Methodology for Deriving Ambient

Water Quality Criteria for the

Protection of Human Health (2000)

United States Office of Water EPA-822-B-00-004

Environmental Protection Office of Science and Technology October 2000

Agency 4304
EPA 822-B-00-004

October 2000

Methodology for Deriving

Ambient Water Quality Criteria

for the Protection of 

Human Health (2000)

Final

Office of Science and Technology

Office of Water

U.S. Environmental Protection Agency

Washington, DC 20460
NOTICE

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

EPA methods 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 based upon the circumstances.

This document has been reviewed in accordance with U.S. Environmental Protection

Agency policy and approved for publication. Mention of trade names or commercial products

does not constitute endorsement or recommendation for use.

ii
iii

FOREWORD

This document presents EPA’s recommended Methodology for developing ambient water

quality criteria as required under Section 304(a) of the Clean Water Act (CWA). The

Methodology is guidance for scientific human health assessments used by EPA to develop,

publish, and from time to time revise, recommended criteria for water quality accurately

reflecting the latest scientific knowledge. The recommended criteria serve States and Tribes’

needs in their development of water quality standards under Section 303(c) of the CWA.

The term “water quality criteria” is used in two sections of the Clean Water Act, Section

304(a)(1) and Section 303(c)(2). The term has a different program impact in each section. In

Section 304, the term represents a scientific assessment of ecological and human health effects

that EPA recommends to States and authorized Tribes for establishing water quality standards

that ultimately provide a basis for controlling discharges or releases of pollutants. Ambient

water quality criteria associated with specific stream uses when adopted as State or Tribal water

quality standards under Section 303 define the maximum levels of a pollutant necessary to

protect designated uses in ambient waters. The water quality criteria adopted in the State or

Tribal water quality standards could have the same numerical limits as the criteria developed

under Section 304. However, in many situations States and authorized Tribes may want to

adjust water quality criteria developed under Section 304 to reflect local environmental

conditions and human exposure patterns before incorporation into water quality standards. 

When adopting their water quality criteria, States and authorized Tribes have four options: (1)

adopt EPA’s 304(a) recommendations; (2) adopt 304(a) criteria modified to reflect site-specific

conditions; (3) develop criteria based on other scientifically defensible methods; or (4) establish

narrative criteria where numeric criteria cannot be determined.

EPA will use this Methodology to develop new ambient water quality criteria and to

revise existing recommended water quality criteria. It also provides States and authorized Tribes

the necessary guidance to adjust water quality criteria developed under Section 304 to reflect

local conditions or to develop their own water quality criteria using scientifically defensible

methods consistent with this Methodology. EPA encourages States and authorized Tribes to use

this Methodology to develop or revise water quality criteria to appropriately reflect local

conditions. EPA believes that ambient water quality criteria inherently require several risk

management decisions that are, in many cases, better made at the State, Tribal, or regional level. 

Additional guidance to assist States and authorized Tribes in the modification of criteria based

on the Methodology will accompany this document in the form of three companion Technical

Support Documents on Risk Assessment, Exposure Assessment, and Bioaccumulation

Assessment.

___________________________

Geoffrey H. Grubbs

Director

Office of Science and Technology
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ACKNOWLEDGMENTS

Project Leader

Denis Borum U.S. EPA Office of Science and Technology

Coauthors

Risk Assessment

Joyce M. Donohue, Ph.D.* U.S. EPA Office of Science and Technology

Julie T. Du, Ph.D.* U.S. EPA Office of Science and Technology

Charles O. Abernathy, Ph.D. U.S. EPA Office of Science and Technology

Exposure

Denis Borum * U.S. EPA Office of Science and Technology

Helen Jacobs, M.S. U.S. EPA Office of Science and Technology

Henry Kahn, D.Sc. U.S. EPA Office of Science and Technology

Bioaccumulation

Keith G. Sappington, M.S.* U.S. EPA Office of Science and Technology

Lawrence P. Burkhard, Ph.D. U.S. EPA Office of Research and Development

Philip M. Cook, Ph.D. U.S. EPA Office of Research and Development

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

U.S. EPA Technical Reviewers

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 Wastewater 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

Denise Hakowski U.S. EPA Region 3

Joel Hansel U.S. EPA Region 4

Wayne Jackson U.S. EPA Region 2

Annie Jarabek U.S. EPA Office of Research and Development

William Jordan U.S. EPA Office of Prevention, Pesticides, and Toxic Substances
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Margaret Kelly U.S. EPA Office of Children’s Health Protection

Henry Lee U.S. EPA Office of Research and Development

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

Jennifer Mclain U.S. EPA Office of Ground Water and Drinking Water

Bruce Mintz U.S. EPA Office of Research and Development

Dave Moon U.S. EPA Region 8

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

Jacqueline Moya U.S. EPA Office of Research and Development

Deirdre Murphy U.S. EPA Office of 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 Office of Research and Development

Lynn Papa U.S. EPA Office of Research and Development

Robert Pepin U.S. EPA Region 5

David Pfeifer U.S. EPA Region 5

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

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

Linda Teuschler U.S. EPA Office of Research and Development

David Tomey U.S. EPA Region 1

Fritz Wagener U.S. EPA Region 4

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

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

Gary Wolinsky U.S. EPA Region 9

Philip Woods U.S. EPA Region 9

William Wuerthele U.S. EPA Region 8

* Principal U.S. EPA Author and Contact 
vii

EXTERNAL 

PEER REVIEW WORKGROUP

The following professionals were part of the External Peer Review 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.

Kenneth T. Bogen, Ph.D. Lawrence Livermore National Laboratory

Paul E. Brubaker, Ph.D. P.E. Brubaker Associates

Peter L. DeFur, Ph.D. Virginia Commonwealth University

Karen Erstfeld, Ph.D. Rutgers University

Bob Fares, Ph.D. Environmental Standards, Inc.

Laura Green, Ph.D. Cambridge Environmental, Inc.

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

Brendan Hickie, Ph.D. Trent University

Ernest Hodgson, Ph.D. North Carolina State University

Paul Locke, Ph.D. Johns Hopkins University

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

Erik Rifkin, Ph.D. Rifkin and Associates, Inc.

Damian Shea, Ph.D. North Carolina State University

Nga Tran, Ph.D. Johns Hopkins University

Curtis Travis, Ph.D. Project Performance Corp.

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

and review of their current affiliations. No conflicts of interest were identified.
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TABLE OF CONTENTS

Page

NOTICE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii

FOREWORD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

ACKNOWLEDGMENTS .......................................................v

EXTERNAL PEER REVIEW WORKGROUP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

CONTENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

TABLES AND FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv

LIST OF ACRONYMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv

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

1.1 Water Quality Criteria and Standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1

1.2 Purpose of This Document . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1

1.3 History of the Ambient Water Quality Criteria (AWQC) Methodology . . . . . . . . . . . . 1-2

1.4 Relationship of Water Quality Standards to AWQC . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-4

1.5 Need for the AWQC Methodology Revisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-4

1.5.1 Group C Chemicals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-6

1.5.2 Consideration of Non-Water Sources of Exposure . . . . . . . . . . . . . . . . . . . . . . 1-7

1.5.3 Cancer Risk Ranges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-8

1.6 Overview of the AWQC Methodology Revisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-9

1.7 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-13

2. CLARIFICATIONS ON THE METHODOLOGY, RISK CHARACTERIZATION,

AND OTHER ISSUES FOR DEVELOPING CRITERIA . . . . . . . . . . . . . . . . . . . 2-1

2.1 Identifying the Population Subgroup that the AWQC Should Protect . . . . . . . . . . . . . 2-1

2.2 Science, Science Policy, and Risk Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-3

2.3 Setting Criteria to Protect Against Multiple Exposures From Multiple Chemicals

(Cumulative Risk) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4

2.4 Cancer Risk Range . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-6

2.5 Microbiological Ambient Water Quality Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-7

2.6 Risk Characterization Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-9

2.7 Discussion of Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-11

2.7.1 Observed Range of Toxicity Versus Range of Environmental Exposure . . . . 2-11

2.7.2 Continuum of Preferred Data/Use of Defaults . . . . . . . . . . . . . . . . . . . . . . . . . 2-11

2.7.3 Significant Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-11

2.8 Other Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-13

2.8.1 Minimum Data Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-13

2.8.2 Site-Specific Criterion Calculation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-13

2.8.3 Organoleptic Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-14

2.8.4 Criteria for Chemical Classes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-15

2.8.5 Criteria for Essential Elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-16

2.9 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-16
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3. RISK ASSESSMENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1

3.1 Cancer Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1

3.1.1 Background on EPA Cancer Risk Assessment Guidelines . . . . . . . . . . . . . . . 3-1

3.1.2 EPA's Proposed Guidelines for Carcinogen Risk Assessment and the Subsequent

July, 1999 Draft Revised Cancer Guidelines . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-2

3.1.3 Methodology for Deriving AWQCby the 1999 Draft Revised 

Cancer Guidelines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-4

3.1.3.1 Weight-of-Evidence Narrative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-5

3.1.3.2 Mode of Action-General Considerations and Framework for

Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-6

3.1.3.3 Dose Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-7

A. Determining the Human Equivalent Dose . . . . . . . . . . . . . . . . . . . 3-7

B. Dose-Response Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-7

3.1.3.4 Characterizing Dose-Response Relationships in the Range of Observation

and at Low Environmentally Relevant Doses . . . . . . . . . . . . . . . . . . . . 3-8

A. Extrapolation to Low, Environmentally Relevant Doses . . . . . . . . 3-9

B. Biologically-Based Modeling Approaches . . . . . . . . . . . . . . . . . . 3-9

C. Default Linear Extrapolation Approach . . . . . . . . . . . . . . . . . . . 3-10

D. Default Nonlinear Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-11

E. Both Linear and Nonlinear Approaches . . . . . . . . . . . . . . . . . . . 3-13

3.1.3.5 AWQC Calculation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-13

A. Linear Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-13

B. Nonlinear Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-14

3.1.3.6 Risk Characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-14

3.1.3.7 Use of Toxicity Equivalence Factors (TEF) and Relative Potency

Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-15

3.1.4 References for Cancer Section . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-16

3.2 Noncancer Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-17

3.2.1 1980 AWQC National Guidelines for Noncancer Effects . . . . . . . . . . . . . . . . 3-17

3.2.2 Noncancer Risk Assessment Developments Since 1980 . . . . . . . . . . . . . . . . . 3-18

3.2.3 Issues and Recommendations Concerning the Derivation of AWQC for

Noncarcinogens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-20

3.2.3.1 Using the Current NOAEL/UF-Based RfD Approach or Adopting More

Quantitative Approaches for Noncancer Risk Assessment . . . . . . . . . 3-20

A. The Benchmark Dose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-22

B. Categorical Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-24

C. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-25

3.2.3.2 Presenting the RfD as a Single Point or as a Range for Deriving 

AWQC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-25

3.2.3.3 Guidelines to be Adopted for Derivation of Noncancer Health Effects

Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-27

3.2.3.4 Treatment of Uncertainty Factors/Severity of Effects During the RfD

Derivation and Verification Process . . . . . . . . . . . . . . . . . . . . . . . . . . 3-27

3.2.3.5 Use of Less-Than-90-Day Studies to Derive RfDs . . . . . . . . . . . . . . . 3-27
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3.2.3.6 Use of Reproductive/Developmental, Immunotoxicity, and Neurotoxicity

Data as the Basis for Deriving RfDs . . . . . . . . . . . . . . . . . . . . . . . . . . 3-28

3.2.3.7 Applicability of Toxicokinetic Data in Risk Assessment . . . . . . . . . . 3-28

3.2.3.8 Consideration of Linearity (or Lack of a Threshold) for Noncarcinogenic

Chemicals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-29

3.2.3.9 Minimum Data Guidance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-29

3.2.4 References for Noncancer Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-30

4. EXPOSURE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1

4.1. Exposure Policy Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1

4.1.1 Sources of Exposure Associated with Ambient Water . . . . . . . . . . . . . . . . . . . 4-2

4.1.1.1 Appropriateness of Including the Drinking Water Pathway in 

AWQC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-2

4.1.1.2 Setting Separate AWQC for Drinking Water and Fish 

Consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-2

4.1.1.3 Incidental Ingestion from Ambient Surface Waters . . . . . . . . . . . . . . . 4-3

4.2. Consideration of Non-Water Sources of Exposure When Setting AWQC . . . . . . . . . . 4-3

4.2.1 Policy Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-3

4.2.2 The Exposure Decision Tree Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-5

4.2.2.1 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-9

4.2.2.2 Data Adequacy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-10

4.2.2.3 Regulatory Actions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-13

4.2.2.4 Apportionment Decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-13

4.2.3 Additional Points of Clarification on the Exposure Decision Tree Approach for

Setting AWQC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-15

4.2.4 Quantification of Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-16

4.2.5 Inclusion of Inhalation and Dermal Exposures . . . . . . . . . . . . . . . . . . . . . . . . 4-16

4.3 Exposure Factors Used in the AWQC Computation . . . . . . . . . . . . . . . . . . . . . . . . . . 4-17

4.3.1 Human Body Weight Values for Dose Calculations . . . . . . . . . . . . . . . . . . . . 4-18

4.3.1.1 Rate Protective of Human Health from Chronic Exposure . . . . . . . . . 4-19

4.3.1.2 Rates Protective of Developmental Human Health Effects . . . . . . . . . 4-20

4.3.2 Drinking Water Intake Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-21

4.3.2.1 Rate Protective of Human Health from Chronic Exposure . . . . . . . . . 4-23

4.3.2.2 Rates Protective of Developmental Human Health Effects . . . . . . . . . 4-24

4.3.2.3 Rates Based on Combining Drinking Water Intake and Body 

Weight . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-24

4.3.3 Fish Intake Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-25

4.3.3.1 Rates Protective of Human Health from Chronic Exposure . . . . . . . . 4-25

4.3.3.2 Rates Protective of Developmental Human Health Effects . . . . . . . . . 4-29

4.3.3.3 Rates Based on Combining Fish Intake and Body Weight . . . . . . . . . 4-30

4.4 References for Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-30

5. BIOACCUMULATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1
xii

5.1.1 Important Bioaccumulation and Bioconcentration Concepts . . . . . . . . . . . . . . 5-2

5.1.2 Goal of the National BAF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-3

5.1.3 Changes to the 1980 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-3

5.1.3.1 Overall Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-4

5.1.3.2 Lipid Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-4

5.1.3.3 Bioavailability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-5

5.1.3.4 Trophic Level Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-5

5.1.3.5 Site-Specific Adjustments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-5

5.1.4 Organization of This Section . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-6

5.2 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-6

5.3 Framework for Determining National Bioaccumulation Factors . . . . . . . . . . . . . . . 5-10

5.3.1 Four Different Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-10

5.3.2 Overview of BAF Derivation Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-12

5.3.3 Defining the Chemical of Concern . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-14

5.3.4 Collecting and Reviewing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-14

5.3.5 Classifying the Chemical of Concern . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-15

5.4 National Bioaccumulation Factors for Nonionic Organic Chemicals . . . . . . . . . . . . . 5-16

5.4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-16

5.4.2 Selecting the BAF Derivation Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-18

5.4.2.1 Chemicals with Moderate to High Hydrophobicity . . . . . . . . . . . . . . . 5-18

5.4.2.2 Chemicals with Low Hydrophobicity . . . . . . . . . . . . . . . . . . . . . . . . . 5-19

5.4.2.3 Assessing Metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-20

5.4.3 Deriving National BAFs Using Procedure #1 . . . . . . . . . . . . . . . . . . . . . . . . . 5-22

5.4.3.1 Calculating Individual Baseline BAFR

fds . . . . . . . . . . . . . . . . . . . . . . . 5-23

A. Baseline BAFR

fd from Field-Measured BAFs . . . . . . . . . . . . . . . . 5-23

B. Baseline BAFR

fd Derived from BSAFs . . . . . . . . . . . . . . . . . . . . . 5-28

C. Baseline BAFR

fd from a Laboratory-Measured BCFt

T

and FCM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-32

D. Baseline BAFR

fd from a Kow and FCM . . . . . . . . . . . . . . . . . . . . . . 5-38

5.4.3.2 Selecting Final Baseline BAFR

fds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-39

5.4.3.3 Calculating National BAFs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-41

5.4.4 Deriving National BAFs Using Procedure #2 . . . . . . . . . . . . . . . . . . . . . . . . . 5-44

5.4.4.1 Calculating Individual Baseline BAFR

fds . . . . . . . . . . . . . . . . . . . . . . . 5-45

A. Baseline BAFR

fd from Field-Measured BAFs . . . . . . . . . . . . . . . . 5-45

B. Baseline BAFR

fd Derived from Field-Measured BSAFs . . . . . . . . 5-46

C. Baseline BAFR

fd from a Laboratory-Measured BCF . . . . . . . . . . 5-46

5.4.4.2 Selecting Final Baseline BAFR

fds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-46

5.4.4.3 Calculating the National BAFs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-47

5.4.5 Deriving National BAFs Using Procedure #3 . . . . . . . . . . . . . . . . . . . . . . . . . 5-47

5.4.5.1 Calculating Individual Baseline BAFR

fds . . . . . . . . . . . . . . . . . . . . . . . 5-47

A. Baseline BAFR

fd from Field-Measured BAFs . . . . . . . . . . . . . . . . 5-48

B. Baseline BAFR

fd from a Laboratory-Measured BCF . . . . . . . . . . 5-48

C. Baseline BAFR

fd from a Kow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-49

5.4.5.2 Selecting Final Baseline BAFR

fds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-49

5.4.5.3 Calculating the National BAFs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-50

5.4.6 Deriving National BAFs Using Procedure #4 . . . . . . . . . . . . . . . . . . . . . . . . . 5-51
xiii

5.4.6.1 Calculating Individual Baseline BAFR

fds . . . . . . . . . . . . . . . . . . . . . . . 5-52

A. Baseline BAFR

fd from Field-measured BAFs . . . . . . . . . . . . . . . . 5-52

B. Baseline BAFR

fd from a Laboratory-measured BCF . . . . . . . . . . . 5-53

5.4.6.2 Selecting Final Baseline BAFR

fds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-53

5.4.6.3 Calculating National BAFs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-54

5.5 National Bioaccumulation Factors for Ionic Organic Chemicals . . . . . . . . . . . . . . . . 5-55

5.6 National Bioaccumulation Factors for Inorganic and Organometallic Chemicals . . . 5-57

5.6.1 Selecting the BAF Derivation Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-57

5.6.2 Bioavailability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-58

5.6.3 Deriving BAFs Using Procedure #5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-58

5.6.3.1 Determining Field-Measured BAFs . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-59

5.6.3.2 Determining Laboratory-Measured BCFs . . . . . . . . . . . . . . . . . . . . . . 5-60

5.6.3.3 Determining the National BAFs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-60

5.6.4 Deriving BAFs Using Procedure #6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-61

5.6.4.1 Determining Field-Measured BAFs . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-62

5.6.4.2 Determining Laboratory-Measured BCFs . . . . . . . . . . . . . . . . . . . . . . 5-62

5.6.4.3 Determining the National BAF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-62

5.7 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-63
xiv

TABLES AND FIGURES

Page

Table 3-1. Uncertainty Factors and the Modifying Factor . . . . . . . . . . . . . . . . . . . . . . . . 3-19

Figure 4-1. Exposure Decision Tree for Defining Proposed RfD (or POD/UF) 

Apportionment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-8

Figure 5-1 Framework for Deriving a National BAF . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-13

Figure 5-2 BAF Derivation for Nonionic Organic Chemicals . . . . . . . . . . . . . . . . . . . . . 5-17

Table 5-1 Food-Chain Multipliers for Trophic Levels 2, 3 and 4 . . . . . . . . . . . . . . . . . . 5-36
xv

LIST OF ACRONYMS

ADI Acceptable Daily Intake

ARAR Applicable or Relevant and Appropriate Requirements 

ASTM American Society of Testing and Materials

AWQC Ambient Water Quality Criteria

BAF Bioaccumulation Factor

BAFR

fd Baseline Bioaccumulation Factor

BCF Bioconcentration Factor

BCFR

fd Baseline Bioconcentration Factor

BCFt

T Bioconcentration Factor Based on Total Concentrations in 

Tissue and Water

BMD Benchmark Dose

BMDL Lower-Bound Confidence Limit on the BMD

BMF Biomagnification Factor

BMR Benchmark Response

BSAF Biota-Sediment Accumulation Factors

BW Body Weight

CR Lipid-normalized Concentration

Csoc Organic Carbon-normalized Concentration

Ct Concentration of the Chemical in the Specified Wet Tissue

Cw Concentration of the Chemical in Water

CDC U.S. Centers for Disease Control and Prevention

CSFII Continuing Survey of Food Intake by Individuals

CWA Clean Water Act

DDT 1,1,1-trichloro-2,2-bis(p-chlorophenyl)ethane

DDE 1,1-dichloro-2,2-bis(p-chlorophenyl)ethylene

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

DI Drinking Water Intake

DNA Deoxyribonucleic Acid

DNOC 2,4-dinitro-o-cresol

DOC Dissolved Organic Carbon

ED10 Dose Associated with a 10 Percent Extra Risk

EPA Environmental Protection Agency

ffd Fraction Freely Dissolved

fR Fraction Lipid

FCM Food Chain Multiplier

FEL Frank Effect Level

FI Fish Intake

FIFRA Federal Insecticide, Fungicide, and Rodenticide Act

GLI Great Lakes Water Quality Initiative

HCBD Hexachlorobutadiene

IARC International Agency for Research on Cancer

II Incidental Ingestion

ILSI International Life Sciences Institute
xvi

IRIS Integration Risk Information System

kg kilogram

Kow Octanol-Water Partition Coefficient

L Liter

LAS Linear Alkylbenzesulfonate

LED10 The Lower 95 Percent Confidence Limit on a Dose Associated with a 10

Percent Extra Risk

LMS Linear Multistage Model

LOAEL Lowest Observed Adverse Effect Level

MR Mass of Lipid in Specified Tissue

Mt Mass of Specified Tissue (Wet Weight)

MCL Maximum Contaminant Level

MCLG Maximum Contaminant Level Goal

MF Modifying Factor

mg Milligrams

ml Milliliters

MOA Mode of Action

MOE Margin of Exposure

NCHS National Center for Health Statistics

NCI National Cancer Institute

NFCS Nationwide Food Consumption Survey

NHANES National Health and Nutrition Examination Survey

NOAEL No Observed Adverse Effect Level

NOEL No Observed Effect Level

NPDES National Pollutant Discharge Elimination System

PAH Polycyclic Aromatic Hydrocarbon

PCB Polychlorinated Biphenyls

POD Point of Departure

POC Particulate Organic Carbon

RDA Recommended Daily Allowance

RfC Reference Concentration

RfD Reference Dose

RfDDT Reference Dose for Developmental Effects

RPF Relative Potency Factor

RSC Relative Source Contribution

RSD Risk-Specific Dose

SAB Science Advisory Board

SDWA Safe Drinking Water Act

SF Safety Factor 

STORET Storage Retrieval 

TEAM Total Exposure Assessment Methodology

TEF Toxicity Equivalency Factor

TMDL Total Maximum Daily Load

TSD Technical Support Document

USDA United States Department of Agriculture

USEPA United States Environmental Protection Agency
xvii

UF Uncertainty Factor

WQBEL Water Quality-Based Effluent Limits
xviii

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1. INTRODUCTION

1.1 WATER QUALITY CRITERIA AND STANDARDS 

Pursuant to Section 304(a)(1) of the Clean Water Act (CWA), the U.S. Environmental

Protection Agency (EPA) is required to publish, and from time to time thereafter revise, criteria

for water quality accurately reflecting the latest scientific knowledge on the kind and extent of

all identifiable effects on human health which may be expected from the presence of pollutants

in any body of water.

Historically, the ambient water quality criteria (AWQC or 304(a) criteria) provided two

essential types of information: (1) discussions of available scientific data on the effects of the

pollutants on public health and welfare, aquatic life, and recreation; and (2) quantitative

concentrations or qualitative assessments of the levels of pollutants in water which, if not

exceeded, will generally ensure adequate water quality for a specified water use. Water quality

criteria developed under Section 304(a) are based solely on data and scientific judgments on the

relationship between pollutant concentrations and environmental and human health effects. The

304(a) criteria do not reflect consideration of economic impacts or the technological feasibility

of meeting the criteria in ambient water. These 304(a) criteria may be used as guidance by

States and authorized Tribes to establish water quality standards, which ultimately provide a

basis for controlling discharges or releases of pollutants into ambient waters.

In 1980, AWQC were derived for 64 pollutants using guidelines developed by the

Agency for calculating the impact of waterborne pollutants on aquatic organisms and on human

health. Those guidelines consisted of systematic procedures for assessing valid and appropriate

data concerning a pollutant’s acute and chronic adverse effects on aquatic organisms, nonhuman

mammals, and humans.

1.2 PURPOSE OF THIS DOCUMENT

The Methodology for Deriving Ambient Water Quality Criteria for the Protection of

Human Health (2000) (hereafter the “2000 Human Health Methodology”) addresses the

development of AWQC to protect human health. The Agency intends to use the 2000 Human

Health Methodology both to develop new AWQC for additional pollutants and to revise existing

AWQC. Within the next several years, EPA intends to focus on deriving AWQC for chemicals

of high priority (including, but not limited to, mercury, arsenic, PCBs, and dioxin). Furthermore,

EPA anticipates that 304(a) criteria development in the future will be for bioaccumulative

chemicals and pollutants considered highest priority by the Agency. The 2000 Human Health

Methodology is also intended to provide States and authorized Tribes flexibility in establishing

water quality standards by providing scientifically valid options for developing their own water

quality criteria that consider local conditions. States and authorized Tribes are strongly

encouraged to use this Methodology to derive their own AWQC. However, the 2000 Human

Health Methodology also defines the default factors EPA intends to use in evaluating and

determining consistency of State water quality standards with the requirements of the CWA. 

The Agency intends to use these default factors to calculate national water quality criteria under
1-2

Section 304(a) of the Act. EPA will also use this Methodology as guidance when promulgating

water quality standards for a State or Tribe under Section 303(c) of the CWA.

 

This Methodology does not substitute for the CWA or EPA’s regulations; nor is it a

regulation itself. Thus, the 2000 Human Health Methodology cannot impose legally-binding

requirements on EPA, States, Tribes or the regulated community, and may not apply to a

particular situation based upon the circumstances. EPA and State/Tribal decision-makers retain

the discretion to use different, scientifically defensible, methodologies to develop human health

criteria on a case-by-case basis that differ from this Methodology where appropriate. EPA may

change the Methodology in the future through intermittent refinements as advances in science or

changes in Agency policy occur.

The 2000 Human Health Methodology incorporates scientific advancements made over

the past two decades. The use of this Methodology is an important component of the Agency’s

efforts to improve the quality of the Nation’s waters. EPA believes the Methodology will

enhance the overall scientific basis of water quality criteria. Further, the Methodology should

help States and Tribes address their unique water quality issues and risk management decisions,

and afford them greater flexibility in developing their water quality programs.

There are three companion Technical Support Document (TSD) volumes for the 2000

Human Health Methodology: a Risk Assessment TSD; an Exposure Assessment TSD; and a

Bioaccumulation TSD. These documents are intended to further support States and Tribes in

developing AWQC to reflect local conditions. The Risk Assessment TSD (USEPA, 2000) is

being published concurrently with this Methodology. Publication of the Exposure Assessment

and Bioaccumulation TSDs are anticipated in 2001. 

1.3 HISTORY OF THE AMBIENT WATER QUALITY CRITERIA (AWQC)

METHODOLOGY

In 1980, EPA published AWQC for 64 pollutants/pollutant classes identified in Section

307(a) of the CWA and provided a methodology for deriving the criteria (USEPA, 1980). These

1980 AWQC National Guidelines (or the “1980 Methodology”) for developing AWQC for the

protection of human health addressed three types of endpoints: noncancer, cancer, and

organoleptic (taste and odor) effects. Criteria for protection against noncancer and cancer effects

were estimated by using risk assessment-based procedures, including extrapolation from animal

toxicity or human epidemiological studies. Basic human exposure assumptions were applied to

the criterion equation.

The risk assessment-based procedures used to derive the AWQC to protect human health

were specific to whether the endpoint was cancer or noncancer. When using cancer as the

critical risk assessment endpoint (which had been assumed not to have a threshold), the AWQC

were presented as a range of concentrations associated with specified incremental lifetime risk
1

Throughout this document, the term “risk level” regarding a cancer assessment using linear approach refers to an

upper-bound estimate of excess lifetime cancer risk.

1-3

levels1

. When using noncancer effects as the critical endpoint, the AWQC reflected an

assessment of a “no-effect” level, since noncancer effects were assumed to have a threshold. 

The key features of each procedure are described briefly in the following paragraphs.

Cancer effects. If human or animal studies on a contaminant indicated that it induced a

statistically significant carcinogenic response, the 1980 AWQC National Guidelines treated the

contaminant as a carcinogen and derived a low-dose cancer potency factor from available animal

data using the linearized multistage model (LMS). The LMS, which uses a linear, nonthreshold

assumption for low-dose risk, was used by the Agency as a science policy choice in protecting

public health, and represented a plausible upper limit for low-dose risk. The cancer potency

factor, which expresses incremental, lifetime risk as a function of the rate of intake of the

contaminant, was then combined with exposure assumptions to express that risk in terms of an

ambient water concentration. In the 1980 AWQC National Guidelines, the Agency presented a

range of contaminant concentrations corresponding to incremental cancer risks of 10-7 to 10-5

(that is, a risk of one additional case of cancer in a population of ten million to one additional

cancer case in a population of one hundred thousand, respectively). 

Noncancer effects. If the pollutant was not considered to have the potential for causing

cancer in humans (later defined as a known, probable, or possible human carcinogen by the 1986

Guidelines for Carcinogen Risk Assessment, USEPA, 1986d), the 1980 AWQC National

Guidelines treated the contaminant as a noncarcinogen; a criterion was derived using a threshold

concentration for noncancer adverse effects. The criteria derived from noncancer data were

based on the Acceptable Daily Intake (ADI) (now termed the reference dose [RfD]). ADI values

were generally derived using a no-observed-adverse-effect level (NOAEL) from animal studies,

although human data were used whenever available. The ADI was calculated by dividing the

NOAEL by an uncertainty factor to account for uncertainties inherent in extrapolating limited

toxicological data to humans. In accordance with the National Research Council

recommendations of 1977 (NRC, 1977), safety factors (SFs) (later redefined as uncertainty

factors) of 10, 100, or 1,000 were used, depending on the quality of the data.

Organoleptic effects. Organoleptic characteristics were also used in developing criteria

for some contaminants to control undesirable taste and/or odor imparted by them to ambient

water. In some cases, a water quality criterion based on organoleptic effects would be more

stringent than a criterion based on toxicologic endpoints. The 1980 AWQC National Guidelines

emphasized that criteria derived for organoleptic endpoints are not based on toxicological

information, have no direct relationship to adverse human health effects and, therefore, do not

necessarily represent approximations of acceptable risk levels for humans.
1-4

1.4 RELATIONSHIP OF WATER QUALITY STANDARDS TO AWQC

Under Section 303(c) of the CWA, States have the primary responsibility for establishing

water quality standards, defined under the Act as designated beneficial uses of a water segment

and the water quality criteria necessary to support those uses. Additionally, Native American

Tribes authorized to administer the water quality standards program under 40 CFR 131.8

establish water quality standards for waters within their jurisdictions. This statutory framework

allows States and authorized Tribes to work with local communities to adopt appropriate

designated uses and to adopt criteria to protect those designated uses. Section 303(c) provides

for EPA review of water quality standards and for promulgation of a superseding Federal rule in

cases where State or Tribal standards are not consistent with the applicable requirements of the

CWA and the implementing Federal regulations, or where the Agency determines Federal

standards are necessary to meet the requirements of the Act. Section 303(c)(2)(B) specifically

requires States and authorized Tribes to adopt water quality criteria for toxics for which EPA has

published criteria under Section 304(a) and for which the discharge or presence could reasonably

be expected to interfere with the designated use adopted by the State or Tribe. In adopting such

criteria, States and authorized Tribes must establish numerical values based on one of the

following: (1) 304(a) criteria; (2) 304(a) criteria modified to reflect site-specific conditions; or,

(3) other scientifically defensible methods. In addition, States and authorized Tribes can

establish narrative criteria where numeric criteria cannot be determined.

It must be recognized that the Act uses the term “criteria” in two different ways. In

Section 303(c), the term is part of the definition of a water quality standard. Specifically, a water

quality standard is composed of designated uses and the criteria necessary to protect those uses. 

Thus, States and authorized Tribes are required to adopt regulations which contain legally

enforceable criteria. However, in Section 304(a) the term criteria is used to describe the

scientific information that EPA develops to be used as guidance by States, authorized Tribes and

EPA when establishing water quality standards pursuant to 303(c). Thus, two distinct purposes

are served by the 304(a) criteria. The first is as guidance to the States and authorized Tribes in

the development and adoption of water quality criteria which will protect designated uses, and

the second is as the basis for promulgation of a superseding Federal rule when such action is

necessary.

1.5 NEED FOR THE AWQC METHODOLOGY REVISIONS

Since 1980, EPA risk assessment practices have evolved significantly in all of the major

Methodology areas: that is, cancer and noncancer risk assessments, exposure assessments, and

bioaccumulation. When the 1980 Methodology was developed, EPA had not yet developed

formal cancer or noncancer risk assessment guidelines. Since then, EPA has published several

risk assessment guidelines. In cancer risk assessment, there have been advances in the use of

mode of action (MOA) information to support both the identification of potential human

carcinogens and the selection of procedures to characterize risk at low, environmentally relevant

exposure levels. EPA published Proposed Guidelines for Carcinogen Risk Assessment (USEPA,

1996a, hereafter the “1996 proposed cancer guidelines”). These guidelines presented revised

procedures to quantify cancer risk at low doses, replacing the current default use of the LMS

model. Following review by the Agency’s Science Advisory Board (SAB), EPA published the
1-5

revised Guidelines for Carcinogen Risk Assessment–Review Draft in July 1999 (USEPA, 1999a,

hereafter the “1999 draft revised cancer guidelines”). In noncancer risk assessment, the Agency

is moving toward the use of the benchmark dose (BMD) and other dose-response approaches in

place of the traditional NOAEL approach to estimate an RfD or Reference Concentration (RfC). 

Guidelines for Mutagenicity Risk Assessment were published in 1986 (USEPA, 1986b). In 1991,

the Agency published Guidelines for Developmental Toxicity Risk Assessment (USEPA, 1991),

and it issued Guidelines for Reproductive Toxicity Risk Assessment in 1996 (USEPA, 1996b). In

1998, EPA published final Guidelines for Neurotoxicity Risk Assessment (USEPA, 1998), and in

1999 it issued the draft Guidance for Conducting Health Risk Assessment of Chemical Mixtures

(USEPA, 1999b). 

In 1986, the Agency made available to the public the Integrated Risk Information System

(IRIS). IRIS is a database that contains risk information on the cancer and noncancer effects of

chemicals. The IRIS assessments are peer reviewed and represent EPA consensus positions

across the Agency’s program and regional offices. 

New studies have addressed water consumption and fish tissue consumption. These

studies provide a more current and comprehensive description of national, regional, and specialpopulation consumption patterns that EPA has reflected in the 2000 Human Health

Methodology. In addition, more formalized procedures are now available to account for human

exposure from multiple sources when setting health goals such as AWQC that address only one

exposure source. In 1986, the Agency published the Total Exposure Assessment Methodology

(TEAM) Study: Summary and Analysis, Volume I, Final Report (USEPA, 1986c), which presents

a process for conducting comprehensive evaluation of human exposures. In 1992, EPA

published the revised Guidelines for Exposure Assessment (USEPA, 1992), which describe

general concepts of exposure assessment, including definitions and associated units, and provide

guidance on planning and conducting an exposure assessment. The Exposure Factors Handbook

was updated in 1997 (USEPA, 1997a). Also in 1997, EPA developed Guiding Principles for

Monte Carlo Analysis (USEPA, 1997b) and published its Policy for Use of Probabilistic

Analysis in Risk Assessment (see http://www.epa.gov/ncea/mcpolicy.htm). The Monte Carlo

guidance can be applied to exposure assessments and risk assessments. The Agency has recently

developed the Relative Source Contribution (RSC) Policy for assessing total human exposure to

a contaminant and apportioning the RfD among the media of concern, published for the first time

in this Methodology.

The Agency has moved toward the use of a bioaccumulation factor (BAF) to reflect the

uptake of a contaminant from all sources (e.g., ingestion, sediment) by fish and shellfish, rather

than just from the water column as reflected by the use of a bioconcentration factor (BCF) in the

1980 Methodology. The Agency has also developed detailed procedures and guidelines for

estimating BAF values.

Another reason for the 2000 Human Health Methodology is the need to bridge the gap

between the differences in the risk assessment and risk management approaches used by EPA’s

Office of Water for the derivation of AWQC under the authority of the CWA and Maximum

Contaminant Level Goals (MCLGs) under the Safe Drinking Water Act (SDWA). Three notable

differences are the treatment of chemicals designated as Group C, possible human carcinogens
1-6

under the 1996 proposed cancer guidelines, the consideration of non-water sources of exposure

when setting an AWQC or MCLG for a noncarcinogen, and cancer risk ranges. Those three

differences are described in the three subsections below, respectively.

1.5.1 Group C Chemicals 

Chemicals were typically classified as Group C–i.e., possible human carcinogens–under

the existing (1986) EPA cancer classification scheme for any of the following reasons:

1) Carcinogenicity has been documented in only one test species and/or only one

cancer bioassay and the results do not meet the requirements of “sufficient

evidence.”

2) Tumor response is of marginal statistical significance due to inadequate design or

reporting.

3) Benign, but not malignant, tumors occur with an agent showing no response in a

variety of short-term tests for mutagenicity.

4) There are responses of marginal statistical significance in a tissue known to have

a high or variable background rate.

The 1986 Guidelines for Carcinogen Risk Assessment (hereafter the “1986 cancer

guidelines”) specifically recognized the need for flexibility with respect to quantifying the risk of

Group C, possible human carcinogens. The 1986 cancer guidelines noted that agents judged to

be in Group C, possible human carcinogens, may generally be regarded as suitable for

quantitative risk assessment, but that case-by-case judgments may be made in this regard.

The EPA Office of Water has historically treated Group C chemicals differently under

the CWA and the SDWA. It is important to note that the 1980 AWQC National Guidelines for

setting AWQC under the CWA predated EPA’s carcinogen classification system, which was

proposed in 1984 (USEPA, 1984) and finalized in 1986 (USEPA, 1986a). The 1980 AWQC

National Guidelines did not explicitly differentiate among agents with respect to the weight of

evidence for characterizing them as likely to be carcinogenic to humans. For all pollutants

judged as having adequate data for quantifying carcinogenic risk–including those now classified

as Group C–AWQC were derived based on data on cancer incidence. In the1980 AWQC

National Guidelines, EPA emphasized that the AWQC for carcinogens should state that the

recommended concentration for maximum protection of human health is zero. At the same time,

the criteria published for specific carcinogens presented water concentrations for these pollutants

corresponding to individual lifetime excess cancer risk levels in the range of 10-7 to 10-5.

In the development of national primary drinking water regulations under the SDWA,

EPA is required to promulgate a health-based MCLG for each contaminant. The Agency policy

has been to set the MCLG at zero for chemicals with strong evidence of carcinogenicity

associated with exposure from water. For chemicals with limited evidence of carcinogenicity,

including many Group C agents, the MCLG was usually obtained using an RfD based on the
1-7

pollutant’s noncancer effects with the application of an additional uncertainty factor of 1 to 10 to

account for carcinogenic potential of the chemical. If valid noncancer data for a Group C agent

were not available to establish an RfD but adequate data are available to quantify the cancer risk,

then the MCLG was based upon a nominal lifetime excess cancer risk in the range of 10-6 to10-5

(ranging from one case in a population of one million to one case in a population of one hundred

thousand). Even in those cases where the RfD approach has been used for the derivation of the

MCLG for a Group C agent, the drinking water concentrations associated with excess cancer

risks in the range of 10-6 to 10-5 were also provided for comparison.

It should also be noted that EPA’s pesticides program has applied both of the previously

described methods for addressing Group C chemicals in actions taken under the Federal

Insecticide, Fungicide, and Rodenticide Act (FIFRA) and finds both methods applicable on a

case-by-case basis. Unlike the drinking water program, however, the pesticides program does

not add an extra uncertainty factor to account for potential carcinogenicity when using the RfD

approach.

In the 1999 draft revised cancer guidelines, there are no more alphanumeric categories. 

Instead, there will be longer narratives for hazard characterization that will use consistent

descriptive terms when assessing cancer risk.

1.5.2 Consideration of Non-water Sources of Exposure

The 1980 AWQC National Guidelines recommended that contributions from non-water

sources, namely air and non-fish dietary intake, be subtracted from the Acceptable Daily Intake

(ADI), thus reducing the amount of the ADI “available” for water-related sources of intake. In

practice, however, when calculating human health criteria, these other exposures were generally

not considered because reliable data on these exposure pathways were not available. 

Consequently, the AWQC were usually derived such that drinking water and fish ingestion

accounted for the entire ADI (now called RfD).

In the drinking water program, a similar “subtraction” method was used in the derivation

of MCLGs proposed and promulgated in drinking water regulations through the mid-1980s. 

More recently, the drinking water program has used a “percentage” method in the derivation of

MCLGs for noncarcinogens. In this approach, the percentage of total exposure typically

accounted for by drinking water, referred to as the relative source contribution (RSC), is applied

to the RfD to determine the maximum amount of the RfD “apportioned” to drinking water

reflected by the MCLG value. In using this percentage procedure, the drinking water program

also applies a ceiling level of 80 percent of the RfD and a floor level of 20 percent of the RfD. 

That is, the MCLG cannot account for more than 80 percent of the RfD, nor less than 20 percent

of the RfD.

The drinking water program usually takes a conservative approach to public health by

applying an RSC factor of 20 percent to the RfD when adequate exposure data do not exist,

assuming that the major portion (80 percent) of the total exposure comes from other sources,

such as diet.
1-8

In the 2000 Human Health Methodology, guidance for the routine consideration of nonwater sources of exposure [both ingestion exposures (e.g., food) and exposures other than the

oral route (e.g., inhalation)] is presented. The approach is called the Exposure Decision Tree. 

Relative source contribution estimates will be made by EPA using this approach, which allows

for use of either the subtraction or percentage methods, depending on chemical-specific

circumstances, within the 20 to 80 percent range described above.

1.5.3 Cancer Risk Ranges

In addition to the different risk assessment approaches discussed above for deriving

AWQC and MCLGs for Group C agents, there have been different risk management approaches

by the drinking water and surface water programs on lifetime excess risk values when setting

health-based criteria for carcinogens. The surface water program has derived AWQC for

carcinogens that generally corresponded to lifetime excess cancer risk levels of 10-7 to 10-5. The

drinking water program has set MCLGs for Group C agents based on a slightly less stringent risk

range of 10-6 to 10-5, while MCLGs for chemicals with strong evidence of carcinogenicity (that

is, classified as Group A, known, or B probable, human carcinogen) are set at zero. The drinking

water program is now following the principles of the 1999 draft revised cancer guidelines to

determine the type of low-dose extrapolation based on mode of action.

It is also important to note that under the drinking water program, for those substances

having an MCLG of zero, enforceable Maximum Contaminant Levels (MCLs) have generally

been promulgated to correspond with cancer risk levels ranging from 10-6 to 10-4. Unlike AWQC

and MCLGs which are strictly health-based criteria, MCLs are developed with consideration

given to the costs and technological feasibility of reducing contaminant levels in water to meet

those standards.

With the 2000 Human Health Methodology, EPA will publish its national 304(a) water

quality criteria at a 10-6 risk level, which EPA considers appropriate for the general population. 

EPA is increasing the degree of consistency between the drinking water and ambient water

programs, given the somewhat different requirements of the CWA and SDWA. 
2 Although appearing in this equation as a factor to be multiplied, the RSC can also be an amount subtracted. Refer to

the explanation key below the equations.

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(Equation 1-1)

(Equation 1-2)

1.6 OVERVIEW OF THE AWQC METHODOLOGY REVISIONS

The following equations for deriving AWQC include toxicological and exposure

assessment parameters which are derived from scientific analysis, science policy, and risk

management decisions. For example, values for parameters such as a field-measured BAF or a

point of departure from an animal study [in the form of a lowest-observed-adverse-effect level

(LOAEL)/no-observed -adverse-effect level (NOAEL)/lower 95 percent confidence limit on a

dose associated with a 10 percent extra risk (LED10)] are empirically measured using scientific

methods. By contrast, the decision to use animal effects as surrogates for human effects involves

judgment on the part of the EPA (and similarly, by other agencies) as to the best practice to

follow when human data are lacking. Such a decision is, therefore, a matter of science policy. 

The choice of default fish consumption rates for protection of a certain percentage (i.e., the 90th

percentile) of the general population is clearly a risk management decision. In many cases, the

Agency has selected parameter values using its best judgment regarding the overall protection

afforded by the resulting AWQC when all parameters are combined. For a longer discussion of

the differences between science, science policy, and risk management, please refer to Section 2

of this document. Section 2 also provides further details with regard to risk characterization for

this Methodology, with emphasis placed on explaining the uncertainties in the overall risk

assessment.

The generalized equations for deriving AWQC based on noncancer effects are:

Noncancer Effects2

Cancer Effects: Nonlinear Low-Dose Extrapolation 
1-10

(Equation 1-3)

Cancer Effects: Linear Low-Dose Extrapolation 

where:

AWQC = Ambient Water Quality Criterion (mg/L)

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

POD = Point of departure for carcinogens based on a nonlinear low-dose

extrapolation (mg/kg-day), usually a LOAEL, NOAEL, or LED10

UF = Uncertainty Factor for carcinogens based on a nonlinear low-dose 

extrapolation (unitless)

RSD = Risk-specific dose for carcinogens based on a linear low-dose

extrapolation (mg/kg-day) (dose associated with a target risk, such

as 10-6)

RSC = Relative source contribution factor to account for non-water

sources of exposure. (Not used for linear carcinogens.) May be

either a percentage (multiplied) or amount subtracted, depending

on whether multiple criteria are relevant to the chemical.

BW = Human body weight (default = 70 kg for adults)

DI = Drinking water intake (default = 2 L/day for adults)

FIi = Fish intake at trophic level (TL) I (I = 2, 3, and 4) (defaults for

total intake = 0.0175 kg/day for general adult population and sport

anglers, and 0.1424 kg/day for subsistence fishers). Trophic level

breakouts for the general adult population and sport anglers are:

TL2 = 0.0038 kg/day; TL3 = 0.0080 kg/day; and TL4 = 0.0057

kg/day.

BAFi = Bioaccumulation factor at trophic level I (I=2, 3 and 4), lipid

normalized (L/kg)

For highly bioaccumulative chemicals where ingestion from water might be considered

negligible, EPA is currently evaluating the feasibility of developing and implementing AWQCs

that are expressed in terms of concentrations in tissues of aquatic organisms. Such tissue residue

criteria might be used as an alternative to AWQCs which are expressed as concentrations in

water, particularly in situations where AWQCs are at or below the practical limits for

quantifying a chemical in water. Even though tissue residue criteria would not require the use of

a BAF in their derivation, implementing such criteria would still require a mechanism for

relating chemical loads and concentrations in water and sediment to concentrations in tissues of

appropriate fish and shellfish (e.g., a BAF or bioaccumulation model). At this time, no revisions

are planned to the Methodology to provide specific guidance on developing fish tissue-based

water quality criteria. However, guidance may be provided in the future either as a separate

document or integrated in a specific 304(a) water quality criteria document for a chemical that

warrants such an approach.
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AWQC for the protection of human health are designed to minimize the risk of adverse

effects occurring to humans from chronic (lifetime) exposure to substances through the ingestion

of drinking water and consumption of fish obtained from surface waters. The Agency is not

recommending the development of additional water quality criteria similar to the “drinking water

health advisories” that focus on acute or short-term effects; these are not seen as routinely having

a meaningful role in the water quality criteria and standards program. However, as discussed

below, there may be some instances where the consideration of acute or short-term toxicity and

exposure in the derivation of AWQC is warranted.

