acceptability. As risks increase above this benchmark, they become
presumptively less acceptable under CAA section 112, and would be
weighed with the other health risk measures and information in making
an overall judgment on acceptability. Or, the Agency may find, in a
particular case, that a risk that includes an MIR less than the
presumptively acceptable level is unacceptable in the light of other
health risk factors.'' Id. at 38045. Similarly, with regard to the
ample margin of safety analysis, the EPA stated in the Benzene NESHAP
that: ``EPA believes the relative weight of the many factors that can
be considered in selecting an ample margin of safety can only be
determined for each specific source category. This occurs mainly
because technological and economic factors (along with the health-
related factors) vary from source category to source category.'' Id. at
38061. We also consider the uncertainties associated with the various
risk analyses, as discussed earlier in this preamble, in our
determinations of acceptability and ample margin of safety.
    The EPA notes that it has not considered certain health information
to date in making residual risk determinations. At this time, we do not
attempt to quantify the HAP risk that may be associated with emissions
from other facilities that do not include the

source category under review, mobile source emissions, natural source
emissions, persistent environmental pollution, or atmospheric
transformation in the vicinity of the sources in the category.
    The EPA understands the potential importance of considering an
individual's total exposure to HAP in addition to considering exposure
to HAP emissions from the source category and facility. We recognize
that such consideration may be particularly important when assessing
noncancer risk, where pollutant-specific exposure health reference
levels (e.g., reference concentrations (RfCs)) are based on the
assumption that thresholds exist for adverse health effects. For
example, the EPA recognizes that, although exposures attributable to
emissions from a source category or facility alone may not indicate the
potential for increased risk of adverse noncancer health effects in a
population, the exposures resulting from emissions from the facility in
combination with emissions from all of the other sources (e.g., other
facilities) to which an individual is exposed may be sufficient to
result in an increased risk of adverse noncancer health effects. In May
2010, the Science Advisory Board (SAB) advised the EPA ``that RTR
assessments will be most useful to decision makers and communities if
results are presented in the broader context of aggregate and
cumulative risks, including background concentrations and contributions
from other sources in the area.'' \29\
---------------------------------------------------------------------------

    \29\ Recommendations of the SAB RTR Panel are provided in their
report, which is available at https://yosemite.epa.gov/sab/
sabproduct.nsf/4AB3966E263D943A8525771F00668381/$File/EPA-SAB-10-
007-unsigned.pdf.
---------------------------------------------------------------------------

    In response to the SAB recommendations, the EPA incorporates
cumulative risk analyses into its RTR risk assessments, including those
reflected in this proposal. The Agency (1) conducts facility-wide
assessments, which include source category emission points, as well as
other emission points within the facilities; (2) combines exposures
from multiple sources in the same category that could affect the same
individuals; and (3) for some persistent and bioaccumulative
pollutants, analyzes the ingestion route of exposure. In addition, the
RTR risk assessments consider aggregate cancer risk from all
carcinogens and aggregated noncancer HQs for all noncarcinogens
affecting the same target organ or target organ system.
    Although we are interested in placing source category and facility-
wide HAP risk in the context of total HAP risk from all sources
combined in the vicinity of each source, we are concerned about the
uncertainties of doing so. Estimates of total HAP risk from emission
sources other than those that we have studied in depth during this RTR
review would have significantly greater associated uncertainties than
the source category or facility-wide estimates. Such aggregate or
cumulative assessments would compound those uncertainties, making the
assessments too unreliable.

B. How do we perform the technology review?

    Our technology review focuses on the identification and evaluation
of developments in practices, processes, and control technologies that
have occurred since the MACT standards were promulgated. Where we
identify such developments, we analyze their technical feasibility,
estimated costs, energy implications, and non-air environmental
impacts. We also consider the emission reductions associated with
applying each development. This analysis informs our decision of
whether it is ``necessary'' to revise the emissions standards. In
addition, we consider the appropriateness of applying controls to new
sources versus retrofitting existing sources. For this exercise, we
consider any of the following to be a ``development'':

     Any add-on control technology or other equipment that
was not identified and considered during development of the original
MACT standards;
     Any improvements in add-on control technology or other
equipment (that were identified and considered during development of
the original MACT standards) that could result in additional
emissions reduction;
     Any work practice or operational procedure that was not
identified or considered during development of the original MACT
standards;
     Any process change or pollution prevention alternative
that could be broadly applied to the industry and that was not
identified or considered during development of the original MACT
standards; and
     Any significant changes in the cost (including cost
effectiveness) of applying controls (including controls the EPA
considered during the development of the original MACT standards).

    In addition to reviewing the practices, processes, and control
technologies that were considered at the time we originally developed
(or last updated) the NESHAP, we review a variety of data sources in
our investigation of potential practices, processes, or controls to
consider. See sections IV.C and IV. D of this preamble for information
on the specific data sources that were reviewed as part of the
technology review.

C. How do we estimate post-MACT risk posed by the source category?

    In this section, we provide a complete description of the types of
analyses that we generally perform during the risk assessment process.
In some cases, we do not perform a specific analysis because it is not
relevant. For example, in the absence of emissions of HAP known to be
persistent and bioaccumulative in the environment (PB-HAP), we would
not perform a multipathway exposure assessment. Where we do not perform
an analysis, we state that we do not and provide the reason. While we
present all of our risk assessment methods, we only present risk
assessment results for the analyses actually conducted (see section
VI.B of this preamble).
    The EPA conducts a risk assessment that provides estimates of the
MIR for cancer posed by the HAP emissions from each source in the
source category, the HI for chronic exposures to HAP with the potential
to cause noncancer health effects, and the HQ for acute exposures to
HAP with the potential to cause noncancer health effects. The
assessment also provides estimates of the distribution of cancer risk
within the exposed populations, cancer incidence, and an evaluation of
the potential for an adverse environmental effect. The seven sections
that follow this paragraph describe how we estimated emissions and
conducted the risk assessment. The docket for this rulemaking contains
the following document which provides more information on the risk
assessment inputs and models: Residual Risk Assessment for the Coal-
and Oil-Fired EGU Source Category in Support of the 2019 Risk and
Technology Review Proposed Rule (risk document). The methods used to
assess risk (as described in the seven primary steps below) are
consistent with those described by the EPA in the document reviewed by
a panel of the EPA's SAB in 2009;\30\ and described in the SAB review
report issued in 2010. They are also consistent with the key
recommendations contained in that report.
---------------------------------------------------------------------------

    \30\ U.S. EPA. June 2009. Risk and Technology Review (RTR) Risk
Assessment Methodologies: For Review by the EPA's Science Advisory
Board with Case Studies--MACT I Petroleum Refining Sources and
Portland Cement Manufacturing (EPA-452/R-09-006). Available at
https://www3.epa.gov/airtoxics/rrisk/rtrpg.html.

---------------------------------------------------------------------------

1. How did we estimate actual emissions and identify the emissions
release characteristics?
    Data for existing EGUs were used to create the RTR emissions
dataset for the risk review as described in section IV.D of this
preamble. The RTR emissions dataset includes information for 608
emission release points (i.e., stacks). Because in some cases multiple
EGUs are routed to a single stack or a single EGU is routed to two
stacks, the number of stacks is not the same as the number of EGUs. The
MATS rule regulates emissions of HAP in four pollutant categories: Hg,
non-Hg metals, acid gases, and organics. As described in section IV.B
of this preamble, EGUs in six subcategories are subject to numeric
emission limits for specific HAP, or surrogates for those HAP, in the
three pollutant categories of Hg, non-Hg metals, and acid gases. EGUs
are not required to meet numeric emission limits for organic HAP or to
test and report organic HAP emissions. During the 2010 ICR effort of
the original MATS rulemaking process, most of the organic HAP emissions
data for EGUs were at or below the detection levels of the prescribed
test methods, even when long duration test runs (i.e., approximately 8
hours) were required. In developing the RTR emissions dataset, the EPA
reviewed the available organic HAP test results from the 2010 ICR. For
each organic HAP tested, if 40 percent or more of the available test
data were above test method detection limits, emissions estimates for
that HAP were included in the modeling file. Emissions of the following
HAP in each of the four pollutant categories were estimated for each
emission release point and included in the RTR emissions dataset:

     Hg: elemental gaseous Hg, gaseous divalent Hg,
particulate divalent Hg;
     Non-Hg metals: antimony, arsenic, beryllium, cadmium,
hexavalent chromium, trivalent chromium, cobalt, lead, manganese,
nickel, selenium;
     Acid gases: HCl, HF; and
     Organics: formaldehyde, naphthalene, 2-
methylnaphthalene, phenanthrene, two dioxin congeners, three furan
congeners, and seven polychlorinated biphenyls congeners.

    As explained in section IV.D of this preamble, emissions estimates
for the RTR emissions dataset were based primarily on data submitted
via the EPA's ECMPS by facilities with affected EGUs. Calendar year
2017 data were used where available because all affected EGUs subject
to numeric emission limits would be required to submit compliance data
by then. Where calendar year 2017 data were not available, the most
recent data available were used. CEMS emissions data for Hg, HCl, and
SO2 reported to the EPA's ECMPS were available as 2017
actual annual values (i.e., pounds per year or tpy).
    Some emissions data for Hg, non-Hg HAP metals, HCl, and fPM was
submitted to the EPA's ECMPS, but maintained in the WebFIRE database.
For those sources, the EPA extracted data associated with operations in
summer 2017, when EGUs would be expected to operate more frequently
given increased demand for electricity, and used those summertime
emissions to estimate annual emissions of the pollutants of interest.
Specifically, test averages from third quarter performance stack tests
(i.e., conducted between July and September 2017) for any pollutant and
30-day rolling average values as of June 30, 2017, for PM CEMS and PM
CPMS were extracted and then converted from pounds of pollutant per
million British thermal units or trillion British thermal units (lb/
MMBtu or lb/TBtu) or pounds of pollutant per megawatt-hour or gigawatt-
hour (lb/MWh or lb/GWh) to actual annual emissions using 2017 total
heat input (MMBtu) or total gross load (MWh) values, as appropriate.
When ECMPS-submitted data for HAP in the RTR emissions dataset were not
available, actual annual emissions estimates were based on data from
the 2014 NEI and the June 2018 EPRI technical report. Some annual
emissions estimates were also generated using the ratio of non-Hg
metals to fPM and acid gases to SO2 from the 2010 ICR in
conjunction with more recent fPM or SO2 emissions data.
Emissions data from the 2010 ICR were used to develop emission factors
for the non-Hg metals and acid gases included in the RTR emissions
dataset and to develop ratios based on each of those emission factors
divided by average fPM or SO2 values, respectively.
Emissions data for EGUs no longer operating were excluded in the
calculation of emission factors or average fPM or SO2
values. In addition, we included in each emission factor and ratio
calculation only the 2010 ICR data for EGUs where data for both the
non-Hg metal HAP (e.g., antimony) and fPM, or the acid gas HAP (e.g.,
HCl) and SO2, were available. Emission factors and emission
factor-based ratios were developed for the various combinations of fuel
types and emissions control device types. Actual annual HAP-specific
emissions for each stack were then estimated by multiplying each
emission factor-based ratio by the most recent fPM or SO2
annual emissions value (e.g., 2017 ECMPS or WebFIRE data or 2014 NEI
data). Because EGUs in the subcategory of limited-use liquid oil-fired
EGUs are not subject to any numeric emission limits, actual annual HAP-
specific emissions were estimated using 2014 NEI data or emission
factor-based ratios along with 2014 NEI data for PM and SO2.
Development of the emission factors and emission factor-based ratios is
explained in the memorandum, Emission Factor Development for RTR Risk
Modeling Dataset for Coal- and Oil-fired EGUs, which is available in
the docket for this action.
    The majority of the total (i.e., non-speciated) Hg actual annual
emissions estimates were based on data maintained in the EPA's ECMPS
for CEMS data or sorbent traps or in WebFIRE for performance stack
tests along with 2017 total heat input or total gross load values, as
appropriate. Where such data were not available, total Hg actual annual
emissions were estimated using the 2014 NEI or the June 2018 EPRI
technical report. For a small number of oil-fired EGUs, EPA-developed
emission factors and emission factor-based ratios were used to estimate
total Hg actual annual emissions. Hg emissions were modeled as three
different species: elemental gaseous Hg, gaseous divalent Hg, and
particulate divalent Hg. The EPA utilized Hg speciation factors--
percentages based on fuel type and installed emissions control
equipment--that were updated versions of those that had been used in
the development of the MATS rule.\31\ Total Hg emissions were then
multiplied by the factors to develop the speciated Hg actual annual
emissions estimates.
---------------------------------------------------------------------------

