UNITED STATES ENVIRONMENTAL PROTECTION AGENCY
WASHINGTON D.C. 20460
OFFICE OF THE ADMINISTRATOR
SCIENCE ADVISORY BOARD
September 29, 2011
EPA-SAB-11-017
The Honorable Lisa P. Jackson
Administrator
U.S. Environmental Protection Agency
1200 Pennsylvania Avenue, N.W.
Washington, D.C. 20460
Dear Administrator Jackson:
EPA's Office of Air and Radiation requested that the Science Advisory Board (SAB) review a draft
Technical Support Document: National-Scale Mercury Risk Assessment Supporting the Appropriate and
Necessary Finding for Coal and Oil-Fired Electric Generating Units - March 2011. The goal of this
draft document is to characterize human health exposure and risk associated with U.S. electrical
generating unit (EGU) mercury emissions with a focus on a highly exposed subpopulation, subsistence
fishers. The SAB was asked to comment on the risk assessment, including the overall design and
approach, as well as the use of specific models and key assumptions. The SAB was also asked to
comment on the extent to which specific facets of the assessment are well characterized in the Technical
Support Document.
The SAB could not evaluate the risk assessment based solely upon information provided in the
Technical Support Document. Important elements of the methods and findings are missing or poorly
explained. Additional information provided by EPA representatives during an SAB public meeting on
June 15-17, 2011 and a public teleconference on July 12, 2011 allowed the SAB to gain a sufficient
understanding of the risk assessment to conduct this review.
The risk assessment is designed to assess how a reduction in mercury emissions will translate to
reductions in fish tissue methylmercury concentrations, and in turn, to a reduction in potential risk to
subsistence fishers that would result from the consumption of self-caught fish from inland watersheds.
EPA sought advice from the SAB on key components of its analysis. In response, the SAB reviewed
available information and has made the following findings. The SAB considers the spatial resolution of
the modeling of mercury deposition to watersheds to be appropriate for the analysis. There is agreement
that the approach used to identify watersheds to include in the assessment is reasonable. This approach
is based upon the availability of fish tissue methylmercury data and census data on target populations
with potential subsistence fishers. The SAB agrees that EPA's calculation of a hazard quotient for each
watershed is appropriate as the primary means of expressing risk because it is based on an established
reference dose for methylmercury that reflects a range of potential neurobehavioral effects. Intelligence
Subject: Review of EPA's Draft National-Scale Mercury Risk Assessment
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Quotient (IQ) loss is also used in the assessment to evaluate risk. The SAB considers IQ loss to be an
insensitive indicator of methylmercury neurobehavioral effects and is concerned that its use as an
endpoint could underestimate risk. The SAB recommends that IQ loss be de-emphasized in the risk
assessment and explored as one of several possible secondary public health endpoints as a supplement to
the main analysis. Although the SAB considers the number of watersheds included in the assessment
adequate, some watersheds in areas with relatively high mercury deposition from U.S. EGUs were
under-sampled due to lack of fish tissue methymercury data. The SAB encourages the Agency to contact
states with these watersheds to determine if additional fish tissue methylmercury data are available to
improve coverage of the assessment.
The SAB identifies additional sources of variability and uncertainty in the risk assessment, as well as
limitations imposed by the availability of data. The uncertainties are appropriate for a screening-level
public health assessment. The SAB regards the design of the risk assessment as suitable for its intended
purpose, to inform decision-making regarding an "appropriate and necessary finding" for regulation of
hazardous air pollutants from coal and oil-fired EGUs, provided that our recommendations are fully
considered in the revision of the assessment.
In summary, based on its review of the draft Technical Support Document and additional information
provided by EPA representatives during the public meetings, the SAB supports the overall design of and
approach to the risk assessment and finds that it should provide an objective, reasonable, and credible
determination of the potential for a public health hazard from mercury emitted from U.S. EGUs. The
SAB finds the current draft document deficient, however, because of its lack of transparency in
describing key analytical methods and findings. We urge the Agency to revise the document based on
our recommendations.
We appreciate the opportunity to review the draft mercury risk assessment. We look forward to your
response.
Sincerely,
/Signed/
/Signed/
Dr. Deborah L. Swackhamer
Chair
Dr. Stephen M. Roberts
Chair
SAB Mercury Review Panel
Science Advisory Board
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NOTICE
This report has been written as part of the activities of the EPA Science Advisory Board (SAB), a public
advisory group providing extramural scientific information and advice to the Administrator and other
officials of the Environmental Protection Agency. The SAB is structured to provide balanced, expert
assessment of scientific matters related to problems facing the Agency. This report has not been
reviewed for approval by the Agency, and, hence, the contents of this report do not necessarily represent
the views and policies of the Environmental Protection Agency, nor of other agencies in the Executive
Branch of the Federal government. Mention of trade names of commercial products does not constitute a
recommendation for use. Reports of the SAB are posted on the EPA website at http://www.epa. gov/sab.
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U.S. Environmental Protection Agency
Science Advisory Board
Mercury Review Panel
CHAIR
Dr. Stephen M. Roberts, Professor, Department of Physiological Sciences, Director, Center for
Environmental and Human Toxicology, University of Florida, Gainesville, FL
MEMBERS
Dr. David T. Allen, Professor, Department of Chemical Engineering, University of Texas, Austin, TX
Dr. Thomas Burbacher, Professor of Environmental and Occupational Health Sciences, Director of the
Infant Primate Research Laboratory, Department of Environmental and Occupational Health Sciences,
School of Public Health, University of Washington , Seattle, WA
Dr. James Burch, Associate Professor, Department of Epidemiology and Statistics, Arnold School of
Public Health, University of South Carolina, Columbia, SC
Dr. Hillary Carpenter, Toxicologist, Health Risk Assessment, Environmental Health, Minnesota
Department of Health, St. Paul, MN
Dr. Celia Chen, Research Professor, Department of Biological Sciences, Dartmouth College, Hanover,
NH
Dr. Miriam L. Diamond, Professor, Department of Geography, University of Toronto, Toronto,
Ontario, CANADA
Dr. Charles T. Driscoll, Jr., Professor, Department of Civil and Environmental Engineering, College of
Engineering and Computer Science, Syracuse University, Syracuse, NY
Dr. Thomas M. Holsen, Professor, Department of Civil and Environmental Engineering, Clarkson
University, Potsdam, NY
Dr. James Hurley, Director, Environmental Health Division, Wisconsin State Laboratory of Hygiene,
and Associate Professor, Department of Civil and Environmental Engineering, University of Wisconsin-
Madison, Madison, WI
Dr. David Krabbenhoft, Research Scientist, Wisconsin Water Science Center, U.S. Geological Survey,
Middleton, WI
Dr. Leonard Levin, Technical Executive, Air Toxics Health & Risk Assessment, Environment Sector,
Electric Power Research Institute, Palo Alto, CA
Dr. C. Jerry Lin, Department of Civil Engineering, Lamar University, Beaumont, TX
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Dr. Jana Milford, Professor, Department of Mechanical Engineering, University of Colorado, Boulder,
CO
Dr. M. Christopher Newland, Alumni Professor, Department of Psychology, Auburn University,
Auburn, AL
Dr. Nicholas Ralston, Human Health Effects Research Group Leader, Energy & Environmental
Research Center (EERC), University of North Dakota , Grand Forks, ND
Dr. Stephen L. Rathbun, Professor of Biostatistics, Department of Epidemiology and Biostatistics,
University of Georgia, Athens, GA
Dr. Eric P. Smith, Professor, Department of Statistics, 406A Hutcheson Hall, Virginia Polytechnic
Institute and State University, Blacksburgh, VA
Dr. Alan Stern, Section Chief-Risk Assessment/ Adjunct Associate Professor, Division of Science,
Research & Technology/Dept. of Environmental & Occupational Health, New Jersey Department of
Environmental Protection/University of Medicine and Dentistry of New Jersey-Robert Wood Johnson
Medical School., Trenton, NJ (Affiliation for identification purposes only)
Dr. Edward Swain, Research Scientist, Minnesota Pollution Control Agency, Saint Paul, MN
Dr. Edwin van Wijngaarden, Associate Professor, Community and Preventive Medicine,
Environmental Medicine, and Dentistry, School of Medicine and Dentistry, University of Rochester,
Rochester, NY
Dr. Robert Wright, Associate Professor, Pediatrics, Division of Environmental Health, Harvard School
of Public Health, Boston, MA
SCIENCE ADVISORY BOARD STAFF
Dr. Angela Nugent, Designated Federal Officer, U.S. Environmental Protection Agency, Science
Advisory Board (1400R), 1200 Pennsylvania Avenue, NW, Washington, DC, Phone: 202-564-2218,
Fax: 202-565-2098, (nugent.angela@epa.gov)
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U.S. Environmental Protection Agency
Science Advisory Board
CHAIR
Dr. Deborah L. Swackhamer, Professor and Charles M. Denny, Jr., Chair in Science, Technology and
Public Policy, Hubert H. Humphrey School of Public Affairs and Co-Director of the Water Resources
Center, University of Minnesota, St. Paul, MN
SAB MEMBERS
Dr. David T. Allen, Professor, Department of Chemical Engineering, University of Texas, Austin, TX
Dr. Claudia Benitez-Nelson, Full Professor and Director of the Marine Science Program, Department
of Earth and Ocean Sciences , University of South Carolina, Columbia, SC
Dr. Timothy Buckley, Associate Professor and Chair, Division of Environmental Health Sciences,
College of Public Health, The Ohio State University, Columbus, OH
Dr. Patricia Buffler, Professor of Epidemiology and Dean Emerita, Department of Epidemiology,
School of Public Health, University of California, Berkeley, CA
Dr. Ingrid Burke, Director, Haub School and Ruckelshaus Institute of Environment and Natural
Resources, University of Wyoming, Laramie, WY
Dr. Thomas Burke, Professor, Department of Health Policy and Management, Johns Hopkins
Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD
Dr. Terry Daniel, Professor of Psychology and Natural Resources, Department of Psychology, School
of Natural Resources, University of Arizona, Tucson, AZ
Dr. George Daston, Victor Mills Society Research Fellow, Product Safety and Regulatory Affairs,
Procter & Gamble, Cincinnati, OH
Dr. Costel Denson, Managing Member, Costech Technologies, LLC, Newark, DE
Dr. Otto C. Doering III, Professor, Department of Agricultural Economics, Purdue University, W.
Lafayette, IN
Dr. David A. Dzombak, Walter J. Blenko Sr. Professor of Environmental Engineering , Department of
Civil and Environmental Engineering, College of Engineering, Carnegie Mellon University, Pittsburgh,
PA
Dr. T. Taylor Eighmy, Vice President for Research, Office of the Vice President for Research, Texas
Tech University, Lubbock, TX
Dr. Elaine Faustman, Professor, Department of Environmental and Occupational Health Sciences,
School of Public Health and Community Medicine, University of Washington, Seattle, WA
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Dr. John P. Giesy, Professor and Canada Research Chair, Veterinary Biomedical Sciences and
Toxicology Centre, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
Dr. Jeffrey Griffiths, Associate Professor, Department of Public Health and Community Medicine,
School of Medicine, Tufts University, Boston, MA
Dr. James K. Hammitt, Professor, Center for Risk Analysis, Harvard University, Boston, MA
Dr. Bernd Kahn, Professor Emeritus and Associate Director, Environmental Radiation Center, Georgia
Institute of Technology, Atlanta, GA
Dr. Agnes Kane, Professor and Chair, Department of Pathology and Laboratory Medicine, Brown
University, Providence, RI
Dr. Madhu Khanna, Professor, Department of Agricultural and Consumer Economics, University of
Illinois at Urbana-Champaign, Urbana, IL
Dr. Nancy K. Kim, Senior Executive, Health Research, Inc., Troy, NY
Dr. Kai Lee, Program Officer, Conservation and Science Program, David & Lucile Packard Foundation,
Los Altos, CA (affiliation listed for identification purposes only)
Dr. Cecil Lue-Hing, President, Cecil Lue-Hing & Assoc. Inc., Burr Ridge, IL
Dr. Floyd Malveaux, Executive Director, Merck Childhood Asthma Network, Inc., Washington, DC
Dr. Lee D. McMullen, Water Resources Practice Leader, Snyder & Associates, Inc., Ankeny, IA
Dr. Judith L. Meyer, Professor Emeritus, Odum School of Ecology, University of Georgia, Lopez
Island, WA
Dr. James R. Mihelcic, Professor, Civil and Environmental Engineering, State of Florida 21st Century
World Class Scholar, University of South Florida, Tampa, FL
Dr. Jana Milford, Professor, Department of Mechanical Engineering, University of Colorado, Boulder,
CO
Dr. Christine Moe, Eugene J. Gangarosa Professor, Hubert Department of Global Health, Rollins
School of Public Health, Emory University, Atlanta, GA
Dr. Horace Moo-Young, Dean and Professor, College of Engineering, Computer Science, and
Technology, California State University, Los Angeles, CA
Dr. Eileen Murphy, Grants Facilitator, Ernest Mario School of Pharmacy, Rutgers University,
Piscataway, NJ
Dr. Duncan Patten, Research Professor, Hydroecology Research Program , Department of Land
Resources and Environmental Sciences, Montana State University, Bozeman, MT
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Dr. Stephen Polasky, Fesler-Lampert Professor of Ecological/Environmental Economics, Department
of Applied Economics, University of Minnesota, St. Paul, MN
Dr. Arden Pope, Professor, Department of Economics, Brigham Young University, Provo, UT
Dr. Stephen M. Roberts, Professor, Department of Physiological Sciences, Director, Center for
Environmental and Human Toxicology, University of Florida, Gainesville, FL
Dr. Amanda Rodewald, Professor of Wildlife Ecology, School of Environment and Natural Resources,
The Ohio State University, Columbus, OH
Dr. Jonathan M. Samet, Professor and Flora L. Thornton Chair, Department of Preventive Medicine,
University of Southern California, Los Angeles, CA
Dr. James Sanders, Director and Professor, Skidaway Institute of Oceanography, Savannah, GA
Dr. Jerald Schnoor, Allen S. Henry Chair Professor, Department of Civil and Environmental
Engineering, Co-Director, Center for Global and Regional Environmental Research, University of Iowa,
Iowa City, IA
Dr. Kathleen Segerson, Philip E. Austin Professor of Economics , Department of Economics,
University of Connecticut, Storrs, CT
Dr. Herman Taylor, Director, Principal Investigator, Jackson Heart Study, University of Mississippi
Medical Center, Jackson, MS
Dr. Barton H. (Buzz) Thompson, Jr., Robert E. Paradise Professor of Natural Resources Law at the
Stanford Law School and Perry L. McCarty Director, Woods Institute for the Environment, Stanford
University, Stanford, CA
Dr. Paige Tolbert, Professor and Chair, Department of Environmental Health, Rollins School of Public
Health, Emory University, Atlanta, GA
Dr. John Vena, Professor and Department Head, Department of Epidemiology and Biostatistics,
College of Public Health, University of Georgia, Athens, GA
Dr. Thomas S. Wallsten, Professor and Chair, Department of Psychology, University of Maryland,
College Park, MD
Dr. Robert Watts, Professor of Mechanical Engineering Emeritus, Tulane University, Annapolis, MD
Dr. R. Thomas Zoeller, Professor, Department of Biology, University of Massachusetts, Amherst, MA
SCIENCE ADVISORY BOARD STAFF
Dr. Angela Nugent, Designated Federal Officer, U.S. Environmental Protection Agency, Science
Advisory Board (1400R), 1200 Pennsylvania Avenue, NW, Washington, DC, Phone: 202-564-2218,
Fax: 202-565-2098, (nugent.angela@epa.gov)
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Table of Contents
Acronyms, Abbreviations and Definitions of Terms viii
1. Executive Summary 1
2. Introduction 6
3. Response to charge questions 7
3.1. Overall design 7
3.2. Critical health endpoints besides IQ loss 7
3.3. Use of an IQ loss metric benchmark 9
3.4. Spatial scale of watersheds 9
3.5. Measured fish tissue mercury concentrations 10
3.6. Use of the 75th percentile fish tissue methylmercury value 12
3.7. Consumption rates and location for high-end consumers 15
3.8. Use of Census data to identify high-end fish consuming populations 16
3.9. Use of the Mercury Map s approach 16
3.10. Exclusion of watersheds with significant non-air loadings 19
3.11. Concentration-response function used in modeling IQ loss 19
3.12. Uncertainty and variability 21
3.13. Discussion of analytical results 25
3.14. Responsiveness to the goals of the study 31
3.15. Confidence in the analysis 31
4. Summary list of recommendations 32
References 40
Appendix A: Agency Charge Questions A-l
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ACRONYMS, ABBREVIATIONS AND DEFINITIONS OF TERMS
CASAC Clean Air Scientific Advisory Committee
CMAQ Community Multiscale Air Quality Modeling
System
EGU Electrical Generating Unit
EPA Environmental Protection Agency
GEOS-Chem A global 3-D chemical transport model (CTM) for
atmospheric composition driven by meteorological
input from the Goddard Earth Observing System
(GEOS) of the NASA Global Modeling and
Assimilation Office.
HAP Hazardous Air Pollutant
Hg Mercury
HQ Hazard Quotient
HUC Hydrologic Unit Codes
IQ Intelligence Quotient
MDN Mercury Deposition Network
MeHg Methylmercury
MMAP Mercury Maps
PUFA Polyunsaturated Fatty Acid
RfD Reference Dose
R-MCM Regional Mercury Cycling Model
SAB Science Advisory Board
TRI Toxic Release Inventory
TSD Technical Support Document
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1. Executive Summary
EPA has proposed National Emission Standards for Hazardous Air Pollutants for coal- and oil-fired
Electric Utility Steam Generating Units (EGUs). These proposed standards would require EGUs to
decrease emissions of mercury and other hazardous air pollutants (HAP). In order to regulate HAP
emissions under the Clean Air Act, Section 112(b), the Agency must make a determination that such
regulation is appropriate and necessary based upon a study of the hazards to public health reasonably
anticipated from HAP emissions. As part of this determination, hazards to public health from U.S. EGU
mercury emissions are evaluated in a draft national-scale risk assessment entitled Technical Support
Document: National-Scale Mercury Risk Assessment Supporting the Appropriate and Necessary Finding
for Coal and Oil-FiredElectric Generating Units (March 2011). This SAB report uses the terms "risk
assessment" and "Technical Support Document" interchangeably to refer to EPA's draft document.
