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Guidance on the Development of Modeled
Emission Rates for Precursors (MERPs) as a
Tier 1 Demonstration Tool for Ozone and PM25
under the PSD Permitting Program
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EPA-454/R-16-006
December 2016
Guidance on the Development of Modeled Emission Rates for Precursors
(MERPs) as a Tier 1 Demonstration Tool for Ozone and PM2.5 under the PSD
Permitting Program
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Air Quality Modeling Group
Research Triangle Park, NC
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Contents
1 Background 4
2 Ozone and secondary PM2.5 formation in the atmosphere 6
3 Photochemical model application for single source secondary impacts 8
4 Single source precursor emissions and downwind O3 and secondary PM2.5 impacts 9
4.1 Annual and Daily PM2.5 12
4.2 8-hour Ozone 16
5 Framework for Developing MERPs as a Tier 1 Demonstration Tool 20
5.1 Annual and Daily PM2.5 21
5.2 8-hour Ozone 25
6 Recommended Method for Developing MERPs as a Tier 1 Demonstration Tool 27
6.1 Developing Area Specific MERPs 27
7 Illustrative MERP Tier 1 Demonstrations Based on EPA Modeling for Example PSD Permit
Scenarios 29
7.1. Application of the EPA Assessment and Illustrative MERPs to Individual Permit
Applications 33
8 Acknowledgements 34
9 References 35
APPENDIX A. Relationship between hypothetical sources and maximum downwind impacts... 37
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1 Bac
EPA has proposed revisions to the Guideline on Air Quality Models (published as Appendix W to
40 CFR Part 51) to establish a recommended two-tiered approach for addressing single-source
impacts on ozone (O3) or secondary particulate matter less than 2.5 microns in diameter (PM2.5)
(U.S. Environmental Protection Agency, 2015a). The first tier (or Tier 1) involves use of
appropriate and technically credible relationships between emissions and ambient impacts
developed from existing modeling studies deemed sufficient for evaluating a project source's
impacts. The second tier (or Tier 2) involves more sophisticated case-specific application of
chemical transport modeling (e.g., with an Eulerian grid or Lagrangian model). This guidance
document is intended to provide a detailed framework that applicants may choose to apply, in
consultation with the appropriate permitting authority, to estimate single-source impacts on
secondary pollutants under the first tier approach put forth in the 2015 proposed revisions to
the Guideline (i.e., Sections 5.3.2.b and 5.4.2.b). This guidance document does not require the
use, nor does it require acceptance of the use, of this framework or any result using this
framework by a permit applicant or a permitting authority. Permit applicants and permitting
authorities retain the discretion to use other methods to complete a first tier assessment under
Sections 5.3.2.b and 5.4.2.b. of Appendix W. This document is not a final agency action, and
does not create any binding requirements on EPA, permitting authorities, permit applicants, or
the public.
For Tier 1 assessments, EPA generally expects that applicants would use existing empirical
relationships between precursors and secondary impacts based on modeling systems
appropriate for this purpose. The use of existing credible technical information that
appropriately characterize the emissions to air quality relationships will need to be determined
on a case-by-case basis. Examples of existing relevant technical information that may be used
by a permit applicant, in consultation with the appropriate permitting authority, include air
quality modeling conducted for the relevant geographic area reflecting emissions changes for
similar source types as part of a State Implementation Plan (SIP) demonstration, other permit
action, or similar policy assessment as well air quality modeling of hypothetical industrial
sources with similar source characteristics and emission rates of precursors that are located in
similar atmospheric environments and for time periods that are conducive to the formation of
O3 or secondary PM2.5. The applicant should describe how the existing modeling reflects the
formation of O3 or PM2.5 in that particular area. Where the existing technical information is
based on chemical and physical conditions less similar to the project source and key receptors,
a more conservative estimate of impacts using demonstration tools may be necessary.
Information that could be used to describe the comparability of two different geographic areas
include average and peak temperatures, humidity, terrain, rural or urban nature of the area,
nearby regional sources of pollutants (e.g., biogenics, other industry), and ambient
concentrations of relevant pollutants where available.
In the preamble of the Appendix W NPRM, EPA briefly discussed plans to develop a new
demonstration tool for ozone and PM2.5 precursors called Modeled Emission Rates for
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Precursors (MERPs). MERPs can be viewed as a type of Tier 1 demonstration tool under the
Prevention of Significant Deterioration (PSD) permitting program that provides a simple way to
relate maximum downwind impacts with a critical air quality threshold. EPA had initially
planned to establish generally-applicable MERPs through a future rulemaking. However, after
further consideration, EPA believes it is preferable for permit applicants and permitting
authorities to consider site-specific conditions when deriving MERPs and to obtain experience
with the development and application of locally and regionally appropriate values in the
permitting process. Thus, instead of deriving generally-applicable MERP values, the EPA is
providing this guidance document for consideration and use by permitting authorities and
permit applicants on a case-by-case basis.
This guidance is relevant for the PSD program and only addresses assessing the effects of
precursors of PM2.5 and 03 for purposes of that program. The term Modeled Emissions Rate for
Precursors (MERP) may be used to describe an emission rate of a precursor that is expected to
result in a change in ambient ozone or PM2.5 that would be less than a specific air quality
concentration threshold for ozone or PM2.5 that a permitting authority chooses to use to
determine whether an impact causes or contributes to a violation of the NAAQS for ozone or
PM2.5. EPA contemplates that MERPs would relate a specific precursor of ozone and/or PM2.5
and would not provide a single demonstration for all NAAQS pollutants. For example, for PSD,
separate MERPs could be developed to relate volatile organic compounds (VOCs) to O3,
nitrogen oxides (NOx) to O3, sulfur dioxide (SO2) to secondary PM2.5, and NOx to secondary
PM2.5.
If approved by the permitting authority as a Tier 1 demonstration tool for a PM2.5 PSD source in
a PM2.5 attainment or unclassifiable area, a finding that projected increases in the PM2.5
precursor emissions of NOx and SO2 from a proposed construction are below the respective
MERPs could be part of a sufficient demonstration that the construction will not cause or
contribute to violation of the appropriate NAAQS (hereinafter "demonstration of compliance"
or "compliance demonstration"). Similarly, for the O3 NAAQS, an appropriate Tier 1
demonstration may include a finding that the projected increases in O3 precursor emissions of
NOx and VOC are below the respective MERPs. Where project sources emit multiple precursors,
the impacts should be estimated in a relative sense in comparison to the critical air quality
threshold such that the sum of precursor impacts would need to be lower than the critical air
quality threshold for a sufficient demonstration of compliance. Examples of combining
precursor impacts are provided in section 7 of this document. Further, where project sources
emit both primary PM2.5 and precursors of secondary PM2.5, EPA expects that applicants will
need to combine the primary and secondary impacts to determine total PM2.5 impacts as part
of the PSD compliance demonstration.
The purpose of this document is to provide a framework for permitting authorities and permit
applicants on how air quality modeling can be used to develop relationships between
precursors and maximum downwind impacts for the purposes of establishing MERPs as a Tier 1
demonstration tool. We also present hypothetical single source impacts on O3 and secondary
PM2.5 to illustrate how this framework can be implemented by stakeholders. The relationships
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presented here in some cases may provide relevant technical information to assist or inform an
applicant in providing a first tier demonstration and also as a template for stakeholders and/or
state or local agencies to develop information relevant to a specific area or source type. Based
on the EPA modeling conducted to inform these illustrative MERPs, it is clear that such values
will vary across the nation reflecting different sensitivities of an area's air quality level to
precursor emissions thereby providing an appropriate basis for evaluating the impacts of these
precursors to PM2.5 and ozone formation because they reflect the regional or local atmospheric
conditions for particular situations.
2 oh. i\)>< > 4i" TM.'j, ('iiriMii'Hfi ih 11¦¦ .limo1 i-li-'i-'
A conceptual understanding of an area's emissions sources and which precursor emissions limit
the formation of secondary pollutants such as O3 and PM2.5 is useful for interpreting modeled
and ambient impacts due to changes in emissions to that area. The formation regime favoring a
particular precursor may vary day to day and by hour of the day. It is important to understand
how the atmosphere will respond to changes in emissions to make informed decisions about
changes in emissions from a source might have on ambient pollutant concentrations. Typically,
reductions in emissions of primary pollutants or precursors to secondary pollutants result in
some level of reduction in ambient pollutant concentrations.
Secondary PM2.5 and O3 are closely related to each other in that they share common sources of
emissions and are formed in the atmosphere from chemical reactions with similar precursors
(U.S. Environmental Protection Agency, 2005). Air pollutants formed through chemical
reactions in the atmosphere are referred to as secondary pollutants. For example, ground-level
ozone is predominantly a secondary pollutant formed through photochemical reactions driven
by emissions of NOx and VOCs in the presence of sunlight. Ozone formation is a complicated
nonlinear process that depends on meteorological conditions in addition to VOC and NOx
concentrations (Seinfeld and Pandis, 2012). Warm temperatures, clear skies (abundant levels of
solar radiation), and stagnant air masses (low wind speeds) increase ozone formation potential
(Seinfeld and Pandis, 2012).
Ozone formation may be limited by either NOx or VOC emissions depending on the
meteorological conditions and the relative mix of these pollutants. When ozone concentrations
increase (decrease) as a result of increases (decreases) in NOx emissions, the ozone formation
regime is termed "NOx limited". Alternatively, the ozone formation regime is termed "VOC
limited" when ambient ozone concentrations are very sensitive to changes in ambient VOC. The
VOC-limited regime is sometimes referred to as "radical-limited" or "oxidant-limited" because
reactions involving VOCs produce peroxy radicals that can lead to ozone formation by
converting NO to NO2 in the presence of sunlight. In a NOx-limited regime, ozone decreases
with decreasing NOx and has very little response to changes in VOC. The NOx-limited formation
regime is more common in rural areas of the U.S. where high levels of biogenic VOC exist and
relatively few man-made, or anthropogenic, NOx emissions occur. Ozone decreases with
decreasing VOC in a VOC-limited formation regime. The ozone formation regime for many
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urban areas in the U.S. is VOC-limited during daytime hours due to large NOx emissions from
mobile and industrial sources and relatively smaller amount of biogenic and anthropogenic VOC
emissions.
In the case of PM2.5, or fine PM, total mass is often categorized into two groups: primary (i.e.
emitted directly as PIVh.sfrom sources) and secondary (i.e., PM2.5 formed in the atmosphere by
precursor emissions from sources). The ratio of primary to secondary PM2.5 varies by location
and season. In the U.S., PM2.5 is dominated by a variety of chemical components: ammonium,
sulfate, nitrate, organic carbon (OC), elemental carbon (EC), crustal elements, sea-spray
constituents, and oxidized metals. PM2.5 EC, crustal elements, and sea spray are directly
emitted into the atmosphere from primary sources. PM2.5 OC is directly emitted from primary
sources but is also formed secondarily in the atmosphere by reactions involving VOCs. PM2.5
sulfate, nitrate, and ammonium are predominantly the result of chemical reactions of the
oxidized products of SO2 and NOx emissions and direct ammonia (NH3) emissions (Seinfeld and
Pandis, 2012).
Sulfur dioxide emissions are oxidized in the atmosphere and form sulfuric acid, which has a very
low vapor pressure and tends to exist in the particulate phase. Particulate sulfuric acid reacts
with ammonia to form ammonium bisulfate and ammonium sulfate. Aqueous phase reactions
are also an important pathway for particulate sulfate formation. SO2 dissolves into cloud and
fog droplets and is oxidized to sulfate via reaction pathways involving hydrogen peroxide,
ozone, and other oxidants. Since sulfate is essentially non-volatile under atmospheric
conditions, sulfate formed in clouds persists as particulate sulfate after the cloud evaporates.
Sulfur dioxide emissions reductions lead to reductions in particulate sulfate. The process is not
completely linear, especially when aqueous phase production is significant, and so changes in
SO2 emissions may not result in the same proportion of change in PM2.5 sulfate concentration.
Emissions of NOx are chemically transformed to nitric acid (HNO3) through gas-phase and
heterogeneous reactions. Nitric acid may condense onto particles to form particulate nitrate
depending on the conditions. Condensation of nitric acid onto particles is favored by low
temperature, high relative humidity, and relatively less acidic conditions associated with high
levels of ammonia and particulate cations. Nitric acid formation may be oxidant or NOx-limited,
and PM2.5 ammonium nitrate formation may be limited by the availability of either nitric acid or
ammonia or by meteorological conditions. When PM2.5 ammonium nitrate is limited by the
availability of ammonia, the formation regime is termed "ammonia-limited", and the formation
regime is termed "nitric acid-limited" when the opposite situation exists (Stockwell et al., 2000).
In general, a decrease in NOx emissions will result in a decrease in PM2.5 nitrate concentration
(Pun et al., 2007). Since PM2.5 ammonium nitrate formation is preferred under low temperature
and high relative humidity conditions and in the presence of ammonia, ammonium nitrate
concentrations tend to be greater during colder months and in areas with significant ammonia
emissions. NOx emissions changes during warm temperatures may result less change in
ambient PM2.5 compared to cold months due to nitric acid staying in the gas rather than particle
phase due to higher temperatures. Additionally, NOx emissions changes in places with very little
or no ambient ammonia will cause little change in ambient PM2.5 ammonium nitrate.
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3 Pli'"«ioi li« miK »l II »|'|-llih%oi',-ii ioi * ii -> «' »ii J >i»
i impacts
Publicly available and fully documented Eulerian photochemical grid models such as the
Comprehensive Air Quality Model with Extensions (CAMx) (ENVIRON, 2014) and the
Community Multiscale Air Quality (CMAQ) (Byun and Schere, 2006) model treat emissions,
chemical transformation, transport, and deposition using time and space variant meteorology.
These modeling systems simulate primarily emitted species and secondarily formed pollutants
such as ozone and PM2.5 (Chen et al., 2014; Civerolo et al., 2010; Russell, 2008; Tesche et al.,
2006). Even though single source emissions are injected into a grid volume, photochemical
transport models have been shown to adequately capture single source impacts when
compared with downwind in-plume measurements (Baker and Kelly, 2014; Zhou et al., 2012).
Where set up appropriately for the purposes of assessing the contribution of single sources to
primary and secondarily formed pollutants, photochemical grid models could be used with a
variety of approaches to estimate these impacts. These approaches generally fall into the
categories of source sensitivity (how air quality changes due to changes in emissions) and
source apportionment (what air quality impacts are related to certain emissions).
The simplest source sensitivity approach, commonly referred to as a brute-force change to
emissions, would be to simulate two sets of conditions, one with all emission sources and a
subsequent simulation with all emissions sources and the post-construction characteristics of
the new or modifying project source being the only difference from the original baseline
simulation (Cohan and Napelenok, 2011). The difference between these model simulations
provides an estimate of the air quality change related to the change in emissions from the
project source. In addition to the brute force approach, some photochemical models have been
"instrumented" with techniques that allow tracking or account for ambient contributions from
the emissions of a particular sector or source. These instrumented techniques provide a source
sensitivity approach within the model to differentiate the impacts of single sources on changes
in model predicted air quality. One sensitivity approach is the decoupled direct method (DDM),
which tracks the sensitivity of an emissions source through all chemical and physical processes
in the modeling system (Dunker et al., 2002). Sensitivity coefficients relating source emissions
to air quality are estimated during the model simulation and output at the resolution of the
host model. Unlike the brute force approach, a second simulation is not necessary when using
DDM, although additional resources are required as part of the baseline simulation when DDM
is also applied. Furthermore, some photochemical models have been instrumented with source
apportionment capabilities, which tracks emissions from specific sources through chemical
transformation, transport, and deposition processes to estimate a contribution to predicted air
quality at downwind receptors (Kwok et al., 2015; Kwok et al., 2013).
Source apportionment has been used to differentiate the contribution from single sources on
model predicted ozone and PM2.5 (Baker and Foley, 2011; Baker and Kelly, 2014). DDM has also
been used to estimate O3 and PM2.5 impacts from specific sources (Baker and Kelly, 2014;
Bergin et al., 2008; Kelly et al., 2015) as well as the simpler brute-force sensitivity approach
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(Baker and Kelly, 2014; Bergin et al., 2008; Kelly et al., 2015; Zhou et al., 2012). Limited
comparison of single source impacts between models (Baker et al., 2013) and approaches to
differentiate single source impacts (Baker and Kelly, 2014; Baker et al., 2013) show generally
similar downwind spatial gradients and impacts. Near-source in-plume aircraft based
measurement field studies provide an approach for evaluating model estimates of (near-
source) downwind transport and chemical impacts from single stationary point sources
(ENVIRON, 2012). Photochemical grid model source apportionment and source sensitivity
simulation of single-source downwind impacts compare well against field study primary and
secondary ambient measurements made in Tennessee and Texas (Baker and Kelly, 2014;
ENVIRON, 2012). This work indicates photochemical grid models using source apportionment or
source sensitivity approaches provide meaningful estimates of single source impacts.
4 Single source precursor emissions and downwind O3 and
sea
This section presents hypothetical single source impacts on downwind O3 and secondary PM2.5.
Hypothetical sources included here are detailed in Table 4-1 and shown in Figure 4-1. As shown,
these source types are located throughout the continental U.S. and reflect different release
heights and multiple emissions rates. For the broader regions (i.e., eastern, central, and
western US), the details on the specific locations modeled are provided in Appendix Table A-l
(ozone), A-2 (daily PM2.5), and A-3 (annual PM2.5).
Source release type "L" refers to low-level sources modeled with surface level emissions
releases: stack height of 1 m, stack diameter of 5 m, exit temperature of 311 K, exit velocity of
27 m/s, and flow rate of 537 m3/s. Source release type "H" refers to high elevation sources
modeled with elevated emissions releases: stack height of 90 m, stack diameter of 5 m, exit
temperature of 311 K, exit velocity of 27 m/s, and flow rate of 537 m3/s. Hypothetical sources
included in this assessment type is then modeled at multiple emission rates: 100, 300, 500,
1000, and 3000 tpy.
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Table 4-1. List of hypothetical sources included in the EPA's modeling assessment.
NAAQS & Precursors Modeled
# hypothetical
Geographic sources within Release Emission
Region
the region
Type
Rate (tpy)
8-hr 03
Daily PM2.5
Annual PM2.