    \31\ See Attachment E of the Risk Modeling Dataset Memo for the
list of Hg speciation factors utilized in compiling the RTR
emissions dataset for the risk review, available in the docket for
this action. See Appendix G of the Technical Support Document for
the Proposed Rule Emissions Inventories (available in the rulemaking
docket at EPA-HQ-OAR-2009-0234-19908) for Hg speciation factors used
in the development of MATS.
---------------------------------------------------------------------------

    For the several EGUs that submitted individual non-Hg HAP metals
data to the EPA, actual annual emissions were estimated using the stack
test values maintained in WebFIRE and 2017 total heat input or total
gross load values, as appropriate. The majority of the non-Hg HAP
metals actual annual emissions estimates were based on emission factor-
based ratios for non-Hg HAP metals and fPM annual emissions values.
Chromium emissions were modeled as hexavalent chromium (Cr(VI)) and
trivalent chromium (Cr(III)). Actual annual emissions of Cr(VI) and
Cr(III) were estimated by multiplying total chromium emissions by the

speciation factors for coal or oil, as appropriate.
    Actual annual emissions estimates for HCl for EGUs that submitted
data to the EPA's ECMPS were based on those ECMPS CEMS data or WebFIRE
performance stack test data and 2017 total heat input or total gross
load values, as appropriate. Where acid gas HAP data were not available
in the WebFIRE database, but SO2 data were available in the
ECMPS for requirements other than those in the MATS rule (e.g., the
acid rain rule), emission factor-based ratios for the acid gas HAP
(i.e., HCl and HF) and SO2 annual emissions values were used
to estimate actual annual HCl and HF emissions. In some instances,
actual annual HCl and HF emissions were estimated based on emission
factor-based ratios and 2014 NEI data for SO2. In a small
number of other instances, actual annual HCl and HF emissions were
estimated using the June 2018 EPRI technical report.
    As previously explained, EGUs are not required to meet numeric
emission limits for organic HAP or to test and report organic HAP
emissions. Actual annual emissions for the 16 organic HAP included in
the RTR emissions dataset are based on EPA-developed representative
detection level (RDL) equivalent emissions values (lb/MMBtu) based on
fuel type. RDL equivalent emissions values for 15 of the organic HAP
are based on the averages of better-performing unit method detection
levels across many source categories. Because we did not have an RDL
analysis across source categories for formaldehyde, detection levels
from the 2010 ICR data were used to develop the RDL equivalent
emissions value for formaldehyde. Actual annual emissions of the 16
organic HAP were estimated by multiplying the RDL equivalent emissions
values by 2017 total heat input. Development of the RDL equivalent
emissions values is explained in the memorandum, Development of
Representative Detection Levels of Certain Organic HAP Expressed as
Pounds per Million British Thermal Units of Fuel Input for RTR Risk
Modeling Dataset for Coal- and Oil-fired EGUs, which is available in
the docket for this action.
    Stack parameter values and locations for each emissions release
point included in the RTR emissions dataset were primarily based on
information reported to the ECMPS and generator-level specific
information about existing generators and their associated
environmental equipment that is collected under Form EIA-860.
Specifically, the ECMPS was the primary source for stack height,
diameter, and latitude/longitude coordinates, and the EIA-860 database
was the primary source for stack temperature, velocity, and flow rate.
Other sources of information that were used to fill gaps in the site-
specific emissions release point data included the 2014 NEI, parameters
from similar stacks at a specific facility, and default parameter
values based on MACT source category 2014 NEI information.
    The RTR emissions dataset was refined as necessary following a
quality assurance check of source locations, emissions release
characteristics, and annual emissions estimates. Latitude and longitude
coordinates were checked using Google Earth[supreg] to ensure that
stack locations were correct. Stack parameters were checked to ensure
that they were within acceptable quality assurance range check
boundaries. Emissions estimates were reviewed for completeness and
accuracy. Additional details on the data and methods used to develop
``actual'' emissions estimates for the RTR emissions dataset are
provided in the memorandum, Development of the RTR Risk Modeling
Dataset for the Coal- and Oil-Fired EGU Source Category (Risk Modeling
Dataset Memo), included as Appendix 1 of the risk document, which is
available in the docket for this action.
    A comparison of the actual annual HAP emissions in 2017 to the
annual HAP emissions prior to promulgation of the MATS rule shows a 96-
percent reduction in total HAP emissions from coal- and oil-fired EGUs.
The actual emissions from coal- and oil-fired EGUs for 2017 and
estimated emissions from 2010 are shown in Table 4. Estimates of pre-
MATS emissions of organic HAP were not available. As discussed
previously in this section, the 2017 emissions of organic HAP are based
on RDL equivalent emissions values; the actual 2017 emissions are
likely lower than the estimate of 3 tpy.

                     Table 4--HAP Emissions From Coal- and Oil-Fired EGUs Pre- and Post-MATS
----------------------------------------------------------------------------------------------------------------
                                                                  2010 Emissions  2017 Emissions
                            Pollutant                               (tons) \32\       (tons)       Reduction (%)
----------------------------------------------------------------------------------------------------------------
Hg..............................................................              29               4              86
Acid Gases......................................................         125,708           4,831              96
Non-Hg Metals...................................................           1,170             221              81
Organic HAP.....................................................               *              <3               *
                                                                 -----------------------------------------------
    Total.......................................................         126,907           5,059              96
----------------------------------------------------------------------------------------------------------------
Note: The compliance date for the vast majority of affected EGUs was on or before April 16, 2016.
* Not available.

2. How did we estimate MACT-allowable emissions?
---------------------------------------------------------------------------

    \32\ Memorandum: Emissions Overview: Hazardous Air Pollutants in
Support of the Final Mercury and Air Toxics Standard. EPA-454/R-11-
014. November 2011; Docket ID No. EPA-HQ-OAR-2009-0234-19914.
---------------------------------------------------------------------------

    The available emissions data in the RTR emissions dataset include
estimates of the mass of HAP emitted during a specified annual time
period. These ``actual'' emission levels are often lower than the
emission levels allowed under the requirements of the current MACT
standards. The emissions allowed under the MACT standards are referred
to as the ``MACT-allowable'' emissions. We discussed the consideration
of both MACT-allowable and actual emissions in the final Coke Oven
Batteries RTR (70 FR 19998-19999, April 15, 2005) and in the proposed
and final Hazardous Organic NESHAP RTR (71 FR 34428, June 14, 2006, and
71 FR 76609, December 21, 2006, respectively). In those actions, we
noted that assessing the risk at the MACT-allowable level is inherently
reasonable since that risk reflects the maximum level facilities could
emit and still comply with national emission standards. We also
explained that it is reasonable to consider actual emissions, where
such data are available, in both steps of the risk analysis, in
accordance with the Benzene NESHAP approach. (54 FR 38044, September
14, 1989.)

    MACT-allowable annual emissions of Hg, non-Hg HAP metals, and acid
gas HAP were estimated using numeric emission limits for existing
sources in the MATS rule along with 2017 total heat input. For Hg,
allowable annual emissions of total Hg were estimated by multiplying
subcategory-specific Hg emission limits by 2017 total heat input.
Allowable annual emissions of elemental gaseous Hg, gaseous divalent
Hg, and particulate divalent Hg were estimated by multiplying annual
emissions of total Hg by EPA-developed Hg speciation factors which are
based on fuel type and emissions control device type.
    With regard to non-Hg HAP metals, performance stack test data in
almost all instances was for fPM, a surrogate for non-Hg HAP metals,
and, as such, allowable annual emissions were estimated using the MATS
rule's fPM emission limits. Specifically, allowable annual emissions of
the non-Hg HAP metals were estimated by multiplying subcategory-based
fPM emission limits by 2017 total heat input and by the emission
factor-based ratios for non-Hg HAP metals that were calculated by the
EPA. Allowable annual emissions of chromium as Cr(VI) and Cr(III) were
estimated by multiplying the total chromium allowable emissions
estimates by the chromium speciation factors for coal or oil, as
appropriate.
    For acid gas HAP, allowable annual emissions of HCl and HF from
oil-fired EGUs were estimated by multiplying subcategory-specific HCl
and HF emission limits by 2017 total heat input. With regard to acid
gas HAP for coal-fired EGUs, some coal-fired sources submitted data for
HCl, a surrogate for acid gas HAP, whereas other sources submitted data
for SO2, a surrogate for acid gas HAP for certain coal-fired
EGUs. Allowable annual emissions of HCl and HF from coal-fired EGUs
were estimated two different ways--one based on the MATS rule's HCl
emission limits and HF actual emissions adjusted using an HCl emissions
ratio and the other based on the MATS rule's SO2 emission
limits and emission factor-based ratios--and the more conservative
estimate was used. In the first approach, allowable annual emissions of
HCl were estimated by multiplying subcategory-specific HCl emission
limits by 2017 total heat input, and allowable annual emissions of HF
were estimated by multiplying actual annual emissions of HF by a ratio
of HCl allowable annual emissions to HCl actual annual emissions. In
the second approach, allowable annual emissions of HCl and HF were
estimated by multiplying subcategory-based SO2 emission
limits by 2017 total heat input and by the emission factor-based ratios
for HCl and HF that were calculated by the EPA.
    Because there are no numeric emission limits for organic HAP,
allowable annual emissions for the 16 organic HAP were assumed equal to
the actual annual emissions estimates for the 16 organic HAP. The Risk
Modeling Dataset Memo, available in the docket for this action,
contains additional information on the development of estimated MACT-
allowable emissions.
3. How do we conduct dispersion modeling, determine inhalation
exposures, and estimate individual and population inhalation risk?
    Both long-term and short-term inhalation exposure concentrations
and health risk from the source category addressed in this proposal
were estimated using the Human Exposure Model (HEM-3).\33\ The HEM-3
performs three primary risk assessment activities: (1) Conducting
dispersion modeling to estimate the concentrations of HAP in ambient
air, (2) estimating long-term and short-term inhalation exposures to
individuals residing within 50 kilometers (km) of the modeled sources,
and (3) estimating individual and population-level inhalation risk
using the exposure estimates and quantitative dose-response
information.
---------------------------------------------------------------------------

    \33\ For more information about HEM-3, go to https://www.epa.gov/fera/risk-assessment-and-modeling-human-exposure-model-hem.
---------------------------------------------------------------------------

a. Dispersion Modeling
    The air dispersion model AERMOD, used by the HEM-3 model, is one of
the EPA's preferred models for assessing air pollutant concentrations
from industrial facilities.\34\ To perform the dispersion modeling and
to develop the preliminary risk estimates, HEM-3 draws on three data
libraries. The first is a library of meteorological data, which is used
for dispersion calculations. This library includes 1 year (2016) of
hourly surface and upper air observations from 824 meteorological
stations, selected to provide coverage of the United States and Puerto
Rico. A second library of United States Census Bureau census block\35\
internal point locations and populations provides the basis of human
exposure calculations (U.S. Census, 2010). In addition, for each census
block, the census library includes the elevation and controlling hill
height, which are also used in dispersion calculations. A third library
of pollutant-specific dose-response values is used to estimate health
risk. These are discussed below.
---------------------------------------------------------------------------

    \34\ U.S. EPA. Revision to the Guideline on Air Quality Models:
Adoption of a Preferred General Purpose (Flat and Complex Terrain)
Dispersion Model and Other Revisions (70 FR 68218, November 9,
2005).
    \35\ A census block is the smallest geographic area for which
census statistics are tabulated.
---------------------------------------------------------------------------

b. Risk From Chronic Exposure to HAP
    In developing the risk assessment for chronic exposures, we use the
estimated annual average ambient air concentrations of each HAP emitted
by each source in the source category. The HAP air concentrations at
each nearby census block centroid located within 50 km of the facility
are a surrogate for the chronic inhalation exposure concentration for
all the people who reside in that census block. A distance of 50 km is
consistent with both the analysis supporting the 1989 Benzene NESHAP
(54 FR 38044, September 14, 1989) and the limitations of Gaussian
dispersion models, including AERMOD.
    For each facility, we calculate the MIR as the cancer risk
associated with a continuous lifetime (24 hours per day, 7 days per
week, 52 weeks per year, 70 years) exposure to the maximum
concentration at the centroid of each inhabited census block. We
calculate individual cancer risk by multiplying the estimated lifetime
exposure to the ambient concentration of each HAP (in micrograms per
cubic meter ([mu]g/m\3\)) by its unit risk estimate (URE). The URE is
an upper-bound estimate of an individual's incremental risk of
contracting cancer over a lifetime of exposure to a concentration of 1
microgram of the pollutant per cubic meter of air. For residual risk
assessments, we generally use UREs from the EPA's Integrated Risk
Information System (IRIS). For carcinogenic pollutants without IRIS
values, we look to other reputable sources of cancer dose-response
values, often using California EPA (CalEPA) UREs, where available. In
cases where new, scientifically credible dose-response values have been
developed in a manner consistent with EPA guidelines and have undergone
a peer review process similar to that used by the EPA, we may use such
dose-response values in place of, or in addition to, other values, if
appropriate. The pollutant-specific dose-response values used to
estimate health risk are available at https://www.epa.gov/fera/dose-response-assessment-assessing-health-risks-associated-exposure-hazardous-air-pollutants.To estimate individual lifetime cancer risks
associated with exposure to HAP emissions from each facility in the
source category, we sum the risks for

each of the carcinogenic HAP \36\ emitted by the modeled facility. We
estimate cancer risk at every census block within 50 km of every
facility in the source category. The MIR is the highest individual
lifetime cancer risk estimated for any of those census blocks. In
addition to calculating the MIR, we estimate the distribution of
individual cancer risks for the source category by summing the number
of individuals within 50 km of the sources whose estimated risk falls
within a specified risk range. We also estimate annual cancer incidence
by multiplying the estimated lifetime cancer risk at each census block
by the number of people residing in that block, summing results for all
of the census blocks, and then dividing this result by a 70-year
lifetime.
---------------------------------------------------------------------------

