United States Office of Water EPA-822-R-03-030

Environmental Protection Office of Science and Technology December 2003

Agency 4304

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-R-03-030

Methodology for Deriving 

Ambient Water Quality Criteria 

for the Protection of 

Human Health (2000)

Technical Support Document

Volume 2: 

Development of 

National Bioaccumulation Factors

Final

Office of Science and Technology

Office of Water

U.S. Environmental Protection Agency

Washington, DC 20460
ii

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.
iii
iv

ACKNOWLEDGMENTS

Project Leader

Erik L. Winchester U.S. EPA Office of Science and Technology

Tala R. Henry 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

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 Human Health Methodology Workgroup

William Beckwith U.S. EPA Region 1

Jeff Bigler U.S. EPA Office of Science and Technology

Sally Brough U.S. EPA Region 10

Karen Clark U.S. EPA Office of General Counsel

Gregory Currey U.S. EPA Office of Waste Management

Vicki Dellarco U.S. EPA Office of Prevention, Pesticides, and Toxic Substances

Charles Delos U.S. EPA Office of Science and Technology

Arnold Den U.S. EPA Region 9

Catherine Eiden U.S. EPA Office of Prevention, Pesticides, and Toxic Substances

Michael Firestone U.S. EPA Office of Prevention, Pesticides, and Toxic Substances

Steven Galson U.S. EPA Office of Prevention, Pesticides, and Toxic Substances

Sue Gilbertson U.S. EPA Office of Science and Technology

Joel Hansel U.S. EPA Region 4

Wayne Jackson U.S. EPA Region 2

William Jordan U.S. EPA Office of Prevention, Pesticides, and Toxic Substances

Margaret Kelly U.S. EPA Office of Children’s Health Protection

Sharon Lin U.S. EPA Office of Wetlands, Oceans, and Watersheds

Roseanne Lorenzana U.S. EPA Region 10

Gregory McCabe U.S. EPA Region 7

Bruce Mintz U.S. EPA National Environmental Research Laboratory-RTP

William Morrow U.S. EPA Office of Science and Technology

Jacqueline Moya U.S. EPA National Center for Environmental Assessment

Deirdre Murphy U.S. EPA Air Quality Planning and Standards

Joseph Nabholz U.S. EPA Office of Prevention, Pesticides, and Toxic Substances

Russell Nelson U.S. EPA Region 6

Jennifer Orme-Zavaleta U.S. EPA National Health and Environmental Effects Research

Laboratory

Lynn Papa U.S. EPA National Center for Environmental Assessment

Robert Pepin U.S. EPA Region 5

David Pfeifer U.S. EPA Region 5
v

Rita Schoeny U.S. EPA Office of Science and Technology

Charles Stephan U.S. EPA Office of Research and Development

David Tomey U.S. EPA Region 1

Fritz Wagener U.S. EPA Region 4

Jennnifer Wigal U.S. EPA Office of Science and Technology

Jeanette Wiltse U.S. EPA Office of Science and Technology

Gary Wolinsky U.S. EPA Region 9

Philip Woods U.S. EPA Region 9

William Wuerthele U.S. EPA Region 8

U.S. EPA Technical Reviewers

Sidney Abel U.S. EPA Office of Pesticide Programs

Charlotte Bertrand U.S. EPA Office of Policy, Economics and Innovation

James Breithaupt U.S. EPA Office of Pesticide Programs

Stephanie Irene U.S. EPA Office of Pesticide Programs

Patricia Jennings U.S. EPA Office of Pesticide Programs

Ann Johnson U.S. EPA Office of Policy, Economics and Innovation

Michael Kravitz U.S. EPA National Center for Environmental Assessment

Matthew Lorber U.S. EPA National Center for Environmental Assessment

Deirdre Murphy U.S. EPA Office of Air Quality Planning and Standards

Lucy Shanaman U.S. EPA Office of Pesticide Programs
vi

EXTERNAL

PEER REVIEW WORKGROUP

The following professionals were part of the External Peer Review

Workgroup–Bioaccumulation Workgroup, that provided technical and scientific review regarding

the content and technical approach in the July 1998 Draft Ambient Water Quality Criteria

Derivation Methodology: Human Health. Their comments were reviewed and incorporated

where appropriate to develop this final document.

Damian Shea, Ph.D. NC State University

Brendan Hickie, Ph.D. Trent University

Robert Hale, Ph.D. Virginia Institute of Marine Science

Karen Erstfeld, Ph.D. Rutgers University

Lynn McCarty, Ph.D. LS McCarty Scientific Research and Consulting

Potential areas for conflict of interest were investigated via direct inquiry with the peer

reviewers and review of their current affiliations. No conflicts of interest were identified.
vii

TABLE OF CONTENTS

Notice .......................................................................... ii

Foreword ...................................................................... iii

Acknowledgments .............................................................. iv

External Peer Review Workgroup .................................................. vi

1. Introduction ............................................................ 1-1

1.1 Purpose ............................................................... 1-2

1.2 Scope .................................................................. 1-2

1.3 Important Bioaccumulation and Bioconcentration Concepts ..................... 1-3

2. Definitions ............................................................. 2-1

3. Overview of the National BAF Methodology ................................ 3-1

3.1 Summary of Four Bioaccumulation Methods ................................. 3-3

3.1.1 Field-Measured BAFs .............................................. 3-3

3.1.2 BAFs Predicted from a Field-Measured BSAF .......................... 3-3

3.1.3 BAF Predicted from Laboratory-Measured BCFs ........................ 3-4

3.1.4 BAFs Predicted from KOW ........................................... 3-4

3.1.5 Advantages and Limitations of BAF Methods .......................... 3-5

3.2 Framework for Deriving National BAFs ...................................... 3-5

3.2.1 BAF Derivation Procedures for Inorganic and Organometallic Chemicals .... 3-7

3.2.2 BAF Derivation Procedures for Ionic Organic Chemicals ................. 3-7

3.2.3 BAF Derivation Procedures for Nonionic Organic Chemicals .............. 3-8

4. Background Information on Lipid Normalization, Bioavailability, and

Biomagnification ........................................................ 4-1

4.1 Lipid Normalization ...................................................... 4-1

4.1.1. Background and Theory ............................................ 4-1

4.1.2 Assumptions and Limitations ........................................ 4-2

4.2 Technical Basis of Freely Dissolved Normalization of Chemical Concentration

in Water ......................................................... 4-5

4.2.1 Background Theory and Basic Equation ............................... 4-5

4.2.2 Effects of Chemical Sorption to DOC and POC on Chemical Bioavailability . . 4-8

4.2.3 Sorptive Behavior of Nonionic Organic Chemicals with DOC and POC

(Including Plankton) in the Water Column ............................ 4-10

4.2.4 Values for the Particulate and Dissolved Organic Carbon Partition 

Coefficients Kpoc and Kdoc ........................................... 4-12

4.2.5 Selection of Appropriate Kows for Partitioning (Bioavailability) Predictions . . 4-20

4.3 Importance of Sediment-Water Concentration Quotient (Jsocw) .................. 4-21
viii

4.4 Derivation and Use of Food Chain Multipliers ................................ 4-24

4.4.1 Derivation of FCMs Using a Food Web Model ......................... 4-25

4.4.2 Derivation of Food Chain Multipliers Using Field Data .................. 4-40

4.4.3 Food Chain Multiplier Uncertainties .................................. 4-41

5. Calculating Baseline BAFs for Nonionic Organic Chemicals Using 

the Four Methods ....................................................... 5-1

5.1 Method 1: Deriving Baseline BAFs from Total BAFs (BAFt

Ts) .................... 5-1

5.1.1 Sampling and Data Quality Considerations ............................. 5-2

5.1.2 Assumptions and Limitations ........................................ 5-4

5.1.3 Validation of Method 1 ............................................. 5-5

5.2 Method 2: Deriving Baseline BAFs from BSAFs ............................... 5-8

5.2.1 Determination of BSAF Values ..................................... 5-10

5.2.2 Relationship of Baseline BAFs to BSAFs ............................. 5-11

5.2.3 Derivation of the Baseline BAF Equation for Method 2 .................. 5-12

5.2.4 Sampling and Data Quality Considerations ............................ 5-13

5.2.5 Assumptions and Limitations ....................................... 5-14

5.2.6 Validation of Method 2 ............................................ 5-15

5.3 Method 3: Deriving Baseline BAFs from Laboratory-Measured BCFs (BCFt

Ts)

and FCMs ............................................................. 5-19

5.3.1 Sampling and Data Quality Considerations ............................ 5-19

5.3.2 Assumptions and Limitations ....................................... 5-21

5.3.3 Validation of Method 3 ............................................ 5-22

5.4 Method 4: Baseline BAF Derived from KOW × FCM ........................... 5-23

5.4.1 Assumptions and Limitations ....................................... 5-23

5.4.2 Validation of Method 4 ............................................ 5-24

6. Derivation of National BAFs for Nonionic Organic Chemicals ................. 6-1

6.1 Selecting Final Baseline BAFs .............................................. 6-2

6.1.1 Calculating Species-Mean Baseline BAFs for Each BAF Method ........... 6-3

6.1.2 Calculating the Trophic Level–Mean Baseline BAF for Each BAF Method ... 6-7

6.1.3 Selecting the Final Trophic Level–Mean Baseline BAFs .................. 6-7

6.2 Basis for the National Default Lipid Fraction (fR) of Commonly Consumed Fish and 

Shellfish ................................................................ 6-9

6.2.1 Variability in Lipid Content .......................................... 6-9

6.2.2 Data Sources .................................................... 6-10

6.2.3 Data Analysis .................................................... 6-15

6.2.4 Uncertainty and Sensitivity Analysis ................................. 6-28

6.3 Basis for the National Default Values of DOC and POC ........................ 6-37

6.3.1 Data Sources .................................................... 6-38

6.3.2 Data Retrieval and Screening ....................................... 6-39

6.3.3 Results ......................................................... 6-41

6.3.4 Uncertainty/Limitations in National Default Values ..................... 6-42
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7. Examples of BAF Calculations ............................................... 7-1

7.1 Example 1: Calculation of a National BAF from a Field-Measured BAF (BAFt

T)

(Method 1) ............................................................. 7-1

7.1.1 Calculating a Total BAF (BAFt

T) ...................................... 7-1

7.1.2 Calculating a Baseline BAF .......................................... 7-1

7.1.3 Calculating a National BAF .......................................... 7-3

7.2 Example 2: Calculation of a National BAF from Field-Measured BSAFs 

(Method 2) ............................................................. 7-4

7.2.1 Calculating a Field-Measured BSAF .................................. 7-4

7.2.2 Determining a Sediment-Water Column Concentration Quotient (Jsocw) ..... 7-5

7.2.3 Calculating a Baseline BAF .......................................... 7-5

7.2.4 Calculating a National BAF .......................................... 7-6

7.3 Example 3: Calculation of a National BAF for Chemical i from BCFt

T × FCM 

(Method 3) ............................................................. 7-7

7.3.1 Calculating a Laboratory-Measured BCFt

T .............................. 7-7

7.3.2 Calculating a Baseline BAF ......................................... 7-7

7.3.3 Calculating a National BAF ......................................... 7-8

7.4 Example 4: Calculation of a National BAF for Chemical i from Kow × FCM 

(Method 4) ............................................................ 7-10

7.4.1 Selecting a Kow and FCM ........................................... 7-10

7.4.2 Calculating a Baseline BAF ......................................... 7-10

7.4.3 Calculating a National BAF ........................................ 7-10

8. References ............................................................. 8-1

Appendix A ................................................................... A-1

Appendix B ................................................................... B-1
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TABLES

Table 3-1. Strengths and Limitations of the Four BAF Methods for Deriving National

BAFs ............................................................ 3-5

Table 4-1. Kpoc Data ........................................................ 4-15

Table 4-2. Regression Equations for Dependence of Kdoc (Geometric Means) 

on KOW .......................................................... 4-18

Table 4-3. Geometric Mean Regression Equations (log Jsocw = A C log KOW + B) for

Polychlorinated Biphenyls (PCBs) and Chlorinated Pesticides ............ 4-30

Table 4-4. Average Jsocw/KOW Ratios for Three Different Ecosystems ............... 4-33

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

Gobas, 1993) .................................................... 4-37

Table 4-6. Food-Chain Multipliers for Trophic Levels 2, 3, and 4 ................... 4-39

Table 4-7. Environmental Parameters and Conditions Used for Determining FCMs

for the National BAF Methodology .................................. 4-39

Table 5-1. BAFt

Ts and Baseline BAFs Exceedance Limit Ratios for Green Bay 

(All Zones Combined) .............................................. 5-7

Table 5-2. Validation Statistics for Method 2: Ratio of Baseline BAFpredicted/Baseline

BAFmeasured ....................................................... 5-17

Table 5-3. Exceedance Levels for Ratio of Method 2-Predicted Baseline BAFs 

(Geometric Mean) to BAFt

Ts from Green Bay and Hudson River .......... 5-17

Table 5-4. Validation Statistics for Method 4: Ratio of Baseline 

BAFpredicted/Baseline BAFmeasured ...................................... 5-25

Table 5-5. Summary Statistics: Differences Between Log Baseline BAFs Predicted

with Method 4 and Log Baseline BAFs Measured from Lake Ontario

(Oliver and Niimi, 1988) for Chemicals with Log KOWs Exceeding 4 ........ 5-26

Table 6-1. Data Preference Hierarchy for Selecting Final Baseline BAFs for Nonionic

Organic Chemicals ................................................. 6-8

Table 6-2. Categories and Mean Per Capita Consumption Rates from the USDA 

CSFII .......................................................... 6-12
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Table 6-3. Lipid Content of Aquatic Organisms Used to Derive National Default 

Values of Lipid Fraction (fR) ........................................ 6-18

Table 6-4. Trophic Level Assignment of Aquatic Species Corresponding to CSFII

Consumption Categories ........................................... 6-22

Table 6-5. National Default Values of Lipid Fraction ............................. 6-28

Table 6-6. Calculation of National Default Values of Consumption-Weighted Mean 

Lipid Fraction .................................................... 6-29

Table 6-7. Sensitivity of National Default Values of Lipid Fraction to Different 

Weighting Assumptions Among Species .............................. 6-31

Table 6-8. Sensitivity of National Default Values of Lipid Fraction to Different 

Weighting Assumptions Among Trophic Levels ........................ 6-33

Table 6-9. Descriptive Statistics of Monte Carlo Simulation of National Default 

Values of Lipid Fraction (10,000 Iterations) ............................ 6-37

Table 6-10. National Default Values for POC and DOC in U.S. Fresh and Estuarine 

Surface Waters ................................................... 6-42

Table 6-11. Descriptive Statistics from the State-Level DOC Distributions ............ 6-46

Table 6-12. Descriptive Statistics from the Ecoregion-Level DOC Distributions ........ 6-47

Table 6-13. Effect of DOC and POC Concentrations on the Freely Dissolved 

Fraction (ffd) Relative to National Default Values of DOC and POC ........ 6-50
xii

FIGURES

Figure 1-1. A. Daily concentrations of a hypothetical nonionic organic chemical over time 

in the water column, predicted using a simple dilution model and daily flow 

data for the Mississippi River at St. Paul, Minnesota ..................... 1-5

Figure 1-1. B. Daily chemical concentrations in piscivorous fish found using the kinetic

food web models of Gobas (1993) with the daily chemical concentrations in

the water column for nonionic organic chemicals with log n-octanol-water

partition coefficients (log KOWs) of 2, 3, ... and 9 ......................... 1-5

Figure 3-1. Framework for selection of methods for deriving national BAFs ............ 3-2

Figure 3-2. BAF derivation for nonionic organic chemicals ......................... 3-10

Figure 4-1. Kpocs determined using the reverse-phase, sparging, ultrafiltration, and

model derived techniques .......................................... 4-16

Figure 4-2. Kdocs determined using the reverse phase, equilibrium dialysis, sparging,

calculated/model derived, fluorescence quenching, solubility enhancement,

biological, and solid-phase microextraction techniques for DOCs from 

different sources .................................................. 4-17

Figure 4-3. Average Kdocs for individual chemicals for different DOC sources: humic 

and fulvic acids, sediment porewaters, soil porewaters and groundwaters, 

and surface waters ................................................ 4-19

Figure 4-4. The residuals between measured log Kdocs and log Kdocs predicted using

relationship of Kdoc = 0.08 KOW. DOC sources: humic and fulvic acids 

without Aldrich humic acid, sediment porewaters, soil porewaters and 

groundwaters, and surface waters .................................... 4-20

Figure 4-5. The sediment-water chemical concentration quotient (Jsocw) for three 

different chemical loading scenarios .................................. 4-23

Figure 4-6. Measured baseline BAFs for PCBs, chlorinated pesticides, and chlorinated

benzenes, chlorinated toluenes, and hexachlorobutadiene from the data of 

Oliver and Niimi (1988) and BAFf

R

d

s predicted using the Gobas and 

Thomann models plotted against Kow for all organisms .................. 4-27

Figure 4-7. Sediment-water column concentration quotient (Jsocw) for PCBs in five 

different geographical zones in Green Bay, Lake Michigan ............... 4-31

Figure 4-8. Average sediment-water column concentration quotients (Jsocw) for

individual PCB congeners across the five different geographical zones

in Green Bay, Lake Michigan ....................................... 4-32
xiii

Figure 4-9. Sediment-water column concentration quotient (Jsocw) for PCBs at 

river miles 189 and 194 ............................................ 4-32

Figure 4-10. The ratio of the 90th to 10th percentile baseline BAF predictions for

piscivorous fish from 100,000 Monte Carlo simulations using the Gobas

model as a function of n-octanol-water partition coefficient (KOW) ......... 4-43

Figure 4-11. FCMs for purely pelagic and purely benthic food webs derived by

modifying the Lake Ontario food web ................................ 4-43

Figure 4-12. FCMs predicted using the Lake Ontario food web with disequilibriums

of 11.5, 23, and 46 ................................................ 4-43

Figure 5-1. BAFt

Ts and Baseline BAFs for PCB congener 149 (2,2',3,4',5',6-

hexachlorobiphenyl) (±1 sd) for adult alewife for different Green

Bay zones ........................................................ 5-6

Figure 5-2. Box plots comparing baseline and field-measured BAFs for six PCB

congeners from Green Bay, Lake Ontario, and Hudson River ecosystems

for 13 fish species with samples segregated according to year classes and

sampling location .................................................. 5-9

Figure 5-3. Relationship between measured and predicted baseline BAFs for

method 3 ........................................................ 5-22

Figure 6-1. A schematic illustrating the aggregation of baseline BAF data by species, 

trophic level, and BAF method type for nonionic organic chemicals under

Procedure 1 ....................................................... 6-3

Figure 6-2. Frequency distribution of national default values of lipid fraction (10,000

iterations) ....................................................... 6-36

Figure 6-3. Reverse cumulative comparison of national default values of lipid fraction

(10,000 iterations) ................................................. 6-36

Figure 6-4. Ecoregion-level DOC distributions for rivers and streams from EPA’s

WATSTORE/LDC and EMAP databases ............................. 6-45

Figure 6-5. Ecoregion-level DOC distributions for rivers and streams from EPA’s

WATSTORE/LDC and EMAP databases ............................. 6-46

Figure 6-6. Effect of DOC, POC, and Kow on the freely dissolved fraction (ffd) ......... 6-50
1-1

1.INTRODUCTION

In 2000, the U.S. Environmental Protection Agency (EPA) published the Methodology

for Deriving Ambient Water Quality Criteria for the Protection of Human Health (USEPA,

2000a). That document (referred to here as the 2000 Human Health Methodology) presents

technical guidance and the steps that EPA will follow for deriving 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. The 2000 Human Health Methodology includes guidance

on chemical risk assessment, exposure, and bioaccumulation. To supplement the 2000 Human

Health Methodology, EPA is developing series of Technical Support Documents (TSD) on Risk

Assessment, Exposure Assessment, and Bioaccumulation. The first volume, (Volume 1: Risk

Assessment; EPA-822-B-00-005), was published with the 2000 Methodology in October 2000.

This volume (Volume 2) of the Technical Support Document (TSD) focuses on the technical

components of the 2000 Human Health Methodology that pertain to the assessment of chemical

bioaccumulation.

The 2000 Human Health Methodology incorporates a number of scientific advancements

made over the past two decades. One of these advancements is in the assessment of chemical

exposure to humans through the aquatic food web pathway. For certain chemicals, exposure via

the aquatic food web is more important than exposure from ingestion of water. Such chemicals

tend to be highly hydrophobic, to partition in aquatic environments to surficial sediments, and to

accumulate in high concentrations in fish and shellfish through the process of bioaccumulation.

One method for incorporating chemical exposure to humans through the aquatic food web

involves estimating the amount of a chemical expected to bioaccumulate in fish and shellfish that

are commonly consumed by populations in the United States. Previously, EPA primarily used

bioconcentration factors (BCFs) to estimate chemical accumulation of waterborne chemicals by

aquatic organisms. The BCF reflects contaminant exposure and accumulation by fish and

shellfish only through the water column. Over the past two decades, however, science has shown

that all the routes (e.g., food, sediment, and water) by which fish and shellfish are exposed to

highly bioaccumulative chemicals may be important in determining the chemical accumulation in

the organism’s body, and that these chemicals can be transferred to humans when they consume

contaminated fish and shellfish. The EPA’s approach to estimating uptake into fish and shellfish

now emphasizes the use of a bioaccumulation factors (BAFs), which account for chemical

accumulation from all potential exposure routes.

The generalized ambient water quality criterion (AWQC) formula for noncancer effects is

shown below (Equation 1-1) as an example of how the BAFs are used in the calculation of a

recommended national AWQC for the protection of human health (USEPA, 2000a). In Equation

1-1, trophic-level specific BAFs are used in the denominator, along with information on the

amount of fish consumed on a daily basis (FI) for each trophic level (i), to estimate human

exposure to contaminants through the aquatic food web.
1-2

(Equation 1-1)

where:

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

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

BW = human body weight (kg)

DI = drinking water intake (L/day)

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

BAFi = bioaccumulation factor (L/kg) at trophic level i (i = 2, 3, 4)

1.1 PURPOSE

This TSD volume:

• Presents the technical basis for the EPA’s approach to developing national BAFs

for the different trophic levels of fish and shellfish commonly consumed by

humans,

• Discusses the underlying assumptions and uncertainties inherent in the approach,

and

• Provides further detail on applying the BAF component of the 2000 Human

Health Methodology.

As indicated in Equation 1-1 of Section 1, the national, trophic level–specific BAFs for a

given contaminant are used by the EPA in the derivation of AWQC for the protection of human

health. A subsequent volume (Volume 3: Development of Site-Specific Bioaccumulation Factors)

provides guidance to States and authorized Tribes for developing site-specific BAFs for the

various trophic levels when BAFs that are more representative of local conditions are preferred.

Neither of the bioaccumulation TSDs should be used alone to derive BAFs, but rather in

conjunction with the 2000 Human Health Methodology. The intended audience for both of these

documents includes the EPA scientists who are responsible for deriving water quality criteria,

State and Tribal risk assessors and stakeholders interested in the technical basis of EPA’s national

BAF methodology, and other users interested in bioaccumulation issues for other applications.

1.2 SCOPE

The goal of EPA’s approach for developing national BAFs is to represent the long-term

average bioaccumulation potential of a pollutant in aquatic organisms that are commonly

consumed by humans throughout the United States. National BAFs are not intended to reflect
1-3

fluctuations in bioaccumulation over short periods (e.g., a few days) because human health

AWQCs are generally designed to protect humans from long-term exposures (over a lifetime) to

waterborne chemicals. 

National BAFs are also intended to account for some major chemical, biological, and

ecological attributes that can affect bioaccumulation in bodies of water across the United States.

For this reason, EPA’s approach includes separate procedures for deriving national BAFs

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. Ionic organic chemicals undergo ionization in water, the extent of

which depends on the pH and the pKa of the water. 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. This TSD

document 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. Therefore, both

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

their application warrant further explanation. 

In addition, EPA’s national BAFs are derived separately for each trophic level to account

for potential biomagnification of some chemicals in aquatic food webs and broad physiological

differences among organisms that may influence bioaccumulation. As discussed in Chapter 3,

lipid contents of aquatic organisms and the amounts of organic carbon in ambient waters affect

bioaccumulation of nonionic organic chemicals in aquatic food webs. National trophic-level

specific BAFs incorporate adjustments for the lipid content of commonly consumed fish and

shellfish and for the freely dissolved fraction of the chemical in ambient water by using

nationwide averages for these two parameters. Further discussion of these parameters is provided

in Section 4.

1.3 IMPORTANT BIOACCUMULATION AND BIOCONCENTRATION CONCEPTS

Several attributes of the bioaccumulation process are important to understanding the

approach used to develop national BAFs used in setting national recommended AWQCs for the

protection of human health. First, the term bioaccumulation refers to the uptake and retention of

a chemical by an aquatic organism from all surrounding media (e.g., water, food, sediment). The

term bioconcentration refers to the uptake and retention of a chemical by an aquatic organism

from water only. For some chemicals (particularly those that are highly persistent and

hydrophobic), the magnitude of bioaccumulation by aquatic organisms can be substantially

greater than the magnitude of bioconcentration. For such chemicals, an assessment of
1-4

bioconcentration alone will underestimate the extent of accumulation in aquatic biota.

Accordingly, EPA’s 2000 Human Health Methodology emphasizes the consideration of chemical

bioaccumulation by aquatic organisms, whereas EPA’s 1980 Methodology emphasized the

measurement of bioconcentration.

Another important aspect of the bioaccumulation process is the steady-state condition.

Specifically, bioaccumulation can be viewed simply as the result of competing rates of chemical

uptake and depuration (chemical loss) by an aquatic organism. The rates of chemical uptake and

depuration can be affected by various factors, including the properties of the chemical, the

physiology of the organism in question, water quality and other environmental conditions, the

ecological characteristics of the water body (e.g., food web structure), and the concentration and

loadings history of the chemical. When the rates of chemical uptake and depuration are equal,

tissue concentrations remain constant over time and the distribution of the chemical between the

organism and its source(s) is said to be at steady state. For constant chemical exposures and

other conditions, the steady-state concentration in the organism represents the highest

accumulation potential of the chemical in that organism under those conditions. The time needed

for a chemical to achieve steady state in the organism has been shown to vary according to the

properties of the chemical, the variability of environmental conditions, and other factors. For

example, some highly hydrophobic chemicals can require long periods (e.g., many months) to

reach steady state between environmental compartments, whereas highly hydrophilic chemicals

usually reach steady state relatively quickly (e.g., hours to days). 

National recommended AWQCs for the protection of human health are typically

designed to protect humans from harmful lifetime or long-term exposures to waterborne

contaminants. Given this goal, assessing bioaccumulation that equals or approximates steadystate accumulation is one of the principles underlying the derivation of national BAFs. For

chemicals that require relatively long periods to reach steady state in aquatic organisms, changes

in the concentration of the chemical in the water column may occur much more rapidly than

corresponding changes in concentrations in tissue. Thus, if the system departs substantially from

steady-state conditions and water concentrations are not averaged over a sufficient time period,

the ratio of the chemical concentration in tissue of organisms to that in water (i.e., the BAF) may

have little resemblance to the steady-state ratio and have little predictive value for long-term

bioaccumulation potential. Therefore, BAF measurements should be based on chemical

concentrations in the water column, averaged over a sufficient period for the chemical of interest.

In addition, the BAFs used in deriving national recommended AWQCs for the protection of

human health should be based on adequate spatial averaging of chemical concentrations in both

tissue of consumed organisms and the water column.

The concept of proper temporal averaging for the determination of BAFs is illustrated in

Figure 1-1 (taken from Burkhard, 2003). Figure 1-1A shows the daily concentrations of a

hypothetical nonionic organic chemical, using a simple dilution model and daily flow data for the

Mississippi River at St. Paul, Minnesota. These daily chemical concentrations in the river can be

transformed into daily chemical concentrations in fish by using the kinetic models of Gobas

(1993). Figure 1-1B shows the results of these transformations in piscivorous fish for chemicals
1-5

Concentration

in Water

A

Day

Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul Oct Jan

Scaled Concentration

(% of largest value)

B

Log

K o w

9

8

7

6

5

4

3

2

Figure 1-1 (A). Daily concentrations of a hypothetical nonionic organic chemical over time in the water column,

predicted using a simple dilution model and daily flow data for the Mississippi River at St. Paul, Minnesota.

(B) Daily chemical concentrations in piscivorous fish found using the kinetic food web models of Gobas (1993) with

the daily chemical concentrations in the water column for nonionic organic chemicals with log n-octanol-water

with log n-octanol-water partition coefficients (Kows) ranging from 2 to 9 for a simple 

hypothetical food web. Together, Figures 1-1A and 1-1B show that concentrations of nonionic

organic chemicals in fish change over time, relative to the concentration of the chemical in the

ambient water, at speeds dependent upon the hydrophobicity of the chemical, i.e., the chemical’s

Kow. The response is graded in magnitude, and the rate of change decreases with increasing Kow.

For chemicals with low Kows (e.g., log Kows of 2 and 3), the speed of change is very fast, such that

concentrations of the chemical in fish mimic the trends of the chemical concentration in ambient

water. For chemicals with large Kows (e.g., log Kows of 6 and 7), concentrations of the chemical in

fish change slowly relative to those in the water, and in general, the concentrations in fish follow

the long-term trends for the chemical concentration in the water. 

Clearly, BAFs based on inappropriate temporal averaging of chemical concentrations in

the water will have little predictive power; thus, BAFs should be based on concentrations in the

water column that are averaged over a sufficient period of time that is appropriate for the

chemical of interest. For this reason, a BAF was defined in the 2000 Human Health Methodology

as representing the ratio (in liters per kilogram) of the concentration of a chemical in the tissue of

an aquatic organism to its concentration in the ambient water in situations where the organism

and its food are exposed and the ratio does not change substantially over time (i.e., the ratio

reflects bioaccumulation at or near steady state). Similarly, a BCF was defined as the ratio (in

liters per kilogram) of the concentration of a chemical in the tissue of an aquatic organism to the

chemical’s concentration in the ambient water, in situations where the organism is exposed

through the water only and the ratio does not change substantially over time. 
1-6

From the perspective of sampling for determining 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; current

chemical concentrations in the water provide a good predictor of the chemical concentration in

the fish. Burkhard (2003) provides additional details on BAF sampling design and EPA will

provide additional information on field sampling designs for determination of BAFs in TSD

Volume 3: Development of Site-Specific Bioaccumulation Factors. 
2-1

(Equation 2-1)

(Equation 2-2)

2. DEFINITIONS

The following terms and their definitions are used throughout this document. 

2.1 BIOACCUMULATION

Bioaccumulation. The net accumulation of a chemical by an aquatic organism as a result of

uptake from all environmental sources.

Bioaccumulation factor (BAF). The ratio (in liters per kilogram of tissue) 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 BAF is calculated as:

where:

Ct = concentration of chemical in tissue 

Cw = concentration of chemical in water

Because chemical concentrations in tissue and water can be defined in terms of chemical

partitioning to different biological or chemical phases (e.g., total concentrations in tissue or water,

concentration in lipid, concentration that is freely dissolved in water), the general equation for

BAF (Equation 2-1) is further refined below to delineate among these different phases.

Total bioaccumulation factor (BAFt

T). A BAF based on the total concentration of chemical in

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

either a specific tissue or a whole organism and is based on wet tissue. The total concentration of

the chemical in water includes chemical associated with particulate organic carbon, chemical

associated with dissolved organic carbon, and chemical freely dissolved in the water. A BAFt

T is

often referred to as a “field-measured” BAF because it is derived from analysis of tissue and

water samples collected from the field. The BAFt

T is expressed in liters per kilogram of lipid. The

BAFt

T is calculated as:

where:

Ct = total concentration of chemical in tissue 

Cw = total concentration of chemical in water
1As described in Section 3.2.2 and illustrated by Figure 3-1, baseline 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. 

2-2

(Equation 2-3)

(Equation 2-4)

Baseline bioaccumulation factor (Baseline BAF or BAF f

L

d

). For nonionic organic chemicals

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

behavior applies1), a BAF 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 BAF is

expressed in liters per kilogram of lipid. The baseline BAF is determined using the equation:

where:

BAFt

T = Total BAF

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

dissolved

fR = fraction of tissue that is lipid

Baseline BAF can also be defined as:

where:

BAFf

R

d = lipid-normalized and freely dissolved-based bioaccumulation factor (see

definition below)

fR = fraction of tissue that is lipid

Note: Appendix A presents the derivation of the baseline BAF and refers to it as

“BAFL

f d

.” The subscript “L” signifies concentration of the chemical specifically in

lipid, in contrast to “R,” which refers to lipid normalization in which the

concentration of the chemical in total tissue is divided by the fraction of the tissue

that is lipid (fR). The superscript “fd” signifies the chemical that is freely dissolved

in water rather than total chemical in water. Based on an equilibrium partitioning

assumption for the chemical’s distribution in both the organism and the water,

concentrations based on the “L” and “fd” chemical expressions can be calculated

using measured or predicted values of the fraction of tissue that is lipid and

fraction of total chemical that is freely dissolved in water, respectively (see
2-3

(Equation 2-5)

(Equation 2-6)

Appendix A). This avoids practical limitations associated with the direct analytical

measurement of concentrations of total chemical in lipid and freely dissolved

chemical in water.

Lipid-normalized and freely dissolved-based bioaccumulation factor (BAFf

R

d

). The ratio (in

liters per kilogram of lipid) 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 and its food are exposed and the ratio does not change substantially over time. The

BAFf

R

d

 is calculated as:

where:

CR = lipid-normalized concentration of chemical in tissues

C

f

w

d = concentration of chemical that is freely dissolved in water

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

nationwide average lipid content for trophic level “n” and nationwide average organic carbon in

ambient waters. The national BAF(TL n) is expressed in liters per kilogram wet tissue. The national

BAF(TL n) is calculated using the equation:

where:

Final Baseline BAFTL n = mean baseline BAF for trophic level “n”

fR(TL n) = fraction of tissue that is lipid in aquatic organisms at trophic level “n”

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

dissolved

2.2 BIOCONCENTRATION

Bioconcentration. The net accumulation of a chemical by an aquatic organism as a result of

uptake directly from the ambient water, through gill membranes or other external body surfaces.

Bioconcentration factor (BCF). The ratio (in liters per kilogram of tissue) 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. The BCF is calculated as:
2As described in Section 3.2.2 and illustrated by Figure 3-1, baseline BCFs 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. 

2-4

(Equation 2-7)

(Equation 2-8)

where:

Ct = concentration of chemical in tissue

Cw = concentration of chemical in water

Because chemical concentrations in tissue and water can be defined in terms of chemical

partitioning to different biological or chemical phases (e.g., total concentrations in tissue or water,

concentration in lipid, concentration that is freely dissolved in water), the general equation for

BCF (Equation 2-7) is further refined below to delineate among these different phases.

Total bioconcentration factor (BCFT

t

). A BCF based on the total concentration of chemical in

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

either a specific tissue or a whole organism and is based on wet tissue. The total concentration of

the chemical in water includes chemical associated with particulate organic carbon, chemical

associated with dissolved organic carbon, and chemical freely dissolved chemical in the water. A

BCF is often referred to as a “laboratory-measured BCF” because it can be measured only in the

laboratory. The BCFt

T is expressed in liters per kilogram of lipid. The BCFt

T is calculated as:

where:

Ct = total concentration of chemical in tissue 

Cw = total concentration of chemical in water

Baseline bioconcentration factor (Baseline BCF or BCF f

L

d

). For nonionic organic chemicals

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

behavior applies2), 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. The baseline BCF is determined using the equation:
2-5

(Equation 2-9)

(Equation 2-10)

where:

BCFt

T = Total BCF

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

dissolved

fR = fraction of tissue that is lipid

Baseline BCF can also be defined as:

where:

BCFf

R

d = lipid-normalized and freely dissolved-based bioconcentration factor (see

definition below)

fR = fraction of tissue that is lipid

Note: Appendix A presents the derivation of the baseline BCF and refers to it as

“BCFL

f d

.” The subscript “L” signifies concentration of the chemical specifically in

lipid, in contrast to “R,” which refers to lipid normalization in which the

concentration of the chemical in total tissue is divided by the fraction of the tissue

that is lipid (f R). The superscript “fd” signifies the chemical that is freely dissolved

in water rather than total chemical in water. Based on an equilibrium partitioning

assumption for the chemical’s distribution in both the organism and the water,

concentrations based on the “L” and “fd” chemical expressions can be calculated

using measured or predicted values of the fraction of tissue that is lipid and

fraction of total chemical that is freely dissolved in water, respectively (see

Appendix A). This avoids practical limitations associated with the direct analytical

measurement of concentrations of total chemical in lipid and freely dissolved

chemical in water.

Lipid-normalized and freely dissolved-based bioconcentration factor (BCFf

R

d

). The ratio (in

liters per kilogram of lipid) 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

R

d

 is calculated as:
2-6

(Equation 2-11)

(Equation 2-12)

(Equation 2-13)

where:

CR = lipid-normalized concentration of chemical in tissues

C

f

w

d = concentration of chemical that is freely dissolved in water

2.3 ADDITIONAL TERMS

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 factor (BMF). The ratio (unitless) of the concentration of a chemical in a

predator organism at a particular 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. 

For nonionic organic chemicals (and certain ionic organic chemicals to which similar lipid and

organic carbon partitioning behavior applies), a BMF can be calculated using lipid-normalized

concentrations of chemical in the tissue of organisms at two successive trophic levels as: 

where:

BMF(TL, n) = biomagnification factor for trophic level “n” (TL “n”)

CR (TL, n) = lipid-normalized concentration of chemical in tissue of predator

organism at a given trophic level (TL “n”)

CR (TL, n-1) = lipid-normalized concentration of chemical in tissue of prey organism

at the next lower trophic level from the predator (TL “n–1") 

For those inorganic, organometallic, and ionic organic chemicals for which lipid and organic

carbon partitioning does not apply (see Section 5.6), a BMF can be calculated using chemical

concentrations in the tissue of organisms at two successive trophic levels as: 
2-7

(Equation 2-14)

where:

BMF(TL, n) = biomagnification factor for trophic level “n” (TL “n”)

Ct (TL, n) = concentration of chemical in tissue of predator organism at a given

trophic level (TL “n”)

Ct (TL, n-1) = concentration of chemical in tissue of prey organism at the next lower

trophic level from the predator (TL “n-1")

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 (in kilograms of sediment organic carbon per kilogram of lipid) of the lipidnormalized concentration of a chemical in tissue of an aquatic organism to its organic carbonnormalized concentration in surface sediment, in situations where the ratio does not change

substantially over time, both the organism and its food are exposed, and the surface sediment is

representative of average surface sediment in the vicinity of the organism. The BSAF is calculated

as:

where:

CR = lipid-normalized concentration of chemical in tissue

Csoc = concentration of chemical in dry sediment, normalized to sediment organic

carbon

Depuration. Loss of a chemical from an organism as a result of any active or passive 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 (FCM). For nonionic organic chemicals (and certain ionic organic

chemicals to which similar lipid and organic carbon partitioning behavior applies), the ratio of a

baseline BAF for an organism of a particular trophic level to the baseline BCF (usually

determined for organisms in trophic level one). For inorganic, organometallic, and certain ionic

organic chemicals to which lipid and organic carbon partitioning does not apply, a FCM can be

derived based on total (wet or dry weight) concentrations of the chemical in tissue as described in

Sections 4.4.1 and 4.4.2. 

Freely dissolved concentration (Cf

w

d

). For nonionic organic chemicals, the concentration of the

chemical that is dissolved in ambient water, excluding the portion sorbed onto particulate or

dissolved organic carbon (POC or DOC). The freely dissolved chemical concentration is

considered to represent the most bioavailable form of an organic chemical in water and therefore
2-8

(Equation 2-15)

(Equation 2-16)

(Equation 2-17)

is the form that best predicts bioaccumulation. The freely dissolved concentration can be

determined as:

where:

Cw

t = total concentration of chemical in water

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

dissolved

Hydrophilic. Having affinity for water; the extent to which a chemical is attracted to partitioning

into the water phase. Hydrophilic organic chemicals have a greater tendency to partition into

polar phases (e.g., water) than do hydrophobic chemicals.

Hydrophobic. Lacking affinity for water; the extent to which a chemical avoids partitioning into

the water phase. Highly hydrophobic organic chemicals have a greater tendency to partition into

nonpolar phases (e.g., lipid, organic carbon) than do hydrophilic chemicals.

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

organism divided by the fraction of that tissue or whole organism that is lipid. The lipidnormalized concentration can be calculated as:

where:

Ct = concentration of chemical in tissue

fR = fraction of tissue that is lipid

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

n-octanol phase to its concentration in the aqueous phase in an equilibrated two-phase n-octanolwater system. For log Kow, the log of the n-octanol-water partition coefficient is a base 10

logarithm.

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. The sediment organic carbon-normalized concentration can be calculated as: 
2-9

(Equation 2-18)

where:

Cs = concentration of chemical in dry sediment

fsoc = fraction of dry sediment that is organic carbon

Sediment-water column concentration quotient (Jsocw). The ratio (in liters per kilogram of

organic carbon) of the concentration of chemical in the sediment, on an organic carbon basis, to

that in the water column, on a freely dissolved basis. Πsocw when divided by the Kow of the

chemical provides a measure, for a given ecosystem, of the chemical’s thermodynamic gradient

between the sediment and the water column. The sediment-water column concentration quotient

is calculated as:

where:

Csoc = concentration of chemical in dry sediment, normalized to sediment organic

carbon

C

f

w

d = concentration of chemical that is freely dissolved in water

Steady state. A condition reached by a system when rates of chemical movement between

phases and reactions within phases are constant 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.

Uptake. Movement of chemical from the environment into an organism as the result of any

active or passive process.
3-1

3. OVERVIEW OF THE NATIONAL BAF METHODOLOGY 

This section provides an overview of the methodology EPA will use for deriving national

BAFs for setting AWQCs for the protection of human health. As mentioned in Section 1,

national BAFs are intended to account for some major chemical, biological, and ecological

attributes that can affect bioaccumulation in bodies of water across the United States. Therefore,

EPA will use separate procedures for deriving national BAFs depending on the type of chemical

(i.e., nonionic organic, ionic organic, inorganic, and organometallic). In addition, to account for

other factors, such as biomagnification and broad physiological differences between trophic

levels, EPA’s national BAFs are derived separately for each trophic level. The methodology

results in three national trophic level–specific BAFs for each chemical, one specific for each of

trophic levels 2, 3, and 4 (BAF2, BAF3, and BAF4).

BAFs can be measured or estimated with a variety of methods, ranging from empirically

driven approaches that rely on measurements of chemical concentrations in aquatic organisms

and their surrounding environmental media (water and sediment) to mechanistically driven

approaches that rely on food web models in combination with information about the properties

of chemicals and ecosystems to estimate bioaccumulation. The four methods that EPA will use

for deriving national BAFs are described in the following sections. For a given chemical, the

choice of which method to use 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 multiple evaluation steps are involved in selecting the

most appropriate BAF method(s) for a given chemical and data set, EPA has developed a

decision framework for deriving national BAFs (Figure 3-1). 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 3-1) 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

following subsections are a prelude to the detailed discussion of the national BAF methodology

provided in Sections 4 through 7. Section 3.1 introduces each of the four methods available for

deriving national BAFs, including a discussion of their relative strengths and limitations. Section

3.2 provides additional discussion and explanation of the BAF derivation framework that applies

to all chemical types. 
3-2

CLASSIFY CHEMICAL 

OF CONCERN

Nonionic Organic Ionic Organic Inorganic & 

Organometallic 

PROCEDURE #4

HYDROPHOBICITY?

METABOLISM?

PROCEDURE #3

PROCEDURE #2

PROCEDURE #1

BIOMAGNIFICATION?

PROCEDURE #6

COLLECT & REVIEW 

DATA

DEFINE CHEMICAL 

OF CONCERN

PROCEDURE #5

 IONIZATION 

NEGLIGIBLE?

 

Moderate-High

(Log K 4) OW >

Low

(Log K < 4) OW

Yes No No Yes

METABOLISM?

Low/

Unknown

Low/

Unknown

High High

1. Field BAF

2. BSAF

3. LAB BCF*FCM

4. KOW* FCM

1. Field BAF

2. BSAF

3. Lab BCF

1. Field BAF or 

 Lab BCF

2. KOW

1. Field BAF or 

 Lab BCF

1. Field BAF or 

 Lab BCF

1. Field BAF

2. Lab BCF*FCM

Figure 3-1. Framework for selection of methods for deriving national BAFs.
3-3

3.1 SUMMARY OF FOUR BIOACCUMULATION METHODS

Bioaccumulation factors used to derive national trophic level–specific BAFs can be

measured or predicted using one or more of the following four methods, depending on the type

of chemical and its properties:

1. Measured BAFs derived from data obtained from a field study (i.e., fieldmeasured BAFs)

2. BAFs predicted from biota-sediment accumulation factors (BSAFs) obtained from

a field study (i.e., field-measured BSAFs)

3. BAFs predicted from laboratory-measured BCFs, with or without adjustment by a

food-chain multiplier

4. BAFs predicted from a chemical’s n-octanol-water partition coefficient (Kow), with

or without adjustment by a food-chain multiplier

Each of the four methods is summarized below. Details of each of the four methods are

described in Section 5.

3.1.1 Field-Measured BAFs

A BAF derived from data obtained from field-collected samples of tissue and water—

referred to here as a “field-measured BAF”—is the most direct measure of bioaccumulation. A

field-measured BAF is determined from measured chemical concentrations in an aquatic

organism and the ambient water collected from the same field location. 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 fieldmeasured BAF also reflects factors that influence the bioavailability and metabolism of a

chemical that might occur 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.

3.1.2 BAFs Predicted from a Field-Measured BSAF

For nonionic organic chemicals (and certain ionic organic chemicals to which similar lipid

and organic carbon partitioning behavior applies), BAFs can also be predicted from BSAFs. A

BSAF is similar to a field-measured BAF in that the concentration of a chemical in biota is

measured from field-collected samples and it reflects an organism’s exposure to all relevant

exposure routes. A BSAF also accounts for bioavailability and chemical metabolism that might

occur in the aquatic organism or its food web. A BSAF references the concentration of the

chemical in an organism to the concentration of chemical in sediment, but it may be converted to

a BAF when the chemical’s distribution between sediments and water can be estimated. The

BSAF procedure is used only to predict a BAF for moderate to highly hydrophobic organic

chemicals.
3-4

3.1.3 BAFs Predicted from Laboratory-Measured BCFs

A laboratory-measured BCF can be used to estimate a BAF for organic and inorganic

chemicals either with or without adjustment with a food-chain multiplier, depending on the

importance of nonaqueous exposure routes. However, unlike a field-measured BAF or one

predicted from a field-measured BSAF, a laboratory-measured BCF typically reflects only the

accumulation of chemical through the water exposure route. A laboratory-measured BCF may

therefore underpredict BAFs for chemicals for which accumulation from sediment or dietary

sources is important. In these cases, laboratory-measured BCF can be adjusted by a factor known

as a food-chain multiplier (FCM) to better reflect accumulation through the food web from

dietary exposures. Because a laboratory-measured BCF is determined by using the measured

concentration of a chemical in an aquatic organism and its surrounding water, a laboratorymeasured BCF often reflects metabolism of the chemical that occurs in the organism during the

BCF measurement, but not in the food web.

3.1.4 BAFs Predicted from Kow

A chemical’s Kow (measured or predicted) can also be used to predict a BAF for nonionic

organic chemicals. This procedure is appropriate for nonionic organic chemicals but can also be

applied to certain ionic chemicals that have lipid and organic carbon partitioning behavior similar

to that of nonionic organics. The Kow is strongly correlated with the BCF for nonionic organic

chemicals, in particular those chemicals that are poorly metabolized by aquatic organisms. For

nonionic organic chemicals where food web exposure is important, use of the Kow alone, as an

estimate of BCF, will underpredict the BAF because the BCF accounts only for chemical

exposure from water. In such cases, the Kow is adjusted with an FCM as described for the BAF

method in Section 3.1.3.

3.1.5 Advantages and Limitations of BAF Methods

Each BAF derivation method summarized above has strengths and limitations associated

with it that will be considered and balanced when deriving national BAFs. These strengths and

limitations, as summarized in Table 3-1, form the basis for the Framework for selecting methods

for deriving national BAFs (Figure 3-1) that is described in Section 3.2. For example, use of the

field-measured BAF method is advantageous in that it applies to all chemical types, and accounts

for site-specific factors that affect bioavailability, biomagnification, and metabolism. However,

the current database of acceptable field-measured BAFs is relatively limited, in terms of both 

number of sites and chemicals for which they have been derived. Furthermore, field-measured

BAFs cannot be readily determined for chemicals that are very difficult to accurately measure in

the water column (e.g., 2,3,7,8-TCDD). 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 method is

the only field-based method that can be used for chemicals such as 2,3,7,8-TCDD that are

difficult to measure in ambient water. In EPA’s framework, however, application of the BSAF

method is currently limited to nonionic organic chemicals of moderate to high hydrophobicity.

BAFs predicted from laboratory-measured BCF can be applied to all chemical types, and data are
3-5

generally more plentiful than with field-measured BAFs. However, laboratory-based BCFs by

themselves do not address chemical biomagnification in food webs unless they are adjusted with

a field- or model-derived FCM. In addition, 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). Finally, the model-derived BAF

derivation method (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 and is currently constrained by the lack of in vivo data on

chemical metabolism.

