# 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 Consum ption of Self

- caught Freshwater Fish In Support of the Appropriate and Necessary Finding for Coal
- and Oil
Fired Electric Generating Units

U.S. Environmental Protection Agency Office of Air Quality

Planning and Standards Health and Environmental Impacts Division Research Triangle Park, North Carolinaiii **DISCLAIMER** This document has been prepared b y 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 necessaril y 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 S ummary ... .... ..................................................................................................... v iii 1 Review of Anal ys is 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 Analy sis
................................ ................................ ........................ 5 1.3 Overview of Risk Metrics and the Risk Characterization Framework
.......................... 10 1.4 Overview of Anal ytical 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 anal ys is
............................ 31 Id entify 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 anal ys is
................................ ................................ ....................... 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 analy sis and the current anal ys is in support of the Appropriate and Necessary Finding
................................ ................ 48 1.6 Detailed Example Calculation (watershed
- level risk HQ)
................................ ............. 50 2 Discussion of Anal ytical 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 at risk due to U.S. EGU mercury emissions (2016 scenario)
................................ ................... 83 2.6 Sensitivity

Anal ys es ................................ ................................ ................................ ....... 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 (I Q loss estimates)
................................ ............. 118 Appendix C. SAB Mercury Panel Peer Review Letter: Review of EPA's Draft National
- Scale Mercury Risk Assessment
................................ ................................ ................................ ........ 120v

**List of Tables** Table 1
- 1 Summary

Statistics for the 2011 MFT HUC
- level Data
................................ ............. 25 Table 1
- 2 S ummary

of 75 th Percentile Hg Concentrations in Fish Tissue Samples by Number of Observations and Number of Sites per HUC
................................ ................................ ................ 27 Table 1
- 3 Summary

of 50 th Percentile Hg Concentrations in Fish Tissue Samples by Number of Observations and Number of Sites per HUC
................................ ................................ ................ 29 Ta ble 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 anal ys is
................................ ................................ ............................. 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. EGU
attributable 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) f

or 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 anal ys is (2016 scenario) (for both total and U.S. EGU incremental RfD
- based HQ)*
................................ ................................ ................................ ................................ ............. 81 Tabl e 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

analy sis 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

analy sis 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

analy sis 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)
................................ ............................... 90vi

Table 2
- 14 Key

sources of variability

associated with the anal ys is and d egree to which they are reflected in the design of the anal ys is
................................ ................................ ........................... 93 Table 2
- 15 Key

sources of uncertainty

associated with the anal ys is, the nature of their potential impact on risk estimates, and degree to which they are characterized as part of the anal ys is
...... 96 **List of Figures** Figure 1
- 1. 2
- Stage Risk Characterization Framework
................................ ............................... 13 Figure 1
- 2 Flow Diagram of Risk Analy sis (for the 2016 air quality scenario) Including Major Analy tical Steps and Associated Modeling Elements
................................ ................................ .. 14 Figure 1
- 3 Diagram I llustrating 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 I llustrating Step
- wise Procedure Used to Develop the Augmentation Mercury Fish Tissue (MFT) Dataset
................................ ................................ ............................. 22 Figure 1
- 5 Diagram I llustrating Step
- wise Procedure Used to Combine the 2010 MFT and Augmentation MFT Datasets
................................ ................................ ................................ ........ 23 Figure 1
- 6 Diagram I llustrating 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 75 th 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/m 2 ) for the 2005 Scenario
........................... 56 Figure 2
- 2 Total Mercury Deposition by

HUC (μg/m 2 ) for the 2016 Scenario
........................... 57 Figure 2
- 3 U.S EGU
- Attributable Mercury Deposition by

HUC (μg/m 2 ) for the 2005 Scenario

................................ ................................ ................................ ................................ ....................... 58 Figure 2
- 4 U.S EGU
- Attributable Mercury Deposition by

HUC (μg/m 2 ) for the 2016 Scenario 59 Figure 2
- 5 Mercury

Wet Deposition by

12km Grid Cell (μg/m 2 ) 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/m 2 ) for the 2016 Scenario
............. 62 Figure 2
- 8 Mercury

Dry Deposition by

12km Grid Cell (μg/m 2 ) 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 In cluded in the Risk Assessment
................................ ................................ ................................ .. 68 Figure 2
- 11 Total Fish Tissue Mercury Concentrations for 2005 Scenario (HUC12
- level 75 th percentile values, ppm)
................................ ................................ ................................ ................. 69 Figure 2
- 12 Total Fish Tissue Mercury Concentrations Projected for 2016 Scenario (HUC12
- level 75 th 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

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

Figure 2
- 15 Comparison of L ocations of Watersheds with Fish Tissue Hg Data* with Pattern of U.S. EGU
- attributable Hg Deposition (2005 scenario)
................................ ................................ 76 Figure 2
- 16 Comparison of L ocations 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 mo deling the high
- end female consumer population as contrasted with all 88,000 watersheds (2016 Scenario).
................................ ................................ ........................ 78viii

**Executive Summary** The Technical Support Document is a revised version of the TSD for EPA’s National
scale Mercury

Risk Assessment completed in March 2011. EPA used the previous version of this report as support for the 2011 finding th at 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 b y 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 anal ys is 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 eventuall y 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 mercury
contaminated fish b y 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 neurops ychological measures. We focused on consumption from inland freshwater waterbodies in the U.S rather than estuarine or marine water bodies because the U.S. EGU
- attributable mercury deposition is a larger fraction of total mercury deposition, particularly

for waterbodies with elevated U.S. EGU
attributable mercury

deposition such as the Ohio River Valley . We focused on subsistence fishi ng populations because they typically have substantially higher consumption rates of self
caught fish than recreational fishers and therefore, experience higher risk. Because we do not have data available on the distribution of subsistence fishing populati ons in all watersheds in the U.S., we modeled a h ypothetical 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 75 th percentile fish tissue mercury concentratio ns at watersheds with fish tissue data 4. Project 75 th 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. Id entify

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 ris k 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. EGU
attributable MeHg in fish and to project fish tissue data to 2016. We use the 75 th 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 o f freshwater self
- caught fish, we appl y 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 EP A, 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
- attributab le 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 thex

total number and percent of watersheds where female subsistence consumers are potentiall y at risk
. 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 inl and 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 anal ysis 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) inc orporating 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, ty pe of fish consumption rates used and application of a cooking/preparation adjustment factor, (f) inclusion of sensitivity anal ys is involving median watershed
- level fish tissue mercury levels , (g) expand ed 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 Analy sis” to a separate TSD titled “ Potential for Excess L ocal 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 90 th percentile to the 99 th 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 99 th 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 potentiall y at - risk due to total deposition with at least 5% of that total deposition attributable to U.S. EGUs (based on the 99 th % 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 thesexi

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). Th at - risk assessment is documented in the Technical Support Document: National
- Scale Mercury

Risk Assessment Supporting the Approp riate 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
- re viewed, 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 questi ons 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 national
scale risk assessment and developed a revised version of the risk assessment reflecting implementation of a number of the recommendations made b y the SAB. This document (hereafter referred to as the “Mercury Risk TSD (revised)” or “Revised TSD” desc ribes the technical approach used in completing the revised version of the risk assessment. While the underlying technical approach used in assessing risk remains essentiall y 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 significantl y 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 signi ficant 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 anal ys is ( s ection 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 policy
relevant context (section 1.3). In section 1.4, we provide an overview of the analy tical approach used in the anal ys is, with subsections addressing specific elements of the analy sis. Section 1.5 describes differences between the current risk assessment and the assessment completed in 2005 (the 2005 Section 112(n) Revision Rule analy sis ). 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.pdf2

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 calcul ations come together in generating watershed
- level hazard quotient (HQ)
- based risk estimates. Section 2 describes anal ytical results of the risk assessment including both intermediate modeling results and the risk estimates that are generated (a detailed o verview of the subsections in section 2 is presented at the beginning of that section). Section 3 presents a summary of key observations from the anal ys is. **1.1 Summary of Significant SAB Peer Review Panel Recommendations and**  **Revisions Reflected in this R evised 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 anal ys is is focused on assessing risk for subsistence
- level consumers of self
- caught fish evaluated at the watershed
- level. 3 Specificall y, we are modeling risk for a set of female subsistence fish consumer scenarios at those waterbodies where (a) we have measured fis h tissue mercury ( Hg) data and (b) it is reasonable to assume that high
- end fishing activity could occur. We emphasize the point that the analy sis is not a representative population
- weighted assessment of risk. Rather, it is based on evaluating exposure sc enarios.  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 accordingl y , 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 fo r selecting potentially impacted watersheds . We revised this discussion accordingl y (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 ris k 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 rev iew 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 subsis tence - levels of fish that they catch or are caught by relatives/acquaintances and shared with them (hereafter referred to as female subsistence fish consumer s ). As discussed in section 1.4 and 1.4.3, we model a number of female subsistence fish consumer s cenarios that are differentiated to provide coverage for different socio
- economic status (SES) differentiated groups. In this context, “s ubsistence ” 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 self
caught 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 an d 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 b y 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 concentration s for fis h 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, N ew Jersey, Kentucky and Illinois where U.S. EGUs Hg deposition may be more significant. We expanded the fish tissue dataset b y 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, i t is reasonable to assume that more than 90% of fish tissue Hg is methyl mercury (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 T able 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., s anitary sewer discharges) in screening out HUCs. We expanded the uncertainty discussion presented in T able 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 tissu e 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 T able 2
- 15 entry

C i n section 2.7 , as well as the discussion in section 1.4.2).  Include a sens itivity analysis based on use of the median fish tissue Hg value (as contrasted with the 75 th percentile value) in generating risk estimates : We also included a sensitivity

anal ys is that used the 50 th 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 75 th percentile value used in th e core anal ysis (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
- 1 5 entries F and G i n 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 sec tion 1.4.4, we carefully reviewed the studies underl yi ng 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 analy sis (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 concentration s, including more recent studies supporting the proportional relati onship 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 concentration s (see Table 2
- 15 entries I through M in section 2.7 for additional uncertainty discussion and section 1.4.6 for discussi on 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 assessmen t. : 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 releva nt if IQ loss functions derived from saltwater fish consumption are used to model risk associated with freshwater fish consumption . We expanded the discussion of5

uncertainty on modeling IQ loss to address this issue (see Table B
- 2 entry B in Appendix B )
.  A dditional 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 consumer s, 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 th is might have on exposure estimates. o Preparation and cooking methods which effects the conversion of fish tissue Hg concentration s (as measured) into “as consumed” values. We expanded the discussion of sources of variability present ed 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) us ed 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 M

ercury 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 concentration s. o Proportionality assumption used to relate changes in Hg deposition to changes in fish tissue Hg concentration s at the watershed
- level. o Characterizing the spatial location of female subsistence fish consume r populations (including degree to which these provide coverage for high
consuming recreational fishers). o Application of the RfD to low SES populations and concerns that this co uld low
bias the risk estimates
. We expanded the discussion of sources of uncertainty present ed 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 descrip tion 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 Cl ean Air Act (CAA). The appropriate and necessary finding is based, in part, on an assessment of the potential public health hazard6

