EPA

Office of Air Quality

Pla nnin g a nd S ta nd a rds

C Air

lean

United States Office of Air Quality EPA-453/R-98-004a

Environmental Protection Planning and Standards February 1998

Agency Research Triangle Park, NC 27711

Air

Study of Hazardous Air Pollutant

Emissions from Electric Utility Steam

Generating Units -- Final Report to

Congress

Volume 1.
mm
ACKNOWLEDGMENTS

The U.S. Environmental Protection Agency would like to

acknowledge numerous technical authors for their significant

contributions to the development of this report. Many of the primary

authors are listed below. The EPA would also like to thank the

numerous individuals from various organizations for their invaluable

review, comments, and inputs for the development of this report. 

These individuals are too numerous to list here, but they include

representatives from many organizations, including: U.S. EPA work

group members and other EPA staff; other Federal Agencies; Electric

Power Industry; State and local Agencies; Environmental Groups;

scientific peer reviewers from academia and consulting firms; and

other public reviewers. In addition, the EPA would like to thank the

Research Triangle Institute, EC/R Incorporated, and S. Cohen &

Associates for the valuable technical support provided for the

development of this report.

U.S. EPA AUTHORS

Charles L. French, Risk and Exposure Assessment Group

Office of Air Quality Planning and Standards

William H. Maxwell, Combustion Group

Office of Air Quality Planning and Standards

Warren D. Peters, Air Quality Modeling Group

Office of Air Quality Planning and Standards

Glenn E. Rice, National Center for Environmental Assessment - Cincinnati

Office of Research and Development

O. Russell Bullock, Atmospheric Sciences Modeling Division

National Oceanic and Atmospheric Administration

on assignment to the U.S. EPA National Exposure Research Laboratory

Amy B. Vasu, Risk and Exposure Assessment Group

Office of Air Quality Planning and Standards

Robert Hetes, Office of Air Quality Planning and Standards

Albert Colli, Office of Radiation and Indoor Air

Christopher Nelson, Office of Radiation and Indoor Air

Bradford F. Lyons, Oak Ridge National Laboratory (non-EPA)

Additional EPA staff that contributed to this report include: 

Ted Palma, Mike Dusetzina, George Duggan, Dianne Byrne, Stan Durkee,

Martha Keating, Neal Nelson, and others.

iii

TABLE OF CONTENTS

Volume 1

Section Page

Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxv

Executive Summary ........................ ES-1

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

1.1 LEGISLATIVE MANDATE . . . . . . . . . . . . . . . . . 1-1

1.2 CAA PROVISIONS AND STUDIES RELATED TO THIS STUDY . . 1-2

1. 1.2.1 Nonattainment Provisions . . . . . . . . . . . . . . 1-2

1.2.2 Acid Deposition Control . . . . . . . . . . 1-3

1.2.3 New Source Performance Standards . . . . . . 1-5

1.2.4 Hazardous Air Pollutants . . . . . . . . . 1-6

1.2.5 Other Studies . . . . . . . . . . . . . . . 1-6

1.3 OVERVIEW AND APPROACH OF ELECTRIC UTILITY HAP STUDY . 1-8

1.4 REFERENCES ..................... 1-10

2.0 CHARACTERIZATION OF THE INDUSTRY . . . . . . . . . . . . . 2-1

2.1 INDUSTRY BACKGROUND . . . . . . . . . . . . . . . . . 2-1

2.2 FOSSIL-FUEL-FIRED ELECTRIC UTILITY STEAM-GENERATING 

UNITS . . . . . . . . . . . . . . . . . . . . . . . . 2-3

2.2.1 Types of Electric Utility Facilities . . . . 2-3

2.2.2 Types of Ownership . . . . . . . . . . . . . 2-4

2.3 DESIGN OF ELECTRIC UTILITY UNITS . . . . . . . . . . 2-5

2.3.1 Furnace Types . . . . . . . . . . . . . . . 2-5

2.3.2 Bottom Types . . . . . . . . . . . . . . . . 2-9

2.3.3 Cogeneration . . . . . . . . . . . . . . . . 2-9

2.3.4 Combined-Cycle Systems . . . . . . . . . . . 2-9

2.4 PARTICULATE MATTER CONTROL . . . . . . . . . . . . . 2-9

2.4.1 Mechanical Collectors ........... 2-10

2.4.2 Electrostatic Precipitators ........ 2-12

2.4.3 Particulate Matter Scrubbers ........ 2-12

2.4.4 Fabric Filters ............... 2-13

2.4.5 Comparison of Particle Collectors ..... 2-13

2.5 SULFUR DIOXIDE CONTROL ............... 2-14

2.5.1 Precombustion Control: Fuel Options .... 2-15

2.5.2 Postcombustion Control: Flue Gas Scrubbing

for SO Control 2 .............. 2-17

2.6 NO CONTROL x ..................... 2-21

2.6.1 Combustion Control ............. 2-23

2.6.2 Postcombustion Control ........... 2-24

2.7 UTILITY INDUSTRY AFTER IMPLEMENTATION OF 1990 

AMENDMENTS ..................... 2-25

2.7.1 Industry Growth .............. 2-26

2.7.2 Title I and Title IV, Phase I and Phase II,

Compliance Strategy Impact ......... 2-31

2.7.3 Compliance Strategy Impacts of Other 

Activities ................. 2-33

2.8 REFERENCES ..................... 2-34
TABLE OF CONTENTS (continued)

Volume 1

Section Page

iv

3.0 EMISSION DATA GATHERING AND ANALYSIS . . . . . . . . . . . 3-1

3.1 LITERATURE REVIEW AND BACKGROUND . . . . . . . . . . 3-1

3.2 POLLUTANTS STUDIED . . . . . . . . . . . . . . . . . 3-3

3.3 DESCRIPTION OF EMISSION TEST PROGRAMS . . . . . . . . 3-3

3.4 DEVELOPMENT OF HAP EMISSION TOTALS . . . . . . . . . 3-4

3.4.1 Program Operation . . . . . . . . . . . . . 3-4

3.4.2 Data Sources . . . . . . . . . . . . . . . . 3-4

3.4.3 Operational Status of Boilers . . . . . . . 3-8

3.4.4 Trace Element Concentration in Fuel . . . . 3-8

3.4.5 HCl and HF Concentration in Fuel . . . . . . 3-9

3.4.6 Emission Modification Factors for 

Inorganic HAPs ............... 3-10

3.4.7 Acid Gas HAPs ............... 3-11

3.4.8 Organic HAPs ................ 3-12

3.4.9 Model Estimates for the Year 2010 ..... 3-12

3.5 SELECTED ESTIMATED NATIONWIDE HAP EMISSIONS ..... 3-14

3.6 COMPARISON OF EFP ESTIMATES WITH TEST DATA ..... 3-14

3.7 CHARACTERISTIC PLANT EMISSIONS ........... 3-16

3.8 UNCERTAINTY ANALYSIS OF EMISSION FACTOR PROGRAM . . . 3-19

3.9 REFERENCES ..................... 3-20

4.0 INTRODUCTION FOR THE HEALTH HAZARD RISK ANALYSIS . . . . . 4-1

4.1 INTRODUCTION AND BACKGROUND . . . . . . . . . . . . . 4-1

4.1.1 Principles of Risk Assessment . . . . . . . 4-1

4.1.2 U.S. EPA Risk Assessment Guidelines . . . . 4-3

4.1.3 Risk Assessment Council (RAC) Guidance . . . 4-3

4.1.4 NAS Report Science and Judgement in Risk

Assessment . . . . . . . . . . . . . . . . . 4-3

4.1.5 SPC's Guidance for Risk Characterization . . 4-4

4.2 GENERAL APPROACH AND METHODS FOR THE UTILITY HEALTH 

HAZARD RISK ASSESSMENT . . . . . . . . . . . . . . . 4-4

4.3 HEALTH EFFECTS DATA: HAZARD IDENTIFICATION AND DOSE

RESPONSE . . . . . . . . . . . . . . . . . . . . . . 4-5

4.3.1 Hazard Identification for Carcinogens . . . 4-5

4.3.2 General Discussion of Dose Response . . . . 4-5

4.3.3 Dose-Response Evaluation for Carcinogens . . 4-7

4.3.4 Long-Term Noncancer Health Effects Data . . 4-8

4.3.5 Short-Term Noncancer Health Effects Data . . 4-9

4.3.6 Summary of Health Effects Data Sources . . . 4-9

4.4 METHODOLOGY FOR ESTIMATING INHALATION EXPOSURE FOR

LOCAL ANALYSIS . . . . . . . . . . . . . . . . . . . 4-9

4.4.1 Emissions Characterization . . . . . . . . . 4-9

4.4.2 Atmospheric Fate and Transport ....... 4-10

4.4.3 Characterization of Study Population .... 4-10

4.4.4 Exposure Calculations ........... 4-11
TABLE OF CONTENTS (continued)

Volume 1

Section Page

v

4.5 METHODOLOGY FOR ESTIMATING QUANTITATIVE INHALATION

RISKS ........................ 4-11

4.5.1 Estimating Cancer Inhalation Risks ..... 4-11

4.5.2 Individual Cancer Risk ........... 4-12

4.5.3 Population Cancer Risk ........... 4-12

4.5.4 Distribution of Individual Risk within a 

Population ................. 4-13

4.5.5 Aggregate Inhalation Cancer Risk ...... 4-13

4.5.6 Estimating Noncancer Inhalation Risks . . . 4-13

4.5.7 Inhalation Hazard Quotient (HQ) ...... 4-14

4.5.8 Total Risk for Noncancer Effects ...... 4-14

4.5.9 Direct Inhalation Exposure and Risk 

Default Options .............. 4-15

4.6 REFERENCES ..................... 4-17

5.0 SCREENING ASSESSMENT TO DETERMINE PRIORITY HAPS . . . . . . 5-1

5.1 MODELING DESCRIPTION . . . . . . . . . . . . . . . . 5-1

5.2 SCREENING CRITERIA . . . . . . . . . . . . . . . . . 5-1

5.3 INHALATION SCREENING ASSESSMENT FOR COAL-FIRED

UTILITIES . . . . . . . . . . . . . . . . . . . . . . 5-2

5.4 INHALATION SCREENING ASSESSMENT FOR OIL- AND GASFIRED UTILITIES . . . . . . . . . . . . . . . . . . . 5-2

5.5 MULTIPATHWAY SCREENING ANALYSIS FOR NONRADIONUCLIDE

HAPS ........................ 5-13

5.5.1 Overview .................. 5-13

5.5.2 Prioritization of HAPs for Multipathway 

Exposure Assessment ............ 5-14

5.6 SELECTION OF HAPS FOR FURTHER ANALYSIS ....... 5-18

5.7 LIMITATIONS OF SCREENING ASSESSMENT ......... 5-19

5.8 REFERENCES ..................... 5-20

6.0 INHALATION RISK ASSESSMENT . . . . . . . . . . . . . . . . 6-1

6.1 BASELINE ASSESSMENT OF INHALATION EXPOSURES AND 

RISKS FOR 13 PRIORITY POLLUTANTS . . . . . . . . . . 6-1

6.1.1 Baseline Inhalation Risks for Coal-Fired

Utilities for Priority HAPs . . . . . . . . 6-2

6.1.2 Baseline Inhalation Risks for Oil-Fired

Utilities . . . . . . . . . . . . . . . . . 6-7

6.1.3 Baseline Risks from Gas-Fired Utilities . . 6-12

6.2 DISTINGUISHING BETWEEN URBAN AND RURAL LOCATIONS . . 6-12

6.3 INHALATION RISK ESTIMATES FOR THE YEAR 2010 ..... 6-15

6.4 ASSESSMENT OF POTENTIAL RISKS DUE TO SHORT-TERM

EXPOSURE ...................... 6-15

6.4.1 Methodology ................ 6-17

6.4.2 Results .................. 6-18

6.5 OVERLAPPING PLUMES/DOUBLE COUNTING ......... 6-18
TABLE OF CONTENTS (continued)

Volume 1

Section Page

vi

6.6 ASSESSMENT OF EXPOSURE DUE TO LONG-RANGE TRANSPORT . 6-20

6.6.1 History and Background Information ..... 6-20

6.6.2 RELMAP Modeling Approach for Particulate

Metals ................... 6-21

6.6.3 Model Parameterizations .......... 6-23

6.6.4 Exposure and Risk Estimates ........ 6-25

6.7 DISCUSSION OF BACKGROUND EXPOSURES ......... 6-43

6.7.1 Arsenic .................. 6-43

6.7.2 Chromium, Nickel, Manganese, and HCl .... 6-45

6.8 CHROMIUM SPECIATION UNCERTAINTY AND IMPACT ON RISK 

ESTIMATES ...................... 6-45

6.9 ISSUES WITH ARSENIC CANCER UNIT RISK ESTIMATE AND 

IMPACT ON INHALATION RISK ESTIMATES ......... 6-47

6.10 NICKEL SPECIATION UNCERTAINTY AND IMPACT ON RISK 

ESTIMATES ...................... 6-47

6.10.1 Alternative Analysis for Estimating 

Population Risks .............. 6-49

6.11 POTENTIAL INCREASED DIOXIN EMISSIONS FROM UTILITIES

WITH ELECTROSTATIC PRECIPITATORS .......... 6-50

6.12 DISCUSSION OF UNCERTAINTY AND ASSUMPTIONS FOR DOSERESPONSE ASSESSMENT FOR CARCINOGENS ......... 6-50

6.12.1 Default Options .............. 6-52

6.12.2 Models, Methods, and Data ......... 6-53

6.12.3 Discussion of Uncertainty in IUREs ..... 6-56

6.12.4 Variability in Cancer Dose-Response

Assessment ................. 6-57

6.13 PRELIMINARY QUANTITATIVE UNCERTAINTY AND VARIABILITY

ANALYSIS FOR INHALATION EXPOSURE AND RISK

ASSESSMENT ..................... 6-58

6.13.1 Introduction ................ 6-58

6.13.2 Approach to Quantitative Uncertainty

Analysis .................. 6-59

6.13.3 Discussion of Results of the Quantitative

Uncertainty Analysis ............ 6-70

6.14 QUALITATIVE DISCUSSION OF ADDITIONAL UNCERTAINTIES . 6-72

6.14.1 Uncertainty Using IUREs .......... 6-72

6.14.2 Residence Time and Activity Patterns .... 6-72

6.15 REFERENCES ..................... 6-74

7.0 MERCURY ASSESSMENT . . . . . . . . . . . . . . . . . . . . 7-1

7.1 OVERVIEW . . . . . . . . . . . . . . . . . . . . . . 7-1

7.1.1 The Mercury Cycle . . . . . . . . . . . . . 7-1

7.1.2 Atmospheric Processes . . . . . . . . . . . 7-6

7.1.3 Terrestrial and Aquatic Fate of Mercury . . 7-13

7.2 MERCURY HEALTH EFFECTS ............... 7-16

7.2.1 Toxicokinetics ............... 7-16

7.2.2 Biological Effects ............. 7-17
TABLE OF CONTENTS (continued)

Volume 1

Section Page

vii

7.2.3 Sensitive Subpopulations .......... 7-18

7.2.4 Interactions ................ 7-18

7.2.5 Hazard Identification/Dose-Response

Assessment ................. 7-18

7.2.6 Ongoing Research .............. 7-19

7.2.7 Research Needs ............... 7-19

7.3 MERCURY CONCENTRATIONS IN BIOTA ........... 7-21

7.4 MEASUREMENT DATA NEAR UTILITIES ........... 7-22

7.5 MODEL FRAMEWORK ................... 7-22

7.5.1 Models Used ................ 7-23

7.5.2 Modeling of Long-Range Fate and Transport 

of Mercury ................. 7-23

7.5.3 Modeling the Local Transport of Mercury in 

the Atmosphere ............... 7-28

7.5.4 Modeling Mercury in a Watershed ...... 7-29

7.5.5 Exposure Modeling ............. 7-34

7.6 RESULTS ....................... 7-35

7.7 CONCLUSIONS ..................... 7-40

7.8 DISCUSSION OF POTENTIAL CONCERNS OF MERCURY 

EMISSIONS FROM UTILITIES .............. 7-45

7.9 REFERENCES ..................... 7-46

8.0 QUALITATIVE MULTIPATHWAY ASSESSMENT FOR LEAD AND CADMIUM . 8-1

8.1 BACKGROUND . . . . . . . . . . . . . . . . . . . . . 8-1

8.2 LEAD COMPOUNDS . . . . . . . . . . . . . . . . . . . 8-1

8.3 CADMIUM COMPOUNDS . . . . . . . . . . . . . . . . . . 8-3

8.4 OVERALL SUMMARY ................... 8-16

8.5 REFERENCES ..................... 8-17

9.0 MULTIPATHWAY EXPOSURE AND RISK ASSESSMENT FOR 

RADIONUCLIDES . . . . . . . . . . . . . . . . . . . . . . . 9-1

9.1 SUMMARY OF RADIONUCLIDE ANALYSIS . . . . . . . . . . 9-1

9.1.1 Natural Radionuclide Content in Fossil

Fuels: Coal . . . . . . . . . . . . . . . . 9-2

9.1.2 Natural Radionuclide Content in Fossil

Fuels: Natural Gas . . . . . . . . . . . . 9-2

9.1.3 Natural Radionuclide Content in Fossil

Fuels: Oil . . . . . . . . . . . . . . . . 9-3

9.1.4 Radionuclide Emissions from Fossil-Fueled 

Plants . . . . . . . . . . . . . . . . . . . 9-4

9.1.5 Summary of CAP-93 Model . . . . . . . . . . 9-5

9.1.6 Estimates of Population Health Risks . . . . 9-8

9.2 RADIONUCLIDE UNCERTAINTY ANALYSIS .......... 9-10

9.3 SUMMARY FINDINGS .................. 9-12

9.4 REFERENCES ..................... 9-17
TABLE OF CONTENTS (continued)

Volume 1

Section Page

viii

10.0 SCREENING LEVEL ASSESSMENT OF MULTIPATHWAY EXPOSURES AND

RISKS TO ARSENIC EMISSIONS ................ 10-1

10.1 PURPOSE AND SCOPE .................. 10-1

10.1.1 Rational and Usefulness of Model Plant 

Approach .................. 10-1

10.2 BACKGROUND INFORMATION ON ARSENIC .......... 10-2

10.2.1 Forms of Arsenic in the Environment .... 10-2

10.2.2 Sources of Arsenic ............. 10-4

10.2.3 Arsenic in the Atmosphere ......... 10-4

10.2.4 Arsenic in Water .............. 10-4

10.2.5 Arsenic in Sediments ............ 10-6

10.2.6 Arsenic in Soil .............. 10-6

10.2.7 Arsenic in Terrestrial Plants ....... 10-7

10.2.8 Arsenic in Aquatic Plants ......... 10-7

10.2.9 Arsenic in Terrestrial Animals ....... 10-7

10.2.10 Arsenic in Fish .............. 10-7

10.2.11 Speciation of Arsenic in Food Products . . 10-10

10.2.12 Arsenic Near Anthropogenic Sources .... 10-10

10.3 SUMMARY OF MODELS AND APPROACH .......... 10-10

10.3.1 Source Classes Considered and Model 

Plant Approach .............. 10-12

10.3.2 Atmospheric Transport Modeling ...... 10-12

10.3.3 Indirect Exposure Modeling ........ 10-12

10.3.4 Determination of Background Values .... 10-19

10.4 MODELING RESULTS ................. 10-21

10.4.1 Air Modeling Results/Comparison with

Measured Data .............. 10-21

10.4.2 Indirect Exposure Modeling ........ 10-23

10.5 HAZARD IDENTIFICATION AND DOSE-RESPONSE FOR

ARSENIC ...................... 10-34

10.5.1 Introduction ............... 10-34

10.5.2 Cancer Effects of Arsenic ........ 10-34

10.5.3 Noncancer Effects of Arsenic ....... 10-36

10.6 RISK CHARACTERIZATION ............... 10-36

10.6.1 Discussion of Cancer Risk Assessment

Results ................. 10-37

10.6.2 Discussion of the Noncancer Risk 

Assessment Results ............ 10-43

10.7 CONCLUSIONS .................... 10-43

10.7.1 Contribution of Arsenic Emissions from 

Utilities to Concentrations in 

Environmental Media and Biota ...... 10-43

10.7.2 Determination of Dominant Pathways of

Potential Exposure to Anthropogenic 

Arsenic Emissions ............ 10-43
TABLE OF CONTENTS (continued)

Volume 1

Section Page

ix

10.8 UNCERTAINTIES AND LIMITATIONS ........... 10-44

10.8.1 Limitations and Uncertainties for the

Multipathway Exposure Modeling ...... 10-44

10.8.2 Limitations and Uncertainties for the 

Risk Characterization .......... 10-44

10.9 RESEARCH NEEDS .................. 10-45

10.10 REFERENCES .................... 10-46

11.0 A MULTIPATHWAY SCREENING-LEVEL ASSESSMENT FOR DIOXINS/

FURANS .......................... 11-1

11.1 INTRODUCTION .................... 11-1

11.2 LONG-RANGE TRANSPORT MODELING ............ 11-6

11.3 RISK ASSESSMENT METHODOLOGY ............ 11-10

11.3.1 Emissions Sources ............ 11-10

11.3.2 Local Air Dispersion Modeling ...... 11-10

11.3.3 Exposure Modeling and Risk Calculation . . 11-14

11.4 DISPERSION, EXPOSURE, AND RISK RESULTS ...... 11-18

11.5 UNCERTAINTY AND SENSITIVITY ANALYSIS ....... 11-21

11.5.1 Model Elasticity ............. 11-22

11.5.2 Fish Consumption Pathway Sensitivity

Analysis ................. 11-23

11.5.3 Sensitivity Analysis of Plume Impaction . 11-25

11.6 SUMMARY OF RESULTS ................ 11-27

11.7 REFERENCES .................... 11-30

12.0 LITERATURE REVIEW ON THE POTENTIAL IMPACTS OF HYDROGEN

CHLORIDE AND HYDROGEN FLUORIDE EMISSIONS ......... 12-1

12.1 OVERVIEW ...................... 12-1

12.2 SUMMARY OF FINDINGS ................. 12-1

12.2.1 Hydrogen Chloride ............. 12-1

12.2.2 Hydrogen Fluoride ............. 12-3

12.3 REFERENCES ..................... 12-6

13.0 ALTERNATIVE CONTROL STRATEGIES FOR HAZARDOUS AIR 

POLLUTANT EMISSIONS REDUCTIONS .............. 13-1

13.1 PRECOMBUSTION CONTROLS ............... 13-1

13.1.1 Fuel Switching ............... 13-1

13.1.2 Coal Cleaning ............... 13-5

13.1.3 Coal Gasification ............ 13-11

13.2 COMBUSTION CONTROL ................ 13-12

13.3 POSTCOMBUSTION CONTROL .............. 13-22

13.3.1 Particulate Phase Controls ........ 13-22

13.3.2 Vapor Phase Controls ........... 13-28

13.3.3 Acid Gas Control ............. 13-28

13.3.4 Carbon Adsorption ............ 13-31

13.4 ALTERNATIVE CONTROLS ............... 13-31
TABLE OF CONTENTS (continued)

Volume 1

Section Page

x

13.5 POLLUTANT TRADEOFFS ................ 13-32

13.5.1 HAP Increase/Decrease .......... 13-32

13.5.2 Water/Solid Waste Considerations ..... 13-34

13.6 AVAILABLE CONTROL TECHNOLOGY AND STRATEGIES FOR

MERCURY CONTROL .................. 13-36

13.6.1 Pre-Combustion Strategies ........ 13-36

13.6.2 Post-Combustion Strategies ........ 13-37

13.7 REFERENCES .................... 13-48

14.0 SUMMARY OF RESULTS, TECHNICAL FINDINGS, AND RESEARCH 

NEEDS ........................... 14-1

14.1 INDUSTRY GROWTH AND HAP EMISSIONS .......... 14-1

14.2 INHALATION RISK ASSESSMENT ............. 14-1

14.3 MERCURY ....................... 14-3

14.4 DIOXINS AND ARSENIC ................. 14-5

14.5 RADIONUCLIDE ANALYSIS ................ 14-6

14.6 ALTERNATIVE CONTROL STRATEGIES ........... 14-6

14.7 AREAS FOR FURTHER RESEARCH AND ANALYSIS ....... 14-8

14.7.1 Emissions Data for Dioxins ......... 14-8

14.7.2 Speciation of Nickel and Chromium ..... 14-8

14.7.3 Multipathway Risk Assessment ........ 14-8

14.7.4 Long-range Transport Exposures ....... 14-8

14.7.5 Mercury Issues ............... 14-8

14.7.6 Projections to the Year 2010 ........ 14-9

14.7.7 Ecological Risks .............. 14-9

14.7.8 Criteria Pollutant and Acid Rain Programs 14-10

14.7.9 Short-term Emissions ........... 14-10
TABLE OF CONTENTS (Continued)

Volume 2

Section Page

xi

Appendix A Median Emission Factors, Determined from Test

Report Data, and Total 1990, 1994, and 2010

Emissions, Projected with the Emission Factor

Program . . . . . . . . . . . . . . . . . . . . . . A-1

Appendix B Matrix of Electric Utility Steam-Generating Units

and Emission Test Sites . . . . . . . . . . . . . . B-1

Appendix C Listing of Emission Modification Factors for Trace

Elements Used In the Individual Boiler Analysis . . C-1

Appendix D Discussion of the Methodology Used to Develop

Nationwide Emission Totals . . . . . . . . . . . . . D-1

Appendix E Health Effects Summaries: Overview . . . . . . . . . E-1

Appendix F Documentation of the Inhalation Human Exposure

Modeling for the Utility Study . . . . . . . . . . . F-1

Appendix G Data Tables for Dioxin Multipathway Assessment . . . G-1

Appendix H Literature Review of the Potential Impacts of

Hydrogen Chloride and Hydrogen Fluoride . . . . . . H-1

Appendix I Mercury Control Technologies . . . . . . . . . . . . I-1
xii

LIST OF TABLES

Table Page

ES-1. Nationwide Utility Emissions for Thirteen Priority HAPs ES-5

ES-2. Estimated Emissions for Nine Priority HAPs from

Characteristic Utility Units (1990; tons per year) . . . ES-6

ES-3. Summary of High-End Inhalation Cancer Risk Estimates

from Local Analysis for Coal-Fired Utilities for

the Year 1990 .................... ES-11

ES-4. Summary of High-End Inhalation Cancer Risk Estimates

Based on Local Analysis for Oil-Fired Utilities for

the Year 1990 .................... ES-12

ES-5. Summary of High-End Inhalation Risk Estimates Due to

Local and Long-Range Transport ............ ES-13

2-1. Comparison of Particulate Matter Collection Systems . . 2-14

2-2. Distribution of SO Control Technologies in 1994 2 .... 2-18

2-3. Distribution of NO Control by Fuel Burned, by Unit, x

in 1994 ........................ 2-26

2-4. Fuel Use in the Electric Utility Industry by Fuel Type,

Quadrillion Btu/yr ................... 2-29

3-1. Assigned Chloride and HCl Concentrations in Coal,

by State of Coal Origin ................ 3-11

3-2. Average Higher Heating Values of Coal ......... 3-13

3-3. Selected Nationwide HAP Emissions ........... 3-15

3-4. Comparison of Utility Boiler Emissions from EFP

Estimates and from Tests ................ 3-17

3-5. Emissions from a Characteristic Coal-Fired Electric

Utility Plant (1994) .................. 3-18

3-6. Emissions from a Characteristic Oil-Fired Electric

Utility Plant (1994) .................. 3-19

3-7. Emissions from a Characteristic Natural Gas-Fired

Electric Utility Plant (1994) ............. 3-19

4-1. Weight-of-Evidence (WOE) Classification . . . . . . . . 4-6

5-1. Inhalation Screening Assessment for Carcinogenic HAPs

from Coal-Fired Utilities for Which Quantitative Cancer

Risk Estimates Were Available . . . . . . . . . . . . . 5-3

5-2. Inhalation Screening Assessment for Noncancer Effects of

HAPs Emitted from Coal-Fired Utilities for Which

Inhalation Reference Concentrations Are Available . . . 5-5

5-3. Inhalation Screening Assessment for HAPS Emitted from

Coal-Fired Utilities for Which No EPA-Verified Health

Benchmarks Are Available (Comparison of Highest

Modeled Air Concentration to Various Non-EPA Health

Benchmarks) . . . . . . . . . . . . . . . . . . . . . . 5-7
LIST OF TABLES (continued)

Table Page

xiii

5-4. Inhalation Screening Assessment for Carcinogenic HAPS

from Oil-Fired Utilities for Which Quantitative Cancer

Risk Estimates Were Available . . . . . . . . . . . . . 5-9

5-5. Inhalation Screening Assessment for Noncancer Effects

of HAPS Emitted from Oil-Fired Utilities for Which

EPA-Verified Inhalation Reference Concentrations Are

Available ....................... 5-11

5-6. Inhalation Screening Assessment for HAPS Emitted from

Oil-Fired Utilities for Which No EPA-Verified Health

Benchmarks Are Available (Comparison of Highest Modeled

Concentration to Various Non-EPA Health Benchmarks) . . 5-12

5-7. Inhalation Screening Assessment for HAPS Emitted from

Gas-Fired Utilities .................. 5-13

5-8. Thirteen HAPs Selected from the Hazard-Based

Multipathway Ranking (shown in order of ranking), and

the Overall and Individual Criterion Scores Assigned

to Each ........................ 5-16

5-9. Comparison of Cancer and Noncancer Effects Benchmarks

and Emissions Estimates for 13 Selected HAPs ...... 5-17

5-10. Pollutants Considered Priority for Further Analysis

Based on Results of Screening Assessment ........ 5-19

6-1. Summary of High-End Risk Estimates from Chronic

Inhalation Exposure by HAP for 424 U.S. Coal-Fired

Utilities Based on the Baseline Inhalation Risk

Assessment . . . . . . . . . . . . . . . . . . . . . . . 6-3

6-2. Summary of High-End Estimates of Population Exposed at

Various Levels of Inhalation Risk or Greater by HAP:

Coal-Fired Utilities . . . . . . . . . . . . . . . . . . 6-7

6-3. Summary of the High-End Risk Estimates from Inhalation

Exposure for Priority HAPs for 137 U.S. Oil-Fired

Utilities Based on the Baseline Risk Assessment . . . . 6-8

6-4. Summary of High-End Estimates of Population Exposed

Through Inhalation at Various Levels of Risk or Greater

from Oil-Fired Utilities ................ 6-12

6-5. Summary of High-End Inhalation Risk Estimates for

Gas-Fired Utilities .................. 6-13

6-6. Comparison of High-End Inhalation Cancer Risk Estimates

Based on (1) HEM Modeling Using Urban Default Assumption

and (2) HEM Modeling Using Urban vs. Rural Distinction . 6-14

6-7. Comparison of High-End Inhalation Noncancer Risk

Estimates Based on (1) HEM Modeling Using Urban Default

Assumption and (2) HEM Modeling Using Urban vs. Rural

Distinction ...................... 6-14

6-8. Estimated High-End Inhalation Cancer Risks for the

Year 2010 Compared to 1990 for Coal- and Oil-Fired

Utilities ....................... 6-16
LIST OF TABLES (continued)

Table Page

xiv

6-9. Estimated High-End Inhalation Noncancer Risks for

Coal-Fired Utilities for the Year 2010 Compared to

the Year 1990 ..................... 6-16

6-10. Noncancer Reference Exposure Levels (Acute) from CAPCOA 6-17

6-11. Sample Stack Parameters for Typical Utility Plant . . . 6-18

6-12. Stack and Emission Values Input to TSCREEN ....... 6-19

6-13. Results of the TSCREEN Model .............. 6-19

6-14. Comparison of Risk Estimates for Single-Count Versus

Double-Count Runs to Assess the Impact of Overlapping

Plumes ......................... 6-21

6-15. Windspeeds Used for Each Pasquill Stability Category in

CARB Subroutine Calculations .............. 6-25

6-16. Roughness Length Used for Each Land-Use Category in

CARB Subroutine Calculations .............. 6-25

6-17. RELMAP Predicted Air Concentrations .......... 6-26

6-18. Predicted Exposure and High-End Risk Estimates Based on

RELMAP Modeling of Particulate Metal Emissions from All

Oil- and Coal-Fired Utilities in the United States. . . 6-41

6-19. Summary of the High-End Estimates of the Inhalation Risk

Estimates Due to Local and Long-Range Transport .... 6-42

6-20. Chromium Speciation Analysis for Coal-Fired Utilities: 

Inhalation Risk Estimates due to Chromium Emissions

Based on Various Assumptions of Percent Chromium VI . . 6-46

6-21. Chromium Speciation Analysis for Oil-Fired Utilities:

Inhalation Risk Estimates due to Chromium Based on

Various Assumptions of Percent Chromium VI ....... 6-46

6-22. High-End Arsenic Inhalation Risk Estimates: Comparison

of Results Using the EPRI, EPA-Verified, and Canadian

IUREs ......................... 6-48

6-23. Nickel from Oil-Fired Utilities: Inhalation Cancer Risk

Estimates Based On Various Assumptions of Speciation and

Cancer Potency ..................... 6-50

6-24. Comparison of Nickel Exposure to Various Noncancer

Health Benchmarks ................... 6-52

6-25. Summary of Basic Parameters Used in the Inhalation Risk

Assessment for Electric Utilities ........... 6-61

6-26. Summary of Results for Monte Carlo Simulation of HAP

Emissions (kg/year) from Oil-Fired Plant No. 29 .... 6-66

6-27. Distribution of MIR: Plant No. 29: Comparison of FCEM

and SGS Concentration Data ............... 6-71

7-1. Best Point Estimates of National Mercury Emission Rates

by Category . . . . . . . . . . . . . . . . . . . . . . 7-8

7-2. Summary of U.S. EPA Hazard Identification/Dose-Response

Assessment for Methylmercury .............. 7-20

7-3. Models Used to Predict Mercury Air Concentrations,

Deposition Fluxes, and Environmental Concentrations . . 7-24
LIST OF TABLES (continued)

Table Page

xv

7-4. Mercury Emissions Inventory Used in the RELMAP Modeling

(Based on the 1994-95 Estimates) ............ 7-24

7-5. Process Parameters for Model Plants .......... 7-30

7-6. Percentiles of the Methylmercury Bioaccumulation Factor 7-34

7-7. Fish Consumption Rates for Columbia River Tribes .... 7-36

7-8. Daily Fish Consumption Rates Among Adults in the

Columbia River Tribes ................. 7-36

7-9. Fish Consumption Rates Used in This Study ....... 7-36

7-10. Model Results for Eastern Site, RELMAP 50 Percentile th

(utilities only) .................... 7-37

7-11. Model Results for Western Site, RELMAP 50 Percentile th

(utilities only) .................... 7-37

7-12. Predicted Exposure Results for Eastern Site, RELMAP 50th

Percentile (utilities only) .............. 7-38

7-13. Predicted Exposure Results for Western Site, RELMAP 50th

Percentile (utilities only) .............. 7-38

8-1. Concentration of Lead in Various Food Products . . . . . 8-4

8-2. Concentration of Cadmium in Various Food Products . . . 8-12

9-1. Utilization and Radionuclide Content by Coal Rank . . . 9-3

9-2. Estimates of Average Radionuclide Concentrations in

42 Residual Fuel Oil Samples . . . . . . . . . . . . . . 9-5

9-3. Average Annual Radionuclide Emissions per Operating

Boiler Unit and per Billion Kilowatt-Hour Electricity

Generated . . . . . . . . . . . . . . . . . . . . . . . 9-6

9-4. Frequency Distribution of Lifetime Fatal Cancer Risks

for All Plants ..................... 9-13

9-5. Plants with the Highest Estimated Maximum Individual Risk

(MIR) ......................... 9-13

9-6 Average Background Radiation Doses (effective dose 

equivalent excluding inhaled radon pogeny) ....... 9-15

9-7 Average Annual Background Exposures Due To Radon Pogency 9-15

10-1. Common Arsenic Compounds, and Classification by Valence

State and Organic/Inorganic. .............. 10-3

10-2. National Arsenic Atmospheric Emission Estimates by

Source Category. .................... 10-5

10-3. Reported Arsenic Air Concentrations. .......... 10-5

10-4. Measured Arsenic Deposition Rates. ........... 10-6

10-5. Measured Arsenic Concentrations in Plants. ....... 10-8

10-6. Measured Arsenic Concentrations in Meat and Other Animal

Products ........................ 10-9

10-7. Total Arsenic Concentrations in Freshwater Fish in the

United States .................... 10-10
LIST OF TABLES (continued)

Table Page

xvi

10-8. Percentage of Inorganic Arsenic Compared to Total

Arsenic in Selected Foods .............. 10-11

10-9. Environmental Concentrations near Facilities ..... 10-11

10-10. Summary of Model Plants and Emission Rates Used for

the Assessment. ................... 10-13

10-11. Summary of Human Exposure Scenarios Considered. . . . 10-17

10-12. Default Values of Scenario-Independent Exposure

Parameters ..................... 10-17

10-13. Values for Scenario-Dependent Exposure Parameters. . . 10-18

10-14. Fraction of Arsenic Emissions Predicted to Be

Deposited Within 50 km in an Arid Site. ....... 10-22

10-15. Fraction of Arsenic Emissions Predicted to Be

Deposited Within 50 km in a Humid Site. ....... 10-22

10-16. RELMAP Air Modeling Results. ............. 10-23

10-17. Predicted Surface Water and Benthic Sediment

Concentrations for the Hypothetical Water Bodies. . . 10-24

10-18. Modeled Arsenic Concentrations. ........... 10-24

10-19. Predicted Total Arsenic Exposure for Hypothetical

Receptors. ...................... 10-25

10-20. Predicted Total Inorganic Arsenic Exposure for

Hypothetical Receptors. ............... 10-25

10-21. Watershed Air Concentration ............. 10-27

10-22. Watershed Deposition Rate .............. 10-27

10-23. Surface Water Concentration ............. 10-28

10-24. Untilled Soil Concentration ............. 10-28

10-25. Grain Concentration ................. 10-29

10-26. Inorganic Arsenic Intake via Ingestion for Pica Child 10-30

10-27. Inorganic Arsenic Intake via Ingestion for Subsistence

Farmer Adult ..................... 10-30

10-28. Inorganic Arsenic Intake via Ingestion for Subsistence

Farmer Child ..................... 10-31

10-29. Inorganic Arsenic Intake via Ingestion for Subsistence

Fisher Adult ..................... 10-31

10-30. Inorganic Arsenic Intake via Ingestion for Subsistence

Fisher Child ..................... 10-32

10-31. Inorganic Arsenic Intake via Inhalation for Pica Child 10-32

10-32. Inorganic Arsenic Intake via Inhalation for 

Subsistence Farmer Adult and Subsistence Fisher Adult 10-33

10-33. Inorganic Arsenic Intake via Inhalation for 

Subsistence Farmer Child and Subsistence Fisher Child. 10-33

10-34. Inorganic Arsenic Intake, Predicted Cancer Risk, and

Noncancer Hazard Quotient (HQ) for Pica Child .... 10-38

10-35. Inorganic Arsenic Intake, Predicted Cancer Risk, and

Noncancer Hazards for Subsistence Farmer Adult .... 10-38

10-36. Inorganic Arsenic Intake, Predicted Cancer Risk, and

Noncancer Hazards for Subsistence Farmer Child .... 10-39
LIST OF TABLES (continued)

Table Page

xvii

10-37. Inorganic Arsenic Intake, Predicted Cancer Risk, and

Noncancer Hazards for Subsistence Fisher Adult .... 10-39

10-38. Inorganic Arsenic Intake, Predicted Cancer Risk, and

Noncancer Hazards for Subsistence Fisher Child .... 10-40

10-39. Inorganic Arsenic Intake via Inhalation and Predicted

Cancer Risks for Pica Child ............. 10-40

10-40. Inorganic Arsenic Intake via Inhalation for 

Subsistence Farmer Adult and Subsistence Fisher Adult 

and Predicted Cancer Risks .............. 10-41

10-41. Inorganic Arsenic Intake via Inhalation for 

Subsistence Farmer Child and Predicted Cancer Risks . 10-41

11-1. Congener-Specific Emissions Rates for Model

Plants (kg/yr) .................... 11-11

11-2. Process Parameters for Model Plants ......... 11-11

11-3. Naming Scheme for Eight Model Plants ......... 11-12

11-4. Process-Specific Depletion Parameters ........ 11-13

11-5. Other Emissions Source Information .......... 11-13

11-6. Summary of Receptor Scenarios and Pathways ...... 11-17

11-7. The 16 Hypothetical Scenarios Included in the

Screening Level, Model Plant, Dioxin Multipathway

Exposure and Risk Assessment ............. 11-17

11-8. Summary of Predicted Cancer Risks from the Screening

Level Multipathway Assessment for Model Plants, for 

16 Hypothetical Scenarios .............. 11-20

11-9. Fish Consumption Pathway Sensitivity Analysis Inputs

and Results ..................... 11-26

11-10. Maximum Dispersion Modeling Locations and

Concentrations .................... 11-28

11-11 Dispersion Modeling Concentrations at Specified

Distances ...................... 11-29

13-1. Comparison of Average Concentrations of Trace Elements

in Utility Fuels .................... 13-2

13-2. Trace Element Reductions Achieved Through Conventional

Coal Cleaning ..................... 13-9

13-3. Emissions from an Air-Blown, Fixed-Bed Gasifier . . . 13-13

13-4. Comparison of Electric Utility Emissions Before and

After Application of NO Control or Application of x

Greater NO Control on a Unit With Lesser NO Control x x

(lb/trillion Btu) .................. 13-14

13-5. Comparison of Wet Bottom vs. Dry Bottom Electric

Utility Boilers Metallic HAP Emissions, Trace

Element Removal, and Trace Metal Concentrations in

Feed Coal ...................... 13-16

13-6. Descriptive Statistics for HAP Removal Efficiencies

Shown in Figures 13-7 and 13-8 ............ 13-23
LIST OF TABLES (continued)

Table Page

xviii

13-7. Descriptive Statistics for HAP Removal Efficiencies

Shown in Figures 13-9 and 13-10 ........... 13-24

13-8. Descriptive Statistics for HAP Removal Efficiencies

Shown in Figures 13-11 and 13-12 ........... 13-25

13-9. Descriptive Statistics for HAP Removal Efficiencies

Shown in Figures 13-13 and 13-14 .......... 13-26

13-10. Particulate Metallic HAP Removal Percentage from ESPs

and FFs (Excluding Mercury) ............. 13-27

13-11. Descriptive Statistics for HAP Removal Efficiencies

Shown in Figures 13-15 and 13-16 ........... 13-29

13-12. Descriptive Statistics for HAP Removal Efficiencies

Shown in Figures 13-17 and 13-18 ........... 13-30

13-13. Qualitative Effects of Different Control Strategies 

on Air Emissions of HAPs ............... 13-33

13-14. Comparison of Typical Uncontrolled Flue Gas Parameters

at Utilities and MWCs ................ 13-42
xix

LIST OF FIGURES

Figure Page

ES-1. Summary of the utility air toxics study ...... ES-3

ES-2. Number of coal-fired utilities posing various levels

of maximum individual risks (by levels of MIR) . . . ES-9

ES-3. Number of oil-fired utilities posing various levels

of maximum individual risks (by levels of MIR) . . ES-10

2-1. Fossil fuel use in the utility industry in 1994 . . 2-2

2-2. Unit types in the utility industry by fuel type

in 1994 . . . . . . . . . . . . . . . . . . . . . . 2-8

2-3. Particulate control in the utility industry by fuel

type in 1994 .................... 2-11

2-4. SO control in the utility industry in 1994 (coal- 2

fired boilers only). ................ 2-16

2-5. Nitrogen oxide control in the utility industry by

fuel type in 1994 ................. 2-22

2-6. Fuel use in the utility industry by fuel type in

1990 and projections for the year 2010 ....... 2-27

2-7. Projected use of fuels by 2010 for utility industry 2-30

3-1. Trace elements in coal . . . . . . . . . . . . . . . 3-5

3-2. Trace elements in oil and natural gas . . . . . . . 3-6

3-3. Organic emissions . . . . . . . . . . . . . . . . . 3-7

6-1a. Number of coal-fired utilities posing various levels

of maximum individual risks (by levels of MIR) . . . 6-5

6-1b. Number of coal-fired utilities posing various levels

of maximum individual risk (by levels of MIR) . . . 6-6

6-2a. Number of oil-fired utilities posing various levels

of maximum individual risk (by levels of MIR) . . . 6-10

6-2b. Number of oil-fired utilities posing various levels

of maximum individual risk (by levels of MIR) . . . 6-11

6-3. Results of the RELMAP modeling analysis from 1990

emissions estimates for arsenic from coal utilities: 

predicted air concentration of arsenic,

units: ng/m .................... 6-27 3

6-4. Results of the RELMAP modeling analysis from 1990

emissions estimates for arsenic from oil utilities:

predicted air concentration of arsenic, 

units: ng/m .................... 6-28 3

6-5. Results of the RELMAP modeling analysis from 1990

emissions estimates for arsenic from coal and oil

utilities: predicted air concentration of arsenic,

units: ng/m .................... 6-29 3

6-6. Results of the RELMAP modeling analysis from 1990

emissions estimates for cadmium from coal utilities:

predicted air concentration of cadmium, 

units: ng/m .................... 6-30 3
LIST OF FIGURES (continued)

Figure Page

xx

6-7. Results of the RELMAP modeling analysis from 1990

emissions estimates for cadmium from oil utilities:

predicted air concentration of cadmium,

units: ng/m .................... 6-31 3

6-8. Results of the RELMAP modeling analysis from 1990

emissions estimates for cadmium from coal and oil

utilities: predicted air concentration of cadmium,

units: ng/m .................... 6-32 3

6-9. Results of the RELMAP modeling analysis from 1990

emissions estimates for chromium from coal utilities:

predicted air concentration of chromium,

units: ng/m .................... 6-33 3

6-10. Results of the RELMAP modeling analysis from 1990

emissions estimates for chromium from oil utilities:

predicted air concentration of chromium,

units: ng/m .................... 6-34 3

6-11. Results of the RELMAP modeling analysis from 1990

emissions estimates for chromium from coal and oil

utilities: predicted air concentration of chromium,

units: ng/m .................... 6-35 3

6-12. Results of the RELMAP modeling analysis from 1990

emissions estimates for nickel from coal utilities:

predicted air concentration of nickel,

units: ng/m .................... 6-36 3

6-13. Results of the RELMAP modeling analysis from 1990

emissions estimates for nickel from oil utilities:

predicted air concentration of nickel,

units: ng/m .................... 6-37 3

6-14. Results of the RELMAP modeling analysis from 1990

emissions estimates for nickel from coal and oil

utilities: predicted air concentration of nickel,

units: ng/m .................... 6-38 3

6-15. Estimates of annual cancer incidence due to

inhalation exposure to HAP emissions from oil-fired

electric utilities based on the local analysis using

alternative UREs for nickel (as % of nickel

subsulfide) .................... 6-51

6-16. Depiction of combining component uncertainty

distributions (i.e., emissions, dispersion, and

exposure-response) into an overall distribution of

uncertainty (e.g., MIR) .............. 6-65

6-17. Summary of results of Monte Carlo simulation of HAP

emissions from oil-fired plant no. 29 ....... 6-67

7-1. Comparison of estimated current and pre-industrial

mercury budgets and fluxes . . . . . . . . . . . . . 7-3
LIST OF FIGURES (continued)

Figure Page

xxi

7-2. Total modeled mercury deposits from wet and dry

deposition from coal utilities based on 1994 emissions

estimates as modeled with RELMAP,

units: g/m /yr . . . . . . . . . . . . . . . . . . 7-25 2

7-3. Total modeled mercury deposits from wet and dry

deposition from oil utilities based on 1994

emissions estimates as modeled with RELMAP,

units: g/m /yr .................. 7-26 2

7-4. Total modeled mercury deposits from wet and dry

deposition from coal and oil utilities based on 1994

emissions estimates as modeled with RELMAP,

units: g/m /yr . . . . . . . . . . . . . . . . . . 7-27 2

7-5. Overview of the IEM-2M watershed modules ...... 7-31

7-6. Configuration of hypothetical water body and

watershed relative to local source ......... 7-32

8-1. Results of the RELMAP modeling analysis from 1990

emissions estimates for lead from coal utilities:

predicted air concentration of lead, units: ng/m . 8-5 3

8-2. Results of the RELMAP modeling analysis from 1990

emissions estimates for lead from oil utilities:

predicted air concentration of lead, units: ng/m . 8-6 3

8-3 Results of the RELMAP modeling analysis from 1990

emissions estimates for lead from coal and oil

utilities: predicted air concentration of lead,

units: ng/m . . . . . . . . . . . . . . . . . . . . 8-7 3

8-4. Predicted lead wet and dry deposition from coal

utilities based on 1990 emissions estimates as

modeled with RELMAP, units: g/m /yr . . . . . . . . 8-8 2

8-5. Predicted lead wet and dry deposition from oil

utilities based on 1990 emissions estimates as

modeled with RELMAP, units: g/m /yr . . . . . . . . 8-9 2

8-6. Predicted lead wet and dry deposition from coal

and oil utilities based on 1990 emissions

estimates as modeled with RELMAP,

units: g/m /yr . . . . . . . . . . . . . . . . . . 8-10 2

8-7. Predicted cadmium wet and dry deposition from coal

utilities based on 1990 emissions estimates as

modeled with RELMAP, units: g/m /yr . . . . . . . . 8-13 2

8-8. Predicted cadmium wet and dry deposition from oil

utilities based on 1990 emissions estimates as

modeled with RELMAP, units: g/m /yr . . . . . . . . 8-14 2

8-9. Predicted cadmium wet and dry deposition from coal

and oil utilities based on 1990 emissions estimates

as modeled with RELMAP, units: g/m /yr . . . . . . 8-15 2
LIST OF FIGURES (continued)

Figure Page

xxii

10-1. The generalized geochemical cycle for arsenic . . . 10-3

10-2. Predicted arsenic wet and dry deposition from coal

utilities based on 1990 emissions estimates as

modeled with RELMAP, units: g/m /yr . . . . . . . 10-14 2

10-3. Predicted arsenic wet and dry deposition from oil

utilities based on 1990 emissions estimates as

modeled with RELMAP, units: g/m /yr . . . . . . . 10-15 2

10-4. Predicted arsenic wet and dry deposition from coal

and oil utilities based on 1990 emissions estimates

as modeled with RELMAP, units: g/m /yr . . . . . 10-16 2

10-5. Location of waterbody considered within watershed 10-20

11-1. Results of the RELMAP modeling analysis from 1990

emissions estimates for total dioxin (incl TEQ

factors) from coal and oil utilities: predicted

air concentration of total dioxin, units: attograms

(10 grams)/m .................. 11-7 -18 3

11-2. Predicted total dioxin (incl TEQ factors) wet

deposition from coal and oil utilities based on

1990 emissions estimates as modeled with RELMAP,

units: picograms (10 grams)/m .......... 11-8 -12 3

11-3. Predicted total dioxin (incl TEQ factors) dry

deposition from coal and oil utilities based on 1990

emissions estimates as modeled with RELMAP,

units: picograms (10 grams)/m ......... 11-9 -12 3

11-4. Flow chart of multipathway processes ....... 11-15

11-5. Risk model sensitivity to changes in meat and fish

factors ..................... 11-24

11-6. Risk model sensitivity to changes in soil loss rate

constant ..................... 11-24

11-7. Sensitivity of predicted risk to the subsistence

fisher to changes in parameter values ...... 11-26

13-1. Relation between the concentrations of mercury and

Sulfur in 153 samples of coal shipments ...... 13-4

13-2(a-g). Relation between concentration of selected trace

Elements and sulfur in modified USGS data ..... 13-6

13-3. Coal gasification combined cycle technology . . . 13-12

13-4(a-c). Average coal-fired boiler emissions, trace metal

removal, and average trace element concentration

in feed coal vs. bottom type (bituminous and

subbituminous coal) ............... 13-19

13-5(a-c). Average coal-fired boiler emissions, trace metal

removal, and average trace element concentration

in feed coal vs. bottom type (bituminous coal-fired

only) ...................... 13-20
LIST OF FIGURES (continued)

Figure Page

xxiii

13-6(a-c). Average coal-fired boiler emissions, trace metal

removal, and average trace element concentration

in feed coal vs. bottom type (subbituminous

coal-fired only) ................. 13-21

13-7. Removal of metallic HAPs by electrostatic

precipitators (cold-side, coal) (includes, arsenic,

beryllium, cadmium, chromium, lead, and manganese) 13-23

13-8. Removal of mercury by electrostatic precipitators

(cold-side, coal) ................ 13-23

13-9. Removal of metallic HAPs by electrostatic

precipitators (hot-side, coal) (includes, arsenic,

beryllium, cadmium, chromium, lead, and manganese) 13-24

13-10. Removal of mercury by electrostatic precipitators

(hot-side, coal) ................. 13-24

13-11. Removal of metallic HAPs by an electrostatic

precipitator (oil) (includes, arsenic, lead, and

nickel) ..................... 13-25

13-12. Removal of mercury by an electrostatic

precipitator (oil) ................ 13-25

13-13. Removal of metallic HAPs by a fabric filter(coal)

(includes, arsenic, beryllium, cadmium, chromium,

lead, and manganese) ............... 13-26

13-14. Removal of mercury by a fabric filter (coal) . . . 13-26

13-15. Removal of metallic HAPs by an FGD (coal) (includes,

arsenic, beryllium, cadmium, chromium, lead, and

manganese) .................... 13-29

13-16. Removal of mercury by an FGD (coal) ....... 13-29

13-17. Removal of metallic HAPs by a spray dryer

adsorber/fabric filter (coal) (includes, arsenic,

beryllium, cadmium, chromium, lead, and manganese) 13-30

13-18 Removal of mercury by a spray dryer adsorber/

fabric filter (coal) ............... 13-30
x
xxv

Glossary

AALG ambient air level goal

AC activated carbon

AECDP Advanced Emissions Control Development Program (Babcock

& Wilcox)

AFBC atmospheric fluidized-bed combustor

APCD air pollution control device

ACGIH American Conference of Government Industrial Hygienists

ARD Acid Rain Division (EPA)

ATSDR Agency for Toxic Substances and Disease Registry

BAF bioaccumulation factor

BAP benzo[a]pyrene

BBF biased burner firing

BCF bioconcentration factor

BFB bubbling fluidized bed

BOO burners out of service

BSAF biota sediment accumulation factor

CAA Clean Air Act

CAP Clean Air Act Assessment Package

CAPCOA California Air Pollution Control Officers Association

CARB California Air Resource Board

CCT clean coal technology

CEDF Clean Environment Development Facility (Babcock &

Wilcox)

CFB circulating fluidized bed

CLD certainly lethal dose

CNS central nervous system

CSF cancer slope factor

DNA deoxyribonucleic acid

DOC dissolved organic carbon

DOE Department of Energy

DSM demand side management

E (ratio of) exposure

ECTC Environmental Control Test Center (EPRI)
xxvi

EEI Edison Electric Institute

EFP emission factor program

EIA Energy Information Administration (DOE)

E-LIDS Enhanced Limestone Injection Dry Scrubbing TM

EMF emission modification factor

ENAMAP Eastern North American Model of Air Pollution

EPA Environmental Protection Agency

EPRI Electric Power Research Institute

ESP electrostatic precipitator

EURMAP European Regional Model of Air Pollution

FBC fluidized-bed combustor

FCEM field chemical emissions monitoring

FETC Federal Energy Technology Center

FF fabric filter

FGD flue gas desulfurization

FGR fluidized gas recirculation

FTIR Fourier transform infrared

GEIA Global Emissions Inventory Activity

GI gastrointestinal

GIS Geographic Information System

HAP hazardous air pollutant

HEC human equivalent concentration

HEM Human Exposure Model

HI hazard index

HQ hazard quotient

IARC International Agency for Research on Cancer

ICRP International Commission on Radiological Protection

IDLH immediately dangerous to life and health

IEM indirect exposure methodology

IGCC integrated gasification combined cycle

IPP independent power producer

IRIS Integrated Risk Information System

IRP Inerts Ranking Program

ISC3 Industrial Source Complex Version 3

ISCLT2 Industrial Source Complex Long Term Version 2
xxvii

ISCLT3 Industrial Source Complex Long Term Version 3

ISCST3 Industrial Source Complex Short Term Version 3

IURE inhalation unit risk estimate

LADD lifetime averaged daily dose

LC /LD lethal concentration/dose that kills 50% of test animals 50 50

LCUB large coal-fired utility boiler

LET linear energy transfer

L/G liquid-to-gas

LOAEL lowest-observed-adverse-effect level

LRT long-range transport

MACT maximum achievable control technology

MCL maximum contaminant level

MCLG maximum contaminant level goal

MCUB medium coal-fired utility boiler

MDL minimum detectable level

MEI maximally exposed individual

MFP monofluorophosphate

MIR maximum individual risk

MLE maximum likelihood estimate

MRL minimal risk level

MWC municipal waste combustor

MWe megawatts electric

MWI medical waste incinerator

NAAQS national ambient air quality standards

NAPAP National Acid Precipitation Assessment Program

NAREL National Air and Radiation Environmental Laboratory

NAS National Academy of Science

NASN National Air Surveillance Network

NGM nested grid model

NIEHS National Institute of Environmental Health Sciences

NIOSH National Institute for Occupational Safety and Health

NIST National Institute of Standards and Technology

NMHC nonmethane hydrocarbon

NOAA National Oceanographic and Atmospheric Administration

NOAEL no-observed-adverse-effect level
xxviii

NOEC no-observed-effect concentration

NRC National Research Council

NSPC Northern States Power Company

NSPS new source performance standards

NTP National Toxicology Program

NWS National Weather Service

OAQPS Office of Air Quality Planning and Standards (EPA)

OAR Office of Air and Radiation (EPA)

OFA overfire air

ORD Office of Research and Development (EPA)

ORIA Office of Radiation and Indoor Air (EPA)

OSF off-stoichiometric firing

OSHA Occupational Safety and Health Administration

OTAG Ozone Transport Assessment Group

OURE oral unit risk estimate

PAB Pollutant Assessment Branch (EPA)

PAH polycyclic aromatic hydrocarbon

PBPK physiologically based pharmacokinetic

PCB polycyclic biphenyl

PCDD polychlorinated dibenzo-p-dioxin

PCDF polychlorinated dibenzofuran

PEL permissible exposure limit

PFBC pressurized fluidized-bed combustor

PM particulate matter

PSCCo Public Services Company of Colorado

PURPA Public Utility Regulatory Policies Act

RAC Risk Assessment Council (EPA)

RBC risk based concentration

RCRA Resource Conservation and Recovery Act

REL reference exposure level

RELMAP Regional Lagrangian Model of Air Pollution

RfC reference concentration

RfD reference dose

SAB Science Advisory Board

SAMSON Solar and Meteorological Surface Observation Network
xxix

SBS small boiler simulator

SCR selective catalytic reduction

SCRAM-BBS Support Center for Regulatory Air Models Bulletin Board

System

SCUB small coal-fired utility boiler

SDA spray dryer absorber

SE standard error

SGU steam generating unit

SIP State Implementation Plan

SNCR selective noncatalytic reduction

SPC Science Policy Council (EPA)

STAR STability ARray

TCDD tetrachlorodibenzo-p-dioxin

TE trace element

TEF toxicity (or toxic) equivalency factor

TEQ toxicity equivalent

TLV threshold limit value

TOC total organic compounds

tpy tons per year

TRI Toxics Release Inventory

TSP total suspended particulate

TTN Technology Transfer Network

UARG Utility Air Regulatory Group

UDI Utility Data Institute

UNDEERC University of North Dakota Energy and Environmental

Research Center

URE unit risk estimate

USGS U.S. Geological Survey

USPHS U.S. Public Health Service

VOC volatile organic compound

WHO World Health Organization

WL working level

WLM working level month

WOE weight of evidence

ES-1

EXECUTIVE SUMMARY

ES.1 LEGISLATIVE MANDATE

In section 112(n)(1)(A) of the Clean Air Act, as amended (the

Act), Congress directs the United States Environmental Protection

Agency (EPA) to:

"... perform a study of the hazards to public health

reasonably anticipated to occur as a result of emissions by

electric utility steam generating units of ... [hazardous

air pollutants] ... after imposition of the requirements of

this Act."

Section 112(a)(8) of the Act defines an "electric utility

steam-generating unit" as "any fossil-fuel–fired combustion unit of

more than 25 megawatts electric (MWe) that serves a generator that

produces electricity for sale." A unit that cogenerates steam and

electricity and supplies more than one-third of its potential electric

output capacity and more than 25 MWe output to any utility power

distribution system for sale is also considered an electric utility

steam-generating unit (i.e., utility unit).

Section 112(n)(1)(A) also requires that:

 The EPA develop and describe alternative control strategies

for hazardous air pollutants (HAPs) that may warrant

regulation under section 112; and

 The EPA proceed with rulemaking activities under section 112

to control HAP emissions from utilities if EPA finds such

regulation is appropriate and necessary after considering

the results of the study.

ES.2 REGULATORY DETERMINATION

This report does not contain a determination as to whether or not

regulations to control HAP emissions from utility units are

appropriate and necessary. The Agency has deferred the regulatory

determination until a later date.

ES.3 OVERVIEW APPROACH TO COMPLETING THE STUDY

 The study included numerous separate and interrelated analyses. 

First, HAP emissions test data were gathered from 52 utility units

(i.e., boilers), including a range of coal-, oil-, and natural

gas-fired utility units. Second, the emissions test data along with

facility specific information (e.g., boiler type, control device, fuel

usage) were used to estimate HAP emissions from all 684 utility plants

in the United States (U.S.). Third, a screening level hazard/risk

assessment was completed to prioritize the HAPs for further analyses. 

Fourth, various priority HAPs were analyzed for inhalation and
ES-2

multipathway exposures and risks and other potential impacts. In

addition, potential control strategies were analyzed for the priority

HAPs. The overall summary of the study is presented in Figure ES-1.

This report presents the findings of the study. The primary

components of this report are: (1) a description of the industry;

(2) an analysis of emissions data; (3) an assessment of hazards and

risks due to inhalation exposures to 67 HAPs; (4) assessments of risks

due to multipathway (inhalation plus non-inhalation) exposures to four

HAPs (radionuclides, mercury, arsenic, and dioxins); and (5) a

discussion of alternative control strategies.

The study was based primarily on two scenarios: (1) 1990 base

year emissions; and (2) 2010 emissions. In addition, emissions for

1994 were estimated using the most recent data. The 1990 scenario was

chosen since that was the year the Amendments to the Act were passed

and was the latest year for which utility operational data were

available at the time the study was initiated. The 2010 scenario was

selected to meet the section 112(n)(1)(A) mandate to evaluate hazards

“after imposition of the requirements of the Act.” Primarily, this

meant assessing the hazards after the acid rain program is in place. 

The 2010 scenario also included estimated changes in HAP emissions

resulting from projected trends in fuel choices and projected

increases in electric power demands. However, the effects of other

on-going or potential activities that were not factored into the 2010

projections (e.g., industry restructuring, new ozone and particulate

matter [PM] standards, global climate change programs) may result in

the 2010 projections being either underestimated or overestimated.

ES.4 EMISSIONS DATA ANALYSIS

A total of 684 utility plants (i.e., utilities) were identified

as meeting the criteria for the study in 1990 in the U.S. These

utilities are fueled primarily by coal (59 percent of total units),

oil (12 percent), or natural gas (29 percent). Many plants have two

or more units and several plants burn more than one type of fuel

(e.g., contain both coal- and oil-fired units). In 1990, there were

426 plants that burned coal as one of their fuels, 137 plants that

burned oil, and 267 plants that burned natural gas.

Emission estimates for the years 1990, 1994, and 2010 were based

on emissions test data from 52 units obtained from extensive emission

tests by the Electric Power Research Institute (EPRI), the Department

of Energy (DOE), the Northern States Power Company, and the EPA. The

testing program was designed to test a wide range of facility types

with a variety of control scenarios; therefore, the data are

considered generally representative of the industry. However, there

are uncertainties in the data because of the small sample sizes for

specific boiler types and control scenarios.
Figure ES-1. Overall Structure of Utility Air Toxics Study Analyses

Screening Assessment

to Prioritize HAPs

-14 HAPs identified

Alternative Control

Strategies Analysis

- Mercury

- Metals

- Dioxins

- HCl/HF

Multipathway Exposure/Risk Assessment

Local Model (CAP 93)

- Radionuclides only

- All Plants

- MIR 3E-05

- Cancer Incidence 0.3

 cases/yr (coal & oil)

Regional Model ( RELMAP)

- All Plants

- 7 HAPs (Hg, As, Dioxins,

 Cd, Cr, Ni, Pb)

Inhalation Exposure/Risk Assessment

Local Analysis

- Within 50 km of Each Plant

- Modeled 67 HAPs from All

 Plants with Local Model

 (HEM)

Risk Assessment Results

- Ni, As, Cr, Rads Primary HAPs

- Coal Incidence up to 0.2 cases/yr

- Oil Incidence up to 0.5 cases/yr

- Highest Predicted Cancer MIR

 for Ni up to 5E-05

- Most Risks Predicted

 to be < 1E-06

Local plus Long-range

Transport

- Beyond 50 km to U.S. Borders

- Regional Model (RELMAP)

- Modeled 4 HAPs from All

 Plants (Ni, As, Cd, Cr)

Risk Assessment Results

- Coal Incidence Increased 7 Fold, up

 to 1.3 cases/yr

- Oil Incidence up to 0.5 cases/yr

- No Change in Predicted Cancer

 MIRs

Arsenic - Results

- Background Dominates

 Exposure

- Highest Predicted Cancer

 MIR up to 1E-04 ("pica child")

- Most Risks Predicted

 to be < 1E-05

Mercury - Results

- Fish was Dominant

 Exposure Pathway

- Effect of Concern is

 Devel. Neuro. Toxicity

- Plausible Link

Dioxins - Results

- Highly Toxic at Low Levels

- Highest Predicted Cancer

 MIR up to 2E-04 (coal)

- Majority of Risks Predicted

 to be < 1E-05

Emissions Data Analysis

- Test Data from 52 Units

- 67 HAPs Identified

- Emissions Estimates for 684 Plants in U.S.

HCl and HF

- Literature Review

of Potential Impacts

 Deposition & Air

Concentration

Representative Plant Analyses

- 4 Model Plants

- 3 HAPs (Hg, As, Dioxins)

- Local Dep. Model (ISC3)

- Food Web Model (IEM)

- Hypothetical Exposure Scenarios

Estimated Air

Concentration in

U.S. from All

Plants Air Concentration

Estimated Air

Concentration and

Depostion in U.S.

from All Plants

MAPS

MAPS

ES-3
ES-4

These test data provided the basis for estimating average annual

emissions for each of the 684 plants. A total of 67 of the 188 HAPs

listed in section 112 of the Act were identified in the emissions

testing program as potentially being emitted by utilities. Tables

ES-1 and ES-2 present estimated emissions for, respectively, a subset

of priority HAPs for 1990, 1994, and 2010, and for a set of

characteristic boilers for 1994.

Although the EPA used average annual emissions estimates in

assessing long-term exposures to individual HAPs on a national basis,

emissions test data were not available for each utility in the U.S. 

Therefore, estimates for individual plants are particularly uncertain. 

Based on an uncertainty analysis, the average annual emissions

estimates are expected to be roughly within a factor of plus or minus

three of actual annual emissions. However, even this uncertainty

analysis had limitations. For example, the uncertainty analysis did

not include data on potential upsets or unusual operating conditions;

therefore, the range of uncertainty could be greater.

ES.5 GENERAL APPROACH TO EXPOSURE AND RISK ASSESSMENT

Most of the risk assessment focused on inhalation exposure. All

67 HAPs were assessed for inhalation exposures, at least at a

screening level. For many of the 67 HAPs, inhalation exposure is

believed to be the dominant exposure pathway. However, for HAPs that

are persistent and/or bioaccumulate, and are toxic by ingestion (or

are radioactive), the non-inhalation exposure pathways could be more

important. Based on a screening and prioritization assessment, which

is described below, the EPA identified four high priority HAPs

(radionuclides, mercury, arsenic, dioxins) to assess for noninhalation exposures. In addition, cadmium and lead were identified

as next highest priority. Multipathway assessments are presented for

radionuclides, mercury, arsenic, and dioxins. The other two HAPs

(lead and cadmium) were examined qualitatively for their potential for

multipathway hazards. 

ES.6 SCREENING ASSESSMENT

As outlined in Figure ES-1, EPA initially conducted a screening

assessment that considered inhalation and non-inhalation exposure

routes for all 67 HAPs to identify priority HAPs for more detailed

assessment. To screen for inhalation exposures, the EPA used the

Human Exposure Model (HEM) to model the 67 HAPs from all 684 utility

plants utilizing generally conservative assumptions (i.e., assumptions

that are more likely to overestimate rather than underestimate risks)

to estimate inhalation risks for maximally exposed individuals (MEIs). 

 
ES-5

Table ES-1. Nationwide Utility Emissions for Thirteen Priority HAPsa

HAP

Nationwide HAP emission estimates (tons per year) b

Coal Oil Na

1990 1994 2010 1990 1994 2010 1990 

Arsenic 61 56 71 5 4 3 0.15 Beryllium 7.1 7.9 8.2 0.46 0.4 0.23 NM 

c

Cadmium 3.3 3.2 3.8 1.7 1.1 0.9 - Chromium 73 62 87 4.7 3.9 2.4 - Lead 75 62 87 11 8.9 5.4 0.43 Manganese 164 168 219 9.3 7.3 4.7 - Mercury 46 51 60 0.25 0.2 0.13 0.0015 Nickel 58 52 69 390 320 200 2.2 Hydrogen chloride 143,000 134,000 155,000 2,900 2,100 1,500 NM Hydrogen fluoride 20,000 23,000 26,000 140 280 73 NM Acrolein 25 27 34 NM NM NM NM Dioxins 0.000097 0.00012 0.00020 1 x 10 9 x 10 3 x 10 NM 

d -5 -6 -6

Formaldehyde 35 29 45 19 9.3 9.5 36 

Radionuclides are the one priority HAP not included on this table because radionuclide emissions are measured in different units (i.e., 



therefore, would not provide a relevant comparison to the other HAPs shown. Radionuclide emissions are presented in chapter 9.

The emissions estimates in this table are derived from model projections based on a limited sample of specific boiler types and contro

b

there are uncertainties in these numbers (see section ES.4 for discussion).NM = Not measured. cThese emissions estimates were calculated using the toxic equivalency (TEQ) approach, which is based on the summation of the emi

d

after adjusting for toxicity relative to 2,3,7,8-tetrachlorodibenzo-p-dioxin (i.e., 2,3,7,8-TCDD).
The RfC is an estimate (with uncertainty spanning perhaps an order of a

magnitude) of the daily inhalation exposure of the human population

(including sensitive subgroups) that is likely to be without appreciable

risk of deleterious effects during a lifetime.

ES-6

Table ES-2. Estimated Emissions for Nine Priority HAPs from

Characteristic Utility Units (1994; tons per year)a

Fuel: Coal Oil Natural gas

Unit size (MWe): 325 160 240

Arsenic 0.0050 0.0062 0.0003

Cadmium 0.0023 0.0014 NCb

Chromium 0.11 0.0062 NC

Lead 0.021 0.014 NC

Mercury 0.05 0.0012 NC

Hydrogen chloride 190 9.4 NC

Hydrogen fluoride 14 NC NC

Dioxins 0.00000013 0.000000023 NC c

Nickel NC 1.7 0.004

There are uncertainties in these numbers. Based on an uncertainty analysis, the EPA predicts that the emissions a

estimates are generally within a factor of roughly three of actual emissions.

NC = Not calculated. b

See footnote d of Table ES-1 c

If the MEI risk was above a minimum measure (e.g., exposure greater

than one-tenth the inhalation reference concentration [RfC] or cancer a

risk greater than 1 chance in 10 million), then the HAP was chosen for

more study. For non-inhalation exposures, the 67 HAPs were

prioritized by considering five criteria: (1) persistence;

(2) tendency to bioaccumulate; (3) toxicity; (4) emissions quantity;

and (5) radioactivity.

Based on this screening assessment, a total of 14 HAPs were

identified as priority. Twelve HAPs (arsenic, beryllium, cadmium,

chromium, manganese, nickel, hydrogen chloride [HCl], hydrogen

fluoride [HF], acrolein, dioxins, formaldehyde, and radionuclides)

were identified as priority pollutants for further study based on

potential for inhalation exposures and risks. Four of these 12 HAPs

(arsenic, cadmium, dioxins, and radionuclides) plus 2 additional HAPs

(mercury and lead) were considered priority for multipathway

exposure); of these 6 HAPs, 4 (arsenic, mercury, dioxins, and

radionuclides) were identified as the highest priority to assess for
ES-7

multipathway exposures and risks. Overall, a total of 14 of the 67

HAPs were considered priority. The other 53 HAPs were not evaluated

beyond the screening assessment.

ES.7 INHALATION RISK ASSESSMENT -- LOCAL ANALYSIS

The EPA estimated inhalation exposures and risks due to

dispersion of HAP emissions within 50 kilometers (km) of each of the

684 plants (i.e., local analysis). For 13 of the 14 priority HAPs,

the HEM was used; for radionuclides, the Clean Air Act Assessment

Package-1993 (CAP-93) model was used. The HEM exposure modeling

conducted for the inhalation risk assessment was very similar to the

modeling conducted for the screening assessment. The same default

options and same input data were used. However, there is one

important difference. For the inhalation risk assessment, a

distinction was made between urban and rural locations. If a plant is

located in an urban area, it was modeled using the urban mode (i.e.,

dispersion is assumed to be characteristic of emissions emitted by a

facility in an urban location where there are buildings nearby). 

Dispersion of the pollutant plume in an urban area is expected to

exhibit greater turbulence because of heat transfer and obstacles

(i.e., large buildings). If a plant is located in a rural location,

it was modeled using the rural mode (i.e., dispersion is assumed to be

characteristic of a facility located in a rural location). In the

screening assessment, all plants were modeled using the urban default

because using the urban default typically leads to more conservative

(i.e., higher) estimates of human exposures, which is appropriate for

a screening assessment. However, using the urban and rural

distinction is believed to reflect more realistic conditions.

The cancer risks for all gas-fired plants were well below one

chance in one million (i.e., < 1 x 10 ) and no noncancer hazards were -6

identified. Therefore, gas-fired plants are omitted from the

following discussions.

In cases where data were missing or incomplete, the EPA had to

make various assumptions. A few of these assumptions are more likely

to overestimate risks. Other assumptions used are likely to

underestimate risks. Based on an uncertainty analysis conducted for

this study, it is estimated that these assumptions taken together lead

to a reasonable high-end estimate (i.e., conservative, but within the

bounds of reasonable estimates) of the risks due to inhalation

exposure within 50 km of plants. Within the limits of current

scientific information, this approach is, therefore, most likely to

overestimate health risks for these pollutants. The uncertainty

analysis suggests that the most likely estimated inhalation MIRs

(i.e., central tendency MIRs) may be roughly 2 to 10 times lower than

the high-end MIRs presented below. The average individual risks due

to inhalation exposure to utility HAP emissions for the total exposed

U.S. population (roughly 200,000,000 people) are predicted to be

roughly 100 to 1000 times lower than the high-end inhalation MIRs.
ES-8

ES.7.1 Inhalation Cancer Risks for Coal-Fired Utilities Based on

Local Analysis (1990)

The vast majority of coal-fired plants (424 of the 426 plants)

are estimated to pose lifetime cancer risks (i.e., increased

probability of an exposed person getting cancer during a lifetime) of

less than 1 x 10 due to inhalation exposure to utility HAP emissions. -6

Only two of the 426 plants are estimated to potentially pose

inhalation risks greater than 1 x 10 (see Figure ES-2). -6

The increased lifetime cancer MIR due to inhalation exposure to

coal-fired utility HAP emissions, based on the local analysis, is

estimated to be no greater than 3 x 10 . Arsenic and chromium are the -6

HAPs contributing most to the inhalation risks (see Table ES-3). All

other HAPs, including radionuclides, were estimated to present

inhalation risks less than 1 x 10 for coal-fired units. -6

The cancer incidence in the U.S. due to inhalation exposure to

HAPs (including radionuclides) from all 426 coal-fired plants based on

the local analysis is estimated to be no greater than approximately

0.2 cancer case per year (cases/yr), or 1 case every 5 years. 

However, as described in later sections, the consideration of longrange dispersion of HAPs (beyond 50 km) results in increased estimates

for cancer incidence.

ES.7.2 Inhalation Cancer Risks for Oil-Fired Utilities Based on Local

Analysis (1990)

The majority of the oil-fired plants (125 of the 137 plants) are

estimated to pose inhalation cancer MIRs less than 1 x 10 . However, -6

up to 11 of the 137 oil-fired plants are estimated to potentially

present inhalation MIRs above 1 x 10 (see Figure ES-3). Nickel, -6

arsenic, radionuclides, and chromium are the primary contributors to

these cancer risks.

For oil-fired utilities, the highest contribution to the MIRs is

from nickel. However, there are substantial uncertainties with the

nickel risk estimates. Nickel is emitted in several different forms

(e.g., nickel oxides, soluble nickel, sulfidic nickel) and the health

effects of these different forms vary, and for some forms are unknown

or uncertain. Nickel subsulfide (which is one of the possible forms

of sulfidic nickel) is a known human carcinogen and appears to be the

most carcinogenic form based on available data. Based on limited

data, 3 to 26 percent of the nickel emissions are believed to be

sulfidic nickel. It is not known how much of the sulfidic nickel

emissions are nickel subsulfide. Several other nickel species (e.g.,

nickel oxides) are also potentially carcinogenic but the potencies are

not known.
0

50

100150200250300350

Maximum individual risk

Number of coal-fired plants

<1E-8 1E-8 to 1E-7 1E-7 to 1E-6 1E-6 to 1E-5

44

289

91

2

ES-9

	




 !"#$% !&

'(((
()'

Note: The high-end maximum individual risks (MIRs) are presented as exponents in this figure. For example, an increased cancer risk o

million (i.e., 1 x 10 ) is shown as 1E-6 in this Figure. The figure shows that 91 plants are estimated to pose an MIR between 1 x 10 and 

-6 -7 
Maximum individual risk

Number of oil-fired plants 26

48

52

9

2

0

10203040506070

<1E-8 1E-8 to 1E-7 1E-7 to 1E-6 1E-6 to 1E-5 

<1E-8 1E-8 to 1E-7 1E-7 to 1E-6 1E-6 to 1E-5 1E-5 to 1E-4

ES-10

	
*
+


 !"#$% !&

'(((
()'

Note: The MIRs are presented as exponents in this figure. For example, an increased cancer risk of 1 x 10 is shown as 1E-6 here. Th

-6

9 oil-fired plants with estimated MIRs between 1 x 10 and 1 x 10 . -6 -5
ES-11

Table ES-3. Summary of High-End Inhalation Cancer Risk Estimates

from Local Analysis for Coal-Fired Utilities for the Year 1990

HAP Cancer MIR > 1 x 10 MIR > 1 x 10

Highest Population with lifetime risk Number plants with a -6 -6

Arsenic 2 x 10 850 2 -6

Chromium 1 x 10 110 1 -6

Total (Aggregate of HAPs) 3 x 10 850 2 b -6

Estimated lifetime maximum individual risk (MIR) due to inhalation exposure for the highest riskcoal-fired plant. 

Based on an uncertainty analysis, these estimates are considered reasonable high-end estimates (see section

ES.7.4 for discussion).

Estimated risk due to inhalation of the aggregate of HAPs assuming additivity of risk for 26 individual carcinogenic b

HAPs.

To evaluate the range of potential risks due to nickel emissions,

the EPA estimated risks using various assumptions for nickel cancer

potency (presented in chapter 6). For example, assuming the nickel

mix is 50 percent as carcinogenic as nickel subsulfide, the highest

inhalation cancer MIR due to the aggregate of HAP emissions from the

highest risk oil-fired utility plant is estimated to be 6 x 10 . -5

Assuming the nickel mix is 10 percent as carcinogenic as nickel

subsulfide, the highest inhalation cancer MIR due to the aggregate of

HAP emissions from the highest risk oil-fired utility plant is

approximately 3 x 10 . The values in Table ES-4 and Figure ES-3 are -5

based on the conservative assumption that the nickel mix is 50 percent

as carcinogenic as nickel subsulfide.

Estimated risks due to inhalation exposure for a subset of HAPs

based on the local analysis are presented in Table ES-4. All other

HAPs analyzed were estimated to pose inhalation cancer risks below

1 x 10 for all 137 oil-fired plants. -6

The cancer incidence in the U.S. due to inhalation exposure to

HAP emissions (including radionuclides) from all 137 oil-fired

utilities, based on the local analysis, is estimated to be no greater

than 0.5 cancer case/yr.

ES.7.3 Inhalation Cancer Risks Based on Long-Range Transport

In addition to the above analyses, the EPA conducted long-range

transport analyses to assess emissions dispersion and exposures on a

national scale for 1990. The Regional Lagrangian Model of Air

Pollution (RELMAP) was used to estimate the dispersion of HAP

emissions from the facility stack out to the borders of the

continental U.S. This is in contrast to the HEM, which estimates

dispersion and air concentrations within 50 km of the source. 
ES-12

Table ES-4. Summary of High-end Inhalation Cancer Risk Estimates

Based on Local Analysis for Oil-Fired Utilities for the Year 1990

HAP Highest MIR > 1 x 10 > 1 x 10 a

Population with lifetime risk Number plants with MIR -6 -6

Nickel 5 x 10 110,000 11 b -5

Arsenic 1 x 10 2,400 2 -5

Radionuclides 1 x 10 2,400 2 -5

Chromium 5 x 10 2,300 1 -6

Cadmium 2 x 10 45 1 -6

Total (aggregate) 6 x 10 110,000 11 c -5

Estimated lifetime maximum individual risk (MIR) due to inhalation exposure for the highest riskoil-fired plant. 

Based on an uncertainty analysis, these estimates are considered reasonable high-end estimates (see section

ES.7.4 for discussion).

The estimates for nickel and total HAPs are based on the assumption that the mix of nickel compounds is 50 b

percent as carcinogenic as nickel subsulfide.

Estimated risk due to inhalation of the aggregate of HAPs assuming additivity of risk for 14 individual carcinogenic c

HAPs.

The RELMAP modeling was conducted for all coal- and oil-fired

utilities, but was limited to mercury, cadmium, chromium, arsenic,

nickel, lead, and dioxins. Only inhalation exposures to the

carcinogenic HAPs are discussed in this section. Deposition and

multipathway concerns are discussed elsewhere in this report. The

long-range transport modeling indicates that the local HEM analysis

alone does not account for a substantial percentage of the population

exposures due to coal-fired utility emissions. A comparison of the

HEM results to the RELMAP results indicates a significant portion of

emissions disperse further than 50 km, as would be expected for these

HAPs, which are mostly fine particulate substances emitted from

elevated stacks.

The RELMAP results for arsenic, cadmium, chromium, and nickel

(which are emitted mainly as PM) were used to estimate the potential

long-range transport inhalation exposures for other carcinogenic HAPs. 

Using this methodology, the highest cancer incidence due to inhalation

exposure to HAPs from coal-fired utilities considering both local and

long-range transport is estimated to be up to 1.3 cases/yr, which is

about 7 times greater than the incidence estimated in the local

analysis alone. The cancer incidence for oil-fired utilities did not

change (see Table ES-5).
ES-13

Table ES-5. Summary of High-End Inhalation Risk Estimates Due to

Local and Long-Range Transport 

LOCAL IMPACTS (dispersion within 50 km of each utility plant) d

OIL-FIRED PLANTS COAL-FIRED PLANTS

Pollutant Maximum individual Annual increased Maximum individual risk Annual increased

risk (MIR) cancer Incidence (MIR) cancer incidence

Radionuclides 1 x 10 0.2 2 x 10 0.1 -5 -8

Nickel 5 x 10 0.2 7 x 10 0.005 a -5 -7

Chromium 5 x 10 0.02 1 x 10 0.02 -6 -6

Arsenic 1 x 10 0.04 2 x 10 0.05 -5 -6

Cadmium 2 x 10 0.005 2 x 10 0.0006 -6 -7

All Others 8 x 10 0.005 8 x 10 0.004 b -7 -7

Total 6 x 10 0.5 3 x 10 0.2 c -5 -6

LOCAL PLUS LONG-RANGE IMPACTS (dispersion from utility emission points to borders of continental U.S.)

OIL-FIRED PLANTS COAL-FIRED PLANTS

Pollutant Maximum individual Annual increased Maximum individual risk Annual increased

risk (MIR) cancer incidence (MIR) cancer incidence

Radionuclides 1 x 10 0.2 Not estimated 0.7 -5

Nickel 5 x 10 0.2 1 x 10 0.038 a -5 -8

Chromium 5 x 10 0.02 2 x 10 0.15 -6 -6

Arsenic 1 x 10 0.05 3 x 10 0.37 -5 -6

Cadmium 2 x 10 0.006 3 x 10 0.005 -6 -7

All Others 8 x 10 0.006 1 x 10 0.028 b -7 -6

Total 6 x 10 0.5 4 x 10 1.3 c -5 -6

Assumes that the nickel mixture is 50 percent as carcinogenic as nickel subsulfide. 

Estimated risks due to exposure to all remaining HAPs analyzed (i.e., excluding nickel, arsenic, chromium, b

cadmium, and radionuclides).

Aggregate risk (risk due to inhalation exposure to all carcinogenic HAPs, assuming additivity of risks). c

There are uncertainties associated with these risk estimates. See sections ES.7.4 for discussion. d

A comparison between the HEM local dispersion results and the

long-range transport modeling results indicates that long-range

transport is much less important for the MIR than it is for cancer

incidence. For example, the MIR from the local analyses for coalfired utilities (i.e., inhalation risk of 3 x 10 ) is predicted to -6
ES-14

increase by roughly 10 to 20 percent to about 4 x 10 when ambient -6

concentrations are added from long-range transport of arsenic from all 

other utilities in the continental U.S. For oil-fired utilities, the

long-range transport of HAPs has no impact on the highest inhalation

MIR because of the remote location of the two highest risk oil-fired

plants.

ES.7.4 Uncertainties with the Inhalation Cancer Risk Assessment

There are several areas of uncertainty in the inhalation risk

assessment including: (1) the impacts of long-range transport;

(2) the emissions and health effects of different forms of chromium

and nickel; (3) the use of a linear non-threshold high-to-low dose 

extrapolation model for estimating cancer risks at low exposure

concentrations; (4) the impacts of episodic releases resulting from

upsets or unusual operating conditions; (5) how residence times and

activity patterns impact the exposures; (6) the impacts on sensitive

subpopulations; (7) the impacts of background exposures; and (8) the

risk of complex pollutant mixtures.

The uncertainty analysis indicates that the inhalation cancer

MIRs and incidence estimates presented above are reasonable high-end

estimates of the risks due to inhalation exposure within 50 km of each

plant. That is, the estimates are considered generally conservative

(i.e., predicted to be roughly the 90th to 95th percentile). The

uncertainty analysis suggests that the most likely estimated

inhalation MIRs (i.e., central tendency MIRs) may be roughly 2 to 10

times lower than the high-end MIRs presented above. The average

individual risks due to inhalation exposure to utility HAP emissions

for the total exposed U.S. population (roughly 200,000,000 people) are

predicted to be roughly 100 to 1,000 times lower than the high-end

inhalation MIRs.

ES.7.5 Summary of the Inhalation Cancer Risks

For the majority of utility plants (approximately 671 of the 684

plants), the estimated inhalation cancer risks due to HAP emissions

are less than 1 x 10 . However, several plants (2 coal plants and up -6

to 11 oil plants) are estimated to potentially pose inhalation cancer

risks above 1 x 10 . One oil plant is estimated to pose a high-end -6

inhalation cancer MIR of up to 6 x 10 . Based on the assessment, no -5

greater than 1.8 cancer cases/yr are estimated to occur in the U.S.

due to inhalation exposure to HAP emissions from all coal- and oilfired utilities. Further research and evaluation may be needed to

more comprehensively assess the inhalation cancer risks, especially to

reduce the uncertainties associated with the nickel risk estimates.

ES.7.6 Inhalation Noncancer Risks

The EPA also assessed noncancer risks (i.e., health effects other

than cancer) due to short- and long-term inhalation exposure. 

Manganese, HCl, HF, and acrolein were found to be the four HAPs of

highest potential concern for noncancer effects.
ES-15

Based on modeling HAPs for all 684 plants with the HEM, estimated

long-term ambient HAP concentrations were generally 100 to 10,000

times below the RfC or similar benchmark. The highest estimated longterm ambient HAP concentration was 10 times below the RfC.

Using a short-term air dispersion model that considers all

reasonable meteorological conditions, EPA modeled maximum one-hour

concentrations for three HAPs (HCl, HF, and acrolein). The highest

short-term exposure was 140 times below the acute reference level.

ES.8 MERCURY MULTIPATHWAY ASSESSMENT

ES.8.1 Background Discussion for Mercury

Mercury cycles in the environment as a result of natural and

human (anthropogenic) activities. The amount of mercury mobilized and

released into the biosphere has increased since the beginning of the

industrial age. Most of the mercury in the atmosphere is elemental

mercury vapor, which circulates in the atmosphere for up to a year,

and hence can be widely dispersed and transported thousands of miles

from likely sources of emission. After it deposits, mercury commonly

is emitted back to the atmosphere either as a gas or associated with

particles, to be re-deposited elsewhere. As it cycles between the

atmosphere, land, and water, mercury undergoes a series of complex

chemical and physical transformations, many of which are not

completely understood.

Mercury is a persistent element and bioaccumulates in the food

web. Mercury accumulates most efficiently in the aquatic food web. 

Predatory organisms at the top of the food web generally have higher

mercury concentrations. Nearly all of the mercury that accumulates in

fish tissue is methylmercury. Inorganic mercury, which is less

efficiently absorbed and more readily eliminated from the body than

methylmercury, does not tend to bioaccumulate.

Fish consumption dominates the pathway for human and wildlife

exposure to methylmercury. The EPA’s 1997 Mercury Study Report to

Congress supports a plausible link between anthropogenic releases of

mercury from industrial and combustion sources in the U.S. and

methylmercury in fish. However, these fish methylmercury

concentrations also result from existing background concentrations of

mercury (which may consist of mercury from natural sources, as well as

mercury which has been re-emitted from the oceans or soils) and

deposition from the global reservoir (which includes mercury emitted

by other countries). Given the current scientific understanding of

the environmental fate and transport of this element, it is not

possible to quantify how much of the methylmercury in fish consumed by

the U.S. population is contributed by U.S. emissions relative to other

sources of mercury (such as natural sources and re-emissions from the

global pool). As a result, it cannot be assumed that a change in

total mercury emissions will be linearly related to any resulting

change in methylmercury in fish, nor over what time period these

changes would occur. This is an area of ongoing study.
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ES.8.2 Methylmercury Health Effects

Epidemics of mercury poisoning following high-dose exposures to

methylmercury in Japan and Iraq demonstrated that neurotoxicity is the

health effect of greatest concern when methylmercury exposure occurs

to the developing fetus. Dietary methylmercury is almost completely

absorbed into the blood and distributed to all tissues including the

brain; it also readily passes through the placenta to the fetus and

fetal brain. The reference dose (RfD) is an amount of methylmercury,

which when ingested daily over a lifetime is anticipated to be without

adverse health effects to humans, including sensitive subpopulations. 

At the RfD or below, exposures are expected to be safe. The risk

following exposures above the RfD is uncertain, but risk increases as

exposures to methylmercury increase. 

Extrapolating from the high-dose exposures that occurred in the

Iraq incident, the U.S. EPA derived a RfD for methylmercury of 0.1

microgram per kilogram body weight per day (μg/kg bw/day). While the

U.S. EPA was advised by scientific reviewers to employ this RfD for

this analysis, new data are emerging. Currently ongoing are two large

epidemiology studies in the Seychelle Islands and in the Faroe Islands

that were designed to evaluate childhood development and neurotoxicity

in relation to fetal exposures to methylmercury in fish-consuming

populations. Because of various limitations and uncertainties in all

of the available data, the U.S. EPA and other Federal agencies intend

to participate in an interagency review of the human data on

methylmercury, including the most recent studies from the Seychelle

Islands and the Faroe Islands. The purposes of this review are to

refine the estimates of the level of exposure to mercury associated

with subtle neurological endpoints and to further consensus between

all of the Federal agencies. After this process, the U.S. EPA will

determine if a change in the RfD for methylmercury is warranted.

(Note: see the 1997 EPA Mercury Study Report to Congress for further

discussion and assessment of mercury health effects and public health

impacts).

ES.8.3 Mercury Multipathway Exposure Assessment

Mercury was considered highest priority for multipathway exposure

analysis. To assess the transport and deposition of mercury emissions

from utilities and to estimate concentrations in environmental media

and biota, three modeling efforts were undertaken: (1) long-range

modeling, (2) local scale modeling, and (3) modeling of environmental

concentrations. The RELMAP was used to predict long-range dispersion

and deposition across the U.S. For the local analysis, a model

designed to predict deposition of HAPs within 50 km, the Industrial

Source Complex Version 3 (ISC3) air dispersion model, was used. Next,

the EPA’s Indirect Exposure Model Version 2M (IEM-2M) was used to

estimate mercury environmental concentrations and human exposures. 

Hypothetical exposure scenarios were evaluated for four model plants

(a large coal-fired, a medium coal-fired, a small coal-fired, and a

medium oil-fired utility boiler). The analysis included three types

of plant locations: (1) rural (agricultural), (2) near lakes
ES-17

(lacustrine), and (3) urban. Three human fish consumption scenarios

were considered.

The modeling provided information on whether local and/or longrange transport of mercury is significant in a variety of scenarios. 

The models indicate that most of the mercury from utilities is

transported further than 50 km from the source. The fate and

transport models provided an assessment of potential inhalation and

ingestion exposures.

ES.8.4 Summary of Mercury Assessment Results for Utilities

Recent estimates of annual total global mercury emissions from

all sources (natural and anthropogenic) are about 5,000 to 5,500 tons

per year (tpy). Of this total, about 1,000 tpy are estimated to be

natural emissions and about 2,000 tpy are estimated to be

contributions through the natural global cycle of re-emissions of

mercury associated with past anthropogenic activity. Current

anthropogenic emissions account for the remaining 2,000 tpy. Point

sources such as fuel combustion; waste incineration; industrial

processes (e.g., chlor-alkali plants); and metal ore roasting,

refining, and processing are the largest point source categories on a

world-wide basis. 

For the year 1994, coal-fired utilities were estimated to emit

approximately 51 tpy of mercury in the U.S., which is estimated to be

33 percent of the 158 tpy of airborne anthropogenic emissions of

mercury in the U.S. If one assumes that current anthropogenic

activity represents between 40 and 75 percent of the total airborne

emissions (anthropogenic plus other emissions [e.g., natural

emissions]), one can calculate that U.S. utilities emit roughly 13 to

26 percent of the total (natural plus anthropogenic) airborne

emissions of mercury in the U.S.

Given the global estimates of 5,000 to 5,500 tpy (which are 

highly uncertain), U.S. anthropogenic mercury emissions are estimated

to account for roughly 3 percent of the global total, and U.S.

utilities are estimated to account for roughly 1 percent of total

global emissions.

A computer simulation of long-range transport of mercury

emissions from all U.S. sources conducted for the EPA’s 1997 Mercury

Study Report to Congress suggests that about one-third (~ 52 tons) of

the 158 tpy of U.S. anthropogenic emissions are deposited, through wet

and dry deposition, within the lower 48 States. The remaining twothirds (~ 107 tons) is transported outside of U.S. borders where it

diffuses into the global reservoir. In addition, the computer

simulation suggests that another 35 tons of mercury from the global

reservoir is deposited for a total deposition of roughly 87 tpy in the

U.S. Although this type of modeling is uncertain, the simulation

suggests that about three times as much mercury is being added to the

global reservoir from U.S. sources as is being deposited from it. 

What is not uncertain is that additional emissions to air will
ES-18

contribute to levels in the global reservoir and deposition to water

bodies.

Long-range transport modeling conducted as part of this Utility

Study predicts that approximately 30 percent (15 tpy) of the utility

mercury emissions deposit in the continental U.S. The estimated

annual deposition rates resulting from utility mercury emissions range

from 0.5 to greater than 10 micrograms per square meter. Long-range

transport modeling also predicts that the highest deposition occurs in

the eastern half of the U.S., particularly areas such as southeastern

Great Lakes and Ohio River Valley, central and western Pennsylvania,

large urban areas in the eastern U.S. (e.g., Washington, D.C., New

York City) and various locations in the vicinity of large coal-fired

utilities. Based on the limited available receptor monitoring data,

the RELMAP model seems to be accurate within a factor of plus or minus

2. That is, the RELMAP model seems to over- and underestimate mercury

values within a factor of two and appears to be relatively unbiased in

its predictions.

The modeling assessment in conjunction with available scientific

knowledge, supports a plausible link between anthropogenic mercury

emissions and mercury found in freshwater fish. As noted above, there

are many sources of mercury emissions worldwide, both natural and

anthropogenic. The coal-fired utilities are one category of the

mercury sources.

Mercury is considered the highest priority for multipathway

analyses because it is an environmentally persistent, toxic element. 

Mercury is deposited to soil and terrestrial vegetation but at levels

that do not result in human exposures likely to be detrimental to

health through terrestrial exposure pathways. However, in its

methylated form mercury bioaccumulates in the food web (especially the

aquatic food web). Modeling results suggest that most of the mercury

emitted to the atmosphere is deposited more than 50 km away from the

source, especially sources that have tall stacks. As stated above,

the modeling assessment from the Mercury Study in conjunction with

available scientific knowledge, supports a plausible link between 

anthropogenic mercury emissions and mercury found in freshwater fish. 

Additional emissions to air will contribute to levels in the global

reservoir and deposition to water bodies. As a result, mercury

emissions from utility units may add to the existing environmental

burden.

At this time, the available information, on balance, indicates

that utility mercury emissions are of sufficient potential concern for

public health to merit further research and monitoring. The EPA

recognizes that there are substantial uncertainties that make it

difficult to quantify the magnitude of the risks due to utility

mercury emissions, and that further research and/or evaluation would

be needed to reduce these uncertainties. Remaining questions include

the following: (1) what is the quantitative relationship between a

change in U.S. mercury emissions and the resulting change in
ES-19

methylmercury levels in fish; (2) what are the actual consumption

patterns and estimated methylmercury exposures of the subpopulations

of concern; (3) what are the actual mercury levels in a statistically

valid and representative sample of the U.S. population and susceptible

subpopulations; (4) what exposure levels are likely to result in

adverse health effects; (5) what affects the formation of

methylmercury in waterbodies and its bioaccumulation in fish; (6) how

much mercury is emitted from natural sources and past anthropogenic

sources; and (7) how much mercury is removed during coal cleaning and

other ongoing practices for pollution control. New data that could

reduce some of the uncertainties are likely to become available in the

next several years, and EPA plans to review and consider these data,

as appropriate, in future decisions.

Regarding potential methods for reducing mercury emissions, the

EPA has not identified any demonstrated add-on control technologies

currently in use in the U.S. that effectively remove mercury from

utility emissions. (However, there may be add-on control technologies

used in other source categories that effectively reduce mercury

emissions.) Based on available data, total mercury removal by

existing PM control devices on coal-fired utilities varies

considerably, ranging from 0 to 82 percent removal (with a median

efficiency of 15 percent removal) for cold-side electrostatic

precipitators (ESPs), and from 0 to 73 percent removal (with a median

efficiency of 8 percent removal) for fabric filters. Also, hot-side

ESPs exhibited no mercury control. Existing flue gas desulfurization

(FGD) units exhibit limited mercury control, ranging from 0 to 62

percent removal, with a median removal of 23 percent. The mercury

control efficiency of FGD units is a function of several factors

including temperature, plant configuration, and type of coal. Pilotscale studies have shown that mercury removal can be enhanced through

the use of activated carbon injection. However, the limited results

to date utilizing carbon injection are inconsistent and more data and

research are needed. Other various pollution prevention strategies,

such as coal cleaning, have shown some effectiveness in reducing

utility emissions of mercury. Conventional coal cleaning removes, on

average, approximately 21 percent of the mercury contained in the

coal. Also, fuel switching, such as switching from coal to natural

gas, would result in decreased emissions of mercury.

ES.9 SCREENING LEVEL MULTIPATHWAY ASSESSMENT FOR ARSENIC

Arsenic is a naturally occurring element found normally, in

various concentrations, in soil. In addition, arsenic can also be

naturally present in other media (e.g., various food sources and

water). Arsenic levels have been measured in a variety of foods. 

Even though shellfish and other marine foods contain the greatest

concentrations of total arsenic, much of the arsenic present in fish

and shellfish exists in the less toxic organic form. Other food

products, such as meats, rice, and cereals, contain higher

percentages, and often higher total amounts, of inorganic arsenic,

which is the form of primary toxicological concern.
ES-20

Arsenic is also naturally present in trace amounts in coal and

oil. When coal or oil are burned, some of this naturally occuring

arsenic is released to the atmosphere. The quantity of arsenic

released from any utility plant is dependent on many factors including

the concentration of arsenic in the fuel, control device efficiency,

and other factors.

Utilities emit about 62 tpy of arsenic nationwide, about 3 to 4

percent of the total anthropogenic arsenic emissions in the U.S. 

Because of its chemical and physical characteristics, arsenic emitted

to the atmosphere may be transported to other environmental media

(soil or water), thus allowing non-inhalation exposures to occur.

ES.9.1 Exposure Modeling

It was not possible to model every utility plant for arsenic

multipathway exposures. Therefore, a screening level model plant

approach was used. Four model plants (i.e., a large coal-fired, a

medium coal-fired, a small coal-fired, and a medium oil-fired utility

boiler) were designed to characterize typical utility plants. 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 arsenic as well as the exposures. However, the assessment was

useful for estimating potential risks due to utility arsenic

emissions. Three models were used to predict environmental arsenic

concentrations and exposure: the RELMAP, the ISC3, and the Indirect

Exposure Model Version 2 (IEM-2). These models were used to predict

the fate and transport of arsenic emissions and to estimate human

exposures to arsenic through multiple exposure routes, including food

consumption, water ingestion, and inhalation. Three basic exposure

scenarios were considered: a subsistence farmer (adult and child), a

subsistence fisher (adult and child), and a pica child (i.e., a child

that ingests significant quantities of soil). These scenarios were

considered because they represent possible high-end scenarios for

exposure to arsenic.

ES.9.2 Health Effects of Arsenic

Inhalation exposure to inorganic arsenic has been strongly

associated with lung cancer in humans. Human exposure to inorganic

arsenic, via ingestion, has been associated with an increased risk of

several types of cancer, including skin, bladder, liver, and lung

cancers. Oral exposure to inorganic arsenic has also been associated

with noncancer effects, including effects to the central nervous

system, cardiovascular system, liver, kidney, and blood.

ES.9.3 Approach for Estimating Screening Level Arsenic Risks

Increased cancer risks were estimated for each hypothetical

scenario, for the four model plants, each of which was placed in two

different hypothetical locations (i.e., an eastern humid site and a

dry western site). For each of the exposure scenarios, except for the

pica child, it is assumed that the hypothetical person is exposed for

30 years. For the pica child, it is assumed that exposure occurs for
ES-21

7 years. Risks were estimated by multiplying the estimated intakes of

arsenic by the EPA’s cancer potency factor for arsenic.

ES.9.4 Screening Level Arsenic Risk Assessment Results

The results of the screening level multipathway arsenic exposure

assessment provide an indication of the potential hazards and risks

that may occur due to emissions from a utility plant. However, the

results are not applicable to any particular plant. There are

uncertainties and limitations to the analysis.

Exposures to inorganic arsenic due to background levels and due

to emissions from the model utility boilers were predicted to be

mainly through the ingestion of grains. Exposure to inorganic arsenic

through the ingestion of fish was not predicted to be a major pathway

of exposure because there is considerable evidence that little of the

total arsenic in fish tissue is inorganic arsenic. Soil ingestion is

the major route of exposure to inorganic arsenic for the pica child.

ES.9.4.1 Arsenic Cancer Risks. The cancer risks due to

multipathway exposures to inorganic arsenic, as estimated in the model

plant analysis using hypothetical scenarios, due to utility emissions

alone (no background) were estimated to range from 4 x 10 to 1 x 10 . -7 -4

The highest estimated risk (1 x 10 ) was for a pica child assumed to -4

be living at the point of maximum deposition. The arsenic emissions

from the large coal-fired model utility boiler at the eastern humid

site were estimated to pose this highest risk for the pica child. 

When the risk from background exposure (2 x 10 ) is added to the -4

maximum risk from utility exposure, the risk for the pica child is

estimated to be up to 3 x 10 . The “pica child” is considered a high- -4

end, conservative scenario.

Background exposures were estimated to dominate the exposures and

risks in all scenarios. When considering only the arsenic emissions

from the model utility units (not including background), in all

scenarios it was the large coal-fired unit that was estimated to pose

the greatest multipathway risks and the medium coal-fired unit was

estimated to pose the next highest risks. The small coal-fired unit

and the oil-fired unit were estimated to present lower risks.

ES.9.4.2 Uncertainty Discussion. There are uncertainties

associated with the cancer risk estimates from arsenic. The analysis

was based on model plants and hypothetical constructs; therefore, the

results are not applicable for any specific utility plant. Further

analyses are needed to better characterize the risks posed by arsenic

emissions from utilities. A few uncertainties are discussed here.

Exposure to arsenic through the ingestion of tap or well water

was not included in this assessment. The exposure modeling assessment

was based on a model plant analysis, hypothetical scenarios, and

incorporated data with varying degrees of uncertainty. Also, there

are uncertainties associated with the health effects data for arsenic. 
ES-22

For example, the animal ingestion studies have not clearly shown an

association between arsenic ingestion exposure and cancer.

ES.10 DIOXIN SCREENING LEVEL MULTIPATHWAY ASSESSMENT

The highest MEI inhalation cancer risk due to dioxin emissions

from any utility plant based on the HEM analysis (described in section

ES.7) was estimated to be 1 x 10 . The EPA estimates that coal-fired -7

utilities emit 0.2 pounds per year (lb/yr) of dioxin (toxic

equivalents, TEQ) and that oil-fired utilities emit 0.01 lb/yr. These

estimates combined are roughly 1 percent of the nationwide

anthropogenic dioxin emissions. However, dioxin emissions data were

only available for twelve utility plants and 42 percent of the

measurements were below the minimum detection limit. Moreover,

dioxins are not part of the naturally occuring fossil fuel. They are

formed in highly complicated reactions which may occur with unknown

frequency during combustion. Therefore, the emissions data for

dioxins from utilities, which are the basis of exposure modeling, are

considered more uncertain than the emissions data for many of the

other HAPs.

For the screening level multipathway analysis, the transport,

deposition, multipathway exposures, and human cancer risks were

assessed for utility emissions of polychlorinated dibenzo-p-dioxins

(PCDDs) and polychlorinated dibenzofurans (PCDFs), collectively

referred to as dioxins. Atmospheric deposition of dioxin emissions

can be important because dioxins tend to persist in the environment

and bioaccumulate in the food web. Environmental persistence and

bioaccumulation, coupled with carcinogenic effects at very low levels,

make multipathway exposure an important consideration for dioxins.

ES.10.1 Methods

The basic approach for estimating screening level multipathway

exposures to dioxins was similar to the methods described above for

mercury and arsenic. However, there were some differences. The EPA’s

ISCST3 model was used to predict deposition and air concentrations of

dioxins within 50 km of each of four model plants. Model plants were

selected to represent both large and small coal- and oil-fired

utilities. A modified version of the IEM spreadsheet model was used

to estimate environmental concentrations, exposures to the

environmental concentrations for 16 hypothetical human scenarios, and

the resulting cancer risks. Pathways assessed include inhalation,

dermal contact with soil, and ingestion of water, soil, fish, plants,

and animals.

ES.10.2 Results

Since the analysis was based on model plants, using hypothetical

scenarios, the results are not applicable to any specific plant and

contain substantial uncertainties about the risks due to dioxin

emissions. Total modeled screening level lifetime cancer risks

related to multipathway exposure to dioxins for the four-model plant

analysis ranged from 1 x 10 to 2 x 10 . The results of this -10 -4
ES-23

analysis indicate that the exposures and risks due to fish consumption

are the highest of all pathways considered. The highest modeled

result of 2 x 10 lifetime cancer risk was obtained for the subsistence -4 

fisher exposure scenario. In all modeled scenarios, the noninhalation exposures were at least one order of magnitude larger than

the inhalation exposures, thus demonstrating the potential

significance of including multipathway exposure analysis in the risk

assessments for pollutants that are environmentally persistent and

tend to bioaccumulate. Also, unlike the results for arsenic, modeled

exposures to dioxins for each pathway exceed the background exposure

estimates for dioxins.

ES.10.3 Uncertainty Discussion

Several sensitivity analyses were completed for the screening

level multipathway assessment of utility dioxin risks to assess the

reasonableness of the results. The assumptions with the greatest

impact on the predicted risk to the subsistence fisher were those made

about the biota-sediment accumulation factor. This sensitivity

analysis suggests that the modeling results are reasonable for a

screening level analysis.

ES.11 MULTIPATHWAY ASSESSMENT FOR RADIONUCLIDES

Radionuclide emissions from utilities may result in human

exposure from multiple pathways including: (1) external radiation

exposure from radionuclides suspended in air or deposited on the

ground, and (2) internal exposure from the inhalation of airborne

contaminants or ingestion of contaminated food. The CAP-93 model was

used to estimate multipathway exposures and risks due to radionuclide

emissions to humans within 50 km of all 684 utilities. However, this

assessment did not use site-specific data for the non-inhalation

exposure analysis, but rather relied on various generic assumptions

and general input data.

Based on the CAP-93 modeling, 667 of the 684 plants are estimated

to pose multipathway risks less than 1 x 10 . The highest estimated -5

multipathway radiation exposure for the MEI due to radionuclide

emissions from utilities was predicted to be 1.5 millirems (mRems) per

year, which is estimated to pose an increased cancer risk of 3 x 10 . -5

Seventeen plants (13 coal- and 4 oil-fired plants) were estimated to

pose multipathway risks between 1 x 10 and 3 x 10 . The estimated -5 -5

cancer incidence in the U.S., due to emissions and dispersion of

radionuclides within 50 km of each utility, is estimated to be 0.3

cancer deaths/yr. The cancer incidence appears to be mostly due to

inhalation exposure. The non-inhalation exposures contribute only

slightly to the incidence. The non-inhalation exposure pathways have

a greater impact on the MEIs, especially for coal-fired plants.

The risks due to exposure to radionuclides from utilities are

substantially lower than the risks due to natural background

radiation. The average exposure to natural background radiation

(excluding radon) for the U.S. population has been estimated to be
ES-24

roughly about 100 mRems per year, which is about 67 times higher than

the highest exposure due to utility radionuclide emissions.

ES.12 QUALITATIVE MULTIPATHWAY EXPOSURE ASSESSMENT

The EPA recognizes that non-inhalation exposure pathways could be

important for additional HAPs that are persistent and tend to

bioaccumulate. A few additional HAPs that were not modeled for

multipathway exposures are discussed below.

ES.12.1 Cadmium and Lead

Cadmium emissions from the vast majority of plants (683 of the

684 plants) are estimated to pose inhalation risks less than 10 , and -6

the highest modeled air concentration of lead was 200 times below the

national ambient air quality standard (NAAQS). Cadmium and lead are

persistent, may bioaccumulate, and are toxic by ingestion. However,

since the emission quantities and inhalation risks are relatively low,

the EPA does not plan to conduct future evaluations of multipathway

exposures of cadmium and lead from utilities.

ES.12.2 Nickel and Chromium

Nickel and chromium were not considered to be priority for noninhalation exposures. At relatively high oral doses, nickel and

chromium do cause noncancer toxicity. However, there are considerable

uncertainties about the noncancer toxicity of nickel and chromium at

relatively low ingestion doses (below the toxic threshold). Also, it

is uncertain whether they pose a carcinogenic risk by ingestion. 

Hence, EPA does not plan to assess multipathway exposures for nickel

and chromium for utilities.

ES.13 POTENTIAL IMPACTS OF HYDROGEN CHLORIDE AND FLUORIDE

No exceedances of the health benchmarks (e.g., RfCs) for HCl or

HF were identified in the inhalation exposure assessment. However,

emissions of HCL and HF may contribute to acid deposition and, to a

lesser extent to PM fine and visibility problems. To the extent that

these emissions may contribute to such problems, they could be

addressed through other Titles of the Act. 

ES.14 ALTERNATIVE CONTROL AND PREVENTION STRATEGIES

There are numerous potential alternative control strategies for

reducing HAPs. These include precombustion controls (e.g., fuel

switching, coal switching, coal cleaning, coal gasification),

combustion controls, post combustion controls (e.g., PM controls, SO 2

controls), and approaches that prevent pollution by improving

efficiency in supply (e.g., promoting energy efficiency in combustion)

or demand (e.g., demand side management [DSM], pollution prevention,

energy conservation). The degree of feasibility, cost, and

effectiveness of each of these potential control technologies varies. 

For example, coal cleaning tends to remove at least some of all the

trace metals, with lead concentrations being removed to the greatest
ES-25

extent (averaging approximately 55 percent removal) and mercury being

removed the least (averaging approximately 21 percent). Existing PM

controls tend to effectively remove the trace metals (with the

exception of mercury) while FGD units remove trace metals less

effectively and exhibit more variability. Fuel switching (e.g.,

switching from coal to natural gas) could result in substantial

reductions in HAP emissions. There are few existing data that show

the HAP reduction effectiveness of DSM, pollution prevention, and

energy conservation. These control strategies need to be examined

further for technical and economic considerations.

ES.15 OTHER ISSUES AND FINDINGS

ES.15.1 Emissions and Risks for the Year 2010

In addition to the 1990 analysis, the EPA also estimated

emissions and inhalation risks for the year 2010. There are

substantial data gaps and uncertainties in the projections to the year

2010. However, the approach utilized is reasonable given the

limitations of data to complete such projections.

Based on EPA’s assessment for this report, HAP emissions from

coal-fired utilities are predicted to increase by 10 to 30 percent by

the year 2010. Predicted changes that were included in the 2010

emissions projections include the installation of scrubbers for a

small number of facilities, the closing of a few facilities, and an

increase in fuel consumption of other facilities. However, based on

EPA’s exposure modeling analysis for the year 2010, the inhalation

risks in 2010 for coal-fired utilities are estimated to be roughly

equivalent to the 1990 inhalation risks. For oil-fired plants,

emissions and inhalation risks are estimated to decrease by 30 to 50

percent by the year 2010. Multipathway risks for 2010 were not

assessed. Utilization of add-on controls to comply with the acid rain

program are not expected to significantly impact on HAP emissions due

to their limited numbers and limited HAP control efficiency

improvement. However, if additional actions are taken to reduce

emissions of criteria pollutants, acid rain precursors, or global

warming compounds (e.g., use of fuel switching or add-on controls to

reduce SO , NO , and/or carbon dioxide emissions), these actions could x x

result in reductions in HAP emissions. For example, analyses

performed to assess compliance with the revised NAAQS for ozone and PM

indicate that mercury emissions in 2010 may be reduced by

approximately 16 percent (11 tpy) over those projected in this report. 

Other potential (but unknown) actions (e.g., repowering,

restructuring) may have a significant impact on HAP emissions;

however, these unknowns were not included in the 2010 projection.

ES.15.2 Peer Review

Draft versions of Chapters 1 through 9 and 13 of this report and

draft technical support documents were reviewed by many non-EPA

scientists representing industry, environmental groups, academia, and

other parties. Chapters 10, 11, and 12 are new chapters produced in

response to major comments from the reviewers. EPA held a scientific
ES-26

peer review meeting and also a public meeting in July 1995 to obtain

comments from reviewers. In February, April, and September 1996, all

sections of the draft report underwent additional review by EPA, State

and local Agencies, and other Federal Agencies. Additional review

occurred during 1997. The EPA has revised the report, as appropriate,

based on the reviewers’ comments. However, there were several

comments that could not be fully addressed because of limitations in

data, methods, and resources. In addition, there were some comments

that EPA did not agree with. Also, the new chapters (10 to 12) have

only undergone a limited review. Draft versions of this report, along

with all the comments received, have been submitted to the public

docket (A-92-55) at the following address: U.S. EPA, Air and Radiation

Docket and Information Center, mail code 6102, 401 M Street, S.W.,

Washington, D.C. 20460; telephone number (202) 260-7548. Materials

are available for public review at the docket center or copies may be

mailed (for a fee) on request by calling the above number.