Although the AWQC are based on chronic health effects data (both cancer and noncancer

effects), the criteria are intended to also be protective against adverse effects that may reasonably

be expected to occur as a result of elevated acute or short-term exposures. That is, through the

use of conservative assumptions with respect to both toxicity and exposure parameters, the

resulting AWQC should provide adequate protection not only for the general population over a

lifetime of exposure, but also for special subpopulations who, because of high water- or fishintake rates, or because of biological sensitivities, have an increased risk of receiving a dose that

would elicit adverse effects. The Agency recognizes that there may be some cases where the

AWQC based on chronic toxicity may not provide adequate protection for a subpopulation at

special risk from shorter-term exposures. The Agency encourages States, Tribes, and others

employing the 2000 Human Health Methodology to give consideration to such circumstances in

deriving criteria to ensure that adequate protection is afforded to all identifiable subpopulations. 

(See Section 4.3, Factors Used in the AWQC Computation, for additional discussion of these

subpopulations.)

The EPA is in the process of revising its cancer guidelines, including its descriptions of

human carcinogenic potential. Once final guidelines are published, they will be the basis for

assessment under this methodology. In the meanwhile, the 1986 guidelines are used and

extended with principles discussed in EPA’s 1999 Guidelines for Carcinogen Risk Assessment -

Review Draft (hereafter “1999 draft revised cancer guidelines”). These principles arise from

new science about cancer discovered in the last 15 years and from EPA policy of recent years

supporting full characterization of hazard and risk both for the general population and

potentially sensitive groups such as children. These principles are incorporated in recent and

ongoing assessments such as the reassessment of dioxin, consistent with the 1986 guidelines. 

Until final guidelines are published, information is presented to describe risk under both the old

guidelines and draft revisions. Dose-response assessment under the 1986 guidelines employs a

linearized multistage model to extrapolate tumor dose-response observed in animal or human

studies down to zero dose, zero extra risk. The dose-response assessment under EPA’s 1999

draft revised cancer guidelines is a two-step process. In the first step, the response data are

modeled in the range of empirical observation. Modeling in the observed range is done with

biologically based or appropriate curve-fitting modeling. In the second step, extrapolation below

the range of observation is accomplished by biologically based modeling if there are sufficient

data or by a default procedure (linear, nonlinear, or both). A point of departure (POD) for

extrapolation is estimated from modeling observed data. The lower 95 percent confidence limit

on a dose associated with 10 percent extra risk (LED10) is the standard POD for low-dose

extrapolation. The linear default procedure is a straight line extrapolation to the origin (i.e., zero

dose, zero extra risk) from the POD, which is the LED10 identified in the observable response
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range. The result of this procedure is generally comparable (within 2-fold) to that of using a

linearized multistage model under existing, 1986 guidelines. The linear low-dose extrapolation

applies to agents that are best characterized by the assumption of linearity (e.g., direct DNA

reactive mutagens) for their MOA. A linear approach would also be applied when inadequate or

no information is available to explain the carcinogenic MOA; this is a science policy choice in

the interest of public health. If it is determined that the MOA understanding fully supports a

nonlinear extrapolation, the AWQC is derived using the nonlinear default which is based on a

margin of exposure (MOE) analysis using the LED10 as the POD and applying uncertainty

factors (UFs) to arrive at an acceptable MOE. There may be situations where it is appropriate to

apply both the linear and nonlinear default procedures (e.g., for an agent that is both DNA

reactive and active as a promoter at higher doses).

For substances that are carcinogenic, particularly those for which the MOA suggests

nonlinearity at low doses, the Agency recommends that an integrated approach be taken in

looking at cancer and noncancer effects. If one effect does not predominate, AWQC values

should be determined for both carcinogenic and noncarcinogenic endpoints. The lower of the

resulting values should be used for the AWQC.

When deriving AWQC for noncarcinogens and carcinogens based on a nonlinear lowdose extrapolation, a factor is included to account for other non-water exposure sources [both

ingestion exposures (e.g., food) and exposures other than the oral route (e.g., inhalation)] so that

the entire RfD, or POD/UF, is not apportioned to drinking water and fish consumption alone. 

Guidance is provided in the 2000 Human Health Methodology for determining the factor (i.e.,

the RSC) to be used for a particular chemical. The Agency is recommending the use of an

Exposure Decision Tree procedure to support the determination of the appropriate RSC value for

a given water contaminant. In the absence of data, the Agency intends to use 20 percent of the

RfD (or POD/UF) as the default RSC in calculating 304(a) criteria or promulgating State or

Tribal water quality standards under Section 303(c).

With AWQC derived for carcinogens based on a linear low-dose extrapolation, the

Agency will publish recommended criteria values at a 10-6 risk level. States and authorized

Tribes can always choose a more stringent risk level, such as 10-7. EPA also believes that

criteria based on a 10-5 risk level are acceptable for the general population as long as States and

authorized Tribes ensure that the risk to more highly exposed subgroups (sportfishers or

subsistence fishers) does not exceed the 10-4 level. Clarification on this risk management

decision is provided in Section 2 of this document.

The default fish consumption value for the general adult population in the 2000 Human

Health Methodology is 17.5 grams/day, which represents an estimate of the 90th percentile

consumption rate for the U.S. adult population based on the U.S. Department of Agriculture’s

(USDA’s) Continuing Survey of Food Intake by Individuals (CSFII) 1994-96 data (USDA,

1998). EPA will use this default intake rate with future national 304(a) criteria derivations or

revisions. This default value is chosen to be protective of the majority of the general population. 

However, States and authorized Tribes are urged to use a fish intake level derived from local

data on fish consumption in place of this default value when deriving AWQC, ensuring that the

fish intake level chosen is protective of highly exposed individuals in the population. EPA has
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provided default values for States and authorized Tribes that do not have adequate information

on local or regional consumption patterns, based on numerous studies that EPA has reviewed on

sport anglers and subsistence fishers. EPA’s defaults for these population groups are estimates

of their average consumption. EPA recommends a default of 17.5 grams/day for sport anglers as

an approximation of their average consumption and 142.4 grams/day for subsistence fishers,

which falls within the range of averages for this group. Consumption rates for women of

childbearing age and children younger than 14 are also provided to maximize protection in those

cases where these subpopulations may be at greatest risk. 

In the 2000 Human Health Methodology, criteria are derived using a BAF rather than a

BCF. To derive the BAF, States and authorized Tribes may use EPA’s Methodology or any

method consistent with this Methodology. EPA’s highest preference in developing BAFs are

BAFs based on field-measured data from local/regional fish.

1.7 REFERENCES

NRC (National Research Council). 1977. Drinking Water and Health. Safe Drinking Water 

Committee. National Academy of Sciences, National Academy Press. Washington, DC.

USDA. 1998. U.S. Department of Agriculture. 1994–1996 Continuing Survey of Food Intakes 

by Individuals and 1994–1996 Diet and Health Knowledge Survey. Agricultural

Research Service, USDA. NTIS CD–ROM, accession number PB98–500457. 

USEPA (U.S. Environmental Protection Agency). 1980. Guidelines and methodology used in 

the preparation of health effect assessment chapters of the consent decree water criteria

documents. Federal Register 45: 79347, Appendix 3.

USEPA (U.S. Environmental Protection Agency). 1984. Proposed guidelines for carcinogen

risk assessment. Federal Register 49:46294.

USEPA (U.S. Environmental Protection Agency). 1986a. Guidelines for carcinogen risk 

assessment. Federal Register 51:33992-34003.

USEPA (U.S. Environmental Protection Agency). 1986b. Guidelines for mutagenicity risk

assessment. Federal Register 51:34006-34012.

USEPA (U.S. Environmental Protection Agency). 1986c. Total Exposure Assessment Model 

(TEAM) Study: Summary and Analysis, Volume I. Final Report. EPA/600/6-87/002a.

USEPA (U.S. Environmental Protection Agency). 1986d. Guidelines for exposure assessment. 

Federal Register 51:34042-34054.

USEPA (U.S. Environmental Protection Agency). 1991. Guidelines for developmental toxicity

risk assessment. Federal Register 56:63789- 63826.
1-14

USEPA (U.S. Environmental Protection Agency). 1992. Guidelines for exposure assessment.

Federal Register 57:22888-22938.

USEPA (U.S. Environmental Protection Agency). 1996a. Proposed guidelines for carcinogen

risk assessment. Federal Register 61:17960-18011.

USEPA (U.S. Environmental Protection Agency). 1996b. Guidelines for reproductive toxicity

risk assessment. Federal Register 61: 6274-56322.

USEPA (U.S. Environmental Protection Agency). 1997a. Exposure Factors Handbook. Office

of Research and Development. Washington, DC. EPA/600/P-95/002Fa..

USEPA (U.S. Environmental Protection Agency). 1997b. Guiding Principles for Monte Carlo

Analysis. Risk Assessment Forum. Washington, DC. EPA/630/R-97/001.

USEPA (U.S. Environmental Protection Agency). 1998. Guidelines for neurotoxicity risk

assessment. Federal Register 63: 26926.

USEPA (U.S. Environmental Protection Agency). 1999a. 1999 Guidelines for Carcinogen Risk

Assessment. Review Draft. Office of Research and Development. Washington, DC. 

NCEA-F-0644.

USEPA (U.S. Environmental Protection Agency). 1999b. Guidance for Conducting Health Risk

Assessment of Chemical Mixtures. Final Draft. Risk Assessment Forum Technical

Panel. Washington, DC. EPA/NCEA-C-0148. September. Website:

http://www.epa.gov/ncea/raf/rafpub.htm

USEPA (U.S. Environmental Protection Agency). 2000. Methodology for Deriving Ambient 

Water Quality Criteria for the Protection of Human Health (2000). Technical Support

Document Volume 1: Risk Assessment. Office of Science and Technology, Office of

Water. Washington, DC. EPA-822-B-00-005. August.
2-1

2. CLARIFICATIONS ON THE METHODOLOGY, RISK CHARACTERIZATION,

AND OTHER ISSUES FOR DEVELOPING CRITERIA

2.1 IDENTIFYING THE POPULATION SUBGROUP THAT THE AWQC SHOULD

PROTECT

Water quality criteria are derived to establish ambient concentrations of pollutants which,

if not exceeded, will protect the general population from adverse health impacts from those

pollutants due to consumption of aquatic organisms and water, including incidental water

consumption related to recreational activities. For each pollutant, chronic criteria are derived to

reflect long-term consumption of food and water. An important decision to make when setting

AWQC is the choice of the particular population to protect. For instance, criteria could be set to

protect those individuals who have average or “typical” exposures, or the criteria could be set so

that they offer greater protection to those individuals who are more highly exposed. EPA has

selected default parameter values that are representative of several defined populations: adults in

the general population; sport (recreational) fishers; subsistence fishers; women of childbearing

age (defined as ages 15-44); and children (up to the age of 14). In deciding on default parameter

values, EPA is aware that multiple parameters are used in combination when calculating AWQC

(e.g., intake rates and body weight). EPA describes the estimated population percentiles that are

represented by each of the default exposure parameter values in Section 4. 

EPA’s national 304(a) criteria are usually derived to protect the majority of the general

population from chronic adverse health effects. EPA has used a combination of median values,

mean values, and percentile estimates for the parameter value defaults to calculate its national

304(a) criteria. EPA believes that its assumptions afford an overall level of protection targeted at

the high end of the general population (i.e., the target population or the criteria-basis population). 

EPA also believes that this is reasonably conservative and appropriate to meet the goals of the

CWA and the 304(a) criteria program. EPA considers that its target protection goal is satisfied if

the population as a whole will be adequately protected by the human health criteria when the

criteria are met in ambient water. However, associating the derived criteria with a specific

population percentile is far more difficult, and such a quantitative descriptor typically requires

detailed distributional exposure and dose information. EPA’s Guidelines For Exposure

Assessment (USEPA, 1992) describes the extreme difficulty in making accurate estimates of

exposures and indicates that uncertainties at the more extreme ends of the distribution increase

greatly. On quantifying population exposures/risks, the guidelines specifically state:

In practice, it is difficult even to establish an accurate mean health effect risk for

a population. This is due to many complications, including uncertainties in using

animal data for human dose-response relationships, nonlinearities in the doseresponse curve, projecting incidence data from one group to another dissimilar

group, etc. Although it has been common practice to estimate the number of

cases of disease, especially cancer, for populations exposed to chemicals, it

should be understood that these estimates are not meant to be accurate estimates

of real (or actuarial) cases of disease. The estimate’s value lies in framing
2-2

hypothetical risk in an understandable way rather than in any literal

interpretation of the term “cases.”

Although it is not possible to subject the estimates to such a rigorous analysis (say, for

example, to determine what criterion value provides protection of exactly the 90th percentile of

the population), EPA believes that the combination of parameter value assumptions achieves its

target goal, without being inordinately conservative. The standard assumptions made for the

national 304(a) criteria are as follows. The assumed body weight value used is an arithmetic

mean, as are the RSC intake estimates of other exposures (e.g., non-fish dietary), when data are

available. The BAF component data (e.g., for lipid values, for particulate and dissolved organic

carbon) are based on median (i.e., 50th percentile) values. The drinking water intake values are

approximately 90th percentile estimates and fish intake values are 90th percentile estimates. EPA

believes the use of these values will result in 304(a) criteria that are protective of a majority of

the population; this is EPA’s goal. 

However, EPA also strongly believes that States and authorized Tribes should have the

flexibility to develop criteria, on a site-specific basis, that provide additional protection

appropriate for highly exposed populations. EPA is aware that exposure patterns in general, and

fish consumption in particular, vary substantially. EPA understands that highly exposed

populations may be widely distributed geographically throughout a given State or Tribal area. 

EPA recommends that priority be given to identifying and adequately protecting the most highly

exposed population. Thus, if the State or Tribe determines that a highly exposed population is

at greater risk and would not be adequately protected by criteria based on the general population,

and by the national 304(a) criteria in particular, EPA recommends that the State or Tribe adopt

more stringent criteria using alternative exposure assumptions.

EPA has provided recommended default intake rates for various population groups for

State and Tribal consideration. EPA does not intend for these alternative default values to be

prescriptive. EPA strongly emphasizes its preference that States and Tribes use local or regional

data over EPA’s defaults, if they so choose, as being more representative of their population

groups of concern.

In the course of updating the 2000 Human Health Methodology, EPA received some

questions regarding the population groups for which the criteria would be developed. EPA does

not intend to derive multiple 304(a) criteria for all subpopulation groups for every chemical. As

stated above, criteria that address chronic adverse health effects are most applicable to the CWA

Section 304(a) criteria program and the chemicals evaluated for this program. If EPA

determined that pregnant women/fetuses or young children were the target population (or criteria

basis population) of a chemical’s RfD or POD/UF, then the 304(a) criteria would be developed

using exposure parameters for that subgroup. This would only be relevant for acute or

subchronic toxicity situations. This does not conflict with the fact that chronic health effects

potentially reflect a person’s exposure during both childhood and adult years. 

For RfD-based and POD/UF-based chemicals, EPA’s policy is that, in general, the RfD

(or POD/UF) should not be exceeded and the exposure assumptions used should reflect the

population of concern. It is recommended that when a State or authorized Tribe sets a
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waterbody-specific AWQC, they consider the populations most exposed via water and fish. 

EPA’s policy on cancer risk management goals is discussed in Section 2.4.

Health Risks to Children

In recognition that children have a special vulnerability to many toxic substances, EPA’s

Administrator directed the Agency in 1995 to explicitly and consistently take into account

environmental health risks to infants and children in all risk assessments, risk characterizations,

and public health standards set for the United States. In April 1997, President Clinton signed

Executive Order 13045 on the protection of children from environmental health risks, which

assigned a high priority to addressing risks to children. In May 1997, EPA established the Office

of Children’s Health Protection to ensure the implementation of the President’s Executive Order. 

EPA has increased efforts to ensure its guidance and regulations take into account risks to

children. Circumstances where risks to children should be considered in the context of the 2000

Human Health Methodology are discussed in the Section 3.2, Noncancer Effects (in terms of

developmental and reproductive toxicity) and in Section 4, Exposure (for appropriate exposure

intake parameters). 

Details on risk characterization and the guiding principles stated above are included in

EPA’s March 21, 1995 policy statement and the discussion of risk characterization (USEPA,

1995) and the 1999 Guidelines for Carcinogen Risk Assessment. Review Draft (USEPA, 1999a)

and the Reproductive and Toxicity Risk Assessment Guidelines of 1996 (USEPA, 1996b).

2.2 SCIENCE, SCIENCE POLICY, AND RISK MANAGEMENT

An important part of risk characterization, as described later in Section 2.7, is to make

risk assessments transparent. This means that conclusions drawn from the science are identified

separately from policy judgments and risk management decisions, and that the use of default

values or methods, as well as the use of assumptions in risk assessments, are clearly articulated. 

In this Methodology, EPA has attempted to separate scientific analysis from science policy and

risk management decisions for clarity. This should allow States and Tribes (who are also

prospective users of this Methodology) to understand the elements of the Methodology

accurately and clearly, and to easily separate out the scientific decisions from the science policy

and risk management decisions. This is important so that when questions are asked regarding

the scientific merit, validity, or apparent stringency or leniency of AWQC, the implementer of

the criteria can clearly explain what judgments were made to develop the criterion in question

and to what degree these judgments were based on science, science policy, or risk management. 

To some extent this process will also be displayed in future AWQC documents.

When EPA speaks of science or scientific analysis, it is referring to the extraction of data

from toxicological or exposure studies and surveys with a minimum of judgment being used to

make inferences from the available evidence. For example, if EPA is describing a POD from an

animal study (e.g., a LOAEL), this is usually determined as a lowest dose that produces an

observable adverse effect. This would constitute a scientific determination. Judgments applying

science policy, however, may enter this determination. For example, several scientists may

differ in their opinion of what is adverse, and this in turn can influence the selection of a LOAEL
2-4

in a given study. The use of an animal study to predict effects in a human in the absence of

human data is an inherent science policy decision. The selection of specific UFs when

developing an RfD is another example of science policy. In any risk assessment, a number of

decision points occur where risk to humans can only be inferred from the available evidence. 

Both scientific judgments and policy choices may be involved in selecting from among several

possible inferences when conducting a risk assessment.

Risk management is the process of selecting the most appropriate guidance or regulatory

actions by integrating the results of risk assessment with engineering data and with social,

economic, and political concerns to reach a decision. In this Methodology, the choice of a

default fish consumption rate which is protective of 90 percent of the general population is a risk

management decision. The choice of an acceptable cancer risk by a State or Tribe is a risk

management decision.

Many of the components in the 2000 Human Health Methodology are an amalgam of

science, science policy, and/or risk management. For example, most of the default values chosen

by EPA are based on examination of scientific data and application of either science policy or

risk management. This includes the default assumption of 2 liters a day of drinking water; the

assumption of 70 kilograms for an adult body weight; the use of default percent lipid and

particulate organic carbon/dissolved organic carbon (POC/DOC) for developing national BAFs;

the default fish consumption rates for the general population and sport and subsistence anglers;

and the choice of a default cancer risk level. Some decisions are more grounded in science and

science policy (such as the choice of default BAFs) and others are more obviously risk

management decisions (such as the determination of default fish consumption rates and cancer

risk levels). Throughout the 2000 Human Health Methodology, EPA has identified the kind of

decision necessary to develop defaults and what the basis for the decision was. More details on

the concepts of science analysis, science policy, risk management, and how they are introduced

into risk assessments are included in Risk Assessment in the Federal Government: Managing the

Process (NRC, 1983). 

2.3 SETTING CRITERIA TO PROTECT AGAINST MULTIPLE EXPOSURES

FROM MULTIPLE CHEMICALS (CUMULATIVE RISK)

EPA is very much aware of the complex issues and implications of cumulative risk and

has endeavored to begin developing an overall approach at the Agency-wide level. Assuming

that multiple exposures to multiple chemicals are additive is scientifically sound if they exhibit

the same toxic endpoints and modes of action. There are numerous publications relevant to

cumulative risk that can assist States and Tribes in understanding the complex issues associated

with cumulative risk. These include the following:

< Durkin, P.R., R.C. Hertzberg, W. Stiteler, and M. Mumtaz. 1995. The identification and

testing of interaction patterns. Toxicol. Letters 79:251-264.

< Hertzberg, R.C., G. Rice, and L.K. Teuschler. 1999. Methods for health risk assessment

of combustion mixtures. In: Hazardous Waste Incineration: Evaluating the Human
2-5

Health and Environmental Risks. S. Roberts, C. Teaf and J. Bean, (eds). CRC Press

LLC, Boca Raton, FL. Pp. 105-148.

< Rice, G., J. Swartout, E. Brady-Roberts, D. Reisman, K. Mahaffey, and B. Lyon. 1999.

Characterization of risks posed by combustor emissions. Drug and Chem. Tox. 22:221-

240.

< USEPA. 1999. Guidance for Conducting Health Risk Assessment of Chemical Mixtures. 

Final Draft. Risk Assessment Forum Technical Panel. Washington, DC. NCEA-C0148. September. Web site: http://www.epa.gov/ncea/raf/rafpub.htm

< USEPA. 1998. Methodology for Assessing Health Risks Associated with Multiple

Pathways of Exposure to Combustor Emissions. (Update to EPA/600/6-90/003

Methodology for Assessing Health Risks Associated with Indirect Exposure to Combustor

Emissions). National Center for Environmental Assessment. Washington, DC. EPA600-R-98-137. Website http://www.epa.gov/ncea/combust.htm

< USEPA. 1996. PCBs: Cancer Dose-Response Assessment and Application to

Environmental Mixtures. National Center for Environmental Assessment. Washington,

DC. EPA/600/P-96/001F.

< USEPA. 1993. Review Draft Addendum to the Methodology for Assessing Health Risks

Associated with Indirect Exposure to Combustor Emissions. Office of Health and

Environmental Assessment, Office of Research and Development. Washington, DC. 

EPA/600/AP-93/003. November.

< USEPA. 1993. Provisional Guidance for Quantitative Risk Assessment of Polycyclic

Aromatic Hydrocarbons. Office of Research and Development. Washington, DC.

EPA/600/R-93/089. July.

< USEPA. 1990. Technical Support Document on Health Risk Assessment of Chemical

Mixtures. Office of Research and Development. Washington, DC. EPA/600/8/90/064.

August.

< USEPA. 1989a. Risk Assessment Guidance for Superfund. Vol. 1. Human Health

Evaluation Manual (Part A). Office of Emergency and Remedial Response. 

Washington, DC. EPA/540/1-89/002.

< USEPA. 1989b. Interim Procedures for Estimating Risks Associated with Exposures to

Mixtures of Chlorinated Dibenzo-p-Dioxins and -Dibenzofurans (CDDs and CDFs) and

1989 Update. Risk Assessment Forum. Washington, DC. EPA/625/3-89/016. March.

The Agency’s program offices are also engaged in on-going discussions of the great

complexities, methodological challenges, data adequacy needs and other information gaps, as

well as the science policy and risk management decisions that will need to be made, as they

pursue developing a sound strategy and, eventually, specific guidance for addressing cumulative
2-6

risks. As a matter of internal policy, EPA is committed to refining the Methodology as advances

in relevant aspects of the science improve, as part of the water quality criteria program.

2.4 CANCER RISK RANGE

For deriving 304(a) criteria or promulgating water quality criteria for States and Tribes

under Section 303(c) based on the 2000 Human Health Methodology, EPA intends to use the 10-

6

 risk level, which the Agency believes reflects an appropriate risk for the general population. 

EPA’s program office guidance and regulatory actions have evolved in recent years to target a

10!6 risk level as an appropriate risk for the general population. EPA has recently reviewed the

policies and regulatory language of other Agency mandates (e.g., the Clean Air Act

Amendments of 1990, the Food Quality Protection Act) and believes the target of a 10!6

 risk

level is consistent with Agency-wide practice.

EPA believes that both 10!6

 and 10!5 may be acceptable for the general population and

that highly exposed populations should not exceed a 10!4

 risk level. States or Tribes that have

adopted standards based on criteria at the 10!5 risk level can continue to do so, if the highly

exposed groups would at least be protected at the 10!4

 risk level. However, EPA is not

automatically assuming that 10!5 will protect “the highest consumers” at the 10!4 risk level. Nor

is EPA advocating that States and Tribes automatically set criteria based on assumptions for

highly exposed population groups at the 10!4 risk level. The Agency is simply endeavoring to

add that a specific determination should be made to ensure that highly exposed groups do not

exceed a 10!4 risk level. EPA understands that fish consumption rates vary considerably,

especially among subsistence populations, and it is such great variation among these population

groups that may make either 10!6 or 10!5 protective of those groups at a 10!4 risk level. 

Therefore, depending on the consumption patterns in a given State or Tribal jurisdiction, a 10!6

or 10!5 risk level could be appropriate. In cases where fish consumption among highly exposed

population groups is of a magnitude that a 10!4

 risk level would be exceeded, a more protective

risk level should be chosen. Such determinations should be made by the State or Tribal

authorities and are subject to EPA’s review and approval or disapproval under Section 303(c) of

the CWA.

Adoption of a 10!6

 or 10!5 risk level, both of which States and authorized Tribes have

chosen in adopting water quality standards to date, represents a generally acceptable risk

management decision, and EPA intends to continue providing this flexibility to States and

Tribes. EPA believes that such State or Tribal decisions are consistent with Section 303(c) if the

State or authorized Tribe has identified the most highly exposed subpopulation, has

demonstrated that the chosen risk level is adequately protective of the most highly exposed

subpopulation, and has completed all necessary public participation. States and authorized

Tribes also have flexibility in how they demonstrate this protectiveness and obtain such

information. A State or authorized Tribe may use existing information as well as collect new

information in making this determination. In addition, if a State or authorized Tribe does not

believe that the 10!6

 risk level adequately protects the exposed subpopulations, water quality

criteria based on a more stringent risk level may be adopted. This discretion includes combining

the 10!6

 risk level with fish consumption rates for highly exposed population groups.
2-7

It is important to understand that criteria for carcinogens are based on chosen risk levels

that inherently reflect, in part, the exposure parameters used to derive those values. Therefore,

changing the exposure parameters also changes the risk. Specifically, the incremental cancer

risk levels are relative, meaning that any given criterion associated with a particular cancer risk

level is also associated with specific exposure parameter assumptions (e.g., intake rates, body

weights). When these exposure parameter values change, so does the relative risk. For a

criterion derived on the basis of a cancer risk level of 10!6

, individuals consuming up to 10 times

the assumed fish intake rate would not exceed a 10!5 risk level. Similarly, individuals

consuming up to 100 times the assumed rate would not exceed a 10!4 risk level. Thus, for a

criterion based on EPA’s default fish intake rate (17.5 gm/day) and a risk level of 10!6, those

consuming a pound per day (i.e., 454 grams/day) would potentially experience between a 10!5

and a 10!4 risk level (closer to a 10!5 risk level). (Note: Fish consumers of up to 1,750 gm/day

would not exceed the 10!4 risk level.) If a criterion were based on high-end intake rates and the

relative risk of 10!6, then an average fish consumer would be protected at a cancer risk level of

approximately 10!8

. The point is that the risks for different population groups are not the same.

2.5 MICROBIOLOGICAL AMBIENT WATER QUALITY CRITERIA

Guidance for deriving microbiological AWQC is not a part of this Methodology. In

1986, EPA published Ambient Water Quality Criteria for Bacteria - 1986 (USEPA, 1986a),

which updated and revised bacteriological criteria previously published in 1976 in Quality

Criteria for Water (USEPA, 1976). The inclusion of guidance for deriving microbiological

AWQC was considered in the 1992 national workshop that initiated the effort to revise the 1980

Methodology and was recommended by the SAB in 1993. Since that time, however, efforts

separate from these Methodology revisions have addressed microbiological AWQC concerns. 

The purpose of this section is to briefly describe EPA’s current recommendations and activities.

EPA’s Ambient Water Quality Criteria for Bacteria - 1986 recommends the use of

Escherichia coli and enterococci rather than fecal coliforms (USEPA, 1986a). EPA’s criteria

recommendations are:

• Fresh water: E. coli not to exceed 126/100 ml or enterococci not to exceed 33/100 ml;

and

• Marine water: enterococci not to exceed 35/100 ml.

These criteria should be calculated as the geometric mean based on five equally spaced samples

taken over a 30-day period. 

In addition, EPA recommends that States adopt a single sample maximum, based on the

expected frequency of use. No sample taken should exceed this value. EPA specifies

appropriate single sample maximum values in the 1986 criteria document. 

Current Activities and Plans for Future Work
2-8

EPA has identified development of microbial water quality criteria as part of its strategy

to control waterborne microbial disease, by controlling pathogens in waterbodies and by

protecting designated uses, such as recreation and public water supplies. The program fosters an

integrated approach to protect both ground-water and surface water sources. EPA plans to

conduct additional monitoring for Cryptosporidium parvum and E. coli, and determine action

plans in accordance with the results of this monitoring.

EPA recommends no change at this time in the stringency of its bacterial criteria for

recreational waters; existing criteria and methodologies from 1986 will still apply. The

recommended methods for E. coli and enterococci have been improved. As outlined in the

Action Plan for Beaches and Recreational Waters (Beach Action Plan, see below), the Agency

plans to conduct national studies on improving indicators together with epidemiology studies for

new criteria development (USEPA, 1999b). The Agency is also planning to establish improved

temporal and spatial monitoring protocols.

In the Beach Action Plan, EPA identifies a multi-year strategy for monitoring

recreational water quality and communicating public health risks associated with potentially

pathogen-contaminated recreational rivers, lakes, and ocean beaches. It articulates the Agency’s

rationale and goals in addressing specific problems and integrates all associated program, policy,

and research needs and directions. The Beach Action Plan also provides information on timing,

products and lead organization for each activity. These include activities and products in the

areas of program development, risk communication, water quality indicator research, modeling

and monitoring research, and exposure and health effects research.

Recently, EPA approved new 24-hour E. coli and enterococcus tests for recreational

waters that may be used as an alternative to the 48-hour test (USEPA, 1997). EPA anticipates

proposing these methods for inclusion in the 40 CRF 136 in the Fall of 2000. EPA has also

published a video with accompanying manual on the original and newer methods for enterococci

and E. coli (USEPA, 2000).

As part of the Beach Action Plan, EPA made the following recommendations for further

Agency study:

• Future criteria development should consider the risk of diseases other than

gastroenteritis. EPA intends to consider and evaluate such water-related exposure routes

as inhalation and dermal absorption when addressing microbial health effects. The

nature and significance of other than the classical waterborne pathogens are to some

degree tied to the particular type of waste sources. 

• A new set of indicator organisms may need to be developed for tropical water if it is

proven that the current fecal indicators can maintain viable cell populations in the soil

and water for significant periods of time in uniform tropical conditions. Some potential

alternative indicators to be fully explored are coliphage, other bacteriophage, and

Clostridium perfringens.
2-9

• Because animal sources of pathogens of concern for human infection such as Giardia

lamblia, Cryptosporidium parvum, and Escherichia coli 0157:H7 may be waterborne or

washed into water and thus become a potential source for infection, they should not be

ignored in risk assessment. A likely approach would be phylogenetic differentiation; that

is, indicators that are specific to, or can discriminate among, animal sources.

• EPA intends to develop additional data on secondary infection routes and infection rates

from prospective epidemiology studies and outbreaks from various types of exposure

(e.g., shellfish consumption, drinking water, recreational exposure).

• EPA needs to improve sampling strategies for recreational water monitoring including

consideration of rainfall and pollution events to trigger sampling.

2.6 RISK CHARACTERIZATION CONSIDERATIONS

On March 21, 1995, EPA’s Administrator issued the EPA Risk Characterization Policy

and Guidance (USEPA, 1995). This policy and guidance is intended to ensure that

characterization information from each stage of a risk assessment is used in forming conclusions

about risk and that this information is communicated from risk assessors to risk managers, and

from EPA to the public. The policy also provides the basis for greater clarity, transparency,

reasonableness, and consistency in risk assessments across EPA programs. The fundamental

principles which form the basis for a risk characterization are as follows:

• Risk assessments should be transparent, in that the conclusions drawn from the science

are identified separately from policy judgments, and the use of default values or methods

and the use of assumptions in the risk assessment are clearly articulated.

• Risk characterizations should include a summary of the key issues and conclusions of

each of the other components of the risk assessments, as well as describe the likelihood

of harm. The summary should include a description of the overall strengths and

limitations (including uncertainties) of the assessment and conclusions.

• Risk characterizations should be consistent in general format, but recognize the unique

characteristics of each specific situation.

• Risk characterizations should include, at least in a qualitative sense, a discussion of how

a specific risk and its context compares with similar risks. This may be accomplished by

comparisons with other pollutants or situations on which the Agency has decided to act,

or other situations with which the public may be familiar. The discussion should

highlight the limitations of such comparisons.

• Risk characterization is a key component of risk communication, which is an interactive

process involving exchange of information and expert opinion among individuals,

groups, and institutions.

Additional guiding principles include:
2-10

• The risk characterization integrates the information from the hazard identification, doseresponse, and exposure assessments, using a combination of qualitative information,

quantitative information, and information regarding uncertainties.

• The risk characterization includes a discussion of uncertainty and variability in the risk

assessment.

• Well-balanced risk characterizations present conclusions and information regarding the

strengths and limitations of the assessment for other risk assessors, EPA decision-makers,

and the public.

In developing the methodology presented here, EPA has closely followed the risk

characterization guiding principles listed above. As States and Tribes adopt criteria using the

2000 Human Health Methodology, they are strongly encouraged to follow EPA’s risk

characterization guidance. There are a number of areas within the Methodology and criteria

development process where risk characterization principles apply:

• Integration of cancer and noncancer assessments with exposure assessments, including

bioaccumulation potential determinations, in essence, weighing the strengths and

weaknesses of the risk assessment as a whole when developing a criterion.

• Selecting a fish consumption rate, either locally derived or the national default value,

within the context of a target population (e.g., sensitive subpopulations) as compared to

the general population.

• Presenting cancer and/or noncancer risk assessment options.

• Describing the uncertainty and variability in the hazard identification, the dose-response,

and the exposure assessment.

2.7 DISCUSSION OF UNCERTAINTY

2.7.1 Observed Range of Toxicity Versus Range of Environmental Exposure 

When characterizing a risk assessment, an important distinction to make is between the

observed range of adverse effects (from an epidemiology or animal study) and the

environmentally observed range of exposure (or anticipated human exposure) to the

contaminant. In many cases, EPA intends to apply default factors to account for uncertainties or

incomplete knowledge in developing RfDs or cancer risk assessments using nonlinear low-dose

extrapolation to provide a margin of protection. In reality, the actual effect level and the

environmental exposure levels may be separated by several orders of magnitude. The difference

between the dose causing some observed response and the anticipated human exposure should be

described by risk assessors and managers, especially when comparing criteria to environmental

levels of a contaminant.

2.7.2 Continuum of Preferred Data/Use of Defaults
2-11

In both toxicological and exposure assessments, EPA has defined a continuum of

preferred data for toxicological assessments ranging from a highest preference for chronic

human data (e.g., studies that examine a long-term exposure of humans to a chemical, usually

from occupational and/or residential exposure) and actual field data for many of the exposure

parameter values (e.g., locally derived fish consumption rates, waterbody-specific

bioaccumulation rates), to default values which are at the lower end of the preference continuum. 

EPA has supplied default values for all of the risk assessment parameters in the 2000 Human

Health Methodology; however, it is important to note that when default values are used, the

uncertainty in the final risk assessment may be higher, and the final resulting criterion may not

be as applicable to local conditions, than is a risk assessment derived from human/field data. 

Using defaults assumes generalized conditions and may not capture the actual variability in the

population (e.g., sensitive subpopulations/high-end consumers). If defaults are chosen as the

basis for criteria, these inherent uncertainties should be communicated to the risk manager and

the public. While this continuum is an expression of preference on the part of EPA, it does not

imply in any way that any of the choices are unacceptable or scientifically indefensible.

2.7.3 Significant Figures

The number of significant figures in a numeric value is the number of certain digits plus

one estimated digit. Digits should not be confused with decimal places. For example, 15.1,

0.0151, and 0.0150 all have 3 significant figures. Decimal places may have been used to

maintain the correct number of significant figures, but in themselves they do not indicate

significant figures (Brinker, 1984). Since the number of significant figures must include only

one estimated digit, the sources of input parameters (e.g., fish consumption and water

consumption rates) should be checked to determine the number of significant figures associated

with data they provide. However, the original measured values may not be available to

determine the number of significant figures in the input parameters. In these situations, EPA

recommends utilizing the data as presented.

When developing criteria, EPA recommends rounding the number of significant figures

at the end of the criterion calculation to the same number of significant figures in the least

precise parameter. This is a generally accepted practice which can be found described in greater

detail in APHA (1992) and Brinker (1984). The general rule is that for multiplication or

division, the resulting value should not possess any more significant figures than is associated

with the factor in the calculation with the least precision. When numbers are added or

subtracted, the number that has the fewest decimal places, not necessarily the fewest significant

figures, puts the limit on the number of places that justifiably may be carried in the sum or

difference. Rounding off a number is the process of dropping one or more digits so that the

value contains only those digits that are significant or necessary in subsequent computations

(Brinker, 1984). The following rounding procedures are recommended: (1) if the digit 6, 7, 8, or

9 is dropped, increase the preceding digit by one unit; (2) if the digit 0, 1, 2, 3, or 4 is dropped,

do not alter the preceding digit; and (3) if the digit 5 is dropped, round off the preceding digit to

the nearest even number (e.g., 2.25 becomes 2.2 and 2.35 becomes 2.4) (APHA, 1992; Brinker,

1984).
2-12

(Equation 2-1)

EPA recommends that calculations of water quality criteria be performed without

rounding of intermediate step values. The resulting criterion may be rounded to a manageable

number of decimal places. However, in no case should the number of digits presented exceed

the number of significant figures implied in the data and calculations performed on them. The

term “intermediate step values” refers to values of the parameters in Equations 1-1 through 1-3. 

The final step is considered the resulting AWQC. Although AWQC are, in turn, used for

purposes of establishing water quality-based effluent limits (WQBELs) in National Pollutant

Discharge Elimination System (NPDES) permits, calculating total maximum daily loads

(TMDLs), and applicable or relevant and appropriate requirements (ARARs) for Superfund, they

are considered the final step of this Methodology and, for the purpose of this discussion, where

the rounding should occur.

The determination of appropriate significant figures inevitably involves some judgment

given that some of the equation parameters are adopted default exposure values. Specifically,

the default drinking water intake rate of 2 L/day is a value adopted to represent a majority of the

population over the course of a lifetime. Although supported by drinking water consumption

survey data, this value was adopted as a policy decision and, as such, does not have to be

considered in determining the parameter with the least precision. That is, the resulting AWQC

need not always be reduced to one significant digit. Similarly, the 70-kg adult body weight has

been adopted Agency-wide and represents a default policy decision.

The following example with a simplified AWQC equation illustrates the rule described

above. The example is for hexachlorobutadiene (HCBD), which EPA used to demonstrate the

1998 draft Methodology revisions (USEPA, 1998b). The parameters that were calculated (i.e.,

not policy adopted values) include values with significant figures of two (the POD and RSC),

three (the UF), and four (the FI and BAF). Based on the 2000 Human Health Methodology, the

final criterion should be rounded to two significant figures. The bold numbers in parentheses

indicate the number of significant figures and those with asterisks also indicate Agency adopted

policy values.

Example [Refer to draft HCBD document for details on the POD/UF, RSC and BAF data (EPA

822-R-98-004). Also note that the fish intake rate in this example is the revised value.]:
2-13

AWQC = 7.3 × 10-5 mg/L (0.073 μg/L, rounded from 7.285 × 10-2 μg/L)

* represents Agency adopted policy value

A number of the values used in the equation may result in intermediate step values that

have more than four figures past the decimal place and may be carried throughout the

calculation. However, carrying more than four figures past the decimal place (equivalent to the

most precise parameter) is unnecessary as it has no effect on the resulting criterion value.

2.8 OTHER CONSIDERATIONS

2.8.1 Minimum Data Considerations

For many of the preceding technical areas, considerations have been presented for data

quality in developing toxicological and exposure assessments. For greater detail and discussion

of minimum data recommendations, the reader is referred to the specific sections in the

Methodology on cancer and noncancer risk assessments (and especially to the referenced EPA

risk assessment guidelines documents), exposure assessment, and bioaccumulation assessment,

in addition to the TSD volumes for each. 

2.8.2 Site-Specific Criterion Calculation

The 2000 Human Health Methodology allows for site-specific modifications by States

and Tribes to reflect local environmental conditions and human exposure patterns. “Local” may

refer to any appropriate geographic area where common aquatic environmental or exposure

patterns exist. Thus “local” may signify Statewide, regional, a river reach, or an entire river.

Such site-specific criteria may be developed as long as the site-specific data, either

toxicological or exposure-related, is justifiable. For example, when using a site-specific fish

consumption rate, a State should use a value that represents at least the central tendency of the

population surveyed (either sport or subsistence, or both). If a site-specific fish consumption rate

for sport anglers or subsistence anglers is lower than an EPA default value, it may be used in

calculating AWQC. However, to justify such a level (either higher or lower than EPA defaults),

the State should assemble appropriate survey data to arrive at a defensible site-specific fish

consumption rate. 

Such data must also be submitted to EPA for its review when approving or disapproving

State or Tribal water quality standards under Section 303(c). The same conditions apply to sitespecific calculations of BAF, percent fish lipid, or the RSC. In the case of deviations from

toxicological values (i.e., IRIS values: verified noncancer and cancer assessments), EPA strongly

recommends that the data upon which the deviation is based be presented to and approved by the

Agency before a criterion is developed.

Additional guidance on site-specific modifications to the 2000 Human Health

Methodology is provided in each of the three TSD volumes.

2.8.3 Organoleptic Criteria
2-14

Organoleptic criteria define concentrations of chemicals or materials which impart

undesirable taste and/or odor to water. Organoleptic effects, while significant from an aesthetic

standpoint, are not a significant health concern. In developing and utilizing such criteria, two

factors must be appreciated: (1) the limitations of most organoleptic data; and (2) the human

health significance of organoleptic properties. In the past, EPA has developed organoleptic

criteria if organoleptic data were available for a specific contaminant. The 1980 AWQC

National Guidelines made a clear distinction that organoleptic criteria and toxicity-based criteria

are derived from completely different endpoints, and that organoleptic criteria have no

demonstrated relationship to potential adverse human health effects because there is no

toxicological basis. EPA acknowledges that if organoleptic effects (i.e., objectionable taste and

odor) cause people to reject the water and its designated uses, then the public is effectively

deprived of the natural resource. It is also possible that intense organoleptic characteristics could

result in depressed fluid intake which, in turn, might lead to an indirect human health effect via

decreased fluid consumption. Although EPA has developed organoleptic criteria in the past and

may potentially do so in the future, this will not be a significant part of the water quality criteria

program. EPA encourages the development of organoleptic criteria when States and Tribes

believe they are needed. However, EPA cautions States and Tribes that the quality of

organoleptic data is often significantly less than that of toxicologic data used in establishing

health-based criteria. Therefore, a comprehensive evaluation of available organoleptic data

should be made, and the selection of the most appropriate database for the criterion should be

based on sound scientific judgment.

In 1980, EPA provided recommended criteria summary language when both types of data

are available. The following format was used and is repeated here:

For comparison purposes, two approaches were used to derive criterion levels for

____. Based on available toxicity data, for the protection of public health the

derived level is ____. Using available organoleptic data, for controlling

undesirable taste and odor quality of ambient water the estimated level is ____. 

It should be recognized that organoleptic data as a basis for establishing a water

quality criteria have no demonstrated relationship to potential adverse human

health effects.

Similarly, the 1980 Methodology recommended that in those instances where a level to

limit toxicity cannot be derived, the following statement should be provided:

Sufficient data are not available for ____ to derive a level which would protect

against the potential toxicity of this compound.

2.8.4 Criteria for Chemical Classes

The 2000 Human Health Methodology also allows for the development of a criterion for

classes of chemicals, as long as a justification is provided through the analysis of mechanistic

data, toxicokinetic data, structure-activity relationship data, and limited acute and chronic

toxicity data. When potency differences between members of a class is great (such as in the case
2-15

of chlorinated dioxins and furans), toxicity equivalency factors (TEFs) may be more

appropriately developed than one class criterion. 

A chemical class is defined as any group of chemical compounds which are similar in

chemical structure and biological activity, and which frequently occur together in the

environment usually because they are generated by the same commercial process. In criterion

development, isomers should be regarded as part of a chemical class rather than as a single

compound. A class criterion, therefore, is an estimate of risk/safety which applies to more than

one member of a class. It involves the use of available data on one or more chemicals of a class

to derive criteria for other compounds of the same class in the event that there are insufficient

data available to derive compound-specific criteria. The health-based criterion may apply to the

water concentration of each member of the class, or may apply to the sum of the water

concentrations of the compounds within the class. Because relatively minor structural changes

within the class of compounds can have pronounced effects on their biological activities, reliance

on class criteria should be minimized depending on the data available. 

The following guidance should also be followed when considering the development of a

class criterion.

• A detailed review of the chemical and physical properties of the chemicals within the

group should be made. A close relationship within the class with respect to chemical

activity would suggest a similar potential to reach common biological sites within tissues. 

Likewise, similar lipid solubilities would suggest the possibility of comparable

absorption and distribution.

• Qualitative and quantitative toxicological data for chemicals within the group should be

examined. Adequate toxicological data on a number of compounds within a group

provides a more reasonable basis for extrapolation to other chemicals of the same class

than minimal data on one chemical or a few chemicals within the group.

• Similarities in the nature of the toxicological response to chemicals in the class provides

additional support for the prediction that the response to other members of the class may

be similar. In contrast, where the biological response has been shown to differ markedly

on a qualitative and quantitative basis for chemicals within a class, the extrapolation of a

criterion to other members is not appropriate.

• Additional support for the validity of extrapolation of a criterion to other members of a

class could be provided by evidence of similar metabolic and toxicokinetic data for some

members of the class.

Additional guidance is described in the Technical Support Document on Health Risk

Assessment of Chemical Mixtures (USEPA, 1990).
2-16

2.9.5 Criteria for Essential Elements
2-17

Developing criteria for essential elements, particularly metals, must be a balancing act

between toxicity and the requirement for good health. The AWQC must consider essentiality

and cannot be established at levels that would result in deficiency of the element in the human

population. The difference between the recommended daily allowance (RDA) and the daily

doses causing a specified risk level for carcinogens or the RfDs for noncarcinogens defines the

spread of daily doses within which the criterion may be derived. Because errors are inherent in

defining both essential and adverse-effect levels, the criterion is derived from a dose level near

the center of such dose ranges.

The process for developing criteria for essential elements should be similar to that used

for any other chemical with minor modifications. The RfD represents concern for one end of the

exposure spectrum (toxicity), whereas the RDA represents the other end (minimum essentiality). 

While the RDA and RfD values might occasionally appear to be similar in magnitude to one

another, it does not imply incompatibility of the two methodological approaches, nor does it

imply inaccuracy or error in either calculation.

2.9 REFERENCES

APHA. American Public Health Association. 1992. Standard Methods: For the Examination

of Water and Wastewater. 18th Edition. Prepared and published jointly by: American

Public Health Association, American Water Works Association, and Water Environment

Federation. Washington, DC.

Brinker, R.C. 1984. Elementary Surveying. 7th Edition. Cliff Robichaud and Robert Greiner,

Eds. Harper and Row Publishers, Inc. New York, NY.

NRC (National Research Council). 1983. Risk Assessment in the Federal Government:

Managing the Process. National Academy Press. Washington, DC. 

USEPA (U.S. Environmental Protection Agency). 1976. Quality Criteria for Water. Office of

Water and Hazardous Materials. Washington, DC. July.

USEPA (U.S. Environmental Protection Agency). 1986a. Ambient Water Quality Criteria for

Bacteria – 1986. Office of Water Regulations and Standards. Washington, DC. 