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. EGU s )
. 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 anal ys is 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 anal ys is. As such , the primary obj ective 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 co mprehensive assessment of risk for all segments of the population potentially exposed to Hg emitted from U.S. EGUs
. Itis 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 anal ys is, 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 exp eriencing 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 emi ssions in 2016 considering potential reductions in EGU Hg emissions attributable to CAA requirements ? 6 and (c) H ow 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 ov erall design and scope of the risk assessment reflects consideration of a number of important technical factors related to air
sourced Hg, including, in particular, Hg released from U.S. EGUs:

 While Hg exposure and risk can occur through a variety of pathway s, the dominant human exposure pathway associated with ambient air emissions is through the consumption of fish that have bioaccumulated Hg originall y deposited to watersheds following atmospheric release and transport. Deposition of Hg to watersheds in cludes Hg originating from local/regional sources, combined with Hg that has been transported
5 76 FR 24976 6 For purpose s 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 globall y from global anthropogenic and natural sources. Generall y 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 he alth 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
- sour ced 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 highl y 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 ty

pe of fish consump tion 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 relativel y el evated U.S. EGU Hg deposition. S ome w atersheds 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 (Hg 0 ), oxidized Hg compounds (Hg +2 ), and particle
- bound Hg (Hg P ). 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 relat e hair mercury levels in mothers (a surrogate for exposure in utero ) or mercury in c ord blood (a direct measure of fetal exposure) to deficits in children’s performance on a range of neuro
cognitive 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 200 5), 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 r esidents in the US experience through consumption of commercially caught foreign
sourced 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 wo uld 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 qua lity 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 near
coastal 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 ha bitats makes it difficult to quantify the EGU contribution to fish tissue Hg concentrations in these locations. Due to the greater uncertainty associated with mo deling near coastal watersheds, we have elected not to simulate this pathway in the risk assess ment. 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. EGU
attributable MeHg exposures and risk are focused on assessing risk for those areas that have (a) relatively elevated fish tissue Hg concentration s and (b) relativel y 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 Secti on 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 analy sis is most relevant for this rulemak ing. 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 consumer s 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 b y Hg emitted from U.S . EGUs. This translates into a focus on women of child
bearing age who consume subsi s tence - level s 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
- attributa ble Hg deposition (see Figure 2
- 4 ) and consequentl y the greatest U.S. EGU
- attributable fish tissue Hg concentration s. B y focusing on subsistence
- level fish consumption scenarios, we focus on those self
- caught fish consumers with the highest intake rates an d therefore, those who will experience the greatest Hg exposures at a given watershed. In defining the high
- end fisher populations to include in the anal ys is, 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 analy sis, 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 Specificall y, we generate a set of risk estimates for each watershed reflecting the ty pe of subsistence fish er 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 watersh ed
- level risk estimate generated
. 14 This focus on subsistence fishing activity and associated consumption reflects the fact that this is a screening
- level anal ys is for risk associated with mercury emitted from U.S. EGU s and is not intended to provide a comprehensive picture of the distribution of risk across all ty pes 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 self
caught 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 r ole in affecting Hg levels in these fish , a s 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 co nsumer 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 simi lar 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, w e 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 esti m ate each of the se 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 purpos es of generating population
- we ighted 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 con sidered 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 Rf D. 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 b y EPA staff that IQ loss may not fu lly 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 fisher s . 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 o f full
- scale IQ as the modeled health endpoint, could underestimate the neur o developmental 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 suc h 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 eff ects potentially associated with Hg exposure has also been highlighted by Grandjean et al., (1997) w ho 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 base d 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 , M cCarthy 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 95 th percentile of risk across watersheds ) . In this context, the distribution of m odeled 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 distribut ion are for the population of watersheds, rather than the population of subsistence fishers. Reflecting the goals of the anal ys is presented in Section 1.2, the HQ risk metrics are calculated for both total risk and U.S . EGU
- attributable risk . In consideri ng U.S. EGU
attributable risk, we generate two types of risk estimates:

 The percent or fraction of total risk attributable to U.S. EGUs at watershed s 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. EG U 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. Specificall y, 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 watershed s 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 b y 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 addi tion, 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 ris k 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 anal ys es 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 depositi on 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 b y 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 ty picall y dominate the U.S. EGU increment across watersheds. **Stage 2** 
**–** calculate the combined ( tota l ) number of wa tersheds and percentage of watersheds where populations may be at- riskfrom U.S. EGU
- attributable Hg: Here we combine estimates from Stages 1 a and 1b to consider watersheds where populations may be at risk 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 separatel y, or as a portion of total risk, represents a potential public health hazard. More specificall y, 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 A pproach**  This section provides an overview of the anal ytical 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 anal ytical 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
. **Sta ge**  **1 a**  : wa te rs he ds whe re t ota l Me Hg exp osur e exceed s the Me Hg Rf D an d whe re U S EG Us mak e a substa nti al co ntr ib uti on (e.g ., 5%) t o tha t e xp osur e. **Stage 1**  **b** : wa te rs he ds wher e U .S. EG U de posi ti on, ifc onsi de re d in is ol ation, c oul d re sul t i n Me Hg exp osur e th at exc eed s the Me Hg Rf D .

**Stage 2**  : tota l n umbe r ofw ate rs he ds wi th popul ations pote nti al ly a t - ris k due t o Stage 1 a or S tage 1 b analy se s (i .e ., uni on of A and B) : 

### St age2 = A+ B - C Somew aters he ds arei

de ntif iedi n both stages (these aren ot d oubl e co untedi n thetotal

S ta ge 2 numbe r) . 

# B **C A** 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 consumer s 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 onl y estimated for the 2016 scenario since **Spe ci**  **fy ing**  **sp ati**  **al scal**  **e of**  **wa tersh**  **ed s**  (sect ion 1. 4. 1) **Ch aracteri**  **zing measu**  **red f**  **is h**  **tis su**  **e Hg**  **co nc**  **en trati**  **ons a**  **t the**  **w atersh**  **ed l**  **evel** (sect ion 1. 4. 2) Characterize fem ale

s ubsisten ce consum er sce narios to m odel

**Esti mati**  **ng tota l f**  **is h**  **co ns**  **um pti**  **on**   ** rel** **ated MeHg**  **exp os**  **ur e**  for f ishers/ consum ers act ive at

e ac h watershed (sect ion 1. 4. 4) Stu dies

characte rizing fi sh consum pt ion rates for f reshwater subsistence fi sher populat ions **Def ining f** **emal e**  **su bs**  **is te**  **nc e**  **co ns**  **um er**  **scen ari**  **os t**  **o inc**  **lude in**  **the an**  **al ys**  **is** (sect ion 1 .4 .3 )

**Esti mat**  **ion of**

**U .S**  **. EG**  **U**   ** attri** **buta ble**  **r is**  **k** at

eac h watershed fo r th e 2016 scenario (sect ion 1. 4. 6) **CMA Q m**  **ercu ry**  **de pos**  **it ion**  **mo de**  **ling** fo r to ta l and US EGU Hg deposit ion at

e ach watershed for

t he 2005 and 2016 air

q ualit y scenarios (se ct ion 1. 4. 6. 1) **Esti mate**  **ri sk**  **( Rf**  **D -**  **ba sed**  **ha zard**  **q uotie**  **nt)** at

e ach watershed for

t he 2016 scenario ( sect ion 1. 4. 5) **Mercu ry**  **RfD for** **H Q**  **esti mat**  **ion Exp**  **os ur**  **e mo**  **de ling** 

**Ri sk**  **mo de**  **ling**

decision dat a input analysis st ep **KE Y:**  Id ent if y which watersheds

will be mo deled for

e ach of th e SES - dif ferent iated fe ma le subsistence consum er sce narios *

* A mo re g en er al ized ty pi ca l female subs is tenc e cons umer sce nar ioi s al soi nc lu ded in t he a na ly si s , wh ic h is ev al uated a t al l water sh eds w it h fi sh t issu e Hg da ta wi thout con sid er ati on fo r th e pr es ence of

a s ou rce po pul ati on . Due to i ts b ro ad erg eogr aphi c co ve ra ge, th is sce nar ior ec ei ves t he g re atest fo cu s in p re se nti ng ri sk e st im ates
. **Pr oje**  **ct fi**  **sh t**  **is su**  **e Hg**  **co nc**  **en trati**  **ons f** **or t**  **he 2**  **016 scen**  **ari o**  at

e ach watershed (sect ion 1. 4. 6)15

underlying U.S. EGU emissions used in mo deling this scenario are closer to actual 2010 emissions than are emissions estimates used in modeling the 2005 scenario. H owever, 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). W e provide a brief description of each anal ytical step of the risk anal ys is reflected in Figure 1
- 2 : 11. Model mercury deposition: We modeled total an d 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 approp riate 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 (75 th percentile) at each watershed : We estimate the 75 th percentile fish tissue Hg concentration s based on measurement data collected primaril y b y 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 analy sis. (See section 1.4.2) . The fish tissue data did not include saltwater or estuarine fish. 14. Project 75 th 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 projec t fish tissue Hg concentrations for the 2016 scenario. Specificall y, we used the ratio of 2016 deposition to 2005 deposition at a given watershed to adjust the 75 th 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 anal ys is focused on females who consume subsistence
- levels of f ish that were caught at inland freshwater waterbodies. Our literature review identified seven female subsistence fish consumer scenarios that we selected to include in the anal ys is: (1) ty pical 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 su bsistence 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 p resent 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 l iterature to identify self
- caught fish consumption rates for each female subsistence fish consumer scen ario . In each case, at the high end (e.g., 90 th to 99 th percentiles), consumption rates reached subsistence levels, ranging from an 8oz self
caught fish meal every

few day s 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 intak e at the watershed
- level for each female subsistence fish consumer scenario potentially active at a given watershed
. These estimates of exposure use the projected 75 th percentile fish ti ssue Hg concentrations for 2016 described in Step 4 above. We were careful to match the fish sampling data with ty pe of sampling (e.g., filet skin on, whole fish), cooking adjustment, and ty pe of consumption rates (e.g., as purchased, as consumed). The exp osure 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 expo sure 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 proportional ity assumption discussed in Step 4, together with total and U.S EGU
attributable 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
. Specif