ES.15.3 Industry Report

If alternative methods and assumptions were used to study the HAP

emissions from utilities, the results would likely be somewhat

different. To assess the impact of using alternative assumptions and

methods, it is useful to compare the EPA study with a similar study

completed by the EPRI.

The EPRI prepared a report, entitled “Electric Utility Trace

Substances Synthesis Report,” (November 1994) that paralleled the

EPA’s study. Many of the same emissions data were used and similar

risk assessment methods were utilized. The EPRI study concluded that

cancer inhalation risks are below 1 x 10 for all utilities, and -6

noncancer inhalation risks are well below Federal threshold levels for

all utilities. Population inhalation risks were determined by the

EPRI to be insignificant (less than 0.1 cancer case/year). Case

studies at four plants found that multimedia risks, including mercury,

are below levels of concern.

The EPRI’s risk estimates are generally similar to, but in

several cases lower than, those of EPA. Differences between the

studies include: (1) EPA’s use of a higher unit risk factor for

arsenic; (2) EPA’s assumption that nickel was carcinogenic (EPRI

assumed nickel was not carcinogenic); (3) EPA’s evaluation of exposure

beyond 50 km to all locations in the U.S. (EPRI did not attempt this

analysis); (4) EPRI’s radionuclide analysis was based on several model

plants, while the EPA evaluated every plant in the U.S.; and (5) the

EPRI assumed that chromium emissions were five percent chromium VI

(the carcinogenic form), while EPA assumed that 11 percent (for coalfired plants) and 18 percent (for oil-fired plants) were chromium VI. 

In addition, the EPRI mercury multimedia study considered only the

local impact from four plants (not worst-case) and did not include

potential impacts of total nationwide utility mercury emissions and

contributions to total environmental loadings.
ES-27

ES.15.4 Potential Environmental Impacts Not Included in Study

There are other potential environmental issues associated with

utilities not assessed in this report. These include: (1) the

impacts of criteria pollutants (SO , NO , PM, carbon monoxide, and 2 x

ozone) or acid rain precursors (SO and NO ), which are studied and 2 x

regulated under other sections of the Act; (2)an assessment of

ecological impacts of HAPs; (3) the impacts of carbon dioxide

emissions and climate; and (4) the impacts resulting from

restructuring, mining, drilling, solid waste disposal, transmission,

transportation, or other activities associated with electric power

generation. These issues and potential impacts were not assessed

because they were considered beyond the scope of this study as

mandated by Section 112(n) of the Act.

ES.15.5 Link to Particulate Matter

Arsenic, cadmium, chromium, lead, nickel, radionuclides, and

several other HAPs are emitted primarily as PM. Consequently, these

HAPs may contribute to PM emissions and PM health concerns, especially

from poorly controlled coal-fired units and uncontrolled oil-fired

units (about two-thirds of oil-fired units are uncontrolled for PM). 

Impacts for PM were not addressed in this study, but are being studied

under Title I of the Act. If additional controls of PM emissions are

utilized, this could result in reductions in HAP emissions.

ES.16 OVERALL TECHNICAL SUMMARY AND CONCLUSIONS

Based on available information and current analyses, the EPA 

believes that mercury from coal-fired utilities is the HAP of greatest

potential concern and merits additional research and monitoring. 

There are uncertainties regarding the extent of risks due to mercury

exposures including those from utility emissions. Further research

and evaluation are needed to gain a better understanding of the risks

and impacts of utility mercury emissions. In addition, further

research and evaluation of potential control technologies and

strategies for mercury are needed.

For a few other HAPs, there also are still some remaining

potential concerns and uncertainties that may need further study. 

First, the screening multipathway assessments for dioxins and arsenic

suggest that these two HAPs are of potential concern (primarily from

coal-fired plants); however, further evaluations and review are needed

to better characterize the impacts of dioxins and arsenic emissions

from utilities. Second, nickel emissions from oil-fired utilities are

of potential concern, but significant uncertainties still exist with

regards to the nickel forms emitted from utilities and the health

effects of those various forms. The impacts due to HAP emissions from

gas-fired utilities are negligible based on the results of this study;

therefore, the EPA feels that there is no need for further evaluation

of the risks of HAP emissions from natural gas-fired utilities.
ES-28

ES.17 AREAS FOR FURTHER RESEARCH AND ANALYSIS

There are many uncertainties and data gaps described throughout

this report. This section summarizes several important areas in which

further research or scientific work may be needed.

ES.17.1 Emissions Data for Dioxins

Emissions data for dioxin compounds were available from less than

12 utility plants. Many of the measurements were near the detection

limits. Therefore, there are greater uncertainties with the dioxin

emissions than for the other HAPs. Research may be needed to gain a

better understanding of the dioxin emissions from utilities and the

dioxin formation, if any, in various utility boiler types (e.g., units

with cold-side or hot-side ESPs). 

ES.17.2 Speciation of Nickel

There are significant uncertainties regarding the forms of nickel

emitted from oil-fired utilities and their associated health effects. 

Research would be useful to determine the emissions quantities of

various nickel forms and the health effects of various nickel forms.

ES.1.7.3 Multipathway Risk Assessment

Further work may be needed to better characterize the risks due

to multipathway exposure to certain HAPs (e.g., arsenic and dioxins).

ES.17.4 Local, Regional, and Long-range Transport Exposures

Further modeling and evaluation may be needed to better

characterize the impacts of local, regional, and long-range transport

of HAPs from utilities.

ES.17.5 Mercury

There are numerous areas regarding mercury that may need further

research, study, or evaluation. A few potential areas for further

study include the following: 

(1) additional data on mercury content of various types of coal;

(2) improved methods for measuring mercury levels in water; 

(3) the impact of reducing mercury emissions from coal-fired

facilities on the bioaccumulation of mercury in fish; 

(4) statistically valid and reliable estimates of methylmercury

exposure levels in the U.S. population and susceptible

subpopulations, as measured in human hair;

(5) the occupational, dietary and behavioral factors that affect

mercury exposures for people who are determined to be

exposed above a threshold of concern;

(6) the human health and environmental benefits that would be

expected by reducing mercury emissions from U.S. utilities; 
ES-29

(7) control technologies or pollution prevention options that

are available, or will be available, that could potentially

reduce mercury emissions and what are the costs of those

options;

(8) how do other regulations, programs and activities (e.g.,

acid rain program, electricity restructuring, NAAQSs, and

climate change) affect mercury emissions; and

(9) additional data on mercury emissions (e.g., how much is

emitted from various types of units, how much is divalent vs

elemental mercury, and how do factors such as control device,

fuel type, and plant configuration affect emissions and

speciation).

Several additional uncertainties and potential areas for further

research on mercury are discussed in other sections of this report.

ES.17.6 Projections to the Year 2010

There are significant uncertainties and unknowns in the emissions

and risk projections made to the year 2010 (e.g., impact of

electricity restructuring; impact of State efforts to regulate such

restructuring; impact of any climate change abatement initiatives). 

Research and evaluation in these areas may be needed.

ES.17.7 Ecological Risks

The effects of HAPs on wildlife, endangered species, and

terrestrial and aquatic ecosystems were not evaluated in this study. 

Although not mandated by section 112(n)(1)(A), further evaluation of

ecological risks due to HAP emissions would be needed to fully

evaluate the impacts of utility HAP emissions.

ES.17.8 Criteria Pollutant and Acid Rain Programs

Further evaluation is needed to assess the impacts of the Acid

Rain and Criteria Pollutant programs (e.g., impact of revisions to the

PM-fine and ozone NAAQS; impact of Ozone Transport Assessment Group

[OTAG] activities) on HAP emissions, especially for mercury.
mm
1-1

1.0 INTRODUCTION

This chapter presents an introduction to the study of hazardous

air pollutant (HAP) emissions from electric utility steam-generating

units (i.e., utilities). The chapter is divided into three main

sections: the legislative mandate that requires this report, the

provisions of the 1990 amendments to the Clean Air Act (CAA or the

Act) related to this study, and an overview of the utility study and

its approach to meeting the provisions of the Act.

1.1 LEGISLATIVE MANDATE

In section 112(n)(1)(A) of the Act, Congress directs the U.S.

Environmental Protection Agency (EPA) to:

"... perform a study of the hazards to public health

reasonably anticipated to occur as a result of emissions by

electric utility steam generating units of ... [HAPs] ...

after imposition of the requirements of this Act."

Section 112(a)(8) of the Act defines an "electric utility

steam-generating unit" as "any fossil-fuel–fired combustion unit of

more than 25 megawatts electric (MWe) that serves a generator that

produces electricity for sale." A unit that cogenerates steam and

electricity and supplies more than one-third of its potential electric

output capacity and more than 25 MWe output to any utility power

distribution system for sale is also considered an electric utility

steam-generating unit.

Section 112(n)(1)(A) also requires that:

 Results of this study be presented in a report to Congress

by November 1993;

 The EPA develop and describe alternative control strategies

for HAPs that may warrant regulation under section 112; and

 The EPA proceed with rulemaking activities under section 112

to control HAP emissions from utilities if it determines

from the study that such regulation is appropriate and

necessary.

Section 112(n)(1)(A) does not include a requirement to analyze

the cost(s) of alternative control strategies in the study. 

Therefore, no cost analyses (e.g., control costs, economic, costbenefit) have been performed as a part of this study. These analyses

would be conducted as part of the rulemaking process should EPA

determine that regulations are appropriate and necessary. 

The EPA began work in 1991 to develop and collect the information

and data needed to prepare this study of HAP emissions from electric

utilities. At that time, only a small amount of reliable data on HAP
1-2

emissions from utilities were available. In October 1996, the Agency

published a three-volume report, Study of Hazardous Air Pollutant

Emissions from Electric Utility Steam Generating Units--Interim Final

Report. (EPA-453/R-96-013). This final report incorporates

additional analyses and includes more recent data on emissions,

control technologies, and health effects. 

This report discusses the possible impact of pollution controls

required by other Federal regulations or sections of the Act,

estimates which HAPs are present in utility unit emissions, and

estimates exposures and risk to humans from the emission of these

HAPs.

1.2 CAA PROVISIONS AND STUDIES RELATED TO THIS STUDY

The CAA contains several provisions relating to electric

utilities that will impact the industry well into the future. 

Environmental regulations implementing many of these requirements are

now in effect; others have been established since the date of the last

report; and others are under development.

This section summarizes the major provisions of the Act affecting

electric utilities and their relevance to this study. These include

nonattainment provisions, acid deposition control programs, and new

source performance standards (NSPS) discussed in sections 1.2.1

through 1.2.3. The development of regulations for HAP under section

112 of the Act and other related studies required by section 112 are

discussed in sections 1.2.4 and 1.2.5, respectively.

1.2.1 Nonattainment Provisions

Title I of the Act includes requirements for attaining and

maintaining the national ambient air quality standards (NAAQS). 

Section 108 of the Act directs EPA to identify certain pollutants

which may reasonably be anticipated to endanger public health and

welfare. Section 109 directs the Administrator to establish primary

and secondary NAAQS for the identified pollutants. Under section 110

of the Act and related provisions, States are primarily responsible

for ensuring attainment and maintenance of the ambient standards. The

EPA has established NAAQS for six criteria pollutants: ozone (O ), 3

carbon monoxide (CO), particulate matter (PM), lead, sulfur dioxide

(SO ), and nitrogen oxides (NO ) under Title 40, Part 50 of the Code of 2 x

Federal Regulations (40 CFR Part 50). Electric utilities are

significant emitters of SO and NO ; NO emissions from electric 2 xx

utilities account for about one-third of nationwide emissions. 1

Electric utilities also emit other criteria pollutants such as PM as

well as air toxics.

The EPA issued revised NAAQS for O and PM on July 18, 1997 3

(Federal Register, volume 62, page 38856 [62 FR 38856]). The new

rules strengthened the primary standard for O , added standards for PM 3

less than 2.5 microns in size (PM ) to supplement the PM primary 2.5 10

standard, and revised secondary standards. As part of this 2
1-3

rulemaking, the EPA also proposed rules requiring States to develop

programs to reduce regional haze.

To achieve the new standards, EPA has developed an integrated

strategy that will require reductions in NO and SO as well as x 2

volatile organic compounds (VOC) and PM. Nitrogen oxide is a

precursor to the formation of ground-level O , and SO is a precursor 3 2

to the formation of PM in the atmosphere. Electric utilities will be

affected as States reduce emissions to meet the new standards. One

EPA study predicts that nationwide NO reductions ranging from 25 to 90 x

percent, depending on the particular State or non-attainment area,

will be needed to attain the revised O ambient standard. 3 3

The Regulatory Impact Analysis for the revised standards assumes

that much of the needed emission reductions would be achieved through

the Acid Deposition Program for SO and NO discussed in section 1.2.2 2 x

and through the revised NSPS discussed in section 1.2.3. According to

the analysis, SO and NO emissions from utilities will be reduced by 2 x

approximately 40 and 50 percent, respectively, by the year 2010. 4

These analyses also estimate a 16 percent reduction (approximately 11

tons per year) in utility mercury emissions (in 2010) as a result of

compliance with the revised NAAQS (primarily related to the impact of

the SO strategy to meet the PM NAAQS). 2 5

In a related action, EPA proposed rules requiring 22 States and

the District of Columbia to submit State Implementation Plans (SIPs)

that address the regional transport of ground-level O . The proposed 3

rule would decrease ozone transport in the eastern half of the United

States by reducing NO emissions. Under the proposed rule, States may x

reduce emissions from sources they choose, although utility and large

nonutility point sources are expected to be affected. Implementation

of the proposed rule would reduce total emissions of NO by 35 percent x

based on analyses by EPA and the Ozone Transport Assessment Group

(OTAG). The EPA estimates that this action will bring areas into

attainment with the revised O standard without additional local 3

controls. Many of these States are expected to reduce NO emissions by x

participating in the cap-and-trade program discussed in section 1.2.2. 6

1.2.2 Acid Deposition Control

Title IV of the Act sets as its primary goal the reduction of

annual SO emissions by 10 million tons below 1980 levels. To achieve 2

these reductions, the law requires a two-phase tightening of the

restrictions placed on fossil-fuel power plants (i.e., utilities). 

Phase I of EPA’s SO Program (40 CFR Parts 72 through 75) began in 1995 2

and affects 263 units at 110 mostly coal-burning electric utility

plants in 21 States. An additional 182 units joined Phase I as

substitution or compensating units, bringing the total of Phase I

units to 445. Emissions data indicate that 1995 SO emissions at these 2

units nationwide were reduced by almost 40 percent below their

required level of 8.7 million tons. The second phase begins in the 7

year 2000 and covers an additional 1,600 boilers. The EPA believes

the 10 million ton goal will be met before the year 2010. To reduce
1-4

SO emissions, an affected source may: (1) install flue gas scrubbers, 2

(2) switch to a fuel that contains less sulfur, or (3) purchase

emission allowances. The control option a utility selects to comply

with the SO reduction requirements may also have an effect on HAP 2

emissions.

Under the SO program, affected units are allocated allowances 2

based on their historic fuel consumption and a specific emissions

rate. Each allowance permits a unit to emit one ton of SO per year. 2

For each ton discharged, one allowance is retired. Allowances may be

bought, sold, or banked and are tracked through a computerized system. 

However, no source can emit at a level violating Federal or State

limits set under Title I of the Act. Sources also must obtain a

permit and meet continuous emission monitoring requirements for SO ,2

NO , and carbon dioxide (CO ), as well as volumetric flow and opacity x 2

monitoring requirements.

Section 407 of the Act establishes the NO Emission Reduction x

Program with the goal of reducing emissions by 2 million tons from

1980 levels. Like the SO emission reduction program, the NO program 2 x

is implemented in two phases beginning in 1996 and 2000. Under Phase

I rules (40 CFR Part 76), approximately 277 dry-bottom wall-fired

boilers and tangentially-fired boilers (Group I) must meet applicable

annual average emission rates of 0.45 pound per million British

thermal units (lb/MMBtu) and 0.50 lb/MMBtu, respectively, by January 1,

1996. Utilities can meet the limits by installing low–NO burner x

technology or other combustion control technology or by averaging

emissions among several units. An affected unit also may obtain an

alternative emission limit under specified conditions. Implementation

of Phase I will decrease annual NO emissions by over 400,000 tons per x

year (tpy) between 1996 and 1999 (60 FR 18751, April 13, 1995).8,9

The EPA issued final rules implementing Phase II of the program

in late 1996 (61 FR 67112, December 19, 1996). In these rules, EPA

determined that more effective low NO burner technology is available x

to establish more stringent standards for Phase II, Group I boilers

than those established for Phase I. Emission limits for Group II

boilers (wet bottom, cyclones, cell burners, and vertically-fired

boilers) were also established based on NO control technologies x

comparable in cost to low NO burners (selective catalytic reduction x

[SCR]). Selective catalytic reduction is a commercially available

flue gas treatment technology that injects ammonia into the flue gas

in the presence of a catalyst. The catalyst promotes reactions that

convert NO to nitrogen and water. By the year 2000, the Phase II rule x

(affecting 775 units) will achieve an additional reduction of 1.17

million tons of NO per year. Phase I and Phase II together are x

estimated to decrease nationwide annual NO emissions by 2.06 million x

tpy beginning in the year 2000.10

The final Phase II rule includes an option allowing a State or

group of States to petition EPA to accept an emissions cap-and-trade

program as a substitute for compliance with the Group 2 limits and
1-5

additional reductions required for Group 1 boilers. The petition may

be granted if the Administrator finds that alternative compliance

through the cap-and-trade program will achieve lower total NOx

emissions from Group 1 and Group 2 boilers than if the new limits were

applicable. The Phase I limits established in 1995 would apply to

Group 1 boilers in a cap-and-trade program. This provision is

expected to affect boilers located in the OTAG region which contains

about 87 percent of the units covered by the Phase II rule.10

In related developments, the EPA is currently developing a model

cap-and-trade program to facilitate NO emission reductions from large x

stationary sources choosing to participate. The Agency intends to

propose the rule in early 1998 and finalize the action in conjunction

with the ozone transport rulemaking in September 1998. 11

1.2.3 New Source Performance Standards

Section 111 of the Act requires the development of NSPS for newly

constructed or modified affected facilities. Section 403 of the Act,

as amended, revised the definition of the term "standard of

performance" to mean:

"...a standard for emissions of air pollutants which reflects the

degree of emission reduction achievable through the application

of the best system of emission reduction which (taking into the

cost of achieving such reduction and any nonair quality health

and environmental impact and energy requirements) the

Administrator determines has been adequately demonstrated." 

New source performance standards currently provide the major

regulatory authority for the control of air emissions from utilities. 

Fossil-fuel-fired steam generating units greater than 73 MW heat input

that were constructed or modified after August 17, 1971, are subject

to requirements of 40 CFR Part 60, Subpart D; units constructed or

modified after September 18, 1987, are subject to 40 CFR Part 60,

Subpart Da. These rules define "fossil fuel" as "natural gas,

petroleum, coal, and any form of solid, liquid, or gaseous fuel

derived from such material for the purpose of creating useful heat." 

Fossil fuels include coal (bituminous, subbituminous, anthracite,

lignite), oil (Nos. 2, 4, and 6), and natural gas. Subparts D and Da

include limits for emissions of SO , NO , and PM based primarily on the 2 x

use of scrubbers or low sulfur coal, combustion modification

techniques (overfire air, low excess air, and reduced heat release

rate), and PM control devices. Provisions also are included for the

use of continuous opacity monitoring systems and continuous emission

monitoring systems for SO and NO and oxygen (O ) or CO . 2x 22

Section 407 of the Act requires EPA to revise the NSPS for NOx

emissions from utility and nonutility units to reflect improvements in

emission reduction methods. The EPA proposed revisions to the NO x

limit for utility units (i.e., boilers) in 40 CFR Part 60, Subpart Da

(regardless of fuel type) based on coal-firing and the performance of

SCR control technology, in combination with combustion controls (62 FR
1-6

36947; July 9, 1997). Thus, units can meet the proposed standards by

using clean fuels such as natural gas or by installing more effective

control systems. The proposed rule also revised the emission limit to

incorporate an output-based format that will encourage unit operating

efficiency and pollution prevention. The EPA estimates that about

43,600 tons of NO per year would be emitted from 17 new utility x

boilers expected to be constructed over the next 5 years. The

proposed revised standards would reduce these emissions by about

25,800 tpy.12

The NSPS program results indirectly in the control of some HAPs. 

For example, NSPS that limit emissions of PM will also control HAPs

that are PM or that condense onto the PM in the affected gas streams. 

Furthermore, the use of SO scrubbers (currently on about 14 percent of 2

the units) will also control some vapor-phase HAPs, such as hydrogen

chloride (HCl) and hydrogen fluoride (HF), in addition to providing

some control of mercury.

1.2.4 Hazardous Air Pollutants

Section 112(d) of the Act requires that EPA promulgate

regulations for the control of HAPs listed in section 112(b) of the

Act from both new and existing major sources. A "major" source means

a source that:

"... emits or has the potential to emit, considering

controls, 10 tons per year or more of any HAP or 25 tons per

year or more of any combination of HAPs."

Regulations developed under section 112(d) must reflect the

maximum degree of reduction in emissions of HAP that is achievable,

taking into consideration the cost of achieving the emissions

reduction, and any non-air quality health and environmental reduction

and energy requirements. This level of control is commonly known as

the maximum achievable control technology (MACT). For new sources,

MACT standards cannot be less stringent than the emission control that

is achieved in practice by the best-controlled similar source. The

MACT standards for existing sources cannot be less stringent than the

average emission limitation achieved by the best-performing 12 percent

of existing sources for categories and subcategories with 30 or more

sources, or the best-performing 5 sources for categories or

subcategories with fewer than 30 sources. Section 112(d) also

provides that the Administrator may distinguish among classes, types,

and sizes of sources within a source category when establishing

standards. Regulations for the control of HAP emissions from

utilities will be developed under this authority if such regulations

are determined to be necessary and appropriate.

1.2.5 Other Studies 

The 1990 amendments to section 112 of the Act also mandate five

other related studies: (1) the mercury study, (2) the National

Institute of Environmental Health Sciences (NIEHS) health effects of

mercury study, (3) the National Academy of Sciences (NAS) risk
1-7

assessment methodologies study, (4) the Great Waters study, and

(5) the Presidential Risk Commission.

1.2.5.1 Mercury Study. Section 112(n)(1)(B) requires the EPA to

complete a study of mercury emissions from utilities, municipal waste

combustion units, and other sources, including area sources, by

November 15, 1994. The study is to consider the rate and mass of

mercury emissions, the health and environmental effects of such

emissions, technologies that are available to control such emissions,

and the costs of such technologies. The EPA just recently (December

19, 1997) published the Final Mercury Study Report to Congress. The

3-year delay was necessary to allow sufficient time for data

gathering, analyses, writing, and extensive peer review. The Mercury

Study is closely related to this Utility Study because utilities are

the largest anthropogenic source of mercury emissions. Utilities

(primarily coal-fired utilities) are estimated to emit approximately

33 percent of the airborne anthropogenic mercury in the United States. 

Several analyses and conclusions contained in the Mercury Study are

applicable to utilities, and are discussed in Chapter 7 of this

report. 

1.2.5.2 NIEHS Health Effects of Mercury Study. Under section

112(n)(1)(c), the NIEHS is required to perform a study identifying the

threshold level of mercury exposure that would not adversely affect

human health. A report on the NIEHS study was published in 1993. 13

1.2.5.3 NAS Risk Assessment Methodologies Study. In January

1995, the NAS finalized a report on the risk assessment methodologies 14

used by the EPA. The results of the NAS study were consulted to help

develop the methodologies for the risk assessment portions of this

study.

1.2.5.4 The Great Waters Study. In response to section 112(m),

the EPA finalized a report in May 1994 on the atmospheric deposition

of pollutants to the “Great Waters,” namely, the Great Lakes,

Chesapeake Bay, Lake Champlain, and coastal waters. The pollutants 15

of concern to the Great Waters study that are emitted from utilities

include lead, cadmium, dioxins, and, in particular, mercury. The

report discussed the following:

 The contribution of atmospheric deposition to pollutant

loadings in these waters

 Environmental and public health effects of atmospheric

pollution that is deposited to these waters

 Sources of pollutants deposited to these waters.

The May 1994 report noted that the Great Waters are polluted by

HAPs that originate from local and distant sources; however, more data

are needed to identify sources of the pollutants. The recommendations

of the May 1994 Great Waters report were: (1) the EPA should strive
1-8

to reduce emissions of the pollutants of concern through

implementation of the Act; (2) a comprehensive approach should be

taken, both within the EPA and with other agencies, to reduce and

preferably prevent pollution in air, water, and soil; and (3) the EPA

should continue to support research for emissions inventories, risk

assessment, and regulatory benefits assessment.

Following the first Report to Congress, the EPA published the

"Final Water Quality Guidance for the Great Lakes System” required by

section 118(c)(2) of the Clean Water Act (60 FR 15366, March 23,

1995). This guidance document established minimum water quality

criteria, methodologies, policies, and procedures for the Great Lakes

System. States and Tribes in the Great Lakes Basin were required to

incorporate these provisions into their water quality standards and

National Permit Discharge Elimination System permit programs by March

1997. In the guidance, EPA recognized that non-point sources of

mercury, particularly by air deposition, are the most significant

remaining contributors of mercury to the Great Lakes System. The EPA

followed the guidance with the "Final Water Quality Guidance for the

Great Lakes System Draft Mercury Permitting Strategy,” released for

public comment in June 1997 (62 FR 31025). The final permitting

strategy will be finalized in the near future.

The second report to Congress on the atmospheric deposition of

pollutants to the Great Waters was completed in June 1997. The report

confirmed, and provided additional support for, the findings of the

first Report to Congress that persistent and bioaccumulative toxic

pollutants and excessive nitrogen can adversely affect the

environmental condition of the Great Waters. Electric utilities and

mobile sources are identified, in modeling studies and emission data,

as major contributors of NO to the Chesapeake Bay and its watershed. x 16

1.2.5.5 Presidential Risk Commission. In section 303 of Title

III of the 1990 amendments to the Act, Congress directed that the

President form a Commission whose mandate would be to “...make a full

investigation of the policy implications and appropriate uses of risk

assessment and risk management in regulatory programs under various

Federal laws to prevent cancer and other chronic human health effects

which may result from exposure to hazardous substances.” This

Commission has issued the report in two volumes. Volume 1 entitled,

"Framework for Environmental Health Risk Assessment," was issued in

February 1997. Volume 2 entitled, "Risk Assessment and Risk

Management in Regulatory Decision-Making," was issued in April 1997.

1.3 OVERVIEW AND APPROACH OF ELECTRIC UTILITY HAP STUDY

This report is the result of the work of government and

nongovernment personnel. Emissions testing and emission estimation

issues were discussed among numerous branches within the EPA and among

representatives of industry, the Electric Power Research Institute

(EPRI), and the Department of Energy (DOE). In particular, EPRI, DOE,

and the EPA coordinated their utility emissions testing to cover more
Reviewers provided comment through a variety of venues (e.g., EPA Work a

Group, scientific peer review, Federal interagency review, public

comment period). However, participation by a reviewer did not imply

agreement with the methodology or conclusions presented by the EPA. All

comments were considered during revision of the document.

1-9

plant configurations and obtain as much information as possible for

the assessment. Portions of this report, and the data and

methodologies utilized, were reviewed by numerous scientific experts

within and outside the Agency. Outside reviewers included

representatives from industry, other Federal agencies, State and local

agencies, academia, and environmental organizations.a

The report is organized as follows. The electric utility

industry is described in Chapter 2, including the types of fossil

fuels, boilers, and air pollution control devices in use in the year

1990, as well as changes in control devices and fuel usage expected

for the year 2010. Chapter 3 describes emissions testing conducted

since 1990, the determination of emission modification factors (EMFs)

from test reports, and the estimation of emissions for several

characteristic units using a computer emission factor program. 

Chapter 4 introduces the health hazard assessment. The screening risk

assessment used to determine the priority HAPs is described in

Chapter 5. Chapter 6 discusses the inhalation route for HAP exposure,

while Chapters 7 through 11 address multipathway exposures to mercury,

lead, cadmium, radionuclides, arsenic, and dioxins. Chapter 12

discusses the potential impacts of HCl and HF. Alternative control

strategies for HAP emissions reductions are given in Chapter 13. 

Chapter 14 presents the conclusions of the study. Additional

supporting material is provided in the appendices.
1-10

1. U.S. Environmental Protection Agency. Nitrogen Oxides: Impacts

on Public Health and the Environment. Office of Air and

Radiation. Washington, DC. August 1997. p. 15. Report

downloaded from EPA’s Technology Transfer Network.

2. U.S. Environmental Protection Agency. Fact Sheets for EPA’s

Revised Ozone Standard and Revised Particulate Matter Standards. 

Office of Air and Radiation. Washington, DC. July 17, 1997.

3. Reference 1. pp. 44-46.

4. U.S. Environmental Protection Agency. Regulatory Impact Analysis

for the Particulate Matter and Ozone National Ambient Air Quality

Standards and Proposed Regional Haze Rule. Appendix A: 

Emissions and Air Quality and Appendix H: Economic Impact

Analysis and Supporting Information. Office of Air Quality

Planning and Standards. Research Triangle Park, NC. July 16,

1997.

5. Reference 4. p. 12-77.

6. U.S. Environmental Protection Agency. Fact Sheet: Proposed Rule

for Reducing Regional Transport of Ground-Level Ozone (Smog). 

October 10, 1997.

7. U.S. Environmental Protection Agency. Fact Sheet: Acid Rain

Program Overview. Washington, DC. Taken from EPA web site on

November 14, 1997.

8. U.S. Environmental Protection Agency. Fact Sheet: Phase I of

the NO Reduction Program. Acid Rain Program. Washington, DC. x

Taken from EPA web site on November 14, 1997.

9. Reference 1. p. 140.

10. U.S. Environmental Protection Agency. Nitrogen Oxides Reduction

Program Final Rule for Phase II (Group 1 and Group 2 Boilers). 

Acid Rain Program. Washington, DC. Taken from EPA web site on

November 14, 1997.

11. U.S. Environmental Protection Agency. Fact Sheet: EPA’s Model

NO Trading Rule Development. Acid Rain Program. Washington, DC. x

1997.

12. U.S. Environmental Protection Agency. Fact Sheet: Proposed

Revision of Standards of Performance for Nitrogen Oxide Emissions

from New Fossil-Fuel Fired Steam Generating Units and Proposed

Revisions to Reporting Requirements for Standards of Performance

for New Fossil Fuel-Fired Steam Generating Units. July 1, 1997. 

Downloaded from EPA’s Technology Transfer Network.

1.4 REFERENCES
1-11

13. U.S. Department of Health and Human Services, National Institutes

of Health. Report to Congress on Methylmercury. NIEHS, Research

Triangle Park, NC. 1993.

14. National Academy of Sciences, National Research Council. Science

and Judgement in Risk Assessment. Washington, DC. 1994.

15. U.S. Environmental Protection Agency. Deposition of Air

Pollutants to the Great Waters: First Report to Congress. EPA453/R-93-055. Office of Air Quality Planning and Standards,

Research Triangle Park, NC. May 1994. 

16. U.S. Environmental Protection Agency. Deposition of Air

Pollutants to the Great Waters: Second Report to Congress. EPA453/R-97-011. Office of Air Quality Planning and Standards,

Research Triangle Park, NC. June 30, 1997, pp. i-vi, 1-14.
mm
1994 was chosen as the analysis year for this final report because that is a

the last year for which complete, plant-specific data were available from

the Utility Data Institute (UDI), the same data source as was used for the

1990 analyses and upon which the 2010 projections are based. The UDI

database is based on the same data as that used by the Energy Information

Administration (which may have more recent information) but, because of

varying uses and means of data verification, the data sets may not agree

completely.

It should be noted that the 1994 data for the number of individual b

combined-cycle turbine systems were not available. However, the number of

plants (34, all gas-fired) and their total number of megawatts (10,047.87)

was available, and these totals were included in Figure 2-1. This would

have the effect of biasing the gas-fired boiler unit numbers lower than

normal, but would have no effect on the gas-fired boiler MW numbers.

2-1

2.0 CHARACTERIZATION OF THE INDUSTRY

This chapter presents a characterization of the fossil-fuel-fired

utility industry. This is provided as a brief background for those

who may not be familiar with the industry. In addition, some

components of the process itself (e.g., type of boiler, method of

firing, type of emission control) may impact on the generation or

emissions of HAPs. These process components are introduced in this

chapter and their impact on HAPs is discussed in chapter 13. The

chapter is divided into seven main sections: background of the

industry, types and ownership of utilities, utility furnace design, PM

control, SO control, NO control, and a projected characterization of 2 x

the utility industry after implementation of the 1990 amendments to

the Act. All of the sections except the last describe the utility

industry as it existed in 1994. The last section projects conditions a

that are expected to exist in 2010, after the amendments are fully

implemented.

2.1 INDUSTRY BACKGROUND

An electric utility steam-generating unit is defined (section

112(a)(8) of the Act) as any fossil-fuel-fired combustion unit of more

than 25 megawatts electric (MWe) that serves a generator producing

electricity for sale. It can also be defined as a unit that

cogenerates steam and electricity and supplies more than one-third of

its potential electric output capacity and more than 25 MWe output to

any utility power distribution system for sale.

Fossil fuel-fired electric utility steam-generating units are

fueled primarily by coal, oil, or natural gas. Figure 2-1 shows the

1994 distribution of fossil fuels burned by the electric utility

industry by unit (i.e., individual boiler) and by total megawatts. 1

Coal-fired boilers account for the largest portion of the industry by

number of units (1,026 units, 61 percent), representing 68 percent of

the industry’s total megawatts. Gas-fired boilers make up 30 percent

of the industry’s units (493 units) and account for 23 percent of the

total megawatts. Oil-fired boilers account for 9 percent of the units b
61%

30%

9 %

68%

9 %

By unit By megaw

Coal Gas Oil

Figure 2-1. Fossil fuel use in the utility industry in 199

2-2
2-3

(149 units) and represent 9 percent of the megawatts. Fossil-fuelfired electric utility steam generating units accounted for 81 percent

of the total industry in 1994. Other fuels utilized include biomass 1

at 0.4 percent and “other” (including nuclear, geothermal,

hydroelectric, etc.) at 17.9 percent. Units less than 25 MWe (the

defining limit set by the Act) comprised 0.7 percent of the industry.

2.2 FOSSIL-FUEL-FIRED ELECTRIC UTILITY STEAM-GENERATING UNITS

This section describes the two basic types of utility facilities

and the types of ownership in the industry.

2.2.1 Types of Electric Utility Facilities

There are two basic types of facilities in the utility industry: 

conventional utility power facilities and cogeneration facilities. 

Although both types of facilities share similar designs, their major

difference is that conventional utility power facilities produce their

power solely for commercial power production whereas cogeneration

facilities produce their power primarily for an industrial purpose and

sell excess steam or electricity equal to more than one-third of their

potential electric output capacity and more than 25 MWe output to any

utility power distribution system.

Conventional facilities consist of units that produce heat in a

boiler to make high-pressure steam, which in turn powers units that

produce electricity through a combined cycle turbine system or a steam

turbine (see section 2.3.4). In both systems, the steam is recycled

without being used for any other purpose. Conventional facilities

account for most of the utility steam-generating units in the United

States. In 1994, there were 1,668 conventional utility steamgenerating units in the United States, with 1,026 burning coal of some

type. The total output was 464.8 gigawatts electrical (GWe).1

Cogeneration is defined as the simultaneous production of power

(usually electricity) and another form of useful thermal energy

(usually steam or hot water) from a single fuel-consuming process. 2

Cogeneration facilities can also consist of units that produce heat in

a boiler to make high-pressure steam that powers a steam turbine to

produce electricity or units that produce electricity through a

combined-cycle turbine system. Because of their primary uses as

industrial power and steam sources, however, they normally are too

small to fit the regulatory definition of a utility boiler. There

were 218 fossil-fuel-fired cogeneration facilities rated at 25 MWe or

greater that provided at least one-third of their excess power to a

grid operating in the United States as of 1990. These cogeneration

facilities consist of coal-, oil-, and gas-fired steam turbines and

combined-cycle turbines that provide 21,053 MWe of capacity. This

megawatt capacity was less than 5 percent of the total conventional

utility capacity in 1990 and was made up of only 54 coal-fired plants

(providing 5,098 MWe of capacity) and 12 oil-fired plants (providing

756 MWe of capacity). Thus, the electrical capacity of the coal- and
2-4

oil-fired cogeneration facilities represented less than 1.2 percent of

total utility capacity in 1990.3

2.2.2 Types of Ownership

There are four basic types of electric power ownership in the

utility industry: publicly owned utility companies, Federal power

agencies, rural electric cooperatives, and investor-owned utility

generating companies. Publicly owned utilities are not-for-profit and

are operated by municipalities, counties, States, or other bodies such

as public utility districts. Federal power agencies are Federal

government agencies that provide electric power, usually to rural or

remote areas. Rural electric cooperatives are private, not-for-profit

corporations owned by their members who are also the customers they

serve; the cooperatives are not a part of the municipal government. 

Investor-owned utility generating companies are owned by their

investors and sell electricity to make a profit.4

The oldest and largest companies (based on total megawatts

electric capacity) are the investor-owned utilities. Although

numbering only approximately 244 separate companies, investor-owned

utilities provided 74.3 percent of kilowatt hour (kWh) generation of

electric power to the Nation in 1994. Publicly owned utility

companies, which consist of approximately 2,020 separate companies,

represent 10.8 percent of the Nation’s electric power supply. The

10 Federal power agencies generate 9.2 percent of the Nation’s

electric power supply. Rural electric cooperatives, numbering

approximately 931 separate companies, provides 5.7 percent of the

Nation’s electric power supply. These utilities maintain jointly

owned electric power grids to which electric power is supplied and

then sold to other utilities, industries, and individual customers. 5

One of the fastest growing areas of the electric utility industry

has been nonutility generators. Nonutility generating units are

generally smaller than other utility units, of newer design

technology, and built to fill a specific need for power in their

geographic area(s). Nonutility generating units are usually privately

owned (although some are sponsored by larger publicly or investorowned utilities) and sell their power to private customers and the

jointly owned electric power grids. 

Ownership of nonutility generators can be further divided into

ownership by:
A qualifying facility, under the Public Utility Regulatory Policies Act c

(PURPA), Sections 292.303 and 292.305, may buy or sell energy to the

local utility or indirectly to other utilities. The local utility is

obligated to purchase or sell the energy at a price that is “just,

reasonable, and in the public interest” and does not “discriminate

against any qualifying facility in comparison to rates for sales to

other customers served by the electric utility.”

2-5

 Units that cogenerate steam and electricity (qualifying

facilities ); 6 c

 Small power producers (<80 MWe) that generate electricity

primarily from a renewable source; and

 Other nonutility generators (e.g., independent power

producers [IPPs], units that cogenerate steam and

electricity [nonqualifying facilities], and other commercial

and industrial units).

In the last few years, the electric utility industry has

undergone a large restructuring brought on by the impending

deregulation of the United States electric utility industry. Under

this deregulation, consumers will be able to buy their electric power

from any supplier willing to provide power in their area, thus

breaking up the virtual monopolies that certain power companies and

agencies have had since electric power was first provided.

Many older performing units are being sold or shut down for the

sake of efficiency so that electric utility companies have an

inventory of newer, more efficient units with modern pollution

controls. Also, some companies are buying units in States where they

traditionally have never supplied power in the past. In the next

decade, these reorganizations will substantially change the makeup of

electric power ownership in the utility industry.

2.3 DESIGN OF ELECTRIC UTILITY UNITS

This section contains a summary of unit designs used in the

utility industry. Hazardous air pollutants are either formed during

combustion or introduced into the combustion unit (e.g., trace

constituents in the fuel). Thus, the design and operation of a unit

may impact on the generation and emission of HAPs.

2.3.1 Furnace Types

Utility furnace-fired boilers can be divided into five basic

firing types: stoker-, cyclone-, tangential-, and wall-fired boilers

and fluidized-bed combustors (FBCs).

2.3.1.1 Stoker-Fired Boilers. Stoker firing is one of the

oldest furnace firing methods still in use. In this process, fuel is

deposited on a moving or stationary grate or spread mechanically or

pneumatically from points usually 10 to 20 feet above the grate. The 7
2-6

process utilizes both the combustion of fine coal powder in air and

the combustion of larger particles that fall and burn in the fuel bed

on the grate. Because of their design, stokers are used only for 8

smaller furnaces firing coal.

2.3.1.2 Cyclone-Fired Boilers. Cyclone firing uses several

water-cooled horizontal burners that produce high-temperature flames

that circulate in a cyclonic pattern. The burner design and placement

cause the ash to become a molten slag that is collected below the furn

ace. Because of this slagging system, cyclone-firing furnaces are

almost exclusively coal-fired; however, some units can fire oil.9

2.3.1.3 Tangential-Fired Boilers. Tangential-fired boilers are

based on the concept of a single flame envelope and project both fuel

and combustion air from the corners of the furnace. The flames are

directed on a line tangent to a small circle lying in a horizontal

plane at the center of the furnace. This action produces a fireball

that moves in a cyclonic motion and expands to fill the furnace. 10

Tangential-fired boilers can fire coal, gas, or oil.

2.3.1.4 Wall-Fired Boilers. Wall-fired boilers are

characterized by rows of burners on the wall(s) of the furnace. The

two basic forms of wall-fired furnaces are single wall (having burners

on one wall) or opposed (having burners on walls that face each

other). Circular register burners and cell burners are types of

burner configurations found in single-wall or opposed-wall-fired

units. A circular register burner is a single burner mounted in the

furnace wall, separated from other burners so that it has a separate,

distinct flame zone. Cell burners are several circular register

burners grouped closely together to concentrate their distinct flame

zones. This use of a distinct flame zone is in contrast to the

fireball effect created by the tangentially fired furnace. 11

Wall-fired boilers can fire coal, gas, or oil.

2.3.1.5 Fluidized-bed Combustors. In a typical FBC, combustion

occurs when coal, together with inert material (e.g., sand, silica,

alumina, or ash) and/or a sorbent such as limestone, are suspended

through the action of primary combustion air distributed below the

combustor floor. "Fluidized" refers to the state of the bed of 12

material (fuel or fuel and inert material [or sorbent]) as gas passes

through the bed. As the gas flow rate is increased, the forces on the

particles become just sufficient to cause buoyancy. The gas cushion

between the solids allows the particles to move freely, giving the bed

a liquid-like characteristic.13

Fluidized-bed combustors can be further divided into circulating

fluidized-bed (CFB) and bubbling fluidized-bed (BFB) steam generators. 

The main difference between these two types is the state of

fluidization, which in turn depends mainly on the bed particle

diameter and fluidizing velocity. The CFB combustors have relatively

high velocities and fine bed particle size, whereas the BFB combustors

have relatively low velocities and coarse bed-particle size.14,15
It should be noted that the 1994 data for the number of individual d

combined-cycle turbine systems were not available. However, the number

of plants (34, all gas-fired) and their total number of megawatts

(10,047.87) was available, and these totals were included in Figure 2-2. 

This would have the effect of biasing the gas-fired boiler unit numbers

lower than normal, but would have no effect on the gas-fired boiler MW

numbers.

2-7

Most FBCs are of the atmospheric fluidized-bed combustor (AFBC)

type, which, as the name suggests, operate at atmospheric pressure. A

newer type of FBC is the pressurized fluidized-bed combustor (PFBC). 

These combustors are physically smaller (yet maintain the same

megawatt capacity as equivalent AFBCs), operate at 10 to 20 times

atmospheric pressure, and incorporate a gas turbine in their power

production cycle. Because of these features, PFBCs offer a

potentially significant gain in overall thermal efficiency over

AFBCs.16

2.3.1.6 Distribution of Furnace Types. Figure 2-2 shows the

1994 distribution of furnace types by fuel in the utility industry by

unit and by total megawatts. Wall-fired designs account for the 1

largest portion of the coal-fired units by number of units (48.8

percent), which represents 48.3 percent of the coal-fired units’ total

megawatts. The second and third most common designs are the

tangential-fired and cyclone-fired units. Tangential firing is used

in 41.2 percent of the units (43.3 percent of the total megawatts),

and cyclone firing is used in 8.5 percent of the units (8 percent of

the total megawatts). Stoker-fired boilers and FBCs account for about

1.5 percent of designs among the coal-fired units (0.4 percent of the

total coal-fired megawatts). Wall-fired designs represent the largest

portion of gas- and oil-fired units by number of units (66 percent),

which represents 62.1 percent of the total megawatts. The second most

common design is the tangential-fired unit. Tangential-fired units

represent 28.5 percent (31 percent of the total megawatts) of the gasand oil-fired units, and combined-cycle gas turbine units account for

about 5.3 percent (6.9 percent of the total megawatts) of designs for

gas- and oil-fired units. There is one known cyclone-fired unit d

fueled by oil. This unit represents 0.2 percent (0.1 percent of the

total megawatts) of the gas- and oil-fired units.

2.3.1.7 Effects of Furnace Type on HAP Emissions. Many of the

organic HAPs leaving a furnace in the gas stream are produced in the

combustion zone and succeeding parts of the gas path. Factors

expected to affect the types and quantities of HAPs produced and

emitted include temperature, residence time, fuel characteristics,

firing scheme, bottom-ash and/or fly-ash partitioning, and adsorption

onto ash. By comparison, essentially all elemental HAPs leaving the

furnace enter with the fuel. The proportion of elemental HAPs in the 

gas stream depends primarily on the bottom-ash and/or fly-ash

partitioning and adsorption onto ash. For both cases, furnace type

appears to influence the HAPs that leave the furnace and continue to a

control device or stack. Chapter 13 provides a discussion, from
Figure 2-2. Unit types in the utility industry by fuel type in 1994.1

48.8%

8.5% 0.8%

41.2%

0.7%

43.3%

0.3%

48.3%

8.0%

0.1%

62.1%

0.1%

31.0%

6.9%

66.0%

5.3%

28.5%

0.2%

Coal by Unit Coal by Megawatt

Gas and Oil by Unit Gas and Oil by Megawatt

2-8
2-9

limited data, suggesting that, for example, organic HAP emissions are

increased as furnace conditions are changed. Similarly for elemental

HAPs, chapter 13 shows data suggesting that, for example, cyclone

boilers emit some elemental HAPs at lower rates than tangential

boilers, and tangential boilers emit at lower rates than cyclone

boilers for other HAPs. Although tentative, furnace type

characterizations as related to HAP emissions are used for the

Nationwide emission factor program described later in this report. 

Appendix D describes the construction of the program and the manner in

which HAP emissions are assigned to each furnace type.

2.3.2 Bottom Types

There are two types of furnace bottoms, wet and dry. The type of

bottom used depends on the type of fuel to be burned and on the

engineering requirements of the furnace. Wet-bottom furnaces sweep

the flame across the furnace floor at all firing rates to maintain the

ash in a molten state. Because of the ash handling and temperature

limitations of wet-bottom boilers, dry-bottom furnaces are the only

type currently used in new furnace construction.

In dry-bottom boilers, the ash reaches the melting point but

cools when deposited on the furnace walls; thus, it can be removed in

a dry state. This type of bottom is used in furnaces with tilting

fuel nozzles. It can provide a wider steam temperature control range

and can burn coals with widely varying ash characteristics.17

2.3.3 Cogeneration

Units that cogenerate steam and electricity can be classified as

topping or bottoming systems. Topping systems produce electricity

first, and all or part of their exhaust heat is subsequently used in

an industrial process. A bottoming system uses waste heat from a

boiler (or other high-temperature thermal process) to run a steam

turbine and/or generating unit to produce electricity. 18-20 18,19,20

2.3.4 Combined-Cycle Systems

The use of one source of hot gas to produce electricity by the

means of two separate thermal cycles and associated turbines is known

as combined cycle. An example would be a combustion gas turbine’s

exhaust gas used to create steam for a steam turbine. Only systems

that incorporate a steam turbine as one of the two cycles are

considered in this study. Simple-cycle gas turbines with waste gas

vented directly to the atmosphere are not considered. Combined-cycle

systems consisting of a gas turbine with exhaust gases serving a heat

recovery steam generator are considered if they otherwise meet the

definition of an electric utility steam generating unit.

2.4 PARTICULATE MATTER CONTROL

This section describes the four major types of PM controls used

on utility boilers: mechanical collectors, electrostatic

precipitators (ESPs), particle scrubbers, and fabric filters (FFs). 
2-10

Figure 2-3 illustrates the 1994 distribution of PM control by fuel in

the utility industry by unit and by total MW.1

In 1994, ESPs accounted for the largest portion of the PM control

technology used on coal-fired units by number of units (91 percent)

and by total megawatts (91 percent). The second most common control

technology was the FF (also referred to as a baghouse). Fabric

filters were used on 7 percent of the coal-fired units (6 percent of

total megawatts). Particle scrubbers were used on 2 percent of the

coal-fired units (approximately 3 percent of the total megawatts).

Uncontrolled units represented the largest portion of the oilfired units (56 percent) and accounted for 48 percent of the oil-fired

industry’s total MWs. Electrostatic precipitators were used on

22 percent of the oil-fired units or at 27.4 percent of the MWe

capacity of the oil-fired industry. Mechanical controls (cyclones)

were used on 21 percent of the oil-fired units (24.5 percent of the

total MWs). There is one known oil-fired unit controlled by a fabric

filter. This unit represents 1 percent (0.1 percent of the total

megawatts) of the oil-fired units. Gas-fired units had no PM

controls. 1

As PM is formed during the combustion process and moves through

the boiler system, HAPs can be condensed or adsorbed on particle

surfaces. Although most particles are formed in the 3-micrometers

(μm) to 50-μm range (on a mass basis), HAPs tend to concentrate 21

preferentially on particles smaller than about 7 μm, and especially on

those around 0.3 μm. Because of this preferential concentration, 22

high collection efficiency for fine particles is an important factor

in evaluating HAP control from PM collection devices. Each of the

four major control devices is described here, along with its method of

operation and collection efficiency by particle size. Much of the

efficiency data by particle size originates from extensive studies

performed by the EPA expressly for the purpose of comparing field

performance of FFs, ESPs, and particle scrubbers applied to combustion

sources. Special care was taken to provide accurate measurements for

particles smaller than about 10 μm (PM ). 10

2.4.1 Mechanical Collectors

Mechanical collectors are the oldest, simplest, and least

efficient of the four types of PM control devices. The collectors

used for utility boilers are generally in the form of groups of

cylinders with conical bottoms (multicyclones). Particles in the

entering gas stream are hurled to the outside of the cylinder by 

centrifugal force and are discharged at the bottom of the cone. 

Collection efficiency for a typical multicyclone may be about 70 to

75 percent for 10-μm particles, but may drop to less than 20 percent

for 1-μm particles. Thus, the multicyclone would be the least 23

effective of the four devices discussed here for reducing HAPs emitted

into the atmosphere as small particles or attached to small particles

(and was assumed to have no control effect on HAPs in the

calculations). 24
Figure 2-3. Particulate control in the utility industry by fuel type in 1994.1

91%

2 % 7 % 6 %

91%

3 %

48%

0.1%

27.4%

24.5%

21% 56%

22%

1 %

Coal by Unit Coal by Megawatt

Oil by Unit Oil by Megawatt

EF

PMN

2-11
2-12

2.4.2 Electrostatic Precipitators

Electrostatic precipitators have been used on boilers for about

80 years, can be designed for high efficiencies (>99 percent, but at

the cost of increased unit size), and are the most frequently used PM

control devices on utility boilers. They operate by imparting an

electrical charge to incoming particles, then attracting the particles

to oppositely charged plates for collection. The collected particles

are periodically dislodged in sheets or agglomerates by rapping the

plates. Particle removal in an ESP depends largely on the electrical

resistivity of the particles being collected. An optimum value exists

for any ash; above and below this value, particles become less

effectively charged and collected. Coal that contains a moderate to

high amount of sulfur (more than about 3 percent) produces an easily

collected fly ash. Low-sulfur coal produces a high-resistivity fly

ash that is difficult to collect. Resistivity of the fly ash can be

changed by operating the boiler at a different temperature or by

conditioning the particles upstream of the ESP with sulfur trioxide,

sulfuric acid, water, sodium, or ammonia. In addition, efficiency is

not uniform for all particle sizes. For coal fly ash, particles

larger than about 1 to 8 μm and smaller than about 0.3 μm (not to be

confused with total PM) are typically collected with efficiencies from

95 to 99.9 percent. Particles near the 0.3 μm size are in a charging 25

transition region that reduces collection efficiency. These 26

particles have been shown to have lower collection efficiency (about

80 to 95 percent). However, for particles in the 1- to 8-μm size

range, the reasons for poorer collection efficiency are not as well

understood. There is often a penetration peak in this size range. 27

If these particles escape capture by the ESP, boiler emissions are

likely to show an increase in smaller particles that may be enriched

with HAPs. As mentioned above, ESPs can be designed to control 28

particulate emissions to high efficiencies. On a total mass basis,

these efficiencies can be equivalent to those of FFs. However, on a

fine particulate basis, the ESP may not be quite as effective as an

FF. Because designing for higher overall efficiencies in an ESP

requires increasing the size (and cost) of the device, past practice

has been to design to meet regulatory requirements. Further study is

required to determine the capabilities of ESPs for higher overall HAP

removal compared to other control systems.

2.4.3 Particulate Matter Scrubbers

The use of wet scrubbers for PM collection has three distinct

disadvantages: high energy consumption when high efficiency is

required, the presence of a wet effluent to be disposed of, and

difficulty in obtaining high collection efficiencies for fine

particles. Scrubbers operate by shattering streams of water into

small droplets that collide with and trap PM contained in the flue gas

or by forcing the flue gas into intimate contact with water films. 

The particle-laden droplets or water films coalesce and are collected

in a sump at the bottom of the scrubber. The three common types of

scrubbers for fly ash control are venturi, preformed spray, and moving

bed. Venturi scrubbers, the type most commonly used for utility

systems, transport particle-laden flue gas through a constriction at
2-13

which violent mixing takes place. Water is introduced either at, or

upstream of, the constriction. Preformed spray scrubbers are usually

vertical cylinders with flue gas passing upward through droplets

sprayed from nozzles near the top of the unit. Moving-bed scrubbers

have an upper chamber in which a bed of low-density spheres (often

plastic) is irrigated by streams of water from above. Gas passing

upward through the bed agitates the wetted spheres, which continually

expose fresh liquid surfaces for particle transfer. Particle

collection efficiency in scrubbers is generally size and energy

dependent. Although some scrubbers collect particles at high

efficiency with low energy consumption, venturi scrubbers are normally

energy intensive compared to ESPs or FFs. Particles larger than a few

micrometers can be collected with efficiencies greater than 99

percent, but, at sizes smaller than about 1 or 2 m, efficiency may be

reduced to less than 50 percent. Because of this low collection 29

efficiency, the emission of HAP-laden particles from scrubbers is

expected to be greater than for ESPs. However, water in the scrubber

may remove water-soluble HAPs. 30

2.4.4 Fabric Filters

Fabric filters have been used on utility boilers for about

20 years. They are inherently efficient and are effective when

high-efficiency PM collection is required. Unlike ESPs, their size is

not a strong function of desired efficiency. They must be designed

and operated carefully to ensure that the fabric tubes (bags) inside

the collector are not damaged or destroyed by adverse operating

conditions. Fabric filters collect PM by placing a fabric barrier in

the flue gas path. Gas passes freely through the fabric, but

particles are trapped and retained for periodic removal. Data from a

small utility boiler show collection efficiencies not lower than 99.6

percent across all particle sizes from 0.3 m to about 10 m (the

range of the measuring equipment). Because of its high collection 31

efficiency for small particles, the baghouse should be particularly

effective for removing particles that have been enriched with HAPs. 32,33

However, further study is required to determine if baghouses can

remove significantly greater quantities of HAPs than are removed by

other control systems.

2.4.5 Comparison of Particle Collectors

Table 2-1 compares the characteristics and capabilities of the

four particle collection devices described. Fabric filters and ESPs

appear to provide the highest mass collection efficiency for fly ash. 

Fabric filters appear to be the best of the four devices for capturing

small particles that may be enriched with HAPs. Examination of

Tables 13-6 and 13-9, which compare HAP removal by cold-side ESPs and

FFs on utility boilers, also suggests that FFs may be more effective. 

However, further study is required for confirmation.
2-14

Table 2-1. Comparison of Particulate Matter Collection Systems34

Collector efficiency, % 0.3 m, %

Typical mass Efficiency at Energy consumption, in. H O2

Collector System

Multicyclone 70 - 90 0 - 15 4 - 10 7 - 13

ESPs 99 - 99.7 80 - 95 0.5 - 1 3.5 - 4

Particle scrubber 95 - 99 30 - 85 2 - 70 5 - 73

FFs 99 - 99.9 99 - 99.8 5 - 10 8 - 13

ESPs = Electrostatic precipitators.

FFs = Fabric filters.

2.5 SULFUR DIOXIDE CONTROL

Sulfur dioxide emissions are controlled through either

(1) precombustion measures, namely, the combustion of fuels that

contain lesser amounts of sulfur; (2) combustion measures, such as an

FBC system that combusts coal and limestone (or an inert material);

and (3) postcombustion measures, such as the use of flue gas scrubbing

(or flue gas desulfurization [FGD]) devices.

Precombustion measures may include the use of compliance fuels

(fuels having characteristics, such as low sulfur content, that allow

the user to comply with emission limitations solely by switching to

the fuel) to meet State implementation plans (SIPs) or NSPS. The use

of SIP or NSPS fuels means that the sulfur content in the fuel is

sufficiently low that add-on controls or postcombustion controls are

not required. As of 1994, all oil- and gas-fired units burn

compliance fuel, whereas approximately 85 percent of the coal-fired

units burn compliance coal.1

Compliance coal may be obtained through the mining of

lower-sulfur coals, coal washing, and/or coal blending. (Because coal

washing is reviewed in section 2.5.1, it is only briefly mentioned

here.) Most bituminous coals are cleaned in order to meet customer

specifications on sulfur, ash, and heating content. In the process of

cleaning, the sulfur and ash content of the coal are reduced, while

the heating content may be increased. Consequently, less of the

cleaned coal, containing less sulfur, is needed to achieve a given

heating rate. Compliance coal may also be obtained through coal

blending, in which higher-sulfur coals are blended with lower-sulfur

coals.

Combustion measures control emissions of SO from six coal-fired 2

units, representing a total capacity of 815.6 MWe. These units are

FBCs and control SO in the combustion zone by using limestone as a 2

sorbent.1
2-15

Figure 2-4 shows SO control devices used in coal-fired utilities 2

in 1994 based on the number of units and total MW capacity. As shown 1

in Figure 2-4, 15 percent of the units, representing about 22 percent

of the coal-fired generation capacity, used postcombustion flue gas

scrubbing to comply with SO regulations. A wet FGD was used at 2

approximately 14 percent of the units (approximately 21 percent of the

coal-fired total electric capacity), whereas a spray dryer adsorber/FF

(SDA/FF, also called a dry scrubber) system was used at approximately

1 percent of the coal-fired units (approximately 1 percent of the

coal-fired total electric capacity).

Sulfur dioxide emission standards for utility steam generators

vary according to the size, age, and location of a facility. Existing

boilers are regulated by SIPs. Plants built after 1971 are subject to

NSPS SO emission limits of 1.2 pounds per million British thermal 2

units heat input (lb/MMBtu). Plants built after 1978 are additionally

required to reduce their SO emissions by 70 to 90 percent. 2

The extent of postcombustion SO control used by the utility 2

industry will increase in response to Title IV of the 1990 amendments

to the Act, which require SO reduction in two phases. The likely mix 2

of SO control approaches that will be used to comply with the Phase I 2

and Phase II requirements is discussed in section 2.7.

2.5.1 Precombustion Control: Fuel Options

By using coal with an appropriately low sulfur content,

85 percent of the coal-based utility units currently comply with SO 2

emission limits. Compliance coals may be mined from the ground or may

be obtained by cleaning or blending mined coal.

Physical coal cleaning typically involves (1) size reduction and

screening, (2) gravity separation of coal from sulfur-bearing mineral

impurities, and (3) dewatering and drying. Approximately 77 percent 35

of the eastern and midwestern bituminous coal shipments are subjected

to some physical cleaning process. Subbituminous and lignite coals 36

are not routinely cleaned. The primary purpose of physical 37,38

cleaning has been to remove ash; coal cleaning has the consequence of

increasing the heating value of the coal and reducing the sulfur

content in the coal. Bituminous coals from the eastern United 39

States, cleaned with a 1.6 specific gravity separation, were found to

provide reductions of 48 percent ash, 65 percent pyritic sulfur, 43

percent total sulfur, and 48 percent SO emissions at a Btu recovery 2

rate of 94 percent.40

As with sulfur, many trace elements may be both organically bound

and present as a part of a mineral in the same coal. Thus, physical

coal cleaning has the potential to remove some of the trace elements

associated with the mineral matter. Recent experimental studies

showed significant reductions of a number of trace elements. The 35,41

reduction percentages were found to depend on the type of coal and the

trace element’s nature within the coal. For a few trace elements, an

enrichment effect was observed for some of the coal samples; however,
Figure 2-4. SO2 control in the utility industry in 1994 (coal-fired boilers 

No FGSD

14%

By Unit By Megawatt

1 %

85%

1 %

21%

78%

2-16
2-17

when expressed on a Btu basis, physical cleaning will always reduce,

to some extent, the amount of trace elements present in coal. The

effectiveness of coal cleaning in reducing concentrations of trace

elements in coal is discussed in section 13.1.2.

2.5.2 Postcombustion Control: Flue Gas Scrubbing for SO2

 Control

According to the 1995 compilation of the Edison Electric

Institute’s (EEI) Power Statistics database (examining 1994 data),

scrubbers were installed on 152 boiler units (out of about

1,026 coal-fired units in the United States) with a total rated

capacity of 70,458 MWe. Table 2-2 lists the different types of 1

scrubbing installations used in United States utility power plants. 

As shown in Table 2-2, wet limestone/lime slurry scrubbing represents

the most prevalent scrubber type with almost 80 percent of the total

flue gas scrubbing capacity.1

2.5.2.1 Wet Limestone. In a wet limestone scrubber, flue gas

containing SO is brought into contact with a limestone-water slurry. 2

The SO is absorbed into the slurry and reacts with limestone to form 2

an insoluble sludge. The sludge, mostly calcium sulfite hemihydrate

and gypsum, is usually disposed of in a pond specifically constructed

for the purpose.42

The two common absorber designs include fixed packing and

horizontal or vertical spray towers, with spray towers being the most

prevalent. The absorber must be constructed of materials that resist

corrosion, erosion, and scaling. To reduce corrosion and erosion

problems, a scrubber is located downstream of a PM collection device. 

A flue gas cooler and humidifier are used to cool the flue gases,

generally to 50 C (122 F), prior to absorption. The size and number o o

of scrubber modules are directly related to boiler size, load

fluctuations, and system availability and compliance requirements.

Auxiliary equipment includes a demister to remove entrained

droplets from the scrubber outlet gas, a heat exchanger system to

reheat the outlet gas prior to exhaust, a slurry preparation system,

and a disposal system for the large quantities of sludge produced. 

Sludge disposal needs can be very site specific and depend upon the

local climate and soil conditions.43

The basic wet limestone scrubbing process is simple and well

established. Limestone sorbent is cheap and generally locally

available in the United States. The SO removal efficiencies of 2

existing wet limestone scrubbers range from 31 to 97 percent, with an

average of 78 percent. Operating parameters affecting SO removal 1 2

efficiency include liquid-to-gas ratio, pH of the scrubbing medium,

and the ratio of calcium sorbent to SO . Periodic maintenance is 2

needed because of scaling, erosion, and plugging problems.
2-18

Table 2-2. Distribution of SO Control Technologies in 1994 2 1

Scrubber type No. of boiler units (MWe) installed FGD capacity

Installed FGD capacity Total percent of

(%)

Wet limestone 70 35,101 49.8

Wet lime 44 21,172 30.0

Dry lime/SDA 15 5,615 8.0

Sodium carbonate 9 3,181 4.5

Dual-alkali 6 2,267 3.2

Wellman-Lord 4 1,779 2.5

Mag-Ox 3 895 1.3

Dry aqueous carbonate 1 450 0.7

Total 100.0

FGD = Flue gas desulfurization.

SDA = Spray dryer adsorber.

Recent advancements include the use of additives or design

changes to promote SO absorption or to reduce scaling and 2

precipitation problems. Gypsum can now be recovered as a salable

byproduct. Extensive operating experience has increased industry

confidence in designing larger, more reliable limestone scrubber

modules. In 1994, wet limestone scrubbers were used at 70 units, or

at 35,101 MWe of the total scrubbing capacity.1

2.5.2.2 Wet Lime. In a wet lime scrubber, flue gas containing

SO is contacted with hydrated lime-water slurry; the SO is absorbed 2 2

into the slurry and reacts with hydrated lime to form an insoluble

sludge. The hydrated lime provides greater alkalinity (higher pH) and

reactivity than limestone.44

Wet lime scrubbing is a proven technology; considerable operating

experience has been gained in 44 utility units. These units 1

represented 21,172 MWe of the total scrubbing capacity in 1994. The

SO removal efficiencies of existing wet lime scrubbers range from 30 2

to 95 percent. Recent advances include the use of additives to

improve performance, reduce scaling problems, and produce a salable

gypsum byproduct. Lime scrubbing processes require appropriate

disposal of large quantities of waste sludge.

2.5.2.3 Dry Lime/Spray Dryer Adsorber. This process produces

dry reaction waste products for easy disposal. In this process, flue

gas at air preheater outlet temperatures of 121to 177C (250to
2-19

350F) is contacted with fine spray droplets of hydrated lime slurry

in a spray dryer vessel. The SO is absorbed in the slurry and reacts 2

with the hydrated lime reagent to form solid calcium sulfite and

calcium sulfate as in a wet lime scrubber. The water is evaporated 45

by the heat of the flue gas. The dried solids are entrained in the

flue gas, along with fly ash, and are collected in a PM collection

device. Most of the SO removal occurs in the spray dryer vessel 2

itself, although some additional SO capture has also been observed in 2

downstream PM collection devices, especially baghouses.

The primary operating parameters affecting SO removal are the 2

calcium-reagent-to-sulfur stoichiometric ratio and the approach to

saturation in the spray dryer. To increase overall sorbent

utilization, the solids collected in the spray dryer and the PM

collection device may be recycled. The SO removal efficiencies of the 2

existing lime spray dryer systems range from 60 to 85 percent. Spray 1

dryers were used at 15 units and constituted 5,615 MWe of scrubbing

capacity in 1994.

2.5.2.4 Wet Sodium Carbonate. Flue gas scrubbing with sodium

carbonate solution minimizes the operation and maintenance problems

related to lime and/or limestone slurry scrubbers. However, the

process uses a reagent that is relatively expensive unless it can be

found as a byproduct from another process or as a locally mined

material (trona). There were nine units (in 1994) using wet sodium

carbonate scrubbing in the United States, representing 3,181 MWe of

the total scrubbing capacity. Waste products of this process include 1

sodium sulfite and sodium sulfate.

Due to the higher solubility and greater reactivity of the sodium

carbonate compared to lime and/or limestone, a smaller size scrubber

can be used. The primary operating parameters are liquid-to-gas ratio

and the reagent stoichiometric ratios used. Sorbent utilizations are

high. The SO removal efficiencies reported for this process range 2

from 75 to 90 percent. The soluble reaction products must be treated 1

before disposal. The treated flue gas is demisted and reheated before

exhausting through a stack.

2.5.2.5 Dual Alkali. A dual alkali system combines the

operational advantage of a sodium-based solution scrubbing system with

the economic advantage of a lime and/or limestone-based system. As

practiced in the United States, a dual (or double) alkali system uses

a sodium sulfite solution to absorb SO from flue gas and to form 2

sodium bisulfite. The spent sorbent is reacted with lime to

precipitate calcium sulfite and to regenerate the active sodium

sulfite sorbent. The precipitated calcium salts are separated and 46

dewatered for disposal. The treated flue gas is demisted and reheated

before it is exhausted through a stack.

The dual alkali process has been installed (1994) on six boiler

units in the United States with a combined capacity of 2,267 MWe. The

SO removal efficiencies at these plants range from 77 to 93 percent. 2 1
2-20

This process also requires appropriate disposal of large quantities of

waste calcium salts. Recent advances in this process include forced

oxidation of calcium sulfite to a salable gypsum byproduct, which

reduces the waste disposal load.

2.5.2.6 Wellman-Lord. In the Wellman-Lord process, SO from the 2

flue gas is absorbed in a sodium sulfite solution to form sodium

bisulfite as in the dual-alkali process. The spent sorbent is,

however, thermally regenerated by reversing the absorption reaction. 

Regenerated sodium sulfite crystals are dissolved and returned to the

absorber. The concentrated, stripped SO stream is converted to 2

salable sulfuric acid, elemental sulfur, or liquid SO . The treated 2 47

flue gas is demisted and reheated before it is exhausted through a

stack. The Wellman-Lord process has been installed on four United

States boiler units with a combined capacity of 1,779 MWe (1994), with

SO removal efficiencies ranging from 65 to 74 percent. 2 1

2.5.2.7 Magnesium Oxide. Similar to Wellman-Lord, the magnesium

oxide (MAG-OX) FGD process is regenerable. The SO in the flue gas is 2

absorbed by a magnesium oxide slurry, and the resulting magnesium

sulfite is calcined to regenerate magnesium oxide that is slurried and

recycled back to the absorber. The SO -rich gas produced in the 2

regeneration step is processed further to produce a salable product

such as sulfuric acid or elemental sulfur.48

Because of the high-temperature regeneration step at 800to

1,000C (1,472to 1,832F), energy requirements for this process are

high. However, due to the regenerative nature of the process, reagent

and disposal costs are small. Scrubber plugging and scaling problems

are reduced compared to a limestone scrubbing system. The corrosion

and/or erosion problems related to a slurry operation are still

significant. The magnesium oxide process has been installed on three

boiler units in the United States with a combined capacity of 895 MWe

(1994). The SO removal efficiencies at these plants range from 85 to 2

94 percent.1

2.5.2.8 Dry Aqueous Carbonate. In the dry aqueous carbonate

process, the flue gas is contacted with an aqueous sodium carbonate

solution in a spray dryer. The sodium carbonate reacts with and

removes SO from the flue gases, then the solution is evaporated to 2

dryness by the hot flue gases. The dry reaction products (sodium

sulfite, sodium sulfate, and unreacted sodium carbonate) are removed

from the flue gases by passage through multicyclones and an ESP. 

Subsequent processing of the reaction products with crushed coal

yields regenerated sodium carbonate and hydrogen sulfide gas. The

sodium carbonate is recycled to the spray absorber, and hydrogen

sulfide gas is converted to salable sulfur. Only one unit, of 450 49

MWe capacity, uses the dry aqueous carbonate system for FGD (1994); it

has a 70 percent SO removal efficiency. 2 1
2-21

2.6 NO CONTROL x

This section provides a brief review of the formation and control

of NO emissions, as well as the general types of NO control used in x x

the utility industry. Detailed information on the formation and

control of NO can be found in four major technical documents. x 50-51 523

Figure 2-5 shows NO control approaches used in 1994 based on the x

number of units and total MW capacity. Around 67 percent of 1

coal-fired plants, representing about 50.4 percent of the coal-fired

MW capacity, had no NO control, whereas around 33 percent of the x

units, representing about 49.6 percent of the coal-fired MW capacity,

used some kind of NO control. Approximately 72 percent of the gas- x

and oil-fired units, with about 61 percent of the MW capacity, did not

use NO control, whereas approximately 28 percent of the units, x

representing about 39 percent of the gas- and oil-fired MW capacity,

used some kind of NO control. The gas- and oil-fired portion of x

Figure 2-5 does not contain data from combined-cycle turbine systems.

 The chemical species nitrogen dioxide (NO ) and nitric oxide 2

(NO) are collectively called NO . In general, NO from combustion x x

consists of about 95 percent NO and 5 percent NO ; however, NO is 2 x

reported as NO . Nitrogen oxides are primarily formed during fossil 2 54

fuel combustion in one of two ways: (1) oxidation of nitrogen in the

combustion air to give thermal NO , or (2) oxidation of nitrogen x

contained in the fuel to give fuel NO . There is a third form of NO , x x

namely prompt NO , that is formed by the reaction of nitrogen and x

hydrocarbons in the fuel, but prompt NO has a lifetime of several x

microseconds. Thermal NO is the predominant form during the 55 x

combustion of fuels that contain relatively little fuel-bound nitrogen

(such as natural gas and distillate oil). Both thermal and fuel NO x

are formed during the combustion of fuels that contain fuel-bound

nitrogen (such as residual oil and coal). Fuel switching, then, may 56

yield reduced NO emissions. x

 The formation of NO in coal-fired units depends on factors such x

as the type of boiler, type of burner, and facility operation. Any 57

of these factors that increase temperature or residence time at high

temperature will promote NO formation. In general, cyclone and other x 58

wet-bottom boilers have relatively higher NO emissions, with an x

approximate range of 1 to 2 lb/MMBtu, than do dry-bottom boilers,

which have an approximate range of 0.4 to 1.5 lb/MMBtu. With regard 59

to the type of burner, wall-fired wet-bottom boilers have relatively

higher NO emissions with an approximate range of 1.6 to 2 lb/MMBtu, x

wall-fired dry-bottom boilers have moderate NO emissions with an x

approximate range of 0.5 to 1.45 lb/MMBtu, and tangential-fired drybottom boilers have relatively lower NO emissions at approximately x

0.4 to 0.9 lb/MMBtu. Because of their low combustion temperatures, 60

an FBC’s thermal NO is essentially zero. Design features such as x

staged combustion can significantly reduce fuel NO , leading to low NO x x

emissions.61
Figure 2-5. Nitrogen oxide control in the utility industry by fuel type in 1994 

67%

49.6%

50.4%

33%

39%

72% 61%

28%

Coal by Unit Coal by Megawatt

Gas and Oil by Unit Gas and Oil by Megawatt

2-22
2-23

The reduction of NO emissions is important for controlling acid x

rain and ozone formation. Techniques used to reduce NO formation 62 x

include those for combustion and postcombustion control. Combustion

control techniques regulate the amount of combustion air and may also

control the flame temperature at different stages of the combustion

process; postcombustion control involves the removal of NO from the x

flue gas. More than one form of combustion control may be used for a 63

given unit.

2.6.1 Combustion Control

Control can be achieved through staged combustion (also called

air staging). With staged combustion, the primary combustion zone is

fired with most of the air needed for complete combustion. The

remaining air needed is introduced into the products of the incomplete

combustion in a second combustion zone. Air staging lowers the peak

flame temperature, thereby reducing thermal NO , and reduces the x

production of fuel NO by reducing the oxygen available for combination x

with the fuel nitrogen. Staged combustion may be achieved through 64

low NO burners, overfire air (OFA), off-stoichiometric firing (OSF), x

selective or biased burner firing (BBF), and burners-out-ofservice (BOOS). Each of these methods requires modifying equipment 65

or operating conditions so that a fuel-rich condition exists near the

burners. In cyclone boilers, combustion occurs with a molten ash

layer and the combustion gases flow to the main furnace; this design

precludes the use of low NO burners and air staging. x 66

Low NO burners may be used in coal-, oil-, and gas-fired boilers x

to lower NO emissions by about 25 to 55 percent. Overfire air may be x 67

used as a single NO control technique, with NO reductions of 15 to x x

50 percent. When OFA is combined with low NO burners, reductions 68 69 , x

of up to 60 percent may result. The actual NO reduction achieved 70 x

with a given control technique may vary from site to site. 71

Just as the combustion air to the primary combustion zone may be

reduced, part of the fuel may be diverted to create a secondary flame

with fuel-rich conditions downstream of the primary combustion zone. 

This combustion technique is termed reburn and involves injecting

10 to 20 percent of the fuel after the primary combustion zone and

completing the combustion with OFA. The fuel injected downstream is 72

not necessarily the same as that used in the preliminary combustion

zone. In most applications of reburn, the primary fuel is coal and

the reburn fuel is natural gas. Natural gas reburn has been

successfully demonstrated in several field tests in the United States

and abroad. Reburn with other fuels, primarily coal, is currently 73,74

under development, as are improvements in the process.75

Other ways to reduce NO formation by reducing peak flame x

temperature include using flue gas recirculation (FGR), reducing

amounts of OFA, injecting steam or water into the primary combustion

zone, and increasing spacing between burners. By using FGR to return 76

part of the flue gas to the primary combustion zone, the flame

temperature and the concentration of oxygen in the primary combustion
2-24

zone are reduced. Flue gas recirculation is usually used with natural

gas and distillate oil combustion. The peak temperature may also be

reduced in natural gas and distillate fuel oil combustion units by

reducing the amount of combustion air that is preheated; however, the

unit efficiency will also be reduced.

Temperatures may also be reduced in the primary combustion zone

by increasing the spacing between burners for greater heat transfer to

heat-absorbing surfaces. Another combustion control technique 77

involves reducing the boiler load. In this case, the formation of

thermal NO generally decreases x directly with decreases in heat release

rate; however, reducing the load may cause poor air and fuel mixing

and increase carbon monoxide (CO) and soot emissions.78

2.6.2 Postcombustion Control

Postcombustion control involves the removal of NO from the flue x

gas downstream of the combustion zone and is achieved either by

reducing NO emissions only (selective noncatalytic reduction [SNCR]) x

or by reducing combined emissions of CO, hydrocarbons, and NOx

(selective catalytic reduction [SCR]). Postcombustion control had, 79

up to 1994, seen limited use in new coal-fired units with the

application concentrated in California, where SCR is used at

cogeneration plants and with gas-fired turbines and where SNCR is used

at FBCs, two pulverized coal-fired units, and a gas-fired unit

boiler. Since 1994, SCR has been installed on seven utility boiler 80

units, five of which are cogeneration units.81

 

With SCR, ammonia or another reducing agent is diluted with air

or steam, and the mixture is injected into the flue gas upstream of a

vanadium, titanium, platinum, or zeolite catalyst bed. The NO is x

reduced to molecular nitrogen on the catalyst surface. Selective 82

catalytic reduction units provide up to 70 to 90 percent NO reduction x 83

and are usually located between the economizer outlet and air heater

flue-gas inlet, where temperatures are 230 to 400 C (450 to 750 F). 84

Selective noncatalytic reduction is currently achieved

commercially in one of two ways: THERMAL DeNO , an Exxon process, or x

®

NO OUT , an EPRI process. THERMAL DeNO reduces NO to nitrogen x x x ® ®

through injection of ammonia into the air-rich flue gas. NO OUT x ®

achieves NO reduction by injecting urea into the oxygen-rich and/or x

high-temperature convection part of the boiler.85

The necessity of using nitrogen-based reagents requires SCR and

SNCR systems to closely monitor and control the rate of reagent

injection. If injection rates are too high, NO emissions may increase x

(in SNCR systems), and stack emissions of ammonia may also occur in

concentrations of 10 to 50 ppm. A portion (usually around 5 percent)

of the NO reduction by SNCR systems is due to transformation of NO to

N O, which is a global warming gas. 2
2-25

Table 2-3 presents a general breakdown of utility industry NOx

control usage according to the 1995 EEI power statistics database

(1994 data). As shown in Table 2-3, most of the utility industry has 1

no NO control; 64 percent of the dry-bottom coal-fired boiler units, x

87 percent of the wet-bottom coal-fired boiler units, 76 percent of

the oil-fired boiler units, 70 percent of the gas-fired boiler units,

and 100 percent of the combined-cycle turbine units had no NO control x

in 1994 (see Note b in Table 2-3). Units that had NO control x

equipment used various types of staged combustion techniques,

including low-NO burners, OFA, OSF, BBF, and BOOS. Staged combustion x

control was used in 33 percent of the dry-bottom coal-fired units,

11 percent of the wet-bottom coal-fired units, 24 percent of the oilfired units, and 30 percent of the gas-fired boiler units. Table 2-3

also shows that approximately 3 percent of the dry-bottom coal-fired

units and 2 percent of the wet-bottom coal-fired units had boiler

design as a NO control method. x

2.7 UTILITY INDUSTRY AFTER IMPLEMENTATION OF 1990 AMENDMENTS

This section describes the changes in the utility industry

expected during the 1990–2010 time frame. The effect of planned

generation capacity growth on the fuel use and technologies that will

be used for steam and power generation is discussed in section 2.7.1. 

Title IV of the Act requires the utility industry to reduce SO2

emissions in two phases. The effect of SO control measures likely to 2

be used to comply with the Phase I and Phase II requirements on the

overall mix of utility SO control technology is discussed in 2

section 2.7.2. For the purposes of this analysis, the projected

compliance date for Phase II was determined to be 2010. This year was

chosen after discussions with Agency, nonagency, and industry sources

concerning possible delays written into Title IV of the Act.

Title IV also contains other provisions that will affect utility

responses to regulations. These revisions include topics such as

permitting, monitoring, enforcement, repowering, and penalties. 

Although these provisions affect the manner in which the utility

industry will respond to regulations, they are generally subsidiary to

emissions estimates based on fuel usage. These provisions are not

discussed further here.

Since the Interim Final Report to Congress, the EPA has obtained

and analyzed current information and future projections on energy

production (by fuel) in the electric utility industry. It appears 86

that the fuel usage projections listed below are being met and are

proceeding toward what the EPA (using the Acid Rain Division’s [ARD]

model projections) predicted in the Interim Final Report to Congress. 

Therefore this section and the 2010 projected emissions were not

changed in this Final Report.
2-26

Table 2-3. Distribution of NO Control by Fuel Burned, by Unit, x

in 1994 1

Percent NO control x

a

Fuel None Staged combustion Boiler design

Coal, boiler bottom

 Dry 64 (46) 33 (49) 3 (5)

 Wet 87 (85) 11 (12) 2 (1)

Oil 76 (66) 24 (34) --

Gas 70 (60) 30 (40) --

Combined-cycle turbine 100 (100) -- -- b

 Values listed in parentheses are the percent distribution by MWe for each type of fuel. a 

To cool combustion gases, steam or water may be injected with the fuel, with the air, or directly into the b

combustion zone. This technique is used for gas-fired turbines due to the relatively low efficiency penalty

(typically 1 percent). However, this technique is not used for utility boilers because it has a high efficiency 87

penalty (about 10 percent). Steam or water injection was present in the 1990 utility data but seems to missing 87

from the 1994 utility data set. In the 1990 data, approximately 36 percent of the combined-cycle turbine units used

steam or water injection for NO control, whereas only approximately 2 percent of the boilers reported using this x

technique. The EPA believes that this technique is still being used but the companies that were surveyed

neglected to include this in the responses.

2.7.1 Industry Growth

The publicly owned utility companies, Federal power agencies,

rural electric cooperatives, and investor-owned utility generating

companies are projected to increase their new generating capacity in

service or scheduled for service in the 1990-2010 time frame by

750 billion kWh, from 1,940 to 2,690 billion kWh. These and other 88

projections for utility industry configuration and growth were taken

from a study titled, Economic Analysis of the Title IV Requirements of

The 1990 Clean Air Act Amendments, produced for the ARD of the EPA’s

Office of Air and Radiation (OAR) by ICF Resources Incorporated. This

single projection is used by the Office of Air Quality Planning and

Standards (OAQPS) in this report to maintain consistency with the ARD.

Figure 2-6 compares utility fossil fuel consumption, on a Btu

basis, for 1990 and projected use for 2010 (publicly owned utility

companies, rural electric cooperatives, investor-owned utility

generating companies only). On this basis, the predominant fossil 89

fuel both in 1990 and projected for 2010 is coal, at approximately 81

percent of the total industry fossil-fuel usage (22 quadrillion Btu/yr

[Quads] in 2010). Oil and gas consumption in 1990 were, respectively,

6 and 13 percent of the total industry fossil-fuel usage on a Btu/yr

basis. For 2010, oil consumption was projected to decrease to

2 percent (0.6 Quads), and gas consumption was projected to increase

to 17 percent (4.5 Quads) on a Btu/yr basis for the total industry

fossil-fuel usage. Based on the ARD model projections, coal 90

consumption in 2010 is expected to be the same percentage of the total

utility fossil-fuel usage as in 1990 (81 percent).
81%

Utility Fuel Usage in 2010

(Btu/yr)

13%

6%

81%

17%

2%

Utility Fuel Usage in 1990

(Btu/yr)

Coal Gas Oil

1990

Fuel Btu/yr

Coal

Oil

Gas

1.7 x 1016

1.2 x 1015

2.8 x 1015

2010

Fuel Btu/yr

Coal

Oil

Gas

2.2 x 1016

6.2 x 1014

4.5 x 1015

Figure 2-6. Fuel use in the utility industry by fuel type

in 1990 and projections for the year 2010. 89,90

2-27
2-28

Recent projections by the United States Department of Energy’s

(DOE) Energy Information Administration (EIA) were reviewed to

determine the current validity of this projected fuel use scenario

(see Table 2-4). Based on the 1998 Annual Energy Outlook, coal use 86

in the electric utility industry in 2010 is projected to be 21.34

Quads (73 percent of total utility fossil-fuel usage), oil use 0.35

Quads (1 percent), and natural gas use 7.38 Quads (25 percent). The

EIA projections include consideration of issues related to decreased

electricity production from nuclear power, lower coal prices, lower

capital costs for coal-fired generating technologies, higher

electricity demand, and industry restructuring. Although the share of

coal generation declines in the 1998 estimate relative to earlier

projections (primarily due to restructuring considerations), the

projection for coal-fired fuel use in 2010 is higher than the 1997

projection and is essentially the same as that of EPA’s ARD. Thus,

the Agency has not changed its projections related to emissions in

2010.

Figure 2-7 shows the projected growth of each utility fuel

between 1990 and 2010. Between 1990 and 2010, fuel consumption is 90

projected to change as follows: coal will increase by 29 percent, oil

will decrease by 48 percent, and natural gas will increase by

61 percent. Based on the recent EIA projection noted above, coal

consumption will increase by 26 percent, oil consumption will decrease

by 71 percent, and natural gas consumption will increase by 164

percent. 

The projected increase or decrease in nationwide fuel consumption

noted above has been apportioned to only those units projected to be

in existence in 2010. The actual increased consumption (coal) would,

in most cases, be distributed among new units (existing units not

being able to increase their capacity factors to account for the

majority of the growth). These new units could be of various sizes

and be located at new or existing sites. However, since the Agency

can estimate neither the size nor the location of the new units, the

increased consumption has been allocated to existing units (in 2010)

for the analyses. This allocation is believed reasonable because

(1) many new units would be built on the site of existing utility

facilities (thus, “co-locating” the emissions) and (2) the analyses

are based on total fuel used (rather than on capacity factor, etc.).

The decrease in oil consumption could result in (or result from)

units being retired or in a decrease in capacity factor, or a mix. 

The decreased consumption has been allocated among those oil-fired

units EPA believes will be operative in 2010. 

Any new units built to accommodate the increased consumption

would be required to comply with all applicable NSPS and State and

local regulations. However, for the purposes of the analyses the

controls currently in use on the unit were considered to be in place

for the same unit with increased fuel consumption.
2-29

Table 2-4. Fuel Use in the Electric Utility Industry by Fuel Type, Quadril

Fuel EPA, projection EIA, actual EIA, pro

1990 2010 1990 1991 1992 1993 1994 1995 1996 2010 

aabbbbbbb c 

Reference Forecast 

case

Coal 17 22 16.19 16.03 16.21 16.79 16.90 16.99 17.93 19.91 19.31 - Oil 1.2 0.62 1.25 1.18 0.95 1.05 0.97 0.66 0.73 0.57 0.56 - Gas 2.8 4.5 2.88 2.86 2.83 2.74 3.05 3.28 2.80 7.09 6.37 - 

Utility Data Institute. a 91

U.S. Department of Energy, Energy Information Agency. b 92Annual Energy Outlook. 1997. c 93Annual Energy Outlook. 1998. d 94
0

5

10

15

20

25

30

1990 2010

Figure 2-7. Projected use of fuels by 2010 for utility industry. 89

Years

Quads (10 15 Btus)

Gas Oil Coal

2-30
2-31

The Agency projects that 135 units will be retired during the

period from 1990 to 2010. These units have been removed from the

2010 analyses.

2.7.2 Title I and Title IV, Phase I and Phase II, Compliance

Strategy Impact

Phase I and Phase II requirements of the Acid Rain Program

establish a cap on the national, annual SO emissions. To achieve 2

compliance with the requirements, utilities may use one or any

combination of the following strategies (among others) at any given

unit: (1) install flue gas scrubbers, (2) switch to a fuel that

contains less sulfur, and (3) reduce the capacity factor of the

Phase I unit to the extent that the unit is in compliance and provide

plans for replacing the reduced capacity. This reduction can be

accomplished by either: (1) energy conservation, (2) improved boiler

efficiency, (3) use of a designated sulfur-free (nuclear or hydro, but

not natural gas-fired) replacement, (4) use of a Phase II compensating

unit, or (5) purchase of emission allowances. 

The Phase I requirements affected 261 generating units (435 with

substitution or compensating generating units). The 174 substitution 95

or compensating generating units are not included in the following

discussion. Examining the method used by the 261 Phase I generating

units to comply with the provisions, the following was found:96

 53 percent (136 units accounting for 59 percent of the 1995

SO emission reductions) switched to a coal that contains 2

less sulfur

 27 percent (83 units accounting for 9 percent of the 1995

SO emission reductions) purchased additional emission 2

allowances

 16 percent (27 units accounting for 28 percent of the 1995

SO emission reductions) installed flue gas scrubbers 2

 2 percent (7 units accounting for 2 percent of the 1995 SO2

emission reductions) were retired

 2 percent (8 units accounting for 2 percent of the 1995 SO2

emission reductions) either repowered using new boiler

technology, or switched to natural gas or low sulfur oil. 

Each of the 27 units known to be installing scrubber units was

modeled with the scrubber unit in place for the 2010 scenario. The

EPA modeled the remaining 234 units by increasing their coal

consumption in proportion to ICF Resources, Inc. (2010) projections. 

These increases were also weighted by the expected increased use of

western, low-sulfur coal.

Under Phase II of the Acid Rain Program, an additional

approximately 1,600 generating units will be covered by the year 2000. 
2-32

Although industry projections suggest an additional 25 units (at 10

plants) will install scrubbers to comply with Phase II, the EPA

believes that these units will comply with Phase II requirements by

using alternate methods. This assumption is based on several factors 97

including: (1) the increased availability of low-sulfur coal at

favorable prices; (2) the introduction of processes that reduce SO 2

emissions by 20 to 50 percent through partial cleaning of higher

sulfur coal, which allows for a variety of coal types to be utilized

(although the impact of these processes as Title IV control options is

uncertain at this time); and (3) the increased age and small size of

the affected units, giving the utility companies little incentive to

spend large amounts of capital on installing scrubbers.98

Many utility units will be implementing NO controls to comply x

with both Title I and IV requirements. This control may involve

switching from coal- or oil-firing to natural gas-firing (for at least

a portion of the year), improved combustion controls, or installation

of low-NO burners, among other activities. If a fuel switch was x

known, that switch was accounted for in the 1990 versus 2010 analysis. 

No change in a unit’s burner configuration (i.e., “old” versus new

low-NO ) was included in the 2010 scenario. The impact of low-NO x x

burner installation is discussed in chapter 13.

Under the Acid Rain Program, the rules for NO control (40 CFR x

Part 7b) require tangential-fired and dry-bottom wall-fired boilers

subject to Phase I SO reduction requirements to meet annual average 2

NO emission limits of 0.45 lb/MMBtu and 0.50 lb/MMBtu, respectively, x

by January 1, 1996. Utilities can meet the Title I and IV

requirements by installing low-NO burner technology or by averaging x

emissions among several units.

Since the Interim Report to Congress, additional rules (e.g.,

revised NAAQS, revised NSPS, and Acid Rain Program for NO and SO ) x x

have been promulgated that could pressure the electric utility

industry to consider options beyond those considered earlier. These

include additional fuel switching or the adoption of SCR or SNCR

rather than the addition of low-NO burners because of tightening NO x x

emission standards. Since limited data were available assessing the

HAP removal potential of SCR and SNCR, they were not addressed in the

2010 program output. Also, additional FGD units may be installed to

comply with the revised PM NAAQS (which impact on sulfate rather than

traditional PM). However, as no area has been determined to be in

non-compliance yet, no units have been planned and such installations

were not addressed in the 2010 analyses. To the extent that

additional FGD units are installed (for NSPS or NAAQS compliance) the

overall effect could be lower HAP emissions in 2010.

Under Phase II of the Acid Rain Program, the EPA established NOx

emission limits for all other boilers, including wet-bottom wall-fired

boilers and cyclones, by January 1, 1997; affected units must be in

compliance by January 1, 2000. EPA also revised the emission limits 99

for dry-bottom wall-fired boilers and tangential-fired Phase II units. 
2-33

Particulate control devices may also need to be upgraded at

individual utility units to account for the different ash qualities of

any new coal being utilized to comply with SO requirements or to 2

account for installation of low-NO burners. In late 1993, the Utility x

Data Institute (UDI) conducted a particulate control equipment survey

to identify those utility facilities that were either in the process

of upgrading their PM removal equipment or had definite plans to do so

in the near future. The survey was mailed to 286 utilities and 97

received a 68 percent response. No information was received for 831

units; 1,215 units indicated that no PM control equipment

modifications were planned. Modification plans were received for

132 units. The data received were analyzed for any potential impact

on HAP emissions. From the data, it appears that the modifications 100

are being made strictly to account for differences in ash quality as

coals are switched and not to effect an overall increase in PM control

efficiency. Therefore, for the 2010 scenario analysis, it has been

assumed that no change in PM control efficiency will occur since the

actual reported values do not vary significantly. In addition, this

assumption will account for any future degradation in PM control

performance. The validity of this assumption is borne out by

indications that some utility units are experiencing emissions

increases (as evidenced by continuous emission monitor excess emission

reports) following switches to lower sulfur coal and/or installation

of low-NO burners. It is not known how transient these excess x 101

emissions will be.

2.7.3 Compliance Strategy Impacts of Other Activities

Other activities, not directly related to CAA mandates, will also

impact on electric utility industry control strategies and emissions. 

These include developments related to electricity industry

restructuring, such the Federal Energy Regulatory Commission’s (FERC)

Open Access Rule (Order 888) finalized in April 1996, changes in the

energy production from other sources (e.g., accelerated retirement of

nuclear plants, increases in the use of biomass), overall national

demand for electricity, relative differences in fuel costs, and any

future “global warming” abatement initiatives. Responses to these

activities (i.e., specific control strategies) were not included in

the analyses for this report. As can be seen from the EIA projections

presented earlier, it is expected that, over the long-term, natural

gas will increase its share of the fossil-fuel generation. However,

it should be noted (see Table 2-4) that between 1995 and 1996 (the

first year of industry restructuring), coal consumption for

electricity generation increased by approximately one quad while

natural gas consumption decreased by approximately 0.5 quad. 

Preliminary data for 1997 indicate that coal use continues to increase

while natural gas use has leveled off. It is not known what factors 102

are involved in this trend (e.g., nuclear outages, relative fuel

prices, seasonal weather conditions) but some parties believe that

restructuring is playing a role. In addition, it is not known how 103

this short-term trend may ultimately factor into long-term

projections.
2-34

 1. Utility Data Institute. EEI Power Statistics Data Base. 

Washington, DC. 1995. (excluding cogeneration facilities)

 2. Singer, J. G. (ed.). Combustion, Fossil Power. Fourth Edition. 

Combustion Engineering, Inc., Windsor, CT. 1991. p. 1-12.

3. Utility Data Institute. EEI Non-Utility Power Plant Data Base. 

Washington, DC. 1993.

 4. National Energy Foundation. Getting to Know Public Power. Salt

Lake City, UT. 1993.

 5. U.S. Electric Utility Statistics. Public Power. American Public

Power Association, Washington, DC. January-February 1997. 

pp. 53, 54.

 6. §292.202, Subpart B-Qualifying Cogeneration and Small Power

Production Facilities. Part 292-Regulations under Sections 201

and 210 of the Public Utility Regulatory Policies Act of 1978

with regard to Small Power Production and Cogeneration. CFR 18,

Parts 280 to 399, revised as of April 1, 1992.

 7. Ref. 2, p. 4-29.

 8. Ref. 2, p. 12-15.

 9. Babcock & Wilcox. Steam, Its Generation and Use. Babcock &

Wilcox. New York. 1978. p. 10-3.

10. Ref. 2, p. 12-4.

11. Ref. 2, p. 12-2.

12. Elliot, T. C. Standard Handbook of Powerplant Engineering. 

McGraw-Hill, Inc., New York. 1989. p. 4.50.

13. Ref. 2, p. 9-2.

14. Ref. 2, p. 9-4.

15. Ref. 2, p. 9-5.

16. Ref. 2, p. 9-30.

17. Ref. 2, p. 7-6.

18. Ref. 2, p. 1-13.

2.8 REFERENCES
2-35

19. Federal Energy Regulatory Commission. Cogeneration. 

Washington, DC. 1985. pp. 3, 5.

20. Ref. 2, p. 1-16.

21. Damle, A. S., D. S. Ensor, and M. B. Ranade. Coal Combustion

Aerosol Formation Mechanisms: A Review. Aerosol Science and

Technology. Volume 1, No. 1. 1982. pp. 119-132.

22. Tumati, P. R., and M. S. Devito. Retention of Condensed/Solid

Phase Trace Elements in an Electrostatic Precipitator. Presented

at International Air Toxics Conference, Washington, DC. Electric

Power Research Institute, Palo Alto, CA. November 4-6, 1991. 

p. 20.

23. U.S. Environmental Protection Agency. Control Techniques for

Particulate Emissions from Stationary Sources -- Volumes 1 and

2. EPA-450/3-81-005a, b. Office of Air Quality Planning and

Standards, Research Triangle Park, NC. 1982. p. 4.2-23.

24. Leith, D., and D. Mehta. Cyclone Performance and Design. 

Atmospheric Environ. Volume 7. 1973. pp. 527-549. 

25. Ref. 23, Vol. 1, pp. 4.3-20 and 4.3-22.

26. Buonicore, A. J., and W. T. Davis (eds.). Air Pollution

Engineering Manual. Air and Waste Management Association, Van

Nostrand Reinhold, New York. 1992. p. 95.

27. Ref. 23, Vol. 1, pp. 4.3-14 to 4.3-23.

28. White, H. J. Industrial Electrostatic Precipitation. AddisonWesley, Reading, MA. 1963.

29. Ref. 23, Vol. 1, pp. 4.5-22 to 4.5-29.

30. Calvert, S., J. Goldshmid, D. Leith, and D. Mehta. Wet Scrubber

System Study, Vol I: Scrubber Handbook. NTIS No. PB 213-016. 

U.S. Environmental Protection Agency, Research Triangle Park, NC. 

1972. 

31. Ref. 23, Vol. 1, p. 4.4-12.

32. McKenna, J. D., and J. H. Turner. Fabric Filter - Baghouses I,

Theory, Design, and Selection (A Reference Text). ETS, Inc.,

Roanoke, VA. 1989. 

33. Donovan, R. P. Fabric Filtration for Combustion Sources: 

Fundamentals and Basic Technology. Marcel Dekker, New York. 

1985.
2-36

34. Memorandum from J. H. Turner, RTI, to J. D. Cole, RTI. 

March 17, 1993. Table presenting a comparison of particulate

matter collection systems.

35. Khoury, D. L. Coal Cleaning Technology. Noyes Data Corporation,

Park Ridge, NJ. 1981. p. 24.

36. Akers, D., C. Raleigh, G. Shirey, and R. Dospoy. The Effect of

Coal Cleaning on Trace Elements, Draft Report, Application of

Algorithms. Prepared for EPRI by CQ, Inc. February 11, 1994.

37. Letter from Burke, F. P., of CONSOL Inc., to W. H. Maxwell, EPA. 

May 28, 1993. Use of USGS data in estimating the emissions of

air toxics.

38. Cavallaro, J. A., A. W. Deurbrouck, R. P. Killmeyer, and W.

Fuchs. Sulfur and Ash Reduction Potential and Selected Chemical

and Physical Properties of United States Coals, Executive

Summary. U.S. DOE Report DOE/PETC/TR-91/6 (DE91015938). 

Pittsburgh, PA. September 1991. pp. 7-9.

39. Ref. 35, p. 21.

40. Ref. 38, p. 1.

41. Akers, D. J. Coal Cleaning: A Trace Element Control Option. 

Paper presented at EPRI Symposium on Managing Hazardous Air

Pollutants: State of the Art. Washington, DC. November 1991.

42. Satriana, M. New Developments in Flue Gas Desulfurization

Technology. Noyes Data Corporation, Park Ridge, NJ. 1981. 

p. 10.

43. Ref. 42, pp. 13-14.

44. Ref. 42, p. 15.

45. Ref. 42, pp. 180-181.

46. Ref. 42, pp. 85-88.

47. Ref. 42, pp. 155-161.

48. Ref. 42, pp. 142-146.

49. Ref. 42, pp. 75-78.

50. Ref. 2, pp. 4-30 to 4-34, 12-6 to 12-8, 15-64 to 15-68.
2-37

51. U.S. Environmental Protection Agency. Evaluation and Costing of

No Controls for Existing Utility Boilers in the NESCAUM Region. x

EPA-453/R-92-010. Office of Air Quality Planning and Standards,

Research Triangle Park, NC. December 1992.

52. U.S. Environmental Protection Agency. Summary of No Control x

Technologies and Their Availability and Extent of Application. 

EPA-450/3-92-004. Office of Air Quality Planning and Standards,

Research Triangle Park, NC. February 1992. pp. 1-1 to 3-25.

53. 1991 Joint Symposium on Stationary Combustion No Control, x

EPA/EPRI. March 25-28. 1991.

54. Ref. 2, p. 4-30.

55. Ref. 53, p. 2-3.

56. Radian Corporation. Combustion Modification NO Controls for x

Wall-Fired and Tangential-Fired Boilers. EPA contract No. 68-DO0125. Prepared for Air and Radiation Division, U.S.

Environmental Protection Agency. July 1991. p. 1.

57. Ref. 56, p. 1. 

58. Low-NO Combustion Retrofit Projects, Pittsburgh Energy x

Technology Center. Pittsburgh, PA. Review, 6. Summer 1992. 

p. 9.

59. Ref. 58, p. 9, Figure 2. Values represent emissions before the

1993 new source performance standards.

60. Ref. 58, p. 9.

61. Ref. 2, p. 9-4.

62. Ref. 2, p. 4-30.

63. Ref. 53, p. 2-1. 

64. Ref. 56, p. 5. 

65. Ref. 53, p. 2-4.

66. Ref. 26, p. 241.

67. Ref. 52, pp. 4-4, 4-25.

68. Ref. 52, p. 4-4.

69. Ref. 53, p. 3-19.

70. Ref. 52, p. 4-4. 
2-38

71. Ref. 53, p. 3-8. 

72. Ref. 53, p. 2-8. 

73. Borio, R., R. Lewis, D. Steen, and A. Lookman. Long-Term NO x

Emissions Results with Natural Gas Reburning on a Coal-Fired

Cyclone Boiler. Presented at the EPA/EPRI Joint Symposium on

Stationary Combustion NO Control, Bal Harbor, Florida. May 24- x

27, 1993. 

74. LaFlesh, R. C., R. D. Lewis, R. E. Hall, V. R. Kolter, and Y. M.

Mospan. Three-Stage Combustion (Reburning) Test Results from a

300-MWe Boiler in the Ukraine. Presented at the EPA/EPRI Joint

Symposium on Stationary Combustion NO Control, Bal Harbor, x

Florida. May 24-27, 1993. 

75. Ref. 56, p. 2.

76. Ref. 53, pp. 2-7 to 2-14.

77. Ref. 53, p. 2-14.

78. Ref. 53, pp. 2-14, 2-15. 

79. Ref. 53, p. 2-16. 

80. Ref. 52, p. 4-32. 

81. U.S. Department of Energy. Control of Nitrogen Oxide Emissions: 

Selective Catalytic Reduction (SCR). Clean Coal Technology,

Topical Report Number 9. July 1997. pp. 14-15.

82. Ref. 53, p. 2-16. 

83. Ref. 52, p. 4-35. 

84. Ref. 52, pp. 4-35, 4-37.

85. Ref. 53, p. 2-17.

86. U.S. Department of Energy - Energy Information Agency (EIA). 

Early Release of the Annual Energy Outlook 1998. Obtained from

EIAs website, “http://www.eia.doe.gov/oiaf/aeo98/

earlyrel.html” Washington, DC. November 1997. Figure 5. p. 5.

87. Ref. 53, p. 2-10. 

88. ICF Resources Incorporated. Economic Analysis of the Title IV

Requirements of the 1990 Clean Air Act Amendments. Prepared for

the U.S. EPA, Office of Air and Radiation, Acid Rain Division. 

Washington, DC. February 1994. p. 8.
2-39

89. Utility Data Institute. EEI Power Statistics Data Base. 

Washington, DC. 1992. (excluding cogeneration facilities)

90. Ref. 88, p. C-1.

91. Utility Data Institute. Interim Final Utility Toxics Report,

Chapter 2, Reference 1; ICR Resources, Incorporated. Interim

Final Utility Toxics Report, Chapter 2.

92. U.S. Department of Energy, Energy Information Agency. Table 2.6. 

Energy Input at Electric Utilities. Obtained from EIAs website,

“ftp://ftp.eia.doe.gov/pub/energy.overview/ monthly.energy/ 

mer2-6" Washington, DC. January 1998.

93. Annual Energy Outlook. 1997. DOE/EIA-0383(97). December 1996. 

Table B2, p. 126.

94. Annual Energy Outlook. 1998. EIA. December 1997. Table 2.

95. U.S. Department of Energy - Energy Information Agency (EIA). The

Effects of Title IV of the Clean Air Act Ammendments of 1990 on

Electric Utilities: An Update. DOE/EIA-0582(97) Office of Coal,

Nuclear, Electric and Alternate Fuels. Washington, DC. March

1997. p. 5.

96. Ref. 95, Table 2. p. 6.

97. Letter from Zeugin, L. B., Hunton & Williams, to W. H. Maxwell,

EPA. April 11, 1994. UDI Particulate Control Equipment Survey.

98. J. Makansi. New Supply Lines, Tools Keep Coal Switching in

Forefront Power. May 1994. p. 41.

99. Federal Register, 57(228), 1992. p. 55633.

100. Memorandum from W. H. Maxwell, EPA, to Electric Utility Air

Toxics Project Files. August 18, 1995. Comparison of PM control

efficiencies: new or upgraded vs. old units.

101. Letter from Schultz, D., EPA (Region 5), to W. H. Maxwell, EPA. 

July 18, 1995. Future year (2010) projection of utility HAP

emissions.

102. Ref. 94.

103. Sullivan, Edward, Environmental Commissioner of the State of

Maine, et al., to Browner, Carol, EPA/AX. Letter and

attachments. Comments on industry restructuring. January 16,

1998.
mm
3-1

3.0 EMISSION DATA GATHERING AND ANALYSIS

3.1 LITERATURE REVIEW AND BACKGROUND

Prior to the beginning of this study, the Agency conducted a

literature search of available nonradionuclide HAP emission and

control information and assessed the usefulness of these data. Much

of the data had been gathered over an extended time period using a

wide variety of dated, and sometimes ill-defined, sampling and

analytical techniques. Many of these techniques, including the method

for mercury, have since been replaced with more accurate methods. The

data in the literature exhibited extensive variability in the reported

concentrations of HAPs in emissions (sometimes varying by several

orders of magnitude). There was often insufficient documentation of

the techniques and assumptions used to distinguish the reliable data

from the unreliable data.

In addition, many of these literature data were gathered at

laboratory or pilot-scale installations or from utility units that did

not reflect the configurations of the current utility unit population. 

Again, there was often insufficient documentation of the design

parameters or process operating conditions to assess the validity of

the data or the impact of the process operating conditions on the

nonradionuclide emissions.

Because of these deficiencies, the Agency was not able to use the

prior existing data (prior to 1990) in control strategy analysis or to

project the data for nationwide application in the health hazard

assessment. The EPRI and DOE conducted major test programs during the

period beginning in 1990 to obtain HAP data from the utility industry

and coordinated these programs and test methodology processes with the

EPA. These new data from field testing became available for this

report beginning in late 1993.

To obtain the necessary nonradionuclide chemical HAP emission

test data, two avenues were followed. The first was to pursue

cooperation with industry and DOE test programs, and the second

involved Agency testing for HAP organics. The EPRI performed 36

emissions tests at 34 locations of their member companies for

approximately 25 of the 189 HAPs listed in section 112(b) of the Act. 

Of these locations, test reports were available for 29 sites (and

particulate control data for one additional site) in time for

inclusion in the health hazard assessment. These tests encompassed

coal-, oil-, and gas-fired boilers of several firing types and

emission control technologies. Emission test sites were selected

based on industry utilization (e.g., the largest percentage of coalfired units are dry bottom, use bituminous coal, with ESPs for PM

control). This approach allowed the acquisition of data for the

broadest spectrum of the utility industry in the most cost-effective

manner. Some of the EPRI emission test sites were DOE Clean Coal

Technology (CCT) sites, which provided for the acquisition of HAP data

before and after installation of controls for NO , an important element x
3-2

in the acid rain program (under Title IV of the Act). This

information will be helpful in determining the implications of the

acid rain program on HAP emissions. In the test programs, samples

were collected before and after each emission control device when

feasible. The Northern States Power Company (NSPC) also provided

eight test reports from five of its coal-fired plants for testing

performed from 1990 to 1992.

The DOE, through what is now its Federal Energy Technology Center

(FETC), initiated contract activities in mid-1992 for a HAP emission

sampling program at eight coal-fired utility units. The contracts

were awarded in early 1993 and the sampling was completed at seven

sites over the period from June to December of 1993. The DOE program

was similar in nature and scope to that of EPRI, although the number

of facilities evaluated was much smaller under the DOE program. The

timing of the DOE program was such that the data were available

concurrently with those from the EPRI studies and could be analyzed

for this report.

The EPA was involved with the design and test method selection

for both the EPRI and the DOE test programs. The Agency also cofunded

a field validation of several mercury emission test methods at a coalfired utility boiler with EPRI, including those methods that measure

the various species of mercury that may be emitted from a utility

boiler.1

For the EPRI program, the Agency independently developed a matrix

of the industry and established that the types of plants selected for

the EPRI program were the same types that would have been selected for

an EPA emission test program. For the DOE program, the Agency had the

opportunity to provide input into the type of plants that should be

selected. All emission test reports from both programs, and from

individual company tests, were reviewed by the EPA for completeness,

adherence to accepted sampling and analytical techniques, and proper

unit operations (typical information missing from the existing

literature-based database). The Agency provided support for the

onsite quality assurance/quality control activities performed during

several of the DOE emission tests.

The EPA also completed the initial development of the Fourier

transform infrared (FTIR) spectrometry field testing system and system

validation for real-time, simultaneous measurement of approximately

120 gaseous organic HAPs. Validation tests for the FTIR at a coalfired unit were conducted in February 1993. The FTIR system was 2

utilized in emission testing by the EPA at five utility sites. The

FTIR system is a lower-cost and much more flexible measurement

technology than those currently available for sources of organic HAPs. 

To examine the magnitude of HAP emissions from utility units, the EPA

conducted emissions testing with FTIR as a screening level analysis. 

Since few HAPs were detected with FTIR testing at these five utility

sites, the EPA decided not to use the FTIR test results to estimate

HAP emissions from utility units at this time.
3-3

For inclusion in this report, a total of 58 tests were conducted

at 52 sites using FTIR and conventional sampling and analysis methods

from the EPRI, the DOE, the NSPC, and the EPA. Although 58 test

reports were received by EPA in time for inclusion in this study,

4 contained data that could not be used in the emission factor program

(EFP) described in section 3.4. These reports were excluded because

measurements were not made between the boiler and the PM control

device. This exclusion resulted in a test containing only a fuel

analysis and stack emission numbers, which did not allow analysis of

control system effectiveness. For draft versions of this report,

emissions were estimated for 1990 based on the 48 usable tests then

available. Estimates for 1994 are based on the 54 usable tests. 

Emission estimates for 1994 use the same modeling procedure as for

1990, but with minor revisions as noted hereafter. 

Data reliability and the precision and accuracy of the analytical

techniques for each test were addressed by the individual test

contractors in their test reports. Where the contractor had major

concerns about the quality of the data or found gross departures from

expected precision or accuracy of a particular test analysis, the EPA

refrained from using the data in its computations.

3.2 POLLUTANTS STUDIED

As many as possible of the 189 HAPs listed in section 112(b) were

included in this study. Table A-1 (Appendix A) lists the organic HAPs

that were detected at least once in the utility test data (excluding

FTIR-detected data), the estimated nationwide HAP emissions in 1990

and 1994, and the projected nationwide emissions for 2010.

The Agency’s Office of Radiation and Indoor Air (ORIA) has

completed a report on radionuclide emissions from the utility

industry. The results of this study, along with their impact on

public health, are included in chapter 9.

3.3 DESCRIPTION OF EMISSION TEST PROGRAMS

At the beginning of this study, the utility industry was

characterized. Through the use of the EEI Power Statistics Database

(1991) from the UDI, a matrix was developed showing the ranking of 3

utility unit configurations from the most to the least prevalent. 

Table B-1 (Appendix B) shows these unit configurations down to a unit

type that accounts for only 1.05 percent of the fossil-fuel–fired unit

megawatts in the United States (plus any additional unit types tested

that were below this cutoff). The matrix was then used only as a

guide to gather data on the largest number of unit configurations

possible with the available resources by targeting the most prevalent

unit types. It should be noted that the totals in Table B-1 were

taken from the 1991 EEI Power Statistics Database and do not correlate

with the 1994 industry statistics given in chapter 2.
3-4

The emission test reports used in this study were produced for

various government agencies as well as for nongovernment and industry

groups (discussed above). Although various test contractors performed

this emission testing, certain specific testing protocols were

followed. Table B-2 (Appendix B) provides a list of all the sites

that were available for this report and were tested under the DOE, the

NSPC, the EPRI, and the EPA test programs. The table also shows the

type of fuel burned and the emission controls applied to the boiler

system. In some cases, the controls are pilot-scale units applied to

a slipstream from the boiler flue gas system. The contractor who

tested the boiler and the date of the test report are also given. 

3.4 DEVELOPMENT OF HAP EMISSION TOTALS

To estimate emissions of HAPs from fossil-fuel–fired electric

utility units (>25 MWe), the EPA developed the EFP. This program

incorporates unit configuration data from individual units as well as

emission testing data to compute estimated emissions. An explanation

of the program and several assumptions about the data and how they

were used are described here.

3.4.1 Program Operation

Emissions of HAPs considered in this study consist of two types:

trace elements and organic compounds. Trace elements exist in the

fuel when fired, whereas the organic HAPs are mostly formed during

combustion and postcombustion processes. Different programming

methods are required for handling the two types of HAPs. Program

diagrams for modeling trace element emissions are shown in Figure 3-1

for coal and Figure 3-2 for oil and gas. The two figures differ only

in treatment of the fuel before trace elements reach the boiler. 

Figure 3-3 shows the program diagrams for modeling organic HAP

emissions.

3.4.2 Data Sources

The EFP was built to accept data from two sources. The first

source of data is a data input file containing plant configurations,

unit fuel usage, and stack parameters. This input file was based on

the UDI/EEI Power Statistics database (1991 and 1994 editions) and an

extract from Production Costs, U.S. Gas Turbine and Combined-Cycle

Power Plants (for 1994 estimates). These databases were composed of

responses from electric utilities to the yearly updated DOE Energy

Information Administration (EIA) Form EIA-767.

The second data file is an emissions modification factor (EMF)

database. This database contains information from emissions tests

conducted by EPRI, DOE, and the electric utility industry. 

The program first searches the input file for the type of fuel

burned and the amount of fuel consumed per year in an individual unit. 

If the fuel type is coal, the EFP then looks for the coal’s State of

origin. Origin is important because the trace elements in coal are

addressed by coal type (bituminous, subbituminous, and lignite) and 
*Taken from UDI/EEI data.