Table 3-1. Strengths and Limitations of the Four BAF Methods for Deriving National BAFs

BAF derivation

method 

Strengths Limitations

1. Fieldmeasured

BAF

• Applicable to all chemical types

• Incorporates chemical biomagnification

and metabolism

• Reflects site-specific attributes that affect

bioavailability and dietary exposure

• High-quality data currently limited to few

sites and chemicals

• Representative chemical concentration in

water may be difficult to quantify

2. BAF

predicted

from fieldmeasured

BSAF

• Incorporates chemical biomagnification

and metabolism

• Reflects site-specific attributes that affect

bioavailability and dietary exposure

• Useful for chemicals that are difficult to

analyze in water

• Use of chemical concentrations in

sediment reduces temporal variability

• Limited to nonionic organic chemicals

with log Kow $ 4 

• High-quality data currently limited to few

chemicals and sites

• Accuracy depends on representativeness

and quality of estimate of chemical

distribution between sediment and water

3. BAF

predicted

from labmeasured

BCF × FCM

• Applicable to all chemical types

• BCF may account for chemical

metabolism in test organisms

• Large BCF database available

• Standardized test methods

• Chemical metabolism, when present in

food web, generally not accounted for

• High-quality data currently limited for

highly hydrophobic chemicals, in part

because of lack of ancillary data that affect

bioavailability

4. BAF

predicted

from a Kow ×

FCM

• Readily applied with minimal input data • Limited to nonionic organic chemicals

• Chemical metabolism, when present, not

accounted for

• Accuracy depends on accuracy of Kow

3.2 FRAMEWORK FOR DERIVING NATIONAL BAFs

The EPA’s framework for deriving national BAFs is depicted in Figure 3-1. The goal of

this framework and the BAF guidance presented in the 2000 Human Health Methodology is to

facilitate the full use of available data and methods for deriving national BAFs while prioritizing

and restricting the use of certain BAF methods based on their inherent strengths and limitations,

as summarized in Section 3.1. Use of this decision framework results in selection, based on the

class and properties of the chemical, of one of six “Procedures,” each of which can be used to
3-6

derive national BAFs for a chemical having the specified class and properties. Each procedure

includes one or more of the methods described in Section 3.1. Within a procedure, the number

next to each BAF method indicates its general order of preference in the hierarchy for calculating

national BAFs. For example, a field-measured BAF is generally given the highest preference for

deriving a national BAF using Procedure 1, followed by a BAF predicted from a BSAF, a BAF

predicted from a BCF x FCM, and, finally, a BAF predicted from a Kow × FCM However, the

hierarchy of methods within each procedure is not intended to be inflexible, as explained in

Section 6.1 and in the 2000 Human Health Methodology. Some situations may indicate that

greater uncertainty is likely to occur when applying a BAF derived from a “more highly

preferred” method (e.g., a field-measured BAF within Procedure 1) than with a “less preferred”

method (e.g., BAF predicted from BCF × FCM within Procedure 1), for example, when data

from the more preferred method are limited in terms of their representativeness, quantity, or

quality relative to the lower-tier method. In these situations, data from the lesser preferred, but

least uncertain, method should be used to derive the national BAFs.

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 3.1 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. 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. Ionic organic chemicals undergo ionization in water, the extent of

which depends on the pH 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 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. 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. These steps are summarized below for each of the three chemical

categories.
3-7

3.2.1 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 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 this 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 laboratorymeasured BCF adjusted with an FCM. If biomagnification is determined to be unlikely, fieldmeasured BAFs and laboratory-measured 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. As a result of the complexity of assessing the fate and effects of metals in the

environment, EPA has embarked on an initiative to provide additional guidance on conducting

metal assessments, including metals bioaccumulation by aquatic organisms (USEPA, 2002).

3.2.2 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. 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 deriving national default 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 organic chemicals does not

apply. For the perfluorinated alkyl acids, Procedure 6 (Figure 3-1) would be used to derive

national default BAFs.
3-8

3.2.3 BAF Derivation Procedures for Nonionic Organic Chemicals

Deriving national BAFs for nonionic organic chemicals is somewhat more complex than

for the other two chemical classes. First, four national BAF derivation procedures are applicable

to nonionic organic chemicals. Second, selecting the most appropriate derivation procedure

depends greatly on chemical properties, which are evaluated in two decision steps (see Figure 

3-1). Finally, once the derivation procedure is selected, additional adjustments are made to the

BAFs in order to account for differences in factors that affect bioaccumulation of this group of

chemicals in aquatic organisms (e.g., lipid content in test organisms and organic carbon content

in water). 

Figure 3-2 shows the national BAF derivation process for nonionic organic chemicals.

This process is divided into four steps:

Step 1. Selecting the BAF derivation procedure

Step 2. Calculating individual baseline BAFs

Step 3. Selecting final baseline BAFs

Step 4. Calculating national BAFs from the final baseline BAFs

A summary of each step follows. 

Step 1: Selecting a BAF Derivation Procedure

Step 1 of the approach determines which of the four BAF procedures described in

Section 3-1 will be appropriate for deriving the national BAF for a given nonionic organic

chemical. As shown in Figure 3-1, there are two decision points. The first decision point requires

knowledge of the chemical’s hydrophobicity (i.e., the Kow of the chemical). The Kow provides an

initial basis for assessing whether nonaqueous (e.g., food web, sediment) exposure and

biomagnification may be a concern for nonionic organic chemicals. Knowledge of the likely

importance of nonaqueous routes of exposure determines whether or not some methods (e.g.,

lab-measured BCF, Kow-derived BAF) require additional adjustments to account for this

exposure. Guidance for selecting the Kow for a chemical is provided in Appendix B of this TSD.

For the purposes of the 2000 Human Health Methodology, nonionic organic chemicals with log

Kow values equal to or greater than 4.0 are classified as “moderately to highly hydrophobic.” For

moderately to highly hydrophobic nonionic organic chemicals, available data indicate that

exposure through the diet and other nonaqueous routes can become important in determining

chemical residues in aquatic organisms (e.g., Russell et al., 1999; Fisk et al., 1998; Oliver and

Niimi, 1983, 1988; Niimi, 1985; Swackhammer and Hites, 1988). Below a log Kow of 4, available

information indicates that nonaqueous exposure to these chemicals is not likely to be important. 

The second decision point involves assessing the importance that chemical metabolism

might have in determining chemical concentrations in aquatic organisms. Assessing metabolism

is important because it affects the degree to which a chemical bioaccumulates (and biomagnifies)

in aquatic food webs. For example, some polynuclear aromatic hydrocarbons have Kow values

that would warrant initial concern for biomagnification (i.e., log Kow > 4), but chemical
3-9

metabolism by higher organisms (primarily fish) often results in reduced concentrations in fish

(Endicott and Cook, 1994; Burkhard, 2000). Guidance for assessing whether a high or low rate of

metabolism is likely for a given chemical is provided in Section 5.4.2.3 of the 2000 Human Health

Methodology.

Together, the hydrophobicity and metabolism decision points lead to the selection of one

of four BAF procedures. Procedure 1 applies to chemicals with moderate to high Kows, where

(1) the influence of chemical metabolism is suspected to be minor (e.g., polychlorinated

biphenyls [PCBs], dichlorodiphenyltrichloroethane [DDT], dieldrin, etc.) or (2) there are

insufficient data on chemical metabolism to make a determination (this reflects a policy decision

to err on the side of public health protection in the absence of data). Procedure 2 applies to

moderate-to-high Kow chemicals for which the influence of chemical metabolism on

bioaccumulation is considered to be important (e.g., selected polynuclear aromatic

hydrocarbons). Within this procedure, the use of Kow-based estimates (with or without FCMs) of

BAFs is restricted because the Kow may substantially overpredict bioaccumulation for chemicals

that are metabolized. Procedure 3 applies to low-Kow chemicals for which chemical metabolism is

not considered significant. For such chemicals, no preference is given to field-measured BAFs

over laboratory-measured BCF (i.e., both methods are appropriate), since biomagnification is not

considered important for low-Kow chemicals. Procedure 4 applies to low-Kow chemicals for which

metabolism is considered to be important. In this procedure as in Procedure 2, use of Kowpredicted BAFs is not recommended because the Kow may substantially overpredict

bioaccumulation.

Step 2: Calculating Individual Baseline BAFs

Step 2 involves calculating individual, species-specific baseline BAFs using all of the

methods available within the selected BAF derivation procedure. Calculating an individual

baseline BAF involves normalizing the field-measured BAFt

T (or laboratory-measured BCFt

T),

which are typically based on total concentrations in tissue and water by the lipid content of the

study organism and the fraction of total chemical that is freely dissolved in the ambient water.

Both the lipid content in the organism and the freely dissolved chemical concentration (as

influenced by organic carbon in water) have been shown to be important factors that influence

the bioaccumulation of nonionic organic chemicals (e.g., Mackay, 1982; Connolly and Pederson,

1988; Thomann, 1989; Suffet et al., 1994). Therefore, baseline BAFs, which are expressed on the

basis of the chemical concentration in the lipid fraction of tissue and freely dissolved in water, are

considered more amenable to being applied across different species and bodies of water than are

BAFs or BCF expressed on the basis of the total concentrations in the tissue and water. Because

bioaccumulation can be strongly influenced by the trophic position of aquatic organisms

(through either biomagnification or physiological differences), extrapolation of baseline BAFs

should not be performed between species of different trophic levels. An example of how a

baseline BAF is calculated from a field-measured BAFt

T is shown by Equation 3-1. Equations for

calculating baseline BAFs differ according to the BAF derivation method. Examples of baseline

BAF equations for other BAF derivation methods are provided in Sections 5.1 through 5.4 of this

TSD and in Sections 5.4.3 through 5.4.6 of the 2000 Human Health Methodology.
3-10

(Equation 3-1)

CHEMICAL 

PROPERTY & 

METABOLISM DATA

Step 1. 

Select BAF 

Procedure

Step 2.

Calculate 

Individual 

Baseline BAFs

Step 3. 

Select Final 

Baseline BAF(s)

Step 4. 

Calculate 

National BAF(s)

LIPID CONTENT OF 

STUDY ORGANISM

FREELY DISSOLVED 

FRACTION IN STUDY 

WATER

DATA PREFERENCE 

HIERARCHY

UNCERTAINTY IN 

BASELINE BAFs

LIPID CONTENT OF 

CONSUMED 

ORGANISMS

FREELY DISSOLVED 

FRACTION IN AWQC 

WATERS

BIOMAGNIFICATION 

DATA

where:

BAFt

T = Total BAF

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

dissolved

fR = fraction of the tissue that is lipid

Figure 3-2. BAF derivation for nonionic organic chemicals.
3-11

(Equation 3-2)

Step 3: Selecting Final Baseline BAFs 

Step 3 of the methodology consists of selecting the final baseline BAFs from the

individual baseline 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). The individual baseline BAFs should

be calculated using as many of the methods as possible under the appropriate BAF derivation

procedure. As described earlier, the data preference hierarchy discussed in Section 5.4.2 of the

2000 Human Health Methodology is not inflexible. Rather, it is intended to be a guide for

selecting the most appropriate final BAF when the uncertainty is similar between two individual

baseline BAFs calculated using different methods. Section 6.1 of this TSD and Section 5.4.3.2 of

the 2000 Human Health Methodology provide more detailed discussions of this step.

Step 4: Calculating National BAFs

The fourth and final step in calculating national BAFs for nonionic organic chemicals

involves calculating three trophic-level specific BAFs that will be used in the equation to calculate

national recommended AWQC for the protection of human health. This step involves adjusting

final baseline BAFs to reflect the average lipid content of commonly consumed fish and shellfish

and bioavailability of the chemical in waters to which the national recommended AWQC will

apply. Converting baseline BAFs to national BAFs requires information on (1) the percent lipid

of the aquatic organisms commonly consumed in the United States and (2) the fraction of

chemical that is freely dissolved that is expected to be present in the ambient waters of interest.

Baseline BAFs are not used directly in the derivation of the national AWQC because they do not

reflect the conditions that affect chemical bioavailability in U.S. waters or chemical accumulation

due to lipid content of the fish and shellfish residing in U.S. waters. The equation for calculating a

national BAF for each trophic level is:

where:

Final Baseline BAFTL n = mean baseline BAF for trophic level “n”

fR(TL n) = fraction of tissue that is lipid in aquatic organisms at trophic

level “n”

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

freely dissolved

The technical basis of Equation 3-2 is provided in Section 4. Procedures EPA will use for

determining each component of Equation 3-2 are provided in Sections 6.4 and 6.5.
4-1

4. BACKGROUND INFORMATION ON LIPID NORMALIZATION,

BIOAVAILABILITY, AND BIOMAGNIFICATION

National trophic-level specific BAFs are intended to represent the long-term, average

bioaccumulation potential of a pollutant in aquatic organisms of a particular trophic level

(i.e., 2, 3, or 4) that are commonly consumed by humans throughout the United States. For

certain chemicals (e.g., nonionic organics), chemical bioavailability, biota lipid content, and

trophic transfer can affect bioaccumulation potential and ultimately the magnitude of BAFs.

Because chemical bioavailability, biota lipid content, and trophic transfer can vary across

locations and species, these factors should be accounted for in the derivation of national BAFs.

Figure 3-2 in Section 3.2.3 presents EPA’s stepwise process for developing national BAFs for

nonionic organics, of which a key step is derivation of baseline BAFs. 

The scientific basis for the lipid and freely dissolved fraction normalizations for nonionic

organic chemicals are presented in Sections 4.1 through 4.3. Section 4.4 presents a discussion on

how biomagnification is incorporated into the baseline BAFs in certain BAF methods.

4.1 LIPID NORMALIZATION

4.1.1 Background and Theory

The importance of lipid content in influencing the bioaccumulation of nonionic organic

chemicals in aquatic organisms is well documented. Early work by Reinert (1969) and Reinert et

al. (1972) demonstrated that nonionic organic chemicals concentrate in the lipids of organisms,

and that differences in DDT concentrations between species and size groups are reduced when

the concentrations of chemicals are normalized by lipid content. Numerous other studies have

confirmed the role of lipid content in the bioconcentration and bioaccumulation of organic

chemicals by aquatic organisms (e.g., Baron, 1990; van den Heuvel, et al., 1991; Leblanc, 1995;

Stow et al., 1997). The lipid compartment is fundamental to equilibrium partitioning theory and to

most bioaccumulation models of organic chemicals, wherein bioconcentration is described as a

chemical partitioning process between the lipid and water compartments (e.g., Mackay, 1982;

Barber et al., 1991; Gobas, 1993; Thomann, 1989; Di Toro et al., 1991). Although other

compartments are assumed to exist in aquatic organisms (e.g., interstitial water, nonlipid

biological material), partitioning to lipids becomes increasingly important as chemical

hydrophobicity increases.

Recognition of the importance of lipids when assessing and predicting bioaccumulation

of nonionic organic chemicals has led to the practice of normalizing chemical concentrations in

tissue by lipid content. Lipid normalization, which is the process of dividing the total

concentration of a chemical in tissue by the fraction of the tissue that is lipid (fR), is usually

performed to account for variation in bioaccumulation between species (or individuals within a

species) that results from differences in lipid content alone. Although quantifying chemical

concentrations in lipids would be a direct measure of chemical partitioning to lipids, it is

technically difficult to do this because of the diffuse nature of lipids in tissues of aquatic

organisms. Lipid normalization has been conducted since at least the 1980s for deriving AWQC
4-2

for the protection of human health (USEPA, 1980), and more recently in developing Equilibrium

Partitioning Sediment Benchmarks (USEPA, 2000b).

In the 2000 Human Health Methodology, EPA continues to recommend that BAFs be

adjusted by the fraction of tissue that is lipid in order to account for differences in

bioaccumulation that result from variation in lipid content among aquatic species (USEPA,

2000a). As depicted by Figure 3-2, BAFs are adjusted by lipid fraction (fR) in two separate steps.

In the first step, data on the fraction of tissue that is lipid (fR) and data on the freely dissolved

chemical concentration in water (Cf

w

d

) are used to calculate a baseline BAF from a field-measured

BAFt

T or a lab-measured BCFtT. This step is illustrated by Equation 3-1 for field-measured BAFtTs.

Here, lipid normalization is conducted to enable more precise estimates of BAFs across multiple

sites and species within a trophic level by accounting for the confounding influence of lipid

variability on BAFt

Ts. In the second step, the final baseline BAF calculated in step 1 is converted

to a national BAF that reflects the lipid fraction of commonly consumed aquatic organisms (and

the fraction of chemical that is freely dissolved calculated for U.S. surface waters). This step is

illustrated by Equation 3-2 in Section 3.2.3. 

4.1.2 Assumptions and Limitations

Although theory and empirical evidence support the concept of adjusting BAFs and

BCFs by lipid content 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. These assumptions 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 amount or composition of lipids present in tissue.

As described in Section 4.1.1, the first assumption is generally supported by the empirical

evidence and underlying theory that supports many widely used bioaccumulation models. This

assumption is also supported by the findings that for organic chemicals that are not metabolized,

BCF is strongly correlated with Kow. (e.g., Veith et al., 1979b; Isnard and Lambert, 1988; de Wolf

et al., 1992). In determining Kows, n-octanol is considered to be a surrogate for lipid. Chiou (1985)

used triolein (glyceryl trioleate) as a surrogate for lipid and also found good agreement between

BCFs and triolein/water partition coefficients. Evidence of the utility of lipid normalization is

presented in Section 5.1.3, Figure 5-2, where it is shown that normalization by the fraction of

tissue that is lipid (fR) and the fraction of chemical in water that is freely dissolved (ffd)

substantially reduces variation in BAFt

Ts.

Although the general utility of lipid normalization has been well established, this

adjustment does not account for all of the variation in BAFs that may occur. Bioaccumulation

can be affected by other factors that differ between species, such as composition of diet, growth
4-3

rate, chemical metabolism, and trophic position. Ecosystem factors, such as chemical loading

history, food web structure, and bioavailability also contribute to variation in BAFs (EPA’s

calculation of baseline and national BAFs address some differences in trophic position and

bioavailability). Therefore, the effectiveness of lipid normalization in reducing variability in BAFs

and BCFs is likely to be greatest when conducted between species (or individuals within a

species) that are substantially different in lipid content but have experienced similar chemical

exposure conditions. In situations where the difference in lipid content between species is

minimal, or when the aforementioned factors (e.g., loadings history, food web structure) differ

substantially between sites, the efficacy of lipid normalization may be substantially reduced or

masked. Such situations may have contributed to reports of little or questionable benefit derived

from lipid normalization in some field studies (e.g., Amrhein et al., 1999; Bergen et al., 2001).

Other procedures, such as analysis of covariance, have been proposed to improve the statistical

basis of lipid normalization (Hebert and Keenleyside, 1995). When sufficient data exist, analysis

of covariance may improve in the statistical basis for lipid-normalizing BAFs. However, the

limited data associated with typical BAF/BCF studies often restrict application of this approach

for deriving national BAFs.

The second assumption pertains to the utility of the total lipid content as a normalizing

factor for species and tissues with widely varying lipid fractions and lipid compositions. The

process of normalizing BAFs and BCFs on the basis of the total fraction of tissue that is lipid

assumes that lipids are a single, uniform compartment. In reality, total lipid content in fish

includes different lipid classes, including relatively polar phospholipids, which are common in

cell membranes, and generally nonpolar triacylglycerols, which are common in storage lipids

(Henderson and Tocher, 1987). The variation in lipid-partitioning behavior of nonionic organic

chemicals is thought to be a function of differences in polarity of lipid classes, as fewer chemicals

become associated with the more polar “membrane-bound” lipids than storage lipids (Ewald and

Larsson, 1994; van Wezel and Opperhuizen, 1995; Randall et al., 1998). 

In practical terms, the potential impact that differences in lipid composition might have on

chemical partitioning and lipid normalization seems to be most relevant for very lean tissues (e.g.,

those less than 1%–2% total lipids). This suggestion is based on observations that lean tissues of

some fish species contain a much greater proportion of polar phospholipids (24%–65%) than do

“fatty” tissues (1.5%–8.7%; Ewald and Larsson, 1994). Similar observations have been made

with populations of ribbed mussels, for which Bergen et al. (2001) reported significantly higher

fractions of polar lipids in leaner populations compared with fatter populations. Because of their

greater polarity with respect to lipid content, 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 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.
4-4

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 acetonehexane). 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. Notably, other attributes (e.g., high temperature, pH, lipid decomposition due to

exposure to light and oxygen) can also affect lipid extractions, but these have been less studied

than has extraction solvent.

Although a variety of solvent systems that extract various lipid classes have been

proposed for use in normalizing tissue chemical concentrations by lipid content, a clear

consensus has not emerged on which method is most appropriate for all tissues, species, and

nonionic organic chemicals. Although it is desirable to have one standardized lipid extraction

method for normalizing concentrations of nonionic organic chemicals, it seems possible that no

single method would be equally appropriate for all chemical and tissue types, because different

tissues have different lipid compositions that, in turn, may alter the chemical/lipid partitioning

process. From a toxicological perspective, the science is not presently clear on which classes of

lipids (e.g., phospholipids, free fatty acids, mono-, di-, and triglycerides) are most relevant with

respect to different organic chemicals. For example, DDT has been reported to bind to relatively

polar membrane–bound lipids, which suggests membrane lipids might be relevant to DDT

toxicity (Chefurka and Gnidec, 1987). Randall et al. (1998) reported that 27% of extractable PCBs

were associated with the more polar, membrane–bound lipid pool (i.e., extractable with

chloroform/methanol), whereas 73% were associated with the neutral lipid pool (i.e., extractable

with hexane). Similarly, de Boer (1988) reported that chlorobiphenyls were associated with both

bound (membrane) and unbound (storage) lipid pools in fish. These findings further suggest that

membrane-bound lipids should not be ignored when selecting lipid extraction methods.

To promote consistency in measuring BAFs and BSAFs in field studies, EPA

recommends the continued use of the Bligh and Dyer (1959) chloroform/methanol extraction

method (or the less toxic solvent system of Hara and Radin (1978), in which hexane/isopropanol)

in combination with gravimetric measurement of lipid. The Bligh-Dyer method is recommended

because it is widely used for lipid measurements and has been well characterized in terms of the

types of lipids extracted. The Bligh-Dyer method also extracts both polar and nonpolar lipids.

Based on these and other considerations, Randall et al. (1998) also 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
4-5

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. When using exiting data on lipid fraction, EPA may

consider it appropriate to exclude certain data when differences in baseline BAFs or BCFs are

substantial and are believed to be caused largely by differences in lipid extraction methods.

4.2 TECHNICAL BASIS OF FREELY DISSOLVED NORMALIZATION OF

CHEMICAL CONCENTRATION IN WATER

The 2000 Human Health Methodology for deriving trophic-level specific national BAFs

for nonionic organic chemicals uses baseline BAFs in an intermediate step. The baseline BAFs

are based on the concentration of chemical that is freely dissolved in water (Cw

f d

) and the fraction

of the organism that is lipid (fR). EPA uses these adjustments because they express the BAF on a

thermodynamic or fugacity basis and allow better extrapolation of BAFs from one ecosystem to

another. 

By basing the baseline BAFs on Cf

w

d

, EPA does not ignore the chemical-associated

dissolved organic carbon (DOC) and particulate organic carbon (POC) in the water column. As

discussed in the following sections, a 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

(an assumption made by EPA; see Section 4.2.3). 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. Therefore, a BAF could be based on any combination of the

three phases and include the influences of the other water column phases.

The relationship among the freely dissolved chemical and the chemical associated with

DOC and POC, presented below, assumes equilibrium among these phases. For a given

ecosystem, DOC and POC define the partitioning of the chemical among the three phases.

National BAFs, calculated from the baseline BAFs, require both the average lipid content of fish

and shellfish consumed by the U.S. population as well as average DOC and POC values for the

nation’s waters. These required parameters result in expression of national BAFs on the basis of

the weight of fish/shellfish tissue and total chemical concentration in the water column,

i.e., (micrograms of chemical per kilogram of wet tissue) / (micrograms of chemical per liter of

water).

4.2.1 Background Theory and Basic Equation

Experimental evidence shows that hydrophobic organic chemicals exist in water in three

phases: (1) the freely dissolved phase, (2) sorbed to suspended solids (particulate organic

carbon), and (3) sorbed to dissolved organic matter (Hassett and Anderson, 1979; Carter and

Suffet, 1982; Landrum et al., 1984; Gschwend and Wu, 1985; McCarthy and Jimenez, 1985a;
4-6

(Equation 4-1)

(Equation 4-2)

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):

where:

C

t

w = total concentration of chemical in water

C

f

w

d = concentration of chemical that is freely dissolved in water

Cpoc = concentration of the chemical partitioned to the particulate organic

carbon in the ambient water

Cdoc = concentration of the chemical sorbed to the dissolved organic carbon

in the water

POC = concentration of particulate organic carbon in water (kilograms of

particulate organic carbon per liter of water)

DOC = concentration of dissolved organic carbon in water (kilograms of

dissolved organic carbon per liter of water)

The above equation can also be expressed using partitioning relationships as:

where:

Kpoc = Cpoc / Cf

w

d

Kdoc = Cdoc / Cf

w

d

Kpoc = equilibrium partition coefficient of the chemical between POC phase

and the freely dissolved phase of water (liters of water per kilogram of

particulate organic carbon)

Kdoc = equilibrium partition coefficient of the chemical between DOC phase

and the freely dissolved phase of water (liters of water per kilogram of

dissolved organic carbon)

From Equation 4-2, the fraction of the chemical that is freely dissolved in the water can be

calculated using the following equation:
4-7

(Equation 4-3)

(Equation 4-4)

(Equation 4-5)

(Equation 4-6)

Experimental investigations by Eadie et al. (1990, 1992), Landrum et al. (1984), Yin and

Hassett (1986, 1989), Chin and Gschwend (1992), and Herbert et al. (1993) have shown that Kdoc

is directly proportional to the Kow of the chemical and is less than the Kow. When measured values

of Kdoc are not available, it can be estimated using the following equation:

Experimental investigations by Eadie at al. (1990, 1992) and Dean et al. (1993) have

shown that Kpoc is approximately equal to the Kow of the chemical. When measured Kpoc values

are not available, it can be estimated using the following equation:

By substituting Equations 4-5 and 4-6 into Equation 4-4, the following equation is

obtained:

Burkhard et al. (1997) evaluated the utility of using Equation 4-6 to derive baseline BAFs

that are applicable to multiple sites. In their study, Burkhard et al. (1997) measured BAFs for

various chlorinated butadienes, chlorinated benzenes, and hexachloroethane for three species of

forage fish and blue crab in Bayou d’Inde of the Calcasieu River system in Louisiana. Using

Equation 4-6, field-measured BAFs were converted to baseline BAFs and compared to baseline

BAFs determined for other trophic level three species in two other field studies (Pereira et al.,
4-8

1988; Oliver and Niimi, 1988). One of these field studies, by Pereira et al. (1988), was conducted

in different sites within the Calcasieu River system; the other study, by Oliver and Niimi (1988),

was carried out in Lake Ontario. Burkhard et al. (1997) found no significant difference between

baseline BAFs determined in their own study and those determined by Pereira et al. (1988)

(Tukey’s, " = 0.05). However, for one chemical (hexachlorobutadiene), a difference between the

two studies of about 1 order of magnitude was observed in the baseline BAFs. Burkhard et al.

(1997) further noted that their own baseline BAFs were not substantially different from those

derived for similar trophic level fish in Lake Ontario, suggesting broader applicability of properly

derived baseline BAFs.

The EPA’s BAF methodology incorporates four decisions/assumptions associated with

the three-phase partitioning model for estimating the concentrations of nonionic organic

chemicals that are freely dissolved in ambient waters. These four decisions/assumptions are:

1. Sorption of the chemical to DOC and POC reduces chemical bioavailability to

aquatic organisms.

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.

3. Kpoc = Kow

4. Kdoc = 0.08 C Kow

These assumptions are based on experimental evidence referenced above. Detailed

discussions of the evidence and other information supporting these assumptions are presented in

the following subsections: assumption 1 in Section 4.2.2, assumption 2 in Section 4.2.3, and

assumptions 3 and 4 in Sections 4.2.4 and 4.2.5. 

4.2.2 Effects of Chemical Sorption to DOC and POC on Chemical Bioavailability

Numerous reports demonstrate the partitioning of hydrophobic nonionic organic

chemicals to POC and DOC (see Section 4.2.4). Concurrent with the research on partitioning of

hydrophobic nonionic organic chemicals to POC and DOC, research efforts have focused on

bioavailability of hydrophobic nonionic organic chemicals to fish and other aquatic organisms in

the presence of DOC and POC. The results of this research show that the concentration of

chemical that is freely dissolved in sediment porewaters and ambient surface waters is the best

measure currently available of the fraction of nonionic organic chemicals available for uptake by

aquatic organisms (Suffet et al., 1994; DiToro et al., 1991).

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
4-9

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 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). 

There are a few reports in the scientific literature of increases in the bioavailability of

nonionic organic chemicals to aquatic organisms in the presence of low concentrations of DOC

(see Haitzer et al., 1998). In their review, Haitzer et al. (1998) compared BCFs determined using

laboratory waters with those determined using lake waters and laboratory waters with added

DOC as the exposure media. When BCFs derived from laboratory water experiments were

smaller than those derived from the other waters, the authors concluded that increased

bioavailability had occurred. The EPA believes that some of these findings are artifacts of the

experimental design. For example, Haitzer et al. (1998) reported that bioavailability of hepta- and

octa-chlorodibenzo-p-dioxins to rainbow trout was enhanced when DOC was low. Careful

examination of the original report by Servos et al. (1989), however, reveals that the solubility

limits for the hepta- and octa-chlorodibenzo-p-dioxins were exceeded in the experiment, and

therefore any conclusions about increased bioavailability are clearly suspect. The EPA also

believes that other factors could explain reports of apparent increases in bioavailability. Verhaar

et al. (1999) and others have pointed out that in performing any experiment from which BCFs

will be derived, the organisms will introduce DOC—for example, from mucous layers, feces, and

urine—into the aqueous phase. Because the measurements made to determine BCF do not

typically measure ‘bioavailable’ chemical (i.e., the concentration of chemical that is freely

dissolved) but rather the total concentration of chemical that is in the exposure water, addition of

DOC by the organisms during the experiment most certainly confounds the assumption that

DOC concentrations were actually low. It is entirely possible that the concentration of chemical

that was freely dissolved in experiments using laboratory waters was substantially different from

that in experiments using lake waters and laboratory waters with added DOC. A recent report by

the authors of the 1998 review (i.e., Haitzer et al.) supports EPA’s belief that the increased

bioavailability in the presence of low concentrations of DOC for BCF measurements is caused by

experimental artifacts. After very careful study of BCF measurements performed with low DOC

concentrations, Haitzer et al. (2001) concluded that “... BCF enhancements that have been

reported in the literature are more likely the result of random, experimental variations than the

result of systematic enhancement of bioconcentration.”

On the basis of the information presented above, EPA has assumed that the

bioavailability of nonionic organic chemicals to aquatic organisms is reduced in the presence of

DOC and POC. EPA acknowledges that there are a few reports of increased bioavailability in the
4-10

scientific literature and believes that the causes of the increased bioavailability are, in all

likelihood, experimental artifacts of the BCF measurements.

4.2.3 Sorptive Behavior of Nonionic Organic Chemicals with DOC and POC (Including

Plankton) in the Water Column

In using the three-phase partitioning model to determine the concentration of chemical

that is freely dissolved in the water column, EPA has assumed that the chemical freely dissolved

in the water column is in equilibrium with the chemical associated with DOC and POC. The basis

for this assumption is presented below.

In the development of the fluorescence quenching technique for measuring partitioning

between the freely dissolved chemical and DOC, investigators have studied the time required for

PAHs to equilibrate with DOC. Gauther et al. (1986), McCarthy and Jimenez (1985a), and

Schlautman and Morgan (1993) have reported times ranging from less than 1 minute to

approximately 10 minutes for PAHs to equilibrate with DOC. These very short equilibration

times suggest that equilibrium conditions should exist between nonionic organic chemicals and

DOC in the environment. 

Kpoc data are quite limited, however, and EPA is unaware of any research efforts studying

the kinetics of partitioning of nonionic organic chemicals between the POC and the freely

dissolved phases of water. Insights into the behavior and kinetics of partitioning with POC can be

gained by examining the experimental evidence on partitioning of nonionic organic chemicals in

sediments/soils. Karickhoff et al. (1979) and Gschwend and Wu (1985) have shown that sorption

and desorption of nonionic organic chemicals to sediment and soil organic carbon are reversible.

In the 1980s, most investigators believed that time periods on the order of hours to a few days

were required for chemical to equilibrate between the freely dissolved and organic carbon phases

(Tomson and Pignatello, 1999). More recently, it had been found that attainment of steadystate/equilibrium conditions in these systems takes substantially longer periods of time (e.g.,

upwards of 100 days), and the time period is dependent on the concentration of suspended solids

in the system (Jepsen et al., 1995).

Numerous investigations have studied the kinetics of sorption and desorption of nonionic

organic chemicals to sediment and soil organic carbon, and these studies suggest the existence of

fast and slow sorption and desorption phases (Pignatello and Xing, 1996). The desorption process

can be characterized as having a fast initial release of chemical followed by a slow, prolonged

release of the chemical. Numerous models have been developed to explain this behavior (Chen et

al., 1999). Many investigators have modeled the sorption process at the surface of the organic

carbon as a quick equilibrium process between the organic carbon surface and the chemical freely

dissolved in the water column (Schwarzenbach et al., 1993). Examples include the retarded

diffusion model of Lick and Rapaka (1996) and the radial diffusion model of Wu and Gschwend

(1986).

There is no clear consensus on a kinetic model for describing the partitioning of nonionic

organic chemicals between POC (or sediment/soil particles) and the freely dissolved phases (see
4-11

Chen et al. [1999] for a listing of models). Such a model would have to account for both fast and

slow sorption/desorption processes. Given the limited amounts of Kpoc data, as well as kinetic

data for sorption/desorption processes with POC, the selection of an appropriate kinetic model is

clearly problematic. From an uncertainty standpoint, modeling nonequilibrium conditions using

equilibrium condition assumptions would cause the freely dissolved concentration of the

chemical to be too small, because the Kpoc for kinetic conditions would be less than that for

equilibrium conditions.

In some situations, the concentration of chemical that is determined to be freely dissolved

using the three-phase model might be too large. As discussed by Gustafsson et al. (1997), PAHs

partition more strongly to soot (i.e., organic carbon derived from incomplete combustion) than to

organic carbon in sediments, whereas other chemical classes, such as PCBs, do not appear to be

influenced by the soot phase. Unfortunately, the soot contents of natural waters are largely

unknown. In situations where significant amounts of soot exist, the three-phase model could be

modified to include a fourth phase consisting of soot. Gustafsson et al. (1997) describe a

methodology for estimating the partition coefficients for soot.

By definition, POC is material retained by filtering or by centrifugation. Therefore, POC

includes plankton. Because EPA assumes that the chemical associated with POC is in equilibrium

with the chemical freely dissolved in water, chemical in plankton retained by the filter must be in

equilibrium with the chemical freely dissolved in water. Under certain conditions—for example,

algal blooms—the plankton is probably not in equilibrium with the chemical freely dissolved in

water. However, because EPA’s basis for deriving AWQC involves long-term average, steadystate, or near steady-state conditions, EPA believes that it is reasonable to assume that chemicals

associated with plankton (in the POC) is on average in equilibrium with the chemicals freely

dissolved in water. It should be noted that larger plankton is not included in POC samples

because a prefiltering step is generally used to remove larger particulates before filtering or

centrifuging to separate POC; see, for example, Broman et al. (1991), who used a 100-μm

prefilter to define the upper size cutoff for POC.

The EPA, in using the three-phase model for determining the concentration of chemical

that is freely dissolved, assumes equilibrium exists between the chemical associated with the

POC and the chemical that is freely dissolved. This assumption is based on the consideration that

POC in the environment is constantly exposed to the chemical of interest, and the sorption and

desorption processes for the chemical to and from the POC are dominated by fast-phase kinetics

specifically, the quick equilibrium process occurring at the surface of the organic carbon

(Schwarzenbach et al., 1993). Because of fast-phase kinetics, short-term fluctuations in ambient

concentrations are quickly accounted for in natural waters. Furthermore, AWQCs are developed

with the assumption that conditions in ambient waters are representative of long-term averages,

which are best captured using steady-state or near steady-state conditions. EPA believes that the

three-phase partitioning model provides a reasonable approximation of these types of conditions.

Errors associated with calculation of the freely dissolved concentration are somewhat

offset by using the three-phase partitioning model twice in EPA’s methodology for developing

national BAFs. First, a baseline BAF is calculated from a measured BAFt

T wherein the measured
4-12

BAFt

T is lipid- normalized and corrected for bioavailability considerations using the fraction of

chemical that is freely dissolved. Second, a national BAFt

T is calculated using the average lipid

content for species consumed in the United States and the fraction of chemical that is freely

dissolved in U.S. waters. EPA believes that use of the freely dissolved concentration for

converting both to and from the intermediate baseline BAF value, that is, from measured BAF-tobaseline BAF and again from baseline BAF-to-national BAF offsets the error associated with

calculating the freely dissolved concentration. 

Given the above considerations, EPA has decided to use the three-phase partitioning

model with the assumption of equilibrium conditions for the calculation of the freely dissolved

concentrations for nonionic organic chemicals for the following reasons: 

1. Available data indicate complete, rapid partitioning to DOC;

2. Initial partitioning to POC is also rapid and near complete and although some

kinetic limitations on chemical partitioning with POC occur, these are not likely to

be important on the time scale applicable to human health water quality criteria;

3. No consensus exists on available kinetic models specific to POC; and

4. Use of the freely dissolved fraction twice in derivation of national BAFs offsets

model error.

4.2.4 Values for the Particulate and Dissolved Organic Carbon Partition Coefficients Kpoc

and Kdoc

In using the three-phase partitioning model for calculating the fraction of a chemical’s

concentration that is freely dissolved in water (ffd), EPA will define Kpoc and Kdoc as follows:

Kpoc = Kow with 95% conference limits of a factor of 8 in either direction.

Kdoc = 0.08 C Kow with 95% confidence limits of a factor of 20 in either direction.

The basis for these relationships is presented below. 

The separation of POC from DOC in water samples is operationally defined by filtering or

centrifugation. With both techniques, the operational cutoffs between POC and DOC fractions

can differ depending upon membrane selection and hardware; for example, a membrane with a

0.45-μm cutoff may be used in one study, whereas centrifugation that retains all particles with a

size of 1.0 μm or greater may be used in another study. Typically, the size cutoff between POC

and DOC fractions is 0.1-1 μm. 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. Organic carbon and chemical-specific analyses are performed on the POC fraction
4-13

to determine POC and Cpoc, respectively. The DOC fraction is defined as the ambient water

remaining after filtration or centrifugation is performed. The DOC fraction contains both the

chemicals that are freely dissolved and the chemicals associated with the DOC. To determine the

concentration of chemical that is freely dissolved in the DOC fraction, a variety of analytical

techniques are available, for example, fluorescence quenching, purging or sparging techniques,

solid phase microextraction (SPME), equilibrium dialysis, solubility enhancement, ultrafiltration,

reverse-phase separation, size exclusion chromatography, and liquid-liquid extraction. Some of

these techniques directly measure the concentration of chemical that is freely dissolved, whereas

others physically separate the DOC-bound chemical fraction from the freely dissolved chemical

fraction.

All of the methods for measuring freely dissolved chemical in water have limitations that

can lead to uncertainties in the Kpoc and Koc. However, limitations associated with some methods

can lead to larger uncertainties than others. The methods that appear to have smaller biases are

sparging, fluorescence quenching, SPME, and possibly equilibrium dialysis. An excellent

discussion on the individual techniques (except SPME) and their limitations is presented by

Suffet et al. (1994). The reader can refer to Poerschmann et al. (1997) or Ramos et al. (1998) for

further information on the SPME method.

A review of the scientific literature reveals that Kpoc measurements are not as prevalent as

Kdoc and Koc measurements. Koc is defined as the partition coefficient of the chemical

concentration on soils or sediments (on an organic carbon basis) to the chemical concentration in

water after the removal of the solid phase. Koc is expressed as liters of water per kilogram of

organic carbon. Kpoc is defined as the partition coefficient of the chemical concentration on the

water column particles (on an organic carbon basis) to the chemical concentration freely

dissolved in the water. Kpoc is expressed as liters of water per kilogram of organic carbon.

Measured Koc values should not be assumed to be equal to Kpoc, because (1) the types of organic

carbon in the soils and sediments can be very different from those in the water column, and

(2) their denominators are different. Sediments and soils tend to be more weathered than water

column particulates, because the latter include organic matter derived from sources such as

recently deceased as well as live plankton and algae and fecal matter from aquatic organisms.

However, in some cases, the organic matter composing the Koc and Kpoc might be very similar,

because sediment resuspension and erosional inputs could be responsible for a majority of the

particulates in the water column. In all cases, the chemical concentrations in water used in

measuring Koc and Kpoc are different. The determination of Kpoc is based on the concentration of

chemical freely dissolved in the water (see Section 4.2.1 for derivation), whereas Koc is

determined by using an operational definition of “dissolved” in water. This operational definition

includes both freely dissolved chemical and chemical sorbed to DOC in the aqueous phase.

Data for Kpoc are limited for a number of reasons. First, the measurement of Kpoc in field

situations is often very difficult because of the extremely low concentrations of hydrophobic

pollutants in natural waters, often 1 ppt or less on a total basis. With low concentrations, large

volumes of water must be processed in order to obtain enough of the chemical to measure. For

example, Broman et al. (1991) processed approximately 2,000 L of Baltic Sea water to obtain

measurable amounts of polychlorinated dioxins and furans on the particulates retained by
4-14

filtering and in the water passing through the filter. Second, the techniques developed for

measuring freely dissolved concentrations of chemical in natural waters are not amenable to field

sampling situations in which large volumes of water need to be processed. Third, in laboratory

studies, many investigators use sediment particles as a surrogate for naturally occurring water

column particulates, and it is somewhat tenuous to assume that sediment particles are equivalent

to water column particulates. Fourth, because of operational and analytical factors, Kocs and Kds

(Kocs expressed on the basis of dry weight rather than organic carbon on the solids, 

i.e., Koc = Kd/foc) are more often reported than Kpoc values, because Kocs and Kds are much easier

to determine.

Kpoc measurements found in a search of the scientific literature are reported in Table 4-1

and plotted in Figure 4-1. These values were determined by using the reverse-phase, sparging,

and ultrafiltration method with samples primarily from Great Lakes ecosystems. An equation of

the form log Kpoc = a + b C log Kow was computed by using the geometric mean regression

technique (Ricker, 1973); this equation is:

log Kpoc = + 1.19 (±2.18) + 0.81 (±0.11) C log Kow df = 14, r = 0.84, sxy = 0.40

The geometric mean regression technique was used because the X variable (log Kow) was

measured with error. The equation and its 95% confidence limits for any single predicted log Kpoc

are plotted in Figure 4-1. The slope of this regression line is not significantly different from 1.0 ("

= 0.05). Assuming a slope of 1.0 results in an equation of the form log Kpoc= log Kow+ B. This

equation, by rearrangement, results in B = log Kpoc– log Kow= log (Kpoc/Kow), and “B” can be

found by averaging the differences of the log Kpoc and log Kowfor the individual chemicals or by

averaging the logarithms of the ratio of the Kpoc to Kow for the individual chemicals. For this data

set, an average difference (standard deviation, number of data points) of 0.023 (0.426, 16) was

obtained. Transforming the average difference to an antilog scale results in predictive relationship

of Kpoc= 1.05 C Kow with 95% confidence limits of a factor of 8 [10(standard deviation C t(" = 5%, df = 15)) =

10(0.426 C 2.131)] in either direction from the mean predicted Kpoc.

Based on the data presented above, EPA will use the following relationship for

determining Kpoc values for use in the three-phase partitioning model: 

Kpoc = 1.0 C Kow with 95% confidence limits for a predicted Kpoc of a factor of 8 in either

direction from the mean predicted Kpoc.
Table 4-1. Kpoc Data

Compound Ecosystem Log Kow Log Kpoc sd n

MeasurementMethod Reference

Mirex Lake Ontario 6.89 6.81 Sparging Yin & Hassett, 1989Mirex Lake Ontario 6.89 6.54 Sparging Yin & Hassett, 1989Mirex Oswego River 6.89 6.72 Sparging Yin & Hassett, 1989Mirex Oswego River 6.89 6.38 Sparging Yin & Hassett, 19894-Chlorobiphenyl Lake Michigan 4.69 4.71 0.50 73 Reverse phase Eadie et al., 1990Pyrene Lake Michigan 5.11 5.65 0.46 73 Reverse phase Eadie et al., 19902,2',5,5'-Tetrachlorobiphenyl Lake Michigan 5.84 5.83 0.40 73 Reverse phase Eadie et al., 1990Benzo[a]pyrene Lake Michigan 6.13 6.66 0.40 73 Reverse phase Eadie et al., 1990p,p'-DDT Lake Michigan 6.91 6.36 0.31 73 Reverse phase Eadie et al., 19902,2',4,4',5,5'-Hexachlorobiphenyl Lake Michigan 6.92 6.76 0.30 73 Reverse phase Eadie et al., 19904-Dichlorobiphenyl Green Bay 4.69 4.70 0.29 23 Reverse phase Eadie et al., 1992Pyrene Green Bay 5.11 5.79 0.19 23 Reverse phase Eadie et al., 19922,2',5,5'-Tetrachlorobiphenyl Green Bay 5.84 5.74 0.19 23 Reverse phase Eadie et al., 1992Benzo[a]pyrene Green Bay 6.13 6.38 0.15 23 Reverse phase Eadie et al., 1992p,p'-DDT Green Bay 6.91 6.24 0.12 23 Reverse phase Eadie et al., 1992

Lake Michigan 5.85 6.78 0.11 2 Ultrafiltration Dean et al., 1993

2-Chlorobiphenyl Hudson River 4.46 5.65 Calculated Butcher et al., 19982,4'-Dichlorobiphenyl Hudson River 5.07 5.67 Calculated Butcher et al., 19982,2',5-Trichlorobiphenyl Hudson River 5.24 5.40 Calculated Butcher et al., 19982,4,4'-Trichlorobiphenyl Hudson River 5.67 5.84 Calculated Butcher et al., 19982,4',5-Trichlorobiphenyl Hudson River 5.67 5.80 Calculated Butcher et al., 19982,2',3,3'-Tetrachlorobiphenyl Hudson River 5.75 5.85 Calculated Butcher et al., 19982,2',5,6'-Tetrachlorobiphenyl Hudson River 5.62 5.82 Calculated Butcher et al., 19982,3',4,4'-Tetrachlorobiphenyl Hudson River 6.20 6.27 Calculated Butcher et al., 19982,3',4',5-Tetrachlorobiphenyl Hudson River 6.20 6.15 Calculated Butcher et al., 19982,2',4,5,5'-Pentachlorobiphenyl Hudson River 6.38 6.18 Calculated Butcher et al., 19982,3',4,4',5-Pentachlorobiphenyl Hudson River 6.74 6.41 Calculated Butcher et al., 19982,2',3,4,4',5-Hexachlorobiphenyl Hudson River 6.83 6.43 Calculated Butcher et al., 19982,2',3,5,5',6-Hexachlorobiphenyl Hudson River 6.64 6.55 Calculated Butcher et al., 19982,2',4,4',5,5'-Hexachlorobiphenyl Hudson River 6.92 6.38 Calculated Butcher et al., 1998
4-16

Log Kow

3 4 5 6 7 8

Log Kpoc

3

4

5

6

7

8

The data used to derive the above relationship were primarily from the Great Lakes ecosystem.

Kpoc data from Butcher et al. (1998) for the Hudson River, which are not used in deriving the

predictive relationship, are also plotted in Figure 4-1 (circles) and are in good agreement with the

Kpoc data from the Great Lakes. The comparability of the Hudson River data to those obtained

from the Great Lakes ecosystem indicates that the above relationship for determining Kpoc has

broader applicability than just the Great Lakes ecosystem.

Figure 4-1. Kpocs determined using the reverse phase

(downwards triangle), sparging (square),

ultrafiltration (upwards triangle), and model-derived

(open circles) techniques. The geometric mean

regression and their 95% prediction confidence limits

are plotted.

In comparison to Kpoc data, numerous Kdoc measurements are reported in the scientific

literature. Sorption of hydrophobic organic chemicals to DOC has been studied by using DOC

from a variety of sources. Sources of DOC include ambient waters; sediment and soil pore

waters; and humic and fulvic acids isolated from ambient waters, sediments, and soils. In general,

humic substances are the major component of the natural organic carbon in water and soil

systems, e.g., 50–80% (Perminova et al., 1999; Thurman, 1985). For this reason, much research

has focused on determining the sorption and binding affinity of hydrophobic organic chemicals

to humic substances, specifically as related to composition and structure of the humic

substances, e.g., the aromaticity, hydrogen-to-carbon (H/C)and oxygen-to-carbon (O/C) atomic

ratios, ultraviolet absorptivity, pH, and molecular weight. 

To develop a predictive relationship for Kdoc, a literature search for Kdoc data for nonionic

organic chemicals was performed and Kdocs from more than 70 references collected (Burkhard,

2000). A predictive relationship, Kdoc = 0.08 Kow with 95% confidence limits of a factor of 20 in

either direction, was developed using Kdoc data based on naturally occurring dissolved organic

carbon. The following text provides a brief summary of the literature search and the development

of the predictive equation. Further details and a complete listing of the Kdoc data can be found in

Burkhard, 2000.

