United States

Environmental Protection

Agency

Air

EPA-452/R-97-005

December 1997

Mercury Study

Report to Congress

c7o032-1-1

Office of Air Quality Planning & Standards

and

Office of Research and Development

Volume III:

Fate and Transport of

Mercury in the Environment
MERCURY STUDY REPORT TO CONGRESS

VOLUME III:

FATE AND TRANSPORT OF MERCURY IN THE ENVIRONMENT

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 ................................................................ v

SCIENTIFIC PEER REVIEWERS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi

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

LIST OF TABLES ................................................................... x

LIST OF FIGURES ................................................................. xiii

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

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

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

1.1 Long Range Atmospheric Transport Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-4

1.2 Local Atmospheric Transport Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-4

1.3 Modeling Terrestrial and Aquatic Fate of Mercury . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-5

1.4 Exposure Modeling Rationale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-6

1.5 Factors Important in Modeling of Mercury Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . 1-7

1.6 Definition of Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-9

2. OVERVIEW OF MERCURY FATE AND TRANSPORT . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1

2.1 Mercury in the Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1

2.1.1 Chemistry of Mercury . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-2

2.1.2 The Mercury Cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-2

2.1.2.1 The Global Mercury Cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-3

2.1.2.2 Regional and Local Mercury Cycles . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4

2.2 Atmospheric Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-6

2.2.1 Emissions of Mercury . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-6

2.2.2 Mercury Transformation and Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-7

2.2.3 Deposition of Mercury . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-9

2.2.4 Re-emissions of Mercury into the Atmosphere ....................... 2-11

2.3 Terrestrial and Aquatic Fate of Mercury ................................... 2-11

2.3.1 Mercury in Soil ................................................ 2-11

2.3.2 Plant and Animal Uptake of Mercury ............................... 2-12

2.3.3 Mercury in the Freshwater Ecosystem .............................. 2-12

2.4 Fate of Mercury in Marine Environments .................................. 2-14

2.4.1 Models of Mercury in the Oceans ................................. 2-15

2.4.2 Modeling Estuaries and Coastal Regions ............................ 2-16

2.4.3 Mercury Budget for a Coastal Waterbody in U.S.A. ................... 2-18

3. MEASURED CONCENTRATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1

3.1 Analytic Measurement Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1

3.2 Measurement Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1

3.2.1 Mercury Air Concentrations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1

3.2.2 Mercury Concentrations in Precipitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-4

3.2.3 Mercury Deposition Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-5

3.2.4 Mercury Concentrations in Water . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-8
TABLE OF CONTENTS (continued)

Page

ii

3.2.5 Mercury Concentrations in Soil/Sediment ........................... 3-10

3.2.6 Mercury Concentrations in Biota .................................. 3-13

3.3 Measurement Data from Remote Locations ................................ 3-30

3.3.1 Elevated Atmospheric Mercury Concentrations over Remote Locations . . . 3-30

3.3.2 Elevated Soil Mercury Concentrations in Locations Remote from Emission

Sources ...................................................... 3-30

3.3.3 Elevated Mercury Concentrations in Aquatic Sediments and Fish from Remote

Water Bodies ................................................. 3-31

3.4 Measurement Data Near Anthropogenic Sources of Concern ................... 3-31

3.4.1 Municipal Waste Combustors ..................................... 3-32

3.4.2 Chlor-Alkali Plants ............................................. 3-33

3.4.3 Coal-Fired Utilities ............................................. 3-33

3.4.4 Mercury Mines ................................................ 3-34

3.4.5 Mercury Near Multiple Local Sources .............................. 3-34

4. MODEL FRAMEWORK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1

4.1 Models Used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1

4.2 Modeling of Long-Range Fate and Transport of Mercury . . . . . . . . . . . . . . . . . . . . . . 4-1

4.2.1 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1

4.2.2 Estimating Impacts from Regional Anthropogenic Sources of Mercury . . . . . 4-2

4.2.3 Description of the RELMAP Mercury Model . . . . . . . . . . . . . . . . . . . . . . . . . 4-2

4.2.3.1 Physical Model Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-3

4.2.3.2 Mercury Emissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-3

4.2.3.3 Carbon Aerosol Emissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-9

4.2.3.4 Ozone Concentration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-9

4.2.3.5 Lagrangian Transport and Deposition ........................ 4-10

4.2.4 Model Parameterizations ........................................ 4-10

4.2.4.1 Chemical Transformation and Wet Deposition ................. 4-10

4.2.4.2 Dry Deposition .......................................... 4-12

4.2.4.3 Vertical Exchange of Mass with the Free Atmosphere ........... 4-15

4.3 Modeling the Local Atmospheric Transport of Mercury in Source Emissions ...... 4-16

4.3.1 Phase and Oxidation State of Emitted Mercury ....................... 4-16

4.3.2 Modeling the Deposition of Mercury ............................... 4-17

4.3.3 Rationale and Utility of Model Plant Approach ....................... 4-21

4.3.4 Development and Description of Model Plants ....................... 4-21

4.3.5 Hypothetical Locations of Model Plants ............................ 4-22

4.4 Modeling Mercury in a Watershed ....................................... 4-24

4.4.1 Overview of the Watershed Model ................................. 4-24

4.4.2 Description of the Watershed Soil Module .......................... 4-26

4.4.2.1 Development of Soil Mass Balance Equations ................. 4-28

4.4.2.2 Loads to Watershed Soils ................................. 4-30

4.4.2.3 Equilibrium Speciation Reactions ........................... 4-31

4.4.2.4 Transformation Processes in Watershed Soils ................. 4-32

4.4.2.5 Transport and Transfer Processes in Watershed Soils ........... 4-32

4.4.3 Description of the Water Body Module ............................. 4-34
TABLE OF CONTENTS (continued)

Page

iii

4.4.3.1 The Water Body Equations ................................ 4-35

4.4.3.2 The Solids Balance Equations .............................. 4-39

4.4.3.3 Loads to the Water Body .................................. 4-40

4.4.3.4 Equilibrium Speciation Reactions ........................... 4-42

4.4.3.5 Transformation Processes in the Water Body .................. 4-43

4.4.3.6 Transport and Transfer Processes in the Water Body ............ 4-44

5. ATMOSPHERIC FATE AND TRANSPORT MODELING RESULTS . . . . . . . . . . . . . . . . . 5-1

5.1 Long Range Atmospheric Fate/Transport Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1

5.1.1 Mass Balances of Mercury within the Long-range Model Domain . . . . . . . . . 5-1

5.1.2 Qualitative Description of Mercury Concentration Results . . . . . . . . . . . . . . . 5-2

5.1.3 Description of Mercury Wet Deposition Simulation Results . . . . . . . . . . . . . . 5-8

5.1.4 Qualitative Description of Mercury Dry Deposition Results ............. 5-19

5.1.5 Qualitative Description of Total Mercury Deposition Results ............ 5-24

5.1.6 General Data Interpretations of the RELMAP Modeling ................ 5-26

5.2 Overview of Local Scale Analysis: Background Concentrations ............... 5-27

5.2.1 Introduction ................................................... 5-27

5.2.2 The Pre-anthropogenicMercury Cycle ........................... 5-28

5.2.3 Estimating Current Background Mercury Concentrations ............... 5-30

5.3 Local Atmospheric Transport Modeling ................................... 5-36

5.3.1 Air Concentrations ............................................. 5-40

5.3.2 Deposition Rates ............................................... 5-40

5.3.3 Mass Balances within the Local-Scale Domain ....................... 5-41

5.3.4 Uncertainty and Sensitivity Analyses ............................... 5-47

5.3.4.1 Dry Deposition .......................................... 5-47

5.3.4.2 Wet Deposition ......................................... 5-53

5.3.4.3 Sensitivity to Emissions Speciation .......................... 5-53

5.3.4.4 Effect of Terrain on Results of Local Scale Modeling ........... 5-54

6. WATERSHED FATE AND TRANSPORT MODELING . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-1

6.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-1

6.2 Watershed and Waterbody Results for Pre-anthropogenic Mercury Cycle and the Current

Mercury Cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-1

6.3 Watershed/Waterbody Model Results for Local Scale Analysis . . . . . . . . . . . . . . . . . . 6-2

6.4 Variability and Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-8

6.4.1 Variability and Parameter Sensitivity Analysis for IEM-2M . . . . . . . . . . . . . . 6-9

6.4.1.1 Description of Base Simulation and Analysis of Variability . . . . . . . 6-9

6.4.1.2 Sensitivity of Soil Mercury to Model Parameters ............... 6-13

6.4.1.3 Sensitivity of Water Column Mercury to Model Parameters ...... 6-15

6.4.1.4 Sensitivity of Fish Mercury to Model Parameters ............... 6-15

6.4.2 Variability and Parameter Sensitivity Analysis for R-MCM ............. 6-18

6.4.2.1 Overview of R-MCM .................................... 6-18

6.4.2.2 Analysis of In-Lake Variability in Mercury Levels ............. 6-19

6.4.2.3 Sensitivity of Water Column Mercury to Model Parameters ...... 6-26

6.4.2.4 Sensitivity of Fish Mercury to Model Parameters ............... 6-28

6.4.3 Summary and Conclusions ....................................... 6-30
TABLE OF CONTENTS (continued)

Page

iv

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

8. RESEARCH NEEDS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-1

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

APPENDIX A ATMOSPHERIC MODELING PARAMETERS . . . . . . . . . . . . . . . . . . . . . . . . . . A-1

APPENDIX B WATERSHED AND WATERBODY MODELING PARAMETERS . . . . . . . . . . B-1

APPENDIX C DESCRIPTION OF MODEL PLANTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-1

APPENDIX D AQUATIC BIOACCUMULATION FACTOR DEVELOPMENT AND

UNCERTAINTY ANALYSIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-1
v

U.S. EPA AUTHORS

Principal Authors:

Glenn E. Rice O. Russell Bullock, Jr.

National Center for Environmental Assessment- Atmospheric Sciences Modeling Division

Cincinnati Air Resources Laboratory

Office of Research and Development National Oceanic and Atmospheric

Cincinnati, OH Administration

Robert B. Ambrose, Jr., P.E.

Ecosystems Research Division

National Exposure Research Laboratory

Athens, GA

Contributing Authors:

William G. Benjey, Ph.D.

Atmospheric Sciences Modeling Division

Air Resources Laboratory

National Oceanic and Atmospheric

Administration

Research Triangle Park, NC

on assignment to the

U.S. EPA National Exposure Research Laboratory

Terry Clark, Ph.D. Kathryn R. Mahaffey, Ph.D. a

Atmospheric Sciences Modeling Division National Center for Environmental AssessmentAir Resources Laboratory Cincinnati

National Oceanic and Atmospheric Office of Research and Development

Administration Cincinnati, OH

Research Triangle Park, NC

David H. Cleverly Mid-Continent Ecology Division

National Center for Environmental Assessment Office of Research and Development

Office of Research and Development Duluth, MN

Washington, DC

Stanley Durkee National Center for Environmental AssessmentOffice of Research and Science Integration Cincinnati

Washington, DC Office of Research and Development

 

 Deceased a

Research Triangle Park, NC

on assignment to the

U.S. EPA National Exposure Research Laboratory

Jeff Swartout

National Center for Environmental AssessmentCincinnati

Office of Research and Development

Cincinnati, OH

Martha H. Keating

Office of Air Quality Planning and Standards

Research Triangle Park, NC

James D.Kilgroe, Ph.D.

National Environmental Research Laboratory

Office of Research and Development

Research Triangle Park, NC

John W. Nichols, Ph.D.

Rita Schoeny, Ph.D.

Cincinnati, OH
vi

SCIENTIFIC PEER REVIEWERS

Dr. William J. Adams* Dr. Rick Canady

Kennecott Utah Corporation Agency for Toxic Substances and Disease

Dr. Brian J. Allee

Harza Northwest, Incorporated Dr. Rufus Chaney

Dr. Thomas D. Atkeson

Florida Department of Environmental Dr. Joan Daisey*

Protection Lawrence Berkeley National Laboratory

Dr. Donald G. Barnes* Dr. John A. Dellinger*

U.S. EPA Science Advisory Board Medical College of Wisconsin

Dr. Steven M. Bartell Dr. Kim N. Dietrich*

SENES Oak Ridge, Inc. University of Cincinnati

Dr. David Bellinger* Dr. Tim Eder

Children’s Hospital, Boston Great Lakes Natural Resource Center

Dr. Nicolas Bloom* States of Michigan and Ohio

Frontier Geosciences, Inc.

Dr. Mike Bolger Michigan State University

U.S. Food and Drug Administration

Dr. Peter Botros University of Connecticut

U.S. Department of Energy Avery Point

Federal Energy Technology Center

Thomas D. Brown U.S. EPA Science Advisory Board

U.S. Department of Energy

Federal Energy Technology Center Dr. Katherine Flegal

Dr. Dallas Burtraw*

Resources for the Future Dr. Bruce A. Fowler*

Dr. Thomas Burbacher*

University of Washington Dr. Steven G. Gilbert*

Seattle Biosupport, Inc.

Dr. James P. Butler Dr. Cynthia C. Gilmour*

University of Chicago The Academy of Natural Sciences

Argonne National Laboratory

Elizabeth Campbell National Institute of Environmental Health

U.S. Department of Energy Sciences

Policy Office, Washington DC

Registry

U.S. Department of Agriculture

National Wildlife Federation for the

Dr. Lawrence J. Fischer*

Dr. William F. Fitzgerald

A. Robert Flaak*

National Center for Health Studies

University of Maryland at Baltimore

Dr. Robert Goyer
SCIENTIFIC PEER REVIEWERS (continued)

vii

Dr. George Gray Dr. Thomas McKone*

Harvard School of Public Health University of California

Dr. Terry Haines

National Biological Service Dr. Malcolm Meaburn

Dr. Gary Heinz* Administration

Patuxent Wildlife Research Center U.S. Department of Commerce

Joann L. Held Dr. Michael W. Meyer*

New Jersey Department of Environmental Wisconsin Department of Natural Resources

Protection & Energy

Dr. Robert E. Hueter* University of Texas Science Center at Houston

Mote Marine Laboratory

Dr. Harold E. B. Humphrey* PB Associates

Michigan Department of Community Health

Dr. James P. Hurley* U.S. Department of Energy

University of Wisconsin Federal Energy Technology Center

Madison

Dr. Joseph L. Jacobson* Auburn University

Wayne State University

Dr. Gerald J. Keeler The University of Michigan

University of Michigan Ann Arbor

Ann Arbor

Dr. Ronald J. Kendall* U.S. Department of Energy

Clemson University Federal Energy Technology Center

Dr. Lynda P. Knobeloch* Dr. W. Steven Otwell*

Wisconsin Division of Health University of Florida

Dr. Leonard Levin

Electric Power Research Institute Dr. Jozef M. Pacyna

Dr. Steven E. Lindberg*

Oak Ridge National Laboratory Dr. Ruth Patterson

Dr. Genevieve M. Matanoski* Fred Gutchinson Cancer Research Center

The Johns Hopkins University

Berkeley

National Oceanic and Atmospheric

Dr. Maria Morandi*

Dr. Paul Mushak

Harvey Ness

Dr. Christopher Newland*

Dr. Jerome O. Nriagu*

William O’Dowd

Gainesville

Norwegian Institute for Air Research

Cancer Prevention Research Program

Dr. Donald Porcella

Electric Power Research Institute
SCIENTIFIC PEER REVIEWERS (continued)

viii

Dr. Deborah C. Rice* Dr. Alan H. Stern

Toxicology Research Center New Jersey Department of Environmental

Samuel R. Rondberg*

U.S. EPA Science Advisory Board Dr. David G. Strimaitis*

Charles Schmidt

U.S. Department of Energy Dr. Edward B. Swain

Dr. Pamela Shubat

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

Dennis Smith

U.S. Department of Energy

Federal Energy Technology Center

Dr. Ann Spacie*

Purdue University

*With EPA’s Science Advisory Board, Mercury

Review Subcommitte

Protection & Energy

Earth Tech

Minnesota Pollution Control Agency

Princeton University

Los Angeles
ix

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
x

LIST OF TABLES

Page

1-1 Factors Potentially Important in Estimating Mercury Exposure and How They are 

Addressed in This Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-8

2-1 Annual Estimates of Mercury Release by Various Combustion and 

Manufacturing Source Classes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-8

3-1 Summary of Measured Mercury Concentration in Air (U.S. EPA, 1993) . . . . . . . . . . . . . . . . . 3-2

3-2 Measured Vapor- and Particulate-Phase Atmospheric Mercury Concentrations . . . . . . . . . . . . 3-3

3-3 Measured Mercury Concentrations in Precipitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-4

3-4 Measured Mercury Concentrations in Rain Which Include Methylmercury Estimates (ng/L) . 3-5

3-5 Mercury Wet Deposition Rates (ug/m /yr) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-7 2

3-6 Estimated Mercury Total Deposition Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-8

3-7 Measured Mercury Concentrations in Surface Fresh Water (ng/L) . . . . . . . . . . . . . . . . . . . . . . 3-9

3-8 Measured Mercury Concentrations in Ground/Drinking Water (ng/L) . . . . . . . . . . . . . . . . . . . 3-9

3-9 Measured Mercury Concentrations in Ocean Water (ng/L) ........................... 3-10

3-10 Measured Mercury Concentration in Soil ......................................... 3-11

3-11 Measured Mercury Concentrations in Freshwater Aquatic Sediment ................... 3-12

3-12 Mercury Concentration in Sediments from NS&T Sites (1984-1991) ................... 3-13

3-13 Freshwater Fish Mercury Concentrations from Nationwide Studies .................... 3-16

3-14 Mercury Concentration in Bivalve Mollusks from Mussel Watch Sites (1986-1993) ....... 3-17

3-15 Nationwide Geometric Mean Concentrations of Mercury in Bivalve Mollusks (1986-1993) . 3-17

3-16 Trends in Mercury Concentrations in Bivalve Mollusks (1986-1993) ................... 3-18

3-17 Measured Mercury Concentrations Freshwater Sportfish (Total Mercury, ug/g wet wt.) .... 3-20

3-18 Measured Mercury Concentrations in Saltwater Commercial Fish and Shellfish 

(ug/g wet wt.) .............................................................. 3-22

3-19 Mercury Concentrations in Marine Finfish ....................................... 3-23

3-20 Mercury Concentrations in Marine Shellfish ...................................... 3-24

3-21 Mercury Concentrations in Marine Molluscan Cephalopods .......................... 3-25

3-22 Mercury Concentrations in Biota from the New York-New Jersey Harbor estuary (1993) . . 3-26

3-23 Measured Mercury Concentration in Meats ....................................... 3-28

3-24 Measured Mercury Concentrations in Garden Produce/Crops ......................... 3-39

3-25 Mean Background Total Mercury Levels for Plants in the Netherlands ................. 3-29

3-26 Range of Mercury Concentrations in Selected Grain Products ........................ 3-30

3-27 Mercury Concentrations in the Atmosphere and Mercury Measured in Rainwater Collected 

in Broward County, FL ....................................................... 3-35

3-28 Mercury Concentrations Measured at Two Sites in the Atmosphere Over Detroit, MI ...... 3-35

4-1 Models Used to Predict Mercury Air Concentrations, Deposition Fluxes, and Environmental 

Concentrations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1

4-2 Mercury Emissions Inventory Used in the RELMAP Modeling . . . . . . . . . . . . . . . . . . . . . . . . 4-5

4-3 Dry Deposition Velocity (cm/s) for Divalent Mercury (Hg ) ......................... 4-14 2+

4-4 Wind Speeds Used for Each Pasquill Stability Category in the CARB Subroutine 

Calculations ............................................................... 4-15

4-5 Roughness Length Used for Each Land-Use Category in the CARB Subroutine 

Calculations ............................................................... 4-15

4-6 Representative Particle Sizes and Size Distribution Assumed for Divalent Mercury Particulate

Emissions ................................................................. 4-17

4-7 Parameter Values Assumed for Calculation of Dry Deposition Velocities for Divalent Mercury 

Vapor .................................................................... 4-18
LIST OF TABLES (continued)

Page

xi

4-8 Air Modeling Parameter Values Used in the Exposure Assessment: Generic 

Parameters ................................................................. 4-20

4-9 Process Parameters for Model Plants ............................................ 4-23

5-1 Mercury Mass Budget in Metric Tons from RELMAP Simulation . . . . . . . . . . . . . . . . . . . . . . 5-1

5-2 Percentile Analysis of RELMAP Simulated Concentration Results for the Continental U. S. . 5-8

5-3 Observed Mercury Deposition in Precipitation from the Transition Phase Data Report 

of the Mercury Deposition Network (MDN) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-16

5-4 Estimates of 1989 Mercury Wet Deposition in Precipitation from MDN Data 

and Comparison to Modeled Wet Deposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-17

5-5 Percentile Analysis of RELMAP Simulated Wet Deposition for the Continental U. S. . . . . . 5-18

5-6 Percentile Analysis of RELMAP Simulated Dry Deposition for the Continental U. S. . . . . . 5-19

5-7 Percentile Analysis of RELMAP Simulated Total Depositions for the Continental U. S. . . . . 5-24

5-8 Mercury Wet Deposition Rates (ug/m /yr) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-31 2

5-9 Estimated Mercury Total Deposition Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-32

5-10 Inputs to IEM2M Model for the two time periods modeled . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-33

5-11 Predicted Air Concentrations and Deposition Rates for Eastern Site 

(Local + RELMAP 50th) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-36

5-12 Predicted Air Concentrations and Deposition Rates for Eastern Site 

(Local + RELMAP 90th) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-37

5-13 Predicted Air Concentrations and Deposition Rates for Western Site 

(Local + RELMAP 50th) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-38

5-14 Predicted Air Concentrations and Deposition Rates for Western Site 

(Local + RELMAP 90th) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-39

5-15 Mass Balance of Mercury Emissions for each Facility in the Humid Site Using the 

ISC3 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-42

5-16 Mass Balance of Mercury Emissions for each Facility in the Arid Site Using the ISC3

Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-43

5-17 Area-Averaged Mercury Deposition Rates for each Facility in the Humid Site . . . . . . . . . . . 5-45

5-18 Area-Averaged Mercury Deposition Rates for each Facility in the Arid Site . . . . . . . . . . . . . 5-46

5-19 Sensitivity of Total Mercury Deposition Rate to Emissions Speciation for Municipal Waste

Combustors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-54

5-20 Ratios of Total Deposition of Mercury at Receptors at Different Elevations to Total Deposition

when the Elevation is Zero . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-55

6-1 Assumed Mercury Air Concentrations and Atmospheric Deposition Rates for pre-Anthropogenic

and Current Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-1

6-2 Total Mercury Concentrations Predicted by IEM-2M Model for the Pre-Anthropogenic Time

Period . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-2

6-3 Total mercury concentrations predicted by IEM-2M Model for the Current (Post-Industrial)

Time Period . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-2

6-4 Predicted Values for Eastern Site (Local + RELMAP 50th) . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-4

6-5 Predicted Values for Eastern Site (Local + RELMAP 90th) . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-5

6-6 Predicted Values for Western Site (Local + RELMAP 50th) . . . . . . . . . . . . . . . . . . . . . . . . . . 6-6

6-7 Predicted Values for Western Site (Local + RELMAP 90th) . . . . . . . . . . . . . . . . . . . . . . . . . . 6-7

6-8 General Properties of the IEM-2M Eastern Lake ................................... 6-10

6-9 Effect of Watershed Size on Total Mercury Concentrations .......................... 6-11

6-10 Effect of Watershed Erosion on Total Mercury Concentrations ....................... 6-12
LIST OF TABLES (continued)

Page

xii

6-11 Effect of Soil Mercury Retention on Total Mercury Concentrations .................... 6-13

6-12 IEM-2M Parameter Sensitivity for Total Soil Mercury .............................. 6-14

6-13 IEM-2M Parameter Sensitivity for Total Water Column Mercury ..................... 6-16

6-14 IEM-2M Parameter Sensitivity for Predatory Fish Mercury .......................... 6-17

6-15 General Properties of the Four R-MCM Lakes .................................... 6-21

6-16 Variable Properties of the Four R-MCM Lakes .................................... 6-22

6-17 Partition Coefficients and First-order Rate Constants for Simulations .................. 6-23

6-18 Simulation Results, No Watershed .............................................. 6-24

6-19 Simulation Results with Standard Watershed ...................................... 6-24

6-20 Simulation Results with Hypolimnion, No Watershed ............................... 6-25

6-21 Simulation Results for 5 Combinations of Watershed and Hydrology .................. 6-25

6-22 R-MCM Parameter Sensitivity for Total Water Column Mercury ...................... 6-27

6-23 R-MCM Parameter Sensitivity for Upper Trophic Level Fish Mercury ................. 6-29
xiii

LIST OF FIGURES

Page

1-1 Fate, Transport and Exposure Modeling Conducted in the Combined ISC3 and RELMAP 

Local Impact Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-2

2-1 The Mercury Cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1

2-2 Comparison of Current and Pre-Industrial Mercury Budgets and Fluxes . . . . . . . . . . . . . . . . . 2-5

3-1 Mercury Fish Consumption Advisories of the U.S. ................................. 3-14

4-1 Hg(0) Emissions from All Anthropogenic Sources (Base) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-6

4-2 Hg Emissions from All Anthropogenic Sources (Base) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-7 2+

4-3 Hg(p) Emissions from All Anthropogenic Sources (Base) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-8

4-4 Wet Deposition Scavenging Ratios Used in Local Scale Air Modeling for Particulate-Bound 

Mercury (Jindal and Heinold 1991) ............................................. 4-19

4-5 Configuration of Hypothetical Water Body and Watershed Relative to Local Source ...... 4-24

4-6 Overview of the IEM-2M Watershed Modules .................................... 4-25

4-7 Overview of the IEM2 Soils Processes .......................................... 4-27

5-1 Average Hg(0) Concentration Excluding Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-4

5-2 Average Hg Concentration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-5 2+

5-3 Average Hg(p) Concentration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-6

5-4 Hg(0) Wet Deposition Excluding Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-9

5-5 Hg(0) Wet Deposition (Background Only) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-10

5-6 Hg Wet Deposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-12 2+

5-7 Hg(p) Wet Deposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-13

5-8 Total Hg Wet Deposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-14

5-9 Hg Dry Deposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-21 2+

5-10 Hg(p) Dry Deposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-22

5-11 Total Hg Dry Deposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-23

5-12 Total Hg Wet+Dry Deposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-25

5-13 IEM-2M results for pre-industrial and industrial periods for Eastern site . . . . . . . . . . . . . . . . 5-34

5-14 IEM-2M results for pre-industrial and industrial periods for Western site . . . . . . . . . . . . . . . 5-35

5-15 Influence of the Compensation Point on the Dry Deposition of Elemental Mercury: 

2,500 Meters NE of a Chlor-alkali Plant at the Eastern Site . . . . . . . . . . . . . . . . . . . . . . . . . . 5-48

5-16 Influence of the Compensation Point on the Dry Deposition of Elemental Mercury: 

10,000 Meters NE of a Chlor-alkali Plant at the Eastern Site . . . . . . . . . . . . . . . . . . . . . . . . . 5-49

5-17 Influence of the Compensation Point on the Dry Deposition of Elemental Mercury: 

25,000 Meters NE of a Chlor-alkali Plant at the Eastern Site . . . . . . . . . . . . . . . . . . . . . . . . . 5-50

5-18 Comparison of Sensitivity of Total Deposition Rate to Divalent Mercury Reactivity . . . . . . . 5-52
xiv

LIST OF SYMBOLS, UNITS AND ACRONYMS

AIRS Automatic Information Retrieval System

ANC Acid Neutralizing Capacity

APCD Air Pollution Control Device

BAF Bioaccumulation Factor

BCF Bioconcentration Factor

BW Body Weight

CAA Clean Air Act as Amended in 1990

CAP Chlor-Alkali Plants

CARB California Air Resources Board

CMWI Continuous MWI Model Plant

COMPDEP COMPlex terrain and DEPosition air dispersion model

DIC Dissolved Inorganic Carbon

DOC Dissolve Organic Carbon

EMAP Environmental Monitoring and Assessment Program

EMEP European Monitoring and Evaluation Programme

EPA U.S. Environmental Protection Agency

EPRI Electric Power Research Institute

ESP Electrostatic Precipitator

FWS U.S. Fish and Wildlife Service

Hg Mercury

Hg Elemental mercury o

Hg Divalent or mercuric mercury 2+

Hg Mercurous mercury 2

2+

Hg(II) Divalent or mercuric mercury

HgCl Mercuric chloride 2

HgI Mercury iodide

Ht Height

IED Indirect Exposure Document (U.S. EPA, 1990. Methodology for Assessing Health Risks

Associated with Indirect Exposure to Combustor Emissions, EPA 600/6-90/003)

IEM2 Modified version of U.S. EPA’s Indirect Exposure Methodology

IEM-2M The aquatic and terrestrial fate, transport, and exposure model

IMWI Intermittent Medical Waste Incinerator Model Plant

ISC3 Industrial Source Code Dispersion Model

LCUB Large Coal-Fired Utility Boiler

LMWC Large Municipal Waste Combustor

MCUB Medium Coal-Fired Utility Boiler

MHg Methylmercury

Mg Megagram

MMHg MonoMethylmercury

MOUB Median Oil-Fired Utility Boiler

MW MegaWatt

MWC Municipal Waste Combustors

MWI Medical Waste Incinerators

NAPAP National Acidic Precipitation Assessment Program

ng Nanogram (1 x 10 gram) -9

NGM Nested Grid Model

NIEHS National Institute of Environmental Health and Safety

NJDEPE New Jersey Department of Environmental Protection
LIST OF SYMBOLS, UNITS AND ACRONYMS (continued)

xv

NMFS National Marine Fisheries Science

NOAA National Oceanic and Atmospheric Administration

NS&T National Status and Trends

O Ozone 3

OAQPS Office of Air Quality Planning and Standards

PBL Planetary Boundary Layer

PCS Primary Copper Smelter

pg Picogram (1 x 10 gram) -12

PLS Primary Lead Smelter

ppb Parts Per Billion

ppm Parts Per Million

RADM Regional Acid Deposition Model

RELMAP Regional Lagrangian Model of Air Pollution

RHG Rural Home Gardener

R-MCM Regional Mercury Cycling Model

RSF Rural Subsistence Farmer

SCRAM Support Center for Regulatory Air Models

SCUB Small Coal-Fired Utility Boiler

SMWC Small Municipal Waste Combustor

SWMC Solid Waste Management Council

TOC Total Organic Carbon

μg Microgram (1x10 gram) -6

UR Urban Resident

USFDA United States Food and Drug Administration

USGS United States Geological Survey

V/P ratio Vapor/Particle Ratio
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 (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 a review of mercury fate and transport (Volume III of the Report). The fate

and transport assessment is one component of the risk assessment of U.S. anthropogenic mercury

emissions. The modeling summarized in this volume is paired with an assessment of exposure to human

and wildlife populations (Volume IV). Conclusions drawn from these analyses are then integrated with

information in Volumes V and VI relating to human and wildlife health impacts of mercury in the Risk

Characterization Volume (Volume VII) of the Report.

Assessment Approach for Fate of Mercury

This assessment addresses atmospheric mercury emissions from selected, major anthropogenic

combustion and manufacturing source categories: municipal waste combustors (MWCs), medical waste

incinerators (MWIs), coal- and oil-fired utility boilers, and chlor-alkali plants. It does not address all

anthropogenic emission sources.

Extant mercury monitoring data for particular sources indicate that there is a relationship

between emissions and increased mercury concentrations in environmental media. Available mercury

monitoring data around these sources are extremely limited, however, and no comprehensive data base

describing environmental concentrations has been developed. To determine if there is a connection

between the above sources and increased environmental levels, EPA utilized exposure modeling

techniques to address many major scientific uncertainties.

The individual exposure assessment in this Report (Volume IV), which relies on the modeling

results presented in this volume, is considered to be a qualitative study based partly on quantitative

analyses; it is considered qualitative because of inherent uncertainties. The exposure assessment draws

upon the available scientific information and develops two quantitative transport analyses, a long range

transport analysis and a local impact analysis. It was intended that these two types of analyses would

provide a more complete estimate of the nation-wide impact of anthropogenic emission sources than

either analysis could provide individually.

The assessment of Volume III draws upon the available scientific information and presents

quantitative modeling analyses which examine the following: (1) the long range transport of mercury

from emissions sources through the atmosphere; (2) the transport of mercury from emission sources

through the local atmosphere; and (3) the aquatic and terrestrial fate and transport of mercury at

hypothetical sites. The results from these analyses are then applied in the exposure assessment of

Volume IV to determine the resulting exposures to hypothetical humans and animals that inhabit these

sites.
ES-2

Long Range Atmospheric Transport Analysis

The long range transport modeling was undertaken to estimate the regional and national impacts

of mercury emissions. It focusses on the long range atmospheric transport of mercury and estimates the

impact of mercury across the continental U.S. This type of modeling was conducted based on the

atmospheric chemistry of emitted elemental mercury (Petersen et al., 1995) and the numerous studies

linking increased mercury concentrations in air, soil, sediments, and biota at remote sites to distant

anthropogenic mercury release followed by long range transport. Details of several studies which

demonstrate the long range transport of mercury are presented in Chapter 2. These provide ample

evidence to justify this assessment of long range mercury transport.

The long range transport of mercury was modeled using site-specific, anthropogenic emission

source data (presented in Volume II of this Report) to generate mean, annual atmospheric mercury

concentrations and deposition values across the continental U.S. The Regional Lagrangian Model of Air

Pollution (RELMAP) atmospheric model was utilized to model cumulative mercury emissions from

multiple mercury emission sources. Assumptions were made concerning the form and species of mercury

emitted from each source class. The results of the RELMAP modeling were combined with a local scale

atmospheric model to assess average annual atmospheric mercury concentrations in air and annual

deposition rates. The continental U.S. was divided into Western and Eastern halves along the line of 90

degrees west longitude. The 50th and 90th percentiles of the predicted atmospheric concentrations and

deposition rates were then used. 

Analysis of Local-Scale Fate of Atmospheric Mercury

The local atmospheric transport of mercury released from anthropogenic emission sources was

undertaken to estimate the impacts of mercury from selected, individual sources. The Industrial Source

Code air dispersion model (ISC3) was utilized to model these processes. Model plants, defined as

hypothetical facilities which were developed to represent actual emissions from existing industrial

processes and combustion sources, were located in hypothetical locations intended to simulate a site in

either the Western or Eastern U.S. This approach was selected because some environmental monitoring

studies suggest that measured mercury levels in environmental media and biota may be elevated in areas

around stationary industrial and combustion sources known to emit mercury. 

Assessment of Watershed Fate and Transport

Atmospheric concentrations and deposition rates were used as inputs to a series of terrestrial and

aquatic models referred to as IEM-2M. IEM-2M is composed of two integrated modules that simulate

mercury fate using mass balance equations describing watershed soils and a shallow lake. The results of

these terrestrial and aquatic models were used to predict mercury exposure to hypothetical humans

through inhalation, consumption of drinking water, and ingestion of soil, farm products (e.g., beef

products and vegetables), and fish. These models were also used to predict mercury exposure in

hypothetical piscivorous (i.e., fish-eating) birds and mammals through their consumption of fish. The

results of these models are utilized in the exposure assessment completed in Volume IV.
ES-3

Conclusions

 The present study in conjunction with available scientific knowledge supports a plausible link

between mercury emissions from anthropogenic combustion and industrial sources and mercury

concentrations in air, soil, water and sediments. The critical variables contributing to this

linkage are these:

a) the species of mercury that are emitted from the sources;

b) the overall amount of mercury emitted from a combustion source;

c) atmospheric and climatic conditions;

d) reduction rates in the soil and water body;

e) erosion rates within the watershed; and

f) solids deposition and burial in the water body.

 The present study, in conjunction with available scientific knowledge, supports a plausible link

between mercury emissions from anthropogenic combustion and industrial sources and

methylmercury concentrations in freshwater fish. The additional critical variables contributing

to this linkage are the following:

a) the extent (magnitude) of mercury methylation and demethylation in the water

body; and

b) the degree of complexation of mercury with DOC and solids.

 Mercury is a natural constituent of the environment; concentrations of mercury in many

environmental media appear to have increased over the last 500 years.

 There is a lack of adequate mercury measurement data near the anthropogenic atmospheric

mercury sources considered in this report. The lack of such measured data preclude a

comparison of the modeling results with measured data around these sources. This shortage of 

data includes measured mercury deposition rates as well as measured concentrations in the local

atmosphere, soils, water bodies, and biota.

 From the atmospheric modeling analyses of mercury deposition and on a comparative basis, a

facility located in a humid climate has a higher annual rate of mercury deposition than a facility

located in an arid climate. The critical variables are the estimated washout ratios of elemental

and divalent mercury as well as the annual amount of precipitation. Precipitation removes

various forms of mercury from the atmosphere and deposits mercury to the surface of the earth. 

Of the species of mercury that are emitted, divalent mercury is predicted to generally deposit to

local environments near sources. Elemental mercury is predicted to generally remain in the

atmosphere until atmospheric conversion to divalent species or uptake and retention by plant

leaves and the subsequent deposition as divalent species in litter fall.
ES-4

 On a national scale, an apportionment between specific sources of mercury and mercury in

environmental media and biota at particular locations cannot be described in quantitative terms

with the current scientific understanding of the environmental fate of mercury.

 

 From the modeling analysis and a review of field measurement studies, it is concluded that

mercury deposition appears to be ubiquitous across the continental U.S. and at, or above,

detection limits when measured with current analytic methods.

 Based on the RELMAP modeling analysis and a review of recent measurement data published in

peer-reviewed scientific literature, there is predicted to be a wide range of mercury deposition

rates across the continental U.S. The highest predicted rates (i.e., above 90th percentile) are

about 20 times higher than the lowest predicted rates (i.e., below the 10th percentile). Three

principal factors contribute to these modeled and observed deposition patterns:

a) emission source locations;

b) amount of divalent and particulate mercury emitted or formed in the atmosphere;

and

c) climate and meteorology.

 Based on the modeling analysis of the transport and deposition of stationary point source and

area source air emissions of mercury from the continental U.S., it is concluded that the following

geographical areas have the highest annual rate of deposition of mercury in all forms (above the

levels predicted at the 90th percentile):

a) the southern Great Lakes and Ohio River Valley;

b) the Northeast and southern New England; and

c) scattered areas in the South with the most elevated deposition occurring in the

Miami and Tampa areas.

Measured deposition estimates are limited, but are available for certain geographic regions. The

data that are available corroborate the RELMAP modeling results for specific areas.

 Based on modeling analysis of the transport and deposition of stationary point source and area

source air emissions of mercury from the continental U.S., it is concluded that the following

geographical areas have the lowest annual rate of deposition of mercury in all forms (below the

levels predicted at the 10th percentile):

a) the less populated areas of the Great Basin, including southern Idaho,

southeastern Oregon, most of southern and western Utah, most of Nevada, and

portions of western New Mexico; and

b) Western Texas other than near El Paso, and most of northeastern Montana.

 Based on limited monitoring data, the RELMAP model predictions of atmospheric mercury

concentrations and wet deposition across the U.S. are comparable with typically measured data.
ES-5

 A number of factors appear to affect the local-scale atmospheric fate of mercury emitted by/from

major anthropogenic sources as well as the quantity of mercury predicted to deposit. These

factors include the following:

a) the amounts of divalent and particulate mercury emitted;

b) parameters that influence the plume height, primarily the stack height and stack

exit gas velocity;

c) meteorology; and 

d) terrain.

 From the analysis of deposition and on a comparative basis, the deposition of divalent mercury

close to an emission source is greater for receptors in elevated terrain (i.e., terrain above the

elevation of the stack base) than for receptors located in flat terrain (i.e., terrain below the

elevation of the stack base). The critical variables are parameters that influence the plume

height, primarily the stack height and stack exit gas velocity.

 Modeling estimates of the transport and deposition of stationary point source and area source air

emissions of mercury from the continental U.S. have revealed the following partial mass balance.

-- Of the total amount of elemental mercury vapor that is emitted, about 1 percent

(0.9 metric tons/yr) may be atmospherically transformed into divalent mercury

by tropospheric ozone and adsorbed to particulate soot in the air and

subsequently deposited in rainfall and snowfall to the surface of the continental

U.S. The vast majority of emitted elemental mercury does not readily deposit

and is transported outside the U.S. or vertically diffused to the free atmosphere

to become part of the global cycle.

-- Nearly all of the elemental mercury vapor emitted from other sources around the

globe also enters the global cycle and can be deposited slowly to the U.S. Over

30 times as much elemental mercury vapor is deposited from these other sources

than from stationary point sources and area sources within the continental U.S. 

-- Of the total amount of divalent mercury vapor that is emitted, about 70 percent

(36.8 metric tons/year) deposits to the surface through wet or dry processes

within the continental U.S. The remaining 30 percent is transported outside the

U.S. or is vertically diffused to the free atmosphere to become part of the global

cycle.

-- Of the total amount of particulate mercury that is emitted, about 38 percent (10.0

metric tons/year) deposits to the surface through wet or dry processes within the

continental U.S. The remaining 62 percent is transported outside the U.S. or is

vertically diffused to the free atmosphere to become part of the global cycle.

 Given the simulated deposition efficiencies for each form of mercury air emission (namely;

elemental mercury - 1 percent, divalent mercury vapor - 70 percent, and particulate mercury - 38

percent) the relative source contributions to the total anthropogenic mercury deposited to the
ES-6

continental U.S. are strongly and positively correlated to the mass of emissions in oxidized form. 

This oxidized mercury occurs in both gaseous (Hg ) and particulate (Hg ) forms. While coal 2+

p

combustion is responsible for more than half of all emissions of mercury in the inventory of U.S.

anthropogenic sources, the fraction of coal combustion emissions in oxidized form is thought to

be less than that from waste incineration and combustion. The true speciation of mercury

emissions from the various source types modeled is still uncertain and is thought to vary, not

only among source types, but also for individual plants as feed stock and operating conditions

change. With further research, it may be possible to make a confident ranking of relative source

contributions to mercury deposition in the continental U.S. However, no such confident ranking

is possible at this time. Given the total mass of mercury thought by EPA to be emitted from all

anthropogenic sources and EPA’s modeling of the atmospheric transport of emitted mercury,

coal combustion and waste disposal most likely bear the greatest responsibility for direct

anthropogenic mercury deposition to the continental U.S.

 Based on the local scale atmospheric modeling results in flat terrain, at least 75 percent of the

emitted mercury from each facility is predicted to be transported more than 50 km from the

facility.

 The models used in the analysis as well as the assumptions implemented concerning the species

of mercury emitted and the wet and dry deposition velocities associated with atmospheric

mercury species indicate that deposition within 10 km of a facility may be dominated by

emissions from the local source rather than from emissions transported from regional mercury

emissions sources, with some exceptions. Specifically, the models predict that in the Eastern

U.S., individual large anthropogenic sources dominate predicted mercury deposition within 2.5

km; chlor-alkali facilities are predicted to dominate up to 10 km from the source. In the western

site, the models predict that the dominance of local source mercury deposition in emissions

extends beyond the predicted range of the eastern site.

 Of the mercury deposited to watershed soils, a small fraction is ultimately transported to the

water body. Deposition to and evasion from soils as well as the amount of reduction in upper

soil layers are important factors in the determining soil concentration of mercury. In forested

watersheds canopy interactions can provide significant fluxes both to and from the atmosphere.

Mercury from litter fall may be an important source of mercury to some soils and water bodies,

but the magnitude of the contribution from this source is uncertain at this time. 

 The net mercury methylation rate (the net result of methylation and demethylation) for most soils

appears to be quite low with much of the measured methylmercury in soils potentially resulting

from wet fall. A significant and important exception to this appears to be wetlands. Wetlands

appear to convert a small but significant fraction of the deposited mercury into methylmercury;

which can be exported to nearby water bodies and potentially bioaccumulated in the aquatic food

chain.

 Both watershed erosion and direct atmospheric deposition can be important sources of mercury

to the water body depending on the relative sizes of the water body and the watershed.

 There appears to be a great deal of variability in the processing of mercury among bodies of

water. This variability extends to water bodies that have similar and dissimilar physical

characteristics. Important properties influencing the levels of total mercury and methylmercury

in a water body include: pH, anoxia, DOC, productivity, turbidity, and the presence of wetlands. 
ES-7

 Some of the mercury entering a water body is methylated predominately through biotic processes

forming methylmercury (predominately monomethylmercury). Methylmercury is accumulated

and retained by aquatic organisms. Important factors influencing bioavailability of

methylmercury to aquatic organisms include DOC and solids, which complex methylmercury and

reduce the bioavailable pool. 

 Methylmercury is bioaccumulated in predatory species of the aquatic food chain. The

concentrations of methylmercury in fish muscle tissue are highly variable across water bodies. 

Within a given body of water methylmercury concentrations generally increase with fish size and

position within the trophic structure.

To improve the quantitative environmental fate component of the risk assessment for mercury and

mercury compounds, U.S. EPA would need more and better mercury emissions data and measured

mercury data near sources of concern, as well as a better quantitative understanding of mercury

chemistry in the emissions plume, the atmosphere, soils, water bodies and biota. Specific needs

include these.

Mercury in the Atmosphere

 aqueous oxidation-reduction kinetics in atmospheric water droplets

 physical adsorption and condensation of divalent mercury gas to ambient

particulate matter

 photolytic reduction of particle-bound divalent mercury by sunlight

 convincing evidence that gas-phase oxidation of mercury is insignificant

Mercury in Soils and Water Bodies

 uptake and release kinetics of mercury from terrestrial and aquatic plants

 biogeochemical mercury transport and transformation kinetics in benthic

sediments

 methylation, demethylation, and reduction kinetics in water bodies

 sorption coefficients to soils, suspended solids, and benthic solids

 complexation to organic matter in water bodies

 more data to better discern seasonal trends

 reduction kinetics in soils

 mercury mass balance studies in wetlands
ES-8

Information Leading to an Improved Quantitative Understanding of Aquatic Bioaccumulation

Processes and Kinetics

 uptake kinetics by aquatic plants and phytoplankton

 partitioning and binding behavior of mercury species within organisms

 metabolic transformations of mercury, and the effect on uptake, internal

distribution, and excretion

 more measurements of methylmercury concentrations in fish for better

identification of the range in fish species.

 more measurements of methylmercury concentrations in other biotic components

of the aquatic environment such as benthic and macro invertebrates and aquatic

macrophytes 
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, which constitutes Volume III of the Report to Congress, is a review of processes

involving the environmental fate of mercury. This analysis is one element of the human health and

ecological risk assessment of U.S. anthropogenic mercury (Hg) emissions. The fate and transport of

mercury in the atmosphere as well as through watershed compartments is modeled here; the model results

are paired with an assessment of exposure to humans and wildlife in Volumes IV and VI, respectively. 

The information in these two documents is then integrated with information relating to human and

wildlife health impacts of mercury in Volume VII of the report.

This assessment addresses the atmospheric fate and transport of atmospheric mercury emissions

from selected, major anthropogenic combustion and manufacturing sources: municipal waste combustors

(MWC), medical waste incinerators (MWI), coal- and oil-fired utility boilers, and chlor-alkali plants

(CAP). This volume does not address all anthropogenic emission sources discussed in Volume II.

Volume III is composed of nine chapters and four appendices. The Introduction is followed by

Chapter 2, which summarizes the natural environmental fate processes that comprise the mercury cycle

and lead to the dispersion of anthropogenic mercury in environmental media (i.e., air, rain water, soil and

surface waters and benthic sediments) and biota (i.e., plants and animals). Chapter 3 briefly describes the

measured mercury concentrations in these media.

The fate and transport modeling of mercury is presented in Chapters 4, 5 and 6. Chapter 4

describes the models and modeling approach utilized in this analysis. Figure 1-1 provides an overview of

the fate and transport models used and the exposure routes considered. These models include the long

range atmospheric transport model (RELMAP), the local scale atmospheric transport model (ISC3) and

the aquatic and terrestrial fate, transport, and exposure models (IEM-2M).
1-2

Figure 1-1

Fate, Transport and Exposure Modeling Conducted in the Combined ISC3 and RELMAP Local Impac
Local Hg Source

Figure 1-1

Fate, Transport and Expsoure Modeling Conducted in the Combined ISC3 and RELMAP Local Impact Analysis

1-2
1-3 

Results obtained from modeling the local and long range atmospheric dynamics of mercury using

ISC3 and RELMAP are discussed in Chapter 5. Chapter 6 describes the results of the fate of mercury as

modeled in terrestrial and aquatic environments. To accomplish this, model plants were developed to

represent major anthropogenic combustion and manufacturing sources: MWCs, MWIs, coal- and oilfired utility boilers, and CAPs. The atmospheric fate and transport processes of the mercury emissions

from these representative model plants were modeled on a local scale by the ISC3 model. The 50th and

90th percentiles of atmospheric mercury concentrations and the deposition rates that were predicted by

the RELMAP model for the Eastern and Western halves of the U.S. were added to the predicted mercury

air concentrations and deposition rates that result from individual model plants at 2.5, 10, and 25

kilometers. These sums were used as inputs to the aquatic and terrestrial fate models (IEM-2M) at the

hypothetical Western and Eastern U.S. sites (see Figure 1-1).

Chapter 7 summarizes the conclusions of this volume, including the extent to which the analysis

demonstrates a plausible link between anthropogenic mercury sources and mercury contamination in the

environment. Further research needs are specified in Chapter 8. Chapter 9 lists all references cited in

this volume.

The four appendices to Volume III are as follows: Atmospheric Modeling Parameters,

Watershed and Waterbody Modeling Parameters, Model Plant Descriptions, and Bioaccumulation Factor

Development and Uncertainty Analysis.

Extant mercury monitoring data for particular sources indicate that there is a relationship

between emissions and increased mercury in environmental media. Available mercury monitoring data

around these sources are extremely limited, however, and no comprehensive data base describing

environmental concentrations has been developed. To determine if there is a connection between the

above sources and increased environmental mercury concentrations, EPA utilized current modeling

techniques to address many major scientific uncertainties. Because of the major uncertainties inherent to

these techniques, the modeling component of this report is essentially a qualitative study based partly on

quantitative analyses. Uncertainties include the following:

 Comprehensive emission data for various anthropogenic and natural sources are not

available. This reflects the current developmental nature of emission speciation

methods, resulting in few data on the various species and proportions of mercury in

vapor and solid forms emitted. Both elemental and divalent mercury species as well as

gaseous and particulate forms are known to be emitted from point sources.

 Atmospheric chemistry data are incomplete. Some atmospheric reactions of mercury,

such as the oxidation of elemental mercury to divalent mercury in cloud water droplets

have been reported. There may exist other chemical reactions in the atmosphere that

reduce divalent species to elemental mercury that have not been reported.

 There is inadequate information on the atmospheric processes that affect wet and dry

deposition of mercury. Atmospheric particulate forms and divalent species of mercury

are thought to wet and dry deposit more rapidly than elemental mercury; however, the

relative rates of deposition are uncertain. 

 There is no validated local air pollution model that estimates wet and dry deposition of

vapor-phase compounds.
1-4 

 There is significant uncertainty regarding the reduction and revolatilization of deposited

mercury in soils and water bodies.

 There is a lack of information concerning the movement of mercury from watershed soils

to water bodies.

 More data are needed quantifying the kinetics of mercury methylation and demethylation

in different types of water bodies.

 There is a lack of data on the transfer of mercury between environmental compartments

and biologic compartments; for example, the link between the amount of mercury in the

water body and the levels in fish appears to vary from water body to water body.

The assessment draws upon the available scientific information and presents quantitative

modeling analyses that examine: (1) the long range transport of mercury through the atmosphere; (2) the

transport of mercury through the local atmosphere; (3) the aquatic and terrestrial fate and transport of

mercury at hypothetical sites; and finally (4) the resulting exposures to hypothetical humans and animals

that inhabit these sites. It was intended that these analyses would provide a more complete estimate of

the impact of anthropogenic emission sources than an individual analysis.

1.1 Long-Range Atmospheric Transport Modeling

The long range transport modeling was undertaken to estimate the regional and national impacts

of mercury emissions. It focusses on the long range atmospheric transport of mercury and estimates the

impact of mercury across the continental U.S. This type of modeling was conducted based on the

atmospheric chemistry of emitted elemental mercury (Petersen et al., 1995) and the numerous studies

linking increased mercury concentrations in air, soil, sediments, and biota at remote sites to distant

anthropogenic mercury release followed by long-range transport. Details of several studies that

demonstrate the long range transport of mercury are presented in Chapter 2. These provide ample

evidence to justify this assessment of long-range mercury transport.

The long range transport of mercury was modeled using site-specific, anthropogenic emission

source data (presented in Volume II of this Report) to generate mean, annual atmospheric mercury

concentrations and deposition values across the continental U.S. The Regional Lagrangian Model of Air

Pollution (RELMAP) atmospheric model was utilized to model cumulative mercury emissions from

multiple mercury emission sources. Assumptions were made concerning the form and species of

mercury emitted from each source class. The results of the RELMAP modeling were combined with a

local scale atmospheric model to assess average annual atmospheric mercury concentrations in air and

annual deposition rates. The continental U.S. was divided into Western and Eastern halves along the line

of 90west longitude. The 50th and 90th percentile of the predicted atmospheric concentrations and

deposition rates were then used.

1.2 Local Atmospheric Transport Modeling

The local atmospheric transport of mercury released from anthropogenic emission sources was

undertaken to estimate the impacts of mercury from selected, individual sources. The Industrial Source

Code air dispersion model (ISC3) was utilized to model these processes. Model plants, defined as

hypothetical facilities that were developed to represent actual emissions from existing industrial

processes and combustion sources, were located in hypothetical locations intended to simulate a site in
1-5 

either the Western or Eastern U.S. This approach was selected because some environmental monitoring

studies suggest that measured mercury levels in environmental media and biota may be elevated in areas

around stationary industrial and combustion sources known to emit mercury. 

1.3 Modeling Terrestrial and Aquatic Fate of Mercury 

Chapter 6 uses atmospheric concentrations and deposition rates from this volume as inputs to a

series of terrestrial and aquatic models. These were initially described in U.S. EPA's (1990) Methodology

for Assessing Health Risks to Indirect Exposure from Combustor Emissions and a 1994 Addendum. In

response to reviewer comments, these models have been updated and are now identified collectively as

IEM-2M. 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 methyl mercury (MHg). The mass 0

balances are performed for each mercury component, with internal transformation rates linking Hg ,0

HgII, and MHg. Sources include wetfall and dryfall loadings of each component to watershed soils and

to the water body as well as diffusion of atmospheric Hg vapor to watershed soils and the water body. 0

Sinks 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 0

component out of the lake.

At the core of IEM-2M are 9 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 are 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. MHg

concentrations in fish are derived from dissolved MHg water concentrations using bioaccumulation

factors (BAF).

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

chains can be adequately represented using four trophic levels. 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). 

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. 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. In addition, this simplified structure ignores

several important groupings of organisms, including benthic detritivores, macroinvertebrates, and

herbivorous fishes. A 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 levels 3 and 4. The BAFs

selected for these calculations were estimated from existing field data. Respectively, these BAFs

(dissolved methylmercury basis) are 1.6 x 10 and 6.8 x 10 . 6 6
1-6 

The results of these terrestrial and aquatic models were used to predict mercury concentrations in

environmental media and biota.

1.4 Exposure Modeling Rationale

The results of these modeling efforts are used in Volumes IV and VI to predict exposures to

hypothetical humans and wildlife. This section explains the decision to estimate mercury exposure based

on the results of environmental fate modeling of stack emissions from anthropogenic sources rather than

attempting an assessment based on monitoring data.

Exposure to mercury for the purpose of this assessment may be broadly defined as chemical

contact with the outer boundary of an organism (also called a receptor). An organism's contact with

mercury may occur through several different exposure routes, including dermal, inhalation, and oral. The

assessment of mercury exposure is complicated by the physical and chemical properties of this naturally

occurring element; factors include the different physical forms manifested in the environment, the

different oxidative states exhibited, and the duality of its environmental behavior as both a metallic and

an organic compound. Mercury is present in many different potential contact media. In addition, the

uncertain accuracy of analytical techniques, particularly at low environmental concentrations, and

problems with contamination during environmental sampling complicate an assessment of exposure.

Mercury is generally present as a low-level contaminant in combustion materials such as coal,

medical wastes, and municipal solid wastes. Unlike dioxin it is not created during the combustion

process but is released by it. At temperatures typical of many combustion and manufacturing processes,

mercury is emitted in a gaseous form rather than a particulate form; therefore, it is difficult to control

mercury emissions from the source.

Anthropogenic mercury emissions are not the only source of mercury to the atmosphere. 

Mercury, under certain conditions, may be introduced into the atmosphere through volatilization from

natural sources such as lakes and soils; for example, some areas in the western U.S. appear to have

naturally elevated mercury levels. Consequently, it is difficult to trace the source(s) of the mercury in

environmental media and biota and estimate the impact of any one source type.

Existing environmental concentrations are a potential source of mercury exposure to both

humans and animal species. These existing environmental concentrations, often referred to as

background mercury concentrations, were estimated and included in this effort.

Mercury has always been present at varying levels in environmental media and biota, and all

mercury is, in a sense, naturally occurring; that is, mercury is not a substance of human origin. 

Anthropogenic activities are thought to redistribute mercury from its original matrix through the

atmosphere to other environmental media. Numerous studies indicate that the amount of mercury being

deposited from the atmosphere has increased since the onset of the industrial age (Nater and Grigal,

1992; Johansson et al., 1991; Swain et al., 1992). Some of the deposited mercury arises from natural

sources and some from anthropogenic activities. 

Many different yet valid approaches may be used to obtain estimates of mercury exposure. 

These include: direct measurement of mercury concentrations in source emissions (e.g., stack monitoring

data), environmental media (e.g., air, soil or water monitoring), and biota (e.g., fish and flora); direct

measurement of mercury concentrations at the expected points of receptor contact (e.g., house or office

air monitoring data, measurement of receptor food sources or drinking water); and direct measurements
1-7 

of mercury concentrations in the tissues of human and wildlife receptors (e.g., hair samples, feather

samples, muscle samples and leaf samples).

It was decided to model the emissions data from the stacks of combustion sources and industrial

processes rather than use the existing measurement data alone. Extant measured mercury data alone were

judged insufficient to assess adequately the impact of anthropogenic mercury releases on human and

wildlife exposures, the primary goal of the study. The discomfort with the available data arose from the

lack of extensive measurement data near U.S. anthropogenic sources of concern. It is likely that these

data will be available in the near future.

This assessment utilizes the results of measured mercury emissions from selected anthropogenic

sources to estimate exposure. The emissions inventory used in this assessment is found in Volume II of

the Report to Congress. Using a series of environmental fate models and hypothetical scenarios, the

mercury concentrations in environmental media and pertinent biota were estimated. Ultimately in

Volume IV and VI mercury contact with human and wildlife receptors was estimated. In Volume IV of

this document an effort was made to estimate the amount of contact with mercury as well as the oxidative

state and form of mercury contacted. No attempt was made to estimate an internal dose for either the

animal or human receptors.

There is a great deal of uncertainty in the modeling approach selected to estimate exposure. 

There is uncertainty in both the predicted fate and transport of this metal and the ultimate estimates of

exposure. This uncertainty can be divided into modeling uncertainty and parameter uncertainty. 

Parameter uncertainty can be further subdivided into uncertainty and variability depending upon the

degree to which a particular parameter is understood. Research needs are identified toward reducing

these key uncertainties and are presented in Chapter 8.

1.5 Factors Important in Modeling of Mercury Exposure

Factors important in the estimation of mercury exposures modeled in this study are listed in

Table 1-1. This table briefly describes the possible effects of these factors on the fate, transport and

exposure to mercury and the means by which these were addressed. More details are provided in

subsequent sections describing the modeling analyses.
1-8 

Table 1-1

Factors Potentially Important in Estimating Mercury Exposure and

How They Are Addressed in This Study

Factor on Mercury Exposure in this Study

Importance and Possible Effect Means of Addressing

Type of anthropogenic Different combustion and industrial process sources Four main source categories, with a total of 11

source of mercury are anticipated to have different local scale impacts different source types, selected based on their

due to physical source characteristics (e.g., stack estimated annual mercury emissions or potential to

height), the method of waste generation (e.g., be localized point sources of concern.

incineration or mass burn) or mercury control devices

and their effectiveness.

Mercury emission rates Increased emissions will result in a greater chance of Emissions of model plants based on emissions

from stack adverse impacts on environment. inventory.

Mercury species More soluble species will tend to deposit closer to the Two species considered to be emitted from source:

emitted from stack source. elemental and divalent mercury

Form of mercury Transport properties can be highly dependent on Both vapor and particle-bound fractions considered.

emitted from stack form.

Deposition differences Vapor-phase forms may deposit significantly faster Deposition (wet and dry) of vapor-phase forms

between vapor and than particulate-bound forms. calculated separately from particulate-bound

particulate-bound deposition.

mercury

Transformations of Relatively nontoxic forms emitted from source may Equilibrium fractions estimated in all environmental

mercury after emission be transformed into more toxic compounds. media for three mercury species: elemental mercury,

from source divalent species, and methylmercury.

Transformation of Reduction and revolatilization of mercury in soil Rate constants are estimated from measured evasion

mercury in watershed limits the buildup of concentration. fluxes at three locations, as reported in scientific

soil literature.

Transport of mercury Mercury in watershed soils can be a significant Runoff and erosion rates are calculated as a function

from watershed soils to source to water bodies and subsequently to fish. of average meteorological and watershed

water body characteristics.

Transformation of Reduction, methylation, and demethylation of Rate constants are estimated from the scientific

mercury in water body mercury in water bodies affect the overall literature, and are calibrated to give the average

concentration and the MHg fraction, which is observed MHg fraction.

bioaccumulated in fish.

Facility locations Effects of meteorology and terrain may be significant. Both a humid and less humid site considered. Effect

of terrain on results addressed separately.

Location relative to Receptors located downwind are more likely to have Three distances in downwind direction considered.

local mercury source higher exposures. Influence of distance depends on

source type.

Contribution from non- Important to keep predicted impacts of local sources Results of local mercury source are combined with

local sources of in perspective. estimate of impact from non-local sources from

mercury RELMAP.

Uncertainty Reduces confidence in ability to estimate exposure Probabilistic capabilities possible for any

accurately. combination of sources and scenarios. In the current

study, limited uncertainty analyses conducted for

major aspects of atmospheric transport modeling .
1-9 

1.6 Definition of Terms

Definitions for the following terms related to the fate and transport of mercury were largely

adapted from the Expert Panel on Mercury Atmospheric Processes (1994), and EPA (1975, 1976).

Anthropogenic Mercury Emissions

The mobilization or release of mercury by human activity that results in a mass transfer of

mercury to the atmosphere.

Bioaccumulation Factor

The equilibrium concentration of a chemical in a biological medium divided by the equilibrium

concentration of a chemical in an environmental medium. While similar to a bioconcentration

factor, a bioaccumulation factor is designed not only to predict chemical uptake through direct

contact with or uptake from an environmental medium, but also to account for any food chain

pathways that may in some manner connect the environmental medium to the biological medium

of interest.

Bioavailability

The state of being capable of being absorbed and available to interact with the metabolic

processes of an organism. Bioavailability is typically a function of chemical properties, the

physical state of the material to which an organism is exposed, and the ability of the individual

organism to physiologically take up the chemical. 

Bioconcentration Factor

The equilibrium concentration of a chemical in a biological medium divided by the equilibrium

concentration of a chemical in an environmental medium. The parameter is typically used to

predict chemical uptake through contact with or uptake from an environmental medium. 

Biotransfer Factor

The equilibrium concentration of a chemical in animal tissue divided by the daily intake of the

chemical.

Contact Rate

The frequency of an exposure. Generally expressed as the product of an amount of a medium per

event and the number of events per a given unit of time.

Current Background Mercury Concentrations

Concentrations of mercury in the abiotic and biotic components of the environment that have

resulted from natural mercury concentrations and anthropogenic activities.

Erosion

The removal of soil particles by wind and water. Water erosion is usually characterized by one

or more of the following types of erosion: raindrop erosion, sheet erosion, rill erosion, gully

erosion, and streambank erosion. Raindrops start soil erosion by detaching soil particles. They

aggravate soil erosion by compacting the soil surface and reducing its ability to infiltrate water. 

Sheet erosion is the removal of a thin layer of soil resulting from sheet flow of water. It has a

high transport capability. Rill erosion is on steeper slopes where channels with depths of up to

one foot are formed. Gully erosion represents an advanced form of soil erosion from
1-10 

concentrated storm runoff. Streambank erosion is the erosion of soil from stream channels, both

on the banks and on the stream beds.

Exposure

Contact of a chemical, physical or biological agent with the outer boundary of an organism. 

Exposure is quantified as the concentration of the agent in the medium in contact, integrated

over the time duration of the contact.

Exposure Scenario

A set of facts, assumptions, and inferences about how exposure takes place that aids the

exposure assessor in evaluating estimating, or quantifying exposures.

Natural Background Mercury Concentrations

Concentrations of mercury in the abiotic and biotic components of the environment that resulted

from natural mercury concentrations. These concentrations existed prior to the onset of

anthropogenic activities.

Natural Mercury Emissions

The mobilization or release of mercury from environmental sources by natural biotic or abiotic

activities that results in a mass transfer of mercury to the atmosphere.

Pathway

The physical course a chemical or pollutant takes from the source to the exposed organism.

Re-emitted Mercury

Mass transfer of mercury to the atmosphere by biotic and geological processes drawing on a

pool of mercury that was deposited to the earth's surface after initial mobilization by either

anthropogenic or natural activities.

Mercury Dry Deposition

Mass transfers of gaseous, aerosol or particulate mercury species from the atmosphere to the

earth's surface (either aquatic or terrestrial, including vegetation) in the absence of

precipitation.

Mercury Wet Deposition

Mass transfers of dissolved gaseous or suspended particulate mercury species from the

atmosphere to the earth's surface (either aquatic or terrestrial) by precipitation.

Local Scale

A relative term, used to describe the area within which emissions travel within one diurnal cycle

(generally 100 Km from source but for this analysis 50 Km from the source). Local influences

are characterized by measurable pollutant concentration gradients with relatively large

fluctuations in air concentrations caused by meteorological factors such as wind direction.

Regional Scale

A relative term, used to describe the area within which emissions travel in more than one diurnal

cycle (generally 100 to 2000 Km from a source). The regional scale describes areas sufficiently

remote or distant from large emission sources so that concentration fields are rather

homogeneous, lacking measurable gradients.
1-11 

Runoff

That portion of the precipitation that appears in surface streams. Surface runoff (or overland

flow) is water that travels over the ground surface. Subsurface runoff (interflow, storm seepage)

is water that has infiltrated the surface soil and moved laterally through the upper soil horizons. 

Groundwater runoff is water that has infiltrated the surface soil, percolated to the general

groundwater table, and then moved laterally to the water body.

2-2

2.1.1 Chemistry of Mercury

Elemental mercury is a heavy, silvery-white liquid metal at typical ambient temperatures and

pressures. The vapor pressure of mercury metal is strongly dependent upon temperature, and it vaporizes

readily under ambient conditions. Its saturation vapor pressure of 14 mg/m greatly exceeds the average 3

permissible concentrations for occupational (0.05 mg/m ) or continuous environmental exposure (0.015 3

mg/m ) (Nriagu, 1979; WHO, 1976). Elemental mercury partitions strongly to air in the environment 3

and is not found in nature as a pure, confined liquid. Most of the mercury encountered in the atmosphere

is elemental mercury vapor.

Mercury can exist in three oxidation states: Hg (metallic), Hg (mercurous), and Hg 0 2+ 2+

2

(mercuric-Hg(II)). The properties and chemical behavior of mercury strongly depend on the oxidation

state. Mercurous and mercuric mercury can form numerous inorganic and organic chemical compounds;

however, mercurous mercury is rarely stable under ordinary environmental conditions. Mercury is

unusual among metals because it tends to form covalent rather than ionic bonds. Most of the mercury

encountered in water/soil/sediments/biota (all environmental media except the atmosphere) is in the form

of inorganic mercuric salts and organomercurics. Organomercurics are defined by the presence of a

covalent C-Hg bond. The presence of a covalent C-Hg bond differentiates organomercurics from

inorganic mercury compounds that merely associate with the organic material in the environment but do

not have the C-Hg bond. The compounds most likely to be found under environmental conditions are

these: the mercuric salts HgCl , Hg(OH) and HgS; the methylmercury compounds, methylmercuric 2 2

chloride (CH HgCl) and methylmercuric hydroxide (CH HgOH); and, in small fractions, other 3 3

organomercurics (i.e., dimethylmercury and phenylmercury). 

Mercury compounds in the aqueous phase often remain as undisassociated molecules, and the

reported solubility values reflect this. Solubility values for mercury compounds which do not

disassociate are not based on the ionic product. Most organomercurics are not soluble and do not react

with weak acids or bases due to the low affinity of the mercury for oxygen bonded to carbon. 

CH HgOH, however, is highly soluble due to the strong hydrogen bonding capability of the hydroxide 3

group. The mercuric salts vary widely in solubility. For example HgCl is readily soluble in water, and 2

HgS is as unreactive as the organomercurics due to the high affinity of mercury for sulfur. A detailed

discussion of mercury chemistry can be found in Nriagu (1979) and Mason et al. (1994).

2.1.2 The Mercury Cycle

Given the present understanding of the mercury cycle, the flux of mercury from the atmosphere

to land or water at any one location is comprised of contributions from:

 The natural global cycle,

 The global cycle perturbed by human activities,

 Regional sources, and

 Local sources.

Recent advances allow for a general understanding of the global mercury cycle and the impact of

anthropogenic sources. It is more difficult to make accurate generalizations of the fluxes on a regional or

local scale due to the site-specific nature of emission and deposition processes.
2-3

2.1.2.1 The Global Mercury Cycle

As a naturally occurring element, mercury is present throughout the environment in both

environmental media and biota. Nriagu (1979) estimated the global distribution of mercury and

concluded that by far the largest repository is ocean sediments. Nriagu estimated that the ocean

sediments may contain about 10 g of mercury, mainly as HgS. Nriagu also estimated that ocean waters 17

contain around 10 g, soils and freshwater sediments 10 g, the biosphere 10 g (mostly in land biota), 13 13 11

the atmosphere 10 g and freshwater on the order of 10 g. This budget excludes "unavailable" mercury 8 7

in mines and other subterranean repositories. A more recent estimate of the global atmospheric

repository by Fitzgerald (1994) is 25 Mmol or approximately 5×10 g. The estimate of Fitzgerald (1994) 9 

is 50 times the previous estimate of Nriagu (1979) and illustrates how rapidly the scientific

understanding of environmental mercury has changed in recent years.

Several authors have used a number of different techniques to estimate the pre-industrial

mercury concentrations in environmental media before anthropogenic emissions became a part of the

global mercury cycle. It is difficult to separate current mercury concentrations by origin (i.e.,

anthropogenic or natural) because of the continuous cycling of the element in the environment. For

example, anthropogenic releases of elemental mercury may be oxidized and deposit as divalent mercury

far from the source; the deposited mercury may be reduced and re-emitted as elemental mercury only to

be deposited again continents away. Not surprisingly, there is a broad range of estimates and a great deal

of uncertainty with each. When the estimates are combined, they indicate that between 40 and 75

percent of the current atmospheric mercury concentrations are the result of anthropogenic releases. The

Expert Panel on Mercury Atmospheric Processes (1994) concluded that pre-industrial atmospheric

concentrations constitute approximately one-third of the current atmospheric concentrations. The panel

estimated that anthropogenic emissions may currently account for 50 - 75 percent of the total annual

input to the global atmosphere (Expert Panel on Mercury Atmospheric Processes, 1994). The estimates

of the panel are corroborated by Lindqvist et al., (1991), who estimated that 60 percent of the current

atmospheric concentrations are the result of anthropogenic emissions and Porcella (1994), who estimated

that this fraction was 50 percent. Horvat et al., (1993b) assessed the anthropogenic fraction as

constituting 40 to 50 percent of the current total. This overall range appears to be in agreement with the

several fold increase noted in inferred deposition rates (Swain et al., 1992; Engstrom et al., 1994; Benoit

et al., 1994). The percentage of current total atmospheric mercury which is of anthropogenic origin may

be much higher near mercury emissions sources.

A better understanding of the relative contribution of mercury from anthropogenic sources is

limited by substantial remaining uncertainties regarding the level of natural emissions as well as the

amount and original source of mercury that is re-emitted to the atmosphere from soils, watersheds, and

ocean waters. Recent estimates indicate that of the approximately 200,000 tons of mercury emitted to

the atmosphere since 1890, about 95 percent resides in terrestrial soils, about 3 percent in the ocean

surface waters, and 2 percent in the atmosphere (Expert Panel, 1994). More study is needed before it is

possible to accurately differentiate natural fluxes from these soils, watersheds, and ocean waters from reemissions of mercury which originated from anthropogenic sources. For instance, approximately onethird of total current global mercury emissions are thought to cycle from the oceans to the atmosphere

and back again to the oceans, but a major fraction of the emissions from oceans consists of recycled

anthropogenic mercury. According to the Expert Panel on Mercury Atmospheric Processes (1994) 20 to

30 percent of the current oceanic emissions are from mercury originally mobilized by natural sources

(Fitzgerald and Mason, 1996). Similarly, a potentially large fraction of terrestrial and vegetative

emissions consists of recycled mercury from previously deposited anthropogenic and natural emissions

(Expert Panel, 1994).
2-4

Comparisons of contemporary (within the last 15-20 years) measurements and historical records

indicate that the total global atmospheric mercury burden has increased since the beginning of the

industrialized period by a factor of between two and five (see Figure 2-2). For example, analysis of

sediments from Swedish lakes shows mercury concentrations in the upper layers that are two to five

times higher than those associated with pre-industrialized times. In Minnesota and Wisconsin, an

investigation of whole-lake mercury accumulation indicates that the annual deposition of atmospheric

mercury has increased by a factor of three to four since pre-industrial times. Similar increases have been

noted in other studies of lake and peat cores from this region, and results from remote lakes in southeast

Alaska also show an increase, though somewhat lower than found in the upper midwest U.S. (Expert

Panel, 1994).

Although it is accepted that atmospheric mercury burdens have increased substantially since the

preindustrial period, it is uncertain whether overall atmospheric mercury levels are currently increasing,

decreasing, or remaining stable. Measurements over remote areas of the Atlantic Ocean show increasing

levels up until 1990 and a decrease for the period 1990-1994 (Slemr, 1996). Measurements of deposition

rates suggest decreased deposition at some localities formerly subject to local or regional deposition (see

Section 2.1.2.2 below). However, other measurements at remote sites in northern Canada and Alaska

show deposition rates that continue to increase (Lucotte et al., 1995; Engstrom and Swain, 1997). Since

these sites are subject to global long-range sources and few regional sources, these measurements may

indicate a still increasing global atmospheric burden. More research is necessary; a multi-year, worldwide atmospheric mercury measurement program may help to better determine current global trends

(Fitzgerald, 1995).

2.1.2.2 Regional and Local Mercury Cycles

According to one estimate, about half of total anthropogenic mercury emissions eventually enter

the global atmospheric cycle (Mason et al., 1994); the remainder is removed through local or regional

cycles. An estimated 5 to 10 percent of primary Hg(II) emissions are deposited within 100 km of the

point of emission and a larger fraction on a regional scale. Hg(0) that is emitted may be removed on a

local and regional scale to the extent that it is oxidized to Hg(II). Some Hg(0) may also be taken up

directly by foliage; most Hg(0) that is not oxidized will undergo long-range transport due to the

insolubility of Hg(0) in water. In general, primary Hg(II) emissions will be deposited on a local and

regional scale to the degree that wet deposition processes remove the soluble Hg(II). Dry deposition

may also account for some removal of atmospheric Hg(II). Assuming constant emission rates, the

quantity of mercury deposited on a regional and local scale can vary depending on source characteristics

(especially the species of mercury emitted), meteorological and topographical attributes, and other

factors (Expert Panel, 1994). For example, deposition rates at some locations have been correlated with

wind trajectories and precipitation amounts (Jensen and Iverfeldt, 1994; Dvonch et al., 1995). Although

these variations prohibit generalizations of local and regional cycles, such cycles may be established for

specific locations. For example, unique mercury cycles have been defined for Siberia on a regional scale

(Sukhenko and Vasiliev, 1996) and for the area downwind of a German chlor-alkali plant on a local scale

(Ebinghaus and Kruger, 1996). Mercury cycles dependent on local and regional sources have also been

established for the Upper Great Lakes region (Glass et al., 1991; Lamborg et al., 1995) and the Nordic

countries (Jensen and Iverfeldt, 1994). 

2-6

While the overall trend in the global mercury burden since pre-industrial times appears to be

increasing, there is some evidence that mercury concentrations in the environment in certain locations

have been stable or decreasing over the past few decades. For example, preliminary results for eastern

red cedar growing near industrial sources (chlor-alkali, nuclear weapons production) show peak mercury

concentrations in wood formed in the 1950s and 1960s, with stable or decreasing concentrations in the

past decade (Expert Panel, 1994). Some results from peat cores and lake sediment cores also suggest

that peak mercury deposition in some regions occurred prior to 1970 and may now be decreasing (Swain

et al., 1992; Benoit et al., 1994; Engstrom et al., 1994; Engstrom and Swain, 1997). Data collected over

25 years from many locations in the United Kingdom on liver mercury concentrations in two raptor

species and a fish-eating grey heron indicate that peak concentrations occurred prior to 1970. The sharp

decline in liver mercury concentrations in the early 1970s suggests that local sources, such as agricultural

uses of fungicides, may have led to elevated mercury levels two to three decades ago (Newton et al.,

1993). Similar trends have been noted for mercury levels in eggs of the common loon collected from

New York and New Hampshire (McIntyre et al., 1993). The downward trend in mercury concentrations

observed in the environment in some geographic locations over the last few decades generally

corresponds to regional mercury use and consumption patterns over the same time frame (consumption

patterns are discussed in Volume II).

2.2 Atmospheric Processes

Basic processes involved in the atmospheric fate and transport of mercury include: (1) emissions

to the atmosphere; (2) transformation and transport in the atmosphere; (3) deposition from the air; and

then (4) re-emission to the atmosphere. Each of these processes is briefly described below.

2.2.1 Emissions of Mercury

As discussed fully in Volume II, mercury is emitted to the atmosphere through both naturally

occurring and anthropogenic processes. Natural processes include volatilization of mercury in marine

and aquatic environments, volatilization from vegetation, degassing of geologic materials (e.g., soils) and

volcanic emissions. The natural emissions are thought to be primarily in the elemental mercury form. 

Conceptually, the current natural emissions can arise from two components: mercury present as part of

the pre-industrial equilibrium and mercury mobilized from deep geologic deposits and added to the

global cycle by human activity. Based on estimates of the total annual global input to the atmosphere

from all sources (i.e., 5000 Mg from anthropogenic, natural, and oceanic emissions), U.S. sources are

estimated to contribute about 3 percent, based on 1995 emissions estimates as described below.

Anthropogenic mercury releases are thought to be dominated on the national scale by industrial

processes and combustion sources that release mercury into the atmosphere. Stack emissions are thought

to include both gaseous and particulate forms of mercury. Gaseous mercury emissions are thought to

include both elemental and oxidized chemical forms, while particulate mercury emissions are thought to

be composed primarily of oxidized compounds due to the relatively high vapor pressure of elemental

mercury. The analytic methods for mercury speciation of exit gasses and emission plumes are being

refined, and there is still controversy in this field. Chemical reactions occurring in the emission plume

are also possible. The speciation of mercury emissions is thought to depend on the fuel used (e.g., coal,

oil, municipal waste), flue gas cleaning and operating temperature. The exit stream is thought to range

from almost all divalent mercury to nearly all elemental mercury. Most of the mercury emitted at the

stack outlet is found in the gas phase although exit streams containing soot can bind up some fraction of

the mercury. The divalent fraction is split between gaseous and particle bound phases (Lindqvist et al.,
2-7

1991, Chapter 4). Much of this divalent mercury is thought to be HgCl (Michigan Environmental 2

Science Board, 1993).

An emission factor-based approach was used to develop the nationwide emission estimates for

the source categories presented in Table 2-1. The emission factors presented are estimates based on

ratios of mass mercury emissions to measures of source activities and nation-wide source activity levels. 

Details of the emission factor approach are described in Volume II of this Report to Congress. The

reader should note that the data presented in this table are estimates; uncertainties include the precision

of measurement techniques and the calculation of emission factors, estimates of pollutant control

efficiency, and nation-wide source class activity levels. The estimates may also be based on limited

information for a particular source class, thereby increasing the uncertainty in the estimate further. Due

to these and other uncertainties, other sources have calculated different total emissions estimates using

similar methods (for example, see Porcella et al., 1996).

Some anthropogenic processes no longer used still result in significant environmental releases

from historically contaminated areas which continue to release mercury to surface water runoff,

groundwater and the atmosphere. It is estimated that the mercury content of typical lakes and rivers has

been increased by a factor of two to four since the onset of the industrial age (Nriagu, 1979). More 

recently, researchers in Sweden estimate that mercury concentrations in soil, water and lake sediments

have increased by a factor of four to seven in southern Sweden and two to three in northern Sweden in

the 20th century (Swedish EPA 1991). It is estimated that present day mercury deposition is two to five

times greater now than in preindustrial times (Lindqvist et al., 1991).

2.2.2 Mercury Transformation and Transport

Hg(0) has an average residence time in the atmosphere of about one year and will thus be

distributed fairly evenly in the troposphere. Oxidized mercury (Hg(II)) may be deposited relatively

quickly by wet and dry deposition processes, leading to a residence time of hours to months. Longer

residence times are possible as well; the atmospheric residence time for some Hg(II) associated with fine

particles may approach that of Hg (Porcella et al., 1996). 0

The transformation of Hg (g) to Hg(II)(aq) and Hg(II)(p) in cloud water demonstrates a possible 0

mechanism by which natural and anthropogenic sources of Hg to air can result in mercury deposition to 0

land and water. This deposition can occur far from the source due to the slow rate of Hg (g) uptake in 0

cloud water. It has been suggested that this mechanism is important in a global sense for mercury

pollution, while direct wet deposition of anthropogenic Hg(II) is the most important locally (Fitzgerald,

1994; Lindqvist et al., 1991, Chapter 6). Gaseous Hg(II) is expected to deposit at a faster rate after

release than particulate Hg(II) assuming that most of the particulate matter is less than 1 μm in diameter. 

An atmospheric residence time of 1⁄2 - 2 years for elemental mercury compared to as little as hours for

some Hg(II) species (Lindqvist and Rodhe, 1985) is expected. This behavior is observed in the modeling

results presented in this effort as well. It is possible that dry deposition of Hg can occur from ozone 0

mediated oxidation of elemental mercury taking place on wet surfaces, but this is not expected to be

comparable in magnitude to the cloud droplet mediated processes.