EUS
19
H
3000
NOX,
VOC
NOX,
S02
NOX, S02
(eastern US)
19
H
1000
NOX,
VOC
NOX,
S02
NOX, S02
19
H
500
NOX,
VOC
NOX,
S02
NOX, S02
19
L
500
NOX,
VOC
NOX,
S02
NOX, S02
CUS
25
H
3000
NOX,
VOC
NOX,
S02
NOX, S02
(ce ntra 1 US)
25
H
1000
NOX,
VOC
NOX,
S02
NOX, S02
25
L
1000
NOX,
VOC
NOX,
S02
NOX, S02
25
L
500
NOX,
VOC
NOX,
S02
NOX, S02
WUS
26
H
3000
NOX,
VOC
NOX,
S02
NOX, S02
(western US)
26
H
1000
NOX,
VOC
NOX,
S02
NOX, S02
26
H
500
NOX,
VOC
NOX,
S02
NOX, S02
26
L
500
NOX,
VOC
NOX,
S02
NOX, S02
Atl a nta
1
L
300
NOX,
VOC
NOX,
S02
NOX, S02
1
L
100
NOX,
VOC
NOX,
S02
NOX, S02
Detroit
1
L
300
NOX,
VOC
NOX,
S02
NOX, S02
1
L
100
NOX,
VOC
NOX,
S02
NOX, S02
S. Ba kersfiel d
1
H
100
NOX,
VOC
NOX,
S02
1
L
100
NOX,
VOC
NOX,
S02
1
H
500
NOX,
VOC
NOX,
S02
1
L
500
NOX,
VOC
NOX,
S02
1
H
2000
NOX,
VOC
NOX,
S02
1
L
2000
NOX,
VOC
NOX,
S02
Ba kersfi el d
1
H
100
NOX,
VOC
NOX,
S02
1
L
100
NOX,
VOC
NOX,
S02
1
H
500
NOX,
VOC
NOX,
S02
1
L
500
NOX,
VOC
NOX,
S02
1
H
2000
NOX,
VOC
NOX,
S02
1
L
2000
NOX,
VOC
NOX,
S02
Shafter
1
H
100
NOX,
VOC
NOX,
S02
1
L
100
NOX,
VOC
NOX,
S02
1
H
500
NOX,
VOC
NOX,
S02
1
L
500
NOX,
VOC
NOX,
S02
1
H
2000
NOX,
VOC
NOX,
S02
1
L
2000
NOX,
VOC
NOX,
S02
LA
1
H
100
NOX,
VOC
NOX,
S02
1
L
100
NOX,
VOC
NOX,
S02
1
H
500
NOX,
VOC
NOX,
S02
1
L
500
NOX,
VOC
NOX,
S02
1
H
2000
NOX,
VOC
NOX,
S02
1
L
2000
NOX,
VOC
NOX,
S02
Riverside
1
H
100
NOX,
VOC
NOX,
S02
1
L
100
NOX,
VOC
NOX,
S02
1
H
500
NOX,
VOC
NOX,
S02
1
L
500
NOX,
VOC
NOX,
S02
1
H
2000
NOX,
VOC
NOX,
S02
1
L
2000
NOX,
VOC
NOX,
S02
Pomona
1
H
100
NOX,
VOC
NOX,
S02
1
L
100
NOX,
VOC
NOX,
S02
1
H
500
NOX,
VOC
NOX,
S02
1
L
500
NOX,
VOC
NOX,
S02
1
H
2000
NOX,
VOC
NOX,
S02
1
L
2000
NOX,
VOC
NOX,
S02
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Figure 4-1. Hypothetical sources modeled for downwind secondary air quality impacts
included in this assessment.
Hypothetical Sources
Baker etal, 2015
Kelly etal, 2015
IWAGM3-NFI Report 2015
New work presented here
-2000
2000
The single source impacts detailed in this section are collected from various photochemical grid
model based assessments of hypothetical sources and report downwind O3 and secondary
PM2.5 impacts and EPA modeling results that are being presented here for the first time. The
resulting relationships are based on photochemical modeling studies that estimated single
source impacts in California (Kelly et al., 2015), the Detroit and Atlanta urban areas (U.S.
Environmental Protection Agency, 2016), and at rural and suburban locations in the central and
eastern United States (Baker et al., 2015a). Additional photochemical modeling was conducted
by EPA consistent with the approach described in Baker et al., 2015 for hypothetical sources in
the western, central, and eastern U.S. to provide broader geographic coverage across the
nation.
The relationships shown here for these hypothetical sources are not intended to provide an
exhaustive representation of all combinations of source type, chemical, and physical source
environments but rather provide insightful information about secondary pollutant impacts from
single sources in different parts of the U.S. The maximum impacts for daily PM2.5, annual PM2.5
and daily maximum 8-hr average O3 are shown in the following sub-sections for the
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hypothetical sources modeled for an entire year (Baker et al., 2015b; U.S. Environmental
Protection Agency, 2016).
4.1 (/12.5
The maximum daily average PM2.5 sulfate ion from SO2 emissions and maximum daily average
PM2.5 nitrate ion from NOx emissions are shown in Figure 4-2 by emission rate and area.
Downwind maximum PM2.5 impacts generally increase as rates of precursor emissions increase.
However, differences in chemical (e.g. NOx/VOC ratio, ammonia concentrations) and physical
(e.g. terrain and meteorology) regimes among these hypothetical sources result in differences
in downwind impacts even for similar types of sources. Differences in maximum impacts can
also be seen between the different areas and studies. Atlanta and Detroit both include a single
hypothetical source modeled at 4 km horizontal grid resolution. The California sources were
also modeled at 4 km but only include a sub-set of an entire year meaning the maximum impact
from those hypothetical sources may not be realized as part of that study design. The western,
central, and eastern U.S. sources were modeled at 12 km horizontal grid resolution for the
entire year of 2011. Therefore, it is possible that the maximum impacts from each of these
hypothetical sources may not have been realized using this specific year of meteorology and
that another year with more conducive meteorology for secondary formation of O3 and/or
PM2.5 might be more appropriate.
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Figure 4-2. Maximum daily average secondary PM2.5 sulfate ion impacts from SO2 emissions and
PM2.5 nitrate ion impacts from NOx emissions. Note: These impacts are from multiple modeling
studies estimating downwind impact from hypothetical sources.
S02 to period peak 24-hr PM2.5 sulfate ion
E
CD
M.I I
rr>
3
Emissions (TPY)
S02 to period peak 24-hr PM2.5 sulfate Ion
NOX to period peak 24-hr PM2.5 nitrate Ion
e ;
*CD
A
.§ S H
500
Emissions (TPY)
NOX to period peak 24-hr PM2.5 nitrate Ion
E
CD
A
CP NT
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The distance from the source of maximum daily average secondary PM2.5 impact is shown in
Figure 4-3. Peak impacts tend to be in proximity to the source and become less common as
distance from the source increases. Figure 4-4 shows maximum annual average impacts from
SO2 emissions on modeled PM2.5 sulfate ion and NOX emissions on modeled PM2.5 nitrate ion.
Downwind impacts tend to increase as emissions of precursors increase. Also, impacts vary
from area to area. Here, for the annual form of the NAAQS, the episodic California sources are
not included since an entire year was not modeled as part of that project source.
Figure 4-3. Maximum daily average secondary PM2.5 sulfate ion impacts from SO2 emissions
(left panels) and PM2.5 nitrate ion impacts from NOx emissions (right panels) shown by
distance from the source.
S02 to period peak 24-hr PM2.5 sulfate ion
NOX to period peak 24-hr PM2.5 nitrate ion
CT>
o
4
£
J1
s=
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Figure 4-4. Maximum annual average secondary PM2.5 sulfate ion impacts from SO2
emissions and PM2.5 nitrate ion impacts from iSlOx emissions. Note: These impacts are from
multiple modeling studies estimating downwind impact from hypothetical sources.
S02 to period peak 24-hr PM2.5 sulfate ion
rC
j=
a.
A
"E
8
8
HOC
Emissions (TPY)
S02 to period peak 24-hr PM2.5 sulfate Ion
rC*
E
A 2 _
3
NOX to period peak 24-hr PM2.5 nitrate Ion
if
.E
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4.2 8-houi ?
Maximum 8-hr O3 impacts are shown in Figure 4-5 compared to single source precursor
emission rates. These relationships are based on photochemical modeling studies that
estimated single source impacts on downwind PM2.5 in California (Kelly et al., 2015), the Detroit
and Atlanta urban areas (U.S. Environmental Protection Agency, 2016), and at rural and
suburban locations in the central and eastern United States (Baker et al., 2015a). Additional
modeling was conducted consistent with the approach described in Baker et al., 2015 for
hypothetical sources in the western and eastern U.S. to provide broader geographic coverage
of the U.S. Downwind maximum 8-hr O3 impacts generally increase as rates of precursor
emissions increase. However, differences in chemical (e.g. NOx/VOC ratio, radical
concentrations) and physical (e.g. terrain and meteorology) regimes among these hypothetical
sources result in differences in downwind impacts even for similar types of sources.
Each of the hypothetical source impacts modeled as part of EPA's assessment used a typical
industrial assumption for speciation of VOC emissions. To better understand the influence of
VOC speciation, as a sensitivity analysis, EPA modeled a set of hypothetical sources with near-
surface releases in the western and eastern U.S. with an alternative VOC emissions speciation
that assumed 100% of the VOC emissions were emitted as formaldehyde to provide a more
reactive profile than typically used. Figure 4-6 shows a comparison of the downwind maximum
daily 8-hr average O3 impacts of the typical hypothetical near-surface release sources in the
western and eastern U.S. with impacts where these same sources with formaldehyde-only VOC
emissions. For both sets of emissions scenarios, a total of 500 tpy of VOC was emitted, the only
difference being the VOC speciation. The formaldehyde-only simulations for these sources
generally resulted in higher downwind O3 impacts than the simulations of hypothetical sources
with typical speciation of VOC emissions. The increases in impacts are typically between 1.5 and
2 times higher.
Since VOC reactivity can be important, some areas may want to develop separate VOC to O3
relationships using typical VOC profiles and also VOC profiles that may be more reflective of
certain types of sources that exist in that area or are anticipated to operate in that area in the
future.
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Figure 4-5. Maximum 8-hr ozone impacts from NOx emissions and from VOC emissions.
Note: These impacts are from multiple modeling studies estimating downwind impact from
hypothetical sources.
NOX to period peak max 8-hr ozone
~1 1 1 i i 1
100 300 500 1000 2000 3000
Emissions (TPY)
NOX to period peak max 8-hr ozone
CENT
VOC to period peak max 8-hr ozone
Emissions (TPY)
VOC to period peak max 8-hr ozone
CENT
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Figure 4-6. Maximum 8-hr ozone impacts from 500 tpy of near-surface VOC emissions using
a typical industrial VOC speciation profile and assuming all VOC emissions are
formaldehyde.
Note: these impacts are for the eastern and western U.S. hypothetical sources presented here and do
not include information from any other studies.
VOC & FORM to period peak max 8-hr ozone
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The distance from the source of the maximum daily 8-hr average O3 impacts are shown in
Figure 4-7. Similar to maximum daily PM2.5 impacts, maximum daily 8-hr average O3 impacts
tend to be in close proximity to the source and are less frequent as distance from the source
increases. This is particularly notable where distance from the source exceeds 50 km.
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Figure 4-7. Maximum 8-hr ozone impacts from NOx emissions and from VOC emissions by
distance from the source. Note: These impacts are from multiple modeling studies estimating
downwind impact from hypothetical sources.
NOX to period peak max 8-hr ozone
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5 Framework for Developing IIV1 IERIPs as a Tier 1 Demonstration Tool
A Tier 1 demonstration tool as described in the 2015 proposed revision to the Guideline consists
of technically credible air quality modeling done to relate precursor emissions and peak
secondary pollutant impacts from specific or hypothetical sources. Permit applicants should
provide a narrative explanation describing how project source post-construction emissions
relate to the information provided as part of the Tier 1 demonstration tool. It should be made
clear how the chemical and physical environments modeled as part of an existing set of
information included in the Tier 1 demonstration tool are relevant to the geographic area of the
source and key receptors. With appropriate supporting information, permitting authorities may
derive and use MERP values as a particular type of Tier 1 demonstration tool. Such values
should be based on existing air quality modeling that would be technically credible under the
2015 proposed revision to the Guideline. Properly-supported MERPs may provide a simple way
to relate maximum downwind impacts with an air quality concentration threshold that is used
to determine if such an impact causes or contributes to a violation of the appropriate NAAQS.
In the discussion that follows, we will refer to the latter threshold as the "critical air quality
threshold."
To derive a MERP value, the model predicted relationship between precursor emissions from
hypothetical sources and their downwind maximum impacts can be combined with a critical air
quality threshold using the following equation:
MERP = Critical Air Quality Threshold * (Modeled emission rate from hypothetical
source / Modeled air quality impact from hypothetical source)
For PM2.5, the modeled air quality impact of an increase in precursor emissions from the
hypothetical source is expressed in units of ng/m3-. For O3, the modeled air quality impact is
expressed in ppb or ppm. As discussed in Section 4, these modeled impacts would reflect the
maximum downwind impacts for PM2.5 and 03. The critical air quality threshold is separately
defined (as discussed below) and expressed as a concentration for PM2.5 (in ng/m3) or O3 (in ppb
or ppm). Consistent with the modeled emissions rates that are input to the air quality model to
predict a change in pollutant concentration, MERPs are expressed as an annual emissions rate
in tons per year.
As illustrated in this section, separate MERPs can be developed for specific precursors and
secondary pollutant impacts: SO2 to PM2.5, NOx to PM2.5, NOx to O3, and VOC to O3. The
following sub-sections provide examples of developing a suitable Tier 1 demonstration tool for
each precursor and secondary pollutant. In this assessment, the maximum downwind impact
from each source is chosen over the length of the model simulation period and matched with
the annual emission rate. The maximum impact is selected since a single year of meteorology
(or less in some instances) is used to generate these relationships. Additional or alternative
meteorological patterns may result in higher impacts in some areas. For the purposes of this
example, the critical air quality thresholds are based on the draft Significant Impact Levels (SILs)
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that EPA has derived in a separate exercise. Nothing in this guidance requires the use of such
values. Consistent with EPA's draft guidance containing these SIL values, to the extent a
permitting authority elects to use a SIL to quantify a level of impact that causes or contributes
to a violation of the NAAQS or PSD increment(s), such values will need to be identified and
justified on a case-by-case basis.
5,1 /12.5
Based on the modeling results across all hypothetical sources presented in Section 4 and
detailed in the Appendix of this document, Figure 5-1 shows NOx to annual maximum daily
average PM2.5 nitrate ion and SO2 to annual maximum daily average PM2.5 sulfate ion MERPs
that illustrate the range of potential values for these sources and time period. Neither PM2.5
sulfate nor PM2.5 nitrate are assumed to be neutralized by ammonium. For this illustrative
example, consistent with EPA's draft SILs guidance, a critical air quality threshold of 1.2 ng/m3
was used to estimate daily average PM2.5 MERPs. The illustrative MERPs for NOx to daily PM2.5
range from 1,075 to just over 100,000, while the illustrative MERPs for SO2 to daily PM2.5 range
from 210 to just over 27,000 for the hypothetical sources presented here based on the selected
air quality threshold. The variation from source to source is related to different chemical and
meteorological environments around the source that range in terms of conduciveness toward
secondary PM2.5 formation.
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Figure 5-1. SO2 (top panels) and NOx (bottom panels) daily average PM? 5 MERPs estimated
from single source hypothetical emissions impacts on PM2.5 nitrate ion and PM2.5 sulfate
ion respectively.
Note: Daily PM2.5 MERPs derived here based on critical air quality threshold of 1.2 |ig/m3 and
neither PM2.5 sulfate nor nitrate is assumed to be neutralized by ammonia.
Daily avg. PM2.5 MERPs - S02 precursor
All Sources
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8 -
WEST
CENTRAL
EAST
Dally avg. PM2.5 MERPs - NOX precursor
All Sources
9 -
WEST
CENTRAL
EAST
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Figure 5-2. SO2 (top panels) and NOx (bottom panels) annual average PM2.5 MERPS shown
by geographic region.
Note: Annual PM2.5 MERPs derived here based on critical air quality threshold of 0.2 ng/m3 and
neither PM2.5 sulfate nor nitrate is assumed to be neutralized by ammonia.
Annual avg. PM2.5 MERPs - S02 precursor All Sources
I §
1 9
WEST
CENTRAL
EAST
Annual avg. PM2.5 MERPs - NOX precursor All Sources
WEST CENTRAL EAST
Similarly, based on EPA's modeling results of hypothetical sources, Figure 5-2 shows NOx to
maximum annual average PM2.5 nitrate ion and SO2 to maximum annual average PM2.5 sulfate
ion MERPs to illustrate the range of potential values for these sources and this time period. As
done for the daily PM2.5 values, neither PM2.5 sulfate nor PM2.5 nitrate are assumed to be
neutralized by ammonium. For this illustrative example, consistent with EPA's draft SILs
guidance, a critical air quality threshold of 0.2 ng/m3 was used to estimate annual average
PM2.5 MERPs. The illustrative MERPs for NOx to annual PM2.5 range from 3,184 tpy to just over
779,000 tpy, while the illustrative MERPs for SO2 to annual PM2.5 range from 1,795 tpy to just
over 75,500 tpy for the hypothetical sources presented here based on the selected air quality
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threshold. The variation from source to source is related to different chemical and
meteorological environments around the source that range in terms of conduciveness toward
secondary PM2.5 formation.
As shown, the illustrative MERPs are generally lower for SO2 than NOx reflecting that SO2 tends
to form PM2.5 more efficiently than NOx. This is consistent with the conceptual model of
secondary PM2.5 formation in many parts of the United States reflecting that the PM2.5 sulfate
ion has a lower vapor pressure than PM2.5 nitrate ion and tends to stay in the particulate phase
in a greater range of meteorological conditions. The distribution of illustrative MERPs for both
SO2 and NOx to daily PM2.5 are shown to vary between regions of the United States. This is
expected since the chemical (e.g., oxidants, neutralizing agents) and physical (e.g., terrain)
environments vary regionally in the United States.
Figure 5-3 shows illustrative MERPs estimated for these sources for the daily and annual
average forms of the PM2.5 NAAQS. Given the critical air quality thresholds used as part of this
illustrative exercise, annual PM2.5 MERPs are consistently higher than for the daily PM2.5 NAAQS
for each hypothetical source modeled across all regions of the nation.
Figure 5-3. Illustrative PM2.5 MERPs for SO2 (left panel) and NOx (right panel) estimated from
single source hypothetical emissions impacts on PM2.5 nitrate ion and PM2.5 sulfate ion
respectively.
Note: Daily average PM2.5 MERPs are directly compared with annual average PM2.5 MERPs. Neither PM2.5
sulfate nor nitrate is assumed to be neutralized by ammonia.
S02 to PM2.5 suflate ion
NOX to PM2.5 nitrate ion
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5.2 8-houi ?
Figure 5-4 shows illustrative MERPs for NOx and VOC to daily maximum 8-hr average O3 to
illustrate the variability between regions/studies for the hypothetical sources included in this
assessment. The modeled impacts reflect the highest annual 8-hr O3 impacts from various
hypothetical sources presented in this assessment (Baker et al., 2015b; Kelly et al., 2015; U.S.
