United States

Environmental Protection

Agency

Air

EPA-452/R-97-006

December 1997

Mercury Study

Report to Congress

c7o032-1-1

Office of Air Quality Planning & Standards

and

Office of Research and Development

Volume IV:

An Assessment of Exposure

to Mercury in the United States
MERCURY STUDY REPORT TO CONGRESS

VOLUME IV:

AN ASSESSMENT OF EXPOSURE TO MERCURY

IN THE UNITED STATES

December 1997

Office of Air Quality Planning and Standards

and

Office of Research and Development

U.S. Environmental Protection Agency
i

TABLE OF CONTENTS

Page

U.S. EPA AUTHORS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv

SCIENTIFIC PEER REVIEWERS ...................................................... v

WORK GROUP AND U.S. EPA/ORD REVIEWERS ...................................... viii

LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv

LIST OF SYMBOLS, UNITS AND ACRONYMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv

EXECUTIVE SUMMARY ......................................................... ES-1

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

2. APPROACH TO EXPOSURE ASSESSMENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1

2.1 Modeling Exposures near Mercury Emissions Sources . . . . . . . . . . . . . . . . . . . . . . . . 2-1

2.1.1 Description of Computer Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1

2.1.2 Estimates of Background Mercury . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-3

2.2 Description of Hypothetical Exposure Scenarios for Humans . . . . . . . . . . . . . . . . . . . 2-4

2.2.1 Hypothetical Location Descriptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4

2.2.2 Description of Hypothetical Human Exposure Scenarios . . . . . . . . . . . . . . . . 2-5

2.3 Summary of Exposure Parameter Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-9

2.4 Emissions Sources .................................................... 2-12

2.5 Predicted Concentrations in Environmental Media ........................... 2-12

3. PREDICTED INDIVIDUAL EXPOSURE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1

3.1 Illustration of Exposure Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1

3.1.1 Concentrations in Environmental Media and Biota . . . . . . . . . . . . . . . . . . . . . 3-2

3.1.2. Results for Hypothetical Exposure Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . 3-6

3.2 Results of Combining Local and Regional Models - Predicted Human Exposure . . . 3-12

3.2.1 Inhalation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-12

3.2.2 Agricultural Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-12

3.2.3 Urban Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-37

3.2.4 Fish Ingestion Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-37

3.3 Issues Related to Predicted Mercury Exposure Estimates . . . . . . . . . . . . . . . . . . . . . . 3-38

3.4 Summary Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-39

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

4.1 Fish Consumption among the General U.S. Population . . . . . . . . . . . . . . . . . . . . . . . . 4-1

4.1.1 Patterns of Fish Consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1

4.1.2 Frequency of Consumption of Fish Based on Surveys of Individuals . . . . . 4-13

4.1.3 Subpopulations with Potentially Higher Consumption Rates . . . . . . . . . . . . 4-22

4.1.4 Summary of Hawaiian Island Fish Consumption Data . . . . . . . . . . . . . . . . . 4-42

4.1.5 Summary of Alaskan Fish Consumption Data . . . . . . . . . . . . . . . . . . . . . . . . 4-43

4.1.6 Summary of Canadian Data on Mercury Intake from Fish and Marine 

 Mammals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-48

4.2 Trends in Fish and Shellfish Consumption in the United States . . . . . . . . . . . . . . . . . 4-50

4.2.1 Fish and Shellfish Consumption: United States, 1975 to 1995 . . . . . . . . . . . 4-50
TABLE OF CONTENTS (continued)

Page

ii

4.2.2 Current Market Trends, 1996 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-53

4.2.3 Patterns in Fish and Shellfish Consumption: United States, 1996 . . . . . . . . . 4-53

4.2.4 Production Patterns and Mercury Concentrations for Specific Fish and Shellfish

Species . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-57

4.3 Mercury Concentrations In Fish . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-59

4.3.1 National Marine Fisheries Service Data Base . . . . . . . . . . . . . . . . . . . . . . . . 4-59

4.3.2 Mercury Concentrations in Marine Fish . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-66

4.3.3 Freshwater Fish Mercury Data Base . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-70

4.3.4 Mercury Concentrations In Freshwater Fish . . . . . . . . . . . . . . . . . . . . . . . . . 4-70

4.3.5 Calculation of Mercury Concentrations in Fish Dishes . . . . . . . . . . . . . . . . . 4-73

4.4 Intake of Methylmercury from Fish/fish Dishes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-75

4.4.1 Intakes "per User" and "per Capita" . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-75

4.4.2 Methylmercury Intake from Fish and Shellfish among Women of Child-bearing

Age and Children . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-78

4.4.3 Month-Long Estimates for Consumers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-82

4.4.4 Habitat of Fish Consumed and Mercury Exposure from Fish of Marine,

Estuarine and Freshwater Origin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-86

4.4.5 Methylmercury Consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-87

4.5 Conclusions on Methylmercury Intake from Fish . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-87

5. POPULATION EXPOSURES - NON-DIETARY SOURCES . . . . . . . . . . . . . . . . . . . . . . . . . 5-1

5.1 Dental Amalgams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1

5.2 Occupational Exposures to Mercury . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1

5.3 Miscellaneous Sources of Mercury Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-3

5.4 Cases of Mercury Poisoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-3

6. COMPARISON OF ESTIMATED EXPOSURE WITH BIOMONITORING . . . . . . . . . . . . . 6-1

6.1 Biomarkers of Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-1

6.2 Biomarkers of Exposure Predictive of Intake of Methylmercury . . . . . . . . . . . . . . . . . 6-1

6.3 Sample Handling and Analysis of Blood Samples for Mercury . . . . . . . . . . . . . . . . . . 6-2

6.4 Association of Blood Mercury with Fish Consumption . . . . . . . . . . . . . . . . . . . . . . . . 6-2

6.4.1 Half-Times of Methylmercury in Blood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-2

6.4.2 Fraction of Total Blood Mercury that Is Organic or Methylmercury . . . . . . . 6-3

6.4.3 Methylmercury Consumption from Fish and Blood Mercury Values . . . . . . . 6-3

6.4.4 North American Reports on Blood Mercury Concentrations . . . . . . . . . . . . . . 6-4

6.5 Hair Mercury as a Biomarker of Methylmercury Exposure . . . . . . . . . . . . . . . . . . . . . 6-8

6.5.1 Hair Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-8

6.5.2 Hair Mercury Concentrations in North America . . . . . . . . . . . . . . . . . . . . . . 6-10

6.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-15

6.6.1 Blood Mercury Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-15

6.6.2 Hair Mercury Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-15

7. CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-1

8. RESEARCH NEEDS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-1
TABLE OF CONTENTS (continued)

Page

iii

9. REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-1

APPENDIX A EXPOSURE PARAMETER JUSTIFICATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1

APPENDIX B ESTIMATED NATIONAL AND REGIONAL POPULATIONS OF 

WOMEN OF CHILD-BEARING AGE: UNITED STATES, 1990 . . . . . . . . . . . . . B-1

APPENDIX C ANALYSIS OF MERCURY LEVELS IN FISH AND SHELLFISH

REPORTED IN NATIONAL MARINE FISHERIES SERVICE SURVEY

OF TRACE ELEMENTS IN THE FISHERY RESERVE . . . . . . . . . . . . . . . . . . . . . C-1

APPENDIX D HUMAN FISH CONSUMPTION AND MERCURY 

INGESTION DISTRIBUTIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-1
iv

U.S. EPA AUTHORS

Principal Authors

Kathryn R. Mahaffey, Ph.D. Glenn E. Rice

National Center for Environmental National Center for Environmental

 Assessment - Washington Assessment - Cincinnati

Office of Research and Development Office of Research and Development

Washington, DC Cincinnati, Ohio

Contributing Author

Jeff Swartout

National Center for Environmental 

 Assessment - Cincinnati

Office of Research and Development

Cincinnati, Ohio
v

SCIENTIFIC PEER REVIEWERS

Dr. William J. Adams* Dr. Joan Daisey*

Kennecott Utah Corporation Lawrence Berkeley National Laboratory

Dr. Brian J. Allee Dr. John A. Dellinger*

Harza Northwest, Incorporated Medical College of Wisconsin

Dr. Thomas D. Atkeson Dr. Kim N. Dietrich*

Florida Department of Environmental University of Cincinnati

Protection

Dr. Donald G. Barnes* Great Lakes Natural Resource Center

U.S. EPA Science Advisory Board National Wildlife Federation for the

Dr. Steven M. Bartell

SENES Oak Ridge, Inc. Dr. Lawrence J. Fischer*

Dr. David Bellinger*

Children’s Hospital, Boston Dr. William F. Fitzgerald

Dr. Nicolas Bloom* Avery Point

Frontier Geosciences, Inc.

Dr. Mike Bolger U.S. EPA Science Advisory Board

U.S. Food and Drug Administration

Dr. Dallas Burtraw* National Center for Health Statistics

Resources for the Future

Dr. Thomas Burbacher* University of Maryland at Baltimore

University of Washington

Seattle Dr. Steven G. Gilbert*

Dr. James P. Butler

University of Chicago Dr. Cynthia C. Gilmour*

Argonne National Laboratory The Academy of Natural Sciences

Dr. Rick Canady Dr. Robert Goyer

Agency for Toxic Substances and Disease National Institute of Environmental Health

Registry Sciences

Dr. Rufus Chaney Dr. George Gray

U.S. Department of Agriculture Harvard School of Public Health

Dr. Tim Eder

States of Michigan and Ohio

Michigan State University

University of Connecticut

A. Robert Flaak*

Dr. Katharine Flegal

Dr. Bruce A. Fowler*

Biosupport, Inc.
SCIENTIFIC PEER REVIEWERS (continued)

vi

Dr. Terry Haines Berkeley

National Biological Service

Dr. Gary Heinz* National Oceanic and Atmospheric

Patuxent Wildlife Research Center Administration

Joann L. Held

New Jersey Department of Environmental Dr. Michael W. Meyer*

Protection & Energy Wisconsin Department of Natural Resources

Dr. Robert E. Hueter* Dr. Maria Morandi*

Mote Marine Laboratory University of Texas Science Center at Houston

Dr. Harold E. B. Humphrey* Dr. Paul Mushak

Michigan Department of Community Health PB Associates

Dr. James P. Hurley* Dr. Christopher Newland*

University of Wisconsin Auburn University

Madison

Dr. Joseph L. Jacobson* The University of Michigan

Wayne State University Ann Arbor

Dr. Gerald J. Keeler Dr. W. Steven Otwell*

University of Michigan University of Florida

Ann Arbor Gainesville

Dr. Ronald J. Kendall* Dr. Jozef M. Pacyna

Clemson University Norwegian Institute for Air Research

Dr. Lynda P. Knobeloch* Dr. Ruth Patterson

Wisconsin Division of Health Cancer Prevention Research Program

Dr. Leonard Levin

Electric Power Research Institute Dr. Donald Porcella

Dr. Steven E. Lindberg*

Oak Ridge National Laboratory Dr. Deborah C. Rice*

Dr. Genevieve M. Matanoski*

The Johns Hopkins University Samuel R. Rondberg*

Dr. Margaret McDowell

National Center for Health Statistics Charles Schmidt

Dr. Thomas McKone*

University of California Dr. Pamela Shubat

Dr. Malcolm Meaburn

U.S. Department of Commerce

Dr. Jerome O. Nriagu*

Fred Gutchinson Cancer Research Center

Electric Power Research Institute

Toxicology Research Center

U.S. EPA Science Advisory Board

U.S. Department of Energy
SCIENTIFIC PEER REVIEWERS (continued)

vii

Minnesota Department of Health Dr. Valerie Thomas*

Dr. Ellen K. Silbergeld*

University of Maryland Dr. M. Anthony Verity

Baltimore University of California

Dr. Howard A. Simonin*

NYSDEC Aquatic Toxicant Research Unit

Dr. Ann Spacie*

Purdue University

Dr. Alan H. Stern

New Jersey Department of Environmental

Protection & Energy 

Dr. David G. Strimaitis*

Earth Tech

Dr. Edward B. Swain

Minnesota Pollution Control Agency

Princeton University

Los Angeles

*With EPA’s Science Advisory Board, Mercury Review Subcommitte
viii

WORK GROUP AND U.S. EPA/ORD REVIEWERS

Core Work Group Reviewers: U.S. EPA/ORD Reviewers:

Dan Axelrad, U.S. EPA Robert Beliles, Ph.D., D.A.B.T.

Office of Policy, Planning and Evaluation National Center for Environmental Assessment

Angela Bandemehr, U.S. EPA

Region 5 Eletha Brady-Roberts

Jim Darr, U.S. EPA Cincinnati, OH

Office of Pollution Prevention and Toxic

Substances Annie M. Jarabek

Thomas Gentile, State of New York Research Triangle Park, NC

Department of Environmental Conservation

Arnie Kuzmack, U.S. EPA National Center for Environmental Assessment

Office of Water Washington, DC

David Layland, U.S. EPA Susan Braen Norton

Office of Solid Waste and Emergency Response National Center for Environmental Assessment

Karen Levy, U.S. EPA

Office of Policy Analysis and Review Terry Harvey, D.V.M.

Steve Levy, U.S. EPA Cincinnati, OH

Office of Solid Waste and Emergency Response

Lorraine Randecker, U.S. EPA

Office of Pollution Prevention and Toxic

Substances

Joy Taylor, State of Michigan

Department of Natural Resources

Washington, DC

National Center for Environmental Assessment

National Center for Environmental Assessment

Matthew Lorber

Washington, DC

National Center for Environmental Assessment
ix

LIST OF TABLES

Page

2-1 Models Used to Predict Mercury Air Concentrations, Deposition Fluxes and Environmental

Concentrations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-2

2-2 Percentiles of the Methylmercury Bioaccumulation Factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-3

2-3 Inputs to IEM-2M Model for the Two Time Periods Modeled . . . . . . . . . . . . . . . . . . . . . . . . . 2-4

2-4 Summary of Human Exposure Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-6

2-5 Fish Consumption Rates for Columbia River Tribes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-8

2-6 Daily Fish Consumption Rates Among Adults Fish Consumption by Columbia River Tribes . 2-8

2-7 Fish Consumption Rates used in this Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-9

2-8 Potential Dependency of Exposure Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-9

2-9 Default Values of Scenario-Independent Exposure Parameters . . . . . . . . . . . . . . . . . . . . . . . . 2-10

2-10 Values for Scenario-Dependent Exposure Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-11

2-11 Process Parameters for the Model Plants Considered in the Local Impact Analysis . . . . . . . . 2-13

2-12 Predicted Mercury Values for Environmental Media at Eastern Site

 (Local + RELMAP 50th) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-15

2-13 Predicted Mercury Values for Environmental Media at Eastern Site

 (Local + RELMAP 90th) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-16

2-14 Predicted Mercury Values in Water Column and Biota for Eastern Site

 (Local + RELMAP 50th) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-17

2-15 Predicted Mercury Values in Water Column and Biota for Eastern Site

 (Local + RELMAP 90th) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-18

2-16 Predicted Mercury Values for Environmental Media at Western Site

 (Local + RELMAP 50th) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-19

2-17 Predicted Mercury Values for Environmental Media at Western Site

 (Local + RELMAP 90th) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-21

2-18 Predicted Mercury Values in Water Column and Biota for Western Site

 (Local + RELMAP 50th) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-22

2-19 Predicted Mercury Values in Water Column and Biota for Western Site

(Local + RELMAP 90th) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-23

3-1 Predicted Mercury Concentrations after Pre-facility Simulations Performed for Eastern Site . 3-2

3-2 Modeled results for Large Hospital HMI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-2

3-3 Predicted Mercury Exposure for Subsistence Farmer Scenario . . . . . . . . . . . . . . . . . . . . . . . . . 3-8

3-4 Predicted Mercury Exposure for Rural Home Gardener . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-9

3-5 Predicted Mercury Exposure for Urban Average Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-10

3-6 Predicted Mercury Exposure for Urban High-end Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . 3-10

3-7 Predicted Mercury Exposure for High-end Fish Consumption Scenario . . . . . . . . . . . . . . . . . 3-11

3-8 Predicted Mercury Exposure for Recreational Angler Scenario . . . . . . . . . . . . . . . . . . . . . . . 3-12

3-9 Eastern Site — RELMAP 50th and 90th Percentiles Predicted Ingestion (mg/kg/day) for

Subsistence Farmer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-13

3-10 Eastern Site — RELMAP 50th and 90th Percentiles Predicted Ingestion (mg/kg/day) for Rural

Home Gardner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-15

3-11 Eastern Site — RELMAP 50th and 90th Percentiles Predicted Ingestion (mg/kg/day) for Urban

Average . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-17

3-12 Eastern Site — RELMAP 50th and 90th Percentiles Predicted Ingestion (mg/kg/day) for Urban

High End . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-19

3-13 Eastern Site — RELMAP 50th and 90th Percentiles Predicted Ingestion (mg/kg/day) for

Subsistence Fisher . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-21
LIST OF TABLES (continued)

Page

x

3-14 Eastern Site — RELMAP 50th and 90th Percentiles Predicted Ingestion (mg/kg/day) for

Recreational Angler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-23

3-15 Western Site — RELMAP 50th and 90th Percentiles Predicted Ingestion (mg/kg/day) for

Subsistence Farmer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-24

3-16 Western Site — RELMAP 50th and 90th Percentiles Predicted Ingestion (mg/kg/day) for Rural

Home Gardner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-26

3-17 Western Site — RELMAP 50th and 90th Percentiles Predicted Ingestion (mg/kg/day) for Urban

Average . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-28

3-18 Western Site — RELMAP 50th and 90th Percentiles Predicted Ingestion (mg/kg/day) for Urban

High End . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-30

3-19 Western Site — RELMAP 50th and 90th Percentiles Predicted Ingestion (mg/kg/day) for

Subsistence Fisher . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-32

3-20 Western Site — RELMAP 50th and 90th Percentiles Predicted Ingestion (mg/kg/day) for

Recreational Angler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-34

3-21 Eastern Site — RELMAP 50th and 90th Percentiles Predicted Inhalation . . . . . . . . . . . . . . . 3-35

3-22 Western Site — RELMAP 50th and 90th Percentiles Predicted Inhalation . . . . . . . . . . . . . . 3-36

4-1 Average Serving Size (gms) for Seafood from USDA Handbook # 11 Used to Calculate

Fish Intake by FDA (1978) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-5

4-2 Fish Species and Number of Persons Using the Species of Fish.

(Adapted from Rupp et al., 1980) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-6

4-3 Fish Consumption from the NPD 1973-1974 Survey

(Modified from Rupp et al., 1980) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-6

4-4 Distribution of Fish Consumption for Females by Age*

Consumption Category (gms/day) (from SRI, 1980) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-7

4-5 CSFII 89-91 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-8

4-6 CSFII 1994 Data — Days 1 and 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-9

4-7 CSFII 1995 Data — Days 1 and 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-10

4-8 Fish Consumption (gms) by Season for Respondents Reporting Seafood Consumption

CFSII 1994 — Day 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-10

4-9 All Age Groups NHANES III . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-12

4-10 NHANES III Adult Respondents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-12

4-11 NHANES III Child Respondents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-13

4-12 Consumption of Fish and Shellfish (gms/day), and Self-Reported Body Weight (kg)

in Respondents of the 1989-1991 CSFII Survey. "Per Capita" 

Data for All Survey Respondents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-15

4-13 Consumption of Fish and Shellfish (gms/day), and

Self-Reported Body Weight (kg) in Respondents of the 1989-1991 CSFII Survey . . . . . . . . 4-15

4-14 Frequency of Fish/Shellfish Ingestion and Percent of Respondents . . . . . . . . . . . . . . . . . . . . 4-17

4-15a Frequency of Fish and Shellfish Consumption by Percent among 

All Adults, Both Genders, Weighted Data, NHANES III . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-18

4-15b Frequency of Fish and Shellfish Consumption by Race/Ethnicity, 

Women Aged 15-45 Years, Weighted Data, NHANES III . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-18

4-16a Distribution of the Frequency of Fish and Shellfish Consumption by Race/Ethnicity

All Adults, Both Genders, Weighted Data, NHANES III . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-19

4-16b Distribution of the Frequency of Fish and Shellfish Consumption by Race/Ethnicity

Among Adult Women Ages 15-45 Years, Weighted Data, NHANES III . . . . . . . . . . . . . . . . 4-19
LIST OF TABLES (continued)

Page

xi

4-17 Classification of Fish Species by Habitat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-20

4-18 Weighted Estimates of Fish and Shellfish Consumed (gms) for Females and Males Aged 15-44

Years Reported in NHANES III (Per User) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-21

4-19 Weighted Estimates for Fish and Shellfish Consumed (gms) by Female and Male Respondents

Aged 15 — 44 Years Reported in the NHANES III Survey by

Habitat of Species Consumed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-21

4-20 Consumption of Fish and Shellfish (gms/day) among Ethnically Diverse Groups . . . . . . . . . 4-24

4-21 Fish Consumption of an Urban “Subsistent” Group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-26

4-22 High Fish Consumption among Urban Subjects: Case Report . . . . . . . . . . . . . . . . . . . . . . . . . 4-26

4-23 Compilation of the Angler Consumption Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-28

4-24 Median Recreationally Caught Fish Consumption Rate Estimates

by Ethnic Group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-31

4-25 Freshwater Fish Consumption Estimates of Turcotte (1983) . . . . . . . . . . . . . . . . . . . . . . . . . . 4-31

4-26 Daily Intake of Sportfish and Total Fish for the Fish-consuming Portion

of the Population Studied by Fiore et al. (1989) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-32

4-27 Fish Consumption Rate Data for Groups Identified in

Hovinga et al. (1992) as Eaters and Controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-33

4-28 Fish Consumption Rates for Maine Anglers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-34

4-29 Fish Consumption Rates of Florida Anglers Who Receive Food Stamps . . . . . . . . . . . . . . . . 4-34

4-30 Fish Consumption by Native U.S. Populations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-35

4-31 Fish Consumption by Columbia River Tribes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-39

4-32 Daily Fish Consumption Rates by Adults of Columbia River Tribes . . . . . . . . . . . . . . . . . . . 4-39

4-33 Fish Consumption (gms/kg bw/day) by the Tulalip and Squaxin Island Tribes . . . . . . . . . . . 4-40

4-34 Local Fish Meals Consumed By Time Period for the

Mohawk and Comparison Nursing Mothers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-41

4-35 Species Composition of Hawaii's Retail Seafood Trade — 1981 Purchases . . . . . . . . . . . . . . 4-43

4-36 Mean Per Capita Harvest of Fish and Marine Mammals (g/day) . . . . . . . . . . . . . . . . . . . . . . . 4-46

4-37 Estimated Daily Intake of Food and Mercury for Arctic Inuit . . . . . . . . . . . . . . . . . . . . . . . . . 4-47

4-38 Mercury Concentrations (μg Hg/g wet weight) in Traditional Foods Consumed

by Canadian Aboriginal Peoples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-49

4-39 Estimated Daily Intake of Mercury Using Contaminant Data Base and Dietary Information from

Dene and Inuit Communities in Canada . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-49

4-40 Percent of Fish/Shellfish by Processing Type between 1910 and 1995 . . . . . . . . . . . . . . . . . . 4-50

4-41 U.S. Supply of Edible Commercial Fishery Products: 1990 and 1995 . . . . . . . . . . . . . . . . . . 4-51

4-42 U.S. Annual Per Capita Consumption of Canned Fishery Products: 1990 and 1995

(Pounds Per Capita) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-52

4-43 U.S. Annual Per Capita Consumption (in pounds*) 

of Certain Fishery Items: 1990 and 1995 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-52

4-44 Ten Most Commonly Reported Fish/Shellfish/Mixed Dishes by Season

CSFII 1994 and CSFII 1995 — Day 1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-54

4-45 Regional Popularity of Fish and Shellfish Species . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-56

4-46 Popularity of Fish/Shellfish Species in Restaurants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-56

4-47 Frequencies of Various Fish and Shellfish Food Types

for Children Ages 1 to 5 and 6 to 11 Years by Gender . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-57

4-48 Summary of Mercury Concentrations in Fish Species

(μg Hg/g fresh weight) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-60

4-49 Mercury Concentrations in Marine Finfish . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-67
LIST OF TABLES (continued)

Page

xii

4-50 Mercury Concentrations in Marine Shellfish . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-68

4-51 Mercury Concentrations in Marine Molluscan Cephalopods . . . . . . . . . . . . . . . . . . . . . . . . . . 4-69

4-52 Analyses of Mercury Standard Reference Materials Used by Lowe et al. (1985)

 in Support of Analyses of Freshwater Fish . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-69

4-53 Freshwater Fish Mercury Concentrations from Lowe et al., (1985) . . . . . . . . . . . . . . . . . . . . 4-71

4-54 Mercury Concentrations in Freshwater Fish

U.S. EPA (1992) and Bahnick et al. (1994) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-73

4-55 CSFII 89-91 Number of Respondents - All Age Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-75

4-56 CSFII 89-91 Adult Respondents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-76

4-57 Contemporary Dietary Surveys — 1990s General U.S. Population . . . . . . . . . . . . . . . . . . . . . 4-76

4-58 Per Capita Fish/Shellfish Consumption (gms/day) and 

Mercury Exposure (μg/kg body weight/day) From CSFII 89-91

Based on Average of Three 24-Hour Recalls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-77

4-59 Per Capita Fish/Shellfish Consumption Based on Individual Days of 24-Hour Recall Data

General U.S. Population Surveys — 1990s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-77

4-60 Per User Fish/Shellfish Consumption (grams per day) and Mercury Exposure (μg/kg bw/day)

Based on Average of Three 24-Hour Recalls — CSFII 89-91 . . . . . . . . . . . . . . . . . . . . . . . . . 4-78

4-61 “Per User” Intake of Fish and Shellfish (gms/day) and Exposure to Mercury (μg Hg/kg bw/day)

among Individuals Reporting Consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-78

4-62 “Per Capita” Fish/Shellfish Consumption (grams/day) and Mercury Exposure (μg/kg bw/day)

Based on Average of Three 24-Hour Dietary Recalls — CSFII 89-91 . . . . . . . . . . . . . . . . . . 4-79

4-63 “Per User” Fish/Shellfish Consumption (grams/day) and Mercury Exposure (μg/kg bw/day) 

Based on Average of Three 24-Hour Dietary Recalls — CSFII 89-91 . . . . . . . . . . . . . . . . . . 4-79

4-64 Consumption of Fish and Shellfish (grams/day) and Mercury Exposure (μg Hg/kg bw/day) 

among Different Age Categories of Children . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-80

4-65 Fish and Shellfish Consumption (grams/day) and Mercury Exposure (μg/kg body weight/day)

for Children Aged 14 years and Younger — CSFII 89-91 . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-80

4-66 “Per User” Fish and/or Shellfish Consumption (grams/day) and

Mercury Exposure (μg Hg/kg bw/day) by Children ages 14 and Younger . . . . . . . . . . . . . . . 4-81

4-67 Consumption of Fish and Shellfish (grams/day) and Mercury Exposure (μg Hg/kg bw/day) 

Among Ethnically Diverse Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-81

4-68a Month-Long Estimates of Fish and Shellfish Consumption (gms/day)

General Population by Ethnic/Racial Group

National Estimates Based on NHANES III Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-83

4-68b Month-Long Estimates of Mercury Exposure (μg/kgbw/day)

Population by Ethnic/Racial Group National Estimates Based on NHANES III Data . . . . . . 4-83

4-69 Month-Long Estimates of Exposure to Fish and Shellfish (gms/day)

for Women Ages 15 through 44 Years Combined 

Distributions Based on NHANES III Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-84

4-70 Month-Long Estimates of Mercury Exposure (μg/kgbw/day) for Women Ages 15 through 44

All Subpopulations Combined National Estimates Based on NHANES III Data . . . . . . . . . . 4-84

4-71 Month-Long Estimates of Fish/Shellfish Consumption (gms/day) 

among Children Ages 3 through 6 Years. 

National Estimates Based on NHANES III Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-85

4-72 Month-Long Estimates of Exposure to Fish and Shellfish (gms/day) and 

Mercury (μg/kgbw/day) among Children Ages 3 through 6 Years.

National Estimates for Individual Ethnic/Racial Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-85
LIST OF TABLES (continued)

Page

xiii

4-73 Exposure of Men Ages 15 to 44 Years to Mercury (μg Hg/kg bw/day)

from Fish and Shellfish of Marine, Estuarine, and Freshwater Origin . . . . . . . . . . . . . . . . . . 4-86

4-74 Exposure of Women Aged 15-44 Years to Mercury (μg Hg/kg bw/day) from 

Fish and Shellfish of Marine, Estuarine, and Freshwater Origin . . . . . . . . . . . . . . . . . . . . . . . 4-87

5-1 Occupational Standards for Airborne Mercury Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-2

6-1 Literature Derived Values for Total Mercury Concentrations in Whole Blood . . . . . . . . . . . . . 6-3

6-2 Blood Mercury Concentrations Values Reported for the United States . . . . . . . . . . . . . . . . . . . 6-5

6-3 Hair Mercury Concentrations (μg Hg/gram hair or ppm) from Residents

of Various Communities in the United States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-11

6-4 Association of Hair Mercury Concentrations (μg Hg/gram hair) with

Frequency of Fish Ingestion by Adult Men and Women

Living in 32 Locations within 13 Countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-14
xiv

LIST OF FIGURES

Page

2-1 Configuration of Hypothetical Water Body and Watershed Relative to Local Source . . . . . . . 2-5

4-1 Distribution of Fish Consumption Rates of Various Populations ...................... 4-23
xv

LIST OF SYMBOLS, UNITS AND ACRONYMS

AC Activated carbon

APCD Air pollution control device

ASME American Society of Mechanical Engineers

CAA Clean Air Act as Amended in 1990

CaS Calcium sulfide

cf Cubic feet

CFB Circulating fluidized bed

cm Cubic meter

CRF Capital recovery factor

dscf Dry standard cubic feet

dscm Dry standard cubic meter

ESP Electrostatic precipitator

DSI Dry sorbent injection

EPRI Electric Power Research Institute

FFDCA Federal Food, Drug, Cosmetic Act

FFs Fabric filters

FGD Flue gas desulfurization

FIFRA Federal Insecticide, Fungicide, Rodenticide Act

FWS U.S. Fish and Wildlife Service

GACT Generally available control technology

GLFCATF Great Lakes Fish Consumption Advisory Task Force

GLNPO Great Lakes National Program Office

g Gram

gr Grains

HAPs Hazardous air pollutants

HCl Hydrochloric acid

Hg Mercury

HgCl Mercuric chloride

HgI Mercuric iodide

HgO Mercuric oxide

HgS Mercuric sulfide

HgSe Mercuric selenite

HMTA Hazardous Materials Transportation Act

HVAC Heating, ventilating and air conditioning

IDLH Immediately dangerous to life and health

INGAA Interstate Natural Gas Association Of America

kg Kilogram

kW Kilowatt

MACT Maximum achievable control technology

MB Mass burn

MCL Maximum contaminant level

Mg Megagram

MSW Municipal solid waste

MW Megawatt

MWCs Municipal waste combustors
LIST OF SYMBOLS, UNITS AND ACRONYMS

(continued)

xvi

MWIs Medical waste incinerators

NaCl Sodium chloride

NaOH Sodium hydroxide

ng Nanogram

NIOSH National Institute for Occupational Safety and Health

Nm Normal cubic meter 3

NOAA National Oceanic and Atmospheric Administration

NPDES National Pollutant Discharge Elimination System

NSP Northern States Power

NSPS New source performance standard

OAQPS Office of Air Quality Planning and Standards (U.S. EPA)

OECD Organization for Economic Co-operation and Development

O&M Operation and maintenance

OSHA Occupational Safety and Health Administration

PCBs Polychlorinated biphenyls

PELs Permissible exposure limits

PM Particulate matter

ppm parts per million

ppmv parts per million by volume

RQ Reportable quantity

SARA Superfund Amendments and Reauthorization Act

scf Standard cubic feet

scm Standard cubic meter

SD Spray dryer

SDAs Spray dryer absorbers

TCC Total capital cost

TCLP Toxicity characteristic leaching procedure

TMT Trimercapto-s-triazine

tpd Tons per day

TRI Toxic Release Inventory

μg Microgram

UNDEERC University of North Dakota Energy and Environmental Research Center

WS Wet scrubber

WW Waterwall
ES-1

EXECUTIVE SUMMARY

Section 112(n)(1)(B) of the Clean Air Act (CAA), as amended in 1990, requires the U.S.

Environmental Protection Agency (U.S. EPA) to submit a study on atmospheric mercury emissions to

Congress. The sources of emissions that must be studied include electric utility steam generating units,

municipal waste combustion units and other sources, including area sources. Congress directed that the

Mercury Study evaluate many aspects of mercury emissions, including the rate and mass of emissions,

health and environmental effects, technologies to control such emissions, and the costs of such controls.

In response to this mandate, U.S. EPA has prepared an eight-volume Mercury Study Report to

Congress. This document is the exposure assessment (Volume IV) of the Mercury Study Report to

Congress. The exposure assessment is one component of the risk assessment of U.S. anthropogenic

mercury emissions. The analysis in this volume builds on the fate and transport data compiled in

Volume III of the study. This exposure assessment considers both inhalation and ingestion exposure

routes. For mercury emitted to the atmosphere, ingestion is an indirect route of exposure that results

from mercury deposition onto soil, water bodies and plants and uptake through the food chain. The

analyses in this volume are integrated with information relating to human and wildlife health impacts of

mercury in the Risk Characterization Volume (Volume VII) of the Report.

National Assessment of Mercury Exposure from Fish Consumption

A current assessment of U.S. general population methylmercury exposure through the

consumption of fish is provided in this volume. This assessment was conducted to provide an estimate

of mercury exposure through the consumption of fish to the general U.S. population. It is not a sitespecific assessment but rather a national assessment. This assessment utilizes data from the Continuing

Surveys of Food Intake by Individuals (CSFII 89-91, CSFII 1994, CSFII 1995) and the third National

Heath and Nutrition Examination Survey (NHANES III) to estimate a range of fish consumption rates

among U.S. fish eaters. Both per capita and per user (only individuals who reported fish consumption)

were considered. For each fish-eater, the number of fish meals, the quantities and species of fish

consumed and the self-reported body weights were used to estimate mercury exposure on a body weight

basis. The constitution of the survey population was weighted to reflect the actual U.S. population. 

Results of smaller surveys on high-endfish consumers are also included.

These estimates of fish consumption rates were combined with fish species-specific mean values

for measured mercury concentrations. The fish mercury concentration data were obtained from the

National Marine Fisheries Service, Bahnick et al., (1994), and Lowe et al., (1985). Through the

application of specific fish preparation factors (USDA, 1995), estimates of the range of methylmercury

exposure from the consumption of fish were prepared for the fish-consuming segment of the U.S.

population. Per kilogram body weight estimates of methylmercury exposure were determined by

dividing the total daily methylmercury exposure from this pathway by the self-reported body weights.

Estimates of month-long patterns of fish and shellfish consumption were based on the data

reporting frequency of fish/shellfish consumption obtained in the third National Health and Nutrition

Examination Survey (NHANES III) conducted between 1988 and 1994. Combining these frequency data

with other information on respondents in NHANES III (i.e., 24-hour recall data and self-reported body

weight of subjects), and mean mercury concentrations in fish/shellfish, these projected month-long

estimates of fish/shellfish consumption describe moderate-term mercury exposures for the general United

States population.
ES-2

Conclusions

The following conclusions are presented in approximate order of degree of certainty in the

conclusion, based on the quality of the underlying database. The conclusions progress from

those with greater certainty to those with lesser certainty.

 Consumption of fish is the dominant pathway of exposure to methylmercury for fishconsuming humans. There is a great deal of variability among individuals in these

populations with respect to food sources and fish consumption rates. As a result, there is

a great deal of variability in exposure to methylmercury in these populations. The

anthropogenic contribution to the total amount of methylmercury in fish is, in part, the

result of anthropogenic mercury releases from industrial and combustion sources

increasing mercury body burdens in fish. As a consequence of human consumption of

the affected fish, there is an incremental increase in exposure to methylmercury.

 The critical variables contributing to these different outcomes in measuring exposures

are these:

a) the fish consumption rate;

b) the body weight of the individual in relation to the fish consumption rate;

c) the level of methylmercury found in different fish species consumed; and

d) the frequency of fish consumption.

 The results of the current exposure of the U.S. population from fish consumption

indicate that most of the population consumes fish and is exposed to methylmercury as a

result. Approximately 85% of adults in the United States consume fish and shellfish at

least once a month with about 40% of adults selecting fish and shellfish as part of their

diets at least once a week (based on food frequency data collected among more than

19,000 adult respondents in the NHANES III conducted between 1988 and 1994). This

same survey identified 1-2% of adults who indicated they consume fish and shellfish

almost daily.

 In the nationally-based dietary surveys, the types of fish most frequently reported to be

eaten by consumers are tuna, shrimp, and Alaskan pollock. The importance of these

species is corroborated by U.S. National Marine Fisheries Service data on per capita

consumption rates of commercial fish species.

 National surveys indicate that Asian/Pacific Islander-American and Black-American

subpopulations report more frequent consumption of fish and shellfish than other survey

participants.

 Superimposed on this general pattern of fish and shellfish consumption is freshwater fish

consumption, which may pose a significant source of methylmercury exposure to

consumers of such fish. The magnitude of methylmercury exposure from freshwater fish

varies with local consumption rates and methylmercury concentrations in the fish. The

modeling exercise indicated that some of these methylmercury concentrations in

freshwater fish may be elevated as a result of mercury emissions from anthropogenic
ES-3

sources. Exposures may be elevated among some members of this subpopulation; these

may be evidenced by analyses of blood mercury showing concentrations in excess of 10

micrograms per liter (μg/L) that have been reported among multiple freshwater fishconsumer subpopulations.

 The results of the assessment of current exposure of the U.S. population from fish

consumption as described in this volume. Exposure to methylmercury from

contaminated fish results in an incremental increase in mercury exposure for most U.S.

fish-consumers. Methylmercury exposure rates on a per body weight basis among fishconsuming children are predicted to be higher than for fish-consuming adults. The 50th

percentile exposure rate among fish-consuming children under the age of 10 and younger

is approximately 0.3 μg/kg of body weight per day. The 90th percentile predicted

exposures are approximately three times greater or 0.8-1.0 μg/kg body weight/day. The

predicted average exposure among males and females fish consumers of reproductive age

is 0.1 μg of methylmercury/ kg body weight/day. Given that these are one-day estimates,

it would be inappropriate to compare these values to the RfD except for subpopulations

that eat fish/shellfish almost every day. Fish consumption rates by adult men and women

vary from zero to more than 300 grams per day. These predictions are consistent across

the three major contemporary national food consumption surveys.

 Estimated month-long patterns of fish/shellfish intake and mercury exposures indicate

that fish/shellfish consumption is lowest among “White/NonHispanics” (73 grams/day),

second highest among “Black/NonHispanics” (97 grams/day) and highest among the

category designated as “Other” (123 grams/day). The category “Other” includes persons

of Asian/Pacific Islander ethnicity, NonMexican Hispanics (typically persons of

Caribbean ethnicity), Native American tribal members and Native Alaskans, and

additional persons. Based on these estimates of month-long fish/shellfish consumption

as the basis for determining methylmercury exposure, an estimated 9% of the general

population exceeds the RfD.

Among women of childbearing age, 7% exceeded the RfD based on month-long

projections of fish/shellfish intake. Approximately 1% of women have methylmercury

exposures three-to-four times the RfD. Children in the age group 3-to-6-years have

higher intakes of methylmercury than do adults relative to body weight. Approximately

25% of children exceed the RfD, and 5% of children have methylmercury exposures

from fish/shellfish two-to-three times the RfD (i.e., 0.29 μg/kg body weight/day).

 Blood mercury concentrations and hair mercury levels are biomarkers used to indicate

exposure to mercury. Inorganic mercury exposure occur occupationally and for some

individuals through ritualistic/hobby exposures to inorganic mercury. Dental

restorations with silver/mercury amalgams can also contribute to inorganic mercury

exposures. Methylmercury exposure is almost exclusively through consumption of fish,

shellfish, and marine mammals. Occupational exposures to methylmercury are rare.

Normative data describing blood and/or hair mercury for a population representative of

the United States do not exist, however, some data are available. Blood mercury

concentrations in the United States are usually less than 10 μg/L; however, blood

mercury concentrations in excess of 30 μg/L have been reported and are attributed to fish

consumption. Hair mercury concentrations in the United States are typically less than

1μg/g, however, hair mercury concentration greater than 10μ/g have been reported for
ES-4

women of childbearing age living in the United States. U.S. EPA’s RfD is associated

with a blood mercury concentration of 4-5 μg/L and a hair mercury concentration of

approximately 1μg/g. The “benchmark” dose is associated with mercury concentrations

of 44 μg/L in blood and 11.1 μg/g in hair. The “benchmark” dose for methylmercury is

based on neurotoxic effects observed in Iraqi children exposed in utero to

methylmercury.

 Specialized smaller surveys of subpopulations including anglers and Native American

Tribal members indicate high fish consumption rates and elevated blood/hair mercury

concentrations occur.
1-1

1. INTRODUCTION

Section 112(n)(1)(B) of the Clean Air Act (CAA), as amended in 1990, requires the U.S.

Environmental Protection Agency (EPA) to submit a study on atmospheric mercury emissions to

Congress. The sources of emissions that must be studied include electric utility steam generating units,

municipal waste combustion units, and other sources, including area sources. Congress directed that the

Mercury Study evaluate many aspects of mercury emissions, including the rate and mass of emissions,

health and environmental effects, technologies to control such emissions, and the costs of such controls.

In response to this mandate, EPA has prepared an eight-volume Mercury Study Report to

Congress. The eight volumes are as follows:

I. Executive Summary

II. An Inventory of Anthropogenic Mercury Emissions in the United States

III. Fate and Transport of Mercury in the Environment

IV. An Assessment of Exposure to Mercury in the United States

V. Health Effects of Mercury and Mercury Compounds

VI. An Ecological Assessment for Anthropogenic Mercury Emissions in the United States

VII. Characterization of Human Health and Wildlife Risks from Mercury Exposure in the

United States

VIII. An Evaluation of Mercury Control Technologies and Costs

This document is the exposure assessment (Volume IV) of U.S. EPA's Report to Congress on

Mercury. The exposure assessment is one element of the human health and ecological risk assessment of

U.S. anthropogenic mercury (Hg) emissions. The exposure assessment considers both inhalation and

ingestion exposure routes. For atmospheric mercury emissions, ingestion is an indirect route of exposure

that results from mercury deposition onto soil, water bodies and plants and uptake through the food

chain. The information in this document is integrated with information relating to human and wildlife

health impacts of mercury in Volume VII of the report.

Using deposition values obtained from fate and transport models in Volume III, this assessment

addresses the exposures that result from selected, major anthropogenic combustion and manufacturing

sources. This volume also estimates current exposures to the general U.S. population that result from

mercury concentrations in freshwater and marine fish. This volume does not address all anthropogenic

emission sources, nor does it address emissions from natural sources. 

Volume IV is composed of nine chapters and three appendices. The Introduction is followed by

Chapter 2, which describes the approach utilized to calculate mercury exposures to humans and wildlife. 

Chapter 3 presents estimates of mercury exposure to individuals in the human population and wildlife. 

Chapter 4 describes current U.S. exposures through consumption of fish. The fish methylmercury

concentrations and the human fish consumption rates were developed using measured data. Exposures

through other routes such as dental amalgams and occupational scenarios are summarized in Chapter 5. 

The predicted human exposures are compared to biomonitoring data in Chapter 6.

Chapter 7 presents the conclusions of this Volume. Information needed for better assessment of

exposure to emitted mercury and to current concentrations in media and biota is listed in Chapter 8. 

Finally, Chapter 9 lists all references cited in this volume. 
1-2

There are four appendices to Volume IV: Exposure Parameter Justifications (Appendix A);

Estimated National and Regional Populations of Women of Child-Bearing Age (Appendix B); Analysis

of Mercury Levels in Fish and Shellfish (Appendix C); and Human Fish Consumption and Mercury

Ingestion Distributions (Appendix D).

The assessment of human mercury exposure through the consumption of fish as described in

Chapter 4 utilizes data from the continuing surveys of food intake by individuals (CSFII 89-91, CSFII

1994, CSFII 1995) and the third National Health and Nutrition Examination Survey (NHANES III). 

Both per capita and per user (only individuals who reported fish consumption) were considered. For

each fish-eater, the number of fish meals, the quantities and species of fish consumed and the selfreported body weights were used to estimate mercury exposure on a body weight basis. The constitution

of the survey population was weighted to reflect the actual U.S. population. Results of smaller surveys

on high-endfish consumers are also included. Continuing Surveys of Food Intake by Individuals

(CSFII 89-91) to estimate a range of fish consumption rates among fish eaters. For each fish-eater, the 3-

day CSFII 89-91 study identified the number of fish meals, the quantities and species of fish consumed

and the self-reported body weights of the consumers. The constitution of the survey population was

weighted to reflect the actual U.S. population.

These estimates of fish consumption rates were combined with fish species-specific mean values

for measured methylmercury concentrations. The fish methylmercury concentration data were obtained

from the National Marine Fisheries Service, Bahnick et al., (1994), Lowe et al., (1985), and FDA (1995). 

Through the application of specific fish preparation factors (USDA, 1995), estimates of the range of

methylmercury exposure from the consumption of fish were prepared for the fish-consuming segment of

the U.S. population. Per body weight estimates of methylmercury exposure were determined by dividing

the total daily methylmercury exposure from this pathway by the self-reported.
2-1

2. APPROACH TO EXPOSURE ASSESSMENT

This chapter summarizes the methods employed to calculate exposures of humans to

anthropogenic mercury emissions. These methods utilize the predictions of the environmental fate 

modeling presented in Volume III. The models used for the human exposure assessment are identical to

those used for the wildlife exposure assessment (Volume VI of this Report). For the human exposure

modeling analysis, two hypothetical sites in the eastern and western U.S. were developed. The proximity

of these sites to the source was varied to examine the effect of distance on model predictions. To account

for the long-range transport of emitted mercury, the 50th and 90th percentile RELMAP atmospheric

concentrations and deposition rates were included in the estimates from the local air dispersion model.

To account for other sources of mercury, estimates of background concentrations of mercury were also

included in this exposure assessment. Human exposure estimates were developed through the use of

mathematical models and a series of assumptions about human dietary behaviors and ingestion rates.

Three separate exposure sceanrios pertaining to the types and sources of foods consumed were

developed. Parameters that affected hypothetical human exposure are identified in Sections 2.2 and 2.3;

some of these parameters have the potential to change across scenarios. Appendix A describes the

specific human exposure factors utilized in this volume.

2.1 Modeling Exposures near Mercury Emissions Sources

This section summarizes the computer models used to assess mercury exposure resulting from

hypothetical local source emissions; this includes a description of the environmental fate models

selected. Modeling assumptions related to the presence of backgroundmercury as well as mercury

transported from other regions of the U.S. are also presented. These models and modeling assumptions

are used to predict exposures of hypothetical humans residing in areas around mercury emission sources.

2.1.1 Description of Computer Models

 Atmospheric transport models were used to simulate the deposition of mercury at two different

geographical scales (Table 2-1). A regional-scale analysis was conducted using the Regional Lagrangian

Model of Air Pollution (RELMAP). RELMAP calculates annual mean air concentrations and annual

mean deposition rates for each cell in a 40 km grid. This analysis covered the 48 contiguous states and

was based upon a recent inventory of mercury emissions sources (presented in Volume II of this Report). 

The results of the RELMAP model accounted for the long-range transport of mercury emitted from

anthropogenic sources.

The local-scale exposure analysis was conducted by using both RELMAP and a local air

transport model, GAS-ISC3, to generate hypothetical exposure scenarios for four mercury emission

source classes. GAS-ISC3 uses hourly meteorological data to estimate hourly air concentrations and

deposition fluxes within 50 km of a point source. For each hour, general plume characteristics are

estimated based on the source parameters (gas exit velocity, temperature, stack diameter, stack height,

wind speed at stack top, atmospheric stability conditions) for that hour. GAS-ISC3 was run using one

year of actual meteorological data (1989, the same meteorologic year as was utilized in the RELMAP

modeling). The average annual predicted values for air concentration and deposition rates were then

used as inputs for to IEM-2M model for 30 years, the assumed typical lifetime of a facility. 
2-2

Table 2-1

Models Used to Predict Mercury Air Concentrations,

Deposition Fluxes and Environmental Concentrations

Model Description

RELMAP

Predicts average annual atmospheric mercury concentration and wet

and dry deposition flux for each 40 km grid in the U.S. due to all 2

anthropocentric sources of mercury in the U.S. and a natural

background atmospheric mercury concentration.

GAS-ISC3 Predicts average concentration and deposition fluxes within 50 km of

emission source.

IEM-2M Predicts environmental concentrations based on air concentrations

and deposition rates to watershed and water body.

The IEM-2M model was used to estimate mercury levels in soil, water and biota based on both

regional and local-scale estimates of atmospheric concentrations of mercury and mercury deposition.

IEM-2M is composed of two integrated modules that simulate mercury fate using mass balance equations

describing watershed soils and a shallow lake. IEM-2M simulates three chemical components —

elemental mercury, Hg , divalent mercury, HgII, and methylmercury, MHg. Mass balances are performed 0

for each mercury component, with internal transformation rates linking Hg , HgII, and MHg. Sources 0

include wetfall and dryfall loadings of each component to watershed soils and to the water body. An

additional source is diffusion of atmospheric Hg vapor to watershed soils and the water body. Sinks 0

include leaching of each component from watershed soils, burial of each component from lake sediments,

volatilization of Hg and MHg from the soil and water column, and advection of each component out of 0

the lake.

At the core of IEM-2M are nine differential equations describing the mass balance of each

mercury component in the surficial soil layer, in the water column, and in the surficial benthic sediments. 

The equations are solved for a specified interval of time, and predicted concentrations output at fixed

intervals. For each calculational time step, IEM-2M first performs a terrestrial mass balance to obtain

mercury concentrations in watershed soils. Soil concentrations are used along with vapor concentrations

and deposition rates to calculate concentrations in various food plants. These are used, in turn, to

calculate concentrations in animals. IEM-2M simultaneously performs an aquatic mass balance driven by

direct atmospheric deposition along with runoff and erosion loads from watershed soils. 

Human exposures through inhalation and ingestion of other contaminated food items (as well as

soils) were also evaluated. Levels of atmospheric mercury were estimated by summing the predicted

concentrations of the RELMAP and GAS-ISC3 models. Soil concentrations were derived directly from

estimates of the IEM-2M model. Concentrations in green plants were estimated using soil-to-plant and

air-to-plant biotransfer factors; mercury in these plants was derived from the local and regional scale air

modeling as well as estimates of background mercury (Section 2.1.2). Estimates of the mercury

concentrations in animal tissues and animal products are generally the product of predicted mercury

concentrations in green plants and soils, animal consumption rates, and specific biotransfer factors. 

Mercury in these animals was derived from the local and regional scale air modeling as well as estimates

of background mercury.
2-3

Mercury residues in fish were estimated by making the simplifying assumption that aquatic food

chains can be adequately represented using four trophic levels. Respectively, these trophic levels are the

following: level 1 - phytoplankton (algal producers); level 2 - zooplankton (primary herbivorous

consumers); level 3 - small forage fish (secondary consumers); and level 4 - larger, piscivorous fish

(tertiary consumers), which are eaten by humans. This type of food chain typifies the pelagic

assemblages found in large freshwater lakes, and has been used extensively to model bioaccumulation of

hydrophobic organic compounds (see for example Thomann, 1989; Clark et al., 1990; Gobas, 1993). It is

recognized, however, that food chain structure can vary considerably among aquatic systems resulting in

large differences in bioaccumulation in a given species of fish (Futter, 1994; Cabana et al., 1994a,b). 

The second simplifying assumption utilized in this effort was that methylmercury concentrations in fish

are directly proportional to dissolved methylmercury concentrations in the water column. It is recognized

that this relationship can vary widely among both physically similar and dissimilar water bodies.

Methylmercury concentrations in fish were derived from predicted water column concentrations

of dissolved methylmercury by using BAFs for trophic level 4 fish (Table 2-2). The BAFs selected for

these calculations were estimated from existing field data. The BAF (dissolved methylmercury basis) for

trophic level 4 fish is 1.6 x 10 . Methylmercury was estimated to constitute 7.8% of the total dissolved 6

mercury in the water column, and 65% of this was assumed to be freely dissolved. The technical basis

for these estimates is presented in Volume III, Appendix D. The potential variability around these

predicted fish residue values is highlighted in Table 2-2. Percentile information for the BAF estimates are

presented. 

Table 2-2

Percentiles of the Methylmercury Bioaccumulation Factor

Parameter

Percentile of Distribution

5th 25th 50th 75th 95th

Trophic 4 BAF 3.3x10 5.0x10 6.8x10 9.2x10 1.4x 10 66 6 6 7

2.1.2 Estimates of Background Mercury

In Volume III of this Report it was noted that mercury was a constituent of the environment and

has always been present on the planet. Estimates of atmospheric mercury concentrations and deposition

rates from periods pre-dating large-scale anthropogenic emissions (“pre-anthropogenic”) and from

current data were presented for hypothetical eastern and western sites. These estimates were used as

inputs to the IEM-2M model. The equilibrium results of the IEM-2M model were calculated for both the

eastern and western sites and for both the pre-anthropogenic and current time periods. (Chemical

equilibrium is defined here as “a steady state, in which opposing chemical reactions occur at equal rates."

(Pauling, 1963)). When modeling the pre-anthropogenic period, the initial conditions of all model

compartments except the atmosphere were set to a mercury concentration of zero. The results of running

the pre-anthropogenic conditions to equilibrium in IEM-2M were used as the initial conditions for

estimating the current mercury concentrations. Table 2-3 lists the estimated mercury air concentrations

and deposition rates used at both hypothetical sites and for both time periods.
2-4

Table 2-3

Inputs to IEM-2M Model for the Two Time Periods Modeled

Time Period Eastern Site Western Site

Air Concentration Annual Air Concentration Annual

ng/m Deposition Rate ng/m Deposition Rate 3

μg/m /yr μg/m /yr 2

3

2

Pre- 0.5 3 0.5 1

Anthropogenic

Current* 1.6 10 1.6 2

* This time period does not reflect the potential contributions of local sources. 

2.2 Description of Hypothetical Exposure Scenarios for Humans

In general, exposure scenarios are real or hypothetical situations that define the source of

contamination, the potential receptor populations, the potential pathway(s) of exposure and the variables

that affect the exposure pathways. Mercury exposure in this analysis was assessed for humans residing at

hypothetical locations in the eastern and western United States. The fate of deposited mercury was

examined in three types of settings: rural (agricultural); lacustrine (or water body); and urban. These

three settings were selected because of the variety they encompass and because each is expected to

provide a potentially elevated mercury concentration in environmental media of concern for human

exposure; for example, elevated mercury concentrations are expected in the waters of lakes near mercury

emission sources.

These exposure scenarios included the total amount of food derived from affected areas and the

extent of mercury contamination of these food sources. For an exposure assessment which is meant to

represent a broad base of potential exposures, it is not practical to model many different types of farms,

gardens, etc. As for the rest of the study, a limited number of representative, generalized types of

activities have been modeled.

2.2.1 Hypothetical Location Descriptions

Mercury exposure is assessed for humans hypothetically located at two generic sites: a humid

site east of 90 degrees west longitude, and a more arid site west of 90 degrees west longitude (these are

described in Volume III). Both sites were assumed to be located in relatively flat terrain. Exposure at

each site was assessed for humans residing at 2.5, 10, or 25 km from the emissions source, as shown in

Figure 2-1. The primary physical differences between the two hypothetical sites as parameterized

included the assumed average annual precipitation rate, the assumed erosion characteristics for the

watershed, and the amount of dilution flow from the water body. The eastern site had generally steeper

terrain in the watershed than was assumed for the western site.

The atmospheric mercury concentration over the hypothetical western site was the sum of the

50th or 90th percentile of the RELMAP output for the entire contiguous United States west of 90 degrees

west longitude and the GAS-ISC3 prediction resulting from the local source mercury emissions.

Similarly, the mercury concentration over the hypothetical eastern site was the sum of the 50th or 90th

percentile of the 
2-5

Figure 2-1

Configuration of Hypothetical Water Body and Watershed Relative to Local Source

RELMAP output for the entire contiguous United States east of 90 degrees west longitude and the GASISC3 prediction resulting from the local source mercury emissions. Deposition to both sites were,

similarly, the sum of the predicted depositions for GAS-ISC3 and the 50th or 90th percentile RELMAP

result. 

2.2.2 Description of Hypothetical Human Exposure Scenarios

Human exposure to environmental mercury is the result of mercury concentrations at specific

human exposure points (e.g., ingested fish). For each location and setting, mercury exposure was

estimated for individuals representing several specific subpopulations expected to have both typical and

higher exposure levels. The individuals representing the subpopulations were defined to model average

and high-end exposures in the three settings: rural, urban, and lacustrine. In this section each

subpopulation is discussed. A more detailed description of the values chosen for parameters of the

exposure assessment is given in Appendix A. Table 2-4 summarizes the hypothetical scenarios

considered as well as the exposure pathways considered in each scenario.
2-6

Table 2-4

Summary of Human Exposure Scenarios

Location

Rural Urban Lacustrine 

Subsistence Farmer Gardener Resident Worker/High-end High End Fisherman angler 

Home Rec. 

Adult Child Adult Adult Adult Child Adult Child Adult 

Pica

Airinhalation X X X X XX X X X Soilingestion X X X X XX X X X 

Animalingestion X XVegetable

ingestion X X X XX X 

Local fishingestion X XX 

Local wateringestion XX X XX

Notes:

 Lakes located greater than 50 km from a mercury emission source a

Blank = Pathway not considered.

X = Pathway considered.
2-7

2.2.2.1 Rural Exposure Scenarios

Both a high-end and average rural scenario were evaluated. The high-end scenario consisted of a

subsistence farmer and child who consumed elevated levels of locally grown food products. It was

assumed that each farm was located on a square plot of land with an area 40,000 m (approximately 10 2

acres). The subsistence farmer was assumed to raise livestock and to consume home-grown animal tissue

and animal products, including chickens and eggs as well as beef and dairy cattle. All chicken feed was

assumed to be derived from non-local sources. For cattle, 100% of the hay and corn used for feed was

assumed to be from the local area. It was also assumed that the subsistence farmer collected rainwater in

cisterns for drinking. The typical rural dweller was assumed to raise a small garden and derive some of

his food from that source.

2.2.2.2 Urban Exposure Scenarios

In the urban high end scenario, it was assumed that the person had a small garden similar in size

to that of the average rural scenario. To address the fact that home-grown fruits and vegetables generally

make up a smaller portion of the diet in urban areas, the contact fractions were based on weight ratios of

home-grown to total fruits and vegetables consumed for city households. These fractions can be up to 10

times smaller than the values for rural households, depending on food plant type (see Table 2-4 and

Appendix A). Exposure duration for inhalation was 24 hours per day. The high-end urban scenario

included a pica child.

An average urban scenario consisted of an adult who worked outside of local area. The exposure

duration for inhalation, therefore, was only 16 hours a day compared to the 24 hours a day for the rural

and high-end urban scenarios. The only other pathway considered for this scenario was ingestion of

average levels of soil.

2.2.2.3 Description of Hypothetical Human Exposure Scenarios for Individuals Using Water

Bodies

The fish ingestion pathway was the dominant source of methylmercury intake in exposure

scenarios wherein the fish ingestion pathway was considered appropriate. For this assessment, three

human fish consumption scenarios were considered for the hypothetical lakes: (1) an adult high-end fish

consumer scenario, in which an individual was assumed to ingest large amounts of locally-caught fish as

well as home-grown garden produce (plant ingestion parameters identical to the rural home gardener

scenario), consume drinking water from the affected water body and inhale the air; (2) a child of a highend local fish consumer, assumed to ingest local fish, garden produce, and soil as well as inhale the

affected air; and (3) a recreational angler scenario, in which the exposure pathways evaluated were fish

ingestion, inhalation, and soil ingestion. These consumption scenarios were thought to represent

identified fish-consuming subpopulations in the United States.

Fish for human consumption from local water bodies can be derived from many sources

including self-caught, gifts, and grocery and restaurant purchases. For the purposes of this study, all fish

consumed were assumed to originate from the hypothetical lakes, which were considered to represent

several small lakes that might be present in the type of hypothetical locations considered. No

commercial distribution of locally caught fish was assumed; exposure to locally-caught fish was modeled

for the three fish-consuming subpopulations described above.

Fish consumption rates for the three fish-consuming subpopulations were derived from the

Columbia River Inter-Tribal Fish Commission Report (1994). Other estimates of human fish

consumption rates are reported later in this volume; these estimates highlight the broad variability in
2-8

consumption rates. The Columbia River Inter-Tribal Fish Commission Report (1994) estimated fish

consumption rates for members of four tribes inhabiting the Columbia River Basin. The estimated fish

consumption rates were based on interviews with 513 adult tribe members who lived on or near the

reservation. The participants had been selected from patient registrations lists provided by the Indian

Health Service. Adults interviewed provided information on fish consumption for themselves and for

204 children under 5 years of age.

Fish consumption rates for tribal members are shown in Tables 2-5 and 2-6. The values used in

this study are shown in Table 2-7. The values listed below reflect an annual average, but monthly

variations were also reported. For example, the average daily consumption rate during the two highest

intake months was 107.8 grams/day, and the daily consumption rate during the two lowest consumption

months was 30.7 grams/day. Fish were consumed by over 90% of the surveyed population with only 9%

of the respondents reporting no fish consumption. The maximum daily consumption rate for fish

reported by one member of this group was 972 grams/day. Since most of the population consisted of fish

consumers (“users”), utilization of per capita estimates was considered appropriate.

Table 2-5

Fish Consumption Rates for Columbia River Tribes a

Subpopulation Mean Daily Fish Consumption (g/day)

Total Adult Population, aged 18 years and older 59

Children, aged 5 years and younger 20

Adult Females 56

Adult Males 63

 Columbia River Inter-Tribal Commission, 1994. a

Table 2-6

Daily Fish Consumption Rates Among Adults

Fish Consumption by Columbia River Tribesa

Percentile grams/day

50th 29-32

90th 97-130

95th 170

99th 389

 Columbia River Inter-Tribal Commission, 1994. a
2-9

Table 2-7

Fish Consumption Rates used in this Study

Subpopulation Fish Consumption Rate (g/day)a

High-end Adult 60

High-end Child 20

Recreational Angler 30

 Columbia River Inter-Tribal Commission, 1994. a

The fish consumed by humans in both the hypothetical eastern and western sites were obtained

from lakes. The drainage lakes were assumed to be circular with a diameter of 1.78 km and average

depth of 5 m. A 2 cm benthic sediment depth was assumed for the lakes. The watershed area associated

with each lake was 37.3 km . 2

2.3 Summary of Exposure Parameter Values

To a large degree, there are only a few parameters that vary across these scenarios. Table 2-8

categorizes exposure parameters as invariant or variant with each scenario. A complete list of the values

used and rationale for these values is given in Appendix A.

Table 2-8

Potential Dependency of Exposure Parameters

Parameters Constant Across Scenarios Parameters that Potentially Change Across

Scenarios

Body weight Fish ingestion rates

Exposure duration Contact fractions for vegetables, animal products, and

water 

Inhalation rate Contact time for inhalation

Animal and vegetable consumption rates Child soil ingestion rates

Adult soil ingestion rates

Drinking water ingestion rates

Table 2-9 shows the default values for the scenario-independent parameters for both the child

and adult receptors, and Table 2-10 shows the default values for the scenario-dependent exposure

parameters. The technical bases for these values are in Appendix A. The hypothetical scenarios are

discussed in more detail in the following sections.
2-10

Table 2-9

Default Values of Scenario-Independent Exposure Parameters

Parameter Adult Child

Default Valuea

Body weight (kg) 70 17

Inhalation rate (m /day) 20 16 3

Vegetable consumption rates (g dry weight/kg body weight/day)b

Leafy vegetables 0.028 0.008

Grains and cereals 1.87 3.77

Legumes 0.381 0.666

Potatoes 0.17 0.274

Root vegetables 0.024 0.036

Fruits 0.57 0.223

Fruiting vegetables 0.064 0.12

Animal product consumption rates (g dry weight/kg body weight/day)

Beef (excluding liver) 0.341 0.553

Beef liver 0.066 0.025

Dairy 0.599 2.04

Pork 0.169 0.236

Poultry 0.111 0.214

Eggs 0.073 0.093

Lamb 0.057 0.061

Soil Ingestion rates (g/day) 0.1 Scenariodependent

Water ingestion rate (L/day) 2 1

 See Appendix A for details regarding these parameter values. a

 DW= dry weight; BW = bodyweight. b
2-11

Table 2-10

Values for Scenario-Dependent Exposure Parameters a

Parameter Adult Child Adult Resident Gardener Child Adult Chi

Rural Subsistence Rural Home 

Farmer Gardener Urban Scenarios High End Fishe

Adult Home Pica

Fish Ingestion rates (g/day) NA NA NA NA NA NA 60 20 

c

Soil Ingestion Rate (g/day) 0.1 0.2 0.1 0.1 0.1 7.5 0.1 0.2 Contact time for inhalation (hr/day)

24 24 24 16 24 24 24 24 

Contact fractions (unitless)

Animal products 1 1 NA NA NA NA NA NA Leafy vegetables 1 1 0.058 NA 0.026 NA 0.058 0.05Grains and cereals 1 1 0.667 NA 0.195 NA 0.667 0.66Legumes 1 1 0.8 NA 0.5 NA 0.8 0.8 Potatoes 1 1 0.225 NA 0.031 NA 0.225 0.22Fruits 1 1 0.233 NA 0.076 NA 0.233 0.23Fruiting vegetables 1 1 0.623 NA 0.317 NA 0.623 0.62Root vegetables 1 1 0.268 NA 0.073 NA 0.268 0.26Drinking water 1 1 NA NA NA NA 1 1 

b

 See Appendix A for more details regarding these values. a

 The source of the contaminated drinking water is different for the subsistence farmer and high end fisher scenarios. b

 NA - Not Considered to be Applicable to this assessment. For example, urban residents were assumed to eat no locally caught fish. Any fish ingested 

c

considered to be contaminated by mercury from outside the modeling domain and, thus, not considered.
2-12

Consumption rates, bioconcentration factors, and biotransfer factors may be derived based on tissue

(plant, animal, and dairy) weights on either a wet or dry basis. Wet weight and dry weight are related by

this formula:

Dry Weight = Wet Weight / (1 - moisture content)

It is critical that parameters used together are consistent based on either dry weight or wet weight. Many

plants are nearly 90% water, and a mix of wet and dry weight modeling parameters can result in a tenfold error. The fish BAF and fish consumption rates in this Report were calculated using wet weight

values. Consumption rates, plant bioaccumulation factors, and animal biotransfer factors were all based

upon dry weights of tissues.

Animal and plant consumption rates as well as inhalation rates are constant across exposure

scenarios. The contact fraction changes generally across the exposure scenarios. The contact fraction

represents the fraction of locally-grown or affected food consumed. Typically, in exposure assessments

the higher the contact fraction the greater the exposure.

2.4 Emissions Sources

Model plants (hypothetical anthropogenic mercury emissions sources) representing four source

classes were developed to represent a range of mercury emissions sources. The source categories were

selected for the indirect exposure analysis based on their estimated annual mercury emissions or their

potential to be localized point sources of concern. The categories selected were these: municipal waste

combustors (MWCs), medical waste incinerators (MWIs), utility boilers, and chlor-alkali plants. Table

2-11 shows the process parameters assumed for each of these facilities. The characteristics of the

facilities were derived based on typical rather than extreme representations; the facilities are known as

model plants (See Volume II).

2.5 Predicted Concentrations in Environmental Media

High rates of mercury deposition were associated with proximity to industrial sources emitting

substantial levels of divalent mercury (Tables 2-12 and 2-15). Additional factors that contributed to high

local deposition rates include low stack height and slow stack exit gas velocities. In general, predicted

mercury concentrations in environmental media at 2.5 km were higher than levels predicted at 10 or 25

km. This was due primarily to the dilution of the mercury emissions in the atmosphere. Mercury

concentrations in biota also typically demonstrated the same pattern. When the two hypothetical

locations were compared (western and eastern), higher mercury concentrations were predicted to occur in

the environmental media and biota at the eastern location. This was due primarily to higher levels of

precipitation at the eastern site, which tends to remove mercury from the atmosphere. Also, the

assumptions of background mercury are higher for the eastern than the western site. This is also

attributed to the generally higher precipitation rates in the eastern United States.
2-13

Table 2-11

Process Parameters for the Model Plants Considered in the Local Impact Analysis

Model Plant Plant (% of Height Diameter Rate (Hg /Hg V

Size year) (ft) (ft) (kg/yr) /Hg ) (m

Capacity Stack Stack Emission Percent 

Hg Speciation

0 2+

P

Large Municipal 2,250 90% 230 9.5 220 

Waste tons/day 60/30/10

CombustorsSmall Municipal 200 90% 140 5 20 60/30/10 Waste tons/day

CombustorsLarge 1500 88% 40 2.7 4.58 33/50/17 

Commercial lb/hrHMI capacity

Waste (1000Incinerator lb/hr(Wetscrubber) actual)Large Hospital 1000 39% 40 2.3 23.9 2/73/25 16 

HMI Waste lb/hrIncinerators capacity

(Good (667Combustion) lb/hr

actual)

Small Hospital 100 lb/hr 27% 40 0.9 1.34 2/73/27 10HMI Waste capacityIncinerators (67 lb/hr(1/4 sec. actual)Combustion)Large Hospital 1000 39% 40 2.3 0.84 33/50/17 9.

HMI Waste lb/hrIncinerators capacity

(Wet Scrubber) (667

lb/hractual)
Table 2-11 (continued)

Process Parameters for the Model Plants Considered in the Local Impact Analysis

Model Plant Plant (% of Height Diameter Rate (Hg /Hg V

Size year) (ft) (ft) (kg/yr) /Hg ) (m

Capacity Stack Stack Emission Percent 

Hg Speciation

0 2+

P

2-14

Small Hospital 100 lb/hr 27% 40 0.9 0.05 33/50/17 5.