This great disparity in atmospheric residence time between Hg and the other mercury species 0

leads to very much larger scales of transport and deposition for Hg . Generally, air emissions of Hg 0 0

from anthropogenic sources, fluxes of Hg from contaminated soils and water bodies and natural fluxes 0

of Hg all contribute to a global atmospheric mercury reservoir with a holding time of 1⁄2 to 2 years. 0

Global atmospheric circulation systems can take Hg emissions from their point of origin and carry them 0
2-8

Table 2-1

Annual Estimates of Mercury Release by Various Combustion and

Manufacturing Source Classes (U.S. EPA, 1997)

Source Emission Rate

Annual Mercury

Combustion Sources - Total 125.2 Mg/yr (137.9 tons/yr)

Electric utilities

Oil- and Gas-fired 0.2 Mg/yr (0.2 tons/yr)

Coal-fired 46.9 Mg/yr (51.6 tons/yr)

Incinerators

Municipal waste combustors 26.9 Mg/yr (29.6 tons/yr)

Medical waste incinerators 14.6 Mg/yr (16.0 tons/yr)

Commercial/Industrial boilers 25.8 Mg/yr (28.4 tons/yr)

Chlor-alkali production 6.5 Mg/yr (7.1 tons/yr)

Primary lead smelting 0.1 Mg/yr (0.1 tons/yr)

Primary copper smelting 0.06 Mg/yr (0.06 tons/yr)

Other combustion sources 10.8 Mg/yr (11.9 tons/yr)

Other sources 12.1 Mg/yr (13.3 tons/yr)

anywhere on the globe before transformation and deposition occur. Emissions of all other forms of

mercury are likely to be deposited to the earth's surface before they thoroughly dilute into the global

atmosphere. Continental-scale atmospheric modeling, such as that performed for this study using the

RELMAP, can explicitly simulate the atmospheric lifetime of gaseous and particulate Hg(II) species, but

not Hg . Although Hg is included as a modeled species in the RELMAP analysis, the vast majority of 0 0

Hg emitted in the simulation transports outside the spatial model domain without depositing, and the 0

same is generally thought to happen in the real atmosphere. Natural Hg emissions and anthropogenic 0

Hg emissions from outside the model domain are simulated in the form of a constant background Hg 0 0

concentration of 1.6 ng m , approximating conditions observed in remote oceanic regions (Fitzgerald, -3

1994). This background Hg concentration is subject to simulated wet deposition by the same process as 0

explicitly modeled anthropogenic sources of Hg within the model domain. 0

Explicit numerical models of global-scale atmospheric mercury transport and deposition have

not yet been developed. As our understanding of the global nature of atmospheric mercury pollution

develops, numerical global-scale atmospheric models will surely follow.
2-9

2.2.3 Deposition of Mercury

The divalent species emitted, either in the vapor or particulate phase, are thought to be subject to

much faster atmospheric removal than elemental mercury (Lindberg et al., 1991, Shannon and Voldner,

1994). Both particulate and gaseous divalent mercury are assumed to dry deposit (this is defined as

deposition in the absence of precipitation) at significant rates when and where measurable concentrations

of these mercury species exist. The deposition velocity of particulate mercury is dependent on

atmospheric conditions and particle size. Particulate mercury is also assumed to be subject to wet

deposition due to scavenging by cloud microphysics and precipitation. The gaseous divalent mercury

emitted is also expected to be scavenged readily by precipitation. Divalent mercury species have much

lower Henry's law constants than elemental mercury, and thus are assumed to partition strongly to the

water phase. Dry deposition of gas phase divalent mercury is thought to be significant due to its

reactivity with surface material. Overall, gas phase divalent mercury is more rapidly and effectively

removed by both dry and wet deposition than particulate divalent mercury (Lindberg et al., 1992;

Petersen et al., 1995; Shannon and Voldner, 1994), a result of the reactivity and water solubility of

gaseous divalent mercury.

In contrast, elemental mercury vapor is not thought to be susceptible to any major process of

direct deposition to the earth's surface due to its relatively high vapor pressure and low water solubility. 

On non-assimilating surfaces elemental mercury deposition appears negligible (Lindberg et al., 1992),

and though elemental mercury can be formed in soil and water due to the reduction of divalent mercury

species by various mechanisms, this elemental mercury is expected to volatilize into the atmosphere

(Expert Panel on Mercury Atmospheric Processes 1994). In fact, it has been suggested that in-situ

production and afflux of elemental mercury could provide a buffering role in aqueous systems, as this

would limit the amount of divalent mercury available for methylation (Fitzgerald, 1994). Water does

contain an amount of dissolved gaseous elemental mercury (Fitzgerald et al., 1991), but it is minor in

comparison to the dissolved-oxidized and particulate mercury content.

There appears to be a potential for deposition of elemental mercury via plant-leaf uptake. 

Lindberg et. al. (1992) indicated that forest canopies could accumulate elemental mercury vapor, via gas

exchange at the leaf surface followed by mercury assimilation in the leaf interior during the daylight

hours. This process causes a downward flux of elemental mercury from the atmosphere, resulting in a

deposition velocity. Recent evidence (Hanson et al., 1994) indicates that this does occur but only when

air concentrations of elemental mercury are above an equilibrium level for the local forest ecosystem. At

lower air concentration levels, the forest appears to act as a source of elemental mercury to the

atmosphere, with the measured mercury flux in the upward direction. Lindberg et. al. (1991) noted this

may be explained by the volatilization of elemental mercury from the canopy/soil system, most likely the

soil. Hanson et al. (1994) stated that "dry foliar surfaces in terrestrial forest landscapes may not be a net

sink for atmospheric elemental mercury, but rather a dynamic exchange surface that can function as a

source or sink dependent on current mercury vapor concentrations, leaf temperatures, surface condition

(wet versus dry) and level of atmospheric oxidants." Similarly, Mosbaek et al. (1988) convincingly

showed that most of the mercury in leafy plants is due 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 plant/soil systems accumulate airborne

elemental mercury when air concentrations are higher than the long-term average for the particular

location, and release elemental mercury when air concentrations fall below the local long-term average. 

On regional and global scales, dry deposition of elemental mercury does not appear to be a significant

pathway for removal of atmospheric mercury, although approximately 95% or more of atmospheric

mercury is elemental mercury (Fitzgerald, 1994).
2-10

There is an indirect pathway, however, by which elemental mercury vapor released into the

atmosphere may be removed and deposited to the earth's surface. Chemical reactions occur in the

aqueous phase (cloud droplets) that both oxidize elemental mercury to divalent mercury and reduce the

divalent mercury to elemental mercury. The most important reactions in this aqueous reductionoxidation balance are thought to be oxidation of elemental mercury with ozone, reduction of divalent

mercury by sulfite (SO ) ions, or complexation of divalent mercury with soot to form particulate 3

-2

divalent mercury:

Hg (g) -> Hg (aq) 0 0

Hg (aq) + O (aq) -> Hg(II)(aq) 0

3

Hg(II)(aq) + soot/possible evaporation -> Hg(II)(p)

Hg(II)(aq) + SO (aq) -> Hg (aq) 3

-2 0

(g) = gas phase molecule

(aq) = aqueous phase molecule

(p) = particulate phase molecule

The Hg(II) produced from oxidation of Hg by ozone can be reduced back to Hg by sulfite; however, the 0 0

oxidation of Hg by ozone is a much faster reaction than the reduction of Hg(II) by sulfite. Thus, a 0

steady state concentration of Hg(II)(aq) is built up in the atmosphere and can be expressed as a function

of the concentrations of Hg (g), O (g), H (representing acids) and SO (g) (Lindqvist et al., 1991, 0 +

3 2

Chapter 6). Note that H and SO (g), although not apparent in the listed atmospheric reactions, control +

2

the formation of sulfite.

The Hg(II)(aq) produced would then be susceptible to atmospheric removal via wet deposition. 

The third reaction, however, may transform most of the Hg(II)(aq) into the particulate form, due to the

much greater amounts of soot than mercury in the atmosphere. The soot concentration will not be

limiting compared to the concentration of Hg(II)(aq), and S atoms in the soot matrix will bond readily to

the Hg(II)(aq). The resulting Hg(II)(p) can then be removed from the atmosphere by wet deposition (if

the particle is still associated with the cloud droplet) or dry deposition (following cloud droplet

evaporation). It is possible that dry deposition of Hg can occur from ozone mediated oxidation of 0

elemental mercury taking place on wet surfaces, but this is not expected to be comparable in magnitude

to the cloud droplet mediated processes (Lindberg, 1994).

Mercury released into the atmosphere from natural and anthropogenic sources deposits mainly as

Hg(II), from either direct deposition of emitted Hg(II) or from conversion of emitted elemental Hg to 0

Hg(II) through ozone-mediated reduction. The former process may result in elevated deposition rates

around atmospheric emission sources and the latter process results in regional/global transport followed

by deposition. 

There is still a great deal of uncertainty with respect to the amount of dry deposition of mercury. 

Once deposited, mercury appears to bind tightly to certain soil components. The deposited Hg(II) may

revolatilize through reduction and be released back to the atmosphere as Hg . Soil Hg(II) may also be 0

methylated to form methylmercury; these two forms may remain in the soil or be transported through the

watershed to a water body via runoff and leaching. Mercury enters the water body through direct

deposition on the watershed, and mercury in water bodies has been measured in both the water column

and the sediments. Hg(II) in the waterbody may also be methylated to form methylmercury; both Hg(II)

and methylmercury may be reduced to form Hg which is reintroduced to the atmosphere. 0
2-11

2.2.4 Re-emissions of Mercury into the Atmosphere

Re-emission of deposited mercury results most significantly from the evasion of elemental

mercury from the oceans. In this process, anthropogenically emitted mercury is deposited to the oceans

as Hg(II) and then reduced to volatile Hg(0) and re-emitted. According to one estimate, this process

accounts for approximately 30% (10 Mmol/year) of the total mercury flux to the atmosphere (Mason et

al., 1994). Overall, 70 to 80 % of total current mercury emissions may be related to anthropogenic

activities (Fitzgerald and Mason, 1996). By considering the current global mercury budget and estimates

of the preindustrial mercury fluxes, Mason et al. (1994) estimate that total emissions have increased by a

factor of 4.5 since preindustrial times, which has subsequently increased the atmospheric and oceanic

reservoirs by a factor of 3. The difference is attributed to local deposition near anthropogenic sources. 

Although the estimated residence time of elemental mercury in the atmosphere is about 1 year, the

equilibrium between the atmosphere and ocean waters results in a longer time period needed for overall

change to take place for reservoir amounts. Therefore, by substantially increasing the size of the oceanic

mercury pool, anthropogenic sources have introduced long term perturbations into the global mercury

cycle. Modeled results from Fitzgerald and Mason (1996) estimate that if all anthropogenic emissions

were ceased today, it would take about 15 years for mercury pools in the oceans and the atmosphere to

return to pre-industrial conditions. The Science Advisory Board, in its review of this study, concluded

that it could take significantly longer. The slow release of mercury from terrestrial sinks to freshwater

and coastal waters will likely persist for much longer, though, effectively increasing the lifetime of

anthropogenic mercury further (Fitzgerald and Mason, 1996). This may be particularly significant

considering that surface soils currently contain most of the pollution-derived mercury of the industrial

period. Recently published studies, however, indicate that mercury in soil may be reduced and

revolatilized, in that the capacity of soils to sequester airborne mercury must be reconsidered (Kim et al.,

1995; Lindberg, 1996). Thus, re-emissions of anthropogenic mercury will contribute to long term

influences on the global biogeochemical cycle for mercury.

2.3 Terrestrial and Aquatic Fate of Mercury

2.3.1 Mercury in Soil

Once deposited, the Hg(II) species are subject to a wide array of chemical and biological

reactions. Soil conditions (e.g., pH, temperature and soil humic content) are typically favorable for the

formation of inorganic Hg(II) compounds such as HgCl , Hg(OH) and inorganic Hg(II) compounds 2 2

complexed with organic anions (Schuster 1991). Although inorganic Hg(II) compounds are quite soluble

(and, thus, theoretically mobile) they form complexes with soil organic matter (mainly fulvic and humic

acids) and mineral colloids; the former is the dominating process. This is due largely to the affinity of

Hg(II) and its inorganic compounds for sulfur-containing functional groups. This complexing behavior

greatly limits the mobility of mercury in soil. Much of the mercury in soil is bound to bulk organic

matter and is susceptible to elution in runoff only by being attached to suspended soil or humus. Some

Hg(II), however, will be absorbed onto dissolvable organic ligands and other forms of dissolved organic

carbon (DOC) and may then partition to runoff in the dissolved phase. Currently, the atmospheric input

of mercury to soil is thought to exceed greatly the amount leached from soil, and the amount of mercury

partitioning to runoff is considered to be a small fraction of the amount of mercury stored in soil. The

results of Appendix C, which detail the calibration of soil-water partition coefficients in the watershed

model, are consistent with these observations. The affinity of mercury species for soil results in soil

acting as a large reservoir for anthropogenic mercury emissions (Meili et al., 1991 and Swedish EPA

1991). For example, note the mercury budget proposed by Meili et al., 1991. Even if anthropogenic

emissions were to stop entirely, leaching of mercury from soil would not be expected to diminish for

many years (Swedish EPA, 1991). Hg can be formed in soil by reduction of Hg(II) 0
2-12

compounds/complexes mediated by humic substances (Nriagu, 1979) and by light (Carpi and Lindberg,

1997). This Hg will diffuse through the soil and re-enter the atmosphere. Methylmercury can be 0

formed by various microbial processes acting on Hg(II) substances. Approximately 1-3% of the total

mercury in surface soil is methylmercury, and as is the case for Hg(II) species, it will be bound largely to

organic matter. The other 97-99% of total soil mercury can be considered largely Hg(II) complexes,

although a small fraction of Mercury in typical soil will be Hg (Revis et al., 1990). The methylmercury 0

percentage exceeded 3% (Cappon, 1987) in garden soil with high organic content under slightly acidic

conditions. Contaminated sediments may also contain higher methylmercury percentages compared to

ambient conditions (Wilken and Hintelmann, 1991; Parks et al., 1989).

2.3.2 Plant and Animal Uptake of Mercury

The Hg(II) and methylmercury complexes in soil are available theoretically for plant uptake and

translocation, potentially resulting in transfer through the terrestrial food chain. In reality plant uptake

from ordinary soils, especially to above-ground parts of plants, appears to be insignificant (Schuster,

1991; Lindqvist et al., 1991, Chapter 9). Mosbaek et al. (1988) determined (by spiking soil with Hg ) 203

that the atmospheric contribution of the total mercury content of the leafy parts of plants is on the order

of 90-95% and for roots 30-60%. The concentrations of mercury in leafy vegetables generally exceeds

that of legumes and fruits (Cappon 1981, 1987), where it is not clear whether the mercury content results

from air and/or soil uptake. Most plant uptake studies do not explicitly measure both the surrounding

soil and air concentrations as performed in Mosbaek et al., 1988. Even when this is performed there is

no way to determine whence the mercury in the plant originated. Speciation data do not provide much

information; apparently any Hg absorbed from the air is readily converted to Hg(II) in the plant interior, 0

since even leafy vegetables do not appear to contain any Hg (Cappon, 1987). Plants also have some 0

mercury methylation ability (Fortmann et al., 1978), so the percentage of methylmercury in plants may

not originate from root uptake. Studies which report plant uptake from soil have typically been

conducted on heavily polluted soils near Chlor-alkali plants (Lenka et al., 1992; Temple and Linzon

1977; Lindberg et al., 1979), where the formation of Cl complexes can increase Hg(II) movement -

somewhat. Overall, mercury concentrations in plants, even those whose main uptake appears to be from

the air, are small (see ambient mercury concentrations tables). Accordingly, livestock typically

accumulates little mercury from 

foraging or silage/grain consumption, and mercury content in meat is low (see tables in the ambient

mercury concentrations section). Due to these factors, the terrestrial pathway is not expected to be

significant in comparison to the consumption of fish by humans and wildlife as an exposure pathway of

concern for mercury.

2.3.3 Mercury in the Freshwater Ecosystem

There are a number of pathways by which mercury can enter the freshwater environment: Hg(II)

and methylmercury from atmospheric deposition (wet and dry) can enter water bodies directly; Hg(II)

and methylmercury can be transported to water bodies in runoff (bound to suspended soil/humus or

attached to dissolved organic carbon); or Hg(II) and methylmercury can leach into the water body from

groundwater flow in the upper soil layers. Once in the freshwater system, the same complexation and

transformation processes that occur to mercury species in soil will occur along with additional processes

due to the aqueous environment. Mercury concentrations are typically reported for particular segments

of the water environment, the most common of which are the water column (further partitioned as

dissolved or attached to suspended material), the underlying sediment (further divided into surface

sediments and deep sediments); and biota (particularly fish). Discussion of several detailed studies on

the movement of mercury between soil/water/sediment and how modeling results compare to these data

are presented in Appendix B.
2-13

Partition coefficients have been calculated for the relative affinity of Hg(II) and methylmercury

for sediment or soil over water. Values of the partition coefficient K (concentration of mercury in dry d

sediment, soil or suspended matter divided by the dissolved concentration in water) on the order of 10-

100,000 ml/g soil, 100,000 ml/g sediment and 100,000+ ml/g suspended material are typically found for

Hg(II) and methylmercury (Appendix B), indicating a strong preference for Hg(II) and methylmercury to

remain bound to soil, bottom sediment or suspended matter (increasing affinity in that order). Of course,

a river or lake freshwater system has a larger volume of water than sediment, and a significant amount of

Hg(II) entering a water system may partition to the water column, especially if there is a high

concentration of suspended material in the water column. It is often unclear whether the mercury in

sediment will be HgCl or Hg(OH) organic complexes, which can be considered more susceptible to 2 2

methylation, or will be the more unreactive HgS and HgO forms.

Most of the mercury in the water column (Hg(II) and methylmercury) will be bound to organic

matter, either to dissolved organic carbon (DOC; consisting of fulvic and humic acids, carbohydrates,

carboxylic acids, amino acids and hydrocarbons; (Lindqvist et al., 1991, (Chapter 2)) or to suspended

particulate matter. In most cases, studies that refer to the dissolved mercury in water include mercury

complexes with DOC. Studies indicate that about 25%-60% of Hg(II) and methylmercury organic

complexes are particle-bound in the water column. The rest is in the dissolved and DOC-bound phase

(Nriagu, 1979; Bloom et al., 1991; NAS 1977). Typically, total mercury and methylmercury

concentrations are positively correlated with DOC concentrations in lake waters (Driscoll et al., 1994;

Mierle and Ingram, 1991). Hg is produced in freshwater by humic acid reduction of Hg(II) or 0

demethylation of methylmercury mediated by sunlight. An amount will remain in the dissolved gaseous

state while most will volatilize. As noted previously, Hg constitutes very little of the total mercury in 0

the water column but may provide a significant pathway for the evolution of mercury out of the water

body via Hg(II) or methylmercury -> Hg -> volatilization. For many lakes, however, sedimentation of 0

the Hg(II) and methylmercury bound to particulate matter is expected to be the dominant process for

removal of mercury from the water column (Sorensen et al., 1990; Fitzgerald et al., 1991).

Generally, no more than 25% of the total mercury in a water column exists as a methylmercury

complex; typically, less than 10% is observed (see Appendix B). The water column methylmercury

concentration is a result of methylation of Hg(II) which occurs in the bottom sediment and the water

column by microbial action and abiotic processes. In a number of sediment-water systems, it has been

found that methylmercury concentrations in waters were independent of water column residence time or

time in contact with sediments (Parks et al., 1989). Methylmercury in the water column which is lost

through demethylation, exported downstream or taken up by biota is thought to be replaced by additional

methylation of Hg(II) compounds to sustain equilibrium.

Once entering a water body, mercury can remain in the water column, be lost from the lake

through drainage water, revolatilize into the atmosphere, settle into the sediment or be taken up by

aquatic biota. After entry, the movements of mercury through any specific water body may be unique. 

Mercury in the water column, in the sediment, and in other aquatic biota appears to be available to

aquatic organisms for uptake.

Methylation is a key step in the entrance of mercury into the food chain (Sorenson et al., 1990). 

The biotransformation of inorganic mercury species to methylated organic species in water bodies can

occur in the sediment (Winfrey and Rudd, 1990) and the water column (Xun et al., 1987). Abiotic

processes (e.g., humic and fulvic acids in solution) also appear to methylate the mercuric ion (Nagase et

al., 1982). Not all mercury compounds entering an aquatic ecosystem are methylated, and demethylation

reactions (Xun et al., 1987) as well as volatilization of dimethylmercury decrease the amount of
2-14

methylmercury available in the aquatic environment. There is a large degree of scientific uncertainty

and variability among water bodies concerning the processes that methylate mercury.

Bacterial methylation rates appear to increase under anaerobic conditions, high temperatures

(NJDEPE, 1993) and low pH (Xun et al., 1987; Winfrey and Rudd, 1990). Increased quantities of the

mercuric species, the proper biologic community, and adequate suspended soil load and sedimentation

rate are also important factors (NJDEPE, 1993). Anthropogenic acidification of lakes appears to increase

methylation rates as well (Winfrey and Rudd, 1990).

Methylmercury is very bioavailable and accumulates in fish through the aquatic food web; nearly

100% of the mercury found in fish muscle tissue is methylated (Bloom et al., 1991). Methylmercury

appears to be primarily passed to planktivorous and piscivorous fish via their diets. Larger, longer-lived

fish species at the upper end of the food web typically have the highest concentrations of methylmercury

in a given water body. A relationship exists between methylmercury content in fish and lake pH, with

higher methylmercury content in fish tissue typically found in more acidic lakes (Winfrey and Rudd,

1990; Driscoll et al., 1994). The mechanisms for this behavior are unclear. Most of the total

methylmercury production ends up in biota, particularly fish (Swedish EPA, 1991). In fact,

bioconcentration factors (BCFs) for accumulation of methylmercury in fish (dry weight basis, compared

with the water methylmercury concentration) are on the order of 10 - 10 (Bloom, 1992; Appendix D). 5 6

Overall, methylmercury production and accumulation in the freshwater ecosystem places this pollutant

into a position to be ingested by fish-eating organisms.

This bioaccumulation of methylmercury in fish muscle tissue occurs in water bodies that are

remote from emission sources and seemingly pristine as well as in water bodies that are less isolated. 

Methylmercury appears to be efficiently passed through the aquatic food web to the highest trophic level

consumers in the community (e.g., piscivorous fish). At this point it can be contacted by fish-consuming

wildlife and humans through ingestion. Methylmercury appears to pass from the gastrointestinal tract

into the bloodstream more efficiently than the divalent species. 

2.4 Fate of Mercury in Marine Environments

This section describes the environmental fate of mercury in the marine environment. Two

models are presented here: a composite conceptual model of the whole ocean, largely based on the data

and modeling presented in the literature by Fitzgerald and coworkers; and a model developed for ocean

margins by Cossa et al. (1996). Ocean margins occur at the convergence of continents and oceans; they

include geologic features such as estuaries, inland seas, and continental shelves and are characterized by

high productivity. These models are very general in nature and reflect only a basic understanding of the

movements of mercury in marine environments. Examples of data collected from U.S. coastal waters are

also included here. The arctic marine system is not examined in this section.

As noted earlier, mercury is an atmophillic element and, as such, its global transport occurs

primarily through the atmosphere. Elemental mercury, the principle species found in the atmosphere,

has a high vapor pressure and a low solubility in water. As a result of these properties, the half-life of

atmospheric mercury is thought to be a year or longer. Elemental mercury appears to be deposited to

ocean waters primarily through wet deposition. Oxidizing reactions in the atmosphere may also play a

role in the conversion of elemental mercury to more reactive atmospheric species which are subsequently

deposited.

Mercury found in ocean waters and sediments comprises a large reservoir of the total mercury on

the planet. The conceptualization of oceans as reservoirs of mercury is fitting for they serve both as
2-15

sources of mercury to the atmosphere as well as environmental mercury sinks (Mason and Fitzgerald,

1993; 1996; Cossa et al., 1996). The forms and species of mercury present in the ocean waters and

sediments may be transformed as a result of both biotic and abiotic factors within the ocean. The most

significant species of mercury from an ecologic and human health perspective is monomethylmercury

(MHg). Both monomethyl- and dimethylmercury have been measured in ocean waters (Mason and

Fitzgerald, 1990). MHg shows strong evidence of bioaccumulation and biomagnification in the marine

food web, potentially posing risks to consumer species (particularly apex marine predators and

piscivores).

2.4.1 Models of Mercury in the Oceans

Rolfus and Fitzgerald (1995) employed a simple ocean model to examine mercury deposition

and concentrations in fish. This model is largely derived from data collected by Fitzgerald and

collaborators. In the model, the ocean is divided into 3 compartments: coastal zones, areas of upwelling,

and open ocean. The open ocean accounts for roughly 90 percent of the total area of the oceans but very

little fish production; the coastal and upwelling regions account for roughly 10 percent and 0.1 percent

of the total area, respectively, but almost all fish production (each area accounted for about 50 percent of

the total production). Mercury inputs to their model include atmospheric deposition, flow from riverine

systems, and flow from upwelling regions of the ocean. 

Mercury inputs are assumed to occur to the mixed layer. Corroborating this assumption, Mason

and Fitzgerald (1996) have suggested that there is a relationship between increased concentrations of

reactive mercury in surface waters and predicted atmospheric deposition rates in the North Atlantic. 

From the mixed upper layer of the ocean waters, reactive mercury is transported through attachment to

particulates (i.e., scavenging) to regions or layers of the ocean where methylation occurs (those areas

naturally lower in oxygen). Particles containing mercury are predicted to deteriorate as they descend,

releasing the mercury. The model assumes that monomethylmercury is produced in these low oxygen

regions below the thermocline in the open ocean and upwelling compartments. The mercury is then

transported to the mixed layer at a depth of less than 100 meters where it is incorporated into the lower

levels of the food web (Mason and Fitzgerald, 1990; 1993; 1996). Specifically, after transport from the

mixed layer, most of the reactive mercury is assumed to become methylated to form dimethylmercury in

the subthermocline waters, although direct formation of monomethylmercury from reactive mercury is

also possible. Dimethylmercury is unstable in marine waters, and most dimethylmercury formed there is

assumed to decompose to form monomethylmercury (Mason and Fitzgerald, 1996). Some of the

monomethylmercury is then converted to Hg , which is assumed to be transported to the surface resulting 0

in a supersaturation of the elemental species in surface waters. Elemental mercury thus may evade back

to the atmosphere from the surface waters. It is hypothesized that the transport of reactive mercury from

the mixed layer controls the rate of methylmercury formation; this rate appears to be related positively to

primary productivity (Mason and Fitzgerald, 1996).

In the model, reduction of reactive species to Hg in the mixed layer and subsequent evasion 0

from the mixed layer is a significant mechanism by which mercury is eliminated from marine waters,

with rates of up to one percent per day reported in the open ocean. Reduction is associated with both

abiotic and biotic components of the marine environment (Mason et al., 1995). Roughly 10 to 30 percent

of the reduction has been attributed to abiotic factors. Abiotic reduction may be mediated by sunlight;

Xiao et al. (1994) showed in an experimental aquatic system that the combination of fulvic and humic

acids with synthetic sunlight resulted in reduction of dissolved divalent species. Weber (1993) has

suggested that abiotic reduction may be mediated by the presence of methyltin compounds and humic

acid. The remainder of the reduction of reactive species is the result of biologic activities. Evidence

presented by Mason et al. (1995) indicates that bacteria and cyanobacteria are responsible for much of
2-16

the biologic reduction of mercury in the mixed layer. 

Mercury methylation in the coastal compartment of the model (which includes estuarine regions)

is assumed to occur both in the sediments and in the water column near the oxycline (Rolfus and

Fitzgerald, 1995). It is assumed that methylmercury in this compartment is transported to the mixed

layer and incorporated into the lower trophic levels of the marine food web. The total deposition of

mercury was estimated at 10 Mmoles/year; the input from rivers and estuaries was estimated to be

approximately 10 percent of this value (about 1 Mmole/year). The inputs to the upwelling zone from

cooler, deeper waters is assumed to be 0.5 Mmoles/year.

To sustain fish methylmercury concentrations, Rolfus and Fitzgerald (1995) predict that in the

open ocean 0.02 percent of the total mercury deposited is methylated and transferred via the food web to

fish. Fractions of the total mercury deposited that are necessary for sustaining levels in fish from the

upwelling and coastal regions of the oceans are estimated to be 5.4 percent and 20 percent, respectively. 

Overall, approximately 2 percent of the deposited mercury is needed to maintain fish concentrations of

0.2 ppm. The differences in necessary fractions between the three areas are related to trophic structure,

deposition, and methylmercury production.

The cycling of mercury proposed by Mason et al. (1994) was reexamined and revised by Hudson

et al. (1995). In this model, the yearly deposition of mercury to the oceans was assumed to be roughly

the same as that of Mason et al., 1994 (approximately 10 Mmol/yr); oceanic emissions were reduced by

10 to 25 percent of the Mason value. Oceanic burial fluxes were assumed by Hudson et al. (1995) to be

about half of the value assumed by Mason et al. (1994) (approximately 0.4 vs 1 Mmol/yr, respectively). 

Hudson et al. (1995) also accounted for mixing into the ocean interior, which was neglected by Mason et

al. (1994).

Several authors, including Rolfus and Fitzgerald (1995), conclude that increases in the

deposition of mercury that result from increases in anthropogenic emissions will result in enhanced food

chain bioaccumulation and higher concentrations of mercury in marine fish.

2.4.2 Modeling Estuaries and Coastal Regions

Estuaries and coastal regions may be more highly affected by anthropogenic mercury sources.

These regions are directly affected by mercury transported from freshwater rivers as well as direct

oceanic discharges. These regions are also potentially affected to a greater degree by reactive mercury

species and particulate-bound mercury released to the atmosphere from nearby anthropogenic sources. 

Reactive mercury species in the vapor-phase are assumed to deposit more rapidly than vapor-phase Hg . 0

The atmosphere above these coastal waters may also have higher concentrations of oxidants. For some

mercury-contaminated estuaries, the primary source of mercury contamination appears to be aquatic

discharges rather than atmospheric deposition.

Cossa et al. (1996) have reviewed data collected in coastal regions and constructed a mercury

mass balance model specifically for these areas. In this model, fluxes from river systems to ocean

margins were determined to be the largest input of total mercury to coastal systems. Annually, about 4.8

Mmol of mercury are assumed to be added via riverine systems. Cossa et al. (1996) noted that measured

concentrations of total mercury are highly variable, with the highest measured concentrations found in

rivers passing through urban and industrialized areas. Over 90% of the total mercury transported from

river systems is bound to particles. Much of this transported particle bound mercury appears to be

unreactive and is assumed to be buried in near shore sediments. However, Cossa et al. (1996) also note

the potentially important lack of data describing mercury concentrations in tropical river systems. 
2-17

Atmospheric deposition to coastal waters was estimated to be approximately 2 Mmol/yr. Much

of the deposited mercury in the model is assumed to be chemically reactive, participating in chemical

reactions in the marine environment. Atmospheric deposition of mercury to other parts of the oceans

followed by transport via upwelling to the coastal regions was also thought to be an important source of

total mercury into these areas, accounting for between 2.5 and 3.5 Mmol/yr. The movement of mercury

from sediments to coastal waters is considerably less important than the other inputs for this model. In

total, approximately 3.3 Mmol of mercury is estimated to be present in the coastal waters.

Fluxes of total mercury from coastal waters include (listed in approximate order of significance): 

sedimentation of particle bound mercury derived from both riverine and upwelling regions; export to the

open ocean; and evasion to the atmosphere. On a global scale, evasion is speculated to be balanced by

deposition to the oceans; regional imbalances may occur with deposition exceeding evasion in the

northern latitudes and the converse occurring in the southern latitudes. 

Cossa et al. (1996) have used this model to describe methylmercury in coastal waters. Inputs to

coastal waters occur through upwelling from other parts of the ocean (0.1-0.2 Mmol/yr), atmospheric

deposition (0.02 Mmol/yr), river systems (0.01 Mmol/yr), and sediments (0.001 Mmol/yr). An unknown

quantity is assumed to pass back to the atmosphere through evasion after undergoing demethylation. 

Approximately 0.05 Mmol/yr was predicted to deposit to the sediments and less than 0.04 Mmol/yr was

predicted to pass to deep ocean. The model indicates that more than half of the methylmercury in coastal

aquatic species originated in waters of the deep ocean; the remainder is the result of methylation of

reactive mercury in coastal waters and methylmercury from other sources. Cossa et al. (1996) noted

general agreement between their model and the model proposed by Mason and Fitzgerald (1993) and

Rolfus and Fitzgerald (1995).

Mercury (particularly methylmercury) clearly accumulates in coastal marine food webs. Two

general food webs can be conceptualized: a benthic sediment community which includes

macroinvertebrates; and a community that resides primarily in the water column, which includes

phytoplankton and zooplankton as well as planktivorous and piscivorous fishes. For mercury species to

accumulate within members of these food webs, they must be bioavailable and retained within the

tissues. 

Mercuric ions in anoxic sediments are transformed to monomethylmercury primarily through

biotic processes. The bulk of this activity appears to occur in the top layers of the sediment. Compeau

and Bartha (1985) showed that sulfate-reducing bacteria such as Desulfovibrio desulfuricans are the

primary group of organisms responsible for this reaction. The organic matter content of the sediment

appears to be a factor controlling mercury methylation rates (Choi and Bartha, 1994). Production of

methylmercury in sediments would enable benthic organisms to accumulate this species. Gagnon and

Fisher (1997) have examined bioavailability of particle-bound inorganic divalent mercury and particlebound monomethylmercury to a species of marine mussel and concluded that the assimilation of particlebound methylmercury is greater than particle-bound inorganic mercury. Furthermore, dissolved

methylmercury and divalent mercury are both assimilated in mussels to a greater degree than particlebound species. However, because particulate mercury species dominate dissolved mercury in coastal

waters, Gagnon and Fisher (1997) have concluded that particle-bound methylmercury is likely the major

source of this chemical species to mussels. Other organisms that dwell in the benthos may share these

characteristics with the mussel. Of particular concern are benthic worms (Bryan and Langston, 1992)

and higher invertebrates as well as some species of carnivorous fish (such as Cynoglossus macrostomas)

(Joseph and Srivastava, 1993) and terrestrial vertebrates (such as carnivorous birds) which consume

these benthic organisms. Mercury species may also impact detritivores, as high mercury concentrations

have been associated with decomposing plant materials (Bryan and Langston, 1992). Some benthic
2-18

invertebrates also have been reported to contain elevated levels of inorganic mercury species.

The food web that exists primarily in the water column may be impacted by methylation of

reactive mercury species. Concentrations of methylmercury in predatory piscivorous marine fishes that

inhabit coastal waters (such as sharks) may exceed 1 ppm (see Volume IV of this Report). Much of this

methylmercury is thought to be transferred through the food web. 

2.4.3 Mercury Budget for a Coastal Waterbody in U.S.A.

Mercury fluxes may be defined more precisely for a specific body of water. For example,

Vandel and Fitzgerald (1995) have developed a preliminary mercury budget for Narragansett Bay in

Rhode Island. The average residence time for waters within this estuary was determined to be 24 days. 

River waters which drained into the bay had higher concentrations of total mercury than water in the bay;

a major component of total mercury in the freshwaters was the strongly-bound fraction. Concentrations

of the strongly-bound fraction appeared to decline as the salinity increased. The estimated mercury flux

from the rivers which drain into the bay was 61 g/day (69% of the total flux). Point sources such as

waste water treatment facilities were estimated to contribute 18.5 g/day (21% of total flux), and

atmospheric deposition was estimated to be 10 g/day (10%of total flux). Approximately half of the

mercury entering the bay was predicted to be retained within the estuarine sediments; the remainder of

the mercury is transported to the ocean through tidal exchange. 
3-1

3. MEASURED CONCENTRATIONS

This chapter first presents available measurement data for mercury in environmental media and

biota. This is followed by a discussion of efforts to collect measurement data from remote locations and

near anthropogenic sources of concern. Note that this chapter does include measured mercury

concentrations in wildlife that function as vectors to humans but does not include measured

concentrations in final receptors of concern (i.e., humans and selected wildlife receptors). Measurement

data for people and wildlife are presented in Volumes IV and VI, respectively. 

3.1 Analytic Measurement Methods

A number of methods can be employed to determine mercury concentrations in environmental

media. The concentrations of total mercury, elemental mercury, organic mercury compounds (especially

methylmercury) and chemical properties of various mercuric compounds can be measured, although

speciation among mercuric compounds is not usually attempted. Recent, significant improvements and

standardizations in analytical methodologies enable reliable data on the concentration of methylmercury,

elemental mercury and the mercuric fraction to be separated from the total mercury in environmental

media. It is possible to speciate the mercuric fraction further into reactive, non-reactive and particlebound components. It is generally not possible to determine which mercuric species is present in

environmental media (e.g., HgS or HgCl ). 2

One of the significant advances in mercury analytical methods over the past decade or so has

been in the accurate detection of mercury at low levels (less than 1 μg/g). Over the past two decades

mercury determinations have progressed from detection of μg levels of total mercury to picogram levels

of particular mercury species (Mitra, 1986 and Hovart et al., 1993a and 1993b). Typical detection limits

for data used or presented in this study are on the order of 1 to 2 ng/L for water samples (Sorensen et al.,

1994), 0.1 ng/g for biota (Cappon, 1987; Bloom, 1992) and 0.1 ng/m for atmospheric samples (Lindberg 3

et al., 1992). Mercury contamination of samples has been shown to be a significant problem in past

studies. The use of ultra-clean sampling techniques is critical for the more precise measurements

required for detection of low levels of mercury.

3.2 Measurement Data

Based on the current understanding of the mercury cycle, mercury is thought to be transported

primarily through the atmosphere and distributed to other compartments of the environment (Chapter 2). 

The primary source of mercury in terrestrial, aquatic and oceanic environments appears to be the wet or

dry deposition of atmospheric mercury. Once deposited, the mercury may be revolatilized back to the

atmosphere, incorporated into the medium of deposit or transferred to other abiotic or biotic components

of these environments.

Elemental mercury vapor is the most common form of mercury in the atmosphere and divalent

mercury the most common in soils, sediments and the water column. The most common form in most

biota is Hg(II); the exception is fish in which the most common form is methylmercury.

3.2.1 Mercury Air Concentrations

As noted in section 2.3.1 anthropogenic emissions are currently thought to account for between

40-75% of the total annual input to the global atmosphere (Expert Panel on Mercury Atmospheric
3-2

Processes, 1994; Hovart et al., 1993b). Current air concentrations are thought to be 2 - 3 times preindustrial levels. This is in agreement with the several fold increase noted in inferred deposition rates

(Swain et al., 1992; Engstrom et al., 1994; Benoit et al., 1994).

As shown in Tables 3-1 and 3-2, measured U.S. atmospheric mercury concentrations are

generally very low. The dominant form in the atmosphere is vapor-phase elemental mercury, although

close to emission sources, higher concentrations of the divalent form may be present. Small fractions of

particulate mercury and methylmercury may also be measured in ambient air. In rural areas, airborne

particulate mercury is typically 4% or less of the total (particulate + gas phase) mercury in air (U.S. EPA,

1993; WHO, 1990). Particulate mercury comprises a greater fraction of the total in urban areas U.S.

EPA (1993), and will consist primarily of bound Hg(II) compounds.

There is a substantial body of recent data pertaining to the atmospheric concentrations and

deposition rates of atmospheric mercury collected at specific sites across the U.S. Most of the collected

deposition data are from sites located some distance from large emission sources. The data have been

collected by several different groups of researchers. These data are briefly summarized here.

Keeler et al., (1994) measured vapor- and particulate-phase atmospheric mercury concentrations

from a site in Chicago, IL, two sites in Detroit, MI and a Lake Michigan site. The mean values are

presented along with the range of measurement data. The collection period for these sites was generally

less than one month; for example, the Detroit data were collected during a 10-day period.

Keeler et al., (1995) reported the results of several short-term atmospheric particulate mercury

measurements in Detroit, MI and longer-term (1-year) particulate measurements at rural sites in

Michigan and Vermont. In the Detroit measurements the particulates sampled were divided into two

categories: fine (<2.5 μm) and coarse (>2.5 μm). The average size of the fine particles was 0.68 μm, and

the average size of the coarse particles was 3.78 μm. Most (mean=88%) of the particulate mercury at the

Detroit, MI site was measured on fine particles; the range for individual samples was 60-100% of total

particulate.

Fitzgerald et al., (1991) reported measured mercury concentrations at Little Rock Lake, WI from

May of 1988 through September of 1989 and particulate mercury concentrations at Long Island Sound

(Avery Point, CT).

Table 3-1

Summary of Measured Mercury Concentration

in Air (U.S. EPA, 1993)

Total Atmospheric Mercury (ng/m ) %Hg(II) % Methylmercury 3

Rural areas: 1 - 4 1-25% 0-21%

Urban areas: 10 - 170

a b

 Higher fractions in urban areas a

 Generally % methylmercury on low end of this range b
3-3

Table 3-2

Measured Vapor- and Particulate-Phase Atmospheric Mercury Concentrations

Site Vapor-Phase Mercury Particulate-Phase Mercury Conc. Reference

Conc. in ng/m Mean in n 3 g/m

Mean (Range)

3

(Range)

Chicago, IL 8.7 (1.8-62.7) 0.098 (0.022-0.52) Keeler et al., (1994)

Lake Michigan 2.3 (1.3-4.9) 0.028 (0.009-0.054) Keeler et al., (1994)

South Haven, MI 2.0 (1.8-4.3) 0.019 (0.009-0.029) Keeler et al., (1994)

0.022 (max 0.086) Keeler et al., (1995)

Ann Arbor, MI 2.0 (max 4.4) 0.10 (max 0.21) Keeler et al., (1994)

0.022 (max 0.077) Keeler et al., (1995)

Detroit, MI Site A >40.8 (max >74) 0.34 (max 1.09) Keeler et al., (1994)

0.094 (0.022-0.23) Keeler et al., (1995)

Detroit, MI Site B 3.7 (max 8.5) 0.30 (max 1.23) Keeler et al., (1994)

Pellston, MI 0.011 (max 0.032) Keeler et al., (1995)

Underhill Center, VT 2.0 (1.2-4.2) 0.011 (0.001-0.043) Burke et al., (1995)

Broward County, FL 1.8 0.034 Dvonch et al., (1995) a

Background Site near

Atlantic Ocean (Site 1)

Broward County, FL 3.3 0.051 Dvonch et al., (1995)

Inland (Site 2)

Broward County, FL 2.8 0.049 Dvonch et al., (1995)

Inland (Site 3)

Little Rock Lake, WI 1.6 (1.0-2.5) 0.022 (0.007-0.062) Fitzgerald et al., (1991)

Long Island Sound, Avery (1.4-5.3): 0.062 (0.005-0.18) Particulate: Fitzgerald et al., (1991)

Pt., CT 95-100% elemental; Vapor: Bloom and Fitzgerald et al., (1988) b

0-1% methylmercury

Crab Lake, WI 1.7 Winter 0.006 Lamborg et al., (in press)

Summer 0.014

 Diurnal variations were also noted; elevated concentrations were measured at night. For example at site 2 the average nighttime vapor-phase a

concentration was 4.5 ng/m . This was attributed to little vertical mixing and lower mixing heights that occur in this area at night. 3

 99% of Total Gaseous Mercury is Hg . During 1 month (October) the mean methylmercury concentration was measured to be 12 pg/m with a b 0 3

range of 4-38 pg/m ; 0.7% of the total 3 gaseous mercury was methylmercury. During November it was measured as <10 pg/m and from 3

December through August it was measured below the detection limit (<5 pg/m ). 3
3-4

3.2.2 Mercury Concentrations in Precipitation

Mercury concentrations in precipitation are shown in Table 3-3. Total mercury concentrations in

rainwater are typically higher than in surface water. This is thought to be the result of efficient

scavenging of divalent mercury by rain droplets and the oxidation of elemental mercury to divalent

mercury, while mercury in surface waters can be lost by revolatilization from the water body and

sequestration in the sediment.

Table 3-3

Measured Mercury Concentrations in Precipitation

Site Mean Mercury Concentration in Reference

precipitation, ng/L Mean (Range)

Ely, MN 20 in 1988 1988-89 data: Glass et al., (1992)

51 in 1989 1990 data: Sorensen et al., (1992)

13 in 1990

Duluth, MN 23 in 1988 1988-89 data: Glass et al., (1992)

11 in 1989 1990 data: Sorensen et al., (1992)

13 in 1990

Marcell, MN 18 in 1988 Glass et al., (1992)

18 in 1989

Bethel, MN 13 in 1990 Sorensen et al., (1992)

Cavalier, ND 19 in 1990 Sorensen et al., (1992)

International Falls, MN 9 in 1990 Sorensen et al., (1992)

Lamberton, MN 15 in 1990 Sorensen et al., (1992)

Raco, MN 10 in 1990 Sorensen et al., (1992)

Little Rock Lake, WI 11 (3.2-15) in rain Fitzgerald et al., (1991)

 6 in snow

Crab Lake, WI 7.9 in rain Lamborg et al., (1995)

3.3 in snow

Underhill Center, VT 8.3 Burke et al., (1995) a

Broward County, FL Total: 35 (15-56) Dvonch et al., (1995)

Background Site near Atlantic Ocean Reactive: 1.0 (0.5-1.4)

(Site 1)

Broward County, FL Total: 40 (15-73) Dvonch et al., (1995)

Inland (Site 2) Reactive: 1.9 (0.8-3.3)

Broward County, FL Total: 46 (14-130) Dvonch et al., (1995)

Inland (Site 3) Reactive: 2.0 (1.0-3.2)

Broward County, FL Total: 57 (43-81) Dvonch et al., (1995)

300 m from MWC (Site 4) Reactive: 2.5 (1.7-3.7)

 Both the concentrations of mercury in precipitation and the amount of precipitation deposited/event increased in spring and summer. Most a

(66%) of the mercury in the spring and fall precipitation samples (only ones tested) was dissolved. The mean concentration of reactive mercury

was 1.0 ng/L. Higher particulate concentrations were observed in the winter.
3-5

Total mercury concentrations in precipitation are generally less than 100 ng/L in areas not

directly influenced by an emissions source, including suburban and urban locations. Levels much higher

(greater than 1000 ng/L) however, have been reported for precipitation downwind of anthropogenic

mercury sources (NJDEPE 1993; see also "Measured Mercury Levels from Point Sources" section

below). Areas downwind of mercury sources also show the greatest variability in precipitation

concentrations. Mercury concentrations do not vary much among different precipitation types (snow,

rain, and ice; NJDEPE 1993, Fitzgerald et al., 1991). Mercury precipitation concentrations show a

seasonal pattern, with average concentrations several times higher during the summer than during the

winter months, even in areas with a warm climate (Pollman et al., 1994). Current average precipitation

mercury levels are on the order of 2-4 times greater than pre-industrial levels, based on information on

the increases in mercury deposition rates (Swain et al., 1992; Expert Panel on Mercury Atmospheric

Processes, 1994). The concentration of methylmercury in rain is minor, and its origins are uncertain (see

Table 3-4).

Table 3-4

Measured Mercury Concentrations in Rain Which Include Methylmercury Estimates (ng/L)

Study Description Total Methyl- % Methyl- Reference

Mercury mercury mercury

(ng/L) (ng/L)

Swedish rain: 9 samples and 7.5-89.8 0.04-0.59 0.1-3.7 Lee and Iverfeldt

4 sites. (1991)

6 Samples at Little Rock 3.5-15 0.06-0.22 0.4-6.3 Fitzgerald et al.

Lake, WI. (1991)

N.B. The difference between Total mercury and methylmercury can be considered Hg(II) species (Brosset 1981; U.S.

EPA 1988). This is assumed for all water samples.

3.2.3 Mercury Deposition Rates

Environmental mercury is widely thought to be transported primarily through the atmosphere. 

The primary source of mercury in terrestrial, aquatic and oceanic environments appears to be the wet or

dry deposition of atmospheric mercury. Once deposited, the mercury may be revolatilized back to the

atmosphere, incorporated into the medium of deposit or transferred to abiotic or biotic components of

these environments.

Intensive, site-specific studies of environmental mercury fluxes have been done at only a handful

of U.S. sites. Watras et al., (1994) summarize the collected data and present a conceptualization of

mercury fluxes between abiotic and biotic components of the environment in 7 Northern Wisconsin

seepage Lakes, including Little Rock Lake. Most of the mercury was thought to enter the lakes through

atmospheric deposition with wet deposition of mercury contributing the most to the total. The total

amount deposited was approximately 10 μg/m /yr. Most of the mercury deposited was thought to deposit 2

into the sediment or volatilize back into the atmosphere. There was a net production of methylmercury

in the lakes with most of the produced methylmercury being stored in the tissues of fish. The behavior of
3-6

mercury at most U.S. sites is not characterized to the same degree as at Little Rock Lake, WI. It should

be noted that Little Rock Lake is a rather remote seepage lake and that atmospheric mercury may behave 

differently closer to emission sources. Mercury may also behave differently in different types of

watersheds and waterbodies.

Measured wet deposition rates are given in Table 3-5. Similar measurements of dry deposition

are rare due to limitations of analytical methods. In particular, dry deposition of divalent mercury vapor

has not been measured to date. This is a major source of uncertainly because its high reactivity implies

that it may be efficiently removed from the atmosphere via dry deposition.

Burke et al., (1995) measured mercury concentrations on a precipitation event basis for one year

at a rural site in Vermont. Underhill Center, VT is located near Lake Champlain and was 200 Km away

from a major urban or industrial area.

Dvonch et al., (1995) conducted a 4-location, 20 day mercury study in Broward County, FL. 

Broward county contains the city of Ft. Lauderdale as well as an oil-fired utility boiler and a municipal

waste combustion facility. Daily measurements of atmospheric particulate and vapor-phase mercury

were collected at 3 of the 4 sites, and daily precipitation samples were collected at all sites.

Hoyer et al., (1995) conducted a 2-year study of mercury concentrations in precipitation (by

event) at 3 rural sites (Pellston, South Haven, and Dexter) in the state of Michigan.

Several authors have estimated mercury total deposition (wet and dry) rates by sample coring of

various media. For example, Engstrom et al., (1994) used lake core sediments to estimate a current

deposition rate of 12.5 μg/m /yr and a preindustrial (natural) deposition rate 3.7 μg/m /yr for remote 2 2

lakes located in Minnesota and northern Wisconsin. Benoit et al., (1994) analyzed mercury

concentrations in a peat bog at a Minnesota site. The estimated pre-1900 deposition rate at this site was

7.0 μg/m /yr, and the current mean deposition rate was estimated to be 24.5 μg/m /yr. Estimates of total 2 2

deposition are given in Table 3-6.
3-7

Table 3-5

Mercury Wet Deposition Rates (ug/m /yr) 2

Site Wet Mercury Deposition Rates (ug/m /yr), Reference 2

Means

Ely, MN 17 in 1988 1988-89 data: Glass et al., (1992)

42 in 1989 1990 data: Sorensen et al., (1992)

6.7 in 1990

Duluth, MN 20 in 1988 1988-89 data: Glass et al., (1992)

6.5 in 1989 1990 data: Sorensen et al., (1992)

9.3 in 1990

Marcell, MN 17 in 1988 Glass et al., (1992)

14 in 1989

Bethel, MN 13 in 1990 Sorensen et al., (1992)

Cavalier, ND 6.1 in 1990 Sorensen et al., (1992)

International Falls, 5.5 in 1990 Sorensen et al., (1992)

MN

Lamberton, MN 9.3 in 1990 Sorensen et al., (1992)

Raco, MN 8.9 in 1990 Sorensen et al., (1992)

Little Rock Lake, WI 4.5 from rain Fitzgerald et al., (1991)

2.3 from snow

Crab Lake, WI 4.4 from rain Lamborg et al., (1995)

0.8 from snow

Nothern MN 10-15 Sorensen et al., (1990)

Pellston, MI 5.8 in year 1 Hoyer et al., (1995)

5.5 in year 2

0.07 ug/m (max 0.51) per rainfall event 2

South Haven, MI 9.5 in year 1 Hoyer et al., (1995)

13 in year 2

0.12 ug/m (max 0.85) per rainfall event 2

Dexter, MI 8.7 in year 1 Hoyer et al., (1995

9.1 in year 2

0.10 ug/m (max 0.98) per rainfall event 2

Underhill Center, VT 9.3 Burke et al., (1995)

0.07 ug/m per rainfall event 2
3-8

Table 3-6

Estimated Mercury Total Deposition Rates

Site Estimate of Pre- Estimate of Current Annual Reference

industrial Annual Deposition Rates μg/m /yr

Deposition Rates

μg/m /yr 2

2

Minnesota and northern 3.7 12.5 Swain et al. (1992);

Wisconsin Engstrom et al., (1994) 

Lake core sediments

Minnesota 7.0 24.5 Benoit et al., (1994) 

Peat bog core sampling

Little Rock Lake, WI 10 Fitzgerald et al., (1991) a

Crab Lake, WI 7.0 (86% estimated to Lamborg et al., a

deposit in summer) (1995)

 Data includes previously tabled values of wet deposition plus particulate deposition. Fitzgerald et al., 1991 did not collect a

particulate size data. Assuming a particulate deposition velocity of 0.5 cm/s, a yearly average particulate deposition flux of

3.5+/- 3 ug/m /yr was estimated. Lamborg et al., (1995) noted the smaller particle sizes in the winter and assumed a deposition 2

velocity 0.1 cm/s for the average winter concentrations (7 pg/m ) and a deposition velocity of 0.5 cm/s for average summer 3

concentrations (26 pg/m ). 3

3.2.4 Mercury Concentrations in Water

Tables 3-7 through 3-9 show measured data in surface water, groundwater and ocean water. 

There is a great deal of variability in these data, some of which may be due to the seasonality of the

water concentrations.
3-9

Table 3-7

Measured Mercury Concentrations in Surface Fresh Water (ng/L)

Study Description Total Methyl- % Reference

Mercury mercury Methyl-

(ng/L) (ng/L) mercury

Swedish lakes: 8 sites, 2-4 samples 1.35-15 0.04-0.8 1.0-12 Lee and Iverfeldt (1991)

each.

Swedish mires: 8 sites, 4 samples 2.9-12 0.08-0.73 2-14 Westling (1991)

each.

Lake Cresent, WA 0.163 <0.004 <2.5 Bloom and Watras (1989)

Swedish runoff: 7 sites, 3 samples 2-12 0.04-0.64 1-6 Lee and Iverfeldt (1991)

each.

Little Rock Lake: reference basin. 1.0-1.2 0.045-0.06 mean of 5 Watras and Bloom (1992)

Lake Michigan (total) 7.2 microlayer Cleckner et al. (1995)

8.0 at 0.3m

6.3 at 10m

Lake Champlain (filtered) 3.4 microlayer Cleckner et al. (1995)

3.2 at 0.3m

2.2 at 15m

Lakes 0.04 - 74 NA NA NJDEPE (1993)

Rivers and Streams 1 - 7

Table 3-8

Measured Mercury Concentrations in Ground/Drinking Water (ng/L)

Study Description Total Mercury Reference

Southern New Jersey domestic wells Up to and exceeding 2000 Dooley (1992)

Drinking/Tap water in U.S. 0.3-25 NJDEPE (1993)

Washington State well 0.3 Bloom (1989)
3-10

Table 3-9

Measured Mercury Concentrations in Ocean Water (ng/L)

Study Description Total Mercury Reference

(ng/L)

Review on concentrations of dissolved 0.5 - 3.0 WHO (1989)

mercury: Open ocean

Review on concentrations of dissolved 2 - 15 WHO (1989)

mercury: Coastal sea water

Hg along the Italian coast Dissolved: 1.7-12.2 Seritti et al. (1982)

Particulate: 0.3 - 80

Puget Sound near-shore sea water 0.72 Hovert et al. (1993b)

Total mercury levels in lakes and streams generally are lower than mercury levels found in

precipitation, with levels typically well under 20 ng/L (NJDEPE 1993). Elevated levels may be found in

lakes and streams thought to be impacted by anthropogenic mercury sources but not to the extent that

precipitation levels appear to be. Total lake water mercury concentrations tend to increase with lower

pH and higher humic content (U.S. EPA, 1993). Present-day mercury levels in freshwater are thought to

be 2 - 7 times greater than pre-industrial levels (Swedish EPA, 1991). Methylmercury percentages are

higher that those in precipitation, ranging from 5 - 20%, with levels around 10% being the most

common. Mercury levels continue to increase in many lakes (Swedish EPA, 1991).

It is important to note that much of the data on mercury in drinking water and ground water

report levels as below detection limits (U.S. EPA, 1988), although the detection limit was a somewhat

dated 100 ng/L. Lindqvist and Rodhe (1985) report that the concentration range for mercury in drinking

water is the same as in rain, with an average estimate for total mercury of 25 ng/L. It seems reasonable

to assume similar speciation as no speciation data could be found. Dooley (1992) states that mercury

concentrations in pristine wells are likely to be below that of unpolluted surface waters.

Table 3-9 shows published values for mercury concentrations in ocean water. Limited speciation

data are available. Hovert et al. (1993b) reported that 2.8% of total mercury was methylmercury, which

is not much different from the speciation in fresh water. Total mercury concentrations in ocean and sea

water vary from undetectable to over 1000 ng/l (Nriagu, 1979).

3.2.5 Mercury Concentrations in Soil/Sediment

Table 3-10 presents reported mercury concentrations in soil. The relatively high concentrations

illustrate the strong partitioning of mercury to soils. Based on the soil data presented, it can be inferred

that soil, while not as important as the atmosphere, is a significant reservoir for environmental mercury. 

The concentrations are presented as total mercury and methylmercury. Most of the soil mercury is

thought to be Hg(II).
3-11

Table 3-10

Measured Mercury Concentration in Soil

Study Description Total Methyl- % Reference

Mercury mercury Methyl-

(ng/g dry (ng/g dry mercury

weight) weight)

Discovery Park, Seattle, WA 29 - 133 0.3-1.3 0.6-1.5 Lindqvist et al. (1991)

Wallace Falls, Cascades (WA) 155 - 244 1.0-2.6 0.5-1.2 Lindqvist et al. (1991)

Control Soil, New York State 117 4.9 4.2 Cappon (1981)

Compost, New York State 213 7.3 3.3 Cappon (1987)

Garden soil, New York State 406 22.9 5.3 Cappon (1987)

Typical U.S. Soils 8 - 117 NA NA NJDEPE (1993)

N.B. As in water samples the fraction of Hg , if present at all, will be very small compared to Hg(II) (Revis et al., 1990), and the 0

difference between Total mercury and methylmercury can be considered to be Hg(II) to be species.

Soil mercury levels are usually less than 200 ng/g in the top soil layer, but values exceeding this

level are not uncommon, especially in areas affected by anthropogenic activities (see section 2.6). Soil

mercury levels vary greatly with depth, with nearly all the mercury found in the top 20 cm of soil. 

Mercury levels are also positively correlated with the percentage of organic matter in soil (Nriagu 1979). 

Top soil mercury concentrations are estimated to be a factor of 4-6 (Swedish EPA, 1991) higher now as

compared to pre-industrial concentrations. Methylmercury percentages in soil are typically on the order

of a few percent. Soil mercury levels are continuing to rise (Fitzgerald 1994), and most (up to 95%) of

the anthropogenic mercury released over the past 100 years resides in surface soil (Fitzgerald, 1994;

Expert Panel on Mercury Atmospheric Processes, 1994). Mercury from soil provides in most cases

(depending on watershed characteristics) the main source of mercury to water bodies and fish. Mercury

is very slowly removed from soil, and long after anthropogenic emissions are reduced, soil and water

concentrations can be expected to remain elevated.

Sediment mercury levels are typically higher than soil levels, and concentrations exceeding

200 ng/g are not unusual (see Table 3-11). Sediment mercury levels follow the same trends as soil in

regards to depth, humic matter, and historical increases, and methylmercury percentage. There is some

evidence suggesting that the methylmercury percentage increases with increasing total mercury

contamination (Parks et al., 1989).

Two large-scale monitoring projects have measured mercury levels in coastal sediments: the

Naional Oceanic and Atmospheric Administration’s (NOAA’s) National Status and Trends (NS&T)

Program and EPA’s Environmental Monitoring and Assessment Program (EMAP) for estuaries. These

programs and their findings are discussed below.
3-12

Table 3-11

Measured Mercury Concentrations in Freshwater Aquatic Sediment

Study Description Total Mercury (ng/g Reference

dry weight)

80 MN Lakes 34-753; mean 174 Sorensen et al. (1990)

North Central WI Lakes 90-190 Rada et al. (1989)

Little Rock Lake, WI 10-170 Wiener et al. (1990)

U.S. Lake sediment mean ranges 70-310 NJDEPE (1993)

NOAA’s NS&T Program was initiated in 1984 to determine the status of, and detect changes in,

the environmental quality of our Nation’s estuarine and coastal waters. Currently, the NS&T Program

conducts periodic chemical monitoring for more than 70 organic chemicals and trace elements in benthic

(bottom-dwelling) fish, sediments and bivalve mollusks (mussels and oysters) at more than 300 sites

throughout the United States. The Mussel Watch Project and the Benthic Surveillance Project, which are

components of the NS&T Program, have measured concentrations of mercury in sediments. These data,

collected during the period 1984-1991, are summarized on a regional basis in Table 3-12. 

Concentrations along the North Atlantic, Middle Atlantic, and Pacific coasts (0.14, 0.12, and 0.08 μg/g

dry weight, respectively) are higher relative to those along the South Atlantic and Eastern and Western

Gulf coasts (0.03, 0.05, and 0.04 μg/g dry weight, respectively). The highest concentrations measured

exceed 2.0 μg/g dry weight along the North Atlantic and Pacific coasts (5.00 and 2.20 μg/g dry weight,

respectively). Information on temporal trends is not available.

EMAP is a national program initiated in 1989 in response to the EPA Science Advisory Board’s

recommendation to monitor the status and trends of U.S. ecological resources -- terrestrial, freshwater

and marine. The program is directed by EPA’s Office of Research and Development, with participation

by other federal agencies (e.g., NOAA, U.S. Forest Service, U.S. Fish and Wildlife Service). One of the

original goals of EMAP was to quantitatively evaluate the condition of coastal estuaries by monitoring

environmental conditions, including sediment contamination. More recently, the goals of EMAP have

been refined to emphasize indicator developmentand other methods for translating monitoring data

into assessments of ecological condition. There will be diminished emphaiss on large-scale monitoring.

Nevertheless, sediment contamination levels were measured in the Virginia Province (Cape Cod to

Chesapeake Bay) during the period 1990-1993. For the 1992 samples, the median mercury

concentrations in Chesapeake Bay and Long Island Sound sediments were 0.054 and 0.088 μg/g dry

weight, respectively (Strobel et al. 1994).
3-13

Table 3-12

Mercury Concentration in Sediments from NS&T Sites (1984-1991)

Region States Range Concentration

Concentration Median

(μg/g-dry weight) (μg/g-dry weight)

North Atlantic ME, MA , RI, CT, NY, NJ 0.007-5.00 0.14

Middle Atlantic DE, MD, VA 0.010-0.84 0.12

South Atlantic NC, SC, GA, FL (east coast) 0.002-0.27 0.03

Eastern Gulf of Mexico FL (west coast), AL, MS 0.007-0.98 0.05

Western Gulf of Mexico LA, TX 0.009-0.34 0.04

Pacific CA, OR, WA, HI, AK 0.009-2.20 0.08

3.2.6 Mercury Concentrations in Biota

Elevated mercury concentrations in fish have been measured across the U.S. As seen in Figure

3-1, 35 states have at least one waterbody under mercury advisory, including six states with statewide

mercury advisories. There are differences in the action levels for advisories from state to state. Fish

mercury concentrations are the single greatest concern in regards to the effects of mercury pollution. 

Fish in lakes seemingly far removed from anthropogenic sources have been found to have mercury levels

of concern to human health. Mercury levels in fish vary greatly, often showing little correlation to

proximity to mercury emission sources. In Sweden, fish mercury concentrations in 1 kg pike have risen

from 0.05 - 0.3 μg/g to 0.5 - 1.0 μg/g in southern and central Sweden over the last 100 years. Fish

mercury concentrations in most cases strongly correlate with pH (lower pH resulting in higher

methylmercury concentrations). Other lake characteristics have been found to correlate with fish

mercury levels, but not as strongly as pH, with some factors showing a positive correlation in some lakes

and a negative correlation in others (U.S. EPA, 1993).

It has been so well established that most (>95%) of the total mercury content of fresh and

saltwater fish is methylmercury (Bloom, 1992) that currently some researchers no longer speciate fish

samples (NJDEPE 1994). Thus, only total mercury concentrations are reported here. Approximately

90% of the mercury in shrimp, mussels and copepods from IAEA standards contain other forms of

mercury (only about 10% of total mercury is methylmercury), but rather about 90% of the mercury total

concentration is ethylmercury (Bloom, 1992). (It should be noted that ethylmercury exposure was not

assessed in this document.)

3-15

The data from two studies national in scope are summarized in Table 3-13. Lowe et al. (1985)

reported mercury concentrations in fish from the National Contaminant Biomonitoring Program. The

fresh-water 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 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 g/g wet weight) occurred in the northern squawfish

of the Columbia River.

"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 U.S. 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 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 were detected. 

The mean mercury concentrations in all fish sampled differ by approximately a factor of 2 for

each study. The mean mercury concentration reported by Lowe et al. was 0.11 g/g, whereas the mean

mercury concentration reported by Bahnick et al. was 0.26 g/g. This is difference which 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 will 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.
3-16

Table 3-13

Freshwater Fish Mercury Concentrations from Nationwide Studies

Species Mean Mercury Concentration μg/g (fresh weight)

Lowe et al., (1985) U.S.EPA (1992c) and Bahnick et al., (1994)

Bass 0.157 0.381

Bloater 0.093

Bluegill 0.033

Smallmouth Buffalo 0.096

Carp, Common 0.093 0.11

Catfish 0.088 0.16 2 3

Crappie (black, white) 0.114 0.22

Fresh-water Drum 0.117

Northern Squawfish 0.33

Northern Pike 0.127 0.31

Perch (white and yellow) 0.11

Sauger 0.23

Sucker 0.114 0.167 4 5

Trout (brown, lake, rainbow) 0.149 0.146

Walleye 0.100 0.52

Mean of all measured fish 0.11 0.26

 Average concentration found in white, largemouth and smallmouth bass. 1

 Channel, largemouth, rock, striped, white catfish. 2

 Channel and flathead catfish. 3

 Bridgelip, carpsucker, klamath, largescale, longnose, rivercarpsucker, tahoe sucker. 4

 Mean of average concentrations found in white, redhorse and spotter sucker. 5

 Brown trout only. 6

Another national study of pollutant contamination in biota is the Mussel Watch Project, which is

a component of NOAA’s NS&T Program. The Mussel Watch Project measures concentrations of 

organic and trace metal contaminants in fresh whole soft-parts of bivalave mollusks (i.e., mussels and

oysters) at over 240 coastal and estuarine sites. These data, which are available for 1986-1993, are

summarized in Table 3-14. Concentrations along the North Atlantic, Eastern Gulf, and Pacific coasts

(0.15, 0.14, and 0.11 μg/g dry weight, respectively) are higher relative to those along the Middle Atlantic,

South Atlantic, and Western Gulf coasts (0.06, 0.09, and 0.08 μg/g dry weight, respectively). The

highest concentrations measured exceed 1.0 μg/g dry weight along the Western Gulf and Pacific coasts

(1.80 and 1.01 μg/g dry weight, respectively). 
3-17

Table 3-14

Mercury Concentration in Bivalve Mollusks from Mussel Watch Sites (1986-1993)

Region States Range Concentration

Concentration Median

(μg/g-dry weight) (μg/g-dry weight)

North Atlantic ME, MA , RI, CT, NY, NJ 0.005-0.72 0.15

Middle Atlantic DE, MD, VA 0.003-0.33 0.06

South Atlantic NC, SC, GA, FL (east coast) 0.012-0.98 0.09

Eastern Gulf of Mexico FL (west coast), AL, MS 0.005-0.72 0.14

Western Gulf of Mexico LA, TX 0.002-1.80 0.08

Pacific CA, OR, WA, HI, AK 0.002-1.01 0.11

For the purpose of temporal analysis, annual Mussel Watch data on mercury concentrations in

bivalve mollusks at specific sites have been aggregated to national geometric means (O’Conner and

Beliaeff 1995). The national means, which are shown in Table 3-15, do not show any temporal trend in

mercury concentrations in mussels and oysters for the period 1986-1993.

Table 3-15

Nationwide Geometric Mean Concentrations of Mercury in Bivalve Mollusks (1986-1993)

1986 1987 1988 1989 1990 1991 1992 1993

Mean Mercury Concentration 0.11 0.11 0.11 0.12 0.09 0.11 0.11 0.12

(μg/g-dry weight)

Temporal trend analysis was also conducted on a site-by-site basis for 154 Mussel Watch sites

that had data for at least six years during the period 1986-1993 (O’Conner and Beliaeff 1995). Seven

sites exhibited an increasing trend in mercury concentrations, and eight sites exhibited a decreasing trend

in mercury concentrations, with 95% statistical confidence. The sites with increasing and decreasing

trends are shown in Table 3-16.
3-18

Table 3-16

Trends in Mercury Concentrations in Bivalve Mollusks (1986-1993)

Site Name State

Increasing Trend

Mobile Bay - Hollingers Island Cahnnel AL

Lake Borgne - Malheureux Point LA

Galveston Bay - Confederate Reef TX

Point Loma - Lighthouse CA

San Francisco Bay - Emeryville CA

Point Arena - Lighthouse CA

Crescent - Point St. George CA

Decreasing Trend

Charlotte Harbor - Bord Island FL

Mississippi Sound - Pascagoula Bay MS

Sabine Lake - Blue Buck Point TX

Mission Bay - Ventura Bridge CA

Marina Del Rey - South Jetty CA

Elliott Bay - Four-Mile Rock WA

Sinclair Inlet - Waterman Point WA

Whidbey Island - Possession Point WA

The studies reported by Lowe et al. (1985) and by Bahnick et al. (1994) and the Mussel Watch

Project are systematic, national examinations of pollutant concentrations in biota. Higher mercury

concentrations in biota have been found in other studies that focused on specific locations. These data,

which are presented below, indicate wide variations in mercury levels in biota.

Table 3-17 summarizes measured mercury concentrations in freshwater sportfish as reported by

a number of researchers, and Table 3-18 summarizes available data on measured mercury concentrations

in saltwater commercial fish. In general, the mercury levels in freshwater fish appear to be higher than

the levels in saltwater fish. Several authors report mercury levels that are higher than 1 ug/g (1 g/g) in

the muscle of freshwater fish: NJDEPE (1994); Wren, et al. (1991); Lathrop et al. (1989); MacCrimmon

et al. (1983); Lange et al. (1993); Glass et al. (1990); Sorensen et al. (1990); U.S. EPA (1992a), U.S.

EPA (1992b); Simonin et al. (1994); and Florida DER (1990). Due to the importance of fish mercury

levels, discussions of several of the mercury studies referenced in the tables are summarized here.

The New Jersey Department of Environmental Protection and Energy collected individual fish

samples throughout the state (NJDEPE 1994). Generally larger fish were sampled from New Jersey

rivers, lakes and reservoirs known to be contaminated with mercury or at risk for mercury contamination. 
3-19

Samples were prepared as skin-off fillets, and clean protocols were used throughout the analysis. 

Mercury levels in fish exceeded the FDA Criterion of 1.0 ug/g (wet weight) in 50 of the 313 sampled fish

and at 15 of the 55 sample locations. It is noted that the FDA criterion is applicable to fish sold through

interstate commerce in the United States under the Food, Drug and Cosmetic Act (21 U.S.C. 301). 

Levels of greater than 0.5 ug/g (wet weight) occurred in 108 of the 313 fish. The highest reported

concentration occurred in a largemouth bass taken from the Atlantic City Reservoir at a concentration of

8.94 ug/g. The mercury levels in all six of the largemouth bass sampled from this site were elevated. At

the Atlantic City site the range of mercury concentrations was 3.05 to 8.94 and the mean was 4.5 ug/g. 

The overall study range for largemouth bass was 0.05 to 8.94 ug/g. High levels were also noted in chain

pickerel particularly those obtained from a series of low pH waterbodies. The range of mercury

concentrations reported for chain pickerel was 0.09 to 2.82 ug/g. Levels of greater than 1 ug/g were also

reported in yellow bullheads (maximum reported 1.47 ug/g). Acidity of these waterbodies was also

measured, and reported in Table 3-17 are the ranges of mean fish mercury concentrations for 9 pH

categories.

Simonin et al. (1994) collected yellow perch from 12 drainage lakes located in Adirondack Park,

New York State, during the fall of 1987. The age of the fish was determined from acetate impressions of

the scales, and filets (including the skin and ribs) were analyzed for total mercury. Lake water samples

were taken late in the summer of 1987 and included analysis of pH, dissolved inorganic and organic

carbon (DIC and DOC), conductance, color, acid neutralizing capacity (ANC) and a number of metals

and ligands. A total of 372 fish were collected, with 7 to 53 fish taken per lake. Fish ranged from 2+ to

11+ years of age, with 4+ year old fish being the most common; fish of this age were used in making

comparisons among lakes. It was found that air-equilibrated pH was the best predictor of mercury

concentrations, with lower lake pH resulting in higher mercury levels in perch. This was clear despite

large variations in mercury concentrations from the same lake. Perch mercury concentrations from the

highest pH lake (considering all ages) ranged from 0.07 - 0.27 g/g wet wt., the corresponding range for

the lowest pH lake was 0.63 - 2.28 g/g. Other variables that were highly correlated (p <0.0001) with

fish mercury levels included ANC, DIC, Ca, conductivity, Mg and field pH. Variables less strongly

correlated (p <0.05) include DOC, Na, SO , lake area and watershed area. Variables not correlated with 4

4+ year old yellow perch include color, total phosphorus, Al, Cl-, lake depth, ratio of watershed area to

lake area, ratio of watershed area to lake volume, fish length and fish weight. For a given lake, fish age

was most strongly correlated with mercury concentrations; older fish had the highest concentrations. 

Fish length and weight were also significantly correlated.
3-20 

Table 3-17

Measured Mercury Concentrations Freshwater Sportfish (Total Mercury, ug/g wet wt.)

Study Pike/Pickerel Walleye Bass Bottom Feeders Panfish 

12 Adirondack Lakes 6 year old: 0.41

2 year old: 0.234 year old: 0.36 8 year old: 0.46

10+ year old: 1.65

16 New York Lakes Yellow Perch: 

0.01 - 0.64

Punkin Seed: 

0.01 - 0.19

42 New Jersey Lakes and Rivers, averages of 9pH categories 0.15-1.45 0.15-1.16 0.07-0.72 0.10-0.32 0Historical trends in mean fish concentrations in 1930s (museum): 0.08-0.29 1930s: 0.08-0.20 NE Minnesota Lakes 1980s: 0.12-0.37 1980s: 0.07-0.73Mean concentrations in 65 northern MN lakes 0.14-1.52 0.13-1.75 Ashtabula River, OH; Means LMouth: 0.15 Carp: 0.05 Bluegill:0.07-0.17 

Smouth: 0.12

Saginaw River, MI; Means Fillets: .12 Carp: 0.07 Northern Michigan Lakes 0.10-1.64 0.11-1.00 Large fish above dams in 3 Michigan rivers 0.11 - 0.28 < 0.05 to 0.72 < 0.05 to 0.23 < 0.05 to 0.73 0Mean Concentrations (and ranges) in Fish from 0.28 (0.25-0.31) 0.29 (0.20-0.37) S. Mouth: 0.25 (0.12-0.43) Channel Cat: 0.39 (0.20- the St. Louis River, MN Rock: 0.35 (0.14-0.61) 0.74)

Red H. Sucker: 0.41

(0.32-0.55)

W. Sucker: 0.27 (0.12-

0.37) 

Historical trends in Onondaga lake Smallmouth 1970: 1.96 1975: 1.09 1979: 0.68 1984: 1.04 1987: 1.75 Bass, Syracuse, NY 1972: 1.26 1977: 0.87 1981: 1.23 1985: 1.20 1988: 1.43

1974: 0.81 1978: 0.68 1983: 1.08 1986: 1.05 1989: 1.71

38 Wisconsin Lakes 0.16-1.74 34 Northern Wisconsin Lakes 0.19 - 0.999 53 Florida lakes 0.04-1.90 
Table 3-17 (continued)

Measured Mercury Concentrations Freshwater Sportfish (Total Mercury, g/g wet wt.)

Study Pike/Pickerel Walleye Bass Bottom Feeders Panfish 

3-21 

Florida Surface Waters Lakes: 0.07 - 0.85 

Streams: 0.22 - 2.37

Mean Concentrations (and ranges) in Maine 0.92 (0.58-1.22) LMouth: 0.57 (0.26-0.95) Yellow Perch: Brook: 0

Predatory Fishes SMouth: 0.67 (0.31-1.12) 0.28 (0.18-0.81) Brown: St. Lawrence River Drainage at Massena, NY. 0.21-0.97 0.24-0.93 SMouth: 0.37-0.71 W. Sucker: 0.12-0.55 Punkinseed: 0.10-0.35 Rainb

Rock: 0.34-0.76 B. Bullhead: 0.08-0.32 Y. Perch: 0.18-0.59

9 Canadian Shield Lakes 0

Ontario Lakes 0.07-1.28 0.09-3.24 
3-22

Table 3-18

Measured Mercury Concentrations in Saltwater Commercial Fish and Shellfish (ug/g wet wt.)

Fish

Mean Hg-tot

U.S. EPA Cramer (1992) USDOC (1978) References

(1992c)

Cod 0.03 0.13

Canned Tuna 0.17 0.24

Fish Sticks 0.21

Shrimp 0.18 0.46

Crabs/Lobsters 0.03-0.08 0.25

Salmon 0.05-0.32 .005

Flounder 0.03 0.06

Clams 0.02 0.05

Boston Mackerel (2 samples) 0.03-0.05 NJDEPE (1994)

Porgy (3 samples) 0.08-0.14 NJDEPE (1994)

Spot (5 samples) 0.02-0.06 NJDEPE (1994)

Scallops 0.05 NOAA (1978)

Ocean fish are an important source of mercury exposure. Although these fish appear to have

lower mercury concentrations, humans typically consume higher quantities of these types of fish. 

Wildlife, depending on location, also may typically consume ocean fish species. Data on mercury

concentrations in marine finfish obtained from the National Marine Fisheries Service are summarized in

Table 3-19. Tables 3-20 and 3-21 sumarize data from the National Marine Fisheries Service on mercury

concentrations in marine shellfish and molluscan cephalopods.
3-23

Table 3-19

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

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 g/g). 1

 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 g/g). 3

 The mercury content for flounder is the average of the mean concentrations measured in 9 types of flounder:Gulf (0.147 g/g), summer (0.127 4

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
3-24

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), Pacific (0.091 5

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, and Pacific. 6

 The mercury content for Herring is the average of the mean concentrations measured in 4 types of Herring: blueback (0.0 g/g), Atlantic (0.012 7

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 Northern. 8

 The mercury content for Mackerel is the average of the mean concentrations measured in 3 types of Mackerel: jack (0.138 g/g), chub (0.081 9

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) and silver (0.007 10

g/g).

 The mercury content for Ocean Perch is the average of the mean concentrations measured in 2 types of Ocean Perch: Pacific (0.083 g/g) and 11

Redfish (0.149 g/g)

 The mercury content for Salmon is the average of the mean concentrations measured in 5 types of Salmon: pink (0.019 g/g), chum (0.030 12

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 g/g), 14

(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 g/g), little skate 15

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 (0.218 g/g) and 17

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 g/g), skipjack tuna 18

(0.136 g/g) and yellowfin tuna (0.218 g/g)

`

Table 3-20

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 (0.011 g/g) and 1

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 (0.034 g/g), 2

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), dungeness crab 3

(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) lobster (0.108 4

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 g/g) and 5

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 (0.101 g/g), 6

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 (0.074 g/g), 7

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).
3-25

Table 3-21

Mercury Concentrations in Marine Molluscan Cephalopods

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)

The New York State Department of Environmental Conservation has investigated chemical

residues found in a wide variety of edible aquatic organisms from the New York-New Jersey Harbor

Estuary. In 1993, 23 fish species, six bivalve species, two crustacean species and one cephalopod

species were collected from sixe areas of the Harbor estuary (Skinner et al. 1996). Average total

mercury concentrations in these samples did not equal or exceed 1 μg/g-wet weight for any species;

however, two individual striped bass samples did exceed 1 μg/g-wet weight (1.046 and 1.252 μg/g-wet

weight). An average mercury concentration exceeding 0.5 μg/g-wet weight was found only in striped

bass larger than 762 mm, and an average mercury concentration exceeding 0.25 μg/g-wet weight was

found in striped bass and tautog. In striped bass, there was a significant (p<0.05) correlation between

size (i.e., age) and mercury concentration (Skinner et al. 1996). Non-detectable mercury concentrations

were most prevalent in the six bivalve species, butterfish, winter flounder, the hakes and American eel. 

Mercury concentrations in fish, bivalves, crustaceans and cephalopods exhibited no consistent spatial

variations within the harbor estuary (Skinner et al. 1996). The data are presented in Table 3-22.
3-26

Table 3-22

Mercury Concentrations in Biota from the New York-New Jersey Harbor estuary (1993)

Species

Mercury Concentration (μg/g-wet weight): Mean ± Standard Deviation

Upper Bay East River The Kills Jamaica Bay Lower Bay New York All Areas

Area1 Area 2 Area 3 Area 4 Area 5 Bight Apex

Area 6 Areas 1-6

Fish

American eel 0.202±0.115 0.025 0.338±0.208 0.059±0.047 0.260±0.231 NA 0.167±0.178

Atlantic herring 0.127±0.049 NA NA NA 0.170 76±52 0.119±0.059

Atlantic tomcod 0.059±0.059 0.119±0.046 0.283±0.138 NA NA NA 0.154±0.128

Bluefish 0.126±0.024 NA 0.527 0.130±0.025 NA 0.055 0.151±0.112

<305 mm

305-559 mm NA 0.095 NA 0.151±0.097 0.184±0.049 0.242±0.081 0.183±0.083

559 mm 0.414±0.182 0.338±0.066 NA 0.276±0.033 0.162 0.233±0.076 0.312±0.122

Butterfish 0.073±0.049 0.025 0.043±0.031 NA 0.035±0.017 0.025 0.041±0.030

Cuner 0.256±0.036 0.185±0.083 0.209±0.061 0.275±0.133 0.164±0.011 0.389±0.166 0.246±0.112

Kingfish 0.056 NA NA 0.038±0.023 NA 0.025 0.039±0.020

Northern sea 0.025 NA NA 0.141 NA 0.196 0.139±0.111

robin

Porgy NA 0.071 0.060 0.094±0.040 0.044±0.027 0.037±0.016 0.060±0.035

Rainbow smelt 0.106±0.034 NA NA NA NA NA 0.106±0.34

Red hake NA NA NA NA NA 0.078±0.065 0.078±0.065

Sea bass 0.151 NA NA NA NA 0.097±0.041 0.106±0.043

Silver hake NA NA NA NA NA 0.025 0.025

Spot 0.042±0.029 NA 0.025 NA 0.045±0.035 0.025 0.039±0.026

Spotted hake 0.106±0.026 NA 0.077±0.046 NA 0.044 0.025 0.065±0.042

Striped bass 0.163±0.076 0.295±0.211 0.377±0.130 0.129±0.068 NA NA 0.258±0.160

<457 mm

457-610 mm 0.289±0.122 0.391±0.199 0.325±0.110 0.198±0.050 0.340±0.169 0.150±0.054 0.284±0.150

610-762 mm 0.435±0.359 0.648±0.368 NA 0.299±0.130 0.242±0.163 0.241±0.021 0.389±0.288

762 mm 0.534±0.104 0.524±0.296 NA 0.578 0.437 0.572 0.528±0.261

Striped sea robin NA NA NA 0.209 0.105±0.072 0.357 0.176±0.122

Summer 0.094±0.039 NA NA 0.125±0.038 0.122±0.076 0.065±0.016 0.102±0.050

flounder

Tautog 0.337±0.319 0.353±0.120 NA 0.203±0.087 0.133±0.044 0.320±0.307 0.267±0.197

Weakfish 0.250±0.209 NA 0.177±0.078 0.096 0.154±0.08 0.025 0.167±0.120
Species

Mercury Concentration (μg/g-wet weight): Mean ± Standard Deviation

Upper Bay East River The Kills Jamaica Bay Lower Bay New York All Areas

Area1 Area 2 Area 3 Area 4 Area 5 Bight Apex

Area 6 Areas 1-6

3-27

White perch 0.207±0.054 0.281±0.11 0.397±0.179 0.025 NA NA 0.230±0.172

Windowpane 0.127±0.062 NA 0.105±0.04 NA 0.124±0.047 0.093±0.017 0.112±0.041

flounder

Winter flounder 0.070±0.068 0.033±0.016 0.035±0.018 0.092±0.022 0.056±0.034 0.036±0.019 0.061±0.04

Bivalves

Blue mussel NA 0.025 NA 0.064±0.041 0.025 0.025 0.036±0.026

Eastern oyster 0.059 0.025 0.091±0.021 NA 0.025 NA 0.049±0.031

Hard clam NA NA NA 0.025 0.025 NA 0.025

Horse mussel 0.082±0.021 0.051±0.046 0.042±0.029 NA 0.025 NA 0.055±0.034

Softshell clam 0.025 0.071±0.03 0.101±0.011 0.025 0.025 NA 0.052±0.035

Surf clam NA NA NA NA Na 0.025 0.025

Crustaceans

American NA NA NA NA 0.246 0.162±0.06 0.170±0.062

lobster

-muscle

-hepatopancreas NA NA NA NA 0.068 0.061±0.029 0.062±0.028

Blue crab 0.112±0.068 0.134±0.087 0.199±0.068 0.132±0.148 0.253±0.169 NA 0.166±0.119

-muscle

-hepatopancreas 0.074±0.035 0.140±0.149 0.084±0.068 0.066±0.044 0.100±0.031 NA 0.093±0.077

Cephalopod

Longfin squid NA NA NA NA 0.096±0.075 0.035±0.017 0.065±0.059

The detection limit was 0.050 μg/g; a reported value of 0.025 μg/g represents one-half the detection limit.