Environmental Protection Agency, 2016). Consistent with EPA's draft SILs guidance, a critical air
quality threshold of 1.0 ppb is used for this illustrative example. The illustrative VOC MERPs are
based on single source VOC impacts on downwind daily maximum 8-hr O3, while the illustrative
NOx MERPs are based on single source NOx impacts on downwind daily maximum 8-hr O3. The
illustrative MERPs for NOx to daily maximum 8-hr O3 range from 107 to 5,573, while the
illustrative MERPs for VOC to daily maximum 8-hr O3 range from 814 to approximately 145,000
for the hypothetical sources presented here based on the selected critical air quality threshold.
For this assessment, illustrative MERPs for NOx tend to be lower than VOC which suggests most
areas included in this assessment are NOx limited rather than VOC limited in terms of O3
formation regime. The distribution of illustrative MERPs for both NOx and VOC are shown to
vary between areas modeled as part of this assessment. Similar to PM2.5, this is expected since
the chemical (e.g., oxidants) and physical (e.g., terrain) environments vary regionally in the
United States. The area-to-area availability of oxidants will determine whether O3 production is
NOx or VOC limited which will be an important factor in how much an emissions source of NOx
or VOC will contribute to O3 production.
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Figure 5-4. NOx (top panels) and VOC (bottom panels) MERPS estimated from single source
hypothetical emissions impacts on daily maximum 8-hr O3.
Note: 8-hr 03 MERPs derived here based on critical air quality threshold of 1.0 ppb
8-hr Ozone MERPs - NOX precursor
All Sources
WEST
CENTRAL
EAST
8-hr Ozone MERPs - VOC precursor
All Sources
I
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WEST
CENTRAL
EAST
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6 IRec 1
Demonstration Tool
Given the observed spatial variability in illustrative MERPs for each precursor for PM2.5 and O3,
stakeholders choosing to develop their own Tier 1 demonstration tool will need to conduct
sufficient air quality modeling, as described below in Section 6.1. Therefore, the air quality
modeling should be consistent with the type of modeling system, model inputs, model
application and estimation approach for 03 and secondary PM2.5 recommended in the 2015
proposed revision to the Guideline (U.S. Environmental Protection Agency, 2015a) and the
EPA's Single-Source Modeling guidance (U.S. Environmental Protection Agency, 2015b). The
chosen modeling system should be applied with a design scope similar to that shown in this
document where multiple hypothetical single sources with varying emission rates and stack
release parameters are simulated for a period that includes meteorology conducive to the
formation of O3 and/or secondary PM2.5. A modeling protocol should be developed and shared
with the EPA Regional office that details the planned approach for developing MERPs based on
photochemical modeling to ensure a sound technical basis for development of a suitable Tier 1
demonstration tool. As part of the protocol, the permit applicant should include a narrative that
provides a technical justification that the existing information is relevant for their project
source scenario.
There is no minimum number of hypothetical sources to include in developing a MERPs Tier 1
demonstration tool, but the benefit of including more hypothetical sources is that more
information is available for future sources to use in predicting secondary pollutant impacts from
their post-construction emissions. Permitting authorities or permit applicants should examine
the existing recent (e.g., last 5 to 10 years) permit applications in that area to determine what
types of emission rates and stack characteristics (e.g., surface and elevated release) should be
reflected in the hypothetical project sources included in the model simulations. These model
simulations should include a credible representation of current or post-construction conditions
in the area of the project source and key receptors.
6.1 Developing Area Specific MERPs
Pre-existing modeling conducted for an area by a source, a governmental agency, or some
other entity that is deemed sufficient may be adequate for air agencies to conduct local
demonstrations leading to the development of area-specific MERPs.
8-hr Ozone: The general framework for such developmental efforts for O3 should include the
following steps:
1) Define the geographic area(s)
2) Conduct a series of source sensitivity simulations with appropriate air quality models to
develop a database of modeled O3 impacts associated with emissions of O3 precursors
(e.g., VOC and NOx) from typical industrial point sources within the area of interest.
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3) Extract the highest daily 8-hr average modeled impact anywhere in the domain from the
model simulation.
4) Calculate the MERP estimate(s) using the equation provided in Section 5 of this
document.
5) Conduct quality assurance of the resulting MERP estimate(s) and evaluate the
interpretation and appropriateness given the nature of O3 precursor emissions sources
and chemical formation in the area of interest. This evaluation will likely require
emissions inventory data and observed ambient data for O3 and precursors.
If there are questions about what steps are appropriate in a given instance or how to apply the
steps described above, air agencies should contact their Regional Office modeling contact for
further technical consultation.
Daily PM? s: The general framework for such developmental efforts for daily PM2.5 should
include the following steps:
1) Define the geographic area(s)
2) Conduct a series of source sensitivity simulations with appropriate air quality models to
develop a database of modeled PM2.5 impacts associated with emissions of PM2.5
precursors (e.g., SO2 and NOx) from typical industrial point sources within the area of
interest.
3) Extract the highest daily 24-hr average modeled impact anywhere in the domain from
the model simulation.
4) Calculate the MERP estimate(s) using the equation provided in Section 5 of this
document.
5) Conduct quality assurance of the resulting MERP estimate(s) and evaluate the
interpretation and appropriateness given the nature of PM2.5 precursor emissions
sources and chemical formation in the area of interest. This evaluation will likely require
emissions inventory data and observed ambient data for PM2.5 and precursors.
If there are questions about what steps are appropriate in a given instance and how to apply
the steps described above, air agencies should contact their Regional Office modeling contact
for further technical consultation.
Annual PM7.5: The general framework for such developmental efforts for annual PM2.5 should
include the following steps:
1) Define the geographic area(s)
2) Conduct a series of source sensitivity simulations with appropriate air quality models to
develop a database of modeled PM2.5 impacts associated with emissions of PM2.5
precursors (e.g., SO2 and NOx) from typical industrial point sources within the area of
interest.
3) Extract the highest annual average modeled impact anywhere in the domain from the
model simulation.
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4) Calculate the MERP estimate(s) using the equation provided in Section 5 of this
document.
5) Conduct quality assurance of the resulting MERP estimate(s) and evaluate the
interpretation and appropriateness given the nature of PM2.5 precursor emissions
sources and chemical formation in the area of interest. This evaluation will likely require
emissions inventory data and observed ambient data for PM2.5 and precursors.
If there are questions about what steps are appropriate in a given instance or how to apply the
steps described above, air agencies should contact their Regional Office modeling contact for
further technical consultation.
7 llhi "in in 'e Ml I I II ki 1 HYiii h "in 11 irk" ! , iii III ' II 1c ' In 1
iihi It jn 1 pII ' II" I I 1 inn S v iik 11.
In this section, several example PSD permit application scenarios are presented to illustrate
how modeled emissions and secondary pollutant impacts from EPA's modeling of hypothetical
sources (described in Section 4) could be used to derive a MERP Tier 1 demonstration tool (as
described in section 5) for a given location. Most of these examples assume the proposed new
or modifying sources (hereinafter "project sources") do not emit any primary PM2.5 to
demonstrate how to account for multiple precursor contributions to secondary PM2.5
formation. One scenario (i.e., scenario D) reflects a situation where a project source emits both
primary PM2.5 and precursors to secondary PM2.5. In those situations, applicants should consult
the appropriate sections of the Guideline (U.S. Environmental Protection Agency, 2015a) and
related guidance (U.S. Environmental Protection Agency, 2015b). As illustrated in these
examples, MERPs for each precursor may be based on either the most conservative (lowest)
value across a region/area or the source-specific value derived from a more similar hypothetical
source modeled by a permit applicant, permitting authority or EPA.
For each area, Table 7.1 shows an example of the most conservative (i.e., lowest) illustrative
MERP for each precursor and NAAQS across all sources, areas, and studies. These illustrative
values in Table 7.1 are based on the EPA modeling of hypothetical sources described in section
4 and the critical air quality thresholds presented in Section 5. For reference at the individual
source level, the maximum predicted downwind impacts for each of the hypothetical sources
modeled with annual simulations are provided in Appendix A.
Table 7.1 Most Conservative (Lowest) Illustrative MERP Values (tons per year) by Precursor,
Pollutant and Region. Note: illustrative MERP values are derived based on EPA modeling (as
described in section 4) and critical air quality thresholds (as described in Section 5).
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Precursor Area
8-hr 03
Daily PM Annual PM
NOx Central US
NOx Eastern US
NOx Western US
S02 Central US
S02 Eastern US
S02 Western US
VOC Central US
VOC Eastern US
VOC Western US
948
814
1,049
126
107
184
1,820 7,427
2,467 10,037
1,155 3,184
256 1,795
675 4,013
225 2,289
Scenario A: VOC and NOx precursor assessment for PM2.5 and additive O3 impacts
In this scenario, a facility with a proposed increase in emissions of 0 TPY of primary PM2.5,130
TPY of VOC, 72 TPY of NOx, and 0 TPY of SO2 located in the upper midwest region. Only VOC and
NOx emissions are above the level of the SER and therefore require a PSD compliance
demonstration.
O3 analysis: The NOx and VOC emissions from the project source are well below the lowest
(most conservative) O3 MERP value shown in Table 7-1 of any source modeled by EPA in the
central or any other region in the continental U.S. In this case, air quality impacts of O3 from
this source would be expected to be below the critical air quality threshold.
However, the NOx and VOC precursor contributions to 8-hr daily maximum O3 are considered
together to determine if the source's air quality impact would exceed the critical air quality
threshold. In such a case, the proposed emissions increase can be expressed as a percent of the
lowest MERP for each precursor and then summed. A value less than 100% indicates that the
critical air quality threshold will not be exceeded when considering the combined impacts of
these precursors on 8-hr daily maximum O3.
Example calculation for additive secondary impacts on 8-hr daily maximum O3:
(72 tpy NOx from source/107 tpy NOx 8-hr daily maximum O3 MERP) + (130 tpy VOC from
source/814 TPY VOC 8-hr daily maximum O3 MERP) = .67 + .16 = .83 * 100 = 83%
PM7.5 analysis: The NOx emissions of 72 tpy from the hypothetical project source are also well
below the lowest (most conservative) PM2.5 MERP value for the daily and annual NAAQS shown
in Table 7-1 of any source modeled by EPA across the continental US. In this case, air quality
impacts of PM2.5 from this source are expected to be below the critical air quality threshold.
Scenario B: NOx and SO2 precursor assessment for comparable source O3 impacts and additive
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secondary PM2.5 impacts
In this scenario, a facility with a proposed increase in emissions of 0 TPY of primary PM2.5, 0 TPY
of VOC, 310 TPY of NOx, and 75 TPY of SO2 located in the southeast region. Only NOx and SO2
emissions are above the level of the SER and therefore require a PSD compliance
demonstration.
O3 analysis: The NOx emissions of 310 tpy are larger than the lowest (most conservative) NOx
MERP for 8-hr O3 in the eastern and other regions of the U.S. such that air quality impacts of 03
from this source would be expected to exceed the critical air quality threshold. A comparable
hypothetical source is identified that may be representative of this source (e.g., EUS region,
source 19 with elevated emissions release as shown in Appendix A) and has source derived NOx
MERPs for 8-hr O3 ranging from 327 to 462 TPY, which are both larger than the project source's
post-construction emissions. The general formula for estimating MERPs is provided in section 5.
Here, the equation is used with the modeled emissions rates and air quality impact information
from source 19 of the EUS region with an elevated release (as detailed in Appendix Table A-l).
Since multiple hypothetical sources were modeled at this location with an elevated release the
source with the lowest MERP was selected for comparison with the project source, i.e.,
MERP for source 19 EUS region elevated release (tpy) = 1.0 ppb * (500 tpy /1.52 ppb) = 329 tpy
In this case, based on modeling results for a more similar hypothetical source from Appendix A,
the project source emissions are less than the calculated NOx to 8-hr O3 MERP such that air
quality impacts of O3 from this source would be expected to be less than the critical air quality
threshold.
PM2.5 analysis: Both the NOx and SO2 emissions are well below the lowest (most conservative)
daily and annual PM2.5 MERP values of any source modeled in the eastern or any other region in
the continental U.S. However, the NOx and SO2 precursor contributions to both daily average
PM2.5 are considered together to determine if the source's air quality impact of PM2.5 would
exceed the critical air quality threshold. In this case, the proposed emissions increase can be
expressed as a percent of the lowest MERP for each precursor and then summed. A value less
than 100% indicates that the critical air quality threshold would not be exceeded when
considering the combined impacts of these precursors on daily and/or annual PM2.5.
Example calculation for additive secondary impacts on daily PM2.5:
(310 tpy NOx from source/1155 tpy NOx daily PM2.5 MERP) + (75 tpy SO2 from source/225 TPY
S02 daily PM2.5 MERP) = .27 + .33 = .60 * 100 = 60%
Example calculation for additive secondary impacts on annual PM2.5:
(310 tpy NOx from source/3184 tpy NOx annual PM2.5 MERP) + (75 tpy SO2 from source/2289
TPY S02 annual PM2.5 MERP) = .097 + .033 = .13 * 100 = 13%
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Scenario C: NOx and SO2 precursor assessment for comparable source O3 and PM2.5 impacts
In this scenario, a facility with a proposed increase in emissions of 0 TPY of primary PM2.5, 22
TPY of VOC, 920 TPY of NOx, and 259 TPY of SO2 located in the western region. Only NOx and
SO2 emissions are above the level of the SER and therefore require a PSD compliance
demonstration.
O3 analysis: The NOx emissions of 920 tpy are larger than the lowest (most conservative) NOx
MERP for 8-hr O3 in the western and other regions of the U.S. such that air quality impacts of
03 from this source would be expected to exceed the critical air quality threshold. A
comparable hypothetical source is identified that may be representative of this source (e.g.,
WUS region, source 16 elevated release as shown in Appendix A) had a range of NOx MERPs for
8-hr O3 of 761 to 1,020 TPY, which are all larger than the source emissions modification. The
general formula for estimating MERPs is provided in section 5. Here, the equation is used with
the modeled emissions rates and air quality impact information from source 19 of the EUS
region with an elevated release (as detailed in Appendix Table A-l). Since multiple hypothetical
sources were modeled at this location with an elevated release the source with the lowest
MERP was selected for comparison with the project source, i.e.,
MERP for source 16 WUS region elev. release (tpy) = 1.0 ppb * (1000 tpy /1.31 ppb) = 763 tpy
In this case, based on modeling results for a more similar hypothetical source from Appendix A,
the project source emissions are still greater than the calculated NOx to 8-hr O3 MERP such that
air quality impacts of 03 from this source are expected to exceed the critical air quality
threshold.
PM2.5 analysis: The NOx emissions of 920 are marginally below the lowest (most conservative)
daily and annual PM2.5 MERP value of any source modeled in the continental U.S., while the SO2
emissions of 259 tpy are comparable to the lowest daily PM2.5 MERP value of any source
modeled in the western U.S. region. A hypothetical source considered more similar (e.g., WUS
region, source 16 elevated release as shown in Appendix A) has a lowest NOx MERP for daily
PM2.5 of 16,667 TPY and SO2 MERP for daily PM2.5 of 5,556 TPY, which are both much larger
than the increase in emissions of the project such that the source's impact on PM2.5 would be
expected to be less than the critical air quality threshold.
Scenario D: NOx and SO2 precursor assessment for additive secondary PM2.5 impacts along
with direct PM2.5
In this scenario, a facility with a proposed increase in emissions of 250 TPY of primary PM2.5, 0
TPY of VOC, 310 TPY of NOx, and 75 TPY of SO2 located in the southeast region. Only NOx and
SO2 emissions are above the level of the SER and therefore require a PSD compliance
demonstration. This scenario is similar to Scenario B above, except that the primary PM2.5
32
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Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
emissions must be accounted for in assessing PM2.5 along with the secondary impacts of NOx
and SO2 precursor emissions as part of the Tier 1 demonstration.
O3 analysis: Please see Scenario B above.
PM7.5 analysis: Similar to Scenario B, when considering NOx and SO2 contributions to daily
average PM2.5 together, the proposed emissions increased expressed as a percent of the lowest
(most conservative) MERP and summed is less than 100% indicating the critical air quality
threshold would be not be exceeded when considering the additive impacts of these
precursors. However, in this example, the primary PM2.5 impacts need to be added to the
secondary impacts for an appropriate account of total PM2.5 impacts for the comparison to the
air quality threshold. The primary PM2.5 impacts should be estimated using AERMOD or an
approved alternative model as outlined in the Guideline (U.S. Environmental Protection Agency,
2015a) and consistent with EPA guidance for combining primary and secondary impacts of
PM2.5 for permit program assessments. In this scenario, a representative secondary impact for
this source is added to the appropriately estimated primary PM2.5 impacts. The highest impact
at any receptor for primary PM2.5 should be divided by the air quality threshold to estimate the
percent contribution and determine if that primary contribution exceeds the 40% remaining
after secondary impacts are accounted for using MERPs demonstration tool.
For example, a peak primary PM2.5 impact from AERMOD is estimated to be 0.45 ug/m3 for the
scenario above. Compared with a 1.2 ug/m3 critical air quality threshold means that the
primary impact is 35% of the critical air quality threshold. When this primary impact is summed
with the secondary impacts of 60% the total is 95% which is below 100% suggesting this source
impact is below the critical air quality threshold.
Alternatively, if the peak primary PM2.5 impact from AERMOD is estimated to be 0.8 ug/m3 for
the above scenario then the percent primary contribution to the critical air quality threshold
would be 62%. When summed with the secondary contribution of 60%, the total source impact
exceeds 100% and therefore is greater than the critical air quality threshold.
7,1, ,| | In iiu ii . 1 iIIin \ II '\,\ ses.'iih in -Midi Illustrative I III Til 1 llii !i 1 !u .1
:ations
In July 2015, EPA proposed revisions to the Guideline that recommend a two-tiered approach
for addressing single-source impacts on O3 or secondary PM2.5 (U.S. Environmental Protection
Agency, 2015a) with the first tier (or Tier 1) involving use of appropriate and technically credible
relationships between emissions and ambient impacts developed from existing modeling
studies deemed sufficient for evaluating a project source's impacts. To the extent the final
revisions to the Guideline continue to reflect this two-tiered approach, this guidance document
was developed by EPA to provide a framework that applicants might choose to apply, in
consultation with the appropriate permitting authority, to develop and use MERPs in estimating
single-source impacts on secondary pollutants under the proposed first tier approach (i.e.,
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Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
Sections 5.3.2.b and 5.4.2.b of the proposed Guideline). As illustrated in the examples above,
use of MERPs for each precursor as a Tier 1 demonstration tool could be based on either the
most conservative (lowest) value across a region/area or the source-specific value derived from
a more similar hypothetical source modeled by a permit applicant, permitting authority or EPA.
