Technical Support Document: Methodology Used to Generate Deposition, Fish Tissue
Methylmercury Concentrations, and Exposure for Determining Effectiveness of Utility
Emission Controls:
Analysis of Mercury from Electricity Generating Units:
Section 1. Mercury Emissions from Power Plants
Section 2. Mercury Deposition from Power Plants
Section 3. Fish Tissue Methylmercury Concentrations
Section 4. Fish Consumption
Section 5. Human Health
Section 6. Human Exposure
Section 7. Utility Report to Congress Modeling
Section 8. Data Limitations, Uncertainty, and the Need for Further Work
1. Mercury Emissions from Power Plants
The specific controls anticipated to be adopted by utilities under the Clean Air Interstate
Rule (CAIR) and Clean Air Mercury Rule (CAMR) are expected to preferentially reduce the
forms of mercury that are of concern with respect to local deposition (non-elemental mercury)
(See Control of Mercury Emissions from Coal Fired Electric Utility Boilers: An Update in the
docket).
Based on the analysis of CAIR, EPA's modeling projects that mercury emissions would be
38.0 tons (12 tons of non-elemental mercury) in 2010, 34.4 tons in 2015 (10 tons of
non-elemental mercury), and 34.0 tons in 2020 (9 tons of non-elemental mercury), about a 20 and
30 percent reduction (in 2010 and 2015, respectively) from a 1999 baseline of 48 tons. With
respect to oxidized mercury, emissions in 2020 are 7.9 tons compared to 20.6 tons in 2001. This
62 percent drop in oxidized mercury emissions is particularly important because this species of
mercury deposits more readily. Tables 1.1 and 1.2 highlight the changes in speciated mercury
emissions from the 1999 ICR through the CAIR and CAIR plus CAMR regulatory approaches.
Elemental mercury has an atmospheric half-life of 6-12 months, which makes it an
important contributor to the global pool. Coupled with the fact that elemental mercury is
generally non-water soluble, it's impact on local deposition is minimized. On the other hand,
oxidized mercury has a half-life on the order of days to weeks, is relatively water soluble, and
contributes significantly to nearfield (< 25 km) deposition. Thus, an appropriately designed
control strategy should focus on reductions in oxidized mercury to optimize its ability to impact
local deposition in and around coal-fired power plants.
The graphs in Appendix A demonstrate the local or nearfield depositional aspect of
oxidized and particulate mercury, and the relative unimportance of elemental mercury in the
nearfield. Particulate mercury tends to form in the atmosphere from oxidized mercury in
conjunction with relatively available atmospheric constituents, (e.g., dust and soot particles).
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Given the settling characteristics of particle-bound mercury, this species of mercury also is
important to nearfield deposition; however, not nearly as important in terms of magnitude as
oxidized mercury.
EPA projected future mercury emissions from the power generation sector using the
Integrated Planning Model (IPM). EPA uses IPM to analyze the projected impact of
environmental policies on the electric power sector in the 48 contiguous States and the District of
Columbia. IPM is a multi-regional, dynamic, deterministic linear programming model of the U.S.
electric power sector. EPA used IPM to project both the national level and the unit level of utility
unit mercury emissions under different control scenarios. EPA also used IPM to project the costs
of those controls (see Chapter 7 of CAMR Final RIA for further discussion).
In these IPM runs, EPA assumed that States would implement the mercury requirements
through the mercury cap and trade program that EPA is establishing in today's rulemaking. The
cap-and-trade program is implemented in two phases, with a hard cap of 38 tons in 2010 (set at
the co-benefits reduction under CAIR) and 15 tons in 2018. EPA modeling of section 111
projects banking of allowances due to excess mercury reductions in the 2010 to 2017 timeframe
for compliance with the cap in 2018 and beyond timeframe. A cap-and-trade program assures
that those reductions will be achieved with the least cost. For that reason, EPA believes it
reasonable to assume that States will adopt the program even though they are not required to do
so.
Under the CAMR scenario modeled by EPA, units are projected to install SCR and
scrubbers to meet their S02 and NOx requirements under CAIR and take additional steps to
address the remaining mercury reduction requirements under section 111, including adding
mercury-specific control technologies (model applies Activated Carbon Injection), additional
scrubbers and SCR, dispatch changes, and coal switching. Many of these reductions are projected
to result from large units installing controls and selling excess allowances. Under the cap-and-
trade approach we are projecting that mercury reductions result from units that are most cost
effective to control, which enables those units that are not cost effective to install controls to use
other approaches for compliance including buying allowances, switching fuels, or making
dispatch changes.
Table 1.1 below provides mercury emissions projections organized by plants with certain
categories of total mercury emissions. As presented in the table, the largest emitting plants (those
with total mercury emissions greater than 230 kg/yr) represented the largest total mercury
emissions in 1999, but under CAIR and CAMR, this category of plants have the greatest
reductions in ionic mercury emissions. In addition, as shown in Table 1 and 2, the smallest
emitting category of plants (plant with Hg total less than 90 but greater than 10 kg per year)
actually increases under the CAIR and CAMR options. This increase is consistent with the
conclusion that reductions are projected to result from large units installing controls. As controls
are installed, the large emitting units move to the category of lower emitting units.
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Table 1.1: Mercury Emission Projections (kg/yr)
1999 ICR Emissions
% of total
Mercury
Mercury
Mercury
Mercury
plants
(lป
C++)
(O)
total
Plants with Mercury total > 230 Kg/yr
15%
619
8,824
10,804
20,248
Plants with Mercury total < 230 but > 90 Kg/yr
29%
444
6,182
8,171
14,797
Plants with Mercury total < 90 but >10 kg/yr
56%
263
3,371
4,540
8,174
Total National Emissions
1,346
18,514
23,673
43,533
CAIR 2020 Projected Emissions
% of total
Mercury
Mercury
Mercury
Mercury
plants
(ป)
(++)
(O)
total
Plants with Mercury total > 230 Kg/yr
6%
131
1,292
7,632
9,055
Plants with Mercury total < 230 but > 90 Kg/yr
22%
361
2,985
10,139
13,485
Plants with Mercury total < 90 but >10 kg/yr
72%
331
2,866
7,134
10,331
Total National Emissions
751
7,139
23,337
31,227
CAMR 2020 Projected Emissions
% of total
Mercury
Mercury
Mercury
Mercury
plants
(lป
C++)
(O)
total
Plants with Mercury total > 230 Kg/yr
3%
121
589
2,964
3,674
Plants with Mercury total < 230 but > 90 Kg/yr
17%
267
2,061
5,945
8,273
Plants with Mercury total < 90 but >10 kg/yr
80%
315
2,839
6,570
9,725
Total National Emissions
753
5,960
16,000
22,713
Note: Total national emissions include emissions from plants less than 10 kg/yr.
Table 1.2: Percent Reduction from 1999 Emissions
CAIR 2020
Mercury
Mercury
Mercury
Mercury
fp)
(++)
(O)
total
Plants with Mercury total > 230 Kg/yr
79%
85%
29%
55%
Plants with Mercury total < 230 but > 90 Kg/yr
19%
52%
-24%
9%
Plants with Mercury total < 90 but > 10 kg/yr
-26%
15%
-57%
-26%
Total National Emissions
44%
61%
1%
28%
CAMR 2020
Mercury
Mercury
Mercury
Mercury
(ป)
(++)
(O)
total
Plants with Mercury total > 230 Kg/yr
81%
93%
73%
82%
Plants with Mercury total < 230 but > 90 Kg/yr
40%
67%
27%
44%
Plants with Mercury total < 90 but > 10 kg/yr
-20%
16%
-45%
-19%
Total National Emissions
44%
68%
32%
48%
2. Analysis of Mercury Deposition from Power Plants
The Community Multi-Scale Air Quality model (CMAQ) is a three-dimensional grid-
based Eulerian air quality model designed to estimate pollutant concentrations and depositions
over large spatial scales (e.g., over the contiguous United States). Because it accounts for spatial
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and temporal variations as well as differences in the reactivity of Mercury emissions, CMAQ is
best suited for evaluating the impacts of the CAMR on U.S. mercury depositions. This model
accounts for the atmospheric reactions of specific mercury emissions and their significance to the
levels of deposition as shown through our results here for CAMR. In addition, the boundary and
initial species concentrations are provided by a three-dimensional global atmospheric chemistry
and transport model, (i.e., Harvard's GEOS-CHEM model). The model simulations were
performed based on plant-specific emissions of mercury by species as provided by the Integrated
Planning Model (IPM).
2.1 Deposition Analysis
Regional deposition is best modeled using a grid model that accounts for atmospheric
chemistry, meteorology, and large scale fate and transport, as well as impacts of global emissions
of mercury. For this analysis, the gridded estimates of mercury deposition from application of the
CMAQ model were used to evaluate the impacts of utility mercury controls. The CAMR
Emissions Inventory and Air Quality Modeling Technical Support Document (U.S. EPA, 2005)
discusses the development of the 2001 and 2020 emissions inventories and the CMAQ mercury
deposition modeling in greater detail.
For this analysis, deposition at 36 km grid cells was aggregated to the eight-digit
watershed using the ArcMap spatial join function (ESRI, 2004)1'2. The HUC, Hydrologic Unit
Code, developed by the USGS, spatially delineates watersheds throughout the United States3.
Hydrologic units are available at four levels of aggregation, ranging from a two-digit regional
level (21 units nationwide) to the eight-digit HUC (2,150 distinct units). The eight-digit
HUC-level designation is useful for this analysis because it provides a nationally consistent
approach for grouping waterbodies on a sufficiently local scale (the average HUC area is 1,631 sq
mi). The average deposition for the grid cells that intersect the HUC-8 polygon is then used as
the deposition value for the HUC-8 unit. Averaging over grid cells may result in a smoothing out
of areas of high and low deposition, because the CMAQ grid cells are smaller than many HUCs.
1 Just like states can be subdivided into counties, large watersheds can be subdivided into smaller and
smaller watersheds. For example, the Chesapeake Bay Watershed is composed of 104 small 8digit Hydrologic Unit
Code (HUC). The 8-digit HUC is the smallest USGS Classification. We refer to 8-digit HUCs as 8-digit watersheds
for clarity.
2 While appropriate for regional scale analyses, use of horizontal grids finer than 36 km2 would be expected
to result in higher local dry deposition of reactive gaseous mercury emissions, especially those from surface-level
emissions or from short exhaust stacks. Eulerian grid models such as CMAQ immediately dilute simulated
emissions into the entire grid volume in which they are released. This causes an artificially fast dilution and
under-represents direct deposition from air to surfaces near emission sources. The magnitude of this artificial
dilution depends on a number of factors related to emission source characteristics and atmospheric variables.
3 More information regarding these hydrological units can be found through the USGS Web site
http://water.usgs.gov/GIS/huc.html.
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This approach averages mercury deposition rates projected by CMAQ at the 36 km grid
cell resolution across all model grid cells that occur within a given HUC-8. We consider the
HUC-8 aggregation most appropriate for this regional deposition analysis in light of the following
rationale. First, because much of the mercury deposited on the watershed of different ecosystems
will eventually enter waterbodies through subsurface inflow and runoff, we consider a watershed
scale analysis to be more appropriate than finer scale resolution that may only describe direct
inputs to surface waters. Second, in larger waterbodies, (i.e., the Great Lakes) where there is
substantial fishing activity, the higher trophic level fish species consumed by humans are likely
migratory and the accumulation of mercury by these species will represent an aggregated signal
from deposition over a wider area, (e.g., the entire waterbody). Third, many anglers catching
these higher trophic level fish may not always fish in the same waterbody within a watershed.
Since we are concerned about the cumulative dose over weeks and months from repetitive
consumption of fish containing methylmercury, this fishing behavior should be considered in the
exposure pathway. Based on the above considerations, we conclude that the HUC-8 watershed is
the appropriate unit of measure for analyzing regional deposition patterns. As discussed in
section 3, for the analysis of individual fish sample locations, we use the unaveraged 36 km
CMAQ outputs to provide a better match with the spatial resolution of the sampling data.
Key deposition metrics:
Total Deposition (|ig/m2)
A. 2001 all sources (base case)
B. 2001 utility zero out
C. 2020 utilities with CAIR (2020 base case)
D. 2020 utilities with CAIR plus CAMR Requirements
E. 2020 utilities with CAIR plus CAMR Alternative
F. 2020 utilities zero out
Change in deposition
A. 2020 utilities with CAIR - 2001 base case (approximate estimate of
benefits of CAIR)
B. 2020 utilities with CAIR plus CAMR Requirements - 2020 utilities
with CAIR
C. 2020 utilities with CAIR plus CAMR Alternative - 2020 utilities
with CAIR
D. 2020 utilities zero out - 2020 utilities with CAIR
Utility Attributable Deposition
A. 2001 (2001 base case - 2001 utility zero out)
B. 2020 CAIR (2020 utilities with CAIR - 2020 utilities zero out)
C. 2020 CAIR plus CAMR Requirements (2020 utilities with CAMR
Requirements - 2020 utilities zero out)
D. 2020 CAIR plus CAMR Alternative (2020 utilities with CAMR
Alternative - 2020 utilities zero out)
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Maps of deposition at the HlIC-8 aggregation level are provided to show the spatial
pattern of deposition from all sources and the portion of deposition that is attributable to utilities
after implementation of CAIR and the CAMR requirements and alternative. In addition, maps of
the change in deposition associated with the CAMR requirements and alternative are provided to
show the incremental impact of each. Figure 2.1 displays a map of total mercury deposition from
all air sources in the 2001 base case. Figure 2.2 displays a map of the deposition in 2001 that is
attributable to utilities, based on comparing CMAQ deposition estimates with and without utility
emissions of mercury. This map suggests that most of the current utility attributable deposition
occurs in the Eastern U.S., especially in the Ohio Valley and Pennsylvania. Figure 2.3 displays a
map of projected utility attributable mercury deposition after implementation of the Clean Air
Interstate Rule4. This map (note the change in scale) shows that after CAIR is implemented, the
utility attributable mercury deposition is greatly reduced throughout the Eastern U.S., with
remaining utility attributable deposition highest in parts of the Ohio Valley and around the
Southern Great Lakes.
Figure 2.1. Regional Annual Deposition of Mercury in the 2001 Base Case
4 Note that the Clean Air Interstate Rule as promulgated does not include utilities in New Jersey, Delaware,
or Arkansas. However, a rulemaking has been proposed to include these states in the CAIR region. The modeling
conducted for the Clean Air Mercury Rule anticipated the inclusion of these states in the CAIR region, and thus
included them in CAIR for the purpose of projecting future conditions in 2020. This may lead to a slight
underestimation of utility attributable mercury deposition in the 2020 base case with CAIR. However, it is expected
that implementation of the mercury cap and trade program would result in reductions in emissions such that the
utility attributable deposition after implementation of both CAIR and CAMR would be the same.
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Utility Attributable Deposition
| 10-15
I 15-20
Figure 2.2. Regional Annual Deposition of Mercury Attributable to Electricity Generating
Utilities in the 2001 Base Year
Figure 2.3a. Regional Annual Deposition of Mercury Attributable to Electricity Generating
Utilities in the 2020 Base Case With Implementation of the Clean Air Interstate Rule (2001
Deposition Comparison Scale)
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Utility Attributable Deposition
-------
Figure 2.5. Regional Annual Deposition of Mercury Attributable to Electricity Generating
Utilities in 2020 With Implementation of CAIR and the Clean Air Mercury Rule Alternative
(2020 Deposition Scale)
Figure 2.6. Change in 2020 Regional Annual Deposition Due to Implementation of the
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Clean Air Mercury Rule Requirements (incremental to the CAIR)
Page 10 of 68
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Reduction in Mercury Deposition
(ug/m2)
Note that negative reductions indicate increases in mercury deposition
Figure 2.7. Change in 2020 Regional Annual Deposition Due to Implementation of the
Clean Air Mercury Rule Alternative (incremental to the CAIR)
Reduction in Mercury Deposition
(ug/m2)
f il -01-00
IM~] Q.0-Q5
0 5-1.0
H 1.0 - 15
ฆฆ 15-2.0
Note that negative reductions indicate increases in mercury deposition
Figure 2.8. Incremental Difference in 2020 Regional Annual Mercury Deposition Between
the Promulgated Clean Air Mercury Rule Requirements and the Alternative
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Figure 2.4 shows the utility attributable mercury deposition remaining after
implementation of Requirements for the Clean Air Mercury Rule (in addition to the CAIR).
Figure 2.5 shows the utility attributable mercury deposition remaining after implementation of the
CAMR alternative. To focus on the specific impacts of the CAMR relative to the 2020 base case
with CAIR, Figures 2.6 and 2.7 show the projected reductions in mercury deposition from
implementation of CAMR Requirements and Alternative, respectively. Figure 2.6 shows that
requirements of the CAMR reduces deposition around the Great Lakes, in parts of the Ohio
Valley and along the Mississippi River, and in Pennsylvania. Figure 2.7 shows that, similar to the
CAMR requirements, the CAMR alternative results in reductions in deposition around the Great
Lakes, the Ohio Valley, and Pennsylvania, as well as in parts of Georgia and the Midwest. Figure
2.8 shows the incremental reduction in mercury deposition comparing the CAMR requirements
and CAMR alternative and that the CAMR alternative provides additional reductions in
deposition primarily in the Midwest and in Georgia. However, in most HUCs, the difference
between the two is very small. Note that in a few HUCs, deposition is expected to increase due to
implementation of the CAMR. This occurs due to the trading provisions of the rule. However, it
should also be noted that after implementation of both CAIR and CAMR, none of these HUCs
have increased mercury deposition relative to the levels of utility deposition in the 2001 base
case.
Cumulative distribution graphs are a useful way to summarize the overall impact of
emission controls on mercury deposition throughout the U.S. These graphs indicate the
distribution of deposition across HUC-8 units. Moving from left to right on each graph indicates
the cumulative percentage of HUC-8 units that have deposition less than the value on the x-axis.
