dissolved chemical
Total chemical
In water
In sediment organic carbon
In tissue
In lipid
Reference chemical
Individual chemical of interest
In organism at trophic level /'
C of total chemical in water
C of chemical freely dissolved in water
C of chemical in sediment
C of chemical in sediment organic carbon
C of chemical in lipid
C of chemical in the specified wet tissue
U.S. centers for disease control and prevention
Continuing survey of food intake by individuals
Clean Water Act
1,1,1 -trichloro-2,2-bis(p-chlorophenyl)ethane
l,l-dichloro-2,2-bis(p-chlorophenyl)ethylene
l,l-dichloro-2,2-bis(p-chlorophenyl)ethane
Drinking water intake
Dissolved organic carbon
Environmental Protection Agency
Fraction freely dissolved
Fraction lipid
Fraction organic carbon in sediment
Food chain multiplier
Fish intake at trophic level /'
Great Lakes Water Quality Initiative
International Agency for Research on Cancer
Integration Risk Information System
XVI
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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
R/D Reference dose
RSC Relative source contribution to account for nonwater sources of exposure
SAB Science Advisory Board
• ?ocw Sediment-water concentration quotient
Dk/r Ratio between values of • ?0cw for reference chemical and chemical of interest k
SDWA Safe Drinking Water Act
STORE! 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
XVll
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Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors
Section 1 EPA Review Draft September 4, 2007
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 offish 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 BAF,)1 is a mean BAF, based on
concentrations of total chemical in wet tissue and water, for a specific trophic level"/'". 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 BAF,s
are in the denominator, along with information on the amount offish of each trophic level (i)
consumed on a daily basis (FI;), to estimate human exposure to contaminants through the aquatic
food web (USEPA, 2000).
AWQC = RfD-RSC
BW
2 = 2
(Equation 1-1)
where:
AWQC = ambient water quality criterion (mg chemical/L water)
R/D = 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)
FI,= fish intake (kg/day) at trophic level i (i = 2, 3, 4)
BAF, = bioaccumulation factor (L/kg) at trophic level i (i = 2, 3, 4) based on
concentrations of total chemical in wet tissue and water
For contaminants that bioaccumulate extensively, such as hydrophobic nonionic organic
chemicals, researchers report BAF; 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 B AF 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 B AF,
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.
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 (BAF, ). The ratio of the concentration of a chemical in the tissue of an
aquatic organism to its concentration in water, in situations where both the organism and its food
are exposed and the ratio does not change substantially over time. The subscript i indicates that a
BAF, is trophic level specific; this subscript was not used in TSD Volume 2. Several forms of the
BAF, are used in this document:
Total bioaccumulation factor (BAF.'T). ABAF 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 BAF.^ 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 BAF, or BAF/^). 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 BAF; is trophic level specific, although the subscript i was not used in TSD Volume 2.
The baseline BAF; is expressed in liters per kilogram of lipid.
Field-measured bioaccumulation factor. A BAF^ 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/J? instead.
Lipid-normalized and freely dissolved-based bioaccumulation factor (BAF.^ ). 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.^ 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 BAF,). A BAF based on
nationwide average lipid content for trophic level / and nationwide average organic carbon in
ambient waters. The national BAF, 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 (BCF^ ). 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 BCF^ is expressed in liters per kilogram.
Baseline bioconcentration factor (Baseline BCF or BCF/d). 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 (BCFjd ). 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 BCFf 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 (BMF,). The ratio (unitless) of the concentration of a chemical in a
predator organism at trophic level /' 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 BMFrLn was used for this term.
Biota-sediment accumulation factor (BSAF,). For nonionic organic chemicals (and certain
ionic organic chemicals to which similar lipid and organic carbon partitioning behavior applies),
the BSAF, 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. BSAF,s 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. BSAF,s 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 (BEF^r). For nonionic organic chemicals (and certain
ionic organic chemicals to which similar lipid and organic carbon partitioning behavior applies),
the BEFfc/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 (FGVL). For nonionic organic chemicals (and certain ionic organic
chemicals to which similar lipid and organic carbon partitioning behavior applies), the FCM, is
the unitless ratio of a baseline BAF for an organism at trophic level /' 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 (C™). For nonionic organic chemicals, the concentration of the
chemical that is dissolved in ambient water, excluding the portions sorbed onto paniculate 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 (C.). 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.
fl-Octanol-water partition coefficient (Kow). The ratio of the concentration of a chemical in the
w-octanol phase to its concentration in the aqueous phase in an equilibrated two-phase system of
w-octanol and water. This is usually expressed as log Kow, the base 10 logarithm of the w-octanol-
water 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 (• £ocm). 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. • ?0cw 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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OEHWf CBSWCm J
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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 site-
specific 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 t\\at for aging 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 B AF 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.
' 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 w-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 site-
specific 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)
SSBAF
Derivation
Approach
SSBAF
Method
Strengths
Limitations
Derive new
baseline BAF
values
1. Field measured
SSBAF
2. SS BAF predicted
from measured BSAF
• Preferred method applicable to
all chemical types
• Incorporates chemical
biomagnification and
metabolism
• Reflects site-specific attributes
that affect bioavailability and
dietary exposure
• 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
• 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
• 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. SSBAF
extrapolated from
BSAF (3a) or
BEF (3b)
• Incorporates chemical
biomagnification and
metabolism
• Quality of BSAFs orBEFs
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)
SSBAF
Derivation
Approach
SSBAF
Method
Strengths
Limitations
Adjust existing
national or
baseline BAFs
4a. SS BAF predicted
from BCF and FCM
4b. SS BAF predicted
from Kow and FCM
5. SS BAF
recalculated from
baseline BAF
• 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
• Readily applied with minimal
input data
• Level of effort does not increase
with spatial scale, number and
type of water bodies within site
• Quality of baseline or National
BAFs may be superior to site-
specific measurements
• 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
• 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
• 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 weight-
of-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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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 site-
specific 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. EPA-
600/3-77-083.
Grieb, T.M., Driscoll, C.T., Gloss, S.P., Scholfield, C.L., Bowie, GL. andD.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., 707,853-861.
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 Inter agency Committee.
Florida Game and Fresh Water Fish Commission. Gainsville, FL.
Schwarzenbach, R.P., Gschwend, P.M. and D.M. Imboden. 1993. Environmental Organic
Chemistry (seconded.). John Wiley & Sons, Hoboken, New Jersey.
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USEPA. 200 la. 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. 200 Ib. 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 BAF*T, also referred to as the "field-
measured" BAF. The BAF/T is calculated from the total concentration of chemical in the
appropriate wet tissue of the aquatic organism sampled at trophic level /', and the total
concentration of the chemical in the ambient water at the sampling site:
Total BAF = BAF1T = -^ (Equation 3-1)
'• Cw
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 ug/L, reflects adequate temporal and
spatial averaging, based on the Kowof 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 BAF is calculated using equation 3-
1:
c,,,
(Equation 3-1)
1000//g
Cw kg 49.5//g wg
= 48.5 L/kg
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.^ ) is calculated using the
lipid-normalized concentration in tissue and the freely dissolved chemical concentration in the
water:
Baseline BAF, = BAF^ =L.-- (Equation 3 -2)
Cw ^t
where:
Cf = lipid-normalized concentration of the chemical in tissue
Cf = concentration of chemical that is freely dissolved in water
f^. ... = 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/^f ):
Baseline BAF, = BAFj (Equation 3-3)
r a
The derivation of the baseline BAF and it's relationship to BAF.^ (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 jc). Because this is an organic chemical, the site-
specific 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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dm
als
cor
(D<
obi
Re
ref
(lo
Calculation of site-specific Baseline BAFs from measurements at the site (continued)
ing sampling in 1993, and three composite samples were analyzed. Twelve water samples were
o collected on a near-monthly basis in 1993, As recommended in the Section 3 guidance, lipid
itents were measured in all fish composite samples, and dissolved and particulate organic carbon
DC and POC) concentrations were measured in all water samples. The following data were
.ained from the study:
LARGEMOUTH BASS
Date
August-93
August-93
August-93
Chemical x Concentration (ug/g)
0.797
1.040
0.646
% Lipid
1.16
1.45
1.10
WATER COLUMN
Date
January-93
February-93
March-93
April-93
May-93
May-93
June-93
June-93
July-93
August-93
August-93
September-93
Chemical x
Concentration (ng/L)
1.02
1.01
1.43
2.34
2.24
2.47
3.32
3.74
4.00
2.92
2.89
2.26
DOC Concentration
(mg/L)
5.18
5.47
5.53
4.39
4.41
5.12
4.84
5.32
5.53
4.83
4.95
4.90
POC Concentration
(mg/L)
1.49
1.55
1.98
0.36
0.37
1.46
1.08
1.55
2.31
0.37
1.46
1.38
view of these data indicated that the mean concentration of the chemical in the water column
ects adequate temporal and spatial averaging, based on the hydrophobicity of this chemical
gKow = 5.84), and was representative of the average exposure of chemical x to the target fish.
L
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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 (/J) of each sample:
Q = Ct//c
The lipid-normalized chemical concentration (Q )in each sample is tabulated:
Sampling Date
August-93 (1)
August-93 (2)
August-93 (3)
August sample average
Lipid-normalized
Concentration (^g/g-lipid)
68.7
71.7
58.7
66.4
The lipid-normalized chemical concentrations in largemouth bass were then averaged, to determine
a mean value of 66.4 ug/g-lipid.
The freely dissolved fraction of chemical in the water column (fa) was also calculated for each
sample, using equation 3-6:
ffd = 1/(1+POC -KOM,+0.08-DOC-Kow
(Equation 3-6)
For example, the freely dissolved chemical fraction of the January water sample is:
1
Jfd —'
|
l.49mg-POC
L kg
L
kg W6mg
|
5.lSmg-DOC
L
,L_ kg
kgltf
= 0.431
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Calculation of site-specific Baseline BAFs from measurements at the site (continued)
The freely dissolved chemical concentration ( C^d ) is calculated as:
Cfd - f -C
°w ~ J fd ^w
The freely dissolved fraction and freely dissolved chemical concentration is calculated for each
sample as tabulated below:
Sampling Date
January-93
February-93
March-93
April-93
May-93
May-93
June-93
June-93
July-93
August-93
August-93
September-93
ffd
0.431
0.421
0.374
0.669
0.666
0.436
0.496
0.422
0.345
0.656
0.438
0.449
Freely-dissolved
Concentration (ng/L)
1.76
1.70
2.14
6.25
5.97
4.31
6.59
6.31
5.51
7.66
5.07
4.06
The freely-dissolved chemical concentrations were then averaged, to determine a mean value of
C/rf = 4.78ng/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 CV ( 66.4 ng/g-lipid),./? (1.24%)
and C^d (4.78 ng/L) as shown below:
C 1
Baseline BAF. = —j- - — (Equation 3-2)
Baseline BAF =
rsdseime ±5 AT
= 66'4/'g L 10QQ"g 1QQOg
00124
The site-specific baseline BAF for chemical x in largemouth bass is 1.39 x 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 site-
specific baseline BAF must be converted to a total BAF. Recalling the relationship between the
baseline BAF and the total BAF ( BAF/T ):
Site Specific BAF/r = (ff • Baseline BAF. +1) • ffd (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:
Site-Specific BAF^ = (0.0124-1. 39xl07-^7
The site-specific total BAF for chemical x in largemouth bass is 8.34 x 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 BAF^as shown in Equation 3-4 by using
information on the lipid fraction (f^) 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):
Baseline BAF, =
BAF/
(Equation 3-4)
where:
^r = Total BAF
= 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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the mean concentration. When the chemical is not detected at all (i.e., no response above
instrumental noise), !/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), El-
Shaarawi 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%)?
Investigate detection limits of available analytical
methods
Compare to expected chemical concentrations
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?
Data Quality Objectives (DQO)
process (USEPA, 20QOc)
Bootstrap or Monte Carlo
simulations
Bioaccumulation modeling
4. Biota. Which species should I sample?
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?
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)
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?
Which tissues are most commonly consumed
and how are they prepared?
Does this vary with organism size?
Composite vs. individual samples
Chemical analysis requirements
8. Water Sample Type. What types of water samples should I collect?
Individual grab samples vs. composites?
Temporal averaging
Depth integration
Composite vs. individual samples
Chemical analysis requirements
9. Chemical Analytical Methods. Which analytical methods should I use?
Must be specific for the individual chemical(s) of concern
10. Biota Sampling Methods. How should biota be sampled?
Appropriate methods depend on water body and target
organisms
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11. Water Sampling Methods. How should water be sampled?
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
12. Water/Biota Sampling Correspondence. How should I coordinate biota and water
sampling (e.g., concurrent vs. staggered sampling)?
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
13. Ancillary Measurements. What other chemical, water and biota parameters should I
consider measuring?
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 paniculate 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
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
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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.
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 (Mtp://www.instant.rcf.coni/download-dqo-pro.himl') 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 3 A. 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 offish 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.cpa.gov/ncrlcsd 1 Asc/softwarc.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 site-
specific 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 offish 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
offish samples with each water sample or the collection offish samples with the last
water sample. From a field-sampling perspective, the one-time collection offish 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 offish 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 offish 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., logKow>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
log Kon
Metabolism
Rate
Sampling
Design"
Sampling Events
Minimum number
(depends on temporal
variability)
Minimum spacing
between sampling
events (d)
<3
4
5
>6
<4
5
>6
all
low
low
low
low
medium
medium
medium
high
2nd series
2nd series
1st or 2nd series
1st or 2nd series
2nd series
1st or 2nd series
1st or 2nd series
2nd series
1
2
1, 3, 6b
1, 5, 8b
1
1, 2, 3b
1, 4, 6b
1
7
7
7
30
7
7
30
7
1st series = collection of a series of water samples with the collection offish samples concurrently with the
last water sample; 2nd series = collection of paired fish and water samples with each sampling event.
' 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 Variability11
low variabilit
l7<----------------->-
y^^
Lakes and Reservoirs
Waterbody
Lake Michigan
(dissolved
PCBs)
cvb
0.37
Estuaries and Tidal Rivers
Waterbody
Hudson River
(Aroclor 1254)
James River
estuary
(Kepone)
Green Bay
zone 3 (PCBs)
CV
0.60
0.60
06-08
high variability
Rivers and Streams
Waterbody
Mississippi River,
MS (chloroform)
Naugatuck River,
CT (dissolved
copper)
lower Fox
River, WI
(dissolved PCBs)
congeners 28+31
congener 149
congener 180
Lake Michigan
Tributaries
(PCBs)
CV
0.97
0.60
0.54 - 0.57
0.37-1.11
0.55 -2.19
0.19 - 1.50
' Variability of chemical concentrations may be higher at concentrations approaching the limit of detection.
' 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 poly chlorinated
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 offish 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 -In (L)] (Equation 3-5a)
or
H = exp [3.33 +2.98 -HAB + 0.58- In (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 -In (180 mm)]
= exp(8.80) = 6,624m2
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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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:
H = exp [-2.91 + 3.14-0+1.65 -In (180 mm)] = exp (5.66) = 287 m2
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
m2) in lakes and not exceeding 30 meters in streams.
• Do the organisms of interest exhibit die I (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.
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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 offish 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 fmfish 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 andM 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 offish 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 offish and shellfish (Versar, 1982). This selectivity is a
great advantage for sampling biota for the purpose of measuring a B AF, 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 SuperfundProgram Representative Sampling
Guidance, Volume 5: Water and Sediment, Part/(U SEP A, 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 highly-
bioaccumulative 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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ff =!/(!+ POC • Kow + 0.08 • DOC • Kow ) (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'j^ow is used to estimate partition coefficient between DOC and freely dissolved chemical
(Burkhard, 2000). The applicability of Equation 3-6 has been demonstrated for many nonionic
organic 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 (site-
specific or generic) can also be used to test alternative sampling designs, as was demonstrated in
Section 3.3.3.
3.4.5 Sample Type
Water samples collected to support the 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 low-
level 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
Fie Id 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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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, DJ. 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 (rib) and water (nw) samples. The bootstrap is
applied to resample «& 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 (rib) 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 3 a 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
18
52
149
180
logKoW
5.24
5.84
6.67
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.
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 =
I (BAFobserved - BAFn)
n=l
In,
where:
and
BAF0bServed = 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)
the root mean square error (RMSE), defined as:
RMSE =
I (BAFobsemd - BAFn)2
n=l
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):
1
where set 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:
1
s « -
where:
Ct~ r
Jl
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
-2-r -s -s -Cfd
fd> wd c» fd w
1/2
where SBAF, SQ, and scwfd are the standard deviations for the BAF, CL, and Cf*, respectively; and
r/d is the correlation coefficient4 between C/ and Cf, r/t is the correlation coefficient between Ct
and C/, and rw(j is the correlation coefficient between Cw and fd.
For sufficiently large sample sizes, the approximate (!-• /2)-100 percent confidence limits
for the BAF (total or baseline) are given by:
where • *is a probability of exceedance and z.« 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 offish 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 3 A-l,
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 3 A-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.
c.oe s -so
FISH
I I T
~BO
13
1C 5
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
3PCB 18 data
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1 1
! |
Jli 11 in
i i
1 :
II !J Illllm. ,
1EOO
L1-I
1EOQ
o
O
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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1CC
o
on n ~'C
1500
~o
I'I
TC O
CD
0.0
1000
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 offish 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 offish 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
2
6.65
5.75
5.46
5.32
5.23
5.02
4.95
4.89
4
4.95
4.10
3.81
3.65
3.55
3.34
3.25
3.17
6
4.37
3.55
3.29
3.16
3.07
2.91
2.86
2.82
8
4.04
3.25
3.01
2.87
2.80
2.64
2.59
2.54
10
3.82
3.06
2.81
2.69
2.61
2.46
2.41
2.35
20
3.31
2.59
2.35
2.24
2.17
2.02
1.98
1.93
30
3.13
2.41
2.18
2.06
1.98
1.84
1.79
1.73
60
2.94
2.23
.99
.86
.78
.62
.57
.51
90
2.87
2.16
.92
.79
.71
.55
.49
.42
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10
12 14 16 18 20
number of water samples
22
24
26
28
30
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 offish 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
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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.