UDI/EEI plant configuration

information

Bituminous Subbituminous Lignite

USGS coal data

(by State and coal type)

USGS coal data

(by State and coal type)

Apply coal cleaning

factor

Trace elements (TE) to boiler

No cleaning factor

What is

boiler type?*

Apply boiler TE emission

modification factor

What is

particulate

matter (PM) control

type?*

Apply the PM control TE

emission modification factor

What is

the SO control

type?*

kg/yr of specific trace element

exiting unit stack

Figure 3-1. Trace elements in coal.

2

Apply SO control TE emission

modification factor

2

3-5
*Taken from UDI/EEI data.

Trace elements (TE) to boiler

What is

boiler type?*

Apply boiler TE emission

modification factor

What is

particulate

matter (PM) control

type?*

Apply the PM control TE

emission modification factor

What is

the SO control

type?*

kg/yr of specific trace element

exiting unit stack

Apply SO control TE emission

modification factor

Figure 3-2. Trace elements in oil and natural gas.

2

2

Oil

Used fuel oil No. 6 (residual)

for all oil types

Trace elements in oil taken

from plant testing

Trace elements in natural gas

taken from plant testing

(only two sets of data)

Natural gas

Used a denisty of 8.2 lb/gal for

feed rate calculation

UDI/EEI plant configuration

information

3-6
Type of fuel

burning?

Oil Coal Gas

Obtain unit's fuel

consumption

Obtain unit's fuel

consumption

Obtain unit's fuel

consumption

For individual HAPs,

find the median

lb/trillion Btu emission

factor for a specific

HAP1

For individual HAPs,

find the median

lb/trillion Btu emission

factor for a specific

HAP2

For individual HAPs, find

the geometric mean

kg/10 9 cu ft emission

factor for a specific

HAP3

Individual fuel

consumption x

emission factor x heat

content of 150,000

Btu/gal

Coal type?

Butuminous Subbituminous Lignite

Individual fuel

consumption x

emission factor x

higher heating value

for bituminous coal

(12,688 Btu/lb coal)

Individual fuel

consumption x

emission factor x

higher heating value

for subbituminous

coal (9,967 Btu/lb

coal)

Individual fuel

consumption x

emission factor x

higher heating value

for lignite coal (6,800

Btu/lb coal)

Figure 3-3. Organic emissions.

Convert into kg/yr

stack emission for

HAP

1Only oil-fired units were used to obtain these emission factors.

2Only coal-fired units were used to obtain these emission factors.

3Only gas-fired units were used to obtain these emission factors.

Individual fuel

consumption x

emission factor

3-7
3-8

State of origin in the United States Geological Survey (USGS)

database, which contains analyses of 3,331 core and channel samples of

coal. The samples come from either the top 50 economically feasible

coal seams in the United States during 1990, or from seams associated

with 1991 coal receipts for electric utility plants.

3.4.3 Operational Status of Boilers

The operational status of units was taken from the UNIT_90.dbf

file of the EEI/UDI Power Statistics database (1991 edition addressing

1990 data) or the similar file for 1994 data. Only units that were

listed as either operational or on standby were used in the EFP. It

was found that 151 units were listed as being on standby in the

EEI/UDI Power Statistics database but were actually on indefinite

standby and, thus, did not emit any HAPs. These units were excluded

from the nationwide emissions totals in Appendix A. Other units

listed on indefinite standby (i.e., no fuel burned) were excluded from

1994 emission estimates.

Only coal-fired, oil-fired, and natural gas-fired units were

included in the EFP. This decision was made because units using these

fuels make up an overwhelming majority of the fossil-fuel-fired electric

utility units with a capacity of >25 MWe.

Anthracite was disregarded as a fuel because of the limited

number of units burning this type of coal. Four units burning 4

anthracite coal were assigned to burn bituminous coal for program

computations. 

The 1990 EEI/UDI database had a number of gaps in the fuel

consumption data. Some of these gaps were filled by data supplied

voluntarily by the industry. To address the remaining gaps, the

available data were plotted and point-slope equations were fit to

estimate fuel consumption. These equations involved plotting 5

nameplate megawatts (modified to take into account the unit’s capacity

factor) against fuel usage. If the fuel usage and the unit capacity

factor in 1990 were not given, 1989 fuel consumption data were used. 

If 1989 data were not available, the geometric mean of the 1980–1988

EEI fuel consumption data was used. When all other options had been

tried unsuccessfully, an average fuel consumption of units rated

within ±5 MW of the unit with unknown fuel usage was used. Similar

problems in the 1994 UDI/EEI database were solved by using 1990 data

where possible and by similar methods to those stated above when not

possible.

Utility units may burn coal that originated from several States;

however, in the EFP each coal-fired unit was assigned a single State

of coal origin. The State of origin used in the EFP was the State 6

that contributed the highest percentage of the unit’s coal.

3.4.4 Trace Element Concentration in Fuel

The USGS database contains concentrations of trace elements in

coal that were extracted from the ground but does not include analyses
3-9

of coal shipments. The concentrations of trace elements in coal in

the ground and in coal shipments to utilities may differ because, in

the process of preparing a coal shipment, some of the mineral matter

in coal may be removed. Since approximately 77 percent of the eastern

and midwestern bituminous coal shipments are cleaned to meet customer 7 

specifications on heat, ash, and sulfur content, a coal cleaning

factor was applied to most bituminous coals in the EFP. Two 8

exceptions were bituminous coals from Illinois and Colorado, for which

analyses were on an as-shipped basis representative of the coal to be

fired.

For a unit that burned bituminous coal, the feed rate in

kilograms per year (kg/yr) of trace elements to the boiler was

determined from the average trace element concentration in the coal, a

coal cleaning factor, and the annual fuel consumption rate. No coal

cleaning factors were applied to lignite and subbituminous coals. See

Appendix D for listings of trace elements in coal, coal cleaning

factors, and equations (Nos. 1 and 2 in Table D-2) used in the EFP.

Oil-fired organic HAP exit concentration calculations included a

150,000-Btu/gallon (gal) heating value for oil. An oil density of

8.2 lb/gal was also used.

An emission rate for each organic HAP emitted from gas-fired

units was extracted from the test reports. There were only two test

reports on gas-fired units that analyzed organic HAPs, and a geometric

mean emission rate of each observed organic HAP was used. This rate

in kilogram HAP/10 cubic feet was then multiplied by the unit’s gas 9

consumption to obtain a kilogram HAP/year stack emission rate of each

specific HAP.

3.4.5 HCl and HF Concentration in Fuel

To obtain hydrogen chloride (HCl) or hydrogen fluoride (HF)

emissions from the boiler, emission factors were derived by performing

mass balances for chloride and fluoride, then converting these

balances to the equivalent levels of HCl or HF throughout the boiler

system. For example, for each lb/hr of chloride in the feed coal at 9

one of the test sites, 0.63 lb/hr of HCl was found in the gas stream

leaving the boiler. Similarly for HF, the boiler emissions were 0.64

lb/hr for each lb/hr of fluoride in the coal. For ease of

programming, the HCl and HF emissions were addressed starting in the

fuel. This programming was done by multiplying the chloride and

fluoride concentrations in the fuel constituents by 0.63 or 0.64,

respectively. The resulting numbers allowed direct conversion into

boiler emissions that could be further modified for systems with PM

control or SO control. For the 1990 emission estimates, before 2

obtaining further test reports, the factors were 0.61 for HCl and 0.56

for HF.

The chloride concentrations were not available for coals from the

following States: Alaska, Illinois, Indiana, Iowa, Missouri, Utah,
3-10

and Washington. Chloride concentrations were assigned, as shown in

Table 3-1, for coals originating from these States.10

3.4.6 Emission Modification Factors for Inorganic HAPs

To address the partitioning of the HAP stream through the

combustion and pollution control process, partitioning factors known

as EMFs were developed from inorganic HAP testing data. The EMFs are

fractions of the amount of a HAP compound exiting a device (boiler or

air pollution control device [APCD]) divided by the amount of the same

HAP compound entering that device. These EMFs are averaged by taking 11

the geometric mean of similar devices (e.g., all oil-fired tangential

boilers, all cold-side ESPs). Geometric means are used because of the

presence of outlying data points, the small amount of data, and the

general fit of the data to a log-normal curve. These geometric means

are then applied to the kg/yr feed rates entering the boiler, the

effect of which either reduces or leaves unchanged the emissions that

pass through them. Those EMFs calculated as being greater than 1.0

(i.e., more material exiting a device than entering it) were set to

equal 1.0. 

Nearly all EMFs were computed from three data samples before and

three data samples after the particular device. When all six data

samples for a particular EMF computation were nondetects, the EPA

decided to disregard the EMF. As such, EMFs were computed when there

was at least one detected sample among the six measured samples. The

EMFs developed for 1990 emission estimates were revised to include

additional test report data for 1994 emission estimates. Appendix D

discusses in more detail the methodology used to develop emission

totals.

The EMFs were computed with data from different test reports but

for similar devices (i.e., cold-side ESPs, front-fired boilers in oilfired units). The data from coal-fired units were not segregated by

State of coal origin. The EMFs from devices are generally segregated

into only coal-, oil-, or gas-fired bins.

The EFP itself uses EMFs to partition the emissions as they

proceed from the fuel through the unit to the stack exit as follows. 

The average concentrations of metallic HAPs in an individual fuel by

State (based on USGS data) were multiplied by the amount of fuel that

the unit burned in 1990 or 1994. After accounting for coal cleaning

(bituminous coal only), the emission concentration of an inorganic HAP

was converted to an emission rate in kg/yr entering the boiler. The

emission rate entering the boiler was then modified by EMFs for the

boiler, particulate control device (when applicable), and the

SO control device (when applicable). 2

As stated above, these geometric mean EMFs were then applied to

the fuel HAP concentration estimates and the kg/yr fuel feed rates

entering the boiler, the effect of which either reduced or left

unchanged the emissions that passed through it, depending on the value

of the EMF.
3-11

Table 3-1. Assigned Chloride and HCl Concentrations in Coal, by

State of Coal Origin 10

State assigned HCl ppmw Assigned ppmw HCl in coal

Conversion of assigned ppmw chloride to

Alaska 54 x 0.63 = 34.0

Illinois 1,136 x 0.63 = 715.7

Indiana 1,033 x 0.63 = 650.8

Iowa 1,498 x 0.63 = 943.7

Missouri 1,701 x 0.63 = 1,071.6

Utah 220 x 0.63 = 138.6

Washington 104 x 0.63 = 65.5

Appendix C contains all of the EMFs used to develop the 1990 and

1994 unit emission estimates for inorganic HAPs. 

3.4.7 Acid Gas HAPs

The method used with HCl or HF emissions allowed direct

conversion from coal chlorine or fluorine content into boiler

emissions, as described earlier, that could be further modified for

systems with PM control or SO control. 2

Hydrochloric acid and HF EMFs for PM and SO control devices were 2

developed with data from test reports in which contractors conducted

tests individually for HCl, chlorine, HF, and fluorine before and

after each control device. These tests were in contrast to the

remaining tests for which HCl and HF values were estimated or omitted

rather than measured. 

The next steps after obtaining amounts of HCl or HF leaving the

boiler were to construct EMFs for the PM control device, and then for

the SO control device. Using chlorine as an example, the measured 2

amount of HCl entering the PM control device (in kg/yr with suitable

conversion factors) was compared with the measured amount of HCl

leaving the PM control device. Using these two quantities, an EMF was

formed as described in section 3.4.6. 

In the final step, EMFs were formed for HCl and HF through the

SO control device based on the measured mass of HCl or HF entering 2

that device (leaving the PM control device) and the mass measured at

the exit of the SO control device. However, a modification was 2

required to account for flue-gas bypass around the SO control device. 2

A portion of the flue gas is bypassed to maintain SO removal at the 2

minimum permitted amount. This action is used as a means of reducing

energy required to reheat the flue gas for effective plume rise from

the stack. In developing the HCl and HF EMFs for wet FGDs and dry

scrubbers, the effect of flue gas bypass was treated by analyzing
3-12

utility test data from the four plants (of eight tested) that used

bypasses, reviewing municipal waste incinerator results that showed a

typical HCl or HF removal efficiency of 95 percent, and having

discussions with industry representatives. Based on the 95 percent

removal efficiency coupled with the measured values for quantity of

flue gas bypassed, an industry average effective value for flue gas

bypass in 1994 was estimated. The value was assumed to be 15 percent

(17 percent for 1990 data) for wet FGDs and 14 percent (for 1990 and

1994 data) for dry scrubber systems. These assumptions were used only

in the development of HCl and HF EMFs.12

3.4.8 Organic HAPs

Because organic HAPs were not always tested at the entrance and

exit of each control device in the emissions testing, all organic HAP

emissions were addressed by examining the test data and determining

the concentration of a particular HAP exiting the stack. Organic HAP

concentrations were obtained from emission test reports. 

Organic stack emissions from coal-fired boilers were first

determined on an emission factor basis (lb/trillion Btu) to account

for different coal heating values, then converted to a rate basis

(kg/yr of individual HAP). This procedure was necessary because

different coal ranks had different heating values. For example, it

would require burning more lignite to achieve the same heat input to

the boiler as burning bituminous coal. These values were determined

as averages for each type of coal (see Table 3-2).13

If stack emission or APCD exit emission data were reported as

nondetected, and, if at least one-third of the data samples at the

inlet of the APCD were detected concentrations with values comparable

to the nondetected outlet values, EPA used the inlet data directly as

a measure of outlet concentration at the stack. If the outlet

nondetected values were significantly different from the inlet

detected values, the data were not used. For each individual organic

HAP observed in testing, a median concentration was obtained. This

fuel-specific median concentration was then individually multiplied by

each utility unit’s fuel consumption. The result was a fuel-specific

emission rate for all organic HAPs that were observed at least once

during testing.

3.4.9 Model Estimates for the Year 2010

Emission estimates for 2010 were derived from the same basic 1990

model described above. However, changes to input files were made to

accommodate expected changes in fuel usage by fuel type, generating

capacity, and responses to Phases I and II of the 1990 amendments

under Title IV. The details of these expected changes are described

in section 2.7.

In summary, the input files for the 2010 analyses were modified

to account for the expected increases in nationwide coal and natural

gas, and expected decrease in oil, use in the utility industry

resulting from the industry growth described in section 2.7.1. Units 
3-13

Table 3-2. Average Higher Heating Values of Coal14

Class and group character than than Average a

Agglomerating greater than greater than greater than than

Fixed carbon limits, Volatile matter limits, Calorific value limits,

% (dry, mineral- % (dry, mineral- Btu/lb (moist, mineralmatter-free basis) matter-free basis) matter-free basis)

b

Equal or Less Equal or Less Equal or Less

I. Bituminous

1. Low-volatile bituminous coal commonly 78 86 14 22 --- ---

agglomeratingc

2. Medium-volatile bituminous coal  69 78 22 31 --- ---

3. High-volatile A bituminous coal  --- 69 31 --- 14,000 --- 14,000 d

4. High-volatile B bituminous coal  --- --- --- --- 13,000 14,000 13,500 d

5. High-volatile C bituminous coal  --- --- --- --- 11,500 13,000 12,250

 High-volatile C bituminous coal agglomerating --- --- --- --- 10,500 11,500 11,000

Average of Averages (Value used in EFP for bituminous coal) 12,688

II. Subbituminous

1. Subbituminous A coal nonagglomerating --- --- --- --- 10,500 11,500 11,000

2. Subbituminous B coal  --- --- --- 9,500 10,500 10,000

3. Subbituminous C coal  --- --- --- --- 8,300 9,500 8,900

Average of Averages (Value used in EFP for subbituminous coal) 9,967

III. Lignitic

1. Lignite A nonagglomerating --- --- --- --- 6,300 8,300 7,300

2. Lignite B  --- --- --- --- --- 6,300 6,300

Average of Averages (Value used in EFP for lignite coal) 6,800

This classification does not include a few coals, principally nonbanded varieties, which have unusual physical and chemical a

properties and which come within the limits of fixed carbon or calorific value for high-volatile and subbituminous ranks. All of

these coals either contain less than 48 percent dry, mineral-matter-free fixed carbon or have more than 15,500 moist, mineralmatter-free Btu per pound.

Moist refers to coal containing its natural inherent moisture but not including visible water on the surface of the coal. b

It is recognized that there may be nonagglomerating varieties in these groups of the bituminous class, and there are notable c

exceptions in high-volatile C bituminous group.

Coals having 69 percent or more fixed carbon on the dry, mineral-matter-free basis shall be classified by fixed carbon, regardless d

of calorific value.

known to be retired during the period 1990-2010 were removed from the

input files. Announced options (described in section 2.7.2) taken for

compliance with the Acid Rain Program (e.g., coal switching, FGD

installation) were accounted for in the input files. Thus, emission

changes resulting from these activities were included in the 2010

analyses.
3-14

However, as noted in section 2.7.3, control strategies taken in

response to such activities as industry restructuring and global

warming abatement initiatives were not included in the 2010 analyses. 

To the extent that such strategies include increased use of FGD units

or natural gas, the projected 2010 HAP emissions could be overestimated in this report. For example, analyses performed to assess

compliance with the revised national ambient air quality standards

(NAAQS) for ozone and PM indicate that mercury emissions in 2010 may

be reduced by approximately 16 percent (11 tons per year) over those

projected in this report, primarily due to increased FGD use and fuel

switching. To the extent that such strategies include the increased 15

use of coal, as may be happening at least in the short term under

industry restructuring, the projected 2010 HAP emissions could be

underestimated in this report.

3.5 SELECTED ESTIMATED NATIONWIDE HAP EMISSIONS

Based on the screening assessment presented in chapter 5, a

subset of HAPs was determined to be of potential concern to public

health and was, therefore, given a priority label for further

analysis. Table 3-3 presents estimated 1990, 1994, and 2010

nationwide emissions of this subset of HAPs from utility units (see

also Table A-1, Appendix A). With expected changes in input data and

new test data for EMFs, the opportunity was taken to improve the model

for more effective use in estimating 1994 emissions. Depending on

reported and projected fuel switching and fuel use, units brought on

line or taken off line, the revisions to EMFs between 1990 and 1994,

and improvements in the model’s handling of fuel combustion, estimated

HAP emissions across the 1990 to 2010 span may increase uniformly,

decrease uniformly, peak in 1994, or show a minimum in 1994. These

varied responses among the HAPs are thought to occur primarily because

of industry changes rather than changes to the model.

3.6 COMPARISON OF EFP ESTIMATES WITH TEST DATA

Comparisons were made between test data from 19 utility boiler

stacks and 1994 predicted emissions for the same plants using the

EFP. Results suggest that the EFP performs as expected (i.e., across 16

a range of boilers and constituents the average of the predicted

values agrees well with the average of the reported values). This

close prediction occurs even with large differences between predicted

and reported values for individual boilers and constituents. It

should be stressed that the EFP is designed to provide a reasonable

estimate of nationwide emissions based on summing a large number of

imperfect boiler estimates that are expected to cluster about a

reasonable estimate of the true value. The EFP program also provides

reasonable estimates for the boiler-specific emissions used for the

exposure modeling analyses (see chapter 6). However, there are

uncertainties and, based on an uncertainty analysis, it is estimated

that emission values may be over- or underestimated by as much as

roughly a factor of three for any specific boiler.
 Selected nationwide HAP 

emissions (estimated) in 

tons/year for 1990

 Selected nationwide HAP 

emissions (estimated) in tons/year 

for 1994

 Sele

emission

Coal-fired electric utility plants

Arsenic 60.93 55.81 Beryllium 7.13 7.93 

Cadmium 3.33 3.15 Chromium 73.27 61.60 Lead 75.47 61.77 Manganese 163.97 167.72 

Mercury 45.80 51.34 

Hydrogen chloride 143,000 134,000 

Hydrogen fluoride 19,500 23,100 

2,3,7,8-tetrachlorodibenzo-p-dioxin (TEQ) 0.000097 0.000121 

n-nitrosodimethylamine 5.84 6.09 

Oil-fired electric utility plants

Arsenic 5.02 3.51 Beryllium 0.46 0.40 

Cadmium 1.71 1.09 Chromium 4.74 3.91 Lead 10.58 8.92 Manganese 9.28 7.30 

Mercury 0.25 0.19 

Nickel 392.8 322.0 Hydrogen chloride 2,860 2,100 

2,3,7,8-tetrachlorodibenzo-p-dioxin (TEQ) 0.000016 0.000009 

Natural-gas-fired electric utility plants

Arsenic 0.15 0.18 Nickel 2.19 2.42 Formaldehyde 35.62 39.23 

3-15

Table 3-3. Selected Nationwide HAP Emissions
3-16

For three elements and 19 boilers, averages for estimates of

three individual elements (arsenic, chromium, and nickel) were

different from the test values by +60, -32, and -3 percent,

respectively. The highest individual difference between predicted and

reported values was represented by a factor of 2,600.

Table 3-4 presents comparisons for the individual plants for

arsenic, chromium, and nickel. Averages for each element and for the

combination of all three elements are also given. The reported values

are ratios of EFP estimates to measured values in terms of pounds of

element emitted per trillion Btu heat input. Plants 1 through 17 fire

coal, whereas plants 18 and 19 fire oil. At least one of the plants

fires a combination of coal and petroleum coke. 

Possible reasons were examined for large differences between

projected and actual emissions. In the 1990 EFP, only one fuel (and

if coal, from only one State) was assumed to be burned. Although the

1994 version of the EFP was designed to accommodate multiple types of

fuel, the one-State-of-origin restriction for coal was still used. 

However, at least one of the plants burned combinations of coal and

petroleum coke, but the EFP recognized only coal from one State. The

petroleum coke used by the plant had nickel concentrations that may

have been more than 100 times higher than that found in the Montana

coal assigned to that plant by the EFP, and concentrations of nickel

in the plant’s ash on the order of 1,000 times higher than that found

in coal ash. In this case, the EFP underpredicted actual nickel

emissions by factors of up to 2,600 as mentioned above. The EFP was

not sufficiently detailed to recognize mixtures of coal and petroleum

coke (i.e., the EFP did not include the nickel contributed by the

petroleum coke). Plants burning such mixtures will likely have their

nickel emissions underestimated by the EFP.

3.7 CHARACTERISTIC PLANT EMISSIONS

To give the reader a better grasp of the potential emissions of

selected HAPs from an individual utility unit, a set of characteristic

units was chosen (one for each fuel type). The EFP and organic HAP

stack emission factors were then used to determine the units’

projected HAP emissions of concern (according to the health hazard

assessment). 

In presenting the characteristic coal-fired unit, the EPA looked

for an existing utility unit that had the characteristics of a typical

coal-fired unit in the United States. Once the specific plant was

chosen, its 1994 HAP emissions of concern (projected by the EFP and

organic HAP stack emission factors) were listed (see Table 3-5). 

The most important parameter of the characteristic oil and gasfired plants (see Tables 3-6 and 3-7) is their fuel consumption, as

there are usually no control devices to reduce emissions. The fuel

consumptions chosen are the averages of each fuel type (oil or gas).
3-17

Table 3-4. Comparison of Utility Boiler Emissions from EFP

Estimates and from Testsa

Plant number Arsenic EFP/ test Chromium EFP/ test Nickel EFP/ test

1 0.12 0.14 0.0035

2 0.24 0.10 0.30

3 1.84 0.48 0.13

4 1.66 1.30 1.97

5 9.83 1.63 1.08

6 0.92 0.26 0.36

7 2.00 0.51 0.81

8 0.18 1.17 10.00

9 0.0010 0.0036 0.0004

10 0.65 0.27 0.20

11 0.20 0.19 0.16

12 0.09 0.0041 0.0730

13 0.04 2.40 1.37

14 4.13 1.15 0.17

15 0.01 0.04 0.14

16 6.40 0.72 0.93

17 2.01 1.23 0.65

18 0.05 -- 0.02

19 0.03 -- 0.03

Average: EFP/test 1.60 0.68 0.97

EFP = emission factor program

 Values presented are the ratio of emission factor program estimates to test data in terms of lb/10 Btu. a 12

The characteristic unit emissions in these three tables are

actually projected emission outputs from the EFP for three existing

units. They are chosen for having the most prevalent fuel,

boiler/furnace, and control device type in their fuel class (coal,

oil, or gas). They are also chosen for having megawatt capacities

that are the average for their fuel class.

It should be noted, however, that characteristic emissions are

based on 1994 fuel consumption values, and the emission testing (on

which the EFP is based) was performed under essentially steady-state

conditions (with little or no variation from the baseline operating

condition). Therefore, the characteristic emissions from testing are

a snapshot in time. In reality, emissions of HAPs are not constant,

steady-state values, but fluctuate with operating conditions as well

as changes in fuel. That is why the fuel heat content was used as an

indicator of emissions rather than the plant capacity or utilization.
1994

Megawatts approximately 325 MWe

Coal source Kentucky (bituminous, sulfur content of 1.5 - 3.5 %)Fuel consumption approximately 600,000 tons/yearParticulate matter control device Electrostatic precipitator (cold-side)Sulfur dioxide control device Compliance fuel/State implementation plan (SIP)

 Selected HAP emissions (estimated) in tons/year 

Arsenic 0.050Beryllium 0.0081Cadmium 0.0023Chromium 0.110Lead 0.021Manganese 0.092

Mercury 0.045Hydrogen chloride 191.8

Hydrogen fluoride 14.312,3,7,8-tetrachlorodibenzo-p-dioxin (TEQ) 0.000000103n-nitrosodimethylamine 0.0052

3-18

Table 3-5. Emissions from a Characteristic Coal-Fired Electric Utility Pla
1994

Megawatts approximately 160 MWe

Fuel Residual oil (sulfur content < 1.5 %)

Fuel consumption approximately 640,000 barrels / year

Particulate matter control device None

Sulfur dioxide control device Compliance fuel/State implementation plan (SIP)

 Selected HAP emissions (estimated) in tons/year for 1994

Arsenic 0.0062

Beryllium 0.0002

Cadmium 0.0140

Chromium 0.0062

Lead 0.014

Manganese 0.019

Mercury 0.0012

Nickel 1.69

Hydrogen chloride 9.4

2,3,7,8-tetrachlorodibenzo-p-dioxin (TEQ) 2.27E-08

1994

Megawatts approximately 240 MWe

Fuel consumption approximately 4,600,000,000 cubic feet / year

Particulate matter control device N/A

Sulfur dioxide control device N/A

 Selected HAP emissions (estimated) in tons/year for 1994

Arsenic 0.0003

Nickel 0.0041

Formaldehyde 0.067

3-19

Table 3-6. Emissions from a Characteristic Oil-Fired Electric

Utility Plant (1994)

Table 3-7. Emissions from a Characteristic Natural Gas-Fired

Electric Utility Plant (1994)

3.8 UNCERTAINTY ANALYSIS OF EMISSION FACTOR PROGRAM 

In order to quantify the uncertainty in the EFP output, the EFP

was subjected to an uncertainty analysis using the statistical method

known as a Monte Carlo analysis. The results of this analysis are

presented in section 6-14.
3-20

1. U.S. Environmental Protection Agency. Evaluation of Method 29

for the Measurement of Mercury Emissions in Exhaust Gases from a

Coal Fired Electric Utility. EPA-454/d-95-001. Office of Air

Quality Planning and Standards, Source Characterization Group B.

Research Triangle Park, NC. May 1995. 

2. U.S. Environmental Protection Agency. Fourier Transform Infrared

(FTIR) Method Validation at a Coal-fired Boiler. EPA-454/R-95-

004. Office of Air Quality Planning and Standards, Research

Triangle Park, NC. July 1993.

3. Utility Data Institute. EEI Power Statistics Data Base. 

Washington, DC. 1992. (excluding non utility units)

4. Ref. 3.

5. Memorandum from Cole, J. D., Research Triangle Institute to

Maxwell, W. H., Environmental Protection Agency. February 3,

1993. Addressing fuel consumption gaps in the EEI power

statistics data base data.

6. Memorandum from Heath, E., RTI to Maxwell, W. H., EPA. July 14,

1993. State of coal origin used in the computer emission

program.

7. Akers, D., C. Raleigh, G. Shirley, and R. Dospoy. The Effect of

Coal Cleaning on Trace Elements, Draft Report, Application of

Algorithms, prepared for EPRI by CQ Inc. February 11, 1994.

8. Memorandum from Heath, E., RTI to Maxwell, W. H., EPA. April 5,

1994. Proposed coal cleaning factors.

9. Memorandum from Turner, J. H., RTI, to Cole, J. D., RTI. 

December 7, 1997. Methodology for determining 1994 HCl and HF

concentrations from utility boilers.

10. Memorandum from Heath, E., RTI to Maxwell, W. H., EPA. May 27,

1994. USGS data gaps in chloride concentrations for seven

states.

11. Memorandum from Cole, J. D., RTI, to Maxwell, W. H., EPA. March

31, 1994. Emission factor memorandum.

12. Memorandum from Cole, J. D., RTI to Maxwell, W. H., EPA. May 9,

1994. Emission modification factors for HCl and HF including FGD

system bypass.

13. Singer, J. G., ed. Combustion Fossil Power, 4th ed. Combustion

Engineering, Incorporated, Windsor, CT. 1991. pp. 2-3, modified

table.

3.9 REFERENCES
3-21

14. Ref. 12, p. 2-3.

15. U.S. Environmental Protection Agency. Regulatory Impact Analysis

for the Particulate Matter and Ozone National Ambient Air Quality

Standards and Proposed Regional Haze Rule. Appendix A: 

Emissions and Air Quality and Appendix H: Economic Impact

Analysis and Supporting Information. Office of Air Quality

Planning and Standards. Research Triangle Park, NC. July 16,

1997.

16. Memorandum from Turner, J. H., RTI, to Cole, J. D., RTI. 

December 8, 1997. Comparison of RTI 1994 emission model

projections with test data.
mm
4-1

4.0 INTRODUCTION FOR THE HEALTH HAZARD RISK ANALYSIS

4.1 INTRODUCTION AND BACKGROUND

The EPA partially evaluated the potential hazards and risks for

the year 1990 and for the year 2010. A significant portion of the

analyses focuses on inhalation risks due to utility emissions within a

50 km radius of each facility (i.e., local analysis). The analyses of

long-range transport or regional analysis (i.e., emissions dispersion

and exposure outside of 50 km) and multipathway assessment (e.g., risk

due to ingestion and dermal exposure) were limited, mostly

qualitative, and considered only a few pollutants. This situation

does not necessarily mean that inhalation exposure within 50 km is the

most important route of exposure. For some of the HAPs emitted from

utilities (e.g., mercury and dioxins), noninhalation exposure through

ingestion is likely to be the dominant route of human exposure.1,2

The estimates of risks due to inhalation exposure presented in

this report are the incremental increased risks due to utility

emissions only. For the most part, this assessment does not consider

exposure to emissions from other sources and does not consider

background levels of the HAPs in the environment. However, background

concentrations were evaluated to a limited extent and are discussed

briefly in later sections of this report.

This chapter begins with a summary of risk assessment principles

and guidelines as used by the EPA and discussions of pertinent reports

such as the National Research Council report Science and Judgement in

Risk Assessment and the EPA Science Policy Council’s (SPC’s) Guidance 3

for Risk Characterization. Section 4.2 presents the general approach 4

and methods for this health hazard risk assessment. Section 4.3

discusses health effects data. Section 4.4 describes the methodology

used in the inhalation exposure assessment, and section 4.5 describes

the methodology for estimating inhalation risks.

4.1.1 Principles of Risk Assessment

Risk assessment is a multidisciplinary evaluation of factual

information as a basis for estimating and evaluating the potential

health effects that individuals or populations may experience as a

result of exposure to hazardous substances. Risk assessments

typically involve both qualitative and quantitative information.

Risk estimates describe the nature and likelihood of adverse

effects and the probabilities that these health effects will occur in

an exposed population. Numerical risk estimates can be calculated for

two categories of adverse health effects:

 Risk of developing cancer

 The likelihood of developing adverse health effects other

than cancer (e.g., asthma).
4-2

To derive statements of risk or the likelihood of adverse health

effects, quantitative information on exposure is combined with

information on toxicity. This process is different for carcinogens

and noncarcinogens due to the underlying assumptions that cancer is a

nonthreshold phenomenon and that thresholds exist for adverse health

effects other than cancer (i.e., noncancer effects). 

In 1983, the National Academy of Sciences (NAS) established a

framework to guide risk assessments by Federal agencies. As defined 5

by the NAS, risk assessment consists of four steps:

 Hazard assessment, or hazard identification

 Dose-response assessment

 Exposure assessment

 Risk characterization.

Hazard identification is the review of relevant toxicologic,

biological, and chemical information to determine whether or not a

pollutant may cause adverse health effects. It is a qualitative

assessment of the potential of a pollutant to increase the incidence

of an adverse health effect if exposure to the pollutant occurs.6,7

Dose-response assessment defines the relationship between the

degree of exposure (or amount of dose) observed in animal or human

studies and the magnitude of the observed adverse health effects. 

This usually includes a quantitative measure of adverse health effects

for a range of doses. For carcinogens, dose-response data are used to

calculate quantitative estimates of the increased risk of developing

cancer per unit of exposure (e.g., inhalation unit risk estimates

[IUREs]). For noncarcinogens, dose-response data are used to

calculate "safe" levels (e.g., inhalation reference concentrations

[RfCs]). 

Exposure assessment estimates the extent of pollutant exposure

via various routes (e.g., oral, inhalation, dermal) to individuals or

populations. For air pollutants, this often involves the application

of exposure models.

Risk characterization is the integration of the hazard

identification, dose-response, and exposure assessments to describe

the nature, and often to estimate the magnitude, of the health risk in

a given population. The risk characterization also includes a 6

presentation of the qualitative and quantitative uncertainties in the

assessment.

Risk assessment should not be confused with risk management. 

Risk management is the process of developing and weighing policy

options and selecting appropriate actions. Risk management integrates

the results of the risk assessment with other information such as

economic, engineering, political and social factors and uses this

integrated information to make policy and regulatory decisions. 
4-3

4.1.2 U.S. EPA Risk Assessment Guidelines

Several publications were used to establish the methods for this

risk assessment. The methods generally follow the risk assessment

guidelines published by the U.S. EPA in 1986. Other sources consulted 7

for preparation of this assessment are discussed briefly below. 

4.1.3 Risk Assessment Council (RAC) Guidance

The RAC of the EPA evaluated EPA risk assessment practices in

1992 and recommended guidance on risk assessment focusing on the risk

assessment-risk management interface and risk characterization. Major 8

elements relevant to this study are summarized below:

 Complete presentation of risk is needed including

discussions of uncertainty and statements of confidence

about data and methods used. The assessment should clearly

identify all assumptions, their rationale, and the effect of

reasonable alternative assumptions on the conclusions and

estimates. 

 Assessors should use consistent and comparable risk

descriptors. For example, assessments should include

descriptions of risk to individuals and to populations, and

presentations of central tendency and worst-case portions of

the range of risk; if feasible, highly exposed or highly

susceptible groups should be identified.

4.1.4 NAS Report Science and Judgement in Risk Assessment

In 1994, the National Research Council (NRC) of the NAS released

a report Science and Judgement in Risk Assessment, which contains a

critique of existing EPA methods and several recommendations for

improvements. A few of the recommendations important for the utility 3

assessment are described briefly here.

The NRC stated that default options are a reasonable way to deal

with uncertainty about underlying mechanisms in selecting methods and

models. However, default options should be explicitly identified, and

the basis explained fully.

The NRC believes the EPA should undertake an iterative approach

to risk assessment. An iterative approach starts with relatively

inexpensive screening techniques to estimate chemicals without health

concerns followed by more resource-intensive levels of data gathering

and model application. 

It is appropriate to use "bounding" estimates for screening

assessments to determine whether further levels of analysis are

necessary. For example, if there are no health impacts even in a

worst-case assumption scenario, then it may not be necessary or

desirable to proceed with further analysis.
4-4

4.1.5 SPC’s Guidance for Risk Characterization

In 1995, the SPC of the EPA provided guidance for characterizing

risk. A few points are briefly summarized here. 4

Risk assessors should be sensitive to distinctions between risk

assessment and risk management. Risk assessors are charged with

(1) generating a credible, objective, realistic, and scientifically

balanced analysis; (2) presenting information on hazard, dose

response, exposure, and risk; and (3) clearly describing confidence,

strengths, uncertainties, and assumptions. 

 The risk characterization should include qualitative and

quantitative descriptions of risk. Both high-end and central tendency

descriptors should be used to convey the variability in risk levels

experienced by different individuals in the population. The

assessment should identify and discuss important strengths,

limitations and uncertainties, and degree of confidence in the

estimates and conclusions. The assessment should also include

discussions of data quality and variability.

4.2 GENERAL APPROACH AND METHODS FOR THE UTILITY HEALTH HAZARD RISK

ASSESSMENT

Emissions of HAPs can be a threat to public health if sufficient

exposure occurs. For many HAPs, exposure through inhalation is the

major concern. However, humans can also be exposed to HAPs via

indirect pathways (multipathway) such as through ingestion or dermal

exposure to HAPs through other media such as food, water, or soil that

has been contaminated by the deposition of the HAPs. Indirect

exposure is primarily a concern for HAPs that are persistent and

bioaccumulate.

To assess the public health concerns due to emissions of HAPs

from utilities, the EPA conducted inhalation and multipathway exposure

and risk analyses. First, a screening assessment was conducted on 67

HAPs potentially emitted from utilities to determine priority HAPs. 

After the screening assessment was completed, further analyses were

conducted for the priority HAPs. In addition to the inhalation risk

assessment, the EPA conducted multipathway analyses of radionuclides,

mercury, arsenic, and dioxins; a long-range transport modeling

analysis for mercury, arsenic, chromium, nickel, cadmium, and lead;

and a limited qualitative assessment of the potential hazards due to

multipathway exposure to a few other persistent, bioaccumulative HAPs.

Chapter 5 presents the screening assessment. Chapter 6 presents

the inhalation risk assessment for 14 priority HAPs. Chapter 7

presents an assessment of mercury. Chapter 8 presents a qualitative

discussion of lead and cadmium. Chapter 9 presents the assessment for

radionuclides. Chapter 10 and 11 present screening level multipathway

assessments for arsenic and dioxins, respectively; and Chapter 12

discusses potential impacts of hydrogen chloride and hydrogen fluoride

emissions. 
4-5

4.3 HEALTH EFFECTS DATA: HAZARD IDENTIFICATION AND DOSE RESPONSE

Health effects data include qualitative and quantitative data on

hazard identification and dose response. These data are closely

related and evaluated concurrently in toxicologic studies. Therefore,

this section of the report includes summary discussions of both. For

detailed information on health effects data for seven of the priority

HAPs emitted from utilities, the reader is referred to Appendix E.

Most of the health effects data used were obtained from EPA’s

Integrated Risk Information System (IRIS). IRIS is an online database

maintained by the EPA, which contains chemical- specific health risk

information. The data provided in IRIS have been reviewed by EPA work

groups and represent Agency consensus. Primarily, EPA-verified risk 9

values were used in this study. However, for HAPs without IRIS data,

health data from other toxicologic data sources were used. If other

data sources were used, they are indicated by footnotes in tables, end

notes, or discussed in the text.

4.3.1 Hazard Identification for Carcinogens

Animal and human cancer studies are evaluated to determine the

likelihood that a chemical causes cancer in humans. The evidence for

each chemical is determined to be sufficient, inadequate, or limited. 

Other types of experimental evidence (e.g., in vitro genotoxicity

studies) may be used to support the epidemiological or animal bioassay

results. The EPA uses a weight-of-evidence, three-step procedure to 7,10

classify the likelihood that the chemical causes cancer in humans. In

the first step, the evidence is characterized separately for human

studies and for animal studies. The human studies are examined

considering the validity and representativeness of the populations

studied, any possible confounding factors, and the statistical

significance of the results. The animal studies are evaluated to

decide whether biologically significant responses have occurred and

whether the responses are statistically significant. Second, the

human and animal evidence is combined into an overall classification. 

In the third step, the classification is adjusted upward or downward,

based on an analysis of other supporting evidence. Supporting

evidence includes structure-activity relationships (i.e., the

structural similarity of a chemical to another chemical with known

carcinogenic potential), studies on the metabolism and

pharmacokinetics of a chemical, and short-term genetic toxicity

tests. The result is that each chemical is placed into one of the 6,7

five categories listed in Table 4-1.

4.3.2 General Discussion of Dose Response

The NAS defined dose-response assessment as: 5

“...the process of characterizing the relation

between dose of a chemical administered or

received and the incidence of adverse health

effects in exposed populations and estimating the

incidence of the effect as a function of human 
4-6

Table 4-1. Weight-of-Evidence (WOE) Classification

Group Description

A Known Human Carcinogen

B1 Probable Human Carcinogen, Limited Human Data Are Available

B2 Probable Human Carcinogen, Sufficient Evidence in Animals and Inadequate or No

Evidence in Humans

C Possible Human Carcinogen

D Not Classifiable as to Human Carcinogenicity

E Evidence of Noncarcinogenicity for Humans

exposure to the agent. It takes account of

intensity of exposure, age pattern of exposure,

and possibly other variables that may affect

response, such as sex, lifestyle, and other

modifying factors.” 

In general, as dose increases, so does the probability that an

adverse effect will occur. Critical to a dose-response assessment is

the basic assumption that thresholds exist for particular compounds

and particular health effects, and thus doses below the threshold

would not result in adverse effects. Thresholds may exist if the body

has the ability to detoxify or compensate for exposures to pollutants

or if multiple numbers of cells perform the same function. When doses

increase to the point that the body can no longer accommodate or

compensate for the exposure to pollutants, adverse health effects can

be observed and the likelihood of effect increases with increased

dose. For "nonthreshold" toxicants, it is assumed that there is no

threshold concentration or dose below which health effects do not

appear and that any exposure means an increase in risk. 

The EPA assumes that cancer is a nonthreshold disease; that is,

any exposure to a chemical carcinogen, no matter how low, contributes

to an increased lifetime probability (i.e., risk) of developing

cancer. In contrast, chemicals causing health effects other than

cancer are typically defined as having a threshold exposure

concentration or dose below which adverse health effects are not

expected to occur. The threshold concept influences the way in which

dose-response modeling or dose-response assessment is done. 

Assessments of carcinogens and noncarcinogens are conducted separately

and are based on different assumptions and methods. Information for

carcinogens and noncarcinogens is discussed separately in this

section.
4-7

4.3.3 Dose-Response Evaluation for Carcinogens

For chemicals that have been classified as carcinogens (WOE = A,

B, or C), the dose-response data are evaluated; and, if data are

adequate, then the EPA calculates quantitative estimates of the

increased risk of developing cancer per unit of exposure. For

example, for air pollutants, an IURE is calculated. The IURE for a

pollutant is the estimated increased risk (upper limit probability) of

a person developing cancer from breathing air containing a

concentration of 1 microgram of the pollutant per cubic meter (g/m )3

of air for a lifetime (70 years). The EPA also calculates oral unit

risk estimates for assessing cancer risks from ingestion exposure. 6,7,9

Since risks at low exposure levels cannot be measured directly, a

number of mathematical models have been developed to extrapolate from

high to low dose to calculate the unit risk estimates. The linearized

multistage model, which is the default model generally used by the

EPA, leads to a plausible upper limit to the risk that is consistent

with some proposed mechanisms of carcinogenesis. The true risk is

unlikely to exceed the value predicted by the linear multistage model

and may be lower; as low as zero is a possibility. For most HAPs 7

included in this assessment, the EPA has used the linear multistage

low-dose extrapolation model. However, there are a few important HAPs

with WOE ratings of “A” (e.g., chromium VI, arsenic) for which the EPA

used other linear extrapolation models. The IUREs for these HAPs are

also considered upper limit estimates of the risks at low

concentration because of the use of linear high- to low-dose

extrapolation and other factors. Table 5-1 presents a summary of the

EPA-verified cancer health effects data for HAPs emitted from

utilities. Table 5-1 also contains some health effects data that are

not EPA-verified.

The EPA assumes that, for carcinogenesis, no threshold for doseresponse relationships exists or that, if one does exist, it is very

low and cannot be reliably identified. As a result, any increase in

dose is associated with an increase in risk of developing cancer. 

Although a number of theories exist to explain the process of

carcinogenesis, the multistage process is the most widely accepted. 

The multistage process consists of three distinct stages: initiation,

promotion, and progression. One reason the multistage process is so 11

well accepted is that it has been demonstrated experimentally for a

number of carcinogens and has been shown to adequately describe

carcinogenesis in the cells of some animal tissues, including the

skin, lung, liver, and bladder. Individual carcinogens can affect 12

one or more of these stages.

The method for deriving IUREs based on animal data is different

than the method used for deriving IUREs based on human data. When

animal data are used, EPA typically determines the 95th percentile

confidence limit of the mean of the dose-response curve, then

extrapolates linearly down to zero. When human data are used, EPA

typically determines the “maximum likelihood” estimate of the doseresponse curve, then extrapolates linearly down to zero. 
4-8

There are factors involved with the human occupational data that

may result in high- or low-biasing effects, including uncertainties in

the estimation of individual exposures and the assumption that the

susceptibility of the exposed workers in the epidemiology studies is

equivalent to the susceptibility of the general population.

4.3.4 Long-Term Noncancer Health Effects Data

Pollutants can cause a variety of noncancer effects including

neurological, reproductive, developmental, and immunological toxicity. 

Noncancer effects can be reversible or irreversible and can occur

following acute (short-term) exposure or chronic (long-term) exposure. 6

Subchronic and chronic animal and human studies are evaluated to

determine potential adverse noncancer effects and the estimated doses

or exposure concentrations that cause those effects. If data are

sufficient, the EPA calculates an inhalation RfC, which is an estimate

(with uncertainty spanning perhaps an order of magnitude) of the daily

inhalation exposure of the human population (including sensitive

subgroups) that is likely to be without appreciable risk of

deleterious effects during a lifetime. The RfC is derived based on

the assumption that thresholds exist for certain toxic effects such as

cellular necrosis but may not exist for other toxic effects such as

carcinogenicity. The RfC is calculated as follows: EPA reviews many

human and/or animal studies to determine the highest dose level tested

at which the critical adverse effect does not occur—i.e., the noobserved-adverse-effect level (NOAEL)—or the lowest dose level at

which the critical adverse effect is observed, the lowest-observedadverse-effect level (LOAEL). The NOAEL from an animal study is

adjusted for exposure duration and respiratory tract differences

between animals and humans. EPA then applies uncertainty factors to

adjust for the uncertainties in extrapolating from animal data to

humans (10), and for protecting sensitive subpopulations (10). Also,

a modifying factor is applied to reflect professional judgment of the

entire database. The inhalation RfC considers toxic effects for both

the respiratory system (portal-of-entry) and for effects peripheral to

the respiratory system (extrarespiratory effects). Exposures below

the RfC are not likely to be associated with adverse noncancer health

effects including respiratory, neurologic, reproductive, developmental,

and other effects. In this report the RfC is expressed in micrograms

of pollutant per cubic meter of air (μg/m ). The EPA also calculates a 3

similar value, called the reference dose for assessing ingestion

exposure and noncancer hazards. The RfD is expressed in units of

mg/kg/d. Doses below the RfD are not expected to result in adverse

noncancer health effects. The EPA considers reproductive and

developmental effects when establishing RfCs and RfDs. If data are

absent, an uncertainty factor is often added to adjust the RfC or RfD

downward. Doses or concentrations above the RfD or RfC do not

necessarily indicate that adverse health effects will occur. As 9,13,14

the amount and frequency of exposures exceeding the RfC or RfD

increases, the possible occurrence of adverse effects in the human

population also increases. When exceedances of the RfD or RfC are

predicted, the data on exposure and health effects should be evaluated
4-9

further to determine the data quality, uncertainties, degree of

exceedance, and the likelihood, frequency, and severity of potential

adverse effects. Evaluating this information helps to characterize

the public health concerns. 

The EPA ranks each RfC as either low, medium, or high in three

areas: (1) confidence in the study on which the RfC was based, (2)

confidence in the database, and (3) overall confidence in the RfC. 13,14

Table 5-2 presents the EPA-verified RfCs and other health effects

information for HAPs identified in the emissions data.

4.3.5 Short-Term Noncancer Health Effects Data

Short-term exposure to HAPs can also cause adverse noncancer

health effects. There are no EPA-verified acute health effects

benchmarks available for the priority HAPs. However, reference

exposure levels (RELs) for acute exposures were obtained from the

California Air Resources Board’s Risk Assessment Guidelines for the

Hot Spots Program.15

4.3.6 Summary of Health Effects Data Sources

As mentioned, IRIS was the primary source of information on

health effects. However, other sources were also consulted such as

the Toxicological Profiles published by the Agency for Toxic

Substances and Disease Registry (ATSDR), the monographs published by

the International Agency for Research on Cancer (IARC), and various

EPA and non-EPA documents.

4.4 METHODOLOGY FOR ESTIMATING INHALATION EXPOSURE FOR LOCAL ANALYSIS

Exposure assessment is the determination or estimation

(qualitative or quantitative) of the magnitude, frequency, duration,

and route of exposure. An exposure assessment for air pollutants

typically has four major components:

 Emissions characterization

 Environmental fate and transport

 Characterization of the study population 

 Exposure calculation.

This section summarizes the local inhalation exposure assessment

approach including discussions of the Human Exposure Model (HEM),

data, default options, and limitations. The long-range transport

exposure analysis is explained in section 6.6.

4.4.1 Emissions Characterization

The emissions data gathered from 52 utility units (described in

chapter 3) were used as the basis for estimating emissions of HAPs

from 684 utility plants in the U.S. As described in chapter 3, a

computer program was developed to estimate emissions from each utility

unit based on boiler type, electric output, fuel type, and APCDs. 

This resulted in average annual emissions estimates for each HAP from

all 684 utility plants. The emissions estimates are believed to be
4-10

reasonable estimates of the emissions from the utility plants. 

However, there are uncertainties in the emissions estimates. The

emissions estimates, which were calculated using the geometric mean of

the test data, are believed to be central tendency estimates. That

is, the true emissions could be higher or lower than predicted with

the emissions model. Based on an uncertainty analysis, the EPA

predicts that the emissions estimates for any individual plant are

likely to be within a factor of plus or minus three of actual

emissions (see chapters 3 and 6 for further discussion of emissions

data and emissions estimates). 

4.4.2 Atmospheric Fate and Transport

To arrive at long-term (annual) average ambient air

concentrations within 50 km of the facility, air dispersion modeling

was conducted using the HEM, which utilizes the Industrial Source

Complex Long-Term, version 2 (ISCLT2) dispersion model (see Appendix F

for details). The ISCLT2 was used to estimate atmospheric fate and

transport of HAPs from the point of emission to the location of

exposure. The ISCLT2 uses emissions source characterizations and

meteorological data to estimate the transport and dispersion of HAPs

in the atmosphere and to estimate the ambient HAP concentrations

within 50 km of each source (i.e., local analysis). Plant-specific

parameters needed for modeling (e.g., stack heights, stack

temperature, stack exit velocity, stack diameter, latitude, and

longitude) were obtained from the UDI/EEI database. Emissions

estimates, also needed as input to the model, were obtained from the

analysis described in chapter 3. Long-range transport (beyond 50 km)

was also addressed and is described in chapters 6 and 7.

The ISCLT2 uses meteorological data in the form of STability

ARray (STAR) data summaries. The STAR summaries contain joint

frequencies of occurrence of windspeed, wind direction, and

atmospheric stability. These factors are combined into an overall

frequency distribution. The meteorological database is based on

hourly surface observations obtained mostly from the Office of Air

Quality Planning and Standard’s (OAQPS’s) Technology Transfer Network

(TTN). The TTN contains data files of surface observations from

National Weather Service locations (primarily airports) across the

United States and its Territories. The STAR summaries combine several

available years (typically 6 years) into one long-term estimate of the

location’s dispersion characteristics. In all cases, the

meteorological data from the site (out of a possible 349 sites)

nearest each plant were used in modeling each utility plant’s

emissions. In addition, there are two smaller databases that provide

average mixing height and temperature by atmospheric stability class. 

Every STAR site has a matching temperature database. However, the

mixing height database contains information for only 74 sites; as with

the STAR summaries, the nearest site is always selected. 

4.4.3 Characterization of Study Population

Census data from 1990, which are the most current and

comprehensive data available, were used in estimating population
4-11

exposures. The data were available on the "block” level, containing

6.9 million records. For each plant, all census blocks within 50 km

were identified and used to estimate local exposure.

4.4.4 Exposure Calculations

Exposure is calculated by multiplying the population (i.e.,

number of people) by the estimated air concentration to which that

population is exposed. The HEM exposure algorithms pair the air

concentration estimates produced by ISCLT2 with the census information

contained in the population database.

Within the HEM, the ISCLT2 calculates air concentrations at

numerous grid points within 50 km of each source. For this study,

grid points were placed around the source along 16 radials, spaced

every 22.5 degrees, at distances of 0.2, 0.5, 1.0, 2.0, 5.0, 10.0,

20.0, 30.0, 40.0, and 50.0 km from the source, for a total of 160 grid

points (which is the default setting). Except for receptors that are

very close to the stack, HEM calculates the air concentration at the

population centroid (the population center of the census block) by

interpolating between the values at the receptors surrounding the

centroid. Exposures were calculated by multiplying the number of

people living within a census block and the modeled air concentration

at the centroid of the census block. When the population blocks are

within 0.5 km of the plant, the population is distributed to each grid

point within 0.5 km to more realistically account for actual locations

of people. (In this region, the areas associated with census blocks

are larger than the sections in the polar grid and thus it is logical

to spread people out by assigning people to grid points rather than

block centroids). Exposure is calculated by multiplying the grid

point concentrations by the number of people assigned to the grid

point. For a more detailed description of the HEM, see Appendix F. 

4.5 METHODOLOGY FOR ESTIMATING QUANTITATIVE INHALATION RISKS

Numerical estimates can be calculated for two categories of

adverse health effects:

 Risk of developing cancer

 Likelihood of noncancer health effects.

To derive statements of risk, quantitative information on exposure was

combined with information on dose response. 

4.5.1 Estimating Cancer Inhalation Risks

For this analysis, the HEM calculated the cancer risk from

inhalation using standard EPA risk equations and assumptions. The

risk equation, which is a linear, nonthreshold model, defines the

exposure-response relationship. The estimate of the inhalation

exposure concentration (μg/m ) is multiplied by the IURE to calculate 3

risks for exposed persons who are assumed to be exposed on average to

the modeled ambient concentration of the carcinogen for a lifetime. 

Risks are generally expressed as either individual risk or population
4-12

risk. By the nature of the exposure and risk assessment models, the

estimated risks are expressions of the risks associated only with

exposure to utility emissions.

4.5.2 Individual Cancer Risk

Individual risk is commonly used to express risk and is defined

as the increased probability that an exposed individual would develop

cancer following exposure to a pollutant. Individual cancer risks can

be calculated by multiplying the estimated long-term ambient air

concentration (μg/m ) of a HAP (i.e., exposure estimate) by the IURE. 3

The IURE generally represents an upper bound estimate of the increased

risk of developing cancer for an individual exposed continuously for a

lifetime (70 years) to a specific concentration (e.g., 1 μg/m ) of a 3

pollutant in the air. The true cancer risk due to exposure to any

particular HAP is unknown and unlikely to be higher than that

predicted with the IURE and could be lower, possibly as low as zero.

If the highest modeled ambient air concentration occurs in an

area (i.e., census block) where no people are known to reside, it is

assumed that, theoretically, a person could be exposed to this

concentration (e.g., someone could move to this location); therefore,

the EPA calculates an estimated risk based on the assumption that

someone is exposed to the concentration. The risk calculated in this

situation is termed the MEI risk. Hence, the MEI risk is the

estimated risk to a theoretical individual exposed to the highest

estimated long-term ambient concentration associated with an emission

source. If the highest modeled ambient air concentration occurs in an

area where people are known to reside, the EPA again calculates an

estimated risk. The risk calculated in this situation is termed the

Maximum Individual Risk (MIR) risk. Hence, the MIR represents the

increased cancer risk to an individual exposed at the highest

estimated long-term ambient concentration in the area in which people

are assumed to reside. In this report, both the MEI risk and the MIR

are calculated assuming that a person is exposed to the modeled

long-term ambient pollutant concentration for a lifetime (i.e., 70

years). By the nature of the assumption, the MEI estimates must be

equal to or greater than the MIR. For this study, the MEI and MIRs

were either the same or very similar. For oil-fired utilities, the

MEI and MIR were exactly the same because the highest concentration

occurred in an area where people are known to reside. For coal-fired

utilities, the MEI risk was slightly higher than the MIR risk. For

example, the MEI risk for arsenic from coal-fired utilities was 3 x 10 ,-6

and the MIR for arsenic from coal-fired utilities was 2 x 10 .-6

4.5.3 Population Cancer Risk

Population risk is an estimate that applies to the entire

population within the given area of analysis. Two population risk

descriptors are:

 The probabilistic number of health effects cases estimated

in the population of interest over a specified time period

(e.g., number of cancer cases per year) or cancer incidence
4-13

 The percentage of the population, or the number of persons,

exposed above a specified level of lifetime risk (e.g., 10 ). -6

Each modeled ambient HAP concentration level is multiplied by the

estimated number of people exposed to that level and by the IURE,

providing an estimate of cancer incidence for a 70-year lifetime

exposure. These risk values are summed to give aggregate risks for

the population within the study area (i.e., the total estimated excess

cancer cases in the exposed population). This lifetime risk estimate

is divided by 70 years to calculate annual incidence in units of

cancer cases per year.

4.5.4 Distribution of Individual Risk within a Population 

The HEM estimates a distribution of individual risks throughout

the exposed population. The risk distribution presents an estimate of

the number of people exposed to various levels of risk (e.g., the

number of people who are exposed to individual risk levels above 10 ,-7

10 , 10 , or 10 ). -6 -5 -4

4.5.5 Aggregate Inhalation Cancer Risk 

The HEM calculates risk from individual HAPs and does not

calculate total risk for the mixture of pollutants from a single

source. To calculate total risks from the emissions of the mixture of

HAPs, the MIR and cancer incidence attributed to each individual HAP

were identified for each power plant. The MIR and cancer incidence

were then added across HAPs for each plant. This addition is

consistent with the EPA’s default procedure for assessing mixtures. 7

The highest total MIR across all plants was identified and the total

cancer incidence was summed across all plants. Given the structure of

the HEM output, it is not possible to calculate total risk (summed

across all carcinogens) for the entire exposed population. Therefore,

the population distribution by total risk is not presented in this

report.

4.5.6 Estimating Noncancer Inhalation Risks 

The concepts of individual and population risks also apply to

noncancer risks. However, cancer risks and noncancer risks are

estimated differently. The noncancer result is not a measure of risk,

but rather indicates the possibility for an adverse health effect. To

assess potential noncancer health effects, the EPA evaluated exposure

to the individuals predicted to receive the maximum modeled

concentration.

 Unlike cancer risk characterization, noncancer risks are not

expressed as a probability of an individual suffering an adverse

effect. Instead, the potential for noncancer effects is evaluated by

comparing an exposure concentration over a specified period of time

(e.g., a lifetime) with a toxicity benchmark (e.g., the inhalation

RfC).
4-14

4.5.7 Inhalation Hazard Quotient (HQ)

The HQ, a ratio of exposure (E) to the RfC, is commonly

calculated. The HQ indicates whether the concentration or dose to an

individual has the potential to cause an adverse effect. HQ values at

or less than 1 imply that exposures are at or below the RfC and not

likely to cause adverse effects. An HQ value exceeding 1 implies that

the RfC is exceeded, and the likelihood of adverse effects increases

as the amount and frequency of exposures exceeding the RfC increase.

In risk assessments in which RfCs are used and exposures are

approaching or exceeding the RfC, information about its derivation,

data, assumptions, and uncertainties should be evaluated along with

the HQ values to determine the concerns for public health and

likelihood for adverse effects. For example, the critical health

effect associated with the RfC, the type of epidemiologic or

toxicologic studies considered, the degree of exceedance, the

uncertainty and modifying factors used in deriving the RfC, and the

uncertainties and degree of confidence in the RfC should be evaluated

to characterize the potential concerns for public health.

4.5.8 Total Risk for Noncancer Effects

The Hazard Index (HI) is used to address total risks from

multiple chemicals and is the sum of HQ values for individual

pollutants to which an individual is exposed. As an initial screen,

the individual HQ values are added within a power plant and the

highest HI across all plants is identified. Similar to the HQ, hazard

indexes at or less than 1 indicate that adverse noncancer health

effects are not expected to occur. As the HI increases, approaching

or exceeding 1 (unity), concern for the potential hazard of the

chemical mixture increases. If the HI exceeds unity, the mixture has

exceeded the equivalent of the RfC for the mixture. The HI should not

be interpreted as a probability of risk nor as a strict delineation of

safe and unsafe levels.7,16

The HI approach assumes that simultaneous exposures to several

chemicals (even at subthreshold levels) could, in combination, result

in an adverse health effect. Even if no single compound exceeds its

RfC, the HI for the overall mixture may exceed 1. If the HI exceeds

unity, the HI should be reevaluated using HQ values summed only for

noncarcinogens with similar target organs based on EPA risk assessment

guidelines and assuming that each target organ has a threshold that ?

must be exceeded before adverse effects can occur and that toxicity

among target organs is independent. In addition, the mixture of

pollutants should be assessed for potential synergistic or

antagonistic effect if the HI is near or at unity and if sufficient

data are available. The EPA has produced a database called Mixtox 17

that contains information about potential effects of mixtures of

pollutants. If the HI is at or near unity, then Mixtox can be used to

evaluate the mixture. For this study, only the maximum HI values

associated with a power plant were calculated.
4-15

4.5.9 Direct Inhalation Exposure and Risk Default Options

The EPA’s risk assessment guidelines contain a number of "default

options." These options are used in the absence of convincing

scientific knowledge about which ones of several competing models and

theories are correct. Several of the defaults are generally

conservative (i.e., they represent a choice that, although

scientifically plausible given the existing uncertainty, is more

likely to result in overestimating rather than underestimating risk). 

However, some of the default options are not necessarily conservative,

and may actually lead to an underestimate of the risks. A number of

default options and assumptions were incorporated into the HEM

inhalation exposure assessment. These include:

 The HEM only estimates exposure within 50 km of each plant. 

Exposure due to long-range transport is not considered in

the HEM analysis. (Long-range transport is analyzed in

section 6.6.) 

 Dispersion occurs as predicted by a Gaussian plume model in

flat terrain.

 The closest meteorological station to each utility plant is

assumed to represent the weather patterns at the utility

plant site.

 Exposure is based on centroids of census blocks since 

locations of actual residences are not in the database. 

 For MEIs and MIRs, people are assumed to reside at the same

location for their entire lifetimes (assumed to be 70 years)

 Indoor concentrations are assumed to be the same as outdoor

concentrations. 

 The average lifetime exposure is based on the assumption

that all exposures occurring at home; exposure due to

movement between home, school, work, etc., is assumed to be,

on average, equal to exposure at home.

 Utilities emit HAPs at rates predicted by the emissions

factor program described in chapter 3 at the same level for

a 70-year lifetime of exposure. Only stack emissions were

considered. Fugitive dust from coal piles was not included.

 The HEM only estimates exposure due to direct inhalation. 

The HEM does not estimate exposure from indirect pathways

(i.e., multipathway exposure).

 The population database is not adjusted for population

growth.
4-16

 Varying exposures as a result of differences such as age,

sex, health status, and activity are not considered.

The impacts of using some of these default values (e.g.,

emissions estimates, indoor vs. outdoor concentrations, breathing

rates, closest meteorological station, flat terrain and others) are

evaluated in sections 6.12 and 6.13 and Appendix G of the EPA’s 1996

Interim Utility Air Toxics Report. Other default parameters are

described and discussed in Appendix F and in various sections of

chapter 6 of this report.
4-17

1. U.S. Environmental Protection Agency. Draft Dioxin Reassessment

Report. EPA/600/BP-92/001. Office of Research and Development. 

1994.

2. U.S. Environmental Protection Agency. Deposition of Air

Pollutants to the Great Waters, First Report to Congress. EPA453/R-93-055. Office of Air Quality Planning and Standards. 

Research Triangle Park, NC. 1993.

3. National Research Council. Science and Judgment in Risk

Assessment. National Academy Press, Washington, DC. 1994.

4. U.S. Environmental Protection Agency. Guidance for Risk

Characterization, Science Policy Council. Washington, DC. 

February 1995.

5. National Research Council. Risk Assessment in the Federal

Government: Managing the Process. National Academy Press,

Washington, DC. 1983.

6. U.S. Environmental Protection Agency. A Descriptive Guide to

Risk Assessment Methodologies for Toxic Air Pollutants. EPA453/R-93-038. Office of Air Quality Planning and Standards,

Research Triangle Park, NC. 1993.

7. U.S. Environmental Protection Agency. Risk Assessment Guidelines

of 1986. EPA-600/8-87/045. (Guidelines for Carcinogen Risk

Assessment, Guidelines for Mutagenicity Risk Assessment,

Guidelines for Health Risk Assessment of Chemical Mixtures,

Guidelines for Health Assessment of Suspect Developmental

Toxicants, Guidelines for Estimating Exposures) Office of Health

and Environmental Assessment, Washington, DC. 1987.

8. U.S. Environmental Protection Agency. Guidance on Risk

Characterization for Risk Managers and Risk Assessors. 

Memorandum from F. Henry Habicht, II, Deputy Administration,

Washington, DC. 1992.

9. U.S. Environmental Protection Agency. Integrated Risk

Information System (IRIS) Database. Environmental Criteria and

Assessment Office, Cincinnati, OH. 1994.

10. U.S. Environmental Protection Agency. Risk Assessment Guidance

for Superfund - Human Health Evaluation Manual, Part A.

EPA/540/1-89-002. Office of Solid Waste and Emergency Response,

Washington, DC. July 1989.

4.6 REFERENCES
4-18

11. Weinstein, I. B. The relevance of tumor promotion and multistage

carcinogensis to risk assessment. In: Banbury Report 19: Risk

Quantitation and Regulatory Policy. Edited by Hoel, D. G., R. A.

Merrill, and F. P. Perera. Cold Spring Harbor Laboratory, Cold

Spring Harbor, NY. 1985. 

12. National Research Council, Safe Drinking Water Committee. 

Drinking Water and Health. Volume 6. National Academy Press. 

Washington, DC. 1986.

13. U.S. Environmental Protection Agency. Interim Methods for

Development of Inhalation Reference Concentrations. EPA 600/8-

90/066A. Office of Health and Environmental Assessment,

Washington, DC. August 1990.

14. U.S. Environmental Protection Agency. General Quantitative Risk

Assessment Guidelines for Noncancer Health Effects. ECAO-CIN538. Office of Research and Development, Washington, DC. June

1990.

15. California Air Resources Board, Risk Assessment Guidelines for

the Hot Spots Program. October 1993.

16. U.S. Environmental Protection Agency. General Quantitative Risk

Assessment Guidelines for Noncancer Health Effects. ECAO-CIN538. Office of Research and Development, Cincinnati, OH. June

1990.

17. MIXTOX (version 1.5). An Information System on Toxicological

Interactions for MS-DOS Personal Computer, Environmental Criteria

and Assessment Office, Cincinnati, OH. September 1992.
5-1

5.0 SCREENING ASSESSMENT TO DETERMINE PRIORITY HAPS

The EPA conducted a screening assessment for the 67 HAPs

identified in the emissions database to determine priority HAPs for

further analyses. The HAPs were prioritized based on their potential

to pose hazards and risks through inhalation or multipathway exposure.

5.1 MODELING DESCRIPTION

To screen for inhalation risks, the EPA conducted exposure

modeling, using the Human Exposure Model (HEM), to estimate direct

inhalation exposure within 50 km of each utility plant for 66 of the

67 HAPs identified in the emissions database. The emissions estimates

used for all the HEM modeling presented in this study (chapters 5 and

6) were estimates of annual average emissions for all plants (see

chapter 3 for details on how emissions estimates were calculated). 

The HEM modeling predicted annual average ambient air concentrations

in all the census blocks located within 50 km of each facility (see

chapters 4, 6, and Appendix F for detailed discussions of the HEM

modeling for this study,and the input data, assumptions and default

parameters). Radionuclides could not be modeled adequately using the

HEM. Therefore, radionuclides were screened based on previous studies

conducted in the 1980s. The screening for radionuclides is discussed 1

in section 5.6. A general description of the HEM, input data, and

default options is presented in chapter 4. The HEM is also described

in detail in Appendix F.

Using the average annual emission estimates (discussed in

chapter 3) for each HAP for all 684 plants along with site-specific

parameters as input (e.g., location, stack height, stack exit

velocity, stack temperature, and population data), the HEM was

utilized to estimate inhalation exposures for the maximally exposed

individuals (MEIs). The exposure estimates were then combined with

health effects data to estimate risks due to inhalation exposure for

the MEIs. Based on these MEI risks, priority HAPs were selected for

inhalation risk assessment. As described below, the screening for

multipathway assessment was based on factors such as persistence of

the HAP, bioaccumulation potential, and toxicity by ingestion

exposure.

5.2 SCREENING CRITERIA

First, HAPs were screened based on cancer risk effects due to

inhalation exposure. The maximum modeled air concentrations for each

HAP were multiplied by the IUREs to estimate upper limit increased

lifetime cancer risks to the MEIs. If the highest MEI risk was

greater than 1 in 10 million (i.e., 1 x 10 ), the HAP was considered a -7

priority for further analysis.

Second, HAPs were screened for noncancer effects due to long-term

(chronic) inhalation exposure. The maximum modeled air concentrations

were compared to RfCs. Hazard quotients (HQ) were calculated by

dividing the maximum modeled concentrations by the RfCs. If the
5-2

highest HQ was greater than 0.1, then the HAP was considered a

priority for further analysis.

Third, in addition to the inhalation screening assessment, HAPs

were prioritized for potential multipathway exposure and risks. The

nonradionuclide HAPs were prioritized for multipathway assessment

based on persistence of the HAP, tendency to bioaccumulate, toxicity

by ingestion exposure route, and quantity of emissions. This resulted

in five nonradionuclide HAPs being identified as priorities for

multipathway assessment. 

After HAPs were prioritized based on the above criteria,

additional HAPs were prioritized because of potential concerns for

noncancer effects due to short-term inhalation exposures. Also,

radionuclides were identified as a priority for multipathway

assessment based on results of previous studies. The following

sections present more details about each of the screening analyses.

5.3 INHALATION SCREENING ASSESSMENT FOR COAL-FIRED UTILITIES

Emissions data were available for 66 nonradionuclide HAPs from

coal-fired utilities. Cancer and noncancer quantitative health

effects data were available from IRIS and various EPA documents for 50

of the 66 nonradionuclide HAPs. Table 5-1 presents the results for

HAPs that are considered carcinogens and for which a quantitative

cancer risk estimate was available. Table 5-2 presents results for

the noncancer screening assessment using EPA-verified RfCs. Table 5-3

presents the HAPs for which no EPA-approved quantitative health data

were available for assessment.

5.4 INHALATION SCREENING ASSESSMENT FOR OIL- AND GAS-FIRED UTILITIES

Emissions data were available for 28 HAPs from oil-fired utilities. 

EPA cancer and noncancer quantitative health effects data were available

for 22 of the 28 HAPs. Table 5-4 presents the results for HAPs from oilfired utilities that are considered carcinogens and for which

quantitative cancer risk estimates were available. Table 5-5 presents

results for the noncancer screening assessment for HAPs from oil-fired

utilities for which EPA-approved RfCs were available. Table 5-6 presents

HAPs from oil-fired utilities for which no EPA-verified quantitative

health data were available for assessment.

For gas-fired utilities, emissions data were available for

14 HAPs. Table 5-7 presents the screening results for gas utilities.
5-3

Table 5-1. Inhalation Screening Assessment for Carcinogenic HAPs

from Coal-Fired Utilities for Which Quantitative Cancer Risk

Estimates Were Available

Hazardous air pollutant (μg/m ) WOE (μg/m ) risk inhalation risk >10

Highest Primary type

MEI MEI of cancer

conc. EPA IURE cancer assoc. w/ MEI cancer a

3

b

3 c d -7

Arsenic compounds 0.0014 A 0.0043 > 10 Lung Yes -6

Chromium (11 percent VI) 0.0023 A 0.0016 > 10 Lung Yes e e -6

Beryllium 0.00025 B2 0.0024 > 10 Lung Yes -7

Cadmium 0.00009 B2 0.0018 > 10 Lung Yes -7

Nickel compounds 0.0027 A 0.00048 > 10 Lung & nasal Yes f f -7

Dioxins Tongue, lung, g

2 x 10 B2 30.0 7 x 10 No -9 g -8

nasal, liver

PAHs 0.00002 B2 0.0021 4 x 10 Lung (BAP) No h h -8

Naphthalene 0.00009 C 4 x 10 4 x 10 NA No -6 -10

Hexaclorobenzene 9 x 10 B2 0.00046 4 x 10 NA No -6 -9

Carbon tetrachloride 0.00038 B2 0.000015 6 x 10 Liver No -9

Quinoline 0.000006 C 0.0035 2 x 10 NA No i j -8

Vinylidene chloride 0.0011 C 5 x 10 6 x 10 NA No -5 -8

Formaldehyde 0.00047 B1 1 x 10 6 x 10 Nasal, lung No -5 -7

n-Nitrosodimethylamine 0.00008 B1 0.014 1 x 10 Liver & other Yes j -6 j

1,1,2-Trichloroethane 0.00054 C 2 x 10 9 x 10 NA No -5 -9

Acetaldehyde Nose & 0.00078 B2 2 x 10 2 x 10 No -6 -9

larynx

Benzene 0.00029 A 8 x 10 2 x 10 Leukemia No -6 -9

Benzyl chloride 6 x 10 B2 5 x 10 3 x 10 NA No -7 -5 -11

Bis(2-ethylhexyl)phthalate 0.00047 B2 4 x 10 2 x 10 NA No i -6 i -9

Bromoform 0.00077 B2 1 x 10 9 x 10 NA No -6 -10

Chloroform 0.00037 B2 2 x 10 9 x 10 Kidney & liver No -5 -9

Ethylene dichloride 0.00036 B2 3 x 10 9 x 10 NA No -5 -9

Isophorone 0.003 C 3 x 10 9 x 10 NA No i -7 i -10

Methyl chloride 0.0007 C 2 x 10 1 x 10 Kidney No i -6 i -9

(continued)
Table 5-1. (Continued)

Hazardous air pollutant (μg/m ) WOE (μg/m ) risk inhalation risk >10

Highest Primary type

MEI MEI of cancer

conc. EPA IURE cancer assoc. w/ MEI cancer a

3

b

3 c d -7

5-4

Methylene chloride 0.0015 B2 5 x 10 7 x 10 Liver & lung No -7 -10

Trichloroethylene Lung, liver, & i

0.00036 B2/C 2 x 10 6 x 10 No -6 i -10

testicular

Pentachlorophenol 1 x 10 B2 3 x 10 3 x 10 NA No i -6 -5 i -11

Tetrachloroethylene 0.00036 B2/C 6 x 10 2 x 10 Liver No i -7 i -10

IURE = Inhalation Unit Risk Estimate. The IURE is the estimated increased risk of cancer from breathing 1 μg of

pollutant per cubic meter of air for 70 years. 

MEI = Maximally Exposed Individual.

NA = Not available.

WOE = Weight of Evidence, for carcinogenicity. See section 4.3.1 and Table 4-1. for explanation of WOE.

This is the highest estimated ambient concentration (annual average) due to emissions from the one highest risk a

coal-fired utility based on HEM modeling of all coal-fired utilities in the U.S. 

IUREs obtained from EPA’s Integrated Risk Information System (IRIS), unless indicated otherwise by footnotes. b 2

This is the estimated increased lifetime cancer risk to the highest MEI due to inhalation exposure. c

This column presents the type of cancer observed in experimental animal studies or human studies. For more details see d

Appendix E and/or various references.

For coal-fired utilities it is assumed that 11 percent of chromium is chromium VI and that the remainder is chromium III. e

For oil-fired utilities it is assumed that 18 percent of chromium is chromium VI. This is based on limited speciation data

described in Appendix H. It is assumed that the carcinogenic effects are caused only by the Cr VI fraction. The IURE 3

was calculated by multiplying the IURE on IRIS for Cr VI (1.2 x 10 ) by 0.11 (11 percent). -2

For this screening assessment all nickel was assumed to be as carcinogenic as nickel subsulfide. This assumption is f

considered an "upper bound" conservative assumption. Nickel risk uncertainty issues are discussed more thoroughly in

Chapter 6.

The emissions were estimated using the toxic equivalency (TEQ) approach described in the draft EPA Dioxin g

Reassessment Report. Exposure was estimated by modeling the TEQ emissions with HEM. The IURE is for 2,3,7,8- 4

tetrachlorodibenzo-p-dioxin (TCDD) and was obtained from the draft EPA Dioxin Reassessment Report.

To estimate the potential risk from polycyclic aromatic hydrocarbon (PAH) emissions, first the EPA summed the h

emissions of the 7 PAHs that are classified as B2 carcinogens (WOE = B2). (These are listed in Appendix H). Second, 5 3

exposure was estimated by using the HEM and modeling the sum of the 7 PAHs. Third, the estimated exposure to the 7

B2 PAHs were multiplied by the IURE for benzo[a]pyrene (BAP) (2.1 x 10 ). However, this IURE has not been verified by -3

the EPA and has not been peer reviewed. It is an interim value with significant uncertainties and is intended for screening

assessment only. This IURE was calculated by converting the oral unit risk estimate of 2.1 x 10 per μg/L to inhalation -4

units. The conversion assumes equal absorption and metabolism and assumes equal risk from the different routes of

exposure, which may not be the case.

The IUREs for these HAPs are not EPA-verified and are intended for screening assessment only. Readers must exercise i

caution interpreting the results using these numbers. These IUREs were obtained from Documentation of De Minimis

Emission Rates - Proposed 40 CFR Part 63, Subpart B, Background Document. This document was developed to 6

support the proposed rulemaking pursuant to 112(g) of the Clean Air Act (Federal Register, Volume 59, No. 63, April 1,

1994). There are significant uncertainties associated with these IUREs. They are not EPA-verified. They are interim

screening values intended for the screening assessment only. For further discussion of the health data and uncertainties,

see the de minimis document cited above. 

The risk estimate for n-nitrosodimethylamine is highly suspect and uncertain because the emissions estimates were j

based on one measured value and several nondetect values. Based on available information, the MEI risk estimate

presented here is likely to be a significant overestimate of the true risks posed by n-Nitrosodimethylamine.
5-5

Table 5-2. Inhalation Screening Assessment for Noncancer Effects

of HAPs Emitted from Coal-Fired Utilities for Which Inhalation

Reference Concentrations Are Available

Hazardous air RfC which RfC is Confidence conc. HQ

pollutant (g/m ) based in RfC (g/m ) Max. HQ > 0.1 3

Noncancer Highest

health effect on MEI Highest

a b

c

3

d

2-Chloro-acetophenone 0.03 low 3 x 10 0.001 No Hyperplasia of nasal

 resp. epith. in rats

-5

Acrolein 0.02 inflammation rat nasal med 4 x 10 0.02 No

Metaplasia and

epithel.

-4

Cumene 9.0  NA 0.00003 3 x 10 No e e -6

Ethyl benzene 1000 Developmental effects low 0.00005 5 x 10 No -8

Ethyl chloride 10000 med 0.0003 2 x 10 No Delayed fetal

ossification

-8

Hexane 200 med 0.00009 5 x 10 No CNS & nasal epith.

lesions humans

-6

Hydrogen chloride 20 mucosa & larynx in low 2.3 0.115 Yes

Hyperplasia of nasal

rats

Hydrogen cyanide 3.0 low 0.0033 0.001 No CNS symptoms and

thyroid effects

Lead 1.5 CNS & devel. humans NA 0.007 0.0057 No f,g f

Manganese 0.05 CNS, humans med 0.02 0.4 Yes

Mercury   -- 0.001 -- No g

Methyl bromide 5.0 high 0.0001 2 x 10 No Lesions of olfactory

epithelium

-5

Methyl chloroform 1000 Hepatotoxicity NA 0.0004 4 x 10 No e e -7

Methyl ethyl ketone 1000 low 0.0009 9 x 10 No Decreased fetal birth

weight (mice)

-7

MTBE 3000 med 0.0002 7 x 10 No Increased liver &

kidney weight (rat)

-8

Styrene 1000 CNS in humans med 0.00036 4 x 10 No -7

Toluene 400 degeneration of nasal med 0.0004 1 x 10 No

Neurological effects;

epithelium

-6

(continued)
Table 5-2. (Continued)

Hazardous air RfC which RfC is Confidence conc. HQ

pollutant (g/m ) based in RfC (g/m ) Max. HQ > 0.1 3

Noncancer Highest

health effect on MEI Highest

a b

c

3

d

5-6

1,3-Dichloropropene 20 high 0.00054 3 x 10 No

Hypertrophy/hyperpla

sia

of nasal respiratory

epithelium

-5

Vinyl acetate 200 high 0.00005 3 x 10 No Nasal epithelium

lesions

-7

CNS = Central nervous system.

HQ = Hazard Quotient. The ratio of exposure concentration/RfC. An HQ < 1.0 indicates that no adverse health

effects are expected to occur (see Chapter 4 for discussion of HQ).

MEI = Maximally Exposed Individual. 

NA = Not applicable.

RfC = Reference concentration (inhalation).

This is the critical adverse noncancer health effect that was observed in animal or human studies. a 2,7

This is the overall confidence in the RfC as reported on IRIS. b

This is the highest estimated ambient concentration (annual average) due to coal-fired utility emissions based on c

HEM modeling within 50 km of all coal-fired utilities in the United States.

If HQ > 0.1, this means that the highest modeled concentration is greater than 1/10 of the RfC. This value (0.1) d

was used as criteria in screening assessment. This is not considered a level of concern, but rather it is a

conservative level to ensure that potentially important HAPs are not missed by screen. See text for explanation.

The RfC was obtained from the 1992 EPA Health Effects Summary Tables. It has not been verified by the EPA's e 8

RfC/RfD workgroup.

No RfC is available for lead compounds. Therefore, as a substitute, the lead National Ambient Air Quality f

Standard (1.5 μg/m ) was used in this assessment. However, the lead NAAQS is note considered equivalent to 3 9

an RfC. The lead NAAQS is based on a quarterly average, but the exposure estimates here represent annual

averages. The reader should exercise caution when interpreting the HQ for lead. Lead has also been classified

as a carcinogen.2,6

These compounds may also be a health concern from multipathway exposure. The assessment here considers g

only inhalation exposure. Considering multipathway exposure may increase the risk estimates for these

pollutants. Multipathway screening assessment is discussed in section 5.5.
5-7

Table 5-3. Inhalation Screening Assessment for HAPS Emitted from

Coal-Fired Utilities for Which No EPA-Verified Health Benchmarks

Are Available (Comparison of Highest Modeled Air Concentration to

Various Non-EPA Health Benchmarks)

Hazardous air REL/420 PEL/420 TLV/420 MEI conc. Maximum

pollutant (g/m ) (g/m ) (μg/m ) (g/m ) HQ HQ > 0.1

NIOSH OSHA ACGIH Highest a

3

a

3

a

3

b

3

Acetophenone NA NA NA 0.00008 NA NA

Antimony compounds 1.2 1.2 1.2 0.0005 4 x 10 No -4

Carbon disulfide 7.1 29 74 0.0005 7 x 10 No -5

Chlorobenzene NA 833 830 0.00037 4 x 10 No -7

Cobalt compounds 0.12 0.12 0.12 0.0017 0.014 No

o & p-Cresols 24 52 0.0003 1 x 10 No -5

Cumene 580 580 580 0.00003 3 x 10 No -6

Dibutyl phthalate 11.9 11.9 11.9 0.00033 3 x 10 No -5

Hydrogen fluoride 6.0 6.0 6.0 0.365 0.06 No c

Methyl methacrylate NA 980 980 0.00013 1 x 10 No c -7

MIBK 490 980 490 0.00058 1 x 10 No -6

Phenol 48 45 45 7 x 10 2 x 10 No c -4 -5

Phthalic anhydride 14 14 14 6 x 10 4 x 10 No -4 -5

Phosphorus 0.24 0.24 0.0036 0.015 No

Propion-aldehyde NA NA NA 0.0012 NA NA

Selenium compounds 0.48 0.48 0.48 0.0056 0.012 No c

m,o,p-Xylenes 1000 1000 1000 0.0005 5 x 10 No -7

2,4-Dinitrotoluene 3.6 3.6 0.36 1 x 10 3 x 10 No -6 -6

Methyl iodide 24 67 29 0.00005 2 x 10 No -6

ACGIH = American Conference of Government Industrial Hygienists, which is a professional society, not a

government agency.

HQ = Hazard Quotient. The ratio of exposure concentration/RfC. An HQ < 0.1 indicates that no adverse

health effects are expected to occur (see Chapter 4 for discussion of HQ).

MEI = Maximally Exposed Individual.

NIOSH = National Institute for Occupational Safety and Health, a U.S. government organization that focuses on

research.

OSHA = Occupational Safety and Health Agency, a U.S. Government Agency
5-8

Footnotes for Table 5-3 (continued)

PEL = Permissible Exposure Levels. These are legal limits established by OSHA. 

REL = Recommended Exposure Levels. NIOSH develops these recommended levels to protect workers. 

TLV = Threshold Limit Values. The TLV are established by ACGIH and are used by industrial hygienists in

the work place to assess the potential concerns for worker exposure. 

The NIOSH, OSHA, and ACGIH are primarily involved with the safety and health of workers. The RELs, PELs, a

and TLVs are similar. Breathing concentrations below these levels are expected to be reasonably protective of

health workers, exposed for 8 hours per day, 5 days per week (~40 hours). However, there are uncertainties and

often the data are less then complete. Also, for some of these values (especially the PELs), measurement

techniques and economic factors are sometimes factored in. 10,11,12

Occupational Exposure Limits (OELs) are being used in this study for screening assessment purposes only. For

this screening assessment, the REL, PEL, and TLV were divided by 420 (4.2 x 10 x 10). The 4.2 is the conversion

factor to extrapolate from a 40 hr/week to a 168 hr/week. A factor of 10 is used to adjust for sensitive

subpopulations. Another factor of 10 is used to account for additional uncertainties associated with these values. 

A similar method was used by the California Air Resources Board (CARB) in the Air Toxics Hot Spots Program. 13

CARB also divides the TLV by 420 to calculate some of their noncancer reference exposure levels (4.2 to account

for exposure time adjustment, 10 to account for sensitive individuals, and another 10 because health effects are

sometimes observed at the TLV level).

This is the highest estimated ambient concentration due to coal-fired utility emissions based on HEM modeling b

within 50 km of all coal-fired utilities in the United States.

These values are the same as the CARB Noncancer Reference Exposure Levels used in the Hot Spots c

Program.

13
5-9

Table 5-4. Inhalation Screening Assessment for Carcinogenic HAPS

from Oil-Fired Utilities for Which Quantitative Cancer Risk

Estimates Were Available

Hazardous air pollutant (g/m ) EPA WOE g/m MEI Risk Inhalation risk >10

Highest cancer

MEI conc. IURE per Cancer associated w/ MEI cancer a

3

b

3 c

Primary type of

d -7

Arsenic 0.0032 A 0.0043 > 10 Lung Yes -6

Chromium (18 percent Cr

VI)e 0.0025 A 0.0028 > 10 Lung Yes -6

Beryllium 0.0003 B2 0.0024 > 10 Lung Yes -7

Cadmium 0.0009 B2 0.0018 > 10 Lung Yes -6

Nickel compounds 0.21 A2 0.00048 > 10 Lung & nasal Yes f -6

Dioxins 4 x 10 B2 30.0 1 x 10 nasal, liver, Yes g -9 -7

Tongue, lung,

thyroid

PAHs 0.00003 B2 0.0021 6 x 10 Lung (BAP) No h -8

Formaldehyde 0.007 B1 1.3 x 10 9 x 10 Nasal, lung No -5 -8

Acetaldehyde 0.0019 B2 2.2 x 10 4 x 10 Nasal & laryngeal No -6 -9

Benzene 0.0003 A 8.3 x 10 3 x 10 Leukemia No -6 -9

Methylene chloride 0.008 B2 4.7 x 10 4 x 10 Liver & lung No -7 -9

Naphthalene 0.00008 C 4.2 x 10 3 x 10 -- No -6 -10

Tetrachloroethylene 0.00013 B2/C 5.8 x 10 8 x 10 Liver No i -7 -11

IURE = Inhalation Unit Risk Estimate. The IURE is the estimated increased risk of cancer from breathing 1 μg of

pollutant per cubic meter of air for 70 years. 

MEI = Maximally Exposed Individual.

WOE = Weight of Evidence, for carcinogenicity. See section 4.3.1 and Table 4-1. for explanation of WOE.

This is the highest estimated ambient concentration (annual average) due to emissions from the one highest risk a

coal-fired utility based on HEM modeling of all coal-fired utilities in the U.S. 

IUREs obtained from EPA's Integrated Risk Information System (IRIS) , unless indicated otherwise by footnotes. b 2

This is the estimated increased lifetime cancer risk to the highest MEI due to inhalation exposure. c

This column presents the type of cancer observed in experimental animal studies or human studies. For more details see d

Appendix E and/or various references.

For coal-fired utilities it is assumed that 11 percent of chromium is chromium VI and that the remainder is chromium III. e

For oil-fired utilities it is assumed that 18 percent of chromium is chromium VI. This is based on the limited speciation

data described in Appendix H.3 It is assumed that the carcinogenic effects are caused only by the Cr VI fraction. The

IURE was calculated by multiplying the IURE on IRIS for Cr VI (1.2 x 10 ) by 0.11 (11 percent). -2

For this screening assessment all nickel was assumed to be as carcinogenic as nickel subsulfide. This assumption is f

considered an "upper bound" conservative assumption. Nickel risk uncertainty issues are discussed more thoroughly in

Chapter 6.

The emissions were estimated using the toxic equivalency (TEQ) approach described in the draft EPA Dioxin g

Reassessment Report. Exposure was estimated by modeling the TEQ emissions with HEM. The IURE is for 2,3,7,8- 4

tetrachlorodibenzo-p-dioxin (TCDD) and was obtained from the draft EPA Dioxin Reassessment Report.
Footnotes for Table 5-4. (Continued)

5-10

To estimate the potential risk from polycyclic aromatic hydrocarbon (PAH) emissions, first the EPA summed the h

emissions of the 7 PAHs that are classified as B2 carcinogens (WOE = B2). (These are listed in Appendix H3). Second, 5

exposure was estimated by using the HEM and modeling the sum of the 7 PAHs. Third, the estimated exposure to the 7

B2 PAHs were multiplied by the IURE for benzo[a]pyrene (BAP) (2.1 x 10 ). However, this IURE has not been verified by -3

the EPA and has not been peer reviewed. It is an interim value with significant uncertainties and is intended for screening

assessment only. This IURE was calculated by converting the oral unit risk estimate of 2.1 x 10 per g/L to inhalation -4

units. The conversion assumes equal absorption and metabolism and assumes equal risk from the different routes of

exposure, which may not be the case.

The IUREs for these HAPs are not EPA-verified and are intended for screening assessment only. Readers must exercise i

caution interpreting the results using these numbers. These IUREs were obtained from Documentation of De Minimis

Emission Rates - Proposed 40 CFR Part 63, Subpart B, Background Document. This document was developed to 6

support the proposed rulemaking pursuant to 112(g) of the Clean Air Act (Federal Register, Volume 59, No. 63, April 1,

1994). There are significant uncertainties associated with these IUREs. They are not EPA-verified. They are interim

screening values intended for the screening assessment only. For further discussion of the health data and uncertainties,

see the de minimis document cited above. 
5-11

Table 5-5. Inhalation Screening Assessment for Noncancer Effects

of HAPS Emitted from Oil-Fired Utilities for Which EPA-Verified

Inhalation Reference Concentrations Are Available

Hazardous air RfC Critical noncancer health confidence conc. Highest

pollutant (μg/m ) effect that RfC is based on in RfC (μg/m ) HQ HQ >0.1 3 a

Overall MEI

b

Highest c

3 d

Ethyl benzene 1000 Developmental effects Low 1 x 10 1 x 10 No -4 -7

Hydrogen

chloride 20 mucosa, Low 1.1 0.16 Yes

Hyperplasia of nasal

larynx, and trachea in rats

Lead 1.5 NA 0.005 0.003 No e Neurotoxicity and

developmental in humans

Manganese 0.05 Medium 0.002 0.04 No Neurobehavioral effects in

humans

Mercury -- -- -- 0.00014 -- No

Methyl

chloroformf 1000 Hepatotoxicity NA 0.0018 2 x 10 No f f -6

Toluene 400 Neurological effects Medium 0.002 5 x 10 No -6

Vinyl acetate 200 Nasal lesions High 0.0012 6 x 10 No -6

HQ = Hazard Quotient. The ratio of exposure concentration/RfC. An HQ < 1.0 indicates that no adverse

health effects are expected to occur (see Chapter 4 for discussion of HQ).

MEI = Maximally Exposed Individual. 

NA = Not applicable.

RfC = Reference concentration (inhalation).

This is the critical adverse noncancer health effect that was observed in animal or human studies. a 2,7

This is the overall confidence in the RfC as reported on IRIS. b

This is the highest estimated ambient concentration (annual average) due to coal-fired utility emissions based on c

HEM modeling within 50 km of all coal-fired utilities in the United States.

If HQ > 0.1, this means that the highest modeled concentration is greater than 1/10 of the RfC. This value (0.1) d

was used as criteria in screening assessment. This is not considered a level of concern, but rather it is a

conservative level to ensure that potentially important HAPs are not missed by screen. See text for explanation.

No RfC is available for lead compounds. Therefore, as a substitute, the lead National Ambient Air Quality e

Standard (1.5 μg/m ) was used in this assessment. However, the lead NAAQS is note considered equivalent to 3 9

an RfC. The lead NAAQS is based on a quarterly average, but the exposure estimates here represent annual

averages. The reader should exercise caution when interpreting the HQ for lead. Lead has also been classified

as a carcinogen.2,6

These compounds may also be a health concern from multipathway exposure. The assessment here considers f

only inhalation exposure. Considering multipathway exposure may increase the risk estimates for these

pollutants. Multipathway screening assessment is discussed in section 5.5.
5-12

Table 5-6. Inhalation Screening Assessment for HAPS Emitted from

Oil-Fired Utilities for Which No EPA-Verified Health Benchmarks

Are Available (Comparison of Highest Modeled Concentration to

Various Non-EPA Health Benchmarks)

Pollutant (μg/m ) (μg/m ) (μg/m ) (μg/m ) Max HQ >0.1

NIOSH OSHA ACGIH Highest

REL/420 PEL/420 TLV/420 MEI conc. Max HQ a

3

a

3

a

3 3b

Cobalt compounds 0.12 0.12 0.12 0.0096 0.08 No

Hydrogen fluoride 6.0 6.0 NA 0.03 0.005 No

Phenol 48 45 45 0.006 0.0001 No

Phosphorus 0.24 0.24 0.24 0.026 0.1 No

Selenium compounds 0.48 0.48 NA 0.001 0.002 No

m,o,p-Xylenes 1040 1040 1040 0.0005 5 x 10 No -7

ACGIH = American Conference of Government Industrial Hygienists, which is a professional society, not a

government agency.

HQ = Hazard Quotient. the ratio of exposure concentration/RfC. An HQ < 0.1 indicates that no adverse

health effects are expected to occur (see Chapter 4 for discussion of HQ).

MEI = Maximally Exposed Individual.

NIOSH = National Institute for Occupational Safety and Health, a U.S. government organization that focuses on

research.

OSHA = Occupational Safety and Health Agency, a U.S. Government Agency

PEL = Permissible Exposure Levels. These are legal limits established by OSHA. 

TLV = Threshold Limit Values. The TLV are established by ACGIH and are used by industrial hygienists in

the work place to assess the potential concerns for worker exposure. 

The NIOSH, OSHA, and ACGIH are primarily involved with the safety and health of workers. The RELs, PELs, a

and TLVs are similar. Breathing concentrations below these levels are expected to be reasonably protective of

health workers, exposed for 8 hours per day, 5 days per week (~40 hours). However, there are uncertainties and

often the data are less then complete. Also, for some of these values (especially the PELs), measurement

techniques and economic factors are sometimes factored in. 10,11,12

Occupational Exposure Limits (OELs) are being used in this study for screening assessment purposes only. For

this screening assessment, the REL, PEL, and TLV were divided by 420 (4.2 x 10 x 10). The 4.2 is the conversion

factor to extrapolate from a 40 hr/week to a 168 hr/week. A factor of 10 is used to adjust for sensitive

subpopulations. Another factor of 10 is used to account for additional uncertainties associated with these values. 

A similar method was used by the California Air Resources Board (CARB) in the Air Toxics Hot Spots Program. 13

CARB also divides the TLV by 420 to calculate some of their noncancer reference exposure levels (4.2 to account

for exposure time adjustment, 10 to account for sensitive individuals, and another 10 because health effects are

sometimes observed at the TLV level).

This is the highest estimated ambient concentration due to coal-fired utility emissions based on HEM modeling b

within 50 km of all coal-fired utilities in the United States.
5-13

Table 5-7. Inhalation Screening Assessment for HAPS Emitted from

Gas-Fired Utilities

Hazardous air Highest MEI IURE HEM Cancer RfC

pollutant conc. (μg/m ) (μg/m ) MEI Risk (μg/m ) Highest HQ a 3

b

3 c3 d

Arsenic 2 x 10 0.0043 1 x 10 NA NA -5 -7

Nickel compounds 0.0003 0.00048 2 x 10 NA NA e -7

Naphthalene 0.0001 4 x 10 4 x 10 NA NA -6 -10

Toluene 0.0018 NA NA 400 4.5 x 10-6

Lead 0.00006 NA NA 1.5 4 x 10-5

Formaldehyde 0.008 1.3 x 10 1 x 10 NA NA -5 -7

Mercury 0.0000002 NA NA -- --

Benzene 0.0003 8.3 x 10 2 x 10 NA NA -6 -9

Phosphorus 0.0002 NA NA 0.24 0.0008 f

Cobalt 0.00002 NA NA 0.12 0.0002 f

HEM = Human Exposure Model

HQ = Hazardous Quotient. The ratio of exposure concentration/RfC. An HQ < 1.0 indicates that no adverse

health effects are expected to occur (see Chapter 4 for discussion of HQ).

IURE = Inhalation Unit Risk Estimates

MEI = Maximally Exposed Individual.

This is the highest estimated ambient concentration (annual average) due to emissions from the one highest risk a

coal-fired utility based on HEM modeling of all coal-fired utilities in the U.S.

IUREs obtained from EPA's Integrated Risk Information System (IRIS), unless indicated otherwise by footnotes. b 2

This is the estimated increased lifetime cancer risk to the highest MEI due to inhalation exposure. c

If HQ > 0.1, this means that the highest modeled concentration is greater than 1/10 of the RfC. This value (0.1) d

was used as criteria in screening assessment. This is not considered a level of concern, but rather it is a

conservative level to ensure that potentially important HAPs are not missed by screen. See text for explanation.

For this screening assessment all nickel was assumed to be as carcinogenic as nickel subsulfide. This e

assumption is considered an upper bound conservative assumption. Nickel risk uncertainty issues are

discussed more thoroughly in Chapter 6.

These values are not RfCs. They are TLV/420. See Tables 5-3 and 5-6. f

5.5 MULTIPATHWAY SCREENING ANALYSIS FOR NONRADIONUCLIDE HAPS

5.5.1 Overview

In past years, most analyses of human health risk associated with

atmospheric emissions of nonradionuclide HAPs from combustion sources

have focused only on exposures occurring by inhalation. The

inhalation exposure pathway is generally the significant pathway for

human exposure to air pollutants. In the past decade, though, studies

have linked elevated levels of pollutants in soils, lake sediments,

and cow’s milk to atmospheric transport and deposition of pollutants

from combustion sources. Scientists have collected convincing 14
5-14

evidence showing that toxic chemicals released to air can travel long

distances and be deposited to land and water at locations both near

and far from their original emission sources. Many studies indicate 15

that deposition of atmospherically emitted pollutants can result in

indirect avenues of exposure for humans. For some HAPs, these 16

noninhalation routes of exposure can be as significant, or more

significant, than inhalation. 

Certain HAPs have been associated with significant adverse

effects on human health and wildlife from noninhalation exposure

pathways. HAPs that pose a concern for noninhalation exposure 15

generally have common characteristics. They are persistent in the

environment, have the potential to bioaccumulate, and exhibit toxicity

via ingestion. For lipophilic contaminants such as dioxins, furans,

polychlorinated biphenyls, and certain pesticides and for metals such

as lead and mercury, exposures through food consumption have been

demonstrated to be dominant contributors to total dose for

nonoccupationally exposed populations. It is also likely that 17

atmospheric pollution from combustors and other thermal processes

significantly contributes to the ubiquitous presence of some of the

highly persistent lipophilic compounds.17

Multipathway exposure to HAPs can potentially occur through the

following exposure routes:

- Soil ingestion - Fruit ingestion

- Soil dermal contact - Vegetable ingestion

- Water ingestion - Ingestion of animal fats

- Inhalation - Milk ingestion

- Fish and meat ingestion - Ingestion of other food products.

The following section presents the screening assessment to

prioritize the nonradionuclide HAPs for further analysis of

multipathway exposures and risks. Chapters 7, 8, 10, and 11 present

the multipathway assessments for the selected priority nonradionuclide

HAPs.

5.5.2 Prioritization of HAPs for Multipathway Exposure Assessment

The 66 nonradionuclide HAPs potentially emitted by utilities were

evaluated for their potential to cause health effects through

noninhalation exposure pathways. To select the highest priority HAPs

for multipathway exposure assessment, a four-step process was

followed. This process involved assessing the HAPs for their

potential to be of concern for exposure through noninhalation

pathways, evaluating their toxicity, and considering the emission

levels from utilities. First, a subset of HAPs was selected from the

list of 66 nonradionuclide HAPs by using the HAP ranking presented in

Attachment A (draft Focus Chemicals Report) of the EPA document,

Schedule for Standards: Methodology and Results for Ranking Source

Categories Based on Environmental Effects Data. The four criteria 18

evaluated and used in this ranking were human toxicity, aquatic

toxicity, bioconcentration potential, and environmental persistence. 

Environmental partitioning was not used as a ranking criterion but was
5-15

used as a “qualifying” criterion. The HAP ranking method is a

modified version of the Inerts Ranking Program (IRP) methodology

developed by EPA’s Office of Research and Development, Environmental

Research Laboratory - Duluth, for evaluating pesticide ingredients. 

The IRP scoring method was modified for scoring the environmental

criteria and for determining overall scores for the HAPs. For the

environmental criteria modification, acute aquatic toxicity and

chronic aquatic toxicity were combined into a single aquatic toxicity

criterion that is based strictly on chronic toxicity data when such

data are available. Each criterion, except environmental persistence,

allowed a possible score of 0, 1, 2, or 3. A score of 0 indicates

that no data are available, and scores of 1, 2, and 3 indicate low,

medium, and high concern, respectively. For environmental

persistence, substances were assigned a score of 1 or 3 only, because

data did not support finer discrimination. The method for deriving

the overall score was similar to that for deriving the original IRP

score. For each substance, the overall score was derived by adding

the scores for the four criteria, dividing by the number of criteria

for which there were data, and then multiplying by 10 to produce an

overall score on a scale of 10 to 30.

The HAP ranking in the Focus Chemicals report is a ranking of all

of the HAPs based on the overall score for each HAP. Of the 66 HAPs

potentially emitted to air by utilities, those that ranked the

highest, with overall ranking scores of greater than 23, were selected

for further evaluation. The cutoff score of 23 was selected because,

at this level and below, a HAP would have scores of 1 or 2, indicating

low and medium concern, respectively, for at least two of the four

criteria. Thirteen HAPs were selected on these criteria. The high

ranking reflects that these 13 HAPs are more likely to be highly

persistent in the environment and/or to bioaccumulate, as well as to

potentially be toxic to humans. The 13 HAPs selected and their

ranking scores are listed in Table 5-8. This approach to ranking the

HAPs is a screening-level, hazard-based ranking of chemicals. This

approach yielded a subset of 13 HAPs from which five HAPs were chosen

for further evaluation. 

In the second step of the process, additional information was

gathered for each of the 13 selected HAPs to determine the HAPs that

are most important for multipathway assessment for the utilities. 

Where available and applicable, the RfD, the oral unit risk estimate

(OURE), the EPA WOE classification, and the emissions estimate were

obtained for each of the 13 HAPs. This information is presented in

Table 5-9. 

Several criteria were used to further prioritize HAPs for

multipathway exposure assessment. The six HAPs with the highest

noncancer toxicity (i.e., lowest RfDs [less than 1 x 10 ]), as well as -3

the HAPs with EPA-verified OUREs and a WOE classifica-tion of A or B,

were selected. Also, due to their extremely high toxicity and the

concern that they are “no threshold” or extremely low threshold

chemicals, 2,3,7,8-TCDD and lead compounds were also included. 

Mercury was also selected because mercury is persistent, tends to 
5-16

Table 5-8. Thirteen HAPs Selected from the Hazard-Based

Multipathway Ranking (shown in order of ranking), and the

Overall and Individual Criterion Scores Assigned to Each

 

HAP score score score potential score score

Overall toxicity toxicity Bioconcentration persistence

Human Aquatic Environmental 

2,3,7,8-TCDD (dioxins) 30 3 3 3 3

Cadmium compounds 30 3 3 3 3

Mercury compounds 30 3 3 3 3

Hexachlorobenzene 28.75 2.5 3 3 3

Selenium compounds 28.75 2.5 3 3 3

Lead compounds 27.5 3 3 2 3

Cobalt compounds 26.67 3 2 0 3

Pentachlorophenol 25 2 3 2 3

Arsenic compounds 25 3 2 2 3

POM (PAH) 25 3 3 3 1

Beryllium compounds 23.75 2.5 2 3 3

Cyanide compounds 23.75 2.5 3 3 1

Manganese compounds 23.75 1.5 2 3 3

HAP = Hazardous air pollutant

TCDD = Tetrachlorodibenzo-p-dioxins

PAH = Polycyclic aromatic hydrocarbons

POM = Polycyclic organic matter

bioaccumulate, and is relatively toxic by ingestion exposure. This

second step in the prioritization process resulted in eight HAPs being

selected: 2,3,7,8-TCDD, lead, mercury, arsenic, cadmium,

hexachlorobenzene, beryllium, and polycyclic organic matter (POM). In

the next step in the selection process (the third step), emission

estimates from utilities were examined for each of the eight HAPs. 

For two of the HAPs, POM and hexachlorobenzene, the emissions data for

utilities did not support their inclusion in further assessments. For

hexachlorobenzene, emissions were not considered high (0.7 ton/yr)

nationwide. Also, this estimate was highly uncertain because of the

very limited emissions data for hexachlorobenzene. For POM, the

emission levels of 1.9 ton/yr from coal-fired utilities and less than

1 ton/yr for oil-fired utilities are low relative to other

anthropogenic sources of POM. For the final step, the two lowestranking of the six remaining HAPs (cadmium and beryllium) were

compared with each other in terms of toxicity, emissions, and the

original ranking scores they were assigned. Cadmium was selected for

further assessment, rather than beryllium, because of its higher
5-17

Table 5-9. Comparison of Cancer and Noncancer Effects Benchmarks

and Emissions Estimates for 13 Selected HAPs

HAP (mg/kg/day) (per μg/L) WOE (ton/yr) (ton/yr)

RfD OURE estimates estimates

Coal-fired Oil-fired

emissions emissions a a

2,3,7,8-TCDD (dioxins)(TEQ) NA 3 x 10 B2 9.7 x 10 0.69 x 10 +0 b -5 -5

Lead compounds -- B2 7.5 x 10 11 No

 threshold c

+1

Mercury compounds 1 x 10 -- C 4.6 x 10 0.3 -4 +1

Arsenic compounds 3 x 10 5.0 x 10 A 6.1 x 10 5 d -4 -5 +1

Cadmium compounds 5 x 10 -- B2 3.3 2 -4

Hexachlorobenzene 8 x 10 4.6 x 10 B2 0.7 NA -4 -5

Selenium compounds 5 x 10 -- -- 1.5 x 10 2 -3 +2

Beryllium compounds 5 x 10 1.2 x 10 B2 7.1 0.5 -3 -4

Cyanide compounds 5 x 10 -- -- 2.2 x 10 NA -3 +2

Manganese compounds 5 x 10 -- -- 1.6 x 10 10 -3 +2

Pentachlorophenol NA -- B2 6.4 x 10 NA -2

Cobalt compounds NA -- -- 2.1 x 10 20 +1

POM (PAH) NA 2.1 x 10 B2 1.9 < 1 e -4

HAP = Hazardous air pollutant

NA = Not available

OURE = Oral unit risk estimate

PAH = Polycyclic aromatic hydrocarbons

POM = Polycyclic organic matter

RfD = Reference dose

TEQ = Toxicity equivalent

TCDD = Tetrachlorodibenzo-p-dioxin

WOE = weight of evidence

This is an estimate of total nationwide emissions from the source category. a

This is an unverified oral unit risk estimate. b 4

The Agency has determined that some of the effects of lead, particularly changes in the levels of certain blood c

enzymes and in aspects of children’s neurobehavioral development, may occur at blood lead levels so low as to

be essentially without a threshold.

RfD is for inorganic arsenic. There was not a clear consensus for developing this value. See Appendix E and/or d

the IRIS database for details.

POM emissions were estimated by summing the emissions estimates for each individual PAH listed in Appendix H e

of the Interim Final Utility HAP Report. 3
5-18

ranking scores for human and aquatic toxicity and its lower Rfd,

representing higher noncancer toxicity. This resulted in the

selection of five highest priority HAPs for multipathway exposure

assessment. The five HAPs selected to be highest priority for further

analysis were 2,3,7,8-TCDD, lead compounds, mercury compounds, arsenic

compounds, and cadmium compounds. This prioritization method for HAPs

for multipathway analysis has limitations and uncertainties, was based

on limited data, was not quantitative, and was based largely on

subjective decisions; therefore, the selection of among the most

important to assess for multipathway exposures and are considered a

reasonable starting point for further multipathway analyses.

5.6 SELECTION OF HAPS FOR FURTHER ANALYSIS

In the initial phase of the screening assessment 12 HAPs were

selected as priority. Pollutants were selected as priority in the

initial phase if they met one of the following three criteria: 

(1) the MEI inhalation cancer risk was estimated to be greater than 1

in 10 million (i.e., 1 x 10 ); (2) maximum inhalation exposure -7

concentration was greater than one-tenth the RfC (i.e., if the HQ was

greater than 0.1); or (3) the emitted HAP is persistent in the

environment, tends to bioaccumulate, and emissions are significant

enough that there are potential concerns for human health from

multipathway exposure. The risk levels chosen for the first

two criteria (i.e., 1 x 10 and 1/10 the RfC) are lower than levels -7

that have been considered historically as levels for regulatory and

policy decisions (e.g., 1 x 10 for cancer and RfC for noncancer). -6

These lower values were chosen for screening purposes so that it would

be unlikely that potentially important HAPs would be missed by screen. 

That is, these conservative levels were chosen to ensure that all

potentially important HAPs would be identified by the screen. The

third criterion was primarily chosen to identify HAPs that are

considered a potential concern from multipathway exposure. Based on

these three criteria, 12 HAPs (arsenic, beryllium, cadmium, chromium,

dioxin/furans, nickel, n-nitrosodimethylamine, hydrogen chloride,

manganese, lead, mercury, and formaldehyde) were chosen to be

priorities for further assessment.

Radionuclides were also chosen as a priority for multipathway

assessment because previous risk assessments indicate that

radionuclides from utilities could potentially cause cancer risks

greater than 1 x 10 for MEIs. -6 1

In addition, three HAPs (HCl, HF, and acrolein) were chosen as

priority for assessment of potential noncancer effects due to shortterm (acute) exposures. The prioritization of HAPs for short-term

exposure analysis was based on review of health effects data,

emissions estimates, and recommendations from the peer review

panel. Hydrogen chloride, HF, and acrolein were the three HAPs 2, , 12 13

considered to be of highest potential concern for health effects due

to short-term exposures. Table 5-10 presents the 15 HAPs that were

selected as priority based on the screening assessment.
5-19

Table 5-10. Pollutants Considered Priority for Further Analysis

Based on Results of Screening Assessment

Pollutant for coal for oil for gas risk >10 HQ >0.1 assessment

Priority Priority Priority MEI cancer Inhalation multipathway

Inhalation Noncancer Priority for 

-7

Acrolein Yes No No No No No a

Arsenic Yes Yes Yes Yes NA Yes

Beryllium Yes Yes No Yes NA No

Cadmium Yes Yes No Yes NA Yes

Chromium Yes Yes No Yes NA No

Dioxins/furans Yes Yes No Yes (oil) NA Yes

Nickel Yes Yes Yes Yes NA No

Radionuclides Yes Yes Yes NA NA Yes b b

n-Nitroso- No No No Yes NA No

dimethylaminec

Hydrogen chloride Yes Yes No No Yes No

Hydrogen flouride Yes Yes No No No No a

Manganese Yes Yes No No Yes No

Lead Yes Yes No No No Yes

Mercury Yes Yes No No No Yes

Formaldehyde No No Yes Yes (gas) NA No

HQ = Hazard quotient

MEI = Maximally exposed individual

NA = Not applicable

Acrolein and hydrogen fluoride did not pass screen based on RfC analysis. However, these two HAPs were a

identified as priority because of potential concern for acute exposure.

Radionuclides were considered priority based on previous risk assessments conducted in the 1980s on b

radionuclides from utilities. 1

The MEI risk estimate for n-Nitrosodimethylamine is highly uncertain (and likely to be a high estimate) because c

emission estimates were based on one measured value and several measurements below the maximum detection

limit. Therefore, n-Nitrosodimethylamine was not selected as priority for further assessment.

5.7 LIMITATIONS OF SCREENING ASSESSMENT

The HAPs that were not chosen for further analysis were below the

screening level and not considered priority for this report. These

HAPs are considered lower priority and are not likely to present

significant risks to public health. Based on available data and the

screening analyses, the 53 HAPs that were not chosen for further study

are not likely to be a concern for public health. However, due to

uncertainties and limitations in the data, it is not possible to fully

and conclusively determine that they do not pose a threat to public

health. It is possible (although unlikely) that future data, such as

revised emissions data or new toxicologic data, could possibly warrant

further evaluation of some of these 53 HAPs in the future.
5-20

1. U.S. Environmental Protection Agency. Risk Assessments. 

Environmental Impact Statement. NESHAPS for Radionuclides. 

Background Information Document-Volume 2. EPA/520/1-89-006-1. 

Office of Radiation Programs, Washington, DC. 1989.

2. U.S. Environmental Protection Agency. Integrated Risk

Information System (IRIS) Database, Environmental Criteria and

Assessment Office, Cincinnati, OH. 1994.

3. U.S. Environmental Protection Agency. Study of Hazardous Air

Pollutant Emissions from Electric Utility Steam Generating

Units—-Interim Final Report. Volume III. Appendices H-M. EPA453/R-96-013c. October 1996.

4. U.S. Environmental Protection Agency. Estimating Exposure to

Dioxin-Like Compounds. Volume III: Site Specific Assessment

Procedures. Review Draft. EPA/600/6-88/005C. Office of

Research and Development. 1994.

5. U.S. Environmental Protection Agency. Provisional Guidance for

Qualitative Risk Assessment for Polycyclic Aromatic Hydrocarbons.

EPA/600/R-93/089. Washington, DC. 1993.

6. U.S. Environmental Protection Agency. Documentation of De

Minimis Emission Rates - Proposed 40 CFR Part 63, Subpart B,

Background Document. Research Triangle Park, NC. 1994.

7. U.S. Environmental Protection Agency. Interim Methods for

Development of Inhalation Reference Concentrations. EPA 600/8-

90/066A. Office of Health and Environmental Assessment,

Washington, DC. August 1990.

8. U.S. Environmental Protection Agency. Health Effects Assessment

Summary Tables. Office of Research and Development, Office of

Emergency and Remedial Response. Washington, DC. 1992. 

9. Code of Federal Regulations. Title 40-part 50, Section 50.12. 

Office of the Federal Register, National Archive and Recoreds

Administration. U.S. Government Printing Office. Washington,

DC.