Figure 2.9 shows the impact of utilities on the distribution of total mercury deposition across
HUC-8 units, while Figure 2.10 shows the impact of implementation of CAIR and CAMR on the
distribution of utility attributable mercury deposition. Figure 2.11 show the impact of CAIR and
CAMR on the distribution of the percent of mercury deposition attributable to utility mercury
emissions.
We also provide tables showing specific percentiles of the cumulative distributions, and
the percent of HUC-8 units that fall within particular ranges of total and utility attributable
mercury deposition. These are provided in Tables 2.1 through 2.3.
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Cumulative Distributions of Total Hg Deposition
3 60.00%
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46
Deposition (ug/m2)
-2001 Base
2001 Zero Out
-2020 Base (with CAIR)
"2020 Zero Out
"CAMR Requirements
"CAMR Alternative
52 55 58 61
Figure 2.9. Cumulative Distributions of Total Annual Mercury Deposition (Across HUC-8 Units)
Cumulative Distributions of Utility Attributable Hg Deposition Across HUC-8 Units
1 2 3 4 5 6 7
10 11 12 13 14 15 16 17 18 19 20
Deposition (ug/m2)
-2001 Utility Attributable 2020 Utility Attributable (post CAIR) CAMR Utility Attributable CAMR Alternative Utility Attributable
Figure 2.10. Cumulative Distributions of Annual Mercury Deposition Attributable to Utilities (Across HUC-8
Units)
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0%
1% 4% 7% 10% 13% 16% 19% 22% 25% 28% 31% 34% 37% 40% 43% 46% 49% 52% 55%
Percent of Total Deposition
% UA 2001
^% UA 2020 (post CAIR)
%UACAMR
^% UA CAMR Alternative
Figure 2.11. Cumulative Distributions of Percent Utility Attributable Mercury Deposition (Across HUC-8
Units)
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Table 2.1. Distributions of Total Annual Mercury Deposition
Range
(ng/m2)
2001 Base
Percent Cumulative %
2020 Base (with CAIR)
Percent Cumulative %
2020 CAMR
Requirements
Percent Cumulative %
2020 CAMR Alternative
Percent Cumulative %
0-5
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
5 - 10
5.71%
5.71%
8.26%
8.26%
8.26%
8.26%
8.49%
8.49%
10-15
34.93%
40.63%
47.98%
56.24%
49.98%
58.24%
50.86%
59.35%
15-20
43.00%
83.63%
35.64%
91.88%
33.83%
92.06%
32.71%
92.06%
20-25
11.50%
95.13%
5.89%
97.77%
5.80%
97.87%
5.80%
97.87%
25-30
3.15%
98.28%
1.30%
99.07%
1.21%
99.07%
1.21%
99.07%
Over 30
1.72%
100.00%
0.93%
100.00%
0.93%
100.00%
0.93%
100.00%
Table 2.2. Distributions of Utility Attributable Annual Mercury Deposition
Range
(ng/m2)
2001
Percent Cumulative %
2020 (with CAIR)
Percent Cumulative %
2020 CAMR
Requirements
Percent Cumulative %
2020 CAMR Alternative
Percent Cumulative %
0-2
77.46%
77.46%
97.03%
97.03%
98.52%
98.52%
98.98%
98.98%
2-4
12.06%
89.52%
2.88%
99.91%
1.48%
100.00%
1.02%
100.00%
4-6
5.52%
95.04%
0.09%
100.00%
0.00%
100.00%
0.00%
100.00%
6-8
2.60%
97.63%
0.00%
100.00%
0.00%
100.00%
0.00%
100.00%
8- 10
1.35%
98.98%
0.00%
100.00%
0.00%
100.00%
0.00%
100.00%
10-15
0.74%
99.72%
0.00%
100.00%
0.00%
100.00%
0.00%
100.00%
15-20
0.28%
100.00%
0.00%
100.00%
0.00%
100.00%
0.00%
100.00%
Over 20
0.00%
100.00%
0.00%
100.00%
0.00%
100.00%
0.00%
100.00%
Table 2.3. Distributions of Percent Utility Attributable Mercury Deposition
Range
(ng/m2)
Percent
2001
Cumulative %
2020 (with CAIR)
Percent Cumulative %
2020 CAMR
Requirements
Percent Cumulative %
2020 CAMR Alternative
Percent Cumulative %
0 - 5%
59.04%
59.04%
72.36%
72.36%
78.39%
78.39%
82.98%
82.98%
5 - 10%
15.45%
74.49%
20.59%
92.95%
17.90%
96.29%
14.56%
97.54%
10 - 15%
7.33%
81.82%
6.08%
99.03%
3.20%
99.49%
2.04%
99.58%
15 - 20%
6.68%
88.50%
0.88%
99.91%
0.51%
100.00%
0.42%
100.00%
20 - 25%
5.33%
93.83%
0.09%
100.00%
0.00%
100.00%
0.00%
100.00%
25 - 30%
2.50%
96.34%
0.00%
100.00%
0.00%
100.00%
0.00%
100.00%
30 - 40%
2.83%
99.17%
0.00%
100.00%
0.00%
100.00%
0.00%
100.00%
40 - 50%
0.60%
99.77%
0.00%
100.00%
0.00%
100.00%
0.00%
100.00%
50 - 60%
0.23%
100.00%
0.00%
100.00%
0.00%
100.00%
0.00%
100.00%
Over 60%
0.00%
100.00%
0.00%
100.00%
0.00%
100.00%
0.00%
100.00%
Page 15 of 68
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Utility attributable mercury deposition has a relatively small effect on total mercury
deposition even in the 2001 base case. After implementation of CAIR in 2020, the distribution
of mercury deposition attributable to utilities shows a substantial shift to the left, indicating
significant reductions in deposition attributable to utilities. The disproportionately large shift in
the upper percentiles of the distribution indicates that HUCs with high levels of utility deposition
are receiving a larger reduction in utility attributable mercury deposition relative to HUCs with a
relatively small level of utility attributable deposition. As shown in Table 2.2, in 2001, 77
percent of HUCs have utility attributable deposition of 2 |ig/m2 or less. By 2020, after the
implementation of CAIR, 97 percent of HUCs have utility attributable deposition of 2 |ig/m2 or
less, and after implementation of the CAMR requirements, almost 99 percent have utility
attributable deposition of 2 |ig/m2 or less. Additional reductions in mercury emissions due to the
CAMR requirements or the alternative relative to CAIR result in relatively small additional
shifts in the overall distribution of deposition. The incremental impact of the CAMR alternative
relative to the promulgated requirements is very small.
In terms of the percent of deposition attributable to utility emissions, after
implementation of CAIR, the distribution again shows a substantial shift to the left, indicating
significant reductions in the number of HUCs with a large percentage of deposition attributable
to utilities. As shown in Table 2.3, in 2001, 89 percent of HUCs have 20 percent or less mercury
deposition attributable to utilities. In 2020, after implementation of CAIR, 99.9 percent of HUCs
are projected to have 20 percent or less mercury deposition attributable to utilities, and 93
percent are projected to have 10 percent or less. Additional emissions reductions due to the
CAMR requirements result in a small additional reduction in the number of HUCs with a high
percentage of utility attributable emissions. Incremental impacts of the CAMR alternative
relative to the promulgated requirements are small. In addition to the cumulative distribution
graphs and tables, for each metric, we provide the following summary statistics: minimum,
maximum, 50th percentile, 90th percentile, and 99th percentile for Total mercury (Table 2.4) and
utility attributable mercury deposition (Table 2.5 and Table 2.6).
Table 2.4. Summary Statistics for Total Mercury Deposition (aggregated to the HUC-8
level)
Statistic (|ig/nr:i
2001 Base Case
2001 Utility
2020 Base Case
2020 Utility
2020 CAMR
2020 CAMR
Zero Out
(with CAIR)
Zero Out
Requirements
Alternative
Minimum
6.94
6.94
6.08
5.90
6.08
6.07
Maximum
54.54
54.38
62.76
62.72
62.76
62.75
50th percentile
15.92
14.60
14.59
13.92
14.44
14.39
90th percentile
22.16
19.48
19.46
19.04
19.37
19.33
99th percentile
32.35
27.20
29.15
28.93
28.96
28.95
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Table 2.5. Summary Statistics for Utility Attributable Mercury Deposition (aggregated to
the HUC-8 level)
Statistic (|ig/nr:i
2001 Base Case
2020 Base Case
2020 CAMR
2020 CAMR
(with CAIR)
Requirements
Alternative
Minimum
0.00
0.00
0.00
0.00
Maximum
19.71
4.03
3.85
3.80
50th percentile
0.39
0.31
0.26
0.22
90th percentile
4.08
1.38
1.16
0.99
99th percentile
10.15
2.56
2.17
2.04
Table 2.6. Summary Statistics for Percent Utility Attributable Mercury Deposition
(aggregated to the HUC-8 level)
Statistic
2001 Base Case
2020 Base Case
2020 CAMR
2020 CAMR
(with CAIR)
Requirements
Alternative
Minimum
0.01%
0.02%
0.01%
0.01%
Maximum
55.21%
19.21%
18.79%
18.79%
50th percentile
2.92%
2.39%
2.00%
1.69%
90th percentile
21.14%
8.88%
7.58%
6.45%
99th percentile
39.16%
14.82%
12.81%
12.10%
Summary statistics reveal very similar information. The median deposition level is
reduced by only 8 percent when utilities emissions are zeroed out in 2001, suggesting that
utilities are not a major source of mercury deposition in most HUCs. At HUCs with the highest
deposition levels, zeroing out utilities reduces the 99th percentile deposition level by 15 percent,
suggesting that there are relatively larger impacts of utilities in high deposition areas. CAIR
shifts the distribution of attributable deposition significantly, resulting in a 75 percent reduction
in the 99th percentile of utility attributable deposition, and a 20 percent reduction in the 50th
percentile. CAMR results in small additional reductions in attributable deposition relative to
2001 levels. CAIR also shifts the distribution of percent attributable deposition. In the 2001
base case, 10 percent of HUCs had greater than 20 percent of deposition attributable to utilities.
In the 2020 post-CAIR base case, no HUCs had greater than 20 percent of deposition attributable
to utilities, and 90 percent had less than 9 percent of deposition attributable to utilities.
Table 2.7 presents the frequency and cumulative distributions of the reductions in
deposition associated with the CAMR requirements and the CAMR alternative. We also provide
the reduction in deposition from the 2020 base case with CAIR implemented relative to the 2001
base case. This change (2001 Base - 2020 CAIR) shows that there are both increases and
decreases in deposition. Negative reductions (increases) are due to growth in non-utility
mercury emissions, and growth in utility emissions in areas unaffected by CAIR. Reductions in
deposition are largely due to the implementation of CAIR controls at utilities.
Both the promulgated CAMR requirements and the CAMR alternative result in small
reductions in mercury deposition beyond CAIR. Less than 1 percent of HUCs have a reduction
Page 17 of 68
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in deposition due to the CAMR requirements that is greater than 1 |ig/m2, A small number of
HUCs have increased mercury deposition, due to shifting of mercury emissions due to the
trading provisions of the rule. The CAMR alternative results in slightly more HUCs with a
greater than 1 |ig/m2 reduction in mercury deposition, and a somewhat smaller number of HUCs
with increased mercury deposition. Over 97 percent of HUCs in both cases have reductions in
deposition between 0 and 1 |ig/m2. Table 2.8 provides summary statistics for the change in
mercury deposition for the promulgated CAMR requirements and the CAMR alternative.
Table 2.7. Distributions of Reductions in Total Mercury Deposition
2001 Base -
2020 Base (with CAIR) -
2020 Base (with CAIR) -
2020 CAMR
2020 Base (with CAIR)
2020 CAMR
2020 CAMR Alternative
Requirements
-2020
Requirements
CAMR Alternative
Range
(ng/m2)
Percent
Cumulative %
Percent
Cumulative %
Percent
Cumulative %
Percent Cumulative %
<=0
6.59%
6.59%
2.13%
2.13%
0.83%
0.83%
0.28%
0.28%
0- 1
58.02%
64.61%
97.03%
99.17%
97.87%
98.70%
99.58%
99.86%
1-2
12.06%
76.67%
0.83%
100.00%
1.30%
100.00%
0.14%
100.00%
2-3
7.33%
84.00%
0.00%
100.00%
0.00%
100.00%
0.00%
100.00%
3-4
5.10%
89.10%
0.00%
100.00%
0.00%
100.00%
0.00%
100.00%
4-5
3.71%
92.81%
0.00%
100.00%
0.00%
100.00%
0.00%
100.00%
5 - 10
6.08%
98.89%
0.00%
100.00%
0.00%
100.00%
0.00%
100.00%
10-15
0.88%
99.77%
0.00%
100.00%
0.00%
100.00%
0.00%
100.00%
15-20
0.23%
100.00%
0.00%
100.00%
0.00%
100.00%
0.00%
100.00%
Table 2.8. Summary Statistics for Reductions in Total Mercury Deposition
Statistic
2001 Base -
2020 Base (with CAIR)
2020 Base (with CAIR)
2020 CAMR
((Hg/m2
2020 Base (with CAIR)
- 2020 CAMR
- 2020 CAMR
Requirements - 2020
Requirements
Alternative
CAMR Alternative
Minimum
-22.44
-0.26
-0.18
-0.06
Maximum
17.29
1.65
1.70
1.28
50th percentile
0.57
0.04
0.07
0.03
90th percentile
4.21
0.22
0.37
0.13
99th percentile
10.23
0.88
1.08
0.51
2.2 How would regional deposition results differ between 2020 and 2015?
Although EPA did not directly model the effects of CAIR on mercury deposition in 2015,
the impacts are not expected to differ too much from the modeling for the 2020 baseline with
CAIR. The estimated total mercury emissions of just over 34 tons from EGUs in 2015 with
CAIR will be virtually the same as the estimated total in 2020 with CAIR. The readily deposited
non-elemental mercury emissions from EGUs are estimated to be 10 tons in 2015 but roughly 9
tons in 2020. The non-elemental mercury emissions consists of the sum of the reactive gaseous
mercury and particulate mercury species. It could be inferred that although 2015 was not
modeled here that the mercury deposition levels that are estimated to occur with CAIR in 2020
as shown in Figure 2.3 are similar to those that would occur in 2015 with CAIR. Thus, the
difference in mercury deposition from 2001 to CAIR in 2020 as shown in Figures 2.9 and 2.10
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should also be indicative of the change between 2001 and CAIR in 2015. The similarity between
these scenarios will depend upon the following three
factors:
The spatial distribution of mercury emissions reductions across these years will
influence the spatial nature of mercury deposition. Despite the fact that the level of total
mercury emissions is virtually the same for both years, the spatial distribution of these
emissions reductions across EGUs will likely differ between 2015 and 2020. This
difference should lead to the spatial coverage of reductions in mercury deposition to be
somewhat less in 2015 than 2020 similar to the reduced spatial coverage observed in the
modeling for the 2020 baseline with CAIR compared to 2020 CAMR options.
The level of mercury emissions reductions by species across these years would effect
the modeled levels of mercury deposition. Despite the fact that the level of total mercury
emissions is virtually the same for both years, the more readily deposited non-elemental
emissions are different by roughly 10 percent, or 10 vs 9 tons respectfully for 2015 and
2020. The mercury emissions from sectors other than EGUs are also expected to be
different between these years. In addition to the spatial differences, these differences in
emissions will contribute to an undetermined difference in the spatial coverage of
mercury deposition reductions in 2015 than 2020.
The levels of criteria pollutant emissions are different across these years and would
effect the mercury deposition through the atmospheric reactions accounted for by
CMAQ. However, the potential for these interactions to cause notable differences is
limited as the emission differences are not significant enough for these interactions to be
more than a second-order impact.
3. Analysis of Fish Tissue Methylmercury Concentrations
While deposition changes will occur immediately after reductions in emissions, changes
in fish tissue methylmercury concentrations will not change immediately. Case studies of
individual ecosystems show that the time necessary for aquatic systems to reach a new steady
state after a reduction in mercury deposition rates can be as short as 5 years or as long as 50
years or more. The medium response scenarios also varied widely but were generally on the
order of one to three decades. Overall, we conclude that the most likely appropriate response
times for freshwater ecosystems to be considered in the national scale assessment range between
five and 30 years, while recognizing that some systems will likely take more than 50-100 years
to reach steady state. It is important to note that the analysis presented in this document does not
account for this lag. Results for methylmercury concentrations and corresponding exposure
estimates should be interpreted being at steady state, not in any particular year.
Fish tissue methylmercury concentrations were developed using fish tissue samples
collected as part of two different sampling programs, the National Listing of Fish Advisories
(NLFA) and the National Lake Fish Tissue Survey (NLFTS). These two databases combined
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provide the largest existing database of fish tissue mercury levels. The NLFA collects data from
state sampling of fish (U.S. EPA, 2004a). According to the EPA NLFA website
(http://www.epa.gov/ost/fish/advisories), states monitor their waters by sampling fish tissue for
long-lasting pollutants that bioaccumulate. States issue their advisories and guidelines
voluntarily and have flexibility in what criteria they use and how the data are collected. As a
result, there are significant variations in the numbers of waters tested, the pollutants tested for,
and the threshold for issuing advisories. Based on self-reporting, the national trend is for states
to monitor different waters each year, generally without retesting waters monitored in recent
years. State sampling programs are based on different sampling criteria and may include
samples from locations suspected to be contaminated by mercury from a variety of sources,
including non-air deposition sources such as gold or mercury mines, as well as industrial
locations such as chloralkalai plants5. In general, the States historically sampled waterbodies in
areas of suspected contamination. More recently, States also have focused sampling efforts on
areas of elevated fishing pressure.
The NLFTS is an ongoing program intended to provide a more geographically
representative set of fish tissue data at lakes and reservoirs in the U.S. (U.S. EPA, 2004b,c)6.
The NLFTS provides samples from 500 randomly selected lakes and reservoirs in the U.S.
during the period 2000-2003. The NLFTS excluded the great lakes, and randomly selected lake
sites from over 270,000 possible sites. The sampling design used a geographically stratified
design, and oversampled large lakes to avoid a preponderance of small lakes in the sample. The
NLFTS is a carefully designed sampling protocol and includes consistent sampling methods and
quality assurance procedures.