EPA/440/5-84/002. January.

USEPA (U.S. Environmental Protection Agency). 1986b. Test Methods for Escherichia coli

and Enterococci in Water by the Membrane Filter Procedure. Office of Research and

Development. Cincinnati, OH. EPA/600/4-85/076.

USEPA (U.S. Environmental Protection Agency). 1990. Technical Support Document on

Health Risk Assessment of Chemical Mixtures. Office of Research and Development. 

Washington, DC. EPA/600/8-90/064. August.

USEPA (U.S. Environmental Protection Agency). 1992. Guidelines for exposure assessment.

Federal Register 57:22888-22938.
2-18

USEPA (U.S. Environmental Protection Agency). 1995. Policy for Risk Characterization.

Memorandum of Carol M. Browner, Administrator. March 21, 1995. Washington, DC.

USEPA (U.S. Environmental Protection Agency). 1996a. Draft revisions to guidelines for 

carcinogen risk assessment. Federal Register 61:17960.

USEPA (U.S. Environmental Protection Agency). 1996b. Guidelines for reproductive toxicity

risk assessment. Federal Register 61:6274-56322.

USEPA (U.S. Environmental Protection Agency). 1997. Method 1600: Membrane Filter Test

Method for Enterococci in Water. Office of Water. Washington, DC. EPA/821/R97/004. May.

USEPA (U.S. Environmental Protection Agency). 1998a. Draft Water Quality Criteria

Methodology: Human Health. Office of Water. Washington, DC. EPA-822-Z-98-001. 

(Federal Register 63:43756) 

USEPA (U.S. Environmental Protection Agency). 1998b. Ambient Water Quality Criteria for

the Protection of Human Health. Hexachlorobutadiene (HCBD). Draft. Office of

Water. Washington, DC. EPA 882-R-98-004. July.

USEPA (U.S. Environmental Protection Agency). 1999a. 1999 Guidelines for Carcinogen Risk 

Assessment. Review Draft. Office of Research and Development. Washington, DC. 

NCEA-F-0644.

USEPA (U.S. Environmental Protection Agency). 1999b. Action Plan for Beaches and 

Recreational Waters. Reducing Exposures to Waterborne Pathogens. Office of

Research and Development and Office of Water. Washington, DC. EPA-600-R-98-079. 

March.

USEPA (U.S. Environmental Protection Agency). 2000. Improved Enumeration Methods for

the Recreational Water Quality Indicators: Enterococci and Escherichia coli. Office of

Water, Office of Science and Technology. Washington, DC. EPA-821-R-97-004. 

March.
3-1

3. RISK ASSESSMENT

This section describes the methods used to estimate ambient water quality criteria

(AWQC) for the protection of human health for carcinogenic chemicals (Section 3.1) and for

noncarcinogenic chemicals (Section 3.2). 

3.1 CANCER EFFECTS

3.1.1 Background on EPA Cancer Risk Assessment Guidelines

The current EPA Guidelines for Carcinogen Risk Assessment were published in 1986

(USEPA, 1986a, hereafter the “1986 cancer guidelines”). The 1986 cancer guidelines categorize

chemicals into alpha-numerical Groups: A, known human carcinogen (sufficient evidence from

epidemiological studies or other human studies); B, probable human carcinogen (sufficient

evidence in animals and limited or inadequate evidence in humans); C, possible human

carcinogen (limited evidence of carcinogenicity in animals in the absence of human data); D, not

classifiable (inadequate or no animal evidence of carcinogenicity); and E, evidence of

noncarcinogenicity for humans (no evidence of carcinogenicity in at least two adequate animal

tests in different species or in both adequate epidemiological and animal studies). Within Group

B there are two subgroups, Groups B1 and B2. Group B1 is reserved for agents for which there

is limited evidence of carcinogenicity from epidemiological studies. Group B2 is generally for

agents for which there is sufficient evidence from animal studies and for which there is

inadequate evidence or no data from epidemiological studies (USEPA, 1986). The system was

similar to that used by the International Agency for Research on Cancer (IARC). 

The 1986 cancer guidelines include guidance on what constitutes sufficient, limited, or

inadequate evidence. In epidemiological studies, sufficient evidence indicates a causal

relationship between the agent and human cancer; limited evidence indicates that a causal

relationship is credible, but that alternative explanations, such as chance, bias, or confounding,

could not adequately be excluded; inadequate evidence indicates either lack of pertinent data, or

a causal interpretation is not credible. In general, although a single study may be indicative of a

cause-effect relationship, confidence in inferring a causal association is increased when several

independent studies are concordant in showing the association. In animal studies, sufficient

evidence includes an increased incidence of malignant tumors or combined malignant and

benign tumors:

• In multiple species or strains; 

• In multiple experiments (e.g., with different routes of administration or using different

dose levels);

• To an unusual degree in a single experiment with regard to high incidence, unusual site

or type of tumor, or early age at onset;

• Additional data on dose-response, short-term tests, or structural activity relationships.
3-2

In the 1986 cancer guidelines, hazard identification and the weight-of-evidence process

focus on tumor findings. The weight-of-evidence approach for making judgments about cancer

hazard analyzes human and animal tumor data separately, then combines them to make the

overall conclusion about potential human carcinogenicity. The next step of the hazard analysis

is an evaluation of supporting evidence (e.g., mutagenicity, cell transformation) to determine

whether the overall weight-of-evidence conclusion should be modified.

For cancer risk quantification, the 1986 cancer guidelines recommend the use of

linearized multistage model (LMS) as the only default approach. The 1986 cancer guidelines

also mention that a low-dose extrapolation model other than the LMS might be considered more

appropriate based on biological grounds. However, no guidance is given in choosing other

approaches. The 1986 cancer guidelines recommended the use of body weight raised to the 2/3

power (BW2/3) as a dose scaling factor between species.

3.1.2 EPA’s Proposed Guidelines for Carcinogen Risk Assessment and the

Subsequent July, 1999 Draft Revised Cancer Guidelines

In 1996, EPA published Proposed Guidelines for Carcinogen Risk Assessment (USEPA,

1996a, hereafter the “1996 proposed cancer guidelines”). After the publication of the 1996

proposed cancer guidelines and a February, 1997 and January, 1999 Science Advisory Board

(SAB) review, a revision was made in July, 1999 Guidelines for Carcinogen Risk Assessment -

Review Draft (hereafter the “1999 draft revised cancer guidelines”; USEPA, 1999a), and an SAB

meeting was convened to review this revised document. When final guidelines are published,

they will replace the 1986 cancer guidelines. These revisions are designed to ensure that the

Agency’s cancer risk assessment methods reflect the most current scientific information and

advances in risk assessment methodology. 

In the meanwhile, the 1986 guidelines are used and extended with principles discussed in

the 1999 draft revised cancer guidelines. These principles arise from scientific discoveries

concerning cancer made in the last 15 years and from EPA policy of recent years supporting full

characterization of hazard and risk both for the general population and potentially sensitive

groups such as children. These principles are incorporated in recent and ongoing assessments

such as the reassessment of dioxin, consistent with the 1986 guidelines. Until final guidelines

are published, information is presented to describe risk under both the 1986 guidelines and 1999

draft revisions. 

The 1999 draft revised cancer guidelines call for the full use of all relevant information to

convey the circumstances or conditions under which a particular hazard is expressed (e.g., route,

duration, pattern, or magnitude of exposure). They emphasize understanding the mode of action

(MOA) whereby the agent induces tumors. The MOA underlies the hazard assessment and

provides the rationale for dose-response assessments. 

The key principles in the 1999 draft revised cancer guidelines include:
3-3

a) Hazard assessment is based on the analysis of all biological information rather

than just tumor findings. 

b) An agent’s MOA in causing tumors is emphasized to reduce the uncertainty in

describing the likelihood of harm and in determining the dose-response

approach(es). 

c) The 1999 draft revised cancer guidelines emphasize the conditions under which

the hazard may be expressed (e.g., route, pattern, duration and magnitude of

exposure). Further, the guidelines call for a hazard characterization to integrate

the data analysis of all relevant studies into a weight-of-evidence conclusion of

hazard and to develop a working conclusion regarding the agent’s mode of action

in leading to tumor development.

d) A weight-of-evidence narrative with accompanying descriptors (listed in Section

3.1.3.1 below) would replace the current alphanumeric classification system. The

narrative summarizes the key evidence for carcinogenicity, describes the agent’s

MOA, characterizes the conditions of hazard expression, including route of

exposure, describes any disproportionate effects on subgroups of the human

population (e.g., children), and recommends appropriate dose-response

approach(es). Significant strengths, weaknesses, and uncertainties of contributing

evidence are also highlighted.

e) Biologically based extrapolation models are the preferred approach for

quantifying risk. These models integrate data and conclusions about events in the

carcinogenic process throughout the dose-response range from high to low doses.

It is anticipated, however, that the necessary data for the parameters used in such

models will not be available for most chemicals. The 1999 draft revised cancer

guidelines allow for alternative quantitative methods, including several default

approaches.

f) Dose-response assessment is a two-step process. In the first step, response data

are modeled in the observable range of data and a determination is made of the

point of departure (POD) from the observed range to extrapolate to low doses. 

The second step is extrapolation from the POD to estimate dose-response at lower

doses. In addition to modeling tumor data, the 1999 draft revised cancer

guidelines call for the use and modeling of other kinds of responses if they are

considered to be more informed measures of carcinogenic risk. Nominally, these

responses reflect key events in the carcinogenic process integral to the MOA of

the agent. 

g) Three default approaches are provided–linear, nonlinear, or both when adequate

data are unavailable to generate a biologically based model. As the first step for

all approaches, curve fitting in the observed range is used to determine a POD. A

standard POD is the effective dose corresponding to the lower 95 percent limit on
3

 Use of the LED10 as the point of departure is recommended with this Methodology, as it is with the 1999 draft revised 

cancer guidelines. 

4

 Additional information regarding the revised method for assessing carcinogens may be found in the Methodology for

Deriving Ambient Water Quality Criteria for the Protection of Human Health (2000). Technical Support Document, Volume 1:

Risk Assessment (USEPA, 2000).

3-4

a dose associated with 10 percent extra risk (LED10).3

 Linear: The linear default

is a straight line extrapolation from the response at LED10 to the origin (zero dose,

zero extra risk). Nonlinear: The nonlinear default begins with the identified POD

and provides a margin of exposure (MOE) analysis rather than estimating the

probability of effects at low doses. The MOE analysis is used to determine the

appropriate margin between the POD and the exposure level of interest, in this

Methodology, the AWQC. The key objective of the MOE analysis is to describe

for the risk manager how rapidly responses may decline with dose. Other factors

are also considered in the MOE analysis (i.e., nature of the response, slope of the

dose-response curve, human sensitivity compared with experimental animals,

nature and extent of human variability in sensitivity and human exposure). 

Linear and nonlinear: Section 3.1.3.4E describes the situations when both linear

and nonlinear defaults are used.

h) The approach used to calculate an oral human equivalent dose when assessments

are based on animal bioassays has been refined and includes a change in the

default assumption for interspecies dose scaling. The 1999 draft revised cancer

guidelines use body weight raised to the 3/4 power.

 EPA health risk assessment practices for both cancer and noncancer endpoints are

beginning to come together with recent proposals to emphasize MOA understanding in risk

assessment and to model response data in the observable range to derive PODs for data sets and

benchmark doses (BMDs) for individual studies. The modeling of observed response data to

identify PODs in a standard way will help to harmonize cancer and noncancer dose-response

approaches and permit comparisons of cancer and noncancer risk estimates.

3.1.3 Methodology for Deriving AWQC4

 by the 1999 Draft Revised Cancer Guidelines 

Following the publication of the Draft Water Quality Criteria Methodology: Human

Health (USEPA, 1998a) and the accompanying TSD (USEPA, 1998b), EPA received comments

from the public. EPA also held an external peer review of the draft Methodology. Both the peer

reviewers and the public recommended that EPA incorporate the new approaches into the

AWQC Methodology. 

Until new guidelines are published, the 1986 cancer guidelines will be used along with

principles of the 1999 draft revised cancer guidelines. The 1986 guidelines are the basis for IRIS

risk numbers which were used to derive the current AWQC. Each new assessment applying the

principles of the 1999 draft revised cancer guidelines will be subject to peer review before being

used as the basis of AWQC.
5 The weight-of-evidence narrative is intended for the risk manager, and thus explains in nontechnical language the key

data and conclusions, as well as the conditions for hazard expression. Conclusions about potential human carcinogenicity are

presented by route of exposure. Contained within this narrative are simple likelihood descriptors that essentially distinguish

whether there is enough evidence to make a projection about human hazard (i.e., Carcinogenic to humans; Likely to be

carcinogenic to humans; Suggestive evidence of carcinogenicity but not sufficient to assess human carcinogenic potential; Data

are inadequate for an assessment of human carcinogenic potential; and Not likely to be carcinogenic to humans). Because one

encounters a variety of data sets on agents, these descriptors are not meant to stand alone; rather, the context of the weight-ofevidence narrative is intended to provide a transparent explanation of the biological evidence and how the conclusions were

derived. Moreover, these descriptors should not be viewed as classification categories (like the alphameric system), which often

obscure key scientific differences among chemicals. The new weight-of-evidence narrative also presents conclusions about how

the agent induces tumors and the relevance of the mode of action to humans, and recommends a dose-response approach based

on the MOA understanding (USEPA, 1996a, 1999a).

3-5

The remainder of Section 3 illustrates the methodology for deriving numerical AWQC

for carcinogens applying the 1999 draft revised cancer guidelines (USEPA, 1999a). This

discussion of the revised methodology for carcinogens focuses primarily on the quantitative

aspects of deriving numerical AWQC values. It is important to note that the cancer risk

assessment process outlined in the 1999 draft revised cancer guidelines is not limited to the

quantitative aspects. A numerical AWQC value derived for a carcinogen is to be based on

appropriate hazard characterization and accompanied by risk characterization information. 

This section contains a discussion of the weight-of-evidence narrative, that describes all

information relevant to a cancer risk evaluation, followed by a discussion of the quantitative

aspects of deriving numerical AWQC values for carcinogens. It is assumed that data from an

appropriately conducted animal bioassay or human epidemiological study provide the underlying

basis for deriving the AWQC value. The discussion focuses on the following: (1) the weight-ofevidence narrative; (2) general considerations and framework for analysis of the MOA; (3) dose

estimation; (4) characterizing dose-response relationships in the range of observation and at low,

environmentally relevant doses; (5) calculating the AWQC value; (6) risk characterization; and

(7) use of Toxicity Equivalent Factors (TEF) and Relative Potency Estimates. The first three

topics encompass the quantitative aspects of deriving AWQC for carcinogens.

3.1.3.1 Weight-of-Evidence Narrative5

The 1999 draft revised cancer guidelines include a weight-of-evidence narrative that is

based on an overall judgment of biological and chemical/physical considerations. Hazard

assessment information accompanying an AWQC value for a carcinogen in the form of a weightof-evidence narrative is described in the footnote. Of particular importance is that the weight-ofevidence narrative explicitly provides adequate support based on human studies, animal

bioassays, and other key evidence for the conclusion whether the substance is or is likely to be

carcinogenic to humans from exposures through drinking water and/or fish ingestion. The

Agency emphasizes the importance of providing an explicit discussion of the MOA for the

substance in the weight-of-evidence narrative if data are available, including a discussion that

relates the MOA to the quantitative procedures used in the derivation of the AWQC. 

3.1.3.2 Mode of Action - General Considerations and Framework for Analysis
6

A “key event” is an empirically observable, precursor step that is itself a necessary element of the mode of action, or

is a marker for such an element.

3-6

An MOA is composed of key events and processes starting with the interaction of an

agent with a cell, through operational and anatomical changes, resulting in cancer formation. 

“Mode” of action is contrasted with “mechanism” of action, which implies a more detailed,

molecular description of events than is meant by MOA. 

Mode of action analysis is based on physical, chemical, and biological information that

helps to explain key events6 in an agent’s influence on development of tumors. Inputs to MOA

analysis include tumor data in humans, animals, and among structural analogues as well as the

other key data. 

There are many examples of possible modes of carcinogenic action, such as

mutagenicity, mitogenesis, inhibition of cell death, cytotoxicity with reparative cell proliferation,

and immune suppression. All pertinent studies are reviewed in analyzing an MOA, and an

overall weighing of evidence is performed, laying out the strengths, weaknesses, and

uncertainties of the case as well as potential alternative positions and rationales. Identifying data

gaps and research needs is also part of the assessment.

 Mode of action conclusions are used to address the question of human relevance of

animal tumor responses, to address differences in anticipated response among humans such as

between children and adults or men and women, and as the basis of decisions about the

anticipated shape of the dose-response relationship.

In reaching conclusions, the question of “general acceptance” of an MOA will be tested

as part of the independent peer review that EPA obtains for its assessment and conclusions. 

 Framework for Evaluating a Postulated Carcinogenic Mode(s) of Action

The framework is intended to be an analytic tool for judging whether available data

support a mode of carcinogenic action postulated for an agent and includes nine elements:

1. Summary description of postulated MOA

2. Identification of key events

3. Strength, consistency, specificity of association

4. Dose-response relationship

5. Temporal relationship

6. Biological plausibility and coherence

7. Other modes of action

8. Conclusion

9. Human relevance, including subpopulations

3.1.3.3 Dose Estimation
3-7

A. Determining the Human Equivalent Dose by the Oral Route

An important objective in the dose-response assessment is to use a measure of internal or

delivered dose at the target site where possible. This is particularly important in those cases

where the carcinogenic response information is being extrapolated to humans from animal

studies. Generally, by the oral exposure route, the measure of a dose provided in the underlying

human studies or animal bioassays is the applied dose, typically given in terms of unit mass per

unit body weight per unit time, (e.g., mg/kg-day). When animal bioassay data are used, it is

necessary to make adjustments to the applied dose values to account for differences in

toxicokinetics between animals and humans that affect the relationship between applied dose and

delivered dose at the target organ. 

In the estimation of a human equivalent dose, the 1999 draft revised cancer guidelines

recommend that when adequate data are available, the doses used in animal studies can be

adjusted to equivalent human doses using toxicokinetic information on the particular agent. 

However, in most cases, there are insufficient data available to compare dose between species. 

In these cases, the estimate of a human equivalent dose is based on science policy default

assumptions. To derive an equivalent human oral dose from animal data, the default procedure

in the 1999 draft revised cancer guidelines is to scale daily applied oral doses experienced for a

lifetime in proportion to body weight raised to the 3/4 power (BW3/4). The adjustment factor is

used because metabolic rates, as well as most rates of physiological processes that determine the

disposition of dose, scale this way. Thus, the rationale for this factor rests on the empirical

observation that rates of physiological processes consistently tend to maintain proportionality

with body weight raised to 3/4 power (USEPA, 1992a, 1999a). 

 The use of BW3/4 is a departure from the scaling factor of BW2/3 that was based on

surface area adjustment and was included in the 1980 AWQC National Guidelines as well as the

1986 cancer guidelines.

B. Dose-Response Analysis

If data on the agent are sufficient to support the parameters of a biologically based model

and the purpose of the assessment is such as to justify investing resources supporting its use, this

is the preferred approach for both the observed tumor and related response data and for

extrapolation below the range of observed data in either animal or human studies.

3.1.3.4 Characterizing Dose-Response Relationships in the Range of Observation and at

Low Environmentally Relevant Doses

The first quantitative component in the derivation of AWQC for carcinogens is the doseresponse assessment in the range of observation. For most agents, in the absence of adequate

data to generate a biologically based model, dose-response relationships in the observed range

can be addressed through curve-fitting procedures for response data. It should be noted that the

1999 draft revised cancer guidelines call for modeling of not only tumor data in the observable

range, but also other responses thought to be important events preceding tumor development

(e.g., DNA adducts, cellular proliferation, receptor binding, hormonal changes). The modeling of
3-8

these data is intended to better inform the dose-response assessment by providing insights into

the relationships of exposure (or dose) below the observable range for tumor response. These

non-tumor response data can only play a role in the dose-response assessment if the agent’s

carcinogenic mode of action is reasonably understood, as well as the role of that precursor event.

The 1999 draft revised cancer guidelines recommend calculating the lower 95 percent

confidence limit on a dose associated with an estimated 10 percent increased tumor or relevant

non-tumor response (LED10) for quantitative modeling of dose-response relationships in the

observed range. The estimate of the LED10 is used as the POD for low-dose extrapolations

discussed below. This standard point of departure (LED10) is adopted as a matter of science

policy to remain as consistent and comparable from case to case as possible. It is also a

convenient comparison point for noncancer endpoints. The rationale supporting use of the

LED10 is that a 10 percent response is at or just below the limit of sensitivity for discerning a

statistically significant tumor response in most long-term rodent studies and is within the

observed range for other toxicity studies. Use of lower limit takes experimental variability and

sample size into account. The ED10 (central estimate) is also presented as a reference for

comparison uses, especially for use in relative hazard/potency ranking among agents for priority

setting.

For some data sets, a choice of the POD other than the LED10 may be appropriate. The

objective is to determine the lowest reliable part of the dose-response curve for the beginning of

the second step of the dose-response assessment—determine the extrapolation range. Therefore,

if the observed response is below the LED10, then a lower point may be a better choice (e.g.,

LED5). Human studies more often support a lower POD than animal studies because of greater

sample size.

The POD may be a NOAEL when a margin of exposure analysis is the nonlinear doseresponse approach. The kinds of data available and the circumstances of the assessment both

contribute to deciding to use a NOAEL or LOAEL which is not as rigorous or as ideal as curve

fitting, but can be appropriate. If several data sets for key events and tumor response are

available for an agent, and they are a mixture of continuous and incidence data, the most

practicable way to assess them together is often through a NOAEL/LOAEL approach. 

When an LED value estimated from animal data is used as the POD, it is adjusted to the

human equivalent dose using an interspecies dose adjustment or a toxicokinetic analysis as

described in Section 3.1.3.3.

Analysis of human studies in the observed range is designed on a case-by-case basis

depending on the type of study and how dose and response are measured in the study.

A. Extrapolation to Low, Environmentally Relevant Doses

In most cases, the derivation of an AWQC will require an evaluation of carcinogenic risk

at environmental exposure levels substantially lower than those used in the underlying study. 

Various approaches are used to extrapolate risk outside the range of observed experimental data. 

In the 1999 draft revised cancer guidelines, the choice of extrapolation method is largely
7

 For discussion of the cancer risk range, see Section 2.4.

3-9

dependent on the mode of action. It should be noted that the term “mode of action” (MOA) is

deliberately chosen in the 1999 draft revised cancer guidelines in lieu of the term “mechanism”

to indicate using knowledge that is sufficient to draw a reasonable working conclusion without

having to know the processes in detail as the term mechanism might imply. The 1999 draft

revised cancer guidelines favor the choice of a biologically based model, if the parameters of

such models can be calculated from data sources independent of tumor data. It is anticipated that

the necessary data for such parameters will not be available for most chemicals. Thus, the 1999

draft revised cancer guidelines allow for several default extrapolation approaches (low-dose

linear, nonlinear, or both).

B. Biologically Based Modeling Approaches

If a biologically based approach has been used to characterize the dose-response

relationships in the observed range, and the confidence in the model is high, it may be used to

extrapolate the dose-response relationship to environmentally relevant doses. For the purposes

of deriving AWQC, the environmentally relevant dose would be the risk-specific dose (RSD)

associated with incremental lifetime cancer risks in the 10-6 to 10-4 range for carcinogens for

which a linear extrapolation approach is applied.7

 The use of the RSD and the POD/UF to

compute the AWQC is presented in Section 3.1.3.5, below. Although biologically-based

approaches are appropriate both for characterizing observed dose-response relationships and

extrapolating to environmentally relevant doses, it is not expected that adequate data will be

available to support the use of such approaches for most substances. In the absence of such data,

the default linear approach, the nonlinear (MOE) approach, or both linear and nonlinear

approaches will be used.
3-10

C. Default Linear Extrapolation Approach 

The default linear approach replaces the LMS approach that has served as the default for

EPA cancer risk assessments. Any of the following conclusions leads to selection of a linear

dose-response assessment approach:

C There is an absence of sufficient tumor MOA information.

• The chemical has direct DNA mutagenic reactivity or other indications of DNA 

effects that are consistent with linearity.

• Human exposure or body burden is high and near doses associated with key 

events in the carcinogenic process (e.g., 2,3,7,8-tetrachlorodibenzo-p-dioxin).

• Mode of action analysis does not support direct DNA effects, but the doseresponse relationship is expected to be linear (e.g., certain receptor-mediated 

effects).

The procedures for implementing the default linear approach begin with the estimation of

a POD as described above. The point of departure, LED10, reflects the interspecies conversion to

the human equivalent dose and the other adjustments for less-than-lifetime experimental

duration. In most cases, the extrapolation for estimating response rates at low, environmentally

relevant exposures is accomplished by drawing a straight line between the POD and the origin

(i.e., zero dose, zero extra risk). This is mathematically represented as:

y = mx + b (Equation 3-1) 

b = 0

where:

y = Response or incidence

m = Slope of the line (cancer potency factor) = ay/ax

x = Dose

b = Slope intercept

The slope of the line, “m” (the estimated cancer potency factor at low doses), is

computed as:

(Equation 3-2) 

The RSD is then calculated for a specific incremental targeted lifetime cancer risk (in the range

of 10-6 to 10-4) as:
8

In 1980, the target lifetime cancer risk range was set at 10-7 to 10-5. However, both the expert panel for the AWQC

workshop (USEPA, 1993) and the peer review workshop experts (USEPA,1999c) recommended that EPA change the risk range

to 10-6 to 10-4, to be consistent with SDWA program decisions. See Section 2.4 for more details.

3-11

(Equation 3-3) 

where:

RSD = Risk-specific dose (mg/kg-day)

Target Incremental 

 Cancer Risk8 = Value in the range of 10-6 to10-4

m = Cancer potency factor (mg/kg-day)-1

The use of the RSD to compute the AWQC is described in Section 3.1.3.5 below.

D. Default Nonlinear Approach 

As discussed in the 1999 draft revised cancer guidelines, any of the following

conclusions leads to a selection of a nonlinear (MOE) approach to dose-response assessment:

• A tumor MOA supporting nonlinearity applies (e.g., some cytotoxic and hormonal agents

such as disruptors of hormonal homeostasis), and the chemical does not demonstrate

mutagenic effects consistent with linearity.

• An MOA supporting nonlinearity has been demonstrated, and the chemical has some

indication of mutagenic activity, but it is judged not to play a significant role in tumor

causation.

Thus, a default assumption of nonlinearity is appropriate when there is no evidence for

linearity and sufficient evidence to support an assumption of nonlinearity. The MOA may lead

to a dose-response relationship that is nonlinear, with response falling much more quickly than

linearly with dose, or being most influenced by individual differences in sensitivity. 

Alternatively, the MOA may theoretically have a threshold (e.g., the carcinogenicity may be a

secondary effect of toxicity or of an induced physiological change that is itself a threshold

phenomenon).

The nonlinear approach may be used, for instance, in the case of a bladder tumor inducer,

where the chemical is not mutagenic and causes only stone formation in male rat bladders at high

doses. This dynamic leads to tumor formation only at the high doses. Stone and subsequent

tumor formation are not expected to occur at doses lower than those that induce the

physiological changes that lead to stone formation. (More detail on this chemical is provided in

the cancer section of the Risk Assessment TSD; USEPA, 2000). EPA does not generally try to

distinguish between modes of action that might imply a “true threshold” from others with a
3-12

nonlinear dose-response relationship, because there is usually not sufficient information to

distinguish between those possibilities empirically. 

The nonlinear MOE approach in the 1986 proposed cancer guidelines compares an

observed response rate such as the LED10, NOAEL, or LOAEL with actual or nominal

environmental exposures of interest by computing the ratio between the two. In the context of

deriving AWQC, the environmentally relevant exposures are nominal targets rather than actual

exposures.

If the evidence for an agent indicates nonlinearity (e.g., when carcinogenicity is

secondary to another toxicity for which there is a threshold), the MOE analysis for the toxicity is

similar to what is done for a noncancer endpoint, and an RfD or RfC for that toxicity may also be

estimated and considered in the cancer assessment. However, a threshold of carcinogenic

response is not necessarily assumed. It should be noted that for cancer assessment, the MOE

analysis begins from a POD that is adjusted for toxicokinetic differences between species to give

a human equivalent dose.

To support the use of the MOE approach, risk assessment information provides

evaluation of the current understanding of the phenomena that may be occurring as dose

(exposure) decreases substantially below the observed data. This gives information about the

risk reduction that is expected to accompany a lowering of exposure. The various factors that

influence the selection of the UF in an MOE approach are also discussed below.

 There are two main steps in the MOE approach. The first step is the selection of a POD. 

The POD may be the LED10 for tumor incidence or a precursor, or in some cases, it may also be

appropriate to use a NOAEL or LOAEL value. When animal data are used, the POD is a human

equivalent dose or concentration arrived at by interspecies dose adjustment (as discussed in

Section 3.1.3.3) or toxicokinetic analysis.

The second step in using MOE analysis to establish AWQC is the selection of an

appropriate margin or UF to apply to the POD. This is supported by analyses in the MOE

discussion in the risk assessment. The following issues should be considered when establishing

the overall UF for the derivation of AWQC using the MOE approach (others may be found

appropriate in specific cases):

• The nature of the response used for the dose-response assessment, for instance, whether it

is a precursor effect or a tumor response. The latter may support a greater MOE.

• The slope of the observed dose-response relationship at the POD and its uncertainties and

implications for risk reduction associated with exposure reduction. (A steeper slope

implies a greater reduction in risk as exposure decreases. This may support a smaller

MOE).

• Human sensitivity compared with that of experimental animals.

• Nature and extent of human variability and sensitivity.
3-13

(Equation 3-4)

• Human exposure. The MOE evaluation also takes into account the magnitude,

frequency, and duration of exposure. If the population exposed in a particular scenario is

wholly or largely composed of a subpopulation of special concern (e.g., children) for

whom evidence indicates a special sensitivity to the agent’s MOA, an adequate MOE

would be larger than for general population exposure.

E. Both Linear and Nonlinear Approaches 

Any of the following conclusions leads to selection of both a linear and nonlinear

approach to dose-response assessment. Relative support for each dose-response method and

advice on the use of that information needs to be documented for the AWQC. In some cases,

evidence for one MOA is stronger than for the other, allowing emphasis to be placed on that

dose-response approach. In other cases, both modes of action are equally possible, and both

dose-response approaches should be emphasized. 

C Modes of action for a single tumor type support both linear and nonlinear dose response

in different parts of the dose-response curve (e.g., 4,4' methylene chloride).

C A tumor mode of action supports different approaches at high and low doses; e.g., at high

dose, nonlinearity, but, at low dose, linearity (e.g., formaldehyde).

C The agent is not DNA-reactive and all plausible modes of action are consistent with

nonlinearity, but not fully established.

C Modes of action for different tumor types support differing approaches, e.g., nonlinear

for one tumor type and linear for another due to lack of MOA information (e.g.,

trichloroethylene).

3.1.3.5 AWQC Calculation

A. Linear Approach

The following equation is used for the calculation of the AWQC for carcinogens where

an RSD is obtained from the linear approach:

AWQC = Ambient water quality criterion (mg/L)

RSD = Risk-specific dose (mg/kg-day)

BW = Human body weight (kg)

DI = Drinking water intake (L/day)
9

 Although appearing in this equation as a factor to be multiplied, the RSC can also be an amount subtracted.

3-14

(Equation 3-5)

FIi = Fish intake at trophic level I (I = 2, 3, and 4) (kg/day)

BAFi = Bioaccumulation factor for trophic level I (I = 2, 3, and 4), lipid

normalized (L/kg)

B. Nonlinear Approach

In those cases where the nonlinear, MOE approach is used, a similar equation is used to

calculate the AWQC 9

where variables are defined as for Equation 3-4 and:

POD = Point of departure (mg/kg-day)

UF = Uncertainty factor (unitless)

RSC = Relative source contribution (percentage or subtraction)

Differences between the AWQC values obtained using the linear and nonlinear

approaches should be noted. First, the AWQC value obtained using the default linear approach

corresponds to a specific estimated incremental lifetime cancer risk level in the range of 10-4 to

10-6. In contrast, the AWQC obtained using the nonlinear approach does not describe a specific

cancer risk. The AWQC calculations shown above are appropriate for waterbodies that are used

as sources of drinking water. 

The actual AWQC chosen for the protection of human health is based on a review of all

relevant information, including cancer and noncancer data. The AWQC may, or may not, utilize

the value obtained from the cancer analysis in the final AWQC value. The endpoint selected for

the AWQC will be based on consideration of the weight of evidence and a complete analysis of

all toxicity endpoints.

3.1.3.6 Risk Characterization

Risk assessment is an integrative process that is documented in a risk characterization

summary. Risk characterization is the final step of the risk assessment process in which all

preceding analyses (i.e., hazard, dose-response, and exposure assessments) are tied together to

convey the overall conclusions about potential human risk. This component of the risk

assessment process characterizes the data in nontechnical terms, explaining the extent and

weight of evidence, major points of interpretation and rationale, and strengths and weaknesses of
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the evidence, and discussing alternative approaches, conclusions, uncertainties, and variability

that deserve serious consideration.

 Risk characterization information accompanies the numerical AWQC value and

addresses the major strengths and weaknesses of the assessment arising from the availability of

data and the current limits of understanding the process of cancer causation. Key issues relating

to the confidence in the hazard assessment and the dose-response analysis (including the lowdose extrapolation procedure used) are discussed. Whenever more than one interpretation of the

weight of evidence for carcinogenicity or the dose-response characterization can be supported,

and when choosing among them is difficult, the alternative views are provided along with the

rationale for the interpretation chosen in the derivation of the AWQC value. Where possible,

quantitative uncertainty analyses of the data are provided; at a minimum, a qualitative discussion

of the important uncertainties is presented. 

3.1.3.7 Use of Toxicity Equivalence Factors and Relative Potency Estimates

The 1999 draft revised cancer guidelines state: 

A toxicity equivalence factor (TEF) procedure is one used to derive quantitative

dose-response estimates for agents that are members of a category or class of

agents. TEFs are based on shared characteristics that can be used to order the

class members by carcinogenic potency when cancer bioassay data are

inadequate for this purpose. The ordering is by reference to the characteristics

and potency of a well-studied member or members of the class. Other class

members are indexed to the reference agent(s) by one or more shared

characteristics to generate their TEFs.

In addition, the 1999 draft revised cancer guidelines state that TEFs are generated and used for

the limited purpose of assessment of agents or mixtures of agents in environmental media when

better data are not available. When better data become available for an agent, the TEF should be

replaced or revised. To date, adequate data to support use of TEFs have been found only for

dibenzofurans (dioxins) and coplanar polychlorinated biphenyls (PCBs) (USEPA, 1989, 1999b).

The uncertainties associated with TEFs must be described when this approach is used.

This is a default approach to be used when tumor data are not available for individual

components in a mixture. Relative potency factors (RPFs) can be similarly derived and used for

agents with carcinogenicity or other supporting data. The RPF is conceptually similar to TEFs,

but does not have the same level of data to support it and thus has a less rigorous definition

compared with the TEF. TEFs and RPFs are used only when there is no better alternative. 

When they are used, assumptions and uncertainties associated with them are discussed. As of

today, there are only three classes of compounds for which relative potency approaches have

been examined by EPA: dibenzofurans (dioxins), polychlorinated biphenyls (PCBs), and

polycyclic aromatic hydrocarbons (PAHs). There are limitations to the use of TEF and RFP

approaches, and caution should be exercised when using them. More guidance can be found in

the draft document for conducting health risk assessment of chemical mixtures, published by the

EPA Risk Assessment Forum (USEPA,1999b). 
3-16

3.1.4 References for Cancer Section

Barnes, D.G., G.P Daston, J.S. Evans, A.M. Jarabek, R.J. Kavlock, C.A. Kimmel, C. Park, and

H.L. Spitzer. 1995. Benchmark dose workshop: Criteria for use of a benchmark dose to

estimate a reference dose. Regul. Toxicol. Pharmacol. 21:296-306.

USEPA (U.S. Environmental Protection Agency). 1980. Water quality criteria documents. 

Federal Register 45: 79318-79379.

USEPA (U.S. Environmental Protection Agency). 1986. Guidelines for carcinogen risk

assessment. Federal Register 51:33992-34003.

USEPA (U.S. Environmental Protection Agency). 1989. Interim Procedures for Estimating

Risks Associated with Exposures to Mixtures of Chlorinated Dibenzo-p-dioxins and -

Dibenzofurans (CDDs and CDFs) and 1989 Update. Risk Assessment Forum.

Washington, DC. EPA/625/3-89/016.

USEPA (U.S. Environmental Protection Agency). 1992a. Draft report: a cross-species scaling

factor for carcinogen risk assessment based on equivalence of mg/kg3/4/day. Federal

Register 57: 24152-24173.

USEPA (U.S. Environmental Protection Agency). 1993. Revision of Methodology for Deriving

National Ambient Water Quality Criteria for the Protection of Human Health: Report of

Workshop and EPA’s Preliminary Recommendations for Revision. Submitted to EPA

Science Advisory Board Drinking Water Committee, January 8, 1993. Office of Science

and Technology, Office of Water. Water Docket W-97-20.

USEPA (U.S. Environmental Protection Agency). 1996. Proposed Guidelines for Carcinogen

Risk Assessment. Office of Research and Development. Washington, DC. EPA/600/P92/003C. (Federal Register 61:17960)

USEPA (U.S. Environmental Protection Agency). 1998a. Draft Water Quality Criteria

Methodology: Human Health. Federal Register Notice. Office of Water. Washington,

DC. EPA-822-Z-98-001.

USEPA (U.S. Environmental Protection Agency). 1998b. Ambient Water Quality Criteria

Derivation Methodology - Human Health. Technical Support Document. Final Draft. 

Office of Water. Washington, DC. EPA-822-B-98-005.

USEPA (U.S. Environmental Protection Agency). 1999a. Guidelines for Carcinogen Risk

Assessment. Review Draft. Risk Assessment Forum. Washington, DC. EPA/NCEA-F0644. July.

USEPA (U.S. Environmental Protection Agency). 1999b. Guidance for Conducting Health Risk

Assessment of Chemical Mixtures. External Peer Review Draft. Risk Assessment Forum. 

Washington, DC. EPA/NCEA-C-0148. April.
3-17

USEPA (U.S. Environmental Protection Agency). 1999c. Revisions to the Methodology for

Deriving Ambient Water Quality Criteria for the Protection of Human Health. Peer

Review Workshop Summary Report. Office of Water. Washington, DC. EPA-822-R-99-

015. September.

USEPA (U.S. Environmental Protection Agency). 2000. Methodology for Deriving Ambient

Water Quality Criteria for the Protection of Human Health (2000). Technical Support

Document Volume 1: Risk Assessment. Office of Science and Technology, Office of

Water. Washington, DC. EPA-822-B-00-005. August.

3.2 NONCANCER EFFECTS

3.2.1 1980 AWQC National Guidelines for Noncancer Effects

In the 1980 AWQC National Guidelines, the Agency evaluated noncancer human health

effects from exposure to chemical contaminants using Acceptable Daily Intake (ADI) levels. 

ADIs were calculated by dividing NOAELs by safety factors (SFs) to obtain estimates of doses

of chemicals that would not be expected to cause adverse effects over a lifetime of exposure. In

accordance with the National Research Council report of 1977 (NRC, 1977), EPA used SFs of

10, 100, or 1,000, depending on the quality and quantity of the overall database. In general, a

factor of 10 was suggested when good-quality data identifying a NOAEL from human studies

were available. A factor of 100 was suggested if no human data were available, but the database

contained valid chronic animal data. For chemicals with no human data and scant animal data, a

factor of 1,000 was recommended. Intermediate SFs could also be used for databases that fell

between these categories.

AWQC were calculated using the ADI levels together with standard exposure

assumptions about the rates of human ingestion of water and fish, and also accounting for intake

from other sources (see Equation 1-1 in the Introduction). Surface water concentrations at or

below the calculated criteria concentrations would be expected to result in human exposure

levels at or below the ADI. Inherent in these calculations is the assumption that, generally,

adverse effects from noncarcinogens exhibit a threshold.

3.2.2 Noncancer Risk Assessment Developments Since 1980

Since 1980, the risk assessment of noncarcinogenic chemicals has changed. To remove

the value judgments implied by the words “acceptable” and “safety,” the ADI and SF terms have

been replaced with the terms RfD and UF/modifying factor (MF), respectively. 

For the risk assessment of general systemic toxicity, the Agency currently uses the

guidelines contained in the IRIS background document entitled Reference Dose (RfD):

Description and Use in Health Risk Assessments (hereafter the “IRIS background document”. 

That document defines an RfD as “an estimate (with uncertainty spanning approximately an

order of magnitude) of a daily exposure to the human population (including sensitive subgroups)

that is likely to be without appreciable risk of deleterious effects over a lifetime” (USEPA,
3-18

(Equation 3-6)

1993a). The most common approach for deriving the RfD does not involve dose-response

modeling. Instead, an RfD for a given chemical is usually derived by first identifying the

NOAEL for the most sensitive known toxicity endpoint, that is, the toxic effect that occurs at the

lowest dose. This effect is called the critical effect. Factors such as the study protocol, the

species of experimental animal, the nature of the toxicity endpoint assessed and its relevance to

human effects, the route of exposure, and exposure duration are critically evaluated in order to

select the most appropriate NOAEL from among all available studies in the chemical’s database. 

If no appropriate NOAEL can be identified from any study, then the LOAEL for the critical

effect endpoint is used and an uncertainly factor for LOAEL-to-NOAEL extrapolation is applied. 

Using this approach, the RfD is equal to the NOAEL (or LOAEL) divided by the product of UFs

and, occasionally, an MF:

The definitions and guidance for use of the UFs and the MFs are provided in the IRIS

background document and are repeated in Table 3-1.

The IRIS background document on the RfD (USEPA, 1993a) provides guidance for

critically assessing noncarcinogenic effects of chemicals and for deriving the RfD. Another

reference on this topic is Dourson (1994). Furthermore, the Agency has also published separate

guidelines for assessing specific toxic endpoints, such as developmental toxicity (USEPA,

1991a), reproductive toxicity (USEPA, 1996a), and neurotoxicity risk assessment (USEPA,

1995). These endpoint-specific guidelines will be used for their respective areas in the hazard

assessment step and will complement the overall toxicological assessment. It should be noted,

however, that an RfD, derived using the most sensitive known endpoint, is considered protective

against all noncarcinogenic effects.
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TABLE 3-1. UNCERTAINTY FACTORS AND THE MODIFYING FACTOR

 Uncertainty Factor Definition

UFH Use a 1, 3, or 10-fold factor when extrapolating from valid data in studies

using long-term exposure to average healthy humans. This factor is intended

to account for the variation in sensitivity (intraspecies variation) among the

members of the human population.

UFA Use an additional factor of 1, 3, or 10 when extrapolating from valid results of

long-term studies on experimental animals when results of studies of human

exposure are not available or are inadequate. This factor is intended to account

for the uncertainty involved in extrapolating from animal data to humans

(interspecies variation). 

UFS Use an additional factor of 1, 3, or 10 when extrapolating from less-thanchronic results on experimental animals when there are no useful long-term

human data. This factor is intended to account for the uncertainty involved in

extrapolating from less-than-chronic NOAELs to chronic NOAELs.

UFL Use an additional factor of 1, 3, or 10 when deriving an RfD from a LOAEL,

instead of a NOAEL. This factor is intended to account for the uncertainty

involved in extrapolating from LOAELs to NOAELs.

UFD Use an additional 3- or 10-fold factor when deriving an RfD from an

"incomplete" database. This factor is meant to account for the inability of any

single type of study to consider all toxic endpoints. The intermediate factor of

3 (approximately 1⁄2 log10 unit, i.e., the square root of 10) is often used when

there is a single data gap exclusive of chronic data. It is often designated as

UFD.

Modifying Factor

Use professional judgment to determine the MF, which is an additional uncertainty factor that is

greater than zero and less than or equal to 10. The magnitude of the MF depends upon the

professional assessment of scientific uncertainties of the study and database not explicitly treated

above (e.g., the number of species tested). The default value for the MF is 1.

Note: With each UF or MF assignment, it is recognized that professional scientific judgment must

be used. The total product of the uncertainty factors and modifying factor should not exceed 3,000.
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Similar to the procedure used in the 1980 AWQC National Guidelines, the revised

method of deriving AWQC for noncarcinogens uses the RfD together with various assumptions

concerning intake of the contaminant from both water and non-water sources of exposure. The

objective of an AWQC for noncarcinogens is to ensure that human exposure to a substance

related to its presence in surface water, combined with exposure from other sources, does not

exceed the RfD. The algorithm for deriving AWQC for noncarcinogens using the RfD is

presented as Equation 1-1 in the Introduction.

3.2.3 Issues and Recommendations Concerning the Derivation of AWQC for

Noncarcinogens

During a review of the 1980 AWQC National Guidelines (USEPA, 1993b), the Agency

identified several issues that must be resolved in order to develop a final revised methodology

for deriving AWQC based on noncancer effects. These issues, as discussed below, mainly

concern the derivation of the RfD as the basis for such an AWQC. Foremost among these issues

is whether the Agency should revise the present method or adopt entirely new procedures that

use quantitative dose-response modeling for the derivation of the RfD. Other issues include the

following:

• Presenting the RfD as a single point value or as a range to reflect the inherent imprecision

of the RfD; 

• Selecting specific guidance documents for derivation of noncancer health effect levels;

• Considering severity of effect in the development of the RfD;

• Using less-than-90-day studies as the basis for RfDs;

• Integrating reproductive/developmental, immunotoxicity, and neurotoxicity data into the

RfD calculation;

• Applying toxicokinetic data in risk assessments; and

• Considering the possibility that some noncarcinogenic effects do not exhibit a threshold.

3.2.3.1 Using the Current NOAEL/UF-Based RfD Approach or Adopting More

Quantitative Approaches for Noncancer Risk Assessment

The current NOAEL/UF-based RfD methodology, or its predecessor ADI/SF

methodology, have been used since 1980. This approach assumes that there is a threshold

exposure below which adverse noncancer health effects are not expected to occur. Exposures

above this threshold are believed to pose some risk to exposed individuals; however, the current

approach does not address the nature and magnitude of the risk above the threshold level (i.e.,

the shape of the dose-response curve above the threshold). The NOAEL/UF-based RfD

approach is intended primarily to ensure that the RfD value derived from the available data falls

below the population effects threshold. However, the NOAEL/UF-based RfD procedure has
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limitations. In particular, this method requires that one of the actual experimental doses used by

the researchers in the critical study be selected as the NOAEL or LOAEL value. The

determination that a dose is a NOAEL or LOAEL will depend on the biological endpoints used

and the statistical significance of the data. Statistical significance will depend on the number and

spacing of dose groups and the numbers of animals used in each dose group. Studies using a

small number of animals can limit the ability to distinguish statistically significant differences

among measurable responses seen in dose groups and control groups. Furthermore, the

determination of the NOAEL or LOAEL also depends on the dose spacing of the study. Doses

are often widely spaced, typically differing by factors of three to ten. A study can identify a

NOAEL and a LOAEL from among the doses studied, but the “true” effects threshold cannot be

determined from those results. The study size and dose spacing limitations also limit the ability

to characterize the nature of the expected response to exposures between the observed NOAEL

and LOAEL values.

The limitations of the NOAEL/UF approach have prompted development of alternative

approaches that incorporate more quantitative dose-response information. The traditional

NOAEL approach for noncancer risk assessment has often been a source of controversy and has

been criticized in several ways. For example, experiments involving fewer animals tend to

produce higher NOAELs and, as a consequence, may produce higher RfDs. Larger sample sizes,

on the other hand, should provide greater experimental sensitivity and lower NOAELs. The

focus of the NOAEL approach is only on the dose that is the NOAEL, and the NOAEL must be

one of the experimental doses. It also ignores the shape of the dose-response curve. Thus, the

slope of the dose-response plays little role in determining acceptable exposures for human

beings. Therefore, in addition to the NOAEL/UF-based RfD approach described above, EPA

will accept other approaches that incorporate more quantitative dose-response information in

appropriate situations for the evaluation of noncancer effects and the derivation of RfDs. 