Consistent with the proposed recommendations in EPA's Guideline, the appropriate tier for a
given application should be selected in consultation with the appropriate reviewing authority
(paragraph 3.0(b)) and be consistent with EPA guidance. If the two-tiered approach is included
in the final Guideline revisions, permit applicants could choose to utilize the EPA analytical work
reflected in this draft guidance as part of a Tier 1 demonstration for a specific PSD permit
situation. To do so, one approach could be for the permit applicant to present to the
appropriate permitting authority a technically credible justification that the emissions
characteristics (e.g., stack height) of the specific source described in a permit application and
the chemical and physical environment in the vicinity of that proposed source are adequately
represented by the various hypothetical sources modeled by EPA, such that the most
conservative (lowest) illustrative MERP values in a region or across the US, as shown in Table 7-
1, may be appropriate to use for the Tier 1 demonstration in an individual permit application.
Another possible approach would be for the permit applicant to show the appropriate
permitting authority that the project source is more similar to certain hypothetical sources
modeled as part of the EPA's assessment presented in Chapter 5. If that is the case, then the
detailed results presented in Appendix A may better represent the O3 or PM2.5 impacts of the
project source or specific location. If either of these approaches is contemplated, EPA
recommends that the permit applicant consult with the appropriate permit reviewing authority
in developing a modeling protocol and that both parties confirm, at that time, the
appropriateness of using these modeling results for any particular permitting situation. As part
of the protocol, the permit applicant should include a narrative that provides a technical
justification that the existing information is relevant for the project source scenario.
Acki. ' I ><[\ m> inK
The report includes contributions from Kirk Baker, Tyler Fox, James Kelly, George Bridgers, Chris
Owen, Chuck Buckler, Dan Deroeck, and Jennifer Shaltanis.
34
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Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
9 Fv h-h In '
Baker, K.R., Foley, K.M., 2011. A nonlinear regression model estimating single source
concentrations of primary and secondarily formed PM2.5. Atmospheric Environment 45, 3758-
3767.
Baker, K.R., Kelly, J.T., 2014. Single source impacts estimated with photochemical model
source sensitivity and apportionment approaches. Atmospheric Environment 96, 266-274.
Baker, K.R., Kelly, J.T., Fox, T., 2013. Estimating second pollutant impacts from single sources
Baker, K.R., Kotchenruther, R., Kay, R., 2015a. Estimating 03 and secondary PM2.5 impacts
from hypothetical single source emissions from the central and eastern United States, in
preparation to be submitted to Atmospheric Pollution Research.
Baker, K.R., Kotchenruther, R.A., Hudman, R.C., 2015b. Estimating ozone and secondary PM
2.5 impacts from hypothetical single source emissions in the central and eastern United States.
Atmospheric Pollution Research 7, 122-133.
Bergin, M.S., Russell, A.G., Odman, M.T., Cohan, D.S., Chameldes, W.L., 2008. Single-Source
Impact Analysis Using Three-Dimensional Air Quality Models. Journal of the Air & Waste
Management Association 58, 1351-1359.
Byun, D., Schere, K.L., 2006. Review of the governing equations, computational algorithms, and
other components of the models-3 Community Multiscale Air Quality (CMAQ) modeling
system. Applied Mechanics Reviews 59, 51-77.
Chen, J., Lu, J., Avise, J.C., DaMassa, J.A., Kleeman, M.J., Kaduwela, A.P., 2014. Seasonal
modeling of PM 2.5 in California's San Joaquin Valley. Atmospheric Environment 92, 182-190.
Civerolo, K., Hogrefe, C., Zalewsky, E., Hao, W., Sistla, G., Lynn, B., Rosenzweig, C., Kinney,
P.L., 2010. Evaluation of an 18-year CMAQ simulation: Seasonal variations and long-term
temporal changes in sulfate and nitrate. Atmospheric environment 44, 3745-3752.
Cohan, D.S., Napelenok, S.L., 2011. Air quality response modeling for decision support.
Atmosphere 2, 407-425.
Dunker, A.M., Yarwood, G., Ortmann, J.P., Wilson, G.M., 2002. The decoupled direct method
for sensitivity analysis in a three-dimensional air quality model - Implementation, accuracy, and
efficiency. Environmental Science & Technology 36, 2965-2976.
ENVIRON, 2012. Evaluation of chemical dispersion models using atmospheric plume
measurements from field experiments, EPA Contract No: EP-D-07-102. September 2012. 06-
20443M6.
ENVIRON, 2014. User's Guide Comprehensive Air Quality Model with Extensions version 6,
www.catiix.com. ENVIRON International Corporation, Novato.
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Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
Kelly, J.T., Baker, K.R., Napelenok, S.L., Roselle, S.J., 2015. Examining single-source
secondary impacts estimated from brute-force, decoupled direct method, and advanced plume
treatment approaches. Atmospheric Environment 111, 10-19.
Kwok, R., Baker, K., Napelenok, S., Tonnesen, G., 2015. Photochemical grid model
implementation of VOC, NO x, and O 3 source apportionment. Geoscientific Model
Development 8, 99-114.
Kwok, R., Napelenok, S., Baker, K., 2013. Implementation and evaluation of PM2.5 source
contribution analysis in a photochemical model. Atmospheric Environment 80, 398-407.
Pun, B.K., Seigneur, C., Bailey, E.M., Gautney, L.L., Douglas, S.G., Haney, J.L., Kumar, N.,
2007. Response of atmospheric particulate matter to changes in precursor emissions: A
comparison of three air quality models. Environmental science & technology 42, 831-837.
Russell, A.G., 2008. EPA Supersites program-related emissions-based particulate matter
modeling: initial applications and advances. Journal of the Air & Waste Management
Association 58, 289-302.
Seinfeld, J.H., Pandis, S.N., 2012. Atmospheric chemistry and physics: from air pollution to
climate change. John Wiley & Sons.
Stockwell, W.R., Watson, J.G., Robinson, N.F., Steiner, W., Sylte, W.W., 2000. The ammonium
nitrate particle equivalent of NO x emissions for wintertime conditions in Central California's
San Joaquin Valley. Atmospheric Environment 34, 4711-4717.
Tesche, T., Morris, R., Tonnesen, G., McNally, D., Boylan, J., Brewer, P., 2006. CMAQ/CAMx
annual 2002 performance evaluation over the eastern US. Atmospheric Environment 40, 4906-
4919.
U.S. Environmental Protection Agency, 2005. 40 CFR, Part 51, Appendix W. Revision to the
Guideline on Air Quality Models, 68 FR 68235-68236, November 9, 2005.
U.S. Environmental Protection Agency, 2015a. 40 CFR, Part 51, Revision to the Guideline on
Air Quality Models: Enhancements to the AERMOD Dispersion Modeling System and
Incorporation of Approaches To Address Ozone and Fine Particulate Matter; Proposed Rule, FR
Vol. 80 No. 145, 45340-45387, July 29, 2015.
U.S. Environmental Protection Agency, 2015b. Guidance on the use of models for assessing the
impacts from single sources on secondarily formed pollutants ozone and PM2.5. EPA 454/P-15-
001.
U.S. Environmental Protection Agency, 2016. Interagency Workgroup on Air Quality Modeling
(IWAQM) Phase 3 Summary Report: Near-Field Single Source Secondary Impacts. EPA-454/R-
16-003.
Zhou, W., Cohan, D.S., Pinder, R.W., Neuman, J.A., Holloway, J.S., Peischl, J., Ryerson, T.B.,
Nowak, J.B., Flocke, F., Zheng, W.G., 2012. Observation and modeling of the evolution of
Texas power plant plumes. Atmospheric Chemistry and Physics 12, 455-468.
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Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
I I II HI II w I c\ Iil ill Iii|- II". t /een hyp. ih II h hi¦> ,n ! in , iiiiurn
/inwind impacts
The following table presents the maximum impacts for sources modeled with annual
simulations. The highest 8-hr O3 impacts are shown in Table A-l, daily PM2.5 in Table A-2, and
annual average PM2.5 in Table A-3. These sources are described in more detail elsewhere (Baker
et al., 2015b; U.S. Environmental Protection Agency, 2016). Emissions are shown in tons per
year (tpy) and release heights relate to surface release (L) or elevated release (H). Source type
"L" refers to sources modeled with surface level emissions releases: stack height of 1 m, stack
diameter of 5 m, exit temperature of 311 K, exit velocity of 27 m/s, and flow rate of 537 m3/s.
Source type "H" refers to sources modeled with elevated emissions releases: stack height of 90
m, stack diameter of 5 m, exit temperature of 311 K, exit velocity of 27 m/s, and flow rate of
537 m3/s. The source number are shown by location in the map below (Figures A-l, A-2, and A-
3). With respect to the areas, CUS=central U.S., WUS=western U.S., and EUS=eastern U.S.
Impacts shown are the maximum daily PM2.5 impacts, maximum annual PM2.5 impacts, and
maximum daily 8-hr maximum impacts over annual simulations.
37
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Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
Figure A-l. Hypothetical source locations for the eastern U.S. (EUS) domain.
Model Domain and Hypothetical Sources
O
o
o
o
o
IT)
o
o
lO
I
o
o
o
o
o
in
c
Q
o
ra
?
c
5
O
- 0.6
1.0
- 0.8
- 0.4
- 0.2
0.0
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Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
Figure A-2. Hypothetical source locations for the central U.S. (CUS) domain.
Model Domain and Hypothetical Sources
C
Q
o
ra
?
c
5
O
- 0.8
- 0.6
1.0
0.4
0.2
0.0
39
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Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
Figure A-3. Hypothetical source locations for the western U.S. (WUS) domain.
Model Domain and Hypothetical Sources
O
o
o
o
o
to
o
o
m
i
c
Q
o
ra
?
c
5
O
- 0.8
- 0.6
1.0
0.4
0.2
0.0
40
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Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
Table A-l. Highest daily maximum 8-hr O3 impacts from NOx and VOC sources from multiple
hypothetical source model simulations. Source locations are shown in Figures A1-A3.
Max
Emissions
Impact
Precursor
Area
(tpy)
Height
Source
FIPS
State
County
(PPb)
NOx
cus
500
H
1
18127
Indiana
Porter
1.15
NOx
cus
500
H
2
18037
Indiana
Dubois
2.11
NOx
cus
500
H
3
47055
Tennessee
Giles
3.21
NOx
cus
500
H
4
1001
Alabama
Autauga
2.41
NOx
cus
500
H
5
12005
Florida
Bay
1.04
NOx
cus
500
H
6
17155
Illinois
Putnam
1.34
NOx
cus
500
H
7
17145
Illinois
Perry
3.88
NOx
cus
500
H
8
47157
Tennessee
Shelby
0.70
NOx
cus
500
H
9
28129
Mississippi
Smith
2.60
NOx
cus
500
H
10
22071
Louisiana
Orleans
1.33
NOx
cus
500
H
11
19095
Iowa
Iowa
1.37
NOx
cus
500
H
12
29029
Missouri
Camden
2.12
NOx
cus
500
H
13
5119
Arkansas
Pulaski
0.97
NOx
cus
500
H
14
22061
Louisiana
Lincoln
1.91
NOx
cus
500
H
15
22001
Louisiana
Acadia
2.51
NOx
cus
500
H
16
31055
Nebraska
Douglas
1.43
NOx
cus
500
H
17
20091
Kansas
Johnson
1.19
NOx
cus
500
H
18
40101
Oklahoma
Muskogee
1.43
NOx
cus
500
H
19
48213
Texas
Henderson
1.93
NOx
cus
500
H
20
48201
Texas
Harris
0.78
NOx
cus
500
H
21
31001
Nebraska
Adams
1.27
NOx
cus
500
H
22
20155
Kansas
Reno
1.33
NOx
cus
500
H
23
40017
Oklahoma
Canadian
0.57
NOx
cus
500
H
24
48367
Texas
Parker
1.30
NOx
cus
500
H
25
48187
Texas
Guadalupe
0.72
NOx
cus
500
L
1
18127
Indiana
Porter
1.18
NOx
cus
500
L
2
18037
Indiana
Dubois
2.14
NOx
cus
500
L
3
47055
Tennessee
Giles
3.07
NOx
cus
500
L
4
1001
Alabama
Autauga
2.43
NOx
cus
500
L
5
12005
Florida
Bay
1.13
NOx
cus
500
L
6
17155
Illinois
Putnam
1.40
NOx
cus
500
L
7
17145
Illinois
Perry
3.97
NOx
cus
500
L
8
47157
Tennessee
Shelby
0.69
NOx
cus
500
L
9
28129
Mississippi
Smith
2.63
NOx
cus
500
L
10
22071
Louisiana
Orleans
1.36
NOx
cus
500
L
11
19095
Iowa
Iowa
1.39
NOx
cus
500
L
12
29029
Missouri
Camden
2.29
NOx
cus
500
L
13
5119
Arkansas
Pulaski
0.95
NOx
cus
500
L
14
22061
Louisiana
Lincoln
1.97
NOx
cus
500
L
15
22001
Louisiana
Acadia
2.53
NOx
cus
500
L
16
31055