HMI Waste capacity

Incinerators (67 lb/hr(Wet Scrubber) actual)Large Coal-fired 975 65% 732 27 230 50/30/20 

Utility Boiler Megawat

ts

Medium 375 65% 465 18 90 50/30/20 Coal-fired Megawat

Utility Boiler ts

Small Coal-fired 100 65% 266 12 10 50/30/20 Utility Boiler Megawat

ts

Medium 285 65% 290 14 2 50/30/20 Oil-fired Utility Megawat

Boiler tsChlor-alkali 300 tons 90% 10 0.5 380 70/30/0 plant chlorine/ 

day

 Hg = Elemental Mercury a 0

 Hg = Divalent Vapor Phase Mercury b 2+

 Hg = Particle-Bound Mercury c P
2-15

Table 2-12

Predicted Mercury Values for Environmental Media at Eastern Site (ISC3 + RELMAP 50th)

50th Percentile

Plant Distance Air Concentration %RelMap %ISC Total Deposition %RelMap %ISC Total %B

(ng/m3) (ug/m2/yr) Hg Soil kg

Concent ndration inUntilledSoil(ng/g)

Variant b:Large Municipal Waste Combustor 2.5 km 1.7E+00 97% 3% 4.2E+01 34% 66% 1.0E+02 46

10 km 1.7E+00 98% 2% 2.6E+01 57% 43% 7.4E+01 625 km 1.7E+00 99% 1% 1.9E+01 78% 22% 6.1E+01 7

Variant b:Small Municipal Waste Combustor 2.5 km 1.7E+00 99% 1% 1.9E+01 74% 26% 6.3E+01 74

10 km 1.7E+00 100% 0% 1.6E+01 90% 10% 5.7E+01 825 km 1.7E+00 100% 0% 1.5E+01 97% 3% 5.5E+01 8

Large Commercial HMI 2.5 km 1.7E+00 99% 1% 1.9E+01 76% 24% 6.2E+01 7

10 km 1.7E+00 100% 0% 1.5E+01 95% 5% 5.6E+01 8425 km 1.7E+00 100% 0% 1.5E+01 99% 1% 5.5E+01 8

Large Hospital HMI 2.5 km 1.7E+00 97% 3% 4.4E+01 33% 67% 1.1E+02 44

10 km 1.7E+00 99% 1% 2.0E+01 74% 26% 6.3E+01 7425 km 1.7E+00 100% 0% 1.6E+01 92% 8% 5.7E+01 8

Small Hospital HMI 2.5 km 1.7E+00 100% 0% 1.6E+01 88% 12% 5.8E+01 8

10 km 1.7E+00 100% 0% 1.5E+01 98% 2% 5.5E+01 825 km 1.7E+00 100% 0% 1.5E+01 100% 0% 5.5E+01 8

Large Hospital HMI (wet scrubber) 2.5 km 1.7E+00 100% 0% 1.5E+01 94% 6% 5.6E+01 84

10 km 1.7E+00 100% 0% 1.5E+01 99% 1% 5.5E+01 825 km 1.7E+00 100% 0% 1.5E+01 100% 0% 5.5E+01 8

Small Hospital HMI (wet scrubber) 2.5 km 1.7E+00 100% 0% 1.5E+01 100% 0% 5.5E+01 8

10 km 1.7E+00 100% 0% 1.5E+01 100% 0% 5.4E+01 825 km 1.7E+00 100% 0% 1.5E+01 100% 0% 5.4E+01 8

Large Coal-fired Utility Boiler 2.5 km 1.7E+00 100% 0% 3.0E+01 48% 52% 8.1E+01 5

10 km 1.7E+00 100% 0% 1.7E+01 83% 17% 5.9E+01 725 km 1.7E+00 100% 0% 1.6E+01 93% 7% 5.6E+01 8

Medium Coal-fired Utility Boiler 2.5 km 1.7E+00 100% 0% 2.1E+01 68% 32% 6.6E+01 7

10 km 1.7E+00 100% 0% 1.6E+01 89% 11% 5.8E+01 825 km 1.7E+00 100% 0% 1.5E+01 94% 6% 5.6E+01 84

Small Coal-fired Utility Boiler 2.5 km 1.7E+00 100% 0% 1.6E+01 90% 10% 5.7E+01 82

10 km 1.7E+00 100% 0% 1.5E+01 96% 4% 5.5E+01 8425 km 1.7E+00 100% 0% 1.5E+01 99% 1% 5.5E+01 8

Medium OIl-fired Utility Boiler 2.5 km 1.7E+00 100% 0% 1.5E+01 99% 1% 5.5E+01 85

10 km 1.7E+00 100% 0% 1.5E+01 100% 0% 5.5E+01 825 km 1.7E+00 100% 0% 1.5E+01 100% 0% 5.5E+01 8

Chlor-alkali plant 2.5 km 4.0E+00 42% 58% 2.5E+02 6% 94% 4.5E+02 10

10 km 2.1E+00 79% 21% 4.6E+01 32% 68% 1.1E+02 425 km 1.8E+00 92% 8% 2.2E+01 65% 35% 6.8E+01 6

Table 2-13
2-16

Predicted Mercury Values for Environmental Media at Eastern Site (ISC3 + RELMAP 90th)

90th Percentile

Plant Distance Air Concentration %RelMap %ISC Total Deposition %RelMap %ISC Total Hg Soil %Ba

(ng/m3) (ug/m2/yr) Concentration und 

in Untilled Soil(ng/g)

Variant b:Large Municipal Waste Combustor 2.5 km 1.8E+00 97% 3% 5.5E+01 50% 50% 1.2E+02 3

10 km 1.8E+00 98% 2% 3.8E+01 71% 29% 9.5E+01 425 km 1.7E+00 99% 1% 3.1E+01 87% 13% 8.3E+01 5

Variant b:Small Municipal Waste Combustor 2.5 km 1.7E+00 99% 1% 3.2E+01 85% 15% 8.5E+01 5

10 km 1.7E+00 100% 0% 2.9E+01 95% 5% 7.9E+01 525 km 1.7E+00 100% 0% 2.8E+01 98% 2% 7.7E+01 6

Large Commercial HMI 2.5 km 1.7E+00 99% 1% 3.2E+01 85% 15% 8.4E+01 5

10 km 1.7E+00 100% 0% 2.8E+01 98% 2% 7.7E+01 625 km 1.7E+00 100% 0% 2.7E+01 99% 1% 7.7E+01 6

Large Hospital HMI 2.5 km 1.8E+00 97% 3% 5.7E+01 48% 52% 1.3E+02 3

10 km 1.7E+00 99% 1% 3.2E+01 84% 16% 8.5E+01 525 km 1.7E+00 100% 0% 2.8E+01 96% 4% 7.8E+01 6

Small Hospital HMI 2.5 km 1.7E+00 100% 0% 2.9E+01 93% 7% 8.0E+01 5

10 km 1.7E+00 100% 0% 2.7E+01 99% 1% 7.7E+01 625 km 1.7E+00 100% 0% 2.7E+01 100% 0% 7.6E+01 6

Large Hospital HMI (wet scrubber) 2.5 km 1.7E+00 100% 0% 2.8E+01 97% 3% 7.8E+01 6

10 km 1.7E+00 100% 0% 2.7E+01 100% 0% 7.6E+01 625 km 1.7E+00 100% 0% 2.7E+01 100% 0% 7.6E+01 6

Small Hospital HMI (wet scrubber) 2.5 km 1.7E+00 100% 0% 2.7E+01 100% 0% 7.6E+01 6

10 km 1.7E+00 100% 0% 2.7E+01 100% 0% 7.6E+01 625 km 1.7E+00 100% 0% 2.7E+01 100% 0% 7.6E+01 6

Large Coal-fired Utility Boiler 2.5 km 1.7E+00 100% 0% 4.3E+01 64% 36% 1.0E+02 4

10 km 1.7E+00 100% 0% 3.0E+01 90% 10% 8.1E+01 525 km 1.7E+00 100% 0% 2.8E+01 96% 4% 7.8E+01 6

Medium Coal-fired Utility Boiler 2.5 km 1.7E+00 100% 0% 3.4E+01 80% 20% 8.8E+01 5

10 km 1.7E+00 100% 0% 2.9E+01 94% 6% 7.9E+01 525 km 1.7E+00 100% 0% 2.8E+01 97% 3% 7.8E+01 6

Small Coal-fired Utility Boiler 2.5 km 1.7E+00 100% 0% 2.9E+01 94% 6% 7.9E+01 5

10 km 1.7E+00 100% 0% 2.8E+01 98% 2% 7.7E+01 625 km 1.7E+00 100% 0% 2.7E+01 99% 1% 7.7E+01 6

Medium OIl-fired Utility Boiler 2.5 km 1.7E+00 100% 0% 2.7E+01 99% 1% 7.7E+01 6

10 km 1.7E+00 100% 0% 2.7E+01 100% 0% 7.6E+01 625 km 1.7E+00 100% 0% 2.7E+01 100% 0% 7.6E+01 6

Chlor-alkali plant 2.5 km 4.0E+00 43% 57% 2.6E+02 10% 90% 4.8E+02 1

10 km 2.2E+00 79% 21% 5.9E+01 46% 54% 1.3E+02 325 km 1.9E+00 92% 8% 3.5E+01 77% 23% 9.0E+01 5
2-17

Table 2-14

Predicted Mercury Values in Water Column and Biota for Eastern Site

 (ISC3 + RELMAP 50th)

50th Percentile

MHg Tier 4 %Backgro %Rel %ISC Total Hg Grain %Bac %Rel %ISC

Dissolv Fish und Map Concentration kgrou Map

ed MHg (ng/g) nd

Water Concent

Conc.( ration

ng/l) (ug/g)

Variant b:Large Municipal Waste 2.5 km 1.7E-01 1.1E+00 38% 7% 54% 2.1E+00 93% 3% 4%

Combustor

10 km 1.1E-01 7.6E-01 58% 11% 31% 2.1E+00 94% 3% 2%

25 km 8.9E-02 6.0E-01 73% 14% 13% 2.1E+00 95% 3% 1%

Variant b:Small Municipal Waste 2.5 km 9.5E-02 6.4E-01 68% 13% 18% 2.1E+00 96% 3% 1%

Combustor

10 km 8.2E-02 5.6E-01 79% 15% 6% 2.1E+00 96% 3% 0%

25 km 7.9E-02 5.3E-01 83% 16% 2% 2.1E+00 96% 3% 0%

Large Commercial HMI 2.5 km 9.6E-02 6.5E-01 68% 13% 19% 2.1E+00 96% 3% 1%

10 km 8.0E-02 5.4E-01 82% 16% 3% 2.1E+00 96% 3% 0%

25 km 7.8E-02 5.3E-01 83% 16% 1% 2.1E+00 97% 3% 0%

Large Hospital HMI 2.5 km 1.9E-01 1.3E+00 34% 6% 60% 2.1E+00 93% 3% 4%

10 km 9.4E-02 6.4E-01 69% 13% 18% 2.1E+00 96% 3% 1%

25 km 8.1E-02 5.5E-01 80% 15% 5% 2.1E+00 96% 3% 0%

Small Hospital HMI 2.5 km 8.5E-02 5.8E-01 76% 15% 9% 2.1E+00 96% 3% 0%

10 km 7.8E-02 5.3E-01 83% 16% 1% 2.1E+00 97% 3% 0%

25 km 7.8E-02 5.3E-01 84% 16% 0% 2.1E+00 97% 3% 0%

Large Hospital HMI (wet scrubber) 2.5 km 8.1E-02 5.5E-01 80% 15% 4% 2.1E+00 96% 3% 0%

10 km 7.8E-02 5.3E-01 84% 16% 1% 2.1E+00 97% 3% 0%

25 km 7.7E-02 5.3E-01 84% 16% 0% 2.1E+00 97% 3% 0%

Small Hospital HMI (wet scrubber) 2.5 km 7.8E-02 5.3E-01 84% 16% 0% 2.1E+00 97% 3% 0%

10 km 7.7E-02 5.3E-01 84% 16% 0% 2.1E+00 97% 3% 0%

25 km 7.7E-02 5.3E-01 84% 16% 0% 2.1E+00 97% 3% 0%

Large Coal-fired Utility Boiler 2.5 km 1.3E-01 9.1E-01 48% 9% 42% 2.1E+00 96% 3% 1%

10 km 8.6E-02 5.9E-01 75% 14% 10% 2.1E+00 96% 3% 0%

25 km 8.0E-02 5.5E-01 81% 15% 4% 2.1E+00 96% 3% 0%

Medium Coal-fired Utility Boiler 2.5 km 1.0E-01 6.9E-01 64% 12% 24% 2.1E+00 96% 3% 0%

10 km 8.3E-02 5.6E-01 78% 15% 7% 2.1E+00 96% 3% 0%

25 km 8.0E-02 5.4E-01 81% 16% 3% 2.1E+00 96% 3% 0%

Small Coal-fired Utility Boiler 2.5 km 8.3E-02 5.6E-01 79% 15% 6% 2.1E+00 96% 3% 0%

10 km 7.9E-02 5.4E-01 82% 16% 2% 2.1E+00 96% 3% 0%

25 km 7.8E-02 5.3E-01 83% 16% 1% 2.1E+00 97% 3% 0%

Medium OIl-fired Utility Boiler 2.5 km 7.8E-02 5.3E-01 83% 16% 1% 2.1E+00 97% 3% 0%

10 km 7.8E-02 5.3E-01 84% 16% 0% 2.1E+00 97% 3% 0%

25 km 7.7E-02 5.3E-01 84% 16% 0% 2.1E+00 97% 3% 0%

Chlor-alkali plant 2.5 km 1.0E+0 6.8E+00 6% 1% 92% 4.5E+00 44% 2% 54%

0

10 km 1.8E-01 1.2E+00 37% 7% 56% 2.5E+00 79% 3% 18%

25 km 1.0E-01 6.8E-01 65% 12% 23% 2.2E+00 90% 3% 7%
2-18

Table 2-15

Predicted Mercury Values in Water Column and Biota for Eastern Site

 (ISC3 + RELMAP 90th)

MHg Tier 4 %Backgro %Rel %ISC Total Hg Grain %Bac %Rel %ISC

Dissolv Fish und Map Concentration kgrou Map

ed MHg (ng/g) nd

Water Concent

Conc.( ration

ng/l) (ug/g)

Variant b:Large Municipal Waste 2 . 5 2.0E-01 1.4E+00 32% 23% 45% 2.2E+00 90% 6% 4%

Combustor km 

10 km 1.5E-01 9.9E-01 44% 32% 23% 2.2E+00 91% 6% 2%

25 km 1.2E-01 8.4E-01 52% 38% 9% 2.1E+00 93% 6% 1%

Variant b:Small Municipal Waste 2 . 5 1.3E-01 8.8E-01 50% 36% 13% 2.1E+00 93% 6% 1%

Combustor km 

10 km 1.2E-01 8.0E-01 55% 40% 4% 2.1E+00 93% 6% 0%

25 km 1.1E-01 7.7E-01 57% 42% 1% 2.1E+00 94% 6% 0%

Large Commercial HMI 2 . 5 1.3E-01 8.9E-01 50% 36% 14% 2.1E+00 93% 6% 1%

km 

10 km 1.1E-01 7.8E-01 57% 41% 2% 2.1E+00 94% 6% 0%

25 km 1.1E-01 7.7E-01 58% 42% 0% 2.1E+00 94% 6% 0%

Large Hospital HMI 2 . 5 2.3E-01 1.5E+00 29% 21% 51% 2.2E+00 90% 6% 4%

km 

10 km 1.3E-01 8.8E-01 50% 37% 13% 2.1E+00 93% 6% 1%

25 km 1.2E-01 7.9E-01 56% 41% 3% 2.1E+00 93% 6% 0%

Small Hospital HMI 2 . 5 1.2E-01 8.2E-01 54% 39% 7% 2.1E+00 93% 6% 0%

km 

10 km 1.1E-01 7.7E-01 57% 42% 1% 2.1E+00 94% 6% 0%

25 km 1.1E-01 7.6E-01 58% 42% 0% 2.1E+00 94% 6% 0%

Large Hospital HMI (wet scrubber) 2 . 5 1.2E-01 7.9E-01 56% 41% 3% 2.1E+00 94% 6% 0%

km 

10 km 1.1E-01 7.7E-01 58% 42% 0% 2.1E+00 94% 6% 0%

25 km 1.1E-01 7.6E-01 58% 42% 0% 2.1E+00 94% 6% 0%

Small Hospital HMI (wet scrubber) 2 . 5 1.1E-01 7.6E-01 58% 42% 0% 2.1E+00 94% 6% 0%

km 

10 km 1.1E-01 7.6E-01 58% 42% 0% 2.1E+00 94% 6% 0%

25 km 1.1E-01 7.6E-01 58% 42% 0% 2.1E+00 94% 6% 0%

Large Coal-fired Utility Boiler 2 . 5 1.7E-01 1.1E+00 38% 28% 34% 2.1E+00 93% 6% 1%

km 

10 km 1.2E-01 8.2E-01 54% 39% 7% 2.1E+00 93% 6% 0%

25 km 1.2E-01 7.8E-01 56% 41% 3% 2.1E+00 94% 6% 0%

Medium Coal-fired Utility Boiler 2 . 5 1.4E-01 9.3E-01 48% 35% 18% 2.1E+00 93% 6% 0%

km 

10 km 1.2E-01 8.0E-01 55% 40% 5% 2.1E+00 94% 6% 0%

25 km 1.1E-01 7.8E-01 57% 41% 2% 2.1E+00 94% 6% 0%

Small Coal-fired Utility Boiler 2 . 5 1.2E-01 8.0E-01 55% 40% 5% 2.1E+00 94% 6% 0%

km 

10 km 1.1E-01 7.8E-01 57% 41% 2% 2.1E+00 94% 6% 0%

25 km 1.1E-01 7.7E-01 58% 42% 1% 2.1E+00 94% 6% 0%

Medium OIl-fired Utility Boiler 2 . 5 1.1E-01 7.7E-01 58% 42% 1% 2.1E+00 94% 6% 0%

km 

10 km 1.1E-01 7.6E-01 58% 42% 0% 2.1E+00 94% 6% 0%

25 km 1.1E-01 7.6E-01 58% 42% 0% 2.1E+00 94% 6% 0%

Chlor-alkali plant 2 . 5 1.0E+0 7.1E+00 6% 5% 89% 4.6E+00 43% 3% 54%

km 0

10 km 2.1E-01 1.4E+00 31% 22% 47% 2.6E+00 77% 5% 18%

25 km 1.4E-01 9.2E-01 48% 35% 17% 2.3E+00 88% 6% 6%
Table 2-16 (continued)

Predicted Mercury Values for Environmental Media at Western Site (ISC3 + RELMAP 50th

2-19

Table 2-16

Predicted Mercury Values for Environmental Media at Western Site (ISC3 + RELMAP 50th

50th Percentile

Plant Distance Air Concentration %RelMap %ISC Total Deposition %RelMap %ISC Total %Ba

(ng/m3) (ug/m2/yr) Hg Soil kgro

Concent ndration inUntilledSoil(ng/g)

Variant b:Large Municipal Waste Combustor 2.5 km 1.7E+00 98% 2% 2.0E+01 11% 89% 3.8E+01 20% 

10 km 1.6E+00 98% 2% 1.1E+01 20% 80% 2.3E+01 33% 25 km 1.6E+00 99% 1% 5.6E+00 41% 59% 1.3E+01 56% 

Variant b:Small Municipal Waste Combustor 2.5 km 1.6E+00 99% 1% 6.2E+00 38% 62% 1.4E+01 53% 

10 km 1.6E+00 100% 0% 3.4E+00 68% 32% 9.9E+00 76% 25 km 1.6E+00 100% 0% 2.7E+00 87% 13% 8.6E+00 87% 

Large Commercial HMI 2.5 km 1.6E+00 99% 1% 6.0E+00 38% 62% 1.4E+01 53% 

10 km 1.6E+00 100% 0% 2.8E+00 83% 17% 8.9E+00 85% 25 km 1.6E+00 100% 0% 2.4E+00 95% 5% 8.3E+00 91% 

Large Hospital HMI 2.5 km 1.7E+00 98% 2% 2.7E+01 9% 91% 4.8E+01 16% 

10 km 1.6E+00 99% 1% 5.9E+00 39% 61% 1.4E+01 54% 25 km 1.6E+00 100% 0% 3.3E+00 71% 29% 9.6E+00 79% 

Small Hospital HMI 2.5 km 1.6E+00 100% 0% 3.9E+00 59% 41% 1.1E+01 71% 

10 km 1.6E+00 100% 0% 2.5E+00 92% 8% 8.4E+00 90% 25 km 1.6E+00 100% 0% 2.4E+00 98% 2% 8.2E+00 93% 

Large Hospital HMI (wet scrubber) 2.5 km 1.6E+00 100% 0% 3.0E+00 77% 23% 9.2E+00 82% 

10 km 1.6E+00 100% 0% 2.4E+00 96% 4% 8.2E+00 92% 25 km 1.6E+00 100% 0% 2.3E+00 99% 1% 8.1E+00 93% 

Small Hospital HMI (wet scrubber) 2.5 km 1.6E+00 100% 0% 2.4E+00 98% 2% 8.2E+00 93% 

10 km 1.6E+00 100% 0% 2.3E+00 100% 0% 8.1E+00 93% 25 km 1.6E+00 100% 0% 2.3E+00 100% 0% 8.1E+00 94% 

Large Coal-fired Utility Boiler 2.5 km 1.6E+00 100% 0% 5.8E+00 40% 60% 1.4E+01 55% 

10 km 1.6E+00 100% 0% 3.5E+00 67% 33% 9.9E+00 76% 25 km 1.6E+00 100% 0% 3.3E+00 69% 31% 9.8E+00 78% 

Medium Coal-fired Utility Boiler 2.5 km 1.6E+00 100% 0% 4.3E+00 53% 47% 1.1E+01 66% 

10 km 1.6E+00 100% 0% 3.7E+00 63% 37% 1.0E+01 73% 25 km 1.6E+00 100% 0% 3.2E+00 73% 27% 9.5E+00 79% 

Small Coal-fired Utility Boiler 2.5 km 1.6E+00 100% 0% 3.4E+00 69% 31% 9.8E+00 77% 
Table 2-16 (continued)

Predicted Mercury Values for Environmental Media at Western Site (ISC3 + RELMAP 50th

50th Percentile

Plant Distance Air Concentration %RelMap %ISC Total Deposition %RelMap %ISC Total %Ba

(ng/m3) (ug/m2/yr) Hg Soil kgro

Concent ndration inUntilledSoil(ng/g)

2-20

10 km 1.6E+00 100% 0% 2.8E+00 84% 16% 8.8E+00 86% 

25 km 1.6E+00 100% 0% 2.5E+00 94% 6% 8.3E+00 91% 

Medium OIl-fired Utility Boiler 2.5 km 1.6E+00 100% 0% 2.4E+00 96% 4% 8.2E+00 92% 

10 km 1.6E+00 100% 0% 2.4E+00 97% 3% 8.2E+00 92% 25 km 1.6E+00 100% 0% 2.3E+00 99% 1% 8.1E+00 93% 

Chlor-alkali plant 2.5 km 3.5E+00 46% 54% 1.9E+02 1% 99% 3.2E+02 2% 

10 km 1.9E+00 84% 16% 2.5E+01 9% 91% 4.5E+01 17% 25 km 1.7E+00 94% 6% 8.1E+00 28% 72% 1.8E+01 43% 
2-21

Table 2-17

Predicted Mercury Values for Environmental Media at Western Site (ISC3 + RELMAP 90th

90th Percentile

Plant Distance Air Concentration %RelMap %ISC Total Deposition %RelMap %ISC Total %B

(ng/m3) (ug/m2/yr) Hg Soil kg

Concent ndration inUntilledSoil(ng/g)

Variant b:Large Municipal Waste Combustor 2.5 km 1.7E+00 98% 2% 2.6E+01 31% 69% 4.7E+01 1

10 km 1.7E+00 98% 2% 1.7E+01 47% 53% 3.2E+01 2425 km 1.7E+00 99% 1% 1.1E+01 71% 29% 2.3E+01 3

Variant b:Small Municipal Waste Combustor 2.5 km 1.7E+00 99% 1% 1.2E+01 67% 33% 2.4E+01 32

10 km 1.6E+00 100% 0% 9.1E+00 88% 12% 1.9E+01 325 km 1.6E+00 100% 0% 8.3E+00 96% 4% 1.8E+01 4

Large Commercial HMI 2.5 km 1.7E+00 99% 1% 1.2E+01 68% 32% 2.3E+01 32

10 km 1.6E+00 100% 0% 8.5E+00 94% 6% 1.8E+01 425 km 1.6E+00 100% 0% 8.1E+00 98% 2% 1.8E+01 4

Large Hospital HMI 2.5 km 1.7E+00 98% 2% 3.2E+01 25% 75% 5.7E+01 1

10 km 1.7E+00 99% 1% 1.2E+01 69% 31% 2.3E+01 325 km 1.6E+00 100% 0% 8.9E+00 90% 10% 1.9E+01 40

Small Hospital HMI 2.5 km 1.6E+00 100% 0% 9.6E+00 83% 17% 2.0E+01 3

10 km 1.6E+00 100% 0% 8.2E+00 98% 2% 1.8E+01 425 km 1.6E+00 100% 0% 8.0E+00 99% 1% 1.7E+01 4

Large Hospital HMI (wet scrubber) 2.5 km 1.6E+00 100% 0% 8.7E+00 92% 8% 1.9E+01 4

10 km 1.6E+00 100% 0% 8.1E+00 99% 1% 1.8E+01 425 km 1.6E+00 100% 0% 8.0E+00 100% 0% 1.7E+01 4

Small Hospital HMI (wet scrubber) 2.5 km 1.6E+00 100% 0% 8.0E+00 99% 1% 1.7E+01 43

10 km 1.6E+00 100% 0% 8.0E+00 100% 0% 1.7E+01 4425 km 1.6E+00 100% 0% 8.0E+00 100% 0% 1.7E+01 44

Large Coal-fired Utility Boiler 2.5 km 1.6E+00 100% 0% 1.2E+01 69% 31% 2.3E+01 33

10 km 1.6E+00 100% 0% 9.1E+00 88% 12% 1.9E+01 325 km 1.6E+00 100% 0% 9.0E+00 89% 11% 1.9E+01 40

Medium Coal-fired Utility Boiler 2.5 km 1.6E+00 100% 0% 1.0E+01 80% 20% 2.1E+01 37

10 km 1.6E+00 100% 0% 9.4E+00 85% 15% 2.0E+01 325 km 1.6E+00 100% 0% 8.9E+00 90% 10% 1.9E+01 40

Small Coal-fired Utility Boiler 2.5 km 1.6E+00 100% 0% 9.1E+00 88% 12% 1.9E+01 40

10 km 1.6E+00 100% 0% 8.5E+00 95% 5% 1.8E+01 425 km 1.6E+00 100% 0% 8.2E+00 98% 2% 1.8E+01 4

Medium OIl-fired Utility Boiler 2.5 km 1.6E+00 100% 0% 8.1E+00 99% 1% 1.8E+01 43

10 km 1.6E+00 100% 0% 8.1E+00 99% 1% 1.7E+01 425 km 1.6E+00 100% 0% 8.0E+00 100% 0% 1.7E+01 4

Chlor-alkali plant 2.5 km 3.6E+00 46% 54% 2.0E+02 4% 96% 3.3E+02 2

10 km 1.9E+00 84% 16% 3.0E+01 26% 74% 5.4E+01 1425 km 1.7E+00 94% 6% 1.4E+01 58% 42% 2.7E+01 2
2-22

Table 2-18

Predicted Mercury Values in Water Column and Biota for Western Site

 (ISC3 + RELMAP 50th)

50th Percentile

MHg Tier 4 %Backgro %Rel %ISC Total Hg Grain %Bac %Rel %ISC

Dissolv Fish und Map Concentration kgrou Map

ed MHg (ng/g) nd

Water Concent

Conc.( ration

ng/l) (ug/g)

Variant b:Large Municipal Waste 2 . 5 8.8E-02 6.0E-01 15% 1% 84% 1.7E+00 96% 1% 3%

Combustor km 

10 km 5.5E-02 3.7E-01 24% 2% 74% 1.7E+00 97% 1% 2%

25 km 2.7E-02 1.9E-01 48% 4% 48% 1.7E+00 98% 1% 1%

Variant b:Small Municipal Waste 2 . 5 3.3E-02 2.3E-01 40% 3% 57% 1.6E+00 98% 1% 1%

Combustor km 

10 km 1.9E-02 1.3E-01 68% 6% 26% 1.6E+00 99% 1% 0%

25 km 1.6E-02 1.1E-01 84% 7% 9% 1.6E+00 99% 1% 0%

Large Commercial HMI 2 . 5 3.4E-02 2.3E-01 39% 3% 58% 1.6E+00 98% 1% 1%

km 

10 km 1.7E-02 1.1E-01 80% 7% 14% 1.6E+00 99% 1% 0%

25 km 1.5E-02 1.0E-01 89% 8% 3% 1.6E+00 99% 1% 0%

Large Hospital HMI 2 . 5 1.4E-01 9.6E-01 9% 1% 90% 1.7E+00 95% 1% 4%

km 

10 km 3.1E-02 2.1E-01 42% 4% 54% 1.6E+00 98% 1% 1%

25 km 1.8E-02 1.2E-01 73% 6% 20% 1.6E+00 99% 1% 0%

Small Hospital HMI 2 . 5 2.3E-02 1.5E-01 58% 5% 37% 1.6E+00 99% 1% 0%

km 

10 km 1.5E-02 1.0E-01 87% 7% 6% 1.6E+00 99% 1% 0%

25 km 1.4E-02 9.8E-02 91% 8% 1% 1.6E+00 99% 1% 0%

Large Hospital HMI (wet scrubber) 2 . 5 1.8E-02 1.2E-01 73% 6% 20% 1.6E+00 99% 1% 0%

km 

10 km 1.5E-02 1.0E-01 90% 8% 3% 1.6E+00 99% 1% 0%

25 km 1.4E-02 9.8E-02 92% 8% 1% 1.6E+00 99% 1% 0%

Small Hospital HMI (wet scrubber) 2 . 5 1.5E-02 9.9E-02 91% 8% 2% 1.6E+00 99% 1% 0%

km 

10 km 1.4E-02 9.7E-02 92% 8% 0% 1.6E+00 99% 1% 0%

25 km 1.4E-02 9.7E-02 92% 8% 0% 1.6E+00 99% 1% 0%

Large Coal-fired Utility Boiler 2 . 5 3.1E-02 2.1E-01 43% 4% 53% 1.6E+00 99% 1% 0%

km 

10 km 1.9E-02 1.3E-01 70% 6% 24% 1.6E+00 99% 1% 0%

25 km 1.8E-02 1.2E-01 73% 6% 21% 1.6E+00 99% 1% 0%

Medium Coal-fired Utility Boiler 2 . 5 2.3E-02 1.5E-01 58% 5% 37% 1.6E+00 99% 1% 0%

km 

10 km 2.0E-02 1.4E-01 66% 6% 28% 1.6E+00 99% 1% 0%

25 km 1.8E-02 1.2E-01 74% 6% 19% 1.6E+00 99% 1% 0%

Small Coal-fired Utility Boiler 2 . 5 1.9E-02 1.3E-01 70% 6% 24% 1.6E+00 99% 1% 0%

km 

10 km 1.6E-02 1.1E-01 81% 7% 13% 1.6E+00 99% 1% 0%

25 km 1.5E-02 1.0E-01 88% 7% 4% 1.6E+00 99% 1% 0%

Medium OIl-fired Utility Boiler 2 . 5 1.5E-02 1.0E-01 90% 8% 2% 1.6E+00 99% 1% 0%

km 

10 km 1.5E-02 9.9E-02 91% 8% 2% 1.6E+00 99% 1% 0%

25 km 1.4E-02 9.8E-02 92% 8% 1% 1.6E+00 99% 1% 0%

Chlor-alkali plant 2 . 5 1.0E+0 6.9E+00 1% 0% 99% 3.7E+00 44% 0% 56%

km 0

10 km 1.2E-01 8.0E-01 11% 1% 88% 1.9E+00 83% 1% 16%

25 km 3.7E-02 2.5E-01 36% 3% 61% 1.7E+00 93% 1% 6%
2-23

Table 2-19

Predicted Mercury Values in Water Column and Biota for Western Site

(ISC3 + RELMAP 90th)

90th Percentile

MHg Tier 4 %Backgro %Rel %ISC Total Hg Grain %Bac %Rel %ISC

Dissolv Fish und Map Concentration kgrou Map

ed MHg (ng/g) nd

Water Concent

Conc.( ration

ng/l) (ug/g)

Variant b:Large Municipal Waste 2 . 5 1.1E-01 7.3E-01 12% 19% 68% 1.7E+00 94% 3% 3%

Combustor km 

10 km 7.5E-02 5.1E-01 18% 28% 54% 1.7E+00 95% 3% 2%

25 km 4.7E-02 3.2E-01 28% 45% 28% 1.7E+00 96% 3% 1%

Variant b:Small Municipal Waste 2 . 5 5.3E-02 3.6E-01 25% 39% 36% 1.7E+00 96% 3% 1%

Combustor km 

10 km 3.9E-02 2.7E-01 34% 53% 13% 1.7E+00 97% 3% 0%

25 km 3.5E-02 2.4E-01 37% 59% 4% 1.7E+00 97% 3% 0%

Large Commercial HMI 2 . 5 5.4E-02 3.6E-01 25% 39% 36% 1.7E+00 96% 3% 1%

km 

10 km 3.6E-02 2.5E-01 36% 58% 6% 1.7E+00 97% 3% 0%

25 km 3.5E-02 2.4E-01 38% 61% 1% 1.7E+00 97% 3% 0%

Large Hospital HMI 2 . 5 1.6E-01 1.1E+00 8% 13% 79% 1.7E+00 94% 3% 4%

km 

10 km 5.1E-02 3.5E-01 26% 41% 33% 1.7E+00 96% 3% 1%

25 km 3.8E-02 2.6E-01 35% 55% 10% 1.7E+00 97% 3% 0%

Small Hospital HMI 2 . 5 4.3E-02 2.9E-01 31% 49% 20% 1.7E+00 97% 3% 0%

km 

10 km 3.5E-02 2.4E-01 38% 60% 3% 1.7E+00 97% 3% 0%

25 km 3.4E-02 2.3E-01 38% 61% 1% 1.7E+00 97% 3% 0%

Large Hospital HMI (wet 2 . 5 3.8E-02 2.6E-01 35% 55% 10% 1.7E+00 97% 3% 0%

scrubber) km 

10 km 3.5E-02 2.3E-01 38% 61% 1% 1.7E+00 97% 3% 0%

25 km 3.4E-02 2.3E-01 39% 61% 0% 1.7E+00 97% 3% 0%

Small Hospital HMI (wet 2 . 5 3.4E-02 2.3E-01 38% 61% 1% 1.7E+00 97% 3% 0%

scrubber) km 

10 km 3.4E-02 2.3E-01 39% 61% 0% 1.7E+00 97% 3% 0%

25 km 3.4E-02 2.3E-01 39% 61% 0% 1.7E+00 97% 3% 0%

Large Coal-fired Utility Boiler 2 . 5 5.0E-02 3.4E-01 26% 42% 32% 1.7E+00 97% 3% 0%

km 

10 km 3.9E-02 2.6E-01 34% 54% 12% 1.7E+00 97% 3% 0%

25 km 3.8E-02 2.6E-01 35% 55% 10% 1.7E+00 97% 3% 0%

Medium Coal-fired Utility Boiler 2 . 5 4.3E-02 2.9E-01 31% 49% 20% 1.7E+00 97% 3% 0%

km 

10 km 4.0E-02 2.7E-01 33% 53% 14% 1.7E+00 97% 3% 0%

25 km 3.8E-02 2.6E-01 35% 56% 9% 1.7E+00 97% 3% 0%

Small Coal-fired Utility Boiler 2 . 5 3.9E-02 2.6E-01 34% 54% 12% 1.7E+00 97% 3% 0%

km 

10 km 3.6E-02 2.5E-01 36% 58% 6% 1.7E+00 97% 3% 0%

25 km 3.5E-02 2.4E-01 38% 60% 2% 1.7E+00 97% 3% 0%

Medium OIl-fired Utility Boiler 2 . 5 3.4E-02 2.3E-01 38% 61% 1% 1.7E+00 97% 3% 0%

km 

10 km 3.4E-02 2.3E-01 38% 61% 1% 1.7E+00 97% 3% 0%

25 km 3.4E-02 2.3E-01 38% 61% 0% 1.7E+00 97% 3% 0%

Chlor-alkali plant 2 . 5 1.0E+0 7.1E+00 1% 2% 97% 3.7E+00 43% 1% 55%

km 

10 km 1.4E-01 9.4E-01 10% 15% 75% 2.0E+00 82% 2% 16%

25 km 5.7E-02 3.9E-01 23% 37% 40% 1.8E+00 92% 3% 6%
3-1

3. PREDICTED INDIVIDUAL EXPOSURE

Using the three models, RELMAP, ISC3, and IEM-2M as well as the hypothetical exposure

scenarios described in Chapter 2 of this Volume, estimates of exposure to individuals residing around

local emissions sources were developed. This exposure assessment incorperated many variables

including types of emissions sources, activity patterns of exposed individuals, climate and impact of

regional atmospheric mercury. Different combinations of these variables provide for a number of

potential outputs. This chapter initially presents a description of the results for one such combination;

this is presented to illustrate how the other combinations presented were developed. This section is

followed by a presentation of the results of the modeling.

3.1 Illustration of Exposure Results

The purpose of this section is to illustrate the results of the exposure modeling by discussing the

results for one facility, distance and site. For the purpose of illustration, the large hospital HMI without a

wet scrubber is selected in the eastern site, and the RELMAP 50th percentile is used as as an example of

the contribution of regional anthropogenic mercury sources. It is noted that a complete discussion is not

practical for all facilities; there are 144 possible combinations: 12 model plants, 2 sites, 3 distances, and

two possible RELMAP values (50th percentile or 90th percentile). These results demonstrate the

impacts of the exposure assessment assumptions used for the hypothetical populations inhabiting the

watershed and water body. It also provides a forum to discuss the more general features and implications

of the exposure assumptions. 

The hospital HMI model plant is assumed to emit a total of 24 kg of mercury a year. Of these

mercury emissions, 73% is divalent mercury vapor, 25 is divalent mercury attached to particulates, and

2% is elemental mercury vapor. At 2.5 km from the source, the total area-averaged air concentration is

predicted to be 1.7 ng/m , of which approximately 3% is predicted to be due to the facility and the rest to 3

regional sources addressed with the RELMAP. The total mercury deposition rate on the watershed is

predicted to be 44 μg/m /yr, with about 70% (30 μg/m /yr) due to the facility; the total deposition rate is 2 2

the sum of the predictions of RELMAP (50th percentile) and ISC3 at 2.5 km from the facility in the

prevailing downwind direction. The predicted area-averaged deposition rate onto the waterbody, which

is located on the side closest to the facility, is 88 μg/m /yr. 2

The air concentration and deposition rates predicted for the facility are combined with the 50th

percentile of the results for the RELMAP model and used as inputs for the IEM-2M model. The initial

conditions assumed are the steady-state results after modeling two different periods of constant

deposition and air concentration. The first period reflects pre-industrial conditions, in which case a

mercury air concentration of 0.5 ng/m and deposition rate of 3 μg/m /yr are assumed. The second period 3 2

represents conditions that exist after the pre-industrial period but before the facility is in operation. The

assumed air concentration was 1.6 ng/m and the deposition rate was 10 μg/m /yr. Table 3-1 shows some 3 2

of the media concentrations predicted after these two simulations.
3-2

Table 3-1

Predicted Mercury Concentrations after Pre-facility Simulations Performed for Eastern Site

(these results are used as initial conditions in IEM-2M model for this site)

%Hg0 %Hg2 %MHg

Watershed soil (ng/g) 47 0.02 98 2

Dissolved in water column 0.9 8 85 7

(ng/L)

3.1.1 Concentrations in Environmental Media and Biota

The predicted concentrations of the three mercury species considered are summarized for various

environmental media and biota in the Table 3-2.

Table 3-2

Modeled results for Large Hospital HMI

(Humid Site, 2.5 km ) Using ISC3 + RELMAP (East 50th percentile)

Total %RelMap %ISC

Waterbody Deposition Rate 8.8E+01 16% 84%

(μg/m2/yr)

Watershed Air Concentration 1.7E+00 97% 3%

(ng/m3)

Watershed Deposition Rate 4.4E+01 33% 67%

(μg/m2/yr)

%Relmap %Backgroun %ISC %Hg2 %MHg

d

Total Mercury Dissolved Surface 2.9

Water Concentration (ng/L)

Dissolved Methylmercury 0.19 6% 34% 60% 0% 100%

concentration in water body (ng/L)

Tier 3 Fish 3.1E-01 6% 34% 60% 0% 100%

Tier 4 Fish 1.3E+00 6% 34% 60% 0% 100%

Tilled Soil (ng/g) 5.0E+01 1% 93% 6% 98% 2%

Notill soil (ng/g) 1.1E+02 7% 44% 48% 98% 2%

Produce (μg/g dry weight)

Grain 2.1E-03 3% 93% 4% 92% 8%

Root Uptake 22%

Direct Deposition 0%

Air-to-plant 78%

Legumes 2.5E-03 3% 91% 6% 93% 7%

Root Uptake 31%

Direct Deposition 3%

Air-to-plant 66%

Potatoes 5.1E-03 1% 93% 6% 96% 4%

Root Uptake 100%

Direct Deposition 0%
Total %RelMap %ISC

Waterbody Deposition Rate 8.8E+01 16% 84%

(μg/m2/yr)

Watershed Air Concentration 1.7E+00 97% 3%

(ng/m3)

Watershed Deposition Rate 4.4E+01 33% 67%

(μg/m2/yr)

%Relmap %Backgroun %ISC %Hg2 %MHg

d

3-3

Air-to-plant 0%
Table 3-2 (continued)

Modeled results for Large Hospital HMI

(Humid Site, 2.5 km) Using ISC3 + RELMAP (East 50th percentile)

Total %RelMap %ISC

Waterbody Deposition Rate 8.8E+01 16% 84%

(μg/m2/yr)

Watershed Air Concentration 1.7E+00 97% 3%

(ng/m3)

Watershed Deposition Rate 4.4E+01 33% 67%

(μg/m2/yr)

%Relmap %Backgroun %ISC %Hg2 %MHg

d

3-4

Root Vegetables 1.9E-03 1% 93% 6% 95% 5%

Root Uptake 100%

Direct Deposition 0%

Air-to-plant 0%

Fruits 3.5E-02 4% 92% 4% 95% 5%

Root Uptake 3%

Direct Deposition 1%

Air-to-plant 96%

Fruiting Vegetables 3.5E-02 4% 92% 4% 95% 5%

Root Uptake 3%

Direct Deposition 1%

Air-to-plant 96%

Leafy Vegetables 3.4E-02 4% 91% 5% 79% 21%

Root Uptake 0%

Direct Deposition 2%

Air-to-plant 98%

Animal Products (μg/g dry weight)

Beef 8.6E-03 4% 86% 10% 81% 19%

from grain 0%

from Forage 71%

from Silage 20%

from Soil 9%

Beef Liver 2.2E-02 4% 86% 10% 81% 19%

from grain 0%

from Forage 71%

from Silage 20%

from Soil 9%

Dairy 1.1E-02 4% 87% 9% 81% 19%

from grain 1%

from Forage 70%

from Silage 21%

from Soil 8%

Pork 7.0E-06 4% 89% 7% 82% 18%

from grain 12%

from Silage 81%

from Soil 7%

Poultry 1.2E-04 7% 52% 41% 97% 3%

from grain 15%

from Soil 85%

Eggs 1.2E-04 7% 52% 41% 97% 3%

from grain 15%
Table 3-2 (continued)

Modeled results for Large Hospital HMI

(Humid Site, 2.5 km) Using ISC3 + RELMAP (East 50th percentile)

Total %RelMap %ISC

Waterbody Deposition Rate 8.8E+01 16% 84%

(μg/m2/yr)

Watershed Air Concentration 1.7E+00 97% 3%

(ng/m3)

Watershed Deposition Rate 4.4E+01 33% 67%

(μg/m2/yr)

%Relmap %Backgroun %ISC %Hg2 %MHg

d

3-5

from Soil 85%

Lamb 3.9E-03 4% 84% 11% 81% 19%

from forage 88%

from Soil 12%

Other Produce (μg/g dry weight)

Forage 3.5E-02 4% 90% 6% 79% 21%

Root Uptake 0%

Direct Deposition 4%

Air-to-plant 96%

Silage 3.4E-02 4% 92% 4% 79% 21%

Root Uptake 0%

Direct Deposition 1%

Air-to-plant 99%

3.1.1.1 Methylmercury Concentrations in Fish

The methylmercury concentration in the fish is determined by multiplying the dissolved

methylmercury concentration in water by a BAF (derivation is described in Volume 3 Appendix D). The

facility is predicted to account for more than half of the methylmercury in the fish for the waterbody

located 2.5 km from the source. This is not via the deposition of methylmercury itself; rather, it is due to

the deposition of elemental and divalent mercury which is either predicted to be methylated after direct

deposition in the water body, or is methylated in the watershed soil and subsequently flows into the

waterbody via runoff or erosion.

The “background” is predicted to account for approximately one third of the methylmercury

concentration in fish. This background represents the steady-state conditions that are predicted to exist

prior to both the facility and the sources represented in the RELMAP modeling, and are used as initial

conditions in the IEM-2M modeling to predict biota concentrations and human exposure.

In the four-tier trophic food chain model used in this Report, fish were assumed to feed at two

levels. Trophic level 3 fish were assumed to feed on plankton which are predicted to be contaminated

with comparatively low levels of methylmercury. Trophic level 4 fish were assumed to feed on trophic

level 3 fish, which have higher methylmercury concentrations than the plankton. The median BAF of

1.6e6 L/kg for trophic level 3 fish was estimated using several sets of data on measured mercury

concentrations in fish and water. The media BAF for trophic level 4 of 6.8e6 L/kg) was estimated by

applying a predator-prey factor (of approximately 5) to the bioaccumulation factor estimated for trophic

level 3 fish.
3-6

3.1.1.2 Concentrations in Other Biota

 Plant Concentrations

Three routes by which plants can take up mercury are addressed here: root uptake, whereby the

plant is assumed to take up mercury from the soil; direct deposition, whereby the mercury deposited on

the plantshoot from atmospheric deposition transfers to the plant; and air-to-plant transfer, whereby the

mercury in the air is transported through the stomata into the plant. In all cases, at least 79% of the

mercury in the plant products is predicted to be of the divalent form, with the rest being methylmercury. 

The mercury in potatoes and root vegetables results solely from root uptake since no air uptake

was assumed to occur for these plants (Appendix B of Volume III). For leafy vegetables, all the mercury

is predicted to be from air uptake since no root uptake was assumed to occur. For grains, legumes, fruits

and fruiting vegetables the bulk of mercury is also modeled to come from air uptake of elemental

mercury and transformation to other species; note, however, that the air and soil biotransfer factors were

calculated based on a conservative premise that air and soil uptake should be of comparable strength. 

This was done because the soil concentrations used for this demonstration are several times lower than

the soil concentrations from the Cappon (1981 and 1987) studies from which the soil BCFs were derived. 

For more details pertaining to the plant-soil BCF please see Appendix B of Volume III.

Generally, the facility is predicted to contribute less than 10% to the total mercury plant

concentration. For the plant types for which air-uptake is assumed to be the primary source of mercury,

the facility contribution is similar to the contribution of the facility to the local air concentrations. For

the plant types that uptake mercury primarily from the soil, it is due to the predicted dynamics of the

tilled soil in which the plants are assumed to be grown.

Hanson et al. (1994) stated that "dry foliar surfaces in terrestrial forest landscapes may not be a

net sink for atmospheric Hg , but rather as a dynamic exchange surface that can function as a source or 0

sink dependent on current Hg vapor concentrations, leaf temperatures, surface condition (wet versus dry)

and level of atmospheric oxidants." Similarly, Mosbaek et al. (1988) showed that most of the mercury in

leafy plants is attributable to air-leaf transfer, but that for a given period of time the amount of elemental

mercury released from the plant-soil system greatly exceeds the amount collected from the air by the

plants. It is also likely that many plants accumulate airborne mercury to certain concentrations, after

which net deposition of elemental mercury does not occur. This is also a function of the large area of

uncertainty in deriving soil-to-plant and air-to-plant BCFs for mercury due to the wide variation in values

among different studies. This is described in Appendix B of Volume III, Section B.1.2.2, B.1.2.2.1, and

B.1.2.2.2. 

In general, the plant uptake of mercury is predicted to be dominated by either root uptake or airto-plant transfer. For facilities in which the deposition rate is significantly higher, direct deposition may

be a more important pathway. Similarly, the root uptake pathway may be more important in areas with

higher soil concentrations. 

3.1.1.3 Mercury Concentrations in Animal Products

The concentrations in animal products were calculated by multiplying the total daily intake of a

particular species of mercury by a transfer factor that can depend on the animal species and tissue. The

animals considered may be exposed to mercury via four possible pathways: ingestion of contaminated
3-7

grain, forage, silage, or soil. The contribution from these pathways depends on both the predicted

concentration in the plant or soil and the ingestion rate for a particular pathway.

For beef and dairy products, most of the intake of mercury is from forage and silage because

these plants are assumed to make up over 80% of their total diet (see Appendix A). The predicted

concentration for beef liver is slightly higher than that for beef due to a higher transfer factor for beef

liver. For poultry products, most of the mercury exposure is predicted to occur through the ingestion of

soil (N.B. the untilled soil is assumed to be consumed).

3.1.2. Results for Hypothetical Exposure Scenarios

In this section the predicted biota concentrations are used in conjunction with the hypothetical

exposure scenarios to estimate exposure to the human receptors.

Based on the predicted concentrations in biota and using the hypothetical exposure scenarios

described in the previous sections, the predicted human intake rates for each scenario are shown in

Tables 3-3 through Table 3-8.

In general, exposure to mercury is dominated by indirect exposure for any scenario that includes

an ingestion pathway other than soil. Furthermore, exposure tends to be dominated by either divalent or

methylmercury species rather than elemental mercury. For the agricultural and urban scenarios, divalent

mercury is the dominant form. For the scenarios that include fish ingestion, methylmercury dominates

predicted exposure.

3.1.2.1 Rural Scenarios

For the rural scenarios considered, exposure to divalent mercury accounted for over 90% of the

total mercury exposure. The primary exposure pathway is from plant products which account for 50-

70% of the total mercury exposure. Most of the exposure through plant products is predicted to occur

from consumption of fruits and grains. The rural subsistence farmer receptors are predicted to have

about four times as much exposure to mercury as the rural home gardener.

Exposure to mercury from milk (dairy) dominates exposure from animal products for the high

end rural scenario considered (total of seven types of animal products are assumed to be consumed).

These individuals were assumed not to consume fish; as a consequence, predicted methylmercury

exposures are low. 

The local source is predicted to account for less than 10% of the total mercury exposure for the

rural scenarios.

3.1.2.2 Urban Scenarios

For the urban average scenario, the only exposure pathways considered are inhalation and

ingestion of soil. For the urban high end scenario, the ingestion of home grown produce is considered as

well, although with lower contact fractions than for the rural home gardener scenario. 

For the urban average scenarios, exposure to mercury from the inhalation route was equal to or

exceeded indirect exposure. The urban high-end scenario included a small garden to the urban average
3-8

scenario, with the result that similar contributions to the total divalent mercury and methylmercury

exposures occurred as for the rural home gardeners. The urban high-end adult receptor had a predicted

mercury exposure of about one-half that of the rural home gardener. The high end urban child scenario

consisted of a pica child assumed to ingest 7.5 grams of soil per day. The exposure rate is then

proportional to the assumed untilled soil concentration, which in this case is 100 ng/g.

3.1.2.3 Fish Ingestion Scenarios

It was assumed that the high-end fish consumer eats fish from the affected freshwater lake on a

daily basis; that is, seasonal consumption rate variation was not addressed. This individual is the most

exposed adult to methylmercury in this assessment, and was predicted to be exposed to approximately

twice the level of methylmercury that the recreational angler is exposed. Fish consumption is predicted to

be the primary source of methylmercury in the diet. The high-end fisher was assumed to consume two

times as much fish as the recreational angler (60 g/day vs. 30 g/day). On a gram per bodyweight basis,

the high-end fish-consuming child was the maximally exposed subpopulation. This is based on the

hypothetical child’s fish consumption rate and the bodyweight, and is consistent with the data presented

in the Chapter 4 of this Volume.

 For the fish ingestion scenarios, intake of mercury was mainly the methylmercury species. 

Although intake of methylmercury via plants and soil is considered in the high-end fish consumption

scenario, it accounts for less than 1% of the total methylmercury intake. The recreational angler was

assumed to be exposed to mercury via fish, soil and water consumption. Exposure via soil and water

however, accounted for less than 0.1% of the total mercury intake. 
3-9

Table 3-3

Predicted Mercury Exposure for Subsistence Farmer Scenario

ISC: Large Hospital HMI(Humid Site, 2.5 km ) + RELMAP(East 50th percentile)

Subsistence Farmer

mg/kg/day Adult

Total %Relmap %Background %ISC %Hg2 %MHg

Inhalation 4.9E-07 4% 93% 3% 0% 0%

Ingestion Total 4.1E-05 0% 4% 90% 6% 90% 10%

Fish Ingestion 0.0E+00 0% NA NA NA NA NA

Water Ingestion 5.0E-07 1% 25% 56% 18% 97% 2%

Produce Ingestion 2.9E-05 71% 4% 92% 4% 94% 6%

Grains 4.0E-06 10% 3% 93% 4% 92% 8%

Legumes 9.5E-07 2% 3% 91% 6% 93% 7%

Potatoes 8.7E-07 2% 1% 93% 6% 96% 4%

Root vegetables 4.5E-08 0% 1% 93% 6% 95% 5%

Fruits 2.0E-05 49% 4% 92% 4% 95% 5%

Fruiting vegetables 2.2E-06 5% 4% 92% 4% 95% 5%

Leafy vegetables 9.6E-07 2% 4% 91% 5% 79% 21%

Animal Ingestion 1.1E-05 27% 4% 86% 9% 81% 19%

Beef 2.9E-06 7% 4% 86% 10% 81% 19%

Beef liver 1.4E-06 4% 4% 86% 10% 81% 19%

Dairy 6.5E-06 16% 4% 87% 9% 81% 19%

Pork 1.2E-09 0% 4% 89% 7% 82% 18%

Poultry 1.4E-08 0% 7% 52% 41% 97% 3%

Eggs 9.0E-09 0% 7% 52% 41% 97% 3%

Lamb 2.2E-07 1% 4% 84% 11% 81% 19%

Soil Ingestion 1.5E-07 0% 7% 44% 48% 98% 2%

ISC: Large Hospital HMI(Humid Site, 2.5 km ) + RELMAP(East 50th percentile)

Subsistence Farmer

mg/kg/day Child

Total %Relmap %Background %ISC %Hg2 %MHg

Inhalation 1.6E-06 4% 93% 3% 0% 0%

Total Ingestion 5.3E-05 0% 4% 87% 8% 87% 13%

Fish Ingestion 0.0E+00 0% NA NA NA NA NA

Water Ingestion 1.0E-06 2% 25% 56% 18% 97% 2%

Produce Ingestion 2.3E-05 44% 3% 92% 4% 94% 6%

Grains 8.1E-06 15% 3% 93% 4% 92% 8%

Legumes 1.7E-06 3% 3% 91% 6% 93% 7%

Potatoes 1.4E-06 3% 1% 93% 6% 96% 4%

Root vegetables 6.7E-08 0% 1% 93% 6% 95% 5%

Fruits 7.8E-06 15% 4% 92% 4% 95% 5%

Fruiting vegetables 4.2E-06 8% 4% 92% 4% 95% 5%

Leafy vegetables 2.7E-07 1% 4% 91% 5% 79% 21%

Animal Ingestion 2.8E-05 52% 4% 86% 9% 81% 19%

Beef 4.8E-06 9% 4% 86% 10% 81% 19%

Beef liver 5.4E-07 1% 4% 86% 10% 81% 19%

Dairy 2.2E-05 41% 4% 87% 9% 81% 19%

Pork 1.7E-09 0% 4% 89% 7% 82% 18%

Poultry 2.6E-08 0% 7% 52% 41% 97% 3%

Eggs 1.1E-08 0% 7% 52% 41% 97% 3%

Lamb 2.4E-07 0% 4% 84% 11% 81% 19%

Soil Ingestion 1.2E-06 2% 7% 44% 48% 98% 2%
3-10

Table 3-4

Predicted Mercury Exposure for Rural Home Gardener

ISC: Large Hospital HMI(Humid Site, 2.5 km ) + RELMAP(East 50th percentile)

Rural Home Gardener

mg/kg/day Adult

Total %Relmap %Background %ISC %Hg2 %MHg

Inhalation 4.9E-07 4% 93% 3% 0% 0%

Ingestion Total 9.9E-06 0% 4% 91% 5% 94% 6%

Fish Ingestion 0.0E+00 0% NA NA NA NA NA

Water Ingestion 0.0E+00 0% NA NA NA NA NA

Produce Ingestion 9.7E-06 98% 4% 92% 4% 94% 6%

Grains 2.7E-06 27% 3% 93% 4% 92% 8%

Legumes 7.6E-07 8% 3% 91% 6% 93% 7%

Potatoes 2.0E-07 2% 1% 93% 6% 96% 4%

Root vegetables 1.2E-08 0% 1% 93% 6% 95% 5%

Fruits 4.6E-06 47% 4% 92% 4% 95% 5%

Fruiting vegetables 1.4E-06 14% 4% 92% 4% 95% 5%

Leafy vegetables 5.5E-08 1% 4% 91% 5% 79% 21%

Animal Ingestion 0.0E+00 0% NA NA NA NA NA

Beef 0.0E+00 0% NA NA NA NA NA

Beef liver 0.0E+00 0% NA NA NA NA NA

Dairy 0.0E+00 0% NA NA NA NA NA

Pork 0.0E+00 0% NA NA NA NA NA

Poultry 0.0E+00 0% NA NA NA NA NA

Eggs 0.0E+00 0% NA NA NA NA NA

Lamb 0.0E+00 0% NA NA NA NA NA

Soil Ingestion 1.5E-07 2% 7% 44% 48% 98% 2%

ISC: Large Hospital HMI(Humid Site, 2.5 km ) + RELMAP(East 50th percentile)

Rural Home Gardener

mg/kg/day Child

Total %Relmap %Background %ISC %Hg2 %MHg

Inhalation 1.6E-06 4% 93% 3% 0% 0%

Total Ingestion 1.3E-05 0% 4% 88% 9% 94% 6%

Fish Ingestion 0.0E+00 0% NA NA NA NA NA

Water Ingestion 0.0E+00 0% NA NA NA NA NA

Produce Ingestion 1.1E-05 90% 3% 92% 4% 94% 6%

Grains 5.4E-06 42% 3% 93% 4% 92% 8%

Legumes 1.3E-06 10% 3% 91% 6% 93% 7%

Potatoes 3.2E-07 2% 1% 93% 6% 96% 4%

Root vegetables 1.8E-08 0% 1% 93% 6% 95% 5%

Fruits 1.8E-06 14% 4% 92% 4% 95% 5%

Fruiting vegetables 2.6E-06 20% 4% 92% 4% 95% 5%

Leafy vegetables 1.6E-08 0% 4% 91% 5% 79% 21%

Animal Ingestion 0.0E+00 0% NA NA NA NA NA

Beef 0.0E+00 0% NA NA NA NA NA

Beef liver 0.0E+00 0% NA NA NA NA NA

Dairy 0.0E+00 0% NA NA NA NA NA

Pork 0.0E+00 0% NA NA NA NA NA

Poultry 0.0E+00 0% NA NA NA NA NA

Eggs 0.0E+00 0% NA NA NA NA NA

Lamb 0.0E+00 0% NA NA NA NA NA

Soil Ingestion 1.2E-06 10% 7% 44% 48% 98% 2%
3-11

Table 3-5

Predicted Mercury Exposure for Urban Average Scenario

ISC: Large Hospital HMI(Humid Site, 2.5 km ) + RELMAP(East 50th percentile)

Urban Average

mg/kg/day Adult

Total %Relmap %Background %ISC %Hg2 %MHg

Inhalation 3.3E-07 4% 93% 3% 0% 0%

Ingestion Total 2.0E-07 100% 6% 54% 40% 98% 2%

Soil Ingestion 2.0E-07 100% 6% 54% 40% 98% 2%

ISC: Large Hospital HMI(Humid Site, 2.5 km ) + RELMAP(East 50th percentile)

Urban Average

mg/kg/day Child

Total %Relmap %Background %ISC %Hg2 %MHg

Inhalation 1.6E-06 4% 93% 3% 0% 0%

Total Ingestion 1.6E-06 100% 6% 54% 40% 98% 2%

Soil Ingestion 1.6E-06 100% 6% 54% 40% 98% 2%

Table 3-6

Predicted Mercury Exposure for Urban High-end Scenarios

ISC: Large Hospital HMI(Humid Site, 2.5 km ) + RELMAP(East 50th percentile)

Urban High End

mg/kg/day Adult

Total %Relmap %Background %ISC %Hg2 %MHg

Inhalation 4.9E-07 4% 93% 3% 0% 0%

Ingestion Total 4.0E-06 100% 4% 91% 6% 94% 6%

Fish Ingestion 0.0E+00 0% NA NA NA NA NA

Water Ingestion 0.0E+00 0% NA NA NA NA NA

Produce Ingestion 3.8E-06 95% 3% 93% 4% 94% 6%

Grains 8.8E-07 22% 3% 94% 3% 93% 7%

Legumes 5.6E-07 14% 3% 92% 5% 93% 7%

Potatoes 4.2E-08 1% 1% 95% 4% 96% 4%

Root vegetables 5.1E-09 0% 1% 95% 4% 95% 5%

Fruits 1.5E-06 39% 4% 92% 4% 95% 5%

Fruiting vegetables 7.2E-07 18% 4% 92% 4% 95% 5%

Leafy vegetables 2.5E-08 1% 4% 91% 5% 79% 21%

Soil Ingestion 2.0E-07 5% 6% 54% 40% 98% 2%

ISC: Large Hospital HMI(Humid Site, 2.5 km ) + RELMAP(East 50th percentile)

Urban High End

mg/kg/day Child

Total %Relmap %Background %ISC %Hg2 %MHg

Inhalation 1.6E-06 4% 93% 3% 0% 0%

Total Ingestion 6.1E-05 100% 6% 54% 40% 98% 2%

Soil Ingestion 6.1E-05 100% 6% 54% 40% 98% 2%
3-12

Table 3-7

Predicted Mercury Exposure for High-end Fish Consumption Scenario

ISC: Large Hospital HMI(Humid Site, 2.5 km ) + RELMAP(East 50th percentile)

Subsistence Fisher

mg/kg/day Adult

Total %Relmap %Background %ISC %Hg2 %MHg

Inhalation 4.9E-07 4% 93% 3% 0% 0%

Ingestion Total 1.1E-03 0% 6% 34% 59% 1% 99%

Fish Ingestion 1.1E-03 99% 6% 34% 60% 0% 100%

Water Ingestion 1.0E-07 0% 6% 32% 62% 87% 7%

Produce Ingestion 9.7E-06 1% 4% 92% 4% 94% 6%

Grains 2.7E-06 0% 3% 93% 4% 92% 8%

Legumes 7.6E-07 0% 3% 91% 6% 93% 7%

Potatoes 2.0E-07 0% 1% 93% 6% 96% 4%

Root vegetables 1.2E-08 0% 1% 93% 6% 95% 5%

Fruits 4.6E-06 0% 4% 92% 4% 95% 5%

Fruiting vegetables 1.4E-06 0% 4% 92% 4% 95% 5%

Leafy vegetables 5.5E-08 0% 4% 91% 5% 79% 21%

Animal Ingestion 0.0E+00 0% NA NA NA NA NA

Beef 0.0E+00 0% NA NA NA NA NA

Beef liver 0.0E+00 0% NA NA NA NA NA

Dairy 0.0E+00 0% NA NA NA NA NA

Pork 0.0E+00 0% NA NA NA NA NA

Poultry 0.0E+00 0% NA NA NA NA NA

Eggs 0.0E+00 0% NA NA NA NA NA

Lamb 0.0E+00 0% NA NA NA NA NA

Soil Ingestion 1.5E-07 0% 7% 44% 48% 98% 2%

ISC: Large Hospital HMI(Humid Site, 2.5 km ) + RELMAP(East 50th percentile)

High end Fish Consumer

mg/kg/day Child

Total %Relmap %Background %ISC %Hg2 %MHg

Inhalation 1.6E-06 4% 93% 3% 0% 0%

Total Ingestion 1.6E-03 0% 6% 34% 59% 1% 99%

Fish Ingestion 1.5E-03 99% 6% 34% 60% 0% 100%

Water Ingestion 2.2E-07 0% 6% 32% 62% 87% 7%

Produce Ingestion 1.1E-05 1% 3% 92% 4% 94% 6%

Grains 5.4E-06 0% 3% 93% 4% 92% 8%

Legumes 1.3E-06 0% 3% 91% 6% 93% 7%

Potatoes 3.2E-07 0% 1% 93% 6% 96% 4%

Root vegetables 1.8E-08 0% 1% 93% 6% 95% 5%

Fruits 1.8E-06 0% 4% 92% 4% 95% 5%

Fruiting vegetables 2.6E-06 0% 4% 92% 4% 95% 5%

Leafy vegetables 1.6E-08 0% 4% 91% 5% 79% 21%

Soil Ingestion 1.2E-06 0% 7% 44% 48% 98% 2%
3-13

Table 3-8

Predicted Mercury Exposure for Recreational Angler Scenario

ISC: Large Hospital HMI(Humid Site, 2.5 km ) + RELMAP(East 50th percentile)

Recreational Angler

mg/kg/day Adult

Total %Relmap %Background %ISC %Hg2 %MHg

Inhalation 4.9E-07 4% 93% 3% 0% 0%

Ingestion Total 5.6E-04 0% 6% 34% 60% 0% 100%

Fish Ingestion 5.6E-04 100% 6% 34% 60% 0% 100%

Water Ingestion 1.0E-07 0% 6% 32% 62% 87% 7%

Soil Ingestion 1.5E-07 0% 7% 45% 48% 98% 2%

3.2 Results of Combining Local and Regional Models - Predicted Human Exposure

In this section the results are presented for combining the local and regional impacts of

anthropogenic sources. For both the eastern and western sites, the 50th and 90th percentile of the

predicted air concentrations and deposition rates by the regional air model are used in conjunction with

the air concentrations and deposition rates predicted by the local scale model for each plant to obtain

estimates of environmental concentrations and possible exposure for humans. Background mercury

concentrations in environmental media are also included.

Tables 3-9 through 3-22 show the predicted human intake for each exposure scenario and site. 

The results include receptors located at three distances from the facility (2.5km, 10km, and 25km). In all

cases, the predicted impact of the local source decreases as the distance from the local source increases.

3.2.1 Inhalation

Only for the chlor-alkali plant is the local source predicted to account for more than 50% of total

mercury exposure due to inhalation, and then only for the closest receptor considered (2.5km). The

primary form of mercury that constitutes this exposure is elemental mercury. Further, the inhalation

route is rarely predicted to be the dominant pathway of total mercury exposure when compared to

indirect exposure. The exception is the “urban average” exposure, in which an adult is assumed to ingest

average amounts of soil in the impacted area. The insignificance of exposure through the inhalation

route when compared to ingestion routes was described previously by the WHO (WHO, 1990). 

3.2.2 Agricultural Scenarios

In general, the local source is predicted to account for less than 10% of the total mercury

exposure for the agricultural scenarios, compared to the contribution of the regional sources (RELMAP)

and background. This is because for these scenarios ingestion of plants is the dominant pathway for

mercury exposure, and the plant concentrations are predicted to accumulate mercury from the air more

than via soil uptake. The contribution of the local source is then roughly equivalent to the impact of the

local source on the air concentration. It is only for the chlor-alkali plant that this contribution is more

than 20% (at 2.5km and 10km). The mercury in potatoes and root vegetables results solely from root

uptake since no air uptake was assumed to occur for these plants (Appendix A). For leafy vegetables, all

the mercury is predicted to be from air uptake since no root uptake was assumed to occur. For grains,

legumes, fruits and 
3-14

Table 3-9

Eastern Site — RELMAP 50th and 90th Percentiles

Predicted Ingestion (mg/kg/day) for Subsistence Farmer

Eastern SiteRELMAP 50th percentile

Predicted Ingestion (mg/kg/day) for Subsistence Farmer

2.5km 10 km 

Child Adult Child Adult Child 

Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC 

Variant b:Large 5.4E-05 9% 4.1E-05 6% 5.1E-05 4% 4.0E-05 4% 5.0E-05 2% Municipal WasteCombustorVariant b:Small 5.0E-05 2% 3.9E-05 1% 4.9E-05 1% 3.9E-05 1% 4.9E-05 0% Municipal WasteCombustorLarge Commercial HMI 5.0E-05 2% 3.9E-05 1% 4.9E-05 0% 3.8E-05 0% 4.9E-05 0% Large Hospital HMI 5.3E-05 8% 4.1E-05 6% 5.0E-05 2% 3.9E-05 1% 4.9E-05 1% Small Hospital HMI 4.9E-05 1% 3.8E-05 0% 4.9E-05 0% 3.8E-05 0% 4.9E-05 0% Large Hospital HMI 4.9E-05 0% 3.8E-05 0% 4.9E-05 0% 3.8E-05 0% 4.9E-05 0% (wet scrubber)Small Hospital HMI 4.9E-05 0% 3.8E-05 0% 4.9E-05 0% 3.8E-05 0% 4.9E-05 0% (wet scrubber)Large Coal-fired Utility 5.1E-05 4% 3.9E-05 3% 5.0E-05 1% 3.9E-05 1% 4.9E-05 0% BoilerMedium Coal-fired 5.0E-05 2% 3.9E-05 1% 4.9E-05 1% 3.8E-05 0% 4.9E-05 0% Utility BoilerSmall Coal-fired Utility 4.9E-05 1% 3.8E-05 0% 4.9E-05 0% 3.8E-05 0% 4.9E-05 0% BoilerMedium OIl-fired 4.9E-05 0% 3.8E-05 0% 4.9E-05 0% 3.8E-05 0% 4.9E-05 0% Utility BoilerChlor-alkali plant 1.3E-04 62% 9.6E-05 60% 6.3E-05 23% 4.9E-05 22% 5.3E-05 8% 
3-15

Table 3-9 (continued)

Eastern Site — RELMAP 50th and 90th Percentiles

Predicted Ingestion (mg/kg/day) for Subsistence Farmer

Eastern Site

RELMAP 90th percentile

Predicted Ingestion (mg/kg/day) for Subsistence Farmer

2.5km 10 km 2

Child Adult Child Adult Child 

Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC 

Variant b:Large 5.7E-05 8% 4.3E-05 6% 5.4E-05 4% 4.2E-05 3% 5.3E-05 2% Municipal WasteCombustorVariant b:Small 5.3E-05 2% 4.1E-05 1% 5.3E-05 1% 4.0E-05 1% 5.2E-05 0% Municipal WasteCombustorLarge Commercial HMI 5.3E-05 2% 4.1E-05 1% 5.2E-05 0% 4.0E-05 0% 5.2E-05 0% Large Hospital HMI 5.7E-05 8% 4.3E-05 6% 5.3E-05 2% 4.1E-05 1% 5.2E-05 0% Small Hospital HMI 5.3E-05 1% 4.0E-05 0% 5.2E-05 0% 4.0E-05 0% 5.2E-05 0% Large Hospital HMI 5.2E-05 0% 4.0E-05 0% 5.2E-05 0% 4.0E-05 0% 5.2E-05 0% (wet scrubber)Small Hospital HMI 5.2E-05 0% 4.0E-05 0% 5.2E-05 0% 4.0E-05 0% 5.2E-05 0% (wet scrubber)Large Coal-fired Utility 5.4E-05 4% 4.1E-05 2% 5.3E-05 1% 4.0E-05 1% 5.2E-05 0% BoilerMedium Coal-fired 5.3E-05 2% 4.1E-05 1% 5.2E-05 1% 4.0E-05 0% 5.2E-05 0% Utility BoilerSmall Coal-fired Utility 5.2E-05 1% 4.0E-05 0% 5.2E-05 0% 4.0E-05 0% 5.2E-05 0% BoilerMedium OIl-fired 5.2E-05 0% 4.0E-05 0% 5.2E-05 0% 4.0E-05 0% 5.2E-05 0% Utility BoilerChlor-alkali plant 1.3E-04 61% 9.8E-05 59% 6.7E-05 22% 5.1E-05 21% 5.7E-05 8% 
3-16

Table 3-10

Eastern Site — RELMAP 50th and 90th Percentiles

Predicted Ingestion (mg/kg/day) for Rural Home Gardner

Eastern Site

RELMAP 50th percentile

Predicted Ingestion (mg/kg/day) for Rural Home Gardener

2.5km 10 km 2

Child Adult Child Adult Child 

Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC 

Variant b:Large 1.3E-05 8% 9.9E-06 5% 1.2E-05 4% 9.7E-06 3% 1.2E-05 2% Municipal WasteCombustorVariant b:Small 1.2E-05 2% 9.5E-06 1% 1.2E-05 1% 9.4E-06 1% 1.2E-05 0% Municipal WasteCombustorLarge Commercial HMI 1.2E-05 2% 9.5E-06 1% 1.2E-05 0% 9.4E-06 0% 1.2E-05 0% Large Hospital HMI 1.3E-05 9% 9.9E-06 5% 1.2E-05 2% 9.5E-06 1% 1.2E-05 1% Small Hospital HMI 1.2E-05 1% 9.4E-06 0% 1.2E-05 0% 9.4E-06 0% 1.2E-05 0% Large Hospital HMI 1.2E-05 0% 9.4E-06 0% 1.2E-05 0% 9.4E-06 0% 1.2E-05 0% (wet scrubber)Small Hospital HMI 1.2E-05 0% 9.4E-06 0% 1.2E-05 0% 9.4E-06 0% 1.2E-05 0% (wet scrubber)Large Coal-fired Utility 1.2E-05 3% 9.5E-06 1% 1.2E-05 1% 9.4E-06 0% 1.2E-05 0% BoilerMedium Coal-fired 1.2E-05 2% 9.5E-06 1% 1.2E-05 1% 9.4E-06 0% 1.2E-05 0% Utility BoilerSmall Coal-fired Utility 1.2E-05 1% 9.4E-06 0% 1.2E-05 0% 9.4E-06 0% 1.2E-05 0% BoilerMedium OIl-fired 1.2E-05 0% 9.4E-06 0% 1.2E-05 0% 9.4E-06 0% 1.2E-05 0% Utility BoilerChlor-alkali plant 3.0E-05 62% 2.2E-05 58% 1.5E-05 22% 1.2E-05 20% 1.3E-05 8% 
3-17

Table 3-10 (continued)

Eastern Site — RELMAP 50th and 90th Percentiles

Predicted Ingestion (mg/kg/day) for Rural Home Gardner

Eastern Site

RELMAP 90th percentile

Predicted Ingestion (mg/kg/day) for Rural Home Gardener

2.5km 10 km 2

Child Adult Child Adult Child 

Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC 

Variant b:Large 1.3E-05 8% 1.0E-05 5% 1.3E-05 4% 1.0E-05 3% 1.2E-05 2% Municipal WasteCombustorVariant b:Small 1.2E-05 2% 9.8E-06 1% 1.2E-05 1% 9.8E-06 1% 1.2E-05 0% Municipal WasteCombustorLarge Commercial HMI 1.2E-05 2% 9.8E-06 1% 1.2E-05 0% 9.8E-06 0% 1.2E-05 0% Large Hospital HMI 1.3E-05 8% 1.0E-05 5% 1.2E-05 2% 9.8E-06 1% 1.2E-05 1% Small Hospital HMI 1.2E-05 1% 9.8E-06 0% 1.2E-05 0% 9.7E-06 0% 1.2E-05 0% Large Hospital HMI 1.2E-05 0% 9.8E-06 0% 1.2E-05 0% 9.7E-06 0% 1.2E-05 0% (wet scrubber)Small Hospital HMI 1.2E-05 0% 9.7E-06 0% 1.2E-05 0% 9.7E-06 0% 1.2E-05 0% (wet scrubber)Large Coal-fired Utility 1.3E-05 3% 9.8E-06 1% 1.2E-05 1% 9.8E-06 0% 1.2E-05 0% BoilerMedium Coal-fired 1.2E-05 2% 9.8E-06 1% 1.2E-05 1% 9.8E-06 0% 1.2E-05 0% Utility BoilerSmall Coal-fired Utility 1.2E-05 0% 9.8E-06 0% 1.2E-05 0% 9.7E-06 0% 1.2E-05 0% BoilerMedium OIl-fired 1.2E-05 0% 9.7E-06 0% 1.2E-05 0% 9.7E-06 0% 1.2E-05 0% Utility BoilerChlor-alkali plant 3.1E-05 60% 2.3E-05 57% 1.6E-05 21% 1.2E-05 20% 1.3E-05 7% 
3-18

Table 3-11

Eastern Site — RELMAP 50th and 90th PercentilesPredicted Ingestion (mg/kg/day) for Urban Average

Eastern Site

RELMAP 50th percentile

Predicted Ingestion (mg/kg/day) for Urban Average

2.5km 10 km 2

Child Adult Child Adult Child 

Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC 

Variant b:Large 1.6E-06 38% 1.9E-07 38% 1.2E-06 20% 1.5E-07 20% 1.1E-06 8% Municipal WasteCombustorVariant b:Small 1.1E-06 10% 1.3E-07 10% 1.0E-06 3% 1.2E-07 3% 1.0E-06 1% Municipal WasteCombustorLarge Commercial 1.1E-06 9% 1.3E-07 9% 1.0E-06 2% 1.2E-07 2% 1.0E-06 0% HMILarge Hospital HMI 1.6E-06 40% 2.0E-07 40% 1.1E-06 10% 1.3E-07 10% 1.0E-06 3% Small Hospital HMI 1.0E-06 4% 1.3E-07 4% 1.0E-06 1% 1.2E-07 1% 9.9E-07 0% Large Hospital HMI 1.0E-06 2% 1.2E-07 2% 1.0E-06 0% 1.2E-07 0% 9.9E-07 0% (wet scrubber)Small Hospital HMI 9.9E-07 0% 1.2E-07 0% 9.9E-07 0% 1.2E-07 0% 9.9E-07 0% (wet scrubber)Large Coal-fired 1.3E-06 26% 1.6E-07 26% 1.1E-06 6% 1.3E-07 6% 1.0E-06 2% Utility BoilerMedium Coal-fired 1.1E-06 13% 1.4E-07 13% 1.0E-06 4% 1.3E-07 4% 1.0E-06 2% Utility BoilerSmall Coal-fired 1.0E-06 3% 1.2E-07 3% 1.0E-06 1% 1.2E-07 1% 1.0E-06 0% Utility BoilerMedium OIl-fired 1.0E-06 0% 1.2E-07 0% 9.9E-07 0% 1.2E-07 0% 9.9E-07 0% Utility BoilerChlor-alkali plant 6.1E-06 84% 7.4E-07 84% 1.7E-06 41% 2.0E-07 41% 1.2E-06 15% 
3-19

Table 3-11 (continued)

Eastern Site — RELMAP 50th and 90th PercentilesPredicted Ingestion (mg/kg/day) for Urban Average

Eastern Site

RELMAP 90th percentile

Predicted Ingestion (mg/kg/day) for Urban Average

2.5km 10 km 2

Child Adult Child Adult Child 

Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC 

Variant b:Large 1.9E-06 32% 2.3E-07 32% 1.5E-06 16% 1.8E-07 16% 1.4E-06 7% Municipal WasteCombustorVariant b:Small 1.4E-06 8% 1.7E-07 8% 1.3E-06 3% 1.6E-07 3% 1.3E-06 1% Municipal WasteCombustorLarge Commercial 1.4E-06 7% 1.7E-07 7% 1.3E-06 1% 1.6E-07 1% 1.3E-06 0% HMILarge Hospital HMI 1.9E-06 34% 2.3E-07 34% 1.4E-06 8% 1.7E-07 8% 1.3E-06 2% Small Hospital HMI 1.3E-06 3% 1.6E-07 3% 1.3E-06 0% 1.6E-07 0% 1.3E-06 0% Large Hospital HMI 1.3E-06 1% 1.6E-07 1% 1.3E-06 0% 1.5E-07 0% 1.3E-06 0% (wet scrubber)Small Hospital HMI 1.3E-06 0% 1.5E-07 0% 1.3E-06 0% 1.5E-07 0% 1.3E-06 0% (wet scrubber)Large Coal-fired 1.6E-06 21% 2.0E-07 21% 1.3E-06 5% 1.6E-07 5% 1.3E-06 2% Utility BoilerMedium Coal-fired 1.4E-06 10% 1.7E-07 10% 1.3E-06 3% 1.6E-07 3% 1.3E-06 1% Utility BoilerSmall Coal-fired 1.3E-06 3% 1.6E-07 3% 1.3E-06 1% 1.6E-07 1% 1.3E-06 0% Utility BoilerMedium OIl-fired 1.3E-06 0% 1.5E-07 0% 1.3E-06 0% 1.5E-07 0% 1.3E-06 0% Utility BoilerChlor-alkali plant 6.4E-06 80% 7.7E-07 80% 2.0E-06 35% 2.4E-07 35% 1.4E-06 12% 
3-20

Table 3-12

Eastern Site — RELMAP 50th and 90th Percentiles

Predicted Ingestion (mg/kg/day) for Urban High End

Eastern Site

RELMAP 50th percentile

Predicted Ingestion (mg/kg/day) for Urban High End

2.5km 10 km 2

Child Adult Child Adult Child 

Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC 

Variant b:Large 5.9E-05 38% 3.9E-06 6% 4.6E-05 20% 3.9E-06 3% 4.0E-05 8% Municipal WasteCombustorVariant b:Small 4.1E-05 10% 3.8E-06 1% 3.8E-05 3% 3.7E-06 1% 3.7E-05 1% Municipal WasteCombustorLarge Commercial 4.0E-05 9% 3.8E-06 1% 3.7E-05 2% 3.7E-06 0% 3.7E-05 0% HMILarge Hospital HMI 6.1E-05 40% 4.0E-06 6% 4.1E-05 10% 3.8E-06 1% 3.8E-05 3% Small Hospital HMI 3.8E-05 4% 3.7E-06 0% 3.7E-05 1% 3.7E-06 0% 3.7E-05 0% Large Hospital HMI 3.7E-05 2% 3.7E-06 0% 3.7E-05 0% 3.7E-06 0% 3.7E-05 0% (wet scrubber)Small Hospital HMI 3.7E-05 0% 3.7E-06 0% 3.7E-05 0% 3.7E-06 0% 3.7E-05 0% (wet scrubber)Large Coal-fired 4.9E-05 26% 3.8E-06 2% 3.9E-05 6% 3.7E-06 0% 3.8E-05 2% Utility BoilerMedium Coal-fired 4.2E-05 13% 3.8E-06 1% 3.8E-05 4% 3.7E-06 0% 3.7E-05 2% Utility BoilerSmall Coal-fired 3.8E-05 3% 3.7E-06 0% 3.7E-05 1% 3.7E-06 0% 3.7E-05 0% Utility BoilerMedium OIl-fired 3.7E-05 0% 3.7E-06 0% 3.7E-05 0% 3.7E-06 0% 3.7E-05 0% Utility BoilerChlor-alkali plant 2.3E-04 84% 8.9E-06 58% 6.2E-05 41% 4.7E-06 20% 4.3E-05 15% 
3-21

Table 3-12 (continued)

Eastern Site — RELMAP 50th and 90th Percentiles

Predicted Ingestion (mg/kg/day) for Urban High End

Eastern Site

RELMAP 90th percentile

Predicted Ingestion (mg/kg/day) for Urban High End

2.5km 10 km 2

Child Adult Child Adult Child 

Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC 

Variant b:Large 7.0E-05 33% 4.1E-06 5% 5.6E-05 16% 4.0E-06 3% 5.0E-05 7% Municipal WasteCombustorVariant b:Small 5.1E-05 8% 3.9E-06 1% 4.8E-05 3% 3.9E-06 1% 4.7E-05 1% Municipal WasteCombustorLarge Commercial 5.1E-05 7% 3.9E-06 1% 4.8E-05 1% 3.9E-06 0% 4.7E-05 0% HMILarge Hospital HMI 7.1E-05 34% 4.1E-06 6% 5.1E-05 8% 3.9E-06 1% 4.8E-05 2% Small Hospital HMI 4.9E-05 3% 3.9E-06 0% 4.7E-05 0% 3.9E-06 0% 4.7E-05 0% Large Hospital HMI 4.8E-05 1% 3.9E-06 0% 4.7E-05 0% 3.9E-06 0% 4.7E-05 0% (wet scrubber)Small Hospital HMI 4.7E-05 0% 3.9E-06 0% 4.7E-05 0% 3.9E-06 0% 4.7E-05 0% (wet scrubber)Large Coal-fired 6.0E-05 21% 3.9E-06 2% 4.9E-05 5% 3.9E-06 0% 4.8E-05 2% Utility BoilerMedium Coal-fired 5.3E-05 11% 3.9E-06 1% 4.9E-05 3% 3.9E-06 0% 4.8E-05 1% Utility BoilerSmall Coal-fired 4.8E-05 3% 3.9E-06 0% 4.8E-05 1% 3.9E-06 0% 4.7E-05 0% Utility BoilerMedium OIl-fired 4.7E-05 0% 3.9E-06 0% 4.7E-05 0% 3.9E-06 0% 4.7E-05 0% Utility BoilerChlor-alkali plant 2.4E-04 80% 9.1E-06 57% 7.3E-05 35% 4.8E-06 20% 5.4E-05 12% 
3-22

Table 3-13

Eastern Site — RELMAP 50th and 90th Percentiles

Predicted Ingestion (mg/kg/day) for Subsistence Fisher

Eastern Site

RELMAP 50th percentile

Predicted Ingestion (mg/kg/day) for Subsistence Fisher

2.5km 10 km 2

Child Adult Child Adult Child 

Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC 

Variant b:Large 1.4E-03 54% 1.0E-03 54% 9.0E-04 30% 6.6E-04 30% 7.2E-04 13% Municipal WasteCombustorVariant b:Small 7.7E-04 18% 5.6E-04 18% 6.7E-04 6% 4.9E-04 6% 6.4E-04 2% Municipal WasteCombustorLarge Commercial 7.8E-04 19% 5.7E-04 19% 6.5E-04 3% 4.7E-04 3% 6.3E-04 1% HMILarge Hospital HMI 1.6E-03 59% 1.1E-03 59% 7.6E-04 17% 5.6E-04 17% 6.6E-04 4% Small Hospital HMI 6.9E-04 9% 5.1E-04 9% 6.4E-04 1% 4.7E-04 1% 6.3E-04 0% Large Hospital HMI 6.6E-04 4% 4.8E-04 4% 6.3E-04 1% 4.6E-04 1% 6.3E-04 0% (wet scrubber)Small Hospital HMI 6.3E-04 0% 4.6E-04 0% 6.3E-04 0% 4.6E-04 0% 6.3E-04 0% (wet scrubber)Large Coal-fired 1.1E-03 42% 7.9E-04 42% 7.0E-04 10% 5.1E-04 10% 6.5E-04 4% Utility BoilerMedium Coal-fired 8.2E-04 23% 6.0E-04 23% 6.7E-04 7% 4.9E-04 7% 6.5E-04 3% Utility BoilerSmall Coal-fired 6.7E-04 6% 4.9E-04 6% 6.5E-04 2% 4.7E-04 2% 6.3E-04 1% Utility BoilerMedium OIl-fired 6.4E-04 1% 4.6E-04 1% 6.3E-04 0% 4.6E-04 0% 6.3E-04 0% Utility BoilerChlor-alkali plant 8.0E-03 92% 5.9E-03 92% 1.4E-03 56% 1.0E-03 56% 8.2E-04 23% 
3-23

Table 3-13 (continued)