In Figure 4-2, log Kdocs are plotted as a function of chemical log Kow for five DOC sources:

(1) Aldrich humic acid, (2) humic and fulvic acids isolated from sediments, natural waters, and

soils, (3) sediment porewaters, (4) soil porewaters and groundwaters, and (5) surface waters. For

DOCs consisting of Aldrich humic acid and sediment porewaters, the relationships between log

Kdoc and log Kow were linear, with slopes of approximately 1 for both and correlation coefficients

of 0.77 and 0.64, respectively (Table 4-2). In contrast, for humic and fulvic acids without Aldrich

humic acids (i.e., Aldrich humic acid data excluded), soil porewaters and groundwaters, and

surface water, the log Kdocs are only somewhat dependent on log Kow; that is, the correlation
4-17

Log Kdoc

2

3

4

5

6

7

Log Kdoc

2

3

4

5

6

Log Kdoc

2

3

4

5

6

Log Kdoc

2

3

4

5

6

Log Kow

3 4 5 6 7 8

Log Kdoc

2

3

4

5

6

Aldrich humic acid

Humic and fulvic acids

w/o Aldrich humic acid

Sediment

 porewater

Soil porewater and 

 Groundwater

Surface Water

coefficients for the relationship are rather small, .0.3, although the slopes are similar to 1. The

small dependence of Kdoc on Kow for DOC from natural waters is consistent with the findings of

Kukkonen and Oikari (1991) and Evans (1988), which suggest that factors other than the

hydrophobicity are important in the sorption or association of nonionic organic chemicals with

surface water DOC. 

Figure 4-2. Kdocs determined using the reverse phase

(circle), equilibrium dialysis (open square), sparging

(plus diamond), calculated/model-derived

(downwards triangle), fluorescence quenching

(upwards triangle), solubility enhancement (open

diamond), biological (plus square), and solid phase

microextraction (open plus diamond) techniques for

DOCs from different sources. The geometric mean

regression and their 95% prediction confidence limits

are plotted.
4-18

The Kdoc data from surface water DOC appear to have more variability than that observed

for the other DOC sources (Figure 4-2). On a diagenesis basis, DOC from the water column

might be expected to be more variable than that from sediments and humic and fulvic acids,

because surface water DOC contains detritus from recently deceased plankton and algae,

macrophytes, and so forth. Some of the variability in all five graphs in Figure 4-2 is caused by

differences among the measurement techniques. Comparisons of the reverse-phase and dialysis

techniques by Landrum et al. (1984) and Kukkonen and Pellinen (1994) suggest that differences

of an order of magnitude or more can occur between these two methods, but that typically the

dialysis technique provided values that were a factor of 2-5 higher. Biphenyl with a log Kow of

4.09 provides an example of the differences that are possible between these two techniques.

Landrum et al. (1984) performed side-by-side Kdoc measurements with biphenyl, using water

samples from Lake Erie and Huron River. The Kdoc data derived from the two techniques differed

by a factor of 3 for the Lake Erie samples and by a factor of 34 for the Huron River samples. The

variability in the Kdoc data from these surface water DOC might also be related to the time period

in which the measurements were made. Most of the measurements occurred in the 1980s, when

methods for measuring Kdoc were new and/or evolving. 

Table 4-2. Regression Equations for Dependence of Kdoc (Geometric Means) on Kow

DOC Source Geometric Mean Regression Equation n r sxy

Aldrich humic acid log Kdoc= 0.85 (±0.03)aC log Kow+ 0.27 (±0.20) 269 0.77 0.52

Humic and fulvic acids without Aldrich

humic acid

log Kdoc= 0.88 (±0.06) C log Kow- 0.11 (±0.31) 230 0.29 0.65

Sediment porewaters log Kdoc= 0.99 (±0.04) C log Kow- 0.88 (±0.23) 396 0.64 0.66

Soil porewaters and groundwaters log Kdoc= 0.91 (±0.13) C log Kow- 0.22 (±0.68) 47 0.31 0.61

Surface waters log Kdoc= 0.97 (±0.06) C log Kow- 1.27 (±0.40) 210 0.32 0.99

All DOC including Aldrich humic acid log Kdoc= 0.85 (±0.04) C log Kow- 0.11 (±0.21) 223 0.78 0.52

Naturally occurring DOC (no Aldrich

humic acid)

log Kdoc= 0.85 (±0.06) C log Kow- 0.25 (±0.34) 127 0.67 0.60

n = number of data points, r = correlation coefficient, sxy = standard error of estimate, a (±standard deviation).

To compare the Kdocs for all DOC sources, Kdoc values for each chemical were averaged

across analytical methods within each DOC source and replotted (Figure 4-3). Average Kdoc

values were used in part because of the unevenness in the numbers of measurements per

chemical. For example, biphenyl and benzo[a]pyrene had 4 and 49 Kdoc measurements,

respectively, for DOC from surface waters. The plot of log Kdoc versus log Kow shows

considerable consistency, and a strong dependence of Kdoc upon Kow is apparent (Figure 4-3). The

average Kdocs for the Aldrich humic acids are on average higher than those derived from natural

sources (Figure 4-3). These results are consistent with the differences in affinities for Aldrich

humic acid and naturally occurring organic carbon reported in the literature for nonpolar organic

chemicals (Suffet et al., 1994).
4-19

With Aldrich humic acid

Log Kdoc

2

3

4

5

6

7

Without Aldrich humic acid

Log Kow

3 4 5 6 7 8

Log Kdoc

2

3

4

5

6

Figure 4-3. Average Kdocs for

individual chemicals for different

DOC sources: humic and fulvic

acids (open diamond), sediment

porewaters (downwards triangle),

soil porewaters and groundwaters

(plus square), and surface waters

(upwards triangle). The geometric

mean regression and their 95%

prediction confidence limits are

plotted.

On a theoretical basis, the equation log Kdoc, Kpoc, or Koc= A C log Kow+ B has a slope of 1

when the ratio of the activity coefficients of the chemical in n-octanol to that in the organic

carbon phase is constant for chemicals with different Kows (Seth et al., 1999). The relationships of

log Koc = A C log Kow+ B derived by Seth et al. (1999), DiToro et al. (1991), and Karickhoff (1984)

had slopes of 1 and are clearly consistent with the hypothesis that the ratio of activity coefficients

is constant. Given the above theoretical basis and experimental data, a slope of 1 is assumed for

the relationship in this investigation, that is, log Kdoc = log Kow+ B. This equation, by

rearrangement, results in B = log Kdoc! log Kow = log (Kdoc/Kow), and B can be found by averaging

the differences of the log Kdoc and log Kow for the individual chemicals or by averaging the

logarithms of the ratio of the Kdoc to Kow for the individual chemicals. For the data set consisting

of naturally occurring DOC (no Aldrich humic acid), an average difference (standard deviation,

number of data points) of -1.11 (0.659, 127) was obtained. Transforming the average difference to

an antilog scale results in a predictive relationship of Kdoc = 0.08 Kow, with the 95% confidence

limits of a factor of 20 in either direction from the predicted mean Kdoc. When Aldrich humic

acids are included, an average difference of -0.966 (standard deviation, 0.578; number of data

points, 223) was obtained, resulting in a predictive relationship of Kdoc = 0.11 Kow, with 95%

confidence limits of a factor of 14 in either direction.

On the basis of the above data, EPA will use the following relationship for determining

Kdoc values for use in the three-phase partitioning model:

Kdoc = 0.08 C Kow
4-20

Log Kow

3 4 5 6 7 8

Residual

-3

-2

-1

0

1

2

3

The 95% confidence limits for a predicted log Kdoc are:

± standard deviation C t( " = 5%, df =127) = ± 0.659 C 1.979 = ± 1.30

and transforming the 95% confidence limits to an antilog basis results in a factor of 20 in either

direction from the mean predicted Kdoc value.

In Figure 4-4, the residuals, that is, measured log Kdocs minus log Kdocs predicted by using

the relationship Kdoc = 0.08 C Kow, along with the 95% confidence limits are plotted. The

distribution of the residuals is normally distributed (" = 10%) with 64 negative and 63 positive

residuals. The residuals for the log Kdocs predicted for naturally occurring DOC have a slight

dependence on the Kow (Figure 4-4). 

Figure 4-4. The residuals between measured log Kdocs

and log Kdocs predicted using relationship of Kdoc =

0.08 Kow. DOC sources: humic and fulvic acids

without Aldrich humic acid (open diamond), sediment

porewaters (downwards triangle), soil porewaters and

groundwaters (plus square), and surface waters

(upwards triangle). The 95% confidence limits are

plotted.

In a recent evaluation of the Koc data by Seth et al. (1999), Koc was found to be equal to

0.35 C Kow with 95% confidence boundaries of a factor of 2.5 in either direction. The variability of

the above predictive relationship for Kdocs is much larger than the factor of 2.5 reported by Seth et

al. (1999) and is larger than the variability observed with only the Aldrich humic acid (Figure 4-2),

by approximately a factor of 14 in either direction or (sxy C t(" = 5%, df = 190) = 0.59 C 1.972). This larger

variability is, however, consistent with Kukkonen and Oikari (1991) and others, who have found

that hydrophobicity of the chemical is not the only factor affecting the association of the

nonionic organic chemicals to DOC.

4.2.5 Selection of Appropriate Kows for Partitioning (Bioavailability) Predictions

The basis of partitioning theory and its relationship to log Kows are discussed in Sections 3

and 4.2 and Appendix A. The Kow is used in several components of the BAF methodology, for

example, to estimate BAFs, hydrophobicity, and partitioning in water, as well as other procedures

(including the BSAF method and use of the food web model). Thus, it is important to select the

most appropriate Kowfor a given chemical. A variety of methods are available to estimate or

predict Kow values. The reliability of these methods varies according to the Kow of the chemical.

In the 1998 draft TSD (USEPA, 1998a) EPA proposed and solicited comments on two

options for selecting reliable Kow values. The first option was EPA’s guidance published in the

Great Lakes Water Quality Initiative (USEPA, 1995a). The second option was a more detailed
4-21

(Equation 4-7)

approach to selecting Kow values, which EPA developed and had peer reviewed more recently.

Based on EPA’s own scientific rationales and the comments received from the peer reviewers,

EPA will select Kow values based on the guidance contained in the more recently developed

protocol. EPA’s methodology for selecting Kow values divides the range of Kows into three groups

to reflect the differences in chemical properties and behaviors with differing hydrophobicities.

Specific details of the Kow guidance are presented in Appendix B.

4.3 IMPORTANCE OF SEDIMENT-WATER CONCENTRATION QUOTIENT (Jsocw)

The distribution of a chemical between surface sediments and the water column in an

ecosystem is most effectively described as the sediment-water concentration quotient (Jsocw),

which is further defined below. BAFs for nonionic organic chemicals are sensitive, in proportion

to hydrophobicity, to differences in the chemical’s Jsocw in an ecosystem. Jsocw is either

implicitly (field-measured BAFs) or explicitly (modeled BAFs) involved in each of the four BAF

methods described in Section 5. The national BAF procedure for nonionic organic chemicals

involves setting BAFs to the extent possible on the basis of current national average values for

several key parameters, including Jsocw. In the case of BAF methods 1 and 2, this is done by

averaging measured BAFs from different ecosystems. For methods 3 and 4, a national value for

Jsocw that was selected by EPA on the basis of high-quality measurements from three different

ecosystems (Section 4.5.1) was used with the Gobas food chain model (Gobas, 1993) to

determine food chain multipliers (FCMs). 

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, 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). For a benthic food web,

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, for example, in phytoplankton and diatoms, 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.

The distribution of chemical between the sediment and overlying water in a water body or

a zone of reference within a water body is described by the sediment-water (column)

concentration quotient (Jsocw), which is defined as:
4-22

where:

Csoc = concentration of chemical in dry sediment, normalized to sediment organic

carbon

C

f

w

d = concentration of chemical that is freely dissolved in water

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, this

quotient 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 Jsocw/Kow for the chemical diverges from a value of 1.0

(Jsocw= Kow).

In the aquatic environment, three factors are primarily responsible for causing Jsocw to

differ among ecosystems. First, concentrations 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 particles

and the sediments, and ecosystem hydrodynamic properties. These processes vary among

ecosystems. Second, the chemical loading history to the ecosystem plays an important role in its

Jsocw. For example, increasing the loading of a chemical to the water column causes an

immediate rise in the concentration of the chemical in the water, and over time, the concentration

of the chemical in the sediment will gradually increase through sedimentation processes. If the

loading of a chemical to the water column is 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. Third, differences in organic carbon content in water column

particulates (or suspended solids) and surface sediment vary among ecosystems. The ratio of

organic carbon contents (water column to surface sediment) approximates the steady-state value

of Jsocw/Kow for the ecosystem due to diagenesis processes on the newly deposited surface

sediments.

The importance of chemical loading on Jsocw is illustrated in Figure 4-5 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. These figures

were created by using a two-compartment mass balance model consisting of a sediment surficial

layer and the water column for a nonmetabolizable chemical with a log Kow of 6, using conditions

and parameters for a large lake ecosystem. 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. In all three scenarios, Jsocw/Kow reaches a plateau of a value of 4.91, nearly equal to

the ratio between the percentage of organic carbon in suspended particulates and surface
4-23

Cfd (pg/L)

0

20

40

60

80

100

120

C

soc (ug/g of oc)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Water, freely dissolved

Sediment

Elapsed Time (years)

0 20 40 60 80 100

Psocw

0

1e+6

2e+6

3e+6

4e+6

5e+6

6e+6

7e+6

Psocw / Kow

0

1

2

3

4

5

6

7

a

Cfd (pg/L)

0

50

100

150

200

C

soc (ug/g of oc)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Water, freely dissolved

Sediment

Elapsed Time (years)

0 20 40 60 80 100

Psocw

0

1e+6

2e+6

3e+6

4e+6

5e+6

6e+6

7e+6

Psocw / Kow

0

1

2

3

4

5

6

7

b

Cfd (pg/L)

0

20

40

60

80

100

120

Csoc (ug/g of oc)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Water, freely dissolved

Sediment

Elapsed Time (years)

0 20 40 60 80 100

Psocw

0.0

2.0e+6

4.0e+6

6.0e+6

8.0e+6

1.0e+7

1.2e+7

1.4e+7

Psocw / Kow

0

2

4

6

8

10

12

14

c

w

w

w

sediments. Many chemicals that have been available as sediment contaminants for many years,

such as PCBs and DDTs, which are now no longer manufactured or used, are often found to be

present in sediments in concentrations that exceed thermodynamic equilibrium with the water

column.

Figure 4-5. The sediment-water chemical concentration quotient (Jsocw) 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.

The latter portion of scenario (c) described above (constant loading of chemical to the

ecosystem with and 80% reduction in loading at year 50) illustrates how Jsocw changes over time.

Differences in ecosystem parameters and conditions, such as hydraulic retention rates,

sedimentation and resuspension rates, water column and surficial sediment layer volumes, and

chemical loading rates between ecosystems, affect the specific time scales and slopes of the

changes in Cw

f d

, Csoc, and Jsocw associated with changes in chemical loading over time.

Ecosystems at thermodynamic equilibrium, a condition that rarely exists in nature, should

theoretically have Jsocws equal to the chemical’s Kow. Consequently, ecosystem models typically

characterize Jsocw by using its ratio to Kow as a measure of the degree to which the ecosystem is in

disequilibrium (Thomann et al., 1992), or, alternatively, as a measure of the fugacity ratio

(Campfens and Mackay, 1997). A Jsocw/Kow ratio of 1 is equivalent to equilibrium conditions

between the sediments and the water column. A ratio of 25, which has been typical of Lake

Ontario conditions for PCBs and DDTs since the 1970s, is a disequilibrium condition in which

the chemical is enriched in the sediments relative to the water column because of greater loadings

of the chemical to the ecosystem in the past. For ratios less than 1, the chemical is enriched in the

water column relative to the sediments; in this situation, the aquatic ecosystem is being loaded

with the chemical, but sediments have not reached steady state with the water (Jsocw constant).

With continued loading, sediment contamination increases until a steady-state condition is

reached (Jsocw constant) and the Jsocw/Kow ratio is in the 2–10 range. The lower bound of 2 arises
4-24

(Equation 4-8)

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 effects of chemical gradients

and greater relative organic carbon amounts in the water column. Green Bay, a fairly shallow and

vertically well-mixed ecosystem receiving a continuous load of PCBs from the contaminated Fox

River in Wisconsin, has a Jsocw/Kow ratio of approximately 5. This ratio indicates that the system

is close to steady state and that most or all of the disequilibrium is attributable to differences in

organic carbon in the water and sediments.

Guidelines for sampling and measurement of Jsocw are identical to those for sampling and

measurement of Cf

w

d

 under BAF method 1, as described in Sections 1.2 and 5.1, and Csoc under

BAF method 2, as described in Section 5.2. 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 appropriate average Cf

w

d

 in systems with

temporal fluctuations is the greatest challenge in measurement or estimation of Jsocw. On the

basis of monitoring reports and historical loading data, EPA expects that most persistent nonionic

organic chemicals will have Jsocw/Kow ratios in the range of 2-40. This expectation does not apply

when such chemicals have not been present in an ecosystem long enough to approach expected

steady-state concentrations in surficial sediments. In this case, Jsocw/Kow will be substantially

lower than 2, indicating low exposure potential through the benthic food web. Because the

national BAF methodology assumes that BAFs are determined for approximate long-term

average conditions, Jsocw/Kow values of less than 2 are unlikely to be relevant for persistent,

hydrophobic chemicals.

4.4 DERIVATION AND USE OF FOOD CHAIN MULTIPLIERS

FCMs are used in Procedure 1 (Figure 3-1) to estimate the dietary transfer of a chemical

up the food web for chemicals where metabolism is believed or assumed to be negligible. In

Procedure 1, FCMs are used with two of the four methods for deriving national BAFs. FCMs are

determined using a food web model and/or field data, and FCMs represent a measure of the

chemical’s tendency to biomagnify in aquatic food webs. By definition, an FCM is:

This equation assumes that a BCF that is corrected for growth dilution, lipid normalized,

and corrected for bioavailability considerations—that is, a baseline BCF—is equal to Kow. The

scientific basis for this assumption is presented in Section 5.4.2. Because a baseline 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 certain chemicals in their tissues can exceed those that are due to water

exposure only because of dietary uptake of the chemical. The baseline BCF, when multiplied by

the FCM for the organism’s trophic level, accounts for the influences of dietary uptake by the
4-25

organism. Dietary uptake of the chemical generally becomes important when the chemical’s

hydrophobicity exceeds a log Kow of 4 and the rate of chemical metabolism by the organism is

small. Thus, for nonionic organic chemicals, the third and fourth methods of Procedure 1 are

applicable only to chemicals with log Kows of 4 or greater.

4.4.1 Derivation of FCMs Using a Food Web Model

To derive FCMs using a food web model, a model and its input parameters must be

selected. The following subsections discuss how EPA selected a food web model for use in the

2000 Human Health Methodology. Also described are the parameters used with the model—the

food web structure, Jsocw (or, equivalently, Cf

w

d

 and Csoc), and the chemical metabolism rate in the

food web. Because all food web models require the above input parameters, these input

parameters are not unique to the food web model selected by EPA.

Selection of a Food Web Model

For a food web model to provide useful predictions, it must have the following general

characteristics and qualities. First, the model must include all biotic components of the food web,

that is, plankton, benthic invertebrates, forage fish, and piscivorous fish. Second, the model must

account for chemical uptake and loss from both food and water for all organisms. Third, the

model must 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. Fourth, because AWQCs for the protection of human health are designed for longterm average conditions in ambient waters, steady-state solutions for predicting bioaccumulation

in the food chain model are preferred over time-variant dynamic solutions for the food chain

model (see Section 1.2). 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

is easy to parameterize. 

Food chain models with the characteristics and desirable qualities summarized above

include the models of Gobas (1993) and Thomann et al. (1992). Other models are available, for

example, Levels I, II, and III fugacity models (Mackay, 1991); RIVER/FISH (Abbott et al., 1995);

AQUATOX (USEPA, 2000c,d); Iannuzzi et al. (1996); Ecofate (Gobas et al., 1998); and

BASS/FGETS (Barber, 2000). The AQUATOX (USEPA, 2000c,d), Iannuzzi et al. (1996), and

Ecofate (Gobas et al., 1998) models incorporate the submodels of Gobas (1993) and Thomann et

al. (1992) for modeling chemical uptake and loss. The RIVER/FISH (Abbott et al., 1995),

AQUATOX (USEPA, 2000c,d), Ecofate (Gobas et al., 1998), and BASS/FGETS (Barber, 2000)

models have extensive input data requirements and are principally designed for time and spatially

variant dynamic solutions for the food web. The time and spatially variant models include fate

and transport submodels along with the bioaccumulation submodel, and thus model predictions

include the uncertainties associated with both of the submodels. The fugacity models (Mackay,

1991) are designed for assessing the general behavior of chemical in model environments. On the

basis of the above characteristics and desirable qualities, EPA selected the models of Gobas
4-26

(1993) and Thomann et al. (1992) for further consideration and evaluation for calculation of

FCMs. These two models are widely accepted in the scientific community and are being used in a

number of scientific and regulatory applications.

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. Burkhard (1998) assessed (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. These evaluations were performed with field

data from the Lake Ontario and its food web structure. A brief summary of this evaluation is

provided in this TSD. For further details, the reader can refer to Burkhard (1998).

Model Scope and Theoretical Basis. The Gobas and Thomann models are quite similar

in many ways. Both models include benthic and pelagic food web components, thereby

incorporating exposure of organisms to chemicals from both the sediments and the water

column. Both models contain rate equations for the estimation of steady-state conditions but also

treat some chemical distributions as equilibrium partitioning. Both models require specification of

the food web structure and the lipid contents and weights of the organisms. Both models also

incorporate the organic carbon contents of the sediment and the water column. However, the two

models also have distinct differences. The major difference pertains to the methods used to

predict chemical concentrations in benthic invertebrates and zooplankton. With the Gobas (1993)

model, concentrations are predicted by using equilibrium partitioning, whereas with the Thomann

(1992) model, concentrations are predicted by using uptake and loss rates based on respiration,

dietary consumption, and growth of the organism.

The Gobas and Thomann models do not include solubility limits or controls for the

concentration of the chemical in any compartment. Thus, for a given ratio of the chemical

concentration in sediment to that in the water column, the models will predict the same BAF and

BSAF regardless of the numerical values used for the chemical concentrations, provided the ratio

is maintained. If these models are used to predict chemical concentrations in aquatic organisms,

the actual chemical concentrations in the sediment and water column will be required.

Model Accuracy. Burkhard’s (1998) evaluation using field data from Lake Ontario (Oliver

and Niimi, 1988) demonstrated that the Gobas and Thomann models have similar predictive

ability for all species for chemicals with log Kows ranging from 3 to 8 (Figure 4-6). The baseline

BAFs predicted with the Gobas model were in slightly better agreement with measured baseline

BAFs (using Lake Ontario field data) than those predicted with the Thomann model. For

chemicals with log Kows of 8 or greater, the models provided significantly different predictions.

For the Gobas model, average ratios of the predicted to the measured baseline BAFs were 1.6 for

sculpin, 1.0 for alewife, 1.4 for small smelt, 1.2 for large smelt, and 1.2 for piscivorous fish. For

the Thomann model, average ratios of the predicted to the measured baseline BAFs were 4.0 for

sculpin, 2.2 for alewife, 3.1 for small smelt, 3.0 for large smelt, and 2.5 for piscivorous fish. On

average, the Thomann model predicted slightly higher baseline BAFs than the Gobas model. For

piscivorous fish, the 10th and 90th percentile ratios (predicted/measured) were 0.4 and 5.6 for the

Thomann model and 0.3 and 2.1 for the Gobas model, respectively. Assuming a predicted 
4-27

Log K ow

3 4 5 6 7 8 9

Log K ow

3 4 5 6 7 8 9

Log K ow

3 4 5 6 7 8 9

Log K ow

3 4 5 6 7 8 9

Log Baseline BAF

3

4

5

6

7

8

9

1 0

Log Baseline BAF

3

4

5

6

7

8

9

1 0

O

Alewife Small Smelt Large Smelt Piscivorous Fish

Phytoplankton Zooplankton Diporeia Sculpin

O O O

3

3 3 3

concentration of 5 ppb in piscivorous fish, these ranges in baseline BAFs translate into

concentrations in fish of 2–28 ppb for the Thomann model and 1.5-10.5 ppb for the Gobas

models. These ranges are relatively narrow, varying by a factor of about 10.

Figure 4-6. Measured baseline BAFs for PCBs (•), chlorinated pesticides (‚), and chlorinated benzenes, chlorinated

toluenes, and hexachlorobutadiene (a) from the data of Oliver and Niimi (1988) and BAFf

R

d

s predicted using the

Gobas (– –) and Thomann (—) models plotted against Kow for all organisms (Burkhard 1998). For phytoplankton, the

predicted baseline BAFs were the same for both models and thus both lines coincide in the phytoplankton plot.

Model Sensitivity. A sensitivity analysis was performed to evaluate which input

parameters most affected the model. The sensitivity analysis of the input parameters used by the

Thomann and Gobas models revealed that Jsocw, Kow, lipid contents of the organisms, feeding

preferences of forage fish upon benthic invertebrates, and feeding preferences of the benthic

invertebrates (Thomann model only) were the most sensitive input parameters for the models. 

Sensitivity analyses were performed using a variety of deviations to the input parameters

(i.e., ±10%, ±25%, ±50%, and ±75%). For both models, the magnitude of effect of a given change

in Jsocw, organism weight, and organism feeding preferences on the overall model outcome was

directly proportional to the change in each of the input parameters. Input parameters with

moderate nonproportionalities were lipid content (Thomann model) and temperature (Gobas

model). Input parameters with large nonproportionalities were lipid content (Gobas model) and

Kow for both models. For both models, Jsocw, lipid content, and feeding preferences upon forage

fish showed little or no sensitivity for chemicals with log Kows of less than 4, a steep increase (or

decrease) in sensitivity for chemicals with log Kows between 4 and 6, and sensitivities of

approximately 1 (or –1) for chemicals with log Kows exceeding 6. A sensitivity of 1 means that a
4-28

10% change in an input parameter results in a 10% change in the predicted baseline BAF. The

greater sensitivity of the models to the Kow input parameter is logical, because all submodels use

the hydrophobicity of the chemical to define rates of chemical uptake and loss. In general, a

+10% change in the Kow parameter results in a +10% to +20% change in the chemical

concentration in fish for log Kows up to .7. The Thomann model was also extremely sensitive to

the feeding preference of the benthic invertebrates upon phytoplankton. For log Kows exceeding

4, the sensitivities became very large and approached values of –20 (in response to a +10%

change in the input parameter) for chemicals with log Kows of 6 or greater. The large sensitivity

for this input parameter occurs because the sediments are the predominant source of the chemical

to the benthic invertebrates but are only a minor part of the diet for the organism. Sensitivities for

other input parameters, such as organism weight, temperature (Gobas model), and other feeding

preferences, were relatively small.

Model Uncertainty. Uncertainty analyses performed with Monte Carlo simulations and

the Lake Ontario food web demonstrated that the input parameters Kow and Jsocw were the

dominant sources of uncertainties for the predicted baseline BAFs in piscivorous fish for both

models (Burkhard, 1998). These analyses were performed by using distributions and variances

for each input variable based on field data, and each simulation was performed with 100,000

iterations. To assess the importance of individual as well as groups of individual parameters,

simulations were performed by setting the variances for individual or groups of individual input

parameters to zero and comparing the ranges of the predicted baseline BAFs for the predictions

with the nonzeroed and zeroed variances. 

For piscivorous fish, overall uncertainties in the predicted baseline BAFs ranged from a

factor of 3.3 to 5.5 in the Gobas model and from a factor of 3.3 to 8.7 in the Thomann model for

chemicals with log Kows of less than 7.6 (based on the ratio of the 10th to 90th percentile

predictions in the distribution of possible values). To provide a perspective of the differences in

predictions between the models and their uncertainties, one can assume that for piscivorous fish

the Gobas model predicts concentrations of 4 ppb (with 1.8 and 9.4 ppb for the 10th and 90th

percentile predictions) for a chemical with a log Kow of 5.0 and a concentration of 4 ppb (2.1 and

7.6 ppb) for a chemical with a log Kow of 6.6. The Thomann model would predict a concentration

of 2.6 ppb (1.2 and 4.6 ppb) for a chemical with a log Kow of 5.0 and a concentration of 16.1 ppb

(8.5 and 30.1 ppb) for a chemical with a log Kow of 6.6. In general, these differences are not large,

and from the perspective of quantifying these concentrations analytically, these differences are

almost indistinguishable.

Burkhard’s (1998) evaluation of the Gobas and Thomann food web models reveals that

the models provide quite similar predictions for all organisms in the food web and that the

predictions are not significantly different for piscivorous fish. The comparison of predicted and

measured baseline BAFs based on field data from Lake Ontario suggests that the Gobas model

provides slightly more accurate predictions than the Thomann model. The sensitivities of the

input parameters are similar for both models, with the exception of benthic invertebrate feeding

preferences. The Thomann model was extremely sensitive to small changes in this input

parameter, and the Gobas model, because of its assumption of equilibrium partitioning for

benthic invertebrates, does not use this input parameter. Uncertainty analyses performed with
4-29

both models indicate that Kow and Jsocw are the dominant sources of uncertainty in the predicted

baseline BAFs. These analyses suggested that the uncertainties associated with predictions by the

Gobas model are slightly smaller than those with the Thomann model.

Based on the evaluation of the Gobas and Thomann food web models described above,

EPA will use the Gobas model for calculating food chain multipliers (FCMs) due to the

considerations listed below:

1. The Gobas model includes both benthic and pelagic food webs, thereby

incorporating exposure of organisms to chemicals from both the sediments and

the water column.

2. The input data needed to run the model can be readily defined. 

3. The baseline BAFs predicted using the model are in good agreement with fieldmeasured baseline BAFs for chemicals, even those with very high log Kows. 

4. The Gobas model had smaller uncertainties associated with the baseline BAFs for

fish compared to the Thomann model.

5. The Gobas model is readily available via the Internet in a Windows-based format

at http://www.rem.sfu.ca/toxicology/models.htm.

6. The model predicts chemical concentrations in benthic organisms using

equilibrium partitioning theory, which is consistent with EPA’s draft equilibrium

partitioning sediment benchmarks (ESBs) (USEPA, 2000b).

Because models are continually being refined, in the future EPA may consider the use of

other appropriately validated food web models for the derivation of FCMs. Any model

considered would need to have the characteristics and qualities outlined in Section 4.4. 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. 

Selection of the Sediment-Water Concentration Quotient (Jsocw)

Calculations of FCMs with the Gobas food web model requires the ratio of the chemical

concentrations in the sediments (expressed on an organic carbon basis) to those in the water

column (expressed on a freely dissolved basis). Unfortunately, measured Jsocws are rather limited

in ecosystem type, chemical classes, and quality because of a number of factors. These include

(1) 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; (2) the collection

of sediment and water samples that are not temporally and/or spatially connected; (3) collection

of bulk sediment samples rather than the uppermost 1 or 2 cm of the sediments; (4) the fact that

measurements of POC and DOC were not performed on the water samples analyzed for the
4-30

hydrophobic organic chemicals; (5) the lack of determination of the sediment organic carbon

content; and (6) the fact that studies designed specifically for determining Jsocw are not usually

performed. In addition, combining sediment measurements from one study with water

measurements from another study can result in large biases in Jsocws due to differences in

analytical methodologies (e.g., different surrogates for recovery corrections, different standards).

Review of a number of different data sets, as described in Burkhard (1998), revealed three

data sets of suitable quality for which Jsocws could be determined. These data sets were from

Lake Ontario (Oliver and Niimi, 1988), Hudson River (USEPA, 1997; USEPA, 1998b), 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, toluenes, and butadiene.

The data for the chlorinated benzenes, toluenes, 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 4-7 shows the Jsocws for selected PCB congeners in five different zones of Green

Bay. For the individual PCB congeners, the geometric mean regressions were performed on data

for the five different zones in the Green Bay system because both variables were measured with

error (Ricker, 1973). The slopes of the log Jsocw-log Kow regressions from the different zones were

not significantly different among the five zones (comparison of slope test, " = 5%). Therefore,

average Jsocws were determined for each PCB congener with data from all zones (Figure 4-8). The

geometric mean regression statistics are reported in Table 4-3 for each zone and for the average of

all zones. Examination of Figures 4-7 and 4-8 and Table 4-3 reveals that for PCBs, Jsocw is

strongly dependent on the Kow and slopes of slightly less than 1 were obtained. Examination of

Jsocws for Lake Ontario and Hudson River reveals trends similar to those in Green Bay; a strong

dependence of Jsocw on Kow for the PCBs and chlorinated pesticides (Figures 4-8 and 4-9 and

Table 4-3) and slopes of 1 and slightly less than 1 were obtained.

Table 4-3. Geometric Mean Regression Equations (log Jsocw = A C log Kow+ B) for

Polychlorinated Biphenyls (PCBs) and Chlorinated Pesticides

Ecosystem Slope (±sd) Intercept (±sd) n r sxy

Green Bay (PCBs)

 Zone 1 0.95 (±0.04) 1.21 (±0.22) 46 0.97 0.17

 Zone 2a 0.92 (±0.09) 1.13 (±0.61) 31 0.82 0.34

 Zone 3a 0.87 (±0.06) 1.61 (±0.36) 63 0.86 0.37

 Zone 3b 0.83 (±0.06)a 1.88 (±0.36) 60 0.85 0.33

 Zone 4 0.86 (±0.08) 1.31 (±0.53) 46 0.76 0.46

 All zones, congener averages 0.92 (±0.06) 1.20 (±0.38) 77 0.82 0.43

Hudson River (PCBs)

 RM 189 0.87 (±0.08) 1.81 (±0.45) 32 0.86 0.13

 RM 194 0.72 (±0.08)a 3.16 (±0.42) 27 0.84 0.16

Lake Ontario (PCBs and chlorinated

pesticides) 1.05 (±0.08) 0.83 (±0.49) 55 0.84 0.46

n = number of data points, r = correlation coefficient, sd = standard deviation, sxy = standard error of estimate, a slope significantly

different from 1.0, " = 1%.
4-31

Green Bay Zone 1

Log 

Psocw

4

5

6

7

8

9

10

Green Bay Zone 2a

Log 

Psocw

4

5

6

7

8

9

Green Bay Zone 3a

Log 

Psocw

4

5

6

7

8

9

Green Bay Zone 3b

Log 

Psocw

4

5

6

7

8

9

Green Bay Zone 4

Log Kow

4 5 6 7 8 9

Log 

Psocw

4

5

6

7

8

9

Figure 4-7. Sediment-water column concentration

quotient (Jsocw) for PCBs in five different

geographical zones in Green Bay, Lake Michigan.

The circled data points are the PCB congeners

numbers (log Kow) 18 (5.24), 28 + 31 (5.67), 52

(5.84), 101 (6.38), 118 (6.74), 149 (6.67), 174

(7.11), and 180 (7.36). The geometric mean

regression and their 95% confidence limits are

plotted.
4-32

All Zones,

 Congener Averages

Log Kow

4 5 6 7 8 9

Log 

Psocw

4

5

6

7

8

9

10

Hudson River Mile 189

Log 

Psocw

5

6

7

8

9

10

Hudson River Mile 194

Log Kow

4 5 6 7 8

Log 

Psocw

5

6

7

8

9

Figure 4-8. Average sediment-water column

concentration quotients (Jsocw) for individual PCB

congeners across the five different geographical

zones in Green Bay, Lake Michigan. The circled

data points are the PCB congeners numbers (log

Kow) 18 (5.24), 28 + 31 (5.67), 52 (5.84), 101

(6.38), 118 (6.74), 149 (6.67), 174 (7.11), and

180 (7.36). The geometric mean regression and

their 95% confidence limits are plotted.

In the Green Bay ecosystem, chemical concentrations in both sediments and the water column

decrease with increasing zone number. Zone 1 is at the mouth of the Fox River, the source of

PCBs to the bay, and zone 4 connects the bay to Lake Michigan. Zone 1, the region of highest

chemical concentrations, has much less variability in the measured Jsocws and the largest slope

for the log Jsocw–log Kow relationship among all sampling zones in Green Bay. Comparison of the

variability existing in zones 1 through 4, as illustrated by the 95% confidence intervals in Figure

4-7, 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 tightness,

consistency, and slope of the Jsocws–Kow relationship observed in zone 1 data might be more

illustrative of the underlying Jsocws–Kow relationship than those of the other zones because of

lower uncertainties associated with the analytical measurements.

Figure 4-9. Sediment-water column concentration

quotient (Jsocw) for PCBs at river miles 189 and

194. The circled data points are the PCB

congeners numbers (log Kow) 18 (5.24), 28 + 31

(5.67), 52 (5.84), 101 (6.38), 118 (6.74), 149

(6.67), 174 (7.11), and 180 (7.36). The geometric

mean regression and their 95% confidence limits

are plotted. 
4-33

Table 4-4. Average Jsocw/Kow Ratios for Three Different Ecosystems

Ecosystem Average Ratio (±sd)

Percentile

5% 10% 90% 95%

Green Bay (PCBs)

 Zone 1 9.15 (±4.97) 4.34 5.55 13.8 17.3

 Zone 2a 6.35 (±6.73) 1.24 1.37 13.1 21.0

 Zone 3a 10.3 (±13.3) 1.27 1.88 21.7 25.6

 Zone 3b 9.48 (±10.6) 1.68 2.00 20.1 29.9

 Zone 4 4.49 (±6.68) 0.60 0.75 6.95 8.10

 All zones, congener averages 7.21 (±6.68) 1.01 1.76 13.3 16.5

Hudson River (PCBs)

 RM 189 14.3 (±8.98) 6.03 7.36 23.4 34.7

 RM 194 48.4 (±47.6) 18.9 22.6 69.5 83.6

Lake Ontario (PCBs and chlorinated

pesticides)

23.4 (±25.1) 2.96 3.57 52.6 82.4

Overall average Jsocw/Kow= 23.3 (±18.0)

sd = standard deviation

From a theoretical standpoint, log Jsocw–log Kow relationships will have a slope of 1 if the

ecosystem is at equilibrium. In addition, EPA believes that ecosystems at steady state or with

conditions that approximate the longer term average conditions will also have slopes nearly equal

to 1. A number of factors could cause the slope to be less than 1; these include volatilization

losses (volatilization rates decrease with increasing molecular weight), sorption/desorption

hysteresis (desorption rates decrease with increasing molecular weight), inaccuracies in the

calculation of the concentration of chemical that is freely dissolved in the water column (the

denominator in the Jsocw term), and measurement error in determining the concentrations of

chemical in the sediments and/or water column. The log Jsocw–log Kow relationships for the

Hudson River, Lake Ontario, and Green Bay ecosystems have slopes that are 1 or slightly less

than 1 for PCBs and chlorinated pesticides (Table 4-3). The smallest slopes were observed with

the Hudson River ecosystem data. The Hudson River ecosystem is much more dynamic and

possibly further from steady-state conditions than are the Lake Ontario and Green Bay

ecosystems, because of changing flows over time and recent changes in PCB loadings. Given the

similarity in slopes among all three ecosystems, the conditions in the Hudson River do not appear

to be greatly different from those in the other two ecosystems.

Given that the slopes for the log Jsocw–log Kow relationships in Green Bay, the Hudson

River, and Lake Ontario are close to 1, and the fact that ecosystems tend to move toward the

theoretical slope of 1 over time, EPA assumes a slope one for this relationship. This causes the

log Jsocw = A C log Kow + B relationship to become log Jsocw = log Kow + B. Rearrangement of this

equation gives B = log Kdoc - log Kow= log (Kdoc/Kow), and B can be found by averaging the

differences of the log Kdoc and log Kow for the individual chemicals or by averaging the logarithms

of the ratios of the Kdoc to the Kow for the individual chemicals. This averaging was performed for

the three ecosystems (Table 4-4), yielding average Jsocw/Kow ratios of 7.21 for Green Bay, 14.3

and 48.4 for Hudson River, and 23.4 for Lake Ontario. The large differences in average Jsocw/Kow

ratios between the two Hudson River sampling stations suggest distinctly different behaviors in

the two sampling stations, and, therefore, an overall ratio was not computed for the Hudson
4-34

River. An average Jsocw/Kow ratio was computed for the three ecosystems by using the average

values for Green Bay and Lake Ontario with the two ratios from the Hudson River. An average

Jsocw/Kowratio for the three ecosystems of 23.3 with a standard deviation of 18.0 was obtained

(Table 4-4).

The EPA believes that the differences in average Jsocw/Kow ratios among the three

ecosystems evaluated here illustrate the range of variability that occurs among ecosystems across

the nation. Because Jsocws are a function of both current and past chemical loadings to the

ecosystems, Jsocw/Kow ratios both larger and smaller than those observed exist in the nation. For

highly contaminated sites, for example, Superfund sites with large concentrations of chemicals in

the sediments, Jsocw/Kow ratios could become very large. For new chemicals that are just being

introduced or discharged into the environment, Jsocw/Kow ratios will be small because very little of

the chemicals is present in the sediment. Degradation processes such as hydrolysis, photolysis,

and metabolism can also strongly influence the Jsocw/Kow ratio, depending on where these

processes occur (i.e., the sediment and/or the water column).

Because the degradation rates for the PCBs and chlorinated pesticides in the environment

are extremely slow, the average Jsocw/Kow ratio of 23.3 for the three ecosystems is representative

of chemicals that are very slowly degraded (or have long half-lives in the environment).

Chemicals with higher degradation rates will, in all likelihood, have Jsocw/Kow ratios that are

different from those for the PCBs and chlorinated pesticides, and EPA believes that the

Jsocw/Kow ratios will be smaller for such chemicals, on average, than those for the PCBs and

chlorinated pesticides.

On the basis of the data and information presented above, EPA will use the average value

of 23 for the Jsocw/Kow ratio for deriving FCMs with the Gobas model.

Selection of a Food Web Structure

To determine FCMs with the Gobas model, a food web structure is needed. 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. The sensitivity analysis performed

with the Gobas model indicated that the model predictions were relatively insensitive to organism

weights (largest sensitivity, < 0.1) and feeding preferences of piscivorous fish (largest sensitivity,

< –0.3) for all Kows. The predictions were more sensitive for Jsocw, feeding preferences of forage

fish upon benthic invertebrates, and lipid contents for chemicals with higher log Kows (Burkhard,

1998). The more sensitive input parameters attain sensitivities of approximately 1 or –1 at log

Kows of 6 (Jsocw), 5 (feeding preferences of forage fish upon benthic invertebrates), and 7 (lipid

content). 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. 
4-35

The uncertainty analysis performed with the Gobas model revealed that the Jsocw and Kow

input parameters were the dominant sources of uncertainty associated with the model prediction.

The higher sensitivities associated with the Jsocw and feeding preferences of forage fish are related

to higher sources of uncertainty associated with the Jsocw input parameter. As concluded in the

previous section, due to past chemical loadings and ecosystem interactions, many sediments

across the United States are currently enriched relative to the water column

(i.e., Jsocw/Kow= 23). This thermodynamic difference results in substantially different

concentrations of the chemical in benthic organisms and pelagic organisms (zooplankton). In the

Gobas model, the difference in lipid-normalized chemical concentrations between benthic and

pelagic organisms is a factor of 23, precisely the disequilibrium between the concentrations of

chemical in sediment and the water column. Consequently, small changes in the benthic portion

of the diet of forage fish will result in very different amounts of the chemical in the diets of both

forage fish and their predators. This large concentration difference is responsible for the

overwhelming importance of the benthic/pelagic composition in defining the food web. However,

in ecosystems where the disequilibrium (Jsocw/Kow) is small (or approaches equilibrium

conditions), the differences in lipid-normalized chemical concentrations between the benthic and

pelagic organisms will be much smaller. At equilibrium conditions (Jsocw/Kow = 1), the lipidnormalized chemical concentrations in the benthic and pelagic organisms are equal and differ by

the ratio of the fraction lipid of the organisms (fR), on a wet-weight basis. Therefore, for

ecosystems at or near equilibrium conditions, the benthic/pelagic composition of the food web is

much less important, because there are small differences between the chemical concentrations in

the benthic and pelagic organisms.

Food webs differ widely in their benthic/pelagic compositions among ecosystems, among

individual species, and among different age classes of species within an ecosystem. Of all the

ecosystem types, the purely pelagic food webs might be the least common for piscivorous fish.

However, purely pelagic food webs have been found in remote Ontario lakes for lake trout

(Rasmussen et al., 1990) and in Adirondack lakes for brook trout and yellow perch (Havens,

1992). Purely benthic food webs are more common than purely pelagic food webs, but are still

rather limited in nature. Some examples of purely benthic food webs can be found in tidal and

estuarine ecosystems, such as the food webs for flounder in New Bedford harbor (Connolly,

1991) and striped bass in the tidal Passaic River (Iannuzzi et al., 1996). Mixed food webs are

common in all ecosystems and, EPA believes, far outnumber the purely pelagic and benthic food

webs. There are numerous examples of mixed benthic/pelagic food webs, such as the food webs

for lake trout in the Great Lakes (Flint, 1986; Morrison et al., 1997), lobster in the New Bedford

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).

Attributes of a common aquatic food web might include multiple trophic levels, the

presence of forage and piscivorous fishes, a mixed benthic-pelagic structure, and benthic

invertebrates as important components. From the perspective of the fish and shellfish consumed

on average by the U.S. population, EPA believes that the common food web just described
4-36

provides a reasonable representation of the potential exposures of humans to various segments of

the food web, for example, benthic filter feeders/detritivores such as shrimp and clams and top

predators such as trout and salmon. For birds and wildlife, such a food web might not be

reasonable because their diets, in all likelihood, are different from those of the U.S. human

population.

In selecting a food web structure for determining FCMs used in the third method of

Procedure 1 of the BAF methodology, EPA considered a number of approaches for deriving or

selecting a food web. These approaches included (1) developing a hypothetical food web

structure consistent with the desirable characteristics described above; (2) developing food web

structures for different ecosystem types and then averaging data to derive the typical food web

for the nation; (3) using fish consumption survey data, developing food web structures for

different species consumed by the U.S. population, and then averaging to derive the typical food

web for the nation; and (4) simply selecting an existing food web with the desirable

characteristics described above. In selecting an average or typical food web structure for the

nation, all of these approaches are somewhat problematic because of the large differences in food

webs across the country. For this reason, EPA strongly encourages States and Tribes to make

site-specific modifications to EPA’s national BAFs (USEPA, 2000a).

The use of a purely benthic food web structure for the national BAF methodology with

the Jsocw/Kow value of 23 will result in the largest FCMs for fish. In contrast, the use of a purely

pelagic food web structure for the national BAF methodology with the Jsocw/Kow value of 23 will

result in the smallest FCMs for fish when the Gobas model is used. Because the goal of EPA’s

national BAF methodology is to represent the long-term, average bioaccumulation potential of

pollutants in aquatic organisms that are commonly consumed by humans throughout the United

States, neither the purely benthic nor the purely pelagic food web structure represents average

conditions. Rather, the purely benthic and the purely pelagic food web structures are the

extremes in food web structure resulting in the largest and smallest FCMs, respectively.

On the basis of the above information and discussion, EPA will use the mixed food web

structure from the Lake Ontario ecosystem as the representative food web for determining FCMs

for the national methodology (Table 4-5) (Flint, 1986; Gobas, 1993). This selection is based on

the following considerations:

1. The Lake Ontario food web possesses the characteristics of the average or typical

food web described above, that is, four trophic levels and a benthic/pelagic

composition ration of 55:45 for the piscivorous fish (Table 4-5). 

2. The Lake Ontario food web structure is not overly complex but does include

multiple forage fish with differing diets that are consumed by piscivorous fish (Table

4-5).

3. Comparisons of measured baseline BAFs and baseline BAFs predicted by using

FCMs based on the Lake Ontario data demonstrated good agreement for other

ecosystems, such as Green Bay, Hudson River, and Bayou d’Inde.
4-37

4. None of the other approaches considered by EPA for deriving an average or typical

food web for the nation would have substantially lower uncertainties than those

associated with using the Lake Ontario food web.

5. A detailed investigation of the sensitivities and uncertainties for this specific food

web structure with the Gobas model can be performed (Burkhard, 1998), whereas

use of the other possible approaches described above for selecting food web

structure is not amenable to such analysis.

6. This selected food web does not represent either extreme in benthic/pelagic

composition and thus is consistent with EPA’s goal for the national methodology of

representing the long-term average bioaccumulation potential of pollutants in

aquatic food webs.

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

Species Trophic

Level

Lipid

Content

Weight Diet

Phytoplankton 1 0.5%

Zooplankton (mysids [Mysis relicta]) 2 5.0% 100 mg

Benthic Invertebrates (Diporeia) 2 3.0% 12 mg

Sculpin (Cottus cognatus) 3 8.0% 5.4 g 18% zooplankton, 82% Diporeia

Alewife (Alosa pseudoharengus) 3 7.0% 32 g 60% zooplankton, 40% Diporeia

Smelt (Osmerus mordax) 3-4 4.0% 16 g 54% zooplankton, 21% Diporeia, 25%

sculpin

Salmonids (Salvelinus namaycush,

Oncorhynchus mykiss, Oncorhynchus

velinus namaycush

4 11% 2,410 g 10% sculpin, 50% alewife, 40% smelt

Calculation of Food Chain Multipliers

One additional input parameter is necessary before FCMs can be determined with the

Gobas food web model. This parameter, the rate of metabolism in forage and piscivorous fish, is

difficult to define because of the general lack of data on metabolism rate constants for individual

compounds. Procedure 1 of EPA’s BAF methodology (see Section 3.1, Figure 3-1) assumes that

the rates of metabolism for the chemicals of interest are low. Consequently, EPA assumes no

metabolism; that is, metabolism rates are set equal to zero in the model when FCMs are

calculated for methods 3 and 4 in Procedure 1.

Inputs to the Gobas model (MS-DOS version) include concentrations of chemicals in the

sediment (expressed on a wet-weight basis) and in the water column (expressed on a total basis).