ically , 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 anal ys is 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 whi ch we have measured Hg fish tissue data. Two studies (Knights et al., 2009, Harris et al., 2007) examining the response of aquatic freshwater ecos ys tems to changes in Hg deposition focused on watersheds with dimensions closest to HUC
12. In each of the s tudies, 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 ap plied 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 75 th percentile fish tissue Hg concentration (p rojected 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. EGU
attributable risk for the 2016 scenario. Both approaches would generate the same risk estimate, refle cting 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 effectivel y explored at the level of these more spatiall y refined watersheds. Each of the studies is briefl y summarized below. The Knights et al., 2009 study focused on characterizing the temporal pattern of reductions in fish tissue Hg concentration s 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 li ttle 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 reas onable 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 analy sis). An article b y Harris et al., 2007, which is based on the METALLICUS study (specif ically 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 b y m easurement 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 relativel y 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 asses sment. 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 chang es in fish tissue Hg concentration s 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 linki ng changes in Hg deposition to changes in fish tissue Hg concentration s. Conversel y, 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 depositi on from U.S. EGUs (and therefore, b y 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**  Th e 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 primaril y on state - level data, using a number of filtering steps that are described below, (b) use the fish tissue database to generate percentile estimates (75 th and 50 th percentiles) for each watershed containing fish tissue Hg18

measurement data and (c) filter the set of w atersheds 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 d ataset (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 addition al 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 e ach step to develop the dataset ultimately used in this risk assessment , which is illustrated in Figures 1
- 3 through 1
- 6
. W e als o 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 a ssessment 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 analy sis co ntains 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 Anal ys is (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 anal ys is, 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 nearl y 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 and19 Analy sis 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 nearl y 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 b y 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 typicall y consumed by the general public. However, because the goal of this anal ys is is to determine the potential for a public health hazard from Hg emitted b y U.S. EGU s , it is beneficial to hav e fish tissue measurement data potentiall y biased towards waterbodies with greater Hg impacts as well as species ty picall y 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 suggest s 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 coveri ng 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 fi sh 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 Asse ssment Protocol” available at: http://www.portal.state.pa.us/portal/server.pt/community/fish_consumption/10560 ). Wisconsin selects sampling locations based on pr oviding 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 wat ersheds (Wisconsin Department of Natural Resources, “A Summary of Mercury Concentrations in Fish (edible portions) from Wisconsin Waters 1990
- 2005”, available at: http://dnr.wi.go v/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 ca ses, 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 im pacted watersheds (at least for the fraction of samples collecte d 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 allo w duplicate samples to be easil y identified and removed. In order to identify potentiall y 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 ye ar 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 potential ly 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 long itude 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)
. F or the majority of measurements in the database, we also have information on the ty

pe of waterbody

(river/lake) and ty

pe 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, S ection 5.2.2. Even though we compiled fish tissue Hg sampling data for 1995 to 2009, we decided to re strict data to 2000 to 2009 in the risk assessment (the SAB supported this decision)
. We exclude d fish tissue samples that likely reflected Hg depositio n 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 concentration s 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 b y 27,522 observations (the potential impact o n the risk assessment related to our decision to focus on data from 2000 and l ater 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 s uch 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 dat abase 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 ye ars. 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 th ey 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 co nverted 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 M as ter (72,966) NL FA (45,166)

NRSA (590) USGS Sta tes (39,972) US GS Oth ers (6,098) Ori ginal Da ta Sourc es (>=1 995) **Re as**  **on Dr**  **opped N** 
Mis si ng Lat/Lo ng - 9,177 Mis si ng Dat e - 1,520 Not a Fr eshwa ter Fi sh - 2,638
Not a Fr eshwa ter Lo catio n - 3,192
Len gt h < 7 in ch es - 5,147 Ye ar < 2000 - 27,522 **To ta**  **l -**  **49,196** Dat a Screening

Me rge d NL FA USGS Sta tes (66,278) 2010 M FT Sa mp le Da tas et (23,770) Data Merging22

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 w ith an Augmentation MFT sample dataset containing 11,797 observations in the U.S. 22 For this anal ys is, 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 combi ned 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 heavil y focused in locations east of the Mississippi Ri ver.

**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 elec ted for this revised assessment to include these samples. Aug mentation Mas ter (12,864) MI 2 007 - 2009 (81) NJ 2007 (220) MN 2003 - 2007 (6,012) PA2003 - 2008 (509) Or iginal Data Sources **Re ason**  **Dr opped**  **N** Missing Lat/Long
- 643 Missing Dat e - 0 Not a Fr eshwa te r Fi sh - 13 Not a Fr eshwa te r Lo cat ion - 0 Len gt h < 7 in ch es - 411 Ye ar < 2000 - 0 **To tal**  **- 1,067**  WI 2 000 - 2010 (6,042) Dat a Screening

Dat a M erging

Aug mentati on MF T Sa mple Datas et (11, 797)23

**Figure 1**  **- 5**  **Diagram Illustra ting 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 analy sis, we then spatially aggregated the 2011 MFT sample data to the watershed level. To begin this process, we used the latitude and long itude 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 , supp orted by

the MMaps study is most supportable in those situations where aerial deposition is the dominant source o f 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 contine ntal U.S., where we have Hg deposition modeling, we also excluded 11 samples that were located in 5 HUC
- 12 watersheds in Alaska.

2010 M FTSa mpl e Da ta se t (23,770)

2010 M FT HUC Level Da ta se t* (2,323 HUC s) Augmentation MFT

Sa mple Datas et (11,797)

Au gmentati on MFT HUC Level Datas et (940 HU Cs )** 2011 M FTS am ple Da ta se t (35,567)

2011 M FTH UC - Level Datas et* (3,147) Fish ti ssu e samples

Sp at ial Ag gregat ion (a ssociat in g in divid ual fis h ti ssu e samplin g locat ions wit h HUC12 wa te rsh eds
– completed us in g GIS )

*An a ddit io nal 11 ob serv ati ons were dr opped du e to 5H UC loc ati ons ou tsi de of th e co ntinental U .S.

230 obser va ti ons (asso ciat ed w it h 15 HUC12 wa ter she ds) dr oppe d beca use wa ter she ds co nta in go ld min es or ha ve substa nti al n on - EGU emi ssi ons*

** 116 o f th es e 940 HUCs were al so included in th e 2010 MFT da tas et. Fi sh ti ssue sa mples associated wit h s pecif ic HUC12 wat ersheds24

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 U SGS, 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 25 th percentile annual U.S.
- EGU Hg emission value as characterized in the 2005 NATA. 25 The 25 th 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 rem oving 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 anal ys is. 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 yi elded 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 fis h 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 ea ch 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 (25 th , 50 th , 75 th and 90 th ) fish tissue Hg value s 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 tw o samp ling sites (each with multiple samples) and we wanted a 75 th percentile fish tissue Hg value for that HUC, we would first compute the 75 th percentile values at each sampling site and then take the average of those two 75 th percentile values. I f there is on ly one sampli ng 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 b y 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 (wi thin 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 l esser 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**  **Me an**  **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 75 th percentile fish tissue Hg value does not use a single fish tissue Hg value in instances where there are multiple sites within a watershe d. Instead, we calculate the 75 th 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 re st coming from lakes). For the augmentation dataset, we did not specify river versus lake in developing the fish tissue Hg dataset, since this distinction wa s downplayed by the SAB as a factor significantly impacting application of the proportionality assump tion 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 75 th percentile fish tissue value at each watershed as the main basis for exposure and risk characterization. 28 Selection of the 75 th 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 ty pe of fish reflected in the 75 th percentile sample, risk estimates could be biased accordingl y. Uncertainty related to use of the 75 th percentile fish tissue Hg value in exposure modeling (specifically , potential bias in the estimation of the 75 th percentile value) is discussed in section 2.9, Table 2
- 15 , Entry C
. As recommended by

the SAB, w e also included of a sensitivity analysis using the median fish tissue Hg concentration at each H UC

– 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 onl y 1
- 2 samples , which potential ly bias es low a 75 th percentile estimate for those watersheds. To examine the potential magnitude of low bias in the 75 th percentile estimates, we have summarized the distribution of 75 th percentile

28 We note that the 75 th percentile value is a constructed statistic that reflects the fish tissue samples available at a given watershed. For some watersheds the numb er of samples is very low , 1 or 2 in some cases (see Table 1
- 2), and as a result, estimation of the true 75 th percentile of fish tissue mercury concentrations in those watersheds is likely to be biased low . This is discussed later in this section. Depen ding 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 cons um ption by those fishers. 

# 2,201 824 11 6 2010 MFT Data (# of HU Cs) Augment at ion MFT Data (# of H UCs)



### 201 1 MFT

D ata (Com bi ning 2 010 and Au gm en tati on ) To tal Nu mb er of

H UC12 obs ervati ons= 3,141 

# Nu mb er o f HUC12 Loc at ions27

fi sh 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 di fferent 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 75 th 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 75 th percentile fish tissue Hg value in the risk assessment, the SAB recommend ed a sensitivity anal ys is involving use of a median fish tissue Hg value in place of the 75 th percentile value. This sensitivity analy sis is discussed in section 2.6. However, we hav e provided fish tissue summary statistics here for median fish tissue statistics to support comparison with the 75 th percentile values (Table 1
- 3)
. **Table 1**  **- 2**  **Summary of 75 th**  **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 )**  **Me an**  **N 25**  **th 50**  **th 75**  **th 90**  **th 95**  **th** 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**  **25 th**  **50 th**  **75 th**  **90 th**  **95 th**  **Me an**  **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 75 th percentile values for HUCs with lower sample sizes. Specifically , in Table 1
- 2, the mean 75 th percentile HUC
- level value increases across strata as the sample size increases. This suggests that as the number of samples increases, estimates of the 75 th p ercentile 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 75 th percentile HUC
- level trend) has a positive slope, denoting that higher 75 th

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 50 th percentile HUC
- level statistic across s ample size strata (see Table 1
- 3 ) also tends to be higher with more samples, but the trend is not a s s ubstantial or consistent as for the 75 th 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 75 th percentile. Uncertainty related to sample size and potential bias in the 75 th percentile HUC
- level fish tissue Hg concentration s 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 75 th 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 (#**  **o f HU**  **Cs)**

**Number of Hg**  **Fish Tissue S**  **am ple**  **s in HU**  **C*** **HUC Frequency by**  **Number of**  **Hg Fis**  **h Tis sue**  **Sa mples** 

*When samp le sizes are 20 or greater, a category is us ed 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 75**  **th Percentile HUC**  **- level Fish tissue Hg Levels Against HUC**  **- level Sample Size**  **Table 1**  **- 3**  **Summary of 50 th**  **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** **) Me**  **an N**  **25 th**  **50 th**  **75 th**  **90 th**  **95 th**  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**  **25 th**  **50 th**  **75 th**  **90 th**  **95 th**  **Me an**  **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 inch es . Specificall y, 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 Num be r ofo bser va ti ons pe r w at ers he dWatershed-level75thpercentilefishtissueHglevel(ppm)30

group of subsistence fishers, since we excluded those smaller fish in our modeling. However, it is important to reiterate that this analy sis 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 f

ishers. Given the goal of the analy sis 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 analy sis