Nebraska
Douglas
1.50
NOx
cus
500
L
17
20091
Kansas
Johnson
1.20
NOx
cus
500
L
18
40101
Oklahoma
Muskogee
1.46
NOx
cus
500
L
19
48213
Texas
Henderson
2.00
NOx
cus
500
L
20
48201
Texas
Harris
0.79
NOx
cus
500
L
21
31001
Nebraska
Adams
1.25
NOx
cus
500
L
22
20155
Kansas
Reno
1.35
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Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
NOx
CUS
500
L
23
40017
Oklahoma
Canadian
0.58
NOx
CUS
500
L
24
48367
Texas
Parker
1.29
NOx
CUS
500
L
25
48187
Texas
Guadalupe
0.73
NOx
CUS
1000
H
1
18127
Indiana
Porter
2.04
NOx
CUS
1000
H
2
18037
Indiana
Dubois
3.76
NOx
CUS
1000
H
3
47055
Tennessee
Giles
5.39
NOx
CUS
1000
H
4
1001
Alabama
Autauga
4.20
NOx
CUS
1000
H
5
12005
Florida
Bay
1.94
NOx
CUS
1000
H
6
17155
Illinois
Putnam
2.40
NOx
CUS
1000
H
7
17145
Illinois
Perry
6.49
NOx
CUS
1000
H
8
47157
Tennessee
Shelby
1.29
NOx
CUS
1000
H
9
28129
Mississippi
Smith
4.43
NOx
CUS
1000
H
10
22071
Louisiana
Orleans
2.48
NOx
CUS
1000
H
11
19095
Iowa
Iowa
2.45
NOx
CUS
1000
H
12
29029
Missouri
Camden
3.82
NOx
CUS
1000
H
13
5119
Arkansas
Pulaski
1.85
NOx
CUS
1000
H
14
22061
Louisiana
Lincoln
3.57
NOx
CUS
1000
H
15
22001
Louisiana
Acadia
4.58
NOx
CUS
1000
H
16
31055
Nebraska
Douglas
2.64
NOx
CUS
1000
H
17
20091
Kansas
Johnson
2.25
NOx
CUS
1000
H
18
40101
Oklahoma
Muskogee
2.61
NOx
CUS
1000
H
19
48213
Texas
Henderson
3.46
NOx
CUS
1000
H
20
48201
Texas
Harris
1.35
NOx
CUS
1000
H
21
31001
Nebraska
Adams
1.88
NOx
CUS
1000
H
22
20155
Kansas
Reno
2.40
NOx
CUS
1000
H
23
40017
Oklahoma
Canadian
1.09
NOx
CUS
1000
H
24
48367
Texas
Parker
2.31
NOx
CUS
1000
H
25
48187
Texas
Guadalupe
1.34
NOx
CUS
3000
H
1
18127
Indiana
Porter
2.81
NOx
CUS
3000
H
2
18037
Indiana
Dubois
8.83
NOx
CUS
3000
H
3
47055
Tennessee
Giles
10.36
NOx
CUS
3000
H
4
1001
Alabama
Autauga
9.38
NOx
CUS
3000
H
5
12005
Florida
Bay
4.55
NOx
CUS
3000
H
6
17155
Illinois
Putnam
5.14
NOx
CUS
3000
H
7
17145
Illinois
Perry
12.34
NOx
CUS
3000
H
8
47157
Tennessee
Shelby
2.23
NOx
CUS
3000
H
9
28129
Mississippi
Smith
10.42
NOx
CUS
3000
H
10
22071
Louisiana
Orleans
6.02
NOx
CUS
3000
H
11
19095
Iowa
Iowa
4.43
NOx
CUS
3000
H
12
29029
Missouri
Camden
9.14
NOx
CUS
3000
H
13
5119
Arkansas
Pulaski
4.77
NOx
CUS
3000
H
14
22061
Louisiana
Lincoln
8.41
NOx
CUS
3000
H
15
22001
Louisiana
Acadia
10.52
NOx
CUS
3000
H
16
31055
Nebraska
Douglas
6.22
NOx
CUS
3000
H
17
20091
Kansas
Johnson
5.68
NOx
CUS
3000
H
18
40101
Oklahoma
Muskogee
6.35
NOx
CUS
3000
H
19
48213
Texas
Henderson
8.42
NOx
CUS
3000
H
20
48201
Texas
Harris
2.81
NOx
CUS
3000
H
21
31001
Nebraska
Adams
2.46
NOx
CUS
3000
H
22
20155
Kansas
Reno
4.69
NOx
CUS
3000
H
23
40017
Oklahoma
Canadian
2.79
NOx
CUS
3000
H
24
48367
Texas
Parker
5.14
NOx
CUS
3000
H
25
48187
Texas
Guadalupe
3.06
NOx
EUS
500
H
1
23003
Maine
Aroostook
2.09
42
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Does not represent final agency action;
NOx
EUS
500
H
2
NOx
EUS
500
H
3
NOx
EUS
500
H
4
NOx
EUS
500
H
5
NOx
EUS
500
H
7
NOx
EUS
500
H
8
NOx
EUS
500
H
9
NOx
EUS
500
H
10
NOx
EUS
500
H
11
NOx
EUS
500
H
12
NOx
EUS
500
H
13
NOx
EUS
500
H
14
NOx
EUS
500
H
15
NOx
EUS
500
H
16
NOx
EUS
500
H
17
NOx
EUS
500
H
18
NOx
EUS
500
H
19
NOx
EUS
500
L
1
NOx
EUS
500
L
2
NOx
EUS
500
L
3
NOx
EUS
500
L
4
NOx
EUS
500
L
5
NOx
EUS
500
L
7
NOx
EUS
500
L
8
NOx
EUS
500
L
9
NOx
EUS
500
L
10
NOx
EUS
500
L
11
NOx
EUS
500
L
12
NOx
EUS
500
L
13
NOx
EUS
500
L
14
NOx
EUS
500
L
15
NOx
EUS
500
L
16
NOx
EUS
500
L
17
NOx
EUS
500
L
18
NOx
EUS
500
L
19
NOx
EUS
1000
H
1
NOx
EUS
1000
H
2
NOx
EUS
1000
H
3
NOx
EUS
1000
H
4
NOx
EUS
1000
H
5
NOx
EUS
1000
H
7
NOx
EUS
1000
H
8
NOx
EUS
1000
H
9
NOx
EUS
1000
H
10
NOx
EUS
1000
H
11
NOx
EUS
1000
H
12
NOx
EUS
1000
H
13
NOx
EUS
1000
H
14
NOx
EUS
1000
H
15
NOx
EUS
1000
H
16
NOx
EUS
1000
H
17
NOx
EUS
1000
H
18
NOx
EUS
1000
H
19
NOx
EUS
3000
H
1
Public Review and Comment, 12/01/2016
Maine
York
0.81
Massachusetts
Norfolk
0.72
Massachusetts
Franklin
1.97
New York
Bronx
0.09
New York
Livingston
1.09
Pennsylvania
Adams
1.66
Virginia
Dinwiddie
2.01
South Carolina
Horry
2.06
Michigan
Macomb
0.94
Ohio
Tuscarawas
1.35
North Carolina
Ashe
1.87
South Carolina
Allendale
2.88
Michigan
Marquette
0.52
Michigan
Montcalm
2.16
Indiana
Grant
1.78
Kentucky
Barren
2.95
Alabama
Tallapoosa
1.53
Maine
Aroostook
2.18
Maine
York
0.79
Massachusetts
Norfolk
0.74
Massachusetts
Franklin
1.95
New York
Bronx
0.09
New York
Livingston
1.07
Pennsylvania
Adams
1.67
Virginia
Dinwiddie
2.00
South Carolina
Horry
2.11
Michigan
Macomb
0.94
Ohio
Tuscarawas
1.36
North Carolina
Ashe
1.81
South Carolina
Allendale
2.94
Michigan
Marquette
0.70
Michigan
Montcalm
2.20
Indiana
Grant
1.80
Kentucky
Barren
2.91
Alabama
Tallapoosa
1.87
Maine
Aroostook
2.89
Maine
York
1.42
Massachusetts
Norfolk
1.35
Massachusetts
Franklin
3.42
New York
Bronx
0.18
New York
Livingston
1.98
Pennsylvania
Adams
2.98
Virginia
Dinwiddie
3.41
South Carolina
Horry
3.66
Michigan
Macomb
1.70
Ohio
Tuscarawas
2.44
North Carolina
Ashe
3.14
South Carolina
Allendale
4.99
Michigan
Marquette
0.99
Michigan
Montcalm
3.83
Indiana
Grant
3.17
Kentucky
Barren
5.03
Alabama
Tallapoosa
3.06
Maine
Aroostook
4.74
Draft for
23031
25021
25011
36005
36051
42001
51053
45051
26099
39157
37009
45005
26103
26117
18053
21009
1123
23003
23031
25021
25011
36005
36051
42001
51053
45051
26099
39157
37009
45005
26103
26117
18053
21009
1123
23003
23031
25021
25011
36005
36051
42001
51053
45051
26099
39157
37009
45005
26103
26117
18053
21009
1123
23003
43
-------
Does not represent final agency action;
NOx
EUS
3000
H
2
NOx
EUS
3000
H
3
NOx
EUS
3000
H
4
NOx
EUS
3000
H
5
NOx
EUS
3000
H
7
NOx
EUS
3000
H
8
NOx
EUS
3000
H
9
NOx
EUS
3000
H
10
NOx
EUS
3000
H
11
NOx
EUS
3000
H
12
NOx
EUS
3000
H
13
NOx
EUS
3000
H
14
NOx
EUS
3000
H
15
NOx
EUS
3000
H
16
NOx
EUS
3000
H
17
NOx
EUS
3000
H
18
NOx
EUS
3000
H
19
NOx
WUS
500
H
1
NOx
WUS
500
H
2
NOx
WUS
500
H
3
NOx
WUS
500
H
4
NOx
WUS
500
H
5
NOx
WUS
500
H
6
NOx
WUS
500
H
7
NOx
WUS
500
H
8
NOx
WUS
500
H
9
NOx
WUS
500
H
10
NOx
WUS
500
H
11
NOx
WUS
500
H
12
NOx
WUS
500
H
13
NOx
WUS
500
H
14
NOx
WUS
500
H
15
NOx
WUS
500
H
16
NOx
WUS
500
H
17
NOx
WUS
500
H
18
NOx
WUS
500
H
19
NOx
WUS
500
H
20
NOx
WUS
500
H
21
NOx
WUS
500
H
22
NOx
WUS
500
H
23
NOx
WUS
500
H
24
NOx
WUS
500
H
25
NOx
WUS
500
H
26
NOx
WUS
500
L
1
NOx
WUS
500
L
2
NOx
WUS
500
L
3
NOx
WUS
500
L
4
NOx
WUS
500
L
5
NOx
WUS
500
L
6
NOx
WUS
500
L
7
NOx
WUS
500
L
8
NOx
WUS
500
L
9
NOx
WUS
500
L
10
NOx
WUS
500
L
11
Public Review and Comment, 12/01/2016
Maine
York
3.49
Massachusetts
Norfolk
3.12
Massachusetts
Franklin
6.06
New York
Bronx
0.52
New York
Livingston
4.23
Pennsylvania
Adams
6.61
Virginia
Dinwiddie
6.59
South Carolina
Horry
8.58
Michigan
Macomb
3.43
Ohio
Tuscarawas
4.99
North Carolina
Ashe
6.34
South Carolina
Allendale
11.24
Michigan
Marquette
2.52
Michigan
Montcalm
7.31
Indiana
Grant
4.69
Kentucky
Barren
10.69
Alabama
Tallapoosa
6.49
North Dakota
Mercer
0.15
North Dakota
Morton
2.72
Colorado
Weld
1.73
Colorado
Bent
2.08
Texas
Terry
1.17
Montana
Richland
1.94
Montana
Powder River
1.70
Colorado
Larimer
0.59
Colorado
Saguache
1.93
New Mexico
Otero
1.16
Montana
Yellowstone
1.39
Utah
Duchesne
1.23
Utah
San Juan
1.19
Arizona
Gila
1.18
Utah
Utah
1.40
Utah
Iron
0.82
Arizona
La Paz
2.33
Oregon
Morrow
1.94
Nevada
Churchill
2.25
California
Tulare
1.32
California
Los Angeles
0.84
Washington
Skagit
0.14
Washington
Klickitat
2.32
California
Plumas
1.34
California
Merced
1.22
California
Kern
1.51
North Dakota
Mercer
0.18
North Dakota
Morton
2.71
Colorado
Weld
1.77
Colorado
Bent
2.13
Texas
Terry
1.20
Montana
Richland
1.89
Montana
Powder River
1.51
Colorado
Larimer
0.61
Colorado
Saguache
1.95
New Mexico
Otero
1.21
Montana
Yellowstone
1.41
Draft for
23031
25021
25011
36005
36051
42001
51053
45051
26099
39157
37009
45005
26103
26117
18053
21009
1123
38057
38059
8123
8011
48445
30083
30075
8069
8109
35035
30111
49013
49037
4007
49049
49021
4012
41049
32001
6107
6037
53057
53039
6063
6047
6029
38057
38059
8123
8011
48445
30083
30075
8069
8109
35035
30111
44
-------
Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
NOx
WUS
500
L
12
49013
Utah
Duchesne
1.28
NOx
WUS
500
L
13
49037
Utah
San Juan
1.43
NOx
WUS
500
L
14
4007
Arizona
Gila
1.23
NOx
WUS
500
L
15
49049
Utah
Utah
1.95
NOx
WUS
500
L
16
49021
Utah
Iron
0.69
NOx
WUS
500
L
17
4012
Arizona
La Paz
2.34
NOx
WUS
500
L
18
41049
Oregon
Morrow
1.94
NOx
WUS
500
L
19
32001
Nevada
Churchill
2.29
NOx
WUS
500
L
20
6107
California
Tulare
1.31
NOx
WUS
500
L
21
6037
California
Los Angeles
0.84
NOx
WUS
500
L
22
53057
Washington
Skagit
0.16
NOx
WUS
500
L
23
53039
Washington
Klickitat
2.52
NOx
WUS
500
L
24
6063
California
Plumas
1.33
NOx
WUS
500
L
25
6047
California
Merced
1.15
NOx
WUS
500
L
26
6029
California
Kern
1.46
NOx
WUS
1000
H
1
38057
North Dakota
Mercer
0.29
NOx
WUS
1000
H
2
38059
North Dakota
Morton
4.53
NOx
WUS
1000
H
3
8123
Colorado
Weld
2.95
NOx
WUS
1000
H
4
8011
Colorado
Bent
3.57
NOx
WUS
1000
H
5
48445
Texas
Terry
2.04
NOx
WUS
1000
H
6
30083
Montana
Richland
3.44
NOx
WUS
1000
H
7
30075
Montana
Powder River
2.96
NOx
WUS
1000
H
8
8069
Colorado
Larimer
1.06
NOx
WUS
1000
H
9
8109
Colorado
Saguache
3.38
NOx
WUS
1000
H
10
35035
New Mexico
Otero
1.94
NOx
WUS
1000
H
11
30111
Montana
Yellowstone
2.39
NOx
WUS
1000
H
12
49013
Utah
Duchesne
2.04
NOx
WUS
1000
H
13
49037
Utah
San Juan
2.41
NOx
WUS
1000
H
14
4007
Arizona
Gila
2.17
NOx
WUS
1000
H
15
49049
Utah
Utah
2.15
NOx
WUS
1000
H
16
49021
Utah
Iron
1.31
NOx
WUS
1000
H
17
4012
Arizona
La Paz
4.10
NOx
WUS
1000
H
18
41049
Oregon
Morrow
2.71
NOx
WUS
1000
H
19
32001
Nevada
Churchill
3.81
NOx
WUS
1000
H
20
6107
California
Tulare
2.31
NOx
WUS
1000
H
21
6037
California
Los Angeles
1.46
NOx
WUS
1000
H
22
53057
Washington
Skagit
0.27
NOx
WUS
1000
H
23
53039
Washington
Klickitat
4.19
NOx
WUS
1000
H
24
6063
California
Plumas
2.36
NOx
WUS
1000
H
25
6047
California
Merced
1.57
NOx
WUS
1000
H
26
6029
California
Kern
2.55
NOx
WUS
3000
H
1
38057
North Dakota
Mercer
0.78
NOx
WUS
3000
H
2
38059
North Dakota
Morton
5.39
NOx
WUS
3000
H
3
8123
Colorado
Weld
5.40
NOx
WUS
3000
H
4
8011
Colorado
Bent
6.21
NOx
WUS
3000
H
5
48445
Texas
Terry
4.29
NOx
WUS
3000
H
6
30083
Montana
Richland
4.74
NOx
WUS
3000
H
7
30075
Montana
Powder River
5.90
NOx
WUS
3000
H
8
8069
Colorado
Larimer
2.54
NOx
WUS
3000
H
9
8109
Colorado
Saguache
6.72
NOx
WUS
3000
H
10
35035
New Mexico
Otero
3.87
NOx
WUS
3000
H
11
30111
Montana
Yellowstone
4.44
NOx
WUS
3000
H
12
49013
Utah
Duchesne
3.75
NOx
WUS
3000
H
13
49037
Utah
San Juan
5.24
45
-------
Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
NOx
wus
3000
H
14
4007
Arizona
Gila
4.20
NOx
wus
3000
H
15
49049
Utah
Utah
5.25
NOx
wus
3000
H
16
49021
Utah
Iron
2.94
NOx
wus
3000
H
17
4012
Arizona
La Paz
8.75
NOx
wus
3000
H
18
41049
Oregon
Morrow
4.73
NOx
wus
3000
H
19
32001
Nevada
Churchill
6.92
NOx
wus
3000
H
20
6107
California
Tulare
3.75
NOx
wus
3000
H
21
6037
California
Los Angeles
3.31
NOx
wus
3000
H
22
53057
Washington
Skagit
0.74
NOx
wus
3000
H
23
53039
Washington
Klickitat
7.95
NOx
wus
3000
H
24
6063
California
Plumas
4.93
NOx
wus
3000
H
25
6047
California
Merced
3.21
NOx
wus
3000
H
26
6029
California
Kern
4.05
VOC
cus
500
L
1
18127
Indiana
Porter
0.42
VOC
cus
500
L
2
18037
Indiana
Dubois
0.10
VOC
cus
500
L
3
47055
Tennessee
Giles
0.04
VOC
cus
500
L
4
1001
Alabama
Autauga
0.08
VOC
cus
500
L
5
12005
Florida
Bay
0.28
VOC
cus
500
L
6
17155
Illinois
Putnam
0.13
VOC
cus
500
L
7
17145
Illinois
Perry
0.11
VOC
cus
500
L
8
47157
Tennessee
Shelby
0.30
VOC
cus
500
L
9
28129
Mississippi
Smith
0.02
VOC
cus
500
L
10
22071
Louisiana
Orleans
0.22
VOC
cus
500
L
11
19095
Iowa
Iowa
0.14
VOC
cus
500
L
12
29029
Missouri
Camden
0.05
VOC
cus
500
L
13
5119
Arkansas
Pulaski
0.21
VOC
cus
500
L
14
22061
Louisiana
Lincoln
0.04
VOC
cus
500
L
15
22001
Louisiana
Acadia
0.12
VOC
cus
500
L
16
31055
Nebraska
Douglas
0.23
VOC
cus
500
L
17
20091
Kansas
Johnson
0.08
VOC
cus
500
L
18
40101
Oklahoma
Muskogee
0.14
VOC
cus
500
L
19
48213
Texas
Henderson
0.05
VOC
cus
500
L
20
48201
Texas
Harris
0.14
VOC
cus
500
L
21
31001
Nebraska
Adams
0.35
VOC
cus
500
L
22
20155
Kansas
Reno
0.09
VOC
cus
500