The NLFA database includes data collected from 1990 to 2003, while the NLFTS
includes data collected from 2000 to 2003. Given the significant changes in emissions of
mercury between 1990 and 1999 due to existing Clean Air Act programs (reductions of almost
50%) and the potential for changes in the methylation rates due to changes in lake acidity
affected by the acid rain program, there could be significant differences in the mercury
concentrations found in fish sampled prior to 1999 and fish sampled after 19997. Given that
5 For example, the Pennsylvania Department of Environmental Protection lists the following protocol for
determining sample locations: "Stations are chosen based on: 1) the need for verification (second) samples at
selected sites for possible new advisories or de-listings, 2) the demand of the Water Quality Network (WQN)
rotation, and 3) the need to follow-up on existing advisories. In addition, DEP issues a request for suggested
sampling station locations and target species to DEP Regional Biologists, PA Fish and Boat Commission Area
Fisheries Mangers (AFMs) and the Erie County Department of Health (ECDH). (Pennsylvania Department of
Environmental Protection, 2004)" The Georgia Department of Natural Resources identifies as a key objective the
need "to identify areas where fish tissue contamination may present a health or environmental risk. To satisfy this
objective, sampling sites should target areas suspected of having high contamination. (Fish Tissue Advisory
Committee, 1992)"
6 For more information, visit the EPA NLFTS website at http://epa.gov/waterscience/fishstudy/
7 In most locations, it is not possible to test for this type of trend effect, because many states do not
resample locations on a regular basis. As such, for many sampling sites, there is only one or a few sampling dates.
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CAIR and CAMR rule provisions will not go into effect until 2010, we determined that using the
more current data on fish tissue concentrations will provide a more reasonable characterization
of the current and future mercury risks attributable to utility Mercury emissions. As such, we
use the NLFTS and NLFA samples collected from 1999 forward for the majority of the analyses.
However, we recognize that because many states do not frequently resample locations, excluding
samples collected prior to 1999 will result in some loss of spatial coverage. To examine the
potential impact of this loss in spatial coverage, we also present several sensitivity analyses
using the full set of NLFA data collected between 1990 and 2003.
Many samples in the NLFA databases are for inedible fish, either due to the species type
or due to the size of the sampled fish. In order to prevent data artifacts associated with using
samples from fish that are of a size that is rarely consumed, we filtered the NLFTS and NLFA
data further to remove samples for fish under 7 inches in length, which we determined to be a
reasonable edible size. We also excluded saltwater species and crustaceans. These species were
excluded due to concerns about the methods to attribute methylmercury in these species to utility
mercury emissions. We recognize that all of the filtering criteria will reduce the geographic
coverage of the data, however, it will ensure that only the most reliable data are used in
determining the impacts of the rule.8
For included locations, samples for the same species are averaged across all available
years (post 1998), and then the highest averaged per species concentration is used to represent
the methylmercury concentration for that sample location. For example, if there are two species
at a location, walleye and pike, with three sampling dates for each species, we would first
average over the three sample dates for each species, and then select walleye if the average for
walleye is highest, or select pike if the average for pike is highest. To the extent that an angler
consumes several different species or focuses on a fish type that does not have the highest
concentrations, exposure will be overestimated. This algorithm is used to reflect the variability
in methylmercury within a species at a particular location, and to allow for the possibility that
some anglers may prefer to catch and consume one species of fish over another. Assignment of
the maximum average species concentration recognizes the greater risk to an individual
consuming species with higher accumulation of mercury while respecting the fact that each
sample for an individual species is only an estimate of the true mean concentration in that
species.
The NLFTS and NLFA data were used to determine the 2001 base case methylmercury
levels. Methylmercury levels for other scenarios were calculated as the 2001 base case
multiplied by the ratio of deposition in the scenario to deposition in the base case. For 2020
control options, this indicates a series of multiplications, for example methylmercury in the
CAMR Requirements case is calculated as:
8 Note that sensitivity analyses using the full 1990 - 2003 database did not lead to substantially different
results. There were a few sample locations that had higher methylmercury values, however, these were determined
to be points that were likely heavily influenced by non-air sources. See all_fish_sample_location_analysis.xls in the
docket for this rule for more information on the sensitivity analysis.
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/ \ | 2020 Base Deposition wi thCAIR] ( 2020 CAMR Deposition
MeHg CAAfR = [MeHg 2001 Base) 11
2001 Base Deposition ) \ 2020 Base Deposition with CAIR
In assigning deposition levels to sample locations, we used the predicted CMAQ deposition for
the 36 km grid cell containing the sample location. We use the CMAQ results in this case rather
than the HUC averages because of the point nature of the sampling data. Using a HUC average
would tend to limit the extremes of deposition, and given that many waterbodies where samples
were taken are much smaller than 36 km grid size, it is more appropriate in this case to match the
scale of the waterbody with the scale of the modeling grid. This does not imply that the
deposition at any specific water body should be used to characterize regional deposition patterns
(see previous section).
The scaling method assumes a proportional relationship between reductions in air
deposition of mercury and methylmercury concentrations in fish. This relationship has been
documented in the Mercury Maps Approach (Cocca, 2001). Mercury Maps implements a
simplified form of the IEM-2M model applied in EPA's Mercury Study Report to Congress
(USEPA, 1997b).9 By simplifying the assumptions inherent in the freshwater ecosystem models
that were described in the Report to Congress, the Mercury Maps model showed that these
models converge at a steady-state solution for methylmercury concentrations in fish that are
proportional to changes in mercury inputs from atmospheric deposition, (e.g., over the long term,
fish concentrations are expected to decline proportionally to declines in atmospheric loading to a
waterbody). This solution only applies to situations where air deposition is the only significant
source of mercury to a water body, and the physical, chemical, and biological characteristics of
the ecosystem remain constant over time. EPA recognizes that concentrations of methylmercury
in fish across all ecosystems may not reach steady state and that ecosystem conditions affecting
mercury dynamics are unlikely to remain constant over time. EPA further recognizes that many
water bodies, particularly in areas of historic gold and mercury mining in western states, contain
significant non-air sources of mercury. In addition, some areas have soils with relatively high
mercury levels that contribute to mercury levels in waterbodies. Finally, EPA recognizes that
Mercury Maps does not provide for a calculation of the time lag between a reduction in mercury
deposition and a reduction in the methymercury concentrations in fish. Despite these limitations,
EPA is unaware of any other tool for performing a national-scale assessment of the change in
fish methylmercury concentrations resulting from reductions in atmospheric deposition of
mercury.
As is stated above, the relationship between changes in mercury deposition from air to
the change in fish tissue concentration holds only when air deposition is the predominant source
of the mercury load to a waterbody. Due to this requirement in the model, the national
application of the Mercury Maps approach used for the benefits assessment screened out
watersheds in which sources of mercury other than air deposition were significant. These
9 Note that SERAFM described elsewhere also employs a similar simplifying assumption that, with all
other conditions remaining constant, methylmercury concentrations will respond proportionally to deposition at
steady state.
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watersheds were not screened out for the purposes of the analysis presented in this document.
This may result in higher concentrations of methylmercury in fish being attributed to power
plants than would be the case had we been able to account for the non-air sources. This bias is in
the direction of overestimating exposure attributable to power plants and therefore is a
conservative simplification.
Key fish tissue methymercury metrics:
A. Total methylmercury concentrations
A. 2001 all sources (base case)
B. 2001 utility zero out
C. 2020 utilities with CAIR (2020 base case)
D. 2020 utilities with CAIR plus CAMR Requirements
E. 2020 utilities with CAIR plus CAMR Alternative
F. 2020 utilities zero out
B. Total methylmercury concentration attributable to utilities
A. 2001 (2001 base case - 2001 utility zero out)
B. 2020 CAIR (2020 utilities with CAIR - 2020 utilities zero out)
C. 2020 CAIR plus CAMR Requirements (2020 utilities with CAMR
Requirements - 2020 utilities zero out)
D. 2020 CAIR plus CAMR Alternative (2020 utilities with CAMR
Alternative - 2020 utilities zero out)
C. Change in methylmercury
A. 2020 utilities with CAIR - 2001 base case (approximate estimate of
benefits of CAIR)
B. 2020 utilities with CAIR plus CAMR Requirements - 2020 utilities with
CAIR
C. 2020 utilities with CAIR plus CAMR Alternative - 2020 utilities with
CAIR
D. 2020 utilities zero out - 2020 utilities with CAIR
Fish Sampling Site Maps
Maps of fish tissue methylmercury concentrations at sampling locations are provided
(Fig. 3.1 - 3.3) to show the geographic coverage of sampling sites and spatial patterns in
methylmercury concentrations. Maps and analysis are provided for the post-1999 combined
NLFTS and NLFA database and for the estimated utility attributable fish tissue methylmercury
concentrations in 2001, including after the implementation of CAIR and CAMR.
Figure 3.1 displays a map of the methylmercury concentrations in fish at sampling
locations based on the 1999-2003 NLFTS and NLFA sampling data. This map shows that, based
on the sampled data, many locations currently have methylmercury levels above the EPA water
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quality criterion of 0.3 ppm (mg/kg). Figure 3.2 displays a map of methylmercury
concentrations in fish that are attributable to utility emissions of mercury in the 2001 base year.
This map was derived by reducing the sampled methylmercury concentrations by the ratio of
mercury deposition with utility emissions to mercury deposition without utility emissions. This
map shows that current utility mercury emissions may contribute more than the water quality
criterion level (0.3 ppm) to total fish tissue methylmercury concentrations in a few locations in
South Carolina, New York, and Ohio. Most locations have utility attributable methylmercury
concentrations well below the 0.3 ppm (mg/kg) water quality criterion. Figure 3.3 displays a
map of methylmercury concentrations in fish that are attributable to utility emissions of mercury
in 2020, after implementation of the Clean Air Interstate Rule, which achieves a substantial
reduction in mercury emissions from utilities. This map was derived by reducing the sampled
methylmercury concentrations by the ratio of mercury deposition in 2001 to mercury deposition
in 2020 (with CAIR implemented). This map shows that after implementation of CAIR in 2020,
there are no sample locations with utility attributable methylmercury concentrations above 0.3
ppm (mg/kg). Significant reductions in utility attributable methylmercury occur in the Ohio
Valley, the Northeast, and parts of South Carolina.
Cumulative distribution graphs
These graphs indicate the distribution of methylmercury fish tissue concentrations across
sampling locations. Moving from left to right on each graph indicates the cumulative percentage
of sampling locations that have methylmercury concentrations less than the value on the x-axis.
These graphs are provided only for the filtered set of post-1999 NLFA and NLFTS samples.
Figure 3.4 shows the impact of utilities on the distribution of total fish tissue methylmercury
concentration across sampling locations, while Figure 3.5 shows the impact of implementation of
CAIR and CAMR on the distribution of fish tissue methylmercury attributable to utility mercury
emissions.
We also provide tables (Tables 3.1 and 3.3) showing specific percentiles of the
cumulative distributions, and the percent of sample locations that fall within particular ranges of
total and utility attributable methylmercury concentrations. In addition to the cumulative
distribution graphs and tables, for each metric, we provide the following summary statistics:
minimum, maximum, 50th percentile, 90th percentile, and 99th percentile. Statistical Analysis:
For each metric, we provide the following summary statistics: minimum, maximum, 50th
percentile, 90th percentile, and 99th percentile (Tables 3.2 and 3.4). Together, these statistics
provide a relatively complete picture of the expected impacts of the CAIR and CAMR
regulations on fresh water fish tissue concentrations of methylmercury in the U.S.
Page 24 of 68
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Fish Tissue Methylmercury
(PPm)
O 0.0-0.3
ฎ 0.3-0.5
ป 0.5-1.0
~ 1.0-1.5
* 1.5-5.0
Figure 3.1. Fish Tissue Methylmercury Concentrations at NLFA and NLFTS Sampling Locations (1999-2003 samples)
Page 25 of 68
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Fish Tissue Methylmercury
(ppm)
ฎ 0.00-0.01
ฎ 0.01-0.05
~ 0.05-0.10
~ 0.10-0.30
~ 0.30-0.85
Figure 3.2. Fish Tissue Methylmercury Concentrations Attributable to Utilities in 2001 Base Year
Page 26 of 68
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Fish Tissue Methylmercury
(ppm)
ฐ 0.00-0.01
O 0.01-0.05
* 0.05-0.10
* 0.10-0.30
* 0.30-0.85
Figure 3.3. Fish Tissue Methylmercury Concentrations Attributable to Utilities in 2020 After Implementation of the Clean
Air Interstate Rule
Page 27 of 68
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Cumulative Distributions of Fish Tissue Concentrations Across Sampling Locations
MeHg Concentration (ppm)
|^^2001 Base 2001 Zero Out ^^M2020 Base (with CAIR) ^^2Q2Q Zero Out ^^2020 CAMR Requirements ^^2020 CAMR Alternative
Figure 3.4. Cumulative Distributions of Total Fish Tissue Methylmercury (Across
Sampling Locations)
Table 3.1. Distributions of Total Fish Tissue Methlymercury
Range
(mg/kg)
2001 Base
Percent Cumulative %
2020 Base (with CAIR)
Percent Cumulative %
2020 CAMR
Requirements
Percent Cumulative %
2020 CAMR
Alternative
Percent Cumulative %
0.10-0.19
17.6%
17.6%
23.2%
23.2%
23.3%
17.6%
23.5%
23.5%
0.20 - 0.29
22.7%
40.3%
24.9%
48.1%
25.0%
40.3%
25.0%
48.5%
0.30-0.39
16.4%
56.7%
15.5%
63.6%
15.7%
56.7%
15.6%
64.1%
0.40 - 0.49
11.5%
68.2%
9.6%
73.1%
9.5%
68.2%
9.6%
73.6%
0.50-0.59
8.3%
76.5%
6.8%
79.9%
6.8%
76.5%
6.7%
80.3%
1.00- 1.99
15.1%
91.6%
13.8%
93.6%
13.6%
91.6%
13.4%
93.8%
2.00 - 2.99
7.7%
99.3%
6.1%
99.7%
5.9%
99.3%
6.0%
99.8%
3.00-4.99
0.6%
99.9%
0.3%
99.9%
0.3%
99.9%
0.2%
99.9%
5.00 - Max
0.1%
100.0%
0.1%
100.0%
0.1%
100.0%
0.1%
100.0%
Page 28 of 68
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Table 3.2. Summary Statistics for Total Fish Tissue Methylmercury (Sample Locations)
Statistic (mg/kg)
2001 Base
2001 Utility
2020 Base
2020 Zero Out
2020 CAMR
2020 CAMR
Case
Zero Out
Case
Requirements
Alternative
Minimum
0.00
0.00
0.00
0.00
0.00
0.00
Maximum
4.49
3.64
3.65
3.46
3.63
3.61
50th percentile
0.25
0.21
0.21
0.20
0.21
0.21
90th percentile
0.90
0.81
0.79
0.77
0.79
0.78
99th percentile
1.80
1.65
1.64
1.57
1.63
1.63
Consistent with deposition patterns, utilities emissions of mercury have relatively little
impact on overall fish tissue concentrations in the 2001 base case for the sample locations where
recent samples have been collected. Zeroing out all utility emissions in 2001 results in a less
than 10 percent decrease in the 99th percentile fish tissue concentration and less than a 17 percent
decrease in median fish tissue concentrations. As such, reductions in utility emissions do not
affect the overall distribution of fish tissue methylmercury concentrations. At the 2020 base
case, which includes reductions in utility mercury emissions due to CAIR, the 99th percentile
methylmercury concentration has been reduced by 10 percent, and the median methylmercury
concentration has been reduced by 17 percent. Additional reductions due to either CAMR
requirements or the CAMR alternative result in small additional shifts in the distribution of
methylmercury fish tissue concentrations.
Page 29 of 68
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Cumulative Distribution of Utility Attributable MeHg Across Sampling Locations
Fish Tissue MeHg (ppm)
|^2001 UA ^2020 UA (with CAIR) =^2020 UA CAMR Requirements ^ป2020 UA CAMR Alternative |
Figure 3.5. Cumulative Distributions of Utility Attributable Fish Tissue Methylmercury
(Across Sampling Locations)
Table 3.3. Distributions of Utility Attributable Fish Tissue Methylmercury Concentrations
2001
2020 (with CAIR)
2020 CAMR
2020 CAMR Alternative
Requirements
Range
Percent Cumulative % Percent
Cumulative %
Percent
Cumulative %
Percent
Cumulative %
(mg/kg)
0.0-0.1
87.50%
87.50%
98.94%
98.94%
99.19%
99.19%
99.25%
99.25%
0.1-0.2
9.56%
97.06%
1.00%
99.94%
0.81%
100.00%
0.75%
100.00%
0.2-0.3
2.38%
99.44%
0.06%
100.00%
0.00%
100.00%
0.00%
100.00%
-t
ฉ
1
ฉ
0.44%
99.88%
0.00%
100.00%
0.00%
100.00%
0.00%
100.00%
0.4-0.5
0.06%
99.94%
0.00%
100.00%
0.00%
100.00%
0.00%
100.00%
More
0.06%
100.00%
0.00%
100.00%
0.00%
100.00%
0.00%
100.00%
Table 3.4. Summary Statistics for Utility Attributable Fish Tissue Methylmercury (Across
Sampling Locations)
Statistic (mg/kg) 2001 Base
2020 (with CAIR)
2020 CAMR
Requirements
2020 CAMR
Alternative
Minimum
0.00
0.00
0.00
0.00
Maximum
0.85
0.25
0.19
0.18
50th percentile
0.03
0.01
0.01
0.01
90th percentile
0.11
0.03
0.03
0.03
99th percentile
0.26
0.10
0.09
0.08
CAIR results in a substantial shift in the distribution of utility attributable fish tissue
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concentrations, resulting in a 60 percent reduction in the 99th percentile attributable fish tissue
concentration and a 67 percent reduction in the 50th percentile attributable fish tissue
concentration (Table 3..3). However, it should be noted that this results in only a 0.02 ppm
(mg/kg) reductions in median fish tissue concentrations. Additional reductions due to either the
promulgated CAMR requirements or the CAMR alternative result in small additional reductions
in median utility attributable concentrations (Table 3.4), however, the CAMR requirements
result in an additional 9 percent reduction in the 99th percentile concentration and the CAMR
alternative results in an additional 20 percent reduction in the 99th percentile concentration.