    \36\ The EPA's 2005 Guidelines for Carcinogen Risk Assessment
classifies carcinogens as: ``carcinogenic to humans,'' ``likely to
be carcinogenic to humans,'' and ``suggestive evidence of
carcinogenic potential.'' These classifications also coincide with
the terms ``known carcinogen, probable carcinogen, and possible
carcinogen,'' respectively, which are the terms advocated in the
EPA's Guidelines for Carcinogen Risk Assessment, published in 1986
(51 FR 33992, September 24, 1986). In August 2000, the document,
Supplemental Guidance for Conducting Health Risk Assessment of
Chemical Mixtures (EPA/630/R-00/002), was published as a supplement
to the 1986 document. Copies of both documents can be obtained from
https://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=20533&CFID=70315376&CFTOKEN=71597944. Summing
the risk of these individual compounds to obtain the cumulative
cancer risk is an approach that was recommended by the EPA's SAB in
their 2002 peer review of the EPA's National Air Toxics Assessment
(NATA) titled NATA--Evaluating the National-scale Air Toxics
Assessment 1996 Data--an SAB Advisory, available at https://
yosemite.epa.gov/sab/sabproduct.nsf/
214C6E915BB04E14852570CA007A682C/$File/ecadv02001.pdf.
---------------------------------------------------------------------------

    To assess the risk of noncancer health effects from chronic
exposure to HAP, we calculate either an HQ or a target organ-specific
hazard index (TOSHI). We calculate an HQ when a single noncancer HAP is
emitted. Where more than one noncancer HAP is emitted, we sum the HQ
for each of the HAP that affects a common target organ or target organ
system to obtain a TOSHI. The HQ is the estimated exposure divided by
the chronic noncancer dose-response value, which is a value selected
from one of several sources. The preferred chronic noncancer dose-
response value is the EPA RfC, defined as ``an estimate (with
uncertainty spanning perhaps an order of magnitude) of a continuous
inhalation exposure to the human population (including sensitive
subgroups) that is likely to be without an appreciable risk of
deleterious effects during a lifetime'' (https://iaspub.epa.gov/sor_internet/registry/termreg/searchandretrieve/glossariesandkeywordlists/search.do?details=&vocabName=IRIS%20Glossary). In cases where an RfC
from the EPA's IRIS is not available or where the EPA determines that
using a value other than the RfC is appropriate, the chronic noncancer
dose-response value can be a value from the following prioritized
sources, which define their dose-response values similarly to the EPA:
(1) The Agency for Toxic Substances and Disease Registry (ATSDR)
Minimum Risk Level (https://www.atsdr.cdc.gov/mrls/index.asp); (2) the
CalEPA Chronic Reference Exposure Level (REL) (https://oehha.ca.gov/air/crnr/notice-adoption-air-toxics-hot-spots-program-guidance-manual-preparation-health-risk-0); or (3), as noted above, a scientifically
credible dose-response value that has been developed in a manner
consistent with EPA guidelines and has undergone a peer review process
similar to that used by the EPA. The pollutant-specific dose-response
values used to estimate health risks are available at https://www.epa.gov/fera/dose-response-assessment-assessing-health-risks-associated-exposure-hazardous-air-pollutants.
c. Risk From Acute Exposure to HAP That May Cause Health Effects Other
Than Cancer
    For each HAP for which appropriate acute inhalation dose-response
values are available, the EPA also assesses the potential health risks
due to acute exposure. For these assessments, the EPA makes
conservative assumptions about emission rates, meteorology, and
exposure location. We use the peak hourly emission rate,\37\ worst-case
dispersion conditions, and, in accordance with our mandate under
section 112 of the CAA, the point of highest off-site exposure to
assess the potential risk to the maximally exposed individual.
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    \37\ In the absence of hourly emission data, we develop
estimates of maximum hourly emission rates by multiplying the
average actual annual emissions rates by a factor (either a
category-specific factor or a default factor of 10) to account for
variability. This is documented in Residual Risk Assessment for
Coal- and Oil-Fired EGU Source Category in Support of the 2019 Risk
and Technology Review Proposed Rule and in Appendix 5 of the report:
Analysis of Data on Short-term Emission Rates Relative to Long-term
Emission Rates. Both are available in the docket for this
rulemaking.
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    To characterize the potential health risks associated with
estimated acute inhalation exposures to a HAP, we generally use
multiple acute dose-response values, including acute RELs, acute
exposure guideline levels (AEGLs), and emergency response planning
guidelines (ERPG) for 1-hour exposure durations, if available, to
calculate acute HQs. The acute HQ is calculated by dividing the
estimated acute exposure by the acute dose-response value. For each HAP
for which acute dose-response values are available, the EPA calculates
acute HQs.
    An acute REL is defined as ``the concentration level at or below
which no adverse health effects are anticipated for a specified
exposure duration.'' \38\ Acute RELs are based on the most sensitive,
relevant, adverse health effect reported in the peer-reviewed medical
and toxicological literature. They are designed to protect the most
sensitive individuals, including children and the elderly, in the
population through the inclusion of margins of safety. Because margins
of safety are incorporated to address data gaps and uncertainties,
exceeding the REL does not automatically indicate an adverse health
impact. AEGLs represent threshold exposure limits for the general
public and are applicable to emergency exposures ranging from 10
minutes to 8 hours.\39\ They are guideline levels for ``once-in-a-
lifetime, short-term exposures to airborne concentrations of acutely
toxic, high-priority chemicals.'' Id. at 21. The AEGL-1 is specifically
defined as ``the airborne concentration (expressed as ppm (parts per
million) or mg/m\3\ (milligrams per cubic meter)) of a substance above
which it is predicted that the general population, including
susceptible individuals, could experience notable discomfort,
irritation, or certain asymptomatic nonsensory effects. However, the
effects are not disabling and are transient and reversible upon
cessation of exposure.'' The document also notes that ``Airborne
concentrations below AEGL-1 represent exposure levels that can produce
mild and progressively increasing but transient and nondisabling odor,
taste, and sensory irritation or certain asymptomatic, nonsensory
effects.'' Id. AEGL-2 are defined as ``the airborne

concentration (expressed as parts per million or milligrams per cubic
meter) of a substance above which it is predicted that the general
population, including susceptible individuals, could experience
irreversible or other serious, long-lasting adverse health effects or
an impaired ability to escape.'' Id.
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    \38\ CalEPA issues acute RELs as part of its Air Toxics Hot
Spots Program, and the 1-hour and 8-hour values are documented in
Air Toxics Hot Spots Program Risk Assessment Guidelines, Part I, The
Determination of Acute Reference Exposure Levels for Airborne
Toxicants, which is available at https://oehha.ca.gov/air/general-info/oehha-acute-8-hour-and-chronic-reference-exposure-level-rel-summary.
    \39\ National Academy of Sciences, 2001. Standing Operating
Procedures for Developing Acute Exposure Levels for Hazardous
Chemicals, page 2. Available at https://www.epa.gov/sites/production/files/2015-09/documents/sop_final_standing_operating_procedures_2001.pdf. Note that the
National Advisory Committee for Acute Exposure Guideline Levels for
Hazardous Substances ended in October 2011, but the AEGL program
continues to operate at the EPA and works with the National
Academies to publish final AEGLs, (https://www.epa.gov/aegl).
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    ERPGs are ``developed for emergency planning and are intended as
health-based guideline concentrations for single exposures to
chemicals.'' \40\ Id. at 1. The ERPG-1 is defined as ``the maximum
airborne concentration below which it is believed that nearly all
individuals could be exposed for up to 1 hour without experiencing
other than mild transient adverse health effects or without perceiving
a clearly defined, objectionable odor.'' Id. at 2. Similarly, the ERPG-
2 is defined as ``the maximum airborne concentration below which it is
believed that nearly all individuals could be exposed for up to one
hour without experiencing or developing irreversible or other serious
health effects or symptoms which could impair an individual's ability
to take protective action.'' Id. at 1.
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    \40\ ERPGS Procedures and Responsibilities. March 2014. American
Industrial Hygiene Association. Available at https://www.aiha.org/get-involved/AIHAGuidelineFoundation/EmergencyResponsePlanningGuidelines/Documents/ERPG%20Committee%20Standard%20Operating%20Procedures%20%20-%20March%202014%20Revision%20%28Updated%2010-2-2014%29.pdf.
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    An acute REL for 1-hour exposure durations is typically lower than
its corresponding AEGL-1 and ERPG-1. Even though their definitions are
slightly different, AEGL-1s are often the same as the corresponding
ERPG-1s, and AEGL-2s are often equal to ERPG-2s. The maximum HQs from
our acute inhalation screening risk assessment typically result when we
use the acute REL for a HAP. In cases where the maximum acute HQ
exceeds 1, we also report the HQ based on the next highest acute dose-
response value (usually the AEGL-1 and/or the ERPG-1).
    For the Coal- and Oil-Fired EGU source category, facility-level
acute factors (i.e., multipliers) developed by the EPA were used to
estimate acute emissions and the potential health risks due to acute
exposure. First, 2017 total heat input (MMBtu) and boiler maximum rated
heat input (MMBtu/hr) data were used to calculate an acute factor for
EGUs where both values were available. Next, facility-level acute
factors were calculated using a heat input-weighted average based on
2017 heat input for each EGU located within a facility fenceline. The
facility-level acute factor was used for each stack at the facility.
For units at facilities that did not have a facility-level factor
(e.g., 2017 total heat input and boiler maximum rated heat input were
not available), a default facility-level value of 6 was used. The
default facility-level value of 6 was developed by taking the average
of the calculated facility-level factors. If the calculated facility-
level acute factor was greater than 10 (e.g., in cases where the EGU
had a low 2017 heat input relative to the maximum rated heat input),
the RTR program default acute emission adjustment factor of 10 was
used. The default emission adjustment factor of 10 reflects a Texas
study of short-term emissions variability, which showed that most peak
emission events in a heavily-industrialized four-county area (Harris,
Galveston, Chambers, and Brazoria Counties, Texas) were less than twice
the annual average hourly emissions rate. The highest peak emissions
event was 74 times the annual average hourly emissions rate and the
99th percentile ratio of peak hourly emissions rate to the annual
average hourly emissions rate was 9.\41\ Considering this analysis, to
account for more than 99 percent of the peak hourly emissions, a
conservative screening multiplication factor of 10 is applied to the
average annual hourly emissions rate in the EPA's acute exposure
screening assessments as the default approach. In this analysis, we
inadvertently used allowable emissions (rather than actual emissions,
which is our standard practice) in conjunction with the facility level
acute factors in our screening assessment of acute risk. Because the
results showed acute risks below a level of concern even with acute
emissions being overstated due to the use of allowable emissions, we
did not correct the analysis and consider it to clearly support the
conclusion that acute risks are below a level of concern as shown in
Table 5 of this preamble. A further discussion of the development of
facility-level acute factors and emissions used to estimate acute
exposure for the risk modeling can be found in the Risk Modeling
Dataset Memo, available in the docket for this rulemaking.
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    \41\ Allen, et al., 2004. Variable Industrial VOC Emissions and
their impact on ozone formation in the Houston Galveston Area. Texas
Environmental Research Consortium. Available at https://www.researchgate.net/publication/237593060_Variable_Industrial_VOC_Emissions_and_their_Impact_on_Ozone_Formation_in_the_Houston_Galveston_Area.
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    In our acute inhalation screening risk assessment, acute impacts
are deemed negligible for HAP for which acute HQs are less than or
equal to 1 (even under the conservative assumptions of the screening
assessment), and no further analysis is performed for these HAP. In
cases where an acute HQ from the screening step is greater than 1, we
consider additional site-specific data to develop a more refined
estimate of the potential for acute exposures of concern.
4. How do we conduct the multipathway exposure and risk screening
assessment?
    The EPA conducts a tiered screening assessment examining the
potential for significant human health risks due to exposures via
routes other than inhalation (i.e., ingestion). We first determine
whether any sources in the source category emit any PB-HAP, as
identified in the EPA's Air Toxics Risk Assessment Library (See Volume
1, Appendix D, at https://www2.epa.gov/fera/risk-assessment-and-modeling-air-toxics-risk-assessment-reference-library.
    For the Coal- and Oil-Fired EGU source category, we identified PB-
HAP emissions of lead compounds, arsenic compounds, Hg compounds,
cadmium compounds, polycyclic organic matter (POM), and dioxins, so we
proceeded to the next step of the evaluation. In this step, we
determine whether the facility-specific emission rates of the emitted
PB-HAP are large enough to create the potential for significant human
health risk through ingestion exposure under reasonable worst-case
conditions. To facilitate this step, we use previously developed
screening threshold emission rates for several PB-HAP that are based on
a hypothetical upper-end screening exposure scenario developed for use
in conjunction with the EPA's Total Risk Integrated Methodology.Fate,
Transport, and Ecological Exposure (TRIM.FaTE) model. The PB-HAP with
screening threshold emission rates are arsenic compounds, cadmium
compounds, chlorinated dibenzodioxins and furans, Hg compounds, and
POM. Based on EPA estimates of toxicity and bioaccumulation potential,
the pollutants above represent a conservative list for inclusion in
multipathway risk assessments for RTR rules. (See Volume 1, Appendix D
at https://www.epa.gov/sites/production/files/201308/documents/volume_1_reflibrary.pdf). In this assessment, we compare the facility-
specific emission rates of these PB-HAP to the screening threshold
emission rates for each PB-HAP to assess the potential for significant
human health risks via the ingestion pathway. We call this application
of the TRIM.FaTE model the Tier 1 screening assessment. The ratio of a
facility's actual emission rate to the Tier 1 screening threshold
emission rate is a ``screening value.''