Because the Gobas model does not have solubility limits or controls for the concentration of

chemical in any compartment (i.e., sediment, water, and biota), the chemical concentration in the

water used with the model is arbitrary for determining the BAFs. In other words, the BAF

obtained by using a concentration of chemical of 1 ng/L will be equal to that obtained using a
4-38

(Equation 4-9)

concentration of chemical of 150 μg/L for a specified Kow. Thus, in deriving the FCMs, 1 ng/L

(concentration of chemical freely dissolved in the water column, Cf

w

d

 ) is used and the

corresponding chemical concentration in the sediment is calculated by using Jsocw/Kow = 23

relationship, or Cs = 23 C Kow C (1 ng/L) C foc. 

In applying the Gobas model, EPA does not use Gobas’s method of accounting for

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. In Section 4.2 of this document, the procedure used in the 2000 Human

Health Methodology for determining the concentration of chemical that is freely dissolved in the

ambient water, Cf

w

d

, is presented. To avoid using Gobas’s method of accounting for

bioavailability, EPA set the concentration of the DOC in the model to an extremely small

number, 1.0 × 10-30 kilograms per liter. The Gobas model takes the total concentration of the

chemical in the water that is input to the model and, before doing any predictions, performs a

bioavailability correction by calculating the Cf

w

d

. The Cf

w

d

 is then used in all subsequent

calculations by the model. By setting the concentration of the DOC to 1.0 × 10-30 kilograms per

liter, the total concentration of the chemical put into the model becomes essentially equal to the

C

f

w

d

 , because the bioavailability correction with the method of Gobas is extremely small.

For each value of Kow input to the Gobas model, predicted baseline BAFs are reported by

the model for each organism in the food web. FCMs are calculated from the predicted BAFs

using the following equation:

where:

Baseline BAF = BAF that is based on the concentration of chemical freely dissolved

in water (Cf

w

d

) and the concentration of chemical in the lipid fraction

of tissue 

Kow = n-octanol-water partition coefficient

Using Equation 4-10, FCMs were calculated for each organism in the Lake Ontario food

web with the reported BAFs (Oliver and Niimi, 1988). Table 4-6 lists the FCMs for trophic level 2

(zooplankton), trophic level 3 (forage fish), and trophic level 4 (piscivorous fish). The FCMs for

the forage fish, trophic level 3, were determined by taking the geometric mean of the FCMs for

sculpin and alewife. The FCMs for the smelt were not used in determining the mean FCMs for

the forage fish because the diet of this organism includes small sculpin. This diet causes smelt to

be at a trophic level slightly higher than 3 but less than trophic level 4. In contrast, the diets of the

sculpin and alewife were solely trophic level 2 organisms (i.e., zooplankton and Diporeia sp.)

 FCMs were determined with the Gobas model, the food web structure in Table 4-5, 

Jsocw/Kow= 23, and the environmental parameters and conditions listed in Table 4-7. The resulting

FCMs, used for the national BAF methodology, are shown in Table 4-6.
4-39

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

Benthic Food Web Structure and Jsocw/Kow= 23)

Log

Kow

Trophic Level

2

Trophic Level

3

a

Trophic Level

4

Log

Kow

Trophic Level

2

Trophic Level

3

a

Trophic Level

4

4.0 1.00 1.23 1.07 6.6 1.00 12.9 23.8

4.1 1.00 1.29 1.09 6.7 1.00 13.2 24.4

4.2 1.00 1.36 1.13 6.8 1.00 13.3 24.7

4.3 1.00 1.45 1.17 6.9 1.00 13.3 24.7

4.4 1.00 1.56 1.23 7.0 1.00 13.2 24.3

4.5 1.00 1.70 1.32 7.1 1.00 13.1 23.6

4.6 1.00 1.87 1.44 7.2 1.00 12.8 22.5

4.7 1.00 2.08 1.60 7.3 1.00 12.5 21.2

4.8 1.00 2.33 1.82 7.4 1.00 12.0 19.5

4.9 1.00 2.64 2.12 7.5 1.00 11.5 17.6

5.0 1.00 3.00 2.51 7.6 1.00 10.8 15.5

5.1 1.00 3.43 3.02 7.7 1.00 10.1 13.3

5.2 1.00 3.93 3.68 7.8 1.00 9.31 11.2

5.3 1.00 4.50 4.49 7.9 1.00 8.46 9.11

5.4 1.00 5.14 5.48 8.0 1.00 7.60 7.23

5.5 1.00 5.85 6.65 8.1 1.00 6.73 5.58

5.6 1.00 6.60 8.01 8.2 1.00 5.88 4.19

5.7 1.00 7.40 9.54 8.3 1.00 5.07 3.07

5.8 1.00 8.21 11.2 8.4 1.00 4.33 2.20

5.9 1.00 9.01 13.0 8.5 1.00 3.65 1.54

6.0 1.00 9.79 14.9 8.6 1.00 3.05 1.06

6.1 1.00 10.5 16.7 8.7 1.00 2.52 0.721

6.2 1.00 11.2 18.5 8.8 1.00 2.08 0.483

6.3 1.00 11.7 20.1 8.9 1.00 1.70 0.320

6.4 1.00 12.2 21.6 9.0 1.00 1.38 0.210

6.5 1.00 12.6 22.8

a

 The FCMs for trophic level 3 are the geometric mean of the FCMs for sculpin and alewife.

Table 4-7. Environmental Parameters and Conditions Used for Determining FCMs for the

National BAF Methodology

Mean water temperature: 8°C

Organic carbon content of the sediment: 2.7%

Dissolved organic carbon content of the water column: 1.0E-30 mg/L

Density of lipids: 0.9 kg/L

Density of organic carbon: 0.9 kg/L

Metabolic transformation rate constants (all organisms): 0.0 d-1

Jsocw = 23 C Kow
4-40

4.4.2 Derivation of Food Chain Multipliers Using Field Data

In addition to model-derived estimates of FCMs, field data can also be used to derive

FCMs for nonionic organic chemicals. Compared with the model-based FCMs described

previously, field-derived FCMs account for any metabolism of the pollutant of concern by the

aquatic organisms used to calculate the FCM.

Field-derived FCMs should be calculated with lipid-normalized concentrations of the

nonionic organic chemical in appropriate predator and prey species, using the following

equations: 

FCM TL2 = BMFTL2 (Equation 4-10)

FCM TL3 = (BMFTL3) C (BMF TL2) (Equation 4-11)

FCM TL4 = (BMF TL4) C (BMF TL3) C (BMF TL2) (Equation 4-12)

where:

FCM = food chain multiplier for designated trophic level (TL2, TL3, or TL4).

The basic difference between FCMs and BMFs is that FCMs relate back to trophic level 1

(or trophic level 2, as assumed by the Gobas model [1993]), whereas BMFs always relate back to

the next lowest trophic level. For nonionic organic chemicals, BMFs can be calculated from lipidnormalized concentrations of chemical in tissues of biota at a site according to the following

equations:

BMF TL2 = (CR, TL2) / (CR , TL1) (Equation 4-13)

BMF TL3 = (CR , TL3) / (CR, TL2) (Equation 4-14)

BMF TL4 = (CR , TL4) / (CR , TL3) (Equation 4-15)

where:

CR = lipid-normalized concentration of chemical in tissue or whole organism

at a specified trophic level (TL2, TL3, or TL4). 

In addition to the acceptability guidelines pertaining to field-measured BAFs, 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 from

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 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. General information
4-41

on determining trophic levels of aquatic organisms can be found in USEPA

(2000 e-g).

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.

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.

4.4.3 Food Chain Multiplier Uncertainties 

Uncertainties associated with predictions from the Gobas model have been assessed by

Burkhard (1998) as described in Section 4.4.1. Monte Carlo analyses were conducted by varying

all input parameters except Kow and Jsocw. The results of these analyses are shown in Figure 4-10.

Because the FCMs were calculated from baseline BAFs predicted with the Gobas model, the

uncertainties shown in Figure 4-10 are directly applicable to the FCMs. These results suggest that

the FCMs have fairly low uncertainties, because, in their calculation, Jsocw was fixed and the Kows

were assumed to have no error. For example, for a log Kow of 6.5, the ratio from the Monte Carlo

analysis was 1.74 for the 90th to 10th percentile predicted baseline BAFs (Figure 4-10). For trophic

level 4 fish, the FCM is 22.8 and the 10th and 90th percentile FCMs would be 17.3 and 30.1,

respectively. 

Application of the FCMs, calculated with the assumed food web (Table 4-5) and

disequilibrium (Jsocw/Kow) of 23, to ecosystems and/or organisms with vastly different food webs

and/or disequilibriums can cause substantial biases in the baseline BAFs predicted for use in

methods 3 and 4 of Procedure 1. Although the degree and magnitude of error will vary among

sites, some general statements can be made about the direction and relative uncertainty

associated with these biases. Food webs that are more pelagic-based will tend to have smaller

FCMs, whereas food webs that are more benthic-based will tend to have larger FCMs. In Figure

4-11, FCMs for purely pelagic and purely benthic food webs, created by modifying the assumed

Lake Ontario food web and rerunning the Gobas model, are shown along with the FCMs from
4-42

4-11, FCMs for purely pelagic and purely benthic food webs, created by modifying the assumed

Lake Ontario food web and rerunning the Gobas model, are shown along with the FCMs from

Table 4-5. These two modified food webs represent the extremes in benthic/pelagic composition.

Decreasing the disequilibrium (Jsocw/Kow) will cause the FCMs to become smaller, whereas

increasing the disequilibrium (Jsocw/Kow) will cause the FCMs to become larger (Figure 4-12). The

FCMs for the 2000 Human Health Methodology were derived assuming no metabolism of the

chemical in the food web. If metabolism does exist within the food web, the FCMs will be

smaller than those calculated without metabolism.

FCMs derived from field measurements (see Section 4.4.2) do not have the above biases

because the measurements incorporate the conditions existing at the field site where the

measurements were performed. This includes the existing disequilibrium, chemical metabolism,

and influences due to the structure of the food web (i.e., predator-prey relationships and benthicpelagic components).
4-43

Log Kow

2 3 4 5 6 7 8 9

Food Chain Multipliers

0.01

0.1

1

10

100

Log Kow

Ratio of the 90

2 3 4 5 6 7 8 9

th to 10th Percentile

 Predicted Baseline BAFs

0

1

2

3

4

Log Kow

2 3 4 5 6 7 8 9

Food Chain Multipliers

0.01

0.1

1

10

100

Figure 4-10. The ratio of the 90th to 10th

percentile baseline BAF predictions for

piscivorous fish from 100,000 Monte Carlo

simulations using the Gobas model as a

function of n-octanol-water partition

coefficient (Kow) (Burkhard 1998). The ratio

when all parameters except Kow ()))) and

except Jsocw and Kow () •• )) are varied.

Figure 4-11. FCMs for purely pelagic (CCCC)

and purely benthic (!! !) food webs derived by

modifying the Lake Ontario food web ()))).

Figure 4-12. FCMs predicted using the Lake

Ontario food web with disequilibriums of

11.5 (CCCC), 23 ()))), and 46 (!! !).
5-1

(Equation 5-1)

(Equation 5-2)

5. CALCULATING BASELINE BAFS FOR NONIONIC ORGANIC CHEMICALS USING

THE FOUR METHODS

This section presents each of the four BAF methods as they are applied to nonionic

organic chemicals under Procedure 1. Application of the four BAF methods under Procedure 1 is

generally more complex than for Procedures 2-4, and thus, more detailed discussions are

warranted on how to appropriately apply them. Nonetheless, the same general data quality

considerations, assumptions, strengths, and limitations that apply to the BAF methods under

Procedure 1 are generally relevant to Procedures 2-4, even though each method is not applied in

the same manner (or may not be used at all) under these other three procedures. The equations

for each BAF method under Procedure 1 are shown in this section, and the ability of each

method to predict BAFs is discussed, as are assumptions and limitations inherent to each

method.

5.1 METHOD 1: DERIVING BASELINE BAFs FROM TOTAL BAFs (BAF t

Ts)

As has been noted, BAFs derived from data from samples collected in the field BAFt

T are

the first preference in EPA’s BAF hierarchy for deriving individual baseline BAFs. In Section 2,

the term “total BAF,” denoted BAFt

T , was introduced to refer to “field-measured” BAFs. The

BAFt

T is defined as:

where:

Ct = total concentration of the chemical in tissue

Cw = total concentration of chemical in water

The BAFt

T shown in Equation 5-1 is calculated on the basis of the total concentration of chemical

in the appropriate wet tissue of the aquatic organism sampled and the total concentration of the

chemical in the ambient water at the sampling site.

A baseline BAF is calculated from a BAFt

T as shown in Equation 5-2 by using information

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

total chemical that is freely dissolved in the study water (ffd). Appendix A provides more detailed

information on derivation of the baseline BAF equation.
5-2

where:

BAF = Total BAF (BAFt

T = Ct

/Cw)

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

fR = fraction of tissue that is lipid

In calculating BAFs using method 1, EPA will use appropriate BAFt

T data obtained from

the open literature (e.g., peer-reviewed journals, government reports, professional society

proceedings) when sufficient information is provided to indicate the quality and usability of data.

In general, the bioaccumulation data used should make it possible to calculate reliable BAFt

Ts and

to make some assessment of the overall uncertainty in the BAFt

T value.

5.1.1 Sampling and Data Quality Considerations

The data used to calculate a BAFt

T should be thoroughly reviewed to assess the quality of

the data and the overall uncertainty in the BAF value. The following general criteria apply in

determining the acceptability of BAFt

Ts. Because no guidance can address all of the variation in

experimental designs and data found in the literature, best professional judgment will be

necessary to supplement these data quality guidelines in selecting the best available information

and using it appropriately.

1. Aquatic organisms used to calculate a field-measured BAFt

T should generally be

representative of those aquatic organisms commonly consumed by the general

population in the United States. An aquatic organism that is not commonly

consumed by the general U.S. population can be used to calculate a fieldmeasured BAFt

T provided that the organism is considered to be a reasonable

surrogate for a commonly consumed organism. Information on the ecology,

physiology, and biology of the organism should be reviewed when assessing

whether an organism is a reasonable surrogate for a commonly consumed

organism.

2. The trophic level of the study organism should be determined by taking into

account its life stage, diet, and the food web structure at the study location.

Information from the study site (or similar sites) is preferred when evaluating

trophic status of an organism. If such information is lacking, general information

for assessing trophic status of aquatic organisms can be found in USEPA 

(2000e-g). 

3. In some cases, assessments of size, age, and reproductive status of the organisms

might be useful in assigning appropriate trophic levels for the study organisms.

Additionally, accumulation of chemical can vary as a result of other factors such

as different growth rates and pre-spawning versus post-spawning organisms.

Thus, the above ancillary information might be useful in deciding whether the

study organisms are appropriate representatives for field sites.
5-3

4. The percent lipid of the tissue used to determine the BAFt

T needs to be known,

either as measured in the field study or reliably estimated. This parameter is

necessary to permit lipid normalization of the concentration of chemical in tissue

when deriving baseline BAFs.

5. The study from which the BAFt

T is derived should contain sufficient supporting

information from which to confirm that tissue and water samples were collected

and analyzed according to appropriate, sensitive, accurate, and precise analytical

methods.

6. The site of the field study should not be so unusual that the BAFt

T cannot be

reasonably extrapolated to other locations where the national BAF and resulting

AWQC will apply.

7. The water concentration(s) used to derive the BAFt

T should reflect the average

exposure experienced by the study organism(s). The extent of spatial and

temporal averaging that is necessary for the water samples is a function of the

variability in chemical concentration in the ecosystem. In general, greater temporal

and spatial averaging of chemical concentrations in water will be necessary with

increasing Kow. More water samples over time and space (i.e., more averaging) will

be necessary for chemicals with higher Kows and higher variability in chemical

concentrations than in ecosystems with lower variabilities in chemical

concentrations. For chemicals with higher Kows, BAFt

Ts determined with

composite water samples over time will generally be more accurate than those

measured by individual “grab samples.” For chemicals with lower Kows, BAFt

T s

determined with composite water samples over time will, in general, be more

accurate than those measured with individual “grab samples.”

8. The home range of the organisms that are collected for determining BAFt

T s should

be determined or assessed such that the appropriateness of the spatial sampling

design can be evaluated within the context of the organism’s mobility. For more

mobile organisms, greater spatial averaging will generally be necessary.

9. The concentrations of POC and DOC in the study water should be measured or

reliably estimated so that baseline BAFs can be derived.

10. The field study should not be conducted in an ecosystem that has recently

experienced a major change or disruption in chemical loadings or flows (for

example, a 100-year flood or the removal of a major chemical source) because it

takes time for the ecosystem to return to long-term average or steady-state

conditions. The response times depend on a number of factors, including the

nature of the disruption; the chemical’s loading to and from the ecosystem; the

hydrodynamics and solids transport of the ecosystem; fish-specific parameters,

such as growth rates, chemical uptake, and depuration rates; and the Kow of the

chemical. For chemicals with higher Kows, response times might range from
5-4

months to years, whereas for those with lower Kows, the response times would be

shorter, possibly less than a year.

The EPA is presently developing guidance for designing and conducting field studies for

determining field-measured BAFt

Ts and for determining minimum data quality and quantity

requirements. This guidance will provide detailed information on how to design field sampling

studies that will yield BAFt

Ts that are representative of the long-term average conditions in an

ecosystem and have low bias and good accuracy.

5.1.2 Assumptions and Limitations

Several assumptions and limitations are inherent in the use of BAFt

Ts for deriving national

BAFs for nonionic organic chemicals. First, it is assumed that properly derived BAFt

Ts can

provide a reasonable estimate of the bioaccumulation that would occur under the long-term

conditions that exist in the ecosystem. This assumption is important because human health

AWQCs are generally intended to protect humans from long-term (chronic) exposure to

chemical concentrations in water and fish. To address this assumption, concentrations of

chemicalin water and tissue must be averaged over appropriate temporal and spacial scales so

that a steady-state or long-term BAF can be reasonably approximated. Complications can arise in

situations where variability in chemical concentrations in water is high relative to concentrations

of chemical in tissue (as is usually the case with highly hydrophobic chemicals), when rapid

changes occur in chemical loadings to the ecosystem, and when organisms move between areas

in which they experience greatly differing chemical exposures. As discussed in Section 5.1.1,

achieving the most appropriate temporal and spatial averaging for determining BAFt

Ts can be

specific to the chemical, species, and study site. In this regard, adherence to the aforementioned

sampling and data quality guidelines with respect to temporal and spatial averaging is the best

way to ensure that BAFt

Ts reflect the bioaccumulation that would be expected at or near steady

state.

The second major assumption associated with the use of BAFt

Ts for nonionic chemicals is

that by adjusting the BAFt

T for the organism’s lipid content (fR) and the chemical concentration

that is freely dissolved (ffd), it is possible to make reasonable predictions of bioaccumulation

across different species (within a trophic level) and sites. In reality, other factors influence

bioaccumulation. These factors include differences in chemical loadings histories (i.e., sedimentwater disequilibrium); food web structure; organism health and physiology; water quality factors

such as temperature; and food quality—all of which may vary across ecosystems. 

Burkhard et al. (2003a) have evaluated the effectiveness of adjusting BAFt

Ts by lipid

content and freely dissolved chemical concentration for increasing the reliability of extrapolating

BAFs across ecosystems and species. The results of these comparisons, which are discussed

further in Section 5.1.3, suggest that adjusting by fR and ffd reduces much of the variability in

BAFt

Ts. Furthermore, this analysis suggests that BAFs can be extrapolated among species within

a trophic level and across ecosystems with reasonable accuracy. Nevertheless, some variation in

BAFt

Ts occurs from “other factors,” such as those mentioned above.
5-5

A third assumption involved in the use of BAFt

Ts for deriving national BAFs is that, within

reasonable limits, BAFt

Ts are independent of exposure concentration (i.e., BAFtTs do not vary as a

function of exposure concentrations). This assumption is made when applying BAFt

Ts derived

from one set of chemical exposure concentrations to another set of concentrations (e.g., from

higher to lower chemical concentrations or vice versa). For nonionic chemicals, this assumption

is consistent with the mechanism of chemical uptake (i.e., passive diffusion across cell

membranes) and is widely supported by reports in the literature. However, it is theoretically

possible for BAFt

Ts to become dependent on exposure concentrations if these concentrations are

so high that they affect an organism’s health and, subsequently, its rate of chemical uptake,

elimination, or metabolism. Although this is an issue typically associated with BCF studies in

which exposure concentration is controlled by the investigator, this issue will be considered

during data review in order to avoid using BAFt

Ts from organisms that show overt signs of

toxicity. Although deviation from the concentration-independency assumption may be possible

under some circumstances, EPA is not aware of data that demonstrate the extent to which this

assumption might be violated under environmentally realistic exposure conditions. Furthermore,

it would probably be difficult to measure the extent of deviation from the concentrationindependency assumption, given the presence of other contributors, particularly differences in

bioaccumulation over space and time, to the overall variability on BAFt

Ts.

Currently, the greatest limitation in using BAFt

Ts to derive national BAFs is the paucity of

high-quality field data. The primary deficiencies that limit the use of available BAFt

T data include

the lack of proper spatial and temporal averaging, insufficient ancillary data (e.g., DOC, POC,

lipid content of organisms), and the lack of samples co-located in space and time. These

deficiencies often reflect limitations on available resources, but they also reflect study designs

that are inconsistent with the goals of a BAF study. These data gaps are expected to be filled as

additional field-measured data are generated to meet demands for site-specific BAFs and as

future guidance is developed for properly designing field BAF studies.

5.1.3 Validation of Method 1

As has been mentioned, use of baseline BAFs for nonionic organic chemicals allows

BAFt

Ts to be extrapolated among species and across locations and improves the accuracy of this

extrapolation. To validate this approach, Burkhard et al. (2003a) EPA made two different

evaluations. In the first evaluation, BAFt

Ts for PCBs and several aquatic species from Green Bay,

Lake Michigan, were compared among different geographical areas of the Bay (zones) and across

the entire Bay. In the second evaluation, BAFt

Ts and baseline BAFs for six PCB congeners were

compared among species and across ecosystems.

In the Green Bay evaluation, BAFt

Ts and baseline BAFs for the PCB congeners 18, 52,

149, and 180 in adult alewife, age 4 walleye, and age 10 carp were compared. These species were

selected because they were those species most frequently sampled across the different zones. The

PCB congeners used in the evaluation are major components of the PCB mixture present in

Green Bay, and uncertainties associated with their measurement are low. In addition, these

congeners had hydrophobicities that spanned a wide range: log Kows are 5.24 for PCB 18, 5.84 for

PCB 52, 6.67 for PCB 149 and 7.36 for PCB 180. BAFt

Ts and baseline BAFs were calculated for
5-6

Zone

1 2a 2b 3a 3b 4

Log BAF

4

5

6

7

8

9

six different zones. Comparing across the zones, the baseline BAFs varied less than the BAFt

Ts;

that is, the baseline BAFs were more constant across zones than were the BAFt

Ts (Figure 5-1;

Burkhard et al., 2003a). The BAFt

Ts increased from zone 1 to 4 (Figure 5-1; Burkhard et al.,

2003a), and the differences are more pronounced for the more hydrophobic PCBs 149 and 180,

which is consistent with equilibrium partitioning theory. The observed trend of increasing BAFt

Ts

across zones is due to increasing bioavailability of dissolved PCBs, which is caused by decreasing

POC and DOC across zones. This trend appeared to disappear with the adjustment of the BAFt

Ts

to baseline BAFs (Figure 5-1; Burkhard et al., 2003a) because, as presented in 

Section 4, the baseline BAF adjustment of chemical concentration to that which is freely

dissolved accounts for differences in POC and DOC.

Figure 5-1. BAFt

Ts (!) and Baseline BAFs (#) for PCB congener 149 

(2,2N,3,4N,5N,6-hexachlorobiphenyl) (±1 sd) for adult alewife for different Green Bay zones.

To further evaluate the relative variances associated with BAFt

Ts and baseline BAFs, baywide BAFs were compared. Bay-wide BAFt

Ts and baseline BAFs were calculated using a samplesize weighted average of the BAFs for each of the geographical zones. The variances of the baywide BAFt

Ts 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 5-1 and Burkhard et al., 2003a. Overall, the baseline

BAFs had smaller ratios than the BAFt

Ts and the adjustment/conversion of BAFtTs to baseline

BAFs resulted in an approximately twofold decrease in variability (Burkhard et al., 2003a).
5-7

Table 5-1. BAF t

Ts and Baseline BAFs Exceedance Limit Ratios for Green Bay (All Zones

Combined)

PCB

Congener 

90th to 10th Percentile Exceedance Limit Ratio 95th to 5th Percentile Exceedance Limit Ratio

BAFt

Ts Baseline BAFs BAFtTs Baseline BAFs

Adult alewife

 18 4.98 3.11 7.86 4.30

 52 5.48 2.85 8.90 3.84

 149 3.33 1.88 4.70 2.26

 180 4.08 2.20 6.10 2.76

Age 4 walleye

 18 3.57 3.50 5.14 5.00

 52 4.04 2.74 6.01 3.65

 149 3.11 2.12 4.30 2.62

 180 3.96 2.12 5.87 2.63

Age 10 carp

 18 4.87 4.23 7.65 6.39

 52 6.75 3.49 11.6 4.99

 149 5.96 1.87 9.91 2.24

 180 7.09 2.17 12.4 2.71

To assess across-ecosystem variability, Burkhard et al. (2000a) assembled BAFt

Ts and

baseline BAFs for six PCB congeners––PCBs 22, 52, 85, 118, 146, and 149––from the Green

Bay, Lake Ontario, and Hudson River ecosystems for 13 fish species (Figure 5-2). When

possible, age class BAFt

Ts were assembled, and trophic levels for the different species were

assigned with nominal/rounded trophic levels. These assignments caused species with slightly

lower trophic level positions (e.g., adult gizzard shad, with an average trophic level of 2.5) to be

lumped with species with slightly higher trophic levels (e.g., adult alewife, with an average trophic

level of 3.5) within the nominal trophic levels shown in Figure 5-2. As shown in Figure 5-2, the

baseline BAFs had substantially lower variability than the BAFt

Ts for trophic level 3 and 4 fish.

The coefficients of variation (in arithmetic space) for the trophic level 3 baseline BAFs were 85%

for PCB 22, 73% for PCB 52, 70% for PCB 85, 61% for PCB 118, 92% for PCB 146, and 59% for

PCB 149. For the BAFt

Ts, these values were 116% for PCB 22, 97% for PCB 52, 104% for PCB

85, 104% for PCB 118, 615% for PCB 146, and 68% for PCB 149 (Burkhard et al., 2003a). Similar

differences in the coefficients of variation were found for trophic level 4 fish (Burkhard et al.,

2003a). On average, the 75th/25th and 90th/10th percentile ranges in baseline BAFs were .2x and

.5x smaller than the ranges for BAFt

Ts. These results suggest that the corrections for tissue or

organism lipid content (fR) and the fraction of chemical concentrations that is freely dissolved in

water (ffd) reduce variability when BAFs are extrapolated among species of similar trophic levels

and across ecosystems. The variability (that is, the remaining spread or range in the baseline

BAFs for each trophic level) that was not due to differences in lipid content and freely dissolved

concentration of chemical is shown in Figure 5-2. Sources of the underlying variability could

include differences in nominal versus actual trophic level assignments for the individual species,

differences in disequilibrium of the ecosystem, and differences in age, size, growth rate, and/or

reproductive status of the individual organisms.
5-8

5.2 METHOD 2: DERIVING BASELINE BAFS FROM BSAFs

When acceptable BAFt

Ts are not available for a nonionic organic chemical with a log Kow

of $4, EPA recommends the use of BSAFs to predict the baseline BAF as the second method in

the BAF data preference hierarchy under Procedure 1 or 2. Although BSAFs may be used for

measuring and predicting bioaccumulation directly from concentrations of chemicals in surface

sediment, they also can be used to estimate baseline BAFs (USEPA, 1995b; Cook and Burkhard,

1998). Because BSAFs based on field data incorporate the effects of metabolism,

biomagnification, growth, and other factors, baseline BAFs estimated from BSAFs will also

account for all these factors. The BSAF approach is particularly beneficial for developing

AWQCs for chemicals that are detectable in fish and shellfish tissues but are difficult to detect

and measure in ambient water. The BSAF method is also beneficial for measuring the degree to

which bioaccumulation is reduced for chemicals, such as polychlorinated dibenzo-p-dioxins,

dibenzofurans, certain biphenyl congeners, and polycyclic aromatic hydrocarbons, through

metabolism in food webs or the species of concern.

Prediction of a baseline BAF from a BSAF requires data for one or more reference

chemicals for which concentrations in ambient water, as well as sediment, can be measured,

preferably from a common sediment-water-biota data set. This method, in effect, translates

relative differences between measured BSAFs for two chemicals into relative differences in

baseline BAFs for the chemicals when the baseline BAF for one chemical cannot be measured.

Relative differences in bioaccumulation can be accurately measured when each chemical’s

concentrations are analyzed from the same or equivalent environmental samples collected from a

site suitable for this purpose. BSAFs must be measured for the chemical of interest in order to

provide the basic measure of the chemical’s bioaccumulation potential. Specifically, this method

uses measured sediment-water concentration quotients (Jsocws) for reference chemicals to

estimate values of Cw

f d

 that cannot be measured for the chemical of interest. Each chemical’s Kow

must also be acquired, because the ratio of Jsocw to Kow provides the basis for relating reference

chemicals to the chemicals of interest. The following sections describe more completely the

determination of BSAF values; the relationship of baseline BAFs to BSAFs; the derivation of the

BSAF method equation; sampling and data quality considerations; assumptions and limitations

associated with the method; and the validation of this method for estimating baseline BAFs with

data from Lake Ontario and other ecosystems.
5-9

2,3',4,4',5-pentachlorobiphenyl

(PCB 118) log Kow = 6.74

TL 3 B TL 3 F TL 4 B TL 4 F

Log BAF

3

4

5

6

7

8

9

10

2,2',3,4',5,5'-hexachlorobiphenyl

(PCB 146) log Kow = 6.89

TL 3 B TL 3 F TL 4 B TL 4 F

Log BAF

3

4

5

6

7

8

9

10

2,2',3,4',5',6-hexachlorobiphenyl

(PCB 149) log Kow = 6.67

TL 3 B TL 3 F TL 4 B TL 4 F

Log BAF

3

4

5

6

7

8

9

10

2,3,4'-Trichlorobiphenyl

(PCB 22) log Kow = 5.58

TL 3 B TL 3 F TL 4 B TL 4 F

Log BAF

3

4

5

6

7

8

9

10

2,2',5,5'-tetrachlorobiphenyl

(PCB 52) log Kow = 5.84

TL 3 B TL 3 F TL 4 B TL 4 F

Log BAF

3

4

5

6

7

8

9

10

2,2',3,4,4'-pentachlorobiphenyl

(PCB 85) log Kow = 6.30

TL 3 B TL 3 F TL 4 B TL 4 F

Log BAF

3

4

5

6

7

8

9

10

Figure 5-2. 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 (!).
5-10

(Equation 5-3)

(Equation 5-4)

(Equation 5-5)

5.2.1 Determination of BSAF Values

As shown in Equation 5-3, the BSAF is determined by relating the lipid-normalized

concentration of a chemical in a tissue or organism to the organic carbon-normalized

concentration of the chemical in surface sediment. A BSAF is expressed in grams of organic

carbon in sediment per gram lipid in tissue.

where:

CR = lipid-normalized concentration of chemical in tissues

Csoc = concentration of chemical in dry sediment, normalized to sediment organic

carbon 

The lipid-normalized concentration of a chemical in an organism (CR) is determined by:

where:

Ct = concentration of chemical in tissue

fR = fraction of the tissue that is lipid

The sediment organic carbon-normalized concentration (Csoc) is determined by:

where:

Cs = concentration of chemical in dry sediment

fsoc = fraction of dry sediment that is organic carbon

The appropriate use of BSAFs for calculation of baseline BAFs does not require the

existence of steady state between the chemical mass loading and concentrations in sediments.

However, BSAFs are most useful when measured under conditions in which chemical

concentrations in water are linked to slowly changing concentrations in sediment. BSAFs
5-11

(Equation 5-6)

measured when concentrations in water are rapidly changing, either through onset of

contamination or the abrupt cessation of loading to the water, are likely to be unreliable without

additional modeling to extrapolate the values to longer term or steady-state conditions. 

BSAFs are rarely measured for ecosystems 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 of a BSAF from the expected equilibrium value of

approximately 1-2 is determined by the net effect of all factors that contribute to the

disequilibrium between sediment and aquatic organisms. A value greater than 1-2 can occur

through biomagnification or when surface sediment has not reached steady state with water. A

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). 

5.2.2 Relationship of Baseline BAFs to BSAFs

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. 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. The relationship between a measured BSAF for a chemical

and its baseline BAF depends strictly on the value of the chemical’s sediment-water

concentration quotient, (Jsocw). Method 2 uses measurements of Jsocw for reference chemicals (r),

(Jsocw)rs, to determine the value of (Jsocw) for a chemical of interest, i, which is unmeasurable.

(Jsocw)rs are determined by:

where:

(Csoc)r = concentration of a reference chemical in dry sediment, normalized to

sediment organic carbon

(Cw

f d

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

From the definitions of BAFR

fd

 (Equation 2-5), BSAF (Equation 2-14), and Jsocw, the

sediment-water column concentration quotient (Equation 2-18), the relationship between Jsocw,

BAFR

fd

 and BSAF may be derived for chemical i:
5-12

(Equation 5-7)

(Equation 5-8)

(Equation 5-9)

(Equation 5-10)

Equation 5-7 can be rearranged to give (BAFR

fd

)i = (BSAF)i C (Jsocw)i. Then (BSAF)i C

(Jsocw)i can be substituted for (BAFR

fd

)i in equation 2-4 to express the baseline BAF for chemical i

as:

Equation 5-8 reveals that a baseline BAF could be directly estimated from a BSAF if a

reasonably certain estimate of Jsocw is available for the chemical. Since ecosystems are often not

under steady-state chemical loading conditions, uncertainty is expected to be less when (Jsocw)i is

based on measurements for chemicals with similar Kows.

5.2.3 Derivation of the Baseline BAF Equation for Method 2

In many cases, the fugacity ratios between sediments and water (Jsocw/Kow) 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 evidence exists

for a significant difference, the explicit difference may be represented by Di/r:

Thus,

By substituting Equation 5-10 into Equation 5-8, the method 2 equation (5-11) is

obtained. For each aquatic species for which a field-measured BSAF for a chemical of interest, i,
5-13

(Equation 5-11)

is available, a baseline BAF may be calculated using the following equation with an appropriate

value of ( Jsocw/Kow)r:

where:

(BSAF)i = biota-sediment accumulation factor for chemical of interest “i”

(Jsocw)r = sediment-water column concentration quotient of reference chemical “r”

(Kow)i = n-octanol-water partition coefficient for chemical of interest “i”

(Kow)r = n-octanol-water partition coefficient for reference chemical “r”

Di/r = ratio between Jsocw/Kow for chemicals “i” and “r” 

(normally chosen so that Di/r = 1)

5.2.4 Sampling and Data Quality Considerations

Reference chemicals with Jsocw/Kow similar to that of the chemical of interest are preferred

for method 2 and often are available. Theoretically, the difference between sediment-to-water

fugacity ratios for two chemicals, “i” and “r” (Di/r), can be used when reliable reference chemicals

that meet the fugacity equivalence condition are not available. Nonionic organic chemicals with

concentrations in water at approximate steady state with respect to concentrations in surface

sediments should have similar, if not equal, values of Jsocw/Kow that are related to the fraction of

organic carbon in suspended solids when compared with the fraction of organic carbon in the

surface sediments. When steady-state conditions are not present, as is often the case, Jsocw/Kow

values for related chemicals may be similar. Similarity of Jsocw/Kow for two chemicals can be

indicated on the basis of similarities in molecular structure, which lead to similar physical

chemical behavior in water (persistence, volatilization), similar mass loading histories, and similar

concentration profiles in sediment cores. In many cases, PCBs serve as effective reference

chemicals. 

The following sampling and data quality considerations should be met when fieldmeasured BSAFs are used to predict BAFs:

1. The reference chemicals and the chemical of interest should have similar

physicochemical properties, as well as persistence in water and sediment.

2. When possible, (Jsocw)r data for several reference chemicals with similar Kows

should be obtained from the same water and sediment samples to ensure that

predictions are more robust than those that would be obtained with only one

reference chemical.
5-14

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. (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 4.5 of this TSD.

5. Whenever possible, the loadings history of the reference chemicals and the

chemical of interest should be similar, such that their sediment-water

disequilibrium ratios (Jsocw/Kow) would not be expected to be substantially

different (Di/r ~ 1).

6. Samples of surface sediments (0-1 cm is ideal) should be collected from locations

in which carbonaceous sediment, containing the reference chemicals and the

chemical of interest, is regularly deposited and is representative of average surface

sediment in the vicinity of the organism.

7. All sampling and data quality considerations described in Section 5.1.1 for

determining BAFt

Ts should also be met.

5.2.5 Assumptions and Limitations

Although EPA is currently restricting the application of this method for baseline BAF

derivation to nonionic organic chemicals with a log Kow of $4, this restriction primarily reflects

lack of validation of this method as applied to chemicals with a log Kow of < 4. 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. Future development

and evaluation of this method may lead to its application to a broader range of chemicals.

The primary assumptions and limitations discussed in Section 5.1.1 for method 1 also

apply to method 2. The primary limitation associated with method 2 for calculating and applying

the baseline BAF—namely, variability of Cw

f d—is common to all methods and models for

predicting and measuring BAFs. In deriving Equation 5-10, the assumption is made that Jsocw

values are chosen from a common sediment data set (i.e., both BSAF and Jsocw are based on the

same value for Csoc). In the event that this cannot be done, the relative percent error in the

baseline BAF associated with the Csoc inequality will equal 100 times the difference in Csocs used

for the BSAF and Jsocw, divided by the Csoc used for the BSAF measurement.

Although EPA recommends that Csoc values represent spatially averaged surface sediment

contamination levels in the region affecting the organism’s exposure, method 2 should be

accurate even when the Csoc value used for the BSAF and Jsocw does not well represent spatially 

averaged conditions. This is because the Csoc need only reflect the relative level of contamination

of sediments over time. The magnitude of errors associated with fluctuations in Cw

f d

 will be the
5-15

same for method 2 as for method 1. Temporal changes in Cw

f d

 are responsible for most deviations

from steady state between biota, water, and sediments.

Inaccuracies associated with the use of Jsocw/Kow from reference chemicals to estimate

Cw

f d

s for chemicals of interest under method 2 have a linear impact on the accuracy of baseline

BAFs. For example, if Jsocw/Kow is 10 but the estimate used is 20, the calculated baseline BAF

will be greater than the true value by a factor of 2. The measurements of Jsocw/Kow to date indicate

an expected range of 5–40 for most contamination scenarios. If the data quality considerations

for choosing Jsocw/Kow for the chemical of interest are followed, the magnitude of the errors

associated with the choice of Jsocw/Kow should be no greater than twofold.

The strength of method 2 is that it utilizes measurements of relative (not absolute)

differences in bioaccumulation between chemicals with structural similarity. When properly

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 polychlorinated dibenzo-pdioxins, polychlorinated dibenzofurans, and non-orthochlorinated biphenyls.

5.2.6 Validation of Method 2

For method 2, validation efforts were 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 validation 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 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 salmonids and Lake Ontario brown trout 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 validation 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).

For this TSD, Burkhard et al. (2003a) extended the previous validations 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 validation 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
5-16

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 validation effort included those used in the

previous validations (PCB 52, 105, 118) (USEPA, 1995b) as well as PCBs 18, 28, 149, 174, and

180. This validation was performed using the geometric mean of the baseline BAFs predicted by

using as many as possible of the eight reference PCB congeners listed above. As noted

previously (see Section 5.4.2), 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 validation study when the chemicals of interest and the reference

chemicals had more closely matched Kows (Burkhard et al., 2003a). The recent validation effort by

Burkhard et al. (2003a) also included baseline BAFs for several fish species in addition to

salmonids (e.g., carp, walleye, shad, alewife, yellow perch, white perch, pumpkinseed, redbreasted sunfish, and largemouth bass), some of which spanned several age classes.

A summary of the validation exercise is presented here and a detailed discussion is

provided by Burkhard et al. (2003a). Baseline BAFs predicted with method 2 were plotted against

field-measured baseline BAFs. The geometric mean baseline BAF predicted from BSAFs are

plotted; these differ from the individual reference chemical predictions only in terms of vertical

displacement due to the differences in congener-specific Jsocw. The ratio of predicted-tomeasured congener-specific baseline BAFs (BAFpredicted/BAFmeasured) was used to evaluate the

agreement between method 2-derived baseline BAFs and field-measured baseline BAFs. Table

5-2 presents zone (Green Bay data) and location-specific (Hudson River data) statistics for the

BAFpredicted/BAFmeasured ratio. Table 5-2 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 (95% from Green Bay and 91% from Hudson River) are

within a factor of 5 of the field-measured baseline BAFs. Table 5-3 presents exceedance levels

(i.e., certain points within the data distribution) for the ratio of predicted to measured congenerspecific baseline BAFs (BAFpredicted/BAFmeasured) for each fish species and ecosystem zone/location.

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, although

exceedance levels are noticeably wider at river mile (RM) 169. Method 2 also appears to

overpredict BAFs at RM 122 and 114, the only locations where bias in this method was found.

Overall, these analyses support the use of method 2 to estimate baseline BAFs from fieldmeasured BSAFs.
5-17

Table 5-2. Validation Statistics for Method 2: Ratio of Baseline BAFpredicted/Baseline

BAFmeasured

a

Location Method 2: Exceedance Levels and Comparison Statistics

95% Mean Median 5% % within 2x % within 5x

Green Bay

 Zone 1 0.39 0.88 0.88 1.66 87.6 100

 Zone 2a 0.25 1.27 0.89 3.47 69.8 92.8

 Zone 3a 0.21 1.25 0.73 3.78 51.5 94.1

 Zone 3b 0.16 1.08 0.69 2.71 53 91.4

 Zone 4 0.31 3.33 1.07 3.79 31.9 97.4

 All zones 0.22 1.53 0.81 3.29 55.7 94.5

Hudson River

 RM 194 0.46 1.12 0.99 2.12 81.9 95.2

 RM 189 0.33 1.00 1.03 1.55 87.5 100

 RM 169 0.11 2.01 0.59 9.91 19.0 68.3

 RM 144 0.67 1.19 0.97 2.14 92.3 100

 RM 122 0.70 2.43 2.16 4.81 45.8 95.8

 RM 114 1.20 3.86 3.78 6.91 16.7 83.3

 All stations 0.13 1.50 1.10 4.42 64.9 90.7

a

Includes number of species and range in “n” across sites

RM = river mile.

Table 5-3. Exceedance Levels for Ratio of Method 2-Predicted Baseline BAFs 

(Geometric Mean) to BAF t

Ts from Green Bay and Hudson River

Location

Percentile

5% 10% 90% 95%

Green Bay Zone 1

 Adult alewife 0.34 0.45 1.27 1.39

 Age 1 carp 0.4 0.48 1.21 1.4

 Age 1 walleye 0.4 0.5 1.26 1.61

 Age 3 walleye 0.4 0.49 1.26 1.63

 Age 4 walleye 0.4 0.49 1.2 1.4

Green Bay Zone 2a

 YOY alewife 0.27 0.37 2.88 3.22

 Adult alewife 0.29 0.42 2.89 3.28

 Age 2 carp 0.33 1.42 2.89 3.31

 Age 8 carp 0.27 0.37 2.88 3.22

 YOY shad 0.2 0.3 2.88 3.19

 YOY smelt 0.27 0.36 2.89 3.25

 Adult smelt 0.27 0.37 2.88 3.22

 Age 3 walleye 0.27 0.37 2.88 3.22

 Age 4 walleye 0.27 0.37 2.88 3.22

Green Bay Zone 3a

 YOY alewife 0.22 0.3 2.62 3.87

 Adult alewife 0.22 0.31 2.45 3.25

 Age 1 carp 0.24 0.28 2.44 2.94
Location

Percentile

5% 10% 90% 95%

5-18

 YOY smelt 0.2 0.27 2.74 3.93

 Adult smelt 0.2 0.26 2.53 3.82

 Age 2 brown trout 0.2 0.26 2.45 3.21

 Age 3 brown trout 0.2 0.26 2.59 3.85

 Age 4 walleye 0.2 0.26 2.44 3.17

Green Bay Zone 3b

 Adult alewife 0.15 0.26 2.27 2.66

 Age 8 carp 0.15 0.23 2.18 2.67

 Age 10 carp 0.17 0.27 2.25 2.65

 YOY smelt 0.15 0.23 2.25 2.65

 Adult smelt 0.15 0.23 2.25 2.65

 Age 2 brown trout 0.15 0.23 2.25 2.65

 Age 3 brown trout 0.15 0.23 2.25 2.65

 Age 3 walleye 0.17 0.27 2.26 2.66

 Age 4 walleye 0.15 0.23 2.24 2.65

Green Bay Zone 4

 Adult alewife 0.31 0.33 2.92 3.79

 Age 10 carp 0.33 0.34 2.91 3.37

 YOY smelt 0.31 0.33 2.92 3.44

 Adult smelt 0.29 0.33 3 3.81

 Age 2 brown trout 0.31 0.33 2.91 3.29

 Age 3 brown trout 0.32 0.34 2.96 3.8

 Age 4 walleye 0.32 0.34 2.91 3.34

 Age 5 walleye 0.32 0.34 2.91 3.32

Hudson River RM 194

 Carp 0.46 0.53 1.78 2.02

 Yellow perch 0.51 0.66 1.85 2.06

 Red-breasted sunfish 0.46 0.54 1.77 2.01

 Largemouth bass 0.46 0.54 1.77 2.01

Hudson River RM 189

 Yellow perch 0.32 0.46 1.53 1.78

 Pumpkinseed 0.33 0.48 1.52 1.75

 Red-breasted sunfish 0.33 0.5 1.52 1.73

 Largemouth bass 0.33 0.5 1.52 1.73

Hudson River RM 169

 Yellow perch 0.11 0.12 4.79 6.85

 Pumpkinseed 0.11 0.12 4.97 7.09

 Red-breasted sunfish 0.11 0.12 4.79 6.85

 Largemouth bass 0.11 0.12 4.79 6.85

Hudson River RM 144

 Yellow perch 0.67 0.7 1.82 2.14

Hudson River RM 122

 Yellow perch 0.86 1.05 4.41 5.37

 White perch 0.64 0.77 3.29 4.01

Hudson River RM 114

 White perch 1.2 1.43 4.99 6.91

 RM = river mile; YOY = young of year.
5-19

5.3 METHOD 3: DERIVING BASELINE BAFs FROM LABORATORY-MEASURED

BCFs (BCF t

Ts) AND FCMs

Method 3 in Procedure 1 is appropriate for nonionic organic chemicals that have

moderate-to-high hydrophobicity (log Kow $4) and low potential for being metabolized. For

method 3, a laboratory-measured BCF (BCFt

T) and FCM are used to predict a baseline BAF. The

BCFt

T must be used in conjunction with an FCM because nonaqueous routes of exposure and

subsequent biomagnification are of concern for the types of chemicals to which Procedure 1

applies. Although a BCFt

T accounts for chemical metabolism that occurs in the organism used to

calculate the BCFt

T, it does not account for metabolism that may occur in other organisms of the

aquatic food web. Method 3 uses the following baseline BAF and BCF equations:

(Equation 5-12)

where:

BCFt

T = Total BCF (BCFt

T = Ct

/Cw)

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

dissolved

fR = fraction of the tissue that is lipid

FCM = the food-chain multiplier for the appropriate trophic level, obtained by

linear interpolation (Table 4-6) or from appropriate field data

The technical basis for Equation 5-12 is provided in Appendix A. Presented below are

detailed discussions and information on selecting appropriate BCFt

Ts and FCMs and the

derivation of FCMs using food web models and field data.

5.3.1 Sampling and Data Quality Considerations

The BCFt

T should be calculated by using information on the total concentration of the

chemical in the tissue of the organism and the total concentration of the chemical in the

laboratory test water. The data used to calculate a BCFt

T should be thoroughly reviewed to assess

the quality of the data and the overall uncertainty in the BCF value. The following general criteria

apply in determining the acceptability of BCFt

T. Because no guidance can address all of the

variation in experimental designs and data found in the literature, best professional judgment will

be necessary to supplement these data quality guidelines in selecting the best available

information and using it appropriately.

1. Aquatic organisms used to calculate a BCFt

T should be representative of those

aquatic organisms commonly consumed in the United States. An aquatic

organism that is not commonly consumed in the United States can be used to

calculate an acceptable BCFt

T provided that the organism is considered to be a

reasonable surrogate for a commonly consumed organism. Information on the
5-20

ecology, physiology, and biology of the organism should be reviewed when

assessing whether an organism is a reasonable surrogate.

2. The test organism should not be diseased, unhealthy, or adversely affected by the

concentration of the chemical, because these conditions may alter accumulation of

chemicals.

3. The test organisms should be exposed to the chemical under flow-through or

renewal conditions.

4. The concentrations of the chemical in the laboratory test water must not exceed

the solubility of the chemical in water. Micelles, which indicate the chemical is not

dissolved, should not be present in the exposure water. Older BCFt

T measurements

for highly hydrophobic chemicals, such as those with a log Kow of > .6 are often

unreliable because solubility limits were exceeded or the chemical was present in

the exposure water in the form of micelles.

5. The total concentration of the chemical in the water should be measured and

should be relatively constant during the exposure period.

6. The concentrations of POC and DOC in the study water should be measured or

reliably estimated.

7. The percent of the tissue or organism that is lipid (i.e., fraction lipid, fR ) must be

measured or reliably estimated to permit lipid normalization.