However, the Agency wishes to emphasize that it still believes the NOAEL/UF RfD

methodology is valid and can continue to be used to develop RfDs. 

Two alternative approaches that may have relevance in assisting in the derivation of the

RfD for a chemical are the BMD and the categorical regression approaches. These alternative

approaches may overcome some of the inherent limitations in the NOAEL/UF approach. For

example, the BMD analyses for developmental effects show that NOAELs from studies correlate

well with a 5 percent response level (Allen et al., 1994). The BMD and the categorical

regression approaches usually have greater data requirements than the RfD approach. Thus, it is

unlikely that any one approach will apply to every circumstance; in some cases, different

approaches may be needed to accommodate the varying databases for the range of chemicals for

which water quality criteria must be developed. Acceptable approaches will satisfy the

following criteria: (1) meet the appropriate risk assessment goal; (2) adequately describe the

toxicity database and its quality; (3) characterize the endpoints properly; (4) provide a measure

of the quality of the “fit” of the model when a model is used for dose-response analysis; and (5)

describe the key assumptions and uncertainties.
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A. The Benchmark Dose
3-23

The BMD is defined as the dose estimated to produce a predetermined level of change in

response (the Benchmark Response level, or BMR) relative to control. The BMDL is defined as

the statistical lower confidence limit on the BMD. In the derivation of an RfD, the BMDL is

used as the dose to which uncertainty factors are applied instead of the NOAEL. The BMD

approach first models a dose-response curve for the critical effect(s) using available

experimental data. Several mathematical algorithms can be used to model the dose-response

curve, such as polynomial or Weibull functions. To define a BMD from the modeled curve for

quantal data, the assessor first selects the BMR. The choice of the BMR is critical. For quantal

endpoints, a particular level of response is chosen (e.g., 1 percent, 5 percent, or 10 percent). For

continuous endpoints, the BMR is the degree of change from controls and is based on what is

considered a biologically significant change. The BMD is derived from the BMR dose by

applying the desired confidence limit calculation. The RfD is obtained by dividing the BMD by

one or more uncertainty factors, similar to the NOAEL approach. Because the BMD is used like

the NOAEL to obtain the RfD, the BMR should be selected at or near the low end of the range of

increased risks that can be detected in a study of typical size. Generally, this falls in the range

between the ED01 and the ED10.

The Agency will accept use of a BMD approach to derive RfDs for those agents for

which there is an adequate database. There are a number of technical decisions associated with

the application of the BMD technique. These include the following: 

• The definition of an adverse response;

• Selection of response data to model;

• The form of the data used (continuous versus quantal);

• The choice of the measures of increased risk (extra risk versus additional risk);

• The choice of mathematical model (including use of nonstandard models for unusual data

sets);

• The selection of the BMR;

• Methods for calculating the confidence interval;

• Selection of the appropriate BMD as the basis for the RfD (when multiple endpoints are

modeled from a single study, when multiple models are applied to a single response, and

when multiple BMDs are calculated from different studies); and 

• The use of uncertainty factors with the BMD approach. 

These topics are discussed in detail in Crump et al. (1995) and in the Risk Assessment

TSD Volume (USEPA, 2000). The use of the BMD approach has been discussed in general

terms by several authors (Gaylor, 1983; Crump, 1984; Dourson et al., 1985; Kimmel and Gaylor,

1988; Brown and Erdreich, 1989; Kimmel, 1990). The International Life Sciences Institute
3-24

(ILSI) also held a major workshop on the BMD in September 1993; the workshop proceedings

are summarized in ILSI (1993) and in Barnes et al. (1995). For further information on these

technical issues, the reader is referred to the publications referenced above.

The BMD approach addresses several of the quantitative or statistical criticisms of the

NOAEL approach. These are discussed at greater length in Crump et al. (1995) and are

summarized here. First, the BMD approach uses all the dose-response information in the

selected study rather than just a single data point, such as the NOAEL or LOAEL. By using

response data from all of the dose groups to model a dose-response curve, the BMD approach

allows for consideration of the steepness of the slope of the curve when estimating the ED10. 

The use of the full data set also makes the BMD approach less sensitive to small changes in data

than the NOAEL approach, which relies on the statistical comparison of individual dose groups. 

The BMD approach also allows consistency in the consideration of the level of effect (e.g., a 10

percent response rate) across endpoints.

The BMD approach accounts more appropriately for the size of each dose group than the

NOAEL approach. Laboratory tests with fewer animals per dose group tend to yield higher

NOAELs, and thus higher RfDs, because statistically significant differences in response rates are

harder to detect. Therefore, in the NOAEL approach, dose groups with fewer animals lead to a

higher (less conservative) RfD. In contrast, with the BMD approach, smaller dose groups will

tend to have the effect of extending the confidence interval around the ED10; therefore, the lower

confidence limit on the ED10 (the BMD) will be lower. With the BMD approach, greater

uncertainty (smaller test groups) leads to a lower (more conservative) RfD.

There are some issues to be resolved before the BMD approach is used routinely. These

were identified in a 1996 Peer Consultation Workshop (USEPA, 1996b). Methods for routine

use of the BMD are currently under development by EPA. Several RfCs and RfDs based on the

BMD approach are included in EPA’s IRIS database. These include reference values for

methylmercury based on delayed postnatal development in humans; carbon disulfide based on

neurotoxicity; 1,1,1,2-tetrafluoroethane based on testicular effects in rats; and antimony trioxide

based on chronic pulmonary interstitial inflammation in female rats.

Various mathematical approaches have been proposed for modeling developmental

toxicity data (e.g., Crump, 1984; Kimmel and Gaylor, 1988; Rai and Van Ryzin, 1985; Faustman

et al., 1989), which could be used to calculate a BMD. Similar methods can be used to model

other types of toxicity data, such as neurotoxicity data (Gaylor and Slikker, 1990, 1992; Glowa

and MacPhail, 1995). The choice of the mathematical model may not be critical, as long as

estimation is within the observed dose range. Since the model fits a mathematical equation to

the observed data, the assumptions in a particular model regarding the existence or absence of a

threshold for the effect may not be pertinent (USEPA, 1997). Thus, any model that suitably fits

the empirical data is likely to provide a reasonable estimate of a BMD. However, research has

shown that flexible models that are nonsymmetric (e.g., the Weibull) are superior to symmetric

models (e.g., the probit) in estimating the BMD because the data points at the higher doses have

less influence on the shape of the curve than at low doses. In addition, models should

incorporate fundamental biological factors where such factors are known (e.g., intralitter

correlation for developmental toxicity data) in order to account for as much variability in the
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data as possible. The Agency is currently using the BMD approach in risk assessments where

the data support its use. Draft guidelines for application of the BMD approach also are being

developed by the Agency.

Use of BMD methods involves fitting mathematical models to dose-response data

obtained primarily from toxicology studies. When considering available models to use for a

BMD analysis, it is important to select the model that fits the data the best and is the most

biologically appropriate. EPA has developed software following several years of research and

development, expert peer review, public comment, subsequent revision, and quality assurance

testing. The software (BMDS, Version 1.2) can be downloaded from

http://www.epa.gov/ncea/bmds.htm. BMDS facilitates these operations by providing simple

data-management tools, a comprehensive help manual, an online help system, and an easy-to-use

interface to run multiple models on the same dose-response data.

As part of this software package, EPA has included sixteen (16) different models that are

appropriate for the analysis of dichotomous (quantal) data (Gamma, Logistic, Log-Logistic,

Multistage, Probit, Log-Probit, Quantal-Linear, Quantal-Quadratic, Weibull), continuous data

(Linear, Polynomial, Power, Hill), and nested developmental toxicology data (NLogistic, NCTR,

Rai & Van Ryzin). Results from all models include a reiteration of the model formula and

model run options chosen by the user, goodness-of-fit information, the BMD, and the estimate of

the lower-bound confidence limit on the benchmark dose (BMDL). Model results are presented

in textual and graphical output files which can be printed or saved and incorporated into other

documents.

B. Categorical Regression

Categorical regression is an emerging technique that may have relevance for the

derivation of RfDs or for estimating risk above the RfD (Dourson et al., 1997; Guth et al., 1997). 

The categorical regression approach, like the BMD approach, can be used to estimate a dose that

corresponds to a given probability of adverse effects. This dose would then be divided by UFs to

establish an RfD. However, unlike the BMD approach, the Categorical regression approach can

incorporate information on different health endpoints in a single dose-response analysis. For

those health effects for which studies exist, responses to the substance in question are grouped

into severity categories; for example (1) no effect, (2) no adverse effect, (3) mild-to-moderate

adverse effect, and (4) frank effect. These categories correspond to the dose categories currently

used in setting the RfD, namely, the no-observed-effect level (NOEL), NOAEL, LOAEL, and

frank-effect level (FEL), respectively. Logistic transformation or other applicable mathematical

operations are used to model the probability of experiencing effects in a certain category as a

function of dose (Harrell, 1986; Hertzberg, 1989). The “acceptability” of the fit of the model to

the data can be judged using several statistical measures, including the P2

 statistic, correlation

coefficients, and the statistical significance of its model parameter estimates.

The resulting mathematical equation can be used to find a dose (or the lower confidence

bound on the dose) at which the probability of experiencing adverse effects does not exceed a

selected level, e.g., 10 percent. This dose (like the NOAEL or BMD) would then be divided by
3-26

relevant UFs to calculate an RfD. For more detail on how to employ the categorical regression

approach, see the discussion in the Risk Assessment TSD (USEPA, 2000).

As with the BMD approach, the categorical regression approach has the advantage of

using more of the available dose-response data to account for response variability as well as

accounting for uncertainty due to sample size through the use of confidence intervals. 

Additional advantages of categorical regression include the combining of data sets prior to

modeling, thus allowing the calculation of the slope of a dose-response curve for multiple

adverse effects rather than only one effect at a time. Another advantage is the ability to estimate

risks for different levels of severity from exposures above the RfD.

On the other hand, as with BMD, opinions differ over the amount and adequacy of data

necessary to implement the method. The categorical regression approach also requires

judgments regarding combining data sets, judging goodness-of-fit, and assigning severity to a

particular effect. Furthermore, this approach is still in the developmental stage. It is not

recommended for routine use, but may be used when data are available and justify the extensive

analyses required.

C. Summary

Whether a NOAEL/UF-based methodology, a BMD, a categorical regression model, or

other approach is used to develop the RfD, the dose-response-evaluation step of a risk

assessment process should include additional discussion about the nature of the toxicity data and

its applicability to human exposure and toxicity. The discussion should present the range of

doses that are effective in producing toxicity for a given agent; the route, timing, and duration of

exposure; species specificity of effects; and any toxicokinetic or other considerations relevant to

extrapolation from the toxicity data to human-health-based AWQC. This information should

always accompany the characterization of the adequacy of the data.

3.2.3.2 Presenting the RfD as a Single Point or as a Range for Deriving AWQC

Although the RfD has traditionally been presented and used as a single point, its

definition contains the phrase “. . . an estimate (with uncertainty spanning perhaps an order of

magnitude) . . .” (USEPA, 1993a). Underlying this concept is the reasoning that the selection of

the critical effect and the total uncertainty factor used in the derivation of the RfD is based on the

“best” scientific judgment, and that competent scientists examining the same database could

derive RfDs which varied within an order of magnitude.

In one instance, IRIS presented the RfD as a point value within an accompanying range. 

EPA derived a single number as the RfD for arsenic (0.3 :g/kg-day), but added that “strong

scientific arguments can be made for various values within a factor of 2 or 3 of the currently

recommended RfD value, i.e., 0.1 to 0.8 :g/kg/day” (USEPA, 1993c). EPA noted that

regulatory managers should be aware of the flexibility afforded them through this action.

There are situations in which the risk manager can select an alternative value to use in

place of the RfD in the AWQC calculations. The domain from which this alternative value can
3-27

be selected is restricted to a defined range around the point estimate. As explained further

below, the Agency is recommending that sometimes the use of a value other than the calculated

RfD point estimate is appropriate in characterizing risk. The selection of an alternative value

within an appropriate range must be determined for each individual situation, since several

factors affect the selection of the alternative value. Observing similar effects in several animal

species, including humans, can increase confidence in the selection of the critical effect and

thereby narrow the range of uncertainty. There are other factors that can affect the precision. 

These include the slope of the dose-response curve, seriousness of the observed effect, dose

spacing, and possibly the route for the experimental doses. Dose spacing and the number of

animals in the study groups used in the experiment can also affect the confidence in the RfD.

To derive the AWQC, the calculated point estimate of the RfD is the default. Based on

consideration of the available data, the use of another number within the range defined by the

product of the UF(s) (and MF, if used) could be justified in some specific situations. This means

that there are risk considerations which indicate that some value in the range other than the point

estimate may be more appropriate, based on human health or environmental fate considerations. 

For example, the bioavailability of the contaminant in fish tissues is one factor to consider. If

bioavailability from fish tissues is much lower than that from water and the RfD was derived

from a study in which the contaminant exposure was from drinking water, the alternative to the

calculated RfD could be selected from the high end of the range and justified using the

quantitative difference in bioavailability.

Most inorganic contaminants, particularly divalent cations, have bioavailability values of

20 percent or less from a food matrix, but are much more available (about 80 percent or higher)

from drinking water. Accordingly, the external dose necessary to produce a toxic internal dose

would likely be higher for a study where the exposure occurred through the diet rather than the

drinking water. As a result, the RfD from a dietary study would likely be higher than that for the

drinking water study if equivalent external doses had been used. Conversely, in cases where the

NOAEL that was the basis for the RfD came from a dietary study, the alternative value could be

slightly lower than the calculated RfD.

Because the uncertainty around the dose-response relationship increases as extrapolation

below the observed data increases, the use of an alternative point within the range may be more

appropriate in characterizing the risk than the use of the calculated RfD, especially in situations

when the uncertainty is high. Therefore, as a matter of policy, the 2000 Human Health

Methodology permits the selection of a single point within a range about the calculated RfD to

be used as the basis of the AWQC if an adequate justification of the alternative point is provided. 

More complete discussion of this option, including limitations on the span of the range, is

provided in the Risk Assessment TSD (USEPA, 2000).

3.2.3.3 Guidelines to be Adopted for Derivation of Noncancer Health Effects Values

The Agency currently is using the IRIS background document as the general basis for the

risk assessment of noncarcinogenic effects of chemicals (USEPA, 1993a). EPA recommends

continued use of this document for this purpose. However, it should be noted that the process

for evaluating chemicals for inclusion in IRIS is undergoing revision (USEPA, 1996c). The
3-28

revised assessments for many chemicals are now available on IRIS and can be consulted as

examples of the RfD development process and required supporting documentation. 

3.2.3.4 Treatment of Uncertainty Factors/Severity of Effects During the RfD Derivation

and Verification Process

During the RfD derivation and toxicology review process, EPA considers the uncertainty

in extrapolating between animal species and within individuals of a species, as well as specific

uncertainties associated with the completeness of the database. The Agency’s RfD Work Group

has always considered the severity of the observed effects induced by the chemical under review

when choosing the value of the UF with a LOAEL. For example, during the derivation and

verification of the RfD for zinc (USEPA, 1992), an uncertainty factor less than the standard

factor of 10 (UF of 3) was assigned to the relatively mild decrease in erythrocyte superoxide

dismutase activity in human subjects. EPA recommends that the severity of the critical effect be

assessed when deriving an RfD and that risk managers be made aware of the severity of the

effect and the weight placed on this attribute of the effect when the RfD was derived.

3.2.3.5 Use of Less-Than-90-Day Studies to Derive RfDs

Generally, less-than-90-day experimental studies are not used to derive an RfD. This is

based on the rationale that studies lasting for less than 90 days may be too short to detect various

toxic effects. However, EPA, has in certain circumstances, derived an RfD based on a less-than90-day study. For example, the RfD for nonradioactive effects of uranium is based on a 30-day

rabbit study (USEPA, 1989). The short-term exposure period was used, because it was adequate

for determining doses that cause chronic toxicity. In other cases, it may be appropriate to use a

less-than-90-day study because the critical effect is expressed in less than 90 days. For example,

the RfD for nitrate was derived and verified using studies that were less than 3-months in

duration (USEPA, 1991b). For nitrate, the critical effect of methemoglobinemia in infants

occurs in less than 90 days. When it can be demonstrated from other data in the toxicological

database that the critical adverse effect is expressed within the study period and that a longer

exposure duration would not exacerbate the observed effect or cause the appearance of some

other adverse effect, the Agency may choose to use less-than-90-day studies as the basis of the

RfD. Such values would have to be used with care because of the uncertainty in determining if

other effects might be expressed if exposure was of greater duration than 90 days.

3.2.3.6 Use of Reproductive/Developmental, Immunotoxicity, and Neurotoxicity Data as the

Basis for Deriving RfDs

All relevant toxicity data have some bearing on the RfD derivation and verification and

are considered by EPA. The “critical” effect is the adverse effect most relevant to humans or, in

the absence of an effect known to be relevant to humans, the adverse effect that occurs at the

lowest dose in animal studies. If the critical effect is neurotoxicity, EPA will use that endpoint

as the basis for the derivation and verification of an RfD, as it did for the RfD for acrylamide. 

Moreover, the Agency is continually revising its procedures for noncancer risk assessment. For

example, EPA has released guidelines for deriving developmental RfDs (RfDDT, USEPA,

1991a), for using reproductive toxicity (USEPA, 1996a), and neurotoxicity (USEPA, 1995) data
3-29

in risk assessments. The Agency is currently working on guidelines for using immunotoxicity

data to derive RfDs. In addition, the Agency is proceeding with the process of generating

acceptable emergency health levels for hazardous substances in acute exposure situations based

on established guidelines (NRC, 1993).

3.2.3.7 Applicability of Toxicokinetic Data in Risk Assessment

All pertinent toxicity data should be used in the risk assessment process, including

toxicokinetic and mechanistic data. The Agency has used toxicokinetic data in deriving the RfD

for cadmium and other compounds and currently is using toxicokinetic data to better characterize

human inhalation exposures from animal inhalation experiments during derivation/verification of

RfCs. In analogy to the RfD, the RfC is considered to be an estimate of a concentration in the air

that is not anticipated to cause adverse noncancer effects over a lifetime of inhalation exposure

(USEPA, 1994; Jarabek, 1995a). For RfCs, different dosimetry adjustments are made to account

for the differences between laboratory animals and humans in gas uptake and disposition or in

particle clearance and retention. This procedure results in calculation of a “human equivalent

concentration.” Based on the use of these procedures, an interspecies UF of 3 (i.e.,

approximately 100.5), instead of the standard factor of 10, is used in the RfC derivation (Jarabek,

1995b).

Toxicokinetics and toxicodynamics of a chemical each contribute to a chemical’s

observed toxicity, and specifically, to observed differences among species in sensitivity. 

Toxicokinetics describes the disposition (i.e., deposition, absorption, distribution, metabolism,

and elimination of chemicals in the body) and can be approximated using toxicokinetic models. 

Toxicodynamics describes the toxic interaction of the agent with the target cell. In the absence

of specific data on their relative contributions to the toxic effects observed in species, each is

considered to account for approximately one-half of the difference in observed effects for

humans compared with laboratory animals. The implication of this assumption is that an

interspecies uncertainty factor of 3 rather than 10 could be used for deriving an RfD when valid

toxicokinetic data and models can be applied to obtain an oral “human equivalent applied dose”

(Jarabek, 1995b). If specific data exist on the relative contribution of either element to observed

effects, that proportion will be used. The role exposure duration may play, and whether or not

the chemical or its damage may accumulate over time in a particular scenario, also requires

careful consideration (Jarabek, 1995c).

3.2.3.8 Consideration of Linearity (or Lack of a Threshold) for Noncarcinogenic Chemicals

It is quite possible that there are chemicals with noncarcinogenic endpoints that have no

threshold for effects. For example, in the case of lead, it has not been possible to identify a

threshold for effects on neurological development. Other examples could include genotoxic

teratogens and germline mutagens. Genotoxic teratogens act by causing mutational events

during organogenesis, histogenesis, or other stages of development. Germline mutagens interact

with germ cells to produce mutations which may be transmitted to the zygote and expressed

during one or more stages of development. However, there are few chemicals which currently

have sufficient mechanistic information about these possible modes of action. It should be

recognized that although an MOA consistent with linearity is possible (especially for agents
3-30

known to be mutagenic), this has yet to be reasonably demonstrated for most toxic endpoints

other than cancer.

EPA has recognized the potential for nonthreshold noncarcinogenic endpoints and

discussed this issue in the Guidelines for Developmental Toxicity Risk Assessment (USEPA,

1991a) and in the 1986 Guidelines for Mutagenicity Risk Assessment (USEPA, 1986). An

awareness of the potential for such teratogenic/mutagenic effects should be established in order

to deal with such data. However, without adequate data to support a genetic or mutational basis

for developmental or reproductive effects, the default becomes a UF or MOA approach, which

are procedures utilized for noncarcinogens assumed to have a threshold. Therefore, genotoxic

teratogens and germline mutagens should be considered an exception while the traditional

uncertainty factor approach is the general rule for calculating criteria or values for chemicals

demonstrating developmental/reproductive effects. For the exceptional cases, since there is no

well-established mechanism for calculating criteria protective of human health from the effects

of these agents, criteria will be established on a case-by-case basis. Other types of nonthreshold

noncarcinogens must also be handled on a case-by-case basis.

3.2.3.9 Minimum Data Guidance

For details on minimum data guidance for RfD development, see the Risk Assessment

TSD (USEPA, 2000).
3-31

3.2.4 References for Noncancer Effects

Allen, B.C., R.T. Kavlock, C.A. Kimmel, and E.M. Faustman. 1994. Dose-response assessment

for developmental toxicity. Fund. Appl. Toxicol. 23:496-509.

Barnes, D.G., G.P Daston, J.S. Evans, A.M. Jarabek, R.J. Kavlock, C.A. Kimmel, C. Park, and

H.L. Spitzer. 1995. Benchmark dose workshop: criteria for use of a benchmark dose to

estimate a reference dose. Reg. Toxicol. Pharmacol. 21:296-306.

Brown, K.G. and L.S. Erdreich. 1989. Statistical uncertainty in the no-observed-adverse-effect

level. Fund. Appl. Toxicol. 13:235-244.

Crump, K.S., B. Allen, and E. Faustman. 1995. The Use of the Benchmark Dose Approach in

Health Risk Assessment. Prepared for U.S. Environmental Protection Agency’s Risk

Assessment Forum. EPA/630/R-94/007.

Crump, K.S. 1984. A new method for determining acceptable daily intakes. Fund. Appl.

Toxicol. 4:854-871.

Dourson, M.L. 1994. Methodology for establishing oral reference doses (RfDs). In: Risk

Assessment of Essential Elements. W. Mertz, C.O. Abernathy, and S.S. Olin (eds.) ILSI

Press. Washington, DC. Pp. 51-61.

Dourson, M.L., R.C. Hertzberg, R. Hartung and K. Blackburn. 1985. Novel approaches for the

estimation of acceptable daily intake. Toxicol. Ind. Health 1:23-41.

Dourson, M.L., L.K. Teuschler, P.R. Durkin, and W.M. Stiteler. 1997. Categorical regression

of toxicity data, a case study using aldicarb. Regul. Toxicol. Pharmacol. 25:121-129.

Faustman, E.M., D.G. Wellington, W.P. Smith and C.A. Kimmel. 1989. Characterization of a

developmental toxicity dose-response model. Environ. Health Perspect. 79:229-241.

Gaylor, D.W. 1983. The use of safety factors for controlling risk. J. Toxicol. Environ. Health

11:329-336.

Gaylor, D.W. and W. Slikker. 1990. Risk assessment for neurotoxic effects. Neurotoxicology

11:211-218.

Gaylor, D.W. and W. Slikker. 1992. Risk assessment for neurotoxicants. In: Neurotoxicology.

H. Tilson and C. Mitchel (eds). Raven Press. New York, NY. Pp. 331-343.

Glowa, J.R. and R.C. MacPhail. 1995. Quantitative approaches to risk assessment in

neurotoxicology. In: Neurotoxicology: Approaches and Methods. Academic Press. New

York, NY. Pp. 777-787.
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Guth, D.J., R.J. Carroll, D.G. Simpson, and H. Zhou. 1997. Categorical regression analysis of

acute exposure to tetrachloroethylene. Risk Anal. 17(3):321-332.

Harrell, F. 1986. The logist procedure. SUGI Supplemental Library Users Guide, Ver. 5th ed. 

SAS Institute. Cary, NC.

Hertzberg, R.C. 1989. Fitting a model to categorical response data with application to species

extrapolation of toxicity. Health Physics 57: 405-409.

ILSI (International Life Sciences Institute). 1993. Report of the Benchmark Dose Workshop.

ISLI Risk Science Institute. Washington, DC.

Jarabek, A.M. 1995a. The application of dosimetry models to identify key processes and

parameters for default dose-response assessment approaches. Toxicol. Lett. 79:171-184.

Jarabek, A.M. 1995b. Interspecies extrapolation based on mechanistic determinants of chemical

disposition. Human Eco. Risk Asses. 1(5):41-622.

Jarabek, A.M. 1995c. Consideration of temporal toxicity challenges current default

assumptions. Inhalation Toxicol. 7:927-946.

Kimmel, C.A. 1990. Quantitative approaches to human risk assessment for noncancer health

effects. Neurotoxicology 11: 189-198.

Kimmel, C.A. and D.W. Gaylor. 1988. Issues in qualitative and quantitative risk analysis for

developmental toxicity. Risk Anal. 8: 15-20.

NRC (National Research Council). 1977. Decision Making in the Environmental Protection

Agency. Vol. 2. National Academy of Sciences. Washington, DC. Pp. 32-33 and 241-

242.

NRC (National Research Council). 1993. Guidelines for Developing Emergency Exposure

Levels for Hazardous Substances. Subcommittee on Guidelines for Developing

Community Emergency Exposure Levels (CEELs) for Hazardous Substances. 

Committee on Toxicology, NRC. National Academy Press. Washington, DC.

Rai, K. and J. Van Ryzin. 1985. A dose-response model for teratological experiments involving

quantal responses. Biometrics 41: 1-10.

USEPA (U.S. Environmental Protection Agency). 1986. Guidelines for mutagenicity

assessment. Federal Register 51:34006-34012. September 24.

USEPA (U.S. Environmental Protection Agency). 1989. Reference dose (RfD) for oral exposure

for uranium (soluble salts). Integrated Risk Information System (IRIS). Online. 

(Verification date 10/1/89). Office of Health and Environmental Assessment,

Environmental Criteria and Assessment Office. Cincinnati, OH. 
3-33

USEPA (U.S. Environmental Protection Agency). 1991a. Final guidelines for developmental

toxicity risk assessment. Federal Register 56:63798-63826. December 5.

USEPA (U.S. Environmental Protection Agency). 1991b. Reference dose (RfD) for oral

exposure for nitrate. Integrated Risk Information System (IRIS). Online. (Verification

date 10/01/91). Office of Health and Environmental Assessment, Environmental Criteria

and Assessment Office. Cincinnati, OH.

USEPA (U.S. Environmental Protection Agency). 1992. Reference dose (RfD) for oral

exposure for inorganic zinc. Integrated Risk Information System (IRIS). Online. 

(Verification date 10/1/92). Office of Health and Environmental Assessment,

Environmental Criteria and Assessment Office. Cincinnati, OH.

USEPA (U.S. Environmental Protection Agency). 1993a. Reference dose (RfD): Description

and use in health risk assessments. Integrated Risk Information System (IRIS). Online.

Intra-Agency Reference Dose (RfD) Work Group, Office of Health and Environmental

Assessment, Environmental Criteria and Assessment Office. Cincinnati, OH. March 15.

USEPA (U.S. Environmental Protection Agency). 1993b. Revision of Methodology for

Deriving National Ambient Water Quality Criteria for the Protection of Human Health:

Report of Workshop and EPA’s Preliminary Recommendations for Revision. Submitted

to the EPA Science Advisory Board by the Human Health Risk Assessment Branch,

Health and Ecological Criteria Division, Office of Science and Technology, Office of

Water. Washington, DC. January 8.

USEPA (U.S. Environmental Protection Agency). 1993c. Reference dose (RfD) for oral

exposure for inorganic arsenic. Integrated Risk Information System (IRIS). Online.

(Verification date 02/01/93). Office of Health and Environmental Assessment,

Environmental Criteria and Assessment Office. Cincinnati, OH.

USEPA (U.S. Environmental Protection Agency). 1994. Methods for Derivation of Inhalation

Reference Concentrations and Application of Inhalation Dosimetry. Office of Health and

Environmental Assessment, Environmental Criteria and Assessment Office. Research

Triangle Park, NC. EPA/600/8-90/066F.

USEPA (U.S. Environmental Protection Agency). 1995. Proposed guidelines for neurotoxicity

risk assessment. Federal Register 60:52032-52056. October 4. 

USEPA (U.S. Environmental Protection Agency). 1996a. Reproductive toxicity risk assessment

guidelines. Federal Register 61:56274-56322. October 31.

USEPA (U.S. Environmental Protection Agency). 1996b. Report on the Benchmark Dose Peer

Consultation Workshop. Risk Assessment Forum. Washington, DC. EPA/630/R96/011.
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USEPA (U.S. Environmental Protection Agency). 1996c. Integrated Risk Information System

(IRIS); announcement of pilot program; request for information. Federal Register. 61:

14570. April 2.

USEPA (U.S. Environmental Protection Agency). 1997. Mercury Study: Report to Congress.

Volume 5: Health Effects of Mercury and Mercury Compounds. Office of Air Quality

Planning and Standards, and Office of Research and Development. Research Triangle

Park, NC. EPA-452-R-97-007.

USEPA (U.S. Environmental Protection Agency). 2000. Methodology for Deriving Ambient

Water Quality Criteria for the Protection of Human Health (2000). Technical Support

Document Volume 1: Risk Assessment. Office of Science and Technology, Office of

Water. Washington, DC. EPA-822-B-00-005. August.
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4-1

4. EXPOSURE

The derivation of AWQC for the protection of human health requires information about

both the toxicological endpoints of concern for water pollutants and the pathways of human

exposure to those pollutants. The two primary pathways of human exposure to pollutants

present in a particular ambient waterbody that have been considered in deriving AWQC are

direct ingestion of drinking water obtained from that waterbody and the consumption of

fish/shellfish obtained from that waterbody. The water pathway also includes other exposures

from household uses (e.g., showering). The derivation of an AWQC involves the calculation of

the maximum water concentration for a pollutant (i.e., the water quality criteria level) that

ensures drinking water and/or fish ingestion exposures will not result in human intake of that

pollutant in amounts that exceed a specified level based upon the toxicological endpoint of

concern.

The equation for noncancer effects is presented again here, in simplified form, to

emphasize the exposure-related parameters (in bold). [Note: the RSC parameter also applies to

nonlinear low-dose extrapolation for cancer effects and the other exposure parameters apply to

all three of the equations (see Section 1.6).]

(Equation 4-1) 

( )

[ ] ( ) AWQC RfD BAF

=• •

+ •

RSC

BW

DI FI

where:

AWQC = Ambient Water Quality Criterion (mg/L)

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

RSC = Relative source contribution factor to account for non-water

sources of exposure 

BW = Human body weight (kg)

DI = Drinking water intake (L/day)

FI = Fish intake (kg/day)

BAF = Bioaccumulation factor (L/kg)

The following subsections discuss exposure issues relevant to the 2000 Human Health

Methodology: exposure policy issues; consideration of non-water sources of exposure (the

Relative Source Contribution approach); and the factors used in AWQC computation. In

relevant sections, science policy and risk management decisions made by EPA are discussed.

4.1 EXPOSURE POLICY ISSUES

This section discusses broad policy issues related to exposure concerning the major

objectives that the Agency believes should be met in setting AWQC.
4-2

An Exposure Assessment TSD provides greater detail on numerous topics discussed in

this guidance: suggested sources of contaminant concentration and exposure intake information;

suggestions of survey methods for obtaining and analyzing exposure data necessary for deriving

AWQC; summaries of studies on fish consumption among sport fishers and subsistence fishers;

more detailed presentation of parameter values (e.g., fish consumption rates, body weights); and

additional guidance on the application of the RSC approach.

4.1.1 Sources of Exposure Associated With Ambient Water

4.1.1.1 Appropriateness of Including the Drinking Water Pathway in AWQC

EPA intends to continue including the drinking water exposure pathway in the derivation

of its national default human health criteria (AWQC), as has been done since the 1980 AWQC

National Guidelines were first published.

EPA recommends inclusion of the drinking water exposure pathway where drinking

water is a designated use for the following reasons: (1) Drinking water is a designated use for

surface waters under the CWA and, therefore, criteria are needed to assure that this designated

use can be protected and maintained. (2) Although rare, there are some public water supplies

that provide drinking water from surface water sources without treatment. (3) Even among the

majority of water supplies that do treat surface waters, existing treatments may not necessarily

be effective for reducing levels of particular contaminants. (4) In consideration of the Agency’s

goals of pollution prevention, ambient waters should not be contaminated to a level where the

burden of achieving health objectives is shifted away from those responsible for pollutant

discharges and placed on downstream users to bear the costs of upgraded or supplemental water

treatment.

This policy decision has been supported by the States, most of the public stakeholders,

and by external peer reviewers. As with the other exposure parameters, States and authorized

Tribes have the flexibility to use alternative intake rates if they believe that drinking water

consumption is substantively different than EPA’s recommended default assumptions of 2 L/day

for adults and 1 L/day for children. EPA recommends that States and authorized Tribes use an

intake rate that would be protective of a majority of consumers and will consider whether an

alternative assumption is adequately protective of a State’s or Tribe’s population based on the

information or rationale provided at the time EPA reviews State and Tribal water quality

standards submissions.

4.1.1.2 Setting Separate AWQC for Drinking Water and Fish Consumption

In conjunction with the issue of the appropriateness of including the drinking water

pathway explicitly in the derivation of AWQC for the protection of human health, EPA intends

to continue its practice of setting a single AWQC for both drinking water and fish/shellfish

consumption, and a separate AWQC based on ingestion of fish/shellfish alone. This latter

criterion applies in those cases where the designated uses of a waterbody include supporting

fishable uses under Section 101(a) of the CWA and, thus, fish or shellfish for human

consumption, but not as a drinking water supply source (e.g., non-potable estuarine waters).
4-3

EPA does not believe that national water quality criteria for protection of drinking water

uses only are particularly useful for two reasons. First, State and Tribal standards for human

health are set to protect Section 101(a) uses (e.g., “fishable, swimmable uses”) under the CWA. 

Second, most waters have multiple designated uses. Additionally, the water quality standards

program protects aquatic life. The 2000 Human Health Methodology revisions do not change

EPA’s policy to apply aquatic life criteria to protect aquatic species where they are more

sensitive (i.e., when human health criteria would not be protective enough) or where human

health via fish or water ingestion is not an issue. 

4.1.1.3 Incidental Ingestion from Ambient Surface Waters

The 2000 Human Health Methodology does not routinely include criteria to address

incidental ingestion of water from recreational uses. EPA has considered whether there are cases

where water quality criteria for the protection of human health based only on fish ingestion (or

only criteria for the protection of aquatic life) may not adequately protect recreational users from

health effects resulting from incidental water ingestion.

EPA reviewed information that provided estimates of incidental water ingestion rates

averaged over time. EPA generally believes that the averaged amount is negligible and will not

have any impact on the chemical criteria values representative of both drinking water and fish

ingestion. A lack of impact on the criteria values would likely also be true for chemical criteria

based on fish consumption only, unless the chemical exhibits no bioaccumulation potential. 

However, EPA also believes that incidental/accidental water ingestion could be important for the

development of microbial contaminant water quality criteria, and for either chemical or

microbial criteria for States where recreational uses such as swimming and boating are

substantially higher than the national average. EPA also notes that some States have indicated

they already have established incidental ingestion rates for use in developing criteria. Therefore,

although EPA will not use this intake parameter when deriving its national 304(a) chemical

criteria, limited guidance is provided in the Exposure Assessment TSD volume in order to assist

States and authorized Tribes that face situations where this intake parameter could be of

significance.

4.2 CONSIDERATION OF NON-WATER SOURCES OF EXPOSURE WHEN

SETTING AWQC

4.2.1 Policy Background

The 2000 Human Health Methodology uses different approaches for addressing nonwater exposure pathways in setting AWQC for the protection of human health depending upon

the toxicological endpoint of concern. With those substances for which the appropriate toxic

endpoint is carcinogenicity based on a linear low-dose extrapolation, only the two water sources

(i.e., drinking water and fish ingestion) are considered in the derivation of the AWQC. Nonwater sources are not considered explicitly. In the case of carcinogens based on linear low-dose

extrapolation, the AWQC is being determined with respect to the incremental lifetime risk posed

by a substance’s presence in water, and is not being set with regard to an individual’s total risk

from all sources of exposure. Thus, the AWQC represents the water concentration that would be
4-4

expected to increase an individual’s lifetime risk of carcinogenicity from exposure to the

particular pollutant by no more than one chance in one million, regardless of the additional

lifetime cancer risk due to exposure, if any, to that particular substance from other sources. 

Furthermore, health-based criteria values for one medium based on linear low-dose

extrapolation typically vary from values for other media in terms of the concentration value, and

often the associated risk level. Therefore, the RSC concept could not even theoretically apply

unless all risk assessments for a particular carcinogen based on linear low-dose extrapolation

resulted in the same concentration value and same risk level; that is, an apportionment would

need to be based on a single risk value and level.

In the case of substances for which the AWQC is set on the basis of a carcinogen based

on a nonlinear low-dose extrapolation or for a noncancer endpoint where a threshold is assumed

to exist, non-water exposures are considered when deriving the AWQC using the RSC approach. 

The rationale for this approach is that for pollutants exhibiting threshold effects, the objective of

the AWQC is to ensure that an individual’s total exposure does not exceed that threshold level.

There has been some discussion of whether it is, in fact, necessary in most cases to

explicitly account for other sources of exposure when computing the AWQC for pollutants

exhibiting threshold effects. It has been argued that because of the conservative assumptions

generally incorporated in the calculation of RfDs (or POD/UF values) used as the basis for the

AWQC derivation, total exposures slightly exceeding the RfD are unlikely to produce adverse

effects.

EPA emphasizes that the purpose of the RSC is to ensure that the level of a chemical

allowed by a criterion or multiple criteria, when combined with other identified sources of

exposure common to the population of concern, will not result in exposures that exceed the RfD

or the POD/UF. The policy of considering multiple sources of exposure when deriving healthbased criteria has become common in EPA’s program office risk characterizations and criteria

and standard-setting actions. Numerous EPA workgroups have evaluated the appropriateness of

factoring in such exposures, and the Agency concludes that it is important for adequately

protecting human health. Consequently, EPA risk management policy has evolved significantly

over the last six years. Various EPA program initiatives and policy documents regarding

aggregate exposure and cumulative risk have been developed, including the consideration of

inhalation and dermal exposures. Additionally, accounting for other exposures has been

included in recent mandates (e.g., the Food Quality Protection Act) and, thus, is becoming a

requirement for the Agency. The Exposure Decision Tree approach has been shared with other

EPA offices, and efforts to coordinate policies on aggregate exposure, where appropriate, have

begun. EPA intends to continue developing policy guidance on the RSC issue and guidance to

address the concern that human health may not be adequately protected if criteria allow for

higher levels of exposure that, combined, may exceed the RfD or POD/UF. EPA also intends to

refine the 2000 Human Health Methodology in the future to incorporate additional guidance on

inhalation and dermal exposures. As stated previously, EPA is required to derive national water

quality criteria under Section 304(a) of the CWA and does not intend to derive site-specific

criteria. However, States and authorized Tribes have the flexibility to make alternative exposure

and RSC estimates based on local data, and EPA strongly encourages this.
4-5

Uncertainty factors used in the derivation of the RfD (or POD/UF) to account for intraand interspecies variability and the incompleteness of the toxicity data set(s)/animal studies are

specifically relevant to the chemical’s internal toxicological action, irrespective of the sources of

exposure that humans may be experiencing. The Agency’s policy is to consider and account for

other sources of exposure in order to set protective health criteria. EPA believes that multiple

route exposures may be particularly important when uncertainty factors associated with the RfD

are small. Although EPA is well aware that RfDs are not all equivalent in their derivation, EPA

does not believe that uncertainty in the toxicological data should result in less stringent criteria

by ignoring exposure sources. However, the RSC policy approach does allow less stringent

assumptions when multiple sources of exposure are not anticipated.

The AWQC are designed to be protective criteria, generally applicable to the waters of

the United States. While EPA cannot quantitatively predict the actual human health risk

associated with combined exposures above the RfD or POD/UF, a combination of health criteria

for multiple media exceeding the RfD or POD/UF may not be sufficiently protective. Therefore,

EPA’s policy is to routinely account for all sources and routes of non-occupational exposure

when setting AWQC for noncarcinogens and for carcinogens based on nonlinear low-dose

extrapolations. EPA believes that maintaining total exposure below the RfD (or POD/UF) is a

reasonable health goal and that there are circumstances where health-based criteria for a

chemical should not exceed the RfD (or POD/UF), either alone (if only one criterion is relevant,

along with other intake sources considered as background exposures) or in combination. EPA

believes its RSC policy ensures this goal.

Also, given the inability to reasonably predict future changes in exposure patterns, the

uncertainties in the exposure estimates due to typical data inadequacy, possible unknown sources

of exposure, and the potential for some populations to experience greater exposures than

indicated by the available data, EPA believes that utilizing the entire RfD (or POD/UF) does not

ensure adequate protection. 

4.2.2 The Exposure Decision Tree Approach

As indicated in Section 1, EPA has, in the past, used a “subtraction” method to account

for multiple sources of exposure to pollutants. In the subtraction method, other sources of

exposure (i.e., those other than the drinking water and fish exposures) are subtracted from the

RfD (or POD/UF). However, EPA also previously used a “percentage” method for the same

purpose. In this approach, the percentage of total exposure typically accounted for by the

exposure source for which the criterion is being determined, referred to as the relative source

contribution (RSC), is applied to the RfD to determine the maximum amount of the RfD

“apportioned” to that source. With both procedures, a “ceiling” level of 80 percent of the RfD

and a “floor level” of 20 percent of the RfD are applied. 

The subtraction method is considered acceptable when only one criterion is relevant for a

particular chemical. The percentage method is recommended in the context of the above goals

when multiple media criteria are at issue. The percentage method does not simply depend on the

amount of a contaminant in the prospective criterion source only. It is intended to reflect health

considerations, the relative portions of other sources, and the likelihood for ever-changing levels
4-6

in each of those multiple sources (due to ever-changing sources of emissions and discharges). 

Rather than simply defaulting in every instance, the Agency attempts to compare multiple source

exposures with one another to estimate their relative contribution to the total–given that

understanding the degree to which their concentrations vary, or making any distributional

analysis, is often not possible. The criteria levels, when multiple criteria are at issue, are based

on the actual levels, with an assumption that there may be enough relative variability such that

an apportionment (relating that percentage to the RfD) is a reasonable way of accounting for the

uncertainty regarding that variability. 

The specific RSC approach recommended by EPA, which we will use for the derivation

of AWQC for noncarcinogens and carcinogens assessed using nonlinear low-dose extrapolation,

is called the Exposure Decision Tree and is described below. To account for exposures from

other media when setting an AWQC (i.e., non-drinking water/non-fish ingestion exposures, and

inhalation or dermal exposures), the Exposure Decision Tree for determining proposed RfD or

POD/UF apportionments represents a method of comprehensively assessing a chemical for water

quality criteria development. This method considers the adequacy of available exposure data,

levels of exposure, relevant sources/media of exposure, and regulatory agendas (i.e., whether

there are multiple health-based criteria or regulatory standards for the same chemical). The

Decision Tree addresses most of the disadvantages associated with the exclusive use of either the

percentage or subtraction approaches, because they are not arbitrarily chosen prior to

determining the following: specific population(s) of concern, whether these populations are

relevant to multiple-source exposures for the chemical in question (i.e., whether the population is

actually or potentially experiencing exposure from multiple sources), and whether levels of

exposure, regulatory agendas, or other circumstances make apportionment of the RfD or

POD/UF desirable. Both subtraction and percentage methods are potentially utilized under

different circumstances with the Exposure Decision Tree approach, and the Decision Tree is

recommended with the idea that there is enough flexibility to use other procedures if information

on the contaminant in question suggests it is not appropriate to follow the Decision Tree. EPA

recognizes that there may be other valid approaches in addition to the Exposure Decision Tree. 

The Exposure Decision Tree approach allows flexibility in the RfD (or POD/UF)

apportionment among sources of exposure. When adequate data are available, they are used to

make protective exposure estimates for the population(s) of concern. When other sources or

routes of exposure are anticipated but data are not adequate, there is an even greater need to

make sure that public health protection is achieved. For these circumstances, a series of

qualitative alternatives is used (with the less adequate data or default assumptions) that allow for

the inadequacies of the data while protecting human health. Specifically, the Decision Tree

makes use of chemical information when actual monitoring data are inadequate. It considers

information on the chemical/physical properties, uses of the chemical, and environmental fate

and transformation, as well as the likelihood of occurrence in various media. Review of such

information, when available, and determination of a reasonable exposure characterization for the

chemical will result in a water quality criterion that more accurately reflects exposures than

automatically using a default value. Although the 20 percent default will still generally be used

when information is not adequate, the need for using it should be reduced. There may also be

some situations where EPA would consider the use of an 80 percent default (see Section 4.2.3). 
4-7

The Decision Tree also allows for use of either the subtraction or percentage method to

account for other exposures, depending on whether one or more health-based criterion is relevant

for the chemical in question. The subtraction method is considered acceptable when only one

criterion is relevant for a particular chemical. In these cases, other sources of exposure can be

considered “background” and can be subtracted from the RfD (or POD/UF). 

EPA cautions States and Tribes when using the subtraction method in these

circumstances. The subtraction method results in a criterion allowing the maximum possible 

chemical concentration in water after subtracting other sources. As such, it removes any cushion

between pre-criteria levels (i.e., actual “current” levels) and the RfD, thereby setting criteria at

the highest levels short of exceeding the RfD. It is somewhat counter to the goals of the CWA

for maintaining and restoring the nation’s waters. It is also directly counter to Agency policies,

explicitly stated in numerous programs, regarding pollution prevention. EPA has advocated that

it is good health policy to set criteria such that exposures are kept low when current levels are

already low. The subtraction method generally results in criteria levels of a contaminant in a

particular medium at significantly higher levels than the percentage method and, in this respect,

is contradictory to such goals. In fact, many chemicals have pre-criteria levels in environmental

media substantially lower (compared to the RfD) than the resulting criteria allow.

When more than one criterion is relevant to a particular chemical, apportioning the RfD

(or POD/UF) via the percentage method is considered appropriate to ensure that the combination

of criteria and, thus, the potential for resulting exposures do not exceed the RfD (or POD/UF). 

The Exposure Decision Tree (with numbered boxes) is shown in Figure 4-1. The explanation in

the text on the following pages must be read in tandem with the Decision Tree figure; the text in

each box of the figure only nominally identifies the process and conditions for determining the

outcome for that step of the Decision Tree. The underlying objective is to maintain total

exposure below the RfD (or POD/UF) while generally avoiding an extremely low limit in a

single medium that represents just a nominal fraction of the total exposure. To meet this

objective, all proposed numeric limits lie between 80 percent and 20 percent of the RfD (or

POD/UF). Again, EPA will use the Exposure Decision Tree approach when deriving its AWQC

but also recognizes that departures from the approach may be appropriate in certain cases. EPA

understands that there may be situations where the Decision Tree procedure is not practicable or 
4-8

Are exposures from

multiple sources (due to a

sum of sources or an

individual source)

potentially at levels near

(i.e., over 80%), at or in

excess of the RfD (or

POD/UF)?

