Revised Technical Support Document: 

National-Scale Assessment of Mercury Risk 

to Populations with High Consumption of 

Self-caught Freshwater Fish 

In Support of the Appropriate and Necessary 

Finding for Coal- and Oil-Fired Electric 

Generating Units 
ii

EPA-452/R-11-009

November 2011

Revised Technical Support Document: National-Scale Assessment of 

Mercury Risk to Populations with High Consumption of Self-caught 

Freshwater Fish 

In Support of the Appropriate and Necessary Finding for Coal- and OilFired Electric Generating Units

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Health and Environmental Impacts Division

Research Triangle Park, North Carolina
iii

DISCLAIMER

This document has been prepared by staff from the Office of Air Quality Planning and 

Standards, U.S. Environmental Protection Agency. Any opinions, findings, conclusions, or 

recommendations are those of the authors and do not necessarily reflect the views of the EPA. 

Questions related to this document should be addressed to Dr. Zachary Pekar, U.S. 

Environmental Protection Agency, Office of Air Quality Planning and Standards, C504-06, 

Research Triangle Park, North Carolina 27711 (email: pekar.zachary@epa.gov).
iv

Table of Contents:

Executive Summary............................................................................................................viii

1 Review of Analysis Approach ................................................................................................ 1

1.1 Summary of Significant SAB Peer Review Panel Recommendations and Revisions 

Reflected in this Revised Mercury Risk TSD............................................................................. 2

1.2 Purpose and Scope of Analysis........................................................................................ 5

1.3 Overview of Risk Metrics and the Risk Characterization Framework .......................... 10

1.4 Overview of Analytical Approach ................................................................................. 13

1.4.1 Specifying the spatial scale of watersheds.............................................................. 16

1.4.2 Characterizing measured fish tissue Hg concentrations at the watershed level...... 17

1.4.2.1 Projecting 75th percentile fish tissue Hg concentrations for the 2016 scenario . 31

1.4.3 Defining subsistence fisher scenarios to include in the analysis ............................ 31

Identify subsistence fisher populations..................................................................................... 32

Assess where the subsistence fisher populations might be active ............................................ 33

1.4.4 Estimating total fish consumption-related MeHg exposure (2016 scenario).......... 41

1.4.5 Estimating risk (RfD-based hazard quotient) (2016 scenario) ............................... 43

1.4.6 Estimation of U.S. EGU-attributable risk (2016 scenario)..................................... 43

1.4.6.1 Mercury Maps analysis ....................................................................................... 44

1.4.6.2 Additional research supporting the proportionality assumption and examining the 

issue of temporal response .................................................................................................... 46

1.4.6.3 CMAQ mercury deposition modeling................................................................. 47

1.5 Differences between the 2005 Section 112(n) Revision Rule analysis and the current 

analysis in support of the Appropriate and Necessary Finding ................................................ 48

1.6 Detailed Example Calculation (watershed-level risk HQ)............................................. 50

2 Discussion of Analytical Results.......................................................................................... 53

2.1 Key design elements to consider when reviewing the risk assessment results.............. 53

2.2 Mercury Deposition from U.S. EGUs as Modeled Using CMAQ................................. 54

2.3 Fish Tissue Mercury Concentrations.............................................................................. 65

2.4 Comparing Patterns of Hg Deposition with Hg Fish Tissue Data for the 3,141 

Watersheds Included in the Risk Assessment........................................................................... 74

2.5 Overview of Risk Estimates........................................................................................... 80

2.5.1 Overview of percentile risk estimates (2016 scenario)........................................... 80

2.5.2 Overview of number and percentage of watersheds with populations potentially atrisk due to U.S. EGU mercury emissions (2016 scenario) ................................................... 83

2.6 Sensitivity Analyses....................................................................................................... 87

2.7 Discussion of key sources of variability and uncertainty............................................... 91

3 Summary of Key Observations........................................................................................... 110

Appendices: Additional Technical Detail on Modeling Elements and Presentation of 

Supplemental Risk Estimates...................................................................................................... 117

Appendix A. Technical Approach Used in Modeling IQ Loss............................................... 117

Appendix B. Supplemental Risk Estimates (IQ loss estimates) ............................................. 118

Appendix C. SAB Mercury Panel Peer Review Letter: Review of EPA's Draft National-Scale 

Mercury Risk Assessment........................................................................................................ 120
v

List of Tables

Table 1-1 Summary Statistics for the 2011 MFT HUC-level Data ............................................. 25

Table 1-2 Summary of 75th Percentile Hg Concentrations in Fish Tissue Samples by Number of 

Observations and Number of Sites per HUC................................................................................ 27

Table 1-3 Summary of 50th Percentile Hg Concentrations in Fish Tissue Samples by Number of 

Observations and Number of Sites per HUC................................................................................ 29

Table 1-4 Comparison of HUC-Level Fish Tissue Hg Statistics for (a) Fish Tissue Dataset with 

Fish >7 Inches and (b) Dataset with All fish Lengths Included ................................................... 31

Table 1-5 Spatial Extent and Number of HUCs Reflected in Risk Modeling for the Female 

subsistence fish consumer Scenarios Included in the Risk Assessment....................................... 32

Table 1-6 Fish consumption rates and additional behavior-related information for subsistence 

populations included in the analysis............................................................................................. 38

Table 2-1 Comparison of total and U.S. EGU-attributable mercury deposition (μg/m2) for the 

2005 and 2016 scenarios.*............................................................................................................ 64

Table 2-2 Comparison of percent of total mercury deposition attributable to U.S. EGUs for 2005 

and 2016.*..................................................................................................................................... 64

Table 2-3 Comparison of percent reduction of total mercury deposition, and U.S. EGUattributable deposition, based on comparing the 2016 scenario against the 2005 scenario.*....... 64

Table 2-4 Comparison of HUC-Level Fish Tissue Mercury Concentrations Across Datasets 

Used in the March Version and Current Version of the Risk Assessment ................................... 73

Table 2-5 Comparison of total and U.S. EGU-attributable Hg fish tissue concentrations 

(including % change) for the original fish tissue dataset and 2016 scenarios* ............................ 73

Table 2-6 Percentile risk estimates for the full set of female subsistence fish consumer scenarios 

included in the analysis (2016 scenario) (for both total and U.S. EGU incremental RfD-based 

HQ)* ............................................................................................................................................. 81

Table 2-8 Watersheds with potentially at-risk populations based on consideration for risk based 

on U.S. EGU mercury deposition and resulting exposure considered alone, without taking into 

account other sources of mercury deposition (2016 scenario)...................................................... 85

Table 2-9 Combination of watersheds with potentially at-risk populations based on either 

consideration for (a) U.S. EGU percent contribution to total risk OR (b) risk when U.S. EGU 

mercury deposition is considered alone, without taking into account deposition from other 

sources (2016 scenario)................................................................................................................. 85

Table 2-10 Reflecting the March version of the risk assessment - combination of watersheds 

with potentially at-risk populations based on either consideration for (a) U.S. EGU percent 

contribution to total risk OR (b) risk when U.S. EGU mercury deposition is considered alone, 

without taking into account deposition from other sources (2016 scenario)................................ 86

Table 2-11 Sensitivity analysis results presented as: watersheds with potentially at-risk 

populations based on U.S. EGUs making a specified contribution to total risk (2016 scenario) 88

Table 2-12 Sensitivity analysis results presented as: watersheds with potentially at-risk 

populations based on consideration for U.S. EGU-attributable HQ risk (2016 scenario) (risk 

considering U.S. EGU Hg deposition before considering other sources of Hg deposition) ........ 89

Table 2-13 Sensitivity analysis results presented as: Combination of watersheds with potentially

at-risk populations based on either consideration for (a) U.S. EGU percent contribution to total 

risk OR (b) risk when U.S. EGU mercury deposition is considered alone, without taking into 

account deposition from other sources (2016 scenario) ............................................................... 90
vi

Table 2-14 Key sources of variability associated with the analysis and degree to which they are 

reflected in the design of the analysis........................................................................................... 93

Table 2-15 Key sources of uncertainty associated with the analysis, the nature of their potential 

impact on risk estimates, and degree to which they are characterized as part of the analysis...... 96

List of Figures

Figure 1-1. 2-Stage Risk Characterization Framework ............................................................... 13

Figure 1-2 Flow Diagram of Risk Analysis (for the 2016 air quality scenario) Including Major 

Analytical Steps and Associated Modeling Elements .................................................................. 14

Figure 1-3 Diagram Illustrating Step-wise Procedure Used to Develop 2010 Mercury Fish 

Tissue (MFT) Dataset Used in the 2010 National-Scale Mercury Risk Assessment ................... 21

Figure 1-4 Diagram Illustrating Step-wise Procedure Used to Develop the Augmentation 

Mercury Fish Tissue (MFT) Dataset............................................................................................. 22

Figure 1-5 Diagram Illustrating Step-wise Procedure Used to Combine the 2010 MFT and 

Augmentation MFT Datasets........................................................................................................ 23

Figure 1-6 Diagram Illustrating Number of HUC12s with Fish Tissue Mercury Data (for 2010 

MFT, Augmentation MFT and the Combined 2011 MFT Datasets)............................................ 26

Figure 1-7 Histogram Characterizing Frequency of Sample Sizes Across HUCs Included in the 

Risk Assessment (illustrates fraction of HUCs with small sample size of 1-2) ........................... 28

Figure 1-8 LOESS (locally-weighted scatter plot smoothing) Least-Square Regression of 75th

Percentile HUC-level Fish tissue Hg Levels Against HUC-level Sample Size............................ 29

Figure 1-9 Sample Calculation for watershed-level Risk HQ ..................................................... 52

Figure 2-1 Total Mercury Deposition by HUC (μg/m2) for the 2005 Scenario ........................... 56

Figure 2-2 Total Mercury Deposition by HUC (μg/m2) for the 2016 Scenario ........................... 57

Figure 2-3 U.S EGU-Attributable Mercury Deposition by HUC (μg/m2) for the 2005 Scenario

....................................................................................................................................................... 58

Figure 2-4 U.S EGU-Attributable Mercury Deposition by HUC (μg/m2) for the 2016 Scenario59

Figure 2-5 Mercury Wet Deposition by 12km Grid Cell (μg/m2) for the 2005 Scenario............ 60

Figure 2-6 Mercury Dry Deposition by 12km Grid Cell (μg/m

2

) for the 2005 Scenario ............. 61

Figure 2-7 Mercury Wet Deposition by 12km Grid Cell (μg/m2) for the 2016 Scenario............. 62

Figure 2-8 Mercury Dry Deposition by 12km Grid Cell (μg/m2) for the 2016 Scenario ............. 63

Figure 2-9 Set of 3,141 HUC12s with Fish Tissue Mercury Data Used in the Risk Assessment*

....................................................................................................................................................... 67

Figure 2-10 Fish Tissue Measurement Sampling Frequency for HUCs with Fish Tissue Data 

Included in the Risk Assessment .................................................................................................. 68

Figure 2-11 Total Fish Tissue Mercury Concentrations for 2005 Scenario (HUC12-level 75th

percentile values, ppm)................................................................................................................. 69

Figure 2-12 Total Fish Tissue Mercury Concentrations Projected for 2016 Scenario (HUC12-

level 75th percentile values, ppm)* ............................................................................................... 70

Figure 2-13 EGU-Attributable Fish Tissue Mercury Concentrations for 2005 Scenario............. 71

Figure 2-14 EGU-Attributable Fish Tissue Mercury Concentrations Projected for 2016 Scenario

....................................................................................................................................................... 72
vii

Figure 2-15 Comparison of Locations of Watersheds with Fish Tissue Hg Data* with Pattern of 

U.S. EGU-attributable Hg Deposition (2005 scenario) ................................................................ 76

Figure 2-16 Comparison of Locations of Watersheds with Fish Tissue Hg Data* with Pattern of 

U.S. EGU-attributable Hg Deposition (2016 scenario) ................................................................ 77

Figure 2-17 For the 2005 Scenario, Scatter Plot of Hg Fish Tissue Concentrations Versus Total 

Hg Deposition for the 3,141 Watersheds Included in the Risk Assessment................................. 78

Figure 2-18 Cumulative distribution plots of U.S. EGU-attributable Hg deposition over the 

3,141 watersheds used in modeling the high-end female consumer population as contrasted with 

all 88,000 watersheds (2016 Scenario). ........................................................................................ 78
viii

Executive Summary

The Technical Support Document is a revised version of the TSD for EPA’s Nationalscale Mercury Risk Assessment completed in March 2011. EPA used the previous version of 

this report as support for the 2011 finding that it is appropriate and necessary to regulate coal and 

oil-fired electric utility steam generating units in the United States (U.S. EGUs), pursuant to 

Section 112(n)(1)(A) of the Clean Air Act. 

EPA commissioned a formal peer review of this assessment during the public comment 

period for the regulation through the EPA Science Advisory Board (SAB), which provides 

independent advice and peer review to EPA's Administrator on the scientific and technical 

aspects of environmental issues. The SAB established a 22-member panel with representation 

from academic institutions, industry, federal agencies, and state governments. The SAB 

supported the overall design of the risk assessment, confirmed EPA’s analytical assumptions, 

and concluded that the risk assessment should provide “an objective, reasonable, and credible 

determination” of the potential public health hazard. The SAB made many recommendations for 

improving this TSD, which SAB organized into three general themes: (1) improve clarify of the 

document regarding methods and presentation of results, (2) expand discussion of sources of 

variability and uncertainty, and (3) de-emphasize IQ loss as an endpoint. EPA has responded to 

the peer review by substantially revising this TSD.

Purpose and Scope of Analysis

The goal of this assessment was to determine whether mercury emitted from U.S. EGUs 

poses a potential public health hazard. Therefore, we have designed this risk assessment as a 

screening analysis focused on identifying watersheds where there is a public health hazard 

attributable to U.S. EGU mercury deposition. Mercury emitted from U.S. EGUs, depending on 

the form of mercury emitted and other factors, can deposit locally and regionally in U.S. 

waterbodies, as well as contribute to the global pool of mercury, where it can be transported and 

eventually deposited around the world. This deposited mercury is transformed into 

methylmercury (MeHg) by microorganisms and then bioaccumulates as MeHg in fish. The 

primary pathway of concern from a public health standpoint is consumption of mercurycontaminated fish by women of child bearing age (since mercury can stay in the system for some 

time, both women who are pregnant or about to be pregnant are of concern.) Depending on the 

level of prenatal exposure, children born to those women may then experience a range of 

neurodevelopmental effects including decrements on a number of neuropsychological measures. 

We focused on consumption from inland freshwater waterbodies in the U.S rather than 

estuarine or marine waterbodies because the U.S. EGU-attributable mercury deposition is a 

larger fraction of total mercury deposition, particularly for waterbodies with elevated U.S. EGUattributable mercury deposition such as the Ohio River Valley. We focused on subsistence 

fishing populations because they typically have substantially higher consumption rates of selfcaught fish than recreational fishers and therefore, experience higher risk. Because we do not 

have data available on the distribution of subsistence fishing populations in all watersheds in the 

U.S., we modeled a hypothetical female subsistence consumer at those watersheds where we 

have fish tissue data and where we believe subsistence fishing activity has the potential to occur. 
ix

Risk Assessment Methods

We followed a 10-step process to estimate risk to female subsistence consumers.

1. Model total and U.S. EGU-attributable mercury deposition for the continental U.S. 

2. Interpolate mercury deposition to watersheds 

3. Estimate 75th percentile fish tissue mercury concentrations at watersheds with fish tissue 

data

4. Project 75th percentile fish tissue mercury concentrations for the 2016 scenario, which 

reflects projected Hg deposition without regulation of mercury emissions from U.S. 

EGUs

5. Define female subsistence consumer scenarios 

6. Identify watersheds with subsistence fisher population activity

7. Define self-caught fish consumption rates for the subsistence scenarios 

8. Estimate total fish consumption-related MeHg exposure 

9. Estimate total MeHg risk

10. Estimate U.S. EGU-attributable risk 

We assume a proportional relationship between changes in mercury deposition and 

changes in fish tissue mercury concentrations. This assumption, supported by the Mercury Maps 

assessment and other scientific literature, is used to estimate the fraction of U.S. EGUattributable MeHg in fish and to project fish tissue data to 2016. We use the 75th percentile fish 

tissue sample because we believe it is likely that subsistence fishers may target larger fish (with 

somewhat higher MeHg levels) to supplement family meals. Although we can only calculate risk 

for the 4% (3,100) of the 88,000 watersheds in the continental U.S. for which we have fish tissue 

mercury data, these data cover 48 states including many states with high levels of mercury

deposition and fish tissue mercury concentrations. We excluded watersheds with potentially

significant non-air sources of mercury.

In estimating risk for the most comprehensive scenario reflecting exposures for women of 

childbearing age with subsistence level consumption of freshwater self-caught fish, we apply the 

scenario to all watersheds with fish tissue mercury data, reflecting the potential for these 

populations to obtain and consume fish from any watershed. To estimate this risk, we use the 

EPA’s MeHg reference dose (MeHg RfD), which identifies a level of exposure above which 

there is the risk of adverse health effects. We compare modeled MeHg exposure levels against 

the RfD to generate a hazard quotient (HQ). An HQ>1 represents an exposure above the MeHg 

RfD and EPA, supported by the SAB, considers such an exposure to represent a potential public 

health hazard. We use two risk metrics at the watershed level: (a) number and percent of 

watersheds modeled where the total mercury-based HQ>1 and where U.S. EGU-attributable 

mercury deposition contributes at least 5% of the risk and (b) number and percent of watersheds 

modeled where U.S. EGU-sourced mercury considered alone, without taking account deposition 

from other sources, results in an HQ>1. The combination of these two estimates provides the 
x

total number and percent of watersheds where female subsistence consumers are potentially atrisk. 

We have also modeled an additional set of female subsistence consumer scenarios 

focused on different socioeconomic (SES) groups (e.g., low income Blacks in the Southeast, low 

income Hispanics, Vietnamese). Each of these female subsistence consumer scenarios is 

supported by scientific data on SES specific high-end subsistence-level fish consumption rates 

for self-caught fish from inland freshwater bodies. We model watersheds for each of these 

scenarios if there are at least 25 individuals from the SES group in close proximity to the 

watershed.

Response to SAB Recommendations

In response to the SAB review, we have substantially modified this TSD. Key

modifications include: (a) clarifying that the analysis is watershed-focused and designed to 

assess risk for female subsistence consumers (and not a representative characterization of risk for 

recreational anglers), (b) refining and improving the technical presentation in general, including 

addition of a sample calculation figure clarifying how the watershed-level risk estimates are 

generated, (c) expanding the discussion of how the fish tissue mercury database was developed, 

(d) incorporating additional fish tissue mercury data for states with high levels of U.S. EGU 

mercury deposition, (e) verifying the linkages between type of fish tissue mercury measured, 

type of fish consumption rates used and application of a cooking/preparation adjustment factor, 

(f) inclusion of sensitivity analysis involving median watershed-level fish tissue mercury levels,

(g) expanded discussion of key sources of variability and uncertainty, and (h) de-emphasized the 

IQ loss estimates and moved those estimates to an appendix In response to public comments, we 

have also moved the previously titled “Hotspot Analysis” to a separate TSD titled “Potential for 

Excess Local Deposition of Mercury in Areas near U.S. EGUs”. 

. 

Key Observations

 Based on a combination of the two risk metrics, and reflecting consumption rates ranging 

from the 90th percentile to the 99th percentile, we estimate that 22% to 29% of the 

watersheds modeled have populations that are potentially at-risk due to U.S. EGU Hg 

emissions in 2016.

o Based on U.S. EGU-attributable deposition considered alone, without taking into 

account other sources of deposition, 10% of the modeled watersheds have 

populations that are potentially at-risk in 2016 (based on the 99th percentile 

consumption rate) (2016 is the year when regulations under Section 112 of the 

Clean Air Act would need to be implemented for EGUs).

o In 2016, 24% of the watersheds have populations that are potentially at-risk due to 

total deposition with at least 5% of that total deposition attributable to U.S. EGUs 

(based on the 99th% percentile consumption rate). 

 These risk results reflect U.S. EGU-attributable deposition. In 2016, we estimate that for 

watersheds modeled in the risk assessment, U.S EGUs contribute up to 16% of total 

mercury deposition and related fish tissue mercury concentrations. On average, for these 
xi

modeled watersheds, U.S. EGUs contribute 3% of total mercury deposition and related 

fish tissue mercury concentrations.

 Reducing U.S. EGU-attributable mercury will reduce the magnitude of the risk from total 

mercury exposure. However, a large fraction of modeled watersheds would still have 

populations at-risk due to deposition from global sources of mercury, although the degree 

of risk would be diminished. 
1

1 Review of Analysis Approach

1.1 Introduction

The EPA completed a national-scale risk assessment for mercury (Hg) that informed the 

March 2011 finding that it is appropriate and necessary to regulate electric utility steam 

generating units in the United States (U.S. EGUs), pursuant to Section 112(n)(1)(A) of the Clean 

Air Act (CAA). That-risk assessment is documented in the Technical Support Document: 

National-Scale Mercury Risk Assessment Supporting the Appropriate and Necessary Finding for 

Coal and Oil-Fired Electric Generating Units (U.S.EPA, 2011), hereafter referred to as the 

“Mercury Risk TSD (March publication)” or “March TSD”. In making that finding, EPA 

determined that this assessment should be peer-reviewed, and that the results of the peer review 

and any EPA response to them would be published before the final rule. EPA conducted a 

formal peer review through the EPA Science Advisory Board, which provides independent 

advice and peer review to EPA's Administrator on the scientific and technical aspects of 

environmental issues. The SAB established a 22-member peer review panel with representation 

from academic institutions, industry, federal agencies, and state governments. The peer review 

charge questions and results of the peer review provided in letter form are publically available on 

the EPA Science Advisory Board website,

1

and are included in this document (see Appendix C). 

EPA reviewed the SAB comments and recommendations for strengthening the nationalscale risk assessment and developed a revised version of the risk assessment reflecting 

implementation of a number of the recommendations made by the SAB. This document 

(hereafter referred to as the “Mercury Risk TSD (revised)” or “Revised TSD” describes the 

technical approach used in completing the revised version of the risk assessment. While the 

underlying technical approach used in assessing risk remains essentially the same, some 

elements of the risk model, including some data inputs, have been modified and the presentation 

and discussion of methods and risk estimates has been significantly revised in accordance with 

SAB recommendations. 

The remainder of this revised TSD is organized as follows. We begin this section with a 

summary of the significant recommendations provided by the SAB peer review panel, and an 

overview of the changes to the technical approach made in response to specific SAB 

recommendations (section 1.1). We then provide a description of the purpose and scope of the 

analysis (section 1.2). Next, we provide an overview of the risk metrics generated and the 2-

stage risk characterization framework used to help interpret the risk estimates in a policyrelevant context (section 1.3). In section 1.4, we provide an overview of the analytical approach 

used in the analysis, with subsections addressing specific elements of the analysis. Section 1.5 

describes differences between the current risk assessment and the assessment completed in 2005 

(the 2005 Section 112(n) Revision Rule analysis). Section 1.6 presents a sample calculation that 

 

1 Link to the SAB letter (which includes the charge questions as an appendix): 

http://yosemite.epa.gov/sab/sabproduct.nsf/02ad90b136fc21ef85256eba00436459/BCA23C5B7917F5BF8525791A

0072CCA1/$File/EPA-SAB-11-017-unsigned.pdf
2

walks the reader through the process used to generate risk estimates in the analysis. This detailed 

example calculation should help the reader to see how the various inputs and intermediate 

modeling calculations come together in generating watershed-level hazard quotient (HQ)-based

risk estimates. Section 2 describes analytical results of the risk assessment including both 

intermediate modeling results and the risk estimates that are generated (a detailed overview of 

the subsections in section 2 is presented at the beginning of that section). Section 3 presents a 

summary of key observations from the analysis. 

1.1 Summary of Significant SAB Peer Review Panel Recommendations and 

Revisions Reflected in this Revised Mercury Risk TSD

In response to the comments and recommendations by the SAB as part of their review of 

the March TSD, EPA revised the technical approach and documentation for the national-scale 

Hg risk assessment. These enhancements to the technical approach are briefly described below, 

along with a summary of the original SAB comment in italics.2

 The watershed-focus of the risk assessment should be clearly stated. We revised the 

technical approach section to more clearly state that the analysis is focused on assessing 

risk for subsistence-level consumers of self-caught fish evaluated at the watershed-level.3

Specifically, we are modeling risk for a set of female subsistence fish consumer scenarios 

at those waterbodies where (a) we have measured fish tissue mercury (Hg) data and (b) it 

is reasonable to assume that high-end fishing activity could occur. We emphasize the 

point that the analysis is not a representative population-weighted assessment of risk. 

Rather, it is based on evaluating exposure scenarios. 

 Because IQ does not fully capture the range of neurodevelopmental effects associated 

with Hg exposure, this endpoint should be deemphasized (and covered in an appendix) 

and primary focus should be placed on the MeHg RfD-based hazard quotient metric. We 

modified the structure of the Revised TSD accordingly, including discussion of 

alternative neurodevelopemental endpoints (besides IQ) reflected in the MeHg RfD (see 

section 1.3).

 Clarify the rationale for using an HQ at or above 1.5 as the basis for selecting potentially 

impacted watersheds. We revised this discussion accordingly (see section 1.4.5).

 Additional detail should be provided on the characteristics of the fish tissue Hg dataset, 

including its derivation and the distribution of specific attributes across the dataset (e.g., 

number of fish tissue samples and number of different waterbodies reflected in the 

 

2 We focused this section on those SAB comments that resulted in more significant revisions to the TSD or the risk 

assessment itself. Those comments that are more editorial in nature, or address less substantial technical elements of 

the analysis, are not covered here, although we addressed those comments within the Revised TSD. The charge 

questions for the peer review together with the SAB letter response provided as part of the peer review are included 

in Appendix C (the SAB letter includes the original EPA charge questions as an attachment).

3 The assessment focuses on women of child-bearing age who consume subsistence-levels of fish that they catch or 

are caught by relatives/acquaintances and shared with them (hereafter referred to as female subsistence fish 

consumers). As discussed in section 1.4 and 1.4.3, we model a number of female subsistence fish consumer 

scenarios that are differentiated to provide coverage for different socio-economic status (SES) differentiated groups. 

In this context, “subsistence” refers to individuals who rely on noncommercial fish as a major source of protein in 

their diet (U.S. EPA, 2000). For purposes of this risk assessment, we have interpreted this as representing selfcaught fish consumption ranging from a fish meal (8 ounce) every few days to a large fish meal (12 ounces or more) 

every day.
3

percentiles provided for a given hydrologic unit code (HUC)4, number of species 

reflected across HUCs). We included additional figures and tables describing the 

derivation of the HUC-level fish tissue Hg dataset including the filtering steps to derive 

the fish tissue dataset and to generate the HUC-level percentile estimates (see section 

1.4.2). In addition, we included tables summarizing the distribution of key attributes 

within that dataset highlighted by the SAB (e.g., distribution of fish tissue sample size 

and number of species across the HUC-level estimates). We also provided a table that 

contrasts fish tissue Hg concentrations for fish greater than 7 inches in length with 

concentrations based on all fish, without filtering on size (i.e., the dataset used in the risk 

assessment, versus all fish combined). 

 Determine whether there is additional (more recent) fish tissue data for key states 

including Pennsylvania, New Jersey, Kentucky and Illinois where U.S. EGUs Hg 

deposition may be more significant. We expanded the fish tissue dataset by incorporating 

additional (newer) fish tissue data from the National Listing of Fish Advisory (NLFA) 

(which included additional data for four states including MI, NJ, PA, and MN). We also 

obtained additional data for Wisconsin (see section 1.4.2). 

 Regarding the fish tissue dataset used in the risk assessment, clarify which species of Hg 

is reflected in the underlying samples and discuss the implications of differences across 

states in sampling protocols in introducing bias into the analysis. We clarified that in 

most cases, the fish tissue is measured for total Hg. Furthermore, based on the literature, 

it is reasonable to assume that more than 90% of fish tissue Hg is methylmercury

(MeHg). Therefore, we incorporated a Hg conversion factor of 0.95 into our exposure 

calculations to account for the fraction of total Hg that is MeHg in fish (see section 

1.4.4). We also expanded the discussion of uncertainty (see Table 2-15 entry B in section 

2.7) to include the potential for different sampling protocols across states to introduce 

bias into the risk assessment. 

 Expand the uncertainty discussion related to the screening out of HUCs from the risk 

assessment to address (a) uncertainty in characterizing significant non-air sources of Hg 

loading reflected in the Toxics Release Inventory (TRI) and (b) failure to consider release 

of Hg from larger urban areas (e.g., sanitary sewer discharges) in screening out HUCs. 

We expanded the uncertainty discussion presented in Table 2-15 entry D of section 2.7 to 

address these two sources of uncertainty related to the exclusion criteria for HUCs. 

 While there is support for the use of the 75th percentile fish tissue Hg value in the risk 

assessment, there is concern that the low sampling rates reflected across the HUCs may 

low-bias the 75th percentile estimates. As noted above, we provided additional 

description of the fish tissue dataset including distribution of sample sizes and fish 

species across the HUCs, which includes an improved discussion of uncertainty and 

potential bias related to the derivation and use of the 75th percentile fish tissue levels (see 

Table 2-15 entry C in section 2.7, as well as the discussion in section 1.4.2). 

 Include a sensitivity analysis based on use of the median fish tissue Hg value (as 

contrasted with the 75th percentile value) in generating risk estimates: We also included a 

sensitivity analysis that used the 50th percentile watershed-level fish tissue Hg 

 

4

For the assessment we have selected Hydrologic Unit Code 12 (HUC12) watersheds as the spatial unit of analysis 

(see section 1.4.1). Throughout this revised TSD, unless otherwise stated, the terms “watershed” and “HUC” are 

used interchangeably to refer to HUC12’s. 
4

concentration, in addition to the 75th percentile value used in the core analysis (see 

section 2.6) 

 The SAB was generally supportive of the consumption rates used, but recommended that 

EPA expand its discussion of caveats associated with the fish consumption rates used in 

the analysis (e.g., high-end consumption rates for South Carolina reflect small sample 

sizes, the consumption surveys underlying the studies are older and behavior may have

changed, consumption rates may reflect seasonality even if they are expressed in annual 

terms). We expanded the discussion of uncertainty related to the fish consumption rates 

to address the caveats identified by the SAB (see Table 2-15 entries F and G in section 

2.7).

 The EPA needs to verify whether the consumption rates are daily values expressed as 

annual averages and whether they are “as caught” or “as prepared” (these factors have 

important implications for the exposure calculations). As noted in section 1.4.4, we 

carefully reviewed the studies underlying the fish consumption rates used in the risk 

assessment and verified that the rates are annual-average daily consumption rates and that 

they represent as prepared estimates. We also expanded the explanation of the exposure 

calculations to more completely describe the exposure factors and equation used to 

generate the average daily MeHg intake estimates for the female subsistence fish 

consumer scenarios (see section 1.4.4). 

 Need to explain exclusion of fish smaller than 7 inches in length from analysis. We 

described the rationale for the 7-inch cutoff for edible fish used in the risk analysis (see 

section 1.4.2). 

 The SAB was generally supportive of the approach used for identifying HUCs with the 

potential for subsistence activity; however, they did recommend that we identify the 

number of HUCs excluded from the analysis due to this criterion. We added a table to 

illustrate the number of HUCs with fish tissue Hg data used to model risk for each of the

female subsistence fish consumer scenarios (see section 1.4.3, Table 1-5). 

 Enhance the discussion of the proportionality assumption linking Hg deposition and fish 

tissue Hg concentrations, including more recent studies supporting the proportional 

relationship between changes in Hg deposition and changes in MeHg fish tissue levels. 

We expanded our discussion of uncertainty on the proportionality assumption and added 

citations for the more recent studies supporting the proportionality between changes in 

Hg deposition and changes in fish tissue Hg concentrations (see Table 2-15 entries I 

through M in section 2.7 for additional uncertainty discussion and section 1.4.6 for 

discussion of newer literature supporting the proportionality assumption).

 Given that there are published studies comparing measurements of wet deposition with 

CMAQ-based estimates, the presentation of Hg deposition estimates should include maps 

of wet and dry Hg deposition in addition to the total Hg deposition results used in the risk 

assessment.: We expanded the presentation of Hg modeling results to include maps of 

wet and dry deposition for both the 2005 and 2016 scenarios (see section 2.2). 

 The discussion of the concentration-response (C-R) function used in modeling IQ loss, 

should be expanded to include coverage for the potential masking effect of MeHg-related 

IQ loss due to nutrients (polyunsaturated fatty acids - PUFAs) in fish. This is particularly 

relevant if IQ loss functions derived from saltwater fish consumption are used to model 

risk associated with freshwater fish consumption. We expanded the discussion of 
5

uncertainty on modeling IQ loss to address this issue (see Table B-2 entry B in Appendix 

B).

 Additional sources of variability should be discussed in terms of the degree to which they 

are reflected in the design of the risk assessment and the impact that they might have on 

risk estimates. These include:

o The geographic patterns of populations of female subsistence fish consumers, 

including how this factor interacts with the limited coverage we have for 

watersheds with our fish tissue Hg data.

o The protocols used by states in collecting fish tissue Hg data.

o Fisher body weights and the impact that this might have on exposure estimates.

o Preparation and cooking methods which effects the conversion of fish tissue Hg 

concentrations (as measured) into “as consumed” values.

We expanded the discussion of sources of variability presented in Table 2-14 (in section 

2.7) to more fully address these sources of variability as requested. 

 Additional sources of uncertainty should be discussed in terms of their potential impact 

on risk estimates. These include:

o Emissions inventory (including the non-EGU segment) used in projecting total 

and U.S. EGU-attributable Hg deposition. This includes the projection of 

reduction in U.S. EGU emission for the 2016 scenario.

o Air quality modeling with CMAQ including the prediction of future air quality 

scenarios.

o Ability of the Mercury Maps-based approach for relating Hg deposition to MeHg 

in fish to capture Hg hotspots.

o The limited coverage that we have with fish tissue Hg data for watersheds in the 

U.S. and implications for the risk assessment.

o The preparation factor used to estimate “as consumed” fish tissue Hg 

concentrations.

o Proportionality assumption used to relate changes in Hg deposition to changes in 

fish tissue Hg concentrations at the watershed-level.

o Characterizing the spatial location of female subsistence fish consumer 

populations (including degree to which these provide coverage for highconsuming recreational fishers).

o Application of the RfD to low SES populations and concerns that this could lowbias the risk estimates.

We expanded the discussion of sources of uncertainty presented in Table 2-15 (in section 

2.7) to more fully address these sources of uncertainty and the potential impact on risk 

estimates.

1.2 Purpose and Scope of Analysis

This document is a revision to the March TSD, and provides a revised description of the 

national-scale risk assessment for Hg that was completed to inform the finding that it is 

appropriate and necessary to regulate electric utility steam generating units in the United States 

(U.S. EGUs), pursuant to Section 112(n)(1)(A) of the Clean Air Act (CAA). The appropriate and 

necessary finding is based, in part, on an assessment of the potential public health hazard 
6

associated with Hg emitted from U.S. EGUs (see Section III of the preamble to the proposed 

Mercury and Air Toxics Standard (MATS) for U.S. EGUs).

5 Consequently, the risk assessment 

was designed to assess whether a potential public health hazard is associated with Hg emitted 

from U.S. EGUs. Because the focus of the analysis is to determine whether a potential public 

health hazard exists and not to characterize the full range of risk associated with exposure to Hg 

emitted from U.S. EGUs, this risk assessment can be viewed as a public health hazard screening 

analysis. As such, the primary objective is to determine whether individuals exposed to Hg 

emitted from U.S. EGUs through high-end consumption of freshwater self-caught fish have the 

potential to experience significant risk. Conversely, the risk assessment is not intended to 

represent a comprehensive assessment of risk for all segments of the population potentially

exposed to Hg emitted from U.S. EGUs. It is beyond the scope of this risk assessment to assess 

the residual risk remaining after accounting for emissions reductions anticipated from MATS.

Given the purpose for the analysis, the following policy-relevant questions were 

developed to guide the design of the risk assessment: 

(a) What is the nature and magnitude the potential risk to public health in the 

United States for individuals experiencing reasonable high-end exposure to Hg 

in freshwater, self-caught fish that is attributable to current U.S. EGU Hg 

emissions?

(b) What is the nature and magnitude of the potential risk for this same group of 

individuals based on projected U.S. EGU Hg emissions in 2016 considering 

potential reductions in EGU Hg emissions attributable to CAA requirements?

6

and 

(c) How is total risk from Hg exposure as estimated for both the current and future 

scenarios apportioned between the U.S. EGUs and other sources of Hg? The 

last policy-relevant question reflects the fact that Hg emitted from U.S. EGUs 

does not result in a distinct and isolated exposure pathway, but rather is 

combined with Hg emitted from other sources (domestic and international) in 

contaminating fish. Therefore, to consider U.S. EGU contributions to exposure 

and risk associated with the consumption of fish containing Hg, we determine 

what share of total Hg exposure is attributable to U.S. EGUs. 

In addition to the above policy-relevant questions, the overall design and scope of the risk 

assessment reflects consideration of a number of important technical factors related to airsourced Hg, including, in particular, Hg released from U.S. EGUs:

 While Hg exposure and risk can occur through a variety of pathways, the dominant 

human exposure pathway associated with ambient air emissions is through the 

consumption of fish that have bioaccumulated Hg originally deposited to watersheds 

following atmospheric release and transport. Deposition of Hg to watersheds includes Hg 

originating from local/regional sources, combined with Hg that has been transported 

 

5

76 FR 24976

6

For purposes of this analysis, we focus on 2016 as this is the year when compliance with mercury standards would 

be required to occur.
7

regionally and globally from global anthropogenic and natural sources. Generally 

oxidized (divalent) and particle-bound Hg will deposit relatively closer to the release 

source, while elemental Hg will travel much further, often becoming part of the global 

pool, before being deposited.7

 The scientific literature on mercury health effects provides the strongest support for 

quantifying neurological deficits in children who were exposed to MeHg in utero through 

maternal fish consumption.8

 Mercury emitted by U.S. EGUs is likely to make a small contribution to MeHg in 

foreign-sourced commercial fish consumed in the U.S. and in commercial fish caught 

further off the U.S. coast. Therefore, this risk assessment, while acknowledging these 

sources of exposure to U.S. EGU-sourced Hg, does not quantify these risks since the U.S. 

EGU-attributable portion of these risks is likely to be small and any quantitative 

estimates of U.S. EGU-attributable risk would be highly uncertain.9

 While areas closer to the U.S. coast (including estuarine areas) as well as the Great Lakes 

may have elevated U.S. EGU deposition in some cases, because of uncertainty in 

modeling the linkage between U.S. EGU-attributable deposition and Hg in fish, we have 

not included this commercial consumption pathway in the quantitative risk assessment.10 

 The type of fish consumption likely to result in exposures to Hg with the greatest 

contributions from U.S. EGU Hg emissions is associated with fishing activity at inland 

freshwater rivers and lakes located in regions experiencing relatively elevated U.S. EGU 

Hg deposition. Some watersheds had U.S. EGU contributions ranging up to 11% and 

 

7 Mercury is a persistent, bioaccumulative toxic metal that is emitted from power plants in three forms: Gaseous 

elemental Hg (Hg0), oxidized Hg compounds (Hg+2), and particle-bound Hg (HgP). Elemental Hg does not quickly 

deposit or chemically react in the atmosphere, resulting in residence times that are long enough to contribute to 

global scale deposition. Hg(2+) and Hg(p) deposit quickly from the atmosphere impacting local and regional areas 

in proximity to sources. 