Eastern Site — RELMAP 50th and 90th Percentiles

Predicted Ingestion (mg/kg/day) for Subsistence Fisher

Eastern Site

RELMAP 90th percentile

Predicted Ingestion (mg/kg/day) for Subsistence Fisher

2.5km 10 km 2

Child Adult Child Adult Child 

Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC 

Variant b:Large 1.6E-03 45% 1.2E-03 45% 1.2E-03 23% 8.6E-04 23% 1.0E-03 9% Municipal WasteCombustorVariant b:Small 1.1E-03 13% 7.7E-04 13% 9.5E-04 4% 6.9E-04 4% 9.2E-04 1% Municipal WasteCombustorLarge Commercial 1.1E-03 14% 7.7E-04 14% 9.3E-04 2% 6.8E-04 2% 9.1E-04 0% HMILarge Hospital HMI 1.8E-03 50% 1.3E-03 50% 1.0E-03 13% 7.6E-04 13% 9.4E-04 3% Small Hospital HMI 9.7E-04 6% 7.1E-04 6% 9.2E-04 1% 6.7E-04 1% 9.1E-04 0% Large Hospital HMI 9.4E-04 3% 6.8E-04 3% 9.1E-04 0% 6.7E-04 0% 9.1E-04 0% (wet scrubber)Small Hospital HMI 9.1E-04 0% 6.7E-04 0% 9.1E-04 0% 6.6E-04 0% 9.1E-04 0% (wet scrubber)Large Coal-fired 1.4E-03 33% 1.0E-03 33% 9.8E-04 7% 7.2E-04 7% 9.3E-04 3% Utility BoilerMedium Coal-fired 1.1E-03 17% 8.0E-04 17% 9.5E-04 5% 7.0E-04 5% 9.3E-04 2% Utility BoilerSmall Coal-fired 9.5E-04 4% 7.0E-04 4% 9.3E-04 2% 6.7E-04 2% 9.1E-04 1% Utility BoilerMedium OIl-fired 9.2E-04 1% 6.7E-04 1% 9.1E-04 0% 6.7E-04 0% 9.1E-04 0% Utility BoilerChlor-alkali plant 8.3E-03 89% 6.1E-03 89% 1.7E-03 47% 1.3E-03 47% 1.1E-03 17% 
3-24

Table 3-14

Eastern Site — RELMAP 50th and 90th Percentiles

Predicted Ingestion (mg/kg/day) for Recreational Angler

Eastern Site

RELMAP 50th percentile

Predicted Ingestion (mg/kg/day) for Recreational Angler

2.5km 10 km 2

Child Adult Child Adult Child 

Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC 

Variant b:Large Municipal Waste Combustor 5.0E-04 54% 3.3E-04 30% Variant b:Small Municipal Waste Combustor 2.8E-04 18% 2.4E-04 6% Large Commercial HMI 2.8E-04 19% 2.3E-04 3% Large Hospital HMI 5.6E-04 60% 2.8E-04 18% Small Hospital HMI 2.5E-04 9% 2.3E-04 1% Large Hospital HMI (wet scrubber) 2.4E-04 4% 2.3E-04 1% Small Hospital HMI (wet scrubber) 2.3E-04 0% 2.3E-04 0% Large Coal-fired Utility Boiler 3.9E-04 42% 2.5E-04 10% Medium Coal-fired Utility Boiler 3.0E-04 24% 2.4E-04 7% Small Coal-fired Utility Boiler 2.4E-04 6% 2.3E-04 2% Medium OIl-fired Utility Boiler 2.3E-04 1% 2.3E-04 0% Chlor-alkali plant 2.9E-03 92% 5.2E-04 56% Eastern SiteRELMAP 90th percentile

Predicted Ingestion (mg/kg/day) for Recreational Angler

2.5km 10 km 2

Child Adult Child Adult Child 

Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC 

Variant b:Large Municipal Waste Combustor 6.0E-04 45% 4.3E-04 23% Variant b:Small Municipal Waste Combustor 3.8E-04 13% 3.4E-04 4% Large Commercial HMI 3.8E-04 14% 3.4E-04 2% Large Hospital HMI 6.7E-04 51% 3.8E-04 13% Small Hospital HMI 3.5E-04 7% 3.3E-04 1% Large Hospital HMI (wet scrubber) 3.4E-04 3% 3.3E-04 0% Small Hospital HMI (wet scrubber) 3.3E-04 0% 3.3E-04 0% Large Coal-fired Utility Boiler 4.9E-04 33% 3.6E-04 7% Medium Coal-fired Utility Boiler 4.0E-04 18% 3.5E-04 5% Small Coal-fired Utility Boiler 3.4E-04 5% 3.3E-04 2% Medium OIl-fired Utility Boiler 3.3E-04 1% 3.3E-04 0% Chlor-alkali plant 3.0E-03 89% 6.2E-04 47% 
3-25
3-26

Table 3-15

Western Site — RELMAP 50th and 90th Percentiles

Predicted Ingestion (mg/kg/day) for Subsistence Farmer

Western Site

RELMAP 50th percentile

Predicted Ingestion (mg/kg/day) for Subsistence Farmer

2.5km 10 km 2

Child Adult Child Adult Child 

Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC 

Variant b:Large 4.6E-05 8% 3.6E-05 6% 4.4E-05 4% 3.5E-05 3% 4.3E-05 2% Municipal WasteCombustorVariant b:Small 4.3E-05 2% 3.5E-05 1% 4.3E-05 1% 3.5E-05 0% 4.3E-05 0% Municipal WasteCombustorLarge Commercial HMI 4.3E-05 2% 3.5E-05 1% 4.3E-05 0% 3.4E-05 0% 4.3E-05 0% Large Hospital HMI 4.6E-05 8% 3.6E-05 5% 4.3E-05 1% 3.5E-05 1% 4.3E-05 0% Small Hospital HMI 4.3E-05 1% 3.5E-05 0% 4.3E-05 0% 3.4E-05 0% 4.3E-05 0% Large Hospital HMI 4.3E-05 0% 3.4E-05 0% 4.3E-05 0% 3.4E-05 0% 4.3E-05 0% (wet scrubber)Small Hospital HMI 4.3E-05 0% 3.4E-05 0% 4.3E-05 0% 3.4E-05 0% 4.3E-05 0% (wet scrubber)Large Coal-fired Utility 4.4E-05 3% 3.5E-05 2% 4.3E-05 1% 3.5E-05 0% 4.3E-05 0% BoilerMedium Coal-fired 4.3E-05 1% 3.5E-05 1% 4.3E-05 1% 3.5E-05 0% 4.3E-05 0% Utility BoilerSmall Coal-fired Utility 4.3E-05 0% 3.4E-05 0% 4.3E-05 0% 3.4E-05 0% 4.3E-05 0% BoilerMedium OIl-fired 4.3E-05 0% 3.4E-05 0% 4.3E-05 0% 3.4E-05 0% 4.3E-05 0% Utility BoilerChlor-alkali plant 1.1E-04 61% 8.3E-05 58% 5.2E-05 19% 4.2E-05 17% 4.6E-05 7% 
3-27

Table 3-15 (continued)

Western Site — RELMAP 50th and 90th Percentiles

Predicted Ingestion (mg/kg/day) for Subsistence Farmer

Western Site

RELMAP 90th percentile

Predicted Ingestion (mg/kg/day) for Subsistence Farmer

2.5km 10 km 2

Child Adult Child Adult Child 

Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC 

Variant b:Large 4.9E-05 7% 3.8E-05 5% 4.7E-05 4% 3.7E-05 3% 4.6E-05 2% Municipal WasteCombustorVariant b:Small 4.6E-05 2% 3.6E-05 1% 4.5E-05 1% 3.6E-05 0% 4.5E-05 0% Municipal WasteCombustorLarge Commercial HMI 4.6E-05 2% 3.6E-05 1% 4.5E-05 0% 3.6E-05 0% 4.5E-05 0% Large Hospital HMI 4.9E-05 7% 3.8E-05 5% 4.6E-05 1% 3.6E-05 1% 4.5E-05 0% Small Hospital HMI 4.5E-05 1% 3.6E-05 0% 4.5E-05 0% 3.6E-05 0% 4.5E-05 0% Large Hospital HMI 4.5E-05 0% 3.6E-05 0% 4.5E-05 0% 3.6E-05 0% 4.5E-05 0% (wet scrubber)Small Hospital HMI 4.5E-05 0% 3.6E-05 0% 4.5E-05 0% 3.6E-05 0% 4.5E-05 0% (wet scrubber)Large Coal-fired Utility 4.6E-05 3% 3.6E-05 2% 4.6E-05 1% 3.6E-05 0% 4.5E-05 0% BoilerMedium Coal-fired 4.6E-05 1% 3.6E-05 1% 4.5E-05 1% 3.6E-05 0% 4.5E-05 0% Utility BoilerSmall Coal-fired Utility 4.5E-05 0% 3.6E-05 0% 4.5E-05 0% 3.6E-05 0% 4.5E-05 0% BoilerMedium OIl-fired 4.5E-05 0% 3.6E-05 0% 4.5E-05 0% 3.6E-05 0% 4.5E-05 0% Utility BoilerChlor-alkali plant 1.1E-04 60% 8.4E-05 57% 5.5E-05 18% 4.3E-05 17% 4.8E-05 6% 
3-28

Table 3-16

Western Site — RELMAP 50th and 90th Percentiles

Predicted Ingestion (mg/kg/day) for Rural Home Gardner

Western Site

RELMAP 50th percentile

Predicted Ingestion (mg/kg/day) for Rural Home Gardener

2.5km 10 km 2

Child Adult Child Adult Child 

Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC 

Variant b:Large 9.9E-06 7% 8.4E-06 4% 9.6E-06 4% 8.3E-06 2% 9.3E-06 2% Municipal WasteCombustorVariant b:Small 9.3E-06 2% 8.2E-06 1% 9.2E-06 1% 8.1E-06 0% 9.2E-06 0% Municipal WasteCombustorLarge Commercial HMI 9.3E-06 2% 8.2E-06 1% 9.2E-06 0% 8.1E-06 0% 9.2E-06 0% Large Hospital HMI 1.0E-05 8% 8.5E-06 4% 9.3E-06 2% 8.2E-06 1% 9.2E-06 0% Small Hospital HMI 9.2E-06 1% 8.1E-06 0% 9.2E-06 0% 8.1E-06 0% 9.2E-06 0% Large Hospital HMI 9.2E-06 0% 8.1E-06 0% 9.2E-06 0% 8.1E-06 0% 9.2E-06 0% (wet scrubber)Small Hospital HMI 9.2E-06 0% 8.1E-06 0% 9.2E-06 0% 8.1E-06 0% 9.2E-06 0% (wet scrubber)Large Coal-fired Utility 9.3E-06 1% 8.1E-06 0% 9.2E-06 0% 8.1E-06 0% 9.2E-06 0% BoilerMedium Coal-fired 9.2E-06 1% 8.1E-06 0% 9.2E-06 1% 8.1E-06 0% 9.2E-06 0% Utility BoilerSmall Coal-fired Utility 9.2E-06 0% 8.1E-06 0% 9.2E-06 0% 8.1E-06 0% 9.2E-06 0% BoilerMedium OIl-fired 9.2E-06 0% 8.1E-06 0% 9.2E-06 0% 8.1E-06 0% 9.2E-06 0% Utility BoilerChlor-alkali plant 2.5E-05 63% 1.9E-05 57% 1.1E-05 20% 9.8E-06 17% 9.9E-06 7% 
3-29

Table 3-16 (continued)

Western Site — RELMAP 50th and 90th Percentiles

Predicted Ingestion (mg/kg/day) for Rural Home Gardner

Western Site

RELMAP 90th percentile

Predicted Ingestion (mg/kg/day) for Rural Home Gardener

2.5km 10 km 2

Child Adult Child Adult Child 

Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC 

Variant b:Large 1.0E-05 7% 8.6E-06 4% 9.8E-06 4% 8.5E-06 2% 9.6E-06 2% Municipal WasteCombustorVariant b:Small 9.6E-06 2% 8.4E-06 1% 9.5E-06 1% 8.3E-06 0% 9.5E-06 0% Municipal WasteCombustorLarge Commercial HMI 9.6E-06 2% 8.4E-06 1% 9.5E-06 0% 8.3E-06 0% 9.5E-06 0% Large Hospital HMI 1.0E-05 8% 8.6E-06 4% 9.6E-06 2% 8.3E-06 1% 9.5E-06 0% Small Hospital HMI 9.5E-06 1% 8.3E-06 0% 9.5E-06 0% 8.3E-06 0% 9.5E-06 0% Large Hospital HMI 9.5E-06 0% 8.3E-06 0% 9.5E-06 0% 8.3E-06 0% 9.5E-06 0% (wet scrubber)Small Hospital HMI 9.5E-06 0% 8.3E-06 0% 9.5E-06 0% 8.3E-06 0% 9.5E-06 0% (wet scrubber)Large Coal-fired Utility 9.6E-06 1% 8.3E-06 0% 9.5E-06 0% 8.3E-06 0% 9.5E-06 0% BoilerMedium Coal-fired 9.5E-06 1% 8.3E-06 0% 9.5E-06 1% 8.3E-06 0% 9.5E-06 0% Utility BoilerSmall Coal-fired Utility 9.5E-06 0% 8.3E-06 0% 9.5E-06 0% 8.3E-06 0% 9.5E-06 0% BoilerMedium OIl-fired 9.5E-06 0% 8.3E-06 0% 9.5E-06 0% 8.3E-06 0% 9.5E-06 0% Utility BoilerChlor-alkali plant 2.5E-05 62% 1.9E-05 57% 1.2E-05 19% 9.9E-06 17% 1.0E-05 7% 
3-30

Table 3-17

Western Site — RELMAP 50th and 90th Percentiles

Predicted Ingestion (mg/kg/day) for Urban Average

Western Site

RELMAP 50th percentile

Predicted Ingestion (mg/kg/day) for Urban Average

2.5km 10 km 2

Child Adult Child Adult Child 

Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC 

Variant b:Large 4.9E-07 75% 6.0E-08 75% 3.1E-07 60% 3.7E-08 60% 1.9E-07 35% Municipal WasteCombustorVariant b:Small 2.0E-07 39% 2.5E-08 39% 1.5E-07 15% 1.8E-08 15% 1.3E-07 5% Municipal WasteCombustorLarge Commercial HMI 2.0E-07 38% 2.4E-08 38% 1.3E-07 7% 1.6E-08 7% 1.3E-07 2% Large Hospital HMI 6.2E-07 80% 7.5E-08 80% 2.0E-07 38% 2.4E-08 38% 1.4E-07 13% Small Hospital HMI 1.6E-07 21% 1.9E-08 21% 1.3E-07 3% 1.5E-08 3% 1.2E-07 1% Large Hospital HMI 1.4E-07 10% 1.7E-08 10% 1.2E-07 1% 1.5E-08 1% 1.2E-07 0% (wet scrubber)Small Hospital HMI 1.2E-07 1% 1.5E-08 1% 1.2E-07 0% 1.5E-08 0% 1.2E-07 0% (wet scrubber)Large Coal-fired Utility 2.0E-07 37% 2.4E-08 37% 1.5E-07 16% 1.8E-08 16% 1.4E-07 14% BoilerMedium Coal-fired 1.6E-07 25% 2.0E-08 25% 1.5E-07 19% 1.8E-08 19% 1.4E-07 13% Utility BoilerSmall Coal-fired Utility 1.4E-07 15% 1.8E-08 15% 1.3E-07 7% 1.6E-08 7% 1.3E-07 3% BoilerMedium OIl-fired 1.3E-07 2% 1.5E-08 2% 1.2E-07 1% 1.5E-08 1% 1.2E-07 0% Utility BoilerChlor-alkali plant 4.0E-06 97% 4.9E-07 97% 5.8E-07 79% 7.0E-08 79% 2.4E-07 49% 
3-31

Table 3-17 (continued)

Western Site — RELMAP 50th and 90th Percentiles

Predicted Ingestion (mg/kg/day) for Urban Average

Western Site

RELMAP 90th percentile

Predicted Ingestion (mg/kg/day) for Urban Average

2.5km 10 km 2

Child Adult Child Adult Child 

Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC 

Variant b:Large 6.1E-07 61% 7.4E-08 61% 4.2E-07 44% 5.2E-08 44% 3.1E-07 22% Municipal WasteCombustorVariant b:Small 3.2E-07 25% 3.9E-08 25% 2.6E-07 9% 3.2E-08 9% 2.5E-07 3% Municipal WasteCombustorLarge Commercial HMI 3.2E-07 24% 3.8E-08 24% 2.5E-07 4% 3.0E-08 4% 2.4E-07 1% Large Hospital HMI 7.4E-07 67% 8.9E-08 67% 3.1E-07 24% 3.8E-08 24% 2.6E-07 7% Small Hospital HMI 2.7E-07 12% 3.3E-08 12% 2.4E-07 2% 3.0E-08 2% 2.4E-07 0% Large Hospital HMI 2.5E-07 6% 3.1E-08 6% 2.4E-07 1% 2.9E-08 1% 2.4E-07 0% (wet scrubber)Small Hospital HMI 2.4E-07 0% 2.9E-08 0% 2.4E-07 0% 2.9E-08 0% 2.4E-07 0% (wet scrubber)Large Coal-fired Utility 3.1E-07 23% 3.8E-08 23% 2.6E-07 9% 3.2E-08 9% 2.6E-07 8% BoilerMedium Coal-fired 2.8E-07 15% 3.4E-08 15% 2.7E-07 10% 3.2E-08 10% 2.6E-07 7% Utility BoilerSmall Coal-fired Utility 2.6E-07 8% 3.2E-08 8% 2.5E-07 4% 3.0E-08 4% 2.4E-07 1% BoilerMedium OIl-fired 2.4E-07 1% 2.9E-08 1% 2.4E-07 1% 2.9E-08 1% 2.4E-07 0% Utility BoilerChlor-alkali plant 4.1E-06 94% 5.0E-07 94% 7.0E-07 66% 8.5E-08 66% 3.6E-07 33% 
3-32

Table 3-18

Western Site — RELMAP 50th and 90th Percentiles

Predicted Ingestion (mg/kg/day) for Urban High End

Western Site

RELMAP 50th percentile

Predicted Ingestion (mg/kg/day) for Urban High End

2.5km 10 km 2

Child Adult Child Adult Child 

Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC 

Variant b:Large 1.8E-05 76% 3.1E-06 5% 1.1E-05 61% 3.1E-06 3% 6.9E-06 36% Municipal WasteCombustorVariant b:Small 7.3E-06 40% 3.0E-06 1% 5.2E-06 16% 3.0E-06 0% 4.7E-06 6% Municipal WasteCombustorLarge Commercial HMI 7.2E-06 39% 3.0E-06 1% 4.8E-06 8% 3.0E-06 0% 4.5E-06 2% Large Hospital HMI 2.3E-05 81% 3.2E-06 6% 7.2E-06 38% 3.0E-06 1% 5.1E-06 14% Small Hospital HMI 5.6E-06 22% 3.0E-06 0% 4.6E-06 3% 3.0E-06 0% 4.4E-06 1% Large Hospital HMI 4.9E-06 11% 3.0E-06 0% 4.5E-06 2% 3.0E-06 0% 4.4E-06 0% (wet scrubber)Small Hospital HMI 4.4E-06 1% 3.0E-06 0% 4.4E-06 0% 3.0E-06 0% 4.4E-06 0% (wet scrubber)Large Coal-fired Utility 7.1E-06 38% 3.0E-06 1% 5.3E-06 16% 3.0E-06 0% 5.2E-06 15% BoilerMedium Coal-fired 6.0E-06 26% 3.0E-06 0% 5.4E-06 19% 3.0E-06 0% 5.1E-06 13% Utility BoilerSmall Coal-fired Utility 5.2E-06 15% 3.0E-06 0% 4.8E-06 7% 3.0E-06 0% 4.5E-06 3% BoilerMedium OIl-fired 4.5E-06 2% 3.0E-06 0% 4.5E-06 1% 3.0E-06 0% 4.4E-06 0% Utility BoilerChlor-alkali plant 1.5E-04 97% 7.3E-06 59% 2.1E-05 79% 3.6E-06 18% 8.8E-06 50% 
3-33

Table 3-18 (continued)

Western Site — RELMAP 50th and 90th Percentiles

Predicted Ingestion (mg/kg/day) for Urban High End

Western Site

RELMAP 90th percentile

Predicted Ingestion (mg/kg/day) for Urban High End

2.5km 10 km 2

Child Adult Child Adult Child 

Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC 

Variant b:Large 2.3E-05 61% 3.2E-06 5% 1.6E-05 44% 3.1E-06 3% 1.1E-05 22% Municipal WasteCombustorVariant b:Small 1.2E-05 25% 3.1E-06 1% 9.6E-06 9% 3.1E-06 0% 9.0E-06 3% Municipal WasteCombustorLarge Commercial HMI 1.2E-05 24% 3.1E-06 1% 9.1E-06 4% 3.1E-06 0% 8.8E-06 1% Large Hospital HMI 2.7E-05 68% 3.2E-06 6% 1.1E-05 24% 3.1E-06 1% 9.4E-06 8% Small Hospital HMI 9.9E-06 12% 3.1E-06 0% 8.9E-06 2% 3.1E-06 0% 8.8E-06 0% Large Hospital HMI 9.3E-06 6% 3.1E-06 0% 8.8E-06 1% 3.1E-06 0% 8.7E-06 0% (wet scrubber)Small Hospital HMI 8.8E-06 0% 3.1E-06 0% 8.7E-06 0% 3.1E-06 0% 8.7E-06 0% (wet scrubber)Large Coal-fired Utility 1.1E-05 23% 3.1E-06 1% 9.6E-06 9% 3.1E-06 0% 9.5E-06 8% BoilerMedium Coal-fired 1.0E-05 15% 3.1E-06 0% 9.8E-06 11% 3.1E-06 0% 9.4E-06 7% Utility BoilerSmall Coal-fired Utility 9.5E-06 8% 3.1E-06 0% 9.1E-06 4% 3.1E-06 0% 8.8E-06 1% BoilerMedium OIl-fired 8.8E-06 1% 3.1E-06 0% 8.8E-06 1% 3.1E-06 0% 8.7E-06 0% Utility BoilerChlor-alkali plant 1.5E-04 94% 7.4E-06 58% 2.6E-05 66% 3.7E-06 17% 1.3E-05 34% 
3-34

Table 3-19

Western Site — RELMAP 50th and 90th Percentiles

Predicted Ingestion (mg/kg/day) for Subsistence Fisher

Western Site

RELMAP 50th percentile

Predicted Ingestion (mg/kg/day) for Subsistence Fisher

2.5km 10 km 2

Child Adult Child Adult Child 

Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC 

Variant b:Large 7.2E-04 83% 5.2E-04 83% 4.5E-04 72% 3.3E-04 72% 2.3E-04 46% Municipal WasteCombustorVariant b:Small 2.8E-04 55% 2.0E-04 55% 1.6E-04 25% 1.2E-04 24% 1.3E-04 8% Municipal WasteCombustorLarge Commercial HMI 2.8E-04 56% 2.1E-04 55% 1.4E-04 13% 1.0E-04 13% 1.3E-04 3% Large Hospital HMI 1.1E-03 89% 8.3E-04 89% 2.6E-04 52% 1.9E-04 52% 1.5E-04 19% Small Hospital HMI 1.9E-04 35% 1.4E-04 35% 1.3E-04 6% 9.7E-05 6% 1.3E-04 1% Large Hospital HMI 1.5E-04 19% 1.1E-04 19% 1.3E-04 3% 9.4E-05 3% 1.2E-04 1% (wet scrubber)Small Hospital HMI 1.3E-04 1% 9.3E-05 1% 1.2E-04 0% 9.2E-05 0% 1.2E-04 0% (wet scrubber)Large Coal-fired Utility 2.5E-04 51% 1.9E-04 51% 1.6E-04 23% 1.2E-04 23% 1.5E-04 20% BoilerMedium Coal-fired 1.9E-04 35% 1.4E-04 35% 1.7E-04 27% 1.2E-04 26% 1.5E-04 18% Utility BoilerSmall Coal-fired Utility 1.6E-04 23% 1.2E-04 23% 1.4E-04 12% 1.0E-04 12% 1.3E-04 4% BoilerMedium OIl-fired 1.3E-04 2% 9.3E-05 2% 1.3E-04 2% 9.3E-05 2% 1.2E-04 1% Utility BoilerChlor-alkali plant 8.2E-03 98% 6.0E-03 98% 9.6E-04 87% 7.0E-04 87% 3.1E-04 60% 
3-35

Table 3-19 (continued)

Western Site — RELMAP 50th and 90th Percentiles

Predicted Ingestion (mg/kg/day) for Subsistence Fisher

Western Site

RELMAP 90th percentile

Predicted Ingestion (mg/kg/day) for Subsistence Fisher

2.5km 10 km 2

Child Adult Child Adult Child 

Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC 

Variant b:Large 8.7E-04 68% 6.4E-04 68% 6.1E-04 53% 4.4E-04 53% 3.9E-04 27% Municipal WasteCombustorVariant b:Small 4.3E-04 35% 3.2E-04 35% 3.2E-04 13% 2.4E-04 12% 2.9E-04 4% Municipal WasteCombustorLarge Commercial HMI 4.4E-04 36% 3.2E-04 35% 3.0E-04 6% 2.2E-04 6% 2.9E-04 1% Large Hospital HMI 1.3E-03 78% 9.5E-04 78% 4.2E-04 32% 3.1E-04 32% 3.1E-04 9% Small Hospital HMI 3.5E-04 19% 2.6E-04 19% 2.9E-04 3% 2.1E-04 3% 2.8E-04 1% Large Hospital HMI 3.1E-04 9% 2.3E-04 9% 2.9E-04 1% 2.1E-04 1% 2.8E-04 0% (wet scrubber)Small Hospital HMI 2.8E-04 1% 2.1E-04 1% 2.8E-04 0% 2.1E-04 0% 2.8E-04 0% (wet scrubber)Large Coal-fired Utility 4.1E-04 32% 3.0E-04 31% 3.2E-04 12% 2.3E-04 11% 3.1E-04 10% BoilerMedium Coal-fired 3.5E-04 19% 2.6E-04 19% 3.3E-04 14% 2.4E-04 14% 3.1E-04 9% Utility BoilerSmall Coal-fired Utility 3.2E-04 12% 2.3E-04 11% 3.0E-04 6% 2.2E-04 6% 2.9E-04 2% BoilerMedium OIl-fired 2.9E-04 1% 2.1E-04 1% 2.8E-04 1% 2.1E-04 1% 2.8E-04 0% Utility BoilerChlor-alkali plant 8.3E-03 97% 6.1E-03 97% 1.1E-03 75% 8.2E-04 75% 4.7E-04 39% 
3-36

Table 3-20

Western Site — RELMAP 50th and 90th Percentiles

Predicted Ingestion (mg/kg/day) for Recreational Angler

Western Site

RELMAP 50th percentile

Predicted Ingestion (mg/kg/day) for Recreational Angler

2.5km 10 km 2

Child Adult Child Adult Child 

Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC 

Variant b:Large Municipal Waste Combustor 2.6E-04 83% 1.6E-04 73% Variant b:Small Municipal Waste Combustor 9.9E-05 57% 5.8E-05 26% Large Commercial HMI 1.0E-04 57% 4.9E-05 13% Large Hospital HMI 4.1E-04 90% 9.2E-05 54% Small Hospital HMI 6.8E-05 37% 4.6E-05 6% Large Hospital HMI (wet scrubber) 5.3E-05 20% 4.4E-05 3% Small Hospital HMI (wet scrubber) 4.4E-05 2% 4.3E-05 0% Large Coal-fired Utility Boiler 9.0E-05 53% 5.6E-05 24% Medium Coal-fired Utility Boiler 6.7E-05 36% 5.9E-05 28% Small Coal-fired Utility Boiler 5.6E-05 24% 4.9E-05 12% Medium OIl-fired Utility Boiler 4.4E-05 2% 4.4E-05 2% Chlor-alkali plant 3.0E-03 99% 3.5E-04 88% Western SiteRELMAP 90th percentile

Predicted Ingestion (mg/kg/day) for Recreational Angler

2.5km 10 km 2

Child Adult Child Adult Child 

Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC 

Variant b:Large Municipal Waste Combustor 3.2E-04 68% 2.2E-04 54% Variant b:Small Municipal Waste Combustor 1.6E-04 36% 1.2E-04 13% Large Commercial HMI 1.6E-04 36% 1.1E-04 6% Large Hospital HMI 4.7E-04 79% 1.5E-04 33% Small Hospital HMI 1.3E-04 20% 1.0E-04 3% Large Hospital HMI (wet scrubber) 1.1E-04 10% 1.0E-04 1% Small Hospital HMI (wet scrubber) 1.0E-04 1% 1.0E-04 0% Large Coal-fired Utility Boiler 1.5E-04 32% 1.1E-04 12% Medium Coal-fired Utility Boiler 1.3E-04 20% 1.2E-04 14% Small Coal-fired Utility Boiler 1.1E-04 12% 1.1E-04 6% Medium OIl-fired Utility Boiler 1.0E-04 1% 1.0E-04 1% Chlor-alkali plant 3.0E-03 97% 4.1E-04 75% 
3-37
3-38

Table 3-21

Eastern Site — RELMAP 50th and 90th Percentiles

Predicted Inhalation

Eastern Site Predicted Inhalation for Eastern Site

RELMAP 50th percentile 2.5km 10km 

Child Adult Full time A d u l t Child Adult Full time A d u l t Child 

Part time Part time 

Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC 

Variant b:Large Municipal Waste Combustor 1.6E-06 3% 4.9E-07 3% 3.3E-07 3% 1.6E-06 2% 4.9E-07 2% 3.3E-07 2% 1.6E-06 1% 4Variant b:Small Municipal Waste Combustor 1.6E-06 1% 4.8E-07 1% 3.2E-07 1% 1.6E-06 0% 4.8E-07 0% 3.2E-07 0% 1.6E-06 0% 4Large Commercial HMI 1.6E-06 1% 4.8E-07 1% 3.2E-07 1% 1.6E-06 0% 4.8E-07 0% 3.2E-07 0% 1.6E-06 0% 4Large Hospital HMI 1.6E-06 3% 4.9E-07 3% 3.3E-07 3% 1.6E-06 1% 4.8E-07 1% 3.2E-07 1% 1.6E-06 0% 4Small Hospital HMI 1.6E-06 0% 4.8E-07 0% 3.2E-07 0% 1.6E-06 0% 4.8E-07 0% 3.2E-07 0% 1.6E-06 0% 4Large Hospital HMI (wet scrubber) 1.6E-06 0% 4.8E-07 0% 3.2E-07 0% 1.6E-06 0% 4.8E-07 0% 3.2E-07 0% 1.6E-06 0% 4Small Hospital HMI (wet scrubber) 1.6E-06 0% 4.8E-07 0% 3.2E-07 0% 1.6E-06 0% 4.8E-07 0% 3.2E-07 0% 1.6E-06 0% 4Large Coal-fired Utility Boiler 1.6E-06 0% 4.8E-07 0% 3.2E-07 0% 1.6E-06 0% 4.8E-07 0% 3.2E-07 0% 1.6E-06 0% 4Medium Coal-fired Utility Boiler 1.6E-06 0% 4.8E-07 0% 3.2E-07 0% 1.6E-06 0% 4.8E-07 0% 3.2E-07 0% 1.6E-06 0% 4Small Coal-fired Utility Boiler 1.6E-06 0% 4.8E-07 0% 3.2E-07 0% 1.6E-06 0% 4.8E-07 0% 3.2E-07 0% 1.6E-06 0% 4Medium OIl-fired Utility Boiler 1.6E-06 0% 4.8E-07 0% 3.2E-07 0% 1.6E-06 0% 4.8E-07 0% 3.2E-07 0% 1.6E-06 0% 4Chlor-alkali plant 3.7E-06 58% 1.1E-06 58% 7.6E-07 58% 2.0E-06 21% 6.0E-07 21% 4.0E-07 21% 1.7E-06 8% 5Eastern Site Predicted Inhalation for Eastern SiteRELMAP 90th percentile 2.5km 10km 

Child Adult Full time A d u l t Child Adult Full time A d u l t Child 

Part time Part time 

Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC 

Variant b:Large Municipal Waste Combustor 1.7E-06 3% 5.1E-07 3% 3.4E-07 3% 1.7E-06 2% 5.0E-07 2% 3.4E-07 2% 1.6E-06 1% 5Variant b:Small Municipal Waste Combustor 1.6E-06 1% 5.0E-07 1% 3.3E-07 1% 1.6E-06 0% 4.9E-07 0% 3.3E-07 0% 1.6E-06 0% 4Large Commercial HMI 1.6E-06 1% 5.0E-07 1% 3.3E-07 1% 1.6E-06 0% 4.9E-07 0% 3.3E-07 0% 1.6E-06 0% 4Large Hospital HMI 1.7E-06 3% 5.1E-07 3% 3.4E-07 3% 1.6E-06 1% 5.0E-07 1% 3.3E-07 1% 1.6E-06 0% 4Small Hospital HMI 1.6E-06 0% 4.9E-07 0% 3.3E-07 0% 1.6E-06 0% 4.9E-07 0% 3.3E-07 0% 1.6E-06 0% 4Large Hospital HMI (wet scrubber) 1.6E-06 0% 4.9E-07 0% 3.3E-07 0% 1.6E-06 0% 4.9E-07 0% 3.3E-07 0% 1.6E-06 0% 4Small Hospital HMI (wet scrubber) 1.6E-06 0% 4.9E-07 0% 3.3E-07 0% 1.6E-06 0% 4.9E-07 0% 3.3E-07 0% 1.6E-06 0% 4Large Coal-fired Utility Boiler 1.6E-06 0% 4.9E-07 0% 3.3E-07 0% 1.6E-06 0% 4.9E-07 0% 3.3E-07 0% 1.6E-06 0% 4Medium Coal-fired Utility Boiler 1.6E-06 0% 4.9E-07 0% 3.3E-07 0% 1.6E-06 0% 4.9E-07 0% 3.3E-07 0% 1.6E-06 0% 4Small Coal-fired Utility Boiler 1.6E-06 0% 4.9E-07 0% 3.3E-07 0% 1.6E-06 0% 4.9E-07 0% 3.3E-07 0% 1.6E-06 0% 4Medium OIl-fired Utility Boiler 1.6E-06 0% 4.9E-07 0% 3.3E-07 0% 1.6E-06 0% 4.9E-07 0% 3.3E-07 0% 1.6E-06 0% 4Chlor-alkali plant 3.8E-06 57% 1.1E-06 57% 7.7E-07 57% 2.0E-06 21% 6.2E-07 21% 4.1E-07 21% 1.8E-06 8% 5
3-39

Table 3-22

Western Site — RELMAP 50th and 90th Percentiles

Predicted Inhalation

Western Site Predicted Inhalation for Western SIte

RELMAP 50th percentile 2.5km 10km 

Child Adult Full time Adult Part time Child Adult Full time Adult Part time 

Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC V

Variant b:Large Municipal Waste Combustor 1.6E-06 2% 4.7E-07 2% 3.2E-07 2% 1.5E-06 2% 4.7E-07 2% 3.1E-07 2% 1Variant b:Small Municipal Waste Combustor 1.5E-06 1% 4.6E-07 1% 3.1E-07 1% 1.5E-06 0% 4.6E-07 0% 3.1E-07 0% 1Large Commercial HMI 1.5E-06 1% 4.7E-07 1% 3.1E-07 1% 1.5E-06 0% 4.6E-07 0% 3.1E-07 0% 1Large Hospital HMI 1.6E-06 2% 4.7E-07 2% 3.2E-07 2% 1.5E-06 1% 4.6E-07 1% 3.1E-07 1% 1Small Hospital HMI 1.5E-06 0% 4.6E-07 0% 3.1E-07 0% 1.5E-06 0% 4.6E-07 0% 3.1E-07 0% 1Large Hospital HMI (wet scrubber) 1.5E-06 0% 4.6E-07 0% 3.1E-07 0% 1.5E-06 0% 4.6E-07 0% 3.1E-07 0% 1Small Hospital HMI (wet scrubber) 1.5E-06 0% 4.6E-07 0% 3.1E-07 0% 1.5E-06 0% 4.6E-07 0% 3.1E-07 0% 1Large Coal-fired Utility Boiler 1.5E-06 0% 4.6E-07 0% 3.1E-07 0% 1.5E-06 0% 4.6E-07 0% 3.1E-07 0% 1Medium Coal-fired Utility Boiler 1.5E-06 0% 4.6E-07 0% 3.1E-07 0% 1.5E-06 0% 4.6E-07 0% 3.1E-07 0% 1Small Coal-fired Utility Boiler 1.5E-06 0% 4.6E-07 0% 3.1E-07 0% 1.5E-06 0% 4.6E-07 0% 3.1E-07 0% 1Medium OIl-fired Utility Boiler 1.5E-06 0% 4.6E-07 0% 3.1E-07 0% 1.5E-06 0% 4.6E-07 0% 3.1E-07 0% 1Chlor-alkali plant 3.3E-06 54% 1.0E-06 54% 6.8E-07 54% 1.8E-06 16% 5.5E-07 16% 3.7E-07 16% 1Western SIte Predicted Inhalation for Western SIte

RELMAP 90th percentile 2.5km 10km 

Child Adult Full time Adult Part time Child Adult Full time Adult Part time 

Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC Value %ISC V

Variant b:Large Municipal Waste Combustor 1.6E-06 2% 4.8E-07 2% 3.2E-07 2% 1.6E-06 2% 4.8E-07 2% 3.2E-07 2% 1Variant b:Small Municipal Waste Combustor 1.6E-06 1% 4.7E-07 1% 3.1E-07 1% 1.5E-06 0% 4.7E-07 0% 3.1E-07 0% 1Large Commercial HMI 1.6E-06 1% 4.7E-07 1% 3.2E-07 1% 1.5E-06 0% 4.7E-07 0% 3.1E-07 0% 1Large Hospital HMI 1.6E-06 2% 4.8E-07 2% 3.2E-07 2% 1.6E-06 1% 4.7E-07 1% 3.1E-07 1% 1Small Hospital HMI 1.5E-06 0% 4.7E-07 0% 3.1E-07 0% 1.5E-06 0% 4.7E-07 0% 3.1E-07 0% 1Large Hospital HMI (wet scrubber) 1.5E-06 0% 4.7E-07 0% 3.1E-07 0% 1.5E-06 0% 4.7E-07 0% 3.1E-07 0% 1Small Hospital HMI (wet scrubber) 1.5E-06 0% 4.7E-07 0% 3.1E-07 0% 1.5E-06 0% 4.7E-07 0% 3.1E-07 0% 1Large Coal-fired Utility Boiler 1.5E-06 0% 4.7E-07 0% 3.1E-07 0% 1.5E-06 0% 4.7E-07 0% 3.1E-07 0% 1Medium Coal-fired Utility Boiler 1.5E-06 0% 4.7E-07 0% 3.1E-07 0% 1.5E-06 0% 4.7E-07 0% 3.1E-07 0% 1Small Coal-fired Utility Boiler 1.5E-06 0% 4.7E-07 0% 3.1E-07 0% 1.5E-06 0% 4.7E-07 0% 3.1E-07 0% 1Medium OIl-fired Utility Boiler 1.5E-06 0% 4.7E-07 0% 3.1E-07 0% 1.5E-06 0% 4.7E-07 0% 3.1E-07 0% 1Chlor-alkali plant 3.4E-06 54% 1.0E-06 54% 6.8E-07 54% 1.8E-06 16% 5.6E-07 16% 3.7E-07 16% 1
3-40

fruiting vegetables the bulk of mercury is also modeled to be the result of uptake of mercury from the

atmosphere into the plant.

Although not shown in the tables below, divalent mercury accounts for approximately 90% of the

total mercury intake for the agricultural scenarios, with the rest being methylmercury. This partitioning

is reflective of the predicted speciation of mercury in the ingested plant and animal products. 

The differences between facilities are due to differences in parameters that affect effective stack

height, and the assumed total mercury emission rate. The speciation of mercury emissions is not an

important factor because the speciation only affects the predicted deposition rates, not the total mercury

air concentrations. 

3.2.3 Urban Scenarios

With the exception of the child exhibbiting pica behavior in this scenario (urban high end child),

the predicted mercury exposures in the urban scenarios are generally an order of magnitude lower than

those for the agricultural scenarios. This reflects the lower ingestion rates assumed for locally grown

plant products. As for the agricultural scenarios, divalent mercury is the primary form of mercury to

which they receptors are exposed. 

The larger contribution of the local sources in these scenarios reflects the fact that only for the

urban high end is consumption of plant products assumed: for the other urban scenarios exposure to

mercury from the local source is assumed to be solely through ingestion of soil. The contributions of the

local source shown for the urban scenarios thus reflect the contribution of the local source on the soil

concentrations, which themselves are driven by the mercury deposition rates. The mercury deposition

rates are generally driven by the assumed speciation of mercury emissions.

The contribution of the local source when pica behaviour is exhibbited (urban high end child)

reflects the contribution of the local source to the soil concentration.

3.2.4 Fish Ingestion Scenarios

The predicted mercury exposure in the fish ingestion scenarios (high-end fisher and recreational

angler) is dominated by exposure through ingestion of fish, even though some exposure through ingestion

of plant products is also assumed. Methylmercury is the primary form of mercury to which these

receptors are exposed. The fish concentrations are driven by the predicted dissolved methylmercury

concentrations in the surface water, which themselves are driven by the watershed soil concentrations

and the waterbody atmospheric mercury deposition rate.

For several of the facilities at both the eastern and western sites, the majority of the exposure to

mercury is predicted to be due to the local source for the waterbody located 2.5 km from the facility. 

This is also true for some facilities at both 10 km and 25 km. These results reflect the contribution of the

local source to total mercury deposition onto the waterbody and the watershed soils.

The contribution of the local source is larger at the western site because both the regional and

pre-industrial deposition rates are lower than at the eastern site, while the results for the local source

(using ISC) are more similar. However, the total mercury exposure is approximately twice as low at the

drier western site compared to the eastern site due primarily to differences in meteorology.
3-41

3.3 Issues Related to Predicted Mercury Exposure Estimates

In the modeling effort exposure for six different hypothetical adult humans was

modeled. Atmospheric emissions of anthropogenic origin, local background and regional atmospheric

mercury may not be the only sources of mercury exposure. Individuals can be exposed to mercury from

other sources such as occupation and consumption of non-local (e.g., marine) fish. Quantitative estimates

of these sources are presented in the following chapters of this Volume. This section considers the logic

of adding exposure from these additional sources in an assessment.

Occupational mercury may be an important source of exposure. This source may apply to any

hypothetical adult modeled here with the exception of the subsistence farmer. For a given area with a

relevant industrial base, it may be appropriate to consider these exposures for members of the population,

when assessing mercury exposures.

In the modeling effort several hypothetical humans were assumed not to consume locally-caught

fish. These hypothetical individuals include: a subsistence farmer and child, a rural home gardener, and

the urban dwellers. For these hypothetical individuals, it is reasonable to assume that some fraction of the

individuals they represent will consume marine fish. For this marine fish consuming subset, the ranges of

methylmercury exposure from marine fish consumption that are estimated later in this Volume are

applicable. Methylmercury from marine fish consumption, if considered, is an incremental increase over

the estimated intakes.

In the modeling effort several hypothetical individuals were assumed to consume high levels of

locally caught fish. These individuals include: an angler, who is assumed to consume 60 grams of local

fish/day, a child, who is assumed to consume 20 grams of local fish/day and a recreational angler who is

assumed to consume 30 grams fish/day. Since these hypothetical individuals consume high levels of

local fish, it is probably inappropriate to consider exposure through an additional fish consumption

pathway. Although it is reasonable to assume that some individuals consume both local and other fish;

for example, Fiore et al. (1989) documented the consumption of both self-caught and purchased fish in

U.S. anglers, these data are not combined in this assessment. 

The initial conditions assumed before the facility is modeled (referred to here as “background”)

are potentially critical to the total mercury exposure. This is particularly important because the

magnitude of the contribution of a local source to the total may be used to assess its impact. A delicate

balance is required when including such a “background” in the analysis. This is because it is not just a

matter of a local source’s contribution to this background, but the total impact of background plus the

local source that is ultimately the primary concern. Overestimating the background will result in a

concurrent decrease in the contribution of a given local source, but may result in exceeding thresholds

that would not be exceeded if lower estimates of background are assumed. Resolution of this issue is not

within the objectives of the current report; it is noted, however, that there is no available guidance on

how to incorporate background in exposure assessment. For a local scale mercury exposure assessment it

is important to measure mercury concentrations in various media.

The impact of the uncertainty in the predicted air concentrations and deposition rates for each

facility is most important for the fish ingestion and pica child scenarios. This is because, in general, the

local source does not contribute significantly to the mercury exposure for the agricultural and urban

scenarios. The exception to this pattern is the chlor-alkali model plant. In this case, the low assumed

mercury release height results in the facility having a substantial impact on the mercury air

concentrations close to the facility.
3-42

3.4 Summary Conclusions

 The contribution of the local source, compared to background and the regional contribution, is

larger at the western, drier site than at the eastern site. This is because both the regional impact

and background values are much lower at the western site than is prdicted to occur for the local

source. However, the magnitude of the total exposure at the western site is about half that at the

eastern site due to the drier meteorology at the western site.

 For the agricultural scenarios, it is generally the background or regional sources that account for

the majority of total mercury exposure. This is because the dominant pathway of mercury

exposure in these scenarios is the ingestion of plants, which accumulate most of their mercury

from the air, and most of the local sources are predicted to have little impact on the local average

air concentrations compared to the regional sources.

 Most of the mercury to which the hypothetical receptor is exposed in the agricultural and urban

scenarios is divalent mercury. This is because most of the mercury in plants and soil is predicted

to be of this form. In contrast, in the fish ingestion scenairos methylmercury is the primary form

of mercury to which the receptor is exposed.

 For the fish ingestion scenarios, the local sources are predicted to account for the majority of the

total mercury exposure for waterbodies close to the facility. This is particularly true for the

western site, where the background and regional contribution tothe total mercury deposition are

lower.
4-1

4. POPULATION EXPOSURE — FISH CONSUMPTION

4.1 Fish Consumption among the General U.S. Population

Fish bioaccumulate methylmercury through the freshwater aquatic and marine food-chains.

Mercury-contaminated phytoplankton and zooplankton are consumed by planktivorous fish (referred to in

other parts of this Volume at trophic level 3 fish). Methylmercury is thought to bioaccumulate in this group

as well as in the piscivorous fish. Both marine and freshwater fish bioaccumulate methylmercury in their

muscle tissue. Consumption of these methylmercury-contaminated fish results in exposures to human

populations. Additional data have become available between 1995 and 1997 that permit estimates of mercury

consumption from marine mammals and birds by populations living in the far Northern latitudes.

Consumption of fish is highly variable across the U.S. population unlike consumption of other

dietary components, such as bread or starch, that are almost ubiquitously consumed. This chapter presents

an estimate of the magnitude of fish consumption in both the general U.S. population and in specific

subpopulations (e.g., children and women of child-bearing age). This estimate identified the portion of the

population that consumes fish and shellfish. It also provides estimates of species of fish consumed and the

quantity of fish consumed based on cross-sectional survey data. Use of a national data base differentiates

data in this Chapter from site-specific assessments. Data presented in this Chapter differ from site-specific

assessments in which consumption of contaminated local freshwater fish are included.

Inclusion of fish in the diet varies with geographic location, seasons of the year, ethnicity, and

personal food preferences. Data on fish consumption have been calculated typically as either on “per capita”

or “per user” basis. The former term is obtained by dividing the supply of fish across an entire population

to establish a “per capita” consumption rate. The latter term divides the supply of fish across only the portion

of the population that consumes fish, providing “per user” rates of consumption.

Identifying differences in fish consumption rates for population groups can be achieved through

analysis of dietary survey data for the general United States population and specified subpopulations; e.g.,

some Native American tribes, recreational anglers, women of childbearing age, and children. The United

States Department of Agriculture (USDA) has conducted a series of nationally-based dietary surveys,

including the 1977-1978 Nationwide Food Consumption Survey and the Continuing Surveys of Food Intake

by Individuals (CFSII) over the period 1989 through 1995 (CFSII 89-91; CSFII, 1994; CFSII, 1995). In

addition, data from the third National Health and Nutrition Examination Survey (NHANES III), conducted

between 1988 and 1994, provide estimates of fish consumption patterns in the early 1990s. Analyses of fish

consumption patterns among the general U.S. population and selected age/gender groupings are described

below. Fish consumption rate data from specific Native American tribes and angling populations are

identified and used to corroborate the nationwide fish consumption data. 

4.1.1 Patterns of Fish Consumption

Although the consumption frequency of fish is low compared with staple foods such as grain

products, dietary intake of fish can be estimated from survey data. The initial issue of how to estimate fish

consumption depends to a great extent on the choice of dietary assessment method. Available techniques

include long-term dietary histories, questionnaires to identify typical food intake or short-term dietary recall

techniques and questionnaires on food frequency. The first consideration is to obtain dietary information

that reflects typical fish consumption. A true estimate of methylmercury intake from fish is complicated by

changes in fish intake over time, differences in species of fish consumed, variation in the methylmercury

concentration in a species of fish, and broad changes in the sources of fish entering the U.S. market place.

For example, increases in aquaculture or fishfarming and increased reliance on imported fish for domestic

consumption may affect consumption estimates. Temporal variation in dietary patterns is an issue to
4-2

consider in the evaluation of short-term recall/record data. For epidemiological studies that seek to

understand the relationship of long-term dietary patterns to chronic disease, typical food intake is the relevant

parameter to evaluate (Willett, 1990).

Because methylmercury is a developmental toxin that may produce adverse effects following a

comparatively brief exposure period (i.e., a few months rather than decades), comparatively short-term

dietary patterns can have importance. Consequently, estimation of recent patterns of methylmercury

consumption from fish is the relevant exposure for the health endpoint of concern. Because it is not possible

to precisely identify the period of development during which mercury is likely to damage the nervous system

of the developing fetus or growing child, exposure of women of childbearing age or your children to mercury

via consumption of fish is a cause for concern.

This chapter describes the distribution of fish intakes for the general population and for

subpopulations defined by age or gender; e.g., women of child-bearing age. Estimates of the number of

women who are pregnant in any given year are based on methods shown in Appendix B. The analysis is not

intended to estimate fish consumption by an individual and relate it to an individual’s health outcomes.

Dietary questionnaires or dietary histories may identify broad patterns of fish consumption, but these

techniques provide less specific recollection of foods consumed such as the species of fish eaten. Likewise

estimates of the quantity of fish consumed become less precise as the eating event becomes more remote in

time. The selection of a dietary survey method to describe fish intakes by the subpopulation of interest

requires a balancing the specificity of information collected with the generalization of short-term dietary

patterns to longer-term food intakes.

After the appropriate period of fish intake is selected, the second area of concern becomes the

variation in the methylmercury concentrations of the fish consumed. A central feature of food intake among

subjects with a free choice of foods is the day-to-day variability in foods consumed superimposed on an

underlying food intake pattern (Willett, 1990). In epidemiology studies, an individual's true intake of a food

such as fish could be considered as the mean intake for a large number of days. Collectively, the true intakes

by these individuals define a frequency distribution for the study population as a whole (Willett, 1990). It

is rarely possible to measure a large number of days of dietary intake for individual subjects; consequently,

a sample of one or several days is used to represent the true intake (Willett, 1990). The effect of this

sampling is to increase artifically the standard deviation, i.e., to broaden the tails of the distribution (Willett,

1990). This results in estimates of intake that are both larger and smaller than the true long-term averages

for any subject. Overall, authorities in nutritional epidemiology (among others see Willett, 1990) conclude

that "measurements of dietary intake based on a single or small number of 24-hour recalls per subject may

provide a reasonable (unbiased) estimate of the mean of a group, but the standard deviation will be greatly

overestimated."

Assessment of recent dietary intakes can be achieved through dietary records for various periods

(typically 7-day records or 3-day records) or dietary recall (typically 24-hour recalls or 3-day recalls) (among

others see Witschi, 1990). Questions on food frequency in dietary histories can be used to estimate how

often a population consumes fish and shellfish. Research is currently in progress to estimate usual intake

distributions that account for intake data of foods that are not consumed on a daily basis (among others see

Nusser et al. 1996). In 1996, Nusser et al. published a statistical approach to estimating moderate-term (e.g.,

months) patterns of food consumption based on multiple 24-hour dietary recalls obtained from the same

individual.

 

Sources of error in short-term recalls and records affect all dietary survey methodologies. These

include errors made by the respondent or recorder of dietary information as well as the interviewer or

reviewer. Information used to calculate the intake of the chemical of interest is another source of error. The

detection limit of the analyte, the frequency of zero and trace values, and how such values are managed can

statistically influence the accuracy of the mean mercury concentration for a fish species. The third source

of error in dietary assessments is the data base used to calculate intakes of the chemical from the food

consumed, for example the data may no longer reflect current concentrations of the chemical in foods.
4-3

The ability of the subject to remember the food consumed and in what quantities it was consumed

is central to these methods (among many others see Witschi, 1990). In an analysis of data from the National

Health and Nutrition Evaluation Survey (NHANES), the largest source of error was uncertainty of subjects

about foods consumed on the recall day (Youland and Engle, 1976). Fish consumption appears to be more

accurately remembered than most other food groups. Karvetti and Knuts (1985) observed the actual intake

of 140 subjects and later interviewed them by 24-hour recall. They found that fish was omitted from the

dietary recall less than 5% of the time and erroneously recalled approximately 7% of the time. The validity

of 24-hour recalls for fish consumption was greater than all other food groups. Interviewer and reviewer

errors can be reasonably predicted to be consistent for a given survey and unlikely to affect reporting of fish

consumption selectively.

4.1.1.1 Estimates of Fish Intake for Populations

Data on fish consumption have been calculated typically as either "per capita" or "per user". The

former term is obtained by dividing the supply of fish across an entire population to establish a "per capita"

consumption rate. The latter term divides the supply of fish across only the portion of the population that

consumes fish; i.e., "per user" rates of consumption. 

Survey methods can broadly be classified into longitudinal methods or cross-sectional surveys.

Typically long-term or longitudinal estimates of intake can be used to reflect patterns for individuals (e.g.,

dietary histories); or longitudinal estimates of moderate duration (e.g., month-long periods) for individuals

or groups. Cross-sectional data are used to give a "snap shot" in time and are typically used to provide

information on the distribution of intakes for groups within the population of interest. Cross-sectional data

typically are for 24-hour or 3-day sampling periods and consist of recall of foods consumed in response to

questioning by a trained interviewer, or they may be taken from written records of foods consumed.

During the past decade, reviewers of dietary survey methodology (for example, the Food and

Nutrition Board of the National Research Council/National Academy of Sciences; the Life Sciences Research

Office of the Federation of American Societies of Experimental Biology) have evaluated various techniques

with regard to their suitability for estimating exposure to contaminants and intake of nutrients. The Food

and Nutrition Board of the National Research Council/National Academy of Sciences in their 1986

publication on Nutrient Adequacy Assessment Using Food Consumption Surveys noted that dietary intake

of an individual is not constant from day to day, but varies on a daily basis both in amount and in type of

foods eaten (intraindividual variation). Variations between persons in their usual food intake averaged over

time is referred to as interindividual variation. Among North American populations, the intraindividual

variation is usually considered to be as large as or greater than the interindividual variation. Having

evaluated a number of data sets, the Academy's Subcommittee concluded that three days of observation may

be more than is required for the derivation of the distribution of usual intakes.

Major sources of data on dietary intake of fish used in preparing this Report to Congress are the

cross-sectional data from the USDA CSFII conducted from 1989 through 1995 (CSFII 89-91; CSFII 1994;

and CSFII 1995); on cross-sectional data from the NHANES III conducted between 1988 and 1994; and the

longer-term data on fish consumption based on recorded fish consumption for various numbers of one-month

periods of time during the years 1973-1974 by the National Purchase Diary (NPD 73-74) conducted by the

Market Research Corporation. Longer-term data on fish consumption has also been obtained from questions

on frequency of fish consumption that were included in the NHANES III survey and in CSFII 1994 and CSFII

1995. 

Identifying differences in fish consumption rates for population groups can be achieved through

analysis of dietary survey data for the general U.S. population and specified subpopulations; e.g., some tribes

of Native Americans including Alaskan tribes, and recreational anglers. The USDA has conducted a series

of nationally-based dietary surveys including the 1977-1978 Nationwide Food Consumption Survey and the

Continuing Surveys of Food Intake by Individuals over the period 1989 through 1991 (CSFII 89-91, CSFII
4-4

1994, and CSFII 1995), as well as the National Center for Health Statistics stratified population based

examination survey conducted between 1988 and 1994 (NHANES III). Analyses of fish consumption

patterns among the general U.S. population are described below.

4.1.1.2 Estimates of Month-Long Fish and Shellfish Consumption from Cross-sectional Data

The adverse developmental effects of methylmercury ingestion are closely associated with the

cumulative quantity of methylmercury consumed. The period of development that is critical to the

expression of adverse developmental effects is not known with precision. In humans, the critical exposure

period is thought to be comparatively short-term based on the methylmercury poisoning outbreak in Iraq and

various case reports of in utero methylmercury poisoning (see the Human Health and Risk Characterization

Volumes for additional information). Consequently, it is important to be able to predict moderate-term

exposures from cross-sectional data on methylmercury exposure.

Estimates of a single day’s exposure to methylmercury can be calculated from 24-hour recall data.

The quantity of fish/shellfish (portion size) and species of fish/shellfish consumed by an individual over a

day can be used to calculate daily intake of fish/shellfish. The 24-hour recall data describe portion size and

species of fish consumed. By including the amount of mercury present in this amount of fish, an estimate

of mercury ingestion can be made. This provides the distribution of mercury intakes for a 24-hour or 1-day

period. Dividing total mercury intake per day by the person’s body weight permits calculation of μg

Hg/kgbw/day. Ranking these estimates by increasing quantity permits identification of various percentiles;

e.g., 50th, 90th, 95th, etc. These rankings are the basis for “per user” percentiles.

The projection of daily dietary exposure to methylmercury (i.e., μg/kgbw/day) to exposure for a

moderate period of time (e.g., months) has been a well-recognized complication of using dietary data. If

multiple 24-hour recall data for an individual are available, Nusser et al. (1996) have described a statistical

method for projecting moderate-term dietary intakes. Publication of this methodology is comparatively

recent and the computer software/hardware requirements for these statistical analyses are somewhat complex.

Consequently, another approach for projecting month-long fish/shellfish consumption and methylmercury

exposures was needed.

The number of days per month that an individual consumes methylmercury from diet can be

estimated from data on frequency of fish/shellfish consumption. The NHANES III included questions on

how often per day/week/month, over the past 12-months, an individual consumed fish and shellfish. These

data are described below (Section 4.1.2.2) for persons 12 years of age and older. Children under 12 years-ofage were not part of the respondents in NHANES III who were asked about frequency of fish and shellfish

consumption. Accordingly, the authors of this report have made the simplifying assumption that the

frequency of fish consumption for adults from the same ethnic, racial, and economic groups can be applied

to estimates of fish and shellfish intake for children. Estimates of mercury exposure based on a single day’s

intake (μg/kgbw/day) specific for individual child survey participants were available from the 24-hour recall

data in NHANES III. These data and the adult’s frequency of fish consumption data were used to estimate

month-long projections of methylmercury exposures for children.

4.1.1.3 1973 and 1974 National Purchase Diary Data

The National Purchase Diary 1973-74 (NPD 73-74) data are based on a sample of 7,662 families

(25,165 individuals) out of 9,590 families sampled between September 1973 and August 1974 (SRI

International Contract Report to U.S. EPA, 1980; Rupp et al., 1980). Available reports are not entirely clear

on how the subsample of 7,662 was chosen. Fish consumption was based on questionnaires completed by
4-5

 the female head of the household in which she recorded the date of any meal containing fish, the type of fish

(species), the packaging of the fish (canned, frozen, fresh, dried, or smoked, or eaten out), whether fresh fish

was recreationally caught or commercially purchased, the amount of fish prepared for the meal, the number

of servings consumed by each family member and any guests, and the amount of fish not consumed during

the meal. Meals eaten both at home and away from home were recorded. Ninety-four percent of the

respondents reported consuming seafood during the sampling period.

Use of these data to estimate intake of fish or mercury on a body weight basis is limited by the

following data gaps:

1. This survey did not include data on the quantity of fish represented by a serving and

information to calculate actual fish consumption from entries described as breaded fish or

fish mixed with other ingredients. Portion size was estimated by using average portion size

for seafood from the USDA Handbook # 11, Table 10, page 40-41. The average serving

sizes from this USDA source are shown in Table 4-1.

Table 4-1

Average Serving Size (gms) for Seafood from

USDA Handbook # 11 Used to Calculate

Fish Intake by FDA (1978)

Age Group Male Female

(years) Subjects Subjects

(gms) (gms)

0-1 20 20

1-5 66 66

6-11 95 95

12-17 131 100

18-54 158 125

55-75 159 130

Over 75 180 139

2. There may have been systematic under-recording of fish intake as it was noted that typical

intakes declined 30% between the first survey period and the last survey period among

persons who completed four survey diaries (Crispin-Smith et al., 1985).

3. There have been changes in the quantities and types of fish consumed between 1973-1974

and present. The USDA (Putnam, 1991) indicated that, on average, fish consumption

increased 27% between 1970 to 1974 and 1990. This increase is also noted by the National

Academy of Sciences in Seafood Safety (1991). Whether or not this increase applies to the

highest percentiles of fish consumption (e.g., 95th or 99th percentile) was not described in

the USDA publication.

Changes in the types of fish consumed have been noted. For example, Heuter et al. (1995)

noted that there is currently a much greater U.S. consumption of shark compared to past

decades.
4-6

4. Although the NPD data with the sample weights were used to project these data to the

general U.S. population (SRI International under U.S. EPA Contract 68-01-3887), in 1980,

U.S. EPA was subsequently informed that the sample weights were not longer available.

Consequently, additional analyses with these data, in a manner than can be projected to the

general population, no longer appear to be possible.

5. Body weights of the individuals surveyed do not appear in published materials. If body

weights of the individuals participating in this survey were recorded these data do not appear

to have been used in subsequent analyses. 

Data on fish consumption from the NPD 73-74 survey have been published by Rupp et al. (1980)

and analyzed by U.S. EPA's contractor SRI International (1980). These data indicate that when a month-long

survey period is used, 94% of the surveyed population consumed fish. The species of fish most commonly

consumed are shown in Table 4-2.

Table 4-2

Fish Species and Number of Persons Using the Species of Fish.

(Adapted from Rupp et al., 1980)

Category Number of Individuals Consuming Fish

Based on 24,652 Replies*

Tuna, light 16,817

Shrimp 5,808

Flounders 3,327

Not reported (or identified) 3,117

Perch (Marine) 2,519

Salmon 2,454

Clams 2,242

Cod 1,492

Pollock 1,466

* More than one species of fish may be eaten by an individual.

Rupp et al. (1980) also estimated quantities of fish and shellfish consumed by teenagers aged 12-18

years and by adults aged 18 to 98 years. These data are shown in Table 4-3. The distribution of fish

consumption for age groups that included women of child-bearing ages are shown in Table 4-4.

Table 4-3

Fish Consumption from the NPD 1973-1974 Survey

(Modified from Rupp et al., 1980)

Age Group 50th Percentile 90th Percentile 99th Percentile Maximum

Teenagers Aged 1.88 kg/year 8.66 kg/year 25.03 kg/year 62.12 kg/year

12-18 Years or 69 grams/day

Adults Aged 18 2.66 kg/year 14.53 kg/year 40.93 kg/year 167.20 kg/year

to 98 Years or 112 grams/day
4-7

Table 4-4

Distribution of Fish Consumption for Females by Age*

Consumption Category (gms/day) (from SRI, 1980)

Age (years) 47.6-60.0 60.1-122.5 Over 122.5

10-19 0.2 0.4 0.0

20-29 0.9 0.9 0.0

30-39 1.9 1.7 0.1

40-49 3.4 2.1 0.2

* The percentage of females in an age bracket who consume, on average, a specified amount (grams) of fish per day.

The calculations in this table were based upon the respondents to the NPD survey who consumed fish in the month of

the survey. The NPD Research estimates that these respondents represent, on a weighted basis, 94.0% of the population

of U.S. residents (from Table 6, SRI Report, 1980).

4.1.1.4 Nationwide Food Consumption Survey of 1977-78

Fish consumption is not evenly divided across the U.S. population. Analysis of patterns of fish

consumption have been performed on data obtained from dietary surveys of nationally representative

populations. For example, Crochetti and Guthrie (1982) analyzed the food consumption patterns of persons

who participated in the Nationwide Food Consumption Survey of 1977-78. Populations specifically excluded

from this analysis were children under four years of age, pregnant and nursing women, vegetarians,

individuals categorized by race as "other" (i.e., not "white" and not "black"), individuals not related to other

members of the household in which they lived, and individuals with incomplete records. After these

exclusions, the study population consisted on 24,085 individual dietary records for a 3-day period. 

Persons reporting consumption of fish, shellfish, and seafood at least once in their 3-day dietary

record were categorized as fish consumers. Combinations of fish, shellfish, or seafood with vegetables

and/or starches (e.g., rice, pasta) or fish sandwiches were categorized as consumers of fish "combinations".

Among the overall population, 25.0% of respondents reported consumption of fish with an additional 9.6%

reporting consumption of fish "combinations" in the 3-day period for a total of 34.6% reporting consumption

of fish and/or fish combinations. Frequency of consumption was comparable for male and female

respondents with 24.1% of men and 25.7% of women reporting consumption of fish in their 3-day dietary

records. Fish "combinations" were reported as dietary items by 11.2% of women and 9.9% of men. Both

these food categories were consumed typically as mid-day and evening meals, rather than as breakfast or as

snacks. For persons who listed fish in their 3-day dietary records, 89.7% listed fish in one meal only with

10.1% of respondents consuming fish in two meals and 0.1% consuming fish in three meals. For dishes that

combined fish and other foods (i.e., fish "combinations"), among persons who reported eating fish

combinations, 93.4% reported this food in one meal only with 6.5% of individuals consuming two meals

containing fish "combinations."

There appears to be little difference between men and women in their likelihood of consuming fish

based on patterns observed in this national survey (Crochetti and Guthrie, 1982). Based on this analysis,

allocation of fish consumption on a "per capita" basis does not adequately reflect the fish consumption

patterns of the general population of the United States. While "per capita" estimates resulted in an

overestimate of fish consumption for the approximately 65% of the U.S. population who did not report

consuming fish, these types of estimates by their nature substantially underestimated fish consumption rates

by persons who consume fish. This pattern of underestimation is important in an assessment of impact of

infrequently consumed foods such as fish.
4-8

4.1.1.5 CSFII 1989-1991

The second set of nation-wide data (CSFII 89-91) are presented in Table 4-5, including an age/gender

analysis of the fish-consuming population. Based on analysis of 11,706 respondents who supplied 3-days

of dietary record in the CSFII of 1989-1991, the frequency of fish consumption within the 3-day period was

determined. Analyses of these dietary records indicate that 30.9% of respondents consumed fish, either alone

or as part of a dish that contained fish. Most respondents eating fish consumed one fish meal within the 3-

day period. Two percent (2%) of respondents reported consuming fish two or more times during the 3-day

period, and 0.5% of these fish-eating respondents reported fish consumption three or more times during the

3-day study period. Among persons who reported eating fish within the 3-day period of the survey, 44.1%

reported eating marine finfish (other than or in addition to tuna, shark, barracuda, and swordfish). Marine

finfish were more frequently consumed than freshwater fish. Of the 1593 people who reported eating finfish,

492 (30.9%) identified these as freshwater fish. 

Table 4-5

CSFII 89-91 Data

Gender Aged 14 Years Aged 15 through Aged 45 Years Total for All Age

or Younger 44 Years or Older Groups

Number of Individuals With 3 Days of Dietary Records

Males 1497 (51.7%) 2131 (42.9%) 1537 (40.0%) 5,165 (44.1%)

Females 1396 (48.3%) 2837 (57.1%) 2308 (60.0%) 6,541 (55.9%)

Total 2893 (24.7%) 4968 (42.4%) 3845 (32.8%) 11,706

Respondents Reporting Consumption of All Fish and Shellfish

(Data weighted to be representative of the U.S. population.)

Males 380 (52.8%) 646 (42.8%) 556 (39.3%) 1582 (43.8%)

Females 340 (47.2%) 864 (57.2%) 828 (58.5%) 2032 (56.2%)

Total 720 (19.9%) 1510 (41.8%) 1415 (39.2%) 3614 (30.9%)

4.1.1.6 CSFII 1994 and CSFII 1995

Analyses in 1994 were based on 5296 respondents on day 1 and 5293 respondents on day 2. A

change in survey methods resulted in food consumption data being collected for two days rather than for

three days as in the 1989-91 survey. Dietary records included fish or shellfish for 598 individuals on day 1

and 596 individuals for day 2. These days were not necessarily sequential. Fish/shellfish consumption by

age and gender categories for CSFII 1994 and CSFII 1995 are shown in Tables 4-6 and 4-7, respectively.

Overall, 11.3% of respondents reported fish or shellfish consumption. The rate was lower among children

under 15 years of age and higher among adults aged 45 years and older.
4-9

Table 4-6

CSFII 1994 Data — Days 1 and 2

Gender Aged 14 Years Aged 15 Aged 15 and Total for All

or Younger through 44 Older Age Groups

Number of Individuals with Dietary Recalls — Day 1

Males 932 852 869 2653

Females 942 842 859 2643

Total 1874 1694 1728 5296

% consumption fish 7.9 10.9 15.4 11.3

Respondents Reporting Consumption of All Fish and Shellfish — Day 1

Males 65 90 138 293

Females 83 94 128 305

Total 148 184 266 598

Number of Individuals with Dietary Recalls — Day 2*

Males 993 852 868 2653

Females 941 840 859 2640

Total 1874 1692 1727 5293

% consumption fish 8.6 10.2 15.1 11.3

Respondents Reporting Consumption of All Fish and Shellfish — Day 2

Males 74 86 132 292

Females 88 87 129 304

Total 162 173 261 596

*Methodology changes based on two 24-hour recalls, not necessarily sequential.

To assess whether or not there were seasonal differences in fish and shellfish consumption, the

year was divided into six two-month intervals. Fish intake data was analyzed by season. These values

are shown in Table 4-8.
4-10

Table 4-7

CSFII 1995 Data — Days 1 and 2

Gender Aged 14 Years Aged 15 through Aged 15 and Total for All Age

or Younger 44 Older Groups

Number of Individuals with Dietary Recalls — Day 1

Males 863 649 1,067 2,579

Females 808 635 1,041 2,484

Total 1,671 1,284 2,108 5,063

% Consuming

Fish

7.5 11.7 15.4 11.9

Respondents Reporting Consumption of All Fish and Shellfish — Day 1

Males 63 77 170 310

Females 63 73 155 291

Total 126 150 325 601

Number of Individuals with Dietary Recalls — Day 2

Males 862 648 1,067 2,577

Females 809 634 1,042 2,485

Total 1,671 1,282 2,109 5,062

% Consuming

Fish

8.8 12.9 14.5 12.2

Respondents Reporting Consumption of All Fish and Shellfish — Day 2

Males 81 82 168 331

Females 67 84 138 289

Total 148 166 306 620

Table 4-8

Fish Consumption (gms) by Season for Respondents Reporting Seafood Consumption

CFSII 1994 — Day 1

Statistics Season

Jan/Feb Mar/Apr May/Jun Jul/Aug Sep/Oct Nov/Dec

 Mean 102 92 92 107 100 105

Std. Dev* 74 74 82 87 77 77

Minimum 212122

Maximum 373 488 960 903 413 517
Table 4-8 (continued)

Fish Consumption (grams) by Season for Respondents Reporting Seafood Consumption

CFS II 1994 — Day 1

Statistics Season

Jan/Feb Mar/Apr May/Jun Jul/Aug Sep/Oct Nov/Dec

Although children are oversampled in the survey design, not all assessmsents were carried out among

1

young children. For example, 24-hour dietary recall data were obtained for children, however, frequency of fish

consumption information was not obtained.

4-11

Percentiles

5th 14 10 22 21 12 14

10th 28 19 28 28 23 24

25th 50 51 42 53 49 48

Median 86 73 57 85 79 85

75th 114 123 118 139 129 165

90th 202 173 190 196 204 189

95th 293 227 295 272 253 235

Observations 183 219 210 242 191 163

Sum of Weights (000s) 10,197 11,383 11,817 11,506 9,573 9,113

* The values in these cells are the weighted standard deviations of the individual observations. Estimates

of the standard errors of the means were not calculated.

4.1.1.7 NHANES III General Description

The NHANES III, conducted between 1988 and 1994, used a multistage probability design that

involved selection of primary sampling units, segments (clusters of households) within these units,

households, eligible persons, and finally sample persons. Primary sampling units typically were

composed of a county or group of contiguous counties. Certain subgroups in the population that were of

special interest for nutritional assessment were oversampled: preschool children (six months through five

years old) , persons 60 through 74 years old, and the poor (persons living in areas defined as poor by the 1

United States Bureau of the Census for the 1990 census). The U.S. Bureau of the Census selected the

NHANES III sample according to rigorous specifications from the National Center for Health Statistics

so that the probability of selection for each person in the sample could be determined.

The statistics presented in the report are population estimates. The findings for each person in

the sample were inflated by the reciprocal of selection probabilities, adjusted to account for persons who

were not examined, and stratified afterward according to race, sex and age, so that the final weighted

population estimates closely approximated the civilian noninstitutionalized population of the United

States as estimated independently by the U.S. Bureau of the Census at the midpoint of the survey, March

1, 1990.
4-12

Although NHANES III was conducted between 1988 and 1994, data on food consumption only

became available in 1996. The survey includes one 24-hour recall obtained by a trained interviewer. 

This data base contains 29,973 dietary records including 3864 individuals who consumed fish and

shellfish (Table 4-9). Consumption of fish differed by age. Overall 12.9% of respondents included fish

or shellfish in their 24-hour dietary recall. As observed in CSFII 1994, the data among children aged 14

years and younger was about half the percentages of fish consumption for ages 45 and older (Tables 4-10

and 4-11). There were questions on frequency of fish/shellfish consumption in the CSFII 1994 and

CSFII 1995 data bases; however, the specific information obtained excluded canned fish. Consequently,

these data were not used to estimate month-long fish consumption. The 24-hour recall data were

analyzed for both children and adults.

Table 4-9

All Age Groups NHANES III

Ages 14 and Ages 15 Ages 45 and Total

Younger through 44 Older

Years

Total 12,048 10,041 7,884 29,973

Fish Consumption 1060 1527 1274 3861

% Consumption Fish 8.8 15.2 16.2 12.9

Table 4-10

NHANES III Adult Respondents

Gender Ages 15 to 44 Age 45 Years Total for All Age

Years and Older Groups

Total Respondents

Males 4,620 3,783 8,403

Females 5,421 4,101 9,522

Total 10,041 7,884 29,989

Respondents Reporting Fish Consumption

Males 664 605 1269

Females 883 645 1528

Total 1527 1274 2801
4-13

Table 4-11

NHANES III Child Respondents

Age Group Total Fish Consumers % Reporting Fish

1-5 Years 7595 626 8.2

6-11 Years 3217 323 10.0

12-14 Years Female 660 58 8.8

12-14 Years Male 576 53 9.2

Total 12,048 1060 8.8

4.1.2 Frequency of Consumption of Fish Based on Surveys of Individuals

4.1.2.1 CSFII 1989-1991

In the USDA 1989 through 1991 Continuing Surveys of Food Intake by Individuals (CSFII 89-

91), food consumption data were obtained from nationally representative samples of individuals. These

surveys included women of child-bearing age — 15 through 44 years of age. Data from the CSFII for the

period including 1989 and 1991 were used to calculate fish intake by the general population and women

of child-bearing age. This subpopulation included pregnant women, which are a subpopulation of

interest in the Mercury Study: Report to Congress, because of the potential developmental toxicity to the

fetus accompanying ingestion of methylmercury. Analysis of Vital and Health Statistics data from 1990

indicated that 9.5% of women in this age group can be predicted to be pregnant in a given year. The size

of this population has been estimated using the methodology described in the Addendum to this chapter,

entitled "Estimated National and Regional Populations of United States Women of Child-Bearing Age."

The data described in this section were obtained from nationally representative samples of

individuals and were weighted to reflect the U.S. population using the sampling weights provided by

USDA. The basic survey was designed to provide a multistage stratified area probability sample

representative of the 48 conterminous states. Weighting for the 1989, 1990 and 1991 data sets was done

in two stages. In the first phase a fundamental sampling weight (the inverse of the probability of

selection) was computed and the responding weight (the inverse of the probability of selection) was

computed for each responding household. This fundamental sampling weight was then adjusted to

account for non-response at the area segment level. The second phase of computations used the weights

produced in the first phase as the starting point of a reweighing process that used regression techniques to

calibrate the sample to match characteristics thought to be correlated with eating behavior.

The weights used in this analysis reflect CSFII individuals providing intakes for three days. 

Weights for the 3-day individual intake sample were constructed separately for each of the three genderage groups: males ages 20 and over, females ages 20 and over and persons aged less than 20 years. 

Characteristics used in weight construction included day of the week, month of the year, region,

urbanization, income as a percent of poverty, food stamp use, home ownership, household composition,

race, ethnicity and age of the individual. The individual's employment status for the previous week was

used for persons ages 20 and older, and the employment status of the female head of household was used

for individuals less than 20 years of age. The end result of this dual weighting process was to provide

consumption estimates which are representative of the U.S. population.

Respondents were drawn from stratified area probability samples of noninstitutionalized U.S.

households. Survey respondents were surveyed across all four seasons of the year, and data were
4-14

obtained across all seven days of the week. The dietary assessment methodology consisted of assessment

of three consecutive days of food intake, measured through one 24-hour-recall and two 1-day food

records. For this analysis, the sample was limited to those individuals who provided records or recalls of

three days of dietary intake.

For purposes of interpretability, it should be noted that assessment of fish consumption patterns

by recall/record assessment methods will probably differ from assessments based on food frequency

methods (See Section 4.1.2.3, below). In order to be designated a consumer or "user" of fish for

purposes of the present analysis, an individual would need to have reported consumption of one or more

fish/shellfish products at some time during the three days when dietary intake was assessed. Since fish is

not a frequently consumed food for the majority of individuals, this dietary assessment method will likely

underestimate the extent of fish consumption, because some individuals who normally consume fish will

be missed if they did not consume fish during the three days of assessment. In contrast, such users would

be picked up by a food frequency questionnaire. The recall/record dietary assessment method does have

the advantage, however, of providing more precise estimates of the quantities of fish consumed that

would be obtained with a food frequency record.

The information that follows comes from the CSFII 1989-1991 and was provided under contract

to U.S. EPA by Dr. Pamela Haines of the Department of Nutrition of the University of North Carolina

School of Public Health. Data are presented for following groups of individuals surveyed by USDA in

the CSFII: data for the total population, data grouped by gender, and for data grouped by age-gender

categories for the age groups 14 years or younger, 15 through 44 years, and 45 years and older (Table 4-

5).

Fish consumption was defined to reflect consumption of approximately 250 individual "Fish

only" food codes and approximately 165 "Mixed dish-fish" food codes present in the 1994 version of the

USDA food composition tables. The USDA maintains a data base (called the "Recipe File") that

describes all food ingredients that are part of a particular food. Through consultation with Dr. Betty

Perloff, an USDA expert in the USDA recipe file, and Dr. Jacob Exler, an USDA expert in food

composition, the USDA recipe file was searched for food codes containing fish or shellfish. The recipe

was then scanned to determine fish codes that were present in the recipe reported as consumed by the

survey respondent. The percent of the recipe that was fish by weight was determined by dividing the

weight of the fish/shellfish in the dish by the total weight of the dish.