Exposure Decision Tree for Defining Proposed RfD (or POD/UF) Apportionment

1. Identify population(s) of 

concern.

2. Identify relevant exposure

sources/pathways. *

3.

4. Are there sufficient data, physical/chemical

property information, fate and transport

information, and/or generalized information

available to characterize the likelihood of

exposure to relevant sources?

Is there some information

available on each source

to make a characterization of exposure?

Apportion the RfD (or

POD/ UF) including

80% ceiling/20% floor

using the percentage

approach (with ceiling

and floor).

Is there more than one regulatory action

(i.e., criteria, standard, guidance) relevant

for the chemical in question?

Describe exposures,

uncertainties, toxicityrelated information,

control issues, and

other information for

management decision.

Perform calculations

associated with Boxes

12 or 13 as applicable. No

Yes

9.

Yes

10.

11.

Use subtraction of appropriate

intake levels from sources other

than source of concern, including

80% ceiling/20% floor.

12.

Are there significant known or 13.

potential uses/sources other

than the source of concern?

Use 50% of

the RfD (or

POD/UF).

7.

8A.

No

No

Yes Yes

No Yes

Are adequate data available

to describe central

tendencies and high-ends

for relevant exposure

sources/pathways?

No

Problem

Formulation

Use 20% of the RfD

(or POD/UF).

8B. No Yes 8C.

5A.

6.

Figure 4-1

Perform apportionment as described in

Box 12 or 13, with a 50% ceiling/

20% floor.

5B.

Gather

more

information

and rereview

Use

20% of

the RfD

or

POD/UF

OR

* Sources and

pathways include both

ingestion and routes

other than oral for

water-related

exposures, and

nonwater sources of

exposure, including

ingestion exposures

(e.g., food), inhalation,

and/or dermal.
4-9

may be simply irrelevant after considering the properties, uses, and sources of the chemical in

question. EPA endorses such flexibility by States and authorized Tribes when developing

alternative water quality criteria in order to choose other procedures that are more appropriate

for setting health-based criteria and, perhaps, apportioning the RfD or POD/UF, as long as

reasons are given as to why it is not appropriate to follow the Exposure Decision Tree approach

and as long as the steps taken to evaluate the potential sources and levels of exposure are clearly

described. Often, however, the common situation of multiple exposure sources for a chemical is

likely to merit a Decision Tree evaluation for the purpose of developing human health water

quality criteria for a given chemical. 

It is clear that this will be an interactive process; input by exposure assessors will be

provided to, and received from, risk managers throughout the process, given that there may be

significant implications regarding control issues (i.e., cost/feasibility), environmental justice

issues, etc. In cases where the Decision Tree is not chosen, communication and concurrence

about the decision rationale and the alternative water quality criteria are of great importance.

Descriptions of the boxes within the Decision Tree are separated by the following

process headings to facilitate an understanding of the major considerations involved. The

decision to perform, or not to perform, an apportionment could actually be made at several points

during the Decision Tree process. Working through the process is most helpful for identifying

possible exposure sources and the potential for exposure, determining the relevancy of the

Decision Tree to developing an AWQC for a particular chemical and, possibly, determining the

appropriateness of using an alternative approach to account for overall exposure. “Relevancy”

here means determining whether more than one criterion, standard, or other guidance is being

planned or is in existence for the chemical in question. Additional guidance for States and

Tribes that wish to use the Exposure Decision Tree is provided in the Exposure Assessment

TSD. 

4.2.2.1 Problem Formulation

Initial Decision Tree discussion centers around the first two boxes: identification of

population(s) of concern (Box 1) and identification of relevant exposure sources and pathways

(Box 2). The term “problem formulation” refers to evaluating the population(s) and sources of

exposure in a manner that allows determination of the potential for the population of concern to

experience exposures from multiple sources for the chemical in question. Also, the data for the

chemical in question must be representative of each source/medium of exposure and be relevant

to the identified population(s). Evaluation includes determining whether the levels, multiple

criteria or regulatory standards, or other circumstances make apportionment of the RfD or

POD/UF reasonable. The initial problem formulation also determines the exposure parameters

chosen, the intake assumptions chosen for each route, and any environmental justice or other

social issues that aid in determining the population of concern. The term “data,” as used here

and discussed throughout this section, refers to ambient sampling data (whether from Federal,

regional, State, or area-specific studies) and not internal human exposure measurements.
4-10

4.2.2.2 Data Adequacy

In Box 3, it is necessary that adequate data exist for the relevant sources/pathways of

exposure if one is to avoid using default procedures. The adequacy of data is a professional

judgment for each individual chemical of concern, but EPA recommends that the minimum

acceptable data for Box 3 are exposure distributions that can be used to determine, with an

acceptable 95 percent confidence interval, the central tendency and high-end exposure levels for

each source. In fact, distributional data may exist for some or most of the sources of exposure.

There are numerous factors to consider in order to determine whether a dataset is

adequate. These include: (1) sample size (i.e., the number of data points); (2) whether the data

set is a random sample representative of the target population (if not, estimates drawn from it

may be biased no matter how large the sample); (3) the magnitude of the error that can be

tolerated in the estimate (estimator precision); (4) the sample size needed to achieve a given

precision for a given parameter (e.g., a larger sample is needed to precisely estimate an upper

percentile than a mean or median value); (5) an acceptable analytical method detection limit; and

(6) the functional form and variability of the underlying distribution, which determines the

estimator precision (e.g., whether the distribution is normal or lognormal and whether the

standard deviation is 1 or 10). Lack of information may prevent assessment of each of these

factors; monitoring study reports often fail to include background information or sufficient

summary statistics (and rarely the raw data) to completely characterize data adequacy. Thus, a

case-by-case determination of data adequacy may be necessary.

That being stated, there are some guidelines, as presented below, that lead to a rough

rule-of-thumb on what constitutes an “adequate” sample size for exposure assessment. Again,

first and foremost, the representativeness of the data for the population evaluated and the

analytical quality of the data must be acceptable. If so, the primary objective then becomes

estimating an upper percentile (e.g., say the 90th) and a central tendency value of some exposure

distribution based on a random sample from the distribution. Assuming that the distribution of

exposures is unknown, a nonparametric estimate of the 90th percentile is required. The required

estimate, based on a random sample of n observations from a target population, is obtained by

ranking the data from smallest to largest and selecting the observation whose rank is 1 greater

than the largest integer in the product of 0.9 times n. For example, in a data set of 25 points, the

nonparametric estimate of the 90th percentile is the 23rd largest observation.

In addition to this point estimate, it is useful to have an upper confidence bound on the

90th percentile. To find the rank of the order statistic that gives an upper 95 percent confidence

limit on the 90th percentile, the smallest value of r that satisfies the following formula is

determined: 

(Equation 4-2)
4-11

where:

r = the rank order of the observation

n = the number of observations

I = integer from 0 to r - 1

For relatively small data sets, the above formula will lead to selecting the largest

observation as the upper confidence limit on the 90th percentile. However, the problem with

using the maximum is that, in many environmental datasets, the largest observation is an outlier

and would provide an unrealistic upper bound on the 90th percentile. It would, therefore, be

preferable if the sample size n were large enough so that the formula yielded the second largest

observation as the confidence limit (see for example Gibbons, 1971).

This motivates establishing the following criterion for setting an “adequate” sample size: 

pick the smallest n such that the nonparametric upper 95 percent confidence limit on the 90th

percentile is the second largest value. Application of the above formula with r set to n-1 yields n

= 45 for this minimum sample size.

For the upper 95 percent confidence limit to be a useful indicator of a high-end exposure,

it must not be overly conservative (too large relative to the 90th percentile). It is, therefore, of

interest to estimate the expected magnitude of the ratio of the upper 95 percent confidence limit

to the 90th percentile. This quantity generally cannot be computed, since it is a function of the

unknown distribution. However, to get a rough idea of its value, consider the particular case of a

normal distribution. If the coefficient of variation (i.e., the standard deviation divided by the

mean) is between 0.5 and 2.0, the expected value of the ratio in samples of 45 will be

approximately 1.17 to 1.31; i.e., the upper 95 percent confidence limit will be only about 17 to

31 percent greater than the 90th percentile on the average.

It should be noted that the nonparametric estimate of the 95 percent upper confidence

limit based on the second largest value can be obtained even if the data set has only two detects

(it is assumed that the two detects are greater than the detection limit associated with all nondetects). This is an argument for using nonparametric rather than parametric estimation, since

use of parametric methods would require more detected values. On the other hand, if nondetects were not a problem and the underlying distribution were known, a parametric estimate of

the 90th percentile would generally be more precise.

As stated above, adequacy also depends on whether the samples are relevant to and

representative of the population at risk. Data may, therefore, be adequate for some decisions and

inadequate for others; this determination requires some professional judgment.

If the answer to Box 3 is no, based on the above determination of adequacy, then the

decision tree moves to Box 4. As suggested by the separate boxes, the available data that will be

reviewed as part of Box 4 do not meet the requirements necessary for Box 3. In Box 4, any

limited data that are available (in addition to information about the chemical/physical properties,

uses, and environmental fate and transformation, as well as any other information that would

characterize the likelihood of exposure from various media for the chemical) are evaluated to
4-12

make a qualitative determination of the relation of one exposure source to another. Although

this information should always be reviewed at the outset, it is recommended that this information

also be used to estimate the health-based water quality criteria. The estimate should be rather

conservative (as indicated in the Decision Tree), given that it is either not based on actual

monitoring data or is based on data that has been considered to be inadequate for a more accurate

quantitative estimate. Therefore, greater uncertainties exist and accounting for variability is not

really possible. Whether the available data are adequate and sufficiently representative will

likely vary from chemical to chemical and may depend on the population of concern. If there are

some data and/or other information to make a characterization of exposure, a determination can

be made as to whether there are significant known or potential uses for the chemical/sources of

exposure other than the source of concern (i.e., in this case, the drinking water and fish intakes

relevant to developing an AWQC) that would allow one to anticipate/quantify those exposures

(Box 6). If there are not, then it is recommended that 50 percent of the RfD or POD/UF can be

safely apportioned to the source of concern (Box 7). While this leaves half of the RfD or

POD/UF unapportioned, it is recommended as the maximum apportionment due to the lack of

data needed to more accurately quantify actual or potential exposures. If the answer to the

question in Box 6 is yes (there is multiple source information available for the exposures of

concern), and some information is available on each source of exposure (Box 8A), apply the

procedure in either Box 12 or Box 13 (depending on whether one or more criterion is relevant to

the chemical), using a 50 percent ceiling (Box 8C)–again due to the lack of adequate data. If the

answer to the question in Box 8A is no (there is no available information to characterize

exposure), then the 20 percent default of the RfD or POD/UF is used (Box 8B).

If the answer to the question in Box 4 is no; that is, there are not sufficient

data/information to characterize exposure, EPA intends to generally use the “default” assumption

of 20 percent of the RfD or POD/UF (Box 5A) when deriving or revising the AWQC. It may be

better to gather more data or information and re-review when this information becomes available

(Box 5B). EPA has done this on occasion when resources permit the acquisition of additional

data to enable better estimates of exposure instead of the default. If this is not possible, then the 

assumption of 20 percent of the RfD or POD/UF (Box 5A) should be used. Box 5A is likely to

be used infrequently with the Exposure Decision Tree approach, given that the information

described in Box 4 should be available in most cases. However, EPA intends to use 20 percent

of the RfD (or POD/UF), which has also been used in past water program regulations, as the

default value.

4.2.2.3 Regulatory Actions

If there are adequate data available to describe the central tendencies and high ends from

each exposure source/pathway, then the levels of exposure relative to the RfD or POD/UF are

compared (Box 9). If the levels of exposure for the chemical in question are not near (currently

defined as greater than 80 percent), at, or in excess of the RfD or POD/UF, then a subsequent

determination is made (Box 11) as to whether there is more than one health-based criterion or

regulatory action relevant for the given chemical (i.e., more than one medium-specific criterion,
4-13

standard or other guidance being planned, performed or in existence for the chemical). The

subtraction method is considered acceptable when only one criterion (standard, etc.) is relevant

for a particular chemical. In these cases, other sources of exposure can be considered

“background” and can be subtracted from the RfD (or POD/UF). When more than one criterion

is relevant to a particular chemical, apportioning the RfD (or POD/UF) via the percentage

method is considered appropriate to ensure that the combination of health criteria, and thus the

potential for resulting exposures, do not exceed the RfD (or POD/UF).

As indicated in Section 2, for EPA’s national 304(a) criteria, the RSC intake estimates of

non-water exposures (e.g., non-fish dietary exposures) will be based on arithmetic mean values

when data are available. The assumed body weight used in calculating the national criteria will

also be based on average values. The drinking water and fish intake values are 90th percentile

estimates. EPA believes that these assumptions will be protective of a majority of the population

and recommends them for State and Tribal use. However, States and authorized Tribes have the

flexibility to choose alternative intake rate and exposure estimate assumptions to protect specific

population groups that they have chosen.

4.2.2.4 Apportionment Decisions

If the answer to the question in Box 11 is no (there is not more than one relevant

medium-specific criterion/regulatory action), then the recommended method for setting a healthbased water quality criterion is to utilize a subtraction calculation (Box 12). Specifically,

appropriate intake values for each exposure source other than the source of concern are

subtracted out. EPA will rely on average values commonly used in the Agency for food

ingestion and inhalation rates, combined with mean contaminant concentration values, for

calculating RSC estimates to subtract. Alternatively, contaminant concentrations could be

selected based on the variability associated with those concentrations for each source. This

implies that a case-by-case determination of the variability and the resulting intake chosen would

be made, as each chemical evaluated can be expected to have different variations in

concentration associated with each source of intake. However, EPA anticipates that the

available data for most contaminants will not allow this for determination (based on past

experience). Guidance addressing this possibility is addressed in the Exposure Assessment TSD. 

EPA does not recommend that high-end intakes be subtracted for every exposure source, since

the combination may not be representative of any actually exposed population or individual. 

The subtraction method would also include an 80 percent ceiling and a 20 percent floor. 

If the answer to the question in Box 11 is yes (there is more than one medium-specific

criterion/regulation relevant), then the recommended method for setting health-based water

quality criteria is to apportion the RfD or POD/UF among those sources for which health-based

criteria are being set (Box 13). This is done via a percentage approach (with a ceiling and floor). 

This simply refers to the percentage of overall exposure contributed by an individual exposure

source. For example, if for a particular chemical, drinking water were to represent half of total

exposure and diet were to represent the other half, then the drinking water contribution (or RSC)

would be 50 percent. The health-based criteria would, in turn, be set at 50 percent of the RfD or

POD/UF. This method also utilizes an appropriate combination of intake values for each
4-14

exposure source based on values commonly used in the Agency for food ingestion and inhalation

rates, combined with mean contaminant concentration values. 

Finally, if the levels of exposure for the chemical in question are near (currently defined

as greater than 80 percent), at, or in excess of the RfD or POD/UF (i.e., the answer in Box 9 is

yes), then the estimates of exposures and related uncertainties, recommended apportionment

(either box 12 or 13), toxicity-related information, control issues, and other information are to be

presented to managers for a decision (Box 10). The high levels referred to in Box 9 may be due

to one source contributing that high level (while other sources contribute relatively little) or due

to more than one source contributing levels that, in combination, approach or exceed the RfD or

POD/UF. Management input may be necessary due to the control issues (i.e., cost and feasibility

concerns), especially when multiple criteria are at issue. In practice, risk managers are routinely

a part of decisions regarding regulatory actions and will be involved with any recommended

outcome of the Exposure Decision Tree or, for that matter, any alternative to the Exposure

Decision Tree. However, because exposures approach or exceed the RfD or POD/UF and

because the feasibility of controlling different sources of exposure are complicated issues, risk

managers will especially need to be directly involved in final decisions in these circumstances.

It is emphasized here that the procedures in these circumstances are not different than the

procedures when exposures are not at or above the RfD (or POD/UF). Therefore, in these cases,

estimates should be performed as with Boxes 11, 12, and 13. The recommendation should be

made based on health-based considerations only, just as when the chemical in question was not a

Box 10 situation. If the chemical is relevant to one health criterion or regulatory action only, the

other sources of exposure could be subtracted from the RfD or POD/UF to determine if there is

any leftover amount for setting the criterion. If the chemical is a multiple media criteria issue,

then an apportionment should be made, even though it is possible that all sources would need to

be reduced. Regardless of the outcome of Box 9, all apportionments made (via the methods of

Boxes 12 or 13) should include a presentation of the uncertainty in the estimate and in the RfD

or POD/UF for a more complete characterization.

The process for a Box 10 situation (versus a situation that is not) differs in that the

presentations for Boxes 12 and 13 are based on apportionments (following the review of

available information and a determination of appropriate exposure parameters) that must address

additional control issues and may result in more selective reductions. With Box 10, one or

several criteria possibilities (“scenarios”) could be presented for comparison along with

implications of the effects of various control options. It is appropriate to present information in

this manner to risk managers given the complexity of these additional control issues.

4.2.3 Additional Points of Clarification on the Exposure Decision Tree Approach for

Setting AWQC

As with Box 9, if a determination is made in Box 8A (i.e., information is available to

characterize exposure) that exposures are near, at, or above the RfD (or POD/UF) based on the

available information, the apportionments made need to be presented to risk managers for

decision. If information is lacking on some of the multiple exposure sources, then EPA would

use a default of 20 percent of the RfD or POD/UF (Box 8B).
4-15

Results of both Boxes 12 and 13 rely on the 80 percent ceiling and 20 percent floor. The

80 percent ceiling was implemented to ensure that the health-based goal will be low enough to

provide adequate protection for individuals whose total exposure to a contaminant is, due to any

of the exposure sources, higher than currently indicated by the available data. This also

increases the margin of safety to account for possible unknown sources of exposure. The 20

percent floor has been traditionally rationalized to prevent a situation where small fractional

exposures are being controlled. That is, below that point, it is more appropriate to reduce other

sources of exposure, rather than promulgating standards for de minimus reductions in overall

exposure. 

If it can be demonstrated that other sources and routes of exposure are not anticipated for

the pollutant in question (based on information about its known/anticipated uses and

chemical/physical properties), then EPA would use the 80 percent ceiling. EPA qualifies this

policy with the understanding that as its policy on cumulative risk assessment continues to

develop, the 80 percent RSC may prove to be underprotective.

In the cases of pollutants for which substantial data sets describing exposures across all

anticipated pathways of exposure exist, and probabilistic analyses have been conducted based on

those data, consideration will be given to the results of those assessments as part of the Exposure

Decision Tree approach for setting AWQC.

For many chemicals, the rate of absorption from ingestion can differ substantially from

absorption by inhalation. There is also available information for some chemicals that

demonstrates appreciable differences in gastrointestinal absorption depending on whether the

chemical is ingested from water, soil, or food. For some contaminants, the absorption of the

contaminant from food can differ appreciably for plant compared with animal food products. 

Regardless of the apportionment approach used, EPA recommends using existing data on

differences in bioavailability between water, air, soils, and different foods when estimating total

exposure for use in apportioning the RfD or POD/UF. The Agency has developed such exposure

estimates for cadmium (USEPA, 1994). In the absence of data, EPA will assume equal rates of

absorption from different routes and sources of exposure. 

4.2.4 Quantification of Exposure

When selecting contaminant concentration values in environmental media and exposure

intake values for the RSC analysis, it is important to realize that each value selected (including

those recommended as default assumptions in the AWQC equation) may be associated with a

distribution of values for that parameter. Determining how various subgroups fall within the

distributions of overall exposure and how the combination of exposure variables defines what

population is being protected is a complicated and, perhaps, unmanageable task, depending on

the amount of information available on each exposure factor included. Many times, the default

assumptions used in EPA risk assessments are derived from the evaluation of numerous studies

and are considered to generally represent a particular population group or a national average. 

Therefore, describing with certainty the exact percentile of a particular population that is

protected with a resulting criteria is often not possible.
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By and large, the AWQC are derived to protect the majority of the general population

from chronic adverse health effects. However, as stated above in Section 4.1.1.1, States and

authorized Tribes are encouraged to consider protecting population groups that they determine

are at greater risk and, thus, would be better protected using alternative exposure assumptions. 

The ultimate choice of the contaminant concentrations used in the RSC estimate and the

exposure intake rates requires the use of professional judgment. This is discussed in greater

detail in the Exposure Assessment TSD.

4.2.5 Inclusion of Inhalation and Dermal Exposures

EPA intends to develop policy guidelines to apply to this Methodology for explicitly

incorporating inhalation and dermal exposures. When estimating overall exposure to pollutants

for AWQC development, EPA believes that the sources of inhalation and dermal exposures

considered should include, on a case-by-case basis, both non-oral exposures from water and

other inhalation and dermal sources (e.g., ambient or indoor air, soil). When the policy

guidelines are completed, this Methodology will be refined to include that guidance.

A number of drinking water contaminants are volatile and thus diffuse from water into

the air where they may be inhaled. In addition, drinking water is used for bathing and, thus,

there is at least the possibility that some contaminants in water may be dermally absorbed. 

Volatilization may increase exposure via inhalation and decrease exposure via ingestion and

dermal absorption. The net effect of volatilization and dermal absorption upon total exposure to

volatile drinking water contaminants is unclear in some cases and varies from chemical to

chemical. Dermal exposures are also important to consider for certain population groups, such

as children and other groups with high soil contact. 

With regard to additional non-water related exposures, it is clear that the type and

magnitude of toxicity produced via inhalation, ingestion, and dermal contact may differ; that is,

the route of exposure can affect absorption of a chemical and can otherwise modify its toxicity. 

For example, an inhaled chemical such as hydrogen fluoride may produce localized effects on

the lung that are not observed (or only observed at much higher doses) when the chemical is

administered orally. Also, the active form of a chemical (and principal toxicity) can be the

parent compound and/or one or more metabolites. With this Methodology, EPA recommends

that differences in absorption and toxicity by different routes of exposure be determined and

accounted for in dose estimates and applied to the exposure assessment. EPA acknowledges that

the issue of whether the doses received from inhalation and ingestion exposures are cumulative

(i.e., toward the same threshold of toxicity) is complicated. Such a determination involves

evaluating the chemical’s physical characteristics, speciation, and reactivity. A chemical may

also exhibit different metabolism by inhalation versus oral exposure and may not typically be

metabolized by all tissues. In addition, a metabolite may be much more or much less toxic than

the parent compound. Certainly with a systemic effect, if the chemical absorbed via different

routes enters the bloodstream, then there is some likelihood that it will contact the same target

organ. Attention also needs to be given to the fact that both the RfD and RfC are derived based

on the administered level. Toxicologists generally believe that the effective concentration of the

active form of a chemical(s) at the site(s) of action determines the toxicity. If specific

differences between routes of exposure are not known, it may be reasonable to assume that the
4-17

internal concentration at the site from any route contributes as much to the same effect as any

other route. A default of assuming equal absorption has often been used. However, for many of

the chemicals that the Agency has reviewed, there is a substantial amount of information already

known to determine differences in rates of absorption. For example, absorption is, in part, a

function of blood solubility (i.e., Henry’s Constant) and better estimations than the default can

be made. 

The RSC analyses that accompany the 2000 Human Health Methodology accommodate

inclusion of inhalation exposures. Even if different target organs are involved between different

routes of exposure, a conservative policy may be appropriate to keep all exposures below a

certain level. A possible alternative is to set allowable levels (via an equation) such that the total

of ingestion exposures over the ingestion RfD added to the total of inhalation exposures over the

inhalation RfC is not greater than 1 (Note: the RfD is typically presented in mg/kg-day and the

RfC is in mg/m3

). Again, EPA intends to develop guidance for this Methodology to explicitly

incorporate inhalation and dermal exposures, and will refine the Methodology when that

guidance is completed. 

4.3 EXPOSURE FACTORS USED IN THE AWQC COMPUTATION

This section presents values for the specific exposure factors that EPA will use in the

derivation of AWQC. These include human body weight, drinking water consumption rates, and

fish ingestion rates. 

When choosing exposure factor values to include in the derivation of a criterion for a

given pollutant, EPA recommends considering values that are relevant to population(s) that is

(are) most susceptible to that pollutant. In addition, highly exposed populations should be

considered when setting criteria. In general, exposure factor values specific to adults and

relevant to lifetime exposures are the most appropriate values to consider when determining

criteria to protect against effects from long-term exposure which, by and large, the human health

criteria are derived to protect. However, infants and children may have higher rates of water and

food consumption per unit body weight compared with adults and also may be more susceptible

to some pollutants than adults (USEPA, 1997a). There may be instances where acute or

subchronic developmental toxicity makes children the population group of concern. In addition,

exposure of pregnant women to certain toxic chemicals may cause developmental effects in the

fetus (USEPA, 1997b). Exposures resulting in developmental effects may be of concern for

some contaminants and should be considered along with information applicable to long-term

health effects when setting AWQC. (See Section 3.2 for further discussion of this issue.) Shortterm exposure may include multiple intermittent or continuous exposures occurring over a week

or so. Exposure factor values relevant for considering chronic toxicity, as well as exposure

factor values relevant for short-term exposure developmental concerns, that could result in

adverse health effects are discussed in the sections below. In appropriate situations, EPA may

consider developing criteria for developmental health effects based on exposure factor values

specific to children or to women of childbearing age. EPA encourages States and Tribes to do

the same when health risks are associated with short-term exposures. 
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EPA believes that the recommended exposure factor default intakes for adults in chronic

exposure situations are adequately protective of the population over a lifetime. In providing

additional exposure intake values for highly exposed subpopulations (e.g., sport anglers,

subsistence fishers), EPA is providing flexibility for States and authorized Tribes to establish

criteria specifically targeted to provide additional protection using adjusted values for exposure

parameters for body weight, drinking water intake, and fish consumption. The exposure factor

values provided for women of childbearing age and children would only be used in the

circumstances indicated above. 

Each of the following sections recommends exposure parameter values for use in

developing AWQC. These are based on both science policy decisions that consider the best

available data, as well as risk management judgments regarding the overall protection afforded

by the choice in the derivation of AWQC. These will be used by EPA to derive new, or revise

existing, 304(a) national criteria. 

4.3.1 Human Body Weight Values for Dose Calculations

The source of data for default human body weights used in deriving the AWQC is the

third National Health and Nutrition Examination Survey (NHANES III). NHANES III

represents a very large interview and examination endeavor of the National Center for Health

Statistics (NCHS) and included participation from the Centers for Disease Control (CDC). The

NHANES III was conducted on a nationwide probability sample of over 30,000 persons from the

civilian, non-institutionalized population of the United States. The survey began in October

1988 and was completed in October 1994 (WESTAT, 2000; McDowell, 2000). Body weight

data were taken from the NHANES III Examination Data File. Sampling weights were applied

to all persons examined in the Mobile Examination Centers (MECs) or at home, as was

recommended by the NHANES data analysts (WESTAT, 2000).

The NHANES III survey has numerous strengths and very few weaknesses. Its primary

strengths are the national representativeness, large sample size, and precise estimates due to this

large sample size. Another strength is its high response rate; the examination rate was 73

percent overall, 89 percent for children under 1 year old, and approximately 85 percent for

children 1 to 5 years old (McDowell, 2000). Interview response rates were even higher, but the

body weight data come from the NHANES examinations; that is, all body weights were carefully

measured by survey staff, rather than the use of self-reported body weights. The only significant

potential weakness of the NHANES data is the fact that the data are now between 6 and 12 years

old. Given that there were upward trends in body weight from NHANES II to NHANES III, and

that NCHS has indicated the prevalence of overweight people increased in all age groups, the

data could underestimate current body weights if that trend has continued (WESTAT, 2000).

The NHANES III collected standard body measurements of sample subjects, including

height and weight, that were made at various times of the day and in different seasons of the

year. This technique was used because one’s weight may vary between winter and summer and

may fluctuate with recency of food and water intake and other daily activities (McDowell, 2000).
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As with the other exposure assumptions, States and authorized Tribes are encouraged to

use alternative body weight assumptions for population groups other than the general population

and to use local or regional data over default values as more representative of their target

population group(s).

4.3.1.1 Rate Protective of Human Health from Chronic Exposure

 EPA recommends maintaining the default body weight of 70 kg for calculating AWQC

as a representative average value for both male and female adults. As previously indicated,

exposure factor values specific to adults are recommended to protect against effects from longterm exposure. The value of 70 kg is based on the following information. In the analysis of the

NHANES III database, median and mean values for female adults 18-74 years old are 65.8 and

69.5 kg, respectively (WESTAT, 2000). For males in the same age range, the median and mean

values are 79.9 and 82.1 kg, respectively. The mean body weight value for men and women ages

18 to 74 years old from this survey is 75.6 kg (WESTAT, 2000). This mean value is higher than

the mean value for adults ages 20-64 years old of 70.5 kg from a study by the National Cancer

Institute (NCI) which primarily measured drinking water intake (Ershow and Cantor, 1989). The

NCI study is described in the subsection on Drinking Water Intake Rates that follows (Section

4.3.2). The value from the NHANES III database is also higher than the value given in the

revised EPA Exposure Factors Handbook (USEPA, 1997b), which recommends 71.8 kg for

adults, based on the older NHANES II data. The Handbook also acknowledges the commonly

used 70 kg value and encourages risk assessors to use values which most accurately reflect the

exposed population. However, the point is also made that the 70 kg value is used in the

derivation of cancer slope factors and unit risks that appear in IRIS. Consistency is advocated

between the dose-response relationship and exposure factors assumed. Therefore, if a value

higher than 70 kg is used, the assessor needs to adjust the dose-response relationship as

described in the Appendix to Chapter 1, Volume 1 of the Handbook (USEPA, 1997b).

4.3.1.2 Rates Protective of Developmental Human Health Effects

As noted above, pregnant women may represent a more appropriate population for which

to assess risks from exposure to chemicals in ambient waters in some cases, because of the

potential for developmental effects in fetuses. In these cases, body weights representative of

women of childbearing age may be appropriate to adequately protect offspring from such health

effects. To determine a mean body weight value appropriate to this population, separate body

weight values for women in individual age groups within the range of 15 to 44 years old were

analyzed from the NHANES III data (WESTAT, 2000). The resulting median and mean body

weight values are 63.2 and 67.3 kg, respectively. Ershow and Cantor (1989) present body

weight values specifically for pregnant women included in the survey; median and mean weights

are 64.4 and 65.8 kilograms, respectively. Ershow and Cantor (1989), however, do not indicate

the ages of these pregnant women. Based on this information for women of childbearing age and

pregnant women, EPA recommends use of a body weight value of 67 kg in cases where pregnant

women are the specific population of concern and the chemical of concern exhibits reproductive

and/or developmental effects (i.e., the critical effect upon which the RfD or POD/UF is based). 

Using the 67 kg assumption would result in lower (more protective) criteria than criteria based

on 70 kg.
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As discussed earlier, because infants and children generally have a higher rate of water

and food consumption per unit body weight compared with adults, a higher intake rate per unit

body weight may be needed when comparing estimated exposure doses with critical doses when

RfDs are based on health effects in children. To calculate intake rates relevant to such effects,

the body weight of children should be used. As with the default body weight for pregnant

women, EPA is not recommending the development of additional AWQC (i.e., similar to

drinking water health advisories) that focus on acute or short-term effects, since these are not

seen routinely as having a meaningful role in the water quality criteria program. However, there

may be circumstances where the consideration of exposures for these groups is warranted. 

Although the AWQC generally are based on chronic health effects data, they are intended to also

be protective with respect to adverse effects that may reasonably be expected to occur as a result

of elevated shorter-term exposures. EPA acknowledges this as a potential course of action and

is, therefore, recommending these default values which EPA would consider in an appropriate

circumstance and for States and authorized Tribes to utilize in such situations.

EPA is recommending an assumption of 30 kg as a default child’s body weight to

calculate AWQC to provide additional protection for children when the chemical of concern

indicates health effects in children are of predominant concern (i.e., test results show children are

more susceptible due to less developed immune systems, neurological systems, and/or lower

body weights). The value is based on the mean body weight value of 29.9 kg for children ages 1

to14 years old, which combines body weight values for individual age groups within this larger

group. The mean value is based on body weight information from NHANES III for individualyear age groups between one and 14 years old (WESTAT, 2000). A mean body weight of 28 kg

is obtained using body weight values from Ershow and Cantor (1989) for five age groups within

this range of 0-14 years and applying a weighting method for different ages by population

percentages from the U.S. Bureau of the Census. The 30 kg assumption is also consistent with

the age range for children used with the estimated fish intake rates. Unfortunately, fish intake

rates for finer age group divisions are not possible due to the limited sampling base from the fish

intake survey; there is limited confidence in calculated values (e.g., the mean) for such fine age

groups. Given this limitation, the broad age category of body weight for children is suitable for

use with the default fish intake assumption. 

Given the hierarchy of preferences regarding the use of fish intake information (see

Section 4.3.3), States may have more comprehensive data and prefer to target a more narrow,

younger age group. If States choose to specifically evaluate toddlers, EPA recommends using 13

kg as a default body weight assumption for children ages 1 to 3 years old. The median and mean

values of body weight for children 1 to 3 years old are 13.2 and 13.1 kg, respectively, based on

an analysis of the NHANES III database (WESTAT, 2000). The NHANES III median and mean

values for females between 1 and 3 years old are 13.0 and 12.9 kg, respectively, and are 13.4 and

13.4 kg for males, respectively. Median and mean body weight values from the earlier Ershow

and Cantor (1989) study for children ages 1 to 3 years old were 13.6 and 14.1 kg, respectively. 

Finally, if infants are specifically evaluated, EPA recommends a default body weight of 7 kg

based on the NHANES III analysis. Median and mean body weights for both male and female

infants (combined) 2 months old were 6.3 and 6.3 kg, respectively, and for infants 3 months old

were 7.0 and 6.9 kg, respectively. With the broader age category of males and females 2 to 6

months old, median and mean body weights were 7.4 and 7.4 kg, respectively. The NHANES
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analysis did not include infants under 2 months of age. Although EPA is not recommending

body weight values for newborns, the NCHS National Vital Statistics Report indicates that, for

1997, the median birth weight ranged from 3 to 3.5 kg, according to WESTAT (2000).

Body weight values for individual ages within the larger range of 0-14 years are listed in

the Exposure Assessment TSD for those States and authorized Tribes who wish to use body

weight values for these individual groups. States and Tribes may wish to consider certain

general developmental ages (e.g., infants, pre-adolescents, etc.), or certain specific

developmental landmarks (e.g., neurological development in the first four years), depending on

the chemical of concern. EPA encourages States and authorized Tribes to choose a body weight

intake from the tables presented in the TSD, if they believe a particular age subgroup is more

appropriate.

4.3.2 Drinking Water Intake Rates

The basis for the drinking water intake rates (also for the fish intake rates presented in

Section 4.3.3) is the 1994-96 Continuing Survey of Food Intake by Individuals (CSFII)

conducted by the U.S. Department of Agriculture (USDA, 1998). The CSFII survey collects

dietary intake information from nationally representative samples of non-institutionalized

persons residing in United States households. Households in these national surveys are sampled

from the 50 states and the District of Columbia. Each survey collects daily consumption records

for approximately 10,000 food codes across nine food groups. These food groups are (1) milk

and milk products; (2) meat, poultry, and fish; (3) eggs; (4) dry beans, peas, legumes, nuts, and

seeds; (5) grain products; (6) fruit; (7) vegetables; (8) fats, oils, and salad dressings; and (9)

sweets, sugars, and beverages. The survey also asks each respondent how many fluid ounces of

plain drinking water he or she drank during each of the survey days. In addition, the CSFII

collects household information, including the source of plain drinking water, water used to

prepare beverages, and water used to prepare foods. Data provide “up-to-date information on

food intakes by Americans for use in policy formation, regulation, program planning and

evaluation, education, and research.” The survey is “the cornerstone of the National Nutritional

Monitoring and Related Research Program, a set of related federal activities intended to provide

regular information on the nutritional status of the United States population” (USDA, 1998).

The 1994-96 CSFII was conducted according to a stratified, multi-area probability

sample organized using estimates of the 1990 United States population. Stratification accounted

for geographic location, degree of urbanization, and socioeconomics. Each year of the survey

consisted of one sample with oversampling for low-income households.

Survey participants provided two non-consecutive, 24-hour days of dietary data. Both

days’ dietary recall information was collected by an in-home interviewer. Interviewers provided

participants with an instructional booklet and standard measuring cups and spoons to assist them

in adequately describing the type and amount of food ingested. If the respondent referred to a

cup or bowl in their own home, a 2-cup measuring cup was provided to aid in the calculation of

the amount consumed. The sample person could fill their own bowl or cup with water to

represent the amount eaten or drunk, and the interviewer could then measure the amount

consumed by pouring it into the 2-cup measure. The Day 2 interview occurred three to 10 days
4-22

after the Day 1 interview, but not on the same day of the week. The interviews allowed

participants “three passes” through the daily intake record to maximize recall (USDA, 1998). 

Proxy interviews were conducted for children aged six and younger and sampled individuals

unable to report due to mental or physical limitations. The average questionnaire administration

time for Day 1 intake was 30 minutes, while Day 2 averaged 27 minutes.

Two days of dietary recall data were provided by 15,303 individuals across the three

survey years. This constitutes an overall two-day response rate of 75.9 percent. Survey weights

were corrected by the USDA for nonresponse.

All three 1994-96 CSFII surveys are multistage, stratified-cluster samples. Sample

weights, which project the data from a sampled individual to the population, are based on the

probability of an individual being sampled at each stage of the sampling design. The sample

weights associated with each individual reporting two days of consumption data were adjusted to

correct for nonresponse bias. 

The 1994-96 CSFII surveys have advantages and limitations for estimating per capita

water (or fish) consumption. The primary advantage of the CSFII surveys is that they were

designed and conducted by the USDA to support unbiased estimation of food consumption

across the population in the United States and the District of Columbia. Second, the survey is

designed to record daily intakes of foods and nutrients and support estimation of food

consumption.

One limitation of the 1994-96 CSFII surveys is that individual food consumption data

were collected for only two days–a brief period which does not necessarily depict “usual intake.” 

Usual dietary intake is defined as “the long-run average of daily intakes by an individual.” 

Upper percentile estimates may differ for short-term and longer-term data because short-term

food consumption data tend to be inherently more variable. It is important to note, however, that

variability due to duration of the survey does not result in bias of estimates of overall mean

consumption levels. Also, the multistage survey design does not support interval estimates for

many of the subpopulations of interest because of sparse representation in the sample. 

Subpopulations with sparse representation include Native Americans on reservations and certain

ethnic groups. While these individuals are participants in the survey, they are not present in

sufficient numbers to support consumption estimates. 

Despite these limitations, the CSFII is considered one of the best sources of current

information on consumption of water and fish-containing foods. The objective of estimating per

capita water and fish consumption by the United States population is compatible with the

statistical design and scope of the CSFII survey.

4.3.2.1 Rate Protective of Human Health from Chronic Exposure

EPA recommends maintaining the default drinking water intake rate of 2 L/day to protect

most consumers from contaminants in drinking water. EPA believes that the 2 L/day assumption

is representative of a majority of the population over the course of a lifetime. EPA also notes

that there is comparatively little variability in water intake within the population compared with
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fish intake (i.e., drinking water intake varies, by and large, by about a three-fold range, whereas

fish intake can vary by 100-fold). EPA believes that the 2 L/day assumption continues to

represent an appropriate risk management decision. The results of the 1994-96 CSFII analysis

indicate that the arithmetic mean, 75th, and 90th percentile values for adults 20 years and older are

1.1, 1.5, and 2.2 L/day, respectively (USEPA, 2000a). The 2 L/day value represents the 86th

percentile for adults. These values can also be compared to data from an older National Cancer

Institute (NCI) study, which estimated intakes of tapwater in the United States based on the

USDA’s 1977-78 Nationwide Food Consumption Survey (NFCS). The arithmetic mean, 75th,

and 90th percentile values for adults 20 - 64 years old were 1.4, 1.7, and 2.3 L/day, respectively

(Ershow and Cantor, 1989). The 2 L/day value represents the 88th percentile for adults from the

NCI study. 

The 2 L/day assumption was used with the original 1980 AWQC National Guidelines and

has also been used in EPA’s drinking water program. EPA believes that the newer studies

continue to support the use of 2 L/day as a reasonable and protective consumption rate that

represents the intake of most water consumers in the general population. However, individuals

who work or exercise in hot climates could have water consumption rates significantly above 2

L/day, and EPA believes that States and Tribes should consider regional or occupational

variations in water consumption. 

4.3.2.2 Rates Protective of Developmental Human Health Effects

Based on the 1994-96 CSFII study data, EPA also recommends 2 L/day for women of

childbearing age. The analysis for women of childbearing age (ages 15-44) indicate mean, 75th,

and 90th percentile values of 0.9, 1.3, and 2.0 L/day, respectively. These rates compare well with

those based on an analysis of tapwater intake by pregnant and lactating women by Ershow et al.

(1991), based on the older USDA data, for women ages 15-49. Arithmetic mean, 75th and 90th

percentile values were 1.2, 1.5, and 2.2 L/day, respectively, for pregnant women. For lactating

women, the arithmetic mean, 75th and 90th percentile values were 1.3, 1.7, and 1.9 L/day,

respectively.

As noted above, because infants and children have a higher daily water intake per unit

body weight compared with adults, a water consumption rate measured for children is

recommended for use when RfDs are based on health effects in children. Use of this water

consumption rate should result in adequate protection for infants and children when setting

criteria based on health effects for this target population. EPA recommends a drinking water

intake of 1 L/day to, again, represent a majority of the population of children that consume

drinking water. The results of the 1994-96 CSFII analysis indicate that for children from 1 to 10

years of age, the arithmetic mean, 75th, and 90th percentile values are 0.4, 0.6, and 0.9 L/day,

respectively (USEPA, 2000a). The 1 L/day value represents the 93rd percentile for this group. 

The arithmetic mean, 75th, and 90th percentile values for smaller children, ages 1 to 3 years, are

0.3, 0.5, and 0.7 L/day, respectively. The 1 L/day value represents the 97th percentile of the

group ages 1 to 3 years old. For the category of infants under 1 year of age, the arithmetic mean,

75th, and 90th percentile values are 0.3, 0.7, and 0.9 L/day, respectively. These data can similarly

be compared to those of the older National Cancer Institute (NCI) study. The arithmetic mean,

75th, and 90th percentile values for children 1 to 10 years old were 0.74, 0.96, and 1.3 L/day,
4-24

respectively. The mean, 75th, and 90th percentile values for children 1 to 3 years old in the NCI

study were 0.6, 0.8, and 1.2 L/day, respectively. Finally, the mean, 75th, and 90th percentile

values for infants less than 6 months old were 0.3, 0.3, and 0.6 L/day, respectively (Ershow and

Cantor, 1989). 

4.3.2.3 Rates Based on Combining Drinking Water Intake and Body Weight

As an alternative to considering body weight and drinking water intake rates separately,

EPA is providing rates based on intake per unit body weight data (in units of ml/kg) in the

Exposure Assessment TSD, with additional discussion on their use. These rates are based on

self-reported body weights from the CSFII survey respondents for the 1994-96 data. While EPA

intends to derive or revise national default criteria on the separate intake values and body

weights, in part due to the strong input received from its State stakeholders, the ml/kg-BW/day

values are provided in the TSD for States or authorized Tribes that prefer their use. It should be

noted that in their 1993 review, EPA’s Science Advisory Board (SAB) felt that using drinking

water intake rate assumptions on a per unit body weight basis would be more accurate, but did

not believe this change would appreciably affect the criteria values (USEPA, 1993).

4.3.3 Fish Intake Rates

The basis for the fish intake rates is the 1994-96 CSFII conducted by the USDA, and

described above in Section 4.3.2.

4.3.3.1 Rates Protective of Human Health from Chronic Exposure

EPA recommends a default fish intake rate of 17.5 grams/day to adequately protect the

general population of fish consumers, based on the 1994 to 1996 data from the USDA’s CSFII

Survey. EPA will use this value when deriving or revising its national 304(a) criteria. This

value represents the 90th percentile of the 1994-96 CSFII data. This value also represents the

uncooked weight estimated from the CSFII data, and represents intake of freshwater and

estuarine finfish and shellfish only. For deriving AWQC, EPA has also considered the States’

and Tribes’ needs to provide adequate protection from adverse health effects to highly exposed

populations such as recreational and subsistence fishers, in addition to the general population. 

Based on available studies that characterize consumers of fish, recreational fishers and

subsistence fishers are two distinct groups whose intake rates may be greater than the general

population. It is, therefore, EPA’s decision to discuss intakes for these two groups, in addition to

the general population. 

EPA recommends default fish intake rates for recreational and subsistence fishers of 17.5

grams/day and 142.4 grams/day, respectively. These rates are also based on uncooked weights

for fresh/estuarine finfish and shellfish only. However, because the level of fish intake in highly

exposed populations varies by geographical location, EPA suggests a four preference hierarchy

for States and authorized Tribes to follow when deriving consumption rates that encourages use

of the best local, State, or regional data available. A thorough discussion of the development of

this policy method and relevant data sources is contained in the Exposure Assessment TSD. The

hierarchy is also presented here because EPA strongly emphasizes that States and authorized
4-25

Tribes should consider developing criteria to protect highly exposed population groups and use

local or regional data over the default values as more representative of their target population

group(s). The four preference hierarchy is: (1) use of local data; (2) use of data reflecting similar

geography/population groups; (3) use of data from national surveys; and (4) use of EPA’s default

intake rates.

The recommended four preference hierarchy is intended for use in evaluating fish intake

from fresh and estuarine species only. Therefore, to protect humans who additionally consume

marine species of fish, the marine portion should be considered an other source of exposure

when calculating an RSC for dietary intake. Refer to the Exposure Assessment TSD for further

discussion. States and Tribes need to ensure that when evaluating overall exposure to a

contaminant, marine fish intake is not double-counted with the other dietary intake estimate

used. Coastal States and authorized Tribes that believe accounting for total fish consumption

(i.e., fresh/estuarine and marine species) is more appropriate for protecting the population of

concern may do so, provided that the marine intake component is not double-counted with the

RSC estimate. Tables of fish consumption intakes based on the CSFII in the TSD provide rates

for fresh/estuarine species, marine species, and total (combined) values to facilitate this option

for States and Tribes. Throughout this section, the terms “fish intake” or “fish consumption” are

used. These terms refer to the consumption of finfish and shellfish, and the CSFII survey

includes both. States and Tribes should ensure that when selecting local or regionally-specific

studies, both finfish and shellfish are included when the population exposed are consumers of

both types.

EPA’s first preference is that States and authorized Tribes use the results from fish intake

surveys of local watersheds within the State or Tribal jurisdiction to establish fish intake rates

that are representative of the defined populations being addressed for the particular waterbody. 

Again, EPA recommends that data indicative of fresh/estuarine species only be used which is, by

and large, most appropriate for developing AWQC. EPA also recommends the use of uncooked

weight intake values, which is discussed in greater detail with the fourth preference. States and

authorized Tribes may use either high-end values (such as the 90th or 95th percentile values) or

average values for an identified population that they plan to protect (e.g., subsistence fishers,

sport fishers, or the general population). EPA generally recommends that arithmetic mean

values should be the lowest value considered by States or Tribes when choosing intake rates for

use in criteria derivation. When considering geometric mean (median) values from fish

consumption studies, States and authorized Tribes need to ensure that the distribution is based on

survey respondents who reported consuming fish because surveys based on both consumers and

nonconsumers can often result in median values of zero. If a State or Tribe chooses values

(whether the central tendency or high-end values) from studies that particularly target high-end

consumers, these values should be compared to high-end fish intake rates for the general

population to make sure that the high-end consumers within the general population would be

protected by the chosen intake rates. EPA believes this is a reasonable procedure and is also

consistent with the recent Great Lakes Water Quality Initiative (known as the “GLI”) (USEPA,

1995). States and authorized Tribes may wish to conduct their own surveys of fish intake, and

EPA guidance is available on methods to conduct such studies in Guidance for Conducting Fish

and Wildlife Consumption Surveys (USEPA, 1998). Results from broader geographic regions in

which the State or Tribe is located can also be used, but may not be as applicable as results from
4-26

local watersheds. Since such studies would ultimately form the basis of a State or Tribe’s

AWQC, EPA would review any surveys of fish intake for consistency with the principles of

EPA’s guidance as part of the Agency’s review of water quality standards under Section 303(c).