8 The EPA’s health benchmark for methylmercury exposure (the reference dose or RfD) is based on three 

epidemiological studies. These studies relate hair mercury levels in mothers (a surrogate for exposure in utero) or 

mercury in cord blood (a direct measure of fetal exposure) to deficits in children’s performance on a range of neurocognitive tests (see section 1.4.5).

9 While mercury emitted from U.S. EGUs does contribute to contamination of foreign-sourced commercial fish, the 

fraction contributed by U.S. EGUs is small. Current estimates of U.S. EGU mercury emissions are ~29 tons per year 

(see section 2.3), compared with global anthropogenic mercury emissions (for 2005), excluding biomass burning, 

estimated at approximately 2,320 tons (Pirrone et al., 2010; UNEP, 2010). Since the mercury in commercially 

caught foreign-sourced fish also includes contributions by natural sources and re-emitted natural and anthropogenic

sources, estimated to be as high as 5,207 metric tons/year (Pironne et al, 2010; UNEP, 2010), the fraction 

contributed by U.S. EGUs is quite small. Therefore, particularly in the context of estimating individual risk, U.S. 

EGU contributions to risk that residents in the US experience through consumption of commercially caught foreignsourced fish, at present, is expected to be too small to characterize, given the uncertainties in determining the 

fraction of mercury that is from U.S. EGUs. This observation would also likely hold for the U.S. EGU contribution 

to commercial fish sourced from further off the U.S. coast, where total mercury loading is likely to also be 

dominated by non-U.S. anthropogenic emissions which are globally transported.

10 While air quality modeling does suggest that some near coastal areas (e.g. the Chesapeake Bay) and portions of 

the Great Lakes may have elevated U.S. EGU deposition relative to the average levels in the continental U.S., 

several factors make modeling how changes in mercury deposition affect fish tissue Hg concentrations in these nearcoastal areas and the Great Lakes challenging and uncertain. Specifically, the size of these waterbodies relative to 

inland lakes and rivers and the potential for fish to have larger habitats makes it difficult to quantify the EGU 

contribution to fish tissue Hg concentrations in these locations. Due to the greater uncertainty associated with 

modeling near coastal watersheds, we have elected not to simulate this pathway in the risk assessment. Because we 

have not assessed this pathway, this risk assessment may underestimate the number of at-risk watersheds in the U.S. 
8

higher in 2016 (see Section 2.4). Therefore, efforts to identify areas with high U.S. EGUattributable MeHg exposures and risk are focused on assessing risk for those areas that

have (a) relatively elevated fish tissue Hg concentrations and (b) relatively elevated levels 

of U.S. EGU Hg deposition. 

Reflecting consideration of the policy questions and technical factors discussed above, 

the scope of the national-scale Hg risk assessment is as follows. 

 Hg deposition was modeled for two years, 2005 and 2016: The 2005 deposition is used 

to represent deposition that is reflected in the sampled fish tissue MeHg levels for 2000 –

2009. The projected 2016 deposition reflects emissions after imposition of CAA 

requirements. The available emissions data (see Section 2.3) suggest that current 2010 

U.S. EGU emissions are closer to levels reflected in the projected 2016 Scenario and 

substantially lower than levels reflected in the 2005 Scenario. As a result, the 2016 

Scenario analysis is most relevant for this rulemaking. Further modeling of future 

emissions indicates that in the absence of binding federal regulations U.S. EGU 

emissions are not likely to be substantially reduced between 2010 and 2016 (although the 

CAA requires the Agency to consider only federal CAA requirements in estimating 

future HAP emissions and attendant risks associated with EGUs). The 2016 Scenario is 

thus used to estimate risks related to both current emissions and emissions after 

implementation of the CAA requirements and risks are not generated for the 2005 

scenario.11 The estimated Hg emissions for U.S. EGUs in 2016, used in the deposition 

modeling for this TSD, is 29 tons. 

 Focus on assessing risk for female subsistence fish consumers of self-caught fish obtained 

from inland freshwater watersheds: Given the goal of determining whether a public 

health hazard is associated with U.S. EGU emissions, we have assessed risk for those 

individuals likely to experience the greatest exposure and risk from consuming fish 

impacted by Hg emitted from U.S. EGUs. This translates into a focus on women of childbearing age who consume subsistence-levels of fish caught from inland freshwater 

waterbodies.

12 By focusing on inland waterbodies, we are focusing on those locations 

with the greatest U.S. EGU-attributable Hg deposition (see Figure 2-4) and consequently 

the greatest U.S. EGU-attributable fish tissue Hg concentrations. By focusing on 

subsistence-level fish consumption scenarios, we focus on those self-caught fish 

consumers with the highest intake rates and therefore, those who will experience the 

greatest Hg exposures at a given watershed. In defining the high-end fisher populations 

to include in the analysis, we have used peer-reviewed study data to characterize 

consumption rates for a variety of high-consuming fisher populations that reflect different 

SES groups and are active in different regions of the country (e.g., Laotians, Great Lakes 

Tribal populations, Black and White anglers active in the Southeast - see Section 1.4.3).

As noted earlier in section 1.1 for the analysis, we focus on women of child-bearing age 

 

11 Deposition estimates for the 2005 scenario are used in scaling fish tissue Hg concentrations to represent future 

fish tissue levels in 2016, which are then used in turn to model total risk for the 2016 scenario (see section 1.4.6).

12 While subsistence-level consumption can reflect consumption by individuals whose SES status compels them to 

supplement their diet with self-caught fish, elevated levels of inland freshwater self-caught fish consumption can 

also be experienced by recreational anglers who fish often and consume a large amount of the fish they catch.
9

since the MeHg RfD used in assessing risk is based on neurodevelopmental effects in 

children exposed to mercury in utero.

 Watershed-level assessment based on modeling female subsistence fish consumer risk at 

watersheds where there is the potential for this type of high fish-consuming activity and 

where we have fish tissue Hg data: The risk assessment is conducted at the watershed 

level, focusing on the subset of inland watersheds in the U.S. where we have fish tissue 

Hg data and where we believe high fish-consuming subsistence fisher populations could 

be active.13 Specifically, we generate a set of risk estimates for each watershed reflecting 

the type of subsistence fisher activity that could potentially exist at that watershed. 

Because it is not possible to enumerate these high-end fisher populations, the risk 

estimates that are generated are not population-weighted and instead are given a uniform 

weight for each watershed-level risk estimate generated.

14 This focus on subsistence 

fishing activity and associated consumption reflects the fact that this is a screening-level 

analysis for risk associated with mercury emitted from U.S. EGUs and is not intended to 

provide a comprehensive picture of the distribution of risk across all types of fishers 

potentially active at watersheds where we have Hg fish tissue data. 

 Exclude commercial fish consumption from the quantitative risk analysis. Although risk 

associated with commercial fish consumption may be a potential public health concern 

under certain circumstances, the relatively low contribution of U.S. EGU Hg to this 

source of dietary fish (relative to non-US Hg emissions), leads us to exclude this 

consumption pathway from the risk assessment and to focus instead, on high-end selfcaught fish consumers. In the specific case of commercial fish sourced from near the U.S.

coast (e.g. Chesapeake Bay) and the Great Lakes, while there is the potential for U.S. 

EGUs to have a greater role in affecting Hg levels in these fish, as noted earlier, 

uncertainty associated with modeling the linkage between U.S. EGU Hg deposition and 

Hg exposure and risk for this dietary pathway precludes us from including this pathway

in the risk assessment. We do not expect that exclusion of these scenarios will result in 

significant downward bias in our overall estimates of the proportion of watersheds with 

populations potentially at-risk due to U.S. EGU emissions of Hg, although the absolute 

number of watersheds will be understated.

 

13

 With the exception of the typical female subsistence fish consumer scenario (which is assessed across all 

watersheds with fish tissue Hg data), the potential for the other SES-differentiated female subsistence fish consumer 

scenarios to be active at a given watersheds is based on determining whether a group of similar SES-differentiated 

individuals (referred to as a source population) lives in the vicinity of the watershed (see section 1.4.3). 

14 In order to enumerate risk estimates generated for the female high-end consumer scenario used in this risk 

assessment, we would need to have the following types of specific information: (a) the fraction of anglers who 

consume at the subsistence-levels modeled for this population specifically at inland freshwater waterbodies, (b) for 

this population, the fraction that focus their activity at individual watersheds, and target somewhat larger fish to 

supplement their diet, and (c) for this subgroup, the fraction of female consumers of childbearing age who either fish 

themselves and consume at this level, or obtain and regularly consume fish provided by fishers who focus their 

fishing efforts at the individual watershed.. However, currently available information does not allow us to estimate 

each of these subgroups of high-consuming fishers. Specifically, while we have data on the frequency of 

recreational angling within the U.S., this covers general recreational fishing and not subsistence fishing. In this 

analysis, we have focused on a subset of female subsistence-level fish consumers that we believe (a) could 

potentially exist at a subset of watersheds evaluated in this analysis and (b) are likely to experience higher risk due 

to their behavior (i.e., favor larger fish as a dietary source, likely to consume fish obtained primarily from individual 

watersheds, consume larger amounts of self-caught fish). While we believe it is reasonable to assume that a subset 

of high-end fishers would have these attributes, it is not possible at this point to definitively state at which 

waterbodies they are active or to enumerate them for purposes of generating population-weighted risk distributions.
10

 Include estimates of total risk from all Hg deposition sources, as well as the U.S. EGU 

incremental contribution to total risk. As discussed below (Section 1.2), we focus on two 

aspects of MeHg-related risk: (a) total Hg risk, with an estimate of the percent of that 

total risk contributed by U.S. EGUs (i.e., the fraction of total risk associated with U.S. 

EGUs) and (b) risk when deposition from U.S. EGUs is considered alone, before taking 

into account deposition and exposures resulting from other sources of Hg. These two risk 

metrics reflect the cumulative burden of Hg exposures and incremental contribution that 

the U.S. EGU-attributable deposition makes to overall exposures to MeHg.15 

1.3 Overview of Risk Metrics and the Risk Characterization Framework

The risk assessment generates hazard quotient (HQ) estimates by comparing estimates of 

modeled potential exposure for subsistence fisher populations to the MeHg RfD. In addition to 

the HQ estimates, Appendix A provides estimates of IQ loss in children born to mothers from 

these high fish-consuming subsistence fishing populations. Because of concerns expressed both 

by the SAB and by EPA staff that IQ loss may not fully capture the full range of 

neurodevelopmental deficits in children exposed to MeHg in utero, we focus on the HQ 

estimates in making our determination of the public health hazard associated with U.S. EGU Hg 

emissions.16

As mentioned above in section 1.2 and discussed in greater detail in sections 1.4 and 

1.4.6, we generate risk estimates at the watershed-level for the subset of watersheds in the U.S. 

where we have Hg fish tissue data and where we believe the potential exists for high-end fish 

consumption due to the presence of subsistence fishers. As noted earlier, limitations in 

quantifying the number of high-consuming fishers active across the set of modeled watersheds 

prevents us from generating population-weighted risk distributions. However, we do use the 

watershed-level risk estimates (with uniform weighting across watersheds) to generate risk 

 

15 When exposures are to be compared to the EPA’s reference dose (RfD) for MeHg in order to generate a hazard 

quotient (HQ), we must first consider total MeHg exposure given the definition of the RfD, which is intended to be 

compared against total exposure to a given hazardous air pollutant. Once an HQ reflecting total exposure is 

calculated, we can then consider the U.S. EGU incremental contribution to that total risk. However, U.S. EGU

incremental risk in the form of an HQ should not be considered in isolation without considering total risk associated 

with MeHg in consumed fish. 

16 Concerns have been raised in the literature that if mercury affects a set of specific neurological functions, then use 

of full-scale IQ as the modeled health endpoint, could underestimate the neurodevelopmental impacts on other 

targeted functions (Axelrad et al., 2007). In addition, two of the most sensitive endpoints in the Faroe Islands study 

were the Boston Naming Test and California Verbal Learning Test, both of which can represent a significant 

educational risk depending on severity, and those tests are not directly assessed as part of measuring IQ in children. 

In addition, IQ does not cover other neurologic domains such as motor skills and attention/behavior and therefore, 

risk estimates based on IQ will not cover these additional endpoints and could further underestimate overall 

neurodevelopmental impacts (Axelrad et al., 2007). The wide range of neuropsychological effects potentially 

associated with Hg exposure has also been highlighted by Grandjean et al., (1997) who described developmental 

delays in verbal skills, learning and short-term memory, and more recently by Wijngaarden et al., (2006), who 

provided benchmark dose calculations for 26 endpoints including a number of neuropsychological measures. These 

studies highlight the range of neurodevelopmental effects in children potentially associated with Hg exposure. In 

contrast to the IQ loss metric, the HQ metric based on the RfD reflects consideration for a wider array of 

neurodevelopmental effects in children (e.g., Boston Naming Test, Continuous Performance Test, California Verbal 

Learning Test, McCarthy Perceived Performance, McCarthy Motor Test, finger tap – U.S. EPA’s Integrated Risk 

Information System in 2001 - http://www.epa.gov/iris/subst/0073.htm ).
11

distributions from which we can identify specific risk percentiles, (e.g. the 95th percentile of risk 

across watersheds). In this context, the distribution of modeled subsistence fisher risk estimates 

represents the range of risk across modeled watersheds based on assessing the same subsistence 

fisher scenario at each watershed (without population-weighting). Percentile risk estimates 

obtained from this distribution are for the population of watersheds, rather than the population of 

subsistence fishers. 

Reflecting the goals of the analysis presented in Section 1.2, the HQ risk metrics are 

calculated for both total risk and U.S. EGU-attributable risk. In considering U.S. EGUattributable risk, we generate two types of risk estimates:

 The percent or fraction of total risk attributable to U.S. EGUs at watersheds where total 

risk is considered to pose a potential public health hazard: We consider the magnitude of 

total HQ risk, identifying those watersheds with total MeHg exposure exceeding the RfD 

(i.e., an HQ>1) and then estimate the fraction (or percent) of that total risk that is 

attributable to U.S. EGUs. 

 Risk focusing on U.S. EGU deposition and excluding other non-U.S. EGU sources: For 

this metric, we estimate risk based on the U.S. EGU incremental contribution to total 

exposure. Specifically, for HQ, we compare U.S. EGU-attributable exposure against the 

MeHg RfD.

In assessing the potential public health significance of HQ estimates, we considered total 

MeHg exposures above the RfD to represent a potential public health hazard.17

Risk Characterization Framework

We have developed a 2-stage framework for using the risk metrics described above to 

address the policy-relevant questions outlined in section 1.2. This 2-stage framework is 

illustrated in Figure 1-1 and each of the stages is also described below (throughout this 

document, this will be referred to as the “risk characterization framework”):

Stage 1 – Identify watersheds with populations potentially at-risk due to U.S. EGU Hg based 

on application of the two risk metrics:

a) The percent or fraction of total risk attributable to U.S. EGUs at watersheds 

where total risk is considered to pose a potential public health hazard : Here we 

identify watersheds with populations potentially at-risk due to U.S. EGU Hg by 

identifying (a) those watersheds where total risk meets or exceeds levels 

 

17 EPA’s interpretation for this assessment is that any exposures to MeHg above the RfD are of concern given the 

nature of the data available for Hg that is not available for many other chemicals, where exposures have often had to 

be significantly above the RfD before they might be considered as causing a hazard to public health. The scientific

basis for the mercury RfD includes extensive human data and extensive data on sensitive subpopulations including

children exposed in utero ; therefore, the RfD does not include extrapolations from animals to humans, and for 

database deficiencies. In addition, there is no evidence for a biological threshold observed for critical effect of 

neurological deficits in children studied in the principal studies of the IRIS assessment for MeHg. This additional 

confidence in the basis for the RfD suggests that all exposures above the RfD can be interpreted with more 

confidence as causing a potential hazard to public health.
12

considered to represent a potential public health hazard (i.e., HQ > 1); and (b) 

U.S. EGUs contribute to total risk at this subset of watersheds with elevated risk 

(we have considered various increments of U.S. EGU contribution ranging from 5 

to 15%). Any contribution of Hg emissions from U.S. EGUs to watersheds where 

potential exposures from total Hg deposition exceed the RfD is a hazard to public 

health, but for purposes of our analyses we evaluated only those watersheds 

where we determined U.S. EGUs contributed 5 percent or more to Hg deposition 

in the watershed. EPA believes this is a conservative approach given the 

increasing risks associated with incremental exposures above the RfD. 

b) Risk focusing on U.S. EGU deposition and excluding other non-U.S. EGU 

sources:: Here we identify watersheds with populations potentially at-risk due to 

U.S. EGU-attributable risk (prior to considering Hg contributed by other sources). 

Although this metric focuses on U.S. EGU exposure, it is important to keep this 

incremental exposure in perspective with regard to total MeHg exposure in which 

non-U.S. EGU sources of deposition typically dominate the U.S. EGU increment 

across watersheds.

Stage 2 – calculate the combined (total) number of watersheds and percentage of watersheds

where populations may be at-risk from U.S. EGU-attributable Hg: Here we combine 

estimates from Stages 1a and 1b to consider watersheds where populations may be atrisk due to (a) U.S. EGUs contributing to exposures at watersheds where total risk 

potentially poses a potential public health hazard or (b) U.S. EGUs making an 

incremental contribution to total Hg exposure which, when considered separate from 

total Hg exposure, represents a potential public health hazard.

This framework allows us to identify watersheds where U.S. EGU-related exposure 

considered separately, or as a portion of total risk, represents a potential public health hazard. 

More specifically, it allows us to estimate the number and percentage of watersheds where 

populations may be at-risk due to U.S. EGU-related Hg emissions. 
13

Figure 1-1. 2-Stage Risk Characterization Framework

1.4 Overview of Analytical Approach

This section provides an overview of the analytical approach for this risk assessment, 

which we illustrate in Figure 1-2. In this figure, we identify the subsections of the TSD with 

more detailed technical information on each of the analytical steps. We also provide a detailed 

example calculation of the total HQ and U.S. EGU incremental HQ at the watershed-level in

section 1.6.

Stage 1a: watersheds where total 

MeHg exposure exceeds the MeHg

RfD and where US EGUs make a 

substantial contribution (e.g., 5%) to 

that exposure.

Stage 1b: watersheds where U.S. 

EGU deposition, if considered in

isolation, could result in MeHg

exposure that exceeds the MeHgRfD.

Stage 2: total number of watersheds with 

populations potentially at-risk due to Stage 1a 

or Stage 1b analyses (i.e., union of A and B) :

Stage 2 = A + B - C

Some watersheds are identified in 

both stages (these are not doublecounted in the total Stage 2 

number).

A C B
14

Figure 1-2 Flow Diagram of Risk Analysis (for the 2016 air quality scenario) Including 

Major Analytical Steps and Associated Modeling Elements

The risk assessment assesses risk to female subsistence fish consumers from potential 

exposure to MeHg from consuming fish caught in U.S. watersheds where we have measured fish 

tissue Hg concentration data and where we have determined that subsistence fishing activity

could occur. As noted earlier in section 1.2, risks are only estimated for the 2016 scenario since 

Specifying 

spatial

scale of 

watersheds

(section 

1.4.1) Characterizing 

measured fish 

tissue Hg

concentrations at 

the watershed level

(section 1.4.2) Characterize female subsistence 

consumer scenarios to model

Estimating total fish consumptionrelated MeHg exposure for fishers/

consumers active at each watershed

(section 1.4.4)

Studies 

characterizing fish 

consumption rates 

for freshwater 

subsistence fisher 

populations

Defining female subsistence consumer scenarios to 

include in the analysis (section 1.4.3)

Estimation of U.S. EGUattributable risk at each watershed 

for the 2016 scenario

(section 1.4.6)

CMAQ mercury

deposition modeling for 

total and US EGU Hg 

deposition at each 

watershed for the 2005 

and 2016 air quality 

scenarios

(section 1.4.6.1)

Estimate risk (RfD-based

hazard quotient) at each 

watershed for the 2016 scenario 

(section 1.4.5)

Mercury

RfD for HQ 

estimation

Exposure modeling

Risk modeling

KEY: decision data input analysis step

Identify which watersheds 

will be modeled for each 

of the SES-differentiated 

female subsistence 

consumer scenarios* 

* A more generalized typical female subsistence consumer scenario is also included in the analysis , which is 

evaluated at all watersheds with fish tissue Hg data without consideration for the presence of a source population . 

Due to its broader geographic coverage, this scenario receives the greatest focus in presenting risk estimates. 

Project fish tissue Hg

concentrations for the 2016 

scenario at each watershed

(section 1.4.6)
15

underlying U.S. EGU emissions used in modeling this scenario are closer to actual 2010 

emissions than are emissions estimates used in modeling the 2005 scenario. However, as 

discussed below, Hg deposition estimates for the 2005 scenario are used in scaling fish tissue Hg 

levels to represent future levels associated with the 2016 scenario (which are in turn used in 

modeling risk for the 2016 scenario). We provide a brief description of each analytical step of 

the risk analysis reflected in Figure 1-2:

11. Model mercury deposition: We modeled total and U.S. EGU-attributable mercury

deposition for the continental U.S. for 2005 and 2016 using the CMAQ model at 12 km 

grid resolution. (See section 1.4.6.3).

12. Specify the spatial scale of the watersheds: We selected Hydrologic Unit Code (HUC) 12 

as the appropriate spatial scale for watersheds, which tend to measure a few kilometers 

(km) on a side and match up with the spatial resolution of the mercury deposition 

modeling. We interpolated the gridded mercury deposition estimates to deposition for 

each watershed in the continental U.S. (See section 1.4.1)

13. Characterize measured fish tissue Hg concentrations (75th percentile) at each watershed: 

We estimate the 75th percentile fish tissue Hg concentrations based on measurement data 

collected primarily by the states collected from 2000-2010 for inland freshwater fish 

species larger than 7 inches. We excluded watersheds without fish tissue data from 

remainder of the analysis. (See section 1.4.2). The fish tissue data did not include 

saltwater or estuarine fish.

14. Project 75th percentile fish tissue Hg concentrations for the 2016 scenario: We used the 

assumption of a proportional relationship between Hg deposition and fish tissue Hg 

concentrations together with deposition estimates for the 2005 and 2016 scenarios to 

project fish tissue Hg concentrations for the 2016 scenario. Specifically, we used the ratio 

of 2016 deposition to 2005 deposition at a given watershed to adjust the 75th percentile 

fish tissue Hg concentration discussed in Step 3, to represent a 2016 concentration for 

that watershed (see section 1.4.2.1).

15. Define female subsistence fish consumer scenarios: The analysis focused on females who 

consume subsistence-levels of fish that were caught at inland freshwater waterbodies. 

Our literature review identified seven female subsistence fish consumer scenarios that we 

selected to include in the analysis: (1) typical female subsistence fish consumer, (2) low 

income White fishers in the southeast, (3) low income Black fishers in the southeast, (4) 

low income Hispanic fishers, (5) Vietnamese fishers, (6) Laotian fishers, and (7) Tribal 

fishers (Chippewa active near the Great Lakes). (See section 1.4.3). 

16. Identify watersheds with subsistence fisher population activity: For the typical female 

subsistence fish consumer scenario, we assume subsistence fisher population activity at 

all watersheds where we have fish tissue Hg data. For the Tribal fishers, we assume 

activity within all watersheds with fish tissue Hg data located within territories ceded to 

these tribes. For the remaining scenarios, we use demographic data to determine if at least 

25 individuals with SES attributes matching those of the subsistence consumer 

population are present in the vicinity of a watershed, and if so, we assume that 

subsistence fishing activity might occur at that watershed. (See section 1.4.3)
16

17. Define self-caught fish consumption rates for the subsistence scenarios: We used survey

data published in the literature to identify self-caught fish consumption rates for each 

female subsistence fish consumer scenario. In each case, at the high end (e.g., 90th to 99th

percentiles), consumption rates reached subsistence levels, ranging from an 8oz selfcaught fish meal every few days to an 8oz meal every day and higher in some cases. (See 

section 1.4.3). 

18. Estimate total fish consumption-related MeHg exposure (2016 scenario): We estimated 

exposure in the form of daily-average MeHg intake at the watershed-level for each

female subsistence fish consumer scenario potentially active at a given watershed. These 

estimates of exposure use the projected 75th percentile fish tissue Hg concentrations for 

2016 described in Step 4 above. We were careful to match the fish sampling data with 

type of sampling (e.g., filet skin on, whole fish), cooking adjustment, and type of 

consumption rates (e.g., as purchased, as consumed). The exposure estimates represent 

potential exposure to MeHg from fish caught in the watershed, not population-weighted 

exposures. (See section 1.4.4)

19. Estimate of total MeHg risk (RfD-based HQ) at each watershed (2016 scenario): We 

compare the watershed-level exposure estimates modeled for each female subsistence 

fish consumer scenario (for 2016) to the MeHg RfD to generate HQ estimates for total 

MeHg exposure. (See section 1.4.5)

20. Estimate of U.S. EGU-attributable risk (2016 scenario): We used the same

proportionality assumption discussed in Step 4, together with total and U.S EGUattributable Hg deposition estimates (generated at the watershed-level) for the 2016 

scenario to estimate the fraction of total HQ (for the 2016 scenario) attributable to U.S. 

EGUs. Specifically, we used the ratio of U.S. EGU-related deposition to total deposition 

(for the 2016 scenario) at a given watershed calculate the fraction of total risk (generated 

in Step 9) that is attributable to U.S. EGUs (see section 1.4.6).18

1.4.1 Specifying the spatial scale of watersheds 

The first step in designing the analysis was to specify the spatial scale of the watersheds 

to use as the basis for risk characterization. As noted above, this risk assessment is based on 

estimating risk at watersheds for which we have measured Hg fish tissue data. Two studies 

(Knights et al., 2009, Harris et al., 2007) examining the response of aquatic freshwater 

ecosystems to changes in Hg deposition focused on watersheds with dimensions closest to HUC12. In each of the studies, researchers used watersheds reflecting a fairly refined spatial scale 

 

18 Given the assumption that fish tissue Hg concentrations and exposure and risk are linearly related, the 

proportionality assumption (together with the ratio of U.S. EGU-related to total Hg deposition for the 2016 scenario) 

could be applied either to the fish tissue Hg concentrations or to total risk in order to generate U.S. EGU-attributable 

risk. For the analysis, as reflected in Step 10, we applied the proportionality assumption and deposition ratio once 

total risk (for the 2016 scenario) had been generated. However, we could have used the proportionality assumption 

to estimate the fraction of the 75th percentile fish tissue Hg concentration (projected for the 2016 scenario) that was 

attributable to U.S. EGUs at each watershed and then used that fractional fish tissue value to estimate U.S. EGUattributable risk for the 2016 scenario. Both approaches would generate the same risk estimate, reflecting the 

underlying linearities in the exposure and risk models.
17

(approximately 5-10 km on a side). This suggests that, at least in the context of these studies, 

researchers believed that the relationship between changes in Hg deposition and changes in 

MeHg levels in aquatic biota could be effectively explored at the level of these more spatially 

refined watersheds. Each of the studies is briefly summarized below.

The Knights et al., 2009 study focused on characterizing the temporal pattern of

reductions in fish tissue Hg concentrations following reductions in Hg deposition over 

waterbodies and associated watersheds. This study relied on modeling and included simulation 

of five different types of waterbodies ranging from a seepage lake (with little watershed loading) 

in Florida to a stratified drainage lake in NH. The scale of the five watersheds included in the 

Knights et al., 2009 study range from 20 by 100 km (for the coastal plain river location in GA) to 

5 by 10km (for the Lake Waccamaw NC site). Three of the five locations had watersheds in the 

10 by 10km range (see Figure 2 in the article). Given that the majority of locations in the study

had smaller watersheds (i.e., in the 10 by 10km range), we conclude that this would represent a 

reasonable watershed spatial scale to use in linking changes in aerial deposition to changes in 

fish tissue levels (i.e., as the basis for risk characterization in the analysis).

An article by Harris et al., 2007, which is based on the METALLICUS study (specifically

lake 658 catchment in northwestern Ontario, Canada), also examined the temporal profile 

associated with changes in media and biota Hg levels following a change in Hg deposition. In 

this study, a 3-yr loading of radio-labeled Hg to the waterbody and watershed (separate labeled 

Hg applied to each location) was followed by measurement of Hg in various media and biota to 

see how long it took for the loaded Hg to impact different media compartments. The single 

watershed involved in this study is relatively small (only a few km on a side). Therefore, the 

spatial scale of the watershed involved in this study also supports use of a more refined spatial 

scale for watersheds in the risk assessment. 

In addition to considering the scale of watershed reflected in these two studies of Hg

loading response, use of a more refined spatial scale (i.e., use of HUC12s rather than a coarser 

scale of watershed) in linking changes in Hg deposition to changes in fish tissue Hg 

concentrations also reduces the potential for averaging out areas of high Hg deposition. The 

HUC12 represents the most refined scale of watershed currently available at the national level 

and therefore was chosen as the basis for linking changes in Hg deposition to changes in fish 

tissue Hg concentrations. Conversely, use of larger watersheds, while allowing us to model 

more of the country in the risk assessment, could result in the unwarranted dilution of areas of 

elevated Hg deposition from U.S. EGUs (and therefore, by association, the dilution of U.S. 

EGU-attributable Hg exposure and risk). The SAB expressed support for the use of HUC12-scale 

watersheds.

1.4.2 Characterizing measured fish tissue Hg concentrations at the watershed level

The next step in the analysis was to characterize fish tissue Hg concentrations at the 

watershed level using measured fish tissue Hg data. This process involved three tasks: (a) 

develop the database of fish tissue Hg concentrations based primarily on state-level data, using a 

number of filtering steps that are described below, (b) use the fish tissue database to generate 

percentile estimates (75th and 50th percentiles) for each watershed containing fish tissue Hg 
18

measurement data and (c) filter the set of watersheds with fish tissue Hg percentile estimates to 

exclude locations potentially impacted by non-atmospheric Hg sources. 

The SAB recommended that EPA evaluate whether there were additional fish tissue data 

that could be used to update the fish tissue dataset (see Section 1.1). We implemented this 

recommendation, and as a result, the fish tissue Hg dataset used in the revised national-scale risk 

assessment is an augmented version of that used in the March assessment. Because the fish 

tissue Hg dataset is different from the March TSD, this section provides an overview of the 2010 

Mercury Fish Tissue (MFT) dataset as well as the augmentation steps of the 2010 MFT dataset 

based on additional fish tissue data identified for a handful of states. We have also included 

additional detail on the derivation of the fish tissue Hg dataset in response to an SAB 

recommendation. We describe each step to develop the dataset ultimately used in this risk 

assessment, which is illustrated in Figures 1-3 through 1-6. We also provide the number of fish 

tissue Hg measurements and watershed-level estimates associated with each step in the filtering 

process. 

Development of the 2010 Mercury Fish Tissue (MFT) database used in the March version of the 

national-scale mercury risk assessment

To develop the 2010 MRT dataset, we began with fish tissue samples from three main 

sources: 

 National Listing of Fish Advisory (NLFA) database. The NLFA, managed by EPA 

(http://water.epa.gov/scitech/swguidance/fishshellfish/fishadvisories/), collects and 

compiles fish tissue sample data from all 50 states and from tribes across the United 

States. In particular, the 2010 version of the NLFA used in this analysis contains data for 

over 45,000 Hg fish tissue samples collected from 1995 to 2007. 

 U.S. Geologic Survey (USGS) compilation of mercury datasets. As part of its 

Environmental Mercury Mapping and Analysis (EMMA) program, USGS compiled Hg 

fish tissue sample data from a wide variety of sources (including the NLFA) and has 

posted these data at http://emmma.usgs.gov/datasets.aspx. To avoid duplication in our 

analysis, we excluded all of the USGS data originating from the NLFA. As shown in 

Figure1-3, we included data from the USGS compilation that originated from two main 

categories of sources: 

(1) state-agency collected and reported data (including Delaware, Iowa, Indiana, 

Louisiana, Minnesota, Ohio, South Carolina, Virginia, Wisconsin, and West Virginia) 

from nearly 40,000 fish tissue samples, covering the period 1995 to 2007 (referred to 

as “USGS States” in Figure 1-3) 

(2) over 6,000 fish tissue samples from several other sources, including the National Fish 

Tissue Survey, the National Pesticide Monitoring Program (NPMP), the National 

Contaminant Biomonitoring Program (NCBP), the Biomonitoring of Environmental 

Status and Trends (BEST) datasets of the USFWS and USGS 

(http://www.cerc.cr.usgs.gov/data/data.htm), and the Environmental Monitoring and 
19

Analysis Program (EMAP) (http://www.epa.gov/emap/). (referred to as “USGS 

Other” in Figure 1-3) 

 EPA’s National River and Stream Assessment (NRSA) study data. These data include 

nearly 600 fish tissue Hg samples collected at randomly selected freshwater sites across 

the United States during the period 2008 to 2009.

All of the measurements in the NLFA database were obtained from state fish advisory

programs managed by the health department, natural resource department or environmental 

protection agency in each state. In many cases, these data were not sampled randomly but instead 

reflect protocols intended to target (1) areas know to support recreational and or commercial 

fishing and (2) areas believed to have elevated levels of chemical contamination (i.e., MeHg) in 

fish.19 Therefore, the state-level data do not provide a representative characterization of the 

distribution of MeHg concentrations in fish tissue at waterbodies across the state and are instead, 

likely to be biased towards locations with higher Hg fish tissue concentrations as well as species 

that are typically consumed by the general public. However, because the goal of this analysis is 

to determine the potential for a public health hazard from Hg emitted by U.S. EGUs, it is 

beneficial to have fish tissue measurement data potentially biased towards waterbodies with 

greater Hg impacts as well as species typically consumed by recreational or commercial fishers 

because this reduces the likelihood that high risk watersheds would be omitted due to gaps in fish 

sampling. 

The majority of fish tissue Hg measurements in these datasets are for total Hg and not 

MeHg. However, research published in the literature suggests that 90 to 95 (or greater) percent 

of total Hg in fish tissue is MeHg (U.S. EPA, 2000). Based on this research, for purposes of 

assessing exposure and risk, we have assumed that 95% of each fish tissue Hg concentration is 

MeHg (see section 1.4.4).

Data from the four datasets shown in Figure 1-3 were combined into a single “2010 

Master” Hg fish tissue (MFT) sample dataset covering the period 1995 to 2009. One problem 

encountered in combining these datasets is the potential duplication of samples in the NLFA and 

 

19 We reviewed approaches used by Pennsylvania and Wisconsin for determining where to collect fish tissue Hg 

measurements. These states were chosen because they either have relatively high fish tissue Hg concentrations 

(Wisconsin), or relatively elevated U.S. EGU-related Hg deposition (Pennsylvania). Pennsylvania identifies 

sampling locations based on (a) a need to verify or delist specific locations for fish advisories, (b) provide additional

data points for their Water Quality Network and (c) respond to requests from selected State Environmental and 

Wildlife officials to sample locations of potential concern (Pennsylvania Department of Environmental Protection’s 

“Fish Tissue Sampling and Assessment Protocol” available at: 

http://www.portal.state.pa.us/portal/server.pt/community/fish_consumption/10560 ). Wisconsin selects sampling 

locations based on providing coverage for (a) northern parts of the state where high Hg fish tissue levels have been 

found, (b) areas were pollutions impacts are suspected, (c) areas with high fishing pressure and (d) locations that are 

considered “indicators” for specific watersheds (Wisconsin Department of Natural Resources, “A Summary of 

Mercury Concentrations in Fish (edible portions) from Wisconsin Waters 1990-2005”, available at:

http://dnr.wi.gov/fish/consumption/1990-2005mercurysummary.pdf ). While the details of these two strategies do 

differ somewhat, generally both states are targeting areas either suspected of having a potential Hg problem, or areas 

with elevated fishing activity. In both cases, the set of fish tissue Hg concentrations are not going to be generally 

representative of trends across watersheds in the state and instead, are likely to favor coverage of more heavily 

impacted watersheds (at least for the fraction of samples collected to cover areas of potential concern with regard to 

mercury impacts). 
20

USGS-States data. Unfortunately, these two datasets do not contain directly comparable and 

unique identifiers that allow duplicate samples to be easily identified and removed. In order to 

identify potentially duplicative samples, we subdivided the samples from these two datasets into 

data groups according to the year and state in which they were collected. If both datasets

contained a data group for the same year and the same state, then the data group with the fewer 

number of observations was not included in the master data. This process excluded 18,860 

potentially duplicate samples from the master dataset.

In finalizing the 2010 MFT sample dataset, the following filters were used to further 

screen the fish tissue samples: 

(a) excluded samples that were not geo-referenced (i.e., did not include latitude and 

longitude information)

(b) excluded samples with missing date information

(c) excluded samples that were not from freshwater fish species or freshwater locations 

(i.e., excluded estuarine locations), 

(d) excluded samples from fish smaller than 7 inches in length.

20 Additional discussion 

of uncertainty related to this filtering step is presented later in this section. 

These filters excluded a total of 21,674 samples from the dataset (in addition to the 18,860 

duplicates referenced earlier). For the majority of measurements in the database, we also have 

information on the type of waterbody (river/lake) and type of sampling method used (e.g., filet 

skin on). Additional detail on the process used to develop the master fish tissue dataset can be 

found in U.S. EPA, 2011, Section 5.2.2. 

Even though we compiled fish tissue Hg sampling data for 1995 to 2009, we decided to 

restrict data to 2000 to 2009 in the risk assessment (the SAB supported this decision). We 

excluded fish tissue samples that likely reflected Hg deposition levels from the 1990’s when 

anthropogenic emissions in the U.S. were higher than after 2000. We recognize the complex 

spatial and temporal nature of the response of fish tissue Hg concentrations to changes in Hg 

deposition and loading and acknowledge that a portion of the sampling data from 2000 to 2009 

could still reflect higher Hg loading rates from earlier periods. Excluding the samples collected 

before 2000 further reduced the size of the initial dataset by 27,522 observations (the potential 

impact on the risk assessment related to our decision to focus on data from 2000 and later is 

discussed in section 2.7, Table 2-15, Entry I).

After completing all of these filtering steps, the 2010 MFT sample dataset contained 

23,770 observations (samples) in the U.S.

 

20 Seven inches represents a minimum size limit for a number of key edible freshwater fish species established at the 

State-level. For example, Pennsylvania establishes 7 inches as the minimum size limit for both trout and salmon 

(other edible fish species such as bass, walleye and northern pike have higher minimum size limits (Summary Book: 

2001 Pennsylvania Fishing Laws and Regulations available at: 

http://fishandboat.com/fishpub/summary/inland.html). 
21

Figure 1-3 Diagram Illustrating Step-wise Procedure Used to Develop 2010 Mercury 

Fish Tissue (MFT) Dataset Used in the 2010 National-Scale Mercury Risk 

Assessment

Augmentation of the 2010 MFT database with additional fish tissue mercury data obtained for a 

subset of states (i.e., MI, NJ, MN, PA, and WI).

Since the March TSD, we augmented the 2010 MFT sample database with additional 

sampling data from selected states and years. The composition and processing of these 

“augmentation” data are shown in Figure 1-4. All of these data were provided to EPA by the 

individual states, but they have not yet been incorporated into the most recent versions of the 

NLFA. The main criteria for selecting these particular 5 states are: (1) a majority of the sample 

data they provided to EPA were geo-referenced and included the year in which they were 

sampled (more recent data for a number of the other states did not have these critical descriptors) 

and (2) the states are located in areas of the country with relatively high levels of Hg deposition. 

To avoid potential duplication of samples with those included in the initial Hg fish tissue 

database, the augmentation data only included samples for the years 2003 to 2009 for MI, NJ, 

MN, and PA, and for 2000 to 2010 for WI (Figure 1-4 provides more detail).21 All of the data 

were reorganized and converted into Microsoft Access databases, using the NLFA fields and 

 

21 The version of the NLFA used in developing the 2010 MFT dataset did not include samples collected between 

2003 and 2009 for MI, NJ, MN or PA. Similarly, that earlier version of the NLFA did not include any WI samples 

collected between 2000 and 2010. Therefore, addition of samples from the latest version of the NLFA from these 

years for these 5 states would supplement the MFT without duplication.