-40
-35 -
-30 -
-20 -
-15 -
-10 -
-5 -
congener:
-»-PCB18
PCB 149
PCB 180
246
10 12 14 16 18 20
number of water samples
22
24
26
28
30
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.
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10
12 14 16 18 20
number of water samples
22
24
26
28
30
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 offish and water
samples.
Taylor estimates confirm Bootstrap results
Table 3 A-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 offish (nb) and water (nw) concentrations. This agreement confirms
that the Bootstrap algorithm was performing properly.
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How does compositing affect the relationship between number of samples and the
accuracy of the BAF?
Compositing offish 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 site-
specific data are limited.
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Table 3A-2. Taylor Series Approximation of Confidence Limits for Baseline BAF/s in Green Bay Zone 3
CHEMICAL
AND
STATISTIC
PCB 18:
Average
St. deviation
St. error
PCB 52:
Average
St. deviation
St. error
PCB 149:
Average
St. deviation
St. error
PCB 180:
Average
St. deviation
St. error
CONCENTRATION
Predator Forage Water
Fish Ct Fish Q Cfd
(ng/g-t) (ng/g-t) (ng^)
183 84.0 0.0601
243 57.5 0.0388
1401 649 0.0590
931 331 0.0393
537 240 0.00659
329 103 0.00579
544 211 0.00102
350 104 0.000949
PREDATOR FISH
Baseline 90% 90% 90%
BAF¥ LCLa UCLb CLRC
3,051 1,970 4,132 2.10
657
23,760 18,928 28,592 1.51
2,938
81,502 63,924 99,081 1.55
10,686
534,098 412,216 655,979 1.59
74,092
FORAGE FISH
Baseline 90% 90% 90%
BAF5 LCL UCL CLR
1,399 1,151 1,647 1.43
151
11,009 9,317 12,700 1.36
1,028
36,349 30,053 42,645 1.42
3,827
207,284 168,325 246,242 1.46
23,683
a LCL: Lower confidence limit; 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
5/1/1989
5/2/1989
5/3/1989
5/3/1989
5/3/1989
5/3/1989
5/3/1989
5/3/1989
5/4/1989
5/4/1989
5/4/1989
5/4/1989
6/11/1989
6/11/1989
6/11/1989
6/11/1989
6/11/1989
6/12/1989
6/12/1989
6/12/1989
6/12/1989
6/12/1989
6/12/1989
6/12/1989
6/12/1989
7/30/1989
7/30/1989
7/30/1989
7/30/1989
7/30/1989
7/30/1989
7/30/1989
7/31/1989
7/31/1989
7/31/1989
7/31/1989
7/31/1989
7/31/1989
7/31/1989
7/31/1989
7/31/1989
9/15/1989
9/15/1989
9/15/1989
9/15/1989
9/15/1989
9/16/1989
9/16/1989
Zone
GBOZ3A
GBOZ3B
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3B
GBOZ3A
GBOZ3B
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3B
GBOZ3A
GBOZ3B
GBOZ3B
GBOZ3A
GBOZ3B
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3B
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3B
GBOZ3A
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3A
GBOZ3B
GBOZ3B
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3B
GBOZ3A
GBOZ3A
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3A
GBOZ3A
GBOZ3B
GBOZ3A
GBOZ3A
Station
GB0019
GB0018
GB0013
GB0013
GB0014
GB0015
GB0016
GB0017
GB0008
GB0009
GB0010
GB0011
GB0016
GB0017
GB0018
GB0019
GB0020
GB0008
GB0009
GB0010
GB0011
GB0013
GB0013
GB0014
GB0015
GB0016
GB0017
GB0018
GB0018
GB0019
GB0020
GB0020
GB0008
GB0008
GB0009
GB0010
GB0010
GB0011
GB0013
GB0014
GB0015
GB0018
GB0018
GB0019
GB0019
GB0020
GB0009
GB0013
Layer"
EPI
HYP
EPI
HYP
EPI
HYP
PCB 149 (ng/L)
0.0038
0.0097
0.0047
0.0131
0.0067
0.0037
0.0108
0.0074
0.0061
0.0065
0.0046
0.0052
0.0075
0.0047
0.0046
0.0047
0.0035
0.0057
0.0044
0.0058
0.0078
0.0062
0.0061
0.0041
0.0044
0.0046
0.0038
0.0037
0.0050
0.0050
0.0037
0.0033
0.0054
0.0072
0.0048
0.0064
0.0061
0.0067
0.0040
0.0035
0.0056
0.0030
0.0071
0.0023
0.0039
0.0035
0.0087
0.0053
PCB 18 (ng/L)
0.0165
0.0487
0.0190
0.0578
0.0551
0.0897
0.0294
0.1026
0.0797
0.0472
0.0764
0.1074
0.0217
0.0828
0.0277
0.0204
0.0233
0.0709
0.0459
0.0795
0.1040
0.0374
0.0376
0.0369
0.1018
0.0248
0.0470
0.0239
0.0282
0.0244
0.0290
0.0240
0.1248
0.1811
0.0536
0.1477
0.1442
0.1395
0.0360
0.0478
0.0333
0.0188
0.0337
0.0204
0.0243
0.0211
0.1206
0.0573
PCB 180 (ng/L)
0.00045
0.00070
0.00098
0.00599
0.00088
0.00080
0.00332
0.00089
0.00074
0.00065
0.00051
0.00049
0.00094
0.00059
0.00047
0.00041
0.00052
0.00068
0.00059
0.00088
0.00102
0.00079
0.00080
0.00048
0.00066
0.00074
0.00069
0.00025
0.00104
0.00060
0.00055
0.00050
0.00081
0.00089
0.00048
0.00072
0.00081
0.00090
0.00056
0.00063
0.00072
0.00006
0.00107
0.00033
0.00052
0.00071
0.00094
0.00074
PCB 52 (ng/L)
0.0233
0.0416
0.0346
0.0935
0.0617
0.0817
0.0418
0.0979
0.0716
0.0426
0.0719
0.0941
0.0191
0.0708
0.0244
0.0140
0.0185
0.0674
0.0486
0.0767
0.0974
0.0415
0.0399
0.0374
0.0901
0.0251
0.0499
0.0197
0.0384
0.0216
0.0309
0.0291
0.1055
0.1817
0.0614
0.1468
0.1455
0.1505
0.0597
0.0528
0.0352
0.0145
0.0263
0.0135
0.0278
0.0158
0.0840
0.0392
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Date
9/16/1989
9/16/1989
9/16/1989
9/16/1989
9/16/1989
9/16/1989
9/16/1989
9/16/1989
9/17/1989
9/17/1989
9/17/1989
10/22/1989
10/22/1989
10/22/1989
10/22/1989
10/23/1989
10/23/1989
10/23/1989
10/23/1989
10/23/1989
10/23/1989
10/23/1989
10/23/1989
10/24/1989
2/8/1990
2/9/1990
2/9/1990
2/10/1990
2/10/1990
2/10/1990
2/11/1990
4/28/1990
4/28/1990
4/28/1990
4/28/1990
4/28/1990
4/29/1990
4/29/1990
4/29/1990
4/29/1990
4/29/1990
4/29/1990
4/29/1990
4/29/1990
4/29/1990
Zone
GBOZ3A
GBOZ3A
GBOZ3B
GBOZ3B
GBOZ3A
GBOZ3A
GBOZ3B
GBOZ3B
GBOZ3A
GBOZ3A
GBOZ3B
GBOZ3A
GBOZ3B
GBOZ3A
GBOZ3B
GBOZ3A
GBOZ3A
GBOZ3B
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3B
GBOZ3B
GBOZ3A
GBOZ3B
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3B
GBOZ3A
GBOZ3A
GBOZ3B
GBOZ3B
GBOZ3A
GBOZ3B
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3B
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3B
Station
GB0013
GB0014
GB0015
GB0015
GB0016
GB0016
GB0017
GB0017
GB0008
GB0010
GB0011
GB0016
GB0018
GB0019
GB0020
GB0009
GB0010
GB0011
GB0013
GB0013
GB0014
GB0015
GB0017
GB0008
GB0015
GB0013
GB0014
GB0009
GB0010
GB0011
GB0008
GB0016
GB0017
GB0018
GB0019
GB0020
GB0008
GB0009
GB0010
GB0010
GB0011
GB0013
GB0014
GB0014
GB0015
Layer"
EPI
HYP
EPI
HYP
EPI
HYP
PCB 149 (ng/L)
0.0058
0.0055
0.0039
0.0044
0.0040
0.0057
0.0035
0.0038
0.0064
0.0053
0.0061
0.0072
0.0041
0.0061
0.0057
0.0029
0.0046
0.0047
0.0044
0.0095
0.0065
0.0043
0.0038
0.0052
0.0229
0.0266
0.0274
0.0267
0.0256
0.0307
0.0179
0.0034
0.0044
0.0027
0.0045
0.0051
0.0033
0.0033
0.0037
0.0025
0.0025
0.0029
0.0031
0.0029
0.0038
PCB 18 (ng/L)
0.0627
0.0507
0.0336
0.0432
0.0293
0.0412
0.0267
0.0263
0.1256
0.0910
0.1034
0.0386
0.0503
0.0402
0.0297
0.0321
0.1067
0.1153
0.0452
0.0506
0.1133
0.0927
0.0429
0.1508
0.0673
0.0512
0.0713
0.0785
0.1139
0.1613
0.1048
0.0321
0.0590
0.0233
0.0205
0.0247
0.0733
0.0415
0.0331
0.0339
0.0479
0.0389
0.0369
0.0355
0.0699
PCB 180 (ng/L)
0.00117
0.00078
0.00091
0.00108
0.00066
0.00108
0.00063
0.00072
0.00091
0.00079
0.00084
0.00132
0.00112
0.00163
0.00133
0.00092
0.00111
0.00125
0.00143
0.00123
0.00126
0.00099
0.00100
0.00085
0.00257
0.00395
0.00340
0.00379
0.00378
0.00322
0.00233
0.00039
0.00044
0.00039
0.00089
0.00006
0.00077
0.00037
0.00040
0.00064
0.00023
0.00036
0.00028
0.00028
0.00063
PCB 52 (ng/L)
0.0414
0.0387
0.0260
0.0366
0.0215
0.0313
0.0220
0.0219
0.1105
0.0704
0.0932
0.0360
0.0485
0.0380
0.0238
0.0436
0.1312
0.1246
0.0518
0.0548
0.1132
0.0961
0.0459
0.1543
0.0948
0.0725
0.0994
0.0891
0.1360
0.1400
0.1148
0.0280
0.0493
0.0216
0.0175
0.0201
0.0618
0.0330
0.0293
0.0241
0.0412
0.0326
0.0340
0.0344
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
ALE WIFE
ALE WIFE
ALEWIFE
ALE WIFE
ALEWIFE
ALEWIFE
ALEWIFE
ALEWIFE
ALEWIFE
ALEWIFE
ALEWIFE
ALEWIFE
ALEWIFE
ALEWIFE
ALEWIFE
ALEWIFE
ALEWIFE
ALEWIFE
ALEWIFE
ALEWIFE
ALEWIFE
ALEWIFE
ALEWIFE
ALEWIFE
ALEWIFE
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
Life
Stage"
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
Y
Y
Y
Y
Y
Y
Y
Y
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
Y
Y
Y
Y
Y
Y
Y
Y
Y
Zone
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
Date
6/10/1989
6/10/1989
6/10/1989
10/3/1989
6/10/1989
8/8/1989
10/3/1989
10/3/1989
8/8/1989
6/10/1989
6/5/1989
10/4/1989
10/4/1989
10/4/1989
8/22/1989
9/13/1989
9/13/1989
6/10/1989
6/16/1989
10/3/1989
6/20/1989
6/16/1989
9/12/1989
9/12/1989
9/12/1989
10/3/1989
6/16/1989
5/17/1989
5/1/1989
6/16/1989
8/9/1989
8/9/1989
8/9/1989
10/3/1989
10/3/1989
5/17/1989
5/1/1989
5/1/1989
10/4/1989
5/1/1989
5/1/1989
5/1/1989
8/9/1989
10/4/1989
8/9/1989
10/4/1989
8/9/1989
6/16/1989
6/20/1989
6/16/1989
6/20/1989
9/14/1989
8/9/1989
10/3/1989
9/14/1989
10/3/1989
PCB 149 (ng/g-1)
288
454
513
197
352
402
197
258
292
326
326
206
229
240
465
157
188
311
275
88
195
286
58
137
40
232
152
479
235
171
192
223
229
298
204
376
304
325
184
440
387
427
235
234
231
267
195
135
100
103
125
187
162
239
198
160
PCB 18 (ng/g-1)
13
91
37
132
110
44
68
53
36
33
42
266
165
146
323
99
139
54
25
29
47
49
36
232
22
79
23
115
54
63
73
117
69
141
80
97
103
172
64
101
113
140
112
79
121
103
97
30
21
11
24
43
48
59
23
45
PCB 180 (ng/g-1)
240
522
587
184
288
366
197
258
292
274
357
188
182
218
374
157
172
199
213
64
129
245
43
87
32
232
159
479
220
133
167
223
229
215
172
279
234
240
184
377
327
305
199
249
190
200
154
111
84
79
89
199
157
256
198
135
PCB 52 (ng/g-1)
367
295
611
724
1072
512
518
549
551
223
403
786
882
874
1200
619
694
466
296
201
570
495
228
847
134
732
184
1310
568
255
627
881
758
1242
768
940
1125
1266
485
1289
1488
1463
866
685
883
867
832
175
197
165
204
474
392
657
455
451
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Common Name
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
RAINBOW SMELT
Life
Stage"
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Zone
GBOZ3A
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
Date
10/3/1989
5/1/1989
5/1/1989
5/1/1989
11/2/1989
9/5/1989
8/9/1989
11/2/1989
8/9/1989
11/2/1989
PCB 149 (ng/g-1)
155
161
169
143
156
235
198
193
272
225
PCB 18 (ng/g-1)
83
109
113
77
78
67
66
66
91
87
PCB 180 (ng/g-1)
179
100
96
101
147
216
144
162
211
225
PCB 52 (ng/g-1)
361
697
733
568
459
603
519
761
603
734
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
BROWN TROUT
BROWN TROUT
BROWN TROUT
BROWN TROUT
BROWN TROUT
BROWN TROUT
BROWN TROUT
BROWN TROUT
BROWN TROUT
BROWN TROUT
BROWN TROUT
BROWN TROUT
BROWN TROUT
BROWN TROUT
BROWN TROUT
BROWN TROUT
BROWN TROUT
BROWN TROUT
BROWN TROUT
BROWN TROUT
BROWN TROUT
BROWN TROUT
BROWN TROUT
BROWN TROUT
BROWN TROUT
WALLEYE
WALLEYE
WALLEYE
WALLEYE
WALLEYE
WALLEYE
WALLEYE
WALLEYE
WALLEYE
WALLEYE
WALLEYE
WALLEYE
WALLEYE
WALLEYE
WALLEYE
WALLEYE
WALLEYE
Age
2
2
2
2
2
2
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
4
4
4
4
4
4
4
4
4
4
4
Zone
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
Date
9/26/1989
6/9/1989
6/9/1989
9/11/1989
7/30/1989
7/22/1989
9/26/1989
10/10/1989
6/17/1989
8/22/1989
10/10/1989
9/9/1989
10/10/1989
9/26/1989
6/6/1989
9/11/1989
7/22/1989
9/26/1989
9/11/1989
10/10/1989
10/18/1989
10/18/1989
5/17/1989
9/12/1989
9/8/1989
10/22/1989
5/11/1989
10/21/1989
5/2/1989
7/12/1989
8/22/1989
6/19/1989
11/9/1989
9/11/1989
11/9/1989
8/31/1989
5/2/1989
10/21/1989
7/12/1989
7/12/1989
10/22/1989
5/2/1989
PCB 149 (ng/g-1)
380
251
269
523
298
531
120
618
252
730
775
412
657
541
531
561
363
884
386
467
480
667
369
579
466
115
444
603
587
544
545
563
141
333
620
439
560
738
701
660
2292
568
PCB 18 (ng/g-1)
101
120
72
133
169
72
391
163
35
1265
164
79
144
123
116
89
85
198
95
71
69
91
141
113
76
278
102
86
147
97
79
188
66
81
1089
69
93
114
116
156
542
220
PCB 180 (ng/g-1)
397
215
239
413
264
493
80
618
229
652
760
461
657
484
558
507
357
976
332
484
516
694
336
632
519
431
389
619
587
575
529
563
562
360
544
439
412
631
701
546
2500
568
PCB 52 (ng/g-1)
930
902
595
1253
1220
830
879
1410
387
3891
1639
889
1540
1233
1157
1095
907
2375
813
710
720
1129
1142
1129
916
2011
1019
1253
1602
1109
1184
1578
1171
752
3183
986
994
1342
1590
1921
5625
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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number of fish
samples/analysis
method:
« 2B
5 4B
- 8B
« SOB
--O--2MC
: 4MC
: SMC
••• 30MC
246
10
12 14 16 18 20
number of water samples
22
24
26
28
30
Figure 3C-1. Comparison of Monte Carlo and Bootstrap results: Ratio of 90%
confidence limits for BAF as a function of numbers offish and water samples for PCB
congener 149 in Green Bay Zone 3 predator fish. Number of water samples are varied
across x-axis; number offish samples plotted as separate curves. Bootstrap resampling
results are plotted as solid lines; Monte Carlo results as dashed lines.