If surveys conducted in the geographic area of the State or Tribe are not available, EPA’s

second preference is that States and authorized Tribes consider results from existing fish intake

surveys that reflect similar geography and population groups (e.g., from a neighboring State or

Tribe or a similar watershed type), and follow the method described above regarding target

values to derive a fish intake rate. Again, EPA recommends the use of uncooked weight intake

values and the use of fresh/estuarine species data only. Results of existing local and regional

surveys are discussed in greater detail in the TSD.

If applicable consumption rates are not available from local, State, or regional surveys,

EPA’s third preference is that States and authorized Tribes select intake rate assumptions for

different population groups from national food consumption surveys. EPA has analyzed one

such national survey, the 1994-96 CSFII. As described in Section 4.3.2, this survey, conducted

annually by the USDA, collects food consumption information from a probability sample of the

population of all 50 states. Respondents to the survey provide two days of dietary recall data. A

detailed description of the combined 1994-96 CSFII survey, the statistical methodology, and the

results and uncertainties of the EPA analyses are provided in a separate EPA report (USEPA,

2000b). The Exposure Assessment TSD for this Methodology presents selected results from this

report including point and interval estimates of combined finfish and shellfish consumption for

the mean, 50th (median), 90th, 95th, and 99th percentiles. The estimated fish consumption rates are

by fish habitat (i.e., freshwater/estuarine, marine and all habitats) for the following population

groups: (1) all individuals; (2) individuals age 18 and over; (3) women ages 15-44; and (4)

children age 14 and under. Three kinds of estimated fish consumption rates are provided: (1) per

capita rates (i.e., rates based on consumers and nonconsumers of fish from the survey period– 

refer to the TSD for further discussion); (2) consumers-only rates (i.e., rates based on

respondents who reported consuming finfish or shellfish during the two-day reporting period);

and (3) per capita consumption by body weight (i.e., per capita rates reported as milligrams of

fish per kilogram of body weight per day). 

EPA’s fourth preference is that States and authorized Tribes use as fish intake

assumptions the following default rates, based on the 1994-96 CSFII data, that EPA believes are

representative of fish intake for different population groups: 17.5 grams/day for the general adult

population and sport fishers, and 142.4 grams/day for subsistence fishers. These are risk

management decisions that EPA has made after evaluating numerous fish intake surveys. These

values represent the uncooked weight intake of freshwater/estuarine finfish and shellfish. As

with the other preferences, EPA requests that States and authorized Tribes routinely consider

whether there is a substantial population of sport fishers or subsistence fishers when developing

site-specific estimates, rather than automatically basing them on the typical individual. Because

the combined 1994-96 CSFII survey is national in scope, EPA will use the results from this

survey to estimate fish intake for deriving national criteria. EPA has recognized the data gaps

and uncertainties associated with the analysis of the 1994-96 CSFII survey in the process of

making its default recommendations. The estimated mean of freshwater and estuarine fish

ingestion for adults is 7.50 grams/day, and the median is 0 grams/day. The estimated 90th
4-27

percentile is 17.53 grams/day; the estimated 95th percentile is 49.59 grams/day; and the estimated

99th percentile is 142.41 grams/day. The median value of 0 grams/day may reflect the portion of

individuals in the population who never eat fish as well as the limited reporting period (2 days)

over which intake was measured. By applying as a default 17.5 grams/day for the general adult

population, EPA intends to select an intake rate that is protective of a majority of the population

(again, the 90th percentile of consumers and nonconsumers according to the 1994-96 CSFII

survey data). Trophic level breakouts are: TL2 = 3.8 grams/day; TL3 = 8.0 grams/day; and TL4

= 5.7 grams/day. EPA further considers 17.5 grams/day to be indicative of the average

consumption among sport fishers based on averages in the studies reviewed, which are presented

in the Exposure Assessment TSD. Similarly, EPA believes that the assumption of 142.4

grams/day is within the range of average consumption estimates for subsistence fishers based on

the studies reviewed. Experts at the 1992 National Workshop that initiated the effort to revise

this Methodology acknowledged that the national survey high-end values are representative of

average rates for highly exposed groups such as subsistence fishermen, specific ethnic groups, or

other highly exposed people. EPA is aware that some local and regional studies indicate greater

consumption among Native American, Pacific Asian American, and other subsistence

consumers, and recommends the use of those studies in appropriate cases, as indicated by the

first and second preferences. Again, States and authorized Tribes have the flexibility to choose

intake rates higher than an average value for these population groups. If a State or authorized

Tribe has not identified a separate well-defined population of high-end consumers and believes

that the national data from the 1994-96 CSFII are representative, they may choose these

recommended rates.

As indicated above, the default intake values are based on the uncooked weights of the

fish analyzed. There has been some question regarding whether to use cooked or uncooked

weights of fish intake for deriving the AWQC. Studies show that, typically, with a filet or steak

of fish, the weight loss in cooking is about 20 percent; that is, the uncooked weight is

approximately 20 percent higher (Jacobs et al., 1998). This obviously means that using

uncooked weights results in a slightly higher intake rate and slightly more stringent AWQC. In

researching consumption surveys for this proposal, EPA has found that some surveys have

reported rates for cooked fish, others have reported uncooked rates, and many more are unclear

as to whether cooked or uncooked rates are used. The basis of the CSFII survey was prepared or

as consumed intakes; that is, the survey respondents estimated the weight of fish that they

consumed. This was also true with the GLI (which was specifically based on studies describing

consumption rates of cooked fish) and, by and large, cooked fish is what people consume. 

However, EPA’s Guidance For Assessing Chemical Contaminant Data For Use In Fish

Advisories recommends analysis and advisories based on uncooked fish (USEPA, 1997a). EPA

considered the potential confusion over the fact that the uncooked weights are used in the fish

advisory program. Further, the measures of a contaminant in fish tissue samples that are

applicable to compliance monitoring and the permitting program are related to the uncooked

weights. The choice of intakes is also complicated by factors such as the effect of the cooking

process, the different parts of a fish where a chemical may accumulate, and the method of

preparation.

After considering all of the above (in addition to public input received), EPA will derive

its national default criteria based on the uncooked weight fish intakes. The Exposure
4-28

Assessment TSD provides additional guidance on site-specific modifications. Specifically, an

alternate approach is described for calculating AWQC with the as consumed weight–which is

more directly associated with human exposure and risk–and then adjusting the value by the

approximate 20 percent loss to an uncooked equivalent (thereby representing the same relative

risk as the as consumed value). This approach results in a different AWQC value (than using the

uncooked weights) and represents a more direct translation of the as consumed risk to the

uncooked equivalent. However, EPA understands that it is more scientifically rigorous and may

be too intensive of a process for States and Tribes to rely on. The option is presented in the TSD

to offer States and authorized Tribes greater flexibility with their water quality standards

program.

The default fish intake values also reflect specific designations of species classified in

accordance with information regarding the life history of the species or based on landings

information form the National Marine Fisheries Service. Most significantly, salmon has been

reclassified from a freshwater/estuarine species to a marine species. As marine harvested salmon

represents approximately 99 percent of salmon consumption in the 1994-96 CSFII Survey,

removal reduces the overall fresh/estuarine fish consumption rate by 13 percent. Although they

represent a very small percentage of freshwater/estuarine intake, land-locked and farm-raised

salmon consumed by 1994-96 CSFII respondents are still included. The rationale for the default

intake species designations is explained in the Exposure Assessment TSD. Once again, EPA

emphasizes the flexibility for States and authorized Tribes to use alternative assumptions based

on local or regional data to better represent their population groups of concern. 

4.3.3.2 Rates Protective of Developmental Human Health Effects

Exposures resulting in health effects in children or developmental effects in fetuses may

be of primary concern. As discussed at the beginning of this section on exposure factors used, in

a situation where acute or sub-chronic toxicity and exposure are the basis of an RfD (or

POD/UF), EPA will consider basing its national default criteria on children or women of

childbearing age, depending on the target population at greatest risk. EPA recommends that

States and authorized Tribes use exposure factors for children or women of childbearing age in

these situations. As stated previously, EPA is not recommending the development of additional

AWQC but is acknowledging that basing a criterion on these population groups is a potential

course of action and is, therefore, recommending the following default intake rates for such

situations.

EPA’s preferences for States and authorized Tribes in selecting values for intake rates

relevant for children is the same as that discussed above for establishing values for average daily

consumption rates for chronic effects; i.e., in decreasing order of preference, results from fish

intake surveys of local watersheds, results from existing fish intake surveys that reflect similar

geography and population groups, the distribution of intake rates from nationally based surveys

(e.g., the CSFII), or lastly, the EPA default rates. When an RfD is based on health effects in

children, EPA recommends a default intake rate of 156.3 grams/day for assessing those

contaminants that exhibit adverse effects. This represents the 90th percentile consumption rate

for actual consumers of freshwater/estuarine finfish and shellfish for children ages 14 and under

using the combined 1994 to 1996 results from the CSFII survey. The value was calculated based
4-29

on data for only those children who ate fish during the 2-day survey period, and the intake was

averaged over the number of days during which fish was actually consumed. EPA believes that

by selecting the data for consumers only, the 90th percentile is a reasonable intake rate to

approximate consumption of fresh/estuarine finfish and shellfish within a short period of time for

use in assessments where adverse effects in children are of primary concern. As discussed

previously, EPA will use a default body weight of 30 kg to address potential acute or subchronic

effects from fish consumption by children. EPA is also providing these default intake values for

States and authorized Tribes that choose to provide additional protection when developing

criteria that they believe should be based on health effects in children. This is consistent with

the rationale in the recent GLI (USEPA, 1995) and is an approach that EPA believes is

reasonable. Distributional information on intake values relevant for assessing exposure when

health effects to children are of concern is presented in the Exposure Assessment TSD. 

There are also cases in which pregnant women may be the population of most concern,

due to the possibility of developmental effects that may result from exposures of the mother to

toxicants. In these cases, fish intake rates specific to females of childbearing age are most

appropriate when assessing exposures to developmental toxicants. When an RfD is based on

developmental toxicity, EPA proposes a default intake rate of 165.5 grams/day for assessing

exposures for women of childbearing age from contaminants that cause developmental effects. 

This is equivalent to the 90th percentile consumption rate for actual consumers of freshwater/

estuarine finfish and shellfish for women ages 15 to 44 using the combined 1994 to1996 results

from the CSFII survey. As with the rate for children, this value represents only those women

who ate fish during the 2-day survey period. As discussed previously, EPA will use a default

body weight of 67 kg for women of childbearing age.

4.3.3.3 Rates Based on Combining Fish Intake and Body Weight

As with the drinking water intake values, EPA is providing values for fish intake based

on a per unit body weight basis (in units of mg/kg) in the Exposure Assessment TSD. These

rates use the self-reported body weights of the 1994-96 CSFII survey. Again, while EPA intends

to derive or revise national default criteria on the separate intake values and body weights, the

mg/kg-BW/day values are provided in the TSD for States or authorized Tribes that prefer their

use. 

4.4 REFERENCES FOR EXPOSURE

Ershow A.G., Brown L.M. and Cantor K.P. 1991. Intake of tapwater and total water by

pregnant and lactating women. Am. J. Public Health. 81:328-334. 

Ershow A.G. and K.P. Cantor. 1989. Total Water and Tap Water Intake in the United States:

Population-based Estimates of Quantities and Sources. National Cancer Institute. 

Bethesda, MD. Order #263-MD-810264.

Gibbons, J.D. 1971. Nonparametric Statistical Inference. Chapter 2: Order Statistics. 

McGraw-Hill, Inc. New York, NY.
4-30

Jacobs, H.L., H.D. Kahn, K.A. Stralka, and D.B. Phan. 1998. Estimates of per capita fish

consumption in the U.S. based on the continuing survey of food intake by individuals 

(CSFII). Risk Analysis: An International Journal 18(3).

McDowell, M. 2000. Personal communication between Denis R. Borum, U.S. Environmental

Protection Agency, and Margaret McDowell, Health Statistician, National Health and

Nutrition Examination Survey, National Center for Health Statistics. March 24, 2000.

USDA. 1998. U.S. Department of Agriculture. 1994–1996 Continuing Survey of Food Intakes

by Individuals and 1994–1996 Diet and Health Knowledge Survey. Agricultural

Research Service, USDA. NTIS CD–ROM, accession number PB98–500457. 

[Available from the National Technical Information Service, 5285 Port Royal Road,

Springfield, VA 22161. Phone: (703) 487–4650.] 

USEPA. 1993. Review of the Methodology for Developing Ambient Water Quality Criteria for 

the Protection of Human Health. Prepared by the Drinking Water Committee of the

Science Advisory Board. EPA-SAB-DWC

USEPA. 1994. Reference dose (RfD) for oral exposure for cadmium. Integrated Risk

Information System (IRIS). Online. (Verification date 02/01/94.) Office of Health and

Environmental Assessment, Environmental Criteria and Assessment Office. Cincinnati,

OH.

USEPA. 1995. Great Lakes Water Quality Initiative Technical Support Document for the

Procedure to Determine Bioaccumulation Factors. Office of Water. Washington, DC. 

EPA/820/B-95/005. 

USEPA. 1997a. Guidance for Assessing Chemical Contaminant Data for Use in Fish

Advisories. Volume II: Risk Assessment and Fish Consumption Limits. Second Edition. 

Office of Water. Washington DC. EPA/823/B-97/009.

USEPA. 1997b. Exposure Factors Handbook. National Center for Environmental Assessment,

Office of Research and Development. Washington, DC. EPA/600/P-95/002Fa. August.

USEPA. 1998. Guidance for Conducting Fish and Wildlife Consumption Surveys. Office of

Science and Technology, Office of Water. Washington, DC. EPA-823-B-98-007. 

November. 

USEPA. 2000a. Estimated Per Capita Water Ingestion in the United States: Based on Data

Collected by the United States Department of Agriculture’s 1994-96 Continuing Survey

of Food Intakes by Individuals. Office of Science and Technology, Office of Water. 

Washington, DC. EPA-822-00-008. April.

USEPA. 2000b. Estimated Per Capita Fish Consumption in the United States: Based on Data

Collected by the United States Department of Agriculture’s 1994-1996 Continuing
4-31

Survey of Food Intake by Individuals. Office of Science and Technology, Office of

Water, Washington, DC. March.

WESTAT. 2000. Memorandum on Body Weight Estimates Based on NHANES III data,

Including Data Tables and Graphs. Analysis conducted and prepared by WESTAT,

under EPA Contract No. 68-C-99-242. March 3, 2000.
5-1

(Equation 5-1)

5. BIOACCUMULATION 

5.1 INTRODUCTION

Aquatic organisms can accumulate certain chemicals in their bodies when exposed to

these chemicals through water, their diet, and other sources. This process is called

bioaccumulation. The magnitude of bioaccumulation by aquatic organisms varies widely

depending on the chemical but can be extremely high for some highly persistent and

hydrophobic chemicals. For such highly bioaccumulative chemicals, concentrations in aquatic

organisms may pose unacceptable human health risks from fish and shellfish consumption even

when concentrations in water are too low to cause unacceptable health risks from drinking water

consumption alone. These chemicals may also biomagnify in aquatic food webs, a process

whereby chemical concentrations increase in aquatic organisms of each successive trophic level

due to increasing dietary exposures (e.g., increasing concentrations from algae, to zooplankton,

to forage fish, to predatory fish).

In order to prevent harmful exposures to waterborne chemicals through the consumption

of contaminated fish and shellfish, national 304(a) water quality criteria for the protection of

human health must address the process of chemical bioaccumulation in aquatic organisms. For

deriving national 304(a) criteria to protect human health, EPA accounts for potential

bioaccumulation of chemicals in fish and shellfish through the use of national bioaccumulation

factors (BAFs). A national BAF is a ratio (in L/kg) that relates the concentration of a chemical

in water to its expected concentration in commonly consumed aquatic organisms in a specified

trophic level. An illustration of how national BAFs are used in the derivation of 304(a) criteria

for carcinogens using linear low-dose extrapolation is shown in the following equation:

where:

RSD = Risk specific dose (mg/kg-day) 

BW = Human body weight (kg)

DI = Drinking water intake (L/day)

FIi = Fish intake at trophic level I, where I=2, 3, and 4; 

BAFi = National bioaccumulation factor at trophic level I, 

where I=2, 3, and 4

The purpose of this chapter is to present EPA’s recommended methodology for deriving

national bioaccumulation factors for setting national 304(a) water quality criteria to protect

human health. A detailed scientific basis of the recommended national BAF methodology is

provided in the Bioaccumulation TSD. While the methodology detailed in this chapter is
5-2

intended to be used by EPA for deriving national BAFs, EPA encourages States and authorized

Tribes to derive BAFs that are specific to certain regions or waterbodies, where appropriate. 

Guidance to States and authorized Tribes for deriving site-specific BAFs is provided in the

Biaccumulation TSD.

5.1.1 Important Bioaccumulation and Bioconcentration Concepts

Several attributes of the bioaccumulation process are important to understand when

deriving national BAFs for use in setting national 304(a) criteria. 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. Thus,

an assessment of bioconcentration alone would underestimate the extent of accumulation in

aquatic biota for these chemicals. Accordingly, EPA’s guidelines presented in this chapter

emphasize the measurement of chemical bioaccumulation by aquatic organisms, whereas EPA’s

1980 Methodology emphasized the measurement of bioconcentration. 

 Another noteworthy aspect of bioaccumulation process is the issue of steady-state

conditions. Specifically, both bioaccumulation and bioconcentration 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, ecological characteristics of the waterbody (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 steadystate. 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 required for a chemical to achieve steady state has been shown

to vary according to the properties of the chemical and other factors. For example, some highly

hydrophobic chemicals can require long periods of time to reach steady state between

environmental compartments (e.g., many months), while highly hydrophilic chemicals usually

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

Since national 304(a) criteria for the protection of human health are typically designed to

protect humans from harmful lifetime or long-term exposures to waterborne contaminants, the

assessment of bioaccumulation that equals or approximates steady-state accumulation is one of

the principles underlying the derivation of national BAFs. For some chemicals that require

relatively long periods of time to reach steady-state in tissues of aquatic organisms, changes in

water column concentrations may occur on a much more rapid time scale compared to the

corresponding changes in tissue concentrations. 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 tissue concentration to a water concentration may have little resemblance to the

steady-state ratio and have little predictive value of long-term bioaccumulation potential. 
5-3

Therefore, BAF measurements should be based on water column concentrations which are

averaged over a sufficient period of time (e.g., a duration comparable to the time required for the

chemical to reach steady-state). In addition, BAF measurements should be based on adequate

spatial averaging of both tissue and water column concentrations for use in deriving 304(a)

criteria for the protection of human health.

For this reason, a BAF is defined in this Methodology as representing the ratio (in L/kgtissue) of a concentration of a chemical in tissue to its concentration in the surrounding water in

situations where the organism and its food are exposed and the ratio does not change

substantially over time (i.e., the ratio which reflects bioaccumulation at or near steady-state). A

bioconcentration factor (BCF) is the ratio (in L/kg-tissue) of the concentration of a substance in

tissue of an aquatic organism to its 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.

5.1.2 Goal of the National BAF

The goal of EPA’s national BAF is to represent the long-term, average bioaccumulation

potential of a chemical in edible tissues of aquatic organisms that are commonly consumed by

humans throughout the United States. National BAFs are not intended to reflect fluctuations in

bioaccumulation over short time periods (e.g., a few days) because 304(a) human health criteria

are generally designed to protect humans from long-term exposures 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

example, separate procedures are provided for deriving national BAFs depending on the type of

chemical (i.e., nonionic organic, ionic organic, inorganic and organometallic). 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

between trophic levels that may influence bioaccumulation. Because lipid content of aquatic

organisms and the amount of organic carbon in the water column have been shown to affect

bioaccumulation of nonionic organic chemicals, EPA’s national BAFs are adjusted to reflect the

lipid content of commonly consumed fish and shellfish and the freely dissolved fraction of the

chemical in ambient water for these chemicals.

5.1.3 Changes to the 1980 Methodology

Numerous scientific advances have occurred in the area of bioaccumulation since the

publication of the 1980 Methodology for deriving AWQC for the protection of human health

(USEPA, 1980). These advances have significantly increased our ability to assess and predict

the bioaccumulation of chemicals in aquatic biota. As a result, EPA has revised the

bioaccumulation portion of the 1980 Methodology to reflect the current state of the science and

to improve accuracy in assessing bioaccumulation for setting 304(a) criteria for the protection of

human health. The changes contained in the bioaccumulation portion of the 2000 Human Health

Methodology are mostly designed to: 
5-4

C Improve the ability to incorporate chemical exposure from sediments and aquatic food

webs in assessing bioaccumulation potential,

C Expand the ability to account for site-specific factors which affect bioaccumulation, and 

C Incorporate new data and assessment tools into the bioaccumulation assessment process.

A summary of the key changes that have been incorporated into the bioaccumulation

portion of the 2000 Human Health Methodology and appropriate comparisons to the1980

Methodology are provided below. 

5.1.3.1 Overall Approach

The 1980 Methodology for deriving 304(a) criteria for the protection of human health

emphasized the assessment of bioconcentration (uptake from water only) through the use of the

BCF. Based on the 1980 Methodology, measured BCFs were usually determined from

laboratory data unless field data demonstrated consistently higher or lower accumulation

compared with laboratory data. In these cases, “field BCFs” (currently termed field-measured

BAFs) were recommended for use. For lipophilic chemicals where lab or field-measured data

were unavailable, EPA recommended predicting BCFs from the octanol-water partition

coefficient and the following equation from Veith et al. (1979): “log BCF = (0.85 log Kow) -

0.70". 

The 2000 Human Health Methodology revisions contained in this chapter emphasize the

measurement of bioaccumulation (uptake from water, sediment, and diet) through the use of the

BAF. Consistent with the 1980 Methodology, measured data are preferred over predictive

approaches for determining the BAF (i.e., field-measured BAFs are generally preferred over

predicted BAFs). However, the 2000 Human Health Methodology contains additional methods

for deriving a national BAF that were not available in 1980. The preference for using the BAF

methods also differs depending on the type and properties of the chemical. For example, the

BAF derivation procedure differs for each of three broadly defined chemical categories: (1)

nonionic organic, (2) ionic organic, and (3) inorganic and organometallic chemicals. 

Furthermore, within the category of nonionic organic chemicals, different procedures are used to

derive the BAF depending on a chemicals’ hydrophobicity and extent of chemical metabolism

that would be expected to occur in aquatic biota. 

5.1.3.2 Lipid Normalization

In the 1980 Methodology, BCFs for lipophilic chemicals were normalized by the lipid

fraction in the tissue of fish and shellfish used to determine the BCF. Lipid normalization

enabled BCFs to be averaged across tissues and organisms. Once the average lipid-normalized

BCF was determined, it was adjusted by the consumption-weighted lipid content of commonly

consumed aquatic organisms in the United States to obtain an overall consumption-weighted

BCF. A similar procedure has been retained in the 2000 Human Health Methodology, whereby

BAFs for nonionic organic chemicals are lipid normalized and adjusted by the consumptionweighted lipid content of commonly consumed organisms to obtain a BAF for criteria
5-5

calculations. However, the 2000 Human Health Methodology uses more up-to-date lipid data

and consumption data for deriving the consumption-weighted BAFs. 

5.1.3.3 Bioavailability

Bioconcentration factors derived according to the 1980 Methodology were based on the

total concentration of the chemical in water, for both lipophilic and nonlipophilic chemicals. In

the 2000 Human Health Methodology, BAFs for nonionic organic chemicals are derived using

the most bioavailable fraction (i.e., the freely dissolved fraction) to account for the influence of

particulate and dissolved organic carbon on a chemical’s bioavailability. Such BAFs are then

adjusted to reflect the expected bioavailability at the sites of interest (i.e., by adjusting for

organic carbon concentrations at the sites of interest). Procedures for accounting for the effect of

organic carbon on bioaccumulation were published previously by EPA under the Great Lakes

Water Quality Initiative (GLWQI or GLI) rulemaking (USEPA, 1995a,b). Bioavailability is also

considered in developing BAFs for the other chemical classes defined in the 2000 Human Health

Methodology (e.g., ionic organics, inorganics/organometallics) but is done so on a chemical-bychemical basis. 

5.1.3.4 Trophic Level Considerations 

In the 1980 Methodology, BCFs were determined and used for criteria derivation without

explicit regard to the trophic level of the aquatic organism (e.g., benthic filter feeder, forage fish,

predatory fish). Over the past two decades, much information has been assembled which

demonstrates that an organism’s trophic position in the aquatic food web can have an important

effect on the magnitude of bioaccumulation of certain chemicals. In order to account for the

variation in bioaccumulation that is due to trophic position of the organism, the 2000 Human

Health Methodology recommends that BAFs be determined and applied on a trophic levelspecific basis. 

5.1.3.5 Site-Specific Adjustments

The 1980 Methodology contained little guidance for making adjustments to the national

BCFs to reflect site- or region-specific conditions. The 2000 Human Health Methodology has

greatly expanded the guidance to States and authorized Tribes for making adjustments to

national BAFs to reflect local conditions. This guidance is contained in the Bioaccumulation

TSD. In the Bioaccumulation TSD, guidance and data are provided for adjusting national BAFs

to reflect the lipid content in locally consumed aquatic biota and the organic carbon content in

the waterbodies of concern. This guidance also allows the use of appropriate bioaccumulation

models for deriving site-specific BAFs. EPA also plans to publish detailed guidance on

designing and conducting field bioaccumulation studies for measuring BAFs and biota-sediment

accumulation factors (BSAFs). In general, EPA encourages States and authorized Tribes to

make site-specific modifications to EPA’s national BAFs provided such adjustments are

scientifically defensible and adequately protect the designated use of the waterbody.

While the aforementioned revisions are new to EPA’s Methodology for deriving national

304(a) criteria for the protection of human health, many of these refinements have been
5-6

(Equation 5-2)

incorporated in prior Agency guidance and regulations. For example, the use of food chain

multipliers to account for the biomagnification of nonionic organic chemicals in aquatic food

webs when measured data are unavailable was introduced by EPA in three documents: Technical

Support Document for Water Quality-Based Toxics Control (USEPA, 1991), a draft document

entitled Assessment and Control of Bioconcentratable Contaminants in Surface Waters (USEPA,

1993), and in the Great Lakes Water Quality Initiative (GLI) (USEPA, 1995b). Similarly,

procedures for predicting BAFs using BSAFsand incorporating the effect of organic carbon on

bioavailability were used to derive water quality criteria under the GLI.

5.1.4 Organization of This Section 

The methodology for deriving national BAFs for use in deriving National 304(a) Human

Health AWQC is provided in the following sections. Important terms used throughout this

chapter are defined in Section 5.2. Section 5.3 provides an overview of the BAF derivation

guidelines. Detailed procedures for deriving national BAFs are provided in Section 5.4 for

nonionic organic chemicals, in Section 5.5 for ionic organic chemicals, and in Section 5.6 for

inorganics and organometallic chemicals. Literature cited is provided in Section 5.7.

5.2 DEFINITIONS

The following terms and definitions are used throughout this chapter.

Bioaccumulation. The net accumulation of a substance by an organism as a result of uptake 

from all environmental sources.

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

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

Bioaccumulation Factor (BAF). The ratio (in L/kg-tissue) of the concentration of a substance

in tissue to its concentration in the ambient 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 the chemical in the specified wet tissue

Cw = Concentration of chemical in water
5-7

(Equation 5-3)

(Equation 5-4)

Bioconcentration Factor (BCF). The ratio (in L/kg-tissue) of the concentration of a substance

in tissue of an aquatic organism to its 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. The BCF is calculated as:

where:

Ct

 = Concentration of the chemical in the specified wet tissue

Cw = Concentration of chemical in water

Baseline BAF (BAFR

fd). For nonionic organic chemicals (and certain ionic organic chemicals

where similar lipid and organic carbon partitioning behavior applies), a BAF (in L/kg-lipid) that

is based on the concentration of freely dissolved chemical in the ambient water and the lipid

normalized concentration in tissue.

Baseline BCF (BCFR

fd). For nonionic organic chemicals (and certain ionic organic chemicals

where similar lipid and organic carbon partitioning behavior applies), a BCF (in L/kg-lipid) that

is based on the concentration of freely dissolved chemical in the ambient water and the lipid

normalized concentration in tissue.

Biomagnification. The increase in tissue concentration of a chemical in organisms at successive

trophic levels through a series of predator-prey associations, primarily through the mechanism of

dietary accumulation.

Biomagnification Factor (BMF). The ratio (unitless) of the tissue concentration of a chemical

in a predator at a particular trophic level to the tissue concentration in its prey at the next lower

trophic level for a given waterbody and chemical exposure. For nonionic organic chemicals (and

certain ionic organic chemicals where similar lipid and organic carbon partitioning behavior

applies), a BMF can be calculated using lipid-normalized concentrations in the tissue of

organisms at two successive trophic levels as: 

where:

CR (TL, n) = Lipid-normalized concentration in appropriate tissue of predator organism at

a given trophic level (TL “n”)
5-8

(Equation 5-5)

(Equation 5-6)

CR (TL, n-1) = Lipid-normalized concentration in appropriate tissue of prey

organism at the next lower trophic level from the predator (TL “n-1”)

For inorganic, organometallic, and certain ionic organic chemicals where lipid and organic

carbon partitioning does not apply, a BMF can be calculated using chemical concentrations in

the tissue of organisms at two successive trophic levels as: 

where:

Ct (TL, n) = Concentration in appropriate tissue of predator organism at trophic

level “n” (may be either wet weight or dry weight concentration so

long as both the predator and prey concentrations are expressed in the

same manner)

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

trophic level from the predator (may be either wet weight or dry

weight concentration so long as both the predator and prey

concentrations are expressed in the same manner)

Biota-Sediment Accumulation Factor (BSAF). For nonionic organic chemicals (and certain

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

the ratio of the lipid-normalized concentration of a substance in tissue of an aquatic organism to

its organic carbon-normalized concentration in surface sediment (expressed as kg of sediment

organic carbon per kg of lipid), 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 defined as:

where:

CR = The lipid-normalized concentration of the chemical in tissues of the biota

(μg/g lipid)

Csoc = The organic carbon-normalized concentration of the chemical in the

surface sediment (μg/g sediment organic carbon)

Depuration. The loss of a substance from an organism as a result of any active or passive

process.
5-9

(Equation 5-7)

(Equation 5-8)

Food Chain Multiplier (FCM). For nonionic organic chemicals (and certain ionic organic

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

baseline BAFR

fd for an organism of a particular trophic level to the baseline BCFRfd (usually

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

organic chemicals where lipid and organic carbon partitioning does not apply, a FCM is based on

total (wet or dry weight) concentrations of the chemical in tissue. 

Freely Dissolved Concentration. 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. The freely dissolved concentration is considered to represent the most

bioavailable form of an organic chemical in water and, thus, is the form that best predicts

bioaccumulation. The freely dissolved concentration can be determined as:

where:

Cw

 f d = Freely dissolved concentration of the organic chemical in ambient water

Cw

 t = Total concentration of the organic chemical in ambient water

ffd = Fraction of the total chemical in ambient water that is freely dissolved

Hydrophilic. A term that refers to 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) compared to chemicals of hydrophobic chemicals. 

Hydrophobic. A term that refers to 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) compared with chemicals of lower hydrophobicity. 

Lipid-normalized Concentration (CR

). The total concentration of a contaminant in a tissue or

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

where:

Ct

 = Concentration of the chemical in the wet tissue (either whole organism or

specified tissue)
5-10

(Equation 5-9)

fR = Fraction lipid content in the organism or specified tissue

Octanol-water Partition Coefficient (Kow). The ratio of the concentration of a substance in the

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

logarithm.

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 organic

carbon-normalized concentration can be calculated as: 

where:

Cs = Concentration of chemical in sediment

foc = Fraction organic carbon in sediment

Uptake. Acquisition by an organism of a substance from the environment as a result of any

active or passive process.

5.3 FRAMEWORK FOR DETERMINING NATIONAL BIOACCUMULATION

FACTORS

5.3.1 Four Different Methods

Bioaccumulation factors used to derive national BAFs can be measured or predicted

using some or all of the following four methods, depending on the type of chemical and its

properties. These methods are:

(1) a measured BAF obtained from a field study (i.e., a field-measured BAF);

(2) a BAF predicted from a field-measured BSAF;

(3) a BAF predicted from a laboratory-measured BCF (with or without adjustment by an

FCM); and

(4) a BAF predicted from a chemical’s octanol-water partition coefficient (K ow ), with or

without adjustment using an FCM.
5-11

A brief summary of each of the four methods is provided below. Additional details on

the use of these four methods is provided in Section 5.4 (for nonionic organics), Section 5.5 (for

ionic organics) and Section 5.6 (for inorganics and organometallics).

1. Field-Measured BAF. Use of a field-measured BAF, which is the most direct measure

of bioaccumulation, is the only method that can be used to derive a national BAF for all

types of chemicals (i.e., nonionic organic, ionic organic, and inorganic and

organometallic chemicals). A field-measured BAF is determined from a field study using

measured chemical concentrations in the aquatic organism and its surrounding water. 

Because field studies are conducted in natural aquatic ecosystems, a field-measured BAF

reflects an organism’s exposure to a chemical through all relevant exposure pathways

(i.e., water, sediment, and diet). A field-measured BAF also reflects any metabolism of a

chemical that might occur in the aquatic organism or its food web. Therefore, fieldmeasured BAFs are appropriate for all chemicals, regardless of the extent of chemical

metabolism in biota. 

2. Field-measured BSAF. For nonionic organic chemicals (and certain ionic organic

chemicals where similar lipid and organic carbon partitioning behavior applies), a BAF

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 in the field and reflects an

organism’s exposure to all relevant exposure routes. A BSAF also reflects any chemical

metabolism that might occur in the aquatic organism or its food web. However, unlike a

field-measured BAF which references the biota concentration to the water concentration,

a BSAF references the biota concentration to the sediment concentration. Use of the

BSAF procedure is restricted to organic chemicals which are classified as being

moderately to highly hydrophobic.

3. Lab-measured BCF. A laboratory-measured BCF can also be used to estimate a BAF

for organic and inorganic chemicals. However, unlike a field-measured BAF or a BAF

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

accumulation of chemical through the water exposure route. Laboratory-measured BCFs

may therefore under estimate BAFs for chemicals where accumulation from sediment or

dietary sources is important. In these cases, laboratory-measured BCFs can be multiplied

by a FCM to reflect accumulation from non-aqueous (i.e., food chain) pathways of

exposure. Since a laboratory-measured BCF is determined using the measured

concentration of a chemical in an aquatic organism and its surrounding water, a

laboratory-measured BCF reflects any metabolism of the chemical that occurs in the

organism, but not in the food web. 

4. Kow. A chemical’s octanol-water partition coefficient, or Kow, can also be used to predict

a BAF for nonionic organic chemicals. This procedure is appropriate only for nonionic

organic chemicals (and certain ionic organic chemicals where similar lipid and organic

carbon partitioning behavior applies). The Kow has been extensively correlated with the

BCF for nonionic organic chemicals that are poorly metabolized by aquatic organisms. 

Therefore, where substantial metabolism is known to occur in biota, the Kow is not used
5-12

to predict the BAF. For nonionic organic chemicals where chemical exposure through

the food web is important, use of the Kow alone will under predict the BAF. In such

cases, the Kow is adjusted with a FCM similar to the BCF procedure above. 

5.3.2 Overview of BAF Derivation Framework

Although up to four methods can be used to derive a BAF as described in the previous

section, it is evident that these methods do not apply equally to all types of chemicals. In

addition, experience demonstrates that the required data will usually not be available to derive a

BAF value using all of the applicable methods. As a result, EPA has developed the following

guidelines to direct users in selecting the most appropriate method(s) for deriving a national

BAF. 

Figure 5-1 shows the overall framework of EPA’s national BAF methodology. This

framework illustrates the major steps and decisions that will ultimately lead to calculating a

national BAF using one of six hierarchical procedures shown at the bottom of Figure 5-1. Each 

procedure contains a hierarchy of the BAF derivation methods discussed above, the composition

of which depends on the chemical type and certain chemical properties (e.g., its degree of

hydrophobicity and expected degree of metabolism and biomagnification). The number assigned

to each BAF method within a procedure indicates its general order of preference for deriving a

national BAF value. The goal of the framework and accompanying guidelines is to enable full

use of available data and methods for deriving a national BAF value while appropriately

restricting the use of certain methods to reflect their inherent limitations. 

The first step in the framework is to define the chemical of concern. As described in

Section 5.3.3, the chemical used to derive the national BAF should be consistent with the

chemical used to derive the critical health assessment value. The second step is to collect and

review all relevant data on bioconcentration and bioaccumulation of the chemical of concern

(see Section 5.3.4). Once pertinent data are reviewed, the third step is to classify the chemical of

concern into one of three broadly defined chemical categories: (1) nonionic organic chemicals,

(2) ionic organic chemicals, and (3) and inorganic and organometallic chemicals. Guidance for

classifying chemicals into these three categories is provided in Section 5.3.5. 

After a chemical has been classified into one of the three categories, other information is

used to select one of six hierarchical procedures to derive the national BAF. The specific

procedures for deriving a BAF for each chemical group are discussed in Section 5.4 for nonionic

organics, Section 5.5 for ionic organics, and Section 5.6 for inorganics and organometallics. 
5-13

CLASSIFY CHEMICAL 

OF CONCERN

Nonionic Organic 

Figure 5-1. Framework for Deriving a National BAF

Ionic Organic Inorganic & 

Organometalic 

Yes

(Log KOW >4) (Log KOW <4)

PROCEDURE #4

HYDROPHOBICITY?

METABOLISM?

Low High Low High

METABOLISM?

1. Field BAF or 

PROCEDURE #3

Lab BCF

2. KOW

1. Field BAF

PROCEDURE #2

3. Lab BCF

1. Field BAF

PROCEDURE #1

2. BSAF

4. KOW*FCM 

BIOMAGNIFICATION?

PROCEDURE #6

3. Lab BCF*FCM

Low

COLLECT & REVIEW 

DATA

DEFINE CHEMICAL 

OF CONCERN

Moderate-High

PROCEDURE #5

No

 IONIZATION 

NEGLIGIBLE?

Yes No

2. BSAF

1. Field BAF or 

Lab BCF

1. Field BAF or 

Lab BCF

1. Field BAF

2. Lab BCF*FCM
5-14

Detailed guidance concerning the first three steps of the derivation process (i.e, defining the

chemical of concern, collecting and reviewing data, and classifying the chemical of concern) is

provided in the following three sections.

5.3.3 Defining the Chemical of Concern

Defining the chemical of concern is the first step in deriving a national BAF. This step

involves precisely defining the form(s) of the chemical upon which the national BAF value will

be derived. Although this step is usually straightforward for single chemicals, complications can 

arise when the chemical of concern occurs as a mixture. The following guidelines should be

followed for defining the chemical of concern.

1. Information for defining the chemical of concern should be obtained from the health and

exposure assessment portions of the criteria derivation effort. The chemical(s) used to

derive the national BAF should be consistent with the chemical(s) used to derive the

reference dose (RfD), point of departure/uncertainty factor (POD/UF), or cancer potency

factor. 

2. In most cases, the RfD, POD/UF, or cancer potency factor will be based on a single

chemical. In some cases, the RfD, POD/UF, or cancer potency factor will be based on a

mixture of compounds, typically within the same chemical class (e.g., toxaphene,

chlordane). In these situations, the national BAF should be derived in a manner that is

consistent with the mixture used to express the health assessment.

a. If sufficient data are available to reliably assess the bioaccumulation of each

relevant compound contained in the mixture, then the national BAF(s) should be

derived using the BAFs for the individual compounds of the mixture and

appropriately weighted to reflect the mixture composition used to establish the

RfD, POD/UF, or cancer potency factor. An example of this approach is shown

in the derivation of BAFs for PCBs in the GLI Rulemaking (USEPA, 1997). 

b. If sufficient data are not available to reliably assess the bioaccumulation of

individual compounds of the mixture, then the national BAF(s) should be derived

using BAFs for the same or appropriately similar chemical mixture as that used to

establish the RfD, POD/UF, or cancer potency value.

5.3.4 Collecting and Reviewing Data 

The second step in deriving a national BAF is to collect and review all relevant

bioaccumulation data for the chemical of concern. The following guidance should be followed

for collecting and reviewing bioaccumulation data for deriving national BAFs.

1. All data on the occurrence and accumulation of the chemical of concern in aquatic

animals and plants should be collected and reviewed for adequacy.
5-15

2. A comprehensive literature search strategy should be used for gathering

bioaccumulation-related data. An example of a comprehensive literature search strategy

is provided in the Bioaccumulation TSD. 

3. All data that are used should contain sufficient supporting information to indicate that

acceptable measurement procedures were used and that the results are probably reliable. 

In some cases it may be appropriate to obtain additional written information from the

investigator. 

4. Questionable data, whether published or unpublished, should not be used. Guidance for

assessing the acceptability of bioaccumulation and bioconcentration studies is found in

Sections 5.4, 5.5, and 5.6. 

5.3.5 Classifying the Chemical of Concern 

The next step in deriving a national BAF consists of classifying the chemical of concern

into one of three categories: nonionic organic, ionic organic, and inorganic and organometallic

(Figure 5-1). This step helps to determine which of the four methods described in Section 5.3.1

are appropriate for deriving BAFs. The following guidance applies for classifying the chemical

of concern.

1. Nonionic Organic Chemicals. For the purposes of the 2000 Human Health

Methodology, nonionic organic chemicals are those 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. Due to their neutrality, nonionic

organic chemicals tend to associate with other neutral (or near neutral) compartments in

aquatic ecosystems (e.g., lipid, organic carbon). Examples of nonionic organic chemicals

which have been widely studied in terms of their bioaccumulation include

polychlorinated biphenyls (PCBs), polychlorinated dibenzo-p-dioxins and furans, many

chlorinated pesticides, and polynuclear aromatic hydrocarbons (PAHs). Procedures for

deriving a national BAF for nonionic organic chemicals are provided in Section 5.4.

2. Ionic Organic Chemicals. For the purposes of the 2000 Human Health Methodology,

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

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

functional groups that readily accept protons such as amino and aromatic heterocyclic

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

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

of these chemicals behave differently from the neutral species, separate guidance is

provided for deriving BAFs for ionic organic chemicals. Procedures for deriving

national BAFs for ionic organic chemicals are provided in Section 5.5. 

3. Inorganic and Organometallic Chemicals. The inorganic and organometallic category

is considered to include inorganic minerals, other inorganic compounds and elements,

metals (e.g., copper, cadmium, chromium, zinc), metalloids (selenium, arsenic) and
5-16

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

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

5.6.

5.4 NATIONAL BIOACCUMULATION FACTORS FOR NONIONIC ORGANIC

CHEMICALS

5.4.1 Overview

This section contains the methodology for deriving national BAFs for nonionic organic

chemicals as defined in Section 5.3.5. The four general steps of this methodology are: 

1. Selecting the BAF derivation procedure,

2. Calculating individual baseline BAFR

fds, 

3. Selecting the final baseline BAFR

fds, and 

4. Calculating the national BAFs from the final baseline BAFR

fds.

A schematic of this four-step process is shown in Figure 5-2.

Step 1 of the methodology (selecting the BAF derivation procedure) determines which of

the four BAF procedures summarized in Figure 5-1 will be appropriate for deriving the national

BAF. Step 2 involves calculating individual, species-specific BAFR

fds using all of the methods

available within the selected BAF derivation procedure. Calculating the individual baseline

BAFR

fds involves using data from the field site or laboratory where the original data were

collected to account for site-specific factors which affect the bioavailability of the chemical to

aquatic organisms (e.g., lipid content of study organisms and freely dissolved concentration in

study water). Step 3 of the methodology consists of selecting the final baseline BAFR

fds from the

individual baseline BAFR

fds by taking into account the uncertainty in the individual BAFs and the

data preference hierarchy selected in Step 1. The final step is to calculate a BAF (or BAFs) that

will be used in the derivation of 304(a) criteria (i.e., referred to as the national BAF). This step

involves adjusting the final baseline BAFR

fd(s) to reflect certain factors that affect bioavailablity

of the chemical to aquatic organisms in waters to which the national 304(a) criteria will apply

(e.g., the freely dissolved fraction expected in U.S. waters and the lipid content of consumed

aquatic organisms). Baseline BAFR

fds are not used directly in the derivation of the 304(a) criteria

because they do not reflect the conditions that affect bioavailability in U.S. waters. 

Section 5.4.2 below provides detailed guidance for selecting the appropriate BAF

derivation procedure (Step 1 of the process). Guidance on calculating individual baseline

BAFR

fds, selecting the final baseline BAF, and calculating the national BAF (Steps 2 through 4 of

the process) is provided in separate sections under each of the four BAF derivation procedures. 
5-17
5-18
5-19

5.4.2 Selecting the BAF Derivation Procedure 

 This section describes the decisions that should be made to select one of the four

available hierarchical procedures for deriving a national BAF for nonionic organic chemicals

(Procedures #1 through #4 of Figure 5-1). As shown in Figure 5-1, two decision points exist in

selecting the BAF derivation procedure. The first decision point requires knowledge of the

chemical’s hydrophobicity (i.e., the Kow of the chemical). Guidance for selecting the Kow for a

chemical is provided in the Bioaccumulation TSD. The Kow provides an initial basis for

assessing whether biomagnification may be a concern for nonionic organic chemicals. The

second decision point is based on the rate of metabolism for the chemical in the target organism. 

Guidance for assessing whether a high or low rate of metabolism is likely for a chemical of

concern is provided below in Section 5.4.2.3. With the appropriate information for these two

decision points, the BAF derivation procedure should be selected using the following guidelines.

5.4.2.1 Chemicals with Moderate to High Hydrophobicity

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

with log Kow values equal to or greater than 4.0 should be classified as moderately to

highly hydrophobic. For moderately to highly hydrophobic nonionic organic chemicals,

available data indicate that exposure through the diet and other non-aqueous 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; Oliver and Niimi, 1988; Niimi, 1985;

Swackhammer and Hites, 1988). Dietary and other non-aqueous exposure can become

extremely important for those nonionic organic chemicals that are poorly metabolized by

aquatic biota (e.g., certain PCB congeners, chlorinated pesticides, and polychlorinated

dibenzo-p-dioxins and furans).

2. Procedure #1 should be used to derive national BAFs for moderately to highly

hydrophobic nonionic organic chemicals in cases where: 

(a) the rate of chemical metabolism by target aquatic organisms is expected to be

sufficiently low such that biomagnification is of concern, or 

(b) the rate of chemical metabolism by target aquatic organisms is not sufficiently

known.

Procedure #1 accounts for non-aqueous exposure and the potential for biomagnification

in aquatic food webs through the use of field-measured values for bioaccumulation (i.e.,

field measured BAF or BSAF) and FCMs when appropriate field data are unavailable.

Guidance on deriving national BAFs using Procedure #1 is found below in Section 5.4.3. 

3. Procedure #2 should be used to derive the national BAFs for moderately to highly

hydrophobic nonionic organic chemicals in cases where:

(a) the rate of chemical metabolism by target aquatic organisms is expected to be

sufficiently high such that biomagnification is not of concern.
5-20

Procedure #2 relaxes the requirement of using FCMs and eliminates the use of Kow-based

estimates of the BAF, two procedures that are most appropriate for poorly metabolized

nonionic organic chemicals. Guidance on deriving national BAFs using Procedure #2 is

found below in Section 5.4.4. 

5.4.2.2 Chemicals with Low Hydrophobicity

1. For the purposes of these guidelines, nonionic organic chemicals with log Kow values less

than 4.0 should be classified as exhibiting low hydrophobicity. For nonionic organic

chemicals that exhibit low hydrophobicity (i.e., log Kow < 4.0), available information

indicates that non-aqueous exposure to these chemicals is not likely to be important in

determining chemical residues in aquatic organisms (e.g., Fisk et al., 1998; Gobas et al.,

1993; Connolly and Pedersen, 1988; Thomann, 1989). For this group of chemicals,

laboratory-measured BCFs and Kow-predicted BCFs do not require adjustment with

FCMs for determining the national BAF (Procedures #3 and #4), unless other appropriate

data indicate differently.