2010 Master

(72,966)

NLFA

(45,166)

NRSA

(590)

USGS States

(39,972)

USGS Others

(6,098)

Original Data 

Sources 

(>=1995) Reason 

Dropped

N

Missing

Lat/Long

-9,177

Missing Date -1,520

Not a 

Freshwater

Fish

-2,638

Not a 

Freshwater

Location

-3,192

Length < 7 

inches

-5,147

Year < 2000 -27,522

Total -49,196

Data Screening

Merged NLFAUSGS States

(66,278)

2010 MFT 

Sample 

Dataset

(23,770)

Data Merging
22

formats. They were then combined into the “Augmentation Master” dataset shown in Figure 1-4, 

which includes a total of 12,864 samples.

Using the same set of screening steps, we ended up with an Augmentation MFT sample 

dataset containing 11,797 observations in the U.S.22

For this analysis, we then combined the 2010 MFT sample dataset with the Augmentation 

MFT dataset (as shown in Figure 1-5) to create the 2011 MFT sample dataset. This combined 

dataset includes 35,567 fish tissue Hg samples from years 2000-2010. As with the original 2010 

MFT database, samples from the 2011 MFT database are located across the U.S.; however, they

are more heavily focused in locations east of the Mississippi River.

Figure 1-4 Diagram Illustrating Step-wise Procedure Used to Develop the Augmentation 

Mercury Fish Tissue (MFT) Dataset

 

22 The data fish tissue sample data from PA did not include a measure of fish length; therefore, all of the samples 

were retained and assumed to be above the 7 inch threshold. In the March assessment, these samples were excluded, 

however, in order to provide a more complete spatial coverage in PA, which has high levels of U.S. EGU Hg 

deposition, we elected for this revised assessment to include these samples.

Augmentation

Master

(12,864)

MI 2007-2009

(81)

NJ 2007

(220)

MN 2003-2007

(6,012)

PA 2003-2008

(509)

Original Data 

Sources

Reason 

Dropped

N

Missing Lat/Long -643

Missing Date -0

Not a Freshwater 

Fish

-13

Not a Freshwater

Location

-0

Length < 7 inches -411

Year < 2000 -0

Total -1,067

WI 2000-2010

(6,042)

Data Merging Data Screening

Augmentation 

MFT Sample 

Dataset

(11,797)
23

Figure 1-5 Diagram Illustrating Step-wise Procedure Used to Combine the 2010 MFT and 

Augmentation MFT Datasets

Use the fish tissue database to generate percentile estimates (75th and medians) for each HUC-12 

watershed containing fish tissue Hg measurement data

To conduct the risk and exposure analysis, we then spatially aggregated the 2011 MFT 

sample data to the watershed level. To begin this process, we used the latitude and longitude

information from each sampling location to identify the HUC-12 watersheds in which each site 

was located. 

As a final data screening step, we then excluded HUC-12 watersheds (and the samples 

located within those watersheds) that either contained active gold mines or had other substantial 

non-U.S. EGU anthropogenic emissions of Hg.23 These watersheds were excluded because the 

assumption of linear proportionality between Hg deposition and fish tissue Hg concentrations, 

supported by the MMaps study is most supportable in those situations where aerial deposition is 

the dominant source of Hg loading to a watershed. We identified watersheds with gold mines 

 

23 The filtering out of locations with potentially significant non-air mercury loadings was also completed in 

generating the 2010 MFT database. At this stage, to restrict the analysis to the continental U.S., where we have Hg 

deposition modeling, we also excluded 11 samples that were located in 5 HUC-12 watersheds in Alaska. 

2010 MFT Sample 

Dataset

(23,770)

2010 MFT HUCLevel Dataset*

(2,323 HUCs)

Augmentation MFT 

Sample Dataset

(11,797)

Augmentation 

MFT HUCLevel Dataset

(940 HUCs)**

2011 MFT Sample

Dataset

(35,567)

2011 MFT HUC-Level

Dataset*

(3,147)

Fish tissue 

samples

Spatial 

Aggregation 

(associating individual fish 

tissue sampling locations 

with HUC12 watersheds –

completed using GIS)

*An additional 11 observations were dropped due to 5 HUC locations outside of the continental U.S.

230 observations (associated with 15 HUC12 

watersheds) dropped because watersheds contain

goldmines or have substantial non-EGU emissions*

** 116 of these 940 HUCs were also included in the 2010 MFT dataset.

Fish tissue 

samples 

associated 

with specific

HUC12 

watersheds
24

using a USGS data set characterizing mineral and metal operations in the U.S. (USGS, 2005). 

The data represent commodities monitored by the National Minerals Information Center of the 

USGS, and the operations included are those considered active in 2003. We identified 

watersheds with substantial non-EGU anthropogenic emissions using a TRI-net query for 2008 

for non-EGU Hg sources with total annual on-site Hg emissions (all media) of 39.7 pounds or 

more.24 This threshold value corresponds to the 25th percentile annual U.S.-EGU Hg emission 

value as characterized in the 2005 NATA.25 The 25th percentile U.S.-EGU emission level was 

selected as a reasonable screen for additional substantial non-U.S. EGU emissions to a given 

watershed. The SAB endorsed this as a sound approach, while noting that the degree of 

conservatism implied by this approach is unknown. The SAB also commented that while other 

screening criteria could be applied, such as removing watersheds near urban areas with potential 

waste runoff, these would be unlikely to substantially change the results. In addition, applying 

additional screening criteria would have the negative impact of further reducing the geographic 

scope of the analysis. 

Application of the filtering described here (i.e., excluding locations with active gold 

mines or other substantial non-U.S. EGU anthropogenic emissions of Hg) resulted in 15 HUC12 

watersheds, containing a total of 230 observations, being excluded from the 2011 MFT dataset 

(see Figure 1-5). The final number of HUC12s with fish tissue Hg data included in the risk 

assessment is 3,141 (containing 35,567 samples), which represents a 33% increase in the number 

of watersheds assessed since the March TSD, as summarized in Figure 1-5.

As shown in Figure 1-5, this approach yielded 3,141 HUC-level sets of fish tissue Hg 

estimates for the augmented (full) dataset used in the revised national-scale mercury risk 

assessment (this compares with 2,317 HUC-level fish tissue Hg estimates used in the March 

version of the risk assessment). 

Because most HUC-12 watersheds with measured fish tissue Hg data have multiple 

sampled values, often distributed over multiple sampling sites, we needed to identify and 

calculate summary statistics for each watershed, in order to represent fish tissue Hg levels in 

estimating exposure and risk. To do this, we generated summary statistics (means and 

percentiles) for MeHg concentrations in each HUC-12 and compiled these summary data into a 

separate HUC-level dataset. The following two-step procedure was used to generate HUC-level 

statistics: (a) calculate mean and percentile (25th, 50th, 75th and 90th) fish tissue Hg values for 

each sampling site within a HUC and (b) for the mean, or a given percentile, take the average of 

the applicable values across the sampling sites within a HUC to generate a single HUC-level 

estimates (for the mean, or specific percentile). For example, if we had two sampling sites (each 

with multiple samples) and we wanted a 75th percentile fish tissue Hg value for that HUC, we 

would first compute the 75th percentile values at each sampling site and then take the average of 

those two 75th percentile values. If there is only one sampling site in a given HUC, then we 

simply compute the mean or percentile of interest from the measurements at that site and that 

statistic is used to represent the HUC. This approach reflects a fisher who fairly consistently

targets the same size (represented by percentile) fish at each location where they fish within a 

 

24 U.S. EPA Toxics Release Inventory (TRI) accessed at: http://www.epa.gov/tri/ .

25 U.S. EPA 2005 National Air Toxics Assessment (2005 NATA) accessed at: 

http://www.epa.gov/ttn/atw/nata2005/..
25

watershed. We are then constructing a representative fish tissue Hg value for them, by taking the 

average of the fish they are simulated to catch across their fishing locations (within a given 

HUC). As noted earlier, our simulation of female subsistence fish consumer exposure and risk 

reflects the assumption that fishing activity is targeted within a given watershed.26

Summary statistics for the 2011 MFT HUC-level datasets are reported in Table 1-1.27 The 

average number of fish tissue measurements for the period 2000 to 2009 for the 3,141 

watersheds is 11.23, although some watersheds contained up to 360 measurements. Most 

watersheds also have multiple species (mean of 2.69) and to a lesser extent, multiple sampling 

sites within the watershed (mean of 1.41). Multiple sampling sites in this context can be different 

streams and/or lakes located within the same watershed. Distributions of other HUC-level 

percentiles (and the mean) are also presented for purposes of comparison.

Table 1-1 Summary Statistics for the 2011 MFT HUC-level Data

Distribution of various attributes across the 3,141 HUCs Mean

Std. 

Dev. Min Max

Number of samples per HUC 11.23 21.05 1 360

Number of species per HUC 2.69 2.19 1 17

Number of sampling sites per HUC 1.41 1.25 1 33

Average of different HUC-level Hg concentration 

percentiles (including the mean) across the 3,141 HUCs

Average of location-specific mean Hg concentrations (ppm) 

in HUC 0.27 0.24 0 3.56

Average of location-specific 25th percentile Hg 

concentrations (ppm) in HUC 0.19 0.19 0 2.20

Average of location-specific 50th percentile Hg 

concentrations (ppm) in HUC 0.25 0.23 0 3.56

Average of location-specific 75th percentile Hg 

concentrations (ppm) in HUC * 0.32 0.31 0 6.61

Average of location-specific 90th percentile Hg 

concentrations (ppm) in HUC 0.40 0.41 0 7.35

* 75th percentile row bolded since this is the statistic used in the risk assessment

 

26 The SAB recommended that a single fish tissue Hg value not be used to represent a watershed when there are 

multiple measurement sites (e.g., multiple lakes) within that watershed. This recommendation reflected concern that 

waterbodies, even when in close proximity within the same watershed, can display different methylation rates such 

that they are likely to respond differently to a unit change in Hg deposition. The approach outlined here for 

calculating the 75th percentile fish tissue Hg value does not use a single fish tissue Hg value in instances where there 

are multiple sites within a watershed. Instead, we calculate the 75th percentile fish tissue Hg for each site (i.e., with 

each value representing a specific sampling site, which often translates into a distinct waterbody) and then take the 

average of those values, reflecting fishing activity that is distributed across the sampling sites, or waterbodies within 

a specific watershed. 

27 In the 2010 MFT dataset used in the March version of the risk assessment, approximately 48% of the fish tissue 

Hg samples were obtained from rivers (with the rest coming from lakes). For the augmentation dataset, we did not 

specify river versus lake in developing the fish tissue Hg dataset, since this distinction was downplayed by the SAB

as a factor significantly impacting application of the proportionality assumption in the context of estimating the U.S. 

EGU-attributable portion of exposure and risk.
26

As noted in Section 1.4, we selected the 75th percentile fish tissue value at each watershed 

as the main basis for exposure and risk characterization.28 Selection of the 75th percentile value 

was based on the assumption that a subset of subsistence fishers would favor larger fish which 

have the potential for higher bioaccumulation (i.e., use of a median or mean value could low-bias 

likely catch-related Hg levels). There is uncertainty associated with this assumption and should 

fishers at a particular watershed favor fish that are either larger or smaller than the type of fish 

reflected in the 75th percentile sample, risk estimates could be biased accordingly. Uncertainty 

related to use of the 75th percentile fish tissue Hg value in exposure modeling (specifically, 

potential bias in the estimation of the 75th percentile value) is discussed in section 2.9, Table 2-

15, Entry C. As recommended by the SAB, we also included of a sensitivity analysis using the 

median fish tissue Hg concentration at each HUC – see section 2.8). 

Figure 1-6 Diagram Illustrating Number of HUC12s with Fish Tissue Mercury Data (for 

2010 MFT, Augmentation MFT and the Combined 2011 MFT Datasets)

Uncertainty related to low sample size for a substantial fraction of the HUC12s and exclusion of 

fish <7 inches in length in calculation the HUC-level percentile estimates

While the majority of the 3,141 watersheds included in the risk assessment do have 

multiple measurements, a substantial fraction (41%) have only 1-2 samples, which potentially

biases low a 75th percentile estimate for those watersheds. To examine the potential magnitude of 

low bias in the 75th percentile estimates, we have summarized the distribution of 75th percentile 

 

28 We note that the 75th percentile value is a constructed statistic that reflects the fish tissue samples available at a 

given watershed. For some watersheds the number of samples is very low, 1 or 2 in some cases (see Table 1-2), and 

as a result, estimation of the true 75th percentile of fish tissue mercury concentrations in those watersheds is likely to 

be biased low. This is discussed later in this section. Depending on the degree to which the underlying fish tissue 

data are representative of actual fish targeted by subsistence fishers active at that waterbody, this statistic may or 

may not actually represent fish tissue Hg concentrations associated with fish consumption by those fishers.

2,201 824

116

2010 MFT Data

(# of HUCs)

Augmentation MFT Data

(# of HUCs)

2011 MFT Data (Combining 2010 and Augmentation)

Total Number of HUC12 observations = 3,141

Number of HUC12 Locations
27

fish tissue values (as used in the risk assessment) for various sample-size-based strata of the 

3,141 watersheds (see Table 1-2). We have also included two plots (as recommended in the 

SAB peer review) including (a) a histogram comparing the frequency of different sample sizes 

across the HUCs included in the risk assessment (Figure 1-7) and (b) a LOESS (locally-weighted 

scatter plot smoothing)-based fitted curve relating the 75th percentile HUC-level values and the 

frequency of samples sizes by HUC (Figure 1-8).29 The number of sampling sites also varies 

across watersheds included in the risk assessment, with some watersheds having up to 30 or 

more distinct sampling sites. 

While supporting the use of the 75th percentile fish tissue Hg value in the risk assessment, 

the SAB recommended a sensitivity analysis involving use of a median fish tissue Hg value in 

place of the 75th percentile value. This sensitivity analysis is discussed in section 2.6. However, 

we have provided fish tissue summary statistics here for median fish tissue statistics to support 

comparison with the 75th percentile values (Table 1-3). 

Table 1-2 Summary of 75th Percentile Hg Concentrations in Fish Tissue Samples by 

Number of Observations and Number of Sites per HUC

Number of 

Observations per HUC

Percentiles (of the 75th percentile HUC-level fish tissue 

Hg concentration - ppm)

25 Mean N

th 50th 75th 90th 95th

1 Observation 0.077 0.1564 0.2887 0.493 0.67834 0.226803 838

2 Observations 0.09995 0.1775 0.315 0.5135 0.765 0.256943 458

3-5 Observations 0.12 0.205625 0.339 0.577 0.8 0.283682 468

6-10 Observations 0.185 0.282 0.464 0.743 0.985 0.374177 506

> 10 Observations 0.25 0.37 0.553 0.85 1.083412 0.444645 871

Total 0.1294 0.245 0.422684 0.673355 0.893 0.324052 3141

Number of sampling 

sites per HUC 25th 50th 75th 90th 95th Mean N

1 Site 0.12 0.233 0.4 0.672 0.87 0.31308 2371

2 Sites 0.160967 0.276706 0.45044 0.71 1.047125 0.360398 553

3-5 Sites 0.179823 0.299482 0.478662 0.603667 0.738476 0.350198 196

6-10 Sites 0.125972 0.224625 0.479822 0.693108 0.8652 0.325642 19

> 10 Sites 0.2698 0.410415 0.494276 0.847259 0.847259 0.419069 8

Total 0.1294 0.245 0.422684 0.673355 0.893 0.324052 3147

Based on the observations-related data presented in Table 1-2 and Figure 1-8, we 

acknowledge the potential for low-bias in the 75th percentile values for HUCs with lower sample 

sizes. Specifically, in Table 1-2, the mean 75th percentile HUC-level value increases across 

strata as the sample size increases. This suggests that as the number of samples increases, 

estimates of the 75th percentile will be more likely to approach the true value, and thus are more 

likely to represent higher end fish tissue levels. Similarly, in Figure 1-8, the smoothed regression 

line (for the 75th percentile HUC-level trend) has a positive slope, denoting that higher 75th

 

29 The LOESS procedure involves fitting of a least-squares curve based on application of a smoothing (or 

bandwidth) parameter. Following the example provided by the SAB, we used a smoothing parameter of 0.2 in our 

application of the LOESS procedure. The LOESS-based curve presented in Figure 1-8 reflects exclusion of two 

outliers.
28

percentile values are associated with higher HUC-level sample sizes.30 Interestingly, the mean 

50th percentile HUC-level statistic across sample size strata (see Table 1-3) also tends to be 

higher with more samples, but the trend is not as substantial or consistent as for the 75th

percentile estimates. This is expected because the median is a central tendency statistic, and 

therefore any specific sample is likely to be closer to the central tendency than to other 

percentiles. As a result, the small sample size issue is not anticipated to introduce significant 

bias in the context of generating median estimates, but it may underestimate higher-end values 

such as the 75th percentile. Uncertainty related to sample size and potential bias in the 75th

percentile HUC-level fish tissue Hg concentrations is also discussed in section 2.7, Table 2-15.

Interestingly, we do not see a consistent trend across the sampling site strata (i.e., the fish 

tissue Hg statistic does not increase in a consistent manner with increasing number of sites – see 

Table 1-2). This is not unexpected since the number of sampling sites is not directly correlated 

with the number of observations at a given watershed (e.g., there are watersheds that have a few 

sites each with a large number of observations and there are watersheds with a larger number of 

sites, but each with only 1-2 observations). 

Figure 1-7 Histogram Characterizing Frequency of Sample Sizes Across HUCs Included in 

the Risk Assessment (illustrates fraction of HUCs with small sample size of 1-2)

 

30

 This trend (higher sampled HUCs having higher 75th percentile fish tissue Hg values) could simply reflect the fact 

that some states may target sampling at locations believed to have higher mercury impacts based on previous 

sampling. 

0

100

200

300

400

500

600

700

800

900

Frequency

(# of HUCs)

Number of Hg Fish Tissue Samples in HUC*

HUC Frequency by Number of Hg Fish Tissue Samples

*When sample sizes are 20 or greater, a category is used i.e. 20 corresponds to 20 to 25, 25 corresponds to 26 to 30, etc.
29

Figure 1-8 LOESS (locally-weighted scatter plot smoothing) Least-Square Regression of 

75th Percentile HUC-level Fish tissue Hg Levels Against HUC-level Sample Size

Table 1-3 Summary of 50th Percentile Hg Concentrations in Fish Tissue Samples by 

Number of Observations and Number of Sites per HUC

Number of 

Observations per HUC

Percentiles (of the 50th percentile HUC-level fish tissue 

Hg concentration - ppm)

25 Mean N

th 50th 75th 90th 95th

1 Observation 0.077 0.1564 0.2887 0.493 0.67834 0.226803 838

2 Observations 0.08055 0.1362 0.25 0.408 0.5235 0.195935 458

3-5 Observations 0.08403 0.1443 0.235 0.425 0.639 0.205501 468

6-10 Observations 0.1295 0.207913 0.36 0.524 0.72 0.274554 506

> 10 Observations 0.178 0.252444 0.376818 0.59 0.728846 0.310015 877

Total 0.104 0.19 0.3203 0.5033 0.674 0.25001 3147

Number of sampling 

sites per HUC 25th 50th 75th 90th 95th Mean N

1 Site 0.1 0.1815 0.31 0.505 0.6633 0.245554 2371

2 Sites 0.1165 0.2105 0.347335 0.531 0.7371 0.269281 553

3-5 Sites 0.134761 0.205034 0.32434 0.439024 0.496111 0.247729 196

6-10 Sites 0.100278 0.175125 0.338674 0.491046 0.638407 0.236554 19

> 10 Sites 0.199375 0.314303 0.41785 0.633312 0.633312 0.326412 8

Total 0.104 0.19 0.3203 0.5033 0.674 0.25001 3147

There is also uncertainty due to excluding fish samples smaller than 7 inches. 

Specifically, if subsistence fishers also target fish smaller than 7 inches in (and if those smaller 

fish have lower MeHg concentrations), then the risk estimates could be high-biased for this 

Number of observations per watershed

Watershed-level 75

th percentile fish tissue Hg level (ppm)
30

group of subsistence fishers, since we excluded those smaller fish in our modeling. However, it 

is important to reiterate that this analysis is not intended to generate a representative assessment 

of the distribution of exposure and risk across the entire set of higher consuming self-caught 

freshwater fishers. Given the goal of the analysis is to determine whether the potential exists for 

adverse health impacts linked to self-caught fish consumption, it is reasonable (and indeed a 

stated element of the scope of the analysis – see section 1.2) to focus on those behaviors by

subsistence fishers that would place them at greater exposure and risk. Given this goal, it is 

reasonable to assume that a subset of subsistence fishers could focus on larger fish (i.e., > 7 

inches) as they attempt to supplement their diet. By basing exposure and risk modeling on the 

75th percentile fish and excluding fish smaller than 7 inches, we have targeted this subset of 

subsistence fishers in this risk assessment. 

While a reasonable assumption is that a subset of high-consuming subsistence fishers will 

choose to catch and eat fish larger than 7 inches, reflecting recommendations by SAB, we 

include a sensitivity analysis to examine the degree to which exclusion of fish smaller than 7 

inches affects risk estimates. Table 1-4 compares trends in different HUC-level percentile fish 

tissue Hg concentrations (and the mean) based on consideration for (a) only fish > 7 inches in 

length (used in the national-scale mercury risk assessment), and (b) fish of all lengths. Of 

particular interest are the bolded rows in the table that provide results based on the 75th percentile 

sample, which is the value used in the risk assessment. The results provided in Table 1-4 suggest 

that excluding fish smaller 7 inches did not have a large impact on the HUC-level percentiles of 

fish tissue Hg. This can be seen by comparing the 75th percentile HUC-level estimates using only

the fish larger than 7 inches with the 75th percentile estimates based on all fish combined. For 

example, if we compare the average of the HUC-level 75th percentile values when only fish >7 

inches in length were included (0.32 ppm) with the average of the HUC-level 75th percentile 

values when all lengths are included (0.31 ppm) we see that the two values are similar. This 

observation holds even if we look at higher end percentiles of the HUC-level 75th percentile 

values (e.g., the 95th percentile of the HUC-level 75th percentile values are 0.89 ppm and 0.86 

ppm, for the >7 inch and all lengths included, datasets, respectively). One likely reason that the 

exclusion of fish smaller than 7 inches did not have a large impact on the 75th percentile HUClevel estimates is that this subset of smaller fish represents a relatively small fraction of the entire 

fish tissue sample dataset considered for this analysis (i.e., ~3,400 measurements for <7 inch fish 

versus 35,600 measurements for > 7inch fish, or roughly ten times more samples for >7 inch 

fish). Because the impact on fish tissue concentrations is small, we do not carry forward this 

sensitivity analysis through the calculation of risks, as the impact on risk is also likely to be 

small.
31

Table 1-4 Comparison of HUC-Level Fish Tissue Hg Statistics for (a) Fish Tissue Dataset 

with Fish >7 Inches and (b) Dataset with All fish Lengths Included 

HUC-level statistic N

Fish tissue Hg concentration (ppm) - as relevant

Mean 25th % 50th % 75th % 90th % 95th % 99th %

(a) Fish Length > 7 Inches (as used in the national-scale Hg risk assessment)

mean 3,147 0.27 0.11 0.20 0.35 0.54 0.71 1.09

25th % 3,147 0.19 0.08 0.14 0.25 0.40 0.52 0.89

50th % 3,147 0.25 0.10 0.19 0.32 0.50 0.67 1.06

75th % 3,147 0.32 0.13 0.25 0.42 0.67 0.89 1.36

90th % 3,147 0.40 0.14 0.29 0.53 0.86 1.13 1.86

Number of samples per HUC 3,147 11.23 1 4 12 28 44 106

Number of species per HUC 3,147 2.69 1 2 4 6 7 10

Number of sample sites per 

HUC 3,147 1.41 1 1 1 2 3 5

(b) All fish lengths

mean 3,268 0.25 0.10 0.19 0.33 0.52 0.68 1.05

25th % 3,268 0.18 0.07 0.13 0.24 0.38 0.50 0.85

50th % 3,268 0.24 0.10 0.18 0.30 0.48 0.65 1.02

75th % 3,268 0.31 0.12 0.23 0.40 0.65 0.86 1.34

90th % 3,268 0.39 0.13 0.27 0.51 0.83 1.10 1.82

Number of samples per HUC 3,268 11.83 1 4 13 29 46 123

Number of species per HUC 3,268 2.85 1 2 4 6 8 11

Number of sampling sites per 

HUC 3,268 1.41 1 1 1 2 3 5

1.4.2.1 Projecting 75th percentile fish tissue Hg concentrations for the 2016 scenario

Once 75th percentile fish tissue Hg concentrations are generated for each of the 

watersheds, we then adjust these estimates to represent 75th percentile fish tissue Hg 

concentrations for the 2016 scenario which are used in modeling risk for this scenario. As 

mentioned in section 1.4 (Step 4 associated with Figure 1-2), projection of fish tissue Hg 

concentrations for 2016 is based on application of the proportionality assumption combined with 

Hg deposition estimates for the 2005 and 2016 scenarios. Specifically, for a given watershed, we 

multiply the 75th percentile measured fish tissue Hg concentration, by the ratio of total Hg 

deposition in 2016 to total Hg deposition in 2005. This results in a projection of 2016 fish tissue 

Hg concentrations. That 2016 projected total fish tissue Hg concentration is then used to model 

HQ for the 2016 scenario in that watershed. Detail on the proportionality assumption (supported 

by Mercury Maps simulations and more recent literature) is presented in section 1.4.6. 

1.4.3 Defining subsistence fisher scenarios to include in the analysis

As described in section 1.4, defining subsistence fisher scenarios included in this analysis 

requires three tasks: (a) identify subsistence fisher populations, (b) assess where they might be 
32

active and (c) define fish consumption rates for female subsistence fish consumers associated 

with those populations. These three tasks are described below. 

Identify subsistence fisher populations 

As discussed in sections 1.2 and 1.4, this analysis estimates risk for scenarios 

representing female subsistence fish consumers who have the potential to consume fish caught at 

inland freshwater locations, since these populations are expected to experience the greatest U.S. 

EGU-attributable risks. Therefore, in reviewing studies of fishing behavior, we prioritized

surveys characterizing fishing activity for populations with these characteristics. In addition, we 

needed studies that provided statistically rigorous estimates of annual-average daily fish 

consumption rates. Subsistence levels of fish consumption are likely only experienced by 

relatively small fractions of the study populations surveyed (e.g., 90th to 99th percentile 

consumption rates). 

Although we reviewed many studies, we ultimately identified three studies that met our 

criteria for characterizing subsistence-level self-caught freshwater fish consumption. These three 

studies included several subsistence populations differentiated by ethnicity and SES status 

including: (a) White and Black populations (including female and low income strata) surveyed in 

South Carolina (Burger et al., 2002), (b) Hispanic, Vietnamese and Laotian populations surveyed 

in California (Shilling et al., 2010) and (c) Great Lakes Tribal populations (Chippewa and 

Ojibwe) active around the Great Lakes (Dellinger et al., 2004). 

These studies were used to characterize behavior for female subsistence fish consumers 

assumed to be associated with the subsistence fisher populations described in the studies.31

Specifically, based on these studies, we characterized behavior for seven distinct female 

subsistence fish consumer scenarios which were included in the risk assessment. These scenarios 

are listed (and briefly described) in Table 1-5. 

Table 1-5 Spatial Extent and Number of HUCs Reflected in Risk Modeling for the Female 

subsistence fish consumer Scenarios Included in the Risk Assessment

Female 

subsistence 

fish consumer

Scenario Description Spatial Extent 

Number of 

watersheds 

(HUC12s) 

Assessed For 

Risk

Typical 

(non-SES 

differentiated)

Generalized (non-SES differentiated) female subsistence 

fish consumers associated with fishing activity that 

could occur at any of the watersheds across the nation

with fish tissue Hg data (i.e., source population 

construct is not applied here).

National (all HUCs 

with fish tissue Hg 

measurements)

3,141

Low income Low income White female subsistence fish consumers Southeast 860

 

31 With the exception of the Burger et al., 2002 study, which did provide consumption rates for females, we assumed 

that rates provided for specific SES-differentiated populations could apply to pregnant females associated with those 

fishers (e.g., either as family members or acquaintances). We recognize that there is uncertainty associated with this 

assumption, but would also point out that the female consumption rates provided in Burger et al., 2002 (particularly 

for the higher percentiles) are in the range of values seen for males and for non-sex differentiated survey groups both 

in that study and in the other two studies of high-end self-caught fish consumption cited here.
33

Female 

subsistence 

fish consumer

Scenario Description Spatial Extent 

Number of 

watersheds 

(HUC12s) 

Assessed For 

Risk

White

(southeast)

active in the Southeast; in watersheds with fish tissue 

Hg data where there are at least 25 White individuals 

below the poverty line.

Low income

Black

(southeast)

Low income Black female subsistence fish consumers 

active in the Southeast, in watersheds with fish tissue 

Hg data where there are at least 25 Black individuals 

below the poverty line.

Southeast 756

Low income

Hispanic

Low income Hispanic female subsistence fish 

consumers active at any watersheds in the continental 

U.S. with fish tissue Hg data where there are at least 25 

Hispanic individuals below the poverty line.

National (where 

there are low

income Hispanics)

1,325

Great Lakes 

Tribal

Tribal (Chippewa/Ojibwe) female subsistence fish 

consumers active at any watersheds with fish tissue Hg 

data within the ceded territories in the vicinity of the 

Great Lakes (i.e., source population construct not 

applied here).

Vicinity of Great 

Lakes (ceded 

territories)

361

Laotian

Laotian female subsistence fish consumers active at any 

watersheds in the continental U.S. with fish tissue Hg 

data where there are at least 25 Laotians.

National (where 

there are Laotians) 131

Vietnamese

Vietnamese female subsistence fish consumers active at 

any watersheds in the continental U.S. with fish tissue 

Hg data where there are at least 25 Vietnamese.

National (where 

there are 

Vietnamese)

336

Assess where the subsistence fisher populations might be active

The following approach was used to identify the spatial extent of activity for each of the 

seven female subsistence fish consumer scenarios. For all of the scenarios (with the exception of 

the Tribal scenario associated with Great Lakes), we assumed that high-end fishing behavior

could be generalized beyond the specific geographic areas covered in a particular study. This 

type of generalization was necessary to provide sufficient coverage for the continental U.S. and 

in particular, the eastern part of the U.S. where U.S. EGU-attributable deposition is higher and 

where we have more measured Hg fish tissue data. In deciding how to extend coverage for each 

fisher population, we considered several factors including (a) the potential for high-end fishing 

activity to be culturally-related and therefore more likely to be followed by populations of a 

given ethnicity living across the U.S. and (b) the potential that subsistence fishing activity (and 

related consumption) might be driven by economic need. Because we assumed that fishing 

activity is culturally related, we generalized fishing activity by Hispanics, Laotians and 

Vietnamese beyond California, where the study providing consumption rates was conducted, to 

the nation. Similarly, we assumed that surveys of high-end fish consumption by low income

Whites and low income Blacks in South Carolina might be generalized to the southeast, 

reflecting similar cultural practices by these groups within that region. 

Once we identified the regions of the country where a particular subsistence fisher 

population might be active, we then identified the specific subset of watersheds within that 

region where the high-end fishing activity (and consumption by female subsistence fish 

consumers associated with those subsistence fishers) might occur. This task was challenging 
34

because we do not have data characterizing the number and location of subsistence fishers for 

any of the fishing populations under consideration. Therefore, we developed and applied a 

source population construct to guide identification of areas (watersheds) where a given 

subsistence scenario might be active.32 This approach requires that in order for a subsistence 

fisher scenario to have the potential to be active at a given watershed, that watershed must be in 

close proximity to a source population matching the ethnic/SES composition of the subsistence 

fishers being represented. In this case, we specified that a watershed would have to intersect a 

U.S. Census tract with at least 25 individuals with the same ethnic/SES composition as the 

subsistence fisher scenario (i.e., a source population) for there to be the potential for that type of 

fishing at that watershed. So, for example the Laotian subsistence fisher scenario was only 

assessed at those watersheds intersecting U.S. Census tracts with at least 25 Laotians. In the case 

of low income White and Black subsistence fishers in the southeast as well as Hispanics assessed 

nationally, we required that the watershed intersect U.S. Census tracts with at least 25 

individuals from that ethnic group who fall below the poverty line. The SAB peer review panel 

agreed that the criterion of using 25 persons within a source population to identify watersheds 

with the potential for subsistence fishing activity is a reasonable approach. The specific approach 

used to extrapolate coverage regionally and then determine the subset of watersheds to include in 

risk modeling for each subsistence fisher scenario is described both in Table 1-5 and in greater 

detail in Table 1-6 (in Table 1-6 see columns “C” and “E”). As described in Table 1-6 (columns 

“C” and “E”), the approach used for the Tribal population and typical female subsistence fish

consumer scenarios differs from those used for the other scenarios. In the case of the Tribal 

subsistence fisher scenario, given the potential for fishing activity to be closely associated with 

heritage cultural practices, we have not extrapolated fishing activity for the Chippewa/Ojibwe 

outside of their ceded territories, although we do assume that all watersheds within those 

territories with fish tissue Hg data have the potential for fishing activity by this group. 

The typical female subsistence fish consumer scenario differs from the other scenarios in 

that it is not SES-differentiated and is assessed nationally at all watersheds with fish tissue Hg 

data (i.e., the source population construct is not used to constrain its spatial extent). The 

generalized nature of this scenario means that it provides broader coverage for subsistence fisher 

activity and for this reason we assume that this scenario (together with female subsistence 

consumption associated with this fishing activity) could potentially occur at any watershed with 

fish tissue Hg data.33 As noted in section 1.2 and 1.4, because the typical female subsistence fish 

consumer is the most generalized of the scenarios and has the greatest spatial extent, we place 

greater emphasis on this scenario in presenting risk estimates.34 Application of the typical female 

 

32 As described in section 1.4, a source population is a group of individuals with demographic attributes matching 

those of the subsistence fisher group being evaluated. While we cannot enumerate the subsistence fishers directly, 

we can use demographic data to determine if the underlying source population is present in the vicinity of a 

watershed with fish tissue Hg data.

33 We are not suggesting that we expect the scenario to occur as a certainty at all watersheds, or that we can predict 

the level of activity (number of fishers or consumers) at each watershed. Rather, for this analysis, we are stating that 

we believe it reasonable to assume that the typical female subsistence fish consumer scenario (and associated fishing 

activity) could potentially occur at some subset of the watersheds with fish tissue Hg data.

34

 The typical female subsistence scenario included in this revised risk assessment is similar to the high consuming 

female scenario included in the March version of the Mercury Risk TSD. However, the typical female subsistence 

scenario is applied without consideration for a source population, while in the case of the high-consuming female 

angler; we did consider a source population based on poverty. Because the typical female subsistence scenario does 

not use a source population (and is applied uniformly to all watersheds with fish tissue Hg data except for those 
35

subsistence fish consumers scenario to all watersheds with fish tissue Hg data also helps to 

address the concern raised by the SAB that the March version of the national-scale Hg risk 

assessment may have excluded more remote water bodies that could be fished by subsistence 

anglers (leading to an underestimation of the percent of watersheds where Hg exposure from 

U.S. EGU sources is a risk). As mentioned earlier, Table 1-5 lists the seven female subsistence 

fish consumer scenarios and presents the number of watersheds assessed for risk for each 

scenario.

Define self-caught fish consumption rates for the subsistence scenarios

After identifying the set of female subsistence fish consumer scenarios to include in the 

analysis and determining where those scenarios should be applied, next we defined high-end 

(subsistence) self caught fish consumption rates for those scenarios. The three studies referenced 

in Table 1-6 provided either subsistence-level consumption rates at the 90th to 99th percentile, or 

the statistical parameters necessary to calculate those percentiles (e.g., median and standard 

deviations).35 In establishing consumption rates for the seven female subsistence fish consumer

scenarios, we either used rates from the studies directly (as in the case of the typical female 

subsistence fish consumer which uses female consumptions rates from the Burger et al., 2002 

study) or we used non-sex differentiated consumption rates, assuming that they applied to 

women of childbearing age. As noted earlier in this section, there is uncertainty associated with 

the assumption that non-sex differentiated consumption rates could apply to women of childbearing age (see footnote 33).

The subsistence-level consumption rates used in modeling risk for these scenarios are 

presented in Table 1-6, along with notes relevant to their interpretation in the context of this risk 

assessment. Of particular interest was whether the consumption rates represented annualaverages of daily “as consumed/prepared” values. As noted earlier in 1.4, this issue is critical to 

ensuring the appropriate technical linkages between the types of fish tissue Hg measurements 

made, application of preparation adjustment factors and ultimately, calculation of exposure and 

risk. This issue is discussed in greater length in the next section. 

The fish consumption rates presented in Table 1-6 clearly represent subsistence behavior 

since they reflect an individual making significant contributions to their diet through self-caught 

fish consumption. In most instances, the rates are equivalent to between one 8oz fish meal every

few days to a larger fish meal (12oz or more) every day. While these consumption scenarios are 

identified as “subsistence” due to their magnitude, they could be experienced by individuals 

purposefully supplementing their diet due to economic need or due to high levels of recreational 

fishing activity. While these scenarios could represent either subsistence or high-end recreational

fishing, the resulting exposure and risk characterization would be similar. For typical female 

subsistence fish consumers, as mentioned earlier, this scenario is applied uniformly across all 

watersheds with fish tissue Hg data, and whether we define consumption as subsistence or highend recreational fishing does not affect the exposure and risk characterization. In either case, the 

 

excluded due to potential impacts from non-air deposition), this scenario does provide greater coverage 

geographically than did the high-consuming female which was only applied to watersheds with fish tissue Hg data 

and at least 25 members of the source population (individuals living below the poverty line).

35 In those instances where a specific percentile was not provided, we estimated that value using the statistical 

parameters provided together with the assumption that the underlying fish consumption distribution was lognormal.
36

risk estimates represent risk experienced by an individual residing in the vicinity of a particular 

watershed who consumes self-caught fish at a subsistence level.

36 In addition to the three studies 

we used to define subsistence fish consumption rates, we also reviewed additional studies 

characterizing higher-levels of self-caught fish consumption in the U.S. While these additional 

studies had limitations that prevented their use in this risk assessment, they generally support the 

rates of self-caught fish consumption modeled in the analysis. 

A subset of these “supporting” studies is briefly described below:

 A study by Burger et al., 1999, examined recreational and subsistence fishing activity

along the Savannah river in Georgia to determine the role played by socio-economic 

status (SES) factors (including race, education and income) and determined levels of selfcaught fish consumption in this study area. The study suggested that all three factors are 

associated with levels of fishing activity. Specifically, in the case of race, the study

showed that Blacks tend to have much higher rates of fish consumption than Whites. For 

both groups, the study suggested that upper-end percentile consumption rates could be 

high enough to approach subsistence levels. For example, a ~200 g/day fish consumption 

rate represented the 98th percentile for Whites, but only the 92nd percentile for Blacks. 

This study supports the presence of high-end consumption rates for both Blacks and 

Whites that approach or meet subsistence levels in this area of the country. While this 

study did provide support for high self-caught fish consumption rates and SES 

differentiation of fish consuming populations, because of its geographically limited scope 

(i.e., focus on Savannah River), we elected not to use it directly in defining consumption 

rates for the risk assessment. 

 A study by Moya et al., 2008 examined factors associated with regional differences in 

patterns of fish consumption, including age, ethnicity (including Tribal affiliation), 

socioeconomic status (e.g., income, education), and type/source of fish consumed 

(freshwater, marine, and estuarine obtained from commercial sources versus self-caught). 