L
23
40017
Oklahoma
Canadian
0.07
VOC
cus
500
L
24
48367
Texas
Parker
0.17
VOC
cus
500
L
25
48187
Texas
Guadalupe
0.16
VOC
cus
1000
H
1
18127
Indiana
Porter
0.78
VOC
cus
1000
H
2
18037
Indiana
Dubois
0.20
VOC
cus
1000
H
3
47055
Tennessee
Giles
0.10
VOC
cus
1000
H
4
1001
Alabama
Autauga
0.12
VOC
cus
1000
H
5
12005
Florida
Bay
0.49
VOC
cus
1000
H
6
17155
Illinois
Putnam
0.29
VOC
cus
1000
H
7
17145
Illinois
Perry
0.22
VOC
cus
1000
H
8
47157
Tennessee
Shelby
0.64
VOC
cus
1000
H
9
28129
Mississippi
Smith
0.07
VOC
cus
1000
H
10
22071
Louisiana
Orleans
0.43
VOC
cus
1000
H
11
19095
Iowa
Iowa
0.37
VOC
cus
1000
H
12
29029
Missouri
Camden
0.09
VOC
cus
1000
H
13
5119
Arkansas
Pulaski
0.46
VOC
cus
1000
H
14
22061
Louisiana
Lincoln
0.09
VOC
cus
1000
H
15
22001
Louisiana
Acadia
0.22
VOC
cus
1000
H
16
31055
Nebraska
Douglas
0.59
46
-------
Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
voc
cus
1000
H
17
20091
Kansas
Johnson
0.16
voc
cus
1000
H
18
40101
Oklahoma
Muskogee
0.28
voc
cus
1000
H
19
48213
Texas
Henderson
0.15
voc
cus
1000
H
20
48201
Texas
Harris
0.29
voc
cus
1000
H
21
31001
Nebraska
Adams
0.69
voc
cus
1000
H
22
20155
Kansas
Reno
0.18
voc
cus
1000
H
23
40017
Oklahoma
Canadian
0.14
voc
cus
1000
H
24
48367
Texas
Parker
0.33
voc
cus
1000
H
25
48187
Texas
Guadalupe
0.34
voc
cus
1000
L
1
18127
Indiana
Porter
0.81
voc
cus
1000
L
2
18037
Indiana
Dubois
0.20
voc
cus
1000
L
3
47055
Tennessee
Giles
0.09
voc
cus
1000
L
4
1001
Alabama
Autauga
0.16
voc
cus
1000
L
5
12005
Florida
Bay
0.55
voc
cus
1000
L
6
17155
Illinois
Putnam
0.31
voc
cus
1000
L
7
17145
Illinois
Perry
0.36
voc
cus
1000
L
8
47157
Tennessee
Shelby
0.65
voc
cus
1000
L
9
28129
Mississippi
Smith
0.11
voc
cus
1000
L
10
22071
Louisiana
Orleans
0.47
voc
cus
1000
L
11
19095
Iowa
Iowa
0.42
voc
cus
1000
L
12
29029
Missouri
Camden
0.09
voc
cus
1000
L
13
5119
Arkansas
Pulaski
0.44
voc
cus
1000
L
14
22061
Louisiana
Lincoln
0.09
voc
cus
1000
L
15
22001
Louisiana
Acadia
0.26
voc
cus
1000
L
16
31055
Nebraska
Douglas
0.54
voc
cus
1000
L
17
20091
Kansas
Johnson
0.17
voc
cus
1000
L
18
40101
Oklahoma
Muskogee
0.29
voc
cus
1000
L
19
48213
Texas
Henderson
0.10
voc
cus
1000
L
20
48201
Texas
Harris
0.27
voc
cus
1000
L
21
31001
Nebraska
Adams
0.77
voc
cus
1000
L
22
20155
Kansas
Reno
0.19
voc
cus
1000
L
23
40017
Oklahoma
Canadian
0.15
voc
cus
1000
L
24
48367
Texas
Parker
0.33
voc
cus
1000
L
25
48187
Texas
Guadalupe
0.36
voc
cus
3000
H
1
18127
Indiana
Porter
2.00
voc
cus
3000
H
2
18037
Indiana
Dubois
0.89
voc
cus
3000
H
3
47055
Tennessee
Giles
0.75
voc
cus
3000
H
4
1001
Alabama
Autauga
0.66
voc
cus
3000
H
5
12005
Florida
Bay
1.29
voc
cus
3000
H
6
17155
Illinois
Putnam
1.70
voc
cus
3000
H
7
17145
Illinois
Perry
1.61
voc
cus
3000
H
8
47157
Tennessee
Shelby
2.24
voc
cus
3000
H
9
28129
Mississippi
Smith
0.61
voc
cus
3000
H
10
22071
Louisiana
Orleans
1.43
voc
cus
3000
H
11
19095
Iowa
Iowa
1.55
voc
cus
3000
H
12
29029
Missouri
Camden
0.35
voc
cus
3000
H
13
5119
Arkansas
Pulaski
1.59
voc
cus
3000
H
14
22061
Louisiana
Lincoln
0.29
voc
cus
3000
H
15
22001
Louisiana
Acadia
1.02
voc
cus
3000
H
16
31055
Nebraska
Douglas
2.05
voc
cus
3000
H
17
20091
Kansas
Johnson
0.58
voc
cus
3000
H
18
40101
Oklahoma
Muskogee
0.84
voc
cus
3000
H
19
48213
Texas
Henderson
0.51
voc
cus
3000
H
20
48201
Texas
Harris
1.09
47
-------
Does not represent final agency action;
voc
cus
3000
H
21
voc
cus
3000
H
22
voc
cus
3000
H
23
voc
cus
3000
H
24
voc
cus
3000
H
25
voc
EUS
500
H
1
voc
EUS
500
H
2
voc
EUS
500
H
3
voc
EUS
500
H
4
voc
EUS
500
H
5
voc
EUS
500
H
7
voc
EUS
500
H
8
voc
EUS
500
H
9
voc
EUS
500
H
10
voc
EUS
500
H
11
voc
EUS
500
H
12
voc
EUS
500
H
13
voc
EUS
500
H
14
voc
EUS
500
H
15
voc
EUS
500
H
16
voc
EUS
500
H
17
voc
EUS
500
H
18
voc
EUS
500
H
19
voc
EUS
500
L
1
voc
EUS
500
L
2
voc
EUS
500
L
3
voc
EUS
500
L
4
voc
EUS
500
L
5
voc
EUS
500
L
7
voc
EUS
500
L
8
voc
EUS
500
L
9
voc
EUS
500
L
10
voc
EUS
500
L
11
voc
EUS
500
L
12
voc
EUS
500
L
13
voc
EUS
500
L
14
voc
EUS
500
L
15
voc
EUS
500
L
16
voc
EUS
500
L
17
voc
EUS
500
L
18
voc
EUS
500
L
19
voc
EUS
1000
H
1
voc
EUS
1000
H
2
voc
EUS
1000
H
3
voc
EUS
1000
H
4
voc
EUS
1000
H
5
voc
EUS
1000
H
7
voc
EUS
1000
H
8
voc
EUS
1000
H
9
voc
EUS
1000
H
10
voc
EUS
1000
H
11
voc
EUS
1000
H
12
voc
EUS
1000
H
13
voc
EUS
1000
H
14
Public Review and Comment, 12/01/2016
Nebraska
Adams
3.16
Kansas
Reno
0.76
Oklahoma
Canadian
0.44
Texas
Parker
1.31
Texas
Guadalupe
1.29
Maine
Aroostook
0.12
Maine
York
0.22
Massachusetts
Norfolk
0.14
Massachusetts
Franklin
0.10
New York
Bronx
0.08
New York
Livingston
0.10
Pennsylvania
Adams
0.16
Virginia
Dinwiddie
0.07
South Carolina
Horry
0.03
Michigan
Macomb
0.28
Ohio
Tuscarawas
0.17
North Carolina
Ashe
0.03
South Carolina
Allendale
0.01
Michigan
Marquette
0.32
Michigan
Montcalm
0.20
Indiana
Grant
0.39
Kentucky
Barren
0.06
Alabama
Tallapoosa
0.05
Maine
Aroostook
0.14
Maine
York
0.23
Massachusetts
Norfolk
0.14
Massachusetts
Franklin
0.11
New York
Bronx
0.09
New York
Livingston
0.13
Pennsylvania
Adams
0.16
Virginia
Dinwiddie
0.06
South Carolina
Horry
0.03
Michigan
Macomb
0.25
Ohio
Tuscarawas
0.18
North Carolina
Ashe
0.06
South Carolina
Allendale
0.02
Michigan
Marquette
0.32
Michigan
Montcalm
0.22
Indiana
Grant
0.43
Kentucky
Barren
0.06
Alabama
Tallapoosa
0.06
Maine
Aroostook
0.26
Maine
York
0.44
Massachusetts
Norfolk
0.28
Massachusetts
Franklin
0.17
New York
Bronx
0.16
New York
Livingston
0.20
Pennsylvania
Adams
0.30
Virginia
Dinwiddie
0.16
South Carolina
Horry
0.07
Michigan
Macomb
0.51
Ohio
Tuscarawas
0.37
North Carolina
Ashe
0.08
South Carolina
Allendale
0.06
Draft for
31001
20155
40017
48367
48187
23003
23031
25021
25011
36005
36051
42001
51053
45051
26099
39157
37009
45005
26103
26117
18053
21009
1123
23003
23031
25021
25011
36005
36051
42001
51053
45051
26099
39157
37009
45005
26103
26117
18053
21009
1123
23003
23031
25021
25011
36005
36051
42001
51053
45051
26099
39157
37009
45005
48
-------
Does not represent final agency action;
voc
EUS
1000
H
15
voc
EUS
1000
H
16
voc
EUS
1000
H
17
voc
EUS
1000
H
18
voc
EUS
1000
H
19
voc
EUS
3000
H
1
voc
EUS
3000
H
2
voc
EUS
3000
H
3
voc
EUS
3000
H
4
voc
EUS
3000
H
5
voc
EUS
3000
H
7
voc
EUS
3000
H
8
voc
EUS
3000
H
9
voc
EUS
3000
H
10
voc
EUS
3000
H
11
voc
EUS
3000
H
12
voc
EUS
3000
H
13
voc
EUS
3000
H
14
voc
EUS
3000
H
15
voc
EUS
3000
H
16
voc
EUS
3000
H
17
voc
EUS
3000
H
18
voc
EUS
3000
H
19
voc
WUS
500
H
1
voc
WUS
500
H
2
voc
WUS
500
H
3
voc
WUS
500
H
4
voc
WUS
500
H
5
voc
WUS
500
H
6
voc
WUS
500
H
7
voc
WUS
500
H
8
voc
WUS
500
H
9
voc
WUS
500
H
10
voc
WUS
500
H
11
voc
WUS
500
H
12
voc
WUS
500
H
13
voc
WUS
500
H
14
voc
WUS
500
H
15
voc
WUS
500
H
16
voc
WUS
500
H
17
voc
WUS
500
H
18
voc
WUS
500
H
19
voc
WUS
500
H
20
voc
WUS
500
H
21
voc
WUS
500
H
22
voc
WUS
500
H
23
voc
WUS
500
H
24
voc
WUS
500
H
25
voc
WUS
500
H
26
voc
WUS
500
L
1
voc
WUS
500
L
2
voc
WUS
500
L
3
voc
WUS
500
L
4
voc
WUS
500
L
5
Public Review and Comment, 12/01/2016
Michigan
Marquette
0.60
Michigan
Montcalm
0.49
Indiana
Grant
0.75
Kentucky
Barren
0.13
Alabama
Tallapoosa
0.10
Maine
Aroostook
1.45
Maine
York
1.31
Massachusetts
Norfolk
0.80
Massachusetts
Franklin
0.47
New York
Bronx
0.48
New York
Livingston
1.16
Pennsylvania
Adams
0.76
Virginia
Dinwiddie
0.81
South Carolina
Horry
0.34
Michigan
Macomb
1.14
Ohio
Tuscarawas
1.15
North Carolina
Ashe
0.36
South Carolina
Allendale
0.43
Michigan
Marquette
1.41
Michigan
Montcalm
1.52
Indiana
Grant
2.01
Kentucky
Barren
0.90
Alabama
Tallapoosa
0.55
North Dakota
Mercer
0.21
North Dakota
Morton
0.29
Colorado
Weld
0.08
Colorado
Bent
0.05
Texas
Terry
0.03
Montana
Richland
0.15
Montana
Powder River
0.08
Colorado
Larimer
0.02
Colorado
Saguache
0.04
New Mexico
Otero
0.00
Montana
Yellowstone
0.13
Utah
Duchesne
0.05
Utah
San Juan
0.03
Arizona
Gila
0.02
Utah
Utah
0.29
Utah
Iron
0.04
Arizona
La Paz
0.02
Oregon
Morrow
0.46
Nevada
Churchill
0.16
California
Tulare
0.31
California
Los Angeles
0.06
Washington
Skagit
0.22
Washington
Klickitat
0.03
California
Plumas
0.03
California
Merced
0.30
California
Kern
0.38
North Dakota
Mercer
0.21
North Dakota
Morton
0.35
Colorado
Weld
0.08
Colorado
Bent
0.06
Texas
Terry
0.03
Draft for
26103
26117
18053
21009
1123
23003
23031
25021
25011
36005
36051
42001
51053
45051
26099
39157
37009
45005
26103
26117
18053
21009
1123
38057
38059
8123
8011
48445
30083
30075
8069
8109
35035
30111
49013
49037
4007
49049
49021
4012
41049
32001
6107
6037
53057
53039
6063
6047
6029
38057
38059
8123
8011
48445
49
-------
Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
voc
wus
500
L
6
30083
Montana
Richland
0.16
voc
wus
500
L
7
30075
Montana
Powder River
0.05
voc
wus
500
L
8
8069
Colorado
Larimer
0.02
voc
wus
500
L
9
8109
Colorado
Saguache
0.04
voc
wus
500
L
10
35035
New Mexico
Otero
0.01
voc
wus
500
L
11
30111
Montana
Yellowstone
0.16
voc
wus
500
L
12
49013
Utah
Duchesne
0.06
voc
wus
500
L
13
49037
Utah
San Juan
0.03
voc
wus
500
L
14
4007
Arizona
Gila
0.02
voc
wus
500
L
15
49049
Utah
Utah
0.46
voc
wus
500
L
16
49021
Utah
Iron
0.05
voc
wus
500
L
17
4012
Arizona
La Paz
0.02
voc
wus
500
L
18
41049
Oregon
Morrow
0.46
voc
wus
500
L
19
32001
Nevada
Churchill
0.11
voc
wus
500
L
20
6107
California
Tulare
0.29
voc
wus
500
L
21
6037
California
Los Angeles
0.06
voc
wus
500
L
22
53057
Washington
Skagit
0.19
voc
wus
500
L
23
53039
Washington
Klickitat
0.04
voc
wus
500
L
24
6063
California
Plumas
0.03
voc
wus
500
L
25
6047
California
Merced
0.32
voc
wus
500
L
26
6029
California
Kern
0.35
voc
wus
1000
H
1
38057
North Dakota
Mercer
0.41
voc
wus
1000
H
2
38059
North Dakota
Morton
0.59
voc
wus
1000
H
3
8123
Colorado
Weld
0.19
voc
wus
1000
H
4
8011
Colorado
Bent
0.12
voc
wus
1000
H
5
48445
Texas
Terry
0.06
voc
wus
1000
H
6
30083
Montana
Richland
0.34
voc
wus
1000
H
7
30075
Montana
Powder River
0.35
voc
wus
1000
H
8
8069
Colorado
Larimer
0.04
voc
wus
1000
H
9
8109
Colorado
Saguache
0.10
voc
wus
1000
H
10
35035
New Mexico
Otero
0.03
voc
wus
1000
H
11
30111
Montana
Yellowstone
0.29
voc
wus
1000
H
12
49013
Utah
Duchesne
0.18
voc
wus
1000
H
13
49037
Utah
San Juan
0.11
voc
wus
1000
H
14
4007
Arizona
Gila
0.05
voc
wus
1000
H
15
49049
Utah
Utah
0.60
voc
wus
1000
H
16
49021
Utah
Iron
0.11
voc
wus
1000
H
17
4012
Arizona
La Paz
0.06
voc
wus
1000
H
18
41049
Oregon
Morrow
0.95
voc
wus
1000
H
19
32001
Nevada
Churchill
0.40
voc
wus
1000
H
20
6107
California
Tulare
0.66
voc
wus
1000
H
21
6037
California
Los Angeles
0.12
voc
wus
1000
H
22
53057
Washington
Skagit
0.43
voc
wus
1000
H
23
53039
Washington
Klickitat
0.13
voc
wus
1000
H
24
6063
California
Plumas
0.06
voc
wus
1000
H
25
6047
California
Merced
0.78
voc
wus
1000
H
26
6029
California
Kern
0.75
voc
wus
3000
H
1
38057
North Dakota
Mercer
1.11
voc
wus
3000
H
2
38059
North Dakota
Morton
1.63
voc
wus
3000
H
3
8123
Colorado
Weld
0.87
voc
wus
3000
H
4
8011
Colorado
Bent
0.84
voc
wus
3000
H
5
48445
Texas
Terry
0.22
voc
wus
3000
H
6
30083
Montana
Richland
2.17
voc
wus
3000
H
7
30075
Montana
Powder River
1.25
50
-------
Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
voc
wus
3000
H
8
8069
Colorado
Larimer
0.18
voc
wus
3000
H
9
8109
Colorado
Saguache
0.64
voc
wus
3000
H
10
35035
New Mexico
Otero
0.41
voc
wus
3000
H
11
30111
Montana
Yellowstone
1.36
voc
wus
3000
H
12
49013
Utah
Duchesne
1.12
voc
wus
3000
H
13
49037
Utah
San Juan
0.55
voc
wus
3000
H
14
4007
Arizona
Gila
0.50
voc
wus
3000
H
15
49049
Utah
Utah
1.95
voc
wus
3000
H
16
49021
Utah
Iron
0.69
voc
wus
3000
H
17
4012
Arizona
La Paz
0.66
voc
wus
3000
H
18
41049
Oregon
Morrow
2.38
voc
wus
3000
H
19
32001
Nevada
Churchill
2.03
voc
wus
3000
H
20
6107
California
Tulare
1.92
voc
wus
3000
H
21
6037
California
Los Angeles
0.68
voc
wus
3000
H
22
53057
Washington
Skagit
1.21
voc
wus
3000
H
23
53039
Washington
Klickitat
1.00
voc
wus
3000
H
24
6063
California
Plumas
0.93
voc
wus
3000
H
25
6047
California
Merced
2.74
voc
wus
3000
H
26
6029
California
Kern
2.01
51
-------
Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
Table A-2. Highest daily 24-hr PM2.5 impacts from NOx and SO2 sources from multiple
hypothetical source model simulations. Source locations are shown in Figures A1-A3.
Max.