However, this equates to only a 0.01 ppm (mg/kg) and 0.02 ppm (mg/kg) reduction for the
promulgated CAMR requirements and the alternative, respectively.
Certain important findings can be drawn from this analysis. These include:
No sample locations have utility attributable fish tissue concentrations exceeding 0.3 ppm
(mg/kg) after CAIR in 2020.
Less than 1 percent of locations have utility attributable fish tissue concentrations
exceeding 0.1 ppm (mg/kg) after CAIR in 2020.
Less than 10 percent of locations have utility attributable fish tissue concentrations
exceeding 0.05 ppm (mg/kg) after CAIR in 2020.
Table 3.5 presents the frequency and cumulative distributions of the reductions in fish
tissue methylmercury concentrations associated with the CAMR requirements and the CAMR
alternative. We also provide the reduction in methylmercury concentrations from the 2020 base
case with CAIR implemented relative to the 2001 base case. This change (2001 Base - 2020
CAIR) shows that there are both increases and decreases in methylmercury concentrations.
Negative reductions (increases) are due to growth in non-utility mercury emissions, and growth
in utility emissions in areas unaffected by CAIR. Reductions in methylmercury concentrations
are largely due to the implementation of CAIR controls at utilities. The reductions between 2001
and 2020 with CAIR are small for most of the sample locations (the median change is only 0.03
ppm). However, 15 percent of locations show an improvement of 0.1 ppm or greater.
Both the promulgated CAMR requirements and the CAMR alternative result in small
reductions in fish tissue methylmercury concentrations. Less than 1 percent of sample locations
have a reduction in fish tissue concentrations due to the CAMR requirements that is greater than
0.05 ppm. A small number of sample locations have increased mercury deposition, due to
shifting of mercury emissions due to the trading provisions of the rule. The CAMR alternative
results in slightly more sample locations with a greater than 0.05 ppm reduction in fish tissue
concentrations. Over 99 percent of sample locations in both cases have reductions in fish tissue
concentrations between 0 and 0.05 ppm. Table 3.6 provides summary statistics for the change in
fish tissue methylmercury for the promulgated CAMR requirements and the CAMR alternative.
From this table it is clear that even at the 99th percentile, the CAMR requirements do not result in
a substantial reduction in fish tissue concentrations over that achieved by CAIR.
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Table 3.5. Distributions of Reductions in Fish Tissue Methylmercury Concentrations
Range
(mg/kg)
2001 Base -
2020 Base (with CAIR)
Percent Cumulative %
2020 Base (with CAIR)
- 2020 CAMR
Requirements
Percent Cumulative %
2020 Base (with CAIR)
- 2020 CAMR
Alternative
Percent Cumulative %
2020 CAMR
Requirements - 2020
CAMR Alternative
Percent Cumulative %
<0.00 -
0.05
0.05-0.10
0.10-0.20
0.20-0.30
0.30-0.40
0.40 - 0.50
0.50 - 1.00
65.75% 65.75%
20.19% 85.94%
11.00% 96.94%
2.44% 99.38%
0.25% 99.63%
0.19% 99.81%
0.19% 100.00%
99.94% 99.94%
0.00% 99.94%
0.00% 99.94%
0.06% 100.00%
0.00% 100.00%
0.00% 100.00%
0.00% 100.00%
99.81% 99.81%
0.13% 99.94%
0.00% 99.94%
0.06% 100.00%
0.00% 100.00%
0.00% 100.00%
0.00% 100.00%
99.94% 99.94%
0.06% 100.00%
0.00% 100.00%
0.00% 100.00%
0.00% 100.00%
0.00% 100.00%
0.00% 100.00%
Table 3.6. Summary Statistics for Reductions in Fish Tissue Methylmercury
Concentrations
Statistic
2001 Base -
2020 Base (with CAIR) -
2020 Base (with
2020 CAMR
(mg/kg)
2020 Base (with
2020 CAMR
CAIR) - 2020 CAMR
Requirements - 2020
CAIR)
Requirements
Alternative
CAMR Alternative
Minimum
-0.22
-0.03
-0.03
0.00
Maximum
0.84
0.20
0.21
0.05
50th percentile
0.03
0.00
0.00
0.00
90th percentile
0.12
0.00
0.01
0.00
99th percentile
0.25
0.01
0.02
0.02
Note: There has been some discussion about whether inclusion of pre-1999 samples would alter
the conclusions. We have also analyzed the data with pre-1999 samples included, and the results
are substantively unchanged. Tables 3.7 and 3.8 show the distributions and sample statistics for
utility attributable fish tissue methylmercury using the full post-1990 set of NLFA and NLFTS
data. There is one sample location in New Jersey in 2020 with a utility attributable concentration
of 0.42 mg/kg, however, it is located in an area that may be influenced by a chloralkali plant, so
the magnitude of the utility attributable portion may be overstated. One other location in South
Carolina has a utility attributable portion that exceeds 0.3 mg/kg in the pre-1999 sample set.
This location is located near several mining operations and thus is likely to have been influenced
by non-air sources. As noted earlier in this document, the proportionality assumption based on
Mercury Maps is not valid for locations that are significantly influenced by non-air sources. In
addition, the higher value is influenced mainly by samples collected in 1993. After 1999, the
samples all have much lower values. Given changes in mercury emissions and mining practices,
it is not recommended that earlier samples be used when more recent data are available.
Excluding these two locations, the conclusions remain generally the same.
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Table 3.7. Sensitivity Analysis: Distributions of Utility Attributable Fish Tissue
Methylmercury Concentrations Using All Post-1990 NLFA and NLFTS Samples
Range
(mg/kg)
Percent
2001
Cumulative %
2020 (with CAIR)
Percent Cumulative %
2020 CAMR
Requirements
Percent Cumulative %
2020 CAMR Alternative
Percent Cumulative %
<0.00
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
0.00-
72.0%
72.1%
94.0%
94.1%
95.2%
95.3%
96.2%
96.3%
0.05
0.00-
17.0%
89.2%
4.5%
98.6%
3.7%
99.0%
2.9%
99.2%
0.10
0.10-
8.0%
97.1%
1.3%
99.8%
0.9%
99.9%
0.7%
99.9%
0.20
0.20-
2.1%
99.3%
0.1%
100.0%
0.1%
100.0%
0.1%
100.0%
0.30
0.30-
0.3%
99.6%
0.0%
100.0%
0.0%
100.0%
0.0%
100.0%
0.40
0.40-
0.1%
99.7%
0.0%
100.0%
0.0%
100.0%
0.0%
100.0%
0.50
0.50-
0.3%
100.0%
0.0%
100.0%
0.0%
100.0%
0.0%
100.0%
1.00
Table 3.8. Sensitivity Analysis: Summary Statistics for Utility Attributable Fish Tissue
Methylmercury Concentrations Using All Post-1990 NLFA and NLFTS Samples
Statistic
2001
2020 (with CAIR)
2020 CAMR
2020 CAMR
(mg/kg)
Requirements
Alternative
Minimum
0.00
0.00
0.00
0.00
Maximum
1.63
0.42
0.36
0.34
50th percentile
0.02
0.01
0.01
0.01
90th percentile
0.10
0.04
0.03
0.03
99th percentile
0.28
0.12
0.10
0.09
4. Fish Consumption
Modeling exposure to methylmercury through fish consumption requires characterizing
dietary fish consumption rates for specific populations such as recreational anglers and
subpopulations exhibiting higher fish consumption rates including Native American subsistence
fishers or other high-consumption (subsistence-like) groups. The primary concern is for the
consumption of self-caught freshwater fish, (i.e., fish caught and consumed from rivers and lakes
by members of the public). This reflects the fact that mercury emitted from coal-fired power
plants is likely to primarily impact domestic inland waterbodies in the eastern part of the country
through local and regional deposition (see Section 2). In addition, because the RfD for mercury
is based on a long-term average daily exposure rate for methylmercury, studies that characterize
long-term fish consumption patterns are most relevant to modeling exposure to methylmercury.
Many available studies on fish consumption focus on short-term consumption patterns (i.e., 2
day dietary recall) without considering meal frequencies over longer periods ranging from
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months to a year or more. These short-term studies can over-estimate consumption rates for
higher percentiles of the population (i.e., higher consuming individuals) compared with longer-
term studies that consider meal frequencies in characterizing high-end consumption rates10.
Two broad categories of fishers can be considered in the context of modeling exposure to
methylmercury through fish consumption: (a) recreational freshwater anglers,(i.e., the majority
of fishers in the nation who typically purchase fishing licenses) and (b) higher consuming
individuals who through choice, socio-cultural practices or necessity, (i.e., dietary
supplementation) consume relatively larger amounts of freshwater fish. It is important to note
that these two populations are not mutually exclusive and there is some degree of overlap. For
example, available fish consumption data suggests that the higher percentiles of the recreational
fisher population, (i.e., >95th %) may have consumption rates that are similar to the average
subsistence consumer such as Native American subsistence fishing populations. Therefore,
while members of group "b" above (higher consuming individuals) are generally considered to
be of greatest concern for exposure to methylmercury due to their high fish consumption rates,
recreational anglers with extreme high-end consumption rates may also consume large amounts
of self-caught freshwater fish; in the same range as subsistence fishers11.
As indicated above, our analysis focused on assessing exposure to self-caught freshwater
fish because available information indicated that U.S. utility mercury emissions might contribute
to the methylmercury concentrations in these fish. EPA also considered the following fish
consumption pathways: consumption of fish from commercial sources (including marine,
freshwater, and estuarine fish from domestic and foreign sources), consumption of recreationally
caught marine fish, consumption of recreationally-caught estuarine fish, and consumption of
commercial fish raised at fish farms (aquaculture). For a number of reasons, EPA does not
believe that these latter pathways would result in significant exposure to utility-attributable
methylmercury. In addition, an important analytical tool, the Mercury Maps approach that has
been applied in freshwater systems to correlate the amount of deposition with level of fish tissue
methylmercury, is more uncertain in saltwater systems (estuaries, coastal, and deep ocean).
Specifically, EPA does not believe that commercial fish are adversely affected by
mercury emissions from U.S. utilities to any significant degree. Imports account for over half of
10 EPA notes that, while mean consumption rates for dietary items characterized using short-term dietary
recall data are reasonable and will typically match rates generated using longer-term surveys, high-end consumption
rates based on short-term dietary recall studies can be biased high when used to represent upper percentile long-term
average consumption rates. This applies only for dietary items which are consumed sporadically, (i.e., are not
consumed day-to-day at a relatively constant rate) which would seem to apply to most types of self-caught fish
consumption except high-end subsistence which may approach a meal a day. (Exposure Factors Handbook, Section
9.2.1, EPA, 1997)
11 Surveys of licensed recreation anglers have shown that extreme high-end consumption rates for self-
caught freshwater fish can meet or exceed the EPA's recommended mean value for freshwater subsistence fishers.
Fiore et al (1999) as reported in U.S. EPA (1997) provides a 100th percentile rate of 150 g/day for recreational
anglers in Wisconsin which exceeds the EPA's recommended subsistence freshwater value of 60 g/day.
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the U.S. commercial fish supply. Moreover, of the commercial fish landed domestically, the
majority (61%) are caught in the deep ocean (3-200 miles offshore) in the Pacific and Atlantic
Oceans, and the Gulf Coast. In 2002, the Pacific coast region alone accounted for 65% of U.S
total commercial landings, with Alaska and California accounting for the largest portion. In
contrast, our modeling shows that U.S. utility-attributable mercury deposition is concentrated in
the midwestern and northeastern portion of the U.S., that is, in areas geographically removed
from the majority of commercial fishing activity. For the same reasons, EPA does not expect
that self-caught, (e.g., recreational) fish from marine waters will be affected significantly by U.S.
utilities.
Individuals also may consume estuarine fish and fish derived from aquaculture. We
determined that it was not critical to assess the impact of utility mercury emissions on
commercial or self-caught estuarine fish. Based on our deposition modeling, we expect that any
impact of utility-attributable mercury on estuarine environments will be limited.
Similarly, we believe that the overall impact of utility-derived deposition on U.S.
aquaculture will be minimal. U.S. aquaculture is very small part of commercial fish production,
comprising, in 2002, about 5% of total commercial fish production. Much of U.S. aquaculture
occurs outside of the northeast and midwest, and therefore is not much impacted by utility
attributable mercury emissions. We expect that potentially significant health impacts could only
occur in the event that individuals consistently consume a particular aquaculture-derived fish
type that is supplied from a single geographic location significantly impacted by utility mercury
emissions. We do not have adequate information to allow us to predict the number of
individuals who would be subject to such a confluence of events, and we suspect that the number
would be quite small. In any event, even under these circumstances, we do not have sufficient
information to characterize the impact of utility emissions on aquaculture. We do not believe
that there will be a clear or consistent relationship between the mercury deposition onto waters
supporting aquaculture and the methylmercury concentrations in fish. Such an analysis would be
complicated by the fact that the diets of fish raised in aquacultural environments often consist of,
or are supplemented with, commercial feeds12. For all these reasons, we believe that the
focusing our analysis on non-commercial freshwater fish is reasonable and appropriate.
Recreational Angler Consumption Rates
EPA has recommended both average and high-end consumption rates for self-caught
freshwater fish for recreational anglers (8 g/day and 25 g/day as the mean and 95th %,
respectively) (EPA, 1997a). These values are based on several of the best available peer-
12 The belief that farm-raised fish often contain less contamination than wild caught fish is currently an
issue under debate in the published literature. The main issue in determining the difference in tissue methylmercury
concentrations between farm raised and wild fish appears to be the duration of exposure and source of food
consumed by the fish.
Farm raised fish species bred for human consumption are fed a controlled food source. Recent studies have
found varying levels of contamination in fish feed (Hites et al. 2004a & b). In situations where the food source is
not contaminated, the threat of bioaccumulation of contaminants in farm raised fish should be lessened.
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reviewed studies focusing on freshwater recreational fisher populations located in the Midwest
and Northeast. All of these studies are based on surveys of licensed fishers and therefore,
exclude non-licensed fishers which may include individuals engaging in high-end subsistence-
like activity.
Because these studies support characterization of high-end consumption by recreational
anglers (through the 95th % of 25 g/day), it is possible to fit a consumption rate distribution for
recreational anglers using these recommended values (lognormal distributions as well as other
statistical models can be fitted using these datapoints, although the discussion here references the
fitting of a lognormal distribution13). A lognormal distribution fitted to these datapoints can be
used both for (a) interpolating percentile consumption rates between these two percentiles (the
mean and 95th %) and (b) extrapolating to higher rates of consumption beyond the 95th %. The
former application is important in supporting population-level modeling of recreational fisher
exposure where individual fish consumption rates are sampled from this distribution and
assigned to modeled fishers. The latter application allows projections of high-end percentiles
(e.g., 99th % and above), which allows modeling of extreme high-end recreational angler
consumption that can approach or even reach subsistence levels of consumption. Values in the
range of 50-60 g/day have been identified for the 99th % recreational fisher using this fitted
lognormal distribution approach. This "derived" 99th percentile value is in-line with similar
percentiles obtained from specific survey's of freshwater recreational anglers (e.g., the 41 g/day
to 150 g/day range reported for the 98th percentile to 100th % in a study of recreational anglers in
Wisconsin, (Fiore et al, 1989 as reported in USEPA, 1997a)).
Use of the 95th percentile recreational freshwater consumption rate recommended by the
EPA (25 g/day) provides coverage for the majority of recreational fishers. However, based on
consideration of the lognormal distribution described above, as well as published data (i.e., the
98th-100th % values published by Fiore referenced above), some recreational anglers would have
fish consumption rates above this level, including individuals with consumption rates near or at
subsistence-levels, (i.e., at or above the 60 g/day mean level recommended by the EPA for
freshwater subsistence fishers - see below).
In the context of CAMR, which is focused on recreational consumption of self-caught
freshwater fish, the 17.5 g/day value recommended by the Office of Water (as representing the
90th % of general population fish consumption) and used in calculating the Ambient Water
Quality Criteria for mercury (0.3 mg/kg) may over-estimate exposure for the average
recreational angler and underestimate exposure for the high-end recreational anglers. This is
because the 17.5 g/day value is larger than the EPA's recommended freshwater self-caught fish
13 The lognormal model is often used in fitting distributions to point value data (e.g., means, 90th %, 95th %)
for purposes of more completely characterizing inter-individual variability in dietary consumption for modeled
populations. There is precedent for using the lognormal distribution for representing inter-individual variability in
fish consumption rates in supporting risk assessment conducted as part of national-scale regulatory development by
EPA (Section 6.3.2 of Human Health and Ecological Risk Assessment Support to the Development of Technical
Standards for Emissions from Combustion Units Burning Hazardous Waste, Background Document, US
Environmental Protection Agency, Office of Solid Waste, Washington, DC, July 1999.)
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mean consumption rate of 8 g/day and smaller than the EPA's recommended 95th % for that same
pathway of 25 g/day. It is important to note also that the 17.5 g/day includes consumption of
commercially-purchased fish and estuarine fish (in addition to self-caught freshwater fish), both
of which are not being considered in this analysis.
EPA recognizes that recreational fisher consumption rates will vary geographically
reflecting different patterns of fishing activity. Ideally, studies covering activity at the local or
regional level would be combined to develop an integrated set (i.e., patchwork) of fish
consumption rates that, when viewed collectively, provided a more representative
characterization of national recreational fisher behavior. However, many of these regional/local
studies do not meet the criteria described above, (i.e., characterizing long-term daily
consumption rates of self-caught freshwater fish). In addition, available local- and regional-scale
studies that meet these criteria do not, as a whole, provide reasonable coverage for the entire
study area. Consequently, EPA favors the use of the recommended recreational fisher
consumption rates of 8 g/day (mean) and 25 g/day (95th %) for our analyses.