As with most dietary assessment studies, multiple days of intake were averaged to reflect usual

dietary intake better. Intakes reported over the three-day period were summed and then divided by three

to provide consumption estimates on a per person, per day basis.

Fish consumption was defined within the following categories.

1. Fish and Shellfish, all types reflected consumption of any fish food code.

2. Marine Finfish, included fish not further specified (e.g., tuna) and processed fish sticks,

as well as anchovy, cod, croaker, eel, flounder, haddock, hake, herring, mackerel, mullet,

ocean perch, pompano, porgy, ray, salmon, sardines, sea bass, skate, smelt, sturgeon,

whiting.

3. Marine Shellfish included abalone, clams, crab, crayfish, lobster, mussels, oysters,

scallops, shrimp and snails.

4. Tuna, contained only tuna.

5. Shark, Barracuda, and Swordfish contained just these three species of fish.

6. Freshwater Fish contained carp, catfish, perch, pike, trout and bass.

The analysis was stratified to reflect "per capita" (Table 4-12), as well as "per user" (Table 4-13),

consumption patterns. A "consumer" of Fish and Shellfish, all types was one who consumed any of the
4-15

included fish only or mixed-fish dish foods. A Marine Finfish consumer was one who consumed any of

the species of fish included within the marine finfish category, and so on for each category. The percent

of the population or subpopulation consuming fish was listed for the entire population, as well as gender

specific values, and age-gender category specific values.

Table 4-12

Consumption of Fish and Shellfish (gms/day), and Self-Reported Body Weight (kg)

in Respondents of the 1989-1991 CSFII Survey.

"Per Capita" Data for All Survey Respondents

(Data are weighted to be representative of the U.S. population.)

Gender Aged 14 Years or Aged 15 through Aged 45 Years or Total

Younger 44 Years Older

Mean SD kg Mean SD kg Mean SD Kg Mean SD kg bw bw bw bw

Males 9 20 26 19 35 73 20 36 90 17 33 68

Females 8 18 24 14 28 63 18 30 67 14 27 58 

Table 4-13

Consumption of Fish and Shellfish (gms/day), and

Self-Reported Body Weight (kg) in Respondents of the 1989-1991 CSFII Survey

(Data for "Users" Only. Data are weighted to be representative of the U.S. population.)

Gender Aged 14 Years or Aged 15 through Aged 45 Years or Total

Younger 44 Years Older

Mean SD kg Mean SD kg Mean SD Kg Mean SD kg bw bw bw bw

Males 32 27 28 54 39 80 51 42 83 49 39 59

Females 29 24 24 41 35 63 42 34 68 40 33 54

 Consumption of fish-only and mixed-fish-dishes was summed across the three available days of

dietary intake data. This sum was then divided by three to create average per day fish consumption

figures. In the tables that describe fish intake, information is presented on sample size, percent of the

population who consumed any product within the specified fish category, the mean grams consumed per

day and the mean grams consumed per kilogram body weight (based on self-reported body weights),

standard deviation, minimum, maximum, and the population intake levels at the 5th, 25th, 50th (median),

75th, and 95th percentiles of the intake distribution for each age-gender category. The means and

standard deviations were determined using a SAS program. Survey sample weights were applied. 

Analysis with SAS does not take design effects into account, so the estimates of variance may differ from

those obtained if SUDAAN or such packages had been used. It should be noted, however, that the point

estimates of consumption (grams per consumer per day, grams per consumer per kilogram of body

weight) will be exactly the same between the two statistical analysis packages. Thus, the point estimates

reported are accurate and appropriate for interpretation on a national level. 

Data were obtained for 11,706 individuals reporting 3-days of diet in the 1989-1991 CSFII

survey. Analyses were based on data weighted through statistical procedures (as described previously) to

be representative of the U.S. population. The total group of respondents reporting consumption of finfish

and/or shellfish during the 3-day period were grouped as a subpopulation who consumed fish, as can be

observed in Table 4-13. Fish and shellfish (total fish consumption) were reported to be eaten by 3614
4-16

persons (30.9%) of the 11,706 of the survey respondents (see Tables 4-12 and 4-13). The subpopulation

considered to be of greatest interest in this Mercury Study: Report to Congress were women of childbearing age (15 through 44 year-old females). Among this group of women ages 15 through 44 years,

864 women of the 2837 surveyed (30.5%) reported consuming fish (see Tables 4-12 and 4-13). Within

this group, 334 women reported consumption of finfish during the 3-day survey period. 

Consumption of fish and shellfish varied by species of fish. Overall, marine finfish (not

including tuna, swordfish, barracuda, and shark) and tuna were consumed by more individuals and in

greater quantity than were shellfish. Tuna fish was the most frequently consumed fish product, and

separate tables are provided that identify quantity of tuna fish consumed. Two other categories of finfish

were identified: freshwater fish and a category comprised of swordfish, barracuda, and shark. 

Freshwater fish were of interest because U.S. EPA's analysis of the fate and transport of ambient,

anthropogenic mercury emissions from sources of concern in this report indicates that fish may

bioaccumulate emitted mercury. Swordfish, barracuda, and shark were also identified as a separate

category. These are predatory, highly migratory species that spend much of their lives at the high end of

marine food web. These fish are large and accumulate higher concentrations of mercury than do lower

trophic level, smaller fish. 

4.1.2.2 Estimated Frequency of Fish/shellfish Consumption Based on Food Frequency Questions

in CSFII 1994 and NHANES III

Both surveys included questions on frequency of consumption of fish and shellfish. The specific

wording of the questions are shown in the box. The wording of CSFII 1994 separated canned fish from

fish making it difficult to provide an overall estimate of fish consumption because no separate question

addressed frequency of consumption of canned fish. The CSFII survey also provided a separate question

on whether of not any of the fish the respondent ate was caught by the respondent or someone known to

the respondent. Among those respondents who ate non-canned fish during the past 12-month period

(84.1% of respondents), 37.5% indicated that they had consumed fish caught by themselves or a person

known to them. Shellfish were reported to have been consumed by 62.2% of respondents during the past

12-month period.
4-17

Fish Consumption Survey Questions 

CFSII 1994

During the past 12 months, that is, since last (NAME OF MONTH), (have you/has NAME) eaten any

(FOOD) in any form?

Yes No

Shellfish . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . 1 2

Fish, other than shellfish or canned fish . . . . . . . . . . . 1 2

 IF YES: Was any of the fish you ate caught by you or 

 someone you know? . . . . . . . . . . . . . . . . . . . . . . . . . 1 2

NHANES III

N2. MAIN DISHES, MEAT, FISH, CHICKEN, AND EGGS

Times Day Week Month Never or DK

g. Shrimp, clams, oysters, 

 crabs, and lobster ____ per 1D 2W 3M 4N or 9DK

h. Fish including fillets, fish sticks

 fish sandwiches, and tuna fish ___ per 1D 2W 3M 4N or 9DK

In the CSFII 1994 survey, subjects who consumed fish other than shellfish or canned fish were to

select the answer “yes.” Because canned fish (e.g., tuna, sardines) represent major food items, a portion

of the fish consumers would indicate they were nonconsumers if they ate canned fish only. 

Consequently, using the results from the CSFII 1994 question would underestimate the frequency of

consumption of fish. 

NHANES III included two questions on fish and shellfish consumption as part of the household

interview portion of the survey. The specific format and wording are shown below. Questions N2g and

N2h addressed shrimp/shellfish and fish separately. Respondents were asked to indicate their frequency

of consumption: never, or how often daily, weekly, or monthly they consumed shrimp/shellfish (g) or fish

(h). Analyses of data from these questions provided the estimates of frequency of fish and shellfish

consumption shown in Table 4-14.

Table 4-14

Frequency of Fish/Shellfish Ingestion and Percent of Respondents*

(NHANES III, Food Frequency Questionnaire, Weighted Data)

Number of times All Adults Women Aged Men Aged Women Aged 45 Men Aged 45

per month 15 — 44 Years 15 —44 Years Years and Older Years and Older

0 12 14 11 11 9

1 or more 88 86 89 89 91

2 or more 79 78 81 80 83

4 or more 58 56 58 61 63

8 or more 23 25 29 30 31

12 or more 13 12 14 15 14

24 or more 33 3 2 3

30 or more 12 2 1 2

*Adult subjects only. Food frequency data were not collected for children ages 11 and younger.
4-18

Frequency of fish and shellfish consumption data have also been calculated by ethnic/racial

grouping. The groups were: Non-Hispanic whites (“Whites”), Non-Hispanic blacks (“Blacks”) and

persons designated as “Other” who included persons of Asian/Pacific Islander ethinicity, Native

Americans, Non-Mexican Hispanics (predominately persons from Puerto Rica and other Carribean

Islands), and additional groups not in the categories “Whites” or “Blacks”. Food frequency data for these

groups is shown in Tables 4-15 and 4-16.

Table 4-15a

Frequency of Fish and Shellfish Consumption by Percent among 

All Adults, Both Genders, Weighted Data, NHANES III*

(Estimated Frequency Per Month)

Frequency per Month White Black Other

Zero 11.8 11.3 15.1

Once a Month or More 88.2 88.7 84.9

Once a Week or More 57.1 63.5 60.3

Twice a Week or More 25.9 31.9 31.2

Three-Times a Week or More 11.6 15.0 22.9

Approximately Daily (6 Times 1.9 3.3 8.9

Per Week)

* Adult subjects only. Food frequency data were not collected for children aged 11 years and younger.

Table 4-15b

Frequency of Fish and Shellfish Consumption by Race/Ethnicity, 

Women Aged 15-44 Years, Weighted Data, NHANES III

(Estimated Frequency Per Month)

Frequency per Month White Black Other

Zero 13.2 10.1 19.1

Once a Month or More 86.8 89.9 80.9

Once a Week or More 54.5 62.8 59.3

Twice a Week or More 22.0 31.7 35.6

Three-Times a Week or More 9.5 15.9 22.7

Approximately Daily (6 Times 1.7 3.2 9.2

Per Week)
4-19

Table 4-16a

Distribution of the Frequency of Fish and Shellfish Consumption by Race/Ethnicity

All Adults, Both Genders, Weighted Data, NHANES III

Percentile Whites Blacks Other

50th 4 4 5

75th 8 8 10

90th 13 13 22

95th 17 19 32

Table 4-16b

Distribution of the Frequency of Fish and Shellfish Consumption By Race/Ethnicity

Among Adult Women Aged 15-44, Weighted Data, NHANES III

Percentile Whites Blacks Other

50th 4 4 5

75th 7 8 10

90th 11 14 23

95th 15 20 31

Overall 88% of all adults consume fish and shellfish at least once a month with 58% of adults

consuming fish at least once a week. Between 13% and 23% consume fish/shellfish two or three times

per week. An estimated 3% indicate they consume fish and shellfish six times a week with 1% of all

respondents indicating they eat fish and shellfish daily. Comparatively small differences exist based on

age and gender of adults. Two percent of women of reproductive age and 2% of men in the age range 15

through 44 years indicate they consume fish/shellfish daily.

Among diverse subpopulations those designated as “Other” consume fish and shellfish more

frequently than do individuals in groups identified as “White” and “Black”. In the “Other” category 5%

of individuals consume fish and shellfish daily (95th percentile value). Approximately 10% of the

subpopulation of “Whites” consume fish and shellfish three-times or more per week with approximately

23% of persons in the “Other” classification consuming fish and shellfish three-times a week or more. 

4.1.2.3 Frequency of Consumption of Various Fish Species by Respondents in NHANES III

Grouping of fish and shellfish species by habitat (i.e., freshwater, estuarine, and marine) was

done based on an organization developed by US EPA’s Office of Water. Table 4-17 shows which

species were grouped into these three habitat categories.
4-20

Table 4-17

Classification of Fish Species by Habitat*

Marine Estuarine Freshwater

Abalone Anchovy Carp

Barracuda Clams (8%) Catfish

Clams (92%) Crab (46%) Pike

Cod Croaker Salmon (1%)

Crab (54%) Flatfish (29%) Trout

Flatfish (71%) Flounder

Haddock Herring

Halibut Mullet

Lobster Oyster

Mackerel Perch

Mussels Scallop (1%)

Ocean Perch Scup

Octopus Shrimp

Pollock Smelts

Pompano Sturgeon

Porgy

Salmon (99%)

Sardine

Scallop (99%)

Sea Bass

Seafood (e.g., fish sauce)

Shark

Snapper

Swordfish

Sole

Squid

Tuna

Whitefish

Whiting

*Unprocessed fish (Food Codes 2815061 and 2815065) were not classified by habitat.

Mean consumption rates for only males and females who reported consuming fish/shellfish in the

NHANES III data set are shown in Table 4-18. Consumption rates for species grouped as marine,

estuarine, and freshwater are shown in Table 4-19. Marine fish are the most frequently consumed

followed by estuarine and freshwater fish. However, when freshwater fish are consumed the portion size

is larger than for marine or estuarine fish. Males consumed larger portions of any of the fish groups than

did female subjects.
4-21

Table 4-18

Weighted Estimates of Fish and Shellfish Consumed (gms) for Females and Males Aged 15 - 44

Years Reported in NHANES III (Per User)

Statistic Females Males

Mean 103 146

Standard Deviation 116 149

Minimum 1 1

Maximum 117 1097

Percentiles

5th 12 14

10th 20 28

25th 37 51

Median 73 97

75th 131 185

90th 228 345

95th 288 435

Observations 883 645

Sum of Weights (000s) 1,162 9,223

Table 4-19

Weighted Estimates for Fish and Shellfish Consumed (gms) by Female and Male Respondents

Aged 15 - 44 Years Reported in the NHANES III Survey by Habitat of Species Consumed

Statistic Marine Fish Estuarine Fish Freshwater Fish 

Females Males Females Males Females Males

Mean 86 113 69 122 158 274

Std. Dev 86 122 64 131 138 268

Minimum 0 0 0 0 7 14

Maximum 957 1004 517 981 740 1097

Percentiles

5th 8 1 8 5 13 42

10th 14 12 9 8 26 42

25th 37 44 22 29 50 123

Median 55 84 47 64 127 185

75th 109 153 101 175 235 313

90th 209 204 168 355 330 617

95th 247 351 202 357 330 929
Table 4-19 (continued)

Weighted Estimates for Fish and Shellfish Consumed (gms) by Female and Male Respondents

Aged 15 - 44 Years Reported in the NHANES III Survey by Habitat of Species Consumed

Statistic Marine Fish Estuarine Fish Freshwater Fish 

Females Males Females Males Females Males

4-22

Observations 519 387 221 198 82 60

Sum of Weights (000s) 6,457 5,999 2,653 2,477 516 588

4.1.3 Subpopulations with Potentially Higher Consumption Rates

The purpose of this section is to document fish consumption rates among U.S. subpopulations

thought to have higher rates of fish consumption. These subpopulations include residents of the States of

Alaska and Hawaii, Native American Tribes, Asian/Pacific Island ethnic groups, anglers, and children;

these groups were selected for analysis because of potentially elevated fish consumption rates rather than

because they were thought to have a high innate sensitivity to methylmercury. The presented estimates

are the results of fish consumption surveys conducted on the specific populations. The surveys use

several different techniques and illustrate a broad range of consumption rates among these

subpopulations. In several studies the fish consumption rates of the subpopulations corroborate the highend (90th percentile and above) fish consumption estimates of the the nationwide food consumption

surveys.

Many of the surveys of fish consumption conducted on high-end fish consumers also included

analyses for mercury in hair and blood of the people who were subjects. These data on biological

monitoring provide an additional bases to estimate mercury exposure.

4.1.3.1 Subpopulations Included in Nationally Representative Food Consumption Surveys

Contemporary food consumption surveys designed to be representative of the U.S. population as

a whole included identifiers for ethnically diverse subpopulations. Publicly available data from the

NHANES III combined three subpopulations of interest with regard to level of fish consumption:

Asian/Pacific origin, Native American origin, and others. By contrast, the CSFII 1994 and CSFII 1995

surveys provided separate estimates for identified ethnic subpopulations: white, black, Asian and Pacific

Islander, Native American and Alaskan Native, and other (see Figure 4-1).

 The 50th, 90th and 95th percentiles for all survey participants in CSFII 1994 and CSFII 1995 for

“Day 1" and “Day 2" recall data are shown in Table 4-20. The number of 24-hour recall food

consumption reports for each group is noted in the table food note. Data are presented for both “per

capita” and “per user.” The subpopulation self-designated as “white” has the smallest intake of

fish/shellfish and mercury at the 50th percentile. “Blacks” have higher levels of intake and Asian and

Pacific Islanders have the highest intake of fish/shellfish. Similar patterns are observed at the 90th and

95th percentile.

If the data are calculated for only those persons who reported consuming fish and shellfish, a

somewhat different pattern emerges. A median intake of fish/shellfish is the lowest among Asian and

Pacific Islanders, intermediate among “whites” and highest among “blacks.” The number of observations

among Native Americans and Alaska Natives are too small to produce reliable estimates. 
7.5

14

18

26

27.7

30.8

32

34 37

43

59

63

100

109

112

150

156

170

200

229

250

300

350

390

400

452 Wolfe & Walker '87 Highest Response Group Mean in AK

CRITFC '94 99th %ile Adult

Puffer '89 90th %ile

LEGEND - POPULATIONS

GENERAL U.S. POPULATION

NPD 73/74

CSFII

RECREATIONAL ANGLERS

PUFFER

FIORE

CONNELY

SUBSISTENCE FISHERS

WOLFE & WALKER

NATIVE AMERICANS

CRITFC

TOY, TULALIP

NOBMAN

EPA '92 WI TRIBES

CRITFC '94 95th %ile

Toy '95 Tulalip Tribe 90th %ile

NPD 73/74 Adult 99th %ile

Nobmann '92 AK Tribes Mean

Fiore '89 95th %ile

WI Anglers

CRITFC '94 Adult Mean

Toy '95 Tulalip Tribe Median

Puffer '81 Median

Fiore '89 75th %ile WI Anglers

EPA '92 WI Tribes

NPD 73/74 Adult 90th %ile

Fiore '89 WI Anglers Connoly '90 NY Anglers Mean

CSFII Age 15-44 Mean and NPD 73/74 Adult 50th %ile

Figure 4-1

Distribution of Fish Consumption Rates of Various Populations

0

4-23
4-24

Table 4-20

Consumption of Fish and Shellfish (gms/day) among Ethnically Diverse Groups

(Source: CSFII 1994 and CSFII 1995)

Ethnic Group Fish Consumption (grams/day)

Per Capita Per User 1 2

White

 50th Percentile

 90th Percentile

 95th Percentile 80 243

Zero 72

24 192

Black

 50th Percentile

 90th Percentile

 95th Percentile 104 302

Zero 82

48 228

Asian and Pacific Islander

 50th Percentile

 90th Percentile

 95th Percentile 127 292

Zero 62

80 189

Native American and Alaska Native

 50th Percentile

 90th Percentile Zero of small numbers of

 95th Percentile

Zero Estimate not made because

56 respondents. 

Other

 50th Percentile

 90th Percentile

 95th Percentile

Zero 83

Zero 294

62 327

Total number of 24-hour food consumption recall reports: White (16,241); Black (2,580); Asian and 1

Pacific Islander (532); Native American and Alaska Native (166): and Other (1,195).

Number of 24-hour food consumption recall reports: White (1,821); Black (329); Asian and Pacific 2

Islander (155); Native American and Alaska Native (12); and Other (98).

4.1.3.2 Specialized Surveys

During the past decade, data describing the quantities of fish consumed by angler, economically

subsistent, and North American Tribal groups have been published (Tables 4-23 and 4-30). 

Subpopulations of particular concern because of exposure patterns are Native Americans, sport anglers,

the urban poor, and children. Data on fish consumption for these groups indicate that exposures for these

subgroups exceed those of the general population of adults. If North American data, including those

from Canada, are considered, mercury exposures from the marine food web (especially if marine

mammals are consumed) exceed limits such as the Tolerable Daily Intake established by Health Canada

(Chan, 1997) and the Acceptable Daily Intake established by the U.S. Food and Drug Administration.

The data cited below on specific subpopulations are not utilized in this Report as the basis of a

site-specific assessment. In a site-specific assessment the fish consumption rates among a surveyed

population would be combined with specific measurements of methylmercury concentrations in the local

fish actually consumed to estimate the human contact rate. Ideally, some follow-up analysis such as

concentrations of mercury in human blood or hair would ensue.
4-25

Analytic and survey methods to estimate the fish consumption rates of the respondents are

described for each population. This chapter does not constitute an exhaustive review of the methods

employed. An attempt was made to characterize the population surveyed. Additionally, to characterize

the entire range of fish consumption rates in the surveyed populations, the consumption rates of both

average and high-end consumers as well as other specific angler subpopulations (e.g., fish consumption

by angler race or age) are presented.

The sources of consumed fish are also identified in the summaries. Fish consumed by humans

can be derived from many sources; these include self-caught, gift, as well as grocery and restaurant

purchases. Some studies describe only the consumption rates for self-caught fish or freshwater fish,

others estimate total fish consumption, and some delineate each source of fish. Humans also consume

fish from many different types of water bodies. When described by the reporting authors, these are also

identified.

Assumptions concerning fish consumption made by the study authors are also identified. 

Humans generally do not eat the entire fish; however, the species and body parts of fish which are

consumed may be highly variable among angler populations (for example, see Toy et al. 1995). Anglers

do not eat their entire catch, and, some species of fish are typically not eaten by specific angling

subpopulations. For example, Ebert et al. (1993) noted that some types and parts of harvested fish are

used as bait, fed to pets or simply discarded. Study authors account for the differences between catch

weight and number in a variety of different ways. Typically, a consumption factor was applied. These

assumptions impact the author's consumption rate estimates.

Data from angler and indigenous populations are useful in that they corroborate the ranges

identified in the 3-day fish consumption data. The data are not utilized in this Report as the basis of a

site-specific assessment. In a site-specific assessment the fish consumption rates among a surveyed

population would be combined with specific measurements of methylmercury concentrations in the local

fish actually consumed to estimate the human contact rate. Ideally, some follow-up analysis such as

concentrations in human blood or hair would ensue.

 

4.1.3.3 U.S. Subsistent Populations

Large urban populations include individuals who obtain some of their food by catching and

eating fish from local urban waters. For example, Waller et al. (1996) identified populations living along

the lake shore of Chicago who have ready access to fishing waters of Lake Michigan along the break

waters, the harbors, and in the park lagoons adjacent to Lake Michigan (Table 4-21). Similar situations

occur for many water bodies in urban areas throughout the United States.
4-26

Table 4-21

Fish Consumption of an Urban “Subsistent” Group

Study Description of Fish Consumption Pattern Notes

Group

Waller et al., 484 pregnant African- 45 of 444 ate no fish; 46 of 444 Types of fish eaten most frequently

1996 American, urban poor consumed sport-caught fish; 34 in descending order: catfish, perch,

women of the women who consumed buffalo, silver bass, and whiting. 

sport-caught fish also consumed Others included: bull heads,

store-bought fish. sunfish, bluegills, and crappie. 

Most catfish consumed was storebought. Generally fisheaters did

not consume only one type of fish. 

Most of the individuals eating

sport-caught fish also ate wild fowl

and other game (duck, raccoon,

opossum, squirrel, turkey, goose,

and other fowl.

Another group of urban consumers who subsist on fish are persons who are not limited in

income, but individuals who choose to consume a large proportion of their dietary protein from fish

because of taste preference or pursuit of health benefits attributed to fish. For an undetermined number

of these individuals, a particular species of fish may be preferred (e.g., swordfish, sea bass, etc.) and

consumed extensively. Depending on the mercury concentration of the preferred fish, the result of

consuming diets high in fish from one source can be substantially increased exposure to mercury. For

example, Knobeloch et al. (1996) provide cases reports of a family whose blood mercury concentrations

increased about ten-fold following long-term consumption of a particular commercial source of imported

fish (Table 4-22). Likewise, investigation by state authorities in Maine of elevated blood mercury

concentrations thought to result from occupational exposures to mercury, in fact, resulted from frequent

consumption of fish (Dr. Allison Hawkes, 1997). After following physician’s advise to reduce fish

consumption the blood mercury levels decreased.

Table 4-22

High Fish Consumption among Urban Subjects: Case Report

Study Description of Fish Consumption Pattern Notes

Group

Knobeloch et Family consuming Wisconsin family consumed two Family members had blood mercury

al., 1995 commercially available meals/week of seabass imported levels elevated to 37 and 58 μg/L

fish. from Chile and obtained and hair mercury values of 10 and 12

commercially which had a mercury μg/g. Cessation of fish consumption

concentration between 0.5 and 0.7 for 200 days reduced blood mercury

μg/g. Other fish having low mercury levels to 3 and 5 μg/L.

concentrations (<0.05 μg/g) were

also consumed. The father

consumed an average of 113 g of

fish/day, the mother and son

consumed approximately 75 and 37

grams of fish/day, respectively. 

Calculated mercury intakes ranged

from 9 μg/day (young child) to 52

μg/day for the father in the

household.
4-27

4.1.3.4 U.S. Immigrant Populations

Subpopulations of recent immigrants to the United States retain food patterns characteristic of

their cultures with adaptations based on the available food supply. In the 1980s and 1990s, the

proportion of the U.S. population whose ancestry was Southeast Asian or Caribbean origin increased. 

The people of rural Cambodia, Laos, and Vietnam supplemented their agricultural resources by hunting

and fishing (Shubat et al., 1996) and many continue to do so in the United States. Puffer (1981) found

that Oriental/Samoan recreational anglers had fish consumption rates twice the mean value for all anglers

in the survey. Specialized fish advisories for chemical contaminants and outreach programs for

Southeast Asian communities have been developed (Shubat et al., 1996). Increased consumption of

purchased frozen fish, as well as self-caught fish, among Southeast Asians has been reported (Shatenstein

et al., 1997). Overall, these subpopulations have higher fish consumption than does the general U.S.

population.

4.1.3.5 U.S. Angling Population Size Estimate and Behaviors

Many citizens catch and consume fish from U.S. waters. The U.S. Fish and Wildlife Service

(U.S. FWS, 1988) reported that in 1985, 26% of the U.S. population fished; over 46 million people in the

U.S. spent time fishing during 1985. Within the U.S. population fishing rates ranged from a low of 17%

for the population in the Middle Atlantic states up to 36% in the West North Central States. These

angling subpopulations included both licensed and non-licensed fishers, hook and line anglers as well as

those who utilized special angling techniques (e.g., bow and arrows, spears or ice-fishing).

U.S. FWS (1988) also noted the harvest and consumption of fish from water bodies where

fishing is prohibited. This disregard or ignorance of fish advisories is corroborated in other U.S. angler

surveys. For example, Fiore et al. (1989) noted that 72% of the respondents in a Wisconsin angler survey

were familiar with the State of Wisconsin Fish Consumption Health Advisory, and 57% of the

respondents reported changing their fishing or fish consumption habits based on the advisory. West et al.

(1989) noted that 87.3% of respondents were "aware or generally aware" of Michigan State's fish

consumption advisories. Finally, Connelly et al. (1990) reported that 82% of respondents knew about the

New York State fish health advisories. They also noted a specific example in which angler consumption

exceeded an advisory. The State of New York State recommends the consumption of no more than 12

fish meals/year of contaminated Lake Ontario fish species; yet, 15% of the anglers, who fished this lake,

reported eating more than 12 fish meals of the contaminated species from the lake in that year.

The extent of the angling population can also be judged from a question included in the USDA’s

CSFII for the years 1994 and 1995. In response to a question of whether or not they had eaten fish within

the past 12 months, 84% of individuals indicated they had. Of those who had eaten fish, 38% indicated

that the fish they had eaten was caught by themselves or someone known to the respondent.

4.1.3.6 U.S. Angler Surveys

Summary of Angler Surveys

The results of the fish consumption surveys are compiled in Table 4-23. These results illustrate

the range of fish consumption rates identified in angler consumption surveys. There is a broad range of

fish consumption rates reported for angling populations. The range extends from 2 g/day to greater than

200 g/day. The variability is the result of differences in the study designs and purposes as well as

differences in the populations surveyed.
4-28

Table 4-23

Compilation of the Angler Consumption Studies

Source Population Percentile Daily Fish Notes

Consumption

g/day

Soldat, 1970 Columbia Mean 2 Estimate of average finfish

River consumption from river.

Anglers

Puffer, 1981; Los Angeles Median 37 Estimates for anglers and

as cited in U.S. area coastal 90th Percentile 225 family members who consume

EPA, 1990 anglers their catch. Consumption rate

Ethnic Subpopulation includes ingestion of both

Medians finfish and shellfish.

African-American 24

Caucasian 46

Mexican-American 33

Oriental/Samoan 71

Pierce et al., Commence- 50th Percentile 23 Finfish only

1981; as cited in ment Bay in 90th Percentile 54

EPA, 1990 Tacoma, WA Maximum Reported 381

Fiore et al., 1989 Licensed WI Mean 12 Fish-Eaters, Daily Sportfish

Anglers 75th Percentile 16 Intake

95th Percentile 37

Mean 26 Fish-Eaters, Total Fish Intake

75th Percentile 34

95th Percentile 63

West et al., 1989 Licensed MI Mean 19 Daily Sportfish Intake

Anglers Mean for Minorities 22

Maximum Reported >200

West et al., 1993 Licensed MI Mean 15 Daily sportfish intake

Anglers 43

Turcotte, 1983 GA anglers Child 10 Estimates of Freshwater Fish

Teenager 23 Intake from the Savannah River

Average Angler 31

Maximum Angler 58

Hovinga et al., Caucasians Maximum Reported 132 Re-examination of Previously

1992 and 1993 living along Identified High-End Fish

Lake Consuming Population

Michigan

Ebert et al., 1993 ME anglers Mean 6 Sportfish Intake

licensed to 50th Percentile 2

fish inland 75th Percentile 6

waters 90th Percentile 13

95th Percentile 26
Table 4-23 (continued)

Compilation of the Angler Consumption Studies

Source Population Percentile Daily Fish Notes

Consumption

g/day

4-29

Courval et al., Data on 46% of Approximately 30% of female

1996 1,950 respondents respondents consumed no

question- reported eating sport-caught fish - about double

naires from sport-caught fish that of male respondents. In

Michigan 1-12 times: 20% the 1 to 12 meal/month range

anglers aged reported eating males and females about

18-34 years. no sport-caught equally represented. More than

fish; 20% 13 meals/month exposure

consumed 13 to category had a higher

24 meals. proportion of males.

Approximately

10% consumed

25 to more than

49 meals/month.

Meredith and 29 locations Compared Survey to determine

Malvestuto, 1996 in Alabama. harvest method consumption rates of anglers

Seasonal and serving-size yielded comparable estimates

estimates of methods of of grams/day consumed.

freshwater estimating However, serving size method

fish consumption. yielded four-times as many

consumption consumers.

Harvest method

yielded estimates

of 43 grams/day

fish consumed

from all sites in

Alabama

(number = 563).

Serving-size

method

estimates 46

grams/day from

all sites in

Alabama

(number = 1311)

Consumption

lowest in the

Spring
Table 4-23 (continued)

Compilation of the Angler Consumption Studies

Source Population Percentile Daily Fish Notes

Consumption

g/day

4-30

Shubat et al., 30 Hmong Respondents ate Consumption of caught fish

1996 anglers an average of only. No information about

(residents of 3.3±3.0 fish size of meals. Species most

St. Paul and meals per month frequently caught: crappie,

Minneapolis) (range 0.5 to white bass and walleye, other

fishing St. 12). Median 2 bass (largemouth and

Croix or meals per month smallmouth), northern pike,

Mississippi and 8.8 meals at trout, bluegill and catfish.

Rivers. Ages 90th percentile.

17-88.

Sekerke et al., FL residents Male Mean 60 Total Home Fish Consumption

1994 receiving Female Mean 40

foodstamps

Anglers of the Columbia River, Washington

Soldat (1970) measured fishing activity along the Columbia River during the daylight hours of

one calendar year (1967-68). The average angler in the sampled population made 4.7 fishing trips per

year and caught an average of 1 fish per trip. Assuming 200 g of fish consumed per meal, Soldat

estimated an average of 0.7 fish meals were harvested per trip; this results in an average of 3.3 Columbia

River fish meals/year. The product of 3.3 meals/year and 200 g/meal is 660 g/year; an estimate of 1.8

g/day results. While not reporting the high-end harvesting or consumption rates, Soldat reported that

approximately 15% of the 1400 anglers interviewed caught 90% of the fish.

Los Angeles, California Anglers

The results of studies from Puffer (1981) and Pierce et al. (1981) are described in U.S. EPA

(1989). Puffer (1981) conducted 1,059 interviews with anglers in the coastal Los Angeles area for an

entire year. Consumption rates were estimated for anglers who ate their catch. These estimates were

based on angling frequency and the assumption of equal fish consumption among all fish-eating family

members. The median consumption rate for fish and shellfish was 37 g/day. The 90th percentile was

224.8 g/day. Table 4-24 notes the higher consumption rate estimates among Orientals and Samoans.
4-31

Table 4-24

Median Recreationally Caught Fish Consumption Rate Estimates

by Ethnic Group (Puffer, 1981)

Ethnic Group Median Consumption Rate

(g/day)

African-American 24

Caucasian 46

Mexican-American 33

Oriental/Samoan 71

Total 37

Anglers of the Commencement Bay Area in Tacoma, Washington

Pierce et al. (1981), as reported in the U.S. EPA 1990 Exposure Factors Handbook, conducted a

total of 509 interviews in the summer and fall around Commencement Bay in Tacoma, Washington. 

They assumed that 49% of the live fish weight was edible and that 98% of the total catch was eaten. The

estimated 50th percentile consumption rate was 23 g/day and the estimated 90th percentile consumption

rate 54 g/day. The maximum estimated consumption rate was 381 g/day based on daily angling.

Anglers of the Savannah River in Georgia

Turcotte (1983) estimated fish consumption from the Savannah River based on total harvest,

population studies and a Georgia fishery survey (Table 4-25). The angler survey data, which included

the number of fishing trips per year as well as the number and weights of fish harvested per trip, were

used to estimate the average consumption rate in the angler population. Several techniques including the

use of the angler survey data were used to estimate the maximum fish consumption in the angler

population. Estimates of average fish consumption for children and teens was also provided.

Table 4-25

Freshwater Fish Consumption Estimates of Turcotte (1983)

Georgia Estimated Freshwater Fish

Subpopulation Consumption Rate (g/day)

Child 10

Teen-ager 23

Average Angler 31

Maximum Angler 58

Alabama Anglers

Meredith and Malestuto (1996) studied anglers in 29 locations in Alabama to estimate freshwater

fish consumption (Table 4-23). The purpose of their study had been to compare two methods of
4-32

estimating fish consumption: The harvest or krill survey compared with the serving-size method of

estimating fish consumption. These two techniques yielded comparable estimates of mean fish intake

(43 and 46 gms/person/day, respectively). The serving size method identified 1311 consumers while the

harvest method identified only 563 consumers.

Wisconsin Anglers

Fiore et al. (1989) surveyed the fishing and fish consumption habits of 801 licensed Wisconsin

anglers. The respondents were divided into 2 groups: fish eaters and non-eaters. The fish eaters group

was further subdivided into four groups: those who consumed 0-1.8 kg fish/yr, 1.9-4.5 kg fish/yr, 4.6-

10.9 kg fish/yr and 10.9 < kg fish/yr. Using an assumption of 8 oz. (227 grams) fish consumed/meal, the

authors estimated that the mean number of sport fish meals/year for all respondents (including noneaters) was 18. The mean number of other fish meals/year including non-eaters was 24. The total

number of fish meals/year was 41 for fish eaters and non-eaters combined and 42 for fish eaters only. 

Recreational anglers were found to consume both commercial fish as well as sport fish. The estimated

daily consumption rates of the eaters-only are presented in Table 4-26.

Table 4-26

Daily Intake of Sportfish and Total Fish for the Fish-consuming Portion

of the Population Studied by Fiore et al. (1989)

Percentile Daily Sport-Fish Intake Daily Total Fish

Intake

Mean 12 g/day 26 g/day

75th 16 g/day 34 g/day

95th 37 g/day 63 g/day

Michigan Anglers

West et al. (1989) used a mail survey to conduct a 7-day fish consumption recall study for

licensed Michigan anglers. The respondents numbered 1104, and the response rate was 47.3%. The

mean fish consumption rate for anglers and other fish-eating members of their households was 18.3

g/day, and the standard deviation was 26.8 g. Because the study was conducted from January through

June, an off-season for some forms of angling in Michigan, higher rates of fish consumption would be

expected during the summer and fall months. A full-year's mean fish consumption rate of 19.2 g/day was

estimated from seasonal data. The mean fish consumption rate for minorities was estimated to be 21.7

g/day. The highest consumption rates reported were over 200 g/day; this occurred in 0.1% of the

population surveyed. Overall, fish consumption rates increased with angler age and lower education

levels. Lower income and education level groups were found to be the only group which consumed

bottom-feeders.

New York State Anglers

Connelly et al. (1990) reported the results of a statewide survey of New York anglers. The

10,314 respondents (62.4% response rate) reported a mean of 20.5 days spent fishing/year. Of the

respondents, 84% fished the inland waters of New York State, and 42% reported fishing in the Great

Lakes. An overall mean of 45.2 fish meals per year was determined for New York anglers. The authors

assumed an average meal size of 8 oz. (227 g) of fish and estimated a yearly consumption rate of 10.1 kg
4-33

fish (27.7 g fish/day). Unlike the Michigan angler study (West et al., 1989), the overall mean number of

fish meals consumed increased with education level of the angler. Fish consumption also increased with

increasing income; respondents earning more than $50,000/year consumed a mean of 54.3 meals per

year, and those with some post-graduate education consumed a mean of 56.2 meals per year. The highest

reported regional mean consumption rates (58.8 meals/year) occurred in the Suffolk and Nassau Counties

of New York State. 

Anglers of Lake Michigan

As part of a larger effort, Hovinga et al. (1992 and 1993) re-examined 115 eaters of Great Lakes

fish and 127 controls, who consumed smaller quantities of fish, originally identified in a 1982 effort. 

Both more recent (1989) as well as 1982 consumption rates of Great Lakes sportfish were estimated. All

of the participants in the study were Caucasian and resided in 11 communities along Lake Michigan. The

population was divided into eaters (defined as individuals consuming 10.9 kg (30 g/day) or greater) and

controls (defined as individuals consuming no more than 2.72 kg/yr). The consumption rates for the

groups are reported in Table 4-27.

Table 4-27

Fish Consumption Rate Data for Groups Identified in

Hovinga et al. (1992) as Eaters and Controls

Groups 1982 1982 Consumption 1989 1989 Consumption

Meals/Year Rates (kg/yr) Meals/Year Rates (kg/yr)

Mean (Range) Mean (Range) Mean (Range) Mean (Range)

Eaters 54 (24-132) 18 (11-53) 38 (0-108) 10 (0-48)

Controls -- -- 4.1 (0-52) 0.73 (0-8.8)

Anglers of Inland Waters in the State of Maine

Ebert et al. (1993) examined freshwater fish consumption rates of 1,612 anglers licensed to fish

the inland (fresh) waters of Maine. They only analyzed fish caught and eaten by the anglers. Anglers

were asked to recall the number, species and average length of fish eaten in the previous year; the actual

fish consumption rates were estimated based on an estimate of edible portion of the fish. The 78% of

respondents who fished in the previous year and 7% who did not fish but did consume freshwater fish

were combined for the analysis. Anglers who practiced ice-fishing as well as fish caught in both standing

and flowing waters were included. Twenty-three percent of the anglers consumed no freshwater fish. If

the authors assumed that the fish were shared evenly among all fish consumers in the angler's family, a

mean consumption rate of 3.7 g/day was estimated for each consumer. Table 4-28 provides the fish

consumption rates for Maine anglers.
4-34

Table 4-28

Fish Consumption Rates for Maine Anglers

Percentile All Anglers Fish-consuming

Anglers

Mean 5.0 6.4

50th (median) 1.1 2.0

75th 4.2 5.8

90th 11 13

95th 21 26

Florida Anglers Who Receive Food Stamps

As part of a larger effort the Florida Department of Environmental Regulation attempted to

identify fish consumption rates of anglers who were thought to consume higher rates of fish. Face-toface interviews were conducted at five Florida food stamp distribution centers. The selected food stamp

distribution centers were located in counties either thought to have a high likelihood of subsistence

anglers or where pollutant concentrations in fish were known. Interviews with twenty-five household's

primary seafood preparer were conducted at each center per quarter for an entire year. A total of 500

interviews was collected. The interviewed were asked to recall fish consumption within the last 7 days. 

Specifically, the respondents were asked to recall the species, sources and quantities of fish consumed. 

Note that the respondents were only asked to recall fish meals prepared at home (actual consumption

rates may have been higher if the respondents consumed seafood elsewhere) and that the sources of fish

were from both salt and freshwater. The results of the survey conducted by Sekerke et al. (1994) are in

Table 4-29.

Table 4-29

Fish Consumption Rates of Florida Anglers Who Receive Food Stamps

Respondent No. Average Finfish Average Shellfish

Consumption Consumption

Adult Males 366 60 g/day 50 g/day

Adult Females 596 40 g/day 30 g/day

4.1.3.7 Indigenous Populations of the United States

The tribes and ethnic groups who comprise the indigenous populations of the United States show

wide variability in fish consumption patterns. Although some tribes, such as the Navajo, consume

minimal amounts of fish as part of their traditional culture, other native groups — such as the Eskimos,

Indians, and Aleuts of Alaska, or the tribes of Puget Sound — traditionally consume high quantities of

fish and fish products. The U.S. indigenous populations are widely distributed geographically. For

example, a U.S. EPA report (1992b) identified 281 Federal Indian reservations that cover 54 million

acres in the United States. Treaty rights to graze livestock, hunt, and fish are held by native peoples for

an additional 100 to 125 million acres. There are an estimated two million American Indians in the
4-35

United States (U.S. EPA, 1992b). Forty-five percent of these two million native people live on or near

reservations and trust lands. High-end fish consuming groups include Alaska natives who number

between 85,000 and 86,000 people (Nobmann et al., 1992).

Fish products consumed by indigenous populations may rely on preparation methods that differ

from ones typically encountered in the diet of the general U.S. population. By way of illustration, food

intake data obtained from Alaskan natives were used to calculate nutrient intakes using a computer and

software program. These computerized databases had been developed by the U.S. Veterans

Administration (VA) for patients in the national Veteran's Administration hospital system. Nobmann et

al. (1992) found they needed to add data for 210 dietary items consumed by Alaskan Natives to the 2400

food items in the VA files.

In the mid-1990s data on fish consumption by indigenous populations of the United States were

reported for Alaska Natives (Nobmann et al., 1992), Wisconsin Tribes (U.S. EPA, 1992), the Columbia

River Tribes (Columbia River Inter-Tribal Fish Commission, 1994) and selected Puget Sound Tribes

(Toy et al. 1995). Findings from these studies can be used to assess differences in fish consumption

between these indigenous groups and the general U.S. population.

Summary of Native American Angler Surveys

Table 4-30 summarizes the reported consumption rates of Native Americans detailed here.

Although not all Native American tribal groups traditionally include fish as part of their diets, groups

living near rivers, lakes, and coastal areas consume a vide variety of fish and shellfish. The highest

levels of fish and shellfish consumption are thought to occur among tribal groups living along the Pacific

Coast and in Alaska. Tribal groups in the Great Lakes region also include fish as part of their typical

diet. The data base to estimate quantities of fish consumed has been greatly enhanced over the past five

years with the publication of a number of dietary assessments conducted as part of activities to determine

exposure to chemical contaminants in fish.

Surveys of Native American anglers in the United States indicate an average fish/shellfish

consumption in the rage of 30 to 80 grams per day (U.S. EPA, 1992b; Harnly et al., 1997; Toy et al.,

1995) with 90th percentile consumption of about 150 grams/day or higher (Toy et al., 1995). Inclusion

of data on Alaskan Native Americans results in still higher levels of fish and shellfish intake. For

example, Nobmann et al. (1992) reported mean fish consumption estimates in excess of 100 grams/day.

Table 4-30

Fish Consumption by Native U.S. Populations

Source Population Percentile Fish-Meals Notes

Consumed or Fish

Consumption (gms)

Nobmann 351 Alaska Native Mean 109 gms of fish and

et al., 1992 adults (Eskimos, shellfish per day.

Indians, Aleuts)

U.S. EPA, Wisconsin Tribes 11 Mean 32 gms of fish per day

1992b Native American

Indian Tribes
Table 4-30 (continued)

Fish Consumption by Native U.S. Populations

Source Population Percentile Fish-Meals Notes

Consumed or Fish

Consumption (gms)

4-36

Peterson et 323 Chippewa adults Mean = 1.7 fish

al., 1995 > 18 years of age. meals/week.

(1.9 and 1.5 fish

meals/week for male

and for female

respondents,

respectively).

0.26% of males and

0.15% of females

reported eating 3 or

more fish-meals per

week.

50% of respondents

ate one or less fish

meals per week.

21% of respondents

ate three or more fish

meals per week.

2% of respondents ate

fish-meals each day.

Toy et al., Tulalip and Squaxin 50th percentile: Report contains

1995 Island Tribes. 263 Finfish, 22 gms/day; data for

adult subjects. total fish consumed, anadromous fish,

43 gms/day. pelagic, bottom

90th percentile: Data are based on

Finfish, 88 gms/day; an average body

total fish, 156 weight of 70

gms/day. kg/day.

and shell fish. 

Fitzgerald 97 nursing Mohawk 24.7% ate 1-9 local Study conducted

et al., 1995 women fish meals/year during from 1986-1992

pregnancy; in area where fish

10.3% ate >9 local are contaminated

fish meals/year during with PCB

pregnancy;

41.2% ate 1-9 local

fish meals/year one

year prior to

pregnancy;

15.4% ate >9 local

fish meals/year one

year prior to

pregnancy;
Table 4-30 (continued)

Fish Consumption by Native U.S. Populations

Source Population Percentile Fish-Meals Notes

Consumed or Fish

Consumption (gms)

4-37

Centers for Miccouskee Indian Local fish: 31% (58 Blue gill most

Disease Tribes of South persons) reported common species

Control, Florida (1993), 2 eating fish from of local fish

1993 children and 183 Everglades during consumed. 

adults completed previous 6 months. Largemouth bass

dietary questionaires Maximum daily consumed in

consumption: 168 greatest quantity

grams Median daily

consumption: 3.5 grams

Marine fish: 57% (105 commonly

persons) consumed consumed (by all

marine fish during 105 of marine

previous 6 months. consumers) and

Nonlocal freshwater amounts (7.0

fish: 1 individual, 25 grams/day

grams/day median level)

Local wildlife: 65% consumed: deer

(120 participants) (57% of

consumed local game. participants),

Canned tuna most

in the largest

Local game

wildboar (10%),

redbelly turtle

(10%), frog (5%)

and alligator

(3%)

Gerstenber 89 Ojibwa Tribal 35% of respondents ate Most frequently

ger et al., members from the Lake Superior fish consumed fish

1997 Great Lakes Region 1x/week. 6.7% ate from Lake

Lake Superior fish Superior: lake

2x/week. trout (37%),

Consumption of fish whitefish (27%).

from other lakes:

12.5% ate these lakes: Walleye.

1x/week

5.7% ate these 2x/week Highest fish

89 respondents April, May, and

averaged 29 fish June

meals/year (range zero

to 150 fish meals/year)

walleye (27%),

From inland

consumption in
Table 4-30 (continued)

Fish Consumption by Native U.S. Populations

Source Population Percentile Fish-Meals Notes

Consumed or Fish

Consumption (gms)

4-38

Harnly et Native Americans Fish-consuming Sportfish species:

al., 1997 living near Clear Lake participants averaged catfish, perch,

California 60 g/day of sportfish hitch, bass, carp

and 24 g/day of

commercial fish. Commercial fish:

10% of adults salmon, crab,

consumed Hg intakes > shrimp.

30 μg/day

snapper, tuna,

Wisconsin Tribes

An U.S. EPA report entitled Tribes at Risk (The Wisconsin Tribes Comparative Risk Project)

(US EPA, 1992) reported an average total daily fish intake for Native Americans living in Wisconsin of

35 gms/day. The average daily intake of locally harvested fish was 31.5 grams.

Peterson et al. (1995) surveyed 323 Chippewa adults over 18 years of age living on the Chippewa

reservation in Wisconsin. The survey was conducted by interview and included questions about season,

species and source of fish consumed. The survey was carried out in May. Fish consumption was found

to be seasonal with the highest fish consumption occurring in April and May. Fish species typically

consumed were walleye and northern pike, muskellunge and bass. During the months in which the

Chippewa ate the most fish, 50% of respondents reported eating one or fewer fish meals per week, 21%

reported eating three or more fish meals per week, and 2% reported daily fish consumption. The mean

number of fish meals per week during the peak consumption period was 1.7 meals; this is approximately

42% higher than the 1.2 fish meals per week that respondents reported as their usual fish consumption. 

Higher levels of fish consumption were reported by males (1.9 meals per week) than by females (1.5

meals per week). Among male respondents 0.26% ate 3 or more fish meals per week, whereas 0.15% of

female respondents ate 3 or more meals of fish per week. Unemployed persons typically had higher fish

consumption rates. 

Columbia River Tribes

The Columbia River Inter-Tribal Fish Commission (1994) estimated fish consumption rates

based on interviews with 513 adult tribal members of four tribes inhabiting the Columbia River Basin

(see Tables 4-31 and 4-32). The participants had been selected from patient registration lists provided by

the Indian Health Service. Data on fish consumption by 204 children under 5 years of age were obtained

by interviewing the adults.

Fish were consumed by over 90% of the population with only 9% of the respondents reporting no

fish consumption. The average daily consumption rate during the two highest intake months was 108

grams/day, and the daily consumption rate during the two highest and lowest intake months were 108

g/day and 31 g/day, respectively. Members who were aged 60 years and older had an average daily

consumption rate of 74 grams/day. During the past two decades, a decrease in fish consumption was
4-39

generally noted among respondents in this survey. The maximum daily consumption rate for fish

reported for this group was approximately 970 grams/day.

Table 4-31

Fish Consumption by Columbia River Tribes

(Columbia River Inter-Tribal Commission, 1994)

Subpopulation Mean Daily Fish Consumption (g/day)

Total Adult Population, aged 18 years and older 59

Children, aged 5 years and younger 20

Adult Females 56

Adult Males 63

Table 4-32

Daily Fish Consumption Rates by Adults of Columbia River Tribes

(Columbia River Inter-Tribal Commission, 1994)

Percentile Amount (g/day)

50th 29-32

90th 97-130

95th 170

99th 389

Tribes of Puget Sound

A study of fish consumption among the Tulalip and Squaxin Island Tribes of Puget Sound was

completed in November 1994 (Toy et al., 1995). The Tulalip and Squaxin Island Tribes live

predominantly on reservations near Puget Sound, Washington. Both tribes rely on commercial fishing as

an important part of tribal income. Subsistence fishing and shell-fishing are significant parts of tribal

members economies and diets. 

The study was conducted between February and April in 1994. Fish consumption practices were

assessed by questionnaire and interview using dietary recall methods, food models and a food frequency

questionnaire. The food frequency questionnaire was aimed as identifying seasonal variability. 

Questions in the interview included food preparation methods and obtained information on the parts of

the fish consumed. Fish consumed were categorized into anadromous fish (king salmon, sockeye salmon,

coho salmon, chum salmon, pink salmon, steelhead salmon, salmon unidentified and smelt); pelagic fish

(cod, pollock, sable fish, spiny dogfish, rockfish, greenling, herring and perch); bottom fish (halibut,

sole/flounder and sturgeon); and shell fish (manila clams, little clams, horse clams, butter clams, cockles,

oysters, mussels, shrimp, dungeness crab, red rock crab, scallops, squid, sea urchin, sea cucumbers and

moon snails).
4-40

Among consumers of anadromous fish, local waters (i.e., Puget Sound) supplied a mean of 80%

of the fish consumed. Respondents from the Tulalip Tribes purchased a mean of approximately twothirds of fish from grocery stores or restaurants, while among the Squaxin Island Tribe, the source of fish

was about 50% self-caught and 50% purchased from grocery stores or restaurants. For bottom fish,

members of both tribes caught about half of the fish they consumed. Anadromous fish were much more

likely to be consumed with the skin attached. Most other fish were consumed minus the skin. 

Approximately 10% of the respondents consumed parts of the fish other than muscle; i.e., head, bones,

eggs.

Data on fish consumption were obtained for 263 members from the Tulalip and Squaxin Island

tribes. The mean consumption rate for women of both tribes was between 10-and-12-times higher than

the default rate of 6.5 grams/day used by some parts of the U.S. government to estimate fish intake. 

Among male members of both tribes, the consumption rate was approximately 14-times higher than the

default rate. The 50th percentile consumption rate for finfish for both tribes combined was 32 grams/kg

body weight/day. Male members of the Tulalip and Squaxin Island tribes had average body weights of

189 pounds and 204 pounds, respectively. Female members of the Tulalip and Squaxin Island tribes

weighed on average 166 pounds and 150 pounds, respectively. If an average body weight is assumed to

be 70 kg, the daily fish consumption rate for both tribes for adults was 73 grams per day with a 90th

percentile value of 156 grams per day for total fish. Fish consumption data for selected categories of fish

are shown in Table 4-33.

Table 4-33

Fish Consumption (gms/kg bw/day) by the Tulalip and Squaxin Island Tribes

(Toy et al., 1995)

Type of 5th 50th 90th 95th Mean SE 95th

Fish Percentile Percentile Percentile Percentile Percent CI

Anadromous .0087 .2281 1.2026 1.9127 .4600 .0345 .3925, 0.5275

Pelagic .0000 .0068 .1026 .2248 .0390 .0046 .0300, 0.0480

Bottom .0000 .0152 .1095 .2408 .0482 .0060 .0364, 0.4375

Shell .0000 .1795 1.0743 1.4475 .3701 .0343 .3027, 0.4375

Fish

Other .0000 .0000 .0489 .1488 .0210 .0029 .0152, 0.0268

Fish

Total .0200 .3200 .1350 2.1800 .5745 .0458 .4847, 0.6643

Finfish

Total .0495 .6081 2.2267 3.2292 1.0151 .0865 .8456, 1.1846

All Fish

During the survey period, 21 of the 263 tribal members surveyed reported fish consumption rates

greater than three standard deviations from the mean consumption rate. For example, six subjects

reported consumptions of 5.85, 6.26, 9.85, 11.0, 22.6 and 11.2 grams of finfish and shell fish/kg body

weight/day. If a 70-kg body weight is assumed these consumption rates correspond to 410, 438, 690, 770

and 1582 grams per day.
4-41

Mohawk Tribe

A study of fish consumption among 97 nursing Mohawk women in rural New York State was

conducted from 1986 to 1992 (Fitzgerald et al., 1995). Fish consumption advisories had been issued in

the area due to polychlorinated biphenyl (PCB) contamination of the local water body. Using food

frequency history and a long-term dietary history, the women were asked about their consumption of

locally caught fish during three specific periods of time: during pregnancy, the year prior to pregnancy,

and more than a year before pregnancy. For comparison, the study also surveyed fish consumption rates

among 154 nursing (primarily caucasian) women from neighboring counties. The socioeconomic status

of the women of the control group were similar to that of the Mohawk women. The fish in these counties

had background PCB concentrations. 

The results (Table 4-34) showed that the Mohawk women had a higher prevalence of consuming

locally caught fish than the comparison group in the two intervals assessed prior to the pregnancy; the

prevalence of local fish consumption during pregnancy for the two groups was comparable. A decrease

in local fish consumption rates was also noted over time; these may be related to the issuance of

advisories. 

Table 4-34

Local Fish Meals Consumed By Time Period for the

Mohawk and Comparison Nursing Mothers (Fitzgerald et al., 1995)

Fish During Pregnancy 1 Year Before Pregnancy >1 Year Before Pregnancy

Meals/

Year Mohawk Control Mohawk Control Mohawk Control

0 64.9% 70.8% 43.3% 64.3% 20.6% 60.4%

1-9 24.7% 15.6% 41.2% 20.1% 43.3% 22.7%

10-19 5.2% 4.5% 4.1% 3.9% 6.2% 5.2%

>19 5.1% 9.1% 11.3% 11.7% 29.9% 11.7%

Native Americans near Clear Lake, California

Harnly et al. (1997) found that Native Americans living near Clear Lake, California consumed an

average of 84 grams of fish/day (60 g/day sport fish plus 24 g/day of commercial fish). Ten percent of

adults reported mercury intakes over 30 μg/day. The most popular species of sportfish were: catfish,

perch, hitch, bass, and carp. Commercial species most commonly eaten were: snapper, tuna, salmon,

crab, and shrimp.

Great Lakes Tribes

Members of the Ojibwa live in the Great Lakes region of the United States and Canada. 

Gerstenberger et al. (1997) reported that approximately 35% of the respondents (89 members of the

Ojibwa Tribes) consumed Lake Superior fish at least once a week with 7% of this group consuming Lake

Superior fish at least twice a week. The most commonly consumed Lake Superior-origin fish were lake

trout, walleye, and whitefish. In addition, fish were consumed from inland lakes with 12% of
4-42

reponsdnets eating inland lake fish once a week and 6% consuming these fish twice a week. Walleye

was the most common species of fish consumed from these inland lake sources.

4.1.4 Summary of Hawaiian Island Fish Consumption Data

The CSFII 1989-1991 did not include the Hawaiian Islands. To the knowledge of the authors of

the Mercury Study Report to Congress, data describing fish consumption by the general Hawaiian

population that estimate Island-wide levels of consumption have not been reported. However, reports on

commercial utilization of seafood (Higuchi and Pooley, 1985; Hudgins, 1980) and analysis of

epidemiology data (Wilkens and Hankin, personal communication, 1996) provide a basis to describe

general patterns of consumption. Overall, seafood consumption in Hawaii is much higher than in the

contiguous United States. On a per capita basis, the United States as a whole consumed 5.45 kg and 5.91

kg (12 and 13 pounds) of seafood in 1973 and 1977, respectively (Hudgins, 1980). By contrast Hawaiian

per capita consumption for all fish products was 11.14 kg (24.5 pounds) in 1972 and 8.77 kg (19.3

pounds) in 1974. 

The most popular species of fish and shellfish consumed were moderately comparable between

Hawaii and the contiguous 48 states. The methods of food preparation differed, however, with raw fish

being far more commonly consumed in Hawaii. Sampled at the retail trade level the most commonly

purchased fish were: tuna, mahimahi, and shellfish [see Table 4-35 which is based on data in Higuchi

and Pooley (1985)]. A survey of seafood consumption by families was identified. In 1987, the

Department of Business and Economic Development (State of Hawaii, 1987) conducted a survey of 400

residents selected on a random digit dialing basis of a population representing 80% of total state seafood

consumption. All data were collected in July and August, 1987 and would not reflect any seasonal

differences in fish/shellfish consumption. The respondents were asked to describe seafood consumption

by their families. Shrimp was the most popular seafood with mahimahi or dolphin fish as the second

most popular (Hawaii Seafood, 1988). Reports on fish consumption in Hawaii separate various species

of tuna: ahi (Hawaiian yellowfin tuna, bigeye tuna & albacore tuna), aku (Hawaiian skipjack tuna), and

tuna. In 1987, nearly 66% of the 400 families surveyed had seafood at least once a week and 30% twice

a week. Only 4% did not report consuming seafood during the previous week based on a telephone

survey. 

Wilkens and Hankin (personal communication, 28 February 1996) analyzed fish intake from

1856 control subjects from Oahu who participated in research studies conducted by the Epidemiology

Program of the Cancer Research Center of Hawaii, University of Hawaii at Manoa. These subjects were

asked about consumption over a one-year period prior to the interview. Within this group the most

commonly consumed fish was tuna [canned with tuna species undesignated (70.8 % of subjects reporting

consumption)]; shrimp (47.7% of subjects); tuna (yellowfin fresh designated aku, ahi with 42.2% of

subjects reporting consumption); mahimahi [(or dolphin) with 32.5% of respondents reporting

consumption]; and canned sardines (with 29.1% of subjects reporting consumption). 
4-43

Table 4-35

Species Composition of Hawaii's Retail Seafood Trade — 1981 Purchases

Higuchi and Pooley (1985)

Fish/Shellfish Pounds Purchased Percent of Total Purchases

Tuna 11,600,000 20.9

Ahi (Hawaiian yellowfin, bigeye & albacore) (5,400,000)

Billfish (including swordfish) 5,900,000 11.3

and shark

Mahimahi and ono (wahoo) 9,900,000 17.7

Akule (Hawaiian bigeye scad) 4,00,000 6.9

and opelu 

Bottom fish 2,600,000 7.0

Reef fish 3,500,000 5.3

Shellfish 8,200,000 15.5

Shrimp (4,200,000)

Lobster (900,000) 

Other species 8,300,000 15.4

Salmon/trout (1,500,000)

Snapper (1,800,000)

Frozen filets (2,300,000)

Frozen sticks/blocks (1,400,000)

Total 54,000,000 100.0

4.1.5 Summary of Alaskan Fish Consumption Data

The CSFII analyses of food intake by the USDA include the 48 contiguous states but do not

include Alaska or Hawaii. A number of investigators have published data on fish consumption in Alaska

by members of native populations (e.g., Inuits, Eskimos) and persons living in isolated surroundings. 

These reports focus on nutritional/health benefits of high levels of fish consumption, food habits of

native populations, and/or effects of bioaccumulation of chemicals in the aquatic food web.

4.1.5.1 General Population

After contacting professionals from the Alaskan health departments and representatives of the

U.S. Centers for Disease Control in Anchorage, the authors of this report have not identified general

population data on fish consumption among Alaskan residents who are not part of native population

groups, subsistence fishers/hunters, or persons living in remote sites. Patterns of fish consumption

among urban residents (e.g., Juneau, Nome, Anchorage) appear not to be documented in the published

literature.
4-44

4.1.5.2 Non-urban Alaskan Populations

Native people living in the Arctic rely on traditional or "country" foods for cultural and

economic reasons. The purpose of the current discussion is not to assess the comparative risks and

benefits of these foods. The risks and benefits of these food consumption habits have been compared by

many investigators and health professionals (among others see Wormworth, 1995; Kinloch et al., 1992;

Bjerregaard, 1995). 

 Despite a degree of acculturation in the area of foods, native foods were still eaten frequently by

Alaskan Native peoples based on results of the 1987-1988 survey. Diets that include major quantities of

fish (especially salmon) and sea mammals retain a major place in the lives of Alaskan Native peoples. 

The consumption of traditional preparations of salmon and other fish continues; this includes fermented

foods such as salmon heads and eggs, other fish and their eggs, seal, beaver, caribou and whale. 

Diets of Native Alaskans differ from the general population and rely more extensively on fish

and marine mammals. These are population groups that are characterized by patterns of food

consumption that reflect availability of locally available foods and include food preparation techniques

that differ from those usually identified in nutrient data bases. For example, Nobmann et al. (1992)

surveyed a population of Alaska Natives that included Eskimos (53%), Indians (34%), and Aleuts (13%). 

The distribution of study participants was proportional to the distribution of Alaska Natives reported in

the 1980 Census. The 1990 Census identified an overall population of 85,698 persons as Alaska Natives.

Nobmann et al. (1992) indicated that Alaska Natives have traditionally subsisted on fish; marine

mammals; game; a few plants such as seaweed, willow leaves, and sourdock; and berries such as

blueberries and salmonberries rather than on a plant-based diet. In preparing a nutrient analysis of the

food consumed in eleven communities that represented different ethnic and socioeconomic regions of

Alaska, these investigators added nutrient values for 210 foods consumed by Alaska Natives in addition

to the 2400 foods present in the Veteran's Administration's nutrient data base. Nobmann et al. (1992)

found fish were an important part of the diet. The mean daily intake of fish and shellfish of Alaska

Natives was 109 grams/day. Fish consumption was more frequent in the summer and fall and game meat

was eaten more often in the winter. 

Quantitative information on dietary intakes of Native Alaskan populations are few. Estimates

can be derived from harvest survey data, but these have limitations because not all harvested animals are

consumed nor are all edible portions consumed. Other edible portions may be fed to domestic animals

(e.g., sled dogs). Substantial variability in intake of foods including ringed seal, bearded seal, muktuk

(beluga skin with an underlying thin layer of fat) and walrus has been reported (Ayotte et al., 1995).

Dietary analyses on seasonal food intakes of 351 Alaska Native adults from eleven communities

were performed during 1987-1988 (Nobmann et al., 1992). Alaska Natives include Eskimos, Indians and

Aleuts. There is no main agricultural crop in Alaska which when combined with a short growing season,

results in limited availability of edible plants. Alaska Natives have traditionally relied on a diet of fish,

sea mammals, game and a few native plants (seaweed, willow leaves, and sourdock) and berries (such as,

blueberries and salmon berries). Although consumption of significant amounts of commercially

produced foods occurs, use of subsistence foods continues. 

The survey sample of 351 adults, aged 21-60 years, was drawn from eleven communities. 

Information was obtained using 24-hour dietary recalls during five seasons over an 18-month period. 

Fish were consumed much more frequently by Alaska Natives than by the general U.S. population. Fish

ranked as the fourth most frequently consumed food by Alaska Natives compared with the 39th most
4-45

frequently consumed food by participants in the nationally representative Second National Health and

Nutrition Assessment Survey (NHANES II). The mean daily intake of fish and shellfish for Alaska

Natives was 109 grams/day contrasted with an intake of 17 grams per day for the general U.S. population

described in NHANES II. Among Alaska Natives fish was consumed more frequently in the summer and

fall months. 

Several extensive data sets on mercury concentrations in marine mammals consumed by

indigenous populations living in the circumpolar regions have been published (Wagemann et al., 1996;

Caurant et al., 1996; Dietz et al., 1996). Analyses that determined chemically speciated mercury have

shown that mercury present in muscle tissue is largely (>75%) organic mercury (i.e., methylmercury

(Caurant et al., 1996)). By contrast, mercury present in organs such as liver and kidney is predominantly

in an inorganic form (Caurant et al., 1996).

4.1.5.3 Alaskans from Subsistence Economies

Wolfe and Walker (1987) described the productivity and geographic distribution of subsistence

economies in Alaska during the 1980s. Based on a sample of 98 communities, the economic

contributions of harvests of fish, land mammals, marine mammals and other wild resources were

analyzed. Noncommercial fishing and hunting play a major role in the economic and social lives of

persons living in these communities. Harvest sizes in these communities were established by detailed

retrospective interviews with harvesters from a sample of households within each community. Harvests

were estimated for a 12-month period. Data were collected in pounds of dressed weight per capita per

year. Although it varies by community and wildlife species, generally "dressed weight" is approximately

70 to 75% of the round weight for fish and 20 to 60% of round weight for marine animals. Dressed

weight is the portion of the kill brought into the kitchen for use, including bones for particular species. 

The category "fish" contains species including salmon, whitefish, herring, char, halibut, and pike. "Land

mammals" included species such as moose, caribou, deer, black bear, snowshoe and tundra hare, beaver

and porcupines. "Marine mammals" consisted of seal, walrus and whale. "Other" contained birds,

marine invertebrates, and certain plant products such as berries. 

Substantial community-to-community variability in the harvesting of fish, land mammals, marine

mammals and other wild resources were noted (Wolfe and Walker, 1985). Units are pounds "dressed

weight" per capita per year. The median harvest was 252 pounds with the highest value approximately

1500 pounds. Wild harvests (quantities of fish, land mammals and marine mammals) in 46% of the

sampled Alaskan communities exceeded the western U.S. consumption of meat, fish, and poultry. These

communities have been grouped by general ecological zones which correspond to historic/cultural areas: 

Arctic-Subarctic Coast, Aleutian-Pacific Coast, Subarctic Interior, Northwest Coast and contemporary

urban population centers. The Arctic-Subarctic Coast displayed the greatest subsistence harvests of the

five ecological zones (610 pounds per capita), due primarily to the relatively greater harvests of fish and

marine animals. For all regions the fishing output is greater than the hunting; fishing comprises 57 - 68%

of total subsistence output. Above 60 north latitude fishing predominates other wildlife harvests, except

for the extreme Arctic coastal sea mammal-caribou hunting communities. Resource harvests of fish

("dressed weight" on a per capita basis) by ecological zone (and cultural area) were these: ArcticSubarctic Coast (Inupiaq-Yup'ik), 363 pounds/year or 452 grams/day; Aleutian-Pacific Coast (AleutSugpiaq), 251 pounds/year or 312 grams/day; Subarctic Interior (Athapaskan), 256 pounds/year or 318

grams/day; Northwest Coast (Tingit-Haida), 122 pounds/year or 152 grams/day; and Other (Anchorage,

Fairbanks, Juneau, Matanuska-Susitna Borough, and Southern Cook Inlet), 28 pounds/year or 35

grams/day.

Consumption of marine mammals was reported among Yupik Eskimos living in either a coastal

or river village of southwest Alaska (Parkinson et al., 1994). Concentrations of plasma omega-3 fatty
4-46

acids were elevated (between 6.8 and 13 times) among the Yupic-speaking Eskimos living in two

separate villages compared with non-Native control subjects (Parkinson et al., 1994). Concentrations of

omega-3 fatty acids in plasma phospholipid has been shown to be a valid surrogate of fish consumption

(Silverman et al., 1990). Among coastal-village participants the concentrations of eicosapentaonoic and

docosahexaenoic acids reflected higher consumption of marine fish and marine mammals and the use of

seal oil in food preparation. Among river village natives, the increase reflected higher consumption of

salmon.

The Division of Subsistence of the Alaska Department of Fish and Game (Robert J. Wolfe,

personal communications, 1997) has provided estimates of the mean per capita harvests of subsistence

fish, shellfish, and marine mammals in rural Alaska areas (Table 4-36). Combined harvests of

fish/shellfish/marine mammals averaged approximately 350 grams/day for all rural areas combined. The

highest intakes were found in the Western, Interior and Arctic regions with harvests of 693, 577, and 482

grams/day, respectively. Marine mammal consumption was particularly high in the Arctic region with an

average of approximately 270 grams/day consumed. Comparable estimates of marine mammal

consumption were reported by Chan (1997) for an Inuit community based on dietary information

gathered by the Centre for Indigenous Peoples’ Nutrition and the Environment (Table 4-37). Using the

Centre’s database for contaminants, Chan estimated that mercury intakes were 185 μg mercury/day with

170 μg of mercury coming from marine mammal meat.

Consumption of marine mammals results in very high exposures to methylmercury. Wolfe

(1997) provided data on mean per capita harvest of marine mammals in the Arctic region of rural Alaska

of about 290 grams/day. Greater details of types of marine mammals consumed, mercury concentrations

found in these mammals, and estimates of quantities of mammals consumed have been published by

Canadian investigators (Jensen et al. 1997; Chan, 1997) and by the investigators in Greenland and

Denmark (Dietz et al., 1996).

Table 4-36

Mean Per Capita Harvest of Fish and Marine Mammals (g/day)

(Wolfe, personal communication, 1997)

Alaska Rural Area Fish Shellfish Marine Fish/Shellfish Fish/Shellfish/

Mammals Marine

Mammals

Southcentral-Prince 114 7 4 122 126

William Sound

Kodiak Island 132 17 2 149 152

Southeast 119 32 7 152 159

Southwest-Aleutian 299 7 12 307 319

Interior 577 0 0 577 577

Arctic 194 1 267 195 482

Western 605 0 88 605 693

All Rural Areas 276 11 65 267 352

Combined
4-47

Table 4-37

Estimated Daily Intake of Food and Mercury for Arctic Inuit

(Adapted from Chan, 1997)

Food group Food (g/day) Mercury (μg/day)

Marine mammal meat 199 170

Marine mammal blubber 30 2.4

Terrestrial mammal meat 147 4.0

Terrestial mammal organs 1 0.9

Fish 42 6.6

Birds 2 0.8

Plants 2 0.0

Total 423 185

Marine mammals are primarily exposed to methylmercury (Caurant et al., 1996). Mercury

present in flesh of marine mammals is largely methylmercury. For example, Caurant et al. (1996)

identified an average of 78% organic mercury in muscle of pilot whales (Globicepala melas) and 23%

organic mercury in pilot whale liver. Mercury in organs such as liver and kidney appears to be

demethylated and stored in a form combined with selenium, which has been regarded as a detoxification

mechanism for the marine mammals (Caurant et al., 1996). Detailed date on mercury concentration in

the northern marine ecosystem were reported by Dietz et al. (1996) including information on mercury

concentration in molluscs, crustaceans, fish, seabirds, seals, whales, and polar bears.

Among the Inuit in coastal communities of the Canadian Arctic and Greenland, marine mammals

are an important source of food. Food items include the flesh and some organs of ringed seals (Phoca

hispida) and the flesh, but preferentially skin meat and liver of ringed seals and muktuk and blubber of

whales are eaten raw or cooked. Muktuk and the flesh, liver, intestines, and blubber of walrus are also

eaten after fermentation (Wagemann et al., 1996).

Throughout the Arctic, the mean mercury concentration in muscle of beluga whale averaged

between 0.7 and 1.34 μg mercury/gram wet weight of tissue (Wagemann et al., 1996). Muktuk (skin as a

whole) of beluga averaged between approximately 0.6 and 0.8 μg mercury/g wet weight. The skin of

cetaceans (whales, dolphins, porpoises) consists of four layers with the mercury concentration increasing

toward the outermost layers of skin. In this outermost layer of skin, mercury concentration were about

1.5 μg/gram. During molting, about 20% of the total mercury in skin is lost annually. Muscle tissue of

narwhal averaged between 0.8 and 1.0 μg/g, while muktuk averaged around 0.6 μg/g wet weight

(Wagemann et al., 1996). Muscle flesh of ringed seals had average mercury concentrations in the range

of 0.4 and 0.7 μg/g with most of the mercury present as methylmercury. Liver mercury concentrations

averaged in the range of 20 to 30 μg/g, but this was primarily present as inorganic mercury. Kidney

contained between 1 and 3 ppm mercury (Wagemann et al., 1996).
4-48

Overall, groups consuming muscle and muktuk from marine mammals have much higher

exposures to methylmercury that do groups who consume primarily fish and/or terrestrial mammals. 

Chan (in press) estimated exposures over 180 μg mercury/day for Arctic Inuits. To whatever extent

organs (specifically liver and kidney) are consumed, these typically contain higher concentrations of

mercury but with a lower fraction of methylmercury than found in muscle tissue. 

4.1.6 Summary of Canadian Data on Mercury Intake from Fish and Marine Mammals

The Northern Contaminants Program on the Department of Indian Affairs and Northern

Development of the Canadian Government published a compilation of contaminant data including

mercury concentrations in fish and marine mammals (Jensen et al., 1997). Most of the traditionally

harvested fish and land and marine animals consumed are long-lived and are from the higher trophic

levels of the food chain which contain greater concentrations of methylmercury than are found in

nonpredatory fish.

Several extensive data sets on mercury concentrations in marine mammals consumed by

indigenous populations living in the circumpolar regions have been published (Wagemann et al., 1996;

Caurant et al., 1996; Dietz et al., 1996). Analyses that determined chemically speciated mercury have

shown that mercury present in muscle tissue is largely (>75%) organic mercury (i.e., methylmercury)

(Caurant et al., 1996). By contrast, mercury present in organs such as liver and kidney is predominantly

in an inorganic form (Caurant et al., 1996).

Wagemann et al. (1997) have provided an overview of mercury concentrations in Arctic whales

and ringed seals. The Inuit in coastal communities of the Canadian Arctic and Greenland hunt and

consume marine mammals for food. The flesh and some organs of ringed seals (Phoca hispida) and flesh

but preferentially skin (muktuk) of belugas (Delphinapterus leucas) and narwal (Monodon monoceros)

contribute significantly to the Inuit diet. Throughout the Arctic, the mean concentrations in Beluga

muscle averaged 0.70 to 1.34 μg mercury/gram wet weight (Wagemann et al., 1996). Mean mercury

concentrations in the muktuk (skin as a whole) of belugas sampled in the western (1993-1994) and the

eastern Arctic (1993-1994) were 0.78 and 0.59 μg mercury/gram wet weight (Wagemann et al., 1996). 

Mean mercury concentrations for narwhal samples collected in the period 1992-1994 were 0.59, 1.03,

10.8, and 1.93 μg mercury/gram wet weight in muktuk, muscle, liver, and kidney, respectively

(Wagemann et al., 1996). Muscle tissue of ringed seals contained mercury in concentrations averaging

between 0.4 and approximately 0.7 μg mercury/gram wet weight. Liver tissue averaged between

approximately 8 and 30 μg mercury/gram wet weight. Kidney tissues averaged between 1.5 and 3.2 μg

mercury/gram wet weight.

 

Extensive data on mercury concentrations in multiple tissues from a wide variety of molluscs,

crustacea, fish, seabirds, and marine mammals (seals, whales, and porpoises), and polar bears collected in

Greenland were published by Dietz et al. (1996). Chemically speciated mercury concentrations in tissues

of pilot whales have been determined by Caurant et al. (1996). The percent organic mercury (i.e.,

methylmercury) in muscle tissue averaged over 75%. Liver contained a smaller fraction organic

mercury, averaging approximately 23% organic mercury. Marine mammals are principally exposed to

methylmercury, which is the main physico-chemical form of storage in fish (Caurant et al., 1996). 

Although demethylation by liver may serve as a means of protecting the marine mammal against adverse

effects of methylmercury, the presence of organic mercury in the marine mammal’s muscle means that

consumption of flesh from these mammals will result in exposure to organic mercury.

Jensen et al. (1997) in the Canadian Arctic Contaminants Assessment Report identified wide

variability in the consumption of fish and marine mammals by various aboriginal groups. Chan (1997)

summarized results from an extensive number of dietary surveys of Northern peoples from the Dene
4-49

(registered Indian) communities and the Inuit communities (Tables 4-38 and 4-39). The Dene were

estimated to have a mean consumption of 80 grams/day of fish. The Inuit communities were estimated to

have a fish consumption of 42 grams/day, a marine mammal consumption of approximately 230

grams/day

Table 4-38

Mercury Concentrations (μg Hg/g wet weight) in Traditional Foods Consumed

by Canadian Aboriginal Peoples

(Modified from Chan, 1997)

Food Group Number of Number of Arithmetic Standard Maximum

Sites Samples Mean Deviation

Marine Mammal Meat 32 764 0.85 1.05 33.4

Marine Mammal

Blubber

6 71 0.08 0.05 0.13

Terrestrial Mammal

Meat

6 19 0.03 0.02 0.17

Terrestrial Mammal

Organs

14 254 0.86 0.90 3.06

Fish 799 31,441 0.46 0.52 12.3

Birds 24 216 0.38 0.59 4.4

Plants 8 14 0.02 0.02 0.05

Table 4-39

Estimated Daily Intake of Mercury Using Contaminant Data Base and Dietary Information from

Dene and Inuit Communities in Canada

(Adapted from Chan, 1997)

Food Group Dene Community Inuit Community

Food Mercury Food Mercury

(g/day) (μg/day) (g/day) (μg/day)

Marine Mammal Meat 0 0 199 170

Marine Mammal Blubber 0 0 30 2

Terrestrial Mammal Meat 205 6 147 4

Terrestrial Mammal 23 20 1 1

Organs

Fish 80 13 42 7

Birds 8 1 2 1

Plants 2 0 2 0.0

Total 318 40 423 185
4-50

(199 grams of meat and 30 grams of blubber). These mean consumptions were associated with a mercury

intake of 39 μg mercury/day for the Dene community and 185 μg mercury/day for an Inuit community. 

For the Inuit community, 170 μg mercury/day came from marine mammal meat.