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number of fish
samples/analysis
method:
10
12 14 16 18 20
number of water samples
22
24
26
28
30
•—2B
= 4B
- 8B
™»""\™™» "^nR
""».:-v.:.™»»» OUD
-0--2MC
::: 4MC
.. SMC
... 30MC
Figure 3C-2. Comparison of Monte Carlo and Bootstrap results: Ratio of 90% confidence limits
for BAF as a function of numbers offish and water samples for PCB congener 149 in Green Bay
Zone 3 forage fish. Number of water samples are varied across x-axis; number offish 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
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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 (rib I 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.
number of concentrations sampled
2 fish/2 water
10 fish/10 water
- - * - - 30 fish/30 water
PQ
0.1 0.2 0.3 0.4 0.5 0.6 0.7
Concentration correlation coefficient
0.8
0.9
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.
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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 offish 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
Fish
concentration
CV
0.4
0.5
0.6
0.7
0.8
Water concentration coefficient of variation (CV)
0.4
3.54
4.21
4.86
5.74
6.68
0.50
4.10
4.74
5.41
6.38
7.35
0.60
4.90
5.56
6.40
7.37
8.51
0.70
5.62
6.33
7.26
8.34
9.40
0.80
6.66
7.41
8.34
9.38
10.67
Table 3C-2 is a tabulation of results for 4 fish and 4 water samples. For this case, highly-
variable 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
Fish
Concentration
CV
0.4
0.5
0.6
0.7
0.8
Water concentration coefficient of variation (CV)
0.4
2.47
2.74
3.11
3.49
3.92
0.50
2.77
3.06
3.39
3.79
4.33
0.60
3.07
3.41
3.75
4.18
4.64
0.70
3.49
3.80
4.20
4.59
5.12
0.80
3.89
4.23
4.65
5.02
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
0.4
0.5
0.6
0.7
0.8
Water concentration coefficient of variation (CV)
0.4
2.08
2.29
2.50
2.78
3.11
0.50
2.29
2.49
2.73
3.00
3.33
0.60
2.54
2.71
2.96
3.21
3.56
0.70
2.80
3.00
3.21
3.52
3.82
0.80
3.08
3.25
3.55
3.81
4.10
' 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 • •nv) 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
Fish
Concentration
CV
0.4
0.5
0.6
0.7
0.8
Water concentration coefficient of variation (CV)
0.4
3.01
3.57
4.32
5.05
6.06
0.50
3.32
3.87
4.60
5.49
6.31
0.60
3.64
4.22
4.99
5.79
6.87
0.70
4.09
4.71
5.40
6.30
7.36
0.80
4.53
5.16
5.94
6.95
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
Fish
Concentration
CV
0.4
0.5
0.6
0.7
0.8
Water concentration coefficient of variation (CV)
0.4
2.99
3.31
3.67
4.04
4.48
0.50
3.58
3.93
4.25
4.68
5.20
0.60
4.32
4.61
4.97
5.45
5.94
0.70
5.11
5.49
5.80
6.35
6.82
0.80
6.16
6.44
6.66
7.33
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 • S*ocw/K0w =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-l.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-lb). 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-lb) to the daily
chemical concentrations (Figure 3D-l.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 highly-
hydrophobic 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 offish, 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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C
0
0)
o
o
IS .2
II
§ E
U
TB
W
100
200
300 Log
Day
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 K0wS of 2 and 3, after scaling, are practically
identical; the log K0w 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, • ?0cw/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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Log KOW= 2
second series
second series
second series
LogKow =
first series
second series
= 5
first series
18 12 14
Number of Water Sampling Events
Figure 3D-2. Ratio of the 90th to 10th percentile bioaccumulation factors (BAFs) for field-
sampling designs consisting of daily water samples spaced 1 (• ), 3 (• ), 5 (• ), 7 (• ), 10 (line),
14 (•), 30.Q, 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 K0wS 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 K0ws 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 K0w 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 sediment-
water 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 offish) 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 site-
specific 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 sediment-
water-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
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)
The BSAF is defined as the ratio of the lipid normalized concentration of a chemical in
an organism sampled at trophic level /' to the organic carbon normalized concentration of the
chemical in surficial sediment (Ankley et al., 1992):
BSAF = <^t^i = ^ Equation 4-1
'' C I f C
^s ' J soc ^soc
where:
BSAFi = Biota-sediment accumulation factor for aquatic organism at trophic level /',
Ct = Chemical concentration in the organism [Mchem/Mtissue],
/.. = Lipid fraction of the organism,
Cs = Chemical concentration in surficial sediment [Mchem/Msediment], and
fsoc = Organic carbon fraction of the sediment.
In addition:
C.. = Lipid-normalized concentration of the chemical in whole fish [Mchem/Miipid],
and
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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 similarly-
behaving reference chemicals. Specifically, this method uses sediment-water concentration
quotients (• ?0cwS) for reference chemicals to estimate values of Cf 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 • ?0cw 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:
Site-Specific Baseline BAF = BSAF, \ —^—socw'r m'k - — Equation 4-2
I V -P "
The subscripts k and r refer to the chemical of interest and a reference chemical, respectively.
Also:
* s"ocw = Sediment-water concentration quotient [L3/M]
Dk/r = Ratio of the fugacity gradients (modeled as • jocw/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:
n
socw, r /^ fd
C} Equation 4-3
where:
CSOCir = concentration of a reference chemical in dry sediment, normalized to
sediment organic carbon
f 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 • ?0cw
should equal Kow. Therefore, • ?0cw/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//.
TT / JC
LLSocw,k ' *^ow,k
n IK
socw, r ovf,r
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 • ?OCw for the chemical of
interest directly. This alternative is discussed in Section 4.4. If a reasonably certain estimate of
• ?ocw is available for the chemical, the baseline BAF can be predicted directly from the BSAF
using this simplification of the Method 2 equation:
Baseline BAF, =BSAF! Usocw -- Equation 4-5
Je
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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 • ?OCw for reference chemicals,
it may be difficult or impossible to reliably estimate • ?ocw for the chemical of interest at the site.
EPA prefers equation 4-2 and the use of reference chemical data for • s»ocw 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 (* £ocw/K0w) 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 site-
specific baseline BAFs are shown for PCBs 52 and 105.
chemical
PCB 126
PCB 118
Log Kow
6.9
6.7
C..
12.3 ng/g-»
^soc
3.83 ng/g-soc
555 ng/g-soc
C/d
^w
34pg/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 lipid-
normalized concentrations of the chemical in 5 to 7-year-old lake trout (C.)
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 C..of PCB 126 in age 5-7 lake trout is 12.3 ng/g-
lipid, 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:
KSAF _ Q _
±***~J
-^ '-
King
g-oc
g-tipid
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 (* Vw) 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 • ?ocw, the concentration of reference chemical that is freely
dissolved in water (Of) 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 = 5.5x 106 (log Kow = 6.7).
Using equation 3-6, the freely dissolved fraction of PCB 118 in Lake Ontario
water is calculated as follows:
ffd = 1 / (1 + POC -Kw +0.08- DOC -
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):
1
1 + 0.08
2.0mg-DOC
I
5.5x10° —
kg
L kg
• = 0.53
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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, (Cf )iig = 0.53x34 pg/L = 18 pg/L or l.SxlO"5 • g/L.
The average (CSOc)ii8 = 555 * g/g-SOC (sediment organic carbon). By
substituting these values into equation 4-3, * |ocw for the reference chemical
(PCB 118) is calculated as:
C
y-y soc, r
- ~ ~ Equation 4-3
socw, r
''m C* kg-SOC i.8Kio-V*
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 • |OCW/KOW for each reference
chemical using equation 4-2:
Site-Specific Baseline BAF4>126 = BSAF D™'JI«*»-'K<>»'™
^
Since the loading histories and fugacity ratios of the chemical of interest (PCB
126) and the reference chemicals (PCBs 52, 105 and 1 18) are assumed to be
similar, Dj/r ~ 1 in equation 4-2. To complete the calculation to predict the site-
specific baseline BAF for PCB 126 using reference chemical PCB 118, the
appropriate Kow values for PCB 126 (7.8 x 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:
Site-Specific Baseline BAF4126 = 3.
(s.SxW6^-} 0.20
\ L '
=1.4 x 108L/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 3.7x 108 using congener 52 and 1,6x 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 2.0xl08 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 (BAF?):
Site Specific BAF(T, = (f( • Baseline BAF;+1) • ffd (rearranged Equation 3-4)
For PCB 126, the site-specific baseline BAF at trophic level 4 was
calculated to be 2.0xl08 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:
• = 0.35
* 10s»«r
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 site-
specific baseline BAF:
Site-Specific BAF^ = (o.20 • 2.0 x 10s-^7 + 1)-0.35 = 1.4 x!07Z/£g-
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 Kovi 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^Cow
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 Kovi, 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 • ?0cw, 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 • s*OCw. 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?
Similar bioaceumulation and partitioning
behavior
Similar hydrophobicity (Kow± 0.5)
Structural similarity
Similar loading history and duration is
preferred
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)?
Investigate detection limits of available analytical methods
Compare to expected chemical concentrations
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)?
DQO process (USEPA, 2000c)
Monte Carlo simulations
Bioaccumulation modeling
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4. Biota. Which species should I sample?
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
5. Site Definition. Have I adequately defined my site of interest in terms of spatial extent?
Spatial extent defined by home range of target species
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?
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)
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7. Spatial Variability (i.e., Number of Stations). How many locations should be sampled?
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)
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)?
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
9. Sediment Sample Type. What types of sediment samples should I collect?
Define depth of surficial sediment to sample
Individual grab samples vs. composites?
Chemical analysis requirements
See Section 4,6 for discussion of sediment sampling
10. Chemical Analytical Methods. Which analytical methods should I use?
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?
Appropriate methods depend on water body and organisms
12. Sediment Sampling Methods. How should sediment be sampled?
Appropriate methods depend on contaminant and water
body
Surficial samples vs. vertically-resolved cores
Select proper sampling device
13. Biota/Sediment Sampling Correspondence. How should I coordinate biota and sediment
sampling (e.g., concurrent vs. staggered sampling)?
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)
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14. Ancillary Measurements. What other parameters do I need to measure?
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
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 (• ?ocw) BE DETERMINED?
In Section 4.1, the sediment-water concentration quotient (• ?OCw) 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
• ?ocw, 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 • s*ocw : measuring
concentrations of one or more reference chemicals in water and sediment at the site; estimating
• socw /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 • jocw for a chemical of interest using a properly calibrated and confirmed fate and
transport model.
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Alternatives for determining
Measuring site-specific chemical concentrations in water and sediment for one or more
reference chemicals
Estimating • |ocw /Kovf as (fp0c/fsoc) by assuming steady state
Predicting « |ocw using transport and fate models
The distribution of a chemical between the sediment and overlying water at a site is
described by the sediment-water concentration quotient (• ?0cw), 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, • ?0cw 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 (• S*ocw/Kow) for the chemical diverges from a value of 1.0 (• s»ocw=
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 theKow. 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 • s*ocw 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 • ?0cw. 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
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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 • S*0cw/K0w 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 • s*ocw is based on measurements for
appropriate reference chemicals at the site. As described above, the factors that are primarily
responsible for causing • J0cw/K0w to vary tend to affect related chemicals in similar ways.
Reference chemicals with • S*ocw/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., • ?0cw,k/Kow,k ~ * socw,r/Kow,r) are not
available. Related nonionic organic chemicals, approximately at steady state, should have
similar, if not equal, values of • S*ocw/Kow that are related to the fp0c/fsoc ratio. When steady-state
conditions are not present, as is often the case, • S*ocw/Kow values for related chemicals may be
similar. The similarity of* ?0cw/K0w 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 • s»ocw:
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 (• s»ocw)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
(• socw/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 • ?OCw are identical to those for sampling
and measurement of Cf 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 Cf in systems with temporal fluctuations is the greatest
challenge in measuring • jocw.
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 • |ocw //Tow ~ fp0c/fsoc by Assuming Steady State
Over the long term, the asymptotic (steady state) value for • s»ocw /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, • ?0cw /Kow = 1 at
equilibrium. However, in natural systems the sediments and water column are almost never at
equilibrium and deviations of • ?OCw /Kow from 1 can be used to describe this disequilibrium (or
nonequilibrium) condition. When the disequilibrium is less than one (i.e., • ?OCw IKOVI < 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., • ?0cw /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., • jocw /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 (• jocw
/Kow= 1), but rather a • ?0cw /Kow of approximately the ratio of organic carbon content in
suspended and sediment particles.
The magnitude of the differences in sediment and water column paniculate 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 • s*OCw /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 • jocw /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 • ?OCw 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., • ?0cw) 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 paniculate, dissolved organic carbon, and freely
dissolved compartments was assumed, and paniculate 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 • s»ocw 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, • S*ocw/Kow reaches a plateau at a value of 4.91,
nearly equal to the fp0c/fsoc ratio.
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B>
a.
E 3
O
s
cT
100
80
60
40
20
7e+6
6e+6
5e+6
4e+6
3e+6
2e+6
1e+6
^
r
'-
'r
r
'-
'-
r
...,...,...,...,...,
^7 :
/
___ water, freely dissolved
•"•• Sediment
a
/ '
0.5
0.4
0.3
0.2
0.1
7
6
5
4
3
2
1
8
"5
o
20 40 60 80 100
Elapsed Time (years)
8
"5
o
B>
a.
5 3
O
120
100
80
60
40
20
0
1.4e+7
1.2e+7
1 .Oe+7
8.0e+6
6.0e+6
4.0e+6
2.0e+6
0.0
Water, freely dissolved
•••• Sediment
I I I | I I I | I I I | I I I | I I I
20 40 60 80 100
Elapsed Time (years)
0 20 40 60 80 100
Elapsed Time (years)
0.6
0.5
0.4
0.3
0.2
0.1
0.0
14
12
10
8
6
4
2
0
8
"5
>
3
o
Figure 4-1. The sediment-water concentration quotient (• ?0cw) for three different chemical loading scenarios: (a) constant loading of a
chemical to the ecosystem overtime, (b) a constant loading of chemical to the ecosystem with a doubling of loading at year 50, and (c)
a constant loading of chemical to the ecosystem with an 80% reduction in loading at year 50. Simulations performed for a chemical
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 • s*OCw 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 Cf , Csoc, and • s*ocw 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 B SAP 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 overtime 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 • ?0cw 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 Kp^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 K»w 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 offish 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 overtime 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
low
low
low
medium
medium
medium
high
high
high
Temporal
variability
high
medium
low
high
medium
low
high
medium
low
logKoW
< 3, 4 , 5, > 6
<4,>5
<3,>4
< 5, 6, > 7
<5,>6
<3,>4
all KOWS
all KoWs
all KoWs
Minimum number of sampling events3
8,5,2,1
2,1
2,1
8,4,1
4,1
2,1
9
4
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 (• ?OCw) 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 • s*OCw 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 • ?0cw, particularly for small sediment sample sizes (ns* 6).
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 • ?OCw,
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 • ?0cwS, 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 • s*OCw, 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 B SAP 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 • ?0cwS 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 fine-
grained 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 (USAGE),
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
• USAGE 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 nsocw 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 highly-
concentrated 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)
1
2
4
10
volume per
core (cm3)
45.6
91.2
182
456
number of cores
required to obtain
SOOmL volume
11
6
3
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/^ 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 field-
measured 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 site-
specific BAFs from field-measured BSAFs.
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GO
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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
for all (combined) sampled fish species
Method 2: Exceedance Levels and Comparison Statistics
Location
Green Bay
Zone 1
Zone 2a
Zone 3 a
Zone 3b
Zone 4
All zones
Hudson River
RM194
RM189
RM169
RM144
RM122
RM114
All stations
95%
0.39
0.25
0.21
0.16
0.31
0.22
0.46
0.33
0.11
0.67
0.70
1.20
0.13
Mean
0.88
1.27
1.25
1.08
3.33
1.53
1.12
1.00
2.01
1.19
2.43
3.86
1.50
Median
0.88
0.89
0.73
0.69
1.07
0.81
0.99
1.03
0.59
0.97
2.16
3.78
1.10
5%
1.66
3.47
3.78
2.71
3.79
3.29
2.12
1.55
9.91
2.14
4.81
6.91
4.42
% within
2x
87.6
69.8
51.5
53
31.9
55.7
81.9
87.5
19.0
92.3
45.8
16.7
64.9
% within
5x
100
92.8
94.1
91.4
97.4
94.5
95.2
100
68.3
100
95.8
83.3
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, sediment-
water 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 (IISOCW) 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 • ?ocw
Reliable measurements of* *0cwS 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 • s»ocw are not usually
performed.
In addition, combining sediment measurements from one study with water measurements from
another study can result in large biases in • jocws 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 • s»ocws 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 • s*ocws 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 • jocw-log Kow regressions from the different zones
were not significantly different among the five zones (comparison of slope test, • •= 5%).
Therefore, average • ?OCwS 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, • s*ocw is strongly dependent on the Kow, and slopes of slightly less than 1 were
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obtained. Examination of • ?0cwS for Lake Ontario and Hudson River reveals trends similar to
those in Green Bay; a strong dependence of • ?0cw 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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o>
o
o>
o
0)
o
O)
o
O)
o
Figure 4-3. Sediment-water column
concentration coefficient (• s*OCw) 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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o
o
W
O)
O
10
9
8
7
6
All Zones,
Congener Averages
LogK
8
ow
Figure 4-4. Average sediment-water column concentration coefficients (• ?0cw) 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 • ?0cw = A '"log KoW+ B) for
Polychlorinated Biphenyls (PCBs) and Chlorinated Pesticides
Ecosystem
Green Bay (PCBs)
Zone 1
Zone 2a
Zone 3 a
Zone 3b
Zone 4
All zones, congener averages
Hudson River (PCBs)
RM189
RM194
Lake Ontario
(PCBs and chlorinated pesticides)
Slope (±sd)
0.95 (±0.04)
0.92 (±0.09)
0.87 (±0.06)
0.83 (±0.06)a
0.86 (±0.08)
0.92 (±0.06)
0.87 (±0.08)
0.72 (±0.08)a
1.05 (±0.08)
Intercept (±sd)
1.21 (±0.22)
1.13 (±0.61)
1.61 (±0.36)
1.88 (±0.36)
1.31 (±0.53)
1.20 (±0.38)
1.81 (±0.45)
3. 16 (±0.42)
0.83 (±0.49)
n
46
31
63
60
46
77
32
27
55
r
0.97
0.82
0.86
0.85
0.76
0.82
0.86
0.84
0.84
3s:
0.17
0.34
0.37
0.33
0.46
0.43
0.13
0.16
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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10
o
o
l
O)
o
8
5
9
8
Hudson River Mile 189
Hudson River Mile 194
8
LogK
ow
Figure 4-5. Sediment-water column concentration coefficients (• ?OCw) 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 • s*OCwS and the largest
slope for the log • s*ocw-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 • ?0cw-Kow relationships observed in zone 1 data might be more
illustrative of the underlying • J0cw-K0w 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 • s*OCw-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 • ?0cw term), and measurement error in determining the
concentrations of chemical in the sediments and/or water column. The log • s*OCw-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 • s*ocw-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 • ?0cw/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 • ?0cw/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 • J0cw/K0w
ratios among the three ecosystems evaluated here illustrate the range of variability that occurs
among ecosystems across the nation. Because • ?0cwS are a function of both current and past
chemical loadings to the ecosystems, • ?0cw/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), • ?0cw/Kow ratios could become very large.