The draft risk assessment considers hazards from mercury released from U.S. EGUs and deposited in
watersheds within the continental United States. Mercury deposition is estimated using the Community
Multi-scale Air Quality (CMAQ) model for watersheds classified using 12-digit Hydrologic Unit Codes
(HUC12). The risk assessment focuses on hazard from consumption of methylmercury in self-caught
fish, specifically hazard to children born to women who consume local fresh water fish in a subsistence
manner. Exposure from fish consumption is estimated for watersheds with data on methylmercury
concentrations in fish tissue, and a hazard quotient (HQ) is calculated based upon the current reference
dose (RfD) for methylmercury. The contribution of U.S. EGUs to the HQ for each watershed is
calculated by comparing U.S. EGU deposition rates with total deposition to the watershed, including
other sources, assuming that the contribution of U.S. EGUs to fish tissue concentrations and risk is
proportional to their contribution to total deposition. Intelligence Quotient (IQ) loss is also modeled as a
health endpoint, with a loss of one or more points from methylmercury exposure considered as a public
health concern. Estimated hazards associated with U.S. EGU emissions in 2005 are compared with
estimated hazards expected to remain in 2016 "after imposition of the requirements of the Act."
The SAB Mercury Review Panel was asked to comment on the draft risk assessment, including the
overall design and approach as well as various technical aspects. The Panel was also asked to comment
on the extent to which specific "observations" or conclusions in the risk assessment are supported by the
analytical results. During the course of deliberations, the Panel reviewed background materials provided
by the Office of Air Quality Planning and Standards, as well as public comments on the topic. The SAB
reviewed and approved the report of the Panel. EPA asked the SAB to address fourteen charge
questions, many with multiple parts. This Executive Summary highlights the main findings. Detailed
responses to the individual charge questions are provided in the body of the report.
The SAB finds the Technical Support Document to lack critical details regarding both the methods used
and the results presented. This made the document difficult to review and, in the view of the SAB,
unsuitable in its present form to fully support Agency decision-making. Presentations and information
provided by Agency representatives helped the SAB understand how the risk assessment was conducted,
the rationale for some of the decisions made in approach and the use of data and the translation of the
results. With this additional information, the SAB views the risk assessment favorably, concluding that
it is able to provide an objective, reasonable, and credible determination of the potential for a public
health hazard from mercury emitted from U.S. EGUs. However, the SAB considers the integrity of the
risk assessment to be dependent in part on a transparent description of the analysis, and the Technical
Support Document needs to be strengthened to provide this description.
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This review is based on the text of the report and the additional information provided by the above cited
presentations and discussion provided by EPA representatives. Responses to charge questions indicate
where improvements need to be made, and a summary of the most critical recommendations is provided
in section 4. The SAB's support for the risk assessment is contingent on its recommendations being fully
considered in the revision of the assessment.
Overall design
The Panel finds the overall design and general approach used in the risk assessment to be scientifically
credible. The Technical Support document, however, needs a more detailed description of the modeling
methods and data sources. The report's introduction should make clear from the start that the analysis is
a determination of potential exposure at the scale of watersheds.
Critical health endpoints besides IQ loss; use of an IQ loss metric benchmark; and concentration-
response function used in modeling IQ loss
The SAB supports the use of the HQ approach in the risk assessment. SAB members agree that because
the RfD from which the HQ is calculated is an integrative metric of neurodevelopmental effects of
methylmercury, it constitutes a reasonable basis for assessing risk. Other potential health endpoints were
also considered by the SAB. The SAB notes that a number of measures of potential neurodevelopmental
effects of methylmercury exist, some of which have greater sensitivity to differential mercury exposure
than does IQ loss. However, none are viewed by the SAB as suitable for quantitative risk estimation
with a reasonable degree of scientific certainty at the present time, and consequently none are
recommended for incorporation into the analysis. The SAB does not consider it appropriate for EPA to
use IQ loss in the risk assessment and recommends that this aspect of the analysis be de-emphasized,
moving it to an appendix where IQ loss is discussed along with other possible endpoints not included in
the primary assessment.
While the SAB agrees that the concentration-response function for IQ loss used in the risk assessment
has validity, IQ loss is not a sensitive response endpoint for methylmercury and its use likely
underestimates the impact of reducing methylmercury in water bodies. The SAB agrees that if IQ loss
were retained in the risk assessment despite these reservations, a loss of one or two points on average in
a population would be an appropriate benchmark. The SAB agrees that fish nutrients (e.g., omega-3
fatty acids) can potentially ameliorate neurologic effects associated with methylmercury, but there is not
sufficient information to recommend a quantitative adjustment in health endpoint measures. However,
the SAB agrees that because the RfD from which the HQ is calculated is an integrative metric of risk, it
constitutes a reasonable basis for assessing risk. The RfD is an integrative measure because it considers
the weight of the evidence and determines a quantitative value that is based on the most sensitive
endpoints across multiple studies and endpoints.
Spatial scale of watersheds
The SAB agrees that HUC12 watersheds provide the appropriate level of spatial resolution and offer
advantages over previous assessments at lower resolution (e.g., HUC8). The comparability of this scale
to CMAQ output makes the transferability and applicability of deposition modeling to the watershed
scientifically robust. Further, the finer resolution of HUC12 watersheds is better suited to follow
deposition patterns of a single source such as an EGU and increases the likelihood that measured
deposition within a watershed is homogeneous. The SAB notes that one disadvantage of smaller
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watershed size is that, within a given watershed, the number of fish samples with methylmercury data is
diminished. During the public meeting, the SAB questioned some of the figures with maps showing
modeled deposition across the United States. Some areas showed intense deposition with no obvious
source, leading SAB members to question the accuracy of the modeling or data presentation in the
March draft report. EPA provided clarification and updated maps in July 2011 (Pekar 2011). The SAB
supports EPA's plans to include these updated mercury deposition maps in the revised report so they
correctly reflect total annual mercury deposition per square meter by watershed.
Measured fish tissue mercury concentrations
The SAB agrees that fish tissue methylmercury data are an appropriate basis to estimate the number and
percentage of fish-sampled watersheds where populations may be at risk. Although fish data were only
available for 2,461 HUC12 watersheds out of 88,000 HUC12 watersheds in the continental United
States, this is viewed as sufficient to estimate the number and percentage of fish-sampled watersheds
where populations may be at risk. The SAB notes advantages and disadvantages of the Agency decision
to limit fish tissue concentration data to the period after 1999 but agrees with this approach, given that
older data might not be representative of conditions during the 2005 reference deposition year. The SAB
is concerned about the absence of fish tissue data from some watersheds with higher levels of mercury
deposition. The EPA is encouraged to contact states with these watersheds to determine whether
additional fish tissue data are available to improve coverage of the analysis. The SAB discussed the use
of modeling to estimate fish methylmercury concentrations as a means to include more watersheds. With
further development, this approach could be used for a national scale assessment such as this in the
future but the SAB does not recommend it for the current assessment.
Use of the 75th percentile fish tissue methylmercury value
As a means of selecting methymercury fish concentrations representative of larger, but not the largest,
edible fish, the risk assessment uses the 75th percentile fish concentration for watersheds with one or
more fish concentration value. The SAB considers this percentile reasonable but is concerned that over
half of the watersheds in the assessment have only four or fewer fish samples with methylmercury
concentration, and a significant number of these have a single fish sample. The SAB notes that in
watersheds where only a few fish samples are available, the 75th percentile concentration and exposure
most likely will be underestimated. This should be explained in the report, and a sensitivity analysis
should be conducted using the median fish tissue concentration to better represent the distribution of
concentrations when the sample size is only one fish. The SAB also recommends that the report describe
the sources of fish methylmercury concentration data more fully, including the state sampling programs
that provide most of the data. Discussion of sampling programs should include the types and sizes of
fish obtained, as well as uncertainties associated with this data set, to improve the transparency of the
analysis.
Consumption rates and location for high-end consumers
The SAB finds that the consumption rates and locations for fishing activity for likely highly exposed
consumers, i.e., self-caught fish consuming populations modeled in the analysis, are supported by the
data presented in the document and are generally reasonable and appropriate given the available data. A
diverse range of susceptible populations is represented in the assessment. There are caveats, however,
associated with the sources of fish consumption data, the data sets selected for inclusion, and the
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suitability of data for inclusion in the risk assessment (e.g., in terms of providing annual average intakes
of the edible portion of the fish) that should be acknowledged more fully in the document.
Use of Census data to identify high-end fish consuming populations
The SAB agrees that the criterion of using at least 25 persons per census tract from a given target
subsistence fisher population is a reasonable approach to identify watersheds with potentially highly
exposed fish consuming populations. While other approaches are possible, none is viewed as being more
effective or feasible. The SAB recommends that the document clarify how many watersheds were
eliminated due to this inclusion criterion.
Mercury Maps approach
The SAB agrees with the Mercury Maps approach used in the analysis and has cited additional work that
supports a linear relationship between mercury loading and accumulation in aquatic biota. These studies
suggest that mercury deposited directly to aquatic ecosystems can become quickly available to biota and
accumulated in fish, and reductions in atmospheric mercury deposition should lead to decreases in
methylmercury concentrations in biota. The SAB notes other modeling tools are available to link
deposition to fish concentrations, but does not consider them to be superior for this analysis or
recommend their use. The integration of Community Multiscale Air Quality Modeling System (CMAQ)
deposition modeling to produce estimates of changes in fish tissue concentrations is considered to be
sound. Although the SAB is generally satisfied with the presentation of uncertainties and limitations
associated with the application of the Mercury Maps approach in qualitative terms, it recommends that
the document include quantitative estimates of uncertainty available in the existing literature.
Exclusion of watersheds with significant non-air loadings
In order to reduce uncertainty associated with the Mercury Maps approach, watersheds with significant
non-air loadings of mercury are excluded from the analysis. The SAB agrees with the exclusion criteria
used by the Agency. Additional exclusion criteria were discussed, but their application would be
unlikely to substantially change the results of the assessment. The SAB also recommends that the EPA
provide additional discussion of uncertainties in the mercury emissions from U.S. EGUs and non-EGU
sources and the implications of these uncertainties.
Uncertainty and variability and discussion of analytical results
The SAB discussed the characterization of variability and uncertainty in the Technical Support
Document in detail. Sources of variability and uncertainty in the assessment are summarized in
Appendix F of the draft document. The qualitative nature of this presentation is considered appropriate,
but the identification of important sources of variability and uncertainty is considered incomplete.
Inclusion of several additional sources of variability and uncertainty is recommended. The SAB notes
that the degree of uncertainty associated with the analysis is consistent with a screening level analysis,
and despite the various sources of uncertainty inherent in the approach, the analysis is sound and
reasonable.
The SAB finds that observations in five areas (mercury deposition from U.S. EGUs, fish tissue
methylmercury concentrations, patterns of mercury deposition with mercury fish tissue data, percentile
risk estimates, and number and frequency of watersheds with populations potentially at risk due to U.S.
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EGU mercury emissions) are generally supported by the analytical results presented in the document.
However, there are many examples where results are poorly presented, and in most areas the
uncertainties, variability and data limitations are not well characterized. The SAB has numerous specific
recommendations to improve presentation of findings and observations.
Responsiveness to the goals of the study
The section of the document on Summary of Key Observations does not encapsulate well the critical
issues and significant results of the analysis. The SAB recommends revising this section to link back
directly with the goals of the studies as articulated on Page 13 of the document, i.e.: (a) what is the
nature and magnitude of the potential risk to public health posed by current U.S. EGU mercury
emissions? (b) what is the nature and magnitude of the potential risk posed by U.S. EGU mercury
emissions in 2016 considering potential reductions in EGU Hg emissions attributable to CAA (Clean Air
Act) requirements? and (c) how is risk estimated for both the current and future scenario apportioned
between the incremental contribution from U.S. EGUs and other sources of mercury?
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2. Introduction
EPA's Office of Air and Radiation requested peer review of a Technical Support Document: National-
Scale Mercury Risk Assessment Supporting the Appropriate and Necessary Finding for Coal and Oil-
FiredElectric Generating Units - March 2011, developed to support a proposed rule published in the
Federal Register on March 16, 2011 to regulate emissions of hazardous air pollutants from for coal- and
oil-fired Electric Utility Steam Generating Units (EGUs). Section 112(n)(l) of the Clean Air Act
requires EPA to determine whether it is "appropriate and necessary" to regulate hazardous air pollutants
emissions from EGUs under section 112. The "appropriate and necessary" finding requires EPA to
perform a study of the hazards to public health reasonably anticipated to occur as a result of hazardous
air pollutant emissions, including mercury.
The Science Advisory Board formed an expert ad hoc Panel to peer review the draft Technical
Document. The Panel addressed fourteen Agency charge questions (see Appendix A) and developed the
responses below. The Panel held a public meeting on June 15-17, 2011 to peer review this document and
held a public teleconference on July 20, 2011 to discuss the Panel's draft report. The chartered SAB held
a quality review to approve the draft report on September 7, 2011.
The SAB had difficulty evaluating the Technical Support Document because it lacked critical details.
During the public meeting, presentations and information provided by Agency representatives helped
the SAB understand technical aspects of the analysis. With this additional information and clarification,
the SAB views the risk assessment positively. However, the SAB considers the integrity of the risk
assessment as dependent in part on a transparent description of the methods and findings. The Technical
Support Document needs to better explain what was done and why, translate the results into findings
that relate to the key goals of the analysis and describe where the uncertainties lie. The SAB's support
for the risk assessment is contingent upon a development of a revised document that addresses these
issues.
The body of this report is organized to respond to each of EPA's charge questions. Section 3 provides
responses and specific suggestions and recommendations for revising the Technical Support Document.
Because this SAB report provides many recommendations for strengthening the Technical Support
Document, Section 4 provides a list of the specific recommendations made by the SAB.
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3. Response to Charge Questions
3.1. Overall design
Question 1: Please comment on the scientific credibility of the overall design of the mercury risk
assessment as an approach to characterize human health exposure and risk associated with U.S.
EGU mercury emissions (with a focus on those more highly exposed).
Response: The SAB finds that the overall design and general approach used in the assessment are
scientifically credible.
The overall approach used in the study is to estimate potential risk at a national scale, attributable to
mercury released from U.S. EGUs and deposited to inland waterbodies, for recent (2005) and future
(2016) emissions levels. To accomplish this, the analysis links a series of models and data to estimate
methylmercury exposure via fish consumption and then compares the exposure with a toxicological
benchmark. The series of models allows for the estimation of deposition of mercury emitted by U.S.
EGUs into watersheds. The assessment uses measured concentrations of methylmercury in fish tissue
samples, as well as estimates of future fish methylmercury concentrations, to estimate the number and
percentage of watersheds where populations may be at risk. Human exposure and potential health effects
in these at-risk watersheds are then assessed through the pathway of ingestion of self-caught fish from
inland water bodies for vulnerable subsistence fisher populations.
Although the overall design and general approach are scientifically credible, the SAB has a number of
suggestions and recommendations for enhancing the assessment, based on review of the draft Technical
Support Document and supplementary presentations and information provided by EPA. The responses to
the charge questions below provide those recommendations and suggestions in detail. It will be
important for EPA to address these issues. The Technical Support Document would benefit from a more
detailed description of the modeling methods and data sources, and results need to be presented more
clearly. The Introductory section should make clear, at the earliest possible point, that the analysis is a
determination of watershed impact with exposure addressed as a potential outcome. Despite weaknesses
in the Technical Support Document and uncertainties inherent in an analysis such as this, the SAB
agrees that the risk assessment makes an objective and reasonable determination of the potential for a
public health hazard from mercury emitted from U.S. EGUs.
3.2. Critical health endpoints besides IQ loss
Question 2: Are there any additional critical health endpoint(s) besides IQ loss which could be
quantitatively estimated with a reasonable degree of confidence to supplement the mercury risk
assessment (see section 1.2 of the Mercury Risk TSD for an overview of the risk metrics used in the risk
assessment)?
Response: This charge question raises issues about the use of IQ, as well as use of alternative
quantitative measures. While several alternative approaches were discussed that might supplement IQ
scores, no substitute can be quantitatively estimated with a "reasonable degree of confidence."
Moreover, there are doubts that IQ met this standard. The response to this charge question addresses
both of these issues below.
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Use of 10. There are significant concerns about the use of IQ for identifying the impact of consuming
fish from water bodies with unacceptable levels of methylmercury because IQ will likely result in an
underestimation of potential neurobehavioral impacts, compared to analyses using the hazard quotient
(HQ). Thus, the SAB considers HQ to be a stronger basis for evaluation of methylmercury hazard. The
HQ is based upon the methylmercury reference dose (RfD), which is an integrative measure reflecting a
range of neurobehavioral effects, and it incorporates pharmacokinetic variability. The RfD considers the
weight of the evidence and determines a quantitative value that is based on the most sensitive endpoints
across multiple studies and endpoints. Sensitive endpoints, in this context, are adverse effects that occur
at the lowest exposures.
In contrast, the loss of IQ points is likely to underestimate the impact of reducing methyl mercury in
water bodies. The reason is that IQ score has not been the most sensitive indicator of methylmercury's
neurotoxicity in the populations studied. As noted in the Technical Support Document, in the Faroe
Island study the most sensitive indicators were in the domains of language (Boston Naming Test),
attention (continuous performance) and memory (California Verbal Learning Test). These two tests are
neuropsychological tests that are not subtests of IQ tests and whose relationship with global IQ is not
well characterized. In the Seychelles study, the Psychomotor Development Index was the most sensitive
measure and, while this index is a component of the Bailey Scales of Infant Development, it is not
highly correlated with cognitive measures (Davidson et al. 2008).
Additionally, the use of IQ, or any neuropsychological measure, distracts from the main goal of the
document. The analysis in the document emphasizes the number of fish-sampled water bodies from
which subsistence fishers would be at risk based on an elevated HQ. As is clear in Tables 2-9 to 2-11 in
the Technical Support Document, an analysis based on IQ identifies far fewer water bodies than one
based on the HQ. This is because IQ underestimates hazard, as noted above.
The SAB recommends that EPA reframe the document's discussion of IQ. EPA should incorporate IQ
and other neuropsychological measures as supplemental information and focus on HQ as the primary
critical health endpoint. It is not suggested that the analyses of IQ be removed altogether but rather that
the analyses be framed in an appendix to the report as a secondary analyses of impact of reduced
exposure on potential health-related outcome. The appendix should discuss the basis for selecting a HQ
at or above 1.5 as the criteria for selecting potentially impacted watersheds should be explained. The
appendix should also include discussion of potential effects on other measures like developmental
delays (Grandjean et al. 1997) or neuropsychological tests (as discussed by van Wijngaarden et al.
2006), presented in the overall context of the weight of evidence.