– 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 75 th 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 s ubsistence fishers will choose to catch and eat fish larger than 7 inches, reflecting recommendations by SAB, we include a sensitivity

analy sis to examine the degree to which exclusion of fish smaller than 7 inches a ffects risk estimates. Table 1
- 4 compares trends in different HUC
- level percentile fish tissue Hg concentration s (and the mean) based on consideration for (a) onl y 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 75 th percentile sample, which is the value used in the risk assessment. T he results provided in Table 1
- 4 suggest that exclu ding fish smaller 7 inches did not have a large impact on the HUC
- level percentiles of fish tissue Hg. This can be seen b y comparing the 75 th percentile HUC
- level estimates using only the fish larger than 7 inches with the 75 th percentile estimates based on all fish combin ed
. For example, i f we compare the average of the HUC
- level 75 th percentile values when only fish >7 inches in length were included (0.32 ppm) with the average of the HUC
- level 75 th 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 75 th percentile values (e.g., the 95 th percentile of the HUC
- level 75 th percentile values are 0.89 ppm and 0.86 ppm, for the >7 inch and all lengths included, datasets, respectivel y). One likely reason that the exclusion of fish smaller than 7 inches did not have a large impact on the 75 th percentile HUC
level estimates is that this subset of smaller fish represents a relativel y small fraction of the entire fish tissue sample dataset considered for this anal ysis (i.e., ~3,400 measurements for <7 inch fish versus 35,600 measurements for > 7inch fish, or roughl y ten times more samples for >7 inch fish). Because the impact on fish tissue concentrations is small, we do not carry forward this sensitivity

anal ys is 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 fo r**  **(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**  **Me an**  **25 th**  **% 50**  **th %**  **75 th**  **% 90**  **th %**  **95 th**  **% 99**  **th %**  **(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 25 th % 3,147 0.19 0.08 0.14 0.25 0.40 0.52 0.89 50 th % 3,147 0.25 0.10 0.19 0.32 0.50 0.67 1.06 **75 th**  **% 3,147**  **0.32 0.13**  **0.25 0.42**  **0.67 0.89**  **1.36** 90 th % 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 Project** **ing 75th percentile fish tissue Hg concentrations for the 2016 scenario**  Once 75 th percentile fish tissue Hg concentrations are generated for each of the watersheds, we then adjust these estimates to represent 75 th percentile fish tissue Hg concentrations for the 2016 scenario which are used in modeling risk for this scenario . As mentio ned 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. Specificall y, for a given watershed, we multiply the 75 th percentile measure d 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 subsis**  **tence fisher scenarios to include in the analysis** As described in section 1.4, defining subsistence fisher scenarios included in this analy sis requires three tasks: (a) identify subsistence fisher populations , (b) assess where the y might be32

active and (c ) define fish consumption rates for female subsistence fish consumer s associated with those populations. These three tasks are described below. Id entify subsistence fisher populations As discussed in sections 1.2 and 1.4, this analysis estimates risk for scenarios representing female subsistence fish consumer s 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 stu dies of fishing behavior, we prioritized survey s characterizing fishing activity for populations with these characteristics. In addition, we needed studies that provide d statistically rigorous estimates of annual
- average daily fish consumption rates. Subsistence levels of fish consumption are likely onl y experienced b y relativel y small fractions of the study populations surveyed (e.g., 90 th to 99 th 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 differentiat ed b y 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., 2 010) 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 consumer s assumed to be associated with the subsistence fisher populations described in the studies. 31 Specificall y, based on these studies, we characterize d behavior for seven distinct female subsistence fish consumer scenario s which were included in the risk assessment. These scenarios are listed (and briefl y described) in Table 1
- 5. **Table 1**  **- 5**  **Spatial Extent and Number of HUCs Refl ected in Risk Modeling for the**  **Female subsistence fish consumer**  **Scenarios Included in the Risk Assessment**
**Fem ale**  **subsistence fish**  **consumer Scenario**  **Description Spatial Extent**  **Number of watersheds**  **(HUC12s) Assessed For**  **Risk** Ty pical (non
- SES differentiated) Generalized (non
- SES differentiated) female subsistence

fish consumer s 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 Lo w income Lo w income White female subsistence fish consumer s Southe ast 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 popul ations 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 provi ded 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

**Fem ale**  **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. Lo w income Black (southeast) Lo w income Black female subsistence fish consumer s

active in the Southeast , in watersheds with fish tissue Hg data where there are at least 25 Black individuals below the poverty line. Southe ast 756 Lo w income Hispanic Lo w income Hispanic female subsistence fish

consumer s 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

consumer s 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
Laoti an Laotian female subsistence fish consumer s active at any

watersheds in the continental U.S. with fish tissue Hg data w here there are at least 25 Laotians. National (where there are Laotians) 131 Vietnamese Vietnamese female subsistence fish consumer s 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 h igh
- end fishing behavior could be generalized beyond the specific geographic areas covered in a particular study . This ty pe 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 as sumed that fishing activity is culturall y related, we generaliz ed fishing activity by Hispanics, Laotians and Vietnamese beyond California, where the study providing consumption rates was conducted, to the nation. Similarly, we assumed that survey s of high
- end fish consumption by low income White s and low income Black s in South Carolina might be generalized to the southeast, reflecting similar cultural practices b y these groups within that region. Once we identified the regions of the country where a parti cular 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 consumer s associated with those subsistence fishers ) might occur. This task was challenging34

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. Inthis case, we specified that a watershed would have to intersect a U.S. Census tract with at least 25 individuals with the same ethnic/SE S 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 onl y assessed at those watersheds intersecting U.S. Census tracts with at least 25 L aotians. In the case of low income White and Black subsistence fishers in the southeast as well as Hispanics ass essed 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 popul ation 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 subsis tence fisher scenario is described both in Table 1
- 5 and in greater detail in Table 1
- 6 ( in Table 1
- 6 see column s “C” and “E” ). As described in Table 1
- 6 (columns “C” and “E”) , the approach used for the Tribal population and ty pical female subsistence fish consumer scenarios differ s 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 b y 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 generaliz ed 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 ty pical female subsistence fish consumer is the most generalized of the scenarios and has the gr eatest spatial extent , we place greater emphasis on this scenario in p resenting risk estimates. 34 Application of the ty pical 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 cons umer 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 s cenario 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 those35

subsistence fish consumer s scenario to all watersheds with fish tissue Hg data also helps to address the co ncern 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 su bsistence 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 identif

yi ng the set of female subsistence fish consumer scenarios to includ e in the anal ys is and determining where those scenarios should be applied, next we define d 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 90 th to 99 th 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 ty

pical 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 ap plied 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 appl y to women of ch ild
bearing age (see footnote 3 3 ). The subsi stence - level consumption rates used in modeling risk for these scen arios 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 annual
averag es of dail y “as consumed/prepared” values. As noted earlier in 1.4, this issue is critical to ensuring the appropriate technical linkages between the ty pes of fish tissue Hg measurements made, application of preparation adjustment factors and ultimately , c alculation of exposure and risk. This issue is discussed in greater length in the next section. The fish consumption rates presented in Table 1
- 6 clearl y 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 day s to a larger fish meal (12oz or more) every day . While these consumption scenarios are identified as “subsistence” due t o their magnitude, they could be experienced by individuals purposefull y 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 ty pical female subsistence fish consumer s , as mentioned earlier, this scenario is applied uniformly across all watersheds with fish tissue Hg data, and whether we define consumption as subsistence or high
end 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 wa s lognormal.36

risk estimates represent risk experienced b y an individual residing in the vicinity of a particular watershed who consumes se lf
- caught fish at a subsistence level
. 36 Inaddition to the three studies we used to define subsistence fish consumption rates , we also reviewed additional studies characterizing higher
- level s of self
- caught fish consumption in the U.S. While these additional studies had limitations that prevented their use in th is risk assessment , they generall y support the rates of self

- caught fish consumption modeled in the anal ys is. 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 b y socio
- economic status (SES) factors (including race, education and income) and determined levels of self
caught fi sh 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 Black s tend to have much higher rates of fish consumption than White s. Fo r 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 98 th percentile for Whites, but only the 92 nd 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 SE S 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 ty pe/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 gen eral 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 effectivel y capture the small fraction of the overall population likely engaging in high
end subsistence lev els of self
- caught fishing behavior. This study supports the existence of subsistence fishing populations in FLand for Tribes in MN. However, failure to capture similar behavior in ND and CT does not necessarily suggest that this ty

pe of
36 To generate population
- weighted risk e stimates, it is im portant 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
- we ighted 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 Caroli na) 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 consumer**  **s) might be active**  **(D) Self**  **- caught fish consumpti**  **on rates (m**  **ean, 90 th**  **, 95 th**  **, 99 th**  **) 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 Sout heast 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 wo men (in considering exposures for pregnant women in particular), we extrapolat ed 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 99 th consumption rate (therefore, this high
- end percentile was not included in the risk assessment) C onsumption 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 Black s, 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 (95 th 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 (90 th , 95 th and 99 th rates are: 285, 429 and 590 g/day). This observation forms the ba sis 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 consumer**  **s) might be active**  **(D) Self**  **- caught fish consumpti**  **on rates (m**  **ean, 90 th**  **, 95 th**  **, 99 th**  **) 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 i n U.S. Census tracts with at least 25 low income me mb ers of the ethnic populations (e.g., the Hispanic consumption rates would b e 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, N C *

- Vietnamese: 27 .1, 99.1, 152.4, N C * * 95 th percentile values were provided in the study. 90 th percentile values were calculated using Crystal Ball (based on the median and standard deviations provided) assuming a log
normality of the consumption rate distributions. 99 th percentile consumption rates were not provided (or derived) for any of these populations due to small sample sizes of the study populations. C onsumption 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 in line 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 consumer**  **s) might be active**  **(D) Self**  **- caught fish consumpti**  **on rates (m**  **ean, 90 th**  **, 95 th**  **, 99 th**  **) 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 self

reported fish consumption rates by Tribes in the Great Lakes area with “actual” fish consumption rates c ollected 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 (90 th

– 99 th) were derived using Crystal Ball (based on median and standard deviations and an assumption of log
- normally distributed variability in consumption rates) C onsumption 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 annual
average s (but it is not explicitly stated). I ngestion 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 t he 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 i n 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 sam pling 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. Th ere 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 fis**  **h consumption -**  **related MeHg exposure (2016 scenario)**  N ext , we estimate d total exposure to Hg for the set of seven female subsistence fish consumer scenarios being evaluated (for the 2016 scenario)
. To generate e stimates of total Hg exposure , we combined the 75 th 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 fi sh and MeHg levels in fish and (c) a food preparation adjustment (cooking fish can increase Me Hg 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 mus t fac tor in cooking/preparation in specifying the fish tissue Hg concentration s. 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, i ncluding 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):** W e calculated the 75 th percentile fish tissue Hg estimate fo r 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):** W e 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 Mer cury