For new chemicals that are just being introduced or discharged into the environment, • S*ocw/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
• socw/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 • ?0cw/KoW Ratios for Three Different Ecosystems
Ecosystem
Green Bay (PCBs)
Zone 1
Zone 2a
Zone 3 a
Zone 3b
Zone 4
All zones, congener averages
Hudson River (PCBs)
RM189
RM194
Lake Ontario
(PCBs and chlorinated pesticides)
Overall average • ?0cw/Kow
Average Ratio Percentile
(±sd) 5% 10% 90%
9.15 (±4.97) 4.34 5.55 13.8
6.35 (±6.73) 1.24 1.37 13.1
10.3 (±13.3) 1.27 1.88 21.7
9.48 (±10.6) 1.68 2.00 20.1
4.49 (±6.68) 0.60 0.75 6.95
7.21 (±6.68) 1.01 1.76 13.3
14.3 (±8.98) 6.03 7.36 23.4
48.4 (±47.6) 18.9 22.6 69.5
23.4 (±25.1) 2.96 3.57 52.6
23.3 (±18.0)
95%
17.3
21.0
25.6
29.9
8.10
16.5
34.7
83.6
82.4
sd = standard deviation
Because the degradation rates for the observed PCBs and chlorinated pesticides in the
environment are extremely slow, the average • ?0cw/K0w 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 • J0cw/K0w
ratios that are different from those for the PCBs and chlorinated pesticides, and EPA believes
that the • ?0cw/K0w 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 • ?0cw 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 • ?0cwS equal to the chemical's Kow. Consequently, ecosystem models typically
characterize • ?0cw 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 • S*ocw/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
(• socw constant). With continued loading, sediment contamination increases until a steady-state
condition is reached (• s*ocw constant) and the • J0cw/K0w 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 • ?0cw/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 • ?0cw/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,
* SOCW/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 Cf - is common to both methods. Temporal changes in Cf are responsible for
most deviations from steady state between biota, water, and sediments. The magnitude of errors
associated with fluctuations in Cf 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 • s*ocw 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 • ?OCw 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 • ?0cw 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 • S*ocw/Kow from reference chemicals to estimate
Cf s for chemicals of interest under Method 2 have a linear impact on the accuracy of baseline
BAFs. For example, if • S*ocw/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 • ?0cw/Kow to date
indicate an expected range of 5-40 for most contamination scenarios. If the data quality
considerations for choosing • Jocw/Kow for the chemical of interest are followed, the magnitude of
the errors associated with the choice of • ?0cw/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
• socw/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 • ?0cw/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 1 Ix, 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 • S*ocw/Kow approximated by the ratio of
organic carbon contents (foc^ater/foc^ediment) assumes that the makeup of organic carbon in the
water column and sediment are similar. Gschwend and others (Gustafson et al. 1997; Accardi-
Dey 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 • S*ocw/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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both absorption into organic carbon and adsorption onto black carbon. Environ. Sci. Technol. 37,
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Ankley GT, Cook PM, Carlson AR, Call DJ, Sorensen JA, Corcoran HF and RA Hoke. 1992.
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Bierman, V.J., Jr. 1990. Equilibrium partitioning and biomagnification of organic chemicals in
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Burkhard, L.P., D.E. Armstrong and A.W. Andren. 1985. Partitioning behavior of
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Burkhard, L.P. 1998. Comparison of two models for predicting bioaccumulation of hydrophobic
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Burkhard, L.P. 2003. Factors Influencing the Design of Bioaccumulation Factor and
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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.
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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
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Campfens J, Mackay D. 1997. Fugacity-based model of PCB bioaccumulation in complex
aquatic food webs. Environ Sci Technol. 31:577-583.
Chapra, SC. 1997. Surface Water-Quality Modeling. WCB/McGraw-Hill, Boston, MA.
Chiou, C.T. and D.E. Kile. 1998. Deviations from Sorption Linearity on Soils of Polar and
Nonpolar Organic Compounds at Low Relative Concentrations. Environ. Sci. Technol. 32, 338.
Cornelissen, G., Gustafsson, O., Bucheli, T.D., Jonker, M.T.O., Koelmans, A.A. and P.C.M. Van
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DePinto J.V., Lui, S., Young, T.C.Y. and Booty, W.G 1998. Development of LOTOX2: Solids
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ecosystem to the virtual elimination of PCBs. Environ. Sci. Technol. 29(8):2038-2046.
Gobas FAPC, MacLean LG. 2003. Sediment-water distribution of organic contaminants in
aquatic ecosystems: The role of organic carbon mineralization. Environ Sci Technol 37:735-741.
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Gustafsson O, Haghseta F, Chan C, MacFarlane J, Gschwend PM. 1997. Quantification of the
dilute sedimentary soot phase: Implications for PAH speciation and bioavailability. Environ Sci
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Karickhoff, S.W. and K.R. Morris. 1985. Sorption Dynamics of Hydrophobic Pollutants in
Sediment Suspensions. Environ. Toxicol. Chem. 4,469.
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seven laboratory-spiked sediments. Environ. Sci. Technol., 37, 4656 - 4663.
Lake, J.L.; Rubinstein, N.; Lee II, H.; Lake, C.A.; Heltshe, J.; Pavignano, S. 1990. Accumulation
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Chemicals in Aquatic Systems; Dickson, K.L., Maki, A.W., Brungs, W.A., Eds.; Pergamon: New
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Service. 584 pp.
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McElroy, A.E.; Means, J.C. 1988. In Aquatic Toxicology and Hazard Assessment, Vol. 10;
Adams, W.J., Chapman. G.A., Landis, W.G., Eds.; American Society for Testing and Materials:
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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 • Jocw/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 ^ows 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.
(0
CO
i
'••
°> 1.6
O 1,4
•S 1-2
I 1.0
0,8
0.6
0,4
0.2
0,0
LogKow =
Log KQW ~ 4 ..
Log
Log KOW = 6 •-
Log KOW = 7 ;-
Log KOW = 8 ;-
Log KOW = 9
10 12 14 0 2 4 6 B 10 12 14 0 2 4 6 8 10 12 14 0 2 4
Number of Sampling Events
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 (• ), 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 benthic-
pelagic food web and (b) • J0cw/K0w = 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 (• ?0cw) 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 site-
specific 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 log-
transformed 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 (rib),
sediment (ns} and water (nw) concentrations were sampled and averaged, to simultaneously
compute BSAFs, • s*ocws and predicted BAFs. This procedure was repeated many times, until
stable distributions of BSAF, • s»ocw 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,
• socw, 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 «&, 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, lipid-
normalized 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 BSAF s, • ?OCwS and BAFs in each simulation.
How is the Uncertainty of BSAF and • ?ocw Affected by the Number of Sediment Samples
and Different Chemical Concentration Variances?
The uncertainty of BSAFs and • s*OCw 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 • jocw. 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 • jocw 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 • s*ocw , up to a sample size of 6. Collecting
additional sediment samples (i.e., greater than 10) has little effect on the uncertainty of • s*ocw s.
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o
'I
+J
|
o
o
o
GO
PQ
18
16 ^
14
12 -
10 -
8 -
6 -
4
2 -J
0
0
10
15
20
25
30
35
Number of sediment samples
Figure 4B-1. 90% confidence interval ratio for B SAP 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).
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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
2
4
6
10
30
CV=0.6
6.43
5.12
4.68
4.34
4.00
CV=0.9
9.62
6.53
5.60
4.88
4.17
CV=1.2
14.2
8.68
6.98
5.68
4.43
CV=1.5
19.5
10.8
8.34
6.48
4.71
B. Chemical Concentrations Measured in 4 Fish Samples
Number Of Sediment
Samples
2
4
6
10
30
CV=0.6
5.05
3.82
3.42
3.10
2.77
CV=0.9
7.99
5.11
4.26
3.59
2.93
CV=1.2
12.0
7.00
5.49
4.32
3.18
CV=1.5
16.7
8.91
6.74
5.06
3.42
C. Chemical Concentrations Measured in 6 Fish Samples
Number Of Sediment
Samples
2
4
6
10
30
CV=0.6
4.59
3.40
3.01
2.69
2.36
CV=0.9
7.44
4.68
3.83
3.17
2.52
CV=1.2
11.4
6.47
5.00
3.87
2.77
CV=1.5
16.1
8.43
6.26
4.58
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
2
4
6
10
30
CV=0.6
4.24
3.06
2.67
2.35
2.02
CV=0.9
7.02
4.34
3.50
2.84
2.19
CV=1.2
10.8
6.04
4.63
3.53
2.43
CV=1.5
15.3
7.97
5.84
4.20
2.67
E. Chemical Concentrations Measured in 30 Fish Samples
Number Of Sediment
Samples
2
4
6
10
30
CV=0.6
3.89
2.72
2.33
2.00
1.64
CV=0.9
6.59
4.01
3.19
2.53
1.83
CV=1.2
10.3
5.65
4.27
3.18
2.09
CV=1.5
15.0
7.47
5.48
3.87
2.33
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s a
"S -^
.0
(L)
GO
18
16
14
12
10-
8
6-
4
2
10 15 20 25
Number of sediment samnles
30
35
Figure 4B-2. 90% confidence interval ratio for • s»ocw as function of number of sediment
samples. (Average results for 4 PCB congeners are plotted; • ?0cwS were calculated using 6
water samples drawn from Green Bay data).
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Table 4B-2. 90% Confidence Interval Ratios for • socw as Function of the Variability in
Chemical Concentrations in Sediment
A. Chemical Concentrations Measured in 2 Water Samples
Number Of
Sediment Samples
2
4
6
10
30
CV=0.6
7.46
6.04
5.55
5.18
4.82
CV=0.9
11.2
7.76
6.68
5.86
5.04
CV=1.2
15.7
9.70
7.89
6.52
5.24
CV=1.5
21.9
12.2
9.51
7.49
5.56
B. Chemical Concentrations Measured in 4 Water Samples
Number Of
Sediment Samples
2
4
6
10
30
CV=0.6
5.52
4.25
3.83
3.50
3.18
CV=0.9
8.58
5.72
4.79
4.07
3.37
CV=1.2
12.6
7.39
5.85
4.70
3.56
CV=1.5
17.9
9.71
7.27
5.53
3.85
C. Chemical Concentrations Measured in 6 Water Samples
Number Of
Sediment Samples
2
4
6
10
30
CV=0.6
4.91
3.69
3.28
2.96
2.63
CV=0.9
7.81
5.06
4.19
3.51
2.82
CV=1.2
11.7
6.71
5.21
4.10
3.02
CV=1.5
16.8
8.99
6.67
4.92
3.28
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Table 4B-2 (continued). 90% Confidence Interval Ratios for • socw as Function of the
Variability in Chemical Concentrations in Sediment
D Chemical Concentrations Measured in 10 Water Samples
Number Of
Sediment Samples
2
4
6
10
30
CV=0.6
4.43
3.23
2.84
2.52
2.19
CV=0.9
7.23
4.56
3.72
3.06
2.38
CV=1.2
11.0
6.17
4.73
3.63
2.58
CV=1.5
15.8
8.25
6.10
4.42
2.86
E Chemical Concentrations Measured in 30 Water Samples
Number Of
Sediment Samples
2
4
6
10
30
CV=0.6
3.95
2.78
2.39
2.06
1.71
CV=0.9
6.66
4.08
3.26
2.60
1.92
CV=1.2
10.3
5.69
4.30
3.21
2.12
CV=1.5
15.1
7.60
5.52
3.95
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.
o
"ro
1 -
20
5?
in
5?
CD
O
c
CD
T3
C IS
O
CD
CQ
o
2 10
5
10 15 20 25
number of sediment samples
30
35
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.
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o
'-t-t
CO
O
o
CD
<
CQ
25
20
= 15
CD
O
C
CD
T3
10
5
0
10 15 20 25
number of sediment samples
30
35
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.
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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 • ?0cw, particularly for small sediment sample sizes (ns* 6). For Method
2 BAFs, the results are similar to those for BSAF and • ?0cw, 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, • jocw 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 offish 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 sub-
tables 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 • s*ocws, 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.
Ratio of Confidence Limits for Method 2 BAF Predictions for PCBcongener 149
average BAF for 3 reference chemicals & varying sediment concentration CVs
60 ,
50-
g 40
"ro
8
30
20-
O 10
O
CD
GO
10
15
20
25
30
35
Number of sediment samples
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.
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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
2
4
6
10
30
CV=0.6
15.5
11.3
9.96
8.96
8.01
CV=0.9
27.6
16.0
12.8
10.5
8.36
CV=1.2
47.2
22.9
16.9
12.7
9.11
CV=1.5
75.3
33.7
23.2
16.1
10.0
B. Chemical Concentrations Measured in 4 Fish and 4 Water Samples
Number Of
Sediment Samples
2
4
6
10
30
CV=0.6
10.5
7.18
6.17
5.41
4.65
CV=0.9
20.0
11.0
8.47
6.64
4.96
CV=1.2
35.9
16.4
11.7
8.4
5.55
CV=1.5
60.9
25.4
16.8
11.1
6.29
C. Chemical Concentrations Measured in 6 Fish and 6 Water Samples
Number Of
Sediment Samples
2
4
6
10
30
CV=0.6
9.05
5.98
5.06
4.34
3.64
CV=0.9
17.9
9.46
7.2
5.5
3.96
CV=1.2
32.6
14.6
10.1
7.1
4.48
CV=1.5
56.3
22.8
14.9
9.61
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
2
4
6
10
30
CV=0.6
7.95
5.07
4.2
3.53
2.87
CV=0.9
16.2
8.33
6.2
4.65
3.19
CV=1.2
30.0
13.1
8.97
6.12
3.67
CV=1.5
53.1
20.7
13.3
8.49
4.33
E. Chemical Concentrations Measured in 30 Fish and 30 Water Samples
Number Of
Sediment Samples
2
4
6
10
30
CV=0.6
6.87
4.2
3.39
2.74
2.08
CV=0.9
14.7
7.25
5.25
3.82
2.44
CV=1.2
27.6
11.7
7.87
5.22
2.87
CV=1.5
49.6
18.8
11.8
7.29
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 • s*OCw, the 0.5 correlation reduced
the CLRby 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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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.
25 ^
.9 o-l
"ro
E
= 5
§
CD
T3
O
O
0
O
CD 5-j
LL
<
CQ
uncorrelated concentrations
within-chemical correlation
-within-media correlation
10
15
20
25
30
35
Number of sediment samples
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.
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Appendix 4C
Green Bay Mass Balance PCB Congener Concentrations:
Organic Carbon-Normalized Surficial (0-1 cm) Sediment
year
1989
1989
1990
1988
1987
1988
1988
1988
1988
1989
1988
1987
1990
1988
1988
1987
1987
1987
1987
1989
1987
1989
1987
1987
1987
1987
1987
1988
1988
1988
1989
1987
1988
1989
1987
1989
1988
1987
1988
1988
zone
GBOZ3B
GBOZ3A
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3A
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3A
GBOZ3A
GBOZ3B
GBOZ3B
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3B
GBOZ3B
GBOZ3B
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3B
GBOZ3A
GBOZ3B
GBOZ3A
GBOZ3A
GBOZ3A
GBOZ3B
GBOZ3B
sediment
station
58
54
52
48
47
44
43
43
42
40
39
39
38
33
32
32A
32A
31
30
27
27A
26
24
22
22
22
21
20
20
18
17
17
16
13
12
11
10
10A
9
8
percent organic
carbon
5.09
5.82
4.78
9.53
NA
7.76
9.28
9.28
8.49
3.83
9.03
9.03
8.27
8.23
8.08
7.39
7.39
7.41
7.89
7.95
6.99
8.01
6.39
6.52
6.52
6.52
7.82
6.83
6.83
7.51
7.54
7.54
6.91
4.12
5.56
0.17
1.51
3.84
4.43
5.45
PCB 18
(ng/g-SOC)
41
33
37
40
22
76
113
61
40
110
132
75
66
69
55
134
198
169
171
150
228
240
238
173
304
237
296
243
85
139
271
416
177
545
155
262
326
315
505
PCB52(ng/g-
SOC)
61
48
54
59
27
117
147
108
57
155
155
134
115
90
105
229
284
223
223
275
6.4*
289
367
336
414
314
339
199
141
207
357
628
183
755
28*
344
381
483
702
PCB 149 (ng/g-
SOC)
21
20
18
25
8.6
45
51
41
18
50
47
34
43
31
29
31
86
62
71
67
107
67
78
55
134
83
71
38
40
49
84
123
38
143
94
80
84
95
126
PCB 180 (ng/g-
SOC)
12
14
14
28
11
47
31*
43
14
31
49
6.2
17
17
9.1
15*
72
7.6
38
33
32
31
26*
87
58*
77
97
19
38
38
72
97
28
139
73*
69
109
104
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. 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:
1 Extrapolating site-specific BAFs from BSAFs measured at another
site (Method 3, with 2 options)
3a. BSAF extrapolation
3b. BEF extrapolation
1 Predicting BAFs using a BCF coupled with food chain multipliers
(Method 4, with 2 options):
4a. Laboratory-measured BCFs
4b. BCFs estimated using Kows
1 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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because these estimation methods may not capture all of the site-, chemical- and/or species-
specific 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, • ?0cw/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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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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Site-Specific BAF Method 3
Extrapolating site-specific BAFs from BSAFs measured
at another site, with 2 options:
3a. BSAF extrapolation
3b, BEF extrapolation
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Figure 5-1. Decision framework for selecting a site-specific BAF derivation method based on
BSAFs
Has a BSAF been determined for the
chemical of interest (COI) at the site?