Other appropriate data include studies clearly indicating that non-aqueous exposure is

important such that use of a BCF would substantially underestimate residues in aquatic

organisms. In these cases, Procedure #1 should be used to derive the BAF for nonionic

organic chemicals with log Kow < 4.0. Furthermore, the data supporting the Kow

determination should be carefully reviewed for accuracy and appropriate interpretation,

since the apparent discrepancy may be due to errors in determining Kow. 

2. Procedure #3 should be used to derive national BAFs for nonionic organic chemicals of

low hydrophobicity in cases where:

(a) the rate of chemical metabolism by target aquatic organisms is expected to be

negligible, such that tissue residues of the chemical of concern are not

substantially reduced compared to an assumption of no metabolism, or

(b) the rate of chemical metabolism by target aquatic organisms is not sufficiently

known. 

Procedure #3 includes the use of Kow-based estimates of the BCF to be used when lab or

field data are absent. Guidance on deriving national BAFs using Procedure #3 is found

below in Section 5.4.5. 

3. Procedure #4 should be used to derive national BAFs for nonionic organic chemicals of

low hydrophobicity in cases where:

(a) the rate of chemical metabolism by target aquatic organisms is expected to be

sufficiently high, such that tissue residues of the chemical of concern are

substantially reduced compared with an assumption of no metabolism. 
5-21

Procedure #4 eliminates the option of using Kow-based estimates of the BAF because the

Kow may over-predict accumulation when a chemical is metabolized substantially by an

aquatic organism. Guidance on deriving national BAFs using Procedure #4 is found

below in Section 5.4.6. 

5.4.2.3 Assessing Metabolism

Currently, assessing the degree to which a chemical is metabolized by aquatic organisms

is confounded by a variety of factors. First, conclusive data on chemical metabolism in aquatic

biota are largely lacking. Such data include whole organism studies where the metabolic rates

and breakdown products are quantified in fish and other aquatic organisms relevant to human

consumption. However, the majority of information on metabolism is derived from in vitro liver

microsomal preparations in which primary and secondary metabolites may be identified and their

rates of formation may or may not be quantified. Extrapolating results from in vitro studies to

the whole organism involves considerable uncertainty. Second, there are no generally accepted

procedures for reliably predicting chemical metabolism by aquatic organisms in the absence of

measured data. Third, the rate at which a chemical is metabolized by aquatic organisms can be

species and temperature dependent. For example, PAHs are known to be metabolized readily by

vertebrate aquatic species (primarily fish), although at rates much less than those observed for

mammals. However, the degree of metabolism in invertebrate species is generally much less

than the degree in vertebrate species (James, 1989). One hypothesis for this difference is that the

invertebrate species lack the detoxifying enzymes and pathways that are present in many

vertebrate species. 

Given the current limitations on assessing the degree of chemical metabolism by aquatic

organisms, the assessment of metabolism should be made on a case-by-case basis using a

weight-of-evidence approach. When assessing a chemical’s likelihood to undergo substantial

metabolism in a target aquatic organism, the following data should be carefully evaluated:

(1) in vivo chemical metabolism data,

(2) bioconcentration and bioaccumulation data,

(3) data on chemical occurrence in target aquatic biota, and

(4) in vitro chemical metabolism data.

1. In vivo Data. In vivo data on metabolism in aquatic organisms are from studies of

chemical metabolism using whole organisms. These studies are usually conducted using

large fish from which blood, bile, urine, and individual tissues can be collected for the

identification and quantification of metabolites formed over time. In vivo studies are

considered the most useful for evaluating a chemical’s degree of metabolism in an

organism because both oxidative (Phase I) and conjugative (Phase II) metabolism can be

assessed in these studies. Mass-balance studies, in which parent compound elimination is

quantified separately from biotransformation and elimination of metabolites, allow

calculation of conversion rate of parent to metabolite as well as metabolite elimination.

This information might be used to estimate loss due to metabolism separately from that

due to elimination of the parent compound for adjustment of Kow-predicted BAFs.

However, due to the analytical and experimental challenges these studies pose, data of
5-22

this type are limited. Less rigorous in vivo metabolism studies might include the use of

metabolic blockers to demonstrate the influence of metabolism on parent compound

kinetics. However, caution should be used in interpretation of absolute rates from these

data due to the lack of specificity of mammalian derived blockers in aquatic species

(Miranda et al., 1998).

2. Bioconcentration or Bioaccumulation Data. Data on chemical bioconcentration or

bioaccumulation in aquatic organisms can be used indirectly for assessing metabolism. 

This assessment involves comparing acceptable lab-measured BCFs or field-measured

BAFs (after converting to baseline values using procedures below) with the chemical’s

predicted value based on Kow. The theoretical basis of bioconcentration and

bioaccumulation for nonionic organic chemicals indicates that a chemical’s baseline BCF

should be similar to its Kow-predicted value if metabolism is not occurring or is minimal

(see the Bioaccumulation TSD). This theory also indicates that baseline BAFs should be

similar to or higher than the Kow for poorly metabolized organic chemicals, with highly

hydrophobic chemicals often exhibiting higher baseline BAFs than Kow values. Thus, if a

chemical’s baseline BCF or BAF is substantially lower than its Kow, this may be an

indication that the chemical is being metabolized by the aquatic organism of concern. 

Note, however, that this difference may also indicate problems in the experimental design

or analytical chemistry, and that it may be difficult to discern the difference. 

3. Chemical Occurrence Data. Although by no means definitive, data on the occurrence

of chemicals in aquatic biota (i.e., residue studies) may offer another useful line of

evidence for evaluating a chemical’s likelihood to undergo substantial metabolism. Such

studies are most useful if they have been conducted repeatedly over time and over wide

geographical areas. Such studies might indicate a chemical is poorly metabolized if data

show that the chemical is being biomagnified in the aquatic food web (i.e., higher lipidnormalized residues in successive trophic levels). Conversely, such studies might

indicate a chemical is being metabolized substantially if residue data show a decline in

residues with increasing trophic level. Again, other reasons for increases or decreases in

concentrations with increasing trophic level might exist and should be carefully evaluated

(e.g., incorrect food web assumptions, differences in exposure concentrations).

4. In vitro Data. In vitro metabolism data include data from studies where specific subcellular fractions (e.g., microsomal, cytosolic), cells, or tissues from an organism are

tested outside the body (i.e., in test-tubes, cell- or tissue-culture). Compared with in vivo

studies of chemical metabolism in aquatic organisms, in vitro studies are much more

plentiful in the literature, with the majority of studies characterizing oxidative (Phase I)

reactions de-coupled from conjugative (Phase II) metabolism. Cell, tissue, or organ level

in vitro studies are less common but provide a more complete assessment of metabolism. 

While such studies are particularly useful for identifying the pathways, rates of

formation, and metabolites formed, as well as the enzymes involved and differences in

the temperature dependence of metabolism across aquatic species, they suffer from

uncertainty when results are extrapolated to the whole organism. This uncertainty results

from the fact that dosimetry (i.e., delivery of the toxicant to, and removal of metabolite
5-23

from, the target tissue) cannot currently be adequately reproduced in the laboratory or

easily modeled.

When assessing chemical metabolism using the above information, the following

guidelines apply.

a. A finding of substantial metabolism should be supported by two or more lines of

evidence identified using the data described above. 

b. At least one of the lines of evidence should be supported by either in vivo metabolism

data or acceptable bioconcentration or bioaccumulation data. 

c. A finding of substantial metabolism in one organism should not be extrapolated to

another organism or another group of organisms unless data indicate similar metabolic

pathways exist (or are very likely to exist) in both organisms. In vitro data may be

particularly useful in cross-species extrapolations.

d. Finally, in situations where sufficient data are not available to properly assess the

likelihood of significant metabolism in aquatic biota of concern, the chemical should be

assumed to undergo little or no metabolism. This assumptions reflects a policy decision

by EPA to err on the side of public health protection when sufficient information on

metabolism is lacking. 

5.4.3 Deriving National BAFs Using Procedure #1

This section contains guidance for calculating national BAFs for nonionic organic

chemicals using Procedure #1 shown in Figure 5-1. The types of nonionic organic chemicals for

which Procedure #1 is most appropriate are those that are classified as moderately to highly

hydrophobic and subject to low (or unknown) rates of metabolism by aquatic biota (see Section

5.4.2 above). Non-aqueous contaminant exposure and subsequent biomagnification in aquatic

food webs are of concern for chemicals that are classified in this category. Some examples of

nonionic organic chemicals for which Procedure #1 is considered appropriate include: 

C tetra-, penta- & hexachlorobenzenes;

C PCBs;

C octachlorostyrene;

C hexachlorobutadiene;

C endrin, dieldrin, aldrin; 

C mirex, photomirex; 

C DDT, DDE, DDD; and

C heptachlor, chlordane, nonachlor.

Under Procedure #1, the following four methods may be used in deriving a national BAF: 

C using a BAF from an acceptable field study (i.e., a field-measured BAF);

C predicting a BAF from an acceptable field-measured BSAF;
5-24

(Equation 5-10)

C predicting a BAF from an acceptable laboratory-measured BCF and FCM; and

C predicting a BAF from an acceptable Kow and FCM.

As shown in Figure 5-2, once the derivation procedure has been selected, the next steps

in deriving a national BAF for a given trophic level include: calculating individual baseline

BAFR

fds (step 2), selecting the final baseline BAFRfd (step 3), and calculating the national BAF

from the final baseline BAFR

fd (step 4). Each of these three steps is discussed separately below.

5.4.3.1 Calculating Individual Baseline BAFR

fds

Calculating an individual baseline BAFR

fd involves normalizing the field-measured BAFt

T

(or laboratory-measured BCFt

T) which are based on total concentrations in tissue and water by

the lipid content of the study organisms and the freely dissolved concentration in the study water. 

Both the lipid content in the organism and the freely dissolved 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 BAFR

fds (which are expressed on

a freely dissolved and lipid-normalized basis) are considered more amenable to extrapolating

between different species and bodies of water compared to BAFs expressed using the total

concentration in the tissue and water. Because bioaccumulation can be strongly influenced by

the trophic position of aquatic organisms (either due to biomagnification or physiological

differences), extrapolation of baseline BAFR

fds should not be performed between species of

different trophic levels.

1. For each species for which acceptable data are available, calculate all possible baseline

BAFR

fds using each of the four methods shown above for Procedure #1. 

2. Individual baseline BAFR

fds should be calculated from field-measured BAFt

Ts, fieldmeasured BSAFs, laboratory BCFt

Ts, and the Kow according to the following procedures.

A. Baseline BAFR

fds from Field-Measured BAFs 

A baseline BAFR

fd should be calculated from each field-measured BAFt

T using information

on the lipid fraction in the tissue of concern for the study organism and the fraction of the total

chemical that is freely dissolved in the study water. 

1. Baseline BAFR

fd Equation. For each acceptable field-measured BAFt

T, calculate a

baseline BAFR

fd using the following equation:

where:
5-25

(Equation 5-11)

Baseline BAFR

fd = BAF expressed on a freely dissolved and lipid-normalized

basis

Measured BAFt

T = BAF based on total concentration in tissue and water

fR = Fraction of the tissue that is lipid

ffd = Fraction of the total chemical that is freely dissolved in the

ambient water

The technical basis of Equation 5-10 is provided in the Bioaccumulation TSD. Guidance for

determining each component of Equation 5-10 is provided below.

2. Determining the Measured BAFt

T. The field-measured BAFt

T shown in Equation 5-10

should be calculated based on the total concentration of the chemical in the appropriate

tissue of the aquatic organism and the total concentration of the chemical in ambient

water at the site of sampling. The equation to derive a measured BAFt

T is:

where:

Ct = Total concentration of the chemical in the specified wet tissue

Cw = Total concentration of chemical in water

The data used to calculate a field-measured BAFt

T should be reviewed thoroughly 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 field-measured BAFs that are

being considered for deriving national BAFs using Procedure #1.

a. Aquatic organisms used to calculate a field-measured BAFt

T should be

representative of aquatic organisms that are 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 field-measured 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

of a commonly consumed organism. 

b. The trophic level of the study organism should be determined by taking into

account its life stage, diet, size, and the food web structure at the study location. 

Information from the study site (or similar sites) is preferred when evaluating

trophic status. If such information is lacking, general information for assessing

trophic status of aquatic organisms can be found in USEPA (2000a,b,c). 
5-26

c. The percent lipid of the tissue used to determine the field-measured BAFt

T should

be either measured or reliably estimated to permit lipid-normalization of the

chemical’s tissue concentration. 

d. The study from which the field-measured BAFt

T is derived should contain

sufficient supporting information from which to determine that tissue and water

samples were collected and analyzed using appropriate, sensitive, accurate, and

precise analytical methods.

e. The site of the field study should not be so unique that the BAF cannot be

reasonably extrapolated to other locations where the BAF and resulting criteria

will apply.

f. The water concentration(s) used to derive the BAF should reflect the average

exposure of the aquatic organism that corresponds to the concentration measured

in its tissue of concern. For nonionic organic chemicals, greater temporal and

spatial averaging of chemical concentrations is required as the Kow increases. In

addition, as variability in water concentrations increase, greater temporal and

spatial averaging is also generally required. Greater spatial averaging is also

generally required for more mobile organisms.

g. The concentrations of particulate organic carbon and dissolved organic carbon in

the study water should be measured or reliably estimated.

EPA is currently developing guidance for designing and conducting field studies for

determining field-measured BAFt

Ts, including recommendations for minimum data

requirements. A more detailed discussion of factors that should be considered when

determining field-measured BAFt

Ts is provided in the Bioaccumulation TSD.

3. Determining the Fraction Freely Dissolved (ffd). As illustrated by Equation 5-10, the

fraction of the nonionic organic chemical that is freely dissolved in the study water is

required for calculating a baseline BAFR

fd from a field-measured BAFt

T. The freely

dissolved fraction is the portion of the nonionic organic chemical that is not bound to

particulate organic carbon or dissolved organic carbon. Together, the concentration of a

nonionic organic chemical that is freely dissolved, bound to dissolved organic carbon,

and bound to particulate organic carbon constitute its total concentration in water. As

discussed further in the Bioaccumulation TSD, the freely dissolved fraction of a chemical

is considered to be the best expression of the bioavailable form of nonionic organic

chemicals to aquatic organisms (e.g., Suffet et al., 1994; USEPA, 1995b). Because the

fraction of a nonionic organic chemical that is freely dissolved may vary among different

bodies of water as a result of differences in dissolved and particulate organic carbon in

the water, the bioavailability of the total chemical concentration in water is expected to

vary from one body of water to another. Therefore, BAFs which are based on the freely

dissolved concentration in water (rather than the total concentration in water) are

considered to be more reliable for extrapolating and aggregating BAFs among different

bodies of water. Currently, availability of BAFs based on measured freely dissolved
5-27

(Equation 5-12)

concentrations is very limited, partly because of difficulties in analytically measuring the

freely dissolved concentration. Thus, if a BAF based on the total water concentration is

reported in a given study, the fraction of the chemical that is freely dissolved should be

predicted using information on the organic carbon content in the study water. 

a. Equation for Determining the Freely Dissolved Fraction. If reliable measured

data are unavailable to directly determine the freely dissolved fraction of the

chemical in water, the freely dissolved fraction should be estimated using the

following equation. 

where:

POC = concentration of particulate organic carbon (kg/L)

DOC = concentration of dissolved organic carbon (kg/L)

Kow = n-octanol water partition coefficient for the chemical

In Equation 5-12, Kow is being used to estimate the partition coefficient to POC

(i.e., KPOC in L/kg) and 0.08@Kow is being used to estimate the partition coefficient

to DOC (i.e., the KDOC in L/kg). A discussion of the technical basis, assumptions,

and uncertainty associated with the derivation and application of Equation 5-12 is

provided in the Bioaccumulation TSD.

b. POC and DOC Values. When converting from the total concentration of a

chemical to a freely dissolved concentration using Equation 5-12 above, the POC

and DOC concentrations should be obtained from the original study from which

the field-measured BAF is determined. If POC and DOC concentrations are not

reported in the BAF study, reliable estimates of POC and DOC might be obtained

from other studies of the same site used in the BAF study or closely related site(s)

within the same water body. When using POC/DOC data from other studies of

the same water body, care should be taken to ensure that environmental and

hydrological conditions that might affect POC or DOC concentrations (i.e., runoff

events, proximity to ground water or surface water inputs, sampling season) are

reasonably similar to those in the BAF study. Additional information related to

selecting POC and DOC values is provided in the Bioaccumulation TSD.

In some cases, BAFs are reported using the concentration of the chemical in

filtered or centrifuged water. When converting these BAFs to a freely dissolved

basis, the concentration of POC should be set equal to zero when using Equation

5-12. Particulates are removed from water samples by filtering or centrifuging

the sample.
5-28

(Equation 5-13)

c. Selecting Kow Values. A variety of techniques are available to measure or predict

Kow values. The reliability of these techniques depends to a large extent on the

Kow of the chemical. Because Kow is an important input parameter for calculating

the freely dissolved concentration of nonionic organic chemicals and for deriving

BAFs using the other three methods of Procedure #1, care should be taken in

selecting the most reliable Kow value. The value of Kow for use in estimating the

freely dissolved fraction and other procedures used to derive national BAFs

should be selected based on the guidance presented in the Bioaccumulation TSD. 

 

 4. Determining the Fraction Lipid (fR). Calculating a baseline BAFR

fd for a nonionic

organic chemical using Equation 5-10 also requires that the total chemical concentration

measured in the tissue used to determine the field-measured BAFt

T be normalized by the

lipid fraction (fR

) in that same tissue. Lipid normalization of tissue concentrations reflects

the assumption that BAFs (and BCFs) for nonionic organic chemicals are directly

proportional to the percent lipid in the tissue upon which they are based. This

assumption means that an organism with a two percent lipid content would be expected

to accumulate twice the amount of a chemical at steady state compared with an organism

with one percent lipid content, all else being equal. The assumption that aquatic

organisms accumulate nonionic organic chemicals in proportion to their lipid content has

been extensively evaluated in the literature (Mackay, 1982; Connell, 1988; Barron, 1990)

and is generally accepted. Because the lipid content in aquatic organisms can vary both

within and across species, BAFs that are expressed using the lipid-normalized

concentration (rather than the total concentration in tissue) are considered to be the most

reliable for aggregating multiple BAF values for a given species. Additional discussion

of technical basis, assumptions, and uncertainties involved in lipid normalization is

provided in the Bioaccumulation TSD. 

a. The lipid fraction fR, is routinely reported in bioaccumulation studies involving

nonionic organic chemicals. If the lipid fraction is not reported in the BAF study,

it can be calculated using the following equation if the appropriate data are

reported:

where:

MR = Mass of lipid in specified tissue 

Mt = Mass of specified tissue (wet weight)

b. Because lipid content can vary within an aquatic organism (and among tissues

within that organism) due to several factors including the age and sex of the

organism, changes in dietary composition, season of sampling and reproductive

status, the lipid fraction used to calculate a baseline BAFR

fd should be measured in
5-29

the same tissue and organisms used to determine the field-measured BAFt

T, unless

comparability is demonstrated across organisms.

c. Experience has shown that different solvent systems used to extract lipids for

analytical measurement can result in different quantities of lipids being extracted

and measured in aquatic organisms (e.g., Randall et al.,1991, 1998). As a result,

lipid measurements determined using different solvent systems might lead to

apparent differences in lipid-normalized concentrations and lipid-normalized

BAFs. The extent to which different solvent systems might affect lipid

extractions (and lipid-normalized concentrations) is thought to vary depending on

the solvent, chemical of concern, and lipid composition of the tissue being

extracted. Guidance on measurement of lipid content, including the choice of

solvent system and how different solvent systems may affect lipid content, is

provided in the Bioaccumulation TSD. 

B. Baseline BAFR

fd Derived from BSAFs

The second method of determining a baseline BAFR

fd for the chemical of concern in

Procedure #1 involves the use of BSAFs. Although BSAFs may be used for measuring and

predicting bioaccumulation directly from concentrations of chemicals in surface sediment, they

may also be used to estimate BAFs (USEPA, 1995b; Cook and Burkhard, 1998). Since BSAFs

are based on field data and incorporate effects of chemical bioavailability, food web structure,

metabolism, biomagnification, growth, and other factors, BAFs estimated from BSAFs will

incorporate the net effect of all these factors. The BSAF approach is particularly beneficial for

developing water quality criteria for chemicals which are detectable in fish tissues and

sediments, but are difficult to detect or measure precisely in the water column.

As shown by Equation 5-14 below, predicting baseline BAFR

fds using BSAFs requires that

certain types of data be used for the chemicals of interest (for which BAFs are to be determined)

and reference chemicals (for which BAFs are measured) from a common sediment-waterorganism data set. Differences between BSAFs for different organic chemicals are good

measures of the relative bioaccumulation potentials of the chemicals. When calculated from a

common organism-sediment sample set, chemical-specific differences in BSAFs reflect the net

effect of biomagnification, metabolism, food chain, bioenergetics, and bioavailability factors on

the degree of each chemical’s equilibrium/disequilibrium between sediment and biota. At

equilibrium, BSAFs are expected to be approximately 1.0. However, deviations from 1.0

(reflecting disequilibrium) are common due to: conditions where water is not at equilibrium with

surface sediment; differences in organic carbon content of water and sediment; kinetic

limitations for chemical transfer between sediments and water associated with specific biota;

biomagnification; or biological processes such as growth or biotransformation. BSAFs are most

useful (i.e., most predictable from one site to another) when measured under steady-state (or near

steady-state) conditions. The use of non-steady-state BSAFs, such as found with new chemical

loadings or rapid increases in loadings, increases uncertainty in this method for the relative

degree of disequilibrium between the reference chemicals and the chemicals of interest. In

general, the fact that concentrations of hydrophobic chemicals in sediment are less sensitive than

concentrations in water to fluctuations in chemical loading and distribution makes the BSAF
5-30

(Equation 5-15)

method robust for estimating BAFs. Results from validation of the BAF procedure in Lake

Ontario, the Fox River and Green Bay, Wisconsin, and the Hudson River, New York,

demonstrate good agreement between observed and BSAF-predicted BAFs in the vast majority

of comparisons made. Detailed results of the validation studies for the BSAF procedure are

provided in the Bioaccumulation TSD.

Baseline BAFR

fds should be calculated using acceptable BSAFs for chemicals of interest 

and appropriate sediment-to-water fugacity (disequilibrium) ratios (Jsocw)r /(Kow)r for reference

chemicals under the following guidelines.

1. Baseline BAFR

fd Equation. For each species with an acceptable field measured (BSAF)I

,

a baseline BAFR

fd for the chemical of interest may be calculated using the following

equation with an appropriate value of ( Jsocw)r /(Kow)r: 

 

(Equation 5-14)

where:

(Baseline BAFR

fd)I = BAF expressed on a freely dissolved and lipidnormalized basis for chemical of interest “I”

(BSAF)I = Biota-sediment accumulation factor for chemical of

interest “I”

(Jsocw)r = sediment organic carbon to water freely dissolved

concentration ratio of reference chemical “r”

(Kow)I = octanol-water partition coefficient for chemical of

interest “I”

(Kow)r = octanol-water partition coefficient for the reference

chemical “r”

Di/r = ratio between Jsocw / Kow for chemicals “I” and “r”

(normally chosen so that Di/r = 1)

The technical basis, assumptions, and uncertainties associated with Equation 5-14 are provided

in the Bioaccumulation TSD. Guidance for determining each component of Equation 5-14 is

provided below.

2. Determining Field-Measured BSAFs. BSAFs should be determined by relating lipidnormalized concentrations of chemicals in an organism (CR

) to organic carbon-normalized

concentrations of the chemicals in surface sediment samples (Csoc) using the following

equation: 
5-31

(Equation 5-16)

(Equation 5-17)

a. Lipid-Normalized Concentration. The lipid-normalized concentration of a

chemical in an organism should be determined by:

where:

Ct = Concentration of the chemical in the wet tissue (either

whole organism or specified tissue) (μg/g)

fR = Fraction lipid content in the tissue

b. Organic Carbon-Normalized Concentration. The organic carbon-normalized

concentration of a chemical in sediment should be determined by:

where:

Cs = Concentration of chemical in sediment (μg/g sediment)

foc = Fraction organic carbon in sediment

The organic carbon-normalized concentrations of the chemicals in surface

sediment samples should be associated with the average exposure environment of

the organism.

3. Sediment-to-Water Partition Coefficient (Jsocw)r

. Sediment-to-water partition

coefficients for reference chemicals should be determined by:

(Equation 5-18)

where:

(Csoc)r = Concentration of a reference chemical in sediment normalized to

sediment organic carbon

( Cw

f d)r = Concentration of the reference chemical freely dissolved in water

4. Selecting Reference Chemicals. Reference chemicals with (Jsocw) / (Kow) similar to that

of the chemical of interest are preferred for this method. Theoretically, knowledge of the
5-32

difference between sediment-to-water fugacity ratios for two chemicals, “I” and “r” (Di/r),

could be used when reliable reference chemicals that meet the fugacity equivalence

condition are not available. Similarity of (Jsocw) / (Kow) for two chemicals can be

indicated on the basis of similar physical-chemical behavior in water (persistence,

volatilization), similar mass loading histories, and similar concentration profiles in

sediment cores.

Validation studies have demonstrated that choosing reference chemicals with well

quantified concentrations in water is important because the uncertainty associated with

measurement of barely detected chemicals is large (see the Bioaccumulation TSD). 

Similarity between Kow values of the reference and target chemicals is generally

desirable, although recent validation studies indicate that the accuracy of the method is

not substantially decreased through use of reference chemicals with large differences in

Kow , as long as the chemicals are structurally similar and have similar persistence

behavior in water and sediments.

5. The following data, procedural, and quality assurance requirements should be met for

predicting baseline BAFR

fds using field-measured BSAFs:

a. Data on the reference chemicals and chemicals of interest should come from a

common organism-water-sediment data set at a particular site. 

b. The chemicals of interest and reference chemicals should have similar

physicochemical properties and persistence in water and sediment.

c. The loadings history of the reference chemicals and chemicals of interest should

be similar such that their expected sediment-water disequilibrium ratios 

(Jsocw/Kow) would not be expected to be substantially different (i.e., Di/r ~ 1).

d. The use of multiple reference chemicals is generally preferred for determining the

value of ( Jsocw)r

 so long as the concentrations are well quantified and the

aforementioned conditions for selecting reference chemicals are met. In some

cases, use of a single reference chemical may be necessary because of limited

data.

e. Samples of surface sediments (0-1 cm is ideal) should be from locations in which

sediment is regularly deposited and is representative of average surface sediment

in the vicinity of the organism.

f. The Kow value for the target and reference chemicals should be selected as

described in the Bioaccumulation TSD.

g. All other data quality and procedural guidelines described earlier for determining

field-measured BAFs in Section 5.4.3.1(A) should be met. 
5-33

(Equation 5-20)

Further details on the requirements for predicting BAFs from BSAF measurements,

including the data, assumptions, and limitations of this approach are provided in the

Bioaccumulation TSD.

C. Baseline BAFR

fd from a Laboratory-Measured BCFt

T and FCM

The third method in Procedure #1 consists of using a laboratory-measured BCFt

T (i.e., a

BCF based on total concentrations in tissue and water) and FCMs to predict a baseline BAFR

fd for

the chemical of concern. The BCFt

T is used in conjunction with an FCM because non-aqueous

routes of exposure and subsequent biomagnification is of concern for the types of chemicals

applicable to Procedure #1. A laboratory-measured BCF inherently accounts for the effects of

chemical metabolism that occurs in the organism used to calculate the BCF, but does not account

for metabolism which may occur in other organisms of the aquatic food web. 

1. Baseline BAFR

fd Equation. For each acceptable laboratory-measured BCFt

T, calculate a

baseline BAFR

fd using the following equation: 

(Equation 5-19)

where:

Baseline BAFR

fd = BAF expressed on a freely dissolved and lipidnormalized basis

Measured BCFt

T = BCF based on total concentration in tissue and

water

fR = Fraction of the tissue that is lipid

ffd = Fraction of the total chemical in the test water that

is freely dissolved

FCM = The food chain multiplier either obtained from

Table 5-1 by linear interpolation for the appropriate

trophic level, or from appropriate field data

The technical basis for Equation 5-19 is provided in the Bioaccumulation TSD. 

Guidance for determining each component of Equation 5-19 is provided below.

2. Determining the Measured BCFt

T. The laboratory-measured BCFt

T shown in Equation

5-19 should be calculated 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 equation to derive a measured BCFt

T is:
5-34

where:

Ct = Total concentration of the chemical in the specified wet tissue

Cw = Total concentration of chemical in the laboratory test water

The data used to calculate a laboratory-measured BCFt

T should be reviewed thoroughly 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 laboratory-measured BCFt

T. 

a. The test organism should not be diseased, unhealthy, or adversely affected by the

concentration of the chemical because these attributes may alter accumulation of

chemicals compared with healthy organisms.

b. The total concentration of the chemical in the water should be measured and

should be relatively constant during the exposure period.

c. The organisms should be exposed to the chemical using a flow-through or

renewal procedure.

d. The percent lipid of the tissue used to normalize the BCFt

T should be either

measured or reliably estimated to permit lipid normalization of chemical

concentrations.

e. The concentrations of particulate organic carbon and dissolved organic carbon in

the study water should be measured or reliably estimated.

f. Aquatic organisms used to calculate a laboratory-measured BCFt

T should be

representative of those aquatic organisms that are commonly consumed in the

United States. An aquatic organism which is not commonly consumed in the

United States can be used to calculate an acceptable laboratory-measured BCFt

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 of a commonly consumed organism. 

g. BCFs may be based on measurement of radioactivity from radiolabeled parent

compounds only when the BCF is intended to include metabolites, when there is

confidence that there is no interference due to metabolites of the parent

compounds, or when studies are conducted to determine the extent of metabolism,

thus allowing for a proper correction.

h. The calculation of the BCFt

T should appropriately address growth dilution, which

can be particularly important in affecting BCFt

T determinations for poorly

depurated chemicals.
5-35

I. Other aspects of the methodology used should be similar to those described by the

American Society of Testing and Materials (ASTM, 1999) and USEPA

Ecological Effects Test Guidelines (USEPA, 1996).

j. In addition, the magnitude of the Kow and the availability of corroborating BCF

data should be considered. For example, if the steady-state method is used for the

BCFt

T determination, exposure periods longer than 28 days will generally be

required for highly hydrophobic chemicals to reach steady state between the

water and the organism.

k. If a baseline BCFR

fd derived from a laboratory-measured BCFt

T consistently

increases or decreases as the chemical concentration increases in the test solutions

for the test organisms, the BCFt

T should be selected from the test concentration(s)

that would most closely correspond to the 304(a) criterion. Note: a BCFt

T should

not be calculated from a control treatment. 

3. Selecting Food Chain Multipliers. An FCM reflects a chemical’s tendency to

biomagnify in the aquatic food web. Values of FCMs greater than 1.0 are indicative of

biomagnification and typically apply to organic chemicals with log Kow values between

4.0 and 9.0. For a given chemical, FCMs tend to be greater at higher trophic levels,

although FCMs for trophic level three can be higher than those for trophic level four. 

Food chain multipliers used to derive baseline BAFR

fds using Procedure #1 can be selected

from model-derived or field-derived estimates. 

a. Model-Derived FCMs. For nonionic organic chemicals appropriate for

Procedure #1, EPA has calculated FCMs for various Kow values and trophic levels

using the bioaccumulation model of Gobas (1993). The FCMs shown in 

Table 5-1 were calculated using the Gobas model as the ratio of the baseline

 BAFR

fds for trophic levels 2, 3, and 4 to the baseline BCFRfd. 

EPA recommends using the biomagnification model by Gobas (1993) to derive

FCMs for nonionic organic chemicals for several reasons. First, the Gobas model 

includes both benthic and pelagic food chains, thereby incorporating exposure of

organisms to chemicals from both the sediment and the water column. Second,

the input data needed to run the model can be readily defined. Third, the

predicted BAFs using the model are in agreement with field-measured BAFs for

chemicals, even those with very high log Kows. Finally, the model predicts

chemical residues in benthic organisms using equilibrium partitioning theory,

which is consistent with EPA’s equilibrium partitioning sediment guidelines

(USEPA, 2000d). 

The Gobas model requires input of specific data on the structure of the food chain

and the water quality characteristics of the water body of interest. For calculating 

national BAFs, a mixed pelagic/benthic food web structure consisting of four

trophic levels is assumed. Trophic level 1 is phytoplankton, trophic level 2 is
5-36

zooplankton, trophic level 3 is forage fish (e.g., sculpin and smelt), and trophic

level 4 are predatory fish (e.g., salmonids). Additional assumptions are made

regarding the composition of the aquatic species’ diets (e.g., salmonids consume

10 percent sculpin, 50 percent alewives, and 40 percent smelt), the physical

parameters of the aquatic species (e.g., lipid values), and the water quality

characteristics (e.g., water temperature, sediment organic carbon). 

A mixed pelagic/benthic food web structure has been assumed for the purpose of

calculating FCMs because it is considered to be most representative of the types

of food webs that occur in aquatic ecosystems. FCMs derived using the mixed

pelagic/benthic structure are also about mid-range in magnitude between a 100%

pelagic and 100% benthic driven food web (see the Bioaccumulation TSD). The

validity of FCMs derived using the mixed pelagic/benthic food web structure has 
5-37

Table 5-1

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

Trophic

Level 4

Log

KOW

Trophic

Level 2

Trophic

Level 3

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

been evaluated in several different ecosystems including Lake Ontario, the tidally

influenced Bayou D’Inde in Louisiana, the Fox River and Green Bay, Wisconsin,

and the Hudson River in New York. Additional details of the validation of EPA’s

national default FCMs and the assumptions, uncertainties, and input parameters

for the model are provided in the Bioaccumulation TSD. 
5-38

Although EPA uses the FCMs in Table 5-1 to derive its national 304(a) criteria,

EPA recognizes that food webs of other waterbodies might differ from the

assumptions used to calculate national BAFs. In these situations, States and

authorized Tribes may wish to use alternate food web structures for calculating

FCMs for use in setting State or Tribal water quality criteria. Additional guidance

on the use of alternate food web structures for calculating State, Tribal, or sitespecific criteria is provided in the Bioaccumulation TSD. 

b. Field-Derived FCMs. In addition to model-derived estimates of FCMs, field

data may also be used to derive FCMs. Currently, the use of field-derived FCMs

is the only method recommended for estimating FCMs for inorganic and

organometalic chemicals because appropriate model-derived estimates are not yet

available (see Section 5.6). In contrast to the model-based FCMs described

previously, field-derived FCMs account for any metabolism of the chemical of

concern by the aquatic organisms used to calculate the FCM. 

Field-derived FCMs should be calculated using lipid-normalized concentrations

of the nonionic organic chemical in appropriate predator and prey species using

the following equations. 

FCM TL2 = BMFTL2 (Equation 5-21) 

FCM TL3 = (BMFTL3) (BMF TL2) (Equation 5-22) 

FCM TL4 = (BMF TL4) (BMF TL3) (BMF TL2) (Equation 5-23) 

where:

FCM = Food chain multiplier for designated trophic level (TL2, TL3,

or TL4)

BMF = Biomagnification factor for designated trophic level (TL2,

TL3, or TL4)

The basic difference between FCMs and BMFs is that FCMs relate back to

trophic level one (or trophic level two as assumed by the Gobas (1993) model),

whereas BMFs always relate back to the next lowest trophic level. For nonionic

organic chemicals, BMFs can be calculated from tissue residue concentrations

determined in biota at a site according to the following equations.

BMF TL2 = (CR, TL2) / (CR , TL1) (Equation 5-24) 

BMF TL3 = (CR , TL3) / (CR, TL2) (Equation 5-25) 

BMF TL4 = (CR , TL4) / (CR , TL3) (Equation 5-26) 

where:
5-39

CR = Lipid-normalized concentration of chemical in tissue of

appropriate biota that occupy the specified trophic level

(TL2, TL3, or TL4)

In addition to the acceptability guidelines pertaining to field-measured BAFs, the

following procedural and quality assurance requirements 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. General information on

determining trophic levels of aquatic organisms can be found in USEPA

2000a,b,c. 

(2) The aquatic organisms sampled from each trophic level should reflect the

most important exposure pathways leading to human exposure via

consumption of aquatic organisms. For higher trophic levels (e.g., 3 and

4), aquatic species should also reflect those that are commonly consumed

by humans.

(3) The studies from which the FCMs are derived should contain sufficient

supporting information from which to determine that tissue samples were

collected and analyzed using appropriate, sensitive, accurate, and precise

methods.

(4) The percent lipid should be either measured or reliably estimated for the

tissue used to determine the FCM. 

(5) The tissue concentrations should reflect average exposure over the

approximate time required to achieve steady-state in the target species. 

D. Baseline BAFR

fd from a Kow and FCM

The fourth method in Procedure #1 consists of using a Kow and an appropriate FCM for

estimating the baseline BAFR

fd. In this method, the Kow is assumed to be equal to the baseline

BCFR

fd. Numerous investigations have demonstrated a linear relationship between the logarithm

of the BCF and the logarithm of the octanol-water partition coefficient (Kow ) for organic

chemicals for fish and other aquatic organisms. Isnard and Lambert (1988) list various

regression equations that illustrate this linear relationship. When the regression equations are

constructed using lipid-normalized BCFs, the slopes and intercepts are not significantly different

from one and zero, respectively (e.g., de Wolf, et al., 1992). The underlying assumption for the

linear relationship between the BCF and Kow is that the bioconcentration process can be viewed

as the partitioning of a chemical between the lipid of the aquatic organisms and water and that

the Kow is a useful surrogate for this partitioning process (Mackay, 1982). To account for

biomagnification, Procedure #1 requires the Kow value be used in conjunction with an

appropriate FCM. 
5-40

(Equation 5-27)

1. Baseline BAFR

fd Equation. For each acceptable Kow value and FCM for the chemical of

concern, calculate a baseline BAFR

fd using the following equation.

where:

 Baseline BAFR

fd = BAF expressed on a freely dissolved and lipid-normalized

basis for a given trophic level

FCM = The food chain multiplier for the appropriate trophic level

obtained from Table 5-1 by linear interpolation or from

appropriate field data (used with Procedure #1 only)

Kow = Octanol-water partition coefficient

The BCF-Kow relationship has been developed primarily for nonionic organic chemicals

that are not readily metabolized by aquatic organisms and thus is most appropriate for

poorly-metabolized nonionic organic chemicals (i.e., Procedures #1 and #3 as depicted in

Figure 5-1). For poorly-metabolized nonionic organic chemicals with large log Kows (i.e.,

> 6), reported log BCFs are often not equal to log Kow. EPA believes that this

nonlinearity is primarily due to not accounting for several factors which affect the BCF

determination. These factors include not basing BCFs on the freely dissolved

concentration in water, not accounting for growth dilution, not assessing BCFs at steadystate, inaccuracies in measurements of uptake and elimination rate constants, and

complications from the use of solvent carriers in the exposure. Application of Equation 5-

27 for predicting BAFs has been conducted in several different ecosystems including

Lake Ontario, the tidally influenced Bayou D’Inde in Louisiana, the Fox River and Green

Bay, Wisconsin, and the Hudson River in New York. Additional detail on the validation,

technical basis, assumptions, and uncertainty associated with Equation 5-27 and is

provided in the Bioaccumulation TSD. 

2. FCMs and Kows. Food chain multipliers and Kow values should be selected as described

previously in Procedure #1.

5.4.3.2 Selecting Final Baseline BAFR

fds

After calculating individual baseline BAFR

fds using as many of the methods in Procedure

#1 as possible, the next step is to determine a final baseline BAFR

fd for each trophic level from the

individual baseline BAFR

fds (see Figures 5-1 and 5-2). The final baseline BAFRfd will be used in

the last step to determine the national BAF for each trophic level. The final baseline BAFR

fd for

each trophic level should be determined from the individual baseline BAFR

fds by considering the

data preference hierarchy defined by Procedure #1 and uncertainty in the data. The data

preference hierarchy for Procedure #1 is (in order of preference): 

1. a baseline BAFR

fd from an acceptable field-measured BAF (method 1)
5-41

2. a baseline BAFR

fd predicted from an acceptable field-measured BSAF (method 2),

3. a baseline BAFR

fd predicted from an acceptable BCF and FCM (method 3), or

4. a baseline BAFR

fd predicted from an acceptable Kow and FCM (method 4).

This data preference hierarchy reflects EPA’s preference for BAFs based on field-measurements

of bioaccumulation (methods 1 and 2) over those based on laboratory-measurements and/or

predictions of bioaccumulation (methods 3 and 4). However, this data preference hierarchy

should not be considered inflexible. Rather, it should be used as a guide for selecting the final

baseline BAFR

fds when the uncertainty is similar among two or more baseline BAFRfds derived

using different methods. The following steps and guidelines should be followed for selecting the

final baseline BAFR

fds using Procedure #1. 

1. Calculate Species-Mean Baseline BAFR

fds. For each BAF method where more than one

acceptable baseline BAFR

fd is available for a given species, calculate a species-mean

baseline BAFR

fd as the geometric mean of all available individual baseline BAFRfds. When

calculating a species-mean baseline BAFR

fd, individual baseline BAFRfds should be

reviewed carefully to assess the uncertainty in the BAF values. For highly hydrophobic

chemicals applicable to Procedure #1, particular attention should be paid to whether

sufficient spatial and temporal averaging of water and tissue concentrations was likely

achieved in the BAF, BSAF, or BCF study. Highly uncertain baseline BAFR

fds should not

be used. Large differences in individual baseline BAFR

fds for a given species (e.g., greater

than a factor of 10) should be investigated further. In such cases, some or all of the

baseline BAFR

fds for a given species might not be used. Additional discussion on

evaluating acceptability of BAF values is provided in the Bioaccumulation TSD. 

2. Calculate Trophic-Level-Mean Baseline BAFR

fds. For each BAF method where more

than one acceptable species-mean baseline BAFR

fd is available within a given trophic

level, calculate a trophic-level-mean baseline BAFR

fd as the geometric mean of acceptable

species-mean baseline BAFR

fds in that trophic level. Trophic-level-mean baseline BAFRfds

should be calculated for trophic levels two, three, and four because available data on U.S.

consumers of fish and shellfish indicate significant consumption of organisms in these

trophic levels. 

3. Select a Final Baseline BAFR

fd for Each Trophic Level. For each trophic level, select

the final baseline BAFR

fd using best professional judgment by considering: (1) the data

preference hierarchy shown previously, (2) the relative uncertainty in the trophic-levelmean baseline BAFR

fds derived using different methods, and (3) the weight of evidence

among the four methods. 

a. In general, when more than one trophic-level-mean baseline BAFR

fd is available for

a given trophic level, the final trophic-level-mean baseline BAFR

fd should be

selected from the most preferred BAF method defined by the data preference

hierarchy for Procedure #1. 

b. If uncertainty in a trophic-level-mean baseline BAF based on a higher tier (more

preferred) method is judged to be substantially greater than a trophic-level-mean
5-42

(Equation 5-28)

baseline BAF from a lower tier method, and the weight of evidence among the

various methods suggests that a BAF value from lower tier method is likely to be

more accurate, then the final baseline BAFR

fd should be selected using a trophic

level-mean baseline BAFR

fd from a lower tier method. 

c. When considering the weight of evidence among the various BAF methods,

greater confidence in the final baseline BAFR

fd is generally assigned when BAFs

from a greater number of methods are in agreement for a given trophic level. 

However, lack of agreement among 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

BAF value, one would expect disagreement between a field-measured BAF (the

highest priority data) and a predicted BAF using a Kow and model-derived FCM. 

Thus, field-measured BAFs should generally be given the greatest weight among

methods because they reflect direct measures of bioaccumulation and incorporate

any metabolism which might occur in the organism and its food web. 

d. The above steps should be performed for each trophic level until a final baseline

BAFR

fd is selected for trophic levels two, three, and four.

5.4.3.3 Calculating National BAFs

The last step in deriving a national BAF for each trophic level is to convert the final

baseline BAFR

fd determined in the previous step to a BAF that reflects conditions to which the

national 304(a) criteria will apply (Figure 5-2). Since a baseline BAFR

fd is by definition

normalized by lipid content and expressed on a freely dissolved basis, it needs to be adjusted to

reflect the lipid fraction of aquatic organisms commonly consumed in the U.S. and the freely

dissolved fraction expected in U.S. bodies of water. Converting a final baseline BAFR

fd to a

national BAF requires information on: (1) the percent lipid of the aquatic organisms commonly

consumed by humans, and (2) the freely dissolved fraction of the chemical of concern that would

be expected in the ambient waters of interest. For each trophic level, a national BAF should be

determined from a final baseline BAFR

fd according to the following guidelines.

1. National BAF Equation. For each trophic level, calculate a national BAF using the

following equation.

where:

Final Baseline BAFR

fd = Final trophic-level-mean baseline BAF expressed

on a freely dissolved and lipid-normalized basis for

trophic level “n”
5-43

fR(TL n) = Lipid fraction of aquatic species consumed at

trophic level “n”

 ffd = Fraction of the total chemical in water that is freely

dissolved

The technical basis of Equation 5-28 is provided in the Bioaccumulation TSD. Guidance

for determining each component of Equation 5-28 is provided below.

2. Determining the Final Baseline BAFR

fd. The final trophic-level-mean baseline BAFR

fds

used in this equation are those which have been determined using the guidance presented

in Section 5.4.3.2 for selecting the final baseline BAFR

fds. 

3. Lipid Content of Commonly Consumed Aquatic Species. As illustrated by Equation

5-28, the percent lipid of the aquatic species consumed by humans is needed to

accurately characterize the potential exposure to a chemical from ingestion of aquatic

organisms. 

a. National Default Lipid Values. For the purposes of calculating a national

304(a) criterion, the following national default values for lipid fraction should be

used: 1.9% (for trophic level two organisms), 2.6% (for trophic level three

organisms), and 3.0% (for trophic level four organisms).

These national default values for lipid content reflect national per capita average

patterns of fish consumption in the United States. Specifically, they were

calculated using the consumption-weighted mean lipid content of commonly

consumed fish and shellfish as identified by the USDA Continuing Survey of

Food Intake by Individuals (CSFII) for 1994 through 1996. This same national

survey data was used to derive national default values of fish consumption. To

maintain consistency with the fish consumption assumptions, only freshwater and

estuarine organisms were included in the derivation of the national default lipid

values. Additional details on the technical basis, assumptions, and uncertainty in

the national default values of lipid fraction are provided in the Bioaccumulation

TSD. 

Although national default lipid values are used by EPA to set national 304(a)

criteria, EPA encourages States and authorized Tribes to use local or regional data

on lipid content of consumed aquatic species when adopting criteria into their

water quality standards because local or regional consumption patterns (and lipid

content) can differ from national consumption patterns. Additional guidance on

developing site-specific values of lipid content, including a database of lipid

content for many commonly consumed aquatic organisms, is found in the

Bioaccumulation TSD.