Alternative quantitative measures. One alternative is developmental delay as described by Grandjean et
al. (1997). Here, an estimate of the number of months of delay in verbal skills as tapped by the Boston
Naming Test or in learning and short-term memory as tapped by the California Verbal Learning Test
was made based on regression coefficients describing the relationship among age, methylmercury
exposure, and scores on these tests. The delays were on the order of five to seven months associated
with a 10-fold increase in cord blood methylmercury.
A recent analysis by van Wijngaarden et al. (2006) derived Benchmark Dose Level-Lower 95%
confidence interval values for 26 endpoints, including IQ and other neuropsychological measures from
the nine-year follow up of the Seychelles child development study main cohort. This paper could be
cited in a discussion of markers of health impacts of lowering mercury deposition and reducing intake
by subsistence fishers.
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One SAB member suggests the use of blood markers of selenium-dependent enzyme function, noting
that methylmercury irreversibly inhibits selenium-dependent enzymes that are required to support vital-
but-vulnerable metabolic pathways in the brain and endocrine system. Impaired selenoenzyme activities
would be observed in the blood before they would be observed in brain, but the effect is also expected to
be transitory. The use of these measures is a minority view among the SAB members.
The SAB recommends that the Technical Support Document acknowledge and discuss alternative
quantitative measures but does not recommend a re-analysis based on these measures.
3 .3. Use of an IQ loss metric benchmark
Question 3: Please comment on the benchmark used for identifying a potentially significant public
health impact in the context of interpreting the IQ loss risk metric (i.e., an IQ loss of 1 to 2 points or
more representing a potential public health hazard). Is there any scientifically credible alternate
decrement in IQ that should be considered as a benchmark to guide interpretation of the IQ risk
estimates (see section 1.2 of the Mercury Risk TSD for additional detail on the benchmark usedfor
interpreting the IQ loss estimates).
Response: The consensus is that if IQ were to be used, then a loss of 1 or 2 points as a population
average is a credible decrement to use for this risk assessment. This metric seems to be derived from the
lead literature and was peer-reviewed by the Clean Air Scientific Advisory Committee (U.S. EPA
CASAC 2007). While its applicability to methylmercury is questionable, the size of the decrement is
justified based on the extensive analyses available from the literature reviewed by CASAC. The support
for the model of the relationship between IQ and methylmercury exposure comes from Axelrad and
Bellinger (2007) and from a whitepaper produced by Bellinger (2005).
The analysis in Table 2-10 showing the effect of using a one- or two -point loss is helpful in evaluating
the sensitivity of this measure to the magnitude of the decrement.
3 .4. Spatial scale of watersheds
Question 4: Please comment on the spatial scale used in defining watersheds that formed the basis for
risk estimates generatedfor the analysis (i.e., use of 12-digit hydrologic unit code classification). To
what extent do HUC12 watersheds capture the appropriate level of spatial resolution in the relationship
between changes in mercury deposition and changes inMeHgfish tissue levels? (see section 1.3 and
Appendix A of the Mercury Risk TSD for additional detail on specifying the spatial scale of watersheds
used in the analysis).
Response: The choice of using the HUC12 (Hydrologic Unit Code) watershed delineation of the
contiguous 48 United States for this risk assessment is more appropriate and offers at least two distinct
advantages over the 2001 Mercury Maps study report that employed the larger-scale HUC8 delineation.
First, HUC8s are "cataloguing units" delineation and do not actually represent true watersheds (areas of
land where surface water drainage accumulates to an outflow location). Instead, many HUC8 areas have
flow lines that cross the unit boundaries, thus making this larger scale delineation not technically correct
for any mass accounting procedure like Mercury Maps. The use of HUC12s, which are true watershed
delineations, does not violate this mass accounting assumption. A second strength of the use of
HUC12's is that they have a similar physical scale to the spatial resolution of the CMAQ output (12 km
CMAQ square grid compared to the HUC12 watersheds that are typically about 5-10 km on a side).
Comparable scales make the transferability and applicability of deposition modeling to the watershed
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more scientifically robust. The use of finer scale watersheds enables modeling and deposition runs that
have the detail to follow deposition patterns from a single source, including EGUs. The fine-scale
watershed resolution decreases the likelihood that there is a significant deposition gradient within the
HUC. Further, the relative biogeochemical and ecological homogeneity of an individual HUC12
watershed allows better validity for ascribing fish concentrations to a specific watershed and that those
fish will respond in proportion to changes in atmospheric mercury deposition. The SAB notes, however,
that one potential disadvantage of HUC12 is that a number of HUC12 watersheds contain a very limited
number of fish samples because of their inherent small size, but other factors described in this response
override this concern.
The Technical Support document acknowledges and this SAB agrees that the fish distribution data are
highly skewed toward the Eastern United States. That said, the legend of Figure 2-6 in the Technical
Support Document indicates that 2,170 out of 2,461 watersheds were from the Eastern United States,
leaving approximately 300 samples from Western sites. Given the apparent distribution of high
deposition zones in CMAQ modeling runs displayed in Figures 2-1 and 2-2 in the Technical Support
Document that are not ground-truthed in Mercury Deposition Network deposition measurement, the
SAB is concerned not only about the reality of the identified intense deposition zones (i.e., whether they
are truly intense deposition zones, for example, in the state of Nevada), but also whether these
watersheds were included in this report's analysis. Fish distribution data appear to overlap with some of
these zones of modeled high mercury deposition, and, with 300 fish samples from the Western United
States, there is a high probability for overlap.
The SAB is concerned about the possibility that in some watersheds, multiple small lakes may be
included within a single HUC12. In some cases, lakes within a small geographic zone have been shown
to have quite different chemistry and biological productivity. For instance, within Voyageurs National
Park in northern Minnesota, the mercury content of similarly-sized fish of a given species in about 20
lakes varies by a factor of 10 (Wiener et al. 2006), indicating that even lakes near each other can
bioaccumulate mercury to greatly differing degrees. In HUCs with multiple lakes, the SAB recommends
against using a single fish methylmercury value to describe the HUC. In response to this concern and
other charge questions, the SAB recommends that the authors provide a summary table describing the
characteristics of the watersheds where fish were collected, including the fraction of fish samples
collected from rivers versus lakes, and whether from single or multiple sites.
3 .5. Measured fish tissue mercury concentrations
Question 5: Please comment on the extent to which the fish tissue data used as the basis for the risk
assessment are appropriate and sufficient given the goals of the analysis. Please comment on the extent
to which focusing on data from the period after 1999 increases confidence that the fish tissue data used
are more likely to reflect more contemporaneous patterns of mercury deposition and less likely to reflect
earlier patterns of mercury deposition. Are there any additional sources offish tissue MeHg data that
would be appropriate for inclusion in the risk assessment?
Response: The measured fish tissue data serve as an appropriate basis for the mercury risk assessment
because they are widely available and reflect the actual environmental conditions that influence fish
methylmercury concentrations and human exposure to methylmercury by the target populations. The
SAB notes that the relevant form of mercury in fish tissue for this risk assessment is methylmercury, but
there is sometimes ambiguity as to the mercury form actually measured in surveys from which the fish
tissue data were taken. Many surveys measure total mercury and assume all mercury present in fish is in
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the methyl form. Although empirical data available are largely supportive of this assumption, the
Technical Support Document needs to clearly acknowledge this aspect of the fish tissue data.
While it is always desirable to have a larger sample size, the sample size of 2,461 HUC12 watersheds is
adequate for the goals of the risk assessment. However, as detailed below, the SAB is concerned about
the sources of bias and uncertainty resulting from the state sampling designs used to select watersheds
where fish tissue samples were obtained. For purposes of hazard assessment, it is reasonable to have an
over-representation of HUC12s in the eastern part of the country given the prevalence of EGUs in the
East. However, the description of the character of the data, as well as the selection of analyzable data
(e.g., sizes, distribution of fish sizes across watersheds), should be better detailed in the report.
There are advantages and disadvantages to using fish methylmercury data prior to 1999 for the risk
assessment. The advantage is that considerable fish data were obtained prior to 1999 and the use of these
data could increase the information available for the national risk assessment. The disadvantage is that
fish methylmercury concentrations may have changed since 1999 and these older data may not be
representative of conditions during the 2005 reference deposition year. Unfortunately, there are few high
quality time series data of fish methylmercury concentrations, so it is difficult to quantify the extent to
which fish methylmercury concentrations have changed since the 1990s. As a result, the SAB
recommends that the EPA utilize fish methylmercury data collected since 1999 for the risk assessment.
Given the spatial distribution of mercury deposition from EGUs and the density of fish methylmercury
measurements (Figure 2-15), there are some states that receive what the Technical Support Document
terms "relatively elevated" mercury deposition from U.S. EGU emissions and have limited fish
methylmercury measurements. These states include Pennsylvania, New Jersey, Kentucky and Illinois.
The SAB suggests that the EPA contact these states to investigate if additional recent (since 1999) fish
methylmercury data are available to improve the coverage for the mercury risk assessment. For example,
the Pennsylvania Department of Environmental Protection, Pennsylvania Fish Monitoring Program has
700 sites for the measurement of the methylmercury content of recreational sport fish, with samples
collected from 1979-2007.
EPA's reliance on the National Listing of Fish Advisory and U.S. Geological Survey (USGS)
compilation of methylmercury data sets contributes to uncertainty because these data were collected by
state agencies with various sampling designs and state protocols. Most of the data are not from
probability-based sampling designs, so it is not entirely clear what population the fish tissue samples
represent. The direction of impact on the risk assessment of this variation in sampling designs cannot be
ascertained. Moreover, some states have greater sampling efforts than others. Particularly strong
sampling efforts were observed in South Carolina, Louisiana, Indiana, Iowa, West Virginia and Virginia.
As a consequence of this variability in fish-tissue sampling effort, the risk assessment will be strongly
influenced by states with high sampling efforts. Moreover, Figure 2-18 suggests that the sample is
biased in favor of watersheds with higher mercury deposition and higher EGU-attributable deposition as
predicted by the CMAQ model. This bias could in part be due to the over-representation of HUC12s in
the East but could also occur if states with high deposition also have high fish-tissue sampling effort.
Nevertheless, as per the limitations of the available data, the risk assessment focuses on that portion of
the fish-sampled watersheds at risk, rather than attempting to make inferences to the larger population of
all 88,000 HUC12 watersheds.
Researchers have developed empirical models for fish methylmercury concentrations using water
chemistry and land cover data (Chen et al. 2005; Driscoll et al. 2007; and Watras et al. 1998). These
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empirical relationships have been used to estimate methylmercury concentrations for different fish
species at state and regional spatial scales. Such an empirical modeling approach could be used to
provide more comprehensive estimates of fish methylmercury concentrations across water resources and
potentially improve the extent of future mercury risk assessments. However, if this empirical modeling
approach was to be used in a risk assessment such as this, it would need to be developed and evaluated
at a national scale. Moreover, empirical models would contribute additional uncertainty in the estimation
of fish methylmercury concentration. The SAB is not recommending that this approach be used for the
current risk assessment. Rather, the EPA might consider use of empirical modeling to improve the
information available related to fish methylmercury concentrations in future assessments.
To strengthen the Technical Support Document the SAB recommends that it be revised to provide a
better description of the character of the data, as well as the selection of analyzable data (e.g., sizes,
distribution of fish sizes across watersheds). The SAB also recommends that EPA contact some states
that receive what the Technical Support Document terms "relatively elevated" mercury deposition from
U.S. EGU emissions and have limited fish methylmercury measurements to investigate if additional
recent (since 1999) fish methylmercury data are available to improve the coverage for the mercury risk
assessment.
3 .6. Use of the 75th percentile fish tissue methylmercury value
Question 6: Given the stated goal of estimating potential risks to highly exposed populations, please
comment on the use of the 75th percentile fish tissue MeHg value (reflecting targeting of larger but not
the largest fish for subsistence consumption) as the basis for estimating risk at each watershed. Are
there scientifically credible alternatives to use of the 75th percentile in representing potential
population exposures at the watershed level?
Response: Using the 75th percentile of fish tissue values as a reflection of consumption of larger, but not
the largest, fish among sport and subsistence fishers is a reasonable approach and is consistent with
published and unpublished data on predominant types of fish consumed. While the choice of the 75th
percentile is reasonable for the estimation of the methymercury levels of consumed fish, the
appropriateness of this approach depends on the data from which the value is derived. The SAB is
concerned that around 29% of watersheds sampled have only one fish sample with a fish tissue
methylmercury concentration available. Figure 1 below shows a plot of the number of fish tissue
samples available for rivers (N= 1551 samples from rivers, 41.5% have one fish measurement) using data
provided to the SAB by EPA. There is clear evidence of a very high proportion of samples with only one
fish.
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600
400 -
O
§
g.
i i r
^ J" (T
I il li ii 11—11 ii—n i.
Ir—il
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0 50 100 150 200
Number of samples per watershed
o 75th percentile fish tissue MeHg concentration (ppm) Loess, Smooth=0.05
Figure 2. Comparison of 75th percentiles of fish methylmercury concentrations for fish samples across watersheds
with different sample sizes, using Excel data on rivers provided to the SAB. The fitted curve is based on a Loess
smoother with smoothing parameter set to 0.2.
The SAB recommends inclusion of a graph depicting the number of tissue samples available for analysis
by tissue concentration. The SAB also recommends that the document discuss this source of uncertainty,
including adding a table with the distribution of the number of available fish samples and the fish size
from which they were obtained across watersheds to indicate the extent of the problem. The Technical
Support Document should describe in more detail why including fish tissue concentrations from one fish
sample is likely to result in an underestimate of the number of watersheds at risk. Furthermore, the SAB
recommends that EPA should also conduct a sensitivity analysis using the median fish tissue
concentration to better represent the distribution of fish tissue methylmercury levels where the sample
size is one and provide a bound on the risk assessment. The use of other percentiles in the sensitivity
analysis is not recommended given the limitations of the fish tissue data available.
The SAB acknowledges that fish sampling programs can result in the collection of fish sizes that can be
either larger or smaller than the actual ecosystem distribution depending on sample collection methods
and objectives (e.g., states may focus on collection of larger predator fish or areas where higher mercury
levels tend to be found). The SAB recommends that the document describe more clearly the source of
the fish methylmercury data and provide at least a general discussion of how fish sampling programs
differ in ways that can contribute variability and uncertainty to the data set, such as fish capture methods
and criteria for selecting fish to measure methylmercury concentrations. Given that fish sizes are likely a
variable in most datasets, the report should also include information on the sizes of fish that were
analyzed. In doing so, the Technical Support Document may be able to quantify the impact, if any, of
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the size of fish sampled in watersheds with few fish tissue samples available on estimated mercury
concentrations. The SAB also recommends that the Technical Support Document clarify that the 75th
percentile represents available fish tissue data that may or may not represent the fish in the watershed or
the fish consumed.
3.7. Consumption rates and location for high-end consumers
Question 7: Please comment on the extent to which characterization of consumption rates and the
potential location for fishing activity for high-end self-caught fish consuming populations modeled in the
analysis are supported by the available study data cited in the Mercury Risk TSD. In addition, please
comment on the extent to which consumption rates documented in Section 1.3 and in Appendix C of the
Mercury Risk TSD provide appropriate representation of high-endfish consumption by the subsistence
population scenarios used in modeling exposures and risk Are there additional data on consumption
behavior in subsistence populations active at inlandfreshwater water bodies within the continental
U.S.?
Response: The SAB finds that the consumption rates and locations for fishing activity are supported by
data presented in Section 1.3 and in Appendix C of the Technical Support Document. In addition, the
targeted locations and fish consumption data used in the analysis are generally appropriate and
reasonable given the available data. The risk assessment uses sources that reported daily consumption
for populations of low socioeconomic status African- and European-Americans females as the target
population for the risk assessment. In addition, consumption rates from a study that targeted Laotian-
and Vietnamese-Americans, previously identified in the central valley of California, are included in the
assessment, as well as those from a study of Great Lakes tribes. Thus, a diverse range of susceptible
populations is represented in the assessment.
The SAB recommends that a few caveats should be acknowledged more fully in the document. The
main consumption estimates comes from a relatively small survey of individuals attending a fishing
convention in South Carolina, so the consumption estimates reported in the Burger 2002 study may be
imprecise, in particular for women. The SAB recommends that the Technical Support Document
acknowledge that, while several estimates of fish consumption rates are used in the risk assessment,
other estimates reported by Burger could be used. For example, median fish consumption estimates may
better represent the distribution of fish consumption data than mean estimates. It should also be
acknowledged that the Burger survey was conducted in 1998, and that fish consumption rates even in
subsistence populations may have changed.
Another issue raised by the SAB focuses on the seasonality of fish consumption. Data on consumption
generated from Southern states (e.g., Burger 2002 data from South Carolina) may reflect year-round
consumption, whereas fishers in Northern states may only fish for nine months a year or less. While
failure to take seasonality of fishing into consideration could result in overestimation of fish-derived
methylmercury exposure for some regions, the SAB notes that some communities preserve fish for
consumption outside the fishing season. It is important to be certain that fish consumption rates used in
the risk assessment are in the form of annual averages, e.g., consumption rate expressed in terms of
grams of fish consumed per week per year. Also, it is unclear whether the risk assessment uses
annualized fish consumption rates and whether fish consumption is based on concentrations that are "as
caught" or "as prepared." The SAB recommends that this information concerning seasonality be
clarified in the Technical Support Document. There is a general agreement that the Technical Support
Document adequately utilizes existing data to identify consumption rates and target populations that are
representative of the most highly exposed susceptible populations.
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Regarding alternative approaches, the SAB notes that population-based fish consumption rates could be
applied, although these data tend to show lower fish consumption rates than surveys focusing on
subsistence and sport-caught fish (Knobeloch et al. 2005). This would tend to underestimate risks and
would not be consistent with the Technical Support Document objective to target sensitive, highly
exposed individuals. Therefore, this alternative is not recommended.
In regard to fish consumption generally, the SAB recommends that EPA better explain its rationale for
assuming that subsistence consumers eat fish larger than seven inches in length and asks EPA to provide
references supporting its assumptions and to discuss uncertainties associated with this assumption.
3.8. Use of Census data to identify high-end fish consuming populations
Question 8: Please comment on the approach used in the risk assessment of assuming that a high-end
fish consuming population could be active at a watershed if the "source population "for that fishing
population is associated with that watershed (e.g. at least 25 individuals of that population are present
in a U.S. Census tract intersecting that watershed). Please identify any additional alternative
approaches for identifying the potential for population exposures in watersheds and the strengths and
limitations associated with these alternative approaches (additional detail on how EPA assessed where
specific high-consuming fisher populations might be active is provided in section 1.3 and Appendix C of
the Mercury Risk TSD).