Method 2
Has a BSAF been determined for a
reference chemical (RC) at the site?
Has a BEF been determined
for the RC and COI at a
reference site?
Has a BSAF been determined
for the COI at a reference site?
Method 3h
Method 3a
Determine a BEF for
the RC and COI at a
reference site, and apply
MethodSb; or, select a
different method
Collect additional
data (e.g., determine
a BSAF for the COI
at the site); or, select
a different method
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,
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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, • ?0cw, 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 offish 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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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/?,/? '-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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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.
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10
w
00
0.1
w
00
0.1
w
00
0.1
BSAFs for white suckers at various sites
White Sucker
from 413345072531001
r = 95%
slope = 0.64 •-0.08 (6)
p = 0.99 a = 0.0003
O
WhiteSucker
frbm 402108076363701
r = 74%
slope = 0.78--0.18 (8)
p = 0.67 a = 0.071
WhiteSucker
from 01191000
r = 93%
slope = 0.57 •-0.09 (6)
p = 0.94 a = 0.0048
White Sucker
from 400037076423701
r = 92%
slope = 1.58 "0.21 (8)
p = 0.90 a = 0.0020
White Sucker
from 01171500
O
r = 95%
slope = 0.70 • -0.09 (6)
p = 0.83 a = 0.042
White Sucker
from 01573560
r = 27%
slope = 0.96 • -0.35 (7)
p = 0.29 a = 0.53
100
10
V)
00
0.1
0.01
10
w
00
0.1
10
w
00
0.1
0.1 1
BSAFs for white suckers
from sampling station 01208869
0.1 1
BSAFs for white suckers
from sampling station 01208869
10
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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OT
00
OT
00
10
1
0.1
0.01
0.001
0.0001
0.00001
1
0.1
0.01
0.001
0.0001
Tokyo Bay
Sea Bass (12)
0.00001
r = 93%
slope = 1.05 •-0.095 (16) ,
p = 0.94 a = <0.0001
Lake Shinji
Sea Bass (11)
0.0001
r = 96%
slope = 1.20 ••0.089 (16) f.
p = 0.96 a = <0.0001
Ya-Er Lake (13-15)
Carp, ponds 3,4 & 5
r = 83%
slope = 0.580 •-0.083 (15)
p = 0.80 a =0.0003
Lake Kasumigaura
Carp (10)
r = 92%
slope =1.05 ••0.099 (16)
p = 0.93 oc= <0.0001
10
1
0.1
0.01
0.001
0.0001
0.00001
1
0.1
0.01
0.001
0.0001
0.00001
OT
00
OT
00
0.001
0.01
0.1
0.0001
0.001
0.01
0.1
BSAFs for 6 year old lake trout
southern Lake Michigan (8)
BSAFs for 6 year old lake trout
southern Lake Michigan (8)
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.
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100
10
LL.
SS
m
0.1
10
0.1
10
w i
m
0.1
Green Bay, Zone 1
Adult Alewife (6)
r = 82%
slope = 0.97--0.13(18)
p = 0.76 a = 0.0002
Green Bay, Zone 1
Walleye, 4 yrs (6)
r = 80%
slope = 1.04--0.15 (18)
p = 0.65 a = 0.0033
Green Bay, Zone 3b
Walleye, 4 yrs (6)
r = 92%
slope = 1.29--0.13(16)
p = 0.84
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(A
LL
00
100
10
1
0.1
0.01
0.001
0.0001
Lake Ontario
Lake Trout (7)
r = 96%
slope = 0.79 • -0.041 (32)
p = 0.96 a = <0.0001
Tokyo Bay
Sea Bass (9)
0.0001 0.001 0.01
0.1
1
10
slope =1.13 "0.045 (26)
1000
100
10
1
0.1
0.01
0.001
100 0.001
0.01
10
100
BSAFs for 6 year old lake trout
southern Lake Michigan (8)
BSAFs for 6 year old lake trout
southern Lake Michigan (8)
(A
LL
CO
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), PCB-
126 (purple), PCB-156 (turquoise), PCB-167 (red), and PCB 169 (green).
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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 (• ?OCw) for the site to obtain a trophic level-specific
baseline BAF:
Baseline BAF, = BSAF,; Usocw -— (Equation 5-1)
Ji
The terms in this equation are defined as follows:
Baseline BAF, = Baseline BAF estimated for the site and the chemical of
interest, for organism at trophic level /' (defined in Equation
3-2);
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BSAFy = BSAF for chemical of interest for organism at trophic level /
extrapolated from ecosystemy (defined in Equation 4-1);
Ilsocw = Sediment-water concentration quotient for the site and the
chemical of interest (defined in Equation 4-3);
ff = Lipid content (fraction) of the target species or tissue.
In most cases, • s*OCw 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 • ?0cw for reference
chemicals are used in Method 2. Equation 5-2 is used to calculate the site-specific baseline BAF
for chemical k, when • jocw is based on measurements for reference chemicals r.
Baseline BAF!,=BSAF! . Dklr "^ K™-k - — (Equation 5-2)
K*».r ft
where:
Dk/r = Ratio of the fugacity gradient (modeled as • s*OCw/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 • ?OCw) 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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variability in BSAFs. The only site-specific adjustment made by the investigator using Method 3a
involves selecting a value for • jocw, 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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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)
6 year old
8 year old
9 year old
BSAF for PCB 101
7.53
6.40
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, • |ocw 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 (* ?ocw,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
52
105
• •
socw
4.24xl06
2.37xl07
logK™
5.84
6.65
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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 * focw is based on measurements for reference chemicals r:
BaselineBAFa =BSAF;/Dt"^T"Ka"* - —
Jt
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:
D n K i
Baseline BAF4101 = BSAF4 S2 —- - —-
Kow,5, Jt
A)(4.24xl06)(2.4Qxl06)
^ - - - L - 1
-. - - -- —
(6.92xl05) 0.11
Likewise, a site-specific baseline BAF for PCB 101 can also be calculated
with reference chemical PCB 105:
Baseline BAF4101 = BSAFA m
K -f
J t
(I)(2.37xl07)(2.40xl06) i
= (7.71)^ -. ^^ }- — = 8.36xlQ7L/fe-I
V ; (5.25xl06) 0.11
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
9.73xl07L/kg-lipid.
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r, the ratio of the fugacity gradient (• ?0cw/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 • jocw, 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 • jocw 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 (Percaflavescens) 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 5-1 A 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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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 site-
specific 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 3 a 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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Table 5-1. Method 3a BSAF Extrapolation Example Using PCS 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
18
28/31
52
110
118
149
180
174
196/203
Log
•">QW
5.24
5.67
5.84
6.48
6.74
6.67
7.36
7.11
7.65
LMLT6
BSAF
1.38
1.50
5.16
4.75
5.57
9.05
11.8
8.30
9.15
GBBT3
BSAF
3.29
4.15
23.2
13.4
11.0
23.5
13.9
14.2
16.4
BSAF
Extrapolation
Error
-58%
-64%
-78%
-65%
-49%
-61%
-15%
-42%
-44%
* focw
Measured in
Green Bay
Zone 4
l.llxlO6
3.89xl06
2.00 xlO6
7.33 xlO6
2.99 xlO7
3.10xl06
1.32 xlO7
9.64 xlO6
8.12xl07
Predicted
Site-specific
Baseline BAF
1.53 xlO6
5.83xl06
1.03 xlO7
3.48 xlO7
1.66 xlO8
2.81 xlO7
1.56 xlO8
8.00 xlO7
7.43 xlO8
Site-specific
Log Baseline
BAF
6.18
6.77
7.01
7.54
8.22
7.45
8.19
7.90
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
18
28/31
52
110
118
149
180
174
196/203
Log
"-OW
5.24
5.67
5.84
6.48
6.74
6.67
7.36
7.11
7.65
LMLT6
BSAF
1.38
1.50
5.16
4.75
5.57
9.05
11.8
8.30
9.15
GBW4
BSAF
2.67
2.99
26.5
16.1
14.3
22.2
39.2
18.7
20.3
BSAF
Extrapolation
Error
-48%
-50%
-81%
-70%
-61%
-59%
-70%
-56%
-55%
socw
Measured in
Green Bay
Zone 4
l.llxlO6
3.89xl06
2.00 xlO6
7.33 xlO6
2.99 xlO7
3.10xl06
1.32 xlO7
9.64 xlO6
8.12 xlO7
Predicted
Site-specific
Baseline BAF
1.53 xlO6
5.83xl06
1.03 xlO7
3.48 xlO7
1.66 xlO8
2.81 xlO7
1.56 xlO8
8.00 xlO7
7.43 xlO8
Site-specific
Log Baseline
BAF
6.18
6.77
7.01
7.54
8.22
7.45
8.19
7.90
8.87
5-20
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Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors
Section 5 EPA Review Draft September 4, 2007
Table 5-1 (Continued). Method 3a BSAF Extrapolation Example Using PCS 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
18
28/31
52
110
118
149
180
174
196/203
Log
•">QW
5.24
5.67
5.84
6.48
6.74
6.67
7.36
7.11
7.65
LMLT6
BSAF
1.38
1.5
5.16
4.75
5.57
9.05
11.8
8.3
9.15
GBW4
BSAF
0.58
0.47
2.07
2.05
3.15
3.06
2.92
3.70
ND
BSAF
Extrapolation
Error
140%
216%
149%
131%
77%
196%
304%
124%
• *
socw
Measured in
Green Bay
Zone 1
1.57 xlO6
5.64 xlO6
3.98 xlO6
1.89 xlO7
4.87 xlO7
2.51 xlO7
1.68 xlO8
5.36 xlO7
ND
Predicted
Site-specific
Baseline BAF
2.16 xlO6
8.47 xlO6
2.06 xlO7
8.98 xlO7
2.71 xlO8
2.27 xlO8
1.99 xlO9
4.45 xlO8
Site-specific
Log Baseline
BAF
6.34
6.93
7.31
7.95
8.43
8.36
9.30
8.65
D. Extrapolating Lake Michigan Lake Trout (LM LT6) BSAFs to Largemouth Bass at
Hudson River mile 189 (RM 189 LMB)
PCB
Congener
18
28/31
52
110
118
149
180
174
196/203*
Log
KOW
5.24
5.67
5.84
6.48
6.74
6.67
7.36
7.11
7.65
LMLT6
BSAF
1.38
1.50
5.16
4.75
5.57
9.05
11.8
8.30
9.15
RM189
LMB BSAF
1.29
2.46
6.56
11.5
17.1
18.4
24.1
20.0
30.4
BSAF
Extrapolation
Error
7%
-39%
-21%
-59%
-67%
-51%
-51%
-59%
-70%
• •
socw
Measured
at Hudson
River Mile
189
3.07 xlO6
6.18 xlO6
5.24 xlO6
1.85xl07
ND
ND
ND
ND
ND
Predicted
Site-specific
Baseline BAF
4.23 xlO6
9.27 xlO6
2.70 xlO7
8.77 xlO7
Site-specific
Log Baseline
BAF
6.63
6.97
7.43
7.94
5-21
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Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors
Section 5 EPA Review Draft September 4, 2007
Table 5-1 (Continued). Method 3a BSAF Extrapolation Example Using PCS 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
18
28/31
52
110
118
149
180
174
196/203*
Log
"OW
5.24
5.67
5.84
6.48
6.74
6.67
7.36
7.11
7.65
LMLT6
BSAF
1.38
1.50
5.16
4.75
5.57
9.05
11.8
8.30
9.15
RM194
LMB BSAF
0.368
0.921
2.82
4.11
5.77
6.36
7.28
5.23
8.92
BSAF
Extrapolation
Error
275%
63%
83%
15%
-3%
42%
62%
59%
3%
SOCW
Measured
at Hudson
River Mile
194
1.04 xlO7
1.60 xlO7
1.60 xlO7
5.32 xlO7
ND
ND
ND
ND
ND
Predicted
Site-specific
Baseline BAF
1.43 xlO7
2.40 xlO7
8.25 xlO7
2.52 xlO8
Site-specific
Log Baseline
BAF
7.16
7.38
7.92
8.40
5-22
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Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors
Section 5 EPA Review Draft September 4, 2007
100
10 -
m
T3
-S
JS
o
Q.
co
1 -
0.1
GB z4 BT3
GB z4 W4
GB z1 W4
HRRM189LMB
HRRM194LMB
5x
2x
-1:1 line
1/2
1/5
0.1
10
100
measured BSAF
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; zl = 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.
5-23
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Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors
Section 5 EPA Review Draft September 4, 2007
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 • s*OCw for the chemical of interest k at the site:
Baseline BAF, = BSAF,, BEF IT t (Equation 5-3)
/ i,r j yT socw,K r
J i
As was the case for Method 3a, • ?OCw 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 • ?ocw 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 • jocw is based on measurements for reference chemicals r.
Baseline BAF. = BSAF. r BEF,. klr Dklr U*ocwr K™>k - — (Equation 5-4)
m-r ft
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 BEF/,*/,. between the chemical of interest k and the
reference chemical r at another site (j) is:
BSAF t (Equation 5-5)
~
BSAF,.,
where:
BSAF for chemical of interest k determined by measurements at
another site j, and
5-24
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Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors
Section 5 EPA Review Draft September 4, 2007
BSAFyr = BSAP for reference chemical r determined by measurements at
another site/
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:
BSAF, r = BSAF for reference chemical determined by measurements of
organism at trophic level / at the site (equation 4-1);
• 'ocw.k = Sediment-water concentration quotient for the site and the chemical
of interest, / (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 • s*ocw) 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 • jocw, 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 (» |ocw) is available for the chemical of
interest at the site. PCB 52 was chosen as a reference chemical for this example;
both • |ocw (4,24x 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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Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors
Section 5 EPA Review Draft September 4, 2007
Extrapolating a Site-Specific BAF From BEFs
Determined at Another Site (Method 3b, continued)
Lake Trout Composite
Sample (age)
6 year old
8 year old
9 year old
BSAF for
PCB89
4.80
4.59
6.21
BSAF for
PCB52
5.16
5.34
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:
(BEF,) =
V ] /k/r
(BSAF,),
(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,,,)
V LM / 89/52
89
BSAF^J
52
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)
6 year old
8 year old
9 year old
BIOACCUMULATION
EQUIVALENCY FACTOR
(BEF)
0.929
0.860
0.890
5-26
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Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors
Section 5 EPA Review Draft September 4, 2007
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 » jocw is based on measurements for reference chemical r and the BEFj;k/r
is extrapolated from another site:
D IT K 1
Baseline BAF, = BSAFiirBEFM/r klr ^w'r ow'k -—
^aw,r Je
The Kow of the chemical of interest, PCB 89, is 1.17x 1Q6; 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:
D n K i
T\ 1' T-* A T-" net A T^ Tir-'T-' 89/52 SO'(W,S2 GfF;89 •*•
Baseline BAF, = BSAF t)1BEF mil!>m —
aw,si J &
. .. V(l)(4.24xl0
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Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors
Section 5 EPA Review Draft September 4, 2007
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 lexicologically 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.
5-28
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Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors
Section 5 EPA Review Draft September 4, 2007
Table 5-2. TCDD Bioaccumulation Equivalency Factors (BEFs) Derived For
lexicologically Important PCDDs And PCDFs From Lakewide Averages Of
Concentrations In Lake Ontario Lake Trout And Surface Sediment In
Depositional Areas.
Congener
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Log KoWa
7.02
7.50
7.80
7.80
7.80
8.20
8.60
6.5b
7.0b
7.0b
7.5b
7.5b
7.5b
7.5b
8.0b
8.0b
8.80
BSAF
0.059
0.054
0.018
0.0073
0.0081
0.0031
0.00074
0.047
0.013
0.095
0.0045
0.011
0.040
0.037
0.00065
0.023
0.001
TCDD BEF
1.0
0.92
0.31
0.12
0.14
0.051
0.012
0.80
0.22
1.6
0.076
0.19
0.67
0.63
0.011
0.39
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.
5-29
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Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors
Section 5 EPA Review Draft September 4, 2007
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 5-3 A 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).