4.2 Trends in Fish and Shellfish Consumption in the United States

Description of long-term trends in fish and shellfish consumption are based on data provided by

the National Marine Fisheries Service of the U.S. Department of Commerce. Detailed information on

trends in the 1990s, and forecasts for future production and consumption of fish and shellfish, are based

on projections described in the Annual Report on the United States Seafood Industry published by H.M.

Johnson & Associates (1997).

4.2.1 Fish and Shellfish Consumption: United States, 1975 to 1995

Data for the U.S. consumption and utilization of fish and shellfish have been obtained from the

World Wide Web (http://remora.ssp.mnfs.gov/commercial/landings/index.html). Landings are reported

in pounds of round (i.e., live) weight for all species or groups except univalve and bivalve molluscs, such

as clams, oysters, and scallops. For the univalves and bivalve molluscs, landings are reported as pounds

of meat which excludes shell weight. Landings to not include aquaculture products except for clams and

oysters. Aquaculture products are an increasing source of fish and shellfish for some species of seafood

(Johnson 1997).

U.S. per capita consumption of commercial fish and shellfish has increased from the early part of

this century until present. The major increases occurred post-1970. In 1910, for example, U.S. citizens

consumed an average of 11.0 pounds (edible meats) of commercial fish and shellfish. The consumption

in 1970 was 11.8 pounds per capita, but by 1990 had increased to 15.0 pounds per capita.

Two major differences are associated with this trend. First, there was a major increase in

population from 92.2 million persons in 1910, to 201.9 individuals in 1970s, and 247.8 million citizens in

1990. In 1995 (the latest year this source provide statistics), the civilian resident population was

estimated at 261.4 million persons. Combined with increased consumption on a per capita basis, the

seafood market has dramatically increased throughout this century.

The second major change was in availability of transportation and in food processing. Changes

between 1910 and 1995 are shown in Table 4-40. Consumption of cured fish dramatically decreased

from about 36% of per capita intake in 1910, to 2.0% in 1990. Fresh or frozen fish were about 40% of

per capita intake in 1910 and increased to about 67% (two-thirds) of fish and shellfish intake by 1990 and

1995. The consumption of canned fish and shellfish changed the least representing about one-fourth of

all fish/shellfish intake in 1910 and about one-third of intake in 1990 and 1995.

Table 4-40

Percent of Fish/Shellfish by Processing Type between 1910 and 1995

(Source: National Marine Fisheries Service, 1997)

Year Fresh/Frozen Canned Cured

1910 39.1 24.5 36.4

1970 58.5 38.1 4.0
4-51

1990 64.7 33.3 2.0

1995 66.7 31.3 2.0

4.2.1.1 United States: Major Imports and Exports of Fish and Shellfish

During the period 1990 through 1994 the United States was the second largest importer of seven

fishery commodity groups, as well as the second largest exporter of these groups. The largest importer

was Japan and the third largest importer (after the United States) was France followed by Spain,

Germany, and Italy. On a value basis, Canada in the second largest trading partner for the United States

after Japan (Johnson, 1997).

The top five exporters of seafood were Thailand, United States, Norway, Denmark, and China. 

Thailand is the leading supplier of seafood to the United States on a value basis, shipping primarily

shrimp (Johnson, 1997). Canada was the leading seafood supplier on a volume basis (Johnson, 1997). 

The seven fishery commodity groups are:

1. Fish, fresh, chilled or frozen;

2. Fish, dried, salted, or smoked;

3. Crustaceans and mollusks, fresh, dried, salted, etc.;

4. Fish products and preparations, whether or not in airtight containers;

5. Crustacean and mollusk products and preparations, whether or not in airtight containers;

6. Oils and fats, crude or refined, or aquatic animal origin; and

7. Meals, soluble and similar animal food stuffs of aquatic animal origin.

4.2.1.2 U.S. Supply of Edible Commercial Fishery Products: 1990 and 1995

The supply of the products shown in Table 4-41 is expressed as round or live weight. Any

comparison of these values with food consumption data must consider that the edible portion is smaller

than the live weight. Factors for edible portion compared with live/round weight were published in the

National Research Council’s report on Seafood Safety (NRC/NAS, 1990). Total U.S. consumption of

fish and shellfish must also include self-caught and recreationally caught fish, as well as other sources

that are not tabulated through commercial channels.

Table 4-41

U.S. Supply of Edible Commercial Fishery Products: 1990 and 1995

(Round or Live Weight in Million Pounds)

Source: National Marine Fisheries Service

Year Domestic Commercial Imports Total

Landings

Million Percent Million Percent Million

Pounds Pounds Pounds

1990 7,041 55.6 5,621 44.4 12,662

1995 7,783 56.8 5,917 43.2 13,700
4-52

4.2.1.3 U.S. Annual Per Capita Consumption of Canned Fishery Products: 1990 and 1995

Canned tuna is the predominant type of canned fish consumed in the United States averaging

72.4% of all canned fish consumed per capita. Table 4-42 shows U.S. annual per capita consumption of

canned fishery products in 1990 and 1995.

Table 4-42

U.S. Annual Per Capita Consumption of Canned Fishery Products: 1990 and 1995

(Pounds Per Capita)

Year Salmon Sardines Tuna Shellfish Other Total

1990 0.4 0.3 3.7 0.3 0.4 5.1

1995 0.5 0.2 3.4 0.3 0.3 4.7

4.2.1.4 U.S. Annual Per Capita Consumption of Fish Items: 1990 and 1995

In fresh and frozen fish products and shrimp, per capita consumption in these categories is shown

in Table 4-43 based on data from the National Marine Fisheries Service. 

Table 4-43

U.S. Annual Per Capita Consumption (in pounds*) of Certain Fishery Items: 1990 and 1995

Year Fillet and Steaks ** Sticks and Portions Shrimp 

(All Preparations)

1990 3.1 1.5 2.2

1995 2.9 1.2 2.5

* Product weight of fillets and steaks and sticks and portions, edible (meat) weight of shrimp.

** Data include ground fish and other species. Data do not include blocks, but fillets could be made into blocks

from which sticks and portions could be produced.

4.2.1.5 Major Imported Fish and Shellfish Products

The two major fish/shellfish products imported into the United States in 1994 and 1995

(expressed by weight) were shrimp (621,618,000 pounds in 1994 and 590,634,000 pounds in 1995), and

tuna (including albacore, canned tuna, and other tuna: 707,426,000 pounds in 1994 and 711,241,000

pounds in 1995). Approximately 28% of imported tuna was imported as albacore tuna and about 33%

was imported as canned tuna. Shrimp imports were not differentiated by species of shrimp or country of

origin in the national Marine Fisheries Service statistics.
4-53

4.2.2 Current Market Trends, 1996

The following data on current market trends in the seafood industry are abstracted from the 1997

Annual Report on the United States Seafood Industry describing 1996 data on seafood by H.M. Johnson

& Associates (Johnson, 1997). 

The world commercial fish and shellfish supplies increased from 109.6 thousand metric tons in

1994 to 112.0 thousand metric tons in 1995. Aquaculture provided the largest boost to world supply in

1995 increasing 13.6% over the previous year. During this period (1995 to 1996) capture fisheries

declined by 0.1 metric tons. Aquaculture represents 26% of all world food fish (total supply less

reduction fish) products.

The Food and Agriculture Organization examined long-term trends in 77 major fish resources

(representing 77% of the world marine fish landings) are concluded that 35% of the resources were

“overfished,” 25% were “fully fished,” and 40% had remaining capacity for expansion (FAO, 1996; as

cited by Johnson, 1997).

Aquaculture

World aquaculture continued to increase with 1995 production increased by 14% to 20.9 million

metric tons (Johnson, 1997). Five Asian countries (China, India, Japan, Republic of Korea, and the

Philippines) supplied 80% of aquaculture-raised fish/shellfish. World-wise aquaculture is predicted by

the Food and Agriculture Organization to continued to increase fish and shellfish production beyond the

years 2000.

Within the United States, domestic finfish aquaculture increased in 1996. The major increases

were in catfish production. Catfish production very much dominates the U.S. finfish aquaculture

production yielding approximately 475 million pounds round weight/year. Tilapia harvests were higher

in 1996, however, trout and salmon production declined. Salmon, trout, and tilapia production are

substantially smaller than catfish production. Yields from U.S. aquaculture for salmon, trout, and tilapia

were under 50 million pounds for each of these species.

4.2.3 Patterns in Fish and Shellfish Consumption: United States, 1996

4.2.3.1 Overall Patterns

Between 1995 and 1996 there was a 0.2 pound decrease in per capita consumption of seafood in

the United States. The principal decline was in canned tuna. The top ten seafoods consumed (expressed

as pounds consumed per capita) were: canned tuna (3.2), shrimp (2.5), Alaska Pollock (1.6); salmon

(1.4); cod (just under 1 pound); catfish (approximately 0.9 pounds); clams (approximately 0.5 pounds),

flatfish (0.4 pounds), crab (approximately 0.3), and scallops (0.3). The source of these data are the

National Marine Fisheries Service and the 1997 Annual Report on the United States Seafood Industry

(Johnson, 1997).

4.2.3.2 Fish Intake among Adults

Analysis of the frequency of reporting of fish/shellfish and menu items containing fish and

shellfish was carried out using data from CSFII 1994 and CSFII 1995. Seasons were grouped into six

two-month intervals; i.e., Jan/Feb, Mar/Apr, etc. Data for the 10 most commonly consumed menu items

are shown in Table 4-44. The most frequently reported menu items are “seafood salads and seafood and

vegetable dishes.” Although other fishery products are possible, this menu category typically describes
4-54

dishes made with tuna, surimi (i.e., Alaska pollock), crab, salmon or other canned fish or shellfish. 

Overall, these dishes represent about 20% of overall seafood consumption. This major group is followed

by shrimp, canned tuna, the group “Seafood cakes, fritters, and casseroles without vegetables”. 

Identified finfish commonly consumed include salmon, cod, catfish, flounder, trout, seabass, ocean

perch, haddock, and porgy. Although specific finfish are identified as among the top ten consumed over

six seasons, they follow consumption of processed fishery products; e.g., salads, fritters, “fast food”

fillets, and shrimp.

Table 4-44

Ten Most Commonly Reported Fish/Shellfish/Mixed Dishes by Season

CSFII 1994 and CSFII 1995 — Day 1 Data

Ranking

Season

Jan/Feb Mar/Apr May/Jun Jul/Aug Sep/Oct Nov/Dec

1st Seafood Seafood Seafood Seafood Seafood Seafood

salads, & salads, & salads, & salads, & salads, & salads, &

seafood & seafood & seafood & seafood & seafood & seafood &

vegetable vegetable vegetable vegetable vegetable vegetable

dishes, dishes, dishes, dishes, dishes, dishes,

17.6% 16.9% 24.5% 23.2% 15.4% 20.0%

2nd Shrimp, Shrimp, Shrimp, 9.5% Seafood Tuna, canned Shrimp,

11.2% 10.5% cakes, fritters, 12.0% 11.1%

& casseroles

w/o

vegetables,

7.9%

3rd Seafood Tuna, canned, Tuna, canned, Tuna, canned Shrimp, Seafood

cakes, fritters 10.1% 6.8% 7.5% 11.5% cakes, fritters

& casseroles & casseroles

w/o w/o

vegetables, vegetables,

8.8% 10.0%

4th Catfish, 8.3% Seafood Seafood Salmon, 6.8% Seafood Fish

cakes, fritters, cakes, fritters, cakes, fritters, stick/fillet,

& casseroles & casseroles & casseroles 9.4%

w/o w/o w/o

vegetables, vegetables, vegetables,

8.1% 6.4% 8.7%

5th Fish Cod, 5.6% Fish Shrimp, 6.4% Fish Fish

stick/fillet stick,fillet stick/fillet, stick/fillet,

7.8% 5.5% 6.7% 9.4%
Table 4-44 (continued)

Ten Most Commonly Reported Fish/Shellfish/Mixed Dishes by Season

CSFII 1994 and CSFII 1995 — Day 1 Data

Ranking

Season

Jan/Feb Mar/Apr May/Jun Jul/Aug Sep/Oct Nov/Dec

4-55

6th Tuna, canned, Salmon, Salmon, 4.5% Fish Cod, Tuna, canned

6.3% 5.2%, stick/fillet, 6.3% 7.8%

5.4%

7th Salmon, 3 Fish, Seafood Catfish, Fish, Salmon, 4.4%

unspecified, sandwiches, 4.6% unspecified

4.8% 4.1% 4.8%

8th Trout, 2.4% Seafood Fish, Cod, Flounder, Fish

sandwiches, unspecified 4.6% 4.3% unspecified,

4.0% 3.6% 4.4%

9th Shellfish Seafood Sea bass, Ocean perch, Salmon, Haddock,

dishes in soups & 3.2% 3.2% 3.4% 3.9%,

sauce, 2.4% casseroles Frozen

with seafood

vegetables, dinners, 3.9%

3.6%

10th Frozen Porgy, 3.6% Trout, Perch, Catfish, 2.9% Flounder,

seafood 2.7% 3.2% 3.3%

dinners, 2.4%

Communications with experts in the seafood industry as well as the import/export and

productions statistics published by the National Marine Fisheries Service and the Food and Agriculture

Organization) indicate the predominant species of fish and shellfish are the various species of tuna,

shrimp, and the Alaskan pollock. Superimposed on these broad national trends in fish/shellfish

consumption, are regional trends in fish/shellfish consumption. Table 4-45 describes regional popularity

of fish species within the United States.
4-56

Table 4-45

Regional Popularity of Fish and Shellfish Species

Region Most Popular Fish Consumed

East Coast haddock, cod*, flounder, lobster, blue crab,

shrimp

South shrimp, catfish, grouper, red snapper, blue crab

West Coast salmon, dungeness crab, shrimp, rockfish

Mid-West Perch, Walleye, Chubs, Multiple Varities of

Freshwater Fish

*In the late 1990s, cod has been replaced on menus largely by Alaskan pollock.

 

These impressions are supported by descriptions of the best-selling fish/shellfish species in

various types of restaurants as shown in Table 4-46 (Seafood Business magazine cited by Johnson, 1997,

page 71).

Table 4-46

Popularity of Fish/Shellfish Species in Restaurants

Rank First Second Third

By Region:

 North East Salmon Shrimp Swordfish

 South Shrimp Salmon Catfish

 Midwest Salmon Shrimp Cod*

 West/Pacific Salmon Shrimp Halibut

By Restaurant Style:

 “Fast Food” Cod*/Shrimp Clams/Scallops Tuna

 “Dinnerhouse” Shrimp Salmon Lobster

 “White Tablecloth” Salmon Shrimp Swordfish

By Overall Sales:

 1996 Shrimp Salmon Cod*

 1995 Cod* Shrimp/Salmon Swordfish

 1994 Cod* Shrimp/Salmon Swordfish

 1993 Cod* (& All Shrimp Hoki

Whitefish)
Rank First Second Third

4-57

 1992 Cod* (& All Shrimp Crab

Whitefish)

*In the late 1990s, cod has been replaced on menus largely by Alaskan pollock.

Although the species shown in Tables 4-45 and 4-46 are popular regionally, for the United States

as a whole, the national statistics indicate major fish consumed are: tuna, shrimp, and Alaskan pollock. 

4.2.3.3 Fish and Shellfish Consumption by Children

The NHANES III data were analyzed to determine the species of fish and shellfish consumed by

children in the age categories 1-to-5 years, 6-to-11 years, and 12-to-14 years for male and female survey

respondents. Specific choices by age groups are shown in Table 4-47. The top four fish dishes for all

age categories of children were: 

• fish sticks and patties,

• tuna salad and canned tuna,

• shrimp, and 

• catfish. 

Table 4-47

Frequencies of Various Fish and Shellfish Food Types

for Children Ages 1 to 5 and 6 to 11 Years by Gender

(Source: NHANES III)

Food Type Frequency of Various Food Types

Ages 1-5 Years Ages 6-11 Ages 12-14

Females Males Females Males Females Males

Fish Sticks/Patties 23% 21% 23% 25% 21% 21%

Tuna Salad/

Canned Tuna

33% 27% 26% 19% 25%

28%

Shrimp 8% 6% 11% 10% 12% 12%

Catfish 5% 5% 5% 10% 9% 4%

All Other fish and Shellfish 31% 41% 35% 36% 30% 33%

Total 100% 100% 100% 100% 100% 100%

4.2.4 Production Patterns and Mercury Concentrations for Specific Fish and Shellfish Species

Four species of fish are important predictors of methylmercury exposure because of the

frequency with which these are consumed by the overall population.

4.2.4.1 Tuna
4-58

Although consumption of canned tuna continues to fall (Johnson, 1997), tuna (canned and fresh

or frozen) continues to be the most commonly consumed fish based on data from contemporary surveys

of food intake by individuals. The mercury concentration of tuna varies with species reflecting

variability in fish size and geographic location.

The mean mercury concentration in tuna is 0.206 μg/gram based on data from NMFS. This

represents an average for the mean concentrations measured in three types of tuna: albacore tuna (0.264

μg/g), skipjack tuna (0.136 μg/g, and yellowfin tuna (0.218 μg/g). Data cited by U.S. FDA (1978)

indicate the following mean (maximum) values in μg/g for various tuna species: tuna, light skipjack,

0.144 (0.385); tuna light yellow, 0.271 (0.870); tuna, white 0.350 (0.904). Cramer (1994) observed that

recent U.S. FDA surveys indicated that the mean mercury content of 1973 samples of canned tuna was

0.21 μg/g, whereas a 1990s survey of 245 samples of canned tuna was 0.17 μg/g mercury. 

4.2.4.2 Shrimp

Shrimp consumption based on contemporary nationally representative surveys in the United

States continues to be a top-ten seafood choice by both adults and children. World shrimp supplies are in

excess of 3,000,000 metric tons (Johnson, 1997) with approximately one-sixth of the production grown

by aquaculture. This amounts to approximately 500,000 metric tons grown by aquaculture. The United

States is a net importer of shrimp with major suppliers (in order of the quantity imported into the United

States) Thailand, Ecuador, Mexico, and India (Johnson, 1997).

The overall averaged mercury concentration in marine shrimp reported by the NMFS is 0.047

μg/g. This is an average of the mean concentrations measured in seven types of shrimp: royal red shrimp

(0.074 μg/g), white shrimp (0.054 μg/g), brown shrimp; (0.048 μg/g), ocean shrimp (0.053 μg/g), pink

shrimp (0.031 μg/g), pink northern shrimp (0.024 μg/g), and Alaska (sidestripe) shrimp (0.042 μg/g). 

Data cited by U.S. FDA (1978) indicate a mean value of 0.040 with a maximum of 0.440 μg/g. 

Shrimp consumed in the United States are predominantly imported from Thailand, Ecuador, and

India. The authors of the Report to Congress have not identified data specifically reporting mercury

concentrations in shrimp from the countries which are currently the major suppliers of shrimp to the

United States.

4.2.4.3 Pollock

The Alaskan pollock dominates the U.S. seafood industry. In 1996, pollock landings totaled 2.6

billion pounds (Johnson, 1997). Pollock is the fish species used in preparation of fish sticks, fish

sandwiches served by various “fast food” restaurant franchises in the United States, artificial “crab” or

surimi.

The mercury concentration attributed to pollock is 0.15 μg/g based on NMFS data. Data cited by

U.S. FDA indicate a mean mercury concentration for pollock of 0.141 (maximum value, 0.96 μg/g). 

4.2.4.4 Salmon

Salmon is a highly important fish species based on frequency of consumption of both the canned

and fresh product. Species include: chinook, coho, chum, sockeye, and pink. Production has declined in

the United States between 1995 and 1996, although the world supply of salmon has continued to grow. 

Salmon is one of the major fish species grown by aquaculture with production of approximately 50

million pounds per year in the United States.
4-59

The mercury content used for salmon was the average of the mean concentrations measured in

five types of salmon: pink (0.019 μg/g), chum (0.030 μg/g), coho (0.038 μg/g), sockeye (0.027 μg/g), and

chinook (0.063 μg/g). Salmon that is raised by aquaculture based on consumption of corn and soy

products may have lower mercury concentrations because of the low mercury concentration of the

vegetable products fed to the aquaculture-raised salmon. Data cited by U.S. FDA (1978) indicated a

mean value for salmon of 0.040 (maximum 0.201).

4.2.4.5 Catfish

Catfish ranks in the top ten fish produced and consumed. Catfish dominates the aquaculture

production in the United States with production of approximately 475 million pounds round (i.e., live)

weight. The mercury concentration of freshwater catfish used in the Mercury Study Report to Congress

was 0.088 μg/g. Data cited by U.S. FDA (1978) indicate a mean value of 0.146 μg/g (with a maximum

value of 0.38 μg/g). As with salmon, catfish raised by aquaculture on vegetable products (e.g., corn and

soy) are predicted to have lower mercury concentrations than capture catfish. 

4.3 Mercury Concentrations In Fish

Mercury concentrations in marine, estuarine, and freshwater fish were obtained from data bases

maintained for marine and estuarine fish and shellfish (National Marine Fisheries Service, 1978) and

freshwater fish (Lowe et al., 1985; and Bahnick et al., 1994). These data combined with estimates of

fish/shellfish consumption from various dietary surveys form the basis for predicted mercury exposures

through fish and shellfish.

4.3.1 National Marine Fisheries Service Data Base

Analyses of total mercury concentrations in marine and estuarine fish and shellfish have been

carried out over the past two to three decades. Data describing methylmercury concentrations in marine

fish were predominantly based on the National Marine Fisheries’ Service (NMFS) data base, the largest

publicly available data base on mercury concentrations in marine fish. In the early 1970s, the NMFS

conducted testing for total mercury on over 200 seafood species of commercial and recreational interest

(Hall et al., 1978). The determination of mercury in fish was based on flameless (cold vapor) atomic

absorption spectrophotometry following chemical digestion of the fish sample. These methods were

described in Hall et al. (1978). 

Although the NMFS data were initially compiled beginning in the 1970s, comparisons of the

mercury concentration identified in the NMFS’s data base with compliance samples obtained by the U.S.

FDA indicate that the NMFS data are appropriate to use in estimating intake of mercury from fish at the

national level of data aggregation. Cramer (1994) of the Office of Seafood of the Center for Food Safety

and Applied Nutrition of the U.S. FDA reported on Exposure of U.S. Consumers to Methylmercury from

Fish. He noted that recent information from NMFS indicated that the fish mercury concentrations

reported in the 1978 report do not appear to have changed significantly. The U.S. FDA continues to

monitor methylmercury concentration in seafood. Cramer (1994) observed that results of recent U.S.

FDA surveys indicate results parallel to earlier findings by U.S. FDA and NMFS. To illustrate, Cramer

estimated the mean methylmercury content of the 1973 samples of canned tuna at 0.21 μg/g mercury,

whereas a recently completed survey of 245 samples of canned tuna was 0.17 μg/g mercury. These data

are considered to be comparable, although the small decrease reported between these two studies may

reflect increased use in canned tuna of tuna species with slightly lower average methylmercury

concentrations. The National Academy of Sciences’ National Research Council’s Subcommittee on

Seafood Safety (1991) also assessed the applicability of the NMFS' 1970s data base to current estimates

of mercury concentrations in fish. This subcommittee also concluded that the 1978 data base differed
4-60

little in mercury concentrations from U.S. FDA compliance samples estimating mercury concentrations

in fish.

Assessment of this data base by persons with expertise in analytical chemistry and patterns of

mercury contamination of the environment have indicated that temporal patterns in mercury

concentrations in fish do not preclude use of this data base in the present risk assessment (US EPA’s

Science Advisory Board’s ad hoc Mercury Subcommittee; Interagency Peer Review Group, External Peer

Review Group). One issue that did arise, however, concerned how zero and trace values were entered

into calculation of mean mercury concentrations. This has been evaluated statistically through

comparison of mean values when different approaches were taken to mathematically calculated means

under different assumptions of inclusion of zero and trace values. 

The NMFS Report provided data on number of samples, number of nondetects, and mean,

standard deviation, minimum and maximum mercury levels (in parts per million wet weight) for 1,333

combinations of fish/shellfish species, variety, location caught, and tissue (Hall et al., 1978). This data

base includes 777 fish/shellfish species for which mercury concentration data are provided. This

represents 5,707 analyses of fish and shellfish tissues for total mercury, of which 1,467 or 26%, are

reported as nondetectable levels. Because the mercury concentration data are used in our analyses at the

species level, not at the more detailed species/variety/location/tissue level, the data have been grouped to

reflect 35 different fish/shellfish species.

The frequency of nondetectable or “zero” values differs with the mercury concentration. When

mean mercury levels are relatively “large”, there are few, if any, nondetects, so the methodology

employed to handle nondetects is irrelevant. When mean mercury levels are small, there are relatively

large numbers of nondetectable values. Because the method of including/excluding nondetectable values

in the calculation has the greatest impact only when mercury concentrations are very low, the overall

impact on estimated mercury exposure is small.

A statistical assessment of these potential differences was carried out by Westat Corporation

(Memo from Robert Clickner, September 26, 1997). A description of the statistical basis for the

comparison is shown in Appendix C. To determine the detection limit applicable to the data base, the

lowest of all detected analytical values was presumed to be the detection limit. This value is 0.010 μg/g

wet weight. The major conclusion of this analysis is that different methods of handling nondetects have

negligible impact on the reported mean concentrations. Consequently the mean values as reported by the

NMFS will be used in preparing estimates of mercury intake from marine and estuarine fish and

shellfish. 

Mercury concentration in various fish species are shown in Table 4-48.

Table 4-48

Summary of Mercury Concentrations in Fish Species

(μg Hg/g fresh weight)
Table 4-48 (continued)

Summary of Mercury Concentrations in Fish Species

(μg Hg/g fresh weight)

4-61

Data Used by USEPA Data Used by US FDA Data Used by Stern et al.

Mercury Study Report to Report on the Chance of U.S. 1996

Congress* Seafood Consumers Exceeding "The Current

1997 Regulatory Controls"

Daily Intake for Mercury and Recommended

1978

Fish Species Average Fish Species Average Maximum Fish Average

(g Hg/g) (g Hg/g) (g Hg/g) Species (g Hg/g)

Abalone 0.016 Abalone 0.018 0.120 Not

Reported

(NR)

Anchovies 0.047 Anchovies 0.039 0.210 NR

Bass, Avgs.= 0.157 Bass, 0.752 2.000 Bass, 0.41

Freshwater (Lowe et al., Striped freshwater

1985) and

0.38 (Bahnick

et al., 1994)

Bass, Sea Not Reported Bass, Sea 0.157 0.575 Bass, Sea 0.25

Bluefish Not Reported Bluefish 0.370 1.255 Bluefish 0.35

Bluegills 0.033 Bluegills 0.259 1.010 NR

Bonito Not Reported Bonito 0.302 0.470 NR

(below

3197)

Bonito Not Reported Bonito 0.382 0.740 NR

(above

3197)

Butterfish Not Reported Butterfish 0.021 0.190 Butterfish 0.05

Carp, 0.093 Carp 0.181 0.540 Catfish, 0.15

Common freshwater

Catfish 0.088 Catfish 0.146 0.380 Clams 0.05

(channel,large (freshwater)

mouth, rock,

striped, white)

Catfish Not Reported Catfish 0.475 1.200 Cod/Scrod 0.15

(Marine) (Marine)

Clams 0.023 Clams 0.049 0.260 See crab.

Cod 0.121 Cod 0.125 0.590 Crab 0.15

Crab, King 0.070; Crab, King 0.070 0.240 NR

Calculations

based on 5

species of crab

combined at

0.117
Table 4-48 (continued)

Summary of Mercury Concentrations in Fish Species

(μg Hg/g fresh weight)

Data Used by USEPA Data Used by US FDA Data Used by Stern et al.

Mercury Study Report to Report on the Chance of U.S. 1996

Congress* Seafood Consumers Exceeding "The Current

1997 Regulatory Controls"

Daily Intake for Mercury and Recommended

1978

Fish Species Average Fish Species Average Maximum Fish Average

(g Hg/g) (g Hg/g) (g Hg/g) Species (g Hg/g)

4-62

Crab 0.117 Crab, other 0.140 0.610 NR

than King

Crappie 0.114 Crappie 0.262 1.390 NR

(black, white)

Croaker 0.125 Croaker 0.124 0.810 NR

Dolphin Not Reported Dolphin 0.144 0.530 Dolphin 0.25

(Mahimahi)

Drums, 0.117 Drums 0.150 0.800 NR

Freshwater

Flounders 0.092 Flounders 0.096 0.880 Flounder 0.10

Groupers Groupers 0.595 2.450 NR

Haddock 0.089 Haddock 0.109 0.368 Haddock 0.05

Hake 0.145 Hake 0.100 1.100 Hake 0.10

Halibut 0.250 Halibut 4 0.187 1.000 Halibut 0.25

Halibut 0.250 Halibut 3 0.284 1.260 Halibut 0.25

Halibut 0.250 Halibut 2H 0.440 1.460 Halibut 0.25

Halibut 0.250 Halibut 25 0.534 1.430 Halibut 0.25

Herring 0.013 Herring 0.023 0.260 Herring 0.05

Kingfish 0.100 Kingfish 0.078 0.330 Kingfish 0.05

Lobster 0.232 Lobster, 0.339 1.603 Lobster 0.25

Northern 11

Lobster 0.232 Lobster 0.509 2.310 Lobster 0.25

Northern 10

Lobster 0.232; Lobster,Spin 0.113 0.370 Lobster 0.25

Spiny Includes spiny y

(Pacific)

lobster=0.210 
Table 4-48 (continued)

Summary of Mercury Concentrations in Fish Species

(μg Hg/g fresh weight)

Data Used by USEPA Data Used by US FDA Data Used by Stern et al.

Mercury Study Report to Report on the Chance of U.S. 1996

Congress* Seafood Consumers Exceeding "The Current

1997 Regulatory Controls"

Daily Intake for Mercury and Recommended

1978

Fish Species Average Fish Species Average Maximum Fish Average

(g Hg/g) (g Hg/g) (g Hg/g) Species (g Hg/g)

4-63

Mackerel 0.081; Mackerel, 0.048 0.190 Mackerel 0.28

Averaged Atlantic

Chub = 0.081;

Atlantic=

0.025;

Jack=0.138

Mackerel 0.081 Mackerel, 0.267 0.510 Mackerel 0.28

Jack

Mackerel 0.081 Mackerel, 0.823 2.730 Mackerel 0.28

King (Gulf)

Mackerel 0.081 Mackerel, 1.128 2.900 Mackerel 0.28

King (other)

Mackerel 0.081 Mackerel, 0.542 2.470 Mackerel 0.28

Spanish 16

Mackerel 0.081 Mackerel, 0.825 1.605 Mackerel 0.28

Spanish 10

Mullet 0.009 Mullet 0.016 0.280 Mullet 0.05

Oysters 0.023 Oysters 0.027 0.460 NR

Perch, 0.110 Perch, 0.290 0.880 Perch 0.18

White and Freshwater

Yellow

Perch, 0.116 Perch, 0.133 0.590 NR

Ocean Marine

Pike, 0.310 Pike 0.810 1.710 NR

Northern 0.127

Pollock 0.150 Pollock 0.141 0.960 NR

Pompano 0.104 Pompano 0.104 8.420 NR

Rockfish Not Reported Rockfish 0.340 0.930 NR

Sablefish Not Reported Sablefish 0.201 0.700 NR

Salmon 0.035 Salmon 0.040 0.210 Salmon 0.05

Scallops 0.042 Scallops 0.058 0.220 NR

Scup Not Reported Scup 0.106 0.520 NR
Table 4-48 (continued)

Summary of Mercury Concentrations in Fish Species

(μg Hg/g fresh weight)

Data Used by USEPA Data Used by US FDA Data Used by Stern et al.

Mercury Study Report to Report on the Chance of U.S. 1996

Congress* Seafood Consumers Exceeding "The Current

1997 Regulatory Controls"

Daily Intake for Mercury and Recommended

1978

Fish Species Average Fish Species Average Maximum Fish Average

(g Hg/g) (g Hg/g) (g Hg/g) Species (g Hg/g)

4-64

Sharks 1.327 Sharks 1.244 4.528 Shark 1.11

Shrimp 0.047 Shrimp 0.040 0.440 Shrimp 0.11

Smelt 0.100 Smelt 0.016 0.058 Smelts 0.05

Snapper 0.25 Snapper,Red 0.454 2.170 Snapper 0.31

Snapper 0.25 Snapper, 0.362 1.840 Snapper 0.31

Other

Snook Not Reported Snook 0.701 1.640 NR

Spot Not Reported Spot 0.041 0.180 Spotfish 0.05

Squid 0.026 Squid and 0.031 0.400 Squid 0.05

Octopi

Octopi 0.029 Squid and 0.031 0.400 NR

Octopi

Sunfish Not Reported Sunfish 0.312 1.200 NR

Swordfish 0.95 Swordfish 1.218 2.720 Swordfish 0.93

Tillefish Not Reported Tillefish 1.607 3.730 NR

Trout, 0.149 Trout, 0.417 1.220 Trout 0.05

Freshwater

Trout 0.149 Trout, 0.212 1.190 Trout 0.05

Marine

Tuna 0.206; Tuna, 0.144 0.385 Tuna, 0.17

Averaged: Light fresh

Tuna, light Skipjack

skipjack=0.13

6Tuna,light

yellow=0.218;

Albacore=0.2

64

Tuna 0.206 Tuna, 0.271 0.870 Tuna, 0.17

Light fresh

Yellow

Tuna 0.206 Tuna, White 0.350 0.904 Tuna, 0.17

fresh

Whitefish Not Reported Whitefish 0.054 0.230 Whitefish 0.04
Table 4-48 (continued)

Summary of Mercury Concentrations in Fish Species

(μg Hg/g fresh weight)

Data Used by USEPA Data Used by US FDA Data Used by Stern et al.

Mercury Study Report to Report on the Chance of U.S. 1996

Congress* Seafood Consumers Exceeding "The Current

1997 Regulatory Controls"

Daily Intake for Mercury and Recommended

1978

Fish Species Average Fish Species Average Maximum Fish Average

(g Hg/g) (g Hg/g) (g Hg/g) Species (g Hg/g)

4-65

Other finfish Other finfish 0.287 1.020 Finfish, 0.17

other

Other shellfish Not Shellfish, 0.12

Reported other

Fish Species (Freshwater) Not Reported by FDA, 1978

Bloater 0.0.93

Smallmouth 0.096

Buffalo

Northern 0.33

Squawfish

Sauger 0.23

Sucker 0.114 (Lowe

et al., 1985;

0.167

(Bahnick et

al., 1994).

Walleye 0.100 (Lowe

et al., 1985)

and 0.52

(Bahnick et

al., 1994).

Trout (brown, 0.149 (Lowe

lake, rainbow) et al., 1985)

and 0.14

(Bahnick et

al., 1994 for

brown trout).

Fish Species Reported by the State of New Jersey 

and Not Reported by EPA or FDA

Blowfish 0.05

Orange roughy 0.5

Sole 0.12

Weakfish 0.15

Porgy 0.55
Table 4-48 (continued)

Summary of Mercury Concentrations in Fish Species

(μg Hg/g fresh weight)

Data Used by USEPA Data Used by US FDA Data Used by Stern et al.

Mercury Study Report to Report on the Chance of U.S. 1996

Congress* Seafood Consumers Exceeding "The Current

1997 Regulatory Controls"

Daily Intake for Mercury and Recommended

1978

Fish Species Average Fish Species Average Maximum Fish Average

(g Hg/g) (g Hg/g) (g Hg/g) Species (g Hg/g)

4-66

Blackfish 0.25

Whiting 0.05

Turbot 0.10

Sardines 0.05

Tilapia 0.05

* See Sections 4.3.1 and 4.3.2 for data on marine species, and Section 4.3.3 for data on freshwater fish.

4.3.2 Mercury Concentrations in Marine Fish

Data supplied by NMFS give the mercury concentrations in fresh weight of fish muscle of

numerous marine fish, shellfish, and other molluscan/crustacean species shown in Table 4-49, 4-50 and

4-51.

Table 4-49

Mercury Concentrations in Marine Finfish

Fish Mercury Concentration Source of Data

(g/g, wet weight)

Anchovy 0.047 NMFS 1

Barracuda, Pacific 0.177 NMFS 2

Cod 0.121 NMFS 3

Croaker, Atlantic 0.125 NMFS

Eel, American 0.213 NMFS

Flounder 0.092 NMFS 4

Haddock 0.089 NMFS

Hake 0.145 NMFS 5

Halibut 0.25 NMFS 6

Herring 0.013 NMFS 7

Kingfish 0.10 NMFS 8
Table 4-49 (continued)

Mercury Concentrations in Marine Finfish

Fish Mercury Concentration Source of Data

(g/g, wet weight)

4-67

Mackerel 0.081 NMFS 9

Mullet 0.009 NMFS 10

Ocean Perch 0.116 NMFS 11

Pollack 0.15 NMFS

Pompano 0.104 NMFS

Porgy 0.522 NMFS

Ray 0.176 NMFS

Salmon 0.035 NMFS 12

Sardines 0.1 NMFS 13

Sea Bass 0.135 NMFS

Shark 1.327 NMFS 14

Skate 0.176 NMFS 15

Smelt, Rainbow 0.1 NMFS

Snapper 0.25 NMFS 16

Sturgeon 0.235 NMFS 17

Swordfish 0.95 FDA Compliance Testing

Tuna 0.206 NMFS 18

Whiting (silver hake) 0.041 NMFS

 This is the average of NMFS mean mercury concentrations for both striped anchovy (0.082 g/g) and northern anchovy (0.010 1

g/g).

 USDA data base specified the consumption of the Pacific barracuda and not the Atlantic barracuda. 2

 The mercury content for cod is the average of the mean concentrations in Atlantic cod (0.114 g/g and the Pacific cod (0.127 3

g/g).

 The mercury content for flounder is the average of the mean concentrations measured in 9 types of flounder: Gulf (0.147 g/g), 4

summer (0.127 g/g), southern (0.078 g/g), four-spot (0.090 g/g), windowpane (0.151 g/g), arrowtooth (0.020 g/g), witch

(0.083 g/g), yellowtail (0.067 g/g), and winter (0.066 g/g).

 The mercury content for hake is the average of the mean concentrations measured in 6 types of hake: silver (0.041 g/g), 5

Pacific (0.091 g/g), spotted (0.042 g/g), red (0.076 g/g), white (0.112 g/g), and blue (0.405 g/g).

 The mercury content for halibut is the average of the mean concentrations measured in 3 types of halibut: Greenland, Atlantic, 6

and Pacific. 

 The mercury content for herring is the average of the mean concentrations measured in 4 types of herring: blueback (0.0 g/g), 7

Atlantic (0.012 g/g), Pacific (0.030 g/g), and round (0.008 g/g).

 The mercury content for kingfish is the average of the mean concentrations measured in 3 types of kingfish: southern, Gulf, and 8

northern. 

 The mercury content for mackerel is the average of the mean concentrations measured in 3 types of mackerel: jack (0.138 g/g), 9

chub (0.081 g/g), and Atlantic (0.025 g/g). 

 The mercury content for mullet is the average of the mean concentrations measured in 2 types of mullet: striped (0.011 g/g) 10

and silver (0.007 g/g).

 The mercury content for ocean perch is the average of the mean concentrations measured in 2 types of ocean perch: Pacific 11

(0.083 g/g) and redfish (0.149 g/g).
4-68

 The mercury content for salmon is the average of the mean concentrations measured in 5 types of salmon: pink (0.019 g/g), 12

chum (0.030 g/g), coho (0.038 g/g), sockeye (0.027 g/g), and chinook (0.063 g/g).

 Sardines were estimated from mercury concentrations in small Atlantic herring. 13

 The mercury content for shark is the average of the mean concentrations measured in 9 types of shark: spiny dogfish (0.607 14

g/g), (unclassified) dogfish (0.477 g/g), smooth dogfish (0.991 g/g), scalloped hammerhead (2.088 g/g), smooth

hammerhead (2.663 g/g), shortfin mako (2.539 g/g), blacktip shark (0.703 g/g), sandbar shark (1.397 g/g), and thresher

shark (0.481 g/g).

 The mercury content for skate is the average of the mean concentrations measured in 3 types of skate: thorny skate (0.200 15

g/g), little skate 0.135 g/g) and the winter skate (0.193 g/g).

 The mercury content for snapper is the average of the mean concentrations measured in types of snapper: 16

 The mercury content for sturgeon is the average of the mean concentrations measured in 2 types of sturgeon:green sturgeon 17

(0.218 g/g) and white sturgeon (0.251 g/g).

 The mercury content for tuna is the average of the mean concentrations measured in 3 types of tuna: albacore tuna (0.264 18

g/g), skipjack tuna (0.136 g/g) and yellowfin tuna (0.218 g/g).

Table 4-50

Mercury Concentrations in Marine Shellfish

Shellfish Mercury Concentration Source of Data

 (g/g, wet weight)

Abalone 0.016 NMFS 1

Clam 0.023 NMFS 2

Crab 0.117 NMFS 3

Lobster 0.232 NMFS 4

Oysters 0.023 NMFS 5

Scallop 0.042 NMFS 6

Shrimp 0.047 NMFS 7

 The mercury content for abalone is the average of the mean concentrations measured in 2 types of abalone: green abalone 1

(0.011 g/g) and red abalone (0.021 g/g).

 The mercury content for clam is the average of the mean concentrations measured in 4 types of clam: hard (or quahog) clam 2

(0.034 g/g), Pacific littleneck clam (0 g/g), soft clam (0.027 g/g), and geoduck clam (0.032 g/g).

 The mercury content for crab is the average of the mean concentrations measured in 5 types of crab: blue crab (0.140 g/g), 3

dungeness crab (0.183 g/g), king crab (0.070 g/g), tanner crab (C.opilio) (0.088 g/g), and tanner crab (C.bairdi) (0.102 g/g).

 The mercury content for lobster is the average of the mean concentrations measured in 3 types of lobster: spiny (Atlantic) 4

lobster (0.108 g/g), spiny (Pacific) lobster (0.210 g/g) and northern (American) lobster (0.378 g/g).

 The mercury content for oyster is the average of the mean concentrations measured in 2 types of oyster: eastern oyster (0.022 5

g/g) and Pacific (giant) oyster (0.023 g/g).

 The mercury content for scallop is the average of the mean concentrations measured in 4 types of scallop: sea (smooth) scallop 6

(0.101 g/g), Atlantic Bay scallop (0.038 g/g), calico scallop (0.026 g/g), and pink scallop (0.004 g/g).

 The mercury content for shrimp is the average of the mean concentrations measured in 7 types of shrimp: royal red shrimp 7

(0.074 g/g), white shrimp (0.054 g/g), brown shrimp (0.048 g/g), ocean shrimp (0.053 g/g), pink shrimp (0.031 g/g), pink

northern shrimp (0.024 g/g) and Alaska (sidestripe) shrimp (0.042 g/g).
4-69

Table 4-51

Mercury Concentrations in Marine Molluscan Cephalopods

Cephalopod Mercury Concentration Source of Data

(g/g wet wt.)

Octopus 0.029 NMFS

Squid 0.026 NMFS 1

 The mercury content for squid is the average of the mean concentrations measured in 3 types of squid: Atlantic 1

longfinned squid (0.025 g/g), short-finned squid (0.034 g/g), and Pacific squid (0.018 g/g)

4.3.3 Freshwater Fish Mercury Data Base

Freshwater fish mercury concentrations were reported by Lowe et al. (1985) and by Bahnick et

al. (1994). Details of their analyses are presented separately from those on marine fish. Lowe et al.

(1985) used flameless cold vapor technique absorption spectrophotometry in their analyses. Mean

recovery for mercury averaged 96.6±14.4 (SD) based on 72 analyses of spiked samples. Duplicate

analyses showed a percent difference of 10.6±14.4 (SD) based on 51 duplicates. Values were reported as

the geometric means, minimum, and maximum of elemental mercury concentrations during 1978 to 1979

and during 1980 to 1981. The limit of detection for mercury was 0.01 μg/g wet weight. Standard

reference materials were included and resulted of their analysis are shown in Table 4-52.

Table 4-52

Analyses of Mercury Standard Reference Materials Used by Lowe et al. (1985)

 in Support of Analyses of Freshwater Fish

Mercury Reference Certified Number of Samples Measured

Material Concentration Range Analyzed Concentrations (μg/g:

(μg/g) mean ± 1SD)

bovine liver 0.016±0.002 53 0.021±0.007

oyster 0.057±0.015 14 0.050±0.005

tuna 0.95±0.10 32 0.86±0.07

Values of 0.01 μg mercury/g fish tissue are routinely reported in this data base. Samples were

handled as individual fish. Mercury residues were reported for all species and all locations. The

geometric mean mercury concentrations for all freshwater fish species was 0.11 μg/g in 1978 to 1979 and

0.11 μg/g in 1980-1981. The minimum value for both time periods was 0.01 μg/g and the maximum

value was 1.10 μg/g in 1978-1979 and 0.77 μg/g in 1980-1981. The 85th percentile value in both time

periods was 0.18 μg/g.

Bahnick et al. (1994) used cold mercury vapor flameless atomic absorption and detected mercury

in 92.2% of the fish sampled. Non-detects were reported as a zero value and averaged as zeros. Two

separate detection limits were reported. Prior to 1990, 465 samples were analyzed using a method having

a detection limit of 0.05 μg/g. Modification of the method for the final 195 samples produced a detection 
4-70

limit of 0.0013 μg/g. The estimated standard deviation for replicate samples was 0.047 μg/g in the

concentration range of 0.08 to 1.79 μg/g. Analysis of EPA reference fish having a reported experimental

mean value of 2.52 μg/g (s=0.64) produced a mean value for mercury of 2.87 (s=0.08) in this study. The

mean value for the overall data set for 669 samples was 0.26 μg/g. Mercury was detected in fish

collected from the 374 sites.

Because mercury emissions from the ambient sources considered in the current Report to

Congress have different impacts on global and local deposition, it was considered important to separate

fish species by habitat. Specifically, global mercury cycling was judged to have its greatest impact on

marine species, whereas local deposition was considered more likely to affect estuarine and freshwater

fish and shellfish species. The species were classified as shown in Table 4-14 on a classification system

described by Jacobs et al. (in press).

Central tendency estimates of seafood mercury concentrations were utilized in the report. This

seems appropriate since commercial seafood is widely distributed across the United States (Seafood

Safety, 1991). The source of a particular fish purchase is generally not noted by the consumer (e.g.,

canned tuna). As a result, a randomness and averaging may be achieved. Additionally, only common

names of commercial seafood were utilized; specific species which could be considered to be that type of

fish were included in the central tendency estimate. Again, typical consumers were assumed to generally

not be aware of the species of fish they were consuming, rather just the type.

As noted above, there are other estimates of mercury concentrations in seafood. After the

analysis of mercury exposure from seafood was completed for this Report, two other databases were

obtained: U.S. FDA and Stern et al. (1996). These data are presented in Table 4-51 for comparison with

those data used for this analysis.

4.3.4 Mercury Concentrations In Freshwater Fish

Estimation of average mercury concentrations in freshwater finfish from across the United States

required a compilation of measurements of fish mercury concentrations from randomly selected U.S.

water bodies. A large number of sources of mercury concentrations in fish were not used in this part of

the assessment. Mercury concentrations in fish have been analyzed for a number of years in many local

or regional water bodies in the United States; several of these studies are detailed in this Report. Data

described in this body of literature are a collection of individual studies which characterize mercury

concentrations in fish from specific geographic regions such as individual water bodies or in individual

states. Many of the studies were initiated because of a problem, perceived or otherwise, with mercury

concentrations in the fish or the water body. Thus, the sample presented by a compilation of these data

may be biased toward the high-end of the distribution of mercury concentrations in freshwater fish. 

Additionally, the methods varied from study to study, and there is no way of determining the consistency

of the reported data from study to study.

Two studies, more national in scope, are thought to provide a more complete picture of mercury

concentrations in U.S. freshwater finfish populations: "National Contaminant Biomonitoring Program: 

Concentrations of Seven Elements in Freshwater Fish, 1978-1981" by Lowe et al. (1985) and "A

National Study of Chemical Residues in Fish" conducted by U.S. EPA (1992) and also reported in

Bahnick et al. (1994).

Lowe et al. (1985) reported mercury concentrations in fish from the National Contaminant

Biomonitoring Program. The freshwater fish data were collected between 1978-1981 at 112 stations

located across the United States. Mercury was measured by a flameless cold vapor technique, and the

detection limit was 0.01 μg/g wet weight. Most of the sampled fish were taken from rivers (93 of the 112
4-71

sample sites were rivers); the other 19 sites included larger lakes, canals, and streams. Fish weights and

lengths were consistently recorded. A wide variety of types of fishes were sampled: most commonly

carp, large mouth bass and white sucker. The geometric mean mercury concentration of all sampled fish

was 0.11 μg/g wet weight; the minimum and maximum concentrations reported were 0.01 and 0.77 μg/g

wet weight, respectively. The highest reported mercury concentrations (0.77 μg/g wet weight) occurred

in the northern squawfish of the Columbia River. See Table 4-53 for mean mercury concentrations by

fish species.

Table 4-53

Freshwater Fish Mercury Concentrations from Lowe et al., (1985)

Species Mean Mercury Concentration μg/g 

(fresh weight)

Bass 0.157

Bloater 0.093

Bluegill 0.033

Smallmouth Buffalo 0.096

Carp, Common 0.093

Catfish (channel, largemouth, rock, striped, white) 0.088

Crappie (black, white) 0.114

Fresh-water Drum 0.117

Northern Squawfish 0.33

Northern Pike 0.127

Perch (white and yellow) 0.11

Sauger 0.23

Sucker (bridgelip, carpsucker, klamath, largescale, longnose, 0.114

rivercarpsucker, tahoe)

Trout (brown, lake, rainbow) 0.149

Walleye 0.100

Mean of all measured fish 0.11

"A National Study of Chemical Residues in Fish" was conducted by U.S. EPA (1992) and also

reported by Bahnick et al. (1994). In this study mercury concentrations in fish tissue were analyzed. 

Five bottom feeders (e.g., carp) and five game fish (e.g., bass) were sampled at each of the 314 sampling

sites in the United States. The sites were selected based on proximity to either point or non-point

pollution sources. Thirty-five "remote" sites among the 314 were included to provide background

pollutant concentrations. The study primarily targeted sites that were expected to be impacted by

increased dioxin levels. The point sources proximate to sites of fish collection included the following: 

pulp and paper mills, Superfund sites, publicly owned treatment works and other industrial sites. Data

describing fish age, weight, and sex were not consistently collected. Whole body mercury concentrations

were determined for bottom feeders and mercury concentrations in fillets were analyzed for the game
4-72

fish. Total mercury levels were analyzed using flameless atomic absorption; the reported detection limits

were 0.05 μg/g early in the study and 0.0013 μg/g as analytical technique improved later in the analysis. 

Mercury was detected in fish at 92% of the sample sites. The maximum mercury level detected was 1.8

μg/g, and the mean across all fish and all sites was 0.26 μg/g. The highest measurements occurred in

walleye, large mouth bass and carp. The mercury concentrations in fish around publicly owned treatment

works were highest of all point source data; the median value measured were 0.61 μg/g. Paper mills

were located near many of the sites where mercury-laden fish was detected. Table 4-54 contains the

mean mercury concentrations of the species collected by Bahnick et al. (1994).

Both the studies reported by Lowe et al. (1985) and by Bahnick et al. (1994) appear to be

systematic, national collections of fish pollutant concentration data. Clearly, higher mercury

concentrations in fish have been detected in other analyses, and the values obtained in these studies

should be interpreted as a rough approximation of the mean concentrations in freshwater finfishes. As

indicated in the range of data presented in Tables 4-53 and 4-54, as well as the aforementioned Tables in

Chapter 2, wide variations are expected in data on mercury concentrations in freshwater fish.

The mean mercury concentrations in all fish sampled vary by a factor of two between the studies. 

The mean mercury concentration reported by Lowe et al.(1985) was 0.11 μg/g, whereas the mean

mercury concentration reported by Bahnick et al. (1994) was 0.26 μg/g. This difference can be extended

to the highest reported mean concentrations in fish species. Note that the average mercury

concentrations in bass and walleye reported by Bahnick's data are higher than the northern squawfish,

which is the species with the highest mean concentration of mercury identified by Lowe et al. (1985).

The bases for these differences in methylmercury concentrations are not immediately obvious. 

The trophic positions of the species sampled, the sizes of the fish, or ages of fish sampled could

significantly increase or decrease the reported mean mercury concentration. Older and larger fish, which

occupy higher trophic positions in the aquatic food chain, would, all other factors being equal, be

expected to have higher mercury concentrations. The sources of the fish also influence fish mercury

concentrations. Most of the fish obtained by Lowe et al. (1985) were from rivers. The fate and transport

of mercury in river systems is less well characterized than in small lakes. Most of the data collected by

Bahnick et al. (1994) were collected with a bias toward more contaminated/industrialized sites, although

not sites specifically contaminated with mercury. It could be that there is more mercury available to the

aquatic food chains at the sites reported by Bahnick et al. (1994). Finally, the increase in the more recent

data as reported in Bahnick et al. (1994) could be the result of temporal increases in mercury

concentrations.

There is a degree of uncertainty in the mercury concentrations selected for this assessment. This

uncertainty reflects both the adequacy of the sampling protocol for this application and the known

variability in fish body burden. The variability in these data is as broad as the range of reported

concentrations, which extends from non-detect (below 0.01 μg/g wet weight) up to 9 μg/g wet weight. 

Where possible, when specific freshwater fish species are described in the USDA 3-day consumption

studies, the mean methylmercury concentration for that particular species was derived in two separate

calculations based on the data on methylmercury concentration in the fish reported by Lowe et al. (1985)

and by Bahnick et al. (1994).

Data for mean mercury concentration in freshwater fish from Bahnick et al. (1994) were

combined with the U.S. consumption rates for freshwater fish from the CSFII 89-91, CSFII 1994, CSFII

1995, and NHANES III to estimate methylmercury intakes for the population. The concentrations in the

fish utilized are shown in Table 4-54. The exposure estimates for freshwater fin fish consumption are

found in Table 4-55. Bahnick et al. (1994) freshwater fish concentration data were utilized, along with

data on mercury concentrations in marine fish and shellfish (Tables 4-48, 4-49, 4-50) to calculate total
4-73

exposure, for general U.S. population, to mercury through consumption of fish and shellfish (shown in

Table 4-55).

Some species of freshwater fishes were not sampled by Bahnick et al. (1994), and some

respondents in the USDA CSFII 89-91 survey did not identify the type of freshwater fish consumed. In

these situations, it was assumed that the fish consumed contained 0.26 μg methylmercury/g, which

is the average of all sampled fish Bahnick et al. (1994). It is important to note that the freshwater fish

data are for wild populations not farm-raised fish.

Table 4-54

Mercury Concentrations in Freshwater Fish

U.S. EPA (1992) and Bahnick et al. (1994)

Freshwater Fish Average Mercury Concentration (g/g, wet weight) 

Carp 0.11

Sucker 0.167 1

Catfish, Channel and Flathead 0.16

Bass 0.38 2

Walleye 0.52

Northern Pike 0.31

Crappie 0.22

Brown Trout 0.14

Mean All Fish Sampled 0.26

 The value presented is the mean of the average concentrations found in three types of sucker fish (white, redhorse and spotter). 1

 The value presented is the mean of the average concentrations found in three types of bass (white, largemouth and smallmouth). 2

4.3.5 Calculation of Mercury Concentrations in Fish Dishes

To estimate the mercury intake from fish and fish dishes reported as consumed by respondents in

the CSFII surveys and NHANES III survey, several steps were taken. Using the Recipe File available

from USDA, the fish species for a particular reported food was identified. The average mercury

concentration in fish tissue on a fresh (or wet) weight basis was identified using the NMFS data or the

data reported by Bahnick et al. (1994). The food intake of the U.S. population includes a large number of

components of aquatic origin. A few of these appear not to have been analyzed for mercury

concentrations. Methylmercury concentration data were not available for some infrequently consumed

food items; e.g., turtle, roe or jelly fish. Data on the quantity of fish present in commercially prepared

soups were also not available and were excluded from the analysis. 

Physical changes occur to a food when it is processed and/or cooked. The NMFS and Bahnick et

al. (1994) data bases were used to estimate mercury intake report mercury concentrations on a g

mercury per gram of fresh tissue basis. Earlier research (Bloom, 1992) indicated that over 90% of

mercury present in fish and shellfish is chemically speciated as methylmercury which is bound to protein
4-74

in fish tissue. Morgan et al. (1994) indicated that over 90% of mercury present in fish and shellfish is

chemically speciated as methylmercury. Consequently the quantity of methylmercury present in the fish

tissue in the raw state will remain in the cooked or processed fish. In cooking or processing raw fish,

there is typically a reduction in the percent moisture in the food. Thus, mercury concentration data were

recalculated to reflect the loss of moisture during food processing, as well as retention of methylmercury

in the remaining lowered-moisture content fish tissues. Standard estimates of cooking/processing-related

weight reductions were provided by Dr. Betty Perloff and Dr. Jacob Exler, experts in the USDA recipe

file and in USDA's food composition. Percent moisture lost for baked or broiled fish was 25%. Fried

fish products lose weight through loss of moisture but add weight from fat added during frying for a total

weight loss of minus 12%. The percent moisture in fish that were dried, pickled or smoked was

identified for individual fish species (e.g., herring, cod, trout, etc.) from USDA handbooks of food

composition. Information on the percent moisture in the raw, and in the dried, smoked or pickled fish

was obtained. The methylmercury concentration in the fish was recalculated to reflect the increased

methylmercury concentration of the fish as the percent moisture decreased in the drying, pickling or

smoking process.

The mean mercury concentrations for all fish from Lowe et al. (1985) and Bahnick et al. (1994)

were combined with the freshwater fish consumption data to estimate a range of exposure from total fish

consumption. Given the human fish consumption rates and the differences between the mercury

concentrations in the two data sets, it is important to use data from both studies of mercury exposures to

assess mean concentrations in fish. For purposes of comparison both sets of data were utilized to

illustrate the predicted methylmercury exposure. For this comparison, the average mercury

concentrations for fish in the Lowe and the Bahnick data were analyzed separately by combining the

freshwater fish data with the data in Tables 4-48 through 4-50. The bodyweight data and the freshwater

fish consumption rates were obtained from Table 4-12. Exposure to methylmercury based on an

assumption of 0.11 μg methylmercury/g fish tissue (wet weight) (Lowe et al., 1985). These values are

estimated on a body weight basis. Tables 4-53 and 4-54 were developed using the mean data on mercury

concentrations for all fishes sampled for these two studies.

Human mercury intake from fish was estimated by combining data on mercury concentration in

fish species with the reported quantities and types of fish species reported as consumed by "users" in the

national food consumption surveys. The mercury concentrations in the consumed fish reported by the

national surveys were estimated using data on mercury concentration in fish expressed as micrograms of

mercury per gram fresh-weight of fish tissue.

The CSFII 89-91, CSFII 1994, and CSFII 1995 are three of the USDA's food consumption

surveys. An additional nationally-based food consumption survey is the third National Health and

Nutrition Examination Survey. The food items reported by individuals interviewed in these surveys are

identified by 7-digit food codes. The USDA has developed a recipe file identifying the primary

components that make up the food or dish reported "as Eaten" by a survey respondent. The total weight

of a fish-containing food is typically not 100% fish. The food code specifies a preparation method and

gives additional ingredients used in preparation of the dish. For example, in the Recipe File "Fish,

floured or breaded, fried" contains 84% fish, by weight. Fish dishes contained a wide range of fish; from

approximately 5% for a frozen "shrimp chow mein dinner with egg roll and peppers" to 100% for fish

consumed raw, such as raw shark.
4-75

4.4 Intake of Methylmercury from Fish/fish Dishes

Estimates of methylmercury intake from fish and shellfish have been made based on dietary

survey data from the nationally representative surveys (CSFII 89-91, CSFII 94, CSFII 95, and NHANES

III). Projected month-long estimates of fish/shellfish intake and mercury exposure have been developed

from the NHANES III frequency of fish consumption data using data from the adult participants in

NHANES III and the 24-hour recall data from children and adults in NHANES III. These month-long

projections are considered to be the descriptions of mercury exposure from fish and shellfish that are

most relevant to the health endpoint used as the basis for the RfD; i.e. developmental deficits in children

following maternal exposure to methylmercury. Based on input from the interagency review a

determination has been made that comparison of 24-hour “per user” data is generally inappropriate and

will not be done except when describing subpopulations who eat fish/shellfish almost every day.

4.4.1 Intakes "per User" and "per Capita"

The data from major nationally based surveys of the general population are from CSFII 89-91,

CSFII 1994, CSFII 1995, and NHANES III conducted between 1988 and 1994. CSFII 89-91 obtained 3-

days of dietary history based on 24-hour recall interviews. CSFII 1994 and CSFII 1995 obtained two

days of dietary history also obtained by 24-hour recall interview techniques. These two days of dietary

recalls were not necessarily sequential days. Interviewers in NHANES III obtained the respondents’

estimate of the number of times per day, per week, and per months the respondent consumed

fish/shellfish over the past 12-month period. These data were obtained only for persons 12 years of age

and older. In addition, recall data on fish/shellfish consumption were obtained on the same respondents

as were questionnaire responses of the frequency of food consumption. These recall data cover the 24-

hour period prior to the interview. 

The number and percent of respondents reporting consumption of fish and/or shellfish in these

surveys in shown in Tables 4-55 to 4-57. Intake data can be expressed on a “per capita” basis which

reports the statistics calculated for all survey participants whether or not they reported consuming fish

and/or shellfish during the recall period. By contrast, “per user” statistics are calculated for only those

individuals who reported consuming fish and/or shellfish during the recall periods. The percent of

survey respondents who reported consuming fish and/or shellfish on one 24-hour recall ranged from 11.3

to 12.9% in the nationally-based contemporary food consumption surveys (Table 4-54).

Table 4-55

CSFII 89-91 Number of Respondents - All Age Groups

Ages 14 and Ages 15 through Ages 46 and Total

Younger 45 Older

Total 2893 4968 3545 11,706

Fish Consumers 720 1510 1384 3614
4-76

Table 4-56

CSFII 89-91 Adult Respondents

Gender Ages 15 to 45 Years Ages 46 Years and Older Total for All Age

Groups

Males 2131 1537 3668

Females 2837 2308 5145

Respondents Reporting Fish Consumption

Gender Ages 15 to 45 Years Ages 46 Years and Older Total for All Age

Groups

Males 646 556 1202

Females 864 828 1692

Total 1510 1384 2894

Table 4-57

Contemporary Dietary Surveys — 1990s

General U.S. Population

Survey Total Number of Number Reporting Percent Consuming

Subjects Fish/shellfish Fish/shellfish

Consumption

NHANES III 29,989 3864 12.9

CSFII 94 - Day 1 5,296 598 11.3

CSFII 94 - Day 2 5,293 596 11.3

CSFII 95 - Day 1 5063 601 11.9

CSFII 95 - Day 2 5062 620 12.2

4.4.1.1 “Per Capita” Consumption

 “Per capita” data for CSFII 89-91 are shown in Table 4-58. Data in CSFII 89-91 reflect averages

calculated from three days of 24-hour recall data. Data for the more-recently conducted national surveys

are shown in Table 4-59. These data were obtained by interview and describe fish/shellfish consumption

in the previous 24-hour period. Interviewers describe two 24-hour recalls per respondent.
4-77

Table 4-58

Per Capita Fish/Shellfish Consumption (gms/day) and 

Mercury Exposure (μg/kg body weight/day) From CSFII 89-91

Based on Average of Three 24-Hour Recalls

25th 50th 75th 95th Maximum

Fish/shellfish

Consumption

Zero Zero 16 73 461

Mercury

Exposure

Zero Zero 0.04 0.24 2.76

Table 4-59

Per Capita Fish/Shellfish Consumption 

Based on Individual Days of 24-Hour Recall Data

General U.S. Population Surveys — 1990s

Survey 10th 50th 90th 95th Maximum

CSFII 94 - Day 1 Zero Zero 32 85 457

0.03 0.13 3.76

CSFII 94 - Day 2 Zero Zero 37 85 606

0.03 0.14 4.03

CSFII 95 - Day 1 Zero Zero 43 105 960

0.04 0.13 5.93

CSFII 95 - Day 2 Zero Zero 43 98 1084

0.05 0.17 2.63

NHANES III Zero Zero 56 114 1260

0.08 0.19 6.96

 

4.4.1.2 “Per User” Consumption

If statistics are calculated only on those individuals who reported consuming fish and/or shellfish

during the recall period “per user” values are calculated. Data from the average (i.e., mean) of three days

of 24-hour recalls reported in the CSFII 1989-1991 survey are shown in Table 4-60. Data for the

individual two days recorded in CSFII 1994 and in CSFII 1995, and for the single day’s 24-hour recall in

NHANES III are shown in Table 4-61.
4-78

Table 4-60

Per User Fish/Shellfish Consumption (grams per day) and

 Mercury Exposure (μg/kg bw/day) Based

on Average of Three 24-Hour Recalls — CSFII 89-91

25th 50th 75th 95th Maximum

Fish/shellfish

Consumption

19 32 57 117 461

Mercury

Exposure

0.04 0.09 0.18 0.45 2.76

Table 4-61

“Per User” Intake of Fish and Shellfish (gms/day) and Exposure to Mercury (μg Hg/kg bw/day)

Among Individuals Reporting Consumption, Based on Individual Day Recall Data

Study 10th 50th 90th 95th Maximum

CSFII 94 - Day 1 28 76 186 252 458

n=598 0.02 0.11 0.43 0.65 3.76

CSFII 94 - Day 2 26 74 200 282 606

n=596 0.03 0.11 0.40 0.65 1.03

CSFII 95 - Day 1 28 84 197 261 960

n=601 0.03 0.10 0.42 0.61 5.93

CSFII 95 - Day 2 24 79 216 285 1084

n = 620 0.03 0.12 0.47 0.64 2.63

NHANES III 22 73 242 336 1260

n=3,864 0.01 0.11 0.44 0.63 6.95

4.4.2 Methylmercury Intake from Fish and Shellfish among Women of Child-bearing Age and

Children

Subgroups at increased risk of exposure and/or toxic effects of mercury among the general

population include women of childbearing age and children. Exposures to women of childbearing age

are of particular interest because methylmercury is a developmental toxin (Tables 4-62 and 4-63). 
4-79

Table 4-62

“Per Capita” Fish/Shellfish Consumption (grams/day) and

 Mercury Exposure (μg/kg bw/day) Based

 on Average of Three 24-Hour Dietary Recalls — CSFII 89-91

Females Aged 15-45 25th 50th 75th 95th Maximum

Value

Fish/shellfish Consumption Zero Zero 15 72 461

Mercury Exposure Zero Zero 0.03 0.20 2.76

Table 4-63

“Per User” Fish/Shellfish Consumption (grams/day) and

 Mercury Exposure (μg/kg bw/day) Based

 on Average of Three 24-Hour Dietary Recalls — CSFII 89-91

Females Aged 15-45 25th 50th 75th 95th Maximum

Value

Fish/shellfish Consumption 19 31 56 113 461

Mercury Exposure 0.04 0.08 0.16 0.33 2.76

Children consume more food on a body weight basis than do adults. Consequently children have

higher exposures to a variety of food contaminants (National Academy of Sciences, 1993 ) including

mercury. Overall, approximately 11 to 13 % of adults report fish/shellfish consumption in short-term

consumption estimates based on single 24-hour recall data. For children, the percent who report fish

consumption in similar surveys is about 8 to 9%.

Looking at the quantity of fish consumed and the intake of mercury on a body weight basis (i.e.,

μg Hg/kg body weight/day), the highest environmental dose of mercury from consumption of fish and

shellfish is found among children (Tables 4-64 and 4-65) based on fish intake and mercury exposures

estimated from single-day estimates. Exposure (on a per kg/bw basis) among children ages 10 and

younger are elevated compared with adult values. Children in the age range 11 through 14 years have

mercury doses (μg Hg/kg body weight/day) more comparable to adult values than to those of younger

children. When the NHANES III data are grouped by age category, exposure patterns shown in Table 4-

64 are identified. Higher doses of mercury relative to body weight (μg/kg body weight/day) were also

observed in data from CSFII 94 and CSFII 95 (Table 4-66).
4-80

Table 4-64

Consumption of Fish and Shellfish (grams/day) and Mercury Exposure (μg Hg/kg bw/day) among

 Different Age Categories of Children, Based on Individual Day Data

(Data from the NHANES III, 1988-1994)

Age Group, Years Fish Consumption Mercury Exposure

grams/day μg/kg body weight/day

Less than 2 years

 50th Percentile 29 0.33

 90th Percentile 95 0.98

 95th Percentile 115 1.33

3 through 6 years

 50th Percentile 43 0.28

 90th Percentile 113 0.77

 95th Percentile 151 1.08

7 through 10 years

 50th Percentile 77 0.31

 90th Percentile 178 0.86

 95th Percentile 270 1.08

11 through 14 years

 50th Percentile 63 0.15

 90th Percentile 158 0.42

 95th Percentile 215 0.68

Table 4-65

Fish and Shellfish Consumption (grams/day) and Mercury Exposure (μg/kg body weight/day)

for Children Aged 14 years and Younger — CSFII 89-91

Based on Average of Three 24-Hour Recalls

Gender 25th 50th 75th 95th Maximum

Value

“Per User”

Females 13 24 38 75 154

0.08 0.17 0.34 0.85 1.69

Males 14 23 43 87 139

0.09 0.17 0.29 0.63 1.51

“Per Capita”

Females Zero Zero 7 43 155

Zero Zero Zero 0.39 1.69

Males Zero Zero 5 52 139

Zero Zero 0.01 0.33 1.51
4-81

Table 4-66

“Per User” Fish and/or Shellfish Consumption (grams/day) and

Mercury Exposure (μg Hg/kg bw/day) by Children ages 14 and Younger

Based on Individual Day Data.

Survey 10th 50th 90th 95th Maximum

CSFII 94 - Day 1 15 53 127 176 293

n=148 0.04 0.13 0.77 1.06 1.56

CSFII 94 - Day 2 16 53 156 171 384

n=162 0.07 0.20 0.67 0.91 2.70

CSFII 95 - Day 1 16 57 185 204 305

n=126 0.04 0.23 0.69 0.81 5.93

CSFII 95 - Day 2 13 53 170 243 305

n=148 0.03 0.23 1.00 1.98 2.63

NHANES III 14 51 155 185 915

1988-1994 0.04 0.25 0.83 1.08 6.95

n=1,062

Comparison of the “per capita” and “per user” values indicate that Asian Americans and Pacific

Islanders consume fish and shellfish more frequently than other subpopulations. However, the quantity

of fish and shellfish consumed per person is actually smaller than for the other subpopulations Table 4-

67). If mercury exposure is expressed on a body weight basis (μg Hg/kg body weight), the exposures are

more comparable although Asian Americans/Pacific Islanders have lower exposure to mercury (on a

body weight basis) than do other ethnically diverse subpopulations (Table 4-67).

Table 4-67

Consumption of Fish and Shellfish (grams/day) and Mercury Exposure (μg Hg/kg bw/day) 

Among Ethnically Diverse Groups, Based on Individual Day Recalls

(Source: CSFII 94 and CSFII 95)

Ethnic Group Per Capita Per User 1 2

Fish Mercury Fish Mercury

Consumption Exposure Consumption Exposure

grams/day μg/kg bw/day grams/day μg/kg bw/day

White

 50th Percentile

 90th Percentile

 95th Percentile 80 0.14 243 0.67

Zero Zero 72 0.12

24 0.03 192 0.46

Black

 50th Percentile

 90th Percentile

 95th Percentile 104 0.19 302 0.96

Zero Zero 82 0.14

48 0.05 228 0.54
Table 4-67 (continued)

Consumption of Fish and Shellfish (grams/day) and Mercury Exposure (μg Hg/kg bw/day) 

Among Ethnically Diverse Groups, Based on Individual Day Recalls

Ethnic Group Per Capita Per User 1 2

Fish Mercury Fish Mercury

Consumption Exposure Consumption Exposure

grams/day μg/kg bw/day grams/day μg/kg bw/day

4-82

Asian and Pacific Islander

 50th Percentile

 90th Percentile

 95th Percentile 127 0.30 292 0.56

Zero Zero 62 0.10

80 0.15 189 0.39

Native American and Alaska

Native

 50th Percentile

 90th Percentile Zero Zero of small of small

 95th Percentile

Zero Zero made because made because

56 0.03 numbers of numbers of

Estimate not Exposures not

respondents. respondents.

Other

 50th Percentile

 90th Percentile

 95th Percentile

Zero Zero 83 0.18

Zero Zero 294 0.64

62 0.13 327 0.81

Total number of 24-hour food consumption recall reports: White (16,241); Black (2,580); Asian and 1

Pacific Islander (532); Native American and Alaska Native (166): and Other (1,195).

Number of 24-hour food consumption recall reports: White (1,821); Black (329); Asian and Pacific 2

Islander (155); Native American and Alaska Native (12); and Other (98).

4.4.3 Month-Long Estimates for Consumers

The third NHANES included survey questions on the frequency of consumption of fish and

shellfish that permitted nationally based estimates on how frequently people in the general United States

population consume fish and shellfish over a month-long period. The typical frequency of consumption

combined with a “snap shot” of typical consumption on any single day as shown in the “per user” 24-

hour recall data permit projection of moderate-term patterns of fish/shellfish consumption. It is these

moderate-term patterns that are the most relevant exposure period for the health-based endpoint that

formed the basis of the RfD - i.e., developmental deficits in children following maternal exposure to

methylmercury. Additional description of the particular importance of moderate-term patterns of

mercury exposure from fish/shellfish intakes is found in Section 4.1.1 (page 4-1 through 4-3 of this

Volume).

The frequency of fish and shellfish consumption can be determined from the food frequency data

obtained in NHANES III. By combining the number of times per month a person eats fish and shellfish

with the 24-hour recall data that provide an estimate of portion size and species of fish/shellfish selected,
4-83

a projection can be made of the consumption pattern over a month. The statistical methods describing

how these two frequency distributions were combined is presented in Appendix D. The month-long

projection of fish/shellfish consumption for the general population is shown in Table 4-68a and 4-68b;

the estimate for women of childbearing age (assumed to be 15 through 44 years) is shown in Tables 4-69

and 4-70, and the estimates for children are shown in Tables 4-71 and 4-72. 

Table 4-68a

Month-Long Estimates of Fish and Shellfish Consumption (gms/day)

General Population by Ethnic/Racial Group

National Estimates Based on NHANES III Data

White/NonHispanic Black/NonHispanic Other

Percentile Fish/Shellfish Percentile Fish/Shellfish Percentile Fish/Shellfish

gms/day gms/day gms/day

50th 8 50th 10 50th 9

75th 19 75th 25 75th 27

90th 44 90th 58 90th 70

95th 73 95th 97 95th 123

Percentile at Percentile at Percentile at

which 97.3th which 95.1th which 94.6th

consumption Percentile consumption Percentile consumption percentile

equals equals equals

approximately approximately approximately

100 grams/day. 100 grams/day. 100 grams/day.

Table 4-68b

Month-Long Estimates of Mercury Exposure (μg/kgbw/day)

Population by Ethnic/Racial Group

National Estimates Based on NHANES III Data

White/NonHispanic Black/NonHispanic Other

Percentile Mercury Percentile Mercury Percentile Mercury

Exposure Exposure Exposure

μg/kgbw/day μg/kgbw/day μg/kgbw/day

50th 0.02 50th 0.02 50th 0.02

75th 0.04 75th 0.05 75th 0.06

90th 0.09 90th 0.13 90th 0.17

95th 0.15 95th 0.21 95th 0.31
4-84

Table 4-69

Month-Long Estimates of Exposure to Fish and Shellfish (gms/day)

for Women Ages 15 through 44 Years

Combined Distributions Based on NHANES III Data

Percentile Fish/Shellfish

(gms/day)

50th 9

75th 21

90th 46

95th 77

Percentile at which consumption

exceeds approximately 100 grams/day 97th percentile

based on month-long projections

Table 4-70

Month-Long Estimates of Mercury Exposure (μg/kgbw/day) for Women Ages 15 through 44

All Subpopulations Combined

National Estimates Based on NHANES III Data

Percentiles Mercury Exposure

μg/kgbw/day

Month-Long Estimates

50th 0.01

75th 0.03

90th 0.08

95th 0.13

99th 0.37
4-85

Table 4-71

Month-Long Estimates of Fish/Shellfish Consumption (gms/day) 

among Children Ages 3 through 6 Years.

National Estimates Based on NHANES III Data

Percentile

Per User Month-Long Estimate

Fish/Shellfish Consumption Mercury Exposure

(grams/day) (μg/kgbw/day)

50th 5 0.03

75th 12 0.08

90th 25 0.18

95th 39 0.29

Table 4-72

Month-Long Estimates of Exposure to Fish and Shellfish (gms/day) and 

Mercury (μg/kgbw/day) among Children Ages 3 through 6 Years

National Estimates for Individual Ethnic/Racial Groups

Percentile All Groups White/ Black/ Other

NonHispanic NonHispanic

50th Fish 

(grams/day)

5567

Mercury

(μg/kgbw/day)

0.03 0.03 0.03 0.04

75th Fish 

(grams/day)

12 11 13 17

Mercury

(μg/kgbw/day)

0.08 0.08 0.08 0.11

90th Fish 

(grams/day)

25 24 28 36

Mercury

(μg/kgbw/day)

0.18 0.17 0.19 0.25

95th Fish

(grams/day)

39 37 44 57

Mercury

(μg/kgbw/day)

0.29 0.28 0.33 0.42
4-86

4.4.4 Habitat of Fish Consumed and Mercury Exposure from Fish of Marine, Estuarine and Freshwater

Origin

Fish and shellfish species have been grouped into those inhabiting marine, estuarine, and

freshwater environments. This classification was developed by US EPA’s Office of Water based on

advise from fisheries biologists. Categories of fish and shellfish into those primarily inhabiting marine,

estuarine, and freshwater environments was shown in Table 4-17.

State and local authorities frequently have obtained data on mercury concentrations in fish in

waterways within their boundaries. Thirty-eight states in the United States have issued advisories

regarding mercury exposures that will occur through consumption of these fish. Nine states have statewide advisories that either are based primarily upon or include concern for mercury exposures from these

fish. At a local level, the mercury concentrations in fish vary widely. Exposures to methylmercury will

vary with the proportion of fish obtained from local sources and from interstate commerce. 

Estimates have been made of a national pattern indicating the mixture of marine, estuarine, and

freshwater source of fish and shellfish. Tables 4-73 and 4-74 are based on the fish/shellfish consumption

data from NHANES III combined with the mercury concentration data of the NMFS and data reported by

Bahnick et al. (1988) on mercury concentrations in freshwater fish coming from a nationally based

sample of fish and shellfish. Consumption of fish and shellfish from a particular geographic site may

result in higher or lower exposures to methylmercury.

Among the three habitat types, overall consumption of freshwater fish and shellfish resulted in

the highest mercury exposure per kilogram body weight, followed by marine and estuarine fish and

shellfish. Men reported higher mercury exposures from freshwater fish than did women. The higher

external doses from freshwater fish are, in part, a reflection of larger serving sizes reported when

freshwater species are consumed.