Response: Overall, the SAB agrees that the criterion of using at least 25 persons per census tract from a
given target population (Laotian, poor Hispanic, American Indian populations, amongst others) is a
reasonable approach. The approach is driven by the necessity of using existing data to identify
watersheds with susceptible proximal populations. While the source population selected is somewhat
arbitrary, the SAB agrees that it is a reasonable approach, and that other approaches may not be as
effective or feasible. Regardless of what number is chosen, the prevalence of subsistence fishing in the
target communities remains unknown. EPA indicated that a sample of 25 individuals or greater was
selected to be reasonably certain that at least one subsistence fisher is potentially active at the watershed.
No major concerns are raised by the SAB concerning this issue. However, the SAB recommends that the
Technical Support Document clarify how many census tracts were eliminated due to the use of this cut
point. The Technical Support Document should also include information on the relative distribution of
the sample size of the susceptible populations in the census tracts that were targeted. That is, an absolute
sample of 25 may represent different proportions of the total target population in a given census tract,
which may reflect differences in subsistence fishing behavior. The Technical Support Document should
also discuss the possibility that more remote waterways are fished by subsistence anglers as well and the
potential of this uncertainty for underestimating exposures
3 .9. Use of the Mercury Maps approach
Question 9: Please comment on the draft risk assessment's characterization of the limitations and
uncertainty associated with application of the Mercury Maps approach (including the assumption of
proportionality between changes in mercury deposition over watersheds and associated changes in fish
tissue MeHg levels) in the risk assessment. Please comment on how the output of CMAQ modeling has
been integrated into the analysis to estimate changes in fish tissue MeHg levels and in the exposures and
risks associated with the EGU-relatedfish tissue MeHg fraction (e.g., matching of spatial and temporal
resolution between CMAQ modeling and HUC12 watersheds). Given the national scale of the analysis,
are there recommended alternatives to the Mercury Maps approach that could have been used to link
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modeled estimates of mercury deposition to monitored MeHgfish tissue levels for all the watersheds
evaluated? (additional detail on the Mercury Maps approach and its application in the risk assessment
is presented in section 1.3 and Appendix E of the Mercury Risk TSD).
Response: Limitations/uncertainty associated with Mercury Maps (MMaps) approach and
proportionality assumption. The risk assessment's qualitative characterization of the limitations and
uncertainty in the application of Mercury Maps approach is appropriate. The SAB recommends that
EPA also summarize quantitative estimates of the uncertainty published in the existing literature in
Appendix F of the Technical Support Document. CMAQ is considered to be the appropriate tool for
providing the link between EGU emissions and mercury deposition to HUC12 watersheds with
methylmercury fish data. There are quite a few comparisons, for example, between mercury wet
deposition as modeled by CMAQ and as observed by the Mercury Deposition Network (e.g., Lin et al.
2007, Prongprueksa et al. 2008, and Bullock et al. 2009). A similar search of the literature for other
components of this risk assessment would allow at least partial quantification of the variability or
uncertainty in this risk assessment, including any literature relating to the time lag in the response of
waterbodies to changes in mercury deposition (e.g., Munthe et al. 2007).
The Mercury Maps model states that for steady-state conditions, reductions in fish tissue concentrations
are expected to track linearly with reductions in air deposition to a watershed with an intercept of zero
for watersheds receiving mercury input exclusively via atmospheric deposition. This proportionality
assumption is extended for the Technical Support Document study so that methylmercury levels in fish
could be apportioned among mercury sources based on the associated apportionment of mercury
deposition within a given watershed. The model is a reduced form of the IEM-2M watershed model used
in the Mercury Study Report to Congress (U.S. EPA, 1997), whereby the equations of these models are
reduced to steady state and consolidated into a single equation relating the ratio of current/future air
deposition rates to current/future fish tissue concentrations.
Given these assumptions, Mercury Maps will work only with watersheds in which air deposition is the
sole significant source of mercury and steady-state conditions are assumed. This indicates that the
extension of the proportionality is valid only when other factors influencing methylation potential and
catch profiles (species and trophic levels) remain relatively constant in a given watershed. Watersheds in
which mercury input sources other than air deposition, such as mineral recovery operations using
mercury, mercury cell chloralkali facilities and geologically high mercury inputs, are present and
contribute loads that are significant relative to the air deposition load to that watershed are set aside from
analysis in this risk assessment.
Since the Mercury Maps approach was developed, several recent publications have supported the
finding of a linear relationship between mercury loading and accumulation in aquatic biota (Orihel et al.
2007; Orihel et al. 2008; Harris et al. 2007). These studies suggested that mercury deposited directly to
aquatic ecosystems can become quickly available to biota and accumulated in fish, and that reductions in
atmospheric mercury deposition should lead to decreases in methylmercury concentrations in biota.
These results substantiate EPA's assumption that proportionality between air deposition changes and
fish tissue methylmercury level changes is sufficiently robust for its application in this risk assessment.
Regarding the limitations and uncertainty associated with the application of Mercury Maps, it is
acknowledged that the Mercury Maps approach (i.e., the assumption of proportionality between input
changes and fish response) represents both a critical element of the analysis and a potentially important
source of uncertainty. The sensitivity analyses conducted in the risk assessment addresses two specific
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uncertainties related to application of Mercury Maps: (1) concerns over including watersheds that may
be disproportionately impacted by non-air mercury sources, and (2) application of the Mercury Maps to
both flowing and stationary freshwater bodies to verify if the two scenarios would produce different
results. The results of these sensitivity analyses suggest that uncertainty related to the Mercury Maps
approach is unlikely to substantially alter the assessment result that mercury emissions from U.S. EGUs
potentially constitute a public health concern.
Integration of CMAQ data to HUC12 watersheds for estimating changes in fishMeHg, exposures and
risks). The use of 12-km spatial resolution in CMAQ modeling is a significant refinement of the
previous analysis, which was conducted using 36-km resolution. The integration of CMAQ data at this
finer resolution into the analysis for estimating changes in fish tissue methylmercury levels is sound,
provided that the proportionality assumption holds true (discussed in the previous response to this
charge question).
CMAQ modeling at a 12-km spatial resolution is used to estimate total annual mercury deposition
caused by U.S. and non-U.S. anthropogenic and natural sources over each watershed. For the purposes
of the risk analysis, watersheds are classified using HUC12 codes (USGS, 2009), representing a fairly
refined level of spatial resolution with watersheds generally 5 to 10 km on a side, which is consistent
with research on the relationship between changes in mercury deposition and changes in methylmercury
levels in aquatic biota. Although interpolating the deposition data from a coarser model grid (CMAQ) to
a finer watershed grid (HUC12) will somewhat diffuse the peak deposition near large point sources, the
data integration approach is sound.
The CMAQ modeling at 12-km resolution is a considerable (nine-fold) spatial refinement of the
modeling conducted to support the Clean Air Mercury Rule (36-km resolution). Modeling results at
finer resolution can be used to better resolve deposition patterns near point sources. The confidence in
applying the 12-km resolution CMAQ results for estimating fish tissue methylmercury changes and its
associated exposure/risk is heavily dependent on the robustness of the proportionality assumption in the
Mercury Maps approach. The limitation and uncertainty of this assumption has been elaborated on in the
response to the first part of this charge question.
Alternatives to the Mercury Maps approach linking modeled deposition to monitoredMeHgfish tissue
levels. The SAB agrees with the application of Mercury Maps in this assessment. There are other
modeling tools capable of making a national scale assessment, such as the Regional Mercury Cycling
Model (R-MCM). However, the R-MCM is more data intensive and the results produced by the two
model approaches should be equivalent.
The R-MCM, a steady-state version of the time-dependent Dynamic Mercury Cycling Model, has been
publicly available to and used by the EPA (Region 4, Athens, Environmental Research Laboratory) for a
number of years. R-MCM requires more detail on water chemistry, methylation potential, etc., and
yields more information as well. Substantial data support the Mercury Maps and the R-MCM steady-
state results, so that the results of the sensitivity analysis and the outcomes from using the alternative
models would be equivalent between the two modeling approaches. Though running an alternative
model framework may provide additional reassurance that the Mercury Maps "base case" approach is a
valid one, it is unlikely that substantial additional insight would be gained with the alternative model
framework.
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3.10. Exclusion of watersheds with significant non-air loadings
Question 10: Please comment on the EPA 's approach of excluding watersheds with significant non-air
loadings of mercury as a method to reduce uncertainty associated with application of the Mercury Maps
approach. Are there additional criteria that should be considered in including or excluding watersheds?
Response: The technique used to exclude watersheds that may have substantial non-air inputs is sound.
Although additional criteria could be applied, they are unlikely to substantially change the results.
EPA excludes those watersheds that either contained active gold mines or had other substantial non-U.S.
EGU anthropogenic releases of mercury. Identification of watersheds with gold mines is based on a
2005 USGS data set characterizing mineral and metal operations in the United States. The data represent
commodities monitored by the National Minerals Information Center of the USGS, and the operations
included are those considered active in 2003. The identification of watersheds with substantial non-EGU
anthropogenic emissions is based on a Toxic Release Inventory (TRI) net query for 2008 non-EGU
mercury sources with total annual on-site mercury emissions (all media) of 39.7 pounds or more. This
threshold value corresponds to the 25th percentile annual U.S. EGU mercury emission value as
characterized in the 2005 National Air Toxics Assessment. The EPA team considers the 25th percentile
U.S. EGU emission level to be a reasonable screen for additional substantial non-U.S. EGU releases to a
given watershed.
This appears to be a sound approach. The caveat is that TRI reporting may be biased high or low by the
reporting entities, so it is not possible to judge whether the exclusion is reasonably conservative or not.
There is no particular step EPA can take to rectify this uncertainty, although sensitivity tests could be
run on different reporting thresholds and the number (and area) of excluded watersheds that result. As a
minimum, the uncertainty in the TRI should be acknowledged, and the number of watersheds excluded
in the base case and the uncertainty analysis should be explicitly stated.
Other criteria that EPA could consider for exclusion of particular watersheds are:
• Watersheds that are near urban areas, since those may have significant mercury inputs from
runoff which are not included in the TRI reporting database, and
• Watersheds that are excessively polluted, for example by sanitary sewer discharges or highly
anoxic conditions that might deter overall consumer fishing by many users.
3.11. Concentration-response function used in modeling IQ loss
Question 11: Please comment on the specification of the concentration-response function used in
modeling IQ loss. Please comment on whether EPA, as part of uncertainty characterization, should
consider alternative concentration-response functions in addition to the model used in the risk
assessment. Please comment on the extent to which available data and methods support a quantitative
treatment of the potential masking effect of fish nutrients (e.g. omega-3 fatty acids and selenium) on the
adverse neurological effects associated with mercury exposure, including IQ loss. Detail on the
concentration-response function used in modeling IQ loss can be found in section 1.3 of the Mercury
Risk TSD.
Response: As noted in the response to questions 2 and 3, the analyses of IQ should assume a less
important role in the final document than in the present one. Question 11 contains three questions
pertaining to the concentration-response function describing methylmercury's effect on IQ. The
response to the first question is that the rationale for the concentration-response function is appropriate,
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but with qualifications noted below. The response to the second question is that there is no alternative
concentration response function that should be considered, but the analysis should be tempered,
qualitatively, by factors that could influence the shape of the concentration function. The response to the
third question is that masking by fish nutrients could influence the shape of the concentration response
function, but there is not sufficient information to recommend a quantitative adjustment. These three
responses are expanded upon in order below.
The specification of the concentration-response function. The function used comes from a paper by
Axelrad and Bellinger (2007) that seeks to define a relationship between methylmercury exposure and
IQ. A whitepaper by Bellinger (Bellinger 2005) describes the sequence of steps in relating
methylmercury exposure to maternal hair mercury and then hair mercury to IQ. The Technical Support
Document furthers notes that IQ has shown utility in describing the health effects of other
neurotoxicants. These are appropriate bases for examining a potential impact of reducing methylmercury
on IQ, but the SAB does not consider these compelling reasons for using IQ as a primary driver of the
risk assessment. Instead, IQ should serve as a secondary measure along with other measures discussed in
the responses to questions 2 and 3. The modeling of the impact of IQ should be placed in the appendix
and accompanied by the qualifications noted below.
Alternative Concentration Response functions. The concentration-response function derived by Axelrad
and Bellinger (2007) is acceptable for use in supplementary analyses in the Technical Support
Document. It should be noted, however, that this function is likely to underestimate the effect on IQ of
reducing mercury deposition for the reasons itemized here and in the response to charge question 2.
There is another reason that a model based on a linear relationship between exposure and
neurobehavioral effect may underestimate the true effect of reducing exposure. It is evident from animal
studies conducted under highly controlled conditions that the relationship between daily intake and brain
mercury (the most suitable biomarker of exposure) is not linear, but rather is a power function with a
power coefficient that is greater than 1.0; the power coefficient was 1.3 in a review of animal studies
(Newland et al. 2008). This means that a decrease in intake will produce a greater-than-linear decrease
in brain concentration. Thus, the impact of any reductions produced by reducing mercury emissions
could be underestimated by the linear model used in the document. This observation is not intended to
suggest that a new model be used, only that a qualitative argument should be made that the potential
health impact may be underestimated.
A quantitative treatment of the mitigating impact of nutrients. The factors listed in this section could
mitigate the concentration-effect relationship and should be mentioned in the Technical Support
Document, but there is not enough known about their quantitative impact to support a recommendation
of a re-analysis.
There is evidence from the Seychelles study that nutrients can mask effects of prenatal methylmercury
exposures. Davidson et al. (2008), Strain et al. (2008) and Stokes-Riner et al. (2011), demonstrated that
maternal hair mercury was associated with protein disulfide isomerase only after controlling for the
effects of maternal omega-3 polyunsaturated fatty acid (PUFA) status. Controlling for omega-3 PUFAs
steepened the slope of the concentration effect relationship (Strain et al. 2008). These nutrients are found
in many marine fish species, but less is known about their concentration in freshwater fish and the
concentrations may be lower. This issue is important because the concentration-effect relationship used
in the Technical Support Document analysis derives from the consumption of marine fish but it is
applied in the Technical Support Document to the consumption of freshwater fish. Since the slope might
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be steeper with freshwater fish, it is possible that the analysis in the Technical Support Document
underestimates the impact of reducing mercury deposition on consumers of freshwater fish.
Not only do omega-3 PUFAs mask methylmercury's neurotoxicity, but they confer benefits of their own
that are of direct interest in considering the health impact of fish consumption. The studies by Oken et
al. (2005, 2008) directly compared the benefits of fish consumption with the hazards associated with
methylmercury exposure. These provide further evidence that the benefits of consuming marine fish
may mask methylmercury's effects, a conclusion that is directly relevant to freshwater fish.
One SAB member points out that methylmercury is a potent inhibitor of multiple families of selenium-
dependent enzymes that are required by the brain and endocrine system (Carvalho et al. 2008; 2011;
Seppanen et al. 2004; Ralston and Raymond 2010). Therefore, the adverse effects of high
methylmercury exposures on these enzymes could be accentuated among populations with poor
selenium nutritional intakes and diminished among those with rich selenium status. Since the
subsistence fish consumers that form the focus of this study are at notable risk of having poor nutrition,
mercury exposures may be non-linearly related to toxicity risks. Other SAB members note that effects of
selenium on methylmercury toxicity are based primarily on observations in animals, and there is
disagreement in the scientific community regarding the significance of these observations to humans.
The same SAB member also suggests that since selenium abundance is largely observed to be inversely
related to mercury bioaccumulation (Chen et al. 2001; Paulsson and Lindberg 1989; Belzile et al. 2004),
diminishments in fish methylmercury concentrations following reductions in mercury deposition will
not be uniform across watersheds. Selenium's inverse relationships to methylmercury bioaccumulation
and toxicity may interact to exacerbate mercury exposure risks in watersheds with low selenium
availability. This SAB member thinks that special consideration should be given to evaluating potential
health risks from consumption of fish with high mercury contents that originate from watersheds in low
selenium regions. Other SAB members note that the Mercury Maps (proportional response) approach is
not affected by spatial differences in fish methylmercury content, and in fact this is one of the strengths
of this approach. Changes in fish methylmercury concentrations may differ among aquatic ecosystems in
absolute terms when mercury loading declines, depending upon whether initial fish concentrations are
high or low. However, the reductions in fish methylmercury concentrations within these watersheds are
nonetheless expected to be proportional to the decreases in loading.
Additional Point. Finally a statement on Page 84, Table F-2 references the Seychelles study instead of
the New Zealand study. This should be corrected. The statement is: "Regarding outliers, when an outlier
data point from the New Zealand 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 mercury, to -
0.125)." This uncertainty should be acknowledged more explicitly in the body of the document rather
than being merely mentioned in detail in a table in the Appendix. No additional analyses in the
Technical Support Document are necessary; it could just be mentioned in the section on limitations and
uncertainties that risk assessment estimates would be reduced by 25%.
3.12. Uncertainty and variability
Question 12: Please comment on the degree to which key sources of uncertainty and variability
associated with the risk assessment have been identified and the degree to which they are sufficiently
characterized.
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Response: To answer this question, the SAB defines variability and uncertainty according to EPA's
standard usage, which is consistent with the definitions given by Cullen & Frey, 1999. These definitions
are as follows:
"Variability refers to temporal, spatial, or interindividual differences (heterogeneity) in
the value of an input. In general, variability cannot be reduced by additional study or
measurement."
"Uncertainty may be thought of as a measure of the incompleteness of one's knowledge
or information about an unknown quantity whose true value could be established if a
perfect measuring device were available."
The Technical Support Document presents a qualitative overview of variability and uncertainty in
Appendix F. The qualitative nature of the discussion is appropriate since this is a conditional analysis.
However, the SAB recommends an expanded discussion in Appendix F of variability and uncertainty to
make explicit the uncertainties associated with the Agency's key analytical choices, which the SAB
supports. This discussion could be organized according to the figures depicting sample calculations of
high and low EGU impact that were provided at the SAB's public meeting on June 15, 2011 and
reproduced below (see Figures 3 and 4, next page). The SAB recommends that these figures be added to
the report along with an explanation of how the calculations were conducted.