5-30
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Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors
Section 5 EPA Review Draft September 4, 2007
Table 5-3. Method 3b BEF Extrapolation Example Using PCS 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
18
28/31
52
110
118
149
180
174
196/203
Log
"OW
5.24
5.67
5.84
6.48
6.74
6.67
7.36
7.11
7.65
LM
LT6
BSAF
1.38
1.50
5.16
4.75
5.57
9.05
11.8
8.30
9.15
GBBT3
BSAF
11.0
BEF
(PCB
118)
0.248
0.269
0.926
0.853
1.00
1.62
2.12
1.49
1.64
Method 3b
BSAF
Prediction
2.73
2.96
10.2
9.38
17.9
23.3
16.4
18.1
Measured
GBBT3
BSAF
3.29
4.15
23
13
24
14
14
16
Method 3b
BSAF
Error
-17%
-29%
-56%
-30%
-24%
68%
15%
10%
socw
Measured
in Green
Bay Zone
4
l.llxlO6
3.89xl06
2.00 xlO6
7.33 xlO6
2.99 xlO7
3.10xl06
1.32 xlO7
9.64 xlO6
8.12 xlO7
Predicted
Site-specific
Log Baseline
BAF
6.48
7.06
7.31
7.84
7.74
8.49
8.20
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
18
28/31
52
110
118
149
180
174
196/203
Log
**>QW
5.24
5.67
5.84
6.48
6.74
6.67
7.36
7.11
7.65
LM
LT6
BSAF
1.38
1.50
5.16
4.75
5.57
9.05
11.8
8.30
9.15
GBW4
BSAF
14.3
BEF
(PCB
118)
0.248
0.269
0.926
0.853
1.00
1.62
2.12
1.49
1.64
Method 3b
BSAF
Prediction
3.54
3.85
13.2
12.2
23.2
30.3
21.3
23.5
Measured
GBW4
BSAF
2.67
2.99
26.5
16
22
39
19
20
Method 3b
BSAF
Error
33%
29%
-50%
-24%
5%
-23%
14%
16%
socw
Measured
in Green
Bay Zone
4
l.llxlO6
3.89xl06
2.00 xlO6
7.33 xlO6
2.99 xlO7
3.10xl06
1.32 xlO7
9.64 xlO6
8.12 xlO7
Predicted
Site-specific
Log Baseline
BAF
6.59
7.18
7.42
7.95
7.86
8.60
8.31
9.28
5-31
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Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors
Section 5 EPA Review Draft September 4, 2007
Table 5-3 (Continued). Method 3b BEF Extrapolation Example Using PCS 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)
PCS
Congener
18
28/31
52
110
118
149
180
174
196/203
Log
J^ow
5.24
5.67
5.84
6.48
6.74
6.67
7.36
7.11
7.65
LM
LT6
BSAF
1.38
1.50
5.16
4.75
5.57
9.05
11.8
8.30
9.15
GBW4
BSAF
3.15
BEF
(PCB
118)
0.248
0.269
0.926
0.853
1.00
1.62
2.12
1.49
1.64
Method 3b
BSAF
Prediction
0.78
0.85
2.92
2.69
5.13
6.68
4.70
5.18
Measured
GBW4
BSAF
0.575
0.474
2.07
2.05
3.06
2.92
3.70
ND
Method 3b
BSAF
Error
36%
79%
41%
31%
68%
129%
27%
socw
Measured
in Green
Bay Zone
1
l.llxlO6
3.89xl06
2.00 xlO6
7.33 xlO6
2.99 xlO7
3.10xl06
1.32 xlO7
9.64 xlO6
8.12xl07
Predicted
Site-specific
Log
Baseline
BAF
5.94
6.52
6.77
7.29
7.20
7.95
7.66
8.62
D. Extrapolating Lake Michigan Lake Trout (LM LT6) BEFs to Largemouth Bass at
Hudson River mile 189 (RM 189 LMB)
PCB
Congener
18
28/31
52
110
118
149
180
174
196/203
Log
**>QW
5.24
5.67
5.84
6.48
6.74
6.67
7.36
7.11
7.65
LM
LT6
BSAF
1.38
1.50
5.16
4.75
5.57
9.05
11.8
8.30
9.15
RM189
LMB
BSAF
17.1
BEF
(PCB
118)
0.248
0.269
0.926
0.853
1.00
1.62
2.12
1.49
1.64
Method 3b
BSAF
Prediction
4.22
4.59
15.8
14.5
27.7
36.1
25.4
28.0
Measured
RM189
LMB
BSAF
1.29
2.46
6.56
11.5
18.4
24.1
20.0
30.4
Method 3b
BSAF
Error
228%
86%
141%
26%
50%
50%
27%
-8%
SOCW
Measured
at Hudson
River
Mile 189
3.07 xlO6
6.18 xlO6
5.24 xlO6
1.85xl07
ND
ND
ND
ND
ND
Predicted
Site-specific
Log Baseline
BAF
7.11
7.45
7.92
8.43
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Table 5-3 (Continued). Method 3b BEF Extrapolation Example Using PCS 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
18
28/31
52
110
118
149
180
174
196/203
Log
ow
5.24
5.67
5.84
6.48
6.74
6.67
7.36
7.11
7.65
LM
LT6
BSAF
1.38
1.50
5.16
4.75
5.57
9.05
11.8
8.30
9.15
RM194
LMB
BSAF
5.77
BEF
(PCB
118)
0.248
0.269
0.926
0.853
1.00
1.62
2.12
1.49
1.64
Method 3b
BSAF
Prediction
1.43
1.55
5.34
4.92
9.37
12.2
8.60
9.48
Measured
RM194
LMB
BSAF
0.368
0.921
2.82
4.11
6.36
7.28
5.23
8.92
Method 3b
BSAF
Error
289%
69%
89%
20%
47%
68%
64%
6%
socw
Measured
at Hudson
River Mile
194
1.04 xlO7
1.60 xlO7
1.60 xlO7
5.32 xlO7
ND
ND
ND
ND
ND
Predicted
Site-specific
Log
Baseline
BAF
7.17
7.39
7.93
8.42
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100
UJ
m
1!
o
Q.
01
E
2
14-
u.
<
m
10 -
1 -
0.1
0.1
GB z4 BT3
GB z4 W4
GB z1 W4
HRRM189LMB
HRRM194LMB
5x
2x
-1:1 line
1/2
1/5
10
100
measured BSAF
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; zl = 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.
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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 site-
specific 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 site-
specific 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
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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 site-
specific 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:
Baseline BAF =FCM •
--1
(Equation 5-6)
where:
= Total BCF (BCF = Ct/Cw)
ffd = fraction of the total concentration of chemical in BCF test water that is
freely dissolved
f.. = fraction of the tissue that is lipid in the test organism
FCM, = the food-chain multiplier for trophic level /', 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.
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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:
BCFl =-^ =
L/kg
o
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
Sediment
Phytoplankton
Zebra mussels
Crayfish
Trophic
level
1
1
2
3
Concentration of
chemical k (• |/kg)
1.95
1.2
1.3
1.7
Lipid or organic
carbon (%)
7.4
1.2
1.3
1.7
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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:
,2 = (C..TL2)/(C..TLi) (Equation 5- 11)
BMFTL3 = (C,.TL3)/(C..TL2) (Equation 5-12)
Since zebra mussels consume both phytoplankton and sediment, the BMP
must be calculated using a weighted average of chemical concentrations in
their diet items, sediment and phytoplankton:
0.43 Ifjg, kg
kg Q.Ql3kg- lipid
-
0.25-r'""* kg ! , n T* f °-35«g kS
kg 0.074% -SOC) ' kg OM2kg -lipid
Likewise, the BMP for crayfish must be calculated using the weighted average
of chemical concentrations in their diet items, phytoplankton and zebra
mussels:
0.392/jg kg
I f\ "» S. . .~
(1/3)
BMFTL3(cf) = —— .kg r""1* ,T.:. : -=0.725
kg 0.012kg-lipid kg 0.013kg-lipid
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
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Site-Specific BAF Predicted Usin£ the Product of a
Laboratory-Measured BCF and a FCM (Method 4a, continued)
Predicting a site-specific baseline BAF
The BCF]: is converted to a baseline BAF 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 (f.), 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:
Baseline BAF = FCM, • ^^L-l .—
/*
Determining the fraction of chemical k that is freely dissolved in the
bioconcentration test water (fyy) 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
(5.0xlQ~7 kg/L) and the median DOC concentration is 10 mg/L (l.Qx 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 SCF^s.
The Kow for chemical k is 2x 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:
10*.
L kg 106mg L kg Wemg
The f..of the fish species sampled in the laboratory in this example was 2%
(0.02). Using this f., the FCM measured at the site, and the BCF* and ffd
calculated above, a site-specific baseline BAF of 8.8><103 L/kg-lipid is
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Site-Specific BAF Predicted Usin£ the Product of a
Laboratory-Measured BCF and a FCM (Method 4a, continued)
calculated as follows:
" 203 -r-
BaselineBAF, =0.844 •
3
0,975
0.02
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:
3.5mg-DOC 2xl()4 L kg
- = 0.984
L kg 10 mg L kg 10 mg
The lipid content for crayfish of harvestable size at the site is 1.7%. The site-
specific total BAF can then be recalculated from the site-specific baseline
BAF:
Site- Specific BAFtT = (o.017-8.8x 103-^+ l). 0.984= 147X/%
The site-specific total BAF for chemical k in crayfish is 147 L/kg.
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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
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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).
6 -
CD
0)
K
(A 5
TO
CD
O)
O
(A
s
3 -
3456
Predicted Log Baseline BAF
FIGURE 5-8. Relationship between baseline BAFs measured at Bayou d'Indie and BAFs
predicted using Method 4a. The dotted and dashed lines represent a factor of 3 and 5 difference
between the measured and predicted baseline BAFs, respectively. Baseline BAFs measured
using Callinectes sapidus (• ), Micropoganias undulatus (• ), Fundulus heteroclitus (• ), and
Brevoortia patronus (• ).
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 • i (no biomagnification) up to about 25 (significant biomagnification),
depending upon the chemical, organism, and food web. Because FCMs can vary due to site-
specific 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
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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:
(Equation 5-7)
FCM, =
Baseline BAF Baseline BAF
BCF;
Baseline BCF
_
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
hexaehlorobenzene (HCB) in trophic level 3 and 4 fish using equation 5-7.
According to the Arnot and Gob as (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
Alewife
Lake trout
Trophic
level
3
4
HCB concentration
(ng/g)
20
90
Lipid
content (%)
7.0
17.4
Chemical concentrations in fish (Cf) were normalized by the lipid content (ft) of each
sample: Q=Ct//e
The lipid-normalized chemical concentration in each sample is tabulated:
fish
Alewife
Lake
Trout
HCB Concentration (ng/g-lipid)
286
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 (#rf) can be calculated using equation 3-6:
ffd = 1/(1+POC-KOW+0.08-DOC-K0J
The log Kow for HCB is 5.73 (Arnot and Gobas, 2006), so Kow = 5,37xl05. The
water samples were centrifuged to remove particulates prior to extraction, so the POC
concentration is (presumably) zero:
J fd ~'
1
L
kg W6mg
= 0.92
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Calculation of food chain multipliers (continued)
The freely dissolved chemical concentration ( C;f ) is calculated as:
cfd - f • r
^w ~ J fit l-'w
= 0.92 • 150pg/L=138pg/L
Baseline BAFs were then calculated for alewife and lake trout, using Equation 3-2:
Baseline BAE=-%- —
1 Cf f,
RAF =
DAT
2Kag L W00ps 1000g ' - 2 07 x 1 06 T ! hr-
g_lipid mpg lng kg 007 -z.u/ xiu LI Kg
= 517"g
1000g
g_tipid
lag
0174
Equation 5-7 can then be used to calculate FCMs for alewife and lake trout:
pCM = Baseline BAF;
* Baseline BCF
FCM3 =2.07xl06/ 4.15xl05 =5.0
FCM4 =3.74xl06/ 4.15xlOs =9.0
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 Kowof 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:
FCM; = food chain multiplier for trophic level /' and
BMP; = Biomagnification factor for trophic level /'.
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 = C..2/C..1 (Equation 5-11)
BMF3 = C..3/C..2 (Equation 5-12)
BMF4 = C..4/C..3 (Equation 5-13)
where:
CV! = lipid-normalized concentration of chemical in tissue or whole
organism at a specified trophic level (/' = 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 • ^3C, • ^5N, and • ^4S, 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, • ?OCw (or, equivalently, Cf* 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 (SET AC)] 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 Q APP for
modeling (USEPA, 2002).
lrThe 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/AQUAWEBvl.2_BIOvl.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, Cf . 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., IxlO"30). By doing so, the total concentration of the chemical
! The Gobas food chain model is also known as Aquaweb; the current version is Aquaweb vl.2.
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input to the model becomes essentially equal to the Cf , 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:
FCM, =
Baseline BAF.
(Equation 5-14)
Table 5-4. Food Web Structure for National BAF Methodology (Flint, 1986; Gobas, 1993)
Species
Phytoplankton
Zooplankton (mysids [Mysis relictaj)
Benthic Invertebrates {Diporeia)
Sculpin (Cottus cognatus)
Alewife (Alosa pseudoharengus)
Smelt (Osmerus mordax)
Salmonids (Salvelinus namaycush,
Oncorhynchus mykiss, Oncorhynchus
velinus namaycush)
Trophic
Level
1
2
2
3
3
3-4
4
Lipid
Content
0.5%
5.0%
3.0%
8.0%
7.0%
4.0%
11%
Weight
100 mg
12 mg
5.4 g
32 g
16 g
2,410 g
Diet
Phytoplankton
Sediment/Detritus
18% zooplankton, 82%
Diporeia
60% zooplankton, 40%
Diporeia
54% zooplankton, 21%
Diporeia,
25% sculpin
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
Mean water temperature
Organic carbon content of the sediment
Metabolic transformation rate constants
(all organisms)
• »OCW'-"flW
Value
8C
2.7%
0.0 d'1
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 and« • |0cw/KoW = 23)
LogKoW
4.0
4.1
4.2
4.3
4.4
4.5
4.6
4.7
4.8
4.9
5.0
5.1
5.2
5.3
5.4
5.5
5.6
5.7
5.8
5.9
6.0
6.1
6.2
6.3
6.4
6.5
TL2
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
TL3a
1.23
1.29
1.36
1.45
1.56
1.70
1.87
2.08
2.33
2.64
3.00
3.43
3.93
4.50
5.14
5.85
6.60
7.40
8.21
9.01
9.79
10.5
11.2
11.7
12.2
12.6
TL4
1.07
1.09
1.13
1.17
1.23
1.32
1.44
1.60
1.82
2.12
2.51
3.02
3.68
4.49
5.48
6.65
8.01
9.54
11.2
13.0
14.9
16.7
18.5
20.1
21.6
22.8
LogKow
6.6
6.7
6.8
6.9
7.0
7.1
7.2
7.3
7.4
7.5
7.6
7.7
7.8
7.9
8.0
8.1
8.2
8.3
8.4
8.5
8.6
8.7
8.8
8.9
9.0
TL2
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
TL3a
12.9
13.2
13.3
13.3
13.2
13.1
12.8
12.5
12.0
11.5
10.8
10.1
9.31
8.46
7.60
6.73
5.88
5.07
4.33
3.65
3.05
2.52
2.08
1.70
1.38
TL4
23.8
24.4
24.7
24.7
24.3
23.6
22.5
21.2
19.5
17.6
15.5
13.3
11.2
9.11
7.23
5.58
4.19
3.07
2.20
1.54
1.06
0.721
0.483
0.320
0.210
a The FCMs for trophic level 3 are the geometric mean of the FCMs for sculpin and alewife.
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30
25 -
O 20 -
Q.
IB
o
"§ 10
£
5 -
trophic level 2
trophic level 3
trophic level 4
6 7
log Kow
FIGURE 5-9. The FCMs determined for the national BAF methodology for trophic levels 2
through 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., • J0cw/K0w 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 • s»ocw, 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 (lannuzzi 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. = Kow • FCM. (Equation 5-15)
where:
FCM, = the food-chain multiplier for trophic level /', determined from appropriate
field data or predicted for site-specific conditions
KOW = w-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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This exar
again usii
using Me
and a FC
of the tar
baseline '.
appropria
to accour
For this
crayfish
example,
Site-Specific BAF Predicted Using the Product of
the Chemical K™, and a FCM (Method 4b)
nple illustrates the prediction of a site-specific BAF using Method 4b,
tig hydrophobic nonionic chemical k. To predict a site-specific BAF
thod 4b, the investigator uses the product of the K<,w for the chemical
VI, which must be determined for the chemical, site, and trophic level
get organism. In this method, Kow is assumed to be equal to the
3CF, as discussed in Section 5.2.3. Method 4 requires selection of an
ite Kow for the chemical, which is multiplied by an appropriate FCM
it for biomagnification.
example, Method 4b will be used to predict a site-specific BAF for
using the same food chain structure presented in the previous
based on the following data from the site:
MEASUREMENT
Chemical k Filtered Water
Concentration
Chemical k Sediment Concentration
Water Column POC
Water Column DOC
Sediment Organic Carbon
Phytoplankton Lipid
Zebra Mussel Lipid
Crayfish Lipid
AVERAGE VALUE
0.16ng/L
1.95-g/kg
0.54 mg/L
3.5 mg/L
7.4%
1.2%
1.3%
1.7%
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Site-Specific BAF Predicted Using the Product of
the Chemical K^w and a FCM (Method 4b continued)
Determining the chemical KW
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 2x 1Q4 (log Kow= 4.3) will again be used for chemical
k.
Calculating the Freely Dissolved Chemical Concentration and Sediment-
Water Fugacity Gradient
The freely dissolved chemical concentration (Cf* ) and the sediment-water
fugacity gradient (• |OCW/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):
( f \ = _ . _ = o 994
V*). -POC Sm-DOC '
kg \tfmg L kg \tfmg
Of = ffl-Cw = 0.994- (0.16 ng/L) = 0.159 ng/L
The sediment- water fugacity gradient can then be calculated using equation 4-
3:
(\.95fjg kg }
(Csoc) { kg 0.074%-SQCJ WOOng 5
l = ±7—77r = - — = 1.65x10 Like-SOC
M59ng/L m
l.6Sxl05L
g~SOC 2.0xl04I
77=8.25
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Site-Specific BAF Predicted Using the Product of
the Chemical Knw 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 ( » ?OCW/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/toxicologv/models/AQUAWEBvl .2_BIOvl .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 kin 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 =
0.0557ne
1000g-%'rf
g-wet O.OY7g-tipid 0.159ng 1kg-lipid
= 5.01xl03I/%-/
and a FCM of 0.25 (using equation 5-14):
T7r<\jt Baseline BAF S.OlxlO L «g~' AOC
^LM = KZ = ie-l '2.0XW4! = U'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 x FCM (Equation 5-15)
= (2,Qx 104 L/kg-lipid) x 0.25
5.0 xlO3 L/kg-lipid
The site-specific baseline BAF for chemical k in crayfish is predicted by
Method 4b to be S.QxlO3 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 (ki) 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.