NA = No samples available or none analyzed

By comparing the mercury concentration in fish with concentrations in other biota (Tables 3-23

through 3-24), it is noted that fish appear to have the highest concentrations of methylmercury in the

environment.

The little recent data available on mercury in meat products show concentrations to be very low

(near the detection limits) for both Hg(II) and methylmercury. It is not thought that meat consumption is

a major concern with regards to mercury exposure, especially in comparison to concentration in fish

tissues. Surprisingly few data, however, are available on meat mercury levels.

Plant mercury levels are generally very low and of little concern, as with meats (see Tables 3-25

and 3-26). Levels tend to be highest in leafy vegetables, and plants grown in mercury contaminated

conditions (in air and/or soil) do accumulate more mercury than plants in background areas. There are

no other noticeable trends in plant concentrations, with mercury levels varying widely among plants and

studies. For further information, see plant BCFs in Appendix B.
3-28

Table 3-23

Measured Mercury Concentration in Meats

Study Description Total Mercury Approx. Total % Methyl- Reference

(ng/g wet weight) Mercury (ng/g mercury

dry weight)1

6 Saginaw River, MI "roaster" ducks 48 124.7 NA U.S. EPA (1992b)

Japan background levels

Chicken 12 31.2 NA Shitara and Yasumasa

(1976)

Beef 5 13.0 NA Shitara and Yasumasa

(1976)

Pork 21 54.5 NA Shitara and Yasumasa

(1976)

Wild Deer (Northern Wisconsin) 5-14 13 -36 11-57 % Bloom and Kuhn (1994)

Beef

Raw < 1 < 2.6 > 10% Bloom and Kuhn (1994)

Lunch Meat 21 54.5 4% Bloom and Kuhn (1994)

Frank <1 < 2.6 > 60% Bloom and Kuhn (1994)

Beef Muscle - Control group 2-3 5.2 - 7.8 NA Vreman et al. (1986)*

Beef Muscle - Exposed group 1-4 2.6 - 10.4 NA Vreman et al. (1986)*

Beef Liver - Control group 3000 - 7000 7800 - 18000 NA Vreman et al. (1986)*

Beef Liver - Exposed group 9000 - 26000 23400- 67000 NA Vreman et al. (1986)*

Pork (raw and sausage) < 1 < 2.6 0-70% Bloom and Kuhn (1994)

Chicken (raw and lunch meat) < 1 to 29 < 2.6 to 75.4 20-67% Bloom and Kuhn (1994)

Turkey (lunch meat) < 1 < 2.6 >20% Bloom and Kuhn (1994)

* See Appendix A for a more complete discussion of this study.

 Based on an assumed water content of 0.615, which is average for beef (Baes et al., 1984) 1
3-29

Table 3-24

Measured Mercury Concentrations in Garden Produce/Crops

Study Description Total Methyl- % Reference

Mercury mercury Methyl-

(ng/g dry mercury

weight)

NY Garden conditions: Leafy vegetables 64-139 9.5-30 15-23 Cappon (1987)

NY Garden conditions: Tuberous plants 11-36 0.3-6.6 11-36 Cappon (1987)

NY Garden conditions: Cole 50-64 8.8-12 18 Cappon (1987)

NY Garden conditions: Fruiting vegetables 2.9-27 0-2.4 0-9.1 Cappon (1987)

NY Garden conditions: Beans 4.3 0 0 Cappon (1987)

Herbs; Garden samples from Belgium 130 Temmerman et al. (1986)

background

a

N.B. No Hg was detected in plants (Cappon, 1987). 0

Conversion to dry wt. assuming 90% water by wt. a

Table 3-25

Mean Background Total Mercury Levels for Plants in the Netherlands

(Wiersma et al., 1986)

Plant Concentration Content (from Baes et Concentration

Total Mercury Approximate Water Total Mercury

(ng/g wet weight) al., 1984) (ng/g dry weight)

Lettuce, greenhouse 2 0.948 38.5

Tomato, greenhouse 1.3 0.941 22.0

Cucumber, greenhouse 0.3 0.961 7.7

Spinach 5 0.927 68.5

Carrot 2 0.882 16.9

Potato 3 0.778 13.5

Wheat 5 0.125 5.7

Barley 6 0.111 6.7

Oats 8 0.083 8.7

Apples 1 0.841 6.3
3-30

Table 3-26

Range of Mercury Concentrations in Selected Grain Products

Grain product Range (ng/g wet weight) Range (ng/g dry weight) Reference 1

Wheat < 0.1 - 30 < 0.1 - 34

Wiersma et al., (1986) Barley 1 - 30 1.1 - 34

Oats <0.1 - 20 < 0.1 - 22

Maize 1.5 - 6.5 1.7 - 7.3 Szymczak and Grajeta

(1992)

 Calculated assuming water content of 0.112 (Baes et al., 1984). 1

3.3 Measurement Data from Remote Locations

The Long Range Transport Analysis (Chapter 5) focusses on the long range atmospheric

transport of mercury and estimates its impact at remote sites. This type of analysis was selected based on

the atmospheric chemistry of emitted elemental mercury (Petersen et al., 1995) and the numerous studies

linking increased mercury levels in air, soil, sediments, and biota at remote sites to distant anthropogenic

mercury release followed by long-range transport. Details of several of the many studies which

demonstrate the long range transport of mercury follow. These provide evidence to support this

assessment of long-range mercury transport.

3.3.1 Elevated Atmospheric Mercury Concentrations over Remote Locations

Olmez et al. (1994) correlated elevated atmospheric levels of particulate mercury at rural U.S.

sites to long range transport from distant sources. Briefly, Olmez et al. (1994) collected ambient

particulates of two sizes (< 2.5μm and between 2.5μm and 10μm) for two years at five rural sites in New

York State and measured levels of numerous pollutants. Using a pollutant fingerprinting technique, the

collected data were evaluated to identify the pollutant sources. Mercury was considered to be a tracer

pollutant for mixed industry and coal combustion. There were no local anthropogenic mercury sources

at these sites. At the five sites the average sub- 2.5μm particulate mercury concentrations ranged from

0.051 to 0.089 ng/m , and the 90th percentile particulate mercury levels ranged from 0.21 to 0.10 ng/m . 3 3

The highest values reported were 0.63 ng/m . Elevated mercury levels were attributed to long-range 3

transport from industrial sources in Canada as well as parts of New York State and occasionally the

midwest U.S. The authors noted that only 1-10% of the total mercury in remote areas is generally

thought to be found on particles. Preliminary vapor-phase analysis (on samples collected for months)

indicated that the mercury attached to these small particulates accounted for only 1.8% of the total

mercury at these rural sites.

Glass et al. (1991) reported that mercury released from distant sources (up to 2500 km distant)

contribute to mercury levels in rain water deposited on remote sites in northern Minnesota.

3.3.2 Elevated Soil Mercury Concentrations in Locations Remote from Emission Sources

Increased concentrations of mercury have been reported in both remote U.S. (Nater and Grigal,

1992) and Swedish soils (as reviewed in Johansson et al., 1991 and by the Swedish Environmental
3-31

Protection Agency, 1991). These elevated concentrations have been correlated with regional transport

and deposition of mercury to soil. Nater and Grigal (1992) found an increasing mercury gradient from

west to east in soils across the upper midwest U.S. This increase was also found to correlate with

increasing regional industrialization. Briefly, soils were sampled in 155 different forest stands

representing five types of forested stands. Mercury levels were measured in three layers: the surface

detritus, surface soil (0-25 cm) and deep mineral soil (75-100 cm). Increases were observed along the

west-east gradient in the upper two layers. The highest values reported for the detritus layer and the

surface soil layer were >150 ng Hg/g detritus and >200 ng Hg/g soil, respectively. Differences in the

ability of various soil types to bind mercury was discounted as a possible reason for the range of mercury

values. The authors felt that their results implicated regional source contributions. Data summarized in

Johansson et al. (1991) and the Swedish Environmental Protection Agency (1991) indicates that mercury

levels in remote soils of southern Sweden are elevated when compared to those in the north. The

increase observed in the soils of southern Sweden is related to emissions from regional Swedish industry

and East European industry (Hakanson et al., 1990).

3.3.3 Elevated Mercury Concentrations in Aquatic Sediments and Fish from Remote Water Bodies

Elevated mercury levels in remote water body bed sediments have been widely reported and well

characterized in many different parts of the world. These elevated levels are related to increased levels

of atmospheric mercury which have been linked to anthropogenic activities. For example Swain et al.

(1992) showed that, based on the vertical distribution of mercury in sediment, mercury deposition from

the atmosphere over Wisconsin and Minnesota had increased from approximately 3.7 to 12.5 μg/m since 2

1850 causing increases in sediment levels. For similar data from remote Wisconsin lakes, remote lakes

in Ontario (Canada) and from remote Scandinavian bogs see Rada et al. (1989), Evans (1986), and

Jensen and Jensen (1991), respectively. Some of the sediment analysis data for Sweden is presented in

the report on mercury by the Swedish Environmental Protection Agency (1991).

The regional and widespread nature of mercury pollution was first identified when elevated

levels of mercury in fish were discovered. These elevated levels in fish were evidence of the efficient

transfer of mercury from prey to predator through the aquatic food chain (Watras and Bloom, 1992). In

fact, the bioaccumulative nature of the mercury in fish has generated much of the interest in the

measurement of mercury in other environmental media. It should be noted that the data of Hakanson et

al. (1990) indicate that mercury levels in Swedish piscivorous fish continue to increase.

Elevated mercury concentrations in fish, particularly higher trophic level fish (e.g., northern

pike) have been measured at sites distant from anthropogenic sources in Sweden (Hakanson et al., 1988;

Swedish Environmental Protection Agency, 1991) and across the U.S. (e.g., Grieb et al., 1990; Sorensen

et al., 1990 and Weiner et al., 1990). The report by Cunningham et al. (1994) illustrates the widespread

nature of mercury fish advisories across the U.S.

3.4 Measurement Data Near Anthropogenic Sources of Concern

Measured mercury levels in environmental media around a single anthropogenic source are

briefly summarized in this section. These data are not derived from a comprehensive study for mercury

around the sources of interest. Despite the obvious needs for such an effort, such a study does not appear

to exist. The quality of the following studies has not been assessed in this Report. The data do not

appear to be directly comparable among themselves because of differences in analytic techniques and

collection methods used. Finally, some of these studies are dated and may not reflect current mercury

emissions from the sources described below.
3-32

These data collectively indicate that mercury concentrations near these anthropogenic sources

are generally elevated when compared with data collected at greater distances from the sources. 

However, because these data do not conclusively demonstrate or refute a connection between

anthropogenic mercury emissions and elevated environmental levels, a modeling exercise was

undertaken to examine further this possible connection. This exercise is described in Chapters 4,5, and 6

of this document. The conclusions are discussed in Chapter 7. Materials in Appendices A-G support the

modeling effort.

3.4.1 Municipal Waste Combustors

Bache et al. (1991) measured mercury concentrations in grasses located upwind and downwind

from a modular mass-burn municipal waste combustor located in a rural area. The facility reportedly

had no pollution control equipment and had been operating for about seven years when the grasses were

sampled. Mercury levels were measured in air-dried grass samples by the flameless atomic absorption

method developed by Hatch and Ott (1968). The sensitivity and detection limit of the method were not

reported. Mercury levels in grass located downwind (along the prevailing wind direction) from the

stack decreased with distance beginning at 100 m and continuing through 900 m. The highest value

recorded downwind of the facility was 0.2 g mercury/g grass (dry weight) at 100 m. The highest

reported value upwind (225 meters in the opposite direction from the prevailing wind direction) of the

facility was 0.11 μg/g (dry weight). All other upwind values including measurements closer to the

facility were 0.05 μg/g or less.

In response to a Congressional mandate, U.S. EPA assessed the "environmental impact of

municipal waste incineration facilities" (U.S. EPA, 1991). Background levels of mercury were measured

in air, soil, water and biota in the area around an MWC in Vermont. The facility, which had a 50 m

stack, was not yet operational when the initial set of measurements were made. Pollution control

equipment included an electrostatic precipitator (ESP) and a wet scrubber. After the facility had begun

operating, pollutant levels were again measured. After the start-up of operations mercury emissions were

measured at approximately 2 x 10 g/s. Mercury levels above the analytical detection limits or above -4 

background levels were not observed in this analysis. Problems were noted with some of the analytical

equipment used for ambient air monitoring. The MWC was also not operational during some of the time

after start-up, and there was a short time (10 months) between operation start-up and environmental

measurement data collection.

Greenberg et al. (1992) measured mercury levels in rainwater near a rural New Jersey municipal

resource recovery facility (MWC). The measurement protocols developed by Glass et al. (1990) were

employed in the analysis. The 2-stack MWC had a 400-ton/day capacity, and pollution control included

a dry fabric filter. The maximum allowable mercury emissions were 0.05 pounds/hour/stack (22.7

grams/hour/stack). During one collection period, state-mandated stack testing indicated that the facility

was emitting mercury at levels slightly lower than the maximum allowable emission rates. Rain water

was collected and analyzed on three separate 2-day time periods; the facility was not operating during

one collection period. Collection sites were generally located in the prevailing wind directions. Mercury

concentrations in rain water appeared to be elevated near the facility in the prevailing wind directions

when compared with measurements taken when the facility was not operating and with measurements at

more remote sites (>2 km). Mercury concentrations in rain water measured up to 2 km from the facility

while it was not operating exhibited a range of 26 - 62 ng mercury/L rainwater (26-62 ppt). Mercury

measurements at sites 3 - 5 km downwind did not exceed 63 ng mercury/L rain water. During facility

operation the highest measured mercury concentration was 606 ng/L. The measurement was taken 2 km

in the prevailing wind direction. Several other measurements of greater than 100 ng mercury/L rain

water were also collected within 2 km of the facility.
3-33

Carpi et al. (1994) measured mercury levels in moss and grass samples around a MWC in rural

New Jersey (same facility as Greenberg et al., 1992 studied). Pollution control equipment on the MWC

reportedly included a spray dryer and a fabric filter. Samples were collected at sites up to 5 km from the

source and mercury levels measured by a cold vapor atomic absorption spectroscopy method described

in U.S. EPA (1991). Statistically significant elevations in mercury concentrations were measured in

moss samples located within 1.7 km of the facility with the highest mercury measured levels exceeding

240 parts per billion (ppb). Oven-dried moss samples had lower levels of mercury than those samples

that were not oven-dried. This was attributed to the loss of volatile mercury species during drying. The

decrease in total mercury was most notable in moss samples at more distant sites (beyond 2 km from the

facility). The authors felt that this might indicate the uptake and retention of different species during

drying. The results of the analysis of grass samples were not presented. They were termed

"inconclusive" in that they did not appear to exhibit point source influence.

3.4.2 Chlor-Alkali Plants

Temple and Linzon (1977) sampled the mercury content of foliage, soil, fresh fruits, vegetables

and snow around a large chlor-alkali plant in an urban-residential area. This facility produced 160 tons

of chlorine/day, resulting in approximately 0.8 kg/day of mercury emissions. Resulting mercury

concentrations were compared to background levels from an urban area 16 km to the west. Mercury

levels averaged 15 g/g (300 times the background level of 0.05 g/g) in maple foliage up to 260 m

downwind, and concentrations 10 times background were found 1.8 km downwind. Mercury levels in

soil averaged 3 g/g (75 times the background level of 0.04 g/g) within 300 m of the plant, and soil

concentrations averaged 6 times background 1.8 km downwind. The mercury levels in snow ranged

from 0.9-16 g/L within 500 m of the plant dropping to 0.10 g/l 3 km downwind. The background

level was found to average 0.03 g/L. Leafy crops were found to accumulate the highest mercury among

garden produce. One lettuce sample contained 99 ng/g (wet w.) of mercury (background: <0.6 ng/g),

and a sample of beet greens contained 37 ng/g (wet w.) (background: 3 ng/g). Tomatoes and cucumbers

within 400 m averaged 2 and 4.5 ng/g (wet w.) of mercury. Background levels in each case measured 1

ng/g.

In one of the earliest reports which measured mercury levels around an industrial emission

source, Jernelov and Wallin (1973) found elevated levels of mercury in the snow around five chlor-alkali

facilities in Sweden. As distance from the facility increased, the amount of mercury detected decreased. 

They linked the elevated levels to source emissions.

Tamura et al. (1985) measured mercury concentrations in plant leaves and humus from areas

with and without mercury emission sources in Japan. Data on total mercury concentrations were

determined by cold flameless atomic absorption. Mercury concentrations were determined at four sites

within 2 km of a currently operating chlor-alkali electrolysis plant. This facility was estimated to release

10-20 kg of mercury per year. Mercury concentrations at the four sites near this area ranged from 0.04-

0.71 g/g in woody plant leaves, 0.05-0.59 g/g in herbaceous plants, and 0.11-2.74 g/g in humus. In

contrast, mercury levels for identical species of plants in the uncontaminated area (three sites) ranged

from 0.02-0.07 g/g in woody plant leaves, 0.02-0.08 g/g in herbs, and 0.02-0.59 g/g in humus. 

Values are typically on the order of 5-10 times less than mercury levels from the contaminated area,

showing significant mercury contamination of plant biota can result from local point sources.

3.4.3 Coal-Fired Utilities

Crockett and Kinnison (1979) sampled the arid soils around a 2,150 megawatt (MW) coal-fired

power plant in New Mexico in 1974. The four stack (two stacks 76 m high and two 91 m high) facility
3-34

had been operational since 1963 with an estimated mercury release rate of 850 kg/year. The rainfall in

the area averaged 15-20 cm/year. Although a mercury distribution pattern was noted, soil mercury levels

near the facility did not differ significantly from background. Given the high amounts of mercury

released by the facility and the insignificant amounts detected, the authors speculated that much of the

mercury emitted was transported over a large area, rather than depositing locally.

Anderson and Smith (1977) measured mercury levels in environmental media and biota around a

200 MW coal-fired power plant in Illinois. The facility used two 152 m high smokestacks and was

equipped with an electrostatic precipitator. Commercial operations at the facility had been ongoing for 6

years when sampling was conducted (from 1973 through 1974). Elevated levels of mercury detected in

atmospheric particulate samples collected 4.8 and 9.6 km downwind of the facility were not statistically

significant when compared with samples collected 4.8 km upwind of the site. Elevated mercury levels

detected in samples from the upper 2 cm of downwind agricultural soils (sample mean 0.022 ug/g

mercury) were statistically significantly elevated when compared with upwind samples (0.015 ug/g

mercury). Core sediment sampling from a nearby lakebed showed statistically significant elevations in

sediment mercury concentrations after plant operations began (sample mean 0.049 ug/g mercury) when

compared with sediment deposits prior to operation (0.037 ug/g mercury). No increases were observed

in mercury levels in fish from the nearby lake when compared with fish from remote lakes. Mercury

levels in local duck muscle samples and aquatic plant samples were also reported but not compared to

background or data from remote areas.

3.4.4 Mercury Mines

Lindberg et al. (1979) compared soil concentrations and plant uptake of mercury in samples

taken one Km west of a mine/smelter operation in Almaden, Spain, to levels found in control soils (20

Km east of the smelter). The most significant mercury release from the Almaden complex was from the

ore roaster via a 30 m high stack; however, estimates of annual mercury releases were unavailable. Mine

soils contained 97 g/g of mercury compared to the control soil level of 2.3 g/g, a 40 fold increase. 

Alfalfa was grown on these soils under controlled conditions. Comparing plant mercury concentrations

(grown under conditions of no fertilizer or lime treatment), the above ground parts of alfalfa contained

1.4 and 2.3 g/g of mercury in the control and mine soils, respectively. The roots of alfalfa contained

0.53 and 9.8 g/g of mercury in the control and mine soils, respectively. The control levels in this

experiment were found to exceed the worldwide average for grass crops by about 10 times; perhaps not

surprising, since the control soil mercury content is also quite high. Nevertheless, additional mercury

from the mine was found to elevate mercury content in surrounding soil and plant material significantly.

3.4.5 Mercury Near Multiple Local Sources

There are two recent reports of atmospheric mercury measurements in the vicinity of multiple

anthropogenic emissions sources. Both are of studies are of short duration but show elevated mercury

concentrations in the local atmosphere or locally collected rain.

Dvonch et al., (1994) conducted a 4-site, 20 day mercury study during August and September of

1993 in Broward County, FL. This county contains the city of Ft. Lauderdale as well as an oil-fired

utility boiler and a municipal waste combustion facility. One of the sample collection sites (site 4) was

located 300 m southwest of the municipal waste combustion facility. Daily measurements of

atmospheric particulate and vapor-phase mercury were collected at 3 of the 4 sites; (daily atmospheric

concentrations were not collected at the site near the municipal waste combustor (site 4)), and daily

precipitation samples were collected at all sites. As shown in Table 3-27, the average vapor and

particulate phase atmospheric mercury concentrations were higher at the inland sites than at the site near
3-35

the Atlantic Ocean, which was considered by the authors to represent background site. Diurnal

variations were also noted; elevated concentrations were measured at night. For example at site 2, an

inland site, the average nighttime vapor-phase concentration was 4.5 ng/m . This was attributed to little 3

vertical mixing and lower mixing heights that occur in this area at night. Particulate mercury comprised

less than 5% of the total (vaporous + particulate) atmospheric mercury. Mercury concentrations in

precipitation samples at the 4 sites were variable; the highest mean concentrations were measured at the

inland sites. Given the high levels of precipitation in this area of the U.S. and short collection period, it

is not appropriate to extend these analysis beyond the time frame measured. These mercury

concentrations are nonetheless elevated.

Table 3-27

Mercury Concentrations in the Atmosphere and Mercury Measured in Rainwater Collected in

Broward County, FL

Site Description Avg. Vapor-phase Avg. Particulate Avg. Total Mercury Avg. Reactive

Mercury Conc., Mercury Conc. pg/m conc. in rain, ng/L Mercury conc. in

ng/m (Range) rain, ng/L (Range) 3

3

Background Near 1.8 34 35 (15-56) 1.0 (0.5-1.4)

Atlantic Ocean (Site 1)

Inland (Site 2) 3.3 51 40 (15-73) 1.9 (0.8-3.3)

Inland (Site 3) 2.8 49 46 (14-130) 2.0 (1.0-3.2)

Inland (Site 4), 300 m - - 57 (43-81) 2.5 (1.7-3.7)

from MWC

Keeler et., al. (1994) and Lamborg et al., (1994) reported results of a 10-day atmospheric

mercury measurement at 2 sites (labeled as sites A and B) in Detroit, MI (see Table 3-28). There is a

large MWC 9 Km from site A and a sludge combustor 5 Km from site B. It should be noted that other

mercury emission sources such as coal-fired utility boiler and steel manufacturing occur in the city as

well. The vapor-phase mercury concentration encountered at site B during the first days of the

experiment exceeded the capacity of the measurement device. Subsequent analyses indicated that the

concentrations of mercury encountered were significantly higher than other reported U.S. observations.

Table 3-28

Mercury Concentrations Measured at Two Sites in the Atmosphere Over Detroit, MI

Site Mean Vapor-Phase Mercury Mean Particulate-Phase Mercury

Concentrations in, ng/m (Maximum Concentrations in pg/m , (Maximum 3

Measured Value) Measure Value)

3

Detroit, MI Site A >40.8, (>74) 341 (1086)

Detroit, MI Site B 3.7, (8.5) 297 (1230)
4-1

4. MODEL FRAMEWORK

This section describes the models and modeling scenarios used to predict the environmental fate

of mercury. Measured mercury concentrations in environmental media were used when available to

parameterize these models. Human and wildlife exposures to mercury were predicted based on modeling

results.

4.1 Models Used

The extant measured mercury data alone were judged insufficient for a national assessment of 

mercury exposure for humans and wildlife. Thus, the decision was made to model the mercury emissions

data from the stacks of combustion sources. In this study, there were three major types of modeling

efforts: (1) modeling of mercury atmospheric transport on a regional basis; (2) modeling of mercury

atmospheric transport on a local scale (within 50 km of source); and (3) modeling of mercury fate in

soils and water bodies into biota, as well as the resulting exposures to human and selected wildlife

species. The models used for these aspects of this study are described in Table 4-1.

Table 4-1

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.

ISC3 Predicts annual average atmospheric concentrations and deposition

fluxes within 50 km of mercury emission source

IEM-2M Predicts environmental mercury concentrations based on air

concentrations and deposition rates to watershed and water body.

4.2 Modeling of Long-Range Fate and Transport of Mercury

4.2.1 Objectives

The goal of this analysis was to model the emission, transport, and fate of airborne mercury over

the continental U.S. using the meteorologic data for the year of 1989 and the most current emissions

data. The results of the simulation were intended to be used to answer a number of fundamental

questions. Probably the most general question was "How much mercury is emitted to the air annually

over the United States, and how much of that is then deposited back to U.S. soils and water bodies over a

typical year?" It is known that year-to-year variations in accumulated precipitation and wind flow

patterns affect the observed quantity of mercury deposited to the surface at any given location. 

Meteorological data for the year of 1989 was used since most of the continental U.S. experienced near
4-2

normal average weather conditions during that year. A secondary question was that of the contribution

by source category to the total amount of mercury emitted and the amount deposited within the U.S. In

order to answer the questions about the source relative depositions, information on chemical and physical

forms of the mercury emissions from the various source categories was needed since these characteristics

determine the rate and location of the wet and dry deposition processes for mercury.

The intent of the analysis was to determine which geographical areas of the United States have

the highest and lowest amounts of deposition from sources using the overall results of the long-range

transport modeling effort nation-wide. This analysis was expected to contribute understanding of the key

variables, such as source location, chemical/physical form of emission, or meteorology, that might

contribute to the outcomes. These long-range modeling efforts were also intended to be used for

comparison with local impact modeling results, essentially to estimate the effects of hypothetical new

local sources in relation to the estimated effects from long-range transport.

4.2.2 Estimating Impacts from Regional Anthropogenic Sources of Mercury

The impact of mercury emissions from stationary, anthropogenic U.S. sources is not entirely

limited to the local area around the facility. To account for impacts of mercury emitted from many of

these other non-local sources on the area around a specific source, the long-range transport of mercury

from all selected sources has been modeled using the RELMAP (Regional Lagrangian Model of Air

Pollution) model. The RELMAP model was used to predict the average annual atmospheric mercury

concentration and the wet and dry deposition flux for each 1⁄2 degree longitude by  degree latitude grid

cell (approximately 40 km square) in the continental U.S. The emission, transport, and fate of airborne

mercury over the continental U.S. was modeled using meteorological data for the year of 1989. Over

10,000 mercury emitting cells within the U.S. were addressed; the emission data used were those

presented in Volume II, Inventory of Anthropogenic Mercury Emissions.

The RELMAP model was originally developed to estimate concentrations of sulfur and sulfur

compounds in the atmosphere and rainwater in the eastern U.S. The primary modification of RELMAP

for this study was the handling of three species of mercury (elemental, divalent, and particulate) and

carbon soot (or total carbon aerosol). Carbon soot was included as a modeled pollutant because carbon

soot concentrations are important in the modeling estimates of the wet deposition of elemental mercury

(Iverfeldt, 1991; Brosset and Lord, 1991; Lindqvist et al., 1991).

4.2.3 Description of the RELMAP Mercury Model

Previous versions of the RELMAP are described and evaluated in Eder et al. (1986) and Clark et

al. (1992) and a separate description of the initial development of the RELMAP mercury modeling is

provided in Bullock et al. (1997a). Modifications to the RELMAP for atmospheric mercury simulation

were heavily based on recent Lagrangian model developments in Europe (Petersen et al., 1995). The

mercury version of the RELMAP was developed to handle three species of mercury: elemental vapor

(Hg ), divalent vapor (the mercuric ion, Hg ) and particulate Hg (Hg ), and also aerosol carbon soot. 0 2+

P

Recent experimental work indicates that ozone (Munthe, 1992) and carbon soot (Iverfeldt, 1991; Brosset

and Lord, 1991; Linqvist et al., 1991) are both important in determining the wet deposition of Hg . 0

Carbon soot, or total carbon aerosol, was included as a modeled pollutant in the mercury version of

RELMAP to provide necessary information for the Hg wet deposition parameterization. Observed 0

ozone (O ) air concentration data for the simulation period were obtained from EPA's Aerometric 3

Information Retrieval System (AIRS) data base. Thus, it was not necessary to include O as an explicitly 3

modeled pollutant. Methyl mercury was not included in the mercury version of RELMAP because it is
4-3

not yet known if it has a primary natural or anthropogenic source, or if it is produced in the atmosphere. 

Unless specified otherwise in the following sections, the modeling concepts and parameterizations

described in the EPA users' guide (Eder et al., 1986) were preserved for the RELMAP mercury modeling

study.

4.2.3.1 Physical Model Structure

RELMAP simulations were originally limited to the area bounded by 25 and 55 degrees north

latitude and 60 and 105 degrees west longitude, and had a minimum spatial resolution of 1 degree in both

latitude and longitude. For this study, the western limit of the RELMAP modeling domain was moved

out to 130 degrees west longitude, and the modeling grid resolution was reduced to 1⁄2 degree longitude

by  degree latitude (approximately 40 km square) to provide high-resolution coverage over the entire

continental U.S.

The original 3-layer puff structure of the RELMAP has been replaced by a 4-layer structure. The

following model layer definitions were used for the RELMAP mercury simulations to account for the

development of deeper nocturnal inversion layers during autumn and winter and higher convective

mixing heights in the spring and summer:

Layer 1 top - 30 to 50 meters above the surface (season-dependent)

Layer 2 top - 200 meters above the surface

Layer 3 top - 700 meters above the surface

Layer 4 top - 700 to 1500 meters above the surface (month-dependent)

4.2.3.2 Mercury Emissions

Area source emissions were introduced into the model in the lowest layer. Point source

emissions were introduced into model layer 2 to account for the effective stack height of the point source

type in question. Effective stack height is the actual stack height plus the estimated plume rise. The

layer of emission is inconsequential during the daytime when complete vertical mixing is imposed

throughout the 4 layers. At night, since there is no vertical mixing, area source emissions to layer 1 are

subject to dry deposition while point source emissions to layer 2 are not. Large industrial emission

sources and sources with very hot stack emissions tend to have a larger plume rise, and their effective

stack heights might actually be larger than the top of layer 2. Since, however the layers of the pollutant

puffs remain vertically aligned during advection, the only significant process effected by the layer of

emission is nighttime dry deposition. 

Mercury emissions data were grouped into eleven different point-source types and a general

area-source type. The area source emissions data describe those sources that are too small to be

accounted for individually in pollutant emission surveys. For the RELMAP mercury modeling study,

area sources were assumed to emit mercury entirely in the form of Hg gas, while ten of the point source 0

types were each assigned mercury speciation profiles based on previous European research (Petersen et

al., 1995) and the results of stack testing at a medical waste incinerator in Dade County, Florida (Stevens

et al., 1996). These speciation profiles defined the estimated fraction of mercury emitted as Hg , Hg , 0 2+

and Hg . For medical waste incinerator speciation estimates, it was assumed that one-quarter of the Hg P

2+

emissions measured in the hot stack exhaust would quickly convert to Hg form upon cooling and P

dilution in ambient air. Municipal waste combustors and medical waste incinerators were further

grouped by the types of air pollution control devices (APCDs) indicated for each plant in the inventory

and separate speciation profiles were assumed for each group based on the assumption that Hg and Hg 2+

P
4-4

are preferentially extracted from the waste stream and that Hg is extracted only after all Hg and Hg is 0 2+

P

removed. The total mercury extraction efficiency for each APCD configuration was based on

information presented in Volume II, Inventory of Anthropogenic Mercury Emissions. The mercury

emissions inventory for hazardous waste combustors included estimates of the emission speciation for

each plant and no general assumption of speciation profile was required. 

There remains considerable uncertainty as to the actual speciation factors for each point source

type. A wide variety of alternate emission speciations have been simulated for important groups of

atmospheric mercury sources in order to test the sensitivity of the RELMAP results to the speciation

profiles used (Bullock et al., 1997b). This work showed that the RELMAP modeling results are very

strongly dependent on the assumed emission speciations. The emission speciation profiles used for this

study are shown in Table 4-2. The total (non-speciated) mercury emissions inventory used is that

described in Volume II of this Report. Figures 4-1, 4-2 and 4-3 show the total mercury emissions from

all anthropogenic sources in the form of Hg , Hg and Hg , respectively. Speciated data derived from 0 2+

P

actual monitoring of sources are a critical research need. These data are needed to establish a clear

causal link between mercury originating from anthropogenic sources and mercury concentrations

(projected or actual) in environmental media and/or biota.

Global-scale natural emissions, recycled anthropogenic emissions and current emissions from

anthropogenic sources outside the RELMAP model domain were accounted for by superimposing an

ambient atmospheric concentration of Hg gas of 1.6 ng/m . This use of a constant background 0 3

concentration to account for global-scale and external anthropogenic emissions is the same technique

used by Petersen et al. (1995). Functional limitations of Lagrangian pollutant parcel modeling prevent

any explicit treatment of emission sources located outside the spatial domain of the RELMAP as no

external starting point for parcel trajectories can be defined. Natural and recycled emissions from soils

and water bodies within the model domain cannot be treated explicitly due to the number of simulated

pollutant parcels that would originate from all locations. Even if these natural and recycled parcels were

explicitly modeled, the prevailing west-to-east atmospheric flow of the mid-latitude northern hemisphere

would produce an artificial west-to-east gradient in the simulated effects from these pollutant parcels. 

The deposition parameterizations described in section 4.2.4.1 were used to simulate the scavenging of

Hg from the constant background concentration throughout the entire RELMAP model domain. The 0

result was used as an estimate of the deposition of mercury from all natural sources and anthropogenic

sources outside the model domain.
4-5

Table 4-2

Mercury Emissions Inventory Used in the RELMAP Modeling

Mercury Emission Source Type Emissions

(kg/yr)

Speciation Percentages

Hg Hg Hg 0 a 2+ b

P

c

Electric Utility Boilers (coal, oil and gas) 46,183 50 30 20

Municipal Waste 50% Control 9,099 40 45 15

Combustors 85% Control 219 100 0 0

Standard 17,393 20 60 20

Total 26,711

Commercial and Industrial Boilers 25,650 50 30 20

Medical Waste Incinerators 94% Control 1,365 33 50 17

Standard 13,177 2 73 25

Total 14,542

Chlor-Alkali Factories 6,482 70 30 0

Hazardous Waste Incinerators 6,435 58 20 22 

Portland Cement Manufacturing 4,355 80 10 10

Residential Boilers 3,244 50 30 20

Pulp and Paper Plants 1,651 50 30 20

Sewage Sludge Incinerators 799 20 60 20

Other Point Sources 3,072 80 10 10

Area Sources 2,721 100 0 0

 Hg represents elemental mercury gas a 0

 Hg represents divalent mercury gas b 2+

 Hg represents particulate mercury c

P

 The inventory included emissions speciation for each plant. Speciation percentages shown are cumulative for all 

hazardous waste incinerators in the inventory.


4-8

Figure 4-3

Hg(p) Emissions from All Anthropogenic Sources (Base)

4-9

4.2.3.3 Carbon Aerosol Emissions

Penner et al. (1993) concluded that total carbon air concentrations are highly correlated with

sulfur dioxide (SO ) air concentrations from minor sources. They concluded that the emissions of total 2

carbon and SO from minor point sources are correlated as well, since both pollutants result from the 2

combustion of fossil fuel. Their data indicate a 35% proportionality constant for total carbon air

concentrations versus SO air concentrations. The RELMAP mercury model estimated total carbon 2

aerosol emissions using this 35% proportionality constant and SO emissions data for minor sources 2

obtained by the National Acidic Precipitation Assessment Program (NAPAP) for the year 1988. Much of

these SO emissions data had been previously analyzed for use by the Regional Acid Deposition Model 2

(RADM). For the portion of the RELMAP mercury model domain not covered by the RADM domain,

state by state totals of SO emissions were apportioned to the county level on the basis of weekday 2

vehicle-miles-traveled data since recent air measurement studies have indicated that aerosol elemental

carbon can be attributed mainly to transportation source types (Keeler et al., 1990). The county level

data were then apportioned by area to the individual RELMAP grid cells. Total carbon soot was

assumed to be emitted into the lowest layer of the model.

4.2.3.4 Ozone Concentration

Ozone concentration data were obtained from U.S. EPA's Aerometric Information Retrieval

System (AIRS) and the Acidmodes experimental air sampling network. AIRS and Acidmodes data were

available hourly. For each observation site in the AIRS database, the ozone concentrations were

computed for the two mid-day RELMAP time steps by using the mean concentration value during the

two corresponding time periods (1000-1300 and 1400-1600 local time). The mean of these two mid-day

values was used to estimate the ozone concentration for the time steps after 1600 local time and before

1000 local time the next morning. This previous-day average was used at night since ground-level ozone

data are not valid for the levels aloft where the wet removal of elemental mercury was assumed to be

occurring. Finally, an objective interpolation scheme using 1/r weighted averaging was used to produce 2 

complete ozone concentration grids from observational data for each time step, with a minimum value of

20 ppb imposed.

It is recognized that, by estimating nighttime elevated ozone concentrations from observed

ground-level ozone concentrations of the previous day, we do not resolve any possible advection of

ozone concentration gradients during the nighttime hours. Since it is well documented that ground level

observations of ozone concentration do not correlate well with actual elevated concentrations at night,

we opted not to use nighttime surface-level data. Since observations of elevated ozone concentration

were not available except in rare instances, we believe that estimation based on previous-day

observations were our only recourse short of explicit modeling of ozone advection and chemistry within

the RELMAP which is not currently possible. By not resolving the advection of ozone gradients, it is

possible that nighttime precipitation could co-locate with erroneous estimates of ozone concentration. 

Given that high ozone concentrations do not normally occur with precipitation, these erroneous estimates

of ozone concentration would most likely be too high, leading to an artificial bias toward high simulated

Hg oxidation and subsequent wet deposition. Since there remains considerable uncertainty about the 0

true nature of Hg oxidation in cloud water and its controlling effect on wet deposition, the risk of 0

modeling errors related to nighttime ozone concentration estimates from previous-day observations was

deemed acceptable.
4-10

4.2.3.5 Lagrangian Transport and Deposition

In the model, each pollutant puff begins with an initial mass equal to the total emission rate of all

sources in the source cell multiplied by the model time-step length. For mercury, as for most other

pollutants previously modeling using RELMAP, emission rates for each source cell were defined from

emission inventory data, and a time step of three hours was used. The initial horizontal area of each puff

was set to 1200 km , instead of the standard initial size of 2500 km , in order to accommodate the finer 2 2

grid resolution used for the mercury modeling study; however, the standard horizontal expansion rate of

339 km per hour was not changed. Although each puff was defined with four separate vertical layers, 2

each layer of an individual puff was advected through the model cell array by the same wind velocity

field. Thus, the layers of each puff always remained vertically stacked. Wind field initialization data for

a National Weather Service prognostic model, the Nested Grid Model (NGM), were obtained for each

0000 and 1200 GMT initialization time during the year of 1989. Wind analyses for the  =0.897 vertical p

level of the NGM were used to define the translation of the puffs across the model grid, except during the

months of January, February, and December, when the  =0.943 vertical level was used to reflect a more p

shallow mixed layer.  is a pressure-based vertical coordinate equal to (p-p )/(p -p ). The p top surface top

 =0.897 and  =0.943 levels approximate elevations above ground level of 1000 m and 500 m, p p

respectively. These wind fields at 12-h intervals were linearly interpolated in time to produce the wind

fields used to define puff motion for each 3-h RELMAP time step.

Pollutant mass was removed from each puff by the processes of wet deposition, dry deposition,

diffusive air exchange between the surface-based mixed layer and the free atmosphere, and, in the case

of reactive species, chemical transformation. The model parameterizations for these processes are

discussed in Section 4.2.4. Hourly precipitation data for the entire year of 1989 from the TD-3240 data

set of the U.S. National Climatic Data Center were used to estimate the wet removal of all pollutant

species modeled. A spatial and temporal sub-grid-scale analysis of these hourly observations was

performed using a process previously developed for sulfur wet deposition modeling (Bullock, 1994). 

This process provides resolution of precipitation variability not obtainable by simple numerical

averaging of all observations within each grid cell and time step. Wet and dry deposition mass totals

were accumulated and average surface-level concentrations were calculated on a monthly basis for each

model cell designated as a receptor. Except for cells in the far southwest and eastern corners of the

model domain where there were no wind data, all cells were designated as receptors for the mercury

simulation. When the mass of pollutant in a puff declines through deposition, vertical diffusion or

transformation to a user-defined minimum value, or when a puff moves out of the model grid, the puff

and its pollutant load is no longer tracked. The amount of pollutant in the terminated puff is taken into

account in monthly mass balance calculations so that the integrity of the model simulation is assured. 

Output data from the model includes monthly wet and dry deposition totals and monthly average air

concentration for each modeled pollutant, in every receptor cell. 

4.2.4 Model Parameterizations

4.2.4.1 Chemical Transformation and Wet Deposition

The simplest type of pollutant to model with RELMAP is the inert type. To model inert

pollutants, one can simply omit chemical transformation calculations for them, and not be concerned

with chemical interactions with the other chemical species in the model. In the mercury version of

RELMAP, particulate mercury and total carbon were each modeled explicitly as inert pollutant species. 

Reactive pollutants are normally handled by a chemical transformation algorithm. RELMAP was

originally developed to simulate sulfur deposition, and the algorithm for transformation of sulfur dioxide
W(Hg 0

)  k1

k2

 1

HHg

 [O3]aq  (1 K3  csoot

r

)

4-11

to sulfate was independent of wet deposition. For gaseous mercury, however, the situation is more

complex. Since there are no gaseous chemical reactions of mercury in the atmosphere which appear to

be significant (Petersen et al., 1995), for this modeling study mercury was assumed to be reactive only in

the aqueous medium. Elemental mercury has a very low solubility in water, while oxidized forms of

mercury and particle bound mercury readily find their way into the aqueous medium through dissolution

and particle scavenging, respectively. Worldwide observations of atmospheric mercury, however,

indicate that particulate mercury is generally a minor constituent of the total mercury loading (Iverfeldt,

1991) and that gaseous elemental mercury (Hg ) is, by far, the major component. Swedish measurements 0

of large north-to-south gradients of mercury concentration in rainwater without corresponding gradients

of atmospheric mercury concentration suggest the presence of physical and chemical interactions with

other pollutants in the precipitation scavenging process (Iverfeldt, 1991). Aqueous chemical reactions

incorporated into the mercury version of RELMAP were based on research efforts in Sweden (Iverfeldt

and Lindqvist, 1986; Lindqvist et al., 1991; Munthe et al., 1991; Munthe and McElroy, 1992; Munthe,

1992) and Canada (Schroeder and Jackson, 1987; Schroeder et al., 1991).

Unlike other pollutants that have been modeled with RELMAP, mercury has wet deposition and

chemical transformation processes that are interdependent. A combined transformation/wet-removal

scheme proposed by Petersen et al. (1995) was used. In this scheme, the following aqueous chemical

processes were modeled when and where precipitation is present:

1) oxidation of dissolved Hg by ozone yielding Hg 0 2+

2) catalytic reduction of this Hg by sulfite ions 2+

3) adsorption of Hg onto carbon soot particles suspended in the aqueous medium 2+

Petersen et al. (1995) shows that these three simultaneous reactions can be considered in the formulation

of a scavenging ratio for elemental mercury gas as follows:

where, 

k is the second order rate constant for the aqueous oxidation of Hg by O equal to 1 3

0

4.7 x 10 M s , 7 -1 -1

k is the first order rate constant for the aqueous reduction of Hg by sulfite ions equal 2

2+

to 4.0 x 10 s , -4 -1

HHg is the dimensionless Henry's Law coefficient for Hg (0.18 in winter, 0.22 in spring and 0

autumn, and 0.25 in summer as calculated from Sanemasa (1975)),

[O ] is the aqueous concentration of ozone, 3 aq

K is a model specific adsorption equilibrium constant (5.0 x 10 m g ), 3

-6 4 -1

csoot is the total carbon soot aqueous concentration, and

r is the assumed mean radius of soot particles (5.0 x 10 m). -7
[O3]aq  [O3]gas

HO3

4-12

[O ] is obtained from this equation: 3 aq

where HO3 is the dimensionless Henry's Law coefficient for ozone (0.448 in winter, 0.382 in spring and

autumn, and 0.317 in summer as calculated from Seinfeld (1986)). csoot is obtained from the simulated

atmospheric concentration of total carbon aerosol using a scavenging ratio of 5.0 x 10 .5

The model used by Petersen et al. (1995) defined one-layer cylindrical puffs, and the Hg0

scavenging layer was defined as the entire vertical extent of the model. The RELMAP defines 4-layer

puffs to allow special treatment of surface-layer and nocturnal inversion-layer processes. It was believed

that, due to the low solubility of Hg in water, the scavenging process outlined above would only take 0

place effectively in the cloud regime, where the water droplet surface-area to volume ratio is high, and

not in falling raindrops. Thus the Hg wet scavenging process was applied only in the top two layers on 0

RELMAP, which extends from 200 meters above the surface to the model top.

For the modeling study described in Petersen et al. (1995), the wet deposition of Hg was treated 2+

separately from that of Hg . Obviously, any Hg dissolved into the water droplet directly from the air 0 2+

could affect the reduction-oxidation balance between the total concentration of Hg and Hg in the 0 2+

droplet. Since the solubility and scavenging ratio for Hg is much larger than that for Hg , and since air 2+ 0

concentrations of Hg are typically larger than those of Hg , separate treatment of Hg wet deposition 0 2+ 2+

was deemed acceptable for this modeling study also. Thus, process 2 above was only considered as a

moderating factor for the oxidation of dissolved Hg .0

In the exposure analysis in Volume III, there was no attempt to develop a new interacting

chemical mechanism for simultaneous Hg and Hg wet deposition. Although Hg was recognized as a 0 2+ 2+

reactive species in aqueous phase redox reactions, it was, in essence, modeled as an inert species just like

particulate mercury and total carbon soot. With the rapid rate at which the aqueous Hg reduction 2+

reaction is believed to occur in the presence of sulfite, it is possible that an interactive cloud-water

chemical mechanism might produce significant conversion of scavenged Hg to Hg , with possible 2+ 0

release of that Hg into the gaseous medium. 0

Wet deposition of Hg , particulate mercury, and total carbon soot in the mercury version of 2+

RELMAP were modeled with the same scavenging ratios used by Petersen et al. (1995). The gaseous

nitric acid scavenging ratio of 1.6 x 10 has been applied for Hg since the water solubilities of these -6 2+

two pollutant species are similar. For particulate mercury, a scavenging ratio of 5.0 x 10 was used, -5

based on experiences in long-range modeling of lead in northern Europe. As previously mentioned, a

scavenging ratio of 5.0 x 10 was also used for total carbon soot. These scavenging ratios for Hg , -5 2+

particulate mercury, and total carbon soot were applied to all four layers of the RELMAP in the

calculation of pollutant mass scavenging by precipitation. 

4.2.4.2 Dry Deposition

Recent experimental data indicate that elemental mercury vapor does not exhibit a net dry

depositional flux to vegetation until the atmospheric concentration exceeds a rather high compensation

point well above the global background concentration of 1.6 ng m (Hanson et al., 1994). This -3
4-13

compensation point is apparently dependent on the surface or vegetation type and represents a balance

between emission from humic soils and dry deposition to leaf surfaces (Lindberg et al., 1992). Since the

emission of mercury from soils was accounted for with a global-scale ambient concentration and not an

actual emission of Hg , for consistency, there was no explicit simulation of the dry deposition of Hg . 0 0

For Hg during daylight hours, a dry deposition velocity table previously developed based on 2+

HNO data (Walcek et al., 1985; Wesely, 1986) was used. The dry deposition characteristics of HNO 3 3

and Hg should be similar since their water solubilities are similar and gaseous dry deposition to 2+

vegetation involves solution into moist plant tissue. This dry deposition velocity data, shown in Table 4-

3, provided season-dependent values for 11 land-use types under six different Pasquill stability

categories. Based on the predominant land-use type and climatological Pasquill stability estimate of

each RELMAP grid cell, and the season for the month being modeled, the dry deposition velocity values

shown in Table 4-3 were used for the daytime only. For nighttime, a value of 0.3 cm/s was used for all

grid cells since the RELMAP does not have the capability of applying land-use dependent dry deposition

at night. Since the nighttime dry deposition was applied only to the lowest layer of the model and no

vertical mixing is assumed for nighttime hours, all Hg modeled would be quickly depleted from the 2+

lowest model layer by larger dry deposition velocities.

For Hg , Petersen et al. (1995) used a dry deposition velocity of 0.2 cm/s at all times and P

locations. Lindberg et al. (1991) suggests that the dry deposition of Hg seems to be dependent on foliar P

activity. In the RELMAP mercury model, daytime dry deposition velocities for Hg were calculated P

using a FORTRAN subroutine developed by the California Air Resources Board (CARB, 1987). A

particle density of 2.0 g cm and diameter of 0.3 μm was assumed. Table 4-4 shows the wind speed (u) -3

used for each Pasquill stability category in the calculation of deposition velocity from the CARB

subroutine, while Table 4-5 shows the roughness length (z ) used for each land-use category. During 0

simulated night, all cells used 0.02 cm/s as the dry deposition velocity for Hg . Lindberg et al. (1991) P

suggested a value of 0.003 cm/s for non-vegetated land, but since the RELMAP can not model land-use

dependent dry deposition at night, the value of 0.02 cm/s was used for these cells by necessity.

For total carbon soot, daytime dry deposition velocities were also calculated using the CARB

subroutine. A particle density of 1.0 g/cm and radius of 0.5 μm was assumed. For nighttime, a dry 3

deposition velocity of 0.07 cm/s was used for all seasons and land-use types.

Petersen et al. (1995) used local dry deposition factors for Hg and Hg in addition to the normal 2+

P

model treatments to remedy an assumed underestimation of the dry deposition rate for mercury species

emitted near the ground due to an underestimation of the ground-level concentration from instantaneous

complete vertical mixing in their model. This local deposition factor was the fraction of the emissions

from a grid cell that were assumed to dry deposit within that grid cell by processes not otherwise

simulated by the dry deposition parameterization. In the RELMAP mercury model, we compensated for

this underestimation of local deposition by simulating all depositions before pollutant parcel transport in

each model timestep. In essence, the parcel was held over its location of origin for 3 h before being

transported away by the horizontal wind. Thus, no use of a local deposition factor in the RELMAP

modeling was deemed necessary.
4-14

Table 4-3

Dry Deposition Velocity (cm/s) for Divalent Mercury (Hg ) 2+

Season Land-Use Category Pasquill Stability Category

ABCDEF

Winter

Urban 4.83 4.80 4.61 4.30 2.79 0.36

Agricultural 1.32 1.30 1.20 1.05 0.46 0.15

Range 1.89 1.86 1.73 1.52 0.73 0.19

Deciduous Forest 3.61 3.57 3.34 3.02 1.68 0.29

Coniferous Forest 3.61 3.57 3.34 3.02 1.68 0.29

Mixed Forest/Wetland 3.49 3.46 3.27 2.99 1.77 0.29

Water 1.09 1.07 0.98 0.85 0.38 0.13

Barren Land 1.16 1.14 1.06 0.92 0.39 0.31

Non-forested Wetland 2.02 2.00 1.89 1.70 0.96 0.21

Mixed Agricultural/Range 1.62 1.60 1.48 1.30 0.60 0.17

Rocky Open Areas 1.98 1.95 1.81 1.58 0.73 0.20

Spring

Urban 4.59 4.54 4.35 4.05 2.49 0.36

Agricultural 1.60 1.56 1.46 1.28 0.53 0.18

Range 1.49 1.46 1.36 1.19 0.48 0.17

Deciduous Forest 3.42 3.36 3.13 2.81 1.42 0.29

Coniferous Forest 3.42 3.36 3.13 2.81 1.42 0.29

Mixed Forest/Wetland 3.28 3.23 3.05 2.78 1.55 0.29

Water 0.98 0.96 0.89 0.77 0.31 0.13

Barren Land 1.05 1.04 0.97 0.85 0.30 0.13

Non-forested Wetland 1.85 1.82 1.73 1.56 0.84 0.21

Mixed Agricultural/Range 1.60 1.56 1.46 1.28 0.53 0.18

Rocky Open Areas 1.84 1.81 1.67 1.46 0.58 0.20

Summer

Urban 4.47 4.41 4.12 3.73 2.07 0.36

Agricultural 2.29 2.25 2.04 1.76 0.72 0.24

Range 1.67 1.64 1.48 1.26 0.41 0.19

Deciduous Forest 3.32 3.26 2.95 2.57 1.04 0.29

Coniferous Forest 3.32 3.26 2.95 2.57 1.04 0.29

Mixed Forest/Wetland 3.17 3.12 2.86 2.53 1.27 0.29

Water 0.92 0.90 0.81 0.69 0.22 0.13

Barren Land 0.98 0.98 0.89 0.76 0.23 0.13

Non-forested Wetland 1.91 1.88 1.73 1.52 0.77 0.22

Mixed Agricultural/Range 1.90 1.87 1.69 1.44 0.52 0.21

Rocky Open Areas 1.95 1.91 1.71 1.46 0.42 0.21

Autumn

Urban 4.64 4.59 4.35 4.05 2.49 0.36

Agricultural 2.02 1.98 1.81 1.60 0.73 0.21

Range 1.78 1.74 1.59 1.40 0.60 0.19

Deciduous Forest 3.46 3.40 3.13 2.81 1.42 0.29

Coniferous Forest 3.46 3.40 3.13 2.81 1.42 0.29

Mixed Forest/Wetland 3.32 3.27 3.05 2.78 1.55 0.29

Water 1.00 0.98 0.89 0.77 0.31 0.13

Barren Land 1.07 1.06 0.97 0.85 0.30 0.13

Non-forested Wetland 1.88 1.86 1.73 1.56 0.84 0.21

Mixed Agricultural/Range 1.93 1.90 1.74 1.53 0.68 0.20

Rocky Open Areas 1.97 1.94 1.76 1.54 0.63 0.20
4-15

Table 4-4

Wind Speeds Used for Each Pasquill Stability Category

in the CARB Subroutine Calculations

Stability Category Wind Speed (m/s)

A 2.0

B 3.0

C 4.0

D 5.0

E 3.0

F 2.0

Table 4-5

Roughness Length Used for Each Land-Use Category

in the CARB Subroutine Calculations

Land-Use Category

Roughness Length (meters)

spring-summer autumn-winter

Urban 0.5 0.5

Agricultural 0.15 0.05

Range 0.12 0.1

Deciduous Forest 0.5 0.5

Coniferous Forest 0.5 0.5

Mixed Forest/Wetland 0.4 0.4

Water 10 10

Barren Land 0.1 0.1

Non-forested Wetland 0.2 0.2

Mixed Agricultural/Range 0.135 0.075

Rocky Open Areas 0.1 0.1

-6 -6

4.2.4.3 Vertical Exchange of Mass with the Free Atmosphere

Due to the long atmospheric lifetime of mercury, the RELMAP was adapted to simulate a

continuous exchange of mass between the surface-based mixed layer and the free atmosphere above. In

the modeling of Petersen et al. (1995), a depletion of pollutant from the mixed layer was simulated at the

end of the day based on estimates of the subsidence of the mixed-layer top due to the horizontal

divergence of the wind field. For the RELMAP modeling, a pollutant depletion rate of 5 percent per 3-

hour timestep was chosen to represent this diffusive mass exchange. When compounded over a 24-hour

period, this depletion rate removes 33.6% of an inert, non-depositing pollutant. This compares to an

average diffusive mass loss of 30-40% per day in the European modeling study (Petersen, personal

communication). Since a portion of all modeled species of mercury can deposit to the surface before this
4-16

diffusive mass loss is calculated in the RELMAP, the effective mass loss is somewhat less than 33.6%

per day from this process.

4.3 Modeling the Local Atmospheric Transport of Mercury in Source Emissions

The program used to model the transport of the anthropogenic mercury within 50 km of an

emissions source was the ISC3 gas deposition model, obtained from USEPA’s Support Center for

Regulatory Air Models (SCRAM) website (the program is called GDISCDFT). This model has a gas dry

deposition model that was applied in this study. 

4.3.1 Phase and Oxidation State of Emitted Mercury

Reports describe several forms of mercury detected in the emissions from the selected sources. 

Primarily, these include elemental mercury (Hg ) and inorganic mercuric (Hg ). Generally, only total 0 2+

mercury has been measured in emission analyses. The reports of MHg in emissions are imprecise. It is

believed that, if MHg is emitted from industrial processes and combustion sources, the quantities emitted

are much smaller than emissions of Hg and Hg . Only Hg and Hg were considered in the air 0 +2 0 +2

dispersion modeling.

The two types of mercury species considered in the emissions are expected to behave quite

differently once emitted from the stack. Hg , due to its high vapor pressure and low water solubility, is 0

not expected to deposit close to the facility. In contrast, Hg , because of differences in these properties, 2+

is expected to deposit in greater quantities closer to the emission sources. 

At the point of stack emission and during atmospheric transport, the contaminant is partitioned

between two physical phases: vapor and particle-bound. The mechanisms of transport of these two

phases are quite different. Particle-bound contaminants can be removed from the atmosphere by both

wet deposition (precipitation scavenging) and dry deposition (gravitational settling, Brownian diffusion). 

Vapor phase contaminants may also be depleted by these processes, although historically their main

impacts were considered to be through absorption into plant tissues (air-to-leaf transfer) and human

exposure occurred through inhalation.

For the present analysis, the vapor/particle (V/P) ratio was assumed to be equal to the V/P ratio

as it would exist in the emissions plume. It is recognized that this is a simplification of reality, as the ratio

when emitted from the stack is likely to change as the distance from the stack increases. It was assumed

that 25% of the divalent emissions from an individual source would attach to particles in the plume. The

uncertainty in this estimate is acknowledged. Essentially, particulate mercury is not measured at stack tip

but has been measured in plumes downwind from local sources. It is assumed that the divalent fraction

binds to sulfur particles.

The particle-size distribution may differ from one combustion process to another, depending on

the type of furnace and design of combustion chamber, composition of feed/fuel, particulate matter

removal efficiency and design of air pollution control equipment, and amount of air in excess of

stoichiometric amounts that is used to sustain the temperature of combustion. The particle size

distribution used is as estimate of the distribution within an ambient air aerosol mass and not at stack tip. 

Based on this assumption, an aerosol particle distribution based on data collected by Whitby (1978) was

used. This distribution is split between two modes: accumulation and coarse particles. The geometric

mean diameter of several hundred measurements indicates that the accumulation mode dominates particle

size, and a representative particle diameter for this mode is 0.3 microns. The coarse particles are formed
4-17

largely from mechanical processes that suspend dust, sea spray and soil particles in the air. A

representative diameter for coarse particles is 5.7 microns. The fraction of particle emissions assigned to

each particle class is approximated based on the determination of the density of surface area of each

representative particle size relative to total surface area of the aerosol mass. Using this method,

approximately 93% and 7% of the total surface area is estimated to be in the 0.3 and 5.7 micron diameter

particles, respectively.

Table 4-6

Representative Particle Sizes and Size Distribution 

Assumed for Divalent Mercury Particulate Emissions

Representative Particle Size Assumed Fraction of Particle

(microns)* Emissions in Size Category

0.3 0.93

5.7 0.07

*These values are based on the geometric means of aerosol particle

 distribution measurements as described in Whitby (1978).

The speciation estimates for the model plants were made from thermal-chemical modeling of

mercury compounds in flue gas, from the interpretation of bench and pilot scale combustor experiments

and from interpretation of available field test results. The amount of uncertainty surrounding the

emission rates data varies for each source. There is also a great deal of uncertainty with respect to the

species of mercury emitted.

Although the speciation may change with distance from the local source, for this analysis it was

assumed that there were no plume reactions that significantly modified the speciation at the local source. 

Because of the differences in deposition characteristics of the two forms of mercury considered, the

assumption of no plume chemistry is a particularly important source of uncertainty. 

4.3.2 Modeling the Deposition of Mercury

Once emitted from a source, the mercury may be deposited to the ground via two main processes:

wet and dry deposition. Wet deposition refers to the mass transfer of dissolved gaseous or suspended

particulate mercury species from the atmosphere to the earth's surface by precipitation, while dry

deposition refers to such mass transfer in the absence of precipitation. 

The deposition properties of the two species of mercury addressed in stack emissions, elemental

and divalent mercury, are considered to be quite different. Due to its higher solubility, divalent mercury

vapor is thought to deposit much more rapidly than elemental mercury. However, at this time no

conclusive data exist to support accurate quantification of the deposition rate of divalent mercury vapor. 

In this analysis, nitric acid vapor is used as a surrogate for Hg vapor based on their similar solubility in ++

water. Whether a pollutant is in the vapor form or particle-bound is also important for estimating

deposition, and each is treated separately.

Dry deposition is estimated by multiplying the predicted air concentration at ground level by a

deposition velocity. For particles, the dry deposition velocity is estimated using the CARB algorithms
4-18

(CARB 1986) that represent empirical relationships for transfer resistances as a function of particle size,

density, surface area, and friction velocity. For the vapor phase fraction for elemental mercury, a single

dry deposition velocity of 0.06 cm/s is assumed. This is based on the average of the winter and summer

deposition velocities presented in Lindberg et al. (1992) for forests. Although it is generally

acknowledged that elemental mercury dry deposits with a (net) rate much lower than divalent mercury

vapor, the precise value is uncertain, and there can be considerable variability with season and time of

day. Additionally, dry deposition of elemental mercury may not occur at all unless the air concentration

is sufficiently high. Preliminary research (Hanson et al.1995) indicates that under some experimental

conditions no dry deposition occurs unless the air concentration is at least 10 ng/m ; this was termed a 3

compensation point (the value at which dry deposition would being to occur is expected to depend on

many factors, including time of year and the type of flora present). These preliminary results were not

specifically addressed in the current study; however, sensitivity analyses were conducted in order to

determine the possible impact that such a compensation point might have on the predicted dry deposition

of elemental mercury (see Section 5.3.4.1 below).

In ISC-GAS, the dry deposition of divalent mercury vapor was modeled by calculating a dry

deposition velocity for each hour using the assumptions usually made for nitric acid for the input

parameters (see EPA 1996; User’s Guide for the Gas Dry Deposition Model, page inserts to the User’s

Guide for the Industrial Source Complex (ISC3) Dispersion Models). Ultimately, using the assumptions

here, the average predicted dry deposition velocity was about 2.9 cm/s for divalent mercury vapor, which

is essentially the average of the values used in the RELMAP modeling for coniferous forests.

Table 4-7

Parameter Values Assumed for Calculation of Dry Deposition Velocities for 

Divalent Mercury Vapor

Parameter Value

Molecular diffusivity (cm2/sec) 0.1628

Solubility enhancement factor 109

Pollutant reactivity 800

Mesophyll resistance 0

Henry’s law coefficient 2.7e-7

Wet deposition is estimated by assuming that the wet deposition rate is characterized by a

scavenging coefficient which depends on precipitation intensity and particle size. For particles, the

scavenging ratios used are from Jindal and Heinold (1991) (see Figure 4-4). For the vapor phase

fraction, a scavenging coefficient is also used, but it is calculated using estimates for the washout ratio,

which is the ratio of the concentration of the chemical in surface-level precipitation to the concentration

in surface-level air. Because of its higher solubility, divalent mercury vapor is assumed to be washed out

at significantly higher rates than elemental mercury vapor. The washout ratio for divalent mercury vapor

was selected based on an assumed similarity between scavenging for divalent mercury and gaseous nitric

acid. This is based on Peterssen (1995), and the value used for the washout ratio for divalent vapor was
4-19

1.6x10 . The washout ratio for elemental mercury vapor was assumed to be 1200. This is a calculated 6

value based on the model of Peterssen et al. (1995), with a soot concentration 0.

Figure 4-4 

Wet Deposition Scavenging Ratios Used in Local Scale Air Modeling for Particulate-Bound

Mercury (Jindal and Heinold 1991)
4-20

Table 4-8

Air Modeling Parameter Values Used in the Exposure Assessment: Generic Parameters

Parameter Value Used in Study

Particle Density (g/cm ) 1.8 3

Surface Roughness Length (m)* 0.30

Anemometer Height (m) 10

Wind Speed Profile Exponents

Stability Class A 0.07

Stability Class B 0.07

Stability Class C 0.10

Stability Class D 0.15

Stability Class E 0.35

Stability Class F 0.55

Terrain Adjustment Factors

Stability Class A 0.5

Stability Class B 0.5

Stability Class C 0.5

Stability Class D 0.5

Stability Class E 0

Stability Class F 0

Distance Limit for Plume Centerline (m) 10

Model Run Options

Terrain Adjustment Yes

Stack-tip Downwash No

Building Wake Effects No

Transitional Plume Rise Yes

Buoyancy-induced dispersion Yes

Calms Processing Option No

 This is used to estimate deposition velocities for particles. a
4-21

4.3.3 Rationale and Utility of Model Plant Approach

Mercury is generally present as a low-level contaminant in combustion materials (e.g., coal or

municipal solid waste) and industrial material (for more information on mercury in emissions refer to

Volume II of this Report). During combustion and high-temperature industrial processes, mercury is

volatilized from these materials. Because of its high volatility, it is difficult to remove mercury from the

post-combustion air stream. As a consequence, mercury is released to the atmosphere. As noted

previously, anthropogenic mercury emissions are not the only source of mercury to the atmosphere. 

Mercury may be introduced into the atmosphere through volatilization from natural sources such as lakes

and soils. Consequently, it is difficult to trace the source(s) of mercury concentrations in environmental

media and biota. For this reason it is also difficult to gain an understanding of contribution to those

concentrations.

For this assessment it was not possible to model the emission impact of every mercury emission

source in each selected industrial and combustion class. Consequently, the actual mercury emission data

and facility characteristics for any specific source were not modeled. Instead, a model plant approach, as

described in Appendix C, was utilized to develop facilities which represent actual sources. Model plants

were developed to represent four source categories; namely municipal waste combustors, coal and oilfired boilers, medical waste incinerators, and chlor-alkali plants. The model plants were designed to

characterize the mercury emission rates as well as the atmospheric release processes exhibited by actual

facilities in the source class. The modeled facilities were not designed to exhibit extreme sources (e.g.,

the facility with the highest mercury emission rate) but rather to serve as a representative of the

industrial/combustion source class.

This assessment took as its starting point the results of measured mercury emissions from

selected anthropogenic sources. Using a series of fate models and hypothetical constructs, mercury

concentrations in environmental media, pertinent biota and ultimately mercury contact with human and

wildlife receptors were predicted. An effort was made to estimate the amount of receptor contact with

mercury as well as the oxidative state and form of mercury contacted.

In taking the model plant approach, it was realized that there would be a great deal of uncertainty

surrounding the predicted fate and transport of mercury as well as the ultimate estimates of exposure. 

The uncertainty can be divided into modeling uncertainty and parameter uncertainty. Parameter

uncertainty can be further subdivided into uncertainty and variability depending on the level to which a

particular model parameter is understood. A limited quantitative analysis of uncertainty is presented. It

is also hoped that the direction of future research can be influenced toward reducing the identified

uncertainties which significantly impact key results.

4.3.4 Development and Description of Model Plants

Model plants representing four source classes were developed to represent a range of mercury

emissions sources. The source categories were selected for the exposure assessment based on their

estimated annual mercury emissions as a class or their potential to be localized point sources of concern. 

The categories selected were these:
4-22 

municipal waste combustors (MWCs),

medical waste incinerators (MWIs),

utility boilers, and 

chlor-alkali plants (CAP).

Parameters for each model plant were selected after evaluation of the characteristics of a given

source category and current knowledge of mercury emissions from that source category. Important

variables for the mercury risk assessment included mercury emission rates, mercury speciation and

mercury transport/deposition rates. Important model plant parameters included stack height, stack

diameter, stack volumetric flow rate, stack gas temperature, plant capacity factor (relative average

operating hours per year), stack mercury concentration, and mercury speciation. Emission estimates

were assumed to represent typical emission levels emitted from existing sources. Table 4-9 shows the

process parameters assumed for each model plant considered in this analysis (for details regarding these

values, see Appendix C). 

4.3.5 Hypothetical Locations of Model Plants

There are a variety of geographic aspects that can influence the impacts of mercury emissions

from an anthropogenic source. These aspects include factors that affect the environmental chemistry of a

pollutant and the physics of plume dispersion. Environmental chemistry can include factors such as the

amount of wet deposition in a given area. Factors affecting plume dispersion include terrain, wind

direction and average wind speed.

Because wet deposition may be an important factor leading to mercury exposures, especially for

the more soluble species emitted, the meteorology of a location was used as a selection criterion. Two

different types of meteorology were deemed necessary to characterize the environmental fate and

transport of mercury: an arid/semi-arid site and a humid site. The humidity of an area was based on total

yearly rainfall. (See Appendix B).

Terrain features refer to the variability of the receptor height with respect to a local source. 

Broadly speaking, there were two main types of terrain used in the modeling: simple, and complex. 

Simple terrain is defined as a study area that is relatively level and well below stack top (rather, the

effective stack height). Complex terrain referred to terrain that is not simple, such as source located in a

valley or a source located near a hill. This included receptors that are above or below the top of the stack

of the source. Complex terrain can effect concentrations, plume trajectory, and deposition. Due to the

complicated nature of plume flow in complex terrain, it is probably not possible to predict impacts in

complex terrain as accurately as for simple terrain. In view of the wide range of uncertainty inherent in

accurately modeling the deposition of the mercury species considered, the impacts posed by complex

terrain were not incorporated in the local scale analysis.

Two generic sites are considered: a humid site east of 90 degrees west longitude, and a more

arid site west of 90 degrees west longitude (these are described in Appendix B). The primary differences

between the two sites as parameterized were 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 for the other site. A circular drainage lake with a diameter of 1.78 km and average depth

of 5 m, with a 2 cm benthic sediment depth was modeled at both sites. The watershed area was 37.3 km . 2
4-23 

Table 4-9

Process Parameters for Model Plants

Model Plant Plant Size (% of (ft) Diameter Rate Percent Velocity (°F)

Capacity Stack Height Stack Hg Emission Speciation Exit Exit Temp.

year) (ft) (kg/yr) (Hg /Hg /Hg ) (m/sec) 0 2+ P

Large Municipal 2,250 90% 230 9.5 220 60/30/10 21.9 285

Waste tons/day

Combustors

Small Municipal 200 tons/day 90% 140 5 20 60/30/10 21.9 375

Waste

Combustors

Large 1500 lb/hr 88% 40 2.7 4.58 33/50/17 9.4 175

CommercialHMI capacity

Waste Incinerator (1000 lb/hr

(Wetscrubber) actual)

Large Hospital 1000 lb/hr 39% 40 2.3 23.9 2/73/25 16 1500

HMI Waste capacity

Incinerators (667 lb/hr

(Good actual)

Combustion)

Small Hospital 100 lb/hr 27% 40 0.9 1.34 2/73/27 10.4 1500

HMI Waste capacity

Incinerators (67 lb/hr

(1/4 actual)

sec.Combustion)

Large Hospital 1000 lb/hr 39% 40 2.3 0.84 33/50/17 9.0 175

HMI Waste capacity

Incinerators (667 lb/hr

(Wet Scrubber) actual)

Small Hospital 100 lb/hr 27% 40 0.9 0.05 33/50/17 5.6 175

HMI Waste capacity

Incinerators (Wet (67 lb/hr

Scrubber) actual)

Large Coal-fired 975 65% 732 27 230 50/30/20 31.1 273

Utility Boiler Megawatts

Medium 375 65% 465 18 90 50/30/20 26.7 275

Coal-fired Utility Megawatts

Boiler

Small Coal-fired 100 65% 266 12 10 50/30/20 6.6 295

Utility Boiler Megawatts

Medium Oil-fired 285 65% 290 14 2 50/30/20 20.7 322

Utility Boiler Megawatts

Chlor-alkali plant 300 tons 90% 10 0.5 380 70/30/0 0.1 Ambient

chlorine/day

 Hg = Elemental Mercury a 0

 Hg = Divalent Vapor Phase Mercury b 2+

 Hg = Particle-Bound Mercury c

P
4-24 

4.4 Modeling Mercury in a Watershed

Atmospheric mercury concentrations and deposition rates estimated from RELMAP and ISC3

drive the calculations of mercury in watershed soils and surface waters. The soil and water

concentrations, in turn, drive calculations of concentrations in the associated biota and fish, which

humans and other animals are assumed to consume. The watershed model used for this report, IEM-2M,

was adapted from the more general IEM-2 methodology (U.S. EPA, 1990; U.S. EPA, 1994, external

review draft) to handle mercury fate in soils and water bodies. 

4.4.1 Overview of the Watershed Model

IEM-2M simulates three chemical components -- elemental mercury, Hg (C ), divalent mercury, 0

1

HgII (C ), and methyl mercury, MHg (C ). In the previous version of IEM-2, these components were 2 3

assumed to be in a fixed ratio with each other as specified by the fraction elemental (f ) and fraction 1

methyl (f ). This updated version calculates the fractions in each component based on specified or 3

calculated rate constants. The equations and parameters are described below, and implemented in an

Excel spreadsheet. The model is parameterized for several hypothetical scenarios as described in

Chapter 5.

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

equations describing watershed soils and a shallow lake, as illustrated in Figures 4-5 and 4-6. The mass

balances are performed for each mercury component, with internal transformation rates linking Hg ,0

HgII, and MHg. Sources 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 0

water 

Figure 4-5

Configuration of Hypothetical Water Body and Watershed Relative to Local Source
4-25 

Definitions for Figure 4-6

C total mercury concentration in upper soil ng/g soil

C total mercury concentration in water body ng/L w

C vapor phase mercury concentration in air ng/m atm

3

D average dry deposition to watershed g/m -yr yds

2

D average wet deposition to watershed g/m -yr yws

2

Figure 4-6

Overview of the IEM-2M Watershed Modules
4-26 

body. Sinks 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 0

component out of the lake.

At the core of IEM-2M are 9 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 are 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 next performs an aquatic mass balance driven by direct

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

in fish are derived from dissolved MHg water concentrations using bioaccumulation factors (BAF).

IEM-2 was developed to handle individual chemicals, or chemicals linked by kinetic

transformation reactions. IEM-2M is expanded to include specific kinetic transformation rates affecting

mercury components in soil, water, and sediments -- oxidation, reduction, methylation, and

demethylation. These transformation rates are driven by specified rate constants. Volatilization kinetics

are included as a transfer reaction driven by specified chemical properties and environmental conditions.

The nature of this methodology is quasi-steady with respect to time and homogeneous with

respect to space. While it tracks the buildup of soil and water concentrations over the years given a

steady depositional load and long-term average hydrological behavior, it does not respond to unsteady

loading or meteorological events. There are, thus, limitations on the analysis and interpretations imposed

by these simplifications. The model's calculations of average water body concentrations are less reliable

for unsteady environments, such as streams, than for more steady environments, such as lakes.

 4.4.2 Description of the Watershed Soil Module

The IEM-2M watershed soil module calculates surface soil concentrations, including dissolved,

sorbed, and gas phases, as illustrated in Figure 4-7. The model accounts for three routes of contaminant

entry into the soil: deposition of particle-bound contaminant through dryfall; deposition through wetfall;

and diffusion of vapor phase contaminant into the soil surface. The model also accounts for four

dissipation processes that remove mercury from the surface soils: volatilization (diffusion of gas phase

out of the soil surface); runoff of dissolved phase from the soil surface; leaching of the dissolved phase

through the soil horizon; and erosion of particulate phase from the soil surface. Key assumptions in the

watershed soil module were these:

Soil concentrations within a depositional area are assumed to be uniform within the area,

and can be estimated by the following key parameters: dry and wet contaminant

deposition rates, a diffusion-driven gaseous exchange rate with the atmosphere, a set of

soil transformation rates, a soil bulk density, and a soil mixing depth.

The partitioning of mercury components among soil water, soil particle, and soil gas

phases can be described by partition coefficients and Henry’s Law constants.
4-27 

Definitions for Figure 4-7

C vapor phase chemical concentration in air μg/m atm

3

D average dry deposition to watershed mg/yr yds

D average wet deposition to watershed mg/yr yws

C total chemical concentration in soil mg/L st

C reaction product concentration in soil mg/L st

'

C background chemical concentration in soil mg/L b

C chemical concentration in soil gas μg/m gs

3

CDs chemical concentration in soil water mg/L

C chemical concentration on soil particles μg/g ps

H Henry's Law constant atm-m /mole 3

R universal gas constant atm-m /mole-K 3

T temperature K

K soil/water partition coefficient L/kg ds

Figure 4-7

Overview of the IEM2 Soils Processes
Csi  Sci  BD

Vs dCsi

dt  LSD,i STs,i  SLs,i

STs,i  ksT  Vs  Csj

SLs,i  ksx  Vs  Csi

Cig  Ciw  Cis

4-28 

4.4.2.1 Development of Soil Mass Balance Equations

The concentration of constituent iin watershed soils can be expressed per unit volume (C , in si

mg/L) or per unit mass (Sc , in mg/kg), where BD is soil bulk density (dry weight basis), in kg/L: i

Given constant steady depositional loading L (g/yr) onto a surficial soil layer, the following mass SD,i 

balance equation governs the mass response:

where:

V = watershed soil volume (m ) s

3

S = total transformation source in the soil layer (g/yr) Ts,i

S = total transport and transformation loss in the soil layer (g/yr). Ls,i

A simple first-order transformation source from constituent C would be given by: sj

where: 

ks = first-order transformation rate constant (yr ) T

-1

Similarly, a simple first-order loss process would be given by: 

where: 

ks = total first-order loss rate constant (yr ) x

-1

The basic mass balance equation is applied to three interacting mercury components. For each

component i,three phases in local equilibrium are calculated -- gas phase (C , g/m ), aqueous phase ig 

3

(C , mg/L), and solid phase (C , g/g): iw is

The fraction of each component iin each phase -- f , f , and f -- is calculated using partition ig iw is

coefficients and properties of the soil, as described in a section below.
ksr

C1T  C2T

kso

ksdm

C2T  C3T

ksm

C3T  C1T

ksmd

Cig  Cia

ksdiff

4-29 

The three mercury components are linked by a set of transformation reactions, including

oxidation of total elemental mercury, reduction and methylation of total divalent mercury, and

demethylation of total methyl mercury by two pathways:

These are modeled as first-order processes, each a function of environmental conditions, where: 

ks = oxidation rate constant (yr ) o

-1

ks = reduction rate constant (yr ) r

-1

ks = methylation rate constant (yr ) m

-1

ks = demethylation rate constant (yr ) dm

-1 

ks = md mer cleavage demethylation rate constant (yr ) -1

Each mercury component is also subject to a set of transport processes, including leaching and

runoff of dissolved phase, erosion of particulate phase, and volatilization of gas phase. These are

modeled as first-order processes, where ks is the leaching rate constant (yr ), ks is the runoff rate L R

-1

constant (yr ), and ks is the erosion rate constant (yr ). Each of these rate constants is a function of -1 -1

e

environmental conditions. While leaching, runoff, and erosion are strictly loss processes from the soil,

volatilization is a diffusive exchange process between soil and atmosphere:

where: 

ks = diffusive exchange volume (m /yr) diff

3

This diffusive exchange leads to an atmospheric loading that partially balances the volatile loss from the

soil. The net loss rate is the product of ks and the concentration gradient (C -C ). For modeling diff ig ia

purposes, it is convenient to divide this process into a diffusive loading term and a first-order loss term,

where ks is the volatilization loss rate constant (yr ). These are developed in the sections below. v

-1

Using the calculated phase fractions, three differential equations can be written to describe the

mercury mass balance in soil:
Vs  dCs1

dt  LSD,1 ksrVs  Cs2 ksmd Vs  Cs3

 ( ksv,1ksoksRO ksLkse

)  Vs  Cs1

Vs  dCs2

dt  LSD,2 kso Vs  Cs1 ksdm Vs  Cs3

 (ksrksm ksRO ksLkse

)  Vs  Cs2

Vs  dCs3

dt  LSD,3 ksm Vs  Cs2

 ( ksv,3ksdm ksmdksRO ksLkse

)  Vs  Cs3

LSD,i  (Dydwi Dywwi LDIF,i

)  As

LDIF,i  ksdiff,i

As

 Ca,i  106

4-30 

The major model coefficients are described in more detail in the sections below.

4.4.2.2 Loads to Watershed Soils

The total atmospheric loading term for component i-- L in the mass balance equations -- is SD,i

the sum of the wetfall, dryfall, and vapor diffusion fluxes:

where: 

Dydw = yearly-average dry depositional flux of component i(g/m -yr) i

2

Dyww = yearly-average wet depositional flux of component i(g/m -yr) i

2

L = yearly-average vapor diffusion flux of component i(g/m -yr) DIF,i

2

The vapor diffusion flux is calculated from the diffusion volume and the atmospheric concentration,

normalized to the surface area:

where: 

ks = diffusive exchange volume (m /yr) diff,i

3

A = surface area of the watershed soil element (m ) s

2

C = vapor phase atmospheric concentration for component i(μg/m ) a,i

3
ksdiff  Di  As  v

zr

 3.15×107  104

Cig  (Hi

/RTK)  Ciw

Cis  Kdi  Ciw

CiT  Cig v Ciw w CisBD

4-31 

The diffusive exchange volume is calculated from the atmospheric diffusion coefficient:

where: 

D = atmospheric diffusion coefficient for component i(cm /sec) i

2

v = soil void fraction

z = characteristic diffusion reference depth (m) r

 3.15×10 = units conversion factor (sec/yr) 7

10 = units conversion factor (m /cm ) -4 2 2

The product A represents the cross-sectional area within the soil through which diffusion occurs. s v

4.4.2.3 Equilibrium Speciation Reactions

The gas and solid phase concentrations are calculated from the aqueous phase concentration:

where: 

H = Henry’s Law constant (atm-m /mole) i

3

R = universal gas constant (atm-m /mole-K) 3

T = temperature (K) K

K = solids partition coefficient (L/kg) di

The total amount of mercury in component iis the summation across all phases:

where: 

= soil void fraction (L/L) v

= soil water fraction (L/L) w

BD = soil bulk density (kg/L) on a dry weight basis. 

From these equations, the fraction of component iin each phase can be calculated:
f

ig  (Hi

/RTK)v

(Hi/RTK)v w Kdi BD

f

iw  w

(Hi/RTK)v w Kdi BD

f

is  Kdi BD

(Hi/RTK)v w Kdi BD

ksRO,i  Ro

zs

 f

iw

w

4-32 

4.4.2.4 Transformation Processes in Watershed Soils

As described above, five transformation reactions are modeled as first-order rates. Rate

constants are directly specified and applied to the bulk concentration (all phases) to give internal mass

transformation loadings. The oxidation loading ks V C is subtracted from the Hg mass balance o s s1

0

equation and added to the HgII equation. The reduction loading ksV C is subtracted from the HgII r s s2

equation and added to the Hg equation. The methylation loading ks V C is subtracted from the HgII 0

m s s2

equation and added to the MHg equation. The demethylation loading ks V C is subtracted from the dm s s3

MHg equation and added to the HgII equation. Finally, the mer demethylation loading ksmd s s3 V C is

subtracted from the MHg equation and added to the Hg equation. 0

There is evidence that reduction in soil is mediated by sunlight, is proportional to soil water

content, and occurs most rapidly within the upper 5 mm of the soil surface (Carpi and Lindberg, 1997). 