High-Level Fish Consumption Rates
Characterization of fish consumption rates for high fish consuming people, (e.g., certain
segments of Native American and other ethnic populations exhibiting high-end consumption) in
the context of a larger regional or national analysis is technically challenging for a number of
reasons. Peer reviewed study data on these populations are relatively limited in geographical
coverage, especially when subjected to the criteria outlined above, (i.e., meal frequencies
averaged over longer periods). This means that, while subsistence-level fish consumption has
been characterized for some populations, many portions of the country where these populations
may be present are not covered by existing data. In addition, many of the high consumption
populations that have been studied are located near the ocean and consequently have a
significant fraction of their overall exposure comprised of saltwater fish. Since analyses
conducted in support of CAMR focus on self-caught freshwater fish consumption by various
populations, inclusion of saltwater fish consumption in many of these studies prevent them from
being considered (since the fractional contribution of saltwater relative to freshwater in these
studies can not be determined). In addition, some of these studies provide details on seasonal
consumption rates, but do not integrate these rates to provide an overall mean annual-averaged
consumption rate relevant to an analysis of longer term exposure to methylmercury.
In addition, while many of these studies provide mean consumption rates, few have
identified specific high-end percentile values (e.g., 90th, 95th or 99th consumption rates). Instead,
many studies, including a number of non-peer reviewed sources, cite non-specific high-end or
bounding point estimates, (e.g., the range of consumption rates for the Ojibwe submitted for the
CAMR NODA - see below). While these point values can be used in developing high-end
bounding scenarios for evaluating risk to these sub populations, they do not support population-
level analysis of exposure since they can not be used to fit distributions characterizing variability
in fish consumption rates across these sub-populations (as noted above, modeling of population-
level exposures requires that distributions characterizing fish consumption rates across a
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particular population be developed).
An additional challenge in characterizing high-level fish consumption is that care needs
to be taken in extrapolating study results from one sub-population to other sub-populations. This
reflects the fact that high-level fish consumption is often tied to socio-cultural practices and
consequently consumption rates for a study population can not be easily transferred to other
populations which may have different practices, (e.g., practices for one Native American tribe
may not be relevant to another and consequently behavior regarding fish consumption may not
be generalized).
Despite these challenges in characterizing high-level consumption, EPA has developed a
recommended high-level fish consumption rates of 60 g/day (mean) and 170 g/day (95th %)
(EPA, 1997a). These values are based on a study of several Native American tribes located
along the Columbia River in Washington state that exhibit high fish consumption behavior.
Although these consumption rates are specific to the tribes included in the study and reflect their
particular socio-cultural practices (including seasonality and target fish species), EPA believes
that this study does provide a reasonable characterization of high-consuming subsistence-like
freshwater fishing behavior. Therefore, in the absence of data on local practices, EPA
recommends that these consumption rates be used to model high-consuming (subsistence) groups
in other locations. It is important to note that, as explained above, application of these
consumption rates outside of the original Columbia River study area to populations that could
have different fishing behavior prevents these consumption rates from being used in modeling
population-level exposure. As noted earlier, population-level exposure modeling requires a fish
consumption variability distribution matched to the population under consideration.
Consequently, because it is not known how representative the percentile consumption rates from
the Columbia River tribes are of other high-consumption groups, they can not be transferred to
other groups and used in population-level modeling. However, it is possible to use the Columbia
River consumption rates to conduct scenario-based analysis of consumption rate groups, (e.g.,
modeling average or high-end exposures without attempting to specify a true range of exposures
across the modeled sub-population). As stated on page 10-27 of the Exposure Factors Handbook
(EPA, 1997a), "[i]t should be emphasized that the above recommendations refer only to Native
American subsistence fishing populations, not the Native American general population."
Although these high-level consumption rates are recommended by EPA, a number of
sources (including NODA comments obtained for this rule), have identified alternate
consumption rates for specific high-consuming groups (including Native Americans) that are in
some instances, higher than these recommended values. For example, a survey by the Great
Lakes Indian Fish and Wildlife Commission (GLIFWC) (as referenced in comments to the
CAMR NODA) indicates that consumption rates by members of Ojibwe Great Lakes tribes
during fall spearing season may range from 155.8-240.7 g/day and may range from 189.6 - 393.8
g/day during the spring. EPA has reviewed these data, and the Agency does not believe they are
suitable for use in this analysis. The Ojibwe consumption data, while useful in providing
perspective on subsistence-fishing, can not be readily translated into an annual-averaged
consumption rate which can be associated with a specific percentile of the population. By
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contrast, the EPA's recommended subsistence consumption value of 170 g/day is a longer-term
averaged value and is identified as clearly representing the high-end of a particular subsistence
fishing group, (i.e., the 95th % of the Columbia River groups). In addition , the locations where
the Ojibwe tribes reside do not appear to be significantly impacted by utility emissions of
mercury, decreasing the relevance of the Ojibwe data in covering subsistence fishing groups
located in those areas of the country with higher relative rates of EGU deposition after the rule.
Therefore, despite these higher consumption rates referenced by GLIFWC, EPA believes that the
Columbia River-based consumption rates provide reasonable coverage for high-level
consumption sub-populations, such as Native American subsistence fishing groups, in other
locations. Also, we can not readily translate these ranges into clear population percentiles
characterizing long-term (annual-averaged) fish consumption rates for Native Americans located
in that part of this country or for other subsistence Native Americans. This final point prevents
these NODA-related data from being used as part of a population-level analysis of exposure in
this analysis.
In the context of CAMR with its emphasis on self-caught freshwater fish consumption,
EPA has concluded that the 142 g/day value recommended by the Office of Water for
subsistence populations (calculated as the 99th percentile of the general population) is not
appropriate for this analysis for two reasons. First, it includes commercial-source fish and
estuaries fish, and the current analysis is focused on freshwater fish. Second, the methodology
used in calculating the distribution of fish consumption rates has a known bias that causes the
tails (90th and 99th percentiles) of the distribution to be overestimated by an unknown but
possibly large amount or possibly small amount. The methodology used a distribution of
short-term consumption rates to extrapolate to distribution of long-term (annual) consumption
rates without adjusting for the larger variability that occurs in short-term consumption rates.14
This bias does not occur for the mean values.
14 This bias was noted in the Estimated Per Capita Fish Consumption in the United States
(U.S. EPA, 2002). See http://www.epa.gov/waterscience/fish/consumption report.pdf at page ix: "The CSFII
surveys have advantages and limitations for estimating per capita fish consumption. The primary advantage of the
CSFII surveys is that they were designed and conducted by the USD A to support unbiased estimation of food
consumption across the population in the United States and the District of Columbia. One limitation of the CSFII
surveys is that individual food consumption data were collected for only two daysa brief period which does not
necessarily depict "usual intake." Usual dietary intake is defined as "the long-run average of daily intakes by an
individual." Upper percentile estimates may differ for short-term and long-term data because short term food
consumption data tend to be inherently more variable. It is important to note, however, that variability due to
duration of the survey does not result in bias of estimates of overall mean consumption levels."
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5. Human Health Benchmarks
The primary route of methylmercury exposure in the US is through the consumption of
fish. It is important to understand that mercury must be transformed into methylmercury for it to
bioaccumulate in fish and for it to be bioavailable to humans.15
EPA has set a health-based ingestion rate for chronic oral exposure to methylmercury,
termed an oral Reference Dose (RfD). The RfD is an estimate (with uncertainty spanning
perhaps an order of magnitude) of a daily exposure to the human population (including sensitive
subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime
(EPA 2002a). EPA believes that exposures near or below the RfD are very unlikely to be
associated with appreciable risk of deleterious effects. It is important to note, however, that the
RfD does not define an exposure level corresponding to zero risk; mercury exposure at or below
the RfD could pose a very low level of risk which EPA deems to be non-appreciable. It is also
important to note that the RfD does not define a bright line above which individuals are at risk of
adverse effect.
In 1995, EPA set an oral RfD for methylmercury at 0.0001 mg/kg-day (0.1 ug/kg-day)
based on a study of the Iraqi poisoning episode (Marsh et al. 1987). Subsequent research from
large epidemiological studies in the Seychelles, Faroe Islands, and New Zealand added
substantially to the body of knowledge on neurological effects from methylmercury exposure.
Per Congressional direction via the House Appropriations Report for Fiscal Year 1999, the
National Research Council (NRC) of the National Academy of Science was contracted by EPA
to examine these data and, if appropriate, make recommendations for deriving a revised RfD.
NRC's analysis concluded that the Iraqi study should no longer be considered the critical study
for the derivation of the RfD and also provided specific recommendations to EPA regarding
methylmercury based on analyses of the three large epidemiological studies (NRC 2000). EPA's
current assessment of the methylmercury RfD, revised in 2001, relied on the quantitative
analyses performed by the NRC.
In their analysis, NRC examined in detail the epidemiological data from the Seychelles,
the Faroe Islands, and New Zealand, as well as other toxicological data on methylmercury
(Crump et al. 1998; Grandjean et al., 1997; Myers et al., 2003). In determining a recommended
point of departure (i.e., the specific dose on which health criteria should be based), NRC
recommended a benchmark dose approach, which applies mathematical models to the available
data to identify the point of departure. The BMD is the exposure level at which a particular level
of response, (i.e., the benchmark response, or BMR) for some outcome of concern is predicted to
occur. In their assessment of the epidemiological data, NRC proposed that the Faroe Islands
cohort was the most appropriate study for defining an RfD, and specifically selected children's
performance on the Boston Naming Test (a neurobehavioral test) as the key endpoint. They
recommended a BMR of 0.05, (i.e., the level at which would result in a doubling in the number
15 Note that as such, the relevant risk that we are addressing is not from exposure to mercury vapors, but is
the exposure to methylmercury through the consumption of fish.
Page 40 of 68
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of children with a response at the 5th percentile of the population).16 On the basis of this study
cohort and that test, NRC identified a BMD of 85 ppb in cord blood. The NRC also estimated
the 95% lower confidence limit for the BMD, (i.e., the BMDL) for this endpoint to be 58 ppb.
The BMDL is a conservative estimate which is used as a point of departure in risk assessment.
Although this BMDL was specifically recommended by NRC as appropriate for deriving the
RfD, NRC also conducted BMD analyses on other endpoints in the Faroe cohort and several
endpoints in the other two populations, as well as an integrative analysis of data from all three
studies (NRC 2000).
In updating the RfD, EPA considered BMD analyses completed by NRC involving
endpoints of neuropsychological development from the Faroe Islands cohort (including results
for the Boston Naming Test), the New Zealand cohort, and the NRC's integrative analysis of all
three studies. The BMDLs for these endpoints, measured as concentrations of mercury in
umbilical cord blood, were considered. For the purposes of calculating the RfD, EPA converted
these BMDLs to maternal daily dietary intake in mg/kg-day using a one-compartment model.17
The BMDLs for these analyses (measured in terms of mercury in cord blood) were all observed
to be within a relatively close range, and, after application of lOx factor to account for variability
and uncertainty, the calculated RfDs converge at about 0.0001 mg/kg-day. Specifically, BMDLs
for a number of neurological endpoints based on tests that gauge a child's ability to learn and
process information, (i.e., Boston Naming Test, Continuous Performance Test, California Verbal
Learning Test, McCarthy Perceived Performance, and McCarthy Motor Test) were calculated by
NRC to range from about 25 to 100 ppb mercury in cord blood. These exposures were converted
to dietary exposures of about 0.0005 mg/kg-bw/day to 0.0019 mg/kg-day, with most dietary
exposures estimated to be about 0.001 mg/kg-bw/day. The integrative BMDL (taking into
account data from all three studies) was calculated by NRC to be 32 ppb mercury in cord blood,
or an exposure of about 0.6 ug/kg-day. All of these results were considered in defining the RfD;
as stated in the IRIS summary for methylmercury18:
"Rather than choose a single measure for the RfD critical endpoint, EPA based this RfD
for this assessment on several scores from the Faroes measures, with supporting analyses
from the New Zealand study, and the integrative analysis of all three studies."
16 As noted by NRC in reference to data from the Seychelles, Faroe Islands, and New Zealand, "because
those data are epidemiological, and exposure is measured on a continuous scale, there is no generally accepted
procedure for determining a dose at which no adverse effects occur." The NRC chose a 5% response level in the
BMD analysis for test results in the lower 5% of the distribution.
17 The one-compartment toxicokinetic model employed by EPA is described by NRC (2000); it represents
all maternal body compartments as a single pool with a relatively small set of parameters, and assumes steady-state
conditions in the maternal system. Methylmercury dose levels were measured as concentrations in umbilical cord
blood (analysts have assumed that methylmercury concentration in cord blood is roughly equal to that in maternal
blood).
18
The IRIS summary may be found at the following website;
http ://www. epa. gov/iris/sub st/0073. htm#reforal
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After identifying a BMDL, consistent with NAS guidance, EPA then applied an
uncertainty factor of 10 to account for interindividual toxicokinetic variability and
pharmicodynamic variability and uncertainty. On this basis, EPA defined the updated RfD of
0.0001 mg/kg-day (or, equivalently, 5.8 ppb in blood) in 2001. Although derived from a more
complete data set and with a somewhat different methodology, the current RfD is the same as the
previous (1995) RfD.
In addition, to put these exposure levels in perspective, it is useful to consider typical
mercury exposure levels in the U.S. measured in the National Health and Nutrition Examination
Survey (NHANES, (CDC, 2001)). This survey is conducted by the National Center for Health
Statistics via standardized interviews to provide continuous health data for the general U.S.
population, and it has included measurements of mercury in blood and hair as biomarkers of
mercury exposure. Based on NHANES data for blood collected for 1999-2002, the overall
distribution of blood mercury concentrations for women of child-bearing age, (i.e., between 16
and 49 years of age) has been estimated for the U.S. population (see Figure 5.1). The RfD and
BMDL derived from the Faroe cohort effect level are included on this chart for reference.
Although all observed exposures are below the BMDL, and most of the exposures fall below the
RfD, about 6% of the population exposures were above the RfD19. The geometric mean blood
mercury concentration in the NHANES data for 1999-2002 is 0.92 ppb, and the range of
observed concentrations was from 0.07 to 38.90 ppb.
19 CDC Morbidity Mortality Weekly Report (MMWR) Vol. 53/NO. 43
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Figure 5.1. Probability Distribution Function of Blood Mercury Levels in US Women of
Childbearing Age (NHANES Data 1999-2002)
0 10 20 30 40 50 60 70 80 90
EPA RfD NRC BMDL NRC BMD
Hg in Blood (ppb)
Note: Cumulative frequency (y-axis) refers to the fraction of the population exposed at or below a given blood
mercury level. EPA's RfD for methylmercury is 0.1 ug/kg-day, which is approximately equivalent to a concentration
of 5.8 ppb in blood.
Methylmercury is a developmental neurotoxicant and the greatest biological sensitivity is
in utero exposure. The developing fetus is most sensitive to mercury exposure because
methylmercury easily passes the placenta and the blood-brain barrier and because, in general, the
developing nervous system is more susceptible to toxicants. However, there are currently no
studies designed specifically to identify the relationship between childhood fish consumption
and developmental outcomes. Thus, we do not have data upon which to determine whether
childhood exposures alone (without fetal exposure) contributed to neurodevelopmental deficits.
Data from what is considered to be the most sensitive subpopulation were used as the basis for
the RfD; thus, its use is thought to be protective of all life stages without additional uncertainty
factors or adjustments. The exposed subpopulations of interest to this rulemaking are women of
childbearing age who consume large quantities of fish caught from vulnerable water bodies with
significant utility attributable mercury deposition.
Using the 2001 RfD and information on mercury exposure routes, EPA published a
recommended ambient water quality criterion for the States' and Tribes' use in setting water
quality standards designed to protect human health. EPA issued the methylmercury water
quality criterion in 2001. (EPA 2001a.) Because of the wide variability in methylmercury
bioaccumulation among waterbodies, EPA set the criterion as a fish tissue level rather than as a
water concentration. The criterion is 0.3 mg/kg (milligram methylmercury per kilogram of wet-
weight fish tissue). The criterion is a risk assessment number which States and authorized Tribes
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may use in their programs for protection of designated uses.
6. Human Exposure to Methylmercury Through Fish Consumption
As described above in Section 3, it is important to note that the analysis presented in this
document does not account for the time lag between changes in deposition and changes in
methylmercury fish tissue concentrations. Results for methylmercury concentrations and
corresponding exposure estimates should be interpreted being at steady state, not in any
particular year.
The RfD provides a useful reference point for comparisons with measured or modeled
exposure. The Agency defines the RfD as an exposure level below which the Agency believes
exposures are likely to be without an appreciable risk over a lifetime of exposure. For the
purposes of assessing population exposure due to power plants, we create an index of daily
intake (DDI). The DDI is defined as the ratio of exposure due solely to power plants to an
exposure of 0. lug/kg bw/day. The DDI is defined so that an DDI of 1 is equal to an incremental
exposure equal to the RfD level, recognizing that the RfD is an absolute level, while the IDI is
based on incremental exposure without regard to absolute levels. Note that an IDI value of 1
would represent an absolute exposure greater than the RfD when background exposures are
considered.
The available data on fish tissue methylmercury concentrations and fish consumption in
the population of recreational anglers does not support an analysis of the specific number of
individuals that are exposed to any particular IDI value. In order to calculate these population
estimates, we would require additional data on the specific fishing locations for individual
anglers, matched with the consumption rates for freshwater fish for those anglers. We do not
currently have this type of detailed, location specific information. In the absence of such
information, we can calculate the likelihood of an individual angler at particular IDI values by
examining the distributions of fish tissue methylmercury concentrations and fish consumption
rates. We can also estimate the general size of the population that might be exposed to various
IDI values by examining the conditional probabilities given specific percentiles of
methylmercury concentrations or consumption rates. The data on consumption rates differs
between the general population of freshwater anglers and subsistence fishing populations. As
such, this discussion is presented in two parts dealing with consumption by the general
freshwater fishing population and the subsistence fishing population.