10. U.S. Department of Health Services. NIOSH Pocket Guide to

Chemical Hazards. Public Health Service, Centers for Disease

Control, National Institute for Occupational Safety and Health,

Cincinnati, OH. 1994.

11. American Conference of Governmental Industrial Hygienists. 

Threshold Limit Values. Cincinnati, Ohio. 1993-94.

5.8 REFERENCES
5-21

12. Calabrese and Kenyon. Air Toxics and Risk Assessment. Lewis

Publishers, Inc., Chelsea, MI. 1991.

13. California Air Pollution Control Officers Association. Air

Toxics “Hot Spots” Program. Revised 1992. Risk Assessment

Guidelines. California. October 1993.

14. Fradkin, L., R. J. F. Bruins, D. H. Cleverly, et al. Assessing

the risk of incinerating municipal solid waste: The development

and application of a methodology. In: Municipal Waste

Combustion and Human health. CRC Press, Palm Beach, FL. 1988.

15. U.S. Environmental Protection Agency. Deposition of Air

Pollutants to the Great Waters: First Report to Congress. EPA453/R-93-055. Office of Air Quality Planning and Standards,

Research Triangle park, NC. 1994.

16. U.S. Environmental Protection Agency. Methodology for Assessing

Health Risks Associated with Indirect Exposure to Combustor

Emissions, Interim Final. EPA/600/6-90/003. Office of Health

and Environmental Assessment, Washington, DC. 1990.

17. U.S. Environmental Protection Agency. Memo to Carol M. Browner,

July 29, 1994. “Draft ‘Addendum to the Methodology for Assessing

Health risks Associated with Indirect Exposure to Combustor

Emissions’.” EPA-SAB-IAQC-94-009b. Science Advisory Board. 

Washington, DC. 1994.

18. U.S. Environmental Protection Agency. Schedule for Standards: 

Methodology and Results for Ranking Source Categories Based on

Environmental Effects Data. Attachment A. EPA-453/R-93-053. 

Office of Air Quality Planning and Standards, Research Triangle

Park, NC. 1993.
mm
6-1

6.0 INHALATION RISK ASSESSMENT

6.1 BASELINE ASSESSMENT OF INHALATION EXPOSURES AND RISKS FOR

13 PRIORITY POLLUTANTS 

This chapter presents estimates of risks due to inhalation

exposure to 13 of the 14 priority HAPs identified in the screening

assessment (chapter 5). The assessment of risks presented in this

chapter is more refined and complete than the screening level

assessment presented in chapter 5. The risk estimates presented in

this chapter are believed to be more accurate and more comprehensive

than those presented in chapter 5. However, it is important for the

reader to understand that the risk estimates presented in this chapter

are still generally considered high-end estimates, and there are still

substantial uncertainties and data gaps in the risk assessment

presented in this chapter. Further assessment would be needed in

several areas to gain a better understanding of the actual risks posed

by electric utilities. 

Radionuclides were the one priority HAP not included in this

chapter because the analysis for radionuclides requires an air

dispersion model that predicts the impacts of the radioactive decay

process. The radionuclide analysis is presented in chapter 9. In

this section, for the 13 priority HAPs, risks have been calculated

using the HEM for HAP emissions from all 684 utilities, and using the

standard HEM default options and assumptions described in chapter 4. 

The HEM estimates ambient air concentrations within 50 km of each

utility. Therefore, the baseline risk estimates reflect only

inhalation exposure within 50 km of each utility (i.e., local

analysis). In addition, the baseline risks presented in section 6.1

do not account for background levels, long-range transport, complex

terrain, indirect exposures, or overlapping plumes. These issues are

analyzed and discussed in later sections of the report.

Not incorporating the above factors may lead to underestimating

risks. However, there are several important assumptions that were

incorporated into the baseline assessment that are generally

conservative (i.e., more likely to overestimate rather than

underestimate risks). For example, the baseline assessment assumes

that MEIs are exposed to the modeled concentrations for 70 years. 

Also, the cancer potency values (i.e., IUREs) that were used in this

assessment are considered "upper limit" estimates. The IUREs 1

represent upper bound estimates of the cancer risks posed by these

HAPs. The true risks are not known and could be as low as zero. 

These are just a few of the assumptions and uncertainties associated

with the baseline assessment. Later sections describe many of the

data inputs and default assumptions and discuss various issues and

uncertainties.

The HEM exposure modeling conducted for the screening assessment

(chapter 5) was very similar to the HEM exposure modeling conducted

for the baseline assessment (this section). The same default options
6-2

described in chapter 4 and same input data were used. However, there

is one important difference. For the baseline assessment, a

distinction was made between urban and rural locations. If a utility

plant is located in an urban area, it was modeled using the urban mode

(i.e., dispersion is assumed to be characteristic of emissions emitted

by a facility in an urban location where there are buildings nearby). 

If a utility plant is located in a rural location, it was modeled

using the rural mode (i.e., dispersion is assumed to be characteristic

of a facility located in a rural location). Dispersion of the

pollutant plume in an urban area is expected to exhibit greater

turbulence because of heat transfer and obstacles (i.e., large

buildings). Therefore, using the urban default setting typically

results in higher predicted air concentrations as compared to the

concentrations predicted using the rural default setting. In the

screening assessment, all plants were modeled using the urban default

because using the urban default typically leads to more conservative

estimates of human exposures and risks. However, using the urban and

rural distinction is believed to reflect more realistic conditions;

therefore, it was considered appropriate to use the urban versus rural

distinction in the baseline assessment, and in subsequent HEM modeling

analyses presented throughout chapter 6. The urban and rural options

(which differ in the assumptions for surface roughness) and their

impact on the risk estimates are discussed in detail in section 6.2.

The uncertainty analysis (presented in later sections of

chapter 6) suggests that the baseline risk estimates are generally

conservative, but within the range of reasonable estimates. 

Therefore, the results presented in this section (baseline risk

estimates) are generally considered reasonably high-end estimates of

the risks due to inhalation exposure of utility HAP emissions within

50 km of the utility plants. This conservatism is considered

appropriate given EPA’s mandate of public health protection. Central

tendency estimates of risks as well as background exposures and risks

are discussed, and analyzed to a limited degree, in later sections of

this chapter.

6.1.1 Baseline Inhalation Risks for Coal-Fired Utilities

 for Priority HAPs

A total of 426 coal-fired units were modeled with the HEM using

1990 emissions and population data. Two of the plants resulted in

individual risks less than 1 x 10 and were excluded from the -9

presentation of results, thus reducing the total number of plants to

424. Table 6-1 summarizes the following: the predicted high-end MEI

risks; high-end MIRs; the high-end estimate of the number of persons

predicted to be exposed above individual cancer risk levels of 1

chance in 1 million (i.e., 1 x 10 ); the number of plants whose -6

emissions result in those risk levels; and the maximum HQs.

6.1.1.1 Individual Cancer Risk. Table 6-1 presents the

estimated risks due to inhalation exposure within 50 km for each HAP

across all coal-fired plants. As stated previously, the MEI is
6-3

Table 6-1. Summary of High-End Risk Estimates from Chronic

Inhalation Exposure by HAP for 424 U.S. Coal-Fired Utilities

Based on the Baseline Inhalation Risk Assessment

Pollutant risk MIR >10 >10 (cases/yr) Max. HQ

Carcinogens Noncarcinogen

Highest Highest Population # Plants Cancer

MEI cancer cancer with risk with MIR incidence a b -6 -6 c

Arsenic 3 x10 2 x 10 850 2 0.05 NA -6 -6

Beryllium 3 x10 2 x 10 0 0 0.004 NA -7 -7

Cadmium 2 x10 1 x 10 0 0 0.0006 NA -7 -7

Chromium 2 x10 1 x 10 110 1 0.02 NA d -6 -6

Dioxin/furans 5 x10 3 x 10 0 0 0.001 NA -8 -8

Hydrogen chloride NA NA NA NA NA 0.1

Lead NA NA NA NA NA 0.001

Manganese NA NA NA NA NA 0.05

Mercury NA NA 0 0 NA --

Nickel 4 x 10 2 x 10 0 0 0.003 NA e -7 -7

Total 4 x10 3 x 10 NA 2 0.1 0.1 to 0.2 -6 -6

HQ = Hazard quotient, the ratio of exposure concentration to the reference concentration (RfC). HQ values below 1 are not

expected to result in adverse effects.

MEI = Maximally exposed individual, which is calculated using the highest annual average concentration predicted with the

HEM. An individual may or may not be exposed at that point. This value may be greater than the MIR.

MIR = Maximum individual risk is the highest risk identified at the centroid of a census tract to which a population is assigned. 

See chapter 4 for description of MEI and MIR.

NA = Not available.

Total = Total MEI are the sum of MEI for individual HAPs within a plant. The total HQ (=HI) is the sum of the HQs within a

plant.

Of all 424 coal-fired plants modeled with the HEM, this is the estimated increased inhalation cancer risk for a theoretical person a

assumed to be exposed for 70 years to the highest modeled HAP ambient air concentration around any of the 424 plants.

Of all 424 coal-fired plants modeled with the HEM, this is the highest MIR. b

The cancer incidence could be up to roughly 7 times greater when considering the impacts of long-range transport (i.e., exposure c

outside of 50 km) from all coal-fired utilities combined. See section 6.6 for discussion of long-range transport.

Assumes that 11 percent of total chromium emitted is hexavalent chromium, the species of chromium responsible for d

carcinogenic potential. Trivalent chromium, which would also be present, is assumed not to have carcinogenic effects. 

The nickel emitted is a mixture of various nickel compounds such as soluble nickel. This analysis assumes that the mix of nickel e

compounds emitted is 50% as carcinogenic as nickel subsulfide.

calculated based on the maximum modeled ambient concentration even

though a person may or may not reside in the vicinity of the maximum

concentration. The MEI risk was highest for arsenic (a Class A, human

carcinogen) at 3 x 10 for the “highest-risk” coal-fired plant. The -6

highest estimated MIR at a single plant was 2 x 10 for arsenic. -6

Table 6-1 shows that arsenic emissions from two plants resulted in
6-4

MIRs greater than or equal to 10 . The MIRs for the remaining 424 -6

coal-fired plants were lower than 1 x 10 . Figures 6-1a and 6-1b show -6

that most inhalation risks were considerably lower than 1 x 10 . The -6

risk for chromium assumes that 11 percent of total chromium is

hexavalent chromium, (a Class A, human carcinogen). The limited

emissions speciation data (described in Appendix H of the EPA Interim

Final Utility Report, Volume III) found hexavalent chromium between 2

0.3 and 34 percent of total chromium. The average percentage of

chromium VI based on limited speciation data was 11 percent. The

other HAPs do not appear to make a significant contribution to the MIR

from coal-fired plants. Figures 6-1a and 6-1b present the 

distribution of plants at different MIR levels for the major

carcinogens. Arsenic and chromium are the major contributors of

inhalation cancer risks from coal-fired utilities. Of the 424 coalfired plants, the median MIR is 2 x 10 for arsenic and 2 x 10 for -8 -9

chromium. The 90th percentile MIR of all 424 plants modeled is 

1 x 10 for arsenic and 4 x 10 for chromium (that is, 10 percent of -7 -8

the plants have MIR levels above, and 90 percent of the plants have

MIR levels below, these risk levels).

The total MIR due to inhalation exposure to the aggregate of HAPs

for each plant was calculated by summing the MIR for each HAP for each

plant. There are two coal-fired plants with total MIRs greater than 

1 x 10 . The highest total MIR for a single coal-fired plant is -6

3 x 10 . Of the 424 coal-fired plants modeled, the median total MIR -6

is 5 x 10 , and the 90th percentile is 2 x 10 (that is, 10 percent of -8 -7

plants pose an MIR greater than 2 x 10 ). -7

6.1.1.2 Population Cancer Risk. The population distribution at

various risk levels is shown in Table 6-2 for each of the five major

carcinogenic HAPs. As with the MIR, arsenic and chromium are the

major contributors. The high-end estimate of number of people exposed

to risks of 1 x 10 or greater from exposure to arsenic is 850 and -6

from exposure to chromium is about 107. That is, based on this

assessment, it is unlikely that more than 850 people are exposed to

inhalation risks greater than 1 x 10 due to coal-fired utility -6

nonradionuclide HAP emissions, and most likely fewer people are

exposed (possibly as few as zero) to this level of inhalation risk.

The HEM also calculated the annual incidence of cancer expected

for each of the HAPs due to inhalation exposure within 50 km. As

shown in Table 6.1, the high-end estimate for total cancer incidence

from the nonradionuclide carcinogenic HAPs was estimated, using the

HEM, to be as high as 0.1 cancer case per year for coal-fired plants

due to emissions within 50 km of each plant. However, the estimate

for incidence increases by about a factor of seven when considering

emissions dispersion beyond 50 km (see section 6.6). Arsenic and

chromium are again the major contributors and account for almost 90

percent of the estimated cancer incidences.
Arsenic

148

235

40

2

0

50

100

150

200

250

<1E-8 1E-8 to 1E-7 1E-7 to 1E-6 1E-6 to 1E-5

Maximum Individual Risk (MIR)

Number of Coal-Fired Plants

Chromium

218

196

10 1

0

50

100

150

200

250

<1E-8 1E-8 to 1E-7 1E-7 to 1E-6 1E-6 to 1E-5

Maximum Individual Risk (MIR)

Number of Coal-Fired Plants

Total: All Carcinogenic HAPs

44

289

91

2

0

50

100

150

200

250

300

350

<1E-8 1E-8 to 1E-7 1E-7 to 1E-6 1E-6 to 1E-5

Maximum Individual Risk (MIR)

Number of Coal-Fired Plants

Figure 6-1a. Number of Coal-Fired Utilities Posing Various Levels

of Maximum Individual Risk (by levels of MIR)
Figure 6-1b. Number of Coal-Fired Utilities Posing Various Lev

of Maximum Individual Risk (by levels of MIR)

415

218

148

44

9

196

235

1 10

40

0 1 2 

0

50

100150200250300350400450

Cadmium Chromium Arsenic Tota

Maximum Individual Risk (MIR)

Number of Coal-Fired Plants

<1E-8 1E-8 to 1E-7 1E-7 to 1E-6 1E-6 to 1E-5
6-7

Table 6-2. Summary of High-End Estimates of Population Exposed

at Various Levels of Inhalation Risk or Greater by HAP: CoalFired Utilities

Risk level Arsenic Chromium Beryllium Cadmium Dioxins/ furans

5 x 10 0 0 0 0 0 -6

2.5 x 10 0 0 0 0 0 -6

1 x 10 852 107 0 0 0 -6

5 x 10 5,990 2,160 0 0 0 -7

2.5 x 10 88,800 8,630 0 0 0 -7

1 x 10 1,710,000 80,500 1,280 107 0 -7

6.1.1.3 Noncancer Risk. The maximum HQ estimated for

noncarcinogenic HAPs emitted from coal-fired power plants was 0.1 for

HCl. The next highest was 0.05 for manganese. HQ values for all

other HAPs were at least an order of magnitude lower. This assessment

does not include background concentrations due to other sources.

6.1.2 Baseline Inhalation Risks for Oil-Fired Utilities

A total of 137 oil-fired plants were modeled using 1990 HAP

emissions and population data. The HEM estimated the high-end

individual and population risks for each of the HAPs evaluated. Eight

plants had risks less than 1 x 10 and were excluded from the -9

presentation. Table 6-3 presents the results.

6.1.2.1 Individual Cancer Risk. For oil-fired utilities, the

HEM predicts that people live in the location of highest modeled

ambient air concentration; therefore, the MEI and the MIR are equal. 

The maximum MEI/MIR estimated for a single carcinogenic HAP across all

plants was 5 x 10 from inhalation of nickel compounds. -5

There are numerous uncertainties that are discussed and analyzed in

later sections, but the EPA believes that the uncertainties associated

with nickel speciation are worth discussing here. There are substantial

uncertainties associated with nickel speciation. In this analysis, as a

conservative assumption, the mix of nickel compounds emitted by oil-fired

utilities was assumed to be 50 percent as carcinogenic as nickel

subsulfide, which is a Class A human carcinogen and has the highest

cancer potency of all nickel compounds evaluated by the EPA. The limited

speciation data indicate that 3 to 26 percent of nickel emissions (from

oil-fired utilities) are sulfidic nickel. It is not known how much of 2,3

the sulfidic nickel is in form of nickel subsulfide. The remainder of

the nickel is a combination of various nickel compounds for which the EPA

has not yet determined the carcinogenic potential. Many nickel compounds

are thought to have some carcinogenic potential via inhalation exposure

although the potency is not known. This issue is discussed further in

section 6.10. 
6-8

Table 6-3. Summary of the High-End Risk Estimates from

Inhalation Exposure for Priority HAPs for 137 U.S. Oil-Fired

Utilities Based on the Baseline Risk Assessment

Pollutant Cancer MIR >10 >10 (cases/yr) MAX HQ

Carcinogens Noncarcinogen

Highest with risk MIR incidence

Population # Plants with Cancer

-6 -6

a

Arsenic 1 x 10 2,400 2 0.04 NA -5

Beryllium 7 x10 0 0 0.002 NA -7

Cadmium 2 x10 45 1 0.005 NA -6

Chromium 5 x10 2,300 1 0.02 NA b -6

Dioxin/furans 1 x 10 0 0 0.0007 NA -7

Hydrogen chloride NA 0 0 NA 0.06

Lead NA 0 0 NA 0.004

Manganese NA 0 0 NA 0.04

Mercury NA 0 0 NA --

Nickel 5 x 10 110,000 11 0.2 NA c -5

Total 6 x 10 NA 11 0.3 NA -5

HQ = Hazard quotient, which is the ratio of exposure concentration to the reference concentration (RfC). HQ values below 1

are not expected to result in adverse effects.

MIR = Maximum individual risk is the highest risk identified at the centroid of a census tract to which a population is assigned.

See chapter 4 for description of MIR and MEI.

NA = Not available.

Total = Total MIR is the sum of the MIR for individual HAPs within a plant. The total HQ (=HI) is the sum of the HQs within a

plant.

The cancer incidence could be up to roughly 7 times greater when considering the impacts of long-range transport (i.e., exposure a

outside of 50 km) from all utilities combined. See section 6.6 for discussion of long-range transport.

Assumes that 18 percent of total chromium emitted is hexavalent chromium, the species of chromium responsible for b

carcinogenic potential. Trivalent chromium, which would also be present, is assumed to be noncarcinogenic. 

This analysis conservatively assumes that all nickel emitted from utilities is 50 percent as carcinogenic as nickel subsulfide (the c

highest potency of nickel compounds tested). However, the nickel emitted is a mixture of various nickel compounds such as

soluble nickel, nickel oxides, and sulfidic nickel. Emissions tests indicate that 3 to 26 percent of the nickel emissions are sulfidic

nickel. Nickel subsulfide is one of the possible forms of sulfidic nickel. It is not known how much of the sulfidic nickel is in the

form of nickel subsulfide. Many nickel compounds are thought to have carcinogenic potential although the potency is not known.

See section 6.10 for further discussion and analysis of nickel speciation uncertainty. 
6-9

Figures 6-2a and 6-2b show the distribution of plants at

different MIR levels for the major carcinogenic HAPs. The median MIR

across all plants is 1 x 10 for nickel and 1 x 10 for arsenic. The -7 -8

90th percentile for MIR among plants is 5 x 10 for nickel (that is, -7

90 percent of plants are estimated to pose risks less than 5 x 10 due -7

to nickel emissions) and 1 x 10 for arsenic. -7

The total MIR was calculated for each facility by summing the

MIRs for individual HAPs. The highest total high-end MIR from the sum

of high-end risks for each carcinogen is 6 in 100,000 (6 x 10 ) at -5

only one plant. The second and third highest-risk oil-fired plants

pose MEI inhalation risks of 2 x 10 and 1 x 10 , respectively. The -5 -5

total high-end MIR exceeded 1 x 10 as a result of HAP emissions from -6

11 oil-fired plants. The median total MIR for all plants is

approximately 4 x 10 , and the 90th percentile is approximately -7

2 x 10 . However, these estimates are considered conservative, high- -6

end estimates because they are mainly due to nickel emissions and the

assumption that the mix of nickel compounds is 50 percent as

carcinogenic as nickel subsulfide (see section 6.10 for discussion).

6.1.2.2 Population Cancer Risk. The population distribution at

various risk levels is shown in Table 6-4 for each of six carcinogens. 

As with the MIR, nickel, arsenic, and chromium are the major

contributors to the total population exposed to risk levels of 1 in

1,000,000 (1 x 10 ) or more. The high-end estimate for number of -6

people exposed to risks of 1 x 10 or greater is 110,000 for nickel, -6

and about 2,400 for arsenic and chromium. That is, based on this

assessment, it is unlikely that more than 110,000 people are exposed

to inhalation risks greater than 1 x 10 due to oil-fired utility -6

nonradionuclide HAP emissions, and most likely fewer people are

exposed to this level of inhalation risk.

Incidences from each nonradionuclide HAP were summed to estimate

total cancer incidence, which was estimated to be as high as 0.3 case

per year from these 137 oil-fired plants. Nickel accounts for over 60

percent of the total annual incidence and arsenic contributes roughly

about 15 percent. 

As with individual risk estimates for oil-fired plants, there are

significant uncertainties associated with these population risk

estimates because of the uncertainties associated with nickel

speciation and other uncertainties as discussed in later sections of

this report.

6.1.2.3 Noncancer Risks Due to Chronic Exposures. The highest

HQ resulting from oil-fired utility emissions was 0.04 for manganese.
Arsenic

58

49

20

1 1 0

0

20

40

60

80

<1E-8 1E-8 to 1E-7 1E-7 to 1E-6 1E-6 to 1E-5 1E-5 to 1E-4 1E-4 to 1E-3

Maximum Individual Risk (MIR)

Number of Oil-Fired Plants

Chromium

72

47

9

1 0 0

0

20

40

60

80

<1E-8 1E-8 to 1E-7 1E-7 to 1E-6 1E-6 to 1E-5 1E-5 to 1E-4 1E-4 to 1E-3

Maximum Individual Risk (MIR)

Number of Oil-Fired Plants

Nickel

28

51 47

9

2 0

0

10

20

30

40

50

60

<1E-8 1E-8 to 1E-7 1E-7 to 1E-6 1E-6 to 1E-5 1E-5 to 1E-4 1E-4 to 1E-3

Maximum Individual Risk (MIR)

Number of Oil-Fired Plants

Total: All Carcinogenic HAPs

26

48 52

9

2 0

0

10

20

30

40

50

60

<1E-8 1E-8 to 1E-7 1E-7 to 1E-6 1E-6 to 1E-5 1E-5 to 1E-4 1E-4 to 1E-3

Maximum Individual Risk (MIR)

Number of Oil-Fired Plants

The nickel risks presented here 

are based on an assumption that

 the mix of nickel compounds 

emitted by utilities are 50 percent 

as carcinogenic as nickel subsulfide.

Figure 6-2a. Number of Oil-Fired Utilities Posing Various Levels

of Maximum Individual Risk (by levels of MIR)
Figure 6-2b. Number of Oil-Fired Utilities Posing Various Leve

of Maximum Individual Risk (by levels of MIR)

98

72

58

28 2

29

47 49 51 

1

9

20

47

111

9 

0 0 1 2 

0

20406080

100120

Cadmium Chromium Arsenic Nickel* T

Maximum Individual Risk (MIR)

Number of Oil-Fired Plants

<1E-8 1E-8 to 1E-7 1E-7 to 1E-6 1E-6 to 1E-5 1E-5 to 1E-4

* The nickel risks presented here 

that the mix of nickel compounds percent as carcinogenic as nicke
6-12

Table 6-4. Summary of High-End Estimates of Population Exposed

Through Inhalation at Various Levels of Risk or Greater from OilFired Utilities

Risk level Nickel Arsenic Chromium Cadmium Beryllium furans

Dioxins/

5 x 10 45 0 0 0 0 0 -5

2.5 x 10 89 0 0 0 0 0 -5

1 x 10 2,200 45 0 0 0 0 -5

5 x 10 2,300 89 45 0 0 0 -6

2.5 x 10 9,900 2,280 89 0 0 0 -6

1 x 10 110,000 2,370 2,280 45 0 0 -6

5 x 10 1,600,000 32,600 2,280 89 45 0 -7

2.5 x 10 7,000,000 287,000 9,490 2,280 89 0 -7

Note: Double counting of population around facilities within 50 km of each other may occur. Exposed individuals

are included in the statistics for each plant within 50 km, presumably at different risk levels. See Section 6.5 and

Appendix F for further discussion of double counting and related issues.

6.1.3 Baseline Risks from Gas-Fired Utilities

Risks were estimated from 267 gas-fired facilities. Table 6-5

summarizes the results. The HAP emissions from only one plant

resulted in high-end risks greater than 1 in 10 million (10 ) with 23 -7

persons exposed above that level. For noncarcinogens, the maximum HQ

was 1 x 10 . The estimated risks due to HAP emissions from gas-fired -7

utilities are low.

6.2 DISTINGUISHING BETWEEN URBAN AND RURAL LOCATIONS

The HEM has two distinct modeling options (urban or rural)

intended to simulate atmospheric dispersion behavior of gases via 

different surface roughness. The urban option assumes that there are 4

buildings near the emission source and that turbulence results because

of these surfaces and other urban effects such as heat transfer from

buildings and roadways. The rural option assumes that there are not

any major buildings nearby, and therefore emissions dispersion plumes

are not as turbulent. Typically, for tall stacks, the urban option

predicts higher exposure concentrations and, therefore, higher risks

to nearby populations.

In the screening assessment(presented in chapter 5), the urban

option was used in all modeling runs. However, to assess the impact

of this default option on the risk assessment results, all of the

priority HAPs were modeled distinguishing between urban and rural

locations. As an option provided by the U.S. EPA Guidelines on Air
6-13

Table 6-5. Summary of High-End Inhalation Risk Estimates for

Gas-Fired Utilities

Pollutant MEI risk >10 > 10 HQ

Carcinogens Noncarcinogen

Population MIR # Plants MIR -6 -6

max

Arsenic 2 x 10 0 0 NA -7

Lead NA NA NA 1 x 10-7

Mercury NA NA NA NA

Nickel 1 x10 0 0 NA a -7

HQ = Hazard quotient, which is the ratio of exposure concentration to the reference concentration (RfC). HQ

values below 1 are not expected to result in adverse effects.

MEI = Maximum exposed individual, which is calculated using the highest annual average concentration. An

individual may or may not be exposed at that point. This value may be greater than the MIR, which is

calculated at the centroid of a census block.

MIR = Maximum individual risk is the highest risk identified at the centroid of a census tract to which a population

is assigned.

NA = Not available

The nickel emitted is a mixture of various nickel compounds. This analysis assumes that all nickel emitted is 50 a

percent as carcinogenic as nickel subsulfide.

Quality Models (40 CFR, Appendix W to Part 51), it was assumed that if 4

21,000 people lived within a 3-km radius of the plant (i.e., density =

750 people/km ), then the area was urban and was modeled using the 2

urban modeling option. If less than 21,000 people lived within a 3-km

radius, then the area was considered rural and the rural modeling

option was chosen. Tables 6-6 and 6-7 present the results. There 4

were some differences in site-by-site estimates. As Tables 6-6 and 

6-7 show, choosing the urban default option versus a more refined

selection of surface roughness options has some impact on the overall

results. There were only slight changes in the results for oil-fired

utilities. Many oil-fired facilities are located in urban areas. The

differences in the risk estimates from coal-fired utilities were

greater. Generally, risk estimates are lower when urban and rural

modeling distinctions are used.

The use of the refined analysis, whereby surface roughness

distinction was made for urban and rural locations, was considered

appropriate for all the inhalation exposure modeling analyses

presented in chapter 6 since it is believed to more realistically

reflect the location of utilities and the impacts of rural and urban

conditions on the dispersion of pollutants. Although the EPA believes

using this distinction is appropriate, there are still uncertainties

and limitations to this approach, which are discussed in later

sections of this report.
6-14

Table 6-6. Comparison of High-End Inhalation Cancer Risk

Estimates Based on (1) HEM Modeling Using Urban Default

Assumption and (2) HEM Modeling Using Urban vs. Rural Distinction

Urban default Rural vs. urban

Pollutant and fuel MEI risk (cases/year) risk >10 MEI risk (cases/year) risk >10

Cancer Population Cancer Population

incidence with cancer incidence with cancer

a

-6

a

-6

As, (Coal) 6 x 10 0.08 21,000 3 x 10 0.05 850 -6 -6

Cr, Coal

(assuming 11% 3 x 10 0.03 890 2 x 10 0.02 110

Cr VI)

-6 -6

Be, Coal 6 x 10 0.006 0.0 3 x 10 0.004 0.0 -7 -7

Cr, Oil (assuming

18% Cr VI) 5 x 10 0.02 2,300 5 x 10 0.02 2,300 -6 -6

Be, Oil 7 x 10 0.002 0.0 7 x 10 0.002 0 -7 -7

Cd, Oil 1.6 x 10 0.007 45 1.6 x 10 0.005 45 -6 -6

Ni, Oil 5 x 10 0.3 155,000 5 x 10 0.2 110,000 -5 -5

As, Oil 1 x 10 0.05 4,600 1 x 10 0.04 2,400 -5 -5

MEI = Maximally exposed individual

 The number of people estimated to be exposed to ambient air concentrations causing a high-end estimated a

 increased risk of cancer of 1 in 1 million or greater.

Table 6-7. Comparison of High-End Inhalation Noncancer Risk

Estimates Based on (1) HEM Modeling Using Urban Default

Assumption and (2) HEM Modeling Using Urban vs. Rural Distinction

Pollutant and fuel MEI HQ MEI HQ

 Selection of appropriate setting

Urban default (rural vs. urban)

 Hcl, from Coal 2.3/20 = 0.12 2.3/20 = 0.12

 Mn, from Coal 0.02/0.05 = 0.4 0.002/0.05 = 0.04

HQ = Hazard quotient

MEI = Maximally exposed individual.
6-15

6.3 INHALATION RISK ESTIMATES FOR THE YEAR 2010

The EPA analyzed potential inhalation risks from utility

emissions for the year 2010. This analysis was conducted to estimate

hazards and risks to public health after imposition of the

requirements of the Clean Air Act Amendments of 1990. The primary

differences between the 1990 and 2010 scenario are increased emissions

from coal-fired utilities and decreased emissions from oil-fired

utilities. Other predicted changes include the installation of

scrubbers for a small number of facilities, the closing of a few

facilities, and an increase in production of other facilities. The

details of the expected changes are explained in chapters 2 and 3. 

Similar to any analyses that predict future events, significant

uncertainties are associated with the method used for projecting risks

of HAP emissions to the year 2010. Moreover, there are several other

potential future actions or programs (e.g., PM and ozone NAAQS

implementation, climate change programs, electricity restructuring),

which could have an impact on HAP emissions, that were not considered

in the projections made for this 2010 analysis because of the

uncertainties and unknowns about how these programs will affect HAP

emissions. However, even with these limitations, the method used by

EPA is considered reasonable given the available data. 

The exposures and risks for the year 2010 were estimated using

the HEM, utilizing the same modeling assumptions, defaults, and inputs

used in the 1990 risk estimates, except that the emissions inputs were

changed to 2010 estimates. Instead of modeling all 15 priority HAPs a

second time, the EPA modeled a subset of HAPs that appear to present

the majority of the inhalation risks from utility emissions. The

analysis of this subset of priority HAPs provides information

regarding the anticipated potential public health risks due to

inhalation for the year 2010.

The results (Tables 6-8 and 6-9) indicate that, based on the

expected changes between 1990 and the year 2010, the inhalation risks

from coal-fired utilities will not change substantially, and the risks

from oil-fired utilities will decrease by roughly a factor of 2.

6.4 ASSESSMENT OF POTENTIAL RISKS DUE TO SHORT-TERM EXPOSURE

The potential for exceeding short-term reference exposure levels

(RELs) was evaluated for compounds emitted from coal- and oil-fired 5

utilities. The RELs (1-hour averages) are set to prevent adverse

acute responses in the exposed population. The pollutants of highest

concern were acrolein, HCl, and HF because these pollutants are

potentially emitted in significant quantities and are toxic due to

short-term (acute) exposures. Although the Agency has not determined

RELs for these compounds, REL values were obtained from the California

Air Pollution Control Officers Association (CAPCOA) Air Toxics ‘Hot

Spots’ Program Risk Assessment Guidelines, October 1993. The CAPCOA 5

RELs are listed in Table 6-10.
6-16

Table 6-8. Estimated High-End Inhalation Cancer Risks for the

Year 2010 Compared to 1990 for Coal- and Oil-Fired Utilities 

Cancer risk 2010 Cancer risk 1990

Pollutant and fuel MEI risk (cases/year) MIR >10 MEI risk (cases/year) MIR >10 a

Cancer Cancer

incidence Population w/ incidence Population w/ -6 a

b

-6

As from Coal 3 x 10 0.051 590 3 x 10 0.045 852 -6 -6

Be from Coal 3 x 10 0.004 0.0 3 x 10 0.0035 0.0 -7 -7

Cd from Coal 3 x 10 0.0007 0.0 2 x 10 0.0006 0.0 -8 -7

Cr from Coal (11%

Cr VI) 1.4 x 10 0.021 399 2 x 10 0.02 107 -6 -6

Dioxins from Coal 6 x 10 0.0012 0.0 5 x 10 0.001 0.0 -8 -8

Ni from Coal 2 x 10 0.003 0.0 4 x 10 0.003 0.0 -7 -7

Cr from Oil (18% Cr

VI) 3 x 10 0.009 89 5 x 10 0.02 2,300 -6 -6

Be from Oil 4 x 10 0.0008 0.0 7 x 10 0.0017 0.0 -7 -7

Cd from Oil 8 x 10 0.0026 0.0 2 x 10 0.0053 45 -7 -6

Ni from Oil 3 x 10 0.1 11,000 5 x 10 0.2 110,000 -5 -5

Dioxins from Oil 7 x 10 0.0004 0.0 1 x 10 0.0007 0.0 -8 -7

As from Oil 7 x 10 0.026 2,300 1 x 10 0.042 2,400 -6 -5

Note: The EPA used urban vs. rural modeling data distinction in this analysis.

MEI = Maximally exposed individual.

MIR = Maximum individual risk is the highest risk identified at the centroid of a census tract to which a population is assigned.

These MEI risk estimates are for the highest riskplant. a

This is the estimated cases of cancer predicted to occur in the United States due to emissions of this HAP from all utilities of that b

fuel type based on the HEM analysis.

Table 6-9. Estimated High-End Inhalation Noncancer Risks for

Coal-Fired Utilities for the Year 2010 Compared to the Year 1990

Pollutant RfC (g/m ) Conc. for 2010 2010 for 1990 1990 3

Highest MEI Maximum HQ for Highest MEI Conc. Maximum HQ for

HCl 20 2.6 g/m 0.1 2.3 g/m 0.1 3 3

Manganese 0.05 0.003 g/m 0.06 0.002 g/m 0.05 3 3

HQ = Hazard quotient.

MEI = Maximally exposed individual.

RfC = Reference concentration.
6-17

Table 6-10. Noncancer Reference Exposure Levels (Acute) from

CAPCOA5

Pollutant REL -- Hourly average concentration (g/m )3

Acrolein 2.5

Hydrochloric acid 3,000

Hydrogen fluoride 580

CAPCOA = California Air Pollution Control Officers Association.

REL = Reference exposure level.

The utilities modeled included the coal-fired and the oil-fired

utility that presented the highest predicted long-term concentrations

as determined from the earlier HEM screening analysis. In addition,

the largest emitter of each compound from a coal-fired and oil-fired

utility was modeled. Note that acrolein was not detected in the

emissions tests for oil-fired utilities.

6.4.1 Methodology. The EPA used a short-term air dispersion

model (called TSCREEN) that considers the potential range in

meteorological conditions at the utility plant site to estimate the

maximum 1 hour concentration of the three compounds in the vicinity of

selected coal- and oil-fired utilities. TSCREEN provides estimates of

1-hour concentrations at various distances from the stack being

analyzed. The user specifies the minimum distance to the stack at

which concentrations will be predicted. For all utilities modeled,

100 meters from the stack was selected. 

The reported concentrations are the maximum predicted from a

range of atmospheric stability classes and windspeeds. The modeler

must also specify whether urban or rural meteorological conditions

exist at the utility site. Urban was selected to maximize the

predicted concentrations.

Each of the selected plants emitted the HAPs from several stacks

at the site. Because the TSCREEN model can evaluate only one emission

point at a time, some adjustments were required for each utility’s

emission parameters. The concept was to select one stack and one

emission rate with one set of stack parameters that would represent

the multiple stacks and their corresponding emissions and stack

parameters. If the stacks at each utility varied in height or other

release characteristics (e.g., stack temperature, stack gas exit

velocity), emissions were assumed to be emitted under conditions to

maximize downwind concentrations: from the shortest stack present,

the lowest temperature among the stack characteristics, and the lowest

exit velocity (see Table 6-11). The emissions rate was calculated by

summing the emissions from each stack.
6-18

Table 6-11. Sample Stack Parameters for Typical Utility Plant

Stack Stack height (m) Exit velocity (m/s) Stack temperature (K)

1 75 15 400

2 75 15 400

3 70 22 390

4 75 12 410

To illustrate this methodology, a sample utility is presented in

Table 6-11. The resulting inputs to the dispersion model for this

sample utility would have been one stack with a stack height of 70 m,

exit velocity of 12 m/s, and temperature of 390 K. An average of the

inside stack diameters for the four stacks would be used (see 

Table 6-12). 

6.4.2 Results

As shown in Table 6-13, for all scenarios and all pollutants

modeled, the predicted maximum concentrations were more than 100 times

lower than the RELs. The emission rate used for each compound

represents an average. The analysis does not address peak short-term

emissions that may result from upsets or other atypical operations. 

Peak emission episodes would reduce the gap between predicted maximum

concentrations and REL, but the peak hourly emission rates are not

expected to be 100-fold higher than the average.

The TSCREEN can also incorporate terrain characteristics. 

Terrain was not considered an important factor in the analysis since

the utilities that caused the highest individual risk in the HEM

analysis were located in relatively flat terrain. (The effects of

terrain are analyzed in Appendix G of the EPA Interim Final Utility

Report, Volume II.) Although hilly terrain can cause an estimated 6

15-fold higher predicted long-term concentration than flat terrain,

this increase would still not result in exceedances of RELs for the

three compounds.

6.5 OVERLAPPING PLUMES/DOUBLE COUNTING

In general, the default standard mode of operation for the HEM is

to evaluate exposure to each source, one at a time, out to 50 km from

the plant. Each source’s exposure is independently estimated, and

detailed exposure estimates are not saved for the next source’s

exposure analysis. Summary information, such as the total numbers of

people who are exposed, is saved. Thus, if two plants are located

very close together, the HEM would independently estimate the total

number of people exposed to each plant’s emission and sum the two

totals even though the same people are being exposed to both plant’s 
6-19

Table 6-12. Stack and Emission Values Input to TSCREEN

Pollutant Stack height (m) velocity (m/s) (m) temperature (K) (g/s)

 Stack gas exit Stack diameter Stack gas Emission rate 

COAL

 HF 49 45.7 2.5 395 0.42

 HCl 49 47.5 2.5 395 9.07

 Acrolein 49 45.7 2.5 395 0.01

OIL

 HF 42 12.3 3.0 396 0.06

 HCl 42 12.3 3.0 396 1.24

Table 6-13. Results of the TSCREEN Model

Pollutant g/m ) (hourly avg g/m ) (hourly avg g/m ) coal oil

Reference

exposure

levels Coal-fired maximum Oil-fired maximum

(hourly avg predicted concentration predicted concentration 333

How much lower?

AAC/Pred

Acrolein 2.5 0.016 not emitted 150

HCl 3,000 21.5 5.5 140 1,200

HF 580 1.0 0.3 580 2,100

Note: Since the largest emissions are generally associated with taller stacks, other analyses indicated that the

estimated concentrations were generally a factor of 2 lower than that presented. 

emissions. In this mode, the HEM will most likely overestimate the

number of people who are exposed when two or more plants are within 50

km of each other. This effect has been called "double-counting." 

Although not intuitive, experience has shown that this effect is not

of great concern when estimating the risks to the MEI and to the

population as a whole. Because of the linear nature of the exposure

and risk models, the population risks (cancer cases per year) are the

same whether one calculates the exposure one plant at a time or

calculates the exposure from nearby plants together; only the number

of people who are estimated to be in the exposed group will differ. 

In the case of the risk to the MEI, nearby plants can only

significantly change the estimated maximum concentration when plants

of equal emission rates are located very close to each other, perhaps

within several hundred meters. This is very unlikely for the utility

industry. 
6-20

There is an option to the HEM, called single-count, which can

provide further insight into this potential problem of double-counting. 

This option still evaluates exposure on a source-by-source basis, but

exposure is calculated for each population census block within 50 km and

this detailed information is saved (stored in the computer memory). 

As each source is considered, the exposure estimate for each census

block is added to the previous source’s exposure estimates at the same

census block. At the end of the computer run, the computer has a

total exposure estimate for each census block in the United States

and, by adding the census block exposure estimates together, provides

a national level estimate of total exposure. For this study, singlecount HEM runs have indicated that individuals may be living within 50

km of up to 12 coal-fired plants or 17 oil-fired plants; thus, a

concern has arisen over multiple exposures to many plants. However,

the single-count analyses conducted for arsenic emissions indicate

that overlapping effects from nearby sources do not significantly

change the estimated risks (see Table 6-14).

6.6 ASSESSMENT OF EXPOSURE DUE TO LONG-RANGE TRANSPORT

6.6.1 History and Background Information

During the mid-1970s, SRI International developed a Lagrangian

puff air pollution model called the EUROPEAN Regional Model of Air

Pollution (EURMAP) for the Federal Environment Office of the Federal

Republic of Germany. This regional model simulated monthly SO and 7 2

sulfate (SO ) concentrations and wet and dry deposition patterns, and 2-

4

generated matrices of international exchanges of sulfur for 13

countries of western and central Europe. In the late 1970s, the EPA

sponsored SRI International to adapt and apply EURMAP to eastern North

America. The adapted version of this model, called Eastern North

American Model of Air Pollution (ENAMAP), also calculated monthly SO 2

and SO concentrations and wet and dry deposition patterns, and 2-

4

generated matrices of interregional exchanges of sulfur for a userdefined configuration of regions. In the early 1980s, EPA modified 7,8

and improved the ENAMAP model to increase its flexibility and

scientific credibility.

By 1985, simple parameterizations of processes involving fine

(diameters < 2.5 m) and coarse (2.5 m < diameters < 10.0 m) PM were

incorporated into the model. This version of the model, renamed the

Regional Lagrangian Model of Air Pollution (RELMAP), is capable of

simulating concentrations and wet and dry deposition patterns of SO ,2

SO , and fine and coarse PM and can also generate source-receptor 2-

4

matrices for user-defined regions. In addition to the main model

program, the complete RELMAP modeling system includes 19 preprocessing

programs that prepare gridded meteorological and emissions data for

use in the main program. A complete scientific specification of the

RELMAP as used at EPA for sulfur modeling is provided in RELMAP: a

Regional Lagrangian Model of Air Pollution - User’s Guide. The next 9

section discusses modifications made to the original sulfur version of

RELMAP to enable the simulation of atmospheric particulate metals

(arsenic, cadmium, chromium, lead and nickel).
6-21

Table 6-14. Comparison of Risk Estimates for Single-Count Versus

Double-Count Runs to Assess the Impact of Overlapping Plumes

HAP, fuel, year MEI risk Incidence risk > 10 MEI risk Incidence risk > 10

Single-count runs Double-count runs

Population w/ Population w/ -6 -6

As, Coal, 1990 3 x 10 0.05 850 3 x 10 0.05 850 -6 -6

As, Oil, 1990 1 x 10 0.04 2,200 1 x 10 0.04 2,400 -5 -5

As, Coal, 2010 3 x 10 0.05 590 3 x 10 0.05 590 -6 -6

As, Oil, 2010 7 x 10 0.03 2,200 7 x 10 0.03 2,300 -6 -6

HAP = Hazardous air pollutants.

MEI = Maximally exposed individual.

6.6.2 RELMAP Modeling Approach for Particulate Metals

6.6.2.1 Introduction. Previous versions of RELMAP have been

described by Eder et al. and Clark et al. The goal of the current 9 10

effort was to model the emission, transport, and fate of airborne

cadmium, arsenic, chromium, nickel, and lead from utilities in the 

continental United States for the year 1989. Modifications to the

RELMAP simulation for arsenic, cadmium, chromium, lead, and nickel

were based on the assumption that these emissions are in particulate

form. 

The RELMAP may be run in either of two modes. In the field mode,

wet deposition, dry deposition, and air concentrations are computed at

user-defined time intervals. In the source-receptor mode, RELMAP also

computes the contribution of each source cell to the deposition and

concentration at each receptor cell. For this study, only the field

mode of RELMAP operation was used. With over 10,000 model cells in

the high-resolution receptor grid and a significant fraction of these

cells also emitting the five metals, the data accounting task of a

source-receptor run for all utility sources could not be performed

with the computing resources and time available. 

Unless specified otherwise in the following sections, the

modeling concepts and parameterizations described by Eder et al. were 9 

preserved for this RELMAP modeling study.

6.6.2.2 Physical Model Structure. Because of the long

atmospheric residence time of fine PM, significant long-range

transport was expected. For this study, RELMAP simulations were

limited to the area bounded by 25 and 55 degrees north latitude and 60

and 130 degrees west longitude and with a minimum spatial resolution

of one-half degree longitude by one-third degree latitude
6-22

(approximately 40 km ) to provide high-resolution coverage over the 2

entire continental United States.

Since the descriptive document by Eder et al. was produced, the 9

original three-layer puff structure of RELMAP was replaced by a fourlayer structure. The following model layer definitions were used for

the RELMAP particulate metal simulations: 

Layer 1 top - 30 to 50 m above the surface

(season-dependent)

Layer 2 top - 200 m above the surface

Layer 3 top - 700 m above the surface

Layer 4 top - 700 to 1,500 m above the surface

(month-dependent).

6.6.2.3 Treatment of Emissions. All of the utilities within

each high-resolution RELMAP grid cell were treated as a single

integrated point source located at the center of the grid cell. As

mentioned in earlier sections of this report, the utility database

contained the necessary information to satisfy the RELMAP data needs,

including long-term particulate metals emission rates, stack

parameters, and plant location. All point source emissions (assumed

to be in steady state) 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

four layers. At night, since there is no vertical mixing, 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 700-m top of layer 2. However, since the layers of the pollutant

puffs remain vertically aligned during advection, the only significant

process affected by the layer of emission is nighttime dry deposition. 

6.6.2.4 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 timestep length. For particulate metals, as for most other pollutants,

emission rates for each source cell were defined from input data and a

time step of three hours was used. The initial horizontal area of

each puff was set to 1,200 km , instead of the standard initial size of 2

2,500 km , in order to accommodate the finer grid resolution used for 2

the modeling study; however, the standard horizontal expansion rate of

339 km /h was not changed. Although each puff was defined with four 2

separate vertical layers, 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. 
6-23

Wind field initialization data for a National Weather Service

prognostic model, the Nested Grid Model (NGM), were obtained from the

National Oceanographic and Atmospheric Administration’s (NOAA’s)

Atmospheric Research Laboratory for the entire year of 1989. Wind

analyses for the vertical level of approximately 1,000 meters above

ground level of the NGM were used to define translation of puffs

across the model grid, except during the months of January, February,

and December when the 600-m vertical level was used to reflect a more

shallow mixed layer. 

Pollutant mass was removed from each puff by the processes of wet

deposition and dry deposition. The model parameterizations for these

processes are discussed in section 6.6.3. Precipitation data for the

entire year of 1989, obtained from the National Climatic Data Center,

were used to estimate the wet removal of all pollutant species

modeled. Wet and dry deposition mass totals are accumulated and

average surface-level concentrations are calculated monthly 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 a receptor for the particulate

metal simulations. When the mass of pollutant on a puff declines to a

user-defined minimum value, or when a puff moves out of the model

grid, the puff and its pollutant load are 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 include monthly wet

and dry deposition totals and monthly average air concentrations for

each modeled pollutant in every receptor cell. 

6.6.2.5 Vertical Exchange of Mass with the Free Atmosphere. To

accurately simulate the long atmospheric lifetime of some pollutants,

the RELMAP was adapted to allow a treatment of the exchange of mass

between the surface-based mixed layer and the free atmosphere above. 

The RELMAP no longer requires that the pollutant mass remain entirely

within the mixed layer. As an intuitive approximation, a pollutant

depletion rate of 5 percent per three-hour time step was chosen to

represent this diffusive mass exchange. This rate of mass exchange

used in the RELMAP was chosen to approximate the average daily

exchange of mass obtained from similar Lagrangian model exercises in

Europe. When compounded over a 24-hour period, the mass exchange rate

of 5 percent every three hours removes 33.6 percent of an inert,

non-depositing pollutant. Since each of the modeled particulate

metals undergo significant wet and dry deposition, their effective

diffusion rate out of the top of the model is less than 33.6 percent

per day. The mass lost through this vertical exchange through the top

of the model is accounted for and is reported as a model output for

mass balance checks.

6.6.3 Model Parameterizations

6.6.3.1 Chemical Transformation. The simplest pollutant type to

model with RELMAP is the inert type. To model inert pollutants, one
6-24

can simply omit chemical transformation calculations and not be

concerned with chemical interactions with other chemical species. 

Arsenic, cadmium, chromium, lead and nickel were treated as inert

pollutants.

6.6.3.2 Dry Deposition. All five metals were assumed to be

totally in particulate form. Since each of these metals and their

compounds make up only a small fraction of the total particulate mass

loading of the atmosphere, we modeled each as a minor component of the

general population of conglomerate aerosol particles. Lead has been

generally associated with fine particle sizes (<1/m diameter), but

there is evidence that larger particles may play a significant role in

its dry deposition in urban areas. Therefore, metal particles were 11,12

modeled in five sizes; 0.1, 0.3, 1.0, 3.0, and 10.0 m diameter. The

results of the RELMAP simulation for each particle size were then used

in a postprocessing operation to estimate effects of dry deposition on

a particle size distribution appropriate for regional-scale air masses

with urban influences.

The semi-empirical dry deposition model as described in Sehmel11

was used to estimate dry deposition velocity for conglomerate

particles in the five size classes. This model requires as input the

particle density, the particle diameter, the friction velocity, the

Monin-Obhukov length, the surface roughness length, and the air

temperature. Assuming that sulfate, nitrate, and organic compounds

make up most of the particulate mass for particles less than 10 m in

diameter, a density value of 2 g/cm was used to represent all 3

particulate metals. Although the metals and their compounds have

densities of over 2 g/cm , it was assumed that they make up only a 3

small part of the conglomerate aerosol particles in the modeled size

range. Dry deposition velocities for particulate metals were

calculated using a FORTRAN subroutine developed by the California Air

Resource Board (CARB). Table 6-15 shows the windspeed (m/s) used for 12

each Pasquill stability category in the calculation of deposition

velocity from the CARB subroutine, and Table 6-16 shows the roughness

length used for each land-use category.

Some of the parameters used for dispersion and deposition

modeling are shown in Tables 6-15 and 6-16. These parameters include

windspeed versus stability category and roughness length versus landuse category.

6.6.3.3 Wet Deposition. A review of literature showed a range

of measured values for the scavenging ratios for the metal

particulates. Considering the source of the metal particulates 13,14

(i.e., utilities) and a review of the literature, a scavenging ratio

of 0.2 x 10 was used for nickel, chromium and lead; 0.3 x 10 for 6 6

arsenic; and 0.4 x 10 for cadmium. 6
6-25

Table 6-15. Windspeeds Used for Each Pasquill Stability

Category in CARB Subroutine Calculations

Stability category Windspeed (m/s)

A 2.0

B 3.0

C 4.0

D 5.0

E 3.0

F 2.0

CARB = California Air Resource Board.

Table 6-16. Roughness Length Used for Each Land-Use Category in

CARB Subroutine Calculations

Land-use category Spring-summer Autumn-winter

Roughness length (ms)

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 -6 -6

Barren land 0.1 0.1

Nonforested wetland 0.2 0.2

Mixed agricultural/range 0.135 0.075

Rocky open areas 0.1 0.1

CARB = California Air Resource Board.

6.6.4 Exposure and Risk Estimates

6.6.4.1 Air HAP Concentration Estimates. Table 6-17 presents the

average and maximum annual air particulate metal concentration for the

continental United States as predicted by the RELMAP analysis for four

metal particulates (i.e., arsenic, cadmium, chromium, and nickel). The

results of the RELMAP modeling for lead are presented in chapter 8. 

Results are presented as both combined impacts (e.g., emissions from both

coal and oil utility combustion combined) and segregated impacts(e.g.,

emissions from coal and oil utilities separately).

Figures 6-3 through 6-14 graphically present the RELMAP air

concentration results for each of the metals and each combustion fuel

type. In general, air concentrations of the four metal particulates,

as a result of utility oil and coal combustion, are predicted by

RELMAP to be maximum in the eastern part of the United States.
6-26

Table 6-17. RELMAP Predicted Air Concentrations

Pollutant Fuel (g/m ) (g/m )

Max concentration Average concentration 3 3

Arsenic Coal 2.5 E-04 8.0 E-06

Oil 4.4 E-05 5.0 E-07

Coal & Oil 2.5 E-04 8.5 E-06

Cadmium Coal 8.0 E-06 3.0 E-07

Oil 1.0 E-05 1.6 E-07

Coal & Oil 1.1 E-05 4.6 E-07

Chromium Coal 2.2 E-04 1.2 E-05

Oil 3.0 E-05 4.7 E-07

Coal & Oil 2.2 E-04 1.2 E-05

Nickel Coal 1.7 E-04 7.8 E-06

Oil 2.6 E-03 3.9 E-05

Coal & Oil 2.6 E-03 4.7 E-05

RELMAP = Regional Lagrangian Model of Air Pollution.

Air concentrations as predicted with RELMAP for emissions from

coal-fired utilities are predicted to be maximum along the western

slopes of the Appalachians Mountains and the northern Ohio River

Valley. Air concentrations as a result of utility oil combustion are

predicted to be maximum along the coastal northeast and the Florida

Peninsula. 

6.6.4.2 Estimating Exposures and Risks. Once the grid cell

concentrations are known (as predicted by RELMAP), public exposure and

risks can be calculated. The population database within the HEM model

contains the centroid coordinates and number of people living within

each census block group. By applying this database to the predicted

RELMAP air concentrations both average and total population exposure

and risk can be estimated by the following six-step approach:

1. For each census block group determine which RELMAP grid cell the

census block group centroid is located within. All the people

living within the census block group are assumed to be exposed to

the predicted RELMAP HAP concentration for that corresponding

grid cell.

C = C (n,pol) (i,j,pol)

where:

C = the concentration of pollutant “pol” at census (n,pol)

block group “n” in g/m .3

C = the RELMAP concentration of pollutant “pol” at (i,j,pol)

grid cell (i,j) in g/m where the centroid of 3

census block group “n” is locate within grid

cell (i,j).












6-39

2. Determine the average individual lifetime cancer risk for people

living in the census block group by multiplying the HAP

concentration for the census block group (determined in step 1)

by the IURE (which is the estimated increase risk of cancer for

an individual exposed to the pollutant concentration of 1 g/m 3

for 70 years). The IURE is explained in detail in chapter 4.

R = C * IURE (n,pol) (n,pol) (pol)

where:

R = the average individual lifetime cancer risk (n,pol)

for pollutant “pol” at census block group “n”

in g/m .3 

IURE = (pol) the estimated increase risk of cancer for an

individual exposed to a concentration of 1 g/m3

of pollutant “pol” for 70 years (1 per g/m ). 3 

3. Determine the lifetime population cancer incidence for each census

block group by multiplying the average individual risk (from step 2)

by the corresponding population of the census block group.

 LCI = R * Pop (n,pol) (n,pol) (n)

where:

LCI = the Lifetime (70 years) Population Cancer (n,pol) 

Incidence for census block group “n” for 

pollutant “pol” in cases/lifetime.

Pop = the 1990 Census total population for census (n) 

block group “n”.

4. The lifetime cancer incidence can be estimated by summing the

lifetime population cancer incidence for each census block group

(from step 3).

LCI = (total,pol) (n,pol) LCI n=1,466,318

where:

LCI = the Lifetime (70 years) Population Cancer (total,pol) 

Incidence for the continental U.S. (466,318

census block groups in the continental United

States) for pollutant “pol” in cases/lifetime.

5. The annual population cancer incidence in the continental U.S. is

predicted by dividing the lifetime cancer incidence (from step 4)

by 70 years. 

ACI = LCI / 70 years (total,pol) (total,pol)

where:

ACI = the Annual Population Cancer Incidence for the (total,pol) 

continental United States. 
6-40

6. The average individual exposure over the entire continental U.S.

population for a pollutant is estimated by dividing the total

population exposure by the total number of people living in the

continental United States. 

AIE = PE / Pop (total,pol) (total,pol) (total)

where:

AIE = the average individual annual exposure for the (total,pol)

continental United States for pollutant “pol”

 PE = the sum of the concentration of pollutant (total,pol)

“pol” for all census block groups 

(C ) in μg/m . (n,pol) n=1,466,318 3

Pop = the 1990 Census total population for the (total)

continental United States. (247,000,000

people)

The results of the exposure and risk estimates are shown in Table 6-18.

To evaluate potential impacts due to long-range transport (LRT),

the coal and oil utility emissions were modeled both together and

separately. By applying the algorithm described above, a combined

(e.g.,oil and coal emissions) value of 0.42 cancer cases/year was

estimated for arsenic emissions over the continental United States. 

The contribution of oil emissions is predicted to be about 11 percent

of the total inhalation risks from arsenic emissions. 

The LRT population exposure and cancer incidence for the four

metals from RELMAP for coal-fired utilities is about seven times

greater (i.e., 600 percent greater) than the population risks

estimated in the local analysis alone (i.e., within 50 km) using the

HEM. However, the population exposure and cancer incidence for oilfired utilities from the RELMAP study are only slightly (about 10

percent) greater than the exposures and incidence predicted with the

HEM analysis (See Table 6-19).

The total (coal and oil)population risk estimate for chromium

emissions is predicted to be 0.22 cases/year. The contribution of oil

fired chromium emissions is predicted to be about 10 percent of the

total risk from chromium emissions. The LRT population risk estimate

for chromium from RELMAP is about five times greater than the

population risks estimated modeling chromium emissions using the HEM

model.

The high-end estimate for total population cancer incidence due

to nickel emissions is predicted to be up to 0.2 cases/year. The

contribution of coal-fired nickel emissions is predicted to be about

10 percent of the total risk from nickel emissions. The LRT population

risk estimate for nickel is about equal to the population risks 
6-41

Table 6-18. Predicted Exposure and High-End Risk Estimates Based

on RELMAP Modeling of Particulate Metal Emissions from All Oiland Coal-Fired Utilities in the United States. 

Pollutant Fuel (g/m3) g/m ) (per g/m ) Risks (cases/yr)

Maximum Average Exposure Unit Risk Maximum Exposed Population

Concentration Concentration Estimate Individual (MEI) Risks 3 3

Arsenic Coal 2.5e-04 2.4e-05 4.3e-03 1.1e-06 0.4

Oil 4.4e-05 3.1e-06 4.3e-03 1.9e-07 0.05

Coal & Oil 2.6e-04 2.8e-05 4.3e-03 1.1e-06 0.4

Cadmium Coal 8.0e-06 7.6e-07 1.8e-03 1.4e-08 0.005

Oil 1.1e-05 9.4e-07 1.8e-03 1.9e-08 0.006

Coal & Oil 1.1e-05 1.7e-06 1.8e-03 1.9e-08 0.01

Chromium Coal 2.2e-04 3.3e-05 1.3e-03 2.9e-07 0.2

Oil 3.0e-05 2.7e-06 2.2e-03 6.6e-08 0.02

Coal & Oil 2.2e-04 3.5e-05 1.7e-03 3.9e-07 0.2

Nickel Coal 1.7e-04 2.2e-05 2.4e-04 4.0e-08 0.02

Oil 2.6e-03 2.4e-04 2.4e-04 5.0e-07 0.2

Coal & Oil 2.6e-03 2.6e-04 2.4e-04 5.0e-07 0.2

MEI = Maximally exposed individual.

RELMAP = Regional Lagrangian Model of Air Pollution.

estimated by modeling nickel emissions using the HEM model because

most of the nickel exposure is due to oil-fired utilities.

The potential impacts to the MEIs appear to be considerably less

than for population exposures for each metal particulate. The maximum

RELMAP ambient concentrations (Table 6-17) are each less than 20

percent of the highest HEM concentration for coal- and oil-fired

utilities (see Tables 5-1 and 5-4). Also, a comparison of results for

MEI risks in Table 6-18 with MEI risks in Tables 6-1 and 6-3 shows the

differences in MEI results from the local versus long-range transport

analyses.

These metal particulates are also associated with other HAP

particulate matter in the utility emissions and probably act in a

similar manner in the atmosphere. In addition, these other HAPs are

generally emitted in roughly proportional quantities for each fuel

type and are emitted from the same set of plant locations. Therefore,

the factor of 7 can be applied to these other HAPs from coal-fired

utilities to roughly estimate the potential impact of long-range

transport of HAPs on the overall cancer incidence. Therefore,

considering local and LRT, the cancer incidence due to inhalation

exposure to HAP emissions is estimated to be as high as about 0.5

cancer cases per year for oil-fired utilities and as high as about 1.3

cases per year for coal-fired utilities. Gas-fired utilities are

estimated to present far less population cancer risks than oil-, and 
6-42

Table 6-19. Summary of the High-End Estimates of the Inhalation

Risk Estimates Due to Local and Long-Range Transport 

LOCAL IMPACTS (dispersion within 50 km of each utility plant) a

Oil-fired plants Coal-fired plants

Pollutant Maximally exposed Annual increased Maximally exposed Annual increased

individual (MEI) cancer Incidence individual (MEI) cancer incidence

Radionuclides 1 x 10 0.2 2 x 10 0.1 b -5 -8

Nickel 5 x 10 0.2 4 x 10 0.003 c -5 -7

Chromium 5 x 10 0.02 2 x 10 0.02 -6 -6

Arsenic 1 x 10 0.04 3 x 10 0.05 -5 -6

Cadmium 2 x 10 0.005 2 x 10 0.0006 -6 -7

All others 8 x 10 0.005 8 x 10 0.004 b,d -7 -7

Total 6 x 10 0.5 4 x 10 0.18 e -5 -6

LOCAL PLUS LONG-RANGE IMPACTS (dispersion from utility emission points to borders of continental U.S.)

Oil-fired plants Coal-fired plants

Pollutant Maximally exposed Annual increased Maximally exposed Annual increased

individual (MEI) cancer incidence individual (MEI) cancer incidenceb,f

Radionuclides 1 x 10 0.2 Not estimated 0.7 b -5

Nickel 5 x 10 0.4 4 x 10 0.02 c -5 -7

Chromium 5 x 10 0.02 3 x 10 0.15 -6 -6

Arsenic 1 x 10 0.05 4 x 10 0.4 -5 -6

Cadmium 2 x 10 0.006 3 x 10 0.005 -6 -7

All others 8 x 10 0.006 1 x 10 0.028 b,d -7 -6

Total 6 x 10 0.5 5 x 10 1.3 e -5 -6

There are uncertainties associated with these risk estimates. See sections 6.4 for discussion. 

Radionuclides and all others were not modeled with RELMAP. The cancer incidence for these HAPs in the b

Local Plus Long-Range Impacts section were estimated by assuming the results from the RELMAP modeling for

Cr, As, Cd, and Ni could be applied to these other HAPs. Hence, the cancer incidence for radionuclides and all

othersfor oil-fired utilities were assumed to be the same as incidence from the local (HEM) analysis, and the

incidence for radionuclides and all othersfrom coal-fired utilities were assumed to increase by a factor of seven.

Assumes that the nickel mixture is 50 percent as carcinogenic as nickel subsulfide. c

Estimated risks due to exposure to all remaining HAPs analyzed (i.e., excluding nickel, arsenic, chromium, d

cadmium, and radionuclides).

Aggregate risk (risk due to inhalation exposure to all carcinogenic HAPs, assuming additivity of risks). e

These population risk estimates are predicted directly from RELMAP which includes the local and regional f

impacts.
6-43

coal-fired utilities. Therefore, adding these estimates (0.5 + 1.3),

it is predicted that up to about 2 cancer cases per year occur due to

inhalation exposure to HAP emissions from all utility plants (coal-,

oil-, and natural gas-fired) in the continental United States. 

However, as stated earlier, the IUREs are upper bound estimates of the

cancer risks posed by HAPs at low exposure levels, and the true risk

is unknown and could be as low as zero. Also, the inhalation high-end

risk estimates are conservative. Therefore, the cancer incidence due

to inhalation exposure to utility HAP emissions is predicted to be no

greater than 2 cases per year in the United States. Most likely fewer

than 2 cases/yr occur in the United States due to inhalation exposure

to utility HAP emissions.

There are numerous uncertainties in the modeling, the

assumptions, the extrapolations, and the resulting cancer incidence

estimates. Since the exposure concentrations for much of the exposed

population are quite low, this analysis relies heavily on the

assumption of cancer being a nonthreshold phenomenon and the

assumption that the dose-response curve for these carcinogens is

linear at very low doses. Also, there are considerable uncertainties

in the risk estimates and incidence estimates for nickel because of

the uncertainties associated with the emissions of different nickel

species and the uncertainties in the health effects for each of those

forms. Further evaluation of the data, models, and methods could be

useful to reduce the uncertainties and to fully evaluate the impacts

of long-range transport. 

6.7 DISCUSSION OF BACKGROUND EXPOSURES

6.7.1 Arsenic

Over 250 sites have reported ambient arsenic data to the EPA’s

Aerometric Information Retrieval System. Up to the year 1987, arsenic

was measured by performing an analysis of the filter catch from 24-

hour high-volume total-suspended-particulate (TSP) sampling devices. 

The Agency compared the results of the dispersion modeling to

available data in 1987 (latest available data) and attempted to

provide insight into typical arsenic concentrations in areas away from

utilities and to provide a check on the credibility of the predicted

concentrations.

 A review of 1987 ambient arsenic data indicated that the minimum

concentration that could be detected was about 3 ng/m . Much of the 3

reported data were at or below the minimum detectable level (MDL); for

instance, 145 of the 261 total sites reported no values above the MDL. 

At sites not located near known, large arsenic emitters, such as

copper smelters, the largest annual concentration reported was about 8

ng/m . Further analysis indicated the large majority (about 3

75 percent) of monitors were located within 50 km of at least one

coal- or oil-fired utility plant, and six sites were located within 50

km of at least 10 plants. On the other hand, there were 59 sites that

were not within 50 km of any coal- or oil-fired utility plant.
6-44

Typical arsenic concentrations can be determined by reviewing the

data from the 59 sites not near utility plants. Of the 59 sites, 8

were known to be near large arsenic sources and were not

representative of typical sites. Only 13 of the remaining 51 sites

recorded annual arsenic concentrations above the MDL. The highest

concentration reported was about 8 ng/m . Thus, based on these data, 3

typical concentrations are probably not much higher than 8 ng/m and 3

are most likely to be lower (or much lower) than the MDL of 3 ng/m . 3

In fact, as seen from review of the data collected near utilities,

this result is typical of all the available arsenic data, when the

monitors are not located near large arsenic-emitting sources. If a

person were exposed to this highest measured concentration of 8 ng/m 3

(or 0.008 g/m ) for a lifetime and the IURE is used to estimate the 3

cancer risk, this person would have an estimated increased cancer risk

of 3 x 10 . However, typical background arsenic inhalation exposures, -5

which are likely to be less than 3 ng/m would likely pose risks lower 3

than 1 x 10 . -5

 Next, a comparison was made between the predicted arsenic

concentrations and the measured values near the plants. The highest

arsenic long-term concentration estimated for any utility plant using

the HEM was about 3 ng/m . The estimated maximum concentrations 3

predicted with the HEM for all the other utility plants were lower or

much lower than 3 ng/m . The monitor nearest the plant that caused the 3

maximum arsenic concentration was about 12 km away, and that monitor

did not register any concentrations above the MDL of 3 ng/m . The air 3

dispersion analysis using the HEM predicted an arsenic concentration

of 0.05 ng/m at that monitoring site, so concentrations due to utility 3

emissions were not expected to register on this monitor. At the site

where the highest arsenic concentration was reported, the air

dispersion analysis predicted arsenic concentrations well below 0.01

ng/m .3

 The information presented above is useful for gaining a general

sense of the potential background air concentrations of arsenic. 

However, it is difficult to draw conclusions from the comparison of

the modeled concentrations and the measurement data. Direct

comparisons between estimated and measured values can be misleading. 

As suggested by the analysis of sites away from where arsenic

concentrations were detected, there are confounding factors. One

confounding factor occurs because arsenic is a naturally occurring

element in the earth’s crust. Some arsenic is expected to be in every

TSP filter catch (i.e., a natural background concentration that would

be present even without nearby anthropogenic sources). There is a

second confounding factor because any other PM-arsenic source in the

area will also have an impact on the monitor. So, for arsenic, the

monitored concentrations are measuring a combination of

concentrations: (1) from natural background, (2) from other arsenic

sources, and (3) from nearby utilities. Thus, the monitored values

are always expected to exceed the impact from the plant’s emissions.
6-45

6.7.2 Chromium, Nickel, Manganese, and HCl

Chromium and nickel ambient data were also available. The

results in analyzing these data led to conclusions similar to those

drawn from the arsenic analysis. Much of the data were below

detectable levels and did not provide much insight into the relative

concentration impacts from utility emissions. However, data presented

in a 1994 EPA Report indicate that chromium levels in some urban 15

areas have been measured to be roughly from 0.8 to 16 ng/m , which 3

would equate to a high-end increased cancer risk of 1 x 10 to 2 x 10 -5 -4

if it is assumed that the chromium is hexavalent and that a person is

exposed to those levels for 70 years (i.e., lifetime). In addition,

based on data presented in the above 1994 EPA report, nickel levels in

a few urban areas are roughly between 0.1 to 20 ng/m . Assuming the 3

nickel mix is 50 percent as carcinogenic as nickel subsulfide and

assuming people are exposed to these levels for a lifetime, this

concentration range would correspond to high-end risks of roughly

between 3 x 10 to 7 x 10 . -8 -6

Based on the HEM modeling, manganese and HCl were the two HAPs

that appear to be of highest potential concern for noncancer effects

due to inhalation exposure. However, in the assessment of noncancer

health effects due to inhalation exposure to HAP emissions from

utilities, the highest HEM-modeled concentrations of manganese and HCl

from the highest-risk plants were estimated to be 10 times lower than

the RfC. All other HEM-modeled concentrations for HCl and manganese

were even lower. Therefore, regardless of background exposure levels,

the emissions of HCl and Mn from utilities are not likely to

contribute significantly to an RfC exceedance. For this reason, the

EPA did not conduct an analysis of ambient air background exposures

for these two HAPs for this report.

6.8 CHROMIUM SPECIATION UNCERTAINTY AND IMPACT ON RISK ESTIMATES

Available health effects data indicate there are significant

differences in toxicity between the trivalent chromium (Cr III) and

the hexavalent chromium (Cr VI). Chromium VI is classified as a human

carcinogen (WOE = A) based on human and animal studies that show an

increase in lung cancer. Available data are not sufficient to

determine the carcinogenicity of Cr III (WOE = D). Cr III appears to

be much less toxic than Cr VI. For more information on chromium 16,17

toxicity see Appendix E.

Data on speciation of chromium were available from 11 test sites. 

The limited emissions speciation data indicate that somewhere between 2

0.4 percent and 34 percent of the emitted chromium is chromium VI. 

The average chromium VI from the coal-fired utilities was 11 percent;

the average from oil-fired utilities was 18 percent.

To assess the potential impact of the range of chromium

speciation on the risk results, the utilities were modeled using the

HEM assuming different speciation percentage assumptions. Tables 6-20

and 6-21 present the results of the assessment. 
6-46

Table 6-20. Chromium Speciation Analysis for Coal-Fired

Utilities: Inhalation Risk Estimates due to Chromium Emissions

Based on Various Assumptions of Percent Chromium VI

% Chromium VI assumption MEI risk MIR lifetime cancer risk (cases/year) a

Lifetime Lifetime Population w/ >10 Cancer incidence -6

Assume 100% Cr VI 2 x 10 1 x 10 69,000 0.2 -5 -5

Assume 23% Cr VI 4 x 10 2 x 10 2,300 0.04 -6 -6

Assume 11% Cr VI 2 x 10 1 x 10 110 0.02 -6 -6

Assume 0.4% Cr VI 7 x 10 4 x 10 0.0 0.0007 -8 -8

MEI = Maximally exposed individual. 

MIR = Maximum individual risk.

Based on speciation data from emissions tests for four coal-fired test sites, the average percent Cr VI was 11 percent, the a

maximum was 23 percent, and the minimum was 0.4 percent. The remaining chromium emissions are assumed to be Cr III. It is

assumed that the cancer risk is due only to Cr VI emissions. Because carcinogenicity data for chromium III are very limited and

uncertain, it was assumed that Cr III does not pose cancer risk. It is not known whether the Cr III emissions contribute to the

cancer risk.

Table 6-21. Chromium Speciation Analysis for Oil-fired

Utilities: Inhalation Risk Estimates due to Chromium Based on

Various Assumptions of Percent Chromium VI 

% Chromium VI Population w/ > 10 Cancer incidence

assumption Lifetime MEI risk Lifetime MIR lifetime cancer risk (cases/year) a

-6

100% Cr VI 3 x 10 3 x 10 40,000 0.1 -5 -5

34% Cr VI 1 x 10 1 x 10 2,300 0.04 -5 -5

18% Cr VI 5 x 10 5 x 10 2,300 0.02 -6 -6

5% Cr VI 1.5 x 10 1.5 x 10 45 0.005 -6 -6

MEI = Maximally exposed individual. 

MIR = Maximum individual risk. 

Based on limited speciation data from emissions tests for seven oil-fired test sites, the average percent Cr VI was 18 percent, a

the maximum was 34 percent, and the minimum was 5 percent Cr VI, it was assumed that chromium III does not pose a cancer

risk. It is assumed that the remainder of the chromium emissions are Cr III. It is assumed that the cancer risk is due only to Cr

VI emissions.
6-47

6.9. ISSUES WITH ARSENIC CANCER UNIT RISK ESTIMATE AND IMPACT ON

 INHALATION RISK ESTIMATES

Arsenic is considered a human carcinogen (WOE = A). The EPA

reviewed the dose-response data in 1986 and established an IURE

of 4.29 x 10 per g/m . This IURE is the EPA-verified value -3 3 17

currently available on IRIS. A more in-depth discussion of the cancer

health effects data is provided in Appendix E.

The EPRI submitted a paper on arsenic carcinogenicity to the EPA

IRIS office. This paper suggested that the IURE should be

approximately three times lower than the current EPA-verified value as

a result of reviewing new data. The EPRI asked the EPA to review the 18

new data and consider revising the arsenic unit risk estimate based on

the most current data and analyses. 

The EPA has initiated the review process. However, to conduct a

thorough review and analysis of the data and to calculate a new risk

estimate is time consuming. A full review and IRIS update could not

be completed in time for this report. However, the EPA has done a

cursory review of the paper submitted by EPRI along with other

relevant data. Based on this initial review by EPA scientists, it

appears that the EPRI-proposed IURE is within the range of plausible

estimates of cancer potency. The Canadians have also reviewed the 19

available data recently and established an IURE of 6 x 10 . The -3 20

Canadian IURE also appears to be within the plausible range of potency

for arsenic. Table 6-22 compares EPRI, EPA-verified, and Canadian 3

inhalation risk estimates.

Since a full review of the unit risk could not be completed in

time for this report, and to help characterize the potential range of

risk due to arsenic exposure, an assessment was conducted that

presents the estimated risks due to inhalation exposure using three

different IUREs (Table 6-22). It should be noted that this

presentation does not present the full range of uncertainty, but

rather presents the impact on the results due to the three different

estimates of the unit risk. 

6.10 NICKEL SPECIATION UNCERTAINTY AND IMPACT ON RISK ESTIMATES 

There are significant uncertainties associated with nickel

speciation. Nickel exists in four different valence states and can be

combined with many other elements to form different nickel compounds. 

Numerous nickel compounds are known to exist. 21

At the time emissions data were being analyzed for this report

(1992 to 1994), total nickel was measured at nearly all sites, but

only two sites (both oil-fired utilities) provided data on speciated

nickel. The species measured were soluble nickel (water-soluble salts

such as nickel sulfate and nickel chloride), sulfidic nickel (such as

nickel subsulfide, nickel monosulfide, and nickel sulfide), metallic

nickel (including alloys), and oxidic nickel (including nickel oxide, 
6-48

Table 6-22. High-End Arsenic Inhalation Risk Estimates: 

Comparison of Results Using the EPRI, EPA-Verified, and Canadian

IUREsa

Risk estimates using EPRI Risk estimates using EPA Risk estimate w/Canadian

IURE (1.4 x 10 per g/m ) IURE (4.3 x 10 per g/m ) IURE (6 x 10 per g/m ) -3 3 -3 3 -3 3

MEI risk # >10 Incidence MIR # >10 Incidence MIR # >10 Incidence -6 -6 -6

Arsenic from

oil-fired utilities 4 x 10 2,200 0.014 1 x 10 2,400 0.042 2 x 10 3,000 0.05 -6 -5 -5

Arsenic from

coal-fired utilities 6 x 10 0.0 0.015 3 x 10 850 0.045 4 x 10 850 0.06 -7 -6 -6

EPA = U.S. Environmental Protection Agency 

EPRI = Electric Power Research Institute

IURE = Inhalation unit risk estimate

MEI = Maximally exposed individual 

MIR = Maximum individual risk

The EPRI IURE for arsenic (1.4 x 10-3 per g/m ) is three times lower than the EPA-verified IURE for arsenic (4.3 x 10 per a 3 -3

g/m ), and, the Canadian value is approximately 35 percent greater than the EPA estimate. 3 17

complex oxides, and silicates). The average values of the two test

sites were: 58 percent soluble nickel, 3 percent sulfidic nickel, and

39 percent nickel oxides. More recently, EPRI submitted a fax to the 2

EPA summarizing nickel speciation data from 5 test sites. Based on 3

the data presented in the fax from EPRI, 25 to 60 percent of nickel

emissions are soluble nickel compounds, 4 to 26 percent are sulfidic

nickel compounds, 0 to 4 percent are metallic nickel compounds, and 27

to 70 percent are oxidic nickel compounds. 

The available health effects data vary significantly from species

to species. Human epidemiologic data indicate that at 

least some forms of nickel are carcinogenic to humans by inhalation

exposure. Nickel refinery dust and nickel subsulfide are 22,23

classified as human carcinogens (WOE = A). The IURE for nickel

refinery dust is 2.4 x 10 . Based on an assumption that nickel -4

subsulfide constitutes 50 percent of the refinery dust, a potency

estimate (IURE) of 4.8 x 10 was assigned to nickel subsulfide. -4

Nickel carbonyl is classified as a probable human carcinogen (WOE =

B2), but no IURE has been established. These are the only species

currently classified by the EPA as carcinogens. The International

Agency for Research on Cancer (IARC) considers nickel monoxide, nickel

hydroxide, and metallic nickel as having sufficient evidence in

experimental animals for carcinogenicity. The IARC considers nickel 24

compounds to be carcinogenic to humans and metallic nickel to be

possibly carcinogenic. The State of California concludes that the

class of nickel compounds is potentially carcinogenic by inhalation. 24

The American Conference of Governmental Industrial Hygienists (ACGIH)

has stated that all nickel compounds should be considered carcinogenic
6-49

for risk management purposes. However, there are still significant 25

uncertainties regarding the carcinogenicity of many of the nickel

compounds. Available data are insufficient to confirm the

carcinogenicity of many nickel compounds. 

Cancer IUREs are only available for nickel subsulfide and nickel

refinery dust. The cancer potency of the other nickel compounds that

may be carcinogenic is not known. Results of animal studies suggest

that nickel subsulfide is the most carcinogenic form. Based on the 23,24

limited speciation data, no more than 10 percent of the nickel

compounds are likely to be nickel subsulfide. Therefore, the nickel

risk estimates presented in previous sections (where it is assumed the

mix of nickel compounds emitted from utilities are 50 percent as

carcinogenic as nickel subsulfide) are considered conservative, highend risk estimates.

To assess the potential impact of the speciation uncertainty, the

EPA conducted an assessment for cancer risks using different

assumptions for speciation and cancer potency. The assessment

(summarized in Table 6-23) provides a range of the potential cancer

risks due to nickel emissions.

6.10.1 Alternative Analysis for Estimating Population Risks. 

Figure 6-15 summarizes the impact of using alternative IURE values for

nickel (as a percent of the nickel subsulfide IURE) on annual cancer

incidence. The estimated annual cancer incidence due to oil-fired

utilities is 0.3 case per year using the assumption that the potency

(IURE) of the mixture of nickel compounds emitted from oil-fired

utilities is 50 percent the potency of nickel subsulfide, about

0.15 case/yr if the IURE is assumed to be 20 percent as potent as

nickel subsulfide, and about 0.1 case per year if the IURE is assumed

to be 10 percent nickel subsulfide. Likewise, there would be changes

in the number of persons potentially exposed at various risk levels. 

If the IURE were 20 percent nickel subsulfide, about 9,930 persons

would be exposed at an MIR > 1 x 10 . Figure 6-15 does not capture -6

the full potential range of estimated population risks. It is

possible that the potency of the mix of nickel compounds emitted from

oil-fired utilities is less than 10 percent as carcinogenic as nickel

subsulfide. Therefore, the cancer incidence could possibly be lower

than that shown in Figure 6-15. The cancer incidence due to nickel

emissions could possibly be as low as zero. 

In addition to the cancer effects, nickel also causes noncancer

health effects, such as allergenicity and respiratory effects. 

Currently, no RfC is available for nickel compounds. However, there

are various health benchmarks in the literature that are useful for

screening purposes to give some idea whether or not the exposure

estimates are likely to cause noncancer health effects. The EPA

conducted such an assessment (see Table 6-24).
6-50

Table 6-23. Nickel from Oil-Fired Utilities: Inhalation Cancer

Risk Estimates Based on Various Assumptions of Speciation and

Cancer Potency

Nickel Speciation (IURE) MIR # People >10 risk Annual incidence a

Cancer potency b -6

100% Ni subsulfide 4.8 x 10 9.6 x 10 1,600,000 0.4 -4 -5

20% Ni subsulfide 9.6 x 10 2 x 10 9,900 0.08 -5 -5

10% Ni subsulfide 4.8 x 10 9.6 x 10 2,300 0.04 -5 -6

1% Ni subsulfide 4.8 x 10 9.6 x 10 0.0 0.004 -6 -7

IURE = Inhalation unit risk estimate

MIR = Maximum individual risk

The limited nickel speciation data indicate that nickel is a combination of nickel oxide, soluble nickel, sulfidic nickel, and a

insoluble nickel. The limited speciation data indicate that less than 10 percent of the nickel is nickel subsulfide.

The Inhalation Unit Risk Estimate (IURE) of 4.8 x 10 is the IURE for nickel subsulfide found on IRIS. For each of these cases, b -4

it is assumed that either 100 percent, 20 percent, 10 percent, or 1 percent of the nickel is nickel subsulfide, and that only this

fraction is contributing to the cancer risk. The cancer risk due to the other nickel compounds is not known. 

6.11 POTENTIAL INCREASED DIOXIN EMISSIONS FROM UTILITIES WITH

ELECTROSTATIC PRECIPITATORS

Emissions data for dioxins and dibenzofurans were available from

only nine test sites. None of these sites have hot-side electrostatic

precipitators (ESPs) installed for controlling emissions. The EPA

discovered that dioxin emissions from municipal waste combustors

(MWCs) with hot-side ESPs could be 5 to 15 times greater than

emissions from a similar source without a hot-side ESP. Since this 26

phenomenon was observed at MWCs, the EPA assumes that it is possible

that the same situation may possibly occur at utilities. However, at

this time, sufficient information is not available to assess the

potential risks due to dioxin emissions from the utility plants with

hot-side ESPs. Currently, the DOE is planning to conduct an emission

test at a facility with a hot-side ESP; however, at this time, no data

are available for dioxins from hot-side ESP units.

6.12 DISCUSSION OF UNCERTAINTY AND ASSUMPTIONS FOR DOSE-RESPONSE

 ASSESSMENT FOR CARCINOGENS

Information related to dose-response assessment for the HAPs is

summarized here to identify the assumptions, methods, data 

used, and uncertainty associated with the dose-response measures. 

This information is useful to place the quantitative risk estimates

into context with respect to their associated uncertainty and

conservatism.
Figure 6-15. Estimates of Annual Cancer Incidence Due to Inhalation Exposure 

from Oil-fired Electric Utilities Based on the Local Analysis Using Alternativ

(as % of Nickel Subsulfide)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Estimated Annual Cancer Incidence (cases per year) Ni URE at 100% Ni URE at 50% Ni URE at 20% 

Nickel Arsenic Cadmium Chromi
6-52

Table 6-24. Comparison of Nickel Exposure to Various Noncancer

Health Benchmarks

Various health benchmarks for nickel compounds CARB REL = 0.24 g/m EPRI value = 2.4 g/m a3 b 3

# People exposed above the benchmark 0.0 0.0 c

Maximum HQ 0.82 0.082 d

CARB = California Air Resources Board

EPRI = Electric Power Research Institute

HQ = Hazard quotient

REL = Reference exposure level

This value was obtained from the CARB Hot Spots Program. CARB calculated this number by dividing the Threshold Limit a 5

Value (TLV) of 0.1 mg/m by 420. The TLV is a level set by the American Conference of Government Industrial Hygienists 3

(ACGIH) as a guideline to protect workers. The 420 accounts for extrapolating from a 40-hour work week to a 168-hour week

(4.2x), extrapolating from healthy workers to sensitive subpopulations (10x), and another factor of 10x because adverse health

effects are often seen at the TLV. 

The EPRI benchmark was calculated by dividing the TLV by 42. The 42 accounts for extrapolating from a 40-hour work week b 27

to a 168-hour week, and a 10x is applied to account for sensitive subpopulations.

The exposed population is estimated from the results of the Inhalation Human Exposure Modeling. c

The HQ is calculated by dividing the modeled concentration by the health benchmark. It is the ratio of the estimated highest d

exposed concentration to the benchmark concentration. A value of 1 or higher indicates that the exposure is above the health

benchmark.

6.12.1 Default Options

The EPA uses default options when dealing with competing

plausible assumptions and uncertainty in estimating cancer unit risks. 

The use of these default options is intended to lead to unit risk

estimates that, although plausible, are believed to be more likely to

overestimate than to underestimate the risks. The use of these

defaults has led EPA scientists to conclude that the resulting unit

risk estimates are upper limits. That is, the actual risks are

unlikely to be greater than these estimates, and may be lower; they

could also be zero. Below are several of the major default options

used in cancer dose-response assessment identified by NRC. However, 28

it must be noted that the preliminary HAPs of interest in this study

for cancer risks (i.e., arsenic, chromium VI, and nickel subsulfide)

have IUREs and WOE that are based on human epidemiology studies;

therefore, many of the assumptions listed below are not relevant for

much of this study.

 Laboratory animals are a surrogate for humans in assessing

cancer risks; positive cancer-bioassay results in

laboratory animals are taken as evidence of a chemical’s

cancer-causing potential in humans.

 Humans are as sensitive as the most sensitive animal

species, strain, or sex evaluated in a bioassay with

appropriate study-design characteristics.
6-53

 Agents that are positive in long-term animal experiments

and also show evidence of promotion or cocarcinogenic

activity should be considered as complete carcinogens.

 Benign tumors are surrogates for malignant tumors, so

benign and malignant tumors are added in evaluating

whether a chemical is carcinogenic and in assessing its

potency.

 Chemicals act like radiation at low exposures (doses) in

inducing cancer; i.e., intake of even one molecule of a

chemical has an associated probability for cancer

induction that can be calculated, so the appropriate model

for relating exposure-response relationships is the

linearized multistage model.

 Important biological parameters, including the rate of

metabolism of chemicals, in humans and laboratory animals

are related to body surface area. When extrapolating

metabolic data from laboratory animals to humans, one may

use the relationship of surface area in the test species

to that in humans in modifying the laboratory animal data.

 A given unit of intake of a chemical has the same effect,

regardless of the time of its intake; chemical intake is

integrated over time, irrespective of intake rate and

duration.

 Unless there are data to the contrary, individual

chemicals act independently of other chemicals in inducing

cancer when multiple chemicals are taken into the body;

when assessing the risks associated with exposures to

mixtures of chemicals, one treats the risks additively.

6.12.2 Models, Methods, and Data

In a dose-response assessment, the likelihood of developing

cancer is determined quantitatively for any given level of exposure to

a carcinogen. The two basic reasons for conducting a cancer 29

dose-response assessment are (1) to extrapolate from high to low

doses, and (2) to extrapolate from animal to human responses. Both

epidemiologic and toxicologic studies are conducted at doses higher

than those normally encountered in the environment. Therefore, in

order to determine response at lower doses, an extrapolation from high

to low dose must be performed. Many models are available for doseresponse estimation and high- to low-dose extrapolation. The

dose-response assessment must also extrapolate from animals to humans

if only animal data are available. This interspecies extrapolation is

carried out by applying a scaling factor to the experimental data or 30

through the use of physiologically based pharmacokinetic (PBPK) data. 

6.12.2.1 Mathematical Dose-Response Extrapolation Models. No

single dose-response model is appropriate in all situations. A
6-54

dose-response model is usually selected on an agent-specific basis. 

However, two categories of dose-response models are generally used in

carcinogen risk assessment—mechanistic models and

tolerance-distribution models.

Mechanistic models describe some mechanism by which

carcinogenesis is believed to occur. All of the mechanistic models

assume that a tumor originates from a single cell that has been

altered by either the agent or one of its metabolites. Examples of 30

mechanistic models are the one-hit, multi-hit, and multistage models. 

The one-hit model assumes that a single hit at a critical site

can result in malignant transformations. This model is conservative

(i.e., reduces the chance of underestimating risk) because it does not

account for cellular or deoxyribonucleic acid (DNA) repair mechanisms. 

The multi-hit mechanistic model, an adaptation of the one-hit model,

assumes more than one chemical exposure or biological event is

required to elicit a carcinogenic response. The linearized multistage

model is the most frequently used of the low-dose extrapolation

models. It corresponds to the most commonly accepted theory of

carcinogenesis (the multistage process) and is the model most

frequently used by EPA in conducting dose-response assessments. This

model assumes that a cell progresses through a number of distinct

stages before becoming malignant. Like the one-hit model, the

multistage model is approximately linear in the low-dose region. 

The second type of dose-response model, the tolerance

distribution model, is an empirical model that assumes for each

individual in a population there is a tolerance level below which that

person will not respond to the exposure. These models assume a 30

variability among individual tolerance levels that can be described in

terms of a probability distribution. This concept of individual

tolerance levels differs from the "threshold" concept used in most

noncancer risk assessment, which posits a general level of exposure

that is "safe" for most of the population. Tolerance distribution

models are actually based on the "nonthreshold" concept of

carcinogenesis because they refer to an infinite number of individual

tolerance levels or thresholds distributed along a curve. The

low-dose extrapolation techniques based on the tolerance distribution

theory include the probit (log-probit), logit (log-logistic), and the

Weibull model. 

 

If animal data are used in the dose-response assessment, scaling

factors are commonly used to calculate a human equivalent dose. These

scaling factors are applied to animal data to account for differences

between humans and animals regarding body size, lifespan, route,

metabolism, and duration of exposure. 1

Standardized dosage scales such as mg/kg body weight/day, ppm, in

the diet or water, and mg/m body surface area/day are commonly used to 2

allow for comparison of data across species. The EPA considers 1

extrapolation on the basis of surface area most appropriate because
6-55

particular pharmacologic effects commonly correlate to surface area. 

Because the body surface area is proportional to the animal’s weight

to the two-thirds power, and because weight is more easily determined

than surface area, equivalent dose can be calculated as follows:

d /bw = d /bw aa hh (2/3) (2/3)

where

d = experimental animal dose (mg) a

d = equivalent human dose (mg) h

bw = weight of experimental animals (kg) a

bw = weight of average human (kg). h

6.12.2.2 Discussion of the Derivation of IUREs. An IURE

represents an upper limit increased cancer risk estimate from a

lifetime (70-year) exposure to a concentration of 1 g/m in the 3

ambient air. This IURE is typically derived from the slope factor,

which is a plausible upper-bound estimate of the availability of a

response per unit intake or exposure concentration of a chemical over

a lifetime. When the slope factor is generated from the linearized 1

multistage model, it is denoted as q *. Slope factors are usually 1

expressed in terms of (mg/kg-day) when derived from oral data and -1

(mg/m ) when derived from inhalation data. The following equation is 3 -1

used to convert a slope factor to an IURE for air contaminants:

IURE = Slope Factor x 1/70 kg x 20 m /d x 10 . 3 -3

To calculate the IURE, it is assumed that a 70-kg individual with a

breathing rate of 20 m /d is exposed to the carcinogen over a 70-year 3

lifespan. The factor of 10 in the IURE equation is required to -3

convert from milligrams to micrograms. The IURE is based on the

assumption of low-dose linearity. If a nonlinear low-dose-response

extrapolation model were used, the unit risk would differ at different

dose levels, and the dose-response assessment output could be

expressed as a dose corresponding to a given level of risk, analogous

to the risk-specific dose, rather than as a single IURE. 

If the IURE is derived from animal data, it usually represents

the upper 95th percent confidence limit of the slope factor as

suggested by the variation within the animal data. Using the upper

95th percent confidence limit reduces the probability of

underestimating the unit risk. 

For four priority HAPs (arsenic, chromium, radionuclide, and

nickel), human epidemiologic data are available and were used to

derive a maximum likelihood estimate (MLE) of the IURE. The MLE is

defined as a statistical best estimate of the value of a parameter

from a given data set. Therefore, the difference between the 30 

upper-bound estimate and the MLE is that the upper bound is a

conservative measure of risk while the MLE is a statistically best

estimate.
6-56

6.12.3 Discussion of Uncertainty in IUREs

Uncertainty is associated with the IURE because many assumptions

have been made in the process of deriving it. Uncertainty arises from

several areas in a dose-response assessment including intra- and

interspecies variability, high- to low-dose extrapolation, route-toroute extrapolation, and the development of equivalent doses. One

type of potential uncertainty is often called the “healthy worker

effect.” This results because the IUREs for some HAPs (e.g., arsenic,

chromium VI) are based on studies of workers exposed during their

working careers. The sensitivity of the workers to developing cancer

may not be the same as the sensitivity of the general population. 

Therefore, there is uncertainty in the representativeness of the

worker population for calculating an IURE for the general population. 

There may also be uncertainties because of truncation of observation

periods in most epidemiology studies. In addition, there are

uncertainties in the estimates of individual dose or exposure in the

epidemiology studies.

When using animal studies to estimate dose-response, the

assumption that administered dose is proportional to delivered dose is

typically used when estimating human equivalent doses. However,

physiological and pharmacokinetic differences between experimental

animal species and humans may result in differences in delivered

target organ dose. Not accounting for these may introduce uncertainty

in the estimation of human equivalent dose. 

Low-dose extrapolation models can result in estimates of risks

that differ by several orders of magnitude. Therefore, selection of

model is critical. Some uncertainties may result in high biases,

others may result in low biases.

The IURE is based on the assumption that exposure to a particular

agent occurs over a 70-yr lifetime under constant conditions and

assumes that risk is independent of dose rate. Actually, the exposed

population is not exposed either continuously or at a constant level. 

It is unknown how the detoxification and repair mechanisms may act at

higher or lower dose rates or with intermittent exposures, thereby

introducing uncertainty in the risk estimate. Variable exposure

concentrations introduce uncertainty. If detoxification and repair

mechanisms are more efficient with intermittent exposures (allowing

for recuperation or repair), the IURE would over-estimate risks when

compared to the total dose received. By contrast, if these mechanisms

were less efficient at an intermittently higher dose rate, the IURE

may underestimate risk when compared to total dose. 

Risks from multiple carcinogens are typically estimated assuming

dose additivity. However, uncertainties are associated with this

approach. The risk summation technique assumes exposures are in the

low-dose range where responses are linear; however, at higher risk

levels, nonlinearity may need to be considered. The additivity

approach also assumes that each chemical acts independently (i.e.,

that there are no synergistic or antagonistic chemical interactions
6-57

and that all chemicals produce the same effect). If these assumptions

are incorrect, over- or underestimation of the actual multiplesubstance risk could occur. Several other limitations to this 31

approach must be acknowledged. Because the IURE is typically an upper

95th percentile estimate of potency and upper 95th percentiles of

probability distributions are not strictly additive, the total cancer

risk estimate might become artificially more conservative as risks

from a number of different carcinogens are summed. However, the

human-derived potency estimates, which are the most important for this

analysis (e.g., radionuclides, arsenic, chromium VI, and nickel

subsulfide), are not based on the upper 95th percentile. These IUREs

are based on a maximum likelihood estimate. Therefore the potential

for artificially conservative estimates resulting from summing risks

of individual HAPs may not be an issue for this risk assessment.

Uncertainty in the breathing rate relates to the level of

activity. The breathing rates in epidemiological studies on which the

cancer slope factors (CSFs) are based are typically higher than the

standard 20 m /d for the general population. Uncertainty in the 3

deposition fractions varies between individuals due to variation in

breathing rates, particle sizes, and the sizes of lung passages. 

Retention half-times typically are distributed lognormally though

there is little information on how they differ between the

(epidemiological) study population and the general population. Lifetime averaged retention half-times should be slightly lower in the

general population due to the inclusion of young ages for which the

retention half-times are usually lower than adult values. 

6.12.4 Variability in Cancer Dose-Response Assessment

Human beings vary substantially in their inherent susceptibility

to carcinogenesis. Person-to-person differences in behavior, genetic

makeup, and life history can influence susceptibilities. Such

interindividual differences can be inherited or acquired. Acquired

differences that can significantly affect an individual’s

susceptibility to carcinogenesis include the presence of concurrent

viral or other infectious diseases, nutritional factors such as

alcohol and fiber intake, and temporal factors such as stress and

aging. Evidence regarding the individual mediators of susceptibility

supports the plausibility of a continuous distribution of

susceptibility in the human population.

Some researchers have attempted to determine the range of

susceptibility due to the general variability in physiological

parameters that may affect target organ dose. Their results indicate

that the difference in susceptibility between the most sensitive 1

percent of the population and the least sensitive 1 percent might be

as small as a factor of 36 (if the logarithmic standard deviation was

0.9) or as large as a factor of 50,000 (if the logarithmic standard

deviation were 2.7).28

Certain groups of individuals within the population are

inherently more sensitive to carcinogen exposure than others. Factors
6-58

that influence susceptibility include age, race, sex, and genetic

predisposition. An example of a sensitive subpopulation is children. 

This subpopulation can be more sensitive to certain chemicals and more

susceptible to cancer for a variety of reasons, including:

 Children have faster breathing rates than adults and,

thus, inhale larger quantities of a pollutant, relative to

their body weights.

 Organs in children are still growing and developing and

are, therefore, more prone to disruption by an

environmental agent.

 Young organisms appear to be inherently susceptible to

many carcinogens. Young experimental animals have been

shown repeatedly to acquire more tumors in a shorter time

with a smaller dose than adult animals.

In most circumstances, as with this study, there are not enough data

available to perform separate quantitative dose-response assessments

for these sensitive subpopulations. Obviously, children are not

included in the work force at plants where much of the epidemiology

data are collected.

As stated above, the IURE is based on the assumption that

exposure to a particular agent occurs over a 70-year lifetime under

the same conditions to which the study group was exposed. For

animals, it is essentially steady and constant exposure over a

lifetime; for humans, it is varying exposure over their working career

at a particular plant. In effect, this assumes that risk is

independent of dose rate. Recent research suggests that cellular

repair mechanisms exist that can reverse the damage caused by a

carcinogen, and it is likely that these mechanisms operate most

effectively after low doses or in the absence of repeated doses. 

Therefore, variability in exposure would also influence or create a

variability in how effective the IURE predicts risk.

6.13 PRELIMINARY QUANTITATIVE UNCERTAINTY AND VARIABILITY

 ANALYSIS FOR INHALATION EXPOSURE AND RISK ASSESSMENT

6.13.1 Introduction

Risk assessment is a complex process, and uncertainty will be

introduced at every step in the analysis. Even using the most

accurate data with the most sophisticated models, uncertainty is

inherent in the process. There are a number of uncertainties

associated with the exposure assessment of emissions from utilities. 

These include parameter estimation (test results), model choice, and

the use of simplifying assumptions. 

Uncertainty in emissions and exposure estimates can result from

uncertainty (i.e., doubt or ignorance of the true value) or from

variability (i.e., known range of values over time, space, or within a
6-59

population). A quantitative uncertainty analysis was conducted for

the direct inhalation exposure part of this risk assessment. The

evaluation of uncertainty in the estimation of emissions, dispersion

and exposure is summarized here. See Appendix G of the EPA Interim

Final Utility Report for details on the uncertainty analysis. This 6

uncertainty evaluation does not include consideration of the impacts

(and associated uncertainties) due to long-range transport and

multipathway exposures. The focus of this particular analysis is the

uncertainties and variability of the inhalation exposure within 50 km

of the plants.

The need for formal uncertainty analysis as a part of any risk

assessment and its aid in conveying results of the risk assessment are

widely accepted, having been proposed in both the EPA Risk

Characterization Guidance and the NRC Committee Report: Science and

Judgement in Risk Assessment. Furthermore, any procedure that relies 28

on a combination of point values (some conservative and some not

conservative) yields a point estimate of exposure and risk that falls

at an unknown percentile of the full distributions of exposure and

risk.

The risk estimates presented in previous sections were derived by

utilizing various input data and assumptions. The results were

presented as point estimates of risks. The following uncertainty

analysis was conducted to determine the degree of conservatism

associated with these point estimates.

The uncertainty analysis focused on the three HAPs (nickel,

arsenic, and chromium) that accounted for over 95 percent of the highend estimate of cancer incidence. An analysis of uncertainty on these

three HAPs accounts for much of the uncertainty in the overall risk

estimates.

6.13.2 Approach to Quantitative Uncertainty Analysis

Uncertainty has been classified into four types (parameter

uncertainty, model uncertainty, decision-rule uncertainty, and

variability). The first two, parameter uncertainty and model

uncertainty, are generally recognized by risk assessors as major

sources of uncertainty. Parameter uncertainty occurs through

measurement errors, random errors, or systematic errors when variables

cannot be measured precisely either because of equipment limitations

or because the quantity being measured varies spatially or temporally. 

Model uncertainty can result from surrogate variables, excluded

variables, abnormal conditions, and/or incorrect model form. 

Decision-rule uncertainty arises out of the need to balance different

social concerns when determining an acceptable level of risk, which

can affect the choice of model, data, or assumptions. Variability is

often used interchangeably with the term "uncertainty," but this is

not strictly correct. Variability is the unchanging and underlying

distribution of a parameter based on physical, chemical, and/or

biological processes (e.g., body weight within a population). Even if
6-60

variability is known (therefore, not in itself uncertain), it still

contributes to overall uncertainty of the risk assessment.

This uncertainty analysis focused on parameter uncertainty within

the models and data available. Other uncertainties which were not

amenable to quantitative evaluation are discussed qualitatively in

section 6.12. Table 6-25 briefly summarizes information regarding the

parameters used in the risk estimation process. Model uncertainties

are not addressed in the quantitative uncertainty analysis, but are

described qualitatively. Variability has been evaluated separately

for exposure-response, but is included in the overall estimate of

uncertainty related to emissions and exposure. The goal of this

uncertainty analysis is to estimate the range of possible risk

estimates considering the parameter uncertainty and variability. It

should also be noted that there are other sources of uncertainty, some

of which may be significant, which could not be evaluated

quantitatively. These uncertainties are qualitatively discussed. 

The approach used in this analysis was to identify the

uncertainty with each of the parameters used in the risk estimation

process. First, the uncertainty associated with each of these

variables was described using an appropriate statistic (e.g., mean and

standard error of means) or as a probability density function (the

relative probability for discrete parameter values). The standard

error of the mean (SE) for each parameter was the estimate of

uncertainty and variability used rather than the standard deviation

for each parameter. Since the available dose-response data are based

on lifetime exposure, and the cancer risk assessment is concerned with

long-term average exposures, the SE is a more appropriate statistic. 

However, it should be noted that using the SE from a sample may be an

overconfident estimate (i.e., too narrow a range) of uncertainty (see

Appendix G of the EPA Interim Final Utility Report for explanation). 6

In general, numerical methods (e.g., Monte Carlo simulation) were

then used to develop a composite uncertainty distribution by combining

the individual distributions. In Monte Carlo simulations, the risk

and/or model equations are repeatedly solved using randomly sampled

values from the specified distributions to calculate a distribution of

risk values. These risk distributions were derived for estimates of

MIR and population risks. Because variability was not specifically

differentiated in the analysis of emissions and dispersion modeling,

uncertainty and variability were simulated together in a onedimensional Monte Carlo simulation.
Table 6-25. SUMMARY OF BASIC PARAMETERS USED IN THE INHALATION RISK ASSESSME

Parameter (and departure from default and reason why) (e.g., source, quality) (quantitative and qualitative) 

Default Option/Assumptions Conservatism Data Uncertainty/Variability 

EMISSIONS CHARACTERIZATIONFuel consumption Coal: Low, average UDI Database: self-reported, Accuracy of self-reported values No

1990 (or 1989, geo mean 1980-8) UDI/EEI Fuel consumption over time due to demand,

data self-reported by the utilities to DOE. Average heating values used for sulfur content, etc...

Adjustments made for heating value of = 12688 BTU/lb, subbit. = 9967

different coals BTU/lb)

Based on total tonnage

Oil: assumed to be residual oil, quantityconsumed in gallons is converted to massbased on an assumption of uniform density. 

value used with no QC or validation 

coal type (lign = 6800 BTU/lb, bit.

Coal State of Origin Assume all from the state where majority of Unknown UDI database identifying Coal from several states may be used at NA 

fuel consumed based on total tonnage majority use one plant, mix of states coal actually used 

Relative composition of state coals due to

availability, cost, sulfur content etc.. maychange over time

Trace element Arithmetic average of coal type for state of Low, average USGS core/channel sampling Coal: Lo

concentration origin which is used most at the facility. value used (extraction) of economically 

Oil: average HAP concentration in test data conc. may occur during processing

of residual fuel oil No. 6 (about 80% of all oilburned). Coal seams measured may not actually

feasible coal seams (n= 3331) Conc. measured in extracted coal, not in 

coal shipments, reductions in trace element being used for shipment

Coal from other states used at plant may

contribute significantly

Variability within a coal seam, between coal

seams within a state.OIL:

Density will vary among No. 6 fuel oils

which means that the volume and massconsumed will vary. Concentration of HAPswithin oil will vary.

Coal cleaning factor Process of preparing coal for shipment may Low, average Testing of coal shipments from Coal cleaning data may not apply to other No

(CCF) reduce some mineral matter. Since about value used Wyoming, Colorado and Illinois types of coal

77% of eastern and midwestern bituminousshipments are cleaned a CCF was applied to Effectiveness of coal cleaning may vary

all bit. coal according to variability in the sulfur and ash

content within a coal seam and the variabilityin processing

EMF: Boiler and Geometric mean of test data, measured in Low, average Coal: 19 facilitestested of varying Were units tested representative of units in BeAPCD gas stream, ash stream was ignored value used configurations, combinations of operation Tr

boiler type and APCD. Unit performance likely to vary over time 

Oil: teesting at 2 facilities. due to fuel and operating parameters. Each test point was at least a triplicate sample 
Table 6-25. Continued

Parameter (and departure from default and reason why) (e.g., source, quality) (quantitative and qualitative) 

Default Option/Assumptions Conservatism Data Uncertainty/Variability 

DISPERSION AND EXPOSURE MODELINGDispersion Gaussian plume Unknown Limited data on other models N

Roughness (rural v. Population density (within 3 km of plant) is Unknown, may Census data on population within Roughness not binary and not always Eaurban) assumed to be an indicator or proxy for not account for 3 km of facility to indicate urban. attributable to population density (e.g., bo

setting of the plant, and that urban and rural values beyond buildings) but other land features as well. are representative of surface roughness. model defaults Binary choice of urban v. rural Urban and rural model default settings may 

not represent the entire range of surface roughness leading to possible extremes not addressed. 

Terrain Assumed to be flat terrain. Low, as shown by Extensive data on terrain Terrain effects can be significant leading to An

Flat terrain used in gaussian plume complex terrain surrounding each facility. minimal dispersion and high exposure sedispersion. analysis However, analysis is very (s

exhaustive and cannot be carried Terrain is not a binary parameter and the

out for all facilities. degree of terrian differences will vary

between plants. 

Meteorology The meteorological data from the nearest Unknown, STAR data are typically five-year Meteorology at plant may be significantly Th

STAR location are used to represent the assumed to averages at 350 airports. different than the nearest plant. Different lometeorology near the plant. represent site met meteorology may not affect the maximum Stability classes are assumed to be conditions concentration but may significantly affect the represented in the STAR data and implicitly number of persons exposed and at risk. addressed in the HEM model. Short-term meteorological conditions (e.g.,

inversion) may affect short-term exposurelevels. 

Meteorological conditions will vary at a site

over time.

Effective Stack Effective stack height is calculated using the Medium, actual Data from UDI database are as The effective stack height may vary HHeight stack height, exit velocity and exit gas data with reported by the facility. significantly from the calculated value due to Lo

temperature as reported in the UDI conservative Little or no data on variability in variation in exit gas temperature and velocity.database. model exit gas temperature orexit which would greatly impact the resulting

velocity exposure concentration. 

Stack height would not vary as it is a

physical parameter.

Location of Exposed Population assigned to the centroid of the Low/Medium 1990 Census Block data. Location of the centroid is estimated, is it AnPopulation census block or, if within 0.5 km, to receptor assignment at 0.5 accurately defined. Uncertainty as to where se

grid location for which concentratio nis km accounts for people are actually located with respect to estimated. Assumed to represent a persons variability the centroid. average exposure ( they may also spend time in areas of higher or lower The location of individuals will vary with concentrations) respect to the centroid, some being in higher

concentrations some in lower concentrations.
Table 6-25. Continued

Parameter (and departure from default and reason why) (e.g., source, quality) (quantitative and qualitative) 

Default Option/Assumptions Conservatism Data Uncertainty/Variability 

EXPOSURE-RESPONSE ASSESSMENTExposure Duration Assumes persons spend 70-year lifetime at High (MEI/MIR) Very little data on mobility which Individuals mobility will affect exposure. No

(Population mobility) the location to which they are assigned. The tends to maximize are applicable to the range of Uncertainty about defining a representative 

concentration at the centroid represents their exposure populations affected. Most have mobility pattern or distribution which also average to which they are exposed. been focused on small subsets accounts for movement to alternatively 

(e.g., residency in apartments). polluted areas. 

Population mobility varies dramatically 

within a population and for an individual over time.

Alternatively can consider time-activity

patterns (e.g., indoor/outdoor, movementwithin area) and residence time (average = 9years, 90th = 30 years).

Exposure Frequency (Indoor/outdoor concentration) High, maximizes Measurements of indoor/outdoor The relationship between indoor and outdoor Lo

Assumes exposure at 100% of outdoor exposure, concentration ratio, time-activity concentration is complex because infiltration vaconcentration. patterns, and exposure. No data is affected by climate, building type, No

specific to locations of electric ventilation etc.. unutility plants. Infiltration will vary over time due to climate

variability (e.g., open windows).

Breathing Rate Assumes that workers breathing rate is Low to Med, Measurements of minute volumes Breathing rates differ greatly by age and Lo

equal to national average. workers may have for different population subgroups. activity. va

higher BR value No

un

Lung Deposition No adjustment between worker and general Low, average Measurement of lung deposition Lung depostion can vary by age and activity Lo

populations. value used fractions. level. va

Noun

Retention Half-life No adjustment between worker and general Low, average Measurement of half-lives, but not Retention half-lives will vary by age, activity Lo

populations. value used for the specific HAPs evaluated. level, and particle size. va

Noun

Slope factors Used EPA-verified slope factors, best Unknown, but Human epidemiological data. Slope factors will differ dramatically based on Lo

estimate linearized function. believed to be high model choice, statistical uncertainty in data. un

NOTE: Quantitative values for all parameters and their distributions are presented in the body of the text.

Key: High = Most likely to overestimate than underestimate. Can represent an upper bound estimate.

Medium = May either under- or over-estimate. With the use of conservative models usually more likely to over- than under-estimate risk. 

Low = Usually an unbiased estimator using the average value. Equally likely to over- or under-estimate risk.

 UDI/EEI: Utility Data Institute, Edison Electric Institute

EMF: Emissions modification FactorAPCD: Air pollution control deviceBeta Distribution: constrained between two distinct values (e.g., 0 and 1), defined by the mean and standard error of mean (SEM) of the originial data. This distribution maint(SEM). The use of a truncated distribution (either normal or log-normal) can lead to a drift in the mean and/or SEM from the original data.
6-64

The uncertainty analysis was conducted on the three major

components of the risk assessment process: emissions

characterization, dispersion and exposure modeling, and

exposure-response assessment. Each of these is summarized briefly

below. Figure 6-16 provides an example of how the uncertainty from

each of these components is combined into an overall distribution. A

detailed uncertainty analysis could not be conducted on all of the

utility plants. Therefore, a total of four plants (two oil-fired and

two coal-fired plants) were selected which contribute most to risk,

the highest estimated incidence and the highest maximum individual

risk. Each of these plants was analyzed for arsenic, nickel, and

chromium. The highest incidence oil-fired plant (Plant No. 29)

accounted for about 7 percent of the annual cancer incidence and,

therefore, was chosen for illustration purposes.

6.13.2.1 Emissions Characterization Uncertainty. An emissions

factor program was developed by EPA to estimate plant-specific

emissions rates based on fuel type, fuel origin, plant configurations,

and emissions testing results. The emission factor program (including

principles and rationale) and the data used are described in chapter 3

and appendix D. This program is based on a mass-balance concept,

reducing concentrations in the fuel due to the impact of the boiler

and control devices.

The parameters used in the emissions characterization were: fuel

consumption (coal: ton/yr, oil: barrel/yr), HAP (trace element)

concentration in fuel, coal cleaning factor (if needed), emissions

modification factors for the boiler (EMF , boiler-specific factor to b

account for amount of HAP entering boiler to that exiting boiler), and

the air pollution control device (APCD), if present (EMF , APCD- a

specific factor to account for amount of HAP entering the APCD to that

exiting APCD). 

It should be noted that two different trace metal concentrations

in oil were used. The original data were from the EPRI’s Field

Chemistry Emissions Monitoring (FCEM). A subsequent data set was

provided by UARG and their contracting lab (SGS Environmental

Laboratories). An analysis of these data indicated that the samples

were discretely different. It is unknown which “sample population” is

most representative of the oil burned. Therefore, the two data sets

are treated as two distinct cases and are assumed to be representative

of the range of oil being burned by utilities. 