Table 4-73

Exposure of Men Ages 15 to 44 Years to Mercury (μg Hg/kg bw/day)

from Fish and Shellfish of Marine, Estuarine, and Freshwater Origin

Based on Individual Day Recalls

(Food Consumption Data from NHANES III and

 Mercury Concentration Data from NMFS and Bahnick et al. (1988))

Statistic Marine Estuarine Freshwater Combined

Origin Origin Origin Origin, i.e., Total

n=386 n = 198 n=60 Exposure

n = 644

Percentiles

 10th 0 0 0.01 0.01

 

 50th 0.10 0.03 0.33 0.11

 90th 0.35 0.30 1.26 0.44

 95th 0.60 0.44 1.37 0.60

Maximum

Values Reported 4.43 0.71 1.91 4.43
4-87

Table 4-74

Exposure of Women Aged 15-44 Years to

 Mercury (μg Hg/kg bw/day) from 

Fish and Shellfish of Marine, Estuarine, and Freshwater Origin

Based on Individual Day Recalls

(Food Consumption Data from NHANES III and 

Mercury Concentration Data from NMFS and Bahnick et al. (1988))

Statistic Marine Estuarine Freshwater Combined

 Origin Origin Origin Origin, i.e., Total

n = 581 n = 221 n = 82 Exposure

n = 882

Percentiles

 10th 0.01 0.01 0.04 0.01

 50th 0.10 0.03 0.18 0.10

 90th 0.41 0.14 0.50 0.39

 95th 0.56 0.23 0.77 0.53

Maximum

Reported Value 3.59 0.39 0.91 3.59

4.4.5 Methylmercury Consumption

Quantities of methylmercury consumed in fish depend upon both the quantity of fish consumed

and the methylmercury concentration of the fish. Although they are infrequently consumed, swordfish,

barracuda and shark have a much higher methylmercury concentration than other marine finfish,

freshwater finfish or shellfish. By contrast most shellfish contain low concentrations of methylmercury

resulting in far lower methylmercury exposures than would occur if finfish species were chosen.

4.5 Conclusions on Methylmercury Intake from Fish

Methylmercury intakes calculated in this chapter have been developed for a nationally based

population rather than site-specific estimates. Food consumption data was provided from the CSFII

89/91, CSFII 94, CSFII 95, and NHANES III surveys. Methylmercury intakes calculated in this chapter

have been developed for a nationally based rather than site-specific estimates. The CSFII 89-91 from

USDA was designed to represent the U.S. population. The concentrations of methylmercury in marine

fish and shellfish were from a data base that is national in scope. Data on freshwater finfish were taken

from two large studies that sampled fish at a number of sites throughout the United States. The extent of

applicability of these data to site-specific assessments must rest with the professional judgments of the

assessor. Because of the magnitude of anthropogenic, ambient mercury contamination, the estimates of

methylmercury from fish do not provide a value that reflects methylmercury from nonindustrial sources. 

"Background" values imply an exposure against which the increments of anthropogenic activity could be

added. This is not the situation due to release of substantial quantities into the environment. 

 Issues dealing with confidence in data on the methylmercury concentration of fish consumed

include the following:
4-88

 In a number of situations individuals cannot identify with accuracy the species of fish

consumed. The USDA Recipe File Data Base has "default" fish species specified if the

respondent does not identify the fish species consumed. There is no way, however to

estimate the magnitude of uncertainty encountered by misidentification of fish species by

the survey respondents.

 The data base used to estimate methylmercury concentrations in marine fish and shellfish

was provided by the NMFS. This data base has been gathered over approximately the

past 20 years and covers a wide number of species of marine fish and shellfish. The

number of fish samples for each species varies but typically exceeds 20 fish per species. 

 The analytical quality of the data base has been evaluated by comparison of these data

with compliance samples run for the U.S. FDA. The National Academy of Sciences'

Report on Seafood Safety and the U.S. FDA have found this data base from NMFS to be

consistent with 1990s data on methylmercury concentrations in fish.

 The methylmercury concentrations in freshwater fish come from two publications, each

giving data that represent freshwater fish from a number of locations. These data were

gathered between the early 1980s and early 1990s. These surveys by U.S. EPA (1992),

Bahnick et al. (1994), and Lowe et al. (1985) report different mean mercury

concentrations; 0.260 μg/g mercury (wet weight) and 0.114 μg/g mercury (wet weight),

respectively. The extent to which either of these data sets represents nationally based

data on freshwater fish methylmercury concentrations remains uncertain. 

 Month-long estimates of mercury exposure from fish and shellfish consumption are

considered the exposure projection most relevant to the health endpoint of concern; i.e.,

developmental deficits among children following maternal fish consumption.

 Because methylmercury is a developmental toxin, a subpopulation of interest is women

of child-bearing age. In this analysis of methylmercury intake, dietary intakes of women

aged 15 through 44 years were used to approximate the diet of the pregnant woman. 

From data on Vital and Health Statistics, it has been determined that 9.5% of women of

reproductive age can be anticipated to be pregnant within a given year. Generally food

intake increases during pregnancy (Naismith, 1980). Information on dietary patterns of

pregnant women has been assessed (among other see Bowen, 1992; Greeley et al.,

1992). Most of these analyses have focussed on intake of nutrients rather than

contaminants. It is uncertain whether or not pregnancy would modify quantities and

frequency of fish consumed beyond any increase that may result from increased energy

(i.e., caloric) intake that typically accompanies pregnancy. 

 Based on available data on fish consumption in the 3 through 6 year age group, it is

estimated that 19 to 26% of these children consume relatively more fish on a per

kilogram per body-weight basis than do adults, which may result in higher mercury

exposure these children. The range reflects differences in mercury exposures between

subpopulations categorized on the basis of race and ethnicity. Persons of Asian/Pacific

Islander, non-Mexican Hispanics (largely persons of Caribbean ethnicity), Native

Americans, and Alaskan Natives have the highest exposures. 

 Because mercury concentrations in fish/shellfish are highly variable, information on

fish/shellfish consumption (grams/day) are also of interest. It is estimated that 3% of
4-89

women have month-long fish/shellfish intakes of 100 grams per day and higher based on

the NHANES III data. 
5-1

5. POPULATION EXPOSURES - NON-DIETARY SOURCES

5.1 Dental Amalgams

Dental amalgams have been the most commonly used restorative material in dentistry. A typical

amalgam consists of approximately 50% mercury by weight. The mercury in the amalgam is

continuously released over time as elemental mercury vapor (Begerow et al., 1994). Research indicates

that this pathway contributes to the total mercury body burden, with mercury levels in some body fluids

correlating with the amount and surface area of fillings for non-occupationally exposed individuals

(Langworth et al., 1991; Olstad et al., 1987; Snapp et al., 1989). For the average individual an intake of

2-20 μg/day of elemental mercury vapor is estimated from this pathway (Begerow et al., 1994). 

Additionally, during and immediately following removal or installation of dental amalgams

supplementary exposures of 1-5 μg/day for several days can be expected (Geurtsen 1990).

Approximately 80% of the elemental mercury vapor released by dental amalgams is expected to

be re-absorbed by the lungs (Begerow et al., 1994). In contrast, dietary inorganic mercury absorption via

the gastrointestinal tract is known the be about 7%. The contribution to the body burden of inorganic

mercury is thus, greater from dental amalgams than from the diet or any other source. The inorganic

mercury is excreted in urine, and methylmercury is mainly excreted in feces. Since urinary mercury

levels will only result from inorganic mercury intake, which occurs almost exclusively from dietary and

dental pathways for members of the general public, it is a reasonable biomonitor of inorganic mercury

exposure. Urinary mercury concentrations from individuals with dental amalgams generally range from

1-5 μg/day, while for persons without these fillings it is generally less than 1 μg/day (Zander et al.,

1990). It can be inferred that the difference represents mercury that originated in dental amalgams.

Begerow et al., (1994) studied the effects of dental amalgams on inhalation intake of elemental

mercury and the resulting body burden of mercury from this pathway. The mercury levels in urine of 17

people aged 28-55 years were monitored before and at varying times after removal of all dental

amalgam fillings (number of fillings was between 4-24 per person). Before amalgam removal, urinary

mercury concentrations averaged 1.44 μg/g creatinine. In the immediate post-removal phase (up to 6

days), concentrations increased by an average of 30%, peaking at 3 days post-removal. After this phase

mercury concentrations in urine decreased continuously and by twelve months had dropped to an average

of 0.36 μg/g creatinine. This represents a four-fold decrease from pre-removal steady-state urinary

mercury levels.

5.2 Occupational Exposures to Mercury

Industries in which occupational exposure to mercury may occur include chemical and drug

synthesis, hospitals, laboratories, dental practices, instrument manufacture, and battery manufacture

(National Institute for Occupational Safety and Health, (NIOSH) 1977). Jobs and processes involving

mercury exposure include manufacture of measuring instruments (barometers, thermometers, etc.),

mercury arc lamps, mercury switches, fluorescent lamps, mercury broilers, mirrors, electric rectifiers,

electrolysis cathodes, pulp and paper, zinc carbon and mercury cell batteries, dental amalgams,

antifouling paints, explosives, photographs, disinfectants, and fur processing. Occupational mercury

exposure can also result from the synthesis and use of metallic mercury, mercury salts, mercury catalysts

(in making urethane and epoxy resins), mercury fulminate, Millon's reagent, chlorine and caustic soda,

pharmaceuticals, and antimicrobial agents (Occupational Safety and Health Administration (OSHA)

1989).
5-2

OSHA (1975) estimated that approximately 150,000 US workers are exposed to mercury in at

least 56 occupations (OSHA 1975). More recently, Campbell et al., (1992) reported that about 70,000

workers are annually exposed to mercury. Inorganic mercury accounts for nearly all occupational

exposures, with airborne elemental mercury vapor the main pathway of concern in most industries, in

particular those with the greatest number of mercury exposures. Occupational exposure to

methylmercury appears to be insignificant. Table 3-10 summarizes workplace standards for airborne

mercury (vapor + particulate).

A number of studies have been reported that monitored workers' exposure to mercury (GonzalezFernandez et al., 1984; Ehrenberg et al., 1991; Cardenas et al., 1993; Kishi et al., 1993, 1994; Yang et al.,

1994). Some studies have reported employees working in areas which contain extremely high air

mercury concentrations: 0.2 to over 1.0 mg/m of mercury. Such workplaces include lamp sock 3

manufacturers in Taiwan (Yang et al., 1994), mercury mines in Japan (Kishi et al., 1993,1994), a small

thermometer and scientific glass manufacturer in the US (Ehrenberg et al., 1991), and a factory

producing mercury glass bubble relays (Gonzalez-Fernandez et al., 1984). High mercury levels have

been reported in blood and urine samples collected from these employees (reportedly over 100 μg/L in

blood and over 200 - 300 μg/L or 100 - 150 μg/g creatinine for urine). At exposures near or over 1.0

mg/m3, workers show clear signs of toxic mercury exposure (fatigue, memory impairment, irritability,

tremors, and mental deterioration). The chronic problems include neurobehavioral deficits that persist

long after blood and urine mercury levels have returned to normal; many workers required hospitalization

and/or drug treatments. With the exception of mercury mines, workplaces producing these mercury

levels are typically small and specialized, often employing only a few workers who were exposed to high

mercury concentrations.

Many other studies have monitored employees’ work areas and reported measured mercury air

concentrations of 0.02 - 0.2 mg/m ; these levels are generally in excess of present occupational standards 3

(see Table 5-1). These mercury levels were most often reported at chlor-alkali plants (Ellingsen et al.,

1993; Dangwal 1993; Barregard et al., 1992; Barregard et al., 1991; Cardenas et al., 1993). Employees at

these facilities had elevated bodily mercury levels of approximately 10-100 μg/L for urine and about 30

μg/L in blood. At these lower levels, chronic problems persisting after retirement included visual

response and peripheral sensory nerve effects.

Exposures to mercury levels under 0.02 mg/m typically result in blood and urine levels 3

statistically higher than the general population, but health effects are usually not observed.

Table 5-1

Occupational Standards for Airborne Mercury Exposure

Concentration Standard Type Mercury Species Reference

Standard (mg/m )3

0.10 STEL inorganic CFR (1989)

0.01 TWA organic CFR (1989)

0.03 STEL alkyl CFR (1989)

0.05 TWA all besides alkyl ACGIH (1986)
Table 5-1 (continued)

Occupational Standards for Airborne Mercury Exposure

Concentration Standard Type Mercury Species Reference

Standard (mg/m )3

5-3

0.01 TWA alkyl ACGIH (1986)

0.03 STEL alkyl ACGIH (1986)

0.10 TWA aryl and inorganic ACGIH (1986)

0.05 TWA all besides alkyl NIOSH (1977)

Abbreviations:

ACGIH - American Conference of Governmental Industrial Hygienists

CFR - Code of Federal Regulations

STEL - Short term exposure limit (15 minutes)

TWA - Time weighted average (8 hour workday)

5.3 Miscellaneous Sources of Mercury Exposure

Inorganic mercury is used in some ritualistic practices (Wendroff, 1995). The extent of this use

in the United States is undocumented, although it is considered to be more commonly encountered in

Hispanic and Latino communities. Inorganic mercury is distributed around the household in a variety of

ways and may result in dermal contact or it potentially be inhaled.

5.4 Cases of Mercury Poisoning

Numerous examples may be found in the literature of unintentional mercury poisoning. The

following examples were taken from Morbidity and Mortality Weekly Report, a publication of the U.S.

Public Health Service, Centers for Disease Control. These cases studies indicate that mercury has

diverse — although, in many cases, illegal — applications. The studies illustrate the wide range of

potential health effects from mercury exposure including death.

Unsafe Levels of Mercury Found in Beauty Cream

Between September 1995 and May 1996, the Texas Department of Health, the New Mexico

Department of Health, and the San Diego County Health Department investigated three cases of mercury

poisoning associated with the use of a mercury-containing beauty cream produced in Mexico. The

cream, marketed as “Crema de Belleza-Manning” for skin cleansing and prevention of acne, has been

produced since 1971. The product listed “calomel” (mercurous chloride) as an ingredient and contained

6% to 10% mercury by weight. Because mercury compounds are readily absorbed through the skin, FDA

regulations restrict the use of these compounds as cosmetic ingredients. Specifically, mercury

compounds can be used only as preservatives in eye-area cosmetics at concentrations not exceeding 65

ppm of mercury; no effective and safe nonmercurial substitute preservative is available for use in such

cosmetics.
5-4

An ongoing investigation of the cream located it in shops and flea markets in the United States

near the U.S.-Mexico border, and identified a U.S. organization in Los Angeles as the distributor. Media

announcements, warning of the mercury containing cream, were then made in Arizona, California, New

Mexico, and Texas. In response to these announcements, 238 people contacted their local health

departments to report using the cream. Urinalysis was conducted for 119 people, and of these, 104 had

elevated mercury levels. Elevated urine mercury levels were also detected in people who did not use the

cream but who were close household contacts of cream users.

Indoor Latex Paint Found to Contain Unsafe Mercury Levels

In August 1989, a previously healthy 4-year-old boy in Michigan was diagnosed with acrodynia,

a rare manifestation of childhood mercury poisoning. A urine mercury level of 65 μg/L was measured in

a urine sample collected over 24 hours. Examinations of his parents and two siblings also revealed

elevated urine mercury levels. The Michigan Department of Public Health (MDPH) determined that

inhalation of mercury-containing vapors from phenylmercuric acetate contained in latex paint was the

probable route of mercury exposure for the family; 17 gallons of the paint had been applied to the inside

of the family’s home during the first week of July. During that month, the air conditioning was turned on

and the windows were closed, so that mercury vapors from the paint were not properly vented. In

addition, samples of the paint contained 930-955 mg/L mercury, while the EPA limit for mercury as a

preservative in interior paint is 300 mg/L. 

In October, the Michigan Department of Agriculture prohibited further sales of the

inappropriately formulated paint, and the MDPH advised people not to use the paint, to thoroughly

ventilate freshly painted areas, and to consult a physician if unexplained health problems occurred. In

November, the MDPH and Centers for Disease Control began an ongoing investigation in selected

communities in southeastern Michigan to assess mercury levels in the air of homes in which this paint

had been applied and in urine samples from the occupants.

Jar of Mercury Spilled in Ohio Apartment

In November 1989, a 15-year-old male from Columbus, Ohio was diagnosed with acrodynia, a

form of mercury poisoning. A 24-hour urine collection detected a mercury level of 840 μg/L in the

patient’s urine. The patient’s sister and both his parents were also found to have elevated mercury urine

levels. Therefore, on November 29, the Columbus Health Department investigated the apartment where

the family had lived since August 26, 1989. Neighbors reported that the previous tenant had spilled a

large jar of elemental mercury within the apartment. Mercury vapor concentrations in seven rooms

ranged from 50-400 μg/m . The Agency for Toxic Substances and Disease Registry’s acceptable 3

residential indoor air mercury concentration is less than or equal to 0.5 μg/m .3

Mercury Vapors Released in House During Smelting Operation

On August 7, 1989, four adults from Michigan, ranging from age 40 to 88, were hospitalized for

acute mercury poisoning. All four patients lived in the same house, where one of the patients had been

smelting dental amalgam in a casting furnace in the basement of the house in an attempt to recover silver. 

Mercury fumes were released during the smelting operation, entered air ducts in the basement, and were

circulated throughout the house. All four patients died of mercury poisoning within 11-24 days after

exposure. 
5-5

Mercury Spilled in Michigan House

During the summer of 1989, a boy in Michigan spilled about 20 cm of liquid mercury in his 3

bedroom. In September of that year, both of his sisters were diagnosed with mercury poisoning, after

exhibiting clinical symptoms associated with such poisoning. The boy, although asymptomatic, was also

tested and was found to have elevated mercury levels.

Florida School Children Find Elemental Mercury in Abandoned Van

During August 1994, five children residing in a neighborhood in Palm Beach County, Florida

found 5 pints of elemental mercury in an abandoned van. During the ensuing 25 days, the children

shared and played with the mercury outdoors, inside homes, and at local schools. On August 25, 1994, a

parent notified local police and fire authorities that her children had brought mercury into the home. 

That same day, 50 homes were immediately vacated and an assessment of environmental and health

impacts was initiated by the State of Florida Department of Environmental Protection, the Health and

Rehabilitation Services of the Palm Beach County Public Health Unit, and the U.S. Environmental

Protection Agency.

A total of 58 residential structures were monitored for indoor mercury vapor concentrations;

unsafe indoor air levels of mercury (>15 μg/m ) were detected in 17. Several classrooms at the local 3

high schools were determined to be contaminated. In addition, 477 people were identified by the survey

as possibly exposed to mercury vapors and were evaluated at the emergency department of the local

hospital or the health department clinic for mercury poisoning. Of these people, 54 were found to have

elevated mercury levels.

Unsafe Mercury Levels Found in North Carolina Home

In July 1988, the Environmental Epidemiology Section of the North Carolina Department of

Environment, Health, and Natural Resources (DEHNR), investigated chronic mercury poisoning

diagnosed in a 3-year-old boy from North Carolina. Results of 24-hour urine specimens for mercury

collected from both the patient and his parents revealed elevated mercury levels. Although the family

reported no known mercury exposures, in April 1988, they had moved into a house whose previous

owner had collected elemental mercury. Several containers of mercury had reportedly been spilled in the

house during the previous owner’s occupancy. As a result of the determination that the house was the

probable source of exposure, the family temporarily relocated.

The DEHNR conducted an extensive investigation of the house. Elevated mercury levels were

detected in five rooms and two bathrooms. The vacuum cleaner filter bag was tested for mercury as well,

and found to have extremely high mercury levels. The carpets were also heavily contaminated with

mercury. When the contaminated carpets were vacuumed, mercury particles and vapor were probably

dispersed throughout the house. Vaporization probably increased with the spread of the mercury and the

onset of warmer weather.
6-1

6. COMPARISON OF ESTIMATED EXPOSURE WITH BIOMONITORING

6.1 Biomarkers of Exposure

Biologic markers, as described by the U.S. National Research Council (NRC, 1989) are

indicators signaling events in biological systems or samples. These are classified as biologic markers of

exposure, effect and susceptibility. A biological marker of exposure is defined by the National Research

Council (1989) an “exogenous substance or its metabolite(s) or the product of an interaction between a

zenobiotic agent and some target molecule or cell that is measured in a compartment within an organism”

(NRC, 1989, pg. 2). Concentrations of mercury and of methylmercury in biological materials are used as

biomarkers of exposure to mercury in the environment. 

Mercury accumulates in body organs. Although concentrations of mercury in organs adversely

affected by mercury (e.g., neural tissue, the kidney) may be more predictive of levels of exposure at the

site of organ system damage, for purposes of monitoring exposures mercury concentrations in tissues less

proximal are relied upon. Typically mercury concentrations in blood, hair, and urine are used to assess

exposure to organic and inorganic mercury. 

6.2 Biomarkers of Exposure Predictive of Intake of Methylmercury

Humans are exposed to both organic (e.g., methylmercury) and inorganic mercury. The

proportion of organic to inorganic mercury exposure depends on exposure conditions. Organic

methylmercury almost exclusively occurs through consumption of fish and shellfish. Occupational

exposure to organic mercury compounds is far less common than are occupational exposures to inorganic

mercury compounds. Within occupations where exposures to organic mercury compounds occur, great

caution must be taken to assure that people handling such compounds do not come into contact with

organic mercury because of its extreme toxicity. Inorganic mercury exposures reflect sources including

dental amalgams and occupational sources with minor contributions from certain hobbies and ritualistic

uses of mercury. Contribution from “minor” sources refers to their overall use in the general population. 

Such “minor” sources can produce highly elevated exposures and poisoning of individuals who use these

products.

Blood and hair concentrations of mercury can be used to back calculate estimates of

methylmercury ingested. Because methylmercury in the diet comes almost exclusively from

consumption of fish and shellfish, methylmercury concentrations in blood and hair are very strong

predictors of methylmercury ingestion from fish and shellfish. 

The fraction of methylmercury absorbed via the gastrointestinal tract from fish and shellfish is

extremely high; typically more than 95% (REFS). After absorption methylmercury is transported in the

blood. There is a strong affinity for the erythrocyte (Aberg et al., 1969; Miettinen, 1971). Standard

reference values for blood mercury concentrations indicate packed cells are 10-times more concentrated

in mercury than is whole blood (Cornelis et al., 1996). Methylmercury is distributed throughout the body

including distribution into the central nervous system. Postabsorption and distribution to tissues,

methylmercury is slowly demethylated and converted to inorganic mercury (Burbacker and Mottet,

1996). 

A portion of the inorganic mercury arising from demethylation of methylmercury is present in

blood (Smith and Farris, 1996). Additional sources of inorganic mercury include dental amalgams in

persons with silver-mercury dental restorations, small amounts of inorganic mercury absorbed from diet,

and for some individuals occupational and/or miscellaneous sources. Although inorganic mercury is
6-2

present in blood, under most conditions the predominant chemical species of mercury in blood is

methylmercury arising from consumption of fish and shellfish.

6.3 Sample Handling and Analysis of Blood Samples for Mercury

The predominant method of chemical analysis of total mercury in blood is based on cold vapor

absorbance techniques (IUPAC, 1996; Nixon et al., 1996). Atomic fluorescence is also a very sensitive

and reliable technique for mercury measurement in blood, serum and urine (IUPAC, 1996). The various

mercury-species are converted by reducing agents to elemental mercury and released as a vapor which is

either directly pumped through the cell of the atomic absorption spectrophotometer or analyzed after

amalgamation and enrichment on gold (IUPAC, 1996).

Sample pretreatment to destroy the organic matter in samples and avoid losses of mercury

through volatilization are key considerations in the analytic procedure for determination of inorganic and

total mercury. Digestion procedures have been developed that permit conversion of organic mercury

compounds and arylmercury to inorganic mercury, but do not convert significant quantities of

alkylmercury (i.e., methylmercury) to inorganic mercury (Nixon et al., 1996). 

The expected concentration cited by IUPAC (1996) for mercury in serum of healthy individuals

is 0.5 μg/L. In packed cells the level is about 5 μg/kg. Standard reference materials for mercury in

whole blood are available in the range of 4 to 14 μg/L. Using the IUPAC (1996) expected concentration,

whole blood mercury would be less than 2.5 μg/L. 

 

Sample handling prior to analysis is always critical in obtaining optimal analytical results. The

Commission of Toxicology of the IUPAC has described an organized system for collection and handling

of human blood and urine for the analysis of trace elements including mercury (1996). 

6.4 Association of Blood Mercury with Fish Consumption

6.4.1 Half-Lives of Methylmercury in Blood

The half-life of mercury in blood varies with prior intake of methylmercury and individual

characteristics. Previous investigations with methylmercury ingestion under controlled conditions

provide estimates of half-lives among adults. Data on half-lives among children do not appear to exist. 

Two studies among adults are particularly informative. Sherlock et al. (1984) evaluated half-lives for

methylmercury ingested via halibut by 14 adult male and 7 adult female volunteers over a period of 96

days. Overall, the half-life for mercury in blood was calculated by Sherlock et al. as 50±1 days

(mean±standard error; range 42 to 70 days) for adult subjects. Another approach is that used by Birke et

al. (1972) based on repeated blood sampling of subjects after termination of chronic ingestion at higher

levels of methylmercury consumption. Data from the study of Birke et al. (1972) showed two subjects

with half-lives of 99 and 120 days in blood cells and 47 and 130 days in plasma. Additional data on halflives of methylmercury ingested via fish were reported by Miettinen et al. (1971) following single

ingestion of radiolabelled fish. Miettinen et al. (1971) using Hg-labelled methylmercury incorporated 203

into burbot (Lota vulgaris) fed as a single dose to 15 adult volunteers determined a mean biological halftime of 50±7 days (mean±standard deviation of the mean) in red blood cells for five male subjects and

one female subject. 

Overall the metabolic data support the use of blood mercury as an indicator of recent

methylmercury intake. The range surround mean half-lives reflect the combined influence of individual

person-to-person characteristics, previous intake of methylmercury, and level of methylmercury
6-3

ingestion. During the 1990s, a number of additional reports on total blood mercury and on organic

methylmercury in blood have confirmed that higher intakes of fish/shellfish are associated with

increasing concentrations of total mercury, and in particular a higher fraction of methylmercury

(Mahaffey and Mergler, in press). 

6.4.2 Fraction of Total Blood Mercury that Is Organic or Methylmercury

Among subjects with blood total mercury levels less than 5 μg/L, Oskarsson et al. (1996)

reporting on 30 women living in northern Sweden found that 26% of blood mercury was organic

mercury. By contrast women who consumed large amounts of seafood had 80% organic mercury at

delivery in maternal blood from Inuit women in Greenland (Hansen et al., 1990), and approximately 83%

organic mercury in Faroese women (Grandjean et al., 1992). High blood levels of total mercury were

reported by Akagi et al. (1995) among residents of the Amazon. In fishing villages where blood total

mercury levels were approximately 100 μg/L, 98% of total mercury was organic (methyl) mercury. Aks

et al. (1995) in another study of adult Amazon villagers, found approximately 90% of total mercury to be

organic mercury when blood levels were approximately 25 to 30 μg/L. Mahaffey and Mergler (in press)

found that there was a linear increase (when the data were log transformed) in the fraction of total blood

mercury that was present as organic mercury over a blood total mercury up to 70 μg/L.

6.4.3 Methylmercury Consumption from Fish and Blood Mercury Values

Increasing frequency of fish consumption is predictive of higher total blood mercury

concentrations; particularly increased concentrations of organic mercury (i.e., methylmercury) in blood

(Brune et al., 1991; Hansen et al., 1990; Svensson et al., 1992; Weihe et al., 1996). Within the nonoccupationally mercury exposed population, frequency, quantity and species of fish consumed produce

differences in methylmercury ingestion and in blood mercury concentrations. Brune et al. (1991)

reviewed the literature on total mercury concentrations in whole blood and associated these with the

number of fish meals/week (Table 6-1). Although there is a clear increase in mean values with increasing

frequency of fish consumption, the ranges of values (e.g., 10th and 90th percentiles) overlap with the

next highest category of consumption. These ranges illustrate some of the difficulty of characterizing

methylmercury intake simply by the reports describing number of fish meals consumed per week.

Table 6-1

Literature Derived Values for Total Mercury Concentrations in Whole Blood

(from Brune et al., 1990)

Level of Fish Mean Value 10th and 90th 25th and 75th Number of

Consumption Percentiles Percentiles Observations

Category I, No Fish

Consumption 20 0, 4.3 0.8, 3.2 223

Category II, < 2 Fish

Meals/Week 4.8 2.4, 7.2 3.5, 6.1 339

Category III,  2-4 Fish

Meals/ Week 8.4 2.6, 14.2 5.4, 11.4 658
Table 6-1 (continued)

Literature Derived Values for Total Mercury Concentrations in Whole Blood

(from Brune et al., 1990)

Level of Fish Mean Value 10th and 90th 25th and 75th Number of

Consumption Percentiles Percentiles Observations

6-4

Category IV, > 4 Fish

Meals/Week 44.4 6.1, 82.7 24.4, 64.4 613

Category V, Unknown

Fish Consumption 5.8 1.2, 10.4 3.4, 8.2 3182

The analysis by Brune et al. (1990) demonstrated the limitations of determining a

methylmercury intake based on the number of fish meals/week. Nonetheless there is an association

between frequency of fish meals and blood mercury levels. If the exposure analysis is further refined to

include a description of the size of the serving of fish consumed, and information on the mercury content

of the fish, the association with blood mercury concentration is strengthened.

6.4.4 North American Reports on Blood Mercury Concentrations

6.4.2.1 United States

Normative data to predict blood mercury concentrations for the United States population are not

available. With a very few exceptions all of the data that have been identified are for adult subjects. The

largest single study appears to be that of former United States Air Force pilots. Kingman et al. (Kingman

et al., in press; Nixon et al., 1996) analyzed urine and blood levels among 1127 Vietnam-era United

States Air Force pilots (all men, average age 53 years at the time of blood collection ) for whom

extensive dental records were available. Blood values were determined for total mercury, inorganic

mercury and organic/methylmercury. Mean total blood mercury concentration was 3.1 μg/L with a range

of “zero” (i.e., detection limit of 0.2) to 44 μg/L. Overall, 75% of total blood mercury was present as

organic/methylmercury. Less than 1% of the variability in total blood mercury was attributable to

variation in the number and size of silver-mercury amalgam dental restorations. Dietary data on the

former pilots were very limited, so typical patterns of fish consumption are not reported.

Additional North American studies have been reported by various individual states in the United

States. These are described below and summarized in Table 6-2.

Arkansas

The Arkansas Department of Health reported on total blood mercury for 236 individuals with a

mean of 10.5 μg/L (range “zero” to 75 μg/L) (Burge and Evans, 1996). Of these, 139 participants had

total blood mercury above 5 μg/L and 36 participants had blood mercury concentrations more than 20

μg/L. To have been included in the survey, subjects had to confirm that their fish consumption rate was

a minimum of two meals per month with eight ounces of fish per meal.
6-5

Table 6-2

Blood Mercury Concentrations Values Reported for the United States

Study Community Measure of Maximum Additional

Central Information on

Tendency Study

Burge and Evans 236 participants Mean: 10.5 μg/L; All subjects: 75 139 participants

(1996) from Arkansas among men: 12.8 μg/L exceeded 5 μg/L.

μg/L; among

women, 6.9 μg/L. Males: 75 μg/L 30 participants in

Median: All Females: 27 μg/L. 75 μg/L or 15%

subjects 7.1 μg/L >20 μg/L.

Men: 9.0 μg/L

Women: 4.8 μg/L 5% of men had >30

the range of 20 to

μg/L. No women

had values > 30

μg/L.

Centers for Disease Micousukee Indian Mean: 2.5 μg/L 13.8 μg/L

Control (1993) Tribe of South Median: 1.6 μg/L

Florida. 50 blood

samples from

subjects with mean

age=34 years

(Range 8 to 86

years).

Gerstenberger et al. 68 Ojibwa Tribal 57 participants < 16 53 μg/L 11 individuals had

(1997) members from the μg/L. Remaining blood mercury in

Great Lakes Region 11 subjects the range 20 to 53

averaged 37 μg/L. μg/L.

Harnly et al. (1997) Native Americans Mean for 44 Tribal Among Tribal 20% of all

living near Clear members: 18.5 μg/L members: Total Hg participants (9

Lake, California. (2.9 μg/L inorganic was 43.5 μg/L (4.7 persons including

Group studied Hg + 15.6 μg/L for μg/L inorganic + four women of

include 44 Tribal organic Hg). 38.8 μg/L organic). childbearing age)

members, and 4 had blood mercury

nontribal members. Mean for 4 For nontribal concentrations  20

nontribal members: members: Total Hg μg/L.

11.5 μg/L (2.7 μg/L 15.6 μg/L (3.4 μg/L

inorganic + 8.8 inorganic + 12.2

μg/L organic Hg). μg/L organic).
Table 6-2 (continued)

Blood Mercury Concentrations Values Reported for the United States

Study Community Measure of Maximum Additional

Central Information on

Tendency Study

6-6

Humphrey and Lake Michigan Algonac, Lake St. Algonac, Lake St. Mercury

Budd (1996) residents studied in Clair: Clair contamination less

1971. Fisheaters (n=42) intense in South

mean 36.4 Fisheaters: 3.0-95.6 Haven compared

compared with 65 μg/L with Algonac.

low fish consumers

having mean of 5.7 Comparison:

μg/L. 1.1 - 20.6 μg/L

South Haven, Lake

Michigan with

lower Hg South Haven, Lake

contamination. Michigan

Fisheaters (n=54)

had mean 11.8 μg/L Fisheaters: 3.7-44.6

and the comparison μg/L

group of low fish

consumers mean Comparison:

(n=42) of 5.2 μg/L 1.6-11.5 μg/L

Knobeloch et al. Family consuming Initial blood values Six months after

(1995) commercially for wife (37 μg/L) family stopped

obtained seafood. and husband (58 consuming seabass,

μg/L) following blood mercury

regular concentrations for

consumption of the wife (3 μg/L)

imported seabass and husband (5

having mercury μg/L) had returned

concentrations to “background”

estimated at 0.5 to concentrations.

0.7 ppm Hg.

Schantz et al. Adult men and 104 fisheaters: Maximum for Questionnaire on

(1996) women aged 50 to mean=2.3 μg Hg/L fisheaters: 20.5 μg fish-eating patterns

90 years. Michigan Hg/L included sportresidents. 84 nonfisheaters: caught Great Lakes

mean=1.1 μg Hg/L. Maximum for fish and purchased

nonfisheaters: 5.0 fish, as well as

μg Hg/L. questions on

patterns of wild

game consumption.
6-7

Great Lakes Region

Schantz et al. (1996) reported on blood mercury levels in an older-adult population (ages 50 to 90

years). Blood mercury levels for non-fisheaters averaged 1.1 μg/L and for fish-eaters the average was 2.3

μg/L.

Gerstenberger et al. (1997) determined blood mercury levels for 57 Ojibwas Tribal Members

from the Great Lakes Region. Among the 68 participants 57 had blood mercury concentrations < 16

μg/L. The remaining 11 subjects had average blood mercury concentrations of 37 μg/L with a maximum

value of 53 μg/L.

Wisconsin

Blood mercury levels among 175 Wisconsin Chippewas Indians who consumed fish from

northern Wisconsin lakes that have fish with high mercury concentrations (>1 ppm) were determined

(Peterson et al., 1994). Values ranged from nondetectable (i.e., < 1 μg/L) to a high of 33 μg/L. Twenty

percent (64 individuals) had blood mercury levels > 5 μg/L. Recent consumption of the fish, walleye,

was associated with elevated blood mercury concentrations.

Knobeloch et al. (1995) investigated mercury exposure in a husband and wife and their two-yearold son living in Wisconsin. The individuals had total blood mercury ranging from 37 to 58 μg/L. The

family’s diet included three to four fish meals per week. The fish was purchased commercially from a

local market. Seabass were found to contain mercury at 0.5 to 0.7 ppm. Six months after the family

stopped consuming the seabass, blood mercury levels in this man and women declined dramatically to 5

and 3 μg/L, respectively.

California

Harnly et al. (1997) determined blood mercury concentrations for 44 members of Native

American tribes and 4 nontribal members living near Clear Lake, California. The mean for the 44 tribal

members was 18.5 μg/L total mercury (15.6 μg/L organic and 2.9 μg/L inorganic). The maximum value

was 43.5 μg/L (38.8 μg/L organic and 4.7 μg/L inorganic). Twenty percent of all participants (including

four women of childbearing age) had blood mercury concentrations  20 μg/L. Among nontribal

members total mercury concentrations were lower with a total mercury value of 11.5 (8.8 organic + 2.7

inorganic) μg/L. The highest value for nontribal members was 15.6 (12.2 organic and 3.4 inorganic)

μg/L. 

Florida

The U.S. Centers for Disease Control (CDC, 1993) conducted a community survey of the tribal

representatives of the Miccousukee Indian Tribe living in South Florida. Blood mercury levels were

determined for 100 participants who were adult tribal members. Fish consumption among this group was

low with a maximum of approximately 170 grams/day and 3.5 grams calculated as a daily average. Total

blood mercury ranged from 0.2 to 13.8 μg/L with median and mean values of 1.6 and 2.5 μg/L,

respectively. There was a correlation between blood mercury levels and consumption of locally caught

fish.

Maine
6-8

An additional source of data on blood mercury levels is the heavy metal profiles (for lead,

arsenic, cadmium, and mercury) conducted as part of occupational surveillance. Typically the persons

who receive this type of screening are expected to have exposures to at least one of these metals.

Occupational surveillance may be based on state requirements or Federal statutes. For example, the State

of Maine has an occupational disease reporting requirement on individuals whose blood mercury

concentrations for total mercury are 5 μg/L and higher and whose urinary total mercury is greater than or

equal to 20 μg/L. The State of Maine evaluated data on occupational screening for heavy metal exposure

and identified a group of adults having total blood mercury concentrations more than 5 ppb. Several

cases of elevated blood mercury concentrations were identified. One case has been reported by Dr.

Allison Hawkes (personal communication, 1997). The individual was identified with a blood mercury of

21.4 μg/L. The subject had no known occupational exposure to mercury, but self-reported eating 3 or 4

fish meals per week. The individual was asked to abstain from consuming fish for 4 or 5 weeks and then

return for follow-up blood testing. On retesting blood total mercury was only 5 μg/L. 

6.4.2.1 Canadian

As in the United States, normative data for the general population of Canada have not been

identified in compiling information for this Report to Congress. By contrast to the United States,

information on mercury exposures in the northern regions of the country has been obtained. The

Department of Indian and Northern Affairs of the Government of Canada reported on Arctic

contaminants in the Canadian Arctic Contaminants Assessment Report in 1997. Methylmercury levels in

blood since 1970. For all Aboriginal Peoples the mean blood mercury concentration was 14.13 

(standard deviation 22.63) and a range of 1 to 660 μg/L (Wheatley and Paradis, 1995) based on 38,571

data points from 514 native communities across Canada. 

Overall, blood mercury concentrations are considered closely tied to consumption of fish and

marine mammals. The highest levels are found among Aboriginal residents with particular high levels

found in northern Quebec and among the northern and eastern Inuit communities. No downward trend

was evident in Inuit blood mercury concentrations between 1975 and 1987, but more recent data (1992 to

1995) indicated lower levels of mercury in some groups (Jensen et al., 1997, page 336). 

Quebec

 Within the values reported in the Canadian Arctic Contaminants Assessment Report (Jensen et

al., 1997) particularly high mean concentrations were observed among the Inuit (Nunavik) of Quebec. 

Mean total mercury concentration of 47 μg/L (SD 33, range 3 to 267 μg/L) was identified among 1114

Inuit of Quebec. The Northern (Cree) had mean values of 34 (SD 41, range 2 to 649 μg/L) among 4,670

blood values and 42.9 (SD 52, range 2 to 649) based on 1,129 blood values. 

North West Territory

The Nunavut (Inuit) of the North West Territory also have elevated blood mercury levels with

mean values during the 1970s through late 1980s averaging between 17 and 40 μg/L (upper extent of this

range going to 226 μg/L). The Western (Dene) population had lower blood mercury levels with means

between 11 and 17 μg/L (upper extent of the Dene range to 138 μg/L). 

6.5 Hair Mercury as a Biomarker of Methylmercury Exposure

6.5.1 Hair Composition
6-9

Hair is approximately 95% proteinaceous and 5% a mixture of lipids, glycoproteins, remnants of

nucleic acids, and in the case of pigmented hairs, of melanin and phaeomelanin. Hair contains a central

core of closely packed spindle-shaped cortical cells, each filled with macrofibrils which in turn consist of

a microfibril/matrix composite. The long axes of the cells and their fibrous constituents are oriented

along the long axis of the hair. The amino acid composition of hair is high in those amino acids with

side-chains (particularly, those containing “reactive” groups such as cystine, cysteine, tyrosine,

tryptophan, acidic and basic amino acids, as well as terminal carboxyl or amino groups). The cortical

core is covered by sheet-like cells of the cuticle. The surfaces of all the cells of the hair shaft have a thin

layer of lipid which is covalently attached to the underlying proteins. 

Hair has been assumed to grow at the rate of one centimeter a month (Kjellstrom et al., 1989; 

Marsh et al., 1980). However, there is variability in the rate of hair growth. Growth determined

experimentally is between 0.9 and 1.3 cm per month (Barman et al., 1963; Munro, 1966; and Saitoh,

1967).

Mercury is incorporated into hair during the growth of hair. Hair mercury concentrations are

presumed to reflect blood mercury concentrations at the moment of hair growth. Whether the

predominant chemical species is inorganic mercury or methylmercury depends on exposure patterns and

on the extent of demethylation of methylmercury. Hair mercury (ug/g) and blood mercury (ng/L) ratios

range from 190:1 up to 370:1 (Skerfving, 1974; Phelps et al., 1980; Turner et al., 1980; Sherlock et al.,

1984). Higher ratios have recently been reported. Additional discussion of the hair to blood mercury

ratio is found in the volume on human health. This is one source of person-to-person variability

considered in selection of uncertainty factors in determining U.S. EPA’s Reference Dose for

methylmercury.

Chemical analyses to determine mercury concentrations in hair determine total mercury rather

than chemical species of mercury. In order to dissolve hair samples, they must be put through an acid

digestion. The process of acid digestion will convert virtually all of the mercury in the biological sample

to inorganic mercury (Nixon et al., 1996). Consequently the fraction of hair mercury that is

methylmercury is only an estimate based on what is known of environmental/occupational exposure

patterns.

The frequency of fish consumption has been used as a guide to differences in hair mercury

concentrations (Airey, 1983). Within a general population as fish consumption increases, hair mercury

concentration will also increase. However, the amount of mercury in hair depends on the concentration

of mercury present in fish consumed. Comparison of recent studies from Bangladesh (Holsbeek et al.,

1996) and Papua New Guinea (Abe et al., 1995) illustrates these differences. Holsbeek et al. (1996)

found a highly significant positive correlation (r=0.88, P<0.001) between fish consumption and hair

mercury concentrations. Total hair mercury concentrations had a mean value of 0.44±0.19 μg/g (range

0.02 to 0.95) and a fish consumption of 2.1 kg/month (range 1.4 to 2.6). The low concentrations in hair

reflect the low concentrations of methylmercury in Bangladesh fish. Abe et al. (1995) evaluated 134

fish-consuming subjects and 13 nonfish-eating subjects in Papua, New Guinea. Among the fish

consumers hair mercury levels had a mean mercury concentrations of 21.9 μg/g (range 3.7 to 71.9). 

Average fish consumption was 280 grams/day (range=52 to 425) or about 8.4 kg/month producing an

average methylmercury intake of 84 μg/day. Among the nonfish consumers the mean hair mercury was

0.75±0.4 μg/g. The difference in hair mercury concentration in Bangladesh and New Guinea were

considerably greater than the differences in fish mercury. 
6-10

6.5.2 Hair Mercury Concentrations in North America

6.5.2.1 United States

Data do not exist describing hair mercury concentrations that are representative of the United

States population as a whole. This is similar to the situation for blood mercury concentrations. Limited

data from smaller studies are described below and summarized in Table 6-3.

U.S. Communities

Crispin-Smith et al. (1997) analyzed hair mercury concentrations in 1431 individuals living in

the United States. The communities in which these individuals resided were not identified. Mean values

in these studies were < 1 μg/g. Fish consumers had slightly higher blood mercury concentrations than

did nonfish consumers (0.52 vs. 0.48). The maximal value reported in this survey was 6.3 μg/g. 

Statistical information on these data is not available currently.

New York Metropolitan Area, New Jersey, Alabama (Birmingham), and North Carolina

(Charlotte)

Creason et al. (1978a, 1978b, and 1978c) evaluated children and adults living in these cities in

the early 1970s. Mean values for all groups of children and adults were less than 1 μg/g. Maximum

values were in the range of 5 to 11.3 μg/g of hair. Adult values were slightly higher than those of

children.

California

Airey (1983) determined hair mercury concentrations among about 100 subjects living in

Southern California (LaJolla and San Diego). Mean values were in the range of 2 to 3 μg Hg/gram, with

maximum values in the range of 4.5 to 6.6 μg/g.. Harnly et al. (1997) determined hair mercury among

Tribal and nontribal group members living near Clear Lake, California. Mean values were typically less

than 1 μg/g., with maximum values of 1.8 μg/g. among Tribal members and 2.3 μg/g among non-Tribal

members. 

Maryland

Airey (1983) found mean concentrations of about 1.5 to 2.3 μg/g in adults living in Maryland

(communities were not identified). Maximum concentrations were 4.5 μg/g.. 

State of Washington

Lazaret et al. (1991) identified hair mercury concentrations < 1 μg/g. and a maximum value of

1.5 μg/g. Earlier Airey (1983) reported mean values of 1.5 to 3.8 μg/g among small numbers of subjects. 

The maximum value reported was 7. 9 μg/g. 

Florida

CDC (1993) surveyed 330 subjects living in the Florida Everglades and determined that average

hair mercury concentrations were 1.3 μg/g.. The maximum value was 15.6 μg/g.
6-11

Wisconsin

Knobeloch et al. (1995) reporting on two individuals with blood mercury concentrations of 38

and >50 μg/g. found the individuals hair mercury concentrations were 11 and 12 μg/g. 

Great Lakes Region

Gerstenberger et al. (1997) determined mean mercury concentrations were less than one μg/g.

among 78 Ojibwa Tribal members. The maximum hair mercury concentration was 2.6 μg/g.

Alaska

Lazaret et al., (1991) reported hair mercury concentrations averaging 1.4 μg/g among 80 women

of childbearing age. The maximum hair mercury concentrations were 15.2 μg/g.

Table 6-3

Hair Mercury Concentrations (ug Hg/gram hair or ppm) from Residents

of Various Communities in the United States

Study Community Mean Maximum Additional

Concentration Concentration Information on

Study

Creason et al., New York Children (n=280); Children, 11.3 ppm Survey conducted

1978a Metropolitan Area 0.67 ppm in 1971 and 1972

Adults (n=203); Adults, 14.0 ppm

0.77

Creason et al., Four communities in Children (n=204), Children, 4.4 ppm Survey conducted

1978b New Jersey: 0.77 ppm in 1972 and 1973

Ridgewood, Adults (n=117), Adults, 5.6 ppm

Fairlawn, Matawan 0.78 ppm

and Elizabeth

Creason et al., Birmingham, Children (n=322), Children, 5.4 ppm; Survey conducted

1978c Alabama, and 0.46 ppm in 1972 and 1973

Charlotte, North Adults (n-117) 0.78 Adults, 7.5 ppm

Carolina ppm
Table 6-3 (continued)

Hair Mercury Concentrations (ug Hg/gram hair or ppm) from Residents

of Various Communities in the United States

Study Community Mean Maximum Additional

Concentration Concentration Information on

Study

6-12

Airey, 1983 U.S. data cited by 1) Males (n=22), 1) 6.2 ppm

Airey, 1983. 2.7 ppm;

Community not (n=16), 2.6 ppm;

identified. 3) Males and 3) 5.6 ppm

2). Females 2) 5.5 ppm

Females (24

subjects), 2.1 ppm;

4) Males and 4) 6.6 ppm

Females (31

subjects), 2.2 ppm;

5) Males and 5) 7.9 ppm

Females 924

subjects) 2.9 ppm;

6) Males and 6) 7.9 ppm

Females (79

subjects), 2.4 ppm.

Airey, 1983 U.S. data cited by 1) 2.4 ppm (13 1) 6.2 ppm

Airey, 1983 men);

Community women);

identified: LaJolla- 3) 2.3 ppm (8 3) 4.5 ppm

San Diego subjects including

2) 2.7 ppm (13 2) 5.5 ppm

men and women);

4) 2.9 ppm (17

subjects including

men and women);

5) 2.6 ppm (5 5) 6.2 ppm

subjects including

men and women);

6) 2.8 ppm (30 6) 6.6 ppm

subjects including

men and women).

Airey, 1983 U.S. data cited by 1) 1.8 ppm (11 1) 3.8 ppm

Airey, 1983. Area subjects, men and

identified: Maryland women);

2) 1.5 ppm (11 2) 3.9 ppm

subjects, men and

women);

3) 2.3 ppm (11 3) 4.5 ppm

subjects, men and

women);

4) 1.9 ppm (33 4) 4.4 ppm

subjects, men and

women).
Table 6-3 (continued)

Hair Mercury Concentrations (ug Hg/gram hair or ppm) from Residents

of Various Communities in the United States

Study Community Mean Maximum Additional

Concentration Concentration Information on

Study

6-13

Airey, 1983 U.S. data cited by 1) 3.3 ppm (9 men); 1) 5.6 ppm

Airey, 1983 2. 2.2 ppm (3 2) 4.1 ppm

Community women);

identified: Seattle. 3) 2.6 ppm (5 3) 5.6 ppm

subjects men and

women);

4) 1.5 ppm (3 4) 2.1 ppm 

subjects, men and

women);

5) 3.8 ppm (8 5) 7.9 ppm

subjects, men and

women);

6) 3.0 ppm (16 6) 7.9 ppm

subjects, men and

women).

Crispin-Smith et U.S., Communities 0.48 ppm (1,431 6.3 ppm The 1009

al., 1997 and distribution not individuals); individuals are a

identified 0.52 ppm (1009 subset of the 1431

individuals subjects.

reporting some

seafood

consumption)

Lasora et al., 1991 Nome, Alaska 1.36 ppm 15.2 ppm

(80 women of

childbearing age)

Lasora et al., 1991 Sequim, Washington 0.70 ppm (7 women 1.5 ppm

of childbearing age)

Fleming et al., Florida Everglades 1.3 ppm (330 15.6 ppm To be included in

1995 subjects, men and the survey the

women) subjects had to have

consumed fish or

wildlife from the

Everglades.

Knobeloch et al., Wisconsin, urban 2 adults (1 man, 1

1995 woman); values 11

and 12 ppm
Table 6-3 (continued)

Hair Mercury Concentrations (ug Hg/gram hair or ppm) from Residents

of Various Communities in the United States

Study Community Mean Maximum Additional

Concentration Concentration Information on

Study

6-14

Gerstenberger et Ojibwa Tribal 47% > 0.28 ppm. 2.6 ppm

al., 1997 members from the Among individuals

Great Lakes Region with values above

the level of

detection, the mean

was 0.83 ppm based

on 78 subjects

Harnly et al., 1997 Native Americans 68 Tribal members. Maximum value for

living near Clear Mean value: 0.64 Tribal members:

Lake, California. ppm. 1.8 ppm

4 non-Tribal Maximum value for

members. Mean non-Tribal

value: 1.6 ppm members: 2.3 ppm

6.5.2.2 Summary of Data on Hair Mercury Concentrations

Available data indicate that mean mercury concentrations in the U.S. population are typically

less than 3 μg/g and often less than 1 μg/g, although, maximum concentrations of more than 15 μg/g are

reported. Hair mercury concentrations of greater than 10 μg/g have been associated with mercury

exposure from fish. The shape of the distribution of hair mercury concentrations in the United States is

not well documented. Comparison of data summarized by Airey (1983) on the association between

frequency of fish meals, mean and range of hair mercury concentrations reveals (see Table 6-4):

• The arithmetic mean of hair mercury from the U.S. surveys is consistent with the lower

bound of the range associated with fish ingestion rates of less than once a month to as

frequent as once a week. 

• The maximum values identified in the surveys are consistent with fish consumption of 

every week to every day.

Table 6-4

Association of Hair Mercury Concentrations (ug Hg/gram hair) with

Frequency of Fish Ingestion by Adult Men and Women

Living in 32 Locations within 13 Countries (Airey, 1983)

Frequency of Fish Meals Arithmetic Mean Range

Once a Month or Less 1.4 0.1-6.2

Twice a Month 1.9 0.2-9.2

Every Week 2.5 0.2-16.2

Every Day 11.6 3.6-24.0
6-15

6.6 Conclusions

6.6.1 Blood Mercury Levels

Mercury in blood is a reflection of exposures in recent days and weeks to environmental

mercury. Typically blood mercury values are reported as total mercury, although chemically speciated

mercury analyses often are included in reports published in the 1990s. Organic mercury in blood

generally reflects methylmercury intake from fish and shellfish. At progressively higher dietary intakes

of fish and shellfish, the fraction of total blood mercury that is organic mercury increases becoming more

than 95% at high levels of fish consumption.

 

Blood mercury concentrations (μg Hg/L) in healthy populations are less than 3 μg/L (5 μg/kg

packed cells and 0.5 μg/L serum) based on values published by the International Union for Pure and

Applied Chemistry (1996). The U.S. EPA RfD is associated with a whole blood mercury concentration

of 4 to 5 μg/L. The “benchmark dose” for methylmercury used in setting the RfD is 44 μg/L based on

neurotoxic effects observed in Iraqi children exposed in utero.

There are no representative data on blood mercury for the U.S. population as a whole. In the

United States (in the peer-reviewed literature published in the 1990s), blood mercury concentrations in

the range of 50 to 95 μg/L have been reported and attributed to the consumption of fish and shellfish. 

Among groups of anglers and Native American Tribal groups, mean blood mercury levels in the range of

10 to 20 μg/L have been reported. Blood mercury concentrations greater than 20 μg/L and attributable to

consumption of fish and shellfish have been identified among women of childbearing age in the United

States. 

6.6.2 Hair Mercury Levels

Mercury is incorporated in hair as it grows. Typically the centimeter of hair nearest the scalp

reflects mercury exposure during the past month. The extent to which the predominant chemical species

in hair is a function of methylmercury exposure depends on environmental exposure patterns. 

Methylmercury in the diet results in elevated hair mercury concentrations. Dietary sources documented

to produce elevated hair mercury concentrations include fish, shellfish, and flesh from marine mammals. 

 

There are no representative data on hair mercury concentrations for the U.S. population as a

whole. Typical values in the United States are less than 1 μg/g. Maximum hair mercury concentrations

of 15 μg/gram and higher have been reported in the United States. Hair mercury concentrations greater

than 10 μg/gram have been reported for women of childbearing age living in the United States. U.S.

EPA’s RfD is associated with a hair mercury concentration of approximately 1 μg/g. The “benchmark”

dose is associated with a hair mercury concentration of 11.1 μg/g and is based on neurotoxic effects

observed in Iraqi children exposed in utero to methylmercury.
7-1

7. CONCLUSIONS

 The results of the current exposure of the U.S. population from fish consumption indicate that

most of the population consumes fish and is exposed to methylmercury as a result.

Approximately 85% of adults in the United States consumer fish and shellfish at least once a

month with about half of adults selecting fish and shellfish as part of their diets at least once a

week (based on food frequency data collected among more than 19,000 adult respondents in the

NHANES III conducted between 1988 and 1994). This same survey identified 1-2% of adults

who indicated they consume fish and shellfish almost daily.

 For the modeled fish ingestion scenarios, the local emission sources are predicted to account for

the majority of the total mercury exposure for water bodies close to the sources. This is

particularly true for the hypothetical western site, where background and regional atmospheric

contributions to the total mercury concentration in the water column are predicted to be lower.

 Consumption of fish is the dominant pathway of exposure to methylmercury for fish-consuming

humans. There is a great deal of variability among individuals in these populations with respect

to food sources and fish consumption rates. As a result, there is a great deal of variability in

exposure to methylmercury in these populations. The anthropogenic contribution to the total

amount of methylmercury in fish is, in part, the result of anthropogenic mercury releases from

industrial and combustion sources which increases mercury body burdens in fish. As a

consequence of human consumption of the affected fish, there is an incremental increase in

exposure to methylmercury. Terrestrial exposures were evaluated in the modeling analysis;

inorganic mercury species were predicted to be the dominant chemical species to which humans

are exposed.

 In the nationally-based dietary surveys, the types of fish most frequently reported to be eaten by

consumers are tuna, shrimp, and Alaskan pollock. The importance of these species is

corroborated by U.S. National Marine Fisheries Service data on per capita consumption rates of

commercial fish species.

 National surveys indicate that Asian/Pacific Islander-American and Black-American

subpopulations report more frequent consumption of fish and shellfish than other survey

participants.

 Superimposed on this general pattern of fish and shellfish consumption is freshwater fish

consumption, which may pose a significant source of methylmercury exposure to consumers of

such fish. The magnitude of methylmercury exposure from freshwater fish varies with local

consumption rates and methylmercury concentrations in the fish. The modeling exercise

indicated that some of these methylmercury concentrations in freshwater fish may be elevated as

a result of mercury emissions from anthropogenic sources. Exposures may be elevated among

some members of this subpopulation; these may be evidenced by analyses of blood mercury

showing concentrations in excess of 10 micrograms per liter (μg/L) that have been reported

among multiple freshwater fish-consumer subpopulations. The mean value of blood mercury in

an Arkansas study was 10μg/L. Because general populations data on the distribution of blood

mercury concentrations have not been gathered, it is not known how common blood mercury

concentration above 10μg/L are.
7-2

 An assessment of consumption of fish and shellfish was based on data obtained from

contemporary nationally based dietary surveys conducted by the United States government: the

third National Health and Nutrition Examination Survey conducted between 1988 and 1994

(National Center for Health Statistics of the Centers for Disease Control) and the 1994 and 1995

Continuing Surveys of Food Intakes by Individuals (United States Department of Agriculture). 

Data on mercury concentrations in fish and shellfish were obtained from national database

compiled by the National Marine Fisheries Service and the U.S. Environment Protection Agency.

The results of the assessment show that the predicted average exposure among make and female

fish consumers of reproductive age is 0.1 micrograms of methylmercury per kilogram of body

weight per day based on a single day’s estimate. The comparable 90th percentile estimate is

approximately four times this level. Median “per user” fish/shellfish consumption values across

these nationally representative surveys were between 73 and 79 grams/day based on single-day

estimates. The comparable 90th percentile values ranged between 186 and 242 grams/day based

on single-day estimates.

The single-day estimates are used to project month-long fish/shellfish consumption when

combined with frequency of fish/shellfish consumption estimates obtained from adult

participants in NHANES III. The single-day estimates of fish/shellfish consumption provide

portion sizes to estimated the impact of intermittent consumption of fish containing mercury at

concentrations considerably above that commonly encountered in the commercial market, e.g.,

approximately 0.5 ppm and higher. Fish with mercury concentrations averaging over 0.5 ppm

include swordfish and shark among marine fish and smallmouth bass, largemouth bass, channel

catfish, walleye, and northern pike among freshwater fish.

 Exposure rates to methylmercury among fish-consuming children are predicted to be higher than

for fish-consuming adults on a body weight basis. The 50th percentile exposure rate among fishconsuming children ages 3 through 6 years is approximately 0.3 micrograms per kilogram of

body weight per day based on single day estimates. Predicted exposures at the 90th percentile

are approximately three-times greater or 0.8 to one microgram of mercury per kilogram of body

weight on a single day. Estimated month long mercury exposures among 3 through 6 year-old

children are 0.03 at the 50th percentile and 0.17 at the 90th percentile using adult data to predict

how often children consume fish and shellfish. It is uncertain how well the adult data are

predictive for children because data for children are not available.

 Exposures among specific subpopulations including anglers, Asian-Americans, and members of

some Native American Tribes indicate that their average exposures to methylmercury may be

more than two-times greater than those experience by the average population.

 Predicted high-end exposures to methylmercury are caused by one or two factors or their

combination: 1) high consumption rates of methylmercury contaminated fish, water and/or 2)

consumption of types of fish which exhibit elevated methylmercury concentrations in their

tissues. Of these two factors the former appears to be more significant for overall population

exposures.

 Blood mercury concentrations and hair mercury levels are biomarkers used to indicate exposure

to mercury. Inorganic mercury exposures occur occupationally and for some individuals through

folk/hobby exposures to inorganic mercury. Dental restorations with silver-mercury amalgams

can also contribute to inorganic mercury exposures. Methylmercury exposure is almost
7-3

exclusively through consumption of fish, shellfish, and marine mammals. Occupational

exposures to methylmercury are rare.

Data describing blood and/or hair mercury for a population representative of the United States do

not exist, however, some data are available. Blood mercury concentrations, attributable to

consumption of fish and shellfish, in excess of 30 μg/L have been reported in the United States. 

Hair mercury concentrations in the United States are typically less than 1μg/g, however, hair

mercury concentration greater than 10μ/g have been reported for women of childbearing age

living in the United States. U.S. EPA’s RfD is associated with a blood mercury concentration of

4-5 μg/L and a hair mercury concentration of approximately 1μg/g. The “benchmark” dose is

associated with mercury concentrations of 44μg/L in blood and 11.1 μg/g in hair. The

“benchmark” dose for methylmercury is based on neurotoxic effects observed in Iraqi children

exposed in utero to methylmercury.

 To improve the quantitative exposure assessment modeling component of the risk assessment for

mercury and mercury compounds, U.S. EPA would need more and better mercury emissions data

and measured data near sources of concern, as well as a better quantitative understanding of

mercury chemistry in the emission plume, the atmosphere, soils, water bodies, and biota.

 To improve the exposure estimated based on surveys of fish consumption, more study is needed

among potentially high-end fish consumers, which examines specific biomarkers indicating

mercury exposure (e.g., blood mercury concentrations and hair mercury concentrations).

 A pharmacokinetic-based understanding of mercury partitioning in children is needed. 

Additional studies of fish intake and methylmercury exposure among children are needed.
8-1

8. RESEARCH NEEDS

 To improve the quantitative exposure assessment modeling component of the risk assessment for

mercury and mercury compounds, U.S. EPA would need more and better mercury emissions data

and measured data near sources of concern, as well as a better quantitative understanding mor

mercury chemistry I the emission plume, the atmosphere, soils, water bodies, and biota.

 To improve the exposure estimated based on surveys of fish consumption, more study in needed

among potentially high-end fish consumers, which examines specific biomarkers indicating

mercury exposure (e.g., blood mercury concentrations and hair mercury concentrations).

 A pharmacokinetic-based understanding of mercury partitioning in children is needed. 

Additional studies of fish intake and methylmercury exposure among children are needed.
9-1

9. REFERENCES

Abe, T., R. Ohtsuka, T. Hongo, T. Suzuki, C. Tohyama, A. Nakano, H. Akagi and T. Akimichi. 1995. 

High hair and urinary mercury levels of fish eaters in the nonpolluted environment of Papua New Guinea. 

Archives of Environmental Health 50:367-373.

Aberg, B., L. Ekman, R. Falk, U. Greitz, G. Persson and J. Snihs. 1969. Metabolism of methyl mercury

( Hg) compounds in man: Excretion and distribution. Arch. Environ. Health 19:478-484. 203

Akagi H., D.C. Mortimer, and D.R. Miller. 1979. Mercury Methylation and Partition in Aquatic

Systems. Bull. Environ. Contam. Toxicol. 23:372-376.

Akagi H, Malm O, Branches FCP., Kinjo Y, Kashima Y, Guimaraes JRD, Oliversia RB, Karaguchi K,

Pfeiffer C, Takizawa Y, and Kato H. 1995. Human exposure to mercury due to goldmining in the Tapajos

River Basin, Amazon, Brazil: speciation of mercury in human hair, blood, and urine. Water, Air & Soil

Pollution 80:85-94.

Aks SE, Erickson TB, Branches FJO, and Hryhorczuk DO. 1995. Blood-mercury concentrations and

renal biomarkers in Amazonian villagers. Royal Swed Acad Sci. Ambio 24; 163-165.

Agency for Toxic Substances and Disease Registry. 1989. Toxicological Profile for Mercury. Atlanta,

Georgia: Centers for Disease Control.

Airey, D. 1983. Mercury in hair due to environment and diet: a review. Environ. Health Perspect. 

52:303-316.

American Conference of Governmental and Industrial Hygienists. 1986. TLVs: threshold limit values

and biological exposure indices for 1986-1987. Cincinnati, OH: American Conference of Governmental

and Industrial Hygienists, Inc.; pp. 253-256.

Anderson, W. L. and K.E. Smith. 1977. Dynamic of mercury at coal-fired utility power plant and

adjacent cooling lake. Environ. Sci and Technol. 11:75.

Ayotte P., Dewailly E., Bruneau S., Careau H., and Vezina A. 1995. Arctic air pollution and human

health: what effects should be expected? Science of the Total Environment 160/161:529-537.

Bache, E., W. Gutenmann, M. Rutzke, G. Chu, D. Elfving and D. Lisk. 1991. Concentrations of Metals

in Grasses in the Vicinity of a Municipal Refuse Incinerator. Arch. Environ. Contam. Toxicol.

20:538-542.

Baes, C., R. Sharp, A. Sjoreen and R. Shor. 1984. A Review and Analysis of parameters for assessing

transport of environmentally released radionuclides through agriculture. Prepared under contract No.

DE-AC05-84OR21400. U.S. Department of Energy, Washington, D.C.

Bahnick, D., C.Sauer, B. Butterworth and D. Kuehl. 1994. A National Study of Mercury Contamination

of Fish. Chemosphere 29(3):537-546.

Barregard, L., G. Sallsten, A Schutz, R. Attewell, S. Skerfving, and B. Jarvholm. 1992. Kinetics of

Mercury in Blood and Urine after Brief Occupational Exposure. Arch. Environ. Health 47(3):176-184.
9-2

Barregard, L., B. Hogstedt, A Schutz, A. Karlsson, G. Sallsten, G. Thiringer. 1991. Effects of

Occupational Exposure to Mercury Vapor on Lymphocyte Micronuclei. Scand. J. Work Environ. Health

17(4):263-268.

Begerow, J., D. Zander, I. Freier, and L. Dunemann. 1994. Long-Term Mercury Excretion in Urine after

removal of Amalgam Fillings. Arch. Occup. Environ. Health 66:209-212.

Benoit, J.M., W.F. Fitzgerald and A.W.H. Damman. 1994. Historical Atmospheric Mercury Deposition

in the Mid-Continental U.S. as Recorded in an Ombrotrophic Peat Bog. Pp. 187-202 in Watras, C.J. and

J.W. Huckabee eds. Mercury Pollution Integration and Synthesis. 

Birke G, Johnels AG, Plantin L-O, Sjostrand, Skerfving S, and Westermark T. 1971. Studies on humans

exposed to methyl mercury through fish consumption. Arch Environ Health 25: 77-91.

Bjerregaard, P. 1995. Health and environment in Greenland and other circumpolar areas. The Science of

the Total Environment 160/161: 521-527.

Bloom, N. and W. F. Fitzgerald. 1988. Determination of Volatile Mercury Species at the Picogram level

by Low-Temperature Gas Chromatography with Cold-Vapor Atomic Fluorescence Detection. Analytica

Chimica Acta, 208:151-161.

Bloom, N. S. 1989. Determination of picogram levels of methyl-mercury by aqueous phase ethylation,

followed by cryogenic gas chromatography with cold-vapor atomic fluorescence detection. Can. J.

Fisher. Aq. Sci. 46:1131-1140.

Bloom, N. S. and C. J. Watras. 1989. Observations of Methylmercury in Precipitation. The Sci. Tot.

Environ. 87/88:191-207.

Bloom, N.S., C.J. Watras, and J.P. Hurley. 1991. Impact of Acidification on the Methylmercury Cycle

of Remote Seepage Lakes. Water, Air, and Soil Poll. 56: 477-491.

Bloom, N.S., C. J. Watras, and J. P. Hurley. 1991. Impact of Acidification on the Methylmercury Cycle

of Remote Seepage Lakes. Water, Air and Soil Poll. 56:477-491.

Bloom, N.S. 1992. On the Chemical Form of Mercury in Edible Fish and Marine Invertebrate Tissue. 

Can. J. Fisher. Aq. Sci. 49:1010-1017.

Bloom, N.S. and E. Kuhn. 1994. Mercury speciation in meat products, personal communication, October

1, 1994.

Bowen, D.J. 1992. Taste and food preference changes across the course of pregnancy. Appetite 19;

233-242.

Bowers, J.R., J.R. Bjorkland and C.S. Cheney. 1979. Industrial Source Complex (ISC) Dispersion

Model User's Guide. Volume I, EPA-450/4-79-030, U.S. Environmental Protection Agency,Research

Triangle Park, North Carolina 27711. 

Briggs, G.A. 1969. Plume Rise, AEC Critical Review Series, TID - 25075, National Technical

Information Service, Springfield, VA., 81pp.
9-3

Briggs, G.A. 1972. Discussion on Chimney Plumes in Neutral and Stable Surroundings. Atmos.

Environ. 6:507-510.

Briggs, G.A. 1973. Diffusion Estimation for Small Emissions, ATDL Contribution File No. 79,

Atmospheric Turbulence and Diffusion Laboratory.

Briggs, G.A. 1973. Diffusion Estimates for Small Emissions, Atmospheric Turbulence and Diffusion

Laboratory, Contribution No. 70 (Draft), Oak Ridge, Tennessee.

Briggs, G.A. 1975. Plume Rise Predications, in Lectures on Air Pollution and Environmental Impact

Analysis, Americal Meteorological Society, Boston, Massachusetts.

Brosset, C. 1981. The Mercury Cycle. Water, Air and Soil Poll. 16:253-255.

Brosset, C. and E. Lord. 1991. Mercury in Precipitation and Ambient Air: A new Scenario. Water, Air

and Soil Poll. 56:493-506.

Brune D, Nordberg GF, Vesterberg O, Gerhardsson L, and Wester PO. 1991. A review of normal

concentrations of mercury in human blood. Sci Total Environ. 100: 235-282.

Burge P, and Evans S. 1994. Mercury contamination in Arkansas gamefish: A public health perspective. 

J. Arkansas Med Soc 90: 542-548.

Buchet J. P., R. Lauwerys, A. Vandevoorde, and J. M. Pycke. 1983. Oral Daily Intake of Cadmium,

Lead, Manganese, Chromium, Mercury, Calcium, Zinc and Arsenic in Belgium. A Duplicate Metal

Study. Food Chem. Tox. 21:19-24.

Burke, J., M. Hoyer, G. Keeler and T. Scherbatskoy. 1995. Wet Deposition of Mercury and Ambient

Mercury Concentrations as a Site in the Lake Champlain Basin. Accepted for Publication in Water, Air

and Soil Pollution. 

Campbell, D., M. Gonzales, and J. B. Sullivan. 1992. Mercury. In: Hazardous Materials Toxicology,

Clinical Principles of Environmental Health, J. B. Sullivan and G. R. Krieger, Eds. Williams and

Wilkins, Baltimore, MD. p. 824-833.

Cappon, C.J. 1981. Mercury and Selenium Content and Chemical Form in Vegetable Crops Grown on

Sludge-Amended Soil. Arch. Environm. Contam. Toxicol. 10: 673-689.

Cappon, C. J. 1987. Uptake and Speciation of Mercury and Selenium in Vegetable Crops Grown on

Compost-Treated Soil. Water, Air and Soil Poll. 34:353-361.

Carpi, A., L. Weinstein and D. Ditz. 1994. Bioaccumulation of Mercury by Sphagnum Moss near a

Municipal Solid Waste Incinerator. Air and Waste 44:669-672.

CARB. 1986. Subroutines for calculating dry deposition velocities using Sehmel's curves. Prepared by

Bart Croes, California Air Resources Board.

Cardenas, A., H. Roels, A. M. Bernard, R. Barbon, J. P. Buchet, R. R. Lauwerys, J. Rosello, G. Hotter,

A. Mutti, I. Franchini, L. M. Fels, H. Stolte, M. E. De-Broe, G. D. Nuyts, S. A. Taylor, and R. G. Price.
9-4

1993. Markers of Early Renal Changes Induced by Industrial Pollutants. I. Applications to Workers

Exposed to Mercury Vapour. Brit. J. Indus. Med. 50(1):17-27.

Catalano, J.A., D.B. Turner, and J.H. Novak. 1987. User's Guide for RAM -- Second Edition. 

EPA/600/8-87/046, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina

27711. 

Caurant F, Navarro M, and Amiard J-C. 1996. Mercury in pilot whales possible limits to the

detoxicification process. The Science of the Total Environment 186:95-104. 

Centers for Disease Control. 1993. Mercury exposure among the Miccousukee Indian Tribe: South

Florida, 1993. Unpublished report. United States Centers for Disease Control. Atlanta, Georgia, USA.

Chan HM. 1997. A database for environmental contaminants in traditional foods in northern and Arctic

Canada: development and applications. Food Additives and Contaminants, in press.

Chapman and Hall. 1984. Dictionary of Organometallic Compounds. Vol. 1, p. 1030.

Cleckner, L. B., E. S. Esseks, P. G. Meier, and G. J. Keeler. 1995. Mercury Concentrations in Two

Great Waters. Accepted for Publication in Water, Air and Soil Pollution. 

Clemente, G. F., G. Ingrao, and G. P. Santaroni. 1982. The Concentration of Some Trace Elements in

Human Milk form Italy. The Sci. Tot. Environ. 24:255-265.

Code of Federal Regulations. 1989. Air contaminants - permissible exposure limits. C.F.R.

29:1910:1000.

Columbia River Iner-Tribal Fish Commission. 1994. A Fish Consumption Survey of the Umtilla, Nez

Perce, Yakama and Warm Springs Tribes of the Columbia River Basin. Technical Report 94-3. 

October, 1994.

Connelly, N.A., T.L. Brown and B.A. Knuth. 1990. New York Statewide Angler Survey 1988. Albany,

NY: New York State Department of Environmental Conservation. 

Cornelis R, Heinzow B, Herber RFM, Cristensen JM, Poulsen OM, Sabbioni E, Templeton DM,

Thomassen Y, Vahter M, and Vesterberg O. 1996. Sample collection guidelines for trace elements in

blood and urine. J. Trace Elements in Medicine and Biology 10: 103-127.

Courval, JM, DeHoog, JV, Holzman, CB, Tay EM, Fischer, L, Humphrey HE, Paneth, NS, Sweeney

AM. 1996. Fish consumption and other characteristics of reproductive-aged Michigan anglers -- a

potential population for studying the effects of consumption of Great Lakes fish on reproductive health. 

Toxicol. Ind. Health 12(3-4):347-359. 

CRC. 1988. Handbook of Chemistry and Physics. 69th ed.

Cramer, G.M. 1992. Interoffice memorandum, April 21, 1992.

Cramer, G. M. 1994. Exposure of U. S. Consumers to Methylmercury from Fish. Presented at the

DOE/FDA/EPA Workshop on Methylmercury and Human Health, Bethesda, MD March 22-23 1994.
9-5

Cramer, H.E. 1957. A Practical Method for Estimating the Dispersal of Atmospheric Contaminants, in

Proceedings of the First National Conference on Applied Meteorology, Sec. C, pp. C-33-C-35, American

Meteorological Society, Hartford, Conn.

Crispin-Smith, J., Turner, M.D., March D.O. 1997. Project III. Hair methylmercury levels in women

of childbearing age.

Crockett, A. and R. Kinnison. 1979. Mercury residues in soil around a coal-fired power-plant. Envir. Sci.

Technol. 13:712-715.

Crochetti AF, Guthrie HA. 1982. Eating Behavior and Associated Nutrient Quality of Diets. Final

Report for US Department of Agriculture Contract 53-22U4-9-192, The Human Nutrition Center,

Science and Education Administration US Department of Agriculture. Anarem Systems Research

Corporation, 31 Union Square West, New York, NY 10003.

CSFII. 1995. Continuing Survey of Food Intake by Individuals. Beltsville Human Nutrition Research

Center. U.S. Department of Agriculture. Available on CD-ROM from NITS (order no. PB97-

500789INC). See also (http://www.ntis.gov/fcpc/cpn7682.htm).

Cunningham, P. S. Smith, J. Tippett and A. Greene. 1994. A National Fish Consumption Advisory Data

Base: A Step Toward Consistency. Fisheries. 19(5):14-23.

Dangwal, S. K. 1993. Evaluation and Control of Mercury Vapor Exposure in the Cell House of Chlor -

Alkali Plants. Environ. Res. 60(2):254-258.

Dietz R, Riget F, and Johansen. 1996. Lead, cadmium, mercury and selenium in Greenland marine

animals. The Science of the Total Environment 186:67-93.

Dooley, J. H. 1992. Natural Sources of Mercury in the Kirkwood-Cohansey Aquifer System of the New

Jersey Coastal Plain. New Jersey Geological Survey, Report 27. 

Driscoll, C. T., C. Yan, C. L. Schofield, R. Munson, and J. Holsapple. 1994. The Mercury Cycle and Fish

in the Adirondack Lakes. ES+T.

Dvonch et al., 1994 and 1995 appears in chapters 2, 5 and 9 both citations should read Dvonch et al., (in

press). In Chapter 9 keep the reference as is just replace 1995 with in press.

Dvonch, J.T., A.F. Vette, G.J. Keeler, G. Evans and R. Stevens. 1995. An intensive Multi-site Pilot

Study Investigating Atmospheric Mercury in Broward County, Florida. Accepted for Publication in

Water, Air and Soil Pollution. 

Ebert, E.S., N.W. Harrington, K.J. Boyle, J.W. Knight and R.E. Keenan. 1993. Estimating Consumption

of Freshwater Fish among Maine Anglers. North American Journal of Fisheries Management. 13:737-

745.

Eder, B. K., D. H. Coventry, T. L. Clark, and C. E. Bollinger, 1986. RELMAP: A regional Lagrangian

model of air pollution - users guide. Project Report, EPA/600/8-86/013, U.S. Environmental Protection

Agency, Research Triangle Park, NC.
9-6

Egan, B.A. 1975. Turbulent Diffusion in Complex Terrain, in Lectures on Air Pollution and

Environmental Impacts Analysis, pp.112-135, D. Haugen (Ed.), American Meteorological Society,

Boston, Mass.

Expert Panel on Mercury Atmospheric Processes. 1994. Mercury Atmospheric Processes: A Synthesis

Report. Report No. TR-104214.

Ehrenberg, R. L., R. L. Vogt, A. B. Smith, J. Brondum, W. S. Brightwell, P. J. Hudson, K. P. McManus,

W. H. Hannon, and F. C. Phipps. 1991. Effects of Elemental Mercury Exposure at a Thermometer Plant.

Amer. J. Indus. Med. 19(4):495-507.

Ellingsen, D. G., T. Moreland, A. Andersen, and H. Kjuus. 1993. Relation between Exposure Related

Indices and Neurological and Neurophysiological Effects in Workers Previously Exposed to Mercury

Vapour. Brit. J. Indus. Med. 50(8):736-744.

Engelmann, R.J. 1968. The Calculation of Precipitation Scavenging, in Meteorology and Atomic

Energy 1968, D.H. Slade, editor. U.S. Atomic Energy Commission.

Engstrom D.R., E.B. Swain, T.A. Henning, M.E. Brigham and P.L. Brezonick. 1994. Atmospheric

Mercury Deposition to Lakes and Watersheds: A Quantitative Reconstruction from Multiple Sediment

Cores. Pp. 33-66 in L.A. Baker (ed). Environmental Chemistry of Lakes and Reservoirs. American

Chemical Society.