6.A. General Population of Freshwater Anglers
The distribution of utility attributable methylmercury concentrations in 2001 shows a 99th
percentile concentration of 0.26 mg/kg (or ppm), representing 16 water bodies (see Table 3.4).
The distribution of utility attributable methylmercury concentrations after implementation of
CAIR in 2020 shows a 99th percentile concentration of 0.102, whereas after CAMR, the 99th
percentile methylmercury concentration is 0.093 (see Table 6.2). By combining these upper end
concentrations with estimates of high-end consumption, it is possible to determine whether there
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is a low probability of an individual exceeding the IDI. For the general population of female
recreational anglers and spouses of male anglers of child-bearing age (around 10.5 million), the
99th percentile consumption level is 47 g/day, based on a lognormal distribution with mean of 8
and 95th percentile at 25 (see section 4 above for more discussion).
In order to calculate the IDI value associated with a particular combination of
methylmercury concentration and fish consumption rate, we use the following equation:
IDI = consumption rate x methylmercury concentration x 1.5
bodyweight
where 1.5 is an adjustment factor to reflect the fact that cooking tends to reduce fish mass by
approximately one-third. Because methylmercury is not volatile and is contained primarily in
the muscle, this translates into a factor increase of 1.5 for concentration of mercury in the cooked
fish (Morgan, Berry, and Graves, 1997). For example, if a person with the 99th percentile of
consumption were to catch and consume fish with the 99th percentile methylmercury
concentration after implementation of CAIR in 2020, that individual would have an exposure
level calculated as:
IDI(60, 0.10) = 47 g/day * 0.1 jig/g * 1.5
= ill
64kg * 0.1 |ig/l
-------
Table 6.1. Index of Daily Intake (IDI) Levels Associated with Upper Percentile
Methylmercury Concentrations and Recreational Angling Fish Consumption Rates
Analytical Scenario
IDI values at 99th
Percentile of
Methylmercury
Concentration and
99th percentile
Consumption Rates
(47 g/day)
Percentile of
Methylmercury
Distribution at Which
the IDI Value is Less
Than 1 (holding
consumption rates at
the 99th percentile)
Percentile of
Consumption Rate
Distribution at Which
the IDI Value is Less
than 1 (holding
methylmercury
concentrations at the
99th percentile)
2001 Base Case
2.81
85.5th
89.7th
2020 Implementation
of CAIR
1.11
98.7th
98.6th
2020 Implementation
of CAMR
1.03
99th
99th
In the 2001 base case, at the 99th percentiles of concentrations and consumption rates, the
IDI value would be almost 3. The IDI value would exceed 1 as long as the methylmercury
concentration exceeded the 85th percentile (holding consumption rates at the 99th percentile) or
the fish consumption rate exceeded the 90th percentile (holding the methylmercury
concentrations at the 99th percentile). There is still a very low probability that an individual
angler would exceed an IDI value of 1 (at the highest consumption levels, the number of anglers
would be 15 percent of 1 percent, or 0.15 percent). However, for those individuals where this
occurs, the IDI value may exceed 1 by several fold. After implementation of CAIR in 2020, the
IDI value at the 99th percentiles of methylmercury and consumption rates is much closer to 1. In
fact, the methylmecury concentrations would only have to be at the 98.7 percentile (holding
consumption at the 99th percentile) or consumption rates at the 98.6th percentile (holding
methymercury concentrations at the 99th percentile) for the IDI value to be reduced to 1. If
consumption is not correlated with the methylmercury concentrations in fish (there is no data to
suggest it is), then the IDI value of 1 would be potentially exceeded only 1 percent of the time in
1 percent of the 10.5 million female consumers of recreationally caught fish. This implies that it
is highly unlikely under this modeling scenario. After implementation of the CAMR
requirements, even at the 99th percentile of methylmercury concentrations and consumption
rates, the IDI value does not exceed 1. It is important to note that these calculations are based on
a mean bodyweight that is not correlated with consumption rates. Increasing the bodyweight (to
reflect a correlation between higher consumption and higher bodyweight) by only 10 percent
(less than one standard deviation from the mean) would reduce the IDI value to 1.
The probability that the IDI value after CAIR or CAMR would exceed 1 is thus much
smaller than 1 percent, as it would require high consumption rates, high fish tissue
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concentrations, and no correlation between bodyweight and consumption rates, all of which are
very unlikely. EPA recognizes that for those few hypothetical individuals where this confluence
of high consumption and high methylmercury concentrations occurs, there may be exposures
above the DDI. In assessing the health implications for exposures for exposure above an IDI of 1,
EPA also looks to the severity of effects which the RfD is designed to prevent as well as the
conservatism built into the parameters used to derive the RfD.
To better understand the implications of the potential effects near or above the RfD it is
helpful to consider the study effects used in establishing the RfD. As mentioned in Section 5, in
updating the RfD, EPA considered endpoints from a battery of neuropsychological tests that
focus on subtle neurological endpoints. We must then interpret what those endpoints will
translate to. The methylmercury-associated performance decrements on the neuropsychological
tests administered in the Faroe Islands and New Zealand studies suggest that prenatal
methylmercury exposure is likely to be associated with poorer school performance. Thus,
children who are exposed to low concentrations of methylmercury prenatally may be at increased
risk of poor performance on neurobehavioral tests, such as those measuring attention, fine motor
function, language skills, visual-spatial abilities (like drawing), and verbal memory.
The underlying parameters chosen to establish the RfD introduce a degree of
conservatism. Choice of the nature and extent of change that would be the appropriate focus for
establishing a reference dose also involved key science policy decisions. Prior to determining a
level that would result in an impact or change in the expression of an adverse effect, the
population baseline rate of the effect must first be identified, (i.e., the probability of the effect
occurring in the general population). After identifying that rate, another informed policy
decision must be made to identify the benchmark response (BMR). In general, risk assessments
for various toxicants based on animal studies have used a BMR of 0.1, because it usually
represents the low range of the observed exposure data. Crump et al. (2000) used a BMR of 0.1,
(i.e., 10% of the population is at risk) in their analyses of the New Zealand and Seychelles
studies. For the end points studied, the baseline rate in the population is 0.05 (P0). Selection of
a BMR of 0.1, therefore, could result in as much as a tripling of the percentage of the population
falling into the abnormal range of neurological performance. In its assessment, the NRC
committee, and subsequently EPA, chose a more health protective P0 of 0.05, and BMR of 0.05,
(i.e., 5% of the population is at risk).
The choice of an "acceptable" risk level is one of policy informed by science. The RfD
does not represent a "bright line" above which individuals are at risk of significant adverse
effects. Rather, it reflects a level where EPA can state with reasonable certainty that risks are
not appreciable. The Agency further notes that a number of other national and international
scientific bodies have assessed the health effects of mthylmercury and have adopted other
health-based benchmarks greater than EPA's RfD. Health Canada established its Tolerable Daily
Intake (TDI) level at twice the EPA's RfD. Their benchmark is 0.2 ug/kg bw/day. The Agency
for Toxic Substances and Disease Registry (ATSDR) has set a Minimal Risk Level (MRI) of 0.3
ug/kg bw/day - three times EPA's RfD level. The World Health Organization's (WHO)
benchmark is set at 0.23 ug/kg bw/day. Of these major agencies, EPA's RfD has established the
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lowest risk benchmark to define levels of exposure that are without appreciable risks. As
exposure levels increase beyond the RfD, the possibility of deleterious effects increases, but the
point at which they become "unacceptable" must be determined on a case-by-case basis. In
making this determination, the Agency considers a number of factors including20:
Confidence in the risk estimate: How certain is the scientific information supporting
the link between possible health effects and exposures?
The effects of concern: How serious are the health effects?
The size of the population at risk, as well as the distribution of risk within the
population.
The Agency has considered these factors in the case of mercury and has concluded that the
exposures above the DDI described elsewhere in this chapter do not constitute an unacceptable
risk.
To examine further the potential impact of correlation between bodyweights and
consumption rates, Table 6.2 shows the distributions of fish consumption rates, bodyweights,
and methylmercury concentration after implementation of CAIR and CAMR. It also shows the
estimated IDI values if the distributions were perfectly correlated, i.e. if the highest percentiles
of each distribution occurred in the same individuals. If this holds, then even at the 99th
percentile of consumption and methylmercury, after either CAIR or CAMR, individuals are not
exposed at an IDI value exceeding 1.
20 See Residual Risk Report to Congress, USEPA, March 1999 EPA-453/R-99-001), page 119.
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Table 6.2. Implied Index of Daily Intake (IDI) Values Associated with Percentiles of the Distributions of Fish Tissue
Methylmercury, Consumption Rates, and Bodyweight
Percentile
(A)
Consumption
Rate (g/day)
(B)
Bodyweight (kg)
(C)
2020 Residual
Utility
Attributable Fish
Tissue
Methylmercury
After CAIR
(D)
2020 Residual
Utility
Attributable Fish
Tissue
Methylmercury
After CAMR
Requirements
2020 Estimated
IDI Value of
Methylmercury
After CAIR
(A*C*1.5/B/0.1)
2020 Estimated
IDI Value of
Methylmercury
After CAMR
(A*D*1.5/B/0.1)
5th
0.95
47.4
0.000
0.000
0.00
0.00
10th
1.38
49.6
0.001
0.001
0.00
0.00
15th
1.76
51.4
0.002
0.002
0.00
0.00
25th
2.50
54.3
0.004
0.004
0.00
0.00
50th
4.88
60.9
0.010
0.009
0.01
0.01
75th
9.48
69.6
0.020
0.017
0.04
0.03
85th
13.61
78.4
0.027
0.024
0.07
0.06
90th
17.29
84.1
0.035
0.032
0.11
0.10
95th
24.46
93.5
0.052
0.047
0.20
0.18
99th
46.6
93.5*
0.102
0.093
0.76
0.70
* The exposure factors handbook does not report the 99th percentile of the bodyweight distribution.
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6.B. Subsistence Fishing Population
As discussed in Section 3 of this document, consumption rates for subsistence fishers are
likely to be much higher than for the general population of recreational anglers. As such, these
populations are more likely to experience an IDI value above 1 if they fish in areas that have a
higher utility attributable methylmercury level. Data on consumption rates for the population of
subsistence fishers is much more limited. In addition, because these populations are located in
very specific areas of the U.S., the general distribution of fish tissue methylmercury
concentrations across the U.S. may not be representative of the fish tissue concentrations in fish
consumed by these population. Specific data on concentrations in fish at waterbodies frequented
by subsistence fishing populations has not been generated. As such we use the overall
distribution of fish tissue methylmercury concentrations, recognizing that it may not capture the
specific distribution for the subsistence subpopulation.
Based on the EPA Exposure Factors Handbook, Table 10-85, the recommended mean
consumption rate for Native American subsistence fishers is 59 grams/day, with a 95th percentile
consumption rate at 170 grams/day. This can be fit to a lognormal distribution which has a mean
of 59 g/day and a standard deviation of 63 g/day. We use this fitted distribution for the
following analysis.
Table 6.3 shows the IDI value for subsistence fishing groups associated with the 99th
percentile of utility attributable methylmercury and 99th percentile of consumption rates,
modeled to be 295 g/day, for the 2001 base case, 2020 after implementation of CAIR, and 2020
after implementation of the CAMR requirements. It also shows the percentile of methylmercury
concentrations at which the IDI value would be equal 1 holding the consumption rate constant,
and the percentile of subsistence fishing consumption rates at which the IDI value would be
equal to 1 holding the methylmercury percentile constant.
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Table 6.3. Average Index of Daily Intake (IDI) Levels Associated with Upper Percentile
Methylmercury Concentrations and Native American Subsistence Fish Consumption Rates
Analytical Scenario
IDI values at 99th
percentiles of
Methylmercury
Exposure and
Consumption Rates
Percentile of
Methylmercury
Distribution at Which
the IDI Values Do
Not Exceed 1
(holding
consumption rates at
the 99th percentile)
Percentile of
Consumption Rate
Distribution at Which
the IDI Value Do Not
Exceed 1 (holding
methylmercury
concentrations at the
99th percentile)
2001 Base Case
17.8
32.5th
16.5th
2020 Implementation
of CAIR
7.05
66.5th
52.0th
2020 Implementation
ofCAMR
6.43
71.8th
55.2th
In the 2001 base case, at the 99th percentiles of concentrations and Native American
subsistence fisher consumption rates the IDI value for subsistence populations would be almost
18. The IDI value would exceed 1 as long as the methylmercury concentration exceeded the 32nd
percentile (holding consumption rates at the 99th percentile) or the fish consumption rate
exceeded the 16th percentile (holding the methylmercury concentrations at the 99th percentile).
Thus for these highly consuming groups, there is a reasonable chance that the IDI value will
exceed 1 in 2001, if those groups are in locations that are moderately affected by mercury
deposition from utilities. After implementation of CAIR, the estimated IDI values associated
with the 99th percentile of methylmercury and consumption rates falls by 60 percent, but is still
an IDI value of 7. After CAIR, the IDI values would exceed 1 as long as the methylmercury
concentrations exceeded the 66th percentile (holding consumption rates at the 99th percentile) or
the fish consumption rate exceeds the 52nd percentile. Thus, even after CAIR, there remains a
chance that the IDI value will exceed 1, if high fish consumption groups fish in locations highly
affected by utility emissions. Using 2000 Census data, Figure 6-1 shows the location of Native
Americans in tribal census tracts relative to utility attributable deposition after implementation of
CAIR in 2020. Tribal census tracts are small, relatively permanent statistical subdivisions of a
federally recognized American Indian reservation and/or off-reservation trust land. The optimum
size for a tribal census tract is 2,500 people; it must contain a minimum of 1,000 people. Visual
inspection shows very few locations where Native Americans live where there is also high
residual deposition due to utilities. This suggests that the 99th percentile of the utility attributable
methylmercury concentrations is likely inappropriate as an upper bound for Native Americans'
subsistence fishing exposures, and thus the IDI based on combining the 99th percentile utility
attributable methylmercury concentration and the Native American subsistence fishers'
consumption rates will overstate the IDI for Native Americans subsistence fishers'.
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Figure 6.1. Locations of Native American Populations in Tribal Census Tracts Relative to
Utility Attributable Mercury Deposition After Implementation of CAIR in 2020
After CAMR, the IDI value exceeds 1 for the cases when the 99th percentile consumption
rate is combined with the 99th percentile methylmercury concentration, and the IDI value exceeds
1 as long as methylmercury concentrations are greater than the 72nd percentile (holding
consumption rates at the 99th percentile) or consumption rates are greater than the 55th percentile
(holding methylmercury concentrations at the 99th percentile). However, as with CAIR, the 99th
percentile of overall utility attributable methylmercury concentrations likely exceeds the 99th
percentile of methylmercury concentrations for fishing locations frequented by Native American
subsistence fishing populations.
It is important to note that these calculations are based on a mean bodyweight that is not
correlated with consumption rates. Increasing the bodyweight (to reflect a correlation between
higher consumption and higher bodyweight) by only 10 percent (less than one standard deviation
from the mean) would reduce the IDI value to 1. In addition, the potential for exposures above
an IDI of 1 should be viewed with consideration of the conservativism built into the derivation of
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the RfD which is the basis for the definition of the IDI reference point.
Table 6.4 shows the estimated IDI for various combinations of alternative consumption
rates and percentiles of methylmercury concentrations. This table suggests that after
implementation of CAIR in 2020, there are only a few circumstances where the IDI exceeds 1.0.
With CAMR there is less chance of an exceedance. These cases occur only at the highest
percentiles of methylmercury and consumption rates. It should be noted that in many elements
of this analysis, we have made assumptions that would likely lead to over-estimates of possible
exposure to utility attributable fish tissue methylmercury. These include the decision to not
screen out sampling locations that may be influenced by non-air deposition sources of mercury,
the use of the maximum averaged species concentration at each sampling location, and the focus
on the extreme percentiles of the methylmercury and consumption rate distributions.