8. The calculation of the BCFt

T should appropriately address growth dilution, which

can be particularly important for poorly depurated chemicals.

9. Other aspects of the methodology used should be similar to those described by

the American Society of Testing and Materials (ASTM, 1990) and U.S. EPA

Ecological Effects Test Guidelines (USEPA, 1996).

10. If BCFt

T consistently increases or decreases as the concentration of the chemical

increases in the test solutions (and this variation is not due to changes in lipid

fraction of organisms, freely dissolved fraction of chemical in water, or changes in

health of the organisms), the BCF determined at the concentration of chemical

that is closest to the expected AWQC concentration should be used in deriving the

AWQC.

11. BCFt

T may be based on measurement of radioactivity only when the BCFtT is

intended to include metabolites, when there is confidence that there is no

interference due to metabolites of the parent chemical, or when studies are

conducted to determine the extent of metabolism, thus allowing for a proper

correction.
5-21

12. All considerations described in Section 4.4.2 for determining FCMs should also be

met.

5.3.2 Assumptions and Limitations

In using method 3, EPA will assume that (1) a high-quality BCFt

T is a better measure of

the bioconcentration potential of a chemical than simply assuming that the baseline BCF is equal

to Kow, an assumption used with method 4, (2) the measured BCFt

T and the baseline BAF

predicted with method 3 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. Assumptions, limitations, and uncertainties associated with FCMs and

concentration independence of BCFt

T and BAFtT are discussed in Sections 4.4.1 and 5.1.4,

respectively. 

BCFt

T 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 BCFt

T measurement. Therefore, the range of effects of metabolism on

BCFt

T will be chemical specific. Nevertheless, EPA believes that high-quality BCFt

Ts provide a

better measure of bioconcentration potential for chemicals than simply assuming the baseline

BCF is equal to the chemical’s Kow because of the potential of the BCFt

T to include the effects of

metabolic processes. Furthermore, BCFt

Ts can be 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.

For method 3, baseline BAFs are calculated with the FCM and the BCFt

T. As discussed in

Section 4.4, FCMs that will be used by EPA in deriving national BAFs are derived using the

Gobas food web model with a number of assumptions and input parameters, namely, no

metabolism, an assumed food web structure, and Jsocw/Kow= 23. Limitations and uncertainties

associated with the FCMs have been discussed previously (see Section 4.4.1), and these

limitations and uncertainties are incorporated into the baseline BAFs derived using method 3. 

The baseline BAFs derived with method 3 for chemicals that are metabolized will not

include the effects of all metabolism processes because of the assumption of no metabolism used

in deriving the FCMs. However, the method will incorporate those metabolism processes or

effects that are captured in the BCF measurement. 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 3 is the current lack of high-quality measured

BCF data for highly hydrophobic chemicals in any organism class. This lack of data is due

principally to the difficulties associated with performing BCF measurements for highly insoluble

(hydrophobic) chemicals. Conditions appropriate for performing these measurements are

described in Section 5.3.1. When evaluating literature BCF data, one often finds measurements
5-22

Predicted Log Baseline BAF

2 3 4 5 6 7

Measured Log Baseline BAF

2

3

4

5

6

7

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 chemical solubilities

in water are exceeded, 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.

5.3.3 Validation of Method 3

To date, EPA has performed only a limited number of evaluations of method 3 because of

a lack of BCF data of the appropriate quality, as described in Section 5.1.2. and in Section 5.3.2.

For example, EPA invested considerable effort in examining the scientific literature for measured

BCFs for PCB congeners and was not able to find BCFs of appropriate quality.

Burkhard et al. (1997) evaluated method 3 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 3 (n = 32) (Figure 5-3). 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 C.

sapidus is suggested as the cause of the poor agreement between the field-measured BAFs and

the baseline BAFs predicted using method 3 (Burkhard et al., 1997).

Figure 5-3. Relationship between measured and predicted

baseline BAFs for method 3. 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 (Ž),

Micropoganias undulatus (•), Fundulus heteroclitus (~),

and Brevoortia patronus (•). 
5-23

5.4 METHOD 4: BASELINE BAF DERIVED FROM Kow × FCM

Method 4 in the tiered hierarchy of Procedure 1 consists of using Kow and an appropriate

FCM for estimating the baseline BAF. In Procedure 1, this method is used only for nonionic,

moderate to highly hydrophobic chemicals whose metabolism is considered negligible or is

unknown. In this method, the Kow is assumed to be equal to the baseline BCF, and thus the

organic carbon and lipid normalization procedures are not needed. To account for

biomagnification in method 4, the Kow value is multiplied by an appropriate FCM. Method 4 uses

the following baseline BAF equation:

Baseline BAF = Kow × FCM (Equation 5-13)

where:

FCM = food-chain multiplier for the appropriate trophic level, obtained by linear

interpolation (Table 4-7) or from appropriate field data

Kow = n-octanol-water partition coefficient

Detailed information on selection of appropriate FCMs and Kow values can be found in

Section 4.4 and Appendix B, respectively.

5.4.1 Assumptions and Limitations

A number of assumptions are associated with baseline BAFs predicted with method 4.

First, it is assumed that the Kow is equal to the chemical’s baseline BCF for a non-metabolized

chemical. Second, it is assumed that there is no metabolism of the chemical in the food web.

Third, the assumptions incorporated into the FCMs—namely, Jsocw/Kow= 23, mixed benthic and

pelagic food web, and adequacy of the Gobas model—are directly incorporated into the

predictions made with method 4. Discussion of these assumptions and limitations is presented

below.

Method 4 assumes that the Kow is equal to the chemical’s baseline BCF. Use of the Kow in

place of the baseline BCF 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) should be similar, if not equal, to the Kow for organic chemicals. Numerous investigations

have provided empirical data to support this theory. 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,
5-24

respectively. For example, de Wolf et al. (1992) adjusted a relationship reported by Mackay

(1982) to a 100% lipid basis (lipid-normalized basis) and obtained the following relationship:

log BCF = 1.00 log Kow + 0.08 (Equation 5-14)

For chemicals with large log Kows (>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 BCFs are reported on a lipid-normalized basis; determined using the

concentration of the chemical that is freely dissolved in the exposure water; corrected for growth

dilution; determined under steady-state conditions or from accurate measurements of the

chemical’s uptake (k1) and elimination (k2) rate constants; and determined with no solvent

carriers in the exposure. If, when reviewing the literature for BCFs, EPA cannot verify that

measured BCFs are measured under the appropriate conditions, as described in Section 5.3.1,

EPA will use the Kow as an approximation of the baseline BCF.

As discussed in Section 3.1.4, method 4 is used only for nonionic organic chemicals with

log Kows greater than or equal to 4 and low rates of metabolism (Figure 3-1). The restriction of the

use of method 4 to only non-metabolized chemicals is based on the fact that the assumption that

Kow equals BCF is valid only for non-metabolizable chemicals. When a chemical is metabolized

by an organism during the measurement of BCF, the measured BCF will be smaller than the Kow.

In addition, as discussed in Section 4.4, FCMs are also calculated using the assumption that no

metabolism of the chemical takes place in the food web. If method 4 is used when metabolism of

the chemical occurs in the food web, predicted BAFs will be larger than field-measured BAFs.

For detailed information on the assumptions incorporated into the FCMs, refer to Section 4.4.

5.4.2 Validation of Method 4

As noted in Section 5.2.6, Burkhard et al. (2003a) have performed exercises to validate the

predictive power of methods 2 and 4. The validation exercises were performed by using data

collected from Lake Ontario, Green Bay/Fox River, the Hudson River, and Bayou d’Inde,

Louisiana. With these data sets, baseline BAFs predicted using method 4 were plotted against

field-measured baseline BAF values. The agreement between baseline BAFs predicted using

method 4 and field-measured baseline BAF values is generally good for Green Bay, although not

as good as was seen for method 2 (see Section 5.2.5 and Burkhard et al., 2003a). In Green Bay,

59% of the baseline BAFs predicted using method 4 are within a factor of 2 and 93% are within a

factor of 5 of the measured baseline BAFs (Table 5-4). The validation exercises using the Green

Bay/Fox River and Hudson River data are described in detail in Burkhard et al. (2003a). 
5-25

Table 5-4. Validation Statistics for Method 4: Ratio of Baseline BAFpredicted/Baseline

BAFmeasured

Location Method 4: Exceedance Levels and Comparison Statistics

95% Mean Median 5% % within 2x % within 5x

Green Bay

 Zone 1 0.32 1.17 0.89 2.75 69.8 98.1

 Zone 2a 0.17 1.17 0.74 3.40 54.6 91

 Zone 2b 0.23 1.18 0.83 3.01 61.0 94.9

 Zone 3a 0.33 1.58 1.05 4.71 64.0 94.7

 Zone 3b 0.23 1.35 0.90 4.15 60.5 94

 Zone 4 0.15 1.43 0.61 5.28 40.5 82.2

 All zones 0.21 1.30 0.84 3.90 58.6 92.7

Hudson River

 RM 194 0.06 0.16 0.11 0.38 3.6 25.3

 RM 189 0.12 0.26 0.20 0.55 9.0 55

 RM 169 0.10 0.95 0.41 1.89 35.3 76.5

 RM 144 0.42 0.72 0.67 1.14 76.5 100

 RM 122 0.40 0.70 0.67 1.27 80.0 100

 RM 114 0.40 0.78 0.73 1.29 76.9 100

 All stations 0.08 0.5 0.24 1.07 26.3 60.7

RM = river mile.

The accuracy of baseline BAFs predicted with method 4 in the Hudson River varied

among sites. Generally, the predicted baseline BAFs are biased low; this is evident in Table 5-4,

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 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 5-4). 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 4. On the other hand, for

the Hudson River data set, the variability associated with baseline BAFs predicted using method

4 was generally smaller than that associated with method 2 (see Section 5.2.5 and Burkhard et al.,

2003a).

Factors that might be involved with the underprediction of the baseline BAFs for river

miles 169, 189, and 194 using method 4 include (1) the use of FCMs (Table 4-7) 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 
5-26

Table 5-5. Summary Statistics: Differences Between Log Baseline BAFs Predicted with

Method 4 and Log Baseline BAFs Measured from Lake Ontario (Oliver and Niimi, 1988)

for Chemicals with Log Kows Exceeding 4

Statistic

Organism

Sculpin Alewife

Small

smelt

Large

smelt Piscivorous fish

Average !0.01 !0.04 0.09 !0.28 !0.08

Standard deviation 0.35 0.36 0.37 0.35 0.36

Count 51 49 46 47 57

Median !0.02 !0.06 0.14 !0.30 !0.08

Within 2x 63% 59% 61% 47% 58%

Within 5x 94% 94% 94% 92% 96%

Negative residual 53% 53% 59% 72% 56%

Positive residual 47% 47% 41% 28% 44%

history due to the collapse of the Allen Mill gate in 1991. Burkhard et al. (2003a) evaluated the

influences of site-specific Jsocw/Kow values, the most important site-specific parameter in

calculating FCMs (Burkhard, 1998), by deriving site-specific FCMs for river miles 169, 189, and

194 using Jsocw/Kow values of 40, 13, and 40, respectively, in comparison to the national value of

23 (see Section 4.4.1). The predictions using method 4 with the site-specific FCMs were still too

small. Increasing the Jsocw/Kow values for river miles189 and 194 to 80 or 120 resulted in

agreement between predicted and measured baseline BAFs comparable to that observed in Green

Bay for method 4 (Table 5-4). The better agreement with much larger Jsocw values is consistent

with the possibility that the samples were not representative of the ecosystem. However, the

collapse of the Allen Mill gate (see below) 2 years prior to the collection of the field samples in

1993 introduces substantial and unresolvable uncertainty into the existing conditions in the river.

The gate collapse introduced elevated and variable PCB concentrations (by orders of

magnitude) into the river for approximately 15 months (QEA, 1999), and average concentrations

in water declined substantially from 1992 to 1993 for all congeners. Since the Allen Mill gate is

upstream of all the river mile locations used in this study, the most dramatic effects of this

episodic loading would be observed at the upstream locations, with a lessening of its effect with

increasing distance downstream. Better agreement with increasing distance downstream was

observed and is consistent with the expected lessening of the effect with increasing distance

downstream. Coincident with the gate failure, the lipid content of large mouth bass (and other

fishes as well) declined to extremely low levels of 0.25% in 1991 and then recovered in 1992 and

1993, to 1.0% and 1.5%, respectively. Although the causative factors are unclear for the rapid

decline in lipid contents, it appears that the food web was significantly disrupted. The overall

importance of the gate collapse and rapidly declining chemical concentrations between 1992 and

1993 and unusual lipid contents in fish for river miles 189 and 194 upon the bias observed with

method 4 at river miles 189 and 194 cannot be fully assessed with available data. At best, one can

conclude that river miles 189 and 194 were strongly influenced by the gate collapse and that

conditions in the river were not stable (e.g., sediment–water column chemical relationships,

chemical concentrations in the water, and fish lipid contents were rapidly changing). In the

derivation of FCMs for method 4, steady-state conditions in the food web are used, and chemical

concentrations in the sediment and water are constant over time. The conditions present in the
5-27

river at river miles 189 and 194 clearly violate the latter assumption of relatively stable chemical

concentrations in sediment and water over time. Some or all of the above factors including

sample representativeness might be responsible for the observed constant biases at river miles

169, 189, and 194. These biases have also been observed using a complex, time-dependent food

web bioaccumulation model, calibrated to site-specific data for food web dynamics, speciesspecific bioenergetics, and chemical uptake and elimination data in the same time period at the

same Hudson River locations as method 4 (QEA, 1999). These results further suggest unusual

conditions in the river.

Burkhard et al. (1997) evaluated the predictiveness of method 4 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 4 across different ecosystems. Because this evaluation of method 4

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, 2000a). 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 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. As was observed in evaluating method 2, the baseline BAFs

predicted with method 4 for hexachloroethane, Z-1,1,2,3,4-pentachlorobuta-1,3-diene, and

hexachlorobuta-1,3-diene for blue crabs (C. sapidus) were smaller than the measured baseline

BAFs. Higher Phase II metabolism activities in this species for this class of chlorinated chemicals

is suggested to contribute to the difference between the predicted and measured BAFs.

The EPA also compared the baseline BAFs predicted with method 4 to measured BAFs

for the Lake Ontario ecosystem (Table 5-5). 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 4 were within a factor of 5 of the measured BAFs. The

residuals (predicted minus measured) 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 4 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 4 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 4 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 4 were much smaller than the
5-28

measured BAFs, but the predictions were consistent with those based on the complex sitespecific time-dependent food web bioaccumulation model. 

Overall, EPA believes that method 4 provides excellent predictions for ecosystems that

have not recently experienced a major change or disruption in chemical loadings or flows. Of all

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 5.1.2 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 4 in ecosystems with extreme temporal dynamics.
6-1

(Equation 6-1)

6. DERIVATION OF NATIONAL BAFS FOR NONIONIC ORGANIC CHEMICALS

This section describes the process and technical documentation for determining national

BAFs for nonionic organic chemicals once all appropriate individual baseline BAFs have been

determined by using the methods described in Section 5. The distinction between national and

baseline BAFs is important and is illustrated by Figure 3-2 in Section 3. Specifically, baseline

BAFs are BAFs that have been adjusted to account for the lipid content of the tissues and the

amount of freely dissolved chemical in water. As explained in Section 4, these two factors are

important in affecting the bioaccumulation of nonionic organic chemicals. However, baseline

BAFs are not directly used to determine national human health AWQC, because they do not

reflect the lipid content of target aquatic organisms and the fraction of chemical that is freely

dissolved in water for the sites to which the AWQC applies. In effect, baseline BAFs are

“normalized” by lipid fraction and are based on the freely dissolved chemical (i.e., expressed on a

100% lipid and 100% freely dissolved basis). Furthermore, baseline BAFs need to be converted

to BAFs, expressed as total concentrations in tissue and water, to be compatible with national

human health AWQCs, which are based on the total concentration of a chemical in water.

To calculate national BAFs from baseline BAFs, two additional steps must be taken. First,

a final baseline BAF must be determined for each trophic level from all appropriate individual

baseline BAFs calculated by the methods described in Section 5. Guidance for determining a final

baseline BAF is provided in Section 6.1. After a final baseline BAF has been selected for each

trophic level, national BAFs are calculated for each trophic level using information on lipid

fraction of consumed aquatic organisms and the fraction of chemical that is freely dissolved in

water at the sites to which the AWQC applies. The calculation of a national BAF from a final

baseline BAF is shown in Equation 6-1.

where:

Final Baseline BAF = mean baseline BAF for trophic level “n”

fR(TL n) = fraction of tissue that is lipid in aquatic organisms at trophic

level “n”

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

freely dissolved

For deriving national BAFs, EPA uses national default values of lipid fraction (fR) that are

specific to each trophic level. The national default values of lipid fraction are:

Trophic Level 2: 0.019

Trophic Level 3: 0.026

Trophic Level 4: 0.030
6-2

These national default values reflect consumption-weighted mean values of the lipid fraction of

aquatic organisms that are commonly consumed throughout the United States. The technical

basis of EPA’s national default values for lipid fraction is presented in Section 6.2.

The same equation used to estimate the fraction of a chemical that is freely dissolved (ffd)

for deriving baseline BAFs (Equation 4-6) is used here to estimate the chemical’s freely dissolved

fraction for deriving national BAFs. For deriving national BAFs, EPA uses national default values

DOC and POC for estimating a representative fraction of chemical that is freely dissolved in U.S.

surface waters. The national default values of DOC and POC are: 

DOC: 2.9 mg/L

POC: 0.5 mg/L

These national default values reflect central tendency estimates of DOC and POC for bodies of

water distributed throughout the United States. The technical basis of EPA’s national default

values for POC and DOC is presented in Section 6.3. 

Once the national BAFs are determined for each trophic level, they are used in Equation 

1-1, 1-2, or 1-3 in the 2000 Human Health Methodology to derive national human health

AWQCs. As discussed earlier in this document, both the fraction of tissue that is lipid (fR) and the

fraction of chemical that is freely dissolved (ffd) are two parameters that may be adjusted by

States and Tribes to reflect local or regional conditions. Details on adjusting national BAFs to

reflect local or region-specific values for lipid fraction of consumed aquatic organisms and the

fraction of chemical that is freely dissolved at the site(s) of interest are provided in a subsequent

volume of this TSD (Volume 3: Development of Site-Specific Bioaccumulation Factors).

6.1 SELECTING FINAL BASELINE BAFs

Once individual baseline BAFs have been determined by using the appropriate BAF

methods within the applicable BAF derivation procedure, the next step in deriving national BAFs

for nonionic organic chemicals consists of selecting the final baseline BAF for each trophic level

(Section 3, Figure 3-2). As shown by Equation 6-1, final baseline BAFs are used to derive

national BAFs by adjusting for the organic carbon content expected in representative U.S. surface

waters and the lipid content of commonly consumed aquatic organisms. Determination of the

final baseline BAF for each trophic level from individual baseline BAFs essentially involves a

series of data aggregation steps. First, for each BAF method and trophic level, the mean of the

corresponding individual baseline BAFs is calculated for each species to produce a set of

“species-mean baseline BAFs.” Next, for each BAF method and trophic level, the mean of the

corresponding species-mean baseline BAFs is calculated to produce a set of “trophic level-mean

baseline BAFs.” Finally, a single “final baseline BAF” is selected or derived for each trophic level

from the available set of trophic level-mean baseline BAFs. Although simple in concept, the

process for calculating final baseline BAFs involves the use of best professional judgment in

combination with other considerations, including the data preference hierarchy, the relative

uncertainty among BAF estimates, and the weight of evidence among BAF methods. A summary

of the steps involved in determining final baseline BAFs is provided in Section 5.4 of the 2000
6-3

Trophic Level

Species Number

2 BAF Method

Mean BAF

1 (Method 1, TL3, sp1)

3

2

3

4

4

3

2

1

Mean BAF

(Method 1, TL4, sp1)

Mean BAF

(Method 1, TL2, sp1)

Mean BAF

(Method 1, TL3, sp2)

Mean BAF

(Method 1, TL2, sp2)

Mean BAF

(Method 1, TL4, sp2)

Mean BAF

(Method 1, TL2, sp3)

Mean BAF

(Method 1, TL3, sp3)

Mean BAF

(Method 1, TL4, sp3)

BAF

BAF

BAF

BAF

BAF

BAF

BAF

BAF

BAF

BAF

BAF

BAF

BAFBAF

BAF

BAF

Figure 6-1. A schematic illustrating the aggregation of baseline BAF data by species, trophic level, and

BAF method type for nonionic organic chemicals under Procedure 1.

Human Health Methodology. Additional guidance for determining final baseline BAFs is

described below.

6.1.1 Calculating Species-Mean Baseline BAFs for Each BAF Method

For each trophic level and BAF method combination, species-mean baseline BAFs are

calculated as the geometric mean of acceptable baseline BAFs. An illustration of this step is

provided in Figure 6-1 for the four BAF methods of Procedure 1. Procedure 1 is the derivation

procedure that applies to nonionic organic chemicals with moderate-to-high hydrophobicity and

negligible or unknown metabolism rates. All four BAF methods can be used in Procedure 1. Each

unique species–trophic level–BAF method combination “sub-cube” in Figure 6-1 may consist of

multiple baseline BAFs (illustrated on the right face of the entire cube in Figure 6-1 for BAF

methods 1, 2, and 3 under trophic level 4). Species delineations are lacking for method 4 (Kow ×

FCM) because this method produces a single baseline BAF for each trophic level. This is

illustrated by the single “baseline BAF column” for each trophic level of method 4 rather than a

sub-cube for each species. For illustration purposes, Figure 6-1 implies that, for each of the

applicable BAF methods, one or more baseline BAFs are available for each sub-cube in trophic

level 4. In practice, acceptable baseline BAFs may not be available for a BAF method or may be

available for only one or two trophic levels for a BAF method. 
6-4

When species-mean baseline BAFs are being calculated, individual baseline BAFs should

be reviewed carefully to assess their quality, variability, and overall uncertainty. This evaluation

will support decisions about whether to exclude certain baseline BAFs from the calculation of

national BAFs. This evaluation will usually be qualitative, because the availability of

bioaccumulation data is currently limited for many chemicals. Highly uncertain baseline BAFs

should not be used. Large differences in individual baseline BAFs for a given species (e.g., greater

than a factor of 10) should be investigated further. Some or all of the baseline BAFs for a given

species might not be used. Although all of the procedural and quality assurance guidelines

described in Section 5 apply for evaluating the quality, variability, and uncertainty of baseline

BAFs, several issues of special concern are discussed below. These issues include:

1. Temporal and spatial averaging of chemical concentrations

2. Spatial and temporal connectivity of samples

3. Chemical loadings history and steady state

4. Differences in food web structure

5. Reliability of lipid and organic carbon measurements

Temporal and Spatial Averaging of Chemical Concentrations

The extent of temporal and spatial averaging of chemical concentrations that are used to

calculate each baseline BAF should be explicitly evaluated as part of determining its overall

reliability (or uncertainty). Sufficient temporal and spatial averaging of chemical concentrations is

critical for accurately determining baseline BAFs from BAFt

Ts and BSAFs (i.e., methods 1 and 2

of Procedure 1). As discussed previously, the extent of averaging that is considered ideal varies

according to the chemical properties (e.g., hydrophobicity) and the variability of chemical

concentrations in compartments of the ecosystem (e.g., water, sediment, tissue). Greater spatial

and temporal averaging will be needed for highly hydrophobic chemicals in ecosystems showing

high variability in chemical concentrations than for chemicals of lower hydrophobicity or those

from ecosystems with low variability in chemical concentrations (see Section 1.2). Spatial

sampling of chemical concentrations should in all likelihood span the immediate/local home

range of the aquatic species. As illustrated by Figure 1-1, variability of chemical concentrations in

water does not necessarily equate to variability of chemical concentrations in sediment and biota,

particularly for highly hydrophobic chemicals where variability of chemical concentrations in

sediments and biota tends to be dampened compared to that in water. Therefore, the extent of

temporal averaging of chemical concentrations required to achieve accurate estimates of baseline

BAFs that reflect steady-state conditions may vary depending on the environmental

compartment. For baseline BAFs derived from BAF methods 1 and 2, which incorporate

measurements of chemical concentrations in ambient water, variability in chemical

concentrations in water may be especially important. 

In situations where variability in chemical concentrations is not well characterized, some

inference may be made from the overall hydrodynamics and chemical loading patterns in an

ecosystem. For example, estuaries and rivers generally display greater hydrologic fluctuations

than do large lake systems and, all else being equal, would generally be expected to have greater

variability in chemical concentrations in water and sediments, and thus potentially in biota.
6-5

Chemical loadings that are highly variable over time and/or are spatially complex (e.g., multiple

point sources at different locations) will also contribute to greater variability in chemical

concentrations in an ecosystem compared with more constant and uniform loading situations

(e.g., continuous releases of similar magnitude over wide geographic areas, such as PCB releases

from sediments in Lake Ontario). For moderately to highly hydrophobic chemicals with low rates

of metabolism, less confidence (greater uncertainty) should usually be assigned to baseline BAFs

derived from studies with minimal or no spatial or temporal averaging than with those from

studies with greater averaging, unless overall variability in concentrations is very small.

Moderately to highly hydrophobic chemicals that lack persistence in the environment may

require less spatial or temporal averaging to the extent that their distributions are known to be

limited with respect to more persistent chemicals of similar hydrophobicity. Concentrations in

water and tissue of chemicals of low hydrophobicity (i.e., log Kow #3) tend to parallel one another

temporally because of rapid uptake and elimination kinetics. Thus, less temporal and spatial

averaging of contemporaneous concentrations in water and tissue is required to produce reliable

BAFs for chemicals of low hydrophobicity. 

Temporal and Spatial Connectivity of Samples

The connectivity of samples in space and time should be evaluated for assessing the

reliability of baseline BAFs for accurately representing steady-state bioaccumulation. Depending

on chemical and ecosystem properties, BAFt

Ts that are derived from tissue samples that are

widely separated in space or time from water samples can be highly uncertain. Special attention

should be paid to situations in which geographic gradients in concentrations are known or

suspected, because geographic asynchrony in tissue and water samples can lead to erroneous or

biased estimates of a BAFt

T under these circumstances. For example, if water samples are

collected from an area of high concentrations in an exposure gradient (e.g., near a discharge) and

fish are collected from a “down gradient” area (i.e., where exposure concentrations in water are

expected to be substantially lower), BAFt

Ts can be underestimated because of overestimated

chemical concentrations in water relative to those in tissue. Even when water, sediment, and

biological samples are co-located in space, the mobility of organisms such as fish can be

problematic when strong gradients in chemical concentrations exist. For highly hydrophobic

chemicals whose metabolism is not important, modeling results suggest that fish tissue should be

sampled toward the end of the water sampling period in order to account for the time lag

associated with the slow accumulation kinetics of these compounds. For chemicals of low

hydrophobicity, rapid uptake and elimination kinetics indicates that tissue and water samples

should be closely connected temporally and spatially to produce reliable estimates of the BAFt

T

(e.g., sampling fish and water at the same time and location).

Chemical Loadings History and Steady State

As discussed in Section 4.3, the history of chemical loadings to an ecosystem has a direct

bearing on the extent of the disequilibrium between chemical concentrations in water and

sediment (Jsocw/Kow). This in turn can affect the magnitude of the BAFt

T for nonionic organic

chemicals (Burkhard et al., 2003b). Furthermore, rapid changes in chemical loadings to an

ecosystem can lead to biased estimates of long-term BAFt

Ts when sufficient time is not allowed
6-6

for tissue, water, and sediment concentrations to approach steady state, or when concentrations

are not averaged properly over space and time. A longer time is generally necessary for highly

hydrophobic chemicals in fish to reach steady state with respect to water than for chemicals with

low hydrophobicity. For BCFt

T measurements, evaluation of steady state is particularly important

when the steady-state method is used, because highly hydrophobic chemicals may require

substantially longer than the 28-day test duration typical of many bioconcentration tests to reach

or approach steady state. Therefore, when variability and uncertainty in baseline BAFs are being

evaluated for a given species, special attention should be paid to differences in chemical loadings

history and the likelihood that BAFt

Ts (or BCFtTs) reflect steady-state conditions as possible

explanatory factors. For highly hydrophobic chemicals (e.g., log Kow of approximately 5 or

greater), baseline BAFs may be highly biased when derived from field measurements in

ecosystems where there has been a recent and substantial change in chemical loadings or

chemical concentrations. Values of BAFt

T that are known or suspected of being substantially

biased with respect to representing steady-state bioaccumulation conditions should not be used

in calculating species-mean baseline BAFs.

Food Web Structure

Another factor to evaluate when comparing baseline BAFs derived from BAFt

Ts or BSAFs

is the difference in food web structure that may exist across sites (or even season) for the same

species. Although there is no one “right” food web structure from which to judge the

acceptability of a baseline BAF, differences in food web structure may help to explain some of

the variation observed in baseline BAFs for a given species across sites. Model predictions and

field observations indicate that food web structure can affect the magnitude of bioaccumulation,

particularly for highly hydrophobic organic chemicals. While the trophic position of a given

organism (and, by extension, the magnitude of its dietary exposure) can vary as a function of its

age, size, and reproductive status, variations in the availability of and competition for prey items

can directly influence dietary exposures. In some cases, individual organisms of the same species

that differ in size or age are classified into separate trophic levels because of size or age-related

differences in feeding preference and diet. Finally, for highly hydrophobic chemicals, for which

significant disequilibrium exists between water and sediment concentrations, models indicate that

a species with a benthic-driven diet tends to accumulate higher concentrations than does the

same species with a pelagic-driven diet (see Section 4.4 and Burkhard et al., 2003b).

Organic Carbon and Lipid Measurements

The reliability of organic carbon (DOC, POC) and lipid measurements is also important to

review when evaluating uncertainty in baseline BAFs. Both are used directly in deriving baseline

BAFs from field or laboratory data. Concentrations of DOC and POC in a body of water are

expected to vary over time as a function of precipitation events, season, hydrodynamics, and

numerous other attributes of a watershed. Thus, sufficient sampling of DOC and POC

concentrations over space and time is needed to achieve representative estimates of baseline

BAFs, the extent of which will vary according to the variability in the particular ecosystem and

the hydrophobicity of the chemical in question. Samples for the analysis of DOC and POC

should be collected simultaneous with water samples collected for the analysis of the chemical of
6-7

interest. For highly hydrophobic chemicals where greater temporal and spatial averaging of

chemical concentrations is needed to determine a BAFt

T that is representative of long-term

conditions, DOC and POC concentrations will need to be similarly averaged when used to

calculate the corresponding baseline BAF for the study site. This is especially important for

highly hydrophobic chemicals because the impact of DOC and POC on calculation of the

baseline BAFs is greatest with these chemicals (see Section 6.3).

In estimating lipid fraction for use in deriving a baseline BAF, 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 4.1, 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-normalized concentrations and, consequently, in baseline BAFs because of the

solvent system used. Of particular concern are differences in the solvent extraction efficiencies of

lipid and chemicals in extremely lean tissues (e.g., <1%-2% lipid). In such tissues, more polar (or

mixed polar/nonpolar) solvent systems tend to extract more lipids than do nonpolar solvent

systems. This phenomenon is believed to result from the proportionately greater fraction of polar

lipids in lean tissues as compared with fatty tissues. Variation in solvent extraction efficiencies

can be exacerbated when solvents of different polarities are used to quantify lipid content and the

target analyte (Randall et al., 1998). Thus, some of the variation in baseline BAFs might be due to

differences in the solvent systems used to extract lipids. It may be appropriate to exclude certain

data for which differences in baseline BAFs are believed to be largely due to differences in

extraction methods.

6.1.2 Calculating the Trophic Level–Mean Baseline BAF for Each BAF Method

After species-mean baseline BAFs have been calculated as described previously, the next

step in determining a final baseline BAF involves calculating a trophic level–mean baseline for

each BAF method, where this is possible. A trophic level–mean baseline BAF is calculated as the

geometric mean of acceptable species-mean baseline BAFs in that trophic level. With Figure 6-1

as an illustration, the result of this calculation is a single mean baseline BAF for each vertical

“column” of Figure 6-1. Trophic level–mean baseline BAFs should be calculated for trophic

levels 2, 3, and 4, because available data on U.S. consumers of fish and shellfish indicate

significant consumption of organisms in these trophic levels. Special attention should be focused

on trophic level assignments, because they can be somewhat ambiguous for a number of aquatic

organisms, particularly because trophic position is actually a continuous function of an

organism’s diet over time, rather than a discrete category.

6.1.3 Selecting the Final Trophic Level–Mean Baseline BAFs

Final baseline BAFs are selected among the trophic level–mean baseline BAFs for each

trophic level using best professional judgment and considerations of:

1. The data preference hierarchy that is applicable to chemical of interest 

2. The uncertainty associated with the baseline BAFs
6-8

3. The weight of evidence suggested by BAFs determined by different BAF

derivation methods.

The data preference hierarchy for each BAF derivation procedure is summarized in Figure

3-1 and further detailed in Table 6-1. It 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 trophic level–mean baseline BAFs are available for more than one BAF

method within a given trophic level, the final trophic level–mean baseline BAF should be selected

from the most preferred BAF method, as defined by the data preference hierarchy for the

applicable derivation procedure (Figure 3-1; Table 6-1). 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. 

Table 6-1. Data Preference Hierarchy for Selecting Final Baseline BAFs for Nonionic

Organic Chemicals

BAF

Derivation

Procedure Applicability Data Preference Hierarchy

1 Kow $4, metabolism

negligible or

unknown 

1. Baseline BAF from an acceptable BAFt

T

 (method 1)

2. Baseline BAF predicted from an acceptable BSAF (method 2)

3. Baseline BAF predicted from an acceptable BCFt

T

 and FCM (method 3)

4. Baseline BAF predicted from an acceptable Kow and FCM (method 4)

2 Kow $4, metabolism

significant 

1. Baseline BAF from an acceptable BAFt

T

 (method 1)

2. Baseline BAF from an acceptable BSAF (method 2)

3. Baseline BAF from an acceptable BCFt

T

 (method 3)

3 Kow <4, metabolism

negligible or

unknown

1. Baseline BAF from an acceptable BAFt

T

 (method 1) or BCFt

T

 (method 3)

2. Baseline BAF predicted from an acceptable Kow value (method 4). 

4 Kow <4, metabolism

significant 

1. Baseline BAF from an acceptable BAFt

T

 (method 1) or BCFt

T

 (method 3)

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

greater confidence in a final baseline BAF is generally assumed when baseline BAFs are in

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

agreement among baseline BAFs derived from different methods does not necessarily indicate

less confidence, if such disagreements can be adequately explained. For example, if the chemical

of concern is metabolized by aquatic organisms represented by a baseline BAF value, one would
6-9

expect disagreement between a baseline BAF derived from a BAFt

T (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. This is particularly relevant because national

BAFs are intended to reflect central tendency estimates of bioaccumulation expected across U.S.

surface waters. 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.

6.2 BASIS FOR THE NATIONAL DEFAULT LIPID FRACTION (fR) OF

COMMONLY CONSUMED FISH AND SHELLFISH

This section provides the technical basis of EPA’s recommended national default values

of lipid fraction (fR) that are used to derive national BAFs for nonionic organic chemicals (0.019

for trophic level 2 organisms, 0.026 for trophic level 3 organisms, and 0.030 for trophic level 4

organisms). As indicated by Equation 6-1 and Figure 2-2, the lipid fraction of commonly

consumed aquatic species is needed to adjust a baseline BAF (which reflects partitioning to 100%

lipids) to a BAF that reflects the lipid fraction of aquatic organisms commonly eaten by U.S.

consumers. Information on lipid content is used to adjust BAFs for nonionic chemicals because it

has been shown to influence the magnitude of bioaccumulation in aquatic organisms (Mackay,

1982; Connolly and Pederson, 1988; Thomann, 1989). Therefore, lipid content in consumed

aquatic organisms is considered to be an important factor for characterizing potential human

exposure to nonionic organic chemicals.

Although EPA uses national default values of lipid fraction to derive national human

health AWQC, EPA encourages States and authorized Tribes 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 data is

encouraged over national-scale data because local or regional consumption patterns of fish and

shellfish (and thus the amount of lipid consumed from aquatic organisms) can differ from

national consumption patterns. Additional guidance on developing local or region-specific values

of lipid fraction, including a database of lipid fraction for many commonly consumed aquatic

organisms, is found in a subsequent volume of this TSD (Volume 3: Development of SiteSpecific Bioaccumulation Factors). Nevertheless, EPA recognizes that there will be situations

when such local or regional data are not available or are inadequate for deriving representative

values of lipid fraction for setting State or Tribal water quality standards. In these cases, EPA

recommends the use of its national default values of lipid fraction for deriving BAFs and resulting

water quality criteria.

6.2.1 Variability in Lipid Content

One issue associated with setting national default values of lipid fraction in consumed

aquatic organisms is how to address intraspecies and interspecies variability in lipid content. 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
6-10

found in fillets of northern pike, Esox lucius (0.7%). Wide variation in lipid content can also

occur within a species. Based on data presented later in this section, 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.

A number of factors can lead to variability in lipid content of aquatic organisms. Many of

these factors are fundamentally related to 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. In addition, the distribution of lipids in a

particular aquatic organism is not uniform across all tissue types, thus 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 (see Section 6.2.2).

For the purposes of deriving national default values of lipid fraction, EPA has addressed

the issue of variability in lipid content in several ways. First, only data for the most commonly

consumed aquatic species in the United States were considered. The identity of these species was

determined by the U.S. Department of Agriculture’s Continuing Survey of Food Intake by

Individuals (CSFII) (USDA, 1998) for 1994 through 1996 (the most recent data at the time of this

analysis). Second, data were limited to the tissues that are most commonly consumed within a

species. Third, when size information was available, data were further limited to sizes of aquatic

species that are typically eaten by U.S. consumers. Finally, national default values of lipid

fraction were determined by weighting individual mean values of lipid fraction for each species

(or group of species) by the appropriate consumption rates determined for the U.S. population. In

this manner, EPA’s national default values of lipid fraction better reflect national consumption

patterns of aquatic organisms in comparison to simply weighting the lipid fraction for each

species equally.

The following sections present the data sources, analysis, assumptions, and uncertainty

associated with the derivation of national default lipid values.

6.2.2 Data Sources

The national default values of lipid fraction (fR) were derived by using three types of

nationally aggregated data:

1. National per capita consumption rates of aquatic organisms

2. Lipid fraction in consumed aquatic organisms

3. Trophic status of consumed aquatic organisms

A summary and description of this information are provided below.
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National Mean Per Capita Fish Consumption Rates

 Information on the types and quantity of aquatic organisms consumed in the United

States was obtained from the CSFII (USDA, 1998). This national survey provides daily mean per

capita estimates of fish consumption for the U.S. population for categories of estuarine,

freshwater, and marine fish and shellfish (among other dietary categories). Although other

regional or local surveys were available, the CSFII was selected because it provided consumption

information on a national basis and contained the most recent data available. Furthermore,

information from the same USDA survey was used to derive national default values of fish

consumption rates for calculating national human health AWQCs. Reliance on the same fish

consumption survey ensures consistency between the derivation of national default values for

lipid fraction and national default values of fish consumption rates.

Table 6-2 shows the habitat classification, CSFII consumption categories, estimated mean

per capita consumption rates, and fraction of total estuarine and freshwater consumption

represented by each category in the CSFII. Mean per capita consumption rates were estimated for

individuals aged 18 years and older for categories of freshwater and estuarine aquatic organisms.

This same category of consumers was used to calculate the national default value of fish

consumption for the general adult population and sport anglers (i.e., a 90th percentile total intake

rate of 17.5 g per person per day, as described in the 2000 Human Health Methodology).

However, sufficient data were not available in the CSFII survey to adequately describe the pattern

of fish consumption (as opposed to the total fish and shellfish intake rate) for individuals

constituting the 90th percentile. Therefore, the consumption pattern represented by the mean per

capita consumption rates was chosen for this analysis. In calculating the estimated mean

consumption rates, two CSFII consumption categories were classified as being unknown by the

survey (i.e., “unknown fish” and “unknown seafood”). By using the same approach adopted for

deriving the national default value for fish consumption, 39% of these two “unknown” fish

consumption categories were apportioned to the freshwater and estuarine consumption

categories on a consumption rate-weighted basis. Further details of the mean per capita

consumption rates from the CSFII survey data and assignment of habitat designations for fish

and shellfish can be found in the Exposure Assessment volume of this Technical Support

Document.

It is apparent from Table 6-2 that estimated mean per capita consumption rates vary

widely across categories and that consumption in a relatively few categories dominates the overall

consumption pattern of freshwater and estuarine organisms. For example, the consumption rate

in the “shrimp category” constitutes about 35% of the total estimated mean per capita

consumption rate of freshwater and estuarine organisms. Similarly, the consumption rates

corresponding to the flounder, catfish (fresh and estuarine), and flatfish categories constitute

approximately 30% of the total consumption rate of freshwater and estuarine organisms. To

account for the disproportional nature of the national per capita consumption rates revealed by

the CSFII survey, the derivation of national default values of lipid fraction were determined on a

consumption-weighted basis, as described later in this section.
6-12

Table 6-2. Categories and Mean Per Capita Consumption Rates from the USDA CSFII 

Habitat

USDA CSFII Consumption

Category

Estimated Mean Consumption

Rate (g/person/day)

Proportion of Total Freshwater and

Estuarine Consumption Rate (%)

Estuarine Shrimp 2.65492 35.4

Flounder 0.73482 9.8

Catfish (estuarine) 0.60335 8.0

Flatfish (estuarine) 0.45173 6.0

Crab (estuarine) 0.42111 5.6

Perch (estuarine) 0.22331 3.0

Croaker 0.17792 2.4

Oyster 0.17485 2.3

Herring 0.16428 2.2

Trout, mixed spp. (estuarine) 0.15305 2.0

Salmon (estuarine) 0.05915 0.8

Anchovy 0.05815 0.8

Rockfish 0.05428 0.7

Mullet 0.04512 0.6

Clam (estuarine) 0.01732 0.2

Smelts (estuarine) 0.00880 0.1

Eel 0.00466 0.06

Scallop (estuarine) 0.00140 0.02

Smelts, rainbow (estuarine) 0.00076 0.01

Sturgeon (estuarine) 0.00018 0.002

Freshwater Catfish (freshwater) 0.60335 8.0

Trout (rainbow) 0.25361 3.4

Perch (freshwater) 0.22331 3.0

Carp 0.19071 2.5

Trout, mixed spp. (freshwater) 0.15305 2.0

Pike 0.04021 0.5

Whitefish (freshwater) 0.01309 0.2

Crayfish 0.01028 0.1

Snails (freshwater) 0.00207 0.03

Cisco 0.00179 0.02

Salmon (freshwater) 0.00118 0.02

Smelts, rainbow (freshwater) 0.00076 0.01

Sturgeon (freshwater) 0.00018 0.002

Total 7.50273 100.0

Source: USDA combined 1994S1996 Continuing Survey of Food Intakes by Individuals (CSFII) (USDA, 1998).

Notes: (1) Estimates were based on 2-day averages and are projected from a sample of 9,596 individuals 18 years of age and

older in the U.S. population of 190,931,846 individuals 18 years of age or older, using 3-year combined survey weights.

Weights are for uncooked fish and shellfish. (2) The fish component of foods containing fish was calculated by using

data from the recipe file of USDA’s Nutrient Database for individual food intake surveys. (3) Values reflect

apportionment of 39% of the consumption rate of “unknown fish” and “unknown seafood” categories to freshwater and

estuarine categories. (4) The number of digits does not imply their statistical significance. See Section 6.2.2 and the

Exposure Assessment volume of this Technical Support Document for additional information about the CSFII and

habitat classification. 
6-13

Lipid Content of Consumed Aquatic Species

In reviewing the available literature, EPA was unable to find a single comprehensive

database containing information on the lipid content of consumed aquatic organisms (fresh and

estuarine). As a result, information on the lipid fraction of aquatic organisms was obtained from a

variety of primary and secondary sources. The following major sources of lipid data were used in

the derivation of national default values of lipid fraction:

• EPA’s National Sediment Quality Survey database (USEPA, 2001a)

• EPA’s National Study of Chemical Residues in Fish (USEPA, 1992a)

• EPA’s Green Bay Mass Balance Study (USEPA, 1992b, 1995c), 

• U.S. Department of Agriculture’s (USDA) Nutrient Data Bank (Exler, 1987)

• A review from National Marine Fisheries Service of the National Oceanic and

Atmospheric Administration (NOAA) (Sidwell, 1981)

• Two California databases (California Toxic Substances Monitoring Program and

Bay Protection and Toxic Cleanup Program)

When insufficient data were available from the above sources for certain species, targeted

literature searches were conducted and data from primary literature were used. Importantly, care

was taken to avoid duplication of data among the various data sources. For example, examination

of the National Sediment Quality Survey lipid data and data from various studies conducted on

the Hudson River revealed significant overlap and apparent duplication. Similarly, multiple

literature reviews (e.g., USDA and NOAA reports) were not used for a given species unless it was

clear that the primary literature upon which each was based was unique. Each of these data

sources is discussed in more detail below.

National Sediment Quality Survey. Data on lipid content were extracted from a

prerelease version of EPA’s National Sediment Quality Survey (NSQS) database from 1980

through 1998, the last year for which data were made available in this version of the database. A

description of the NSQS database can be found in USEPA (2001a). The primary source of data

contained in the NSQS was EPA’s STORET (Storage and Retrieval of U.S. Waterways

Parametric Data) database, recently renamed the Legacy Data Center database. STORET is a

waterway-related monitoring database that contains data from many Federal and State

government agencies. Geographically, the lipid data extracted from the NSQS database were

mostly limited to organisms in freshwater habitats. More than 47,000 records of lipid content

were extracted, representing more than 200 species and taxonomic groupings. Despite a large

number of species represented in the database, the quantity of lipid data was not evenly

distributed among species. For example, 10 species (mostly catfish, bass, perch, and salmonids)

represented 60% of the total number of records. Data on lipid content (e.g., percent lipid, tissue

type), organism attributes (e.g., common name, scientific name, and, where available, age,

weight, length, and sex), and sampling station (e.g., latitude, longitude, sampling date,

investigator names) were extracted and combined in an MS ACCESS® database. Information on

the method of lipid analysis was not reported in the NSQS database.
6-14

National Study of Chemical Residues in Fish (NSCRF). Data on the lipid content of

aquatic organisms were extracted from EPA’s National Study of Chemical Residues in Fish 

(USEPA). This study represents a one-time screening investigation to determine the prevalence of

selected bioaccumulative pollutants in fish. Samples were collected from 388 locations in the

United States that included targeted sites near point and non-point pollution sources, background

sites with minimal expected pollution sources, and a few sites corresponding to the U.S.

Geological Survey’s National Stream Quality Accounting Network (NASQAN) to obtain

nationwide coverage. Species represented in the study generally included a bottom-feeder and a

game-fish species. Carp was the most common species sampled, followed by largemouth bass,

white sucker, channel catfish, and smallmouth bass. Three to five fish collected from one location

were used for each composite sample. For each composite sample, two measurements of the

lipid content were obtained, one from the test for dioxins/furans and one from the test for other

xenobiotics. The average of the two lipid values was used to represent each sample data point in

the lipid database. Location and sampling date information was available, as were the common

name of the species collected and the tissue type sampled (whole body, fillet). Lipids were

measured gravimetrically after extraction with hexane and methylene chloride (1:1).

U.S. Department of Agriculture (USDA). Information on the composition of finfish and

shellfish products (including lipid content) was summarized by Exler (1987), using data from

USDA’s Nutrient Data Bank. These data were particularly useful because they included estuarine

species that were poorly represented by the other databases. Sources of the data summarized by

Exler (1987) included journal articles, technical reports, and other scientific and technical

literature (published and unpublished). Raw data on lipid content were not reported by Exler;

only summary statistics (mean, standard error, and number of samples) were reported. Because it

was not possible to combine the summary data from Exler (1987) with individual records from

other sources and still maintain statistical reliability, the Exler (1987) data were used on an

exclusive basis for a given species. Tissue types were restricted to the “edible portion” of species.

Although data were available on various lipid fractions (e.g., saturated, monosaturated, and

polyunsaturated fatty acids; cholesterol), only total lipid data were used in raw (unprocessed)

samples. Information on the method of lipid analysis used was not reported.

National Marine Fisheries Service. Data on the lipid content of estuarine species from

the National Marine Fisheries Service of NOAA were available from a review by Sidwell (1981).

This review consists of compilations of data from primary literature sources. Information on the

specific location and number of individuals per value was not available in these reviews. Data

were restricted to species collected in North America, when information was available to make

this distinction. Information was available on species’ common and Latin names, tissue type, and

method of preparation (e.g., raw, cooked). Only samples that were indicated as being fresh or raw

(or for which no preparation information was available) were used in the analysis of lipid data.

Other information, such as the number of individuals in a sample and their age, weight, and sex,

was not available. Importantly, the Sidwell (1981) data were not used in combination with the

USDA data for a given species, because both represent reviews of the primary literature and may

contain redundant data. A later review was also available from the National Marine

Fisheries Service (Kryznowek and Murphy, 1987); however, this was not used because its data

were presented in an aggregated format.
6-15

California Toxic Substances Monitoring Program. Lipid data were obtained from the

Toxic Substances Monitoring Program (TSMP) database sponsored by the California

Environmental Protection Agency, California State Water Resources Control Board, via

downloading from their Internet site (www.swrcb.ca.gov). The TSMP was initiated in 1976 to

provide a uniform statewide approach to monitor for toxic substances in freshwater and, to a

limited extent, in estuarine and marine waters, through the analysis of fish tissue and other

aquatic life. Samples are collected annually and composite samples of six fish are collected when

possible. The database provides information on age, sample collection date, location, number of

organisms per sample, weight, and length. Most samples for the species of interest are fillet

samples, although some whole-organism samples are also present. Lipids were measured

gravimetrically with petroleum ether solvent. A complete description of the TSMP database is

found in Rasmussen (1998).