    We derive the Tier 1 screening threshold emission rates for these
PB-HAP (other than lead compounds) to correspond to a maximum excess
lifetime cancer risk of 1-in-1 million (i.e., for arsenic compounds,
polychlorinated dibenzodioxins and furans and POM) or, for HAP that
cause noncancer health effects (i.e., cadmium compounds and Hg
compounds), a maximum HQ of 1. If the emission rate of any one PB-HAP
or combination of carcinogenic PB-HAP in the Tier 1 screening
assessment exceeds the Tier 1 screening threshold emission rate for any
facility (i.e., the screening value is greater than 1), we conduct a
second screening assessment, which we call the Tier 2 screening
assessment.
    In the Tier 2 screening assessment, the location of each facility
that exceeds a Tier 1 screening threshold emission rate is used to
refine the assumptions associated with the Tier 1 fisher and farmer
exposure scenarios at that facility. A key assumption in the Tier 1
screening assessment is that a lake and/or farm is located near the
facility. As part of the Tier 2 screening assessment, we use a United
States Geological Survey (USGS) database to identify actual waterbodies
within 50 km of each facility. We also examine the differences between
local meteorology near the facility and the meteorology used in the
Tier 1 screening assessment. We then adjust the previously-developed
Tier 1 screening threshold emission rates for each PB-HAP for each
facility based on an understanding of how exposure concentrations
estimated for the screening scenario change with the use of local
meteorology and USGS waterbody data. If the PB-HAP emission rates for a
facility exceed the Tier 2 screening threshold emission rates and data
are available, we may conduct a Tier 3 screening assessment. If PB-HAP
emission rates do not exceed a Tier 2 screening value of 1, we consider
those PB-HAP emissions to pose risks below a level of concern.
    There are several analyses that can be included in a Tier 3
screening assessment, depending upon the extent of refinement
warranted, including validating that the lakes are fishable,
considering plume-rise to estimate emissions lost above the mixing
layer, and considering hourly effects of meteorology and plume rise on
chemical fate and transport. If the Tier 3 screening assessment
indicates that risks above levels of concern cannot be ruled out, the
EPA may further refine the screening assessment through a site-specific
assessment.
    In evaluating the potential multipathway risk from emissions of
lead compounds, rather than developing a screening threshold emission
rate, we compare maximum estimated chronic inhalation exposure
concentrations to the level of the current NAAQS for lead.\42\ Values
below the level of the primary (health-based) lead NAAQS are considered
to have a low potential for multipathway risk.
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    \42\ In doing so, the EPA notes that the legal standard for a
primary NAAQS--that a standard is requisite to protect public health
and provide an adequate margin of safety (CAA section 109(b))--
differs from the CAA section 112(f) standard (requiring, among other
things, that the standard provide an ``ample margin of safety to
protect public health''). However, the primary lead NAAQS is a
reasonable measure of determining risk acceptability (i.e., the
first step of the Benzene NESHAP analysis) since it is designed to
protect the most susceptible group in the human population--
children, including children living near major lead emitting
sources. 73 FR 67002/3; 73 FR 67000/3; 73 FR 67005/1. In addition,
applying the level of the primary lead NAAQS at the risk
acceptability step is conservative, since that primary lead NAAQS
reflects an adequate margin of safety.
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    For further information on the multipathway assessment approach,
see the risk document, which is available in the docket for this
action.
5. How do we conduct the environmental risk screening assessment?
a. Adverse Environmental Effect, Environmental HAP, and Ecological
Benchmarks
    The EPA conducts a screening assessment to examine the potential
for an adverse environmental effect as required under section
112(f)(2)(A) of the CAA. Section 112(a)(7) of the CAA defines ``adverse
environmental effect'' as ``any significant and widespread adverse
effect, which may reasonably be anticipated, to wildlife, aquatic life,
or other natural resources, including adverse impacts on populations of
endangered or threatened species or significant degradation of
environmental quality over broad areas.''
    The EPA focuses on eight HAP, which are referred to as
``environmental HAP,'' in its screening assessment: six PB-HAP and two
acid gases. The PB-HAP included in the screening assessment are arsenic
compounds, cadmium compounds, dioxins/furans, POM, Hg (both inorganic
Hg and methyl Hg), and lead compounds. The acid gases included in the
screening assessment are HCl and HF.
    HAP that persist and bioaccumulate are of particular environmental
concern because they accumulate in the soil, sediment, and water. The
acid gases, HCl and HF, are included due to their well-documented
potential to cause direct damage to terrestrial plants. In the
environmental risk screening assessment, we evaluate the following four
exposure media: terrestrial soils, surface water bodies (includes
water-column and benthic sediments), fish consumed by wildlife, and
air. Within these four exposure media, we evaluate nine ecological
assessment endpoints, which are defined by the ecological entity and
its attributes. For PB-HAP (other than lead), both community-level and
population-level endpoints are included. For acid gases, the ecological
assessment evaluated is terrestrial plant communities.
    An ecological benchmark represents a concentration of HAP that has
been linked to a particular environmental effect level. For each
environmental HAP, we identified the available ecological benchmarks
for each assessment endpoint. We identified, where possible, ecological
benchmarks at the following effect levels: probable effect levels,
lowest-observed-adverse-effect level, and no-observed-adverse-effect
level. In cases where multiple effect levels were available for a
particular PB-HAP and assessment endpoint, we use all of the available
effect levels to help us to determine whether ecological risks exist
and, if so, whether the risks could be considered significant and
widespread.
    For further information on how the environmental risk screening
assessment was conducted, including a discussion of the risk metrics
used, how the environmental HAP were identified, and how the ecological
benchmarks were selected, see Appendix 9 of the risk document, which is
available in the docket for this action.
b. Environmental Risk Screening Methodology
    For the environmental risk screening assessment, the EPA first
determined whether any facilities in the Coal- and Oil-Fired EGU source
category emitted any of the environmental HAP. For the Coal- and Oil-
Fired EGU source category, we identified emissions of lead compounds,
arsenic compounds, Hg compounds, cadmium compounds, POM, dioxins, HCl,
and HF. Because one or more of the environmental HAP evaluated are
emitted by at least one facility in the source category, we proceeded
to the second step of the evaluation.
c. PB-HAP Methodology
    The environmental screening assessment includes six PB-HAP, arsenic
compounds, cadmium compounds, dioxins/furans, POM, Hg (both inorganic
Hg and methyl Hg), and lead compounds. With the exception of

lead, the environmental risk screening assessment for PB-HAP consists
of three tiers. The first tier of the environmental risk screening
assessment uses the same health-protective conceptual model that is
used for the Tier 1 human health screening assessment. TRIM.FaTE model
simulations were used to back-calculate Tier 1 screening threshold
emission rates. The screening threshold emission rates represent the
emission rate in tpy that results in media concentrations at the
facility that equal the relevant ecological benchmark. To assess
emissions from each facility in the category, the reported emission
rate for each PB-HAP was compared to the Tier 1 screening threshold
emission rate for that PB-HAP for each assessment endpoint and effect
level. If emissions from a facility do not exceed the Tier 1 screening
threshold emission rate, the facility ``passes'' the screening
assessment, and, therefore, is not evaluated further under the
screening approach. If emissions from a facility exceed the Tier 1
screening threshold emission rate, we evaluate the facility further in
Tier 2.
    In Tier 2 of the environmental screening assessment, the screening
threshold emission rates are adjusted to account for local meteorology
and the actual location of lakes in the vicinity of facilities that did
not pass the Tier 1 screening assessment. For soils, we evaluate the
average soil concentration for all soil parcels within a 7.5-km radius
for each facility and PB-HAP. For the water, sediment, and fish tissue
concentrations, the highest value for each facility for each pollutant
is used. If emission concentrations from a facility do not exceed the
Tier 2 screening threshold emission rate, the facility ``passes'' the
screening assessment and typically is not evaluated further. If
emissions from a facility exceed the Tier 2 screening threshold
emission rate, we evaluate the facility further in Tier 3.
    As in the multipathway human health risk assessment, in Tier 3 of
the environmental screening assessment, we examine the suitability of
the lakes around the facilities to support life and remove those that
are not suitable (e.g., lakes that have been filled in or are
industrial ponds), adjust emissions for plume-rise, and conduct hour-
by-hour time-series assessments. If these Tier 3 adjustments to the
screening threshold emission rates still indicate the potential for an
adverse environmental effect (i.e., facility emission rate exceeds the
screening threshold emission rate), we may elect to conduct a more
refined assessment using more site-specific information. If, after
additional refinement, the facility emission rate still exceeds the
screening threshold emission rate, the facility may have the potential
to cause an adverse environmental effect.
    To evaluate the potential for an adverse environmental effect from
lead, we compared the average modeled air concentrations (from HEM-3)
of lead around each facility in the source category to the level of the
secondary NAAQS for lead. The secondary lead NAAQS is a reasonable
means of evaluating environmental risk because it is set to provide
substantial protection against adverse welfare effects which can
include ``effects on soils, water, crops, vegetation, man-made
materials, animals, wildlife, weather, visibility and climate, damage
to and deterioration of property, and hazards to transportation, as
well as effects on economic values and on personal comfort and well-
being.''
d. Acid Gas Environmental Risk Methodology
    The environmental screening assessment for acid gases evaluates the
potential phytotoxicity and reduced productivity of plants due to
chronic exposure to HF and HCl. The environmental risk screening
methodology for acid gases is a single-tier screening assessment that
compares modeled ambient air concentrations (from AERMOD) to the
ecological benchmarks for each acid gas. To identify a potential
adverse environmental effect (as defined in section 112(a)(7) of the
CAA) from emissions of HF and HCl, we evaluate the following metrics:
The size of the modeled area around each facility that exceeds the
ecological benchmark for each acid gas, in acres and km\2\; the
percentage of the modeled area around each facility that exceeds the
ecological benchmark for each acid gas; and the area-weighted average
screening value around each facility (calculated by dividing the area-
weighted average concentration over the 50-km modeling domain by the
ecological benchmark for each acid gas). For further information on the
environmental screening assessment approach, see Appendix 9 of the risk
document, which is available in the docket for this action.
6. How do we conduct facility-wide assessments?
    To put the source category risks in context, we typically examine
the risks from the entire ``facility,'' where the facility includes all
HAP-emitting operations within a contiguous area and under common
control. In other words, we examine the HAP emissions not only from the
source category emission points of interest, but also emissions of HAP
from all other emission sources at the facility for which we have data.
For this source category, we conducted the facility-wide assessment
using a dataset compiled from the 2014 NEI. The source category records
of that NEI dataset were removed, evaluated, and updated as described
in section IV.D of this preamble: What other relevant background
information and data are available? Once a quality assured source
category dataset was available, it was placed back with the remaining
records from the NEI for that facility. The facility-wide file was then
used to analyze risks due to the inhalation of HAP that are emitted
``facility-wide'' for the populations residing within 50 km of each
facility, consistent with the methods used for the source category
analysis described above. For these facility-wide risk analyses, the
modeled source category risks were compared to the facility-wide risks
to determine the portion of the facility-wide risks that could be
attributed to the source category addressed in this proposal. We also
specifically examined the facility that was associated with the highest
estimate of risk and determined the percentage of that risk
attributable to the source category of interest. The risk document,
available through the docket for this action, provides the methodology
and results of the facility-wide analyses, including all facility-wide
risks and the percentage of source category contribution to facility-
wide risks.
7. How do we consider uncertainties in risk assessment?
    Uncertainty and the potential for bias are inherent in all risk
assessments, including those performed for this proposal. Although
uncertainty exists, we believe that our approach, which used
conservative tools and assumptions, ensures that our decisions are
health and environmentally protective. A brief discussion of the
uncertainties in the RTR emissions dataset, dispersion modeling,
inhalation exposure estimates, and dose-response relationships follows
below. Also included are those uncertainties specific to our acute
screening assessments, multipathway screening assessments, and our
environmental risk screening assessments. A more thorough discussion of
these uncertainties is included in the risk document, which is
available in the docket for this action. If a multipathway site-
specific assessment was performed for this source category, a full
discussion of the uncertainties associated with that assessment can be