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)** : B ecause we use fish consumption rates for “as consumed” portions, we need to adjust the fish tissue Hg concentration s to reflect concentrations after food preparation. C ooking fish ty picall y increase s MeHg levels per unit fish because Hg concentrates in the muscle while preparation involves removal primarily of non muscle elements of the fish (e.g. , water, fat etc) (Morgan et al., 1997). The FPCAF factor used in the anal ys is 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 fact ors 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 asses s ing these studies, we conclude that they do not support a lower42

preparation/cooking loss adjustment factor. The Farias study appear s 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 measure d non - fish components added to dishes (e.g., onions, heav y breading etc) in post
- cooking meas urements, which could provid e the appearance of a cooking loss in Hg while actual fish tissue Hg concentration s could have increased. The Musaiger et al. 2008 study compar ed Hg levels across different ty pes 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 ty picall y removes fat and water, while Hg is located in the meat and therefore will not be reduced. 37 **Fish consumption**  **rate (F C**  **R ):**  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 dail y 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 the se factors were combined to generate estimates of annual
- average dail y MeHg exposure per kg body weight:

IR = FTC (2016) * MCF * FPCAF * F C R

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) F C R = 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 o f fish and the effect on Hg concentration. This literature revie w 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 fac tor 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 scenario s 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 b y the EPA in the Integrated Risk Information Sy stem in 2001 (US EPA, 2001 a) (http://www.epa.gov/iris/subst/0073.htm). Reflecting precedent in interpreting HQ es timates, 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 e xposures 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 underl yi ng these risk estimates. While precision associated with the exposure estimates m ay 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 expresse d 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. EGU s (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 e stimat e 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 concentration s. 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 concentration s 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 spe cific watersheds, we need to also consider whether total risk (i.e., the HQ reflecting total MeHg exposure ) exceeds 1.5 and therefore represents a potentia l public health hazard. If that is the case, the n we would consider the degree to which U.S. EGU s 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 us ed to relate changes in mercury deposition to change in total risk at that HUC.44

anal ys is (U.S. EPA, 2001 b ) . In addition, a number of additional studies (some of which were highlighted b y the SAB in their review of the March TSD) support a proportional relationship between changes in Hg deposition and changes in fish tissue Hg concentration s, although with important caveats related to the temporal response of aquatic s ys tems 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 th e results from total Hg deposition to isolate the U.S. EGU contribution. The CMAQ mo deling for this anal ys is 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. T o 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 estim ate of U.S. EGU
- attributable risk for 2016 is outlined below: **Generate U.S. EGU -**  **attributable HQ risk estimates for the 2016 scenario:** W e use the 2016 total and U.S. EGU
- attributable CMAQ deposition results with the estimated total HQ risk estimates for 2 016 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 anal ys is support the proportionality

assumption (U.S. EPA, 2001 b )
. The Mercury Maps anal ys is 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 ecos ys tem models that were described in the Report to Congress, the Mercury Maps model show ed 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 signi ficant source of Hg to a water body, and the phy sical, chemical, and biological characteristics of the ecos ys tem remain constant over time.45

Consequently , the proportionality assumption used to estimate the U.S. EGU
- attributable fraction of risk would ideal ly onl y 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 ecosy stems may not reach steady

state and that ecosy stem conditions affecting Hg dynamics are unlikely to remain constant over time. EPA further recognizes that many waterbodies, particularly in areas of historic gold and H g mining in western states, contain significant non
- air sources of Hg
. 40 Finall y, 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 revi ew, 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 anal ys is, nor did they

recommend their use. The SAB specifically noted that the Regional Mercury Cycling Model (R
- MCM) c ould also be used for a national assessment , but they also noted that the R
- MCM is more data intensive and the results produced b y the two model approaches should be equivalent. We did not have all of the data inputs (water ch emistry , 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 onl y 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 h ere and addressed in Table 2
- 15
. Appl yi ng Mercury Maps to apportion fish tissue Hg concentration s 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 fairl y 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 s amples 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 dis cussion 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 concentr ations 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 se en in the near
term (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 con centration s 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 fro m industrial activity and mines , we did not consider municipal sewage emissions of Hg to watersheds. This introduces additional uncertainty into the anal ys is, 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 f

raction 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 directl y to aquatic ecos ys tems 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 concentration s following reductions in Hg deposition to a watershed, we are likely

to see a 2
- stage re sponse including: (a) an initial more rapid phase of reduction (ranging from months to a few ye ars) reflecting a decrease in direct loading to the waterbody and subsequent reductions in fish tissue Hg concentration s related to decreased water column Hg lev els 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 su ggest 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 b uffer 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 sy stem), the proportionality assumption likely

holds and we would expect to see a reduction i n 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 mode l used to estimate air quality

(Appel et al., 2008; Appel et al., 2007; By un 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 s ys tems in several peer reviewed publications (Bullock et al., 2008, 2009; L in 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 sy stem (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 o f 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 s ys tem through bound ary 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

dy n amic 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 hourl y boundary concentrations to the 12 km domains. Initial and boun dary

conditions for the projected future year (2016) 36 km simulations are the same as the 2005 base ye ar. The first 10 day s of the 36 km modeling simulation are not used in the anal ys is, which is beyond the number of day s 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 glob al 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 200548

and 2016 simulations based on the consistency in Asian mercury

em issions 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 anal ys es to support revision of the 2000 appropriate and necessary finding. 42 This section identif ies 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 difference s in the interpretation of potential public health significance of the risk estimates generated. Key differences between the two anal ys es include: Higher spatial resolution through use of CMAQ 12km grid cells : In this analy sis, we modeled Hg deposition us ing CMAQ at a 12 km grid resolution , whereas in the 2005 anal ys is, 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 anal ys is. The 12 km resolution also matches up with the more refined HUC12 watersheds now being used in the anal ys is, 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 anal ys is uses HUC12 watersheds as the basis for risk estimation (these watersheds typi cally are 5
- 10 km on a side). By contrast, the 2005 anal ys is 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). In clusion of updated fish tissue data: For this anal ysis, we included measured fish tissue data collected between 2000 and 2010. By contrast, the 2005 anal ys is used data collected between 1999 and 2003 , which was the best availab le 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 p opulations (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 bel ieve that the literature supports the plausibility of high
- end sub sistence
- like fishing activity across the watersheds included in the anal ysis. Additionally , the variety of studies identifying self
- caught fishing activity

at subsistence levels (i.e., a me al every few day s 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 Utili ty Emission Controls
.49

regions of the country , support s assessing this type subsistence fish consumption behavior across the modeled watersheds
. By contrast, in the 2005 anal ys is, 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 readil y 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

onl y applied during specific harvesting periods
- e.g., spearfishing months for Great Lakes Tribes). Therefore, in the 2005 analy sis, we appl ied a high
- end self
- caught percentile values (95 th and 99 th percentiles) based on Tribal fishing practices in the Northwest to watersheds across the country . 43 The updated literature review , for the current anal ys is , led us to revise several of our earlier conclusions regarding high
- end fishing activity . Specificall y, 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 broadl y across the entire study area. Additionally , while some of the studies may include saltwater fishing in addition to freshwater (e.g., Burger, 2002), when tho se studies clearl y covered both saltwater and freshwater self
caught 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 an al ys is, we used the 75 th 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 concentration s 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 analy sis, 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 spec ific cultural practices focused around salmon fishing. There is also significant uncertainty in extrapolating this ty pe 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 SES
differentiated populations are defined more genera lly 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 coa st 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 focusi ng 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 dail y i ntake) metric u sed in the 2005 anal ys is. However an important distinction is that in the current anal ys is, we highlight the fact that this U.S. EGU
- related risk is alway s 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 anal ys is with the IDI). By contrast, for the 2005 anal ys is 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 anal ys is 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 calcul ation 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 step s is summarized below:

 Step 1: Based on the fish tissue Hg measurements available for a given watershed (after filtering), generate the 75 th percentile fish tissue Hg concentration. We provide the median, 75 th and 90 th percentile values in Figure 1
- 9 to i llustrate 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 d eposition). (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 proportionali ty 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. M ost 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

di agram 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 deposi tion 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 t o 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 = **FT C**  * **MC F**  * **FC R**  * **CA F** 

**BW** Examp le – Lo wer K ettl e Cre ek in nor th cen tr al P en nsyl van ia (H UC 12 ID: 2 0502030106)

IR: d aily Me Hg int ak e ra te ( ug/kg
- da y) FTC: Me Hg fish t issue c onc en tr at ion ( ug/g or ppm ) MCR: m ercu ry convers ion fa ct or – fr ac tion fish t issue me rcu ry th at

i s Me Hg ( unit le ss ) FCR: fish cons um pt ion ra te (g/day) FPCA F: f ish pr ep ar at ion/c ooking adjust me nt f ac to r RfD : me th ylme rcu ry RfD Fr ac EGU : fract ion de p fr om U S EGU Fish ti ssue ( ppm ) Me dian : 0. 24 75 th : 0 .3 2 90 th : 0 .3 4 6 samp le s

PR OJ EC TED Fish tissue ( ppm ) **2016** Me an : 0. 20 **75 th**  **: 0**  **.26** 90 th : 0 .2 8 To tal Hg de p ove r wate rs hed (CMA Q): 2005 = 30. 42 2016 = 25. 19 Or a ~17%r ed uct ion 95 th % co nsum pti on rat e: **173 g/day**  Fi sh

pr ep arat io n/ co ok in g adju stm en t Fa ct or : **1.5**

Bo dy we igh t: **64 g**  **IR =**  **1 .01**  **ug /kg**  **- day** 

Me Hg RfD : **0.1 ug**  **/kg -**  **day To**  **tal HQ**  **= IR**  **= 1.01**  **= 10.1**  **RfD 0.1** 

2016 Hg de p ove r wate rs hed (CMA Q): to tal = 25. 19 US EGU = 1 .2 6 US EGU is 5 % **Fra cEGU**  **= 0.050**  **US E**  **GU ri**  **sk =**  **HQ** ***** **Fr acEG**  **U**
**= 10.1**  ***** **0.050 =**  **0.504 (i.e.,**  **<1)** 

## 1

2

3 4 5

7 Mercu ry co nve rs io n fact or : **0.95** #: c alcu lat io n ste p



## 653

**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 full y 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 (I CR). This shows that significant reduction in U.S. EGU Hg emissions has occurred since 20 05, 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 largel y due to regulations and federal enfo rcement actions that achieve Hg reductions as a co
- benefit of controls for NOx and SO 2 emissions. Given these estimates of total Hg emissions, characterization of “current conditions” is better represented b y the 2016 Scenario than the 2005 Base Case, sin ce total Hg emissions for the former is equal to our projection of recent emissions. By contrast, the 2005 anal ys is reflects total Hg emissions (52.9 tons) which are significantly higher than our estimate of recent emissions. For this reason, as mentioned earlier, we onl y 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). Inthis section , we provide a brief overview of critical design elements of the risk anal ys is that the reader should keep in mi nd when reviewing the results (s ection 2.1). We then discuss the intermediate inputs and outputs for individual analy tical steps associated with the risk assessment including: (a) Hg deposition from U.S. EGUs as modeled using CMAQ (section 2.2), (b ) fish tissu e Hg concentrations (section 2.3), (c ) relationship between Hg deposition and Hg fish ti ssue concentrations (s ection 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
- relev ant observations. In section 2.6 , we discuss the results of several sensitivity anal ys es 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 observ ations from the anal ys is, 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 anal ys is should be consid ered when reviewing the results :
 The anal ys is focuses on subsistence
- like fishing activity at inland freshwater bodies. The anal ys is is not intended to capture more generalized recreational fishing activity or to reflect self

- caught fisher exposure associated with saltwater fishi ng 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 fin al 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 analy sis to risks estimated in other contexts, the specific focus on these high
- end populations show s risks substantially higher than risks for recreational fishers.