The study examined fishing activity in four states (i.e., CT, FL, MN, and ND) and 

provided estimates of high-end self-caught fish consumption for populations. Higher, 

subsistence-level consumption rates were identified for fishing populations in FL, MN 

(specifically for Tribes) and CT (for Asian populations, although it is not clear whether 

the rates for Asians are for self-caught fish consumption). Higher-end rates reported for 

ND and for general fishers in CT and MN did not approach the range of subsistence 

levels of consumption. However, the study designs used in these surveys may not 

effectively capture the small fraction of the overall population likely engaging in highend subsistence levels of self-caught fishing behavior. This study supports the existence 

of subsistence fishing populations in FL and for Tribes in MN. However, failure to 

capture similar behavior in ND and CT does not necessarily suggest that this type of 

 

36 To generate population-weighted risk estimates, it is important to differentiate between high-end recreational and 

necessity-based subsistence activity, since the associated populations at any given watershed could differ, leading to 

different population-weighted risk distributions. However, because we lacked other data needed to calculate 

population-weighted risks, this differentiation is not relevant. 
37

behavior is non-existent, but it may suggest that subsistence behavior is less prevalent 

than in FL. We did not elect to use the data for FL to define consumption rates since we 

already had the Burger et al., 2002 study which covers a region of the Southeast (South 

Carolina) where we have a larger number of watersheds being modeled for risk. 
38

Table 1-6 Fish consumption rates and additional behavior-related information for subsistence populations included in the 

analysis

(A) Fish consuming 

populations covered by 

study (and reference 

information)

(B) Overview of 

study

(C) Assumption regarding where 

the subsistence fisher scenario 

(and associated female subsistence 

fish consumers) might be active 

(D) Self-caught fish consumption rates 

(mean, 90th, 95th, 99th) g/day and notes on 

type of consumption rate 

(e.g., temporal averaging period and as 

purchased versus as consumed)

(E) Notes on consumption rate 

information relevant to the risk 

assessment

Higher self-caught fish 

consuming populations 

(White, Black and female) 

surveyed in South 

Carolina 

Citation: Daily 

consumption of wild fish 

and game: Exposures of 

high end recreationalists, 

Burger et al., International 

Journal of Environmental 

Health Research, 12:4, p. 

343-354, July, 2002

Random survey of 

participants in the 

Palmetto 

Sportsmen’s Classic 

in Columbia SC 

(1998). Population 

interested in 

fishing/hunting (not 

general population –

represents outdoor 

enthusiasts in SC)

- the Black and White fisher 

populations were extrapolated to 

cover watersheds modeled for risk 

in the Southeastern states. The 

rationale for this was that fishing 

activity by these two groups could 

be generalized in this region of the 

country. These scenarios were only 

assessed for watersheds in the 

Southeast located within U.S.

Census tracts with at least 25 

individuals from that ethnic group 

below the poverty line.

- given the focus of the risk 

assessment on consumption by 

women (in considering exposures 

for pregnant women in particular), 

we extrapolated the typical female 

consumer scenario to all watersheds 

in the continental U.S. and, given 

the more generalized nature of this 

scenario (no ethnic or SES 

differentiation), we assessed the 

scenario for all watersheds included 

in the risk assessment (i.e., we did 

not apply the source population 

criterion used for the other 

scenarios). 

- Black: 171, 446, 557, NC *

- White: 38.8, 93, 129, 286

- female: 39.1, 123, 173, 373

* the sample size for this population is only 

39, reducing overall confidence in a 99th

consumption rate (therefore, this high-end 

percentile was not included in the risk 

assessment)

Consumption rates are annual-average values 

expressed as meals or portions (as prepared) 

(median, 75th, 90th, 95th, and 99th). Survey 

asked respondents by month for number of 

meals of different type of fish and serving size 

(here models were used to demonstrate 

different meal or serving sizes). Authors 

could then use this to estimate monthly 

consumption rates and convert these into an 

annual average.

Sample size is variable – out of 458 

respondents, 39 are Blacks, 149 are 

female and 98 are low income –

Black n is relatively smaller than the 

other groups, which increases 

uncertainty in higher percentile 

values provided for this group. 

The authors point out that these 

results highlight the considerable 

spread between high-end consumers 

and more typical behavior (95th

percentile is more than 10X greater 

than the mean or median intake rate 

for wild-caught fish).

Results are also provided for low 

income (0-20K$ annual income). 

These consumption rates are 

relatively high particularly for the 

higher percentiles (90th, 95th and 99th

rates are: 285, 429 and 590 g/day). 

This observation forms the basis for 

our decision to assess a number of 

the subsistence populations only for 

watersheds located in U.S. Census 

tracts containing members of source 

populations below the poverty line 

for the White and Black populations.
39

(A) Fish consuming 

populations covered by 

study (and reference 

information)

(B) Overview of 

study

(C) Assumption regarding where 

the subsistence fisher scenario 

(and associated female subsistence 

fish consumers) might be active 

(D) Self-caught fish consumption rates 

(mean, 90th, 95th, 99th) g/day and notes on 

type of consumption rate 

(e.g., temporal averaging period and as 

purchased versus as consumed)

(E) Notes on consumption rate 

information relevant to the risk 

assessment

Higher self-caught fish 

consuming ethnic 

populations including 

Hispanics, Laotians and 

Vietnamese surveyed in 

California 

Citation: Contaminated 

fish consumption in 

California’s Central Valley 

Delta (Shilling et al., 

Environmental Research 

110, p. 334-344 (2010)

Study looks at 

subsistence fishing 

activity among 

ethnic groups 

associated with 

more urbanized 

areas near the 

Sacramento and San 

Joaquin rivers in the 

Central Valley in 

CA. 

- the Hispanic fishing scenario was 

extrapolated to cover watersheds 

located in U.S. Census tracts with at 

least 25 low income members of the 

ethnic populations (e.g., the 

Hispanic consumption rates would 

be applied to the subset of the 3,141

watersheds located in U.S. Census 

tracts with at least 25 low income

Hispanic individuals).

- the Laotian and Vietnamese 

fishing scenarios were extrapolated 

to cover watersheds located in U.S.

Census tracts with at least 25 

members of the underlying ethnic 

group. 

- Hispanic: 25.8, 98, 155.9, NC*

- Lao: 47.2, 144.8, 265.8, NC*

- Vietnamese: 27.1, 99.1, 152.4, NC*

* 95th percentile values were provided in the 

study. 90th percentile values were calculated 

using Crystal Ball (based on the median and 

standard deviations provided) assuming a lognormality of the consumption rate 

distributions. 99thpercentile consumption rates 

were not provided (or derived) for any of these 

populations due to small sample sizes of the 

study populations.

Consumption rates are annual-average 

estimates (cooked weight - as filets) based on 

the fact that they compare their average values

against other rates from the literature 

including rates used by the EPA in the 

regulatory context - all of which are annual 

averages. Survey used different sized models 

of cooked fish filets.

The authors note that many of these 

ethnic groups relied on fishing in 

origin countries and bring that 

practice here (e.g., Cambodian, 

Vietnamese and Mexican). The 

authors also note that fish 

consumption rates reported here for 

specific ethnic groups (specifically 

Southeast Asian) are generally inline with rates seen in WA and OR 

studies.
40

(A) Fish consuming 

populations covered by 

study (and reference 

information)

(B) Overview of 

study

(C) Assumption regarding where 

the subsistence fisher scenario 

(and associated female subsistence 

fish consumers) might be active 

(D) Self-caught fish consumption rates 

(mean, 90th, 95th, 99th) g/day and notes on 

type of consumption rate 

(e.g., temporal averaging period and as 

purchased versus as consumed)

(E) Notes on consumption rate 

information relevant to the risk 

assessment

High-end self-caught fish 

consuming Chippewa and 

Ojibwa Tribal populations 

active in the vicinity of the 

Great Lakes. 

Citation: Exposure 

assessment and initial 

intervention regarding fish 

consumption of tribal 

members in the Upper 

Great Lakes Region in the 

United States. Dellinger , 

Environmental Research 

95 (2004) p. 325-340

This study 

contrasted selfreported fish 

consumption rates 

by Tribes in the 

Great Lakes area 

with “actual” fish 

consumption rates 

collected for a 

subset of the 

original study 

population (147 of 

822 from 4 Tribal 

population/location 

combinations). The 

study found that 

actual fish 

consumption rates 

were lower than 

reported values. 

Activity only assumed to occur in 

areas ceded to the Tribes covered in 

the study (regions in the vicinity of 

the Great Lakes). Because fishing 

activity is highly variable across 

Tribes (and closely associated with 

heritage cultural practices) we have 

not extrapolated fishing behavior for 

these Tribes outside of the specific 

populations and regions covered. 

- reported value for all Tribal areas (in the 

study) combined: 62, 136.2, 213.1, 492.8 

All higher percentiles (90th– 99th) were 

derived using Crystal Ball (based on median 

and standard deviations and an assumption of 

log-normally distributed variability in 

consumption rates)

Consumption rates appear to be annual 

average values. Study includes reference to 

querying for the number of fish meals in a 

year, which suggests that estimates are annualaverages (but it is not explicitly stated). 

Ingestion rates are in-line with other high-end 

consumer rates cited in this table. It also 

appears that estimates are for amount 

consumed (i.e., meal weight), based on the 

fact that the "actual" estimates provided in the 

article focused on this type of consumption 

rate.

While the “actual” consumption 

rates collected for a subset of the 

families were far lower than the 

reported values (often an order of 

magnitude smaller), a number of 

factors resulted in a decision to use 

the reported values rather than the 

actual values in the risk assessment. 

First, and most importantly, the 

sample size is very small for the 

“actual” analysis with n’s ranging 

from 12 to 54 individuals 

(representing a smaller number of 

associated families) for the different 

survey groups. These small 

sampling rates reduce the 

probability of capturing individuals 

with higher consumption rates in the 

broader population. It also appears 

that the actual values may cover 

walleye specifically and not include 

all fish, which could bias these 

values downward. There is concern 

that, even if consumption rates have 

decreased, actual heritage cultural 

practices could still exist (or there 

could be a desire to return to those 

rates), in which case, risks levels 

associated with those higher 

historical consumption rates could 

be important to assess. And finally, 

the high-end percentile consumption 

rates derived based on reported 

mean consumption rates (and 

standard deviations) are in-line with 

subsistence consumption rates seen 

for other populations in the U.S. 

Therefore, these Tribal high-end fish 

consumption rates would general 

comport with subsistence fish 

consumption activity and therefore 

are considered reasonable to include 

in the risk assessment. 
41

1.4.4 Estimating total fish consumption-related MeHg exposure (2016 scenario)

Next, we estimated total exposure to Hg for the set of seven female subsistence fish 

consumer scenarios being evaluated (for the 2016 scenario). To generate estimates of total Hg 

exposure, we combined the 75th percentile fish tissue Hg values (projected for 2016) with three 

exposure-related factors including: (a) the consumption rates for a subsistence fisher scenarios in 

grams of fish per day, (b) a conversion factor between total Hg measurements in fish and MeHg 

levels in fish and (c) a food preparation adjustment (cooking fish can increase MeHg 

concentrations). 

In modeling MeHg exposure through fish consumption, it is important to verify that the 

linkages between factors used in modeling exposure (i.e., the variables in the exposure equation 

listed above) are conceptually correct. For example, we need to convert the total Hg levels in fish 

to MeHg levels in order to calculate the HQ, which is based on MeHg intake. Similarly, if fish 

ingestion rates are specified in terms of “as cooked” or “as consumed” rather than “as 

purchased” or “as market basket,” then we must factor in cooking/preparation in specifying the 

fish tissue Hg concentrations. Failure to correctly link these exposure factors can result in biased 

estimates of MeHg intake and consequently biased risk estimates. Below we discuss each 

exposure factor, including the data used to specify that factor and the linkage of that factor to 

others in the exposure equation. Then, we present the exposure equation.

Mercury fish tissue concentration (FTC): We calculated the 75th percentile fish tissue Hg 

estimate for each watershed and then project those to represent the 2016 scenario (as described in 

section 1.4.2.1). Generally, measured fish tissue Hg is in terms of total Hg. Because we need

MeHg levels in fish, we convert total Hg levels measured in fish into MeHg levels using the 

mercury conversion factor (MCF).

Mercury conversion factor (MCF): We applied a factor of 0.95 (unitless) to convert total Hg 

levels in fish tissue samples into MeHg levels. This conversion factor is based on conclusions 

reached in the Mercury Study Report to Congress (MRTC) (U.S. EPA, 1997) that more than 

90% of Hg in fish is MeHg. The conclusion presented in the MRTC is in turn based on two 

studies (Bloom, 1992 and Morgan et al., 1994). The 0.95 factor represents a median value 

between an assumption that all of the Hg in fish is MeHg and the lower bound value of 0.90 cited 

in the MRTC document.

Food preparation/cooking adjustment factor (FPCAF): Because we use fish consumption 

rates for “as consumed” portions, we need to adjust the fish tissue Hg concentrations to reflect 

concentrations after food preparation. Cooking fish typically increases MeHg levels per unit fish 

because Hg concentrates in the muscle while preparation involves removal primarily of nonmuscle elements of the fish (e.g., water, fat etc) (Morgan et al., 1997). The FPCAF factor used in 

the analysis is 1.5 (i.e., an increase of 50% in the MeHg concentration per unit fish due to 

preparation/cooking). This factor is based on the Morgan et al., 1997 study, which estimated 

factors of between 1.1 and 1.5 for walleye and 1.5 and 2.0 for lake trout. 

The SAB recommended that additional studies be acknowledged as providing information 

regarding the FPCAF, citing two alternative studies (Farias et al., 2010 and Musaiger et al. 

2008). After assessing these studies, we conclude that they do not support a lower 
42

preparation/cooking loss adjustment factor. The Farias study appears to suggest that preparing

fish by Manaus residents in the Amazon could decrease Hg concentrations. Although this 

initially suggests that the 1.5 factor used in the risk assessment is biased high, a closer read of the 

Farias study suggests that the authors may have measured non-fish components added to dishes 

(e.g., onions, heavy breading etc) in post-cooking measurements, which could provide the 

appearance of a cooking loss in Hg while actual fish tissue Hg concentrations could have 

increased. The Musaiger et al. 2008 study compared Hg levels across different types of fish 

meals after preparation rather than pre and post-cooking Hg levels. In fact, the authors state that 

cooking is not a means of reducing Hg because it typically removes fat and water, while Hg is 

located in the meat and therefore will not be reduced.37 

Fish consumption rate (FCR): We reviewed the three studies providing fish consumption rates 

used in the risk assessment to verify that consumption rates (a) are annual-averages and not rates 

reflecting elevated consumption during shorter seasonal periods and (b) represent “as consumed” 

rather than “as purchased” values. Based on the review of the three studies we concluded that the 

consumption rates are annual average daily consumption rates for “as cooked/as prepared.” In

some instances, a study did not explicitly state whether rates were annual-averages and/or “as 

cooked”, and we had to infer this from other information provided in the article. Details 

regarding our assessment of each of the three studies is provided in Table 1-6 (column D). 

The following equation shows how these factors were combined to generate estimates of 

annual-average daily MeHg exposure per kg body weight:

IR = FTC(2016) * MCF * FPCAF * FCR 

 BW 

where, 

IR = daily MeHg intake rate (ug/kg-day)

FTC(2016) = Hg fish tissue concentration (ug/g or ppm of total Hg) projected for the 

2016 scenario

MCF = Hg conversion factor (unitless)

FPCAF = food preparation/cooking adjustment factor (unitless)

FCR = fish consumption rate (g/day)

BW = body weight (kg)

 

37 As part of revising the national-scale Hg risk assessment, we completed a literature review focusing on the issue 

of cooking/preparation of fish and the effect on Hg concentration. This literature review included the two studies 

recommended by the SAB. That review did not identify any studies that argued against use of an adjustment factor 

of 1.5 and consequently, we continue to use that factor in modeling exposure and risk.
43

1.4.5 Estimating risk (RfD-based hazard quotient) (2016 scenario)

Once we estimate the MeHg intake rate for each female subsistence fish consumer

scenarios at each watershed (for 2016), we compare these exposure estimates to the MeHg RfD 

to generate HQ estimates. The MeHg RfD is 0.0001 mg/kg-day (equivalent to 0.1 μg/kg-day) 

and was published by the EPA in the Integrated Risk Information System in 2001 (US EPA, 

2001a) (http://www.epa.gov/iris/subst/0073.htm). 

Reflecting precedent in interpreting HQ estimates, we consider exposures above the RfD 

(i.e., an HQ above one) to represent a potential public health hazard. Consequently, watersheds 

with female subsistence fish consumer exposures above the RfD are considered to have the 

potential for consumers of self-caught fish from that watershed to experience a public health 

hazard due to MeHg exposure. An important factor to consider in interpreting HQ’s is the 

precision underlying these risk estimates. While precision associated with the exposure estimates 

may be higher, the RfD is only reported to one significant digit, which limits the precision in the 

HQ estimate. Consequently, we interpret an HQ of 1.5 or greater as representing an exposure that 

exceeds the RfD (since this value will round to two expressed as a whole number). Conversely, 

exposures of 1.49999 or less are considered not to exceed the RfD given this rounding 

convention for HQs.38

In response to SAB recommendations that the IQ loss endpoint may not fully capture the 

range of neurodevelopmental effects associated with Hg exposure, we have deemphasized this 

category of risk metrics and moved the IQ discussion to the appendices. We discuss the 

approach used to generate IQ loss estimates including the concentration-response function in 

Appendix A and we provide the results and approach used to interpret the public health 

significant of IQ loss in Appendix B.

1.4.6 Estimation of U.S. EGU-attributable risk (2016 scenario)

Next, we estimated the fraction of total risk (HQ) that is attributable to U.S. EGUs (for 

the 2016 scenario). The estimate of U.S. EGU-attributable risk is at the core of the 2-stage risk 

characterization framework (see Section 1.3). We estimate the U.S. EGU-attributable fraction of 

total risk using the linear proportionality assumption linking changes in Hg deposition over 

watersheds with changes in fish tissue Hg concentrations.39 As noted in section 1.4.2.1, this 

proportionality assumption states that, under steady state conditions, a change in Hg deposition 

over a given watershed will result in a proportional change in fish tissue Hg concentrations and 

associated exposure and risk. The proportionality assumption is supported by the Mercury Maps 

 

38 Note, that for the U.S. EGU incremental contribution analysis, an HQ of less than 1.5 does not necessarily 

indicate there is no public health hazard related to U.S. EGU emissions. Rather, it suggests that, for those specific 

watersheds, we need to also consider whether total risk (i.e., the HQ reflecting total MeHg exposure) exceeds 1.5

and therefore represents a potential public health hazard. If that is the case, then we would consider the degree to 

which U.S. EGUs contribute to that total exposure because incremental exposures above the RfD increase the risk.

39 Because risk at the HUC-level is linearly related to the fish tissue Hg concentration (see sections 1.4.4 and 1.4.5), 

this proportionality assumption can be used to relate changes in mercury deposition to change in total risk at that 

HUC.
44

analysis (U.S. EPA, 2001b). In addition, a number of additional studies (some of which were 

highlighted by the SAB in their review of the March TSD) support a proportional relationship 

between changes in Hg deposition and changes in fish tissue Hg concentrations, although with 

important caveats related to the temporal response of aquatic systems to changes in Hg 

deposition/loading. There are also important criteria that must be met for the proportionality

assumption to hold, which we discuss below. 

We used the CMAQ air quality model to estimate Hg deposition over U.S. watersheds for 

all sources of mercury, including U.S. and non-U.S. sources. We also modeled Hg deposition 

directly from U.S. EGUs by zeroing out Hg emissions from U.S. EGU sources and subtracting 

the results from total Hg deposition to isolate the U.S. EGU contribution. The CMAQ modeling 

for this analysis is described in section 1.4.6.3. 

Hg deposition estimates at each watershed coupled with the proportionality assumption 

described above can be used to estimate U.S. EGU-attributable HQ risk for the 2016 scenario. To 

estimate the U.S. EGU-attributable fraction of that 2016 HQ estimate, we use the ratio of U.S. 

EGU Hg deposition versus the estimate for total Hg deposition (both for the 2016 scenario in that 

watershed). The step-wise procedure for completing the estimate of U.S. EGU-attributable risk 

for 2016 is outlined below: 

Generate U.S. EGU-attributable HQ risk estimates for the 2016 scenario: We use 

the 2016 total and U.S. EGU-attributable CMAQ deposition results with the estimated 

total HQ risk estimates for 2016 to estimate the U.S. EGU-attributable HQ risk at each 

watershed as follows:

2016 EGU-HQ = 2016 THQ * (2016 EGU Hg dep / 2016 total Hg dep)

Where:

2016 EGU-HQ: U.S. EGU-attributable HQ risk for the 2016 scenario

2016 THQ: HUC-level total HQ risk for 2016 generated as described in section 1.4.5.

2016 EGU Hg dep: 2016 CMAQ-based projections of U.S. EGU-related Hg deposition 

over a given watershed)

2016 total EGU dep: 2016 CMAQ-based projections of total Hg deposition over a given 

watershed

1.4.6.1 Mercury Maps analysis 

Results of the EPA’s Office of Water’s Mercury Maps analysis support the 

proportionality assumption (U.S. EPA, 2001b). The Mercury Maps analysis used a simplified 

form of the IEM-2M model applied in EPA’s Mercury Study Report to Congress (U.S. EPA, 

1997). By simplifying the assumptions inherent in the freshwater ecosystem models that were 

described in the Report to Congress, the Mercury Maps model showed that these models 

converge at a steady-state solution for MeHg concentrations in fish that are proportional to 

changes in Hg inputs from atmospheric deposition. This steady-state solution only applies in 

situations where air deposition is the only significant source of Hg to a water body, and the 

physical, chemical, and biological characteristics of the ecosystem remain constant over time. 
45

Consequently, the proportionality assumption used to estimate the U.S. EGU-attributable 

fraction of risk would ideally only be applied to watersheds where these criteria have been met. 

Application of the proportionality assumption in situations where these criteria have not been 

met introduces uncertainty in the apportionment of total risk. EPA recognizes that concentrations 

of MeHg in fish across all ecosystems may not reach steady state and that ecosystem conditions 

affecting Hg dynamics are unlikely to remain constant over time. EPA further recognizes that 

many waterbodies, particularly in areas of historic gold and Hg mining in western states, contain 

significant non-air sources of Hg.

40

 Finally, EPA recognizes that Mercury Maps does not 

estimate the time lag between a reduction in Hg deposition and a reduction in the MeHg 

concentrations in fish. 

In their peer review, the SAB noted that there are other modeling tools available to link 

deposition to fish tissue concentrations, but did not consider them to be superior for this analysis, 

nor did they recommend their use. The SAB specifically noted that the Regional Mercury

Cycling Model (R-MCM) could also be used for a national assessment, but they also noted that 

the R-MCM is more data intensive and the results produced by the two model approaches should 

be equivalent. We did not have all of the data inputs (water chemistry, methylation potential, 

etc) that would be required to run the R-MCM, and given the SAB recommendation, and their 

comment that “it is unlikely that substantial additional insight would be gained with the 

alternative model framework,” we elected to use the proportionality assumption as supported by

the Mercury Maps modeling and the peer-reviewed literature. Because the Mercury Maps 

approach only applies in those watersheds where aerial deposition is the dominant source of Hg 

loading, we excluded watersheds with substantial non-U.S. EGU anthropogenic emissions of Hg

from the risk assessment as described in section 1.4.2. 

There are a number of limitations and uncertainties associated with the application of the 

Mercury Maps approach in the context of this risk assessment. These limitations are discussed 

here and addressed in Table 2-15. Applying Mercury Maps to apportion fish tissue Hg 

concentrations and consequently exposure and risk between U.S. EGUs and all other sources of 

Hg at the watershed-level assumes that the relationship between fish tissue levels and Hg 

deposition has remained fairly consistent such that near steady-state conditions have been 

reached. However, in reality, patterns of Hg deposition for the period during which the fish tissue 

samples were collected (2000 to 2009) have not remained constant. In addition, those fish tissue 

concentrations may actually reflect patterns of Hg deposition from earlier time periods (e.g., the 

1990s) when Hg emissions from U.S. sources were experiencing substantial decreases.41 In

 

40 As described below, we have excluded those watersheds containing gold mines or with other non-EGU related 

anthropogenic Hg emissions exceeding specified thresholds.

41 The discussion of model uncertainty provided in the Technical Support Document describing the Mercury Maps 

analysis (MMAPs TSD, U.S. EPA, 2001) also addresses the fact that proportionality between a decrease in Hg 

deposition and changes in fish tissue Hg concentrations may not be fully realized for some time due to the lagging 

effect of Hg that has built up in sediment. Simulations discussed in the MMAPs TSD suggested that substantial 

concentrations of Hg could build up in sediment with these loadings effectively buffering the impact of reductions in 

Hg. This issue does not invalidate the application of the proportionality assumption in the national-scale mercury 

risk assessment, but it does suggest that the full effect of predicted reductions in risk may not be seen in the nearterm (this issue is discussed further in the context of other studies in section 1.4.6.2). The discussion of model 

uncertainty in the MMAPs TSD also addresses potential non-linearity in methylation. Citing the DiPasquale, et al, 

2000 study, the MMAPs TSD notes the potential for reduced methylation at very high Hg loadings typically 

experienced in mining areas. However, since our analysis excluded areas likely impacted by active gold mines, we 

believe that this issue has been largely ameliorated in the context of the national-scale mercury risk assessment. 
46

addition, other factors that can affect rates of Hg methylation (e.g., sulfur deposition to 

waterbodies, pH of the waterbodies) also have not remained constant over the past 1-2 decades 

for most watersheds. The fact that many of these factors related to methylation in fish have not 

remained constant introduces uncertainty into the application of the Mercury Maps based 

proportionality assumption. However, we believe that the Mercury Maps approach for 

apportioning fish tissue Hg concentrations is still appropriate to use, particularly if we are not 

attempting to characterize the temporal response and instead, can assume that sufficient time has 

passed for near steady state conditions to be reached (see section 1.4.6.2 below). Furthermore, 

while we have excluded watersheds with substantial non-air loading of Hg from industrial 

activity and mines, we did not consider municipal sewage emissions of Hg to watersheds. This 

introduces additional uncertainty into the analysis, since watersheds could have a substantial 

fraction of Hg loading originating from municipal sewage treatment. In these instances, failure to 

consider this source of non-air loading could result in a high-bias in the estimates of the U.S. 

EGU-attributable fraction of deposition (and hence risk), since the contribution from this non-air 

source would not have been considered.

1.4.6.2 Additional research supporting the proportionality assumption and 

examining the issue of temporal response 

The SAB commented that several recent publications have supported the finding of a 

linear relationship between Hg loading and accumulation in aquatic biota (Orihel et al., 2007; 

Orihel et al., 2008; Harris et al., 2007). The SAB noted that these studies suggest that that Hg 

deposited directly to aquatic ecosystems can become quickly available to biota and accumulated 

in fish, and reductions in atmospheric Hg deposition should lead to decreases in MeHg 

concentrations in biota. 

EPA has reviewed the studies identified by the SAB, together with a study by Knightes et 

al., (2009). All of these studies, to varying degrees, suggest that when we are considering 

reductions in fish tissue Hg concentrations following reductions in Hg deposition to a watershed, 

we are likely to see a 2-stage response including: (a) an initial more rapid phase of reduction 

(ranging from months to a few years) reflecting a decrease in direct loading to the waterbody and 

subsequent reductions in fish tissue Hg concentrations related to decreased water column Hg 

levels and (b) a second slower phase of reduction (years to decades or more) reflecting longer 

term changes in the rate of erosion/runoff loading to the waterbody and the potential buffering 

effect of historical reservoirs of Hg in sediment. These findings suggest that we might not see an 

equivalent fractional reduction in risk matching the reduction in aerial deposition for near-term 

reductions (less than one year) due to historical reservoirs of Hg in sediment that continue to load 

the benthic food web and buffer the Hg response. However, for reductions over a longer period 

of time (sufficient for steady state, or near steady state conditions to be met in the aquatic 

system), the proportionality assumption likely holds and we would expect to see a reduction in 

risk matching the fractional reduction in Hg deposition. Since we have stated that our risk 

estimates are based on an assumption that steady state, or near steady state conditions are met, 

regardless of how long that takes, then the temporal response is not a factor and we have 

increased confidence in applying the proportionality assumption. 
47

1.4.6.3 CMAQ mercury deposition modeling

We modeled total annual Hg deposition from U.S. and foreign anthropogenic and natural 

sources as well as the fraction of Hg deposition from U.S. EGUs using the CMAQ model. The 

Community Multi-scale Air Quality (CMAQ) model v4.7.1 (www.cmaq-model.org) is a state of 

the science three-dimensional Eulerian “one-atmosphere” photochemical transport model used 

to estimate air quality (Appel et al., 2008; Appel et al., 2007; Byun and Schere, 2006). CMAQ 

simulates the formation and fate of photochemical oxidants, ozone, primary and secondary PM 

concentrations, and air toxics over regional and urban spatial scales for given input sets of 

meteorological conditions and emissions. Mercury oxidation pathways are represented for both 

the gas and aqueous phases in addition to aqueous phase reduction reactions (Bullock and 

Brehme, 2002). Mercury estimates from CMAQ have been compared to observations and other 

mercury modeling systems in several peer reviewed publications (Bullock et al., 2008, 2009; Lin 

et al., 2007). Additional information about the model, model inputs for this assessment, and 

model evaluation are available in the Air Quality Modeling TSD (U.S. Environmental Protection 

Agency, 2011).

The 36km and both 12km modeling domains were modeled for the entire year of 2005. 

The emissions data used in the 2005 base year and 2016 total Hg emissions and U.S. EGU Hg 

emissions zero-out cases are based on the 2005 v4.1 platform. Emissions are processed to 

photochemical model inputs with the SMOKE emissions modeling system (Houyoux et al., 

2000). The 2016 total Hg emissions case is intended to represent the emissions associated with 

growth and controls in that year projected from the 2005 simulation year. Only anthropogenic 

emissions changed between the 2005 and 2016 simulations, all other model inputs are the same 

in both simulations. Other North American emissions of criteria and toxic pollutants (including 

mercury) are based on a 2006 Canadian inventory and 1999 Mexican inventory (U.S. EPA, 

2011). 

Global emissions of criteria and toxic pollutants (including mercury) are included in the 

modeling system through boundary condition inflow. The lateral boundary and initial species 

concentrations are provided by a three-dimensional global atmospheric chemistry model, the 

GEOS-CHEM model (standard version 7-04-11). The GEOS-CHEM predictions were used to 

provide one-way dynamic boundary conditions at three-hour intervals and an initial 

concentration field for the 36 km CMAQ simulations. The 36 km photochemical model 

simulation is used to supply initial and hourly boundary concentrations to the 12 km domains. 

Initial and boundary conditions for the projected future year (2016) 36 km simulations are the 

same as the 2005 base year. The first 10 days of the 36 km modeling simulation are not used in 

the analysis, which is beyond the number of days necessary to remove the influence of initial 

conditions on mercury deposition estimates (Pongprueksa et al., 2008).

The boundary inflow for the CMAQ mercury modeling used in the national-scale 

mercury risk assessment are based on a global model GEOS-CHEM simulation using a 2000 

based global inventory as described in (Selin et al., 2007). A comparison of global mercury

emissions by continent for 2000 and 2006 was recently published in (Streets et al., 2009) and 

show there is no discernable change in mercury emissions from Asia between 2000 and 2006. 

Given these consistent emissions estimates from Asia, the 2005 boundary inflow to the 36 km 

CMAQ domain was not adjusted. Mercury boundary conditions are the same for both the 2005 
48

and 2016 simulations based on the consistency in Asian mercury emissions between 2000 and 

2006, the declining ambient mercury concentrations in the northern hemisphere since 2000 

(Slemr et al, 2011), and the large uncertainties surrounding projected global inventories of 

mercury emissions.

1.5 Differences between the 2005 Section 112(n) Revision Rule analysis and the 

current analysis in support of the Appropriate and Necessary Finding 

In 2005, EPA conducted a set of technical analyses to support revision of the 2000 

appropriate and necessary finding.42 This section identifies key differences between the 

watershed-level risk assessment completed in support of the 2005 revision rule and the current 

risk assessment. These differences include both technical factors related to the design of the 

assessments, as well as differences in the interpretation of potential public health significance of 

the risk estimates generated. Key differences between the two analyses include:

Higher spatial resolution through use of CMAQ 12km grid cells: In this analysis, we modeled 

Hg deposition using CMAQ at a 12 km grid resolution, whereas in the 2005 analysis, we used 

CMAQ modeling with a 36 km grid cell resolution. The more refined spatial resolution at 12 km 

is more appropriate for representing areas of elevated U.S. EGU-attributable deposition (and 

total Hg deposition in general) compared with the 36 km resolution used in the 2005 analysis. 

The 12 km resolution also matches up with the more refined HUC12 watersheds now being used 

in the analysis, thereby allowing a more refined treatment of the intersection of aerial Hg 

deposition and measured fish tissue concentrations at the watershed level.

Application of more refined HUC12 watersheds: The current analysis uses HUC12 watersheds as 

the basis for risk estimation (these watersheds typically are 5-10 km on a side). By contrast, the 

2005 analysis used HUC8s, which are much larger (averaging 40km on a side). The use of more 

spatially refined watersheds increases the potential for capturing areas of elevated aerial Hg 

deposition (combined with measured fish tissue levels). 

Inclusion of updated fish tissue data: For this analysis, we included measured fish tissue data 

collected between 2000 and 2010. By contrast, the 2005 analysis used data collected between 

1999 and 2003, which was the best available data at the time.

Subsistence fisher activity better defined and considered more ubiquitous: Based on an extensive 

review of available literature, we identified studies characterizing high-end self-caught fish 

consumption for a wide variety of source populations (e.g., Hispanic, Vietnamese, Whites and 

Blacks in the southeast, Great Lakes Tribal populations). Although in many cases, it was 

necessary to extrapolate high-end fishing activity to regions beyond those covered in the 

underlying studies, we believe that the literature supports the plausibility of high-end 

subsistence-like fishing activity across the watersheds included in the analysis. Additionally, the 

variety of studies identifying self-caught fishing activity at subsistence levels (i.e., a meal every

few days to a meal every day) for a variety of diverse SES-differentiated populations in different 

 

42

 U.S.EPA. 2005. Technical Support Document: Methodology Used to Generate Deposition, Fish Tissue 

Methylmercury Concentrations, and Exposure for Determining Effectiveness of Utility Emission Controls.
49

regions of the country, supports assessing this type subsistence fish consumption behavior across 

the modeled watersheds. 

By contrast, in the 2005 analysis, we concluded that the study data characterizing fishing activity

available at that time was limited in its ability to support modeling of subsistence fisher activity 

for the following reasons: (a) it characterized regional or local activity that could not be readily 

extrapolated more broadly, (b) fishing activity queried included consumption of saltwater 

species, or (c) specific high-end percentiles were not identified (or if they were, they only 

applied during specific harvesting periods - e.g., spearfishing months for Great Lakes Tribes). 

Therefore, in the 2005 analysis, we applied a high-end self-caught percentile values (95th and 

99th percentiles) based on Tribal fishing practices in the Northwest to watersheds across the 

country.

43 The updated literature review, for the current analysis, led us to revise several of our 

earlier conclusions regarding high-end fishing activity. Specifically, while many of the studies of 

subsistence-like activity are regional in nature, when considered together, we now conclude that 

they support modeling subsistence-like fishing activity more broadly across the entire study area. 

Additionally, while some of the studies may include saltwater fishing in addition to freshwater 

(e.g., Burger, 2002), when those studies clearly covered both saltwater and freshwater selfcaught fish consumption, we concluded that it was reasonable to assume that subsistence-like 

fishing activity could occur both at the coast and inland at freshwater bodies.44

For the current analysis, we used the 75th percentile fish tissue Hg concentration reflecting the 

potential for high-end subsistence fishers to target larger fish, which would have greater 

bioaccumulation potential relative to the average fish. By contrast, in the 2005 risk assessment, 

we used the maximum of the average fish tissue Hg concentrations across species of fish in a 

given HUC8, which is potentially a more conservative approach (i.e., resulting in higher risk, 

other factors equal). 

Calculation of RfD-based HQ estimates including total and U.S. EGU-attributable risk and 

calculation of IQ loss: For this analysis, we compared total exposure to the MeHg RfD to 

generate an HQ estimate based on total Hg exposure for fishers at a given watershed. 

Furthermore, to focus on the U.S. EGU component of that total risk, we have generated two 

related risk metrics: (a) U.S. EGU incremental contribution to total risk which essentially

considers the magnitude of the HQ when deposition from U.S. EGUs is considered before taking 

into account deposition and exposures resulting from other sources of Hg and (b) the percent of 

 

43 These NW Tribal fishing estimates are subject to considerable uncertainty when extrapolated to other areas in the 

U.S. These specific high-end fish consumption rates were derived for Tribes active in the Northwest who engage in 

specific cultural practices focused around salmon fishing. There is also significant uncertainty in extrapolating this 

type of highly-specific cultural-based fishing activity to other Tribes, let alone to other fish consumers in the U.S. 

By contrast, extrapolation of more generalized high-consuming rates (for ethnic groups and Whites and Blacks) to 

cover portions of the U.S. as was done in the current analysis is subject to less uncertainty given that these SESdifferentiated populations are defined more generally and therefore are likely to demonstrate more consistency in 

culturally-related practices (such as subsistence fishing) across the country. 

44 In the situation where a study specifically characterized low income high-end fishing populations, as is done in the 

Burger 2010 study of activity in SC, we considered it reasonable to assume that low income individuals would likely 

conduct their frequent fishing activity near home. In that case, some of these high-end fishers would likely be 

located near the coast and some inland. In the case of subsistence-like fishing activity in the southeast, other studies 

from rivers in that area also showed subsistence-like fish consumption rates when only freshwater rivers were 

considered (e.g., Burger et al., 1999 focusing on fishing activity on the Savannah river in GA). 
50

total HQ risk attributable to U.S. EGUs. The calculation of U.S. EGU incremental contribution 

to total HQ is identical to the IDI (index of daily intake) metric used in the 2005 analysis. 

However an important distinction is that in the current analysis, we highlight the fact that this 

U.S. EGU-related risk is always associated with a total HQ which is generally substantially

larger (i.e., the U.S.-EGU-attributable HQ should not be considered in isolation as was done in 

the 2005 analysis with the IDI). By contrast, for the 2005 analysis both of the risk metrics used 

(i.e., the IDI and the comparison of U.S. EGU-related fish tissue concentrations against EPA’s 

water quality criterion expressed as a Hg fish tissue value) essentially considered the U.S. EGU 

portion of risk in isolation. These risk metrics in the 2005 analysis were not contrasted with the 

much larger fraction of total Hg-related risk associated with the non-U.S. EGU portion of risk. 

1.6 Detailed Example Calculation (watershed-level risk HQ)

This section provides a sample calculation of a watershed-level RfD-based HQ risk 

estimate generated for the 2016 scenario and walks the reader through each of the calculation 

steps associated with generating the estimate (see Figure 1-9). We cross-reference to sections of 

the Revised TSD that cover each of the intermediate calculations and data inputs involved in 

generating the risk estimate.45 Each of the calculation steps is summarized below:

 Step 1: Based on the fish tissue Hg measurements available for a given watershed (after 

filtering), generate the 75th percentile fish tissue Hg concentration. We provide the 

median, 75th and 90th percentile values in Figure 1-9 to illustrate the spread in percentile 

values for this watershed). (see section 1.4.2).

 Step 2: Obtain CMAQ Hg deposition estimates for the watershed for the 2005 and 2016 

scenarios. Compute the ratio of these two factors (i.e., 2016 deposition/2005 deposition). 

(see section 1.4.6.3).