found in Appendix 11 of that document, Site-Specific Human Health
Multipathway Residual Risk Assessment Report.
a. Uncertainties in the RTR Emissions Dataset
    Although the development of the RTR emissions dataset involved
quality assurance/quality control processes, the accuracy of emissions
values will vary depending on the source of the data, the degree to
which data are incomplete or missing, the degree to which assumptions
made to complete the datasets are accurate, errors in emission
estimates, and other factors. The emission estimates considered in this
analysis generally are annual totals for certain years, and they do not
reflect short-term fluctuations during the course of a year or
variations from year to year. The estimates of peak hourly emission
rates for the acute effects screening assessment were based on an
emission adjustment factor applied to the average annual hourly
emission rates, which are intended to account for emission fluctuations
due to normal facility operations.
b. Uncertainties in Dispersion Modeling
    We recognize there is uncertainty in ambient concentration
estimates associated with any model, including the EPA's recommended
regulatory dispersion model, AERMOD. In using a model to estimate
ambient pollutant concentrations, the user chooses certain options to
apply. For RTR assessments, we select some model options that have the
potential to overestimate ambient air concentrations (e.g., not
including plume depletion or pollutant transformation). We select other
model options that have the potential to underestimate ambient impacts
(e.g., not including building downwash). Other options that we select
have the potential to either under- or overestimate ambient levels
(e.g., meteorology and receptor locations). On balance, considering the
directional nature of the uncertainties commonly present in ambient
concentrations estimated by dispersion models, the approach we apply in
the RTR assessments should yield unbiased estimates of ambient HAP
concentrations. We also note that the selection of meteorology dataset
location could have an impact on the risk estimates. As we continue to
update and expand our library of meteorological station data used in
our risk assessments, we expect to reduce this variability.
c. Uncertainties in Inhalation Exposure Assessment
    Although every effort is made to identify all of the relevant
facilities and emission points, as well as to develop accurate
estimates of the annual emission rates for all relevant HAP, the
uncertainties in our emission inventory likely dominate the
uncertainties in the exposure assessment. Some uncertainties in our
exposure assessment include human mobility, using the centroid of each
census block, assuming lifetime exposure, and assuming only outdoor
exposures. For most of these factors, there is neither an underestimate
nor overestimate when looking at the maximum individual risk or the
incidence, but the shape of the distribution of risks may be affected.
With respect to outdoor exposures, actual exposures may not be as high
if people spend time indoors, especially for very reactive pollutants
or larger particles. For all factors, we reduce uncertainty when
possible. For example, with respect to census-block centroids, we
analyze large blocks using aerial imagery and adjust locations of the
block centroids to better represent the population in the blocks. We
also add additional receptor locations where the population of a block
is not well represented by a single location.
d. Uncertainties in Dose-Response Relationships
    There are uncertainties inherent in the development of the dose-
response values used in our risk assessments for cancer effects from
chronic exposures and noncancer effects from both chronic and acute
exposures. Some uncertainties are generally expressed quantitatively,
and others are generally expressed in qualitative terms. We note, as a
preface to this discussion, a point on dose-response uncertainty that
is stated in the EPA's 2005 Guidelines for Carcinogen Risk Assessment;
namely, that ``the primary goal of EPA actions is protection of human
health; accordingly, as an Agency policy, risk assessment procedures,
including default options that are used in the absence of scientific
data to the contrary, should be health protective'' (the EPA's 2005
Guidelines for Carcinogen Risk Assessment, page 1-7). This is the
approach followed here as summarized in the next paragraphs.
    Cancer UREs used in our risk assessments are those that have been
developed to generally provide an upper bound estimate of risk.\43\
That is, they represent a ``plausible upper limit to the true value of
a quantity'' (although this is usually not a true statistical
confidence limit). In some circumstances, the true risk could be as low
as zero; however, in other circumstances the risk could be greater.\44\
Chronic noncancer RfC and reference dose (RfD) values represent chronic
exposure levels that are intended to be health-protective levels. To
derive dose-response values that are intended to be ``without
appreciable risk,'' the methodology relies upon an uncertainty factor
(UF) approach,\45\ which considers uncertainty, variability, and gaps
in the available data. The UFs are applied to derive dose-response
values that are intended to protect against appreciable risk of
deleterious effects.
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    \43\ IRIS glossary (https://ofmpub.epa.gov/sor_internet/registry/termreg/searchandretrieve/glossariesandkeywordlists/search.do?details=&glossaryName=IRIS%20Glossary).
    \44\ An exception to this is the URE for benzene, which is
considered to cover a range of values, each end of which is
considered to be equally plausible, and which is based on maximum
likelihood estimates.
    \45\ See A Review of the Reference Dose and Reference
Concentration Processes, U.S. EPA, December 2002, and Methods for
Derivation of Inhalation Reference Concentrations and Application of
Inhalation Dosimetry, U.S. EPA, 1994.
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    Many of the UFs used to account for variability and uncertainty in
the development of acute dose-response values are quite similar to
those developed for chronic durations. Additional adjustments are often
applied to account for uncertainty in extrapolation from observations
at one exposure duration (e.g., 4 hours) to derive an acute dose-
response value at another exposure duration (e.g., 1 hour). Not all
acute dose-response values are developed for the same purpose, and care
must be taken when interpreting the results of an acute assessment of
human health effects relative to the dose-response value or values
being exceeded. Where relevant to the estimated exposures, the lack of
acute dose-response values at different levels of severity should be
factored into the risk characterization as potential uncertainties.
    Uncertainty also exists in the selection of ecological benchmarks
for the environmental risk screening assessment. We established a
hierarchy of preferred benchmark sources to allow selection of
benchmarks for each environmental HAP at each ecological assessment
endpoint. We searched for benchmarks for three effect levels (i.e., no-
effects level, threshold-effect level, and probable effect level), but
not all combinations of ecological assessment/environmental HAP had
benchmarks for

all three effect levels. Where multiple effect levels were available
for a particular HAP and assessment endpoint, we used all of the
available effect levels to help us determine whether risk exists and
whether the risk could be considered significant and widespread.
    Although we make every effort to identify appropriate human health
effect dose-response values for all pollutants emitted by the sources
in this risk assessment, some HAP emitted by this source category are
lacking dose-response assessments. Accordingly, these pollutants cannot
be included in the quantitative risk assessment, which could result in
quantitative estimates understating HAP risk. To help to alleviate this
potential underestimate, where we conclude similarity with a HAP for
which a dose-response value is available, we use that value as a
surrogate for the assessment of the HAP for which no value is
available. To the extent use of surrogates indicates appreciable risk,
we may identify a need to increase priority for an IRIS assessment for
that substance. We additionally note that, generally speaking, HAP of
greatest concern due to environmental exposures and hazard are those
for which dose-response assessments have been performed, reducing the
likelihood of understating risk. Further, HAP not included in the
quantitative assessment are assessed qualitatively and considered in
the risk characterization that informs the risk management decisions,
including consideration of HAP reductions achieved by various control
options.
    For a group of compounds that are unspeciated (e.g., glycol
ethers), we conservatively use the most protective dose-response value
of an individual compound in that group to estimate risk. Similarly,
for an individual compound in a group (e.g., ethylene glycol diethyl
ether) that does not have a specified dose-response value, we also
apply the most protective dose-response value from the other compounds
in the group to estimate risk.
e. Uncertainties in Acute Inhalation Screening Assessments
    In addition to the uncertainties highlighted above, there are
several factors specific to the acute exposure assessment that the EPA
conducts as part of the risk review under section 112 of the CAA. The
accuracy of an acute inhalation exposure assessment depends on the
simultaneous occurrence of independent factors that may vary greatly,
such as hourly emissions rates, meteorology, and the presence of humans
at the location of the maximum concentration. In the acute screening
assessment that we conduct under the RTR program, we assume that peak
emissions from the source category and worst-case meteorological
conditions co-occur, thus, resulting in maximum ambient concentrations.
These two events are unlikely to occur at the same time, making these
assumptions conservative. We then include the additional assumption
that a person is located at this point during this same time period.
For this source category, these assumptions would tend to be worst-case
actual exposures, as it is unlikely that a person would be located at
the point of maximum exposure during the time when peak emissions and
worst-case meteorological conditions occur simultaneously.
f. Uncertainties in the Multipathway and Environmental Risk Screening
Assessments
    For each source category, we generally rely on site-specific levels
of PB-HAP or environmental HAP emissions to determine whether a refined
assessment of the impacts from multipathway exposures is necessary or
whether it is necessary to perform an environmental screening
assessment. This determination is based on the results of a three-
tiered screening assessment that relies on the outputs from models--
TRIM.FaTE and AERMOD--that estimate environmental pollutant
concentrations and human exposures for five PB-HAP (dioxins, POM, Hg,
cadmium, and arsenic) and two acid gases (HF and HCl). For lead, we use
AERMOD to determine ambient air concentrations, which are then compared
to the secondary NAAQS standard for lead. Two important types of
uncertainty associated with the use of these models in RTR risk
assessments and inherent to any assessment that relies on environmental
modeling are model uncertainty and input uncertainty.\46\
---------------------------------------------------------------------------

    \46\ In the context of this discussion, the term ``uncertainty''
as it pertains to exposure and risk encompasses both variability in
the range of expected inputs and screening results due to existing
spatial, temporal, and other factors, as well as uncertainty in
being able to accurately estimate the true result.
---------------------------------------------------------------------------