6.13.2.2 Plant-Specific Emission Rates. Monte Carlo simulation

was used to develop a distribution of possible plant-specific

emissions rates. Simulations were carried out randomly sampling

values for fuel consumption, HAP concentration, and EMFs. For

illustration purposes, Table 6-26 and Figure 6-17 present the summary

statistics and graphical representation, respectively, of the

emissions predicted for Plant No. 29. This distribution gives some

indication of the degree of uncertainty and the possible range of

emissions estimates that may be experienced. The emissions estimates

Table 6-26. Summary of Results for Monte Carlo Simulation of

HAP Emissions (kg/year) from Oil-Fired Plant No. 29

Arsenic emissions Chromium emissions Nickel emissions

FCEM SGS Combined FCEM SGS Combined FCEM SGS Combined

Mean 44 216 130 69 20 45 6423 8612 7527Initial Point Estimate ------------- 133 ------------- ------------- 69 ------------- ------------- 8170 -------------(Percentile) (93) (22) (58) (64) (95) (80) (76) (59) (68)Percentiles: 0.0% 0.0 13 0.0 4 0.2 0.2 225 507 225(approx.) 2.5% 0.4 66 -- 11 0.5 0.6 934 2001 1125

5.0% 0.8 78 -- 15 0.9 1.5 1227 2389 1462

10% 1.6 98 2.0 20 1.6 2.1 1645 3015 212525% 3.9 142 6.0 33 3.8 8.1 2731 4583 348750% 9.1 197 98 54 8.7 26 4759 7010 596275% 31 269 206 85 20 60 8009 10847 956290% 94 358 303 135 43 106 12736 16034 14625

95.0% 177 417 378 174 71 149 17441 20173 1906097.5% 322 474 455 214 117 208 22936 24544 24750

FCEM = Field Chemical Emissions Monitoring from EPRI program. Original oil concentration data.SGS = Subsequent data, trace metal analysis conducted by Utility Air Regulatory Group (UARG) HAP committee  from samples collected for radionuclide analysis. The concentration was determined by  SGS Environmental Laboratories, a contractor to UARG.Combined = Combined forecasts assuming equal probability of the FCEM and SGS data sets.Initial Point Estimate = The estimate of emissions used in the baseline exposure assessment.  This value was based on the average concentration in the FCEM data. (Percentile) = The percentile of the predicted distribution corresponding to the initial point estimate. 

6-68

used in the baseline risk assessment were generally central tendency

estimates (i.e., geometric means). In general, the 95 percent

confidence range for long-term average emissions estimates are within

a factor of 2 to 3 of the emissions estimates used in the risk

assessment. For example, the 95th percentile of the overall range of

predicted emissions ranged from about 0.9 times the original emissions

estimate from plant No. 343 for nickel to about 2.5 times the original

estimate for arsenic. As shown in Table 6-26, the original estimate

of emissions from the baseline risk assessment ranged from the 22nd to

the 95th percentile of the range of emissions predicted under the

uncertainty analysis. 

A preliminary evaluation of the EFP was conducted. Comparisons

were made of test data from 19 utility boiler stacks (17 coal-fired, 2

oil-fired) against predicted emissions for the Table 6-26. Summary of

Results for Monte Carlo Simulation of HAP Emissions (kg/year) from

Oil-fired Plant #29 same plants using the EFP. For each facility, the

emission estimate from the EFP was divided by the reported value from

the corresponding test report. A value of 1 meant that the EFP

exactly predicted the test results, values lower than 1 indicated

the EFP underpredicted emissions, while values higher than 1 indicated

the EFP overestimated emissions. In general, the results suggested

that the EFP performs reasonably well for predicting emissions on a

national basis. The average of the ratios across all stacks and

constituents was 1.08, while averages for arsenic, chromium, and

nickel were 1.6, 0.68, and 0.97, respectively.

However, while the model did well in predicting overall or

average emissions across a range of utility boilers, large differences

between predicted and reported values were found for a few individual

boilers and constituents. The largest difference was for an

individual boiler for which estimated emissions were about 2,600 times

lower than reported test results. However, it was determined that

this facility was a low-risk plant in the overall analysis; therefore,

increasing emissions of this plant by 2,600 would not change the

overall results. The EFP tended to underestimate rather than

overestimate emissions about 70 percent of the time within this sample

of boilers. A preliminary evaluation of facilities with large

differences between projected and actual emissions found that these

facilities were likely to burn multiple fuel types. In addition,

variability in fuel composition might also lead to large differences

between measured and calculated emissions. Since most of the higher

risk plants do not fall into this category, the differences here are

not expected to impact significantly on the overall risk estimates.

See Appendix G of the EPA Interim Final Utility Report for further

discussion.6

6.13.2.3 Dispersion and Exposure. Air dispersion modeling is

complex and nonlinear, cannot be carried out with the use of

spreadsheets, and requires significant time to conduct the modeling

and process the data for each run. To better estimate percentile

values above 90 percent, a stochastic (Monte Carlo) approach requires
6-69

large numbers (thousands) of repetitive runs (3,000 was used for the

emissions estimates) needed to generate a distribution. Given the

time and resources required for single runs, the Monte Carlo approach

was not feasible and an alternative approach was needed to evaluate

the uncertainty in dispersion and exposure modeling. 

The degree of dispersion and resulting exposure is affected by

three major parameters: plant stack parameters (e.g., stack height,

stack gas temperature, and exit velocity), meteorologic conditions,

and surface roughness (urban vs. rural). The uncertainty analysis,

therefore, focused on these three parameters. The three factors being

evaluated are nonlinear with respect to each other and require a

separate HEM run for each parameter value. Therefore, a test matrix

approach was used to evaluate uncertainty in the exposure modeling

component of the exposure assessment. A limited number of options

were developed to represent the expected range of uncertainty for each

of these three categories of parameters as follows: 

Surface roughness: urban or rural mode 

Stack parameters: represented as high (1.1 x UDI values),

medium (UDI values), and low (0.9 x UDI

values) estimates for stack gas temperature

and flue gas exit velocity 

Meteorology: three closest meteorology locations in the

STAR database.

As a result, for each plant, a total of 18 different HEM runs were

made covering each combination of dispersion parameters. For the

purposes of this uncertainty analysis, it was assumed that there is

insufficient information to determine the relative correctness of each

combination and, therefore, each was considered equally likely to

represent the possible range of values. The coefficients for

estimating maximum concentration and total exposure (per 1 kg/yr

emission) resulting from each of these 18 HEM runs were summarized for

each plant. 

6.13.2.4 Exposure-Response Assessment. The variability of the

quantitative relationship between exposure and the excess probability

of cancer for different humans and the uncertainty in the mean (taken

here also to be the "best estimate" or "maximum likelihood estimate")

quantitative relationship between exposure and the excess probability

of cancer were both addressed. As with the uncertainty analysis for

emissions and dispersion, efforts were limited to arsenic, chromium,

and nickel. Specific parameters, for which uncertainty about the mean

value (or best estimate for a given parameter within the exposed

population) was addressed, include exposure frequency, exposure

duration, breathing rate, deposition fractions, and retention halftimes. Uncertainty related to the IURE focused on data and the use of

epidemiologic data (typically from workers) extrapolated to the

general population. 
6-70

The software program Crystal Ball (Decisioneering, Inc., Denver, ® 

CO) conducted stochastic (Monte Carlo) simulations of the risk

estimates, incorporating the uncertainty for each parameter. A

probability distribution that best represents the variable, its

average value, and a measure of uncertainty about the average value

was developed for each parameter. The simulation consists of

conducting repeated calculations (thousands) of risk using values for

each parameter sampled from the distribution of values for that

parameter. 

The study of variability focused on how parameter values would be

expected to vary among individuals within the general population and

how that would affect the estimation of risk and incidence. The

parameters for which some measure of variability among individuals

within the general population was addressed include exposure duration,

exposure frequency, breathing rate, deposition rate, and retention

times in the lung. No specific measures of variability were available

for how the IURE for these three HAPs may differ among individuals. 

However, limited data indicate that the IURE differs between smokers

and nonsmokers and this difference was incorporated in the analysis.

6.13.3 Discussion of Results of the Quantitative Uncertainty Analysis

The risk estimation process used in the baseline assessment

utilized a combination of parameters, each with varying degrees of

conservatism (the degree of overestimation, or underestimation). In

general, the estimates of maximum individual risk and annual cancer

incidence derived in the baseline risk assessment were conservative,

generally around the 95th percentile on the distribution. The 95th

percentile is roughly 10 times the median and about 5 times the mean. 

The distribution of estimates of MIR for Plant No. 29 are presented in

Table 6-27. The sensitivity analysis indicated that the dispersion

coefficient (surface roughness) was the most significant parameter for

estimating uncertainty MIR and incidence, followed by the EMFs. The

deposition fraction, retention time, and exposure frequency also

contributed significantly in the variability of these estimates.

The EPA risk assessments are generally conservative (more likely

to overestimate than underestimate risks). Often there is a concern

that the use of several conservative assumptions results in risk

estimates that are unrealistic and beyond the range of possible risks

(i.e., overly conservative). The results of the uncertainty analysis

indicate that the baseline inhalation risk estimates are reasonably

conservative (predicted to be roughly around the 90th or 95th

percentile). The uncertainty analysis supports the general conclusion

that the baseline risk estimates are likely to be reasonable high-end

estimates.
Table 6-27. Distribution of MIR: Plant No. 29:

 Comparison of FCEM and SGS Concentration Data

MIR, Plant No. 29

Uncertainty

Arsenic Chromium Nickel

FCEM SGS FCEM SGS FCEM SGS

Mean 1E-07 6E-07 1E-07 4E-08 2E-06 3E-06

Initial Point Estimate 6E-07 2E-07 4E-06

(percentile) (96) (71) (87) (98) (90) (85)

Percentiles:

0.0% 2E-12 1E-09 2E-10 1E-11 2E-09 6E-09

2.5% 3E-09 8E-09 2E-09 7E-10 2E-08 4E-08

5.0% 6E-09 2E-08 3E-09 1E-09 5E-08 6E-08

10% 1E-08 3E-08 5E-09 3E-09 9E-08 1E-07

25% 3E-08 7E-08 1E-08 7E-09 2E-07 3E-07

50% 6E-08 2E-07 4E-08 1E-08 6E-07 9E-07

75% 8E-08 7E-07 1E-07 2E-08 2E-06 3E-06

90% 2E-07 1E-06 3E-07 8E-08 4E-06 6E-06

95.0% 5E-07 2E-06 5E-07 1E-07 7E-06 1E-05

97.5% 1E-06 4E-06 7E-07 2E-07 1E-05 2E-05

Ratio

95th : baseline 0.8 4.1 2.3 0.7 1.7 2.5

95th : median 8.7 10.6 12.8 9.6 11.1 10.4

95th : mean 3.5 3.8 4.2 3.9 4.0 3.8

Variability

Arsenic Chromium Nickel

FCEM SGS FCEM SGS FCEM SGS

Mean 1E-07 6E-07 1E-07 3E-08 2E-06 2E-06

Initial Point Estimate 6E-07 2E-07 4E-06

(percentile) (95) (68) (90) (97) (90) (90)

Percentiles:

0.0% 5E-12 4E-09 4E-10 1E-11 5E-09 2E-08

2.5% 1E-09 1E-08 2E-09 3E-10 3E-08 6E-08

5.0% 3E-09 2E-08 3E-09 7E-10 6E-08 1E-07

10% 6E-09 4E-08 6E-09 1E-09 1E-07 2E-07

25% 1E-08 1E-07 1E-08 3E-09 3E-07 4E-07

50% 3E-08 3E-07 4E-08 7E-09 7E-07 1E-06

75% 6E-08 7E-07 1E-07 3E-08 2E-06 3E-06

90% 2E-07 2E-06 3E-07 7E-08 4E-06 6E-06

95.0% 5E-07 2E-06 4E-07 1E-07 6E-06 9E-06

97.5% 1E-06 3E-06 6E-07 2E-07 9E-06 1E-05

FCEM = Field Chemical Emissions Monitoring from EPRI program. Original oil concentration data.

SGS = Subsequent data, trace metal analysis conducted by Utility Air Regulatory Group (UARG) HAP committee from samples collected

 The concentration was determined by SGS Environmental Laboratories, a contractor to UARG.

Combined = Combined forecasts assuming equal probability of the FCEM and SGS data sets.

Initial Point Estimate = The estimate of emissions used in the baseline exposure assessment. 

This value was based on the average concentration in the FCEM data. 

(Percentile) = The percentile of the predicted distribution corresponding to the initial point estimate. 
6-72

The uncertainty analysis suggests that the most likely inhalation

MIRs (i.e., central tendency MIRs) and most likely cancer incidence

values (i.e., central tendency cancer incidence estimates) may be

roughly 2 to 10 times lower than the high-end MIRs and incidence

estimates presented above. In addition, based on results of the HEM

modeling and the uncertainty analysis, it is predicted that the

average individual risks due to inhalation exposure to utility HAP

emissions for the total exposed U.S. population (roughly 200,000,000

people) are roughly 100 to 1,000 times lower than the high-end MIRs.

However, it should be noted that this analysis has focused only

on parameter uncertainty. Also, not all parameters were included. 

For example, residence time and activity patterns were not assessed

quantitatively in the uncertainty analysis. As a result, the

uncertainty presented here may underestimate the overall uncertainty.

6.14 QUALITATIVE DISCUSSION OF ADDITIONAL UNCERTAINTIES 

There are several areas of uncertainty that were not covered in

the quantitative analysis. Several of these were discussed in

previous sections of this report. Further discussion of two areas of

uncertainty is provided below.

6.14.1 Uncertainty Using IUREs

As discussed in section 6.12, there are uncertainties associated

with the IUREs. Many of these uncertainties were not included in the

quantitative uncertainty analysis because adequate data were not

available. 

6.14.2 Residence Time and Activity Patterns

In the baseline assessment for the MEI risks, it was assumed that

people are exposed to the modeled concentration at their residence for

70 years. This approach assumes that people spend most of their time

at home and that the average concentration at their residence

represents the average concentration to which they are exposed. 

Electric utility plants typically have high stacks compared with many

other air pollutant point sources. As a result, ground-level

concentrations (and concomitant exposures) would tend to vary less

with distance than other sources. Therefore, movement by individuals

within the grid would have minimal impact on exposures. The EPA

realizes that the average person does not live in the same house for

70 years. However, adjusting for exposure due to changes in residence

is no easy task, especially for utilities since plants are located

nationwide and roughly 80 percent of the United States population live

within 50 km of at least one plant.

This uncertainty was not quantified for several reasons. First, 

a person who moves out of one residence may move into another

residence still in the high-concentration area (e.g., person moves

next door). Second, a person may move away from an area for a period

of time, then move back to the same location. Third, since there is

typically more than one person located in the high exposure area, if
6-73

all except one move away (e.g., one person in the census block stays

for 70 years), then the assumption of 70-year residence time holds for

the MEI. And, fourth, a person may move from the area of exposure of

one utility into an exposure area of another. This person’s exposure

may change, but may not become zero. Therefore, 70-year exposure is

considered a conservative, but reasonable, assumption for the MEI. 

However, it is still quite uncertain how much residence time and

activity patterns would affect the risk estimates.
6-74

1. U.S. Environmental Protection Agency. Risk Assessment Guidelines

of 1986. EPA-600/8-87/045. (Guidelines for Carcinogen Risk

Assessment, Guidelines for Mutagenicity Risk Assessment,

Guidelines for Health Risk Assessment of Chemical Mixtures,

Guidelines for Health Assessment of Suspect Developmental

Toxicants, Guidelines for Estimating Exposures) Office of Health

and Environmental Assessment, Washington, DC. 1987.

2. U.S. Environmental Protection Agency. Air Pollutant Emissions

from Electric Utility Steam Generating Units—Interim Final. 

Volume III. Appendices H-M. EPA-453/R-96-013c. 1996.

3. EPRI. Personal communication by fax from Paul Chu to Chuck

French and Bill Maxwell, EPA. November 27, 1995.

4. U.S. Environmental Protection Agency. Guidelines on Air Quality

Models. Code of Federal Regulations, 40, Appendix W to Part 51,

July 1, 1994.

5. California Air Pollution Control Officers Association. Air

Toxics “Hot Spots” Program, Risk Assessment Guidelines. October

1993.

6. U.S. Environmental Protection Agency. Air Pollutant Emissions

from Electric Utility Steam Generating Units—Interim Final.

Volume II. Appendices A-G. EPA-453/R-96-013b. October 1996.

7. Johnson, W. B., Wolf, D. E. and Mancuso, R. L. Long Term

Regional Patterns and Transfrontier Exchanges of Airborne Sulfur

Pollution in Europe. Atmospheric Environment. Volume 12. 1978. 

Pp. 511-527.

8. U.S. Environmental Protection Agency. ENAP-1 Long-Term SO and 2

Sulfate Pollution Model - Adaptation and Application to Eastern

North America. EPA 600/4-80-039. Environmental Sciences

Research Laboratory, Research Triangle Park, NC. May 1980. 

9. U.S. Environmental Protection Agency. RELMAP: A Regional

Lagrangian Model of Air Pollution - User’s Guide. (Eder et al.

1986) Atmospheric Sciences Research Laboratory, Research Triangle

Park, NC. March 1986. 

10. Clark, T. L., Blakley, P., Mapp, G. Model Calculations of the

Annual Atmospheric Deposition of Toxic Metals to Lake Michigan. 

Paper presented at the 85th Annual Meeting of the Air and Waste

Management Association. June 1992.

11. Sehmel, G. A. Particle and Gas Dry Deposition: A Review. 

Atmospheric Environment. Volume 14. Pp. 983-1011. 1980.

6.15 REFERENCES
6-75

12. California Air Resource Board. Deposition Rate Calculations for

Air Toxics Source Assessments. Air Quality Modeling Section,

Technical Support Division, California. September 16, 1987.

13. Alcamo, J., Bartnicki, J., Olendrzynski, K. and Pacyna, J.

Computing Heavy Metals in Europe’s Atmosphere - I. Model

Development and Testing. Atmospheric Environment. volume 26A,

No. 18. Pp. 3355-3369. 1992.

14. Chan, W. H., Tang, A. J. S., Chung, D. H. S., Lusis, M. A. 

Concentration and Deposition of Trace Metals in Ontario - 1982. 

Ontario Ministry of the Environment, Air Resources Branch. 

Toronto, Ontario, Canada. Revised February 5, 1986.

15. U.S. Environmental Protection Agency. A Screening Analysis of

Ambient Monitoring Data for the Urban Area Source Program. EPA453/R-94-075. October 1994.

16. U.S. Environmental Protection Agency. Health Effects Notebook

for Hazardous Air Pollutants. EPA-456-d-94-1003. Air Risk

Information Support Center. Research Triangle Park, NC. 

December 1994.

17. U.S. Environmental Protection Agency. Integrated Risk

Information System (IRIS) Database, Environmental Criteria and

Assessment Office, Cincinnati, OH. 1994.

18. Viren Jr., Silvers A. Unit risk estimates for airborne arsenic

exposure: an updated view based on recent data from two copper

smelters. Regulatory Toxicology and Pharmacology Volume 20. 

1994 pp. 125-138.

19. Memorandum from Hugh McKinnon, M. D., to Seitz, John, EPA. 

April 26, 1995. Advice on unit risk estimate for airborne

arsenic. 

20. Government of Canada. Canadian Environmental Protection Act

Priority Substances List Assessment Report. Arsenic and Its

Compounds. 1993.

21. Handbook of Chemistry and Physics, 60th ed., Chemical Rubber

Company Press, Cleveland, OH. 1980.

22. U.S. Environmental Protection Agency. Health Effects Document

for Nickel and Mercury Compounds, Final Report. EPA/600/8-

83/012ff. September 1986.

23. International Agency for Research on Cancer. IARC Monographs on

the Evolution of Carcinogenic Risks to Humans: Chromium, Nickel

and Welding. Volume 49. Lyon, France. 1990.
6-76

24. California Air Resources Board. Initial Statement of Reasons for

Rulemaking. Proposed Identification of Nickel as a Toxic Air

Contaminant. 1991.

25. American Conference of Governmental Hygienists. Threshold Limit

Values for Chemical Substances and Physical Agents and Biological

Exposure Indices. 1995.

26. Memorandum from Maxwell, William, EPA to the EPA Air Docket No.

A-92-55. March 8, 1996. Dioxin from Hot-side ESP units.

27. Letter and enclosure from Peck, Stephen C., Electric Power

Research Institute, to Maxwell, William H., EPA:ESD. 

September 15, 1995. Transmittal of unlicensed electric utility

trace substances synthesis report.

28. National Research Council. Science and Judgment in Risk 

Assessment. National Academy of Sciences, Washington, DC. 1994.

29. Office of Science and Technology Policy, Executive Office of the

President. Chemical Carcinogens: A Review of the Science and Its

Associated Principles. Washington, DC. 50 Federal Register

10372. March 14, 1985.

30. U.S. Environmental Protection Agency. Guidelines for

Developmental Toxicity Risk Assessment: Office of Health and

Environmental Assessment, 54 Federal Register 6398-63826, 1991.

31. U.S. Environmental Protection Agency. A Descriptive Guide to

Risk Assessment Methodologies for Toxic Air Pollutants. EPA453/R-93-038. Office of Air Quality Planning and Standards,

Research Triangle Park, NC. 1993.
7-1

7.0 MERCURY ASSESSMENT

7.1 OVERVIEW

Mercury is a highly persistent, naturally occurring metal in the

environment. Mercury is typically found in the environment in the

elemental state Hg(0). When it bonds to other chemical elements, it

is commonly found as a cation. The mercuric ion may bind to a number

of inorganic anions; these are generally referred to as species of

divalent mercury (Hg[II]). The mercuric ion may also form one or two

bonds with a methyl group forming either monomethyl- or

dimethylmercury. 

The tendency of this metal to bioaccumulate in aquatic food webs

has been well documented. Mercury is toxic to humans from both the 1-31,2,3

inhalation and oral exposure routes. Mercury is also toxic to other 4 4-64,5,6

mammals and to birds. Questions remain regarding both the quantity , 7-107,8,9,10 11-1811,12,13,14 15,16,17,18

of mercury and the duration of the exposure required to elicit

responses in humans and animals, but it is widely accepted that

exposures to mercury produce neurotoxicity. Mercury contamination of

freshwater fish is a potential concern in the United States as

indicated by numerous fish advisories and mercury-related water 19

quality standards issued by State Agencies. The 1997 EPA Mercury

Study Report to Congress presents a more complete assessment of the 20

health effects, exposures, risks, ecological effects, sources, and

control technologies. This chapter presents an abbreviated assessment

of mercury as it is relevant to utilities, which is largely based on

information presented in EPA’s Mercury Study Report to Congress. 20

Fish consumption dominates the pathway for human and wildlife

exposure to methylmercury. The EPA’s 1997 Mercury Study Report to

Congress supports a plausible link between anthropogenic releases of

mercury from industrial and combustion sources in the United States

and methylmercury in fish. However, these fish methylmercury

concentrations also result from existing background concentrations of

mercury (which may consist of mercury from natural sources, as well as

mercury which has been re-emitted from the oceans or soils) and

deposition from the global reservoir (which includes mercury emitted

by other countries). Given the current scientific understanding of

the environmental fate and transport of this element, it is not

possible to quantify how much of the methylmercury in fish consumed by

the U.S. population is contributed by U.S. emissions relative to other

sources of mercury (such as natural sources and re-emissions from the

global pool). As a result, it cannot be assumed that a change in

total mercury emissions will be linearly related to any resulting

change in methylmercury in fish, nor over what time period these

changes would occur. This is an area of ongoing study.

7.1.1 The Mercury Cycle

Environmental mercury passes through various environmental

compartments and may change physical form and chemical species during
7-2

this process; these movements are conceptualized as a cycle. The

mercury cycle has been studied and described in several recent reports

and its understanding continues to undergo refinement. , 3,21-2421,22,23 24

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.

7.1.1.1 The Global Mercury Cycle Past and Present. As a

naturally occurring element, mercury is present throughout the

environment in both environmental media and biota. In a 1979 report 25

edited by Nriagu, various authors estimated the global distribution of

mercury and concluded that by far the largest repository is ocean

sediments. Ocean sediments contain an estimated 10 g of mercury, 17

mainly as HgS. According to estimates in the report edited by Nriagu,

ocean waters contain around 10 g, soils and freshwater sediments 13

10 g, the biosphere 10 g (mostly in land biota), the atmosphere 13 11

10 g, and freshwater contains on the order of 10 g. This budget 8 7

excludes "unavailable" mercury in mines and other subterranean

repositories. A more recent estimate of the global atmospheric

repository by Fitzgerald is 25 Mmol or approximately 5×10 g; this is 3 9 

50 times the previous estimate of Nriagu.25

Recent estimates of annual total global mercury emissions from

all sources (natural and anthropogenic) are about 5,000 to 5,500 tpy. 26

Of this total, about 1,000 tpy are estimated to be natural emissions

and about 2,000 tpy are estimated to be contributions through the

natural global cycle of re-emissions of mercury associated with past

anthropogenic activity. Current anthropogenic emissions account for

the remaining 2,000 tpy. Point sources such as fuel combustion; waste

incineration; industrial processes (e.g., chlor-alkali plants); and

metal ore roasting, refining, and processing are the largest point

source categories on a world-wide basis. Given the global estimates

of 5,000 to 5,500 tpy (which are highly uncertain), U. S.

anthropogenic mercury emissions are estimated to account for roughly 3

percent of the global total, and U. S. utilities are estimated to

account for roughly 1 percent of total global emissions.

A number of different techniques have been used to estimate the

pre-industrial mercury concentrations in environmental media before

anthropogenic emissions contributed significantly to the global

mercury cycle. Figure 7-1 shows estimated current and preindustrial

budgets and fluxes. 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 

7-4

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 concluded that pre-industrial atmospheric 27

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. The estimates of the panel are 27

corroborated by Lindqvist et al., who estimated that 60 percent of 28

the current atmospheric concentrations are the result of anthropogenic

emissions and Porcella, who estimated that this fraction was 50 29

percent. Horvat et al. assessed the anthropogenic fraction as 30

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. The percentage of current total 31,32,33

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 existing reservoirs. 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. 27

More study is needed before it is possible to accurately differentiate

natural fluxes from these reservoirs from re-emissions of mercury

originally released from anthropogenic sources. For instance,

approximately one-third 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. It is believed that as little as

20 to 30 percent of the current oceanic emissions are from mercury

originally mobilized by natural sources. Similarly, a potentially 34

large fraction of terrestrial and vegetative emissions consists of

recycled mercury from previously deposited anthropogenic and natural

emissions.27

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. 

Contamination from some anthropogenic processes that are no longer in

use produces continuing significant releases to surface water,

groundwater, and the atmosphere. It is estimated that the mercury

content of typical lakes and rivers has been increased by a factor of
7-5

two to four since the onset of the industrial age. For example, 25

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. More recently, researchers

in Sweden estimated 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. In 23

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 preindustrial times. Similar increases have been noted in other studies

of lake and peat cores from this region; results from remote lakes in

southeast Alaska also show an increase, though somewhat lower than

found in the upper midwest United States.27

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. At some locations in the upper 35

Midwest of the United States, measurements of deposition rates suggest

decreased deposition. However, other measurements at remote sites in

northern Canada and Alaska show deposition rates that continue to

increase. Since these sites are subject to global long-range 36,37

sources and few regional sources, these measurements may indicate a

still increasing global atmospheric burden. More research is

necessary; a multi-year, world-wide atmospheric mercury measurement

program may help to better determine current global trends.38

7.1.1.2 Regional and Local Mercury Cycles. According to one

estimate, roughly one half of the total anthropogenic mercury

emissions eventually enter the global atmospheric cycle; the 39

remainder is removed through local or regional cycles. Mercury

emissions from utilities are believed to exist primarily in two forms,

divalent or elemental mercury. Divalent mercury, or Hg(II), is a

positive ion (missing two electrons) with a electric charge of plus 2

(i.e., Hg , or oxidized mercury). Elemental mercury, or Hg(0), has a ++

neutral charge (i.e., Hg ). An estimated 5 to 10 percent of primary 0

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. For 27
7-6

example, deposition rates at some locations have been correlated with

wind trajectories and precipitation amounts. Although these 40,41

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 and 42

for the area downwind of a German chlor-alkali plant on a local

scale. Mercury cycles dependent on local and regional sources have 43

also been established for the Upper Great Lakes region and the 44,45

Nordic countries. 40

While the overall trend in the global mercury burden since preindustrial 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. Some results from peat 27

cores and lake sediment cores also suggest that peak mercury

deposition in some regions occurred prior to 1970 and may now be

decreasing. Data collected over 25 years from many locations in 31,32,33,37

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. Similar trends have been noted for 46

mercury levels in eggs of the common loon collected from New York and

New Hampshire. The downward trend in mercury concentrations observed 47

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 of the Mercury Study Report to Congress).20

7.1.2 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.

7.1.2.1 Emissions of Mercury. As discussed fully in Volume II

of the Mercury Study Report to Congress, mercury is emitted to the 20

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. 
7-7

Anthropogenic mercury releases are thought to be dominated on the

national scale by industrial processes and combustion sources that

release mercury into the atmosphere. Available information indicates

that stack emissions include both gaseous and particulate forms of

mercury. Gaseous mercury, Hg(g), emissions are believed to include

both elemental and oxidized chemical forms, while particulate mercury,

Hg(p), emissions are thought to be composed primarily of oxidized

compounds due to the relatively high vapor pressure of Hg(0). 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. Available information suggests that the speciation of

mercury emissions depend on the fuel used (e.g., coal, oil), flue gas

cleaning and operating temperature, and possibly other factors. 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. 28

Much of this Hg(II) is believed to be mercuric chloride (HgCl ). 2 48

An emission factor-based approach was used to develop the

nationwide emission estimates for the fossil fuel combustion

categories presented in Table 7-1. The emission factors presented are

estimates based on ratios of mass mercury emissions to measures of

source activities and nationwide source activity levels. 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 nationwide 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. 49

7.1.2.2 Transformation and Transport of Atmospheric Mercury. 

Hg(0) has an atmospheric residence time of about one year and will

thus be distributed fairly evenly in the troposphere. Oxidized

mercury may be deposited relatively quickly if it is precipitated out,

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 one year.49

The transformation of Hg(0)(g) to Hg(II)(aqueous) and Hg(II)(p)

in cloud water demonstrates a possible mechanism by which natural and

anthropogenic sources of Hg(0) to air can result in mercury deposition

to land and water. This deposition can occur far from the source due

to the slow rate of Hg(0)(g) uptake in cloud water. It has been

suggested that this mechanism is important in a global sense for Hg 

pollution, while direct wet deposition of anthropogenic Hg(II) is the

most important locally. Gaseous Hg(II) is expected to deposit at a 3,28

faster rate after release than particulate Hg(II) assuming that most
7-8

Table 7-1. Best Point Estimates of National Mercury Emission 

Rates by Category

Sources of mercury 1994-1995 Mg/yr 1994-1995 tons/yr % of Total inventory a b bb

Area sources 3.1 3.4 2.2

 Lamp breakage 1.4 1.5 1.0

 General laboratory use 1.0 1.1 0.7

 Dental preparations 0.6 0.7 0.4

 Landfills <0.1 <0.1 0.0

 Mobile sources c c c

 Paint use c c c

 Agricultural burning c c c

Point Sources 140.9 155.7 97.8

 Combustion sources 125.2 137.9 86.9

 Utility boilers 46.8 51.5 32.6

 Coal (46.7) 51.3 32.5

(0.2) (0.2) (0.1)

 Natural gas (<0.1) (<0.1) (0.0)

 MWCs 26.9 29.6 18.7 h

 Commercial/industrial boilers 25.8 28.4 17.9

 Coal (18.8) (20.7) (13.1)

 Oil (7.0) (7.7) (4.9)

 MWIs 14.6 16.0 10.1 h

 Hazardous waste combustors 6.4 7.1 4.4 e

 Residential boilers 3.3 3.6 2.3

 Oil (2.9) (3.2) (2.0)

 Coal (0.4) (0.5) (0.3)

 SSIs 0.9 1.0 0.6

 Wood-fired boilers 0.2 0.2 0.1 f

 Crematories <0.1 <0.1 0.0

 Manufacturing sources 14.4 15.8 10.0

 Chlor-alkali 6.5 7.1 4.5

 Portland cement 4.4 4.8 3.1 e

 Pulp and paper manufacturing 1.7 1.9 1.2

 Instruments manufacturing 0.5 0.5 0.3

 Secondary Hg production 0.4 0.4 0.3

 Electrical apparatus 0.3 0.3 0.2

 Carbon black 0.3 0.3 0.2

 Lime manufacturing 0.1 0.1 0.1

 Primary lead 0.1 0.1 0.1

 Primary copper <0.1 <0.1 0.0

 Fluorescent lamp recycling <0.1 <0.1 0.0

 Batteries <0.1 <0.1 0.0

 Primary Hg production c c c

 Mercury compounds c c c

 Byproduct coke c c c

 Refineries c c c

 Miscellaneous sources 1.3 1.4 0.9

 Geothermal power 1.3 1.4 0.9

 Turf products g g g

 Pigments, oil, etc. g g g

TOTAL 144 158 100

d

 Oil

MWC = Municipal waste combustor; MWI = medical waste incinerator; SSI = sewage sludge incinerator. a

Numbers do not add exactly because of rounding. b 

Insufficient information to estimate 1994-1995 emissions. c

Parentheses denote subtotal within larger point source category. d

For the purpose of this inventory, cement kilns that burn hazardous waste for fuel are counted as hazardous waste combustors. e 

 Includes boilers only; does not include residential wood combustion (wood stoves). f

Mercury has been phased out of use. g

U.S. EPA has finalized emission guidelines for these source categories which will reduce mercury emissions by at least an additional h

90 percent over 1995 levels.
7-9

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 is expected. 50

This behavior is observed in the modeling results presented in this

effort as well. It is possible that dry deposition of Hg(0) can occur

from ozone 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(0)

and the other mercury species leads to very much larger scales of

transport and deposition for Hg(0). Generally, air emissions of Hg(0)

from anthropogenic sources, fluxes of Hg(0) from contaminated soils

and water bodies, and natural fluxes of Hg(0) all contribute to a

global atmospheric mercury reservoir with a holding time of 1⁄2 to 2

years. Global atmospheric circulation systems can take Hg(0)

emissions from their point of origin and carry them 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 Regional Lagrangian Model of Air Pollution

(RELMAP), can explicitly simulate the atmospheric lifetime of gaseous

and particulate mercury species, but not Hg(0). Although Hg(0) is

included as a modeled species in the RELMAP analysis, the vast

majority of Hg(0) emitted in the simulation transports outside the

spatial model domain without depositing, and the same is generally

thought to happen in the real atmosphere. Natural Hg(0) emissions and

anthropogenic Hg(0) emissions from outside the model domain are

simulated in the form of a constant background Hg(0) concentration of

1.6 ng m , approximating conditions observed in remote oceanic -3

regions. This background Hg(0) concentration is subject to simulated 3

wet deposition by the same process as explicitly modeled anthropogenic

sources of Hg(0) within the model domain.

Explicit numerical models of global-scale atmospheric mercury

transport and deposition have not yet been developed. As the general

understanding of the global nature of atmospheric mercury pollution

develops, numerical global-scale atmospheric models will surely

follow.

7.1.2.3. 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. Both 51,52

particulate and gaseous divalent mercury is 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
7-10

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, a result of the reactivity and water 51,52,53

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, and though elemental mercury can be formed in 51

soil and water due to the reduction of divalent mercury species by

various mechanisms, this elemental mercury is expected to volatilize

into the atmosphere. In fact, it has been suggested that in-situ 27

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. Water does contain an amount of 3

dissolved gaseous elemental mercury, but it is minor in comparison to 54

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. indicated that forest 51

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 indicates that this does occur but only 55

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. noted this may be explained by the 56

volatilization of elemental mercury from the canopy/soil system, most

likely the soil. Hanson et al. stated that "dry foliar surfaces in 55

terrestrial forest landscapes may not be a net sink for atmospheric

elemental mercury, but rather as 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.57

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 Hg(0) does not appear to be a
7-11

significant pathway for removal of atmospheric mercury, although

approximately 95 percent or more of atmospheric mercury is Hg(0). 3

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 reduction-oxidation balance are thought to

be oxidation of elemental mercury with ozone, reduction of divalent

mercury by sulfite (SO ) ions, or complexation of divalent mercury 3

-2

with soot to form particulate divalent mercury:

Hg(0)(g) -> Hg(0)(aq)

Hg(0)(aq) + O (aq) -> Hg(II)(aq) 3

Hg(II)(aq) + soot/possible evaporation -> Hg(II)(p)

Hg(II)(aq) + SO (aq) -> Hg(0)(aq) 3

-2

(g) = gas phase molecule

(aq) = aqueous phase molecule

(p) = particulate phase molecule

The Hg(II) produced from oxidation of Hg(0) by ozone can be reduced

back to Hg(0) by sulfite; however, the oxidation of Hg(0) by ozone is

a much faster reaction than the reduction of Hg(II) by sulfite. Thus,

a 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(0)(g), O (g), H (representing acids) and SO (g). Note that H and 3 2 + 28 +

SO (g), although not apparent in the listed atmospheric reactions, 2

control 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(0) can occur

from ozone 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. 58

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(0) to 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. 
7-12

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(0). Soil Hg(II) may also be 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 water body may also be methylated to form

methylmercury; both Hg(II) and methylmercury may be reduced to form

Hg(0) which is reintroduced to the atmosphere. 

7.1.2.4 Re-emissions of Mercury into the Atmosphere. Reemission 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 percent (10

Mmol/year) of the total mercury flux to the atmosphere. Overall, 70 39

to 80 percent of total current mercury emissions may be related to

anthropogenic activities. By considering the current global mercury 34

budget and estimates of the preindustrial mercury fluxes, Mason et

al. estimate that total emissions have increased by a factor of 4.5 39

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. Fitzgerald and Mason estimate that if all anthropogenic 34

emissions were stopped, 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 the EPA’s

Mercury Study, concluded that it could take significantly longer. 

There is scientific agreement, however, that the slow release of

mercury from terrestrial sinks to freshwater and coastal waters will

persist for a long time, probably decades, which effectively increases

the length of time anthropogenic emissions would impact the

environment. This is particularly significant given that the surface

soils contain most of the pollution-derived mercury of the industrial

period. As a result, it is uncertain at this time how long it would

take after reductions in anthropogenic emissions for mercury levels in

the global environment, including fish levels, to return to true

background levels. The slow release of mercury from terrestrial sinks

to freshwater and coastal waters will likely persist for much longer,

possibly decades, effectively increasing the lifetime of anthropogenic

mercury further. This may be particularly significant considering 34

that surface soils currently contain most of the pollution-derived
7-13

mercury of the industrial period. Thus, re-emissions of past

anthropogenic mercury emissions will contribute to long-term

influences on the global biogeochemical cycle for mercury.

7.1.3 Terrestrial and Aquatic Fate of Mercury

7.1.3.1 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 complexed with 2 2

organic anions. Although inorganic Hg(II) compounds are quite 59

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 sulfurcontaining 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 affinity of

mercury species for soil results in soil acting as a large reservoir

for anthropogenic mercury emissions. For example, note the mercury 60,23

budget proposed by Meili et al. Even if anthropogenic emissions were 60

to stop entirely, leaching of mercury from soil would not be expected

to diminish for many years. Hg(0) can be formed in soil by reduction 23

of Hg(II) compounds/complexes mediated by humic substances. This 25

Hg(0) will vaporize eventually and re-enter the atmosphere. 

Methylmercury can be formed by various microbial processes acting on

Hg(II) substances. Approximately 1-3 percent 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 percent

of total soil mercury can be considered largely Hg(II) complexes,

although a small fraction of Hg in typical soil will be Hg(0). 61

7.1.3.2 Plant and Animal Uptake of Mercury. While there is a

great deal of uncertainty surrounding air-to-plant transfer of

mercury, some evidence indicates that this pathway may be an important

source of mercury to soils via defoliation. Overall, mercury

concentrations in plants, even those whose main uptake appears to be

from the air, are expected from modeling results to be low. This

prediction is corroborated by low reported mercury concentrations in

most green plants, although the data set of these values is not

complete and there are some exceptions. The bulk of the mercury in

plants appears to be inorganic. Livestock typically accumulate 50

little mercury from foraging or silage/grain consumption, and the

mercury content of meat is low. Due to these factors, the terrestrial
7-14

pathway is not expected to be significant, particularly when compared

to the consumption of fish by humans. Since this is not an exposure

pathway of concern for mercury, it was not included in the modeling

that follows.

7.1.3.3 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); and 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 in aquatic media 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

these 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).

Most of the mercury in the water column, Hg(II) and

methylmercury, will be bound to organic matter, either to dissolved

organic carbon or to suspended particulate matter. In most cases, 62,28

studies that refer to the dissolved mercury in water include mercury

complexes with DOC. Studies indicate that about 25-60 percent of

Hg(II) and methylmercury organic complexes are particle-bound in the

water column. The rest is in the dissolved, bound-to-DOC phase. 25,63

Hg(0) is produced in fresh water by humic acid reduction of Hg(II) or

demethylation of methylmercury. Some will remain in the dissolved

gaseous state, but most will volatilize. As noted previously, Hg(0)

constitutes very little of the total mercury in 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(0) -> volatilization. 

For many lakes, however, sedimentation of the Hg(II) and methylmercury

bound to particulate matter is expected to be the dominant process for

removal of mercury from the water column.24

Generally, no more than 25 percent of the total mercury in a

water column exists as a methylmercury complex; typically, less than

10 percent is observed. This is a result of methylation of Hg(II)

which is thought to occur in the bottom sediment and the water column

by microbial action and abiotic processes. An equilibrium is soon

established between Hg(II) and methylmercury in freshwater systems; 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. 64

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.
7-15

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. Only mercury in the water column,

the sediment, and other aquatic biota appears to be available to

aquatic organisms for uptake.

Methylation appears to be a key step in the entrance of mercury

into the food chain. The biotransformation of inorganic mercury 24

species to methylated organic species in water bodies can occur in the

sediment and the water column. Abiotic processes (e.g., humic and

fulvic acids in solution) also appear to methylate the mercuric ion. 

All mercury compounds entering an aquatic ecosystem are not

methylated, and demethylation reactions as well as volatilization of

dimethylmercury decrease the amount of methylmercury available in the

aquatic environment. It is clear that there is a large degree of

scientific uncertainty and variability among water bodies concerning

the processes that methylate mercury.24

Methylmercury is very bioavailable and accumulates in fish

through the aquatic food web; nearly 100 percent of the mercury found

in fish muscle tissue is methylated. Methylmercury appears to be 24

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. Most of the total methylmercury production ends up

in biota, particularly fish. Overall, methylmercury production and

accumulation in the freshwater ecosystem places this pollutant into a

position to be ingested by fish-eating organisms.

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 humans through ingestion. Methylmercury appears to

pass from the human gastrointestinal tract into the bloodstream more

efficiently than the divalent species. 

7.1.3.4 Fate of Mercury in Marine Environments. 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
7-16

sources of mercury to the atmosphere and as environmental mercury

sinks. The forms and species of mercury present in the ocean 65,66,67

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 a human health perspective is monomethylmercury (MHg). 

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).24

7.2 MERCURY HEALTH EFFECTS

A brief summary of the health effects of methylmercury is

presented here. The 1997 EPA Mercury Study Report to Congress68

contains more information on the health effects of mercury and mercury

compounds.

Most of the population of the earth have some exposure to mercury

as a result of normal daily activities. The general population may be

exposed to mercury through inhalation of ambient air; consumption of

contaminated food, water, or soil; and/or dermal exposure to

substances containing mercury. In addition, some quantity of mercury

is released from dental amalgam.

The health effects literature contains many investigations of

populations with potentially high exposure to mercury, including

industrial workers, people living near point sources of mercury

emissions, people who consume large amounts of fish, and dental

professionals. There also are numerous studies of populations exposed

to high levels of mercury, such as the Minamata poisoning episode in

Japan. Volume IV of the EPA’s Mercury Study Report to Congress69

presents measured and predicted mercury exposure for various U.S.

populations.

The form of mercury which is emphasized here is methylmercury

because methylmercury is the form of primary interest for human

exposures for this report. It is acknowledged that humans can be

exposed to elemental and inorganic mercury and that certain

populations can be exposed to many types of organic mercurials, such

as antiseptics and pesticides, which are not discussed here.

7.2.1 Toxicokinetics

The toxicokinetics (i.e., absorption, distribution, metabolism,

and excretion) of mercury is highly dependent on the form of mercury

to which a receptor has been exposed. Below is a brief summary of the

toxicokinetics information for methylmercury.

Methylmercury is rapidly and extensively absorbed through the

gastrointestinal tract. Absorption information following inhalation

exposures is limited. This form of mercury is distributed throughout

the body and easily penetrates the blood-brain and placental barriers

in humans and animals. Methylmercury transport into tissues appears

to be mediated by the formation of a methylmercury-cysteine complex. 
7-17

This complex is structurally similar to methionine and is transported

into cells via a widely distributed neutral amino acid carrier

protein. Methylmercury in the body is considered to be relatively

stable and is only slowly demethylated to form mercuric mercury in

rats. It is hypothesized that methylmercury metabolism may be related

to a latent or silent period observed in epidemiological studies

observed as a delay in the onset of specific adverse effects. 

Methylmercury has a relatively long biological half-life in humans;

estimates range from 44 to 80 days. Excretion occurs via the feces,

breast milk, and urine.

7.2.2 Biological Effects

The primary targets for toxicity of mercury and mercury compounds

are the nervous system, kidney, and developing fetus. Other systems

that may be affected include the respiratory, cardiovascular,

gastrointestinal, hematologic, immune, and reproductive systems. A

brief summary of the biological effects of methylmercury is presented

here. 

Three human studies that examined the relationship between

methylmercury and cancer incidence were considered extremely limited

because of study design inappropriate for risk assessment or

incomplete data reporting. Evidence from animal studies provides

limited evidence of carcinogenicity. Male ICR and B6C3F1 mice exposed

orally to methylmercuric chloride were observed to have an increased

incidence of renal adenomas, adenocarcinomas, and carcinomas. Renal

epithelial cell hyperplasia and tumors, however, were observed only in

the presence of profound nephrotoxicity suggesting that the tumors may

be a consequence of reparative changes to the damaged kidneys. Tumors

were observed at a single site, in a single species and sex.

Methylmercury appears to be clastogenic but not a potent mutagen. 

Studies have also shown evidence that methylmercury may induce

mammalian germ cell chromosome aberrations. There are a number of

studies in both humans and experimental animals that show

methylmercury to be a developmental toxicant. Neurotoxicity in

offspring is the most commonly observed effect and the effect seen at

lowest exposures.

A significant body of human studies exists for evaluating the

potential systemic toxicity of methylmercury. This data base is the

result of studying two large scale poisoning episodes in Japan and

Iraq as well as several epidemiological studies assessing populations

that consume significant quantities of fish. In addition, much

research on the toxicity of methylmercury has been conducted in

animals including non-human primates.

The critical target for methylmercury toxicity is the nervous

system. The developing fetus may be at particular risk from

methylmercury exposure. Offspring born of women exposed to high doses

of methylmercury during pregnancy have exhibited a variety of

developmental neurological abnormalities, including the following: 
7-18

delayed onset of walking, delayed onset of talking, cerebral palsy,

altered muscle tone and deep tendon reflexes, and reduced neurological

test scores. Maternal toxicity may or may not have been present

during pregnancy for those offspring exhibiting adverse effects. For

the general population, the critical effects observed following

methylmercury exposure are multiple central nervous system effects

including ataxia and paresthesia.

 A latent or silent period has been observed in some

epidemiological and animal studies indicating a delay in the onset of

adverse effects. It is hypothesized this delay may be related to

methylmercury metabolism.

7.2.3 Sensitive Subpopulations

A susceptible population is a group that may experience more

severe adverse effects at comparable exposure levels or adverse

effects at lower exposure levels than the general population. The

greater response of these sensitive subpopulations may be a result of

a variety of intrinsic or extrinsic factors. For mercury, the most

sensitive subpopulations may be developing organisms. Data are also

available indicating that other factors may be associated with the

identification of sensitive subpopulations including the following: 

age; gender; dietary insufficiencies of zinc, glutathione, or

antioxidants; predisposition for autoimmune glomerulonephritis; and

predisposition for acrodynia. 

7.2.4 Interactions

There are data demonstrating that a number of substances affect

the pharmacokinetics and/or toxicity of mercury compounds. Of most

interest is the potential interaction of selenium and mercury. 

Selenium is known to bioaccumulate in fish, so exposure to

methylmercury from fish consumption may be associated with exposure to

increased levels of selenium. There are data indicating that selenium

co-administered with methylmercury can form selenium-methylmercury

complexes. The formation of these complexes may temporarily prevent

methylmercury-induced tissue damage but also may delay excretion of

the methylmercury. Thus, formation of selenium-methylmercury

complexes may not reduce methylmercury toxicity but rather may delay

onset of symptoms. More information is needed to understand the

possible interaction of selenium with methylmercury. There is also

potential for interaction between various forms of mercury and

ethanol, thiol compounds, tellurium, potassium dichromate, zinc,

atrazine, and vitamins C and E.

7.2.5 Hazard Identification/Dose-Response Assessment

The available toxicological and epidemiological evidence was

evaluated, and U.S. EPA risk assessment guidelines and methodologies

were applied to hazard identification for various endpoints; namely,

carcinogenicity, germ cell mutagenicity, developmental toxicity, and
The oral RfD is an estimate (with uncertainty spanning perhaps an order a

of magnitude) of a daily exposure to the human population (including

sensitive subpopulations) that is likely to be without an appreciable

risk of deleterious health effects during a lifetime.

7-19

general systemic toxicity. Data supported quantitative assessments of

systemic toxicity. An oral reference dose (RfD ) was calculated for a

methylmercury. U.S. EPA derived the RfD for methylmercury by

extrapolating from the high-dose exposures that occurred in the Iraq

incident. Data for carcinogenicity of inorganic and methylmercury

were judged to be inadequate in humans and limited from animal

bioassays. The carcinogenicity data for methylmercury were not

sufficient to support a quantitative assessment. Table 7-2 summarizes

the hazard identification and dose-response information for organic

mercury.

7.2.6 Ongoing Research

While much data has been collected on the potential toxicity of

mercury and mercury compounds, much is still unknown. Two ongoing

epidemiological studies are now providing critical information on the

developmental toxicity of methylmercury. One study, being conducted

in the Seychelles Islands, is evaluating dose-response relationships

in a human population with dietary exposures (fish) at levels believed

to be in the range of the threshold for developmental toxicity. The

second study, conducted in the Faroe Islands, is assessing mercury

exposure in a population that consumes a relatively large quantity of

marine fish and marine mammals. Children exposed to methylmercury in

utero and followed through 6 years of age have been assessed for

mercury exposure and neurological developmental. Because of various

limitations and uncertainties in all of the available data, the U.S.

EPA and other Federal agencies intend to participate in an interagency

review of the human data on methylmercury, including the most recent

studies from the Seychelle Islands and the Faroe Islands. The

purposes of this review are to refine the estimates of the level of

exposure to mercury associated with subtle neurological endpoints and

to further consensus among all of the Federal agencies. After this

process, the U.S. EPA will determine if a change in the RfD for

methylmercury is warranted.

7.2.7 Research Needs

Specifically, information is needed to reduce the uncertainties

associated with the current oral RfD for methylmercury. More work

with respect to both dose and duration of exposure would also allow

for potentially assessing effects above the RfD. Limited evidence

suggests that methylmercury is a possible human carcinogen. Research

on mode of action in induction of tumors at high doses will be of

particular use in defining the nature of the dose response

relationship for carcinogenicity. 
7-20

Table 7-2. Summary of U.S. EPA Hazard Identification/DoseResponse Assessment for Methylmercury

Form Inhalation weight-of- Cancer toxicity 

of Oral RfD RfC evidence slope Germ cell data base

mercury (mg/kg-day) (mg/m ) rating factor mutagenicity characterization 3

Cancer Developmental

Organic 0.0001 n/a C, possible n/a High weight of Sufficient human a

(methyl- human evidence and animal data

mercury) carcinogen

 Critical effect is neurological toxicity in progeny of exposed women, RfD calculated using a benchmark dose (10%). a

There are many uncertainties associated with the health effects

data analysis, due to an incomplete understanding of the toxicity

of methylmercury. The sources of uncertainty include the

following:

 The data serving as the basis for the methylmercury RfD were

from a population ingesting contaminated seed grain. The

nutritional status of this group may not be similar to that

of U.S. populations. The exposure was for a short, albeit

critical, period of time. It is likely that there is a

range of response among individuals to methylmercury

exposure. The selenium status of the exposed Iraqi

population is not certain, nor is it established the extent

to which selenium has an effect on mercury toxicity.

 There was no NOAEL (no-observable-adverse-effect level) for

estimation of a threshold for all developmental endpoints. 

A benchmark was estimated using a Weibull model on grouped

data. Use of an estimate other than the 95 percent lower

limit on 10 percent response provides alternate estimates. 

Other modeling approaches using data which have not been

grouped provide similar estimates. Benchmark doses, NOAELs,

and LOAELs from other human studies provide support for the

benchmark used in the RfD.

 Ingestion levels of methylmercury associated with measured

mercury in hair were estimated based on pharmacokinetic

parameters derived from evaluation of the extant literature. 

Use of other plausible values for these parameters results

in (relatively small) changes in the exposure estimate.

 While there are data to show that the developing fetus is

more susceptible to methylmercury toxicity than adults,

there are not sufficient data to support calculation of a

separate RfD for children (vs. adults).
7-21

To improve the risk assessment for methylmercury, U.S. EPA would

need the following:

 Results from ongoing studies in human populations with

measurable exposure to methylmercury, and new research on

actual consumption patterns and estimated methylmercury

exposure of the subpopulations of concern, with validation

by analysis of hair samples from a representative sample of

members of this subpopulation. 

 Reproductive studies and analysis.

 Data on mode of action of methylmercury tumor induction.

 Validated physiologically-based pharmacokinetic models for

mercury which include a fetal component.

Based on the extant data and knowledge of developing studies, the

following outcome can be expected:

 Human populations exposed to sufficiently high levels of

methylmercury either in utero or post partum will have

increased incidence of neurotoxic effects.

7.3 MERCURY CONCENTRATIONS IN BIOTA

The Mercury Study Report to Congress documents many

concentrations in animals and plants. Concentrations in abiotic 24

environmental components consist primarily of inorganic species. 

While these concentrations may be elevated in specific areas, fish

concentrations are generally of highest concern when assessing risks

posed by emitted mercury. The concern stems from the consumption of

fish by humans and the form of mercury, methylmercury, which fish

bioaccumulate. Methylmercury, which is the primary form of mercury

found in fish tissue, is a human neurotoxin and is readily absorbed

into the human body through the gastrointestinal tract. Fish

methylmercury concentrations result from existing environmental

concentrations of mercury (which may consist of mercury from

anthropogenic and natural sources, as well as mercury which has been

re-emitted from the oceans or soils) and deposition from the global

reservoir (which includes mercury emitted by other countries). 

Given the current scientific understanding of the environmental

fate and transport of this element, it is not possible to quantify how

much of the methylmercury in fish consumed by the U.S. population is

contributed by U.S. emissions relative to other sources of mercury

(such as natural sources and re-emissions from the global pool). As a

result, it cannot be assumed that a change in total mercury emissions

will be linearly related to any resulting change in methylmercury in

fish, nor over what time period these changes would occur. This is an

area of ongoing study.
7-22

7.4 MEASUREMENT DATA NEAR UTILITIES

The measured mercury concentrations in environmental media around

utilities are briefly summarized in this section. These data are not

derived from a comprehensive study of mercury around utilities. 

Despite the need for this effort, such a study does not appear to

exist. The quality of the following studies has not been assessed by

the U.S. EPA. The data do not appear to be directly comparable among

themselves because of differences in analytic techniques and

collection methods used. Some of these studies are from older

literature and may not reflect current mercury emissions from the

sources described.

Anderson and Smith measured mercury levels in environmental media 70

and biota around a 200-MW coal-fired utility in Illinois. The

facility had 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). Levels of mercury detected in atmospheric particulate

samples collected 4.8 and 9.6 km downwind of the facility were not

statistically significantly elevated when compared with samples

collected 4.8 km upwind of the site. Mercury levels detected in

samples from the upper 2 cm of downwind agricultural soils (sample

mean 0.022 ppm mercury) were statistically significantly elevated when

compared with upwind samples (0.015 ppm mercury). Core sediment

sampling from a nearby lake bed showed statistically significant

elevations in sediment mercury concentrations after plant operations

began (sample mean 0.049 ppm mercury) when compared with sediment

deposits prior to operation (0.037 ppm mercury). No increases were

observed in mercury levels in fish from the nearby lake when compared

with fish from remote lakes. 

Crockett and Kinnison sampled the arid soils around a 2,150-MW 71

coal-fired utility in New Mexico in 1974. The four-stack (two stacks

76 m high and two 91 m high) facility had been operational since 1963

and had an estimated mercury release rate of 850 kg/yr. The rainfall

in the area averaged 15 to 20 cm/yr. 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 larger area, rather than deposited locally. 

Measurement data near other types of anthropogenic sources are

discussed in the 1997 EPA Mercury Study Report to Congress. 24

7.5 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 exposures to mercury were predicted

based on modeling results.
7-23

7.5.1 Models Used

The extant measured mercury data alone were judged insufficient

for a national assessment of mercury exposure for humans from utility

units. Thus, the decision was made to model the mercury emissions. 

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 fish

consumers. The models used are described in Table 7-3.

7.5.2 Modeling of Long-Range Fate and Transport of Mercury

The goal of this analysis was to model the emission, transport,

and fate of airborne mercury over the continental United States using

the meteorologic data for the year of 1989 and the most current

utility mercury 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 emitted by

utility boilers is deposited back to United States 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 1989 was used since most

of the continental United States experienced near average weather

conditions during that year. To estimate the quantity of mercury

emitted by utilities that deposits in the United States, and

specifically which geographic regions may be more highly impacted,

information on chemical and physical forms of the mercury emissions

was needed since these characteristics determine the rate and location

of the wet and dry deposition processes for mercury.

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 United States. The

emission, transport, and fate of airborne mercury over the continental

United States was modeled using meteorological data for the year 1989. 

The utility emission data used were those presented in the Mercury

Study Report to Congress. Emission data are shown in Table 7-4. 20

The RELMAP model was originally developed to estimate

concentrations of sulfur and sulfur compounds in the atmosphere and

rainwater in the eastern United States. The primary modification of

RELMAP was the handling of three species of mercury (elemental,

divalent, and particulate) and carbon soot (or total carbon aerosol). 24

A complete description of the RELMAP mercury model is presented in the

Mercury Study Report to Congress. The results of the RELMAP modeling 24

are shown in Figures 7-2 through 7-4.
7-24

Table 7-3. 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 anthropocentric sources of 2

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. Predicts human exposure based on

these predicted concentrations and human activity patterns.

RELMAP = Regional Lagrangian Model of Air Pollution

IEM = Indirect exposure methodology

ISC = Industrial Source Complex

Table 7-4. Mercury Emissions Inventory Used in the RELMAP

Modeling (Based on the 1994-95 Estimates)

Mercury emission source type

Emissions

(kg/yr) Hg(0) Hg Hg 

Assumed speciation percentages

a 2+ b

P

c

Electric utility boilers (coal, oil and gas) 46,183 50 30 20

 Hg(0) represents elemental mercury gas a

 Hg represents divalent mercury gas b 2+

 Hg represents particulate mercury c

P



7-28

A computer simulation of long-range transport of mercury

emissions from all United States sources conducted for the EPA’s 1997

Mercury Study Report to Congress suggests that about one-third (~ 52

tons) of the 158 tpy of United States anthropogenic emissions are

deposited, through wet and dry deposition, within the lower 48 States. 

The remaining two-thirds (~ 107 tons) is transported outside of United

States borders where it diffuses into the global reservoir. In

addition, the computer simulation suggests that another 35 tons of

mercury from the global reservoir is deposited for a total deposition

of roughly 87 tons. Although this type of modeling is uncertain, the

simulation suggests that about three times as much mercury is being

added to the global reservoir from United States sources as is being

deposited from it. What is not uncertain is that additional emissions

to air will contribute to levels in the global reservoir, and

concomitant deposition to water bodies.

Long-range transport modeling conducted as part of this Utility

Study predicts that approximately 30 percent (i.e., 15 tpy) of the

utility mercury emissions deposit in the continental United States. 

The estimated annual deposition rates resulting from utility mercury

emissions range from 0.5 to greater than 10 g per square meter.

7.5.3 Modeling the Local Transport of Mercury in the Atmosphere

The program used to model the transport of the anthropogenic

mercury within 50km of an emissions source was the Industrial Source 

Complex Version 3 (ISC3) gas deposition model obtained from the United

States EPA’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. The issues related to using

this program to model emitted mercury in the local atmosphere are

detailed in Volume III of the Mercury Study Report to Congress. 24

 The phase and oxidation state of emitted mercury is thought to

be of critical importance in determining atmospheric fate. Only Hg(0)

and Hg were considered in the air dispersion modeling. At the point +2

of stack emission and during atmospheric transport, the contaminant is

partitioned between two physical phases: vapor and particle-bound. 

It was assumed that 25 percent of the divalent emissions from an

individual source would attach to particles in the plume; particle

sizes were assumed to reflect ambient particle data.

7.5.3.1 Development and Description of Model Plants. Model

plants representing four utility boilers were developed to represent a

range of mercury emissions from this source. 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
7-29

speciation (see Table 7-5). Emission estimates were assumed to

represent typical emission levels emitted from existing sources.20,24

7.5.3.2. Hypothetical Locations of Model Plants. There are a

variety of geographic aspects that can influence the dispersion of

mercury emissions from a utility boiler. 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 criterion specifically utilized was total

yearly rainfall. (See Volume III of the Mercury Study Report to

Congress for details.)24

Terrain features refer to the variability of the receptor height

with respect to a local source. Two main types of terrain were 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 refers 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 affect

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 Volume III of the Mercury Study

Report to Congress). The primary differences between the two sites as 24

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 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

7.5.4 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,
7-30

Table 7-5. Process Parameters for Model Plants

Model plant Plant size (% of year) height (ft) diameter (ft) (kg/yr) (Hg(0)/Hg /Hg ) (m/sec) (°F)

Capacity Stack Stack rate percent velocity temperature

Hg emission Speciation Exit Exit

2+ P

Large coal-fired 975 Megawatts 65% 732 27 230 50/30/20 31.1 273

utility boiler

Medium coal-fired 375 Megawatts 65% 465 18 90 50/30/20 26.7 275

utility boiler

Small coal-fired 100 Megawatts 65% 266 12 10 50/30/20 6.6 295

utility boiler

Medium oil-fired 285 Megawatts 65% 290 14 2 50/30/20 20.7 322

utility boiler

Hg(0) = elemental mercury;

Hg = divalent vapor phase mercury; 2+

Hg = particle-bound mercury P

drive calculations of concentrations in the associated biota and fish,

which humans are assumed to consume. The watershed model used for

this report, Indirect Exposure Methodology Version 2M (IEM-2M), was

adapted from the more general IEM-2 methodology to handle mercury 30,31

fate in soils and water bodies. It is described completely in the EPA

Mercury Study Report to Congress, Volume III.24

7.5.4.1 Overview of the Watershed Model. IEM-2M simulates three

chemical components: elemental mercury, Hg(0); divalent mercury,

HgII; and methylmercury, MHg. In the previous version of IEM-2, these

components were assumed to be in a fixed ratio with each other as

specified by the fraction elemental (f ) and fraction methyl (f ). 1 3

This version calculates the fractions in each component based on

specified or calculated rate constants. 

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 7-5 and 7-6. 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(0) vapor to watershed soils and the water body. Sinks include

leaching of each component from watershed soils, burial of each

component from lake sediments, volatilization of Hg(0) and MeHg from

the soil and water column, and advection of each component out of the

lake.

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

the mass balance of each mercury component in the surficial soil

layer, in the water column, and in the surficial benthic sediments. 

The equations are solved for a specified interval of time, and

predicted concentrations output at fixed intervals. For each
7-31

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 7-5. Overview of the IEM-2M Watershed Modules
7-32

Figure 7-6. Configuration of Hypothetical Water Body and Watershed

Relative to Local Source
7-33

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.

The nature of this methodology is basically 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

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. The 24

description includes a “benchmarking” exercise with an independent

model, the Mercury Cycling Model.

Mhg concentrations in fish are derived from dissolved MHg water

concentrations using bioaccumulation factors (BAF). Methylmercury 24

concentrations in fish were derived from predicted water column

concentrations of dissolved methylmercury by using BAFs for trophic

level 4 fish (Table 7-6). The BAFs selected for these calculations

were estimated from existing field data. The BAF (dissolved

methylmercury basis) for trophic level 4 fish is 1.6 x 10 . 6

Methylmercury was estimated to constitute 7.8 percent of the total

dissolved mercury in the water column, and 65 percent of this was

assumed to be freely dissolved. The potential variability around

these predicted fish residue values is highlighted in Table 7-6, which

shows percentile information for the BAF estimates. 

There are several limitations to the modeling analyses. First,

there is a lack of adequate mercury measurement data near the

anthropogenic atmospheric mercury sources considered in this report. 

To assess how well the modeled data predict actual mercury

concentrations in different environmental media at a variety of

geographic locations requires a database against which to make these

comparisons. The lack of such measured data preclude a comparison of

the modeling results with measured data around these sources. These

data include measured mercury deposition rates as well as measured

concentrations in the atmosphere, soils, water bodies and biota. 

Substantial additional monitoring data would facilitate such

comparison. Second, the IEM-2M has not been validated with sitespecific data. The model was benchmarked against the independentlyderived Regional Mercury Cycling Model (R-MCM), which itself has been

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. Additional limitations are discussed in

later sections below. 
7-34

Table 7-6. Percentiles of the Methylmercury Bioaccumulation

Factor

Parameter

Percentile of distribution

5th 25th 50th 75th 95th

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

BAF = bioaccumulation factor

7.5.5 Exposure Modeling

7.5.5.1 Description of Hypothetical Human Exposure Scenarios. 

Human exposure to environmental mercury is the result of mercury

concentrations at specific human exposure points (e.g., ingested

fish). For each location, mercury exposure was estimated only for

individuals representing several specific subpopulations that consumed

the freshwater fish that inhabited one of the three local lakes. The

individuals representing the subpopulations were defined to model both

average and high-end exposures.

The fish ingestion pathway was the only source of methylmercury

intake assessed. For this assessment, four human fish consumption

scenarios were considered for the hypothetical lakes: (1) an adult

subsistence fish consumer who was assumed to ingest large amounts of

locally-caught fish; (2) a child of a subsistence local fish consumer;

(3) a high-end recreational angler; and 4) an average local fish

consumer. These consumption scenarios were thought to represent

identified fish-consuming subpopulations in the United States.

Fish for human consumption from local water bodies can be derived

from many sources including self-caught, gifts, and grocery and

restaurant purchases. For the purposes of this study, all fish 

consumed were assumed to originate from the hypothetical lakes, which

were considered to represent several small lakes that might be present

in the type of hypothetical locations considered. No commercial

distribution of locally caught fish was assumed; exposure to locallycaught fish was modeled for the fish-consuming subpopulations

described above.

Fish consumption rates for the three fish-consuming

subpopulations were derived from the Columbia River Inter-Tribal Fish

Commission report and the draft EPA Exposure Factors Handbook. 72 73

Other estimates of human fish consumption rates are reported in the

Exposure Factors Handbook. The estimates presented highlight the 73

broad variability in consumption rates. The Columbia River InterTribal Fish Commission report estimated fish consumption rates for 72

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
7-35

participants had been selected from patient registrations lists

provided by the Indian Health Service. Adults interviewed provided

information on fish consumption for themselves and for 204 children

under five years of age.

Fish consumption rates for tribal members are shown in Tables 7-7

and 7-8. The values used in this study are shown in Table 7-9. The

values listed below reflect an annual average, but monthly variations

were also reported. For example, the average daily consumption rate

during the two highest intake months was 107.8 g/day, and the daily

consumption rate during the two lowest consumption months was

30.7 g/day. Fish were consumed by over 90 percent of the surveyed

population, with only 9 percent of the respondents reporting no fish

consumption. The maximum daily consumption rate for fish reported by

one member of this group was 972 g/day. Since most of the population

consisted of fish consumers, utilization of per capita estimates was

considered appropriate.

The Exposure Factors Handbook describes many freshwater fish 73

consumption studies. The mean daily consumption rates derived for

recreational freshwater anglers from the compiled studies range from

5-17 g/day; the derived 95th percentile range was 8-25 g/day. The

value of 30 g/day clearly exceeds the 95th percentile; this individual

is a high-end consumer. The recommended mean intake for subsistence

populations was 70 g/day, and the 95th percentile was 170 g/day. The

value of 60 g/day which is used for the subsistence adult is lower 

than the recommended mean. The body weights used for the adult and

child were 70 and 17 kg.74

7.6 RESULTS

Tables 7-10 through 7-13 present the results of the multipathway

modeling analysis. The results are based on a model plant analysis

and are for hypothetical scenarios. Therefore, the results do not

apply to any specific utility plant and contain significant

uncertainties. 

In all cases, the average air concentrations are predicted to be

dominated by the regional contribution of utilities rather than the

single local source modeled. This is largely due to the high

effective stack heights exhibited by the sources. The largest

contribution of 35 percent is for the medium coal-fired utility boiler

(MCUB) in the western site. The western site is predicted to have

lower concentrations of mercury as a result of regional transport.

At both the eastern and western sites using the 50th percentile

RELMAP results, the deposition rates, soil concentrations, and fish

concentrations are usually dominated by the local coal-fired utility

source within 10 km of the source. The small coal-fired utility

boiler (SCUB) at the eastern site is the exception due to the higher

deposition rate from regional sources. In the eastern site regional

sources dominate the deposition rates, soil concentrations, and fish
7-36

Table 7-7. Fish Consumption Rates for Columbia River Tribes 72

Subpopulation Mean daily fish consumption (g/day)

Total adult population, aged 18 years and older 59

Children, aged 5 years and younger 20

Adult females 56

Adult males 63

Table 7-8. Daily Fish Consumption Rates Among Adults in the

Columbia River Tribes72

Percentile g/day

50th 29-32

90th 97-130

95th 170

99th 389

Table 7-9. Fish Consumption Rates Used in This Study

Subpopulation Fish consumption rate (g/day)a

Subsistence adult 60 a

High-end child 20 a

Recreational angler 8

High-end recreational angler 30 a

 Columbia River Inter-Tribal Commission, 1994. a
7-37

Table 7-10. Model Results for Eastern Site, RELMAP 50 Percentile (utilities only

th

Facility Distance (ng/m3) %RELMAP %ISC3 Deposition (μg/m2/yr) %RELMAP %ISC3 (ng/g) %RELMAP %ISC3 (u

Air concentration Soil concentration Tier 4 fish c

Large coal-fired utility 2.5 km 0.026 93% 7% 17.9 13% 87% 31 13% 87% 0boiler

10 km 0.026 91% 9% 5.27 44% 56% 9.1 44% 56% 025 km 0.026 92% 8% 3.4 69% 31% 5.8 69% 31% 0

Medium coal-fired utility 2.5 km 0.027 89% 11% 9.12 26% 74% 16 26% 74% 0boiler

10 km 0.028 88% 12% 4.17 56% 44% 7.2 56% 44% 025 km 0.027 90% 10% 3.19 73% 27% 5.5 73% 27% 0

Small coal-fired utility 2.5 km 0.028 87% 13% 3.94 59% 41% 6.8 59% 41% 0boiler

10 km 0.027 91% 9% 2.93 80% 20% 5 80% 20% 025 km 0.025 96% 4% 2.54 92% 8% 4.4 92% 8% 0

Medium oil-fired utility 2.5 km 0.024 99% 1% 2.53 93% 7% 4.3 93% 7% 0boiler

10 km 0.024 99% 1% 2.41 97% 3% 4.1 97% 3% 025 km 0.024 99% 1% 2.37 99% 1% 4.1 99% 1% 0

Table 7-11. Model Results for Western Site, RELMAP 50 Percentile (utilities only

th

Facility Distance (ng/m3) %RELMAP %ISC3 Deposition (ug/m2/yr) %RELMAP %ISC3 (ng/g) %RelMap %ISC3 (u

Air concentration Soil concentration Tier 4 fish c

Large coal-fired utility 2.5 km 0.0061 87% 13% 3.9 10% 90% 6.4 10% 90% 0

boiler

10 km 0.0066 80% 20% 1.51 25% 75% 2.5 25% 75% 025 km 0.0073 73% 27% 1.4 27% 73% 2.3 27% 73% 0

Medium coal-fired utility 2.5 km 0.007 76% 24% 2.41 16% 84% 3.9 16% 84% 0boiler

10 km 0.0081 65% 35% 1.75 22% 78% 2.9 22% 78% 025 km 0.0076 69% 31% 1.26 30% 70% 2.1 30% 70% 0

Small coal-fired utility boiler 2.5 km 0.0077 69% 31% 1.44 26% 74% 2.4 26% 74% 0

10 km 0.0067 79% 21% 0.836 45% 55% 1.4 45% 55% 025 km 0.006 89% 11% 0.535 71% 29% 0.88 71% 29% 0

Medium oil-fired utility 2.5 km 0.0054 97% 3% 0.471 80% 20% 0.77 80% 20% 0boiler

10 km 0.0054 97% 3% 0.439 86% 14% 0.72 86% 14% 025 km 0.0054 98% 2% 0.405 93% 7% 0.66 93% 7% 0.
7-38

Table 7-12. Predicted Exposure Results for Eastern Site, RELMAP 50th Percentile (

MHg Exposure (mg/kg/day)

Facility Distance Subsistence Fisher Fisher Recreational Angler angler %RELM

Child of Subsistence Average recreational

Large coal-fired utility boiler 2.5 km 3.7E-04 5.1E-04 1.8E-04 4.9E-05 10% 

10 km 9.0E-05 1.2E-04 4.5E-05 1.2E-05 42% 25 km 5.5E-05 7.6E-05 2.8E-05 7.4E-06 68% 

Medium coal-fired utility boiler 2.5 km 1.8E-04 2.4E-04 8.9E-05 2.4E-05 21% 

10 km 7.0E-05 9.6E-05 3.5E-05 9.3E-06 54% 25 km 5.2E-05 7.1E-05 2.6E-05 6.9E-06 73% 

Small coal-fired utility boiler 2.5 km 6.9E-05 9.4E-05 3.4E-05 9.2E-06 55% 

10 km 4.8E-05 6.6E-05 2.4E-05 6.4E-06 78% 25 km 4.1E-05 5.6E-05 2.0E-05 5.5E-06 92% 

Medium oil-fired utility boiler 2.5 km 4.1E-05 5.7E-05 2.1E-05 5.5E-06 91% 

10 km 3.9E-05 5.3E-05 1.9E-05 5.2E-06 97% 25 km 3.8E-05 5.2E-05 1.9E-05 5.1E-06 99% 

Table 7-13. Predicted Exposure Results for Western Site, RELMAP 50th Percentile (

MHg Exposure (mg/kg/day)

Facility Distance Subsistence fisher fisher Recreational angler angler %RE

Child of subsistence Average recreational

Large coal-fired utility boiler 2.5 km 1.0E-04 1.4E-04 5.1E-05 1.4E-05 

10 km 3.5E-05 4.8E-05 1.7E-05 4.6E-06 225 km 3.0E-05 4.2E-05 1.5E-05 4.0E-06 2

Medium coal-fired utility boiler 2.5 km 5.7E-05 7.8E-05 2.8E-05 7.6E-06 1

10 km 4.0E-05 5.5E-05 2.0E-05 5.4E-06 125 km 2.8E-05 3.8E-05 1.4E-05 3.7E-06 2

Small coal-fired utility boiler 2.5 km 3.5E-05 4.7E-05 1.7E-05 4.6E-06 2

10 km 2.0E-05 2.7E-05 9.9E-06 2.6E-06 325 km 1.1E-05 1.6E-05 5.7E-06 1.5E-06 6

Medium oil-fired utility boiler 2.5 km 9.7E-06 1.3E-05 4.9E-06 1.3E-06 7

10 km 9.3E-06 1.3E-05 4.6E-06 1.2E-06 825 km 8.3E-06 1.1E-05 4.2E-06 1.1E-06 9
7-39

concentrations at 25 km from all four sources considered. In the

western site at 25 km the local coal combustion source emissions still

dominate at 25 km except for the SCUB. The deposition rates at both

sites are dominated by the regional sources when compared to the

medium oil-fired utility model plant.

The contribution of the local source is identical for the

deposition and soil concentrations, but this is not true for the fish

concentration. This is because the surface water receives input of

mercury from both direct deposition and from erosion/runoff from the

watershed. The water body is assumed to lie at the end of the

watershed closest to the facility, and so the contribution of the

local source to the deposition rate to the water body is generally

larger than that for the watershed. This results in a slightly higher

contribution of the local source to the predicted fish concentrations.

The multipathway exposure modeling analysis presented in this

chapter contains substantial uncertainties and is based on model

plants and hypothetical scenarios. Therefore, the results do not

apply to any existing utility plant. The analysis and results are

useful for gaining a better qualitative understanding of the potential

environmental fate of mercury emissions from a model utility plant. 

However, the quantitative results are uncertain. Further research and

analyses are needed to gain a more complete understanding of the

mercury exposures due to utility emissions. 

Based on the model plant, multipathway exposure modeling analysis

of hypothetical scenarios, the daily average methylmercury exposure of

the average hypothetical recreational angler (8 g fish consumed per

day) is not predicted to exceed the RfD of 1 x 10 mg/kg/day under any -4

combination of source, site, and distance. The daily average

methylmercury exposure of the high-end hypothetical recreational

angler (30 g fish consumed per day) is predicted to exceed the RfD in

the eastern site at 2.5 km from the large coal-fired utility boiler

(LCUB). The daily average methylmercury exposure of the hypothetical

subsistence angler (60 g fish consumed per day) is predicted to exceed

the RfD in the eastern site at 2.5 km from the LCUB and at 2.5 km from

the MCUB. The daily average methylmercury exposure of the

hypothetical subsistence angler is predicted to exceed the RfD in the

western site at 2.5 km from the LCUB. Fish consumption by children is

predicted to exceed the RfD for several hypothetical general cases: at

2.5 km from the LCUB at either site and at 2.5 km from the MCUB at the

eastern site. Background exposures were not considered in this

analysis. If background exposures due to other anthropogenic and

natural sources were considered, this would obviously result in higher

predicted exposures. Total exposures and background exposures are

discussed and analyzed in the 1997 EPA Mercury Study Report to

Congress.24,26,69
7-40

7.7 CONCLUSIONS

Long-Range Transport Analysis

Based on modeling analysis of the wet and dry deposition of

utility air emissions of all forms of mercury within the continental

United States, the Agency finds that the following geographic areas

have the highest annual rate of total deposition of mercury in all

forms (above the 90th percentile level):

 Southeastern Great Lakes and Ohio River Valley

 Most of central and western Pennsylvania

 The urban corridor from Washington, DC, to New York City.

 In the vicinity of many of the larger cities in the eastern

United States and in numerous isolated locations where

relatively large coal-fired utilities are located.

Areas Predicted to be Least Impacted by Atmospheric Deposition of

Mercury from Utilities

Based on modeling analysis of the wet and dry deposition of

utility emissions of all forms of mercury within the continental

United States, the Agency predicts that the following geographic areas

have the lowest annual rate of total deposition of mercury in all

forms (below the 10th percentile level):

 Most of the Pacific Coast and Great Basin regions

 Parts of the northern Rocky Mountain region.

The three principal factors that contribute to these modeled

deposition patterns are:

 the emission source locations, 

 the amount of divalent and particulate mercury emitted or

formed in the atmosphere, and 

 climate and meteorology. 

A facility located in a humid climate is predicted to have a higher

annual rate of mercury deposition than a facility located in an arid

climate. The critical variables within the model are:

 the estimated washout ratios of elemental and divalent

mercury, and 

 the annual amount of precipitation.
7-41

Precipitation is important because it removes various forms of mercury

from the atmosphere and deposits them to the surface of the earth.

The chemical form of emitted mercury is a critical factor in its

fate, transport, and toxicity in the environment. The form

distributions, or speciation factors, define the estimated fraction of

mercury emitted as elemental mercury (Hg0), divalent mercury (Hg ), or 2+

mercury associated with particulates (Hgp). A wide variety of

alternate speciation scenarios have been investigated to measure the

sensitivity of the RELMAP results to this uncertainty. The results 74,75

show that the total simulated wet and dry deposition of mercury to the

continental United States is strongly and positively correlated to the

fraction of mercury emitted as Hg and Hgp for all major source types. 2+

The differences between the results for the eastern and western

sites are due primarily to the differences in the frequency and

intensity of precipitation. At the eastern site, precipitation occurs

about 12 percent of the year, with about 5 percent of this

precipitation of moderate intensity (0.11 to 0.30 inches per hour). 

By comparison, at the western site, precipitation occurs about 3

percent of the year, with about 2 percent of the precipitation of

moderate intensity.

Assessment of Watershed Fate

The atmospheric mercury concentrations and deposition rates

estimated using the RELMAP and ISC3 were then used as inputs in the

watershed model, IEM-2M, to derive 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 are assumed to consume. 

IEM-2M Model Sensitivity

For a specific 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

Hg(II) 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
7-42

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

reaction 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 appear 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 methylmercury mobility from the

sediments to the water column; these factors include sediment DOC and

sediment-pore water partition coefficients.

Limitations of the Local Scale and Watershed Analyses

There are limitations associated with the fate and transport

analyses. These have to do to a large degree with the current stateof-the-science concerning mercury fate and transport in the

terrestrial and aquatic environments and variability between

waterbodies. A few important limitations were discussed previously. 

Additional important limitations are discussed below.

 There is a lack of information characterizing the movement

of mercury from watershed soils to water bodies and the

rates at which mercury converts from one chemical species to

another. There appears to be a great deal of variability in

these factors among watersheds. 
7-43

 There are not conclusive data on the amount of and rates of

mercury methylation in different types of water bodies. In

addition, 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.

Conclusions Regarding Mercury Fate and Transport in the Environment

The uncertainty inherent in the modeled estimates in this study

arises from many individual assumptions present within the three

models. Because of these uncertainties, EPA interpreted the model

results qualitatively rather than quantitatively as follows.

The analyses conducted for this study as well as for the EPA’s

Mercury Study and available scientific knowledge indicate that human

activities today are adding to the mercury reservoirs that already

exist in land, water, and air, both naturally and as a result of

previous human activities. 

The analysis of mercury fate and transport conducted for this

study, in conjunction with available scientific knowledge, supports a

plausible link between mercury emissions from utility combustion

sources and mercury concentrations in air, soil, water, and sediments. 

The critical variables contributing to this linkage are:

 the species of mercury that are emitted from the sources,

with Hg0 mostly contributing to concentrations in ambient

air and Hg mostly contributing to concentrations in soil, 2+

water and sediments;

 the overall amount of mercury emitted from a combustion

source;

 the watershed soil loss rates, including reduction and

erosion;

 the water body loss rates, including outflow, reduction, and

settling; and

 the climate conditions.

In addition, this study also supports a plausible link between

mercury emissions from utility combustion sources and methylmercury

concentrations in freshwater fish. The critical variables

contributing to this linkage are:

 the species of mercury that are emitted, with emitted

divalent mercury mostly depositing into local watershed

areas and, to a lesser extent the atmospheric conversion of
7-44

elemental mercury to divalent species which are deposited

over greater distances;

 the overall amount of mercury emitted from a source;

 the watershed soil loss rates, including reduction and

erosion;

 the water body loss rates, including outflow, reduction, and

settling;

 the extent of mercury methylation in the water body;

 the extent of food web bioaccumulation in the water body;

and

 the climate conditions.

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

deposition of Hg close to an emission source is greater for receptors 2+

in elevated terrain (i.e., terrain above the elevation of the stack

base) than from 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.

On a national scale, an apportionment between sources of mercury

and mercury in environmental media and biota cannot be described in

quantitative terms with the current scientific understanding of the

environmental fate and transport of this pollutant. 

Human Exposure

The only exposure route considered was the consumption of

freshwater fish. Consumption of fish is the dominant pathway of

exposure to methylmercury for fish-consuming humans. There is a great

deal of variability among individuals in these populations with

respect to fish consumption rates. As a result, there is a great deal

of variability in exposure to methylmercury in these populations. 

While EPA interprets these models qualitatively, some freshwater fishconsuming individuals are predicted to exceed the RfD as a result of

mercury emissions from the sources considered. Measuring

methylmercury concentrations in fish from these waters and more direct

measures of exposure (e.g., hair or blood data) to humans consuming

fish around these sources should be a research priority. 

It is important to note that the utility contribution is only one

component of the total amount of methylmercury in fish. Other

anthropogenic sources, natural sources and the existing background are

expected to influence fish methylmercury levels.
7-45

7.8 DISCUSSION OF POTENTIAL CONCERNS OF MERCURY EMISSIONS FROM

 UTILITIES

Mercury is considered the highest priority for multipathway

analyses because it is an environmentally persistent, toxic element. 

Mercury is deposited to soil and terrestrial vegetation through

terrestrial exposure pathways, but at levels that do not result in

human exposures likely to be detrimental to health. However, in its

methylated form mercury bioaccumulates in the food web (especially the

aquatic food web). Modeling results suggest that most of the mercury

emitted to the atmosphere is deposited more than 50 km away from the

source, especially sources that have tall stacks. As stated above,

the modeling assessment from the Mercury Study, in conjunction with

available scientific knowledge, supports a plausible link between 

anthropogenic mercury emissions and mercury found in freshwater fish. 

Additional emissions to air will contribute to levels in the global

reservoir and deposition to water bodies. As a result, mercury

emissions from utility units may add to the existing environmental

burden.

At this time, the available information, on balance, indicates

that utility mercury emissions are of sufficient potential concern for

public health to merit further research and monitoring. The EPA

recognizes that there are substantial uncertainties that make it

difficult to quantify the magnitude of the risks due to utility

mercury emissions, and that further research and/or evaluation would

be needed to reduce these uncertainties. Remaining questions include

the following: (1) what is the quantitative relationship between a

change in United States mercury emissions and the resulting change in

methylmercury levels in fish; (2) what are the actual consumption

patterns and estimated methylmercury exposures of the subpopulations

of concern; (3) what are the actual mercury levels in a statistically

valid and representative sample of the U.S. population and susceptible

subpopulations; (4) what exposure levels are likely to result in

adverse health effects; (5) what affects the formation of

methylmercury in waterbodies and its bioaccumulation in fish; (6) how

much mercury is emitted from natural sources and past anthropogenic

sources; and (7) how much mercury is removed during coal cleaning and

other ongoing practices for pollution control. New data that could

reduce some of the uncertainties are likely to become available in the

next several years, and EPA plans to review and consider these data,

as appropriate, in future decisions.
7-46

1. Phillips, G. R., T. E. Lenhart, and R. W. Gregory. Relation

between trophic position and mercury accumulation among fishes

from the Tongue River, Montana. Environ. Res. Volume 22. 1980. 

pp. 73-80.

2. Wren, C. D., H. R. MacCrimmon, and B. R. Loescher. Examination

of bioaccumulation and biomagnification of metals in a

precambrian shield lake. Water, Air, Soil Pollut. Volume 19.

1983. pp. 277-291.

3. Fitzgerald, W. F. Global biogeochemical cycling of mercury.

Presented at the DOE/FDA/EPA Workshop on Methylmercury and Human

Health, Bethesda, MD. March 22-23, 1994.

4. U.S. Environmental Protection Agency. Integrated Risk

Information System (IRIS) Database. Environmental Criteria and

Assessment Office, Cincinnati, OH. 1994.

5. Tsubaki, T. and K. Irukayama. Minamata Disease. Methylmercury

Poisoning in Minamata and Niigata, Japan. Kodansha, Ltd and

Elsevier Scientific Publishing Company, Amsterdam. 1977.

6. Bakir, F., S. F. Damluji, L. Amin-Zaki, M. Murtadha, A. Khalidi,

N.Y. Al-Rawi, S. Tikriti, H. I. Dhahir, T. W. Clarkson, J. C.

Smith and R. A. Doherty. Methylmercury poisoning in Iraq. 

Science. Volume 181. 1973. pp. 230-241.

7. O’Connor, D. J. and S. W. Nielsen. Environmental survey of

methylmercury levels in wild mink (Mustela vison) and otter

(Lutra canadensis) from the northeastern United States and

experimental pathology of methylmercurialism in the otter. 

Worldwide Furbearer Conference Proceedings, 1981. 

pp. 1728-1745.

8. Borst, H. A. and C. G. Lieshout. Phenylmercuric acetate

intoxication in mink. Tijdschr. Diergeneesk. Volume 102. 1977. 

pp. 495-503.

9. Wobeser, G., N. D. Nielsen and B. Schiefer. Mercury and mink I: 

The use of mercury contaminated fish as a food for ranch mink. 

Can. J. Comp. Med. Volume 40. 1976. pp. 30-33. 

10. Wobeser, G., N. D. Nielsen and B. Schiefer. Mercury and mink II: 

Experimental methylmercury intoxication. Can. J. Comp. Med.

Volume 40. 1976. pp. 34-45.

11. Fimreite, N. Effects of methylmercury treated feed on the

mortality and growth of leghorn cockerels. Can. J. Anim. Sci.

Volume 50. 1970. pp. 387-389. 

7.9 REFERENCES
7-47

12. Fimreite, N. Effects of methylmercury on ring-necked pheasants. 

Canadian Wildlife Service Occasional Paper Number 9. Department

of the Environment. 1971. p. 39.

13. Fimreite, N. Accumulation and effects of mercury on birds. In: 

The Biogeochemistry of Mercury in the Environment. Edited by

J. O. Nriagu. Elsevier, Amsterdam, The Netherlands. 1979. 

pp. 601-628. 

14. Heinz, G. H. Effects of low dietary levels of methylmercury on

mallard reproduction. Bull. Environ. Contam. Toxicol.

Volume 11. 1974. pp. 386-392.

15. Heinz, G. H. Effects of methylmercury on approach and avoidance

behavior of mallard ducklings. Bull. Environ. Contam. Toxicol.

Volume 13. 1975. pp. 554-564.

16. Heinz, G. H. Methylmercury: Second-year feeding effects on

mallard reproduction and duckling behavior. J. Wildl. Manage.

Volume 40. 1976. pp. 82-90.

17. Heinz, G. H. Methylmercury: Second-generation reproductive and

behavioral effects on mallard ducks. J. Wildl. Manage. Volume 40. 

1976. pp. 710-715.

18. Heinz, G. H. Methylmercury: Reproductive and behavioral effects

on three generations of mallard ducks. J. Wildl. Mgmt. 1979. 

pp. 394-401.

19. U.S. Environmental Protection Agency. U.S. Fish Advisory

Bulletin Board. Fish Contamination Section, Office of Science

and Technology, Office of Water. 1994.

20. U.S. Environmental Protection Agency. Mercury Study Report to

Congress. Volume II. An Inventory of Anthropogenic Mercury

Emissions in the United States. Office of Air Quality Planning

and Standards and Office of Research and Development. 1997.

21. Mitra, S. Mercury in the Ecosystem. Trans Tech Publications

Ltd. Switzerland. 1986.

22. Fitzgerald, W. F. and T. W. Clarkson. Mercury and

monomethylmercury: Present and future concerns. Envir. Health

Perspec. Volume 96. 1991. pp. 159-166. 

23. Swedish Environmental Protection Agency. Mercury in the

Environment: Problems and Remedial Measures in Sweden. 

ISBN 91-620-1105-7. 1991. 
7-48

24. U.S. Environmental Protection Agency. Mercury Study Report to

Congress. Volume III Fate and Transport of Mercury in the

Environment. Office of Air Quality Planning and Standards and

Office of Research and Development. 1997.

25. Nriagu, J. O. The Biogeochemistry of Mercury in the Environment.

Elsevier/North Holland. Biomedical Press: New York. 1979.

26. U.S. Environmental Protection Agency. Mercury Study Report to

Congress. Volume I. Office of Air Quality Planning and

Standards and Office of Research and Development. 1997.

27. Expert Panel on Mercury Atmospheric Processes. Mercury

Atmospheric Processes: A Synthesis Report. Report No. TR104214. 1994.

28. Lindqvist, O., K. Johansson, M. Aastrup, A. Andersson, L.

Bringmark, G. Hovsenius, L. Hakanson, A. Iverfeldt, M. Meili, and

B. Timm. Mercury in the Swedish environment - recent research on

causes, consequences and corrective methods. Water, Air and Soil

Poll. Volume 55. (all chapters).

29. Porcella, D. B. Mercury in the environment: Biogeochemistry. 

In: Mercury Pollution Integration and Synthesis. Edited by

Watras, C.J. and J.W. Huckabee. 1994. 

30. Hovart, M., L. Liang, N. S. Bloom. Comparison of distillation

with other current isolation methods for the determination of

methylmercury compounds in low level environmental samples. 

Part II: Water. Analytica Chimica Acta. Volume 282. 1993. 

pp. 153-168.

31. Swain, E. B., D. A. Engstrom, M. E. Brigham, T. A. Henning, and

P. L. Brezonik. Increasing rates of atmospheric mercury

deposition in midcontinental North America. Science. Volume 257. 

1992. pp. 784-787.

32. Engstrom D. R., E. B. Swain, T. A. Henning, M. E. Brigham and

P. L. Brezonick. Atmospheric mercury deposition to lakes and

watersheds: A quantitative reconstruction from multiple sediment

cores. In: Environmental Chemistry of Lakes and Reservoirs.

Edited by L. A. Baker. American Chemical Society. 1994. 

pp. 33-66.

33. Benoit, J. M., W. F. Fitzgerald, and A. W. H. Damman. Historical

atmospheric mercury deposition in the mid-continental U.S. as

recorded in an ombrotrophic peat bog. In: Mercury Pollution

Integration and Synthesis. Edited by Watras, C. J. and J. W.

Huckabee 1994. pp. 187-202.
7-49

34. Fitzgerald, W. F., and R. P. Mason. The global mercury cycle:

oceanic and anthropogenic aspects. In Baeyens, W., R. Ebinghaus,

and O. Vasiliev, eds., Global and Regional Mercury Cycles:

Sources, Fluxes and Mass Balances. 1996. Pp. 85-108.

35. Slemr, F. Trends in atmospheric mercury concentrations over the

Atlantic Ocean and at the Wank Summit, and the resulting

constraints on the budget of atmospheric mercury. In Baeyens,

W., R. Ebinghaus, and O. Vasiliev, eds., Global and Regional

Mercury Cycles: Sources, Fluxes and Mass Balances. 1996. 

Pp. 33-84. 

36. Lucotte, M., A. Mucci, C. Hillaire-Marcel, P. Pichet, and A.

Grondin. Anthropogenic mercury enrichment in remote lakes of

Northern Quebec (Canada). Water, Air, and Soil Pollution

Volume 80. 1995. pp. 467-476.

37. Engstrom, D. R., and E. B. Swain. Recent Declines in Atmospheric

Mercury Deposition in the Upper Midwest. Environmental Science

and Technology. Volume 31. 1997. pp. 960-967.

38. Fitzgerald, W. F. Is mercury increasing in the atmosphere? The

need for an atmospheric mercury network (AMNET). Water, Air, and

Soil Pollution. Volume 80. 1995. pp. 245-254.

39. Mason, R. P., W. F. Fitzgerald and F.M.M. Morel. The

biogeochemical cycling of elemental mercury: Anthropogenic

influences. Geochimica et Cosmochimicia Acta. Volume 58,

No. 15. 1994. pp. 3191-3198.

40. Jensen, A., and A. Iverfeldt. Atmospheric bulk deposition of

mercury to the southern Baltic Sea area. In Watras, C. J., and

J. W. Huckabee eds. Mercury Pollution: Integration and

Synthesis. Pp. 221-229.

41. Dvonch, J. T., A. F. Vette, G. J. Keeler, G. Evans and

R. Stevens. An intensive multi-site pilot study investigating

atmospheric mercury in Broward County, Florida. Water, Air, and

Soil Pollution. Volume 80. 1995. pp. 169-178.

42. Sukhenko, S. A., and O. F. Vasiliev. A regional mercury budget

for Siberia and the role of the region in global cycling of the

metal. In Baeyens, W., R. Ebinghaus, and O. Vasiliev, eds.,

Global and Regional Mercury Cycles: Sources, Fluxes and Mass

Balances. Pp. 123-133.

43. Ebinghaus, R., and O. Kruger. Emission and local deposition

estimates of atmospheric mercury in North-Western and Central

Europe. In Baeyens, W., R. Ebinghaus, and O. Vasiliev, eds.,

Global and Regional Mercury Cycles: Sources, Fluxes and Mass

Balances. Pp. 135-159.
7-50

44. Glass, G., J. Sorensen, K. Schmidt, G. Rapp, D. Yap, and

D. Fraser. Mercury deposition and sources for the upper Great

Lakes region. Water, Air and Soil Pollution. Volume 56. 1991. 

pp. 235-249.

45. Lamborg, C. H., M. E. Hoyer, G. J. Keeler, I. Olmez, and X.

Huang. Particulate-phase mercury in the atmosphere: 

collection/analysis method development and applications. In: 

Mercury Pollution Integration and Synthesis. Edited by Watras,

C. J. and J. W. Huckabee. 1994. pp. 251-259.

46. Newton, I., I. Wyllie, and A. Asher. Long-term trends in

organochlorine and mercury residues in some predatory birds in

Britain. Environ. Pollut. Volume 79. 1993. pp. 143-151.

47. McIntyre, J. W., J. T. Hickey, K. Karwowski, C. L. Holmquist, and

K. Carr. In: Proceedings of the 1992 Conference on the Loon and

its Ecosystem Status. Edited by L. Morse and M. Pokras. U.S.

Fish and Wildlife Service, Concord, NH. 1993. pp. 73-91.

48. Michigan Environmental Science Board. Mercury in Michigan’s

Environment: Environmental and Human Health Concerns. Report to

Gov. John Engler. 1993.

49. Porcella, D. B., P. Chu, and M. A. Allan. Inventory of North

American Hg emissions to the atmosphere: Relationship to the

global mercury cycle. In Baeyens, W., R. Ebinghaus, and O.

Vasiliev, eds., Global and Regional Mercury Cycles: Sources,

Fluxes and Mass Balances. Pp. 179-190.

50. Lindqvist, O. and H. Rode. Atmospheric mercury-a review. Tellus.

Volume 37B. 1985. pp. 136-159.

51. Lindberg, S. E., T. P. Meyers, G. E. Taylor, R. R. Turner, and

W. H. Schroeder. Atmosphere-surface exchange of mercury to a

forest: Results of modeling and gradient approaches. J. of

Geophy. Res. Volume 97, No. D2. 1992. Pp. 2519-2528.

52. Shannon, J. D., and E. C. Voldner. Modeling; atmospheric

concentrations and deposition of mercury to the Great Lakes. 

Presented at the DOE/FDA/EPA Workshop on Methylmercury and Human

Health, Bethesda, MD. March 22-23, 1994.

53. Petersen, G., Iverfeldt, A. and Munthe, J. 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. Atmos. Env. Volume 29. 

1995. pp. 47-68.
7-51

54. Fitzgerald, W. F., R. P. Mason and G. M. Vandal. Atmospheric

cycling and air-water exchange of mercury over mid-continental

lacustrine regions. Water, Air and Soil Poll. Volume 56. 1991. 

pp. 745-767.

55. Hanson, P. J., S. E. Lindberg, K. H. Kim, J. G. Owens and T. A.

Tabberer. Air/surface exchange of mercury vapor in the forest

canopy, I. Laboratory studies of foliar Hg vapor exchange. 

Presented at the 3rd International Conference on Mercury as a

Global Pollutant. Whistler, BC, Canada. July 10-14, 1994.

56. Lindberg, S. E., R. R. Turner, T. P. Meyers, G. E. Taylor, and

W. H. Schroeder. Atmospheric concentrations and deposition of Hg

to a deciduous forest at Walker Branch Watershed, Tennessee, USA. 

Water, Air and Soil Poll. Volume 56. 1991. pp. 577-594.

57. Mosbaek, H., J. C. Tjell, and T. Sevel. Plant uptake of mercury

in background areas. Chemosphere. Volume 17, No. 6. 1988. 

pp. 1227-1236.

58. Lindberg, S. E. Forests and the global biogeochemical cycle of

mercury: the importance of understanding air/vegetation exchange

processes. W. Baeyans et al. (Eds.), Global and Regional Mercury

Cycles: Sources, Fluxes, and Mass Balances. pp. 359-380.

59. Schuster, E. The behavior of mercury in the soil with special

emphasis on complexation and adsorption processes- a review of

the literature. Water, Air and Soil Poll. Volume 56. 1991. 

pp. 667-680.

60. Meili, M., A. Iverfeldt and L. Hakanson. Mercury in the surface

water of Swedish forest lakes - Concentrations, speciation and

controlling factors. Water, Air, and Soil Pollution. Volume 56. 

1991. pp. 439-453.

61. Revis, N. W., T. R. Osborne, G. Holdsworth, and C. Hadden.

Mercury in soil: A method for assessing acceptable limits. Arch.

Environ. Contam. Toxicol. Volume 19. 1990. pp. 221-226.

62. Lindqvist, O., K. Johansson, M. Aastrup, A. Andersson,

L. Bringmark, G. Hovsenius, L. Hakanson, A. Iverfeldt, M. Meili,

and B. Timm. Mercury in the Swedish environment - recent

research on causes, consequences and corrective methods. Water,

Air and Soil Poll. Volume 55. (Chapter 4). 1991.

63. Bloom, N. S., C. J. Watras, and J. P. Hurley. Impact of

acidification on the methylmercury cycle of remote seepage lakes. 

Water, Air and Soil Poll. Volume 56. 1991. pp. 477-491.
7-52

64. Parks, J. W., A. Lutz, and J. A. Sutton. Water column

methylmercury in the Wabigoon/English River-Lake system: Factors

controlling concentrations, speciation, and net production. Can.

J. Fisher. Aq. Sci. Volume 46. 1989. pp. 2184-2202.

65. Mason, R., and W. Fitzgerald. The distribution and biogeochemical

cycling of mercury in the equatorial Pacific Ocean. Deep Sea

Research. Volume 40, No. 9. 1993. pp. 1897-1924.

66. Mason, R., and W. Fitzgerald. Sources, sinks and biogeochemical

cycling of mercury in the ocean. In: Global and Regional Mercury

Cycles: Sources, Fluxes and Mass Balances. Edited by: W. Baeyens,

R. Ebinghaus, and O.Vasiliev. Kluwer Academic Publishers,

(Netherlands). pp. 249-272.

67. Cossa, D. M. Coquery, C. Gobeil, and J. Martin. Mercury fluxes

at the ocean margins. In: Global and Regional Mercury Cycles:

Sources, Fluxes and Mass Balances. Edited by W. Baeyens,

R. Ebinghaus, and O. Vasiliev. Kluwer Academic Publishers,

Netherlands. 1996. pp. 229-248.

68. U.S. Environmental Protection Agency. Mercury Study Report to

Congress. Volume V. Health Effects of Mercury and Mercury

Compounds. EPA-452/R-97-007. Office of Air Quality Planning and

Standards and Office of Research and Development.

69. U.S. Environmental Protection Agency. Mercury Study Report to

Congress. Volume IV. An Assessment of Exposure to Mercury in

the United States. EPA-452/R-97-006. Office of Air Quality

Planning and Standards and Office of Research and Development. 

1997.

70. Anderson, W. L. and K. E. Smith. Dynamic of mercury at

coal-fired utility power plant and adjacent cooling lake. 

Environ. Sci and Technol. Volume 11. 1977. p. 75.

71. Crockett, A. and R. Kinnison. Mercury residues in soil around a

coal-fired power-plant. Envir. Sci. Technol. Volume 13. 1979. 

pp. 712-715.

72. Columbia River Inter-Tribal Fish Commission. A Fish Consumption

Survey of the Umatilla, Nez Perce, Yakima and Warm Springs Tribes

of the Columbia River Basin. Technical Report 94-3. October

1994.

73. U.S. Environmental Protection Agency. Exposure Factors Handbook,

Volume 2 of 3. Food Ingestion Factors. SAB Review draft. 1996.
7-53

74. Bullock, Jr., O. R., W. G. Benjey and M. H. Keating. Modeling of

regional scale atmospheric mercury transport and deposition using

RELMAP. In: Atmospheric Deposition of Contaminants to the Great

Lakes and Coastal Waters. Edited by J. E. Baker. 1997a. 

pp. 323-247. 

75. Bullock, Jr., O. R., K. A. Brehme and G. R. Mapp. Lagrangian

modeling of mercury air emission, transport and deposition: An

analysis of model sensitivity to emissions uncertainty. Special

Issue on Mercury as a Global Pollutant: Science of the Total

Environment, in press. 1997b.
mm
8-1

8.0 QUALITATIVE MULTIPATHWAY ASSESSMENT FOR LEAD AND CADMIUM

8.1 BACKGROUND

 A multipathway exposure analysis, which is an assessment of

multiple routes of exposure of humans and/or biota to pollutants, is

the appropriate approach for a complete human health risk assessment. 

Though it would have been preferable to perform a quantitative

multipathway exposure assessment of utility emissions of all six

priority HAPs (radionuclides, mercury, arsenic, dioxins, cadmium, and

lead), at the time of this study not enough data had been collected or

were available to do so. However, multipathway assessments were

performed for radionuclides, mercury, arsenic, and dioxins. For the

other two HAPs, lead and cadmium, only qualitative assessments of the

potential concerns to human health from noninhalation exposure were

performed. 

The completion of quantitative assessments of inhalation

exposures for all HAPs and of multipathway exposures for only

radionuclides, mercury, arsenic, and dioxins does not reflect the

significance of noninhalation exposure and risks from utility HAP

emissions. Rather, it is a reflection of the complexity of assessing

multipathway exposure to a HAP. Due to the intensive data

requirements of a quantitative multipathway exposure assessment of a

HAP, and the limited chemical-specific data available (e.g., chemicalspecific air to plant biotransfer factor, chemical-specific plant

uptake rates) for input into the exposure model, quantitative analyses

were not completed for lead or cadmium. Though it is important to

address the noninhalation exposure pathways for select HAPs, there are

complex issues associated with the analysis of all exposure pathways.

 The EPA recognizes that, for some of the utility HAPs

(e.g., mercury, dioxins), noninhalation exposure is a potentially more

significant route of exposure than inhalation exposure. The mercury

assessment suggests that there is a need for further analysis of

noninhalation exposures.

Efforts are underway to collect the chemical-specific data needed

for quantitative multipathway exposure assessment, and further

analyses may be undertaken for additional HAPs in the future, in

recognition of the need to understand the contribution of air

pollutants to risk from noninhalation exposure pathways. For this

report, a qualitative discussion of the potential concerns about

noninhalation exposure to lead and cadmium is presented in the

following sections. 

8.2 LEAD COMPOUNDS

Lead compounds are persistent in the environment and have the

tendency to bioaccumulate (see Table 5-8). Lead is known to be toxic

by both the oral and inhalation routes. For these reasons,
Industrial processes include nonferrous smelters, battery plants, and a

chemical plants.

8-2

noninhalation exposure to lead emissions from utilities are a

potential concern.

For 1990, the estimated lead emissions from all coal-, oil-, and

gas-fired units were 72 ton/yr, 10 ton/yr, and 0.44 ton/yr, respectively. 

To put these emissions estimates into perspective, it was estimated that

a total of 7.2 x 10 metric tons per year of lead were emitted into the 3

atmosphere from anthropogenic point and nonpoint sources during 1989. 1

When this estimate is compared to the 1990 lead emissions estimate for

utilities, it appears that utilities are responsible for emitting

approximately 1 percent of the total amount of lead emitted annually. In

1989, the major contributors of atmospheric lead included industrial

processes (2.3 x 10 metric tons/year), solid waste management (2.3 x 10 3 3

metric tons/year), transportation (2.2 x 10 metric tons/year), and fuel 3

combustion (0.5 x 10 metric tons/year). 3 a

Lead is a naturally occurring metal that can be found in small

amounts in the earth’s crust. However, the primary source of lead

found in the environment is anthropogenic activities. Once emitted to

the atmosphere from stack sources, such as utilities, lead can be

deposited onto environmental media such as soil, water, and

vegetation. In the atmosphere, lead exists primarily as PM in the

form of lead sulfate (PbSO ) and lead carbonate (PbCO ). However, it 4 3

is not clear how the chemical composition of lead changes during

dispersion. Because a typical residence time of particulate lead is

10 days, long-range transport up to thousands of miles can occur. For

example, lead has been found in sediment cores of lakes in Canada that

were not located near any point sources of lead, indicating that

long-range atmospheric transport may have been occurring. The primary

removal mechanism from the atmosphere is wet or dry deposition onto

soil, water, and plants. Atmospheric deposition is the primary source

of lead found in soils. There is evidence that lead enters soil as 2

lead sulfate or quickly converts to lead sulfate at the soil surface. 

Once deposited onto soil, lead tends to sorb strongly to soils and

becomes extremely persistent. Because lead is strongly sorbed to

soils, leaching of significant amounts of lead to groundwater or

surface water is not likely to occur. With the exception of highly

acidic environmental conditions, leaching of lead into groundwater and

surface water occurs very slowly. The major contributors to surface

water lead are atmospheric deposition and urban runoff. Typically in

the aquatic environment, lead is associated with suspended solids and

sediments. The concentration of dissolved lead in water is low

because lead tends to form compounds with low water solubilities that

precipitate out of the water column. The ratio of suspended lead to

dissolved lead is found to vary from 4:1 in rural streams to 27:1 in

urban streams. Many terrestrial plants accumulate lead by root uptake

from soil or by absorption of airborne lead deposited onto plants. 
8-3

However, the bioavailability of lead to plants from soil is limited

due to the strong absorption of lead to soil.

The highest background levels of lead are found in soils (<10 to

30 g/g) and in sediments (the average concentration of lead in river

sediments is 20,000 g/g). In 1988, the average ambient air 2

concentration for 139 sites monitored by the National Air Surveillance

Network (NASN) was determined to be 0.085 g/m . This value is well 3 1

below the NAAQS of 1.5 g/m .3 2

Concentrations of lead found in foods are given in Table 8-1. 

These concentrations range from a low of about 0.002 g/g of food to a

high of more than 0.8 g/g (found in milk). Background levels of lead

in milk can range from 23 to 79 g/kg. The ATSDR states that, for the 3

general population, the highest levels of exposure to lead are most

likely to occur through the ingestion of contaminated food and

drinking water and by the inhalation of lead particles in ambient air. 

Furthermore, fruits, vegetables, and grains may contain levels of lead

in excess of background levels as a result of deposition of lead 

onto plant surfaces and by plant uptake from soil. As seen in

Table 8-1, the average adult dietary intake of lead for the years 

1980-82 was estimated to be 56.5 g/day. However, recent data (1992) 1

indicate that average dietary intakes have decreased significantly

over the past decade to approximately 2-4 g/day. Presumably, this is 4

at least partially due to the phasing out of lead in gasoline over the

past two decades. In general, human exposure to lead is most likely

to occur through the ingestion of contaminated food and drinking water

and by inhalation of lead particulates emitted from an emission source

or reentrainment of lead-contaminated soil. Lead-based paints, dust,

and chips are also potential avenues of significant exposures for

subpopulations (e.g., children) that may ingest these items.

Lead emissions from utilities do not contribute substantially to

the total amount of lead released annually from anthropogenic

activities (i.e., approximately 1 percent). However, lead emissions

from utilities were not modeled for noninhalation exposures. 

Therefore, it is unclear whether the impact of the lead emissions from

utilities is significant. Air concentrations and deposition rates for

lead emissions were estimated using the RELMAP model. The methods and

model are described in detail in chapter 6 (section 6.6). Figures 8-1

through 8-6 show the results of the lead RELMAP modeling.

8.3 CADMIUM COMPOUNDS

As shown in Table 5-8, cadmium is persistent in the environment

and has the potential to bioaccumulate. Health effects data indicate

that cadmium is relatively toxic by both oral and inhalation routes. 

Cadmium is a probable (B1) human carcinogen by the inhalation route

and is relatively potent (IURE = 1.8 x 10 per g/m ). However, there -3 3

are insufficient data to assess the carcinogenicity from oral

exposure. Regarding noncancer effects, cadmium exposure has been

linked to kidney effects, primarily proteinuria. The RfD for cadmium
8-4

Table 8-1. Concentration of Lead in Various Food Products1,2

Food group Concentration (μg/g)

Dairy products 0.003-0.83

Meat, fish, and poultry 0.002-0.159

Grain and cereal products 0.002-0.136

Vegetables 0.005-0.649

Fruit and fruit juices 0.005-0.223

Oils, fats, and shortenings 0.002-0.028

Sugar and adjuncts 0.006-0.073

Beverages 0.002-0.041

is 5 x 10 mg/kg/d, and is associated with a high confidence level -4

since it is based on human data. For these reasons, cadmium emissions

from anthropogenic sources have the potential to be a concern for

noninhalation exposure.

For 1990, the estimated cadmium emissions from all coal-, oil-,

and gas-fired units were 1.9 ton/yr, 1.7 ton/yr, and 0.054 ton/yr,

respectively. To put these estimates into perspective, a recent study

by the EPA estimates that about 233 tpy were emitted in the United

States by anthropogenic sources in 1990. Therefore, utilities are 5

estimated to emit slightly more than 1 percent of anthropogenic

cadmium emissions in the United States.

Cadmium is a naturally occurring metal found in small amounts

throughout the earth’s crust. However, the primary source of cadmium

found in the environment is anthropogenic activities. Once released

from stack sources, such as utilities, cadmium can be deposited to

various environmental media. Cadmium emitted from combustion

processes typically exists in the atmosphere as small PM. Cadmium

oxide is the predominant form of cadmium in the atmosphere. However,

the toxicology of cadmium appears not to be dependent on the

chemical’s form. Because a typical residence time of particulate

cadmium is between 1 and 10 days, long-range transport (up to

thousands of kilometers) can occur. The primary removal mechanism

from the atmosphere is wet or dry deposition onto soil, water, and

plants. Atmospheric deposition can contribute significantly to the

concentration of cadmium in soil in areas surrounding emission

sources, such as incinerators and areas of heavy vehicular traffic. 

Once deposited onto soil, cadmium can be leached into water,

especially under acidic conditions. In the aquatic environment,

cadmium exists primarily as a soluble hydrated ion; as a result, it is

more mobile than other heavy metals, such as lead. However, under

certain environmental conditions, cadmium concentrations have been 

found to be at least 1 order of magnitude higher in sediment than in






8-11

the water column. Accumulation of cadmium in terrestrial and aquatic

plants can occur by root uptake from soil. In addition, terrestrial

plants can be contaminated by absorption of airborne cadmium deposited

onto plants. Accumulation of cadmium in feed crops has the potential

to result in high levels of cadmium in the liver and kidneys of

animals that eat the contaminated feed. Data indicate that cadmium

bioaccumulates in all levels of the food chain. Table 8-2 presents 6

concentrations of cadmium in various foods.

The highest background levels of cadmium are found in soils. 6

The mean concentration of cadmium in uncontaminated soil is 0.25 ppm. 

Topsoil concentrations can be as much as two times higher than

subsurface concentrations due to atmospheric exposition and

contamination (e.g., landfarming of municipal sludge). Average

ambient air concentrations can range from 1 x 10 mg/m in remote areas -6 3

to 4 x 10 in urban areas. Concentrations of cadmium in surface -5

water, groundwater, and drinking water are typically less than 1 g/L. 

In a study conducted in 27 U.S. cities, 12 food groups were tested and

cadmium was detected in nearly all samples. As shown in Table 8-2,

the highest levels of cadmium were found in leafy vegetables and

potatoes and the lowest levels were found in beverages. Liver and

kidney meats and shellfish were associated with higher concentrations

than other types of meats. Cadmium can accumulate in freshwater and

marine animals at levels up to thousands of times higher than the

levels of cadmium found in the water. In the United States, the adult

intake of cadmium attributable to diet is estimated to be

approximately 30 g/d. Assuming a gastrointestinal absorption of 5 to

10 percent, the amount of cadmium absorbed from diet is approximately

1 to 3 g/d. In addition, human exposure can occur at the same level

through cigarette smoking. Cadmium has been found to accumulate in

tobacco plants. The average concentration of cadmium in a cigarette

is between 1 and 2 g/cigarette. A cadmium exposure and absorption

level of 1 to 3 g/d can result from smoking one pack of cigarettes

per day. Based on these data, the ATSDR states that, for the general

nonsmoking population, the highest levels of exposure to cadmium are

most likely to occur through consumption of food. However, smoking 6

can result in double the exposure level. Individuals living near

emission sources may be exposed to above-average exposure levels

through multiple pathways, such as ingestion of contaminated drinking

water or garden vegetables, inhalation of airborne dust, and

incidental ingestion of contaminated soil.