 The ri sk esti mates generated are based on a set of female subsistence fish consumer scenario s evaluated at watershed s where (a) we have fish tissue Hg data and (b) this ty pe of subsistence fisher activity could potentially

occur. Therefore, the risk assessment i s watershed
- focused, does not provide population
- representative risk estimates for subsistence fishers.

 Risks are estimated for 3, 141 watersheds. This watershed coverage (onl y about 4% of U.S. watersheds) leaves much of the country not covered b y the anal y sis, including a substantial number of watersheds with relativel y elevated levels of U.S. EGU
- related Hg deposition. Further, the eastern part of the U.S. is more heavil y represented in the watershed
- level estimates of risk. Given that U.S. EGU Hg deposition is generall y 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 analy sis.

 The anal ys is uses a proportionality assumption to link changes in Hg depositi on (over watersheds) to changes in Hg fish tissue levels. This approach assumes that near steadystate conditions are met, which may take ye ars to decades at a given watershed following changes in Hg deposition.

 The anal ys is estimates risk based on MeHg RfD
- based HQ. The U.S. EGU
- attributable HQ should alway s be considered in the context of total HQ which is ty pically substantially

larger than the U.S. EGU
- attributable HQ (reflecting the large contribution from non
- U.S. EGU sources of Hg). We also assess ed IQ loss in children, however, due to concerns that the IQ loss endpoint may not full y 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 no t feasible to enumerate the female subsistence fish consumer s modeled in this analy sis, we could not generate distributions of popul ation
- 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 survey s of their behavior, that this ty pe of subsistence
- like activity could reasonabl y be expected to occur across some fraction of the 3,141 watersheds included in the analy sis. Therefore, we have asses sed 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 the55

TSD we have also included ma ps 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 ( μ g/m 2 ) 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 ( μ g/m 2 ) at the watershed
- level, for the 2005 and 2016 scenarios, respectivel y.
 Figures 2
- 5 through 2
- 8 : Maps presenting CMAQ modeling results for wet and dry Hg deposition ( μ g/m 2 ) for the 2005 and 2016 scenarios. These estimates are presented at the original CMAQ 12km grid resolution and have not been interpolated to the watershed
level . 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, 50 th , 75 th , 90 th , 95 th and 99 th percentiles) for total Hg deposition and U.S. EGU
- attributable deposition for the 2005 and 2016 scenarios.

 Table 2
- 2: Summary

of statistics (mean, 50 th , 75 th , 90 th , 95 th and 99 th 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, 50 th , 75 th , 90 th , 95 th and 99 th 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 we re 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 h ere are the corrected figures and match those presented in that memo . 48 I nclusion of maps presenting wet and dry deposition modeling results reflects sugges tions made by SAB Panel members.56 **Figure 2**  **- 1**  **Total Mercury Deposition by HUC (μg/m 2**  **) for the 2005 Scenario**57 **Figure 2**  **- 2**  **Total Mercury Deposition by HUC (μg/m 2**  **) for the 2016 Scenario**58 **Figure 2**  **- 3**  **U.S E GU**  **- Attributable Mercury Deposition by HUC (μg/m**  **2 ) for the 2005 Scenario** 59 **Figure 2**  **- 4**  **U.S EGU -**  **Attributable Mercury Deposition by HUC (μg/m 2**  **) for the 2016 Scenario**60 **Figure 2**  **- 5**  **Mercury Wet Deposition by 12km Grid Cell (μg/m 2**  **) for the 2005 Scenario**61 **Figure 2**  **- 6**  **Mercury Dry Deposition by 12km Grid Cell (μg/m 2**  **) for the 2005 Scenario**62 **Figure 2**  **- 7**  **Mercury Wet Deposition by 12km Grid Cell (μg/m 2**  **) for the 2016 Scenario**63 **Figure 2**  **- 8**  **Mercury Dry Deposition by 12km Grid Cell (μg/m 2**  **) for the 2016 Scenario**64

**Table 2**  **- 1**  **Comparison of total and U.S. EGU -**  **attributable mercury deposition ( μ**  **g/m 2**  **) 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 99 th percentile for total Hg deposition will not be the same watershed as the watershed with the 99 th 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 mer cury deposition, and U.S. EGU**  **** **attributable 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 pres ented 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 t otal Hg deposition are distributed around the country (e.g., west coast, areas in Nevada, southern Mississippi, West Virginia, southe astern 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 Oh io 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. EGU
attributable deposition, many of the highest areas (and largest expanses) of U.S. EGU
attributable 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 decreas es 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 th e 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 95 th 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 depositio n in the U.S., this contribution ranged up to 30% for the 99 th percentile watershed (see Table 2
- 2). While overall U.S. EGU

–attributable deposition decreased substantiall y between the 2005 scenario and the 2016 scenario, U.S. EGUs contributed 11% of total Hg deposition for the 99 th percentile watershed in 2016 ( (see Table 2
- 2). **2.3 Fish Tissue Mercury Concentrations**  This section characterizes the sub set 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 75 th 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 depositi on coverages

49 Controls on PM precursors, including directly emitted PM and SO 2 , can significantly reduce divalent and particle
bound 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.ep a.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 betwe en 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 a ssessment, 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 co verage 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 water sheds 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
- 1 1 and 2
- 12 : Maps presenting Total Fish Tissue Mercury Concentrations for the 2005 and 2016 scenarios respectivel y.
 Figures 2
- 13 and 2
- 14 : Maps presenting EGU
- Attributable Fish Tissue Mercury Concentrations for 2005 and 2016 scenarios, respectivel y. 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 fi sh 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. EGU
attributable 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 concentration s and EGU
- attributable fish tissue Hg concentration s. 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 th e 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 o f 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 75 th**  **percentile values, ppm)**70

**Figure 2**  **- 12**  **Total Fish Tissue Mercury Concentrations Projected for 2016 Scenario (HUC12 -**  **level 75 th**  **percentile values, ppm)**  ***** * Values estimated b y 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 75**  **th percentile values, ppm)** * * S cale s 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 75**  **th percentile values, ppm)** * * S cale s 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 75**  **th 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**  **tissue concentration Used for 2005 scenario**  **Projected for 2016 scenario**
**Total U.S. EGU**  ****

**attributable U.S. EGU** 

**as percent of total**  **Total U.S. EGU**  ****

**attributable U.S. EGU** 

**as percent of total**  **Total U.S. EGU**  ****

**attributable** 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% * T hese percentiles are not for matched HUCs (i.e., the 99 th percentile for total Hg fish concentration for the 2005 scenario could occur in a different HUC than the 99 th 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 largel y 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 data74

significantl y expand ed coverage for watersheds in areas with elevated U.S. EGU
attributable Hg deposition (Pennsylvania) and for areas with relativel y elevated fish tissue Hg concentration s (Minnesota and Wisconsin) , the additional data did not substantially

change the overall distribution of fish tissue Hg concentration s. 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 f ish 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 ty

pical 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 concentration s **:** 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 o f reduction seen in Figures 2
- 13 and 2
- 14 for U.S. EGU
- attributable Hg fish tissue levels with the relativel y small er 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 contributio n on fish tissue : Under the 2005 scenario, U.S. EGUs contribute up to 43% of total Hg fish tissue levels (for the 99 th percentile watershed). Under the 2016 Scenario, this pattern is reduced, but U.S. EGUs can still contribute up to 16% of total fish tissu e Hg concentration s (again, for the 99 th 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 compare d spatial patterns of CMAQ modeled Hg depo sition 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 augment ed dataset. These maps show the degree of overlap between fish tissue Hg data and areas of elevated U.S. EGU
- attributable Hg deposi tion (for both 2005 and 2016 scenarios). 51

 Figure 2
- 17 : Plot comparing the fish tissue Hg concentration s versus total Hg deposition (2005 scenario) by

watershed. This plot allows consideration for whether there appears to be a correlation between these tw o factors at the watershed level.

 Figure 2
- 18: C umulative 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 onl y 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 depos ition 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. EGU
attributable Hg deposition based on the average rate of depositio n 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 th e 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 co nsider 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 id entify 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 Wate** **rsheds 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 (** **C M** **A Q** **- b** **a s** **e d** **t o** **t a** **l H** **g d** **e p** **o s** **i t** **i o** **n b** **y H** **U C** **( 2** **0 0** **5 S** **c e** **n a** **r i** **o )** **( u** **g /** **m 3** **)**

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**  **fis h**  **tis su**  **e mercury**  **levels (**  **ppm )**  **fo r**  **3,141 HUC**  **s us**  **ed in ris**  **k as**  **sessmen t**  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**  279

We made the following observations based on the information presented in Figures 2
- 1 5 through 2
- 18 regarding how estimates of Hg deposition estimates relate to measured fish tissue Hg concentration s 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) , especiall y with the augmented dataset for Penns yl vania. 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 larg er (depending on the underl yi ng fish tissue Hg concentration s). 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 concentration s). This is not unexpec ted because the relationship between total Hg deposition and total Hg fish tissue levels is highl y 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 sulfat e 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 depositio n. 53 This can be seen b y 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 to tal Hg deposition than the full set of watersheds. This likel y 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 as sessment, 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
- em phasized, we decided t o only provide these cumulative plots for the 2016 scenario. However, we note that the same relationship noted here (between U.S. EGU
related Hg deposition for all ~88,000 watersheds and for the subset included in the risk assessment), holds for the 2005 s cenario.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 b y 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 Black s and White s 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): W e provide percentile risk estimates for the ty pical female subsistence fish consumer scenario at the national level and summarize percentile risk estimates for the other SES
differentiated female subsistence fish consumer scenarios assessed in the analy sis. The se percentile risk estimates allow us to compare risk (total and U.S. EGU
- attrib utable) across the female subsistence fish consumer s scenarios assessed in the risk assessment.