Emissions
Value
Precursor
Area
(tpy)
Height
Source
FIPS
State
County
(ug/m3)
NOx
cus
500
L
1
18127
Indiana
Porter
0.26
NOx
cus
500
L
2
18037
Indiana
Dubois
0.18
NOx
cus
500
L
3
47055
Tennessee
Giles
0.15
NOx
cus
500
L
4
1001
Alabama
Autauga
0.21
NOx
cus
500
L
6
17155
Illinois
Putnam
0.10
NOx
cus
500
L
7
17145
Illinois
Perry
0.14
NOx
cus
500
L
8
47157
Tennessee
Shelby
0.06
NOx
cus
500
L
9
28129
Mississippi
Smith
0.27
NOx
cus
500
L
10
22071
Louisiana
Orleans
0.29
NOx
cus
500
L
11
19095
Iowa
Iowa
0.12
NOx
cus
500
L
12
29029
Missouri
Camden
0.11
NOx
cus
500
L
13
5119
Arkansas
Pulaski
0.14
NOx
cus
500
L
14
22061
Louisiana
Lincoln
0.11
NOx
cus
500
L
15
22001
Louisiana
Acadia
0.20
NOx
cus
500
L
16
31055
Nebraska
Douglas
0.21
NOx
cus
500
L
17
20091
Kansas
Johnson
0.13
NOx
cus
500
L
18
40101
Oklahoma
Muskogee
0.12
NOx
cus
500
L
19
48213
Texas
Henderson
0.12
NOx
cus
500
L
20
48201
Texas
Harris
0.13
NOx
cus
500
L
21
31001
Nebraska
Adams
0.35
NOx
cus
500
L
22
20155
Kansas
Reno
0.08
NOx
cus
500
L
23
40017
Oklahoma
Canadian
0.06
NOx
cus
500
L
24
48367
Texas
Parker
0.21
NOx
cus
500
L
25
48187
Texas
Guadalupe
0.11
NOx
cus
1000
H
1
18127
Indiana
Porter
0.19
NOx
cus
1000
H
2
18037
Indiana
Dubois
0.13
NOx
cus
1000
H
3
47055
Tennessee
Giles
0.12
NOx
cus
1000
H
4
1001
Alabama
Autauga
0.24
NOx
cus
1000
H
6
17155
Illinois
Putnam
0.11
NOx
cus
1000
H
7
17145
Illinois
Perry
0.19
NOx
cus
1000
H
8
47157
Tennessee
Shelby
0.10
NOx
cus
1000
H
9
28129
Mississippi
Smith
0.14
NOx
cus
1000
H
10
22071
Louisiana
Orleans
0.33
NOx
cus
1000
H
11
19095
Iowa
Iowa
0.13
NOx
cus
1000
H
12
29029
Missouri
Camden
0.09
NOx
cus
1000
H
13
5119
Arkansas
Pulaski
0.24
NOx
cus
1000
H
14
22061
Louisiana
Lincoln
0.11
NOx
cus
1000
H
15
22001
Louisiana
Acadia
0.17
NOx
cus
1000
H
16
31055
Nebraska
Douglas
0.15
NOx
cus
1000
H
17
20091
Kansas
Johnson
0.10
NOx
cus
1000
H
18
40101
Oklahoma
Muskogee
0.15
NOx
cus
1000
H
19
48213
Texas
Henderson
0.08
NOx
cus
1000
H
20
48201
Texas
Harris
0.09
NOx
cus
1000
H
21
31001
Nebraska
Adams
0.21
NOx
cus
1000
H
22
20155
Kansas
Reno
0.11
NOx
cus
1000
H
23
40017
Oklahoma
Canadian
0.05
NOx
cus
1000
H
24
48367
Texas
Parker
0.16
52
-------
Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
NOx
CUS
1000
H
25
48187
Texas
Guadalupe
0.12
NOx
CUS
1000
L
1
18127
Indiana
Porter
0.44
NOx
CUS
1000
L
2
18037
Indiana
Dubois
0.49
NOx
CUS
1000
L
3
47055
Tennessee
Giles
0.30
NOx
CUS
1000
L
4
1001
Alabama
Autauga
0.38
NOx
CUS
1000
L
6
17155
Illinois
Putnam
0.20
NOx
CUS
1000
L
7
17145
Illinois
Perry
0.27
NOx
CUS
1000
L
8
47157
Tennessee
Shelby
0.13
NOx
CUS
1000
L
9
28129
Mississippi
Smith
0.50
NOx
CUS
1000
L
10
22071
Louisiana
Orleans
0.71
NOx
CUS
1000
L
11
19095
Iowa
Iowa
0.22
NOx
CUS
1000
L
12
29029
Missouri
Camden
0.22
NOx
CUS
1000
L
13
5119
Arkansas
Pulaski
0.32
NOx
CUS
1000
L
14
22061
Louisiana
Lincoln
0.22
NOx
CUS
1000
L
15
22001
Louisiana
Acadia
0.40
NOx
CUS
1000
L
16
31055
Nebraska
Douglas
0.41
NOx
CUS
1000
L
17
20091
Kansas
Johnson
0.26
NOx
CUS
1000
L
18
40101
Oklahoma
Muskogee
0.28
NOx
CUS
1000
L
19
48213
Texas
Henderson
0.23
NOx
CUS
1000
L
20
48201
Texas
Harris
0.24
NOx
CUS
1000
L
21
31001
Nebraska
Adams
0.62
NOx
CUS
1000
L
22
20155
Kansas
Reno
0.16
NOx
CUS
1000
L
23
40017
Oklahoma
Canadian
0.10
NOx
CUS
1000
L
24
48367
Texas
Parker
0.48
NOx
CUS
1000
L
25
48187
Texas
Guadalupe
0.24
NOx
CUS
3000
H
1
18127
Indiana
Porter
0.53
NOx
CUS
3000
H
2
18037
Indiana
Dubois
0.53
NOx
CUS
3000
H
3
47055
Tennessee
Giles
0.48
NOx
CUS
3000
H
4
1001
Alabama
Autauga
0.68
NOx
CUS
3000
H
6
17155
Illinois
Putnam
0.32
NOx
CUS
3000
H
7
17145
Illinois
Perry
0.54
NOx
CUS
3000
H
8
47157
Tennessee
Shelby
0.34
NOx
CUS
3000
H
9
28129
Mississippi
Smith
0.54
NOx
CUS
3000
H
10
22071
Louisiana
Orleans
1.09
NOx
CUS
3000
H
11
19095
Iowa
Iowa
0.35
NOx
CUS
3000
H
12
29029
Missouri
Camden
0.37
NOx
CUS
3000
H
13
5119
Arkansas
Pulaski
0.76
NOx
CUS
3000
H
14
22061
Louisiana
Lincoln
0.37
NOx
CUS
3000
H
15
22001
Louisiana
Acadia
0.56
NOx
CUS
3000
H
16
31055
Nebraska
Douglas
0.46
NOx
CUS
3000
H
17
20091
Kansas
Johnson
0.32
NOx
CUS
3000
H
18
40101
Oklahoma
Muskogee
0.53
NOx
CUS
3000
H
19
48213
Texas
Henderson
0.26
NOx
CUS
3000
H
20
48201
Texas
Harris
0.33
NOx
CUS
3000
H
21
31001
Nebraska
Adams
0.82
NOx
CUS
3000
H
22
20155
Kansas
Reno
0.30
NOx
CUS
3000
H
23
40017
Oklahoma
Canadian
0.17
NOx
CUS
3000
H
24
48367
Texas
Parker
0.60
NOx
CUS
3000
H
25
48187
Texas
Guadalupe
0.41
NOx
EUS
500
H
1
23003
Maine
Aroostook
0.06
NOx
EUS
500
H
2
23031
Maine
York
0.04
NOx
EUS
500
H
3
25021
Massachusetts
Norfolk
0.03
NOx
EUS
500
H
4
25011
Massachusetts
Franklin
0.03
NOx
EUS
500
H
6
0
NONE
NONE
0.10
53
-------
Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
NOx
EUS
500
H
7
36051
New York
Livingston
0.11
NOx
EUS
500
H
8
42001
Pennsylvania
Adams
0.05
NOx
EUS
500
H
9
51053
Virginia
Dinwiddie
0.09
NOx
EUS
500
H
10
45051
South Carolina
Horry
0.07
NOx
EUS
500
H
11
26099
Michigan
Macomb
0.06
NOx
EUS
500
H
12
39157
Ohio
Tuscarawas
0.04
NOx
EUS
500
H
13
37009
North Carolina
Ashe
0.04
NOx
EUS
500
H
14
45005
South Carolina
Allendale
0.03
NOx
EUS
500
H
15
26103
Michigan
Marquette
0.02
NOx
EUS
500
H
16
26117
Michigan
Montcalm
0.05
NOx
EUS
500
H
17
18053
Indiana
Grant
0.07
NOx
EUS
500
H
18
21009
Kentucky
Barren
0.05
NOx
EUS
500
H
19
1123
Alabama
Tallapoosa
0.05
NOx
EUS
500
L
1
23003
Maine
Aroostook
0.12
NOx
EUS
500
L
2
23031
Maine
York
0.07
NOx
EUS
500
L
3
25021
Massachusetts
Norfolk
0.05
NOx
EUS
500
L
4
25011
Massachusetts
Franklin
0.05
NOx
EUS
500
L
6
0
NONE
NONE
0.17
NOx
EUS
500
L
7
36051
New York
Livingston
0.26
NOx
EUS
500
L
8
42001
Pennsylvania
Adams
0.10
NOx
EUS
500
L
9
51053
Virginia
Dinwiddie
0.13
NOx
EUS
500
L
10
45051
South Carolina
Horry
0.19
NOx
EUS
500
L
11
26099
Michigan
Macomb
0.13
NOx
EUS
500
L
12
39157
Ohio
Tuscarawas
0.09
NOx
EUS
500
L
13
37009
North Carolina
Ashe
0.05
NOx
EUS
500
L
14
45005
South Carolina
Allendale
0.08
NOx
EUS
500
L
15
26103
Michigan
Marquette
0.04
NOx
EUS
500
L
16
26117
Michigan
Montcalm
0.20
NOx
EUS
500
L
17
18053
Indiana
Grant
0.11
NOx
EUS
500
L
18
21009
Kentucky
Barren
0.11
NOx
EUS
500
L
19
1123
Alabama
Tallapoosa
0.09
NOx
EUS
1000
H
1
23003
Maine
Aroostook
0.10
NOx
EUS
1000
H
2
23031
Maine
York
0.07
NOx
EUS
1000
H
3
25021
Massachusetts
Norfolk
0.05
NOx
EUS
1000
H
4
25011
Massachusetts
Franklin
0.06
NOx
EUS
1000
H
6
0
NONE
NONE
0.18
NOx
EUS
1000
H
7
36051
New York
Livingston
0.23
NOx
EUS
1000
H
8
42001
Pennsylvania
Adams
0.09
NOx
EUS
1000
H
9
51053
Virginia
Dinwiddie
0.18
NOx
EUS
1000
H
10
45051
South Carolina
Horry
0.13
NOx
EUS
1000
H
11
26099
Michigan
Macomb
0.12
NOx
EUS
1000
H
12
39157
Ohio
Tuscarawas
0.08
NOx
EUS
1000
H
13
37009
North Carolina
Ashe
0.08
NOx
EUS
1000
H
14
45005
South Carolina
Allendale
0.05
NOx
EUS
1000
H
15
26103
Michigan
Marquette
0.04
NOx
EUS
1000
H
16
26117
Michigan
Montcalm
0.10
NOx
EUS
1000
H
17
18053
Indiana
Grant
0.14
NOx
EUS
1000
H
18
21009
Kentucky
Barren
0.09
NOx
EUS
1000
H
19
1123
Alabama
Tallapoosa
0.09
NOx
EUS
3000
H
1
23003
Maine
Aroostook
0.24
NOx
EUS
3000
H
2
23031
Maine
York
0.21
NOx
EUS
3000
H
3
25021
Massachusetts
Norfolk
0.14
NOx
EUS
3000
H
4
25011
Massachusetts
Franklin
0.15
NOx
EUS
3000
H
6
0
NONE
NONE
0.46
54
-------
Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
NOx
EUS
3000
H
7
36051
New York
Livingston
0.68
NOx
EUS
3000
H
8
42001
Pennsylvania
Adams
0.27
NOx
EUS
3000
H
9
51053
Virginia
Dinwiddie
0.52
NOx
EUS
3000
H
10
45051
South Carolina
Horry
0.39
NOx
EUS
3000
H
11
26099
Michigan
Macomb
0.33
NOx
EUS
3000
H
12
39157
Ohio
Tuscarawas
0.23
NOx
EUS
3000
H
13
37009
North Carolina
Ashe
0.22
NOx
EUS
3000
H
14
45005
South Carolina
Allendale
0.16
NOx
EUS
3000
H
15
26103
Michigan
Marquette
0.12
NOx
EUS
3000
H
16
26117
Michigan
Montcalm
0.26
NOx
EUS
3000
H
17
18053
Indiana
Grant
0.42
NOx
EUS
3000
H
18
21009
Kentucky
Barren
0.20
NOx
EUS
3000
H
19
1123
Alabama
Tallapoosa
0.21
NOx
WUS
500
H
1
38057
North Dakota
Mercer
0.09
NOx
WUS
500
H
2
38059
North Dakota
Morton
0.04
NOx
WUS
500
H
3
8123
Colorado
Weld
0.06
NOx
WUS
500
H
4
8011
Colorado
Bent
0.06
NOx
WUS
500
H
6
30083
Montana
Richland
Powder
0.08
NOx
WUS
500
H
7
30075
Montana
River
0.05
NOx
WUS
500
H
8
8069
Colorado
Larimer
0.02
NOx
WUS
500
H
9
8109
Colorado
Saguache
0.04
NOx
WUS
500
H
10
35035
New Mexico
Otero
0.01
NOx
WUS
500
H
11
30111
Montana
Yellowstone
0.07
NOx
WUS
500
H
12
49013
Utah
Duchesne
0.03
NOx
WUS
500
H
13
49037
Utah
San Juan
0.01
NOx
WUS
500
H
14
4007
Arizona
Gila
0.01
NOx
WUS
500
H
15
49049
Utah
Utah
0.08
NOx
WUS
500
H
16
49021
Utah
Iron
0.03
NOx
WUS
500
H
17
4012
Arizona
La Paz
0.03
NOx
WUS
500
H
18
41049
Oregon
Morrow
0.15
NOx
WUS
500
H
19
32001
Nevada
Churchill
0.03
NOx
WUS
500
H
20
6107
California
Tulare
0.31
NOx
WUS
500
H
21
6037
California
Los Angeles
0.02
NOx
WUS
500
H
22
53057
Washington
Skagit
0.05
NOx
WUS
500
H
23
53039
Washington
Klickitat
0.03
NOx
WUS
500
H
24
6063
California
Plumas
0.02
NOx
WUS
500
H
25
6047
California
Merced
0.30
NOx
WUS
500
H
26
6029
California
Kern
0.17
NOx
WUS
500
L
1
38057
North Dakota
Mercer
0.11
NOx
WUS
500
L
2
38059
North Dakota
Morton
0.07
NOx
WUS
500
L
3
8123
Colorado
Weld
0.10
NOx
WUS
500
L
4
8011
Colorado
Bent
0.08
NOx
WUS
500
L
6
30083
Montana
Richland
Powder
0.09
NOx
WUS
500
L
7
30075
Montana
River
0.09
NOx
WUS
500
L
8
8069
Colorado
Larimer
0.02
NOx
WUS
500
L
9
8109
Colorado
Saguache
0.04
NOx
WUS
500
L
10
35035
New Mexico
Otero
0.01
NOx
WUS
500
L
11
30111
Montana
Yellowstone
0.09
NOx
WUS
500
L
12
49013
Utah
Duchesne
0.03
NOx
WUS
500
L
13
49037
Utah
San Juan
0.01
NOx
WUS
500
L
14
4007
Arizona
Gila
0.01
NOx
WUS
500
L
15
49049
Utah
Utah
0.09
55
-------
Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
NOx
WUS
500
L
16
49021
Utah
Iron
0.04
NOx
WUS
500
L
17
4012
Arizona
La Paz
0.04
NOx
WUS
500
L
18
41049
Oregon
Morrow
0.20
NOx
WUS
500
L
19
32001
Nevada
Churchill
0.04
NOx
WUS
500
L
20
6107
California
Tulare
0.45
NOx
WUS
500
L
21
6037
California
Los Angeles
0.03
NOx
WUS
500
L
22
53057
Washington
Skagit
0.07
NOx
WUS
500
L
23
53039
Washington
Klickitat
0.04
NOx
WUS
500
L
24
6063
California
Plumas
0.03
NOx
WUS
500
L
25
6047
California
Merced
0.56
NOx
WUS
500
L
26
6029
California
Kern
0.17
NOx
WUS
1000
H
1
38057
North Dakota
Mercer
0.17
NOx
WUS
1000
H
2
38059
North Dakota
Morton
0.08
NOx
WUS
1000
H
3
8123
Colorado
Weld
0.12
NOx
WUS
1000
H
4
8011
Colorado
Bent
0.12
NOx
WUS
1000
H
6
30083
Montana
Richland
Powder
0.16
NOx
WUS
1000
H
7
30075
Montana
River
0.09
NOx
WUS
1000
H
8
8069
Colorado
Larimer
0.04
NOx
WUS
1000
H
9
8109
Colorado
Saguache
0.08
NOx
WUS
1000
H
10
35035
New Mexico
Otero
0.01
NOx
WUS
1000
H
11
30111
Montana
Yellowstone
0.12
NOx
WUS
1000
H
12
49013
Utah
Duchesne
0.06
NOx
WUS
1000
H
13
49037
Utah
San Juan
0.01
NOx
WUS
1000
H
14
4007
Arizona
Gila
0.02
NOx
WUS
1000
H
15
49049
Utah
Utah
0.16
NOx
WUS
1000
H
16
49021
Utah
Iron
0.05
NOx
WUS
1000
H
17
4012
Arizona
La Paz
0.05
NOx
WUS
1000
H
18
41049
Oregon
Morrow
0.30
NOx
WUS
1000
H
19
32001
Nevada
Churchill
0.05
NOx
WUS
1000
H
20
6107
California
Tulare
0.59
NOx
WUS
1000
H
21
6037
California
Los Angeles
0.04
NOx
WUS
1000
H
22
53057
Washington
Skagit
0.11
NOx
WUS
1000
H
23
53039
Washington
Klickitat
0.06
NOx
WUS
1000
H
24
6063
California
Plumas
0.04
NOx
WUS
1000
H
25
6047
California
Merced
0.59
NOx
WUS
1000
H
26
6029
California
Kern
0.34
NOx
WUS
3000
H
1
38057
North Dakota
Mercer
0.44
NOx
WUS
3000
H
2
38059
North Dakota
Morton
0.24
NOx
WUS
3000
H
3
8123
Colorado
Weld
0.31
NOx
WUS
3000
H
4
8011
Colorado
Bent
0.34
NOx
WUS
3000
H
6
30083
Montana
Richland
Powder
0.39
NOx
WUS
3000
H
7
30075
Montana
River
0.21
NOx
WUS
3000
H
8
8069
Colorado
Larimer
0.11
NOx
WUS
3000
H
9
8109
Colorado
Saguache
0.26
NOx
WUS
3000
H
10
35035
New Mexico
Otero
0.04
NOx
WUS
3000
H
11
30111
Montana
Yellowstone
0.28
NOx
WUS
3000
H
12
49013
Utah
Duchesne
0.16
NOx
WUS
3000
H
13
49037
Utah
San Juan
0.04
NOx
WUS
3000
H
14
4007
Arizona
Gila
0.07
NOx
WUS
3000
H
15
49049
Utah
Utah
0.42
NOx
WUS
3000
H
16
49021
Utah
Iron
0.11
NOx
WUS
3000
H
17
4012
Arizona
La Paz
0.13
56
-------
Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
NOx
WUS
3000
H
18
41049
Oregon
Morrow
0.77
NOx
wus
3000
H
19
32001
Nevada
Churchill
0.15
NOx
WUS
3000
H
20
6107
California
Tulare
1.64
NOx
wus
3000
H
21
6037
California
Los Angeles
0.12
NOx
wus
3000
H
22
53057
Washington
Skagit
0.31
NOx
wus
3000
H
23
53039
Washington
Klickitat
0.20
NOx
wus
3000
H
24
6063
California
Plumas
0.13
NOx
wus
3000
H
25
6047
California
Merced
1.69
NOx
wus
3000
H
26
6029
California
Kern
0.93
S02
cus
500
L
1
18127
Indiana
Porter
1.79
S02
cus
500
L
2
18037
Indiana
Dubois
0.26
S02
cus
500
L
3
47055
Tennessee
Giles
0.44
S02
cus
500
L
4
1001
Alabama
Autauga
1.30
S02
cus
500
L
6
17155
Illinois
Putnam
0.54
S02
cus
500
L
7
17145
Illinois
Perry
0.85
S02
cus
500
L
8
47157
Tennessee
Shelby
0.72
S02
cus
500
L
9
28129
Mississippi
Smith
1.99
S02
cus
500
L
10
22071
Louisiana
Orleans
0.88
S02
cus
500
L
11
19095
Iowa
Iowa
1.37
S02
cus
500
L
12
29029
Missouri
Camden
1.06
S02
cus
500
L
13
5119
Arkansas
Pulaski
1.62
S02
cus
500
L
14
22061
Louisiana
Lincoln
0.59
S02
cus
500
L
15
22001
Louisiana
Acadia
1.93
S02
cus
500
L
16
31055
Nebraska
Douglas
0.98
S02
cus
500
L
17