Sample Calc - High US EGU Impact (2016)
Example - lake watershed on OH-WV border
(HUC12 ID: 50302011009)
Cooking adjustment
Factor:
95th% ingestion rate:
172 g/day
IR = 0.65 ug/kg-day
Total HQ = |R_= 0.65 = 6.5
_ RfD 0.1
BW
Body
weight:
\64g
MeHg RfD: ,
0.1 ug/kg-day
US EGU risk = HQ * FracEGU
= 6.5 *0.236 = 1.5
(ppm)2016
75th: 0.16'
90th: 0.27
90th: 0.32
Samples: 44
Fish tissue (ppm) 2005
Total Hg dep
(CMAQ):
2016 Hg dep
(CMAQ):
IR: daily MeHg intake rate (ug/kg-day)
FTC: MeHg fish tissue concentration (ug/g or ppm)
FIR: fish ingestion rate (g/day)
CAF: cooking adjustment factor
RfD: methyl mercury RfD
FracEGU: fraction dep from US EGU
Figure 3: U.S. EPA-provided (June 16,2011) schematic showing sample calculation - high U.S. EGU impact
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Sample Calc- Low US EGU Impact (2016)
Example - river watershed on NY
(HUC12 ID: 20200040205)
Cooking adjustment
Factor:
95th% ingestion rate:
172 g/day
IR = 1.61 ug/kg-day
Total HQ = ]R_= 1.61 =16
_ RfD 0.1
BW
Body
weight:
\64g
MeHg RfD: ,
0.1 ug/kg-day
\7
US EGU risk = HQ * FracEGU
= 16*0.014 = 0.2 (i.e., <1)
75*: 0.40'
90th: 0.44
75th: 0.42
90th: 0.47
5 samples
Fish tissue (ppml 2005
Total Hg dep
(CMAQ):
2016 Hg dep
(CMAQ):
IR: daily MeHg intake rate (ug/kg-day)
FTC: MeHg fish tissue concentration (ug/g or ppm)
FIR: fish ingestion rate (g/day)
CAF: cooking adjustment factor
RfD: m eth yl m ercu ry RfD
FracEGU: fraction dep from US EGU
Figure 4: U.S. EPA-provided (June 16,2011) schematic showing sample calculation - low U.S. EGU impact
In addition to the explicit discussion of variability and uncertainty, the SAB suggests that language be
used throughout the Technical Support Document to clarify the scope of the results vis-a-vis variability
and uncertainty in data and methods. For example, the Technical Support Document should cite the
evaluation of uncertainty in the CMAQ and MMAPs source documents. Notwithstanding the
uncertainties in the approach, the SAB considers the approach presented in the Technical Support
Document sound and reasonable.
Variability. The SAB notes the topics covered in Appendix F regarding variability. The clarity of the
documentation of the impact of individual sources of variability could be improved. Carefully selected
maps and additional figures could be particularly helpful in providing this clarity. The SAB recommends
that the following sources of variability be included in Appendix F to avoid misinterpretation of study
results and outcomes.
• The effect of temporal variability in the following on estimates of mercury deposition
o Appendix F should describe CMAQ boundary conditions that are necessary to
establish in order to run the model for the 2 temporal scenarios
• Variation in geographic patterns of populations of subsistence fishers.
o Appendix F addresses geographic variability in total and U.S. EGU-attributable
mercury deposition and fish tissue concentrations. Appendix F should be expanded to
discuss spatial variability in populations of subsistence fishers, noting the limited
geographic coverage of watersheds with fish tissue concentrations.
• Variability in nature and protocols of state collection of fish data (see the response to
Question 5, also mentioned below).
• Variation in fisher populations; for example, variation in body weights (potentially across
race/ethnicities) and fishing and consumption habits.
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• Variability in the factor used to translate mercury concentration measured at time of
collection (i.e., expressed per unit wet weight) in comparison to mercury concentration at
point of consumption following cooking.
Uncertainty. Appendix F defines sources of uncertainty for several components of the overall approach
and selected parameter characterizations. The level of uncertainty is consistent with a screening level
analysis. The SAB advises EPA to strengthen the discussion of each uncertainty presented by
identifying at least qualitatively the direction of its effect on the overall risk assessment. For example,
the small fish sample sizes results in underestimates of the 75th percentiles, which propagates to
conservative underestimates of risk.
The SAB has discussed some sources of uncertainty in responses to other Charge Questions (e.g.,
Question 9). To summarize, the SAB recommends that Appendix F be expanded to provide a more
complete listing and discussion of key uncertainties associated with the assessment. Additional sources
of uncertainty that should be considered for expanded discussion include:
• Overall emission inventories, especially the non-EGU inventory derived as a modified
version of the National Emissions Inventory (NEI). Appendix F should discuss the
uncertainties in inventory components; whether and how the uncertainty changes between the
2005 to 2016 scenarios, including uncertainties in the TRI database; whether there is bias in
the EGU and non-EGU components of the inventory; and whether the EGU emission
estimates are derived from the best performing facilities or from the complete set of facilities.
• Alternative future scenario forecasts. Appendix F should more clearly describe the variables
that are held constant versus factors that are varied between the two scenarios.
• Uncertainty in location of 2016 emissions reductions. Due to EPA's projection methods,
there is uncertainty about where emissions reductions will occur between 2005 and 2016,
which in turn influences the spatial patterns of deposition from EGUs in the 2016 scenario.
Appendix F should address the uncertainties in the 2016 scenario regarding the specific
geographic locations of reductions in EGU-derived mercury deposition as a fraction of total
mercury deposition.
• Use of CMAQ and performance evaluation of CMAQ. Appendix F should provide more
detailed description of uncertainty in CMAQ, including references to existing evaluations of
the model.
• "Hot spots"
o Appendix F should address whether the Mercury Maps approach, as implemented, is
adequate to characterize the existence and extent of mercury "hot spots."
• Impacts of excluding watersheds from the analysis.
o Appendix F should detail the criteria used for excluding watersheds, characterize the
watersheds excluded by different criteria, and describe the estimated deposition in
these watersheds.
• Representativeness of approximately 2,500 watersheds compared to 88,000 HUC12
nationwide.
o Appendix F should characterize any bias introduced by looking at this subset of
watersheds (e.g., some states are over-represented, such as Indiana and Minnesota,
while others are under-represented such as Pennsylvania).
• Fish populations and fish tissue database (see SAB responses to questions 5, 6 and 13 for
more detail). Appendix F should include discussion of:
o Sample size for characterization of Implications of a data set with a low number of
fish per watershed. Appendix F should identify the distribution of fish samples per
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watershed and the possible implications of this distribution, including the
implications of sample size for characterization of 75th percentile fish tissue
concentration.
o Uncertainty in methylmercury fish tissue concentrations from differences in sampling
and analytical protocols used by States that contribute data and errors introduced by
potential misidentification of locations, etc.
• Adjustment between wet and cooked weight of fish. EPA relies upon a single older study to
derive an adjustment factor of 1.5. Alternative and newer peer reviewed studies of cooking
effects on mercury in fish should be acknowledged (e.g., Musaiger et al. 2008; Farias et al.
2010) and used to discuss uncertainty associated with this assumed value.
o Appendix F should note that this is a constant value applied in the calculation and
thus does not bias but could skew the results.
• Uncertainty of the assumption of proportionality and the MMAPs approach (see SAB
response to Question 9).
• Characterization of susceptible human populations (see SAB responses to Questions 7 and 8)
o Characterizing subsistence fishing activity within high EGU deposition sites.
o Implications of choosing subsistence fishers and excluding high-end sport fishers.
o Census information that may exclude groups such as students, immigrants).
• Fish consumption rates (see SAB Response to Question 7).
o Limitations of the single study used to support the Technical Support Document's
fish consumption rate for female subsistence fishers.
o Size of fish consumed.
• Derivation of the concentration-response relationship and RfD based on data from marine
fish and mammal species, not inland freshwaters.
o Appendix F should discuss the uncertainty introduced by not using RfDs derived
based on studies of consumption of fish from inland freshwaters. (See SAB response
to Question 11).
• Applicability of the concentration-response relationship and RfD for low socio-economic
status populations. This relationship has not been examined.
o Appendix F should discuss how this relationship may bias the report toward
underestimating risk.
• Effect of the nutritional benefits of fish consumption in comparison to risks from mercury.
Appendix F should address how the lack of consideration of this factor that may bias the
analysis toward underestimating risk (see SAB response to Question 11).
3.13. Discussion of analytical results
Question 13: Please comment on the draft Mercury Risk TSD's discussion of analytical results for each
component of the analysis. For each of the components below, please comment on the extent to which
EPA 's observations are supported by the analytical results presented and whether there is a sufficient
characterization of uncertainty, variability, and data limitations, taking into account the models and
data used.
Mercury deposition from U.S. EGUs
Response: EPA's observations in section 2.3 of the Technical Support Document (p. 35)1 are generally
supported by EPA's observations about mercury deposition as depicted in analytical results provided to
1 Section 2.3 observations:
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the SAB by EPA following the SAB meeting in the form of a Memorandum from Zachary Pekar, July 1,
2011, entitled "Clarification and Updating of Mercury Deposition Maps Provided in the Technical
Support Document: National-Scale Mercury Risk Assessment." The SAB supports EPA's plan to
include updated figures from the memorandum in EPA's Technical Support Document as replacements
for Figures 2-1 to 2-4 in the March 2011 draft so that they correctly reflect total annual mercury
deposition per square-meter by watershed. The SAB recommends that the spatial patterns of simulated
deposition shown in Figure 2-1 to 2-4 be better explained and that EPA should characterize data
limitations more effectively.
EPA's observations about mercury deposition as depicted in Technical Support Document Figures 2-1
to 2-4 are supported by analytical results. However the 12-km deposition maps are very different than
previously produced maps on the 36-km scale (for example in Texas and Nevada). The SAB
recommends that EPA explain these differences and that EPA consider including separate maps of wet
and dry deposition and/or aggregating the results into an approximately 36 km grid scale for comparison
to earlier maps and to data plots, such as national deposition maps from the Mercury Deposition
Network.
In general, the uncertainties associated with these results are not well characterized or adequately
quantified. For example, there have been several intercomparison studies among numerical models for
long-range transport of mercury and studies on model uncertainty evaluation that are not discussed or
referenced. The SAB recommends that EPA summarize these references (Bullock et al. 2009;
Pongprueksa et al. 2008; Lin et al. 2007; Ryaboshapko 2007) to help frame the overall uncertainty of the
deposition estimates.
In addition, EPA should discuss more completely the inputs that are kept constant for the 2016 scenario
and the inputs that are varied (and by how much). This information may merit discussion earlier in the
report. In addition, the CMAQ results are very dependent on global boundary conditions that are
supplied by the GEOS-Chem model. Uncertainty in those inputs will be carried through to the results.
This should be noted.
Fish tissue methyl mercury concentrations
Response: The observations listed in section 2.4 of the Technical Support Document (pp. 43-44)2 are
generally supported by the analytical results. The SAB recommends that EPA clarify the text to improve
the description of the analytical results for each bulleted observation as described below.
• Patterns of total and U.S. EGU-related Hg deposition differ considerably.
• US Hg deposition is generally dominated by sources other than U.S. EGUs (with the contribution from U.S. EGUs
decreasing between the 2005 and 2016 scenarios).
• The contribution of U.S. EGU deposition to total deposition does vary across watersheds and can represent a
relatively large fraction in some (more limited) instances.
2 Section 2.4 observations:
• Focus on U.S. EGU-attributable Hg fish tissue levels is in the eastern half of the U.S.
• 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), this reflects the fact that Hg fish tissue samples are focused in the east where U.S. EGU
deposition is greater.
• Relative to the combined impact of other sources, U.S. EGUs represent a smaller, but still potentially important
contributor to total fish tissue MeHg levels.
• Despite the relatively small fraction of total fish tissue MeHg associated with U.S. EGUs on average, for a subset of
watersheds, they can make a substantially larger contribution.
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Although there is sufficient characterization of variability, EPA should characterize uncertainty and data
limitations more fully. Specifically, the small sample sizes of mercury concentrations in fish for the
individual watersheds (-29% of watersheds have n=l) will result in lower estimates of mercury
concentrations in the 75th percentile as shown earlier in Figures 1 and 2 in this SAB report. This data
limitation bias will be propagated to underestimate the hazard in the risk assessment.
The text in the observations should be modified to refer to tissue and mercury "concentrations" rather
than "levels" to be more precise. "Level" is a generic term and can refer to any number of different
metrics. Finally, where the percentages of EGU-contribution to fish methylmercury are mentioned, EPA
should clarify that those values pertain to only fish-sampled watersheds. Given the under-sampling in
watersheds where there are high levels of deposition, the percentages indicated could be higher.
Some figures and tables would also benefit from modification or elimination. Figures 2-7 to 2-10 are
difficult to interpret because the symbols do not reflect the number of observations for that site.
Improved plots should display symbols proportional to sample size and provide color or shading of
symbols to represent observed fish concentrations. In addition, the maps shown in Figures 2-7 to 2-14
need to include the western continental United States. These figures unnecessarily cut off the western
continental United States. While the SAB understands the reason for this omission (there is minimal
expected change in EGU emissions in the western United States), it is important to show the results for
the entire United States in the figures of this national assessment. In the absence of national maps, the
reader (especially someone with interest in the western United States) many be left wondering about
current fish methylmercury concentrations in this region (see Figure 2-6), as well as the model predicted
changes in fish methylmercury for the 2016 scenario.
The legend for Figure 2-8 should make it clear that the 2016 mercury tissue concentrations are computed
by adjusting the 2005 concentrations to account for lower expected deposition as per the Mercury Maps
approach. The third bullet item on page 36 of the Technical Support Document should be corrected to
indicate that Figures 2-7 and 2-8 give concentrations of mercury in fish, not total mercury deposition. In
addition, the figures showing the top 10th percentile (2-11 to 2-14) should be removed since the pattern
of mercury is greatly affected by high sampling effort in South Carolina, Indiana, West Virginia and
Louisiana. The current maps could also result in undue public concern in those states. Finally, the text
describing Table 2-5 needs to be clarified to state that the relationships are not causal.
Patterns of mercury deposition with mercury fish tissue data
Response: Overall the SAB agrees that the observations in section 2.5 of the Technical Support
Document (pp. 48-49)3 are supported by the analytical results presented and there is a sufficient
characterization of uncertainty, variability, and data limitations. However, a number of changes are
needed to better clarify these points. The Technical Support Document should clearly describe the
degree to which the non-uniform, state-specific data availability influences this analysis. For example,
South Carolina, Louisiana and Indiana all have abundant data availability compared to most states. EPA
should discuss how this data availability bias affects the analytical results. The SAB recommends that
3 Section 2.5 observations:
• The fish tissue MeHg sampling data (summarized at the watershed-level) provides limited coverage for areas with
elevated U.S. EGU Hg deposition. Therefore, the number of "at risk" watersheds as characterized in this risk
assessment may be substantially higher than estimated.
• Hg fish tissue levels are not correlated with total Hg deposition (the relationship is highly dependent on methylation
potential of individual waterbodies).
• Hg fish tissue samples were generally collected in regions with elevated total Hg deposition.
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this section be substantively rewritten to improve clarity and to highlight the major relevant points. As
discussed below, EPA should revise the text in footnote 36, which is critical to the understanding of
Figures 2-15 and 2-16, and yet is not clearly enough written for the reader to understand the key
information. Also, the figure legends within each of Figures 2-15 and 2-16 need to be changed because
the "blue areas" are not "water bodies," but rather "watersheds," which include water bodies that
sometimes are more obvious than their watersheds (e.g., the Minnesota portion of Lake Superior, Long
Island Sound and perhaps erroneously, the Canadian portion of Lake Champlain). The SAB
recommends that these two maps be replotted with a third color that clearly identifies the areas of
overlap.
Figure 2-17 is critically important not only to this section, but also to the overall document. The SAB
suggests that this figure could be brought into this document much earlier because it adds value to
understanding the lack of direct relationships between deposition and mercury in fish. In a sense, it
frames the justification for the approach taken in the overall analysis. The SAB recommends that EPA
provide a more complete introduction to Figure 2-17 that would state the important premises of the
analysis applied in this risk assessment - that spatial variability of deposition rates is only one major
driver of spatial variability of fish methylmercury and that variability of ecosystem factors that control
methylation potential (especially wetlands, aqueous organic carbon, pH, and sulfate) also play a key
role. A question was also raised as to whether Figure 2-17 has been truncated, and if so, did it need to
be? That is, are there data above 1.0 ppm fish concentration and 40 ug/m2-yr deposition? The SAB
suspects that there are.
Figure 2-18 could similarly be moved to an earlier section of the document because it indicates that the
analysis identified watersheds with higher rates of deposition than the national (-88,000 HUC 12
watersheds) trend and that the watersheds with available fish data are in fact, those with higher EGU-
derived mercury deposition rates.
The red areas of Figures 2-15 and 2-16 are labeled in each map's legend as "Watersheds with relatively
elevated US EGU Hg dep." Footnote 36 explains how the red areas are identified, an explanation that is
densely written, as follows:
Footnote 36. Areas of "elevated U.S. EGU-related Hg deposition" refer to areas that are at or above
the average deposition level seen in watersheds with U.S. EGU-attributable exposures above the
MeHg RfD. Specifically, we used exposure estimates based on the 95th percentile fish consumption
rate (for the female high consumer scenario assessed nation-wide) to identify watersheds with U.S.
EGU-attributable exposures above the MeHg RfD and then queried for the average U.S. EGU-
related Hg deposition across that subset of watersheds. This average deposition rate differed for the
2005 and 2016 Scenarios (i.e., 3.79 and 1.28 ug/m2, respectively). These values were used as the
basis for identifying watersheds with levels of U.S. EGU-related Hg deposition for the 2005 and
2016 Scenarios presented in Figures 2-13 and 2-14.
It is troublesome that footnote 36 implies that the threshold for what constitutes "relatively elevated U.S.
EGU Hg deposition" is different in the two maps. The red area in Figure 2-15 is characterized as an
average deposition rate of 3.79 and for Figure 2-16, 1.29 ug/m2. The next, and last, sentence is
confusing, and implies that 3.79 and 1.29 are used as thresholds for identifying the red areas: "These
values were used as the basis for identifying watersheds..." This characterization may confuse readers, in
that readers probably expect similarly colored geographic areas in adjacent similar maps to be presented
as portraying quantitatively similar environmental information, an expectation that these maps
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apparently do not meet. The SAB suspects that the deposition rate threshold for inclusion in the map is
probably relatively constant, and communicating the threshold would be a more useful characterization
than describing the average deposition rates, which are different for understandable, but unexplained,
reasons. For any given watershed, the threshold is the EGU-attributable deposition rate that produces
EGU-attributable exposure "above the MeHg RfD." In practical terms for this risk assessment of
subsistence fishers, this threshold is a modeled EGU-attributable increment in fish concentration that is
greater than 0.038 ppm methylmercury, a concentration that does not correspond to a constant mercury
deposition rate because the concentration varies among watersheds in accordance with the
proportionality principle described in the risk assessment. However, the SAB notes that the average
mercury deposition rate that produces this incremental methylmercury concentration will be similar
between the 2005 and 2016 scenarios. If so, the red areas could then be characterized, for example, as
"elevated U.S. EGU-related mercury deposition that refers to areas where deposition from EGU
emissions has the potential, even in the absence of mercury from other sources, to cause exposures
above the methylmercury RfD." The average threshold EGU-attributable mercury deposition rate for
exceeding the threshold could be presented, along with the average deposition in the red area. The
revised document should explain why the average deposition rate is lower in the 2016 scenario red area,
rather than assume that the reader will immediately know why.