    Model uncertainty concerns whether the model adequately represents
the actual processes (e.g., movement and accumulation) that might occur
in the environment. For example, does the model adequately describe the
movement of a pollutant through the soil? This type of uncertainty is
difficult to quantify. However, based on feedback received from
previous EPA SAB reviews and other reviews, we are confident that the
models used in the screening assessments are appropriate and state-of-
the-art for the multipathway and environmental screening risk
assessments conducted in support of RTR.
    Input uncertainty is concerned with how accurately the models have
been configured and parameterized for the assessment at hand. For Tier
1 of the multipathway and environmental screening assessments, we
configured the models to avoid underestimating exposure and risk. This
was accomplished by selecting upper-end values from nationally
representative datasets for the more influential parameters in the
environmental model, including selection and spatial configuration of
the area of interest, lake location and size, meteorology, surface
water, soil characteristics, and structure of the aquatic food web. We
also assume an ingestion exposure scenario and values for human
exposure factors that represent reasonable maximum exposures.
    In Tier 2 of the multipathway and environmental screening
assessments, we refine the model inputs to account for meteorological
patterns in the vicinity of the facility versus using upper-end
national values, and we identify the actual location of lakes near the
facility rather than the default lake location that we apply in Tier 1.
By refining the screening approach in Tier 2 to account for local
geographical and meteorological data, we decrease the likelihood that
concentrations in environmental media are overestimated, thereby
increasing the usefulness of the screening assessment. In Tier 3 of the
screening assessments, we refine the model inputs again to account for
hour-by-hour plume rise and the height of the mixing layer. We can also
use those hour-by-hour meteorological data in a TRIM.FaTE run using the
screening configuration corresponding to the lake location. These
refinements produce a more accurate estimate of chemical concentrations
in the media of interest, thereby reducing the uncertainty with those
estimates. The assumptions and the associated uncertainties regarding
the selected ingestion exposure scenario are the same for all three
tiers.
    For the environmental screening assessment for acid gases, we
employ a single-tiered approach. We use the modeled air concentrations
and compare those with ecological benchmarks.
    For all tiers of the multipathway and environmental screening
assessments, our approach to addressing model input uncertainty is
generally cautious. We

choose model inputs from the upper end of the range of possible values
for the influential parameters used in the models, and we assume that
the exposed individual exhibits ingestion behavior that would lead to a
high total exposure. This approach reduces the likelihood of not
identifying high risks for adverse impacts.
    Despite the uncertainties, when individual pollutants or facilities
do not exceed screening threshold emission rates (i.e., screen out), we
are confident that the potential for adverse multipathway impacts on
human health is very low. On the other hand, when individual pollutants
or facilities do exceed screening threshold emission rates, it does not
mean that impacts are significant, only that we cannot rule out that
possibility and that a refined assessment for the site might be
necessary to obtain a more accurate risk characterization for the
source category.
    The EPA evaluates the following HAP in the multipathway and/or
environmental risk screening assessments, where applicable: Arsenic,
cadmium, dioxins/furans, lead, Hg (both inorganic and methyl Hg), POM,
HCl, and HF. These HAP represent pollutants that can cause adverse
impacts either through direct exposure to HAP in the air or through
exposure to HAP that are deposited from the air onto soils and surface
waters and then through the environment into the food web. These HAP
represent those HAP for which we can conduct a meaningful multipathway
or environmental screening risk assessment. For other HAP not included
in our screening assessments, the model has not been parameterized such
that it can be used for that purpose. In some cases, depending on the
HAP, we may not have appropriate multipathway models that allow us to
predict the concentration of that pollutant. The EPA acknowledges that
other HAP beyond these that we are evaluating may have the potential to
cause adverse effects and, therefore, the EPA may evaluate other
relevant HAP in the future, as modeling science and resources allow.

VI. RTR Analytical Results and Proposed Decisions

A. What are the results of the risk assessment and analyses?

1. Inhalation Risk Assessment Results
    Table 5 of this preamble provides a summary of the results of the
inhalation risk assessment for the source category. More detailed
information on the risk assessment can be found in the risk document,
available in the docket for this action.

                                           Table 5--Coal- and Oil-Fired EGU Inhalation Risk Assessment Results
--------------------------------------------------------------------------------------------------------------------------------------------------------
                     Maximum individual cancer    Population at increased     Annual cancer incidence    Maximum chronic noncancer    Maximum screening
                      risk (in 1 million) \2\     risk of cancer >=1-in-1        (cases per year)                TOSHI \3\           acute noncancer HQ
                   ----------------------------           million          --------------------------------------------------------          \4\
                          Based on . . .       ----------------------------       Based on . . .              Based on . . .       ---------------------
     Number of     ----------------------------       Based on . . .       --------------------------------------------------------
  facilities \1\                               ----------------------------
                       Actual       Allowable      Actual       Allowable      Actual       Allowable      Actual       Allowable      Based on actual
                      emissions     emissions     emissions     emissions     emissions     emissions     emissions     emissions      emissions level
                      level \2\       level       level \2\       level       level \2\       level         level         level
--------------------------------------------------------------------------------------------------------------------------------------------------------
322...............            9            10       193,000       636,000          0.04           0.1           0.2           0.4   HQREL = 0.09
                                                                                (arsenic).
--------------------------------------------------------------------------------------------------------------------------------------------------------
\1\ Number of facilities evaluated in the risk analysis.
\2\ Maximum individual excess lifetime cancer risk due to HAP emissions from the source category.
\3\ Maximum TOSHI. The target organ systems with the highest TOSHI for the source category are neurological and reproductive.
\4\ The maximum estimated acute exposure concentration was divided by available short-term threshold values to develop an array of HQ values. HQ values
  shown use the lowest available acute threshold value, which in most cases is the REL. When an HQ exceeds 1, we also show the HQ using the next lowest
  available acute dose-response value.

    As shown in Table 5 of this preamble, based on actual emissions,
the estimated cancer MIR is 9-in-1 million, and nickel emissions from
oil-fired EGUs are the major contributor to the risk. The total
estimated cancer incidence from this source category is 0.04 excess
cancer cases per year, or one excess case in every 25 years.
Approximately 193,000 people are estimated to have cancer risks at or
above 1-in-1 million from HAP emitted from the facilities in this
source category. The estimated maximum chronic noncancer TOSHI for the
source category is 0.2 (respiratory), which is driven by emissions of
nickel and cobalt from oil-fired EGUs. No one is exposed to TOSHI
levels above 1.
    Based on allowable emissions, the estimated cancer MIR is 10-in-1
million, and, as before, nickel emissions from oil-fired EGUs are the
major contributor to the risk. The total estimated cancer incidence
from this source category is 0.1 excess cancer cases per year, or one
excess case in every 10 years. Approximately 636,000 people are
estimated to have cancer risks at or above 1-in-1 million from HAP
emitted from the facilities in this source category. The estimated
maximum chronic noncancer TOSHI for the source category is 0.4
(respiratory), driven by emissions of nickel and cobalt from oil-fired
EGUs. No one is exposed to TOSHI levels above 1.
2. Acute Risk Results
    Table 5 of this preamble provides the worst-case acute HQ (based on
the REL) of 0.09, driven by actual emissions of arsenic. There are no
facilities that have acute HQs (based on the REL or any other reference
values) greater than 1. For more detailed acute risk results, refer to
the risk document.
3. Multipathway Risk Screening Results
    Potential multipathway health risks under a fisher and gardener
scenario were identified using a three-tier screening assessment of the
PB-HAP emitted by facilities in this source category, and a site-
specific assessment of Hg using TRIM.FaTE for one location. Of the 322
MATS facilities modeled, 307 facilities have reported emissions of
carcinogenic PB-HAP (arsenic, dioxins, and POM) that exceed a Tier 1
cancer screening value of 1, and 235 facilities have reported emissions
of non-carcinogenic PB-HAP (lead, Hg, and cadmium) that exceed a Tier 1
noncancer screening value of 1. For facilities that exceeded a Tier 1
multipathway screening value of 1, we used additional facility site-
specific information to perform an assessment through Tiers 2 and 3, as
necessary, to determine the maximum chronic cancer and noncancer
impacts for the source category. For cancer, the highest Tier 2
screening value was 200. This screening value was reduced to 50 after
the plume rise stage of Tier 3. Because this screening value was much
lower than 100-in-1 million, and because we expect the actual risk to
be lower than the screening value (site-specific assessments typically
lower estimates by an order of magnitude), we did not perform further
assessment for cancer. For noncancer, the highest Tier 2 screening
value was 30 (for Hg), with

four facilities having screening values greater than 20. These
screening values were reduced to 9 or lower after the plume rise stage
of Tier 3.
    An exceedance of a screening value in any of the tiers cannot be
equated with a risk value or an HQ (or HI). Rather, it represents a
high-end estimate of what the risk or hazard may be. For example, a
screening value of 2 for a non-carcinogen can be interpreted to mean
that we are confident that the HQ would be lower than 2. Similarly, a
screening value of 30 for a carcinogen means that we are confident that
the risk is lower than 30-in-1 million. Our confidence comes from the
conservative, or health-protective, assumptions encompassed in the
screening tiers: We choose inputs from the upper end of the range of
possible values for the influential parameters used in the screening
tiers; and we assume that the exposed individual exhibits ingestion
behavior that would lead to a high total exposure.
    In evaluating the potential for multipathway effects from emissions
of lead, we compared modeled maximum annual lead concentrations to the
secondary NAAQS for lead (0.15 [micro]g/m\3\). The modeled maximum
annual lead concentration is below the NAAQS for lead, indicating a low
potential for multipathway impacts of concern due to lead.
4. Multipathway Site-Specific Assessment Results
    Because the final stage of Tier 3 (time-series) was unlikely to
reduce the highest Hg screening values to 1, we conducted a site-
specific multipathway assessment of Hg emissions for this source
category. Analysis of the facilities with the highest Tier 2 and Tier 3
screening values helped identify the location for the site-specific
assessment and the facilities to model with TRIM.FaTE. We also
considered the effect multiple facilities within the source category
could have on common lakes in the modeling domain. The selection of the
facilities for the site-specific assessment also included evaluating
the number and location of lakes impacted, watershed boundaries, and
land-use features around the target lakes, (i.e., elevation changes,
topography, rivers).
    The three facilities selected are located near Underwood, North
Dakota. All three facilities had Tier 2 screening values greater than
or equal to 20. Two of the facilities are near each other (16 km
apart). The third facility is more distant, about 20 to 30 km from the
other facilities, but it was included in the analysis because it is
within the 50-km modeling domain of the other facilities and because it
had an elevated Tier 2 screening value. We expect that the exposure
scenarios we assessed for these facilities are among the highest, if
not the highest, that might be encountered for other facilities in this
source category. The refined site-specific multipathway assessment, as
in the screening assessments, includes some hypothetical elements,
namely the hypothetical human receptor (e.g., the fisher scenario which
did not screen out in the screening assessments). It is important to
note that although the multipathway assessment has been conducted, no
data exist to verify the existence of the hypothetical human receptor.
The refined multipathway assessment produced an HQ of 0.06 for Hg for
the three facilities assessed. This risk assessment likely represents
the maximum hazard for Hg through fish consumption for the source
category and, with an HQ less than 1, is below the level of concern for
exposure to emissions from these sources.
5. Environmental Risk Screening Results
    As described in section V.C of this preamble, we conducted an
environmental risk screening assessment for the Coal- and Oil-Fired EGU
source category for the following pollutants: Arsenic, cadmium,
dioxins/furans, HCl, HF, lead, Hg (methyl Hg and mercuric chloride),
and POMs.
    In the Tier 1 screening analysis for PB-HAP (other than lead, which
was evaluated differently), POM emissions had no exceedances of any of
the ecological benchmarks evaluated. Arsenic and dioxins/furans
emissions had Tier 1 exceedances for surface soil benchmarks. Cadmium
and methyl Hg emissions had Tier 1 exceedances for surface soil and
fish benchmarks. Divalent Hg emissions had Tier 1 exceedances for
sediment and surface soil benchmarks.
    A Tier 2 screening analysis was performed for arsenic, cadmium,
dioxins/furans, divalent Hg, and methyl Hg emissions. In the Tier 2
screening analysis, arsenic, cadmium, and dioxins/furans emissions had
no exceedances of any of the ecological benchmarks evaluated. Divalent
Hg emissions from two facilities exceeded the Tier 2 screen for a
sediment threshold level benchmark by a maximum screening value of 2 at
lake #35731. Methyl Hg emissions from the same two facilities exceeded
the Tier 2 screen for a fish (avian/piscivores) no-observed-adverse-
effect-level (NOAEL) (merganser) benchmark by a maximum screening value
of 2 at the same lake (lake #35731). A Tier 3 screening assessment was
performed to verify the existence of lake #35731. Lake #35731 was found
to be located on-site and is a man-made industrial pond, and,
therefore, was removed from the assessment.
    Methyl Hg emissions from two facilities exceeded the Tier 2 screen
for a surface soil NOAEL for avian ground insectivores (woodcock)
benchmark by a maximum screening value of 2. Other surface soil
benchmarks for methyl Hg, such as the NOAEL for mammalian insectivores
and the threshold level for the invertebrate community, were not
exceeded. Given the low Tier 2 maximum screening value of 2 for methyl
Hg, and the fact that only the most protective benchmark was exceeded,
a Tier 3 environmental risk screen was not conducted for methyl Hg.
    For lead, we did not estimate any exceedances of the secondary lead
NAAQS. For HCl and HF, the average modeled concentration around each
facility (i.e., the average concentration of all off-site data points
in the modeling domain) did not exceed any ecological benchmark. In
addition, each individual modeled concentration of HCl and HF (i.e.,
each off-site data point in the modeling domain) was below the
ecological benchmarks for all facilities.
    Based on the results of the environmental risk screening analysis,
we do not expect an adverse environmental effect as a result of HAP
emissions from this source category.
6. Facility-Wide Risk Results
    Based on facility-wide emissions, the estimated cancer MIR is 9-in-
1 million, and nickel emissions from oil-fired EGUs are the major
contributor to the risk. The total estimated cancer incidence from this
source category is 0.04 excess cancer cases per year, or one excess
case in every 25 years. Approximately 203,000 people are estimated to
have cancer risks at or above 1-in-1 million from HAP emitted from the
facilities in this source category. The estimated maximum chronic
noncancer TOSHI for the source category is 0.2 (respiratory), driven by
emissions of nickel and cobalt from oil-fired EGUs. No one is exposed
to TOSHI levels above 1. These results are very similar to those based
on actual emissions from the source category because there is not
significant collocation of other sources with EGUs.
7. What demographic groups might benefit from this regulation?
    To examine the potential for any environmental justice issues that
might be associated with the source category, we performed a
demographic analysis,

which is an assessment of risk to individual demographic groups of the
populations living within 5 km and within 50 km of the facilities. In
the analysis, we evaluated the distribution of HAP-related cancer and
noncancer risk from the Coal- and Oil-Fired EGU source category across
different demographic groups within the populations living near
facilities.\47\
---------------------------------------------------------------------------

    \47\ Demographic groups included in the analysis are: White,
African American, Native American, other races and multiracial,
Hispanic or Latino, children 17 years of age and under, adults 18 to
64 years of age, adults 65 years of age and over, adults without a
high school diploma, people living below the poverty level, people
living two times the poverty level, and linguistically isolated
people.
---------------------------------------------------------------------------

    The results of the demographic analysis are summarized in Table 6
below. These results, for various demographic groups, are based on the
estimated risk from actual emissions levels for the population living
within 50 km of the facilities.