 Step 3: Multiply the ratio from Step 2 by the fish tissue Hg concentration to project the 

fish tissue Hg concentration value to represent the 2016 scenario for this watershed. This 

step involves application of the proportionality assumption (see section 1.4.2.1).

 Step 4: Calculate intake rate for total Hg for the subsistence fisher scenario being 

assessed in that watershed using equation with fish tissue Hg concentrations calculated in 

Step 3(see section 1.4.4 for a discussion of this exposure calculation).

 Step 5: Compare the exposure estimate generated in step 4 to the MeHg RfD to generate 

a hazard quotient reflecting total Hg exposure for the subsistence fisher at that watershed 

(see section 1.4.5).

 

45 The SAB recommended including a cross reference between the calculation steps outlined in Figure 1-9 and the 

discussion of variability and uncertainty presented in section 2-7. However, the complex interplay of sources of 

uncertainty and variability with the analytical structure used in the risk assessment precludes presentation of a clean 

linkage between sources of uncertainty and variability and the analytical framework used in the risk assessment. 

Most sources of variability and uncertainty impact either directly or indirectly multiple (if not all) of the calculations 

steps involved in generating risk estimates. Careful consideration of the analytical approach reflected in Figure 2-1 

and the flow diagram outlining major analytical steps of the risk assessment presented in Figure 1-2 should allow the 

reader to determine which elements of the risk model are impacted by specific sources of uncertainty and/or 

variability. 
51

 Step 6: Obtain CMAQ Hg deposition estimates for the watershed for both total and U.S. 

EGU-attributable Hg for the 2016 scenario. Compute the ratio of these two factors (i.e., 

U.S. EGU/total deposition). This ratio will be used to estimate the fraction of total risk 

attributable to U.S. EGUs (see section 1.4.6).

 Step 7: Estimate the fraction of the total HQ risk that is attributable to U.S. EGUs (for the 

2016 scenario). This estimate is based on the proportionality assumption and uses the 

ratio developed in step 6 (see section 1.4.6).
52

Figure 1-9 Sample Calculation for watershed-level Risk HQ

IR = FTC * MCF * FCR * CAF

BW 

Example – Lower Kettle Creek in north central Pennsylvania (HUC12 ID: 20502030106)

IR: daily MeHg intake rate (ug/kg-day)

FTC: MeHg fish tissue concentration (ug/g or ppm)

MCR: mercury conversion factor – fraction fish tissue 

mercury that is MeHg (unitless)

FCR: fish consumption rate (g/day)

FPCAF: fish preparation/cooking adjustment factor

RfD: methylmercury RfD

FracEGU: fraction dep from US EGU

Fish tissue (ppm)

Median: 0.24

75th : 0.32

90th : 0.34

6 samples

PROJECTED

Fish tissue

(ppm) 2016

Mean: 0.20

75th : 0.26

90th : 0.28

Total Hg dep

over watershed

(CMAQ):

2005 = 30.42

2016 = 25.19

Or a ~17% reduction

95th% 

consumption 

rate:

173 g/day

Fish 

preparation/

cooking 

adjustment 

Factor:

1.5

Body

weight:

64 g

IR = 1.01 ug/kg-day

MeHg RfD:

0.1 ug/kg-day

Total HQ = IR = 1.01 = 10.1

RfD 0.1

2016 Hg dep

over watershed

(CMAQ):

total = 25.19

US EGU = 1.26

US EGU is 5%

FracEGU = 0.050

US EGU risk = HQ * FracEGU

= 10.1 *0.050 = 0.504 (i.e., <1)

1

2

3

4

5

7

Mercury

conversion 

factor:

0.95

#: calculation step

6
53

2 Discussion of Analytical Results

This chapter provides a discussion of the results of the 2016 simulation, including both

risk estimates as well as intermediate calculations and inputs associated with generating those 

risk estimates. EPA’s projection of total Hg emissions from U.S. EGUs in 2016 (after other 

CAA-related regulations are fully implemented) is 29 tons.

46

 This national estimate is the same 

as the estimate of recent U.S. EGU Hg emissions based on information collected from industry

through the Information Collection Request (ICR). This shows that significant reduction in U.S. 

EGU Hg emissions has occurred since 2005, when Hg emissions were estimated to be 52.9 tons, 

but that few additional reductions are expected to occur without additional regulations to reduce 

mercury emissions. The reductions between 2005 and 2016 are largely due to regulations and 

federal enforcement actions that achieve Hg reductions as a co-benefit of controls for NOx and 

SO2 emissions. Given these estimates of total Hg emissions, characterization of “current 

conditions” is better represented by the 2016 Scenario than the 2005 Base Case, since total Hg 

emissions for the former is equal to our projection of recent emissions. By contrast, the 2005 

analysis reflects total Hg emissions (52.9 tons) which are significantly higher than our estimate 

of recent emissions. For this reason, as mentioned earlier, we only present risk estimates for the 

2016 Scenario and have not generated risk estimates for the 2005 scenario (although the 2005 Hg 

deposition estimates are used in scaling fish tissue Hg concentrations to represent concentrations 

in 2016).

In this section, we provide a brief overview of critical design elements of the risk 

analysis that the reader should keep in mind when reviewing the results (section 2.1). We then 

discuss the intermediate inputs and outputs for individual analytical steps associated with the risk 

assessment including: (a) Hg deposition from U.S. EGUs as modeled using CMAQ (section 2.2), 

(b) fish tissue Hg concentrations (section 2.3), (c) relationship between Hg deposition and Hg 

fish tissue concentrations (section 2.4) and (d) the MeHg RfD-based HQ risk assessment results 

for the 2016 scenario (section 2.5). In discussing each category of results, emphasis is placed on 

identifying key policy-relevant observations. In section 2.6, we discuss the results of several 

sensitivity analyses conducted to characterize the potential impact of specific sources of 

uncertainty on the risk estimates. In section 2.7, we discuss variability and uncertainty related to 

the risk assessment. In Section 3, we provide a summary of critical observations from the 

analysis, which draws on information provided in sections 2.2 through 2.7. 

2.1 Key design elements to consider when reviewing the risk assessment results

The following design elements of the analysis should be considered when reviewing the 

results:

 The analysis focuses on subsistence-like fishing activity at inland freshwater bodies. The 

analysis is not intended to capture more generalized recreational fishing activity or to 

reflect self-caught fisher exposure associated with saltwater fishing or fishing in the Great 

 

46 The modeling conducted for the March risk assessment included a representation of the proposed Cross State Air 

Pollution Rule, which was finalized in June 2011. We have evaluated differences in projected Hg emissions after 

taking into account differences between the proposed and final CSAPR and determined that the difference is less 

than 2 tons nationally, which would have little expected impact on our risk estimates.
54

Lakes. In comparing any risk profiles generated in this analysis to risks estimated in other 

contexts, the specific focus on these high-end populations shows risks substantially

higher than risks for recreational fishers. 

 The risk estimates generated are based on a set of female subsistence fish consumer

scenarios evaluated at watersheds where (a) we have fish tissue Hg data and (b) this type 

of subsistence fisher activity could potentially occur. Therefore, the risk assessment is 

watershed-focused, does not provide population-representative risk estimates for 

subsistence fishers.

 Risks are estimated for 3,141 watersheds. This watershed coverage (only about 4% of 

U.S. watersheds) leaves much of the country not covered by the analysis, including a 

substantial number of watersheds with relatively elevated levels of U.S. EGU-related Hg 

deposition. Further, the eastern part of the U.S. is more heavily represented in the 

watershed-level estimates of risk. Given that U.S. EGU Hg deposition is generally higher 

in the eastern part of the U.S., the fact that the risk assessment is focused on this part of 

the country is considered to be a strength of the analysis. 

 The analysis uses a proportionality assumption to link changes in Hg deposition (over 

watersheds) to changes in Hg fish tissue levels. This approach assumes that near steadystate conditions are met, which may take years to decades at a given watershed following 

changes in Hg deposition. 

 The analysis estimates risk based on MeHg RfD-based HQ. The U.S. EGU-attributable 

HQ should always be considered in the context of total HQ which is typically

substantially larger than the U.S. EGU-attributable HQ (reflecting the large contribution 

from non-U.S. EGU sources of Hg). We also assessed IQ loss in children, however, due 

to concerns that the IQ loss endpoint may not fully capture all of the neurodevelopmental 

effects associated with MeHg exposure, we have moved discussion of the IQ loss 

estimates to Appendices A and B. 

 Because it is not feasible to enumerate the female subsistence fish consumers modeled in 

this analysis, we could not generate distributions of population-weighted risk for specific 

scenarios assessed (e.g., low income Hispanic fishers, or Tribal fishers in the vicinity of

the Great Lakes). However, we do believe, based on surveys of their behavior, that this 

type of subsistence-like activity could reasonably be expected to occur across some 

fraction of the 3,141 watersheds included in the analysis. Therefore, we have assessed 

female subsistence fish consumer risk for each watershed where we have fish tissue Hg 

data. We then consider the fraction of watersheds that meet the risk characterization 

criteria outlined in the risk characterization framework (see Section 1.3). 

2.2 Mercury Deposition from U.S. EGUs as Modeled Using CMAQ

Below we provide the results of the CMAQ modeled Hg deposition for the 2005 and 

2016 scenarios for total deposition and U.S. EGU-attributable deposition.

47 For this Revised 

 

47 During the Science Advisory Board (SAB) review of the March TSD held on June 15-17th, Panel members raised 

questions regarding patterns of mercury deposition reflected in Figures 2-1 though 2-4 of the March version of the 
55

TSD we have also included maps of wet and dry deposition for the 2005 and 2016 scenarios.48 

Unlike the results presented in the subsequent sections that are limited to watersheds with fish 

tissue data, we modeled Hg deposition in all 88,000 watersheds in the continental U.S. We 

present a set of bulleted observations at the end of the section that draws on information 

conveyed in the figures and tables. The set of figures and tables presented include:

 Figure 2-1 and 2-2: Maps presenting CMAQ modeling results for total Hg deposition 

(μg/m2) at the watershed-level, for the 2005 and 2016 scenarios respectively.

 Figures 2-3 and 2-4: Maps presenting CMAQ modeling results for U.S. EGU-attributable 

Hg deposition (μg/m2) at the watershed-level, for the 2005 and 2016 scenarios, 

respectively.

 Figures 2-5 through 2-8: Maps presenting CMAQ modeling results for wet and dry Hg 

deposition (μg/m2) for the 2005 and 2016 scenarios. These estimates are presented at the 

original CMAQ 12km grid resolution and have not been interpolated to the watershedlevel. Maps presenting wet and dry deposition modeling results have been included to 

allow readers to consider patterns of deposition over specific locations and potentially

compare those with measured data, in those instances where relevant data are (or 

become) available. 

 Table 2-1: Summary of statistics (mean, 50th, 75th, 90th, 95th and 99th percentiles) for total 

Hg deposition and U.S. EGU-attributable deposition for the 2005 and 2016 scenarios. 

 Table 2-2: Summary of statistics (mean, 50th, 75th, 90th, 95th and 99th percentiles) for U.S. 

EGU-attributable deposition as a percent of total deposition for the 2005 and 2016 

scenarios. 

 Table 2-3: Summary of statistics (mean, 50th, 75th, 90th, 95th and 99th percentiles) for 

percent reduction of (a) total Hg deposition, and (b) U.S. EGU-attributable deposition, 

based on comparison of the 2016 scenario against the 2005 scenario. 

 

Mercury Risk TSD. The figures in the March TSD were intended to show annual total mercury (Hg) deposition per 

unit area (in units of μg/m2) by watershed, however they actually displayed intermediate calculations that had not 

been adjusted by the waterbody-specific surface areas. We corrected the figures and provided them in a memo to the 

docket (U.S. EPA, 2011). The figures presented here are the corrected figures and match those presented in that 

memo.

48 Inclusion of maps presenting wet and dry deposition modeling results reflects suggestions made by SAB Panel 

members.
56

Figure 2-1 Total Mercury Deposition by HUC (μg/m2) for the 2005 Scenario
57

Figure 2-2 Total Mercury Deposition by HUC (μg/m2) for the 2016 Scenario
58

Figure 2-3 U.S EGU-Attributable Mercury Deposition by HUC (μg/m2) for the 2005 Scenario
59

Figure 2-4 U.S EGU-Attributable Mercury Deposition by HUC (μg/m2) for the 2016 Scenario
60

Figure 2-5 Mercury Wet Deposition by 12km Grid Cell (μg/m2) for the 2005 Scenario
61

Figure 2-6 Mercury Dry Deposition by 12km Grid Cell (μg/m2) for the 2005 Scenario
62

Figure 2-7 Mercury Wet Deposition by 12km Grid Cell (μg/m2) for the 2016 Scenario
63

Figure 2-8 Mercury Dry Deposition by 12km Grid Cell (μg/m2) for the 2016 Scenario
64

Table 2-1 Comparison of total and U.S. EGU-attributable mercury deposition (μg/m2) for 

the 2005 and 2016 scenarios.* 

Statistic

2005 scenario 2016 scenario

Total Hg 

Deposition

U.S. EGU-attributable 

Hg Deposition

Total Hg 

Deposition

U.S. EGU-attributable 

Hg Deposition

Mean 19.41 0.89 18.66 0.34

Median 17.25 0.24 16.59 0.15

75th percentile 23.69 1.07 22.83 0.46

90th percentile 30.78 2.38 29.90 0.85

95th percentile 36.85 3.60 35.16 1.18

99th percentile 58.32 7.77 56.23 2.41

* Values are based on CMAQ results interpolated to the watershed –level and reflect trends across all ~88,000 

watersheds in the continental U.S. Percentiles for total Hg deposition and U.S. EGU attributable Hg 

deposition are not matched, e.g. the watershed with the 99th percentile for total Hg deposition will not be 

the same watershed as the watershed with the 99th percentile for U.S. EGU attributable Hg deposition.

Table 2-2 Comparison of percent of total mercury deposition attributable to U.S. EGUs for 

2005 and 2016.* 

Statistic 2005 scenario 2016 scenario

Mean 5% 2%

Median 1% 1%

75th percentile 6% 3%

90th percentile 13% 5%

95th percentile 18% 6%

99th percentile 30% 11%

* Values are based on CMAQ results interpolated to the watershed –level and reflect trends across all ~88,000 

watersheds in the U.S.

Table 2-3 Comparison of percent reduction of total mercury deposition, and U.S. EGUattributable deposition, based on comparing the 2016 scenario against the 2005 

scenario.*

Statistics

Percent Change in Total 

Hg Deposition

Percent Change in U.S. 

EGU-attributable Hg 

Deposition

Mean -4% NC**

Median -1% -41%

75th percentile -5% -70%

90th percentile -12% -80%

95th percentile -16% -85%

99th percentile -27% -91%

* Values are based on CMAQ results interpolated to the watershed –level and reflect trends across all ~88,000 

watersheds in the U.S.

** A mean value was not calculated (NC) for this category due to presence of a number of watersheds with very 

small U.S. EGU-attributable deposition values which skewed this distribution. 
65

We made the following observations based on the information presented in Figures 2-2 

through 2-4 and in Tables 2-1 through 2-3 regarding estimates of total and U.S. EGU-attributable 

Hg deposition for the 2005 and 2016 scenarios:

 Patterns of total and U.S. EGU-related Hg deposition differ considerably: Areas of 

elevated total Hg deposition are distributed around the country (e.g., west coast, areas in 

Nevada, southern Mississippi, West Virginia, southeastern Georgia) (see Figures 2-1 and 

2-2). By contrast, U.S. EGU Hg deposition is concentrated in the eastern U.S., especially

in the Ohio River Valley (see Figures 2-3 and 2-4). Figures 2-3 and 2-4 also illustrate that 

while some near-coastal areas and portions of the Great Lakes have elevated U.S. EGUattributable deposition, many of the highest areas (and largest expanses) of U.S. EGUattributable deposition occur inland (e.g., Ohio River Valley, areas in northeast Texas and 

along the Mississippi River).

 U.S. Hg deposition is generally dominated by sources other than U.S. EGUs and the 

contribution from U.S. EGUs decreases between the 2005 and 2016 scenarios: On 

average., U.S. EGUs contribute 5% of total Hg deposition for the 2005 scenario, which 

decreases to 2% for the 2016 scenario (see Table 2-2). The remaining Hg deposition (i.e., 

~95% and ~98%, respectively for the two scenarios) originates from other U.S. sources 

of Hg emissions and from international sources (both anthropogenic and natural). U.S. 

EGU-attributable deposition decreases considerably between the 2005 and 2016 

scenarios, primarily from implementation of the Cross State Air Pollution Rule (CSAPR), 

state Hg regulations and Federal enforcement actions.49 The median reduction in U.S. 

EGU-attributable deposition was 41% with reductions ranging up to 85% for the 95th

percentile watershed (see Tables 2-2 and 2-3). 

 The contribution of U.S. EGU-attributable deposition to total deposition varies across 

watersheds and can represent a relatively large fraction in some instances: In the 2005 

scenario, while on average, U.S. EGUs only contributed 5% of total Hg deposition in the 

U.S., this contribution ranged up to 30% for the 99th percentile watershed (see Table 2-2). 

While overall U.S. EGU –attributable deposition decreased substantially between the

2005 scenario and the 2016 scenario, U.S. EGUs contributed 11% of total Hg deposition 

for the 99th percentile watershed in 2016 ( (see Table 2-2). 

2.3 Fish Tissue Mercury Concentrations

This section characterizes the subset of U.S. watersheds with fish tissue Hg data included 

in the current the risk assessment. This dataset includes (a) the original HUC-level fish tissue 

dataset used in the March version of the risk assessment (post augmentation data set with 2,317 

watersheds) and (b) the additional 940 watersheds with fish tissue data identified as part of our 

refinement to the March risk assessment (for a total of 3,141 watersheds). In this section, we 

provide the 75th percentile fish tissue sample for each watershed. As discussed in Sections 

1.4.2.1 and 1.4.6, we used the proportionality assumption together with Hg deposition coverages 

 

49 Controls on PM precursors, including directly emitted PM and SO2, can significantly reduce divalent and particlebound mercury, both of which primarily deposit locally and regionally. For more information on the emission 

reductions from CSAPR, see the final Regulatory Impact Analysis, which is available at 

http://www.epa.gov/airtransport/pdfs/FinalRIA.pdf.
66

for 2005 (total and U.S. EGU-related) and 2016 (total and U.S. EGU-related) to project fish 

tissue Hg concentrations for the 2016 scenario and to estimate the U.S. EGU-attributable 

fraction of total fish Hg. 

We provide figures and tables to summarize the fish tissue data. As discussed in Section 

2.2, most of the areas with elevated U.S. EGU-attributable Hg deposition are located in the 

eastern U.S., where we also have more Hg fish tissue data and most of the U.S. EGU emission 

reductions between 2005 and 2016. We also present a set of bulleted observations based on the 

figures and tables. The set of figures and tables presented include:

 Figure 2-9: Map of 3,141 watersheds with fish tissue sampling data used in the current 

version of the risk assessment, including (a) watersheds included in the previous version 

of the risk assessment (2010 MFT) and (b) additional fish tissue data incorporated to 

enhance the current version of the risk assessment (augmentation). This map illustrates 

the uneven coverage of the fish tissue dataset across the continental U.S and highlights 

which states were included in the additional data incorporated for this version of the risk 

assessment (i.e., the augmentation HUCs shown in red). 

 Figure 2-10: Map of 3,141 watersheds with fish tissue data showing distribution of 

sampling frequency (number of fish tissue measurements) across watersheds. This map 

can be used to show the relationship between trends in sampling frequency and fish tissue 

Hg concentrations. 

 Figure 2-11 and 2-12: Maps presenting Total Fish Tissue Mercury Concentrations for the 

2005 and 2016 scenarios respectively. 

 Figures 2-13 and 2-14: Maps presenting EGU-Attributable Fish Tissue Mercury

Concentrations for 2005 and 2016 scenarios, respectively.

50

 Table 2-4: Comparison of watershed-level fish tissue Hg concentrations (including means 

and various percentiles) for the March and revised version of the national-scale Hg risk 

assessment. Table 2-4 includes percentile data for (a) 2010 MFT dataset (the fish tissue 

data used in the March risk assessment), (b) the augmentation dataset (additional fish 

tissue data collected to supplement data used in the last risk assessment) and (c) the 2011 

MFT dataset (the dataset used in the current revised version of the risk assessment). 

 Table 2-5: Summary of statistics (mean and various percentiles) for both total and U.S. 

EGU-attributable Hg fish tissue levels (for the 2005 and 2016 scenarios). These statistics 

are based on watershed-level data. In addition, this table also presents the percent 

reduction (between the 2005 and 2016 scenarios) for both total and U.S. EGUattributable Hg fish tissue levels. 

 

50 Note, in the March version of the Mercury Risk TSD, we included a series of maps showing fish tissue 

Hg concentrations at the watershed-level for the upper 10 percent of watersheds with regard to total fish 

tissue Hg concentrations and EGU-attributable fish tissue Hg concentrations. However, we decided to 

remove these maps from the revised version of the Mercury Risk TSD given the limited coverage that the 

fish tissue Hg sampling data provides for watersheds across the U.S.
67

Figure 2-9 Set of 3,141 HUC12s with Fish Tissue Mercury Data Used in the Risk Assessment* 

* Includes 2010 Mercury Fish Tissue (MFT) dataset used in the March version of the risk assessment as well as the 

augmentation dataset incorporated for the current version of the risk assessment.
68

Figure 2-10 Fish Tissue Measurement Sampling Frequency for HUCs with Fish Tissue Data Included in the Risk Assessment
69

Figure 2-11 Total Fish Tissue Mercury Concentrations for 2005 Scenario (HUC12-level 75th percentile values, ppm)
70

Figure 2-12 Total Fish Tissue Mercury Concentrations Projected for 2016 Scenario (HUC12-level 75th percentile values, 

ppm)*

* Values estimated by adjusting fish tissue Hg data presented in Figure 2-12 to reflect different patterns of Hg deposition in 

2016 (relative to 2005).
71

Figure 2-13 EGU-Attributable Fish Tissue Mercury Concentrations for 2005 Scenario 

(HUC12-level 75th percentile values, ppm)*

* Scales are different between the EGU-attributable and total Hg fish tissue concentrations maps.
72

Figure 2-14 EGU-Attributable Fish Tissue Mercury Concentrations Projected for 2016 Scenario

(HUC12-level 75th percentile values, ppm)*

* Scales differ between the EGU-attributable and total Hg fish tissue concentrations maps.
73

Table 2-4 Comparison of HUC-Level Fish Tissue Mercury Concentrations Across Datasets 

Used in the March Version and Current Version of the Risk Assessment 

statistic

Hg fish tissue concentration (ppm) 

(HUC12-level 75th percentile)

2010 MFT dataset

(used in the March 

version of the risk 

assessment)

(2,317)

Augmentation 

dataset

(collected as part of 

refining analysis)

(940)

2011 MFT dataset 

(used in the current 

version of the risk 

assessment)

(3,141)

Mean 0.31 0.37 0.32

Median 0.23 0.30 0.25

75th % 0.39 0.47 0.42

90th % 0.67 0.72 0.67

95th % 0.91 0.86 0.89

99th % 1.33 1.44 1.35

Table 2-5 Comparison of total and U.S. EGU-attributable Hg fish tissue concentrations 

(including % change) for the original fish tissue dataset and 2016 scenarios*

 

Statistic

Hg fish tissue concentration (ppm) % change (2016 versus 

original fish tissue 

dataset) in Hg fish 

Used for 2005 scenario Projected for 2016 scenario tissue concentration

Total 

U.S. EGUattributable 

U.S. EGU 

as percent 

of total Total 

U.S. EGUattributable 

U.S. EGU 

as percent 

of total Total

U.S. EGUattributable 

Mean 0.32 0.024 9.1% 0.30 0.008 3.4% -8.0% -43.9%

Median 0.25 0.014 6.0% 0.22 0.005 2.6% -4.9% -61.4%

75th % 0.42 0.032 13.5% 0.40 0.011 4.4% -12.7% -76.0%

90th % 0.67 0.057 20.4% 0.65 0.018 6.6% -18.6% -82.6%

95th % 0.89 0.081 26.2% 0.86 0.026 8.7% -23.0% -85.4%

99th % 1.35 0.152 42.9% 1.30 0.045 16.4% -38.3% -91.7%

* These percentiles are not for matched HUCs (i.e., the 99th percentile for total Hg fish concentration for the 2005 

scenario could occur in a different HUC than the 99th percentile U.S. EGU attributable percent reduction). 

We made the following observations based on Figures 2-10 through 2-14 and in Tables 2-

4 and 2-5:

 U.S. EGU-attributable fish tissue Hg levels are higher in the eastern half of the U.S.:

This reflects primarily the fact that levels of U.S. EGU Hg deposition (that largely drives 

U.S. EGU-attributable Hg fish tissue levels) are much higher in the east (see Figures 2-3 

and 2-4). 

 Augmenting the fish tissue Hg dataset significantly improved coverage for Wisconsin, 

Minnesota, Pennsylvania and New Jersey: Although the additional fish tissue Hg data 
74

significantly expanded coverage for watersheds in areas with elevated U.S. EGUattributable Hg deposition (Pennsylvania) and for areas with relatively elevated fish 

tissue Hg concentrations (Minnesota and Wisconsin), the additional data did not 

substantially change the overall distribution of fish tissue Hg concentrations. This 

conclusion is based on a comparison of percentiles across the different datasets (i.e., 2010 

MFT, augmentation, and 2011 MFT datasets) as presented in Table 2-4. 

 U.S. EGUs contribute a larger fraction to total Hg fish tissue levels in the U.S. than they 

do to total Hg deposition (in terms of percent), reflecting the fact that Hg fish tissue 

samples are focused in the east where U.S. EGU-attributable deposition is greater. While 

U.S. EGUs contribute ~5% of total Hg deposition in the U.S. (for the 2005 scenario – see 

Table 2-2), their contribution to Hg fish tissue levels (summarized at the watershed-level) 

for the 2005 scenario is larger at ~9% (see Table 2-5). This reflects the fact that Hg fish 

tissue samples are heavily weighted in the eastern portion of the U.S. where U.S. EGU 

Hg deposition is typical higher than in the west. By providing greater coverage for the 

eastern half of the country, the Hg fish tissue sampling data generally provides greater 

coverage for regions with potentially greater U.S. EGU-attributable risk.

 Relative to the combined impact of other sources, U.S. EGUs represent a smaller, but 

still potentially important contributor to total fish tissue Hg concentrations: U.S. EGUs 

contribute ~9% of Hg fish tissue levels on average under the 2005 scenario (see Table 2-

5). Under the 2016 scenario, the U.S. EGU contribution decreases to ~ 3% on average 

(see Table 2-5). While U.S. EGU-attributable Hg fish tissue decreases notably between 

the 2005 and 2016 scenarios, the impact on total Hg fish tissue levels is not that 

noticeable given that U.S. EGUs contribute a relatively small fraction on total Hg fish 

tissue levels in general (contrast the pattern of reduction seen in Figures 2-13 and 2-14

for U.S. EGU-attributable Hg fish tissue levels with the relatively smaller changes seen in 

Figures 2-11 and 2-12 for total Hg fish tissue levels). 

 Despite the relatively small fraction of U.S. EGU-attributable fish tissue Hg on average, 

for a subset of watersheds, U.S. EGU-attributable deposition has a significant 

contribution on fish tissue: Under the 2005 scenario, U.S. EGUs contribute up to 43% of 

total Hg fish tissue levels (for the 99th percentile watershed). Under the 2016 Scenario, 

this pattern is reduced, but U.S. EGUs can still contribute up to 16% of total fish tissue 

Hg concentrations (again, for the 99th percentile watershed) (see Table 2-5).

2.4 Comparing Patterns of Hg Deposition with Hg Fish Tissue Data for the 3,141 

Watersheds Included in the Risk Assessment

We also compared spatial patterns of CMAQ modeled Hg deposition and fish tissue Hg 

concentrations, which can inform interpretation of the risk estimates. Specifically we can 

consider: (a) whether the watershed-level Hg fish tissue levels are correlated with total Hg 

deposition, (b) how patterns of Hg deposition for the 3,141 watersheds with Hg fish tissue data 

compare with patterns for the 88,000 watersheds in the continental U.S. and (c) to what extent 

the watersheds for which we have fish tissue Hg data correspond to areas with elevated U.S. 
75

EGU-attributable deposition. To address these questions, we developed a series of figures 

including:

 Figures 2-15 and 2-16: Maps comparing (a) watersheds with fish tissue Hg data with (b) 

the general pattern of U.S. EGU-attributable Hg deposition across the continental U.S. 

(for 2005 and 2016). These maps include both the fish tissue dataset used in the March 

TSD (2010 MFT dataset) and the augmented dataset. These maps show the degree of 

overlap between fish tissue Hg data and areas of elevated U.S. EGU-attributable Hg 

deposition (for both 2005 and 2016 scenarios).51 

 Figure 2-17: Plot comparing the fish tissue Hg concentrations versus total Hg deposition

(2005 scenario) by watershed. This plot allows consideration for whether there appears to 

be a correlation between these two factors at the watershed level.

 Figure 2-18: Cumulative distribution plots comparing U.S. EGU-attributable deposition 

for the 3,141 watersheds used in the risk assessment with U.S. EGU-attributable 

deposition of the 88,000 watersheds in the continental U.S. Given emphasis in the risk 

assessment on the 2016 scenario, we only provided plots for this scenario. These plots 

allow us to consider whether the watersheds with fish tissue Hg data tended to fall in 

regions with higher U.S. EGU-attributable Hg deposition and the degree to which this 

subset of watersheds provided coverage for areas with relatively elevated U.S. EGU Hg 

deposition across the country. 

 

51 These two maps replace Figures 2-15 and 2-16 from the March TSD. Those earlier maps also compared 

watersheds with fish tissue Hg data to areas of the country that had elevated U.S. EGU-attributable Hg deposition. 

However, in identifying areas of elevated U.S. EGU-attributable Hg deposition for those earlier maps, we attempted 

to be much more precise. Specifically, we used a deposition threshold for identifying areas of high U.S. EGUattributable Hg deposition based on the average rate of deposition over watersheds modeled for risk that had U.S. 

EGU-attributable exposure exceeding the MeHg RfD. In other words, we identified a deposition level associated 

generally with watersheds having high U.S. EGU-attributable risks and used this bright line as the basis for 

identifying areas of high U.S. EGU deposition across the U.S.; the implication being, that these could be areas of 

high U.S. EGU-attributable risk. However, elevated U.S. EGU-attributable risk typically reflects both elevated U.S. 

EGU-attributable Hg deposition and elevated baseline fish tissue Hg concentrations. Therefore, attempting to 

consider only U.S. EGU-attributable deposition as a means for identifying areas of potential elevated risk is subject 

to uncertainty, since it does not also consider the baseline fish tissue Hg concentrations. The current set of figures 

seeks to support a more general comparison of the watersheds we have modeled for risk against the national pattern 

of U.S. EGU-attributable Hg deposition, by not attempting to identify a bright line for classifying areas of the 

country with regard to the magnitude of elevated U.S. EGU-attributable Hg deposition.
76

Figure 2-15 Comparison of Locations of Watersheds with Fish Tissue Hg Data* with Pattern of U.S. EGU-attributable Hg 

Deposition (2005 scenario) 

* Fish tissue Hg data includes (a) 2010 MFT data used in March version of the risk assessment and (b) augmentation data 

added for this revised version.
77

Figure 2-16 Comparison of Locations of Watersheds with Fish Tissue Hg Data* with Pattern of U.S. EGU-attributable Hg 

Deposition (2016 scenario)

* Fish tissue Hg data includes (a) 2010 MFT data used in March version of the risk assessment and (b) augmentation data 

added for this revised version.
Figure 2-17 For the 2005 Scenario, Scatter Plot of Hg Fish Tissue Concentrations Versus 

Total Hg Deposition for the 3,141 Watersheds Included in the Risk Assessment

Figure 2-18 Cumulative distribution plots of U.S. EGU

3,141 watersheds used in modeling the high

contrasted with all 88,000 watersheds (

CM

A

Q-based total Hg deposition by HUC

(2005 Scenario) (ug/m3

)

0

20

40

60

80

100

120

140

0 0.2

78

the 2005 Scenario, Scatter Plot of Hg Fish Tissue Concentrations Versus 

Total Hg Deposition for the 3,141 Watersheds Included in the Risk Assessment

Cumulative distribution plots of U.S. EGU-attributable Hg deposition over the 

watersheds used in modeling the high-end female consumer population as 

contrasted with all 88,000 watersheds (2016 Scenario). 

HUC-level fish tissue mercury levels (ppm) 

for 3,141 HUCs used in risk assessment

0.4 0.6 0.8 1 1.2 1.4 1.6 1.8

the 2005 Scenario, Scatter Plot of Hg Fish Tissue Concentrations Versus 

Total Hg Deposition for the 3,141 Watersheds Included in the Risk Assessment

attributable Hg deposition over the 

end female consumer population as 

2
79

We made the following observations based on the information presented in Figures 2-15 

through 2-18 regarding how estimates of Hg deposition estimates relate to measured fish tissue 

Hg concentrations at the watershed-level:

 The fish tissue Hg sampling data (watersheds modeled for risk) provide varying coverage 

for areas with elevated U.S. EGU-attributable Hg deposition. The augmented dataset

significantly expanded coverage in key areas. However, the number of “at-risk” 

watersheds may still be substantially higher than we estimated: As depicted in Figures 2-

15 and 2-16, the 3,141 watersheds used in the risk assessment provide reasonable 

coverage for some of the regions having the highest U.S. EGU-attributable Hg deposition 

(i.e., Ohio River Valley), especially with the augmented dataset for Pennsylvania. 

However, even with the expanded coverage, the majority of areas with elevated U.S. 

EGU-attributable deposition are not covered in the risk assessment. Therefore, we 

believe that the number of watersheds with elevated U.S. EGU-attributable exposure 

and/or risk could be substantially larger (depending on the underlying fish tissue Hg 

concentrations).52 

 Hg fish tissue levels are not correlated with total Hg deposition because the relationship 

is highly dependent on methylation potential of individual waterbodies: As shown in 

Figure 2-17, total Hg fish tissue levels at the watershed-level are not correlated with 

levels of total Hg deposition across watersheds (i.e., the highest total Hg deposition 

watersheds do not always have the highest fish tissue Hg concentrations). This is not 

unexpected because the relationship between total Hg deposition and total Hg fish tissue 

levels is highly dependent on the methylation potential at the waterbody, which is driven 

in part by the presence of wetlands, levels of aqueous organic carbon, Ph and sulfate 

deposition (see section 1.4.6).

 Hg fish tissue samples were generally collected in regions with elevated total Hg 

deposition: As demonstrated in Figure 2-18, Hg fish tissue sampling appears to have 

favored areas with relatively higher total Hg deposition.53 This can be seen by comparing 

cumulative plots of modeled watersheds (where we have fish tissue Hg data) against plots 

for the 88,000 watersheds. This comparison suggests that watersheds where fish tissue 

Hg data were collected tended to have higher total Hg deposition than the full set of 

watersheds. This likely reflects to some extent, the fact that fish tissue sample are focused 

in the eastern U.S., which has elevated total Hg deposition compared to the broad central 

region (see Figures 2-2 and 2-3).

 

52 As noted earlier, in order for a watershed to have elevated U.S. EGU-attributable risk, in the context of this risk 

assessment, that watershed must have high U.S. EGU-attributable Hg deposition (as a fraction of total Hg 

deposition) and elevated baseline fish tissue Hg concentrations.

53 Because the risk estimates for the 2005 scenario are being de-emphasized, we decided to only provide these 

cumulative plots for the 2016 scenario. However, we note that the same relationship noted here (between U.S. EGUrelated Hg deposition for all ~88,000 watersheds and for the subset included in the risk assessment), holds for the 

2005 scenario.
80

2.5 Overview of Risk Estimates

This section provides an overview of risk estimates generated for the 3,141 watersheds 

included in the revised risk assessment. As noted earlier in Section 1.4.3, presentation of risk 

estimates focuses on the typical female subsistence fish consumer scenario assessed at the 

national-level, since this scenario provides the most comprehensive coverage for watersheds with 

Hg fish tissue data across the U.S. and because the consumption rates used to model this scenario 

represent subsistence levels supported by a number of studies. While this scenario is emphasized 

in summarizing risk estimates, we also provide risk estimates for the other scenarios (i.e., low 

income Blacks and Whites in the southeast, Tribal populations near the Great Lakes, Hispanics). 

In summarizing risk estimates, we focus on the 2016 air quality scenario, since as discussed in 

section 2.3, U.S. EGU-related Hg emission levels reflected in this scenario are closest to recent 

conditions. The remainder of this section is organized as follows: 

 Overview of percentile risk estimates generated for the fisher scenarios (Section 2.5.1):

We provide percentile risk estimates for the typical female subsistence fish consumer

scenario at the national level and summarize percentile risk estimates for the other SESdifferentiated female subsistence fish consumer scenarios assessed in the analysis. These

percentile risk estimates allow us to compare risk (total and U.S. EGU-attributable) 

across the female subsistence fish consumers scenarios assessed in the risk assessment. 

 Overview of the number (and frequency) of watersheds with populations potentially atrisk due to U.S. EGU-attributable Hg deposition (section 2.5.2): This set of risk estimates 

provides the main input to the risk characterization framework (see section 1.3). 

Specifically, watersheds with populations potentially at-risk comprise:

o Watersheds where total risk is considered to represent a potential public health 

concern and where U.S. EGUs contribute to that total risk. We considered various 

increments of U.S. EGU contribution including ≥ 5%, ≥10%, ≥15% and ≥20%, 

although, we focus on cases where U.S. EGUs contribute ≥5 % as noted in section 

1.3. 

o Watersheds where risk from U.S EGUs alone (focusing on U.S. EGU deposition 

and excluding other non-U.S. EGU sources) represents a potential public health 

concern.

To support the discussion of risk estimates, we present a series of tables summarizing 

those estimates and a list of observations based on this information. 

2.5.1 Overview of percentile risk estimates (2016 scenario)

This section compares risk percentiles (for total and U.S. EGU-attributable risk) across 

the set of female subsistence fish consumer scenarios included in the risk assessment, with an 

emphasis on the typical female subsistence fish consumer scenario. We also compare and 

contrast risk for the other female consumer scenarios with the more generalized typical female 

subsistence fish consumer scenario. 
81

These percentile estimates are not population-weighted, but instead represent the specific 

watershed-level risk estimates that fall at a specific point within the larger distribution of 

watershed-level risk estimates for each female subsistence fish consumer scenario. In the 

following tables, we sorted the total and U.S. EGU-attributable risk estimates separately for each 

female subsistence fish consumer scenario. This means that the total and U.S. EGU-attributable

risk for a particular percentile are not matched by watershed (i.e., the 90th percentile total and 

U.S. EGU-attributable risk for the Vietnamese subsistence fisher likely occur at different 

watersheds).54 While this risk assessment focuses on subsistence levels of fish consumption, the 

risk tables also include risk estimates based on mean fish consumption rates for these female 

subsistence fish consumer scenarios, which are relatively high compared to general recreational 

angler rates but do not represent true subsistence rates. 

Table 2-6 provides risk percentiles for RfD-based HQs for all of the female subsistence 

fish consumer scenarios assessed in the analysis for the 2016 scenario. 