Bay Protection and Toxic Cleanup Program. Relevant information on lipid content was

extracted from two small databases available from the Bay Protection and Toxic Cleanup

Program (BPTCP) (also at www.swrcb.ca.gov). One database contained samples from San

Francisco Bay, and the second included samples from San Francisco Bay and other California

locations. Lipids were measured gravimetrically with methylene chloride as the extraction

solvent.

Green Bay Mass Balance Study. The 1989–1990 Green Bay Mass Balance Study 

(GBMB) was conducted to generate a comprehensive data set for modeling the fate, transport,

and bioaccumulation of several toxic chemicals, including polychlorinated biphenyls, in Green

Bay, Lake Michigan. Six species were represented in the database for lipid content (walleye,

brown trout, carp, alewife, rainbow smelt, and gizzard shad). All data are from composite

samples of whole-body tissue. Included in the database is information on collection date, zone of

bay in which the sample was collected, age, and number of fish in the composite. Data were

retrieved from the Green Bay Relational Database, which is maintained by EPA’s Office of

Research and Development. Lipids were measured gravimetrically after extraction with

hexane/methylene chloride (1:1).

6.2.3 Data Analysis

The following steps were taken in the calculation of national default values of lipid

fraction:

C Removing suspect data (e.g., duplicate records, extreme values)

C Classifying species into CSFII consumption categories

C Excluding data for tissue types and size ranges not typically consumed

C Calculating mean lipid fraction for CSFII species

C Assigning trophic levels to species and CSFII consumption categories

C Calculating consumption-weighted values of lipid fraction within each trophic

level
6-16

Each of these steps is further described below.

Removing Suspect Data

Data from the NSQS database were screened to ensure that all records in the database

included, at a minimum, fields for common name, scientific name, species code, lipid content,

tissue type, sample data, and sample location. Most NSQS records contained additional

information beyond this subset. Records for common name, scientific name, and species code

were cross-checked for consistency, and erroneous entries were corrected or removed if they

were ambiguous. Furthermore, data were removed from all database sources that were collected

before 1980. Extreme values of lipid content (e.g., zero values and those above 35% for finfish

other than lake trout) were removed to minimize the impact of suspected outliers on the analysis.

For lake trout, values of up to 45% were tolerated because fillets of this species are notoriously

high in fat content, which on rare occasions can approach this value. The trimming of the high

extreme values resulted in the removal of very few records (i.e., fewer than 30 of more than

47,000). Finally, records from multiple sources were compared to identify and remove duplicate

records.

Classifying Species into CSFII Consumption Categories

The next step in calculating the national default values of lipid fraction involved assigning

species to the CSFII consumption categories shown in Table 6-2. This step was conducted to

maintain consistency in the data used to determine national default consumption rates of fish and

shellfish and lipid fraction. In most cases, information was not available from the CSFII to

identify exactly which species were represented by the consumption rates listed in Table 6-2.

Therefore, assignment of a species to a CSFII category was based on several factors, including (1)

its taxonomic and publicly perceived linkage to a CSFII category, (2) its likelihood of being

caught (either recreationally or commercially) and consumed in the United States, and (3) its

likelihood of inhabiting either fresh or estuarine waters for at least some portion of its life cycle.

Information from numerous published sources was used to help determine whether a species met

these criteria. Because several of the CSFII species categories were broad in terms of the types of

species that could be included, some species were assigned to multiple CSFII categories. For

example, flounder species fit into both estuarine flatfish and flounder categories. In such cases,

appropriate records were included in both CSFII categories. Data for species that could not be

unambiguously assigned to a CSFII consumption category were omitted from the analysis.

Notably, this resulted in the exclusion of lipid data for some species that are commonly

consumed in the United States but were not associated with a CSFII category (e.g., largemouth

bass and walleye).

Screening by Tissue Type and Size Ranges

As discussed previously, lipid content can vary widely by the type of tissue in which it is

measured. To derive national default values of lipid fraction and national BAFs that are

representative of human exposure potential, lipid data were screened according to the types of

tissues most commonly consumed by the U.S. population. Because lipid data originated from a
6-17

variety of sources that differed in nomenclature used to classify the type of tissue, a variety of

commonly consumed tissues were accepted, depending on the species and the availability of

data. The following types of tissues reported by various data sources were included in the

derivation of the national default values of lipid fraction:

C Finfish (except anchovy and smelt): standard fillet, fillet (with skin, without skin,

skin unspecified), edible portion

C Anchovy, smelt: whole body

C Clam: whole/raw, adductor muscle

C Crab: edible portion, muscle, standard fillet, muscle and hepatopancreas 

C Crayfish, oyster, scallop, shrimp: edible portion

C Snails: whole/raw

Lipid Content of Species in CSFII Categories

On the basis of lipid data from the aforementioned sources, the average percent lipid was

calculated for each of the species in the CSFII consumption categories (Table 6-3). Next, the

average percent lipid of all species in each CSFII category was determined as the average of the

corresponding individual species-mean lipid values. Average values of lipid content were

determined because national BAFs are designed to reflect central-tendency estimates. Ideally, if

sufficient national consumption data were available at the species level, the overall average lipid

value for each CSFII category would be determined on a consumption-weighted basis. However,

sufficient consumption data were not available below the CSFII category level at a national scale.

Therefore, equal weights were assigned to each species’ mean lipid value. For example, lipid data

were available for several species of trout (e.g., rainbow trout, brown trout, and others), whereas

consumption rates were available from the CSFII only for trout as a group. Thus, mean percent

lipid values for all trout species were averaged and combined with the consumption rate for trout

from the CSFII.

Trophic Level Assignments to Species and CSFII Consumption Categories

National fish and shellfish consumption data from the CSFII (see Table 6-2) indicate that,

on average, individuals consume aquatic organisms from a variety of trophic levels (e.g., oysters

and clams in trophic level 2, whitefish and herring in trophic level 3, perch and trout in trophic

level 4). Because trophic position (in particular, dietary composition) can affect the extent of

bioaccumulation in aquatic organisms, national BAFs are derived separately for each trophic

level. Similarly, because lipid content can vary by species, national default values of lipid fraction

are derived separately for each trophic level. To estimate trophic level–specific values of lipid

fraction, a trophic level designation must be assigned to each of the CSFII consumption rate

categories shown in Table 6-2. This same trophic level assignment is used to discern the fraction

of the national default fish consumption rate (17.5 g/d) that occurs at each trophic level (see the

2000 Human Health Methodology).
Table 6-3. Lipid Content of Aquatic Organisms Used to Derive National Default Values of Lipid Fraction (fR)

CSFII Consumption

Category (Habitat)a Common Name Scientific Name

Species Mean

Lipid Content (%) CVb No. Obs.

Data

Sourcec

CSFII Mean

Lipid (%)

Anchovy (estuarine) Striped anchovy Anchoa hepsetus 2.8 NR 23 1 6.1 

European anchovy Engraulis encrasicholus 4.8 0.34 26 2

Northern anchovy Engraulis mordax 10.7 NR 16 1

Carp (freshwater) Common carp Cyprinus carpio 5.4 0.86 2,792 3 5.4

Catfish (freshwater) White catfish Ameiurus catus 4.3 0.58 204 3, 4, 5 2.9

Black bullhead Ameiurus melas 1.1 0.70 113 3, 4, 5

Yellow bullhead Ameiurus natalis 1.4 0.99 95 3, 5

Brown bullhead Ameiurus nebulosus 2.6 0.72 988 3, 4, 5

Channel catfish Ictalurus punctatus 5.3 0.71 1,427 3, 4, 5

Catfish (estuarine) White catfish Ameiurus catus 4.3 0.58 204 3, 4, 5 4.0

Brown bullhead Ameiurus nebulosus 2.6 0.72 988 3, 4, 5

Channel catfish Ictalurus punctatus 5.3 0.71 1,427 3, 4, 5

Cisco (freshwater) Cisco Coregonus Artedii 1.9 0.65 69 2 1.9

Clam (estuarine) Hard shell clam Mercenaria mercenaria 0.7 NR 47 1, 6 1.3

Soft shell clam Mya arenaria 1.2 NR 3 1

Venus clam (Littleneck

Japanese)

Tapes (venerupis) decussatus 1.2 NR 15 1

Venus clam (Shortneck) Tapes japonica 1.8 NR 3 1

Venus clam (Asari) Tapes philippinarum 2.6 3 1

Venus clam Venus gallina 0.9 NR 29 1

Venus clam (hard) Venus lusoria 0.6 NR 5 1

Crab (estuarine) Blue crab Callinectes sapidus 1.3 1.19 101 3 1.1

Dungeness crab Cancer magister 1.0 0.26 24 2

Queen crab Chionoectes opilio 1.2 0.30 6 2

Crayfish (freshwater) Crayfish (mixed sp.) Astacus and Orconectes 1.1 NR 5 2 1.1

Croaker (estuarine) White croaker Genyonemus lineatus 4.2 0.88 37 4, 5, 6, 7 3.0

Atlantic croaker Micropogonias undulatus 3.2 0.47 8 2

Yellowfin croaker Umbrina roncador 1.8 0.70 3 5

Eel (estuarine) Eel, mixed species Anguilla spp. 11.7 0.28 14 2 11.7

Flatfish (estuarine) Sole and flounder Bothidae and Pleuronectidae 1.2 0.80 596 2 1.2

Flounder (estuarine) Sole and flounder Bothidae and Pleuronectidae 1.2 0.80 596 2 1.2

Herring (estuarine) Blueback herring Alosa aestivalis 7.2 0.45 92 3 10.0

Atlantic herring Clupea harengus 9.0 0.51 2,524 2

Pacific herring Clupea pallasi 13.9 0.39 128 2

Mullet (freshwater) Striped mullet Mugil cephalus 3.8 0.62 43 2 3.8

Oyster (estuarine) Pacific oyster Crassostrea gigas 2.3 0.33 13 2 2.4
CSFII Consumption

Category (Habitat)a Common Name Scientific Name

Species Mean

Lipid Content (%) CVb No. Obs.

Data

Sourcec

CSFII Mean

Lipid (%)

Eastern oyster Crassostrea virginica 2.5 0.56 193 2

Perch (estuarine and

 freshwater)

White perch Morone americana 3.5 0.72 682 3, 4 2.3

Yellow perch Perca flavescens 1.0 0.79 841 3, 5

Pike (freshwater) Northern pike Esox lucius 0.6 1.01 904 3, 4 0.7

Muskellunge Esox Masquinongy 1.1 0.87 35 3

Chain pickerel Esox niger 0.4 0.74 72 3, 4

Rockfish (estuarine) Striped bass Morone saxatilis 5.3 0.59 7,657 3, 4, 5, 7 3.5

Rockfish Sebastes spp. 1.6 NR 81 2

Salmon

(estuarine & freshwater)

Pink salmon Oncorhynchus gorbuscha 3.5 0.49 144 2 4.7

Chum salmon Oncorhynchus keta 3.8 0.62 13 2

Coho salmon Oncorhynchus kistuch 2.9 0.75 617 3

Sockeye salmon Oncorhynchus nerka 8.6 0.32 48 2

Chinook salmon Oncorhynchus tshawytscha 3.4 0.93 873 3

Atlantic salmon Salmo salar 6.3 0.74 7 2

Scallop (estuarine) Scallop, mixed species Pectinidae 0.8 0.35 114 2 0.8

Shrimp (estuarine) Shrimp, mixed species Panaeidae and Pandalidae 1.7 0.39 100 2 1.7

Smelt, rainbow (estuarine

& freshwater)

Rainbow smelt Osmerus mordax mordax 4.1 0.46 130 3, 8 4.1 

Snails (freshwater) Snails, mixed species Vivaparadidae, Helixidaed 1.4 0.75 11 1 1.4

Sturgeon

 (estuarine & freshwater)

Lake sturgeon Acipenser fulvescens 9.4 0.63 51 3 5.4

White sturgeon Acipenser transmontanus 1.3 0.67 7 4, 5, 7

Trout, mixed spp. 

(freshwater)

Rainbow trout Oncorhynchus mykiss 5.1 0.67 556 3, 4, 5 6.0

Cutthroat trout Salmo clarki clarki 1.2 0.79 15 3

Brown trout Salmo trutta 7.4 0.73 615 3, 4, 5

Brook trout Salvelinus fontinalis 4.0 0.56 96 3, 4, 5

Lake trout Salvelinus namaycush 12.3 0.62 910 3, 4, 5

Trout, mixed spp.

(estuarine)

Rainbow trout Oncorhynchus mykiss 5.1 0.67 556 3, 4, 5 3.2 

Cutthroat trout Salmo clarki clarki 1.2 0.79 15 3

Trout, (freshwater)e Rainbow trout Oncorhynchus mykiss 5.1 0.67 556 3, 4, 5 5.1

Whitefish Whitefish, mixed spp. Coregonus spp. 5.9 0.64 68 2 5.9

a Habitat designation (freshwater, estuarine) assigned to the CSFII consumption categories. See the Exposure Assessment volume of this Technical Support Document 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, and Aporrhais.

e Information from the CSFII survey indicates that rainbow trout is appropriate for the “trout, freshwater” category.
6-20

The EPA recognizes that the dietary composition of an aquatic species (and, henceforth,

its trophic position) can vary as a function of size, age, life history, season, and food web

structure of the water body. As in the discussion presented at the beginning of Section 6.2 for

deriving values of lipid fraction in general, States and Tribes are encouraged to use local or

regional information to estimate the trophic position of consumed aquatic organisms for setting

local or regional criteria. The use of such local or regional information is encouraged because

factors that can affect trophic position often vary on a local or regional basis.

In the case of deriving national AWQC, and in situations where sufficient local or regional

data are not available, an assessment of the trophic position of consumed aquatic organisms is

necessary. Estimating the trophic level position of aquatic species requires information on the

identity, size, age, and diets of the individual aquatic species consumed. As previously discussed,

very limited data were available to further delineate the identity and size of species consumed

within each of the CSFII categories in Table 6-2. For most of the CSFII categories, this lack of

information was not viewed as problematic, because rather unambiguous assignments of trophic

position could be made to these categories (e.g., all oysters are considered to be trophic level 2).

However, for other CSFII categories, assignment of trophic position required some assumptions

to be made, which introduces greater uncertainty. To assist in estimating the trophic position of

species represented by the CSFII consumption survey, EPA relied on information summarized in

a report entitled Trophic Level and Exposure Analysis for Selected Piscivorous Birds and

Mammals (USEPA, 2000e-g). Although focused on piscivorous birds and mammals, this report

contains information on dietary composition and trophic status for numerous species in the

aquatic food web by virtue of the fact that the aquatic food web serves as the dietary basis for

piscivorous wildlife. The following procedures were used in assigning trophic position to the

CSFII consumption categories. 

1. Species Trophic Level Assignments. Species trophic level assignments were performed

as follows:

a. For game fish that correspond to the CSFII categories, data were used for edible

size ranges (about 20 cm [8 inches] or larger).

b. For species where multiple size ranges were available, preference was given to the

larger specimens in determining the species trophic level.

c. Trophic level 2 was assigned to a species if appropriate trophic level data ranged

between 1.6 and 2.4; trophic level 3 if trophic level data ranged from 2.5 to 3.4;

and trophic level 4 if trophic level data were 3.5 or higher. This is consistent with

the approach taken in the Great Lakes Water Quality Initiative guidance (USEPA,

1995b).

2. CSFII Consumption Category Trophic Level Assignments. Once trophic levels were

assigned to each species that could reasonably correspond to a CSFII consumption

category, this information was used to assign a trophic level to each CSFII consumption

category, as follows:
6-21

(Equation 6-2)

a. In situations where a CSFII category was represented by the vast majority of

species within a single trophic level, that trophic level was assigned to the CSFII

category (e.g., trout, pike, smelt).

b. For some CSFII consumption categories, the trophic status of representative

species spanned two trophic levels. In this case, consumption rate for that

category was evenly divided between the two trophic levels (e.g., for flounder,

50% to trophic level 3 and 50% to trophic level 4). This situation occurred for

opportunistic species such as flounder and flatfish, catfish, croaker (all evenly

divided between trophic levels 3 and 4), and shrimp (divided between trophic

levels 2 and 3). 

3. The results of the trophic level assignments are shown in Table 6-4.

Calculation of Consumption-Weighted Lipid Content, by Trophic Level. The national

consumption-weighted mean lipid fraction for each trophic level (i.e., the national default values

of lipid fraction) was calculated according to the following equation.

where:

fR = national consumption-weighted mean lipid fraction of consumed aquatic

organisms at a given trophic level

CRi = mean per capita consumption rate of species in CSFII consumption category

“i” at the same trophic level

CRtot = mean per capita consumption rate of species in all CSFII consumption

categories at the same trophic level

fR,i = average lipid fraction of species in CSFII consumption category “i”

Using Equation 6-2, EPA’s national default values of lipid fraction were calculated for

trophic levels 2, 3, and 4 in Table 6-5.

These values were calculated with consumption rate data (CRi and CRtot) that originated

from the USDA CSFII in Table 6-2, average values of lipid fraction for aquatic species described

in Table 6-3, and trophic level designations of each CSFII consumption category described in

Table 6-4. The calculation of the national default values of lipid fraction is illustrated in

Table 6-6. 
Table 6-4. Trophic Level Assignment of Aquatic Species Corresponding to CSFII Consumption Categories

CSFII

Consumption

Category

Common 

Name

Scientific 

Name Size

Trophic

Level (a)

(Mean)

Trophic

Level (a)

(Range) Notes (a)

Species

Assigned

Trophic

Level (b)

CSFII

Assigned

Trophic

Level (c)

Anchovy Bay anchovy Anchoa mitchilli Adult 2.8 — 9% Microinvertebrates, 58%

zooplankton, 33% organic detritus.

3 2(50%)

3(50%)

Northern anchovy Engraulis mordax — 2.3 2.1–2.5 Feeds primarily on phytoplankton and

some zooplankton. 

2

Carp Common carp Cyprinus carpio — — 2.2–3.1 Young feed on zooplankton, small carp

feed on benthic invertebrates and

detritus, larger carp become more

herbivorous.

3 3

10-23 cm 3 2.8–3.1 Up to 23 cm, feed primarily on benthic

invertebrates.

> 23 cm 2.4 2.2–2.6 Larger carp (approx >23 cm) feed

primarily on plants and detritus

(60%–70%), benthic invertebrates

(15–35%), and some zooplankton

(<15%). 

Catfish Black bullhead Ameiurus melas — 3 2.9–3.2 Seem to consume zooplankton and

benthic invertebrates throughout life.

Individuals >15 cm may consume some

small fish, but also plant materials.

3 3 (50%)

4 (50%)

Blue catfish Ictalurus furcatus — 3 — Assumption. 3

Brown bullhead Ameiurus nebulosus — — 2.7–3.3 Diet changes with size. 3

>10 cm 3.0 2.7–3.2 Those >10 cm feed on 20%–30%

plants and 70%–100% benthic

invertebrates (burrowing mayfly, scud,

chironomid types). Some consume

small fish as well.

Channel catfish Ictalurus punctatus 36–54 cm — 2.8–4 Changes with age; can grow up to $50

cm. Three studies indicate it consumes

plants; one other did not.

4

5–30 cm 3.1 — 5–30 cm; consumes largely benthic

invertebrates (60%–80%), detritus

(10%–15%), and zooplankton

(10%–25%).

30–35 cm 3.3 3–3.5 30–35 cm; consumes fish (32%),

benthic invertebrates (40%),

zooplankton (12%), and detritus (15%).

Some populations consume up to 25%

algae.
Table 6-4. Trophic Level Assignment of Aquatic Species Corresponding to CSFII Consumption Categories (continued)

CSFII

Consumption

Category

Common 

Name

Scientific 

Name Size

Trophic

Level (a)

(Mean)

Trophic

Level (a)

(Range) Notes (a)

Species

Assigned

Trophic

Level (b)

CSFII

Assigned

Trophic

Level (c)

Catfish Channel catfish Ictalurus punctatus 35-45 cm 3.8 3.5–3.9 35–45 cm; consumes fish (67%),

benthic invertebrates (25%), and

detritus (8%). Some populations

consume up to 25% algae.

> 45 cm 4 4.0–4.2 >45 cm; consumes fish (100%).

Flat bullhead Ictalurus platycephalus — 3.2 3–4 Can grow to large sizes; feeds on

mollusks (primarily clams), bryozoans,

and worms.

4

Flathead catfish Pylodictis olivaris — 3.8 — Diet consists primarily of fish with

some crayfish, and mollusks.

4

Yellow bullhead Ictalurus natalis 30–46 cm 2.6 — Scavenger; often consumes minnows,

crayfish, insect larvae, worms, and

algae.

3

Cisco Cisco Coregonus artedii 20–30 cm 3.0 3.0–3.1 Primarily a plankton feeder.

Occasionally eats eggs, small fish.

3 3

Clam Clam (general) — — 2.2 2.1–2.4 Filter feeder on plankton, detritus;

includes zooplankton.

2 2

Hardshell clam Mercenaria spp. — 2.2 2.1–2.3 Selective filter feeder, consumes

primarily planktonic microalgae.

2

Softshell clam Mya arenaria — 2.2 2.1–2.3 Nonselective filter feeder. 2

Manila clam Venerupis japonica — 2.2 2.1–2.3 Nonselective filter feeder. 2

Geoduck Panopea abrupta — 2.2 2.1–2.3 Filter feeder. 2

Amethyst gemclam — — 2.2 2.1–2.3 Filter feeder. 2

Atlantic rangia Rangia cuneata — 2.2 2.1–2.3 Filter feeder. 2

Baltic macoma Macoma balthica — 2.2 2.1–2.3 Filter feeder. 2

Dwarf surf clam Mulina lateralis — 2.2 2.1–2.3 Filter feeder. 2

Crab Blue crab Callinectes sapidus — 3.2 3.0–3.4 Feeds primarily on bivalves, organic

debris fish, crustaceans, plants, and

worms.

3 3

Rock crab Cancer spp. — 3.3 3.1–3.5 Feeds on herbivorous snails,

amphipods, shrimp, polychaetes, and

sea urchins.

3

Dungeness crab Cancer magister — 3.5–3.7 3.3–4.1 Carnivorous; shrimp are preferred prey. 4

Stone crab Menippe spp. — 3.4 3.2–3.7 Carnivorous, feeding primarily on

mollusks.

3
Table 6-4. Trophic Level Assignment of Aquatic Species Corresponding to CSFII Consumption Categories (continued)

CSFII

Consumption

Category

Common 

Name

Scientific 

Name Size

Trophic

Level (a)

(Mean)

Trophic

Level (a)

(Range) Notes (a)

Species

Assigned

Trophic

Level (b)

CSFII

Assigned

Trophic

Level (c)

Crayfish Crayfish Astacidae — 2.4 2.0–2.7 Primarily herbivorous, omnivorous;

animal food minor part of diet if

vegetation is available.

2 2

Croaker White (Pacific)

croaker

Genyonemus lineatus — 3.4 3.2–3.7 Opportunistic bottom feeder on small

fish, squid, shrimp, polychaetes, crabs,

and clams.

3 3 (50%)

4 (50%)

Atlantic croaker Micropogonias

undulatus

— 3.7-4.0 3.3–4.7 Opportunistic bottom feeder; feeds

mostly on polychaetes, copepods,

mysids, and small clams.

4

Eel American eel Anguilla rostrata — 3.9 3.7–4.3 Feeds on small fish, young alewives,

salmon and trout fry, shrimp, and

crabs.

4 4

Flatfish and

flounder

Gulf flounder Paralichthys albigutta — 3.5–3.9 3.3–4.1 Paralichthys genus are primarily

piscivorous as adults but also eat

polychaetes, crustacea, echinoderms,

and mollusks.

4 3 (50%)

4 (50%)

California halibut Paralichthys

californicus

>56 cm 3.5 3.3–4.0 Feeds on anchovies and other small

fish.

Summer flounder Paralichthys dentatus — 3.5–3.9 3.3–4.1 See Gulf flounder. 4

Southern flounder Paralichthys

lethostigma

— 3.5–3.9 3.3–4.1 See Gulf flounder. 4

Starry flounder Platichthys stellatus — 3.2–3.6 2.7–3.8 Feeds primarily on small crustacea,

polychaetes, bivalves, and

echinoderms. Few fish.

3

Winter flounder Pseudopleuronectes

americanus

— 3.0–3.6 2.7–3.8 Feeds primarily on benthic polychaetes,

amphipods, coelenterates, shrimp, plant

material, and detritus.

3

Herring Atlantic herring Clupea harengus — 3.2 3.1–3.3 Feeds primarily on copepods and krill. 3 3

Pacific herring Clupea pallasi — 3.2 3.1–3.3 Feeds primarily on copepods and krill. 3

Mullet Mullet Mugil spp. — 2.1 2.0–2.3 Bottom feeding herbivore/detritivore;

consumes some benthic animals.

2 2

Oyster American oyster Crassostrea virginica — 2.2 2.1–2.3 Filter feeder on phytoplankton, detritus,

and bacteria.

2 2

Pacific oyster Crassostrea gigas — 2.2 2.1–2.3 Filter feeder on phytoplankton, detritus,

and bacteria.

2
Table 6-4. Trophic Level Assignment of Aquatic Species Corresponding to CSFII Consumption Categories (continued)

CSFII

Consumption

Category

Common 

Name

Scientific 

Name Size

Trophic

Level (a)

(Mean)

Trophic

Level (a)

(Range) Notes (a)

Species

Assigned

Trophic

Level (b)

CSFII

Assigned

Trophic

Level (c)

Perch Silver perch Bairdiella chrysoura > 8 cm 3.6 3.3–4.2 Feeds on polychaetes, shrimp, and

mollusks; becomes more piscivorous

with age.

4 4

White perch Morone americana adult 3.6 3.3–4.2 Benthic predator, becoming

increasingly piscivorous with age.

4

Yellow perch Perca flavescens 20-30 cm 3.4 3.1–3.8 20–30 cm; consumes 10%

zooplankton, 50% benthic

invertebrates, 34% fish (some

populations); nearly 100% fish in other

populations.

3

Pike Northern pike Esox lucius >10 cm 4 — > 10 cm; diet primarily all fish. 4 4

Pickerel 

(redfin & grass)

Esox americanus larger

specimens

4 — Larger specimens consume small fish. 4

Rockfish Striped bass Morone saxatilis >10 cm 3.9 — > 10 cm consumes 85%–97% fish. 4 4

Salmon Pink salmon Oncorhynchus

gorbuscha

— 3.8 — Feeds at sea; consumes krill,

amphipods, squid, and copepods.

4 4

Coho salmon Oncorhynchus kistuch 45–60 cm 4 4.0–4.5 Adults feed primarily on alewife and

smelt. In Lake Michigan, this could

result in a higher trophic level where

alewife feed on Mysis.

4

Sockeye salmon Oncorhynchus nerka — 4 — Assume trophic level 4 for large

specimens.

4

Chinook salmon Oncorhynchus

tshawytscha

young 3 — Young in fresh water feed on terrestrial

insects taken at water surface.

3

adult 4 — Marine adults consume primarily fish,

some amphipods, and other inverts.

4

Scallop Bay scallop Argopecten irradians — 2.2 2.1–2.3 Smaller feeder than ocean scallop. 2 2

Sea scallop Placopecten

magellanicus

— 2.2 2.1–2.4 Filter feeder, consuming primarily algae

but also zooplankton, bacteria, and

detritus.

2

Shrimp Northern shrimp Pandalus borealis — 2.7 2.3–2.9 Large; feeds on polychaetes,

copepods, benthic and planktonic

microorganisms, and algae.

3 2 (50%)

3 (50%)

Brown shrimp Pandalus aztecus — 2.7 2.3–3.0 Omnivore/predator, consuming

polychaetes, amphipods, detritus, and

algae.

3
Table 6-4. Trophic Level Assignment of Aquatic Species Corresponding to CSFII Consumption Categories (continued)

CSFII

Consumption

Category

Common 

Name

Scientific 

Name Size

Trophic

Level (a)

(Mean)

Trophic

Level (a)

(Range) Notes (a)

Species

Assigned

Trophic

Level (b)

CSFII

Assigned

Trophic

Level (c)

Shrimp Pink shrimp Panaeus duorarum — 2.4 2.1–2.9 Benthic omnivore, consuming

polychaetes, other crustaceans,

mollusks, algae, and vascular plant

detritus.

2

White shrimp Penaeus setiferus — 2.3 2.1–2.7 Omnivore, but consuming more plant

material than do other shrimp.

2

Smelt Jacksmelt Atherinopsis

californiensis

adult 2.5 2.2–2.8 Omnivorous; feeds on algae, diatoms,

detritus, and small crustaceans.

3 3

Surf smelt Hypomesus pretiosus — 3.1 3.0–3.2 Consumes primarily amphipods,

euphausiids, copepods, other

zooplankton.

3

Rainbow smelt Osmerus mordax — 3.4 3.2–3.8 Feeds on krill, amphipods, polychaetes,

plant debris, small fish, including

herring cunner, anchovy, and

silversides.

3

Smelt Eulachon Thaleichthys pacificus adult 3.1 3.0–3.2 Feeds primarily on zooplankton,

including krill and copepods.

3

Snail Snail — — 2 — Most species are strictly herbivorous,

grazing on periphyton or other plant

materials.

2 2

Sturgeon Green sturgeon Acipenser medirostris > 1 m 3.7 3.5–4.0 Benthic carnivore, feeding on

invertebrates and small fish.

4 4

White sturgeon Acipenser

transmontanus

> 1.2 m 3.7 3.5–4.0 Adults are benthic carnivores, feeding

on invertebrates, including shrimp and

bivalves. Larger juveniles and adults

feed on fish, including eulachon,

anchovies, minnows, and suckers.

4

Lake sturgeon Acipenser rubicundus — — 3–4 Can grow up to 100 pounds, averages

about 40–50 pounds for adults;

primarily a bottom feeder, reportedly

feeding on small gastropods,

crustaceans, insect larvae, and small

fishes.

4

Trout Brook trout Salvelinus fontinalis 10–40 cm 3.2 — 10–40 cm; at most, 7%–8% fish in diet;

remainder primarily benthic

invertebrates but also some

zooplankton in some populations.

3 4

Cutthroat trout Salmo clarki < 40 cm 3 — < 40 cm; consumes invertebrates. 4
Table 6-4. Trophic Level Assignment of Aquatic Species Corresponding to CSFII Consumption Categories (continued)

CSFII

Consumption

Category

Common 

Name

Scientific 

Name Size

Trophic

Level (a)

(Mean)

Trophic

Level (a)

(Range) Notes (a)

Species

Assigned

Trophic

Level (b)

CSFII

Assigned

Trophic

Level (c)

> 40 cm 3.2 — > 40 cm; becomes piscivorous.

old adults 4 — Assumption for oldest specimens.

Dolly Varden trout Salvelinus malma — — 3–4 Trophic level changes with age. 4

10–30 cm 3 — 10–30 cm; diet 100% benthic

invertebrates.

Dolly Varden trout Salvelinus malma 30–40 cm 3.75 — 30–40 cm; diet 75% fish, 17% benthic

invertebrates.

> 40 cm 4 — > 40 cm; diet consists of 100% fish.

Lake trout Salvelinus namaycush 20–30 cm 3.7 3.5-4.0 20–30 cm; feeds primarily on small fish

(70%) and benthic invertebrates (30%).

4

30–40 cm 3.9 3.7-4.1 30–40 cm; feeds primarily on fish

(90%) and some benthic invertebrates

(10%).

>40 cm 4.2 4.0-4.5 > 40 cm; feeds entirely on fish; in Lake

Michigan, feed on alewives, which feed

on Mysis, which feed on zooplankton.

Trout Rainbow trout Oncorhynchus mykiss < 30 cm 3 — < 30 cm; diet completely of benthic

invertebrates or both invertebrates and

zooplankton.

4

30–50 cm 3.6 — 30–50 cm; diet 35–90% fish, 25–75%

benthic invertebrates, zooplankton,

terrestrial insects.

> 50 cm 4 — > 50 cm; diet of 100% fish.

Whitefish Round whitefish Prosopium

cylindraceum

20–30 cm 3 — Young probably feed on zooplankton;

adults feed primarily on benthic

invertebrates.

3 3

Mountain whitefish Prosopium williamsoni > 30 cm 3.5 — Greater than 30 cm; consumes large

invertebrates and small fish.

4

a Unless otherwise specified, information on trophic status was obtained from USEPA (2000e-g). Game fish data were limited to specimens considered to be representative of the

edible size range (i.e., 20 cm or larger).

b

In determining species trophic level assignments, preference was given to data on larger specimens. Trophic level 4 was assigned to a species with data indicating trophic level 3.5

or higher; trophic level 3 was assigned to a species with data indicating trophic level 2.5–3.4; trophic level 2 was assigned for trophic level 1.5–2.4. 

c

In determining CSFII category trophic level assignments, best professional judgment was used. For example, the CSFII category for catfish includes four species that are assigned

to trophic level 3 and three species assigned to trophic level 4. Thus, it is assumed that half (50%) of consumption in the catfish CSFII category is from TL3 and half from TL4.

Except for shrimp, all other CSFII categories included species that either were exclusively or predominately one trophic level (e.g., trout, estuarine flatfish, smelt).
6-28

Table 6-5. National Default Values of Lipid Fraction

Trophic Level National Default Value 

(percentage)

2 1.9%

3 2.6%

4 3.0%

6.2.4 Uncertainty and Sensitivity Analysis 

This section discusses the uncertainty and sensitivity associated with the calculation of

national default values of lipid fraction described in the previous section. The objective here is to

identify the major sources of uncertainty in the present analysis and, where possible, provide

some insight into their potential magnitude and direction of impact on the national default values

of lipid fraction. In this way, the overall confidence in the default lipid values can be assessed (at

least qualitatively) and steps to reduce this uncertainty can be identified. Although ideally one

would attempt to address each source of uncertainty quantitatively, available data and resources

did not permit complete quantitative analysis of uncertainty. A quantitative analysis of selected

sources of uncertainty is provided at the end of this section.

Qualitative Analysis

Applicability of Fish Consumption Rate Data. An integral part of EPA’s calculation of

national default values of lipid fraction involved the estimation of the type and quantity of fish

and shellfish consumed by the U.S. population. As described previously, data on fish and

shellfish consumption were obtained from USDA’s CSFII for the years 1994–1996 (USDA,

1998). A number of uncertainties are associated with the use of the CSFII consumption data,

some of which are described in more detail in the Exposure Assessment volume of this Technical

Support Document. First and foremost, the mean per capita rates of fish and shellfish

consumption derived from the CSFII are national in scope. As a result, the national pattern of fish

and shellfish consumption developed from the CSFII may differ from the consumption patterns

represented by various human subpopulations, particularly on local or regional scales. Although

the magnitude of this uncertainty has not been quantified here, its impact on the national default

values of lipid fraction is believed to be bidirectional (i.e., resulting in an overestimation or

underestimation of lipid fraction applicable to local or regional scenarios). Because the CSFII

consumption rates are weighted toward leaner aquatic organisms (e.g., shrimp, flounder, flatfish),

it is conceivable that they may lead to a greater tendency in the national default values of lipid

fraction to underestimate lipid fraction associated with some local and regional consumption

patterns compared with overestimating lipid fraction. The magnitude of uncertainty in applying

the CSFII consumption rates to local or regional situations will depend on the extent to which

local consumption patterns differ from the pattern represented by the CSFII.
Table 6-6. Calculation of National Default Values of Consumption-Weighted Mean Lipid Fraction

Habitat

CSFII

Consumption

Category

Assigned

Trophic

Level

Trophic Level

Weighting Factor

Mean

Percent

Lipid

Mean

Consumption

Rate 

(g/person/d)

Trophic Level

Weighted

Consumption Rate

(g/person/day )

CSFII Category

Weights

Consumption

Weighted Percent

Lipid Values

Estuarine Anchovy 2 0.5 6.1 0.05815 0.02907 0.01809 0.11057

Estuarine Clam 2 1 1.3 0.01732 0.01732 0.01077 0.01380

Freshwater Crayfish 2 1 1.1 0.01028 0.01028 0.00640 0.00678

Estuarine Mullet 2 1 3.8 0.04512 0.04512 0.02807 0.10638

Estuarine Oyster 2 1 2.4 0.17485 0.17485 0.10877 0.25941

Estuarine Scallop 2 1 0.8 0.00140 0.00140 0.00087 0.00066

Estuarine Shrimp 2 0.5 1.7 2.65492 1.32746 0.82575 1.42855

Freshwater Snails 2 1 1.4 0.00207 0.00207 0.00129 0.00181

Estuarine Anchovy 3 0.5 6.1 0.05815 0.02907 0.00844 0.05162

Freshwater Carp 3 1 5.4 0.19071 0.19071 0.05538 0.29671

Estuarine Catfish 3 0.5 4.0 0.60335 0.30168 0.08761 0.35292

Freshwater Catfish 3 0.5 2.9 0.60335 0.30168 0.08761 0.25541

Freshwater Cisco 3 1 1.9 0.00179 0.00179 0.00052 0.00099

Estuarine Crab 3 1 1.1 0.42111 0.42111 0.12229 0.13928

Estuarine Croaker 3 0.5 3.0 0.17792 0.08896 0.02584 0.07865

Estuarine Flatfish 3 0.5 1.2 0.45173 0.22586 0.06559 0.07806

Estuarine Flounder 3 0.5 1.2 0.73482 0.36741 0.10670 0.12697

Estuarine Herring 3 1 10.0 0.16428 0.16428 0.04771 0.47898

Estuarine Shrimp 3 0.5 1.7 2.65492 1.32746 0.38551 0.66693

Estuarine Smelts 3 1 4.1 0.00880 0.00880 0.00256 0.01048

Estuarine Smelts, rainbow 3 1 4.1 0.00076 0.00076 0.00022 0.00090

Freshwater Smelts, rainbow 3 1 4.1 0.00076 0.00076 0.00022 0.00090

Freshwater Whitefish 3 1 5.9 0.01309 0.01309 0.00380 0.02228

Estuarine Catfish 4 0.5 4.0 0.60335 0.30168 0.12305 0.49566

Freshwater Catfish 4 0.5 2.9 0.60335 0.30168 0.12305 0.35871

Estuarine Croaker 4 0.5 3.0 0.17792 0.08896 0.03629 0.11046

Estuarine Eel 4 1 11.7 0.00466 0.00466 0.00190 0.02218

Estuarine Flatfish 4 0.5 1.2 0.45173 0.22586 0.09212 0.10963

Estuarine Flounder 4 0.5 1.2 0.73482 0.36741 0.14986 0.17833

Estuarine Perch 4 1 2.3 0.22331 0.22331 0.09108 0.20599

Freshwater Perch 4 1 2.3 0.22331 0.22331 0.09108 0.20599

Freshwater Pike 4 1 0.7 0.04021 0.04021 0.01640 0.01190

Estuarine Rockfish 4 1 3.5 0.05428 0.05428 0.02214 0.07638
Table 6-6. Calculation of National Default Values of Consumption-Weighted Mean Lipid Fraction (continued)

Habitat

CSFII

Consumption

Category

Assigned

Trophic

Level

Trophic Level

Weighting Factor

Mean

Percent

Lipid

Mean

Consumption

Rate 

(g/person/d)

Trophic Level

Weighted

Consumption Rate

(g/person/day )

CSFII Category

Weights

Consumption

Weighted Percent

Lipid Values

Estuarine Salmon 4 1 4.7 0.05915 0.05915 0.02412 0.11420

Freshwater Salmon 4 1 4.7 0.00118 0.00118 0.00048 0.00227

Estuarine Sturgeon 4 1 5.3 0.00018 0.00018 0.00007 0.00039

Freshwater Sturgeon 4 1 5.3 0.00018 0.00018 0.00007 0.00039

Freshwater Trout (rainbow) 4 1 5.1 0.25361 0.25361 0.10344 0.53028

Estuarine Trout, mixed sp. 4 1 3.2 0.15305 0.15305 0.06242 0.19788

Freshwater Trout, mixed sp. 4 1 6.0 0.15305 0.15305 0.06242 0.37425

Trophic Level

Consumption Rate

(g/person/day) Sum of Weights

ConsumptionWeighted Mean

Percent Lipid

2 1.60757 1.00000 1.9

3 3.44341 1.00000 2.6

4 2.45175 1.00000 3.0

Total 7.50273 — —
6-31

Of particular importance will be the extent to which the lipid contents of locally consumed

aquatic organisms differ from those corresponding to species that drive EPA’s calculation of the

national default lipid values (e.g., shrimp, flounder, flatfish, catfish). Interestingly, an analogous

derivation of consumption-weighted values of lipid fraction specific for the Great Lakes region

resulted in estimates that were similar to the national default values (i.e., 1.8% for trophic level 3,

3.0% for trophic level 4), despite considerable differences in consumption patterns (USEPA,

1995b). To address uncertainty associated with potential differences in consumption patterns and

associated lipid fraction, EPA recommends that States and Tribes use local or regional

information on fish and shellfish consumption to calculate values for lipid fraction whenever

possible.

Specificity of Fish Consumption Rate Data. Another attribute associated with the

CSFII-derived consumption rates that leads to uncertainty in the national default values of lipid

fraction is the lack of specificity in the consumption rate categories (trout, flatfish, catfish).

Specifically, consumption rate information was not available at the species level (e.g., lake trout,

brook trout). Therefore, within a CSFII consumption category, equal weighting of lipid fraction

among species was assumed. Obviously, to the extent that this assumption is violated,

uncertainty will be introduced into the default values of lipid fraction.

To provide some insight into the effect of violating EPA’s assumption of equal weighting

among species lipid values on the national default values of lipid fraction, the default values of

lipid fraction were recalculated assuming 100% weighting to the species with the highest mean

lipid value within a CSFII consumption category, and again assuming 100% weighting to the

species with the lowest mean lipid value. Values of lipid fraction calculated with the species with

the lowest and highest mean lipid values are provided in Table 6-7, along with EPA’s national

default values of lipid fraction (calculated with the average of species mean lipid values).

It is apparent from this exercise that substantially different assumptions about the

weighting of species mean lipid values within a CSFII consumption category have relatively little

impact on the national default values of lipid fraction (i.e., <50% increase or decrease). However,

this analysis was constrained by the limited availability of lipid data for multiple species within a

CSFII consumption category. Specifically, of the consumption categories

Table 6-7. Sensitivity of National Default Values of Lipid Fraction to Different Weighting

Assumptions Among Species 

Trophic Level Calculated Using Lowest

Species Mean Lipid Values

Calculated Using Average of

Species Mean Lipid Valuesa

Calculated Using Highest

Species Mean Lipid Values

2 1.9 1.9 2.0

3 2.1 2.6 3.1

4 1.8 3.0 4.4

a

 Weighting assumption used for calculating the national default values of lipid fraction.
6-32

constituting the bulk of the total consumption rate (shrimp, catfish, flounder, flatfish), only the

catfish category was represented by multiple species. Therefore, the impact of different

assumptions about the weighting of species mean lipid values is likely to be underestimated by

the present analysis.

Model Uncertainty, Measurement Error, and Variability in CSFII-Derived Fish

Consumption Rates. Other sources of uncertainty in EPA’s national default values of lipid

fraction include model uncertainty, measurement error, and variability associated with the

estimates of mean fish consumption rates from the CSFII study. Even though data and resource

limitations prevented EPA from assessing the magnitude and direction of these sources of

uncertainty, it is still considered instructive to discuss their overall characteristics. The term model

uncertainty is used here to represent uncertainty originating from the design of the CSFII study

and its application to AWQC derivation. Specifically, the fish consumption rates from the CSFII

study were based on a 2-day dietary recall from a stratified random sample of the U.S.

population. In many situations, AWQCs are derived to protect against adverse effects from longterm (chronic) exposures to chemicals from sources including fish consumption. Under these

AWQC applications, so-called model uncertainty is introduced in the estimated fish consumption

rates to the extent that daily mean per capita consumption rates estimated over a 2-day period

deviate from the “true” daily consumption rates over the long term.

The term measurement error refers to the error associated with recalling from memory

the type and quantity of fish and shellfish actually consumed. Interestingly, although the previous

discussion of model uncertainty might lead one to favor survey designs with longer recall periods

(e.g., weekly, monthly), measurement error can increase substantially for longer survey recall

periods.

Finally, one can expect the variability associated with the estimated mean per capita

consumption rates to affect the derivation of national default values of lipid fraction. This

variability would reflect variation in the amount and types of fish and shellfish actually consumed

across individuals, in addition to differences in the ability of individuals to recall what they ate in

the past (measurement error). To assess the effect of this source of variability on the default

consumption rates, one would need some estimate of the variance of mean per capita

consumption rates for each CSFII category. However, limitations in the CSFII study prevent

accurate estimates of this variance at the CSFII category level.

For a more detailed discussion of uncertainty associated with the use of data from the

CSFII study, see the Exposure Assessment volume of the Technical Support Document.

Uncertainty in Trophic Level Classification. As illustrated by Table 6-4, variation exists

in the trophic position of commonly consumed aquatic organisms. Sources of this variability can

be attributed to numerous factors, including the size and life stage of the organism, the season,

the organism’s life history (e.g., migratory behavior), and spatial heterogeneity in the food web

structure. To calculate national default values of lipid fraction, EPA relied on a synthesis of data

on the trophic position of aquatic organisms (USEPA, 2000e-g). Data from these syntheses of

trophic positions were ultimately rounded to nominal values (e.g., 1, 2, 3, 4), when in reality a
6-33

continuum exists for an organism’s trophic position (e.g., 1.3, 2.6, 3.7). To the extent that the

trophic position of consumed aquatic organisms at various locations differs from EPA’s

assessment of trophic position, uncertainty will be introduced in derivation of the national default

lipid values. For some organisms (e.g., clams, oysters, scallops), the variability in trophic position

appears small (Table 6-3) and the likely impact on the default values of lipid fraction is expected

to be minimal. For other groups of organisms, (e.g., anchovy, catfish, croaker, flatfish, flounder,

shrimp), wider variation exists in trophic position within and across species, in part because of

their opportunistic feeding style. For these CSFII categories, EPA weighted the consumption

rates equally between multiple trophic levels (Table 6-6). By chance, these more “opportunistic”

species make up a large fraction of the total rate of consumption of freshwater and estuarine

species. To assess the sensitivity of EPA’s national default values of lipid fraction to the

assumption of equal weighting of consumption rates across trophic levels for selected species

(anchovy, catfish, croaker, flatfish, flounder, and shrimp categories), national default values of

lipid fraction were recalculated by assuming that 100% of the consumption occurred in the lower

trophic level, and again assuming that 100% of the consumption occurred in the higher trophic

level. For example, calculations were performed assuming that all of the consumption of catfish

species occurred at trophic level 3 and again at trophic level 4. Results from this sensitivity

analysis are shown in Table 6-8.

It is apparent from Table 6-8 that the national default values of lipid fraction are relatively

insensitive to assumptions made about the trophic position of those species for which the trophic

position is particularly variable (anchovy, catfish, croaker, flatfish, flounder, and shrimp).

Table 6-8. Sensitivity of National Default Values of Lipid Fraction to Different Weighting

Assumptions Among Trophic Levelsa

Trophic Level Calculated Assuming 100%

Consumption at the Lower

Trophic Level

Calculated Assuming 50%

Consumption at Lower and

Higher Trophic Levelb

Calculated Assuming 100%

Consumption at the Higher

Trophic Level

2 2.5 1.9 1.9

3 2.3 2.6 2.8

4 2.8 3.0 3.7

a Different weighting assumptions were made for anchovy, catfish, croaker, flatfish, flounder, and shrimp CSFII consumption

categories.

b Weighting assumption chosen for calculating the national default values of lipid fraction.

Limitations in the Lipid Data. A number of limitations in the lipid data contribute to

uncertainty in the national default values of lipid fraction. First, the lipid data used to calculate the

national default values of lipid fraction were originally generated for a variety of purposes and by

a variety of methods. These data almost certainly do not represent a random sampling of aquatic

organisms that is properly stratified over potentially important variables such as age, tissue type,

and season. As a result, the data set may contain hidden biases that are difficult to assess without

a comparison with a truly random, stratified sample. For example, some species data sets may be

overrepresented by one or more tissue types, where multiple tissue types are being consumed
6-34

(e.g., fillet with and without skin). Other species data sets might contain biases that result from

overrepresentation of certain lipid measurement methods. Second, the sample size is small for a

number of species (i.e., <10 for certain species of clam, crayfish, croaker, sturgeon, and salmon).

In general, the lower sample sizes for these species result in lower confidence in the species mean

values of lipid fraction. Some commonly consumed species may not be represented at all in a

given CSFII consumption category because lipid data were simply unavailable. Third, the number

of species represented in certain CSFII consumption categories is small or unknown. The latter

situation occurred for shrimp, flounder, flatfish, crayfish, eel, and whitefish categories, where

lipid data were available only in an aggregated form (e.g., at the family level for shrimp). Finally,

the quality of some of the lipid data (in particular, the STORET-based NSQS data) was not

documented and could not be verified directly.

Quantitative Analysis

Variability in Lipid Data. As shown in Table 6-3, estimates of variation around the

species mean value of lipid content are available for most species. Multiple sources of variation

are believed to contribute to the observed variation in species lipid content. Within a species,

these sources include measurement error; differences in lipid extraction and quantification

methods; inclusion of data from different tissue types, ages, and sizes of organisms; and different

dietary habits among individuals within a species, to name a few. 

To assess how these and other sources of variability in lipid content affect the uncertainty

in EPA’s calculation of national default values of lipid fraction, a probabilistic-based uncertainty

analysis was conducted, using the estimated variance and mean values shown in Table 6-2. This

analysis relied on several assumptions:

1. Mean and coefficient of variations in species lipid content were defined from the

data summarized in Table 6-3.