As a result, the input reduction rate constant k is normalized to a reference depth z of 5 mm and to rs r

100% water content. The actual reduction rate constant ks used in the model is the product of k , the r rs

soil water content and the ratio of the reference depth to the depth of the soil layer z /z . w r s

4.4.2.5 Transport and Transfer Processes in Watershed Soils

The total transport loss of component ifrom the soil is the sum of the loss rates due to

leaching, runoff, erosion, and volatilization. In the governing mass balance equations, these loss rates

are expressed as the product of a loss rate constant, the total component concentration, and the soil

volume. The runoff loss constant is a function of the runoff volume and the dissolved fraction of

component i:

where: 

Ro = average annual runoff (m/yr) 

z = upper soil layer depth (m) s

= volumetric water content (dimensionless; cm /cm ) w

3 3
ksL,i  P I  Ro  EV

zs

 f

iw

s

kse,i  Xe  SD  ER

zs

 f

is

1000  BD

ksv  Vs  Csi  ksdiff,i  Cig

ksv  ksdiff,i

Vs

 f

ig

v

4-33 

f = aqueous fraction of component iconcentration. iw

The first term times the soil volume is the annual runoff volume in m /yr, while the second term times 3

the total concentration is the aqueous concentration in the runoff in g/m .3

The leaching loss constant is a function of the leaching volume and the dissolved fraction of

component i:

where: 

P = average annual precipitation (m/yr)

I = average annual irrigation (m/yr)

EV = average annual evapotranspiration (m/yr). 

The first term times the soil volume is the annual percolation volume in m /yr , while the second term 3

times the total concentration is the aqueous concentration in percolating water, in g/m .3

The erosion loss constant is a function of the erosion mass and the particulate fraction of

component i:

where: 

X = unit soil loss (kg/m -yr; see Eq [9-3], IED; Wischmeier and Smith, 1978) e

2

SD = sediment delivery ratio 

ER = particle enrichment ratio 

BD = soil dry density (g/cm ) 3

f = sorbed fraction of the total component iconcentration . is

The first term times the soil volume is the annual erosion mass in kg/yr, while the second term times the

total concentration is the sorbed concentration on the eroding particles in g/kg.

The volatilization rate constant ks can be derived from the diffusive exchange volume and gas v

phase concentration:

where: 

ks = diffusive exchange volume (m /yr) diff,i

3

V = soil layer volume (m ) s

3
ksv  3.15×103 Di  v

zs  zr

 f

ig

v

4-34 

= void fraction. v

Substituting in the expression for ks gives the volatilization rate constant in terms of the atmospheric diff,i

diffusivity and the gaseous fraction of component iin the soil:

where: 

D = atmospheric diffusivity (cm /sec) i

2

z = soil thickness (m) s

z = characteristic diffusive mixing depth (m) r

 3.15×10 = units conversion factor (sec/yr  m /cm ) 3 22

v = soil void fraction

f = gaseous fraction of the total component iconcentration. ig

The first term times the soil volume is the annual gas diffusion volume in m /yr, while the second term 3

times the total concentration is the gas phase concentration in the void space in g/m .3

4.4.3 Description of the Water Body Module

The IEM-2M water body module estimates water column as well as bed sediment concentrations

in a shallow lake. Water column concentrations included dissolved, sorbed to suspended sediments and

total (sorbed plus dissolved, or total contaminant divided by total water volume). This framework also

provides three concentrations for the bed sediments: dissolved in pore water, sorbed to bed sediments,

and total. The model accounts for five routes of contaminant entry into the water body: erosion of

mercury sorbed to soil particles; runoff of dissolved mercury in runoff water; deposition of particlebound mercury through wetfall; deposition of particle-bound mercury through dryfall; and diffusion of

vapor phase Hg into the water body. The model also accounts for three dissipation processes that 0

remove mercury from the water body: volatilization of dissolved phase Hg and MHg from the water 0

column; removal of total mercury via "burial" from the surficial bed sediment layer; and advection of

total mercury from the water column via outflow. The burial rate is a function of the deposition of biotic

and abiotic solids from the water column to the bed; it accounts for the fact that much of the soil eroding

into a water body annually is incorporated into bottom sediment. The impact to the water body was

assumed to be uniform. This tends to be more realistic for smaller water bodies as compared to large

rivers or lakes. Key features and assumptions in the surface water body module include the following.

The partitioning of mercury components between the water column and suspended biotic and

abiotic solids, and between pore water and sediment particles is in local equilibrium as described

by a set of partition coefficients.

Atmospheric mercury wetfall and dryfall loads are handled as a constant average flux.

Surface runoff mercury loadings are estimated as a function of the dissolved concentration of

mercury in the surficial soil water (calculated by the soil module as a function of time) and the

specified annual water runoff.
Vw dCwt

dt  LT  Vfx Cwt Rsw (CdbCdw)

 [vsCsw vsB CBw  vrsCbt]Aw

Swt  kv Aw Cdw

Vb dCbt

dt   Rsw (CdbCdw) Sbt

[vsCsw vsB CBw  (vrsvb)Csb]Aw

4-35 

Soil erosion mercury loadings are calculated as a function of the sorbed concentration of

mercury in the surficial soil layer (calculated by the soil module as a function of time), together

with the calculated annual soil erosion, a sediment delivery ratio, and an enrichment ratio. The

sediment delivery ratio serves to reduce the total potential amount of soil erosion (where the total

potential erosion equals the unit erosion rate in kg/m multiplied by the watershed area, in m ) 2 2

reaching the water body. This parameter accounts for the observation that most of the eroded

particles mobilized within a watershed during a year deposit prior to reaching the water body. 

The enrichment ratio accounts for the fact that eroding soils tend to be lighter in texture, more

abundant in surface area, and higher in organic carbon. All these characteristics lead to

concentrations in eroded soils that tend to be higher than those in situ soils.

Diffusive mercury loadings from the atmosphere are calculated as a function of a specified

atmospheric vapor concentration, the calculated dissolved water column concentration, and the

calculated transfer velocity. The dissolved concentration in a water body is driven toward

equilibrium with the vapor phase concentration above the water body. At equilibrium, gaseous

diffusion into the water body is matched by volatilization out of the water body. This specified

air concentration is an output of the atmospheric transport model.

The rate of contaminant burial in bed sediments is estimated as a function of the rate at which

biotic and abiotic solids deposit from the water column onto the surficial sediment layer minus

the rate at which they resuspend to the water column. Burial represents a permanent sink of

eroded soil and mercury concentrations scavenged from the water column. 

Separate transformation rate constants allow for the calculation of mercury component fractions

in the water column and benthic sediments.

In the following sections, the mass balance equations and the equilibrium state equations that

link the concentrations are developed.

4.4.3.1 The Water Body Equations

Given the loading of mercury from atmospheric deposition and the surrounding watershed, the

following mass balance equations govern the concentration response in the water column and surficial

benthic sediment layer of a shallow lake:

where: 
Rsw  Esw  Aw  bs

zb

Cis  Ciw  CiB

4-36 

C = total water column concentration (mg/L) wt

C = total benthic concentration (mg/L) bt

C = dissolved water column concentration (mg/L) dw

C = dissolved benthic concentration (mg/L) db

C = particulate abiotic water column concentration (mg/L) sw

C = particulate biotic water column concentration (mg/L) bw

C = particulate (sorbed) benthic concentration (mg/L) sb

C = atmospheric concentration (g/m ) a

3

V = water column volume (m ) w

3

V = benthic volume (m ) b

3

L = total loading (g/year) T

Vf = dilution flow (m /year) x

3

R = pore water diffusion volume (m /year) sw

3

A = surface area (m ) w

2

v = settling velocity for abiotic solids (m/year) s

v = settling velocity for biotic solids (m/year) sB

v = resuspension velocity (m/year) rs

v = burial velocity (m/year) b

S = net transformation source in the water column (g/yr) wt

S = net transformation source in the benthic sediment layer (g/yr) bt

The pore water diffusion volume is calculated as:

where:

E = pore water diffusion coefficient (m /year) sw

2

 = benthic porosity (L /L) bs w

z = surficial benthic layer depth (m) b

The first term in the water column mass balance equation describes external loading, while the

second term describes advective export. The third term covers net pore water exchange with the surficial

benthic layer, and is also present in the benthic sediment equation. The fourth term gives net deposition

of particulate mercury, including settling of abiotic and biotic solids and resuspension of benthic solids. 

These processes are also represented in the benthic sediment equation. The fifth term in the water

column equation gives the net internal transformation source, while the last term gives the net

volatilization loss.

The benthic sediment mass balance equation contains terms for pore water exchange, internal

transformation source, and net solids transport, which includes deposition, resuspension, and burial.

These basic equations are applied to three interacting mercury components. For each component

iin the water column, three phases in local equilibrium are calculated -- aqueous phase (C , mg/L), iw 

abiotic solid phase (C , g/g), and biotic solid phase (C , g/g): is iB
kwr kbr

Cwt,1  Cwt,2 Cbt,1  Cbt,2

kwo kbo

kwdm kbdm

Cwt,2  Cwt,3 Cbt,2  Cbt,3

kwm kbm

Cwt,3  Cwt,1 Cbt,3  Cbt,1

kwmd kbmd

4-37 

In the sediments, two phases in local equilibrium are calculated -- aqueous pore water phase (C , mg/L) db

and sediment phase (C , sb g/g). The fraction of each component iin each phase -- f , f , and f in dw,i sw,i Bw,i

the water column and f and f in the sediments -- is calculated using partition coefficients and db,i sb,i

properties of the solids and sediment, as described in a section below.

The three mercury components are linked by a set of first-order transformation reactions,

including oxidation of total elemental mercury, reduction and methylation of total divalent mercury, and

demethylation of total methyl mercury by two pathways:

where: 

kw = water column oxidation rate constant (yr ) o 

-1

kb = benthic oxidation rate constant (yr ) o

-1

kw = water column reduction rate constant (yr ) r

-1

kb = benthic reduction rate constant (yr ) r

-1

kw = water column methylation rate constant (yr ) m

-1

kb = benthic methylation rate constant (yr ) m

-1

kw = water column demethylation rate constant (yr ) dm

-1

kb = benthic demethylation rate constant (yr ) dm

-1

kw = water column md mer cleavage demethylation rate constant (yr )-1

kb = benthic md mer cleavage demethylation rate constant (yr )-1

Each of these rate constants is a function of environmental conditions. Their values are specified as input

to the IEM-2M model.

Each mercury component is also subject to a set of transport processes, including advective

export of all phases in the water column, volatilization and pore water exchange of dissolved phase, and

settling, resuspension, and burial of particulate phase. These are modeled as first-order processes as

described in the general water column and benthic sediment mass balance equations above. 

Volatilization is modeled as a surficial thin-filmexchange process in which the dissolved concentration

in the water column is driven toward equilibrium with the atmosphere:
Vw dCw,i

dt volatilization  Kv,i  Aw  Cdw,i  Ca,i  106Hi

/RTK

Vw  dCwt,1

dt  LT1 Vs(ksRO kse) Cs1 kwrVw Cwt,2 kwmd Vw Cwt,3

 Vfx Rsw fdw,1 (kwv,1 kwo)Vw (vsfsw,1 vsB fBw,1)Aw Cwt,1

Rsw (fdb,1 /bs) vrsfsb,1 Aw Cbt,1

Vw  dCwt,2

dt  LT2 Vs(ksRO kse) Cs2 kwo Vw Cwt,1 kwdm Vw Cwt,3

 Vfx Rsw fdw,2 (kwr kwm)Vw (vsfsw,2 vsB fBw,2)Aw Cwt,2

Rsw (fdb,2 /bs) vrsfsb,2 Aw Cbt,2

Vw  dCwt,3

dt  LT3 Vs(ksRO kse) Cs3 kwm Vw Cwt,2

 Vfx Rsw fdw,3 (kwv,3 kwmd kwdm)Vw (vsfsw,3 vsB fBw,3)Aw Cwt,3

Rsw (fdb,3 /bs) vrsfsb,3 Aw Cbt,3

4-38 

where: 

K = conductivity of component ithrough the air-water interface (m/yr) v,i

H = Henry’s Law constant for component i(m -atm/mole) i

3

R = Universal Gas constant (atm-m /mole-K) 3

T = water temperature (K) K

C = component i, vapor phase air concentration (μg/m ) a,i

3

For modeling purposes, it is convenient to divide this process into a diffusive loading term and a firstorder loss term, where kw is the volatilization loss rate constant (yr ). These terms are developed in v,i

-1

the sections below.

Using the calculated phase fractions, six differential equations for the three mercury components

in water column and sediments can be expressed in their mass balance form, grouping constants and

model parameters within brackets:
Vb  dCbt,1

dt  Rswf

dw,1 (vsfsw,1 vsB fBw,1)Aw Cwt,1 kbrVb Cbt,2

kbmd Vb Cbt,3  Rsw(f

db,1 /bs) (vrs vb)fsb,1 Aw (kbo)Vb Cbt,1

Vb  dCbt,2

dt  Rswf

dw,2 (vsfsw,2 vsB fBw,2)Aw Cwt,2 kbo Vb  Cbt,1

kbdm Vb Cbt,3  Rsw(f

db,2 /bs) (vrs vb)fsb,2 Aw (kbr kbm)Vb Cbt,2

Vb  dCbt,3

dt  Rswf

dw,3 (vsfsw,3 vsB fBw,3)Aw Cwt,3 kbm Vb  Cbt,2

 Rsw(f

db,3 /bs) (vrs vb)fsb,3 Aw (kbmd kbdm)Vb Cbt,3

Vw  d Sw

d t  LSe As103  Vfx vsAw Sw vrsAw SB

Vw  d SBio

d t  LSB Aw  Vfx kmort Vw vsB Aw SBio

Vb  d SB

d t  vsSw vsB SBio Aw  (vrs vmin vb)Aw SB

4-39 

Combined with the soil mercury equations expressed in the previous section, there are nine mass

balance differential equations to solve for nine state variables -- elemental, divalent, and methyl mercury

in the soil, water column, and benthic sediments. These differential equations have constant parameters

and can be solved for specified time intervals using a standard ODE solver. The following sections

describe how the model parameters in the water body differential equations are calculated or specified.

4.4.3.2 The Solids Balance Equations

The abiotic and biotic solids must be modeled in order to predict the dissolved and particulate

mercury fractions. These determine the amount of mercury lost through deposition and burial, and also

influence the bioavailability of methyl mercury in the water column. The differential equations

describing the fate of watershed-derived solids in the water column (S ), internally-generated biotic w

solids in the water column (S ), and total solids in the benthic sediments (S ) are given by: Bio B
Sw  LSe As103 vrsAwSB

Vfx vsAw

SBio  LSB Aw

Vfx kmort Vw vsB Aw

vB  vsSw vsB SBio (vrs vmin)SB

SB

LT,i  LDep,i LRI,i LR,i LE,i LDif,i

4-40 

where: 

L = watershed solids erosion load (kg/m -yr) Se

2

L = net internal production of biotic solids (g/m -year) SB

2

k = phytoplankton mortality rate (yr ) mort

-1

v = mineralization rate for upper benthic solids (m/year) min

For long-term average calculations using average watershed loading and waterbody productivity, S , w

S , and S can be assumed to be at steady-state. If S , v , v , v , and k are specified, then the water Bio B B s sB rs mort

column solids concentrations and the burial velocity can be calculated from:

4.4.3.3 Loads to the Water Body

The total chemical load term L in the mass balance equation is the sum of the loadings for each T

component "i." Component loadings included wet and dry deposition, impervious and pervious runoff,

erosion, and atmospheric diffusion:

where:

L = total component "i" load to the water body (g/yr) T,i

L = deposition of particle bound component "i" (g/yr) Dep,i

L = runoff load from impervious surfaces (g/yr) RI,i

L = runoff load from pervious surfaces (g/yr) R,i

L = soil erosion load (g/yr) E,i

L = diffusion load for vapor phase component "i" (g/yr) Dif,i

The runoff and erosion loads required estimation of average contaminant concentration in

watershed soils that comprise the depositional area. These concentrations were developed in terrestrial

sections above.

Load due to direct deposition -- The load to surface waters via direct deposition is solved as follows:
LDep,i  (Dyds,i D

yws,i

)  Aw

LRI,i  (D

yww,i Dydw,i

)  AI

LR,i  ksRO,i  Vs  Csi

LE,i  kse,i  Vs  Csi

4-41 

where:

L = direct component "i" deposition load (g/yr) Dep,i

D = yearly dry deposition rate of component "i" onto surface water body (g yds,i

pollutant/m -yr) 2

D = yearly wet deposition rate of component "i" onto surface water body (g yws,i

pollutant/m -yr) 2

A = water body area (m ) w

2

Load due to impervious surface runoff -- A fraction of the wet and dry chemical deposition in the

watershed will be to impervious surfaces. Dry deposition may accumulate and be washed off during rain

events. If the impervious surface includes gutters, the pollutant load will be transported to surface

waters, bypassing the watershed soils. The average load from such impervious surfaces is given by this

equation:

where:

L = impervious surface runoff load for component "i" (g/yr) RI,i

A = impervious watershed area receiving pollutant deposition (m ) I

2

D = yearly wet deposition flux of component "i" onto the watershed (g/m -yr) yww,i

2

D = yearly dry deposition flux of component "i" onto the watershed (g/m -yr) ydw,i

2

Load due to pervious surface runoff -- Most of the chemical deposition to a watershed will be to

pervious soil surfaces. These loads are accounted for in the soil mass balance equation. During periodic

runoff events, dissolved chemical concentrations in the soil are transported to surface waters as given by

this equation:

where:

L = pervious surface runoff load for component "i" (g/yr) R,i

ks = soil runoff rate constant for component i(yr ) RO,i

-1

C = component "i" concentration in watershed soils (g/m ) si

3

V = volume of pervious soil layer = A  z (m ) s ss

3

A = surface area of pervious soil layer = WA - WA (m ) s L I

2

WA = total watershed area receiving pollutant deposition (m ) L

2

WA = impervious watershed area receiving pollutant deposition (m ) I

2

z = depth of surface soil layer (m) s

Load due to soil erosion -- During periodic erosion events, particulate chemical concentrations in the

soil are transported to surface waters as described by this relationship:

where:

L = soil erosion load for component "i" (g/yr) E,i
LDif,i  Kv,i  Aw  Ca,i  106

Hi

/RTK

f

dw,i  1

1 Kdw,i

Sw106 KBio,iSBio 106

f

sw,i  Kdw,i

Sw106

1 Kdw,i

Sw106 KBio,iSBio 106

f

Bw,i  KBio,i

SBio 106

1 Kdw,i

Sw106 KBio,iSBio 106

4-42 

ks = soil erosion loss rate constant for component i(yr ) e,i

-1

Load due to gaseous diffusion -- The volatilization equation presented above is divided into a diffusive

loading term and a loss term in IEM-2M. The diffusive loading term is given by:

where:

L = diffusive loading rate for component "i" (g/yr) Dif,i

K = overall transfer rate, or conductivity for component "i" (m/yr) v,i

A = surface area of water body (m ) w

2

C = component "i" vapor phase air concentration over water body (μg/m ) a,i

3

H = component "i" Henry's Constant (atm-m /mole) i

3

R = universal gas constant, 8.206·10 atm-m /mole-K -5 3

T = water body temperature (K) K

10 = units conversion factor (g/μg) -6

This treatment of volatilization is based on the well-known two-film theory (Whitman, 1923), as

implemented in standard chemical fate models (Burns, et al., 1982, Ambrose, et al., 1988). The

equations for K are presented in the volatilization loss section below. v,i

4.4.3.4 Equilibrium Speciation Reactions

In the previous methodology, all mercury components and phases were assumed to be in

equilibrium. The equations presented here drop the assumption of equilibrium among components and

between water column and underlying sediments. For each mercury component, the fractions in the

aqueous phase and on the particulate phases are calculated for the water column and for the benthic

sediments from partition coefficients, solids concentrations, and porosities:
f

db,i  bs

bs Kdb,iSb 106

f

sb,i  Kdb,i

Sb 106

bs Kdb,iSb 106

4-43 

where:

f = aqueous phase fraction for component “i” in the water column dw,i

f = aqueous phase fractions for component “i” in the benthic sediments db,i

f = abiotic particulate fraction in the water column sw,i

 f = biotic particulate fraction in the water column Bw,i

f = particulate fraction in the sediments sb,i

S = abiotic solids concentration in the water column (g/m ) w

3

S = biotic solids concentration in the water column (g/m ) Bio

3

S = solids concentration (dry density) in the benthic sediments (g/m ) b

3

K = partition coefficient for abiotic solids in the water column (L /kg ) dws,i w s

K = partition coefficient for biotic solids in the water column (L /kg ) Bio w Bio

K = partition coefficient for benthic solids (L /kg ) db,i w s

 = porosity of the upper sediment bed (L /L). bs w

4.4.3.5 Transformation Processes in the Water Body

As described above, five transformation reactions are modeled as first-order rates. Rate

constants are directly specified and applied to the bulk concentration (all phases) in the water column and

in the benthic sediments to give internal mass transformation loadings. The oxidation loadings --

kw V C in the water column and kb V C in the sediments -- are subtracted from the Hg mass o w wt,1 o b bt,1

0

balance equations and added to the HgII equations. The reduction loadings, kw V C and kb V C , r w wt,2 r b bt,2

are subtracted from the HgII equations and added to the Hg equations. The methylation loadings, 0

kwm w wt,2 m b bt,2 V C and kb V C , are subtracted from the HgII equations and added to the MHg equations. 

The demethylation loadings, kwdm w wt,3 dm b bt,3 V C and kb V C , are subtracted from the MHg equations and

added to the HgII equations. Finally, the mer demethylation loadings, kwmd w wt,3 md b bt,3 V C and kb V C , are

subtracted from the MHg equations and added to the Hg equations. 0

While the transformation rate constants are specified as input to the IEM-2M model, it is

understood that their values may be affected by several environmental properties, including pH, DOC,

anoxia, sulfate concentrations, and water clarity. Many of these dependencies are under investigation by

the scientific community; some are built into the Regional Mercury Cycling Model (R-MCM). It was

decided not to program the environmental rate dependencies into the IEM-2M, but rather to require their

consideration external to the model. The rate dependencies and values for the rate constants are iscussed

in Appendix B, with citations to the current scientific literature. A qualitative summary is given here.
Rsw  f

dw,i

Cwt,i  (f

db,i

/bs)Cbt,i

Rsw  Esw  Aw  bs

zb

 3.15×107

4-44 

Low pH conditions should favor oxidation of Hg , while high pH conditions should favor 0

reduction of HgII. Lower pH, then, should lead to lower volatilization loss and higher levels of HgII that

can be methylated. Reduction and demethylation in the water column appear to be mediated by sunlight. 

Low water clarity due to high concentrations of DOC and solids should lead to slower reduction and

subsequent volatilization loss, and higher levels of total and methyl mercury. Methylation is mediated by

anaerobic bacteria, predominantly sulfate reducers. Anoxic conditions and moderate concentrations of

sulfate should lead to more rapid methylation. DOC affects mercury in several ways. High levels of

DOC compete with solids in complexing HgII and MHg, thus promoting more mobility of these

components from the benthic sediments to the water column. The DOC-complexed mercury does not

volatilize, further promoting higher levels of mercury in the water column. Other reactions, however,

may also be retarded by DOC complexation, including bioaccumulation in the aquatic food web.

4.4.3.6 Transport and Transfer Processes in the Water Body

Mercury components are transferred between the water column and benthic sediment

compartments of a water body through pore water diffusion, solids deposition, and resuspension. They

are lost from the water body due to advection, burial, and volatilization.

Advection -- Advective flow from the water body removes all phases of component iat a rate

proportional to the average volumetric flow rate, Vf , and the water column concentration C . An x wt,i

impacted water body derives its annual flow from its watershed or effective drainage area. Flow and

watershed area, then, are related, and compatible values should be specified by the user. Given the area

of drainage, one way to estimate annual flow volume is to multiply total drainage area (in length squared

units) by a unit surface water runoff (in length per time). The Water Atlas of the United States (Geraghty

et al., 1973) provides maps with isolines of annual average surface water runoff, which is defined as all

flow contributions to surface water bodies, including direct runoff, shallow interflow, and groundwater

recharge. The values ranged from 5 to 40 in/yr (0.13 to 1.0 m/yr) in various parts of the United States.

Pore Water Diffusion -- Pore water diffusion exchanges dissolved constituents between the water

column and the benthic sediments. As expressed in the mass balance equations above, this exchange is

the product of the pore water diffusive volume and the dissolved phase concentration gradient between

the pore water and the water column:

where:

R = pore water diffusive volume (m /yr) sw

3

= benthic porosity (L /L) bs w

The diffusive volume is calculated from the pore water diffusion coefficient:

where:

E = pore water diffusion coefficient (m /sec) sw

2

A = water body surface area (m ) w

2

= benthic porosity (L /L) bs w
vs  Aw  f

sw,i  Cwt,i vsB  Aw  fBw,i  Cwt,i

vrs  Aw  f

sb,i  Cbt,i

vb  Aw  f

sb,i  Cbt,i

4-45 

z = benthic layer depth, taken as the characteristic mixing length (m) b

 3.15×10 = units conversion factor (sec/yr) 7

The product Aw bs represents the cross-sectional area within the sediment layer through which diffusion

occurs.

Solids Movement -- The sorbed fraction of component imoves at the same velocity as its particulate

carrier. The solids balance equations include the processes of abiotic and biotic deposition,

resuspension, and burial. As expressed in the mercury mass balance equations above, abiotic and biotic

deposition is the product of the solids deposition velocity, the particulate phase concentrations in the

water column, and the surface area:

where:

A = water body surface area (m ) w

2

v = abiotic solids deposition velocity (m/yr) s

v = biotic solids deposition velocity (m/yr) sB

C = total component iconcentration (g/m ) wt,i

3

f = fraction of component iconcentration sorbed to abiotic solids sw,i

f = fraction of component iconcentration sorbed to biotic solids Bw,i

In a similar manner, resuspension is the product of the sediment resuspension velocity, the

particulate phase concentration in the sediments, and the surface area:

where:

A = water body surface area (m ) w

2

v = sediment resuspension velocity (m/yr) rs

C = total component iconcentration in the sediment (g/m ) bt,i

3

f = fraction of component iconcentration sorbed to sediment solids sb,i

Finally, burial is the product of the sediment burial velocity, the particulate phase concentration in the

sediments, and the surface area:

where:

A = water body surface area (m ) w

2

v = sediment burial velocity (m/yr) b

C = total component iconcentration in the sediment (g/m ) bt,i

3

f = fraction of component iconcentration sorbed to sediment solids sb,i
Kv,i  Aw  Cdw,i  Kv,i

zw

 Vw  f

dw,i  Cwt,i

kwv,i  Kv,i  f

dw,i

zw

Kv,i  (RL,i RG,i)

1  K1

L,i KG,i

Hi

R TK

1 1

4-46 

Volatilization -- The volatilization equation presented above is divided into a diffusive loading term and

a loss term in IEM-2M. The volatilization loss term is given by:

where:

K = overall transfer rate, or conductivity for component i(m/yr) v,i

A = surface area of water body (m ) w

2

z = depth of water column (m) w

V = volume of water column (m ) w

3

C = dissolved concentration of component iin water column (g/m ) dw,i

3

f = dissolved fraction of component dw,i iin water column

C = total concentration of component iin water column (g/m ) wt,i

3

From this equation, the rate constant for volatilization loss used in the mass balance equation can be

derived:

where:

kw = water column volatilization loss rate constant for component i(yr ) v,i

-1

K = overall transfer rate, or conductivity for component i(m/yr) v,i

f = fraction of component dw,i iin the water column that is dissolved

z = water body depth (m) w

The overall transfer rate, K or conductivity, was determined by the two-layer resistance model v,i

(Whitman, 1923; or see Burns, et al., 1982 or Ambrose, et al., 1988). The two-resistance method

assumes that two stagnant filmsat the air-water interface are bounded on either side by well mixed

compartments. Concentration differences serve as the driving force for the water layer diffusion. 

Pressure differences drive the diffusion for the air layer. From mass balance considerations, it is obvious

that the same mass must pass through both films; thus, the two resistances combine in series, so that the

conductivity is the reciprocal of the total resistance:

where:

R = liquid phase resistance (year/m) L,i

K = liquid phase transfer coefficient (m/year) L,i

R = gas phase resistance (year/m) G,i

K = gas phase transfer coefficient (m/year) G,i

R = universal gas constant (atm-m /mole-K) 3

H = Henry's law constant for component "i" (atm-m /mole) i

 3
Kv,i,T  Kv,i,20 (T20)

KL,i  u  a

w

0.5

k 0.33

2

Sc 0.67

w,i (3.15x10 7 )

KG,i  u  k 0.33

2

Sc 0.67

a,i (3.15x10 7 )

u   Cd

0.5 W

Sca,i  μa

a Da,i

 a

Da,i

4-47 

T = water body temperature (K) K

The value of K , the conductivity, depends on the intensity of turbulence in a water body and in v,i

the overlying atmosphere. As the Henry's Law constant increases, the conductivity tends to be

increasingly influenced by the intensity of turbulence in water. As the Henry's Law constant decreases,

the value of the conductivity tends to be increasingly influenced by the intensity of atmospheric

turbulence.

Because Henry's Law constant generally increases with increasing vapor pressure of a compound

and generally decreases with increasing solubility of a compound, highly volatile low solubility

compounds are most likely to exhibit mass transfer limitations in water, and relatively nonvolatile high

solubility compounds are more likely to exhibit mass transfer limitations in the air. Volatilization is

usually of relatively less magnitude in lakes and reservoirs than in rivers and streams.

The estimated volatilization rate constant was for a nominal temperature of 20 C. It is adjusted

for the actual water temperature using the equation:

where:

= temperature correction factor, set to 1.026.

T = water body temperature (C)

 There have been a variety of methods proposed to compute the liquid (K ) and gas phase (K ) L,i G,i

transfer coefficients. For a stagnant system, the transfer coefficients are controlled by wind-induced

turbulence. For stagnant systems, the liquid film transfer coefficient (K ) is computed using the L,i

O'Connor (1983) equations:

where:
Da,i  1.9

MW 2/3

i

a  (1.32 0.009 Ta) × 101

Scw,i  μw

w Dw,i

Dw,i  22×105

MW 2/3

i

w  1  8.8×105 Tw

log(μw)  1301

998.333 8.1855(Tw20) 0.00585(Tw20)2  3.0233

4-48 

and:

u = shear velocity (m/s) *

C = drag coefficient (= 0.0011) d

W = wind velocity, 10 m above water surface (m/s)

 = density of air corresponding to the air temperature (g/cm ) a

 3

 = density of water corresponding to the water temperature (g/cm ) w

 3

k = von Karman's constant (= 0.4)

 = dimensionless viscous sublayer thickness (= 4) 2

Sc = air Schmidt number for component "i" (dimensionless) a,i

Sc = water Schmidt number for component "i" (dimensionless) w,i

D = diffusivity of component "i" in air (cm /sec) a,i

 2

D = diffusivity of component "i" in water (cm /sec) w,i

 2

μ = viscosity of air corresponding to the air temperature (g/cm-s) a

μ = viscosity of water corresponding to the water temperature (g/cm-s) w

 = dynamic viscosity of air (cm /sec) a

2

MW = molecular weight of component "i" i

T = air temperature (C) a

T = water temperature (C) w

 3.15x10 = units conversion factor (sec/yr) 7
5-1

5. ATMOSPHERIC FATE AND TRANSPORT MODELING RESULTS

This chapter summarizes the results of the atmospheric fate and transport modeling of mercury

using the long-range and local models.

5.1 Long-Range Atmospheric Fate/Transport Modeling

5.1.1 Mass Balances of Mercury within the Long-Range Model Domain

The general mass balance of elemental mercury gas, divalent mercury gas, and particle-bound

mercury from the RELMAP simulation results using the assumed emission speciation profiles are shown

in Table 5-1. The mass-balance accounting for the simulation using the meteorological data from the

year 1989 shows a total of 141.8 metric tons of mercury emitted to the atmosphere from anthropogenic

sources. This simulated emission total differs slightly from the national totals indicated in Volume II

since the states of Alaska and Hawaii are not within the model domain. The RELMAP simulation

indicates that 47.6 metric tons of anthropogenic mercury emissions are deposited within the model

domain and 0.4 metric tons remain in the air within the model domain at the end of the simulation. The

remainder, about 93.8 metric tons, is transported outside the model domain and probably diffuses into the

global atmospheric reservoir. The simulation also shows 32.0 metric tons of mercury is deposited within

the model domain from the global atmospheric reservoir, suggesting that about three times as much

mercury is being added to the global reservoir as is being deposited from it. The total amount of mercury

deposited in the model domain annually from U.S. anthropogenic emissions and from the global

background concentration is estimated to be 79.6 metric tons, or slightly more than one-half of the mass

of all atmospheric emissions from anthropogenic sources in the lower 48 United States.

Table 5-1

Mercury Mass Budget in Metric Tons from RELMAP Simulation

Source/Fate Hg Hg Hg 0 a 2+ b

p 

c Total

Mercury

Total U.S. anthropogenic emissions 63.5 52.3 26.0 141.8

Mass advected from model domain 62.3 15.5 16.0 93.8

Dry deposited anthropogenic emissions 0.0 22.9 0.5 23.4

Wet deposited anthropogenic emissions 0.9 13.8 9.5 24.2

Remaining in air at end of simulation 0.3 <0.1 <0.1 0.4

Total deposited anthropogenic emissions 0.9 36.8 10.0 47.6

Deposition from background Hg 32.0 - - 32.0 0

Mercury deposited from all sources 32.9 36.8 10.0 79.6

(All figures rounded to the nearest tenth of a metric ton)

 Hg = Elemental Mercury a 0

 Hg = Divalent Vapor-phase Mercury b 2+

 Hg = Particle-Bound/Mercury c

p
5-2

A variety of emission speciation profiles have been tested for all source types in the RELMAP

mercury model to evaluate the model's sensitivity to the assumed chemical and physical forms of the

mercury air emissions (Bullock et al., 1997b). The results of this study showed a strong positive

correlation between the fraction of emissions in Hg and Hg forms and total simulated wet deposition. 2+

P

A specific alternate speciation has been tested using the RELMAP where all Hg emissions were 2+

assumed to convert to the particulate form (Bullock et al., 1997a). The results of this simulation showed

that the total mass of mercury deposited in all forms was reduced by 40% compared to the base-case

simulation. It is generally known that gaseous Hg can adsorb to the surface of particulate matter, 2+

especially carbon soot. However, there exists no evidence of complete conversion to the particulate

form. This case of complete Hg -to-Hg transfer was modeled only as a sensitivity test for the 2+

P

RELMAP mercury model. There remains considerable uncertainty about the actual speciation of

atmospheric mercury emissions from most anthropogenic sources. As current studies of this subject

progress, our confidence in the results of atmospheric model simulations will increase. Based on the

results in Bullock et al. (1997a), we estimate an emission speciation modeling uncertainty of +/- 40% for

RELMAP-derived mass balance estimates.

Of the total anthropogenic mercury mass deposited to the surface in the model domain, 77% is

estimated by the RELMAP simulation to come from Hg emissions, 21% from Hg emissions and 2% 2+

P

from Hg emissions. When the deposition of Hg from the global background is considered in addition 0 0

to anthropogenic sources in the lower 48 states, the species fractions of total deposition become 46%

Hg , 41% Hg and 13% Hg . The vast majority of mercury already in the global atmosphere is in the 2+ 0

P

form of Hg and, in general, the anthropogenic Hg emissions do not greatly increase the existing Hg 0 0 0

concentration. Although Hg is removed from the atmosphere very slowly, the global background 0

reservoir is large and total deposition from it is significant. It should be noted here that dry deposition of

Hg is thought to be significant only on the local scale and has not been included in the RELMAP 0

simulations. Wet deposition is the only major pathway for removal of Hg from the atmosphere. This 0

removal pathway simulated by the RELMAP involves oxidation of mercury by ozone in an aqueous

solution; thus, the Hg that is extracted from the atmosphere by the modeled precipitation process would 0

actually be deposited primarily in the form of Hg . 2+

Results from the RELMAP simulation show that of the 63.5 metric tons of anthropogenic Hg0

emitted in the lower 48 states, only 0.9 tons (1.4%) is deposited within the model domain, while of the

52.3 metric tons of Hg emitted, about 36.8 tons (70.4%) is deposited. Ninety-eight percent of the 2+

deposited anthropogenic mercury was emitted in the form of Hg or Hg . Thus, a strong argument can 2+

P

be made that the combined Hg and Hg component of anthropogenic mercury emissions can be used as 2+

P

an indicator of eventual deposition of those emissions to the lower 48 states and surrounding areas. The

emission inventory and estimated chemical/physical speciation profiles indicate that of all combined

Hg and Hg emissions, 29% is from electric utility boilers, 25% is from municipal waste combustion, 2+

P

18% is from medical waste incineration, 16% is from commercial and industrial boilers, and 12% is from

all other modeled sources.

5.1.2 Qualitative Description of Mercury Concentration Results

Average surface-level concentration fields for elemental mercury, divalent mercury, and

particulate mercury have been calculated from the RELMAP simulation using the meteorological data

for the year 1989 and current air emission estimates. Figure 5-1 shows the annual average elemental

mercury (Hg ) concentration at ground level from anthropogenic sources obtained by using the source- 0

based emission speciation profiles described in chapter 4, section 2. It shows that anthropogenic Hg0
5-3

concentrations remain less than 0.1 ng/m over nearly all of the modeled area. The areas where the 3

average anthropogenic Hg concentrations exceed 0.1 ng/m are mostly confined to the highly 0 3

industrialized regions of the eastern Mid-west and the North-east. Compared to the estimated average

global background concentration of 1.6 ng/m , this 0.1 ng/m elevation of Hg concentration by 33 0

anthropogenic emissions is rather small. 

Figure 5-2 shows annual average divalent mercury vapor (Hg ) air concentrations, also using the 2+

base case emissions. These values are significantly lower than for anthropogenic Hg , and there are 0

some new areas of higher concentration. The highest concentration areas have values from 0.05 to 0.1

ng/m and are mostly confined to the Midwest and the Northeast corridor, but two high concentration 3

areas are also located near Tampa and Miami, Florida. The background atmospheric mercury loading is

assumed to be completely in the elemental form, so there is no background contribution to the Hg2+

concentrations. In most areas, the anthropogenic component of the Hg concentrations shown in Figure 0

5-1 are at least 4 times higher than the Hg concentrations shown in Figure 5-2. For the assumed 2+

emission speciation, Hg vapor is a minor component of the total mercury emissions from some source 2+

types, but it is a significant part of the total mercury emissions from most waste incineration (60%) and

fossil fuel combustion (30%). Since the total Hg emissions are similar to those for Hg , these much 2+ 0

lower average annual Hg concentrations cannot be attributed to the emissions. The lower simulated air 2+

concentrations of Hg vapor are due to its more rapid removal from the atmosphere than for Hg . 2+ 0

The RELMAP Hg and Hg air concentration results taken together with the assumed 0 2+

background Hg concentration of 1.6 ng/m agree well with observations of vapor-phase Hg air 0 3

concentration in Minnesota by Fitzgerald et al. (1991), in Vermont by Burke et al. (1995) and in

Wisconsin by Lamborg et al. (1995). These works showed that annual average vapor-phase Hg

concentrations were near the levels found over other remote locations in the northern hemisphere, from

1.6 to 2.0 ng/m . Measurements taken for a two-week period at three sites in Broward County, Florida, 3

(Dvonch et al., 1995) show slightly elevated vapor-phase Hg air concentrations for two of those sites

downwind of industrial activities. These two sites had average vapor-phase Hg air concentrations of 3.3

and 2.8 ng/m . The RELMAP simulation results for the Fort Lauderdale area show only about a 0.1 3

ng/m elevation of the annual average vapor-phase Hg (Hg plus Hg ) concentration over the 1.6 ng/m 3 0 2+ 3

background value assumed. The measurements of Dvonch et al. (1995), however, did not extend for a

significant portion of the year and there was no discrimination between Hg and Hg forms. The third 0 2+

site for their observations had an average vapor-phase air concentration of 1.8 ng/m , which is much 3

closer to the RELMAP simulation results. A more comprehensive air monitoring program is required

before an evaluation of the RELMAP results in Florida can be performed. 

Particulate mercury (Hg ) emissions are thought to be a small fraction of the total for most P

source types. For the base-case emission speciation, 20% is the largest particulate fraction of mercury

emissions for any source type. Figure 5-3 shows that the simulated annual average Hg concentrations P

were even lower than those for Hg vapor. The maximum annual average values are around 50 pg/m 2+ 3

(0.05 ng/m ) in the urban centers of the Northeast. Keeler et al. (1995) found instantaneous Hg 3

P

concentrations in urban Detroit during March of 1992 of over 1 ng/m and average concentrations over 3

an 18-day period of 94 pg/m . Given the 40-km horizontal scale of the RELMAP computational grid, 3

however, one cannot expect the simulation to reflect these extreme local-scale measurement results. The

RELMAP simulation suggests an annual average Hg concentration in the Detroit area of about 40 P

pg/m . Dvonch et al. (1995) found average Hg concentrations in Broward County, Florida, of between 3

P

34 and 51 pg/m at three sites 3



5-7

from 25 August to 7 September of 1993. The RELMAP simulation resulted in a value of around 25

pg/m near the city of Fort Lauderdale. Keeler et al. (1995) found annual average Hg air concentrations 3

P

of 10.5 pg/m in Pellston, Michigan, 22.4 pg/m in South Haven, Michigan, and 21.9 pg/m in Ann 33 3

Arbor, Michigan, from April 1993 to April 1994, and 11.2 pg/m in Underhill, Vermont, for the year of 3

1993. The RELMAP simulation showed 8.0 pg/m for Pellston, Michigan, 15.2 pg/m in South Haven, 3 3

Michigan, 21.2 pg/m in Ann Arbor, Michigan, and 10.6 pg/m in Underhill, Vermont, indicating a slight 3 3

tendency of the model to under-estimate particulate mercury air concentrations in these locations, but by

no more than 25%.

Table 5-2 shows a percentile analysis of the simulated concentration results from the RELMAP

grid cells over the entire continental United States and for two subsets of this area east and west of 90 W

longitude. This table shows that the Hg concentrations never exceeded the assumed background level of 0

1.6 ng/m by a large relative amount. It also shows that Hg and to a lesser degree Hg air 3 2+

P

concentrations were highly elevated in only a few grid cells. Over the entire continental U.S., there is

nearly an order of magnitude difference between the modeled Hg concentrations at the 90th percentile 2+

level and those at the maximum level, with approximately a factor of 4 difference for Hg .P
5-8

Table 5-2

Percentile Analysis of RELMAP Simulated Concentration Results 

for the Continental U. S.

Variable Min 10th 50th 90th Max

Full Area

Hg concentration (ng/m ) 1.602 1.606 1.619 1.662 1.903 0a 3

Hg concentration (pg/m ) 0.101 0.329 1.825 9.144 72.71 2+b 3

Hg concentration (pg/m ) 0.156 0.699 3.753 13.87 51.18 P

c 3

Total mercury (ng/m ) 1.602 1.607 1.624 1.685 1.995 3

East of 90 W longitude 

Hg concentration (ng/m ) 1.612 1.629 1.651 1.687 1.903 0a 3

Hg concentration (pg/m ) 0.787 2.750 6.426 14.92 72.71 2+b 3

Hg concentration (pg/m ) 2.789 6.324 10.89 19.29 51.18 P

c 3

Total mercury (ng/m ) 1.616 1.640 1.668 1.720 1.995 3

West of 90 W longitude 

Hg concentration (ng/m ) 1.602 1.605 1.613 1.632 1.702 0a 3

Hg concentration (pg/m ) 0.101 0.271 1.100 3.481 26.95 2+b 3

Hg concentration (pg/m ) 0.158 0.595 2.146 6.392 27.43 P

c 3

Total mercury (ng/m ) 1.602 1.606 1.616 1.642 1.743 3

 Hg = Elemental Mercury a 0

 Hg = Divalent Vapor-phase Mercury b 2+

 Hg = Particle-Bound/Mercury c

p

5.1.3 Description of Mercury Wet Deposition Simulation Results

Figure 5-4 shows the total simulated wet deposition of Hg from U. S. anthropogenic sources 0

using the meteorological data for the year 1989 and current air emission estimates. Figure 5-5 shows the

total simulated wet deposition of Hg assuming only a non-depleting global background concentration of 0

1.6 ng/m . Both of these wet deposition results are influenced by ozone and soot concentrations due to 3

the chemical transformations modeled by the RELMAP. Emission patterns influence the primary

anthropogenic Hg wet deposition pattern, and it is obvious that total annual precipitation is a strong 0

factor in wet deposition from the global background concentration with heaviest wet deposition in areas

with the


5-11

highest annual precipitation. It is widely accepted that deposition of measurable quantities of mercury

occurs on continental and global scales, and the RELMAP simulation shows areas of Hg wet deposition 0

occurring in remote areas. The simulated annual wet deposition for Hg vapor shown in Figure 5-6 2+

show high deposition areas that are much more local to the emission source areas. There are a few

model cells in urban areas with wet deposition totals of Hg vapor over 30 μg/m while most of the cells 2+ 2

in the non-urban areas have wet depositions of less than 3 μg/m . This indicates the Hg vapor wet 2 2+

deposits more on the local scale and less on regional or global scales and that its wet removal from the

atmosphere is much more rapid than for Hg . This is an expected result due to the higher water solubility 0

of most mercuric salts compared to mercury in the elemental form. Figure 5-7 shows that the maximum

simulated wet deposition of Hg is about one-third of that for Hg vapor. This is partly due to P

2+

differences in the total mass of Hg emitted compared to Hg , but it is also due to the less efficient wet P

2+

scavenging that is assumed for Hg versus Hg . The areas of Hg wet deposition are also more widely P P

2+

distributed than for Hg due to the slower wet scavenging of Hg and, thus, a greater opportunity for 2+

P

long-range transport.

The simulated total wet deposition of mercury from anthropogenic emissions in all three forms

and from the global background is shown in Figure 5-8. This illustration shows significant wet

deposition of mercury over most of the eastern half of the U.S. For the simulated meteorological year of

1989, the entire eastern half of the nation has a wet deposition total of over 3 μg/m and values exceed 10 2

μg/m over most of the Ohio Valley and Northeast U.S. In fact, the largest simulated wet deposition is 2

slightly over 80 μg/m in the grid cell containing New York City. At this time, these highest wet 2

deposition rates for total mercury cannot be substantiated by observations. In the RELMAP simulation

the most impacted areas are subjected to wet deposition of mercury mainly from emissions of Hg2+

vapor. It is likely that the RELMAP model for mercury may still be significantly incomplete, and that

other chemical and/or physical transformations may occur which moderate the wet deposition of Hg2+

vapor and possibly Hg .P

There exist only limited data with which to compare the RELMAP simulation results. 

Measurements of mercury wet deposition at three locations in northeastern Minnesota during 1989 by

Glass et al. (1991) indicated annual wet deposition rates of 6.5 μg/m at Duluth, 13.5 μg/m at Marcell 2 2

and 41.9 μg/m at Ely. A later study by Sorensen et al. (1994) measuring annual wet deposition of 2

mercury during 1990, 1991 and 1992 at Ely, Duluth and seven other sites in Minnesota, upper Michigan

and northeastern North Dakota found all annual wet deposition totals to be within the range of 3.8 to 9.7

μg/m , bringing into question the Ely observation of 41.9 μg/m in 1989 by Glass et al. (1991). 2 2

Measurements by Fitzgerald et al. (1991) at Little Rock Lake, in northern Wisconsin, of mercury in snow

during February and March, 1989, and in rain from May to August, 1989, have been used to estimate

annual mercury depositions in rain and snow of 4.5 and 2.3 μg/m , respectively. This suggests a total 2

annual mercury wet deposition of 6.8 μg/m at Little Rock Lake. Measurements at Presque Isle, also in 2

northern Wisconsin, from 1993 to 1994 by Lamborg et al. (1995) suggested a wet deposition rate for

total mercury of 5.2 μg/m /yr, somewhat less than the measurements by Fitzgerald et al. (1991). The 2

extremely heavy rainfall during the summer of 1993 in the mid-west states to the south and west of

Presque Isle may be responsible for the lower wet deposition. The RELMAP simulation results using the

meteorological data for 1989 indicate around 4 μg/m wet deposition of total mercury over northern 2

Minnesota and 5 μg/m wet deposition of total mercury over northern Wisconsin. However, due to the 2

varying years of observation, these data cannot be used to confidently evaluate the RELMAP model

performance for the 1989 simulation period.



5-15

There were also some mercury wet deposition measurement programs conducted during the early

1990's in somewhat less remote sites in Michigan and Vermont. Observations by Hoyer et al. (1995)

during two years of event precipitation sampling at three sites in Michigan show evidence for a north-tosouth gradient in mercury wet deposition. From March 1992 to March 1993, the total mercury wet

deposition observed at South Haven, in southwest Michigan, was 9.45 μg/m . At Pellston, in the 2

northern part of the lower peninsula of Michigan, the wet deposition was 5.79 μg/m . At Dexter, in 2

southeast Michigan about 100 km west of Detroit, the wet deposition was 8.66 μg/m . From March 1993 2

to March 1994, wet deposition at South Haven was 12.67 μg/m , significantly higher than for the 2

previous year, while measurements at Pellston and Dexter remained about constant at 5.54 and 9.11

μg/m , respectively. Hoyer et al. (1995) attribute the higher second-year wet deposition at South Haven 2

to an increased precipitation rate and cite the measurements by Burke et al (1995) at Underhill, Vermont,

as further evidence of the importance of precipitation amount. From December 1992 to December 1993,

the average volume-weighted mercury concentration at Underhill (8.3 ng/L) was similar to that observed

at Pellston (7.9 ng/L). However, with more precipitation during that period the total mercury wet

deposition at Underhill was 9.26 μg/m , significantly higher than at Pellston. The RELMAP simulation 2

results show 6.63 μg/m wet deposition of total mercury at the Pellston site, about 20% larger than the 2

1992 to 1994 observations. At Underhill, the RELMAP simulation indicates 11.86 μg/m wet deposition, 2

about 25% larger than the observation in 1993. At the South Haven site, the RELMAP simulation

showed 11.57 μg/m wet deposition of total mercury, which closely approximates the measurements 2

taken there from 1992 to 1994. At Dexter, the RELMAP simulation showed 12.84 μg/m wet deposition 2

of total mercury, about 40% above the observed values from 1992 to 1994. Overall, this comparison

seems to indicate a slight tendency of the RELMAP mercury model to over-estimate wet deposition. 

However, one should not expect the RELMAP simulation using 1989 meteorology to exactly match

observed wet deposition values from 1992 to 1994 due to differences in annual precipitation from year to

year. Nonetheless, the agreement between simulated and observed annual wet deposition of total

mercury provides some evidence that the most important atmospheric processes for deposition of

mercury in precipitation are being accounted for.

The very large total mercury wet deposition values (>30 μg/m ) from the RELMAP simulation 2

for some of the larger urban centers in the Great Lakes, Ohio Valley and Northeast regions cannot be

evaluated thoroughly due to a lack of long-term precipitation event sampling at those locations. A data

report recently obtained by EPA's Great Lakes National Program Office (Keeler, 1997) showed a total

mercury wet deposition of 30.3 μg/m at the Illinois Institute of Technology in Chicago between July 1, 2

1994 and October 31, 1995, a period of 16 months. The 12-month RELMAP simulation produced 37.5

μg/m at this location. A study by Dvonch et al. (1995) describes precipitation event sampling from 19 2

August to 7 September of 1993 at 4 sites in Broward County, Florida, in and around the city of Fort

Lauderdale. During the 20-day sampling period, total mercury mean concentrations in precipitation were

35, 57, 40 and 46 ng/L at the 4 sites. Given the average annual precipitation of 150 cm per year typical

of that area, the resulting annual wet deposition estimates at these 4 sites would 52.5, 85.5, 60 and 69

μg/m . Since most of the annual rainfall in Broward County occurs in warm tropical conditions of the 2

March to October wet season, this extrapolation from 20 days during the wet season to an annual

estimate is not totally without basis. However, additional urban measurement studies are required to

allow any credible evaluation of RELMAP wet deposition results in heavily populated, industrialized

area.

Observational mercury wet deposition data obtained over the World Wide Web from the

Mercury Deposition Network (Lindberg and Vermette, 1995; Vermette et al., 1995) have been used to

estimate 1989 mercury wet deposition totals for 12 locations in the eastern U. S. Table 5-3 shows the

data obtained from the MDN transitional data set at 17 locations during 1994 and 1995. Only those sites
5-16

with 50% or more data completeness were used to estimate 1989 wet deposition totals. The observed

volume-weighted mercury concentration at the MDN site (ng/L) was multiplied by the total accumulated

precipitation depth (m) as modeled for 1989 at each location to obtain estimates of total wet deposition

of mercury during the 1989 simulation period (μg/m ). These estimates of total 1989 mercury wet 2

deposition are shown along with the RELMAP-simulated values and percent differences on Table 5-4. 

For all locations but two, the RELMAP simulation produced 20 to 50% less wet deposition than the

estimated 1989 values. It should be noted that the Sturgeon Point (NY97) site, where the model

difference is most positive, and the Mulberry Flat (KY99) site, where the model difference is most

negative, are both located very close to the edge of their model cells. They are also located in areas

where large point sources of atmospheric mercury are known to exist. Thus the indicated model error

may be due more to horizontal resolution problems than inaccurate atmospheric process modeling. 

Overall, this comparison seems to contradict the previous comparison to 1993 and 1994 observations in

Michigan and Vermont and indicates a tendency for the RELMAP mercury model to under-estimate wet

deposition.

Table 5-3

Observed Mercury Deposition in Precipitation from the Transition Phase Data Report 

of the Mercury Deposition Network (MDN)

(from http://nadp.nrel.colostate.edu/nadp/mdn/mdn.html)

Station Conc. Complete- Latitude Longitude

(ddmmss) (ddmmss)

Vol. Wt. Data

(ng/L) ness

Lewes (DE02) 384620 750557 8.28 94%

Everglades Nat'l Pk. (FL11) 252324 804048 7.90 42%

Bondville (IL11) 400312 882219 13.56 50%

Mulberry Flat (KY99) 365405 880049 12.56 88%

Wye (MD13) 385447 760909 8.59 46%

Acadia Nat'l Pk. (ME98) 442226 681538 3.62 40%

Marcell Exp. Forest (MN16) 473152 932807 8.40 94%

Fernberg (MN18) 475647 912946 9.0 79%

Waccamaw State Pk. (NC08) 341000 782500 9.18 96%

Pettigrew State Pk. (NC42) 354500 762200 8.79 87%

Sturgeon Point (NY97) 424100 790200 12.75 75%

Congaree Swamp (SC19) 335200 805200 12.83 94%

Longview (TX21) 322243 944242 8.13 27%

Olympic Nat'l Pk. (WA14) 475136 1235555 4.18 37%

Brule River (WI08) 464500 913000 10.04 85%

Popple River (WI09) 454747 882358 12.52 96%

Trout Lake (WI36) 460310 893911 10.53 96%
5-17

Table 5-4

Estimates of 1989 Mercury Wet Deposition in Precipitation from MDN Data 

and Comparison to Modeled Wet Deposition 

Station Conc. Precip. Hg Dep. Dep. % difference

Vol. Wt. Modeled Estimated Modeled Hg

(ng/L) (meters) (μg/m ) (μg/m ) 2 2

DE02 8.28 1.525 12.63 12.47 +1

IL11 13.56 0.857 11.62 8.51 -27

KY99 12.56 1.271 15.97 7.46 -53

MN16 8.40 0.707 5.94 3.37 -43

MN18 9.00 0.683 6.15 3.52 -43

NC08 9.18 1.336 12.26 8.38 -32

NC42 8.79 2.209 19.41 11.52 -41

NY97 12.75 0.888 11.33 14.47 +28

SC19 12.83 1.257 16.12 12.91 -20

WI08 10.04 0.685 6.88 4.25 -38

WI09 12.52 0.631 7.91 4.48 -43

WI36 10.53 0.751 7.91 4.70 -41

The percentile analysis of the wet deposition simulation results in Table 5-5 shows that

50 percent of the continental U.S. had an annual wet deposition of total Hg of 2.9 μg/m or more, and 10 2

percent of the area had 12.4 μg/m or more. However, due to rapid wet deposition of Hg and Hg there 2 2+

P

are select areas where wet deposition may be significantly higher. In the eastern U.S., east of 90 degrees

west longitude, the 50th and 90th percentile levels for total Hg wet deposition are considerably higher

than those for the entire continental U.S., about 10 and 18 μg/m , respectively. 2
5-18

Table 5-5

Percentile Analysis of RELMAP Simulated Wet Deposition for the Continental U. S.

Variable Min 10th 50th 90th Max

Full Area

Hg wet dep. (μg/m /yr) <0.001 0.002 0.029 0.247 1.011 0a 2

Hg wet dep. (μg/m /yr) <0.001 0.031 0.401 3.880 54.42 2+b 2

Hg wet dep. (μg/m /yr) <0.001 0.030 0.371 2.613 19.41 P

c 2

Background Hg (μg/m /yr) 0.022 0.584 2.111 5.994 9.722 2

Total mercury (μg/m /yr) 0.022 0.697 2.858 12.42 80.31 2

East of 90 W longitude 

Hg wet dep. (μg/m /yr) 0.007 0.060 0.181 0.385 1.011 0a 2

Hg wet dep. (μg/m /yr) 0.123 1.053 2.652 7.056 54.42 2+b 2

Hg wet dep. (μg/m /yr) 0.134 0.848 1.956 4.368 19.41 P

c 2

Background Hg (μg/m /yr) 0.524 2.908 5.138 6.944 9.722 2

Total mercury (μg/m /yr) 0.795 5.455 10.26 18.42 80.31 2

West of 90 W longitude 

Hg wet dep. (μg/m /yr) <0.001 0.002 0.011 0.090 0.465 0a 2

Hg wet dep. (μg/m /yr) <0.001 0.022 0.174 0.967 10.81 2+b 2

Hg wet dep. (μg/m /yr) <0.001 0.022 0.175 0.855 6.306 P

c 2

Background Hg (μg/m /yr) 0.022 0.507 1.317 3.889 7.627 2

Total mercury (μg/m /yr) 0.022 0.597 1.765 5.856 21.94 2

 Hg = Elemental Mercury from U.S. sources a 0

 Hg = Divalent Vapor-phase Mercury from U.S. sources b 2+

 Hg = Particle-Bound/Mercury from U.S. sources c

p
5-19

5.1.4 Qualitative Description of Mercury Dry Deposition Results

As described in the section on the RELMAP mercury model parameterizations, it was assumed

that Hg was not effectively dry deposited due to its high vapor pressure and very low water solubility at 0

normal atmospheric temperatures. Therefore, only Hg vapor and Hg were dry deposited in the 2+

P

RELMAP simulation. The percentile analysis of the simulated dry deposition using the assumed

emission speciation profiles is shown in Table 5-6. The statistics on this table indicate the strong local

dry deposition of Hg vapor as parameterized in the RELMAP mercury model. There is considerable 2+

uncertainty regarding the dry deposition velocity of Hg and in the extremely high local depositions 2+

indicated from the simulation.

Table 5-6

Percentile Analysis of RELMAP Simulated Dry Deposition for the Continental U. S.

Variable Min 10th 50th 90th Max

Full Area

Hg dry dep. (μg/m /yr) 0.047 0.175 0.864 5.46 62.24 2+a 2

Hg dry dep. (μg/m /yr) <0.001 0.005 0.026 0.096 0.418 P

b 2

Total mercury (μg/m /yr) 0.050 0.183 0.887 5.56 62.61 2

East of 90 W longitude 

Hg dry dep. (μg/m /yr) 0.246 1.595 4.101 9.342 62.24 2+a 2

Hg dry dep. (μg/m /yr) 0.011 0.036 0.078 0.139 0.418 P

b 2

Total mercury (μg/m /yr) 0.258 1.629 4.175 9.479 62.61 2

West of 90 W longitude 

Hg dry dep. (μg/m /yr) 0.047 0.139 0.546 2.092 17.73 2+a 2

Hg dry dep. (μg/m /yr) <0.001 0.004 0.016 0.047 0.210 P

b 2

Total mercury (μg/m /yr) 0.050 0.145 0.564 2.136 17.93 2

 Hg = Divalent Vapor-phase Mercury from U.S. sources a 2+

 Hg = Particle-Bound/Mercury from U.S. sources b

p
5-20

Figure 5-9 shows the simulated annual dry deposition totals for Hg . Dry deposition of Hg 2+ 2+

appears to occur primarily on the local scale, the majority occurring within one or two grid cells from the

source (40-80 km), much like the wet deposition. The magnitude of the dry deposition of Hg is similar 2+

to that for wet deposition, with major urban areas showing values in excess of 30 μg/m . Simulated dry 2

deposition of Hg vapor in heavily industrialized urban centers is very intense, exceeding 60 μg/m in 2+ 2

the model grid cell containing New York City. Again, it must be stressed that dry deposition of Hg2+

vapor is not well understood. The simulation used nitric acid vapor data as a surrogate for Hg vapor 2+

based on similar water solubilities. The Agency has been unable to find observations of the dry

deposition of Hg vapor with which to compare to the RELMAP simulation results. Dry deposition 2+

rates for vapor-phase Hg have been estimated from vertical eddy flux calculations at a single site

(Lindberg et al., 1992), but these calculations estimate the combined effects of both Hg and Hg vapors. 0 2+

The relatively high solubility and reactivity of Hg compounds suggests that dry deposition of total 2+

vapor-phase mercury may be strongly driven by the Hg component of the total vapor-phase mercury 2+

concentration.

Figure 5-10 shows the simulated annual dry deposition totals for Hg . As described in Appendix P

D, the dry deposition velocity estimates for Hg have been made based on the assumption that the P

particulate mass is concentrated around a 0.3 μm diameter size. The patterns show much less intense dry

deposition of Hg than for Hg , but the dry deposition still appears to occur primarily within a few P

2+

hundred km of the source areas. This slower dry deposition combined with relatively smaller quantities

of Hg emission result in maximum dry deposition values of only around 0.4 μg/m . In urban areas P

2

where larger particle sizes are more prevalent, these estimates of Hg dry deposition are probably too P

low, but the RELMAP could treat only one particle size. Since the focus of this modeling was on the

regional scale, 0.3 μm was chosen as the most appropriate diameter size.

Figure 5-11 shows the simulated annual dry deposition for all forms of mercury. This graphic

looks nearly identical to the simulated dry deposition of Hg shown in Figure 5-9, indicating that the 2+

simulated dry deposition is strongly driven by the Hg component of the air concentration of total 2+

mercury. Total dry deposition of mercury in all forms would be greatly reduced if significant transfer of

Hg to Hg is occurring through particle adsorption or condensation. Thus, it is very important that our 2+

P

understanding of the physical transformations of Hg in the atmosphere be complete and accurate.



5-24

5.1.5 Qualitative Description of Total Mercury Deposition Results

Since both wet and dry deposition of mercury can affect human and ecosystem health, an

analysis of the simulated total deposition of all forms of mercury has been performed. Table 5-7 shows a

percentile analysis of total deposition of mercury in all modeled forms. The strong bias toward mercury

deposition in the eastern U.S. is immediately obvious. Also obvious is the order of magnitude difference

between the 90th percentile level and the maximum values in the nationwide and eastern U.S. analyses. 

The extremely high simulated deposition totals over heavily populated urban centers cannot be

substantiated by observations at this time. Due to the high degree of uncertainty regarding the emission

speciations and possible rapid chemical and physical transformations immediately after emission, it is

recommended that these maximum simulated deposition values should be considered highly uncertain

until further research is conducted to reduce these uncertainties.

Figure 5-12 shows the RELMAP-simulated total deposition of mercury to the Earth's surface

from U.S. anthropogenic sources and the global atmospheric background concentration combined. These

results show deposition totals of over 10 μg/m throughout most of the continental U. S. east of 90 W 2 

longitude, with values over 30 μg/m for the northeast corridor and at other major urban centers. 2

Table 5-7

Percentile Analysis of RELMAP Simulated Total Depositions for the Continental U. S.

Area of Analysis Min 10th 50th 90th Max

(μg/m /yr) (μg/m /yr) (μg/m /yr) (μg/m /yr) (μg/m /yr) 22222

Full Area 0.310 1.024 3.718 17.94 142.9

East of 90 W longitude 1.226 7.407 14.50 27.18 142.9 

West of 90 W 0.310 0.861 2.321 8.003 38.56 

5-26

5.1.6 General Data Interpretations of the RELMAP Modeling

At this time there is significant uncertainty regarding the chemical and physical forms of

mercury air emissions and their chemical and physical transformations in the atmosphere. This longrange modeling effort has relied heavily on the assumptions and parameterizations of Petersen et al.

(1995) regarding emission speciation and chemical and physical pathways for mercury deposition. 

These previous mercury modeling results were compared to measurements of Hg and Hg air 0

P

concentration and wet deposition in northern Europe. The comparison showed the European model

results agreed with measurements to within a factor of 2 in nearly all cases. While the climate of

northern Europe may be quite different from that of some locations in North America, it has been

assumed that the predominant chemical and physical mechanisms for mercury transport, transformation

and deposition should be the same for both regions.

The wet deposition results from the RELMAP simulation of atmospheric mercury also seem to

agree with actual measurements within a factor of 2 in most cases. The RELMAP estimate of just over

80 μg/m wet deposition in the grid cell containing New York City seems extraordinarily high, but there 2

currently are no measurement data which can be compared to these results. The simulated wet

deposition in the grid cell immediately east of New York City is about 20 μg/m . Thus, measurements 2

taken outside the central industrial areas cannot be used to evaluate these maximum wet deposition

results. Comparison of modeled mercury wet deposition to observed values during 1993 and 1994 in

Michigan and Vermont suggest that the RELMAP is over-estimating wet deposition, while comparison

to estimates based on observed volume-weighted average concentrations and 1989 precipitation data

suggest the model is under-estimating wet deposition. Overall, the RELMAP mercury model seems to

produce reasonable spatial patterns of annual wet deposition with site-specific agreement to observations

to within a factor of two. A rigorous model evaluation will require better spatial coverage by a long-term

observation network than is now available.

The RELMAP dry deposition results indicate that the importance of dry versus wet deposition

processes may be dependent on the fraction of emitted mercury that eventually becomes particle-bound

before deposition. Very few direct measurements of the dry deposition of gaseous and particulate Hg

have been made to date. Vertical concentration gradients and eddy flux correlations have been used to

estimate the dry flux of total gaseous mercury by Lindberg et al. (1992), but no discrimination was made

between Hg and Hg forms. At this time, no scientifically credible model evaluation for dry deposition 0 2+

of mercury is possible. Once techniques for the measurement of gaseous dry deposition of mercury

become available and observational networks employing them are developed and operated, model

evaluation and subsequent refinement should rapidly follow.

Many of the measurement studies performed up until the 1980's are now suspected of having

been subject to laboratory contamination. It is only recently that, by employing ultra-clean laboratory

techniques, mercury measurement studies have been able to assess accurately atmospheric concentrations

and deposition quantities of mercury in near-background conditions. Even now, it is very difficult to

obtain an accurate assessment of the chemical forms of mercury in typical ambient air samples. The

RELMAP air concentration results seem quite plausible, with the vast majority of atmospheric mercury

estimated to be in the elemental vapor form, but the precise concentrations of Hg and Hg cannot be 2+

P

simulated with much confidence until a more complete understanding is established of all pertinent

chemical and physical processes in the atmosphere.

There are some limitations of the RELMAP and other Lagrangian puff models that may

negatively affect the accuracy of atmospheric mercury modeling. The simulated pollutant puff must
5-27

move as an integral volume, and differences in wind direction or speed at various heights above the

surface are not treated. The pollutant puff is currently simulated with a predefined vertical top, through

which turbulent exchanges of air and pollutants are set at an arbitrary value. For pollutants such as Hg0

that remain in the atmosphere for a long time, significant transfer of mass between the PBL and the rest

of the atmosphere is inevitable. These exchanges can be attributed not only to turbulent processes but

also larger-scale vertical atmospheric motions, both rising and sinking. Finally, Lagrangian puff models

have no straightforward way to treat the horizontal boundary flux of pollutant into the model domain. 

Hg vapor is known to be transported in the atmosphere on a global scale, but adequate methods are 0

unavailable to model its transport from other parts of the earth into the model domain. The U.S. EPA is

working to develop a general purpose air-quality modeling system employing an Eulerian reference

frame which should prove more suitable for mercury transport and deposition simulations; completion of

this model is expected in 1998.

5.2 Overview of Local Scale Analysis: Background Concentrations

5.2.1 Introduction

Background concentrations arising from numerous and varied sources are clearly of concern

when evaluating contaminants such as mercury that are ubiquitous and exist as natural constituents of the

environment. Some of the mercury on the planet has by virtue of its location alone been inaccessible to

living things; for example, if undisturbed, mercury contained in deep crustal materials is expected to

cycle extremely slowly (if at all) through the environment. Other forms of mercury, although more

accessible, are tightly bound chemically and are expected to cycle slowly. By comparison, mercury in the

upper layers of the soil cycles more rapidly in the environment. Some human activities have liberated

mercury which was formerly sequestered; as a result, mercury concentrations in the atmosphere, soils,

water bodies and sediments have increased over time due to natural and human activities. For example,

the mercury concentration measured in a given soil sample is potentially the result of a combination of

the naturalconstituent mercury, previous mining emissions, as well as current emissions; additionally

mercury could have volatilized from other soils or bodies of water and deposited to the soil being

sampled.

Hudson et al., (1995) recognized the occurrence of three separate historical periods of mercury

cycling in the Americas: 1) paleochemicalwhich occurred prior to 1550 AD, 2) a period of miningrelated emissions which occurred roughly between 1550 - 1900 AD, and finally, 3) the period of 1900-

present (which includes past anthropogenic increases from mining as well as current increases related to

industrial emissions). In their model current mercury levels in the environment are influenced by all

three time periods.

The purpose of this section is to estimate existingmercury concentrations in the U.S. over two

different time periods. These estimates are used as inputs to the local dispersion analysis so that an

assessment can be made of the impacts of a local anthropogenic source currently emitting mercury

relative to existing background concentrations. This type of analysis enables an examination of mercury

concentrations and potential exposures near emissions sources in a more comprehensive manner because

both current and past releases of mercury are accounted. The reader should note that these are imprecise

estimates designed to examine a typical site and perhaps typical conditions. Local concentrations of

mercury in environmental compartments are highly variable, with perhaps the exception of current

atmospheric mercury concentrations located distant to point sources. 

Two separate estimates are presented. The first is an estimate of the mercury biogeochemical

cycle in the U.S. prior to major influences from anthropogenic sources; this is prior to a period of
5-28

mining-related mercury emissions in the Americas. A representation of the pre-anthropogenicmercury

cycle will be developed by inputting to the IEM-2M model an average atmospheric mercury

concentration and average annual atmospheric mercury deposition rates for the hypothetical eastern and

western sites. The estimate of the pre-anthropogenicatmospheric mercury concentration is based on

work by Mason et. al (1994). The estimate of an average annual deposition rate from this period is e

derived from coring samples of geologic materials which can be correlated to specific time periods. The

mercury concentrations contained in the core samples are assumed to reflect deposition patterns. Using

these inputs the local dispersion model will be run until equilibrium is achieved for the model

compartments. Chemical equilibrium is defined as a steady state, in which opposing chemical reactions

occur at equal rates" (Pauling, 1963). The hypothetical sites of the modeling are a shallow lake and

watershed described previously as existing in the Eastern U.S. and the Western U.S. 

The second estimate is an approximation of the mercury biogeochemical cycle as it currently

exists at the two hypothetical U.S. sites remote from emission sources. This second estimation of the

mercury cycle will roughly correspond to the current conditions. The predicted mercury concentrations

from the pre-anthropogenicIEM-2M modeling results will be used as inputs for the approximation of

the current mercury cycle. The atmospheric concentrations and the annual rate of mercury deposition are

thought to have increased from the pre-anthropogenicperiod. Elevated concentration and deposition

rates will be used as inputs to the IEM-2M. The deposition rate and an atmospheric air concentration will

be assumed to have existed for a period of time sufficient to reach equilibrium. (This is clearly a

simplifying and uncertain assumption. The current mercury cycle may not be in equilibrium). The

predicted equilibrium mercury concentrations from this second approximation will be used as inputs to

the IEM-2M model to estimate current exposures near emission point sources. These emissions sources

will be assumed to be operational for 30 years. 

5.2.2 The Pre-anthropogenicMercury Cycle 

The purpose of this section is to determine an approximate natural background concentration of

mercury in the atmosphere and approximate atmospheric deposition rates for the hypothetical eastern and

western sites prior to anthropogenic influences on the mercury cycle. The pre-anthropogenic

atmosphere concentrations and deposition rates will be utilized as inputs to the IEM-2M model for the

purposes of predicting mercury concentrations in soils and other media and biota associated with the

hypothetical shallow lakes and their respective watersheds prior to anthropogenic emissions.

The Expert Panel on Mercury Atmospheric Processes (1994) described the current background

mercury concentration in the atmosphere over the northern hemisphere as ranging from 1.5 - 2.0 ng/m .3

Fitzgerald (1994) reported that the value used in their modeling of 25 Mmol of mercury in the

atmosphere represents an average concentration of 1.6 ng/m which is comparable to the average 3

measured concentration of mercury over oceans. Mason et al., 1994 estimate that pre-industrial

atmospheric mercury levels were roughly one third of current levels. Assuming that 1.6 ng/m is a 3

reasonable estimate of the current continental atmospheric background, an estimate of 0.5 ng/m is a 3

crude estimate of a pre-anthropogenic background. 

While the levels of mercury circulating in the atmosphere are directly influenced by emissions,

deposition rates appear to be influenced by a number of factors including: the atmospheric mercury

concentration, rainfall, particulate concentration in the atmosphere, and levels of atmospheric oxidants. 
5-29

To estimate deposition rates, Mason et al. (1994) present a pre-anthropogenic global mercury

budget in which the predicted atmospheric deposition to both terrestrial and marine environments is

equal to the evasion of mercury from the two compartments. Given the extensive time period over which

the pre-anthropogenic mercury cycle operated, it is logical when modeling this time period that the

system be in chemical equilibrium. 

Since there are no measured mercury concentration data from this time period, other sources of

data providing indirect measurements must be examined to infer a record of mercury deposition. Core

samples of sedimented materials from lake beds, ombrotrophic bogs and oceans present opportunities to

evaluate the history of mercury deposition from the atmosphere. Other sources such as ice sheets and tree

ring analyses may also evidence patterns of atmospheric mercury concentrations or deposition rates

(Expert Panel on Mercury Atmospheric Processes, (1994). It is important to note that these types of data

only provide inferred deposition rates. These data are primarily interpreted by the authors to describe

trends in mercury deposition rather than precise deposition rates. They do offer one advantage to current

measurement data in that they account for both wet and dry deposition of mercury. 

Rada et al., (1989) analyzed sediment mercury concentrations from Wisconsin (U.S.A.) lakes

and concluded that atmospheric mercury deposition to these lakes had increased significantly when

compared to deposition from earlier time period. Several authors have estimated the total annual

deposition (wet and dry) rates of mercury by sample coring of various media. Swain et al., (1992) and

Engstrom et al., (1994) analyzed sediment cores from remote lakes in Wisconsin (U.S.A.) and Minnesota

(U.S.A.). They concluded that the annual atmospheric deposition rate of mercury was 3.7 μg/m /yr 2

around the year 1850 in this region of the U.S. Benoit et al., (1994) analyzed mercury concentrations in

a peat bog at a Minnesota site. The estimated pre-1900 deposition rate at this site was 7 μg/m /yr. 2

Meili (1995) re-evaluated these types of estimates and concluded that the inferred rates were

higher than actual deposition rates. Meili speculated that lake sediment and peat profiles may

overestimate atmospheric deposition rates and that this could be the result of focussing of mercury from

the catchment to a comparatively smaller area of sediment bed and the movement of fine sediments to

deeper zones within a lake. In re-evaluating the data of Swain et al., 1992, Meili poses that as the ratio of

catchment to lake area increases, there is a smaller likelihood of mercury not being retained by the lake

(i.e., it is removed through outflow). In his reevaluation Meili (1995) suggested that a total mercury

deposition rate of approximately 2 μg/m /yr is perhaps more accurate. This estimate is similar to that 2

inferred by Meili et al., 1991(WASP 56:333-347) for Europe prior to mercury releases of large

anthropogenic sources. Additionally, the studies of Benoit et al., as well as Swain et al., and Engstrom et

al. estimate mercury deposition rates only back to 1850; these estimates would postdate mercury

emissions from mining in the Americas (Hudson et al., 1995) and these inferred estimates could include

an anthropogenic component. Meili’s other analyses indicate a deposition rate of between 1 and 5

μg/m /yr. Based on these data and considerations, the EPA believes a deposition rate of 3 μg/m /yr is a 2 2

reasonable estimate of yearly pre-anthropogenic deposition rate for Eastern site (this would also be an

appropriate estimate for northern U.S. sites.) 

Most of the data used to infer deposition rates have been collected at sites which experience

higher levels of precipitation. The pre-anthropogenicdeposition rate of 3 μg/m /yr is probably more 2

appropriate to Eastern sites rather than Western sites. Given the lower rates of precipitation in the West,

a lower end value from Meili (1995) of 1 μg/m /yr is believed by the EPA to be an appropriate estimate 2

of deposition for the pre-anthropogenicdeposition at the hypothetical Western site.
5-30

Based on the studies described above, in this analysis EPA is using an atmospheric concentration

of 0.5 ng/m for both hypothetical sites as a model input. The deposition rate of 3 μg/m /yr and 1 3 2

μg/m /yr will be used for the hypothetical Eastern site and Western sites respectively as inputs to the 2

model. The IEM-2M model will be used to estimate the equilibrium concentrations in the hypothetical

shallow water bodies of the eastern and western sites. The initial quantity of mercury in soil, sediments,

water column, and biota is assumed to be zero. The predicted values are EPA’S estimates of the preanthropogenic background concentrations.

5.2.3 Estimating Current Background Mercury Concentrations

Current environmental mercury levels are the result of both natural and human events. The

mercury cycle has changed over the last 100 years. Most agree that there has been a sharp increase in the

loadings of mercury to the atmosphere over this period of time as a direct result of anthropogenic activity

(e.g., Expert Panel on Mercury Atmospheric Processes, 1994, Mason et al., 1994, Hudson et al., 1995).

There is also some evidence indicating that recent deposition rates have declined in some areas.

As noted previously the current background mercury concentration over the northern hemisphere

is considered to be between 1.5 - 2.0 ng/m (Expert Panel on Mercury Atmospheric Processes, 1994). A 3

background atmospheric concentration of elemental mercury gas of 1.6 ng/m was put forth by Fitzgerald 3

(1994). This value will be used as an estimate of the current background atmospheric mercury

concentration across the U.S. This concentration reflects contributions from many sources: natural

sources, volatilization and evasion of previously deposited mercury from both natural and old

anthropogenic sources, as well as current mercury emissions and evasion of mercury from water bodies

contaminated by mercury in effluents.

Current deposition rates at remote sites are difficult to assess. Most measured data are collected

for only a short period of time. Measured data typically only include mercury deposited through wet

deposition, although some researchers have included estimates of dry particulate-bound mercury (e.g.,

Fitzgerald et al., 1991). Methods to measure potential dry deposition of vapor phase mercury are still

being developed. The current measured data may not include this fraction of the total mercury deposited.

Watras et al., (1994) summarized the collected data and presented a conceptualization of

mercury fluxes between abiotic and biotic components of the environment in 7 Northern Wisconsin

seepage Lakes, including Little Rock Lake. Most of the mercury was thought to enter the lakes through

atmospheric deposition with wet deposition of mercury contributing the most to the total. The total

amount deposited was approximately 10 μg/m /yr. Additional estimates of annual mercury deposition 2

rates are listed in Table 5-8 below. Most of the mercury deposited at Little Rock Lake was thought to

either deposit into the sediment or volatilize back into the atmosphere. The behavior of mercury at most

U.S. sites is not characterized to the same degree as at Little Rock Lake. It should be noted that Little

Rock Lake is a remote seepage lake and that the atmospheric chemistry of mercury may be different

closer to emission sources and under different atmospheric conditions. The chemistry of mercury may

also be different across a spectrum of watersheds and water bodies. Other current deposition rates

measured primarily through mercury concentrations in rainfall are presented in the table below. At

remote sites the primary pathway of atmospheric mercury deposition is presumed to be the result of wet

deposition. The average annual deposition rate across the sites and years is roughly 11 μg/m /yr. 2
5-31

Table 5-8

Mercury Wet Deposition Rates (ug/m /yr) 2

Site Wet Mercury Deposition Rates Reference

(ug/m /yr), Means 2

Ely, MN 17 in 1988 1988-89 data: Glass et al., (1992)

42 in 1989 1990 data: Sorensen et al., (1992)

6.7 in 1990

Duluth, MN 20 in 1988 1988-89 data: Glass et al., (1992)

6.5 in 1989 1990 data: Sorensen et al., (1992)

9.3 in 1990

Marcell, MN 17 in 1988 Glass et al., (1992)

14 in 1989

Bethel, MN 13 in 1990 Sorensen et al., (1992)

Cavalier, ND 6.1 in 1990 Sorensen et al., (1992)

International Falls, 5.5 in 1990 Sorensen et al., (1992)

MN

Lamberton, MN 9.3 in 1990 Sorensen et al., (1992)

Raco, MN 8.9 in 1990 Sorensen et al., (1992)

Little Rock Lake, 4.5 from rain Fitzgerald et al., (1991)

WI 2.3 from snow

Crab Lake, WI 4.4 from rain Lamborg et al., (1995)

0.8 from snow

Northern MN 10-15 Sorensen et al., (1990)

Pellston, MI 5.8 in year 1 Hoyer et al., (1995)

5.5 in year 2

0.07 ug/m (max 0.51) per rainfall event 2

South Haven, MI 9.5 in year 1 Hoyer et al., (1995)

13 in year 2

0.12 ug/m (max 0.85) per rainfall event 2

Dexter, MI 8.7 in year 1 Hoyer et al., (1995

9.1 in year 2

0.10 ug/m (max 0.98) per rainfall event 2

Underhill Center, 9.3 Burke et al., (1995)

VT 0.07 ug/m per rainfall event 2
5-32

Several authors have estimated current mercury total deposition (wet and dry) rates by sample

coring of various media (see Table 5-9). For example, Swain et al., (1992) and Engstrom et al., (1994)

used lake core sediments to estimate a current deposition rate of 12.5 μg/m /yr for remote lakes located 2

in Minnesota and northern Wisconsin. Benoit et al., (1994) estimated current mean mercury deposition

rate to be 24.5 μg/m /yr in Minnesota. Engstrom and Swain (1997)evaluated sediment core data from 2

lakes located in Minnesota and Alaska. They concluded that there has been a recent (over approximately

the last 20 years) decline in the deposition rate of atmospheric mercury to some lakes. Specifically, they

showed declines in the mercury deposition rate in four Eastern Minnesota lakes and in four lakes around

Minneapolis, MN, but not in three Alaskan lakes and four lakes located in Western Minnesota. The

authors suggested that the observed declines were the result of decreased regional emissions. The lakes

that did not exhibit a decrease were believed to be more influenced by the global concentrations. Benoit

et al., (1994) also noted a recent decline in inferred deposition rates over the last 10 year interval

measured. The deposition rate of 24.5 μg/m /yr is actually one third of the rate reported for the previous 2

10 year interval. 