Table 2-6 Percentile risk estimates for the full set of female subsistence fish consumer

scenarios included in the analysis (2016 scenario) (for both total and U.S. EGU 

incremental RfD-based HQ)*

Fisher 

consumption rate 

(g/day) and 

percentile**

Watershed percentile risk (RfD-based HQ)*

Total U.S. EGU

50th 75th 90th 95th 99th 50th 75th 90th 95th 99th

typical female subsistence fish consumer assessed nationally

39 (mean) 1.9 3.5 5.6 7.4 11.2 - 0.1 0.2 0.2 0.4

123 (90

th) 6.1 10.9 17.7 23.4 35.4 0.2 0.3 0.5 0.7 1.2

173 (95th) 8.5 15.3 24.9 32.9 49.7 0.2 0.4 0.7 1 1.7

373 (99th) 18.4 33 53.7 70.9 107.2 0.5 0.9 1.5 2.1 3.7

Low income White female subsistence fish consumer in the Southeast

39 (mean) 2.1 4.1 6.8 9.3 12.5 0.1 0.1 0.2 0.3 0.6

93 (90th) 4.9 9.8 16.3 22.4 29.9 0.2 0.3 0.5 0.7 1.4

129 (95th) 6.8 13.6 22.7 31 41.5 0.2 0.4 0.8 1 2

286 (99th) 15.2 30.2 50.3 68.8 92 0.5 1 1.7 2.3 4.3

Low income Black female subsistence fish consumer in the Southeast

171 (mean) 9.4 19.4 32.8 42.2 56.4 0.3 0.6 1 1.4 2.6

446 (90th) 24.6 50.6 85.6 110.1 147.2 0.8 1.6 2.7 3.6 6.8

557 (95th) 30.8 63.2 106.9 137.5 183.8 0.9 1.9 3.4 4.4 8.4

(99th) NC NC

Low income Hispanic female subsistence fish consumer evaluated nationally

 

54 Presentation of non-matched percentile risk estimates represents a change from the March TSD. In that version, 

we used risk bands to match trends in total risk to a specific U.S. EGU-attributable risk percentile. However, given 

that total and U.S. EGU-attributable risk are not closely correlated, we determined that attempting to present 

matched percentiles was of little utility. Instead, we have decided for this version of the Mercury Risk TSD to 

simply provide percentile total and U.S. EGU-attributable risk estimates based on direct queries of the underlying 

risk distributions. 
82

Fisher 

consumption rate 

(g/day) and 

percentile**

Watershed percentile risk (RfD-based HQ)*

Total U.S. EGU

50th 75th 90th 95th 99th 50th 75th 90th 95th 99th

26 (mean) 1.2 2.2 3.5 4.9 8.3 0 0.1 0.1 0.2 0.3

98 (90th) 4.5 8.3 13.3 18.6 31.5 0.1 0.2 0.4 0.6 1.1

156 (95th) 7.2 13.2 21.2 29.6 50.1 0.2 0.4 0.7 0.9 1.7

(99th) NC NC

Vietnamese female subsistence fish consumer

27 (mean) 1.1 2 3 4.1 6.8 0 0 0.1 0.1 0.3

99 (90th) 4.1 7.2 11.1 15 24.9 0.1 0.2 0.3 0.5 1

152 (95th) 6.3 11 17.1 23 38.3 0.2 0.3 0.5 0.8 1.5

(99th) NC NC

Laotians female subsistence fish consumer

47 (mean) 1.9 3.7 5.3 5.9 7.7 0.1 0.1 0.2 0.3 0.9

145 (90th) 5.9 11.3 16.1 18 23.7 0.2 0.3 0.6 0.9 2.6

226 (95th) 10.9 20.8 29.6 33 43.4 0.3 0.5 1.2 1.7 4.9

(99th) NC NC

Tribal (near Great Lakes) female subsistence fish consumer

62 (mean) 5.4 7.7 11 13.2 17.5 0.1 0.1 0.2 0.3 0.4

136 (90th) 11.8 17 24.1 29.1 38.4 0.2 0.3 0.4 0.6 0.9

213 (95th) 18.5 26.6 37.7 45.5 60.2 0.3 0.4 0.7 0.9 1.3

493 (99th) 42.8 61.4 87.3 105.2 139.1 0.6 1 1.5 2.1 3.1

* Percentile risk estimates presented for “total” and “U.S. EGU” are not matched by watershed (e.g., the 90th 

percentile total and U.S. EGU-attributable risk estimates for a particular female subsistence fish consumer

scenario may are unlikely to be from the same watershed).

** Means are provided along with upper-end percentile values.

“-“: RfD-based HQ is ≤ 0.1.

NC: It was not possible to derive a 99th percentile consumption rate for this population due to insufficient sample 

size in the underlying study. Consequently, risk estimates for the 99th percentile consumption rates were not 

generated.

We made several observations based on the information in Table 2-6 regarding the 

potential health hazard associated with MeHg RfD-based HQ estimates (see Section 1.4.5).:

 Risk estimates for the typical female subsistence fish consumer (assessed at the nationallevel) generally provide coverage for the Hispanic, Vietnamese and Tribal scenarios: 

Risk estimates (for both total and U.S. EGU-attributable) generated for the typical female 

subsistence fish consumer scenario are generally higher than estimates generated for the 

Hispanic and Vietnamese scenarios. Risk estimates for the typical female subsistence fish 

consumer scenario are also higher than risks for the Tribal population for U.S. EGUattributable risk although the Tribal populations tend to have higher total risks. Given 

emphasis on U.S. EGU-attributable risk in the policy context, these trends would suggest 
83

that the typical female subsistence fish consumer scenario provides general coverage for 

the other three scenarios (Hispanic, Vietnamese and Tribal), since the typical female 

subsistence population tends to have higher U.S. EGU-attributable risk estimates. 

 Risk estimates for the typical female subsistence fish consumer (assessed at the nationallevel) may not provide full coverage for risks experienced by low income Blacks and low 

income Whites in the Southeast, or for Laotians assessed at the national-level: U.S. 

EGU-attributable risk estimates for the Southeastern low income White and low income

Black scenarios and for the Laotian scenario are higher than those for the typical female 

subsistence fish consumer. In the case of low income Whites, this likely reflects the fact 

that some regions in the Southeast (specifically South Carolina) have relatively elevated 

total and U.S. EGU-attributable fish tissue Hg concentrations compared with the national 

distribution (see Figures 2-13 and 2-15). In the case of low income Blacks, higher fish 

tissue Hg concentrations in South Carolina are combined with fish consumption rates that 

are substantially higher than those used for the typical female subsistence fish consumer

scenario (see Table 1-6). While both the low income White and low income Black

subsistence fisher scenarios in the Southeast have higher risk estimates than the typical 

female subsistence fish consumer scenario (assessed nationally), because the typical 

female subsistence scenario is assessed at all 3,141 watersheds with fish tissue Hg data, 

we focus on the typical female subsistence fish consumer scenario to provided policyrelevant risk information to support this rulemaking (i.e., we have greater overall 

confidence in risk estimates generated for the typical female subsistence fish consumer 

scenario because it is assessed at a larger number of watersheds with broader spatial 

coverage). However, the higher risks estimates for these two Southeastern subsistence 

fisher scenarios should also be considered in the context of interpreting the results of this 

risk assessment in a science-policy context. In the case of the Laotian scenario, while 

U.S. EGU-related risks are higher than for the typical female subsistence fish consumer

scenario, this scenario was assessed for a small number of watersheds (131 of the 3,141 

watersheds with fish tissue Hg data). Specifically, risk estimates generated for the 

Laotian scenario may reflect relatively rare localized interactions between fish tissue Hg 

concentrations and U.S. EGU-related Hg deposition, resulting in areas of elevated U.S. 

EGU-attributable exposure and risk. 

2.5.2 Overview of number and percentage of watersheds with populations 

potentially at-risk due to U.S. EGU mercury emissions (2016 scenario)

This section discusses risk estimates based on identifying the number and percent of 

watersheds with fish tissue Hg samples with populations potentially at-risk due to Hg emitted

from U.S. EGUs (for the 2016 scenario). As noted in Section 1.3, the “at-risk population” 

classification is based on identifying watersheds where: (a) U.S. EGUs contribute to total risk at 

watersheds where that total risk is considered to represent a potential public health hazard and/or 

(b) risk at the watershed-level represents a potential public health hazard from U.S. EGUattributable deposition when considered alone, without taking into account deposition from other 

sources. The estimates of watersheds with at-risk populations discussed in this section are used 

in the 2-Stage risk characterization framework described in Section 1.3 for interpreting risk 

estimates. Specifically, the first category of at-risk populations described above comprise Stage 

1a of the 2-Stage approach, while the second category comprises Stage 1b (see Figure 1-1). The 
84

combination (i.e., mathematical union) of these two groups of watersheds with at-risk

populations comprises the set of watersheds represented in Stage 2 of the framework. 

In summarizing risk estimates in this section, we focus on estimates generated using the 

current risk assessment that reflects refinements to the risk model (e.g., inclusion of additional 

fish tissue data and evaluating the typical female subsistence fish consumer for all watersheds 

with fish tissue Hg data). However, when we present the Stage 2 risk estimates, we also include a 

summary of Stage 2 estimates from the March version of the Mercury Risk TSD (then referred to 

as “stage 3” results) for comparison. 

With regard to the HQ estimates, any contribution of Hg from EGUs to watersheds with 

exposures exceeding the MeHg RfD represents a potential hazard to public health, but for 

purposes of this analysis, we have focused on those waterbodies where we determined EGUs 

contributed 5% or more to the hazard. This is a reasonable, conservative approach because risks 

are associated with all exposures above the MeHg RfD.

The watersheds with potentially at-risk populations are all based on the underlying risk 

estimates generated for the typical female subsistence fish consumer scenario. The estimates of 

watersheds with potentially at-risk populations are summarized in tables described below, 

followed by a set of observations regarding these risk estimates. 

 Tables 2-7: Identifies watersheds with potentially at-risk populations based on 

consideration for different degrees of U.S. EGU contribution (i.e., ≥5, ≥10, ≥15 and 

≥20%) at watersheds where total risk is considered to represent a potential public health 

hazard (i.e. HQ>1). For reference purposes, the table also identifies the total number of 

watersheds (out of the 3,141 assessed for the typical female subsistence fish consumer) 

with total risk exceeding the HQ threshold, regardless of the U.S. EGU percent 

contribution (see the “≥ 0%” row of results in the table). In presenting results, the tables 

include both the number and percent of watersheds with fish tissue Hg samples meeting 

specific criteria that this represents. (Stage 1a of the 2-Stage framework)

 Table 2-8: Identifies watersheds with potentially at-risk populations based on U.S. EGUattributable deposition considered alone, without taking into account other sources of Hg 

(Stage 1b of the 2-Stage framework)

 Table 2-9: Presents the union of the two categories of watersheds with potentially at-risk 

populations (i.e., mathematical union of the Stage 1 and 2 estimates presented in Tables 

2-7 and 2-8, see Figure 1-1 for explanation). Table 2-9 considers the number and percent 

of watersheds that have (a) U.S. EGUs contributing to total risk of an HQ>1, OR (b) an

HQ>1 based on considering U.S. EGU Hg deposition alone, without taking into account 

deposition from other sources (i.e., U.S. EGU-attributable HQ>1). This represents Stage 

2 of the risk characterization framework. 

 Table 2-10: Presents summary of the same Stage 2 risk metric as Table 2-9 with the 

March Mercury Risk TSD results.
85

Table 2-7 Watersheds with potentially at-risk populations based on consideration for 

various degrees of U.S. EGU contribution to total risk (2016 scenario)

EGU risk 

threshold

Number and percentage of HUCs meeting risk threshold 

criteria

(RfD-based HQ > 1)*

2016 analysis

90th percentile fish 

consumption

95th percentile

fish 

consumption

99th percentile fish 

consumption

≥ 0% 2853 (91%) 2983 (95%) 3112 (99%)

≥ 5% 679 (22%) 726 (23%) 754 (24%)

≥ 10% 104 (3%) 114 (4%) 119 (4%)

≥ 15% 37 - 40 - 40 -

≥ 20% 15 - 17 - 17 -

* Following convention for reporting HQ estimates to one significant digit, this requires an HQ ≥ 1.5 (see 

section 1.4.5). Results presented here for different levels of fish consumption are each rank-ordered 

separately (i.e., results are not matched for the same watershed across a given row).

Table 2-8 Watersheds with potentially at-risk populations based on consideration for risk 

based on U.S. EGU mercury deposition and resulting exposure considered alone,

without taking into account other sources of mercury deposition (2016 scenario)

EGU risk 

threshold

Number and percentage of 3,141 HUCs meeting risk 

threshold criteria

(RfD-based HQ risk)*

2016 analysis

90th fish 

consumption

95th percentile

fish 

consumption

99th percentile fish 

consumption

≥ 1.5* 17 - 52 (2%) 327 (10%)

* Following convention for reporting HQ estimates to one significant digit, this requires an HQ ≥ 1.5 (see 

section 1.4.5). Results presented here for different levels of fish consumption are each rank-ordered 

separately (i.e., results are not matched for the same watershed across a the “>1.5” row).

Table 2-9 Combination of watersheds with potentially at-risk populations based on either 

consideration for (a) U.S. EGU percent contribution to total risk OR (b) risk when 

U.S. EGU mercury deposition is considered alone, without taking into account 

deposition from other sources (2016 scenario)

EGU risk 

threshold

Number and percentage of HUCs meeting risk threshold criteria

2016 analysis

90th percentile fish 

consumption

95th percentile fish 

consumption

99th percentile fish 

consumption

U.S. EGU-attributable risk ≥1.5* HQ OR total risk ≥1.5* HQ and U.S. EGU contribution of:

≥ 5% 679 (22%) 744 (24%) 917 (29%)

≥ 10% 107 (3%) 141 (4%) 385 (12%)

≥ 15% 47 - 80 (3%) 339 (11%)

≥ 20% 29 - 64 (2%) 331 (11%)

*** Following convention for reporting HQ estimates to one significant digit, this requires an HQ ≥ 1.5 

(see section 1.4.5). Results presented here for different levels of fish consumption are each rank-ordered 

separately (i.e., results are not matched for the same watershed across a given row).
86

Table 2-10 Reflecting the March version of the risk assessment - combination of 

watersheds with potentially at-risk populations based on either consideration for (a) 

U.S. EGU percent contribution to total risk OR (b) risk when U.S. EGU mercury 

deposition is considered alone, without taking into account deposition from other 

sources (2016 scenario)

EGU risk 

threshold

Number and percentage of HUCs meeting risk threshold criteria

90th percentile fish 

consumption

95th percentile fish 

consumption

99th percentile fish 

consumption

U.S. EGU-attributable risk ≥1.5* HQ OR total risk ≥1.5* HQ and U.S. EGU contribution of:

≥ 5% 484 (20%) 524 (22%) 672 (28%)

≥ 10% 96 (4%) 121 (5%) 325 (14%)

≥ 15% 49 (2%) 76 (3%) 292 (12%)

≥ 20% 34 - 61 (3%) 286 (12%)

* Following convention for reporting HQ estimates to one significant digit, this requires an HQ ≥ 1.5 (see 

section 1.4.5). Results presented here for different levels of fish consumption are each rank-ordered 

separately (i.e., results are not matched for the same watershed across a given row).

We made several observations regarding watersheds with potentially at-risk populations 

due to U.S. EGU–attributable Hg deposition based on the information in Tables 2-7 through 2-

10, oriented around the 2-Stage risk characterization framework.

 Depending on the percentile fish consumption rate, between 3 and 24% of those 

watersheds with total risk HQs >1 have U.S. EGUs contributing at least 5% of total Hg 

deposition (2016 scenario): With this risk metric, we consider the degree to which U.S. 

EGUs contribute to total risk at watersheds where total risk represents a potential public 

health hazard (i.e., total exposure leads to an HQ > 1). Considering a 5% U.S. EGU 

contribution at watersheds where total risk is considered a potential public health hazard, 

we have up to 24% of the watersheds falling into this category (with the 24% value 

reflecting risk modeled using the 99th percentile fish consumption rate for the high-end 

female consumer – see Table 2-7). It is important when considering this risk metric to 

reiterate that any exposure above the MeHg RfD represents a potential public health 

hazard. 

 Depending on the percentile fish consumption rate, between 2 and 12% of the watersheds 

have HQs > 1, based on U.S. EGU Hg deposition before factoring in any other sources 

of Hg (i.e., an U.S. EGU-attributable HQ>1) (2016 scenario): Our analysis suggests that 

between 2 and 12% of the 3,141 watersheds modeled in the risk assessment for high-end 

female consumers could have an HQ >1 from U.S. EGU-attributable Hg deposition when 

considered alone, without taking into account other sources of deposition. The low end of 

the range reflects the 95th percentile consumption rate, and the high end reflects the 99th

percentile consumption rate (again for the typical female subsistence fish consumer). 

 Depending on the percentile fish consumption rate, between 22 and 29% of the 

watersheds are at-risk based on at least one of the risk characterization criteria (2016 

scenario): Combining the two categories of watersheds with populations at-risk due to 

U.S. EGU Hg emissions summarized in the last two bullets, we get a total estimate 

ranging from 22 to 29% of watersheds at-risk. These estimates are sensitive to the 
87

specification of the percent U.S. EGU contribution. If a 10 percent contribution threshold 

is applied, the range of watersheds at-risk ranges from 3% to 12%. The ranges for each 

percent contribution threshold also reflect the different fish consumption rates considered 

for the high-end female consumer (i.e., 90th, 95th and 99th percentile fish consumption 

rates). The results summarized here for total “at-risk” watersheds map to Stage 2 of the 

2-stage risk characterization framework.

 The revised risk assessment and the March Mercury Risk TSD generate very similar 

Stage 2 risk estimates (2016 scenario): Comparison of risk estimates presented in Tables 

2-9 and 2-10 suggest that refinements to the risk assessment implemented since the 

March TSD have not substantially affected overall Stage 2 results (formerly known as 

“Stage 3” results in the March version of the Mercury Risk TSD). 

2.6 Sensitivity Analyses 

This section discusses several sensitivity analyses conducted to assess the potential 

impact of sources of uncertainty related to: (a) the application of the proportionality assumption 

in linking Hg deposition and fish tissue Hg concentrations (see sections 1.4.2.1 and 1.4.6) and (b) 

use of the watershed-level 75th percentile fish tissue Hg concentration as the basis for generating 

watershed-level risk estimates (see section 1.4.2). Given the emphasis placed in the nationalscale mercury risk assessment on risk estimates generated for the 2016 scenario, we have also 

based the sensitivity analyses on simulating risks for the 2016 scenario.

To address uncertainty in applying the proportionality assumption, we conducted two 

sensitivity analyses focused on concerns that the risk assessment may have included some 

watersheds that are disproportionately impacted by non-air Hg sources. We included the 

proportionality assumption in the sensitivity analysis because it represents a critical element of 

the analysis and is acknowledged as representing a potentially important source of uncertainty 

(see Table 2-15 in section 2.7).55 As noted in section 1.4.6, the proportionality assumption 

linking Hg deposition over watersheds with fish tissue Hg concentrations, only holds for 

watersheds where aerial Hg deposition is the primary source of loading. The two sensitivity

analyses include: (a) excluding four states where we have concerns over the potential for non-air 

Hg playing a greater role (ME, MN, SC and LA)56 and (b) constraining the risk analysis to only 

include those watersheds in the upper 25th percentile of total Hg deposition (i.e., watersheds with 

 

55 In the March TSD, we included a sensitivity analysis on the proportionality assumption for flowing versus 

stationary waterbodies. However, comments by the SAB suggested that this issue was unlikely to represent a 

significant source of uncertainty in the analysis and consequently we have not repeated it here.

56 We excluded ME, MN and LA in this sensitivity analysis for reasons specific to each state. ME was excluded 

because Hg fish tissue levels there are fairly high, while Hg deposition is not relatively elevated (compared to other 

eastern states) – this raises the concern that other factor may affected fish tissue Hg (e.g., other non-air sources, or 

increased methylation potential). MN was excluded because taconite mining could contribute non-air Hg loading. 

Finally LA was excluded due to substantial industrial activity that could contribute non-air Hg loading. We note 

that, in the March TSD, we excluded SC from this sensitivity analysis because of higher fish Hg levels and Hg air 

deposition that (while elevated in some locations) is not uniformly higher than other states. As part of our 

explanation for the higher fish tissue Hg concentrations in SC we pointed to a history of gold mining in SC (i.e., the 

potential that significant non-air Hg loading may be a factor). SAB panel members with expertise in this area did 

point out that the methylation potential of many of the waterbodies in SC (particular central and eastern SC) is 

relatively high and that this is likely the reason for the higher fish tissue Hg concentrations.
88

relatively elevated levels of total Hg deposition so we have increased confidence that aerial 

deposition plays a significant role in loading). 

To address uncertainty in use of the watershed-level 75

th percentile fish tissue Hg 

concentration in risk modeling, we conducted a sensitivity analysis using the watershed-level 

median fish tissue Hg. This sensitivity analysis, which was recommended by SAB, considers the 

situation in which subsistence fishers do not necessarily target the larger (higher trophic level) 

fish and consequently could catch fish with lower fish tissue Hg concentrations. 

The results of both sensitivity analyses are presented in terms of their impact on the 2-

Stage Risk Characterization Framework results (i.e., their impact on estimates of the number and 

percent of watersheds with potentially at-risk populations – section 1.3). Therefore, we provide 

three tables (Tables 2-11, 2-12 and 2-13) that correspond to each stages of the 2-Stage Risk 

Characterization Framework. Each table first presents core results, followed by results for each 

of the three sensitivity analyses (this allows the results of each sensitivity analysis to be readily 

compared against the core analysis). We present observations from the sensitivity analyses at the 

end of this section. All results presented in the three tables are based on simulating risk for the 

typical female subsistence fish consumer scenario. 

Table 2-11 Sensitivity analysis results presented as: watersheds with potentially at-risk

populations based on U.S. EGUs making a specified contribution to total risk (2016 

scenario)

EGU risk threshold

(percent U.S. EGU 

contribution to risk)

Number and percentage of watersheds meeting risk threshold 

criteria

(Total HQ ≥ 1.5 and U.S. EGU percent contribution as dimensioned 

below)*

90th percentile fish 

consumption

95th percentile fish 

consumption

99th percentile fish 

consumption

All watersheds (core analysis)

≥ 0% 2853 (91%) 2983 (95%) 3112 (99%)

≥ 5% 679 (22%) 726 (23%) 754 (24%)

≥ 10% 104 (3%) 114 (4%) 119 (4%)

≥ 15% 37 - 40 - 40 -

≥ 20% 15 - 17 - 17 -

Sensitivity analysis A (exclude watersheds in MN, LA, SC and ME)

≥ 0% 2104 (89%) 2224 (94%) 2347 (99%)

≥ 5% 638 (27%) 685 (29%) 713 (30%)

≥ 10% 90 (4%) 100 (4%) 105 (4%)

≥ 15% 32 - 35 - 35 -

≥ 20% 14 - 16 - 16 -

Sensitivity analysis B (include watersheds in top 25th percentile with regard to total Hg 

deposition)

≥ 0% 712 (91%) 740 (94%) 776 (99%)

≥ 5% 154 (20%) 166 (21%) 178 (23%)

≥ 10% 39 (5%) 42 (5%) 45 (6%)
89

EGU risk threshold

(percent U.S. EGU 

contribution to risk)

Number and percentage of watersheds meeting risk threshold 

criteria

(Total HQ ≥ 1.5 and U.S. EGU percent contribution as dimensioned 

below)*

90th percentile fish 

consumption

95th percentile fish 

consumption

99th percentile fish 

consumption

≥ 15% 20 (3%) 20 (3%) 20 (3%)

≥ 20% 9 - 9 - 9 -

Sensitivity analysis C (generate risks using median watershed-level fish tissue Hg 

concentration)

≥ 0% 2774 (88%) 2932 (93%) 3095 (99%)

≥ 5% 659 (21%) 717 (23%) 751 (24%)

≥ 10% 100 (3%) 111 (4%) 118 (4%)

≥ 15% 37 - 39 - 40 -

≥ 20% 15 - 17 - 17 -

* Following convention for reporting HQ estimates to one significant digit, this requires an HQ ≥ 1.5 (see 

section 1.4.5). 

Table 2-12 Sensitivity analysis results presented as: watersheds with potentially at-risk 

populations based on consideration for U.S. EGU-attributable HQ risk (2016 scenario)

(risk considering U.S. EGU Hg deposition before considering other sources of Hg 

deposition)

U.S. EGU-attributable 

risk threshold

Number and percentage of 3,141 watersheds meeting risk 

threshold criteria

(U.S. EGU-attributable HQ)*

90th percentile fish 

consumption

95th percentile

fish 

consumption

99th percentile fish 

consumption

All watersheds (core analysis)

≥ 1.5* 17 - 52 (2%) 327 (10%)

Sensitivity analysis A (exclude watersheds in MN, LA, SC and ME)

≥ 1.5* 7 - 22 - 209 (9%)

Sensitivity analysis B (include watersheds in top 25th percentile with regard to total Hg 

deposition)

≥ 1.5* 9 - 25 (3%) 104 (13%)

Sensitivity analysis C (generate risks using median watershed-level fish tissue Hg 

concentration)

≥ 1.5* 11 - 24 - 173 (6%)

* Following convention for reporting HQ estimates to one significant digit, this requires an HQ ≥ 1.5 (see 

section 1.4.5). 
90

Table 2-13 Sensitivity analysis results presented as: Combination of watersheds with 

potentially at-risk populations based on either consideration for (a) U.S. EGU 

percent contribution to total risk OR (b) risk when U.S. EGU mercury deposition is 

considered alone, without taking into account deposition from other sources (2016 

scenario)

EGU risk threshold

(percent U.S. EGU 

contribution to risk)

Number and percentage of watersheds meeting risk threshold 

criteria

(U.S. EGU-attributable risk ≥1.5* HQ OR total risk ≥1.5* HQ and 

U.S. EGU: percent contribution as dimensioned below)*

90th percentile fish 

consumption

95th percentile fish 

consumption

99th percentile fish 

consumption

All watersheds (core analysis)

≥ 5% 679 (22%) 744 (24%) 917 (29%)

≥ 10% 107 (3%) 141 (4%) 385 (12%)

≥ 15% 47 - 80 (3%) 339 (11%)

≥ 20% 29 - 64 (2%) 331 (11%)

Sensitivity analysis A (exclude watersheds in MN, LA, SC and ME)

≥ 5% 638 (27%) 689 (29%) 782 (33%)

≥ 10% 90 (4%) 108 (5%) 267 (11%)

≥ 15% 34 - 49 (2%) 221 (9%)

≥ 20% 19 - 34 - 213 (9%)

Sensitivity analysis B (include watersheds in top 25th percentile with regard to total Hg 

deposition)

≥ 5% 154 (20%) 174 (22%) 232 (30%)

≥ 10% 41 (5%) 54 (7%) 122 (16%)

≥ 15% 26 (3%) 38 (5%) 108 (14%)

≥ 20% 16 (2%) 30 (4%) 105 (13%)

Sensitivity analysis C (generate risks using median watershed-level fish tissue Hg 

concentration)

≥ 5% 659 (21%) 721 (23%) 822 (26%)

≥ 10% 101 (3%) 118 (4%) 241 (8%)

≥ 15% 42 - 53 (2%) 188 (6%)

≥ 20% 23 - 36 - 179 (6%)

* Following convention for reporting HQ estimates to one significant digit, this requires an HQ ≥ 1.5 (see 

section 1.4.5). 

Observations regarding the sensitivity analyses include:

 Generating risk estimates excluding watersheds located in four states (AL, SC, ME and 

MN): This sensitivity analysis resulted in different effects on the Stage 1a and Stage 1b

risk estimates, although neither was substantially different than the core estimates. 

Estimates of the number of watersheds with at-risk populations due to U.S. EGUs 

contributing at least 5% to total risk (Stage 1a) demonstrated a range of moderate 

increases compared with the core analysis, depending on the “percent contribution” 

category considered (see Table 2-11). Similarly, estimates of the percentage of 
91

watersheds with at-risk populations when considering U.S. EGU Hg deposition (without 

other sources – Stage 1b) based on dropping out the four states was 9% as compared with 

10% when considering all states (for the 99th percentile fish consumption rate - see Table 

2-12). This sensitivity analysis suggests that the risk estimates are relatively robust to 

exclusion of the four states considered in this sensitivity analysis.

 Generating risk estimates including only those watersheds falling in the top 25th

percentile with regard to total Hg deposition: These sensitivity analysis results suggest 

that focusing on those watersheds with higher total deposition results in a mixture of 

impacts on both Stage 1a and Stage 1b risk metrics. Estimates of the number of 

watersheds with at-risk populations due to U.S. EGUs contributing between 5% and 10% 

is lower for the sensitivity analysis than for the core analysis. However, estimates for 

contributions of ≥ 10% and ≥20% are notably higher than for the core analysis (see Table 

2-11). Similarly, estimates of the percentage of watersheds with at-risk populations when 

considering U.S. EGU Hg deposition alone (without taking into account deposition from 

other sources – Stage 1b) is 13% as compared with 10% when considering all watersheds 

(for the 99th percentile fish consumption rate - see Table 2-11). This sensitivity analysis 

suggests that the risk estimates would be somewhat higher (particularly for the tail of the 

risk distribution) if we focus on those watersheds receiving the greatest amount of 

atmospherically deposited Hg. However, the magnitude of risk is not substantially 

different from the core analysis. 

 Generating risk estimates using the median watershed-level fish tissue Hg concentration 

(instead of the 75thpercentile as used in the core analysis): These sensitivity analysis 

results are notably different for the two categories of risk metric considered. Estimates of 

the number of watersheds with at-risk populations due to U.S. EGUs contributing ≥ 5% 

are essentially the same as the core risk estimates. However, estimates of the percentage 

of watersheds with at-risk populations when considering U.S. EGU Hg deposition 

(without other sources – Stage 1b) is substantially lower than the core analysis (i.e., 5% 

when the median fish tissue Hg concentration is used as compared with 10% with the 

core analysis). These results suggest that for some risk metrics, the results of the analysis 

would be significantly affected were we to use the median fish tissue Hg concentration

rather than the 75th percentile. The SAB peer-review panel concluded that “using the 75th

percentile of fish tissue values as a reflection of consumption of larger, but not the 

largest, fish among sport and subsistence fishers is a reasonable approach and is 

consistent with published and unpublished data on predominant types of fish consumed.” 

2.7 Discussion of key sources of variability and uncertainty 

This section provides a qualitative discussion of variability and uncertainty associated 

with the risk assessment. Regarding variability, we focus on identifying the key sources of 

variability associated with modeling risk for the scenarios included in the analysis and then 

discuss the degree to which those sources are reflected in the design of the risk assessment and 

implications for the risk estimates generated. The risk assessment has been designed to reflect 

critical sources of variability to the extent allowed by available methods and data and given the 

resources and time available. Key sources of variability associated with the analysis include: 
92

 The pattern of total and U.S. EGU-attributable Hg deposition across watersheds in the 

U.S. (including variation in the global inflow of Hg into the U.S.)

 The patterns of fish tissue Hg concentrations across watersheds within the U.S.

 The sampling protocols for measuring fish tissue Hg concentrations used by States which 

results in varying degrees of spatial coverage of fish tissue Hg concentrations across the 

states.

 The response of watersheds to Hg loading including variation in the temporal response 

and degree to which fish tissue Hg concentrations change in response to altered Hg 

loadings.

 The degree of subsistence fishing activity by different groups across watersheds.

 Subsistence fishing behavior including: consumption rates, species harvested, degree to 

which activity is focused on one or more waterbodies and differences in 

cooking/preparation practices. Variation in bodyweight can also impact exposure and risk 

estimates. 

These sources of variability and the degree to which each is reflected in the design of the 

analysis are identified and described in Table 2-14. 

Regarding uncertainty, we focus first on identifying potentially significant sources of 

uncertainty impacting the analysis. Then we characterize (a) the nature of the impact of each 

source on risk estimates and (b) the degree to which the potential impact of the source of 

uncertainty is characterized as part of the analysis (including whether sensitivity analyses 

completed for the risk assessment address a particular source of uncertainty). The results of this 

qualitative assessment are presented in Table 2-15. 

The SAB commented extensively on the sources of variability and uncertainty that should 

be addressed in the revised version of this section and those recommendations are reflected in 

Tables 2-14 and 2-15.
93

Table 2-14 Key sources of variability associated with the analysis and degree to which they are reflected in the design of the 

analysis 

Source of variability Description 

Degree to which source is reflected in design of risk analysis and implications 

for the risk estimates generated

(A) 2005 Hg emissions Emissions approaches have been applied to create a 2005 

Hg inventory for separate categories of emissions (e.g., 

Coal utilities, Portland Cement, Electric Arc Furnaces). 

Within each category, types of variability for total Hg 

include (1) annual variability related to product demand, 

(2) sub-annual (e.g., hourly) temporal variability from 

production related fluctuations at each facility, (3) 

variability of emissions control systems over time 

depending on the control device age and process stages 

such as startup/shutdown and operating conditions even 

during standard operations, (4) fuel characteristics 

including the Hg content of the fuel, and (5) differences in 

similar pieces of equipment that are relevant when 

emission factors are applied to a class of process (e.g., not 

all fluidized bed boilers will have the same emissions 

potential). Further, the speciation characteristics of the 

total Hg has variability based on many of the same factors 

as for total Hg (e.g., production parameters, control 

systems, fuels, physical properties of individual units)

The analysis accounts for annual variability in that estimates of Hg emissions were 

developed for the particular 2005 year as the midpoint of the fish tissue data 

projected to 2016 to estimate risks in the future baseline. The analysis attempted to 

estimate sub-annual variability through use of (a) hourly EGU demand data that is 

developed from several years of hourly Continuous Emissions Monitoring (CEM) 

heat input data and (b) monthly, day-of-week, and diurnal temporal allocation factor 

for non-EGU sources. The approach for estimating 2005 EGU emissions was tied to 

1999 control assumptions and fuels, which creates some uncertainty described in 

Table 2-15. The variability associated with different pieces of equipment in a class 

of process was not accounted for explicitly, but the Hg emission factors developed 

for EGU boilers and other unit types were developed to represent, on average, 

emissions from a given unit type by averaging emissions test data over a many units 

at facilities within a given class. The emission factors used to compute Hg for U.S. 

EGUs were assigned based on boiler type, fuel (coal or oil), and control device 

configuration. The same characteristics were used to assign speciation fractions.

(B) 2016 Hg emissions The sources of variability identified for the 2005 

emissions also apply to 2016; however, they are addressed 

differently because of the differences in approach 

associated with estimating emissions for a future year.

Annual variability is accounted for by the Integrated Planning Model’s 2016 estimate 

of Hg emissions. This model accounts for expected electricity demand, controls in 

place before 2016, and forecasts of fuel prices resulting in predicted choices of fuels. 

Further, the model predicts the Hg content of the fuel used in each unit in the future 

based on forecasts of which coal mine will supply coal to each unit. Thus, IPM does 

a more complete job than is done in 2005 for including control and fuel information. 

As in 2005, sub-annual variability uses a) hourly EGU demand data that is developed 

from several years of hourly Continuous Emissions Monitoring (CEM) heat input 

data and (b) monthly, day-of-week, and diurnal temporal allocation factor for nonEGU sources. IPM further accounts for differences in equipment using Emissions 

Modification Factors (EMFs), which adjust the Hg emissions based on the boiler 

type and control device configuration. The EMFs are not specific to each unit, but 

rather were developed to represent, on average, emissions changes due to boiler and 

control device types. Finally, the speciation fractions used in 2005 were also applied 

in 2016, but the fractions were assigned based on the 2016 unit type, fuel type, and 

control devices, rather than the base year configuration.

(C) Pattern of total and 

U.S. EGU-attributable 

Hg deposition across 

Patterns of annual deposition of Hg including total (all 

source) and estimates of the U.S. EGU fraction (by 

watershed) displays considerable spatial variability across 

By extrapolating CMAQ grid cell results at the more spatially refined HUC12 

watershed level, we retain the greater degree of spatial resolution in characterizing 

Hg deposition obtained through the use of the 12k CMAQ grid cell simulations. 
94

Source of variability Description 

Degree to which source is reflected in design of risk analysis and implications 

for the risk estimates generated

watersheds in the US 

(including variation in 

the global inflow of Hg 

into the U.S.)

the U.S. based on the results of 12 km grid cell CMAQ 

modeling (see Section 2.4). This variability includes 

changes over time in global-scale ambient mercury 

concentrations due to changes in global emissions and 

chemistry. 

Regarding variation in the global inflow of Hg, the global GEOS-CHEM simulation 

does provide ambient Hg inflow estimates used in the CMAQ model domain that 

vary spatially in the horizontal direction and vertically from the surface through the 

troposphere. The Hg inflow concentrations also vary for each day of the year and 

within each day. However, the ambient speciated Hg inflow is kept constant between 

2005 and 2016 scenarios.

(D) Patterns of fish 

tissue Hg 

concentrations across 

watersheds within the 

U.S.

Mercury fish tissue measurements can display 

considerable spatial variability across watersheds (see 

section 2.3).

We have fish tissue measurements for roughly 4% of the watersheds in the U.S. (i.e., 

3,141 watersheds with measured values out of 88,000 based on data from 2000-

2010). These measurements are concentrated in the Eastern part of the U.S., although 

there are some measurements in almost all states. While our measured fish tissue 

levels do generally provide some degree of coverage for areas with elevated Hg 

deposition (in terms of both total and U.S. EGU-attributable), this coverage is limited 

and there are a large number of watersheds with high total and U.S. EGU-attributable 

Hg deposition for which we do not have fish tissue measurements (see section 2.4). 

(E) Sampling protocols 

for measuring fish 

tissue Hg 

concentrations used by 

States differ, which 

results in varying 

degrees of spatial 

coverage of fish tissue 

Hg concentrations 

across the states.

States utilize varied approaches for targeting waterbodies 

for fish sampling and Hg measurement. In virtually all 

cases, sampling strategies are not intended to provide a 

representative characterization of fish tissue Hg 

concentrations across the state and instead are intended to 

identify and then target areas suspected of having higher 

Hg levels, or higher fishing activity.

The sampling protocol used by states in collecting fish tissue Hg measurements is 

directly reflected in the fish tissue dataset used in the risk assessment. Therefore, any 

potential bias in the collection of fish tissue data (i.e., tendency to capture more

highly impacted waterbodies) is reflected in that dataset. However, having fish tissue 

Hg data that are potentially biased towards more heavily Hg-impacted waterbodies is 

not problematic given the focus of the risk assessment. Specifically, given that we are 

attempting to capture reasonable high-end subsistence fisher risk, having fish tissue 

data biased towards more highly impacted waterbodies is actually preferable, since it 

increase the potential that we will identify areas of higher exposure and risk related 

to the subsistence fisher scenario (it would only be problematic if we were attempting 

to generate a representative picture of more generalized recreational fisher risk). 

(F) Response of 

watersheds to Hg 

loading including 

variation in the 

temporal response and 

degree to which fish 

tissue Hg 

concentrations change 

in response to altered 

Hg loadings.

The response of fish tissue Hg concentrations to mercury 

deposition can vary greatly depending on a number of 

factors (e.g., role of watershed in loading to waterbody, 

methylation potential of the waterbody, rates of sulfate 

deposition, nature of aquatic biotic foodweb including mix 

of upper-level trophic fish etc). These factors can also 

affect the temporal profile of that response.

Variation in methylation potential is reflected directly in the variation in the 

measured Hg levels in fish across the different sampling locations. This variation is 

very important, and in fact is a primary determinant of variation in fish tissue Hg 

concentrations across watersheds. This will have a large impact on the variability in 

total Hg exposure, and as shown in Figure 2-17, results in a low correlation between 

total Hg deposition and fish tissue Hg across watersheds. However, this variability 

will not have an impact on the attribution of exposure at any specific watershed, 

because the scientific literature supports a linear relationship between changes in 

deposition of Hg at a watershed and changes in fish tissue Hg at the same watershed. 

Furthermore, because we are not predicting temporal trends in fish tissue levels and 

instead consider a future point in time (once near-steady state conditions are 

reached), variation in the temporal profile of changes in fish tissue levels related to 

differences in methylation potential of different watersheds is also minimized as a 

factor in the analysis. 