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Table 6.4. Fish Tissue Concentration Percenti
es and EPA EFH and OW Fish Consumption Rates and Resulting IDI values
Consumption
Fish Tissue Methylmercury Percentile
Rate
5th
10th
15th
25th
50th
75th
85th
90th
95th
99th
2001 Base Case Methylmercury
0.000
0.002
0.004
0.008
0.030
0.064
0.09
0.113
0.164
0.257
EPA EFH Mean Recreational Fisher
8
0.00
0.00
0.01
0.02
0.06
0.12
0.17
0.21
0.31
0.48
EPA OW 90th Percentile General Population
17.5
0.00
0.01
0.02
0.03
0.12
0.26
0.37
0.46
0.67
1.05
EPA EFH 95th Percentile Recreational Fisher
25
0.00
0.01
0.02
0.05
0.18
0.38
0.53
0.66
0.96
1.51
EPA EFH 99th Percentile Recreational Fisher
47
0.00
0.02
0.04
0.09
0.33
0.71
0.99
1.24
1.81
2.83
EPA EFH Mean Subsistence Native American
60
0.00
0.03
0.06
0.11
0.42
0.90
1.27
1.59
2.31
3.61
EPA EFH 95th Percentile Subsistence Native American
170
0.00
0.08
0.16
0.32
1.20
2.55
3.59
4.50
6.53
10.24
EPA EFH 99th Percentile Subsistence Native American
295
0.00
0.14
0.28
0.55
2.07
4.43
6.22
7.81
11.34
17.77
2020 with CAIR Methylmercury
0.000
0.001
0.002
0.004
0.010
0.020
0.027
0.035
0.052
0.102
EPA EFH Mean Recreational Fisher
8
0.00
0.00
0.00
0.01
0.02
0.04
0.05
0.07
0.10
0.19
EPA OW 90th Percentile General Population
17.5
0.00
0.00
0.01
0.02
0.04
0.08
0.11
0.14
0.21
0.42
EPA EFH 95th Percentile Recreational Fisher
25
0.00
0.01
0.01
0.02
0.06
0.12
0.16
0.21
0.30
0.60
EPA EFH 99th Percentile Recreational Fisher
47
0.00
0.01
0.02
0.04
0.11
0.22
0.30
0.39
0.57
1.12
EPA EFH Mean Subsistence Native American
60
0.00
0.01
0.03
0.06
0.14
0.28
0.38
0.49
0.73
1.43
EPA EFH 95th Percentile Subsistence Native American
170
0.00
0.04
0.08
0.16
0.40
0.80
1.08
1.39
2.07
4.06
EPA EFH 99th Percentile Subsistence Native American
295
0.00
0.07
0.14
0.28
0.69
1.38
1.87
2.42
3.60
7.05
2020 with CAIR + CAMR Requirements Methylmercury
0.000
0.001
0.002
0.004
0.009
0.017
0.024
0.031
0.047
0.092
EPA EFH Mean Recreational Fisher
8
0.00
0.00
0.00
0.01
0.02
0.03
0.05
0.06
0.09
0.17
EPA OW 90th Percentile General Population
17.5
0.00
0.00
0.01
0.02
0.04
0.07
0.10
0.13
0.19
0.38
EPA EFH 95th Percentile Recreational Fisher
25
0.00
0.01
0.01
0.02
0.05
0.10
0.14
0.18
0.28
0.54
EPA EFH 99th Percentile Recreational Fisher
47
0.00
0.01
0.02
0.04
0.10
0.19
0.26
0.34
0.52
1.01
EPA EFH Mean Subsistence Native American
60
0.00
0.01
0.03
0.06
0.13
0.24
0.34
0.44
0.66
1.29
EPA EFH 95th Percentile Subsistence Native American
170
0.00
0.04
0.08
0.16
0.36
0.68
0.96
1.24
1.87
3.67
EPA EFH 99th Percentile Subsistence Native American
295
0.00
0.07
0.14
0.28
0.62
1.18
1.66
2.14
3.25
6.36
Note: EFH is Exposures Factors Handbook. See http://www.epa.gov/ncea/pdfs/efli/front.pdf.
7. Utility Report to Congress Modeling
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In the Study of Hazardous Air Pollutant Emissions from Electric Utility Steam
Generating Units Final Report to Congress (U.S. EPA, 1998), referred to hereafter as the
Utility RtC, EPA conducted a screening level analysis of deposition patterns likely to result from
utility emissions of mercury. That analysis found that some mercury emissions have the
potential to deposit locally and accumulate in fish tissue of local water bodies. However, the
analysis was based on hypothetical situations and was not suitable for estimating the frequency
of any given fish tissue or exposure level. The Utility RtC presented results from modeling that
are superseded by the modeling results presented above. In particular, the Agency views the
application of a more robust modeling approach as critical and required for assessing the
mercury deposition associated with CAMR because of the density and properties of mercury and
its complex transport and reactions in the atmosphere. The recently developed CMAQ modeling
system meets our requirements and the recommendations of the Report to Congress for a "single
air quality model" to address Mercury deposition.
Based on our current modeling we now recognize that the Utility RtC model plant
analysis modeling does not appear to represent the central tendency nor did it attempt to describe
the distribution of impacts nationally. The conclusion of the Utility RtC (with regard to the
230kg/day model facility) is currently thought to overstate the impact in the majority of locations
nationally. In some (small) set of locations, however, conditions may be similar to that projected
by the Utility RtC scenario. As described below, the Agency is pursuing additional research to
better understand these locations and any potential for "utility hotspots."
In responding to the numerous comments received by the Agency on the January 2004
proposal, the March 2004 Supplemental Notice of Proposed Rulemaking (SNPR) and the
December 2004 Notice of Data Availability (NODA), we performed model plant analyses
similar to those done in the Utility RtC in that they employed the same dispersion model (ISC3),
but these new analyses differ from the Utility RtC analysis in certain ways intended to help us
better understand important factors influencing near-field deposition. The Utility RtC
assessment also included watershed modeling of the projected local deposition. That modeling
and more recent watershed modeling of mercury deposition are described in ecosystem modeling
below.
7.1 Speciation Profile of Mercury Emissions
The Agency has learned much more about mercury emissions from the power plant
sector since the Utility RtC. Pursuant to areas of further research identified in the Utility RtC in
1998, the Agency conducted an Information Collection Request (ICR) for all coal-fired power
plants in the U.S. which were greater than 25 MW. To that end, the Agency sampled incoming
coal shipments for approximately 450 coal-fired power plants for calendar year 1999 to
determine the concentration of mercury and chlorine in these coals, and to quantify the heat
content (British Thermal Units, Btu) and ash content. Additionally, the Agency selected 81
individual plants for emissions testing during 1999, which were representative of the larger fleet
of sources. These two pieces of data greatly refined the Agency's understanding of mercury
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emissions from coal-fired power plants. Among the most notable was the overall speciation of
mercury emissions from coal-fired power plants. Table 7.1. summarizes the differences in the
speciation profiles for both the Utility RtC and the 1999 ICR.
Table 7.1: Speciation Assumptions in Utility Report compared to results of 1999 ICR
Utility Report values for model plant
scenarios
1999 ICR (average of 81
plants)
Percent
Mercury(O)
50%
54%
Percent
Mercury (++)1
30%
43%
Percent
Mercury (p)1
20%
3%
1 Divalent mercury occurs in the ionic form, abbreviated here as Mercury(++) and in particle-bound
compounds, abbreviated here as Mercury(p).
As can be seen from Table 7.1, the mean percentage of elemental mercury from the 1999 ICR
matches fairly well with the values used in the Utility model plant, while the percentages of the
ionic and particulate divalent mercury differ somewhat.
7.2 Industrial Source Complex Version 3 (ISC) modeling
In the Industrial Source Complex Version 3 (ISC) modeling done at the time of the
Utility RtC, it was found that the highest deposition and associated concern about localized
exposure occurred when a lake was located 2.5 km from a large coal-fired power plant
theoretically sited in a humid eastern U.S. location, (i.e., subject to the meteorology of this
location). This lake and surrounding watershed was estimated to receive an average deposition
rate of 15.5 ug per square meter per year from the large coal-fired power plant21. One concern
raised in the Utility RtC is summarized in the Table 7-10 (page 7-37) of that report. The report
found that, under a hypothetical but not impossible scenario, a subsistence fisher could be
exposed to an IDI level of 3.7. Our more recent modeling allows us to better assess the
probability of this event (See Section 6).
In the Utility RtCt, a model plant was sited in two different locations, one a humid
eastern site and the second an arid western site. In the more recent work, we modeled air
quality and deposition for four locations representing four different meteorological regimes
across the U.S. - Phoenix, Arizona; Kansas City , Missouri; Indianapolis, Indiana; and, Tampa,
Florida. Phoenix, Arizona is located in the Sonoran Desert in the Southwestern U.S., and is
21 See table 7-10. 17.9ug/m2/year from all power plants, 87% of which is from the single facility.
17.9*.87= 15.5 ug/m2/year.
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representative of an arid climatic regime. Temperatures range from very hot in the summer
months to relatively mild during winter, and generally experience large diurnal temperature
fluctuations and light winds. Kansas City, Missouri is located near the geographical center of
the continental U.S. and exhibits a modified continental climate. Indianapolis, Indiana is located
in the western reaches of the Ohio Valley, and has a classic temperate climatic regime - very hot
during summer, and often bitterly cold during winter. Tampa, Florida is located in the central
Gulf Coast region of the Florida peninsula, and exhibits a strong subtropical climatic regime.
The local climate is modified extensively by the existence of daily land and sea breezes. Thus,
these 4 climatic regimes typify the range of conditions expected at the majority of major U.S.
power plant sites.
The 1997 Mercury Study Report to Congress (EPA, 1997b) noted that "a single air
quality model which was capable of modeling both the local as well as regional fate of mercury
was not identified." Thus, the modeling approach used for the Mercury RtC employed two
models: 1) the Regional Lagrangian Model of Air Pollution (RELMAP) to address regional-scale
atmospheric transport, and 2) the Industrial Source Code model (ISC3) to address local-scale
analyses (i.e., within 50 km of source). The ISC3 model discussed here is a Gaussian plume
dispersion model routinely used by the Agency for local-scale air quality modeling for air toxics.
It differs in many ways from other models used for modeling mercury distribution and
deposition nationally. The current state-of-the-science national models include
three-dimensional eulerian grid models such as the Community Multi-Scale Air Quality
(CMAQ) model and the Regional Modeling System for Aerosols and Deposition (REMSAD).
As described in the December 2004 NOD A, the Agency has continued to refine its models and
scientific understanding of the fate, transport and deposition of mercury and the subsequent
cycling through the terrestrial and aquatic ecosystems. See the CAMR RIA Chapter 3
(Ecosystem) and Appendix A (Case studies) for a discussion of the ecosystem modeling.
As mentioned above the objective in the more recent ISC3 modeling was to assess the
relative importance of geography, emissions profile and stack height on localized/nearfield
deposition patterns. Specifically, we have applied these modeling scenarios to the four climatic
regimes described above. The model results were based on a hypothetical coal-fired power plant
emitting 1 kilogram (kg) of either speciated mercury (mercury(++), mercury(p) or mercury(O))
per day from either a 50 m, 250 m or 500m stack. The design of the scenarios was such that the
results would yield evidence on the relative importance of the species of mercury being emitted,
and the sensitivity of the local/nearfield (< 50 km) deposition to both stack height and local
meteorological conditions.
The results show that the highest deposition occurs when 100% mercury(++) is emitted
from the stack in the more humid climatic regions (e.g., Kansas City, Indianapolis and Tampa)
and this deposition is dominated by wet deposition in the 5-10 km downwind distance. In
general, beyond 10-15 km downwind, dry deposition becomes the dominant form of local
deposition (See Figures 1,2 and 3 in Appendix A). In general, the deposition patterns associated
with a 100 % release of mercury(p) showed depositional patterns approximately half of the rates
observed for the oxidized mercury analyses. Consistent with discussion in the Utility Report,
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deposition patterns for emission of 100 % mercury(O) yielded extremely low (< 5 g/m2/yr)
deposition totals for all four sites. In assessing the changes in deposition in arid climatic
conditions, dry deposition becomes the dominant mode of deposition, as would be expected, and
the total deposition is somewhat reduced (see Figure 1 in Appendix A). These results are
consistent with the Utility RtC which indicated that wet deposition was believed to be the
dominant form of mercury deposition in the continental U.S.
Discussion of stack height importance:
In analyzing atmospheric transport and deposition, several physical parameters are of key
importance - wind speed, stack temperature and stack exit velocity. Stack temperature and exit
velocity contribute to plume rise, or the buoyancy in the plume as it enters the ambient
atmosphere. In determining the importance of a source to contributing to local deposition, plume
buoyancy and stack height are of key importance.
Wind profiles near the surface are logarithmic in nature, with wind speeds increasing
exponentially as one moves away from the surface. Thus, the higher the stack height (or source
of the emissions) the greater the wind speed encountered by the mercury emissions being
released from the stack. The impact of higher wind speeds on deposition is to effectively carry
the emissions further downwind, or farther from the source. Holding stack temperature and exit
velocity constant/similar (constant buoyancy), the height of the mercury emissions release is a
key factor in defining the ambient wind speed encountered by the mercury emissions and thus
the downwind transport and deposition of the emissions.
Appendix A documents the importance of these concepts by examining the depositional
difference for oxidized mercury (the most important species of mercury for local-scale
deposition) given 3 different stack heights - 50 m, 250 m and 500 m. In the figures, for four
distinct geographic regions (Tampa, Florida; Phoenix, Arizona; Indianapolis, Indiana; Kansas
City, Missouri), it is evident that at the lower stack heights (50 m), holding other emissions
characteristics constant, nearfield deposition is increased by an order of 3 times (based on the
mercury deposition index, explained elsewhere in this document). When reviewing the figures
for the higher stack height (500 m), the maximum deposition is shifted downwind because of the
higher release. The figures for the 250 m stack are generally representative of a normal
coal-fired power plant in the U.S.
Assessing the impact of stack height on local/nearfield deposition indicated that for the
scenario of 100 % mercury(++), a stack height of 50 m resulted in 3-5-fold increase in nearfield
deposition (<15 km downwind) over the deposition observed with a 250m stack height and
deposition was completely dominated by the dry deposited fraction. On the other hand, an
increase in stack height from 250m to 500 m resulted in only minor changes in the downwind
deposition patterns for the three eastern-most sites. For the Phoenix location, the higher stack
resulted in approximately a 50 % reduction in nearfield deposition. In general, the results of
these revised analyses are supportive of the conclusions reached in the Utility RtC which
indicated that localized deposition was significantly impacted by the physical parameters of the
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coal-fired power plant (e.g., stack height, emissions rate, etc.), local climatic conditions and
intensity of precipitation events.
7.3 Ecosystem Modeling
Assessment of the IEM-2M RtC Model and other Ecosystem Scale Modeling
In the Utility RtC, EPA modeled a "hypothetical" ecosystem at both an eastern and a western
site. In the recent work (Chapter 3 of the RIA), EPA considers the relationship between declines
in deposition and fish mercury concentrations at five real ecosystems and considers a range of
ecological characteristics that affect the magnitude and response times of different ecosystems.
The ecosystems modeled include: Eagle Butte, SD; Lake Waccamaw, NC; Lake Barco, FL; Brier
Creek, GA; and Pawtuckaway Lake, NH. Since the RtC was released in 1997, EPA has
developed and refined a set of watershed, waterbody, and food web models that describe the
speciation, transport, and bioaccumulation of mercury as a function of the physical, chemical and
biological properties of different ecosystems. For consistency with previous work, an updated
version of the original IEM-2M model applied in the RtC (SERAFM) was applied with several
other models to describe the fate and bioaccumulation of mercury in the systems listed above.
We also compared projected fish mercury levels at the "hypothetical ecosystem" described in the
RtC using the IEM model to the SERAFM forecasted values. These results are described in
greater detail below.
State variables in both the IEM-2M and SERAFM models include three mercury species,
mercury(O), mercury(++), and methylmercury. SERAFM includes four solids types and
dissolved organic carbon, DOC. Both IEM-2M and SERAFM simulations are driven by external
mercury loadings delivered from the atmosphere, from watershed tributaries, and from point
sources, or by internal loadings from contaminated sediments. However, at the time the
IEM-2M model was developed, inputs of methylmercury from the watershed were not
quantified. This can be a significant source of methylmercury in may aquatic ecosystems (see
Chapter 2, RIA for more detail).
The SERAFM model incorporates more recent advances in scientific understanding
described above and implements an updated set of the IEM-2M solids and mercury fate
algorithms described in detail in the Mercury Study RtC (USEPA, 1997b). These updates
provide more realistic representations of the processes governing mercury fate and transport in
aquatic systems. Major differences between the SERAFM model and the IEM-2M model are as
follows:
Dynamic calculations: SERAFM can describe the temporal response of fish mercury
concentrations to changes in mercury loading, while the IEM-2M model calculated
expected fish tissue mercury concentrations at steady state.
Watershed Loading: Both IEM-2M and SERAFM model soil erosion into the water
body using the Revised Universal Soil Loss Equation (RUSLE). However, in SERAFM
mercury loading from the watershed to the water body is modeled using run-off
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coefficients. SERAFM defines four land-use types: impervious, upland, riparian, and
wetland/forest. The user defines the percentage of each type in the watershed. The
model uses run-off coefficients to describe mercury from atmospheric deposition to each
land type as loadings to the water body. IEM-2M calculates mercury concentrations in
soils, and calculates erosion and transport to the water body.
Two-Laver: SERAFM has the capability to model a layered lake system with an
epilimnion and hypolimnion, while IEM-2M used a single, well mixed layer to represent
the water column.
Photo-reactions: Recent research has demonstrated the photo-reactions of mercury.
These have been incorporated into SERAFM but were not part of the original IEM-2M
model. The oxidation and reduction of mercury as functions of visible and UV-B light
are included.
Speciation: Speciation of mercury with hydroxides, chlorides, and sulfides has been
included in the SERAFM model but was not incorporated in the IEM-2M model. The
abiotic oxidation rate constant for Mercury++ is multiplied by the fraction of dissolved
divalent mercury and the fraction of Mercury++ present as Mercury(OH)2.
Equilibrium Partitioning: SERAFM models equilibrium partitioning between multiple
compartments or phases: aqueous phase, abiotic particles (silts/fines), biotic particles
(phytoplankton, zooplankton, seston), and DOC-complexation. In SERAFM, the biotic
demethylation rate constant is multiplied by the sum of the fraction dissolved and the
fraction DOC-complexed, as suggested by previous research (Matilainen and Verta,
1995).
Trophic status: Trophic status of the lake has been incorporated into the SERAFM
model and was not a component of the IEM-2M model. Trophic status is used to
calculate visible light attenuation in the lake, the turnover of biomass, and the
phytoplankton and zooplankton concentration in the SERAFM model framework.
Suspended particle types in the water column: The SERAFM model accounts for both
zooplankton and phytoplankton as biotic materials in the system, while IEM-2M only
accounted for one biotic particle type.
Reaction rates: The SERAFM model incorporates more recent reaction rate
coefficients, and the understanding of the variability of these rates with different
conditions.
Partition coefficients: The SERAFM model incorporates more recent values for
mercury partition coefficients for each mercury species. Future versions of the SERAFM
model will calculate site-specific partitioning as a function of sediment organic matter
and the organic carbon content of suspended materials.