Location
Method 4b: Exceedance Levels and Comparison Statistics
95%
Mean
Median
5%
% within 2x
% within 5x
Green Bay
Zonel
Zone 2a
Zone 2b
Zone 3a
Zone 3b
Zone 4
All Zones
0.32
0.17
0.23
0.33
0.23
0.15
0.21
1.17
1.17
1.18
1.58
1.35
1.43
1.30
0.89
0.74
0.83
1.05
0.90
0.61
0.84
2.75
3.40
3.01
4.71
4.15
5.28
3.90
69.8
54.6
61.0
64.0
60.5
40.5
58.6
98.1
91
94.9
94.7
94
82.2
92.7
Hudson River
RM194
RM189
RM169
RM144
RM122
RM114
All Stations
0.06
0.12
0.10
0.42
0.40
0.4
0.08
0.16
0.26
0.95
0.72
0.70
0.78
0.50
0.11
0.20
0.41
0.67
0.67
0.73
0.24
0.38
0.55
1.89
1.14
1.27
1.29
1.07
3.6
9.0
35.3
76.5
80.0
76.9
26.3
25.3
55
76.5
100
100
100
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
Statistic
Average
Standard
Deviation
Count
Median
Within 2x
Within 5x
Negative Residual
Positive Residual
Organism
Sculpin
0.01
0.35
51
0.02
63%
94%
53%
47%
Alewife
0.04
0.36
49
0.06
59%
94%
53%
47%
Small
Smelt
0.09
0.37
46
0.14
61%
94%
59%
41%
Large
Smelt
0.28
0.35
47
0.30
47%
92%
72%
28%
Piscivorous
Fish
0.08
0.36
57
0.08
58%
96%
56%
44%
Burkhard et al. (1997) also evaluated the predictiveness of Method 4b against field-
measured 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 K/mS 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 Kowfor 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 • • h?g Kow • • ?•, 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 B AF 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:
Site Specific BAF/J = (fe • Baseline BAF;+1) • ffd
(Equation 5-17)
where:
ffd = fraction of the total concentration of chemical that is freely dissolved in the
water column at the site
f.. = fraction of the organism tissue that is lipid
Equation 5-17 is a rearrangement of the 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 offish 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, w-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 l%-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 Cf*, EPA does not ignore the chemical associated with
dissolved organic carbon (DOC) and paniculate 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.
j_L
00
O)
O
•P
8 -
7 -
^"~ - - "- ' ;
.:. "" *~
6 ] T T- l^-^—^f
5 -j &^ i i i
A ! 1 1 1 1 1
2a 2b 3a 3b
4
Zone
FIGURE 5-10. BAF'xS (•) 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)
PCB
Congener
90th to 10th Percentile CLR
BAF
Baseline BAF
95th to 5th Percentile CLR
BAF
Baseline BAF
Adult alewife
18
52
149
180
4.98
5.48
3.33
4.08
3.11
2.85
1.88
2.20
7.86
8.90
4.70
6.10
4.3
3.84
2.26
2.76
Age 4 walleye
18
52
149
180
3.57
4.04
3.11
3.96
3.50
2.74
2.12
2.12
5.14
6.01
4.30
5.87
5.00
3.65
2.62
2.63
Age 10 carp
18
52
149
180
4.87
6.75
5.96
7.09
4.23
3.49
1.87
2.17
7.65
11.6
9.91
12.4
6.39
4.99
2.24
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 75ih/25ih and 90th/10th percentile ranges
were smaller by • 2x and • *>x, 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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2,3,4'-Trichlorobiphenyl
(PCB 22) log tt,. = 5.58
TL5B ILSF TL41 TL4F
2,2',5,5'-letrachlorobiphenyf
(PCB 52) log Kow = 5.84
TL3B TL S F Till TL. i F
i,2*,3,44*-pe«tecbJe»rebipheefI
It SB fi3f TL4B TL4F
2,2',3,4',5',6-hexachIorobifjtienyf
(PCB 149) log Kj., = 6.67
^j
i -
4 ~
i
TL 3 B TL 3 f Jt4B Ti 4 f
2,3*,4,4*.5-pent»chiorobiphenfl
log ^, = 6.74
ft SB H3f TLii TL4F
2,2*,3,4',5,5*-liexachloriphenyf
|PCB 14SJ log ^K = i.S3
Tt 3 B TL S F Tt J i Ti J F
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 andPederson, 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 offish 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.,
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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 Bligh-
Dyer 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, 200la)
• 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:
ffd = 1 / (1 + POC • Kow + 0.08 • DOC • Kow ) (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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1 -
0.9
0.8
|
§ 0.7
•4=
o
ro
± 0.6
ro
o
I
«! 0.5
> 0.4
0.3
0.2
0.1
national median POC and DOC
10% organic carbon
90% organic carbon
6 7
log Kow
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
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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 (StandardMethods #53 IOC) is generally preferred to
the wet oxidation method (StandardMethods #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 uncertainly 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 periodic 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 pre-
secondary 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
Statistic
Median
Mean
Std.
CV
n
5th
10th
25th
75th
90th
95th
95th/5th
DOC (mg/L)
All
Types
2.9
4.6
5.1
111%
111,059
0.8
1.2
2.0
5.4
9.7
14
17.5
Stream/
River
3.8
5.6
5.9
105%
69,589
0.7
1.0
2.1
6.9
11.6
16.5
23.6
Lake/
Reservoir
2.1
2.9
3.0
103%
25,704
1.0
1.4
1.8
2.6
5.0
7.8
7.8
Estuary
2.7
3.4
2.6
76%
15,766
1.7
2.0
2.3
3.2
5.0
9
5.3
All
Types
0.5
1.0
2.0
200%
86,540
Oa
0
0.2
1.1
2.3
3.9
—
POC (mg/L)
Stream/
River
0.6
1.3
2.5
192%
48,238
Oa
Oa
0.2
1.4
3.1
5
—
Lake/
Reservoir
0.3
0.5
1.0
200%
23,483
0.08
0.1
0.2
0.5
0.8
1.3
16.3
Estuary
0.9
1.2
1.8
150%
14,819
0.1
0.3
0.5
1.4
2.2
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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REFERENCES
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Campfens, J. and D. Mackay. 1997. Fugacity-BasedModel ofPCB Bioaccumulation in Complex
Aquatic Food Webs. Environ. Sci. Technol. 31:577-583.
Chapra, S.C. 1997. Surface Water-Quality Modeling. WCB McGraw-Hill. Boston, MA.
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application of a model ofPCBs in the Green Bay, Lake Michigan walleye and brown trout and their
food webs. Report for Large Lakes Research Station, U.S. Environmental Protection Agency, Grosse
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Cook, P.M. and Burkhard, L.P. 1995. Development of Bioaccumulation Factors for Protection of
Fish and Wildlife in the Great Lakes. In: U.S. Environmental Protection Agency. National
Sediment Bioaccumulation Conference Proceedings. 823/R-98/002. Washington, DC.
de Wolf, W., de Bruijn JHM, Seinen W. and J.L.M. Hermens. 1992. Influence of
biotransformation on the relationship between bioconcentration factors and octanol-water
partition coefficients. Environ Sci Technol26:1197-1201.( 1992)
Flint, R. 1986. Hypothesized carbon flow through the deep water Lake Ontario food web. J
Great Lakes Res 12:344-354.
Gobas, F.A.P.C.. 1993. A model for predicting the bioaccumulation of hydrophobic organic
chemicals in aquatic food-webs: Application to Lake Ontario. EcolModel 69:1-17'.
Gobas FAPC, Pasternak JP, Lien, K. and R.K. Duncan. 1998. Development and field-validation
of a multi-media exposure assessment model for waste load allocation in aquatic ecosystems:
Application to TCDD and TCDF in the Fraser River Watershed. Environ Sci Technol
32:2442-2449.
Havens K. 1992. Scale and structure in natural food webs. Science 257:1107-1109.
Honeycutt, M.E., McFarland V.A. and D.D. McCant. 1995. Comparison of three lipid extraction
methods for fish. Bull Environ Contam Toxicol. (3):469-72.
lannuzzi TJ, Harrington NW, Shear NM, Curry CL, Carlson-Lynch H, Henning MH, Su, S.H.
and D.E. Rabbe. 1996. Distribution of key exposure factors controlling the uptake of xenobiotic
chemicals in an estuarine food web. Environ Toxicol Chem 15:1979-1992.
Isnard, P. and S. Lambert S. 1988. Estimating bioconcentration factors from octanol-water
partition coefficients and aqueous solubility. Chemosphere 17:21-34.
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Jardine,T.D., Kidd, K.A. and A.T. Fisk. 2006. Applications, Considerations, and Sources of
Uncertainty When Using Stable Isotope Analysis in Ecotoxicology. Environ. Sci. Technol, 40
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Kleeman, J. M.; Olson, J. R.; Peterson, R. E. 1988. Species differences in 2,3,7,8-
tetrachlorodibenzo-p-dioxin toxicity and biotransformation in fish. Fundament. Appl. Toxicol. 10
(2), 206-213.
Leadley, T. A.; Balch, G.; Metcalfe, C. D.; Lazar, R.; Mazak, E.; Habowsky, J.; Haffner, G. D.
1998. Chemical accumulation and toxicological stress in three brown bullhead (Ameiurus
nebulosus) populations of the Detroit River, Michigan, USA. Environ. Toxicol. Chem. 17 (9),
1756-1766.
Linkov, I, Ames, M.R., Crouch, E.A.C. and F. Satterstrom. 2005. Uncertainty in Octanol-Water
Partition Coefficient: Implications for Risk Assessment and Remedial Costs. Environ. Sci.
Technol., 39 (18), 6917-6922.
Lodge, K., P.M. Cook, D.R. Marklund, S.W. Kohlbry, J. Libal, C. Harper, B.C. Butterworth and
A.G. Kizlauskas. 1994. Accumulation of polychlorinated dibenzo-p-dioxins (PCDDs) and
dibenzofurans (PCDFs) in sediments and fishes of Lake Ontario. In preparation.
Mackay, D. 1982. Correlation of bioconcentration factors. Environ Sci Technol 16:274-278.
Martinez, N.D. 1991. Artifacts or attributes? Effects of resolution on the Little Rock Lake food
web. EcologMonogr 61:367-392.
McCutchan, J. H. Jr, Lewis, W. M., Kendall, C. and McGrath, C. C. 2003. Variation
in trophic shift for stable isotope ratios of carbon, nitrogen, and sulfur. Oikos 102: 378-390.
Morrison, H.A., Gobas, F.A.P.C., Rodica, L. and G.D. Haffner. 1996. Development and
Verification of a Bioaccumulation model for organic contaminants in benthic invertebrates.
Environ. Sci Technol. 30:3377-3384.
Morrison HA, Gobas FAPC, Lazar R, Whittle D.M. and G.D. Haffner. 1997. A food web
bioaccumulation model for organic contaminants in western Lake Erie. Environ Sci Technol
31:3267-3273.
Naito, W.; Jin, J.; Kang, Y.-S.; Yamamuro, M.; Masunaga, S.; Nakanishi, J. 2003. Dynamics of
PCDDs/DFs and coplanar-PCBs in an aquatic food chain of Tokyo Bay. Chemosphere. 53 (4),
347-362.
Niimi AJ. and E.G. Oliver. 1989. Distribution of polychlorinated biphenyl congeners and other
halocarbonsin whole fish and muscle among Lake Ontario salmonids. Environ. Sci. Technol.
23(1): 83-88.
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Oliver E.G. and AJ. Niimi. 1988. Trophodynamic analysis of polychlorinated biphenyl
congeners and other chlorinated hydrocarbons in the Lake Ontario ecosystem. Environ Sci
Technol 22:388-397.
Opperhuizen, A.; Sijm, D. T. H. M. 1990. Bioaccumulation and biotransformation of
polychlorinated dibenzo-p-dioxins, and dibenzofurans in fish. Environ. Toxicol. Chem. 9 (2),
175-186.
Peterson, B. and Fry, B. 1987. Stable isotopes in ecosystem studies. Annu. Rev. Ecol. Syst. 18:
293-320.
Pontolillo, J.; Eganhouse, R.P. 2002. The Search for Reliable Aqueous Solubility (Sw) and
Octanol-Water Partition Coefficient (Kow) Data for Hydrophobic Organic Compounds: DDT
and DDE as a Case Study. USGS Water-Resources Investigations Report 01-4202.
QEA.1999. PCBs in the Upper Hudson River. Volume 2- A Model ofPCB Fate, Transport, and
Bioaccumulation. for General Electric, Albany, New York. May 1999.
Rasmussen JB, Rowan DJ, Lean, D.R.S. and J.H. Carey. 1990. Food chain structure and Ontario
Lakes determines PCB levels in lake trout (Salvelinus namaycush) and other pelagic fish. Can J
Fish Aquat Sci 47:2030-2038.
Thomann, R.V., Connolly, J.P. and T.F. Parkerton. 1992. An equilibrium model of organic
chemical accumulation in aquatic food webs with sediment interaction. Environ. Toxicol. Chem.
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Thomann, R.V. and J.A. Meuller. 1987. Principles of Surface Water Quality Modeling and
Control. Harper & Row, Publishers, Inc.
Thomann, R. V. and J. P. Connolly. 1984. Model of PCB in the Lake Michigan Lake Trout food
chain. Environ. Sci. Technol. 18(2):65-71.
Thomann, R. V. 1989. Bioaccumulation model of organic chemical distribution in aquatic food
chains. Environ. Sci. Technol. 23(6):699-707.
USEPA, 1995a. Great Lakes Water Quality Initiative Technical Support Document for the
Procedure to Determine Bioaccumulation Factors. EPA-820-B-95-005. Office of Water,
Washington, D.C.
USEPA. 1995b. Guidance for Assessing Chemical Contaminant Data for Use in Fish Advisories,
Volume I: Fish Sampling and Analysis, Second Edition
USEPA. 1999. Aquatic Food Web Module, Background, and Implementation for the Multimedia,
Multipathway, and Multireceptor Risk Assessment (3MRA) for HWIR99. In: Federal Register.
November 19, 1999, 6¥(223), 63381-63461.
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USEPA. 2000a. Trophic Level and Exposure Analyses for Selected Piscivorous Birds and
Mammals. Volume I: Analyses of Species for the Great Lakes. Draft. Office of Water,
Washington, DC.
USEPA. 2000b. Trophic Level and Exposure Analyses for Selected Piscivorous Birds and
Mammals. Volume II: Analyses of Species in the Conterminous United States. Draft. Office of
Water, Washington, DC.
USEPA. 2000c. Trophic Level and Exposure Analyses for Selected Piscivorous Birds and
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20460, December 2002 (EPA/240/R-02/007).
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Human Health (2000). Technical Support Document Volume 2: Development of National
Bioaccumulation Factors. EPA-822-B-03-030. Office of Water, Washington, D.C., 2003.
USEPA. 2003b. Draft Guidance on Development, Evaluation, and Application of Regulatory
Environmental Models. Council of Regulatory Environmental Modeling, Office of Science
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Wong, C, S.; Capel, P. D.; Nowell, L. H. 2001. National-scale, field-based evaluation of the
biota-sediment accumulation factor model. Environ. Sci. Technol. 35 (9), 1709-1715.
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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 STORE! 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.