               Table 6--Coal- and Oil-Fired EGU Source Category Demographic Risk Analysis Results
----------------------------------------------------------------------------------------------------------------
                                                                                Population
                                                                                with cancer
                                                                                risk greater     Population
                                                                                than or equal      with HI
                                                                                to 1-in-1    greater than 1
                                                                                million
----------------------------------------------------------------------------------------------------------------
                                                                      Nationwide          Source Category
----------------------------------------------------------------------------------------------------------------
Total Population................................................     317,746,049         193,000               0
----------------------------------------------------------------------------------------------------------------
                                                                           White and Minority by Percent
----------------------------------------------------------------------------------------------------------------
White...........................................................              62               1               0
Minority........................................................              38            * 99               0
----------------------------------------------------------------------------------------------------------------
                                                                                Minority by Percent
----------------------------------------------------------------------------------------------------------------
African American................................................              12               0               0
Native American.................................................             0.8               0               0
Hispanic or Latino (includes white and nonwhite)................              18            * 99               0
Other and Multiracial...........................................               7               0               0
----------------------------------------------------------------------------------------------------------------
                                                                                Income by Percent
----------------------------------------------------------------------------------------------------------------
Below Poverty Level.............................................              14              40               0
Above Poverty Level.............................................              86              60               0
----------------------------------------------------------------------------------------------------------------
                                                                               Education by Percent
----------------------------------------------------------------------------------------------------------------
Over 25 and without a High School Diploma.......................              14              25               0
Over 25 and with a High School Diploma..........................              86              75               0
----------------------------------------------------------------------------------------------------------------
                                                                        Linguistically Isolated by Percent
----------------------------------------------------------------------------------------------------------------
Linguistically Isolated.........................................               6            * 67               0
----------------------------------------------------------------------------------------------------------------
* Note: All the people with a cancer risk greater than or equal to 1 in 1 million reside in Puerto Rico.

    The results of the Coal- and Oil-Fired EGU source category
demographic analysis indicate that emissions from the source category
expose approximately 193,000 people to a cancer risk at or above 1-in-1
million and no people to a chronic noncancer TOSHI greater than 1.
There are only 4 facilities in the source category with cancer risk at
or above 1-in-1 million, and all of them are located in Puerto Rico.
Consequently, all of the percentages of the at-risk population in each
demographic group associated with the Puerto Rican population are much
higher than their respective nationwide percentages, and those not
associated with Puerto Rico are much lower than their respective
nationwide percentages.
    The methodology and the results of the demographic analysis are
presented in a technical report, Risk and Technology Review--Analysis
of Demographic Factors for Populations Living Near Coal- and Oil-Fired
EGUs, available in the docket for this action.

B. What are our proposed decisions regarding risk acceptability, ample
margin of safety, and adverse environmental effect?

1. Risk Acceptability
    As noted in section V.A of this preamble, the EPA sets standards
under CAA section 112(f)(2) using ``a two-step standard-setting
approach, with an analytical first step to determine an `acceptable
risk' that considers all health information, including risk estimation
uncertainty, and includes a presumptive limit on MIR of approximately
1-in-10 thousand.'' (54 FR 38045, September 14, 1989). In this
proposal, the EPA estimated risks based on actual and allowable
emissions from coal- and oil-fired EGU sources, and we considered these
in determining acceptability.
    The estimated inhalation cancer risk to the individual most exposed
to actual emissions from the source category is 9-in-1 million. The
estimated incidence of cancer due to inhalation exposures is 0.04
excess cancer cases per year, or one excess case every 25 years.
Approximately 190,000 people face an increased cancer risk at or above
1-in-1 million due to inhalation exposure to HAP emissions from this
source category. The estimated maximum chronic noncancer TOSHI from

inhalation exposure for this source category is 0.2. Based on allowable
emissions, the estimated inhalation cancer risk to the individual most
exposed is 10-in-1 million, and the estimated incidence of cancer due
to inhalation exposures is 0.1 excess cancer cases per year, or one
excess case every 10 years. Approximately 640,000 people face an
increased cancer risk at or above 1-in-1 million due to inhalation
exposure to allowable HAP emissions from this source category. The
maximum chronic noncancer TOSHI from inhalation exposure is 0.4 based
on allowable emissions. The screening assessment of worst-case acute
inhalation impacts indicates that no facilities have actual emissions
that result in an acute HQ greater than 1 for any pollutant, with an
estimated worst-case maximum acute HQ of 0.09 for arsenic based on the
1-hour REL.
    Potential multipathway human health risks were estimated using a
three-tier screening assessment of the PB-HAP emitted by facilities in
this source category. The only pollutants with elevated screening
values are arsenic (cancer) and Hg (noncancer). The highest Tier 3
cancer screening value is 50, mostly driven by arsenic. The highest
Tier 3 noncancer screening value is 9, for Hg. We performed a site-
specific multipathway assessment which indicates that the highest Hg HQ
expected from any facility in the source category is much less than 1.
In evaluating the potential for multipathway effects from emissions of
lead from the source category, we compared modeled maximum annual lead
concentrations to the primary NAAQS for lead (0.15 [mu]g/m\3\). Results
of this analysis estimate that the NAAQS for lead would not be exceeded
at any off-site locations.
    In determining whether risks are acceptable for this source
category, the EPA considered all available health information and risk
estimation uncertainty as described above. The risk results indicate
that both the actual and allowable inhalation cancer risks to the
individual most exposed are well below 100-in-1 million, which is the
presumptive limit of acceptability. Also, the highest chronic noncancer
TOSHI, and the highest acute noncancer HQ, are well below 1, indicating
low likelihood of adverse noncancer effects from inhalation exposures.
There are also low risks associated with ingestion, with the highest
cancer risk being less than 50-in-1 million based on a conservative
screening assessment, and the highest noncancer hazard being less than
1 based on a site-specific multipathway assessment.
    Considering all of the health risk information and factors
discussed above, including the uncertainties discussed in section V of
this preamble, the EPA proposes that the risks are acceptable for this
source category.
2. Ample Margin of Safety Analysis
    As directed by CAA section 112(f)(2), we conducted an analysis to
determine if the current emissions standards provide an ample margin of
safety to protect public health. Under the ample margin of safety
analysis, the EPA considers all health factors evaluated in the risk
assessment and evaluates the cost and feasibility of available control
technologies and other measures (including the controls, measures, and
costs reviewed under the technology review) that could be applied to
this source category to further reduce the risks (or potential risks)
due to emissions of HAP identified in our risk assessment. In this
analysis, we considered the results of the technology review, risk
assessment, and other aspects of our MACT rule review to determine
whether there are any cost-effective controls or other measures that
would reduce emissions further to provide an ample margin of safety
with respect to the risks associated with these emissions.
    Our risk analysis indicated the risks from the source category are
low for both cancer and noncancer health effects, and, therefore, any
risk reductions from further available control options would result in
minimal health benefits. Moreover, as noted in our discussion of the
technology review in section VI.C of this preamble, no additional
measures were identified for reducing HAP emissions from affected
sources in the Coal- and Oil-Fired EGU source category. Thus, we are
proposing that the current MATS requirements provide an ample margin of
safety to protect public health.
3. Adverse Environmental Effects
    Based on the results of our environmental risk screening
assessment, we conclude that there is not an adverse environmental
effect from the Coal- and Oil-Fired EGU source category. We are
proposing that it is not necessary to set a more stringent standard to
prevent, taking into consideration costs, energy, safety, and other
relevant factors, an adverse environmental effect.
C. What are the results and proposed decisions based on our technology
review?
    As described in section V.B of this preamble, our technology review
focused on identifying developments in practices, processes, and
control technologies that have occurred since the MATS rule was
promulgated. Control technologies typically used to minimize emissions
of pollutants that have numeric emission limits under the MATS rule
include electrostatic precipitators and fabric filters for control of
PM and non-Hg HAP metals; wet scrubbers and dry scrubbers for control
of acid gases (SO2, HCl, and HF); and activated carbon
injection for control of Hg. The existing air pollution control
technologies that are currently in use are well-established and provide
the capture efficiencies necessary for compliance with the MATS
emission limits. Based on the effectiveness and proven reliability of
these control technologies, and the relatively short period of time
since the promulgation of the MATS rule, no developments in practices,
processes, or control technologies, nor any new technologies or
practices were identified for the control of non-Hg HAP metals, acid
gas HAP, or Hg. Organic HAP, including emissions of dioxins and furans,
are regulated by a work practice standard that requires periodic burner
tune-ups to ensure good combustion. This work practice continues to be
a practical approach to ensuring that combustion equipment is
maintained and optimized to run to reduce emissions of organic HAP, and
continues to be expected to be more effective than establishing a
numeric standard that cannot reliably be measured or monitored. Based
on the effectiveness and proven reliability of the work practice
standard, and the relatively short amount of time since the
promulgation of the MATS rule, no developments in work practices nor
any new work practices or operational procedures have been identified
for this source category regarding the additional control of organic
HAP. Consequently, we propose that no revisions to the MATS rule are
necessary pursuant to CAA section 112(d)(6). Additional details of our
technology review can be found in the memorandum, Technology Review for
the Coal- and Oil-fired EGU Source Category, which is available in the
docket for this action.

VII. Consideration of Separate Subcategory and Acid Gas Standard for
Existing EGUs That Fire Eastern Bituminous Coal Refuse

    The EPA is considering establishing a subcategory for emissions of
acid gas HAP from existing EGUs firing eastern bituminous coal refuse.
In this action, the EPA is soliciting comment on whether establishment
of such a subcategory is needed (Comment C-11)

and on the acid gas HAP emission standards that would be established if
we create this subcategory (Comment C-12).