Table 5-9

Estimated Mercury Total Deposition Rates

Site Estimate of Pre- Estimate of Current Reference

industrial Annual Annual Deposition Rates

Deposition Rates μg/m /yr

μg/m /yr 2

2

Minnesota and 3.7 12.5 Swain et al. (1992);

northern Wisconsin Engstrom et al., (1994) 

Lake core sediments

Minnesota 7.0 24.5 Benoit et al., (1994) 

Peat bog core

sampling

Little Rock Lake, WI 10 Fitzgerald et al., a

(1991)

Crab Lake, WI 7.0 (86% estimated to Lamborg et al., a

deposit in summer) (1995)

 Data includes previously tabled values of wet deposition plus particulate deposition. Fitzgerald et al., 1991 did not a

collect particulate size data. Assuming a particulate deposition velocity of 0.5 cm/s, a yearly average particulate

deposition flux of 3.5+/- 3 ug/m /yr was estimated. Lamborg et al., (1995) noted the smaller particle sizes in the 2

winter and assumed a deposition velocity 0.1 cm/s for the average winter concentrations (7 pg/m ) and a deposition 3

velocity of 0.5 cm/s for average summer concentrations (26 pg/m ). 3

Given these data, a reasonable estimate of annual deposition at Eastern and Northern sites remote

from sources is 10 μg/m /yr. This value will be input into the model. It is intended that this value account 2

for pre-anthropogenicmercury as well as the cycling of mercury emitted from old anthropogenic

sources that has been previously deposited. There is a collection bias associated with these data;

specifically, the data primarily examine mercury deposition at remote sites from the Northern and

Eastern parts of the U.S. Mercury deposition at these sites is thought to be primarily derived by climactic
5-33

factors such as wet deposition. The total deposition in this region of the U.S. is thought to be

approximately a factor of 5 lower; this is based roughly on the decrease in precipitation rates. These

deposition rates and the air concentration rate will be input to the model until equilibrium is achieved.

The final predicted concentrations will serve as initial inputs to the local scale modeling effort with the

model plants at both hypothetical sites. 

Table 5-10 summarizes the inputs to the IEM-2M model which have been derived form the data

discussed above. It is important to recognize that many alternative approaches could have been

developed to examine the influence of backgroundmercury. This option was utilized because of the

internal consistency it offered. Additionally, it conveys a more general rather than a specific or exact

understanding of the influences on the mercury cycle that is more consistent with the current level of

scientific understanding. Figures 5-13 and 5-14 show schematically how these inputs have been utilized

to establish initial conditions for analysis.

Table 5-10

Inputs to IEM-2M Model for the two time periods modeled

Time Period Parameter East West

Pre-industrial Air Concentration (ng/m3) 0.5 0.5

Deposition Rate (ug/m2/yr) 3.0 1

Predicted Watershed soil concentration 14 4

(ng/g)

Predicted Total Hg Water 0.35 0.1

Concentration (ng/L)

Predicted Trophic Level 4 Fish 0.13 0.04

Concentration (ug/g)

Industrial Air Concentration (ng/m3) 1.6 1.6

Deposition Rate (ug/m2/yr) 10.0 2.0

Predicted Watershed soil concentration 47 8.0

(ng/g)

Predicted Total Hg Water 1.2 0.2

Concentration (ng/L)

Predicted Trophic Level 4 Fish 0.44 0.09

Concentration (ug/g)
5-34

Industrial Period Steady-State Conditions (usconditions for model plant analysis)

Pre-industrial Period Steady-State Conditions (used as initialconditions for industrial period)

Figure 5-13 IEM-2M results for pre-industrial and industrial periods for Eastern site
5-35

Industrial Period Steady-State Conditions (usconditions for model plant analysis)

Pre-industrial Period Steady-State Conditions (used as initialconditions for industrial period)

Figure 5-14 IEM-2M results for pre-industrial and industrial periods for Western site
5-36

5.3 Local Atmospheric Transport Modeling

Tables 5-11 through 5-14 show the predicted air concentrations and deposition rates for each facility in

each site, for both the RELMAP 50th and RELMAP 90th percentiles. These results are discussed in sections

5.3.1 and 5.3.2 below.

Table 5-11

Predicted Air Concentrations and Deposition Rates for Eastern Site (Local + RELMAP 50th

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

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

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

10 km 1.7E+00 98% 2% 2.6E+01 57% 43%

25 km 1.7E+00 99% 1% 1.9E+01 78% 22%

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

10 km 1.7E+00 100% 0% 1.6E+01 90% 10%

25 km 1.7E+00 100% 0% 1.5E+01 97% 3%

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

10 km 1.7E+00 100% 0% 1.5E+01 95% 5%

25 km 1.7E+00 100% 0% 1.5E+01 99% 1%

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

10 km 1.7E+00 99% 1% 2.0E+01 74% 26%

25 km 1.7E+00 100% 0% 1.6E+01 92% 8%

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

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

25 km 1.7E+00 100% 0% 1.5E+01 100% 0%

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

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

25 km 1.7E+00 100% 0% 1.5E+01 100% 0%

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

10 km 1.7E+00 100% 0% 1.5E+01 100% 0%

25 km 1.7E+00 100% 0% 1.5E+01 100% 0%

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

10 km 1.7E+00 100% 0% 1.7E+01 83% 17%

25 km 1.7E+00 100% 0% 1.6E+01 93% 7%

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

10 km 1.7E+00 100% 0% 1.6E+01 89% 11%

25 km 1.7E+00 100% 0% 1.5E+01 94% 6%

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

10 km 1.7E+00 100% 0% 1.5E+01 96% 4%

25 km 1.7E+00 100% 0% 1.5E+01 99% 1%

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

10 km 1.7E+00 100% 0% 1.5E+01 100% 0%

25 km 1.7E+00 100% 0% 1.5E+01 100% 0%

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

10 km 2.1E+00 79% 21% 4.6E+01 32% 68%

25 km 1.8E+00 92% 8% 2.2E+01 65% 35%
5-37

Table 5-12

Predicted Air Concentrations and Deposition Rates for Eastern Site (Local 

+

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

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

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

10 km 1.8E+00 98% 2% 3.8E+01 71% 29%

25 km 1.7E+00 99% 1% 3.1E+01 87% 13%

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

10 km 1.7E+00 100% 0% 2.9E+01 95% 5%

25 km 1.7E+00 100% 0% 2.8E+01 98% 2%

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

10 km 1.7E+00 100% 0% 2.8E+01 98% 2%

25 km 1.7E+00 100% 0% 2.7E+01 99% 1%

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

10 km 1.7E+00 99% 1% 3.2E+01 84% 16%

25 km 1.7E+00 100% 0% 2.8E+01 96% 4%

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

10 km 1.7E+00 100% 0% 2.7E+01 99% 1%

25 km 1.7E+00 100% 0% 2.7E+01 100% 0%

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

10 km 1.7E+00 100% 0% 2.7E+01 100% 0%

25 km 1.7E+00 100% 0% 2.7E+01 100% 0%

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

10 km 1.7E+00 100% 0% 2.7E+01 100% 0%

25 km 1.7E+00 100% 0% 2.7E+01 100% 0%

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

10 km 1.7E+00 100% 0% 3.0E+01 90% 10%

25 km 1.7E+00 100% 0% 2.8E+01 96% 4%

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

10 km 1.7E+00 100% 0% 2.9E+01 94% 6%

25 km 1.7E+00 100% 0% 2.8E+01 97% 3%

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

10 km 1.7E+00 100% 0% 2.8E+01 98% 2%

25 km 1.7E+00 100% 0% 2.7E+01 99% 1%

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

10 km 1.7E+00 100% 0% 2.7E+01 100% 0%

25 km 1.7E+00 100% 0% 2.7E+01 100% 0%

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

10 km 2.2E+00 79% 21% 5.9E+01 46% 54%

25 km 1.9E+00 92% 8% 3.5E+01 77% 23%
5-38

Table 5-13

Predicted Air Concentrations and Deposition Rates for Western Site (Local 

+

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

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

Variant b:Large Municipal Waste Combustor 2.5 km 1.7E+00 98% 2% 2.0E+01 11% 89%

10 km 1.6E+00 98% 2% 1.1E+01 20% 80%

25 km 1.6E+00 99% 1% 5.6E+00 41% 59%

Variant b:Small Municipal Waste Combustor 2.5 km 1.6E+00 99% 1% 6.2E+00 38% 62%

10 km 1.6E+00 100% 0% 3.4E+00 68% 32%

25 km 1.6E+00 100% 0% 2.7E+00 87% 13%

Large Commercial HMI 2.5 km 1.6E+00 99% 1% 6.0E+00 38% 62%

10 km 1.6E+00 100% 0% 2.8E+00 83% 17%

25 km 1.6E+00 100% 0% 2.4E+00 95% 5%

Large Hospital HMI 2.5 km 1.7E+00 98% 2% 2.7E+01 9% 91%

10 km 1.6E+00 99% 1% 5.9E+00 39% 61%

25 km 1.6E+00 100% 0% 3.3E+00 71% 29%

Small Hospital HMI 2.5 km 1.6E+00 100% 0% 3.9E+00 59% 41%

10 km 1.6E+00 100% 0% 2.5E+00 92% 8%

25 km 1.6E+00 100% 0% 2.4E+00 98% 2%

Large Hospital HMI (wet scrubber) 2.5 km 1.6E+00 100% 0% 3.0E+00 77% 23%

10 km 1.6E+00 100% 0% 2.4E+00 96% 4%

25 km 1.6E+00 100% 0% 2.3E+00 99% 1%

Small Hospital HMI (wet scrubber) 2.5 km 1.6E+00 100% 0% 2.4E+00 98% 2%

10 km 1.6E+00 100% 0% 2.3E+00 100% 0%

25 km 1.6E+00 100% 0% 2.3E+00 100% 0%

Large Coal-fired Utility Boiler 2.5 km 1.6E+00 100% 0% 5.8E+00 40% 60%

10 km 1.6E+00 100% 0% 3.5E+00 67% 33%

25 km 1.6E+00 100% 0% 3.3E+00 69% 31%

Medium Coal-fired Utility Boiler 2.5 km 1.6E+00 100% 0% 4.3E+00 53% 47%

10 km 1.6E+00 100% 0% 3.7E+00 63% 37%

25 km 1.6E+00 100% 0% 3.2E+00 73% 27%

Small Coal-fired Utility Boiler 2.5 km 1.6E+00 100% 0% 3.4E+00 69% 31%

10 km 1.6E+00 100% 0% 2.8E+00 84% 16%

25 km 1.6E+00 100% 0% 2.5E+00 94% 6%

Medium OIl-fired Utility Boiler 2.5 km 1.6E+00 100% 0% 2.4E+00 96% 4%

10 km 1.6E+00 100% 0% 2.4E+00 97% 3%

25 km 1.6E+00 100% 0% 2.3E+00 99% 1%

Chlor-alkali plant 2.5 km 3.5E+00 46% 54% 1.9E+02 1% 99%

10 km 1.9E+00 84% 16% 2.5E+01 9% 91%

25 km 1.7E+00 94% 6% 8.1E+00 28% 72%
5-39

Table 5-14

Predicted Air Concentrations and Deposition Rates for Western Site (Local 

+

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

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

Variant b:Large Municipal Waste Combustor 2.5 km 1.7E+00 98% 2% 2.6E+01 31% 69%

10 km 1.7E+00 98% 2% 1.7E+01 47% 53%

25 km 1.7E+00 99% 1% 1.1E+01 71% 29%

Variant b:Small Municipal Waste Combustor 2.5 km 1.7E+00 99% 1% 1.2E+01 67% 33%

10 km 1.6E+00 100% 0% 9.1E+00 88% 12%

25 km 1.6E+00 100% 0% 8.3E+00 96% 4%

Large Commercial HMI 2.5 km 1.7E+00 99% 1% 1.2E+01 68% 32%

10 km 1.6E+00 100% 0% 8.5E+00 94% 6%

25 km 1.6E+00 100% 0% 8.1E+00 98% 2%

Large Hospital HMI 2.5 km 1.7E+00 98% 2% 3.2E+01 25% 75%

10 km 1.7E+00 99% 1% 1.2E+01 69% 31%

25 km 1.6E+00 100% 0% 8.9E+00 90% 10%

Small Hospital HMI 2.5 km 1.6E+00 100% 0% 9.6E+00 83% 17%

10 km 1.6E+00 100% 0% 8.2E+00 98% 2%

25 km 1.6E+00 100% 0% 8.0E+00 99% 1%

Large Hospital HMI (wet scrubber) 2.5 km 1.6E+00 100% 0% 8.7E+00 92% 8%

10 km 1.6E+00 100% 0% 8.1E+00 99% 1%

25 km 1.6E+00 100% 0% 8.0E+00 100% 0%

Small Hospital HMI (wet scrubber) 2.5 km 1.6E+00 100% 0% 8.0E+00 99% 1%

10 km 1.6E+00 100% 0% 8.0E+00 100% 0%

25 km 1.6E+00 100% 0% 8.0E+00 100% 0%

Large Coal-fired Utility Boiler 2.5 km 1.6E+00 100% 0% 1.2E+01 69% 31%

10 km 1.6E+00 100% 0% 9.1E+00 88% 12%

25 km 1.6E+00 100% 0% 9.0E+00 89% 11%

Medium Coal-fired Utility Boiler 2.5 km 1.6E+00 100% 0% 1.0E+01 80% 20%

10 km 1.6E+00 100% 0% 9.4E+00 85% 15%

25 km 1.6E+00 100% 0% 8.9E+00 90% 10%

Small Coal-fired Utility Boiler 2.5 km 1.6E+00 100% 0% 9.1E+00 88% 12%

10 km 1.6E+00 100% 0% 8.5E+00 95% 5%

25 km 1.6E+00 100% 0% 8.2E+00 98% 2%

Medium OIl-fired Utility Boiler 2.5 km 1.6E+00 100% 0% 8.1E+00 99% 1%

10 km 1.6E+00 100% 0% 8.1E+00 99% 1%

25 km 1.6E+00 100% 0% 8.0E+00 100% 0%

Chlor-alkali plant 2.5 km 3.6E+00 46% 54% 2.0E+02 4% 96%

10 km 1.9E+00 84% 16% 3.0E+01 26% 74%

25 km 1.7E+00 94% 6% 1.4E+01 58% 42%
5-40

5.3.1 Air Concentrations

In analyzing the air concentrations predicted by the ISC3 model, it is important to observe that in

a typical year the predicted air concentration due to the local source at any receptor is zero a rather

substantial fraction of the time. There are two basic reasons for this. First, in order to predict a non-zero

air concentration for a given hour the receptor must be in the downwind direction. This means that the

wind must be blowing in a direction within 90 degrees of the receptor itself. For most sites this only

occurs about 50% of the time for the direction with the highest frequency. Second, even if the receptor is

downwind, the predicted air concentration will be significant only if the wind is blowing in a direction

within about 10 degrees of the receptor's direction relative to the facility. For most sites this occurs for

the prevailing downwind direction only about 10 to 15 percent of the time. Because the air

concentrations are averaged over the year, this results in (usually) low average air concentrations.

The predicted air concentrations are typically dominated by the regional values, even for the

watersheds relatively close to the facility. The only exception to this is the chlor-alkali plant, for which

larger air concentrations are predicted (this is due to the low stack height and assumed stack gas exit

velocity). The predicted air concentrations are similar for both sites, and none of the predicted air

concentrations exceed 4 ng/m . 3

The differences in predicted air concentrations across source classes depend mainly on three key

parameters: the total mercury emission rate, the stack height, and the exit velocity of the plume from the

source. The sensitivity of the air model to the emission rate is to be expected because the predicted air

concentrations are linear with the total mercury emission rate, and the model plants are assumed to have

a wide range of emission rates (from less than 1 kg/yr up to 380 kg/yr). Both the stack height and exit

velocity are used in calculating the effective stack height, which is the height to which the plume rises

from the stack top. The importance of the effective stack height on air concentrations is well known, and

is demonstrated here by the predicted air concentrations for the chlor-alkali plant, which clearly

dominates the values as a whole. This is due to a combination of a low stack height (10 feet) and slow

stack gas exit velocity (0.1 m/s) and a comparatively high assumed total mercury emissions rate of about

380 kg/yr. The low stack parameters result in predicted low plumes that are not as vertically dispersed at

the receptor when compared with the facilities with higher stacks, thereby enhancing air concentrations.

In general, the predicted average air concentrations are quite low. The only source class for

which significantly elevated air concentrations are predicted is the chlor-alkali facility. This is due to a

very low stack height coupled with a high assumed mercury emission rate. The low stack height results

in predicted plumes that are close to the receptors considered, and so there is less dispersion of the plume

compared to the other facilities. 

5.3.2 Deposition Rates

In contrast to the predicted air concentrations, the annual deposition rates are cumulative; they

represent the sum of any deposition that occurs during the year, and hence are not affected by long

periods of little deposition. Further, the ISC3 model predicts that significant deposition events occur

infrequently, and it is these relatively rare events that are responsible for the majority of the annual

deposition rate.

Because dry deposition is calculated by multiplying the predicted air concentration for the hour

by the deposition velocity, significant dry deposition events only occur when, for the reasons discussed

above, there is a "spike" of predicted high air concentration for a given hour. Annual dry deposition
5-41

tends to be dominated by these peak values when the wind is blowing within a few degrees of the

receptor's direction.

For any site with appreciable precipitation, wet deposition can dominate the total deposition for

receptors close to the source. Single wet deposition events can deposit 300 times more Hg than a high

dry deposition event. These events are even rarer than significant dry deposition events because not only

must the wind direction be within a few degrees of the receptor's direction, but precipitation must be

occurring as well.

The predicted dry deposition rates depend ultimately on the predicted air concentrations. For

this reason, dry deposition accounts for most of the total deposition for the facility with the highest

predicted air concentrations, the chlor-alkali plant. In complex terrain, dry deposition can play a larger

but uncertain role than in the results presented here.

5.3.3 Mass Balances within the Local-Scale Domain

In this section the fraction of the mercury emitted from each hypothetical facility that is

predicted to deposit within 50 km is estimated. The area-averaged wet and dry deposition rates are also

estimated based on the fraction from the single source that is predicted to deposit within 50 km.

Tables 5-15 and 5-16 show the results for all facilities at both sites. These results were obtained

by using a total of 480 receptors for each facility and site. The receptors were placed in 16 directions

around the facility and 30 distances, from 0.5 km to 50 km. 

In general, 7-45 percent of the total mercury emitted is predicted to deposit within 50 km at the

humid site in flat terrain, while 2-38 percent is predicted to deposit at the arid site. (The ranges represent

values from the different sources considered.) This implies that at least 55 percent of the total mercury

emissions is transported more than 50 km from any of the sources considered, and is consistent with the

RELMAP results that predict that mercury may be transported across considerable distances. 

The differences between the results for the two sites are due primarily to the differences in the

frequency and intensity of precipitation. At the humid site, precipitation occurs about 12 percent of the

year, with about 5 percent of this precipitation of moderate intensity (0.11 to 0.30 in/hr). At the arid site,

precipitation occurs about 3 percent of the year, with about 2 percent of the precipitation of moderate

intensity.
5-42

Table 5-15

Mass Balance of Mercury Emissions for each Facility in the Humid Site Using the ISC3 Model

Percent of Total Mercury Em

Eastern Site Speciation of Emissions Total Hg0 Vapor 

Facility Stack height (m) Hg Emission Hg0 Vapor Hg0 Particulate Hg(II) Vapor Hg(II) Particulate Dry Wet Dry Wet 

(kg/yr) Dep Dep Dep Dep 

LMWC_b 70 220 60% 0% 30% 10% 6.7% 6.2% 0.5% 3.9% SMWC_b 43 20 60% 0% 30% 10% 10.5% 6.1% 0.9% 3.9% LCMHI 12 5 33% 0% 50% 17% 27.5% 5.2% 1.3% 2.1% LHMHI 12 24 2% 0% 73% 25% 35.3% 5.0% 0.1% 0.1% SHMHI 12 1 2% 0% 73% 25% 38.8% 4.5% 0.1% 0.1% LHMHI_Scru 12 1 33% 0% 50% 17% 27.6% 5.2% 1.3% 2.1% bberSHMHI_Scru 12 0 33% 0% 50% 17% 28.1% 5.0% 1.4% 2.1% bberLCUB 223 230 50% 0% 30% 20% 0.8% 5.9% 0.0% 3.2% MCUB 142 90 50% 0% 30% 20% 2.5% 6.0% 0.1% 3.3% SCUB 81 10 50% 0% 30% 20% 7.8% 5.9% 0.5% 3.2% MOUB 88 2 50% 0% 30% 20% 4.6% 6.0% 0.2% 3.3% CAP 3 380 70% 0% 30% 0% 17.2% 5.5% 3.5% 4.5% 
5-43

Table 5-16

Mass Balance of Mercury Emissions for each Facility in the Arid Site Using the ISC3 Model

Estimated Percent of Total Merc

Western Site Speciation of Emissions Total Hg0 Vapor 

Facility Stack height (m) Assumed Hg Hg0 Vapor Hg0 Particulate Hg(II) Vapor Hg(II) Particulate Dry Wet Dry Wet 

Emission (kg/yr) Dep Dep Dep Dep 

LMWC_b 70 220 60% 0% 30% 10% 6.7% 1.0% 0.5% 0.6% SMWC_b 43 20 60% 0% 30% 10% 10.0% 1.0% 0.8% 0.7% LCMHI 12 5 33% 0% 50% 17% 26.0% 0.9% 1.2% 0.4% LHMHI 12 24 2% 0% 73% 25% 33.5% 0.8% 0.1% 0.0% SHMHI 12 1 2% 0% 73% 25% 36.9% 0.7% 0.1% 0.0% LHMHI_Scrubb 12 1 33% 0% 50% 17% 26.3% 0.9% 1.3% 0.4% erSHMHI_Scrubb 12 0 33% 0% 50% 17% 27.0% 0.8% 1.4% 0.4% erLCUB 223 230 50% 0% 30% 20% 1.2% 0.9% 0.0% 0.5% MCUB 142 90 50% 0% 30% 20% 2.8% 0.9% 0.1% 0.5% SCUB 81 10 50% 0% 30% 20% 7.5% 1.0% 0.4% 0.5% MOUB 88 2 50% 0% 30% 20% 4.8% 1.0% 0.2% 0.5% CAP 3 380 70% 0% 30% 0% 16.7% 1.0% 3.6% 0.8% 
5-44

The percentage of mercury deposited within 50 km depends on two main factors: facility

characteristics that influence effective stack height (stack height plus plume rise) and the fraction of mercury

emissions that is divalent mercury. In most cases, the effective stack height affects only the air concentrations,

and hence dry deposition.

The differences between the results for the LMWC and SMWC are primarily due to differences in the

parameters used to estimate the effective stack height (stack height plus plume rise): stack height, stack

diameter, and exit temperature. The effective stack height is used to estimate dry deposition. The lower

plumes predicted for the SMWC result in higher air concentrations, and hence higher predicted dry deposition. 

About twice as much of the emitted mercury is predicted to dry deposit for the SMWC than for the LMWC. 

This difference is roughly the same as the ratio of the stack heights (LMWC stack is about twice as high as that

of the SMWC). Wet deposition is only affected by differences in wind speed at stack top, and in this case the

ultimate effects are minimal. The wind speed at stack top is extrapolated from the height at which it was

measured using wind profile exponents.

Differences between the results for the utility boilers are due primarily to the difference in stack

heights. For all utility boilers, less than 15 percent of the total mercury emitted is predicted to deposit within

50 km. Again, this is a reflection of the high effective stacks predicted for this source class.

The deposition rates averaged over the entire 50 km radius region surrounding each facility are given in

Tables 5-17 and 5-18. These values are comparable to or well below typically reported deposition rates (see

Section 2). 
5-45

Table 5-17

Area-Averaged Mercury Deposition Rates for each Facility in the Humid Site

Eastern Site

Speciation of Emissions Area-Averaged Values w

Facility Stack height (m) Hg Emission (kg/yr) Hg0 Vapor Hg0 Hg(II) Vapor Hg(II) Total Deposition Dry Dep

Particulate Particulate Rate Rat

LMWC_b 70.1 220.0 60% 0% 30% 10% 3.6 1.9 SMWC_b 42.7 20.0 60% 0% 30% 10% 0.4 0.3 LCMHI 12.2 4.6 33% 0% 50% 17% 0.2 0.2 LHMHI 12.2 23.9 2% 0% 73% 25% 1.2 1.1 SHMHI 12.2 1.3 2% 0% 73% 25% 0.1 0.1 LHMHI_Scrubber 12.2 0.8 33% 0% 50% 17% 0.0 0.0 SHMHI_Scrubber 12.2 0.1 33% 0% 50% 17% 0.0 0.0 LCUB 223.1 230.0 50% 0% 30% 20% 2.0 0.2 MCUB 141.7 90.0 50% 0% 30% 20% 1.0 0.3 SCUB 81.1 10.0 50% 0% 30% 20% 0.2 0.1 MOUB 88.4 2.0 50% 0% 30% 20% 0.1 0.1 CAP 3.0 380.0 70% 0% 30% 0% 11.0 8.3 
5-46

Table 5-18

Area-Averaged Mercury Deposition Rates for each Facility in the Arid Site

Western Site

Assumed Speciation of Emissions Area-Averaged Va

(ug/m

Facility Stack height (m) Assumed Hg Hg0 Vapor Hg0 Hg(II) Vapor Hg(II) Total Dr

Emission Particulate Particulate Deposition Depos

(kg/yr) Rate Rat

LMWC_b 70.1 220.0 60% 0% 30% 10% 2.2 1.9 SMWC_b 42.7 20.0 60% 0% 30% 10% 0.3 0.3 LCMHI 12.2 4.6 33% 0% 50% 17% 0.2 0.2 LHMHI 12.2 23.9 2% 0% 73% 25% 1.0 1.0 SHMHI 12.2 1.3 2% 0% 73% 25% 0.1 0.LHMHI_Scrubber 12.2 0.8 33% 0% 50% 17% 0.0 0.0 SHMHI_Scrubber 12.2 0.1 33% 0% 50% 17% 0.0 0.0 LCUB 223.1 230.0 50% 0% 30% 20% 0.6 0.4 MCUB 141.7 90.0 50% 0% 30% 20% 0.4 0.3 SCUB 81.1 10.0 50% 0% 30% 20% 0.1 0.MOUB 88.4 2.0 50% 0% 30% 20% 0.0 0.0 CAP 3.0 380.0 70% 0% 30% 0% 8.5 8.
5-47

5.3.4 Uncertainty and Sensitivity Analyses

As has been noted previously, the behavior of atmospheric mercury close to the point of release

has not been studied extensively. This alone results in a significant degree of uncertainty implicit in the

preceding modeling exercises. In this section, several of these assumptions along with other possible

behaviors are examined to illustrate the implications of these potential properties of atmospheric mercury

in the near-field. 

5.3.4.1 Dry Deposition

Impact of a Compensation Point for Dry Deposition of Elemental Mercury

It has been suggested that dry deposition of elemental mercury to plants may not occur at all

unless the air concentration is above a certain threshold value, which is termed the compensation point.

Results of Hanson et al. (1995) suggest that this threshold is at least about 10 ng/m , although there are 3

lingering uncertainties due to the possible dependence of the compensation point on the type of

vegetation, season, and time of day. Sensitivity analyses were conducted to examine the possible impact

of a compensation point on the ISC3 analysis. This analysis represents one of the first efforts ever to

investigate the possible impact of a compensation point on the dry deposition of elemental mercury.

Figures 5-15, 5-16, and 5-17 show the predicted dry deposition of elemental mercury at 2.5 km,

10 km, and 25 km, respectively, northeast (the direction of maximum deposition) of the model plant

chlor-alkali facility located at the eastern site. These results were generated assuming a dry deposition

velocity of 0.06 cm/s for elemental mercury (the same value used in the analyses); however, if the

calculated air concentration for a given hour is not above the compensation point, no dry deposition is

allowed to occur. The figures show the sensitivity of the total dry deposition rate to different

compensation points. 
Influence of the Compensation Point on the Dry Deposition of Elemental Mercury: 

2,500 Meters NE of a Chlor-alkali Plant at the Eastern Site

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20

Compensation Point (nanograms per cubic meter)

Dry Deposition (micrograms per square meter)

Neglected dry deposition due to compensation

point

Dry deposition

5-48

Figure 5-15
Influence of the Compensation Point on the Dry Deposition of Elemental Mercury: 

10,000 Meters NE of a Chlor-alkali Plant at the Eastern Site

0

2

4

6

8

10

12

0 5 10 15 20

Compensation Point (nanograms per cubic meter)

Dry Deposition (micrograms per square 

meter)

Neglected dry deposition due to

compensation point

Dry deposition

5-49

Figure 5-16
Influence of the Compensation Point on the Dry Deposition of Elemental Mercury: 

25,000 Meters NE of a Chlor-alkali Plant at the Eastern Site

0

2

4

6

8

10

12

0 5 10 15 20

Compensation Point (nanograms per cubic meter)

Dry Deposition (micrograms per square meter)

Neglected dry deposition due to

compensation point

Dry deposition

5-50

Figure 5-17
5-51

As a percentage of the total dry deposition, there is little impact for the receptor located at 2.5 km from

the facility. This indicates that almost all of the predicted dry deposition occurs during hours when the

predicted air concentration is above 20 ng/m . This suggests that the existence of a compensation point 3

will not have a large impact on the results for receptors close to this facility unless it is above 20 ng/m . 3

For receptors farther from the facility, the compensation point is predicted to have more of an

impact, in terms of the fraction of the total dry deposition. This impact indicates that most of the dry

deposition is predicted to occur when the air concentration is low. However, in these cases the total dry

deposition rate is then correspondingly low. For example, at 25 km, by not assuming a compensation

point there is the possibility of overestimating the dry deposition of elemental mercury by up to a factor

of 2, depending on what the compensation point is; however, this amounts to less than 2 μg/m /yr. 2

Sensitivity of Dry Deposition of Divalent Mercury to Reactivity

The gas deposition module of the ISC3 model utilizes several parameters to estimate the dry

deposition velocity for gases. In this section, the sensitivity of the results to the pollutant reactivity

parameter is investigated. The pollutant reactivity is used to estimate the resistance through the

vegetative canopy (EPA 1996; page inserts to ISC3 Dispersion Model User’s Guide). In particular, this

resistance is obtained by scaling the reference resistance for SO by (A /A ), where A is the default 2 ref poll ref

reference reactivity for SO of 8 (EPA 1996), and A is the reactivity for the pollutant of interest. In 2 poll

this analysis, nitric acid has been used as a surrogate for divalent mercury vapor, and for this reason a

large value for the reactivity (i.e., a low value for canopy resistance) is assumed. A precise estimate is

not available for nitric acid. Previous applications of similar models (e.g., CALPUFF) suggested a

default value of 18 for the reactivity for nitric acid, although no references are available to support this

value. More recent discussions with the model developers suggested that the reactivity of nitric acid

should be considerably higher than 18 in order to reduce the canopy resistance. Based on discussions

with the model developers, in the present analysis reactivity of 800 was assumed. This results in average

dry deposition velocities of about 3 cm/s for the eastern site.

Figure 5-18 shows the predicted dry deposition assuming different reactivities for divalent

mercury vapor. These results indicate that increasing the reactivity higher than 800 will have little effect

on the predicted deposition. However, there is a substantial difference between the deposition results

using a reactivity of 18 instead 800. Indeed, the predicted dry deposition velocities average about

0.6 cm/s if a reactivity of 18 is assumed.
0

5

10

15

20

25

30

35

40

45

50

Total Mercury Deposition 

(ug/m2/yr)

2.5 km 10 km 25 km

Receptor

Comparison of Sensitivity of Total Deposition Rate to Divalent Mercury Reactivity

Reactivity=18

Reactivity=800

Reactivity=8e8

5-52

Figure 5-18

The above sensitivity analysis shows that if more empirical data would show that the pollutant reactivity

is 18 or less for divalent mercury vapor, then our use of the number 800 in the present analysis has led to

overestimation of mercury deposition by, at most, about a factor of five. An observation in support of

the use of a reactivity near 800 is that the average predicted dry deposition velocity for divalent mercury

vapor of about 3 cm/s is consistent with the table of values used by RELMAP for coniferous forests.
5-53

5.3.4.2 Wet Deposition

In the local impact analysis, wet deposition of particulate mercury is estimated by calculating a

scavenging coefficient that depends on particle size and precipitation intensity, while wet deposition of

vapor is estimated by converting a washout ratio to a scavenging coefficient. The washout ratio is the

ratio of the concentration in surface-level precipitation to the concentration in surface level air (Slinn

1984). Because most facilities are assumed to emit primarily vapor-phase mercury (elemental of

divalent), in this section the possible impacts of uncertainty in wet deposition of vapor are briefly

discussed. 

Due to its higher solubility, divalent mercury is thought to wet deposit at much higher rates than

that of elemental mercury vapor. Determination of washout ratios for divalent mercury vapor has

precluded by the limitations of current analytical measurement techniques: it has not been possible to

obtain measurements of divalent vapor air concentrations, and hence there are no reported values in the

peer-reviewed literature. For this reason, the washout ratio used for divalent mercury vapor is based on

an assumed similarity between divalent mercury and nitric acid, for which washout ratios are available

(Petersen 1995). In particular, a value of 1.6x10 is used. Comparisons of concentrations in 6

precipitation calculated using this value agree quite well for nitric acid. However, the applicability of

this value for divalent mercury vapor is uncertain, as there may be other processes specific to mercury

that would result in a smaller washout ratio.

Because the washout ratio is used to calculate a scavenging coefficient, the effect of the

uncertainty in the washout ratio is not strictly linear. A larger washout ratio results in a larger

scavenging coefficient, which results in more of the plume being depleted closer to the source. Thus, at

larger distances from the source, the predicted wet deposition may be higher using a smaller washout

ratio, and the uncertainty in the washout ratio will primarily affect predictions of deposition close to the

facility. These predictions are of course the most critical, and at present cannot be validated due to a lack

of available measured data near the facilities of concern. In the end, the total deposited within 50 km is

actually not sensitive to the washout ratio assumed. This is indicated when the mass balance results for

elemental and divalent mercury are compared: the fraction of total elemental mercury emitted that is

deposited within 50 km via wet deposition is similar to that for divalent mercury (both at about 6%),

despite the fact that the washout ratio for divalent mercury is 10,000 times larger than that for elemental

mercury. 

5.3.4.3 Sensitivity to Emissions Speciation

For the two municipal waste combustors, two additional emissions speciations were utilized to

investigate the sensitivity of the deposition rates to the speciation. These results are summarized in

Table 5-19.
5-54

Table 5-19

Sensitivity of Total Mercury Deposition Rate to Emissions Speciation for 

Municipal Waste Combustors

Mercury % Hg0 % Hg2 Vapor % Hg2 Particulate Total Hg Total Hg

Emissions Deposition rate at Deposition

Scenario 2.5 km (ug/m /yr) rate at 10 km 2

(ug/m /yr) 2

LMWC_A 30 50 20 46.0 18.1

LMWC_B (used 60 30 10 26.7 11.2

in analyses)

LMWC_C 90 10 0 9.46 4.19

SMWC_A 30 50 20 8.15 2.47

SMWC_B (used 60 30 10 4.98 1.54

in analyses)

SMWC_C 90 10 0 1.81 0.62

The results are qualitatively similar for both facilities: the predicted total mercury deposition rate

is roughly proportional to the fraction of emissions that is assumed to be divalent mercury vapor. This

indicates the significance of the assumption regarding emissions speciation for these facilities.

5.3.4.4 Effect of Terrain on Results of Local Scale Modeling

The ISC3 modeling in this report has assumed that the model plants were placed in simple

terrain, with the receptors all located at the same elevation as the stack base. In reality, many of these

emission sources may actually be located in rolling topography, which may ultimately affect the

predicted media concentrations near the facility. In this section a limited analysis of the effect of terrain

on the total deposition of mercury is reported. 

For this analysis, the ISC3 model was run with receptors located at the following heights: same

height as stack base, half the height of the stack, the same height as the stack, and 1.5 times the height of

the stack. Analyses were made at 2.5 km, 10.0 km, and 25.0 km northeast of a large municipal waste

combustor (variant b, with 60% elemental mercury, 30% divalent mercury vapor, and 10% divalent

mercury in particulates) located at the eastern site. The direction of maximum deposition for this site is

northeast of the plant, and the stack height for this model plant is 70.104 meters. At each receptor

location (three distances and four elevations), the total depositions were combined for elemental mercury

and for divalent mercury both in the vapor and particulate forms. It is important to realize that the total

depositions evaluated in this analysis included both wet and dry depositions. Table 5-20 shows the

extent of the increase observed in total deposition with increase in elevation at each of the 3 distances

from the stack. This table shows the dimensionless ratio of the predicted value at a given height and the

predicted value for a receptor at the same elevation as the stack base. The value for height of 0, yielding

a ratio of 1.0, is included to make the meaning of the ratio explicitly clear. 
5-55

Table 5-20

Ratios of Total Deposition of Mercury at Receptors at Different Elevations 

to Total Deposition when the Elevation is Zero

Elevation of receptor Calculated for the following three distances

in meters 2.5 km from stack 10.0 km from stack 25.0 km from stack

0 1.00 1.00 1.00

35.052 (i.e. half stack height) 1.48 1.26 1.13

70.104 (i.e., stack height) 1.81 1.47 1.27

105.156 (i.e., 1.5 x stack height) 2.79 1.72 1.36

At 25 km, the difference as a function of receptor height is not as extreme because more

dispersion has occurred: the vertical change in air concentrations is not as great as it is for closer

receptors, thereby resulting in less deposition. The maximum increase noted, which is at 2.5 km, is less

than three fold. 
6-1

6. WATERSHED FATE AND TRANSPORT MODELING

6.1 Overview

The purpose of this section is to present the summary results for the watershed fate and transport

modeling. In section 6.2 the watershed results are summarized for the pre-anthropogenic and current

condition periods. The results of the latter analysis are used as the initial conditions for evaluating the

potential impact of the model plants considered in this report that emit mercury. In section 6.3, selected

watershed/waterbody output are presented for all model plants. In section 6.4, the results of sensitivity

analyses conducted with the IEM-2M model are discussed.

6.2 Watershed and Waterbody Results for Pre-Anthropogenic Mercury Cycle and the Current 

Mercury Cycle

Mercury has always been an environmental constituent. Table 6-1 compares pre-anthropogenic

and current cycle values for average air concentrations and average annual deposition rates for the

hypothetical Eastern and Western U.S. Table 6-2 lists predicted total mercury concentrations in the

watershed soils, grain, the water column, and trophic level 4 fish for both sites. Predicted concentrations

at the eastern site are higher than those in the West. This is the result of higher estimated deposition

rates due to differences in annual precipitation rates. Over 90% of the total mercury in grain and soils is

predicted to be the inorganic divalent species. Over 80% of the total mercury in the water column is

predicted to be inorganic divalent. All of the mercury in trophic level 4 fish is methylated.

The predictions of the IEM-2M model for the pre-anthropogenic mercury cycle were used as

inputs to the current cycle. Table 6-3 lists the results for the current cycle. These values were used as

inputs to the Local Scale Analysis. The predictions for the western site are much lower than those for the

eastern site.

Table 6-1

Assumed Mercury Air Concentrations and Atmospheric Deposition Rates for Pre-Anthropogenic

and Current Conditions

Eastern Site Western Site

Period Air Concentration Annual Deposition Air Annual

ng/m Rate Concentration Deposition Rate 3

μg/m /yr ng/m μg/m /yr 2 32

pre- 0.5 3 0.5 1

Anthropogenic

Current Cycle 1.6 10 1.6 2
6-2

Table 6-2

Total Mercury Concentrations Predicted by IEM-2M Model for the 

Pre-Anthropogenic Time Period

Media Eastern Site Western Site

 watershed soils (ng/g) 14 4

grain (ng/g) 0.6 0.5

Dissolved water column(ng/L) 0.3 0.1

Trophic level 4 fish ppm 0.13 0.04

Table 6-3

Total Mercury Concentrations Predicted by IEM-2M Model for the Current 

(Post-Industrial) Time Period

Media Eastern Site Western Site

 watershed soils (ng/g) 47 8

grain (ng/g) 2 1.6

Dissolved water column(ng/L) 0.9 0.2

Trophic level 4 fish ppm 0.44 0.09

6.3 Watershed/Waterbody Model Results for Local Scale Analysis

Tables 6-4 through 6-7 show selected results for all facilities at both sites, using both the

RELMAP 50th and 90th percentiles. The columns labeled backgroundrepresent the fraction of the

total value that were predicted prior to modeling the facility. For example, for the eastern site the total

predicted watershed soil concentration assumed before the facility was modeled was 47 ng/g (see Table

6-3 above). When the facility and RELMAP are modeled for an additional 30 years, the predicted soil

concentration at 2.5 km is 102 ng/g; the percent backgroundis 46 percent. Similarly, the percent

RELMAPis the ratio of the value predicted using RELMAP for 30 years without the facility, with the

total value (using the same initial conditions; i.e., Table 6-3). The percent ISCis the remaining

fraction.

For all facilities, the contribution of the local source decreases as the distance from the facility

increases. With the exception of the chlor-alkali plant, the facilities are generally predicted to contribute

less than 50% to the total watershed soil concentration, with regional anthropogenic sources contributing

up to 15% for the RELMAP 50th percentiles and up to 60% for the RELMAP 90th percentiles. 

The results for the methylmercury water concentrations and trophic level 4 fish concentrations

show a slightly higher contribution from the local sources. While the fractions are similar to those for
6-3

watershed soil since the watershed serves as a mercury source for the waterbody, these values are

slightly higher due to the direct deposition onto the waterbody.

The predicted fruit, leafy vegetable, and beef concentrations are generally dominated by the

background values. For plants, these products are assumed to take up most of the mercury from the air,

and therefor the local source usually does not impact the local air concentrations significantly. The

exception is the chlor-alkali plant for which the low stack results in higher mercury air concentrations. 

The results for the beef concentrations are similar; however, there is a slightly higher contribution from

the local source because the cattle are exposed through the ingestion of soil.
6-4

Table 6-4

Predicted Values for Eastern Site (Local + RELMAP 50th)

Watershe %Backgr %RelMap %ISC MHg Tier 4 %Backgr %RelMap %ISC Total Hg %Backgr %RelMap %ISC Total Hg %Backgr %RelM

d Soil ound Dissolved Fish MHg ound Fruit ound Leafy ound 

Concentra Water Concentra Concentra Vegetable 

tion (ng/g) Conc.(ng/l tion (ug/g) tion (ng/g) Concentra 

) tion (ng/g)

Variant b:Large Municipal Waste 2.5 km 1.0E+02 46% 8% 47% 1.7E-01 1.1E+00 38% 7% 54% 3.5E+01 92% 4% 4% 3.4E+01 91% 4% Combustor

10 km 7.4E+01 63% 11% 26% 1.1E-01 7.6E-01 58% 11% 31% 3.4E+01 93% 4% 3% 3.3E+01 93% 4% 25 km 6.1E+01 76% 13% 11% 8.9E-02 6.0E-01 73% 14% 13% 3.4E+01 95% 4% 1% 3.3E+01 94% 4% 

Variant b:Small Municipal Waste 2.5 km 6.3E+01 74% 12% 14% 9.5E-02 6.4E-01 68% 13% 18% 3.4E+01 95% 4% 1% 3.3E+01 95% 4% Combustor

10 km 5.7E+01 82% 14% 5% 8.2E-02 5.6E-01 79% 15% 6% 3.4E+01 95% 4% 1% 3.3E+01 95% 4% 25 km 5.5E+01 85% 14% 1% 7.9E-02 5.3E-01 83% 16% 2% 3.4E+01 96% 4% 0% 3.3E+01 96% 4% 

Large Commercial HMI 2.5 km 6.2E+01 75% 12% 13% 9.6E-02 6.5E-01 68% 13% 19% 3.4E+01 95% 4% 1% 3.3E+01 95% 4% 

10 km 5.6E+01 84% 14% 2% 8.0E-02 5.4E-01 82% 16% 3% 3.4E+01 96% 4% 0% 3.3E+01 95% 4% 25 km 5.5E+01 85% 14% 1% 7.8E-02 5.3E-01 83% 16% 1% 3.4E+01 96% 4% 0% 3.2E+01 96% 4% 

Large Hospital HMI 2.5 km 1.1E+02 44% 7% 48% 1.9E-01 1.3E+00 34% 6% 60% 3.5E+01 92% 4% 4% 3.4E+01 91% 4% 

10 km 6.3E+01 74% 12% 14% 9.4E-02 6.4E-01 69% 13% 18% 3.4E+01 95% 4% 1% 3.3E+01 95% 4% 25 km 5.7E+01 82% 14% 4% 8.1E-02 5.5E-01 80% 15% 5% 3.4E+01 96% 4% 0% 3.3E+01 95% 4% 

Small Hospital HMI 2.5 km 5.8E+01 81% 13% 6% 8.5E-02 5.8E-01 76% 15% 9% 3.4E+01 96% 4% 0% 3.3E+01 95% 4% 

10 km 5.5E+01 85% 14% 1% 7.8E-02 5.3E-01 83% 16% 1% 3.4E+01 96% 4% 0% 3.2E+01 96% 4% 25 km 5.5E+01 86% 14% 0% 7.8E-02 5.3E-01 84% 16% 0% 3.3E+01 96% 4% 0% 3.2E+01 96% 4% 

Large Hospital HMI (wet scrubber) 2.5 km 5.6E+01 84% 14% 3% 8.1E-02 5.5E-01 80% 15% 4% 3.4E+01 96% 4% 0% 3.3E+01 96% 4% 

10 km 5.5E+01 85% 14% 0% 7.8E-02 5.3E-01 84% 16% 1% 3.3E+01 96% 4% 0% 3.2E+01 96% 4% 25 km 5.5E+01 86% 14% 0% 7.7E-02 5.3E-01 84% 16% 0% 3.3E+01 96% 4% 0% 3.2E+01 96% 4% 

Small Hospital HMI (wet scrubber) 2.5 km 5.5E+01 86% 14% 0% 7.8E-02 5.3E-01 84% 16% 0% 3.3E+01 96% 4% 0% 3.2E+01 96% 4% 

10 km 5.4E+01 86% 14% 0% 7.7E-02 5.3E-01 84% 16% 0% 3.3E+01 96% 4% 0% 3.2E+01 96% 4% 25 km 5.4E+01 86% 14% 0% 7.7E-02 5.3E-01 84% 16% 0% 3.3E+01 96% 4% 0% 3.2E+01 96% 4% 

Large Coal-fired Utility Boiler 2.5 km 8.1E+01 58% 10% 33% 1.3E-01 9.1E-01 48% 9% 42% 3.4E+01 95% 4% 1% 3.3E+01 95% 4% 

10 km 5.9E+01 79% 13% 8% 8.6E-02 5.9E-01 75% 14% 10% 3.4E+01 96% 4% 0% 3.3E+01 95% 4% 25 km 5.6E+01 83% 14% 3% 8.0E-02 5.5E-01 81% 15% 4% 3.4E+01 96% 4% 0% 3.2E+01 96% 4% 

Medium Coal-fired Utility Boiler 2.5 km 6.6E+01 71% 12% 18% 1.0E-01 6.9E-01 64% 12% 24% 3.4E+01 96% 4% 0% 3.3E+01 95% 4% 

10 km 5.8E+01 81% 13% 5% 8.3E-02 5.6E-01 78% 15% 7% 3.4E+01 96% 4% 0% 3.3E+01 95% 4% 25 km 5.6E+01 84% 14% 3% 8.0E-02 5.4E-01 81% 16% 3% 3.4E+01 96% 4% 0% 3.2E+01 96% 4% 

Small Coal-fired Utility Boiler 2.5 km 5.7E+01 82% 14% 5% 8.3E-02 5.6E-01 79% 15% 6% 3.4E+01 96% 4% 0% 3.3E+01 95% 4% 

10 km 5.5E+01 84% 14% 2% 7.9E-02 5.4E-01 82% 16% 2% 3.4E+01 96% 4% 0% 3.2E+01 96% 4% 25 km 5.5E+01 85% 14% 1% 7.8E-02 5.3E-01 83% 16% 1% 3.4E+01 96% 4% 0% 3.2E+01 96% 4% 

Medium OIl-fired Utility Boiler 2.5 km 5.5E+01 85% 14% 1% 7.8E-02 5.3E-01 83% 16% 1% 3.3E+01 96% 4% 0% 3.2E+01 96% 4% 

10 km 5.5E+01 86% 14% 0% 7.8E-02 5.3E-01 84% 16% 0% 3.3E+01 96% 4% 0% 3.2E+01 96% 4% 25 km 5.5E+01 86% 14% 0% 7.7E-02 5.3E-01 84% 16% 0% 3.3E+01 96% 4% 0% 3.2E+01 96% 4% 

Chlor-alkali plant 2.5 km 4.5E+02 10% 2% 88% 1.0E+00 6.8E+00 6% 1% 92% 8.1E+01 39% 2% 59% 8.2E+01 38% 2% 

10 km 1.1E+02 43% 7% 50% 1.8E-01 1.2E+00 37% 7% 56% 4.3E+01 75% 3% 21% 4.2E+01 74% 3% 25 km 6.8E+01 69% 11% 20% 1.0E-01 6.8E-01 65% 12% 23% 3.6E+01 88% 4% 8% 3.5E+01 88% 4% 
6-5

Table 6-5

Predicted Values for Eastern Site (Local + RELMAP 90th)

Watershe %Backgr %RelMap %ISC MHg Tier 4 %Backgr %RelMap %ISC Total Hg %Backgr %RelMap %ISC Total Hg %Backgr %RelM

d Soil ound Dissolved Fish MHg ound Fruit ound Leafy ound 

Concentra Water Concentra Concentra Vegetable 

tion (ng/g) Conc.(ng/l tion (ug/g) tion (ng/g) Concentra 

) tion (ng/g)

Variant b:Large Municipal Waste 2.5 km 1.2E+02 38% 24% 38% 2.0E-01 1.4E+00 32% 23% 45% 3.6E+01 89% 7% 4% 3.5E+01 88% 7% 

Combustor

10 km 9.5E+01 49% 31% 20% 1.5E-01 9.9E-01 44% 32% 23% 3.6E+01 90% 7% 3% 3.5E+01 90% 7% 25 km 8.3E+01 56% 35% 8% 1.2E-01 8.4E-01 52% 38% 9% 3.5E+01 91% 7% 1% 3.4E+01 91% 7% 

Variant b:Small Municipal Waste 2.5 km 8.5E+01 55% 35% 10% 1.3E-01 8.8E-01 50% 36% 13% 3.5E+01 92% 7% 1% 3.4E+01 91% 7% Combustor

10 km 7.9E+01 59% 37% 3% 1.2E-01 8.0E-01 55% 40% 4% 3.5E+01 92% 7% 1% 3.4E+01 92% 8% 25 km 7.7E+01 61% 38% 1% 1.1E-01 7.7E-01 57% 42% 1% 3.5E+01 93% 7% 0% 3.4E+01 92% 8% 

Large Commercial HMI 2.5 km 8.4E+01 55% 35% 9% 1.3E-01 8.9E-01 50% 36% 14% 3.5E+01 92% 7% 1% 3.4E+01 91% 7% 

10 km 7.7E+01 60% 38% 2% 1.1E-01 7.8E-01 57% 41% 2% 3.5E+01 93% 7% 0% 3.4E+01 92% 8% 25 km 7.7E+01 61% 39% 0% 1.1E-01 7.7E-01 58% 42% 0% 3.5E+01 93% 7% 0% 3.4E+01 92% 8% 

Large Hospital HMI 2.5 km 1.3E+02 37% 23% 40% 2.3E-01 1.5E+00 29% 21% 51% 3.6E+01 89% 7% 4% 3.5E+01 88% 7% 

10 km 8.5E+01 55% 35% 10% 1.3E-01 8.8E-01 50% 37% 13% 3.5E+01 92% 7% 1% 3.4E+01 91% 7% 25 km 7.8E+01 60% 38% 3% 1.2E-01 7.9E-01 56% 41% 3% 3.5E+01 92% 7% 0% 3.4E+01 92% 8% 

Small Hospital HMI 2.5 km 8.0E+01 59% 37% 4% 1.2E-01 8.2E-01 54% 39% 7% 3.5E+01 92% 7% 0% 3.4E+01 92% 8% 

10 km 7.7E+01 61% 38% 1% 1.1E-01 7.7E-01 57% 42% 1% 3.5E+01 93% 7% 0% 3.4E+01 92% 8% 25 km 7.6E+01 61% 39% 0% 1.1E-01 7.6E-01 58% 42% 0% 3.5E+01 93% 7% 0% 3.4E+01 92% 8% 

Large Hospital HMI (wet scrubber) 2.5 km 7.8E+01 60% 38% 2% 1.2E-01 7.9E-01 56% 41% 3% 3.5E+01 93% 7% 0% 3.4E+01 92% 8% 

10 km 7.6E+01 61% 39% 0% 1.1E-01 7.7E-01 58% 42% 0% 3.5E+01 93% 7% 0% 3.4E+01 92% 8% 25 km 7.6E+01 61% 39% 0% 1.1E-01 7.6E-01 58% 42% 0% 3.5E+01 93% 7% 0% 3.4E+01 92% 8% 

Small Hospital HMI (wet scrubber) 2.5 km 7.6E+01 61% 39% 0% 1.1E-01 7.6E-01 58% 42% 0% 3.5E+01 93% 7% 0% 3.4E+01 92% 8% 

10 km 7.6E+01 61% 39% 0% 1.1E-01 7.6E-01 58% 42% 0% 3.5E+01 93% 7% 0% 3.4E+01 92% 8% 25 km 7.6E+01 61% 39% 0% 1.1E-01 7.6E-01 58% 42% 0% 3.5E+01 93% 7% 0% 3.4E+01 92% 8% 

Large Coal-fired Utility Boiler 2.5 km 1.0E+02 45% 29% 26% 1.7E-01 1.1E+00 38% 28% 34% 3.5E+01 92% 7% 0% 3.4E+01 92% 7% 

10 km 8.1E+01 57% 36% 6% 1.2E-01 8.2E-01 54% 39% 7% 3.5E+01 93% 7% 0% 3.4E+01 92% 8% 25 km 7.8E+01 60% 38% 2% 1.2E-01 7.8E-01 56% 41% 3% 3.5E+01 93% 7% 0% 3.4E+01 92% 8% 

Medium Coal-fired Utility Boiler 2.5 km 8.8E+01 53% 34% 13% 1.4E-01 9.3E-01 48% 35% 18% 3.5E+01 92% 7% 0% 3.4E+01 92% 8% 

10 km 7.9E+01 59% 37% 4% 1.2E-01 8.0E-01 55% 40% 5% 3.5E+01 93% 7% 0% 3.4E+01 92% 8% 25 km 7.8E+01 60% 38% 2% 1.1E-01 7.8E-01 57% 41% 2% 3.5E+01 93% 7% 0% 3.4E+01 92% 8% 

Small Coal-fired Utility Boiler 2.5 km 7.9E+01 59% 37% 3% 1.2E-01 8.0E-01 55% 40% 5% 3.5E+01 93% 7% 0% 3.4E+01 92% 8% 

10 km 7.7E+01 60% 38% 1% 1.1E-01 7.8E-01 57% 41% 2% 3.5E+01 93% 7% 0% 3.4E+01 92% 8% 25 km 7.7E+01 61% 39% 0% 1.1E-01 7.7E-01 58% 42% 1% 3.5E+01 93% 7% 0% 3.4E+01 92% 8% 

Medium OIl-fired Utility Boiler 2.5 km 7.7E+01 61% 39% 0% 1.1E-01 7.7E-01 58% 42% 1% 3.5E+01 93% 7% 0% 3.4E+01 92% 8% 

10 km 7.6E+01 61% 39% 0% 1.1E-01 7.6E-01 58% 42% 0% 3.5E+01 93% 7% 0% 3.4E+01 92% 8% 25 km 7.6E+01 61% 39% 0% 1.1E-01 7.6E-01 58% 42% 0% 3.5E+01 93% 7% 0% 3.4E+01 92% 8% 

Chlor-alkali plant 2.5 km 4.8E+02 10% 6% 84% 1.0E+00 7.1E+00 6% 5% 89% 8.2E+01 39% 3% 58% 8.3E+01 38% 3% 

10 km 1.3E+02 36% 23% 41% 2.1E-01 1.4E+00 31% 22% 47% 4.4E+01 73% 6% 21% 4.3E+01 72% 6% 25 km 9.0E+01 52% 33% 15% 1.4E-01 9.2E-01 48% 35% 17% 3.7E+01 86% 7% 8% 3.6E+01 85% 7% 
6-6

Table 6-6

Predicted Values for Western Site (Local + RELMAP 50th)

Watershe %Backgro %RelMap %ISC MHg Tier 4 %Backgr %RelMap %ISC Total Hg %Backgr %RelMap %ISC Total Hg %Backgr %RelM

d Soil und Dissolved Fish MHg ound Fruit ound Leafy ound 

Concentr Water Concentra Concentra Vegetable 

ation Conc.(ng/l tion (ug/g) tion (ng/g) Concentra 

(ng/g) ) tion (ng/g)

Variant b:Large Municipal Waste 2.5 km 3.8E+01 20% 1% 79% 8.8E-02 6.0E-01 15% 1% 84% 3.3E+01 96% 1% 3% 3.2E+01 96% 1% 

Combustor

10 km 2.3E+01 33% 2% 65% 5.5E-02 3.7E-01 24% 2% 74% 3.2E+01 97% 1% 2% 3.2E+01 97% 1% 25 km 1.3E+01 56% 4% 40% 2.7E-02 1.9E-01 48% 4% 48% 3.2E+01 98% 1% 1% 3.2E+01 98% 1% 

Variant b:Small Municipal Waste 2.5 km 1.4E+01 53% 4% 44% 3.3E-02 2.3E-01 40% 3% 57% 3.2E+01 98% 1% 1% 3.2E+01 98% 1% Combustor

10 km 9.9E+00 76% 5% 18% 1.9E-02 1.3E-01 68% 6% 26% 3.2E+01 99% 1% 0% 3.1E+01 99% 1% 25 km 8.6E+00 87% 6% 6% 1.6E-02 1.1E-01 84% 7% 9% 3.2E+01 99% 1% 0% 3.1E+01 99% 1% 

Large Commercial HMI 2.5 km 1.4E+01 53% 4% 43% 3.4E-02 2.3E-01 39% 3% 58% 3.2E+01 98% 1% 1% 3.2E+01 98% 1% 

10 km 8.9E+00 85% 6% 9% 1.7E-02 1.1E-01 80% 7% 14% 3.2E+01 99% 1% 0% 3.1E+01 99% 1% 25 km 8.3E+00 91% 6% 2% 1.5E-02 1.0E-01 89% 8% 3% 3.2E+01 99% 1% 0% 3.1E+01 99% 1% 

Large Hospital HMI 2.5 km 4.8E+01 16% 1% 83% 1.4E-01 9.6E-01 9% 1% 90% 3.3E+01 96% 1% 3% 3.3E+01 95% 1% 

10 km 1.4E+01 54% 4% 42% 3.1E-02 2.1E-01 42% 4% 54% 3.2E+01 98% 1% 1% 3.2E+01 98% 1% 25 km 9.6E+00 79% 5% 16% 1.8E-02 1.2E-01 73% 6% 20% 3.2E+01 99% 1% 0% 3.1E+01 99% 1% 

Small Hospital HMI 2.5 km 1.1E+01 71% 5% 24% 2.3E-02 1.5E-01 58% 5% 37% 3.2E+01 99% 1% 0% 3.1E+01 99% 1% 

10 km 8.4E+00 90% 6% 4% 1.5E-02 1.0E-01 87% 7% 6% 3.2E+01 99% 1% 0% 3.1E+01 99% 1% 25 km 8.2E+00 93% 6% 1% 1.4E-02 9.8E-02 91% 8% 1% 3.2E+01 99% 1% 0% 3.1E+01 99% 1% 

Large Hospital HMI (wet scrubber) 2.5 km 9.2E+00 82% 6% 12% 1.8E-02 1.2E-01 73% 6% 20% 3.2E+01 99% 1% 0% 3.1E+01 99% 1% 

10 km 8.2E+00 92% 6% 2% 1.5E-02 1.0E-01 90% 8% 3% 3.2E+01 99% 1% 0% 3.1E+01 99% 1% 25 km 8.1E+00 93% 6% 0% 1.4E-02 9.8E-02 92% 8% 1% 3.2E+01 99% 1% 0% 3.1E+01 99% 1% 

Small Hospital HMI (wet scrubber) 2.5 km 8.2E+00 93% 6% 1% 1.5E-02 9.9E-02 91% 8% 2% 3.2E+01 99% 1% 0% 3.1E+01 99% 1% 

10 km 8.1E+00 93% 6% 0% 1.4E-02 9.7E-02 92% 8% 0% 3.2E+01 99% 1% 0% 3.1E+01 99% 1% 25 km 8.1E+00 94% 6% 0% 1.4E-02 9.7E-02 92% 8% 0% 3.2E+01 99% 1% 0% 3.1E+01 99% 1% 

Large Coal-fired Utility Boiler 2.5 km 1.4E+01 55% 4% 42% 3.1E-02 2.1E-01 43% 4% 53% 3.2E+01 99% 1% 0% 3.1E+01 99% 1% 

10 km 9.9E+00 76% 5% 19% 1.9E-02 1.3E-01 70% 6% 24% 3.2E+01 99% 1% 0% 3.1E+01 99% 1% 25 km 9.8E+00 78% 5% 17% 1.8E-02 1.2E-01 73% 6% 21% 3.2E+01 99% 1% 0% 3.1E+01 99% 1% 

Medium Coal-fired Utility Boiler 2.5 km 1.1E+01 66% 5% 29% 2.3E-02 1.5E-01 58% 5% 37% 3.2E+01 99% 1% 0% 3.1E+01 99% 1% 

10 km 1.0E+01 73% 5% 22% 2.0E-02 1.4E-01 66% 6% 28% 3.2E+01 99% 1% 0% 3.1E+01 99% 1% 25 km 9.5E+00 79% 5% 15% 1.8E-02 1.2E-01 74% 6% 19% 3.2E+01 99% 1% 0% 3.1E+01 99% 1% 

Small Coal-fired Utility Boiler 2.5 km 9.8E+00 77% 5% 18% 1.9E-02 1.3E-01 70% 6% 24% 3.2E+01 99% 1% 0% 3.1E+01 99% 1% 

10 km 8.8E+00 86% 6% 8% 1.6E-02 1.1E-01 81% 7% 13% 3.2E+01 99% 1% 0% 3.1E+01 99% 1% 25 km 8.3E+00 91% 6% 3% 1.5E-02 1.0E-01 88% 7% 4% 3.2E+01 99% 1% 0% 3.1E+01 99% 1% 

Medium OIl-fired Utility Boiler 2.5 km 8.2E+00 92% 6% 2% 1.5E-02 1.0E-01 90% 8% 2% 3.2E+01 99% 1% 0% 3.1E+01 99% 1% 

10 km 8.2E+00 92% 6% 1% 1.5E-02 9.9E-02 91% 8% 2% 3.2E+01 99% 1% 0% 3.1E+01 99% 1% 25 km 8.1E+00 93% 6% 1% 1.4E-02 9.8E-02 92% 8% 1% 3.2E+01 99% 1% 0% 3.1E+01 99% 1% 

Chlor-alkali plant 2.5 km 3.2E+02 2% 0% 97% 1.0E+00 6.9E+00 1% 0% 99% 7.2E+01 44% 0% 56% 7.3E+01 43% 0% 

10 km 4.5E+01 17% 1% 82% 1.2E-01 8.0E-01 11% 1% 88% 3.8E+01 83% 1% 16% 3.8E+01 82% 1% 25 km 1.8E+01 43% 3% 54% 3.7E-02 2.5E-01 36% 3% 61% 3.4E+01 93% 1% 6% 3.3E+01 93% 1% 
6-7

Table 6-7

Predicted Values for Western Site (Local + RELMAP 90th)

Watershe %Backgr %RelMap %ISC MHg Tier 4 %Backgr %RelMap %ISC Total Hg %Backgr %RelMap %ISC Total Hg %Backgr %RelM

d Soil ound Dissolved Fish MHg ound Fruit ound Leafy ound 

Concentra Water Concentra Concentra Vegetable 

tion (ng/g) Conc.(ng/l tion (ug/g) tion (ng/g) Concentra 

) tion (ng/g)

Variant b:Large Municipal Waste 2.5 km 4.7E+01 16% 21% 63% 1.1E-01 7.3E-01 12% 19% 68% 3.3E+01 94% 3% 3% 3.3E+01 94% 3% 

Combustor

10 km 3.2E+01 24% 31% 46% 7.5E-02 5.1E-01 18% 28% 54% 3.3E+01 95% 3% 2% 3.3E+01 95% 3% 25 km 2.3E+01 33% 43% 24% 4.7E-02 3.2E-01 28% 45% 28% 3.3E+01 96% 3% 1% 3.2E+01 96% 3% 

Variant b:Small Municipal Waste 2.5 km 2.4E+01 32% 41% 27% 5.3E-02 3.6E-01 25% 39% 36% 3.2E+01 97% 3% 1% 3.2E+01 96% 3% Combustor

10 km 1.9E+01 39% 51% 9% 3.9E-02 2.7E-01 34% 53% 13% 3.2E+01 97% 3% 0% 3.2E+01 97% 3% 25 km 1.8E+01 42% 55% 3% 3.5E-02 2.4E-01 37% 59% 4% 3.2E+01 97% 3% 0% 3.2E+01 97% 3% 

Large Commercial HMI 2.5 km 2.3E+01 32% 42% 26% 5.4E-02 3.6E-01 25% 39% 36% 3.3E+01 96% 3% 1% 3.2E+01 96% 3% 

10 km 1.8E+01 42% 54% 4% 3.6E-02 2.5E-01 36% 58% 6% 3.2E+01 97% 3% 0% 3.2E+01 97% 3% 25 km 1.8E+01 43% 56% 1% 3.5E-02 2.4E-01 38% 61% 1% 3.2E+01 97% 3% 0% 3.2E+01 97% 3% 

Large Hospital HMI 2.5 km 5.7E+01 13% 17% 70% 1.6E-01 1.1E+00 8% 13% 79% 3.3E+01 94% 3% 3% 3.3E+01 93% 3% 

10 km 2.3E+01 32% 42% 25% 5.1E-02 3.5E-01 26% 41% 33% 3.2E+01 97% 3% 1% 3.2E+01 96% 3% 25 km 1.9E+01 40% 52% 8% 3.8E-02 2.6E-01 35% 55% 10% 3.2E+01 97% 3% 0% 3.2E+01 97% 3% 

Small Hospital HMI 2.5 km 2.0E+01 38% 49% 13% 4.3E-02 2.9E-01 31% 49% 20% 3.2E+01 97% 3% 0% 3.2E+01 97% 3% 

10 km 1.8E+01 43% 55% 2% 3.5E-02 2.4E-01 38% 60% 3% 3.2E+01 97% 3% 0% 3.2E+01 97% 3% 25 km 1.7E+01 43% 56% 0% 3.4E-02 2.3E-01 38% 61% 1% 3.2E+01 97% 3% 0% 3.2E+01 97% 3% 

Large Hospital HMI (wet scrubber) 2.5 km 1.9E+01 41% 53% 6% 3.8E-02 2.6E-01 35% 55% 10% 3.2E+01 97% 3% 0% 3.2E+01 97% 3% 

10 km 1.8E+01 43% 56% 1% 3.5E-02 2.3E-01 38% 61% 1% 3.2E+01 97% 3% 0% 3.2E+01 97% 3% 25 km 1.7E+01 43% 56% 0% 3.4E-02 2.3E-01 39% 61% 0% 3.2E+01 97% 3% 0% 3.2E+01 97% 3% 

Small Hospital HMI (wet scrubber) 2.5 km 1.7E+01 43% 56% 0% 3.4E-02 2.3E-01 38% 61% 1% 3.2E+01 97% 3% 0% 3.2E+01 97% 3% 

10 km 1.7E+01 44% 56% 0% 3.4E-02 2.3E-01 39% 61% 0% 3.2E+01 97% 3% 0% 3.2E+01 97% 3% 25 km 1.7E+01 44% 56% 0% 3.4E-02 2.3E-01 39% 61% 0% 3.2E+01 97% 3% 0% 3.2E+01 97% 3% 

Large Coal-fired Utility Boiler 2.5 km 2.3E+01 33% 42% 25% 5.0E-02 3.4E-01 26% 42% 32% 3.2E+01 97% 3% 0% 3.2E+01 97% 3% 

10 km 1.9E+01 39% 51% 10% 3.9E-02 2.6E-01 34% 54% 12% 3.2E+01 97% 3% 0% 3.2E+01 97% 3% 25 km 1.9E+01 40% 52% 9% 3.8E-02 2.6E-01 35% 55% 10% 3.2E+01 97% 3% 0% 3.2E+01 97% 3% 

Medium Coal-fired Utility Boiler 2.5 km 2.1E+01 37% 47% 16% 4.3E-02 2.9E-01 31% 49% 20% 3.2E+01 97% 3% 0% 3.2E+01 97% 3% 

10 km 2.0E+01 39% 50% 11% 4.0E-02 2.7E-01 33% 53% 14% 3.2E+01 97% 3% 0% 3.2E+01 97% 3% 25 km 1.9E+01 40% 52% 8% 3.8E-02 2.6E-01 35% 56% 9% 3.2E+01 97% 3% 0% 3.2E+01 97% 3% 

Small Coal-fired Utility Boiler 2.5 km 1.9E+01 40% 51% 9% 3.9E-02 2.6E-01 34% 54% 12% 3.2E+01 97% 3% 0% 3.2E+01 97% 3% 

10 km 1.8E+01 42% 54% 4% 3.6E-02 2.5E-01 36% 58% 6% 3.2E+01 97% 3% 0% 3.2E+01 97% 3% 25 km 1.8E+01 43% 56% 1% 3.5E-02 2.4E-01 38% 60% 2% 3.2E+01 97% 3% 0% 3.2E+01 97% 3% 

Medium OIl-fired Utility Boiler 2.5 km 1.8E+01 43% 56% 1% 3.4E-02 2.3E-01 38% 61% 1% 3.2E+01 97% 3% 0% 3.2E+01 97% 3% 

10 km 1.7E+01 43% 56% 1% 3.4E-02 2.3E-01 38% 61% 1% 3.2E+01 97% 3% 0% 3.2E+01 97% 3% 25 km 1.7E+01 43% 56% 0% 3.4E-02 2.3E-01 38% 61% 0% 3.2E+01 97% 3% 0% 3.2E+01 97% 3% 

Chlor-alkali plant 2.5 km 3.3E+02 2% 3% 95% 1.0E+00 7.1E+00 1% 2% 97% 7.2E+01 43% 1% 56% 7.3E+01 42% 1% 

10 km 5.4E+01 14% 18% 68% 1.4E-01 9.4E-01 10% 15% 75% 3.8E+01 82% 2% 16% 3.8E+01 81% 2% 25 km 2.7E+01 28% 36% 35% 5.7E-02 3.9E-01 23% 37% 40% 3.4E+01 91% 3% 6% 3.4E+01 91% 3% 
(x,p)  100 

Xp XB

XB

ppB

pB

6-8

6.4 Variability and Sensitivity Analysis

The main thrust of this chapter has been to establish a plausible link between mercury emissions

and mercury concentrations in soil, surface water bodies, and fish. It is well established, however, that

watershed and water body characteristics significantly influence these concentrations as well. Several

reports document variability among water bodies within a region in which atmospheric deposition is

presumably constant (Watras, et al., 1995, Schofield, et al., 1994, Verta and Matilainen, 1995, Hurley et

al., 1995, St. Louis et al., 1996).

To explore the effects of watershed and water body characteristics on mercury levels, a series of

model variability and sensitivity analyses were performed. Variability analyses were used to determine

an expected range of mercury concentrations in soil, water, and fish due to a reasonable range of

watershed or water body characteristics. These analyses presented the opportunity to benchmark the

IEM-2M water body module against the independently-derived R-MCM (Harris, et al., 1996), which is

described below. Calculated input and output from the two models are compared in several tables. This

exercise provides a degree of model verification testing for IEM-2M.

Sensitivity analyses were conducted to better understand the importance of various model

parameters. First, a representative base simulation was run and calculated concentrations were noted. 

Next, a series of sensitivity simulations were run, each with a single model parameter increased or

decreased by 50%. The resulting changes in calculated concentrations were noted. The model sensitivity

to a parameter change is defined as the relative change in the mercury concentration divided by the

relative change in the parameter value, and is expressed as a percentage:

where:

 (x,p) = sensitivity of model output “x” to parameter “p” (percent)

x = model output of interest, such as total water column mercury concentration

p = model parameter being varied

X = calculated value of model output in base simulation B

X = calculated value of model output for a change in parameter “p” p

p = model parameter value in base simulation B

p = model parameter value in sensitivity simulation 

The calculated concentrations of interest are the total mercury concentration in soil, the total mercury

concentration in the water column, and the predatory (trophic level 4) fish concentration. Model

sensitivity results are summarized in tables, in which model parameters are presented in the order of their

sensitivity and grouped into four categories: extra strongly sensitive (> 100%), strongly sensitive (50% -

99%), moderately sensitive (25% - 49%), and weakly sensitive (<25%).
6-9

6.4.1 Variability and Parameter Sensitivity Analysis for IEM-2M

6.4.1.1 Description of Base Simulation and Analysis of Variability

The IEM-2M is set up to represent a shallow drainage lake and its adjoining watershed. For the

model sensitivity analysis, we used the environmental parameters for the eastern lake scenario, as

summarized in Table 6-8. The atmospheric deposition flux of 10 g/m -yr represents a typical 2

preindustrial loading. Because the model is linear with respect to loading, the choice of atmospheric

deposition does not affect the analysis of model sensitivity.

For the model parameters shown, IEM-2M predicts a steady-state total soil concentration of 46.8

ng/g. The total water column concentration is 1.16 ng/L, 7% of which is methyl mercury. 

Concentrations in prey and predator fish are 100 and 440 ng/g, respectively.

Several sets of model simulations were conducted to explore how variable watershed

characteristics affect mercury concentrations in the lake. Three major watershed characteristics are

expected to influence mercury levels: watershed size, watershed erosion potential, and soil mercury

retention. 

Variability due to watershed size -- In the first set of simulations, watershed size was varied from 10%

of the lake surface area to 50 times the lake surface area (0.25 - 125 km ). The sediment delivery ratio 2

was adjusted for watershed size following the Vanoni (1975) relationship as presented in Mills et al.

(1985). Results are summarized in Table 6-9.

As watershed size increases by a factor of 500, average soil concentrations increase by a factor of

2 because smaller sediment delivery ratios from larger surface areas yield lower net erosion loss rates

from the watershed. Similarly, while erosion loads of mercury from the watershed increase by a factor of

500, water body concentrations increase by a factor of just over 2. This is because the increased load of

solids to the water body causes increased settling and burial loss of mercury from the water body. 

Furthermore, the higher solids levels cause lower dissolved MHg fractions and thus lower bioavailability

of the mercury. Consequently, fish levels increase by less than a factor of 2. Large variations in

watershed size, then, should cause small but significant variations in water body mercury levels.
6-10

Table 6-8

General Properties of the IEM-2M Eastern Lake

Water Body

Volume 1.245 × 10 m7 3

Surface Area 2.49 × 10 m6 2

Average Depth 5 m

Advective Flow 0.46 m /sec 3

Upper Sediment Depth 0.02 m

Solids Density 1.5 g/mL

Benthic Porosity 0.95

Primary Productivity 100 mg-C/m -day 2

Biotic Solids Settling Velocity 0.2 m/day

Abiotic Solids Settling Velocity 2 m/day

Biotic Solids Concentration (calculated) 0.7 mg/L

Abiotic Solids Concentration (calculated) 1.2 mg/L

Watershed

Atmospheric Deposition 10 g/m -yr 2

Watershed Area 3.74 × 10 m7 2

Watershed Soil Depth 0.01 m

Soil Dry Density 1.4 g/mL

Soil Moisture Content 0.1

Precipitation 0.8 m/yr

Runoff 0.18 m/yr

Erosivity Factor (R) 200 yr-1

Erodibility Factor (K) 0.3 tons/acre

Topographic Factor (LS) 2.5

Cover Management Factor (C) 0.006

Sediment Delivery Ratio 0.2

Enrichment Ratio 2
Xe  1.29  (R1.735)  K  LS  C

6-11

Table 6-9

Effect of Watershed Size on Total Mercury Concentrations

Watershed Size, km : 0.25 2.5 12.5 37.3 124.5 2

Sediment Delivery Ratio: 0.5 0.3 0.22 0.18 0.12

Soil, ng/g 30 39 45 49 55

Water Column, ng/L 0.65 0.73 0.95 1.18 1.55

Sediment, ng/g 51 57 72 86 106

Predatory Fish, ng/g 330 360 430 460 460

Variability due to watershed erosion -- Watershed erosion is the major pathway of mercury transport

from soil to water body. IEM-2M calculates soil erosion using the Universal Soil Loss Equations

(USLE), in which:

where:

X = soil erosion (tonnes/ha-yr) e

R = rainfall erosivity index (yr )-1

K = soil erosivity factor (tonnes/ha)

LS = topographic (slope-length) factor

C = cover-management factor

1.29 = units conversion factor

1.735 = units conversion factor for R

The default values for these parameters used in the IEM-2M eastern lake give a soil erosion value of 1.2

tonnes/ha-yr. To explore the feasible range of values for X , five diverse watershed types were defined: e

A: Northern (R=100), sand (K=0.05), moderate slope (slope-length=3%-500 m, LS=0.66),

undisturbed forest (75% cover, C=0.001); X = 0.0074 tonnes/ha-yr. e

B: Mideastern (R=175), sandy loam (K=0.24), hilly (slope-length=10%-100 m, LS=1.17),

undisturbed forest (75% cover, C=0.001); X = 0.11 tonnes/ha-yr. e

C: Western (R=20), fine sand (K=0.16), moderate slope (slope-length=3%-500 m, LS=0.66),

brush and weeds (20% cover, C=0.2); X = 0.95 tonnes/ha-yr. e

D: Southeastern (R=350), clay loam (K=0.25), hilly (slope-length=10%-100 m, LS=2.47), trees,

brush, and grass (80% cover, C=0.013); X = 6.3 tonnes/ha-yr. e
6-12

E: Midwestern (R=175), silty loam (K=0.42), moderate slope (slope-length=3%-500 m,

LS=0.66), row crops (C=0.4); X = 43 tonnes/ha-yr. e

These watershed erosion characteristics were specified for the standard 12.43 km watershed. Calculated 2

mercury concentrations are presented in Table 6-10.

Table 6-10

Effect of Watershed Erosion on Total Mercury Concentrations

Watershed (see text): A B C D E

Erosion Loss, tonnes/ha: 0.0072 0.11 0.95 6.3 43

Soil, ng/g 75 72 58 26 5

Water Column, ng/L 0.87 1.00 1.24 0.81 0.36

Sediment, ng/g 69 78 93 52 11

Predatory Fish, ng/g 450 510 560 190 20

As watershed erosion increases by a factor of 100 between watershed A and C, soil mercury

concentrations decline only slightly. In this range, losses from soil due to reduction and volatilization are

more important than losses due to erosion. Water body and fish concentrations increase slightly in

response to the large loading increase. The large increases in mercury loading are partially

counterbalanced by increases in settling and burial loss. As erosion losses increase by a factor of 50

between watershed C and E, soil concentrations decline by a factor of 10. In this range, mercury losses

from soil due to erosion become important relative to the reduction and volatilization loss. In response to

declining soil levels, water column concentrations decline by a factor of 4. Fish concentrations decline

by a factor of 25 due to the lower water concentrations and the markedly lower bioavailable dissolved

fraction. Variability in watershed erosion characteristics, then, is expected to cause significant variability

in water body mercury levels, particularly for more disturbed watersheds with high levels of erosion.

Variability due to soil mercury retention -- Soil mercury retention in a watershed depends upon

several transport and transformation processes, some of which are not well understood. The IEM-2M

includes simple algorithms for leaching, runoff, and volatilization, which is driven by reduction of HgII. 

Erosion carries soil solids and associated mercury away from the watershed. Leaching and runoff losses

of mercury are small because of its strong partitioning to solids. The soil partition coefficient itself is a

relatively insensitive parameter. Changes in its value do not significantly affect the absolute amount of

mercury lost from the upper soil layer.

Soil retention of mercury in IEM-2M, then, is controlled by reduction, which is formulated as a

first-order reaction in the upper 5 mm that is proportional to soil water content, as described in Appendix

B. Rate constants characterizing a forest and a field, derived from data presented in Carpi and Lindberg

(1997) and Lindberg (1996), are 1×10 and 1.3×10 L/L -day, respectively. A value of 5×10 L/L -day -4 -3 -4

w w

was selected for the base IEM-2M simulations. To examine the water body response to variable soil
6-13

mercury retention, reduction rate constants were varied from 5×10 to 5×10 L/L -day. Resulting -5 -3

w

mercury concentrations are summarized in Table 6-11.

Table 6-11

Effect of Soil Mercury Retention on Total Mercury Concentrations

Reduction Rate Constant (day ): 0.00001 0.00025 0.0005 0.0025 0.005 -1

Soil, ng/g 99 66 47 14 8

Water Column, ng/L 2.25 1.56 1.16 0.47 0.33

Sediment, ng/g 164 114 84 34 24

Predatory Fish, ng/g 880 600 440 170 110

As reduction rates increase 2 orders of magnitude, soil concentrations decrease by a factor of 10

and water body concentrations decrease by a factor of 8. Soil retention of mercury, then, is an uncertain

component of watershed variability that could cause large variations in mercury response.

6.4.1.2 Sensitivity of Soil Mercury to Model Parameters

The relative sensitivity of mercury concentrations in the upper soil layer to the model parameters

is summarized in Table 6-12. Total soil mercury is strongly sensitive to total atmospheric deposition. 

In these simulations, wetfall and dryfall are not separated. Increases in loading lead to increases in total

mercury concentrations. 

The main loss pathway for soil mercury is volatilization of Hg , which is controlled by the HgII 0

reduction rate. This rate is proportional to the soil reduction rate constant and the soil moisture

content, which are both strongly sensitive parameters. Increases in the rate constant or soil moisture lead

to strongly-increased loss rates and lower soil concentrations.

Soil erosion is another significant loss pathway for soil mercury. Several model parameters

contribute to the calculation of bulk erosion, including rainfall erosivity, soil erodibility, the

topographic factor, the cover factor, and the sediment delivery ratio. The enrichment ratio

contributes to the calculation of mercury concentrations on eroded soil. Increases in these parameters

lead to moderately-increased loss rates and lower soil concentrations. The soil-water partition coefficient

also contributes to the calculation of mercury concentrations on eroded soil. Its value is high enough,

however, so that increases or decreases lead to only slightly higher or lower particulate soil

concentrations.

Mercury loss from runoff and leaching is relatively insignificant. For this reason, model

parameters related to these processes, such as runoff curve number, are not sensitive. On the other

hand, the exchange of gas phase Hg is relatively rapid. Moderate changes in model parameters 0

contributing to this process, such as soil void fraction and atmospheric diffusivity, are insensitive.
6-14

Table 6-12

IEM-2M Parameter Sensitivity for Total Soil Mercury *

Model Parameter Eastern Lake

decrease increase

 Total Atmospheric Deposition -100 +100

Soil Water Content +82 -74

Soil Reduction Rate Constant +82 -45

Soil Enrichment Ratio +46 -32

Soil Erosion Factors +46 -32 **

Soil Water Partition Coefficient - 8 +3

Soil Demethylation Rate Constant +2 - 1

Soil Methylation Rate Constant 0 0

Runoff Curve Number 0 0

Soil Void Fraction 0 0

Atmospheric Diffusivity 0 0

Sensitivity is expressed as relative change in total water column mercury concentration divided by the relative *

change in the model parameter, in percent.