(G) Differences in the 

spatial distribution and 

Studies reviewed in developing the approach for this 

analysis suggests that there can be considerable variation 

Surveys of high- self-caught fish consuming populations allow us to clearly define 

this type of activity for specific areas covered by those surveys (e.g., Hispanic, 
95

Source of variability Description 

Degree to which source is reflected in design of risk analysis and implications 

for the risk estimates generated

extent of subsistence 

fishing activity by 

different groups across 

watersheds in the U.S.

in the nature of high-end self-caught fisher populations 

across regions of the country. This variation reflects ethnic 

and cultural practices and can also be driven be socioeconomic status (SES) related factors (e.g., poverty, levels 

of income). In addition, we would expect that access to 

freshwater vs saltwater fishing locations would also play a 

role.

Vietnamese and Laotian fishing activity in specific regions of California, high-end 

fishing activity by Blacks and Whites in South Carolina and Tribal activity near the 

Great Lakes). However, available studies on this type of high-end fishing activity at 

inland freshwater bodies do not provide comprehensive coverage for all regions in 

the U.S. Therefore, we extrapolated coverage from areas reflected in these studies to 

other portions of the U.S. There is uncertainty associated with this process. As 

discussed in Table 2-15 (entry E) a lack of comprehensive survey information on 

subsistence fishing activity for all regions in the U.S. is a limitation of the analysis. 

This also prevents us from generating population-weighted estimates of risk.

(H) Subsistence fishing 

behavior including: 

consumption rates, 

species harvested, 

degree to which 

activity is focused on 

one or more 

waterbodies and 

differences in 

cooking/preparation 

practices. Variation in 

bodyweight can also 

impact exposure and 

risk estimates.

Subsistence fisher populations can display significant 

variability in their fishing behavior, which can impact 

overall exposure and risk. Variation in consumption rates 

has a direct impact on exposure, as do differences in the 

types of fish (size and species) potentially targeted. 

Tendency to focus fishing activity in one spot, versus 

distributing it across different waterbodies can also impact 

risk. Specifically, activity distributed across multiple 

waterbodies will tend to dilute out locations with higher 

fish tissue Hg concentrations, while focused activity 

means that one or more fishers may get all of their fish 

from the more impacted lake. Differences in 

cooking/preparation can also impact Hg levels on a per 

unit of fish basis, which, when combined with “as cooked” 

consumption rates can effected modeled exposure and 

risk. Body weight will also affect the calculation of 

exposure, when standardized by body weight (e.g., a 

heavier individual ingesting the same amount of fish will 

have a lower body-weighted adjusted exposure than a 

lighter individual ingesting the same amount of fish). 

While we have fairly comprehensive information on consumption rates for the set of 

female subsistence fish consumer scenarios we included in the analysis, we do not 

have information (for those fishers) characterizing several of the other behaviorrelated factors including types of species and size of fish targeted and the degree to 

which fishers target a single waterbody, versus distribute their activity across 

multiple waterbodies. However, given the focus of this assessment on capturing 

reasonable high-end fish consumption by subsistence fishers, we have modeled these 

scenarios assuming that (a) they tend to favor somewhat larger fish (use of the 75th

percentile watershed-level fish tissue Hg concentration) and (b) they focus their 

fishing activity on a single watershed. While we do not know how representative 

these types of higher-exposure related behaviors are within the subsistence fisher 

populations covered in the risk assessment, we do believe it is reasonable to assume 

that some subset of each population could engage in this type of behavior. With 

regard to fish cooking/preparation, we have not considered variability in this 

behavior, although the factors used (1.5 – see section 1.4.4), is a value that reflects 

the range of adjustment factors seen with different types of fish preparation methods 

for different types of fish. Similarly for body weight, we have used a central 

tendency factor and not explicitly modeled variability in this factor. 
96

Table 2-15 Key sources of uncertainty associated with the analysis, the nature of their potential impact on risk estimates, and 

degree to which they are characterized as part of the analysis 

Source of 

uncertainty Description 

Nature of potential impact on the exposure and risk 

estimates

Degree to which the potential impact of the source of 

uncertainty is characterized as part of the analysis

Characterizing the pattern of fish tissue Hg concentrations across watersheds in the U.S. (reflects small fraction of total watersheds with fish tissue measurement data, 

differences in state-level protocols for collection of fish tissue data, substantial number of watersheds with relatively low sample sizes (e.g., 1-2 samples), uncertainty associated with 

filtering watersheds to exclude locations with potentially significant non-air Hg impacts)

(A) Relatively small 

fraction of U.S. 

watersheds with 

measured fish tissue 

Hg data

Fish tissue Hg measurements are 

available for 3,141 of the ~88,000 

watersheds in the U.S. (i.e., ~4% 

of the watersheds). This relatively 

low coverage raises concerns that 

the risk assessment may miss 

areas of elevated U.S. EGU Hg 

deposition and/or areas with 

elevated fish tissue Hg 

concentrations, both of which 

could result in a low-biased 

assessment of U.S. EGU-related 

risk. 

Supplemental data for Pennsylvania that has been 

incorporated into the fish tissue dataset to enhance the 

current version of the risk assessment reduces 

somewhat concerns over missing areas of high U.S. 

EGU deposition (Pennsylvania has high U.S. EGU 

deposition and in the March version of the risk 

assessment had very little coverage by fish tissue Hg 

data). Similarly, additional data for Wisconsin and 

Minnesota reduces concerns over missing areas with 

elevated fish tissue Hg concentrations. However, there 

is still the potential that areas of high U.S. EGU Hg 

deposition and/or areas with high fish tissue Hg 

concentrations have been excluded from the analysis. In 

addition, there is still an overall low bias in our 

estimates of the number of watersheds with specified 

levels of risk since only a fraction of watersheds have 

fish tissue Hg measurements. 

We have semi-quantitatively examined the issue of 

potentially missing Hg deposition hot spots by presenting 

Figures 2-15 and 2-16, which show coverage by fish tissue 

Hg data for areas of the country with elevated U.S. EGU 

Hg deposition. Examination of these maps suggests, for 

example, that while Pennsylvania is now fairly well 

covered by available fish tissue data, Illinois, Kentucky 

and Ohio are less well covered. It is also important to point 

out that even while general coverage for high U.S. EGU 

Hg deposition areas by measured fish tissue data has 

improved for this version of the risk assessment, the 

potential still exists that substantial numbers of highimpact watersheds have not been included in the risk 

assessment due to a lack of fish tissue sampling at those 

locations. 

(B) Differences in 

state-level protocols 

for collection of fish 

tissue data which 

results in differing 

degrees of spatial 

coverage across states 

and differing degrees 

of potential bias in the 

samples collected

States use different strategies for 

determining where fish tissue 

samples will be taken 

geographically as well as the 

types of fish that will be measured 

(and the methods of collection). 

Generally, however, there is likely 

to be a bias towards targeting 

waterbodies suspected of having 

elevated fish tissue Hg 

concentrations for most states. 

Differences in the strategies used 

to collect fish tissue data together 

with the potential for bias 

favoring more highly impacted 

waterbodies, introduces 

uncertainty into the risk 

assessment. Specifically, there is 

Because the goal of the risk assessment is to capture 

risk for subsistence fishers that are likely to experience 

elevated U.S. EGU-attributable risk, having fish tissue 

Hg concentrations that likely reflect targeting of more 

highly impacted waterbodies (i.e., biased towards 

higher risk locations) is not problematic and in fact, is 

preferable. If it is reasonable to assume that subsistence 

fisher activity could occur at these sampled watersheds 

that have relatively higher fish tissue Hg concentrations, 

then having risk estimates based on these measurements 

strengthens the analysis from a science policy 

standpoint. By contrast, if the analysis had been 

designed to generate a fully representative populationweighted assessment of subsistence fisher risk, then 

bias in the fish tissue Hg concentrations would be 

problematic (since it would high-bias an assessment 

targeted at capturing “typical” risk for these highconsumers). 

Given that potential high-bias in the characterization of 

fish tissue Hg concentrations across the watersheds 

included in this risk assessment is not problematic and 

may in fact be preferable, we did not attempt to quantify 

the impact of this factor on risk estimates. 
97

Source of 

uncertainty Description 

Nature of potential impact on the exposure and risk 

estimates

Degree to which the potential impact of the source of 

uncertainty is characterized as part of the analysis

likely to be varying degrees of 

high-bias in the characterization 

of fish tissue Hg concentrations 

across states.

(C) Substantial 

number of watersheds 

with relatively low 

sample sizes (e.g., 1-2 

samples)

Only 28% of the watersheds with 

fish tissue Hg data have at least 

10 samples (~41% of the 

watersheds have only 1 or 2 

samples). Generating 75th

percentile risk estimates for 

watersheds with a small number 

of samples (e.g., <10 to 20) is 

subject to uncertainty and will on 

average understate the true 75th

percentiles across watersheds, 

although the bias in the estimate 

of the 75th percentile for any 

specific watershed is unknown. 

The potential that a substantial fraction of the 3,141 

watersheds included in the risk assessment could have 

low bias in the 75th percentile fish tissue Hg 

concentrations used in modeling risk means that in turn, 

risk estimates for a substantial number of watersheds 

could be low-biased. For the very low samples sizes 

(e.g., 1-2 samples per watershed), it is likely that, as a 

trend, the estimates of the 75th percentile values are 

actually more likely representative of the central 

tendency, e.g. the median. 

We examined trends in the watershed-level 75th percentile 

estimates across different strata of watersheds, where those 

strata were based on sample size (see Table 1-2). As 

discussed in section 1.4.2, the average of the 75th

percentile estimates increases substantially across these 

strata (i.e., as the number of samples available to 

calculated a 75th percentile increases, the trend in 75th

percentiles also increases). This suggests that other factors 

equal (see below), there is a distinct potential for low bias 

in the 75th percentile fish tissue Hg levels used in risk 

characterization for those watersheds with lower sample 

sizes. The important caveat to this is that this observation 

assumes that there is no correlation between absolute level 

of fish tissue Hg concentrations and sample size across 

watersheds. This may not be the case if, for example, 

waterbodies with higher absolute fish tissue Hg 

concentrations are also targeted more for sampling (which 

is likely the case). Therefore, the potential for low-bias in 

the 75th percentile estimates for watersheds with 1-2 

samples probably needs to be softened slightly since there 

is the potential for these watersheds to have lower “actual” 

measured fish tissue Hg levels. 

(D) Filtering 

watersheds to exclude 

locations with 

potentially significant 

non-air Hg impacts

As described in section 1.4.2, we 

filtered watersheds (with fish 

tissue Hg data) to exclude those 

potentially impacted by non-air 

Hg sources including gold mines 

and any industrial sources 

meeting a specified Hg release 

threshold. However, we did not 

filter out watersheds located near 

large urban areas which have the 

potential to release Hg through 

waste water treatments effluent. 

Recall that the proportionality 

assumption used as the basis for 

generating U.S. EGU-relevant 

exposure and risk does not hold in 

In those instances where risk is modeled for a watershed 

assuming that air Hg deposition is dominant, when in 

reality, municipal wastewater discharge may be 

contributing significantly to Hg loading, then estimates 

of U.S. EGU-attributable risk for that watershed could 

be biased high. In this case, we would not have assigned 

a portion of risk to the municipal discharges and instead 

would have over-estimated the U.S. EGU-attributable 

fraction of risk. 

Although we did not explicitly consider the issue of urban 

wastewater Hg loading in our sensitivity analyses, two of 

the sensitivity analyses did examine the broader issue of 

uncertainty in filtering our watersheds to exclude areas 

with substantial non-air Hg loading (se section 2.6). These 

two sensitivity analyses suggested that the risk estimates 

generated in the core analysis are relatively robust even 

when we (a) exclude a set of states that may have 

substantial non-air Hg loadings across their watersheds (or 

have higher methylation potentials in their waterbodies) or 

(b) focus risk modeling on those watershed in the upper 

quartile with regard total Hg loading (and therefore are 

more likely to be the dominant source of Hg to a 

watershed). 
98

Source of 

uncertainty Description 

Nature of potential impact on the exposure and risk 

estimates

Degree to which the potential impact of the source of 

uncertainty is characterized as part of the analysis

instances where non-air Hg 

loading is substantial.

Characterizing subsistence fishing activity across areas of high U.S. EGU mercury deposition (includes assessing potential locations for subsistence fishing activity as well as 

details regarding actual fishing and fish consumption behavior)

(E) Predicting which 

watersheds are likely 

to experience 

subsistence fishing 

activity 

There is uncertainty associated 

with estimating which of the 

watersheds for which we have 

fish tissue Hg data are likely to 

experience subsistence fishing 

activity associated with the female 

subsistence fish consumer

scenarios considered in the 

analysis. For the typical female 

subsistence fish consumer

scenario, we assumed that activity 

could occur at any of the 

watersheds with fish tissue data. 

However, for the remainder of the 

female subsistence fish consumer

scenarios, we used the concept of 

“source population” (see section 

1.4.3) to guide identification of 

watersheds with the potential for 

subsistence fisher activity by 

these specific SES-differentiated 

groups of fishers. 

Because the goal of the analysis is to determine whether 

there is the potential for significant risk for female 

subsistence fish consumers and not to generate 

representative population-weighted risk distributions for 

those populations, the importance of rigorously 

assessing where these populations are active (and the 

numbers of fishers at each location) is reduced 

somewhat. However, bias could be introduced into the 

analysis if we have modeled risk for high-impact 

watersheds when in reality there is no likelihood of 

subsistence fishing activity at those locations. The 

potential impact of this source of uncertainty is difficult 

to assess. However, as discussed above, because we are 

not attempting to generate population-weighted risk 

distributions, it is not expected to substantially impact 

the risk assessment. 

We did not explicitly examine this source of uncertainty 

related to where specific subsistence fisher populations 

may be active.

(F) Focus on risks to 

subsistence fishing 

populations and 

associated female 

subsistence fish 

consumers (concern 

that analysis does not 

cover high-end 

recreational angler 

risk and risk by other 

potentially highconsuming groups)

The SAB raised concerns that by 

focusing on subsistence fishers, 

we may have not provided 

coverage in the risk assessment 

for other high-consuming 

populations including the subset 

of recreational anglers who 

frequently fish and consume the 

fish they catch, and thus have near 

subsistence level consumption 

rates. 

The typical female subsistence fish consumer scenario 

is actually characterized (in terms of consumption rates) 

using a survey of recreational anglers conducted in 

South Carolina (Burger et al., 2002). This population is 

characterized as “subsistence” for purposes of this 

analysis due to the magnitude of the upper percentile 

consumption rates used (i.e., the 90th to 99th percentile 

values) which are considered subsistence since they 

represent a substantial contribution to dietary intake of 

protein. Furthermore, given that in this revised analysis, 

the typical female subsistence fish consumer scenario 

was assessed at all watersheds with fish tissue Hg data 

(excluding those filtered due to high-non air Hg 

impacts), this scenario would provide complete 

coverage for high-end recreational anglers, both in 

terms of consumption rate and potential location of 

We did not examine this issue of providing coverage for 

high-end recreational anglers because, as discussed in the 

cell to the left, the typical female subsistence fish 

consumer scenario provides coverage for a high-end 

recreational angler in the context of this assessment (i.e., 

given the goal of assessing subsistence-level fish 

consumption at each watershed without population 

weighting). 
99

Source of 

uncertainty Description 

Nature of potential impact on the exposure and risk 

estimates

Degree to which the potential impact of the source of 

uncertainty is characterized as part of the analysis

activity. There is no systematic bias introduced into the 

analysis due to its focus on female subsistence fish 

consumer scenarios that is consistent with the overall 

goal of the analysis (i.e., to characterize risk for the 

most highly impacted fisher populations).

(G) Characterizing 

subsistence fisher 

behavior (including 

consumption rates, 

types/size of fish 

targeted and the 

degree to which 

activity is focused at 

specific waterbodies) 

There is uncertainty associated 

with characterizing subsistence 

fisher behavior including 

consumption rates, types and size 

of fish targeted and the degree to 

which fishers target specific 

watersheds rather than distribute 

their activity across multiple 

watersheds.

There are relatively comprehensive survey data 

available for characterizing fish consumption rates for 

the subsistence scenarios included in the analysis. In 

fact, the subsistence fisher scenarios included in the 

analysis were selected, in large part, based on the 

availability of survey data to characterize consumption 

rates. In addition, the consumption rates display a 

reasonable degree of consistency in terms of magnitude 

across the studies (see Table 1-6). Therefore, we believe 

that there is relatively low uncertainty associated with 

characterizing this aspect of subsistence fisher behavior

and little concern for the introduction of bias into the 

analysis.

57

However, the other critical aspects (degree to which 

fishers target specific sizes and species of fish and the 

degree to which their activity is focused at one or more 

watersheds) are not well characterized in the literature 

and therefore are subject to considerable uncertainty. 

However, because the analysis is focused on modeling 

reasonable high-end risk, the importance of uncertainty 

associated with these additional behavior-related factors 

is reduced substantially. Specifically, if it is reasonable 

to assume that a subset of subsistence fishers would 

Given that the focus of this analysis is on assessing risk for 

populations likely to experience the highest reasonable 

U.S. EGU-attributable risk, we concluded that because it is 

reasonable to assume that some fraction of high-end 

fishing populations could focus their activity on a single 

watershed (and could favor larger fish), we can model this 

behavior in our analysis without introducing substantial 

uncertainty. If these assumptions regarding behavior are 

relaxed (i.e., fishers were assumed to eat smaller fish and 

distribute their activity across multiple watersheds), then 

risk will be reduced.* However, we did not explicitly 

model these alternative behavioral profiles given our focus 

on capturing reasonable estimates of high-end risk. 

* The observation that distributing fishing activity between 

watersheds will reduce risk needs additional clarification. 

While distributing fishing activity between watersheds 

may or may not impact central tendency estimates of risk 

across a group of fishers, high-end risk (risk for 

individuals at the upper tail of a simulated distribution) 

will be lower, since instances of having an individual fish 

at a single high Hg watershed will be removed, with 

fishing activity at that high Hg watershed now being 

averaged with activity at other less-impacted watersheds. 

 

57 Concerns have been raised by the SAB that some of the studies providing the consumption rates are older and that therefore, these studies may not capture current fishing 

practices. In addition, concerns were raised that sampling frequencies characterizing higher consumption levels for some of the fishing populations were relatively low (e.g., Black 

fishers in the Burger et al., 2002 study). The sources of uncertainty described here would have a potentially significant impact on the risk assessment if we were attempting to 

generate a representative picture of the actual distribution of risk across the full body of fishers reflected in each scenario. In that case, errors related to the study being old, or 

having insufficient survey samples to full characterize specific high-end percentiles of consumption would be a significant concern. However, when the goal of the analysis is to 

identify a set of high-end subsistence-like fish consumption rates that we believe could exist for a subset of each fishing population (and not to generate a representative 

population-weighted picture of risk), then the potential impact of these sources of uncertainty is reduced. Concerns were also raised by the SAB that the fish consumption surveys 

could reflect seasonally-differentiated consumption rates (i.e., higher rates may only occurred for part of the year during increased fishing activity). Regarding this issue of 

seasonality in the consumption rates, our evaluation of all three studies suggests that they provide annualized consumption rates (see Table 1-6) and therefore, we believe that the 

likely impact of this source of uncertainty is limited.
100

Source of 

uncertainty Description 

Nature of potential impact on the exposure and risk 

estimates

Degree to which the potential impact of the source of 

uncertainty is characterized as part of the analysis

target larger fish and focus their fishing activity at a 

specific watershed, then uncertainty in these two factors 

is largely ameliorated. 

(H) Estimating unit 

concentration of 

MeHg in cooked fish 

(application of 

adjustment factors for 

the fraction of total 

Hg that is MeHg in 

fish and cooking/ 

preparation). 

Two adjustment factors are 

applied to the 75th percentile fish 

tissue Hg concentration at a given 

watershed to generate an estimate 

of MeHg in the cooked fish 

serving (in ppm). These include a 

Hg conversion factor for 

estimating the fraction of Hg in 

fish that is MeHg and a fish 

preparation/cooking adjustment

factor for adjusting the MeHg 

concentration in the fish to reflect 

preparation of the fish. There is 

uncertainty associated with both 

of these factors. 

Mercury conversion factor: the factor used in this 

analysis (0.95) reflects consideration for the range of 

values provided in the Mercury Study Report to 

Congress (U.S. EPA, 1997) (i.e., ≥0 .90) (see section 

1.4.4). It would be possible to include a sensitivity 

analysis considering 0.90 and 0.99, but given that this 

factor is linearly related to both exposure and risk, the 

outcome would be similarly modest: approximately 5% 

lower and higher risk, respectively for a given 

watershed-specific estimate. Given available

information, we do not believe that any systematic bias 

is introduced into the analysis due to the use of this 

parameter value.

Fish preparation/cooking adjustment factor: The factor 

used in this analysis (1.5) reflects consideration for the 

range of preparation factors provided in Morgan et al., 

1997) (i.e., 1.1 to 1.5 for walleye and 1.5 to 2.0 for lake 

trout - see section 1.4.4). Again, as with the Hg 

conversion factor, we could consider a range of factors 

as a sensitivity analysis, but again, given that exposure 

and risk are linearly related to this factor, the outcome 

would is predictable. In this case, the impact on risk 

could be more pronounced, since the potential factors 

range from 1.0 to 2.0. The SAB identified several 

alternative studies for characterizing this factor, 

suggesting that those studies would support a smaller 

(or no) positive adjustment of the MeHg levels in fish 

reflecting preparation. However, as discussed in section 

1.4.4, close analysis of these studies resulted in a 

conclusion that they in fact, did not support application 

of alternative (lower) factors. Furthermore, given 

available information, we do not believe that any 

systematic bias is introduced into the analysis due to the 

use of this parameter value.

Uncertainty in these factors was not quantitatively 

assessed as part of the sensitivity analysis, however, as 

noted in the cell to the left, the linear relationship between 

both factors and exposure and risk allows us to readily 

determine the magnitude of impact that uncertainty in 

these factors could have on risk at the watershed-level.

Application of proportionality assumption in generating estimates of the U.S. EGU-attributable fraction of risk

(I) Fish tissue 

measurement data 

Concerns that the proportionality 

assumption linking Hg deposition 

If air deposition patterns from the 1990s are reflected in 

some of the Hg fish tissue measurements we are using 

We did not quantitatively assess this potential source of 

uncertainty and its impact on risk. However, our decision 
101

Source of 

uncertainty Description 

Nature of potential impact on the exposure and risk 

estimates

Degree to which the potential impact of the source of 

uncertainty is characterized as part of the analysis

used in the analysis 

may still reflect 

earlier historical 

patterns of Hg 

deposition (from the 

1990’s) more so than 

deposition from the 

simulation period 

(i.e., ~2005)

and fish tissue Hg concentrations 

may not hold if fish tissue Hg 

concentrations actually reflect an 

earlier (higher Hg deposition) 

period. In this case, bias could be 

introduced into estimates of 

reduction in fish tissue Hg 

concentrations (based on the 

proportionality assumption) since 

the effect of changes in current 

Hg deposition on fish tissue Hg 

concentrations could be 

overstated (i.e., there could be an 

effective buffering of the fish 

tissue Hg concentrations by the 

residual effects of earlier higher 

Hg deposition). 

the implications can vary depending on the nature of 

that difference. If fish tissue levels for a watershed still 

reflect higher 1990 deposition values, then there may be 

an upward bias to the estimates of U.S. EGUattributable risk, since in reality we would expect the 

underlying fish tissue levels to decrease as the impact of 

those earlier higher deposition values dissipates. 

Conversely, if total deposition remains the same, but 

only the source distribution has changed since the 

1990’s, then the effect on our risk assessment may not 

be that significant, since we are making projections 

based on the current source-mix (or future source-mix) 

assuming near steady-state assumptions are reached

given those specific source mixes. Given currently 

available information, we cannot evaluate the interplay 

of these two factors and the way in which they come 

together to introduce potential bias into estimates of 

U.S. EGU-attributable risk (we can only discuss each as 

a source of uncertainty as has been done here).

to use only fish tissue samples from after 1999 is intended 

to reduce this source of uncertainty.

(J) Watersheds can 

display substantially 

different methylation 

potentials, resulting in 

differences in the 

impact of a unit 

change in Hg 

deposition on fish 

tissue Hg 

concentrations

Differences in methylation 

potential cause variation in fish 

tissue Hg concentrations across 

waterbodies (even when those 

waterbodies have similar rates of 

Hg loading). In addition, 

differences in methylation 

potential can also translate into 

different temporal profiles for the 

response of fish tissue Hg 

concentrations to changes in Hg 

loading. Failure to reflect these 

sources of variability in 

methylation potential can 

introduce uncertainty into the 

analysis. 

Differences in methylation potential across watersheds, 

when present, are likely to be reflected in the underlying 

fish tissue Hg concentrations themselves. Therefore, 

these methylation differences will not to have a 

substantial impact on our analysis and are not expected 

to introduce systematic bias (the time sequence of 

changes in fish tissue levels will depend on differences 

in methylation potentials, but we are not attempting to 

predict these temporal profiles). 

We did not quantitatively assess uncertainty related to this 

issue. Our determination is that this is not an important 

source of uncertainty for our risk estimates.

(K) Factors related to 

methylation in 

watersheds (e.g., 

sulfate deposition, 

pH) have not 

remained constant 

This source of uncertainty is 

distinct from the issue of 

methylation potential discussed 

above (here we talk about factors 

which can interact with the 

underlying methylation potential 

As with methylation potential discussed above, any 

changes in factors affecting the underlying methylation 

potential of a waterbody would eventually be reflected 

in fish tissue Hg concentrations and therefore not 

introduce systematic bias into the analysis. For this 

reason, our concern over this issue is reduced. However, 

We did not quantitatively assess uncertainty related to this 

issue.
102

Source of 

uncertainty Description 

Nature of potential impact on the exposure and risk 

estimates

Degree to which the potential impact of the source of 

uncertainty is characterized as part of the analysis

over time, resulting in 

variation in the 

methylation potential 

of watersheds over 

time.

to increase or decrease it). If these 

factors (pH and sulfate deposition 

for example), have changed for a 

given watershed, then the 

methylation rate of that waterbody 

may also change, thereby 

impacting the degree to which 

fish bioconcentrate and ultimately 

(for higher trophic levels) 

bioaccumulate Hg. Changes in 

methylation factors can also 

impact the temporal profile for 

changes in fish tissue Hg 

concentrations. 

if changes in methylation factors are continuous and 

extend into the current simulation period, then there is 

more concern, since there could be more of a gradual 

change in methylation across our simulation period (a 

change that would not be fully reflected in the fish 

tissue Hg values we have in the dataset). However, it is 

difficult to characterize the magnitude of the potential 

impact of this source of uncertainty on risk estimates, 

including the direction and magnitude of any bias, given 

the complex interplay of the factors involved (i.e., 

potential for ongoing changes in pH and sulfate to 

produce methylation rates that vary over time).

(L) Effort to exclude 

watersheds with 

substantial non-air Hg 

deposition58

Despite efforts to exclude 

watersheds with substantial nonair sources of Hg loading (see 

section 1.4.2), some watersheds 

with substantial non-air impacts 

may have been retained in the 

analysis.

The potential that we may have failed to exclude 

watersheds with significant non-air Hg loadings could

introduce high-bias into our estimates of U.S. EGUattributable risk, since we would overstate the role of 

U.S. EGUs in contributing to risk, by overlooking the 

other non-air sources. 

We completed two sensitivity analyses exploring this issue 

and its potential impact on risk including: (a) an analysis 

of risks when watersheds falling in LA, SC, MN, and ME, 

are excluded (there is concern that these four states may 

have significant non-air Hg releases and/or increased 

methylation potential) and (b) an assessment of risk based 

only on those watersheds falling in the upper 25th 

percentile with regard to total Hg deposition (i.e., 

estimating risk when we focus on those watersheds where 

we are more confident that aerial Hg deposition plays a 

dominant role). The sensitivity analysis results suggest that 

exclusion of the four states where we have concerns over 

non-air Hg loading does not substantially impact risk. 

However, when we estimated risk focusing on the 

watersheds with the highest overall Hg loading (the upper 

25th percentile), we did see a moderate increase in risk 

estimates. 

(M) Potential that the 

modeling framework 

is not sufficiently 

refined from a spatial 

standpoint to capture 

elevated levels of Hg 

If the overall approach for linking 

Hg deposition (specifically the 

fraction attributable to U.S. 

EGUs) is not sufficiently spatially 

refined, then areas of high U.S. 

EGU Hg impact and resulting risk 

We believe the level of uncertainty associated with 

precision in modeling exposure and specifically the 

ability to identify U.S. EGU-related risk “hot spots” 

(again, using SAB terminology to refer to areas of 

elevated Hg-related risk) is moderate. CMAQ modeling 

is provided at the 12km grid resolution which matches 

The issue of spatial scale in capturing high-end subsistence 

fisher risk has not been quantitatively assessed as part of 

the risk assessment. 

 

58

 For the March version of the Mercury Risk TSD, we also explored the issue of whether the proportionality assumption was more applicable to stationary 

waterbodies (lakes/ponds) then flowing waterbodies (rivers/streams). However, for this version of the Mercury Risk TSD, reflecting recommendations made by 

SAB that the proportionality assumption can be readily applied for either category of waterbody, we have not repeated that sensitivity analysis, or discussed this 

source of uncertainty.
103

Source of 

uncertainty Description 

Nature of potential impact on the exposure and risk 

estimates

Degree to which the potential impact of the source of 

uncertainty is characterized as part of the analysis

deposition (“hot 

spots” as termed in 

the SAB comments) 

(Note, this source of 

uncertainty also 

involves CMAQ 

modeling)

may not be reflected in the risk 

assessment. This issue speaks to 

spatial precision in (a) the CMAQ 

air quality modeling, (b) scale of 

the watersheds (and degree to 

which subsistence activity is 

focused within specific 

watersheds) and (c) applicability 

of the proportionality assumption 

in relating changes in Hg 

deposition to changes in fish 

tissue Hg concentrations (or 

relating fraction of total Hg 

deposition coming from U.S. 

EGUs to the fraction of fish tissue 

Hg attributable to U.S. EGUs). 

well the watershed scale used in the analysis (i.e., the 

HUC12). The HUC12 watersheds, in turn, represent a 

small watershed which is appropriate if we assume that 

the subsistence fisher being modeled focuses their 

activity primarily at the same waterbody (i.e., within a 

given HUC12 watershed). Therefore, given the focus of 

this analysis on a reasonable highly-exposed 

subsistence fisher (i.e., an individual who focuses their 

activity at a given waterbody), then the modeling 

framework, including the spatial scale of the various 

modeling elements (CMAQ modeling, watershed, fish 

tissue data), would seem appropriate. For this reason, 

we believe there is little concern for the introduction of 

bias into the risk assessment due to the precision of the 

modeling and our ability to capture potential hot spots.

Factors relating to the estimation of mercury deposition over watersheds using the CMAQ model (e.g., estimating Hg emissions from U.S. EGUs and other sources, chemistry 

associated with Hg fate and transport, prediction of wet and dry deposition, and global inflow of Hg into the U.S.)

(N) Hg 2005 U.S. 

EGU emissions, 

uncertainties and 

sources of bias

2005 Hg emissions from EGUs 

used for this analysis are from the 

2005 inventory developed for the 

National Air Toxics Assessment 

(NATA). These data were 

calculated by EPA by scaling Hg 

data from the 1999 National 

Emissions Inventory (NEI) using 

the ratio of 2005 heat input to 

1999 heat input. Thus, 

uncertainties and bias in the 2005 

data fall into two classes – those 

that are due to the 1999 estimates 

and those resulting from the 

scaling approach used to estimate 

2005 emissions. 

The 1999 NEI data uncertainties 

include uncertainties traditionally 

associated with emissions 

estimation, such as uncertainties 

in the accuracy of test data, the 

applicability of the test data, the 

representativeness of emission 

The approach taken for the 1999 NEI used estimates 

developed from the data collected for the ICR done for 

the Clean Air Mercury Rule. These data were 

preferentially used over other estimates available from 

states, local agencies, or tribes. While these approaches 

include some uncertainties, they used best available 

practices and data available from the very rich data 

source of the 1999 ICR. In general, emissions estimates 

relying on test data will result in the best available 

emission estimates for sources with such data, and the 

uncertainties associated with measurement error are 

considered negligible in comparison to the other sources 

of uncertainty. Estimates based on emission factors will 

have greater uncertainty but will not significantly 

contribute to bias unless the emission factors are not 

representative. Care was taken to apply the most 

representative emission factors data to the sources in the 

cases where this was needed. A great deal was known 

about throughput at these sources from the ICR data, 

and so that is not considered a large source of 

uncertainty. These uncertainties can cause both 

overestimates and underestimates of emissions, but 

since there is no way to assess that, it is not possible to 

quantify the potential impact (including the direction 

The impact of this source of uncertainty was not 

quantitatively evaluated.
104

Source of 

uncertainty Description 

Nature of potential impact on the exposure and risk 

estimates

Degree to which the potential impact of the source of 

uncertainty is characterized as part of the analysis

factors for specific units, the 

oversimplification occurring when 

emission factors are used rather 

than continuous monitoring, and 

the accuracy of throughput 

estimates.

The 2005 approach adds 

uncertainty as well as bias to 

emissions estimates because it 

does not include the impacts of 

controls added to sources between 

1999 and 2005 or changes in 

fuels, including the Hg content of 

the fuel. In addition, uncertainties 

in the throughput information 

used to scale the emissions can 

affect the emissions estimates. 

Lastly, the temporal and 

speciation representation of 

emissions in the 2005 model 

inputs is another source of 

uncertainty. However, as noted in 

column to right, the net effect of 

high-bias in 2005 emissions 

would be to low-bias risk 

estimates for the 2016 scenario.

and magnitude of bias) on risk estimates.

The 2005 scaling approach will tend to overestimate 

emissions in 2005 because of additions of control 

technologies and coal used between 1999 and 2005 that 

would have reduced emissions. These reductions were 

not included because only the changes to throughput 

were considered in the approach. This most likely 

causes uncertainties that result in a high bias 

(overestimate) for the emissions in 2005. Since the 

2005 deposition results are used in the denominator of 

the risk scaling approach to predict risk in 2016, 

overestimates of 2005 emissions are expected to result 

in an underestimate of risk in 2016. The uncertainties 

associated with the heat input data in 2005 are very low, 

since most units included in the mercury estimates had 

CEM data to record and report heat input for 2005. 

The speciation approach used test data from the 1999 

data collection, which assigned speciation factors based 

on boiler type, fuel type, and control configuration. 

While imperfect, these data will tend to minimize 

inaccuracies in speciation assigned since the dataset 

used to calculate the fractions was fairly large, and was 

certainly the most comprehensive Hg speciation data 

available. Finally, the temporal allocation approach was 

kept the same between 2005 and 2016 so that any small 

impacts of these assumptions on deposition results will 

cancel out using the scaling approach for risk.

(O) Hg 2016 U.S. 

EGU emissions, 

uncertainties and 

sources of bias

There are three key variables that 

figure in the modeling of 2016 Hg 

emissions from U.S. electric 

generating units (U.S. EGUs): the 

mercury content of the fuel 

combusted (essentially various 

ranks and grades of coal), the 

extent of coal consumed, and type 

and performance characteristics of 

the controls to reduce mercury 

emissions at a given U.S. EGU. 

Uncertainty may figure in each of 

these key variables.

As a result of uncertainty, the 2016 mercury emissions 

from EGUs may be over or under-estimated. However, 

the extent of the impact of uncertainty is considerably 

reduced (as is concern over bias that may impact risk 

estimates) due to the number of directly measured and 

quality assured data points that are used to characterize 

the variables described in the previous column, by the 

mature nature of the technologies for mercury emission 

control, and by the commensurate experience and 

expertise in characterizing their performance as a result 

of more than a decade of in-field operation of the 

controls.

The impact of this source of uncertainty was not 

quantitatively evaluated.
105

Source of 

uncertainty Description 

Nature of potential impact on the exposure and risk 

estimates

Degree to which the potential impact of the source of 

uncertainty is characterized as part of the analysis

(P) Hg Non-EGU

emissions, general 

uncertainties and

consideration of 

potential bias

The 2005 NATA inventory for 

non-EGU sources is a compilation 

of state data and data associated 

with sector-specific rulemaking 

activities at EPA. Uncertainties 

include uncertainties traditionally 

associated with emissions 

estimation, such as uncertainties 

in the accuracy of test data, the 

applicability of the test data, the 

representativeness of emission 

factors for specific units, the 

oversimplification occurring when 

emission factors are used rather 

than continuous monitoring, and 

the accuracy of throughput 

estimates. In general, the data 

based on source tests and 

individual source reporting are of 

higher quality with less 

uncertainty. In addition, 

speciation and temporal allocation 

considerations add sources of 

uncertainty. These uncertainties 

are also applicable to the 2016 

emissions, since these emissions 

are derived by scaling the 2005 

emissions based on expected 

reductions between 2005 and 

2016.

Mercury emissions based on facility test data have been 

used whenever possible for the 2005 NATA inventory, 

focused on the categories with the highest emissions. 

For example, emissions sources with 2 or more tons in 

2005 include Portland cement, electric arc furnaces, 

boilers, chemical manufacturing, hazardous waste 

incineration, chlor-alkali plants, gold mining, and 

municipal waste combustors. For these sources, which 

represent 36 tons out of 47 tons non-EGU Hg 

emissions, 65% of these sources used data developed 

for rules associated with these sectors. The rule data 

arein large part based on test data and have otherwise 

had extensive review by industry experts and the 

facilities themselves. In combination with the U.S. 

EGU data, emissions from these higher quality data 

sources comprises 76% of the total anthropogenic U.S. 

Hg inventory. Thus, EPA’s approach reflects an 

inventory with much lower uncertainty than if it had 

been based solely on more generic emission factors. 

Speciation data used for this analysis are highly 

uncertain, but reflect best available information. Since 

the analysis was not quantifying the impact of non-EGU 

sources, the speciation of non-EGU sources tends to 

contribute less to the impact on the analysis. 

Additionally, while temporal allocation uncertainties are 

also high, the temporal allocation approaches for each 

sector are used consistently in 2005 and 2016, so their 

impacts are lessened through the use of the scaling 

approach.

There are no known sources of bias related to 

estimation of emissions from non-EGU sources in this 

analysis. Every attempt was made to use the best 

available emissions data in 2005 and future year 

projections to 2016.

However, a possible (but not likely) source of non-EGU

emissions underestimates is from natural sources. 

Public comments on EPA’s approach suggest that some 

natural sources were not considered. However, EPA 

does include natural elemental Hg emissions from 

volcanoes, oceans, and land. Further estimates of 

recycled Hg emissions from oceans and land are 

The impact of this source of uncertainty was not 

quantitatively evaluated.
106

Source of 

uncertainty Description 

Nature of potential impact on the exposure and risk 

estimates

Degree to which the potential impact of the source of 

uncertainty is characterized as part of the analysis

included. One source of uncertainty is the treatment of 

Hg from fires. While EPA did not explicitly include 

these emissions in the analysis as fires, the model 

calibration approach used to estimate the recycled land 

emissions captures such emissions (Seigneur, et. al. 

2004). To the extent that there is any underestimation 

bias, it would reduce the fractional contribution of U.S. 

EGU sources, however, given the low likelihood of 

these sources of bias, the impacts on risk are expected 

to be small. 

(Q)Hg Emissions, 

changes in 

uncertainties and bias 

between 2005 and 

2016

The sources of change between 

2005 and 2016 emissions are the 

U.S. EGU emissions and the 

stationary non-EGU emissions. 

All other emissions sources 

(mobile and non-anthropogenic) 

stayed constant between the two 

years. For U.S. EGUs, different

sources of uncertainties exist in 

the two different approaches for 

2005 and 2016 emissions, as 

described above. The possible 

changes in uncertainties from 

2005 to 2016 are caused by the 

changes in methods.