A study that examines the transfer of metals to bovine milk

indicates that human exposure to cadmium through the consumption of

milk may not be of concern. Because the contribution to human

exposure through the food chain has not been thoroughly examined, a

study was undertaken to estimate the steady-state bovine milk 

biotransfer factors (i.e., the rate at which the compounds are 
8-12

Table 8-2. Concentration of Cadmium in Various Food Products6

Food group Average concentration (ppm) Range of concentrations (ppm)

Potatoes 0.0421 0.016 to 0.142

Leafy vegetables 0.0328 0.016 to 0.061

Grain and cereal products 0.0237 0.002 to 0.033

Root vegetables 0.0159 trace-0.028

Garden vegetables 0.0171 trace-0.093

Oils and fats 0.0108 trace-0.033

Sugars and adjuncts 0.0109 trace-0.053

Meat, fish, and poultry 0.0057 trace-0.014

Legume vegetables 0.0044 trace-0.016

Dairy products 0.0035 trace-0.016

Fruits 0.0021 trace-0.012

Beverages 0.0013 trace

All groups trace-0.142

transferred to milk) for six metals: arsenic, cadmium, chromium,

lead, mercury, and nickel. Results from this study indicated that, of 3

the metals studied, lead and arsenic transferred to milk to the

greatest extent and cadmium to a lesser extent. The bioconcentration

factor estimated for cadmium is 1.3 x 10 L/kg. To put this value -6

into perspective, the author points out the estimated biotransfer

factor of TCDD is 2.6 x 10 L/kg and that this particular food chain -2

pathway may be less important for these metals than it is for TCDD.

In general, human exposure to cadmium is most likely to occur

through the consumption of food products for nonsmokers and through

the consumption of food products and cigarette smoking for smokers. 

Because cadmium emissions from utilities do not contribute

significantly to the total amount of cadmium released annually from

anthropogenic activities (i.e., approximately 1 percent) and because

there are numerous units dispersed throughout the country, it is

unclear whether the impact of the cadmium emissions on the background

concentration of cadmium in the various media is significant. 

Exposure resulting from utility emissions of cadmium in excess of

background levels cannot readily be determined. Deposition rates were

estimated for cadmium using the RELMAP model. The methods and model

are described in detail in chapter 6 (section 6.6). Figures 8-7

through 8-9 show the results of the cadmium RELMAP modeling.



8-16

8.4 OVERALL SUMMARY

Further quantitative analysis of noninhalation exposure to

HAPs that are persistent in the environment and that have the

potential to bioaccumulate may be appropriate in future studies. 

Due, in part, to low emissions of these HAPs from utilities

relative to other anthropogenic sources, a quantitative

assessment of noninhalation exposure to lead and/or cadmium has

not been given as high a priority as arsenic, radionuclides,

dioxins, and mercury for multipathway assessment. However, lead

and cadmium are persistent in the environment, have a tendency to

bioaccumulate, and are toxic by the inhalation and ingestion

routes of exposure. Therefore, further assessment of the

multipathway exposures and risks may be appropriate in future

studies to more comprehensively evaluate the impacts of emissions

of lead and cadmium from utilities.
8-17

1. Agency for Toxic Substances and Disease Registry Toxicological

Profile for Lead. TP-92/12. U.S. Public Health Service. 

Atlanta, GA. 1993.

2. U.S. Environmental Protection Agency. Air Quality Criteria for

Lead. EPA 600/8-83-028f. Washington, DC. 1983.

3. Stevens, J. B. Disposition of toxic metals in the agricultural

food chain. 1. Steady-state bovine milk biotransfer factors. 

Environmental Science and Technology. Volume 25, No. 7. 1991. 

pp. 1289-1294.

4. Bolger, P. M., Yess, N. J., Gunderson, E. L., Troxell, T. C., and

Carrington, C. D. Identification and reduction of sources of

dietary lead in the United States. Food Additives and

Contaminants, Volume 13, No. 1. 1996. pp. 53-60.

5. U.S. Environmental Protection Agency. 1990 Emissions Inventory

of Forty Section 112 (k) Pollutants. September 1997. 

6. Agency for Toxic Substances and Disease Registry. Toxicology

Profile for Cadmium. TP-92/06. U.S. Public Health Service. 

Atlanta, GA. 1993.

8.5 REFERENCES
mm
9-1

9.0 MULTIPATHWAY EXPOSURE AND RISK ASSESSMENT FOR RADIONUCLIDES

9.1 SUMMARY OF RADIONUCLIDE ANALYSIS

The EPA assessed the exposure and risks due to radionuclide

emissions from coal-, oil-, and gas-fired utilities. The details of

this assessment are contained in an EPA report entitled: Estimates of

Health Risks Associated with Radionuclide Emissions from Fossil-Fueled

Steam Electric Generating Plants. This section summarizes that 1

report.

Shortly after the discovery of radioactivity at the turn of the

century, investigators became aware that nearly all natural materials

contained trace quantities of radioactivity. Natural radioactivity is

derived from two sources. A small percentage of natural radioactivity

is derived from the interaction of cosmic radiation with specific

elements (e.g., carbon-14, tritium). The majority of naturally

occurring radionuclides are classified as primordial radioisotopes or

their radioactive decay products. Primordial radionuclides are

believed to have been formed, along with all other terrestrial

elements except hydrogen by nuclear fusion reactions, neutron

absorption, and beta decay in a former star that exploded as a super

nova.2

The behavior of primordial radionuclides and their decay products

in the environment is complex. Pathways leading to significant human

exposures include external radiation from the emission of gamma rays

from the ground and building materials. Internal exposure may result

from the transfer of radioactivity through root uptake by plants that

serve as food for domestic animals or are directly ingested by humans. 

Internal exposure may also result from the inhalation of airborne

radioactivity.

The three major fossil fuels—coal, oil, and natural gas—contain

varying quantities of the naturally occurring radionuclides of the

uranium-238 and thorium-232 series and potassium-40. When these fuels

are burned to produce steam in the production of electricity,

radionuclides are entrained in the combustion gases and may be emitted

into the environment. As early as 1954, Anderson, Mayneord, and

Turner suggested that human activities, particularly the burning of 3

coal, might significantly perturb the natural radiation environment by

transferring additional radioactivity into the air, where it is more

readily available for human intake by inhalation.

Radionuclides are among the HAPs included in section 112(b). 

Over the years, EPA has reviewed available information and provided

estimates regarding the radionuclide content of fossil fuels,

environmental emissions, human exposure, and health risks. This

information has been reported by the EPA in several earlier reports,

including the Background Information Document supporting the decision

not to regulate radionuclide emissions from coal-fired boilers issued

in 1989. The EPA report summarized in this chapter updates previously 4

published data and estimates with more recently available information
9-2

regarding the radionuclide contents of fossil fuels, associated

emissions by utilities, and potential health effects to exposed

population groups.

9.1.1 Natural Radionuclide Content in Fossil Fuels: Coal

The decay series of uranium and thorium constitute the major

radionuclides contained in coal. Uranium-238 has 13 major radioactive

decay products and thorium-232 has 9. For coal, it is generally

assumed that primary members within each of the two decay series are

in secular equilibrium. Secular equilibrium means that the

radioactivity concentrations among primary decay chain members are

constant. A national database of nearly 7,000 coal samples was

analyzed with regard to uranium and thorium content of the major ranks

of coal used by utilities. Concentrations spanned a wide range of

values that were lognormally distributed. Table 9-1 summarizes the

data by providing the geometric mean concentration values expressed in

units of parts per million and identifies the relative percent utility

consumption of coal types.

Concentration values expressed in parts per million are readily

converted to radioactivity concentrations by means of the specific

activity values for uranium-238 and thorium-232. For U-238, 1 ppm is

equal to 0.33 pCi/g of coal; for Th-232, 1 ppm is equal to 0.11 pCi/g

of coal. For example, in bituminous coal with an average content of

1.24 ppm uranium and 2.18 ppm thorium there is a corresponding

activity of 0.41 pCi/g for each member of the U-238 series and 0.24

pCi/g for each member of the Th-232 series.

The radionuclide content of coal is not unique when compared to

other natural materials. In fact, it is generally assumed that the

average radioactivity of the earth’s crust (i.e., soil and rocks) is

about twice that of coal.

9.1.2 Natural Radionuclide Content in Fossil Fuels: Natural Gas

Radioactivity in natural gas is almost exclusively radon-222,

which migrates from proximal geologic formations into gas reservoirs. 

In 1989, the American Gas Association identified 262,482 production

wells that yielded more than 18 trillion cubic feet (Tcf) of natural

gas. An additional 1.53 Tcf of gas were imported primarily from

Canada. About 2.77 Tcf of gas were consumed by utilities to produce

electricity.

The radon content of natural gas at the wellhead has been

measured in thousands of wells over several decades. However, these

measurements are of limited use for estimating radon concentrations at

the point of consumption for several reasons. Radon concentrations

vary by geographic location and over time. Also, radon content is

markedly reduced when natural gas is processed to remove commercially

valuable heavier hydrocarbons (ethane, propane, butane). Further

reductions in radon concentrations reflect the natural decay that

occurs during the gathering, processing, and distribution/storage
9-3

Table 9-1. Utilization and Radionuclide Content by Coal Rank

Coal rank utilization (ppm) Average thorium (ppm)

Percent Average uranium

Bituminous 69.0 1.24 2.18

Subbituminous 24.7 1.07 2.28

Lignite 6.3 1.41 2.38

of gas prior to consumption. The main radon isotope, Rn-222, has a

half-life of about 4 days; the other isotopes have half-lives of less

than 1 minute.

A more meaningful approach is to assess the radon content in gas

distribution lines. Analysis of gas in the distribution lines

eliminates well-to-well variations and accounts for radon reduction

from processing and natural decay. Radon measurements of natural gas

in distribution lines near the point of consumption suggest an average

value of 20 pCi/L. In this report, therefore, estimated radon

emissions from gas-fired boilers are based on a radon concentration of

20 pCi/L of processed gas.

9.1.3 Natural Radionuclide Content in Fossil Fuels: Oil

Residual fuel oil is a general classification of fuel obtained as

liquid still bottoms from the distillation of crude oil. 

Nonradiometric analyses show that crude oil and various petroleum

products may contain as many as 60 different metals in measurable

quantities. Uranium and thorium are among the trace metals commonly

found in crude oil and petroleum products. The presence of these two

radioactive trace metals also implies the presence of their

radioactive decay products.

A comprehensive literature search, however, revealed that data

specific to the radionuclide content of residual fuel oil are not only

sparse but are considerably more difficult to interpret than those for

coal or gas. Contributing to the difficulty in data interpretation is

the absence of secular equilibrium among primary members of the U-238

and Th-232 decay chains. Due to the paucity of data, the EPA

concluded that there was a need for additional data and conducted its

own study.

The EPA enlisted the help of the Utility Air Regulatory Group

(UARG) and the EPRI to solicit the voluntary participation of

individual utilities in providing samples of residual oils for

radioanalysis. The selection of a utility was based on the utility’s

geographic location, along with its generator nameplate capacity,

capacity factor, and/or annual fuel-oil consumption. Selection,

therefore, favored larger facilities with the highest capacity

factors/fuel consumption and accounted for radionuclide variability

based on origin of crude oil.
9-4

In total, 12 utilities provided 42 samples of residual fuel oil

for analysis. Participating utilities represented major regions of

the United States where fuel oil serves as a primary fuel source. 

Quantitatively, the 12 utilities had an annual consumption of about

2 billion gallons, which was estimated to be about 24 percent of the

fuel oil consumed by all U.S. oil-fired units.

Radionuclide analysis, data interpretation, and data verification

involved the efforts of a major commercial analytical laboratory, the

EPA’s National Air and Radiation Environmental Laboratory (NAREL), and

the National Institute of Standards and Technology (NIST).

Table 9-2 provides estimates of the average radionuclide values

of the 42 residual fuel oil samples evaluated in the EPA study. 

Values are well within the range of the limited study data reported by

others and support the conclusion that the radionuclide content of

residual fuel oil is low relative to coal.

9.1.4 Radionuclide Emissions from Fossil-Fueled Plants

Radionuclide emissions from utilities are affected by the

radionuclide content in fossil fuel, by plant design features, and by

operating parameters. Important design features involve the size of

the plant, type of furnace used, and the emission control systems

designed to remove pollutants from the flue gas. The most significant

operational factors, which dictate the rate of fuel consumption,

involve the percentage of time a plant is operating, the power level,

and the efficiency by which a plant converts thermal energy to

electric energy.

In this report, estimates of radionuclide emissions and

associated human health risks are based on fossil-fired boiler units

with generating capacities of 25 MWe or more. The 25-MWe selection

criterion reflects the low probability of significant emissions for

small plants, regardless of unit-specific operating parameters. Of

the Nation’s 2,298 boiler units (Table 9-3), 1,748 units have a

generating capacity of 25 MWe or more.

From data reported to the EEI that include annual fuel

consumption and particulate removal efficiencies, emissions were

estimated for each of the 1,748 boiler units and aggregated by plant

affiliation. (The 1,748 fossil-fired boiler units represent a total

of 684 utility plants.) These unit- and plant-specific emission data

are contained in a separate addendum to the EPA report. Table 9-3 5

provides average annual emissions per operating boiler unit, as well

as per billion kilowatt-hour of electricity generated. For coal-fired

units, the average annual emissions for particulates range from a

fraction of a millicurie (mCi) to several millicuries among primary

radionuclides.
9-5

Table 9-2. Estimates of Average Radionuclide Concentrations in

42 Residual Fuel Oil Samples

U-238 Series Concentration (pCi/g)

U-238 0.0018

Th-234 0.0018

Pa-234 0.0018

U-234 0.0034

Th-230 0.0068

Ra-226 0.0043

Rn-222 0.0043

Po-218 0.0043

Pb-214 0.0043

Bi-214 0.0043

Po-214 0.0043

Pb-210 0.44

Bi-210 0.44

Po-210 0.44

Th-232 Series Concentration (pCi/g)

Th-232 0.0030

Ra-228 0.068

Ac-228 0.068

Th-228 0.068

Ra-224 0.068

Rn-220 0.068

Po-216 0.068

Pb-212 0.068

Po-212 0.068

Although the average radionuclide content of residual fuel oil is

2 to 3 orders of magnitude lower than that of coal, Table 9-3 reveals

that average emission rates are nearly comparable. This is explained

by the fact that, unlike coal-fired units, the majority of oil-fired

units lack particulate emission control systems that remove

radionuclides from the flue gas with efficiencies of 95 percent or

more. Due to the fact that coal-fired units on average have a higher

capacity factor, the degree of comparability between coal-fired and

oil-fired units is further enhanced when emissions are defined per

unit of billion kilowatt-hours.

Particulate emissions for units designated as gas-fired are

generally small when compared to either coal- or oil-fired units. 

Moreover, radionuclide emissions other than radon from units

designated as gas-fired principally result from the combustion of a

secondary fuel.

9.1.5 Summary of CAP-93 Model

The Clean Air Act Assessment Package-1993 (CAP-93) is the most

recent version of a computer model used for population dose and risk

assessment for radionuclide air emissions. For a given facility,
9-6

Table 9-3. Average Annual Radionuclide Emissions per Operating

Boiler Unit and per Billion Kilowatt-Hour Electricity Generated

Radionuclide Coal Gas Oil Coal Gas Oil

Emission rates

Per operating unit (mCi/y) Per billion kWe-h generated (mCi/y)

U-238 2.3 x 10 1.3 x 10 1.1 x 10 1.5 x 10 2.6 x 10 1.8 x 10 0 2 -1 0 -2 -1

Th-234 1.2 x 10 1.3 x 10 1.1 x 10 7.7 x 10 2.6 x 10 1.8 x 10 0 -2 -1 -1 -2 -1

Pa-234m 1.2 x 10 1.3 x 10 1.1 x 10 7.7 x 10 2.6 x 10 1.8 x 10 0 -2 -1 -1 -2 -1

Pa-234 1.2 x 10 1.3 x 10 1.1 x 10 7.7 x 10 2.6 x 10 1.8 x 10 0 -2 -1 -1 -2 -1

U-234 2.3 x 10 2.5 x 10 2.1 x 10 1.5 x 10 4.9 x 10 3.4 x 10 0 -2 -1 0 -2 -1

Th-230 1.2 x 10 4.9 x 10 4.1 x 10 7.7 x 10 9.5 x 10 6.7 x 10 0 -2 -1 -1 -2 -1

Ra-226 1.7 x 10 2.9 x 10 2.6 x 10 1.2 x 10 5.7 x 10 4.3 x 10 0 -2 -1 0 -2 -1

Rn-222 3.0 x 10 2.5 x 10 3.8 x 10 2.0 x 10 4.9 x 10 6.2 x 10 232232

Po-218 5.6 x 10 3.1 x 10 2.7 x 10 3.8 x 10 6.0 x 10 4.4 x 10 0 -2 -1 0 -2 -1

Pb-214 5.6 x 10 3.1 x 10 2.7 x 10 3.8 x 10 6.0 x 10 4.4 x 10 0 -2 -1 0 -2 -1

Bi-214 1.2 x 10 3.1 x 10 2.7 x 10 7.7 x 10 6.0 x 10 4.4 x 10 0 -2 -1 -1 -2 -1

Po-214 5.6 x 10 3.1 x 10 2.7 x 10 3.8 x 10 6.0 x 10 4.4 x 10 0 -2 -1 0 -2 -1

Pb-210 5.6 x 10 3.1 x 10 2.7 x 10 3.8 x 10 6.0 x 10 4.4 x 10 001001

Bi-210 1.2 x 10 3.1 x 10 2.7 x 10 7.7 x 10 6.0 x 10 4.4 x 10 0 0 1 -1 0 1

Po-210 5.6 x 10 3.1 x 10 2.7 x 10 3.8 x 10 6.0 x 10 4.4 x 10 001001

Th-232 7.1 x 10 2.1 x 10 1.8 x 10 4.7 x 10 4.1 x 10 3.0 x 10 -1 -2 1 -1 -2 -1

Ra-228 1.0 x 10 4.7 x 10 4.1 x 10 7.1 x 10 9.1 x 10 6.7 x 10 0 -1 0 -1 -1 0

Ac-228 7.1 x 10 4.7 x 10 4.1 x 10 4.7 x 10 9.1 x 10 6.7 x 10 -1 -1 0 -1 -1 0

Th-228 7.1 x 10 4.7 x 10 4.1 x 10 4.7 x 10 9.1 x 10 6.7 x 10 -1 -1 0 -1 -1 0

Ra-224 1.0 x 10 4.7 x 10 4.1 x 10 7.1 x 10 9.1 x 10 6.7 x 10 0 -1 0 -1 -1 0

Rn-220 1.6 x 10 5.7 x 10 8.4 x 10 1.1 x 10 1.1 x 10 1.4 x 10 2 -1 0 2 0 1

Po-216 3.5 x 10 4.7 x 10 4.1 x 10 2.4 x 10 9.1 x 10 6.7 x 10 0 -1 0 0 -1 0

Pb-212 3.5 x 10 4.7 x 10 4.1 x 10 2.4 x 10 9.1 x 10 6.7 x 10 0 -1 0 0 -1 0

Bi-212 7.1 x 10 4.7 x 10 4.1 x 10 4.7 x 10 9.1 x 10 6.7 x 10 -1 -1 0 -1 -1 0

Tl-208 2.1 x 10 1.4 x 10 1.2 x 10 1.4 x 10 2.7 x 10 1.9 x 10 -1 -1 0 -1 -1 0

K-40 7.8 x 10 6.2 x 10 5.2 x 10 5.3 x 10 1.2 x 10 8.5 x 10 0 -3 -3 0 -2 -3
9-7

atmospheric releases may be modeled for as many as six independent

sources. Plume rise can be calculated assuming either a momentum- or

buoyancy-driven plume that reflects facility-specific plant

parameters. Plume dispersion is based on a modified Gaussian plume

equation and accounts for plume depletion that includes precipitation

scavenging and dry deposition. Primary model parameters for plume

dispersion and depletion are based on available site-specific

meteorological data. (A library of meteorological data that include

wind data files, annual precipitation, ambient temperatures, and lidheight for all major cities is provided by the code of CAP-93). 

From plume dispersion and plume depletion calculations, the CAP93 program computes radionuclide concentrations in air and rates of

deposition and buildup on ground surfaces and in soil. Estimates of

the radionuclide concentrations in produce, leafy vegetables, milk,

and meat are made by coupling the output of the atmospheric transport

models with the terrestrial food-chain models defined in the U.S.

Nuclear Regulatory Commission’s Regulatory Guide 1.109. The

quantities of foodstuff produced locally are based on the average

agricultural productivity data of the State in which the assessment

area is located.

For dose and risk estimates, the population distribution at each

of the 684 assessed sites was developed by means of the GENPOP

computer code and 1990 Census Bureau data. Dose estimates reflect the

exposure from external (air immersion and ground surface) and internal

(inhalation and ingestion) sources. For low-LET external radiation,

CAP-93 employs the nominal risk coefficient of 3.9 x 10 fatal cancers -4

per rem.

For internal exposures, dose and risk estimates are defined by

ICRP tissue/organ weighting factors that account for route of entry,

clearance class, and transfer factors within body compartments. In

summary, dose and cancer risks can be tabulated for individual

exposure pathways, radionuclides, and tissues/ organs. All risk

estimates pertain to the risk of fatal cancer and assume that exposure

occurs over the lifetime of individuals within the assessed

population.

EPA’s methodology for estimating risks from Rn-222 emissions is

based on an extrapolation of epidemiologic findings of underground

miners exposed to radon. CAP-93 calculates working levels (WL), not 1,5

concentrations of specific radon daughter products. A WL is defined

as any combination of short-lived radon decay products in 1 liter of

air that will result in the emission of 1.3 x 10 MeV of alpha-particle 5

energy. Risk is not derived from dose but from time-integrated

exposure expressed in working level months (WLM). Under typical

residential exposure conditions, it is assumed that 1 WLM corresponds
Recently, the Agency revised its estimates of radiogenic cancer risks to a

reflect the current epidemiological data and scientific consensus on

extrapolations from the available data to chronic low dose exposures. 6

The revised estimates yield a nominal value of 5.1 x 10 fatal cancers per -4

rad for uniform whole body exposure to low-LET radiation and 2.2 x 10-4

fatal lung cancers per WLM for exposure to radon-222 and its decay

products. The radon risks reported in this study can be adjusted to the

new radon risk coefficient simply by applying a correction factor of about

0.6. No simple adjustment can be made to the non-radon risks to reflect

the Agency’s current values. However, since the ground surface pathway

dominates the risk for maximally exposed individuals, an upward adjustment

of approximately 30 percent would bound their risks.

9-8

to 170 hours of exposure at 200 pCi/L of radon gas. CAP-93 employs a

risk coefficient of 3.6 x 10 fatal lung cancers per WLM. -4 a6

CAP-93 assesses risk for a circular grid that is defined by

sixteen sectors and up to 20 radial distances around a specified

facility. For this study radial distances of 400, 1,500, 3,500,

7,500, 10,000, 15,000, 25,000, 35,000, 45,000, and 50,000 meters were

used. Risk to the population is determined by summing individual

risks by distance and section for the 0- to 50-km grid around each

assessed facility. Risk to the maximally exposed individual(s)

corresponds to that location (i.e., distance and sector of highest

exposure) where individuals are believed to reside.

The population risk frequency distribution identifies the number

of people at various levels of risk. The risk categories are divided

into powers of 10, in which the individual lifetime cancer risk ranges

from one chance in ten to less than one chance in a million. Risk

data for each of the 684 assessed plants are provided in the

previously identified Addendum. Only a summary of these data is

provided below.

9.1.6 Estimates of Population Health Risks

Radionuclide emissions from utilities may result in public

exposure from multiple pathways that include (1) external radiation

from activity suspended in air or deposited on the ground and

(2) internal exposure from the inhalation of airborne contaminants or

ingestion of contaminated food products. Although the potential

health risks are essentially independent of whether a dose was

internal or external, the assessment of internal exposures is

considerably more complex. For ingested or inhaled radionuclides,

dose assessment requires biokinetic information that describes the

distribution and retention of individual nuclides, the type of

radiation emitted, and the amount of energy absorbed by individual

target tissues/organs.

Estimates of population doses from chronic atmospheric releases

require the use of a computer code that accounts for atmospheric

dispersion, radionuclide concentrations in environmental media, and

radionuclide intakes by inhalation and ingestion. In support of
9-9

National Emission Standards for Hazardous Air Pollutants, the EPA,

with support from Oak Ridge National Laboratory, developed the CAP-88

computer model. The CAP-88 (Clean Air Act Assessment Package-1988)

computer model is a composite of computer programs, databases, and

associated utility programs.

The CAP-88 programs are considered among the best available

verified models for population dose and risk assessment for

radionuclide air emissions. For a given facility, atmospheric

releases and dose assessment may be modeled for up to six independent

sources that take into account plant- and site-specific model

parameters.

Since it was first introduced, CAP-88 has been revised

periodically to reflect changes in database information and improved

risk methodologies. For this study, the most recent version of the

code, designated as CAP-93, was used. The CAP-93 contains a

correction to the procedure used to calculate wet deposition of

radionuclides from the plume.

For low doses of radiation, potential health effects may not

appear for years or even decades following exposure. Such delayed

effects are termed “stochastic” and are thought to result from highly

selective molecular changes in individual cell(s). Although these

highly selective changes occur rarely, when they do, the altered cell

may develop into cancer. Among the stochastic effects that have been

associated with radiation exposure, medical scientists consider cancer

induction the primary health effect of concern.

A key characteristic of a stochastic effect is that the severity

of the effect is not dose-dependent. However, the probability that a

stochastic event (i.e., cancer) may occur is dictated by the radiation

dose. The stochastic nature of low-dose radiation is not unique but

is universal to all carcinogenic agents that act by primary genetic

mechanisms.

The current method of estimating radiation risks relies on select

human studies in which cancer rates were observed at a higher

incidence among exposed individuals than would normally occur

spontaneously. The most intensely studied people are the Japanese

atomic bomb survivors of Hiroshima and Nagasaki. Data through 1985

show that, among the 76,000 individuals studied, 5,935 survivors have

died of cancer from all causes. It is estimated that about 340 of

these cancers (80 leukemias and 260 nonleukemias) were the result of

radiation exposure.

The data also define a dose response in which increasing doses

yielded an increased percentage of excess cancers, especially for

leukemia. However, some numerical estimates embody substantial

statistical uncertainties about the number of cancer deaths induced by

radiation. Thus, for doses less than 50,000 mrem (50 rem), the small

number of excess cancers above normal expected levels may reflect
9-10

random fluctuations that are not linked to radiation exposure. When

doses exceed 50,000 mrem (50 rem), the number of excess cancers is

sufficient to support a causal link to human cancers.

For low-dose exposures, a causal link and a quantitative

relationship between radiation dose and cancer has not been

established. Yet, scientists conservatively assume that any dose of

radiation, no matter how small, may pose a risk to human health. 

Estimates of health risks from low-level radiation are, therefore,

derived by extrapolating risks from high doses to lower doses using a

linear nonthreshold dose-response model contained in the CAP-88 and

CAP-93 computer codes.

9.2 RADIONUCLIDE UNCERTAINTY ANALYSIS

Uncertainties in the estimates of risk presented for radionuclides

emitted from fossil-fuel-fired steam electric- generating units were

assessed using both qualitative judgments and quantitative techniques. 7

As in almost all assessments of environmental health risk, the risk

estimates were based on modeling rather than direct measurements of

exposure and risk; therefore, the results were subject to uncertainties

in modeling, completeness, and parameter values.

Modeling uncertainties pertain to the formulation of the

mathematical models used to predict risk and the degree to which they

accurately represent reality. Completeness uncertainties pertain to

whether or not all significant radionuclides and pathways of exposure

are addressed. Parameter uncertainties pertain to the specific values

assigned to the parameters that are input to the calculational models.

Census-tract, air dispersion, environmental transport, metabolic,

and dose-response models were used to predict the location of

individuals around the plants; the dispersion of the pollutants in the

environment; their concentrations in soil and air at receptor

locations; their accumulation and removal from soil; their uptake and

transfer from soil to foodstuffs; their intakes, translocations,

accumulations, and removal from the various organs and tissues of the

body; and the resulting risks to the individuals in the exposed

population. 

The modeling uncertainty associated with the use of the GENPOP

census-tract model used to locate the individuals within 50 km of each

plant was limited to its ability to properly place individuals living

in proximity to the plant. The potential magnitude of this

uncertainty was partially assessed in a quantitative manner using

field-verification techniques to identify actual locations of nearby

individuals for the plants with the highest estimated maximum

individual risks. The results of these plant-specific assessments

indicated that, on a plant-by-plant basis, the reported MIR might be

high by an order of magnitude. However, they also demonstrated that

the reported MIR of about 10 is correct when viewed as an upper bound -5

for the entire population of electric utility steam-generating units.
9-11

Modeling uncertainties associated with the air dispersion,

environmental transport, metabolic, and dose-response models were

considered via model input parameters. Significant model inputs were

included in a rigorous Monte Carlo analysis of parameter uncertainties

associated with two plants with the highest estimated MIRs. For the

dispersion and environmental transport models that were used, which

are widely recognized as appropriate for the physical processes that

govern dispersion and environmental concentration, assessment of

parameter uncertainty only was clearly appropriate. For the metabolic

and dose-response models, the parameter uncertainty relied on lumped

parameters. This approach reflected the limited data available on

the uptake and retention of radionuclides within the various organs

and tissues of the body and the necessity of extrapolating the

dose-response relationship from data reflecting much higher (orders of

magnitude) exposures.

Uncertainties in completeness are limited to source terms and

exposure pathways. Because the source terms for utilities are well

characterized, there is very little likelihood that significant

unaccounted for radionuclide releases are occurring at these

facilities. With respect to pathways of exposure, the analysis

assumed that four pathways of exposure (ingestion of milk, meat, and

vegetables; inhalation; immersion in contaminated air; and exposure to

contaminated ground) were present at all sites. The ground water

pathway was not included because the deposited material is on the

ground surface in a physical and chemical form that minimizes its

potential to leach to ground water.

Exposure to multiple sources is one potentially significant

exposure pathway that was not accounted for by the air dispersion and

environmental transport modeling. To evaluate the potential

significance of this pathway, explicit hand calculations were

performed for the plants with the highest estimated MIRs to estimate

the impacts from all plants within 50 km. The results of these

explicit calculations showed that omission of multiple plants from the

estimates resulted in less than a 5 percent error in the MIRs. A

related completeness uncertainty was the impact on individuals

residing beyond the 50-km assessment area around each plant. A

semiquantitative assessment of this uncertainty, which affects the

estimate of deaths per year in the exposed populations, indicated that

the risk was not understated by more than a factor of 3. Given these

results, completeness uncertainties are not judged to be a significant

contributor to the overall uncertainty in the analysis.

The largest uncertainties were associated with the parameter values

used in the assessment. As noted above, a rigorous assessment of

parameter uncertainties was conducted for the two plants with the highest

estimated MIRs. Nominal values used in the assessment were assigned a

distribution and range based on available data and expert judgment. 

Based on this analysis, it was determined that the 90 percent confidence

interval for the reported MIR values of approximately 10 ranges from -5

about 10 to 10 . -6 -4
9-12

9.3 SUMMARY FINDINGS

Table 9-4 gives the distribution of fatal cancer risks to the

combined populations residing within the 50-km (35-mile) radii of the

684 utility plants. The aggregate of assessed populations living

within a 50-km radius of a plant is estimated to be 196.1 million,

which represents approximately 75 percent of the U.S. population. The

individual lifetime risk of fatal cancer to more than 99.9 percent of

the assessed population (i.e., 196.1 million) is less than one chance

in a million. The data further suggest that, under current operating

conditions, there are no instances in which the release of

radioactivity is likely to result in a lifetime fatal cancer risk to

any one person that is equal to or greater than 1 chance in 10,000. 

It is estimated that about 1,027 individuals residing within a 50-km

distance of a plant may receive radiation exposures for which the

lifetime risk is between 1 in 10,000 and 1 in 100,000 (i.e., 1 x 10 -4

to 1 x 10 ). -5

It must also be pointed out that the distribution of individual

risks within each risk range is heavily skewed toward the lower value. 

This is evidenced by the fact that the average individual lifetime

risk is a small fraction of the midpoint value within each of the risk

ranges. Correspondingly, the probability of a single fatal cancer

occurrence within the highest risk group of 1,027 individuals is less

than 2 chances in 10,000 per year. For the entire assessed population

of 196,100,000 within 50 km of these plants, the estimated cancer risk

attributable to radionuclide emissions from electric utility steam

generating units (SGUs) (includes coal-, oil-, and gas-fired utilities

)is less than 1 cancer death per year (i.e., 3.36 x 10 deaths/year is -1

the risk equivalent of about 1 in 3 chances that a single cancer death

will occur in a year). Exposures and risks to individuals residing

beyond 50 km are not explicitly evaluated. However, using the

assumption that radionuclides dispersion and exposure beyond 50 km

would be similar to that of arsenic, which was modeled with the RELMAP

(see chapter 6), EPA estimates that the overall cancer incidence may

be seven times greater. That is, considering both local and longrange transport, the cancer incidence could be roughly 2 cases per

year (i.e., 0.3 x 7). Most (approximately 99 percent) of the cancer

incidence is due to inhalation exposure. The EPA estimates that coalfired utilities are contributing about 25 percent of the cancer

incidence and oil-fired utilities the other 75 percent.

Based on radionuclide emissions and plant-specific/ site-specific

data, CAP-93 also calculates the MIR for each of the 684 plants. 

Table 9-5 characterizes those plants with the highest estimated MIR

values expressed in lifetime fatal cancer risk. There were a total of

17 plants for which the lifetime risk of fatal cancer to the MIR is

estimated to exceed 1 x 10 due to multipathway exposures to -5

radionuclide emissions from utilities. The highest MIR value of 

3 x 10 corresponds to a five-unit coal-fired facility that generated -5

3,340 MW of electricity in 1990. Of the 17 plants with the highest

MIR values, 11 are exclusively designated as coal boilers. Only
9-13

Table 9-4. Frequency Distribution of Lifetime Fatal Cancer Risks

for All Plants

Lifetime cancer Number of individual Deaths per year in Death per year in this

risk range people lifetime risk this risk range risk range or higher

Average

1 x 10 to 1 x 10 0 0 0 0 0 -1

1 x 10 to 1 x 10 0 0 0 0 -1 -2

1 x 10 to 1 x 10 0 0 0 0 -2 -3

1 x 10 to 1 x 10 0 0 0 0 -3 -4

1 x 10 to 1 x 10 1,027 1.3 x 10 1.92 x 10 1.92 x 10 -4 -5 -5 -4 -4

1 x 10 to 1 x 10 95,745 2.2 x 10 3.06 x 10 3.26 x 10 -5 -6 -6 -3 -3

Less than 1 x 10 196,000,000 1.2 x 10 3.32 x 10 3.36 x 10 -6 -7 -1 -1

Table 9-5. Plants with the Highest Estimated Maximum Individual

Risk (MIR)

Plant name MIR Units MWe Units MWe Units MWe

Coal-fired Gas-fired Oil-fired

Plant #222 3 x 10 5 3,340 -5

Plant #247 3 x 10 4 900 -5

Plant #60 2 x 10 4 3,160 -5

Plant #301 2 x 10 2 750 3 262 -5

Plant #251 2 x 10 4 1,540 -5

Plant #406 2 x 10 4 2,777 -5

Plant #256 2 x 10 3 1,728 -5

Plant #17 2 x 10 2 1,112 -5

Plant #133 2 x 10 2 1,135 2 66 -5

Plant #318 1 x 10 6 1,100 -5

Plant #672 1 x 10 8 1,965 -5

Plant #668 1 x 10 7 2,304 -5

Plant #82 1 x 10 2 804 -5

Plant #207 1 x 10 2 558 -5

Plant #253 1 x 10 3 2,052 -5

Plant #489 1 x 10 4 1,872 -5

Plant #651 1 x 10 6 372 -5

MIR = maximum individual risk expressed as lifetime fatal cancer risk
9-14

two facilities are identified as exclusively oil-fired plants.

The remaining four plants are represented by a combination of boilers,

where coal is at least one of the designated primary fuels.

The MEI risk due to inhalation exposure to radionuclides from the

highest risk oil-fired plant is estimated to be 1 x 10 . The MEI risk -5

due to inhalation exposure to radionuclides from the second highest

risk oil-fired utility is estimated to be 3 x 10 . The other 135 oil- -6

fired utilities and all coal-fired utilities are estimated to pose

cancer risks less than 1 x 10 due to inhalation exposure to -6

radionuclides.

Background Radiation Exposures

The risks due to exposure to radionuclide emissions from

utilities are substantially lower than the risks due to exposure to

natural background radiation. As shown in Tables 9-6 and 9-7 the

average exposure to natural background radiation (excluding radon

progeny) for the U.S. population has been estimated to be roughly

about 100 millirems (mRems) per year.8,9

Background radiation exposure can come from internal or external

sources. External sources include cosmic (extraterrestrial) and

terrestrial (radionuclides in soil and rock). Internal sources

include inhaled and ingested radionuclides retained in the body, with

inhaled radon progeny treated as a separate problem. Radiation from

consumer products and fallout from weapons tests make minor

contributions to background. Average doses are listed in Table 9-6.

Background radon exposure is assessed on the basis of exposure to

its progeny. The estimate is based on the distribution of short halflife radioactive progeny of radon in the inspired air. Progeny

estimates must be calculated for each environment independently. The

values in Table 9-7 are average population values and do not reflect

the ranges possible.

Risks of background radiation exposure in the United States can

be calculated using the average annual dose from external and internal

sources and the average annual radon exposures and risk conversion

factors from appropriate references.

The age averaged lifetime risk of fatal cancer associated with

the average annual dose of 100 mrem (Table 9-6) is 5.7 x 10 . -5 10

Continual lifetime exposure at 100 mrem/y yields a risk of 4.3 x 10 . -3

The lifetime risk of fatal lung cancer associated with the average

annual exposure of 0.257 WLM (Table 9-7) is 5.8 10 . Continuous -5 11

lifetime exposure (about 75 years) at 0.257 WLM/y yields a risk of

4.4 x 10 .-3
9-15

Table 9-6. Average Background Radiation Doses (effective dose

equivalent excluding inhaled radon progeny)

Source Annual Dose (mrem)

External

Cosmic 28.4 12

Terrestrial 23.6 13

Internal14

Ingested and inhaled 39

Fallout 1 14

Building materials and consumer products 8 14

Total 100

Notes:

An individual is expected to spend 87.9% of his/her time indoors (estimates of fraction of time in the indoor and

outdoor environments were adapted from data in the Draft Exposure Factors Handbook, U.S. EPA, Washington,

D.C., 1997.)

For terrestrial sources, the indoor dose rate in air is 0.8 times the outdoor value and the effective dose equivalent

(mrem) is 0.7 times the absorbed dose (mrad) in air. 13

The range of background exposure in the United States from the sources listed in Table 9-6 is from about 75

mrem/year to about 200 mrem/year.

Table 9-7. Average Annual Background Exposures Due to Radon

Progeny

Source Annual Exposure (WLM)

Residence 0.242 11

Outdoors 0.015

 Total 0.257

Notes:

WLM = working level month

This radon estimate is incomplete since there is no estimate of the average exposure level inside structures other

than residences. The estimated average exposure and risk can only be higher than listed here.

The estimates of average radon concentration used were: 1.25 pCi/l in residences and 0.3 pCi/l outdoors.

The estimated fraction of time in the outdoor environment (0.121) was adapted from data in the Draft Exposure

Factors Handbook, U.S. EPA, Washington, D.C., 1997. The equilibrium fraction was assumed to be: 0.5 indoors

and 0.8 outdoors.
9-16

Because of limitations in the GENPOP computer code used for

identifying locations of individuals, the MIRs shown for each plant

should be viewed with caution; errors of a few hundred meters in the

location of individuals can result in an over- or underestimate of

risk by factors of 2 or more. The UARG reestimated the risks for the

17 plants with the highest MIRs using refined population grids. Their

results show lower MIRs for the majority of these plants, but their

highest MIR of 1 x 10 is consistent with the EPA’s estimates. Thus, -5

the EPA believes the GENPOP methodology is sufficiently accurate to

establish the magnitude of MIRs for all utilities.
9-17

1. U.S. Environmental Protection Agency. Estimates of Health Risks

Associated with Radionuclide Emissions from Fossil-Fueled SteamElectric Generating Plants. EPA 402/R-95-16. Office of Radiation

and Indoor Air, Washington, D.C. August 1995.

2. Reeves, H. Stellar Evolution and Nucleosynthesis. Gordon and

Breach Science Publishers. New York. 1968.

3. W. Anderson, Mayneord W. V., and Turner R. C. The Radon Content

of the Atmosphere Nature. Volume 174. p. 424. 1954. 

4. U.S. Environmental Protection Agency. Background Information

Document for Environmental Impact Statement on NESHAPS for

Radionuclides. EPA-550/1-89-006-1. 1989.

5. U.S. Environmental Protection Agency. Estimates of Health Risks

Associated with Radionuclide Emissions from Fossil-Fueled SteamElectric Generating Plants-Addendum. EPA 402/R-95-16a. Office of

Radiation and Indoor Air, Washington, D.C. August 1995.

6. U.S. Environmental Protection Agency. Estimating Radiogenic

Cancer Risks. EPA-402-R-93-076. Office of Radiation and Indoor

Air. Washington, D.C. 1994.

7. National Council on Radiation Protection and Measurements. A

Guide for Uncertainty Analysis in Dose and Risk Assessment

Related to Environmental Contamination. NCRP Commentary #14,

May 10, 1996. p. 54.

8. National Council on Radiation Protection and Measurements. NCRP

Report 93, 1993.

9. National Council on Radiation Protection and Measurements. 

Exposures for the Population of the U.S. and Canada from Natural

Background Radiation. NCRP Report 94, 1994.

10. Health Risks from Low-Level Environmental Exposure to

Radionuclides, Federal Guidance Report No. 13-Part 1, Interim

Version, EPA 402-R-97-014, U.S. EPA, Washington, D.C. 1998.

11. Technical Support Document for the 1992 Citizen’s Guide to Radon,

EPA 400-R-92-011, U.S. EPA, Washington, D.C. 1992.

12. Bogen, K. T. and Goldin, A. S. Population Exposures to External

Natural Radiation Background in the United States, Technical Note

ORP/SEPD-80-12, U.S. EPA, Washington, D.C. 1981.

9.4 REFERENCES
9-18

13. Sources and Effects of Ionizing Radiation, United Nations

Scientific Committee on the Effects of Atomic Radiation, United

Nations, New York, NY 1993, and K. M. Miller, Measurements of

External Radiation in United States Buildings. Radiat. Prot.

Dosim. Volume 45. 1992. pp. 535-539.

14. Ionizing Radiation Exposure of the Population of the United

States, NCRP Report No. 93, National Council on Radiation

Protection and Measurements, Bethesda, MD. 1987.
10-1

10.0 SCREENING LEVEL ASSESSMENT OF MULTIPATHWAY EXPOSURES 

AND RISKS TO ARSENIC EMISSIONS

10.1 PURPOSE AND SCOPE

Though arsenic is a naturally occurring element that is found in

environmental media (air, water, and soil), as well as in biota, it is

also released to the environment by anthropogenic sources, including

fossil-fuel-fired electric utility plants (i.e., utilities). Since

arsenic compounds are known to cause health effects in humans from

inhalation and ingestion exposures, and since arsenic has been found

at relatively high concentrations in animals and plants that are food

sources for humans, the potential impact of arsenic emissions from

utilities on human health was evaluated. Human exposure to arsenic

through multiple exposure pathways was assessed. Though inhalation

exposure assessments have been performed for arsenic emissions from

many types of sources, few assessments have examined non-inhalation

exposures to arsenic emissions from anthropogenic sources. Specific

objectives of this analysis were: (1) to assess the magnitude of the

contribution of arsenic emissions from utilities to concentrations in

environmental media and biota; (2) to assess potential human exposure

to arsenic emissions through multiple exposure pathways, including

ingestion; (3) to identify dominant pathways of potential exposure to

arsenic emitted from coal-fired and oil-fired utilities, and (4) to

characterize potential human health risks from exposure to arsenic

emissions from utilities.

10.1.1 Rationale and Usefulness of Model Plant Approach

Arsenic is generally present as a low-level contaminant in coal

and oil. During combustion arsenic is volatilized from coal and oil

and released to the atmosphere. For this assessment, it was not

possible to model the emission impact of every utility plant.

Consequently, the actual arsenic emission data and facility

characteristics for any specific source were not modeled. Instead, a

model plant approach was used to represent actual sources. The model

plants were designed to characterize the arsenic emission rates and

the atmospheric release processes exhibited by typical facilities in

each of the four source classes considered. The modeled facilities

were not designed to exhibit extreme sources (e.g., facilities with

the highest arsenic emission rates) but rather to serve as

representatives of the combustion source class.

In taking the model plant approach, it was realized that there

would be a great deal of uncertainty about the predicted fate and

transport of arsenic and about 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 that significantly impact key results.
10-2

For the assessment, a series of fate, transport, and exposure

models were used to predict arsenic concentrations in environmental

media, pertinent biota, and arsenic contact with humans. An effort

was made to estimate the amount of receptor contact with arsenic as

well as the oxidative state and form of arsenic contacted. No attempt

was made to estimate an internal dose.

Three models were used to predict environmental arsenic

concentrations and exposure: the Regional Lagrangian Model of Air

Pollution (RELMAP), the Industrial Source Complex Short-Term Version 3

(ISCST3), and the Indirect Exposure Model 2 (IEM2).

10.2 BACKGROUND INFORMATION ON ARSENIC

10.2.1 Forms of Arsenic in the Environment

Arsenic has five electrons in its outer shell; hence, it has four

possible oxidation states: +5, +3, 0, and -3. Arsenic is rarely

found in the environment as a free element. The two primary valence

states of arsenic are the trivalent state, which is denoted by As /As 3+ 3-

or As(III), and the pentavalent state, which is denoted by As or 5+

As(V). Arsenic in each of these valence states forms both organic and

inorganic compounds (an organic arsenic compound is one in which the

arsenic atom is covalently attached to at least one carbon atom). 

Arsenic compounds are typically classified in two different manners: 

(1) according to the oxidation state of arsenic (As , As , and As ), 3- 3+ 5+

or (2) according to whether or not arsenic is in the organic form. 

Table 10-1 shows common arsenic compounds and their classification by

valence state and organic/inorganic form.

 

As(III) is more mobile and soluble than the other common form,

As(V). Organic arsenic is present in most soils and is found most 1 2

often in the environment in combination with oxygen, chlorine, and

sulfur. Inorganic arsenic occurs naturally in geologic formations,

where its most common form is arsenopyrite (FeAsS).

Figure 10-1 presents a generalized scheme for the geochemical

cycling of arsenic through various compartments of the environment. 

The atmosphere is a major conduit for arsenic emitted from

anthropogenic sources to the balance of the cycle via the wet and dry

deposition process. Dry and wet deposition from the atmosphere onto 3

soils may be followed by movement through soils either into

groundwater or surface water. Passage of arsenic into surface waters

may then be followed by further transfer to sediments.

The existence of chemical and biochemical transformations that

occur within the cycle makes the environmental cycling of arsenic more

complicated. Trivalent arsenic in the atmosphere can undergo

oxidation to the pentavalent state. Such conversion can also occur in

aerated surface waters. On the other hand, pentavalent arsenic in an

aqueous medium which is somewhat acidic is an oxidant, and, in the

presence of oxidizable material, will react to form trivalent arsenic. 4
LITHOSPHERE

ROCKS

ARSENIC-BEARING

DEPOSITS

HYDROSPHERE

WATER SEDIMENTS

ATMOSPHERE

BIOSPHERE

PLANTS ANIMALS

PEDOSPHERE

SOILS

GLACIAL MATERIALS

VAPORIZATION

 PRECIPITATION

 PRECIPITATION

 SOLUTION

DEGRADATION

AND

SOLUTION

ABSORPTION

AND

ADSORPTION

CHEMICAL

PRECIPITATION AND

SEDIMENTATION OF

SOLIDS

SOLUTION AND

MECHANICAL

WEATHERING

DUST

SOLUTION AND

MECHANICAL

ENGINEERING

CHEMICAL

PRECIPITATION

PRECIPITATION AND

CONSOLIDATION OF SOLIDS

INHALATION OF DUST

AND GASEOUS FORMS

OF ARSENIC

DEGRADATION

ABSORPTION AND

ADSORPTION

10-3

Figure 10-1. The generalized geochemical cycle for arsenic.8,9

Table 10-1. Common Arsenic Compounds, and Classification by

Valence State and Organic/Inorganic5,6

Type As(III) As(V)

Inorganic Arsenopyrite Arsenate

Arsenite Arsenic acid

Arsenous acid Arsenic pentoxide

Arsenic trioxide

Organic Arsenobetaine Monomethylarsonic acid

Arsenocholine Dimethylarsinic acid

Tetramethylarsonium ion Trimethylarsine oxide

Dimethylarsinylethanol

,7 8
10-4

10.2.2 Sources of Arsenic

The primary source of arsenic emissions in commercial processes

is as a by-product of the treatment of copper, lead, cobalt, and gold

ores. It is used in the production of commercial products such as

agricultural products, wood preservatives, animal feed additives,

medicine, ceramics, and glass, and in the dying and printing

processes. Estimates of the impact of anthropogenic activities that 9

release arsenic on the global arsenic cycle vary widely, but the

impacts on the local arsenic cycle can be significant.

As noted in Table 10-2, hazardous waste incineration is the most

significant source of anthropogenic emissions of arsenic to air for

the United States. Hazardous waste incineration is estimated to

result in 1,742 tons of arsenic emissions annually, 88 percent of the

total national atmospheric arsenic emissions from anthropogenic

sources. Industrial source combustion, followed by utility

combustion, are the second and third highest emitters of arsenic,

respectively. Industrial source combustors emit an estimated 67 tons

of arsenic annually, and utilities emit approximately 65 tons of

arsenic each year.

10.2.3 Arsenic in the Atmosphere

Arsenic air concentrations in unimpacted areas are generally

below several nanograms per cubic meter. However, values near some

combustion facilities can be significantly higher, as

evidenced by measurements taken outside of the United States. 

Reported values in the literature are provided in Table 10-3.

Two studies document initial results from monitoring for wet and

dry deposition of selected trace elements at two Maryland 

shoreline sites in close proximity to Chesapeake Bay waters. Both 10,11

sites are located in rural settings but receive trace metal inputs

from the Baltimore-Washington DC metro area and the Ohio Valley. The

Ohio Valley is heavily industrialized, and sources in the Baltimore

and Washington DC area include several coal and oil-fired utilities as

well as other non-utility sources. Measured deposition rates for

arsenic are summarized in Table 10-4. 

10.2.4 Arsenic in Water

Arsenic concentrations in most U.S. rivers and lakes range from

less than 10 to over 1000 g/L. A summary of measured values found 12

in precipitation, groundwater, lakes, rivers, and oceans is provided

in the EPA draft Screening Level Multipathway Exposure Analysis for

Arsenic.13

It is expected that As(V) will dominate fresh water bodies that

are approximately at equilibrium, and that a large fraction of the

arsenic carried to the water body will be deposited in the benthic

sediment. As(V) is significantly more soluble in water than As(III) 4

(e.g., 630 g of arsenic pentoxide will dissolve in 100 g of water,

compared to only about 2 g for arsenic tridoxide at typical ambient

temperatures). 4
10-5

Table 10-2. National Arsenic Atmospheric Emission Estimates by

Source Category14

Facility type Tons/year Percent

Hazardous waste incineration 1,742 88

Industrial combustion 67 3.4

Utility combustion 65 3.3

Primary copper smelting 47 2.4

Commercial/institutional combustion 32 1.6

Copper ores, mining 13 0.65

Pressed and blown glass and glassware manufacturing 6.1 0.31

Primary nonferrous metals 6.0 0.31

Landfill waste gas flares 5.9 0.3

Secondary nonferrous metals 1.6 0.081

Turbines - distillate oil 0.81 0.041

Agricultural chemicals manufacturing 0.47 0.023

Sewage sludge incineration 0.41 0.021

Wood preserving 0.41 0.021

Medical waste incineration 0.21 0.011

Municipal waste combustion 0.20 0.010

Industrial inorganic chemicals manufacturing 0.20 0.010

Other anthropogenic sources 0.46 0.029

Total 1,989 100

Table 10-3. Reported Arsenic Air Concentrations

Type Mean (ng/m ) Max (ng/m ) Reference 3 3

Remote areas <21 Eisler 1994

Kaarvatn, N.Europe 0.5 1 Pacyna et al. 1989

 Nordmoen, N. Europe 1.2 2

 Prestebaake, N. Europe 0.5 1.8

 Chesapeake Bay (Wye, Elms) 0.69, 0.625 1.96, 1.56 Wu et al. 1994

Near coal-fired power plant (Czech) 19000-69000 Eisler 1994

Urban areas <160
10-6

Table 10-4. Measured Arsenic Deposition Ratesa

Location (g/m /yr) (g/m /yr) Reference

Arsenic dry deposition rate Arsenic wet deposition rate 2 2

Lake Michigan 35 - Wu et al. 1994

Chesapeake Bay 54-150 - Wu et al. 1994

Chesapeake Bay - 45-52 Scudlark et al.

1994

These deposition rates are the result of emissions from a combination of natural and anthropogenic sources. a

Utilities are only one of the many source categories that may be contributing to the deposition rates.

10.2.5 Arsenic in Sediments

Arsenic concentrations in sediments of various rivers and harbors

in the United States have been reported by Brannon and Patrick (1987)

as 1.0 to 8.8 g/g dry weight, and much higher concentrations have

been observed at contaminated sites. Sediments often act as a sink

for arsenic in natural systems and can hold high concentrations of

arsenic. At contaminated sites, such as Sugar Creek, arsenic

concentrations ranging from 4,470 to 66,700 g/g dry weight were

measured. A summary of measured sediment concentrations is provided 4

in the EPA draft Screening Level Multipathway Exposure Analysis for

Arsenic.13

10.2.6 Arsenic in Soil

The majority of soils contain levels of arsenic that vary between

1 and 5 mg/kg, with much higher concentrations being observed around

anthropogenic sources and areas with high background sources. Arsenic

also ends up in soil as a result of the historical direct applications

of arsenical pesticides (which are being phased out in some areas and

are prohibited in others), landfarming of sewage sludge, deposition

from air pollution, and as waste materials from industrial processes. 

Soil concentrations have been found to decrease rapidly with distance

from an elevated point source and have been reported to decline to

background concentrations within 8 to 16 km of the source.9

Ambient soil concentrations given in Bowen range from 0.1 to 40

mg/kg, with a mean of 6; and Vinogradov estimated a range of 1 to 50 15 16

mg/kg with a mean of 5. A range in the United States was given by 16

Stater, et al. of 1 to 20 mg/kg, with a mean of 7.5 mg/kg (n=52), and 17

by Conner and Shacklette as 1.6 to 72 mg/kg, with a mean of 7.5 mg/kg

(n=1215). Wood and Duda compiled background arsenic concentrations 18 19

in soils sampled from the southeastern United States (NC, SC, GA, and

AL) and found that mean arsenic concentrations ranged from 0.69 to

13.3 mg/kg. Cullen and Reimer report that the average arsenic 19

concentration in the continental crust is 3 g/g. 20

There are few data on either the speciation of arsenic in soil or

the determination of the fraction of arsenic in soil that is inorganic. 
10-7

Changes in season, temperature, pH, and relative reducing/oxidizing

conditions will initiate kinetically slow conversions between As(III)

and As(V) species. In aerobic soils, As(V) is the dominant species,

while As(III) may dominate in reduced soils (such as those that are

temporarily flooded or groundwater wells). In very reduced soils

(such as swamps), arsenic can be found in its elemental state, arsine,

or as methylated arsenic. The results in Takamatsu et al. and 21

Bombach et al. indicate that generally less than 20 percent of the 22

arsenic in soil is As (V)21,22

The bioavailability of arsenic in soils contaminated either

through smelter operation or mine tailings has been assessed. In a 23

recent study, immature swine were dosed with the contaminated soils

(immature swine were selected because of the similarity of their

gastrointestinal (GI) tracts to those of human children). The

absorption from the GI tract into the bloodstream was monitored. The

relative (mean) bioavailability of soil arsenic was 78 percent 

(C.I. = 56-111 percent) and the absolute (mean) was 52 percent

(C.I. = 44-61 percent).

10.2.7 Arsenic in Terrestrial Plants

Arsenic does not readily translocate to the shoots of plants and

is found mostly in the roots. In general, arsenic uptake values are

low. Studies have found that, to achieve 1 mg/kg of arsenic in fresh

weight plants, a soil concentration of 200-300 mg/kg is necessary. 24,25-27,26,

Measured concentrations in a variety of food and other plants are

shown in Table 10-5.

10.2.8 Arsenic in Aquatic Plants

Arsenic is not a major contaminant of aquatic plants, except in

severe cases of pollution. Highest reported concentrations in aquatic

plants are the result of mine and smelter wastes, reaching levels as

high as 1,450 mg/kg dry weight. Arsenic concentrations in some 28

untreated areas have been measured as ranging from 1.4 to 13 mg/kg dry

weight. 28

10.2.9 Arsenic in Terrestrial Animals

Some studies have found arsenic can accumulate in meat/dairy

products with most arsenic compounds accumulating in the liver and

kidney. However, reported concentrations are rare. Table 10-6 29,30

shows values reported in the literature.

10.2.10 Arsenic in Fish

Arsenic does not appear to bioaccumulate or bioconcentrate in

freshwater finfish. However, it does appear to bioconcentrate in

freshwater bivalves. Lower trophic level organisms generally appear

to have higher concentrations of arsenic than predatory or omnivorous

aquatic and marine species. 
10-8

Table 10-5. Measured Arsenic Concentrations in Plants

Terrestrial plant (range)] Reference

Concentration (dry

weight mg/kg) [mean,

Edible plants

Lettuce

0.056 (0.012-0.68) Wiersma et al. 1986 a

0.72* Wiersma et al. 1986 (Barudi and Bielig 1980) 31

0.076 (0 - 0.20) Wiersma et al. 1986 (Jelinek and Corneliussen a

1977)

Spinach 0.056 (0.024 - 0.12) Wiersma et al. 1986 a

1.16 Wiersma et al. 1986 (Barudi and Bielig 1980) a

Tomato 0.004 (0.0012 - 0.012) Wiersma et al. 1986 a

0.4 Wiersma et al. 1986 (Barudi and Bielig 1980) a

Carrot 0.12 (0.024 - 0.44) Wiersma et al. 1986 (Jelinek and Corneliussen a

1977)

Potato

0.064 (0.012 - 0.20) Wiersma et al. 1986 a

0.04 (0 - 1.16) Wiersma et al. 1986 (Jelinek and Corneliussen a

1977)

Wheat 0.24 (0.024 - 1.44) Wiersma et al. 1986 a

< 0.24 Wiersma et al. 1986 (Varo et al. 1980) a 32

Barley 0.32 (0.024 - 1.88) Wiersma et al. 1986 a

Oats 0.92 (0.44 - 2.72) Wiersma et al. 1986 a

<0.24 (0.16 - 0.4) Varo et al. 1980 a

Apple 0.08 (0.008 - 1.08) Wiersma et al. 1986 a

0.12 (<0.04 - 1.92) Reinhard 1974 a

Impacted/arsenic-treated areas

Alfalfa from Montana smelter area (2.0-28.4) Jenkins 1980 a 28

Various species from impacted soils (80 (<0.2-5.8) Merry et al. 1986

mg/kg)

33

Grass/fodder crops

Colonial bent grass on low arsenic soil (0.3-3) Jenkins 1980

Grasses on non-treated areas (0.1-0.9) NRC 1977

Scotch heather on low-arsenic soil 0.3 Jenkins 1980

Grass (soil concentration 1-38 g/g dry 1.4 (0.36 - 5.56) Wiersma et al. 1986

weight, mean 11 g/g)

a

Silage maize (soil concentration 1-110 0.88 (0.28 - 2.36) Wiersma et al. 1986

g/g dry weight, mean 10 g/g)

Sugar beet crowns & leaves (soil 2.56 (0.44 - 8.0) Wiersma et al. 1986

concentration 1-36 g/g dry weight, 

mean 14 g/g)

 Converted from freshweight values assuming water content of 0.8. a

 These values are for the Netherlands. A range of mean soil concentrations of 5 - 12 g/g dry weight is reported. b
10-9

Table 10-6. Measured Arsenic Concentrations in Meat and Other

Animal Products

Meat Wet weight Assumed moisture Dry weight

product (g/g) content (g/g) Reference

Beef 0.005 ±0.001 0.615 0.013 ±0.003 Vreman et al. 1986 a

Beef Liver 0.008 ±0.001 0.70 0.027 ±0.003 Vreman et al. 1986 a

Milk <0.001 0.87 <0.003 Vreman et al. 1986 a

Pork <0.1 0.615 <0.3 Jelinek and Corneliussen b

1977c

Poultry 0 - 0.5 0.615 0 - 1.30 Jelinek and Corneliussen b

1977c

Eggs 0 - 0.2 ? Jelinek and Corneliussen

1977c

Lamb 0.002 ±0.001 0.67 0.006 ±0.003 van der Veen and Vreman

1986

 Baes et al. 1984 a

 Assumed same as beef. b

 Detection limit of 0.1 g/g. Measurement is for As O . c

2 3

The species of arsenic present in fish appear to be somewhat

variable. Marine organisms can convert inorganic arsenic into organic

arsenic species, with arsenobetaine being both the major form

identified in fish and the suspected metabolic endpoint for arsenic in

the marine environment. Arsenobetaine, or “fish-arsenic,” an organic

arsenic species, is a relatively nontoxic form of arsenic. There is

little evidence of human toxicity due to arsenobetaine exposure. 

Arsenocholine and inorganic arsenic species have also been identified

as forms of arsenic in fish. Arsenocholine may be more toxic than

arsenobetaine, but these results are questionable. Inorganic

arsenicals are rapidly converted to less toxic organic forms of

arsenic in fish. The primary arsenic form in marine fish is 34

arsenobetaine, which comes from arsenosugars present in algae and not

from the formation of trimethylarsine oxide. 

Table 10-7 shows a summary of measured freshwater fish

concentrations of total arsenic. Even though shellfish and other

marine foods contain the greatest arsenic concentrations, much of the

arsenic present in fish and shellfish exists in the less toxic organic

form. In shellfish, 0-2.9 percent of the total arsenic is generally 35

measured to be in inorganic forms; in finfish, 0-9.5 percent is

generally measured as inorganic. 
10-10

Table 10-7. Total Arsenic Concentrations in Freshwater Fish in

the United States

Freshwater fish (Wet weight mg/kg) Reference

Concentration range

Bass - muscle 0.0 - 0.51 Jenkins 198028

Bluegill- nationwide- whole 0.05-0.4 Jenkins 1980; Wiener et al. 198436

Catfish - native- muscle 0.0-0.3 Jenkins 1980

Catfish - cultured- muscle 0.2-3.1 Jenkins 1980

Common carp - nationwide - muscle 0.0-0.2 Jenkins 1980

Northern pike - northern U.S. - muscle <0.01- 0.1 Jenkins 1980

Coho salmon- USA- muscle 0.07 - 0.17 Jenkins 1980

Atlantic salmon - muscle oil 0.8-3.1 Jenkins 1980

Rainbow trout- all tissues <0.4 Jenkins 1980

Various species - USA, 1976 - 1984  0.14 - 2.9 Lima et al. 1984 ; Schmitt & Brumbaugh 37

White sucker - Muscle 0.03-0.13 Jenkins 1980

Lake trout- nationwide- whole 0.06-0.68 Jenkins 1980

10.2.11 Speciation of Arsenic in Food Products

Though total arsenic levels have been measured in a variety of

food products, limited data are available on concentrations of

particular arsenic species in food products. The chemical forms of

arsenic in foods are varied and complex. Table 10-8 lists reported

percentages of inorganic arsenic in foods. The form found in fish and

marine foods was discussed above. Other food products, such as meats,

rice, and cereals, contain higher percentages, and often higher total

amounts, of inorganic arsenic.

10.2.12 Arsenic Near Anthropogenic Sources

Increased arsenic concentrations have been measured in

environmental media surrounding anthropogenic atmospheric 

emission sources, including copper and lead smelters. The measured

data indicate an elevation of arsenic concentrations that is related

to the emission of arsenic and subsequent deposition to the

terrestrial environment. Table 10-9 shows a summary of values. Many

of these data were collected at sources that operated prior to the use

of modern particulate control technology, and the facilities are not

of the type considered in this report. However, they are included 38

because the data indicate that some arsenic sources can have an

observable impact on their local environment.

10.3 SUMMARY OF MODELS AND APPROACH

In order to address the objectives of this study, a series of

modeling efforts were performed. The Regional Lagrangian Model of Air

Pollution (RELMAP) model was used to model the long-range transport of

arsenic from coal- and oil-fired utilities. A separate effort

involved modeling the atmospheric dispersion and deposition of arsenic

on a local scale (within 50 km), using the Industrial Source Complex 
10-11

Table 10-8. Percentage of Inorganic Arsenic Compared to Total

Arsenic in Selected Foods

Food Percent Inorganic As

Milk and dairy products 75%

Meat-- beef and pork 75%

Poultry 65%

Saltwater fisha 0%

Freshwater fish (0-9.5 percent) 10% a

Cereals 65%

Rice 35%

Vegetables 0.5%

Potatoes 10%

Fruits 10%

 The values presented for fish are not widely accepted. a

Source: Weiler (1987) in Borum and Abernathy (1994) as cited in IF Kaiser, 1996.

Table 10-9. Environmental Concentrations near Facilities38

Facility Value(s) Reference

Air (ng/m )3

Former USSR 500-1900 Pershagen, G. and Vahter, M. 1979

Texas max. 1400 Pershagen, G. and Vahter, M. 1979

Tacoma, Washington max. 1500 Pershagen, G. and Vahter, M. 1979

Romania max. 1600 Pershagen, G. and Vahter, M. 1979

Germany 900-1500 Pershagen, G. and Vahter, M. 1979

Drinking water (g/L)

Mexico, from plant producing As O 4000-6000 Pershagen, G. and Vahter, M. 1979 2 3

Japan, near factory producing As sulfide 3000 Pershagen, G. and Vahter, M. 1979

Dust (mg/kg or g/g)

Tacoma, WA, near smelter 1300 (remote from NRC 1977

smelter 70)

Sediments (mg/kg dry weight, g/g)

Near sewer outfall 35 NRCC 1978

Soil (mg/kg or g/g) dry weight

 Tacoma, WA, near smelter Max. 380 Pershagen, G. and Vahter, M. 1979

Japan max. 2470 Pershagen, G. and Vahter, M. 1979

Fish, near smelter (water arsenic 2.3-2.9 g/L), g/g freshweight

Total arsenic 0.05 - 0.24 Norin et al. 1985. 

Inorganic arsenic 0.01 - 0.02 Norin, H., M. Vahter, A. Christakopoulos, and 

M. Sandstroem 1985. 

Source: Eisler (1994)
10-12

Short-Term Model 3 (ISCST3). Finally, using the predicted air

concentrations and deposition rates, the Indirect Exposure Model 2

(IEM2) model was used to predict environmental concentrations and

subsequent exposure for several hypothetical exposure scenarios.

10.3.1 Source Classes Considered and Model Plant Approach

Four different arsenic source facilities were considered in the

local-scale aspect of this study: three coal-fired utilities (a

small, a medium, and a large facility) and an oil-fired utility. A

list of these facilities and their emission rates is provided in

Table 10-10. Details about these facilities and how the

parameterizations for the model plants were performed are provided in

Appendix A of the EPA Draft Screening Level Multipathway Exposure

Analysis for Arsenic.13

10.3.2 Atmospheric Transport Modeling

10.3.2.1 Local Scale Modeling. The ISCST3 was used to estimate

the atmospheric dispersion and deposition of emitted arsenic within a

50 km radius of the facilities. The ISCST3 uses hourly 39,40

meteorological data to estimate the ambient air concentrations of an

emitted pollutant, as well as the wet and dry deposition rates. The

dry deposition of the air pollutant is calculated, based on particle

size, atmospheric conditions, and gravitational settling velocities. 

Wet deposition of the pollutant is based on scavenging coefficients

that depend on particle size and the precipitation rate. 

For each of the four facilities considered, ISCST3 was run in

both a humid and an arid site, with receptors placed at 16 directions

around the facility and 30 distances between 200 m and 50 km, for a

total of 480 receptors. Simple (i.e., flat) terrain was assumed. 

Values at other locations were estimated using linear interpolation,

as discussed in Appendix A of EPA Draft Screening Level Multipathway

Exposure Analysis for Arsenic. Area-averaged air concentrations and 13

deposition rates were estimated, for use in the exposure modeling.

10.3.2.2 Regional Transport Modeling. Long-range transport was

modeled for arsenic. The methods, model, and approach are described

in detail in chapter 6 (section 6.6). Figures 10-2 through 10-4 show

the results of the arsenic RELMAP modeling.

10.3.3 Indirect Exposure Modeling

Because of its chemical and physical characteristics, arsenic

emitted to the atmosphere may be transported to other environmental

media (i.e., soil or water), thus allowing noninhalation exposures to

arsenic to occur. The IEM2 was used to predict the terrestrial and

aquatic fate and transport of arsenic deposited in the region of

interest, as well as the human exposure to arsenic. The IEM2

calculated arsenic concentrations in watershed soils; these

concentrations were then used in calculating concentrations in various

food plants. The waterbody component of the IEM2 model calculated

arsenic concentrations in surface water and in aquatic organisms. In 
10-13

Table 10-10. Summary of Model Plants and Emission Rates Used for

the Assessment

Model plant Stack/source kg/yr g/s

Total arsenic emissions 

Large coal-fired utility boiler Stack 2 74.0 2.35e-3

Stack 1 85.0 2.70e-3

Stack 3 98.0 3.11e-3

Medium coal-fired utility boiler

Stack 1 32.0 1.01e-3

Stack 2 30.0 9.51e-4

Small coal-fired utility boiler

Stack 1 15.4 4.88e-4

Stack 2 8.8 2.79e-4

Oil-fired utility boiler Stack 1 14.6 4.63e-4

Stack 2 18.8 5.96e-4

addition, the IEM2 model calculated human exposures for selected

exposure scenarios through multiple exposure routes, including food

consumption, water ingestion, and inhalation.

10.3.3.1 Description of Exposure Scenarios. Three basic

exposure scenarios were considered: a subsistence farmer (adult and

child), a subsistence fisher (adult and child), and a pica child. 

These scenarios were considered because they represent possible highend scenarios for exposure to arsenic. Table 10-11 summarizes the

exposure pathways considered for each of these scenarios. 

Table 10-12 shows the default values for the scenario-independent

parameters for both the child and adult receptors, and Table 10-13 shows

the default values for the scenario-dependent exposure parameters (the

technical bases for these values are provided in Appendix B of the EPA

Draft Screening Level Multipathway Exposure Analysis for Arsenic). 13

The subsistence farmer scenario consists of a subsistence farmer

and child who consume elevated levels of locally-grown food products. 

It was assumed that each farm was located on approximately 10 acres. 

The subsistence farmer was assumed to raise livestock and to consume 

home-grown animal tissue and animal products, including chickens,

eggs, beef, and dairy products. All chicken feed was assumed to be

derived from non-local sources (and is, therefore, not contaminated

with arsenic). For bovine consumption of contaminated feed, 100

percent of the hay and corn used for feed was assumed to be from the

affected area. It was also assumed that the drinking water for the

subsistence farmer comes from rainwater collected in cisterns. Though

rainwater collected in cisterns may not be the primary source of

drinking water for most farmers, some are still expected to use

rainwater collected in cisterns as the primary source. Since

rainwater is likely to have the highest arsenic levels due to arsenic

emissions from anthropogenic sources, this assumption is consistent

with the high-end exposure scenario for this screening level assessment.



10-17

Table 10-11. Summary of Human Exposure Scenarios Considered

Exposure route child Adult Child Adult Child

Subsistence farmer Subsistence fisher

Pica

Air inhalation X X X X X

Soil Ingestion X X X X X

Animal ingestion X X

Vegetable ingestion X X X X

Fish ingestion X X

Water ingestion X X X X

Blank = Pathway not considered; X = Pathway considered.

Table 10-12. Default Values of Scenario-Independent Exposure

Parameters

Parameter Adult Child a

Valueb

Body weight (kg) 70 17

Exposure duration (years) 30 18

Inhalation rate (m /day) 20 16 3

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

Leafy vegetables 0.028 0.008

Grains and cereals 1.87 3.77

Legumes 0.381 0.666

Potatoes 0.17 0.274

Fruits 0.57 0.223

Fruiting vegetables 0.064 0.12

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

Beef (excluding liver) 0.341 0.553

Beef liver 0.066 0.025

Dairy 0.599 2.04

Pork 0.169 0.236

Poultry 0.111 0.214

Eggs 0.093 0.073

Lamb 0.057 0.061

Soil ingestion rates (g/day) 0.1 Scenario-dependent

Water ingestion rate (L/day) 2 1

All human consumption rates except for soil and water are reported as dry weight. a

See Appendix B of the EPA draft Screening Level Multipathway Exposure Analysis for details regarding these parameter values. b
10-18

Table 10-13. Values for Scenario-Dependent Exposure Parametersa

Parameter Child

Subsistence farmer Subsistence fisher 

Pica

b

Adult Child Adult Child

Finfish ingestion rates (g/day) NA NA NA 60 20

Soil Ingestion rate (g/day) 0.1 0.2 7.5 0.1 0.2

Contact time for inhalation (hr/day) 24 24 24 24 24

Contact fractions (unitless)c

Animal products 1 1 NA NA NA

Leafy vegetables 1 1 NA 0.058 0.058

Grains and cereals 1 1 NA 0.667 0.667

Legumes 1 1 NA 0.8 0.8

Potatoes 1 1 NA 0.225 0.225

Fruits 1 1 NA 0.233 0.233

Fruiting vegetables 1 1 NA 0.623 0.623

Root vegetables 1 1 NA 0.268 0.268

Drinking water 1 1 NA 1 1 d

See Appendix B of the EPA draft Screening Level Multipathway Exposure Analysis for Arsenic for more details regarding these a

values.

The subsistence fisher scenario does not consider consumption of freshwater bivalves or marine organisms. b

Contact fraction is the fraction of the total consumption of a food product from the study site. c

The source of the contaminated drinking water is different for the subsistence farmer and fisherperson scenarios. d

NA - Not considered to be applicable to this assessment.

For the urban high-end scenario, a pica child was defined as

consuming 7.5 g of soil per day at the location of maximum deposition

of arsenic. Estimates of the rate of soil ingestion by pica children

range from 5 g/day to 50 g/day. The data of Calabrese et al. (1989)

show a range of 5-10 g/day. A value of 7.5 grams was considered

appropriate to represent this subpopulation of children.

The subsistence fisher scenario consisted of a subsistence fisher

and child whose fish and water consumption scenarios were associated

with the hypothetical lake setting. The high-end fish consumer

scenario represented an individual who was assumed to ingest large

amounts of locally-caught fish, as well as home-grown garden produce

(plant ingestion parameters identical to the rural home gardener

scenario) and drinking water from the affected lake. These
10-19

consumption scenarios were thought to represent identified fishconsuming subpopulations in the United States. No commercial

distribution of locally caught fish was assumed. Fish consumption

rates for the fish-consuming subpopulations were derived from the

Columbia River Inter-Tribal Fish Commission Report.41

All arsenic was assumed to be of the inorganic form when

estimating exposure, except for fish consumption. Based on the

estimates of 0.5 to 75 percent inorganic arsenic in a variety of food

products (see Table 10-8), particularly percentages of 65-75 percent

for meat, dairy products, grains, and cereals, a conservative estimate

of 100 percent was selected, since the assessment is being performed

as a screening-level assessment. For freshwater fish, for which more

measurements have been taken, it is assumed that 10 percent of the

arsenic is inorganic arsenic. This is also a conservative estimate,

since lower levels of inorganic arsenic are generally measured in

fish, but it is appropriate to select a conservative number for this

screening-level assessment. 

10.3.3.2 Description of Waterbody/Watershed Configuration. The

watershed was assumed to be a circular region with a radius of 3.5 km,

and the waterbody was assumed to be a small circular lake with a

radius of 0.9 km (i.e., the ratio of watershed to waterbody is 15). 

Three different locations of the lake within the watershed were

considered; these locations are shown in Figure 10-5. The areaaveraged values for the watersheds and waterbodies were calculated at

five distances (0.2, 2, 5, 10 and 25 km; where “distance” is the

distance between the closest point on the watershed and the facility)

and for 16 directions around each facility; thus, there are a total of

240 different watershed/waterbody configurations per facility.

10.3.4 Determination of Background Values

To assist in determining the reasonableness of the IEM2 model

parameterization, model runs were performed using background soil

concentrations, air concentrations, and deposition rates. Of these

parameters, the soil concentration was found to be the most important

because it was critical to the estimation of arsenic concentrations in

plants and to the estimation of the flux to the water body. In this

section, the values assumed for arsenic background soil

concentrations, air concentrations, and deposition rates are

discussed.

In this assessment, background arsenic concentrations were

defined as the natural arsenic levels in the soil and air that would

exist without any anthropogenic input of arsenic. Determination of

such a background level presents a formidable challenge: it is

difficult to quantify; there can be considerable variability in what

constitutes background; and, at present, there is little guidance on

how background should be determined. However, due to the naturally

high concentrations of arsenic in many soils, it was considered 
10-20

Figure 10-5. Location of waterbody considered within watershed.
10-21

critical that such background concentrations be addressed, even if

only in a simple, screening-level manner. It is hoped that this will

serve as a starting place for future arsenic assessments. 

Natural and anthropogenic arsenic soil concentrations show a wide

range of variation. Even crustal levels of arsenic can range from 0.1

to several hundred mg/kg. Therefore, it is difficult to establish

typical ambient levels of arsenic in soil without respect to a

particular geological formation and geographical area, and general

comparisons must be made with caution. For the purpose of this

modeling exercise, a background soil concentration of 3 g/g was

assumed. This is the average arsenic concentration for the

continental crust reported in Cullen and Reimer.20

Chilvers and Peterson reported that the current natural levels 7

of arsenic in the atmosphere account for approximately 60 percent of

the total atmospheric load, due primarily to volcanic sources and 

biological methylation. Based on this, the mean air concentration of

0.7 ng/m , reported in Wu et al. for the Chesapeake Bay and assumed to 3 10

be representative for a relatively unimpacted area, was multiplied by

0.6 to obtain a background arsenic air concentration of 0.4 ng/m . It 3

is important to note that the ultimate impact of the air concentration in

this case is minimal, as the assumed soil concentration drives the

concentrations in biota and the water body. The dry deposition rate was

assumed to be 60 g/m /yr (=0.6*100; see Table 10-4), and the wet 2 10

deposition rate was assumed to be 30 g/m /yr (=0.6*50; see Table 10-4). 2 42

Few data were located on the speciation of arsenic in the atmosphere. 

Andreae reports that the As(V):As(III) ratio was 2:1 in rainfall, and 43

so it was assumed that this same relationship holds for the background

dry deposition rate and air concentration.

10.4 MODELING RESULTS

The following is a presentation of the results of this

assessment. Included is a discussion of the air modeling results, a

comparison of these results with measured data, and the potential

impact of these on hypothetical receptors.

10.4.1 Air Modeling Results/Comparison with Measured Data

10.4.1.1 Local Scale Modeling. The fraction of arsenic

emissions predicted to be deposited within 50 km of the source are

presented in Tables 10-14 and 10-15, representing the two different

meteorological regimes that were modeled. In general, less than 50

percent of the emissions are predicted to be deposited within 50 km,

and facilities with shorter stacks have a higher fraction of emissions

deposited within 50 km of the facility. Except for the large coalfired utility, dry deposition is greater than wet deposition. This is

the opposite of what has been concluded for regional models (e.g., the

regional results presented in this report and those in Alcamo et al.) 44
10-22

Table 10-14. Fraction of Arsenic Emissions Predicted to Be

Deposited Within 50 km in an Arid Site

Boiler type Stack (m) Total Dry Wet a

 Release

 height

Fraction of emissions deposited within 50 km

Oil-fired utility boiler Stack 1 87 13% 12% 1%

Stack 2 83 14% 13% 1%

Large coal-fired utility boiler Stack 1 198 5% 4% 1%

Stack 2 191 5% 4% 1%

Stack 3 173 6% 5% 1%

Medium coal-fired utility boiler Stack 1 147 7% 6% 1%

Stack 2 122 9% 8% 1%

Small coal-fired utility boiler Stack 1 84 14% 13% 1%

Stack 2 79 14% 13% 1%

 Size distribution for stacks is mass based. a

Table 10-15. Fraction of Arsenic Emissions Predicted to Be

Deposited Within 50 km in a Humid Site

Release

height (m)

Fraction of emissions deposited within 50 km

Boiler type Stack Total Dry Wet a

Oil-fired utility boiler Stack 1 87 21% 15% 6%

Stack 2 83 21% 16% 6%

Large coal-fired utility boiler Stack 1 198 10% 4% 7%

Stack 2 191 11% 4% 7%

Stack 3 173 12% 5% 7%

Medium coal-fired utility boiler Stack 1 147 14% 7% 7%

Stack 2 122 16% 10% 7%

Small coal-fired utility boiler Stack 1 84 23% 16% 7%

Stack 2 79 24% 17% 7%

 Size distribution for stacks is mass based. a

For the large coal-fired utility, the high stacks result in a

significantly lower fraction deposited by dry deposition. The

predicted wet deposition is not as sensitive to stack height and,

therefore, does not change. 

10.4.1.2 Regional Scale Modeling. RELMAP was used to model the

long-range transport of arsenic. Modifications to RELMAP for

atmospheric arsenic simulation were based on the assumption that all

arsenic emissions are in particulate form. Only the field mode of

RELMAP was used in this assessment (see Section 6.6 of Chapter 6 for

description of field mode and other details of the RELMAP). The RELMAP

regional-scale air modeling results are presented in Table 10-16.
10-23

Table 10-16. RELMAP Air Modeling Results

Result

Western United States (>90longitude) (>90longitude)

Eastern United States 

50 percentile 90 percentile 50 percentile 90 percentile th th th th

Arsenic air concentration (ng/m ) 9.00e-04 6.10e-03 1.40e-02 3.90e-02 3

Arsenic dry deposition rate (g/m /yr) 9.80e-03 8.50e-02 7.40e-02 7.60e-01 2

Arsenic wet deposition rate (g/m /yr) 7.70e-02 7.70e-01 1.90e+00 7.40e+00 2

10.4.2 Indirect Exposure Modeling

10.4.2.1 Comparison of Predicted Values with Measured Values. 

Table 10-17 compares the results for the eastern and western sites. 

The more humid climate of the eastern site results in slightly more 

runoff, and, therefore, more influx of arsenic into the water body. 

A critical factor in the water body calculations is the soil-water

partition coefficient. The value used here is 100 L/kg (see Appendix B

of the EPA draft Screening Level Multipathway Exposure Analysis for

Arsenic for Explanation) and is based on a combination of curve fits 13

to more sophisticated partitioning modeling and other literature data. 

The surface water concentrations are essentially inversely

proportional to the soil-water partition coefficient. The surface

water and sediment concentrations are within the range of values

reported for lakes in Section 2. It is noted that the sediment

concentrations are not used for any subsequent modeling.

To assist in determining the reasonableness of the model

predictions, typical values for input parameters were used in the

model, and then the predicted concentrations were compared with

measured values. The predicted plant and animal concentrations are 

shown in Table 10-18. The predicted concentrations are comparable

with those presented in (Tables 10-5, 10-6, and 10-7), although the

values are not consistently at one end of the range for all food

types. For example, the predicted beef values are slightly higher 

(by about 6 ng/g), while most of the other food types are in the

middle or lower end of the range of concentrations reported in the

literature for what are assumed to be “unimpacted” values. Most of

the arsenic in the beef (60 percent) is predicted to come from the

ingestion of grain/forage/silage, while the rest is predicted to come from the

ingestion of soil.

The fish and bivalve concentrations are calculated from the

arsenic water concentration. The difference in predicted values 

is due entirely to the difference in bioconcentration factors used

(1 L/kg for finfish, 350 L/kg for bivalves).
10-24

Table 10-17. Predicted Surface Water and Benthic Sediment

Concentrations for the Hypothetical Water Bodies

Concentrations Eastern site Western site a

Total arsenic water concentration (g/L) 14 9

Percent of arsenic dissolved 97 90

Predicted suspended sediment concentration (mg/L) 0.5 2.2

Total arsenic benthic sediment concentration (μg/g dry weight) 27 17

Assuming arsenic air concentration of 0.4 ng/m , deposition rate of 90 μg/m /yr, and soil concentration of 3 μg/g. a 3 2

Table 10-18. Modeled Arsenic Concentrationsa

Biota Modeled concentration (g/g dry weight) range (g/g dry weight)

Background concentrationb

Grain 0.181 0.024 - 2.72

Fruits 0.002 0.008 - 1.92

Fruiting vegetables 0.001 

Leafy vegetables 0.009 0.056 - 1.16

Beef 0.022 0.01 - 0.016

Dairy 0.001 < 0.003

Pork 0.008 < 0.3

Poultry 0.008 0 - 1.30

Freshwater fish (fresh weight) 0.013 0 - 3.1

Freshwater bivalves (fresh weight) 4.7

Assuming a background soil concentration (3 g/g), air concentration (0.4 ng/m ), and deposition rate (90 g/m /yr). a 3 2

Background concentration ranges were taken from data in Tables 5 and 6. b

The predicted exposures for the hypothetical receptors are shown

in Tables 10-19 (total arsenic) and 10-20 (inorganic arsenic). The

intake of total arsenic is predicted to be dominated by the ingestion

of grains for all scenarios in which grains are consumed, except for

the adult subsistence fisher. In the case of the adult subsistence

fisher, most of the exposure to arsenic is predicted to be from the

ingestion of freshwater bivalves. Because only 10 percent of the

total arsenic in fish is assumed to be inorganic, ingestion of grains

is predicted to be the dominant food ingestion exposure to inorganic

arsenic, even for the adult subsistence fisher scenario. Exposure to

arsenic for the pica child scenario, in which only exposure to arsenic

through soil ingestion is considered, is predicted to be approximately

twice as large as that for the subsistence farmer child.
10-25

Table 10-19. Predicted Total Arsenic Exposure for Hypothetical Receptorsa

Scenario Results for Total As % for Receptor

Eastern site (humid) intake (mg/kg/day) intake (mg/kg/day) Water Grains Legumes Potatoes vegetables Fruits vegetables vegetables Beef liver Dairy Pork Poultry E

Total inhalation Total ingestion Root_ Fruiting_ Leafy_ Beef_ 

Subsistence Farmer Adult 1E-07 4E-04 0 94 1 0 0 0 0 0 2 1 0 0 0 Subsistence Farmer Child 4E-07 7E-04 0 91 0 0 0 0 0 0 2 0 0 0 0 Pica Child 4E-07 1E-03Subsistence Fisher Adult 1E-07 5E-04 0 44 0 0 0 0 0 0 0 0 0 0 0 Subsistence Fisher Child 4E-07 5E-04 0 89 1 0 0 0 0 0 0 0 0 0 0 Western site (arid)Subsistence Farmer Adult 1E-07 4E-04 1 93 1 0 0 0 0 0 2 1 0 0 0 

Subsistence Farmer Child 4E-07 8E-04 1 91 0 0 0 0 0 0 2 0 0 0 0 Pica Child 4E-07 1E-03Subsistence Fisher Adult 1E-07 4E-04 1 56 0 0 0 0 0 0 0 0 0 0 0 Subsistence Fisher Child 4E-07 5E-04 2 89 1 0 0 0 0 0 0 0 0 0 0 

Assuming background arsenic soil concentration of 3 g/g, air concentration of 0.4 ng/m , and deposition rate of 90 g/m /yr. a 3 2

Table 10-20. Predicted Total Inorganic Arsenic Exposure for Hypothetical R

Scenario Results for Inorganic As % for Receptor

Eastern site (humid) intake (mg/kg/day) intake (mg/kg/day) Water Grains Legumes Potatoes vegetables Fruits vegetables s Beef liver Dairy Pork Poultry E

Total inhalation Total ingestion Root_ Fruiting_ vegetable Beef_ 

Leafy_

Subsistence Farmer Adult 1E-07 4E-04 0 94 1 0 0 0 0 0 2 1 0 0 0 

Subsistence Farmer Child 4E-07 7E-04 0 91 0 0 0 0 0 0 2 0 0 0 0 Pica Child 4E-07 1E-03Subsistence Fisher Adult 1E-07 3E-04 0 86 1 0 0 0 0 0 0 0 0 0 0 Subsistence Fisher Child 4E-07 5E-04 0 91 1 0 0 0 0 0 0 0 0 0 0 

Western site (arid)Subsistence Farmer Adult 1E-07 4E-04 1 93 1 0 0 0 0 0 2 1 0 0 0 

Subsistence Farmer Child 4E-07 8E-04 1 91 0 0 0 0 0 0 2 0 0 0 0 Pica Child 4E-07 1E-03Subsistence Fisher Adult 1E-07 3E-04 2 89 1 0 0 0 0 0 0 0 0 0 0 Subsistence Fisher Child 4E-07 5E-04 2 90 1 0 0 0 0 0 0 0 0 0 0 

Assuming background arsenic soil concentration of 3 g/g, air concentration of 0.4 ng/m , and deposition rate of 90 g/m /yr. a 3 2
10-26

10.4.2.2 Combined Results

10.4.2.2.1 Contribution of individual and regional sources to

total concentrations. One of the objectives of this analysis was to

assess the magnitude of the contribution of arsenic emissions from the

four types of utility boilers, as compared to concentrations in

environmental media and biota. This objective was addressed by

calculating the concentrations in media and biota with and without an

estimate of background, and by including the regional contribution of

nationwide utilities. These results are shown in Tables 10-21 through

10-25.

When background levels are included and watershed area-averaged

media concentration values are used, concentrations in media are

usually dominated by background estimates. This is because the

concentrations in most media are strongly dependent on the arsenic

soil concentration, and the build-up of arsenic in soil, due to

deposition of arsenic from the facilities over 30 years, is predicted

to be only a fraction of typical background soil concentrations. The

large coal-fired utility burner (LCUB) was predicted to account for

slightly more than 50 percent of the total deposition on the watershed

considered, but the build-up in the soil after 30 years is still

predicted to be less than 5 percent of the total soil concentration. 

The facilities’ contribution to the watershed air concentration is

small relative to the assumed background concentration of 0.7 ng/m . 3

As mentioned above, none of the facilities is predicted to

contribute more than 10 percent to the total area-averaged arsenic

soil concentration in the watershed after a 30-year build-up period. 

The LCUB has the largest contribution. A possibly important

uncertainty, though, is that it is not known if the deposited arsenic

from any of the facilities considered is more bioavailable than that

typically found naturally in the soil. If the deposited arsenic is

significantly more bioavailable, then the contribution to the total

exposure could be more than the predicted level.

The concentrations in grain were also examined because the

ingestion of grain is predicted to be a dominant pathway for exposure

to arsenic. The contribution of each facility to the arsenic

concentration in grain followed the same pattern as that for soil,

because the grain was predicted to accumulate arsenic mostly from the

soil.

It is important to note that the regional contribution of

utilities, as estimated by the RELMAP model, is generally less than 10

percent of the total background concentration or deposition. However,

this contribution is larger than that for some types of facilities

considered on the local scale when the upper 90th percentile is used.
10-27

Table 10-21. Watershed Air Concentration 

Watershed air concentration (μg/m )3 a

Facility only background Background + RELMAP90th Background RELMAP

Facility Facility + % Facility + background % %

Eastern site (humid)

Large coal-fired utility boiler 2.2E-06 4.0E-04 99 4.4E-04 91 9

Medium coal-fired utility boiler 1.4E-06 4.0E-04 100 4.4E-04 91 9

Small coal-fired utility boiler 2.3E-06 4.0E-04 99 4.4E-04 91 9

Oil-fired utility boiler 2.0E-06 4.0E-04 100 4.4E-04 91 9

Western site (arid)

Large coal-fired utility boiler 1.5E-06 4.0E-04 100 4.1E-04 98 1

Medium coal-fired utility boiler 1.1E-06 4.0E-04 100 4.1E-04 98 1

Small coal-fired utility boiler 1.9E-06 4.0E-04 100 4.1E-04 98 1

Oil-fired utility boiler 1.9E-06 4.0E-04 100 4.1E-04 98 1

 Edge of watershed, 200 m from source a

Table 10-22. Watershed Deposition Rate

Watershed deposition rate (μg/m /yr) 2 a

Facility only background Background + RELMAP90th Background RELMAP

Facility Facility + % Facility + background % % 

Eastern site (humid)

Large coal-fired utility boiler 1.2E+02 2.1E+02 42 2.2E+02 41 4

Medium coal-fired utility boiler 3.1E+01 1.2E+02 74 1.3E+02 70 6

Small coal-fired utility boiler 1.4E+01 1.0E+02 86 1.1E+02 80 7

Oil-fired utility boiler 1.5E+01 1.1E+02 86 1.1E+02 79 7

Western site (arid)

Large coal-fired utility boiler 1.6E+01 1.1E+02 85 1.1E+02 85 1

Medium coal-fired utility boiler 4.8E+00 9.5E+01 95 9.6E+01 94 1

Small coal-fired utility boiler 3.9E+00 9.4E+01 96 9.5E+01 95 1

Oil-fired utility boiler 3.5E+00 9.4E+01 96 9.4E+01 95 1

 Edge of watershed 200 m from source a
10-28

Table 10-23. Surface Water Concentration

Surface water concentration (μg/L) a

Facility only background Background + RELMAP90th Background RELMAP

Facility Facility + % Facility + background % %

Eastern site (humid)

Large coal-fired utility boiler 3.1E-01 1.4E+01 97 1.4E+01 97 1

Medium coal-fired utility boiler 7.8E-02 1.4E+01 98 1.4E+01 98 1

Small coal-fired utility boiler 3.4E-02 1.4E+01 99 1.4E+01 99 1

Oil-fired utility boiler 3.8E-02 1.4E+01 99 1.4E+01 98 1

Western site (arid)

Large coal-fired utility boiler 1.0E-01 9.4E+00 96 9.4E+00 96 3

Medium coal-fired utility boiler 2.9E-02 9.3E+00 97 9.3E+00 97 3

Small coal-fired utility boiler 1.9E-02 9.3E+00 97 9.3E+00 97 3

Oil-fired utility boiler 1.8E-02 9.3E+00 97 9.3E+00 97 3

 Edge of watershed 200 m from source. a

Table 10-24. Untilled Soil Concentration

Untilled soil concentration (μg/g)a

Facility only background Background + RELMAP90th Background RELMAP

Facility Facility + % Facility + background % %

Eastern site (humid)

Large coal-fired utility boiler 4.7E-02 3.1E+00 97 3.1E+00 97 1

Medium coal-fired utility boiler 1.2E-02 3.0E+00 98 3.0E+00 98 1

Small coal-fired utility boiler 5.4E-03 3.0E+00 99 3.0E+00 99 1

Oil-fired utility boiler 5.8E-03 3.0E+00 99 3.0E+00 99 1

Western site (arid)

Large coal-fired utility boiler 1.5e-02 3.1e+00 97 3.1e+00 97 3

Medium coal-fired utility boiler 4.8E-03 3.1E+00 97 3.1E+00 97 3

Small coal-fired utility boiler 3.8E-03 3.1E+00 97 3.1E+00 97 3

Oil-fired utility boiler 3.5E-03 3.1E+00 97 3.1E+00 97 3

 Edge of watershed 200 m from source. a
10-29

Table 10-25. Grain Concentration

Grain (μg/g dry weight)a

Facility only background Background + RELMAP90th Background RELMAP

Facility Facility + % Facility + background % % 

Eastern site (humid)

Large coal-fired utility boiler 6.4E-04 1.8E-01 99 1.8E-01 99 0

Medium coal-fired utility boiler 1.6E-04 1.8E-01 100 1.8E-01 100 0

Small coal-fired utility boiler 7.3E-05 1.8E-01 100 1.8E-01 100 0

Oil-fired utility boiler 7.9E-05 1.8E-01 100 1.8E-01 100 0

Western site (arid) 

Large coal-fired utility boiler 7.2E-05 1.8E-01 100 1.8E-01 100 0

Medium coal-fired utility boiler 2.2E-05 1.8E-01 100 1.8E-01 100 0

Small coal-fired utility boiler 1.8E-05 1.8E-01 100 1.8E-01 100 0

Oil-fired utility boiler 1.6E-05 1.8E-01 100 1.8E-01 100 0

 Edge of watershed 200 m from source. a

10.4.2.2.2 Contribution of individual and regional sources to

inorganic arsenic exposure. The predicted exposures to inorganic

arsenic are summarized in Tables 10-26 through 10-33 for the

hypothetical exposure scenarios considered. Tables 10-26 through 

10-29 represent the ingestion exposure pathway for the hypothetical

exposure scenarios, and Tables 10-30 through 10-33 represent the

inhalation exposure pathway for the exposure scenarios. In general,

less than 10 percent of the total inorganic arsenic exposure through

ingestion of food products is predicted to be attributable to any of

the local sources, for the hypothetical scenarios considered. This is

a reflection of the individual sources’ contribution to the total soil

concentration, because the concentrations in media and biota, and,

hence, exposure to these media and biota, is strongly dependent on the

soil concentration. The exception to the trend is the pica child

scenario, in which a pica child is assumed to be exposed at the

location of maximum deposition. In this case, the LCUB is predicted

to contribute up to approximately 40 percent of the total inorganic

exposure. The other facilities are predicted to contribute less than

15 percent. While this scenario is considered to be rare, it does

indicate that is possible for some of the types of facilities

considered to result in exposures comparable to background exposure.
10-30

Table 10-26. Inorganic Arsenic Intake via Ingestion for Pica

Child

Inorganic arsenic intake (mg/kg/day) for pica childa

Facility only background Background + RELMAP90th Background RELMAP

Facility Facility + % Facility +background % %

Eastern site (humid)

Large coal-fired utility boiler 7.3E-04 2.1E-03 64 2.1E-03 64 1

Medium coal-fired utility boiler 1.8E-04 1.5E-03 87 1.5E-03 87 1

Small coal-fired utility boiler 7.7E-05 1.4E-03 93 1.4E-03 93 1

Oil-fired utility boiler 8.8E-05 1.4E-03 93 1.4E-03 93 1

Western site (arid)

Large coal-fired utility boiler 5.3E-05 1.4E-03 93 1.4E-03 93 3

Medium coal-fired utility boiler 3.5E-05 1.4E-03 95 1.4E-03 95 3

Small coal-fired utility boiler 1.6E-05 1.4E-03 96 1.4E-03 96 3

Oil-fired utility boiler 1.7E-05 1.4E-03 96 1.4E-03 96 3

Receptor point based on location of maximum deposition. a 

Table 10-27. Inorganic Arsenic Intake via Ingestion for

Subsistence Farmer Adult

Inorganic arsenic intake (mg/kg/day) for subsistence farmer adult a

Facility only background Background + RELMAP90th Background RELMAP

Facility Facility + % Facility + background % %

Eastern site (humid)

Large coal-fired utility boiler 5.4E-06 3.7E-04 98 3.7E-04 98 0

Medium coal-fired utility boiler 1.3E-06 3.6E-04 99 3.6E-04 99 0

Small coal-fired utility boiler 5.8E-07 3.6E-04 100 3.6E-04 99 0

Oil-fired utility boiler 6.3E-07 3.6E-04 100 3.6E-04 99 0

Western site (arid)

Large coal-fired utility boiler 2.3E-05 3.9E-04 94 3.9E-04 94 0

Medium coal-fired utility boiler 5.2E-06 3.7E-04 98 3.7E-04 98 0

Small coal-fired utility boiler 1.8E-06 3.7E-04 99 3.7E-04 99 0

Oil-fired utility boiler 2.3E-06 3.7E-04 99 3.7E-04 99 0

 Edge of watershed 200 m from source. a
10-31

Table 10-28. Inorganic Arsenic Intake via Ingestion for

Subsistence Farmer Child

Inorganic arsenic intake (mg/kg/day) for subsistence farmer child a

Facility only background Background + RELMAP90th Background RELMAP

Facility Facility + % Facility + background % %

Eastern site (humid)

Large coal-fired utility boiler 1.0E-05 7.5E-04 98 7.5E-04 98 0

Medium coal-fired utility boiler 2.5E-06 7.5E-04 99 7.5E-04 99 0

Small coal-fired utility boiler 1.1E-06 7.4E-04 100 7.5E-04 99 0

Oil-fired utility boiler 1.2E-06 7.5E-04 100 7.5E-04 99 0

Western site (arid)

Large coal-fired utility boiler 4.6E-05 8.0E-04 94 8.0E-04 94 0

Medium coal-fired utility boiler 1.0E-05 7.6E-04 98 7.6E-04 98 0

Small coal-fired utility boiler 3.7E-06 7.5E-04 99 7.5E-04 99 0

Oil-fired utility boiler 4.7E-06 7.5E-04 99 7.6E-04 99 0

 Edge of watershed 200 m from source. a

Table 10-29. Inorganic Arsenic Intake via Ingestion for

Subsistence Fisher Adult

Inorganic arsenic intake (mg/kg/day) for subsistence fisher adult a

Facility only background Background + RELMAP90th Background RELMAP

Facility Facility + % Facility + background % %

Eastern site (humid)

Large coal-fired utility boiler 5.0E-06 2.7E-04 98 2.7E-04 98 0

Medium coal-fired utility boiler 1.3E-06 2.6E-04 99 2.6E-04 99 0

Small coal-fired utility boiler 5.3E-07 2.6E-04 99 2.6E-04 99 1

Oil-fired utility boiler 5.8E-07 2.6E-04 99 2.6E-04 99 1

Western site (arid)

Large coal-fired utility boiler 2.3E-05 2.8E-04 91 2.8E-04 91 1

Medium coal-fired utility boiler 5.3E-06 2.6E-04 97 2.6E-04 97 1

Small coal-fired utility boiler 1.9E-06 2.6E-04 99 2.6E-04 99 1

Oil-fired utility boiler 2.4E-06 2.6E-04 99 2.6E-04 99 1

 Edge of watershed 200 m from source. a
10-32

Table 10-30. Inorganic Arsenic Intake via Ingestion for

Subsistence Fisher Child

Inorganic arsenic intake (mg/kg/day) for subsistence fisher childa

Facility only background Background + RELMAP90th Background RELMAP

Facility Facility + % Facility + background % %

Eastern site (humid)

Large coal-fired utility boiler 9.3E-06 5.1E-04 98 5.1E-04 98 0

Medium coal-fired utility boiler 2.3E-06 5.0E-04 99 5.0E-04 99 0

Small coal-fired utility boiler 9.7E-07 5.0E-04 99 5.0E-04 99 0

Oil-fired utility boiler 1.1E-06 5.0E-04 99 5.0E-04 99 0

Western site (arid)

Large Coal-fired utility boiler 4.7E-05 5.5E-04 91 5.5E-04 91 0

Medium coal-fired utility boiler 1.1E-05 5.1E-04 97 5.1E-04 97 1

Small coal-fired utility boiler 3.7E-06 5.1E-04 99 5.1E-04 99 1

Oil-fired utility boiler 4.8E-06 5.1E-04 99 5.1E-04 99 1

 Edge of watershed 200 m from source. a

Table 10-31. Inorganic Arsenic Intake via Inhalation for Pica

Child

Inorganic arsenic intake via inhalation (mg/kg body wt./day) for pica child a

Facility only background Background + RELMAP90th Background RELMAP

Facility Facility + % Facility + background % %

Eastern site (humid)

Large coal-fired utility boiler 4.1E-21 3.8E-07 100 4.1E-07 91 9

Medium coal-fired utility boiler 1.7E-18 3.8E-07 100 4.1E-07 91 9

Small coal-fired utility boiler 1.7E-16 3.8E-07 100 4.1E-07 91 9

Oil-fired utility boiler 8.9E-16 3.8E-07 100 4.1E-07 91 9

Western site (arid)

Large coal-fired utility boiler 4.9E-18 3.8E-07 100 3.8E-07 98 2

Medium coal-fired utility boiler 8.6E-24 3.8E-07 100 3.8E-07 98 2

Small coal-fired utility boiler 6.8E-19 3.8E-07 100 3.8E-07 98 2

Oil-fired utility boiler 1.9E-18 3.8E-07 100 3.8E-07 98 2

 Receptor point based on location of maximum deposition. a
10-33

Table 10-32. Inorganic Arsenic Intake via Inhalation for

Subsistence Farmer Adult and Subsistence Fisher Adult

Inorganic arsenic intake via inhalation (mg/kg body wt./day) for subsistence farmer and fisher adults a

Facility only background Background + RELMAP90th Background RELMAP

Facility Facility + % Facility + background % %

Eastern site (humid)

Large coal-fired utility boiler 6.2E-10 1.1E-07 99 1.3E-07 91 9

Medium coal-fired utility boiler 4.1E-10 1.1E-07 100 1.3E-07 91 9

Small coal-fired utility boiler 6.4E-10 1.1E-07 99 1.3E-07 91 9

Oil-fired utility boiler 5.7E-10 1.1E-07 100 1.3E-07 91 9

Western site (arid)

Large coal-fired utility boiler 0.0E+00 1.1E-07 100 1.2E-07 98 2

Medium coal-fired utility boiler 4.3E-23 1.1E-07 100 1.2E-07 98 2

Small coal-fired utility boiler 1.3E-18 1.1E-07 100 1.2E-07 98 2

Oil-fired utility boiler 4.7E-18 1.1E-07 100 1.2E-07 98 2

 Edge of watershed 200 m from source. a

Table 10-33. Inorganic Arsenic Intake via Inhalation for

Subsistence Farmer Child and Subsistence Fisher Child

Inorganic arsenic intake via inhalation (mg/kg body wt./day) for subsistence farmer and fisher children a

Facility only background Background + RELMAP90th Background RELMAP

Facility Facility + % Facility + background % %

Eastern site (humid)

Large coal-fired utility boiler 2.0E-09 3.8E-07 99 4.2E-07 91 9

Medium coal-fired utility boiler 1.4E-09 3.8E-07 100 4.1E-07 91 9

Small coal-fired utility boiler 2.1E-09 3.8E-07 99 4.2E-07 91 9

Oil-fired utility boiler 1.9E-09 3.8E-07 100 4.2E-07 91 9

Western site (arid) 

Large coal-fired utility boiler 0.0E+00 3.8E-07 100 3.8E-07 98 2

Medium coal-fired utility boiler 1.4E-22 3.8E-07 100 3.8E-07 98 2

Small coal-fired utility boiler 4.2E-18 3.8E-07 100 3.8E-07 98 2

Oil-fired utility boiler 1.5E-17 3.8E-07 100 3.8E-07 98 2

 Edge of watershed 200 m from source. a
10-34

10.5 HAZARD IDENTIFICATION AND DOSE-RESPONSE FOR ARSENIC

10.5.1 Introduction

Inhalation exposure to inorganic arsenic in humans has been

strongly associated with lung cancer. Human oral exposure to 13

inorganic arsenic has been associated with an increased risk of

several types of cancer in humans, including skin, bladder, liver, and

lung cancer. Oral exposure to inorganic arsenic has also been 13,45

associated with noncancer effects, including effects to the central

nervous system, cardiovascular system, gastrointestinal tract, liver,

kidney, and blood. Appendix E of this report contains more 44

information on health effects of arsenic. Chapter 4 contains general

information on terminology, definitions, and guidelines related to

risk assessments. A short summary of the health effects of arsenic is

presented here.

Arsenic in both the trivalent (+3) and pentavalent (+5) oxidation

states may form both inorganic and organic compounds. Both trivalent

and pentavalent species of inorganic arsenic may be taken up by humans

from the gut (as can organic forms); these forms may be found in urine

(after exposure), along with monomethylated arsenic and dimethylated

arsenic. When ingested by humans, pentavalent forms are reduced to

trivalent arsenic which is then subject to methylation-forming

monomethyl- and dimethyl arsenic. Trivalent forms appear to be more

toxic than pentavalent forms. Methylation was thought to result in

detoxification; however, this assumption has been called into question

recently. Despite its toxicity, there is some (weak) evidence to

indicate that arsenic is an essential element in the human diet.5

The toxicological data on organic arsenic are limited. In fish

and shellfish, organic arsenic is absorbed through the gastrointestinal tract and excreted rapidly (60-70 percent within a few

days) in the urine. However, since the inorganic arsenic forms appear 5

to be of primary toxicological significance in humans, the following

discussion of health effects of arsenic and the risk characterization

focus only on inorganic arsenic.

10.5.2 Cancer Effects of Arsenic

There is clear evidence that chronic exposure to inorganic

arsenic in humans increases the risk of cancer. Various studies of

humans have reported that inhalation of arsenic results in an

increased risk of lung cancer. In addition, ingestion of arsenic has

been associated with an increased risk of non-melanoma skin cancer and

bladder, liver, and lung cancer. Animal studies have not clearly

associated arsenic exposure, via ingestion exposure, with cancer. No

studies have investigated the risk of cancer in animals as a result of

inhalation or dermal exposure. 46

EPA has classified inorganic arsenic in Group A - Known Human

Carcinogen. The Group A classification was based on the increased

incidence in humans of lung cancer through inhalation exposure and the
10-35

increased risk of skin, bladder, liver, and lung cancer through

drinking water exposure. 45

10.5.2.1 Inhalation Cancer Risk for Arsenic. EPA used the

absolute-risk linear extrapolation model to estimate the inhalation

unit risk for inorganic arsenic. Five studies on arsenic-exposed

copper smelter workers were modeled for excess cancer risk. Using the

geometric mean of these data, EPA calculated an inhalation unit risk

estimate (IURE) of 4.29 x 10 per (g/m ). The IURE is an upper-bound -3 3

estimate of the increased probability of a person developing cancer

from breathing air containing a concentration of 1 g/m3 of air for

70 years. EPA has high confidence in the arsenic cancer IURE because 47

the studies examined a large number of people, the exposure

assessments included air measurements and urinary arsenic

measurements, and lung cancer incidence was significantly increased

over expected values. The inhalation cancer slope factor is 45

1.5 x 10 per (mg/kg/day). The inhalation slope factor is an upper +1

bound estimate of the increased risk of developing cancer due to an

average inhalation intake of 1 mg/kg/day of arsenic over a lifetime

(70 years).46

10.5.2.2 Oral Cancer Risk for Arsenic. To estimate the risks

posed by ingesting arsenic, EPA obtained data in Taiwan concerning

skin cancer incidence, age, and level of exposure via drinking water. 

In 37 villages that had obtained drinking water for 45 years from

artesian wells with various elevated levels of arsenic, 40,421

individuals were examined for hyperpigmentation, keratosis, skin

cancer, and blackfoot disease (gangrene of the extremities caused by

injury to the peripheral vasculature). The local well waters were

analyzed for arsenic, and the age-specific cancer prevalence rates

were found to correlate with both local arsenic concentrations and age

(duration of exposure). Based on these data, although EPA has not

presented the calculations for the oral unit risk estimate for

arsenic, they did propose that a unit risk estimate of 5 x 10 (μg/L) -5 -1

from oral exposure to arsenic in drinking water be used. This 45

equates to an oral cancer slope factor of 1.5E + 00 per (mg/kg/day). 

The oral cancer slope factor is an upper bound estimate of the

increased risk of developing cancer due to an average oral intake of

1 mg/kg/day of arsenic over a lifetime (70 years). 45,46

The Taiwan cancer data have several limitations: (1) the water

was contaminated with substances such as bacteria and ergot alkaloids,

in addition to arsenic; (2) total arsenic exposure was uncertain

because of intake from the diet and other sources; (3) early deaths

from blackfoot disease may have led to an underestimate of prevalence;

and (4) there was uncertainty concerning exposure durations. Due to

these limitations, and also because the diet, economic status, and

mobility of individuals in Taiwan are different from those of most

United States citizens, EPA has stated that "the uncertainties

associated with ingested inorganic arsenic are such that estimates

could be modified downwards as much as an order of magnitude, relative

to risk estimates associated with most other carcinogens.”45
10-36

10.5.3 Noncancer Effects of Arsenic

10.5.3.1 Chronic (Long-Term) Effects for Arsenic. The primary

noncancer effects noted in humans from chronic exposure to arsenic,

through both inhalation and oral exposure, are effects on the skin. 

The inhalation route has resulted primarily in irritation of the skin

and mucous membranes (dermatitis, conjunctivitis, pharyngitis, and

rhinitis), while chronic oral exposure has resulted in a pattern of

skin changes that include the formation of warts or corns on the palms

and soles along with areas of darkened skin on the face, neck, and

back. Other effects noted from chronic oral exposure include

peripheral neuropathy, cardiovascular disorders, liver and kidney

disorders, and blackfoot disease.45

EPA has established an RfD for inorganic arsenic of 0.0003

mg/kg/day, based on a NOAEL (adjusted to include arsenic exposure from

food) of 0.0008 mg/kg/day, an uncertainty factor of 3, and a modifying

factor of 1. The EPA has not established a RfC for inorganic 45

arsenic.45

10.6 RISK CHARACTERIZATION

In this section, the information on hazard, dose-response, and

exposure are combined to characterize the potential risks due to

arsenic emissions from the model utility plants. As stated above,

inorganic arsenic is the form of arsenic considered to be of primary

concern for causing adverse health effects. Therefore, risks and

hazards have been estimated only for the inorganic arsenic. 

Increased cancer risk for each hypothetical person, under each

hypothetical scenario, for four different model plants placed in two

locations has been estimated. The general method of estimating the

increased risk of cancer due to multipathway exposure to inorganic

arsenic, is to multiply the predicted intakes (mg/kg/day) from the

multipathway exposure modeling by the EPA’s Oral Cancer Potency Factor

for inorganic arsenic (1.51E+00 per [mg/kg]/day) and then to multiply

by 30/70 to adjust for the assumed duration of exposure. The potency

factor is based on lifetime exposure (i.e., 70 years). For all of the

exposure scenarios, except for the pica child, it is assumed that the

hypothetical person is exposed for 30 years. A 30-year exposure

assumption is considered appropriate for most exposure scenarios

considered in this analysis. However, for the pica child, it is

assumed that exposure only occurs for 7 years (i.e., duration

adjustment factor was 7/70), since it highly unlikely that the pica

child would continue pica behavior for more than 7 years. (Note: the

7-year assumption may be high for pica behavior; however, other

exposures that may occur as the child gets older such as through food

consumption were not considered. Therefore, the 7-year assumption may

be a reasonable assumption for this screening assessment.) For the

subsistence fisher child and subsistence farmer child, a 30-year

exposure is assumed because it is quite possible that the child will

continue these consumption behaviors as they become adults. The doses
10-37

may decrease some because of the changes in body size, however, the

consumption rate is also expected to increase as they become adults.

 A 30-year exposure duration was assumed for the inhalation risk

calculations in this multipathway arsenic assessment. To estimate the

increased cancer risk due to inhalation exposure the predicted intakes

via inhalation (in mg/kg/day) were multiplied by the inhalation cancer

slope factor (1.5E+01 per [mg/kg]/day) and then multiplied by 30/70 to

adjust for the assumed duration of exposure. 

To estimate the noncancer hazard, the predicted intakes are

compared to the EPA’s oral RfD (3E-04 mg/kg/day). The estimated

intakes in mg/kg/day, were divided by the RfD to calculate a hazard

quotient (HQ). Therefore, a HQ greater than 1.0 indicates exceedance

of the RfD. 

It is important to remember that this is a screening level

multipathway analysis. The analysis is based on the use of model

plants placed in hypothetical locations, as well as the use of

hypothetical exposure scenarios and various assumptions, to predict

exposure to arsenic emitted by the model plant. The results give some

indication of the potential hazards and risks that may occur due to

emissions from an electric utility plant. However, the results are

not applicable to any particular plant. There are uncertainties and

limitations to the analyses and results. The risk estimates could be

overestimates or underestimates of the true risk for any particular

facility. There are uncertainties and variabilities in the modeling

inputs and results, exposure estimates, cancer potency estimates, RfD,

and overall risk estimates. The risk estimates presented below are

predicted values based on hypothetical scenarios and are intended for

screening purposes only. The results are shown in Tables 10-34

through 10-41.

10.6.1 Discussion of Cancer Risk Assessment Results

This analysis of multipathway exposures to arsenic emissions is a

screening analysis. Thus, these quantitive exposure and risk results,

because of the many modeling and analytic uncertainties, are very

uncertain and do not, therefore, conclusively demonstrate the

existence of health risks of concern associated with exposures to

utility emissions either on a national scale or from any actual

individual utility. The lack of measured data around these sources

preclude a comparison with modeled results. These results do suggest

that exposures and risks of concern cannot at present be ruled out and

that there is a need for development of additional scientific

information to evaluate whether risk levels of concern may exist.