5-112
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Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors
Sections EPA Review Draft September 4, 2007
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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Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors
Sections EPA Review Draft September 4, 2007
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
PCB 5+8
PCB 6
PCB 7
PCB 16+32
PCB 17
PCB 18
PCB 19
PCB 22
PCB 24+27
PCB 25
PCB 26
PCB 28+31
PCB 29
PCB 33
PCB 37+42
PCB 40
PCB 41+64+71
PCB 43
PCB 45
PCB 46
PCB 47+48
PCB 49
PCB 52
PCB 53
PCB 56+60
PCB 63
PCB 66+95
PCB 70+76
PCB 74
PCB 77+110
PCB 81
PCB 82
PCB 83
PCB 84+92
PCB 85
PCB 87
PCB 89
PCB 91
PCB 97
PCB 99
PCB 100
LOG
KQW
5.02
5.06
5.07
5.30
5.25
5.24
5.02
5.58
5.40
5.67
5.66
5.67
5.60
5.60
5.80
5.66
5.87
5.75
5.53
5.53
5.82
5.85
5.84
5.62
6.11
6.17
6.17
6.17
6.20
6.42
6.36
6.20
6.26
6.20
6.30
6.29
6.07
6.13
6.29
6.39
6.23
AVERAGE
BSAF
0.264
0.405
0.362
0.767
3.11
1.91
0.66
1.04
2.34
1.54
1.78
1.56
0.224
0.686
0.876
1.66
1.90
7.25
2.84
1.03
10.3
5.60
7.65
2.39
2.00
4.12
4.72
2.05
3.63
5.05
11.4
6.09
8.98
4.77
7.38
7.62
4.79
6.78
7.20
6.12
1.58
BSAF CV
1.16
0.489
0.294
0.587
1.55
1.07
0.347
1.23
1.13
0.949
1.01
0.705
0.000
0.937
0.487
0.598
0.585
0.508
0.655
0.614
1.08
1.17
1.12
0.404
0.867
0.614
0.719
1.04
0.816
0.889
0.474
0.987
0.647
0.898
0.710
0.885
0.560
0.747
0.760
0.623
0.487
MINIMUM
BSAF
0.0536
0.140
0.231
0.189
0.274
0.171
0.303
0.148
0.362
0.179
0.204
0.229
0.224
0.108
0.318
0.618
0.541
2.84
0.597
0.398
0.544
0.777
1.02
0.976
0.288
1.09
0.469
0.257
0.614
0.740
3.68
0.806
2.47
1.02
1.03
0.907
1.97
1.16
0.966
1.45
0.563
MAXIMUM
BSAF
1.17
0.728
0.500
1.98
16.0
8.41
1.06
4.66
9.79
5.59
7.40
4.15
0.224
2.39
1.44
4.05
4.32
15.9
7.47
3.05
36.0
27.9
34.53
4.18
8.62
9.10
16.3
10.7
14.1
21.6
28.3
25.5
28.3
16.5
25.8
30.0
9.09
19.9
24.2
18.1
2.61
5-114
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Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors
Sections EPA Review Draft September 4, 2007
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
PCB 101
PCB 105+132+153
PCB 107
PCB 114+134
PCB 118
PCB 119
PCB 124+135+144+147
PCB 128
PCB 129+178
PCB 130
PCB 131
PCB 136
PCB 138+158+163
PCB 141 (+179)
PCB 146
PCB 149
PCB 151
PCB 156+171+202
PCB 167
PCB 170+190
PCB 172+197
PCB 174
PCB 175
PCB 177
PCB 180
PCB 182+187
PCB 183
PCB 189
PCB 191
PCB 193
PCB 194
PCB 195+208
PCB 196+203
PCB 198
PCB 201
PCB 200
PCB 206
PCB 207
PCB 209
AVERAGE (ALL
CONGENERS)
LOG
KOW
6.38
6.72
6.71
6.60
6.74
6.58
6.67
6.74
6.94
6.80
6.58
6.22
6.95
6.78
6.89
6.67
6.64
7.18
7.27
7.37
7.32
7.11
7.17
7.08
7.36
7.19
7.20
7.71
7.55
7.52
7.80
7.64
7.65
7.62
7.62
7.27
8.09
7.74
8.18
AVERAGE
BSAF
8.39
6.06
7.12
156
5.74
2.89
6.04
9.29
5.56
11.2
1.63
6.65
11.07
12.19
8.76
8.68
7.11
26.34
13.41
5.78
23.03
6.30
6.06
9.05
11.85
7.36
7.22
8.45
34.93
13.98
2.60
1.01
8.70
0.35
6.05
6.50
1.44
0.68
0.31
7.85
BSAF CV
0.623
0.828
0.783
1.10
0.808
0.546
0.833
0.546
0.742
0.750
0.366
1.63
0.765
0.860
1.13
0.971
0.596
0.501
0.697
1.03
0.671
0.867
0.652
0.864
1.05
0.931
0.812
0.280
0.953
0.928
0.620
0.991
0.981
0.676
0.651
0.937
1.08
0.672
1.10
0.790
MINIMUM
BSAF
1.18
0.913
1.62
7.94
0.772
0.398
1.14
2.92
0.970
1.62
0.970
0.199
1.19
1.13
0.236
0.889
2.25
9.94
3.06
0.95
4.861
1.09
1.91
1.50
0.722
0.635
1.56
5.66
5.23
1.83
0.428
0.0626
1.65
0.196
2.21
1.15
0.178
0.117
0.0788
1.28
MAXIMUM
BSAF
23.0
25.3
25.6
497
20.0
7.17
17.8
22.2
18.9
31.3
2.53
19.1
40.84
42.94
32.59
38.15
18.13
48.12
40.58
22.41
64.51
25.9
18.7
26.7
50.0
27.5
25.1
14.2
127
49.2
6.97
3.86
33.2
0.876
14.8
25.0
5.98
1.70
1.25
25.4
5-115
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Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors
Sections EPA Review Draft September 4, 2007
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
PCB 1
PCB 3
PCB 4
PCB 6
PCB 8
PCB 9
PCB 10
PCB 15
PCB 16
PCB 17
PCB 18
PCB 19
PCB 20
PCB 22
PCB 25
PCB 26
PCB 27
PCB 28
PCB 31
PCB 32
PCB 33
PCB 34
PCB 37
PCB 40
PCB 41
PCB 42
PCB 44
PCB 45
PCB 47
PCB 48
PCB 49
PCB 51
PCB 52
PCB 53
PCB 56
PCB 60
PCB 63
PCB 64
PCB 66
PCB 67
PCB 70
PCB 74
PCB 77
PCB 82
LOG
KOW
4.46
4.69
4.65
5.06
5.07
5.06
4.84
5.30
5.16
5.25
5.24
5.02
5.57
5.58
5.67
5.66
5.44
5.67
5.67
5.44
5.60
5.66
5.83
5.66
5.69
5.76
5.75
5.53
5.85
5.78
5.85
5.63
5.84
5.62
6.11
6.11
6.17
5.95
6.20
6.20
6.20
6.20
6.36
6.20
AVERAGE
BSAF
0.149
0.0340
0.407
0.144
0.250
0.121
0.655
0.0882
0.570
1.85
1.19
0.877
3.61
1.53
1.08
1.62
1.58
2.34
1.71
1.43
2.46
1.17
0.469
1.82
1.40
5.01
4.63
3.07
3.99
4.05
5.30
2.95
5.24
1.82
4.09
4.99
6.01
5.83
6.67
63.6
6.54
7.78
1.92
7.58
BSAF CV
0.774
0
0.933
0.977
1.02
1.02
1.19
0.956
0.734
0.274
0.876
0.810
0.177
0.552
0.733
0.713
0.420
0.586
0.729
0.963
0.254
1.03
0.867
0.623
1.01
0.198
0.521
0.139
0.778
0.448
0.747
0.942
0.725
0.492
0.574
0.689
0.356
0.884
0.508
0.584
0.564
0.167
0.767
0.731
MINIMUM
BSAF
0.0594
0.0340
0.0324
0.00668
0.00851
0.0173
0.0471
0.00314
0.0769
1.47
0.123
0.0480
3.09
0.496
0.0952
0.124
0.924
0.918
0.171
0.0409
1.91
0.0706
0.0870
0.172
0.247
4.12
1.75
2.59
0.157
1.86
0.361
0.0635
0.468
0.370
1.24
1.30
3.08
0.835
2.67
12.4
2.53
6.33
0.295
3.67
MAXIMUM
BSAF
0.406
0.0340
1.19
0.360
0.733
0.296
3.15
0.220
1.30
2.57
3.75
2.14
4.32
3.12
2.73
4.10
2.16
5.81
4.34
5.38
3.15
4.21
1.31
3.60
2.98
6.19
11.5
3.50
12.1
6.65
16.0
10.7
15.2
2.88
9.93
13.2
8.95
21.5
14.9
119
15.4
9.48
4.71
15.6
5-116
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Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors
Section 5 EPA Review Draft September 4, 2007
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
PCB 83
PCB 84
PCB 85
PCB 87
PCB 101 + 90
PCB 91
PCB 92
PCB 95
PCB 96
PCB 97
PCB 99
PCB 105
PCB 107
PCB 110
PCB 114
PCB 118
PCB 119
PCB 122
PCB 123
PCB 128
PCB 129
PCB 135
PCB 136
PCB 137
PCB 138
PCB 141
PCB 144
PCB 146
PCB 149
PCB 151
PCB 153
PCB 156
PCB 158
PCB 167
PCB 170
PCB 172
PCB 174
PCB 177
PCB 178
PCB 180
PCB 183
LOG
KOW
6.26
6.04
6.30
6.29
6.37
6.13
6.35
6.13
5.71
6.29
6.39
6.65
6.71
6.48
6.65
6.74
6.58
6.64
6.74
6.74
6.73
6.64
6.22
6.83
6.83
6.82
6.67
6.89
6.67
6.64
6.92
7.18
7.02
7.27
7.27
7.33
7.11
7.08
7.14
7.36
7.20
AVERAGE
BSAF
6.53
3.98
11.1
7.82
9.01
6.03
8.40
5.23
3.62
8.81
11.1
10.5
13.6
9.04
20.9
10.9
14.7
15.5
5.90
11.0
7.41
19.1
8.48
13.5
9.96
10.8
24.5
15.1
8.69
8.55
13.6
13.6
13.1
13.6
12.4
11.7
13.0
10.3
1.85
12.5
13.0
BSAF CV
0.444
0.835
0.525
0.332
0.496
0.725
0.758
0.805
0.104
0.418
0.526
0.322
0.414
0.200
0.459
0.454
0.397
0.447
0.105
0.552
0.123
0.266
0.485
0.523
0.575
0.516
0.681
0.635
0.717
0.136
0.529
0.402
0.492
0.493
0.644
0.524
0.396
0.565
0.274
0.608
0.0276
MINIMUM
BSAF
3.38
0.180
5.24
4.03
2.65
0.285
0.981
0.271
3.19
4.98
3.11
7.83
7.34
7.38
6.68
4.14
9.37
10.6
5.46
4.87
6.37
12.8
3.51
8.25
2.13
5.83
4.46
6.63
0.689
7.73
7.33
8.52
8.21
8.60
6.14
6.84
7.77
4.28
1.47
6.48
12.6
MAXIMUM
BSAF
12.6
12.8
26.3
12.3
21.3
13.4
24.2
15.0
3.90
16.7
27.5
15.3
25.2
11.5
38.5
18.8
22.3
20.5
6.34
23.0
8.10
25.0
15.0
21.5
23.3
24.9
59.4
38.5
22.4
9.37
30.3
21.3
27.5
28.6
30.1
23.5
20.0
22.4
2.57
28.4
13.2
5-117
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Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors
Section 5 EPA Review Draft September 4, 2007
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
PCB 187
PCB 201
PCB 202
PCB 203
AVERAGE (ALL
CONGENERS)
LOG
KOW
7.17
7.62
7.24
7.65
AVERAGE
BSAF
16.9
14.7
11.0
11.5
7.66
BSAF CV
0.727
0.531
0.356
0.691
0.565
MINIMUM
BSAF
7.18
7.80
8.20
5.21
3.52
MAXIMUM
BSAF
49.4
30.4
13.7
31.4
15.4
5-118
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Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors
Section 5 EPA Review Draft September 4, 2007
Appendix 5D
Lipid Content of Aquatic Organisms
Table 5D-1. Lipid Content of Aquatic Organisms Used to Derive National Default Values of Lipid Fraction (f.)
CSFII Consumption „ „
ft *. ^TT u-1 .ua Common Name
Category (Habitat)
Anchovy (estuarine)
Carp (freshwater)
Catfish (freshwater)
Catfish (estuarine)
Cisco (freshwater)
Clam (estuarine)
Striped anchovy
European anchovy
Northern anchovy
Common carp
White catfish
Black bullhead
Yellow bullhead
Brown bullhead
Channel catfish
White catfish
Brown bullhead
Channel catfish
Cisco
Hard shell clam
Soft shell clam
Venus clam (Littleneck
Japanese)
Venus clam (Shortneck)
Venus clam (Asari)
Species Mean
Scientific Name Lipid Content
(%)
Anchoa hepsetus
Engraulis encrasicholus
Engraulis mordax
Cyprinus carpio
Ameiurus catus
Ameiurus melas
Ameiurus natalis
Ameiurus nebulosus
Ictalurus punctatus
Ameiurus catus
Ameiurus nebulosus
Ictalurus punctatus
Coregonus Artedii
Mercenaria mercenaria
Mya arenaria
Tapes (venerupis) decussatus
Tapes japonica
Tapes philippinarum
2.8
4.8
10.7
5.4
4.3
1.1
1.4
2.6
5.3
4.3
2.6
5.3
1.9
0.7
1.2
1.2
1.8
2.6
cvb
NR
0.34
NR
0.86
0.58
0.70
0.99
0.72
0.71
0.58
0.72
0.71
0.65
NR
NR
NR
NR
No. Obs.
23
26
16
2,792
204
113
95
988
1,427
204
988
1,427
69
47
o
J
15
o
5
o
5
Data
Source0
1
2
1
o
5
3,4,5
3,4,5
3,5
3,4,5
3,4,5
3,4,5
3,4,5
3,4,5
2
1,6
1
1
1
1
CSFII Mean
Lipid (%)
6.1
5.4
2.9
4.0
1.9
1.3
5-119
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Technical Support Document Volume 3: Development of Site-Specific Bioaccumulation Factors
Section 5 EPA Review Draft September 4, 2007
CSFII Consumption _, mT
/-< A /TI u v ^\» Common Name
Category (Habitat)
Crab (estuarine)
Crayfish (freshwater)
Croaker (estuarine)
Eel (estuarine)
Flatfish (estuarine)
Flounder (estuarine)
Herring (estuarine)
Mullet (freshwater)
Oyster (estuarine)
Perch (estuarine and
freshwater)
Pike (freshwater)
Venus clam
Venus clam (hard)
Blue crab
Dungeness crab
Queen crab
Crayfish (mixed sp.)
White croaker
Atlantic croaker
Yellowfin croaker
Eel, mixed species
Sole and flounder
Sole and flounder
Blueback herring
Atlantic herring
Pacific herring
Striped mullet
Pacific oyster
Eastern oyster
White perch
Yellow perch
Northern pike
Muskellunge
Species Mean
Scientific Name Lipid Content
(%)
Venus gallina
Venus lusoria
Callinectes sapidus
Cancer magister
Chionoectes opilio
Astacus and Orconectes
Genyonemus lineatus
Micropogonias undulatus
Umbrina roncador
Anguilla spp.
Bothidae and Pleuronectidae
Bothidae and Pleuronectidae
Alosa aestivalis
Clupea harengus
Clupea pallasi
Mugil cephalus
Crassostrea gigas
Crassostrea virginica
Morone americana
Perca flavescens
Esox lucius
Esox Masquinongy
0.9
0.6
1.3
1.0
1.2
1.1
4.2
3.2
1.8
11.7
1.2
1.2
7.2
9.0
13.9
3.8
2.3
2.5
3.5
1.0
0.6
1.1
cvb
NR
NR
1.19
0.26
0.30
NR
0.88
0.47
0.70
0.28
0.80
0.80
0.45
0.51
0.39
0.62
0.33
0.56
0.72
0.79
1.01
0.87
No. Obs.
29
5
101
24
6
5
37
8
o
J
14
596
596
92
2,524
128
43
13
193
682
841
904
35
Data CSFII Mean
Source' Lipid (%)
1
1
3
2
2
2
4, 5, 6, 7
2
5
2
2
2
3
2
2
2
2
2
3,4
3,5
3,4
o
6
1.1
1.1
3.0
11.7
1.2
1.2
10.0
3.8
2.4
2.3
0.7
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Section 5 EPA Review Draft September 4, 2007
CSFII Consumption
Category (Habitat)8
Rockfish (estuarine)
Common Name
Chain pickerel
Striped bass
Rockfish
Salmon
(estuarine & freshwater) Pink salmon
Scallop (estuarine)
Shrimp (estuarine)
Smelt, rainbow
(estuarine & freshwater)
Snails (freshwater)
Chum salmon
Coho salmon
Sockeye salmon
Chinook salmon
Atlantic salmon
Scallop, mixed species
Shrimp, mixed species
Rainbow smelt
Snails, mixed species
Sturgeon
(estuarine & freshwater) Lake sturgeon
Trout, mixed spp.
(freshwater)
White sturgeon
Rainbow trout
Cutthroat trout
Brown trout
Brook trout
Species Mean
Scientific Name Lipid Content
(%)
Esox niger
Morone saxatilis
Se bastes spp.
Oncorhynchus gorbuscha
Oncorhynchus keta
Oncorhynchus kistuch
Oncorhynchus nerka
Oncorhynchus tshawytscha
Salmo salar
Pectinidae
Panaeidae and Pandalidae
Osmerus mordax
Vivaparadidae, Helixidaed
Acipenser fulvescens
Acipenser transmontanus
Oncorhynchus mykiss
Salmo clarki
Salmo trutta
Salvelinus fontinalis
0.4
5.3
1.6
3.5
3.8
2.9
8.6
3.4
6.3
0.8
1.7
4.1
1.4
9.4
1.3
5.1
1.2
7.4
4.0
cvb
0.74
0.59
NR
0.49
0.62
0.75
0.32
0.93
0.74
0.35
0.39
0.46
0.75
0.63
0.67
0.67
0.79
0.73
0.56
No. Obs.
72
7,657
81
144
13
617
48
873
7
114
100
130
11
51
7
556
15
615
96
Data CSFII Mean
Source' Lipid (%)
3,4
3,4,5,7
2
2
2
o
3
2
3
2
2
2
3,8
1
o
3
4,5,7
3,4,5
o
3
3,4,5
3,4,5
3.5
4.7
0.8
1.7
4.1
1.4
5.4
6.0
5-121
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Section 5 EPA Review Draft September 4, 2007
CSFII Consumption _, mT
/-< A /TI u v ^\» Common Name
Category (Habitat)
Lake trout
Trout, mixed spp.
(estuarine) Rainbow trout
Cutthroat trout
Trout, (freshwater)e Rainbow trout
Whitefish Whitefish, mixed spp.
Scientific Name
Salvelinus namaycush
Oncorhynchus mykiss
Salmo clarki
Oncorhynchus mykiss
Coregonus spp.
Species Mean
Lipid Content
(%)
12.3
5.1
1.2
5.1
5.9
cvb
0.62
0.67
0.79
0.67
0.64
No. Obs.
910
556
15
556
68
Data
Source'
3,4,5
3,4,5
3
3,4,5
2
CSFII Mean
Lipid (%)
3.2
5.1
5.9
a Habitat designation (freshwater, estuarine) assigned to the CSFII consumption categories. See the Exposure Assessment volume of TSD Volume 2 (USEPA,
2003) for details.
b Coefficient of variation.
c Data sources: 1 = Sidwell (1981), 2 = Exler (1987), 3 = NSI (USEPA, 2001a), 4 = USEPA (1992a), 5 = CATSMP, 6 = primary literature, 7 = BPTCP, 8 =
GBMB. See Section 6.2.2 for a description of data sources.
d In addition to these two families, specific genera represented include Ampullaria, Vivaparus, Achatina, Murex, Thais, Nassa, andAporrhais.
e Information from the CSFII survey indicates that rainbow trout is appropriate for the "trout, freshwater" category.
5-122
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