For non-EGU sources, 

uncertainties in 2016 emissions 

can result from assumptions about 

emissions reductions associated 

with other EPA regulations as 

well as predicting future activity 

levels associated with economic 

or other changes. 

For EGUs, some types of uncertainties could be greater 

in 2016 than in 2005 since the emissions are forecast to 

the future, for example, the future expected electricity 

demand and coal usage. 2005 emissions estimates have 

different uncertainties and it is not possible to determine 

without extensive uncertainty analysis which sources 

are greater than others. As described above, a bias most 

likely exists in the 2005 data that does not exist in the 

2016 data, resulting in a possible underestimate of risk 

from the deposition-based scaling approach used for 

2016 risk estimates.

For non-EGUs, several source categories reduced 

emissions from 2005 to 2016 as a result of other EPA 

regulations. While the reductions are estimated, the 

uncertainties are relatively low given the extensive 

analyses done to develop the regulations and since 

compliance with EPA emissions reductions 

requirements tends to be very high. Uncertainties are 

introduced because EPA does not include co-benefits of 

reductions from controls for other pollutants such as 

PM2.5, which might lead to overestimation of the 2016 

non-EGU emissions, lessening the impacts of U.S. 

EGUs, and therefore lowering the risk estimates from 

EGUs in our analysis. Uncertainty is also introduced in 

this analysis for non-EGU sources because the possible 

growth or reduction in emissions associated with 

economic changes is not included in the 2016 estimates. 

However, it is not possible to determine the impact of 

excluding such considerations since non-EGU industryspecific forecasts are highly uncertain themselves, there 

is a complex relationship between economic changes, 

regulations on emissions, and new source requirements 

The impact of this source of uncertainty was not 

quantitatively evaluated.
107

Source of 

uncertainty Description 

Nature of potential impact on the exposure and risk 

estimates

Degree to which the potential impact of the source of 

uncertainty is characterized as part of the analysis

that can reduce emissions. 

(R) Uncertainty in 

location of 2016 

emissions reductions

Uncertainties in the 2016 scenario 

regarding the specific geographic 

locations of reductions in EGUderived mercury deposition as a 

fraction of total mercury 

deposition

The upstream U.S. EGU sources of Hg emissions are 

provided by EPA’s power sector model, the Integrated 

Planning Model (IPM). The uncertainty in the extent of 

projected Hg emissions from these sources is treated 

above (see entry “O”). Here the focus is on the question 

of uncertainty in the location of the 2016 emissions 

reductions from the standpoint of which EGUs are 

projected to operate and so serve as sources of Hg 

emissions. IPM is an extremely detailed bottom-up 

deterministic linear programming representation of the 

U.S. power sector. There is no stochastic component in 

the model that would allow one to characterize the 

likelihood that the location of EGU sources and the 

extent of emissions from these sources are shifting. 

Therefore, we are not able at this time, to evaluate the 

potential for systematic bias being introduced into risk 

estimates through this step in our modeling. However, 

the model’s rigorous adherence to economic and 

engineering fundamentals and detailed representation of 

all factors affecting power system operation are 

designed to limit uncertainty and ensure that the 

model’s locational projections are reasonable.

The impact of this source of uncertainty was not 

quantitatively evaluated.

(S) Global inflow of 

mercury into the 

continental United 

States

There is considerable uncertainty 

in the global emissions inventory 

for mercury and given the long 

residence time of elemental 

mercury it is possible that inflow 

into the modeling domain may 

reflect deficiencies in the global 

emissions inventory. Some studies 

suggest global mercury emissions 

would increase without control 

implementation (Pacyna et al., 

2009; Streets et al., 2009). Recent 

measurements of ambient 

mercury at remote locations from 

1975 to 2008 suggest global 

mercury burden is steadily 

decreasing (Slemr et al., 2011). 

Given this conflicting information 

and the uncertainty in projecting 

Mercury deposition may be over or under-estimated 

(i.e., potential bias introduced is not clear in terms of 

direction or magnitude).

This analysis did not address this source of uncertainty. 

Mercury inflow was adjusted in another study and was 

shown to have a larger impact on modeled dry deposition 

compared to wet deposition. Boundary condition 

perturbations within realistic bounds resulted in minimally 

changed distributions of total modeled mercury deposition 

(Pongprueksa et al., 2008). This study also determined that 

model spin-up of one week is necessary to remove 

influence of initial conditions (Pongprueksa et al., 2008). 

The CMAQ modeling in this analysis had spin-up of more 

than one week making initial condition influence 

negligible.
108

Source of 

uncertainty Description 

Nature of potential impact on the exposure and risk 

estimates

Degree to which the potential impact of the source of 

uncertainty is characterized as part of the analysis

the current global emission 

inventory, boundary inflow is 

kept constant between 2005 and 

2016. 

(T) Photochemical 

model prediction of 

mercury wet 

deposition estimates

The uncertainty in the wide 

variety of model inputs such as 

emissions, meteorology, global 

inflow to the modeling domain, 

and chemistry manifest in model 

estimates of mercury wet 

deposition. 

Mercury wet deposition may be under-estimated during 

the summer season and over-estimated during the 

winter (i.e., potential bias introduced is not clear in 

terms of direction or magnitude). Model performance is 

described in “Air Quality Modeling Technical Support 

Document: Point Source Sector Rules” (USEPA, 2011).

Model estimated weekly mercury wet deposition is 

compared to observation data to assess model skill 

simulating this component of mercury deposition. Mercury 

wet deposition measurements are weekly totals taken at 

sites that are part of the Mercury Deposition Network 

(http://nadp.sws.uiuc.edu/MDN/) which operates under the 

National Atmospheric Deposition Program. This is 

generally consistent with other published studies that use 

coarser grid resolution, older versions of CMAQ and older 

emission inventories (Bullock et al., 2008; Lin et al., 

2007).

(U) Photochemical 

model prediction of 

mercury dry 

deposition estimates

The uncertainty in the wide 

variety of model inputs such as 

emissions, meteorology, global 

inflow to the modeling domain, 

and chemistry manifest in model 

estimates of mercury dry 

deposition. 

Mercury dry deposition may be over or under-estimated 

(i.e., potential bias introduced is not clear in terms of 

direction or magnitude).

This analysis did not address this source of uncertainty. 

There is a lack of dry deposition observation data that 

makes a direct quantitative or even qualitative comparison 

to modeled estimates impossible. Other studies have 

shown differences in estimated dry deposition based on 

changes in ambient mercury, reaction rate changes, and 

changes to the dry deposition scheme (Bullock et al., 2009; 

Lin et al., 2007; Pongprueksa et al., 2008; Ryaboshapko et 

al., 2007a; Ryaboshapko et al., 2007b). However, given 

that these studies are based on earlier versions of CMAQ 

or other models that are not routinely used for regulatory 

purposes and that mercury chemistry and dry deposition 

has changed through model versions, it is not clear that dry 

deposition estimates in the version of CMAQ used for this 

analysis would be comparable to earlier studies.

(V) Mercury 

Chemistry

The complete set of mercury 

oxidation and reduction reactions 

has not been identified by the 

scientific community. 

Mercury deposition may be over or under-estimated 

(i.e., potential bias introduced is not clear in terms of 

direction or magnitude).

This analysis did not address this source of uncertainty. 

Other studies have shown differences in estimated total 

mercury deposition based on changes in reaction rates 

(Bullock et al., 2009; Lin et al., 2007; Pongprueksa et al., 

2008; Ryaboshapko et al., 2007a; Ryaboshapko et al., 

2007b). However, given that these studies are based on 

earlier versions of CMAQ or other models that are not 

routinely used for regulatory purposes and that mercury 

chemistry and deposition has changed through model 

versions it is not clear that total mercury deposition 

estimates in the version of CMAQ used for this analysis 

would be comparable to earlier studies. 
109

Source of 

uncertainty Description 

Nature of potential impact on the exposure and risk 

estimates

Degree to which the potential impact of the source of 

uncertainty is characterized as part of the analysis

Application of the MeHg RfD in generating hazard quotient (HQ) risk estimates 

(W) Degree to which 

the RfD as calculated 

provides coverage for 

low SES status 

groups which may be 

at greater risk for 

adverse health effects 

following MeHg 

exposure

Given a number of factors (e.g., 

nutritional deficiencies, reduced 

access to health care and healthrelated information), there is 

concern that the MeHg RfD may 

not provide sufficient coverage 

for these low SES populations.

The RfD is defined as the amount of the substance of 

concern that can be consumed without expectation of 

harm for a lifetime by populations including sensitive 

subpopulations. The calculated RfD for MeHg includes 

an uncertainty factor to account for human 

pharmacokinetic variability (3 fold) and uncertainty 

and variability in pharmacodynamics. This may be 

sufficient to account for increased sensitivity to IQ 

decrements or adverse effects on neurobehavioral 

functions in low socio-economic status populations. 

There are no published analyses by EPA or other parties 

that would permit estimation of uncertainty for this 

factor and the potential for resulting bias being 

introduced into the analysis. Two of the human 

populations on which the RfD was based (the Faroese 

and the Seychellois) are relatively homogenous for 

some aspects of SES. 

We did not explore this potential source of uncertainty 

quantitatively. However, as noted in the cell to the left due 

to the method used in calculating the RfD, concerns that 

the RfD may not provide coverage for low SES 

populations is reduced.

(X) The MeHg RfD 

was derived based on 

saltwater fish 

consumption (which 

can involve higher 

levels of nutrients that 

ameliorate the 

adverse effects of 

MeHg). However, the 

RfD is now being 

applied in the context 

of freshwater fish 

consumption (which 

can involve lower 

levels of these 

nutrients). 

Recent studies (e.g. Oken et al 

2008; Choi et al 2008; ) point to 

the potential for nutrients in fish 

(particularly marine fish) to 

ameliorate some of the observed 

adverse effects of MeHg when coexposure occurs. However, there 

was no correction for potential 

confounding by nutrients in 

marine fish and mammals in 

calculation of the benchmark 

doses used in the RfD derivation. 

Therefore, there is the potential 

that these benchmark doses may 

be underestimates, in which case 

the HQ estimates based on the 

RfD could be biased low, 

particularly in the case of 

freshwater fish which may have 

lower levels of these nutrients. 

Failure to consider the effects of fish nutrients as a 

covariate or confounder for nerodevelopmental effects 

associated with MeHg exposure could result in HQ 

estimates being biased low. However, currently, 

available information does not support a rigorous 

adjustment of the RfD to address potential confounding 

by fish nutrients. 

We did not explore this potential source of uncertainty 

quantitatively.
110

3 Summary of Key Observations

This section provides key policy-relevant observations drawn from discussions presented 

in Sections 2.1 through 2.7. It is important to emphasize, that the risk estimates and additional 

supporting information summarized here are intended to inform a determination by the 

Administrator as to whether Hg emitted from U.S. EGUs represents a public health hazard. 

These observations are not intended to be conclusionary in nature and instead, focus on 

characterizing the nature and magnitude of risk associated with U.S. EGU Hg emissions. 

 Estimates of U.S. EGU Hg emissions suggest that the 2016 Scenario is likely closer to 

recent (2010) emissions compared with the 2005 scenario (which has substantially higher 

total Hg emissions for this sector). Therefore, risk estimates have been generated only for 

the 2016 scenario for the revised risk assessment (CMAQ-based Hg deposition estimates 

for the 2005 scenario are used in scaling fish tissue concentrations for use in modeling 

2016 scenario risk).

 Risk characterization is based on estimates of RfD-based HQ. Due to concerns raised by 

the SAB peer-review panel that the IQ loss endpoint may not fully capture the range of 

neurodevelopmental effects associated with MeHg exposure, IQ loss estimates are 

presented in brief summary form in Appendix B and not used in the risk characterization.

 Based on the 2016 scenario, U.S. EGUs can contribute up to 11% of total Hg emissions 

for a subset of watersheds. However, in general, other sources besides U.S. EGUs 

dominate Hg deposition. In 2016 U.S. EGUs contributed on average about 2% of total 

Hg deposition across the country. U.S. EGU-related Hg deposition is higher in the eastern 

part of the country with elevated contributions in a number of specific areas, including 

most notably, the Ohio River valley. U.S. EGU-related Hg deposition estimates show a 

significant reduction between 2005 and 2016 scenarios, reflecting mainly co-benefits 

from implementation of criteria pollutant controls with the average U.S. EGU-attributable 

deposition decreasing from ~5% of total to ~2% for the 2005 and 2016 scenarios, 

respectively. 

 Based on the 2016 scenario, U.S. EGUs can contribute up to 16% of MeHg in fish tissue. 

However, generally, U.S. EGUs contribute a much smaller fraction averaging 3% for the 

2016 Scenario.

 Comparing the magnitude of Hg fish tissue levels with total Hg deposition (as 

characterized at the watershed-level) suggests that there is not a strong correlation. This is 

not surprising given the variety of factors which effect methylation potential; factors 

which can demonstrate substantial spatial variation. However, available evidence 

supports a steady state linear relationship between changes in Hg deposition and changes

in fish tissue Hg concentrations.

 The additional fish tissue Hg data added for the revised risk assessment have significantly

improved coverage for watersheds located in areas with elevated U.S. EGU-related Hg 

deposition such as the Ohio River Valley. However, we still conclude that, generally, our 
111

coverage for high U.S. EGU impact areas remains limited. For this reason, we continue to 

believe that the actual number of “at-risk” watersheds (i.e., watersheds where U.S. EGUs 

could contribute to a public health concern) could be substantially larger than estimated. 

 Based on application of the 2-stage risk characterization framework described in Section 

1.3, we estimate that from 22 to 29% of the watersheds included in this risk assessment 

could be classified as potentially having at-risk populations under the 2016 Scenario. 

This estimate is based on risks modeled for the typical female subsistence fish consumer

and reflects aggregation of results from Stages 1a and 1b of the 2-Stage Risk 

Characterization Framework (i.e., watersheds where mercury released from U.S. EGUs

when considered alone, without taking into account mercury deposition from other 

sources, would produce an HQ >1 or watersheds where total HQ (reflecting Hg from all 

sources) is > 1 and U.S. EGUs make at least at 5% contribution to that-risk). 

 Comparison of risk estimates generated for the typical female subsistence fish consumer

scenario with estimates generated for the other six SES-differentiated female subsistence 

fish consumer scenarios included in this risk assessment results in the following 

observations: (a) total and U.S. EGU-attributable risks for the Hispanic and Vietnamese 

scenarios are generally lower than for the typical female subsistence fish consumer

scenario, (b) U.S. EGU-attributable risks for the Tribal scenario are similar to those for 

the typical female subsistence fish consumer scenario, although total risks are generally 

higher for the Tribal scenario and (c) U.S. EGU-attributable risks for the Laotian and low 

income southeastern White and Black scenarios are generally higher than for the typical 

female subsistence fish consumer scenario, although total risks can be higher or lower 

depending on the scenario. These trends suggest that generally, the typical female 

subsistence fish consumer scenario will provide coverage (in terms of representing risk) 

for Hispanic, Vietnamese and Tribal scenarios. However, the typical female subsistence 

fish consumer scenario may not provide full coverage for the Laotian and low income

southeastern White and Black scenarios, particularly in terms of U.S. EGU-attributable 

risk. 

 If U.S. EGU impacts to watersheds included in the risk assessment were zeroed-out, for a 

significant majority of those watersheds, total exposure would still exceed (and in most 

cases, significantly exceed) the RfD. Reductions in U.S. EGU attributable Hg will 

reduce the magnitude of the risk, although substantial total exposure and risk from Hg 

deposition will remain. 

 Sensitivity analyses conducted primarily to examine uncertainty in applying the 

proportionality assumption linking Hg deposition to Hg fish tissue levels, suggest that 

uncertainty related to the proportionality assumption is unlikely to substantially effect an 

assessment of whether Hg emissions from U.S. EGUs constitute a public health concern. 

Use of watershed-level 50th percentile fish tissue Hg concentrations (instead of the 75th

percentile values used in the core analysis) can result in notable reductions in risk

estimates in some instances, but the SAB peer review panel supports use of the 75th

percentile estimates.
112

Citations:

Appel, K. W., A. B. Gilliland, et al. (2007). "Evaluation of the Community Multiscale Air 

Quality (CMAQ) model version 4.5: Sensitivities impacting model performance Part I -

Ozone." Atmospheric Environment 41(40): 9603-9615.

Appel, K. W., P. V. Bhave, et al. (2008). "Evaluation of the community multiscale air quality

(CMAQ) model version 4.5: Sensitivities impacting model performance; Part II -

particulate matter." Atmospheric Environment 42(24): 6057-6066.

Axelrad, D. A.; Bellinger, D. C.; Ryan, L. M.; Woodruff, T. J. (2007). Dose-response 

relationship of prenatal mercury exposure and IQ: an integrative analysis of 

epidemiologic data. Environmental Health Perspectives. 2007, 115, 609–615.

Bloom, N.S. (1992). On the chemical form of mercury in edible fish and marine invertebrate 

tissue. Can. J. Fish. Aquat. Sci. 49:1010-1017.

Bullock, O. R. and K. A. Brehme (2002). "Atmospheric mercury simulation using the CMAQ 

model: formulation description and analysis of wet deposition results." Atmospheric 

Environment 36(13): 2135-2146.

Bullock, O.R., Atkinson, D., Braverman, T., Civerolo, K., Dastoor, A., Davignon, D., Ku, J.Y., 

Lohman, K., Myers, T.C., Park, R.J., Seigneur, C., Selin, N.E., Sistla, G., 

Vijayaraghavan, K., (2008). The North American Mercury Model Intercomparison Study

(NAMMIS): Study description and model-to-model comparisons. Journal of Geophysical 

Research-Atmospheres 113.

Bullock, O.R., Atkinson, D., Braverman, T., Civerolo, K., Dastoor, A., Davignon, D., Ku, J.Y., 

Lohman, K., Myers, T.C., Park, R.J., Seigneur, C., Selin, N.E., Sistla, G., 

Vijayaraghavan, K., (2009). An analysis of simulated wet deposition of mercury from the 

North American Mercury Model Intercomparison Study. Journal of Geophysical 

Research-Atmospheres 114.

Burger, J. (2002). Daily consumption of wild fish and game: Exposures of high end 

recreationalists, International Journal of Environmental Health Research, 12:4, p. 343-

354, July, 2010.

Burger, J., Stephens, W. L., Boring, C. S., Kuklinski, M., Gibbons, J. W., Gochfeld M. (1999) 

Factors in Exposure Assessment: Ethnic and Socioeconomic Differences in Fishing and 

Soncumption of Fish Caught along the Savannah River. Risk Analysis, Vol. 19, No. 3, p. 

427.

Byun, D. and K. L. Schere (2005). "Review of the governing equations, computational 

algorithms, and other components of the models-3 Community Multiscale Air Quality

(CMAQ) modeling system." Applied Mechanics Reviews 59(1-6): 51-77.

Byun, D., Schere, K.L., 2006. Review of the governing equations, computational algorithms, and 

other components of the models-3 Community Multiscale Air Quality (CMAQ) modeling 

system. Applied Mechanics Reviews 59, 51-77.

Dellinger, J. A., (2004) Exposure assessment and initial intervention regarding fish consumption 

of tribal members in the Upper Great Lakes Region in the United States. Dellinger , 

Environmental Research 95, p. 325-340
113

Dispasquale, M.M., J. Agee, C. McGowan, R.S. Oremland, M. Thomas, D. Krabbenhoft, and 

C.C. Gilmour. (2000) Methyl-Mercury Degradation Pathways: A Comparison Among 

Three Mercury-Impacted Ecosystems. Environ. Sci. Technol., 34, 4908-4916.

Farias L.A., Favaro, D.I., Santos J.O., Vasconcellos M.B., et al., (2010). Cooking Process 

Evaluation on Mercury Content in Fish,‖ Acta Amazonia, 40 (4), 741–748.

Grandjean, P., P. Weihe, R. White, F. Debes, S. Arak, K. Yokoyama, K. Murata, N. Sorensen, R. 

Dahl, and P. Jorgensen. (1997). Cognitive deficit in 7-year-old children with prenatal 

exposure to methylmercury. Neurotoxicol. Teratol. 20:1-12.

Harris., R. C., John W. M. Rudd, Marc Amyot, Christopher L. Babiarz, Ken G. Beaty, Paul J. 

Blanchfield, R. A. Bodaly, Brian A. Branfireun, Cynthia C. Gilmour, Jennifer A. 

Graydon, Andrew Heyes, Holger Hintelmann, James P. Hurley, Carol A. Kelly, David P. 

Krabbenhoft, Steve E. Lindberg, Robert P. Mason, Michael J. Paterson, Cheryl L. 

Podemski, Art Robinson, Ken A. Sandilands, George R. Southworth, Vincent L. St. 

Louis, and Michael T. TateRudd, J. W. M., Amyot M., et al., (2007). Whole-Ecosystem 

study Shows Rapid Fish-Mercury Response to Changes in Mercury Deposition. 

Proceedings of the National Academy of Sciences Early Edition, PNAS 2007 104 (42) 

pp. 16586-16591; (published ahead of print September 27, 2007).

Knights, D. C, Sunderland, E. M., Barber, M. C., Johnston J. M., and Ambrose, R. B., (2009). 

Application of Ecosystem-Scale Fate and Bioaccumulation Models to Predict Fish 

Mercury Response Times to Changes in Atmospheric Deposition. Environmental 

Toxicology and Chemistry, Vol 28, No. 4., pp. 881-893.

Lin, C.J., Pongprueks, P., Rusell Bulock, O., Lindberg, S.E., Pehkonen, S.O., Jang, C., 

Braverman, T., Ho, T.C., (2007). Scientific uncertainties in atmospheric mercury models 

II: Sensitivity analysis in the CONUS domain. Atmospheric Environment 41, 6544-6560.

Morgan, J.N., M.R. Berry, and R.L. Graves. (1997). “Effects of Commonly Used Cooking 

Practices on Total Mercury Concentration in Fish and Their Impact on Exposure 

Assessments.” Journal of Exposure Analysis and Environmental Epidemiology 7(1):119-

133.

Morgan, J.N., M.R. Berry, Jr., and R.L. Graves. (1994). Effects of Native American cooking 

practices on total mercury concentrations in walleye. Presented at ISEE/ISEA Joint 

Conference, September 18-21, 1994.

Moya. J., Itkiin, C., Selevan, S.G., Rogers, J.W., Clickner, R. P., (2008). Estimates of fish 

consumption rates for consumers of bought and self-caught fish in Connecticut, Florida, 

Minnesota, and North Dacota. Science of the Total Environment (2008), 

doi:10.1016/j.scitotenv.2008.05.023.

Musaiger, A. O., R. D’Souza, (2008). The effects of different methods of cooking on proximate, 

mineral and heavy metal composition of fish and shrimps consumed in the Arabian Gulf. 

Archivos LatinoAmericanos De Nutricion, Organo Oficial de la Sociedad 

Latinoamericana de Nutricion., Vol 58, No 1, 2008. 

NMFS, (2003a). National Marine Fisheries Service, Fisheries Statistics Division, National 

Oceanic and Atmospheric Administration. Statistical Highlights: Fisheries of the United 

States.
114

Oken e., Radesky, J.S., Wright, R. O., Bellinger D. C., Amarasiriwardena C. UJ., Kleinman, K. 

P., Hu, H., Gillman, M.W., (2008). Maternal Fish Intake during Pregnancy, Blood 

Mercury Levels, and Child Cognition at Age 3 Years in a US Cohort. American Journal 

of Epmidemiology, Vol. 167, No. 10, p. 1171.

Pacyna, E.G., Pacyna, J.M., Sundseth, K., Munthe, J., Kindbom, K., Wilson, S., Steenhuisen, F., 

Maxson, P., 2009. Global emission of mercury to the atmosphere from anthropogenic 

sources in 2005 and projections to 2020. Atmospheric Environment 44, 2487-2499.

Pirrone N. and Keating T, editors. “Hemispheric Transport of Air Pollution (2010), Part B: 

Mercury. Prepared by the Task Force on Hemispheric Transport of Air Pollution acting 

within the framework of the Convention on Long –range Transboundary Air Pollution. 

United Nations New York and Geneva, 2010.

Pirrone N, Cinnirella S, Feng X, Finkelman RB, Friedli HR, Leaner J, et al., (2010). Global 

mercury emissions to the atmosphere from anthropogenic and natural sources. 

Atmospheric Chemistry and Physics Discussions, 10(2), 4719-4752.

Pongprueksa, P., Lin, C.J., Lindberg, S.E., Jang, C., Braverman, T., Bullock, O.R., Ho, T.C., 

Chu, H.W., (2008). Scientific uncertainties in atmospheric mercury models III: Boundary

and initial conditions, model grid resolution, and Hg(II) reduction mechanism. 

Atmospheric Environment 42, 1828-1845.

Rice GE, Hammitt JK, Evans JS. (2010). A probabilistic characterization of the health benefits of 

reducing methyl mercury intake in the United States. Environmental Science Technology. 

2010 Jul 1;44(13):5216-24

Ryaboshapko, A., Bullock, O.R., Christensen, J., Cohen, M., Dastoor, A., Ilyin, I., Petersen, G., 

Syrakov, D., Artz, R.S., Davignon, D., Draxler, R.R., Munthe, J., (2007a). 

Intercomparison study of atmospheric mercury models: 1. Comparison of models with 

short-term measurements. Science of the Total Environment 376, 228-240.

Ryaboshapko, A., Bullock, O.R., Christensen, J., Cohen, M., Dastoor, A., Ilyin, I., Petersen, G., 

Syrakov, D., Travnikov, O., Artz, R.S., Davignon, D., Draxler, R.R., Munthe, J., Pacyna, 

J., (2007b). Intercomparison study of atmospheric mercury models: 2. Modelling results 

vs. long-term observations and comparison of country deposition budgets. Science of the 

Total Environment 377, 319-333.

Seigneur, C., K . Vigjayaraghavan, K. Lohman, P. Karamchandani, C. Scott. (2004).“Global 

Source Attribution for Mercury Deposition in the United States.” Environ. Sci. Technol. 

38, 555-569.

Selin, N.E., Jacob, D.J., Park, R.J., Yantosca, R.M., Strode, S., Jaegle, L., Jaffe, D., (2007). 

Chemical cycling and deposition of atmospheric mercury: Global constraints from 

observations. Journal of Geophysical Research-Atmospheres 112.

Shilling, Fraser, Aubrey White, Lucas Lippert, Mark Lubell (2010). Contaminated fish 

consumption in California’s Central Valley Delta. Environmental Research 110, p. 334-

344.

Slemr, F., Brunke, E.G., Ebinghaus, R., Kuss, J., (2011). Worldwide trend of atmospheric 

mercury since 1995. Atmospheric Chemistry and Physics 11, 4779-4787.
115

Streets, D.G., Zhang, Q., Wu, Y., (2009). Projections of Global Mercury Emissions in 2050. 

Environmental Science & Technology 43, 2983-2988.

Swartout, J., and G. Rice. (2000). “Uncertainty Analysis of the Estimated Ingestion Rates Used 

to Derive the Methylmercury Reference Dose.” Drug and Chemical Toxicology

23(1):293-306. 11-41

U.S. EPA, U.S. Environmental Protection Agency, (1997). Mercury Study Report to Congress. 

Office of Air Quality Planning and Standards and Office of Research and Development. 

EPA-452/R-97-003, December 1997.

U.S. EPA, U.S. Environmental Protection Agency, (2000). Guidance for Assessing Chemical 

Contaminant Data for Use in Fish Advisories, Volume 1: Fish Sampling and Analysis 

and Volume 3: Overview of Risk Management. Office of Science and Technology, 

Office of Water, U.S. Environmental Protection Agency, Washington, DC. EPA 823-B00-007.

U.S. EPA, U.S. Environmental Protection Agency, (2001a). Water Quality Criterion for the 

Protection of Human Health: Methylmercury - Final. U.S. EPA, Office of Science and 

Technology, Office of Water, EPA-823-R-01-001 January 2001.

U.S. EPA, U.S. Environmental Protection Agency, (2001b). Mercury Maps - A Quantitative 

Spatial Link Between Air Deposition and Fish Tissue Peer Reviewed Final Report. U.S. 

EPA, Office of Water, EPA-823-R-01-009, September, 2001.

U.S. EPA, U.S. Environmental Protection Agency, (2005), Neurobehavioral Assessments 

Conducted in the New Zealand, Faroe Islands, and Seychelles Islands Studies of 

Methylmercury Neurotoxicity in Children. David C. Bellinger, Professor of Neurology, 

Harvard Medical School, March 2005. Report to the U.S. Environmental Protection 

Agency EPA-HQ-OAR-2002-0056-6045[1]

U.S. EPA, U.S. Environmental Protection Agency, (2007a) Review of the National Ambient Air 

Quality Standards for Lead: Policy Assessment of Scientific and Technical Information 

OAQPS Staff Paper. Office of Air Quality Planning and Standards, Research Triangle 

Park, NC. EPA-452/R-07-013 November 2007.

U.S. EPA, U.S. Environmental Protection Agency, (2007b) Lead: Human Exposure and Health 

Risk Assessments for Selected Case Studies, Volume I. Human Exposure and Health 

Risk Assessments—Full-Scale and Volume II. Appendices. Office of Air Quality

Planning and Standards, Research Triangle Park, NC. EPA–452/R–07–014a and EPA–

452/R– 07–014b.

U.S. EPA, U.S. Environmental Protection Agency, (2011) Regulatory Impact Assessment: 

Benefits Associated with Reduced Mercury Air Emissions in the U.S. Utility Sector. 

Office of Air Quality Planning and Standards, Research Triangle Park, NC.

U.S. EPA, U.S. Environmental Protection Agency, (2011). Air Quality Modeling Technical 

Support Document: EGU Mercury Analysis. EPA-454/R-11-008.

UNEP’s Division of Technology, Industry and Economics. (2010). Study on mercury sources 

and emissions and analysis of cost and effectiveness of control measures “UNEP 

Paragraph 29 study”.
116

USGS, U.S. Geological Survey (USGS). 2005. Active mines and mineral plants in the US. 

Reston, VA. Available on the Internet at http://tin.er.usgs.gov/mineplant

USGS, U.S. Geological Survey and U.S. Department of Agriculture, Natural Resources 

Conservation Service, (2009), Federal guidelines, requirements, and procedures for the 

national Watershed Boundary Dataset: U.S. Geological Survey Techniques and Methods 

11–A3, 55 p.

Wijngaarden, Edwin van, Christopher Beck, Conrad F. Shamlaye, Elsa Cernichiari, Philip W. 

Davidson, Gary J. Myers, Thomas W. Clarkson. (2006). Benchmark concentrations for 

methyl mercury obtained from the 9-year follow-up of the Seychelles Child Development 

Study. NeuroToxicology 27 :702–709.
117

Appendices: Additional Technical Detail on Modeling Elements and Presentation of 

Supplemental Risk Estimates

(Citations for the appendices are provided in the citation list for the main document –

see above) 

As noted in section 1.4.5, the IQ loss risk metric has been de-emphasized in presenting 

risk estimates due to concerns that it may not fully capture the range of adverse 

neurodevelopmental effects associated with MeHg exposure. For this reason, both the 

description of the approach used in modeling IQ loss as well as a summary of IQ loss estimates 

and a discussion of uncertainty related to those risk estimates is presented in appendices, with the 

technical approach being described in Appendix A and the summary of IQ loss risk estimates 

and discussion of uncertainty being presented in Appendix B. Appendix C presents the SAB 

Mercury Panel peer review letter (the attachment to that letter provides the original EPA charge 

questions). 

Appendix A. Technical Approach Used in Modeling IQ Loss 

Estimation of IQ loss in children begin with the same exposure estimates used in 

generating HQ estimates (i.e., estimates of MeHg exposure generated for a given subsitence 

fisher scenario - see section 1.4.4 for derivation of these exposure estimates). However, these 

estimates of body weighted-adjusted MeHg exposure need to be converted into an equivalent 

maternal hair concentration since the IQ loss function uses hair Hg as the dose measure. To do 

that, we use a dose-to-hair conversion factor (DHCV) of 12.5 (units ppm per unit μg/kg-day) that 

converts ingested dose (IR) to hair Hg concentration in ppm. The DHCV factor is based on a one 

compartment toxicokinetic model used for deriving the MeHg RfD by Swartout and Rice (2000).

After generating an estimate of maternal hair Hg level for the subsistence fisher (at the 

particular watershed being modeled), we then apply a concentration-response (CR) function 

relating material hair Hg levels to IQ points lost in the child born to that mother. This CR 

function was published in Axelrad et al., 2007and is based on application of a Bayesian 

hierarchical model which integrates data from the three key epidemiological studies (Seychelles, 

New Zealand and Faroe Islands).59

Since the CR function was published in the Axelrad et al., 2007 study, a number of 

authors have raised the possibility that neurological deficits related to Hg exposure through fish 

consumption could be masked to some degree by the neurologically-beneficial effects of fish oil 

consumption. Some authors have suggested that the IQ loss factor should be adjusted upward to 

compensate for this masking effect (see Rice et al., 2010 and Oken, 2008). However, no rigorous 

basis for a specific adjusted estimate has been provided to-date and therefore, we address this 

potential for low-bias as part of our qualitative uncertainty discussion (see Table B-2, Appendix 

B). 

 

59 The IQ loss model uses a linear slope of 0.18 IQ points per ppm hair Hg concentration (Axelrad et al., 2007).
118

Appendix B. Supplemental Risk Estimates (IQ loss estimates)

This section provides summaries of IQ loss risk estimates generated for the 2016 scenario

and discusses key sources of uncertainty associated with the IQ loss estimates. In assessing the 

potential public health significance of the IQ loss risk estimates, based on recommendations 

provided by the Clean Air Science Advisory Committee (CASAC) in the context of the last 

National Ambient Air Quality Standard (NAAQS) review for lead completed in 2008 (US EPA, 

2007a), we interpreted IQ loss estimates of 1-2 points as being clearly of public health 

significance. All of the risk estimates summarized here are based on application of the revised 

version of the risk assessment model. Specific tables include: 

 Table B-1: provides risk percentiles for the IQ loss risk metric for children born to 

members of the typical female subsistence fish consumer scenario for the 2016 scenario. 

 Table B-2: identifies and discusses key sources of uncertainty associated with modeling 

IQ loss (uncertainty related to modeling exposure, which also impacts HQ estimates, is 

covered in Table 2-15).

Table B-1. Percentile IQ loss risk estimates for children born to members of the typical 

female subsistence fish consumer scenario assessed nationally (2016 scenario) (for 

both total and U.S. EGU incremental risk, including IQ loss and MeHg RfD-based HQ 

estimates)

Typical female 

subsistence fish 

consumer rate 

(g/day) and 

percentile/mean

Watershed percentile

Total IQ points lost U.S. EGU-attributable IQ points lost

50th 75th 90th 95th 99th 50th 75th 90th 95th 99th

39 (mean) 0.4 0.8 1.3 1.7 2.5 - - - 0.1 0.1

123 (90th) 1.4 2.4 4 5.3 8* - 0.1 0.1 0.2 0.3

173 (95th) 1.9 3.4 5.6 7.4* 11.2* - 0.1 0.2 0.2 0.4

373 (99th) 4.1 7.4* 12.1* 16* 24.1* 0.1 0.2 0.3 0.5 0.8

- IQ loss is <0.1 point 

* IQ loss estimate subject to greater uncertainty due to application of the underlying concentration-response function 

for IQ loss at levels of exposure above those in the underlying epidemiological studies (see Appendix A)

Table B-2. Key sources of uncertainty related to modeling IQ loss, the nature of their 

potential impact on risk estimates, and the degree to which they are characterized as 

part of the analysis

Source of 

uncertainty Description 

Nature of potential impact on 

the exposure and risk estimates

Degree to which the potential 

impact of the source of 

uncertainty is characterized as 

part of the analysis

(A) IQ may not 

fully capture 

the most 

IQ may not represent the most 

sensitive cognitive endpoint for 

Hg exposure (Axelrad et al., 

Given this concern, we have 

focused the risk assessment on the 

RfD-based HQ risk estimates and 

Because we do not have readily 

available data to support 

quantitative analyses of the first 
119

Source of 

uncertainty Description 

Nature of potential impact on 

the exposure and risk estimates

Degree to which the potential 

impact of the source of 

uncertainty is characterized as 

part of the analysis

sensitive

cognitive 

endpoints 

associated with 

Hg exposure

2007 - see Section 1.2). In 

addition deficits in some 

categories of cognitive 

functioning are not captured by 

IQ. Together, these sources of 

uncertainty suggest that we 

could be under-estimating the 

extent of cognitive impacts 

associated with Hg exposure 

(when we focus on modeling the 

IQ loss endpoint alone).

have deemphasized IQ loss 

estimates. 

two sources of uncertainty (IQ 

loss not capturing all of the 

cognitive effects and potential 

confounding by LCPFAs), we 

could only address these factors 

qualitatively. In both cases, the 

potential effect on the risk 

assessment would be to 

potentially down-bias our 

estimate of cognitive endpointrelated risk for children. 

(B) Potential 

confounding 

from nutrients, 

including longchained 

polyunsaturated 

fatty acids 

(LCPFAs), 

found in fish

This issue is similar to the 

nutrient confounding issue 

discussed above under the RfDbased HQ entry. Specifically, 

there are concerns that fish 

nutrients may mask to some 

extent, the adverse 

neruodevelopmental effects of 

MeHg, resulting in a 

concentration-response function 

for IQ loss which could be 

biased low (particularly in the 

case of freshwater fish 

consumption which may have 

lower levels of relevant fish 

nutrients)

Potential confounding (masking) 

of the neurodevelopmental effects 

of MeHg could result in 

concentration-response functions 

for IQ loss that are low-biased, 

which in turn would mean that 

estimates of IQ loss could be low 

biased. However, at this point we 

do not have the information 

necessary to derive a 

concentration-response function 

for IQ loss that would account for 

this masking effect and therefore, 

cannot quantitatively evaluate this 

source of uncertainty. 

(C) How to 

treat potential 

outliers from 

the 

epidemiological 

datasets used in 

deriving the IQ 

loss functions.

Regarding outliers, when an 

outlier datapoint from the 

Seychelles study was included in 

the integrated derivation of the 

IQ loss slope factor, the factor 

was reduced by 25 percent (from 

-0.18 IQ points per unit ppm hair 

Hg, to -0.125). If in reality, this 

outlier actually reflects the true 

response for a subset of the 

populations, then risks (as 

modeled) could be biased high 

specifically for this 

subpopulation.

In the case of excluding the 

outlier from the Seychelles study, 

we note that the effect (given the 

linear nature of the IQ loss slope) 

would be to simply result in a 

25% reduction in risk, if we were 

to include the outlier in derivation 

of the slope function (i.e., a 

formal rerun of the model with 

this alternative slope is not 

required – we can just consider 

this magnitude of impact on the 

primary risk estimates we 

generate for the analysis).

Although it is possible given the 

linear relationship of exposure 

and IQ loss to readily determine 

the impact of a sensitivity 

analysis involving application of 

an IQ loss slope factor that 

reflects inclusion of the outliers, 

we did not explicitly complete 

such an analysis (primarily 

because the IQ loss risk metric 

has been de-emphasized with 

primary focus being placed on 

the RfD-based HQ metric).
120

Appendix C. SAB Mercury Panel Peer Review Letter: Review of EPA's Draft 

National-Scale Mercury Risk Assessment

This appendix presents a copy of the SAB’s Mercury Panel Peer Review Letter. The 

original charge questions provided by EPA to the SAB are included at the end of the letter as 

Appendix A. 

NOTE – APPENDIX C ADDED AS PDF AFTER WORD FILE CONVERTED.
United States

Environmental Protection

Agency

Office of Air Quality Planning and Standards

Health and Environmental Impacts Division 

Research Triangle Park, NC

EPA-452/R-11-009

November 2011