Erosion factors include rainfall erosivity, soil erodibility, the topographic factor, the cover factor, and the **

sediment delivery ratio
6-15

6.4.1.3 Sensitivity of Water Column Mercury to Model Parameters

The sensitivity of total mercury concentration in the water column to the model parameters is

summarized in Table 6-13. Water column mercury is strongly sensitive to total atmospheric

deposition, which occurs over the watershed as well as directly onto the water body. In these

simulations, wetfall and dryfall are not separated. Increases in loading lead to increases in total mercury

concentrations. 

Water column mercury is strongly sensitive to loading from the watershed, including those

parameters that most influence soil mercury levels -- soil water content, the soil reduction rate

constant, and the soil enrichment ratio. Increases in soil water content and the reduction rate constant

cause strong declines in soil concentrations, erosion loads, and water concentrations. Increases in the

enrichment ratio cause moderate declines in soil concentration, but moderate increases in mercury

erosion loads and subsequent water concentrations. While increased soil erosion factors might be

expected to cause higher erosion loading and water concentrations, the reduced soil concentrations and

increased water column settling loss actually lead to slightly lower water column concentrations. 

Decreases in soil erosion lead first to small increases in water concentrations. Further large reductions in

soil erosion, however, lead to small reductions in water concentrations, as summarized in Table 6-10.

Mercury is lost from the water column through settling and volatilization. Settling loss is

strongly influenced by the solids-water partition coefficient. Increases in solids partitioning lead to

greater settling loss and moderately lower concentrations. Decreases in solids partitioning are strongly

sensitive. Settling velocities for biotic and abiotic solids are weakly sensitive parameters. The water

column reduction rate constant controls the supply of Hg , and thus the volatile loss rate. Increases in 0

reduction lead to moderate decreases in water column mercury levels. Wind speed directly contributes

to the volatilization rate, which is generally much faster than the reduction rate. While increases in wind

speed cause almost insignificant declines in mercury levels, decreases in wind speed are more sensitive.

Benthic mercury fluxes are internal loadings that moderately affect water column concentrations. 

Increased sediment-pore water partition coefficients and decreased pore water diffusion coefficients

cause small declines in net pore water diffusive loading to the water column, and thus slightly lower

water concentrations.

6.4.1.4 Sensitivity of Fish Mercury to Model Parameters

The sensitivity of predatory fish mercury concentration to the IEM-2M model parameters is

summarized in Table 6-14. The sensitivities of fish concentration and total water column concentration

are virtually identical for those parameters controlling atmospheric and watershed loading --

atmospheric deposition, soil water content, soil reduction rate constant, and soil enrichment ratio. 

Increases in loading cause strong increases in fish levels. Increases in the soil erosion parameters,

however, cause moderate declines in fish levels not only because water concentrations are lowered, but

also because the increased solids concentrations cause lower fractions of dissolved MHg, which is

bioavailable. The watershed surface area has a similar effect on fish levels as the soil erosion

parameters because of the increased supply of solids. The soil demethylation rate constant affects the

MHg fraction in the soil erosion loads. Increased soil demethylation leads to lower MHg concentrations

and slightly lower fish concentrations.

The internal sediment-water column exchange processes can provide an important net source of

MHg to the water column. The relatively low MHg partition coefficient in the upper sediment favors the

mobilization of MHg in the pore water. Consequently, both the pore water diffusion coefficient and the
6-16

Table 6-13

IEM-2M Parameter Sensitivity for Total Water Column Mercury *

Model Parameter Eastern Lake

decrease increase

 Total Atmospheric Deposition -100 +100

Soil Water Content +69 -64

Solids-Water Partition Coefficient +74 -36

Soil Reduction Rate Constant +69 -40

Soil Enrichment Ratio -62 +41

Water Reduction Rate Constant +14 -29

Soil Erosion Factors +14 -19 **

Sediment-Pore Water Partition Coefficient +22 -10

Biotic Solids Settling Velocity +12 -9

Abiotic Solids Settling Velocity + 14 -5

Dilution Flow +9 - 9

Watershed Surface Area - 10 +7

Pore Water Diffusion Coefficient - 10 +7

Wind Speed +12 -5

Sediment Mineralization Rate Constant -3 +2

Sediment Resuspension Velocity -3 +2

Sensitivity is expressed as relative change in total water column mercury concentration divided by the relative *

change in the model parameter, in percent.

Erosion factors include rainfall erosivity, soil erodibility, the topographic factor, the cover factor, and the **

sediment delivery ratio.
6-17

Table 6-14

IEM-2M Parameter Sensitivity for Predatory Fish Mercury *

Model Parameter Eastern Lake

decrease increase

 Total Atmospheric Deposition -100 +100

Soil Water Content +73 -64

Solids-Water Partition Coefficient +73 -41

Soil Reduction Rate Constant +73 -41

Soil Enrichment Ratio -64 +45

Pore Water Diffusion Coefficient - 45 +53

Sediment-Pore Water Partition Coefficient +64 -32

Soil Erosion Factors +50 -41 **

Sediment Methylation Rate Constant -45 +45

Water Demethylation Rate Constant +50 -32

Water Methylation Rate Constant -32 +32

Sediment Demethylation Rate Constant + 36 -23

Soil Demethylation Rate Constant +41 - 14

Watershed Surface Area +27 -23

Benthic Solids Concentration -23 +18

Water Reduction Rate Constant +18 -14

Primary Productivity +18 -14

Dilution Flow +14 -14

Sensitivity is expressed as relative change in total water column mercury concentration divided by the relative *

change in the model parameter, in percent.

Erosion factors include rainfall erosivity, soil erodibility, the topographic factor, the cover factor, and the sediment **

delivery ratio.
6-18

sediment-pore water partition coefficient are strongly sensitive parameters affecting fish

concentrations. Higher diffusion and lower sediment partitioning lead to increased diffusion of MHg to

the water column and higher fish concentrations. The sediment methylation and demethylation rate

constants are moderately sensitive parameters that control sediment MHg concentrations, and thus

loading to the water column and fish concentrations.

MHg is lost from the water column through demethylation and settling, and is gained in the water

column through methylation. An increase in the solids-water partition coefficient leads to moderatelyincreased settling loss of MHg, and thus moderately lower fish concentrations. The water column

methylation and demethylation rate constants moderately influence the MHg concentrations, and thus

the levels of mercury in fish. Increases in methylation and decreases in demethylation lead to higher fish

mercury concentrations.

6.4.2 Variability and Parameter Sensitivity Analysis for R-MCM

Given constant atmospheric mercury deposition, in-lake processes can lead to significant

variability in resulting mercury concentrations in water, sediment, and fish. To analyze the potential laketo-lake variability of mercury levels, we conducted a series of sensitivity analyses with the Regional

Mercury Cycling Model (R-MCM) (Harris, et al., 1996). R-MCM is a steady-state model that explicitly

handles most of the factors affecting fish mercury levels in a mechanistic way. Funded by the Electric

Power Research Institute (EPRI) and the Wisconsin Department of Natural Resources, R-MCM was

designed to examine and explain variations in mercury levels among lakes. It is an extension of an

earlier dynamic model developed for a set of seven oligotrophic seepage lakes in Wisconsin (Hudson et

al., 1994). The fish bioaccumulation compartments are based on equations from the Fish Bioenergetics

Model 2 (Hewett and Johnson, 1992). The R-MCM has been developed and calibrated to data from

several lakes in Wisconsin, and can be considered descriptive of these and similar lakes. While it is

presently being extended to lakes in other regions, it has not been validated for general nationwide use.

The Beta Version 1.0b (December 1996) version of the R-MCM was obtained from Tetra Tech,

Inc., with permission from EPRI, and used in these analyses. 

6.4.2.1 Overview of R-MCM

R-MCM is a compartment model that performs a steady-state mass balance for three mercury

components -- elemental mercury (Hg ), divalent mercury (HgII), and methyl mercury (MHg). The lake 0

is divided into two or three physical compartments, including epilimnion, hypolimnion (optional), and

surficial sediments. A simple food chain is simulated, including phytoplankton and benthos at its base,

zooplankton, prey fish, and predator fish. Fish are divided into year classes to which a set of

bioenergetics equations are applied. Specified atmospheric mercury concentrations and fluxes drive the

simulations.

The model simulates a set of simple transport processes, including atmospheric wetfall and

dryfall deposition, inflow and outflow through surface water or groundwater, sorption to biotic and

abiotic solids, settling, resuspension, and burial of abiotic particles and sorbed mercury, volatile

exchange of dissolved and vapor phase mercury at the air-water boundary, and pore water exchange of

dissolved mercury at the sediment-water interface. A set of equilibrium reactions is solved to calculate

the complexation of HgII and MHg with DOC and inorganic ligands. The resulting speciation depends

on the specified environmental characteristics (i.e., pH, DOC, SO , Cl, solids) and the equilibrium 4

constants and partition coefficients in the model database. 
6-19

Abiotic solids, phytoplankton, and zooplankton concentrations are specified as model input

parameters, as are settling and burial velocities and benthic mineralization rate constants. Bulk density is

calculated internally from specified porosity and solids density. Resuspension velocities are computed

using a solids mass balance.

Next, R-MCM solves a set of transformation reactions, including reduction in the water column

and methylation and demethylation in the water column and sediments. Reduction is modeled as a firstorder rate applied to dissolved Hg(OH) , and so is pH-dependent. Higher pH values favor this species, 2

and so promote reduction. Water column and sediment methylation are modeled as second-order rates

applied to all dissolved HgII species. The water column methylation rate constant is multiplied by the

DOC concentration, while the sediment rate constant is multiplied by the sediment TOC concentration. 

The rates are further modified by a temperature correction function that is based on the maximum

monthly temperature, and a Monod function that represents the stimulating effect of sulfate on

methylation. The temperature function doubles the methylation rate for every 10C increase above

15C. The sulfate function is disabled as a default option in this version of R-MCM. Apparently,

differences in sulfate concentrations among the set of Wisconsin lakes were not enough to require the use

of this function to describe differences in lake methylation rates. Demethylation in the water column is

modeled as a second-order rate applied to dissolved MHg species. The water surface demethylation rate

constant is multiplied by the average sunlight, and then attenuated throughout the water column using a

light extinction coefficient to obtain a depth-averaged rate. Demethylation in the sediments is modeled

as a second-order rate applied to the dissolved MHg species in pore water. The rate constant is

multiplied by the sediment TOC concentration, but is not temperature-corrected.

Finally, R-MCM solves a set of bioenergetics and biouptake equations to calculate the

bioaccumulation of mercury in a 5-component food web containing phytoplankton, zooplankton, benthos,

prey fish, and predatory fish. The fish bioenergetics equations are based on the Fish Bioenergetics

Model 2. These are used to calculate the energy requirements for growth and metabolism, from which

consumption, excretion, and gill exchange are derived. These rates are coupled with mercury

concentrations in the water and the diet to calculate mercury food chain fluxes and concentrations. Up to

10 individual year-classes of prey and predator fish with different diets are simulated. The default fish

species of yellow perch and walleye were used here. The model output reports mercury concentrations in

1-year old prey fish and in 5-year old predatory fish.

6.4.2.2 Analysis of In-Lake Variability in Mercury Levels

Representations of several Wisconsin lakes are available in the R-MCM database. To examine

the effects of water body characteristics on mercury levels, we worked with four -- Pallette Lake, Little

Rock Lake, Crystal Lake, and Lake Muskellunge. The model default values for atmospheric deposition

fluxes in this region were used -- 8.5 g/m -yr for wetfall and 3 g/m -yr for dryfall. In order to isolate 2 2

the variability due to intrinsic lake properties, we set the volume, depth, and hydraulic residence time for

these four lakes equal to the characteristic eastern lake analyzed with IEM-2M. The four lakes were

designated as “drainage lakes.” For one set of simulations, the surrounding watersheds were removed so

that the mercury levels respond to direct atmospheric deposition only. Another set of simulations was

conducted with the standard eastern watershed draining to the four lakes. A final set of simulations was

conducted in which the water columns of the four lakes were divided into epilimnion and hypolimnion,

keeping the total lake volume the same. These simulations were run with no watershed in order to

determine the effect of the hypolimnion on fish levels.

In order to study the effects of watershed characteristics on mercury levels, we conducted further

simulations with the modified Pallette Lake. The first was designated a perched lake, fed by rainwater

only, with a surrounding watershed area equal to 10 percent of the lake surface area and no wetlands. 
6-20

The second simulation represented a seepage lake, fed by groundwater, with a watershed area equal to

the lake surface area and 30 percent wetlands. The third simulation represented a drainage lake with the

same watershed properties. The fourth simulation represented a drainage lake with watershed area 5

times the lake surface area and 6 percent wetlands. The final simulation represented a drainage lake with

watershed area 15 times the lake surface area and 2 percent wetlands. Note that the seepage lake and the

drainage lakes all have the same wetland area, 30 percent of the lake surface area.

The general properties and forcing functions used in all four lakes are summarized in Table 6-15. 

The variable properties are given in Table 6-16. The effective partition coefficients and first-order rate

constants calculated internally are summarized in Table 6-17, along with the values used in the IEM-2M

eastern lake simulation. The calculated concentrations in the four R-MCM lakes and the IEM-2M

eastern lake are presented in Table 6-18 (no watershed) and Table 6-19 (standard watershed). The

calculated concentrations in the four R-MCM lakes with added hypolimnion are presented in Table 6-20. 

Finally, the calculated concentrations in the modified Pallette Lake with the five different

watershed/hydrology combinations are given in Table 6-21.

Variability due to in-lake processes -- The primary in-lake characteristics expected to influence

mercury concentrations in water and fish are pH, DOC, and solids. While hypolimnetic anoxia should be

important in deeper lakes, the four lakes represented here are all oligotrophic and shallow. Pallette is

characterized by higher pH, DOC, and sedimentary TOC. Crystal is lower in pH, DOC, and TOC. Little

Rock and Muskellunge have intermediate DOC and TOC levels, with lower and higher pH, respectively. 

Pallette and Little Rock have higher solids concentrations and resuspension velocities.

Predicted total mercury concentrations in the water column varied by a factor of 2 for each series

of simulations. With no watershed, values varied from 0.67 ng/L in Muskellunge to 1.21 ng/L in Little

Rock. With the watershed, values varied from 1.35 ng/L in Pallette to 2.11 ng/L in Muskellunge. IEM2M calculated total water column mercury levels at the low end of these ranges, predicting 1.49 ng/L and

0.63 ng/L with and without a watershed. 

Total sediment mercury concentrations varied by a factor of 6 within each set of simulations. 

With no watershed, values varied from 35 ng/g in Pallette to 205 ng/g in Crystal. With the watershed, the

range was 61 to 359 ng/g. Again, the IEM-2M lake is at the low end of the ranges, with 50 and 116 ng/g,

respectively. Predatory fish concentrations varied by just over a factor of 2. With no watershed, values

varied from 200 ng/g in Pallette to 463 ng/g in Little Rock. With the watershed, the range was 340 to

797 ng/g. With no watershed, IEM-2M predicted fish levels of 330 ng/g -- the middle of the range. With

the watershed, IEM-2M predicted fish levels of 740 ng/g, which is at the high end of the range.

Adding a hypolimnion had the effect of decreasing the variability among the four lakes examined

here. Total mercury varied between 0.75 and 0.81 ng/L in the epilimnion, and between 1.39 and 1.70 in

the hypolimnion. Predatory fish varied between 240 and 366 ng/g. Mercury concentrations in the

epilimnion and fish did not change systematically with the addition of a hypolimnion.

It should be stressed that R-MCM (version 1.0b) is currently parameterized for a limited set of

lakes. Some key processes, such as the effect of anoxia on methylation, are not included in the model 
6-21

Table 6-15

General Properties of the Four R-MCM Lakes

Property Value and Units

Volume 1.245 × 10 m7 3

Surface Area 2.49 × 10 m6 2

Depth 5 m

Hydraulic Residence Time 314 days

Advective Flow 0.46 m /sec 3

Upper Sediment Depth 0.02 m

Wet Deposition 8 g/m -yr 2

Dry Deposition 3.5 g/m -yr 2

Maximum Monthly Temperature 22C

Average Sunlight 318 Einsteins/m -sec 2

Fish Biomass 1 kg/ha

Benthic Biomass 25 g/m2

Solids Density 1.5 g/mL

Benthic Porosity 0.95 - 0.90
6-22

Table 6-16

Variable Properties of the Four R-MCM Lakes

Variable Pallette Little Crystal Muskel- IEM-2M

Rock lunge **

pH 7.2 6.0 6.3 7.2 -

DOC (water), mg/L 5.4 3.3 1.8 3.8 -

DOC (sediment), mg/L 16 10 5 11 -

TOC (sediment), g/m 800 500 275 550 - 2

SO , eq/L 56 65 72 77 - 4

Cl, mg/L .25 .28 .46 .30 -

DO, mg/L 8 8 8 8 -

Ca, mg/L 2.2 .94 1.14 5.8 -

light extinction coefficient, m 1.0 .75 .36 .76 - -1

phytoplankton, mg/L .28 .2 .1 .1

0.4*

zooplankton, mg/L .14 .1 .05 .05

abiotic solids, mg/L .9 .7 .35 .35 0.5*

settling velocity, m/day .5 .5 .5 .5 2

burial velocity, mm/yr .21 .15 .1 .1 .13*

resuspension velocity, mm/yr 2.02 1.35 0.49 0.53 3.7 *** *

sediment mineralization, yr .01 .01 .013 .011 .05 -1

calculated internally ** revised solids parameters for comparison with Wisconsin lakes *
6-23

Table 6-17

Partition Coefficients and First-order Rate Constants for Simulations

Coefficient Pallette Little Crystal Muskel- IEM-2M

Rock lunge

Partition Coefficients, L/kg

HgII - abiotic S 80,000 160,000 270,000 110,000 100,000

HgII - TSS 100,000 190,000 340,000 140,000 100,000

HgII - sediment 30,000 52,000 100,000 43,000 50,000

MHg - abiotic S 94,000 160,000 250,000 130,000 100,000

MHg - TSS 230,000 400,000 670,000 330,000 400,000

MHg - sediment 2,200 3,500 7,800 3,600 3,000

Rate Constants, day-1

reduction in water .0046 .00059 .0021 .0065 .0075

methylation in water .000044 .000027 .000015 .000031 .001

demethylation in water .0063 .0083 .015 .0082 .015

methylation in sediment .000055 .000020 .0000057 .000026 .0001 *

demethylation in sediment .00048 .00018 .000048 .00019 .002 *

applied to the total sediment mercury concentration *
6-24

Table 6-18

Simulation Results, No Watershed

Compartment and Variable Pallette Little Crystal Muskel- IEM-2M

Rock lunge

*

WATER COLUMN

Unfiltered Total, ng/L 0.78 1.21 0.90 0.67 0.63

Dissolved Methyl, ng/L 0.053 0.057 0.024 0.036 .051

Dissolved Elemental, ng/L 0.045 0.022 0.030 0.051 .047

SEDIMENT

Particulate Total, ng/g 35 120 205 42 50

Dissolved Methyl, ng/L 0.45 0.58 0.26 0.23 0.43

BIOTA

Prey Fish, ng/g 35 84 165 57 80

Predatory Fish, ng/g 200 463 336 278 330

*Adjusted watershed size, erosion to yield abiotic solids = 0.5 mg/L; no watershed Hg yield.

Table 6-19

Simulation Results with Standard Watershed

Compartment and Variable Pallette Little Crystal Muskel- IEM-2M

Rock lunge

*

WATER COLUMN

Unfiltered Total, ng/L 1.35 2.11 1.57 1.68 1.49

Dissolved Methyl, ng/L 0.090 0.096 0.039 0.081 0.108

Dissolved Elemental, ng/L 0.071 0.030 0.044 0.113 0.099

SEDIMENT

Particulate Total, ng/g 61 210 359 106 116 

Dissolved Methyl, ng/L 0.77 0.99 0.44 0.56 1.10

BIOTA

Prey Fish, ng/g 59 144 279 138 170

Predatory Fish, ng/g 340 797 572 668 740

* Adjusted watershed erosion by 0.4 to yield abiotic solids = 0.7 mg/L.
6-25

Table 6-20

Simulation Results with Hypolimnion, No Watershed

Compartment and Variable Pallette Little Crystal MuskelRock lunge

WATER COLUMN

Epilimnion Unfiltered Total, ng/L 0.78 0.81 0.75 0.78

Hypolimnion Unfiltered Total, ng/L 1.70 1.27 1.39 1.64

Epilimnion Dissolved Methyl, ng/L 0.057 0.036 0.016 0.037

Hypolimnion Dissolved Methyl, ng/L 0.245 0.159 0.029 0.089

SEDIMENT

Epilimnion Particulate Total, ng/g 56 82 172 52

Hypolimnion Particulate Total, ng/g 36 76 111 21

BIOTA

Prey Fish, ng/g 108 60 39 75

Predatory Fish, ng/g 270 345 240 366

*Same total water body volume as shallow lake.

Table 6-21

Simulation Results for 5 Combinations of Watershed and Hydrology

Compartment and Variable Perched Seepage Drainage Drainage Drainage

0.1A 1 ww w w w A 1A 5A 15A

WATER COLUMN

Unfiltered Total, ng/L 0.68 0.71 0.82 0.98 1.36

Dissolved Methyl, ng/L 0.02 0.03 0.06 0.07 0.10

Dissolved Elemental, ng/L 0.04 0.04 0.05 0.05 0.07

SEDIMENT

Particulate Total, ng/g 26 27 37 44 61

Dissolved Methyl, ng/L 0.13 0.14 0.50 0.59 0.80

BIOTA

Prey Fish, ng/g 12 14 40 46 62

Predatory Fish, ng/g 62 74 225 263 357

structure. Other key processes, such as the effect of sulfate on methylation, are not parameterized for

these lakes. Consequently, the variability due to in-lake processes demonstrated here is somewhat less
6-26

than the actual variability among lakes.

Variability due to watershed and hydrology -- The primary watershed characteristics affecting the RMCM predictions are the surface area, the presence of wetlands, and the nature of the hydrologic

connection with the lake. Wetlands are expected to contribute more methyl mercury to the lake than

other watershed elements. Larger watersheds connected by surface drainage are expected to contribute

more total mercury loading than smaller watersheds and those connected through groundwater.

The five watershed hydrology combinations range from a perched lake with minimal watershed

connection to a large drainage watershed. Total water column and sediment mercury varied by a factor

of 2, from 0.68 ng/L and 26 ng/g in the perched lake to 1.36 ng/L and 61 ng/g in the drainage lake with

the largest watershed. Predatory fish varied by a factor of 6, from 62 to 357 ng/g. The perched lake and

the seepage lake had similar concentrations. There is a significant difference between the seepage lake

and the drainage lake with the same watershed size. Although total mercury did not increase much

between these two lakes, the sediment methyl mercury and fish mercury increased by a factor of 3.

6.4.2.3 Sensitivity of Water Column Mercury to Model Parameters

Following the base simulations for the reconstructed Pallette and Crystal datasets, the major

parameters were systematically varied and the changes in model output were noted. The relative

sensitivity of total water column mercury to these parameters is summarized in Table 6-22. Total water

column mercury is strongly sensitive to wetfall concentration, which represents two-thirds of the total

loading in these simulations. Dryfall loading is a moderately sensitive parameter that represents about a

third of the total loading. Increases in loading lead to increases in total mercury concentrations. 

DOC is a moderately sensitive parameter that complexes mercury and increases its mobility from

the sediments to the water column. Increases in DOC lead to increases in total water column mercury. 

Hydraulic residence time is inversely proportional to advective flow. Decreases in hydraulic residence

time strongly dilute the loading and decrease total mercury levels. Increases in residence time

moderately increase total mercury. 

Particle density and benthic porosity are inversely related. Increases in particle density and

decreases in porosity lead to higher benthic solids concentration, which in R-MCM results in lower

resuspension rates and thus lower water column mercury concentrations. Increased solids partitioning

causes more deposition of mercury from the water column, leading to lower water column concentrations

and higher sediment concentrations. Increased sediment burial velocity is associated with lower

resuspension rates and less transfer of sediment mercury to the water column. These effects are weakly

sensitive for the Pallette data set and moderately sensitive for the Crystal data set. Increases in benthic

mineralization are associated with weak decreases in water column mercury. This is a consequence of

how R-MCM calculates the resuspension velocity by balancing solids deposition, burial, and

mineralization. Given a constant resuspension velocity, increases in benthic mineralization should

actually cause decreases in burial and thus increased mercury concentrations in the sediments and water

column.

Total mercury levels are moderately sensitive to [H ], which is calculated from specified pH +

levels. R-MCM applies the reduction rate constant only to dissolved Hg(OH) . As a result, increased pH 2

(i.e., decreased [H ]) strongly increases Hg(OH) , concentrations causing significantly more reduction of +

2
6-27

Table 6-22

R-MCM Parameter Sensitivity for Total Water Column Mercury

Model Parameter Pallette Crystal

decrease increase decrease increase

Wetfall Concentration -67 +67 -68 +68

DOC -46 +34 -57 +40

Hydraulic Residence Time -53 +27 -59 +33

Dryfall Load -30 +30 -30 +30

Particle Density +10 - 9 +37 -37

Porosity - 9 +10 -37 +38

[H ] -55 +12 -43 - 6 +

Solids Partitioning + 9 - 9 +36 -28

Sediment Burial Velocity +10 - 9 +30 -23

Benthic Mineralization + 1 - 1 +10 -13

Average Sunlight + 6 - 4 + 4 - 2

Biotic Solids Concentration - 4 + 4 - 4 + 4

Light Extinction Coefficient - 5 + 4 - 2 + 2

Abiotic Solids Concentration - 0 + 2 - 6 + 5

Abiotic Solids Settling Velocity + 4 - 2 - 1 + 1

Sediment TOC + 3 - 1 + 2 - 2

* Sensitivity is expressed as relative change in total water column mercury concentration divided by the relative

change in the model parameter, in percent.
6-28

HgII to Hg with subsequent loss due to volatilization. The two datasets are only weakly sensitive to 0

decreasing pH which causes a relatively small decline in reduction and volatilization.

Total mercury concentrations were only weakly sensitive to the other parameters tested,

including sunlight, light extinction, biotic and abiotic solids concentrations, settling velocity, and

sediment TOC. Total mercury levels were insensitive to dissolved oxygen, sulfate, chloride and fish

biomass.

6.4.2.4 Sensitivity of Fish Mercury to Model Parameters

The relative sensitivity of mercury concentration in upper trophic level fish (5-years old) to the

model parameters is summarized in Table 6-23. The most sensitive parameter for fish mercury is the

maximum monthly temperature. The annual-average R-MCM adjusts the base methylation rate

constant using the maximum monthly temperature (i.e., July) to account for the higher fish activity at this

time of year. Demethylation rates are not temperature-corrected due to a lack of data. As a consequence,

an increase in maximum temperature leads to an increase in net methylation, and a subsequent increase in

fish concentrations. Sediment TOC concentrations directly affect methylation rates in the sediment. 

While increases in TOC cause proportional increases in both benthic methylation and demethylation

rates, some of the extra MHg production diffuses to the water column leading to higher fish mercury

levels.

Fish mercury levels are strongly sensitive to solids partitioning. The partition coefficients for

HgII and MHg partitioning to abiotic solids and sediments were adjusted up and down as a group. 

Higher partitioning causes water column mercury levels to decline somewhat and benthic concentrations

to increase strongly. The elevated mercury concentrations in benthic biota enter the food web, ultimately

resulting in higher fish concentrations. The strong sensitivity of fish mercury to DOC operates in the

opposite direction. Higher levels of DOC complex more mercury, leading to higher benthic fluxes and

water column concentrations. The DOC-bound mercury concentrations, however, are not bioavailable,

and thus fish mercury levels strongly decline.

Fish mercury concentrations, like total water column levels, are strongly sensitive to wetfall

concentration and moderately sensitive to dryfall loading. Fish mercury is more sensitive than total

water column mercury to hydraulic residence time. Increases in residence time associated with lower

flow rates provide less dilution not only to external loading of HgII, but also to benthic fluxes of MHg,

leading to significantly higher fish concentrations.

Average sunlight and the light extinction coefficient are moderately sensitive parameters that

affect the water column reduction and demethylation rates. Increased light levels cause increased

reduction and loss of HgII and increased demethylation of MHg, leading to lower MHg concentrations

and thus lower fish mercury concentrations.

The abiotic solids concentration and deposition velocity, the sediment mineralization rate,

and the sediment burial velocity are moderately to weakly sensitive parameters connected through the

R-MCM solids balance. The resuspension velocity is calculated internally from these parameters, along

with porosity and particle density using a solids mass balance. Increases in the abiotic solids

concentration or deposition velocity increase the supply of solids to the sediment layer, which leads to

higher calculated resuspension velocities given the fixed burial and mineralization rates. These higher

resuspension velocities cause higher fluxes of MHg to the water column, and thus higher MHg and fish

mercury concentrations. 
6-29

Table 6-23

R-MCM Parameter Sensitivity for Upper Trophic Level Fish Mercury

Model Parameter Pallette Crystal

decrease increase decrease increase

Maximum Monthly Temperature -160 +180 -160 +180

Solids Partitioning - 91 +100 - 94 + 88

DOC +120 - 54 +120 - 54

Wetfall Concentration - 59 + 59 - 58 + 57

Hydraulic Residence Time - 70 + 40 - 65 + 37

Sediment TOC - 50 + 45 - 74 + 62

Average Sunlight + 46 - 27 - 27 _ 26

[H ] - 64 + 15 - 53 + 19 +

Light Extinction Coefficient - 41 + 27 - 27 + 27

Dryfall Deposition - 28 + 27 - 27 + 26

Abiotic Solids Settling Velocity - 40 + 29 - 18 + 17

Abiotic Solids Concentration - 37 + 26 - 15 + 13

Sediment Burial Velocity + 10 - 9 + 27 - 21

Sediment Mineralization Rate - 6 + 5 - 22 + 29

Benthic Porosity - 10 + 5 - 3 + 3

Biotic Solids Concentration + 5 - 5 + 5 - 5

* Sensitivity is expressed as relative change in level 4 fish mercury concentration divided by the relative change in

the model parameter, in percent.
6-30

Sediment burial velocity affects mercury in a similar way. Decreases in burial lead to increases

in calculated resuspension and thus increased MHg flux to the water column and higher fish mercury

levels. On the other hand, an increase in the sediment remineralization rate causes a lower calculated

resuspension rate, but also causes increased concentration of sediment mercury levels. The net effect is

to increase MHg diffusion flux to the water column leading to higher levels of MHg and fish mercury.

The burial and remineralization effects are more pronounced in the Crystal data set than in the Pallette

data set.

The model was not parameterized to simulate the effects of dissolved oxygen and sulfate on

methylation rates. These parameters are expected to affect fish bioaccumulation significantly in some

lakes.

6.4.3 Summary and Conclusions

For a particular atmospheric deposition rate, mercury concentrations in watersheds and water

bodies can vary significantly. Several intrinsic and extrinsic watershed and water body characteristics

influence the mercury concentrations in soil, water, and fish. These should cause significant variability

in mercury concentrations between regions and among individual lakes within a region.

Mercury concentrations in watershed soils are strongly influenced by atmospheric loading and

soil loss processes. The influence of plant canopy and roots in mediating both the loading to the soil and

the loss from the soil is not well characterized at present, although published studies indicate its potential

importance. Reduction of HgII in the upper soil layer appears to control the volatile loss of mercury, and

variations in this reaction can cause significant variations in soil mercury levels. The factors controlling

mercury reduction are not well characterized at present. Soil erosion from a watershed can vary more

than 3 orders of magnitude depending on rainfall patterns, soil type, topography, and plant cover. High

levels of soil erosion should significantly diminish soil mercury concentrations. Runoff and leaching are

not expected to affect soil mercury concentrations significantly.

Total mercury concentrations in a water body are strongly influenced by atmospheric loading

and, for drainage lakes, by watershed loading. Variations in watershed size and erosion rates can cause

significant variability in lake mercury levels. Hydraulic residence time, the water body volume divided

by total flow, affects the maximum possible level of total water column mercury for a given loading rate. 

Parameters controlling mercury loss through volatilization and net settling can also cause significant

variations among lakes. Mercury loss through settling is affected by in-situ productivity, by the supply of

solids from the watershed, and by the solids-water partition coefficient. DOC concentrations can

significantly affect partitioning, and thus overall mercury levels. Mercury loss through volatilization is

controlled by the reduction rate, which is a function of sunlight and water clarity. Reduction may also be

controlled by pH, with lower values inhibiting this reduction and leading to higher total mercury levels.

Fish mercury levels are strongly influenced by the same factors that control total mercury levels. 

In addition, fish concentrations are sensitive to methylation and demethylation in the water column and

sediments. A set of water body characteristics appears to affect these reactions, including DOC,

sediment TOC, sunlight, and water clarity. Variations in these properties can cause significant variations

in fish concentrations among lakes. Other factors not examined here, such as anoxia and sulfate

concentrations, can stimulate methylation and lead to elevated fish concentrations. Fish mercury levels

are sensitive to factors that promote methyl mercury mobility from the sediments to the water column;

these factors include sediment DOC and sediment-pore water partition coefficients.
6-31

The IEM-2M has not been validated with site-specific data. Here the model is benchmarked

against the independently-derived R-MCM, which itself is calibrated to several Wisconsin lakes. When

driven by the same atmospheric loading and solids concentrations, IEM-2M predictions of mercury

concentrations compare well with those calculated by R-MCM for a set of Wisconsin lakes.

The R-MCM is a useful research model that has been set up to describe mercury dynamics in a

set of Wisconsin lakes. Current work is extending the model to lakes in other regions. This model shows

promise for use as a more general management tool, especially as it is applied and tested in diverse water

bodies around the nation. As of this writing it remains a scientific work in progress.

Based on the variability and sensitivity analyses with IEM-2M and R-MCM, predictions of

average mercury concentrations in particular watersheds and waterbodies should vary about an order of

magnitude based on variable environmental characteristics. Total mercury concentration in a water body

is a function of atmospheric deposition, watershed soil retention, solids erosion, water body flow,

suspended solids partitioning and deposition, and water column reduction and volatilization loss. Fish

mercury concentrations depend upon total mercury concentrations along with net water column and

sediment methylation rates, uptake at the base of the food web, and fish bioaccumulation. To predict

mercury levels in particular water bodies, these environmental factors must be properly parameterized. 

Site-specific calibration data would allow more accurate predictions.

In the analysis relating mercury emissions to water body and fish concentrations, the IEM-2M

model was parameterized for an average shallow lake using data collected from studies across a number

of water bodies. This analysis of general response to atmospheric loadings is national in scope and

appears to be an appropriate application of the model. The variability and sensitivity analyses conducted

here provide a context in which to interpret the predicted average loading response.
7-1

7. CONCLUSIONS

Summary Conclusions

 The present study in conjunction with available scientific knowledge supports a plausible link

between mercury emissions from anthropogenic combustion and industrial sources and mercury

concentrations in air, soil, water and sediments. The critical variables contributing to this

linkage are these:

a) the species of mercury that are emitted from the sources;

b) the overall amount of mercury emitted from a combustion source;

c) atmospheric and climatic conditions;

d) reduction rates in the soil and water body;

e) erosion rates within the watershed; and

f) solids deposition and burial in the water body.

 The present study, in conjunction with available scientific knowledge, supports a plausible link

between mercury emissions from anthropogenic combustion and industrial sources and

methylmercury concentrations in freshwater fish. The additional critical variables contributing

to this linkage are the following:

a) the extent (magnitude) of mercury methylation and demethylation in the water body; and

b) the degree of complexation of mercury with DOC and solids.

 Mercury is a natural constituent of the environment; concentrations of mercury in many 

environmental media appear to have increased over the last 500 years.

 There is a lack of adequate mercury measurement data near the anthropogenic atmospheric

mercury sources considered in this report. Measurement data are needed to assess how well the

modeled data predict acutal mercury concentrations in different environmental media at a variety

of geographic locations. The lack of such measured data preclude a comparison of the modeling

results with measured data around these sources. Missing data includes measured mercury

deposition rates as well as measured concentrations in the local atmosphere, soils, water bodies,

and biota.

 From the atmospheric modeling analyses of mercury deposition and on a comparative basis, a

facility located in a humid climate has a higher annual rate of mercury deposition than a facility

located in an arid climate. The critical variables are the estimated washout ratios of elemental

and divalent mercury as well as the annual amount of precipitation. Precipitation removes

various forms of mercury from the atmosphere and deposits mercury to the surface of the earth. 

Of the species of mercury that are emitted, divalent mercury is predicted to generally deposit to

local environments near sources. Elemental mercury is predicted to generally remain in the
7-2

atmosphere until atmospheric conversion to divalent species or uptake and retention by plant

leaves and the subsequent deposition as divalent species in litter fall.

 On a national scale, an apportionment between specific sources of mercury and mercury in

environmental media and biota at particular locations cannot be described in quantitative terms

with the current scientific understanding of the environmental fate of mercury.

 

 From the modeling analysis and a review of field measurement studies, it is concluded that

mercury deposition appears to be ubiquitous across the continental U.S. and at, or above,

detection limits when measured with current analytic methods.

 Based on the RELMAP modeling analysis and a review of recent measurement data published in

peer-reviewed scientific literature, there is predicted to be a wide range of mercury deposition

rates across the continental U.S. The highest predicted rates (i.e., above 90th percentile) are

about 20 times higher than the lowest predicted rates (i.e., below the 10th percentile). Three

principal factors contribute to these modeled and observed deposition patterns:

a) emission source locations;

b) amount of divalent and particulate mercury emitted or formed in the

atmosphere; and

c) climate and meteorology.

 Based on the modeling analysis of the transport and deposition of stationary point source and

area source air emissions of mercury from the continental U.S., it is concluded that the following

geographical areas have the highest annual rate of deposition of mercury in all forms (above the

levels predicted at the 90th percentile):

a) the southern Great Lakes and Ohio River Valley;

b) the Northeast and southern New England; and

c) scattered areas in the South with the most elevated deposition occurring in the

Miami and Tampa areas.

Measured deposition estimates are limited, but are available for certain geographic regions. The

data that are available corroborate the RELMAP modeling results for specific areas.

 Based on modeling analysis of the transport and deposition of stationary point source and area

source air emissions of mercury from the continental U.S., it is concluded that the following

geographical areas have the lowest annual rate of deposition of mercury in all forms (below the

levels predicted at the 10th percentile):

a) the less populated areas of the Great Basin, including southern Idaho,

southeastern Oregon, most of southern and western Utah, most of Nevada, and

portions of western New Mexico; and

b) western Texas other than near El Paso, and most of northeastern Montana.
7-3

 Based on limited monitoring data, the RELMAP model predictions of atmospheric mercury

concentrations and wet deposition across the U.S. are comparable with typically measured data.

 A number of factors appear to affect the local-scale atmospheric fate of mercury emitted by/from

major anthropogenic sources as well as the quantity of mercury predicted to deposit. These

factors include the following:

a) the amounts of divalent and particulate mercury emitted;

b) parameters that influence the plume height, primarily the stack height and stack exit gas

velocity;

c) meteorology; and 

d) terrain.

 From the analysis of deposition and on a comparative basis, the deposition of divalent mercury

close to an emission source is greater for receptors in elevated terrain (i.e., terrain above the

elevation of the stack base) than for receptors located in flat terrain (i.e., terrain below the

elevation of the stack base). The critical variables are parameters that influence the plume

height, primarily the stack height and stack exit gas velocity.

 Modeling estimates of the transport and deposition of stationary point source and area source air

emissions of mercury from the continental U.S. have revealed the following partial mass balance.

-- Of the total amount of elemental mercury vapor that is emitted, about 1 percent

(0.9 metric tons/yr) may be atmospherically transformed into divalent mercury

by tropospheric ozone and adsorbed to particulate soot in the air and

subsequently deposited in rainfall and snowfall to the surface of the continental

U.S. The vast majority of emitted elemental mercury does not readily deposit

and is transported outside the U.S. or vertically diffused to the free atmosphere

to become part of the global cycle.

-- Nearly all of the elemental mercury vapor emitted from other sources around the

globe also enters the global cycle and can be deposited slowly to the U.S. Over

30 times as much elemental mercury vapor is deposited from these other sources

than from stationary point sources and area sources within the continental U.S.

-- Of the total amount of divalent mercury vapor that is emitted, about 70 percent

(36.8 metric tons/year) deposits to the surface through wet or dry processes

within the continental U.S. The remaining 30 percent is transported outside the

U.S. or is vertically diffused to the free atmosphere to become part of the global

cycle.

-- Of the total amount of particulate mercury that is emitted, about 38 percent (10.0

metric tons/year) deposits to the surface through wet or dry processes within the

continental U.S. The remaining 62 percent is transported outside the U.S. or is

vertically diffused to the free atmosphere to become part of the global cycle.
7-4

 Given the simulated deposition efficiencies for each form of mercury air emission (namely;

elemental mercury - 1 percent, divalent mercury vapor - 70 percent, and particulate mercury - 38

percent) the relative source contributions to the total anthropogenic mercury deposited to the

continental U.S. are strongly and positively correlated to the mass of emissions in oxidized form. 

This oxidized mercury occurs in both gaseous (Hg ) and particulate (Hg ) forms. While coal 2+

p

combustion is responsible for more than half of all emissions of mercury in the inventory of U.S.

anthropogenic sources, the fraction of coal combustion emissions in oxidized form is thought to

be less than that from waste incineration and combustion. The true speciation of mercury

emissions from the various source types modeled is still uncertain and is thought to vary, not

only among source types, but also for individual plants as feed stock and operating conditions

change. With further research, it may be possible to make a confident ranking of relative source

contributions to mercury deposition in the continental U.S. However, no such confident ranking

is possible at this time. Given the total mass of mercury thought by EPA to be emitted from all

anthropogenic sources and EPA’s modeling of the atmospheric transport of emitted mercury,

coal combustion and waste disposal most likely bear the greatest responsibility for direct

anthropogenic mercury deposition to the continental U.S.

 Based on the local scale atmospheric modeling results in flat terrain, at least 75 percent of the

emitted mercury from each facility is predicted to be transported more than 50 km from the

facility.

 The models used in the analysis as well as the assumptions implemented concerning the species

of mercury emitted and the wet and dry deposition velocities associated with atmospheric

mercury species indicate that deposition within 10 km of a facility is may be dominated by

emissions from the local source rather than from emissions transported from regional mercury

emissions sources, with some exceptions. Specifically, the models predict that in the Eastern

U.S., individual large anthropogenic sources dominate predicted mercury deposition within 2.5

km; chlor-alkali facilities are predicted to dominate up to 10 km from the source. In the western

site, the models predict that the dominance of local source mercury deposition in emissions

extends beyond the predicted range of the eastern site.

 Of the mercury deposited to watershed soils, a small fraction is ultimately transported to the

water body. Deposition to and evasion from soils as well as the amount of reduction in upper

soil layers are important factors in the determining soil concentration of mercury. In forested

watersheds canopy interactions can provide significant fluxes both to and from the atmosphere. 

Mercury from litter fall may be an important source of mercury to some soils and water bodies,

but the magnitude of the contribution from this source is uncertain at this time. 

 The net mercury methylation rate (the net result of methylation and demethylation) for most soils

appears to be quite low with much of the measured methylmercury in soils potentially resulting

from wet fall. A significant and important exception to this appears to be wetlands. Wetlands

appear to convert a small but significant fraction of the deposited mercury into methylmercury;

which can be exported to nearby water bodies and potentially bioaccumulated in the aquatic food

chain.

 Both watershed erosion and direct atmospheric deposition can be important sources of mercury

to the water body depending on the relative sizes of the water body and the watershed.
7-5

 There appears to be a great deal of variability in the processing of mercury among bodies of

water. This variability extends to water bodies that have similar and dissimilar physical

characteristics. Important properties influencing the levels of total mercury and methylmercury

in a water body include: pH, anoxia, DOC, productivity, turbidity, and the presence of wetlands. 

 Some of the mercury entering a water body is methylated predominately through biotic processes

forming methylmercury (predominately monomethylmercury). Methylmercury is accumulated

and retained by aquatic organisms. Important factors influencing bioavailability of

methylmercury to aquatic organisms include DOC and solids, which complex methylmercury and

reduce the biovailable pool. 

 Methylmercury is bioaccumulated in predatory species of the aquatic food chain. The

concentrations of methylmercury in fish muscle tissue are highly variable across water bodies.

Within a given body of water methylmercury concentrations generally increase with fish size and

position within the trophic structure.

To improve the quantitative environmental fate component of the risk assessment for mercury and

mercury compounds, U.S. EPA would need more and better mercury emissions data and measured

mercury data near sources of concern, as well as a better quantitative understanding of mercury

chemistry in the emissions plume, the atmosphere, soils, water bodies and biota. Specific needs

include these.

Mercury in the Atmosphere

 aqueous oxidation-reduction kinetics in atmospheric water droplets

 physical adsorption and condensation of divalent mercury gas to ambient

particulate matter

 photolytic reduction of particle-bound divalent mercury by sunlight

 convincing evidence that gas-phase oxidation of mercury is insignificant

Mercury in Soils and Water Bodies

 uptake and release kinetics of mercury from terrestrial and aquatic plants

 biogeochemical mercury transport and transformation kinetics in benthic

sediments

 methylation, demethylation, and reduction kinetics in water bodies

 sorption coefficients to soils, suspended solids, and benthic solids

 complexation to organic matter in water bodies

 more data to better discern seasonal trends

 reduction kinetics in soils
7-6

 mercury mass balance studies in wetlands

Information Leading to an Improved Quantitative Understanding of Aquatic Bioaccumulation

Processes and Kinetics

 uptake kinetics by aquatic plants and phytoplankton

 partitioning and binding behavior of mercury species within organisms

 metabolic transformations of mercury, and the effect on uptake, internal

distribution, and excretion

 more measurements of methylmercury concentrations in fish for better

identification of the range in fish species.

 more measurements of methylmercury concentrations in other biotic components

of the aquatic environment such as benthic and macro invertebrates and aquatic

macrophytes 
8-1

8. RESEARCH NEEDS

During the development of the mercury fate and transport assessment, many areas of uncertainty

and significant data gaps were identified. Many of these have been identified in the document, and

several are presented in the following list.

1. Improved analytical techniques for measuring speciated mercury air emissions as well as total

mercury emissions from major point sources. Laboratory evidence suggests that divalent

mercury gas emissions will wet and dry deposit much more readily than elemental mercury gas. 

Particle-bound mercury is also likely to deposit relatively quickly. Current stack sampling

methods do not provide sound information about the fraction of mercury emissions that are in

oxidized form. While filters are used to determine particulate mercury fractions, high

temperature stack samples may not be indicative of the fraction of mercury that is bound to

particles after dilution and cooling in the first few seconds after emission to the atmosphere. 

Methods for determination of the chemical and physical forms of mercury air emissions after

dilution and cooling need to be developed and used to characterize all known major point

sources.

2. Evaluated Local and Regional Atmospheric Fate and Transport Models are needed. These

models should treat all important chemical and physical transformations which take place in the

atmosphere. The development of these models will require comprehensive field investigations to

determine the important atmospheric transformation pathways (e.g., aqueous cloud chemistry,

gas-phase chemistry, particle attachment, photolytic reduction) for various climatic regions. The

evaluation of these models will require long-term national (possibly international) monitoring

networks to quantify the actual air concentrations and surface deposition rates for the various

chemical and physical forms of mercury.

3. Better understanding of mercury transport from watershed to water body including the soil

chemistry of mercury, the temporal aspects of the soil equilibrium, the impact of low levels of

volatile mercury species in surface soils and water bodies on total mercury concentrations and

equilibrium.

4. Better understanding of foliar uptake of mercury and plant/mercury chemistry. (The most

important questions: Do plants convert elemental or divalent mercury into forms of mercury that

are more readily bioaccumulated? Do plants then emit these different forms to the air?) A better

understanding of the condensation point for mercury is needed.

5. Better understanding of mercury movement from plant into soil (detritus). May need to refine

the models used to account for movement of mercury in leaf litter to soil.

6. The impact of anthropogenic mercury on the "natural," existing mercury levels and species

formed in soil, water, and sediments needs better understanding. How does the addition of

anthropogenic mercury affect "natural" soil and water mercury cycles? Natural emission sources

need to be studied better and their impacts better evaluated.

7. Improved understanding of mercury flux in water bodies and impact of plant and animal biomass

are needed. Unlike many other pollutants, most of the methylmercury in a water body appears to

be in the biological compartment. The sedimentation rate as well as benthic sediment:water
8-2

partition coefficient require field evaluation. Important to consider rivers and other larger water

bodies in these flux analyses.

8. The BAF contains a substantial level of uncertainty. A more appropriate BAF can probably be

developed when the data base upon which the estimate is based is enlarged; i.e., need data from

more than four studies. The availability of more data would enable the possible development of

lake-type adjustment factors for the mercury BAF possibly based on color, acidification

susceptibility, etc., or species-specific BAF adjustment factors for freshwater species most

commonly consumed. Also need a time analysis of fish mercury uptake which could lead to the

development of a dynamic fish model. A mercury BAF for saltwater fish is needed.

9. Need to improve the biotransfer factors for mercury from soil and plants to beef.
9-1

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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.

Xiao, Z., D. Stromberg, and O. Lindqvist (1995) Influence of Humic Substances on photolysis of

divalent mercury in aqueous solution. WASP 80:789-798.

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.
APPENDIX A

ATMOSPHERIC MODELING PARAMETERS
A-i

TABLE OF CONTENTS

Page

A. ATMOSPHERIC MODELING PARAMETERS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1

A.1 Phase and Oxidation State of Emitted Mercury . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1

A.2 Modeling the Deposition of Mercury . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-2

A.3 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-6
A-ii

LIST OF TABLES AND FIGURES

Tables Page

A-1 Representative Particle Sizes and Size Distribution Assumed for Divalent Mercury 

Particulate Emissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-2

A-2 Parameter Values Assumed for Calculation of Dry Deposition Velocities for Divalent 

Mercury Vapor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-3

A-3 Air Modeling Parameter Values Used in the Exposure Assessment: Generic Parameters . . . A-5

Figures Page

A-1 Wet Deposition Scavenging Ratios Used in Local Scale Air Modeling for Particulate-Bound 

Mercury (Jindal and Heinold 1991) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-4
A-1

A. ATMOSPHERIC MODELING PARAMETERS

In this appendix, a summary is provided of the local scale air modeling performed. The program

used to model the transport of the anthropogenic mercury with 50 km was the ISC3 gas deposition model, 

obtained from USEPA’s Support Center for Regulatory Air Models (SCRAM) website (the program is

called GDISCDFT). This model has a gas dry deposition model that was applied in this study. 

A.1 Phase and Oxidation State of Emitted Mercury

Reports describe several forms of mercury detected in the emissions from the selected sources. 

Primarily, these include elemental mercury (Hg ) and inorganic mercuric (Hg ). Generally, only total 0 2+

mercury has been measured in emission analyses. The reports of MHg in emissions are imprecise. It is

believed that, if MHg is emitted from industrial processes and combustion sources, the quantities emitted

are much smaller than emissions of Hg and Hg . Only Hg and Hg were considered in the air 0 +2 0 +2

dispersion modeling.

The two types of mercury species considered in the emissions are expected to behave quite

differently once emitted from the stack. Hg , due to its high vapor pressure and low water solubility, is 0

not expected to deposit close to the facility. In contrast, Hg , because of differences in these properties, 2+

is expected to deposit in greater quantities closer to the emission sources. 

At the point of stack emission and during atmospheric transport, the contaminant is partitioned

between two physical phases: vapor and particle-bound. The mechanisms of transport of these two

phases are quite different. Particle-bound contaminants can be removed from the atmosphere by both

wet deposition (precipitation scavenging) and dry deposition (gravitational settling, Brownian diffusion). 

Vapor phase contaminants may also be depleted by these processes, although historically their main

impacts were considered to be through absorption into plant tissues (air-to-leaf transfer) and human

exposure (which may occur through inhalation).

For the present analysis, the vapor/particle (V/P) ratio was assumed to be equal to the V/P ratio

as it would exist in stack emissions. It is recognized that this is a simplification of actual conditions, as

the ratio when emitted from the stack is likely to change as the distance from the stack increases. The air

concentration used for inhalation was taken as the sum of the vapor and particle air concentrations. 

The particle-size distribution may differ from one combustion process to another, depending on

the type of furnace and design of combustion chamber, composition of feed/fuel, particulate matter

removal efficiency and design of air pollution control equipment, and amount of air in excess of

stoichiometric amounts that is used to sustain the temperature of combustion. The particle size

distribution used is an estimate of the distribution within an ambient air aerosol mass and not at stack tip. 

Based on this assumption, an aerosol particle distribution based on data collected by Whitby (1978) was

used. This distribution is split between two modes: accumulation and coarse particles. The geometric

mean diameter of several hundred measurements indicates that the accumulation mode dominates particle

size, and a representative particle diameter for this mode is 0.3 microns. The coarse particles are formed

largely from mechanical processes that suspend dust, sea spray and soil particles in the air. A

representative diameter for coarse particles is 5.7 microns. The fraction of particle emissions assigned to

each particle class is approximated based on the determination of the density of surface area of each

representative particle size relative to total surface area of the aerosol mass. Using this method,

approximately 93% and 7% of the total surface area is estimated to be in the 0.3 and 5.7 micron diameter

particles, respectively (Table A-1).
A-2

Table A-1

Representative Particle Sizes and Size Distribution 

Assumed for Divalent Mercury Particulate Emissions

Representative Particle Size Assumed Fraction of Particle

(microns)* Emissions in Size Category

0.3 0.93

5.7 0.07

*These values are based on the geometric means of aerosol particle distribution measurements as described in Whitby (1978).

The speciation estimates for the model plants were made from thermal-chemical modeling of

mercury compounds in flue gas, from the interpretation of bench and pilot scale combustor experiments,

and from interpretation of available field test results. The amount of uncertainty surrounding the

emission rates data varies for each source. There is also a great deal of uncertainty with respect to the

species of mercury emitted.

Although the speciation may change with distance from the local source, it was assumed for this

analysis that there were no plume reactions that significantly modified the speciation at the local source. 

Because of the differences in deposition characteristics of the two forms of mercury considered, the

assumption of no plume chemistry is a particularly important source of uncertainty.

A.2 Modeling the Deposition of Mercury

Once emitted from a source, the mercury may be deposited to the ground via two main processes:

wet and dry deposition. Wet deposition refers to the mass transfer of dissolved gaseous or suspended

particulate mercury species from the atmosphere to the earth's surface by precipitation, while dry

deposition refers to such mass transfer in the absence of precipitation. 

The deposition properties of the two species of mercury addressed in stack emissions, elemental

and divalent mercury, are considered to be quite different. Due to its higher solubility, divalent mercury

vapor is thought to deposit much more rapidly than elemental mercury. However, at this time no

conclusive data exist to support accurate quantification of the deposition rate of divalent mercury vapor. 

In this analysis, nitric acid vapor is used as a surrogate for Hg vapor based on their similar solubilities ++

in water. Whether a pollutant is in the vapor form or particle-bound is also important for estimating

deposition, and each is treated separately.

Dry deposition is estimated by multiplying the predicted air concentration at ground level by a

deposition velocity. For particles, the dry deposition velocity is estimated using the CARB algorithms

(CARB 1986) that represent empirical relationships for transfer resistances as a function of particle size,

density, surface area, and friction velocity. For the vapor phase fraction for elemental mercury, a single

dry deposition velocity of 0.06 cm/s is assumed. This is based on the average of the winter and summer

deposition velocities presented in Lindberg et al. (1992) for forests. Although it is generally

acknowledged 
A-3

that elemental mercury dry deposits with a (net) rate much lower than divalent mercury vapor, the precise

value is uncertain, and there can be considerable variability with season and time of day. A further issue

is that dry deposition of elemental mercury may not occur at all unless the air concentration is

sufficiently high. Preliminary research (Hanson et al.1995) indicates that under some experimental

conditions no dry deposition occurs unless the air concentration is at least about 10 ng/m . The value at 3

which dry deposition would begin to occur (the compensation point) is expected to depend on many

factors, including time of year and flora type. These preliminary results were not specifically addressed

in the current study; however, sensitivity analyses were conducted in order to determine the possible

impact that such a compensation point might have on the predicted dry deposition of elemental mercury.

In ISC-GAS, the dry deposition of divalent mercury vapor was modeled by calculating a dry

deposition velocity for each hour using the assumptions usually made for nitric acid for the input

parameters (see EPA 1996; User’s Guide for the Gas Dry Deposition Model, page inserts to the User’s

Guide for the Industrial Source Complex (ISC3) Dispersion Models). The values assumed are provided

in Table A-2 below. Ultimately, using these assumptions, the average predicted dry deposition velocity

was about 2.9 cm/s for divalent mercury vapor, which is essentially the average of the values used in the

RELMAP modeling for coniferous forests.

Table A-2

Parameter Values Assumed for Calculation of Dry Deposition

 Velocities for Divalent Mercury Vapor

Parameter Value

Molecular diffusivity (cm /sec) 0.1628 2

Solubility enhancement factor 109

Pollutant reactivity 800

Mesophyll resistance 0

Henry’s law coefficient 2.7e-7

Wet deposition is estimated assuming that the wet deposition rate is characterized by a

scavenging coefficient that depends on precipitation intensity and particle size. For particles, the

scavenging ratios used are from Jindal and Heinold (1991) (see Figure A-1). For the vapor phase

fraction, a scavenging coefficient is also used, but it is calculated using estimates for the washout ratio,

which is the ratio of the concentration of the chemical in surface-level precipitation to the concentration

in surface-level air. Because of its higher solubility, divalent mercury vapor is assumed to be washed out

at significantly higher rates than elemental mercury vapor. The washout ratio for divalent mercury vapor

was selected based on an assumed similarity between scavenging for divalent mercury and gaseous nitric

acid. This is based on Peterson et al. (1995), and the value used for the washout ratio for divalent vapor

was 1.6 x 10 . The washout ratio for elemental mercury vapor was assumed to be 1200. This is a 6

calculated value based on the model of Peterson et al. (1995), with a soot concentration zero.

Other options utilized in the local air modeling of mercury are summarized in Table A-3.
A-4

Figure A-1

Wet Deposition Scavenging Ratios Used in Local Scale Air Modeling for Particulate-Bound

Mercury (Jindal and Heinold 1991)
A-5

Table A-3

Air Modeling Parameter Values Used in the 

Exposure Assessment: Generic Parameters

Parameter Value Used in Study

Particle Density (g/cm ) 1.8 3

Surface Roughness Length (m) 0.30

Anemometer Height (m) 10

Values used for options:

Final plume rise Default

Stack-tip downwash Default

Buoyancy-induced dispersion Default

Calms processing routine Used

Missing data processing routine Not used

Wind profile exponents Default

Vertical potential temperature gradients Default

Values for supersquat buildings “Upper Bound”

Rural mode setting No exponential decay

State of vegetation Active and Unstressed

Type of Dispersion Rural

Dry depletion setting True

Wet depletion setting True

Type of terrain Flat
A-6

A.3 References

CARB (1986). Subroutines for calculating dry deposition velocities using Sehmel's curves. Prepared by

Bart Croes, California Air Resources Board.

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 Modeling and Gradient Approaches. J. of Geophy.

Res. 97(D2):2519-2528.

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.

Whitby, K. (1978). The physical characteristics of sulfur aerosols. Atmosph. Env. 12:135-159.
APPENDIX B

WATERSHED AND WATERBODY MODELING PARAMETERS
B-i

TABLE OF CONTENTS

Page

LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-iii

DISTRIBUTION NOTATION ....................................................... B-iv

B. WATERSHED AND WATERBODY MODELING PARAMETERS

B.1 SCENARIO INDEPENDENT PARAMETERS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-1

B.1.1 Chemical Independent Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-1

B.1.1.1 Basic Constants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-1

B.1.1.2 Receptor Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-1

B.1.1.2.1 Body Weight . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-1

B.1.1.2.2 Exposure Duration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-2

B.1.1.3 Agricultural Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-3

B.1.1.3.1 Interception Fraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-3

B.1.1.3.2 Length of Plant Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-4

B.1.1.3.3 Plant Yield . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-5

B.1.1.3.4 Plant Ingestion by Animals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-6

B.1.1.3.5 Soil Ingestion by Animals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-7

B.1.1.4 Exposure Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-8

B.1.1.4.1 Inhalation Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-8

B.1.1.4.2 Consumption Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-9

B.1.1.4.3 Soil Ingestion Rate .................................... B-10

B.1.1.4.4 Groundwater Ingestion Rate ............................ B-11

B.1.1.4.5 Fish Ingestion Rate .................................... B-12

B.1.1.4.6 Contact Fractions ..................................... B-14

B.1.2 Chemical Dependent Parameters ........................................ B-15

B.1.2.1 Basic Chemical Properties ...................................... B-15

B.1.2.1.1 Molecular Weight .................................... B-15

B.1.2.1.2 Henry's Law Constant ................................. B-15

B.1.2.1.3 Soil-Water Partition Coefficient ......................... B-16

B.1.2.1.4 Sediment-to-Water Partition Coefficient ................... B-16

B.1.2.1.5 Suspended Sediment-Water Partition Coefficient ............ B-17

B.1.2.1.6 Suspended Biotic Solids-Water Partition Coefficient ......... B-17

B.1.2.1.7 Chemical Diffusivity in Air ............................. B-18

B.1.2.1.8 Chemical Transformation Rate Constants .................. B-18

Reduction in Water Column .............................. B-18

Reduction in Sediments .................................. B-19

Reduction in Soil ....................................... B-19

Methylation in Water Column ............................. B-20

Methylation in Sediments ................................ B-21

Methylation in Soil ..................................... B-21

Demethylation in Water Column ........................... B-21

Demethylation in Sediments .............................. B-22

Demethylation in Soil ................................... B-22

B.1.2.2 Biotransfer Factors ............................................ B-22

B.1.2.2.1 Plant-Soil BCF ....................................... B-24
B-ii

B.1.2.2.2 Air-Plant BCF ....................................... B-27

B.1.2.2.3 Animal BTF ......................................... B-31

B.1.2.2.4 Fish Bioaccumulation Factor ............................ B-34

B.1.2.2.5 Plant Surface Loss Coefficient ........................... B-34

B.1.2.2.6 Fraction of Wet Deposition Adhering ..................... B-35

B.2 SCENARIO DEPENDENT PARAMETERS .................................... B-36

B.2.1 Time of Concentration ................................................ B-37

B.2.2 Average Air Temperature ............................................. B-37

B.2.3 Watershed Area ..................................................... B-38

B.2.4 Average Annual Precipitation .......................................... B-38

B.2.5 Average Annual Irrigation ............................................. B-38

B.2.6 Average Annual Runoff ............................................... B-39

B.2.7 Average Annual Evapotranspiration ..................................... B-39

B.2.8 Wind Speed ........................................................ B-40

B.2.9 Soil Density ........................................................ B-40

B.2.10 Mixing Depth in Watershed Area ....................................... B-41

B.2.11 Mixing Depth for Soil Tillage .......................................... B-41

B.2.12 Soil Volumetric Water Content ......................................... B-41

B.2.13 Soil Erosivity Factor ................................................. B-43

B.2.14 Soil Erodability Factor ................................................ B-43

B.2.15 Topographic Factor .................................................. B-43

B.2.16 Cover Management Factor ............................................ B-44

B.2.17 Sediment Delivery Ratio to Water Body .................................. B-45

B.2.18 Pollutant Enrichment Factor ........................................... B-46

B.2.19 Water Body Surface Area ............................................. B-46

B.2.20 Water Body Volume ................................................. B-47

B.2.21 Long-Term Dilution Flow ............................................. B-47

B.2.22 Abiotic Solids Deposition Velocity ...................................... B-48

B.2.23 Biotic Solids Production Rate .......................................... B-48

B.2.24 Phytoplankton Mortality .............................................. B-48

B.2.25 Biotic Solids Deposition Velocity ....................................... B-49

B.2.26 Surficial Sediment Particle Density and Dry Density (Sediment Concentration) . . . B-49

B.2.27 Upper Benthic Sediment Depth ......................................... B-50

B.2.28 Resuspension Velocity ................................................ B-50

B.3 REFERENCES ............................................................ B-51
B-iii

LIST OF TABLES

B-1 Chemical Independent Constants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-1

B-2 Chemical Transformation Rate Constants ....................................... B-18

B-3 Soil-to-Plant Transfer Coefficients for Mercury .................................. B-25

B-4 Other Values for Soil-to-Plant Transfer Coefficients for Hg ........................ B-26 2+

B-5 Relative Concentration of Mercury in Different Parts of Edible Plants ................. B-28

B-6 Mercury Speciation in Various Plants .......................................... B-30

B-7 Mercury Concentrations in Specific Beef Tissue Media Per Test Group and Dose ....... B-32

B-8 Animal Biotransfer Factors Derived from Vreman et al. (1986) ...................... B-32

B-9 Mercury Concentrations and Resulting BTFs in Lamb Muscle Tissue Per Test Group 

and Dose ................................................................. B-33

B-10 Values From Hoffman et al. (1992) and the Values of Fw Estimated Using Those Values . B-36

B-11 Water Content Per Soil Type ................................................. B-42

B-12 Representative Soil Types For Each Site ........................................ B-42

B-13 Cover Factor Values of Undisturbed Forest Land ................................. B-45

B-14 Long-Term Dilution Flow In In/Yr ............................................ B-47
B-iv

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 this appendix. These

were not utilized in the generation of quantitative 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
B-1

B.1 SCENARIO INDEPENDENT PARAMETERS

This part of Appendix B describes the scenario-independent parameters used in the exposure

modeling for the Mercury Study Report to Congress. Scenario independent parameters are variables

whose values are independent of a particular site and are constant among various site-specific situations. 

Examples of scenario independent parameters are air density, the average height of an adult, or the

average crop yield of a particular food item. These scenario independent parameters may be either

chemical independent or chemical dependent. The following sections present the chemical independent

and chemical dependent parameters used in this study.

B.1.1 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.

B.1.1.1 Basic Constants

Table B-1 lists the chemical independent constants used in the study, their definitions, and

values. 

Table B-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

pa abiotic solids density 2.7 g/cm3

Cdrag drag coefficient 1.1E-3

Von Karman's coefficient 7.40E-1

 boundary thickness 4.0 2

B.1.1.2 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.

B.1.1.2.1 Body Weight

Parameter: BWa, BWc

Definition: Body weights (or masses) of individual human receptors

Units: kg
B-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.

B.1.1.2.2 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.
B-3

B.1.1.3 Agricultural Parameters

B.1.1.3.1 Interception Fraction

Parameter: RPi

Definition: The fraction of the total deposition within a unit area that is initially intercepted by

vegetation.

Units: unitless

Crop Value Distribution Range

Default 

Leafy vegetables 0.15 Log (0.16, 0.10) 0.08 - 0.38

Legume vegetables 0.008 Log(0.008, 0.004) 0.005 - 0.01

Fruiting vegetables 0.05 Log(0.05, 0.05) 0.004 - 0.08

Rooting vegetables 0 N/A N/A

Grains and cereals 0 N/A N/A

Forage 0.47 Norm(0.47, 0.3) 0.02 - 0.89

Silage 0.44 Log (0.44, 0.3)

Fruits 0.05 Log (0.05, 0.05) 0.004 - 0.08

Potatoes 0 N/A N/A

Technical Basis:

For leafy vegetables, Baes et al. (1984) obtained an average interception fraction of 0.15 where it

was emphasized that this value represents a theoretical average over the United States. This value was

calculated assuming a logistic growth pattern for leafy vegetables and taking into account a distribution

of field spacings (for details see Baes et al., p.68). The associated distribution and ranges shown in the

previous table were calculated based on Baes's analyses by Belcher and Travis (1989).

For legumes and fruits, Belcher and Travis (1989) used the exposed produce equation that relates

the interception fraction to the standing crop biomass (also called productivity) and crop biomass values

from Shor et al. (1982) to obtain the range of values given in the previous table. The values for fruiting

vegetables are assumed to be the same as for fruits.

The distribution for forage is based on the work of Hoffman and Baes (1979), who determined

that the values are normally distributed with the parameters presented in the previous table.

The value for silage was calculated in Baes et al. (1984) and is based essentially on sorghum and

corn plantings (Knott, 1957; Rutledge, 1979).

Potatoes, root vegetables and grains are assumed to equal zero since the edible portion of the

plant is protected from direct deposition (grains have a protective husk). 

B.1.1.3.2 Length of Plant Exposure
B-4

Parameter: TPi

Definition: The amount of time that the edible part of an exposed plant is exposed to direct

deposition.

Units: years

Plant Type Default Value Distribution Range

(years) (years)

Leafy vegetables 0.157 U(0.082,0.247) 0.082- 0.247

Legume vegetables 0.123 U(0.082,0.247) 0.082- 0.247

Fruiting vegetables 0.123 U(0.082,0.247) 0.082 - 0.247

Forage 0.123 U(0.082,0.247) 0.082 - 0.247

Silage 0.123 U(0.082,0.247) 0.082 - 0.247

Fruits 0.123 U(0.082,0.247) 0.082 - 0.247

Technical Basis:

Bounding estimates were obtained by assuming an average time between successive harvests of

30 and 90 days. This range is based on the values in Baes et al. (1984) of 60 to 90 days and the reported

values by the South Coast Air Quality Management District (SCAQMD) (1988) of 45 days for tomatoes

and 30-85 days for lettuce.

The default value for leafy vegetables is the midpoint of the range for lettuce. The values for

legumes, fruits and fruiting vegetables are based on the value of 45 days for tomatoes. The value for

forage and silage is the average time between successive hay harvests and successive grazings by cattle

(Baes et al., 1984).
B-5

B.1.1.3.3 Plant Yield

Parameter: YPi

Definition: Yield of the ith plant per unit area.

Units: kg (dry weight)/m2

Type of Crop Default Value (kg Range (kg (dry Distribution

(dry weight)/m ) weight)/m ) 2 2

Leafy vegetables 0.177 0.091 - 0.353 Log (0.177, 0.086)

Legume vegetables 0.104 0.077 - 0.130 Log (0.104, 0.038)

Fruiting vegetables 0.107 0.012 - 0.253 Log(0.107, 0.093)

Rooting vegetables 0.334 0.090 - 0.434 Log(0.334, 0.142)

Grains and cereals 0.3 0.14 - 0.45 Log (0.30, 0.09)

Forage 0.31 0.02- 0.75 0.84482993969

Fruits 0.107 0.012 - 0.253 Log(0.107, 0.093)

Potatoes 0.48 0.405 - 0.555 Log (0.48, 0.106)

Silage 0.84 0.3- 1.34 Log(0.84,0.26)

Technical Basis:

The distributions and ranges shown for all but the silage values are those used in Belcher and

Travis (1989). The distributions selected were chosen based on a probability plot for leafy vegetables

with data in Shor et al. (1982). The default values are the means of the distributions. Silage was not

considered in Belcher and Travis (1989), but the same method by which the default values and

distributions were calculated there were replicated using data from Shor et al. (1982) for the purpose of

this assessment.
B-6

B.1.1.3.4 Plant Ingestion by Animals

Parameter: QPij

Definition: The daily consumption of plants by livestock.

Units: kg dry weight/day

Livestock Consumption Default Value Distribution Range

 of Plants (kg dry weight/day) (kg dry weight/day)

Beef/Beef Liver 

grain 0.97 U(0.5,6.5) 0.5-6.5

forage 8.80 U(2.0,9.0) 2.0-9.0

silage 2.50 U(1,5) 1.0-5.0

Dairy

grain 2.60 U(0.5,6.5) 0.5 - 6.5

 forage 11.0 U(7,15) 7.0-15.0

silage 3.30 U(1,5) 1.0-5.0

Pork

 grain 3.0 U(2,4) 2.0-4.0

 silage 1.3 U(0.5,3) 0.5-3.0

Sheep (lamb)

 forage 1.1 U(0,2) 0.0 - 2.0

Poultry/Eggs

grain 0.08 U(0.04,0.10) 0.04-0.10

Technical Basis:

With the exception of the beef liver, egg and lamb-forage values, the default values are from

U.S. EPA (1990). The value for beef liver is assumed to be the same as for cattle, and the value for eggs

is assumed to be the same as for poultry. The value for lamb-forage is from the National Academy of

Sciences (NAS,1987). 

The ranges shown are based on a combination of the ranges determined by Belcher and Travis

(1989), the U.S. EPA (1990) values, and the objective of capturing all of the most likely values.

Although lognormal distributions were chosen in Belcher and Travis (1989), this was not based

on the actual distribution of the available data; that is, no probability plots were done. For that reason,

uniform distributions are suggested here.
B-7

B.1.1.3.5 Soil Ingestion by Animals

Parameter: QSj

Definition: Quantity of soil ingested daily by the a specific animal.

Units: kg/day

Livestock Default Value Range

(kg/day) (kg/day)

Beef/beef liver 0.39 0.1 - 0.72

Dairy 0.41 0.1 - 0.72

Pork 0.034 0.0 - 0.0688

Sheep (lamb) 0.05 0.01 - 0.15

Poultry/eggs 0.009 0.006 - 0.012

Technical Basis:

The values for beef cattle and dairy cattle are from McKone and Ryan (1989). The value for

beef liver is assumed to be the same as for beef. The value for pork is the mean of the distributions used

in Belcher and Travis (1989) and are based on values in Fries (1987). The sheep value is from Fries

(1982). The value for poultry is the mean of the distribution used in the Hanford Environmental Dose

Reconstruction Project (HEDR, 1992) and is based on values for free-ranging chickens. The range is that

used in HEDR (1992).

For beef, dairy and pork, the ranges are from Belcher and Travis (1989).

The range for sheep is based on the values reported in Fries (1982). The lower end of the range

is for sheep that are fed in a lot, in which case they eat little soil. The upper end is based on sheep

grazing on poor pasture land.
B-8

B.1.1.4 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.

B.1.1.4.1 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 (1989b), 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.
B-9

B.1.1.4.2 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.
B-10

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 recognized 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 U.S. EPA (1989b).

B.1.1.4.3 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 (1989b). 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,

1989b). This range was selected, and the midpoint was chosen as the default value.
B-11

B.1.1.4.4 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 (1989b) 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.
B-12

B.1.1.4.5 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. (Please see Chapter 4 of Volume IV of this Report for a more

complete discussion of reported fish consumption rate variability.)

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.
B-13

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 Chapter 4 of Volume IV of this

Report. 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 Chapter 4 of Volume IV of this Report.)
B-14

B.1.1.4.6 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

(1989b)

Legumes 1 0.8 0.5 Values are for peas from

Table 2-7 in U.S. EPA

(1989b).

Potatoes 1 0.225 0.031 Values are for total fresh

potatoes from Table 2-7 in

U.S. EPA (1989b).

Root Vegetables 1 0.268 0.073 Values are for carrots from

Table 2-7 in U.S. EPA

(1989b).

Fruits 1 0.233 0.076 citrus fruit from Table 2-7

Values are for Total nonin (1989b).

Fruiting 1 0.623 0.317 Values are for tomatoes

Vegetables from Table 2-7 in U.S. EPA

(1989b).

Leafy Vegetables 1 0.058 0.026 Values are for lettuce from

U.S. EPA (1989b).

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 (1989b), 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 zero for consumption of local animal products.
B-15

B.1.2 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.

B.1.2.1 Basic Chemical Properties

The following sections list the chemical properties used in the study, their definitions, and

values.

B.1.2.1.1 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

B.1.2.1.2 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

HgII (HgCl ) 7.1x10 2

-10

Methylmercury 4.7x10-7

Technical Basis:

The Henry’s Law constant is set to 0.0071 atm-m /mole for Hg (Iverfeldt and Persson, 1985) 3 0

and 4.7×10 atm-m /mole for MHg (Lindquist and Rodhe, 1985). -7 3
B-16

B.1.2.1.3 Soil-Water Partition Coefficient

Parameter: Kd

Definition: Equilibrium concentration in soil particulates divided by concentration in soil water.

Units: mL/g

Chemical Default Value (mL/g) Range

Hg 1000 -- 0

HgII 58,000 24,000-270,000

Methylmercury 7,000 2,700-31,000

Technical Basis:

Calculated soil-water partition coefficients for HgII in the upper soil layer are reported to range

between 24,000 and 270,000 mL/g, with a mean value of about 60,000 mL/g ( Lyon, et al., 1997). For

MHg, values range between 2,700 and 31,000 mL/g, with a mean value of about 6,700 mL/g. 

B.1.2.1.4 Sediment-to-Water Partition Coefficient

Parameter: Kdb

Definition: Equilibrium concentration in sediment solid divided by concentration in pore water.

Units: mL/g

Chemical Default Value Range

(mL/g)

Hg 3000 -- 0

HgII 50,000 16,000-990,000

Methylmercury 3000 2,200-7,800

Technical Basis:

Calculated benthic sediment partition coefficients for HgII are reported to range between 16,000

and 990,000 mL/g, with median values between 54,000 and 79,000 mL/g ( Lyon, et al., 1997). For MHg,

values range between 650 and 110,000 mL/g, with median values between 6100 and 9000 mL/g. In the

R-MCM model (Harris, et al, 1996) the partitioning of HgII to benthic solids in four lakes ranges from

30,000 to 100,000 mL/g, while the partitioning of MHg ranges from 2,200 to 7,800 mL/g. (see Table 6-

17, in Chapter 6 of Volume III). The values chosen here are at the lower mid-range of the reported

values.
B-17

B.1.2.1.5 Suspended Sediment-Water Partition Coefficient

Parameter: Kdw

Definition: Suspended sediment-water partition coefficient.

Units: L/kg

Chemical Default Value (L/kg) Range

Hg 1000 -- 0

HgII 100,000 1,380-270,000

Methylmercury 100,000 94,000-250,000

Technical Basis:

The estimated value for both Hg(II) and MHg is consistent with available literature on suspended

sediment partition coefficients, with reported values of 1380-188,000 (Moore and Ramamoduray, 1980),

118,000 (Glass et al. 1990), and 86,800-113,000 (Robinson and Shuman, 1989). In the R-MCM model

(Harris, et al, 1996) the partitioning of HgII to abiotic suspended solids in four lakes ranges from 80,000

to 270,000 L/kg, while the partitioning of MHg ranges from 94,000 to 250,000 L/kg. (see Table 6-17, in

Chapter 6 of Volume III). 

B.1.2.1.6 Suspended Biotic Solids-Water Partition Coefficient

Parameter: KBio

Definition: Suspended biotic solids-water partition coefficient.

Units: L/kg

Chemical Default Value (L/kg)

Hg 1000 0

HgII 200,000

Methylmercury 500,000

Technical Basis:

Because of higher organic matter content, partitioning to biotic solids should be similar to, but

slightly higher than partitioning to abiotic solids. In the R-MCM model (Harris, et al, 1996) the

partitioning of HgII to total (abiotic plus biotic) suspended solids is calculated to be 20-25% higher than

partitioning to abiotic solids (see Table 6-17, in Chapter 6 of Volume III). Given the low ratio of biotic

to total solids, the effective partition coefficients of HgII to biotic solids should be about a factor of 1.5 -

2 higher than the partition coefficients to abiotic solids. Likewise, the partitioning of MHg to total

suspended solids is about 2.5 times higher than partitioning to abiotic solids, giving an effective partition

coefficient of MHg to biotic solids about 5 to 8 times higher than the partition coefficient to abiotic

solids. 
Da,i  1.9

MW 2/3

i

B-18

B.1.2.1.7 Chemical Diffusivity in Air

Volatilization of gas phase Hg and MHg is calculated using a diffusion coefficient of 4700 0

cm /day, based upon a general formula for air diffusivity as a function of molecular weight (Schnoor, et 2

al., 1987):

where D is the atmospheric diffusivity of component “i”, cm /sec, and MW is the molecular weight of a,i i

2

component “i”, in g/mole.

B.1.2.1.8 Chemical Transformation Rate Constants

Fifteen transformation rate constants must be specified or calculated for the soil, water column,

and benthic sediment equations -- oxidation of Hg (ks , kw kb ); reduction of HgII (ks , kw , kb ); 0

o o, o r r r

methylation of HgII (ks , kw , kb ); demeth m m m dm dm dm ylation of MHg to HgII (ks , kw , kb ); and mer cleavage

demethylation of MHg to Hg (ks , kw , kb ). Values (in day ) are summarized in Table B-2 and 0 -1

md md md

discussed below. Calculated volatilization rate constants are included in the table for comparison.

Table B-2

Chemical Transformation Rate Constants

Rate Constants, day Watershed Soil Water Column Benthic Sediments -1

volatilization of Hg 0.082 0.10 0 0* *

oxidation 0 0 0

reduction 0.000025 0.0075 0.000001 *

methylation 0.00005 0.001 0.0001

demethylation to HgII 0.0025 0.015 0.002

mer demethylation to Hg 000 0

 Calculated internally for specified conditions. *

Reduction in Water Column -- The reduction rate constant in the water column, kw , was set to 0.0075 r

day . -1

Technical Basis:

Recent literature has addressed reduction of divalent mercury in the water column due to the

presence of sunlight, heterotrophic bacteria, and some species of phytoplankton. Reduction of aqueous

Hg(II) solutions in the presence of simulated sunlight was observed, with rate constants of 3.5 day and -1

0.05 day for 20% and 80% of the mercury when normalized to sunlight in Stockholm, Sweden (Xiao, et -1

al., 1995). Mason et al. (1994) calculated reduction rate constants between 0.005 and 0.1 day from -1

mass balances in the equatorial Pacific and in Wisconsin seepage lakes. Mason et al. (1995) give
Fr  krs  C2T  B  w  zr

B-19

reduction rate constants in the epilimnion of Mystic Lake between 0.02 and 0.04 day ; this reaction is -1

attributed primarily to heterotrophic bacteria. Rate constants declined to 0.01 day at 9 meters depth, -1

and less than 0.005 day at 17 meters depth. Amyot et al. (1997a) report reduction rate constants of 0.14 -1

day in high Arctic lakes during a 24-hour sunlight period. Under low DOC conditions, these rate -1

constants reached 0.2 to 0.4 day , whereas under high DOC conditions, rate constants varied between -1

0.02 to 0.14 day . Amyot et al. (1997b) report rapid DGM production in the epilimnion of temperate -1

lakes, driven mainly by sunlight. The most likely limiting factor was thought to be the reactive HgII pool

itself; the nature of the photoreducible complexes is unknown. Lower DGM production is observed in

high DOC lakes due to reduced light penetration and increased complexation of HgII. In Pallette Lake,

Vandal et al. (1995) report reduction rate constants in July-August 1993 averaging 0.10 day in the upper -1

3 meters and 0.05 day at 9 meters. In May 1994, observed reduction rate constants increased to 0.22 -1

day in the upper 6 meters. High levels of DGM supersaturation, which indicates significant reduction, -1

exist from mid-May to mid-September.

Maximum surficial reduction rate constants must be adjusted to reflect the attenuation of light

with depth in water bodies with significant light attenuation. In addition, cool temperatures and reduced

sunlight should lead to significantly lower reduction rates during winter months. A yearly-average water

column rate constant of 0.075 day was selected for this study. -1

Reduction in Sediments -- The reduction rate constant in sediments, kb , was set to 10 day . r

-6 -1

Less literature is available for reduction in sediments. Amyot et al. (1994) report an increase in

DGM concentrations near the bottom of Ranger Lake and speculate that this is caused by aerobic and

facultative bacteria reducing HgII in the sediments. Vandal et al. (1995) report porewater Hg0

concentrations of 65 pg/L, or 10% of the total mercury, indicating the presence of reduction in sediments. 

A whole-sediment reduction rate constant of 10 day along with the pore water diffusion and solids -6 -1

resuspension rates used in this model produces pore water Hg concentrations of about 50 pg/L given a 0

partition coefficient of 1000 L/kg for Hg .