Evans, R.D. 1986. Sources of mercury contamination in the sediments of small headwater lakes in

south-central Ontario, Canada. Arch. Environ. Contam. Toxicol. 15:505-512.

Fay, J.M., M. Escudier, and D.P. Hoult. 1969. A Correlation of Field Observations of Plume Rise, Fluid

Mechanics Laboratory Publication No. 69-4, Massachusetts Institute of Technology.

FDA Compliance Testing as described in the NMFS data base.

Fiore, B.J., H.A. Anderson, L.P. Hanrahan, L.J. Olson and W.C. Sonzogni. 1989. Sport Fish

Consumption and Body Burden Levels of Chlorinated Hydrocarbons: A Study of Wisconsin Anglers. 

Arch. Environ. Health. 44(2):82-88.

Fitzgerald, W. F. and T. W. Clarkson. 1991. Mercury and Monomethylmercury: Present and Future

Concerns. Envir. Health Perspec. 96:159-166.

Fitzgerald, W. F., R. P. Mason and G. M. Vandal. 1991. Atmospheric Cycling and Air-Water Exchange

of Mercury over Mid-Continental Lacustrine Regions. Water, Air and Soil Poll. 56:745-767.

Fitzgerald, W. F. 1994. Global Biogeochemical Cycling of Mercury. Presented at the DOE/FDA/EPA

Workshop on Methylmercury and Human Health, Bethesda, MD March 22-23 1994.

Fitzgerald, E.G., S. Hwang, K. Brix, B. Bush, K. Cook, and P. Worsick. 1995. Fish PCB Concentrations

and Consumption Patterns Among Mohawk Women at Akswsanse. J. Expsoure Analysis and

Environmental Epidemiology: 5(1):1-19.
9-7

Fiore, B.J., H.A. Anderson, L.P. Hanrahan, L.J. Olson and W.C. Sonzogni. 1989. Sport Fish

Consumption and Body Burden Levels of Chlorinated Hydrocarbons: A Study of Wisconsin Anglers. 

Arch. Environ. Health. 44(2):82-88.

Florida Department of Environmental Regulation. 1990. Mercury, Largemouth Bass and Water Quality: 

A Preliminary Report.

Foley, R. E., S. L. Jackling, R. L. Sloan, and M. K. Brown. 1988. Organochlorine and Mercury

Residues in Wild Mink and Otter: Comparison with Fish. Environ. Toxicol. Chem. 7:363-374.

Fortmann, L. C., D. D. Gay, and K. O. Wirtz. 1978. Ethylmercury: Formation in Plant Tissues and

Relation to Methylmercury Formation. U.S. EPA Ecological Research Series, EPA-600/3-78-037.

Fouassin, A. and M. Fondu. 1978. Evaluation de la teneur moyenne en mercure de la ration alimentaire

en Belgique. Arch. Belg. Med. Soc. Hyg. Med Trav. Med Leg. 36:481-490.

Fujita, M. and E. Takabatake. 1977. Mercury Levels in Human Maternal and Neonatal Blood, Hair and

Milk. Bull. Environ. Contam. Toxicol. 18:205-209.

Fukuzaki, N. R. Tamuro, Y. Hirano, and Y. Mizushima. 1986. Mercury Emission from a Cement

Factory and its Influence on the Environment. Atmos. Environ. 20(12):2291-2299.

Gerstsenberger, S., J. Pratt-Shelley, M. Beattie and J. Dellinger. 1993. Mercury Concentrations of

Walleye (Stizostedion vitreum vitreum) in 34 Northern Wisconsin Lakes. Bull. Environ. Contam.

Toxicol. 50:612-617.

Gerstenberger SL, Tarvis DR, Hansen LK, Pratt-Shelley J, and Dellinger JA. 1997. Concentrations of

blood and hair mercury and serum PCBs in an Ojibwa population that consumes Great Lakes region fish. 

Clinical Toxicology 35940; 377-386.

Geurtsen W. 1990. Amalgam in der Diskussion - Zur frage der Amalgamtoxizitat. Phillip J. 3:121-128.

Giesy, J., D. Verbrugge, R. Othout, W. Bowerman, M. Mora, et. al. 1994. Contaminants in Fishes

from Great Lakes-influenced Sections and above dams of three Michigan Rivers. I. Concentrations of

organochlorine insecticides, polychlorinated biphenyls, dioxin equivalents and mercury. Arch. Environ.

Contam. Toxicol. 27:202-212.

Gifford, F.A. 1976. Turbulent Diffusion Typing Schemes - A Review, Nucl. Saf., 17:68-86.

Glass, G.E., E.N. Leonard, W.H. Chan and D.B. Orr. 1986. Airborne mercury in precipitation in the Lake

Superior Region. J. Great Lakes Res. 12(1) 37-51.

Glass, G.E., J.A. Sorensen, K.W. Schmidt and G.R. Rapp. 1990. New Source Identification of Mercury

Contamination in the Great Lakes. Environ. Sci. Technol. 24 (7):1059-1069.

Glass, G., J. Sorensen, K. Schmidt, G. Rapp, D. Yap, and D. Fraser. 1991. Mercury Deposition and

sources for the Upper Great Lakes Region. Water, Air and Soil Pollution. 56:235-249.

Glass, G.E., J.A. Sorensen, K.W. Schmidt, G.R. Rapp, D. Yap and D. Fraser. 1992. Mercury Sources

and Distribution in Minnesota's Aquatic Resources: Deposition. Part 1 of Chapter 4 (Mercury Washout
9-8

from Precipitation: Atmospheric Sources). In: Mercury in the St. Louis River, Mississippi River, Crane

Lake and Sand Point Lake: Cycling, Distribution and Sources. Report to the Legislative Commission on

Minnesota Resources. April, 1992. Water Quality Division Minnesota Pollution Control Agency St.

Paul, MN.

Gloss, S. P., T. M. Grieb, C. T. Driscoll, C. L. Scholfield, J. P. Baker, D. H. Landers, and D. B. Porcella.

1990. Mercury levels in fish from the Upper Peninsula of Michigan (ELS Subregion 2B) in Relation to

Lake Acidity. USEPA Corvallis Env. Res. Lab. Corvallis.

Gmelins Handbuch, Der Anorganischen Chemie (34), Queck Silber, 1967 Verlag Chemie. GMBH

Weinheim/Burger.

Gonzalez-Fernandez, E., J. Mena, M. Diaz-Gonzalez, and J. M. Martinez-Gil-De-Arana. 1984. A

Long-Term Study of Environmental, Blood and Urine Mercury Levels and Clinical Findings in Workers

Manufacturing Mercury Relays. Indus. Health 22(2):97-106.

Grandjean P., et al. 1997. Cognitive deficit in 7-year-old children with prenatal exposure to

methylmercury. Neurotoxicol. Teratol. 19(6):417-428.

Grandjean P, Weihe P, Jorgensen PJ. 1992. Impact of maternal seafood diet on fetal exposure to

mercury, selenium, and lead. Arch Environ Health 47: 185-195.

Greeley S, Storbakken L, and Magel R. 1992. Use of a modified food frequency questionnaire during

pregnancy. J. Amer. Col. Nutr. 11: 728-734.

Greenberg, A., I Wojtenko, H. Chen, S. Krivanek, J. Butler, J. Held, P. Weis and N. Reiss. 1992. 

Mercury in Air and Rainwater in the Vicinity of a Municipal Resource Recovery Facility in Western

New Jersey. Presented at International Symposium on Measurement of Toxic and Related Pollutants,

Durham, NC. May 8.

Grieb, T., C. Driscoll, S. Gloss, C. Schofield, G. Bowie and D. Porcella. 1990. Factors Affecting

Mercury Accumulation in Fish in the Upper Michigan Peninsula. Environ. Tox. Chem. 9:919-930.

Hakanson, A. T. Andersson and A. Nilsson. 1990. Mercury in Fish in Swedish Lakes-Linkages to

Domestic and European Sources. Water, Air and Soil Poll. 50:171-191.

Hakanson, L. A. Nilsson and T. Anderson. 1988. Mercury in Fish in Swedish Lakes. Environmental

Pollution. 49:145-162.

Halbach, S. 1985. The Octanal/Water distribution of Mercury Compounds. Arch. Toxicol. 57:139-141.

Halbrook, R. S., J. H. Jenkins, P. B. Bush, and N. D. Seabolt. 1994. Sublethal Concentrations of

Mercury in River Otters: Monitoring Environmental Contamination. Arch. Environ. Contam. Toxicol.

27:306-310.

Hall, R. A., E. G. Zook, and G. M. Meaburn. 1978. National Marine Fisheries Survey of Trace

Elements in the Fishery Resource, NOAA Technical Report NMFS SSRF-721, U.S. Department of

Commerce, Washington, DC.
9-9

Hanna, S.R., G.A. Briggs, and R.P. Hosker, Jr. 1982. Handbook on Atmospheric Diffusion, DOE/TIC11223.

Hansen JC, Tarp U, and Bohm J. 1990. Prenatal exposure to methyl mercury among Greenlandic Polar

Inuits. Arch Environ Health 45; 355-358.

Hanson, P.J., S.E. Lindberg, K.H. Kim, J.G. Owens and T.A. Tabberer. 1994. Air/Surface Exchange of

Mercury Vapor in the Forest Canopy I. Laboratory Studies of Foliar Hg Vapor Exchange. 3rd

International Conference on Mercury as a Global Pollutant. Whistler, BC, Canada (July 10-14, 1994)

Harnly M., et al. 1997. Biological monitoring for mercury within a community with soil and fish

contamination. Environ. Health Perspect. 105(4):424-429.

Hatch, W. and W. Ott. 1968. Determination of submicrogram quantities of mercury by atomic

absorption spectrophotometry. Anal. Chem. 40:2085 - 2087.

Hawkes, Dr. Allison (personal communication, 1997). Maine Bureau of Health occupational Health

Program, Augusta, Maine, USA>

Hicks, B.B., D.D. Baldocchi, T.P Meyers, R.P. Hosker, Jr., and D.R. Matt. 1987. A preliminary multiple 

resistance routine for deriving dry deposition velocities from measured quantities. Water, Air, and Soil

Pollution 36: 311-330.

Higuchi, W.K. and Pooley, S.G. 1985. Hawaii's Retail Seafood Volume. Administrative Report H-85-

6. Southwest Fisheries Center. Honolulu Laboratory, national Marine Fisheries Service. P.O. Box 3830,

Honolulu, HI 96812.

Holzworth, G.C. 1972. Mixing Heights, Wind Speeds and Potential for Urban Air Pollution Throughout

the Contiguous United States. Publication No. AP-101, U.S. Environmental Protection Agency,

Research Triangle Park, North Carolina 27711.

Horvat, M., N. S. Bloom, L. Liang. 1993a. Comparison of distillation with other current isolation

methods for the determination of methylmercury compounds in low level environmental samples. Part I:

Sediments. Analytica Chimica Acta, 281:135-152.

Horvat, M., L. Liang, N. S. Bloom. 1993b. Comparison of distillation with other current isolation

methods for the determination of methylmercury compounds in low level environmental samples. Part II:

Water. Analytica Chimica Acta, 282:153-168.

Hovinga, M.E., M. Sowers and H.B. Humphrey. 1993. Environmental Exposure and Lifestyle

Predictors of Lead, Cadmium, PCB and DDT Levels in Great Lakes Fish Eaters. Archives of

Environmental Health 48(2):98-104.

Hovinga, M.E., M. Sowers and H.B. Humphrey. 1992. Historical Changes in Serum PCB and DDT

Levels in an Environmentally-Exposed Cohort. Arch. Environ. Contam. Toxicol. 22: 362-366.

Hoyer, M., J. Burke and G. Keeler. 1995. Atmospheric Sources, Transport and Deposition of Mercury in

Michigan: Two Years of Event Precipitation. Accepted for Publication in Water, Air and Soil Pollution. 
9-10

Hubert, A.H. and W.H. Snyder. 1976. Building Wake Effects on Short Stack Effluents, in Third

Symposium on Atmospheric Turbulence, Diffusion, and Air Quality, Raleigh, NC, Oct. 19-22, pp. 235-

242, American Meteorological Society, Bostaon, Mass.

Huber, A.H. 1977. Incorporating Building/Terrain Wake Effects on Stack Effluents, in Preprints of

Joint Conference on Applications of Air Pollution Meteorology, Salt Lake City, Nov. 29-Dec. 2,

ppp. 353-356, American Meteorological Society, Boston, Mass. 

Hudgins, L.L. 1980. Per capita annual utilization and consumption of fish and shellfish in Hawaii,

1970-77. Marine Fisheries Review [February; No Volume Cited]; pgs. 16-20.

Humphrey HEB, and Budd ML. 1996. Michigan’s fisheater cohorts: A prospective history of exposure. 

Toxicology and Industrial Health 12 (nos.3/4):499-505.

Humphrey, H. E. B. 1983. In: PCB's: Human and Environmental Hazards D'Itri and Kamrin, eds. Ann

Arbor Science Publications, Ann Arbor, MI.

International Atomic Energy Agency (IAEA). Evaluating the Reliability of Predictions Made Using

Environmental Transfer Models. IAEA Safety Series 100. Vienna, Austria.

Irwin, J. 1979. Estimating Plume Dispersion: A Recommended Generalized Scheme, in Proceedings of

the Fourth Symposium on Turbulence, Diffusion, and Air Pollution, Jan. 15-18, 1979, Reno, Nev.,

pp. 62-69, American Meteorological Society, Boston, Mass.

Iverfeldt, Å. and I. Persson. 1985. The Solvation Thermodynamics of Methylmercury(II) Species

Derived from Measurements of the Heat of Solution and the Henry's Law Constant. Inorganica Chimica

Acta, 103:113-119.

Iverfeldt, Å. 1991. Occupance and turnover of atmospheric mercury over the nordic countries. Water,

Air and Soil Pollution 56:251-265.

Jensen, A. and A. Jensen. 1991. Historical Deposition Rates of Mercury in Scandinavia Estimated by

Dating and Measurement in Cores of Peat Bogs. Water, Air and Soil Poll. 56:769-777.

Jensen J, Adare K, and Shearer R. 1997. Canadian Arctic Contaminants Assessment Report,

Department of Indian Affairs and Northern Development. 10 Wellington, Ottawa, Ontario KIA OH4,

Canada. 1997.

Johansson, K., M. Aastrup, A. Andersson, L. Bringmark and A Iverfeldt. 1991. Mercury in Swedish

Forest Soils and Waters-Assessment of Critical Load. Water, Air and Soil Pollution.56:267-281.

John, M.K. 1972. Mercury Uptake from Soil by Various Plant Species. Bull. Environ. Contam.

Toxicol. 8(2): 77-80.

Johnson, HM and Associates. 1997. Annual Report on the United States Seafood Industry. Bellevue,

Washington, 98015.

Jernelov, A. and T. Wallin. 1973. Air-borne Mercury Fallout on Snow around five Swedish Chlor-alkali

Plants. Atmos. Environ. 7:209-214.
9-11

Karvetti, R. and Knuts, L. 1985. Validity of the 24-hour recall. J. Amer. Dietet. Assoc. 85: 1437-1442.

Keeler, G., M. Hoyer, and C. Lamborg. 1994. Measurements of Atmospheric Mercury in the Great

Lakes Basin. Pp. 231-241 in Watras, C.J. and J.W. Huckabee eds. Mercury Pollution Integration and

Synthesis. 

Keeler, G., G. Glinsorn and N. Pirrone. 1995. Particulate Mercury in the Atmosphere: Its Significance,

Transport, Transformation and Sources. Accepted for Publication in Water, Air and Soil Pollution. 

Kingman, A (in press) Journal of Dental Research. Data cited by permission of author.

Kinloch, D., Kuhnlein, H., and Muir, D.C. 1992. Inuit foods and diet: a preliminary assessment of

benefits and risks. The Science of the total Environment 122: 247-278.

Kishi, R., R. Doi, Y. Fukuchi, H. Satoh, T. Satoh, A. Ono, F. Moriwaka, K. Tashiro, N. Takahata, H.

Sasatani, H. Shriakashi, T. Kamada, and K. Nakagawa. 1994. Residual Neurobehavioural Effects

Associated with Chronic Exposure to Mercury Vapor. Occup. Environ. Med. 51(1):35-41.

Kishi, R., R. Doi, Y. Fukuchi, H. Satoh, T. Satoh, A. Ono, F. Moriwaka, K. Tashiro, N. Takahata. 1993.

Subjective Symptoms and Neurobehavioural Performances of Ex-Mercury Miners at an average of 18

Years after the Cessation of Chronic Exposure to Mercury Vapor. Environ. Res. 62(2):289-302.

Kjellstrom, T., P. Kennedy, S. Wallis, et al. 1989. Physical and mental development of children with

prenatal exposure to mercury from fish: Stage 2: Interviews and psychological tests at age 6. National

Swedish Environmental Protection Board, Report 3642. (Solna, Sweden).

Knobeloch LM, Ziarnik M, Anderson HA, and Dodson VN. 1995. Imported seabass as a source of

mercury exposure: A Wisconsin case study. Environmental Health Perspectives 103:604-606.

Kosta, L., A. R. Byrne, and M. Dermelj. 1983. Trace elements in some human milk samples by

radiochemical neutron activation analysis. The Sci. Tot. Environ. 29:261-268.

Kudo, A., H.Nagase, and Y. Ose. 1982. Proportion of Methylmercury to the Total Mercury in river

waters of Canada and Japan. Water Res. 16:1011-1015.

Lambourg, C., W. Fitzgerald, G. Vandal, and K. Rolfhus. Atmospheric Mercury in Northern Wisconsin:

Sources and Species. (In press).

Lambourg, C.H., M.E. Hoyer, G.J. Keeler, I. Olmez and X. Huang. 1994. Particulate-Phase Mercury in

the Atmosphere: Collection/Analysis Method Development and Applications. Pp. 251-259 in Watras,

C.J. and J.W. Huckabee eds. In: Mercury Pollution Integration and Synthesis. 

Lange, T. R., H. R. Royals, and L. L. Conner. 1993. Influence of Water Chemistry on Mercury

Concentrations in Largemouth Bass from Florida Lakes. Trans. Amer. Fish. Soc. 122:74-84.

Langsworth, S., C. G. Elinder, C. J. Gothe, and O. Vesterburg. 1991. Biological Monitoring of

Environmental and Occupational Exposure to Mercury. Arch. Occup. Environ. Health 63:161-167.

Lasora, B.K. 1992. Trends in Mercury Concentrations in the Hair of Women of Nome Alaska: Evidence

of Seafood Consumption or Abiotic Absorption? Presented at the Mercury as a Global Pollutant:
9-12

Toward Integration and Synthesis, Monterey, California, May 31-June 4. Department of Energy, Pacific

Northwest Laboratory. 

Lathrop, R. C., K. C. Noonan, P. M. Guenther, T. L. Grasino, and P. W. Rasmussen. 1989. Mercury

Levels in Walleyes form Wisconsin Lakes of different Water and Sediment Chemistry Characteristics.

Tech. Bull. No. 163. DNR, State of Wisconsin, Madison.

Lenka, M., K. K. Panda, and B. B. Panda. 1992. Monitoring and Assessment of Mercury Pollution in

the Vicinity of a Chloralkali Plant. IV. Bioconcentration of Mercury in In Situ Aquatic and Terrestrial

Plants at Ganjam, India. Arch. Environ. Contam. Toxicol. 22:195-202.

Lee, Y. and A. Iverfeldt. 1991. Measurement of Methylmercury and Mercury in Run-off, Lake and Rain

Waters. Water, Air and Soil Poll. 56:309-321.

Lindberg, S. E., D. R. Jackson, J. W. Huckabee, S. A. Janzen, M. J. Levin, and J. R. Lund. 1979.

Atmospheric Emission and Plant Uptake of Mercury from Agricultural Soils near the Almaden Mercury

Mine. J. Environ. Qual. 8(4):572-578.

Lindberg, S. E., R. R. Turner, T. P. Meyers, G. E. Taylor, and W. H. Schroeder. 1991. Atmospheric

Concentrations and Deposition of Hg to a Deciduous Forest at Walker Branch Watershed, Tennessee,

USA. Water, Air and Soil Poll. 56:577-594.

Lindberg, S. E., T. P. Meyers, G. E. Taylor, R. R. Turner, and W. H. Schroeder. 1992. AtmosphereSurface Exchange of Mercury to a Forest: Results of Modelling and Gradient Approaches. J. of Geophy.

Res. 97(D2):2519-2528.

Lindqvist, O. and H. Rodhe. 1985. Atmospheric Mercury-a review. Tellus. 37B:136-159.

Lindqvist, O., K. Johansson, M. Aastrup, A. Andersson, L. Bringmark, G. Hovsenius, L. Hakanson, A.

Iverfeldt, M. Meili, and B. Timm. 1991. Mercury in the Swedish Environment - Recent Research on

Causes, Consequences and Corrective Methods. Water, Air and Soil Poll. 55:(all chapters)

Lipfert, F.W., P.D. Moskowitz, V.M. Fthenakis, M.P. DePhillips, J. Viren and L. Saroff. 1994.

Assessment of Mercury Health Risks to Adults from Coal Combustion. Brookhaven National

Laboratory. Upton, NY. 

Lowe TP, May TW, Brumbaugh WG, and Kane DA. 1985. National Contaminant Biomonitoring

Program: Concentrations of seven elements in fresh-water fish, 1978-1981. Arch. Environ. Contamin.

Toxicol. 14: 363-388. 

Mahaffey KR, and Mergler D. Blood levels of total and organic mercury in residents of the upper St.

Lawrence River Basin, Quebec: Association with age, gender, and fish consumption.

Mason, R.P., Fitzgerald, W.F., and Morel, M.M. 1994. The Biogeochemical Cycling of Elemental

Mercury: Anthropogenic Influences. Geochem. Cosmocheim. Acta.

MacCrimmon, H. R., C. D. Wren, and B. L. Gots. 1983. Mercury Uptake by Lake Trout, Salveiinus

namaycush, relative to age, growth and diet in Tadenac Lake with comparative data from other

Precambrian Sheild lakes. Can. J. Fisher. Aq. Sci. 40:114-120.
9-13

McKeown-Eyssen, G.E., J. Ruedy, and A. Neims. 1983. Methylmercury Exposure in Northern Quebec.

II. Neurologic Findings in Children. Amer. J. Epidem. 118:470-479.

Market Research Corporation of America. 1988. 14 Day Survey (5 Year Census, 1982-1987)

Northbrook, IL.

Marsh, D.O., T.W. Clarkson, C. Cox, L. Amin-Zaki and S. Al-Tikriti. 1987. Fetal methyl mercury

poisoning: Relationship between concentration in single strands of maternal hair and child effects. Arch.

Neurol. 44: 1017-1022.

Mason, R.P., W.F. Fitzgerald and F.M.M. Morel. 1994. The Biogeochemical Cycling of Elemental

Mercury: Anthropogenic Influences. Geochimica et Cosmochimicia Acta. 58(15):3191-3198.

Meili, M., A. Iverfeldt and L. Hakanson. 1991. Mercury in the Surface Water of Swedish Forest Lakes -

Concentrations, Speciation and Controlling Factors, Water, Air, and Soil Pollution 56: 439-453.

Meredith EK, and Malvestuto SP. 1996. Evaluation of two on-site survey methods for determining daily

per capita freshwater fish consumption by anglers. American Fisheries Society Symposium 16; 271-276.

Michigan Environmental Science Board. 1993. Mercury in Michigan's Environment: Environmental and

Human Health Concerns. Report to Gov. John Engler.

Mierle, G. and R. Ingram. 1991. The Role of Humic Substances in the Mobilization of Mercury from

Watersheds. Water, Air and Soil Poll. 56:349-357.

Miettinen JK, Rahola T, Hattula T, Rissanen K, and Tillander M. 1971. Elimination of 203 Hgmethylmercury in man. Ann Clin Res 3: 116-122.

Mills, E.L., W.H. Gutenmann, and D.J. Lisk. 1994. Mercury content of small pan fish from New York

State Waters, Chemosphere, Vol. 29, No. 6, pp. 1357-1359.

Mitra, S. 1986. Mercury in the Ecosystem. Trans Tech Publications Ltd. Switzerland.

MMES (Martin Marietta Energy Systems) material safety data sheets, VAX VTX online.

Morgan, J.N., M.R. Berry, Jr., and R.L. Graves. 1994. Effects of Native American cooking practices on

total mercury concentrations in walleye. Presented at ISEE/ISEA Joint Conference September 18-21,

1994.

Mosbaek, H., J. C. Tjell, and T. Sevel. 1988. Plant Uptake of Mercury in Background Areas.

Chemosphere 17(6):1227-1236.

Nagase, H., Y. Ose, T. Sato, and T. Ishikawa. 1982. Methylation of Mercury by Humic Substances in an

Aquatic Environment. Sci. Total Environ. 32:147-156.

Naismith DJ. 1980. Maternal nutrition and the outcome of pregnancy - a critical appraisal. Proc.

Nutrition Society 39; 1-11.

NAS (National Academy of Science). 1977. An Assessment of Mercury in the Environment. Safe

Drinking Water Committee, National Research Council.
9-14

NAS/NRC. 1989. Biologic Markers in ReproductiveToxicology. Washington, DC: National Academy

Press. Committee on Reproductive and Neurodevelopmental Toxicology. Committee on Biologic

Markers.

NAS. 1991. Methyl mercury: FDA Risk Assessment and Current Regulations. In: Seafood Safety. 

Committee on Evaluation of the Safety of Fishery Products, National Academy Press, Washington, DC.

p. 196–221.

Nater, E. and D. Grigal. 1992. Regional Trends in Mercury Distribution across the Great Lakes States,

north central USA. Nature 358:139-141.

National Institute for Occupational Safety and Health (NIOSH). 1977. A Recommended Standard for

Occupational Exposure to Inorganic Mercury. 

National Marine Fisheries Service (NMFS). 1993. Fisheries of the United States, 1992. Current Fishery

Statistics No. 9200, National Oceanic and Atmospheric Administration, U. S. Department of Commerce,

U. S. Government Printing Office, Washington, DC. 

National Research Council/National Academy of Sciences (Committee on Evaluation of the Safety of

Fishery Products). 1991. Seafood Safety. Ed: Ahmed FE. National Academy Press, Washington, DC.

New Jersey Department of Environmental Protection and Energy. 1993. Final Report on Municipal

Solid Waste Incineration. Volume II: Environmental and Health Issues.

New Jersey Department of Environmental Protection and Energy Division of Science and Research.

1994. Preliminary Assessment of Total Mercury Concentrations in Fishes from Rivers, Lakes and

Reservoirs of New Jersey. 93-15F.

New York State Department of Environmental Conservation. 1990. Chemical Contaminants in Fish

from the St. Lawrence River Drainage on Lands of the Mohawk Nation at Akwesasne and Near the

General Motors Corporation /Central Foundry Division Massena, New York Plant. Technical Report 90-

1.

NMFS (National Marine Fisheries Service). 1995. The current publically-available National Marine

Fisheries Service Data base was supplied to U.S. EPA via fax from Malcolm Meaburn (Charleston

Laboratory/Southeast Fisheries Science Center/National Marine Fisheries Service/National Oceanic and

Atmospheric Administration/U.S. Dept. Of Commerce) to Kathryn Mahaffey (Environmental Criteria

and Assessment Office-Cincinnati,OH/Office of Health and Environmental Assessment/Office of

Research and Development/ U.S. Environmental Protection Agency). February 23, 1995.

Nixon DE, Mussmann GV, and Moyer TP. 1996. Inorganic, organic, and total mercury in blood and

urine: Cold vapor analysis with automated flow injection sample delivery. J. Analytical Toxicology

20:17-22.

NOAA (1978) as described in the NMFS data base.

Nobmann ED, Byers T, Lanier AP, Hankin JH, and Jackson MY. 1992. The diet of Alaska Native

adults: 1987-1988. American Journal of Clinical Nutrition 55: 1024-1032.
9-15

Nriagu, J. O. 1979. The Biogeochemistry of Mercury in the Environment. Elsevier/North Holland.

Biomedical Press: New York.

Nusser SM, Corriquiry AL, Dodd KW, and Fuller WA. 1996. Semiparametric transofrmation approach

to estimating usual daily inttake distirbutions. Journal of the American Statistical Assocation 91:1440-

1449.

Occupational Safety and Health Administration, Job Health Hazards Series. 1975. Mercury. OSHA

report 2234.

Occupational Safety and Health Administration. 1989. Industrial Exposure and Control Technologies

for OSHA Regulated Hazardous Substances: Mercury, Aryl and Inorganic Compounds. p 1207-1213.

Olmez, I., G. Keeler and P. Hopke. 1994. Interim Data Interpretation Report of MIT/ALSC Data Set

(MIT Report No. MITNRL-060).

Olstad, M. L., R. I. Hooland, N. Wandel, and P. A. Hengsten. 1987. Correlation between Amalgam

Restorations and Mercury Concentrations in Urine. J. Dent. Res. 66:1179-1182.

Oskarsson A, Schutz A , Skerfving S, Hallen IP, Ohlin B, and Lagerkvist BJ. 1996. Total and inorganic

mercury in breast milk and blood in relation to fish consumption and amalgam fillings in lactating

women. Arch Environmental Health 51;234-241.

Overcamp, T.J. 1977. Modelling of Air Quality for Industrial Pollution Control, Appendix from a

Continuing Education Shortcourse taught at Clemson University on December 7, 1977.

Parkinson, A.J., Cruz, A.L., Heyward, W.L., Bulkow, L.R., Hall, D., Barstaed, L., and Connor, W.E. 

1994. Elevated concentrations of plasma omega-3 polyunsaturated fatty acids among Alaskan Eskimos. 

Amer. J. Clin. Nutr. 59:383-388.

Parks, J. W., A. Lutz, and J. A. Sutton. 1989. Water Column Methylmercury in the Wabigoon/English

River-Lake System: Factors Controlling Concentrations, Speciation, and Net Production. Can. J. Fisher.

Aq. Sci. 46:2184-2202.

Pasquill, F. 1961. The Estimation of Dispersion of Windborne Material, Meteorol. Mag., 90:33-49.

Pasquill, F. 1974. Atmospheric Diffusion, the Dispersion of Windborne Material from Industrial and

other Sources, Ellis Horwood, New York.

Pasquill, F. 1976. Atmospheric Dispersion Parameters in Gaussian Plume Modelling: Part II. Possible

Requirements for Change in Turner Workbook Values, Report EPA-600/4-760306, U.S. Environmental

Protection Agency.

PEI Associates, Inc and H.E. Cramer Company, Inc. 1986. Air quality modeling analysis of municipal

waste combustors. Prepared for Monitoring and Data Analysis Division, Office of Air Quality Planning

and Standards, Research Triangle Park, North Carolina 27711.

Pennington, J. A. T. 1983. Revision of the Total Diet Study Food List and Diets. J. Amer. Dietetic

Assoc. 82:166-173.
9-16

Peterson DE, Kanarek MS, Keykendall MA, Diedrich JM, Anderson HA, Remington PL, and Sheffy TB.

1995. Fish consumption patterns and blood mercury levels in Wisconsin Chippewa Indians. Archives of

Environmental Health 49(11): 53-58.

Petersen, G., Å. Iverfeldt and J. Munthe. 1995. Atmospheric mercury species over Central and

Northern Europe. Model calculations and comparison with observations from the Nordic Air and

Precipitation Network for 1987 and 1988. Atmospheric Environment 29:47-68.

Phelps, R.W., T.W. Clarkson, T.G. Kershaw and B. Wheatley. 1980. Interrelationships of blood and

hair mercury concentrations in a North American population exposed to methyl mercury. Arch. Environ.

Health. 35:161-168.

Pierce, R., D. Noviello and S. Rogers. 1981. Commencement Bay Seafood Consumption Report.

Preliminary Report. Tacoma, WA: Tacoma-Pierce County Health Department. As cited in U.S. EPA

(1989). Exposure Factors Handbook. EPA/600/8-89/043.

Pierce, T.E. and D.B. Turner. 1980. User's Guide for MPTER: A multiple point Gaussian dispersion

algorithm with optional terrain adjustment. EPA-600/8-80-016. U.S. EPA., Research Triangle Park,

N.C.

Poderbarac, D. S. 1984. Pesticide, Metal, and Other Chemical Residues in Adult Total Diet Samples.

(XIV). October 1977 - September 1978. J. Assoc. Off. Anal. Chem. 67:176-185. 

Pollman, C. D., G. A. Gill, W. M. Landing, D. A. Bare, J. Guentzel, D. Porcella, E. Zillioux, and T.

Atkeson. 1994. Overview of the Flordia Atmospheric Mercury Study (FAMS).

Porcella, D.B. 1994. Mercury in the Environment: Biogeochemistry. Pp. 3-19 in Watras, C.J. and J.W.

Huckabee eds. In: Mercury Pollution Integration and Synthesis.

Puffer, H. 1981. Consumption rates of potentially hazardous marine fish caught in the metropolitan Los

Angeles area. EPA Grant #R807 120010.As cited in U.S. EPA (1989). Exposure Factors Handbook. 

EPA/600/8-89/043.

Putnam, J.J. 1991. Food Consumption, 1970-1990. Food Review 14(3):2-12. July-September.

Rada, R., J Wiener, M. Winfrey, and D. Powell. 1989. Recent increases in atmospheric deposition of

mercury to north-central Wisconsin Lakes inferred from sediment Analysis. Arch. Environ. Contam.

Toxicol. 18:175-181.

Randerson, D. 1984. D. Randerson, editor, Atmospheric Science and Power Production,

DOE/TIC-27601). 

Rao, K.S. and L. Satterfield. 1980. A study of the probable environmental impact of fugitive coal dust

emissions at the Ravenswood Power Plant, New York. ATDL Contribution 80/26, NOAA, Oak Ridge,

TN.

Rao, K.S. 1981. Analytical Solutions of a Gradient-Transfer Model for Plume Deposition and

Sedimentation, National Oceanic and Atmospheric Administration, Environmental Research

Laboratories, Technical Memorandum ERL ARL-109.
9-17

Revis, N. W., T. R. Osborne, G. Holdsworth, and C. Hadden. 1990. Mercury in Soil: A Method for

Assessing Acceptable Limits. Arch. Environ. Contam. Toxicol., 19:221-226.

Richardson, M., M. Mitchell, S. Coad and R. Raphael. 1995. Exposure to Mercury in Canada: A

Multimedia Analysis. Water, Air and Soil Pol. 80:21-30.

Rupp, EM, Miller, FI, Baes, CF, 3rd. 1980. Some results of recent surveys of fish and shellfish

consumption by age and region of U.S. regions. Health Phys. 39(2):165-175.

Schantz SL, Sweeney AM, Gardiner JC, Humphrey HE, McCaffrey RJ, Gasior DM, Srikanh KR, and

Budd ML. 1996. Neuropsychological assessment of an aging population of great Lakes Fisheaters. 

Toxicology Industrial Health 12:403-417.

Schuster, E. 1991. The Behavior of Mercury in the Soil with special emphasis on Complexation and

Adsorption processes- A Review of the Literature. Water, Air and Soil Poll. 56:667-680.

Schroeder, W. H. and R. A. Jackson. 1987. Environmental Measurements with an Atmospheric

Mercury Monitor having Specific Capabilities. Chemosphere, 16:183-199.

Seafood Business (September/October 1993). p. 35

Sehmel, G.A. 1984. Deposition and Resuspension, in Atmospheric Science and Power Production, D.

Randerson (Ed.), DOE/TIC-27601.

Sekerke, H.J., R.L. Denger, C.M. Adams and S.D. Moss. 1994. Seafood Consumption Survey in Florida.

Abstract presented at the Society of Toxicology 33rd Annual Meeting (Vol. 14 No. 1. March, 1994.)

Abstract No. 563.

Seritti, A., A. Petrosino, E. Morelli, R. Ferrara, and C. Barghigiani. 1982. The biogeochemical cycling

of mercury in the Mediterranean. Part I: Particulate and dissolved forms of mercury in the northern

Tyrrhenian Sea. Environ. Tech. Lett. 3:251-256.

Shannon, J. D., and E. C. Voldner. 1994. Modelling Atmospheric Concentrations and Deposition of

Mercury to the Great Lakes. Presented at the DOE/FDA/EPA Workshop on Methylmercury and Human

Health, Bethesda, MD March 22-23 1994.

Shatenstein B, Kosatsky T, Tapia M, Kishchuk N, and Marchand Y. 1997. Exploratory assessment of

fish consumption among Asian-origin sportsfishers on the St. Lawrence River in the Montreal region. 

Abstract from the Conference on the Effects of the Environment on Human Health in the Great Lakes

and St. Lawrence River Basins. May 12-15, 1997. Montreal, Quebec, Canada.

Sherlock, R.H. and E.A. Stalker. 1941. A Study of Flow Phenomena in the Wake of Smoke Stacks,

Engineering Research Bulletin 29, University of Michigan, Ann Arbor.

Sherlock J, Hislop J, Newton D, Topping G, and Whittle K. 1984. Evaluation of mercury in human

blood from controlled chronic ingestion of methylmercury in fish. Human Toxicol. 3: 117-131.

Sherlock, J.C. and M.J. Quinn. 1988. Underestimation of dose-response relationship with particular

reference to the relationship between the dietary intake of mercury and its concentration in blood. Hum.

Toxicol. 7(2):129-132.
9-18

Shitara, K., and A. Yasumasa. 1976. Hokkaidoritzu Eigel kenkyushoho Hokkaidorita 26:73-78.

Shubat PJ, Raatz KA, and Olson RA. 1996. Fish consumption advisories and outreach programs for

Southeast Asian immigrant. Toxicology and Industrial Health 12 (Nos 3/4): 427-434.

Siegel, S. M., B. Z. Siegel, C. Barghigani, K. Aratani, P. Penny, and D. Penny. 1987. A Contribution to

the Environmental Biology of Mercury Accumulation in Plants. Water, Air and Soil Poll. 33:65-72.

Silverman, D.L., Reis, G.J., Sacks, F.M., Boucher, T.M., and Pasternak, R.C. 1990. Usefulness of

plasma phospholipid N-3 fatty acid levels in predicting dietary fish intake in patients with coronary

artery disease. Amer. J. Cardiol. 66: 800-862.

Simonin, H. A., S. P. Gloss, C. T. Driscoll, C. L. Schofield, W. A. Kretser, R. W. Karcher, and J.

Symula. 1994. Mercury in Yellow Perch from Adirondack Drainage Lakes (New York, U.S.), In: 

Watras, C. J. and J. W. Huckabee [eds], Mercury Pollution: Integration and Synthesis, Lewis Publishers,

Boca Raton, FL (in press).

Skerfving, S., K. Hansson, C. Mangs, J. Lindsten and N. Ryman. 1974. Methyl mercury-induced

chromosome damage in man. Environ. Res. 7:83-98.

Skerving, S. 1988. Mercury in Women Exposed to Methylmercury Though Fish Consumption and in

Their Newborn Babies and Breast Milk. Bull. Environ. Contam. Toxicol. 41:475-482. 

Slemr, F. and E. Langer. 1992. Increase in Global Atmospheric Concentrations of Mercury inferred

from Measurements over the Atlantic Ocean. Nature 335:434-437.

Slinn, W.G.N. 1984. Precipitation Scavenging, in Atmospheric Science and Power Production, D.

Randerson, ed. DOE/TIC-27601.

Sloan, R. 1990. Trends in Mercury Concentrations of the Fish of Onondaga Lake, in Proceedings of the

Onondaga Lake Remediation Conference, Bolton Landing, NY, February 5-8 1990. 

Smith JC, and Farris FF. 1996. Methyl Mercury Pharmacolkinetics in man: A reevaluation. Tox. Appl.

Pharmacol. 137: 245-252

Smith, M.E. 1951. The Forecasting of Micrometeorological Variables, Meteorol. Monogr., 4:50-55.

Snapp, K. R., D. B. Boyer, L. C. Peterson, and C. W. Svare. 1989. The Contribution of Dental

Amalgams to Mercury in Blood. J. Dent. Res. 68:780-785.

Soldat, J.K. 1970. A statistical study of the habits of fishermen utilizing the Columbia River below

Hanford. Pages 302-308 in W.C. Rienig, editor. Environmental Surveillance in the vicinity of nuclear

facilities. C.C. Thomas, Springfield Illinois.

Sorensen, J., G. Glass, K. Schmidt, J. Huber and G. Rapp. 1990. Airborne Mercury Deposition and

Watershed Characteristics in Relation to Mercury Concentrations in Water, Sediments, Plankton and

Fish of Eighty Northern Minnesota Lakes. Environ. Sci. Technol. 24:1716-1727.
9-19

Sorensen, J., G. Glass, K. Schmidt, and G. Rapp. 1991. Mercury Concentrations in Fish from the St.

Louis River Near the Fond Du Lac-Indian Reservation (Below the Cloquet and White Pine Rivers). First

Year Report on the St. Louis River Water Resources Project.

Sorensen, J.A., G.E. Glass and K.W. Schmidt. 1992. Regional Patterns of Mercury Wet Deposition and

Major Ions. Part 2 of Chapter 4. In: Mercury in the St. Louis River, Mississippi River, Crane Lake and

Sand Point Lake: Cycling, Distribution and Sources. Report to the Legislative Commission on Minnesota

Resources. April, 1992. Water Quality Division Minnesota Pollution Control Agency St. Paul, MN.

Sorensen, J.A., G.E. Glass and K.W. Schmidt. 1994. Regional Patterns of Mercury Wet Deposition.

Environ. Sci. Tech. 28:2025-2032.

Stafford, C. 1994. Mercury Contamination in Maine Predatory Fishes. Masters Thesis Submitted to The

Graduate School, University of Maine.

Stafford, C. P. 1994. Mercury Concentrations in Maine Predatory Fishes. Masters Thesis, University of

Maine.

State of Hawaii, Department of Business and Economic Development. 1988. Hawaii Seafood

Consumption. A Survey of Seafood Consumption in Hawaii. Department of Business and Economic

Development. Honolulu, Hawaii.

Stern, A. 1993. Re-evaluation of the Reference Dose for Methylmercury and Assessment of Current

Exposure Levels. Risk Analysis 13, 3:355-363.

Stern, A.H., Korn, L.R., and Ruppel, B.F. 1996. Estimation of Fish Consumption and Methylmercury

Intake in the New Jersey Population. J. Euro. Anal. Env. Epi. 6:503-525.

Subcommittee on Criteria for Dietary Evaluation. 1986. Coordinating Committee on Evaluation of

Food Consumption Surveys, Food and Nutrition Board, Commmission on Life Sciences, National

Research Council, National Academy Press, Washington, DC. 

Sumo, E., B.R. Singh, A.R. Selmer-Olsen, and K. Steenburg. 1985. Uptake of Hg-labeled Mercury 203

compounds by Wheat and Beans Grown on an oxisol. Plant and Soil 85: 347-355.

Swain, E. B., and D. D. Helwig. 1989. Mercury in Fish from Northeastern Minnesota Lakes: Historical

Trends, Environmental Correlates, and Potential Sources. Journal of the Minnesota Acadamy of Science

55:103-109.

Swain, E. B., D. A. Engstrom, M. E. Brigham, T. A. Henning, and P. L. Brezonik. 1992. Increasing

Rates of Atmospheric Mercury Deposition in Midcontinental North America. Science 257:784-787.

Swedish EPA. 1991. Mercury in the Environment: Problems and Remedial Measures in Sweden. ISBN

91-620-1105-7.

Swedish Expert Group. 1971. Methylmercury in Fish. A toxicological-epidemiological evaluation of

risks. Nord. Hyd. Tidskr. Supp. 4:1-364.

Szymczak, J. and H. Grajeta. 1992. Mercury Concentrations in Soil and Plant Material, Pol. J. Food

Nutr. Sci., Vol 1/42, No.2, pp.31-39.
9-20

Tamura, R., M. Fukuzaki, Y. Hirano, and Y. Mitzushima. 1985. Evaluation of Mercury Contamination

using Plant Leaves and Humus as Indicators. Chemosphere 14(11/12):1687-1693.

Temmerman, L. R., R. Vandeputte, and M. Guns. 1986. Biological Monitoring and Accumulation of

Airborne Mercury in Vegetables. Environ. Poll., 41:139-151.

Temple, P. J. and S. N. Linzon. 1977. Contamination of Vegetation, Soil, Snow and Garden Crops by

Atmospheric Deposition of Mercury from a Chlor-Alkali Plant, in (1977) D. D. Hemphill [ed] In: Trace

Substances in Environmental Health - XI, Univ Missouri, Columbia. p. 389-398.

Tollefson, L., and F. Cordle. 1986. Methylmercury in Fish: A review of Residual Levels, Fish

Consumption and Regulatory Action in the United States. Envir. Health Perspect. 68:203-208.

Toy, K.A., Gawne-Mittelstaedt G.D., Polissar N.L., and Liao S. 1995. A fish consumption survey of the

Tulalip and Squaxin Island tribes of Puget Sound. Seattle, Washington.

Turcotte, S. 1983. Georgia Fishery Study: Implications for Dose Calculations. Technical Division

Savannah River Laboratory. DPST--83--319-Rev.1. Memorandum to H.P. Olson.

Turner , D.B. 1967. Workbook of Atmospheric Dispersion Estimates, Public Health Service 999-AP-26,

Robert A. Taft Sanitary Engineering Center, Cincinnati, Ohio.

Turner, D.B. 1970. Workbook of Atmospheric Dispersion Estimates. Public Health Service Publication

No. 999-AP-26, U.S. EPA, Research Triangle Park, N.C.

Turner, C.J., M.K. Bhatnagar and S. Yamashiro. 1981. Ethanol potentiation of methyl mercury toxicity:

A preliminary report. J. Toxicol. Environ. Health. 7:665-668.

U.S.D.A. 1995. Adjustments to U.S.D.A. recipe files. Fax from Betty Perloff (Agricultural Research

Service/U.S.D.A.) to Kate Mahaffey (Environmental Criteria and Assessment OfficeCincinnati,OH/Office of Health and Environmental Assessment/Office of Research and

Development/U.S. Environmental Protection Agency). March 13, 1995.

U.S. Department of Commerce. 1978. Report on the chance of U.S. seafood consumers exceeding the

current acceptable daily of mercury and on recommended regulatory controls. National Oceanic and

Atmospheric Administration, Washington, DC.

U.S. EPA. 1975. Control of Water Pollution from Cropland: Volume I, A Manual for Guideline

Development. EPA-600/2-75-026a. Office of Research and Development.

U.S. EPA. 1976. Control of Water Pollution from Cropland: Volume I, An Overview. EPA-600/2-75-

026b. Office of Research and Development.

U.S. EPA. 1980. Ambient Water Quality Criteria Documents (various). U.S. Environmental Protection

Agency, Office of Water Regulations and Standards. EPA 440/5-80 Series.

U.S. EPA. 1985. Water Quality Assessment: A Screening Procedure for Toxic and Conventional

Pollutants in Surface and Ground Water (Part 1). Washington, D.C. EPA/600/6-85/002-A.

U.S. EPA. 1988. Drinking Water Criteria Document for Inorganic Mercury. ECAO-CIN-025
9-21

U.S. EPA. 1989. Assessing Human Health Risks from Chemically Contaminated Fish and Shellfish: A

Guidance Manual. EPA/500/3-89-00.

U.S. EPA. 1990. Methodology for Assessing Health Risks Associated with Indirect Exposure to

Combustor Emission. Interim Final. EPA/600/6-90/003.

U.S. EPA. 1991. Feasibility of Environmental Monitoring and Exposure Assessment for a Municipal

Waste Combustor: Rutland, Vermont Pilot Study. EPA/600/8-91/007.

U.S. EPA. 1992. A National Study of Chemical Residues in Fish. (EPA 823-R-92-008a and b.) Office

of Water Regulations and Standards, U.S. EPA. Washington DC Vols. 1 and 2. September 1992.

U.S. EPA. 1992. Tribes at Risk. The Wisconsin Tribes Comparative Risk Project. Office of Policy

Planning and Evaluation and Region 5. EPA 230-R-92-017. October, 1992.

U.S. EPA. 1992a. Assessment and Remediation of Contaminated Sediments (ARCS) Program. EPA

905-R92-007.

U.S. EPA. 1992b. Assessment and Remediation of Contaminated Sediments (ARCS) Program. EPA

905-R92-008.

U.S. EPA. 1992b. Tribes at Risk. The Wisconsin Tribes Comparative Risk Project. Office of Policy

Planning and Evaluation and Region 5. EPA 230-R-92-017. October, 1992.

U.S. EPA. 1992b. Environmental Risk in Indian Country. U.S. EPA Washington, DC. Report No. EPA

171-R-92-014. PB 92-182393.

U.S. EPA. 1992c. National Study of Chemical Residues in Fish. EPA 823-R-92-008a.

U.S. EPA. 1992. User's Guide for the Industrial Source Complex (ISC2) Dispersion Models, Volume II

- Description of Model Algorithms, EPA-450/4-92-008b, U.S. Environmental Protection Agency,

Research Triangle Park, North Carolina 27711.

U.S. EPA. 1993. Great Lakes Water Quality Initiative Criteria Documents for the protection of Wildlife

(proposed). DDT, Mercury; 2,3,7,8-TCDD, PCBs. Office of Water, Office of Science and Technology,

Washington, D.C.

U.S. EPA. 1993. Summary Review of Health Effects Associated with Mercuric Chloride. Office of

Health and Environmental Assessment, Washington, D.C. EPA/600/R-92/1993. September 1993.

U.S. EPA. 1993a. Summary Review of Health Effects Associated with Mercuric Chloride. EPA/600/R92/199.

U.S. EPA. 1993b. Wildlife Exposure Factors Handbook. Prepared by ORD/OHEA. EPA/600/R-93/187a

& b.

U.S. EPA. 1993c. Wildlife Criteria Portions of the Proposed Water Quality Guidance for the Great Lakes

System. (Proposed) Office of Water and Office of Science and Technology. EPA-822-R-93-006.

U.S. EPA. 1995. Mercury Study Report to Congress. Volume II: Inventory of Anthropogenic Mercury

Emissions in the United States.
9-22

Vreman, K., N.J. van der Veen, E.J. van der Molen and W.G. de Ruig. 1986. Transfer of cadmium, lead,

mercury and arsenic from feed into milk and various tissues of dairy cows: chemical and pathological

data. Netherlands Journal of Agricultural Science 34:129-144.

Wagemann R, Innes S, and Richard PR. 1996. Overview and regional and temporal differences of heavy

metals in Arctic whales and ringed seals in the Canadian Arctic. The Science of the Total Environment

186;41-66.

Waller DP, Presperin C, Drum M, Negrusz A, Larsen AK, and van der Ven H, and Hibbard J. 1996. 

Great Lakes fish as a source of maternal and fetal exposure to chlorinated hydrocarbons. Toxicology and

Industrial Health 12 (Nos 3/4): 335-345.

Wark, K. and C.F. Warner. 1981. Air Pollution Its Origin and Control, Harper Collins, 1981.

Watras, C. J. and N. S. Bloom. 1992. Mercury and Methylmercury in Individual Zooplankton:

Implications for bioaccumulation. Limnol. Oceanagr., 37(6):1313-1318. 

Weihle, P., Grandjean, P, Debes, F, White, R. 1996. Health implicaitons for Faroe islanders of heavy

metals and PCBs from pilot whales. Science of the Total Environment 186(1-2):141-148.

Welch, H.E., Bergmann, M.A., Siferd, T.D., Martin, K.A., Curtis, M.F., Crawford, R.E., Conover, R.J.,

and Hop, H. 1992. Energy flow through the marine ecosystem of the Lancaster Sound Region, Arctic

Canada. Arctic 45: 343-357.

Welch, L. J. 1994. Contaminant Burdens and Reproductive Rates of Bald Eagles Breeding in Maine.

Masters Thesis, University of Maine.

Wells, A. E. 1917. Results of Recent Investigations of the Smelter Smoke Problem, Ind. Eng. Chem.,

9:640-646.

Wendroff, AP. 1995. Magico-religious mercury use and cultural sensitivity. Amer. J. Public Health

85:409-410.

West, P.C., J.M. Fly, R. Marans and F. Larkin. 1989. Michigan Sport Anglers Fish Consumption Survey. 

A Report to Michigan Toxic Substance Control Commission by University of Michigan Ann Arbor, MI.

Wheatley B, and Paradis S. 1995. Exposure of Canadian Aboriginal Peoples to methylmercury. Water,

Air and Soil Pollution 80:3-11.

Whitby, K. 1978. The physical characteristics of sulfer aerosols. Atmosph. Env. 12:135-159.

WHO. 1976. World Health Organization. Environmental Health Criteria I, Mercury. Geneva.

WHO. 1989. World Health Organization. Environmental Health Criteria 86: Mercury Environmental

Aspects. Geneva.

WHO. 1990. World Health Organization. Environmental Health Criteria 101: Methylmercury. Geneva.

Wiener, J., W. Fitzgerald, C. Watras and R. Rada. 1990. Partitioning and Bioavailability of Mercury in

an Experimentally Acidified Wisconsin Lake. Environ. Toxicol. Chem. 9:909-918.
9-23

Wiersma, D., B. J. van Goor, and N. G. van der Veen. 1986. Cadmium, Lead, Mercury, and Arsenic

Concentrations in Crops and Corresponding Soils in the Netherlands. J. Agric. Food Chem., 34:1067-

1074.

Wilken, R. D. and H. Hintelmann. 1991. Mercury and Methylmercury in Sediments and Suspended

particles from the River Elbe, North Germany. Water, Air and Soil Poll. 56:427-437.

Wilkins, L. and Hankin, J. Personal Communication. 28 February 1996. University of Hawaii at

Manoa. Cancer Research Center of Hawai'i, Epidemiology Program. 1236 Lauhain Street, Honolulu,

Hawaii.

Willett W. 1990. "Nature of Variation in Diet". pp. 34-51. In: Nutritional Epidemiology. Ed. Willett,

W. Nutritional Epidemiology. Monographs in Epidemiology and Biostatistics, Vol. 15. Oxford

University Press. New York/Oxford.

Winfrey, M. R. and J. W. M. Rudd. 1990. Environmental Factors Affecting the Formation of

Methylmercury in Low pH Lakes. Environ. Toxicol. and Chem., 9:853-869.

Witschi JC. 1990. "Short-Term Dietary Recall and Recording Methods", pp. 52-68. In: Nutritional

Epidemiology. Monographs in Epidemiology and Biostatistics, Volume 15. Oxford University Press. 

New York/Oxford.

Wolfe, R. August 14, 1997. Personal communication, Division of Subsistence, Department of Fish and

Game, State of Alaska. Juneau, Alaska.

Wolfe, R. and R. Walker. 1987. Subsistence Economies in Alaska: Productivity, geography and

developmental impacts. Arctic Anthropol. 24:56-81.

Wormworth, J. 1995. Toxins and tradition: The impact of food-chain contamination on the Inuit of

Northern Quebec. Canadian Medical Association Journal 152(8):1237-1240.

Wren, C. D., W. A. Scheinder, D. L. Wales, B. M. Muncaster, and I. M. Gray. 1991. Relation Between

Mercury Concentrations in Walleye (Sitzostedion vitreum vitreum) and Northern Pike (Esox lucius) in

Ontario Lakes and Influence of Environmental Factors. Can. J. Fisher. Aq. Sci. 44:750-757.

Xun, L., N. Campbell and J.W. Rudd. 1987. Measurements of Specific Rates of Net Methyl Mercury

Production in the Water Column and Surface Sediments of Acidified and Circumneutral Lakes. Can. J.

Fish Aquat. Sci. 44:750-757.

Yang, Y. J., C. C. Huang, T. S. Shih, and S. S. Yang. 1994. Chronic elemental Mercury Intoxication:

Clinical and Field Studies in Lampsocket Manufacturers. Occup. Environ. Med. 51(4):267-270.

Youland DM, and Engle A. 1976. Practices and problems in HANES: Dietary data methodology. J.

Amer. Dietet. Assoc. 68: 22-25.

Zander D., U. Ewers, I. Freier, S. Westerweller, E. Jermann, and A. Brockhaus. 1990. Untersuchungen

zu Quecksilberbelastung der Bevolkerung II. Quecksilberfreisetzung aus Amalgamfullungen. Zbl. Hyg.

190:325-334.

Zeller, K.F. 1984. The Environmental Impact Statement, in Atmospheric Science and Power

Production, D. Randerson (Ed.), DOE/TIC-27601.
APPENDIX A

EXPOSURE PARAMETER JUSTIFICATIONS 
A-i

TABLE OF CONTENTS

DISTRIBUTION NOTATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

A. SCENARIO INDEPENDENT PARAMETERS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1

A.1 Chemical Independent Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1

A.1.1 Basic Constants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1

A.1.2 Receptor Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1

A.1.2.1 Body Weight . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-2

A.1.2.2 Exposure Duration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-2

A.1.3 Agricultural Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-3

A.1.3.1 Interception Fraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-3

A.1.3.2 Length of Plant Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-4

A.1.3.3 Plant Yield . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-5

A.1.3.4 Plant Ingestion by Animals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-6

A.1.3.5 Soil Ingestion by Animals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-7

A.1.4 Exposure Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-7

A.1.4.1 Inhalation Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-8

A.1.4.2 Consumption Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-9

A.1.4.3 Soil Ingestion Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-10

A.1.4.4 Groundwater Ingestion Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-11

A.1.4.5 Fish Ingestion Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-12

A.1.4.6 Contact Fractions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-14

A.2 Chemical Dependent Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-15

A.2.1 Basic Chemical Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-15

A.2.1.1 Molecular Weight . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-15

A.2.1.2 Henry's Law Constant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-15

A.2.1.3 Soil-Water Partition Coefficient . . . . . . . . . . . . . . . . . . . . . . . . . . . A-16

A.2.1.4 Sediment-to-Water Partition Coefficient . . . . . . . . . . . . . . . . . . . . . A-16

A.2.1.5 Suspended Sediment-Water Partition Coefficient . . . . . . . . . . . . . . A-17

A.2.1.6 Soil and Water Loss Degradation Constants . . . . . . . . . . . . . . . . . . A-18

A.2.1.7 Equilibrium Fraction for Chemical in Soil . . . . . . . . . . . . . . . . . . . A-18

A.2.1.8 Equilibrium Fraction for Chemical in Water . . . . . . . . . . . . . . . . . . A-19

A.3 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-35
A-ii

LIST OF TABLES

A-1 Chemical Independent Constants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1
A-iii

DISTRIBUTION NOTATION

A comprehensive uncertainty analysis was not conducted as part of this study. Initially,

preliminary parameter probability distributions were developed. These are listed in Appendicies A and

B. These were not utilized in the generation of quantative exposure estimates. They are provided as a

matter of interest for the reader.

Unless noted otherwise in the text, distribution notations are presented as follows.

Distribution Description

Log (A,B) Lognormal distribution with mean A and standard deviation B

Log*(A,B) Lognormal distribution, but A and B are mean and standard deviation

of underlying normal distribution.

Norm (A,B) Normal distribution with mean A and standard deviation B

U (A,B) Uniform distribution over the range (A,B)

T (A,B,C) Triangular distribution over the range (A,C) with mode of B
A-1

A. EXPOSURE MODEL PARAMETERS

This appendix describes the parameters used in the exposure modeling for the Mercury Study

Report to Congress. For other environmental fate model parameters the reader is referred to Appendicies

A-C of Volume 3.

A.1 Chemical Independent Parameters

Chemical independent parameters are variables that remain constant despite the specific

contaminant being evaluated. The chemical independent variables used in this study are described in the

following sections.

A.1.1 Basic Constants

Table A-1 lists the chemical independent constants used in the study, their definitions, and

values. 

Table A-1

Chemical Independent Constants

Parameter Description Value

R ideal gas constant 8.21E-5 m -atm/mole-K 3

pa air density 1.19E-3 g/cm3

ua viscosity of air 1.84E-4 g/cm-second

Psed solids density 2.7 kg/L

Cdrag drag coefficient 1.1E-3

 Von Karman's coefficient 7.40E-1

boundary thickness 4.0 2

A.1.2 Receptor Parameters

Receptor parameters are variables that reflect information about potential receptors modeled in

the study. These parameters include body weight, exposure duration, and other characteristics of

potential receptors.

A.1.2.1 Body Weight

Parameter: BWa, BWc

Definition: Body weights (or masses) of individual human receptors

Units: kg
A-2

Receptor Default Value (kg)

Child 17

Adult 70

Technical Basis:

The default values for children and adults are those assumed in U.S. EPA, 1990.

A.1.2.2 Exposure Duration

Parameter: ED

Definition: Length of time that exposure occurs.

Units: years

Receptor Default Value Distribution Range

(years) (years)

Child 18 U(1,18) 1-18

Adult 30 U(7,70) 7-70

Technical Basis:

The 18-year exposure duration for the child is based on U.S. EPA guidance for this study. For

adults, the 30-year duration is the assumed lifetime of the facility (U.S. EPA, 1990). It should be noted

for noncarcinogenic chemicals the exposure duration is not used in the calculations. The range and

distribution are arbitrary to determine the relative sensitivity of this variable, when appropriate.

A.1.4 Exposure Parameters

Exposure parameters are variables that directly affect an individual's dose or intake of a

contaminant. Such parameters include inhalation and ingestion rates of air, water and crops and the

surface area of skin for the purposes of dermal contact scenarios.
A-3

A.1.4.1 Inhalation Rate

Parameter: INH

Definition: Rate of inhalation of air containing contaminants.

Units: m /day 3

Receptor Default Value Distribution

(m /day) 3

Infant 5.14 T(1.7,5.14,15.4)

Child 16 T(2.9,16,53.9)

Adult 20 T(6,20,60)

Technical Basis:

The default value for infants is the central value of the distribution used for 1 year olds in

Hanford Environmental Dose Reconstruction Project (HEDR) (1992) and is from Roy and Courtay

(1991). The default value for children is based on U.S. EPA (1990). The default value for adults is that

recommended in U.S. EPA (1991), which states that this value represents a reasonable upper bound for

individuals that spend a majority of time at home.

The range for infants is that used for 1 year olds in HEDR (1992) and was determined by scaling

the value 5.14 by 0.3 and 3.0, respectively. The range for children is the smallest range containing the

values used for 5-, 10-, and 15-year-old children in HEDR (1992). The range for the adult was obtained

by scaling the default value by the same numbers used for infants of 0.3 and 3.0 (we note that HEDR,

1992 used a slightly higher central value of 22 m /day). 3

To prevent a bias towards upper-end inhalation rates, triangular distributions were considered

more appropriate than more arbitrary uniform distributions, with a most likely value equal to the default

value.
A-4

A.1.4.2 Consumption Rates

Parameter: CPi, CAj

Definition: Consumption rate of food product per kg of body weight per day.

Units: g dry weight/kg BW/day

Food Type Child (gDW/kgBW/day) Adult (g DW/kg BW/day)

Leafy Vegetables 0.008 0.0281

Grains and cereals 3.77 1.87

Legumes 0.666 0.381

Potatoes 0.274 0.170

Fruits 0.223 0.570

Fruiting vegetables 0.120 0.064

Rooting Vegetables 0.036 0.024

Beef, excluding liver 0.553 0.341

Beef liver 0.025 0.066 a

Dairy (milk) 2.04 0.599

Pork 0.236 0.169

Poultry 0.214 0.111

Eggs 0.093 0.073

Lamb 0.061 0.057 a

 Only the 95-100 percentile of the data from TAS (1991) was nonzero. a

Technical Basis:

All of the values reported above are given on a gram dry weight per kg of body weight per day

basis. With the exception of the ingestion rates for adults for leafy vegetables and fruits, the values are

either the 50-55 percentile (or the 95-100 percentile if the median was zero) of the data from Technical

Assessment Systems, Inc. (TAS). The values for the percentiles were reported in g DW/kg of body

weight per day. 

TAS conducted this analysis of food consumption habits of the total population and five

population subgroups in the United States. The data used were the results of the Nationwide Food

Consumption Survey (NFCS) of 1987-88 conducted by the United States Department of Agriculture. 

The information in the NFCS was collected during home visits by trained interviewers using one-day

interviewer-recorded recall and a two-day self-administered record. A stratified area-probability sample

of households was drawn in the 48 contiguous states from April 1987 to 1988. More than 10,000

individuals provided information for the basic survey.
A-5

Each individual's intake of food was averaged across the 3 days of the original NFCS survey, and

food consumption for each food group was determined for each individual. Percentiles were then

computed for six population subgroups:

• U.S. population

• males  13 years

• females  13 years

• children 1-6 years

• children 7-12 years

• infants < 1 year.

The values for children in the previous table are based on the data for children between 7 and 12

year of age, while the adult values are for males older than 12 years of age. The males older than

12 years of age were chosen to represent the adult since rates for females are lower; this is recoganized to

be somewhat conservative. The United States population rates include the rates of children which were

considered inappropriate for the hypothetical adult receptors modeled in this analysis.

The values for leafy vegetables and fruits for adults are from (USU.S. EPA 1989).

A.1.4.3 Soil Ingestion Rate

Parameter: Cs

Definition: Amount of soil ingested daily.

Units: g/day

Receptor Default Value (g/day) Distribution Range (g/day)

Pica Child 7.5 U(5,10) 5-10

Child 0.2 U(0.016,0.2) 0.016-0.2

Adult 0.1 U(0.016,0.1) 0.016-0.1

Technical Basis:

Soil ingestion may occur inadvertently through hand-to-mouth contact or intentionally in the case

of a child who engages in pica. The default values for adults and non-pica children are those suggested

for use in U.S. EPA (1989). More recent studies have found that these values are rather conservative. 

For example, Calabrese and Stanek (1991) found that average soil intake by children was found to range

from 0.016 to 0.055 g/day. This range, in conjunction with the suggested U.S. EPA values, was used to

obtain the ranges shown.

Several studies suggest that a pica child may ingest up to 5 to 10 g/day (LaGoy, 1987, U.S. EPA,

1989). This range was selected, and the midpoint was chosen as the default value.
A-6

A.1.4.4 Groundwater Ingestion Rate

Parameter: Cw

Definition: The amount of water consumed each day.

Units: L/day

Receptor Default Values Distribution

(L/day)

Child 1.0 Log*(0.378; 0.079)

Adult 2.0 Log*(0.1; 0.007)

Technical Basis:

The default values for children and adult are those also suggested in U.S. EPA (1989) and were

first published by the Safe Drinking Water Committee of the National Academy of Sciences (NAS,

1977).

The distributions are those computed in Roseberry and Burmaster (1992). In that paper,

lognormal distributions were fit to data collected in a national survey for both total water intake and tap

water intake by children and adults. These data were originally gathered in the 1977-1978 Nationwide

Food Consumption Survey of the United States Department of Agriculture and were analyzed by Ershow

and Cantor (1989).

In Roseberry and Burmaster (1992), distributions were fit to the intake rates for humans ages 0-1

year, 1-11 years, 11-20 years, 20-65 years and older than 65 years. The distribution for children ages 1-

11 was chosen for the child's distribution given in the previous table and the distribution for adults ages

20-65 was used for the adult. For the purpose of the present analysis, the tap water intake was deemed

more appropriate than total water intake. The total water intake included water intrinsic in foods that are

accounted for in the agricultural pathways, while the tap water intake was the sum of water consumed

directly as a beverage and water added to foods and beverages during preparation.

The minima and maxima were selected as the 2.5 and 97.5 percentiles, respectively.
A-7

A.1.4.5 Fish Ingestion Rate

Parameter: Cf

Definition: Quantity of locally - caught fish ingested per day.

Units: g/day

Receptor Default Value (g/day)

High End Fisher 60

Child of high end fisher 20

Recreational Angler 30

Technical Basis:

Because of the bioaccumulation of methylmercury in fish, the fish ingestion rate is an important

parameter for modeling mercury exposure. Fish consumption rates are difficult to determine for a

general population study because individual fish ingestion rates vary widely across the United States. 

This animal protein source may be readily consumed or avoided on a seasonal, social, economic or

demographic basis. Ideally, for an actual site, specific surveys identifying the type, source, and quantity

of fish consumed by area residents would be used. Within the context of this study, it is not possible to

characterize this variability completely.

For this part of the assessment, individuals in three broad groups of exposed populations will be

considered: high end fishers, recreational anglers and the general population. For the general

population, no commercial distribution of locally caught fish was assumed. All consumers of locallycaught fish were assumed to be recreational anglers or subsistence fishers.

In U.S. EPA's 1989 Exposure Factors Handbook, fish consumption data from Puffer (1981) and

Pierce et al. (1981) are suggested as most appropriate for fish consumption of recreational anglers from

large water bodies. The median of this subpopulation is 30 g/day with a 90th percentile of 140 g/day

(340 meals/year). The median was used as the surrogate value for recreational anglers.

For subsistence fishers, human fish consumption data were obtained from the report of the

Columbia River Inter-Tribal Fish Commission (1994), which estimated fish consumption rates for

members of four tribes inhabiting the Columbia River Basin. The estimated fish consumption rates were

based on interviews with 513 adult tribe members who lived on or near the reservation. The participants

had been selected from patient registration lists provided by the Indian Health Service. Adults

interviewed provided information on fish consumption for themselves and for 204 children under 5 years

of age.

During the study fish were consumed by over 90% of the population with only 9% of the

respondents reporting no fish consumption. Monthly variations in consumption rates were reported. The

average daily consumption rate during the two highest intake months was 107.8 grams/day, and the daily

consumption rate during the two lowest consumption months was 30.7 grams/day. Members who were

aged 60 years and older had an average daily consumption rate of 74.4 grams/day. During the past two

decades, a decrease in fish consumption was generally noted among respondents in this survey. The

maximum daily consumption rate for fish reported for this group was 972 grams/day.
A-8

The mean daily fish consumption rate for the total adult population (aged 18 years and older) was

reported to be 59 grams/day. The mean daily fish consumption rate for the adult females surveyed was

56 g/day and the mean daily fish consumption rate for the adult males surveyed was 63 grams. A value

of 60 grams of fish per day was selected for the subsistence angler modeled in this report.

Other fish consumption rate studies for specific subpopulations (i.e., anglers and subsistence

consumers) have been conducted. These studies are briefly described in Volume IV. These studies

demonstrate the wide range of fish consumption rates exhibited across the U.S. population. They also

tend to corroborate the estimates to be used in this analysis. These analyses also illustrate the difficulty

in determining average and high-end consumption rates for subpopulations considered to be more likely

to consume more fish.

In the lacustrine scenarios of this assessment, all fish were assumed to originate from the lakes,

which are considered to represent several small lakes that may be present in a hypothetical location.

The effects of fish preparation for food on extant mercury levels in fish have also been evaluated

(Morgan et al., 1994). Total mercury levels in walleye were found to be constant before and after

preparation; however, mercury concentrations in the cooked fish were increased 1.3 to 2.0 times when

compared to mercury levels in the raw fish. It was suggested that this increase was probably due to water

and fat loss during cooking and fish skin removal. A preparation factor adjustment was noted but not

implemented in this analysis because human consumption levels were measured on uncooked fish. For

more information see Volume IV.
A-9

A.1.4.6 Contact Fractions

Parameter: FPi, Faj

Definition: that fraction of the food type grown or raised on contaminated land

Units: Unitless

Food Subsistence Rural Home Urban Gardener Comment

Farmer Gardener/

Subsistence Fisher

Grains 1 0.667 0.195 Values are for corn from

Table 2-7 in U.S. EPA

(1989)

Legumes 1 0.8 0.5 Values are for peas from

Table 2-7 in U.S. EPA

(1989).

Potatoes 1 0.225 0.031 Values are for total fresh

potatoes from Table 2-7 in

U.S. EPA (1989).

Root Vegetables 1 0.268 0.073 Values are for carrots from

Table 2-7 in U.S. EPA

(1989).

Fruits 1 0.233 0.076 citrus fruit from Table 2-7

Values are for Total nonin U.S. EPA (1989).

Fruiting 1 0.623 0.317 Values are for tomatoes

Vegetables from Table 2-7 in U.S. EPA

(1989).

Leafy Vegetables 1 0.058 0.026 Values are for lettuce from

U.S. EPA (1989).

Beef 1 0 0

Beef liver 1 0 0

Dairy 100

Pork 1 0 0

Poultry 100

Eggs 100

Lamb 1 0 0

Technical Basis:

The values for the subsistence farmer are consistent with the assumptions regarding this scenario. 

The values for the gardeners are from U.S. EPA (1989), per U.S. EPA guidance. Because it is assumed

that only the subsistence farmers will consume contaminated animal products, the contact fractions for

gardeners is 0 for consumption of local animal products.
A-10

A.2 Chemical Dependent Parameters

Chemical dependent parameters are variables that change depending on the specific contaminant

being evaluated. The chemical dependent variables used in this study are described in the following

sections.

A.2.1 Basic Chemical Properties

The following sections list the chemical properties used in the study, their definitions, and

values.

A.2.1.1 Molecular Weight

Parameter: Mw

Definition: The mass in grams of one mole of molecules of a compound.

Units: g/mole

Chemical Default Value (g/mole)

Hg , Hg 201 0 2+

Methylmercury 216

Methyl mercuric chloride 251

Mercuric chloride 272

A.2.1.2 Henry's Law Constant

Parameter: H

Definition: Provides a measure of the extent of chemical partitioning between air and water at

equilibrium.

Units: atm-m /mole 3

Chemical Default Value (atm-m /mole) 3

Hg 7.1x10 0 -3

Hg (HgCl ) 7.1x10 2+

2

-10

Methylmercury 4.7x10-7

Technical Basis:

The higher the Henry's Law Constant, the more likely a chemical is to volatilize than to remain in

the water. The value for Hg is from Iverfeldt and Persson (1985), while the other values are from 0

Lindquist and Rodhe (1985).
A-11

A.3 References

Akagi H., D.C. Mortimer, and D.R. Miller (1979). Mercury Methylation and Partition in Aquatic

Systems. Bull. Environ. Contam. Toxicol. 23: 372-376.

Alberts, J.J., J.E. Schindler, and R.W. Miller. (1974). Elemental mercury evolution mediated by humic

acid.

Albeuts, J.J., J.E. Schindler, and R.W. Miller (1974). Elemental Mercury Evolution Mediated by Humic

Acid. Science 184: 895-897.

Anderson, A., N. Browne, S. Duletsky, J. Ramig, and T. Warn (1985). Development of Statistical

Distributions or Ranges of Standard Factors Used in Exposure Assessments. EPA 600/8-85-101,

U.S. Environmental Protection Agency, Office of Health and Environmental Assessment,

Washington, D.C. 

Baes, C.F., R.D. Sharp, A.L. Sjoreen, and R.W. Shor (1984). A Review and Analysis of Parameters for

Assessing Transport of Environmentally Released Radionuclides Through Agriculture. Prepared

under contract No. DE-AC05-84OR21400. U.S. Department of Energy, Washington, D.C.

Belcher, G.D. and C.C. Travis (1989). Modeling Support for the Rura and Municipal Waste Combustion

Projects: Final Report on Sensitivity and Uncertainty Analysis for the Terrestrial Food Chain

Model. Prepared for the U.S. EPA.

Bloom, N.S. and C.J. Watras (1989). Observations of Methylmercury in Precipitation. The Sci. Tot.

Environ. 87/88: 199-207.

Bloom, N. and S.W. Effler. (1990). Water, Air, and Soil Poll. 53:251-265.

Bloom, N.S., C.J. Watras, and J.P. Hurley (1991). Impact of Acidification on the Methylmercury Cycle 

of Remote Seepage Lakes. Water, Air, and Soil Poll. 56: 477-491.

Bousset, C. (1981). The Mercury Cycle. Water, Air and Soil Pollution 16: 253-255.

Butcher, B., B. Davidoff, M.C. Amacher, C. Hinz, I.K. Iskandar, and H.M. Selim (1989). Correlation of

Freundlich K and retention parameters with soils and elements. Soil Science 148: 370-379. d

Calabrese, E.J. and E.J. Stanek, III (1991). A Guide to Interpreting Soil Ingestion Studies. II. 

Qualitative and Quantitative Evidence of Soil Ingestion. Regul. Toxicol. Pharmacol. 13:278-292.

Cappon, C.J. (1981). Mercury and Selenium Content and Chemical Form in Vegetable Crops Grown on

Sludge-Amended Soil. Arch. Environm. Contam. Toxicol. 10: 673-689.

Cappon, C. (1984). Content and Chemical Form of mercury and selenium in soil, sludge and fertilizer

materials. Water, Air, Soil Pollut 22:95-104.

Cappon, C.J. (1987). Uptake and Speciation of Mercury and Selenium in Vegetable CropsGrown on

Compost-Treated Soil. Water, Air, Soil Poll. 34: 353-361.

Carsel, R.F., C.N. Smith, L.A. Mulkey, J.D. Dean, and P. Jowise (1984). User's Manual for the

Pesticide Root Zone Model (PRZM) Release 1. U.S. EPA, Athens, GA. EPA-600/3-84-109.
A-12

Ershow, A.G. and K.P. Cantor (1989). Total Water and Tapwater Intake in the United States: 

Population-based Estimates of Quantities and Sources. Life Sciences Research Office,

Federation of American Societies for Experimental Biology. Bethesda, Maryland.

Fitzgerald, W. 1986. Cycling of mercury between the atmosphere and oceans, in: The Role of Air-Sea

Exchange in Geochemical Cycling, NATO Advanced Science Institutes Series, P. Buat-Menard

(Ed.), D. Reidel publishers, Dordrecht, pp 363-408.

Fitzgerald, W.F., R.P. Mason, and G.M. Vandal (1991). Atmospheric Cycling and Air-Water Exchange

of Mercury over Mid-Continental Lacustrine Regions. Water, Air, and Soil Pollution 56: 745-

767.

Fortmann, L. C., D. D. Gay, and K. O. Wirtz (1978). Ethylmercury: Formation in Plant Tissues and

Relation to Methylmercury Formation. U.S. EPA Ecological Research Series, EPA-600/3-78-

037.

Fries, G.F. 1982. Potential Polychlorinated Biphenyl Residues in Animal Products from Application of

Contaminated Sewage Sludge to Land. J. Environ. Quality. 11: 14-20.

Fries, G.F. (1987). Assessment of Potential Residues in Food Derived from Animals Exposed to

TCDD-Contaminated Soil. Chemosphere 16: 2123-2128.

Gill, G. And K. Bruland (1990). Mercury Speciation in surface freshwaters systems in California and

other areas. Sci. Total Environ. 24:1392.

Glass, G.E., J.A. Sorenson, K.W. Schmidt, and G.R. Rapp (1990). New Source Identification of Mercury

Contamination in the Great Lakes. Environmental Science and Technology 24:1059-1069.

Hanford Environmental Dose Reconstruction Project (1992). Parameters Used in Environmental

Pathways (DESCARTES) and Radiological Dose (CIDER) Modules of the Hanford

Environmental Dose Reconstruction Integrated Codes (HEDRICF) for the Air Pathway, PNWD2023 HEDR, Pacific Northwest Laboratories, September 1992.

Hattemer-Frey, H. and C.C. Travis (1989). An Overview of Food Chain Impacts from Municipal Waste

Combustion. Prepared for U.S. EPA.

Hawley, J.K. (1985). Assessment of Health Risk from Exposure to Contaminated Soil. Risk Anal. 5(4):

289-302.

Hildebrand, S.G., S.E. Lindberg, R.R. Turner, J.W. Huckabee (1980). Biogeochemistry of Mercury in a

River Reservoir System: Impact of an Inactive Chloralkali Plant on the Holston River. 

ORNL/TM-6141.

Hildebrand, S.G., R.H. Strand, and J.W. Huckabee (1980). Mercury accumulation in fish and

invertebrates of the North Fork Holston river, Virginia and Tennessee. J. Environ. Quan.