2. The values of lipid content for each species were assumed to be log-normally

distributed. This assumption was consistent with the positive skewness (and nonnegative nature) of percentage data and was supported by visual inspection of

frequency distributions from selected lipid data sets.

3. In a few situations (e.g., striped anchovy, northern anchovy, rockfish [Sebastes

spp.]), estimates of variance around the mean value were not available. In these

cases, the coefficient of variation was assumed to be equal to that calculated from

another species in the same CSFII consumption category.

4. For two CSFII consumption categories (clam, crayfish), no information on

variance was available from any species. As a result, no variance was assumed

around the mean values.

5. Trophic level designations and mean per capita consumption were held constant.
6-35

6. All input distributions were assumed to be independent (i.e., no correlation among

distributions).

With the aforementioned information and supporting assumptions, a probabilistic

uncertainty analysis was run, using a Monte Carlo simulation technique for calculating the

national default values of lipid fraction. The Monte Carlo simulation used Crystal Ball® version

4.0 software (Decisioneering, Inc., Denver, Colorado) in combination with a Microsoft Excel® 97

spreadsheet application. For each iteration of the simulation, a consumption-weighted average

value of lipid fraction (i.e., a default value of lipid fraction) was calculated for each trophic level,

using a randomly selected value of lipid fraction from each of the species input distributions. The

simulation was run for 10,000 iterations, thus producing a distribution of default values of lipid

fraction for each trophic level. Repeated simulations indicated that 10,000 iterations produced

highly stable estimates of the mean and extreme percentiles of the default lipid values.

Figures 6-2 and 6-3 show the results of the Monte Carlo simulations. The x-axis of Figure

6-2 refers to the default value of lipid fraction (expressed as a percentage), and the y-axis displays

the probability. It is apparent that the default values of lipid fraction comprise three distinct but

somewhat overlapping distributions. For clarity, Figure 6-2 displays the same information in the

form of a reverse cumulative frequency. The y-axis displays the frequency by which a given

value of the default value of lipid fraction (x-axis) is exceeded in the data set. From Figure 6-3,

one can estimate the likelihood that the default lipid fraction would exceed a particular value. 

Relevant descriptive statistics from the output distributions of default values of lipid

fraction are shown in Table 6-9. As expected, mean values produced from the Monte Carlo

simulations were identical (to two significant digits) to the national default values (Table 6-4)

calculated by using only the species mean values as inputs. Regarding variation surrounding the

mean values, it is often useful to evaluate the range between the 5th and 95th percentiles of the

distribution. From this measure, it is evident that the variability around the mean values of lipid

fraction is relatively small (a factor of #2.5). The most sensitive input distributions to the

calculated default values of lipid fraction are shrimp (for trophic level 2), shrimp and common

carp (for trophic level 3), and rainbow trout (for trophic level 4). Each of these input distributions

contributed approximately 25% or more to the variance in the calculated default lipid values.

Finally, EPA acknowledges that there is similarity among the national default value lipid

fractions across trophic levels (i.e., 1.9, 2.6, 3.0) and that the uncertainty bounds somewhat

overlap. This degree of similarity might support the notion of calculating a single national default

value of lipid fraction rather than maintaining distinctions among trophic levels. Although EPA

considered this option, it was ultimately rejected in favor of maintaining separate national default

values of lipid fraction at each trophic level for various reasons. First, maintaining trophic level

specificity in lipid fraction is consistent with EPA’s derivation of national BAFs, which are

calculated separately for trophic levels 2, 3, and 4. As explained in the 2000 Human Health

Methodology (USEPA, 2000a), trophic level–specific BAFs are derived to account for factors that

can affect bioaccumulation in aquatic organisms occupying different trophic positions in aquatic

food webs (e.g., biomagnification and broad physiological differences such as clams versus fish).

In addition to the improved technical accuracy associated with applying trophic level–specific
6-36

 

.000

.026

.052

.078

.104

0.50 1.75 3.00 4.25 5.50

TL2-cons. wt. lipid

TL3-cons. wt. lipid

TL4 Cons. Wt. Lipid

Overlay Chart

Figure 6-2. Frequency distribution of national default values of lipid fraction (10,000 iterations).

Reverse Cumulative Comparison

.000

.250

.500

.750

1.000

0.50 1.75 3.00 4.25 5.50

TL2-cons. wt. lipid

TL3-cons. wt. lipid

TL4 Cons. Wt. Lipid

Overlay Chart

Figure 6-3. Reverse cumulative comparison of national default values of lipid fraction (10,000 iterations).

values of lipid fraction, maintaining a separate distinction across trophic levels also provides

flexibility to States and Tribes for adjusting EPA’s national default lipid values. Specifically,

adjustments can be made to the calculation of one trophic level–specific value of lipid fraction

without affecting those determined for the other trophic levels. For example, a State or Tribe may

wish to add or subtract lipid data for various top predator species (trophic level 4) without

changing the values of lipid fraction for other trophic levels. Thus, EPA believes that its use of

trophic level–specific values of lipid fraction not only achieves greater technical accuracy than

does a single estimate, but also affords greater flexibility to States and Tribes in making desired

adjustments to EPA’s national default values of lipid fraction.
6-37

Table 6-9. Descriptive Statistics of Monte Carlo Simulation of National Default Values of

Lipid Fraction (10,000 Iterations)

Statistic/Percentile Trophic Level 2 Trophic Level 3 Trophic Level 4

Mean 1.9 2.6 3.0

Median 1.8 2.5 2.9

CV 0.30 0.17 0.17

5

th 1.2 2.0 2.3

95th 3.0 3.4 3.9

95th/5th 2.5 1.7 1.7

Minimum 0.7 1.4 1.6

Maximum 6.1 5.9 6.5

6.3 BASIS FOR THE NATIONAL DEFAULT VALUES OF DOC AND POC 

This section provides the technical basis of EPA’s calculation of national default values of

DOC and POC concentrations in U.S. fresh and estuarine surface waters. As summarized in the

National Human Health AWQC Methodology (USEPA, 2000a), EPA’s national default values of

DOC (2.9 mg/L) and POC (0.5 mg/L) are used in calculating national BAFs for nonionic organic

chemicals. Information on DOC and POC is necessary to adjust a baseline BAF, which reflects

the concentration in the lipid fraction of tissue and the freely dissolved concentration in water, to

a national BAF expressed in terms of total chemical in water and tissue (Section 3, Figure 3-2).

The national BAF incorporates values that are reflective of the lipid content of the fish and

shellfish consumed by the U.S. population and the effects of chemical binding/associating with

DOC and POC in representative U.S. surface waters. For deriving national human health

AWQCs, EPA uses national default values of DOC and POC that are representative of U.S.

surface waters for calculating the freely dissolved fraction of a nonionic organic chemical (see

Equation 4-4).

Although EPA uses national default values of DOC and POC to derive national human

health AWQC for nonionic organic chemicals, EPA encourages States and authorized Tribes to

use local or regional data on the organic carbon content of applicable waters when adopting

criteria into their own water quality standards. The EPA encourages the use of appropriately

derived 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. Additional guidance on

developing local or region-specific values of DOC and POC is found in a subsequent volume of

this TSD (Volume 3: Development of Site-Specific Bioaccumulation Factors). Nevertheless, EPA

recognizes that there will be situations when such local or regional data are not available or are

inadequate for deriving local or regional values of DOC and POC. In these cases, EPA
6-38

recommends that States and Tribes use EPA’s national default values of DOC and POC when

deriving BAFs for use in establishing State or Tribal water quality criteria and standards.

The following sections present the data sources, analysis, and uncertainty associated with

EPA’s derivation of national default values of DOC and POC.

6.3.1 Data Sources

Data on the concentrations of DOC and POC in U.S. surface waters were obtained from

two databases:

1. The U.S. Geological Survey’s (USGS) WATSTORE database

2. EPA’s historical STORET database (recently renamed the Legacy Data Center

[LDC]database)

Although EPA’s historical STORET database (henceforth called “LDC” for consistency

with current EPA nomenclature) contains data from the USGS WATSTORE database, queries

indicated that the USGS data contained in LDC were at least 2 years out of date. Therefore, nonUSGS data were retrieved from the LDC database and USGS data were retrieved from the

WATSTORE database in order to obtain a comprehensive data retrieval without duplicating

records. Each database is described further below.

WATSTORE Database

The USGS developed the WATSTORE (National Water Data Storage and Retrieval

System) for the storage and retrieval of water data collected through its activities. The

WATSTORE database was established in 1972 to provide an effective and efficient means for

processing and maintaining water data collected through USGS activities and to facilitate release

of the data to the public. The system resides on the central computer facilities of the USGS at its

National Center in Reston, Virginia, and consists of related files and databases. The Water

Quality File was searched for retrieval of DOC and POC data. This file contains approximately 2

million analyses of water samples that describe the chemical, physical, biological, and

radiological characteristics of both surface and groundwater. The method for analysis of POC

followed Standard Methods #5310D—“Wet Oxidation Method for Total Organic Carbon”

(APHA, 1995), with two modifications. First, silver filters were used instead of glass fiber filters.

Second, a sonification step was added in 1997 to facilitate complete oxidation of organic carbon

(USGS, 1997; Burkhardt et al., 1999). For analysis of DOC, the wet oxidation method (Standard

Methods #5310D) was also used on filtered samples until approximately 1983. After 1983, the

persulfate-ultraviolet oxidation method was used (Standard Methods #5310C) on filtered

samples, which includes UV radiation with a reduced heating/digestion step (APHA, 1995;

Kammer, 2000).
6-39

LDC Database

EPA’s LDC database is a waterway-related monitoring database that contains data

collected by Federal, State, and local agencies; Indian Tribes; volunteer groups; academics; and

others. All 50 States, territories, and jurisdictions of the United States, along with portions of

Canada and Mexico, are represented in the database. The database used in this analysis was

called the historical, or “old,” STORET database but recently was renamed the Legacy Data

Center (LDC). 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. The database contains raw biological, chemical,

and physical data on surface water and groundwater. Each sampling result is accompanied by

information on where the sample was taken (latitude, longitude, State, county, Hydrologic Unit

Code, and a brief site identification), when the sample was gathered, the medium sampled (e.g.,

water, sediment, fish tissue), and the name of the organization that sponsored the monitoring.

Information on the analytical methods used to quantify DOC and POC was not available in the

LDC database. Although a newer version of STORET was initiated in 1999 that contained this

and other QA/QC information, sufficient data were not available from this database at the time of

this retrieval (i.e., the new STORET database contains data collected beginning in 1999, along

with selected older data that have been properly documented and migrated from the LDC).

6.3.2 Data Retrieval and Screening

Data retrievals from the LDC and WATSTORE databases were conducted in January

2000 and combined into a single relational database. Originally, approximately 800,000 records

containing data on POC, DOC, or total organic carbon (TOC) were retrieved for the period

beginning in 1970 through the latest year data were available (1999 for WATSTORE; 1998 for

LDC). This retrieval was limited to samples taken from ambient surface waters (i.e., samples from

wells, springs, effluents, and other nonambient sources were excluded). Additionally, this initial

retrieval included multiple types of POC and DOC measurements to ensure that the initial data

retrieval would be sufficiently comprehensive.

Once these data were retrieved, the two data retrievals were combined into a single

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.

These processing and screening steps are outlined below.

1. Organic Carbon Parameters. The following parameter codes were retained in the

database: 00680 (Carbon, Total Organic); 00681 (Carbon, Dissolved Organic);

00684 (Carbon, Dissolved Organic-Whatman GF/F); 00689 (Carbon, Suspended

Organic); 80102 (Carbon, Organic Particulate). All units were expressed in

milligrams per liter as C (Carbon).

2. Uncertain Values. Values that were coded in such a way as to suggest uncertainty

in the measurement were deleted from the database. For example, values coded as

“estimated value,” “analyte detected in blank and sample,” “sample held beyond
6-40

normal holding time,” “actual value is known to be greater reported value,” and

similar such indicators were deleted.

3. Water Body and Station Types. The database was further restricted to the

following water body types: estuaries, lakes, reservoirs, and streams (including

rivers). This step excluded other “ambient” surface water types, such as oceans,

freshwater wetlands, and canals. Station types were restricted to those coded as

“ambient” only. This step excluded so-called specialty stations (i.e., those stations

designated for special purposes such as storm water runoff and biological and

sediment monitoring).

4. Sampling Period. Although the initial retrieval contained data dating back to 1970,

the time period for the final database was restricted to 1980 through 1999. Pre1980 data were eliminated because of the greater uncertainty in using these data to

represent present-day conditions that can affect organic carbon concentrations in

surface waters (e.g., secondary treatment of effluents).

5. Detection Levels. Some values for POC and DOC were reported to be below

analytical detection levels. In this situation, the value was assumed to be half of

the reported detection level. Values with “high” detection levels (i.e., >1.0 mg/L

for DOC and >0.2 mg/L for POC) were deleted from the database because of the

greater uncertainty involved in estimating definitive values of DOC and POC in

these situations.

6. Calculated Values. It was clear from reviewing the data that a substantial portion

of samples contained values of DOC and TOC, but not POC. It is apparently not

uncommon to determine the POC concentration by subtracting the DOC

concentration from the TOC concentration determined from a given sample. In

these situations, the parameter of interest (POC or DOC) was calculated by the

difference from the other two measurements (i.e., POC = TOC – DOC; DOC =

TOC – POC). This calculation was performed using data only from the same

sample to avoid introducing error into calculated POC values. The end result was

that about 40% of the total number of POC values in the database were

determined by difference. The opposite condition (i.e., TOC and POC, but no

DOC value) occurred rarely and resulted in only 0.4% of the total DOC samples

being determined by difference.

7. Extreme Values. As a final quality control step, DOC and POC values at the

extreme high end of the cumulative frequency distributions were reviewed for

consistency with extreme values reported in natural surface waters of the United

States. A small fraction of the DOC and POC concentrations in the LDC database

exceeded concentrations considered to represent upper limits of DOC and POC

concentrations reported in U.S. water bodies (i.e., 0.2% exceeded 60 mg/L for

DOC and 0.6% exceeded 30 mg/L for POC). These extreme values were based on

a review of organic carbon data by Thurman (1985), who reported extreme values
6-41

of DOC concentrations of as high as 50 mg/L in distrophic lakes and 60 mg/L in

tributaries draining wetland systems. Concentrations of POC between 1 and 30

mg/L encompass 99% of the world’s river systems and are at the upper range of

POC concentrations reported for U.S. rivers (as reviewed by Thurman, 1985).

The evaluation of extreme values revealed some “negative” values of POC (i.e.,

about 7% of the total number of POC values). These values occurred almost

entirely as an artifact of calculating POC values by difference (see item 6 above)

and the impact of measurement error on this process. For example, if both TOC

and DOC were near analytical detection limits or were otherwise very similar in

magnitude, it would not be surprising for reported values of DOC to be, on

occasion, slightly higher than those for TOC as a result of measurement error. The

vast majority of the negative values were relatively close to zero (i.e., between –1

mg/L and 0 mg/L).

To address concerns about the impact of extreme values on the calculation of

national default values for DOC and POC, the extreme ends of the respective

distributions were truncated. Specifically, values of DOC above 60 mg/L and of

POC above 30 mg/L were omitted from the database. These values represent the

99.8th and 99.4th cumulative percentiles of the respective DOC and POC

distributions. To avoid introducing bias into the median values of DOC and POC

by truncating one side of the distribution, DOC and POC values below the lower

0.2% and 0.6%, respectively, were also omitted. This truncation of the lower tail of

the POC distribution bounds had the impact of eliminating some, but not all, of

the “negative” POC values.

6.3.3 Results

Using the screened databases described previously, national default values of DOC and

POC were calculated to be 2.9 mg/L and 0.5 mg/L, respectively. These values represent median

(50th percentile) values from approximately 110,000 measurements of DOC and 86,000

measurements of POC in U.S. fresh and estuarine surface waters. All 50 States are represented in

the database. The EPA selected median values of DOC and POC for the national default values

for consistency with the goal of national BAFs (i.e., as central-tendency estimates).

Table 6-10 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 6-10 that variation in

DOC and POC concentrations is relatively large. For example, 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,
6-42

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 when sufficient data are available. However, for

deriving national AWQC, and when States and Tribes lack sufficient local or regional data, EPA

recommends the use of its national default values of DOC and POC.

Table 6-10. National Default Values for POC and DOC in U.S. Fresh and Estuarine Surface

Waters

Statistic

DOC (mg/L) POC (mg/L)

All Types Stream/

River

Lake/

Reservoir

Estuary All Types Stream/

River

Lake/

Reservoir

Estuary

Median 2.9 3.8 2.1 2.7 0.5 0.6 0.3 0.9

Mean 4.6 5.6 2.9 3.4 1.0 1.3 0.5 1.2

Std. 5.1 5.9 3.0 2.6 2.0 2.5 1.0 1.8

CV 111% 105% 103% 76% 200% 192% 200% 150%

n 111,059 69,589 25,704 15,766 86,540 48,238 23,483 14,819

5

th 0.8 0.7 1.0 1.7 0a 0a 0.08 0.1

10th 1.2 1.0 1.4 2.0 0 0

a 0.1 0.3

25th 2.0 2.1 1.8 2.3 0.2 0.2 0.2 0.5

75th 5.4 6.9 2.6 3.2 1.1 1.4 0.5 1.4

90th 9.7 11.6 5.0 5.0 2.3 3.1 0.8 2.2

95th 14 16.5 7.8 9 3.9 5 1.3 3

95th/5th 17.5 23.6 7.8 5.3 — — 16.3 30.0

a

 Values calculated to be less than zero because of measurement error; see Section 6.3.2 for explanation.

Source: U.S. EPA LDC and USGS WATSTORE databases. Data retrieval: January 2000; see Sections 6.3.1 and 6.3.2 for

description.

6.3.4 Uncertainty/Limitations in National Default Values

This section describes sources of uncertainty associated with EPA’s derivation of national

default values of DOC and POC for establishing national human health AWQCs. This discussion

of uncertainty is neither exhaustive nor entirely quantitative. Rather, it focuses on sources of

uncertainty that are likely to have the greatest impact on the derivation and application of national

default values of DOC and POC. Sources of uncertainty characterized below are grouped into the

following categories: (1) sampling bias, (2) measurement error, and (3) natural variability in DOC

and POC concentrations. 
6-43

Sampling Bias

The national default values of DOC and POC are intended to represent central tendency

estimates of DOC and POC concentrations in U.S. surface waters. Ideally, the data used to

generate these values should originate from a random sampling of U.S. surface waters and should

be appropriately stratified and weighted by spatial and temporal factors that would be expected to

influence organic carbon concentrations in aquatic ecosystems (e.g., water body type, hydrologic

and watershed characteristics, ecoregion, season). However, this type of database was not

available on a national scale. Therefore, EPA relied on data from USGS’s WATSTORE and

EPA’s LDC databases to calculate its national default DOC and POC values. The strengths of

these databases include their large number of records (e.g., >110,000 DOC values and >86,000

POC values), a representation of DOC and POC values for all 50 States, and the reasonably long

period over which data were collected (1980–1999 for this analysis).

An important limitation of the WATSTORE and LDC databases is the fact that they do

not reflect a random sampling of U.S. surface waters (i.e., they may contain biases because of

sampling design). For example, about half of the DOC and POC values in the databases were

sampled in Maryland, New York, Ohio, Florida, and Delaware. Thus, some States are

disproportionally represented, even when one considers the relative area of surface water area

likely to be contained within each State. In addition, organic carbon data from these databases

were not weighted or aggregated in any way before national default (median) values were

calculated. Given these potential biases in the underlying data, it is important to address the

obvious question: How well do EPA’s national default values of DOC and POC represent

average conditions across the United States?

To address the question of sampling bias and its impact on the representativeness of

EPA’s national default DOC and POC values, two types of comparisons were made with the

WATSTORE/LDC data. First, the national default values were compared with central-tendency

estimates of DOC and POC obtained from independent reviews of the relevant scientific

literature. This was done to provide a qualitative assessment of the comparability of national

default values to “expected” values based on literature accounts. The second comparison was

more quantitative in design and involved contrasting geographically distinct subsets of the

WATSTORE/LDC databases with geographically similar subsets of data produced by EPA’s

Environmental Monitoring and Assessment Program (EMAP). Data contained in the EMAP

databases are sampled by using a stratified, random sampling design that minimizes the effect of

biases in sampling design on resulting statistical distributions of the data. Each of these

comparisons is described below.

Comparisons with Literature Data. Thurman (1985) reviewed the literature on DOC and

POC concentrations in surface waters throughout the world. The concentrations of DOC and

POC were found to vary in surface waters as a function of water body type, trophic status (lakes),

climate, watershed size and vegetation, and season of the year. Specifically, Thurman (1985)

reported that mean values of DOC in some pristine streams range from 1 to 3 mg/L and those in

rivers and lakes typically range from 2 to 10 mg/L. Ranges of DOC concentrations in estuaries are

reported to be highest at the limit of tidal rise (i.e., essentially equivalent to DOC in rivers) and
6-44

lowest as dilution with seawater becomes complete (i.e., approximating 1 mg/L on average). For

swamps, marshes, and bogs, DOC concentrations are reported to range from 10 to 30 mg/L.

Concentrations of POC in lakes reportedly range from about 0.1 to 1.0 mg/L, whereas those in

small streams range from 0.1 to 0.3 mg/L, or about 10% of the DOC. Finally, POC concentrations

in rivers are reported to be about one-half the concentration of DOC (i.e., 1–5 mg/L), although

POC may equal DOC in the largest rivers during times of high discharge. Although the ranges of

DOC and POC concentrations reported by Thurman (1985) include surface waters found beyond

the United States, they also appear to be representative of U.S. surface waters, based on data

summarized by Thurman (1985) that were specific to the United States.

Despite the aforementioned limitations in EPA’s DOC and POC databases with respect to

potential sampling bias, EPA’s national default values of DOC (2.9 mg/L) and POC (0.5 mg/L)

compare favorably with the ranges of DOC and POC concentrations summarized by Thurman

(1985). This comparison suggests that EPA’s national default values of DOC and POC are not

unreasonable in terms of representing typical organic carbon concentrations found in U.S. rivers,

streams, lakes, and estuaries. With respect to wetland areas (marshes, swamps, bogs), it is likely

that the national default values may significantly underestimate DOC and POC concentrations in

these systems, owing to their poor representation in the DOC and POC databases. The impact of

this underestimation on national BAFs will vary as a function of the Kow of the chemical (see

“Natural Variability in DOC and POC Concentrations,” below). For some highly hydrophobic

organic chemicals, this underestimation may result in a conservative estimate of the AWQC for

these systems (i.e., a lower AWQC than what might be necessary) because of a likely

overestimation of the bioavailable fraction and national BAF.

Comparisons with EMAP Data. Data generated by EPA’s EMAP program are based on

a stratified, random sampling strategy that is specifically designed to minimize the influence of

sampling bias on the data and to enable statistically based extrapolations across geographic

regions (Herlihy et al., 2000). Currently, however, the EMAP databases contain DOC

measurements (but not POC measurements) and are limited to smaller geographic scales and

specific water body types. Thus, DOC data from EMAP’s 1997–1998 sampling of mid-Atlantic

streams and rivers (http://www.epa.gov/emap/html/dataI/surfwatr/data) were compared with

similar geographic subsets from the WATSTORE/LDC database. The mid-Atlantic EMAP

database was chosen because sufficient data were available on DOC in rivers and streams to

make meaningful comparisons at the State and ecoregion levels. Similarly, the mid-Atlantic

region is also well represented in the WATSTORE/LDC database.

Figure 6-4 shows the cumulative frequency distributions of DOC contained in the EMAP

mid-Atlantic database (top panel) and the WATSTORE/LDC database (bottom panel) for rivers

and streams in Pennsylvania, Virginia, and West Virginia. Similar comparisons are made for four

mid-Atlantic ecoregions (Piedmont, Ridge and Valley, Central Appalachians, Western Allegheny

Plateau; Figure 6-5). Descriptive statistics are provided in Tables 6-11 and 6-12. From both sets of

comparisons, it is apparent that the agreement between the WATSTORE/LDC and EMAP data is

best at the middle to lower tails of the distributions and poorest at the higher end of the

distributions. At the lower tails of the distributions (e.g., 10th, 25th percentiles), the

WATSTORE/LDC DOC data are generally within 30% of the EMAP data (ecoregion 70 being
6-45

EMAP Data

0

0.2

0.4

0.6

0.8

1

0 1 2 3 4 5 6 7 8 9 10 11 12

DOC (mg/L)

Cumulative Length Proportion

PA VA WV

WATSTORE & LDC Data 

0

0.2

0.4

0.6

0.8

1

0 1 2 3 4 5 6 7 8 9 10 11 12

DOC (mg/L)

Cumulative Frequency

PA VA WV

the only exception). The median DOC values of the WATSTORE/LDC data show a slightly

higher bias compared with median values from the EMAP data but are usually within a factor of

1.5 (ecoregions 47 and 70 are about a factor of 2 greater). For a majority of comparisons made at

the 75th and 90th percentiles (i.e., 11 of 14), the WATSTORE/LDC DOC values are approximately

a factor of 2 greater than the corresponding percentiles from the EMAP data. This result is

expected, given the greater focus of the WATSTORE/LDC sampling sites on larger river and

stream systems and on areas receiving proportionately greater human influence compared with

the EMAP sampling sites.

Figure 6-4. Ecoregion-level DOC distributions for rivers and streams from EPA’s WATSTORE/LDC and EMAP

databases.

Source: EMAP data were taken from EPA’s Environmental Monitoring and Assessment Program, Mid-Atlantic

Integrated Assessment, 1997–98 (http://www.epa.gov/emap/html/dataI/surfwatr/data).

USGS WATSTORE and EPA LDC retrievals are explained in Section 6.3.2.
6-46

EMAP Data

0

0.2

0.4

0.6

0.8

1

0 1 2 3 4 5 6 7 8 9 10 11 12

DOC (mg/L)

Cumulative Length Proportion

Eco Reg. 45 Eco Reg. 67 Eco Reg. 69 Eco Reg. 70

WATSTORE & LDC Data 

0

0.2

0.4

0.6

0.8

1

0 1 2 3 4 5 6 7 8 9 10 11 12

DOC (mg/L)

Cumulative Frequency

45 67 69 70

Figure 6-5. Ecoregion-level DOC distributions for rivers and streams from EPA’s WATSTORE/LDC and EMAP

databases.

Source: EMAP data were taken from EPA’s Environmental Monitoring and Assessment Program, Mid-Atlantic

Integrated Assessment, 1997–98 (http://www.epa.gov/emap/html/dataI/surfwatr/data). Ecoregion 45 = Piedmont; 67

= Ridge & Valley; 69 = Central Appalachians; 70 = Western Allegheny Plateau.

USGS WATSTORE and EPA LDC retrievals are explained in Section 6.3.2.

Table 6-11. Descriptive Statistics from the State-Level DOC Distributions

Statistic EMAP (1997–1998)a WATSTORE & LDC (1980–1999)

PA VA WV PA VA WV

n 89 80 59 1,359 634 682

Mean 1.7 2.0 2.3 3.8 3.1 2.0

10th percentile 0.8 0.8 1.0 0.9 0.7 0.7

25th percentile 1.0 1.0 1.5 1.3 1.0 1.2

50th percentile 1.5 1.5 1.8 2.2 1.8 1.7

75th percentile 2.1 1.9 2.2 4.6 3.7 2.5

90th percentile 2.5 3.2 3.7 8.9 6.5 3.5
6-47

Table 6-12. Descriptive Statistics from the Ecoregion-Level DOC Distributions

Statistic

EMAP MAIA (1997–1998)a WATSTORE & LDC (1980–1999)

Ecoregion Ecoregion

45 67 69 70 45 67 69 70

n 38 64 36 43 309 733 864 1,795

Mean 2.6 1.7 1.7 2.1 4.4 2.2 1.9 4.0

10th percentile 1.1 0.8 0.7 <0.7 1.0 0.6 0.7 1.6

25th percentile 1.6 1.1 1.0 1.4 1.7 1.0 1.1 2.7

50th percentile 1.8 1.3 1.5 1.8 3.4 1.7 1.6 4.1

75th percentile 3.1 2.0 1.8 2.1 5.9 2.8 2.3 5.0

90th percentile 4.0 2.7 2.1 2.7 9.3 4.5 3.3 6.1

a

 Length-weighted statistics for EMAP data. Ecoregion 45 = Piedmont; 67 = Ridge & Valley; 

69 = Central Appalachians; 70 = Western Allegheny Plateau.

Source: EMAP data were taken from EPA’s Environmental Monitoring and Assessment Program, Mid-Atlantic Integrated

Assessment, 1997–98 (http://www.epa.gov/emap/html/dataI/surfwatr/data/).

USGS WATSTORE and EPA LDC retrievals are explained in Section 6.3.2.

The previous comparisons of DOC concentrations from the mid-Atlantic EMAP and

WATSTORE/LDC databases are clearly limited with respect to evaluating the impact of possible

sampling bias on EPA’s national default values of DOC and POC (i.e., comparisons are restricted

to the mid-Atlantic region and no comparisons could be made for POC). Despite these

limitations, this analysis indicates that, at least for the three States that are well represented in the

WATSTORE/LDC database, the degree of sampling bias at median values is not overly

exaggerated. Best agreement between the two databases occurred at percentiles at or below the

median values of the distributions. Assuming the EMAP data represent unbiased results, a

noticeable and somewhat expected bias appears in the WATSTORE/LDC data, primarily at the

higher percentiles. Results are mixed at the ecoregion level; two of the four DOC distributions

compare favorably between the two databases (defined here as percentile values within a factor

of 2). The greater discrepancy between DOC concentrations in ecoregions 45 and 70 appears to

be related to the disproportionate influence of several stations from which large numbers of

measurements were taken relative to the other stations.

Measurement Error. Other sources of uncertainty in EPA’s national default values of

DOC and POC concentrations include error associated with measuring DOC and POC

concentrations. Measurement error refers to error associated with quantifying the particular

variable of interest (e.g., DOC) and includes error associated with sample collection and handling

and analytical techniques. Measurement error varies by analytical method, laboratory, and, to

some extent, each batch of samples analyzed. For the LDC data, the analytical methods used to

determine DOC and POC concentrations were not reported in the database. For analytical

methods underlying the WATSTORE data, estimates of accuracy (percent recovery) and
6-48

precision (relative standard deviation) are available for the analysis of TOC and POC. The mean

percent recovery and relative standard deviation associated with TOC measurements with the wet

oxidation method (Standard Methods #5310D) are reported as 103 ± 3.4% (APHA, 1995).

Similarly, for TOC measurements using persulfate-ultraviolet oxidation (Standard Methods

#5310C), the reported mean percent recovery and relative standard deviation of measurements in

two matrices are 93 ± 7% and 106 ± 6% (APHA, 1995). Finally, the reported mean percent

recovery and standard deviation for POC measurements with the wet oxidation method with

silver filtration and sonification are 97 ± 11% (USGS, 1997; Burkhardt et al., 1999). Relative to

other sources of uncertainty in national default DOC and POC values, error associated with

analytical methods appears to be small, at least where it has been quantified.

Natural Variability in DOC and POC Concentrations. As one would expect, there is

substantial variability in the median values of DOC and POC concentrations in U.S. surface

waters (Table 6-10). Specifically, the range of 95th to 5th percentile estimates approximates or

exceeds a factor of 20 in several types of surface waters. Although measurement error is reflected

in this variability, the bulk of 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). To address uncertainty in 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. Nevertheless, EPA recognizes that appropriate local or regional data will

not always be available in sufficient quantity or quality. Therefore, it is appropriate to explore the

degree to which variability in DOC and POC concentrations has an impact on national BAFs.

Figure 6-6 illustrates the effect of varying concentrations of DOC and POC on the freely

dissolved fraction for nonionic organic chemicals with various Kows. The freely dissolved fraction

was calculated according to Equation 5-12 of the 2000 Human Health Methodology (USEPA,

2000a) and has a 1:1 impact on the resulting national BAF (see Equation 5-28 of the 2000 Human

Health Methodology).

From an examination of Figure 6-6, several observations can be made regarding how

variability in organic carbon concentrations is predicted to affect the freely dissolved fraction (and

subsequently the national BAF) for nonionic organic chemicals. First, the effect of DOC and

POC concentrations on the freely dissolved fraction is highly dependent on Kow. For nonionic

organic chemicals with log Kow values of about 4 or less, changes in DOC and POC

concentrations within the 5th to 95th percentiles have very little impact on the freely dissolved

fraction. Further analysis (not shown here) indicates that this insensitivity holds true for values of

DOC and POC far exceeding the 5th and 95th percentiles. Thus, uncertainty in the DOC or POC

concentrations has very little impact on the resulting national BAFs for low Kow chemicals.
6-49

A second observation is that, for nonionic organic chemicals with higher hydrophobicity

(e.g., log Kow >4), the impact of DOC and POC on the freely dissolved fraction increases as Kow

increases. Although the absolute range in the freely dissolved fraction corresponding to the 5th

and 95th percentiles of DOC and POC peaks at a log Kow of about 6 (i.e., from 0.89 at the 5th

percentile to 0.17 at the 95th percentile), the relative difference in freely dissolved fraction (as

measured by the ratio of freely dissolved fraction at various DOC and POC percentiles) increases

with Kow. Because the freely dissolved fraction is used in a multiplication step to calculate the

national BAF, the relative differences in freely dissolved fraction are more meaningful for

interpreting the impact of variability of organic carbon concentrations on the national BAF.

Table 6-13 illustrates the effect of organic carbon on the differences in freely dissolved

fraction relative to that calculated with national default values of DOC and POC. Here, relative

differences are expressed as ratios of the freely dissolved fraction calculated at various percentiles

of DOC and POC (from Table 6-12) to that calculated at the national default values of DOC and

POC. For chemicals with a log Kow of 5.0, the relative impact of DOC and POC within these

percentiles is still rather minor (i.e., a 10% increase at the 5th percentile DOC and POC values

versus a 30% decrease at the 95th percentile). For chemicals with a log Kow value of 6.0, the impact

of organic carbon is more substantial, resulting in a 50% increase in the freely dissolved fraction

at the 5th percentile DOC and POC values. The freely dissolved fraction associated with the 95th

percentile DOC and POC values drops to 30% of the fraction calculated with the national default

DOC and POC values. The effect of lower DOC and POC concentrations on the freely dissolved

fraction is still somewhat muted compared with higher concentrations, in part because the freely

dissolved fraction calculated with the national default values of DOC and POC is still relatively

high (0.93 at Kow 5.0 and 0.58 at Kow 6.0), and it cannot increase beyond 1.0. The greatest impact

of organic carbon on the freely dissolved fraction is seen at the highest Kow (8.0), where the freely

dissolved fractions calculated at the 5th and 95th percentiles are similar in magnitude to the

changes in DOC and POC values (i.e., a fivefold increase in DOC concentration and an eightfold

increase in POC concentration results in an approximately sevenfold decrease in the freely

dissolved fraction).

A final observation is that for highly hydrophobic chemicals, the freely dissolved fraction

is most sensitive to changes in POC relative to DOC. This fact is clear from examination of

Equation 4-4 and relates to the higher partition coefficient for organic chemicals to POC

(Kpoc = 1.0 C Kow L/kg) as compared with that for DOC (Kdoc = 0.08 C Kow L/kg). Therefore, in

terms of reducing overall uncertainty associated with the application of national default values of

DOC and POC, resources should be directed toward site- or region-specific organic carbon

measurements for chemicals with higher hydrophobicity (e.g., about log Kow of 5 and above).

Although both DOC and POC measurements are needed, results indicate that particular attention

should be paid to quantifying representative measurements of POC. 
6-50

0.0

0.2

0.4

0.6

0.8

1.0

1.0E+03 1.0E+04 1.0E+05 1.0E+06 1.0E+07 1.0E+08

Kow

Fraction Freely Dissolved

Nat'l Default (50th) 5th (DOC & POC*) 10th (DOC & POC*)

25th (DOC & POC) 75th (DOC & POC) 90th (DOC & POC)

95th (DOC & POC)

Figure 6-6. Effect of DOC, POC, and Kow on the freely dissolved fraction (ffd). The solid line (€) is the freely

dissolved fraction that corresponds to EPA’s national default values of DOC (2.9 mg/L) and POC (0.5 mg/L). The

dashed lines reflect various percentiles from the distributions of DOC and POC concentrations used to derive the

national default values (e.g., 5th, 10th, 25th, 75th, 90th, 95th percentiles from Table 6-9). 

 * Estimated value based on statistical parameters from the POC distribution (see Table 6-9), assuming data were log-normally

distributed. 

Table 6-13. Effect of DOC and POC Concentrations on the Freely Dissolved Fraction (ffd)

Relative to National Default Values of DOC and POC

Percentile

DOC

(mg/L)

POC

(mg/L)

Fraction Freely Dissolved (ffd) and [Ratio to National Default]

Log Kow

5.0 6.0 7.0 8.0

50th (National

Default)

2.9 0.5 0.93 0.58 0.12 0.014

5

th 0.8 0.06a 0.99 [1.1] 0.89 [1.5] 0.44 [3.7] 0.08 [5.5]

10th 1.2 0.09a 0.98 [1.1] 0.84 [1.5] 0.35 [2.9] 0.05 [3.8]

25th 2 0.2 0.97 [1.0] 0.74 [1.3] 0.22 [1.8] 0.03 [2.0]

75th 5.4 1.1 0.87 [0.9] 0.40 [0.7] 0.06 [0.5] 0.006 [0.5]

90th 9.7 2.3 0.77 [0.8] 0.25 [0.4] 0.03 [0.25] 0.003 [0.24]

95th 14 3.9 0.67 [0.7] 0.17 [0.3] 0.02 [0.16] 0.002 [0.15]

a

 Estimated value based on statistical parameters from the POC distribution (see text).
7-1

(See Equation 2-1)

7. EXAMPLES OF BAF CALCULATIONS

The examples presented in this section illustrate how national BAFs used in calculation of

human health AWQC are developed for a chemical of interest (chemical i) with the four BAF

methods under Procedure 1. The general process illustrated here is also applicable to chemicals

under Procedures 2–4. The equations used in the examples given here are presented in Sections 4

and 5 and the terms used in the equations are defined in Section 2. For reference, the equation

numbers provided here refer to the section where the equation was initially presented or derived.

7.1 EXAMPLE 1: CALCULATION OF A NATIONAL BAF FROM A FIELDMEASURED BAF (BAF t

T) (METHOD 1)

This example illustrates the development of a national trophic level 4 BAF using 

method 1 for a hydrophobic nonionic chemical (chemical i). Calculating national BAFs using

method 1 requires the use of a BAFt

T (also commonly referred to as a “field-measured” BAF).

Determination of a BAFt

T requires information on the total concentration of chemical i in fish

tissue and the total concentration of chemical i in the ambient water.

7.1.1 Calculating a Total BAF (BAF t

T)

 In this example, data are available from Lake John Doe (a hypothetical lake) on the total

concentration of chemical i in lake trout (100 μg/kg) and in the water column (1.6×108 μg/L). 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 (USEPA,

2000e-g). Data obtained from field studies indicate that the mean concentration of the chemical in

the water column reflects adequate temporal and spatial averaging, based on the Kow of this

chemical, and is representative of the average exposure of chemical i to the target fish. The BAFt

T

calculated for chemical i is 6.2×105 L/kg, as shown below.

7.1.2 Calculating a Baseline BAF

The BAFt

T is converted to a baseline BAF for a specific trophic level by incorporating sitespecific information on the fraction of the chemical that is freely dissolved in the ambient water

(ffd) and the fraction of tissue or aquatic organism sampled that is lipid (fR). The equation for

calculating a baseline BAF from BAFt

T is:
7-2

(See Equation 5-2)

(See Equation 4-6)

(See Equation 5-2)

Determining the fraction of chemical i that is freely dissolved (ffd) in the ambient water

requires information on the POC and DOC in the ambient water where the samples were

collected and the Kow of chemical i. For this example, the median POC concentration from Lake

John Doe is 0.6 mg/L (6.0×10-7 kg/L) and the median DOC concentration is 8.0 mg/L (8.0×10-6

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 body used in the BAF study.

It is not appropriate to use national default POC and DOC concentrations to derive baseline

BAFs from BAFt

Ts. The Kow for chemical i is 1.0×105

, or a log Kow of 5. Based on these data, the

fraction of chemical i that is freely dissolved in water is 0.89, calculated as shown below:

The mean fR of the fish species sampled in Lake John Doe is 0.08 (8%). Using this fR and

the BAFt

T and ffd calculated above, a baseline BAF for lake trout of 8.7×106

 L/kg of lipid is

calculated as follows:

For the purposes of this example, it is assumed that only one acceptable BAF value is

available for trophic level 4 organisms. Thus, the baseline BAF for trophic level 4 is the baseline

BAF for lake trout. If acceptable BAFt

Ts are available for additional trophic level 4 organisms,

baseline BAFs are calculated for each of the trophic level 4 species for which there is acceptable

data and then the baseline BAF for trophic level 4 is calculated as the geometric mean of these

baseline BAFs. Recall that in EPA’s BAF methodology, BAFs are trophic-level specific. Hence,

this calculation would be carried out similarly for each trophic level. 
7-3

(See Equation 3-2)

(See Equation 4-6)

7.1.3 Calculating a National BAF

After deriving all acceptable baseline BAFs for chemical i and selecting a final baseline

BAF for trophic level 4, the next step is to calculate a national BAF for this trophic level that will

be used to derive the AWQC. In this example, it is assumed that the baseline BAF calculated

above represents the final baseline BAF for trophic level 4. For a given trophic level, calculating a

national BAF involves adjusting the final baseline BAF to reflect conditions that are expected to

affect the bioavailability of chemical i in ambient waters of the United States. This is

accomplished through the use of national default values for fR and ffd that are based on national

central tendency estimates. For each trophic level, the general equation for deriving a national

BAF is:

For the purposes of this example, a national BAF is calculated for aquatic organisms at only one

trophic level (trophic level 4). In the 2000 Human Health Methodology, EPA divided the default

fish intake rate into trophic-level 2-, 3-, and 4-specific rates. Hence, in the process of deriving

national AWQC, EPA will derive national BAFs for trophic levels 2, 3, and 4.

For chemical i, the baseline BAF at trophic level 4 was calculated to be 8.7×106 L/kg of

lipid. The freely dissolved fraction of chemical i that is estimated for all water bodies in the United

States is calculated by using Equation 4-6 and the national default values of 5×10-7 kg/L for POC

and 2.9×10-6 kg/L for DOC (Section 6.3), and the Kow of chemical i which is 1.0×105 (log Kow of

5). A value of 0.93 is calculated as shown below:

The national default fR for trophic level 4 is 0.03 (3%; see Section 6.2). Using the ffd calculated

above and the national default fR in the national BAF equation, the national BAF for trophic 

level 4 organisms is calculated to be 2.4×105 L/kg, as shown below:

National BAF for Trophic Level 4

= [(8.7×106 L/kg of lipid) • (0.03 kg of lipid per kg of tissue) + 1] • 0.93

= 2.4×105 L/kg of wet tissue
7-4

(See Equation 5-3)

This national BAF relates the total concentration of chemical in water to the total concentration

of chemical in tissue of trophic level 4 organisms.

7.2 EXAMPLE 2: CALCULATION OF A NATIONAL BAF FROM FIELDMEASURED BSAFs (METHOD 2)

This example illustrates the development of a national trophic level 4 BAF using method 2

for PCB congener 126. Calculating national BAFs using method 2 requires the use of fieldmeasured BSAFs for reference chemicals and the chemical of interest. In section 5.2.4, it is

suggested that multiple reference chemicals be used to calculate a baseline BAF with method 2

because this results in a more accurate baseline BAF prediction (see Section 5.2.4).

In this example, data from Lake Ontario are used to derive baseline BAFs from BSAFs

for chemicals like PCB 126, which cannot be readily detected in water (USEPA, 1995b; Cook and

Burkhard, 1998). To simplify this example, a baseline BAF is derived for only one trophic level 4

organism, that is, age 5–7 lake trout. 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. Previously, the PCB congeners 52, 105, and 118 have been used as the

reference chemicals for calculating baseline BAFs for PCB 126 (USEPA, 1995b; Cook and

Burkhard, 1998). These three congeners were selected 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 126 and thus their 

(Jsocw)r/(Kow)r values 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 baseline

BAFs are shown for PCBs 52 and 105.

7.2.1 Calculating a Field-Measured BSAF

The BSAF for PCB 126 is determined by relating lipid-normalized concentrations of the

chemical in 5 to 7-year-old lake trout (CR) to the average organic carbon-normalized concentration

of the chemical in surface sediment (Csoc), using equation 5-2. On the basis of data collected from

Lake Ontario, the CR of PCB 126 in age 5–7 lake trout is 12.3 ng/g of lipid, and the Csoc of PCB

126 in the sediment is 3.83 ng/g of organic carbon (actual calculations for these normalized values

are not shown here). Therefore:
7-5

(See Equation 4-6)

(See Equation 5-11)

(See Equation 5-6)

7.2.2 Determining a Sediment-Water Column Concentration Quotient (Jsocw)r

Sediment-water column concentration quotients for reference chemicals are determined

by using equation 5-6. For this calculation, the concentration of reference chemical that is freely

dissolved in water, (Cw

f d

)PCB 118, is needed. To calculate the (Cw

f d

)PCB 118 , the fraction of reference

chemical that is freely dissolved in water (ffd)r is needed. The (ffd)r is calculated by using equation

4-6. The measured DOC value is 2.0×10-6 kg/L; POC is set equal to zero (because all particulates

were removed using a filter); and the Kow for PCB 118 = 5.5×106 (log Kow of 6.7). Using Equation

4-6, the fraction of PCB 118 that is freely dissolved in water is calculated as follows:

For this example, the measured concentration of reference congener PCB 118 in filtered

Lake Ontario water is 34 pg/L. Thus, (Cw

f d

)PCB 118 = 34 pg/L × 0.53 = 18 pg/L or 1.8×10-5 μg/L . The

average (Csoc)PCB118 is 555 μg/kg of sediment organic carbon. By substituting these values into

Equation 5-6, Jsocw for the reference chemical, PCB 118, is calculated as:

7.2.3 Calculating a Baseline BAF

For each species having an acceptable field-measured (BSAF)i, a baseline BAF for the

chemical of interest may be calculated with the following equation and an appropriate value of

( Jsocw)r /(Kow)r:

By using a commonly valid assumption—that Di/r ~ 1 for PCB congeners 118 and 126;

substituting the BSAF for PCB 126 (3.2), Jsocw for PCB 118 (3.1×107), the appropriate Kow values

for PCB 126 (7.8×106 or log Kow = 6.9) and PCB 118 (5.5×106 or log Kow = 6.7), and 0.20 (20%)

fraction of lipid for lake trout into the baseline BAF equation (Equation 5-11), the baseline BAF

for PCB 126 may be calculated as:
7-6

(See Equation 5-11)

(See Equation 3-2)

The baseline BAFs using reference PCB congeners 52 and 105 are derived in the same

manner as for PCB 118. The predicted baseline BAFs that result are 3.7×108 using congener 52

and 1.6×108 using congener 105. Once all the baseline BAFs have been derived, the final baseline

BAF is derived by calculating the geometric mean of the three baseline BAFs, which in this case

is 2.0×108 L/kg.

7.2.4 Calculating a National BAF

After deriving all acceptable baseline BAFs for chemical i and selecting a final baseline

BAF for trophic level 4, the next step is to calculate a national BAF for this trophic level that will

be used to derive the AWQC. In this example, it is assumed that the baseline BAF calculated

above represents the final baseline BAF for trophic level 4. For a given trophic level, calculating a

national BAF involves adjusting the final baseline BAF to reflect conditions that are expected to

affect the bioavailability of chemical i in ambient waters of the United States. This is

accomplished through the use of national default values for fR and ffd that are based on national

central tendency estimates. For each trophic level, the general equation for deriving a national

BAF is:

For the purposes of this example, a national BAF is calculated only for aquatic organisms at one

trophic level (trophic level 4). In the 2000 Human Health Methodology, EPA divided the default

fish intake rate into trophic-level 2-, 3-, and 4-specific rates. Hence, in the process of deriving

national AWQC, EPA will derive national BAFs for trophic levels 2, 3, and 4.

For PCB 126, the baseline BAF at trophic level 4 was calculated to be 2.0×108 L/kg of

lipid. The freely dissolved fraction of PCB 126 that is estimated to be applicable to all water

bodies in the United States is calculated by using Equation 4-6 and the national default values of

5×10-7 kg/L for POC and 2.9×10-6 kg/L for DOC (Section 6.3), and the Kow of PCB 126 which is

7.8×106, or a log Kow = 6.9. A value of 0.15 is calculated as shown below:
7-7

(See Equation 4-6)

(See Equation 2-8)

The national default fR for trophic level 4 is 0.03 (3%; see Section 6.2). Using the ffd calculated

above and the national default fR in the national BAF equation, the national BAF for trophic 

level 4 organisms is calculated to be 9.0×105 L/kg, as shown below:

National BAF for Trophic Level 4

= [(2.0×108 L/kg of lipid) • (0.03 kg of lipid per kg of tissue) +1] • (0.15)

= 9.0×105 L/kg of wet tissue 

This example of a national 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. 

7.3 EXAMPLE 3: CALCULATION OF A NATIONAL BAF FOR CHEMICAL i

FROM BCF t

T × FCM (METHOD 3)

This example illustrates the calculation of a national trophic level 4 BAF using method 3

for a hydrophobic nonionic chemical (chemical i). Calculating national BAFs using method 3

requires the use of a BCFt

T (also commonly referred to as a “laboratory-measured BCFt

T“) and a

FCM. Determination of a BCFt

T requires information on the total concentration of chemical i in

fish tissue and the total concentration of chemical i in the laboratory test water.