A. Background

    In the MATS rule proposal, the EPA proposed a single acid gas
emission standard for all coal-fired power plants--using HCl as a
surrogate for all acidic gas HAP. See 76 FR 24976, May 3, 2011. The EPA
also proposed an alternative emission standard for SO2 as a
surrogate for the acid gas HAP. SO2 is also an acidic gas--
though not a HAP--and the controls used for SO2 emission
reduction are also effective for control of the acid gas HAP. Further,
most, if not all, affected EGUs were already measuring and reporting
SO2 emissions as a requirement of the Acid Rain Program.
    The Appalachian Region Independent Power Producers Association
(ARIPPA) \48\ submitted comments on the MATS proposal arguing that the
characteristics of coal refuse made achievement of the standard too
costly for its members and requested that the EPA create a subcategory
for facilities burning coal refuse. The EPA determined that there was
no basis to create such a subcategory and finalized emission standards
for both HCl and SO2 that apply to all coal-fired EGUs. See
77 FR 9304, February 16, 2012. ARIPPA, along with other petitioners,
challenged the EPA's determination in the D.C. Circuit, and the Court
upheld the final rule. White Stallion, 748 F.3d at 1249-50.
---------------------------------------------------------------------------

    \48\ ARIPPA is a non-profit trade association comprised of
independent electric power producers, environmental remediators, and
service providers located in Pennsylvania and West Virginia that use
coal refuse as a primary fuel to generate electricity.
---------------------------------------------------------------------------

    In addition to challenging the final rule, ARIPPA also petitioned
the Agency for reconsideration, again requesting a subcategory for the
acid gas standards for facilities combusting all types of coal refuse.
The EPA denied the petition for reconsideration on grounds that ARIPPA
had adequate opportunity to comment on the ability of coal refuse-
combusting facilities to comply with the final standard. Furthermore,
the EPA determined that the ARIPPA petition did not present any new
information to support a change in the previous determination regarding
the appropriateness of a subcategory for the acid gas HAP standard.
ARIPPA subsequently sought judicial review of the denial of the
petition for reconsideration. ARIPPA v. EPA, No. 15-1180 (D.C.
Cir.).\49\ In petitioner's briefs, ARIPPA claimed that the EPA had
misunderstood its reconsideration petition and pointed to a distinction
between the control of acid gas emissions from units burning anthracite
refuse and those burning bituminous coal refuse. See Industry Pets. Br.
at 35-36, ARIPPA, No. 15-1180 (D.C. Cir. filed Dec. 6, 2016). The EPA
disagrees with the assertion that the Agency misunderstood the basis
for ARIPPA's reconsideration petition as we could not find a single
statement in the rulemaking record that clearly or even vaguely
requested a separate acid gas HAP limit based on the distinction
between anthracite refuse and bituminous coal refuse. Nonetheless, the
Agency recognizes that there are differences in anthracite and
bituminous coal (and, thus, between anthracite refuse and bituminous
coal refuse) and that those differences can influence the acid gas HAP
emissions from EGUs firing those respective fuels. Those differences
may also impact the unit's ability to control those emissions.
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    \49\ ARIPPA's petition for review is currently being held in
abeyance. ARIPPA v. EPA, No. 15-1180, Order, No. 1672985 (April 27,
2017).
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B. Basis for Consideration of a Subcategory

1. Differences Between Anthracite Refuse and Eastern Bituminous Coal
Refuse
    Anthracite (or ``hard coal'') is the highest quality coal as it
contains more carbon and fewer impurities--including sulfur and
chlorine--than lower ranks of coal such as bituminous coal, sub-
bituminous coal, and lignite. Anthracite is rarely used in utility
power plants, but anthracite refuse is used by a small number of EGUs
located in Pennsylvania. Bituminous coal is a middle rank coal between
subbituminous coal and anthracite. Bituminous coal typically has a high
heating value and is commonly used in electricity generation in the
United States. Bituminous coal is mined in the Appalachian region
(northern Alabama through Pennsylvania), the Interior Region (primarily
Illinois basin), and the Western Region (a small amount of bituminous
coal mined primarily in Colorado and Utah). The bituminous coal in the
Interior Region tends to have the highest sulfur content, followed by
bituminous coals from the Appalachian Region. Coals (of all types)
mined in the Western Region tend to have the lowest sulfur and chlorine
content--and the highest content of free alkali (which can act as a
natural sorbent to neutralize acid gases produced in the combustion
process). The EPA is aware of currently operational coal-refuse EGUs
that are firing anthracite refuse (10 units), subbituminous coal refuse
(1 unit), western bituminous coal refuse (1 unit), and eastern
bituminous coal refuse (12 units).
    The existing eastern bituminous coal refuse-fired EGUs that are
currently in operation are listed below in Table 7 (excluding Seward,
as discussed later). The table also lists the units' net summer
capacity.

 Table 7--Eastern Bituminous Coal Refuse-Fired EGUs in Current Operation
                                    *
------------------------------------------------------------------------
    ORIS Plant code              Plant            State    Capacity (MW)
------------------------------------------------------------------------
10143.................  Colver Power Project..  PA                   110
10151.................  Grant Town Power Plant  WV                    40
                         Unit 1A.
10151.................  Grant Town Power Plant  WV                    40
                         Unit 1B.
10603.................  Ebensburg Power.......  PA                    50
10641.................  Cambria Cogen Unit 1..  PA                    44
10641.................  Cambria Cogen Unit 2..  PA                    44
10743.................  Morgantown Energy       WV                    25
                         Facility Unit 1.
10743.................  Morgantown Energy       WV                    25
                         Facility Unit 2.
50974.................  Scrubgrass Generating   PA                    42
                         Company LP Unit 1.
50974.................  Scrubgrass Generating   PA                    42
                         Company LP Unit 2.
------------------------------------------------------------------------
* Excluding the Seward units (as explained later).

2. Control Technologies for Acid Gas HAP
    All coal refuse fuels are fired in fluidized bed combustors (FBC)
that use limestone injection to minimize SO2 emissions and
to increase heat transfer efficiency. This limestone injection
technology may be adequate for EGUs that are firing anthracite refuse,
subbituminous, and western bituminous coal refuse to meet the MATS
alternative (surrogate) emission standard for SO2 because,
as previously mentioned, the anthracite coals are naturally much lower
in impurities (including sulfur and chlorine) and western bituminous
coals (and subbituminous coals) have lower sulfur and chlorine content
and higher free alkalinity. All anthracite coal refuse-fired and
western bituminous coal refuse-fired EGUs are currently emitting
SO2 at rates that are below the final MATS emission standard
for acid gas HAP and the subbituminous coal refuse-fired EGU is
currently emitting HCl at a rate that is below the final MATS emission
standard for acid gas HAP. Therefore, there is no need to consider a
subcategory that would include those units. No anthracite coal refuse-
fired or western bituminous coal refuse-fired EGUs are currently
reporting HCl emissions for compliance purposes; they are all opting
to, instead, report the alternative standard for SO2.
    However, ARIPPA has argued that, for the eastern bituminous coal
refuse-fired EGUs, limestone injection alone is not adequate to meet
the final HCl or SO2 MATS emission standards. Operators
cannot simply continue to inject more limestone to the combustor as
that could negatively affect the operation of the combustor with
limited impact on acid gas emissions.\50\ For this reason, bituminous
coal refuse-fired EGUs are required to install some sort of downstream
acid gas control technology in order to meet the final acid gas MATS
standards. These downstream control devices could include wet FGD
scrubbers, spray dryer absorbers (SDA), or dry sorbent injection (DSI)
systems.
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    \50\ ``[I]ncreased limestone injection consistent with current
design and operational constraints cannot further reduce HCl
emissions . . . to levels consistent with the Utility MACT limit.''
See ARIPPA Petition for Reconsideration, p. 5, See also p. 10,
Docket ID No. EPA-HQ-OAR-2009-0234-20175.
---------------------------------------------------------------------------

    Available information suggests that wet FGD scrubbers and SDA
systems would be particularly expensive retrofit control options for
the small units that are currently firing eastern bituminous coal
refuse. The cost effectiveness--i.e., the cost per incremental ton acid
gas HAP reduced--may be excessive and may be technically and
practically infeasible for these units. The EPA solicits comment on
whether these controls are particularly costly for these units to adopt
(Comment C-13).
    The eastern bituminous coal refuse-fired EGUs can also consider
installation of DSI technology, which is a less costly control option.
A DSI system is used to inject powdered alkaline sorbent (typically
sodium- or calcium-based sorbents) into the flue gas stream. The
alkaline sorbents neutralize acidic gases and the resulting solids are
captured in a downstream PM control device (e.g., a fabric filter). DSI
has been identified as a relatively low-cost technology for control of
acid gases. Some commenters to the original MATS proposal stated that
DSI will not work on units firing bituminous coals. Some commenters
stated that DSI is only suitable for use on low-sulfur, low-chlorine
western coals. In fact, in power sector modeling using the Integrated
Planning Model (IPM) to support the development of MATS, the EPA
restricted the availability of the DSI option to only those units that
use or switch to relatively low-sulfur coal (up to 2 lb/MMBtu
SO2).\51\ Some eastern bituminous coal refuse-fired EGUs
have tested DSI systems and have identified the following problems that
make the technology infeasible. The use of sodium-based sorbents
negatively impacts the usability, and, thus, saleability, of the
captured fly ash which can be utilized in many useful ways. One
beneficial use includes using fly ash in mine reclamation activities.
The increased sodium loading from the injection of sodium-based
sorbents can increase the leachability and mobility of metals from the
fly ash.\52\ Therefore, the saleability of the fly ash may be affected
by the use of DSI. When both calcium-based and sodium-based sorbents
were injected in testing, the emissions of Hg increased considerably
(well above the final MATS emission standard for Hg). This is due to
the alkaline sorbents scavenging free halides from the flue gas
stream--which effectively helps to control acid gas emissions. However,
the free halides are also helpful in oxidizing elemental Hg so that it
can be captured in a downstream PM control device. All coal refuse-
fired EGUs are emitting at levels that are below the final MATS
standard for Hg (and also with the standard for filterable PM). In
fact, FBC units--including those firing coal refuse--are among the best
performers for Hg control.\53\ Therefore, use of DSI technology for
acid gas control (if feasible), would likely also require the
installation of Hg-specific control technology. The EPA is soliciting
comment on the technical feasibility of installing DSI, dry FGD, or
other applicable control technologies at these units and whether the
installation of acid gas HAP controls may create technical
infeasibilities in meeting other MATS emission limits (Comment C-14).
---------------------------------------------------------------------------

    \51\ See 77 FR 9412.
    \52\ See ARIPPA comments on EPA's Proposed Supplemental Finding,
available at Docket ID No. EPA-HQ-OAR-2009-0234-20530.
    \53\ Ibid.
---------------------------------------------------------------------------

    Further, most of the existing eastern bituminous coal refuse-fired
EGUs are small (most are less than 100 MW) and may be constrained by
space or other configurational limitations. However, there are two
eastern bituminous coal refuse-fired EGUs at the Seward Generating
Station in Pennsylvania that the EPA would not consider for inclusion
in a potential subcategory. The Seward units are the newest and, at 260
MW each, are by far the largest EGUs that are firing coal refuse. The
Seward units were constructed with installed downstream acid gas
controls that were part of the original design. The Seward facility,
therefore, did not suffer from space and other configurational
limitations that can affect other smaller existing eastern bituminous
coal refuse-fired EGUs that are attempting to retrofit air pollution
controls. Further, the Seward units were among the best performing
units--with respect to HCl emissions--when the EPA developed the final
MATS emission standards. And, MATS compliance reports submitted by the
Seward EGUs show that the units' HCl emissions are well below the final
MATS standard of 0.0020 lb/MMBtu.
    The EPA has incomplete information on the emissions controls that
are installed at the currently operating eastern bituminous coal
refuse-fired EGUs (i.e., those identified earlier in Table 7). The EPA
solicits information on installed controls at those units, the types
and amount of sorbents or reagents (if any) that are used, and, if
present, the extent of the operation of these emissions controls
(Comment C-15). The EPA also solicits comment on the cost of
retrofitting DSI, dry FGD, or other applicable control technologies
such that eastern bituminous coal refuse-fired EGUs are able to emit at
or below the MATS standard for HCl or SO2 (Comment C-16). To
better understand the economic characteristics of the eastern
bituminous coal refuse-fired EGUs, the EPA additionally solicits
information on the operating costs of these units, availability and
cost

of their fuel supplies, and any planned retirements (Comment C-17).

C. Potential Subcategory Emission Standards

    As mentioned, the EPA is considering establishing acid gas emission
standards for a subcategory of existing EGUs that fire eastern
bituminous coal refuse; and we are soliciting comment on the need for
such a standard (Comment C-18). The EPA has conducted an analysis to
determine what such a numerical emission standard would be. The
analysis is summarized in a separate memorandum available in the
rulemaking docket.\54\ The results of that MACT floor analysis are
shown below in Table 8. After the EPA establishes the MACT floor, it
considers the costs and non-air quality health and environmental
impacts and energy requirements to determine whether a more stringent,
or ``beyond-the-floor,'' level of control should be established. The
average SO2 lb/MMBtu emission rate was determined for each
currently operating eastern bituminous coal refuse-fired EGU using
monthly SO2 data available in the EPA's ECMPS for the period
of January 2015 through June 2018. If the EPA were to establish a
beyond-the-floor SO2 emissions limit, it would likely be in
the range of 0.60--0.70 lb/MMBtu; a limit that, on average, the
currently operating eastern bituminous coal refuse-fired EGUs have
achieved based on their monthly emissions data for January 2015 through
June 2018. Because no HCl emissions data have been submitted for the
currently operating EGUs, and SO2 lb/MWh emissions data are
available for only two of the EGUs, we could not use this same beyond-
the-floor methodology to evaluate beyond-the-floor standards for
SO2 in lb/MWh or for HCl in either lb/MMBtu or lb/MWh. We,
therefore, determined that the beyond-the-floor standards for those
pollutants should reasonably be set based on the same percentage