0

A benthic rate constant of 10 day was selected based on model calibration to general -6 -1

observations of Hg concentration in pore water. 0

Reduction in Soil -- The reduction rate constant in the upper 5 mm soil layer of the watershed,

normalized to water content, is set to 0.0005 L/L -da w y. The reduction rate constant in the upper 5 mm

soil layer of the field site, normalized to water content, is set to 0.0013 L/L -da w y. For water content of

0.1, and depths of 1 and 20 cm, the watershed and field site rate constants are 2.5×10 and 3.25×10 day -5 -6 -

, respectively. 1

Technical Basis:

Assuming that the net flux of Hg is driven by the reduction of HgII, reduction rate constants can 0

be derived from evasion flux measurements over soil:

where F is the net evasion flux in r rs g/m -year, k is the reference reduction rate constant normalized to 2

soil water content, C is the volumetric concentration of H 2T gII in soil in mg/L, L/L - w B yr,  is the soil bulk

density in kg/L, w wr is the soil water content in L /L, and z is the soil layer thickness over which reduction

occurs, in mm. This equation normalizes the reduction rate constant to the soil water in the surficial 5

mm layer, following observations presented in Carpi and Lindberg (1997). To derive the reduction rate

constant ks used in the model, k must be multi r rs plied by the soil water content and averaged over the

entire soil depth (ie, by multiplying k b rs y 0.5 cm and dividing by either 2 or 20 cm for the nontilled and

tilled soils, respectively). 
kwm  kwm,net kwd× CMeHg

CHgII

B-20

From data presented in Lindberg (1996) and Carpi and Lindberg (1997), values for k can be rs

calculated for fields exposed to sunlight and for forests with canopy. These rate constants represent

diurnal and seasonal averages. First, the observed fluxes must be modified to represent annual averages. 

Following the diurnal pattern observed for sludge-amended soils, the summer mid-day fluxes in

background field sites were multiplied by 0.25 to obtain a diurnal average. For the shaded forest site, the

summer daylight flux is multiplied by 0.6 to obtain a diurnal average. Assuming that winter rates are

about half of summer rates, the summer diurnal average fluxes were multiplied by 0.75 to obtain annual

average fluxes. Applying these factors, both field sites yield a value of 0.0013 L/L -da w r y for k , while the

forest site yielded a value of 0.0001 L/L -da w y. Presumably the difference in rate constants is due to the

significant shading under the forest canopy. Any given watershed should experience average rate

constants somewhere between these estimates, depending on the landscape pattern. A value of 0.0005

L/L -da w rs w , y is recommended. The field k , 0.0013 L/L -day should be used for calculating concentrations

in tilled soil. For water content of 0.1 L /L and de w pths of 2 cm and 20 cm for the watershed and field,

the soil reduction rate constants are calculated to be 1.25×10 day and 3.3×10 day , respectively. -5 -1 -6 -1

Methylation in Water Column -- The water column methylation rate constant, kw , was set to m

0.001 day . -1

Technical Basis:

Using radiolabelled mercury, maximum potential methylation rates have been measured in a

number of water bodies. Gilmour and Henry (1991) report maximum potential methylation rate

constants between 0.0001 and 0.003 day in the water column of fresh water bodies. Henry et al. (1995) -1 

report net methylation rates in Onondaga Lake, New York between April and November, 1992; a net

production of 0.003 ng/L-day at 3 m depth, and 0.03 ng/L-day at 9 m was calculated. Given divalent Hg

concentrations averaging about 5 ng/L at these depths (Jacobs, et al., 1995), these production rates would

correspond to net methylation rate constants of about 0.0006 day and 0.006 day , respectively. For the -1 -1

surficial water layers where photodegradation of methyl mercury is significant, the net methylation rate

constant should be adjusted to yield the gross methylation rate constant:

Assuming a demethylation rate constant at 3 m depth of about 0.013 day (see discussion below) and a -1

ratio of MHg to HgII of about 10%, the gross methylation rate constant at 3 m should have been about

0.002 day . At the seasonally-anoxic 15 m depth, the reported net methylation rate of 0.11 ng/L-day -1

yields rate constants between 0.01 and 0.03 day . Matilainen (1995) reports low but detectable non- -1

microbial methylation rate constants of 0.0005 to 0.001 day in the oxic portions of four forest lakes in -1

Finland. Higher rate constants of 0.004 to 0.01day were observed in deeper, anaerobic layers of the -1

hypolimnion. For Pallette Lake, Wisconsin, Watras et al. (1995) report high methylation rates within a

layer of bacterioplankton near the top of the anoxic hypolimnion. This 0.5 to 1 meter layer exhibited rate

constants of about 0.01 to 0.04 day . The methylation rates are linked to sulfate reduction “within -1

anoxic microbial layers, whether in hypolimnetic waters or in sediments underlying oxic water.”

For this case study representing a shallow lake with little or no anoxia, the average water column

methylation rate constant was set to 0.001 day , reflecting observed non-microbial rate constants in oxic -1

waters. For case studies representing deeper lakes with seasonal anoxia, the methylation rate constant in

the hypolimnion should be increased to account for a higher microbial rate constant of 0.01 day within -1

the oxic/anoxic boundary layer.

Methylation in Sediments -- The methylation rate constant in the upper sediment layer, kb , was set to m

0.0001 day . -1
B-21

Technical Basis:

In freshwater sediments, Gilmour and Henry (1991) report maximum potential methylation rate

constants between 10 and 10 day . In studies of Quabbin Reservoir, MA, Gilmour et al. (1992) found -5 -1 -1

that the highest rates of methylation occur at the sediment surface, and conclude that sulfate-reducing

bacteria are important mediators of this reaction. Stordal and Gill (1995) report methylation rate

constants above intact sediment cores of 0.0008 to 0.025 day , reflecting both net methylation and -1

sediment exchange. Gilmour and Riedel (1995) report methylation rates of 0.29 and 0.09 g/m -day in 2

the upper 4 cm of Little Rock Lake sediments. Although rate constants were not reported, the sediment

concentrations can be combined with an assumed dry density of 1.2 g/cm to yield rate constants of 8×10 3 -

 and 2×10 5 -5

day . -1

As a bacterial reaction, sediment methylation should proceed more slowly in the winter months,

giving a yearly-average rate about half of the summer rate due to temperature alone. Gilmour and Henry

(1991) and Henry et al. (1995) report up to 40 times higher methyl mercury production rates from

sediments under anoxic/sulfidic waters than under oxic waters. More oxygenated waters during the

winter are expected to result in result in significantly lowered methylation rates than those observed

during the summer months. Here, the yearly-average methylation rate constant in the upper sediment

layer was set to 0.0001 day on a whole-sediment basis. -1

Methylation in Soil -- The soil methylation rate constant, ks , was set to 5×10 da m y . -5 -1

Technical Basis:

Beckert et al. (1974) provide qualitative evidence showing methylation of mercuric salts in

agricultural soils in Nevada due, presumably, to methanogenic bacteria. Abiotic methylation may also

occur in soils, either by transmethylation from other organometals at contaminated sites or by humic

substances in uncontaminated soils (Gilmour and Henry, 1991). Porvari and Verta (1995) report a

laboratory study of potential methylation and demethylation in flooded soils using radiolabelled mercury. 

While rates varied during the 120-day experiment, maximum potential methylation rate constants in

humus and peat averaged about 10 day under anaerobic conditions and 2×10 day under aerobic -3 -1 -4 -1

conditions.

For this study, a soil methylation rate constant of 5×10 day was chosen. This value is 25% of -5 -1

the potential aerobic rate constants measured by Porvari and Verta (1995) and lies at the lower end of the

range for sediments reported by Gilmour and Henry (1991).

Demethylation in Water Column -- The water column demethylation rate constant, kw , was set to dm

0.015 day . -1

Technical Basis:

Gilmour and Henry (1991) report maximum potential demethylation rate constants in the water

column between 0.001 and 0.025 day . From in-situ incubations in the Ontario Experimental Lakes -1

area, Sellers et al. (1996) derive rate constants for abiotic photodegradation of methyl mercury that are

first-order with respect to light levels and with concentration. From data presented in this report, the rate

equation is k = 0.0017×PAR ( dm photosynthetically-active radiation, in E/m -day). Zepp (1980) gives a 2

mean annual daytime PAR of 95 E/m -day for clear skies at 40N latitude. Kirk, 1994 reports a cloud 2

reduction factor for Europe in the summer of 0.5 to 0.8. Assuming an average cloud reduction factor of

0.65, a surface reflectance of 5%, and a light attenuation factor of 0.75 m through a 5-meter deep lake, -1

an average demethylation rate constant is calculated to be 0.013 day . This rate, of course, would be -1

highest in the epilimnion and during the summer months. For this study, the water column demethylation
B-22

rate constant was set to 0.015 day , producing a MHg fraction close to the observed epilimnetic average -1

of 6%.

Demethylation in Sediments -- The demethylation rate constant in upper sediments, kb , was set to dm

0.002 day . -1

Technical Basis:

In freshwater sediments, maximum potential demethylation rate constants are generally higher

than corresponding methylation rate constants, with observations between 2×10 and 10 day (Gilmour -4 -1 -1

and Henry 1991). Gilmour et al. (1992) report that demethylation is maximal at the sediment-water

interface. While direct measurements of demethylation rates in sediments are lacking in the literature,

rate constants in sediments can be calculated from methylation rate constants and the percent methyl

mercury (%MHg). This percentage is generally lower than the water column. Verta and Matilainen

(1995) report %MHg between 0.03% and 6% in the sediments of four lakes in Finland. Consistent with

reported observations, the demethylation rate constant in upper sediments was set to 0.002 day , -1

producing a sediment MHg fraction of 3%.

Demethylation in Soil -- The soil demethylation rate constant, ks , was set to 0.0025 day . dm

-1

Technical Basis:

 Porvari and Verta (1995) report potential demethylation in flooded soils using radiolabelled

mercury. Maximum potential demethylation rate constants in humus averaged about 0.06 day under -1

anaerobic conditions and 0.03 day under aerobic conditions. -1

For this study, a soil demethylation rate constant of 0.0025 day-1 was chosen. This value is 10%

of the potential aerobic rate constants measured by Porvari and Verta (1995), and is a factor of 50 higher

than the methylation rate constant chosen for this study.

B.1.2.2 Biotransfer Factors

Biotransfer factors reflect the extent of chemical partitioning between a biological medium

(plants, meats or fish) and an external medium (air, soil or water). The following sections describe the

BCFs used in this study.

It is necessary to note the uncertainty inherent in determining BCFs for mercury species with

regard to plant uptake. In general, there seems to be no consensus in the literature on plant

bioconcentration factors for mercury, as values for each crop vary widely among studies. Further, in

many studies the mercury speciation is not determined. In deriving BCFs for plant absorption of mercury

species from the air and soil, it was, therefore sometimes necessary to make assumptions about certain

behaviors of mercury based on whatever information was at hand, as opposed to established scientific

knowledge, which was lacking. These assumptions are described in each Technical Basis section that

follows, but it is useful at this time to identify some of the general uncertainties regarding plant uptake of

mercury.

(1) Plants both absorb and release mercury to the environment. Hanson et al. (1994)

demonstrates clearly that at ambient air concentrations forest foliage usually acts as a

source of elemental mercury to the atmosphere; deposition (plant absorption) only occurs

above a "compensation concentration" at air mercury levels well above background. It is

not yet known from where the mercury released by the plants originates (air uptake

during periods of high mercury air concentrations, root uptake, Hg(II) absorption, etc.). 

Similarly, Mosbaek (1988) found that for a given period of time more elemental mercury
B-23

was released from a plant-soil system than was absorbed by the plant. These cases,

however, in no way indicate that mercury is not bioconcentrated in plants; the above

behaviors are consistent with mercury being collected by plants only to certain levels,

after which any mercury absorbed is simply released.

(2) It is usually not known from where the mercury that is found in plants originated (air vs.

soil). Only one study determined the fractions of total mercury in plants which came

from air and soil (Mosbaek, 1988); in this study, soil was isotopically labeled with Hg. 203

After some time the specific activity in the plant was compared to that in the soil to

ascertain how much of the mercury in the plant came from the soil. Although the

experiment worked well, isotopic equilibrium in the soil was never achieved, and the

number of plants studied was limited.

(3) The speciation of mercury in plants is often not known. If it is known, it is still very

unclear as to how the speciation occurred. The plant speciation may be simply a result

of direct uptake of different mercury species from the environment (but from air or

soil?). It has been shown, however, that a few plants have the ability to change the

species of mercury initially taken up from the environment (Fortmann et al., 1978). Such

behavior may have to be accounted for regarding plant uptake of mercury.
B-24

B.1.2.2.1 Plant-Soil BCF

Parameter: BRi

Definition: The ratio of the contaminant concentration in plants (based on dry weight) to that in the

soil.

Units: Unitless

Crop

Hg Methylmercury 2+

Default Distribution Default Distribution

Value Value

Leafy vegetables 0 None 0 None

Legume vegetables 0.015 0.157) 0.090) 

U(0.00026, 0.031 U(0.0,

Fruiting vegetables 0.018 U(0.007,0.059) 0.024 U(0.0,0.11)

Rooting vegetables 0.036 U(0.011, 0.073) 0.099 U(0.013,0.29)

Grains and cereals 0.0093 U(0.0024,0.057) 0.019 U(0.0048,0.11) a a

Forage 0 None 0 None

Fruits 0.018 U(0.007-0.059) 0.024 U(0.0,0.11) 

Potatoes 0.1 U(0.05,0.2) 0.2 U(0.1,0.4) a a

Silage 0 None 0 None

 Hg values multiplied by 2 a 2+

Technical Basis:

Mosbaek (1988) convincingly showed that for leafy, above-ground parts of plants virtually all of

the mercury uptake was from air; therefore, for leafy vegetables, forage and silage no root uptake was

modeled.

Values in Cappon (1987) and Cappon (1981) were the only data located which measured

methylmercury concentrations in plants, and methylmercury plant-soil BCF's were determined for

rooting vegetables, fruiting vegetables, and legumes. Values were determined for crops grown on

compost (Cappon 1987) and sludge-treated soils (Cappon 1981), and those values considering edible

portions of plants are shown in Table B-3.
B-25

Table B-3

Soil-to-Plant Transfer Coefficients for Mercury

 (from Cappon, 1987 and Cappon, 1981)

Crop 1987 Values 1981 Values

Hg Methylmercury Hg Methylmercury 2+ 2+

Rooting Vegetables

 Beet .055 .227 .017 .11

 Carrot .026 .118 .014 .048

 Onion, Yellow .073 .288 .053 .042

 Onion, Spanish - - .047 .030

 Red Radish .056 .092 .018 .066

 White Radish - - .011 .060

 Turnip .026 .013 - -

Fruiting Vegetables

 Cucumber, slicing - - .015 0

 Cucumber, pickle .007 0 .015 .006

 Pepper .019 .022 .016 .042

 Zucchini .021 0 .014 .018

 Summer Squash - - .007 0

 Acorn Squash - - .016 .012

 Spaghetti Squash - - .016 .024

 Pumpkin - - .008 .006

 Tomato .059 .105 .020 .072

Legumes

 Green Bush Beans .011 0 .014 .020

 Yellow Bush Beans - - .017 .015

 Lima Beans - - .017 .090

It has been shown, however, that mercury taken up into plants from the environment can be

transformed into other mercury species, especially to organomercuric forms such as methylmercury

(Fortmann et al., 1978). The methylmercury in plants, therefore, may not have been directly absorbed

from the environment. For the purposes of this study, considering root uptake, methylmercury

concentrations in plants were treated as though they originated from the soil. It is also important to note

that air-to-plant transfer may have occurred, but the Cappon (1981, 1987) studies were not designed to

measure air-uptake.

Table B-4 shows additional soil-to-plant transfer coefficients for Hg species (it was assumed 2+

that all the mercury in the soil is Hg , which at worst would result in an error of a few percent in the 2+

Hg soil-to-plant transfer coefficients) determined from a number of studies. Temple and Linzon (1977) 2+

sampled garden produce in the vicinity of a chlor-alkali plant. Lenka et al. (1992) also measured

mercury concentrations in soil and plants near a chlor-alkali plant. Somu et al. (1985) determined
B-26

mercury uptake in wheat and beans grown on HgCl contaminated soil. John (1972) determined mercury 2

concentrations in plants grown on soil artificially contaminated with HgCl . Wiersma et al. (1986) 2

measured soil and plant total mercury concentrations from major growing areas in the Netherlands. 

Belcher and Travis (1989) compiled data from EPA (1985). Mosbaek (1988) studied plant

concentrations from soil and air uptake under background conditions. For studies reporting wet weight

plant concentrations, wet weight to dry weight conversion factors in Baes et al. (1984) were used to

convert to dry weight based concentrations.

Table B-4

Other Values for Soil-to-Plant Transfer Coefficients for Hg2+

Crop Values References

Legume vegetables 0.157-1.79, 0.00026-0.0003, al. (1985), John (1972),

0.0005, 0.003-0.03 Belcher and Travis (1989).

Lenka et al. (1992), Somu et

Fruiting vegetables 0.028 Lenka et al. (1992), Belcher

0.013-0.33, 0.127-1.36, 0.0078- Temple and Linzon (1977),

and Travis (1989).

Rooting vegetables 0.09-0.33, 0.090-0.149, 0.0065- Lenka et al. (1992), John

0.013, 0.05-0.2, 1.6-1.9 (1972), Belcher and Travis

Temple and Linzon (1977),

(1989), Mosbaek (1988)

Grains and cereals 0.0024-0.0093, 0.0033, 0.00038- Somu et al. (1985), John

0.057 (1972), Belcher and Travis

(1989).

Fruits 0.0078-0.028 Belcher and Travis (1989).

Potatoes 0.05-0.2 Belcher and Travis (1989).

When possible, default values were chosen based on experiments under reasonable or

background conditions, as opposed to experiments where the soil was "spiked" with large amounts of

mercury or measurements were taken from severely polluted areas. This is actually a conservative

approach; although plants from mercury polluted areas will have greater contaminate levels, the

efficiency of accumulation (quantified in the transfer coefficients) tends to decrease with increasing

contaminate concentrations. Values from Cappon (1987) and Cappon (1981) were used when possible,

since these experiments were conducted under reasonable garden conditions, edible portions of plants

were analyzed separately, and different mercury species were measured. Cappon (1981) analyzed plants

grown in control soil (total mercury soil content of 120 ng/g with 4.2% methylmercury) in addition to the

sludged soil (330 ng/g with 5.1% methylmercury, which is comparable to the 1987 soil levels of 430

ng/g with 5.3% methylmercury). The control soil data were not used since the methylmercury levels

were often undetectable. Note that the compost and sludge-amended soils, although elevated in mercury,

are nonetheless at reasonable concentrations. For fruiting vegetables, rooting vegetables and legumes

values from Cappon (1987) and values derived from the edible potions of plants grown on sludged soil

from Cappon (1981) were pooled and averaged; the results were used as the defaults for these plant

types.
B-27

Default Hg values for grains and cereals are from Somu (1985); the methylmercury values 2+

were assumed to be twice as great in accordance with the overall average trend noted in plants from the

pooled Cappon data. The default values for fruits were assumed to be the same as for fruiting

vegetables. The default Hg value for potatoes was taken from Belcher and Travis (1989); the 2+

methylmercury value for potatoes was assumed to be twice the Hg value. 2+

B.1.2.2.2 Air-Plant BCF

Parameter: BI

Definition: The ratio of the contaminant concentration in plants (based on dry weight) to that in the

air. 

Units: Unitless

Crop

Hg Methylmercury 2+a a

Default Distribution Default Distribution

Value Value

 Leafy vegetables 18000 U[12000,24000] 5000 U[3300,6800]

 Legume vegetables 1050 U[700,1400] 100 U[65,130]

 Fruiting vegetables 22000 U[14000,29000] 1200 U[780,1600]

 Rooting vegetables 0 NA 0 NA

 Grains and cereals 1050 U[700,1400] 100 U[65,130]

 Forage 18000 U[12000,24000] 5000 U[3300,6800]

 Fruits 22000 U[14000,29000] 1200 U[780,1600]

 Potatoes 0 NA 0 NA

 Silage 18000 U[12000,24000] 5000 U[3300,6800]

 Based on elemental mercury air concentration, and speciation of divalent and methylmercury species a

based on Cappon (1981,1987).

Technical Basis:

Mosbaek (1988) determined that mercury concentration in the above-ground, leafy parts of

plants is almost entirely the result of air-to-plant transfer of mercury. Cappon (1987,1981), however,

found only divalent and methylmercury in these types of plants. Fitzgerald (1986) noted that up to 99%

of the total airborne mercury is Hg vapor (Fitzgerald, 1986). It was assumed that any atmospheric 0

elemental mercury taken up by the plant is converted into Hg and methylmercury in the plant tissue. 2+

This is not unreasonable: it has been shown that mercury taken up into plants from the environment can

be transformed into other mercury species (Fortmann et al., 1978).

A strong correlation between mercury soil concentration and concentration in rooting vegetables

has been established (John, 1972; Lenka et al., 1992; Lindberg et al., 1979), and the Mosbaek study

(1988) demonstrated that much of the mercury in rooting vegetables was from the soil. As a result, airto-plant uptake of mercury was not modeled for rooting vegetables and potatoes.
B-28

For grains, fruits, legumes and fruiting vegetables, little correlation between mercury plant

concentrations and either air or soil concentrations has been found; however, non-negligible

concentrations of mercury species in these plants are routinely observed. For this reason, both air-toplant and soil-to-plant uptake was modeled for these plants. Using a conservative approach, the transfer

factors for each accumulation pathway were calculated as if all of the mercury in the plant came only

from that pathway. This has the effect of possibly double-counting the amount of mercury in the plant

tissue. There is a great deal of uncertainty due to the lack of applicable data.

The range of air-plant bioconcentration factors based on Mosbaek et al. (1988) was found to be

15,000 - 31,000, based on total mercury concentration in the plant tissue. Mosbeak et al. (1988)

determined average mercury concentrations due to air uptake in lettuce, radish tops, and grass. 

Concentrations were converted to dry weight according to Baes et al. (1984), and the overall range of 

air-plant bioconcentration factors based on total mercury in the plant tissue was found to be 15,000 -

31,000. Air to plant bioconcentration factors can be derived from other studies only indirectly (by

making a reasonable estimate of the air concentration and assuming all the mercury in plant tissue comes

from air), and the values arrived at for various plant species generally fall into the previous range. Due

to the limited data, it was decided to use the midpoint of the Mosbeak et al. (1988) bioconcentration

values (23,000) as the starting default for all plant species assumed to accumulate mercury from the air. 

This approach was adjusted for the consideration of portions of grains and legumes that are not

directly exposed to the atmosphere. Although atmospherically absorbed mercury can translocate

throughout different portions of the plant, data indicate internal portions of grains and legumes (the

edible portions) do not appear to accumulate mercury to the same degree as plant leaves or vines. Somu

et al. (1985), John (1972), and Cappon (1981) determined mercury concentrations from different portions

of the same plants. Table B-5 below shows the relative concentrations of total mercury found in plant

parts from the portions of these studies representative of noncontaminated conditions.

Table B-5

Relative Concentration of Mercury in Different Parts of Edible Plants

Legumes Beans Peas Beans

(Somu et al. 1985) (John 1972) (Cappon 1981)

vines 1.0

stalks 1.0

pods 0.045 1.0

seeds 0.060 0.0091 0.028 - 0.089

Grains Wheat Oats

(Somu et al. 1985) (John 1972)

leaves 1.0

stalks 1.0 0.063

husks 0.61

grain 0.14 0.051

A clear trend of decreasing mercury concentrations is seen proceeding from leafy to seed

portions of the plants. Based on these data, it was decided to decrease the default air-to-plant

bioconcentration factor of 23,000 by a factor of 20 (to 1200) to account for the decreasing accumulation
B-29

of airborne mercury for the edible portions of these plants as compared to the leafy portions (for which

the bioconcentration factor of 23,000 is applicable). Airborne mercury uptake by fruits may also be

overestimated with the default bioconcentration factor. However, no data are available to explore this

possibility. 

The product of the bioconcentration factors and the atmospheric mercury concentration is the

total mercury in the plant tissue resulting from accumulation of airborne elemental mercury. Plantspecific speciation estimates from Cappon (1981,1987) were used to partition the total mercury

bioconcentration factor (and corresponding range) in order to model the relative fractions of

methylmercury and Hg found in the plant; these are shown in Table B-6; note that the rest of the 2+

mercury was found to be divalent mercury.

Thus, for leafy, fruiting and legume vegetables, the default values for the bioconcentration of

methylmercury based on the elemental mercury concentration in air were assumed to be 23,000 or 1200

multiplied by the average methylmercury percentages in Table B-5; the Hg values were derived 2+

similarly (Hg fraction x 23,000). The values for fruits were assumed to be the same as for fruiting 2+

vegetables. The values for forage and silage were assumed the same as for leafy vegetables, and the

values for grains were assumed to be the same as for legumes (beans).
B-30

Table B-6

Mercury Speciation in Various Plants

Plant Type % Methylmercury Cappon (1981) % Methylmercury Cappon (1987)

Leafy vegetables

Head lettuce 8.8 21.4

Leaf lettuce 16.5 18

Spinach 19.8 23.1

Swiss chard, Fordhook 30.2 14.8

Swiss chard, Ruby Red 28.6 -

Broccoli 33.1 17.8 a

Late Cabbage 28.8 -

Red Cabbage 22.4 -

Savoy King Cabbage 25.2 -

Jersey Wakefield Cabbage - 18 a

Cauliflower 21.2 -

Collards 22.8 -

Average 21.8

Legume vegetables

Green Bush Beans 0 7.2

Yellow Bush Beans - 4.3

Lima Beans - 22.4

Average 8.5

Fruiting vegetables

Cucumber, slicing 0 -

Cucumber, pickle 2.1 0

Pepper 12.5 6.1

Zucchini 6.7 0

Summer Squash 0 -

Acorn Squash 4.1 -

Spaghetti Squash 7.4 -

Pumpkin 4.0 -

Tomato 16.0 9.1

Average 5.2

 These were classified as "cole" in Cappon (1987). a
B-31

B.1.2.2.3 Animal BTF

Parameter: BAj

Definition: The equilibrium concentration of a pollutant in an animal divided by the average daily

intake of the pollutant.

Units: day/kg DW

Livestock Default Value (day/kg DW) Distribution

 beef 0.02 U(0.0008,0.04)

 beef liver 0.05 U(0.02,0.1)

 dairy 0.02 U(0.003,0.09)

 pork 0.00013 U(0.00005,0.00026)

 poultry 0.11 U(0.094,0.13)

 eggs 0.11 U(0.094,0.13)

 lamb 0.09 U(0.009,0.3)

Technical Basis:

Biotransfer factors measure pollutant transfer from the environment to animal tissues and

products. They are defined as the ratio of pollutant concentration in animal tissue to the daily pollutant

intake of an animal. The biotransfer factors for mercury to cattle tissues were estimated based on data

found in Vreman et al. (1986), and biotransfer factors for mercury to lamb were based on data found in

van der Veen and Vreman (1986).

The data collected from Vreman et al. (1986) and van der Veen and Vreman (1986) are not from

single pollutant and single route ingestion studies; rather, the animals in these studies were generally

dosed with elevated levels of several metals in a single wafer. This is not the ideal set of studies for

assessing the transfer of mercury primarily from ingested grass and soil. These studies, however are

multiple dose and long-term experiments which should provide data more representative of the desired

equilibrium situation than a single, very large dose experiment.

In two experiments, Vreman et al. (1986) measured transfer of mercury from diet to tissues and

milk of dairy cattle. In the first experiment 12 lactating cows/group were placed on pasture in 2 groups

for 3 months. The control group was fed uncontaminated wafers and, based on mercury levels in the

pasture grass, were estimated to ingest 0.2 mg mercury/day. The exposed group received wafers treated

with a solution of mercury acetate, lead, cadmium and arsenic pentoxide; the daily mercury ingestion

rate for the exposed group was 1.7 mg/day. During the experiment mercury levels in milk were

measured. After three months on test, four cows/group were slaughtered, and mercury levels were

measured in liver, kidney and muscle samples. In the second study, lactating cows were kept indoors

and divided into 4 groups of 8 for up to 28 months. In addition to the control group, the diets of 3 other

groups were supplemented with the following: wafers containing the same metals (1.7 mg mercury/day),

sludge delivering dietary levels of 3.1 mg mercury/day, and sludge delivering dietary levels of 1.2 mg

mercury/day. Two cows from each group were slaughtered at study termination (except for the group

receiving 3.1 mg mercury/day from sludge in which only one cow was sacrificed). Mean milk mercury
B-32

concentrations in the groups were reported, and mercury levels in the slaughtered cows were measured in

liver, kidney and muscle samples. 

Shown in Table B-7 are data from Vreman et al. (1986) that are relevant to deriving beef and

dairy biotransfer factors. The tissue mercury concentrations presented are in wet weight.

Table B-7

Mercury Concentrations in Specific Beef Tissue

Media Per Test Group and Dose (from Vreman et al, 1986)

Test Group Dose Mercury in Milk Muscle Mercury in Liver

(mg mercury/day) (ug/Kg WW ) (ug/Kg WW ) (ug/Kg WW ) A

Mercury in

A A

Pasture Control 0.2 2.3 3 7

Pasture Treated 1.7 0.9 4 10

Indoor Control 0.2 <0.5 2 3

Indoor Wafer 1.7 0.6 2 26

Indoor 3.1 2.4 1 14

High-Level Sludge

Indoor 1.2 1.3 2 9

Low-Level Sludge

 Wet weight A

The data in Table B-7 can be easily converted into milk, beef and liver biotransfer factors by

converting the tissue concentrations to dry weight and dividing the tissue concentrations by the daily

intake of mercury (after converting the intake from mg/day to ug/day). The moisture content of the

above tissues are reported in Baes et al. (1984): 0.87 for whole milk, 0.615 for beef and 0.70 for liver. 

The biotransfer factors derived are shown in Table B-8.

Table B-8

Animal Biotransfer Factors Derived from Vreman et al. (1986)

Biotransfer Factor (day/kg DW)

Test Group Dairy Beef Beef Liver

Pasture Control 0.09 0.04 0.1

Pasture Treated 0.004 0.006 0.02

Indoor Control 0.02 0.03 0.05

Indoor Wafer 0.003 0.003 0.05

Indoor High-Level 0.006 0.0008 0.02

Sludge

Indoor Low-Level 0.008 0.004 0.03

Sludge
B-33

Using the number of animals sampled for each value in Table B-8, weighted averages for the

Dairy, Beef and Beef Liver Biotransfer factors can be derived. These are chosen as the default values,

with the ranges taken from Table B-8.

In a experiment very similar to Vreman et al. (1986), van der Veen and Vreman (1986) measured

transfer of mercury from diet to tissues of 10 week old fattening lambs. Two groups of 8 lambs were

placed on pasture for 3 months. The control group was fed uncontaminated feed concentrate and based

on mercury levels in the pasture grass and uncontaminated feed were estimated to ingest <0.02 mg

mercury/Kg dry feed-day. The exposed group received feed concentrate treated with a solution of

mercury acetate, lead, cadmium and arsenic pentoxide; the daily mercury ingestion rate for the exposed

group was 0.08 mg/Kg dry feed. Another four groups of 8 lambs were kept indoors and were fed hay

and feed concentrate. A control group was fed uncontaminated feed concentrate, and were estimated to

ingest <0.02 mg mercury/Kg dry feed-day. The 3 other groups were fed feed concentrate contaminated

with, respectively, a soluble solution of the metals, harbor sludge and sewage sludge. Daily mercury

ingestion rates for these groups ranged from 0.14 - 0.27 mg/Kg dry feed. After three months all lambs

were slaughtered and mercury levels were measured in liver, kidney, brain and muscle samples. 

Shown in Table B-9 are data from van der Veen and Vreman et al. (1986) and the biotransfer

factors derived from these data.

Table B-9

Mercury Concentrations and Resulting BTFs in Lamb Muscle Tissue

Per Test Group and Dose (from van der Veen and Vreman 1986)

Test Group mercury/Kg dry Feed Amount Muscle Muscle BTF

Dose (mg mercury in

feed-day) (Kg DW/day) (ug/Kg WW) Dry % (day/Kg DW)

A

Pasture Control <0.02 1.36 1 32.3 0.2

Pasture Treated 0.08 1.36 3 32.8 0.08

Indoor Control <0.02 1.3 2 30.5 0.3

Indoor Wafer 0.14 1.28 1 29.5 0.02

Indoor 0.27 1.39 1 30.5 0.009

High-Level Sludge

Indoor 0.17 1.38 1 29.1 0.02

Low-Level Sludge

 Biotransfer Factor (BTF) A

To calculate the biotransfer factors listed from the data in Table B-9, the daily mercury intake

was calculated from the mercury concentration in dry feed and daily intake of dry feed. van der Veen

and Vreman (1986) reported the dry weight fractions of the muscle samples, and the mercury

concentration in muscle was calculated on a dry weight basis. The biotransfer factor for each group of

lambs was then determined. The average over all groups was chosen as the default value, with the

ranges taken from Table B-9.

In U.S. EPA (1993b), uptake slopes were developed for a number of pollutants found in sludge

including mercury. For pork and poultry, U.S. EPA (1993b) reviewed the literature on concentrations of

metals in meat from studies in which livestock were fed known concentrations of the metals in feed. 

These values were used to obtain the default values (after converting wet-weight values to dry-weight).
B-34

B.1.2.2.4 Fish Bioaccumulation Factor

Parameter: Tier 3 Fish BAF (BAF )3

Tier 4 Fish BAF (BAF )4

Definition: The concentration of the methylmercury in fish divided by the concentration of total

dissolved methylmercury in water

Units: L/kg

Fish Type

Percentiles (L/kg)

Default 5th Median 95th

Value (L/kg) Percentile Percentile

Trophic Level 3 Fish 1.6E6

Trophic Level 4 Fish 6.8E6

Technical Basis:

For a discussion of these values, the reader is referred to Appendix D of this volume.

B.1.2.2.5 Plant Surface Loss Coefficient

Parameter: kp

Definition: A measure of the loss of contaminants deposited on plant surfaces over time as a result

of environmental processes.

Units: /yr

Chemical Default Value Distribution Range

(per year)

Hg 40.41 Log(40.41,17.39) 28.11 - 52.7 0

Hg 40.41 Log(40.41,17.39) 28.11-52.7 2+

Methylmercury 40.41 Log(40.41,17.39) 28.11-52.7

Technical Basis:

The values in the previous table were taken from Belcher and Travis (1989), although no

speciation was provided. The values for all species were assumed to be the same. The default value is

the mean of the lognormal distribution used in Belcher and Travis (1989). The choice of a lognormal

distribution was based on the work of Miller and Hoffman (1983).

B.1.2.2.6 Fraction of Wet Deposition Adhering
B-35

Parameter: Fw

Definition: Fraction of wet deposition that adheres to plant (i.e., is not washed off).

Units: unitless

Default Value Distribution Range

0.6 T(0.1,0.6,0.8) 0.1-0.8

Technical Basis:

The unitless parameter Fw represents the fraction of the pollutant in wet deposition that adheres

to the plant, is not washed off by precipitation and is used to estimate plant pollutant levels. A value of 1

is the most conservative; this implies that all of the pollutant which deposits onto the plant via wet

deposition will adhere to the plant. U.S. EPA (1990) originally used a value of 0.02, which significantly

diminishes the impact of this pathway. A more recent study by Hoffman et al. (1992) suggests an answer

between these extremes for both dissolved pollutants and suspended particulates in simulated rain drops.

Hoffman et al. (1992) attempted to quantify the amount of radiolabeled beryllium (Be) and

Iodine (I) as well as particles of sizes 3, 9, and 25 μm that adhered to three plant types (fescue, clover,

and a typical weeded plot). The radiolabeled pollutants were dissolved or suspended in water, which was

then showered upon the different types of vegetation to simulate precipitation. Two precipitation

intensities were modeled in the experiment: moderate (1-4 cm/hour) and high (4-12 cm/hour). Due to

experimental complications, total deposition and pollutant retention upon the vegetation were estimated

by the authors; these estimates were termed the interception fraction in the Hoffman report. For

example, in the experiment. Beryllium in the form of BeCl was dissolved in the water and then 2

showered upon the vegetation. For the moderate and high intensity precipitation events simulated, the

mean interception fractions were estimated to be 0.28 and 0.15, respectively.

The 1993 Addendum to the Indirection Exposure Methodology (U.S. EPA, 1993a) models

deposition and retention as the product of the interception fraction (Rp ) and Fw. In terms of the U.S. i

EPA model, the Hoffman report estimates the product Rp xFw. To obtain estimates for Fw, the values i

reported in Hoffman et al. (1992) were divided by the interception fraction for forage used in this

assessment (0.47; Baes et al., 1984). This provides estimates of 0.60 and 0.32 for Fw for the moderate

and high precipitation intensities, respectively (see Table B-10).

Table B-10 shows the Hoffman et al. (1992) estimates for the interception and adhesion of

dissolved pollutants and suspended particles in simulated moderate and high intensity precipitation. 

Based on the Hoffman estimates and the assumption of an interception fraction for forage of 0.47, the Fw

for the two pollutants and three particle sizes were estimated for the precipitation intensities studied, and

the means were calculated. No attempt has been made to adjust the final estimate for frequency of the

two precipitation intensities; however, since moderate precipitation intensities are more common, the

unadjusted means are probably an underestimate.
B-36

Table B-10

Values From Hoffman et al. (1992)

and the Values of Fw Estimated Using Those Values

Compound Moderate High for Moderate for High Fw Mean

Rpi

 x Fw for Rp x Fw for Fw Estimate Fw Estimate

Intensity Intensity Intensity Intensity

i

I 0.08 0.05 0.17 0.11 0.14

Beryllium 0.28 0.15 0.60 0.32 0.46

3 μm 0.30 0.24 0.64 0.51 0.58

9 μm 0.33 0.26 0.70 0.55 0.63

25 μm 0.37 0.31 0.79 0.66 0.72

The Fw estimated for beryllium was used as a surrogate for mercury. Be , as a cation, is 2+

assumed to behave in a manner similar to Hg during deposition. Because the moderate intensity is 2+

expected to be more common than the heavy intensity, an Fw of 0.60 is assumed to be a reasonable

estimate of Fw for divalent mercury. This value is higher than the range of 0.1-0.3 presented in McKone

and Ryan (1989). For beryllium, Hoffman noted the appearance of a strong attraction between the cation

and the plant surface, which was assumed to be negatively charged. Beryllium is believed to adsorb to

cation exchange sites in the leaf cuticle. Once dried on the plant surface, beryllium was not easily

removed by subsequent precipitation events. Divalent mercury is assumed to exhibit a similar behavior. 

The range of 0.1-0.8 was used to estimate the sensitivity of this parameter.

The adjusted Hoffman data indicate that the greater the intensity of the precipitation, the smaller

the Fw estimate for both dissolved pollutants and suspended particles. This is intuitively appealing given

the understanding of the physical process. Hoffman et al. (1992) noted that the intensity and amount of

rainfall had approximately the same impact on the estimated values. It should also be noted that the data

indicate that the value of Fw for pollutants that deposit as anions (e.g., I) may be significantly lower than

cations.

B.2 SCENARIO DEPENDENT PARAMETERS

This section of Appendix B describes the scenario dependent parameters used in the exposure

modeling for the Mercury Study Report to Congress. Scenario dependent parameters are variables

whose values are dependent on a particular site and may differ among various site-specific situations. 

For this assessment, three settings are being evaluated: (1) rural, (2) lacustrine, and (3) urban. The

receptors differ for each of these scenarios, as do the parameters. These scenario dependent parameters

may be either chemical independent or chemical dependent. The following sections present the chemical

independent and chemical dependent parameters used in this assessment.

Chemical independent parameters are variables that remain constant despite the specific

contaminant being evaluated. The chemical independent variables used in this assessment are described

in the following sections.

Site physical data include information such as the environmental setting, vegetative cover,

presence of surface water or groundwater, area of source and meteorological and climatological data. 

These parameters are described in the following sections.

B.2.1 Time of Concentration
B-37

Parameter: Tc

Definition: Number of years that the air concentration at the above level persists; equal to the

facility lifetime for calculations from anthropogenic sources

Units: yrs

Scenario Default Value(s) Distribution

(years)

All 30 None

Technical Basis:

The time of concentration is the same as the assumed facility lifetime. The generic value is 30

years. 

B.2.2 Average Air Temperature

Parameter: Ta

Definition: Average air temperature of microscale area

Units: C

Location (Years value is Distribution

Default Value

 based upon) (C)

Eastern Location 11.9 (25) U (8,16)

Western Location 13.4 (47) U (9,17)

Technical Basis:

The values for local airports are reported in the section "U.S. Local Climatological Data

Summaries for 288 Primary Stations throughout the U.S." on CDROM by WeatherDisc Associates

(1992). The distributions are arbitrary to explore the sensitivity of this parameter.

B.2.3 Watershed Area

Parameter: As

Definition: Area of contamination which drains into a water body

Units: Km2
B-38

Location Default Value (Km )2

Eastern Location 37.3

Western Location 37.3

Technical Basis:

 The values for the fish ingestion pathways are based on hypothetical watershed/waterbody

surface area ratio of 15 and a lake diameter of 1.78 km. This parameter was used only to calculate the

erosion and runoff load to the water body.

B.2.4 Average Annual Precipitation

Parameter: P

Definition: Average annual precipitation

Units: cm/yr

Location Default Value Distribution

(cm/yr)

Eastern Location 102 T(82,102,122)

Western Location 21 T(1,21,41)

Technical Basis:

All values are for local airports as reported in the section "U.S. Local Climatological Data

Summaries for 288 Primary Stations throughout the U.S." on CDRom by WeatherDisc Associates

(1992). These were considered the "best estimates" of a triangular distribution, with a range of 20 cm/yr

above and below the mode.

B.2.5 Average Annual Irrigation

Parameter: I

Definition: Average annual irrigation of plants

Units: cm/yr

Location Default Value Distribution

(cm/yr)

Eastern Location 12.5 U(0,25)

Western Location 57.5 U(50,65)

Technical Basis:
B-39

The ranges were approximated from Figure 4.25 in Baes et al. (1984). The tentative default

values are the midpoint of this range. It was assumed that both the farmer and home gardener will

irrigate the same amount if they are in the same area of the country (i.e., irrigation rate does not depend

on size of plot).

B.2.6 Average Annual Runoff

Parameter: Ro

Definition: Average annual runoff

Units: cm/yr

Location Default Value Distribution

(cm/yr)

Eastern Location 18 U(9,27)

Western Location 1 U(0,2)

Technical Basis:

The default values for the eastern location are from Geraghty et al. (1973). The total runoff

values given in that report include groundwater recharge, direct runoff, and shallow interflow. Following

U.S. EPA (1993c), this number was reduced by one-half to represent surface runoff. Because of the

difficulty of hydrologic modeling in the western location, the PRZM-2 model (Carsel, 1984) was used to

estimate the runoff for this area. The estimated value was 1 cm/yr. The distributions are arbitrary to

determine the sensitivity of this parameter.

B.2.7 Average Annual Evapotranspiration

Parameter: Ev

Definition: Average annual loss of water due to evaporation

Units: cm/yr

Location Default Value Distribution

(cm/yr)

Eastern Location 65 U(60,70)

Western Location 13 U(8,18)

Technical Basis:

For the eastern location, the ranges are based on estimates from isopleths given in Figure 4.24 in

Baes et al. (1984). The values presented there were estimated based on local data (average temperature

and precipitation) as well as the maximum possible sunshine for the area. The default value is the
B-40

midpoint of this range. For the western location, the model PRZM-2 was used to estimate the values

given previously.

B.2.8 Wind Speed

Parameter: W

Definition: Wind speed

Units: m/s

Location Default Value (m/s) Distribution

Eastern Location 4.3 U(1,7)

Western Location 4.0 U(1,7)

Technical Basis:

All values were collected for local airports and reported in the section "U.S. Local

Climatological Data Summaries for 288 Primary Stations throughout the U.S." on CDROM by

WeatherDisc Associates (1992). The primary use of this parameter is for estimating volatilization from

soil and water bodies. The distributions are arbitrary to explore the sensitivity of this parameter.

B.2.9 Soil Density

Parameter: BD

Definition: Soil density

Units: g/cm3

Location Default Value (g/cm ) Distribution Range 3

All Sites 1.4 Log(1.4,0.15) 0.93-1.84

Technical Basis:

The distribution is from Belcher and Travis (1989) and is based on a probability plot using data

from Hoffman and Baes (1979). There is little variation in the parameter, despite the fact that more than

200 data points were used. The default value is the mean of the distribution.

B.2.10 Mixing Depth in Watershed Area

Parameter: Zd

Definition: The depth that contaminants are incorporated into soil (no tillage)

Units: cm
B-41

Location Default Value (cm) Distribution

All Sites 1.0 U(0.5,5)

Technical Basis:

The default value is based on U.S. EPA (1990). The distribution is arbitrary to determine the

relative sensitivity of the parameter. 

B.2.11 Mixing Depth for Soil Tillage

Parameter: Ztill

Definition: The depth that contaminants are incorporated into tilled soil

Units: cm

Location Default Value (cm) Distribution

All Sites 20 U(10,30)

Technical Basis:

The default value is based on U.S. EPA (1990). The distribution is arbitrary to determine the

sensitivity of this parameter.

B.2.12 Soil Volumetric Water Content

Parameter: w

Definition: Amount of water that a given volume of soil can hold

Units: ml/cm3

Location Default Value Distribution

(ml/cm )3

Eastern Location 0.30 U(0.15,0.42)

Western Location 0.36 U(0.15,0.42)

Technical Basis:

Values for water content can range from 0.003 to 0.40 ml/cm depending on the type of soil 3

(Hoffman and Baes, 1979). Table B-11 demonstrates the dependency of values on the hydrologic soil

type. These values were derived from the PATRIOT software system (Imhoff et al., 1994), which can be

obtained from the Center for Exposure Assessment Modeling at the U.S. Environmental Protection

Agency, Athens, Georgia.
B-42

Table B-11

Water Content Per Soil Type

Soil Type Water Content

A 0.15

B 0.22

C 0.30

D 0.42

Representative soil types for both sites are shown in Table B-12 and were determined from

Carsel (1984). The soil types were used in conjunction with the previous table to determine the default

value for the soil water content, with the value for the western location being the average of the values

for types C and D.

Table B-12

Representative Soil Types For Each Site

Location Soil Type

Eastern Location C

Western Location C/D

The distribution for all sites is a uniform distribution over the range over all soil types. 
B-43

B.2.13 Soil Erosivity Factor

Parameter: R

Definition: Quantifies local rainfall's ability to cause erosion

Units: kg/km -yr 2

Location Default Value Distribution

(kg/km -yr) 2

Eastern Location 200 U(100,300)

Western Location 53 U(30,75)

Technical Basis:

The ranges were determined based on an isopleth map for the region in USDA (1978). The

upper and lower bounds were determined from this map by finding extremes within a 300-mile radius.

B.2.14 Soil Erodability Factor

Parameter: K

Definition: Quantifies soil's susceptibility to erosion

Units: tons/acre per unit of R

Location Default Value Distribution

(tons/acre)

Eastern Location 0.30 U(0.12,0.48)

Western Location 0.28 U(0.08,0.48)

Technical Basis:

Based on similar soil near the eastern location (loamy sand, loam, and silt loam) and using Table

A2-2 in U.S. EPA (1989a), a range of 0.12 to 0.48 was obtained. A similar analyses has not been

performed for the other sites, but the ranges listed in the previous table are apparently the maximum

range possible based on Table A2-2 in U.S. EPA (1989a); therefore, these ranges encompass all likely

values and can be used for sensitivity analyses. The default values are the midpoint of these ranges.

B.2.15 Topographic Factor

Parameter: LS

Definition: Provides a measure of the length and steepness of the land slope

Units: unitless
B-44

Location Default Value Distribution

Eastern Location 2.5 U(0.25,5)

Western Location 0.4 U(0.1,1.2)

Technical Basis:

The length and steepness of the land slope substantially affect the rate of soil erosion. Table A2-

3 in U.S. EPA (1989a) contains LS values for various slopes and slope lengths and was used in

conjunction with United States Geological Survey (USGS) maps to obtain the ranges given in the

previous table. A 1:24000 map was available for the humid/east/complex I site while only a 1:250000

USGS map was available for all other sites. The default value was chosen as representative of the most

common slope and length in the area.

B.2.16 Cover Management Factor

Parameter: C

Definition: The ratio of soil loss from land cropped under local conditions to the corresponding loss

from clean tilled fallow

Units: unitless

Location Default Value

Eastern Location 0.006

Western Location 0.1

Technical Basis:

The lower end of the range for areas having forests (0.001) is the lower of two values suggested

for woodlands in U.S. EPA (1988). For those areas lacking forests (i.e., western site), the value of 0.1

given for grass in U.S. EPA (1993b) was used.

For the watershed, it was decided to use a cover fraction representative of undisturbed grass or

forested areas, although high-end values were used. It was noted that the cover fraction can vary by

several orders of magnitude, depending on the land use type and soil type. Table B-13 shows estimates

of cover factor values for undisturbed forest land (Wischmeier and Smith, 1978).
Sdel  a WA b

L

B-45

Table B-13

Cover Factor Values of Undisturbed Forest Land

(from WQAM, 1985; original citation Wischmeier and Smith, 1978)

Percent of Area Covered by

Canopy of Trees and Percent of Area Covered by Cover Management

Undergrowth Duff (litter) at least 5 cm deep Factor Value

75-100 90-100 0.0001-0.001

45-70 75-85 0.002-0.004

20-40 40-70 0.003-0.009

Based on the above values and the objectives of this exposure assessment, it was decided that the

high-end values (of those above) would be appropriate; a nominal value of 0.006 (the midpoint of the

high-end range) was chosen.

B.2.17 Sediment Delivery Ratio to Water Body

Parameter: Sdel

Definition: Sediment delivery ratio to water body

Units: unitless

Location Default Value Distribution

Both Locations 0.2 U(0.14,0.23)

Technical Basis:

The sediment delivery ratio is the fraction of soil eroded from the watershed that reaches the

water body. It can be calculated based on the watershed surface area using an approach proposed by

Vanoni (1975):

where WA is watershed area in m , b is an empirical slope coefficient (-0.125) and a is an empirical L

2

intercept coefficient that varies with watershed area. A graph of the sediment delivery ratio as a function

of watershed area is given in the Water Quality Assessment Manual (Mills et al. 1985, pp. 177,178). 
EF  2 0.2 ln(Xe

/Aw)

B-46

B.2.18 Pollutant Enrichment Factor

Parameter: EF

Definition: The pollutant enrichment factor accounts for the fact that the lighter particles susceptible

to erosion tend to have a greater concentration of pollutants attached per mass than what

the average soil concentration may suggest.

Units: unitless

Location Default Value Distribution

Both Locations 2 U(1.5,2.6)

Technical Basis:

Enrichment refers to the fact that erosion favors the lighter soil particles, which have higher

surface area to volume ratios and are higher in organic matter content. Concentrations of hydrophobic

pollutants would be expected to be higher in eroded soil as compared to in-situ soil. While enrichment is

best ascertained with sampling or site-specific expertise, generally it has been assigned values in the

range of 1 to 5 for organic matter, phosphorus, and other soil-bound constituents of concern. 

Mullins et al. (1993, p.6-22) describe the following equation for calculating enrichment ratio for storm

events:

where X is the mass of soil eroded, in metric tons (1 metric ton = 1000 kg), and A is watershed area, in e w

hectares (1 hectare = 10,000 m ). Experience suggests that typical values range from 1.5 to 2.0, 2

reflecting erosion events from 0.08 to 1.0 tonnes per hectare. A very large erosion event of 20 tonnes per

hectare would have a predicted enrichment ratio of 2.6. The default value assumed here is 2.

B.2.19 Water Body Surface Area

Parameter: Aw

Definition: Water body surface area

Units: km2

Location Default Value Distribution

Both Locations 2.49 U(1.5,3)

Technical Basis:

For the purpose of this assessment, it was assumed that the hypothetical water body has a

diameter of 1.78 km, from which the default surface area is calculated.
B-47

B.2.20 Water Body Volume

Parameter: Vw

Definition: Water body volume

Units: m3

Location Default Value Distribution

Both Locations 1.24x10 Constant 7

Technical Basis:

For the purpose of this assessment, it was assumed that the hypothetical water body has a

diameter of 1.78 km and mean depth of 5 m. The corresponding volume is 1.24x10 m (using the 7 3

formula volume= r h). 2

B.2.21 Long-Term Dilution Flow

Parameter: Vfx

Definition: Long term dilution flow

Units: m /yr 3

Location Default Value (m /yr) 3

Eastern Location 1.44x107

Western Location 1.44x105

Technical Basis:

The long-term dilution flow can be estimated from Tables in Mills et al. (1985). The values in

in/yr are given in Table B-14. These were multiplied by the watershed area of 3.3x10 m to obtain the 7 2

default values.

Table B-14

Long-Term Dilution Flow In In/Yr

Location Value (in/yr)

Eastern Location 15

Western Location 0.15
Vs  g

18μ

pa  w d 2

p  3.15107  104

B-48

B.2.22 Abiotic Solids Deposition Velocity

The settling velocity for abiotic solids, v , is represented in the spreadsheet by variable SSDEP. s

The default value is set to 730 m/year (2 m/day).

Technical Basis:

Stoke’s equation can be used to calculate the terminal velocity of a particle settling through the

water column:

where V is Stoke’s velocit s y, m/year, for abiotic particles with diameter d , mm, and densit p pa y  , g/cm ; g 3

is the acceleration of gravity, 981 cm/sec ; μ is the absolute viscosity of water, 0.01 poise (g/cm-sec) at 2

20C; and w is the density of water, 1.0 g/cm at 20C. 3

Values of V for s particle density of 2.7 and diameters representative of clay, very fine silt, fine

silt, and medium silt are 30, 120, 730, and 2900 m/year, respectively. Settling velocities should be set at

or below Stoke’s velocity corresponding to the median suspended particle size, keeping in mind that

pollutants tend to sorb more to the smaller silts and clays than to large silt and sand particles. The value

chosen here is the calculated Stoke’s velocity for fine silt (d = 0.005 mm), and re p presents settling of fine

to medium silts.

B.2.23 Biotic Solids Production Rate

The internal production of biotic solids, L , in g/m -year, is set to a default value of 100. Bio

2

Technical Basis:

The production of phytoplankton carbon, in mg-C/m -day, has been linked to trophic status as 2

follows (Wetzel, 1975):

 

ultra-oligotrophic: < 50

oligotrophic: 50 - 300

mesotrophic: 25 - 1000

eutrophic: >1000

The phytoplankton production is multiplied by 2.7 to get mg-solids/m -day (Mills, et al., 1985, p. 62) and 2

by 0.365 to get g-solids/m -year (since the product of 2.7 and 0.365 is 0.99, the phytoplankton production 2

in mg-C/m -day is numerically equal to the solids production in g-solids/m -year). A value 2 2

representative of oligotrophic water bodies was chosen.

B.2.24 Phytoplankton Mortality

The phytoplankton mortality rate constant k is set to a value of 11 mort yr . -1

Technical Basis:

In calculating biotic solids, the net primary productivity incorporates phytoplankton growth and

respiration. Grazing transfers phytoplankton solids to zooplankton solids, both of which are considered

biotic solids. Settling removes phytoplankton solids from the water column, and is accounted for with a

specific settling velocity. Nonpredatory mortality accounts for phytoplankton loss processes that are not
D  p (1  n)

n  p  B

p  w

D  p (B  w)

p  w

B-49

accounted for in the grazing, respiration, and settling loss. This loss term includes processes such as

senescence, bacterial decomposition of cells, and stress-induced mortality, and is generally modeled

using a first-order rate constant (Bowie, et al., 1985). Values for rate constants cited in Bowie, et al,

1985 range from 0.003 to 0.17 day . A rate constant of 0.03 day (11 yr ) was chosen here as a -1 -1 -1

representative mid-range value. Applying a typical oligotrophic production rate of 100 g/m -year, a 2

settling rate constant of 73 m/yr, and a mortality rate constant of 11 yr to the shallow lake parameterized -1

here, biotic solids are predicted to be 0.75 mg/L. Assuming that about 75% are phytoplankton, this

corresponds with chlorophyll a levels averaging less than 7 g/L, which is reasonable for an oligotrophic

lake.

B.2.25 Biotic Solids Deposition Velocity

The settling velocity of biotic solids, v , is set to a default value of 73 m/year (0.2 m/day). sB

Technical Basis:

The settling of phytoplankton solids depends on several factors, including the density, size,

shape, and physiological state of the cells and the turbulence of the flow (Bowie, et al., 1985). 

Phytoplankton settling is a calibration parameter in most modeling exercises. Most values cited in Bowie

et al. (1985) fall between 0.02 and 2 m/day. A value of 0.2 m/day (73 m/year) is close to mid-range.

B.2.26 Surficial Sediment Particle Density and Dry Density (Sediment Concentration)

Upper benthic sediment particle density, p, is set to a default value of 1.5 g/cm . The 3

corresponding upper benthic sediment concentration, S , in mg/L, is set to a default value of 75,000. B

Technical Basis:

Benthic sediment concentration is equivalent to dry density (D, in g-sediment/cm ), which can 3

be calculated from the particle density (p, in g-sediment/cm -sediment) and sediment porosity (n, in 3 

cm -water /cm ): 3 3

Sediment porosity can be calculated from bulk density (B, in g-sediment+water/cm ), along with particle 3

density and the density of water (w , in g-water/cm -water): 3

Dry density, then, can be related to bulk density, particle density, and water density:

Typical particle densities in sediments range between 2.6 and 2.7 g/cm . At 20C, water density is close 3

to 1.0 g/cm . For these properties, a bulk density value of 1.6 g/cm corresponds to a dry density of 1.0 3 3

g/cm and a porosity of 0.65, which represents consolidated benthic sediment. An analysis of 1680 3

measured bulk densities in marine sediments exhibited a range from 1.25 to 1.8 g/cm and an average 3

particle density of 2.7 (Richards et al., 1974). Some waterbodies contain an upper unconsolidated layer

of sediment with bulk densities of 1.1 to 1.3, which correspond to porosities of 0.94 to 0.82 and dry
vrs  vs 

Sw

SB

B-50

densities of 0.16 to 0.48 g/cm . In this study, we represent pollutant storage in the unconsolidated upper 3

bed layer where the porosity is 0.95 and the particle density is 1.5 g/cm , which would represent a 3

significant percentage of biotic solids. The corresponding bulk density of this layer is 1.025 g/cm , and 3

the dry density is 0.075 g/cm .3

B.2.27 Upper Benthic Sediment Depth

Parameter: zb

Definition: Benthic sediment concentration

Units: m

Scenario Default Value (m) Distribution

Both Locations 0.02 U(0.01,0.03)

Technical Basis:

The total benthic sediment depth can vary from essentially zero in rocky streams to hundreds of

meters in oceans. In the lake environments being modeled here, the total benthic sediment depth usually

exceeds a few centimeters. Here we are modeling only the upper layer that is in partial contact with the

water column through physical mixing and bioturbation; thus, a shallow depth of 2 cm was chosen.

B.2.28 Resuspension Velocity

Resuspension velocity, v , is set to a default value of 0.0037 m/year. . rs

Technical Basis:

Resuspension from a sediment bed is caused by shear stress in the overlying water; the rate of

sediment resuspension for a given shear stress is determined by the shear strength of the upper sediment

layer. Shear stress in streams is caused primarily by flow velocity. In lakes, shear stress may be caused

by wind-driven residual currents and waves. In addition, biotic activity can cause some resuspension. 

For a small lake 5 meters deep with a short fetch (approximately 100 m), wave-induced resuspension

should be almost zero under most wind conditions. 

An empirical approach to parameterizing resuspension velocity is to calculate a value that would

be sufficient to maintain equilibrium suspended solids levels at a given level:

Given a settling velocity of 730 m/year, a benthic solids concentration of 75,000 mg/L, and an

equilibrium suspended solids concentration of 0.4 mg/L, the resuspension velocity is calculated to be

4×10 m/year. -3
B-51

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Vanoni, V.A. (1975). Sedimentation Engineering. American Society of Civil Engineers, New York, NY. 

pp. 460-463.

 

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 dat1. 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.

 

Watras, C.J., N.S. Bloom, S.A. Claas, K.A. Morrison, C.C. Gilmour, and S.R. Craig (1995). 

Methylmercury Production in the Anoxic Hypolimnion of a Dimictic Seepage Lake. Water, Air, and Soil

Pollution, 80:745-745.

 

WeatherDisc Associates (1992). U.S. Local Climatological Data Summaries for 288 Primary Stations

throughout the U.S., on CDROM.

 

Wetzel, R.G. (1975). Limnology. 1st. Edition. W.B. Saunders, Philadelphia, 743 pp.

 
B-59

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.

 

Wiessman, D., B.J. vanGoor, and 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.

 

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.

 

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APPENDIX C

DESCRIPTION OF MODEL PLANTS
C-i

TABLE OF CONTENTS

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

C.2 Relationship of Plant Process Conditions to Emissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-1

C.2.1 Municipal Waste Combustors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-3

C.2.1.1 Description of Source Category . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-3

C.2.1.2 Summary of Available Data on Emissions and Controls . . . . . . . . . . . . . . . . C-4

C.2.1.3 Selection of MWC Model Plant Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . C-6

C.2.2 Medical Waste Incinerators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-7

C.2.2.1 Description of Source Category . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-7

C.2.2.2 Summary of Available Data on Emissions and Controls . . . . . . . . . . . . . . . . C-7

C.2.2.3 Selection of MWI Model Plant Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . C-8

C.2.3 Utility Boilers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-8

C.2.3.1 Description of Source Category . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-8

C.2.3.2 Summary of Available Data on Emissions and Controls . . . . . . . . . . . . . . . . C-9

C.2.3.3 Selection of Model Plant Parameters .............................. C-11

C.2.4 Chlor-Alkali Production .............................................. C-11

C.2.4.1 Description of Source Category .................................. C-11

C.2.4.2 Summary of Available Data on Emissions and Control ................ C-12

C.2.4.3 Selection of Model Plant Parameters .............................. C-12

C.3 References ................................................................ C-13
C-ii

LIST OF TABLES

C-1 Process Parameters for Model Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-2

C-2 Scenarios for Sensitivity of Total Mercury Deposition Rate to Emissions Speciation 

for Municipal Waste Combustors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-7

C-3 Mercury Removal Efficiencies ................................................ C-10
C-iii

LIST OF FIGURES

C-1 Distribution of Mercury in EPA Method 29 Sampling Train, Camden County and Stanislaus

County Carbon Injection Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-5
C-1

C.1 Introduction

Model plants representing six source categories were developed to represent a range of mercury

emission parameters. The source categories were selected for the local impact analysis based on their

estimated annual mercury emissions or their potential to be localized point sources of concern. The

categories were municipal waste combustors (MWCs), medical waste incinerators (MWIs), utility

boilers, and chlor-alkali plants.

Descriptive characteristics, or parameters for each model plant were selected after evaluating the

characteristics of the entire source class. Important variables for mercury risk assessments include the

following: mercury emission rates, mercury speciation, and mercury transport/deposition rates. 

Important model plant parameters included stack height, stack diameter, stack volumetric flow rate, stack

gas temperature, plant capacity factor (relative average operating hours per year), stack concentration,

and mercury speciation.

Table C-1 summarizes the model plant parameters modeled in this analysis. These parameters

represent operating conditions associated with each source type's current mercury control. That is,

mercury emissions reductions being achieved by the air pollution control devices presently in place were

considered for each source category. These parameters are not meant to represent a "worst-case"

emissions scenario; they are believed to be representative of the full range of sources (of a given

category) across the United States. The amount of uncertainty surrounding the emission rates varies for

each model plant. This uncertainty is reflected in Chapter 8 -- Research Needs -- of this Volume.

C.2 Relationship of Plant Process Conditions to Emissions

Mercury speciation estimates for elemental mercury (Hg ) and divalent mercury (Hg ) were 0 2+

made using literature results of thermal-chemical modeling of mercury compounds in flue gas,

interpretation of bench and pilot scale combustor experiments and interpretation of available field test

results.

The amount and speciation of mercury emitted from high temperature process depends on the

composition of the feed material, amount of mercury in the process feed material, process operating

conditions, and process flue gas cleaning techniques (Lindqvist and Schager, 1990).

The inorganic mercury compounds that are considered important in high temperature processes

are HgS(s), HgO(s,g), HgCl (s,g), Hg Cl (s) and HgSO (s). Some organic compounds, such as 2 22 4

methylmercury, CH HgCH , and CH HgCl may also occur (Hall et al., 1991). Thermochemical 33 3

calculations indicate that at combustion temperatures above 700C nearly all mercury is vaporized to

form gaseous Hg . As the flue gas cools, changing equilibrium conditions favor oxidized forms of 0

mercury. When there are significant levels of HCl, Cl , O and SO in the flue gas, all of the above 22 2

oxidized inorganic forms of mercury will tend to occur. In flue gas from coal and peat combustion at

temperatures below 200C, the dominant equilibrium species are HgO and Hg . For combustion wastes 0

containing relatively high levels of chlorine, HgCl will be the dominant mercury compound (Hall et al., 2

1990; Hall 1991, Lindqvist and Schager, 1990).
C-2

Table C-1

Process Parameters for Model Plants

Model Plant Plant Size (% of (ft) Diameter Rate Percent Velocity (°F)

Capacity Stack Height Stack Hg Emission Speciation Exit Exit Temp.

year) (ft) (kg/yr) (Hg /Hg /Hg ) (m/sec) 0 2+ P

Large Municipal 2,250 90% 230 9.5 220 60/30/10 21.9 285

Waste tons/day

Combustors

Small Municipal 200 tons/day 90% 140 5 20 60/30/10 21.9 375

Waste

Combustors

Large 1500 lb/hr 88% 40 2.7 4.58 33/50/17 9.4 175

CommercialHMI capacity

Waste Incinerator (1000 lb/hr

(Wetscrubber) actual)

Large Hospital 1000 lb/hr 39% 40 2.3 23.9 2/73/25 16 1500

HMI Waste capacity

Incinerators (667 lb/hr

(Good actual)

Combustion)

Small Hospital 100 lb/hr 27% 40 0.9 1.34 2/73/27 10.4 1500

HMI Waste capacity

Incinerators (67 lb/hr

(1/4 actual)

sec.Combustion)

Large Hospital 1000 lb/hr 39% 40 2.3 0.84 33/50/17 9.0 175

HMI Waste capacity

Incinerators (667 lb/hr

(Wet Scrubber) actual)

Small Hospital 100 lb/hr 27% 40 0.9 0.05 33/50/17 5.6 175

HMI Waste capacity

Incinerators (Wet (67 lb/hr

Scrubber) actual)

Large Coal-fired 975 65% 732 27 230 50/30/20 31.1 273

Utility Boiler Megawatts

Medium 375 65% 465 18 90 50/30/20 26.7 275

Coal-fired Utility Megawatts

Boiler

Small Coal-fired 100 65% 266 12 10 50/30/20 6.6 295

Utility Boiler Megawatts

Medium Oil-fired 285 65% 290 14 2 50/30/20 20.7 322

Utility Boiler Megawatts

Chlor-alkali plant 300 tons 90% 10 0.5 380 70/30/0 0.1 Ambient

chlorine/day

 Hg = Elemental Mercury a 0

 Hg = Divalent Vapor Phase Mercury b 2+

 Hg = Particle-Bound Mercury c

P
C-3

Experimental evidence shows that the speciation of mercury is more complicated than indicated

by thermochemical equilibrium calculations. For example SO , soot, activated carbon, CaO and iron 2

may promote low temperature reactions that reduce oxidized forms of mercury to Hg (Hall et al., 1990; 0

Hall 1991). The presence of trace gases and particulate in flue gas promote mercury reactions and

provide surfaces for physical and chemical adsorption. Reaction kinetics can also be expected to play an

important role under the changing thermodynamics conditions that exist in high temperature flue gas

streams. Also, some gases such as HCl may not always be or reaction with mercury because of mixing

limitations.

While thermochemical chemical calculations provide information on likely mercury compounds

in flue gas, their presence and relative magnitude have not been confirmed with experimental data. This

shortcoming is primarily the result of difficulties in the sampling and analysis for mercury compounds. 

One study of mercury speciation on a pilot combustor and a full scale municipal waste incinerator found

that the flue gas contained mainly mercury chlorides, and that Hg mercury was present in insignificant 0

amounts (Metzger and Braun, 1987). Another experimental study of mercury sampling methods

concluded that total mercury and Hg can be adequately measured, that the results of the different 0

sampling methods tested for ionic mercury differed significantly and that additional efforts must be

devoted the development of mercury speciation methodologies (Lindqvist and Schager, 1990).

In a third study, conducted in the U.S., measurements with a sampling train designed to provide

information on mercury speciation tentatively indicated the presence of methylmercury in the exhaust of

coal and municipal waste combustors (Bloom, 1993). It was later reported that the methylmercury

reported in that study were the result of artifacts associated with the laboratory analytical procedures. 

Based on these studies it is concluded that the results of tests providing information on total mercury,

Hg and Hg are probably valid, but results of tests for methylmercury and other compounds must be 0 2+

considered suspect until sampling and analysis protocols for those compounds are validated.

The capture of mercury in flue gas cleaning devices depends on the mercury form [e.g,

speciation and phase (gas, liquid or solid)] and the control devices employed. Most metals condense to

form solid particles as the flue gases are cooled so that the metals can be collected as particulate matter

(PM). However, mercury specie such as Hg and HgCl are vapors at flue gas temperatures and are 0

2

difficult to control. Some mercury compounds such as HgCl are soluble in water and can be controlled 2

by wet scrubbers. Some specie such as HgCl2 can be adsorbed onto activated carbon and fly ash carbon

for subsequent collection as PM. Reagents can be used to produce mercury compounds that condense for

collection as PM. Reagents can also be used to produce soluble mercury compounds for scrubber

collection.

C.2.1 Municipal Waste Combustors

C.2.1.1 Description of Source Category

There are three major types of municipal waste combustors (MWC's): mass burn combustors,

refuse-derived fuel (RDF) combustors and modular combustors. There are number of sub-categories of

these three major types, plus some other types of MWCs, such as fluidized bed combustors. These other

types of MWCs constitute a minor fraction of the total MWC population.

As of January 1995, there were over 160 MWC plants in the U.S. with aggregate capacities

ranging from greater than 36 Mg/day (40 tons/day). Most large facilities contain from two to four mass

burn or refuse-derived fuel (RDF) combustors. Approximately 50 percent of the MWC capacity in the
C-4

U.S. now employ spray dryers and fabric filters (SD/FFs) or SD and electrostatic precipitators (SD/ESPs)

for emission control. The remaining large facilities do not have acid gas control equipment and

generally use good combustion practice and electrostatic precipitators (ESPS) for emissions control. 

Few U.S. facilities use wet scrubbers. A number of facilities are planning to use activated carbon to

control mercury emissions as mandated by siting permits. At least one MWC using activated carbon is

in commercial operation.

C.2.1.2 Summary of Available Data on Emissions and Controls

Uncontrolled mercury emissions from MWC's range from less than 200 ug/dscm to more than

1500 ug/dscm depending on the mercury content of wastes being burned. Average uncontrolled flue gas

concentrations in mass burn combustors are estimated to be in the range of 600 to 700 ug/dscm (U.S.

EPA, 1993; White et al., 1992; Nebel et al., 1992; White et al., 1993). Uncontrolled flue gas

concentrations in RDF combustors are somewhat lower since some mercury contained in batteries and

other items is removed in the process that produces RDF.

For combustion sources, the degree of mercury control depends on the flue gas composition, the

amount of fly ash carbon and the flue gas cleaning techniques employed. Well designed and operated

mass burn combustors have little carbon in their fly ash; even when equipped with SD/FFs or SD/ESPs

they exhibit mercury control levels that typically range from 0 to 50 percent (Nebel et al., 1992; White et

al.,1992). When powdered activated carbon is injected into the flue gas upstream of the spray dryer in

mass burn combustors, control levels exceeding 90 percent can be achieved for both SD/FF and SD/ESP

systems (Brown and Felsvang, 1992; White et al., 1992; Nebel et al., 1992; White et al., 1993). 

However, SD/ESP systems require from 2 to 3 times more carbon than SD/FF systems (Kilgroe et al.,

1993).

The RDF combustors contain a relatively high amounts of carbon in the fly ash and exhibit

control efficiencies of approximately 80 percent when equipped with SD/ESPs and above 90 percent

when equipped with SD/FFs (White et al., 1992). Injection of powdered activated carbon can also be

used to augment mercury control in RDF combustion facilities.

Little information is available concerning the performance of flue gas cleaning techniques for

controlling mercury in modular MWCs. It is expected that the performance of flue gas cleaning devices

on modular units will be similar to the performance of comparable equipment installed on conventional

mass burn combustors.

Electrostatic precipitators and wet scrubbers are commonly used to control emissions from

European MWCs. Some European plants have installed activated carbon beds downstream of the

primary air pollution control devices to act as polishing filters for control of metals, dioxins and acid

gases. The use of activated carbon filter beds in combination with conventional control equipment have

demonstrated mercury reductions exceeding 99 percent and mercury outlet concentrations of less than 1

ug/dscm (Hartenstein, 1993).

In conducting risk assessments, it is important to estimate the form and speciation of mercury

emitted in the flue gas. Several studies estimate the speciation of mercury in MWC flue gases. Metzger

and Braun (1987) estimate that nearly all mercury emitted from MWCs at flue gas cleaning temperatures

is in the form of mercury chlorides. Lindqvist and Scahger (1990) estimate that the speciation of

mercury emissions from European waste incinerators consist of 10 percent Hg , 85 percent Hg , and 5 0 2+

percent mercury associated with PM (Hg (PM)). Pacyna (1991) estimates that mercury emissions from

European waste incinerators consist of 10 percent Hg , 85 percent Hg , and 5 percent Hg(PM). 0 2+

There is currently no validated U. S. EPA method for determining the speciation of mercury in

stack gas. Information on the chemical behavior of mercury and the distribution of mercury in EPA's
C-5

multi-metal sampling train (Method 29) can be used to estimate the form and speciation of mercury in

MWC stack gas. Mercury found in the probe and filter can be assumed to have been vapor-phase

mercury adsorbed onto PM or be a solid-phase compound. Both phases are associated with PM. 

Mercuric chloride is soluble in water and mercury found in the KMnO /H SO impingers is probably 42 4

Hg . The distribution of multi-metal train samples collected during the activated carbon injection tests at 0

the Camden County MWC and Stanislaus County tests is shown in Figure C-1 (Nebel et al., 1992; White

et al., 1993).

Figure C-1

Distribution of Mercury in EPA Method 29 Sampling Train,

Camden County and Stanislaus County Carbon Injection Projects
C-6

Tests showing mercury stack concentrations of greater than 100 g/dscm represent either low

carbon injection feed rates or no carbon injection. For these tests, Hg ranged from 2 to 26 percent of 0

total mercury. As carbon injection rates and mercury capture increased, the percentage of Hg as a 0

fraction of total mercury increased. This implies that Hg is more easily captured by activated carbon 2+

than Hg . For mercury stack concentrations less than 50 g/dscm, the fraction of Hg ranged from 0 0

approximately 14 to 72 percent. The fraction of Hg(PM) was generally below detection limits for most

tests. It exceeded 10 percent for only one test and was below 10 percent for all other tests where it was

detected.

At low levels of control, the stack concentration of mercury is probably 15 to 30 percent Hg (v) 0

and the rest is Hg (v). At high levels of control, Hg (v) is selectively removed, increasing the relative 2+ 2+

concentration of Hg (v), and the relative concentration of Hg (v) may be 50 percent or higher. 0 0

For this analysis the speciation profiles for these course types were derived from Petersen et al.,

1995. The profiles are shown below for each model plant.

C.2.1.3 Selection of MWC Model Plant Parameters

In this analysis, the range of MWC plant conditions are represented by a large 2250 tons/day

model plant and a small 200 tons/day model plant. The large model plant consists of 3 conventional 750

tons/day mass burn combustors. The small MWC model plant was assumed to consist of two

conventional 100 tons/day mass burn combustors. 

Large MWC Model Plant

In October 1995, the U.S. EPA finalized emissions guidelines for existing MWCs and New

Source Performance Standards (NSPS) for new facilities. These require new and existing MWCs that

combust more than 39 tons of waste per day to reduce their mercury emissions to no more than 80

ug/dscm. To achieve this emission reduction it is likely that most facilities will use activated carbon

injection as a control measure. Activated carbon injection effectively captures Hg with the result that 2+

the percentage of Hg as a fraction of total mercury increases. In addition, the fraction of mercury 0

associated with particulate matter (Hg (PM)) also decreases. 

The model plants in this analysis reflected these requirements. In the analysis, the speciation

profile utilized for the large MWC model was 60 percent Hg , 30 percent Hg , and 10 percent Hg (see 0 2+ p 

Table C-1).

Small MWC Model Plant

The small MWC model plant was assumed to consist of two state-of-the-art 100 tons/day mass

burn combustors. The waste composition and behavior of mercury in the combustor was assumed to be

similar to that observed in large mass burn combustors. Since the 1995 emissions guidelines and NSPS

apply to the small MWCs as well, it is again likely that most facilities will use activated carbon injection

as a control measure. Stack emissions were therefore assumed to consist of mercury consisting of 60

percent Hg , 30 percent Hg , and 10 percent particulate mercury. 0 2+

Sensitivity to Emissions Speciation

For the two municipal waste combustors, two additional emissions speciations were utilized to

investigate the sensitivity of the deposition rates to the speciation. These scenarios are summarized in

Table C-2.

Table C-2

Scenarios for Sensitivity of Total Mercury Deposition Rate to Emissions Speciation 
C-7

for Municipal Waste Combustors

Mercury % Hg0 % Hg2 Vapor % Hg2 Particulate

Emissions

Scenario

LMWC_A 30 50 20

LMWC_B (used 60 30 10

in analyses)

LMWC_C 90 10 0

SMWC_A 30 50 20

SMWC_B (used 60 30 10

in analyses)

SMWC_C 90 10 0

C.2.2 Medical Waste Incinerators

C.2.2.1 Description of Source Category

Medical waste incinerators (MWIs) are small incinerators that burn from 1 ton/day (0.9 Mg/day)

to 60 tons/day (55 Mg/day) of infectious and noninfectious wastes. These wastes are generated by

various facilities including hospitals, clinics, medical and dental offices, veterinary clinics, nursing

homes, medical laboratories, medical and veterinary schools and research laboratories and funeral

homes.

Approximately 2,400 MWIs currently operate throughout the country; geographic distribution is

relatively even. Of these units, most are hospital incinerators (U.S. EPA, 1996).

The primary function of MWIs is to render the waste biologically innocuous and to reduce the

volume and mass of solids that must be landfilled (by combusting the organic material contained within

the waste). Currently, three major types of MWI operate in the United States: continuous, intermittent,

and batch. All three have two chambers that operate on a similar principle. Waste is fed to a primary

chamber, where it is heated and volatilized. The volatiles and combustion gases are then sent to a

secondary chamber, where combustion of the volatiles is completed by adding air and heat. All mercury

in the waste is assumed to be volatilized during the combustion process and emitted with the combustion

stack gases.

C.2.2.2 Summary of Available Data on Emissions and Controls

A number of air pollution control systems are used to control PM and acid gas emissions from

MWI stacks. Most of these systems fall into the general classes of either wet or dry systems. Wet

systems typically comprise a wet scrubber, designed for PM control (venturi scrubber or rotary

atomizing scrubber), in series with a packed-bed scrubber for acid gas removal and a high efficiency mist

elimination system. Most dry systems use a fabric filter for PM removal, but ESP's have been used on

some of the larger MWIs. All of these systems have limited success in controlling mercury emissions. 

Recent EPA studies indicate that sorbent injection/fabric filtration systems can achieve improved

mercury control by adding activated carbon to the sorbent material (U.S. EPA, 1994).
C-8

C.2.2.3 Selection of MWI Model Plant Parameters

To represent the MWI source category, three model plants were devised: a large commercial

MWI, a large hospital MWI (with good combustion), and a small hospital MWI (with 1/4 sec.

combustion). For the large and small hospital MWIs, two alternate scenarios were examined using the

assumption that wet scrubbers were utilized as a control technology (in accordance with the final

emissions guidelines for MWIs).

Large Commercial Facility

The large commercial MWI was assumed to have a capacity of 1500 lb/hr. This incinerator was

modeled at a mercury emissions rate of 4.58 kg/yr with a speciation profile consisting of 33 percent Hg ,0

50 percent Hg , and 17 percent particulate mercury. 2+

Hospital Facilities

Two hospital facilities were modeled: a large hospital MWI and a small hospital MWI. The

larger facility was assumed to perform with good combustion and a capacity of 1000 lb/hr. It was

modeled at a mercury emissions rate of 23.9 kg/yr with a speciation profile consisting of 2 percent Hg ,0

73 percent Hg , and 25 percent particulate mercury. The smaller hospital MWI, with 1/4 sec. 2+

combustion, was assumed to have a capacity of 100 lb/hr. It was modeled with a speciation profile

consisting of 2 percent Hg , 75 percent Hg , and 23 percent particulate mercury. 0 2+

In developing the alternate speciation profiles for the large and small hospital MWIs, it was

assumed that the wet scrubber would remove 94 percent of the Hg from the flue gas. The effect of this 2+

is that the large and small hospitals with wet scrubbers emit more elemental mercury relative to what

they emit when they are uncontrolled (see Table C-1).

C.2.3 Utility Boilers

C.2.3.1 Description of Source Category

Utility boilers are large boilers used by public and private utilities to generate electricity. There

are approximately 1800 utility boilers in the U.S. which burn coal, oil and natural gas. In 1990, utility

boilers consumed fossil fuel at an annual level of 21 x 10 British thermal units (Btu). About 80 percent 15

of this total energy consumption resulted from coal combustion, 6 percent from oil and petroleum fuels

and 14 percent from natural gas consumption. Ninety-five percent of the coal burned is bituminous and

subbituminous; lignite accounts for 4 percent. Mercury emission estimates were not calculated for

natural gas combustion because reliable test data necessary to calculate an emission factor do not exist. 

Given these factors, the indirect exposure analysis focused only on coal-fired units burning bituminous

coal and residual oil-fired units.
C-9

C.2.3.2 Summary of Available Data on Emissions and Controls

About 80 percent of coal-fired utility boilers use ESPs for PM control. Scrubbers (or flue gas

desulfurization units (FGDs)) are the most commonly used device for sulfur dioxide (SO ) control. 2

Spray dryer absorption (SDA), or dry scrubbing, followed by a PM control device may also be used. 

Mechanical collectors are used infrequently. Coal washing, which separates coal and impurities from

crushed and screened coal by differences in specific gravity, is done routinely to meet customer

specifications for heating value, ash and sulfur content. Advanced coal cleaning techniques may reduce

the concentration of mercury contained in the mineral and organic phases of the coal, but the reliability

and feasibility of these emerging techniques are unknown at this time.

Carbon filter beds are being used successfully in Europe for control of heavy metals, organic

compounds and acid gases (Hartenstein, 1993). Five full-scale applications of carbon beds are currently

in use for utilities, with future applications planned for hazardous waste incinerators and MWCs. 

Activated carbon injection has been used successfully in the U.S. for mercury removal from the stack gas

of MWCs and MWIs. This technology has been tested on a pilot-scale basis in the U.S.. Table C-3

summarizes the control efficiencies for various control technologies for utility boilers, based on pilotscale test data.
C-10

Table C-3

Mercury Removal Efficiencies

Control Technique (percent) (percent) Reference

Range of Removal Median Removal

Efficiency Efficiency

Carbon bed Unknown 99 Hartenstein (1993)

Fabric Filter + AC 76-99 98 Volume VIII, App. A.