However the red area is dealt with, a more complete and understandable explanation needs to be
presented than the current explanation of footnote 36.
Percentile risk estimates
Response. Generally, the percentile risk estimates in 2.6.1 are calculated in a reasonable manner and the
observations on pages 53-54 of the Technical Support Document4 are appropriate. The Technical
Support Document especially provides a useful discussion of the uncertainties of high values in Tables
2-5 and 2-7. The SAB has several suggestions to improve the presentation of the material and results for
other parts of section 2.6.1.
The Technical Support Document should include an explanation of why the values in Tables 2-6 and 2-7
decrease when going from the 50th to 75th percentile. This is likely because the ranked risk values are
not the same as the ranked EGU contributions. This difference should be mentioned. Perhaps the tabled
values should be referred to in some way as averaged.
The values in Tables 2-6 and 2-7 are based on averaging the values that are 2.5% below and 2.5%
above. EPA should consider whether it is better to use a 2.5% range or use the 10 nearest values. EPA
should also describe how the range is selected for the 99th percentile.
4 Section 2.6.1 observations:
• For the high-end female consumer assessed at the national-level, total IQ loss and total HQ estimates do not change
in a systematic way between the 2005 and 2016 Scenarios with these levels often being of potential health concern
across a wide variety of consumption rates and watershed percentiles.
• By contrast (again focusing on the high-end female consumer assessed nationally), both U.S. EGU-incremental IQ
loss and the U.S. EGU increment-based HQ display notable reductions between the 2005 and 2016 Scenarios, but
U.S. EGU-attributable risk still exceeds potential levels of concern for a over a quarter of watersheds.
• Estimates of risks generated for the high-end female consumer population (assessed at the national-level) are
generally higher than risks estimated for the other high-end fisher populations, with the exception of white and black
fisher populations assessed in the southeast.
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In section 2 page 54, the paragraph comparing "risks" for high-end females with other populations is
oversimplified. Depending on the percentiles considered, "risks" for Laotians, Vietnamese and Tribal
fish consumers can also be higher than for high-end females. The highest consumption rates should be
summarized in an appendix.
In section 2 page 55, EPA should provide more information on the gold-mining impacted watersheds in
the Southeast. For example, it seems that gold mining occurred historically in a relatively small region
of South Carolina, and only a few mines have recently been re-activated. Is it really appropriate to
discount or question concerns about EGU affected exposure across the whole Southeast on this basis?
In Tables 2-6 and 2-7, EPA should consider reporting consumption rates and putting the percentiles in
parentheses rather than reporting the percentiles and having the rates in parentheses.
In Table 2-15 and other places, the mean is included. Since the mean is not a percentile, the table header
should be changed or the median used.
Number andfrequency of watersheds with populations potentially at risk due to U.S. EGU mercury
emissions
Response: The SAB has no significant concerns regarding the observations in section 2.6.2 of the
Technical Support Document (pp. 57-58).5 The SAB recommends that language be added regarding the
change in the percentage of watersheds that continue to be above the RfD (or above a change in one to
two IQ points, if this aspect of the risk assessment is retained) after EGU emissions are removed.
Furthermore, on the SAB recommends that the first bullet point on page 57 to change the language
"before taking into account deposition..." to something that does not imply temporality (e.g., "when you
factor out other sources of mercury deposition"). The SAB also recommends that if the document
discusses loss of IQ points, the revised document should refer to this change in relation to "populations
living close to watersheds" rather than "watersheds".
With regard to the target population in a broader context, the size of the potentially impacted population
is a key factor to consider in this risk assessment. This issue is outside the scope of the data available for
the risk assessment, even though it is very relevant to the objectives of the Technical Support Document
and its application to public health policy. The document focuses on subsistence fishing populations as a
target population likely to be the most severely impacted by methylmercury consumption in fish. There
is scant evidence documenting the prevalence or extent of subsistence fishing in the United States. Some
SAB members note similarities in consumption rates among sport fishers and subsistence fishing
populations. The inclusion of sport fishers with relatively higher fish consumption rates could expand
the size and extent of the targeted susceptible population. Similarly, only limited information on the
locations or characteristics of watersheds that are excluded from the analysis is provided (p. 63, bullet 4,
Figs 2-15, 2-16). The SAB suggests that more detailed information be included regarding these
watersheds and the uncertainties associated with their exclusion. In addition, the document should
5 Section 2.6.2 observations:
• Less than 1% of the watersheds have an IQ loss of 1 point when deposition from U.S. EGUs is considered before
taking into account deposition and exposures resulting from other sources of Hg.
• Between 2 and 12% of the watersheds have HQs> 1.5, based on U.S. EGU mercury deposition before factoring in
any other sources of mercury.
• Combining the two categories of watersheds with populations at-risk due to U.S. EGU mercury emissions
summarized in the last two bullets, we get a total estimate ranging from 2 to 28% of watersheds, with this range
reflecting in part the U.S. EGU percent contribution that is considered (e.g., 5, 10, 15 or 20%).
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address the excluded watersheds within the context of predicted mercury deposition patterns. Some
enumeration of the extent to which the target population would be expanded if these factors had been
incorporated into the analysis would help provide important additional information on the potential
scope and magnitude of the hazards estimated in the assessment. The SAB recognizes that some
additional data may be available on the consumption patterns of recreational anglers, but that EPA did
not have time or resources to integrate this information into the current analysis.
3 .14. Responsiveness to the goals of the study
Question 14: Does section 2.8 respond to the goals of the study and does it encapsulate the critical
issues and the significant results of the analysis?
Response: Section 2.8 responds to the goals of the study, but the manner in which it highlights the key
findings could be improved. The section should be revised to explicitly respond to each of the goals of
the study as set out on page 13 of the Technical Support Document:
(a) What is the nature and magnitude of the potential risk to public health posed by current
U.S. EGU mercury emissions?
(b) What is the nature and magnitude of the potential risk posed by U.S. EGU mercury
emissions in 2016 considering potential reductions in EGU mercury emissions
attributable to Clean Air Act requirements? and
(c) How is risk estimated for both the current and future scenario apportioned between the
incremental contribution from U.S. EGU's and other sources of mercury?
In response to these goals, the SAB sees that the major finding of the study is that a reduction in
mercury emissions will translate to reductions in fish tissue methylmercury concentrations, and in turn,
to a reduction in potential risk to subsistence fishers that would result from the consumption of self-
caught fish from inland watersheds. While there are numerous unquantified sources of variability and
uncertainty that are contained in the numerical estimates of potential risk, the variability and uncertainty
do not contradict this basic finding.
3.15. Confidence in the analysis
Question 15: Despite the uncertainties identified, is there sufficient confidence in the analysis for it to
determine whether mercury emissions from U.S. EGUs represent a potential public health hazardfor the
group of fish consumers likely to experience the highest risk attributable to U.S. EGU?
[Note: This question was not among the original charge questions. It was formulated by the SAB as an
alternative to the second subquestion originally posed by EPA for Charge Question 14, which read as
follows: "In addition, please comment on the degree to which the level of confidence and precision in
the overall analysis is sufficient to support use of the risk characterization framework described on page
75."]
Response: Notwithstanding the uncertainties inherent in this analysis, the Technical Support Document,
after incorporation of the recommendations of the SAB, should provide an objective, reasonable and
credible determination of the potential for a public health hazard from mercury emitted from U.S. EGUs.
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4. Summary List of Recommendations
This SAB report contains many recommendations for improving the Technical Support Document
presented in the responses to the charge questions. These recommendations can be organized into three
general themes:
1. Improve clarity of the Technical Support Document in terms of the methods used in the risk
assessment and presentation of results. The reader should be able to understand how risk
calculations were performed, the rationale for key decisions regarding the use of models and
sources of input data, and results obtained from the analysis and the conclusions. SAB support
for the risk assessment is contingent upon this improvement in clarity being accomplished.
2. Expand the discussion of sources of variability and uncertainty in the risk assessment. Several
additional sources of uncertainty should be acknowledged and discussed briefly in the Technical
Support document.
3. De-emphasize IQ loss as an endpoint in the risk assessment.
For convenience, specific recommendations have been extracted from the body of the report and are
listed below.
Questionl: Overall design
• The Introductory section should make clear, at the earliest possible point, that the analysis is a
determination of watershed impact with exposure addressed as a potential outcome.
Question 2: Critical health endpoints besides IQ
• The SAB recommends that EPA reframe the document's discussion of IQ. EPA should
incorporate IQ and other neuropsychological measures as supplemental information and focus on
HQ as the primary critical health endpoint.lt is not suggested that the analyses of IQ be removed
altogether but rather that the analyses be framed in an appendix to the report as a secondary
analysis of impact of reduced exposure on potential health-related outcome. The appendix should
discuss the basis for selecting a HQ at or above 1.5 as the criteria for selecting potentially
impacted watersheds should be explained. The appendix should also include discussion of
potential effects on other measures like developmental delays (Grandjean et al. 1997) or
neuropsychological tests (as discussed by van Wijngaarden et al. 2006), presented in the overall
context of the weight of evidence.
• The SAB recommends that the Technical Support Document acknowledge and discuss
alternative (to HQ) quantitative measures but does not recommend a re-analysis based on these
measures.
Question 4: Spatial scale of watersheds
• In HUCs with multiple lakes, the SAB recommends against using a single fish methylmercury
value to describe the HUC.
• The SAB recommends that the authors provide a summary table describing the characteristics of
the watersheds where fish were collected, including the fraction of fish samples collected from
rivers versus lakes, and whether from single or multiple sites.
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Question 5: Measured fish tissue mercury concentration
• The SAB recommends that the EPA utilize fish methylmercury data collected since 1999 for the
risk assessment.
• The SAB recommends that it be revised to provide a better description of the character of the
data, as well as the selection of analyzable data (e.g., sizes, distribution of fish sizes across
watersheds), should be better detailed in the report.
• The SAB recommends that EPA contact some states that receive what the Technical Support
Document terms "relatively elevated" mercury deposition from U.S. EGU emissions and have
limited fish methylmercury measurements to investigate if additional recent (since 1999) fish
methylmercury data are available to improve the coverage for the mercury risk assessment.
Question 6: Use of the 75th percentile fish tissue methylmercury value
• The SAB recommends inclusion of a graph depicting the number of tissue samples available for
analysis by tissue concentration.
• The SAB also recommends that the document discuss this source of uncertainty, including
adding a table with the distribution of number of available fish samples and the fish size from
which they were obtained across watersheds to indicate the extent of the problem. The Technical
Support Document should describe in more detail why including fish tissue concentrations from
one fish sample is likely to result in an underestimate of the number of watersheds at risk.
• The SAB recommends that EPA should also conduct a sensitivity analysis using the median fish
tissue concentration to better represent the distribution of fish tissue methylmercury levels where
the sample size is one and provide a bound on the risk assessment.
• The use of other percentiles in the sensitivity analysis is not recommended given the limitations
of the fish tissue data available.
• The SAB recommends that the document describe more clearly the source of the fish
methylmercury data and provide at least a general discussion of how fish sampling programs
differ in ways that can contribute variability and uncertainty to the data set, such as fish capture
methods and criteria for selecting fish to measure methylmercury concentrations.
• The report should include information on the sizes of fish that were analyzed. In doing so, the
Technical Support Document may be able to quantify the impact, if any, of the size of fish
sampled in watersheds with few fish tissue samples available on estimated mercury
concentrations.
• The SAB also recommends that the Technical Support Document clarify that the 75th percentile
represents available fish tissue data that may or may not represent the fish in the watershed or the
fish consumed.
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Question 7: Consumption rates and location for high-end consumers
• The SAB recommends that a few caveats should be acknowledged more fully in the document.
The main consumption estimates came from a relatively small survey of individuals attending a
fishing convention in South Carolina, so the consumption estimates reported in the Burger 2002
study may be imprecise, in particular for women. The SAB recommends that the Technical
Support Document acknowledge that, while several estimates of fish consumption rates were
used in the risk assessment, other estimates reported by Burger could have been used. For
example, median fish consumption estimates may better represent the distribution of fish
consumption data than mean estimates. It should also be acknowledged that the Burger survey
was conducted in 1998, and that fish consumption rates even in subsistence populations may
have changed.
• The SAB recommends that this information concerning seasonality be clarified in the Technical
Support Document
• The SAB recommends that EPA better explain its rationale for assuming that subsistence
consumers eat fish larger than seven inches in length and asks EPA to provide references
supporting its assumptions and to discuss uncertainties associated with this assumption.
Question 8: Use of census data to identify high-end fish consuming populations
• The SAB recommends that the Technical Support Document clarify how many census tracts
were eliminated due to the use of the 25 individual cut point.
• The Technical Support Document should include information on the relative distribution of the
sample size of the susceptible populations in the census tracts that were targeted.
• The Technical Support Document should discuss the possibility that more remote waterways are
fished by subsistence anglers as well and the potential of this uncertainty for underestimating
exposures.
Question 9: Use of the Mercury Maps approach
• The SAB recommends that the quantitative estimates of the uncertainty associated with use of
the Mercury Maps approach published in the existing literature be summarized in Appendix F of
the Technical Support Document.
Question 10: Exclusion of watersheds with significant non-air loadings
• The uncertainty in the TRI (screen) should be acknowledged, and the number of watersheds
excluded in the base case and the uncertainty analysis should be explicitly stated.
Question 11: Concentration-response function used in modeling IQ loss
• IQ should serve as a secondary measure along with other measures discussed in the responses to
questions 2 and 3. The modeling of the impact of IQ should be placed in the appendix and
accompanied by the qualifications discussed in section 3.11 of this SAB report.
• A statement on Page 84, Table F-2 references the Seychelles study instead of the New Zealand
study. This should be corrected.
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Question 12: Uncertainty and variability
• The SAB recommends an expanded discussion in Appendix F of variability and uncertainty to
make explicit the uncertainties associated with the Agency's key analytical choices, which the
SAB supports. This discussion could be organized according to the figures depicting sample
calculations of high and low EGU impact that were provided at the SAB's public meeting on
June 15, 2011 and reproduced as Figures 3 and 4. The SAB recommends that these figures be
added to the report along with an explanation of how the calculations were conducted.
• The SAB suggests that language be used throughout the Technical Support Document that
clarifies the scope of the results vis-a-vis variability and uncertainty in data and methods. For
example, the Technical Support Document should cite the evaluation of uncertainty in the
CMAQ and MMAPs source documents.
• The SAB recommends that the following sources of variability to be included in Appendix F to
avoid misinterpretation of study results and outcomes.
o The effect of temporal variability in the following on estimates of mercury deposition,
o Appendix F should describe CMAQ boundary conditions that are necessary to establish
in order to run the model for the 2 temporal scenarios,
o Variation in geographic patterns of populations of subsistence fishers,
o Appendix F addresses geographic variability in total and U.S. EGU-attributable mercury
deposition and fish tissue concentrations. Appendix F should be expanded to discuss
spatial variability in populations of subsistence fishers, noting the limited geographic
coverage of watersheds with fish tissue concentrations,
o Variability in nature and protocols of state collection of fish data (see the response to
Question 5, also mentioned below),
o Variation in fisher populations; for example, variation in body weights (potentially across
race/ethnicities) and fishing and consumption habits,
o Variability in the factor used to translate mercury concentration measured at time of
collection (i.e., expressed per unit wet weight) in comparison to mercury concentration at
point of consumption following cooking.
• The SAB advises EPA to strengthen the discussion of each uncertainty presented by identifying
at least qualitatively the direction of its effect on the overall risk assessment. For example, the
small fish sample sizes results in underestimates of the 75th percentiles, which propagates to
conservative underestimates of risk.
• The SAB recommends that Appendix F be expanded to provide a more complete listing and
discussion of key uncertainties associated with the assessment. Additional sources of uncertainty
that should be considered for expanded discussion include:
o Overall emission inventories, especially the non-EGU inventory derived as a modified
version of the National Emissions Inventory (NEI). Appendix F should discuss the
uncertainties in inventory components; whether and how the uncertainty changes between
the 2005 to 2016 scenarios, including uncertainties in the TRI database; whether there is
bias in the EGU and non-EGU components of the inventory; and whether the EGU
emission estimates were derived from the best performing facilities or from the complete
set of facilities.
o Alternative future scenario forecasts. Appendix F should more clearly describe the
variables that were held constant versus factors that were varied between the two
scenarios.
o Regarding uncertainty in location of 2016 emissions reductions. Due to EPA's projection
methods, there is uncertainty about where emissions reductions will occur between 2005
and 2016, which in turn influences the spatial patterns of deposition from EGUs in the
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2016 scenario. Appendix F should address the uncertainties in the 2016 scenario
regarding the specific geographic locations of reductions in EGU-derived mercury
deposition as a fraction of total mercury deposition.
Use of CMAQ and performance evaluation of CMAQ. Appendix F should provide more
detailed description of uncertainty in CMAQ, including references to existing evaluations
of the model.
Appendix F should address whether the Mercury Maps approach, as implemented, is
adequate to characterize the existence and extent of mercury "hot spots."
Appendix F should detail the criteria used for excluding watersheds, characterize the
watersheds excluded by different criteria, and describe the estimated deposition in these
watersheds.
Regarding representativeness of approximately 2,500 watersheds compared to 88,000
HUC12 nationwide, Appendix F should characterize any bias introduced by looking at
this subset of watersheds (e.g., some states are over-represented, such as Indiana and
Minnesota, while others are under-represented such as Pennsylvania).
Fish populations and fish tissue database (see SAB responses to questions 5, 6 and 13 for
more detail). Appendix F should include discussion of:
¦ Sample size for characterization of Implications of a data set with a low number
of fish per watershed. Appendix F should identify the distribution of fish samples
per watershed and the possible implications of this distribution, including the
implications of sample size for characterization of 75th percentile fish tissue
concentration.
¦ Uncertainty in methylmercury fish tissue concentrations from differences in
sampling and analytical protocols used by States that contribute data and errors
introduced by potential misidentification of locations, etc.