4. Freely Dissolved Fraction. The third piece of information required for deriving a

national BAF is the freely dissolved fraction of the chemical of concern that is expected
5-44

(Equation 5-29)

in waters of the United States. As noted previously, expressing BAFs on the freely

dissolved concentration in water allows a common basis for averaging BAFs from

several studies. However, for use in criteria development, these BAFs should be

converted back to values based on the total concentration in the water to be consistent

with monitored water column and effluent concentrations, which are typically based on

total concentrations of chemicals in the water. This should be done by multiplying the

freely dissolved baseline BAFR

fd by the fraction of the freely dissolved chemical expected

in water bodies of the United States where criteria are to be applied, as shown in

Equation 5-29. 

where:

POC = national default value for the particulate organic carbon

concentration (kg/L)

DOC = national default value for the dissolved organic carbon

concentration (kg/L)

Kow = n-octanol water partition coefficient for the chemical

Equation 5-29 is identical to Equation 5-12, which was used to determine the freely

dissolved fraction for deriving baseline BAFR

fds from field-measured BAFs. However, the

POC and DOC concentrations used in Equation 5-29 reflect those values that are

expected in U.S. bodies of water, not the POC and DOC values in the study water used to

derive the BAF. Guidance for determining each component of Equation 5-29 follows.

a. National Default Values of POC and DOC. For estimating the freely dissolved

fraction of the chemical of concern that is expected in U.S. water bodies, national

default values of 0.5 mg/L (5 × 10-7 kg/L) for POC and 2.9 mg/L (2.9 × 10-6 kg/L)

for DOC should be used. These values are 50th percentile values (medians) based

on an analysis of over 110,000 DOC values and 85,000 POC values contained in

EPA’s STORET database from 1980 through 1999. These default values reflect a

combination of values for streams, lakes and estuaries across the United States. 

Additional details on the technical basis, assumptions, and uncertainty in the

derivation and application of the national default values of POC and DOC are

provided in the Bioaccumulation TSD.

Although national default values of POC and DOC concentrations are used by

EPA to set national 304(a) criteria as described by this document, EPA

encourages States and authorized Tribes to use local or regional data on POC and

DOC when adopting criteria into their water quality standards. EPA encourages

States and Tribes to consider local or regional data on POC and DOC because

local or regional conditions may result in differences in POC or DOC
5-45

concentrations compared with the values used as national defaults. Additional

guidance on developing local or regional values of POC and DOC, including a

database of POC and DOC values segregated by waterbody type, is found in the

Bioaccumulation TSD. 

b. KowValue. The value selected for the Kow of the chemical of concern should be

the same value used in earlier calculations (e.g., for calculating baseline BAFR

fds

and FCMs). Guidance for selecting the Kow value is found in the

Bioaccumulation TSD. 

5.4.4 Deriving National BAFs Using Procedure #2

This section provides guidance for calculating national BAFs for nonionic organic

chemicals using Procedure #2 shown in Figure 5-1. The types of nonionic organic chemicals for

which Procedure #2 is most appropriate are those that are classified as moderately to highly

hydrophobic and subject to high rates of metabolism by aquatic biota (see Section 5.4.2 above). 

Non-aqueous contaminant exposure and subsequent biomagnification in aquatic food webs are

not generally of concern for chemicals that are classified in this category. As a result, FCMs are

not used in this procedure. In addition, Kow -based predictions of bioconcentration are not used

in this procedure since the Kow /BCF relationship is primarily based on poorly metabolized

chemicals. Some nonionic organic chemicals for which Procedure #2 is probably appropriate

include certain PAHs which are believed to be metabolized substantially by fish (e.g.,

benzo[a]pyrene, phenanthrene, fluoranthene, pyrene, benzo[a]anthracene and

chrysene/triphenylene; USEPA, 1980; Burkhard and Lukasewycz, 2000). 

According to Procedure #2, the following three methods can be used in deriving a

national BAF: 

C using a BAF from an acceptable field study (i.e., a field-measured BAF) (method 1),

C predicting a BAF from an acceptable BSAF (method 2), and

C predicting a BAF from an acceptable BCF (method 3).

Each of these three methods relies on measured data for assessing bioaccumulation and

therefore, includes the effects of chemical metabolism by the study organism in the BAF

estimate. The field-measured BAF and BSAF methods also incorporate any metabolism which

occurs in the aquatic food web.

As shown in Figure 5-2, the next steps in deriving a national BAF after selecting the

derivation procedure are: (1) calculating individual baseline BAFR

fds, (2) selecting the final

baseline BAFR

fds, and (3) calculating the national BAFs. Each of these three steps is discussed

separately below.

5.4.4.1 Calculating Individual Baseline BAFR

fds

As described previously in Procedure #1, calculating individual baseline BAFR

fds involves

normalizing the measured BAFt

T or BCFtT (which are based on the total chemical in water and
5-46

tissue) by the lipid content of the study organisms and the freely dissolved fraction of the

chemical in the study water. Converting measured BAFt

T (or BCFtT) values to baseline BAFR

fd (or

BCFR

fd) values is designed to account for variation in measured BAFt

Ts that is caused by

differences in lipid content of study organisms and differences in the freely dissolved fraction of

chemical in study waters. Therefore, baseline BAFR

fds are considered more amenable for

extrapolating and averaging BAFs across different species and different study waters compared

with total BAFt

Ts. 

1. For each species where acceptable data are available, calculate all possible baseline

BAFR

fds using each of the three methods shown above for Procedure #2. 

2. Individual baseline BAFR

fds should be calculated from field-measured BAFt

Ts, fieldmeasured BSAFs, and laboratory BCFt

Ts according to the following procedures.

A. Baseline BAFR

fd from Field-Measured BAFs

1. Except where noted below, a baseline BAFR

fd should be calculated from a field-measured

BAFt

T using the guidance and equations outlined in Section 5.4.3.1(A) for determining

baseline BAFR

fds from field-measured BAFs in Procedure #1. 

2. Because nonionic organic chemicals applicable to Procedure #2 have relatively high rates

of metabolism in aquatic organisms, they will tend to reach steady state more quickly

than nonionic organic chemicals with similar Kow values but which undergo little or no

metabolism. Therefore, less temporal averaging of chemical concentrations would

generally be required for determining field-measured BAFt

Ts with highly metabolizable

chemicals compared with chemicals that are poorly metabolized by aquatic biota. 
5-47

B. Baseline BAFR

fd Derived from Field-measured BSAFs

1. A baseline BAFR

fd should be calculated from a field-measured BSAF using the guidance

and equations outlined in Section 5.4.3.1(B) for determining baseline BAFR

fds from fieldmeasured BSAFs in Procedure #1. 

C. Baseline BAFR

fd from a Laboratory-Measured BCF

1. Except where noted below, a baseline BAFR

fd should be calculated from a laboratorymeasured BCFt

T using the guidance and equations outlined in Section 5.4.3.1(c) for

determining baseline BAFR

fds from a laboratory-measured BCF and FCM in Procedure #1.

2. Because biomagnification is not an overriding concern for nonionic organic chemicals

applicable to Procedure #2, food chain multipliers are not used in the derivation of a

baseline BAFR

fd from a laboratory-measured BCFt

T .

5.4.4.2 Selecting Final Baseline BAFR

fds

After calculating individual, baseline BAFR

fds using as many of the methods in Procedure

#2 as possible, the next step is to determine a final baseline BAFR

fd for each trophic level from the

individual baseline BAFR

fds. The final baseline BAFRfd will be used in the last step to determine

the national BAF for each trophic level. A final baseline BAFR

fd for each trophic level should be

determined from the individual baseline BAFR

fds by considering the data preference hierarchy

defined by Procedure #2 and uncertainty in the data. The data preference hierarchy for

Procedure #2 is (in order of preference): 

1. a baseline BAFR

fd from an acceptable field-measured BAF (method 1), 

2. a baseline BAFR

fd from an acceptable field-measured BSAF (method 2), or

3. a baseline BAFR

fd from an acceptable laboratory-measured BCF (method 3).

This data preference hierarchy reflects EPA’s preference for BAFs based on fieldmeasurements of bioaccumulation (methods 1 and 2) over those based on laboratorymeasurements (method 3). However, as explained in Procedure #1, this data preference

hierarchy should not be considered inflexible. Rather, it should be used as a guide for selecting

the final baseline BAFR

fds when the underlying uncertainty is similar among two or more baseline

BAFR

fds derived using different methods. Although biomagnification is not generally a concern

for chemicals subject to Procedure #2, trophic level differences in bioaccumulation might be

substantial to the extent that the rate of chemical metabolism by organisms in different trophic

levels differs. For example, certain PAHs have been shown to be metabolized to a much greater

extent by some fish compared with some invertebrate species (James, 1989). Therefore, final

baseline BAFR

fds for chemicals applicable to Procedure #2 should be determined on a trophiclevel-specific basis according to the following guidelines.

1. The final baseline BAFR

fds in Procedure #2 should be selected according to the same steps

described in Procedure #1 but with the substitution of the data preference hierarchy

described above for Procedure #2. Specifically, the species-mean baseline BAFR

fds,
5-48

trophic-level-mean baseline BAFR

fds, and the final baseline BAFRfds should be determined

according to the guidelines presented in Procedure #1 (Section 5.4.3.2, Steps 1, 2, and 3). 

5.4.4.3 Calculating the National BAFs

As described in Procedure #1, the last step in deriving national BAFs for nonionic

organic chemicals is to convert the final baseline BAFR

fds determined in the previous step to

BAFs which reflect conditions to which the national 304(a) criteria will apply (Figure 5-2). 

1. For trophic levels two, three, and four, national BAFs should be calculated from the final

baseline BAFR

fds using the same equation and procedures described previously in

Procedure #1 (see Section 5.4.3.3 entitled “Calculating the National BAFs”). 

5.4.5 Deriving National BAFs Using Procedure #3

This section provides guidance for calculating national BAFs for nonionic organic

chemicals using Procedure #3 shown in Figure 5-1. The types of nonionic organic chemicals for

which Procedure #3 is most appropriate are those that are classified as low in hydrophobicity

(i.e., log Kow values less than 4.0) and subject to low (or unknown) rates of metabolism by

aquatic biota (see Section 5.4.2 above). Non-aqueous contaminant exposure and subsequent

biomagnification in aquatic food webs are not generally of concern for chemicals that are

classified in this category (Fisk et al., 1998; Gobas et al., 1993; Connolly and Pedersen, 1988;

Thomann, 1989). As a result, FCMs are not used in this procedure. 

According to Procedure #3, the following three methods can be used in deriving a

national BAF: 

C using a BAF from an acceptable field study (i.e., a field-measured BAF),

C predicting a BAF from an acceptable laboratory-measured BCF, and 

C predicting a BAF from an acceptable Kow.

After selecting the derivation procedure, the next steps in deriving a national BAF at a

given trophic level for nonionic organic chemicals are: (1) calculating individual baseline

BAFR

fds, (2) selecting the final baseline BAFRfd, and (3) calculating the national BAF (Figure 5-2). 

Each of these three steps is discussed separately below.

5.4.5.1 Calculating Individual Baseline BAFR

fds

Calculating individual baseline BAFR

fds involves normalizing each measured BAFt

T or

BCFt

T (which are based on the total chemical in water and tissue) by the lipid content of the study

organism and the freely dissolved fraction of the chemical in the study water. For additional

discussion of the technical basis for calculating baseline BAFR

fds, see Section 5.4.3.1 in Procedure

#1. 
5-49

1. For each species where acceptable data are available, calculate all possible baseline

BAFR

fds using each of the three methods shown above for Procedure #3. 

2. An individual baseline BAFR

fd should be calculated from field-measured BAFt

Ts,

laboratory-measured BCFt

Ts, and Kow values according to the following procedures.

A. Baseline BAFR

fd from Field-Measured BAFs

1. Except where noted below, a baseline BAFR

fd should be calculated from a field-measured

BAFt

T using the guidance and equations outlined in Section 5.4.3.1(A) in Procedure #1. 

2. Freely Dissolved Fraction. Due to their low hydrophobicity (i.e., log Kow < 4.0),

nonionic organic chemicals applicable to Procedure #3 are expected to remain almost

entirely in the freely dissolved form in natural waters with dissolved and particulate

organic carbon concentrations typical of most field BAF studies. Therefore, the freely

dissolved fraction should be assumed to be equal to 1.0, unless the concentrations of

DOC and POC are very high in the field BAF study. For studies with very high DOC or

POC concentrations, (e.g., about 100 mg/L or higher for DOC or 10 mg/L or higher for

POC), the freely dissolved fraction may be substantially lower than 1.0 and therefore

should be calculated using Equation 5-12. 

3. Temporal Averaging of Concentrations. Also due to their low hydrophobicity,

nonionic organic chemicals appropriate to Procedure #3 will also tend to reach steady

state quickly compared with those chemicals to which Procedure #1 applies. Therefore,

the extent of temporal averaging of tissue and water concentrations is typically much less

than that required for highly hydrophobic chemicals to which Procedure #1 is applied. In

addition, field studies used to calculate BAFs for these chemicals should have sampled

water and tissue at similar points in time because tissue concentrations respond more

rapidly to changes in water concentrations. EPA will be providing additional guidance

on appropriate BAF study designs for nonionic organic chemicals (including those

appropriate to Procedure #3) in its forthcoming guidance document on conducting field

BAF and BSAF studies.

B. Baseline BAFR

fd from a Laboratory-Measured BCF

1. Except where noted below, a baseline BAFR

fd should be calculated from a laboratorymeasured BCFt

T using the guidance and equations outlined in Section 5.4.3.1(c) of

Procedure #1.

2. Food Chain Multipliers. Because biomagnification is not an overriding concern for the

minimally hydrophobic chemicals applicable to Procedure #3, FCMs are not used in the

derivation of a baseline BAFR

fd from a laboratory-measured BCFt

T. 

3. Freely Dissolved Fraction. Due to their low hydrophobicity (i.e., log Kow < 4.0),

nonionic organic chemicals to which Procedure #3 is applied are expected to remain
5-50

almost entirely in the freely dissolved form in waters containing dissolved and particulate

organic carbon concentrations typical of laboratory BCF studies. Therefore, the freely

dissolved fraction should usually be assumed equal to 1.0. The freely dissolved fraction

will be substantially less than 1.0 only in situations where unusually high concentrations

of DOC and POC are present in the laboratory BCF study (e.g., above about 100 mg/L

for DOC or about 10 mg/L for POC). In this situation, the freely dissolved fraction

should be calculated according to Equation 5-12. 

C. Baseline BAFR

fd from a Kow

1. Except where noted below, a baseline BAFR

fd should be calculated from an acceptable Kow

using the guidance and equations outlined in Section 5.4.3.1(D) in Procedure #1.

2. Because biomagnification is not an overriding concern for nonionic organic chemicals

with low hydrophobicity (i.e., log Kow < 4.0), food chain multipliers are not used in

Procedure #3 for deriving the baseline BAFR

fd from a Kow. 

5.4.5.2 Selecting Final Baseline BAFR

fds

After calculating individual baseline BAFR

fds using as many of the methods in Procedure

#3 as possible, the next step is to determine a final baseline BAFR

fd for each trophic level from the

individual baseline BAFR

fds (Figure 5-2). The final baseline BAFRfd will be used in the last step to

determine the national BAF for each trophic level. The final baseline BAFR

fd for each trophic

level should be determined from the individual baseline BAFR

fds by considering the data

preference hierarchy defined by Procedure #3 and uncertainty in the data. The data preference

hierarchy for Procedure #3 is (in order of preference): 

1. a baseline BAFR

fd from an acceptable field-measured BAF or laboratory-measured

BCF, or 

2. a baseline BAFR

fd predicted from an acceptable Kow value. 

This data preference hierarchy reflects EPA’s preference for BAFs that are based on

measured data (field-measured BAFs and laboratory-measured BCFs) over BAFs based on

predictive methods (Kow). This data preference hierarchy should be used as a guide for selecting

the final baseline BAFR

fds when the uncertainty is similar among two or more baseline BAFRfds

derived using different methods. Since bioaccumulation via dietary uptake and subsequent

biomagnification generally are not of concern for chemicals subject to Procedure #3, fieldmeasured BAFs and laboratory-measured BCFs are considered equally in determining the

national BAF. 

Final baseline BAFR

fds should be selected for each trophic level using the following steps

and guidelines.

1. Calculate Species-Mean Baseline BAFR

fds. For each BAF method (i.e., field-measured

BAF, BAF from a lab-measured BCF, or BAF from a Kow) where more than one
5-51

acceptable baseline BAFR

fd is available for a given species, calculate a species-mean

baseline BAFR

fd according to the guidance described previously in Procedure #1. 

2. Calculate Trophic-Level-Mean Baseline BAFR

fds. For each BAF method where more

than one acceptable species-mean baseline BAFR

fd is available within a given trophic

level, calculate the trophic-level-mean baseline BAFR

fd as the geometric mean of

acceptable species-mean baseline BAFR

fds in that trophic level. 

3. Select a Final Baseline BAFR

fd for Each Trophic Level. For each trophic level, select

the final baseline BAFR

fd using best professional judgment by considering: (1) the data

preference hierarchy, (2) the relative uncertainties among trophic-level-mean baseline

BAFR

fds derived using different methods, and (3) the weight of evidence among the three

methods. 

a. In general, when more than one trophic-level-mean baseline BAFR

fd is available

within a given trophic level, the final baseline BAFR

fd should be selected from the

most preferred BAF method defined by the data preference hierarchy for

Procedure #3. Within the first data preference tier, field-measured BAFs and

laboratory-measured BCFs are considered equally desirable for deriving a final

trophic-level-mean baseline BAFR

fd using Procedure #3. If a trophic-level-mean

baseline BAFR

fd is available from both a field-measured BAF and a laboratorymeasured BCF, the final baseline BAFR

fd should be selected using the trophiclevel-mean baseline BAFR

fd or BCFRfd with the least overall uncertainty. 

b. If uncertainty in a trophic-level-mean baseline BAFR

fd based on a higher tier (more

preferred) method is judged to be substantially greater than a trophic-level-mean

baseline BAFR

fd from a lower tier method, then the final baseline BAFRfd should be

selected using a trophic-level-mean baseline BAFR

fd from a lower tier method.

c. The above steps should be performed for each trophic level until a final baseline

BAFR

fd is selected for trophic level two, three, and four.

5.4.5.3 Calculating the National BAFs

As described in Procedure #1, the last step in deriving a national BAF for a given trophic

level for nonionic organic chemicals is to convert the final baseline BAFR

fd determined in the

previous step to a BAF that reflect conditions to which the national 304(a) criterion will apply

(Figure 5-2). Each national BAF should be determined from a final baseline BAFR

fd according to

the following guidelines.

1. National BAF Equation. Except where noted below, national BAFs for trophic levels

two, three, and four should be calculated from the final, trophic-level-mean baseline

BAFR

fds using Equation 5-28 and associated guidance described in Procedure #1 (see

Section 5.4.3.3). 
5-52

2. Freely Dissolved Fraction. Due to their low hydrophobicity (i.e., log Kow < 4.0), a

freely dissolved fraction of 1.0 should be assumed for calculating national BAFs for

nonionic organic chemicals using Procedure #3. A freely dissolved fraction of 1.0 should

be assumed because at a log Kow of less than 4.0, nonionic organic chemicals are

expected to remain over 99 percent in the freely dissolved form at POC and DOC

concentrations corresponding to national default values for U.S. bodies of water (i.e., 0.5

mg/L and 2.9 mg/L, respectively).

5.4.6 Deriving National BAFs Using Procedure #4

This section provides guidance for calculating national BAFs for nonionic organic

chemicals using Procedure #4 shown in Figure 5-1. The types of nonionic organic chemicals for

which Procedure #4 is most appropriate are those that are classified as having low

hydrophobicity and subject to high rates of metabolism by aquatic biota (see Section 5.4.2

above). Non-aqueous contaminant exposure and subsequent biomagnification in aquatic food

webs are not generally of concern for chemicals that are classified in this category. As a result,

FCMs are not used in this procedure. In addition, Kow -based predictions of bioconcentration are

not used in this procedure since the Kow /BCF relationship is primarily based on poorly

metabolized chemicals. One example of a nonionic organic chemical for which Procedure #4

appears appropriate is butyl benzyl phthalate in fish. Using radiolabeling techniques with

confirmation by chromatographic analysis, Carr et al. (1997) present evidence that indicates

butyl benzyl phthalate is extensively metabolized in sunfish. Carr et al. (1997) also report

measured BCFs (and subsequently lipid-normalized BCFs) which are substantially below

predicted BCFs based on log Kow. In a study of chlorinated anilines (which would be essentially

un-ionized at ambient pH), de Wolf et al. (1992) reported measured BCFs substantially lower

than those predicted based on Kow. The authors suggested that biotransformation (metabolism)

involving the amine (NH2) was responsible for the lower measured BCFs. 

According to Procedure #4, the following two methods can be used in deriving a national

BAF: 

C using a BAF from an acceptable field study (i.e., a field-measured BAF), and

C predicting a BAF from an acceptable BCF.

After selecting the derivation procedure, the next steps in deriving a national BAF for a

given trophic level for nonionic organic chemicals are: (1) calculating individual baseline

BAFR

fds, (2) selecting the final baseline BAFRfd, and (3) calculating the national BAF (Figure 5-2). 

Each of these three steps is discussed separately below.

5.4.6.1 Calculating Individual Baseline BAFR

fds

Calculating individual baseline BAFR

fds involves normalizing the measured BAFt

T or BCFtT

(which are based on the total chemical in water and tissue) by the lipid content of the study

organism and the freely dissolved fraction of the chemical in the study water. For additional

discussion of the technical basis for calculating baseline BAFR

fds, see Section 5.4.3.1 in Procedure

#1. 
5-53

1. For each species where acceptable data are available, calculate all possible baseline

BAFR

fds using each of the two methods shown above for Procedure #4. 

2. Individual baseline BAFR

fds should be calculated from field-measured BAFt

Ts and

laboratory-measured BCFt

Ts according to the following procedures.

A. Baseline BAFR

fd from Field-Measured BAFs

1. A baseline BAFR

fd should be calculated from a field-measured BAFt

T using the guidance

and equations outlined in Section 5.4.3.1(A) in Procedure #1. 

2. Freely Dissolved Fraction. Due to their low hydrophobicity (i.e., log Kow < 4.0),

nonionic organic chemicals applicable to Procedure #4 are expected to remain almost

entirely in the freely dissolved form in natural waters with dissolved and particulate

organic carbon concentrations typical of most field BAF studies. Therefore, the freely

dissolved fraction should be assumed equal to 1.0 unless the concentrations of DOC and

POC are very high in the field BAF study. For studies with very high DOC or POC

concentrations, (e.g., about 100 mg/L or higher for DOC or 10 mg/L or higher for POC),

the freely dissolved fraction may be substantially lower than 1.0 and therefore should be

calculated using Equation 5-12. 

3. Temporal Averaging of Concentrations. Also due to their low hydrophobicity,

nonionic organic chemicals appropriate to Procedure #4 will also tend to reach steadystate quickly compared with those chemicals to which Procedure #1 applies. Therefore,

the extent of temporal averaging of tissue and water concentrations is typically much less

than that required for highly hydrophobic chemicals to which Procedure #1 is applied. In

addition, field studies used to calculate BAFs for these chemicals should have sampled

water and tissue at similar points in time because tissue concentrations should respond

rapidly to changes in water concentrations. EPA will be providing additional guidance

on appropriate BAF study designs for nonionic organic chemicals (including those

appropriate to Procedure #4) in its forthcoming guidance document on conducting field

BAF and BSAF studies.

B. Baseline BAFR

fd from a Laboratory-Measured BCF

1. Except where noted below, a baseline BAFR

fd should be calculated from a laboratorymeasured BCFt

T using the guidance and equations outlined in Section 5.4.3.1(c) of

Procedure #1.

2. Food Chain Multipliers. Because biomagnification is not an important concern for the

minimally hydrophobic chemicals applicable to Procedure #4, FCMs are not used in the

derivation of a baseline BAFR

fd from a laboratory-measured BCFt

T. 

3. Freely Dissolved Fraction. Due to their low hydrophobicity (i.e., log Kow < 4.0),

nonionic organic chemicals to which Procedure #4 is applied are expected to remain
5-54

almost entirely in the freely dissolved form in waters containing dissolved and particulate

organic carbon concentrations typical of laboratory BCF studies. Therefore, the freely

dissolved fraction should usually be assumed to be equal to 1.0. The freely dissolved

fraction will be substantially less than 1.0 only in situations where unusually high

concentrations of DOC and POC are present in the lab BCF study (e.g., above about 100

mg/L for DOC or about 10 mg/L for POC). In this situation, the freely dissolved fraction

should be calculated according to Equation 5-12. 

5.4.6.2 Selecting Final Baseline BAFR

fds

After calculating individual baseline BAFR

fds using as many of the methods in Procedure

#4 as possible, the next step is to determine a final baseline BAFR

fd for a given trophic level from

the individual baseline BAFR

fds (Figure 5-2). The final baseline BAFRfd will be used in the last step

to determine the national BAF for each trophic level. A final baseline BAFR

fd should be

determined for each trophic level from the individual baseline BAFR

fds by considering the data

preference hierarchy defined by Procedure #4 and uncertainty in the data. The data preference

hierarchy for Procedure #4 is:

1. a baseline BAFR

fd from an acceptable field-measured BAF or predicted from an

acceptable laboratory-measured BCF.

Since bioaccumulation via dietary uptake and subsequent biomagnification generally are

not of concern for chemicals subject to Procedure #4, field-measured BAFs and laboratorymeasured BCFs are considered equally in determining the national BAF. 

Final baseline BAFR

fds should be selected for each trophic level using the following steps

and guidelines.

1. Calculate Species-Mean Baseline BAFR

fds. For each BAF method (i.e., field-measured

BAF or a BAF from a lab-measured BCF) where more than one acceptable baseline

BAFR

fd is available for a given species, calculate a species-mean baseline BAFRfd according

to the guidance described previously in Procedure #1. 

2. Calculate Trophic-Level-Mean Baseline BAFR

fds. For each BAF method where more

than one acceptable species-mean baseline BAFR

fd is available within a given trophic

level, calculate the trophic-level-mean baseline BAFR

fd as the geometric mean of

acceptable species-mean baseline BAFR

fds for that trophic level. 

3. Select a Final Baseline BAFR

fd for Each Trophic Level. For each trophic level, select

the final baseline BAFR

fd using best professional judgment by considering: (1) the data

preference hierarchy, and (2) the relative uncertainties among trophic-level-mean BAFs

derived using different methods.

a. As discussed above, field-measured BAFs and laboratory-measured BCFs are

considered equally desirable for deriving a final trophic-level-mean baseline
5-55

BAFR

fd using Procedure #4. If a trophic-level-mean baseline BAFRfd is available

from both a field-measured BAF and a laboratory-measured BCF, the final

baseline BAFR

fd should be selected using the trophic-level-mean baseline BAFRfd or

BCFR

fd with the least overall uncertainty.

b. The above steps should be performed for each trophic level until a final baseline

BAFR

fd is selected for trophic levels two, three, and four.

5.4.6.3 Calculating National BAFs

As described in Procedure #1, the last step in deriving a national BAF for a given trophic

level for nonionic organic chemicals is to convert the final baseline BAFR

fd determined in the

previous step to a BAF that reflects conditions to which the national 304(a) criterion will apply

(Figure 5-2). Each national BAF should be determined from a final baseline BAFR

fd according to

the following guidelines.

1. National BAF Equation. Except where noted below, national BAFs for trophic-levels

two, three, and four should be calculated from the final, trophic-level-mean baseline

BAFR

fds using the same equation and procedures described previously in Procedure #1

(see Section 5.4.3.3 in Procedure #1). 

2. Freely Dissolved Fraction. Due to their low hydrophobicity (i.e., log Kow < 4.0), a

freely dissolved fraction of 1.0 should be assumed for calculating national BAFs for

nonionic organic chemicals using Procedure #4. A freely dissolved fraction of 1.0

should be assumed because at a log Kow value of less than 4.0, nonionic organic

chemicals are expected to remain over 99 percent in the freely dissolved form at POC and

DOC concentrations corresponding to national default values for U.S. bodies of water

(i.e., 0.5 mg/L and 2.9 mg/L, respectively).

5.5 NATIONAL BIOACCUMULATION FACTORS FOR IONIC ORGANIC

CHEMICALS

This section contains guidelines for deriving national BAFs for ionic organic chemicals

(i.e., organic chemicals which undergo significant ionization in water). As defined in Section

5.3.5, ionic organic chemicals contain functional groups which can either readily donate protons

(e.g., organic acids with hydroxyl, carboxylic, and sulfonic groups) or readily accept protons

(e.g., organic bases with amino and aromatic heterocyclic nitrogen groups). Some examples of

ionic organic compounds include: 

C chlorinated phenols (e.g., 2,4,6-trichlorophenol, pentachlorophenol),

C chlorinated phenoxyalkanoic acids (e.g., 2,4-dichlorophenoxyacetic acid [2,4-D]),

C nitrophenols (e.g., 2-nitrophenol, 2,4,6-trinitrophenol),

C cresols (e.g., 2,4-dinitro-o-cresol [DNOC]),

C pyridines (e.g., 2,4-dimethypyidine),

C aliphatic and aromatic amines (e.g., trimethylamine, aniline), and 
5-56

C linear alkylbenzenesulfonate (LAS) surfactants.

Ionic organic chemicals are considered separately for deriving national BAFs because the

anionic or cationic species of these chemicals behave much differently in the aquatic

environment compared with their neutral (un-ionized) counterparts. The neutral species of ionic

organic chemicals are thought to behave in a similar manner as nonionic organic compounds

(e.g., partitioning to lipids and organic carbon as a function of hydrophobicity). However, the

ionized (cationic, anionic) species exhibit a considerably more complex behavior involving

multiple environmental partitioning mechanisms (e.g., ion exchange, electrostatic, and

hydrophobic interactions) and a dependency on pH and other factors including ionic strength and

ionic composition (Jafvert et al., 1990; Jafvert 1990; Schwarzenbach, et al., 1993). As a

consequence, methods to predict the environmental partitioning of organic cations and anions are

less developed and validated compared with methods for nonionic organic chemicals (Spacie,

1994; Suffet et al., 1994). 

Given the current limitations in the state of the science for predicting the partitioning and

bioaccumulation of the ionized species of ionic organic chemicals, procedures for deriving

national BAFs for these chemicals differ depending on the extent to which the fraction of the

total chemical is likely to be represented by the ionized (cationic, anionic) species in U.S.

surface waters. When a significant fraction of the total chemical concentration is expected to be

present as the ionized species in water, procedures for deriving the national BAF rely on

empirical (measured) methods (i.e., Procedures #5 and 6 in Section 5.6). When an insignificant

fraction of the total chemical is expected to be present as the ionized species (i.e., the chemical

exists essentially in the neutral form), procedures for deriving the national BAF will follow those

established for nonionic organic chemicals (e.g., Procedures #1 through #4 in Section 5.4). The

following guidelines apply for assessing the occurrence of cationic and anionic forms at typical

environmental pH ranges. 

1. For the ionic organic chemical of concern, the dissociation constant, pKa, should be

compared to the range of pH values expected in fresh and estuarine waters of the U.S. At

pH equal to the pKa, 50% of the organic acid or base is expected to be present in the

ionized species. The pH values for U.S. fresh and estuarine waters typically range

between 6 and 9, although somewhat higher and lower values can occur in some bodies

of water (e.g., acidic bogs and lakes, highly alkaline and eutrophic systems, etc.). 

2. For organic acids, the chemical will exist almost entirely in its un-ionized form when pH

is about 2 or more units below the pKa. For organic bases, the chemical will exist almost

entirely in its un-ionized form when pH is about 2 or more units above the pKa. In these

cases, the aqueous behavior of the chemical would be expected to be similar to nonionic

organic chemicals. Therefore, national BAF should usually be derived using Procedures

#1 through #4 in Section 5.4. 

3. When pH is greater than the pKa minus 2 for organic acids (or less than the pKa plus 2

for organic bases), the fraction of the total chemical that is expected to exist in its ionized

form can become significant (i.e., $1% in the ionized). In these cases, the national BAF

should usually be derived using Procedures #5 and #6 in Section 5.6. 
5-57

4. In general, most organic acids (e.g., pentachlorophenol and silvex), exist primarily in the

ionized form in ambient waters because their pKa’s (4.75 and 3.07, respectively) are

much smaller than the pH of the ambient waters. Conversely, most organic bases, (e.g.,

aniline) exist mostly in the un-ionized form in ambient waters because their pKa’s (4.63

for aniline) are much smaller than the pH of the ambient waters. 

5. The above guidelines are intended to be a general guide for deriving national BAFs for

ionic organic chemicals, not an inflexible rule. Modifications to these guidelines should

be considered on a case-by-case basis, particularly when such modifications are strongly

supported by measured bioaccumulation or bioconcentration data. For example, initial

models have been developed for predicting the solid and organic-phase partitioning of

certain organic acids (e.g., Jafvert 1990, Jafvert et al., 1990). As these or other models

become more fully developed and appropriately validated in the future, they should be

considered in the development of national BAFs. In addition, since pH is a controlling

factor for dissociation and subsequent partitioning of ionic organic chemicals,

consideration should be given to expressing BAFs or BCFs as a function of pH (or other

factors) where sufficient data exist to reliably establish such relationships. 
5-58

5.6 NATIONAL BIOACCUMULATION FACTORS FOR INORGANIC AND

ORGANOMETALLIC CHEMICALS

This section contains guidelines for deriving national BAFs for inorganic and

organometallic chemicals as defined in Section 5.3.5. The derivation of BAFs for inorganic and

organometallic chemicals differs in several ways from procedures for nonionic organic

chemicals. First, lipid normalization of chemical concentrations in tissues does not generally

apply for inorganic and organometallic chemicals. Thus, BAFs and BCFs cannot be extrapolated

from one tissue to another based on lipid-normalized concentrations as is done for nonionic

organic chemicals. Second, the bioavailability of inorganics and organometallics in water tends

to be chemical-specific and thus, the techniques for expressing concentrations of nonionic

organic chemicals based on the freely dissolved form do not generally apply. Third, at the

present time there are no generic bioaccumulation models that can be used to predict BAFs for

inorganic and organometallic chemicals as a whole, unlike the existence of Kow-based models for

nonionic organic chemicals. While some chemical-specific bioaccumulation models have been

developed for inorganic and organometallic chemicals (e.g., Mercury Cycling Model by Hudson

et. al, 1994), those models currently tend to require site-specific data for input to the model and

are restricted to site-specific applications. As the models become more fully developed and

validated in the future, they should be considered on a case-by-case basis in conjunction with the

following procedures for deriving national BAFs. 

5.6.1 Selecting the BAF Derivation Procedure

As shown in Figure 5-1, national BAFs can be derived using two procedures for

inorganic and organometallic chemicals (Procedures #5 and #6). The choice of the BAF

derivation procedure depends on whether or not the chemical undergoes biomagnification in

aquatic food webs. 

1. For many inorganic and organometallic chemicals, biomagnification does not occur and

the BCF will be equal to the BAF. For these types of chemicals, Procedure #5 should be

used to derive the national BAF. Procedure #5 considers BAFs and BCFs to be of equal

value in determining the national BAF and does not require the use of FCMs with BCF

measurements. Guidance for deriving BAFs using Procedure #5 is provided in Section

5.6.3. 

2. For some inorganic and organometallic chemicals (e.g., methylmercury),

biomagnification does occur and Procedure #6 should be used to determine the national

BAF. Procedure #6 gives general preference to the use of field-measured BAFs over

laboratory-measured BCFs and requires FCMs to be used with BCF measurements for

predicting BAFs. Guidance for deriving BAFs using Procedure #6 is provided in Section

5.6.4. 

3. Determining whether or not biomagnification occurs for inorganic and organometallic

chemicals requires chemical-specific data on measured concentrations of the chemical in

aquatic organisms and their prey. Concentrations in aquatic organisms that increase

substantially at successive trophic levels of a food web suggest that biomagnification is
5-59

occurring. Concentrations in aquatic organisms that remain about the same or decrease at

successive trophic levels of a food web suggest that biomagnification is not occurring. 

When comparing tissue concentrations for assessing biomagnification, care should be

taken to ensure that the aquatic organisms chosen actually represent functional predatorprey relationships and that all major prey species are considered in the comparisons.

5.6.2 Bioavailability

The chemical-specific nature of inorganic and organometallic bioavailability is likely due

in part to chemical-specific differences in several factors which affect bioavailability and

bioaccumulation. These factors include differences in the mechanisms for chemical uptake by

aquatic organisms (e.g., passive diffusion, facilitated transport, active transport), differences in

sorption affinities to biotic and abiotic ligands, and differences in chemical speciation in water. 

Some inorganic and organometallic chemicals exist in multiple forms and valence states in

aquatic ecosystems that can differ in their bioavailability to aquatic organisms and undergo

conversions between forms. For example, selenium can exist in various forms in aquatic

ecosystems, including inorganic selenite(+4) and selenate(+6) oxyanions, elemental selenium (0

)

under reducing conditions (primarily in sediments), and organoselenium compounds of selenide

(

-2). Dominant forms of mercury in natural, oxic waters include inorganic (+2) mercury

compounds and methylmercury; the latter is generally considered to be substantially more

bioavailable than inorganic mercury compounds to higher trophic level organisms. Although a

generic analogue to the “freely dissolved” conversion for nonionic organic chemicals does not

presently exist for inorganic and organometallic chemicals as a whole, the occurrence and

bioavailability of different forms of these chemicals should be carefully considered when

deriving national BAFs. 

1. If data indicate that: (1) a particular form (or multiple forms) of the chemical of concern

largely governs its bioavailability to target aquatic organisms, and (2) BAFs are more

reliable when derived using the bioavailable form(s) compared with using other form(s)

of the chemical of concern, then BAFs and BCFs should be based on the appropriate

bioavailable form(s). 

2. Because different forms of many inorganic and organometallic chemicals may

interconvert once released to the aquatic environment, regulatory and mass balance

considerations typically require an accounting of the total concentration in water. In

these cases, sufficient data should be available to enable conversion between total

concentrations and the other (presumably more bioavailable) forms in water.

5.6.3 Deriving BAFs Using Procedure #5 

This section contains guidance for calculating national BAFs for inorganic and

organometallic chemicals using Procedure #5 as shown in Figure 5-1. The types of inorganic

and organometallic chemicals for which Procedure #5 is appropriate are those that are not likely

to biomagnify in aquatic food webs (see Section 5.1 above). In Procedure #5, two methods are

available to derive the national BAF for a given trophic level:
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C using a BAF from an acceptable field study (i.e., field-measured BAF), or

C predicting a BAF from an acceptable laboratory-measured BCF.

Individual BAFs should be determined from field-measured BAFs or laboratory-measured BCFs

according to the following guidelines. 

5.6.3.1 Determining Field-Measured BAFs

1. Except where noted below, field-measured BAFs should be determined using the

guidance provided in Section 5.4.3.1(A) of Procedure #1. 

2. As described previously, conversion of field-measured BAFs to baseline BAFR

fds based on

lipid-normalized and freely-dissolved concentrations does not apply for inorganic and

organometallic chemicals. Therefore, the guidance and equations provided in Procedure

#1 which pertain to converting field-measured BAFs to baseline BAFR

fds and subsequently

to national BAFs do not generally apply to inorganic chemicals. As discussed in Section

5.6.2 above, an analogous procedure in concept might be required for converting total

BAFs to BAFs based on the most bioavailable form(s) for some inorganic and

organometallic chemicals of concern. Such procedures should be applied on a chemicalspecific basis.

3. BAFs should be expressed on a wet-weight basis; BAFs reported on a dry-weight basis

can be used only if they are converted to a wet-weight basis using a conversion factor

that is measured or reliably estimated for the tissue used in the determination of the BAF. 

4. BAFs should be based on concentrations in the edible tissue(s) of the biota unless it is

demonstrated that whole-body BAFs are similar to edible tissue BAFs. For some finfish

and shellfish species, whole body is considered to be the edible tissue.

5. The concentrations of an inorganic or organometallic chemical in a bioaccumulation

study should be greater than normal background levels and greater than levels required

for normal nutrition of the test species if the chemical is a micronutrient, but below levels

that adversely affect the species. Bioaccumulation of an inorganic or organometallic

chemical that is essential to the nutrition of aquatic organisms might be overestimated if

concentrations are at or below normal background levels due to selective accumulation

by the organisms to meet their nutritional requirements. 
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5.6.3.2 Determining Laboratory-Measured BCFs

1. Except where noted below, BAFs should be predicted from laboratory-measured BCFs

using the guidance provided in Section 5.4.3.1(c) of Procedure #1. 

2. As described previously, conversion of laboratory-measured BCFs to baseline BCFR

fds

based on lipid-normalized and freely dissolved concentrations does not apply for

inorganic and organometallic chemicals. Therefore, the guidance and equations provided 

in Procedure #1 which pertain to converting laboratory-measured BCFs to baseline

BCFR

fds and subsequently to national BCFs do not generally apply to inorganic and

organometallic chemicals. As discussed in Section 5.6.2 above, an analogous procedure

in concept might be required for converting total BCFs to BCFs based on the most

bioavailable form(s) of some inorganic and organometallic chemicals of concern. Such

procedures should be applied on a chemical-specific basis. In addition, the use of FCMs

with BCFs does not apply to chemicals applicable to Procedure #5. 

3. BCFs should be expressed on a wet-weight basis; BCFs reported on a dry-weight basis

can be used only if they are converted to a wet-weight basis using a conversion factor

that is measured or reliably estimated for the tissue used in the determination of the BCF. 

4. BCFs should be based on concentrations in the edible tissue(s) of the biota unless it is

demonstrated that whole-body BCFs are similar to edible tissue BCFs. For some finfish

and shellfish species, whole body is considered to be the edible tissue.

5. The concentrations of an inorganic or organometallic chemical in a bioconcentration test

should be greater than normal background levels and greater than levels required for

normal nutrition of the test species if the chemical is a micronutrient, but below levels

that adversely affect the species. Bioaccumulation of an inorganic or organometallic

chemical that is essential to the nutrition of aquatic organisms might be overestimated if

concentrations are at or below normal background levels due to selective accumulation

by the organisms to meet their nutritional requirements. 

5.6.3.3 Determining the National BAFs

After calculating individual BAFs using as many of the methods in Procedure #5 as

possible, the next step is to determine national BAFs for each trophic level from the individual

BAFs. The national BAFs will be used to determine the national 304(a) criteria. The national

BAFs should be determined from the individual BAFs by considering the data preference

hierarchy defined for Procedure #5 and uncertainty in the data. The data preference hierarchy

for Procedure #5 is:

1. a BAF from an acceptable field-measured BAF or predicted from an acceptable

laboratory-measured BCF.

Since bioaccumulation via dietary uptake and subsequent biomagnification are not of

concern for chemicals subject to Procedure #5, field-measured BAFs and laboratory-measured
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BCFs are considered equally in determining the national BAFs. The national BAFs should be

selected for each trophic level using the following steps and guidelines.

1. Calculate Species-Mean BAFs. For each BAF method where more than one acceptable

field-measured BAF (or a BAF predicted from a BCF) is available for a given species,

calculate the species-mean BAF as the geometric mean of all acceptable individual

measured or BCF-predicted BAFs. When calculating species-mean BAFs, individual

measured or BCF-predicted BAFs should be reviewed carefully to assess uncertainties in

the BAF values. Highly uncertain BAFs should not be used. Large differences in

individual BAFs for a given species (e.g., greater than a factor of 10) should be

investigated further and in such cases, some or all of the BAFs for a given species might

not be used. Additional discussion on evaluating the acceptability of BAF and BCF

values is provided in the Bioaccumulation TSD.

2. Calculate Trophic-Level-Mean BAFs. For each BAF method where more than one

acceptable species-mean BAF is available within a given trophic level, calculate the

trophic-level-mean BAF as the geometric mean of acceptable species-mean BAFs in that

trophic level. Trophic-level-mean BAFs should be calculated for trophic levels two,

three and four because available data on U.S. consumers of fish and shellfish indicate

significant consumption of organisms in these trophic levels.

3. Select a Final National BAF for Each Trophic Level. For each trophic level, select the

final national BAF using best professional judgment by considering: (1) the data

preference hierarchy in Procedure #5, and (2) the relative uncertainties among trophic

level-mean BAFs derived using different methods.

a. As discussed above, field-measured BAFs and laboratory-measured BCFs are

considered equally desirable for deriving a final national BAF using Procedure

#5. If a trophic-level-mean BAF is available from both a field-measured BAF

and a laboratory-measured BCF, the final national BAF should be selected using

the trophic-level-mean BAF with the least overall uncertainty.

b. The above steps should be performed for each trophic level until a national BAF

is selected for trophic levels two, three, and four.

5.6.4 Deriving BAFs Using Procedure #6 

This section contains guidance for calculating national BAFs for inorganic and

organometallic chemicals using Procedure #6 as shown in Figure 5-1. The types of inorganic

and organometallic chemicals for which Procedure #6 is appropriate are those that are

considered likely to biomagnify in aquatic food webs (see Section 5.6.1 above). Methylmercury

is an example of an organometallic chemical to which Procedure #6 applies. In Procedure #6,

two methods are available to derive the national BAF:

C using a BAF from an acceptable field study (i.e., field-measured BAF), or
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C predicting a BAF from an acceptable laboratory-measured BCF and a FCM.

Individual BAFs should be determined from field-measured BAFs or laboratory-measured BCFs

and FCMs according to the following guidelines. 

5.6.4.1 Determining Field-Measured BAFs

1. Field-measured BAFs should be determined using the guidance provided in Section

5.6.3.1 of Procedure #5. 

5.6.4.2 Determining Laboratory-Measured BCFs

1. Except where noted below, BAFs should be predicted from laboratory-measured BCFs

using the guidance provided in Section 5.6.3.2 of Procedure #5. 

2. Because biomagnification is of concern for chemicals applicable to Procedure #6, BAFs

should be predicted from laboratory-measured BCF using FCMs. Currently, there are no

generic models from which to predict FCMs for inorganic or organometallic chemicals. 

Therefore, FCMs should be determined using field data as described in the section

entitled: “Field-Derived FCMs” in Section 5.4.3.1(c) of Procedure #1. Unlike nonionic

organic chemicals, field-derived FCMs for inorganic and organometallic chemicals are

not based on lipid-normalized concentrations in tissues. For calculating FCMs for

inorganic and organometallic chemicals, concentrations in tissues should be based on the

consistent use of either wet-weight or dry-weight concentrations in edible tissues. FCMs

should be derived for trophic levels two, three, and four.

5.6.4.3 Determining the National BAF

After calculating individual BAFs using as many of the methods in Procedure #6 as

possible, the next step is to determine national BAFs for each trophic level from the individual

BAFs. The national BAFs will be used to determine the national 304(a) criteria. The national

BAFs should be determined from the individual BAFs by considering the data preference

hierarchy defined for Procedure #6 and uncertainty in the data. The data preference hierarchy

for Procedure #6 is (in order of preference): 

1. a BAF from an acceptable field-measured BAF, or 

2. a predicted BAF from an acceptable laboratory-measured BCF and FCM.

This data preference hierarchy reflects EPA’s preference for field-measured BAFs over

BAFs predicted from a laboratory-measured BCF and FCM, because field-measured BAFs are

direct measures of bioaccumulation and biomagnification in aquatic food webs. BAFs predicted

from laboratory-measured BCFs and FCMs indirectly account for biomagnification through the

use of the FCM. For each trophic level, the national BAFs should be determined using the

following steps and guidelines.
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1. Calculate Species-Mean BAFs. For each BAF method where more than one acceptable

field-measured BAF or BAF predicted using a BCF and FCM is available, calculate a

species-mean BAF according to the guidance described previously in Procedure #5.

2. Calculate Trophic Level-Mean BAFs. For each BAF method where more than one

acceptable species-mean BAF is available within a given trophic level, calculate the

trophic level-mean BAF according to guidance described previously in Procedure #5.

3. Select a Final National BAF for Each Trophic Level. For each trophic level, select the

final national BAF using best professional judgment by considering: (1) the data

preference hierarchy in Procedure #6, and (2) the relative uncertainties among trophic

level-mean BAFs derived using different methods.

a. When a trophic-level mean BAF is available using both methods for a given

trophic level (i.e., a field-measured BAF and a BAF predicted from a BCF and

FCM), the national BAF should usually be selected using the field-measured BAF

which is the preferred BAF method in the data preference hierarchy in Procedure

#6.

b. If uncertainty in the trophic-level mean BAF derived using field-measured BAFs

is considered to be substantially greater than a trophic-level mean BAF derived

using a BCF and FCM, the national BAF for that trophic level should be selected

from the second tier (BCF @ FCM) method.

c. The above steps should be performed for each trophic level until a national BAF

is selected for trophic levels two, three, and four.

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