9:393-400.

Hoffman, F.O. and D.F. Baes (1979). A Statistical Analysis of Selected Parameters for Predicting Food 

Chain Transport and Internal Dose of Radionucleotides. ORNL/NUREG/TM-882.

Hoffman, F.O., K.M. Thiessen, M.L. Frank and B.G. Blaylock (1992). Quantification of the interception

and initial retention of radioactive contaminants deposited on pasture grass by simulated rain. 

Atmospheric Environment, 26A (18): 3313-3321.
A-13

Iverfeldt, A. and J. Persson (1985). The solvation thermodynamics of methylmercury (II) species derived

from measurements of the heat of solvation and the Henry's Law constant. Inorganic Chimica

Acta, 103: 113-119.

John, M.K. (1972). Mercury Uptake from Soil by Various Plant Species. Bull. Environ. Contam.

Toxicol. 8(2): 77-80.

Knott, J.E. (1957). Handbook for Vegetable Growers. J. Wiley and Sons, Inc., New York.

LaGoy, L.K. (1987). Estimated Soil Ingestion Rates for Use in Risk Assessment. Risk Anal. 7(3): 355-

359.

Lee, Y., and H. Hultburg (1990). Methylmercury in some Swedish Surface Waters. Environ. Toxicol.

Chem. 9: 833-841.

Lenka, M., K. K. Panda, and B. B. Panda (1992) Monitoring and Assessment of Mercury Pollution in the

Vicinity of a Chloralkali Plant. IV. Bioconcentration of Mercury in In Situ Aquatic and

Terrestrial Plants at Ganjam, India. Arch. Environ. Contam. Toxicol. 22:195-202.

Lenka, M., K.K. Panda, and B.B. Panda (1992a). Monitoring and Assessment of Mercury Pollution in

the Vicinity of a Chloralkali Plant. II. Plant Availability, Tissue Concentration and Genotoxicity

of Mercury from Agricultural Soil Contaminated with Solid Waste Assessed in Barley. Environ.

Poll. 0269-7491/92. pp. 33-42.

Lenka, M., K.K. Panda, and B.B. Panda (1992b). Monitoring and Assessment of Mercury Pollution in the

Vicinity of a Chlor-alkali Plant. IV. Bioconcentration of Mercury in In Situ Aquatic and

Terrestrial Plants at Ganjam, India. Arch. Environ. Contam. Toxicol. 22:195-202.

Lepow, M.L., L. Bruckman, M. Gillette, S. Markowitz, R. Robino, and J. Kapish (1975). Investigations

into Sources of Lead in the Environment of Urban Children. Environ. Res. 10: 415-426.

Lindberg, S. E., D. R. Jackson, J. W. Huckabee, S. A. Janzen, M. J. Levin, and J. R. Lund (1979).

Atmospheric Emission and Plant Uptake of Mercury from Agricultural Soils near the Almaden

Mercury Mine. J. Environ. Qual. 8(4):572-578.

Lindquist, O. and H. Rodhe (1985). Atmospheric Mercury: A Review. Tellus 37B: 136-159.

Lindqvist, O., K. Johansson, M. Aastrup, A. Andersson, L. Bringmark, G. Hovsenius, L. Hakanson, A.

Iverfeldt, M. Meili, and B. Timm (1991). Mercury in the Swedish Environment - Recent

Research on Causes, Consequences and Corrective Methods. Water, Air and Soil Poll. 55:(all

chapters)

Lyman, W.J., W.F. Rheel, and D.H. Rosenblatt (1982). Handbook of Chemical Property Estimation

Methods. New York: McGraw-Hill.

McKone, T.E. and P.B. Ryan (1989). Human Exposure to Chemicals Through Food Chains: An

Uncertainty Analysis. Environ. Sci. Technol. 23(9): 1154-1163.

Miller, C.W. and F.O. Hoffman (1981). An Examination of the Environmental Half-time for

Radionuclides Deposited on Vegetation. Health Phys. 45:731-744.

Miller, C. And F. Hoffman. (1983). An examination of the environmental half-time for radionuclides

deposited on vegetation. Health Physics 45:731.
A-14

Miskimmin, B.M. (1991). Effect of Natural Levels of Dissolved Organic Carbon (DOC) on

Methylmercury Formation and Sediment-Water Partitioning. Bull. Environ. Contam. Toxicol.

47: 743-750.

Moore, J.W. and S. Ramamoorthy (1984). Heavy Metal in Natural Waters - Applied Monitoring in

Impact Assessment. New York, Springer-Verlag.

Morgan, J., M. Berry and R. Graves. (1994) Effects of Native American Cooking Practices on Total

Mercury Concentrations in Walleye. Abstract presented at ISEE/ISEA Joint Conference.

September 18-21, 1994.

Mosbaek, H., J. C. Tjell, and T. Sevel (1988). Plant Uptake of Mercury in Background Areas.

Chemosphere 17(6):1227-1236.

NAS (National Academy of Science) (1977). Safe Drinking Water Committee. An Assessment of

Mercury in the Environment. National Research Council.

NAS (National Academy of Sciences). 1987. Predicting Feed Intake of Food-Producing Animals. 

National Research Council, Committee on Animal Nutrition, Washington, DC.

Nriagu, J.O. (1979). The Biogeochemistry of Mercury in the Environment. Elsevier/North Holland. 

Biomedical Press: New York.

Parks, J.W., A. Luitz, and J.A. Sutton (1989). Water Column Methylmercury in the Wabigoon/English

River-lake System: Factors Controlling Concentrations, Speciation, and Net Production. Can. J.

Fish. Aquat. Sci. 46: 2184-2202.

Pierce, R., D. Noviello and S. Rogers. 1981. Commencement Bay Seafood Consumption Report.

Preliminary Report. Tacoma, WA: Tacoma-Pierce County Health Department. As cited in U.S.

EPA (1991). Exposure Factors Handbook. EPA/600/8-89/043.

Puffer, H. 1981. Consumption rates of potentially hazardous marine fish caught in the metropolitan Los

Angeles area. EPA Grant #R807 120010.As cited in U.S. EPA (1991). Exposure Factors

Handbook. EPA/600/8-89/043.

Robinson, K.G. and M.S. Shuman (1989). Determinination of mercury in surface waters using an

optimized cold vapor spectrophotometric technique. International Journal of Environmental

Chemistry 36: 111-123.

Roels, H.A. , J.P. Buchet, and R.R. Lauwerys (1980). Exposure to Lead by the Oral and Pulmonary

Routes of Children Living in the Vicinity of a Primary Lead Smelter. Environ. Res. 22: 81-94.

Roseberry, A.M. and D.E. Burmaster (1992). Lognormal Distributions for Water Intake by Children and

Adults. Risk Analysis 12: 65-72.

Roy, M. and C. Courtay (1991). Daily Activities and Breathing Parameters for Use in Respiratory Tract

Dosimetry. Radiation Protection Dosimetry 35 (3): 179-186.

Rutledge, A.D. (1979). "Vegetable Garden Guide." Publication 447 (Revised) University of Tennessee

Agricultural Extension Service, The University of Tennessee.

Sanemasa, I. (1975). Solubility of Elemental Mercury Vapor in Water. Bull. Chem. Soc. Jpn. 48(6):

1795-1798.
A-15

Schroeder, W., G. Yarwood and H. Niki (1991). Transformation Processes involving Mercury Species in

the Atmosphere-Results from a Literature Survey. Water, Air and Soil Pollution 56: 653-666.

Shor, R.W. , C.F. Baes , and R.D. Sharp (1982). Agricultural Production in the United States by County: 

A Compilation of Information from the 1974 Census of Agriculture for Use in Terrestrial Food

Chain Transport and Assessment Models. Oak Ridge National Laboratory, ORNL-5786.

South Coast Air Quality Management District (SCAQMD) 1988. Multi-pathway Health Risk

Assessment Input parameters. Guidance Document.

Sumo, E., B.R. Singh, A.R. Selmer-Olsen, and K. Steenburg (1985). Uptake of Hg-labeled Mercury 203

compounds by Wheat and Beans Grown on an oxisol. Plant and Soil 85: 347-355.

Technical Assessment Systems (TAS) (1991). Distribution of Intake of Foods in Sixteen Food

Groupings: grains and cereals; potatoes including sweet potatoes and yams; leafy vegetables,

excluding brassica; brassica vegetables; legume vegetables; fruiting vegetables, including

cucurbits; root vegetables; other vegetables; beef excluding liver; beefliver; sheep; pork; poultry;

eggs; milk; and fruits, excluding cucurbits. Prepared for U.S. EPA.

Temple, P. J. and S. N. Linzon (1977). Contamination of Vegetation, Soil, Snow and Garden Crops by

Atmospheric Deposition of Mercury from a Chlor-Alkali Plant, in (1977) D. D. Hemphill [ed]

Trace Substances in Environmental Health - XI, Univ Missouri, Columbia. p. 389-398.

Turner, R. (1994). Personal communication with Ralph Turner, Environmental Sciences Division, Oak

Ridge National Laboratory.

U.S. Department of Agriculture (USDA) (1978). Handbook No. 537: Predicting Rainfall Erosion

Losses. U.S. Government Printing Office, Washington, D.C.

U.S. Department of Commerce. (1977). 1974 Census of Agriculture. Bureau of the Census, Agriculture

Division, Washington, D.C.

U.S. EPA (1984). Health Assessment Document for Mercury. Prepared by the Office of Health and

Environmental Assessment, Environmental Criteria and Assessment Office, Cincinnati, OH for

the Office of Emergency and Remedial Response, Washington, D.C.

U.S. EPA (1985). Environmental profiles and hazard indices for constituents of municipal sludge: 

Mercury. Washington, D.C.: Office of Water Regulations and Standards.

U.S. EPA (1989). Exposure Factors Handbook. Office of Health and Environmental Assessment,

Exposure Assessment Group, Washington, D.C. EPA 600/8-89-043. NTIS PB90-106774.

U.S. EPA (1990). Methodology for Assessing Health Risks Associated with Indirect Exposure to

Combustor Emissions. Office of Health and Environmental Assessment, Washington, D.C. 

EPA/600/6-90/003.

U.S. EPA (1993a). Assessment of Mercury Occurence in Pristine Freshwater Ecosystems, prepared by

ABT Associates, Inc. Draft as of September 1993.

U.S. EPA. 1993b. Technical Support document or Land Application of Sewage Sludge. Federal

Register. 40 CFR Part 257 et al. Standards for the Use or Disposal of Sewage sludge; Final

Rules. February 19.
A-16

van der Veen, N.G. and K. Vreman (1986). Transfer of cadmium, lead, mercury and arsenic from feed

into various organs and tissues of fattening lambs. Netherlands Journal of Agricultural Science

34: 145-153.

Vreman, K., N.J. van der Veen, E.J. van der Molen and W.G. de Ruig (1986). Transfer of cadmium,

lead, mercury and arsenic from feed into milk and various tissues of dairy cows: chemical and

pathological data. Netherlands Journal of Agricultural Science 34: 129-144.

Watras, C.J. and N.S. Bloom (1992). Mercury and Methylmercury in Individual Zooplankton: 

Implications for Bioaccumulation. Limnol. Oceanogr.37(6): 1313-1318.

Wiessman, D., B.J. vanGoor, amd N.G. van der Veen (1986). Cadmium, Lead, Mercury, and Arsenic

Concentrations in Coops amd Corresponding Soils in the Netherlands. J. Agric. Food Chem. 34:

1067-1074.

Wiersma, D., B. J. van Goor, and N. G. van der Veen (1986). Cadmium, Lead, Mercury, and Arsenic

Concentrations in Crops and Corresponding Soils in the Netherlands. J. Agric. Food Chem.,

34:1067-1074.

Wilken, R.D. and H. Hintelmann (1991). Mercury and Methylmercury in Sediments and Suspended

Particles from the River Elbe, North Germany. Water, Air, and Soil Poll. 56: 427-437.
APPENDIX B

ESTIMATED NATIONAL AND REGIONAL POPULATIONS OF WOMEN OF

CHILD-BEARING AGE: UNITED STATES, 1990
B-1

Estimated National and Regional Populations of

Women of Child-Bearing Age: United States, 1990

Because methylmercury is a developmental toxin, the subpopulation judged of particular concern

in this Mercury Study: Report to Congress was women of child-bearing age. Estimates of the size of the

population of women of reproductive age, number of live births, number of fetal deaths, and number of

legal abortions can be used to predict the percent of the population and number of women of

reproductive age who are pregnant in a given year. This methodology has been previously used in the

Agency for Toxic Substances and Disease Registry's (ATSDR's) Report to Congress on The Nature and

Extent of Lead Poisoning in Children in the United States (Mushak and Crocetti, 1990).

The estimates of number of women of child-bearing age calculated for this Mercury Study: 

Report to Congress were prepared by Dr. A.M. Crocetti under purchase order from the EPA Office of

Air Quality, Planning and Standards (OAQPS). The techniques used by Dr. Crocetti parallel those used

to prepared the 1984 estimates for ATSDR. To estimate the size of this population on a national basis

Vital and Health Statistics data for number of live births (National Center for Health Statistics of the

United States, 1990; Volume I, Natality, Table 1-60, pages 134-140), and fetal deaths (National Center

for Health Statistics of the United States, 1990; Volume II, Mortality; Table 3-10, pages 16, 18, and 20). 

Fetal wastage, that is, spontaneous abortions prior to 20 weeks of gestation were not considered since no

systematically collected, nationally based data exist.

The estimate of number of women of child-bearing age includes some proportion of women who

will never experience pregnancy. However, substitution of the number of pregnancies in a given year

provides some measure of assessing the size of the surrogate population at risk. Estimates of the size of

the population were based on "Estimates of Resident Population of the United States Regions and

Divisions by Age and Sex" (Byerly, 1993). The Census data for 1990 were grouped by age and gender.

The sizes of these populations are shown in Table B-1.

Women ages 15 through 44 are the age group of greatest interest in identifying a subpopulation

of concern for the effects of a developmental toxin such as methylmercury. This population consisted of

58,222,000 women living within the contiguous United States. This population was chosen rather than

for the total United States (population 58,620,000 women ages 15 through 44 years) because the dietary

survey information from CSFII 89-91 did not include Hawaii and Alaska. Based on estimates of fish

consumption data for Alaska by Nobmann et al. (1992) the quantities of fish eaten by Alaskans exceeds

those of the contiguous U.S. population. It is also estimated that residents of the Hawaiian Islands also

have fish consumption patterns that differ from those of the contiguous United States.

The number of pregnancies per year was estimated by combining the number of live births,

number of fetal deaths (past 20 weeks of gestation) and the number of legal abortions. The legal

abortion data were based on information published by Koonin et al. (1993) in Morbidity and Mortality

Weekly Report. These totals are presented in Table B-2. As noted in this table, the total of legal

abortions includes those with unknown age which were not included in the body of each table entry. 

There were 2,929 such cases for the United States in 1990 or 0.2% of all legal abortions. Another

complication in the legal abortion data was for the age group 45 and older. The available data provide

abortion data for 40 years and older only. To estimate the size of the population older than 45 years, the

number of legal abortions for women age 40 years and older were allocated by using the proportions of

Live Births and Fetal Deaths for the two age groups 40-44 and 45 and older.
B-2

It was estimated that within the contiguous United States 9.5% of women ages 15 to 44 years

were pregnant in a given year. The total number of live births reported in 1990 for this age group was

4,112,579 with 30,974 reported fetal deaths and 1,407,830 reported legal abortions. The estimated

number of total pregnancies for women ages 15 to 44 years was 5,551,383 in a population of 58,222,000

women.

REFERENCES

Byerly, E.R. (1993) State Population Estimates by Age and Sex: 1980-1992, U.S. Bureau of the Census,

Current Population Reports P25-1106, U.S. Government Printing Office, Washington, DC.

Koonin, L.M., Smith, J.C., and Ramick, M. (1993) Division of Reproductive Health, National Center for

Chronic Disease Prevention and Health Promotion: Abortion Surveillance - United States, 1990: 

Morbidity Mortality Weekly Report, Vol 42/No. SS-6, pps. 29-57, December 17.

Mushak, P., and Crocetti, A.M. (1988). The Nature and Extent of Lead Poisoning in Children in the

United States: A Report to Congress. Agency for Toxic Substances and Disease Registry, United States

Public Health Service, United States Department of Health and Human Services.

National Center for Health Statistics of the United States (1990) Volume I. Natality: Table 1-60; pages

134-140.

National Center for Health Statistics of the United States (1990) Volume II. Mortality; Table 3-10, pages

16, 18, and 20.

Nobmann, E.D., Byers, T., Lanier, A.P., Hankin, J.H., and Jackson, M.Y. (1992) The diet of Alaska

Native adults: 1987-1988. Amer. J. Clin. Nutr. 55: 1024-1032.
B-3

Table B-1

Resident Population of the United States and Divisions, April 1, 1990

Census by Gender and Age; in Thousands, including Armed Forces Residing in Region

Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and

Age; in Thousands, including Armed Forces Residing in Region.

Division/ Total < 15 Years 15-44 Years > 44 Years

Gender of Age of Age of Age

United States 248,710 53,853 117,610 77,248

Male 121,239 27,570 58,989 34,680

Female 127,471 26,284 58,620 42,567

% Female 51.3 48.8 49.8 55.1

Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and

Age; in Thousands, including Armed Forces Residing in Region.

Division/ Total < 15 Years 15-44 Years > 44 Years

Gender of Age of Age of Age

Contiguous

United States

 247,052 53,462 116,772 76,817

Male 120,385 27,369 58,548 34,467

Female 126,667 26,094 58,222 42,348

% Female 51.3 48.8 49.9 55.1

Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and

Age; in Thousands, including Armed Forces Residing in Region.

Division/ Total < 15 Years 15-44 Years > 44 Years

Gender of Age of Age of Age

New England 13,207 2,590 6,379 4,239

Male 6,380 1,327 3,174 1,878

Female 6,827 1,264 3,202 2,361

% Female 51.7 48.8 50.2 55.7
B-4

Table B-1 (continued)

Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and

Age; in Thousands, including Armed Forces Residing in Region.

Division/ Total < 15 Years 15-44 Years > 45 Years

Gender of Age of Age of Age

Middle

Atlantic

States

 37,602 7,471 17,495 12,638

Male 18,056 3,824 8,676 5,554

Female 19,547 3,645 8,818 7,083

% Female 52 49 50 56

Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and

Age; in Thousands, including Armed Forces Residing in Region.

Division/ Total < 15 Years 15-44 Years > 44 Years

Gender of Age of Age of Age

E North Central 42,009 9,233 19,596 13,180

Male 20,373 4,728 9,744 5,899

Female 21,636 4,505 9,851 7,279

% Female 51.5 48.8 50.3 55.2

Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and

Age; in Thousands, including Armed Forces Residing in Region.

Division/ Total < 15 Years 15-44 Years > 44 Years

Gender of Age of Age of Age

West North

Central

 17,660 3,967 8,017 5,676

Male 8,599 2,032 4,020 2,546

Female 9,061 1,935 3,997 3,129

% Female 51.3 48.8 49.9 55.1
B-5

Table B-1 (continued)

Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and

Age; in Thousands, including Armed Forces Residing in Region.

Division/ Total < 15 Years 15-44 Years > 44 Years

Gender of Age of Age of Age

South

Atlantic

 43,567 8,864 20,579 14,122

Male 21,129 4,531 10,279 6,321

Female 22,438 4,333 10,301 7,804

% Female 51.5 48.9 50.1 55.3

Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and

Age; in Thousands, including Armed Forces Residing in Region.

Division/ Total < 15 Years 15-44 Years > 44 Years

Gender of Age of Age of Age

East South

Central

 15,176 3,316 7,037 4,823

Male 7,301 1,698 3,472 2,132

Female 7,875 1,618 3,565 2,692

% Female 51.9 48.8 50.7 55.8

Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and

Age; in Thousands, including Armed Forces Residing in Region.

Division/ Total < 15 Years 15-44 Years > 44 Years

Gender of Age of Age of Age

West South

Central

 26,703 6,366 12,687 7,651

Male 13,061 3,256 6,359 3,445

Female 13,641 3,110 6,328 4,204

% Female 51.1 48.9 49.9 54.9
B-6

Table B-1 (continued)

Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and

Age; in Thousands, including Armed Forces Residing in Region.

Division/ Total < 15 Years 15-44 Years > 44 Years

Gender of Age of Age of Age

Mountain

States

 13,659 3,313 6,435 3,910

Male 6,779 1,696 3,259 1,825

Female 6,880 1,616 3,176 2,087

% Female 50.4 48.8 49.4 53.4

Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and

Age; in Thousands, including Armed Forces Residing in Region.

Division/ Total < 15 Years 15-44 Years > 44 Years

Gender of Age of Age of Age

West North

Central

 17,660 3,967 8,017 5,676

Male 8,599 2,032 4,020 2,546

Female 9,061 1,935 3,997 3,129

% Female 51.3 48.8 49.9 55.1

Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and

Age; in Thousands, including Armed Forces Residing in Region.

Division/ Total < 15 Years 15-44 Years > 44 Years

Gender of Age of Age of Age

Pacific (5 States

including Alaska

and Hawaii)

 39,127 8,734 19,394 11,011

Male 19,562 4,476 10,004 5,083

Female 19,565 4,258 9,379 5,929

% Female 50.0 48.8 48.4 53.8
B-7

Table B-1 (continued)

Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and

Age; in Thousands, including Armed Forces Residing in Region.

Division/ Total < 15 Years 15-44 Years > 44 Years

Gender of Age of Age of Age

Pacific

(Washington,

Oregon and

California only)

 37,469 8,343 18,546 10,580

Male 18,708 4,275 9,563 4,870

Female 18,761 4,068 8,981 5,710

% Female 50.1 48.8 48.4 54.0
B-8

Table B-2

Pregnancies by Outcome for Resident Females by Divisions and States,

U.S. 1990, by Age

Pregnancies by Outcome for Resident Females by Divisions and States, U.S. 1990, by Age*

United Total** < 15 Years 15-44 Years > 44

States Years***

Females 127,471,000 26,284,000 58,620,000 42,567,000

Live births 4,158,212 11,657 4,144,917 1,638

Fetal Deaths 31,386 174 31,176 36

Legal Abortions 1,429,577 11,819 1,413,992 837

Total

Pregnancies

 5,619,175 23,650 5,590,085 2,511

% Pregnant - 9.5 -

Pregnancies by Outcome for Resident Females by Divisions and States, U.S. 1990, by Age*

Contiguous Total** < 15 Years 15-44 Years > 44 Years

United

States Females 126,667,000 26,094,000 58,222,000 42,348,000

Live births 4,125,821 11,615 4,112,579 1,627

Fetal Deaths 31,183 173 30,974 36

Legal Abortions 1,423,340 11,765 1,407,830 833

Total

Pregnancies

 5,580,344 23,553 5,551,383 2,496

% Pregnant - - 9.5 -

Pregnancies by Outcome for Resident Females by Divisions and States, U.S. 1990, by Age*

New Total** < 15 Years 15-44 Years >44 Years

England

Females 6,827,000 1,264,000 3,202,000 2,361,000

Live births 201,173 270 200,827 76

Fetal Deaths 1,226 4 1,220 2

Legal Abortions 78,347 487 77,358 37

Total

Pregnancies

 280,746 761 279,405 115

% Pregnant - - 8.7 -
B-9

Table B-2 (continued)

Pregnancies by Outcome for Resident Females by Divisions and States, U.S. 1990, by Age*

Middle Total** < 15 Years 15-44 Years > 44 Years

Atlantic

Females 19,547,000 3,645,000 8,818,000 7,083,000

Live births 591,826 1,305 590,238 283

Fetal Deaths 5,653 25 5,622 6

Legal Abortions 252,599 1,912 250,484 157

Total

Pregnancies

 850,078 3,242 846,344 446

% Pregnant 9.6

Pregnancies by Outcome for Resident Females by Divisions and States, U.S. 1990, by Age*

East Total** < 15 Years 15-44 Years > 44 Years

North

Central Females 21,636,000 4,505,000 9,851,000 7,279,000

Live births 675,512 1,838 673,449 225

Fetal Deaths 4,555 14 4,537 4

Legal Abortions 166,897 1,056 165,434 109

Total

Pregnancies

 846,964 2,908 843,420 338

% Pregnant 8.6

Pregnancies by Outcome for Resident Females by Divisions and States, U.S. 1990, by Age*

West Total** < 15 Years 15-44 Years > 44 Years

North

Central Females 9,061,000 1,935,000 3,997,000 3,129,000

Live births 270,331 457 269,792 82

Fetal Deaths 1,741 6 1,733 2

Legal Abortions 57,219 398 56,562 30

Total

Pregnancies

 329,291 861 328,087 114

% Pregnant - 8.2 -
B-10

Table B-2 (continued)

Pregnancies by Outcome for Resident Females by Divisions and States, U.S. 1990, by Age*

South Total** < 15 Years 15-44 Years > 44 Years

Atlantic

Females 22,438,000 4,333,000 10,301,000 7,804,000

Live births 700,285 2,644 697,424 217

Fetal Deaths 6,453 57 6,389 7

Legal Abortions 238,538 2,242 235,536 123

Total

Pregnancies

 945,276 4,943 939,349 347

% Pregnant - 9.1 -

Pregnancies by Outcome for Resident Females by Divisions and States, U.S. 1990, by Age*

East Total** < 15 Years 15-44 Years > 44 Years

South 

Central Females 7,875,000 1,618,000 3,565,000 2,692,000

Live births 236,374 1,143 235,195 36

Fetal Deaths 2,954 25 2,027 2

Legal Abortions 53,919 662 53,030 19

Total

Pregnancies

292,347 1,830 290,252 57

% Pregnant - 8.1 -

Pregnancies by Outcome for Resident Females by Divisions and States, U.S. 1990, by Age*

West Total** < 15 Years 15-44 Years > 44 Years

South

Central Females 13,641,000 3,110,000 6,328,000 4,204,000

Live births 472,721 1,852 470,715 154

Fetal Deaths 3,258 21 3,234 3

Legal Abortions 122,261 781 121,100 90

Total

Pregnancies

 598,240 2,654 595,049 247

% Pregnant - 9.4 -
B-11

Table B-2 (continued)

Pregnancies by Outcome for Resident Females by Divisions and States, U.S. 1990, by Age*

Mountain Total** < 15 Years 15-44 Years > 44 Years

Females 6,880,000 1,616,000 3,176,000 2,087,000

Live births 242,829 500 242,235 94

Fetal Deaths 1,492 6 1,483 3

Legal Abortions 50,880 288 50,330 31

Total

Pregnancies

 295,201 794 294,048 128

% Pregnant - 9.3 -

Pregnancies by Outcome for Resident Females by Divisions and States, U.S. 1990, by Age*

Pacific Total** < 15 Years 15-44 Years > 44 Years

(5 states

including

Alaska and

Hawaii)

Females 19,565,000 4,258,000 9,379,000 5,929,000

Live births 767.161 1,648 765,042 471

Fetal Deaths 4,954 16 4,931 7

Legal Abortions 408,917 3,993 404,158 241

Total

Pregnancies

1,181,032 5,657 1,174,131 719

% Pregnant - 12.5 -

Pregnancies by Outcome for Resident Females by Divisions and States, U.S. 1990, by Age*

Pacific Total** < 15 Years 15-44 Years > 44 Years

(Washington,

Oregon, and

California)

Females 18,761,000 4,068,000 8,981,000 5,710,000

Live births 734,770 1,606 732,704 460

Fetal Deaths 4,751 15 4,729 7

Legal

Abortions

 402,680 3,939 397,996 237

Total

Pregnancies

 1,142,201 5,560 1,135,429 704

% Pregnant - - 12.6 -
C-1

APPENDIX C

ANALYSIS OF MERCURY LEVELS IN FISH AND SHELLFISH

REPORTED IN NATIONAL MARINE FISHERIES SERVICE

SURVEY OF TRACE ELEMENTS IN THE FISHERY RESERVE
C-1

C.1 Introduction

Some reviewers of data on the levels of mercury in fish and shellfish have expressed concern

about the methods used to handle “nondetects” by the investigators who originally reported the data on

the concentrations of mercury in fish and shellfish tissues. Specifically, these reviewers have expressed

concern about the potential impact that different methods of handling nondetects may have on the

reported mean concentrations of mercury. The purpose of this memo is to report the results of a data

analysis performed on the nondetects in the mercury data reported in the report National Marine

Fisheries Service Survey of Trace Elements in the Fishery Reserve, hereinafter referenced as the NMFS

Report.

The major conclusion of this analysis is that different methods of handling nondetects have

negligible impact on the reported mean concentrations. This conclusion follows from two findings from

the data analysis, set forth below. First, when mean mercury levels are relatively “large”, there are few,

if any, nondetects, so the methodology employed to handle nondetects is irrelevant. Second, when mean

mercury levels are small, there are relatively large numbers of nondetects. However, the differences

between different methods of handling nondetects result in small differences in the resultant mean

values.

The NMFS Report reports number of samples, number of nondetects, and mean, standard

deviation, minimum and maximum mercury level in ppm for 1,333 combinations of fish/shellfish species,

variety, location caught, and tissue. Of these, 777 correspond to fish/shellfish species for which we have

mercury concentration data. These 777 combinations form the basis for the analyses reported in this

memorandum. They represent 5,707 analyses of fish and shellfish tissues for mercury, of which 1,467, or

26 percent, are reported as nondetects. Because the mercury concentration data is used in our analyses at

the species level, not at the more detailed species/variety/location/tissue level, we have aggregated, or

pooled, the 777 combinations to 35 different species for the purposes of this analysis.

In the following sections, we first discuss various methods of handling nondetects in calculating

mean mercury concentrations, then the analysis method adopted, and finally the results of that analysis.

C.2 Methods for Handling the Detection Limits

There are five methods commonly used to handle values below the detection limits in calculating

the mean mercury levels.

1. All nondetects are treated as being equal to 0. The total number of samples for which

mercury was measured is used in the mean calculation and it is assumed that the

concentration of mercury is 0.000 whenever the chemical analysis was reported as

“not detected”. This approach may lead to an underestimation of the true mean.

2. All nondetects are excluded from the calculation of the mean. The mean is calculated

as if these samples were not selected. The number of nondetects is subtracted from

the total number of samples for which mercury was measured, and the resulting

number is used to calculate the mean. This method may overestimate the true mean

and always yields a mean estimate greater than that obtained by method 1 (see

formulae in Addendum A).
C-2

3. All nondetects are replaced with a fixed value, usually one-half of the detection limit.

This method is the most widely used and accepted of the five methods. It is difficult

to know whether this method will lead to an underestimation or to an overestimation

of the true mean. But it will always lead to an estimate that falls between the

estimates obtained from method 1 and method 2.

4. All nondetects are replaced with simulated mercury levels randomly selected in the

interval (0, detection limit) according to an appropriate statistical distribution. This

method is close in spirit to method 3 and, like method 3, will lead to an estimate

falling between estimates obtained from method 1 and method 2.

5. All nondetects are replaced with the detection limit. This method may overestimate

the mean as all nondetects are smaller or equal to the detection limit. The mean

calculated by method 5 will also be between the means obtained from method 1 and

method 2.

The NMFS Report says that method 2 -- nondetects dropped from the calculation -- was used to

calculate their reported mean mercury levels. However, an examination of their data indicates that the

investigators did not always use method 2. It appears that other methods, including method 1 --

nondetects set equal to zero -- may have sometimes been used.

C.3 Method of Analysis

The approach adopted amounts to comparing means obtained by two different methods. Since we

do not have access to the raw data, it was necessary to first assume that the reported mean mercury levels

were calculated by one of the five methods mentioned above. Then we calculated the mean that would

have been obtained if another method had been used.

Although it is possible to consider all ten possible combinations of two methods that can be

obtained from the five under analysis, we have confined ourselves to the case where the other methods

are compared with method 3, the latter being the most commonly used in such situations. The following

three scenarios are studied:

 The reported means are assumed to have been calculated by method 1. The

corresponding mean mercury levels that would have been obtained by method 3 were

then calculated. The two sets of corresponding means are then compared. The

calculation method is reported in Addendum A.

 The above analysis was repeated for method 2 and method 3.

 The above analysis was repeated for method 5 and method 3. It should be noted that

if the reported mean is 0 and is assumed to be obtained by method 5 then method 3

might yield a negative value. In that case the mean was set to 0.000.
C-3

It is unlikely that method 4 was used to calculate the reported means since this would likely have

appeared in the NMFS report. Therefore method 4 is ruled out of this analysis. To be able to calculate

the mean mercury level by method 3, a value for the limit of detection is needed. We have been told that

the limit of detection was 0.100 ppm. However, the data reported in the NMFS Report have numerous

reported positive values less than 0.100 ppm. We therefore used the lowest of all detected analytical

values as the presumed limit of detection. This value is 0.010 ppm.

Addendum B lists and graphs the mean mercury levels in ppm by fish and shellfish species, as

reported by NMFS, then as calculated according to the methodology described above. That is, the mean

mercury level that would be obtained by method 3, assuming NMFS used method 1 is presented,

followed by the other two comparisons listed above. Then the mean differences between pairs of

methods are presented.

C.4 Data Analysis Results

The calculations comparing method 1 -- nondetects dropped -- and method 3 -- nondetects set to

one-half the detection limit, viz., 0.005 -- are reported in Figure C-1a and C-1b. The straight line in

Figure C-1a is the line y = x; points on the line correspond to mean values that are the same for both

methods. All points are on the line y = x, or nearly on it; the two methods yield identical results for most

species. This result follows from the fact that when mean mercury levels are relatively large, very few

nondetects were reported (see Figure C-4a).

In order to have a better assessment of the magnitude of the differences between method 1 and

method 3, we plotted the differences between the two methods versus method 1 in Figure C-1b. The

differences between methods 1 and 3 are never as high as 0.004 ppm. Further, they never exceed 0.001

ppm when the mean is above 0.200 ppm.

C-5

The results comparing methods 2 and 3 are in Figures C-2a and C-2b. They lead to the same

conclusions as the comparison of methods 1 and 3. The differences between methods 2 and 3 never

exceed 0.030 ppm in magnitude. Because the differences between methods 2 and 3 are an order of

magnitude greater than the other two comparisons, it was decided to investigate the larger differences

between these methods to see if there were any significant patterns.

The results comparing methods 5 and 3 are in Figures C-3a and C-3b. They lead to the same

conclusions as the two previous comparisons. The differences between methods 5 and 3 never exceed

0.003 ppm in magnitude. They never exceed 0.001 ppm when the mean mercury level is above 0.200

ppm.

These results follow from the fact that the number of nondetects is especially high when the

reported mean is very small. When that mean is larger, there are very few nondetects, so that all methods

yield virtually the same results. This phenomenon is well illustrated in Figures C-4a and C-4b, which

present the number and percentage of nondetects against the mean mercury levels, respectively.



C-9

ADDENDUM A

This addendum provides the formulae used to calculate the mean Mercury levels according

to the four methods used in the analysis.

Let N0 be the total number of samples for which the fish was measured, Nd the total

number of samples in which no Mercury was detected and d0 the limit of detection. Suppose that

xi stands for the Mercury level (ppm) detected in the i

th sample and that XX X 123 , , and X5 are the

mean Mercury levels calculated by methods 1,2,3 and 5 respectively. Then we have that,

X

N

x X

N N

x

X

N

x Nd X

N

x Nd

i

d

i

i

N N

i

N N

i d

i

N N

i d

i

N N

d d

d d

1

0

2

0 1 1

3

0

0

1

5

0

0

1

1 1

1 2 1

0 0

0 0

= = −

= +















 = +

















=

−

=

−

=

−

=

−

∑ ∑

∑ ∑

,

/ ,

Let X3/1 , X3 2/ and X3 5/ be the means calculated by method 3 under the assumption that

the reported data are calculated by method 1, 2 and 5 respectively. These conditional means are obtained

as follows:

X NX N d

N

d d 3

1 0

0

2

/1 = + ×÷ ,

X N NX N d

N

d d 3 2 0 2 0

0

2

/

( ) = − + ×÷

and

X NX N d

N

d 3 5 0 5 0

0

2

/ = − ×÷ .
C-10

ADDENDUM B

Mercury Levels by Species

NMFS Data:

Table and Graphs

Comparisons of Different Methods of Handling Nondetects:

Table and Graphs
C-11

Table C-1

Records in NMFS Report for which the difference between Method 3 and Method 2 is greater than

0.010 (sorted according to the magnitude of the difference, DIFF)

SPECIES VARIETY LOCATION TISSUE NO. N. DET MEAN DIFF

Herring Pacific Pacific NWest whole 20 19 .260 -0.242

Sole Petrale Pacific NWest muscle 11 6 .347 -0.187

Tuna Bigeye Hawaii liver 2 1 .250 -0.123

Squid Atl. Longfinned N. Atlantic mantle, skinless 6 5 .130 -0.104

Cod Atlantic N. Atlantic liver 2 1 .210 -0.103

Crab Tanner (Bairdi) Alaska meat 10 5 .208 -0.102

Squid Atl. Longfinned N. Atlantic mantle, skinless 7 5 .140 -0.096

Shrimp Alaska (Sidestriped) Alaska tail, peeled 7 4 .168 -0.093

Cod Atlantic N. Atlantic liver 6 5 .110 -0.088

Shrimp Ocean Pacific NWest tail, peeled 10 6 .136 -0.079

Cod Atlantic N. Atlantic liver 4 2 .158 -0.077

Clam Butter Pacific NWest shucked, large 10 8 .100 -0.076

Mullet Striped Hawaii muscle 18 16 .090 -0.076

Salmon Coho (Silver) Alaska muscle 10 7 .110 -0.074

Crab Tanner (Bairdi) Alaska meat 10 5 .152 -0.074

Mullet Striped South Atlantic muscle 19 15 .098 -0.073

Oyster Pacific (Giant) California shucked 10 8 .090 -0.068

Scallop Calico S. Atlantic abductor muscle 10 8 .090 -0.068

Clam Hard N. Atlantic shucked, cherrysto 10 5 .141 -0.068

Squid Shortfinned N. Atlantic mantle, skinless 4 2 .135 -0.065

Shrimp Brown Gulf tail, peeled 10 8 .085 -0.064

Oyster Pacific (Giant) California shucked 20 12 .111 -0.064

Squid Atl. Longfinned N. Atlantic mantle, skinless 20 13 .100 -0.062

Squid Shortfinned N. Atlantic mantle, skinless 2 1 .120 -0.058

Tuna Yellowfin Hawaii liver 2 1 .120 -0.058

Clam Razor Alaska shucked 11 8 .083 -0.057

Croaker Atlantic Gulf muscle 9 6 .090 -0.057

Pollock Walleye (Alaska) Alaska muscle 28 12 .135 -0.056

Squid Shortfinned N. Atlantic mantle, skinless 11 6 .105 -0.055

Shrimp Pink Gulf tail, peeled 20 10 .114 -0.055

Salmon Coho (Silver) Pacific NWest liver 2 1 .110 -0.053

Mackerel Jack California headed 4 3 .070 -0.049

Trout (Sea) Silver (White) Gulf muscle 10 5 .100 -0.048

Clam Soft N. Atlantic shucked 19 11 .086 -0.047

Flounder Fourspot N. Atlantic muscle 3 1 .145 -0.047

Mullet Striped Gulf muscle 12 10 .060 -0.046

Shrimp White Gulf tail, peeled 10 8 .060 -0.044

Squid Shortfinned N. Atlantic mantle, skinless 5 4 .060 -0.044

Cod Atlantic N. Atlantic muscle 16 6 .121 -0.044

Pollock N. Atlantic liver 3 2 .070 -0.043

Flounder Winter North Atlantic muscle 10 4 .113 -0.043
C-12

Mackerel King Gulf ROE 9 2 .199 -0.043

Flounder Witch N. Atlantic muscle 2 1 .090 -0.043

Tuna Skipjack Pacific liver 2 1 .090 -0.043

Herring Atlantic North Atlantic whole 12 11 .050 -0.041

Scallop Calico S. Atlantic shucked 10 6 .073 -0.041

Squid Shortfinned N. Atlantic mantle, skinless 10 6 .073 -0.041

Mullet Striped South Atlantic muscle 10 9 .050 -0.041

Shrimp Pink (Northern) Alaska tail, peeled 10 9 .050 -0.041

Flounder Winter North Atlantic muscle 2 1 .085 -0.040

Squid Pacific California whole 29 19 .064 -0.039

Oyster Eastern S. Atlantic shucked 10 3 .133 -0.038

Flounder Winter North Atlantic muscle 5 2 .100 -0.038

Salmon Sockeye (Red) Pacific NWest muscle 12 7 .068 -0.037

Abalone Red California shucked 10 5 .078 -0.037

Oyster Eastern N. Atlantic shucked, std. 10 7 .057 -0.036

Crab Tanner (Bairdi) Alaska meat 10 3 .126 -0.036

Herring Round North Atlantic H & G tailless 10 6 .065 -0.036

Flounder Southern S. Atlantic muscle 10 4 .095 -0.036

Pollock N. Atlantic liver 7 5 .055 -0.036

Squid Atl. Longfinned N. Atlantic mantle, skinless 7 3 .088 -0.036

Scallop Calico S. Atlantic abductor muscle 10 5 .076 -0.036

Flounder Summer (Fluke) S. Atlantic muscle 20 6 .119 -0.034

Trout (Sea) Sand Gulf muscle 5 3 .060 -0.033

Crab Rock N. Atlantic meat 5 1 .169 -0.033

Mullet Striped Gulf muscle 15 14 .040 -0.033

Flounder Winter North Atlantic muscle 4 2 .070 -0.033

Scup North Atlantic muscle 2 1 .070 -0.033

Salmon Chum (Keta) Alaska muscle 10 4 .086 -0.032

Shrimp Pink (Northern) Alaska tail, peeled 9 5 .063 -0.032

Mullet Striped South Atlantic muscle 4 1 .133 -0.032

Squid Shortfinned N. Atlantic mantle, skinless 14 8 .060 -0.031

Clam Surf N. Atlantic shucked, whole 19 9 .070 -0.031

Pollock Walleye (Alaska) Alaska liver 3 2 .050 -0.030

Anchovy Northern California whole 10 4 .080 -0.030

Scallop Sea (smooth) N. Atlantic abductor muscle 10 7 .047 -0.029

Squid Atl. Longfinned N. Atlantic mantle, skinless 10 4 .078 -0.029

Herring Atlantic North Atlantic headed 6 5 .040 -0.029

Herring Atlantic North Atlantic whole 29 14 .065 -0.029

Shrimp Brown Gulf tail, peeled 10 4 .077 -0.029

Salmon Chinock (King) Pacific NWest liver 5 1 .149 -0.029

Snapper Red (EMU) Hawaii muscle 18 1 .522 -0.029

Flounder Witch N. Atlantic muscle 16 3 .156 -0.028

Flounder Yellowtail North Atlantic muscle 10 3 .099 -0.028

Mackerel Atlantic North Atlantic muscle 8 5 .050 -0.028

Oyster Eastern N. Atlantic shucked, select 10 8 .040 -0.028

Shrimp White Gulf tail, peeled 10 8 .040 -0.028

Mullet Striped Hawaii muscle 9 6 .047 -0.028
C-13

Shrimp Pink Gulf tail, peeled 9 2 .130 -0.028

Pollock N. Atlantic liver 14 8 .053 -0.027

Shrimp Pink (Northern) N. Atlantic tail, peeled 11 7 .048 -0.027

Shark Blue North Atlantic liver 9 2 .127 -0.027

Scup North Atlantic muscle 6 2 .086 -0.027

Flounder Winter North Atlantic muscle 10 4 .072 -0.027

Flounder Yellowtail North Atlantic muscle 3 2 .045 -0.027

Salmon Chinock (King) Alaska muscle 10 8 .038 -0.026

Mackerel Spanish South Atlantic muscle 20 3 .181 -0.026

Squid Atl. Longfinned N. Atlantic mantle, skinless 4 3 .040 -0.026

Squid Shortfinned N. Atlantic mantle, skinless 4 3 .040 -0.026

Flounder Gulf Gulf muscle 19 5 .101 -0.025

Trout (Sea) Gray (Weakfish) N. Atlantic whole 10 4 .068 -0.025

Octopus Marmuratus Hawaii mantle, skinless 36 17 .058 -0.025

Flounder Fourspot N. Atlantic muscle 4 2 .055 -0.025

Herring Atlantic North Atlantic whole 3 1 .080 -0.025

Croaker Atlantic N. Atlantic muscle 5 1 .130 -0.025

Perch Ocean (Pacific) Pacific NWest muscle 10 7 .040 -0.025

Shrimp Alaska (Sidestriped) Alaska tail, peeled 10 7 .040 -0.025

Oyster Pacific (Giant) Pacific NWest shucked, medium 9 4 .060 -0.024

Flounder Winter North Atlantic muscle 7 4 .047 -0.024

Flounder Witch N. Atlantic muscle 5 2 .065 -0.024

Flounder Winter North Atlantic muscle 15 9 .045 -0.024

Salmon Chum (Keta) Alaska muscle 9 4 .059 -0.024

Sole Dover Pacific NWest muscle 10 3 .085 -0.024

Flounder Winter North Atlantic muscle 6 1 .147 -0.024

Bass striped N. Atlantic muscle 16 8 .052 -0.024

Cod Atlantic N. Atlantic liver 3 2 .040 -0.023

Halibut Pacific Pacific NWest liver 3 2 .040 -0.023

Mackerel Atlantic North Atlantic muscle 11 4 .069 -0.023

Squid Atl. Longfinned N. Atlantic mantle, skinless 13 7 .048 -0.023

Mullet Striped South Atlantic muscle 2 1 .050 -0.023

Herring Atlantic North Atlantic muscle 10 9 .030 -0.023

Mullet Silver (white) South Atlantic muscle 24 18 .035 -0.023

Oyster Pacific (Giant) Pacific NWest shucked, small 10 5 .050 -0.023

Shrimp Pink Gulf tail, peeled 9 8 .030 -0.022

Herring Round North Atlantic H & G tailless 27 21 .033 -0.022

Bass striped Pacific NWest muscle 40 1 .858 -0.021

Flounder Witch N. Atlantic muscle 15 3 .111 -0.021

Mullet Striped Gulf muscle 20 11 .043 -0.021

Cod Atlantic N. Atlantic liver 5 2 .057 -0.021

Flounder Witch N. Atlantic muscle 4 1 .088 -0.021

Clam Razor Pacific NWest shucked 10 5 .046 -0.021

Flounder Winter North Atlantic muscle 21 6 .076 -0.020

Herring Atlantic North Atlantic muscle 12 8 .035 -0.020

Scup North Atlantic muscle 5 1 .105 -0.020

Sole Petrale Pacific NWest muscle 2 1 .045 -0.020
C-14

Shark Blacktip South Atlantic liver 3 1 .065 -0.020

Squid Pacific California whole 10 6 .038 -0.020

Squid Atl. Longfinned N. Atlantic mantle, skinless 17 7 .053 -0.020

Mackerel Atlantic North Atlantic muscle 36 17 .046 -0.019

Oyster Eastern N. Atlantic shucked 20 9 .048 -0.019

Trout Rainbow/Steelhead Pacific NWest muscle 6 2 .063 -0.019

Trout (Sea) Silver (White) Gulf muscle 10 3 .069 -0.019

Shrimp Brown Gulf tail, peeled 17 3 .113 -0.019

Scup North Atlantic muscle 6 3 .043 -0.019

Croaker Atlantic S. Atlantic muscle 12 4 .061 -0.019

Clam Razor Pacific NWest shucked 10 5 .042 -0.019

Shrimp White S. Atlantic tail, peeled 10 2 .096 -0.018

Shrimp Pink (Northern) N. Atlantic tail, peeled 10 4 .050 -0.018

Salmon Chum (Keta) Pacific NWest muscle 7 5 .030 -0.018

Squid Atl. Longfinned N. Atlantic whole 23 9 .050 -0.018

Flounder Witch N. Atlantic muscle 10 2 .093 -0.018

Scallop Atlantic Bay S. Atlantic abductor muscle 10 6 .034 -0.017

Flounder Fourspot N. Atlantic muscle 6 1 .109 -0.017

Anchovy Northern California whole 10 4 .048 -0.017

Scallop Atlantic Bay S. Atlantic abductor muscle 10 2 .091 -0.017

Halibut Pacific Pacific NWest muscle 10 3 .062 -0.017

Salmon Sockeye (Red) Alaska muscle 19 9 .041 -0.017

Croaker Atlantic N. Atlantic muscle 10 6 .033 -0.017

Cod Pacific (Gray) Alaska liver 5 2 .047 -0.017

Trout (Sea) Silver (White) Gulf muscle 13 2 .114 -0.017

Shrimp Pink (Northern) N. Atlantic tail, peeled 3 1 .055 -0.017

Anchovy Northern California whole 10 8 .025 -0.016

Crab Blue N. Atlantic claw & body meat 10 5 .037 -0.016

Mackerel Atlantic North Atlantic muscle 7 4 .033 -0.016

Squid Shortfinned N. Atlantic mantle, skinless 20 5 .069 -0.016

Flounder Southern Gulf muscle 4 1 .067 -0.016

Flounder Fourspot N. Atlantic muscle 19 3 .103 -0.015

Flounder Winter North Atlantic muscle 12 4 .051 -0.015

Bass striped California muscle 28 1 .432 -0.015

Salmon Pink Alaska muscle 9 4 .039 -0.015

Clam Razor Alaska shucked 8 4 .035 -0.015

Croaker Atlantic Gulf muscle 2 1 .035 -0.015

Halibut Pacific Pacific NWest liver 8 6 .025 -0.015

Clam Hard N. Atlantic shucked, mixed 20 5 .065 -0.015

Oyster Eastern Gulf shucked 11 5 .038 -0.015

Squid Shortfinned N. Atlantic mantle, skinless 5 3 .030 -0.015

Tuna Yellowfin Hawaii muscle 10 3 .054 -0.015

Flounder Fourspot N. Atlantic muscle 6 1 .093 -0.015

Shrimp Pink Gulf tail, peeled 10 5 .034 -0.015

Clam Hard N. Atlantic shucked, littleneck 16 7 .038 -0.014

Abalone Green California shucked 10 6 .029 -0.014

Herring Round North Atlantic H & G tailless 7 5 .025 -0.014
C-15

Shrimp Pink (Northern) N. Atlantic tail, peeled 9 4 .037 -0.014

Herring Atlantic North Atlantic whole 17 16 .020 -0.014

Shrimp White Gulf tail, peeled 17 3 .085 -0.014

Shrimp Brown Gulf tail, peeled 13 7 .031 -0.014

Clam Hard N. Atlantic shucked, cherrysto 30 13 .037 -0.014

Mullet Striped Hawaii muscle 13 12 .020 -0.014

Oyster Eastern Gulf shucked 20 12 .028 -0.014

Oyster Pacific (Giant) Pacific NWest shucked, medium 10 6 .028 -0.014

Scup North Atlantic muscle 11 10 .020 -0.014

Oyster Eastern N. Atlantic shucked, select 16 9 .029 -0.014

Anchovy Northern California whole 10 9 .020 -0.014

Croaker Atlantic Gulf muscle 10 9 .020 -0.014

Salmon Sockeye (Red) Alaska muscle 10 9 .020 -0.014

Oyster Pacific (Giant) Pacific NWest shucked 10 4 .038 -0.013

Clam Hard N. Atlantic shucked, chowder 49 14 .050 -0.013

Croaker Atlantic S. Atlantic muscle 2 1 .030 -0.013

Haddock N. Atlantic liver 2 1 .030 -0.013

Oyster Eastern S.Atlantic shucked 10 5 .030 -0.013

Perch Ocean (Pacific) Pacific NWest liver 8 4 .030 -0.013

Snapper Vermilion South Atlantic muscle 2 1 .030 -0.013

Lobster Atlantic Spiny Gulf tail meat 12 3 .055 -0.013

Tuna Skipjack Pacific muscle 20 3 .088 -0.012

Clam Butter Pacific NWest shucked, ex. large 9 4 .033 -0.012

Salmon Chinock (King) Alaska muscle 9 3 .042 -0.012

Flounder Windowpane N. Atlantic muscle 7 1 .090 -0.012

Salmon Chinock (King) Alaska muscle 10 8 .020 -0.012

Scallop Pink Alaska abductor muscle 5 4 .020 -0.012

Scup North Atlantic muscle 5 3 .025 -0.012

Squid Shortfinned N. Atlantic mantle, skinless 5 4 .020 -0.012

Haddock N. Atlantic muscle 5 1 .065 -0.012

Squid Shortfinned N. Atlantic mantle, skinless 9 1 .112 -0.012

Shrimp Brown Gulf tail, peeled 3 1 .040 -0.012

Flounder Witch N. Atlantic muscle 15 2 .092 -0.012

Salmon Coho (Silver) Pacific NWest muscle 10 5 .028 -0.012

Flounder Fourspot N. Atlantic muscle 18 2 .108 -0.011

Clam Butter Pacific NWest shucked 4 3 .020 -0.011

Shrimp Pink (Northern) N. Atlantic tail, peeled 4 1 .050 -0.011

Salmon Sockeye (Red) Alaska muscle 10 4 .033 -0.011

Perch Ocean (Redfish) North Atlantic muscle 14 1 .161 -0.011

Haddock N. Atlantic muscle 9 1 .105 -0.011

Crab King Alaska meat 9 3 .038 -0.011

Salmon Pink Alaska muscle 10 6 .023 -0.011
APPENDIX D

HUMAN FISH CONSUMPTION AND MERCURY INGESTION

DISTRIBUTIONS 
D-i

TABLE OF CONTENTS

Page

D.1 Introduction .................................................................. 1

D.2 Methods and Assumptions ...................................................... 1

D.3 Population Exposure Equations .................................................. 1

D.4 Input Distributions ............................................................ 2

D.4.1 Mercury Ingestion per Fish Meal (Hg ) MEAL ................................... 2

D.4.2 Fish Consumption per Fish Meal (Fish ) MEAL .................................. 3

D.4.3 Number of Fish Meals per Month (Nmeals) ................................. 4

D.5 Simulation Output ............................................................ 6

D.6 Sensitivity Analysis ........................................................... 8

D.6.1 Adequacy of Input Distribution Fit ......................................... 8

D.6.2 Impact of Assumptions on Simulation Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

D.6.2 Other Sources of Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

D.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

D.8 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
D-ii

LIST OF TABLES

Page

D-1 Hg Distributions for Selected Populations MEAL ....................................... 3

D-2 Fish Distributions for Selected Populations MEAL ...................................... 4

D-3 Nmeals Distributions for Selected Populations ...................................... 5

D-4 Hg Distributions for Selected Populations: Adults DAILY ............................... 6

D-5 Hg Distributions for Selected Populations: Children DAILY .............................. 7

D-6 FC Distributions for Selected Populations: Adults DAILY ............................... 7

D-7 FC Distributions for Selected Populations: Children DAILY .............................. 8

D-8 Comparison of Hg Output for Alternate Fits DAILY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
D-iii

LISTOF FIGURES

Page

D-1 Quantile-Quantile Plots for Hg Distributions MEAL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

D-2 Quantile-Quantile Plots for Fish Distributions MEAL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

D-3 Quantile-Quantile Plots for Nmeals Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

D-4 Quantile-Quantile Plots for Mixtures-Distribution Fits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
HgDAILY  HgMEAL x Nmeals

30

D-1

D.1 Introduction

This Appendix presents an analysis of the third National Health and Nutrition Examination

Survey (NHANES III) data on frequency of fish and shellfish consumption over an one-month interval, 

24-hour recall data for consumption of fish and shellfish, body weight (in kilograms) and mean mercury

concentrations in fish and shellfish. These data were utilized to estimate national exposure distributions

for ingestion of mercury from fish and shellfish for a time period defined as one month or 30 days. 

Mathematical distributions were fit to data addressing the number and size of fish meals and associated

mercury ingestion for several ethnic and racial groups within the general U.S. population. Analyses for

higher-frequency fish consumers, women of child-bearing age and children were also performed.

D.2 Methods and Assumptions

All variables in this analysis were assumed to be lognormally distributed and independent. 

Parameters of the lognormal distributions are expressed as the geometric mean (GM) and the geometric

standard deviation (GSD). The geometric mean (and median) is defined as e , where μ is the mean of the μ

logarithms of the observations. The geometric standard deviation is defined as e , where  is the 

standard deviation of the logarithms of the observations. 

The data available for estimation of distribution parameters were in the form of cumulative

distribution percentiles and moments (arithmetic mean and standard deviation). The primary approach to

fitting lognormal distributions to the data was by the method of moments, in which the sample mean and

sample standard deviation, themselves, are used as estimates of the parameters. For the lognormal, the

parameters are determined in log space (mean and standard deviation of the logs of the observations). In

this analysis, the GM and GSD were estimated from the arithmetic mean and standard deviation using

analytic formulas relating the arithmetic and geometric moments (Evans et al., 1993). In some cases the

arithmetic moments did not provide reasonable estimates of the geometric moments. In these cases

parameter estimation focused on the range between the 50th (median) and 95th percentiles. μ was

assumed to be the log of the median.  was estimated as the average of the difference of the logs of the

75th, 90th and 95th percentiles and μ, divided by the corresponding z-score from the standard unit

normal distribution. Distributions derived by the percentile method should be considered to be less

reliable than by the method of moments. The fit of the distributions to the data in this range was assessed

by graphical analysis and percentile matching. 

D.3 Population Exposure Equations

Daily mercury ingestion from fish consumption is given as Equation 1.

(1)

where 

Hg is daily ingestion of total mercury (μg/kg DAILY bw-day),

Hg is the ingestion of total mercury per fish meal (μg/kg MEAL bw-meal),

Nmeals is the number of fish meals per month (month ) and -1

30 is the number of days per month (days/month).
FCDAILY  FishMEAL x Nmeals

30

D-2

Daily fish consumption is given as Equation 2.

(2)

where 

FC is daily DAILY per capita fish consumption (g/day),

Fish is fish consumption per fish meal (g/meal), MEAL

Nmeals is the number of fish meals per month (month ) and -1

30 is the number of days per month (days/month).

Equations 1 and 2 are solved using analytic methods for multiplying lognormal distributions (Aitchison

and Brown, 1966; see also Appendix D to Volume 3 of this Report).

D.4 Input Distributions

This section presents the development of each of the input distributions for Equations 1 and 2. 

The basis for each distribution is given. Moments and percentiles for all empirical distributions were

based on population weighted frequencies. That is, the sample observation frequencies were projected to

the national population weighted by sex and age frequencies in the national population (NHANES III). 

D.4.1 Mercury Ingestion per Fish Meal (HgMEAL)

Hg distributions were based on 24-hour fish (and shellfish) consumption recall data for MEAL

consumers, only (per user), reported in NHANES III and average mercury concentrations reported for

each fish species consumed. Consumption-mass-weighted mercury concentrations for individual species

were summed across all species consumed by each survey respondent (consumers only) and divided by

the respondent's body weight. Simplifying assumption were made that all the mercury was

methylmercury (MeHg) and was ingested in a single meal. Empirical Hg distributions were MEAL

constructed for six subpopulations: the Caucasian (nonHispanic) general population ("White"), the

African-American (nonHispanic) general population ("Black"), the Mexican-American general

population ("Hispanic"), a more frequent fish-consuming population that included Asians, Pacific

Islanders, Native Americans and Caribbean Islanders ("Other"), 15 to 44 year-old females across all

groups ("Women") and 3 to 6 year-old children across all groups ("Children"). Women of this age group

were selected as the MeHg Reference Dose (RfD) based primarily on effects in offspring of women

exposed to MeHg during pregnancy. This particular age group of children was selected because of its

much higher mercury exposure rate than other child age groups. The Hg empirical distributions and MEAL

lognormal approximations for each of these subpopulations are given in Table D-1. 
D-3

Table D-1

Hg Distributions for Selected Populations MEAL

(μg/kgbw-meal)

Population

Distribution: White Black Hispanic Other Women Children

Empirical

n 1392 1278 914 265 882 415 

mean 0.19 0.23 0.22 0.23 0.17 0.40

std. dev. 43.05 19.69 11.42 50.00 0.28 0.56

50th percentile 0.12* 0.15 0.15 0.12 0.10 0.28

75th percentile 0.26 0.32 0.31 0.32 0.22 0.49

90th percentile 0.50 0.57 0.58 0.61 0.39 0.77

95th percentile 0.73 0.77 0.77 0.97 0.53 1.08

Lognormal 

method percentiles percentiles percentiles percentiles moments moments

GM 0.12 0.15 0.15 0.12 0.09 0.23 a

GSD 3.01 2.82 2.91 3.77 3.14 2.83 b

75th percentile 0.25 0.31 0.30 0.30 0.19 0.47

90th percentile 0.50 0.57 0.58 0.66 0.38 0.88

95th percentile 0.74 0.83 0.85 1.07 0.58 1.29

mean 0.22 0.26 0.26 0.29 - -

std. dev. 0.34 0.36 0.38 0.64 - -

 Geometric Mean (and 50th percentile) a

 Geometric Standard Deviation b

 *Rounded to 2 significant figures.

D.4.2 Fish Consumption per Fish Meal (FishMEAL)

Fish distributions were based on 24-hour fish (and shellfish) consumption recall data for MEAL

consumers, only (per user), reported in NHANES III. A simplifying assumption was made that all the

fish was consumed in a single meal. Fish distributions were constructed for the same five MEAL

subpopulations as for Hg . The Fish empirical distributions and lognormal approximations for MEAL MEAL

each of these subpopulations are given in Table D-2. 
D-4

Table D-2

Fish Distributions for Selected Populations MEAL

(g/meal)

Population

Distribution: White Black Hispanic Other Women Children

Empirical

n 1394 1282 920 266 883 415 

mean 109 128 108 106 103 57

std. dev. 16752 8004 4856 15277 116 55

50th percentile 65.5* 77.5 64.7 67.5 66.0 43.3

75th percentile 126 151 129 122 131 66.2

90th percentile 222 263 222 234 228 113

95th percentile 291 356 318 297 288 151

Lognormal

method percentiles percentiles percentiles percentiles moments moments

Gm 65.5 77.5 64.7 67.5 68.6 40.7 a

GSD 2.57 2.60 2.67 2.50 2.47 2.26 b

75th percentile 124 148 125 125 126 70.6

90th percentile 220 264 228 219 219 116

95th percentile 310 373 326 305 304 156

mean 102 122 105 103 - -

std. dev. 123 150 134 119 - -

 Geometric Mean (and 50th percentile) a

 Geometric Standard Deviation b

 * Rounded to 3 significant figures.

D.4.3 Number of Fish Meals per Month (Nmeals)

Nmeals distributions were based on monthly fish (and shellfish) consumption frequency data for

all respondents (per capita) reported in NHANES III. The frequency of fish meals consumed per month

was treated as a continuous variable for estimation of long-term fish consumption rates. Values at the

reference percentiles (50th, 75th, 90th and 95th) were estimated by linear interpolation from cumulative

discrete frequency distributions. As these data are from the general population (not just fish consumers),

a significant fraction of respondents reported eating no fish in the last month (11-14%). Nmeals
D-5

distributions were constructed for the same subpopulations as for Hg and Fish except for MEAL MEAL

"Women" and "Children," for which data were not available. An Nmeals distribution for the general

population across all other groups ("All") was used as a surrogate for "Women" and "Children." Nmeals

empirical distributions and lognormal approximations for each of these subpopulations are given in Table

D-3. 

Table D-3

Nmeals Distributions for Selected Populations

(month )-1

Population

Distribution White Black Hispanic Other All

Empirical

n 7410 5594 5394 785 19,200 

mean 5.6 6.5 4.7 8.3 5.8

std. dev. 6.2 8.2 5.8 2.6 6.9

50th percentile 3.4* 3.8 2.9 4.1 3.5

75th percentile 7.2 8.0 5.8 9.9 7.4

90th percentile 12 13 11 22 12

95th percentile 16 18 14 31 17

99th percentile 30 31 28 43 30

maximum 150 220 150 61 220

Lognormal

method moments moments moments moments moments

GM 3.7 4.0 3.0 5.3 3.8 a

GSD 2.5 2.7 2.6 2.6 2.5 b

75th percentile 6.8 7.8 5.7 10 7.1

90th percentile 12 14 10 18 12

95th percentile 16 20 14 25 18

99th percentile 30 39 28 19 33

 Geometric Mean (and 50th percentile) a

 Geometric Standard Deviation b

 * Rounded to 2 significant figures.
D-6

D.5 Simulation Output 

The results of the solution of Equation 1 (Hg ) are given for adults and children in Tables DAILY

D-4 and D-5, respectively. The percentile at which the MeHg RfD falls in the Hg output is given DAILY

for adults (Table D-4). Direct comparison to the RfD is most appropriate for women of child-bearing

age, as the MeHg RfD is based, primarily, on effects in the offspring of exposures to their mothers during

pregnancy (see Volume V of this report; also U. S. EPA, 1997). That is, although the effects were

observed in children, the exposure (and it's associated metric) was to the mother. The RfD is designed to

be protective of all sensitive subpopulations. In this case (MeHg), the developing fetus was judged to be

the most sensitive population. An uncertainty factor was included in the RfD to account for the lack of

data on post-natal development, among other factors. 

The results of the solution of Equation 2 (FC ) are given for adults and children in Tables DAILY

D-6 and D-7, respectively. The percentile at which fish ingestion exceeds 100 g/day in the Fish DAILY

output is also shown. 

Table D-4

Hg Distributions for Selected Populations: Adults DAILY

(μg/kgbw-day)

Population

Percentile White Black Hispanic Other Women a b cd e

50th 0.015 0.020 0.015 0.021 0.011

75th 0.039 0.053 0.047 0.064 0.030

90th 0.092 0.13 0.11 0.17 0.074

95th 0.15 0.21 0.18 0.31 0.13

RfD Percentile 91.0 86.8 91.0 82.7 93.2

GM = 0.0149, GSD = 4.145 a 

GM = 0.0204, GSD = 4.153 b 

GM = 0.0145, GSD = 4.216 c 

GM = 0.0214, GSD = 5.123 d 

GM = 0.0111, GSD = 4.382 d 
D-7

Table D-5

Hg Distributions for Selected Populations: Children DAILY

(μg/kgbw-day)

Ethnicity

Percentile All White Black Hispanic Other

Groupsa

b c de

50th 0.029 0.029 0.031 0.023 0.041

75th 0.075 0.072 0.082 0.060 0.11

90th 0.18 0.17 0.19 0.14 0.25

95th 0.29 0.28 0.33 0.24 0.42

GM = 0.0292, GSD = 4.050 a 

GM = 0.0286, GSD = 3.961 b 

GM = 0.0311, GSD = 4.173 c 

GM = 0.0230, GSD = 4.130 d 

GM = 0.0411, GSD = 4.102 e 

Nmeals distributions from general population for each group (not child-specific)

HgMEAL distribution from 3-6 year-old children across ethnicities (not group-specific)

Table D-6

FC Distributions for Selected Populations: Adults DAILY

(g/day)

Population

Percentile White Black Hispanic Other Women a b cc d

50th 8.1 10 6.4 12 8.6

75th 19 26 16 29 21

90th 43 60 37 65 46

95th 69 99 62 105 73

100 g percentile 97.3 95.1 97.7 94.6 97.0

GM = 8.08, GSD = 3.685 a 

GM = 10.4, GSD = 3.925 b 

GM = 6.43, GSD = 3.957 c 

GM = 11.9, GSD = 3.751 c 

GM = 8.63, GSD = 3.668 d 
D-8

Table D-7

FC Distributions for Selected Populations: Children DAILY

(g/day)

Ethnicity

Percentile All White Black Hispanic Other

Groupsa

b c de

50th 5.1 5.0 5.5 4.0 7.2

75th 12 11 13 9.5 17

90th 25 24 28 20 36

95th 39 37 44 32 57

100 g percentile >99 >99 99 >99 98

GM = 5.12, GSD = 3.456 a 

GM = 5.01, GSD = 3.370 b 

GM = 5.46, GSD = 3.573 c 

GM = 4.04, GSD = 3.532 d 

GM = 7.18, GSD = 3.506 e 

Nmeals distributions from general population for each group (not child-specific)

Fish distribution from 3-6 MEAL year-old children across ethnicities (not group-specific)

D.6 Sensitivity Analysis

D.6.1 Adequacy of Input Distribution Fit

A general trend for fitting input distributions by the percentile method was for higher estimates

of  at lower percentiles but with fairly good agreement in the targeted range (75th to 95th percentiles);

coefficients of variation for  estimates for a given data set were in the range of 0.03 to 0.1. 

Distributions fit by this method were not particularly good approximations of the data outside these

percentile ranges. The impact of overestimating the lower end of the input distributions on the output of

Equations 1 and 2 is discussed in the next section.

Quantile-quantile plots (QQ plots) are shown for each of the distributions in Figures D-1, D-2

and D-3, which show the Hg , Fish , and Nmeals distributions, respectively. These figures plot MEAL MEAL

the z-scores of the logs of the observations against the z-scores for the corresponding fitted lognormal

distribution (normal in log space). The z-scores are the number of standard deviations above or below

the median. A z-score of 2 corresponds to about the 95 percentile (z= -2  5 percentile). The 99 and th th th

99.9 percentiles correspond to z-scores of 2.33 and 3.1, respectively. As these plots compare the logs th

of the distributions, zeroes in the raw data are not included. Zeroes were included, however, in the fitting

process for those variables fit by the method of moments. For those distributions fit by the percentile

method, the data points (50 , 75 , 90 and 95 percentiles) used in the fitting process are indicated by th th th th

filled symbols on the Figures. 
D-9

The solid straight lines on the QQ plots represent perfect fits. That is, a perfect fit would result

in all the points lining up along the line. The direction of deviations from the line can be used to assess

the direction of the prediction error. If the points curve below the line at either end, the fitted distribution

will under predict actual values at that end. Conversely, if the points curve above the line, the fitted

distribution will over predict. The tendency to over predict the lower tail can be seen for all of the

variables. This tendency is quite marked for a number of variables, particularly for the ones fitted by the

percentile method. The upper tails of the empirical distributions are all fairly well represented by the

fitted distributions, even for extreme values. Nmeals/Other is an exception, but the poor fit is well

beyond the 99th percentile; the data points above the 99th percentile are single observations. The effect

of over prediction in the lower tail on the analytic solutions of Equations 1 and 2 will be to greatly

exaggerate the lower percentiles. There will also be a tendency to over predict the upper percentiles, but

probably not by a large amount. Deviations from the fit line at z-scores of less than -3 should have no

effect on the output. In general, the magnitude of the over prediction is difficult to assess from the QQ

plots, but will be considerably less than that resulting from over prediction in the upper tails of the input

distributions. The best predictions should be for both outputs for "Women" and "Children," given the

better combined fit for Hg , Fish , and Nmeals for these two groups. MEAL MEAL
empirical z-score

normal z-score

-3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

WHITE

empirical z-score

normal z-score

-7.0 -5.5 -4.0 -2.5 -1.0 0.0 1.0 2.0 3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

BLACK

empirical z-score

normal z-score

-4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

HISPANIC

empirical z-score

normal z-score

-3.0 -2.0 -1.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

OTHER

empirical z-score

normal z-score

-2.0 -1.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

WOMEN

empirical z-score

normal z-score

-3.0 -2.0 -1.0 0.0 1.0 2.0 3.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

CHILDREN

D-10

Figure D-1

Quantile-Quantile Plots for Hg Distributions MEAL
empirical z-score

normal z-score

-4.5 -3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

WHITE

empirical z-score

normal z-score

-5.0 -4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

BLACK

empirical z-score

normal z-score

-3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

HISPANIC

empirical z-score

normal z-score

-4.5 -3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

OTHER

empirical z-score

normal z-score

-4.5 -3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

WOMEN

empirical z-score

normal z-score

-4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

CHILDREN

D-11

Figure D-2

Quantile-Quantile Plots for Fish Distributions MEAL
empirical z-score

normal z-score

-1.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

WHITE

empirical z-score

normal z-score

-1.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

BLACK

empirical z-score

normal z-score

-1.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

HISPANIC

empirical z-score

normal z-score

-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

OTHER

empirical z-score

normal z-score

-1.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

ALL

D-12

Figure D-3

Quantile-Quantile Plots for Nmeals Distributions
D-13

D.6.2 Impact of Assumptions on Simulation Output

The assumption that the 24-hour recall data represent one fish meal is obviously false for all

respondents who reported more than 30 fish meals per month. The assumption will result in

overestimation of both Hg and FC at higher percentiles. The 30 fish meal per month mark falls DAILY DAILY

at the 99th percentile or higher for all groups except “Other,” for which the 95th percentile is 31.4 fish

meals per month. The bias in Hg and FC for groups other than “Other” should not be DAILY DAILY

significant at the 95th percentile and lower, but this assumption was not tested. The results for “Other”

above the 90th percentile should be considered to be conservative.

Correlation of input variables was not considered in this analysis. Data for “Women” suggest

that there is a slight positive correlation between Nmeals and the other two variables, with a more

noticeable difference in Fish for those respondents reporting zero or one fish meal in the last month. MEAL

That is, those individuals who had a low frequency of fish consumption also tended to eat less fish per

meal (70 g/meal vs 108 g/meal for respondents reporting two or more fish meals per month). The result

of this correlation would be an over prediction of FC . The magnitude of the over prediction could DAILY

not be estimated without the specific body weight of the individuals, but was judged to be small. The

correlation of Nmeals and Hg was very weak and was not expected to have any impact on the output. MEAL

The effect of correlations on simulation output is generally smaller than that arising from the form of the

assigned distribution (Bukowski et al., 1995). 

The impact of the simplifying lognormal assumptions on the output of Equations 1 and 2 was

investigated by defining the input distributions as mixtures (mixtures approach) and then solving the

equations by Monte Carlo analysis. That is, separate distributions were fit to discrete segments of the

empirical data rather than assuming a single mathematical form for the entire distribution. For several

data sets where the number of zeroes was high, the proportion of zeroes was modeled as a delta function

(spike), with a lognormal distribution fit to the nonzero data (delta method). For one data set with no

zeroes, a log-triangular distribution was fit to the proportion of the data set that did not appear to be

lognormal (the lower 25%) and a lognormal was fit to the remainder (two-distribution method). In each

case, a composite mixtures distribution was constructed by Monte Carlo simulation. 

Figure D-4 shows the QQ-plots for the mixtures distribution fits to selected variables. Two of

the worst-fitting Hg data sets (Hispanic and Other) were selected for this part of the analysis. The MEAL

corresponding Nmeals data sets were also analyzed so that output distributions (Equation 1) could be

generated. Hg /Hispanic, was fit by the two-distribution method and the rest by the delta method. MEAL

Distribution quantiles, in natural log units, are shown in these plots instead of z-scores, as the fitted

distributions are not entirely lognormal. Otherwise, the visual fit of the distributions can be compared

directly with the corresponding QQ-plots in Figures D-1 and D-3. The mixtures approach provided a

better overall fit for Hg , particularly at the lower end, the lower three points for Hg /Hispanic MEAL MEAL

being an exception. These data points, however, represent less than 1% of the distribution and would

have no effect on the output. Upper percentile estimates for the mixtures approach are similar to those

estimated by the simple lognormal assumptions. The Nmeals distributions estimated by the mixtures

approach showed only slightly better fit (or none at all) in the lower percentiles at the expense of a

slightly poorer fit at the upper extreme. Fits to Nmeals/White and Nmeals/All were similar to

Nmeals/Hispanic. Overall, the mixtures approach did not improve the fit to Nmeals. 
empirical quantiles (natural log)

mixture distribution quantiles (natural log)

-6.5 -5.5 -4.5 -3.5 -2.5 -1.5 -0.5 0.5 1.5

-5.0

-4.5

-4.0

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

HgMeal (Hispanic)

empirical quantiles (natural log)

mixture distribution quantiles (natural log)

-6.0 -5.0 -4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0

-5.0

-4.5

-4.0

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

HgMeal (Other)

empirical quantiles (natural log)

mixture distribution quantiles (natural log)

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Nmeals (Hispanic)

empirical quantiles (natural log)

mixture distribution quantiles (natural log)

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

Nmeals (Other)

D-14

Figure D-4

Quantile-Quantile Plots for Mixtures-Distribution Fits

Results of the Monte Carlo simulations of Equation 1 using the mixtures distributions are given

in Table D-8. The output was simulated with mixtures distributions for both inputs (Hg and Nmeals) MEAL

and for Hg , only, as the mixtures approach did not provide a better fit for Nmeals. The results in MEAL

Table D-8 show little effect from the simple lognormal assumption for the inputs in this limited

comparison. Further analysis using the full data sets and other parametric fitting or nonparametric

methods would be useful for resolving the remaining distribution fit issues. 
D-15

Table D-8

Comparison of Hg Output for Alternate Fits DAILY

(μg/kgbw-day)

Group Hispanic Other

method of simple Hg both simple Hg both

distribution lognormal mixture mixtures lognormal mixture mixtures

fit

a MEALbc a MEALb c

Percentiles

50th 0.015* 0.014 0.019 0.021 0.021 0.020

75th 0.038 0.038 0.047 0.064 0.066 0.071

90th 0.092 0.086 0.11 0.17 0.18 0.20

95th 0.15 0.14 0.18 0.31 0.33 0.36

99th 0.41 0.40 0.45 0.96 0.98 1.1

from Table D-4 a 

mixture for Hg , only; lognormal Nmeals from Table D-3 b 

MEAL

mixtures for both inputs c 

 * Rounded to 2 significant figures.

D.6.2 Other Sources of Uncertainty

Sources of uncertainty or bias that have not been considered in this analysis include fish mercury

concentrations, mercury speciation in fish and shellfish, and population weights. The mercury

concentrations in the fish and shellfish were average concentrations for the identified fish species. Data

were available on the distribution of mercury in each species but were not considered for this analysis. 

These data would provide bounds on the percentile values estimated in this analysis but would not

change the median estimates for each percentile. The mercury in all “fish” species was assumed to be

methylmercury, which is a fairly sound assumption for finfish (Bloom, 1992), but somewhat less so for

shellfish and other species. The impact of this assumption on the simulation output was not investigated

but was assumed to be small. The uncertainty in the population weighting protocol in NHANES III was

not investigated either. 

D.7 Conclusions

The derived distributions are thought to be more characteristic of month-long patterns of fish and

shellfish consumption than are either of the two individual distributions that formed the input variables. 

The resulting derived distribution was done to maximize fit between the 75th and 95th percentiles. 
D-16

D.8 References

Aitchison, J. and J. A. C. Brown (1966). The Lognormal Distribution. University Press, Cambridge.

Barnes, D. G. and M. L. Dourson (1988). “Reference dose (RfD): Description and use in health risk

assessment,” Regul. Toxicol. Pharmacol. 8:471-486. 

Bloom, N.S. (1992). On the chemical form of mercury in edible fish and marine invertebrate tissue. Can.

J. Fish. Aquat. Sci. 49:1010-1017.

Bukowski, J., L. Korn and D. Wartenberg (1995).“Correlated Inputs in Quantitative Risk Assessment:

The Effects of Distributional Shape,” Risk Analysis 15:215-219.

Evans, M., N. Hastings and B. Peacock (1993). Statistical Distributions. 2nd Edition. John Wiley and

Sons, Inc., New York, NY, pp. 42-44.

U. S. EPA (1988). Integrated Risk Information System (IRIS), Background Document 1, National

Center for Environmental Assessment, Cincinnati Office, Cincinnati, OH.

U. S. EPA (1997). Integrated Risk Information System (IRIS), On-Line Assessments, National Center

for Environmental Assessment, Cincinnati Office, Cincinnati, OH.