The cancer risks due to multipathway exposures to inorganic

arsenic from utility emissions alone (no background) are estimated to

be no greater than approximately 1 x 10 (for pica child) based on -4

this screening level analysis. The large coal-fired utility at the

eastern humid site was estimated to pose this highest risk for the

pica child. Considering background exposures alone, the risk for the

pica child is estimated to be as high as 3 x 10 . -4
10-38

Table 10-34. Inorganic Arsenic Intake, Predicted Cancer Risk,

and Noncancer Hazard Quotient (HQ) for Pica Childa

Facility only Facility +background RELMAP90th

Facility + background +

Electric utility model intake increased cancer intake cancer cancer intake increased cancer

Facility (mg/kg/d) cancer risk HQ (mg/kg/d) risk HQ (mg/kg/d) cancer risk HQ

Inorganic Inorganic Estimated Inorganic

arsenic Estimated Non- arsenic increased Non- arsenic Estimated NonEastern site (humid)

Large coal-fired boiler 7.3E-04 1E-04 2.4 2.1E-03 3E-04 7 2.1E-03 3E-04 7

Medium coal-fired boiler 1.8E-04 3E-05 0.6 1.5E-03 2E-04 5 1.5E-03 2E-04 5

Small coal-fired boiler 7.7E-05 1E-05 0.3 1.4E-03 2E-04 5 1.4E-03 2E-04 5

Oil-fired boiler 8.8E-05 1E-05 0.3 1.4E-03 2E-04 5 1.4E-03 2E-04 5

Western site (arid)

Large coal-fired boiler 5.3E-05 8E-06 0.2 1.4E-03 2E-04 5 1.4E-03 2E-04 5

Medium coal-fired boiler 3.5E-05 5E-06 0.1 1.4E-03 2E-04 5 1.4E-03 2E-04 5

Small coal-fired boiler 1.6E-05 2E-06 0.05 1.4E-03 2E-04 5 1.4E-03 2E-04 5

Oil-fired boiler 1.7E-05 3E-06 0.06 1.4E-03 2E-04 5 1.4E-03 2E-04 5

 Receptor point based on location of maximum deposition. a

Table 10-35. Inorganic Arsenic Intake, Predicted Cancer Risk,

and Noncancer Hazards for Subsistence Farmer Adulta

Electric utility model 

facility 

Facility only Facility + background RELMAP90th

Facility + background +

Inorganic Inorganic Inorganic

arsenic Estimated Non- arsenic Estimated Non- arsenic Estimated Nonintake increased cancer intake increased cancer intake increased cancer

(mg/kg/d) cancer risk HQ (mg/kg/d) cancer risk HQ (mg/kg/d) cancer risk HQ

Eastern site (humid)

Large coal-fired boiler 5.4E-06 4E-06 0.03 3.7E-04 3E-04 1.2 3.7E-04 3E-04 1.2

Medium coal-fired boiler 1.3E-06 1E-06 0.004 3.6E-04 2E-04 1.2 3.6E-04 2E-04 1.2

Small coal-fired boiler 5.8E-07 4E-07 0.002 3.6E-04 2E-04 1.2 3.6E-04 2E-04 1.2

Oil-fired boiler 6.3E-07 5E-07 0.003 3.6E-04 2E-04 1.2 3.6E-04 2E-04 1.2

Western site (arid)

Large coal-fired boiler 2.3E-05 1E-05 0.08 3.9E-04 3E-04 1.3 3.9E-04 3E-04 1.3

Medium coal-fired boiler 5.2E-06 4E-06 0.02 3.7E-04 3E-04 1.2 3.7E-04 3E-04 1.2

Small coal-fired boiler 1.8E-06 1E-06 0.006 3.7E-04 3E-04 1.2 3.7E-04 3E-04 1.2

Oil-fired boiler 2.3E-06 1E-06 0.008 3.7E-04 3E-04 1.2 3.7E-04 3E-04 1.2

 Edge of watershed 200 m from source. a
10-39

Table 10-36. Inorganic Arsenic Intake, Predicted Cancer Risk,

and Noncancer Hazards for Subsistence Farmer Childa

Facility only Facility + background RELMAP90th

Facility + background +

Electric utility model intake increased cancer intake Increased cancer intake increased cancer

facility (mg/kg/d) cancer risk HQ (mg/kg/d) cancer risk HQ (mg/kg/d) cancer risk HQ

Inorganic Inorganic Inorganic

arsenic Estimated Non- arsenic Estimated Non- arsenic Estimated NonEastern site (humid)

Large coal-fired boiler 1.0E-05 1E-05 0.03 7.5E-04 5E-04 2.5 7.5E-04 5E-04 2.5

Medium coal-fired boiler 2.5E-06 2E-06 0.008 7.5E-04 5E-04 2.5 7.5E-04 5E-04 2.5

Small coal-fired boiler 1.1E-06 1E-06 0.004 7.4E-04 5E-04 2.5 7.5E-04 5E-04 2.5

Oil-fired boiler 1.2E-06 1E-06 0.004 7.5E-04 5E-04 2.5 7.5E-04 5E-04 2.5

Western site (arid)

Large coal-fired boiler 4.6E-05 3E-05 0.15 8E-04 6E-04 2.7 8.0E-04 6E-04 2.7

Medium coal-fired boiler 1.0E-05 1E-05 0.03 7.6E-04 5E-04 2.5 7.6E-04 5E-04 2.5

Small coal-fired boiler 3.7E-06 3E-06 0.01 7.5E-04 5E-04 2.5 7.5E-04 5E-04 2.5

Oil-fired boiler 4.7E-06 3E-06 0.02 7.5E-04 5E-04 2.5 7.5E-04 5E-04 2.5

 Edge of watershed 200 m from source. a

Table 37. Inorganic Arsenic Intake, Predicted Cancer Risk, and

Noncancer Hazards for Subsistence Fisher Adulta

Facility only Facility + background RELMAP90th

Facility + background +

Electric utility model intake increased cancer intake increased cancer intake increased cancer

facility (mg/kg/d) cancer risk HQ (mg/kg/d) cancer risk HQ (mg/kg/d) cancer risk HQ

Inorganic Inorganic Inorganic

arsenic Estimated Non- arsenic Estimated Non- arsenic Estimated NonEastern site (humid)

Large coal-fired boiler 5.0E-06 4E-06 0.02 2.7E-04 2E-04 0.9 2.7E-04 2E-04 0.9

Medium coal-fired boiler 1.3E-06 1E-06 0.004 2.6E-04 2E-04 0.9 2.6E-04 2E-04 0.9

Small coal-fired boiler 5.3E-07 4E-07 0.002 2.6E-04 2E-04 0.9 2.6E-04 2E-04 0.9

Oil-fired boiler 5.8E-07 4E-07 0.002 2.6E-04 2E-04 0.9 2.6E-04 2E-04 0.9

Western site (arid)

Large coal-fired boiler 2.3E-05 1E-05 0.08 2.8E-04 2E-04 0.9 2.8E-04 2E-04 0.9

Medium coal-fired boiler 5.3E-06 4E-06 0.02 2.6E-04 2E-04 0.9 2.6E-04 2E-04 0.9

Small coal-fired boiler 1.9E-06 1E-06 0.006 2.6E-04 2E-04 0.9 2.6E-04 2E-04 0.9

Oil-fired boiler 2.4E-06 2E-06 0.008 2.6E-04 2E-04 0.9 2.6E-04 2E-04 0.9

 Edge of watershed 200 m from source. a
10-40

Table 10-38. Inorganic Arsenic Intake, Predicted Cancer Risk,

and Noncancer Hazards for Subsistence Fisher Childa

Facility only Facility + background RELMAP90th

Facility + background +

Electric utility model intake increased cancer intake increased cancer Intake increased cancer

facility (mg/kg/d) cancer risk HQ (mg/kg/d) cancer risk HQ (mg/kg/d) cancer risk HQ

Inorganic Inorganic Inorganic

arsenic Estimated Non- arsenic Estimated Non- Arsenic Estimated NonEastern site (humid)

Large coal-fired boiler 9.3E-06 7E-06 0.03 5.1E-04 4E-04 1.7 5.1E-04 4E-04 1.7

Medium coal-fired boiler 2.3E-06 2E-06 0.008 5.0E-04 4E-04 1.7 5.0E-04 4E-04 1.7

Small coal-fired boiler 9.7E-07 7E-07 0.003 5.0E-04 4E-04 1.7 5.0E-04 4E-04 1.7

Oil-fired boiler 1.1E-06 1E-06 0.004 5.0E-04 4E-04 1.7 5.0E-04 4E-04 1.7

Western site (arid)

Large coal-fired boiler 4.7E-05 3E-05 0.16 5.5E-04 4E-04 1.7 5.5E-04 4E-04 1.7

Medium coal-fired boiler 1.1E-05 1E-05 0.04 5.1E-04 4E-04 1.7 5.1E-04 4E-04 1.7

Small coal-fired boiler 3.7E-06 3E-06 0.01 5.1E-04 4E-04 1.7 5.1E-04 4E-04 1.7

Oil-fired boiler 4.8E-06 3E-06 0.02 5.1E-04 4E-04 1.7 5.1E-04 4E-04 1.7

 Edge of watershed 200 m from source. a

Table 10-39. Inorganic Arsenic Intake via Inhalation and

Predicted Cancer Risks for Pica Childa

Facility only Facility + background RELMAP90th

Facility + background +

Electric utility model intake increased arsenic intake increased arsenic intake increased cancer

facility (mg/kg/d) cancer risk (mg/kg/d) cancer risk (mg/kg/d) risk

Inorganic

arsenic Estimated Inorganic Estimated Inorganic Estimated

Eastern site (humid)

Large coal-fired boiler 4.1E-21 3E-20 3.8E-07 3E-06 4.1E-07 3E-06

Medium coal-fired boiler 1.7E-18 1E-17 3.8E-07 3E-06 4.1E-07 3E-06

Small coal-fired boiler 1.7E-16 1E-15 3.8E-07 3E-06 4.1E-07 3E-06

Oil-fired boiler 8.9E-16 7E-15 3.8E-07 3E-06 4.1E-07 3E-06

Western site (arid)

Large coal-fired boiler 4.9E-18 4E-17 3.8E-07 3E-06 3.8E-07 3E-06

Medium coal-fired boiler 8.6E-24 7E-23 3.8E-07 3E-06 3.8E-07 3E-06

Small coal-fired boiler 6.8E-19 6E-18 3.8E-07 3E-06 3.8E-07 3E-06

Oil-fired boiler 1.9E-18 1E-17 3.8E-07 3E-06 3.8E-07 3E-06

Receptor point based on location of maximum deposition. a
10-41

Table 10-40. Inorganic Arsenic Intake via Inhalation for

Subsistence Farmer Adult and Subsistence Fisher Adult and

Predicted Cancer Risksa

Facility only Facility + background RELMAP90th

Facility + background +

Electric utility model arsenic intake increased arsenic intake increased arsenic intake increased

facility (mg/kg/d) cancer risk (mg/kg/d) cancer risk (mg/kg/d) cancer risk

Inorganic Estimated Inorganic Estimated Inorganic Estimated

Eastern site (humid)

Large coal-fired boiler 6.2E-10 4E-09 1.1E-07 1E-06 1.3E-07 1E-06

Medium coal-fired boiler 4.1E-10 3E-09 1.1E-07 1E-06 1.3E-07 1E-06

Small coal-fired boiler 6.4E-10 5E-09 1.1E-07 1E-06 1.3E-07 1E-06

Oil-fired boiler 5.7E-10 4E-09 1.1E-07 1E-06 1.3E-07 1E-06

Western site (arid)

Large coal-fired boiler 0.0E+00 0.0 1.1E-07 1E-06 1.2E-07 1E-06

Medium Coal-fired boiler 4.3E-23 3E-22 1.1E-07 1E-06 1.2E-07 1E-06

Small coal-fired boiler 1.3E-18 1E-17 1.1E-07 1E-06 1.2E-07 1E-06

Oil-fired Boiler 4.7E-18 3E-17 1.1E-07 1E-06 1.2E-07 1E-06

 Edge of watershed 200 m from source. a

Table 10-41. Inorganic Arsenic Intake via Inhalation for

Subsistence Farmer Child and Predicted Cancer Risksa

Facility only Facility + background RELMAP90th

Facility + background +

Electric utility model arsenic intake increased arsenic intake increased arsenic intake increased

facility (mg/kg/d) cancer risk (mg/kg/d) cancer risk (mg/kg/d) cancer risk

Inorganic Estimated Inorganic Estimated Inorganic Estimated

Eastern site (humid)

Large coal-fired boiler 2.0E-09 1E-08 3.8E-07 3E-06 4.2E-07 3E-06

Medium coal-fired boiler 1.4E-09 1E-08 3.8E-07 3E-06 4.1E-07 3E-06

Small coal-fired boiler 2.1E-09 1E-08 3.8E-07 3E-06 4.2E-07 3E-06

Oil-fired boiler 1.9E-09 1E-08 3.8E-07 3E-06 4.2E-07 3E-06

Western site (arid)

Large coal-fired boiler 0.0E+00 0 3.8E-07 3E-06 3.8E-07 3E-06

Medium coal-fired boiler 1.4E-22 1E-21 3.8E-07 3E-06 3.8E-07 3E-06

Small coal-fired boiler 4.2E-18 3E-17 3.8E-07 3E-06 3.8E-07 3E-06

Oil-fired boiler 1.5E-17 1E-16 3.8E-07 3E-06 3.8E-07 3E-06

 Edge of watershed 200 m from source) and predicted cancer risks. a
10-42

The cancer risks due to multipathway exposures to inorganic

arsenic from utility emissions alone (no background) are estimated to

be no greater than approximately 1 x 10 (for pica child) based on -4

this screening level analysis. The large coal-fired utility at the

eastern humid site was estimated to pose this highest risk for the

pica child. Considering background exposures alone, the risk for the

pica child is estimated to be as high as 3 x 10 . -4

The estimated cancer risks for the subsistence farmer adult and

subsistence fisher adult are estimated to be as high as 1 x 10 and -5

the estimated cancer risks for the subsistence farmer child and

subsistence fisher child are estimated to be as high as 3 x 10 . In -5

all scenarios, it was the large coal-fired utility that was estimated

to pose the greatest multipathway risks, the medium coal-fired utility

was estimated to pose the next highest risks, and the small coal-fired

utility and the oil-fired utility were estimated to present very

similar magnitudes of exposures and risks. Background exposures were

estimated to dominate the exposures and risks in all scenarios. 

Including background exposures increases the risks by approximately 2

to 200 times, depending on the receptor and plant scenario analyzed. 

Inhalation risks are predicted to be significantly lower than the

risks from multipathway exposures. The highest inhalation risks due

to the model utility plant emissions only (no background) were

estimated to be 1 x 10 for the hypothetical receptors in this -8

analysis. The RELMAP results contributed slightly (approximately

9 percent) to the total inhalation exposures and risks. Similar to

multipathway exposures, the inhalation exposures to background were

estimated to be substantially higher than the inhalation exposures due

to utilities emissions based on this model plant analysis. However,

it is important to recall that the estimated risks for inhalation

exposures to arsenic from the HEM analyses, described in chapter 6 (in

which every actual plant [684 plants] in the United States was

modeled), were estimated to be up to 3 x 10 , which is about 300 times -6

higher than the inhalation risks predicted in this model plant

analysis. This may be due to several factors. One likely factor is

that many more plants were modeled (684 actual plants, including some

outliers instead of 4 model plants) for the HEM analyses. Also, in

the HEM analyses, 349 meteorological sites were included, whereas only

2 meteorological stations were included in the multipathway analysis. 

In addition, 70-year exposures were assumed for the HEM analyses;

30-year exposures were the primary assumption used for the

multipathway analysis. Moreover, the multipathway analysis was

intended to predict potential high-end exposures due to multipathway

exposures; therefore, the distances from plant were chosen that would

likely result in high-end multipathway exposures. The distances

chosen for this multipathway analysis were mainly based on the

locations of predicted high deposition rates, and not based on high

air concentrations. The 200-meter distance from stack used for most

of the scenarios is estimated to be a location of high deposition;

however, this distance is not likely to be the distance of highest air

concentrations. The distances of high air concentrations are likely
10-43

to be much further from the stack. Therefore, it is not expected that

this model plant analysis would predict inhalation exposures and risks

as high as the HEM analysis.

10.6.2 Discussion of the Noncancer Risk Assessment Results

The highest predicted intake of arsenic due to utility emissions

only was 7E-04 mg/kg/day (predicted for the pica child/large coalfired utility/eastern site scenario), which is 2.4 times higher than

the RfD (3E-04 mg/kg/day) and equates to an HQ of 2.4. All other

scenarios had predicted HQs less than 1.0 when considering utility

emissions only. When background is included, the HQs range from 0.9

to 7. These results suggest that adverse noncancer effects due to

utility emissions alone could possibly be of concern for the pica

child. For all other scenarios analyzed, it appears that utility

emissions alone are not likely to be of concern for noncancer effects. 

However, exposures to current background levels of arsenic, as well as

exposures to current background levels combined with utility emissions

of arsenic, could be a potential concern for adverse noncancer

effects. 

10.7 CONCLUSIONS

10.7.1 Contribution of Arsenic Emissions from Utilities to

Concentrations in Environmental Media and Biota.

The facilities considered were found to contribute less than 10

percent of the total predicted arsenic concentrations in soil, water

and all biota when a background soil concentration of 3 g/g and air

concentration of 0.7 ng/m were assumed, and area-averaged deposition 3

rates were used for a watershed 200 m downwind from the facility. 

Soil and predicted water concentrations are dominated (e.g., >95

percent) by background when background is included. 

10.7.2 Determination of Dominant Pathways of Potential Exposure to

Anthropogenic Arsenic Emissions

Using the hypothetical exposure scenarios discussed above, it was

found that exposure to inorganic arsenic through ingestion was mainly

through the ingestion of grain. This result holds true whether or not

anthropogenic sources are considered because there is not sufficient

evidence to support the assumption that the anthropogenic arsenic

varied chemically from that already in the environment. The highest

dietary exposures to total arsenic for adults not consuming marine

organisms or freshwater bivalves are the result of consumption of

freshwater fish and grains. 

Exposure to inorganic arsenic through the ingestion of fish was

not predicted to be a major pathway of exposure because there is

considerable evidence that little of the total arsenic in fish tissue

is inorganic arsenic. For the subsistence fisher scenario considered,

exposure to inorganic arsenic from grain was larger than that from

fish, even though a high fish consumption rate was assumed.
10-44

Exposure to arsenic through the ingestion of unusually high

amounts of soil (e.g., pica) was shown to be of potential concern. 

Dominant exposure for the pica child is from background soil

concentrations of arsenic, when background levels are considered.

Since this is a screening level analysis, further detailed

analyses are needed to better characterize the risks posed.

10.8. UNCERTAINTIES AND LIMITATIONS

10.8.1 Limitations and Uncertainties for the Multipathway Exposure

Modeling

The following are uncertainties and limitations associated with

this assessment that may affect the results.

If a location is such that the background concentrations are

significantly lower than those assumed in the assessment, then the

contribution of any particular individual source to the total arsenic

concentrations and subsequent exposure will be larger than that

determined here. However, this will also reduce the total arsenic

exposure. If the background concentrations are significantly higher

than that assumed here, then although the contribution of a source to

the total is less, the additional contribution may result in the

exceedance of certain toxic thresholds. At this time, there is little

guidance on the determination of or incorporation of background

concentrations in an assessment. For chemicals which have a threshold

of action, inclusion of an appropriate estimate of background is

critical in the determination of the impacts to public health. 

There are few data on the speciation or bioavailability of

arsenic in environmental media or anthropogenic emissions. In this

assessment, it was assumed that the arsenic emitted from utilities is

similar to that reported in the literature for various media. If the

form of the emitted arsenic is significantly different, then this can

have important impacts on the assessment results and conclusions.

The bioavailability of anthropogenic arsenic is assumed to be the

same as the bioavailability of arsenic that in the environment in this

assessment. If this is not true, then this could impact the

conclusions reached. More research is needed on the bioavailability

of anthropogenic arsenic.

An important uncertainty when identifying a dominant pathway is

the range of pathways which are considered. For example, not

considered here was exposure to arsenic through the ingestion of tap

or well water. Inclusion of this pathway would increase the primary

or background exposure (depending on the level of exposure), and hence

reduce the relative contribution of any particular source.

10.8.2 Limitations and Uncertainties for the Risk Characterization

As discussed in section 10.5 above and in Appendix E, there are

limitations in the health effects data for arsenic. For example,
10-45

although human data are available on the carcinogenicity of arsenic,

there are no animal studies available on the carcinogenicity of

arsenic from inhalation exposure and the animal ingestion studies have

not clearly shown an association between arsenic ingestion exposure

and cancer. 

The oral cancer potency factor (1.5 per mg/kg/day) is a

reasonable estimate based on available information. However, the

available toxicity data are limited and uncertain; hence the cancer

potency estimate is somewhat uncertain. Also, because of differences

in genetics, diet, lifestyle, stress management, and a number of other

host factors, the human population is likely to have considerable

variation in individual sensitivities to developing cancer per unit of

exposure. Therefore, although the oral potency factor may be a

reasonable estimate, it is not likely to accurately represent the

potency for all humans. Many humans may be either more or less

sensitive to developing cancer, than predicted by the oral potency

factor.

The human exposure estimates presented above are uncertain due to

modeling uncertainties (described above), and also because of human

variability and uncertainties associated with several factors such as

breathing rates, food consumption rates, activity patterns, body size,

metabolic differences, and other factors. Variation and uncertainty

in meteorology, environmental fate of arsenic, bioaccumulation rates,

and other factors also contribute to the overall uncertainty in the

risks estimates. Therefore, the risk estimates presented above should

be viewed as reasonable estimates for a screening level assessment,

but should also be viewed as containing significant uncertainties. 

Further analyses are needed to more fully assess the potential risks

due to arsenic exposure from utilities. 

10.9 RESEARCH NEEDS

To improve the multimedia, multipathway exposure assessment for

arsenic, additional data on the speciation of arsenic in air, soil,

and water are needed. Better information on arsenic bioavailability

is also needed. In addition, research to improve the understanding of

watershed dynamics would benefit this study, as well as other efforts

to model pollutant fate and transport. Additional health effects data

are also needed, especially for ingestion exposure route, for cancer

and noncancer effects, to better characterize the hazards of arsenic

exposures.
10-46

1. Deuel, L. E. and A. R. Swoboda. Arsenic solubility in a reduced

environment. Soil Sci. Soc. Am. Proc. Volume 36. 1972. 

pp. 276-278.

2. Braman, R. S. Arsenic in the environment. In: Arsenical

Pesticides. American Chemical Society, Washington, DC. 1975. 

pp. 108-123.

3. U.S. Environmental Protection Agency. Health Assessment Document

for Inorganic Arsenic: Final Report. EPA-600/8-83-021F. Office

of Health and Environmental Assessment, Washington, DC. 1984.

4. National Research Council (NRC). Medical and Biological Effects

of Environmental Pollutants: Arsenic. National Academy of

Sciences, Washington, DC. 1977. pp. 332.

5. IF Kaiser. Toxicity and exposure concerns related to arsenic in

seafood: as arsenic literature review for risk assessments.

Submitted in fulfillment of Region X EAST Work Unit Document 4038

under Technical Instruction Document 10-9601-815 as requested by

Patricia Crone. 1996.

6. Hindmarsh, J. T. and R. F. McCurdy. Clinical and Environmental

Aspects of Arsenic Toxicity. CRC Critical Reviews in Clinical

Laboratory Sciences. Volume 23, No. 4. 1984. pp. 315-347.

7. Chilvers, D. C., and P. J. Peterson. Lead, Mercury, Cadmium and

Arsenic in the Environment. Edited by T.C. Hutchinson and K. M.

Meema. Wiley, New York. 1987. pp. 279.

8. Union Carbide Corporation. Review of the Environmental Effects

of Arsenic. ORNL/EIS-79 Oak Ridge National Laboratory, Oak

Ridge, TN. 1977.

9. Radian Corporation. Draft Report- Locating and Estimating Air

Emissions from Sources of Arsenic and Arsenic Compounds. Office

of Air Quality Planning and Standards, U.S. Environmental

Protection Agency, Research Triangle Park, NC. 1995.

10. Wu, Z. Y, M. Han, Z. C. Lin, and J. M. Ondov. Chesapeake Bay

atmospheric deposition study, year 1: Sources and dry deposition

of selected elements in aerosol particles. Atmospheric

Environment. Volume 28, No. 8. 1994. pp. 1471-1486. 

11. Scudlark, J. R., K. M. Conko, and T.C. Church. Atmospheric wet

deposition of trace elements to Chesapeake Bay: CBAD study year 1

results. Atmospheric Environment. Volume 28, No. 8. 1994. 

pp. 1487-1498.

10.10 REFERENCES
10-47

12. Durum, W. H., J. D. Hem, and S. G. Heidel. Reconnaisance of

Selected Minor Elements in Surface Waters of the United States.

October 1970, Geologic Survey Circ. 643. U.S. Department of the

Interior, Washington, DC. 1971. p. 49.

13. U.S. Environmental Protection Agency. Screening Level

Multipathway Exposure Analysis for Arsenic from Several Model

Electric Utility Plants and Secondary Lead Smelters. EPA Draft

Technical Report. 1997.

14. U.S. Environmental Protection Agency. Locating and Estimating

Air Emissions from Sources of Arsenic and Arsenic Compounds.

Draft Report. July 6 1996.

15. Bowen. Elemental Chemistry of the Elements. Academic Press,

London and New York. 1979. pp. 60-61.

16. Vinogradov, A. P. The Geochemistry of Rare and Dispersed

Chemical Elements in Soils. New York, NY. 1959. pp. 65-70.

17. Stater, C. S., R. S. Holmes, and H. G. Byers. Trace elements in

the soil from the erosion experiment stations, with supplementary

data on other soils. U.S. Department Agric Tech Bulletin. 

Volume 552. 1937. p. 23.

18. Conner and Shacklette. Background Geochemistry of Some Rocks,

Soils, Plants, and Vegetables in the Conterminous United States. 

U.S. Government Printing Office, Washington, DC. 1975.

19. Wood, H. and S. Duda. Background risk for regional mean

inorganic concentrations from southeastern soils. Presented at

the Annual Society of Risk Analysis meeting, New Orleans, LA. 

December 1996.

20. Cullen, W. R. and K. J. Reimer. Arsenic speciation in the

environment. Chemical Review. Volume 89. 1989. pp. 713-764.

21. Takamatsu, T., H. Aoki, and T. Yoshida. Determination of

arsenate, arsenite, monomethylarsonate, and dimethylarsinate in

soil polluted with arsenic. Soil Science. Volume 33. 1982. 

pp. 239-246.

22. Bombach, G., A. Pierra, and W. Klemm. Arsenic in contaminated

soil and river sediment. Fresenius Journal of Analytical

Chemistry. Volume 350. 1994. pp. 49-53.

23. U.S. Environmental Protection Agency. Bioavailability of arsenic

and lead in environmental substrates. EPA 910/R-96-002. 

Region 10. Seattle, WA. 1996.
10-48

24. Aten, C. F., J. B. Bourke, J. H. Martini, and J. C. Walton. 

Arsenic and lead in an orchard environment. Environmental

Toxicology. Volume 24. 1980. pp. 108-115.

25. Jones, J. S. and M. B. Hatch. Spray residues and crop assimilation

of arsenic and lead. Soil Science. Volume 44. 1945. Pp. 37-44.

26. Chisholm, D. Lead, arsenic, and copper content of crops grown on

lead arsenate-treated and untreated soils. Canadian Journal of

Plant Science. Volume 52. 1972. pp. 583-588.

27. Woolson, E. A. Effects of fertilizer material and combinations

on the phytotoxicity, availability, and content of arsenic in

corn. Journal of Science Food Agriculture. Volume 23. 1972. 

pp. 1477-1481.

28. Jenkins, D. W. Biological monitoring of toxic trace metals. 

Vol. 2 Toxic trace metals in plants and animals of the world. 

Part 1. U.S. EPA Agency Report 600/3-80-090, 1980. pp. 30-138.

29. van der Veen, N. G. and K. Vreman. Transfer of cadmium, lead,

mercury and arsenic from feed into various organs and tissues of

fattening lambs. Netherlands Journal of Agricultural Science. 

Volume 34. 1986. pp. 145-153.

30. Vreman, K., N. J. van der Veen, E. J. van der Molen and W. G. de

Ruig. Transfer of cadmium, lead, mercury and arsenic from feed

into milk and various tissues of dairy cows: chemical and

pathological data. Netherlands Journal of Agricultural Science.

Volume 3. 1986. pp. 129-144.

31. Barudi, W. and Bielig, H. J. Heavy metal content (As, Pb, Cd,

Hg) [arsenic, lead, cadmium, mercury] of vegetables which grow

above ground and fruits. Gehalt an Schwermetallen (Arsen, Blei,

Cadmium, Quecksilber) in oberirdisch wachsenden Gemuse- and

Obstarten. SOZ-Lebensm-Unters-Forsch.Munchen, J. F. Bergmann. 

Volume 70, No. 4. 1980. pp. 254-257.

32. Varo et al. Acta Agric. Scand. Suppl. Volume 22. 1980. p. 27.

33. Merry et al. The effects of soil contamination with copper, lead

and arsenic on the growth and composition of plants. Effects of

source of contamination, varying soil pH, and prior waterlogging. 

Plant and Soil. Volume 95, No. 2. 1985. pp. 255-69.

34. Moore, Edmonds, and Fracesconi. 1987. 

35. Jelinek, C. F. and P. E. Corneliussen. Levels of arsenic in the

United States food supply. Environmental Health Perspectives.

Volume 19. 1977. pp. 83-87.
10-49

36. Wiener et al. 1984. Longitudinal distribution of trace elements

(As, Cd, Cr, Hg, Pb, and Se) in fishes and sediment in the Upper

Mississippi River. In: J. G. Wiener, R. V. Anderson, D. R.

McConville (Eds.), Contaminants in Upper Mississippi River. 

Butterworth, Stoneham, MA. 1984. pp. 139-170.

37. Lima et al. Acute and chronic toxicities of arsenic (III) to

fathead minnows, flagfish, daphnids, and an amphipod. ArchEnviron-Contam-Toxicol. New York, NY: Springer-Verlag. 

Volume 13, No. 5. 1984. pp. 595-601.

38. Eisler, R. A review of arsenic hazards to plants and animals

with emphasis on fishery and wildlife resources. Arsenic in the

Environment Part II: Human Health and Ecosystem Effects. Edited

by Nriagu, J. O. Wiley Series in Environmental Science and

Technology. 1994.

39. U.S. Environmental Protection Agency. User’s Guide for the

Industrial Source Complex (ISCST3) Dispersion Models, Volume I -

User’s Instructions. EPA-454/B-95-003a. Office of Air Quality

Planning and Standards. Research Triangle Park, NC. September

1995.

40. U.S. Environmental Protection Agency. User’s Guide for the

Industrial Source Complex (ISCST3) Dispersion Models, Volume IIDescription of Model Algorithms. EPA-454/B-95-003b. Office of

Air Quality Planning and Standards. Research Triangle Park,

North Carolina. September 1995.

41. Columbia River Inter-Tribal Fish Commission. A Fish Consumption

Survey of the Umatilla, Nez Perce, Yakima and Warm Springs Tribes

of the Columbia River Basin. Technical Report 94-3. October

1994.

42. Scudlark, J. R., and T. C. Church. The atmospheric deposition of

arsenic and association with acid precipitation. Atmospheric

Environment. Volume 22, No. 5. 1988. pp. 937-943.

43. Andraea, M. O. Arsenic in rain and the atmospheric balance of

arsenic. Journal of Geophysical Research. Volume 85. 1980. 

pp. 4512-4518.

44. Alcamo, J., J. Bartnicki, K. Olendrzyski, and J. Pacyna. 

Computing heavy metals in Europe’s atmosphere-I. Model

development and testing. Atmospheric Environment. Volume 26A,

No. 18. 1992. pp. 3355-3369. 

45. Agency for Toxic Substances and Disease Registry (ATSDR). 

Toxicological Profile for Arsenic. U.S. Public Health Service,

U.S. Department of Health and Human Services, Atlanta, GA. 1993.
10-50

46. U.S. Environmental Protection Agency. Integrated Risk

Information System on Arsenic. 1995.

47. U.S. Environmental Protection Agency. Guidelines for the

preparation of Office of Water Health Advisories. U.S.

Environmental Protection Agency, Environmental Criteria and

Assessment Office-Cincinnati, OH. 1987.
11-1

11.0 A MULTIPATHWAY SCREENING-LEVEL ASSESSMENT FOR DIOXINS/FURANS

11.1 INTRODUCTION

Polychlorinated dibenzo-p-dioxin (PCDD) and dibenzofuran (PCDF)

emissions were identified as a priority for multipathway exposure

analysis (see chapter 5). PCDDs and PCDFs, which will be referred to

collectively as dioxins, are ubiquitous in the environment. The 1

dioxin and furan compounds, chlorinated in the 2,3,7,8 positions on

the molecule, are highly toxic, environmentally persistent, and have a

tendency to bioaccumulate. Exposure to dioxins is a potential concern

for both cancer and noncancer effects, even at extremely low levels. 

The EPA has concluded that 2,3,7,8-tetrachlorodibenzo-p-dioxin

(2,3,7,8-TCDD) and related compounds (congeners) are probable human

carcinogens. In addition, EPA has concluded that there is adequate 1

evidence to support the inference that humans are likely to respond

with a broad spectrum of noncancer effects from exposure to dioxins,

if exposures are high enough.1

The occurrence of dioxin and dioxin-like compounds in the

environment appears to be primarily the result of human activities. 1,2

The national estimated loading of these compounds from identified

sources into the environment is approximately 12,000 g toxicity

equivalents (TEQ)/yr. When this loading of 12,000 g TEQ/yr is

compared to annual loadings of other hazardous air pollutants (HAPs),

it appears to be relatively low. However, small quantities of dioxin

emissions can be of concern because of the high toxicity and

persistence, and tendency to bioaccumulate. The draft dioxin 1

reassessment report estimated an average of 9,200 g TEQ/yr from known

combustion sources. Based on this study’s estimate of 95 g TEQ/yr,

dioxin emissions from utilities represent about 1 percent of total

dioxin emissions. However, there are substantial uncertainties

associated with the dioxin emissions estimates due to several factors

including the following: emissions test data were available from only

13 utility units; data were not collected from all types of facilities

(e.g., no data were collected from units with hot-side ESPs); and many

measurements were below the minimum detection limit. Therefore, the

estimated emissions for the model plants described below are also

uncertain. The actual dioxin emissions could be greater than, or less

than, the predicted emissions presented in this chapter and other

chapters of this report.

There are several hypotheses on the chemistry and conditions for

the formation of PCDDs and PCDFs, including (1) contaminated feed

stock, (2) formation from precursors, and (3) formation de novo. The 1

contaminated feedstock theory suggests that PCDDs and PCDFs in the

feed material survive the combustion process to become emissions. 

However, this theory is not thought to be the principle explanation

for PCDD/PCDF formation. The second theory states that the formation

of PCDDs/PCDFs from precursors that are structurally related to

PCDDs/PCDFs occurs through thermal breakdown and rearrangement during

the combustion process. Precursors referred to in this theory include
11-2

polychlorinated biphenyls, chlorinated phenols, and chlorinated

benzenes. This process is believed to occur when the precursor

condenses and adsorbs to the surface of fly ash particles in the

temperature range of 250-450 C. The third theory on the chemistry and 

conditions of PCDD/PCDF formation states that creation is from

moieties that bear little structural resemblance to the PCDDs/PCDFs. 

Such compounds may include petroleum products, chlorinated plastics,

non-chlorinated plastics, cellulose, lignin, coke, coal, particulate

carbon, and hydrogen chloride gas. This de novo formation is believed

to occur in the same temperature range used in the precursor theory,

but occurs as a reaction between a chlorine donor and another molecule

forms a chemical intermediate that then serves as a precursor in the

formation of PCDDs and PCDFs.

Emission data for various congeners of dioxins and furans were

obtained from 10 coal- and 2 oil-fired units using EPA Reference

Method 23. Eleven of these 12 tests resulted in one or more congeners

being identified at blank-corrected values above the minimum detection

level (MDL) for each individual test (there were usually three

replicate runs per test). Of the 188 sets of replicate analyses for

the various congeners, 109 (58 percent) had data values above the MDL

for all replicates. The dioxin/furan testing conducted at utility

boilers typically made use of field blanks, laboratory blanks,

calibration spiking, and strict measurement criteria during the

analyses. 

As a larger portion of the dioxin/furan data sets were below the

MDL compared to other analyzed constituents, a summary of the protocol

for dealing with non-detect values is presented here. (See Appendix D

for further details.) Consistent with the methodology used for other

constituents, for those data sets containing at least one value above

the MDL, a run average was obtained by using one-half the MDL for

those values below the MDL in the averaging analysis. If all three

data values for a data set were below the MDL, that data set was not

used in the analyses. Using this approach resulted in a nationwide

dioxin emissions estimate of 95 g/yr TEQ for utilities.

There are other methodologies for handling non-detect values in

analyses. Reference Method 23, written for use on MWCs, indicates

that non-detects are to be handled as zero in the calculations. Use

of the Method 23 approach results in a nationwide dioxin/furan

emissions estimate for utility plants of 58 g/yr TEQ, which is roughly

2 times lower than the nationwide estimate of 95 g/yr TEQ reported

above. The EPA believes that the approach taken (i.e., using one-half

the MDL in the analyses) is appropriate for this analysis.

It has been hypothesized that the primary mechanism by which

dioxins enter the terrestrial food chain is through atmospheric

deposition. The PCDDs and PCDFs have been found throughout the world 1

in all media, including air, soil, water, sediment, and in biota,

including fish and shellfish, and other plants and animals used as

food products. The ubiquitous nature of these compounds can be
11-3

attributed to their stability under most environmental conditions and

also to the great number of sources located throughout the United

States. Once emitted into the atmosphere, the primary removal

mechanisms are degradation and deposition to environmental media, such

as soil, water, and vegetation. In general, the compounds have very

low water solubility and vapor pressures and high octanol/water

partition coefficients (K ) and organic carbon partition coefficients ow

(K ). These measures of chemical properties indicate that dioxins oc

tend to strongly adsorb to soils and, as a result, resist leaching or

volatilization. The fate of dioxins adsorbed to particles includes

burial in place, resuspension into the air, and erosion of soils into

waterbodies. In the aquatic environment, PCDDs and PCDFs remain

adsorbed to particulate matter, and those compounds that enter surface

waters and dissolve will tend to partition to suspended solids or

dissolved organic matter. The primary removal mechanism from the

water column is sedimentation and, ultimately, burial of sediments.

Once PCDDs and PCDFs are deposited and make their way into

various media and into biota, they are available for human uptake

through ingestion. The draft Dioxin Reassessment report states that,

with regard to average intake, humans are currently exposed to

background levels of dioxin-like compounds, including dioxin-like

polychlorinated biphenyls (PCBs), on the order of 3 to 6 pg TEQ/kg

body weight/day. This value is more than 500-fold higher than the 3

EPA’s 1985 risk-specific dose of 0.006 pg TEQ/kg body weight/day

associated with an upper-bound risk of 1 in a million (1 x 10 ) and is -6

several hundredfold higher than the revised risk-specific dose

estimates presented in the draft Dioxin Reassessment report. An 1

inhalation exposure assessment for utility emissions was performed for

priority HAPs using the human exposure model (HEM), as described in

chapter 6 of this report. The cancer risk to the maximum exposed

individual in that assessment was estimated to be 1 x 10 .-7

As part of the draft dioxin reassessment, methodologies for

conducting site-specific indirect exposure modeling are presented. 3

These methodologies are intended for use in evaluating incremental

exposures (i.e., not background exposures) associated with specific

sources of dioxin-like compounds. In the dioxin reassessment

documents, example scenarios were developed and modeling was conducted

for six exposure scenarios to demonstrate these methodologies. Of the

six scenarios, scenarios 4 and 5 focused on indirect exposure

resulting from stack emissions from stationary combustion sources. Of

the emission sources modeled, scenarios 4 and 5 best represent

exposures that could possibly be roughly similar to exposures

resulting from the utility emissions. However, as stated in the draft

dioxin reassessment documents, in evaluating the results from this

modeling effort, it should be noted that the demonstration scenarios

were developed only to illustrate the site-specific methodologies and

that the exposure estimates generated for each scenario are not

generalizable to other sites. 
11-4

The results from the indirect exposure modeling effort presented

in the draft dioxin reassessment report indicate that exposure levels

due to the consumption of fish obtained from an impacted stream

dominated the results generated for scenario 4. Under scenario 5, the

highest levels of exposure occurred through the ingestion of fish and

homegrown beef and milk. In summary, results for the two scenarios

indicate that consumption of fish and consumption of beef and milk can

be significant routes of exposure of humans to dioxin and dioxin-like

compounds. 

The transfer of dioxins from air to plants plays a major role in

the exposure of terrestrial animals to dioxins. A finding in the

draft dioxin reassessment report is that the principal cause for

terrestrial food chain contamination is the transfer of dioxins from

the air to vegetation that animals consume. It has been found that

dioxins in the vapor phase can transfer readily to plants and that

this is the primary pathway by which plants take up dioxins. This is 1

of significance since the uptake of plants by foraging animals,

including cattle, is relevant to the concentrations of dioxins to

which humans are exposed through the ingestion of beef and other

animal products.

In addition to exposure pathways discussed above, exposure

through the consumption of breast milk appears to be of potential

concern for nursing infants. A study of 42 nursing mothers revealed

that an average concentration of 16 ppt of TEQ was found in the lipid

portion of their breast milk. A similar study conducted in Germany

revealed an average level of 29 ppt of TEQ in the lipid portion of the

breast milk. Based on the estimated adult intake of dioxin discussed

above, an exposure duration of 1 year (i.e., the infant nurses for 1

year), an average weight gain of 10 kg during the exposure period, and

a milk concentration of 20 ppt of TEQ, it is estimated that the

average daily dose to the infant over this period is approximately

60 pg of TEQ/kg/day, 20 to 60 times higher than the estimated range

for background exposure to adults (1 to 3 pg of TEQ/kg/day).

Because dioxins tend to accumulate in the environment and because

they are extremely toxic to humans and wildlife, even small amounts of

these compounds emitted from specific sources like utilities may be of

concern. Based on background media concentration data and other

assessments conducted by the Agency, including the one discussed

above, it can be hypothesized that the primary human exposure routes

of concern are those that are related to the ingestion of food

products (e.g., fish, meat, and dairy products). Animal exposure

through these routes is most likely to occur through the consumption

of animals or fish contaminated through the ingestion of contaminated

media or organisms or through the consumption of vegetation

contaminated by atmospheric deposition. 

Dioxins were identified (in chapter 5) as high priority for

multipathway risk assessment. This chapter presents a screening-level

assessment of the multipathway exposures and risks associated with
11-5

dioxins. The local-scale dioxin and furan emissions estimates used in

this screening-level assessment were from four model plant fossilfuel-fired utility boilers, which were developed for this analysis. 

The cumulative effects of long-range transport of utility boiler

PCDD/PCDF emissions are also considered in the screening-level

analysis. The model plants were developed to represent actual utility

coal- and oil-fired boilers, and were designed to characterize

potential emission rates and atmospheric release processes for average

large and small boilers of each fuel type. The assessment presented

in this report used long-range and local atmospheric transport and

deposition modeling and direct and indirect multipathway exposure

modeling to predict cancer risks from inhalation and ingestion of

dioxin- and furan-contaminated air, water, soil, and food. 

Polychlorinated dioxins and furans may also cause noncancer

health effects in humans. Developmental, reproductive, and immune

system endpoints have been reviewed as part of the dioxin reassessment

effort. However, reference concentrations have not been established 1

by EPA for use in human risk assessment. As a result, only the cancer

endpoints have been examined in this study. Additionally, the breast

milk exposure pathway for infants has been recognized as a significant

source of exposure, as described above. However, quantitative

assessment of this pathway is not included in this analysis.

This analysis of noninhalation exposures to dioxin emissions is a

screening analysis. Thus, these quantitative exposure and risk

results, because of the many modeling and analytic uncertainties, are

very uncertain and do not conclusively demonstrate the existence of

health risks of concern associated with exposures to utility emissions

either on a national scale or from any actual individual utility. The

lack of measured data around these sources precludes a comparison with

modeled results. These results do suggest that exposures and risks of

concern cannot at present be ruled out and that there is a need for

development of additional scientific information to evaluate whether

risk levels of concern may exist. 

EPA’s Industrial Source Complex Short Term, Version 3 (ISCST3)

model was used to estimate individual congener ambient air

concentrations and wet/dry deposition rates associated with emissions

from each of the model plants. The ISCST3 uses Gaussian dispersion to 4

calculate air concentration and deposition in the local area (i.e.,

within 50 km of the emissions source). Particle size, atmospheric

conditions and gravitational settling velocities are used to predict

dry deposition. Precipitation rate and particle size are used to

estimate scavenging coefficients which are used to predict wet

deposition. 

Long-range transport of dioxins emitted from multiple power

plants is also a potential cause of increased population risk. 

Therefore, long-range modeling of utility dioxin emissions using the

RELMAP model are summarized below for consideration in the screeninglevel multipathway exposure assessment. 
11-6

After the air concentrations and deposition rates were modeled,

multipathway exposures were estimated using a modified version of the

Indirect Exposure Methodology (IEM) - Spreadsheet provided by EPA

Office of Research and Development. The IEM methodology uses sitespecific exposure scenarios and dispersion model predictions of air

concentrations and deposition rates to calculate pollutant

concentrations in vegetation, soil, water, and the aquatic and

terrestrial food chain. The methodology then calculates human

exposure from data on ingestion and dermal contact. 

The following section (section 11.2) summarizes the RELMAP longrange dispersion modeling for dioxins. Section 11.3 describes the

methods and data used in this risk assessment, including emissions

sources, local dispersion modeling, and multipathway exposure and risk

modeling. In section 11.4 the results of the risk assessment are

presented for the different scenarios and pathways considered. 

Section 11.5 describes how the results were tested for their

sensitivity to changes in various assumptions about the model

parameters. The final section (section 11.6) provides conclusions on

the analysis, limitations, and uncertainties.

11.2 LONG-RANGE TRANSPORT MODELING

Long-range atmospheric transport modeling was performed for the

continental United States using the RELMAP model. RELMAP modeling

allows consideration of emissions contributions from multiple sources

on a national scale. More detailed description of the RELMAP model,

methods, and uncertainties can be found in chapter 6 of this report. 

The methods and emissions sources used in the RELMAP modeling of

dioxins were patterned after the RELMAP modeling described here in

chapter 6. The dioxin RELMAP results described here consider the

combined TEQ emissions from all U.S. coal- and oil-fired utilities.

The RELMAP model was used to generate air concentration, wet

deposition, and dry deposition results for dioxin emissions for the

contiguous 48 states. The results of all three analyses indicate that

long-range transport provides higher air concentrations and deposition

rates for dioxins in the Eastern United States, with the maximum for

each falling in the general region of the Ohio River Valley. 

Figure 11-1 presents the RELMAP results for dioxin air

concentration. Long-range transport of emissions generated air

concentrations ranging from less than 10 to a maximum of 316 

attograms/m , with an average of 19 attograms/m . Figure 11-2 provides 3 3

the RELMAP wet deposition results. The results show that long-range

transport accounted for wet depositions ranging from less than one to

a maximum of 93 picograms per square meter per year (pg/m /yr). The 2

overall average wet deposition for Figure 11-2 was 25 pg/m /yr. The 2

dry deposition model results (Figure 11-3) are slightly less than the

wet deposition results. The dry deposition ranged from less than one

to a maximum deposition of 82 pg/m /yr, with an average of 24 pg/m /yr. 2 2



11-10

11.3 RISK ASSESSMENT METHODOLOGY

11.3.1 Emissions Sources

A model plant approach was used to describe local-scale dioxin

emissions from utility boilers. Four model plants were selected, one

each for: large coal-fired, small coal-fired, large oil-fired, and

small oil-fired. The selection of large and small model plants

represents an upper- and lower-end depiction of dioxin emissions. The

dioxin emissions rates for the model plants were developed based on

dioxin emissions estimates from the EPA database of 426 coal-fired

plants and 145 oil-fired plants. The 426 coal-fired and 137 oil-fired

plants were ranked by 2,3,7,8-TCDD TEQ emissions (kg/y) and then

divided into thirds. The large model plants represent the average

dioxin emissions for the upper ranking third of the plants for each

fuel type (e.g., emissions for the large coal-fired utility model

plant are the average dioxin emissions for the 142 largest emitting

coal-fired utility plants), while the small model plants represent the

average dioxin emissions from the lower third of the plants for each

fuel type. The resulting dioxin and furan congener-specific emissions

rates and process parameters for each for each model plant are shown

in Tables 11-1 and 11-2.5

The emissions information for each of the four model plants was

applied for two locations. One location (case #1) was modeled using 

the emissions from each of the model plants combined with high-end

meteorology and terrain conditions. Albany, New York, was selected to

be the case #1 meteorological location, as this location has

previously been shown to have high-end meteorology for the purpose of

dispersion modeling. Specifically, Albany’s geographic location in

the Hudson River Valley limits wind flow patterns, which generally

limits the dilution effects of varied wind flow patterns. Complex

terrain was combined with the Albany meteorological data to complete

the case #1 application of the model plant data. Similarly, all model

plant data were applied to more average meteorology and simple

terrain. Springfield, Missouri, was selected as a location for

average meteorological conditions (case #2). The application of two

locations and terrain conditions to the model plant data gives a total

of eight model plants considered in the assessment (Table 11-3). 

11.3.2 Local Air Dispersion Modeling

The EPA’s ISCST3 dispersion model was used to predict the localscale (<50 km from the source) atmospheric dispersion of model plant

dioxin emissions. ISCST3 is a Gaussian plume model that has the

capacity to model wet and dry depletion with corresponding annual

deposition rates, as well as ambient air concentrations. A more

detailed description of the ISCST3 model may be found in the other

multipathway assessments of this report (Chapters 7 and 10), or in

other EPA publications. The resulting annual depositions and ambient 4

concentrations are used as inputs to the IEM-spreadsheet model for the

purpose of multipathway exposure and risk analysis. 
11-11

Table 11-1. Congener-Specific Emissions Rates for Model Plants

(kg/yr)

Congener Large coal Large oil Small coal Small oil a

PCDDs: 

 2378 1.24e-05 1.82e-04 1.67e-04 8.71e-06

 12378 3.22e-04 1.61e-04 4.34e-05 7.70e-06

 123478 7.32e-04 3.46e-04 9.87e-05 1.66e-05

 123789 5.54e-04 2.33e-04 7.46e-05 1.12e-05

 123678 4.39e-04 1.52e-04 5.92e-05 7.28e-06

 1234678 4.34e-04 5.54e-04 5.85e-05 2.66e-05

 Octa 4.41e-03 6.47e-04 5.94e-04 3.10e-05

PCDFs: 

 2378 2.99e-04 1.28e-04 4.02e-05 6.15e-06

 23478 7.88e-04 1.34e-04 1.06e-04 6.44e-06

 12378 1.85e-04 1.21e-04 2.50e-05 5.80e-06

 123478 9.77e-04 1.69e-04 1.32e-04 8.09e-06

 123678 3.02e-04 1.07e-04 4.07e-05 5.14e-06

 123789 6.40e-04 1.62e-04 8.63e-05 7.79e-06

 234678 1.23e-03 1.33e-04 1.65e-04 6.38e-06

 1234678 1.49e-03 2.62e-04 2.01e-04 1.26e-05

 1234789 1.31e-02 2.82e-04 1.77e-03 1.35e-05

 Octa 1.25e-03 2.82e-04 1.68e-04 1.35e-05

PCDDs = polychlorinated dibenzo-p-dioxins; PCDFs = polychlorinated dibenzofurans

Congener: Numbers represent chlorine placement on the molecule. a

 Data in this table provided by EPA for this analysis. 5

Table 11-2. Process Parameters for Model Plants

Model plant Stack height (m) Stack diameter (m) Exit velocity (m/s) Exit temp. (K)

Large coal-fired

(800 MW) 211.8 8.290 28 412.6

Small coal-fired

(232 MW) 119.5 4.938 22 399.3

Large oil-fired

(506 MW) 157.0 6.584 26 434.8

Small oil-fired

(174 MW) 70.10 3.962 19 419.8

MW = megawatt

Data in this table provided by EPA for this analysis. 5
11-12

Table 11-3. Naming Scheme for Eight Model Plants

LCH Large Coal-Fired Plant, High-End Meteorology and Complex Terrain

SCH Small Coal-Fired Plant, High-End Meteorology and Complex Terrain

LOH Large Oil-Fired Plant, High-End Meteorology and Complex Terrain

SOH Small Oil-Fired Plant, High-End Meteorology and Complex Terrain

LCC Large Coal-Fired Plant, Central-tendancy Meteorology and Simple Terrain

SCC Small Coal-Fired Plant, Central-tendancy Meteorology and Simple Terrain

LOC Large Oil-Fired Plant, Central-tendancy Meteorology and Simple Terrain

SOC Small Oil-Fired Plant, Central-tendancyMeteorology and Simple Terrain

The ISCST3 model requires inputs related to the processes and

location of modeled facilities. The ISCST3 dispersion modeling

requires various emissions source process parameters for dispersion

and deposition modeling, including stack height, stack diameter, exit

velocity, and exit temperature. The value of each of these inputs for

each of the model plants is presented in Table 11-2. Other process- 5

specific dispersion parameters include particle size distributions,

mass fraction of particles, and wet and dry scavenging coefficients

(Table 11-4). These data were obtained for utility boilers from AP42, Perry’s Chemical Engineering Handbook, and the User’s Guide for 6 7

the Industrial Source Complex Dispersion Models, respectively. 4

Additional emissions source information, such as meteorology, complex

terrain, and land use factors, was varied between case #1 and case #2

(Table 11-5). The ISCST3 meteorological inputs were five years of

data from the Solar and Meteorological Surface Observation Network

(SAMSON) for each of the locations considered in this analysis. The 8

Albany, New York, terrain information was obtained through the United

States Geological Survey.9

ISCST3 modeling was completed using the "default" model options

specified in the Guidelines on Air Quality Models. A unit emissions 10

rate (1 g/s) was used in modeling the ambient air concentrations and

wet and dry deposition rates. A polar array of receptors was used,

with receptors placed 22.5 degrees apart on concentric circles 500 m

(for case #1) and 5,000 m (for case #2) from the source. The case #1

and case #2 receptor distances were patterned after prevous dioxin

indirect exposure assessments. The receptor distances from the 3,11

source were patterned after these studies because experience has shown

that, in general, exposure is increased closer to the source, and

these locations were believed to be reasonable distances for potential

exposure scenarios. The locations were tested for sensitivity

regarding the modeled exposure/risk results (see section 11.5.3,

below), and determined to be adequate for this analysis. 
11-13

Table 11-4. Process-Specific Depletion Parameters

Parameter Value

Gas depletion

 Gas Scavenging coefficient (s ) 1.8 x 10 -1 -4

Particle depletion

 Particle size distribution (microns) 15 10 6 2.5 1

 Mass fraction per particle size: coal-fired .005 0.09 0.20 0.23 0.43 a

 Mass fraction per particle size: oil-fired 0.31 0.115 10.5 10.5 36.5 a

 Particle density (g/cm ) 1.4 1.4 1.4 1.4 1.4 3 a

 Particle scavenging coefficient (s ) 6.6 x 10 6.6 x 10 4.2 x 10 1.8 x 10 4.3 x 10 -1 -4 -4 -4 -4 -5

Particle size distributions by mass taken from AP-42 data for dry bottom boilers burning bituminous coal and residual oil- a 6

burning utility boilers. Particle density estimated for bituminous coal and confirmed with EPA/ORD. 7

Table 11-5. Other Emissions Source Information

Setting type Case #1 Case #2

Location Data

 Surface/upper air Albany, NY/Albany, NY Springfield, MO/Monett, MO

 Anemometer height (m) 10.00 6.10

Preprocessor Inputs

 Land use within 5 km Suburban/rural Suburban/rural

 Minimum Monin-Obukov length (m) 2.00 2.00

 Roughness height (m) 0.50 0.33

 Noon-time albedo (fraction) 0.28 0.22

 Bowen ratio (fraction) 0.50 0.51

 Net radiation absorbed in ground (fraction) 0.15 0.15

 Anthropogenic heat flux (watts/m ) 0.00 5.40 2

ISCST3 was used to generate a wet and dry deposition rate, as

well as an ambient air concentration for both particle and vapor phase

emissions. To determine the separate particle and vapor fractions of

the emissions, each model plant case was run twice: once for particle

phase emissions and once for vapor phase emissions. A particle

modeling run was performed for each model plant using the particle

information in Table 11-4, with the output options of wet and dry

deposition and air concentration. A vapor, or gas phase, modeling

run was also performed for each model plant by omitting particle
11-14

information and allowing only air concentration and wet depletion

outputs. It was assumed that gaseous emissions behaved like small

particles for the purpose of gas scavenging and wet deposition

modeling. Dry deposition of vapors was approximated using a dry

deposition rate of 0.2 cm/s, which was estimated from previous EPA

research and cited in the secondary aluminum smelter indirect 12

exposure analysis for dioxins. For each of the eight model plant 11

cases, the maximum vapor and particle air concentration and wet and

dry deposition rates were converted from the unit emissions results to

congener specific values using the model plants’ individual dioxin

congener emissions rates (Table 11-1). The vapor and particle

congener specific values were then multiplied by the appropriate

vapor/particle fraction for ambient conditions. Additionally, 3

particle air concentrations were modified to reflect only particles

less than approximately 10 microns, based on the initial mass fraction

in Table 11-4, for the inhalation exposure.

11.3.3 Exposure Modeling and Risk Calculation

Exposure and risk modeling were performed using the IEMSpreadsheet. The spreadsheet model is based on the Methodology for

Assessing Health Risks Associated with Indirect Exposure to Combustor

Emissions and its Addendum. The IEM-spreadsheet model uses the 13 14

dispersion modeled annual air concentrations and wet and dry

deposition rates to estimate direct and indirect exposure and risks

associated with dioxin model plant emissions. Direct exposure and

risks are determined from the inhalation of airborne emissions

particles and vapors. Indirect (non-inhalation) exposure and risks

are those associated with human contact with the dioxins that have

accumulated in the environment. The model assesses indirect exposures

and risks by using a simplified mass balance approach to determine the

fate and concentrations of emissions in environmental media including

surface soils, surface water, fish, vegetation, and domestic animals. 

Using estimates of human contact, these environmental media

concentrations are translated to the indirect (non-inhalation) human

exposures and the related cancer risk values. The different routes or

pathways of indirect human contact considered by the IEM-spreadsheet

model include dermal contact with soil and ingestion of water, soil,

fish, plants, and domestic animals. A flow chart diagram of the

processes involved in the multipathway analysis and the IEMspreadsheet model are presented in Figure 11-4. 

The version of the IEM-spreadsheet used in this assessment was

developed for dioxins by the EPA Office of Research and Development

(EPA/ORD). The spreadsheet was modified, as advised by ORD, to be 15

more consistent with the current assumptions and methods used by ORD

in their work on dioxins. The spreadsheet was modified with a single

soil loss rate constant, a biota sediment accumulation factor (BSAF),

and an increased no-till depth of soil incorporation, to suit the

recommendations of EPA/ORD. The single soil loss rate constant has a

value of 0.0277 y , which is based on a new estimate of a 25-year -1
Food

Inge stion

W a te r

Inge stion

Dermal

Conta ct

Direct

Inha la tion

Soil

P roce sse s

Surfa ce

W a te r

P roce sse s

Atm osphe ric

P roce sse s

Direct

Inhalation Risk

Dermal

Contact Risk

Soil Ingestion

Risk

Vegetable

Ingestion Risk

Livestock

Ingestion Risk

W ater

Ingestion Risk

Fish Ingestion

Risk

Total Indirect

Risk

Total Direct

Risk

Model Power

Plant Dioxin

Emissions

11-15

Figure 11-4. Flow Chart of Multipathway Processes

half-life for dioxins in surface soils. The fish accumulation 15

pathway was adjusted to use BSAF calculations because dioxins are

"super-hydrophobic,” and thus are not detected in the water column

even in highly contaminated environments where the aquatic life show

high tissue concentrations. In the IEM-spreadsheet model, the 3

hydrophobic compounds are assumed to be adsorbed to the hydrophobic

components of aquatic sediments. The BSAF is the preferred method of

calculation because it bases the concentration in fish on the

concentration in adsorbed to bottom sediments, thus providing a more

accurate relationship between aquatic environment concentrations and

fish tissue concentrations. The BSAF values used in this assessment

were based on those in Estimating Exposure to Dioxin-Like Compounds3

and additional data from the Great Lakes Water Quality Initiative

Technical Support Document for the Procedure to Determine

Bioaccumulation Factors, provided by EPA/ORD. The depth of no-till 16 15

soil incorporation was increased based on new data, which was 17

recommended by EPA/ORD. In addition to the above stated 15
11-16

modifications, the animal bioaccumulation methodology was modified to

reflect Estimating Exposure to Dioxin-Like Compounds due to the 3

availability of data from this document. Also, the original version

of IEM-Spreadsheet did not calculate direct inhalation exposures and

risk, so the model was modified to calculate direct inhalation risk,

per the Addendum to the IEM Methodology. 14

Aside from the parameters and recommended changes described

above, all other IEM-spreadsheet inputs were chosen specifically for

this study. Each input parameter, its modeled value, and the source

of the data are presented in Appendix G-1 of this report. Every

attempt was made to keep the parameters used in this chapter

consistent with those used in the multipathway assessments for mercury

and arsenic (chapters 7 and 10 of this report). This includes the

description of the watershed, which was assumed to have the same

characteristics as the hypothetical watershed described in the mercury

and arsenic chapters. The IEM-spreadsheet inputs related to human

exposure were obtained from the Mercury Study Report to Congress due 18

to the parallel observed between the mercury multipathway analysis in

the Mercury Study Report to Congress and the mercury multipathway

analysis in Chapter 7. For parameters specific to dioxin and furan

congeners, the preferred source of input data was Estimating Exposure

to Dioxin-Like Compounds. Additional site-specific information was 3

obtained for parameters related to the assumed model plant locations

(see section 11.3.2 above for location descriptions); and for

parameters not specified by the sources listed above, general defaults

were obtained from the methodology supporting the IEM-spreadsheet. 13,14

The indirect exposure methodology and input parameters described

above were used to estimate exposures to dioxin and furans. The case

#2 exposure and risk modeling scenarios used the results of the

dispersion modeling (see section 11.3.2 above) and exposure

assumptions related to case #2 receptors. The case #2 receptors were

based on a resident adult and child scenario. The case #2 scenarios

were used in modeling exposure and cancer risks through the direct

inhalation, soil dermal contact, soil ingestion, home-grown vegetable

ingestion, and surface-supplied drinking water ingestion pathways. 

The case #1 exposure and risk modeling scenarios employed the results

of the case #1 dispersion modeling (see section 11.3.2 above) and

exposure assumptions related to case #1 receptors. The case #1

receptors were based on subsistence fisher and subsistence farmer

scenarios. These case #1 receptors were considered for all the

pathways described for the case #2 receptors. Additionally, the

subsistence farmer scenario considered ingestion of home-grown animal

products and the subsistence fisher scenario considered ingestion of

locally caught fish. The case #1 scenarios also used different

vegetable ingestion contact fractions. The vegetable ingestion

contact fractions represent a subsistence farmer, a rural gardener,

and urban gardener for the subsistence farmer, subsistence fisher, 18

and residents, respectively. Table 11-6 presents a summary of the

scenarios and pathways considered in this assessment, and Table 11-7

presents all 16 hypothetical scenarios included in this assessment. 
11-17

Table 11-6. Summary of Receptor Scenarios and Pathways

Receptor scenario Pathways considered

Adult resident Direct inhalation, soil dermal, soil ingestion, vegetable ingestion,

(used for case #2 modeling scenarios only) and drinking water ingestion

Child resident Direct inhalation, soil dermal, soil ingestion, vegetable ingestion,

(used for case #2 modeling scenarios only) and drinking water ingestion

Subsistence fisher Direct inhalation, soil dermal, soil ingestion, vegetable ingestion,

(used for case #1 modeling scenarios only) drinking water ingestion, and fish ingestion

Subsistence farmer Direct inhalation, soil dermal, soil ingestion, vegetable ingestion,

(used for case #1 modeling scenarios only) drinking water ingestion, and animal product ingestion

Table 11-7. The 16 Hypothetical Scenarios Included in the

Screening Level, Model Plant, Dioxin Multipathway Exposure and

Risk Assessment.

Scenario Description

LCH-fisher Subsistence fisher living near large coal-fired plant, using case #1 modeling assumptions and

inputs

LOH-fisher Subsistence fisher living near large oil-fired plant, using case #1 modeling assumptions and

inputs

SCH-fisher Subsistence fisher living near small coal-fired plant, using case #1 modeling assumptions and

inputs

SOH-fisher Subsistence fisher living near small oil-fired plant, using case #1 modeling assumptions and

inputs

LCH-farmer Subsistence farmer living near large coal-fired plant, using case #1 modeling assumptions and

inputs

LOH-farmer Subsistence farmer living near large oil-fired plant, using case #1 modeling assumptions and

inputs

SCH-farmer Subsistence farmer living near small coal-fired plant, using case #1 modeling assumptions and

inputs

SOH-farmer Subsistence farmer living near small oil-fired plant, using case #1 modeling assumptions and

inputs

LCC-resident Adult resident living near large coal-fired plant, using case #2 modeling assumptions and inputs

LOC-resident Adult resident living near large oil-fired plant, using case #2 modeling assumptions and inputs

SCC-resident Adult resident living near small coal-fired plant, using case #2 modeling assumptions and inputs

SOC-resident Adult resident living near small oil-fired plant, using case #2 modeling assumptions and inputs

LCC-child Child resident living near large coal-fired plant, using case #2 modeling assumptions and inputs

LOC-child Child resident living near large oil-fired plant, using case #2 modeling assumptions and inputs

SCC-child Child resident living near small coal-fired plant, using case #2 modeling assumptions and inputs

SOC-child Child resident living near large oil-fired plant, using case #2 modeling assumptions and inputs
11-18

11.4 DISPERSION, EXPOSURE, AND RISK RESULTS

This section presents the relationship between dioxin emissions

from the model plant stack and human cancer endpoints. Dispersion

modeling provided estimates of ambient air concentrations and wet and

dry deposition rates. The IEM-spreadsheet modeling estimated dioxin

concentrations in environmental media (i.e., soil, water, plant,

animal, and fish). The IEM-spreadsheet also estimated human exposure

to these environmental concentrations to approximate the corresponding

human exposure through various pathways (see Table 11-6) in terms of

lifetime averaged daily doses (LADDs) and cancer risk estimates. The

exposure and risk results of the local-scale and long-range modeling

were compared.

The dispersion modeling results for each model plant at both the

case #1 and case #2 receptor distances are presented in Appendix G-2. 

The data in Appendix G-2 are the maximum vapor and particle phase

ambient air concentrations and wet/dry deposition rates for each model

plant, under both case #1 and case #2 conditions (see sections 11.3.1

and 11.3.2 above for description). The dispersion modeling results

indicate that the case #1 exposures stem entirely from wet deposition,

with the exception of very low air concentration and dry deposition

associated with the small oil-fired model plant. It is likely that

the air concentration and dry deposition rate are zero because the

modeled plume of emissions has not yet settled to ground level at this

distance. From this it can be concluded that the modeled case #1

receptors, located at 500 m, probably are not at the highest direct

inhalation exposure location because receptors are actually too close

to the very tall stacks. However, the 500 m distance has much higher

wet deposition rates, which may account for higher indirect exposures. 

Following the observation of wet deposition versus plume impaction,

the sensitivity of the results to the receptor distance from the stack

was analyzed (see section 11.5.3 below).

The environmental media dioxin concentrations resulting from the

mass balance performed by the IEM-spreadsheet are presented in

Appendix G-3. The concentrations are reported for surface soil,

surface water, whole fish, and plants and animals produced on

contaminated lands. The results indicate that the bioaccumulated

concentrations in organisms (fish, plants, and animals) exceed those

that persist in soil and water. This is a reasonable result, given 

the sequestration of the lipophilic dioxin and furan compounds in the

organisms’ tissues.

The congener-specific LADDs for each of the exposure pathways and

scenarios modeled in this chapter are detailed in Appendix G-4. The

LADDs were calculated by the IEM-spreadsheet model, using the media

concentrations described above and the input parameters related to

human contact and exposure (Appendix G-1). The greatest LADD is seen

in the subsistence fisher fish consumption pathway, with animal

product and vegetable consumption following closely. This observation

is consistent with the modeled media concentrations because each of
11-19

these pathways involves consumption of an organism that has

bioaccumulated dioxins from the environment. 

Background exposures to dioxins in the United States have been

estimated. The estimated exposures range from 0.8 pg/day for soil 2

ingestion to 37.0 pg/day for ingestion of beef and veal. The

background exposure from fish ingestion is listed as 7.8 pg/day, and

the background exposure from inhalation is 0.8 pg/day. After

converting the units on the modeled LADD exposures in Appendix G-3,

the corresponding maximum modeled exposures (which do not take into

account background) are 2.1 pg/day for soil ingestion, 140 pg/day for

animal product ingestion, 420 pg/day for fish ingestion, and 0.0014

pg/day for direct inhalation. In each of the indirect pathways, the

modeled exposures exceed background.

The congener-specific results of cancer risk calculation for each

pathway and each scenario are presented in Appendix G-5. The cancer

risk values were calculated using a slope factor for dioxin of 0.156

kg-d/ng, and the Toxicity Equivalency Factors (TEFs) for 2,3,7,8-

dioxin congeners (see Appendix G-1). This slope factor was adopted

from Estimating Exposure to Dioxin-Like Compounds; the derivation of 3

the factor is described in the report Risk Analysis of TCDD

Contaminated Soil. The calculated risks show the same pattern as the 19

LADDs, with the highest risks generally associated with fish

consumption. A summary of the predicted dioxin TEQ cancer risks for

each hypothetical scenario is provided in Table 11-8. Note that the

zero direct risks in the case #1 exposure scenarios are due to the

selection of 500 m as the receptor location. As described above, at

this location the ambient air concentrations are zero, but the

deposition is near to maximum.

The TEQ cancer risks are greater for the combined indirect (noninhalation) exposures than the direct (inhalation) exposures in every

modeled scenario. In all cases, the indirect risks are at least an

order of magnitude larger. These results demonstrate the need to

consider indirect risks for environmental pollution that is persistent

in the environment, such as dioxins. This is especially evident in

the case #1 analyses where the direct risk is zero (because vapor and

particle air concentrations are zero), yet the indirect risks from the

multipathway exposures based on wet deposition are as high as 2 x 10 .-4

The highest risk, predicted to be 2 x 10 , is to the subsistence -4

fisher hypothetical scenario from the indirect (non-inhalation)

pathways for the large coal-fired model plant. Given the high level

of risk predicted for the subsistence fisher, this value is explored

further in a sensitivity analysis (see section 11.5.2 below). The

maximum direct (inhalation) risk for this model plant was 4 x 10 , -10

which is orders of magnitude less than the risk predicted for the

maximum exposed individual considered by the direct inhalation

assessment in chapter 6 of this report. This large difference in

inhalation risk estimates may be due to the selection of receptor

distance in the model plant analysis. 
11-20

Table 11-8. Summary of Predicted Cancer Risks from the Screening

Level Multipathway Assessment for Model Plants, for 16 Hypothetical

Scenarios

Scenario inhalation exposure inhalation) exposure a

Predicted cancer risk due to direct Predicted risk due to indirect (non- b b

LCH-fisher 0 1E-04 to 2E-04

LOH-fisher 0 5E-05 to 1E-04

SCH-fisher 0 3E-05 to 6E-05

SOH-fisher 1E-13 3E-06 to 6E-06

LCH-farmer 0 1E-05 to 3E-05

LOH-farmer 0 1E-05 to 2E-05

SCH-farmer 0 5E-06 to 1E-05

SOH-farmer 1E-13 5E-07 to 1E-06

LCC-resident 2E-10 2E-08 to 5E-08

LOC-resident 2E-10 1E-08 to 2E-08

SCC-resident 4E-10 1E-08 to 3E-08

SOC-resident 7E-11 3E-9 to 6E-09

LCC-child 1E-10 1E-08 to 2E-08

LOC-child 1E-10 1E-08 to 2E-08

SCC-child 2E-10 1E-08 to 2E-08

SOC-child 5E-11 1E-09 to 2E-09

See Tables 11-3, 11-6, and 11-7 and text for description of scenarios and associated assumptions, methods and a

inputs.

The risks are presented as a range. For each scenario, the higher predicted risks are based on results of the b

modeling method, assumptions, and data inputs as described in this chapter. The lower predicted risks for each

scenario are based on the assumption that the emissions estimates for the model plants (Table 11-1) could

possibly be over estimated by a factor of 2 (see discussion in section 11.1); therefore, it is predicted (based on the

assumption that the exposure and risk calculations are reasonably linear) that the risks for the hypothetical

scenarios could be about 2 times lower than those predicted using inputs and methods described in this chapter if

the emissions rates using in the calculations were 2 times lower than those shown in Table 11-1.

The predicted risks presented in Table 11-8 are screening level

estimates based on model plants and hypothetical scenarios. The

results do not apply to any actual utility plant. There are

substantial uncertainties in these predicted risks because of numerous

assumptions and data inputs with varying levels of uncertainty. For

example, the emissions estimates for the model plants are quite

uncertain and may be overestimated, or possibly underestimated (see
11-21

section 11.1 for discussion of emissions data uncertainty). 

Additional uncertainties and variability in the assessment and data

inputs are discussed in section 11.5.

The multipathway exposures and risks resulting from the localscale ISCST3 dispersion modeling results (described in the preceding

paragraphs) were compared to those resulting from the RELMAP modeling

of long-range transport (Appendix G-6). The comparison was made for

dioxin TEQ rather than the congener specific method used in the localscale modeling, due to the limited data available from RELMAP. To

obtain exposure and risk estimates, the average RELMAP data for air

concentration, and wet and dry deposition were used with the IEM -

spreadsheet model (thus paralleling what was done for the local-scale

ISCST3 dispersion result). It was assumed that the air concentration

results of the RELMAP model (Figure 11-1) were inhalable for the

purpose of inhalation exposure. A similar comparison using background

air concentration and deposition rates was not preformed for this

study due to limited data.

The comparison shows that in most cases the local-scale emissions

exposures are a greater percentage of the total exposures than the

long-range transport exposures. The importance of the local-scale

emissions generally decreases as distance from the facility increases. 

Thus, long-range transport was more influential in the case #2

modeling scenarios, for which local-scale modeling was at a greater

distance from the facility. Long-range transport was most influential

in the direct inhalation pathway and the vegetable ingestion pathway

because the RELMAP modeled air concentrations were relatively large

portions of the total TEQ air concentration.

11.5 UNCERTAINTY AND SENSITIVITY ANALYSIS

The models and assumptions used in this study have been the

subject of significant review and revision as part of other EPA

studies. The reviews have identified limitations of the methods and 20

the data sets from which the parameters are derived. Reviewers have

also cautioned EPA against misuse of the indirect exposure

methodology, including both the dispersion and the intermedia transfer

modeling components. Every effort has been made in the present study

to use exposure models and parameter values consistent with the latest

EPA studies of dioxin and mercury risks.

Specifically, the review of IEM methodology performed by the

Science Advisory Board (SAB) made several recommendations that were 20

considered in this analysis. The SAB recommended accounting for the

cumulative impacts of several sources on the regional or National

level. The national RELMAP modeling results included in sections 2.0

and 4.0 of this chapter respond to this recommendation. The SAB cited

lack of validation results as a scientific uncertainty in the IEM

methodology. The SAB recommends sensitivity analysis as a means of

determining the importance of input values. Sensitivity analyses were

performed for this assessment, and are described in the following
11-22

subsections. However, the SAB notes problems in the model with

conservation of mass and chemical thermodynamics that were not

corrected in the model for this application. It should be noted that

these issues and others add uncertainty to the mass balance estimation

of media concentrations performed by the IEM spreadsheet, as well as

the corresponding modeled indirect exposures and risks.

11.5.1 Model Elasticity

A brief, initial sensitivity analysis was performed for selected

parameters employed in the risk modeling exercise. Using professional

judgement, a limited number of parameters were chosen for their

perceived importance in the model. Additionally, the chosen parameters

are known to have uncertainty in their value, generally due to limited

or variable empirical data. The selected parameters were individually

varied from 90 percent to 110 percent of their initial value, while

all other parameter values were held constant. The resulting changes

in the modeled risks for the pathway(s) impacted were analyzed. All

sensitivity analyses were performed using data from the large coalfired model plant because it demonstrated the largest indirect

exposures and risks. 

The bioconcentration factor (BCF), or biota sediment accumulation

factor (BSAF), for fish was analyzed in the fisher scenario, as it is

recognized that fish consumption is an important factor for estimating

risks to the subsistence fisher, and is the overall driver of the risk

assessment. There is a limited set of data available for

consideration in the selection of BSAF; and thus BSAF was analyzed 3,16

for impact on the fish consumption pathway by percentage variation of

the modeling input value. 

 Similarly, the BCFs for animal products (beef, chicken, dairy,

and eggs) were analyzed for the animal product consumption pathway of

the subsistence farmer scenario, as animal product consumption

provides significant risk in many of the modeled scenarios. Again,

there are limited data available for the bioconcentration of dioxinlike compounds in animal products, and no true distribution of data 3,21

could be determined for analysis. Thus, in order to explore the

impact of changes in the BCFs, percentage changes in the modeling

input value were used. 

The overall soil loss rate constant, which was recommended for

dioxin exposure modeling by EPA/ORD, was analyzed by expanding the

number of pathways considered in the adult resident scenario. The

soil loss rate constant was chosen because it influences the exposure

media concentrations in several pathways, including soil dermal

exposure, soil ingestion, plant ingestion, animal product ingestion,

water ingestion, and fish consumption. There was believed to be

uncertainty in the value of this parameter because the value

recommended by the EPA has changed from 0.0693 y to 0.0277 y . -1 -1 3 15

Again, due to the unavailability of a distribution for the soil loss

rate constant, the sensitivity analysis was performed by percent

change in the value.
11-23

The results of this sensitivity analysis indicate that the IEMspreadsheet model is responsive to each of the parameters addressed in

this sensitivity analysis in differing amounts. None of the tested

cases create a change in risk greater than ten percent as a result of

the ten percent change in the tested parameter. The bioconcentration

and accumulation factors (Figure 11-5) show exactly a ten percent

change in modeled risk due to the ten percent change in the input

value, while the changes in risk, due to the ten percent change in the

soil loss rate constant, vary from 3.7 percent to 0.2 percent for the

different pathways considered (Figure 11-6).

These results indicate that none of the tested parameters will

create a greater percentage change in risk than the percentage change

in the input value. This conclusion is not unreasonable given the

general linearity of the model and the large number of input

parameters and equations playing into the calculated risks.

11.5.2 Fish Consumption Pathway Sensitivity Analysis

In consideration of the risk analysis results, it was observed

that the fish consumption pathway has the highest cancer risks. Thus,

a more extensive sensitivity analysis was undertaken for the input

parameters involved in the fish consumption pathway. The inputs into

the fish consumption pathway were considered, and five parameters were

chosen for this analysis: fish consumption rate of the subsistence

fisher, the fish lipid correction term, the BSAF, the fraction organic

carbon in the bottom sediments, and the organic carbon partition

coefficient (K ). The available data for each of these parameters was oc

examined and high and low values were selected, as described below. 

The IEM-spreadsheet was then used to hold all other parameters

constant while individually considering each of the high and low

values for the fish consumption pathway input variables. This

analysis was performed using the large coal-fired model plant and the

subsistence fisher scenario because this combination generated the

greatest risk in the entire assessment.

The fish consumption rate for the subsistence fisher scenario in

the risk modeling exercise was 0.86 g fish/kg body weight/day. This

value was calculated from the 60 g fish/d recommendation of the

Mercury Study Report to Congress. This value is based on the 18

weighted mean of the data from a 1994 study by the Columbia River

Inter-Tribal Commission (as presented in the EPA Exposure Factors

Handbook). The units of g fish/d were converted by assuming a 22,23

70 kg body weight. It was also assumed that, as a subsistence

population, the fishers caught all of the consumed fish locally. High

and low consumption rates taken from the Columbia River data set have

the values of 0.04 g fish/kg body weight/day (10 percentile) and th

2.0 g fish/kg body weight/day (90 percentile). th

The fish lipid correction term is used as a correction for the

edible portion of fish lipid, as the BSAFs are developed for whole fish

lipid concentration. The fish lipid correction term in the risk modeling

exercise was 0.07, as recommended in Estimating Exposure to Dioxin-Like
Indirect Risk Model Elasticity - Meat and Fish Factors

80.0

90.0

100.0

110.0

120.0

Percent Change in Meat or Fish Factor

Percent Change in Risk Result

Meat Bioconcentration Factor (BCF) 90.0 100.0 110.0 

Fish Biota Sediment Accumulation Factor

(BSAF)

 90.0 100.0 110.0 

90% 100% 110%

Percent Change in Risk Result

11-24

Figure 11-5. Risk Model Sensitivity to Changes in Meat

and Fish Factors

Figure 11-6. Risk Model Sensitivity to Changes in Soil

Loss Rate Constant
11-25

Compounds. This document notes that fish lipid content generally varies 3

from 5 percent to 20 percent, and thus these values were used as the high

and low sensitivity analysis values for this parameter.

The BSAFs used in the risk modeling exercise were the congener

specific values used in the demonstration of methodology in chapter 5

of Estimating Exposure to Dioxin-Like Compounds. The limited set of 3

empirical values for dioxin congener BSAFs was considered in the 3,16

determination of the high and low values for sensitivity analysis. 

The highest and lowest empirical values for each congener were chosen

to use as the high and low values in this sensitivity analysis.

The fraction of organic carbon in the sediment used in the risk

modeling exercise was 0.03, as recommended by Estimating Exposure to

Dioxin-Like Compounds. This value was assumed to be more than the 3

soil organic carbon fraction (0.01) and less than the organic carbon

fraction of suspended solids (0.05). Thus, the assumed values for the

soil and suspended solids organic carbon fractions were used as the

low and high values for variation in sediment organic carbon content.

The K was considered in this sensitivity analysis because it oc

directly affects the concentration of dioxins and furans adsorbed to

the benthic sediments, which is used in the fish pathway. The

concentration of each congener that is adsorbed to the benthic

sediments is dependent on the sediment-water partition coefficient,

and this coefficient was determined by estimation using K . The oc

initial values for K were obtained from oc Estimating Exposure to

Dioxin-Like Compounds. The K for each congener was varied, using the 3 oc

high and low empirical values from Mackay, and assumptions where data 24

was unavailable. The calculation of the adsorbed sediment

concentrations is complex and involves many input parameters;

therefore, the only attempt at analyzing sensitivity to the adsorbed

sediment concentration was through the analysis of K . oc

The results of the fish consumption pathway sensitivity analysis

are presented in Figure 11-7 and Table 11-9. The results indicate

that none of the single changes to low-end parameter values decreased

the fish ingestion risks below 9 x 10 . However, the high assumption -6

for BSAF raised the modeled cancer risks to as high as 1 x 10 , or one -3

in one thousand for the subsistence fisher.

11.5.3 Sensitivity Analysis of Plume Impaction

As previously described in section 11.3, it was recognized that

the risks described for the case #1 farmer and fisher scenarios are

due to only wet deposition concentrations. The zero values for dry

deposition and air concentration at the case #1 location imply that

plume impaction has not occurred at 500 m. Thus, an analysis was

performed to identify areas of plume impaction, with the idea that

potential exposures may exceed even the case #1 modeling scenarios.
11-26

Figure 11-7. Sensitivity of Predicted Risk to the Subsistence

Fisher to Changes in Parameter Values

Table 11-9. Fish Consumption Pathway Sensitivity Analysis Inputs

and Results

Parameter

Parameter Value Risk Result

Low Nominal High Low Nominal High

BSAF (unitless) specific specific specific 2e-04 2e-04 1e-03

congener congener congener

Organic carbon content of

sediment (fraction) 1e-02 3e-02 5e-02 6e-04 2e-04 1e-04

Fraction lipid in fish (fraction)

5e-02 7e-02 2e-01 1e-04 2e-04 6e-04

Fish consumption - Columbia

River (kg/kg/d) 4e-05 2e-03 2e-03 9e-06 2e-04 3e-04

K (L/kg) specific specific specific 2e-04 2e-04 2e-04 oc

congener congener congener

BSAF = biota sediment accumulation factor

ISCST3 dispersion modeling for each of the case #1 model plants

was run again with receptors placed every 1000 m on a polar grid out

to a distance of 25,000 m. Flat terrain was used in the dispersion

remodeling because it was realized that variation in terrain would

influence the location of plume impaction and maximum concentration. 

The resulting data has not been corrected for congener specific

emissions rates (unit emissions are used), and it has not been

proportioned for vapor and particle phase fractions. However, it
11-27

still serves the analysis for determination of maximum values. 

Table 11-10 presents the highest values for each model plant in terms

of wet and dry deposition and air concentration of both particles and

vapors. (Note that dry deposition of vapors is not included in this

table, as it is not an ISCST3 modeled value [see section 11.3.2.]) 

The location of each value in Table 11-10 is indicated by x and y

coordinates in meters. Table 11-11 presents modeled concentrations at

2.5 km, 10 km, and 25 km, as are reported in the mercury multipathway

analysis (chapter 7). The locations in Table 11-11 are described in

terms of degrees from the polar grid origin. For this analysis the

polar origin was North with degrees progressing clockwise.

Due to the disparate locations of the maximum modeled

concentrations for each modeled plant, it is difficult to determine

where the single site of maximum risk would be. It is likely that the

maximum risks would be observed at the 50 m location because that is

the location of maximum wet deposition. In all modeled cases, wet

deposition represents a significantly larger share, by an order of

magnitude or greater, of the environmental concentration than either

dry deposition or air concentration. This data does not indicate that

a re-analysis of the model plant risks is necessary because the effort

required to determine the exact location of a maximum and average

exposure scenario in a multipathway analysis is beyond the scope of

this screening-level analysis. 

11.6 SUMMARY OF RESULTS

The results of this assessment indicate that the predicted cancer

risks associated with case #2 exposures to dioxin emissions from the

model plants are not greater than 1 x 10 for the model adult and -7

child resident scenarios. This is the case for both the direct and

the indirect exposure pathways. However, risks of 1 x 10 or above -6

are predicted for the indirect pathways of the case #1 scenarios. In

particular, the subsistence fisher scenario results in a predicted

risk of 2 x 10 . When factors influencing the magnitude of the risk -4

to the subsistence fisher were examined in a sensitivity analysis, it

appeared that the assumptions about the BSAF had the greatest impact

on the predicted risk. 

Taken one at a time, several other uncertainty factors have the

potential to raise the risk estimate for the subsistence fisher above

2 x 10 ; these include fish consumption rates, the organic carbon -4

content of sediment, and the fraction of fish tissue that is lipid. 

This assessment has been conducted using model plant data and two

geographic locations selected to represent two potential scenarios. 

The location of the case #1 receptors was arbitrarily placed at a

point of high wet deposition. This assumption was made for the

purpose of providing a conservative estimate of the case #1 risk. 

The case #1 scenario represents a sub-population that has the

potential to receive high exposures due to relatively high ingestion

rates, and assumed to be in a location for which the meteorology would 
11-28

Table 11-10. Maximum Dispersion Modeling Locations and

Concentrations

Model plant location in m Wet deposition location in m Dry deposition location in m concentration

phase (x,y) (g/m /y) (x,y) (g/m /y) (x,y) (g/m )

Wet deposition Dry deposition concentration Air

2 2

Air

3

LCH, vapor 0, -500 0.07430 - - 0, 9000 0.00050

LCH, particle 0, -500 0.09158 Several 0.00026 Several 0.00046

SCH, vapor 0, -500 0.08372 - - Several 0.00344

SCH, particle 0, -500 0.10274 8314, -3444 0.00210 -3826, 9238 0.00302

LOH,vapor 0, -500 0.07846 - - Several 0.00105

LOH, particle 0, -500 0.14166 Several 0.00092 Several 0.00084

SOH, vapor 0, -500 0.09083 - - -2296, 5543 0.00951

SOH, particle 0, -500 0.16233 4619, -1813 0.00997 -1913, 4619 0.00792

also tend to result in high exposures for given rates of release of

dioxins. The results of this analysis can be considered a potential

representative example for utility boilers, yet they do not represent

a specific plant or location. The analysis should be considered an

analysis of hypothetical scenarios used for demonstration. 

This analysis of noninhalation exposures to dioxin emissions is a

screening analysis. Thus, these quantitative exposure and risk

results, because of the many modeling and analytic uncertainties, are

very uncertain and do not, therefore, conclusively demonstrate the

existence of health risks of concern associated with exposures to

utility emissions either on a national scale or from any actual

individual utility. The lack of measured data around these sources

precludes a comparison with modeled results. These results do suggest

that exposures and risks of concern cannot at present be ruled out and

that there is a need for development of additional scientific

information to evaluate whether risk levels of concern may exist.
11-29

Table 11-11. Dispersion Modeling Concentrations at Specified

Distances

Model plant, Wet deposition Wet deposition Dry deposition Dry deposition concentration Concentration

phase location ( ) (g/m /y) location ( ) (g/m /y) location ( ) (g/m ) o2 o2

Air Air

o 3

2.5 km concentrations

LCH, vapor 180 0.01342 - - 360 0.00015

LCH, particle 180 0.01414 360 0.00005 360 0.00014

SCH, vapor 180 0.01406 - - 360 0.00040

SCH, particle 180 0.01535 360 0.00088 360 0.00196

LOH, vapor 360 0.01406 - - 360 0.00040

LOH, particle 360 0.01970 360 0.00030 360 0.00036

SOH, vapor 180 0.01577 - - 360 0.00681

SOH, particle 180 0.02141 112.5 0.00768 360 0.00607

10 km concentrations

LCH, vapor 180 0.00217 - - 360 0.00049

LCH, particle 337.5 0.00121 337.5, 360 0.00019 360 0.00045

SCH, vapor 180 0.00225 - - 337.5 0.00344

SCH, particle 180 0.01890 112.5 0.00207 337.5 0.00302

LOH, vapor 180 0.00221 - - 360 0.00100

LOH, particle 180 0.00211 112.5 0.00084 360 0.00083

SOH, vapor 180 0.00233 - - 337.5 0.00852

SOH, particle 180 0.00210 112.5 0.00675 337.5 0.00679

25 km concentrations

LCH, vapor 180 0.00046 - - 337.50 0.00046

LCH, particle 180 0.00037 112.5 0.00026 135 0.00039

SCH, vapor 180 0.00047 - - 337.5 0.00256

SCH, particle 180 0.00036 112.5 0.00099 337.5 0.00205

LOH, vapor 180 0.00046 - - 337.5 0.00103

LOH, particle 180 0.00036 112.5 0.00080 337.5 0.00079

SOH, vapor 180 0.00047 - - 360 0.00444

SOH, particle 180 0.00033 337.5 0.00205 360 0.00355
11-30

1. U.S. Environmental Protection Agency. Health Assessment for

2,3,7,8-TCDD and Related Compounds. EPA/600/BP-92/001c. 1994b.

2. U.S. Environmental Protection Agency. Estimating Exposure to

Dioxin Like Compounds, Vol. II: Properties, Sources, Occurrence,

and Background Exposures-External Review Draft. EPA/600/6-

88/005Cb. Office of Research and Development, Washington, DC. 

1994.

3. U.S. Environmental Protection Agency. Estimating Exposure to

Dioxin-Like Compounds, Vol. III: Site-Specific Assessment

Procedures. External Review Draft. EPA/600/6-88/005Cc. Office

of Research and Development. Washington, DC. 1994a.

4. U.S. Environmental Protection Agency. User’s Guide for the

Industrial Source Complex (ISC3) Dispersion Models, volumes I and

II. EPA-454/B-95-003a. Office of Air Quality Planning and

Standards, Research Triangle Park, NC. 1995b.

5. U.S. Environmental Protection Agency. Memo, from Jim Turner,

Research Triangle Institute, to Chuck French, EPA, regarding

Process Parameters for Utility Boiler Dioxin Modeling, Utility

Boiler Report to Congress. Dated July 14, 1997. 1997a.

6. U.S. Environmental Protection Agency. AP-42. Office of Air

Quality Planning and Standards. Research Triangle Park, NC. 

January 1995a.

7. Perry, R. H., D. W. Green, and J. O. Maloney. Perry’s Chemical

Engineers’ Handbook. McGraw-Hill Book Company, New York, NY. 

1984.

8. SAMSON. Solar and Meteorological Surface Observation Network. 

1961-1990 data, Version 1. United States Department of Commerce,

National Climatic Data Center, Asheville, NC. 1993.

9. USGS, United States Geological Survey. Earth Resources

Observation System Data Center digital elevation model data from

the online Global Land Information System (GLIS) at

http://edcwww.cr.usgs.gov/glis. 1997.

10. U.S. Environmental Protection Agency. Guideline on Air Quality

Models. Office of Air Quality Planning and Standards. Research

Triangle Park, NC. 40 CFR Parts 51 and 52. 1996b. 

11.7 REFERENCES
11-31

11. U.S. Environmental Protection Agency. Secondary Aluminum

Smelters: Screening Level Indirect Risk Assessment. EPA Contract

No. 68-D3-0034, Work Assignment No. III-26. 1997b.

12. U.S. Environmental Protection Agency. Risk Assessment Support to

the Development of Technical Standards for Emissions from

Combustion Units Burning Hazardous Wastes. 1996e. 

13. U.S. Environmental Protection Agency. Methodology for Assessing

Health Risks Associated with Indirect Exposure to Combustor

Emissions. Interim Final Report. EPA/600/6-90/003. Office of

Health and Environmental Assessment. Washington, DC. 1990. 

14. U.S. Environmental Protection Agency. Addendum to the

Methodology for Assessing Health Risks Associated with Indirect

Exposure to Combustor Emissions. External Review Draft. 

EPA/600/AP-93/003. Office of Health and Environmental

Assessment. Washington, DC. 1993. 

15. Lorber, Matthew. EPA Office of Research and Development. 

Personal Communication. 1997.

16. U.S. Environmental Protection Agency. Great Lakes Water Quality

Initiative Technical Support Document for the Procedure to

Determine Bioaccumulation Factors. EPA-820-B-95-005. Office of

Water. March 1995c. 

17. Brzuzy, L. P. and R. A. Hites. Estimating the atmospheric

deposition of polychlorinated dibenzo-p-dioxins and furans from

soils. Environmental Science and Technology. Volume 29. 1995. 

Pp. 2090-2098.

18. U.S. Environmental Protection Agency. Mercury Study Report to

Congress, Volume III: An Assessment of Exposure from

Anthropogenic Mercury Emissions in the United States. SAB Review

Draft. EPA-452/R-96-001c. Office of Air Quality Planning and

Standards and Office of Research and Development. 1996c. 

19. U.S. Environmental Protection Agency. Risk Analysis of TCDD

Contaminated Soil. EPA-600/8-84-031. Office of Health and

Environmental Assessment. 1984. 

20. U.S. Environmental Protection Agency. Review of Draft Addendum

to the methodology for Assessing Health Risks Associated with

Indirect Exposure to Combustor Emissions. EPA-SAB-IAQC-94-009b. 

Science Advisory Board, Washington, DC. 1994.

21. Stephens, R. D., M. S. Petreas, and D. G. Haward. Biotransfer

and Bioaccumulation of Dioxins and Furans from Soil: Chickens as

a Model for Grazing Animals. Science of the Total Environment.

Volume 175. 1995. Pp. 253-273.
11-32

22. Columbia River Inter-Tribal Fish Commission. A Fish Consumption

Survey of the Umtilla, Nez Perce, Yakama and Warm Springs Tribes

of the Columbia River Basin. Technical Report 94-3. October

1994.

23. U.S. Environmental Protection Agency. Exposure Factors Handbook,

Volumes I, II, III. Draft Report. Office of Research and

Development. EPA/600-P-95/002Ba,b,c. Washington, DC. 1996a. 

24. Mackay, Donald, Wan-Ying Shiu, and Kuo-Ching Ma. Illustrated

Handbook of Physical-Chemical Properties and Environmental Fate

for Organic Chemicals. CRC Press, Inc - Lewis Publishers. Boca

Raton, FL. 1995.
12-1

12.0 LITERATURE REVIEW ON THE POTENTIAL IMPACTS OF

HYDROGEN CHLORIDE AND HYDROGEN FLUORIDE EMISSIONS

12.1 OVERVIEW

The information presented in this chapter and in Appendix H was

collected to expand the EPA’s knowledge of the potential impacts of

HCl and HF emissions from utilities. The details and references are

presented in Appendix H. This chapter presents a summary of the

findings. The EPA is updating its current state of knowledge of

potential health impacts; atmospheric chemistry (e.g., half-life,

impacts on the acid rain phenomenon); potential human exposure through

pathways other than direct inhalation; and possible ecological harm. 

The EPA’s goal is to understand the potential impacts from HCl and HF

emissions to any and all health and environmental areas. This chapter

is not intended to provide a detailed, comprehensive treatise on the

above subject area; rather, it is designed to provide general

technical information that will identify possible problem areas that

may call for additional, more detailed research.

Published evidence for potential impacts of HCl and HF was

evaluated from a wide variety of sources. Overall, there is extensive

information available on the toxicology of these two pollutants;

however, literature pertaining specifically to HF and HCl atmospheric

chemistry is relatively scarce, especially that pertaining to fine

particulate matter and acid rain. Literature on HCl and HF from

sources outside the United States and pertaining to emissions sources

other than utilities has also been evaluated. 

This chapter is organized so that the findings for HCl are

presented first, followed by the findings for HF. Within each

section, evidence from the literature for transport and transformation

through atmospheric, terrestrial, and aquatic processes is presented

first, followed by evidence for impacts on human health; vegetation;

and wild, domestic, and aquatic animals.

12.2 SUMMARY OF FINDINGS

This chapter provides a synopsis of the information of interest

found during the literature review on potential impacts (e.g., acid

rain, fine particulate matter, visibility, and toxicity to various

plant and animal species) of hydrogen chloride and hydrogen fluoride

emissions from utilities.

12.2.1 Hydrogen Chloride

12.2.1.1 HCl Emissions and Formation. Utilities emit a

substantial amount of the anthropogenic atmospheric emissions of HCl

in the United States. As shown in chapter 3 of this report, utilities 1

were estimated to emit 146,000 tpy of HCl in the United States in

1990. Other important sources of HCl are industrial coal combustion

and solid waste combustion. Natural sources of HCl emissions include
12-2

volcanic activity, marine plants and microorganisms, and land plant

combustion.

According to available information, ambient concentrations of HCl

in the United State ranged from none detected to 4 g/m . Rural sites 3

can be expected to be at the low end of this range and urban sites are

likely to be toward the high end. Human health effects are discussed

in Appendix E.

HCl can be formed several ways in the atmosphere. Anthropogenic

chlorocarbons can react with OH radicals to produce small amounts of

HCl. Nonanthropogenic HCl can be formed from deliquescent sea-salt in

the marine environment. HCl can also be created or destroyed through

the interaction between fogwater and aerosols. Although information

on HCl formation by-products is scarce, available sources indicate

that reactions generating HCl can produce the following by-products in

the atmosphere: NaNO , Na SO , hydrocarbon radicals, and NH . 3 24 3

12.2.1.2 HCl Atmospheric Processes. The atmospheric lifetime of

HCl is estimated to be between 1 and 5 days. HCl is a highly reactive

gas that is removed from the atmosphere via wet and dry deposition. 

12.2.1.3 HCl Atmospheric Transport. In general, because of its

high solubility, HCl will be removed from the atmosphere much faster

than SO or NO and will be deposited in close proximity to the 2 2

emissions source. However, conditions exist under which SO is subject 2

to further transport. One study found evidence that HCl-enriched

plumes, believed to originate from coal-burning utilities, reached a

rural site two days after emissions release. Evidence was found that

HCl may affect the atmospheric chemistry of mercury, and thus the

toxicity of mercury emissions from utilities. HCl emissions are

believed to contribute to some limited degree to the formation of

atmospheric acidity and acid rain. In addition, HCl appears to

indirectly contribute to some limited degree, to fine PM and

visibility problems. However, there are significant uncertainties as

to the extent of the impacts due to HCl emissions in these areas. 

Further research and evaluation are needed to determine if, and to

what extent HCl contributes to acid rain, fine PM, and visibility

issues.

 

12.2.1.4 HCl Terrestrial Processes. Information on the

terrestrial behavior of HCl is scarce. The references found discuss

the evidence that HCl can lower pH to the point that SO oxidation is 2

delayed, possibly altering the spatial deposition of acid species, and

that gaseous HCl damages limestone.

12.2.1.5 HCl Aquatic Processes. The chemistry of ubiquitous

chlorinated compounds in natural waters is affected by a number of

factors that determine the persistence and toxicity of interim

species. Chloride cycling in watersheds was found to be more complex

than previously thought. The traditional view has been that

atmospherically deposited chloride is rapidly transported. Further
12-3

research of chloride cycling would be needed to gain a better

understanding of the aquatic processes. 

12.2.1.6 HCl Human Health Impacts. Evidence of local irritation

to the upper respiratory tract by HCl was found, and long-term

exposure may cause tooth erosion. The WHO concluded in a review on

HCl that there are no mutagenic, carcinogenic, or teratogenic effects

related to HCl. Appendix E contains more information on health

effects.

12.2.1.7 HCl Vegetation Impacts. Atmospheric and leaf chloride

levels were found to be closely correlated. Atmospheric chloride can

concentrate in and cause damage to foliar tissues. High chloride

concentrations resulted in tissue death in coastal vegetation.

12.2.1.8 HCl Terrestrial Animal Impacts. HCl that reaches

plants and soil via wet and dry deposition is then available for

uptake by animals. The adverse health symptoms of HCl exposure in

animals include eye, nasal, and respiratory tract irritation, with the

respiratory tract being the primary target.

12.2.1.9 HCl Aquatic Animal Impacts. Toxicity of chlorinated

compounds to aquatic biota varies widely. One study reported the 24-

hour LC for one marine species as 0.0018 mg/L, and the LC for one 50 50

sensitive freshwater species as 0.003 mg/L.

12.2.2 Hydrogen Fluoride

12.2.2.1 HF Emissions and Formation. Anthropogenic sources are

responsible for most atmospheric fluoride. Anthropogenic emissions of

hydrogen fluoride (HF) originate from coal combustion and the

aluminum, phosphate, and steel-making industries. As shown in

chapter 3, utilities are estimated to emit approximately 19,600 tpy of

HF nationwide. Volcanoes are the primary natural sources of HF. 

Ocean spray, fires, and dust from soil and rock weathering contribute

relatively minor amounts of fluoride to the atmosphere. Measured

concentrations of HF at one monitoring station in the United States

ranged between 1 g/m and 8 g/m . Atmospheric concentrations of 3 3

fluoride in remote rural areas are reported to be approximately 0.1

g/m , which is at the limit of detection. Estimates of the relative 3

proportions of gaseous to particulate emissions of industrial fluoride

vary. Fluoride particulates range from distinct minerals to alumina

with HF adsorbed to its surface, and particle diameters range from

<0.1 μm to approximately 10 μm. Volcanic emissions are usually not

predominately HF, but rather other fluoride-containing compounds that

react in the atmosphere to form HF. 

12.2.2.2 HF Atmospheric Processes. HF is described as

moderately persistent in the atmosphere, with an estimated lifetime of

approximately 1 to 5 days. Wet and dry deposition are the primary

routes of HF removal from the atmosphere. HF atmospheric reaction

products are primarily fluoride salts. HF does not biodegrade.
12-4

12.2.2.3 HF Atmospheric Transport. Fluoride emissions from

utilities are transported on a regional scale. Fluoride has been used

as an atmospheric tracer because the fluoride to sulfur oxide ratio is

relatively constant in the coal-fired utilities examined, providing a

characteristic utility emissions fingerprint. Evidence was found that

coal-fired utility emissions contribute to measured concentrations of

atmospheric fluoride at distances of up to 500 km. Another study

found that elevated levels (18-21 ppm) of fluorides emitted from a

utility could be detected in grape leaves at distances of up to 37 km. 

However, it has been reported that the measurement of fluoride

transport and deposition have problems of accuracy due to limitations

of analytical methods.

12.2.2.4 HF Terrestrial Processes. Fluoride is lost from the

various surfaces on which it is deposited and leaves ecosystems at a

rapid rate. The volatilization pathway as a route of fluoride export

from ecosystems needs further investigation. Soil can be both a sink

and source of fluoride, but fluoride is not usually available or

labile in soils. Soluble fluoride-containing process water and

leachate of phosphogypsum were shown to dissolve much of the fine soil

clay fraction, as well as the smectite of the coarse clay fraction of

soils. Several conditions are reported to facilitate rapid uptake of

water-soluble fluoride and transport. The natural buffering capacity

of soils or water, or dilution can reduce acidity added by the

presence of HF. Sulfate and fluoride were found to slightly retard

aluminum’s mobility through soils.

12.2.2.5 HF Aquatic Processes. Fluoride is a major component of

seawater, and natural and anthropogenic fluoride may accumulate in

waterbody sediments. In freshwaters with pH greater than 5, fluoride

is mainly present as fluoride ion. 

12.2.2.6 HF Human Health Impacts. Adverse effects of fluoride

on human health include dental fluorosis, gastric disturbances,

reductions in urinary concentrating ability, skeletal fluorosis, and

even death. Optimally fluoridated water has been shown not to be

associated with a detectable risk of cancer in humans. Fluoride

exposure is not associated with birth defects, and there is no

indication that organ systems are affected by chronic, low-level

fluoride exposure. Genotoxicity studies have yielded contradictory

results. There is disagreement about whether the increased prevalence

of dental fluorisis observed in the United States since the 1940s is a

toxic effect. Crippling skeletal fluorosis has not been and is not a

public health problem in the United States. Beneficial effects of

high fluoride regimens in reducing osteoporosis have not been

demonstrated. Further epidemiological studies are required to

determine whether or not an association exists between various levels

of fluoride in the drinking water and bone fractures. Appendix E 2

contains more information on health effects of HF.

12.2.2.7 HF Vegetation Impacts. Inherent differences in the

resistance of some tropical tree species to fluoride may be related to
12-5

their capacity to accumulate aluminum. Although some plant species

are tolerant of elevated fluoride levels, the storage of large amounts

of fluoride in plant tissues may present a risk to ecosystems. Plant

uptake of fluoride was found to be limited to the smaller, watersoluble, and labile fractions. The major pathway of fluoride to

plants is atmospheric deposition.

Atmospheric fluoride is capable of injuring certain plant species

at lower concentrations than any other air pollutant. However, most

plant species are relatively resistant to fluoride. No morphological

damage to lichen species exposed to high concentrations of fluoride

was found. Fluoride was found to be the most important pollutant

contributing to vegetation damage in one section of a tropical

rainforest.

12.2.2.8 HF Terrestrial Animal Impacts. Two studies on the

toxicity of fluoride to several rodent species found the animals to

exhibit visible incisor lesions after fluoride ingestion. Bone

fluoride loads in four predatory bird species were found to be greater

in males of all species examined and higher than average in more

industrial regions. 

Conflicting information was found concerning whether fluoride

accumulates in food chains. Variations in fluoride concentrations

within plant organs can result in animal species with differing

feeding niches ingesting different amounts of fluoride. Contamination

of foliage with soil may constitute an important route of fluoride

transfer to large herbivores in situations where soil has been treated

with phosphate fertilizer or exposed to substantial airborne

deposition of fluoride.

12.2.2.9 HF Aquatic Animal Impacts. Biomagnification in aquatic

animals is reported to be negligible to very slight. Two trout

species were demonstrated to be more resistant to fluoride than

freshwater benthic macroinvertebrates. Diatoms appear to be tolerant

of, and stimulated to grow by, high fluoride concentrations; the

ecological significance of this is uncertain. Limited evidence exists

for fluoride-containing effluent effects on both abundance and

diversity of estuarine/marine organisms at relatively low fluoride

levels.
12-6

1. Placet, M., et al. Emissions Involved In Acidic Deposition

Processes, NAPAP Report 1, Nin: Acidic Deposition: State of

Science and Technology, National Acidic Precipitation Assessment

Program. 1990.

2. Whitford, G. M. The Metabolism and Toxicity of Fluoride (Revised

edition). Karger. 1996.

12.3 REFERENCES
13-1

13.0 ALTERNATIVE CONTROL STRATEGIES FOR HAZARDOUS AIR POLLUTANT

EMISSIONS REDUCTIONS

This chapter presents methods of reducing HAP emissions through

precombustion controls, combustion controls, postcombustion controls,

and alternative controls. Also, strategies for maximizing total HAP

control or minimizing total HAP emissions are reviewed. 

The HAPs of concern include the trace elements identified in

chapter 5 as potential health risks. These consist of arsenic,

cadmium, chromium, lead, manganese, mercury, and nickel; dioxins and

furans (due to the toxicity of the organic chemical); and HCl and HF

(due to the estimated emission quantities of the compounds).

13.1 PRECOMBUSTION CONTROLS

To reduce SO emissions and thereby comply with the Phase II 2

requirements of the Acid Rain Provisions of the Act, some utilities

will switch to fuels that contain lower amounts of sulfur. The

effects of fuel switching on HAP emissions are briefly reviewed in

this section. Emissions of trace elements from coal-fired units may

be controlled through precombustion control techniques such as coal

cleaning and coal gasification. The effectiveness of these control

techniques is also reviewed.

13.1.1 Fuel Switching

Utilities that switch fuel may change from higher to lower

sulfur-containing coal (less than 1.5 weight percent sulfur) or elect

to burn a different type of fuel (e.g., switching from oil to natural

gas combustion). A potential concern with fuel switching is whether

or not it will increase HAP emissions, due to potentially increased

concentrations of trace elements in the fuel and different fly ash

characteristics that impact effective PM and HAP control with existing

APCDs (e.g., ESPs).

The qualitative effects of switching the type of fuel may be

noted through comparisons of the averages of trace element

concentrations in utility fuels. Table 13-1 lists the arithmetic

average, as well as the standard deviation of the average, for trace

element concentrations in coal, residual oil, and natural gas. As

indicated by the magnitude of the standard deviations listed in

Table 13-1, trace element concentrations vary considerably in coal and

residual oil. Some of the standard deviations are large enough that

comparable concentrations of trace elements may occur in some coal and

residual oil samples. For discussion purposes, any overlap in trace

element concentrations was ignored, and generalizations on the effects

of fuel switching were made from comparisons of average trace element

concentrations in the three fuels.
13-2

Table 13-1. Comparison of Average Concentrations of Trace

Elements in Utility Fuelsa,b

Coal Residual Natural gas c d

Sulfur (lb/MMBtu) (lb/MMBtu) (lb/MMBtu)

average 1.24 0.31 0.00006 e

SD (mean) 0.19 0.07 0.00006 f

No. averages 26 13 2

Coal Residual Natural gas c e

Trace elements: (lb/trillion) (lb/trillion) (lb/trillion)

Arsenic

average 660 17 0.19

SD (mean) 120 11 0.06

No. averages 26 6 2

Cadmium

average 60 5.4

SD (mean) 30 3.9 --

No. averages 26 3

Chloride

average 27,000 7,400

SD (mean) 6,600 3,300 --

No. averages 20 11

Chromium

average 600 17

SD (mean) 98 3 --

No. averages 26 11

Fluoride

average 5,300 600

SD (mean) 720 200 --

No. averages 26 3

Lead

average 800 73

SD (mean) 190 43 --

No. averages 26 5

Mercury 1,2

average 7.7 0.6 0.001

SD (mean) 0.6 0.3 1

No. averages 152 4

Nickel

average 700 1,300

SD (mean) 69 200 --

No. averages 26 13

The coal data listed in Table 13-1 were not weighted for coal production by State of coal origin. a

There were only two sets of data for concentrations of trace elements in natural gas in Table 13-1. b

With the exception of the mercury data, coal values were determined from modified U.S. Geological Survey (USGS) data, by c

State of coal origin, and coal shipment dat for coals that originated from three States. Modified USGS data are USGS data that

were modified to account for the effects of bituminous coal cleaning. Mercury data were reported by EPRI for samples of coal

shipments. None of the data were weighted for coal production. 2

Natural gas values were determined from the preliminary EPRI test reports for Sites 120 and 121. The listed values are detected d

concentrations.

Averages of averaged data sets. e

This is the standard deviation of the number of averages directly below. f
13-3

13.1.1.1 Switching to Natural Gas Combustion. As shown in

Table 13-1, natural gas has the lowest average concentrations, on a

lb/trillion Btu basis, of sulfur, arsenic, and mercury when compared

with the corresponding values for residual oil and coal. The averages

listed for coal and residual oil exceed those listed for natural gas

by factors that range from approximately 100 (for the concentration of

arsenic in residual oil) to as much as approximately 21,000 (for the

concentration of sulfur in coal). Thus, of the three utility fuels,

natural gas contains the least amounts of the trace elements, and

switching from coal or residual oil to natural gas combustion would

ultimately reduce emissions of trace elements. Some total and

seasonal conversion of coal- and oil-fired units to natural gas firing

is expected to affect compliance with the various ozone and NO control x

provisions of Titles I and IV of the Act. However, a complete

conversion of all utility boilers to natural gas is not practical. 

Even though the natural gas transmission network is expanding,

delivery of natural gas to each utility unit cannot yet be

accomplished. In addition, there is concern over the long-term

availability of natural gas (particularly with respect to other fuels)

given the projected usage (and increase in usage) in the residential,

commercial, and industrial sectors and the estimates of proved and

supplemental reserves of natural gas. Estimates of “proved reserves”

of natural gas have decreased each year (but one) for the past 10

years.

13.1.1.2 Switching from Coal to Residual Oil Combustion. As

shown in Table 13-1, with the exception of the average concentrations

of nickel, the average concentrations of trace elements listed for

coal exceed those listed for residual oil by factors that range from

approximately 4 (for the concentrations of sulfur and chloride) to as

much as approximately 40 (for the concentration of arsenic). However,

the average concentration of nickel in coal is approximately half the

corresponding value for residual oil. Thus, switching from coal to

residual oil combustion could result in increased emissions of nickel

and decreased emissions of the other trace elements.

13.1.1.3 Switching from Higher to Lower Sulfur Coals. The

effects of coal switching will be reviewed first for mercury and then

for the other trace elements.

Figure 13-1 shows the relationship between the concentrations of

mercury and sulfur in 153 samples of coal shipments. As shown in 2

Figure 13-1, there is no relationship between the sulfur and mercury

content in the sampled coal shipments; mercury concentrations below

approximately 15 lb/trillion Btu are present in coal with both higher

sulfur concentrations (above 2.5 lb/MMBtu) and lower sulfur

concentrations (below 1.5 lb/MMBtu).

A conceivable control strategy would involve blending higher

mercury-containing coals with lower mercury-containing coals to reduce

mercury emissions. Such a practice would be comparable to blending

high and low sulfur-containing coals in order to meet SO emission 2
Figure 13-1. Relation between the concentrations of mercury and sulfur in 153 samples of 

0

102030405060

0 0.5 1 1.5 2 2.5 3 3.5 

Sulfur (lb/MMBtu)

Mercury (lb/trillion Btu)
13-5

limits. However, coal blending for mercury control is not a proven

control strategy. Changes in the electrical resistivity and amount of

flyash resulting from coal blending could reduce PM capture

efficiencies by ESPs and subsequently lead to increased emissions of

PM and HAP metals. However, these effects on ESP performance can be

addressed by gas conditioning and/or modifications to the ESP. 

Blending for mercury control could also increase levels of other HAPs

or sulfur. Another uncertainty with coal blending for mercury control

would be the possibility of changing the distribution of the elemental

and oxidized forms of mercury that could affect mercury control with

existing control devices. Another factor is that the blending of two

different coals might change the higher heating value of the resulting

mixture, with subsequent effects on the quantity of fuel required for

combustion.