Regarding adjustment between wet and cooked weight of fish: EPA relied upon a single
older study to derive an adjustment factor of 1.5. Alternative and newer peer reviewed
studies of cooking effects on mercury in fish should be acknowledged (e.g., Musaiger et
al. 2008; Farias et al. 2010) and used to discuss uncertainty associated with this assumed
value.
¦ Appendix F should note that this is a constant value applied in the calculation and
thus does not bias but could skew the results.
Regarding uncertainty of the assumption of proportionality and the MMAPs approach
(see SAB response to Question 9 for specifics to be discussed in Appendix F).
Characterization of susceptible human populations (see SAB responses to Questions 7
and 8)
¦ Characterizing subsistence fishing activity within high EGU deposition sites.
¦ Implications of choosing subsistence fishers and excluding high-end sport fishers.
¦ Census information that may exclude groups such as students, immigrants).
Fish consumption rates (see SAB Response to Question 7).
¦ Limitations of the single study used to support the Technical Support Document's
fish consumption rate for female subsistence fishers.
¦ Size of fish consumed.
Derivation of the concentration-response relationship and RfD based on data from marine
fish and mammal species, not inland freshwaters.
¦ Appendix F should discuss the uncertainty introduced by not using RfDs derived
based on studies of consumption of fish from inland freshwaters. (See SAB
response to Question 11).
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o Applicability of the concentration-response relationship and RfD for low socio-economic
status populations. This relationship has not been examined.
¦ Appendix F should discuss how this relationship may bias the report toward
underestimating risk.
o Effect of the nutritional benefits of fish consumption in comparison to risks from
mercury. Appendix F should address how the lack of consideration of this factor that may
bias the analysis toward underestimating risk (see SAB response to Question 11).
Question 13: Discussion of analytical results
Mercury deposition from U.S. EGUs
• The SAB recommends that the spatial patterns of simulated deposition shown in Figure 2-1 to 2-
4 be better explained and that EPA should characterize data limitations more effectively.
• The 12-km deposition maps are very different than previously produced maps on the 36-km scale
(for example in Texas and Nevada). The SAB recommends that EPA explain these differences
and that EPA consider including separate maps of wet and dry deposition and/or aggregating the
results into an approximately 36 km grid scale for comparison to earlier maps and to data plots,
such as national deposition maps from the Mercury Deposition Network.
• There have been several intercomparison studies among numerical models for long-range
transport of mercury and studies on model uncertainty evaluation that are not discussed or
referenced. The SAB recommends that EPA summarize these references (Bullock, 2009;
Pongprueksa et al., 2008; Lin et al, 2007; and Ryaboshapko, 2007) to help frame the overall
uncertainty of the deposition estimates.
Fish tissue methyl mercury concentrations
• EPA should characterize uncertainty and data limitations more fully. Specifically, the small
sample sizes of mercury concentrations in fish for the individual watersheds (-29% of
watersheds have n=l) will result in lower estimates of mercury concentrations in the 75th
percentile as shown earlier in Figures 1 and 2 in this document.
• The text in the observations should be modified to refer to tissue and mercury "concentrations"
rather than "levels" to be more precise.
• Where the percentages of EGU-contribution to fish methylmercury are mentioned, EPA should
clarify that those values pertain to only fish-sampled watersheds. Given the under-sampling in
watersheds where there are high levels of deposition, the percentages indicated could be higher.
• EPA should modify or eliminate some figures and tables.
o For figures 2-7 to 2-10, improved plots should display symbols proportional to sample
size and provide color or shading of symbols to represent observed fish concentrations,
o The maps shown in Figures 2-7 to 2-14 need to include the western continental United
States.
o The legend for Figure 2-8 should make it clear that the 2016 mercury tissue
concentrations were computed by adjusting the 2005 concentrations to account for lower
expected deposition as per the Mercury Maps approach,
o The third bullet item on page 36 of the Technical Support Document should be corrected
to indicate that Figures 2-7 and 2-8 give concentrations of mercury in fish, not total
mercury deposition.
o Figures showing the top 10th percentile (2-11 to 2-14) should be removed since the
pattern of mercury is greatly affected by high sampling effort in South Carolina, Indiana,
West Virginia, and Louisiana,
o The text describing Table 2-5 needs to be clarified to state that the relationships are not
causal.
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• Patterns of mercury deposition with mercury fish tissue data. The Technical Support Document
should clearly describe the degree to which the non-uniform, state-specific data availability
influences this analysis
• The SAB recommends that this section be substantively rewritten to improve clarity and to
highlight the major relevant points.
• EPA should revise the text in footnote 36, which is critical to the understanding of Figures 2-15
and 2-16.
• Also, the figure legends within each of Figures 2-15 and 2-16 need to be changed because the
"blue areas" are not "water bodies," but rather "watersheds," which include water bodies that
sometimes are more obvious than their watersheds (e.g., the Minnesota portion of Lake Superior,
Long Island Sound, and perhaps erroneously, the Canadian portion of Lake Champlain). The
SAB recommends that these two maps be replotted with a third color that clearly identifies the
areas of overlap.
• The SAB recommends that EPA provide a more complete introduction to Figure 2-17 that would
state the important premises of the analysis applied in this risk assessment - that spatial
variability of deposition rates is only one major driver of spatial variability of fish
methylmercury and that variability of ecosystem factors that control methylation potential
(especially wetlands, aqueous organic carbon, pH, and sulfate) also play a key role.
• The revised document should explain why the average deposition rate is lower in the 2016
scenario red area.
Percentile risk estimates
• The Technical Support Document should include an explanation of why the values in Tables 2-6
and 2-7 decrease when going from the 50th to 75th percentile. This is likely because the ranked
risk values are not the same as the ranked EGU contributions. This difference should be
mentioned. Perhaps the tabled values should be referred to in some way as averaged.
• The values in Tables 2-6 and 2-7 are based on averaging the values that are 2.5% below and
2.5% above. EPA should consider whether it is better to use a 2.5% range or use the 10 nearest
values. EPA should also describe how the range is selected for the 99th percentile.
• Section 2 page 54: the paragraph comparing "risks" for high-end females with other populations
is oversimplified. Depending on the percentiles considered, "risks" for Laotians, Vietnamese and
Tribal fish consumers can also be higher than for high-end females. The highest consumption
rates should be summarized in an appendix.
• Section 2 page 55: EPA should provide more information on the gold-mining impacted
watersheds in the Southeast. For example, it seems that gold mining occurred historically in a
relatively small region of South Carolina, and only a few mines have recently been re-activated.
Is it really appropriate to discount or question concerns about EGU affected exposure across the
whole Southeast on this basis?
• In Tables 2-6 and 2-7, EPA should consider reporting consumption rates and putting the
percentiles in parentheses rather than reporting the percentiles and having the rates in
parentheses.
• In Table 2-15 and other places, the mean is included. Since the mean is not a percentile, the table
header should be changed or the median used.
Number andfrequency of watersheds with populations potentially at risk due to U.S. EGU mercury
emissions
• The SAB recommends that language be added regarding the change in the percentage of
watersheds that continue to be above the RfD (or above a change in one to two IQ points, if this
aspect of the risk assessment is retained) after EGU emissions are removed.
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• The SAB recommends that the first bullet point on page 57 to change the language "before
taking into account deposition..." to something that does not imply temporality (e.g., "when you
factor out other sources of mercury deposition").
• The SAB also recommends that if the document discusses loss of IQ points, that it should refer to
this change in relation to "populations living close to watersheds" rather than "watersheds."
Question 14: Responsiveness to the goals of the study
• EPA should revise section 2.8 to explicitly respond to each of the goals of the study as set out on
page 13 of the Technical Support Document.
39
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Pekar, Zakary. 2011. Memorandum from Zachary Pekar, July 1, 2011, Clarification and Updating of
Mercury Deposition Maps Provided in the Technical Support Document: National-Scale
Mercury Risk Assessment.
http://vosemite.epa.gov/sab/sabproduct.nsf/7EEF81 lB90F741DF852578C00059FC64/$File/ME
MO-clarification+and+updating+of+mercurv+deposition+maps-Julylst.pdf (accessed 9/9/11).
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Atmospheric Mercury Models III: Boundary and Initial Conditions, Model Grid Resolution, and
Hg(II) Reduction Mechanism. Atmospheric Environment 42, 1828-18451.
Ralston, N.V.C. and L.J.Raymond. 2010. Dietary selenium's protective effects against methylmercury
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Ryaboshapko, A.O., O. R. Bullock Jr., J. Christensen, M. Cohen, A. Dastoor et al. 2007.
Intercomparison study of atmospheric mercury models: 1. Comparison of models with short-term
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methylmercury and fatty acids in the Seychelles. Neurotoxicology and Teratology 33(2), 325-
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Strain J., P.W. Davidson, M.P. Bonham, E.M. Duffy, A. Stokes-Riner et al. 2008. Associations of
maternal long chain polyunsaturated fatty acids, methyl mercury, and infant development in the
Seychelles child development and nutrition study. Journal of Neurotoxicology. 5, 776-82.
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van Wijngaarden, E., C. Beck et al. 2006. Benchmark concentrations for methyl mercury obtained from
the 9-year follow-up of the Seychelles Child Development Study. NeuroToxicology 27(5): 702-
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Wiener, J.G., B.C. Knights, M.B.Sandheinrich, J.D.Jeremiason, M.E. Brigham et al. 2006. Mercury in
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Appendix A: Agency Charge Questions
Background and Charge for the SAB Review of EPA's Technical Support Document: National-
Scale Mercury Risk Assessment Supporting the Appropriate and Necessary Finding for Coal- and
Oil-Fired Electric Generating Units (March 2011)
May 23, 2011
Background
On March 16, 2011, EPA proposed National Emission Standards for Hazardous Air Pollutants
(NESHAP) for coal- and oil-fired Electric Utility Steam Generating Units (EGUs). The proposed
NESHAP would protect air quality and promote public health by reducing emissions from EGUs of the
hazardous air pollutants (HAP) listed in Clean Air Act (CAA) section 112(b), including both mercury
and non-mercury HAP. Specifically, the proposed rule would require EGUs to decrease emissions of
mercury, other metal HAP, organic HAP, and acid gas HAP. Section 112(n)(l) of the CAA requires
EPA to determine whether it is "appropriate and necessary" to regulate HAP emissions from EGUs
under section 112. Before the Agency is authorized to make the appropriate and necessary
determination, section 112(n)(l) requires EPA to perform a study of the hazards to public health
reasonably anticipated to occur as a result of HAP emissions, including mercury, from EGUs after
imposition of the requirements of the CAA. EPA completed the required study in 1998. (Utility Air
Toxics Study, 1998). Based in part on the results of that study , EPA made a finding in December 2000
that it was appropriate and necessary to regulate HAP emissions from coal- and oil-fired EGUs. In the
recently proposed NESHAP, EPA confirmed that finding and concluded that it remains appropriate and
necessary to regulate HAP emissions from coal- and oil- fired EGUs. EPA confirmed the finding in part
by conducting a new analysis of the human health risks posed by consuming freshwater fish containing
mercury that is attributable to U.S. EGU emissions of mercury. EPA is seeking peer review of the data
and methods used in the national scale mercury risk assessment as documented in the Technical Support
Document: National-Scale Mercury Risk Assessment Supporting the Appropriate and Necessary Finding
for Coal and Oil-FiredElectric Generating Units (hereafter referred to as the "Mercury Risk TSD").
In determining whether U.S. EGUs pose a hazard to public health, we developed an approach for
assessing the nature and magnitude of the risk to public health posed by U.S. EGU mercury emissions
(the 2005 scenario). We also estimated the health risks associated with US EGU mercury emissions
estimated to remain "after imposition of the requirements of the Act" (the 2016 scenario). Specifically,
for the 2016 scenario, we looked at certain regulations, including, for example, the proposed Transport
Rule, which have a co-benefit impact on mercury.
Our approach focused on identifying the number of watersheds where the U.S. EGU contribution to
total methylmercury (MeHg) risk is considered to represent a potential public health hazard. To do this,
we focused on estimating risk associated with human exposures at those watersheds in the U.S. where
we have measured data on fish tissue MeHg concentrations (about 4% of the watersheds, or 2,461 out of
-88,000 U.S. watersheds - see section 2.4 and Appendix B of the Mercury Risk TSD). For each of the
2,461 watersheds, we modeled potential risk from high-end (i.e., subsistence-level) self-caught fish
consumption. Specifically, we used the fish tissue MeHg data combined with self-caught fish ingestion
rates to model exposure, and then we translated that into estimates of total MeHg-related risk (see
sections 1.3, 2.1 and Appendices C and D of the Mercury Risk TSD).
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In our analyses, we estimated both total risk associated with emissions from all emissions sources,
including global emissions, and the incremental contribution to the total risk that was attributable to
mercury emissions from U.S. EGUs. We used an assumption of proportionality between mercury
deposition over a watershed and the levels of MeHg in fish (and, by association, the levels of exposure
and risk). This proportionality assumption is based on the U.S. EPA Office of Water's Mercury Maps
assessment (see section 1.3 and Appendix E of the Mercury Risk TSD). Mercury Maps demonstrated
that, under certain conditions, a fractional change in mercury deposition will ultimately translate into a
similar fractional change in MeHg levels in fish. We note that the time delay between changes in
deposition and changes in MeHg levels in fish is not well characterized (there are a range of
assumptions and limitations associated with the Mercury Maps approach which we have considered -
see below). Application of the Mercury Maps approach allowed us to translate any changes in mercury
deposition to changes in MeHg fish tissue levels. It also allowed us to apportion MeHg levels in fish
(and, by association, exposure and risk estimates) based on the proportionality assumption. In other
words, if the estimated U.S. EGU-related emissions compriselO% of total deposition over a watershed,
assuming near steady-state conditions are met, we would assume that eventually 10% of the MeHg in
fish (and, therefore, 10% of the total human exposure and risk) would be attributable to U.S. EGUs.
Mercury deposition modeling was completed for two scenarios: 2005 and 2016. The analysis included
consideration of mercury emitted from (a) US EGUs, (b) other non-EGU sources in the U.S. (including
natural and anthropogenic), and (c) sources outside of the U.S. (both anthropogenic and natural) whose
mercury is deposited in the U.S. following long range atmospheric transport. Estimates of mercury
deposition within the U.S., both of total deposition and of EGU-related deposition, were completed
using the Community Multiscale Air Quality model (CMAQ) version 4.7.1, which generates estimates at
the 12 km grid cell-level of resolution.6'7 CMAQ modeling reflects mercury oxidation pathways for both
the gas and aqueous phases in addition to aqueous phase reduction reactions. Mercury "re-emission" is
not explicitly modeled in this version of CMAQ; however, approximations of these emissions are
included in the CMAQ model and called "recycled" emissions. Speciation of U.S. EGU mercury
emissions is based on a factor approach reflecting coal rank, firing type, boiler/burner type, and post-
combustion emissions controls. Emissions of mercury from sources in Canada and Mexico are based on
the 2006 Canadian inventory and 1999 Mexican inventory, respectively. Estimates of mercury
transported into the U.S. from outside North America (i.e., specification of lateral boundary
concentrations, pollutant inflow into the photochemical modeling domain, and initial species
concentrations) are provided by a three-dimensional global atmospheric chemistry model, the GEOS-
CHEM model (standard version 7-04-11). The GEOS-CHEM predictions were used to provide one-way
dynamic boundary conditions at three-hour intervals and an initial concentration field for the 36 km
CMAQ simulations. The 36 km photochemical model simulation is used to supply initial and hourly
o
boundary concentrations to the 12 km domains. Mercury initial and boundary conditions were based on
a GEOS-CHEM simulation using a 2000 based global anthropogenic emissions inventory that includes
1,278 Mg/yr of Hg(0), 720 Mg/yr of Hg(II), and 192 Mg/yr of particle bound mercury.9 The description
6 Foley, K.M., Roselle, S.J., Appel, K.W., Bhave, P.V., Pleim, J.E., Otte, T.L., Mathur, R., Sarwar, G., Young, J.O., Gilliam,
R.C., Nolte, C.G., Kelly, J.T., Gilliland, A.B., Bash, J.O., 2010. Incremental testing of the Community Multiscale Air Quality
(CMAQ) modeling system version 4.7. Geoscientific Model Development 3, 205-226.
7 Byun, D., Schere, K.L., 2006. Review of the governing equations, computational algorithms, and other components of the
models-3 Community Multiscale Air Quality (CMAQ) modeling system. Applied Mechanics Reviews 59, 51-77.
8 USEPA, 2010. Air Quality Modeling Technical Support Document: Point Source Sector Rules (EPA-454/R-11-003),
Research Triangle Park, North Carolina.
9 Selin, N.E., Jacob, D.J., Park, R.J., Yantosca, R.M., Strode, S., Jaegle, L., Jaffe, D., 2007. Chemical cycling and deposition
of atmospheric mercury: Global constraints from observations. Journal of Geophysical Re search-Atmospheres 112.
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of emissions and modeling presented above pertains to the 2005 scenario evaluated in the risk
assessment. For the 2016 scenario, EPA projected US EGU emissions based on an Integrated Planning
Model (IPM) run.10 Mercury emissions from other U.S. anthropogenic sources are projected to 2016
based on growth factors and known controls (e.g., boilers, cement kilns). The estimates for non-U.S.
global emission sources (i.e., both natural and anthropogenic) were not adjusted for the 2016 scenario.
The risk assessment for mercury focuses on two risk metrics: (a) comparison of estimated exposures to
the MeHg Reference Dose (MeHg RfD) to determine the hazard quotient (HQ) for each watershed
evaluated, and (b) an estimate of the number of IQ points lost to children born to mothers exposed to
MeHg during pregnancy (see 1.2 of the Mercury Risk TSD). The current EPA MeHg RfD reflects the
full range of potential neurodevelopmental impacts including effects on IQ, educational development,
motor skills and attention. For the risk assessment, we did not estimate the incidence of adverse health
effects for health endpoints other than IQ loss, as the literature and available data supporting the
modeling of IQ loss is considered to be the strongest and has received the most review by the scientific
community.
For each of the risk metrics modeled (RfD-based HQ and IQ loss), we identified a benchmark for a
potentially significant public health impact to guide interpretation of the risk estimates. For the RfD-
based HQ, we considered any exposure above the RfD (equal to an HQ of 1) to represent a potential
public health hazard with recognition, as noted above, that the RfD provides coverage for the full range
of neruodevelopmental impacts. In the case of IQ loss, we considered a loss of 1 or more points to
represent a clear public health concern. This benchmark was based on advice received from the Clean
Air Science Advisory Committee (CASAC) in relation to the Pb NAAQS review. It is important to note
that CASAC identified this level of IQ loss in the context of a population-level impact (see 1.2 of the
Mercury Risk TSD for additional detail on the benchmarks used to help interpret risk metrics).