7.3.1 Calculating a Laboratory-Measured BCFt

T

In this example, data are available from John Doe’s laboratory (a hypothetical laboratory)

on the total concentration of chemical i in fish tissue (10 μg/kg) and the laboratory test water

(3.0×103 μg/L). The laboratory-measured BCF calculated for chemical i is 3.3×103 L/kg, as shown

below:

7.3.2 Calculating a Baseline BAF

The BCFt

T is converted to a baseline BAF for a specific trophic level by incorporating sitespecific information on the fraction of the chemical that is freely dissolved in the test water (ffd),

the fraction of tissue or aquatic organism tested that is lipid (fR), and a food-chain multiplier

(FCM) for the chemical. The equation for calculating a baseline BAF from BCFt

T is:
7-8

(See Equation 5-12)

(See Equation 4-6)

(See Equation 5-12)

Determining the fraction of chemical i that is freely dissolved in the test water (ffd) requires

information on the POC and DOC in the test water and the Kow of chemical i. For this example,

the median POC concentration in the test water is 0.6 mg/L (6.0×10-7 kg/L) and the median DOC

concentration is 8.0 mg/L (8.0×10-6 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 national default POC and DOC concentrations

to derive baseline BAFs from BCFt

Ts. The Kow for chemical i is 1×104

, or a log Kow of 4.0. Based

on these data, the fraction of chemical i that is freely dissolved is 0.99, calculated as shown

below:

The fR of the fish species sampled in the laboratory in this example is 0.08 (8%). The

FCM, based on a log Kow of 4, is 1.07, as indicated in Table 4-6 (assuming a mixed benthic and

pelagic food web structure and trophic level 4 for the tested species). Using this fR and FCM with

the BCFt

T and ffd calculated above, a baseline BAF of 4.5×104

 L/kg of lipid is calculated as

follows:

For the purposes of this example, it is assumed that only one acceptable BCF study is

available for trophic level 4 organisms. Thus, the baseline BAF value for trophic level 4 is the

baseline BAF for the tested organism. If acceptable BCFt

Ts are available for additional trophic

level 4 organisms, baseline BAFs are calculated for each of the trophic level 4 species for which

there are acceptable data and then the baseline BAF for trophic level 4 is calculated as the

geometric mean of these baseline BAFs. Recall that in EPA’s BAF methodology, BAFs are

trophic-level specific. Hence, this calculation would be carried out similarly for each trophic level.

7.3.3 Calculating a National BAF

After deriving all acceptable baseline BAFs for chemical i and selecting a final baseline

BAF for trophic level 4, the next step is to calculate a national BAF for this trophic level that will

be used to derive the AWQC. In this example, it is assumed that the baseline BAF calculated

above represents the final baseline BAF for trophic level 4. For a given trophic level, calculating a
7-9

(See Equation 3-2)

(See Equation 4-6)

national BAF involves adjusting the final baseline BAF to reflect conditions that are expected to

affect the bioavailability of chemical i in ambient waters of the United States. This is

accomplished through the use of national default values for fR and ffd that are based on national

central tendency estimates. For each trophic level, the general equation for deriving a national

BAF is:

For the purposes of this example, a national BAF is calculated for aquatic organisms at

only one trophic level (trophic level 4). In the 2000 Human Health Methodology, EPA divided the

default fish intake rate into trophic-level 2-, 3-, and 4-specific rates. Hence, in the process of

deriving national AWQC, EPA will derive national BAFs for trophic levels 2, 3, and 4.

For chemical i, the baseline BAF at trophic level 4 was calculated to be 4.5×104 L/kg of

lipid. The freely dissolved fraction of chemical i that is estimated for all water bodies in the United

States is calculated by using Equation 4-6 and the national default values of 5×10-7 kg/L for POC

and 2.9×10-6 kg/L for DOC (Section 6.3), and the Kow of chemical i which is 1×104 (a log Kow of

4.0). A value of 0.99 is calculated as shown below:

The national default fR for trophic level 4 is 0.03 (3%; see Section 6.2). Using the ffd calculated

above and the national default fR in the national BAF equation, the national BAF for trophic 

level 4 organisms is calculated to be 1.3×103 L/kg, as shown below:

National BAF for Trophic Level 4

= [(4.5×104 L/kg of lipid) • (0.03 kg of lipid per kg of tissue) + 1] • (0.99)

= 1.3×103 L/kg of wet tissue

This national BAF relates the total concentration of chemical in water to the total concentration of

chemical in tissue of trophic level 4 organisms.
7-10

7.4 EXAMPLE 4: CALCULATION OF A NATIONAL BAF FOR CHEMICAL i

FROM Kow × FCM (METHOD 4)

This example illustrates the development of a national trophic level 4 BAF using 

method 4 for a hydrophobic nonionic chemical (chemical i). Calculating national BAFs using

method 4 does not require knowing the fraction of the chemical that is freely dissolved in the test

water (ffd) or the fraction of the species sampled that is lipid (fR). This is because the Kow is

assumed to be equal to the baseline BCF, as discussed in Section 5.4 and Appendix A. Method 4

requires selection of an appropriate Kow for the chemical and that the Kow be multiplied by an

appropriate FCM to account for biomagnification.

7.4.1 Selecting a Kow and FCM

The procedures that EPA will follow in selecting chemical Kows are described in detail in

Appendix B. For the purposes of this example, a Kow value of 1×104 (log Kow= 4.0) has been

selected for chemical i. The FCM, based on a log Kow of 4, is 1.07, as indicated in Table 4-6

(assuming a mixed benthic and pelagic food web structure and trophic level 4 for the tested

species).

7.4.2 Calculating a Baseline BAF

Method 4 does not require adjusting a field- or laboratory-derived bioaccumulation factor

with ffd or fR. The calculation of a baseline BAF, using the selected Kow and FCM, is 

straightforward, as shown below:

Baseline BAF = Kow × FCM (See Equation 5-13)

= (1×104) × 1.07

= 1.1×104 L/kg of lipid

For this example, only one Kow is provided. As discussed in Appendix B, it is possible

that several Kow values may be located. The data quality considerations provided in Appendix B

will be used for judging the quality of various Kow values, and the procedures outlined in

Appendix B will be used for selecting among or combining Kow values of acceptable quality.

Recall that in EPA’s BAF methodology, BAFs are trophic-level specific. Hence, this calculation

would be carried out similarly, with appropriate FCMs, for each trophic level.

7.4.3 Calculating a National BAF

After deriving all acceptable baseline BAFs for chemical i and selecting a final baseline

BAF for trophic level 4, the next step is to calculate a national BAF for this trophic level that will

be used to derive the AWQC. In this example, it is assumed that the baseline BAF calculated

above represents the final baseline BAF for trophic level 4. For a given trophic level, calculating a

national BAF involves adjusting the final baseline BAF to reflect conditions that are expected to

affect the bioavailability of chemical i in ambient waters of the United States. This is

accomplished through the use of national default values for fR and ffd that are based on national
7-11

(See Equation 3-2)

(See Equation 4-4)

central tendency estimates. For each trophic level, the general equation for deriving a national

BAF is:

For the purposes of this example, a national BAF is calculated for aquatic organisms at

only one trophic level (trophic level 4). In the 2000 Human Health Methodology, EPA divided the

default fish intake rate into trophic-level 2-, 3-, and 4-specific rates. Hence, in the process of

deriving national AWQC, EPA will derive national BAFs for trophic levels 2, 3, and 4.

For chemical i, the baseline BAF at trophic level 4 was calculated to be 1.1×104 L/kg of

lipid. The freely dissolved fraction of chemical i that is estimated to be applicable to all water

bodies in the United States is calculated by using Equation 4-6 and the national default values of

5×10-7 kg/L for POC and 2.9×10-6 kg/L for DOC (Section 6.3), and the Kow of chemical i which is

1×104 (log Kow = 4.0). A value of 0.99 is calculated as shown below: 

The national default fR for trophic level 4 is 0.03 (3%; see Section 6.2). Using the ffd calculated

above and the national default fR in the national BAF equation, the national BAF for trophic 

level 4 organisms is calculated to be 1,344 L/kg, as shown below: 

National BAF for Trophic Level 4

= [(1.1×104 L/kg of lipid) • (0.03 kg of lipid per kg of tissue) + 1] • (0.99)

= 3.3×104 L/kg of wet tissue

This national BAF relates the total concentration of chemical in water to the total concentration

of chemical in tissue of trophic level 4 organisms.
8-1

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lipid on the toxicity and toxicokinetics of pentachlorophenol in rainbow trout (Oncorhynchus

mykiss). Aquat Toxicol 20:235-252.

van Wezel AP, Opperhuizen A. 1995. Narcosis due to environmental pollutants in aquatic

organisms: Residue-based toxicity, mechanisms, and membrane burdens. Crit Rev Toxicol

25:255-279.

Vander Zanden MJ, Rasmussen JB. 1996. A trophic position model of pelagic food webs: 

Impact on contaminant bioaccumulation in lake trout. Ecol Monogr 66:451-477.
8-12

Veith GD, Austin NM, Morris RT. 1979a. A rapid method for estimating log P for organic

chemicals. Water Res 13:43-47.

Veith GD, DeFoe DFL, Bergstedt BV. 1979b. Measuring and estimating the bioconcentration

factor in fish. J Fish Res Board Can 36:1040-1045.

Verhaar HJM, de Jongh J, Hermans JLM. 1999. Modeling the bioconcentration of organic

compounds by fish: A novel approach. Environ Sci Technol 33:4069-4072.

Wu S-C, Gschwend PM. 1986. Sorption kinetics of hydrophobic organic compounds to natural

sediments and soils. Environ Sci Technol 20:717-725.

Yin C, Hassett JP. 1986. Gas-partitioning approach for laboratory and field studies of Mirex

fugacity in water. Environ Sci Technol 20:1213-1217.

Yin C, Hassett JP. 1989. Fugacity and phase distribution of Mirex in Oswego River and Lake

Ontario waters. Chemosphere 19:1289-1296.
A-1

APPENDIX A

DERIVATION OF THE BASIC BIOCONCENTRATION AND BIOACCUMULATION

EQUATIONS FOR ORGANIC CHEMICALS
A-2

1. DERIVATION OF THE BASIC BIOCONCENTRATION AND

BIOACCUMULATION EQUATIONS FOR ORGANIC CHEMICALS

This appendix provides a detailed presentation of the derivation of the basic

bioconcentration and bioaccumulation equations for organic chemicals that are the basis for the

methods for deriving BCFs and BAFs in EPA’s BAF methodology. The equations are based on

widely accepted and peer-reviewed scientific principles and theories, as referenced in this

appendix. This appendix was developed to provide additional background for the TSD. 

Therefore, some additional notations (e.g., subscripts and superscripts) have been added to the

equation terms to provide clarity to the discussion of the equation derivations.

A.1.1 Bioconcentration

The basic BCF applicable to all classes of chemicals is defined as:

(Equation A-1)

where:

BCFt

T = total bioconcentration factor (i.e., a BCF that is based on the total

concentrations of the chemical in the water and in the aquatic biota)

C

t

B = total concentration of the chemical in the aquatic biota, based on the wet

weight of the aquatic biota

C

t

w = total concentration of the chemical in the water around the aquatic biota

As more bioconcentration information was generated and reviewed by scientists, it was

realized that extrapolation of BCFs for organic chemicals from one species to another would be

more accurate if the BCFs were normalized on the basis of the amount of lipid in the aquatic

biota exposed in the original bioconcentration test, because many nonpolar organic chemicals are

hydrophobic, accumulating in direct proportion to the amount of lipid in a given aquatic

organism (Mackay, 1982; Connolly and Pederson, 1988; Thomann, 1989). It was also realized

that extrapolation of BCFs for organic chemicals from one water to another would be more

accurate if the BCFs were calculated on the basis of the freely dissolved concentration of the

organic chemical in the water around the aquatic biota. Thus, two additional BCFs were defined

and used:
A-3

(Equation A-2)

(Equation A-3)

where:

BCF = lipid-normalized total BCF (i.e., normalized to 100% lipid and based on the

total concentration of the chemical in the water around the biota)

CR = lipid-normalized concentration of the chemical in the aquatic biota

BCFf

R

d = lipid-normalized and freely dissolved-based BCF

C

f

w

d

 = freely dissolved concentration of chemical in the water around the aquatic

biota

The experimental definition of CR is:

CR =

= (Equation A-4)

where:

B = wet weight of the aquatic biota

L = weight of the lipid in the aquatic biota

fR = fraction of the aquatic biota that is lipid = L/B

Using Equation 4 to substitute for CR in Equation 2 and then using Equation 1:

(Equation A-5)
A-4

(Equation A-7)

If ffd = the fraction of the chemical in the water around the aquatic biota that is freely dissolved,

then:

(Equation A-6)

Using Equations 4 and 6 to substitute for CR and Cf

w

d

 in Equation 3 and then using Equation 1:

Equations 1, 5, and 7 show the relationships among the three different BCFs.

Theoretical justification for use of both lipid normalization and the freely dissolved

concentration of the organic chemical in the ambient water is based on the concept of equilibrium

partitioning, whereas practical justification is provided by the general similarity of the value of

BCFf

R

d

 for an organic chemical across both species and waters. This concept of equilibrium

partitioning is discussed further in the following section. It will be demonstrated in Section A.2,

however, that a more complete application of equilibrium partition theory shows that BCFf

R

d

 

extrapolates well only for chemicals whose Kows are greater than 1,000, whereas a different BCF

(BCFf

L

d

) extrapolates well for organic chemicals whose Kows are greater than 1,000 as well as for

chemicals whose Kows are less than 1,000.

A.2 PARTITION THEORY AND BIOCONCENTRATION

Equilibrium partition theory provides the understanding necessary to ensure proper use of

Kows, BCFs, and BAFs in the derivation of water quality criteria for organic chemicals. For the

purpose of applying partition theory, aquatic biota can be modeled as consisting of water, lipid,

and nonlipid organic matter (Barber et al., 1991). In this model, an organic chemical in aquatic

biota partitions into three phases:

1. The chemical that is freely dissolved in the water that is in the biota.

2. The chemical that is partitioned to the lipid that is in the biota.

3. The chemical that is partitioned to nonlipid organic matter in the biota. 

The total concentration of chemical in the water inside the biota includes chemical that is

partitioned to lipid and nonlipid organic matter in the water.
A-5

According to this model:

(Equation A-8)

where:

fw = fraction of the aquatic biota that is water

Cw

f d

B = freely dissolved concentration of the organic chemical in the water in the

aquatic biota

fR = fraction of the aquatic biota that is lipid

CL = concentration of the organic chemical in the lipid

fN = fraction of the aquatic biota that is nonlipid organic matter

CN = concentration of the organic chemical in the nonlipid organic matter in the

aquatic biota

The most important partitioning of the organic chemical within the aquatic biota is between the

lipid and the water, which is described by the following equation:

where:

(Equation A-9)

KLW = the lipid-water partition coefficient

“KLW” (Gobas, 1993) is used herein because it is more descriptive than “KL” which is

used by DiToro et al. (1991). This partition coefficient is central to the equilibrium partition

approach that is used to derive sediment quality criteria (DiToro et al., 1991), the food chain

multipliers based on the Gobas model, and the equations given here that are used to derive BCFs

and BAFs for the national BAF methodology.

In order for Equations 8 and 9 to be correct, partition theory requires that the

concentration of the organic chemical in the lipid, CL, be defined as:
A-6

(Equation A-11)

(Equation A-12)

(Equation A-13)

(Equation A-14)

It is difficult to determine CL experimentally because it is not easy to measure only the

chemical that is partitioned to the lipid (i.e., it is not easy to separate the three different

compartments within aquatic biota that the chemical partitions into according to the model). 

Because all of the organic chemical in the biota is measured when CR is determined, CR can be

determined easily, and CR is higher than CL.

It is useful to define another BCF as:

(Equation A-10)

because CL is lower than CR, BCFf

L

d

 < BCFf

R

d

.

The only difference between KLW and BCFf

L

d

 is that the denominator in KLW is Cf

w

d

B ,

whereas the denominator in BCFf

L

d

 is C

f

w

d

B. When partition theory applies, however, all phases

are in equilibrium, and so:

Therefore, when the organic chemical is not metabolized by the aquatic biota and when growth

dilution is negligible:

In laboratory experiments it has been shown that the chemical octanol is a useful surrogate for

lipid, thus a reasonable approximation is that:

where:

Kow = the n-octanol-water partition coefficient.

Thus:
A-7

(Equation A-15)

(Equation A-16)

By using Equations 9 and 11 to substitute for CL and Cf

w

d

B in Equation 8:

By using Equation 6 to substitute for Cf

w

d

 in Equation 15:

Dividing by Ct

w gives:

(Equation A-17)

Using Equation 1 and rearranging gives:

(Equation A-18)

Using Equation 6:

(Equation A-19)

Substituting x = fw + (fN)(CN/Cf

w

d

 ) and rearranging gives:

(Equation A-20)

The term “(fR)(BCFf

L

d

)” accounts for the amount of organic chemical that is partitioned to the lipid

in the biota, whereas in “x,” the term “fw” accounts for the amount of organic chemical that is

freely dissolved in the water in the biota and the term “(fN)(CN/C f

W

d

)” accounts for the amount of

organic chemical that is partitioned to nonlipid organic matter in the biota. The relative

magnitudes of these three terms depend on the following:
A-8

C Because of bones and other inorganic matter that make up the total mass of an

organism, the sum of fw + fR + fN must be less than 1.

C fw is usually about 0.7 to 0.9.

C fR must be measured in the organism in question if the BAF or BCF is to be useful,

or estimated from other similar biota; it is usually between 0.03 and 0.15.

C The term “(CN/Cf

w

d

)” is similar to BCFf

L

d

 (see Equation 10) and is therefore

probably related to Kow (see Equation 14), although the affinity of the chemical for

nonlipid organic matter is probably much less than its affinity for lipid.

Although such considerations aid in understanding “x” in Equation 20, the magnitude of

“x” is important only for chemicals whose log Kows are in the range of 1 to 3. For organic

chemicals whose log Kows are about 1, ffd is about 1. In addition, such chemicals distribute

themselves so as to have similar concentrations in water and in the different organic phases in the

aquatic biota, which means that BCFt

T will be approximately 1 if both metabolism and growth

dilution are negligible. An organic chemical whose log Kow is less than 1 will also have a BCFt

T on

the order of 1 because water is the predominant component in aquatic biota. Setting “x” equal to

1 is about right in the range of log Kows in which it is not negligible (see also McCarty et al.,

1992).

Substituting x = 1 into Equation 20:

(Equation A-21)

Rearranging gives:

(Equation A-22)

Because BCFf

L

d

 is normalized for both the aquatic biota lipid content and freely dissolved fraction

of the chemical, it is called the “baseline BCF.” The baseline BCF is the most useful BCF for

extrapolating from one species to another and from one water to another for organic chemicals

with both high and low Kows. The baseline BCF is intended to reference bioconcentration of

organic chemicals to partitioning between lipid and water.

Equations 12, 13, and 22 demonstrate that both Kow and
A-9

are useful approximations of the baseline BCFs. It will probably be possible to improve both

approximations within a few years, but such improvements might not affect the BCFs

substantially and probably will not require changes in the rest of the equations or the

terminology.

When BCFt

T is greater than 1,000, the “-1" in Equation 22 is negligible, and so this

equation becomes equivalent to Equation 7 (i.e., when BCFt

T is large it generally indicates that the

chemical readily partitions to and accumulates in the lipid portion of aquatic biota, and thus the

BCFf

R

d

 is a useful approximation of the baseline BCF).

A.3 DERIVATION OF THE BASIC BAF AND BASELINE BAF EQUATIONS

As has been previously mentioned, bioaccumulation represents uptake and retention of a

chemical from all routes of exposure, including water only (i.e., bioconcentration) and the food

chain, therefore by analogy and substituting BAF for BCF in Equations 21 and 22:

(Equation A-23)

(Equation A-24)

As with the BCF, the BAFf

L

d

 can be called the “baseline BAF” because it normalizes the factor to

the lipid content of the aquatic biota and the freely dissolved fraction of the chemical in water. It

too is the most useful BAF for extrapolating from one species to another and from one water to

another for chemicals with both high and low Kows.

A.4 CALCULATION OF CRITERIA

Baseline BCFs and BAFs can be extrapolated between species and waters, but they

cannot be used directly in the calculation of criteria that are based on the total concentration of

the chemical in the water. The BCFs and BAFs that are needed to calculate such criteria can be

calculated from measured and predicted baseline BCFs and BAFs using the following equations,

which are derived from Equations 21 and 23:

(Equation A-25)
A-10

(Equation A-26)

A.5 DERIVATION OF THE BASIC FCM AND BMF EQUATIONS

Food chain multipliers (FCM) are used in BAF methods 3 and 4 (see Sections 5.3 and 5.4)

for estimating BAFs for chemicals that biomagnify up the food chain. The FCM can be defined

as:

(Equation A-27)

Some of the consequences of Equation 27 are:

1. Substituting Equations 22 and 24 into Equation 27:

(Equation A-28)

Therefore, BAFt

T = (FCM) (BCFt

T) only when ffd is much less than BAFt

T and BCFt

T.

2. When FCM = 1 (as for trophic level 2 in the Gobas model):

(Equation A-29)

3. Predicted Baseline BAFs can be obtained using FCMs and the following rearrangement of

Equation 27:

predicted Baseline BAF = (FCM)(Baseline BCF) (Equation A-30)

a. Using a laboratory-measured BCF in Equation 22:

predicted Baseline BAF = (FCM)(measured Baseline BCF) (Equation A-31)

(Equation A-32)
A-11

b. Using a predicted BCF in Equation 14:

predicted Baseline BAF = (FCM)(predicted BCFf

L

d

) (Equation A-33)

= (FCM)(Kow) (Equation A-34)

The FCMs used to calculate predicted baseline BAFs must be appropriate for the trophic

level of the aquatic biota to which the predicted baseline BAF is intended to apply.

Although BAFs can be related to BCFs using FCMs, BAFs and BCFs can also be related

using biomagnification factors (BMFs). The two systems are entirely compatible, but confusion

can result if the terms are not used consistently and clearly. Because both FCMs and BMFs are

used in the Guidance document and elsewhere, it is appropriate to explain the relation between

the two here. The basic difference is that FCMs always relate back to trophic level one (TL1),

whereas BMFs always relate back to the next trophic level. In the FCM system:

BAFTL1 = BCF

BAFTL2 = (FCMTL2)(BAFTL1)

BAFTL3 = (FCMTL3)(BAFTL1)

BAFTL4 = (FCMTL4)(BAFTL1)

In the BMF system:

BAFTL1 = BCF

BAFTL2 = (BMFTL2)(BAFTL1)

BAFTL3 = (BMFTL3)(BAFTL2)

BAFTL4 = (BMFTL4)(BAFTL3)

Therefore:

BMFTL2 = FCMTL2

BMFTL3 = (FCMTL3)/(FCMTL2)

BMFTL4 = (FCMTL4)/(FCMTL3)

Both metabolism and growth dilution can cause BMFs to be less than 1.
A-12

A.6 REFERENCES

Barber MG, Suarez LA, Lassiter RR. 1991. Modeling bioaccumulation of organic pollutants in

fish with an application to PCBs in Lake Ontario salmonids. Can J Fish Aquat Sci 48:318-337.

Connolly J, Pedersen C. 1988. A thermodynamic-based evaluation of organic chemical

accumulation in aquatic organisms. Environ Sci Technol 22:99-103.

DiToro DM, Zarba CS, Hansen DJ, Berry WJ, Swartz RC, Cowan CE, Pavlou SP, Allen HE,

Thomas NA, Paquin PR. 1991. Technical basis for establishing sediment quality criteria for

nonionic organic chemicals using equilibrium partitioning. Environ Toxicol Chem 10:1541-1583.

Gobas, FAPC. 1993. A model for predicting the bioaccumulation of hydrophobic organic

chemicals in aquatic food-webs: Application to Lake Ontario. Ecol Mod 69:1-17.

Mackay D. 1982. Correlation of bioconcentration factors. Environ Sci Technol 16:274-278.

McCarty LS, Mackay D, Smith AD, Ozburn GW, Dixon DG. 1992. Residue-based

interpretation of toxicity and bioconcentration QSARs from aquatic bioassays: neutral narcotic

organics. Environ Toxicol Chem 11:917-930.

Thomann RV. 1989. Bioaccumulation model of organic chemical distribution in aquatic food

chains. Environ Sci Technol 23:699-707.
B-1

APPENDIX B

PROTOCOL FOR DETERMINING 

OCTANOL-WATER PARTITION COEFFICIENTS (Kow)

FOR COMPOUNDS WITH LOG KOW VALUES > 5
B-2

PROTOCOL FOR DETERMINING 

OCTANOL-WATER PARTITION COEFFICIENTS (Kow)

FOR COMPOUNDS WITH LOG KOW VALUES > 5

1. INTRODUCTION

The octanol-water partition coefficient (Kow) is one of the most widely used chemical

parameters. The Kow of a chemical has been found to be representative of a chemical’s propensity

to partition into biotic and abiotic components of the environment as well as a chemical’s

propensity to accumulate in living organisms. Because of these associations, the Kow is widely

used to predict a chemical’s behavior in the environment and to evaluate a chemical’s impact on

human health. 

The octanol-water partition coefficient (Kow) is a unitless measure and is defined as the

ratio of the equilibrium concentrations, C, of a chemical in the two phases of a system consisting

of n -octanol and water at standard temperature and pressure (STP, 25o C, 1 atm) 

Kow = Coct/Cw

where Coct represents the concentration in the n -octanol phase, and Cw represents the

concentration in the water. The concentrations in the respective phases are expressed in the same

volume-referenced units (i.e., mg/mL, mole/L, etc.), therefore, the Kow is a unitless property.

Since the value of the partition coefficient spans orders of magnitude, it is frequently expressed

on a log scale (base ten) such that a given chemical has a log Kow value which may range from 1

to >8. This parameter is also called the log P value.

Some specific applications of the Kow within the U.S. EPA include: evaluation of a

chemical’s potential to bioaccumulate in aquatic life, wildlife and humans; modeling the fate,

transport and distribution of a chemical in the environment; prediction of the distribution of a

contaminant in a living organism; classification of persistent bioaccumulators for regulatory

actions; derivation of soil screening levels; calculation of water quality benchmarks; and

derivation of Sediment Quality Advisory Levels. 

Although a seemingly simple experimental determination, Kow measurement is beset with

difficulties. The appropriateness and accuracy of laboratory methods to directly measure a Kow

are influenced by a number of factors which include the magnitude of the value itself. For

chemicals with log Kow values at or exceeding 5, common sources of error include: (1) failure to

achieve equilibrium; (2) incomplete phase separation or interphase mixing during sampling;

(3) emulsion effects derived from ?excessive” mixing or induced by contaminants; (4) propensity

of the chemical to self-associate, tautomerize or form hydrates; and (5) the presence of small

quantities of contaminants with a lower Kow value. These errors tend not to be random, but to

give measured numbers lower than the true value, frequently by an order of magnitude or more.

The likelihood and degree of error increases with increasing Kow and also seems to be more

prevalent for certain classes of chemicals (such as halogenated compounds or phthalate esters).

As a result, in addition to direct experimental measurement methods, techniques to indirectly

experimentally measure or estimate Kow values have been developed. 
3

SPARC (SPARC Performs Automated Reasoning in Chemistry) is a mechanistic model developed at the

Ecosystems Research Division of the National Exposure Research Laboratory of the Office of Research and

Development of the U.S. Environmental Protection Agency by Sam Karickhoff, Lionel Carreira, and co-workers. A

prototype version was used for which no performance data for KOW estimation is available. The model complements

the aforementioned models because development, training, and testing were done away from KOW data. (See Hilal,

Carreira, and Karickhoff, 1994, for model description.) 

B-3

1.1 Experimental Measurement Techniques

• Direct experimental measurement techniques include the shake-flask approach,

generator column, and slow-stir methods. 

• The shake-flask method is the classical approach and fairly straight-forward for

chemicals with log Kow values below 5. For chemicals with higher log Kow values,

the shake-flask approach requires large volumes of water and formation of

emulsions becomes a significant impediment to accurate measurements. 

• The generator-column approach was developed to measure the partition

coefficients of more hydrophobic chemicals (those with larger log Kow values). This

is a laborious method which results in more reliable data than the shake-flask

approach for chemicals with higher log Kow values, but some discontinuities in the

data for higher-chlorinated PCB congeners have been observed. 

• A third direct measurement technique is the slow-stir method. In this method,

careful stirring and close temperature control can prevent or limit the formation of

emulsions and reliable very high partition coefficients can be obtained relatively

easily. 

1.2 Indirect Experimental Measurement Techniques

An indirect experimental measurement technique is to incorporate a radioactive label into

the chemical and use a radiotracer assay to evaluate the compound’s distribution between the

octanol and water phases. This approach can be used when you have small amounts of the

compound. However, radiotracer assays do not directly measure the compound, and low Kow

values frequently result from the presence of impurities or instability of the compound.

1.3 Computer-based Estimation Techniques

Because of the difficulty of directly and accurately measuring Kow values, various

computer-based estimation methods exist. These can be divided into two types, those based

upon fundamental chemical thermodynamics, and those requiring a training set of chemicals with

measured Kows.

1.3.1 Technique based on fundamental thermodynamics

Computer methods based on fundamental chemical structure theory and are not limited

by nor do they require a training set of chemicals with measured Kows. For example, the SPARC3

model consists of a set of core models describing intra- and inter-molecular interactions. These
B-4

models are linked by appropriate thermodynamic relationships to provide estimates of reactivity

parameters under desired conditions (e.g., temperature, pressure, solvent). 

1.3.2 Techniques using a training set of chemicals

Methods requiring a training set of chemicals use Quantitative Property-Property

Relationships (QPPRs) or Quantitative Structure Activity Relationships (QSARs) to derive Kows.

In QPPRs, Kow values are correlated with the values for other chemical parameters--either

measured or calculated--using data available from a training set of chemicals. In QSARs, Kow

values are derived from fragment constants obtained from a training set of chemicals.

One application of QPPRs is estimating Kows indirectly from other experimental

measurements. In this approach, the Kow is correlated with another measured property. These

techniques include the use of reversed-phase high performance liquid chromatography (HPLC)

and reversed-phase thin-layer chromatography (TLC). In applying these approaches, Kows are

estimated from linear equations relating retention times on the reversed-phase column to the Kow

values. The equations are developed based on a set of reference chemicals for which Kow values

are well established. These are relatively efficient methods because they do not require

quantification of concentrations, but the linear equations can not be extrapolated beyond the Kow

range represented by the reference chemicals from which the equation was derived. In

application, values for the reference chemicals are usually shake-flask values obtained from the

literature, resulting in unreliable Kow estimates for chemicals with higher log Kow values. 

In addition to direct and indirect measurement methods, QPPRs are also used to establish

correlations between the Kow and calculated properties. For example, Hawker and Connell (1988)

developed a correlative relationship between log Kow and molecular surface area using

approximately two dozen PCBs. They then estimated log Kows for the remaining PCBs by

inputting the molecular surface area of each PCB. This technique is limited to estimating Kows for

chemicals which are similar to the chemicals used in developing the relationship. 

In QSARs, hydrophobic fragment values are derived from a large database of measured

Kows. These fragments constants are used to estimate Kow in two ways: (1) One approach is to

estimate the Kow by adding up the values for all the fragments composing the chemical, either by

atom or by functional group. (2) The other approach is to start with a measured Kow value for a

structurally similar compound and add or subtract the fragment constants for functional groups

or atoms to estimate the Kow for the specific compound. In both these cases, the calculated Kow

value must also be corrected for proximity effects between structurally close substituent groups,

and the Kow value derived is only as good as the data associated with the training set of chemicals.

This method is also limited to predicting Kows for chemicals with structures similar to those within
4 CLOGP is a molecular fragment-based model developed at Pomona College by Albert Leo, Corwin Hansch, and coworkers. This model has undergone extensive development and exhaustive testing; version 3.1 was used in this

exercise. (See Hansch and Leo, 1995, for model description and performance data.)

5 LOGKOW is essentially an expanded CLOGP with more recent training data and additional fragment constants. The

developers were Philip Howard, William Meylan and co-workers at Syracuse Research Corporation; Version 1.51

was used in this exercise. (See Meylan and Howard, 1994, for model details and performance information.)

B-5

the training set. Computer-based models exist which apply QSAR approaches to estimate Kows.

CLOGP4 and LOGKOW5 databases are both applications of this approach. 

1.4 Recommendations

Given the numerous techniques available to determine the Kow and its numerous and

important applications across the Agency, the U.S. EPA has formed an Agency Kow Work Group

to determine recommended Kow values for chemicals of concern to various EPA programs. In

determining these recommended Kow values, the preferable option would be to recommend actual

measured values. For chemicals with log Kow values below 5, the classical shake-flask approach is

adequate to obtain these measurements. However, there is a serious shortage of reliable measured

data for compounds with higher log Kow values (Log Kow > 5) and these chemicals frequently

exhibit a propensity to accumulate in living tissues or bind to soils and sediments. For these

reasons, this protocol has been restricted to chemicals with log Kow values equal to or

exceeding 5. 

2. PROTOCOL FOR DETERMINING RECOMMENDED KOW VALUES

Measured values are preferable to estimated values for determining recommended Kow

values. However, the absence or scarcity of reliable data necessitates the use of estimation

methods in evaluating data and in assigning Kows. Kow estimates used in this exercise include:

calculation methods (e.g., CLOGP, LOGKOW, SPARC, and fragment additions or subtractions)

and QPPRs (e.g., HPLC and TLC methods). All of these approaches except SPARC, an

estimation method based on fundamental chemical structure theory, require measured Kow values

for a training set of chemicals. 

Assigning a Kow from these data will necessarily involve scientific judgement in evaluating

not only the reliability of all data inputs but also the accretion/concretion of evidence in support

of the recommended Kow value. Supporting rationale will be provided for each recommended

value.

2.1 Operational Guidelines 

• “High quality” measured value are preferred over estimates. For chemicals with

log Kow > 5, it is highly unlikely to find multiple “high quality” measurements.

(Note: “high quality” is data judged to be reliable based on the guidelines
B-6

presented in Appendix I). Due to the paucity of “high quality” data, assigning

Kow’s from estimation techniques may be necessary.

• Kow measurements by slow stir are extendable to 108. Shake flask Kow

measurements are extendable to 106 with sufficient attention to micro emulsion

effects; for classes of chemicals that are not highly sensitive to emulsion effects

(i.e., polycyclic nuclear aromatic hydrocarbons) this range may extend to 106.5.

• What is considered reasonable agreement in log Kow data (measured or estimated)

depends primarily on the magnitude of the log Kow value. Therefore, the following

ranges of acceptable variation have been established for this exercise: 0.5 for log

Kow > 7; 0.4 for 6 # log Kow # 7 ; 0.3 for log Kow < 6. 

• Statistical methods should be applied to data as appropriate. However, it is

recognized that application is limited by the paucity of data and the

determinate/methodic nature of most measurement error(s).

2.2 Tiered Procedure for Selecting KOW Values

I. Assemble/evaluate experimental and calculated data (e.g.,CLOGP, LOGKOW,

SPARC).

II. If calculated log Kow’s > 8:

A. Develop independent estimates

1. Liquid Chromatography (LC) methods with “appropriate”

standards. (See Appendix I for guidelines for LC application.)

2. Structure Activity Relationship (SAR) estimates extrapolated from

similar chemicals where “high quality” measurements are available.

3. Property Reactivity Correlation (PRC) estimates based on other

measured properties (solubility, etc.)

B. If calculated data are in reasonable agreement (as defined in section 2.1)

and are supported by independent estimates described above, report the

average calculated value. 

C. If calculated/estimated data do not agree, use professional judgement to

evaluate/blend/weight calculated and estimated data to assign the Kow

value.

D. Document rationale including relevant statistics.
B-7

III. If calculated log Kow’s in range 6 - 8:

A. Look for “high quality” measurements. These will generally be slow stir

measurements, the exception being certain classes of compounds where

micro emulsions tend to be less of a problem (i.e., polycyclic nuclear

aromatic hydrocarbons; for these compounds, shake flask measurements

are good to log Kow of 6.5).

B. If measured data are available with reasonable agreement (both

measurements and calculations), report average measured value.

C. If measured data are in reasonable agreement, but differ from calculated

values, develop independent estimates and apply professional judgement

to evaluate/blend/weight the measured, calculated and estimated data to

assign the Kow value.

D. If measured data are not in reasonable agreement (or if only one

measurement is available), use II A, B, and C to produce a “best estimate”;

use this value to evaluate/screen the measured Kow data. Report the average

value of the screened data. If no measurements agree with the “best

estimate,” apply professional judgement to evaluate/blend/weight the

measured, calculated and estimated data to assign the Kow value. 

E. If measured data are unavailable, proceed through II A, B, C and report the

“best estimate”.

F. Document rationale including relevant statistics.

IV. If calculated log Kow’s < 6:

A. Proceed as in III. Slow stir is the preferred method but shake flask data can

be considered for all chemicals if sufficient attention has been given to

emulsion problems in the measurement.

3. REFERENCES

Hansch, C. and A. Leo, Exploring QSAR, American Chemical Society, 1995.

Hawker, D. W. and D.W. Connell. Environ. Sci. Technol.. 1988, 22, 382-387.

Hilal, S. H., L. A. Carreira and S. W. Karickhoff, Quantitative Treatments of Solute/Solvent

Interactions, Theoretical and Computational Chemistry, Vol. 1, 291-353, 1994 Elsevier Science.

Meylan, W. M. and P. H. Howard, J. Pharm. Sci. 1995, 84, 83-92.
B-8

ATTACHMENT I: GUIDELINES FOR EVALUATING MEASURED AND ESTIMATED

KOW VALUES

1. ASSESSMENT OF MEASURED KOW VALUES

1.1 Molecular Speciation. In order to interpret measured data, it is necessary to understand

the molecular species present in both the octanol and water phases including ionization,

self-association, tautomerization, and hydrate formation. For these reasons, it is difficult

to conduct or interpret such measurements for mixtures of unknown composition or for

single molecules of unknown structure. Solutes composed of more than one molecular

species may also show substantial temperature dependence of Kow reflecting relative

change in speciation in the octanol and water phases.

1.1.1 Ionization. This protocol is directed primarily towards assigning a log Kow value

for neutral (non-ionizable) organic compounds. In the case of weakly acidic or

basic compounds a portion of the molecules may be ionized at environmental pH,

and partitioning into biota or abiota will be correspondingly reduced. For weakly

ionizable molecules, shake flask measurements are conducted in solutions of a

stable, non-extractable buffer to suppress ionization. Measurements for weakly

ionizable molecules can also be performed using a potentiometric titration method

(Avdeef, 1992; 1993, Slater et al. 1994).

1.1.2 Self-Association - This protocol is directed primarily towards neutral (nonionizable) organic compounds where self-association is generally not expected to

be of concern. In some situations, self association can arise for very high log KOW

solutes because rather high concentrations of the solute in very small amounts of

octanol will be required for the delivery of sufficient solute to the water phase for a

successful measurement. For these types of molecules, it will be very difficult, if

not impossible, to be sure that no self-association occurs in the octanol phase. Self

association can also arise for molecules which have H-bonding donor and

acceptor groups that could participate in such self-association at high

concentration in the octanol phase. For the latter group of molecules, e.g., amines,

carboxylic acids, and phenols, especially if cyclic dimers can form, measurements

need to be conducted at a sufficiently low concentration so that Kow reflects only

the unassociated form of the molecule in both water and octanol phases. In either

of the cases of self-association, it is recommended that measurements be

performed using several solute concentrations in the octanol and water phases. No

change in Kow with differing solute concentrations provides an indication that

measurements have been performed using the unassociated form of the molecule.

If the KOW decreases with decreasing solute concentration in octanol, extrapolation

to infinite dilution is suggested. 

1.1.3 Tautomerization - This protocol is directed primarily towards neutral (nonionizable) organic compounds where tautomerization is generally not expected to

be of concern. The most common tautomerism (keto-enol tautomerism) involves
B-9

structures with a -OH attached to a doubly-bonded carbon (enol) which rapidly

convert to the keto structure where the -OH becomes -C=O group and the

hydrogen attaches to the other carbon of previously existing doubly-bonded

carbon. If the molecule is likely to exist in more than one tautomeric forms, the

ratio of tautomers is often quite different in the octanol and water phases. The

measured value for a tautomeric chemical is meaningful. However, this value will

in most cases lie somewhere among the values for the individual tautomeric

forms; in essence, an average value for the ratio of tautomers is measured. The

individual values for the tautomeric forms most often will have to be calculated

because measurements can not be performed for the individual tautomeric forms

since individual tautomeric forms rapidly requilibrate to the tautomeric mixture.

Sometimes molecules exhibit both ionization and tautomerization, leading to

further complications. 

1.1.4 Hydrate Formation - Similar to the case of tautomerization, hydrates may exist to

different degrees in the water and octanol phases thus confounding the

interpretation of the measured value.

1.1.5 Photodegradation - If the compound is expected to be light-sensitive and subject

to photodegradation, care should be taken to protect the substance from light

during the experiment.

1.2 Shake Flask or Slow-Stirring Considerations. (1) Water and octanol phases should be free

of impurities; (2) mixing should be of sufficient duration (e.g., 7 days for dioctyl

phthalate) to reach steady state equilibrium, particularly for very hydrophobic chemicals;

(3) when using volatile solutes, it is particularly important that both phases are analytically

measured; (4) avoid formation of emulsions during mixing and centrifuge before

measuring; (5) experimental protocol should be particularly scrutinized for Kow

measurements 4-6; (6) the ratio of octanol to water should be reduced for high Kow

chemicals; and (7) sorption to glass (e.g., for pyrethroids) during workup can be a

problem.

1.3 General Considerations. Solute should be stable to hydrolysis during the course of the

experiment. If stability can not be ensured, a calculated value may be used. Solutes

should be of high purity as the presence of a less lipophilic impurity exerts a dominant

effect in the measured Kow value. Mixtures such as chlorinated paraffins (containing

thousands of isomers, congeners, and degrees of chlorination) therefore cannot be

determined except by chromatographic methods.

1.4 Indicators of Potential Concern. Inconsistency with other measured values, with

estimated value, or inconsistency among estimated values. The importance of

professional judgement and knowledge of chemistry cannot be overemphasized in

making the best Kow assignments. For example, inconsistency between measured and

predicted may reflect only problems in the training set used based upon poor

experimental values when better data have since become available.
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1.5 References. Listed below are references for the shake flask and slow stir methods for

determining Kow used by various governmental agencies and specified in EPA testing

protocols. Selected references for measurement of ionizable compounds using

potentiometric titration are also included.

OECD. 1994. OECD Guidelines for Testing of Chemicals, 2nd Ed. Method: 107. Partition

Coefficient (n-octanol/water)(Flask-shaking Method (30 May 89). 

US-Environmental Protection Agency. 1996. Product Properties Test Guidelines. OPPTS

830.7550. Partition Coefficient (n-Octanol/water), shake flask method. EPA 712-C-96-310

(1701). Public Draft.

OECD. 1998. OECD Guidelines for Testing of Chemicals, Partition Coefficient

(n-octanol/water), Slow-stirring method, Review Draft.

Avdeef, A. 1992. Quant. Struct.-Act. Relat. 11:510-517.

Avdeef, A. 1993. J. Pharm. Sci. 82:183-190. 

Slater, B., A. McCormack, A.Avdeef, J.E.A. Comer. 1994. J. Pharm. Sci. 83:1280-1283.

2. ASSESSMENT OF KOW VALUES ESTIMATED FROM LIQUID

CHROMATOGRAPHIC TECHNIQUES

An estimated Kow value would be considered “appropriate” provided the following

experimental conditions existed during its determination: 

 2.1 Kow’'s used for the reference compounds consist of “high quality” slow stir

measurements.

2.1.1 Better estimates for Kow's are obtained when reference and test chemicals are

similar. 

2.1.2 When solutes have hydrogen accepting and/or amphiprotic substituents,

predictions of the log Kows from the log capacity factor (using relationships

developed with non-hydrogen bonding solutes) will generally result in predicted

log Kows which are too large and too small, respectively (Yamagami et al. 1994).

The chromatographic behavior for solutes containing hydrogen accepting and/or

amphiprotic substituents for the prediction of log Kow has been extensively studied

by Yamagami and coworkers. These studies concluded that “corrections for

hydrogen-bond effects are required in most cases when polar functional groups

are present” and that solvent composition in the chromatography system can

greatly change the capacity factors for these chemicals relative to non-hydrogen

bonding chemicals.
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 2.2 A minimum of five chemicals are used in developing the log capacity factor (k')- log Kow

calibration relationship. The Kow's of the reference chemicals should be evenly distributed

and should span 3 to 4 orders of magnitude.

2.3 The log k' - log Kow calibration curve is linear and has a correlation coefficient greater than

0.95.

2.4 The Kow estimated for the test chemical is within the range of Kows for the reference

compounds or does not exceed the upper end of the range of Kows for the reference

compounds beyond 0.5 log units without adequate justification.

2.5 Chemical speciation must be accounted for in performing the measurements. For

example, with ionizable chemicals, measurements must be performed on the unionized

form by using an appropriate buffer with a pH below the pK for an acid and above the pK

for a base.

2.6 Reference and test chemicals are of known purity and structure. Independent

confirmation of the identity and purity of the reference and test chemicals is required or

highly desirable. 

2.7 Chemical mixtures can be used as the source of test chemicals provided accurate

identities can be assigned to individual chromatographic components.

2.8 References. These references for liquid chromatography techniques include methods

recommended by various governmental agencies that would provide “appropriate” Kows

when the reference compounds used in the determination are similar to the compound of

interest.

ASTM. 1997. Standard test method for partition coefficient (n-octanol/water) estimation

by liquid chromatography, Designation: E 1147 - 92. Annual Book of ASTM Standards,

Section 11, Water and Environmental Technology, Volume 11.05. ASTM, West

Conshohocken, PA. 

OECD. 1994. OECD Guidelines for Testing of Chemicals, 2nd Ed. Method: 117. Partition

Coefficient (n-octanol/water), HPLC method (30 May 89). 

U.S. Environmental Protection Agency. 1995. Product Properties Test Guidelines. OPPTS

830.7570. Partition Coefficient (n-Octanol/H2O), estimation by liquid chromatography.

EPA 712-C-95-040 (1701). Public Draft.

Yamagami, C., T. Ogura, N. Tako. 1990. J. Chromatogr. 514:123-126.

Yamagami, C., M. Yokota. 1991. Chem. Pharm. Bull. 39:1217-1221.

Yamagami, C., M. Yokota. 1991. Chem. Pharm. Bull. 39:2924-2929.
B-12

Yamagami, C., M. Yokota. 1992. Chem. Pharm. Bull. 40:925-929.

Yamagami, C., M. Yokota. 1993. Chem. Pharm. Bull. 41:694-698.

Yamagami, C., M. Yokota, N. Tako. 1994. J. Chromatogr. 662:49-60.

Yamagami, C., M. Yokota. 1994. Chem. Pharm. Bull. 42:907-912.

Yamagami, C., M. Yokota. 1995. Chem. Pharm. Bull. 43:2238-2242.

Yamagami, C., M. Yokota. 1996. Chem. Pharm. Bull. 44:1338-1343.
B-13

ATTACHMENT II: ESTIMATION OF KOW FROM MOLECULAR FRAGMENTS

For computing thermodynamic properties it is often useful to consider a molecule as a

collection of molecular fragments, each making a distinct contribution to the property of interest,

which is relatively independent of the rest of the molecule. The rationale behind the method is

that a large number of structures can be generated from a relatively small number of fragments,

and thus a large number of estimates can be derived from a small number of experimentally

determined fragment constants. The accuracy of the estimation, however, necessarily improves as

the specificity of the fragment environment increases, which entails an increase in the number of

fragments or corrective factors that must be considered. This approach is applied at different

levels of sophistication. One user may employ a few fragment constants and generate 'first order'

estimates whereas another may make numerous corrections or adjustments reflecting more

fragment specificity for a given molecular environment. For a more complete discussion of group

fragment methods one should consult Hansch and Leo, 1995. For this exercise, these methods

will be used for molecule-to-molecule extrapolation (via addition or subtraction of

fragments\substituents) rather than a priori estimation. 

1. Addition of ring fragments

For condensed ring aromatics, the addition of rings is given by

where are the fragment addition constants for ", $, and (

condensation respectively. 

2. Addition of substituents

The addition of a substituent, S (replacing a H atom) is a primary application of this

method. In this case
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where R is the base molecule and is a substituent constant, which is experimentally

determined. Tables for common substituents are readily available or can be easily determined

from measured data. One must distinguish (i.e., have different substituent constants for)

attachment to aliphatic, ethylenic, acetylenic, and aromatic carbon atoms in 'R'. Also corrections

must be made for multiple substitution if attachment is to the same or adjacent carbons. The

following is an example of Kow estimation. The fragment constant for Cl attached to aromatic

carbon can be derived from:

With this constant, one can derive

An exhaustive list of substituent constants is included in the aforementioned Hansch and Leo

(1995) reference.

It should be noted that B-values are most often illustrated, as above, by replacing a hydrogen

atom on a benzene ‘parent’ molecule. However, substituents that are strong electron donors or

acceptors such as chlorine have different B-values when placed on other ‘parent’ aromatic

molecules. For example, differences in log KOWs of 0.71, 0.99, 0.71 and 0.85 are obtained between

the 4-chloro-analogues and their parent molecules for benzene, aniline, nitrobenzene, and

phenoxyacetic acid, respectively. The literature is replete with calculations making this type of

error and the importance of using the correct B-values for the ‘parent’ molecule can not be under

emphasized.