 Overview of the number (and frequency) of watersheds with populations potentially at risk 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). Specificall y, watersheds with populations potentially at - risk comprise: o Watersheds where total risk is considered to represent a potential public health con cern 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 li st 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 ty

pical female subsistence fish consumer scenario. We also compare and contrast risk for the other female consumer s cenarios with the more generalized ty pical female subsistence fish cons umer scenario.81

These percentile estimates are not population
- weighted , but instead represent the specific watershed
- level risk estimate s that fall at a specific point within the larger distribution of watershed
- level risk estimates for each female subsist ence fish consumer s cenario. 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 t otal and U.S. EGU
- attributable risk for a particular perc entile are not matched b y watershed (i.e., the 90 th percentile total and U.S. EGU
- attributable risk for the Vietnamese subsistence fisher likel y occur at different watersheds). 54 While this risk assessment focus es 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 analy sis for the 2016 scenario. **Table 2**  **- 6**  **Percentile risk estimates for the fu ll set of**  **female subsistence fish consumer scenarios**  **included in the analysis (2016 scenario)** (for both total and U.S. EGU incremental RfD
- based HQ)*

**Fis her**  **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 ( 9 0 th) 6.1 10.9 17.7 23.4 35.4 0.2 0.3 0.5 0.7 1.2 173 ( 9 5th) 8.5 15.3 24.9 32.9 49.7 0.2 0.4 0.7 1 1.7 373 ( 9 9th) 18.4 33 53.7 70.9 107.2 0.5 0.9 1.5 2.1 3.7

**Low income White**  **fem ale 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 ( 90 th) 4.9 9.8 16.3 22.4 29.9 0.2 0.3 0.5 0.7 1.4 129 ( 9 5th) 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 fem**  **ale 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 (9 0th ) 24.6 50.6 85.6 110.1 147.2 0.8 1.6 2.7 3.6 6.8 557 (95 th) 30.8 63.2 106.9 137.5 183.8 0.9 1.9 3.4 4.4 8.4 ( 99 th) 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 **Fis her**  **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 (9 0th) 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 ( 99 th) 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 ( 99 th) NC NC **Laotians fem**  **ale 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 ( 99 th) NC NC

**Tribal (near Great Lakes) fem**  **ale 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 e stimates presented for “total” and “U.S. EGU” are not matched by watershed (e.g., the 90 th 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 perce ntile values.

“
- “: RfD
- based HQ is ≤ 0.1. NC: It was not possible to derive a 99 th percentile consumption rate for this population due to insufficient sample size in the underlying study. Consequently, risk estimates for the 99 th percentile consumption rat es 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 consum er (assessed at the national
level) generally provide coverage for the Hispanic, Vietnamese and Tribal scenarios: Risk estimates (for both total and U.S. EGU
- attributable) generated for the ty pical female subsistence fish consumer scenario are generall y hi gher than estimates generated for the Hispanic and Vietnamese scenarios. Risk estimates for the ty pical female subsistence fish consumer scenario are also higher than risks for the Tribal population for U.S. EGU
attributable 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 suggest83

that the ty pical female subsistence fish consumer scenario provides general coverage for the other three scenarios (Hispanic, Vietnamese and Tribal), since the ty pical 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 national
level) may not provide full co verage for risks experienced by low income Black s 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 ty pical female subsistence fish consumer . In the case of low income White s, 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 concentration s compared with the national distribution (see Figures 2
- 13 and 2
- 15). In the case of low income Black s, higher fish tissue Hg concentration s in South Carolina are combined with fish co nsumption rates that are substantiall y higher than those used for the ty pical 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 es timates than the ty pical female subsistence fish consumer scenario ( assessed nationally) , because the ty pical female subsistence scenario is assessed at all 3,141 watersheds with fish tissue Hg data, we focus on the ty

pical female subsistence fish consumer s cenario to provided policy
relevant risk information to support this rulemaking (i.e., we have greater overall confidence in risk estimates generated for the ty pical 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. Inthe case of the Laotian scenario, while U.S. EGU
- related risks are higher than for the ty pical female subsistence fish consumer scenario, this scenario was assessed for a small number of watersheds (131 of the 3,141 watersh eds with fish tissue Hg data) . S pecifically , risk estimates generated for the Laotian scenario may

reflect relativel y rare localized interactions between fish tissue Hg concentration s 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 ris k 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. EGU
attributable deposition when considered alone , without taking into account deposition from other sources . The estimates of watersheds with at
- risk populations discussed i n this section are used in the 2
- Stage risk characterization framework described in Section 1.3 for interpreting risk estimates. Specificall y, the first category of at - risk populations described above comprise Stage 1 a of the 2
- Stage approach, while the second category comprises Stage 1b (see Figure 1
- 1). The84

combination (i.e., mathematical union) of these two groups of watersheds with at - risk popul ations comprises the set of watersheds repr esented 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 ty pical female subsistence fish consumer for all watersheds with fish tissue Hg data). Howe ver, when we present the Stage 2 risk estimates, we also include a summary of Stage 2 estimates from the March vers ion 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 f or purposes of this anal ys is , 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 ty pical female subsistence fish consumer scenario. The estimates of watersheds with potentially at - risk populations are summarized in table s described below, followed b y 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 ty pical female subsistence fish co nsumer) 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 sample s meeting specific criteria that this represents. (Stage 1 a of the 2
- Stage framework)

 Table 2
- 8: Identifies watersheds with potentially at - risk populations based on U.S. EGU
attributable deposition considered alone, without taking into account other source s 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 sourc es (i.e., U.S. EGU
- attributable HQ>1) . This represents Stage 2 of the risk characterization framework.

 Table 2
- 10: Pres ents 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 criteri**  **a (RfD**  **- based HQ > 1)** * **2016 analysis 90**  **th percentile fish**  **consumption 95**  **th percentile**  **fish consumption**  **99 th**  **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 threshol**  **d Number and percentage of**  **3,141 HUCs meeting risk**  **threshold criteria (RfD**  **- based HQ risk)** * **2016 analysis 90**  **th fish**  **consumption 95**  **th percentile fish**  **consumption 99**  **th 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) r**  **isk 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**  **90 th**  **percentile fish consumption**  **95 th**  **percentile fish**  **consumption 99**  **th 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 watersh**  **eds 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 sce**  **nario)**

**EGU risk threshold**  **Number and percentage of HUCs meeting risk threshold criteria**

**90 th**  **percentile fish consumption**  **95 th**  **percentile fish**  **consumption 99**  **th 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 o bservations 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 , ori ented 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 water sheds 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 99 th percentile fish consumption rate for the high
- end female consum er – 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 h ave 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 anal ys is suggests that between 2 and 12% of the 3,141 w atersheds modeled in the risk assessment for high
- en d 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 95 th percentile consumption rate, and the high end reflect s the 99 th 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 . The se estimates are sensitive to the87

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 reflec t the different fish consumption rates considered for the high
- end female consumer (i.e., 90 th , 95 th and 99 th percentile fish consumption rates). The results summarized here for total “ at - risk” watersheds map to Stage 2 of the 2
- stage risk characterizatio n framework
.  The revised risk assessment and the March Mercury Risk TSD generate very similar Stage 2 risk estimates (2 016 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 a ffected 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 anal ys es conducted to assess the potential impact of sources of uncertainty related to: (a) the application of the proportionality assumption in li nking Hg deposition and fish tissue Hg concentration s (see section s 1.4.2.1 and 1.4.6) and (b) use of the watershed
- level 75 th percentile f ish tissue Hg concentration as the basis for generating watershed
- level risk estimates (see section 1.4.2). Given the emphasis placed in the national
scale mercury

risk assessment on risk estimates generated for the 2016 scenario, we have also based the sensitivity

analy ses on simulating risks for the 2016 scenario. To address uncertainty in applying the proportionality assumption, we conduct ed two sensitivity

anal ys es 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 analy sis because it represents a critical element of the anal ys is 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 concentration s, only holds for watersheds where aerial Hg deposition is the primary source of loading . The two sensitivity anal ys es 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 onl y include those watersheds in the upper 25 th 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 suggeste d 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 bec ause 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 methylati on 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 se nsitivity 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 hist ory 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

relativel y elevated levels of total Hg deposition so we have increased confidence that aerial deposition play s a significant role in loading). To address uncertainty in use of the watershed
- level 7 5 th percentile fish tissue Hg concentration in risk modeling, we conduct ed a sensitivity anal ys is using the watershed
- level median fish tissue Hg. This sensitivity analy sis , 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 concentration s. The results of both sensitivity anal ys es are presented i n 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 p rovide three tables (Tables 2
- 11, 2
- 12 and 2
- 13 ) that correspond to each stages of the 2
- Stag e Risk Characterization Framework. Each table first presents core results, followed by results for each of the three sensitivity

analy ses (this allows the results of each sensitivity analy sis to be readil y compared against the core anal ys is). We present o bservations from the sensitivity anal ys es at the end of this section. All results presented in the three tables are based on simulating risk for the ty pical 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 percent**  **age of watersheds meeting risk threshold criteria**  **( Total HQ**  **≥ 1.5 and U.S. EGU percent contribution as dimensioned below** **)*** **90 th**  **percentile fish consumption**  **95 th**  **percentile fish** 

**consumption 99**  **th 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 25 th**  **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 percent**  **age of watersheds meeting risk threshold criteria**  **( Total HQ**  **≥ 1.5 and U.S. EGU percent contribution as dimensioned below** **)*** **90 th**  **percentile fish consumption**  **95 th**  **percentile fish** 

**consumption 99**  **th 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**  **wa tersheds meeting risk**  **threshold criteria (**  **U.S. EGU**

**- attributable HQ** **)*** **90 th**  **percentile fish consumption**  **95 th**  **percentile fish**  **consumption 99**  **th 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 25**  **th 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**  **, w ithout 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**  **)*** **90 th**  **percentile fish consumption**  **95 th**  **percentile fish** 

**consumption 99**  **th 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 25 th**  **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 anal ys es include:

 Generating risk estimates excluding watersheds located in four states (AL, SC, ME and MN): This sensitivity

analy sis resulted in different effects on the Stage 1 a and Stage 1b risk estimates, although neither was substantiall y different than the core estimates. Estimates of the number of watersheds with at - risk populations due t o U.S. EGUs contributing at least 5% to total risk (Stage 1 a) demonstrated a range of moderate increases compared with the core anal ys is, depending on the “percent contribution” category considered (see Table 2
- 11 ). Similarl y, estimates of the percentage of91

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 99 th percentile fish co nsumption rate
- see Table 2
- 12 ). This sensitivity

analy sis suggests that the risk estimates are relatively robust to exclusion of the four states considered in this sensitivity anal ys is.

 Generating risk estimates including only those watersheds falling in the top 25 th percentile with regard to total Hg deposition: These sensitivity anal ys is results suggest that focusing on those watersheds with higher total deposition results in a mixture of impacts on both Stage 1 a and Stag e 1b risk metrics. Estimates of the number o f watersheds with at - risk populations due to U.S. EGUs contributing between 5% and 10% is lower for the sensitivity anal ys is than for the core anal ys is. However, estimates for contributions of ≥ 10% and ≥20% are notabl y higher than for the core analy sis (s ee 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 99 th percentile fish consumption rate
- see Table 2
- 11). This sensitivity anal ys is suggests that the risk estimates would be somewhat higher (particularl y for the tail of the risk distribution) if we focus on those watersheds recei ving the greatest amount of atmosphericall y deposited Hg. However, the magnitude of risk is not substantially different from the core analy sis.