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A preliminary comparison of the SERAFM model to the IEM-2M model using the
parameter values for the model ecosystem described in the RtC suggests that updates to the
IEM-2M model incorporated into the SERAFM model result in lower values for fish mercury
concentrations (Table 7.2), by up to a factor of two. However, the model ecosystem described in
the RtC uses a lower dry deposition rate than estimated based on more recent understanding and
assumes that there is no watershed methylmercury loading (discussed above). At the time the
RtC modeling was conducted, best estimates of dry deposition were approximately 50% the
magnitude of wet deposition. More recent modeling described in the RIA suggests that on
average, dry deposition is better approximated as 50% of total deposition, a significant increase
over previous estimates. This is clearly a major source of uncertainty when attempting to
quantify inputs of mercury to different aquatic systems and characterize the contribution of
different sources to fish mercury concentrations. When the parameters are updated to reflect
current knowledge, forecasted fish mercury concentrations are higher than the original IEM-2M
results (Table 7-2). We note, however, that because we did not run IEM-2M with these updated
parameters, a clear comparison of the IEM-2M and SERAFM models themselves cannot be
made in this instance (original IEM-2M versus updated SERAFM model runs). The differences
in the predicted concentrations reflect, in part, input assumptions that are not related to these
ecosystem models (e.g. differences in assumptions regarding air deposition). Although this
preliminary comparison provides some perspective on the differences between these two models,
it is difficult to draw from this comparison general conclusions that are applicable across all
ecosystems and scenarios. In part, for these reasons, we conclude, at this point, that the
ecosystem models (e.g. SERAFM) and case studies (described below) are primarily useful as a
tool for evaluating the national-scale assessment described in sections 1- 6 of this document.
Table 7.2. Comparison of SERAFM and IEM-2M
Parameters
RtC Model
Ecosystem
RtC Model
Ecosystem
Updated Parameters
Model
IEM-2M
SERAFM
SERAFM
Water Column Me
mercury Unfiltered
0.08
0.031 NG L-l
0.12 nG L-l
Water Column
MercuryT Unfiltered
1.16 nG L-l
2.50 nG L-l
1.17 nG L-l
Trophic Level 4 Fish
0.44 ug g-1
0.21 ug g-1
0.80 ug g-1
Trophic Level 4 Fish
BAF:
F i shMercury/Me
mercury
6.8x10s
6.8x10s
6.8x10s
Note: Comparison of SERAFM and IEM-2M forecasted mercury concentrations using parameter values for model
ecosystem described in the Mercury Study RtC (EPA, 1997b) and a 50% reduction in atmospheric deposition. The
"Updated Parameters" column refers to modification of the original model ecosystem described in the RtC to
incorporate more recent knowledge on the magnitude of dry deposition and inputs of Me mercury from the
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catchment.
Based on the BAFs considered, the hypothetical ecosystem described in the RtC is more
sensitive than three out of four ecosystems modeled as case studies (see Table 7-3 below and
Chapter 3, RIA), and is less sensitive than one.
Table 7.3. Empirically derived BAFs for each of the ecosystem case studies
BAFs (L/kg)
MeHg
Mercury T
BAF-MeHg
BAF-
(nG/L)
(nG/L)
MercuryT
Eagle Butte
0.82
10.2
8.90E+05
8.73E+04
Lake Barco
0.018
1.03
3.06E+07
5.34E+05
Lake Pawtuckaway
0.19
2.26
1.11E+06
9.29E+04
Lake Waccamaw
0.48
4.79
1.24E+06
3.86E+04
Hypothetical
6.80E+06
5.30E+05
Ecosystem (USEPA
1997)
Comparing these case studies to empirically derived BAFs characterized by the Office of
Water indicates that modeled fish tissue responses in three of four case studies has empirically
derived BAFs that fell between the 5th and 50th percentiles of the geometric mean of
field-measured BAFs for trophic level four species obtained from the published literature (EPA
2000). The model ecosystem described in the RTC fell between the 50th and 95th percentile for
the BAFs identified in the OW Methylmercury Water Quality Criterion Appendix A (U.S. EPA,
2001) (see http://www.epa.gov/waterscience/criteria/methylmercury/document.html) and one of
the case studies (Lake Barco) exceeded the 95th percentile. Some limitations to the BAF
approach deserve mention. Because methylmercury concentrations in the water column are
highly variable, empirically-derived BAFs are inherently undetermined and have limited
predictive power. A more credible approach based on our current knowledge is to forecast
changes in fish mercury concentrations using information on the food web dynamics
("bioenergetics") of different ecosystems. Such a model (BASS) was applied in one of the case
studies described in the RIA. Results showed that the BAFs calculated from the outputs of the
bioenergetics based bioaccumulation model were within a factor of two of the empirically
derived BAF used in the SERAFM model.
Acknowledging the many uncertainties in this analysis, results generally suggest that the
forecasted contribution of US power plants to fish mercury concentrations may have been
overpredicted in the hypothetical ecosystems described in the original RtC relative to the
majority of aquatic systems that could potentially be affected by mercury deposition from US
power plants. However, it is important to note that fish tissue methylmercury concentrations due
to power plants may be higher in some ecosystems (for example, ecosystems similar to Lake
Barco described in Chapter 3 of the RIA).
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8.0 Data Limitations, Uncertainty, and the Need for Further Work
Based on the analyses presented in this document, several areas exist where additional
data may provide for more clear understanding of the potential for continued exposures to utility
attributable methylmercury concentrations after implementation of CAIR and CAMR. Figure
8.1 shows the location of post-1999 fish sampling locations relative to utility attributable
mercury deposition after implementation of CAIR and CAMR in 2020. In areas with residual
utility attributable deposition, including portions of the Ohio Valley, the Southern Great Lakes,
including the area around Chicago, and portions of Missouri and Southern Illinois, including
watersheds surrounding the Missouri and Mississippi Rivers, there are many gaps in the fish
sampling data. As such, there is uncertainty as to the methylmercury fish tissue concentrations
due solely to power plants in these areas.
Post 1999 Fish Sample Location
Figure 8.1. Location of Post-1999 NLFA and NLFTS Sample Sites Relative to 2020 Utility
Attributable Mercury Deposition After Implementation of CAIR and CAMR
Requirements
As with any analysis based on limited data, and especially analyses of future conditions,
there is inherent uncertainty in the estimates of all analytical outputs of our modeling. EPA
typically classifies the major areas of uncertainty in risk assessments as parameter uncertainty,
scenario uncertainty, and model uncertainty. Parameter uncertainty is simply uncertainty about
some parameter used in the analysis. The sources of parameter uncertainty are measurement
errors, sampling errors, variability, and use of generic or surrogate data. Scenario uncertainty is
uncertainty about missing or incomplete information needed to fully define the exposure and
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dose. Scenario uncertainty results from the general impracticality of making actual
measurements of receptors' exposure. The sources of scenario uncertainty are descriptive errors,
aggregation errors, errors in professional judgment, and incomplete analysis. Model uncertainty
results from the fact that models and their mathematical expressions are simplifications of reality
that are used to approximate real-world conditions and processes, and their relationships.
Models do not include all parameters or equations necessary to express real-world conditions
because of the inherent complexity of the natural environment, and the lack of sufficient data to
describe the natural environment. Consequently, models are based on numerous assumptions
and simplifications, and reflect an incomplete understanding of natural processes.
In conducting this analysis, EPA endeavored to use state of the science models and
algorithms, as well as the best available parameter data of known and documented quality.
Nevertheless, EPA understands that all analysts who conduct complex assessments of the type
presented in this document are faced with numerous sources of uncertainty. Throughout this
document we describe sources of uncertainty in our analysis, as well as our efforts to more fully
understand and mitigate those uncertainties. In cases where sophisticated modeling tools or
complete data were not available, we undertook additional effort to identify and understand their
potential impact on our analysis. The analysis described in this document represents a
reasonable and considered approach to evaluating a highly complex problem. We believe that
the decisions we have made in the face of unavoidable uncertainty have reduced the chance that
we have either significantly over- or underestimated the methylmercury exposures to consumers
of noncommercial freshwater fish. In cases where over- or under-estimates exist, we have made
these estimates transparent.
Additional Monitoring Needs
Based on the demonstrated gaps in available fish tissue sampling data, EPA is
encouraging states, utilities, and other interested parties to work with the Agency in collecting
data on mercury deposition and fish samples in and around waterbodies near where utilities are
projected to have residual emissions of those mercury species that contribute to near-field or
regional deposition. Collection of this data will ensure that CAIR and CAMR are resulting in
the reductions in utility attributable deposition and fish tissue methylmercury concentrations as
anticipated. Furthermore, the ongoing collection of data by state agencies under the NLFA and
the EPA NLFTS will continue to provide fish sampling data that can be used to assess the
effectiveness of CAIR and CAMR over time.
EPA also recognizes the potential for certain populations, including subsistence fishers
within the population of Native Americans, to experience greater exposure to utility attributable
methylmercury due to high consumption of fish. While the CAIR and CAMR cannot be
demonstrated to reduce all exposure from utility mercury emissions, Table 6.4 shows that under
most circumstances, the chances of incremental exposures above an IDI value of 1 are small.
EPA will continue to work with Native American tribes to provide more complete information
about levels of methylmercury in fish caught in waterbodies frequented by Native American
subsistence fishing populations.
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EPA also recognizes that there are other subsistence fishing populations outside of the
Native American community, including low income and minority populations located in urban
and rural areas who eat large amounts of self-caught freshwater fish. These populations may
have higher consumption rates then the general population of recreational anglers. However,
very little information is available on the fishing behaviors of this population. As such, while
additional collection of fish samples will allow for a more complete understanding of the extent
of residual fish-tissue methylmercury levels due to utilities, population surveys of fish
consumption in these subpopulations will also be useful in helping to characterize remaining
exposure to utility attributable methylmercury.
Mercury Vulnerability Analysis
Our current national-scale analysis uses the Mercury Maps proportionality assumption to
relate changes in deposition to changes in fish tissue. This approach has a number of limitations
as described above . In part to help address some of these limitations, EPA is currently engaged
in ongoing work to analyze the vulnerability of different ecosystems across the United States to
methylmercury exposure. Different ecosystems exhibit dramatically different responses to
changes in mercury loading depending on their chemical and physical attributes. Using
georeferenced empirical databases that describe some of the watershed and waterbody
characteristics across the United States, we are exploring a preliminary approach to such an
analysis by presenting the gradient of values for some attributes known to be important for
methylmercury formation and bioaccumulation in fish. In this analysis, we will consider sulfate
deposition, organic matter in soils, percent wetland coverage, and total mercury deposition
forecasted using the CMAQ model as proxy indicators for methylation potential. We define
"methylation potential" as a relative indicator of the likelihood that mercury deposited in a given
ecosystem will be converted to methylmercury and bioaccumulate in fish. A number of
variables important for methylmercury formation have not yet been included in this analysis and
the results must therefore be considered a work in progress and are by no means conclusive. We,
therefore, do not present the results here and do not rely on them for this rulemaking.
Different variables and combinations of variables will produce different results for the
most vulnerable regions or enhanced areas of methylmercury production. Developing broad
categories of ecosystem types based on their propensity for methylmercury formation and
bioaccumulation in fish and their frequency of occurrence is an iterative effort. Such work will
help us better characterize case studies and empirical about particular ecosystems within the
range of ecosystems found across the US. It also will allow us to focus better on individual
watersheds or waterbodies that may be of concern as "utility hotspots."
In addition to the above analysis, EPA's Regional Vulnerability Assessment Program
(ReVa) has developed a pilot internal version of an environmental decision toolkit (EDT) to
assess mercury vulnerability across the United States. While recognizing that both our process
based understanding of the mercury exposure pathway and the availability of data to make this
assessment are currently imperfect, the ReVA program develops effective ways to make use of
data and information that currently exist. Given that there is no obvious "right" way to assess the
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risk from methylmercury, a toolkit with the flexibility to consider and compare alternative data,
model inputs, and assumptions, and alternative ways to combine these inputs into indices of
relative risk will allow a broader understanding of where the greatest uncertainties lie and where
there is agreement among data and methods.
The EDT is a statistical toolkit that displays information spatially. The advantage of
using a statistical package over a GIS is that it allows rapid reanalysis of data such that different
combinations of variables can be displayed and compared quickly. This makes it ideal for
problems that have a great deal of uncertainty or where a number of "what if' scenarios might be
explored. Within the Mercury-EDT:
The raw data can be viewed and explored
Choices can be made as to which data or model results are used in determining overall
risk when multiple options exist
Different weights for influential parameters can be set for estimating a methylation
potential index
Comparisons can be made between estimated values and monitored data, and
Distributions of sensitive populations, estimated indices of methylation potential, and
estimated mercury deposition can be integrated into relative rankings of risk from
mercury generated from EGUs.
References:
Center for Disease Control (CDC). 2001: National Report on Human Exposure to Environmental
Chemicals. Publication No. 01-0379
Cocca, P. 2001. Mercury Maps: A Quantitative Spatial Link Between Air Deposition and Fish
Tissue Peer Reviewed Final Report. U.S. Environmental Protection Agency, Office of
Water, EPA Report Number EPA-823-R-01-009.
Crump KS, Kjellstrom T, Shipp AM, Silvers A, Stewart A, 1998. Influence of prenatal mercury
exposure upon scholastic and psychological test performance: Benchmark analysis of a
New Zealand cohort. Risk Analysis, 18:701-713.
Crump KS, Van Landingham C, Shamlaye C, Cox C, Davidson PW, Myers GJ, Clarkson TW.,
2000. Benchmark concentrations for methylmercury obtained from the Seychelles Child
Development Study. Environ Health Perspect. 2000 Mar;108(3):257-63.
ESRI, 2004. ArcMAP, Version 9.0.
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Fish Tissue Advisory Committee, 1992. Recommendation for a fish tissue monitoring strategy
for freshwater lakes, rivers, and streams. Georgia Department of Natural Resources,
Environmental Protection Division & Game and Fish Division.
Grandjean P, Weihe P, White RF, Debes F, Araki S, Yokoyama K, Murata K, Sorensen N, Dahl
R, Jorgensen PJ (1997). Cognitive deficit in 7-year-old children with prenatal exposure to
methylmercury. Neurotoxicology and Teratology, 19:417-428.
Hites, R. A., J. A. Foran, et al. (2004a). "Global assessment of organic contaminants in farmed
salmon." Science 303(5655): 226-9.
Hites, R. A., J. A. Foran, et al. (2004b). "Global assessment of polybrominated diphenyl ethers in
farmed and wild salmon." Environ Sci Technol 38(19): 4945-4949.
Marsh DO, Clarkson TW, Cox C, Myers GJ, Amin-Zaki L, Al-Tikriti S., 1987. Fetal
methylmercury poisoning. Relationship between concentration in single strands of
maternal hair and child effects. Arch Neurol. 1987 Oct;44(10): 1017-22.
Matilainen, T. and M. Verta (1995). Mercury methylation and demethylation in aerobic surface
waters. Canadian Journal of Fisheries & Aquatic Sciences 52(8): 1597-1608.
Morgan, J.N., M.R. Berry, and R.L. Graves. 1997. "Effects of Commonly Used Cooking
Practices on Total Mercury Concentration in Fish and Their Impact on Exposure
Assessments." Journal of Exposure Analysis and Environmental Epidemiology
7(1): 119-133
Myers GJ, Davidson PW, Cox, C, Shamlaye CF, Palumbo D, Cernichiari E, Sloane-Reeves J,
Wilding GE, Kost J, Huang LS, Clarkson TW (2003). Prenatal methylmercury exposure
from ocean fish consumption in the Seychelles child development study. Lancet,
361:1686-1692.
National Research Council (NRC). 2000. Toxicological Effects of Methylmercury. Committee
on the Toxicological Effects of Methylmercury, Board on Environmental Studies and
Toxicology, Commission of Life Sciences, National Research Council. National
Academy Press, Washington, DC.
Pennsylvania Department of Environmental Protection. 2004. Assessment and Listing
Methodology for the 2004 Integrated Water Quality Monitoring and Assessment Report.
U.S. EPA, 1997a. Exposure Factors Handbook. Volume 2: Food Ingestion Factors.
EPA/600/P-95/002Fa. Washington, DC: Office of Research and Development, National
Center for Environmental Assessment. Location:
http://www.epa.gov/ncea/pdfs/efh/front.pdf
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U.S. EPA, 1997b. Mercury Study Report to Congress. Office of Air and Radiation.
EPA-452/R-97-007
U.S. EPA. 1998. Study of Hazardous Air Pollutant Emissions from Electric Utility Steam
Generating Units Final Report to Congress, February 1998 (EPA-453/R-98-004 a&b)
U.S. EPA, 1999, "Human Health and Ecological Risk Assessment Support to the Development
of Technical Standards for Emissions from Combustion Units Burning Hazardous Waste,
Background Document," US Environmental Protection Agency, Office of Solid Waste,
Washington, DC, July 1999. Location:
http://www.epa.gov/epaoswer/hazwaste/combust/rrvol_i.pdf
U.S. EPA, 2001. Water Quality Criterion for the Protection of Human Health: Methylmercury.
EPA-823-R-01-001. Office of Science and Technology, Office of Water, USEPA,
Washington, DC.)
U.S. EPA. 2002a. A review of the Reference Dose and Reference Concentration Process - Final
Report, EPA Report Number EPA/630/P-02/002F
U.S. EPA, 2002b, "Estimated Per Capita Fish Consumption in the United States", US
Environmental Protection Agency, Office of Water, EPA-821-C-02-003, August, 2002.
Location: http://www.epa.gov/waterscience/fish/consumption report.pdf:
U.S. EPA, 2004a. National Listing of Fish Advisories. Office of Water. EPA-823-F-04-016.
U.S. EPA, 2004b. Fact Sheet: 2004 Update, The National Study of Chemical Residues in Lake
Fish Tissue. Office of Water. EPA-823-F-04-021.
U.S. EPA, 2004c. Field Sampling Plan for the National Study of Chemical Residues in Lake
Fish Tissue. Office of Water, EPA report number EPA-823-R-02-004.
U.S. EPA, 2005a. Control of Mercury Emissions from Coal Fired Electric Utility Boilers: An
Update
U.S. EPA, 2005b. Clean Air Mercury Rule Final Regulatory Impact Analysis. Office of Air and
Radiation. EPA Report Number EPA-452/R-05-003.
U.S. EPA, 2005c. CAMR Emissions Inventory and Air Quality Modeling Technical Support
Document. Office of Air Quality Planning and Standards, Emissions Modeling and
Analysis Division.
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