For the risk assessment, we focused on high-end (subsistence) fish consumption by women of child-
bearing age at inland fresh water bodies; the consumption rates used ranged from the 90th to 99th
percentiles and were obtained from peer-reviewed studies of fish consumption by specific populations
active within the continental U.S. (see section 1.3 and Appendix C of the Mercury Risk TSD). This
overall approach reflects our assumption that U.S. EGUs will have the greatest public health impact on
the subset of watersheds in the U.S. that (a) have relatively elevated fish tissue MeHg levels (increasing
overall risk levels associated with MeHg exposure through fish consumption at those watersheds), (b)
have relatively larger mercury deposition from U.S. EGUs (translating into a greater fractional risk
associated with U.S. EGUs), and (c) have subsistence-level fishing activity (resulting in higher self-
caught fish intake and higher risk). We have not focused on recreational fishing activity. Recreational
fishing may be important from a population risk standpoint; however, these fishers consume less fish
overall and will not have the levels of individual-risk likely to be experienced by subsistence fishers.
Furthermore, we have not considered U.S. EGU impacts on commercial fish from international or near
coastal locations. Although MeHg levels can be relatively high in fish from these locations, the U.S.
EGU contribution (as a fraction of overall mercury impacts) is both highly uncertain and likely to be
low. The high degree of uncertainty associated with linking U.S. EGU deposition to MeHg levels in fish
that are either self-caught or commercially harvested near the U.S. shore led us to exclude consideration
of risks linked to consumption of these fish. Specifically, given the greater mobility of these fish and the
greater dilution of deposited mercury in the ocean and near coastal waters, application of the Mercury
10 USEPA, 2010. Air Quality Modeling Technical Support Document: Point Source Sector Rules (EPA-454/R-11-003),
Research Triangle Park, North Carolina.
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Maps approach is subject to significantly greater uncertainty relative to its application to inland fresh
water bodies.
The RfD-based risk characterization was done by developing HQs for each watershed. The HQ is
defined as the estimate of MeHg exposure divided by the MeHg RfD. Generally (both for
methylmercury and for all pollutants) a HQ of 1 or less is considered to represent a level of daily
exposure for the human population (including sensitive subgroups) that is likely to be without an
appreciable risk of deleterious effects during a lifetime. We developed a 3-stage risk characterization
framework to estimate the number of watersheds where the U.S. EGU contribution to total MeHg risk is
considered to represent a potential public health hazard based on consideration of the HQ metric:
Stage 1: estimate the number of watersheds where (a) potential exposure for subsistence level fish
consumers exceeds the RfD (e.g., HQ > 1.0), and (b) U.S. EGUs contribute a specific fraction of
mercury deposition to those watersheds (and by association, a specific fraction of total exposure and
risk). Several fractions of mercury deposition were considered ranging from >5 to >20%.
Stage 2: estimate the number of watersheds where the deposition from U.S. EGUs would result in
exposures to MeHg that exceed the RfD before considering exposures to MeHg attributable to other
sources. While we may consider the U.S. EGU increment of exposure, particularly in the context of
comparing exposure to the MeHg RfD, it is critical to place the U.S. EGU-incremental exposure in
the context of the larger total exposure at a given watershed. This reflects the fact that the MeHg
RfD is for total exposure and not increments of exposure considered in isolation.
Stage 3: estimate the total number of watersheds where populations are at risk from exposures
attributable to U.S. EGU mercury emissions by merging the two sets of watersheds identified in
stages 1 and 2.
(see section 1.2 of the Mercury Risk TSD for additional detail on the 3-stage framework)
The second risk characterization was done by modeling potential IQ loss attributable to U.S. EGU
emissions resulting in increased MeHg exposure (see section 1.2 of the Mercury Risk TSD). In
modeling IQ loss, we first converted annual-average ingested dose estimates for MeHg into equivalent
maternal hair mercury levels, since the CR function for IQ loss is based on estimated exposure
characterized as maternal hair mercury levels. This was accomplished using a factor based on a one
compartment toxicokinetic model used in deriving the methylmercury RfD. Then a CR function relating
hair mercury levels to IQ points lost in children born to mothers whose exposure is modeled in this
analysis was used to predict IQ points lost for those children. This CR function is based on application
of a Bayesian hierarchical model which integrates data from the three key epidemiological studies
(Seychelles, New Zealand and Faroe Islands).
As part of the risk assessment, EPA also addressed both variability and uncertainty. Regarding
variability, we assessed the degree to which key sources of variability associated with the scenarios
being modeled were reflected in the design of the risk model (see sections 1.4, 2.7 and Appendix F,
Table F-l of the Mercury Risk TSD). Regarding uncertainty we included a number of sensitivity
analyses intended to consider the potential impact of key sources of uncertainty (with emphasis on
application of the Mercury Maps assumption). We also qualitatively discussed additional sources of
uncertainty and the nature and magnitude of their potential impact on risk estimates that were generated
(see section 2.7 and Appendix F, Table F-2 of the Mercury Risk TSD).
Figure 1 provides a conceptual diagram for the key steps in the risk assessment.
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This peer review is intended to focus on the linkages of the key data inputs, and the critical inputs
related to fish consumption rates, dose-response information, and fish MeHg levels. Two key inputs to
the risk assessment are the MeHg RfD and the estimates of mercury deposition from CMAQ. We
believe the MeHg RfD is the appropriate indicator to use because it reflects the full range of potential
neurodevelopmental impacts, including effects on IQ, educational development, motor skills, and
attention. We are not requesting that this panel review the scientific basis for the MeHg RfD, rather, this
review is focused on the estimation of potential exposures to MeHg for comparison against the existing
RfD. The current RfD has been subject to extensive peer review and is the EPA reference value for
assessing MeHg ingestion risk.11 In addition, the CMAQ model has been extensively peer reviewed and
the mercury fate and transport algorithms are documented in several peer reviewed publications.12'13'14
Thus, we are not seeking peer review of the mercury components of the CMAQ model. However, as
reflected in the charge questions, we are looking for comment on how CMAQ outputs (i.e., mercury
deposition estimates) are integrated into the risk assessment to estimate changes in fish tissue MeHg
levels and in exposures and risks associated with the EGU-related fish tissue MeHg fraction.
11 U.S. Environmental Protection Agency (U.S. EPA). 2002. Integrated Risk Information System File for Methylmercury.
Research and Development, National Center for Environmental Assessment, Washington, DC. This material is available
electronically at: http://www.epa.gov/iris/subst/0073.htm.
12 Bullock, O. R., Jr., et al. (2008), The North American Mercury Model Intercomparison Study (NAMMIS): Study
description and model-to-model comparisons, J. Geophys. Res., 113, D17310, doi:10.1029/2008JD009803.
13 Bullock, O. R., Jr., et al. (2009), An analysis of simulated wet deposition of mercury from the North American Mercury
Model Intercomparison Study, J. Geophys. Res., 114, D08301, doi: 10.1029/2008JD011224
14 Pongprueksa, P., et al (2008), Scientific uncertainties in atmospheric mercury models III: Boundary and initial conditions,
model grid resolution, and Hg(II) reduction mechanism, Atmospheric Environment 42: 1828-1845
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Exposure modeling
Specifying
spatial scale of
watersheds
Characterizing
measured fish tissue
Hg concentrations at
the watershed-level
Estimating total fish
consumption-related
Hg exposure forfishers
active at each
watershed
CMAQ
modeling
results fortotal
and US EGU Hg
deposition at
each watershed
Apportioning total
exposure between
total and US EGU-
attributable at each
watershed
Defining near-subsistence and
subsistence fisher scenarios
US Demographic data
characterizing source
populations for high-
end fishers at the US
Census tract-level
Defining near-
subsistence and
subsistence fisher
populations by
watershed
5
Studies characterizing fish
consumption rates for
high-consumption fisher
populations
Risk modeling
Mercury RfD for
HQ estimation
Estimation of HQ and
IQ loss at each
watershed including total
riskand US EGU-
attributable fraction
Modeling IQ loss
IQ loss concentration-
response function
Factortranslating Hg
ingestion dose into Hg hair
concentrations (in ppm)
KEY:
decision
data input
analysis step
Figure 1. Flow Diagram of Risk Analysis Including Major Analytical Steps and Associated
Modeling Elements (Note, GEOS-CHEM results are input into CMAQ modeling box)
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Charge Questions
The charge questions presented below are organized by topic and track specific sections within the
Mercury Risk TSD beginning with Purpose and Scope of the Analysis (section 1.1). We have included
brief overviews of the technical focus of each section to help reviewers place each section in context
with regard to the overall risk assessment (Note, we did not include any charge questions addressing
elements of the Executive Summary since all technical content provided in that introductory section is
covered in greater detail in the other sections of the TSD for which we have included charge questions).
Purpose and Scope of the Analysis (section 1.1)
This section presents the policy-related questions that were developed to guide the design of the risk
assessment. It also highlights some important technical factors related to air-sourced mercury, in
particular, mercury released from U.S. EGUs that were considered in designing the risk assessment. And
finally, the section provides an overview of key elements of the scope of the risk assessment.
Question 1. Please comment on the scientific credibility of the overall design of the mercury risk
assessment as an approach to characterize human health exposure and risk associated with U.S.
EGU mercury emissions (with a focus on those more highly exposed).
Overview of Risk Metrics and the Risk Characterization Framework (section 1.2)
This section describes the risk metrics used in the risk assessment (i.e., IQ loss and MeHg RfD-based
HQs, including both total risk and U.S. EGU-attributable risk). The section also presents the 3-stage risk
characterization framework which uses these risk metrics to estimate the number of watersheds where
populations may be at risk due to MeHg exposure with consideration for the U.S. EGU attributable
fraction of that exposure. Questions for this section focus on the IQ calculations. As explained above,
we are not asking for peer review of the current mercury RfD or its suitability as a benchmark for
comparison with mercury exposures.
Question 2. Are there any additional critical health endpoint(s) besides IQ loss which could be
quantitatively estimated with a reasonable degree of confidence to supplement the mercury risk
assessment (see section 1.2 of the Mercury Risk TSD for an overview of the risk metrics used in
the risk assessment)?
Question 3. Please comment on the benchmark used for identifying a potentially significant
public health impact in the context of interpreting the IQ loss risk metric (i.e., an IQ loss of 1 to
2 points or more representing a potential public health hazard). Is there any scientifically
credible alternate decrement in IQ that should be considered as a benchmark to guide
interpretation of the IQ risk estimates (see section 1.2 of the Mercury Risk TSD for additional
detail on the benchmark usedfor interpreting the IQ loss estimates).
Overview of Analytical Approach (section 1.3)
This section of the Mercury Risk TSD (together with the referenced appendices) provides a detailed
overview of the technical design and inputs to the risk assessment, with the section being further divided
into subsections (unnumbered) that address each of the design elements. Charge questions presented
below which address the design of the risk assessment are grouped by each of these design elements.
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Specifying the spatial scale of watersheds (presented within section 1.3)
This section describes the spatial unit used as the basis for the risk assessment (the HUC-12 watershed,
representing a fairly refined level of watersheds approximately 5-10 km on a side) and provides the
rationale for the decision to use that specific spatial scale and spatial unit in the analysis.
Question 4: Please comment on the spatial scale used in defining watersheds that formed the
basis for risk estimates generatedfor the analysis (i.e., use of 12-digit hydrologic unit code
classification). To what extent do HUC12 watersheds capture the appropriate level of spatial
resolution in the relationship between changes in mercury deposition and changes in MeHg fish
tissue levels? (see section 1.3 and Appendix A of the Mercury Risk TSD for additional detail on
specifying the spatial scale of watersheds used in the analysis).
Characterizing measured fish tissue Hg concentrations (presented within section 1.3)
This section describes the fish tissue MeHg sampling data used in the risk assessment, including the
underlying sources of data used in developing the dataset and factors considered in developing the
dataset (e.g., inclusion of data sampled between 2000 and 2009). This section also provides the rationale
for using the 75th percentile fish tissue MeHg value (within a given watershed) as the basis for exposure
and risk characterization.
Question 5: Please comment on the extent to which the fish tissue data used as the basis for the
risk assessment are appropriate and sufficient given the goals of the analysis. Please comment
on the extent to which focusing on data from the period after 1999 increases confidence that the
fish tissue data used are more likely to reflect more contemporaneous patterns of mercury
deposition and less likely to reflect earlier patterns of mercury deposition. Are there any
additional sources offish tissue MeHg data that would be appropriate for inclusion in the risk
assessment?
Question 6: Given the stated goal of estimating potential risks to highly exposed populations,
please comment on the use of the 75th percentile fish tissue MeHg value (reflecting targeting of
larger but not the largest fish for subsistence consumption) as the basis for estimating risk at
each watershed. Are there scientifically credible alternatives to use of the 75th percentile in
representing potential population exposures at the watershed level?
Defining subsistence fisher scenarios (presented within section 1.3)
This section describes the high-end self-caught freshwater fish consuming populations evaluated for
exposure and risk in the risk assessment. The section includes detailed discussion of the self-caught fish
consumption rates used in modeling exposure for these study populations.
Question 7: Please comment on the extent to which characterization of consumption rates and
the potential location for fishing activity for high-end self caught fish consuming populations
modeled in the analysis are supported by the available study data cited in the Mercury Risk TSD.
In addition, please comment on the extent to which consumption rates documented in Section 1.3
and in Appendix C of the Mercury Risk TSD provide appropriate representation of high-endfish
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consumption by the subsistence population scenarios used in modeling exposures and risk. Are
there additional data on consumption behavior in subsistence populations active at inland
freshwater water bodies within the continental U.S. ?
Question 8: Please comment on the approach used in the risk assessment of assuming that a
high-endfish consuming population could be active at a watershed if the "source population "
for that fishing population is associated with that watershed (e.g. at least 25 individuals of that
population are present in a U.S .Census tract intersecting that watershed). Please identify any
additional alternative approaches for identifying the potential for population exposures in
watersheds and the strengths and limitations associated with these alternative approaches
(additional detail on how EPA assessed where specific high-consuming fisher populations might
be active is provided in section 1.3 and Appendix C of the Mercury Risk TSD).
Apportioning total MeHg exposure between total and U.S. EGU-attributable exposure (presented
within section 1.3)
This section describes the application of the Mercury Maps based proportionality assumption to link
changes in mercury deposition (over watersheds) to changes in fish tissue MeHg levels. The section also
discusses the use of CMAQ modeling output (i.e., gridded mercury deposition estimates for both total
mercury and U.S. EGU-attributable mercury) as part of this process of linking changes in U.S. EGU
mercury emissions ultimately, to changes in fish tissue MeHg levels in watersheds assessed for risk in
the risk assessment.
Question 9: Please comment on the draft risk assessment's characterization of the limitations
and uncertainty associated with application of the Mercury Maps approach (including the
assumption of proportionality between changes in mercury deposition over watersheds and
associated changes in fish tissue MeHg levels) in the risk assessment. Please comment on how
the output of CMAQ modeling has been integrated into the analysis to estimate changes in fish
tissue MeHg levels and in the exposures and risks associated with the EGU-relatedfish tissue
MeHg fraction (e.g., matching of spatial and temporal resolution between CMAQ modeling and
HUC12 watersheds). Given the national scale of the analysis, are there recommended
alternatives to the Mercury Maps approach that could have been used to link modeled estimates
of mercury deposition to monitored MeHg fish tissue levels for all the watersheds evaluated?
(additional detail on the Mercury Maps approach and its application in the risk assessment is
presented in section 1.3 and Appendix E of the Mercury Risk TSD).
Question 10: Please comment on the EPA 's approach of excluding watersheds with significant
non-air loadings of mercury as a method to reduce uncertainty associated with application of the
Mercury Maps approach. Are there additional criteria that should be considered in including or
excluding watersheds?
Estimating risk including HQ and 10 loss (presented within section 1.3)
This section describes how exposure estimates generated for the high-end fish consuming populations
modeled in the analysis are translated into risk estimates for those populations (in the form of both
MeHg RfD-based HQs and IQ losses). This section also includes a detailed discussion of the
concentration-response function used in modeling IQ loss.
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Question 11: Please comment on the specification of the concentration-response function used in
modeling IQ loss. Please comment on whether EPA, as part of uncertainty characterization,
should consider alternative concentration-response functions in addition to the model used in the
risk assessment. Please comment on the extent to which available data and methods support a
quantitative treatment of the potential masking effect of fish nutrients (e.g. omega-3 fatty acids
and selenium) on the adverse neurological effects associated with mercury exposure, including
IQ loss, (detail on the concentration-response function used in modeling IQ loss can be found in
section 1.3 of the Mercury Risk TSD).
Discussion of key sources of uncertainty and variability (section 1.4)
This section describes the extent to which the risk assessment design reflects consideration for
potentially important sources of variability associated with the type of exposure being modeled. It also
discusses sources of uncertainty associated with the analysis, including the nature and potential
magnitude of their impact on risk estimates (Note, also that an important part of the analysis - the
sensitivity analyses completed primarily to examine the potential impact of uncertainty related to the
Mercury Maps approach - are discussed in section 2.7 of the Mercury Risk TSD).
Question 12: Please comment on the degree to which key sources of uncertainty and variability
associated with the risk assessment have been identified and the degree to which they are
sufficiently characterized.
Discussion of analytical results (section 2)
This section presents estimates generated as part of the risk assessment, including important
intermediate calculations as well as the risk estimates themselves - subsections include: (a) estimates of
mercury deposition over watersheds (section 2.3), (b) characterization of changes in fish tissue MeHg
levels based on modeling the impact of changes in mercury deposition (section 2.4) and (c) presentation
of MeHg RfD-based HQ estimates and IQ loss risk estimates (section 2.6). Key observations from the
analysis are presented in section 2.8.
Question 13: Please comment on the draft Mercury Risk TSD's discussion of analytical results
for each component of the analysis. For each of the components below, please comment on the
extent to which EPA's observations are supported by the analytical results presented and
whether there is a sufficient characterization of uncertainty, variability, and data limitations,
taking into account the models and data used.
¦ Mercury deposition from U.S. EG Us
¦ Fish tissue methyl mercury concentrations
¦ Patterns of Hg deposition with HG fish tissue data
¦ Percentile risk estimates
¦ Number andfrequency of watersheds with populations potentially at risk due to U.S. EGU
mercury emissions
Question 14: Please comment on the degree to which the final summary of key observations in Section
2.8 is supported by the analytical results presented. In addition, please comment on the degree to which
the level of confidence and precision in the overall analysis is sufficient to support use of the risk
characterization framework described on page 18.
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