 Generating risk estimates using the median watershed
- level fish tissue Hg concentration (instead of the 75 th pe rcentile as used in the core analysis): These sensitivity analy sis results are notabl y 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 analy sis (i.e., 5% when the median fish tissue Hg concentration is used as compared with 10% with the core anal ys is). These results suggest that for some risk metrics, the results of the anal ys is would be significantly

affected were we to use the median fish tissue Hg concentrati on rather than the 75 th percentile. The SAB peer
- review panel concluded that “using the 75 th 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 ty pes 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 analy sis and then discuss the degree to which those sources are reflected in the design of the risk a ssessment 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 anal ys is 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 concentration s across watersheds within the U.S.  The sampling protocols for measuring fish tissue Hg concentration s used by States which results in varying degrees of spatial coverage of fish tissue Hg concentration s across the states.  The response of watersheds to Hg loading including variation in the temporal response and degree to which fish tissue Hg concentration s 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 bodyw eight can also impact exposure and risk estimates. These sources of variab ility and the degree to which each is reflected in the design of the anal ys is are identi fied and described in Table 2
- 14
. Regarding uncertainty , we focus first on identifying potentiall y significant sources of uncertainty impacting the anal ys is. Then we c haracterize (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 anal ys is (including whether sensitivity anal ys es completed for the risk assessment address a particular source of uncertainty). The results of this qualitative assessment are presented in Tabl e 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 sy stems, fuels, physical properties of individual units) The analysis ac counts 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 v ariability 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 approac h 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 explicitl y, 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 fac tors 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 e stimate 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 non EGU sources. IPM further accounts for differences in equipment using Emissions Modifi cation 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 concentration s across watersheds within the U
. S. Mercury fish tissue measurements can display considerable spatial variability ac ross 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 f rom 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 d eposition (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 mea surements (see section 2.4 ). (E ) Sampling protocols for measuring fish tissue Hg concentration s used by States differ, which results in varying degrees of spatial coverage of fish tissue Hg concentration s 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 concentration s across the state and instead are intended to identify and then target areas sus pected 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. H owever, having fish tissue Hg data that are potentially biased towards more heavily Hg
- im pacted 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 concentration s 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 fa ct is a primary determinant of variation in fish tissue Hg concentration s across watersheds. This will have a large impact on the variability in total Hg expos ure, and as shown in Figure 2
- 17 , results in a low correlation between total Hg deposition and f

ish 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 watershe d 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 dif ferent 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 socio
economic 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 rol e. Vietnamese and Laotian fishing activity in specific regions of California, high
- end f ishing activity by Black s and White s 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
- 1 5 (entry E) a lack of comprehensive survey information on subsistence f

ishing 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 distributin g 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 concentration s, while focused activity means that one or more fishers may ge t 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 h ave 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 behavior
related factors inc luding 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 con sumption by subsistence fishers , we have modeled these scenarios assuming that (a) they tend to favor somewhat larger fish (use of the 75 th 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 r easonable 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 unc**  **ertainty 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 depo sition and/or areas with elevated fish tissue Hg concentration s, 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 curr ent 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 concentration s 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, f or 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 mea sured fish tissue data has improved for this version of the risk assessment, the potential still exists that substantial numbers of high
impact watersheds have not been included in the risk assessment due to a lack of fish tissue sampling at those location s. (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 det ermining 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 concentration s 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 concentration s 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 concentration s, 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 population
weighted assessment of subsistence fisher risk, then b ias in the fish tissue Hg concentration s would be problematic (since it would high
-bias an assessment targeted at capturing “typical” risk for these high
consumers). Gi ven that potential high
-bias in the characterization of fish tissue Hg concentration s a cross 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 unc**  **ertainty 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 concentration s 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 75 th 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 75 th percentiles across watersheds, although the bias in the estimate of the 75 th 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 75 th percentile fish tissue Hg concentration s 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 75 th percentile values are actually more likely representative of the central tendency, e.g. the median. We examined trends in the watershed
- level 75 th percenti le 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 75 th percentile estimates increases substantially across these strata (i.e., as the number of samples available to calculated a 75 th percentile increases, the trend in 75 th percentiles also increases). This suggests that other factors equal (see below), there is a distinct potential for low bias in the 75 th 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. Thi s 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 75 th 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 e stimates 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 watershe ds (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 unc**  **ertainty 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**  i nstances where non
- air Hg loading is substantial. **Characterizi ng subsistence fishing activity across areas of high U.S. EGU m**  **ercury deposition** (includes assessing potential locations for subsistence fishing activity as well as details regarding actual fishing and fish consumption behavior) (E) Predicting which water sheds 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 subsist ence 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 assessin g 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 subsist ence 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 subst antially 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 subsistenc e fish consumers (concern that analysis does not cover high
- end recreational angler risk and risk by other potentially high
consuming groups) The SAB raised concerns that by focusing on subsistence fishers, we may have not provided coverage in the risk a ssessment 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 ty pical 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 90 th to 99 th percentile values) which are considered subsistence since they represent a substantial contribution to dietary intake of protein. Furthermore, given that in this revised analy s is, 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 c onsumption 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 unc**  **ertainty 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 ris k 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 i ncluding 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 uncert ainty 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 Gi ven 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 a ssumptions 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 no t 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 remo ved, 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, t hese 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 th at 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 repres entative population
- weighted picture of risk), then the potential impact of these sources of uncertainty is reduced. Co ncerns 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 unc**  **ertainty 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 s pecific 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 MeH g in fish and cooking/ preparation). Two adjustment factors are applied to the 75 th 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 an alysis 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 th at 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 t

hat they in fact, did not support application of alternative (lower) factors. Furthermore, given available information, we do not believe that any sy stematic 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 uncertai nty 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 decision101 **Source of unc**  **ertainty 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 concentration s may not hold if fish tissue Hg concentration s actually reflect an earlier (higher Hg deposition) period. In this case, bias could be introduced into estimates of reduction in fish tissue Hg concentration s (based on the proportionality assumption) since the effect of changes in curre nt Hg deposition on fish tissue Hg concentration s could be overstated (i.e., there could be an effective buffering of the fi sh tissue Hg concentration s 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. EGU
attributable 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 an d the way in which the y 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 concentration s Differences in methylation potential cause variation in fish tissue Hg concentration s across waterbodies (even when those waterbodies have similar rates of Hg loading). In addition, differences in methylation potential can also translate into different tem

poral profiles for the response of fish tissue Hg concentration s 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 concentration s 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 determinati on 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 event ually be reflected in fish tissue Hg concentration s 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 unc**  **ertainty 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. Ch anges 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 diff icult 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 deposition 58 Despite efforts to exclude watersheds with substantial non
air 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. EGU
attributable 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 po tential 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 25 th 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 25 th 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 o f the Mercury Risk TSD, we also explored the issue of whether the proportionality assumption was more applicable to stationar y waterbodies (lakes/ponds) then flowing waterbodies (rivers/streams ). However, for this version of the Mercury Risk TSD, reflectin g 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 unc**  **ertainty 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 concentration s (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 esti mation 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 estim ates 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 tes t 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. E stimates based on emission factor s 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 g reat 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 unc**  **ertainty 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 we ll 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 betw een 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 emissi ons 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 expecte d 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 experi ence 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 unc**  **ertainty 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 monito ring, 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 uncertai nty. 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 p ossible 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 are in l arge 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 ap proaches 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. E very atte mpt 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 r ecycled Hg emissions from oceans and land are The impact of this source of uncertainty was not quantitatively evaluated.106 **Source of unc**  **ertainty 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 recycl ed 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 201 6 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 oth er 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 ex ists 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 - EGU s, 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 hi gh. Uncertainties are introduced because EPA does not include co
- benefits of reductions from controls for other pollutants such as PM 2.5 , which might lead to overestimation of the 2016 non - EGU emissions, lessening the impacts of U.S. EGUs, and therefore l owering 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 industry
specific 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 unc**  **ertainty 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 EGU
derived 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 Pla nning Model (IPM) . The uncertainty in the extent of projected Hg emissions from th ese sources is treated above (see entry “O”). Here the focus is on the question of uncertainty in the location of the 2016 emissions reduction s from the standpoint of which EGUs are projected to operate and so serve as sources of Hg emissions. IP M 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 s ource 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). Gi ven this conflicting information and the uncertainty in projecting Mercury deposition may b e 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 i nflow 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 in itial condition influence negligible.108 **Source of unc**  **ertainty 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 Depositio n 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 versio ns 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 b e 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 mod eled 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., 20 08; 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 b e 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., 2 007a; 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 unc**  **ertainty 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 Gi ven a number of factors (e.g., nutritional deficiencies, reduced access to health care and health
related 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 sen sitive 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
. T wo 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 co
exposure 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. H owever, 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 Observat** **ions** 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 associ ated 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 onl y 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 characteriz ation is based on estimates of RfD
- based HQ. Due to concerns raised b y the SAB peer
- review panel that the IQ loss endpoint may not full y capture the range of neurodevelopmental effects associated with MeHg exposure, IQ loss estimates are presented in brie f 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 deposi tion. 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 notabl y, 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 deposi tion decreasing from ~5% of total to ~2% for the 2005 and 2016 scenarios, respectivel y.
 Based on the 2016 scenario, U.S. EGUs can contribute up to 16% of MeHg in fish tissue. However, generall y, U.S. EGUs contribute a much smaller fraction averaging 3% fo r 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 tissu e 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, generall y, our111

coverage for hi gh 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.

 B ased 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 e stimates 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 observ ations: (a) total and U.S. EGU
- attributable risk s 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 generall y 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 typi cal 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, significantl y 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

analy ses condu cted primaril y to examine uncertainty in appl yi ng 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 50 th percentile fish tissue Hg concentration s (instead of the 75 th percentile values used in the core anal ys is) can result in notable reductions in risk estimates in so me instances , but the SAB peer review panel supports use of the 75 th percentile estimates.112

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**Appendices: Additional Technical Detail on Modeling Elements and Presentation of Supplem**  **ental Risk Estimates** ( C itations 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 n ot 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 Los s**  Estimation of I Q 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). How ever, 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 μ g/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 b y 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 b y the neurologicall y- 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 thi s potential for low
- bias as part of our qualitative uncert ainty 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 scenari o 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 b y 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 clearl y of public health significance. All of the risk estimates summarized he re 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 l oss 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 /m**  **ean 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 estim**  **ates, 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**  **im pact 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., Gi ven 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 qu antitative analyses of the first119 **Source of uncertainty**  **Description Nature of potential impact on**  **the exposure and risk estimates Degree to which the potential**  **im pact of the source of**  **uncertainty is characterized as**  **part of the analysis** sensitiv e 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 e xposure (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. I n both cases, the potential effect on the risk assessm ent would be to potentially down
- bias our estimate of cognitive endpoint
related risk for children. (B) Potential confounding from nutrients, including long
chained polyunsaturated fatty acids (LCPFAs), found in fish This issue is similar to the

nutrient confounding issue discussed above under the RfD
based 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 re ality, 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 effe ct (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

– w e 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 appl ication 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 b y EPA to the SAB are inclu ded 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
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