(Low temp. + Low C injection rate)

Fabric Filter + AC 14-47 29 Volume VIII, App. A

(High temp. + Low C injection rate)

Fabric Filter + AC 95-99 98 Volume VIII, App. A

(Low temp. + High C injection rate)

Fabric Filter + AC 69-91 73 Volume VIII, App. A

High temp. + High C injection rate)

SDA/ESP + AC 75-91 86 Volume VIII, App. A

SDA/FF + AC 50- >99 NA Volume VIII, App. A

Fabric filter 0 - 51 29 Volume II

Scrubber (FGd) 18 - 84 23 Volume II

Dry scubber (SDa) 23 - 83 67 Volume II

ESP 0 - 22 15 Volume II

Mechanical collector 0 0 Volume II

Coal washing -200 - 64 21 (average Volume II

removal)

Advanced coal washing Unknown - Volume VII

Mercury emissions of mercury from utility boilers can vary depending on the mercury content of

the fuel and the control technique used. Based on emissions test data (as described in the mercury

emissions inventory, documented in a separate report), a mercury emission rate of 10 ug/dscm was

chosen to represent emissions from a coal-fired utility with PM control. Two ug/dscm was chosen as the

emission rate for mercury emissions from an uncontrolled residual oil-fired utility. This emission rate is

a worse-case estimate for an oil-fired plant. This high estimate was selected for the modeling because

the impacts from oil-fired boiler were expected to be very small even using the worst-case.

As discussed above, the chemical specie of mercury being emitted affects both the removal

efficiency of the control device and the deposition of mercury from the atmosphere. Based on Petersen

et al., 1995, it was assumed for the local impact analysis that the mercury emitted from the utility model

plants (both coal- and oil-fired) consisted of 50 percent HgO and 30 percent Hg , and 20 percent 2+

Hg(PM).
C-11

C.2.3.3 Selection of Model Plant Parameters

The source of data for selecting the model plant sizes was the Utility Data Institute (UDI)/Edison

Electric Institute (EEI) Power Statistics Database (1991 edition). This database provided information on

fuel use, boiler sizes and stack parameters. The database had information on 1708 units, of which 795

were bituminous coal-fired and about 225 were oil-fired. The remainder were primarily fired with

natural gas although there were some boilers burning lignite and anthracite coals. Given the

predominance of bituminous coal-fired units, those were the units chosen for the indirect exposure

analysis as well as one residual oil-fired unit.

The 795 coal-fired units were divided into 3 size classifications (by megawatt (MW)) according

to 33rd percentiles. The size classes had approximately the same number of units in each. The "large"

group which consisted of units greater than or equal to 575 MW had 262 units. The "medium" group

which consisted of units between 199 MW and 575 MW had 256 units. The "small" group consisting of

units greater than 25 MW but less than 200 MW had 277 units. The model plant parameters were chosen

by evaluating each group separately and taking the average value for each parameter from each group

(e.g., for representative MW, stack height and stack diameter).

Based on this analysis, three boiler sizes were chosen as the basis for the coal-fired model plants:

975 MW, 375 MW, and 100 MW. The same type of analysis for the oil-fired units led to the selection of

a 285 MW residual-oil fired unit as a representative model plant size for the oilfired units.

Coal-Fired Utility Model Plants

All of the coal-fired utility model plants had a capacity factor of 65 percent, and were equipped

with a cold-side ESP. The inlet mercury level (i.e., the amount of mercury entering the emission control

device) was assumed to be 10 /dscm (4.4 gr/million dscf). For emissions, it was assumed that no

mercury control across the ESP was achieved and that the mercury emissions were 10 ug/dscm (4.4

gr/million dscf).

Oil-Fired Utility Model Plant

The oil-fired utility model plant was a 285 MW boiler firing No. 6 fuel oil containing 1 percent

sulfur and 300 ppm chlorine. It was assumed to have a capacity factor of 65 percent, and was not

equipped with any particulate matter control device. The inlet mercury level associated with this model

plant was assumed to be 2 /dscm (1 gr/million dscf). It was assumed that no mercury control was

achieved and that the mercury emissions were 2 ug/dscm (1 gr/million dscf).

C.2.4 Chlor-Alkali Production

C.2.4.1 Description of Source Category

Chlor-alkali production using the mercury cell process, (which is the only chlor-alkali process

that uses mercury), accounted for 14.7 percent of all U.S. chlorine production in 1993 (Dungan, 1994). 

The three primary sources of mercury air emissions from chlor-alkali plants are the byproduct hydrogen

stream, the end box ventilation air and the cell room ventilation air. The byproduct hydrogen stream

from the decomposer is saturated with mercury vapor and may also contain fine droplets of liquid

mercury. The quantity of mercury emitted in the end box ventilation air depends on the degree of

mercury saturation and the volumetric flow rate of the air. The amount of mercury in the cell room

ventilation air is variable and comes from many sources, including end box sampling, removal of

mercury butter from end boxes, maintenance operations, mercury spills, equipment leaks and cell failure

(U.S. EPA, 1984).
C-12

C.2.4.2 Summary of Available Data on Emissions and Control

The most recent source of mercury emission data is from Clean Air Act section 114 survey

questionnaires of the chlor-alkali industry (as referenced in section 4.2.1 of Volume II of this report). 

The industry survey data were reported for 1991; total annual mercury emissions were 6.5 Mg (7.1 tons)

which includes 12 of 14 facilities that were operational in 1996. A previous report by the U.S. EPA was

used to develop plant- specific parameters (U.S. EPA, 1973).

The control techniques that are typically used to reduce the level of mercury in the hydrogen

streams and in the ventilation stream from the end boxes are the following: gas stream cooling, mist

eliminators, scrubbers and adsorption on activated carbon or molecular sieves. Mercury emissions from

the cell room air circulation are not subject to specific emission control measures. Concentrations are

maintained at acceptable worker exposure levels through good housekeeping practices and equipment

maintenance procedures (U.S. EPA, 1984).

Speciated emissions data for chlor-alkali plants are extremely limited. For this analysis was

assumed that the emitted mercury was in the vapor phase and consisted of 70 percent Hg and 30 percent 0

Hg (Peterson et al., 1995). 2+

C.2.4.3 Selection of Model Plant Parameters

For the indirect exposure analysis, one chlor-alkali model plant, which produces 273 Mg (300

tons) of chlorine per day, was devised. This model plant represented the mid-range size of chlor-alkali

plants in operation (U.S. EPA, 1984). The model plant had individual flow rates from the hydrogen and

end-box streams of 4,080 dscm/hr (144,000 dscf/hr) each at 21 percent 02 (combined to equal 8,160

dscm/hr [288,000 dscf/hr]) (U.S. EPA, 1973). A 90 percent capacity factor (operation for 7889 hr/yr)

was assumed.

The typical emissions control scenario for both the hydrogen and end-box streams was assumed

to consist of a heat exchanger to cool the effluent gas, followed by a knockout drum to separate the

condensed mercury from the hydrogen and end-box streams. A mercury level of 1,040 g/day (2.3 lb/day)

was assumed for the purpose of indirect exposure analysis to be consistent with the federally-mandated

mercury standard for the hydrogen and end-box streams at all chlor-alkali plants (U.S. EPA, 1984).
C-13

C.3 References

Bloom, Nicolas S., Eric M. Prestbo, Vesna L. Miklavcic, "Flue Gas Mercury Emissions and Speciation

from Fossil Fuel Combustion," Second International Conference on Managing Hazardous Air Pollutants,

Washington, D.C. July 1993.

Brna, T. G. Toxic Metal Emissions from MWCs and their Control, In Proceedings: 1991 International

Conference on Municipal Waste Combustion, Volume 3, EPA-600/R-92-209c (NTIS PB93-124196), pp

23-39, November 1992.

Brna, T.G., J.D. Kilgroe, and C.A. Miller, Reducing Mercury Emission from Municipal Waste

Combustion with Carbon Injection into Flue Gas, ECO World '92 Conference, Washington, DC, June

1992.

Brown, B. and K.S. Felsvang, Control of Mercury and Dioxin Emissions from United States and

European Municipal Solid Waste Incinerators by Spray Dryer Absorption Systems, In Proceedings, 1991

International Conference on Municipal Waste Combustion, Volume 3, EPA600/R-92-209c (NTIS PB93-

124196), pp 287-317, November 1992.

Buonicore, A.J., and W.T. Davis, , 1992, eds. Air Pollution Engineering Manual. Van Nostrand

Reinhold, New York. 1992.

Dungan, A., 1994. Chlorine Production Routes - U.S. and Canada. Facsimile to Ommen, Roy, Radian

Corporation. April 1994.

Edlund. H., 1993, Boliden Contech, telefax to K. Nebel, Radian Corporation. August 17, 1993.

Hall, B. Reactions of Mercury with Flue Gas Components, Statens Energiverk, National Energy

Administration, Sweden, (STEV-FBT-91-18), 1991.

Hall, B., 0. Lindqvist, and D. Ljungstrom, Mercury Chemistry in Simulated Flue Gases Related to Waste

Incineration Conditions, Environ. Sci. and Tech., 24 (1990), 108-111.

Hartenstein, H.U., 1993. Activated Carbon Filters for Flue Gas Polishing of MWI's. Presented at the

International Conference on Municipal Waste Combustion, Williamsburg, Virginia, March 1993.

Hartenstein, H. U. Activated Carbon Filters for Flue Gas Polishing of MWI'S, In Proceedings 1993

Conference on Municipal Waste Combustion, Air & Waste Management Association (VIP32),

Pittsburgh, PA, 1993, pp 87-105.

Kilgroe, J. D. et al., Camden County MWC Carbon Injection Test Results, Presented at the 1993

International Conference on Municipal Waste Combustion, Williamsburg, VA, March 30 to April 2,

1993.

Kiser, J. V. L. The IWSA Municipal Waste Combustion Directory: 1993 Update of U. S. Plants,

Integrated Waste Services Association, Washington, DC, 1993.

Lerner, B.J., Beco Engineering Company, 1992. Mercury Emissions Control in Medical Waste

Incineration. Presented at the 86th Annual Meeting, Air and Waste Management Association, Denver,

Colorado. June 1992.

Lindqvist, O. and P. Schager, Continuous Measurements of Mercury in Flue Gases from Waste

Incinerators and Combustion Plants, VDI Berichte, (NR. 838), 1990, pp. 401-421.
C-14

Metzger, M. and H. Braun, In-situ Mercury Speciation in Flue Gas by Liquid and Solid Sorption

Systems, Chemosphere, 16 (1987), 821-832.

Midwest Research Institute, 1992. Medical Waste Incinerators -Background Information for Proposed

Standards and Guidelines: Control Technology Performance Report for New and

Existing Facilities. Draft Report. July 1992.

Nebel, K.L. et al., Emission Test Report: OMSS Field Test on Carbon Injection for Mercury Control,

EPA-600/R-92-192 (NTIS PB93-105518), Air and Energy Engineering Research Laboratory, Research

Triangle Park, NC, September 1992.

Pacyna, J. M. Anthropogenic Mercury Emission in Europe, Water, Air and Soil Pollution, 56 (1991), 51-

61.

Peterson, G., A. Iverfeldt, 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. GKSS Research Centre, Institue of Physics, Max-Planck-Str. 1, D-21502,

Geesthacht, Germany.

TRC Environmental Corporation, 1992. Emission Characterization Program. Prepared for Copper

Range Company, White Pine, Michigan. October 15, 1992.

U.S. Environmental Protection Agency, 1996. Standards of Performance for New Stationary Sources

and Emissions Guidelines for Existing Sources: Medical Waste Incinerators, Proposed Rule, Federal

Register, June 20, 1996.

U.S. Environmental Protection Agency, 1994. Medical Waste Incinerators - Background Information for

Proposed Standards and Guidelines: Industry Profile Report for New and Existing Facilities. EPA453/R-94-042a. Office of Air Quality Planning and Standards, Research Triangle Park, NC. July 1994.

U.S. Environmental Protection Agency, 1993. Locating and Estimating Air Emissions form Sources of

Mercury and Mercury Compounds. EPA 454/R-93-023. Office of Air Quality Planning and Standards,

Research Triangle Park, NC. September 1993.

U.S. Environmental Protection Agency, 1992a. Medical Waste Incinerators - Background Paper for New

and Existing Facilities, Draft. U.S., Environmental Protection Agency, Research Triangle Park, NC. 

June 1992.

U.S. Environmental Protection Agency, 1992b. Medical Waste Incineration Emission Test Report --

Borgess Medical Center, Kalamazoo, Michigan. September 1992.

U.S. Environmental Protection Agency, 1991. Medical Waste Incineration Emission Test Report --

Morristown Memorial Hospital, Morristown, New Jersey. EMB Report 91-MWI-8. December 1991.

U.S. Environmental Protection Agency, 1989. Municipal Waste Combustors, Background Information

for Proposed Standards, Post-Combustion Technology Performance, Volume 3, EPA450/-3-89-27c

(NTIS PB90-154865), Research Triangle Park, NC, August 1989.

U.S. Environmental Protection Agency, 1988a. Compilation of Air Pollution Emission Factors, A,P-42,

Fourth Edition, Supplement B. Office of Air Quality Planning and Standards, Research Triangle Park,

NC.
C-15

U.S. Environmental Protection Agency, 1984. Review of National Emissions Standards for Mercury. 

EPA 450/-3-84-014. Office of Air Quality Planning and Standards, Research Triangle Park, NC. 

December 1984.

U.S. Environmental Protection Agency, 1974. Background Information for New Source Performance

Standards: Primary Copper, Zinc and Lead Smelters. EPA-450/2-74-002a. Office of Air Quality

Planning and Standards, Research Triangle Park, NC. October 1974.

U.S. Environmental Protection Agency, 1973. Control Techniques for Mercury Emissions from

Extraction and Chlor-Alkali Plants. Office of Air Quality Planning and Standards, Research Triangle

Park, NC. February 1973.

White, D. M. et al., Field Test of Carbon Injection for Mercury Control, Camden County Municipal

Waste Combustor, EPA-600/R-93-181 (NTIS PB94-101540), September 1993.

White, D.M., K.L. Nebel, and M.G. Johnston, Municipal Waste Combustors: A Survey of Mercury

Emissions and Applicable Control Technologies , In Proceedings, 1991 International Conference on

Municipal Waste Combustion, Volume 3, EPA-600/R-92-209c (NTIS PB93124196), pp 247-257,

November 1992.

White, D.M., et al., Parametric Evaluation of Activated Carbon Injection for Control of Mercury

Emissions from a Municipal Waste Combustor, Paper No. 92-40.06, 1992 Annual Meeting, Air & Waste

Management Association, Kansas City, MO, June 1992.
APPENDIX D

AQUATIC BIOACCUMULATION FACTOR DEVELOPMENT AND 

UNCERTAINTY ANALYSIS
D-i

TABLE OF CONTENTS

Page

D. AQUATIC BIOACCUMULATION FACTOR DEVELOPMENT AND UNCERTAINTY

ANALYSIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-1

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

D.2 Probabilistic Uncertainty Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-1

D.2.1 Sources of Uncertainty and Their Treatment . . . . . . . . . . . . . . . . . . . . . . . . . D-1

D.2.2 Probabilistic Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-2

D.2.3 Selection of Distributions for Input Variables . . . . . . . . . . . . . . . . . . . . . . . . D-2

D.2.4 Conventions for Representation of Distributions in This Appendix . . . . . . . D-3

D.3 Estimation of BAFs for Methylmercury . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-4

D.3.1 Data Quality Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-4

D.3.2 Estimation of BAFs For Mercury Using a Modified GLWQI Approach . . . D-5

D.3.2.1 BAFs Published in the Proposed Guidance . . . . . . . . . . . . . . . . . . . . D-5

D.3.2.2 Inputs and Assumptions for the Present Analysis . . . . . . . . . . . . . . . D-6

D.3.2.3 Results of Probabilistic Analysis of the Modified GLWQI 

Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-13

D.3.3 Estimation of a BAF for Methylmercury Using Measured Values for Trophic 4

Level 3 and a Field-Derived Food Chain Multiplier . . . . . . . . . . . . . . . . . . D-13

D.3.3.1 Bioaccumulation Factors Directly Estimated From Field Data -

Methylmercury in Forage Fish . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-13

D.3.3.2 Results of Probabilistic Simulation of BAF Using Field-Derived MD 4

MD 3 4 BAF and PPF Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-15

D.3.4 Specification of a Distribution for BAF Directly Derived from Field DataD-16 MD 4

D.3.4.1 Bioaccumulation Factors Directly Estimated From Field Data -

Methylmercury in Piscivorous Fish . . . . . . . . . . . . . . . . . . . . . . . . . D-16

D.3.5 Specification of BAF Distributions Based on Dissolved Total Mercury . . . D-18

D.3.5.1 Bioaccumulation Factors Directly Estimated From Field Data - Total

Dissolved Mercury in Forage Fish . . . . . . . . . . . . . . . . . . . . . . . . . D-18

D.3.5.2 Bioaccumulation Factors Directly Estimated From Field Data - Total

Dissolved Mercury in Piscivorous Fish . . . . . . . . . . . . . . . . . . . . . . D-20

D.3.6 Estimation of Methylmercury BAF for Trophic Level 4 from Distributions Based

on Total Mercury . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-21

D.3.6.1 Speciation of Mercury in the Water Column . . . . . . . . . . . . . . . . . . D-22

D.3.6.2 Simulation of a Methylmercury BAF 4 . . . . . . . . . . . . . . . . . . . . . . D-23

D.3.7 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-25

D.3.8 Selection of Bioaccumulation Factors for Trophic Levels 3 and 4 . . . . . . . D-26

D.3.9 Discussion of Uncertainty and Variability in the BAF . . . . . . . . . . . . . . . . . D-29

D.4 Other Variables Concerning the Form of Mercury in the Water Column . . . . . . . . . D-30

D.4.1 Fraction of Methylmercury in the Hypolimnion . . . . . . . . . . . . . . . . . . . . . D-30

D.4.2 Fraction of Dissolved Methylmercury in the Water Column . . . . . . . . . . . . D-30
D-ii

D.5 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-32

LIST OF TABLES AND FIGURES

Tables Page

D-1 Bioconcentration Factor for Methylmercury in Phytoplankton . . . . . . . . . . . . . . . . . . . . . . . . D-6

D-2 Predator-Prey Factor for Trophic Level 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-7

D-3 Predator-Prey Factor for Trophic Level 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-8

D-4 Predator-Prey Factor for Trophic Level 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-11

D-5 Statistics for BAFs Using the Modified GLWQI Methodology . . . . . . . . . . . . . . . . . . . . . . . D-13

D-6 Bioaccumulation Factor for Methylmercury in Trophic Level 3 Fish . . . . . . . . . . . . . . . . . . D-15

D-7 Statistics for Methylmercury BAF : Direct Estimate from Field Data MD 3 . . . . . . . . . . . . . . . D-15

D-8 Statistics for Methylmercury BAF Estimated from BAF x PPF MD 4 MD 3 4 . . . . . . . . . . . . . . . . . D-16

D-9 Bioaccumulation Factors for Methylmercury in Trophic Level 4 Fish . . . . . . . . . . . . . . . . . D-17

D-10 Statistics for Field-Derived BAF MD 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-18

D-11 Bioaccumulation Factor for Dissolved Total Mercury in Trophic Level 3 Fish . . . . . . . . . . D-19

D-12 Statistics for Field-Derived BAF TD 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-20

D-13 Bioaccumulation Factor for Dissolved Total Mercury in Trophic Level 4 Fish . . . . . . . . . . D-20

D-14 Statistics for Field-Derived BAF TD 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-21

D-15 Methylmercury as a Fraction of Total Dissolved Mercury in the Epilimnion . . . . . . . . . . . . D-22

D-16 Statistics for Methylmercury BAFs Estimated from Total Mercury BAFs . . . . . . . . . . . . . . D-24

D-17 Comparison of Lognormal and Normal (Directly Estimated) BAF Distributions MD . . . . . . D-26

D-18 Summary of Methylmercury Bioaccumulation Factors for Trophic Level 3 . . . . . . . . . . . . . D-27

D-19 Summary of Methylmercury Bioaccumulation Factors for Trophic Level 4 . . . . . . . . . . . . . D-28

D-20 Methylmercury as a Fraction of Total Dissolved Mercury in the Hypolimnion . . . . . . . . . . D-30

D-21 Dissolved Methylmercury as a Fraction of Total Methylmercury in the Epilimnion . . . . . . D-31

Figures Page

D-1 Input Distribution for PPF4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-12

D-2 Distribution for fmmw . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-23 E

D-3 BAF Distribution Based on BAF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-24 MD 4 TD 4

D-4 BAF Distribution Based on BAF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-25 MD 4 TD 3

D-5 BAF Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-28 MD 4

D-6 Distribution for fdmw . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-31
D-1

D. AQUATIC BIOACCUMULATION FACTOR DEVELOPMENT AND

UNCERTAINTY ANALYSIS

D.1 Introduction

This appendix describes efforts to estimate bioaccumulation factors (BAFs) for mercury in fish. 

Following the food chain structure described in Section 3.3 of Volume VI, BAFs are estimated for fish

that occupy trophic levels 3 and 4. Respectively, these values are referred to as BAF and BAF . In this 3 4

analysis, BAFs for mercury are derived for methylmercury and total mercury in filtered surface lake

water (dissolved species in the epilimnion, only). The BAF for methylmercury is defined as the

concentration of methylmercury in whole fish divided by the concentration of dissolved MeHg in the the

epilimnion. The BAF for total mercury is defined as the concentration of total mercury in whole fish

divided by the concentration of dissolved total mercury in the the epilimnion. As the primary focus is on

the methylmercury species, several methods are used to estimate methylmercury BAFs. The BAFs serve

as critical inputs to the calculation of wildlife criterion (WC) values and are also used to characterize

human exposure from consumption of contaminated fish. Special emphasis is placed on evaluating

uncertainties associated with these values.

Measures of mercury accumulation that are not treated in this appendix include: (1) BAFs for

trophic levels 1 and 2; (2) biota-sediment bioaccumulation factors (BSAFs) for trophic levels 1 - 4; and

(3) predator-prey factors (PPFs) for piscivorous wildlife (i.e., the concentration of mercury in piscivorous

birds and mammals divided by that of their prey). Readers interested in information concerning these

parameters, including summaries of field data from which estimated values can be derived, are referred

to section 2.3.1 of Volume VI. 

The wildlife criterion methodology used to derive BAFs in the Great Lakes Water Quality

Initiative (GLWQI) served as a starting point for the present analysis. The analysis was then extended to

include an examination of field data from which BAFs could be estimated directly. It was recognized

that considerable natural variability exists with respect of the accumulation of mercury in aquatic food

chains. An effort therefore, was made to incorporate this variability into the analysis. This was

accomplished by using a probabilistic simulation approach. The probabilistic simulation method is

described in Section D.2.2. 

Two other variables are also presented in this appendix that are not directly used in the

calculation of BAFs for methylmercury, but are necessary for other parts of Volume III. Those variables

are 1) the fraction of total methylmercury in the epilimnion that is dissolved and 2) the fraction of total

methylmercury in the hypolimnion that is dissolved. 

D.2 Probabilistic Uncertainty Analysis

D.2.1 Sources of Uncertainty and Their Treatment

Models of environmental phenomena must deal with two basic sources of uncertainty. The first

is uncertainty arising from natural variability, such as the size of individuals in a population or their

differences in xenobiotic metabolism. The second is uncertainty of the value of a parameter or variable

when it is known that there is a single value, such as measurement error in duplicate samples or lack of

knowledge of the true variance of a process. These two sources of uncertainty are formally referred to as

"variability" and "uncertainty", respectively. In the current analysis (this Appendix), variability and
D-2

uncertainty are aggregated in the probabilistic simulation output. That is, there is no attempt to fully

separate variability and uncertainty, except to the extent that variability of within-lake processes are used

to estimate the uncertainty in the means of the among-lake variables. Ideally, each parameter for each

input distribution should be distributions, themselves. The distributions for the input distribution

parameters are determined best by formal expert-elicitation techniques designed to assess subjective

knowledge (Morgan and Henrion, 1990). In this appendix the term "variability" is used in a general

context, comprising both variability and uncertainty. The term "variable" is used to describe model

variables treated as random variates, while the term "parameter" refers to a fixed parameter of the

mathematical form of a specific distribution.

In dealing with the issue of uncertainty, it is important to distinguish between qualitative and

quantitative models. A qualitative analysis can only make descriptive value-judgement statements about

the magnitude of the uncertainty or about the general confidence in the model output, such as "high",

"medium" or "low" and cannot address the statistical properties of the model. A quantitative analysis

allows for a more precise expression of the overall variability, is essential for comparing the results of

different models and is necessary to determine which of the input parameters have the greatest effect on

the model output. The latter procedure, called a sensitivity analysis, allows the model developers to

focus future efforts on the most important aspects of the model and gives the risk assessor or risk

manager valuable perspective for interpreting the results.

D.2.2 Probabilistic Simulation

There are a number of methods for expressing uncertainty in a quantitative fashion, the

discussion of which is beyond the scope of this document. The reader is referred to Morgan and Henrion

(1990) for a description of these techniques. Monte Carlo simulation is an approach that is commonly

used as a means of explicitly treating the variability in the input variables. The Monte Carlo method is

an iterative random sampling technique that mathematically combines prescribed distributions rather than

single numbers and allows for the propagation of variability in each input variable throughout the model;

that is, the variability in each input will be reflected in the output, which is also in the form of a

distribution. 

For models in which all variables are Gaussian ("normal") and all operations are addition or

subtraction, a mathematical ("analytic") solution exists and Monte Carlo simulation is unnecessary

(Aitchison and Brown, 1966). In the analytic solution, the output is a normal distribution with a mean

equal to the sum (or difference) of the means of the inputs and a standard deviation equal to the square

root of the sum of the variances of the input distributions. This solution is also applicable to lognormal

distributions when the mathematical operations are carried out on the logarithms of the inputs (commonly

referred to as "log space"). 

Calculation of a WC value requires that a single BAF value be established for each trophic level

contributing to the analysis. The same is true for estimating human exposure due to ingestion of

contaminated fish. It should be noted, however, that the probabilistic simulation approach yields both a

mean and distribution of BAF values. Although mean values were used for the calculation of WC values,

it is of interest to characterize the statistical variation about this estimate, since it may reflect actual

variation in natural systems. The possible significance of these distributions is discussed in Volume VI.
D-3

D.2.3 Selection of Distributions for Input Variables

Input distributions are based on an analysis of published data and one unpublished report (see

Sec. D.3.1., Data Quality Objectives). In general, an empirical distribution representing both the central

tendency and the extremes of the given data is determined. For analytical convenience the actual input

distributions are given in parametric form for this analysis. That is, formal analytic distributions that are

expressed by a mathematical equation (with specific defining parameters) are assigned to the inputs

rather than using the preliminary empirical forms (a collection of values). The choice of the

mathematical form for each of the variables is somewhat a matter of judgement. The particular set of

parameters chosen for each of the distributions is only one realization of a number of possible choices. 

Lognormal distributions are used almost exclusively in this analysis, partly for analytical

convenience but primarily because many of the factors contributing to the variability of the modeled

processes are likely to be multiplicative rather than additive. Given the complexity and nonlinear nature

of the underlying processes, the actual distribution of model variables is unlikely to follow any simple

closed-form mathematical relationship. The lognormal form is judged to be the most appropriate, but

values from the distribution far outside the range of observations do not necessarily have any real-world

significance. A general rule-of-thumb is that the empirical observations span a fractile range of 0.5/n to

(n - 0.5)/n, where n is the number of observations (Wilk and Gnanedesikan, 1968). That is, for a sample

size of 5, the extreme observations would be considered to be at the 0.1 and 0.9 fractiles (10th and 90th

percentiles). This approach allows for the possibility of more extreme values in larger samples and is

used in this analysis to construct all empirical distributions. In this analysis, the 90% confidence interval

(5th to 95th percentiles) is used extensively for comparisons of approaches. In many cases this interval is

somewhat outside the spread of the empirical distribution. Therefore, no strong significance should be

attached to these extreme values.

Distribution parameters are established 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). 

The only other distribution used in this analysis is the beta. The beta is used for all values that are

expressed as fractions of a whole, primarily because the beta has limits of 0 and 1 and can take on a wide

range of shapes. The beta has two shape parameters ( and ), which, in this analysis, are indirectly

determined by the method of moments. That is, the two equations for  and in terms of the moments

are solved simultaneously using the sample mean and standard deviation (Evans et al., 1993). 

The fundamental data unit in all analyses was defined as the water body (lake). That is, each

lake was treated as an independent unit. In some cases, when data from several lakes were aggregated in

the published study or to avoid over-representation of a specific geographical area, data points were

defined by the average across several lakes.

All simulations were performed on an Intel Pentium /130 CPU in S-PLUS (version 3.1) in the ® ®

MS Windows (version 3.11) environment. ®

D.2.4 Conventions for Representation of Distributions in This Appendix

Parameters of the lognormal distributions are expressed as the geometric mean (GM) and the

geometric standard deviation (GSD). The geometric mean 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 
D-4

standard deviation of the logarithms of the observations. The beta distribution is characterized by two

shape parameters ( and ).

All of the distributions developed in this analysis represent the variability of the mean value of a

specific variable across water bodies. As all of the variables in this analysis deal with estimates for a

randomly-selected single water body, independent of any other water body, the central-tendency estimate

will be given as the median . The arithmetic mean (average of the untransformed values) is given in

some cases for purposes of comparison. The arithmetic mean would be useful only in the case when fish

were consumed equally by a receptor from all of the lakes in the analysis. The arithmetic mean given in

tables showing the output of analytic (lognormal) simulations is the analytic mean of the specified

distribution, calculated directly from the distribution parameters (Evans et al., 1993). The spread of the

distribution will be expressed as the interquartile range (25th to 75th percentiles) and the 90% confidence

interval (5th to 95th percentiles). When possible, a distribution will be represented graphically by an

empirical density histogram overlaid with the fitted probability density function (PDF).

D.3 Estimation of BAFs for Methylmercury

BAF values for methylmercury in aquatic food chains were calculated in three different ways. 

The first method of calculation was a modification of the method used to support WC development in the

GLWQI (U.S. EPA, 1994) and involved multiplication of a weighted bioconcentration factor (BCF) by

appropriate food chain multipliers (FCMs). This method (modified GLWQI method) yielded BAFs for

trophic levels 3 and 4. The second method (direct method or field-derived method) involved direct

estimation of a BAF for trophic level 3 from field data, which was then multiplied by a predatory-prey

factor (PPF) for trophic level 4 to yield a BAF for trophic level 4. A BAF for trophic level 4 was also

estimated directly from field data. The results of all three analyses were then compared. Final BAF

values for trophic levels 3 and 4 were recommended for calculation of WC values and for estimation of

human exposure from consumption of contaminated fish.

More detailed approaches have been proposed to estimate BAFs for mercury in aquatic food

chains (e.g., the mercury cycling model (MCM), developed by the Electric Power Research Institute;

Hudson et al., 1994). Such approaches were considered to be inappropriate, however, in view of the

general lack of understanding of mercury accumulation and the broad geographical focus of this report. 

In particular, it was determined that models requiring calibration to specified food chains and lake water

characteristics were unlikely to yield information that could be applied with confidence to a different

food chain, or in a lake with different biogeochemical characteristics. Instead, the decision was made to

accept that considerable variability exists with respect to mercury concentrations in fish, and to employ

statistical methods that treat this variability quantitatively.

D.3.1 Data Quality Objectives

Preference was given to data published in the peer-reviewed literature. Data from one

unpublished report was also included (Suchanek et al., 1993), which was notable for its scope, level of

detail and quality; this study also provides the only data for a eutrophic water body in a more temperate

climate. Also, because of recent advances in analytical techniques for measurement of methylmercury in

natural waters, primary consideration was given to methylmercury values reported since 1990 (Bloom,

1989; Bloom, 1992). BAFs based on methylmercury from earlier literature tend to be lower due to

higher (biased) reported water concentrations. This restriction does not apply to reported values for total

mercury. An attempt was made to characterize the data as necessary to permit comparisons to be made

between studies. For example, mean values were estimated even if the original authors did not do so. 

When possible BAFs were reported in terms of both the age or size of the fish involved and the mercury

species measured in the fish. 
D-5

D.3.2 Estimation of BAFs For Mercury Using a Modified GLWQI Approach

D.3.2.1 BAFs Published in the Proposed Guidance

BAFs for mercury were estimated to support the development of wildlife criteria values in the

GLWQI (U.S. EPA, 1993). The approach and assumptions used in these calculations were subsequently

modified to incorporate new information (U.S. EPA, 1994) and to provide BAFs for methylmercury

rather than total mercury. The following is a description of the modified approach. 

BAFs were calculated in support of wildlife criterion development to relate methylmercury

concentrations in fish to dissolved methylmercury concentrations in water. The formula for the

calculation of the BAF for a given trophic level is given in equation 1.

BAF = BCF x FCM x fmmf (1) X MeHg X

where

BAF is the bioaccumulation factor for trophic level X (L/kg), X

BCF is the bioconcentration factor for methylmercury at trophic level 1, MeHg

FCM is the food-chain multiplier representing the cumulative biomagnification of mercury X

from trophic level 2 to trophic level X and

fmmf is the fraction of total mercury in fish flesh that is in the methylated form.

The formula for FCM is given in equation 2. 4

FCM = PPF x PPF x PPF (2) 4 234

where

PPF is the predator-prey factor at trophic level 2 representing the biomagnification of 2

mercury in zooplankton as a result of feeding on contaminated phytoplankton,

PPF is the predator-prey factor for forage fish feeding on contaminated zooplankton, and 3

PPF is the predator-prey factor piscivorous fish feeding on forage fish. 4

The estimated inputs for equations 1 and 2 are as follows:

The BCF for methylmercury in aquatic biota is 33,000.

The predator-prey factors (PPFs) for trophic levels 2, 3 and 4 are 6.3, 6.2 and 4.9, respectively. 

The FCM for trophic level 3 is 39 (6.3 x 6.2).

The FCM for trophic level 4 is 195 (6.3 x 6.2 x 5.0).

The estimated BAFs for trophic levels 3 and 4 are as follows:

BAF for trophic level 3 = 1.32 x 10 L/kg 6

BAF for trophic level 4 = 6.52 x 10 L/kg 6

Several key assumptions were made to permit estimation of these values. These assumptions

include the following:

1. The mercury concentration at trophic level 1 is determined by the extent to which mercury

bioconcentrates during an aqueous exposure,
D-6

2. BCFs and PPFs are lognormally distributed,

3. 100% of total mercury in fish exists as methylmercury,

4. Phytoplankton are 90% water by weight, 

5. Zooplankton are 80% water by weight. 

D.3.2.2 Inputs and Assumptions for the Present Analysis

Input variable distributions are presented individually along with the data from which they were

derived. 

Bioconcentration Factor for Methylmercury in Fish

Variable: BCFMHg

Definition: Total mercury concentration in fish divided by dissolved methylmercury in water

Point Estimate: 33,000 (unitless)

Distribution: lognormal (GM = 33,400; GSD = 4.888)

Technical Basis:

Bioconcentration factors (BCFs) for methylmercury in phytoplankton ranging from 3,400 to

133,000 were estimated from field data in the published literature and are presented in Table D-1. As

phytoplankton analysis is done on a dry weight basis an assumption was made that phytoplankton were

90% water (Watras and Bloom, 1992). This assumption applies to all of the values in Table D-1. The

lowest value of 3,400 estimated from Mason and Sullivan (1997) was a result of a very low percentage of 

methylmercury (2.3%) of the total mercury in the sample. The highest value of 133,000 for Little Rock

Lake was converted from a BCF for total methylmercury of 88,900 calculated from data in Watras and

Bloom (1992) by dividing by 0.667, the fraction of total methylmercury in the water column that is

dissolved. The latter value was calculated from mercury speciation data reported in Bloom et al. (1991)

for the same lake. The BCF from Hill et al. (1996) is an average of 4 locations in E. Fork Poplar Creek.

Table D-1

Bioconcentration Factor for Methylmercury in Phytoplankton

Value Reference

3,400 Lake Michigan Mason and Sullivan, 1997

38,400 E. Fork Poplar Cr., TN Hill et al., 1996

107,000 Onondaga Lake, NY Becker and Bigham, 1995

133,000 Little Rock Lake, WI Watras and Bloom, 1992

Predator-Prey Factor for Trophic Level 2 

Variable: PPF2

Definition: Factor by which methylmercury concentrations in trophic level 2 organisms

exceed those in trophic level 1 organisms upon which they prey.

Point Estimate: 6.3 (unitless)
D-7

Distribution: lognormal (GM = 6.34; GSD = 1.273)

Technical Basis:

Three studies were found in the literature for the estimation of PPF . The estimated values are 2

listed in Table D-2. Concentrations of methylmercury in trophic level 1 organisms (phytoplankton) were

estimated as for BCF . That is, phytoplankton were assumed to be 90% water. In addition, to convert MHg

from dry weight to wet weight, zooplankton were assumed to be 80% water (Watras and Bloom, 1992)

for all of the values. The factors contributing to the variability in PPFs were assumed to be

multiplicative and best represented by a lognormal distribution. 

Table D-2

Predator-Prey Factor for Trophic Level 2

Value Location Reference

5.0 Little Rock Lake, WI Watras and Bloom, 1992

6.3 L. Michigan Mason and Sullivan, 1997

8.1 Onondaga L., NY Becker and Bigham, 1995

Predator-Prey Factor for Trophic Level 3

Variable: PPF3

Definition: Factor by which methylmercury concentrations in trophic level 3 organisms

exceed those in trophic level 2 organisms upon which they prey.

Point Estimate: 6.2 (unitless)

Distribution: lognormal (GM = 6.22; GSD = 2.120)

Technical Basis:

Five studies were found in the literature for the estimation of PPF . The estimated values and 3

trophic level 3 species are listed in Table D-3. Concentrations of methylmercury in trophic level 2

organisms (zooplankton) were estimated as for PPF . That is, zooplankton were assumed to be 80% 2

water. Trophic level 3 species were reported only as “planktivorous fishes” in Plourde et al. (1997). The

factors contributing to the variability in PPFs were assumed to be multiplicative and best represented by

a lognormal distribution.
D-8

Table D-3

Predator-Prey Factor for Trophic Level 3

Value Trophic Level 3 Species Location Reference

2.6 gizzard shad, white perch Onondaga L., NY Becker and Bigham, 1995

3.2 yellow perch Little Rock Lake, WI Watras and Bloom, 1992

7.5 bloater L. Michigan Mason and Sullivan, 1997

9.6 “planktivorous fishes” LG2 Res., Quebec Plourde et al., 1997

15.5 “planktivorous fishes” 4 lake aggregate, Quebec Plourde et al., 1997

Predator-Prey Factor for Trophic Level 4

Variable: PPF4

Definition: Factor by which methylmercury concentrations in trophic level 4 organisms

exceed those in trophic level 3 organisms upon which they prey.

Point Estimate: 5.0 (unitless)

Distribution: lognormal (GM = 4.95; GSD = 1.464)

Technical Basis:

PPF s on a total mercury basis ranging from 2.4 to 7.5 were estimated for "standardized" lake 4

trout (60 cm) and rainbow smelt (15 cm) from nine Ontario lakes (MacCrimmon et al., 1983). Values

from very old (20+ years) lake trout from Tadenac Lake exceeded those of age 2+ year-old rainbow smelt

by a factor of 12.3. Levels in trout appeared to increase dramatically when they became large enough

(about 6 years old) to switch from a diet of benthic invertebrates to smelt. The overall average PPF was

5.0.

PPF s (total mercury basis) ranging from 1.2 to 8.4 were calculated from data reported by Wren 4

et al. (1983). These estimates were computed by dividing the average values for three predators

(smallmouth bass, northern pike, and lake trout) by average values for two forage fish (bluntnose minnow

and rainbow smelt). The maximum value was obtained by dividing the value for pike by that for

minnows. The overall average PPF was 3.0.

Data presented by Mathers and Johansen (1985) were used to calculate PPF s of 5.9 and 4.9 for 4

northern pike and walleye, respectively. Each value was calculated by dividing the total mercury residue

in eight-year-old fish by the weighted average total mercury content of the diet for each species. 

Corresponding values for four-year-old fish were 2.8 and 2.2, respectively. Values for both species

tended to increase with fish age and in some very old walleye exceeded 10. The overall average PPF was

4.0.

PPF s ranging from 2.7 to 4.5 were computed by dividing the average total mercury residues in 4

two predators (northern pike and brown trout) by average values for two forage fish (whitefish and smelt)

(Skurdal et al., 1985). The overall average PPF was 3.6.

A PPF of 5.22 was calculated from data presented by Cope et al. (1990). Data for age 5 walleye

were regressed against data from age 2 yellow perch. All fish were collected from northern Wisconsin
D-9

seepage lakes. The PPF was calculated from the regression equation for a perch containing 0.1

micrograms/g of total mercury. It should be noted that in this study mercury levels in muscle from

walleye were compared with whole-body levels in perch.

Residue data given by Jackson (1991) were used to calculate PPF s ranging from 5.5 to 14. 4

Estimates were computed for four lakes in Manitoba by dividing the average values for two predators

(northern pike and walleye) by average values for two forage fish (yellow perch and spottail shiners).

The average value was 9.8. 

A value of 6.8 was obtained by regressing total mercury data for 1 kg northern pike against that

from 8 to 10 cm yellow perch (Lindqvist, 1991). The data are from 43 lakes and are remarkable for the

consistency of the relationship. 

A PPF of 7.4 was calculated by comparing total mercury in 1 kg northern pike to total mercury 4

in 5 to 10 g yellow perch (Meili et al., 1991).

Average concentrations of total mercury were calculated for largemouth bass and silversides in

Clear Lake, California (Suchanek, 1993). The average PPF estimated from these data was 7.1. 4

A PPF of 2.75 was calculated from total mercury residues in lake trout (trophic level 4) and bloater 4

(trophic level 3) in Lake Michigan (Mason and Sullivan, 1997).

A PPF of 3.42 was estimated from data on total mercury levels in predatory fish (pescada and tucunaré) 4

and planktivores (mapará) in the Tucuruí reservoir in Brazil (Porvari, 1995). 

PPFs ranging from 3.52 to 6.72 were estimated for six Canadian Shield lakes from data on total mercury

levels reported by Bodaly et al. (1993). The trophic level 4 fish were northern pike and walleye. The

trophic level 3 fish were white sucker and cisco. The overall average BAF was 5.06.

A PPF of 5.63 was estimated from data on methylmercury levels for walleye and smallmouth bass 4

feeding on gizzard shad and bluegill in Onondaga Lake, NY (Becker and Bigham, 1995).

Summary:

Predator-prey factors reflecting the increase in mercury concentration between trophic levels 3

and 4 range from 1 to 20 considering one-to-one species comparisons only. The overall mean values

from any given water body is more in the range of 3 to 10, with a median of about 5. Interpretation of

predator-prey factors is complicated by the fact that piscivorous fish accumulate mercury throughout

their lifetime; thus, calculation of this value for a given species and system depends to a large extent

upon the age of the fish sampled. In addition, it is well known that the diet of a piscivorous fish changes

with age, tending in many cases to be dominated by invertebrates until fish reach a critical size that

allows them to prey efficiently upon small fish. In general, therefore, the mercury concentration in prey

of a piscivorous fish can be expected to increase with the age or size of the predator. Additional

considerations, including sexual reproduction, prey selection and availability and seasonal changes in

bioenergetics due to changes in water temperature are also likely to be important determinants of

bioaccumulation.

Overall, it can be shown that for most, if not all, piscivorous fish, mercury concentrations

increase in a nearly linear fashion with age. The increase appears to be linear for younger fish but may

be closer to exponential for older fish (Monteiro et al., 1991). The apparent exponential increase results

in ever increasing BAFs as fish get older and larger. An exponential increase hypothesis is supported by
D-10

regression analyses of the association of mercury concentration in fish and fish age or size in the

literature. For those regressions based on the log of the mercury concentrations (Wren and

MacCrimmon, 1986; Monteiro et al., 1991; Kim, 1995; Monteiro et al., 1996), correlation coefficients

averaged 0.84 (r = 0.7). When the regression was based on the untransformed mercury concentrations 2

(Grieb et al., 1990; Lange et al., 1994; Kim, 1995; Porvari, 1995; Hoover et al., 1997), the correlation

coefficients averaged 0.54 (r = 0.3). That is 70% of the variability in the logarithms of fish mercury 2

concentrations for a given species is explained by the variability in fish age/size, while only 30% of the

variability is explained on an untransformed mercury concentration basis. This result suggests a

multiplicative or exponential relationship. For a given system, therefore, it is feasible to extrapolate data

for small predators to larger members of the same species. The mix of species and size ranges in the diet

of a given piscivore would still be required in order to construct a receptor-specific distribution for more

accurate exposure estimation. Only one of the studies described in this section presented specific diets

for piscivorous species (Mathers and Johansen, 1985). 

The values defining the empirical distribution for PPF are given in Table D-4. The GM and 4

GSD of the values were 4.95 and 1.464, respectively. The arithmetic mean and standard deviation were

5.29 and 2.04, respectively. The lowest and highest values fall at the 4 and 96 percentiles, th th

respectively, in the empirical distribution. The median of the empirical distribution is 5.06. The factors

contributing to the variability in PPFs were assumed to be multiplicative and best represented by a

lognormal distribution. The arithmetic mean of the resulting distribution was 5.32. The empirical

distribution and PDF for PPF is shown in Figure D-1. 4
D-11

Table D-4

Predator-Prey Factor for Trophic Level 4

Value TL4 Species TL3 Species Location Reference

2.75 lake trout bloater L. Michigan Mason and Sullivan,

1997

3.0 L. Tadenac, Ontario, Canada Wren et al., 1983 smallmouth bass, bluntnose minnow,

lake trout, n. pike rainbow smelt

3.42 pescada, tucunaré mapará Tucuruí Res., Brazil Porvari, 1995

3.5 35 lake aggregate, upper MI Grieb et al., 1990 northern pike, yellow perch, 

largemouth bass white sucker

3.6 northern pike L. Tyrifjorden, Norway Skurdal et al., 1985 rainbow smelt,

whitefish

4.0 n. pike, walleye L. Simcoe, Canada specific weighted Mathers and

diets Johansen, 1985

5.0 lake trout (60 cm) rainbow smelt (15 9 lake aggregate, Ontario, MacCrimmon et al.,

cm) Canada 1983

5.06 n. pike, walleye white sucker, cisco Bodaly et al., 1993 average of 6 Canadian

Shield lakes

5.22 walleye (age 5) yellow perch (age 2) 10 lake aggregate, WI Cope et al., 1990

5.63 Onondaga L., NY smallmouth bass, gizzard shad, Becker and Bigham,

walleye bluegill 1995

6.8 northern pike (1 kg) y. perch (8-10 cm) 43 lake aggregate, Sweden Lindqvist, 1991

7.1 largemouth bass silversides Clear L., CA Suchanek et al., 1993

7.4 northern pike yellow perch 25 lake aggregate, Sweden Meili et al., 1991

9.8 n. pike, walleye Jackson, 1991 spottail shiner, 4 lake average, Manitoba,

yellow perch Canada
0 5 10 15

0.0 0.05 0.10 0.15 0.20

PPF

probability density

D-12

Figure D-1.

Input Distribution for PPF4

Mercury Speciation in Fish Tissues

Variable: fmmf

Definition: Fraction of total mercury in fish tissues existing as the methylated form.

Point Estimate: 1

Distribution: none

Technical Basis:

Bloom (1992) reported that virtually all (>95%) of the mercury in muscle tissues from

largemouth bass, yellow perch, northern pike and white suckers existed as methylmercury. In addition,

the author suggested that lower values reported in earlier literature are probably erroneous due to

inadequate sampling, analytical and reporting techniques. In particular, even with the best analytical

methods, recovery of methylmercury is, at best, 95%, while recovery of total Hg can be virtually 100%. 

That is, any value greater than 0.9 is probably indistinguishable from 1. Subsequent studies support a

value of >0.95 for this variable (Lasorsa and Allen-Gil, 1995; Akagi et al., 1995; Malm et al., 1995; Kim,

1995; Becker and Bigham, 1995). 

Summary:

Because of continued refinement in mercury analysis methods, more confidence should be placed

in recent values (see Bloom (1992) for a discussion of factors that can result in lower estimates than are

actually present). Collectively, the most recent values suggest that the percentage of mercury in fish that

exists as the methylated form exceeds 95%. Minor differences in reported values may be due the

different types of samples evaluated (e.g., whole fish vs. skin-on fillets vs. skin-off fillets), but are

unlikely to be important in the calculation of a BAF. fmmf was assigned a value of 1.0, as the

contribution of this variable to both the mean and variance of the BAF output was judged to be

negligible.
D-13

D.3.2.3 Results of Probabilistic Analysis of the Modified GLWQI Methodology

Selected statistics for the BAF and BAF distributions generated from the Modified GLWQI 3 4

methodology are given in Table D-5. There was a large variability in the simulation output for both

BAFs as evidenced by the large GSDs. The 90% confidence interval spans a 340-fold range for BAF3

and a 390-fold range for BAF . The large variances also resulted in a large difference between the 4

geometric and arithmetic means.

Table D-5

Statistics for BAFs Using the Modified GLWQI Methodology

BAF BAF 3 4

Statistic

Geometric Mean 1,320,000 6,520,000

Arithmetic Mean 6,350,000 33,700,000

GSD 5.884 6.127

Percentiles:

5th 71,500 331,000

25th 399,000 1,920,000

50th 1,320,000 6,520,000

75th 4,360,000 22,100,000

95th 24,400,000 129,000,000

 

D.3.3 Estimation of a BAF4 for Methylmercury Using Measured Values for Trophic Level 3 and a

Field-Derived Food Chain Multiplier

In this analysis a BAF for trophic level 4 was calculated using a field-derived BAF value for

trophic level 3 and published predator-prey factors for trophic level 4. The distribution of BAFs for

trophic level 3 is presented along with the data from which it was derived. The distribution of predatorprey factors for trophic level 4 was defined previously in Section D.3.2.2. All of the mercury in fish was

assumed to be methylmercury (see Section D.3.2.2). Here, as is the case throughout the document,

MD 3 MD 4 BAF and BAF refer to the concentrations of methylmercury in fish divided by the concentration of

methylmercury in filtered water.

D.3.3.1 Bioaccumulation Factors Directly Estimated From Field Data - Methylmercury in Forage Fish

Variable: BAF MD 3

Definition: Average methylmercury concentrations in planktivorous fish (trophic level 3)

divided by average dissolved methylmercury concentrations in water,

accumulated by all possible routes of exposure.

Point Estimate: 1.6 x 10 L/kg 6

Distribution: lognormal (GM = 1.58 x 10 ; GSD = 2.115) 6
D-14

Technical Basis:

A BAF of 667,000 was estimated for gizzard shad from Onondaga Lake (NY) from data MD 3

reported by Becker and Bigham (1995). The value was derived from an average concentration of 0.2 ppm

methylmercury in shad (age classes 3-4 years) and 0.3 ng/L (3 x 10 ppm) dissolved methylmercury in -7

the water. There is considerable uncertainty in this BAF estimate as water methylmercury concentrations

were determined for a single season (summer) only.

A BAF of 1.46 x 10 for yellow perch from Lake Iso Valkjarvi in Finland was calculated from MD 3

6

data reported by Rask and Verta (1995). Concentrations of total mercury in perch averaged 0.15 ppm

over a 3-year period (1990-1993). The mean concentration of dissolved methylmercury in the epilimnion

was 0.103 ng/L (1.03 x 10 ppm). The water concentrations were determined on a single day (8/24/93). -7

All measurements were taken from the control basin of Iso Valkjarvi as the lake was partitioned for

experimental liming to control pH. 

MD 3 BAF s for silversides and juvenile bass in Clear Lake (CA) were estimated from data tables

providing cross-seasonal measurements for two years (Suchanek et al., 1993). BAFs were estimated MD

from matched fish (total mercury) and water (dissolved surface methylmercury) concentrations for each

measurement for this period across 4 lake areas and up to 5 sampling locations for each area. The

average BAF for silversides was 1.13 x 10 and for juvenile bass was 1.93 x 10 , with an overall mean MD 3

6 6

of 1.53 x 10 .6

A BAF of 4.17 x 10 for bloater in Lake Michigan was calculated from data reported by MD 3

6

Mason and Sullivan (1997). A two-seasonal average (August/October, 1994) of methylmercury in the

surface waters was calculated to be 0.0104 ng/L or 1.04 x 10 ppm (all methylmercury assumed to be -8

monomethylmercury). The average concentration of methylmercury in bloater was calculated to be

0.0434 ppm (assuming that all the mercury was monomethylmercury). 

Summary:

Only two of the data points for BAF incorporated cross-seasonal variability for water MD 3

methylmercury concentrations and the fish species/age/size range was limited or undeterminable for most

of the studies. The geometric mean of 1.58 x 10 , however, was still considered to be the best (unbiased) 6

estimate of central tendency. The values defining the empirical distribution for BAF are given in MD 3

Table D-6. The GM and GSD of the values were 1.58 x 10 and 2.115, respectively. The arithmetic 6

mean and standard deviation were 1.95 x 10 and 1.52 x 10 , respectively. The lowest and highest values 6 6

fall at the 12.5 and 87.5 percentiles, respectively, in the empirical distribution. The median of the th th

empirical distribution is 1.50 x 10 . The factors contributing to the variability in BAFs were assumed to 6

be multiplicative and best represented by a lognormal distribution. Selected statistics for BAF are MD 3

given in Table D-7.
D-15

Table D-6

Bioaccumulation Factor for Methylmercury in Trophic Level 3 Fish

Value Species Location Reference

6.67 x 10 gizzard shad Onondaga L., NY Becker and Bigham, 1995 5

1.46 x 10 yellow perch Iso Valkjarvi, Finland Rask and Verta, 1995 6

1.53 x 10 silversides, juvenile bass Clear L., CA Suchanek et al., 1993 6

4.17 x 10 bloater L. Michigan Mason and Sullivan, 1997 6

Table D-7

Statistics for Methylmercury BAF : MD 3

Direct Estimate from Field Data

Statistic Value

Geometric Mean 1,580,000

Arithmetic Mean 2,090,000

GSD 2.115

Percentiles:

5th 461,000

25th 953,000

50th 1,580,000

75th 2,662,000

95th 5,410,000

D.3.3.2 Results of Probabilistic Simulation of BAF Using Field-Derived BAF and PPF Estimates MD 4 MD 3 4

The formula for the calculation of BAF by this method is given in equation 3. MD 4

MD 4 MD 3 4 BAF = BAF x PPF (3)

where

MD 3 MD BAF is the field-derived distribution for the BAF at trophic level 3

PPF is the predator-prey factor at trophic level 4 representing the biomagnification of mercury in 4

piscivorous fish feeding on forage fish

Selected percentiles from the distribution of BAFs calculated by the analytic solution of

Equation 3 using lognormal distributions for field-derived BAF (§D.3.3.1) and PPF (§D.3.2.2) MD 3 4

estimates are given in Table D-8. 
D-16

Table D-8

Statistics for Methylmercury BAF MD 4

 Estimated from BAF x PPF MD 3 4

Statistic Value

Geometric Mean 4,820,000

Arithmetic Mean 11,100,000

GSD 2.317

Percentiles:

5th 1,960,000

25th 4,440,000

50th 7,820,000

75th 1,380,000

95th 31,100,000

D.3.4 Specification of a Distribution for MD 4 BAF Directly Derived from Field Data

D.3.4.1 Bioaccumulation Factors Directly Estimated From Field Data - Methylmercury in Piscivorous

Fish

Variable: BAF MD 4

Definition: Average methylmercury concentrations in piscivorous fish (trophic level 4)

divided by average dissolved methylmercury concentrations in water,

accumulated by all possible routes of exposure.

Point Estimate: 6.8 x 10 L/kg 6

Distribution: lognormal (GM = 6.81 x 10 ; GSD = 1.564) 6

Technical Basis:

A BAF of 4 x 10 was reported by Becker and Bigham (1995) for methylmercury in MD 4

6

piscivores from Onondaga Lake (NY). The piscivorous species included smallmouth bass (age classes 6-

9 years) and walleye (age classes not given). There is considerable uncertainty in this BAF estimate as

water methylmercury concentrations were determined for a single season (summer) only.

A BAF of 5.86 x 10 was estimated for walleye and northern pike from four lakes in the MD 4

6

Manitoba Province of Canada from data reported by Jackson (1991). Fish and water methylmercury

concentrations were estimated from Figures 2 and 3 (in Jackson, 1991). The BAF is an average of BAFs

ranging from 4.0 x 10 to 7.0 x 10 across the four lakes. An average was used to avoid over 6 6

representation of this one area outside the continental U.S. Mean lengths, only, were reported for

walleye (37-46 cm) and northern pike (55-71 cm). Fish sample collection was in 1980 and dissolved

methylmercury concentrations were determined in June, 1981. As a result, this BAF estimate should MD

be considered to be a minimum value (downward bias arising from pre-1990 analytical methods) and

highly uncertain (no cross-seasonal average).

A BAF for largemouth bass in Clear Lake (CA) was estimated from data reported by MD 4

Suchanek et al. (1993). In this case, fish mercury concentrations (total mercury) were estimated from

figures showing fish mercury levels versus fish weight for each of three lake areas. Mercury
D-17

concentrations were averaged for all fish of 450 grams or higher, ranging up to 4200 grams, suggesting

that a broad range of year classes was represented. The average fish mercury levels in each lake area

were divided by average dissolved methylmercury concentrations in the surface waters for the same area.

The water methylmercury concentrations, in each case, were averaged across seasons over a two year

period. 

A BAF of 1.14 x 10 for lake trout in Lake Michigan was calculated from data reported by MD 3

7

Mason and Sullivan (1997). A two-seasonal average (August/October, 1994) of methylmercury in the

surface waters was calculated to be 0.0104 ng/L or 1.04 x 10 ppm (all methylmercury assumed to be -8

monomethylmercury). The average concentration of methylmercury in lake trout was calculated to be

0.1186 ppm (assuming that all the mercury was monomethylmercury). 

Summary:

Only two of the data points for BAF incorporated cross-seasonal variability for water MD 4

methylmercury concentrations and the fish species/age/size range was limited or undeterminable for most

of the studies. The geometric mean of 6.81 x 10 , however, was still considered to be the best (unbiased) 6

estimate of central tendency. The values defining the empirical distribution for BAF are given in MD 4

Table D-9. The GM and GSD of the values were 6.81 x 10 and 1.564, respectively. The arithmetic mean 6

and standard deviation were 7.33 x 10 and 3.18 x 10 , respectively. The lowest and highest values fall at 6 6

the 12.5 and 87.5 percentiles, respectively, in the empirical distribution. The median of the empirical th th

distribution is 6.96 x 10 . The factors contributing to the variability in BAFs were assumed to be 6

multiplicative and best represented by a lognormal distribution. Selected statistics for BAF are given MD 4

in Table D-10.

Table D-9

Bioaccumulation Factors for Methylmercury in Trophic Level 4 Fish

Value Species Location Reference

4.00 x 10 Onondaga L., NY Becker and Bigham, 1995 6 smallmouth bass,

walleye

5.86 x 10 northern pike, walleye 4 lake average, Manitoba, Can. Jackson, 1991 6

8.06 x 10 largemouth bass Clear L., CA Suchanek et al., 1993 6

1.14 x 10 lake trout L. Michigan Mason and Sullivan, 1997 7
D-18

Table D-10

Statistics for Field-Derived BAF MD 4

Statistic Value

Geometric Mean 6,810,000

Arithmetic Mean 7,530,000

GSD 1.564

Percentiles:

5th 3,260,000

25th 5,040,000

50th 6,810,000

75th 9,210,000

95th 14,200,000

D.3.5 Specification of BAF Distributions Based on Dissolved Total Mercury

D.3.5.1 Bioaccumulation Factors Directly Estimated From Field Data - Total Dissolved Mercury in

Forage Fish

Variable: BAF TD 3

Definition: Average total mercury concentrations in forage fish (trophic level 3) divided by

average dissolved total mercury concentrations in water, accumulated by all

possible routes of exposure.

Point Estimate: 1.2 x 10 L/kg 5

Distribution: lognormal (GM = 1.19 x 10 ; GSD = 1.531) 5

Technical Basis:

BAF s for silversides and juvenile bass in Clear Lake (CA) were estimated from data tables TD 3

providing cross-seasonal measurements for two years (Suchanek et al., 1993). BAFs were estimated MD

from matched fish (total mercury) and water (dissolved surface total mercury) concentrations for each

measurement for this period across two lake areas and up to five sampling locations for each area. The

Oaks Arm total mercury measurements were excluded from the analysis as this area of the lake was the

site of the point-source for mercury contamination (chlor-alkali plant); total mercury levels were

anomalously high in this area (5 times that of other lake areas) and were judged not to be representative. 

The combined average BAF for silversides and juvenile bass was 76,000. TD 3

A BAF of 76,000 was estimated for gizzard shad from Onondaga Lake (NY) from data TD 3

reported by Becker and Bigham (1995) and Henry et al. (1995). The value was derived from an average

concentration of 0.2 ppm mercury in shad (age classes 3-4 years; Becker and Bigham, 1995) and 2.625

ng/L dissolved total mercury in the water (Henry et al., 1995). The water concentration represents an

average across three seasons (spring, summer and fall) for an entire year.

A BAF of 113,000 for yellow perch from Lake Iso Valkjarvi (control basin) in Finland was TD 3

calculated from data reported by Rask and Verta (1995). Concentrations of total mercury in perch
D-19

averaged 0.15 ppm over a 3-year period (1990-1993). The mean concentration of dissolved total mercury

in the epilimnion was 1.33 ng/L. The water concentrations were based on three observations on a single

day (8/24/93). 

A BAF of 212,000 for bloater in Lake Michigan was calculated from data reported by Mason TD 3

and Sullivan (1997). A two-year (1994, 1995) three-season (spring, summer, fall) average of dissolved

total mercury in the surface waters was calculated to be 0.2 ng/L. The average concentration of total

mercury in bloater was calculated to be 0.0434 ppm. 

Summary:

All but one of the data points for BAF incorporated cross-seasonal variability for water TD 3

mercury concentrations but the fish species/age/size ranges were somewhat limited. Overall, the data,

although meager, were somewhat more representative of the definition for this variable than for BAF . MD 3

The values defining the empirical distribution for BAF are given in Table D-11. The GM and GSD of MD 4

the values were 1.19 x 10 and 1.531, respectively. The arithmetic mean and standard deviation were 5

1.28 x 10 and 5.87 x 10 , respectively. The lowest and highest values fall at the 12.5 and 87.5 5 4 th th

percentiles, respectively, in the empirical distribution. The median of the empirical distribution is 1.11 x

10 . The factors contributing to the variability in BAFs were assumed to be multiplicative and best 5

represented by a lognormal distribution. Selected statistics for BAF are given in Table D-12. TD 3

Table D-11

Bioaccumulation Factor for Dissolved Total Mercury in Trophic Level 3 Fish

Value Species Location Reference

7.60 x 10 gizzard shad Onondaga L., NY 4 Becker and Bigham, 1995;

Henry et al., 1995

1.09 x 10 silversides, juvenile bass Clear L., CA Suchanek et al., 1993 5

1.13 x 10 yellow perch Iso Valkjarvi, Finland Rask and Verta, 1995 5

2.12 x 10 bloater L. Michigan Mason and Sullivan, 1997 5
D-20

Table D-12

Statistics for Field-Derived BAF TD 3

Statistic Value

Geometric Mean 119,000

Arithmetic Mean 130,000

GSD 1.531

Percentiles:

5th 59,100

25th 89,300

50th 119,000

75th 159,000

95th 240,000

D.3.5.2 Bioaccumulation Factors Directly Estimated From Field Data - Total Dissolved Mercury in

Piscivorous Fish

Variable: BAF TD 4

Definition: Average total mercury concentrations in Piscivorous fish (trophic level 4)

divided by average dissolved total mercury concentrations in water, accumulated

by all possible routes of exposure.

Point Estimate: 5.0 x 10 L/kg 5

Distribution: lognormal (GM = 4.96 x 10 ; GSD = 1.181) 5

Technical Basis:

A BAF of 4.19 x 10 was estimated for piscivores (smallmouth bass and walleye) in Onondaga TD 4

5

Lake (NY) from data reported by Becker and Bigham (1995) and Henry et al. (1995). The value was

derived from an average concentration of 1.1 ppm mercury in piscivores (Becker and Bigham, 1995) and

2.625 ng/L dissolved total mercury in the water (Henry et al., 1995; see D.3.5.1). 

A BAF of 5.0 x 10 for largemouth bass in Clear Lake (CA) was estimated from data reported TD 4

5

by Suchanek et al. (1993). Estimation of fish mercury concentrations was described in D.3.4.1,

previously. The average fish mercury levels in each of two lake areas were divided by average dissolved

total mercury concentrations in the surface waters (see D.3.5.1) for the same area.

A BAF of 5.84 x 10 for lake trout in Lake Michigan was calculated from data reported by TD 4

5

Mason and Sullivan (1997). A two-year (1994, 1995) three-season (spring, summer, fall) average of

dissolved total mercury in the surface waters was calculated to be 0.2 ng/L. The average concentration of

total mercury in trout was calculated to be 0.1186 ppm. 

Summary:

Only three studies were available for the estimation of BAF . All of the data points, however, TD 4

incorporated cross-seasonal variability for water mercury concentrations. The fish species/age/size range

was undertminable for one study (Mason and Sullivan, 1997) but fairly representative for the other two. 
D-21

Overall, the data, although meager, were more representative of the definition for this variable than for

MD 4 BAF . The values defining the empirical distribution for BAF are given in Table D-13. The GM TD 4

and GSD of the values were 4.96 x 10 and 1.181, respectively. The arithmetic mean and standard 5

deviation were 5.01 x 10 and 8.25 x 10 , respectively. The lowest and highest values fall at the 16.7 5 4 th

and 83.3 percentiles, respectively, in the empirical distribution. The median of the empirical distribution th

is 5.00 x 10 . The factors contributing to the variability in BAFs were assumed to be multiplicative and 5

best represented by a lognormal distribution. Selected statistics for BAF are given in Table D-14. TD 4

Table D-13

Bioaccumulation Factor for Dissolved Total Mercury in Trophic Level 4 Fish

Value Species Location Reference

4.19 x 10 smallmouth bass, walleye Onondaga L., NY 5 Becker and Bigham, 1995;

Henry et al., 1995

5.00 x 10 largemouth bass Clear L., CA Suchanek et al., 1993 5

5.84 x 10 lake trout L. Michigan Mason and Sullivan, 1997 5

Table D-14

Statistics for Field-Derived BAF TD 4

Statistic Value

Geometric Mean 496,000

Arithmetic Mean 503,000

GSD 1.181

Percentiles:

5th 377,000

25th 422,000

50th 496,000

75th 555,000

95th 652,000

D.3.6 Estimation of Methylmercury BAF for Trophic Level 4 from Distributions Based on Total

Mercury

BAFs for methylmercury in piscivorous fish (trophic level 4) were estimated from the BAF and 3

BAF distributions based on dissolved total mercury. The estimation of BAF from BAF required a 4 MD 4 TD 4

second distribution that estimated the fraction of dissolved total mercury in the water column

(epilimnion) that is in the methylated form (fmmw ). The estimation of BAF from BAF required E MD 4 TD 3

the same additional distribution and a third distribution, PPF . In both cases, BAF was estimated by 4 MD 4

means of a Monte Carlo simulation, as there was no analytical solution (fmmw was defined as a beta E 

distribution).

D.3.6.1 Speciation of Mercury in the Water Column
D-22

Mercury Speciation in the Epilimnion

Variable: fmmwE

Definition: Fraction of total dissolved mercury in the epilimnion existing as the methylated

species

Point Estimate: 0.078 (unitless)

Distribution: beta ( = 5.07; = 56.3)

The values defining the empirical distribution for fmmw are given in Table D-15. The beta E

distribution was chosen as best representative of fractions. The distribution was fitted by the method of

moments. The point estimate is the median of the resulting distribution. The empirical distribution and

PDF for fmmw is shown in Figure D-2 E

Table D-15

Methylmercury as a Fraction of Total Dissolved Mercury in the Epilimnion

Value Location Reference

0.046 Pallette L., WI Bloom et al., 1991

0.054 Oregon Pond, NY Driscoll et al., 1995

0.059 Lake Michigan Mason and Sullivan, 1997

0.089 Clear L., CA Suchanek et al., 1993

0.089 Onondaga L., NY Henry et al., 1995

0.092 Iso Valkjarvi, Finland Rask and Verta, 1995

0.15 22 lake aggregate, WI Watras et al., 1995a, 1995b
0.0 0.05 0.10 0.15 0.20

0 2 4 6 8 10 12 14

fraction MeHg

probability density

D-23

Figure D-2

Distribution for fmmwE

D.3.6.2 Simulation of a Methylmercury BAF from a Total Mercury BAF 4 4

The formula for the methylmercury BAF based on BAF and fmmw is given in Equation 4. 4 TD 4 E

MD 4 TD 4 E BAF = BAF ÷ fmmw (4)

The formula for the methylmercury BAF based on BAF , PPF and fmmw is given in 4 TD 3 4 E

Equation 5.

MD 4 TD 4 4 E BAF = BAF x PPF ÷ fmmw (5)

Results of the Monte Carlo simulations (100,000 iterations) for these equations are shown in

Table D-16. The BAF based on BAF (GSD = 1.614) was only slightly more variable than the MD 4 TD 4

directly estimated BAF (GSD = 1.564), largely because of the low variance of BAF , itself. This MD 4 TD 4

result may reflect the greater uncertainty in the direct BAF estimate arising from the more uncertain MD 4

methylmercury water concentrations, or it may just reflect the large variability in GSD estimates from

such small sample sizes. The variability of BAF based on BAF (GSD = 2.067) was intermediate to MD 4 TD 3

the variabilities in the direct BAF estimate and BAF based on BAF x PPF (GSD = 2.317). MD 4 MD 4 MD 3 4
0 5*10^6 10^7 1.5*10^7 2*10^7 2.5*10^7 3*10^7

0 5*10^-8 10^-7 1.5*10^-7

BAF

probability density

D-24

Table D-16

Statistics for Methylmercury BAFs Estimated from Total Mercury BAFs

Method

Statistica BAF ÷ fmmw BAF x PPF ÷ TD 4 E TD 3 4

fmmwE

Geometric Mean 6,580,000 7,810,000

Arithmetic Mean 7,440,000 10,200,000

GSD 1.614 2.067

Percentiles:

5th 3,180,000 2,430,000

25th 4,710,000 4,750,000

50th 6,370,000 7,700,000

75th 8,860,000 12,600,000

95th 15,200,000 26,400,000

 statistics based on 100,000 iterations a

Both of these output distributions were closely approximated by lognormal distributions, which

were fitted by the method of moments. The Monte Carlo simulation output and the fitted distributions

for methylmercury BAF s based on BAF and BAF are shown in Figures D-3 and D-4, respectively. 4 TD 4 TD 3

Figure D-3

MD 4 TD 4 BAF Distribution Based on BAF
0 5*10^6 10^7 1.5*10^7 2*10^7 2.5*10^7 3*10^7

0 2*10^-8 6*10^-8 10^-7

BAF

probability density

D-25

Figure D-4

MD 4 TD 3 BAF Distribution Based on BAF

D.3.7 Sensitivity Analysis

The focus of the sensitivity analysis was on the primary method for BAF estimation, that is,

direct estimation from field data. The impact of the assumption that BAFs were lognormally distributed

was examined by assigning normal (Gaussian) distributions to the variables and comparing the outputs. 

Normal distributions were fit to the BAF and BAF field data (Tables D-7 and D-10, respectively) MD 3 MD 4

by the method of moments. A comparison of the normal and lognormal distributions for each variable

is given in Table D-11. BAF values for the percentiles at the empirical distribution extremes were also

calculated for comparison to the actual observations. The normal distribution for BAF was similar to MD 4

the lognormal; the primary difference was the slightly higher median for the distribution in normal form. 

The negative 5 percentile outcome for BAF illustrated a common problem when assigning normal th

MD 3

distributions to variables that have a lower bound of zero; a truncated form of the distribution must be

used to give meaningful results. Otherwise, the median and 95th percentile for the normal form of

MD 3 BAF were only slightly higher than for the lognormal form. The lognormal forms allow for slightly

larger values in the upper tails of the distributions. The empirical extremes match the lognormal

distributions slightly better than the normal forms.
D-26

Table D-17

Comparison of Lognormal and Normal (Directly Estimated) BAF Distributions MD

MD 3 MD 4 BAF BAF

Percentile

lognormal normal lognormal normal a b cd

5th 461,000 (-545,000) 3,260,000 2,100,000

12.5th 667,000 205,000 4,070,000 3,670,000 e

50th 1,580,000 1,950,000 6,810,000 7,330,000

87.5th 3,740,000 3,700,000 11,400,000 11,000,000 f

95th 5,410,000 4,450,000 14,200,000 12,600,000

 GM = 1.58 x 10 , GSD = 2.115 a 6

 mean = 1.95 x 10 , std. dev. = 1.517 x 10 b6 6

 GM = 6.81 x 10 , GSD = 1.564 c 6

 mean = 7.33 x 10 , std. dev. = 3.181 x 10 d6 6

 100 x (1 - (n - 0.5)/n), n = 4 e

 100 x (n - 0.5)/n, n = 4 f

D.3.8 Selection of Bioaccumulation Factors for Trophic Levels 3 and 4

As all of the BAF distributions in this analysis dealt with estimates for a randomly-selected

single water body, independent of any other water body, the central-tendency estimate was given as the

median (equivalent to the geometric mean for lognormal distributions). That is, the median represents

equal likelihood that the lake-specific BAF will greater or less than the chosen value and is most

appropriate when sampling only once from the distribution. The overall average (arithmetic mean)

would be useful only in the case when fish were consumed equally by a receptor from all of the lakes in

the analysis. That is, the mean value better reflects actual exposure when repeatedly sampling from the

same distribution. If a similar analysis would be conducted for a narrowly-defined region, where fish

consumption across several lakes would be a reasonable scenario, the average BAF across those water

bodies would be more appropriate

Bioaccumulation factors for trophic level 3 estimated using each of the two previously described

methods are given in Table D-17. Bioaccumulation factors for trophic level 4 estimated using each of the

five previously described methods are given in Table D-18. These factors are meant to be applied when

appropriate local site data do not exist. For a particular site of concern, BAFs derived from data

collected at the site are preferred to the estimated values in Tables D-18 and D-19. Median BAF

estimates are within 20% irrespective of the method used. The similarity of the direct and GLWQI

estimates is somewhat surprising, given the greater number of variables for the GLWQI approach and

the relative independence of the data sets. The much greater variability of the results from the GLWQI

approach than those from the direct BAF derivation, however, is expected. As an example, the GSD of

6.13 for the GLWQI BAF reflects a 17-fold greater variance than the GSD of 1.56 for the direct MD 4

estimate of BAF from field data. The result is a 90% confidence interval for the GLWQI BAF that MD 4 MD 4

is 90 times wider than that for the direct estimate of BAF . The BAF derived from BAF and MD 4 MD 4 MD 3

PPF estimates is also more variable, with a 90% confidence interval that is about three times wider than 4

for the direct estimate of BAF . A graphical representation of the BAF distributions for the direct MD 4 MD 4
D-27

estimate and the two most divergent estmates is provided in Figure D-5. The vertical line represents the

recommended value for BAF . MD 4

Most of the data for estimating BAFs are from studies of northern oligotrophic water bodies.

These freshwater systems are all similar, in that they are at risk due to acid deposition and that, by virtue

of average temperature, etc., support similar fish assemblages (typically northern pike, walleye, yellow

perch and spottail shiners). This has the potential to create two possible problems of interpretation.

1) Because data from such systems tend to look similar, it would be easy to

overlook relationships that would be important under a different set of

circumstances.

2) The relative abundance of these data introduce a regional bias into any type of

analysis that is intended for nationwide application.

The one exception, Clear Lake, CA (Suchanek et al., 1993), however, provides BAF estimates for both MD

trophic levels that are similar to the central tendency estimates for all lakes combined. Clear Lake is

warm, highly eutrophic, and receives considerable agricultural runoff from nearby orchards and

vineyards. Lacking any corroborating information, it is not possible to determine how representative the

Clear Lake data are of warm water systems generally. Clearly, however, there is a need for additional

residue data collected from a broader spectrum of freshwater systems.

Table D-18

Summary of Methylmercury Bioaccumulation Factors for Trophic Level 3

(BAF values in units of L/kg)

Recommended 1,600,000

Value

Method Direct Estimate Modified GLWQI

5 pctl 461,000 7,000 th

50 pctl 1,580,000 1,320,000 th

95 pctl 5,410,000 24,400,000 th

GSD 2.152 5.884
BAF

probability density

0 5*10^6 10^7 1.5*10^7 2*10^7 2.5*10^7 3*10^7

0 5*10^-8 10^-7 1.5*10^-7

Modified GLWQI

Direct Estimate

BAF3 x PPF4

D-28

Table D-19

Summary of Methylmercury Bioaccumulation Factors for Trophic Level 4

(BAF values in units of L/kg)

Recommended 6,800,000

Value

Method Direct Estimate BAF ÷ BAF x PPF BAF x PPF Modified TD 4

fmmw ÷ fmmw GLWQI E

MD 3 4 TD 3 4

E

5 pctl 3,260,000 3,180,000 1,960,000 2,430,000 330,000 th

50 pctl 6,810,000 6,370,000 7,820,000 7,700,000 6,520,000 th

95 pctl 14,200,000 15,200,000 31,100,000 26,400,000 129,000,000 th

GSD 1.564 1.614 2.317 2.067 6.127

Figure D-5

MD 4 BAF Distributions

As stated previously, BAFs derived from data collected at the site are preferred to the estimated

values in Tables D-18 and D-19. Otherwise, given the large differences in the variances of the

distributions, BAFs estimated by the direct method are preferred over BAFs calculated using the

modified GLWQI methodology. The median BAF values estimated using the direct approach are MD

therefore recommended for subsequent calculation of a WC for methylmercury (Volume VI) and for an

evaluation of human exposure due to consumption of contaminated fish (Volume IV). The recommended

MDBAFs for methylmercury are 1.6 x 10 L/kg for trophic level 3 and 6.8 x 10 L/kg for trophic level 4. 6 6
D-29

D.3.9 Discussion of Uncertainty and Variability in the BAF

The BAF distributions were designed to estimate an average concentration of methylmercury in

fish of a given trophic level from an average concentration of dissolved methylmercury in the epilimnion

for a (single) randomly-selected lake in the continental U.S. In the overall mercury fate and exposure

model, the input (water concentrations) to this distribution represented an annual average, aggregating

variability in methylmercury concentrations in the epilimnion over an entire year and the output (fish

concentrations) represented the average methylmercury concentration in the diet of a specific receptor. 

Available data were inadequate to satisfy these representations fully. In most cases, water

methylmercury concentrations incorporated limited or no cross-seasonal variability. Also, fish diets for

specific receptors have not been determined. For this analysis a generic receptor was assumed and was

approximated by including a large range of fish age or size classes whenever possible. Also, because of

the general paucity of appropriate data, many studies on lakes in other countries were included in the

analysis; biotic and abiotic processes in these lakes were assumed to be similar to lakes in the continental

U.S. These limitations introduced additional uncertainty in the BAF output that was not quantified in

this analysis. 

Except as will be discussed, there were no distinctions in the BAF distributions as to size of fish,

lake trophic status, lake pH, or relative methylmercury concentrations in the water column. The data,

however, are heavily biased towards northern oligotrophic lakes and somewhat towards smaller

(younger) fish. There was also no distinction made between variability and uncertainty in the BAF and 3

BAF distributions; they are aggregated in the output. Thus, from this analysis, it cannot be determined 4

where natural variability stops and uncertainty starts. The distributions do, however, provide a rough

estimate of the total uncertainty in the aggregate processes and an idea of the precision (or lack thereof)

of these types of estimates.

The large amount of variability evidenced by the data and reflected in the output distributions

arises from several sources, which have not yet been quantified. A primary source of variability in both

BAF and PPF is the dependence of methylmercury bioaccumulation on the age of the fish as discussed 3 4

in Section D-3.2.2 (PPF ). Reported BAF values for a given species will, therefore, vary as a function of 4

the ages of the animals examined. As a result, some researchers have suggested that comparisons

between lakes should be made using "standardized" fish values (i.e., a value for a hypothetical 1 kg

northern pike), typically derived by linear regression of residue data collected from individuals of

varying size and/or age (see discussion in Section D-3.2.2). The available data, however, are too limited

to allow for this kind of analysis. The distributions derived in this appendix include both “standardized”

comparisons and those based on “opportunity” (whatever you catch, you include).

Perhaps the greatest source of variability is that of model uncertainty, that is, uncertainty

introduced by failure of the model to account for significant real-world processes. The simple linear

BAF model relating methylmercury in fish to total mercury in water masks a number of nonlinear

processes leading to the formation of bioavailable methylmercury in the water column. Much of the

variability in field data applicable to the estimation of mercury BAFs can be attributed to differences

between aquatic systems. As an example, in lake surveys conducted within a relatively restricted

geographic region, large differences can exist between lakes with respect to mercury concentrations in a

given species of fish (Cope et al., 1990; Grieb et al., 1990; Sorenson et al., 1990; Jackson, 1991; Lange et

al., 1993). These observations have led to the suggestion that much of this variability is due to

differences in within-lake processes that determine the percentage of total mercury that exists as the

methylated form. Limited data also indicate that within a given water body concentrations of

methylmercury are likely to vary with depth and season. Unfortunately, while the concentration of

methylmercury in fish flesh is presumably a function of these varying concentrations, published BAFs
D-30

are generally estimated from a small number of measured water values, the representativeness of which is

poorly known.

D.4 Other Variables Concerning the Form of Mercury in the Water Column

Two other variables are presented in this section that are not used in the calculation of mercury

BAFs , but are referred to in other parts of Volume III. Those variables are 1) the fraction of dissolved

total mercury in the hypolimnion that is methylmercury and 2) the fraction of total methylmercury in the

water column that is dissolved. 

D.4.1 Fraction of Methylmercury in the Hypolimnion

Mercury Speciation in the Hypolimnion

Variable: fmmwH

Definition: Fraction of total dissolved mercury in the hypolimnion existing as the methylated

species

Point Estimate: 0.36 (unitless)

Distribution: beta ( = 11.0; = 19.6)

The values defining the empirical distribution for fmmw are given in Table D-20. The beta H

distribution was chosen as best representative of fractions. The distribution was fitted by the method of

moments. The point estimate is the median of the resulting distribution. 

Table D-20

Methylmercury as a Fraction of Total Dissolved Mercury in the Hypolimnion

Value Location Reference

0.27 Iso Valkjarvi, Finland Rask and Verta, 1995

0.37 Pallette L., WI Bloom et al., 1991

0.44 Onondaga L., NY Henry et al., 1995

D.4.2 Fraction of Dissolved Methylmercury in the Water Column

Variable: fdmw

Definition: Fraction of total methylmercury in the epilimnion that is dissolved

Point Estimate: 0.69 (unitless)

Distribution: beta ( = 2.55; = 1.29)

The values defining the empirical distribution for fdmw are given in Table D-21. The beta

distribution was chosen as best representative of fractions. The distribution was fitted by the method of

moments. The point estimate is the median of the resulting distribution and is identical to the median of

the empirical distribution. The empirical distribution and PDF for fdmw is shown in Figure D-6
0.0 0.2 0.4 0.6 0.8 1.0

0.0 0.5 1.0 1.5 2.0 2.5

fraction dissolved MeHg

probability density

D-31

Table D-21

Dissolved Methylmercury as a Fraction of Total Methylmercury in the Epilimnion

Value Location Reference

0.303 Vandercook Lake, WI Bloom et al., 1991

0.353 Onondaga Lake, NY Henry et al., 1995

0.577 Pallette Lake, WI Bloom et al., 1991

0.60 Lake Hako, Finland Verta and Matilainen, 1995

0.667 Little Rock Lake, WI Bloom et al., 1991

0.72 Max Lake, WI Bloom et al., 1991

0.762 Lake Michigan Mason and Sullivan, 1997

0.79 Lake Iva, Finland Verta and Matilainen, 1995

0.82 Lake Keha, Finland Verta and Matilainen, 1995

1.02 Clear Lake, CA Suchanek et al., 1993

Figure D-6

Distribution for fdmw

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