The qualitative effects of switching to lower-sulfur-containing

coals on other metallic HAPs are examined in Figure 13-2(a-g) through

plots of the average concentrations of each HAP, excluding mercury,

with sulfur content in coal. As shown in Figure 13-2(a-g), the

average concentration of trace elements in coal shipments, as

approximated by the modified U.S. Geological Survey (USGS) data

(modified for the effect of coal cleaning on bituminous coals),

generally show no clear trends with sulfur content (i.e., decreasing

the sulfur content of coal does not generally lead to reduced

concentrations of trace elements in coal). 

Based upon average concentrations of trace metals in coal from

the modified USGS data, fuel switching to lower-sulfur-containing

coals will not generally result in consistently reduced emissions of

the trace elements. Trace elements associated with the PM (e.g.,

arsenic, cadmium, chromium, lead, and nickel) could be removed from

coal-fired flue gas with a PM control device.

It should be stressed that the effects of coal switching were

drawn from comparisons of average concentrations of trace elements in

modified USGS coal data. The concentrations of trace elements in

actual coal shipments may vary from the USGS averages. 

13.1.2 Coal Cleaning

Approximately 77 percent of eastern and midwestern bituminous 3

coal shipments are cleaned to meet customer specifications on heat,

ash, and sulfur content. Subbituminous and lignite coals are not

routinely cleaned. Conventional coal cleaning removes mineral matter 4

and, in the process, may also remove some of the trace elements

contained in the mineral matter. The mineral matter is removed from

the coal by either crushing and screening or by coal washing (through

the difference in specific gravities of the constituents or by

surface-based floatation). In the process of removing the mineral 5

matter, coal cleaning generates solid refuse that contains trace

elements; the solid refuse must be disposed of properly. Any coal

cleaning liquid wastes will also contain trace elements, but the

liquid wastes may be properly clarified and then recycled.


13-8

Table 13-2 lists the limited amount of available data on trace

element reductions achieved through conventional coal cleaning. In

Table 13-2, some of the trace element reductions are negative. 

Negative percentages occur when part of the coal is removed but the

element is not contained in the extracted portion of the coal, so that

the same weight of the element that was contained in the uncleaned

coal is contained within a relatively smaller weight of the cleaned

coal. Because the weight of the trace element does not change,

negative removal percentages are considered to indicate that no trace

element reduction occurred or that the trace element reduction was

effectively 0 percent. 

As shown in Table 13-2, for the limited amount of available data,

trace element removal percentages may vary for coals obtained from the

same seam. The variability occurs because trace element

concentrations, in the mineral portion of coal, vary from coal to

coal. For the data listed in Table 13-2, the variation in trace

element reductions may also be due to the use of various cleaning

methods, the accuracy of the analytical techniques used to measure

concentrations of trace elements in cleaned and uncleaned coals, and

sample collection methods. With regard to analytical techniques,

CONSOL, Inc., noted the following difficulties in analyzing mercury

concentrations in coal: the volatility of mercury compounds, low

mercury concentrations in coal, large variability of approximately 50

percent in the interlaboratory reproducibility of mercury

concentrations, lack of certified mercury-in-coal standards, and lack

of standard sample preparation and analysis methods.6,7

The average trace element reductions, listed in Table 13-2 for a

limited amount of available data, were determined with the negative

percentages treated as 0 percent removal and the averages not weighted

by coal production since the EPA does not believe sufficient data

exist at this time to follow a production-weighted approach. The

average values for the limited amount of available data indicate that,

in general, lead concentrations were reduced the most (approximately

55 percent) while mercury concentrations were, on average, reduced the

least (approximately 21 percent). It should be stressed that better

and worse trace element reductions may be found for specific coals. 

For instance, mercury removals of at least 50 percent should occur

during conventional cleaning of Upper Freeport coal, based upon the

modes of occurrence of mercury and available conventional coal

cleaning data. In Table 13-2, mercury removals were reported for 8

three samples of Upper Freeport coal; two indicated approximately

62 percent removal while the third indicated effectively no mercury

removal. More research is needed to establish trace element removal

efficiencies, achieved through conventional coal cleaning, on a

statistically representative sampling of commercially viable coal

seams. Additional information on advanced coal cleaning is provided

in section 13.6.1.
Table 13-2. Trace Element Reductions Achieved Through Conventional Coa

Seam State Reference

% Removal 

arsenic

% Removal 

cadmium

% Removal 

chromium

% Removal 

fluoride

% Removal 

lead

% Removal 

mercury

% 

C. App. A a 39 14 75 57 -11 C. App. B a 22 33 67 65 8.3 IL IL e 54 59 21 38 55 IL #6 IL a 47 76 72 35 43 IL #6 IL c 3 32 23 27 37 -8.3 IL 2,3,5 IL d 30 44 41 68 17 IL 2,3,5 IL d 48 46 60 59 42 Ky #11 Ky d 52 60 83 88 Ky #11 Ky d 13 69 92 84 20 Ky #9 & 14 Ky c 40 53 29 42 49 13 Lower Kittanning Pa c 66 43 44 64 59 23 Pittsburgh Pa b 74 71 -459 67 20 Pittsburgh Pa c 53 57 56 67 65 15 Pittsburgh Pa c 27 20 24 30 34 7.7 Pittsburgh A Pa a 65 40 42 37 27 Pittsburgh B Pa a 67 58 66 69 36 Pittsburgh C Pa a 67 63 63 63 7.1 Pittsburgh D Pa a 77 55 64 63 -20 Pittsburgh E Pa a 50 50 71 72 20 Pratt Al d 29 0 48 19 58 3.4 Pratt Al d 7.1 0 54 54 24 29 Pratt/Utley Al d 29 0 41 65 53 21 Sewickley Pa c 36 52 46 57 94 0 Upper Freeport Pa b 38 11 47 53 -200 Upper Freeport Pa d 73 40 50 80 8.3 64 Upper Freeport Pa d 74 42 45 75 61 60 Utley Al d 23 0 17 6.5 28 21 All seams:

min 3.4 0 11 -459 8.3 -200 

max 77 76 75 92 94 64 

average 45 38 49 50 55 21 

a Negative percentages are listed as entries. However, averages were determined with negative percentages treated as zero removal.
13-10

Table 13-2. (continued)ReferencesData for references a - d were taken from the report:

Akers, David, Robert Dospoy, and Clifford Raleigh, The Effect of Coal Cleaning on Trace Elements, Draft Report, Development of A

1993, prepared for EPRI by CQ Inc.

Data for reference e were taken from the report:

Demir, Ilham, Richard D. Harvey, Rodney R. Ruch, Heinz H. Damberger, Chusak Chaven, John D. Steele, Wayne T. Frankie, Ken K

Available Coals from Illinois Mines," draft report, December 28, 1993, Illinois State Geological Survey file number to be assigned.

Specific references that were mentioned in the report by Akers, Dospoy, and Raleigh:

DeVito, M., L. Rosendale, and V. Conrad, "Comparison of Trace Element Contents of Raw and Clean Commercial Coals," presented a

a

Trace Elements in Coal-Fired Power Systems, Scottsdale, AZ, April 1993.Ford, C. and A. Price, "Evaluation of the Effects of Coal Cleaning on Fugitive Elements: Final Report, Phase III," DOE/EV/04427-62, J

c
13-11

Although there is variability in trace element reductions, the

data suggest that coal cleaning techniques may be useful in reducing

trace element concentrations in selected coals. More studies are

needed on diverse samples of coal to establish the effectiveness of

coal cleaning in reducing trace element concentrations and to

determine the causes of variability in cleaning effectiveness.

13.1.3 Coal Gasification

Coal gasification converts coal to a syngas-fuel form that emits

lower quantities of pollutants at the utility boiler than if the coal

were not converted. Although there are some disadvantages to this

process (the cost of gasification and the addition of another

combustion source), the total quantity of air pollutants emitted from

the combination of gasification and combustion is expected to be lower

than burning coal in a conventional system.9,10

The gasification process typically described for near-term

generation projects uses integrated gasification combined cycle (IGCC)

technology and conventional cold-gas cleanup. In this process, gas

from coal is used to generate electricity from both a steam turbine

and a gas turbine. Steps in the process, shown in Figure 13-3,

include coal preparation, coal oxidation and gasification, gas

cooling, and gas cleanup. A large part of the pollutant mass is

transferred to the slag or ash produced during gasification, and more

of the impurities are transferred to water streams used in the gas

cleanup. Slag or ash from the gasification step may be treated for

recovery of salable products, and the stream from gas cleanup may be

treated for recovery of sulfur. Heat transferred from the cooling

step is used to produce steam for the steam turbine generator, while

fuel gas made from the coal is burned to produce more electricity from

the gas turbine generator. The IGCC technology can produce up to 25

percent more electricity from a given amount of coal than is currently

obtained from conventional boilers.9

Statements by the DOE suggest that IGCC technology is almost 9

certain to be one of the lowest-cost fossil fuel options for

generating electricity in the 21st century. When used to refurbish an

existing plant, the technology is less expensive than building a

conventional coal-fired plant. Other claims include higher thermal

efficiency (to about 40 or 45 percent from about 35 percent), higher

plant output (by 50 to 150 percent), and lower SO and NO emissions as 2 x

described above. The IGCC process has been demonstrated in a limited

number of commercial-scale projects. In addition, IGCC is being

utilized in several DOE CCT projects.
13-12

Figure 13-3. Coal gasification combined cycle technology.

There are limited data available on the impact of IGCC on HAP

emissions. Experiments with a different gasifier, an air-blown,

fixed-bed gasifier coupled to a turbine simulator, produced trace

metal concentrations as shown in Table 13-3. The hot gas from the 10

gasifier was treated in a moving bed with zinc titanate sorbent, 

13.2 COMBUSTION CONTROL

Combustion control deals with the effect of furnace type (firing

method and bottom type) and furnace modifications (such as the

addition of low-NO burners) on HAP formation. Since the recent x

emission testing on utility units provided a significant amount of

information on the generation and control of trace metals but

considerably less information on organic HAPs, trace metals are used 
13-13

Table 13-3. Emissions from an Air-Blown, Fixed-Bed Gasifier

Trace metal Emissions to flare, μg/Nm simulator, μg/Nm Total air, μg/Nm 3

Emissions from turbine 3 3

Arsenic 639 8 647

Cadmium 16 0.19 16.2

Chromium 155 20 175

Mercury 20 2 22

Nickel 1,530 26 1,556

Selenium 68 0.56 68.6

to analyze the effect of combustion control. The trace metals for

coal-fired units examined in this section are arsenic, beryllium,

cadmium, chromium, lead, manganese, and mercury; those for oil-fired

units are arsenic, lead, nickel, and mercury.

While the majority of recently collected HAP data has focused on

metals, some small-scale studies have been conducted to evaluate

changes in combustion conditions on organic HAPs. In one test, coal

was burned at normal and elevated excess air levels and with air

staging to simulate combustion modification NO controls. A large x 11

number of organic HAPs were sampled in each case. Some increases in

HAP emissions were noted for the air-staging conditions, but the

conclusion was that this increase would not result in emissions at

significant levels, even for a large utility boiler. During a second

small-scale test, combustion conditions were varied between very high

excess air and substoichiometric conditions. This study concluded 12

that low-NO firing conditions did not necessarily exacerbate emissions x

of organic HAPs. In both studies, the organic emissions were found to

be one or more orders of magnitude less than emissions of any of the

metallic HAPs, even under the worst combustion conditions tested. 

Although these results are from small-scale units and are relatively

limited in their scope, they provide additional information supporting

the position that, in general, organic HAP emissions are not likely to

increase significantly due to the installation of low-NO combustion x

equipment.

The effect of NO control on metallic HAP generation was examined x

by developing an average emission output in lb/trillion Btu from one

oil- and several coal-fired units. These units were tested before and

after the installation of NO control or after the addition of greater x

NO control to an existing NO x x-controlled unit. As shown in Table 13-4,

there appears to be a trend toward reductions in HAP emissions through

the addition of NO control. However, this trend is neither uniform x
Table 13-4. Comparison of Electric Utility Emissions Before and After Application of NO Control x

or Application of Greater NO Control on a Unit With Lesser NO Control (lb/trillion Btu) x x 13-16

13,14,15,16

Trace metal burners) offset air) change control) burners) change control) service) change air) burners) change

EPRI Site EPRI Site 110 EPRI Site 114 EPRI Site 114 EPRI Site EPRI Site 13 (with

110 (with (with low NO 110 (without (with overfire 114 EPRI Site 13 (with burners EPRI Site 13 EPRI Site 16 overfire air EPRI Site 16

low NO burners and percentage NO air and reburn percentage (without NO out of percentage (with overfire and low NO percentage x

x

EPRI Site EPRI Site 16

x x x

Arsenic 141.73 64.15 -121% 137.22 151.4 9% 7.1 3.7 -92% 1789.01 1805.89 1%

Beryllium 43.87 45.26 3% 39.86 36.21 -10% ND ND ND 108.33 130.43 17%

Cadmium 448.83 120.9 -271% 17.38 20.41 15% 10.13 13.84 27% 10.93 21.07 48%

Chromium 745.12 743.02 -0.28% 241.77 164.57 -47% 2.95 8.95 67% 934.26 845.76 -10%

Lead 878.75 503.23 -75% 1365.7 1007.17 -36% 8.02 4.6 -74% 437.31 351.15 -25%

Manganese 1021.84 999.7 -2% 392.1 375.22 -4% 4.7 7.84 40% 745.42 890.91 16%

Mercury 3.58 0.02 -17981% 6.47 4.48 -44% 0.23 0.17 -36% 7.06 10.74 34% a

Nickel 538.55 511.13 -5% 1073 1437.58 25% 1827.15 1355 -35% 664.17 655.97 -1%

Average   -67%   -11%   -15%   10%

Overall

Percentage

Change

Lesser Greater NO Lesser Greater NO Lesser NO Greater NO Lesser NO Greater NO

NO control NO control control control control control x

control control

x

x

x x x x x

ND = This trace metal was below the detection limit in both the coal feed and the boiler exit emissions on both tests.

This percentage difference seems too large and is not used in the average overall percentage change calculation. a
13-15

(see arsenic, beryllium, and cadmium percentage change between

Sites 110 and 114) nor universal (see Site 16 compared to all

other sites). The differences in the percentage change could be

due to the variability of trace metal concentrations in the oil

or in different sources of bituminous coal; differences in the

carbon, chloride, or ash content of the coal burned; differences

in the age or operating condition of the units; or a combination of

all of these or other factors.

The effect of the bottom type (furnace type) on HAP generation

was examined by analyzing an average emission output of coal-fired

units (see Table 13-5) with either a wet bottom furnace or a dry

bottom furnace, both burning pulverized coal. Emissions were further

segregated by coal type. Since there was only one test done on a

conventional, dry bottom, lignite-fired unit, no lignite-fired units

were analyzed. Site 111, which burned a mixture of bituminous and

subbituminous coal, was grouped with the subbituminous coal-fired

units for this analysis. To support the emission results, the EMFs

for these furnaces and the concentrations of the trace metals found in

feed coal are also included in Table 13-5. In this way, the effects

of furnace type and low-NO operation and of trace metal concentration x

in the coal can be observed. Furnace type, with and without low-NO x

operation, may affect the partitioning of ash between bottom ash and

fly ash. Trace metal concentration in the coal affects the trace

metal concentration in the flue gas in either vapor or solid form. 

Oil-fired units could not be separated into wet or dry bottom

configurations, as all oil-fired units use dry bottom furnaces. 

Therefore, the impact on HAP emissions of bottom type is not addressed

for oil-fired boilers. 

When units firing bituminous and subbituminous coal were

analyzed, their averages showed that arsenic, lead, and mercury seemed

to be emitted in higher amounts by wet bottom units while beryllium,

cadmium, chromium, and manganese seemed to be emitted in higher

amounts by dry bottom units (see Figure 13-4a). When units firing

only bituminous coal were analyzed, the same effect was observed (see

Figure 13-5a). When units firing only subbituminous coal were

analyzed, their averages showed that emissions of almost all HAPs

stated above were emitted in higher amounts from dry bottom units than

from wet bottom units (see Figure 13-6a). Mercury was the exception,

being emitted in similar amounts by both bottom types. The percent

removal by bottom type and the concentrations of trace metals in the

feed coal show a logical relationship between the trace metal

concentration observed in the feed coal and the amount emitted from

the boiler. This relationship appears to be a consequence of the ash

and trace metal partitioning in the boiler.
Table 13-5. Comparison of wet bottom vs. dry bottom electric utility boilers metallic HAP emissions, trace element 

removal, and trace metal concentrations in feed coal 13,14,16-33 

Compound

EPRI Site 114 

(without NOx 

control)

EPRI Site 114 

(with overfire air 

and reburn 

burners)

DOE Niles 

test (with 

SNOX SCR) DOE Niles

Northern 

States Power 

Riverside 8

EPRI Site 102 

(same as NSP 

A.S. King) DOE Boswell

Bottom Type WET WET WET WET WET WET WET

Furnace Type Cyclone Cyclone Cyclone Cyclone Cyclone Cyclone Cyclone

Fuel type BIT BIT BIT BIT SUB SUB SUB

Total bituminous & 

subbituminous Bituminous Subbituminous

Boiler emissions (lb/trillion Btu) Average emissions Average emissions Average emissions

Arsenic 137.2 151.4 2349.3 1583.3 32.4 51.4 34.4 620 1055 39

Beryllium 39.9 36.2 55.6 41.3 5.0 3.1 11.7 28 43 7

Cadmium 17.4 20.4 4.8 2.6 2.1 1.9 11.0 9 11 5

Chromium 241.8 164.6 434.0 366.1 54.7 146.0 446.7 265 302 216

Lead 1365.7 1007.2 637.5 593.0 56.3 152.0 176.5 570 901 128

Manganese 392.1 375.2 420.4 322.5 66.4 599.0 6278.5 1208 378 2315

Mercury 6.5 4.5 21.3 19.7 3.0 6.4 4.7 9 13 5

Boiler emission modification factors

Average percent removal by 

boiler (1-average EMF)

Average percent removal by 

boiler (1-average EMF)

Average percent removal by 

boiler (1-average EMF)

Arsenic 0.15 0.25 0.85 0.58 0.51 0.48 0.23 56% 54% 59%

Beryllium 0.15 0.15 0.30 0.26 0.08 0.04 0.60 77% 79% 76%

Cadmium 0.01 0.01 0.20 0.11 0.16 0.02 1.00 79% 92% 61%

Chromium 0.30 0.23 0.35 0.28 0.22 0.25 1.00 63% 71% 51%

Lead 0.50 0.84 0.60 0.56 0.38 0.61 0.42 44% 38% 53%

Manganese 0.20 0.18 0.19 0.15 0.13 0.33 0.57 75% 82% 66%

Mercury 0.73 0.54 1.00 1.00 1.00 1.00 0.87 12% 18% 4%

Trace metal concentrations in feed coal (microgram/gram)

Average trace metal 

concentrations in feed coal 

(microgram/gram)

Average trace metal 

concentrations in feed coal 

(microgram/gram)

Average trace metal 

concentrations in feed coal 

(microgram/gram)

Arsenic 12.0 8.1 34.0 33.3 0.7 1.3 1.4 13.0 21.9 1.1

Beryllium 3.6 3.3 2.3 1.9 0.7 0.9 0.2 1.8 2.8 0.6

Cadmium 39.0 47.0 0.3 0.3 0.2 1.0 0.1 12.5 21.7 0.4

Chromium 11.0 9.4 15.3 16.0 2.7 7.0 3.2 9.2 12.9 4.3

Lead 37.0 16.0 13.0 13.0 1.6 3.0 3.9 12.5 19.8 2.8

Manganese 26.5 27.0 26.7 25.3 5.5 22.0 102.0 33.6 26.4 43.2

Mercury 0.12 0.11 0.26 0.21 0.03 0.06 0.05 0.12 0.17 0.05

BIT = bituminousSUB = subbituminousSNOX = wet sulfuric acid-selective catalytic reduction process
Table 13-5. Continued

Compound

EPRI Site 110 

(w/LNB and 

offset air)

EPRI Site 110 

(w/LNB) DOE Yates EPRI Site 15

DOE 

Cardinal EPRI Site 116 EPRI Site 12 EPRI Site 14

EPRI Site 

115

EPRI Site 16 

OFA

EPRI Site 16 

OFA/LNB

Bottom Type DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY DRY

Furnace Type Tangential Tangential Tangential Tangential Opposed Front Opposed Opposed Vertical Opposed Opposed

Fuel type BIT BIT BIT BIT BIT BIT BIT BIT BIT BIT BIT

Boiler emissions (lb/trillion Btu)

Arsenic 64.2 141.7 393.1 598.9 1043.2 61.5 498.4 230.4 38.2 1789.0 1805.9

Beryllium 45.3 43.9 90.1 45.8 113.0 4.1 58.6 30.1 17.5 108.3 130.4

Cadmium 120.9 448.8 23.8 7.4 32.4 0.6 33.8 5.3 4.0 10.9 21.1

Chromium 743.0 745.1 2798.8 1164.3 976.0 71.8 1252.7 735.6 87.5 934.3 845.8

Lead 503.2 878.8 688.6 811.3 623.0 23.9 381.2 115.2 167.1 437.3 351.2

Manganese 999.7 1021.8 2055.9 1752.6 1350.8 71.8 1723.6 1152.2 334.3 745.4 890.9

Mercury 0.02 12.4 1.7 2.2 7.6 0.8 1.7 7.1 10.7

Boiler emission modification factors

Arsenic 0.89 0.39 1.00 0.60 0.91 0.70 1.00 0.50 0.61 1.00 1.00

Beryllium 0.93 0.43 1.00 0.54 0.96 0.35 1.00 0.92 0.52 1.00 0.82

Cadmium 1.00 0.70 1.00 0.01 1.00 0.12 0.14 0.02 0.58 1.00 0.11

Chromium 1.00 1.00 1.00 0.58 0.61 0.27 1.00 0.67 0.57 0.58 0.69

Lead 1.00 0.36 1.00 1.00 1.00 0.26 1.00 0.79 0.38 1.00 0.66

Manganese 0.71 0.76 1.00 0.81 0.27 0.21 1.00 0.93 0.58 0.60 0.88

Mercury 0.66 1.00 0.41 0.97 0.74 0.74 0.78 0.64 1.00

Trace metal concentrations in feed coal (microgram/gram)

Arsenic 2.0 1.9 2.3 12.9 5.0 6.2 6.3 0.5 17.0 23.0

Beryllium 1.3 0.6 1.1 1.1 1.3 0.7 0.7 0.5 0.2 1.4 2.2

Cadmium 2.1 1.7 0.3 8.0 0.1 0.3 3.4 3.0 0.1 0.1 2.6

Chromium 9.0 5.5 24.8 26.0 18.0 15.0 17.0 15.0 1.1 22.0 17.0

Lead 17.0 5.0 8.0 4.0 6.8 5.3 2.4 2.0 2.1 5.1 7.3

Manganese 16.0 17.0 23.4 28.0 57.3 19.0 18.0 17.0 4.2 17.0 14.0

Mercury 0.08 0.08 0.05 0.13 0.14 0.24 0.02 0.15 0.14

BIT = bituminousSUB = subbituminousSNOX = wet sulfuric acid-selective catalytic reduction process
Table 13-5. Continued

Compound EPRI Site 11 EPRI Site 111

DOE 

Springerville

NSP 

Sherburne 3

NSP 

Riverside 6,7

Bottom Type DRY DRY DRY DRY DRY

Furnace Type Tangential Opposed Tangential Opposed Front

Fuel type SUB SUB (BIT Mixed) SUB SUB SUB

Total bituminous & 

subbituminous Bituminous Subbituminous

Boiler emissions (lb/trillion Btu) Average emissions Average emissions Average emissions

Arsenic 124.1 5.8 81.0 64.4 47.1 437 606 64

Beryllium 9.9 93.4 21.8 9.4 55 62 34

Cadmium 58.7 16.6 172.4 7.0 10.3 61 64 53

Chromium 248.2 607.0 253.6 326.7 786 941 359

Lead 143.1 91.0 525.4 236.1 398 453 249

Manganese 1406.5 5627.6 4978.2 480.0 1639 1100 3123

Mercury 5.4 6.0 4.3 5 5 5

Boiler emission modification factors

Average percent removal by 

boiler (1-average EMF)

Average percent removal by 

boiler (1-average EMF)

Average percent removal by 

boiler (1-average EMF)

Arsenic 0.92 0.11 0.29 0.79 0.99 27% 22% 38%

Beryllium 0.79 0.87 0.58 0.40 24% 23% 34%

Cadmium 0.35 0.05 1.00 0.99 0.25 39% 48% 47%

Chromium 0.72 0.68 0.49 1.00 23% 27% 28%

Lead 1.00 0.19 0.49 0.19 30% 23% 53%

Manganese 0.98 0.72 0.00 0.77 24% 29% 38%

Mercury 1.00 1.00 1.00 19% 23% 0%

Trace metal concentrations in feed coal (microgram/gram)

Average trace metal 

concentrations in feed coal 

(microgram/gram)

Average trace metal 

concentrations in feed coal 

(microgram/gram)

Average trace metal 

concentrations in feed coal 

(microgram/gram)

Arsenic 1.6 0.6 1.5 0.7 0.4 5.5 7.7 1.0

Beryllium 0.2 1.1 0.3 0.2 0.9 1.0 0.4

Cadmium 2.0 4.0 0.5 0.1 0.4 1.8 2.0 1.4

Chromium 4.1 9.2 4.4 2.6 12.7 15.5 5.1

Lead 1.4 5.0 9.1 10.5 6.1 5.9 6.5

Manganese 17.0 80.8 5.4 23.9 21.0 34.4

Mercury 0.04 0.02 0.001 0.09 0.11 0.02

BIT = bituminousSUB = subbituminousSNOX = wet sulfuric acid-selective catalytic reduction process
Figure 13-4 (a-c). Average coal-fired boiler emissions, trace metal removal, and average trace element 

concentration in feed coal vs. bottom type (bituminous and subbituminous coal)

Figure 13-4a. Average boiler emissions

437

55 61

786

398

1639

5

620

28 9

265

570

1208

9

0

200

400

600

800

1000

1200

1400

1600

1800

Arsenic Beryllium Cadmium Chromium Lead Manganese Mercury

lb/trillion Btu

dry bottom

wet bottom

Figure 13-4b. Average trace metal removal by boiler

27% 26%

48%

27% 31%

27%

17%

56%

77% 79%

63%

44%

75%

12%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Arsenic Beryllium Cadmium Chromium Lead Manganese Mercury

percent removal

dry bottom

wet bottom

Figure 13-4c. Average trace metal concentration in feed coal

5.5

0.9

1.8

12.7

6.1

23.9

13.0

1.8

12.5

9.2

12.5

33.6

0.090.12

0

5

10

15

20

25

30

35

40

Arsenic Beryllium Cadmium Chromium Lead Manganese Mercury

microgram/gram

dry bottom

wet bottom

Note: Data taken from Table 13-5.
Figure 13-5 (a-c). Average coal-fired boiler emissions, trace metal removal, and average trace element 

concentration in feed coal vs. bottom type (bituminous coal-fired only)

Figure 13-5a. Average boiler emissions

606

62 64

941

453

1100

5

1055

43 11

302

901

378

13

0

200

400

600

800

1000

1200

Arsenic Beryllium Cadmium Chromium Lead Manganese Mercury

lb/trillion Btu

dry bottom

wet bottom

Figure 13-5b. Average trace metal removal by boiler

22% 23%

48%

27% 23%

29%

23%

54%

79%

92%

71%

38%

82%

18%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Arsenic Beryllium Cadmium Chromium Lead Manganese Mercury

percent removal

dry bottom

wet bottom

Figure 13-5c. Average trace metal concentration in feed coal

7.7

1.0

2.0

15.5

5.9

21.0

21.9

2.8

21.7

12.9

19.8

0.11

0.17

26.4

0

5

10

15

20

25

30

Arsenic Beryllium Cadmium Chromium Lead Manganese Mercury

microgram/gram

dry bottom

wet bottom

Note: Data taken from Table 13-5.
Figure 13-6 (a-c). Average coal-fired boiler emissions, trace metal removal, and average trace element 

concentration in feed coal vs. bottom type (subbituminous coal-fired only)

Figure 13-6a. Average boiler emissions

64 34 53

359 249

3123

39 7 5 5

216 128

2315

5

0

500

1000

1500

2000

2500

3000

3500

Arsenic Beryllium Cadmium Chromium Lead Manganese Mercury

lb/trillion Btu

dry bottom

wet bottom

Figure 13-6b. Average trace metal removal by boiler

38%

34%

47%

28%

53%

18%

0%

59%

76%

61%

51% 53%

66%

4%

0%

10%

20%

30%

40%

50%

60%

70%

80%

Arsenic Beryllium Cadmium Chromium Lead Manganese Mercury

percent removal

dry bottom

wet bottom

Figure 13-6c. Average trace metal concentration in feed coal

1.0

0.4

1.4

5.1

6.5

34.4

1.1

0.6

0.4

4.3

2.8

43.2

0.04

0.05

0

5

10

15

20

25

30

35

40

45

50

Arsenic Beryllium Cadmium Chromium Lead Manganese Mercury

microgram/gram

dry bottom

wet bottom

Note: Data taken from Table 13-5.
13-22

Based on this analysis, wet bottom furnaces seem to have better

trace metal removal than dry bottom furnaces. Trace metal removal in

a furnace is likely due to the partioning mentioned above. A possible

explanation for the effect would be that bottom ash in a wet bottom

furnace is kept in a molten state, and, thus, the trace metals in the

bottom ash are less likely to reentrain into the fly ash. It needs to

be emphasized that these analyses are based on limited data and may

not hold true for all units and coals. More data and analyses are

needed.

It needs to be noted that the averages in Figures 13-4(a and b),

13-5(a and b), and 13-6(a and b) were computed from data with EMFs

limited to a maximum of 1.0, meaning that no more HAP could exit a

device than entered it. All of the data used in these figures had at

least one instance in which an EMF of 1.0 was used. This situation

did not occur in Figures 13-4(c), 13-5(c), or 13-6(c) because these

data were taken directly from the coal feed without modification. The

result of this methodology is several sets of data averages where more

HAP is emitted than was present in the feed coal. These data averages

were composed of a large number of EMFs of 1.0.

13.3 POSTCOMBUSTION CONTROL

To comply with various local, State, and Federal requirements,

utilities routinely use postcombustion technologies for the control of

PM and SO . The following sections assess how different APCDs affect 2

removal of selected HAPs from fossil-fuel–fired electric utility flue gas.

13.3.1 Particulate Phase Controls

Figures 13-7 through 13-14 and Tables 13-6 through 13-9 show the

relationship between the HAP metal removal and PM collection efficiency of

different particulate controls (namely ESPs and FFs). The HAP removal

effectiveness is shown in the tables in this section as percent removal. 

Percent removal is equivalent to 1 minus the EMF (see chapter 3, section

3.4.6). A 90 percent removal indicates that 90 percent of that HAP has

been collected by a PM control device. The HAP metals that exist

primarily in particulate form are readily controlled by PM control

devices. These HAPs include arsenic, beryllium, cadmium, chromium, lead,

and manganese. Table 13-10 shows the percentage of data, for all listed

HAPs other than mercury, with a control device HAP removal efficiency

greater than 90 percent. For example, 90 percent of the particulate from

metallic HAPs data points for cold-side ESPs fall into the 90 percent or

better removal category. For the two oil-fired sites for which ESP

removal data were available, the control of particulate metallic HAPs was

not clear. It should be noted that the concentrations of metallic HAPs in

oil, with the exception of nickel, which is not discussed here, are

significantly lower than those in coal, and the fuel-ash characteristics

are also quite different. These factors could explain the spread of PM

HAP removal of 51 to 93 percent. 




13-27

Table 13-10. Particulate Metallic HAP Removal Percentage from ESPs

and FFs (Excluding Mercury)

Particulate control device Number of data removal efficiency 

(coal) points greater than 90 percent

Percentage of data with a HAP

ESP (cold-side) 108 89

ESP (hot-side) 12 92

FF 33 89

ESP = electrostatic precipitator

FF = fabric filter

HAP = hazardous air pollutant

Mercury, however, is not well controlled by PM APCDs. This

situation would be expected because mercury is emitted as a mixture of

solid and gaseous forms. Mercury removals and current investigations

on the control of mercury are further discussed in section 13.6.

Dioxin removal in utility boiler PM control equipment has been

measured at one coal-fired boiler and one oil-fired boiler. In both

cases, measurements contained many values of uncertain accuracy. 

However, removal efficiencies could be estimated for one dioxin and

three furans at the coal-fired boiler. This unit was a 615-MWe boiler

firing Pennsylvania bituminous coal that had an ESP with an overall PM

efficiency of 99 percent. The ESP’s apparent efficiency for the

following compounds was:

 1,2,3,4,6,7,8-heptachlorodibenzo-p-dioxin 7 percent

 2,3,7,8-tetrachlorodibenzofuran 38 percent

 2,3,4,6,7,8-hexachlorodibenzofuran 35 percent

 1,2,3,4,6,7,8-heptachlorodibenzofuran 29 percent.

The oil-fired boiler was an 850-MWe unit firing residual oil and

had an ESP with an overall PM collection efficiency of 92 percent. 

All of the measurements for this site indicated negative efficiencies

for dioxins and furans in the ESP.

Under certain conditions in MWC systems, dioxins and furans can

be formed in the particulate-laden flue gas stream upstream of or

within the associated PM control equipment at temperatures in the

range of 480to 1,020F (250to 550C). Units that are equipped 42-4442,43,44

with hot-side ESPs (ESPs upstream of the air preheater operating at

temperatures in the range of 600 to 750F [316to 400C]) are of
13-28

particular concern with regard to this formation mechanism since their

operating temperatures typically fall within this range. Little

information is available regarding dioxin formation in utility

particulate removal equipment, however, and additional information is

needed to adequately quantify the potential for dioxin formation in

utility pollution control systems. Thus, at utility plants, any

strategy for dioxin control must consider adequately treating large

volumes of gas in order to remove relatively small concentrations of

dioxin.

13.3.2 Vapor Phase Controls

Figures 13-15 through 13-18 and Tables 13-11 and 13-12 show the ,45 46

relationship between HAP metal removal and the inlet temperature for

SO control devices. The correlation between FGD scrubber inlet 2

temperature and HAP metal removal is difficult to determine. This

difficulty is compounded by having a maximum of eight data sites at

which four of the eight test sites employ flue gas bypass in their

design. A bypass means that part of the flue gas is diverted around

the FGD or SDA/FF while the majority of the flue gas is treated by

these control devices. Bypass is used to minimize the size and the

lime/limestone cost of the FGD unit while still meeting SO emission 2

limits. Another factor is that FGDs usually follow an ESP or an FF;

thus, the concentrations of metallic HAPs that reach the FGDs are

generally less than 10 percent of the amounts intercepted by primary

PM control devices.

The HAP metal removal by SDA/FF-equipped units seems to follow

the results found in FF PM-controlled units, i.e., metallic HAPs are

controlled to approximately 90 percent or better. This situation

follows because an SDA/FF contains an FF.

13.3.3 Acid Gas Control

There was a limited amount of data (using EPA Method 26a)

available on the removal efficiencies for HCl and HF from air

pollution control devices. Since utilities operate with varying

amounts of bypass, removal percentages for FGDs and SDA/FFs were

estimated for operations with 15 percent and 14 percent bypass,

respectively. The test report data indicated that: ESPs removed less

than 6 percent of the acid gases; FFs removed approximately 44 percent

of the HCl and essentially none of the HF; an FGD with 15 percent

bypass was estimated to remove approximately 80 percent of the HCl and

approximately 29 percent of the HF; and an SDA/FF with 14 percent

bypass was estimated to remove approximately 82 percent of each acid

gas. Despite the inconsistencies in removal efficiencies achieved 47

for HCl and HF with FFs and FGDs, the data indicate that the SO2

control devices remove more of the acid gases than do PM controls.


13-31

13.3.4 Carbon Adsorption

A possible way of further reducing the amount of vapor phase HAPs

emitted from utilities is through the use of carbon adsorption. 

Activated carbon (AC) is a specialized form of carbon produced by

pyrolyzing coal or various hard, vegetative materials (e.g., wood) to

remove volatile material. The resulting char then undergoes a steam

or chemical activation process to produce an AC that contains multiple

internal pores and has a very high specific surface area. With this

internal pore structure, the AC can adsorb a broad range of

contaminants. 

Activated carbon can be introduced through the use of a fixedcarbon filter bed, a moving bed, a fluidized carbon filter bed, or

through direct AC injection into the flue gas stream.

Other than for mercury (see section 13.6.1.2), no utility data

were found for AC removal of HAPs. However, for other industries

(e.g., MWCs and medical waste incinerators [MWIs]), dioxin removal

from the flue gas stream has also been achieved by AC injection.

13.4 ALTERNATIVE CONTROLS

An alternative to pollution control systems applied directly to

boiler systems is to use alternative, nontraditional control methods

such as demand side management (DSM) and energy conservation. Demand

side management addresses the issue of reducing potential HAP

emissions by reducing the amount of electricity needed. This

objective can be accomplished by several different methods.

Through the use of progressively higher efficiency electrical

devices (e.g., more efficient home appliances, lighting systems, and

industrial machinery), the overall tonnage of fossil fuel burned would

be reduced. Also, campaigns to reduce the use of fossil-fuel– 51

generated electricity have a positive effect on reducing HAPs by

slowing down the necessity of building additional plants. Research

into more efficient electrical generators and transmission equipment

could produce units that deliver the same amount of energy for less

fuel. 52

Another potential fuel option is the use of liquid or gaseous

fuels derived from biomass. Currently, fuels such as ethanol and

methanol, derived from corn, grains, and other crops, are being used

to produce “gasohol” (a blend of up to 10 percent ethanol in

gasoline). Future use of these fuels by utilities, as well as

hydrogen fuels derived from biomass, could reduce (but perhaps not

eliminate) HAP emissions. However, DOE has estimated that development

of technologies to produce sufficient quantities of biomass-derived

fuels may not be commercially viable until 2030. Other assessments 53

indicate that with realistic investments in research and development
13-32

in both crop production and generation techniques, biomass could be

competitive (without subsidies) in niche markets within 5 years (with

high energy prices, which are not currently forecast) and within 10

years in larger markets.54

Another method would be to switch to a source of renewable energy

(e.g., wind, solar), but to date these methods have been limited to

certain geographic locations only. (However, such campaigns may also

lead to slower introduction of new, more efficient fossil fuel-burning

technologies.)

Future potential electrical transmission equipment could include

the development and use of superconductive power lines, which could

substantially reduce the amount of electricity that needs to be

generated to move the high voltage electricity through power lines

because of the negligible power loss (due to lower resistance). 55

13.5 POLLUTANT TRADEOFFS

13.5.1 HAP Increase/Decrease

The various strategies for limiting HAP emissions, discussed in

sections 13.1 through 13.4, have different effects in controlling air

emissions of all HAPs. Table 13-13 presents the qualitative effects

of the different control strategies on air emissions.

Table 13-13 provides a comparison of HAP removal effectiveness of

different existing and alternative control strategies. As shown in

Table 13-13, the effect on HAP emissions of: (1) switching from a

higher- to a lower-sulfur coal, (2) NO controls, and (3) boiler types x

cannot be predicted from the available data. Techniques that would

reduce emissions of the HAPs of concern include: (1) switching from

coal or oil to natural gas; (2) coal gasification; and (3) alternative

controls, such as energy conservation or DSM. The remaining

strategies control certain HAPs. Existing PM control devices, such as

ESPs and FFs, generally do not remove the vapor-phase HAPs (i.e.,

organics, elemental mercury, HCl, and HF). (However, these controls

do provide some reduction of ionic mercury emissions.) Emissions of

the vapor-phase HAP, dioxin, are not controlled by ESPs but are

controlled to some extent with FFs because dioxins adsorb onto the

filter cake. As noted in section 13.3.1, there is the potential for

dioxins to be produced in hot-side ESPs due to the temperature and

catalytic effects of the fly ash. With the exception of elemental

mercury emissions, the existing SO control devices, namely FGDs and 2

SDA/FFs, tend to reduce emissions of the HAPs and provide some

reduction of emissions of ionic mercury.
There is the potential for dioxins to be produced in hot-side ESPs due to the temper

a

catalytic effects of the fly ash.

13-33

Table 13-13. Qualitative Effects of Different Control Strategies on Air Em

Type of alternative control strategy 

Effect on particulate HAPs Effect on mercury

Primarily Primarily Primarily

organically oxidized elemental

bound mercury mercury

Primarily

inorganically bound

PRECOMBUSTION CONTROLConventional coal cleaning No removal Decrease in emission Decrease in emission De

(highly coal-specific) 

Coal gasification Decrease in emission Cannot predict the effect 

Fuel switching

From a higher to lower sulfur coal Cannot predict the effect Cannot predict the effect To western and certain eastern coals Cannot predict the effect Cannot predict the effect Coal or oil to gas Decrease in emission Decrease in emission 

COMBUSTION CONTROLS

NOx controls Cannot be determined without further testing

Boiler type Cannot be determined without further testing

POSTCOMBUSTION CONTROLS

Particulate phase controls

ESP Decrease in emission No effect 

Some decrease in emission

FF Decrease in emission No effect 

Some decrease 

in emission 

Vapor phase controls

FGD Decrease in emission No effect 

Decrease in emission

SDA/FF Decrease in emission No effect 

Some decrease in emission

Carbon adsorption Decrease in emission. (Elemental and ionic mercury were removed, respectively, by impregnated 

pilot-scale studies.)

NON-TECHNOLOGY-BASED CONTROL OPTIONS 

Demand management Decrease in emission
13-34

Emissions of HAPs could be reduced through energy conservation or

DSM. Finally, pilot-scale studies suggest that ionic and elemental

mercury emissions could be controlled, respectively, with

unimpregnated- and impregnated-carbon adsorption, but full-scale

testing is needed to establish the effectiveness of these techniques.

13.5.2 Water/Solid Waste Considerations

Coal cleaning can produce a variety of waste problems. The 56

process creates a liquid waste containing fine coal particles and

inorganic elements and compounds dissolved from the ash in the coal. 

The large volumes of water used in the cleaning process and the large

amount of suspended solids generated dictate that process water be

clarified and recycled. The usual means of clarification is retention

in large sedimentation ponds. Contamination of surface water or

groundwater can occur from coal wastepiles or storage piles if water

is allowed to infiltrate them. Contaminants such as iron, manganese,

and heavy metals (such as cadmium and silver) may leach from the

wastes.56

Precombustion controls such as fuel switching could have an

effect on reducing this waste. If coal with lower amounts of ash and

sulfur was mined, there would be less need to clean the coal and

therefore fewer tailings would be created at the coal washing site. 

Switching from coal to natural gas would reduce the need to mine and

wash the coal.

In either case, the amount of bottom ash and captured fly ash is

quite large. Because the metals are not destroyed in the combustion

process, the ash will have a higher concentration of metals than the

coal, and water contamination may occur if water runoff from the ash

is not controlled. The amount of metals in ash disposal pits is not

likely to increase significantly if particulate controls are already

in place. If new controls are installed, larger amounts of ash will

require disposal, leading to an increase in the potential for water

contamination from ash leachate.

Coal gasification would not necessarily reduce the waste

potential of coal, but the processing of coal into a gasified form

would tend to localize the waste and transfer it from a gaseous stream

to a solid stream that could be more easily disposed of after being

properly treated. For example, gasifier residue may contain 10

contaminants in a nearly vitreous matrix that is not easily leached. 

In conventional coal combustion, these contaminants would largely

appear in the flue gas stream.

Combustion controls, such as different boiler/furnace types or

the adding of NO controls, would have the effect of changing x

combustion conditions in the coal-fired furnace and, thus, changing
13-35

the ratio of bottom ash to fly ash. Increasing the amount of bottom

ash in the furnace would consequently reduce the amount of fly ash

exiting the furnace. However, if the amount of fly ash exiting the

furnace was increased, there would be greater fly ash loading on the

PM controls. If the PM controls were not designed to accept this

additional load, excess fly ash could cause stack opacity problems and

higher HAP emissions.

The wet FGD processes use a liquid absorbent to absorb SO gases. 2

This absorbent is most likely an alkaline slurry composed of lime or

limestone slurried with water that can be used in a regenerable

process or in a nonregenerable process. Both processes produce a

calcium sludge effluent that must be disposed of properly. The sludge

can be stored in a settling pond or dewatered to take up less space in

a landfill. In addition to removing sulfur oxides, regenerable 57

processes generate a usable product from the sludge, such as gypsum,

which can be used in the manufacture of wallboard.

In a dry FGD system, the flue gas is contacted with an alkaline

material to produce a dry waste product for disposal. The alkaline

material can be added either in the fuel prior to combustion, through

dry injection into the flue gas, or as an alkaline slurry. For 58

example, SDAs inject a lime/limestone alkaline slurry into the flue

gas steam. The reagent droplets absorb SO while simultaneously being 2

dried. In all these methods, the resulting dried reagent and fly ash

are then captured by an FF or ESP and, thus, do not represent an

additional liquid or solid waste problem except that the sorbent may

contaminate salable fly ash.

The use of all forms of carbon adsorption have the potential to

add additional PM loading to existing PM controls. Also, the spent AC

either has to be disposed of as an additional solid waste or

regenerated and reused. Studies on other industries indicate that the

adsorbed HAP (e.g., mercury, dioxin) does not have a tendency to leach

out of the AC.

Section 3001 (b)(3)(c) of the Resource Conservation and Recovery

Act (RCRA) required that the EPA determine, based on the results of a

study required by section 8002(n) of RCRA, whether RCRA subtitle C

regulation of fossil fuel combustion wastes is warranted. On August

9, 1993, the Agency determined that regulation of four large-volume

waste streams resulting from fossil fuel combustion (specifically,

coal combustion in utility steam-generating units) was inappropriate. 

These waste steams are fly ash, bottom ash, boiler slag, and flue gas

emission control waste. However, any change in the characteristics of

these wastes resulting from actions taken to specifically control HAPs

could necessitate a review of that decision by the Agency.59
13-36

Changes in all elements of the alternative controls, from

conservation to technology improvements, can reduce the amount of

waste produced by the utility industry.

13.6 AVAILABLE CONTROL TECHNOLOGY AND STRATEGIES FOR MERCURY

 CONTROL

Typical mercury removal efficiencies for conventional emission

controls are discussed in section 13.3. Conventional controls are

generally inconsistent in their effectiveness, and range from 0 to

more than 83 percent removal (based on emissions testing at full-scale

utility boilers).

Strategies for further reducing mercury emissions from electric

power generation include demand reduction to decrease overall fossil

fuel use, use of other forms of generation (e.g., nuclear power,

biomass), switching to fuels having less mercury (e.g., natural gas),

improving the mercury removal efficiency of conventional controls, and

adding controls that remove mercury more effectively than conventional

controls. The mercury content in utility flue gas ranges from 0.2

g/dry standard cubic meter (g/dscm) to 25 g/dscm at 7 percent

oxygen and standard conditions of 20C and 1 atmosphere, while utility

flue gas flow rates may range from 10,000 to 4,000,000 dscm/min. 60

Thus, at utility plants, any strategy for mercury control must

consider adequately treating large volumes of gas in order to remove

relatively small concentrations of mercury as well as addressing any

resulting impacts on power plant equipment operations (such as

particulate control devices) and on waste disposal issues.

There has been some evidence that a higher carbon content in fly

ash may lead to lower levels of stack mercury emissions, with the

hypothesis being that the mercury is adsorbed by the carbon in the

flue gases. There is other evidence that the chloride content of the

coal impacts on the form and suitability of mercury removal by

conventional control systems. This evidence has led to research on

improving mercury removal from utility flue gas streams.

This section briefly reviews one pre-combustion technology (coal

cleaning) and three classes of post-combustion technologies

(enhancement of existing APCDs, carbon injection, and novel

techniques). Additional information may be found in Appendix I.

 

13.6.1 Pre-Combustion Strategies

As mentioned in section 13.1.2, conventional coal cleaning may be

effective for reducing mercury concentrations only in specific coals

and, at this time, cannot be considered a mercury control technique

for all coals. Advanced coal cleaning techniques are being 63
13-37

investigated for improved mercury removal potential. Like

conventional cleaning techniques, the advanced cleaning techniques

cannot be considered a mercury control technique for all coals at this

time.

Advanced coal cleaning methods such as selective agglomeration

and column froth flotation have the potential to increase the amount

of mercury removed by conventional cleaning alone. Bench-scale

studies indicate that the combination of conventional and advanced

coal cleaning techniques removed from 40 to 82 percent of the mercury

contained in eight samples of raw coal.61,62

Advanced cleaning methods, such as column froth flotation, are

starting to emerge. MicrocelTM is a type of column froth flotation

available through ICF Kaiser and Control International. The company

is the exclusive licensee for the technology in the coal fields east

of the Mississippi River and has sold units for commercial operation

in Virginia, West Virginia, and Kentucky. Ken-FloteTM is another type

of column froth flotation cell.

13.6.2 Post-Combustion Strategies

13.6.2.1 Impact of Fuels and Temperature on Mercury Emissions

Fuels and the temperature of the flue gas can have a significant

impact on the quantity of mercury emissions in the flue gas from a

boiler and on the ability of control systems to remove the mercury.

13.6.2.1.1 Fuels and Mercury Speciation. Mercury is contained

in the coal and oil fuels burned in utility boilers. During

combustion, mercury readily volatilizes from the fuel and is found

predominantly in the vapor phase in the flue gas in one of three 63

forms: (1) elemental, (2) ionic, or (3) organic. 

In the early 1990s, test methods were developed to quantify the

species of mercury present in utility flue gas. Since that time, more

accurate speciation test methods (e.g., Ontario Hydro, EPA draft 101B)

have been developed. Mercury speciation testing indicates that the

distribution of ionic mercury, most likely mercuric chloride (HgCl ), 2

and elemental mercury each varied in the sampled coal-fired utility

flue gas. 

Preliminary test results suggest that the chloride concentration

in the coal and the type of coal (e.g., bituminous, subbituminous, or

lignite) may be associated with a particular speciation of mercury in

the flue gas, but more data are needed to verify these associations. 

Specifically, higher concentrations of ionic mercury were associated

with tested coals containing high chloride concentrations (0.1 to 0.3

weight percent), while 149C (300F) flue gas from tested 64-66646566
13-38

subbituminous coals appeared to contain approximately ten times the

percentage of elemental mercury as compared with flue gas from tested

bituminous coals. The variability in the speciation of vapor-phase 67-69 67,68,69

mercury in coal-fired flue gas may explain the variation in mercury

removal that is seen with existing control devices. 70

The association between fuel chloride content and the

concentration of ionic mercury in the flue gas may also apply to fuel

oil, but this association has not been examined. Analysis of two

samples of flue gas suggests that mercury is predominantly in the

elemental form when the fuel is oil.

It is important to understand mercury speciation because it will

indicate potential ways to reduce mercury emissions. For example, the

ionic mercury form (i.e., Hg ) is water soluble. Wet scrubbing of the ++

flue gas may result in increased ionic mercury removal. 

The scrubbing affinity for ionic mercury has been observed in

pilot-scale studies. Preliminary results from tests of pilot-scale

wet and dry scrubbers indicate that at least 90 percent of the ionic

mercury was captured, while the removal of elemental mercury ranged

from 0 to approximately 70 percent. These preliminary test 65,66,71-73 71,72,73

results suggest that a scrubbing system will have a low mercury

removal efficiency if it treats flue gas from a boiler that fires

subbituminous coal (containing relatively more elemental mercury) and

a high mercury removal efficiency if it treats flue gas from a boiler

that fires bituminous coal (containing relatively more ionic

mercury). Full-scale studies are needed to verify these 74

observations.

Speciation of mercury is important in planning control strategies

but is still under investigation. The speciation information is also

needed to understand what is emitted from utility stacks, how it is

affected by atmospheric chemistry, and the subsequent deposition of

mercury.

13.6.2.1.2 Temperature. Utility flue gas typically has a

temperature range of 121to 177C (250to 350F) after leaving an

air preheater, which is a heat exchanger commonly used to heat

incoming combustion air. Mercury is found predominantly in the vapor 60

phase in utility flue gas. If the vapor-phase mercury was condensed 63

onto PM, the PM could be removed with existing PM control devices. 

Theoretically, cooler temperatures will give relatively more mercury

condensation onto PM and, subsequently, increased mercury removal 63

with existing PM control devices. 

There is limited, preliminary evidence for the temperature

dependence of mercury removal in a pilot-scale FF study. The pilot
13-39

study suggests that mercury removal efficiencies apparently increased

from 27 percent to 51 percent as the temperature of the flue gas

decreased from 107C (225F) to 96C (205F). 75

13.6.2.2 Effectiveness of Mercury Capture Effectiveness of

Existing Pollution Control Technology. Typical mercury removal

efficiencies for conventional emission controls are discussed in

section 13.3. Since conventional controls are generally inconsistent

in their effectiveness, with a range from 0 to more than 83 percent

removal (based on emissions testing at full-scale utility boilers),

conventional controls cannot be considered a mercury control strategy

for all electric utility plants at this time. Due to the limited

effectiveness of conventional controls, research continues on ways to

improve mercury capture by conventional emission control devices,

sorbent injection, and the development of novel techniques. In order

to develop low cost post-combustion mercury strategies for full-scale

utility operation, fundamental research must continue on the chemistry

and interactions of flue gas constituents, fly ash, and mercuryspecies encountered at various flue gas conditions across the utility

industry.

The following sections briefly describe research on enhancing

mercury removal with existing control technologies, activated carbon

injection, and development of novel mercury control technologies.

13.6.2.3 Enhancement of Existing Control Devices. 

13.6.2.3.1 Enhanced Particulate Control. Several approaches to

improving the capture of fine particles and mercury with existing

control devices are being investigated. Studies are underway on

enhancing particulate control with a new Asea Brown Boveri (ABB)

precharger, a wet ESP, flue gas cooling and humidification, and an

advanced power supply (the ABB Switched Integrated Rectifier). Flue

gas cooling and humidification tests are currently in progress. This

approach shows promise in improving the collection of particulatebound mercury, and may also cause vapor-phase mercury to condense on

particulate matter and be captured in the ESP. Research is also 76

underway on optimizing the mercury removal capability of conventional

pollution control technologies.77

13.6.2.3.2 Enhanced Wet Scrubbers. Several approaches to

improving the capture of total and elemental mercury are being

investigated. These include optimizing the liquid-to-gas ratio, wet

FGD tower design, and improved elemental mercury removal with

scrubbing liquid additives and catalysts. Brief descriptions of these

approaches follow.
13-40

Liquid-to-gas ratio. The liquid-to-gas (L/G) ratio of a wet FGD

system is dictated by the desired removal efficiency of SO and impacts 2

the removal efficiency of oxidized mercury. In general, high

efficiency (95 percent SO removal) systems are designed with L/G 2

ratios of 120 gal/1000 acf to 150 gal/1000 acf. In two separate

pilot-scale studies, increasing the L/G ratio from approximately 40

gal/1000 acf to approximately 125 gal/1000 acf increased the removal

efficiency of oxidized mercury from 90 percent to 99 percent. Test 73,78

data suggest that increasing the L/G ratio did not affect the removal

efficiency of elemental mercury, which was close to zero percent. 79

Wet FGD tower design. Most of the existing U.S. wet FGD systems

have open spray tower or tray tower designs. Recent research has 80

shown that tray tower designs are more effective in removing oxidized

mercury from boiler flue gas than tower designs. In one study of wet

FGD systems, where the composition of the flue gas was mostly oxidized

mercury, the tray tower design removed from 85 to 95 percent of the

total mercury, whereas the open spray tower design removed from 70 to

85 percent of the total mercury. 81

Improved elemental mercury removal. Since two studies noted 

higher concentrations of elemental mercury in the outlet of a wet FGD

system compared to the inlet concentrations of elemental mercury, 82,83

research is in progress on ways to convert and capture elemental

mercury. Research currently is investigating scrubbing liquid

additives and catalysts to convert elemental to ionic mercury. 

Several scrubbing liquid additives that combine strong oxidizing

properties with relatively high vapor pressures are being investigated

as techniques to enhance the capture of mercury in a wet scrubber. Of

three halogen (i.e., chlorine, bromine, and iodine) solutions tested

to date, the chlorine solution appears to remove the most elemental

mercury in the presence of SO and NO. Further testing of these and 2

possibly other oxidizing reagents is planned.84

Due to a much higher solubility compared to elemental mercury,

oxidized mercury is readily removed in a wet scrubber. 

Investigations are underway on the conversion of vapor-phase elemental

mercury to more soluble Hg in bench and pilot-scale studies. The ++

effect of flue gas temperature and residence time on the oxidation

potential of a number of catalysts and coal-based fly ashes is being

studied. To date, pilot-scale tests of three iron-based catalysts, a

carbon, a bituminous, and lignite fly ash have shown the carbon-based

catalyst to be the most effective in converting elemental mercury to

Hg . Further testing of the carbon catalysts is planned. ++ 85

13.6.2.4 Activated Carbon Injection. Activated carbon (AC) 

injection is considered a potential control technology for mercury
13-41

emitted from electric utilities, since a form of this technology has

been successfully demonstrated on medical waste incinerators and

municipal waste combustors. 86-89 86,87,88,89

The level of mercury control that might be achieved with AC

injection into utility flue gas may depend upon flue gas

characteristics such as temperature, chloride content (in the fuel),

mercury content (in the flue gas), and the volume of flue gas. As

shown in Table 13-14, these properties distinctly differ from those in

MWC flue gas. In particular, mercury concentrations in MWC flue gas

streams may be up to several orders of magnitude greater than those

seen in utility flue gas streams. 

Due to the differences between the flue gas characteristics at

MWCs and utility units, the application of AC injection to utility

flue gas has not been directly scaled from the application at MWCs. 

At utility plants, the small concentrations of mercury are contained

in a large volume of flue gas, and large amounts of AC may be needed

to provide adequate contact between the carbon particles and mercury. 

Pilot-scale studies of AC injection on utility flue gas have been

conducted, but full-scale testing is needed to determine the

feasibility of using AC at utility plants.

13.6.2.4.1 Factors Affecting Mercury Removal Efficiency.

Preliminary data from various pilot- and bench-scale studies suggest

that factors besides the optimum amount of AC that is injected may

affect mercury removal. These factors are temperature, the speciation

of the vapor-phase mercury and type of activated carbon injected into 67

the flue gas, and flue gas composition.

Temperature. A pilot-scale study of AC injection upstream of an

FF suggests that mercury removal efficiencies and the required amount

of AC injection were apparently temperature dependent. In reducing 75

the temperature from approximately 116C (240F) to approximately 93C

(200F), the mercury removal increased from approximately 80 percent

(with an injection rate of approximately 3,500 g carbon/g of inlet

mercury) to approximately 98 percent (with an injection rate of

approximately 155 g carbon/g of inlet mercury). (The high mercury

removal percentages suggest that flue gas contained mostly ionic

mercury.)

These test results suggest that more mercury is removed and less

carbon is needed at lower flue gas temperatures. However, it may not

be possible to lower the flue gas temperature sufficiently at a given

utility plant because utility plants typically operate with a stack

gas temperature between 121and 177C (250and 351F) upstream of

any PM control device to avoid acid condensation and, consequently,

equipment corrosion. The stack gas temperature may be lowered below
13-42

Table 13-14. Comparison of Typical Uncontrolled Flue Gas Parameters

at Utilities and MWCsa

Uncontrolled flue gas

parameters MWC Coal-fired utility boiler Oil-fired utility boiler 60,90 91,92

b 93,94

Temperature (C) 121 - 177 121 - 177 177 - 299

Mercury content (g/dscm) 1 - 25 0.2 - 2 400 - 1,400

Chloride content (g/dscm) 1,000 - 140,000 1,000 - 3,000 200,000 - 400,000

Flow rate (dscm/min) 11,000 - 4,000,000 10,000 - 2,000,000 80,000 - 200,000

MWC = municipal waste combustion

Standard conditions are 0C and 1 atmosphere. a 

Moisture content in the MWC flue gas was assumed to be 13.2 percent. b 

96C (205F) and still avoid acid condensation, provided low-sulfur

coals (less than about 1 weight percent sulfur) are burned. If the 95

utility burns low-sulfur coal and uses an ESP for PM control, the flue

gas will probably require conditioning to reduce the high resistivity

of the fly ash since high resistivity makes the fly ash hard to

collect in an ESP. 

Speciation of mercury. The effectiveness of AC injection in

recovering different forms of mercury is still under investigation. 

The available data indicate iodide- and sulfur-impregnated AC are

needed for significant elemental mercury removal. 

Studies without AC injection at a pilot-scale SDA/ESP system in

Denmark and a full- and pilot-scale SDA/FF system indicated that

essentially all of the ionic mercury was removed (with greater than 97

percent removal efficiencies) while essentially none of the elemental

mercury was removed (with 0 to approximately 3 percent removal

efficiencies). Studies indicated that the removal of elemental 65

mercury was increased to approximately 50 percent with AC injection

ahead of the SDA/ESP and SDA/FF and to approximately 93 percent with

injection of iodide- and sulfur-impregnated AC ahead of two pilotscale test systems (a SDA/FF system and the University of North Dakota

Energy and Environmental Research Center (UNDEERC) system consisting

of a boiler and baghouse). Pilot-scale testing at the UNDEERC 65,96

system indicated that the percent-removal of elemental mercury with

lignite-based AC was temperature dependent.97

Since mercury speciation affects total mercury removal from

utility flue gas with AC injection and because the speciation of
13-43

mercury is not understood at this time, more data are needed to

establish the factors that affect, and to characterize, mercury

speciation in utility flue gas.

Flue gas composition. Flue gas components such as sulfur oxides,

water, and chlorine compounds can affect the mercury removal

efficiency of carbon. A recent bench-scale study investigated the

effects of SO and HCl on the adsorption of elemental mercury and 2

mercuric chloride (HgCl ) by a lignite-based activated carbon. 2 98

Removing SO from the flue gas increased the equilibrium adsorption 2

capacity for elemental mercury by a factor of about 5.5 compared to

3.5 for mercuric chloride. Removing HCl from the flue gas did not

affect the equilibrium adsorption capacity of the carbon for mercuric

chloride; however, it did prevent the carbon from adsorbing elemental

mercury. With no HCl in the gas, the carbon adsorption capacity for

mercuric chloride was larger than that for elemental mercury. Other

carbons may not be affected by the presence of HCl and SO if the 2

mercury adsorption mechanism is different. 

Research continues on the chemistry and interactions of flue gas,

fly ash, and mercury species. This fundamental research is needed at

various flue gas conditions encountered across the utility industry in

order to develop low cost mercury strategies for full-scale utility

operation. Thus, while AC injection shows promise as a mercury

control technology, more data and research are needed to understand

the factors that affect mercury removal.

13.6.2.5 Emerging Technologies for Controlling Mercury Emissions

from Utilities. Research continues on developing potential

technologies for mercury emission reduction from utility plants. This

research is aimed at either the addition of some type of sorbent

technology or novel technology for mercury control. Emerging

technologies are described below.

13.6.2.5.1 Sorbent technology. Although AC injection has been

shown to be a promising technology, research with impregnated ACs,

sodium sulfide (Na S) injection, and an AC circulating fluidized bed 2

suggest that greater mercury removal is possible. 

Sulfur-impregnated carbon. In sulfur-impregnated AC injection,

the carbon-bound sulfur reacts with mercury to form mercuric sulfide

(HgS) on the carbon and the carbon is removed by a PM control device. 

In a pilot-scale study, sulfur-impregnated carbon increased mercury

removal to 80 percent, an increase of 25 percent over results achieved

with an equal amount of nonimpregnated AC.65

Iodide-impregnated carbon. With iodide-impregnated AC injection,

the carbon-bound iodide reacts with mercury to form mercuric iodide
13-44

(HgI ) on the carbon and the carbon is removed by a PM control device. 2

In pilot-scale studies, iodide-impregnated carbon achieved

approximately 99 percent mercury removal. 65,99

While all testing to date has shown that iodide-impregnated AC

injection has a substantial effect on the mercury removal capability

of AC, further testing has shown that, under certain conditions (with

certain coal types and at temperatures of 177C [350F] and higher),

a portion of the captured mercury (postulated to be mercuric iodide)

may be revolatilized as oxidized mercury.100

Chloride-impregnated carbon. Chloride-impregnated AC injection

has been tested only on MWCs in Europe. The chloride reacts with

mercury to form HgCl on the carbon and the carbon is removed by a PM 2

control device. European MWC experiments have shown that impregnating

AC with chloride salts increases the adsorptive capacity of the AC

300-fold. Although the amount is small, chloride-impregnated AC 108

injection would introduce additional chlorine (a HAP) into the flue

gas stream.

Sorbalit. Another potential method of improving mercury

collection efficiency is to combine calcium hydroxide (lime, Ca(OH) )2

with AC. This reagent, consisting of approximately 95 to 97 percent

lime and 3 to 5 percent AC, is known under the product name Sorbalit. 93

Sorbalit has only been tested on European MWCs and MWIs.

Sulfur-, iodide-, chloride salt-, and Ca(OH) -impregnated ACs 2

show promise for increasing the mercury removal efficiency, but

further testing is needed. The cost of these modified carbons can be

as much as 20 times higher than that of unmodified AC.101

Other sorbents. Numerous studies are underway to develop other

sorbents as economical alternatives to activated carbon. Some of 102,103

the sorbents under investigation include volcanic pumice, sulfur- and

iodide-impregnated carbons, several proprietary sorbents, high-carbon

fly ash, Darco FGD (an activated carbon derived from lignite), an

activated carbon prepared from a bituminous coal, steam-activated

lignite, thermal-activated bituminous coal, chemical-activated

hardwood, iodine impregnated steam-activated coconut shell, and

sulfur-impregnated steam-activated bituminous coal.104

Sorbent Technologies is marketing a sorbent called Mercsorbent. 105

The company claims that the sorbent is effective in removing elemental

mercury at high temperatures typical of utility flue gas, and is

unaffected by common co-existing flue gases, such as SO , HCl, and H O. 2 2

Mercsorbent can be used for sorbent injection or it can be used as a

coating on an FF. A bench-scale duct-injection system at Sorbent

Technologies facilities is now being used to test Mersorbent with this
13-45

approach. The company is also scheduled to demonstrate the sorbent at

the refuse incinerator in Fort Dix, New Jersey; prior compliance

sampling at this facility suggests that a significant amount of its

mercury is in the elemental form. A coal-fired boiler or slipstream

is also being sought for a test of the new sorbent material.

Sodium sulfide injection. Mercury reduction has been achieved at

MWCs through injection of Na S solution into the flue gas prior to the 2

acid gas control device. The resulting solid, HgS, can be collected

by an FF. There are several potential limitations to Na S injection. 106 2

These include reaction of Na S with calcium in the sorbent (as found in 2

Sorbalit) to form calcium sulfide (CaS); reduction of the amount of

sulfur available to react with mercury (CaS can also cause scaling of

the sorbent feed line); corrosion of ductwork (Na S is a corrosive 2

material); clogging and plugging of the screw conveyor due to

solidification of Na S; and sludge formation due to the presence of 2

inorganic salts in the mixing water. At present, full-scale 107

operational injection of Na S has been done only in MWCs. No plans 2

have been announced to test this technology on utility units.

Carbon with circulating fluidized beds (CFBs). Another potential

process for the reduction of mercury emissions is the use of AC in a

CFB. In a CFB, the AC is continuously fed to the reactor, where it 63

is mixed with the flue gas at a relatively high velocity, separated in

the subsequent FF, and recycled to the reactor. A small part of the

used AC is withdrawn from the process and replaced by fresh material. 108

The main advantages of CFBs over fixed carbon beds are the

increased flue gas-to-carbon contact area and the smaller overall

pressure drop. No pilot or full-scale utility boiler testing has yet

been performed with this system although it has been used in Germany

for MWC operation.

In the United States, Environmental Elements Corporation is

developing a CFB that promotes agglomeration of fine particulate

matter, allowing for its capture in an ESP. In addition, activated

carbon is added to the fluid bed to adsorb mercury vapor. High

residence time, due to the recirculation of the particles, allows for

effective utilization of the carbon. Water sprayed within the

circulating bed further promotes the removal of mercury. Results from

bench-scale testing indicate that mercury was significantly reduced

when passed through the fluidized bed of fly ash and activated carbon. 

Based on these tests, a carbon-to-mercury usage was determined for the

system that compares favorably with other sorbent-based mercury

control techniques. There are plans to install a pilot unit and test

at Public Service Electric and Gas’s Mercer Station.109
13-46

 13.6.2.5.2 Novel technologies. Additional potential processes

for controlling mercury emissions include advanced coal cleaning, a

condensing heat exchanger, gold sorbent technology, other sorbent

injection processes, a corona reactor, and mercury amalgamation. 

These technologies are briefly described below.

Condensing heat exchanger. Based on condensing heat exchanger

technology, McDermott Technology (formerly Babcock & Wilcox) is

developing an integrated flue gas treatment system for recovering

waste heat and removing SO , SO , particles, and trace elements from 2 3

coal combustion flue gas. The condensing heat exchanger is a twopass, counter-flow shell and tube heat exchanger. The hot flue gas

enters the top and flows downward through the first cooling stage,

across a horizontal transition region, then upward through the second

cooling stage. An alkali reagent is sprayed from the top of the second

stage to aid in the removal of SO . Testing of the technology is being 2

conducted at McDermott Technology’s research facility in Alliance,

Ohio. Preliminary results indicate that total mercury removal across

both stages of the condensing heat exchanger is about 62 percent when

firing a blend of Ohio coals. Additional testing is planned on two

other bituminous coals.110

Gold sorbent technology. ADA Technologies has begun development

and testing of a process, called Mercu-RE, for the removal and

recovery of vapor-phase mercury from coal-fired utility boilers. The

process takes mercury from flue gases and produces liquid, elemental

mercury with no secondary wastes. Noble metals are used to adsorb

mercury at typical flue gas temperatures. The mercury is then

thermally desorbed. 

Preliminary results from laboratory tests indicate that a goldcoated monolith captured virtually all of the elemental mercury

injected into a simulated flue gas. Pressure drop through the

monolith was low, which is critical to full-scale use. Further

testing of the gold monoliths will include repeated sorption and

desorption cycles. This phase will be followed by testing on a pilotscale coal combustion flue gas at Consol’s research facility in

Library, Pennsylvania.111

Sorbent injection processes. The Enhanced Limestone Injection

Dry Scrubbing (E-LIDSTM) process combines furnace limestone injection

with dry scrubbing to achieve high efficiency SO , particulate, and 2

trace element emissions control. Dry, pulverized limestone is

injected into the upper furnace region of the boiler. The limestone

is calcined to lime and a portion of the sorbent reacts with SO in the 2

flue gas. The flue gas passes through a particulate matter collector

ahead of the dry scrubber to remove some of the solids from the gas
13-47

stream. The solids are mixed with material collected in a baghouse to

produce the SO scrubbing reagent for the spray dryer. 2

Application of the E-LIDSTM system, when firing an Ohio bituminous

coal in the Clean Environment Development Facility (CEDF) at the

Alliance Research Center of McDermott Technology, Incorporated, has

shown efficient emissions control performance. Sulfur dioxide

emissions generated from firing the nominal 3-percent sulfur coal were

reduced by more than 99 percent to less than 0.10 lbs SO /10 Btu. 2 6

Total mercury emissions were reduced from an uncontrolled level of

17.6 g/dscm to less than 0.2 g/dscm for an average total removal

efficiency of greater than 98 percent. The measured performance

confirmed earlier results obtained in the 5 x 10 Btu/hr small boiler 6

simulator (SBS) facility. Mercury measurements upstream of the dry

scrubber indicated that both the limestone injection and operation of

the spray dryer/baghouse system at close to the saturation temperature

contributed to the observed total mercury emissions reduction. The

furnace limestone injection alone reduced mercury emissions to an

average of 3.1 g/dscm.112

Corona reactor. Environmental Elements Corporation is developing

a process for mercury control through DOE’s Small Business Innovative

Research program. The first concept utilizes an intense corona

discharge to convert Hg to mercuric oxide. The process also produces 0

SO to serve as a conditioner for high-resistivity fly ash. A corona 3

discharge in coal combustion flue gas will produce oxidizing radicals,

such as OH and atomic oxygen. Bench-scale results indicate that the

corona reactor, operating at relatively low power levels and short

residence time, yielded high elemental mercury vapor oxidation. The

mercuric oxide, in the form of solid particles, was removed using

conventional particulate control technology. The corona reactor may

also convert mercuric chloride to mercuric oxide, allowing for its

capture as well. The system is currently being tested on a slipstream

at Alabama Power’s Plant Miller.113

Mercury amalgamation. There are plans to investigate the

interaction of mercury with metals such as zinc, silver, tin, and

cadmium. Mercury has been shown to amalgamate with certain metals. 

Both experimental and modeling efforts are planned to determine the

suitability of metals for the capture of mercury.114
13-48

1. Memorandum from Baker, Samuel S., RMB Consulting, to Participants

in the EPRI/UARG Mercury in Coal Study, August 17, 1994. Mercury

in Lignite, Table 3.

2. Baker, Samuel S. EPRI Mercury in Coal Study: A Summary

Report for Utilities That Submitted Samples Update. 

Prepared for Utility Air Regulatory Group by Systems

Applications International, June 1994. pp. D-1 to D-4.

3. Akers, David, Clifford Raleigh, Glenn Shirey, and Robert

Dospoy. The Effect of Coal Cleaning on Trace Elements,

Draft Report, Application of Algorithms. Prepared for EPRI

by CQ, Inc, Homer City, Pennsylvania. February 11, 1994.

4. Letter from Burke, F. P., CONSOL, Inc., to W. H. Maxwell, EPA. 

May 28, 1993. Use of USGS data in estimating the emissions of

air toxics.

5. U.S. Environmental Protection Agency. Assessment of

Physical Coal Cleaning Practices for Sulfur Removal. Final

Report. EPA-600/7-90-013, Research Triangle Park, NC. June

1990.

6. Letter from Burke, F. P., CONSOL Inc., to W. H. Maxwell, EPA. 

March 9, 1994. CONSOL Inc.’s round-robin study of the variation

in determinations of mercury concentrations in coal.

7. Lengyel, John, Matthew S. DeVito, and Richard A. Bilonick.

Interlaboratory and Intralaboratory Variability in the Analysis

of Mercury in Coal. CONSOL, Inc., Library, Pennsylvania. 

March 9, 1994.

8. Letter from Finkelman, R. B., of U.S. Geological Survey, to W. H.

Maxwell, EPA. January 21, 1994. Comments on the concentration

of mercury in USGS and as-fired coal samples.

9. U.S. Department of Energy, Advanced Power Generation Future

Bright With Coal Gasification-Combined Cycle, Clean Coal Today,

DOE/FE-0215P-5, Issue No. 6. Spring 1992.

10. Baker, D. C. Hazardous Air Pollutants and Other Trace

Constituents in the Syngas From the Shell Coal Gasification

Process. IGTI-Vol. 7, ASME COGEN-TURBO, 6th International

Conference on Gas Turbines in Cogeneration and Utility Industrial

and Independent Power Generation, Houston, TX, as modified for

submission to the U.S. Environmental Protection Agency. 

September 1992.

13.7 REFERENCES
13-49

11. Miller, C. A., R. K. Srivastava, and J. V. Ryan. Emissions of

Organic Hazardous Air Pollutants from the Combustion of

Pulverized Coal in a Small-Scale Combustor. Environmental

Science and Technology. 28:1150-1158. 1994.

12. Nsakala, N., D. Raymond, R. Patel, and M. Cohen. Measurement of

Organic Air Toxics Emissions from Coal Firing in a Laminar-Flow

Reactor. Presented at the Pacific Rim International Conference

on Environmental Control of Combustion Processes, Maui, Hawaii. 

October 16-20, 1994.

13. Southern Research Institute. Preliminary draft emissions report

for EPRI Site 110 (baseline and with NO control) for the EPRI x

PISCES Study. SRI report No. SRI-ENV-92-796-7496. October 1993.

14. Radian Corporation. Preliminary draft emissions report for EPRI

Site 114, Field Chemical Emissions Monitoring Project. Prepared

for Electric Power Research Institute. EPRI report No. DCN 92-

213-152-51. May 1994.

15. Radian Corporation Preliminary draft emissions report for EPRI

Site 13, Field Chemical Emissions Monitoring Project. Prepared

for Electric Power Research Institute. EPRI report No. DCN 93-

213-152-36. February 1993.

16. Electric Power Research Institute. Preliminary draft emissions

report for EPRI Site 16 (OFA and OFA/Low NO ) for the Clean Coal x

Technology Project (CCT). Prepared for the Department of

Energy/Pittsburgh Energy Technology Center (DOE/PETC), EPRI

report No. DCN 93-209-061-01. November 1993.

17. Battelle. Preliminary draft emissions report for Niles Station

Boiler No. 2 (Ohio Edison) for the Comprehensive Assessment of

Toxic Emissions from Coal-Fired Power Plants. Prepared for the

Department of Energy/Pittsburgh Energy Technology Center

(DOE/PETC). DOE contract # DE-AC22-93PC93251. December 1993.

18. Battelle. Preliminary draft emissions report for Niles Station

Boiler No. 2 with NOx (Ohio Edison) for the Comprehensive

Assessment of Toxic Emissions from Coal-Fired Power Plants. 

Prepared for the Department of Energy/Pittsburgh Energy

Technology Center (DOE/PETC). DOE contract # DE-AC22-93PC93251. 

December 1993.

19. Radian Corporation. Preliminary draft emissions report for EPRI

Site 102, Field Chemical Emissions Monitoring Project. Prepared

for Electric Power Research Institute. EPRI report No. DCN 92-

213-152-35. February 1993.
13-50

20. Interpoll Laboratories, Inc. Results of the Air Toxic Emission

Study on the No. 8 Boiler at the NSPC Riverside Plant. Prepared

for Northern States Power Company. Report No. 2-3590. September

1992.

21. Electric Power Research Institute. Preliminary draft emissions

report for Plant Yates Unit No. 1 (Georgia Power Company) for the

Comprehensive Assessment of Toxic Emissions from Coal-Fired Power

Plants. Prepared for the Department of Energy/Pittsburgh Energy

Technology Center (DOE/PETC). EPRI Report No. DCN 93-643-004-03. 

December 1993.

22. Southern Research Institute. Preliminary draft emissions report

for Springerville Generating Station Unit No. 2 (Tucson Electric

Power Company) for the Comprehensive Assessment of Toxic

Emissions from Coal-Fired Power Plants. Prepared for the

Department of Energy/Pittsburgh Energy Technology Center

(DOE/PETC). DOE contract # DE-AC22-93PC93254, SRI Report No. SRIENV-93-1049-7960. December 1993.

23. Radian Corporation. Preliminary draft emissions report (and

mercury retest) for EPRI Site 11, Field Chemical Emissions

Monitoring Project. Prepared for Electric Power Research

Institute. EPRI report Nos. DCN 92-213-152-24 and DCN 92-213-

152-48. November 1992/October 1993.

24. Radian Corporation. Preliminary draft emissions report for EPRI

Site 15, Field Chemical Emissions Monitoring Project. Prepared

for Electric Power Research Institute. EPRI report No. DCN 93-

213-152-26. October 1992.

25. Radian Corporation. Preliminary draft emissions report (and

mercury retest) for EPRI Site 12, Field Chemical Emissions

Monitoring Project. Prepared for Electric Power Research

Institute. EPRI report Nos. DCN 92-213-152-27 and DCN 93-213-

152-49. November 1992/October 1993.

26. Energy and Environmental Research Corporation. Preliminary draft

emissions report for Cardinal Station - Unit 1 (American Electric

Power) for the Comprehensive Assessment of Toxic Emissions from

Coal-Fired Power Plants. Prepared for the Department of

Energy/Pittsburgh Energy Technology Center (DOE/PETC), DOE

contract # DE-AC22-93PC93252. December 1993.

27. Roy F. Weston, Inc. Preliminary draft emissions report for

Boswell Energy Center - Unit 2 (Minnesota Power Company) for the

Comprehensive Assessment of Toxic Emissions from Coal-Fired Power

Plants. Prepared for the Department of Energy/Pittsburgh Energy

Technology Center (DOE/PETC), DOE contract # DE-AC22-93PC93255,

Weston project # 10016-011, Weston Report # DOE017G.RP1. 

December 1993.
13-51

28. Interpoll Laboratories, Inc. Results of the Air Toxic Emission

Study on the No. 6 & 7 Boilers at the NSPC Riverside Plant. 

Prepared for NSPC, Report No. 1-3468A. February 1992.

29. Carnot. Preliminary draft emissions report for EPRI Site 115,

Field Chemical Emissions Monitoring Project. Prepared for

Electric Power Research Institute. Carnot report No. EPRI E10106/R022C855.T.

30. Radian Corporation. Preliminary draft emissions report for EPRI

Site 14, Field Chemical Emissions Monitoring Project, Prepared 

for Electric Power Research Institute. EPRI report No. DCN 93-

213-152-28. November 1992.

31. Interpoll Laboratories, Inc. Results of the Air Toxic Emission

Study on the No. 3 Boiler at the NSPC Sherburne Plant. Prepared

for Northern States Power Company. Report No. 0-3005. June 1990/

October 1991.

32. Preliminary draft emissions report for EPRI Site 116, Field

Chemical Emissions Monitoring Project, prepared by Radian

Corporation for EPRI. EPRI report No. DCN 94-213-152-55. 

October 1994.

33. Radian Corporation. Final emissions report for EPRI Site 111,

Field Chemical Emissions Monitoring Project. Prepared for

Electric Power Research Institute. EPRI report No. TR-105631. 

December 1995.

34. Interpoll Laboratories, Inc. Results of the Air Toxic Emission

Study on the No. 1, 3, & 4 Boilers at the NSPC Black Dog Plant. 

Prepared for Northern States Power Company. Report No. 1-3451. 

January 1992.

35. Interpoll Laboratories, Inc. Results of the Air Toxic Emission

Study on the No. 2 Boiler at the NSPC Black Dog Plant. Prepared

for Northern States Power Company. Report No. 2-3496. May 1992.

36. Interpoll Laboratories, Inc. Results of the Air Toxic Emission

Study on the No. 3, 4, 5 & 6 Boilers at the NSPC High Bridge

Plant. Prepared for Northern States Power Company. Report No.

1-3453. January 1992.

37. Roy F. Weston, Inc. Preliminary draft emissions report for

Baldwin Power Station - Unit 2 (Illinois Power Company) for the

Comprehensive Assessment of Toxic Emissions from Coal-Fired Power

Plants. Prepared for the Department of Energy/Pittsburgh Energy

Technology Center (DOE/PETC). DOE contract # DE-AC22-93PC93255,

Weston project # 10016-011, Weston report # DOE018G.RP1. 

December 1993.
13-52

38. Battelle. Preliminary draft emissions report for Coal Creek

Station - Unit 2 (Cooperative Power Association) for the

Comprehensive Assessment of Toxic Emissions from Coal-Fired Power

Plants. Prepared for the Department of Energy/Pittsburgh Energy

Technology Center (DOE/PETC). DOE contract # DE-AC22-93PC93251. 

December 1993.

39. Carnot. Preliminary draft emissions report for EPRI Site 112,

Field Chemical Emissions Monitoring Project. Prepared for

Electric Power Research Institute. Carnot report No. EPRI E10106/R016C374.T. March 1994.

40. Carnot. Preliminary draft emissions report for EPRI Site 118,

Field Chemical Emissions Monitoring Project. Prepared for

Electric Power Research Institute. Report No. EPRI

E-10106/R140C928.T. January 1994.

41. Radian Corporation. Preliminary draft emissions report for EPRI

Site 10, Field Chemical Emissions Monitoring Project. Prepared

for Electric Power Research Institute. EPRI report No. DCN 92-

213-152-35. October 1992.

42. Environment Canada. Environmental Characterization of Mass

Burning Incinerator Technology at Quebec City, Summary Report. 

EPS 3/UP/5. National Incinerator Testing and Evaluation Program,

Ottawa, Canada. June 1988.

43. Goldfarb, T. D. Evidence for Post-Furnace formation of PCDDs and

PCDFs -- Implications for Control. Chemosphere. 18:1051-1055. 

1989.

44. Steiglitz, L., G. Zwick, J. Beck, W. Roth, and H. Vogg. On the

De-Novo Synthesis of PCDD/PCDF on Fly Ash of Municipal Waste

Combustors. Chemosphere. Vol. 18, pp. 1219-1226, 1989.

45. Interpoll Laboratories, Inc. Results of the Mercury Removal

Tests on Units No. 1 & 2, and Unit 3 Scrubber System at the NSPC

Sherburne Plant. Prepared for Northern States Power Company,

Report No. 1-3409. October 1991.

46. Interpoll Laboratories, Inc. Results of the May 1, 1990 Trace

Metal Characterization Study on Units No. 1 & 2 at the NSPC

Sherburne Plant. Prepared for Northern States Power Company,

Report No. 0-3033E. July 1990.

47. Memorandum from Cole, Jeffrey, RTI, to William Maxwell, EPA. 

February 20, 1998. Update of May 11, 1994 memorandum to Systems

Application International (SAI) about HCl & HF emission factor

output.
13-53

48. Preliminary draft emissions report for EPRI Site 20, Field

Chemical Emissions Monitoring Project, prepared by Radian

Corporation for EPRI. EPRI report No. DCN 93-213-152-54. March

1994. 

49. Preliminary draft emissions report for EPRI Site 101, Field

Chemical Emissions Monitoring Project, prepared by Radian

Corporation for EPRI. EPRI report No. DCN 94-643-015-02. 

October 1994.

50. Draft final report for Paradise Fossil Plant for the

Comprehensive Assessment of Air Toxic Emissions, prepared by

Southern Research Institute for the Department of

Energy/Pittsburgh Energy Technology Center (DOE/PETC), SRI report

No. SRI-ENV-95-338-7960. May 1995. 

51. Department of Energy. National Energy Strategy, Powerful Ideas

for America. First Edition. Washington, DC. February 1991. 

p. 44.

52. Ref. 51, p. 8.

53. Ref. 51, p. 127.

54. Memorandum from Gibbons, Jack and Rosina Bierbaum, OSTP, to

William H. Maxwell, EPA, May 6, 1996, Interim Report.

55. Ref. 51, p. 39.

56. Kilgroe, J. D. Cleaned Coal (Chapter 19). In: Handbook on Air

Pollution Control, John Wiley & Sons, Inc. April 1983. pp. 38-39. 

57. U.S. Environmental Protection Agency. Air Pollution Training

Institute Course 415, Control of Gaseous Emissions (Student

Manual). EPA 450/2-81-005. December 1981. pp. 8-7 through 8-9.

58. Ref. 57, p. 8-23.

59. 58 FR 42466. August 9, 1993. Final Regulatory Determination on

Four Large-Volume Wastes from the Combustion of Coal by Electric

Utility Power Plants.

60. Radian Corporation. Preliminary Draft Report on Field Chemical

Emissions Monitoring Project. Prepared for Electric Power

Research Institute from the following reports: Site 10,

October 6, 1992; Site 11, October 6, 1992; Site 12, November, 23,

1992; Site 15, October 6, 1992; Site 21, May 14, 1993.
13-54

61. Smit, F. J., Gene L. Shields, Mahesh, C. Jha. "Reduction of

Toxic Trace Elements in Coal By Advanced Cleaning." Presented at

the Thirteenth Annual International Pittsburgh Coal Conference,

September 3-7, 1996.

62. "Topical Report No. 5 Trace Element Removal Study." Prepared for

U.S. Department of Energy’s Pittsburgh Technology Center by ICF

Kaiser Engineers, March 1995.

63. Clarke, L. B., and L. L. Sloss. Trace Elements-Emissions from

coal combustion and gasification. IEACR/49. IEA Coal Research,

London. July 1992. pp. 29 - 32, 48 - 58.

64. Bloom, Nicolas S., Eric M. Prestbo, and Vesna L. Miklavcic. 

Flue Gas Mercury Emissions and Speciation from Fossil Fuel

Combustion. Second International Conference on Managing

Hazardous Air Pollutants, Washington, DC. July 1993.

65. Felsvang, Karsten, Rick Gleiser, Gary Juip, and Kirsten Kragh

Nielsen. Air Toxics Control by Spray Dryer Absorption Systems. 

Second International Conference on Managing Hazardous Air

Pollutants, Washington, DC. July 1993.

66. Noblett, Jr., J. G., F. B. Meserole, D. M. Seeger, and D. R.

Owens. Control of Air Toxics from Coal-fired Power Plants using

FGD Technology. Second International Conference on Managing

Hazardous Air Pollutants, Washington, DC. July 1993.

67. Letter from Chang, Ramsay, EPRI, to Martha H. Keating, EPA. 

February 7, 1994. Comments on the draft report Mercury Control

Technologies and Costing of Activated Carbon Injection for the

Electric Utility Industry, prepared by RTI, September 1993.

68. Letter from Boyce, P. L., Northern States Power Company, to

Martha Keating, EPA. January 19, 1994. Comments on the draft

report Mercury Control Technologies and Costing of Activated

Carbon Injection for the Electric Utility Industry, prepared by

RTI, September 1993.

69. Chang, R, and D. Owens, 1994. "Developing Mercury Removal

Methods for Power Plants." EPRI Journal, July/August, 1994.

70. DeVito, Matthew S., Prasad R. Tumati, Rachel J. Carlson, and

Nicolas Bloom. Sampling and Analysis of Mercury in Combustion

Flue Gas. Second International Conference on Managing Hazardous

Air Pollutants, Washington, DC. July 1993.

71. Chow, W., et. al., 1994. "Pathways of Trace Elements in Power

Plants: Interim Research Results and Implications." Trace

Element Transformations in Coal-Fired Power Systems, Fuel

Processing Technology, August, 1994, pp. 5-20.
13-55

72. Letter from Kevin E. Redinger of Babcock & Wilcox to William

Maxwell, Emission Standards Division, U.S. EPA. February 7,

1996.

73. Redinger, K. E., A. P. Evans, R. T. Bailey and P. S. Nolan, 1997. 

"Mercury Emissions Control in FGD Systems," Presented at the

EPRI-DOE-EPA Combined Air Pollutant Control Symposium,

Washington, DC. August 25-29, 1997. 

74. Chang, R., and D. Owens, 1994. "Developing Mercury Removal

Methods for Power Plants." EPRI Journal, July/August, 1994.

75. Chang, Ramsay, C. Jean Bustard, Gordon Schott, Terry Hunt, Howard

Noble, and John Cooper. Pilot Scale Evaluation of AC for the

Removal of Mercury at Coal-fired Utility Power Plants. Second

EPRI International Conference on Managing Hazardous Air

Pollutants, Washington, DC. July 1993.

76. Feeley, T. J., III, "An Overview of the U.S. Department of

Energy’s Electric-Utility Mercury Emissions R&D Activities," Acid

Rain & Electric Utilities II Conference, Scottsdale, AZ,

January 21-22, 1997. p. 4.

77. Ref. 76

78. Electric Utility Trace Substances Synthesis Report - Volume 3: 

Appendix O, Mercury in the Environment." EPRI TR-104614-V3,

Project 3081,3297, November, 1994.

79. Ref. 73

80. Ref. 73

81. Ref. 73

82. Hargrove, O. W., 1994. "A Study of Toxic Emissions from a CoalFired Power Plant Demonstrating The ICCT CT-121 FGD Project." 

Tenth Annual Coal Preparation, Utilization, and Environmental

Control Contractors Conference, Pittsburgh, PA, July, 1994,

pp. 267-274.

83. Ref. 73

84. Ref. 76, p. 3.

85. Ref. 76, p. 3.

86. Report, Municipal Waste Combustors–Background Information for

Proposed Standards: Post-Combustion Technology Performance,

EPA:ISB, September 22, 1989.
13-56

87. Report, Emission Test Report OMSS (Ogden Martin Systems of

Stanislaus, Incorporated) Fields Test on Carbon Injection for

Mercury Control, EPA:OAQPS, Publication No. EPA-600/R-92-192,

September 1992.

88. Report, Emission Test Report Field Test of Carbon Injection for

Mercury Control Camden County Municipal Waste Combustor,

EPA:OAQPS, Publication No. EPA-600/R-93-181, September 1993.

89. Report, Medical waste incinerators – background information for

proposed standards and guidelines: Control technology

performance report for new and existing facilities, EPA:OAQPS,

Publication No. EPA-453/R-94-044a, pp. 98 to 99 and B-7 to B-8,

July 1994.

90. Report, A Comprehensive Assessment of Toxic Emissions from CoalFired Power Plants: Phase I Results from the U.S. Department of

Energy Study, DOE:FETC Contract No. DE-FC21-93MC30097 (Subtask

2.3.3). pg. A-14. September 1996.

91. Temperature and flow rate data taken or determined from

Preliminary Draft Report on Field Chemical Emissions Monitoring

Project: Emissions Report for Sites 103 - 109, prepared for

Electric Power Research Institute by Radian Corporation, March 3,

1993. Mercury content taken from Preliminary Draft Report on

Field Chemical Emissions Monitoring Project, prepared for

Electric Power Research Institute by Radian Corporation, from the

following reports: Site 13, February 12, 1993; Site 112,

December 30, 1993; Site 117, January 20, 1994; Site 118,

January 20, 1994.

92. Chloride content data determined from Electric Power Research

Institute (EPRI) test site 13, in the Preliminary Draft Report on

Field Chemical Emissions Monitoring Project: Site 13 Emissions

Report, prepared for Electric Power Research Institute by Radian

Corporation, February 12, 1993.

93. Mercury content data taken from Nebel, K. L. and D. M. White. A

Summary of Mercury Emissions and Applicable Control Technologies

for Municipal Waste Combustors. Radian Corporation. Research

Triangle Park, NC. June 1991. p. 2-1.

94. Temperature, chloride content, and flow rate data taken or

determined from 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,”

proceedings of the ASME/EPRI/AWMA 5 Integrated Environmental th

Control for Power Plants Conference, Figure 2.

95. McKenna, John D., and James H. Turner. Fabric - Filter -

Baghouses I, Theory, Design, and Selection (A Reference Text). 

ETS, Inc., Roanoke, VA. 1989. p. 6-9.
13-57

96. Miller S. J., Dennis L. Laudal, Ramsay Chang, and Perry D.

Bergman. “Laboratory-Scale Investigation of Sorbents for Mercury

Control.” Presented at the 87th Annual Meeting of the Air &

Waste Management Association, Cincinnati, OH, June 19-24, 1994.

97. Ref. 96

98. Carey, T. R., Oliver W. Hargrove Jr., Carl F. Richardson, Ramsay

Chang, Frank B. Meserole. "Factors Affecting Mercury Control in

Utility Flue Gas Using Sorbent Injection." Presented at the Air

& Waste Management Association’s 90 Annual Meeting & Exhibition, th

June 8-13, 1997, Toronto, Ontario, Canada.

99. University of North Dakota Energy and Environment Research Center

(UNDEERC), “Effective Sorbents for Trace Metals,” A study

performed from March 1994 to March 1995. Abstract taken from the

UNDEERC internet web page.

100. Ref. 96, p. 3.

101. Memo from Maxwell, William H., EPA, to Kenneth R. Durkee, EPA. 

August 3, 1993. Second International Conference on Managing

Hazardous Air Pollutants.

102. Ref. 76

103. Ref. 99

104. Ref. 99

105. Nelson Jr., S., Jon Miller, and Deborah Summanen. "Innovative

Mercury Emission Control.,” Presented at the Air & Waster

Management Associations’s 90 Annual Meeting & Exhibition, th

June 8-13, 1997, Toronto, Ontario, Canada.

106. Ref. 93, p. 3-1. (equations given in this reference were balanced)

107. Ref. 93, pp. 3-6 and 3-7.

108. New Jersey Department of Environmental Protection and Energy. 

Task Force on Mercury Emissions Standard Setting Preliminary

Report. Volume III, Technical and Regulatory Issues. July 1993. 

p. 6.9.

109. Ref. 76, p. 5.

110. Ref. 76, p. 5.

111. Ref. 76, p. 5.

112. Ref. 73
13-58

113. Ref. 76, p. 5.

114. Ref. 76, p. 7.
14-1

14.0 SUMMARY OF RESULTS, TECHNICAL FINDINGS, AND RESEARCH NEEDS

The following summary of results, technical findings, and

research needs is based on this study and the currently available

scientific data.

14.1 INDUSTRY GROWTH AND HAP EMISSIONS

 1. Utility units emit a significant number of the 189 HAPs

included on the section 112(b) list, although in most cases

they are responsible for very small percentages of total

anthropogenic emissions. Coal-fired units emit the largest

number of utility-originated HAPs. Coal-, oil-, and gasfired utilities emit a mix of HAPs, including organics

(e.g., polycyclic aromatic hydrocarbons, dioxins) and heavy

metals (e.g., arsenic, lead, cadmium, chromium, nickel,

mercury). Utilities are responsible for approximately 34

percent of the United States anthropogenic airborne

emissions of mercury but no more than 4 percent of other

measured HAPs.

 2. Under the assumptions made in this Report, HAP emissions are

predicted to increase during the period 1990-2010. Over

this period, utility coal consumption is estimated to

increase by approximately 29 percent, oil consumption is

estimated to decrease by approximately 48 percent, and

natural gas consumption is expected to increase by about 61

percent. Coal combustion accounts for the increase in HAP

emissions.

 3. Actions anticipated to be taken by the utility industry to

comply with other provisions of the Act (e.g., acid rain,

NAAQS revisions) or with other initiatives (e.g.,

electricity restructuring, global warming abatement) may

impact HAP emissions.

 4. The emission test program provided valid and representative

results for the purpose of this study. The utility industry

is composed of a wide variety of units employing a mix of

fuel types, burner types, boiler types, and control device

configurations. The HAP emission test data were obtained by

a variety of organizations using common test and analytical

protocols. The EPA helped to establish these protocols.

14.2 INHALATION RISK ASSESSMENT

 5. For the vast majority of the more than 196 million persons

living within 50 km of any utility unit, the lifetime cancer

risk due to inhalation exposure to HAP emissions is likely

to be less than 1 x 10 . -6
14-2

 6. Out of 426 coal-fired plants, EPA’s modeling indicates that

only 2 coal-fired plants pose high-end excess lifetime

cancer risks greater than 1 x 10 due to inhalation -6

exposure to HAP emissions. For no plant does the inhalation

MIR exceed 4 x 10 . More than 50 percent of the inhalation -6

cancer risk is attributable to arsenic. The average

inhalation MIR across all plants is estimated to be roughly

1/10th to 1/100 as large as the highest inhalation MIR. 

Central tendency inhalation risks for all exposed

individuals are predicted to be approximately one to three

orders of magnitude lower than the highest inhalation MIR. 

The population cancer incidence due to inhalation exposure

to coal-fired utility HAP emissions, considering the results

of both the local and long-range transport modeling, is

estimated to be no greater than 1.3 cases per year

nationwide.

 7. For the year 2010, the cancer MIRs from coal-fired utilities

are not expected to be significantly different. However,

due to uncertainties about future demand, industry

operations, government regulation, etc., the EPA has low

confidence in this projection.

 8. Out of 137 oil-fired plants, EPA’s modeling indicates that

only 11 oil-fired plants pose high-end excess lifetime

cancer risks greater than 1 x 10 due to inhalation -6

exposure to HAP emissions. For no plant does the inhalation

MIR exceed 6 x 10 . More than 50 percent of the inhalation -5

cancer risk is attributable to nickel and the assumption

that nickel emissions are 50 percent as carcinogenic as

nickel subsulfide. The average inhalation MIR across all

plants is estimated to be roughly 1/10th to 1/100th as large

as the highest inhalation MIR. Central tendency inhalation

risks for all exposed individuals are predicted to be

roughly two to three orders of magnitude lower than the

highest inhalation MIR. Population cancer incidence due to

inhalation exposure to oil-fired utility HAP emissions

(considering local and long-range transport) is estimated to

be no greater than 0.5 cases per year nationwide.

 9. For the year 2010, the cancer MIRs from oil-fired utilities

are predicted to be approximately 30 to 50 percent lower

than the 1990 estimates. The EPA has good confidence in

this projection because of the well-established trend of

declining oil use by utilities.

10. Based on the quantitative parameter uncertainty analysis

conducted for the inhalation risk assessment, the EPA

estimates that the high-end inhalation risk estimates

presented in this report are conservative (i.e., more likely

to be overestimating rather than underestimating the true

MIR). The quantitative variability and uncertainty of many
14-3

of the input parameters such as emission estimates, stack

height, breathing rates, and exposure duration were

considered in the uncertainty analysis. This resulted in an

estimated range and distribution of potential risks due to

inhalation exposure only. The median (“central tendency

estimate”) inhalation MIR estimates are predicted to be

roughly 2 to 10 times lower than the high-end MIR estimates. 

However, there are limitations to the uncertainty analysis

and limitations in available data and the range of

uncertainty is, most likely, larger than estimated by this

study.

11. The risk estimates contain additional uncertainties that are

not represented in the quantitative uncertainty analysis. 

The impact of using different high-to-low dose extrapolation

models was not quantitatively assessed in this study. In

addition, there are other factors, such as variation in

population sensitivity (e.g., sensitive subpopulations),

residence time, activity patterns, and other uncertainties

and variabilities, that were not quantitatively assessed.

12. With regard to noncancer effects, the highest hazard

quotient for any HAP, considering both short- and long-term

exposures, is approximately 0.1 for HCl from coal-fired

utilities. That is, the highest exposure concentration for

HCl was estimated to be 10 times lower than the Inhalation

Reference Concentration (RfC) for HCl. The highest hazard

index for all HAPs is about 0.2. 

14.3 MERCURY

13. Mercury cycles in the environment as a result of natural and

human (anthropogenic) activities. Most of the mercury in

the atmosphere is elemental mercury vapor, which circulates

in the atmosphere for up to a year, and hence can be widely

dispersed and transported thousands of miles from sources of

emissions. Even after it deposits, mercury commonly is

emitted back to the atmosphere to be re-deposited elsewhere. 

The modeling of long-range transport of mercury suggests

that about one-third of United States utility emissions is

deposited within the lower 48 States. The remaining twothirds are transported outside of United States borders

where it diffuses into the global reservoir. Finally,

predictions of the ISC3 and RELMAP models indicate that most

of the mercury emitted by utilities is transported further

than 50 km from the emission source.

14. The analysis of mercury fate and transport completed for

this study, as well as the analyses completed in the EPA’s

1997 Mercury Study Report to Congress, in conjunction with

available scientific knowledge, supports a plausible link

between mercury emissions from anthropogenic combustion and
14-4

industrial sources and mercury concentrations in air, soil,

water and sediments. The critical variables contributing to

this linkage are these:

 the species of mercury that are emitted from the

sources, with Hg mostly contributing to 0

concentrations in ambient air and Hg mostly 2+

contributing to concentrations in soil, water, and

sediments;

 the overall amount of mercury emitted from a

combustion source;

 the watershed soil loss rates, including reduction and

erosion;

 the water body loss rates, including outflow,

reduction, and settling; and

 the climate conditions.

In addition, the analysis of mercury fate and transport

supports a plausible link between mercury emissions from

anthropogenic combustion and industrial sources and

methylmercury concentrations in freshwater fish. However,

these fish methylmercury concentrations also result from

existing background concentrations of mercury (which may

consist of mercury from natural sources, as well as mercury

which has been re-emitted from the oceans or soils) and

deposition from the global reservoir (which includes mercury

emitted by other countries). Given the current scientific

understanding of the environmental fate and transport of

this element, it is not possible to quantify how much of the

methylmercury in fish consumed by the United States

population is contributed by current United States emissions

relative to other sources of mercury (such as natural

sources and re-emissions from the global pool). The

critical variables contributing to the plausible link

include:

 the species of mercury that are emitted, with emitted

divalent mercury mostly depositing into local

watershed areas and, to a lesser extent the

atmospheric conversion of elemental mercury to

divalent species which are deposited over greater

distances;

 the overall amount of mercury emitted from a source;

 the watershed soil loss rates, including reduction and

erosion;
14-5

 the water body loss rates, including outflow,

reduction, and settling;

 the extent of mercury methylation in the water body;

 the extent of food web bioaccumulation in the water

body; and

 the climate conditions.

From the analysis of deposition and on a comparative basis,

the deposition of Hg close to an emission source is 2+

greater for receptors in elevated terrain (i.e., terrain

above the elevation of the stack base) than from 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.

14.4 DIOXINS AND ARSENIC

15. Based on a screening level multipathway analysis, the

highest estimated individual risks due to utility arsenic

emissions are predicted to be no higher than 1 x 10 and -4

are probably much lower for the vast majority of the exposed

population. The increased cancer risks due to multipathway

exposures to arsenic emissions, based on screening level

model-plant analysis, using hypothetical scenarios, were

predicted to range from less than 4 x 10 up to 1 x 10 . -7 -4

The highest predicted risk (i.e., 1 x 10 ) was for a -4

hypothetical scenario pica child assumed to be living at the

point of maximum deposition, which is considered an upper

bound, conservative scenario. When the risk from background

exposure (2 x 10 ) is added to the maximum risk from -4

utility exposure, the risk for the pica child is estimated

to be up to 3 x 10 . Background exposures were estimated -4

to dominate the exposures and risk. There are substantial

uncertainties associated with this screening level arsenic

multipathway assessment, and the results do not apply to any

specific existing utility plant. Further assessment is

needed to more fully evaluate the risks due to arsenic

emissions from utilities.

16. Based on a screening level multipathway assessment for

dioxins, total modeled lifetime cancer risks related to

indirect exposure to dioxins, based on model-plant analyses,

are predicted to range from 1 x 10 to 2 x 10 . The -10 -4

results of the analyses indicate that the exposures and

risks due to fish consumption are the highest of all

pathways considered. In all scenarios, the noninhalation

(e.g., ingestion) exposures were predicted to be at least

one order of magnitude larger than the inhalation exposures
14-6

and modeled exposures exceed the background exposures for

dioxins. There are substantial uncertainties associated

with this dioxin screening level multipathway assessment and

the results do not apply to any specific existing utility

plant. Further assessment is needed to more fully evaluate

the risks due to dioxin emissions from utilities.

14.5 RADIONUCLIDE ANALYSIS

17. For the vast majority of the more than 196 million persons

living within 50 km of any utility unit, the lifetime fatal

cancer risk due to radionuclide emissions is less than

1 x 10 .-6

18. The highest MIR to any individual, within a 50-km radius,

resulting from multipathway exposure to radionuclide

emissions from utility units is estimated to be up to

3 x 10 , and 17 of the 684 plants were estimated to -5

potentially pose an MIR greater than 1 x 10 .-5

19. Based on the multipathway exposure modeling conducted with

the CAP-93 model, which estimates exposure within 50 km of

each utility unit, the estimated deaths/year resulting from

multipathway exposure to radionuclide emissions from utility

units is approximately 0.3/year.

20. The quantitative uncertainty analysis performed for the

radionuclide analysis indicates that the population risk

estimates are central values of the true probability

distribution.

14.6 ALTERNATIVE CONTROL STRATEGIES

21. There are a number of alternative control strategies that

are effective, based on the data obtained for this report,

in controlling some of the HAPs emitted from utility units. 

These strategies are summarized below.

 Conversion of coal- and oil-fired units to natural gas

firing effectively eliminates emission of HAPs.

 Conversion of coal-fired units to oil combustion will

effect decreases in emissions of some HAPs but could

lead to increased emissions of others (e.g., nickel).

 Because of the wide variability in the trace metal

contents of coals, switching from one coal to another

will not generally result in consistently reduced

overall HAP emissions.

 Current methods of coal cleaning are able to remove

portions of the trace metals contained within the
14-7

coal. These emission reductions range from

approximately 20 percent for mercury to approximately

50 percent for lead. Advanced coal cleaning

technologies show promise in reducing mercury from

coal from approximately 30 to greater than 60 percent. 

Further research is needed in methods of effecting

greater trace metal removals during coal cleaning and

in assessing the various impacts of these methods.

 Newer forms of fuel combustion (e.g., coal

gasification) show promise as being cleaner sources of

electricity but available data are limited and further

research is needed.

 The impact of combustion controls on HAP emissions is

inconclusive given the current level of knowledge. 

While available data appear to indicate that

installation of low-NO burners results in a trend x

toward lower HAP emissions, the trend is neither

universal nor uniform.

 Particulate matter control devices (i.e., FFs, ESPs)

generally effect good control (i.e., greater than 90

percent removal) of the trace metallic HAPs, with the

exception of mercury. Research is underway to further

enhance fine particle removal, including trace

metallic HAPs, from these control devices. Organic

HAPs do not appear to be well controlled by PM control

devices but these compounds are generally found near

the detection limit. Fabric filters appear to

moderately control HCl (i.e., 50 percent removal) but

not HF, and ESPs do not generally effect good acid gas

HAP control.

 Wet acid gas control devices (e.g., FGDs) by

themselves do not uniformly effect good control of the

trace metallic HAPs, including mercury. Based on

limited data, SDA/FF combinations, however, appear to

be as effective as do FFs alone. Research is underway

to further enhance the mercury removal capability of

FGD systems. Flue gas desulfurization units (as

operated on utility units) generally effect good

control (i.e., greater than 80 percent) of HCl, but

control of HF is not uniform.

 Add-on technologies for the control of mercury have

not been demonstrated on utility units in the United

States. Pilot-scale work on activated carbon

injection indicates that mercury removal is possible

but that such removal is inconsistent and variable and

that further research is needed.
14-8

 Pollution prevention methods (i.e., DSM, energy

conservation, repowering for energy efficiency) have

the potential to result in reduced HAP emissions. The

extent that these methods will be utilized by the

industry is not known and, thus, the extent of any

emission reductions cannot be forecast at this time.

Each of these alternative control strategies may have significant

cost, economic, technical, and research implications before they can

be widely utilized in the utility industry.

14.7 AREAS FOR FURTHER RESEARCH AND ANALYSIS

There are numerous uncertainties and data gaps described

throughout this report. This section identifies several of the

important areas in which further research or scientific and technical

work is needed.

14.7.1 Emissions Data for Dioxins

Emissions data for dioxins were available from only eight of the

tested utility plants. Therefore, there are greater uncertainties

with the dioxin emissions than for many of the other HAPs. All types

of utility units were not tested (e.g., there are no data available

from coal-fired units with hot-side ESPs).

14.7.2 Speciation of Nickel and Chromium

There are significant uncertainties regarding the forms of nickel

being emitted from oil-fired utilities and the health effects

associated with those different forms. Therefore, further research

and evaluation of the emissions to determine what forms are being

emitted and the health effects associated with those different forms

would be of value. Further evaluation of chromium speciation is also

needed.

14.7.3 Multipathway Risk Assessment

As mentioned previously, further work is needed to study the

risks due to multipathway exposure to HAPs that are persistent and

bioaccumulate. Arsenic and dioxins are two HAPs identified as

priority for further multipathway assessment.

14.7.4 Long-range Transport Exposures

Uncertainties remain regarding long-range transport of HAPs.

Further modeling and evaluation could be helpful to assess the impacts

of long-range transport of HAPs from utilities.

14.7.5 Mercury Issues

 There are numerous areas regarding mercury that may need further

research, study, or evaluation. A number of potential areas for

further study include the following: 

 review the estimates of the levels of exposure to mercury

associated with subtle neurological endpoints;
14-9

 quantify and/or evaluate the relationship between a change

in United States mercury emissions and the resulting change

in methylmercury levels in fish; 

 evaluate actual consumption patterns and estimates of the

methylmercury exposure of the subpopulations of concern;

 gather additional data on the mercury content of various

types of coal;

 establish improved methods for measuring mercury

concentrations in water; 

 study the occupational, dietary, and behavioral factors that

affect mercury exposures for people who are determined to be

exposed above a threshold of concern;

 study the public health and environmental benefits that

would be expected by reducing mercury emissions from

utilities; 

 evaluate and/or research control technologies or pollution

prevention options that are available, or will be available,

that could potentially reduce mercury emissions and what are

the costs, economic impacts, and feasibility of those

options;

 evaluate how other regulations, programs, and activities

(e.g., acid rain program, electricity restructuring, NAAQS,

climate change) affect mercury emissions; 

 gather additional data on mercury emissions (e.g., how much

is emitted from various types of utility units, how much is

divalent vs elemental mercury, and how do factors such as

control device, fuel type, and plant configuration affect

emissions and speciation); and

 study how much mercury is emitted from natural sources and

past anthropogenic sources.

14.7.6 Projections to the Year 2010

There are significant uncertainties and unknowns in the emissions

and risk projections made to the year 2010 (e.g., impact of industry

restructuring; impact of State efforts to regulate restructuring;

impact of any climate change initiatives). Further research and

evaluation in this area is needed.

14.7.7 Ecological Risks

The effects of HAPs on wildlife, endangered species, and

terrestrial and aquatic ecosystems were not evaluated in this study. 

Although not mandated by section 112(n)(1)(A), further evaluation of
14-10

ecological risks due to HAP emissions would be needed to fully

evaluate the impacts of utility HAP emissions.

14.7.8 Criteria Pollutant and Acid Rain Programs

Further evaluation is needed to assess the impacts of the Acid

Rain and Criteria Pollutant programs (e.g., impact of revisions to the

PM-fine and ozone NAAQS; impact of Ozone Transport Assessment Group

[OTAG] activities) on HAP emissions.

14.7.9 Short-term Emissions

A limited assessment of short-term exposures was completed. 

However, further evaluation of short-term releases, especially highend, peak releases, could be useful to fully assess the potential

impacts to public health due to emissions of HAPs (particularly HCl

and HF) from utilities.
TECHNICAL REPORT DATA

(Please read Instructions on reverse before completing)

1. REPORT NO. 2. 3. RECIPIENT'S ACCESSION NO.

 EPA-453/R-98-004a,-b

4. TITLE AND SUBTITLE 5. REPORT DATE

 Study of Hazardous Air Pollutant Emissions from Electric February 1998

 Utility Steam Generating Units -- Final Report to Congress 6. PERFORMING ORGANIZATION CODE

7. AUTHOR(S) 8. PERFORMING ORGANIZATION REPORT NO.

9. PERFORMING ORGANIZATION NAME AND ADDRESS 10. PROGRAM ELEMENT NO.

 U.S. Environmental Protection Agency

 Emission Standards Division/Air Quality Strategies and

 Standards Division

 Office of Air Quality Planning and Standards

 Research Triangle Park, NC 27711

11. CONTRACT/GRANT NO.

 

12. SPONSORING AGENCY NAME AND ADDRESS 13. TYPE OF REPORT AND PERIOD COVERED

14. SPONSORING AGENCY CODE

15. SUPPLEMENTARY NOTES

16. ABSTRACT

This report has been prepared pursuant to section 112(n)(1)(A) of the Clean Air Act, and provides the

Congress and the public with information regarding the emissions, fate, and transport of utility HAPs. The

primary components of this report are: (1) a description of the industry; (2) an analysis of emissions data;

(3) an assessment of hazards and risks due to inhalation exposures to 67 HAPs; (4) assessments of risks

due to multipathway (inhalation plus non-inhalation) exposures to four HAPs (radionuclides, mercury,

arsenic, and dioxins); and (5) a discussion of alternative control strategies. The assessment for mercury in

this report includes a description of emissions, deposition estimates, control technologies, and a dispersion

and fate modeling assessment which includes predicted levels of mercury in various media (including soil,

water, and freshwater fish) based on modeling from four representative utility plants using hypothetical

scenarios. The EPA has not evaluated human or wildlife exposures to mercury emissions from utilities in

this report. With regard to non-inhalation exposures (e.g., ingestion) to other HAPs, this report presents a

limited qualitative discussion of arsenic, cadmium, dioxins, and lead.

17. KEY WORDS AND DOCUMENT ANALYSIS

a. DESCRIPTORS b. IDENTIFIERS/OPEN ENDED TERMS c. COSATI Field/Group

 Air Pollution Air Pollution Control

 Atmospheric Dispersion Modeling

 Electric Utility Steam Generating Units

 Hazardous Air Pollutants/Air Toxics

18. DISTRIBUTION STATEMENT 19. SECURITY CLASS (Report)

 Release Unlimited

 Unclassified

21. 787

20. SECURITY CLASS (Page) 22. PRICE

 Unclassified

EPA Form 2220-1 (Rev. 4-77) PREVIOUS EDITION IS OBSOLETE
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