20091
Kansas
Johnson
0.65
S02
cus
500
L
18
40101
Oklahoma
Muskogee
1.21
S02
cus
500
L
19
48213
Texas
Henderson
0.37
S02
cus
500
L
20
48201
Texas
Harris
1.65
S02
cus
500
L
21
31001
Nebraska
Adams
1.96
S02
cus
500
L
22
20155
Kansas
Reno
0.46
S02
cus
500
L
23
40017
Oklahoma
Canadian
0.69
S02
cus
500
L
24
48367
Texas
Parker
0.55
S02
cus
500
L
25
48187
Texas
Guadalupe
0.57
S02
cus
1000
H
1
18127
Indiana
Porter
1.13
S02
cus
1000
H
2
18037
Indiana
Dubois
0.34
S02
cus
1000
H
3
47055
Tennessee
Giles
0.89
S02
cus
1000
H
4
1001
Alabama
Autauga
1.62
S02
cus
1000
H
6
17155
Illinois
Putnam
0.35
S02
cus
1000
H
7
17145
Illinois
Perry
0.72
S02
cus
1000
H
8
47157
Tennessee
Shelby
0.62
S02
cus
1000
H
9
28129
Mississippi
Smith
0.49
S02
cus
1000
H
10
22071
Louisiana
Orleans
1.12
S02
cus
1000
H
11
19095
Iowa
Iowa
0.76
S02
cus
1000
H
12
29029
Missouri
Camden
0.65
S02
cus
1000
H
13
5119
Arkansas
Pulaski
1.13
S02
cus
1000
H
14
22061
Louisiana
Lincoln
0.78
S02
cus
1000
H
15
22001
Louisiana
Acadia
1.14
S02
cus
1000
H
16
31055
Nebraska
Douglas
0.56
S02
cus
1000
H
17
20091
Kansas
Johnson
0.45
S02
cus
1000
H
18
40101
Oklahoma
Muskogee
0.69
S02
cus
1000
H
19
48213
Texas
Henderson
0.52
S02
cus
1000
H
20
48201
Texas
Harris
0.89
S02
cus
1000
H
21
31001
Nebraska
Adams
0.65
S02
cus
1000
H
22
20155
Kansas
Reno
0.53
57
-------
Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
S02
CUS
1000
H
23
40017
Oklahoma
Canadian
0.47
S02
CUS
1000
H
24
48367
Texas
Parker
0.82
S02
CUS
1000
H
25
48187
Texas
Guadalupe
0.74
S02
CUS
1000
L
1
18127
Indiana
Porter
3.93
S02
CUS
1000
L
2
18037
Indiana
Dubois
0.80
S02
CUS
1000
L
3
47055
Tennessee
Giles
1.58
S02
CUS
1000
L
4
1001
Alabama
Autauga
3.70
S02
CUS
1000
L
6
17155
Illinois
Putnam
1.33
S02
CUS
1000
L
7
17145
Illinois
Perry
2.48
S02
CUS
1000
L
8
47157
Tennessee
Shelby
1.60
S02
CUS
1000
L
9
28129
Mississippi
Smith
4.79
S02
CUS
1000
L
10
22071
Louisiana
Orleans
3.11
S02
CUS
1000
L
11
19095
Iowa
Iowa
2.76
S02
CUS
1000
L
12
29029
Missouri
Camden
2.42
S02
CUS
1000
L
13
5119
Arkansas
Pulaski
4.14
S02
CUS
1000
L
14
22061
Louisiana
Lincoln
1.76
S02
CUS
1000
L
15
22001
Louisiana
Acadia
4.58
S02
CUS
1000
L
16
31055
Nebraska
Douglas
2.29
S02
CUS
1000
L
17
20091
Kansas
Johnson
1.66
S02
CUS
1000
L
18
40101
Oklahoma
Muskogee
3.33
S02
CUS
1000
L
19
48213
Texas
Henderson
1.06
S02
CUS
1000
L
20
48201
Texas
Harris
3.49
S02
CUS
1000
L
21
31001
Nebraska
Adams
5.05
S02
CUS
1000
L
22
20155
Kansas
Reno
0.96
S02
CUS
1000
L
23
40017
Oklahoma
Canadian
1.74
S02
CUS
1000
L
24
48367
Texas
Parker
2.05
S02
CUS
1000
L
25
48187
Texas
Guadalupe
1.41
S02
CUS
3000
H
1
18127
Indiana
Porter
5.91
S02
CUS
3000
H
2
18037
Indiana
Dubois
1.33
S02
CUS
3000
H
3
47055
Tennessee
Giles
5.40
S02
CUS
3000
H
4
1001
Alabama
Autauga
7.85
S02
CUS
3000
H
6
17155
Illinois
Putnam
1.40
S02
CUS
3000
H
7
17145
Illinois
Perry
3.36
S02
CUS
3000
H
8
47157
Tennessee
Shelby
2.32
S02
CUS
3000
H
9
28129
Mississippi
Smith
1.94
S02
CUS
3000
H
10
22071
Louisiana
Orleans
6.12
S02
CUS
3000
H
11
19095
Iowa
Iowa
3.26
S02
CUS
3000
H
12
29029
Missouri
Camden
3.72
S02
CUS
3000
H
13
5119
Arkansas
Pulaski
4.93
S02
CUS
3000
H
14
22061
Louisiana
Lincoln
3.61
S02
CUS
3000
H
15
22001
Louisiana
Acadia
4.13
S02
CUS
3000
H
16
31055
Nebraska
Douglas
2.26
S02
CUS
3000
H
17
20091
Kansas
Johnson
1.62
S02
CUS
3000
H
18
40101
Oklahoma
Muskogee
3.39
S02
CUS
3000
H
19
48213
Texas
Henderson
2.04
S02
CUS
3000
H
20
48201
Texas
Harris
2.86
S02
CUS
3000
H
21
31001
Nebraska
Adams
2.88
S02
CUS
3000
H
22
20155
Kansas
Reno
2.07
S02
CUS
3000
H
23
40017
Oklahoma
Canadian
1.87
S02
CUS
3000
H
24
48367
Texas
Parker
3.55
S02
CUS
3000
H
25
48187
Texas
Guadalupe
2.71
S02
EUS
500
H
1
23003
Maine
Aroostook
0.39
S02
EUS
500
H
2
23031
Maine
York
0.39
S02
EUS
500
H
3
25021
Massachusetts
Norfolk
0.16
58
-------
Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
S02
EUS
500
H
4
25011
Massachusetts
Franklin
0.19
S02
EUS
500
H
6
0
NONE
NONE
0.42
S02
EUS
500
H
7
36051
New York
Livingston
0.13
S02
EUS
500
H
8
42001
Pennsylvania
Adams
0.10
S02
EUS
500
H
9
51053
Virginia
Dinwiddie
0.27
S02
EUS
500
H
10
45051
South Carolina
Horry
0.22
S02
EUS
500
H
11
26099
Michigan
Macomb
0.24
S02
EUS
500
H
12
39157
Ohio
Tuscarawas
0.08
S02
EUS
500
H
13
37009
North Carolina
Ashe
0.22
S02
EUS
500
H
14
45005
South Carolina
Allendale
0.14
S02
EUS
500
H
15
26103
Michigan
Marquette
0.12
S02
EUS
500
H
16
26117
Michigan
Montcalm
0.22
S02
EUS
500
H
17
18053
Indiana
Grant
0.33
S02
EUS
500
H
18
21009
Kentucky
Barren
0.06
S02
EUS
500
H
19
1123
Alabama
Tallapoosa
0.23
S02
EUS
500
L
1
23003
Maine
Aroostook
0.86
S02
EUS
500
L
2
23031
Maine
York
0.63
S02
EUS
500
L
3
25021
Massachusetts
Norfolk
0.19
S02
EUS
500
L
4
25011
Massachusetts
Franklin
0.25
S02
EUS
500
L
6
0
NONE
NONE
0.62
S02
EUS
500
L
7
36051
New York
Livingston
0.32
S02
EUS
500
L
8
42001
Pennsylvania
Adams
0.36
S02
EUS
500
L
9
51053
Virginia
Dinwiddie
0.56
S02
EUS
500
L
10
45051
South Carolina
Horry
0.63
S02
EUS
500
L
11
26099
Michigan
Macomb
0.29
S02
EUS
500
L
12
39157
Ohio
Tuscarawas
0.24
S02
EUS
500
L
13
37009
North Carolina
Ashe
0.25
S02
EUS
500
L
14
45005
South Carolina
Allendale
0.51
S02
EUS
500
L
15
26103
Michigan
Marquette
0.37
S02
EUS
500
L
16
26117
Michigan
Montcalm
0.53
S02
EUS
500
L
17
18053
Indiana
Grant
0.96
S02
EUS
500
L
18
21009
Kentucky
Barren
0.13
S02
EUS
500
L
19
1123
Alabama
Tallapoosa
0.33
S02
EUS
1000
H
1
23003
Maine
Aroostook
0.76
S02
EUS
1000
H
2
23031
Maine
York
0.65
S02
EUS
1000
H
3
25021
Massachusetts
Norfolk
0.31
S02
EUS
1000
H
4
25011
Massachusetts
Franklin
0.34
S02
EUS
1000
H
6
0
NONE
NONE
0.63
S02
EUS
1000
H
7
36051
New York
Livingston
0.25
S02
EUS
1000
H
8
42001
Pennsylvania
Adams
0.20
S02
EUS
1000
H
9
51053
Virginia
Dinwiddie
0.47
S02
EUS
1000
H
10
45051
South Carolina
Horry
0.35
S02
EUS
1000
H
11
26099
Michigan
Macomb
0.40
S02
EUS
1000
H
12
39157
Ohio
Tuscarawas
0.16
S02
EUS
1000
H
13
37009
North Carolina
Ashe
0.39
S02
EUS
1000
H
14
45005
South Carolina
Allendale
0.27
S02
EUS
1000
H
15
26103
Michigan
Marquette
0.23
S02
EUS
1000
H
16
26117
Michigan
Montcalm
0.39
S02
EUS
1000
H
17
18053
Indiana
Grant
0.65
S02
EUS
1000
H
18
21009
Kentucky
Barren
0.11
S02
EUS
1000
H
19
1123
Alabama
Tallapoosa
0.40
S02
EUS
3000
H
1
23003
Maine
Aroostook
1.51
S02
EUS
3000
H
2
23031
Maine
York
1.41
S02
EUS
3000
H
3
25021
Massachusetts
Norfolk
0.72
59
-------
Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
S02
EUS
3000
H
4
25011
Massachusetts
Franklin
0.78
S02
EUS
3000
H
6
0
NONE
NONE
1.16
S02
EUS
3000
H
7
36051
New York
Livingston
0.62
S02
EUS
3000
H
8
42001
Pennsylvania
Adams
0.56
S02
EUS
3000
H
9
51053
Virginia
Dinwiddie
1.03
S02
EUS
3000
H
10
45051
South Carolina
Horry
0.82
S02
EUS
3000
H
11
26099
Michigan
Macomb
0.79
S02
EUS
3000
H
12
39157
Ohio
Tuscarawas
0.43
S02
EUS
3000
H
13
37009
North Carolina
Ashe
0.81
S02
EUS
3000
H
14
45005
South Carolina
Allendale
0.63
S02
EUS
3000
H
15
26103
Michigan
Marquette
0.63
S02
EUS
3000
H
16
26117
Michigan
Montcalm
0.86
S02
EUS
3000
H
17
18053
Indiana
Grant
1.62
S02
EUS
3000
H
18
21009
Kentucky
Barren
0.26
S02
EUS
3000
H
19
1123
Alabama
Tallapoosa
0.89
S02
WUS
500
H
1
38057
North Dakota
Mercer
0.50
S02
WUS
500
H
2
38059
North Dakota
Morton
0.18
S02
WUS
500
H
3
8123
Colorado
Weld
0.24
S02
WUS
500
H
4
8011
Colorado
Bent
0.18
S02
WUS
500
H
6
30083
Montana
Richland
Powder
0.23
S02
WUS
500
H
7
30075
Montana
River
0.09
S02
WUS
500
H
8
8069
Colorado
Larimer
0.07
S02
WUS
500
H
9
8109
Colorado
Saguache
0.28
S02
WUS
500
H
10
35035
New Mexico
Otero
0.04
S02
WUS
500
H
11
30111
Montana
Yellowstone
0.10
S02
WUS
500
H
12
49013
Utah
Duchesne
0.06
S02
WUS
500
H
13
49037
Utah
San Juan
0.05
S02
WUS
500
H
14
4007
Arizona
Gila
0.04
S02
WUS
500
H
15
49049
Utah
Utah
0.07
S02
WUS
500
H
16
49021
Utah
Iron
0.09
S02
WUS
500
H
17
4012
Arizona
La Paz
0.26
S02
WUS
500
H
18
41049
Oregon
Morrow
0.19
S02
WUS
500
H
19
32001
Nevada
Churchill
0.35
S02
WUS
500
H
20
6107
California
Tulare
0.76
S02
WUS
500
H
21
6037
California
Los Angeles
0.04
S02
WUS
500
H
22
53057
Washington
Skagit
0.08
S02
WUS
500
H
23
53039
Washington
Klickitat
0.24
S02
WUS
500
H
24
6063
California
Plumas
0.16
S02
WUS
500
H
25
6047
California
Merced
0.66
S02
WUS
500
H
26
6029
California
Kern
0.14
S02
WUS
500
L
1
38057
North Dakota
Mercer
1.14
S02
WUS
500
L
2
38059
North Dakota
Morton
0.55
S02
WUS
500
L
3
8123
Colorado
Weld
0.40
S02
WUS
500
L
4
8011
Colorado
Bent
0.26
S02
WUS
500
L
6
30083
Montana
Richland
Powder
0.39
S02
WUS
500
L
7
30075
Montana
River
0.32
S02
WUS
500
L
8
8069
Colorado
Larimer
0.17
S02
WUS
500
L
9
8109
Colorado
Saguache
0.33
S02
WUS
500
L
10
35035
New Mexico
Otero
0.04
S02
WUS
500
L
11
30111
Montana
Yellowstone
0.20
S02
WUS
500
L
12
49013
Utah
Duchesne
0.07
S02
WUS
500
L
13
49037
Utah
San Juan
0.05
60
-------
Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
S02
WUS
500
L
14
4007
Arizona
Gila
0.04
S02
wus
500
L
15
49049
Utah
Utah
0.08
S02
WUS
500
L
16
49021
Utah
Iron
0.10
S02
wus
500
L
17
4012
Arizona
La Paz
0.32
S02
wus
500
L
18
41049
Oregon
Morrow
0.25
S02
wus
500
L
19
32001
Nevada
Churchill
0.48
S02
wus
500
L
20
6107
California
Tulare
2.86
S02
wus
500
L
21
6037
California
Los Angeles
0.08
S02
wus
500
L
22
53057
Washington
Skagit
0.18
S02
wus
500
L
23
53039
Washington
Klickitat
0.56
S02
wus
500
L
24
6063
California
Plumas
0.27
S02
wus
500
L
25
6047
California
Merced
2.04
S02
wus
500
L
26
6029
California
Kern
0.26
S02
wus
1000
H
1
38057
North Dakota
Mercer
0.98
S02
wus
1000
H
2
38059
North Dakota
Morton
0.34
S02
wus
1000
H
3
8123
Colorado
Weld
0.41
S02
wus
1000
H
4
8011
Colorado
Bent
0.30
S02
wus
1000
H
6
30083
Montana
Richland
Powder
0.36
S02
wus
1000
H
7
30075
Montana
River
0.16
S02
wus
1000
H
8
8069
Colorado
Larimer
0.13
S02
wus
1000
H
9
8109
Colorado
Saguache
0.46
S02
wus
1000
H
10
35035
New Mexico
Otero
0.06
S02
wus
1000
H
11
30111
Montana
Yellowstone
0.18
S02
wus
1000
H
12
49013
Utah
Duchesne
0.10
S02
wus
1000
H
13
49037
Utah
San Juan
0.08
S02
wus
1000
H
14
4007
Arizona
Gila
0.08
S02
wus
1000
H
15
49049
Utah
Utah
0.13
S02
wus
1000
H
16
49021
Utah
Iron
0.15
S02
wus
1000
H
17
4012
Arizona
La Paz
0.48
S02
wus
1000
H
18
41049
Oregon
Morrow
0.35
S02
wus
1000
H
19
32001
Nevada
Churchill
0.58
S02
wus
1000
H
20
6107
California
Tulare
1.53
S02
wus
1000
H
21
6037
California
Los Angeles
0.07
S02
wus
1000
H
22
53057
Washington
Skagit
0.15
S02
wus
1000
H
23
53039
Washington
Klickitat
0.45
S02
wus
1000
H
24
6063
California
Plumas
0.28
S02
wus
1000
H
25
6047
California
Merced
1.31
S02
wus
1000
H
26
6029
California
Kern
0.25
S02
wus
3000
H
1
38057
North Dakota
Mercer
2.69
S02
wus
3000
H
2
38059
North Dakota
Morton
0.81
S02
wus
3000
H
3
8123
Colorado
Weld
0.77
S02
wus
3000
H
4
8011
Colorado
Bent
0.60
S02
wus
3000
H
6
30083
Montana
Richland
Powder
0.98
S02
wus
3000
H
7
30075
Montana
River
0.43
S02
wus
3000
H
8
8069
Colorado
Larimer
0.30
S02
wus
3000
H
9
8109
Colorado
Saguache
1.24
S02
wus
3000
H
10
35035
New Mexico
Otero
0.13
S02
wus
3000
H
11
30111
Montana
Yellowstone
0.42
S02
wus
3000
H
12
49013
Utah
Duchesne
0.22
S02
wus
3000
H
13
49037
Utah
San Juan
0.19
S02
wus
3000
H
14
4007
Arizona
Gila
0.23
S02
wus
3000
H
15
49049
Utah
Utah
0.30
61
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Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
S02
WUS
3000
H
16
49021
Utah
Iron
0.34
S02
wus
3000
H
17
4012
Arizona
La Paz
1.02
S02
WUS
3000
H
18
41049
Oregon
Morrow
0.89
S02
wus
3000
H
19
32001
Nevada
Churchill
1.18
S02
wus
3000
H
20
6107
California
Tulare
4.58
S02
wus
3000
H
21
6037
California
Los Angeles
0.14
S02
wus
3000
H
22
53057
Washington
Skagit
0.43
S02
wus
3000
H
23
53039
Washington
Klickitat
1.15
S02
wus
3000
H
24
6063
California
Plumas
0.76
S02
wus
3000
H
25
6047
California
Merced
3.69
S02
wus
3000
H
26
6029
California
Kern
0.53
62
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Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
Table A-3. Highest annual average PM2.5 impacts from NOx and SO2 sources from multiple
hypothetical source model simulations. Source locations are shown in Figures A1-A3.
Max
Emissions
Value
Precursor
Area
(tpy)
Height
Source
FIPS
State
County
(ug/m3)
NOx
cus
500
L
1
18127
Indiana
Porter
0.011
NOx
cus
500
L
2
18037
Indiana
Dubois
0.011
NOx
cus
500
L
3
47055
Tennessee
Giles
0.012
NOx
cus
500
L
4
1001
Alabama
Autauga
0.011
NOx
cus
500
L
6
17155
Illinois
Putnam
0.007
NOx
cus
500
L
7
17145
Illinois
Perry
0.011
NOx
cus
500
L
8
47157
Tennessee
Shelby
0.003
NOx
cus
500
L
9
28129
Mississippi
Smith
0.011
NOx
cus
500
L
10
22071
Louisiana
Orleans
0.009
NOx
cus
500
L
11
19095
Iowa
Iowa
0.010
NOx
cus
500
L
12
29029
Missouri
Camden
0.007
NOx
cus
500
L
13
5119
Arkansas
Pulaski
0.005
NOx
cus
500
L
14
22061
Louisiana
Lincoln
0.005
NOx
cus
500
L
15
22001
Louisiana
Acadia
0.013
NOx
cus
500
L
16
31055
Nebraska
Douglas
0.007
NOx
cus
500
L
17
20091
Kansas
Johnson
0.006
NOx
cus
500
L
18
40101
Oklahoma
Muskogee
0.008
NOx
cus
500
L
19
48213
Texas
Henderson
0.005
NOx
cus
500
L
20
48201
Texas
Harris
0.009
NOx
cus
500
L
21
31001
Nebraska
Adams
0.011
NOx
cus
500
L
22
20155
Kansas
Reno
0.005
NOx
cus
500
L
23
40017
Oklahoma
Canadian
0.004
NOx
cus
500
L
24
48367
Texas
Parker
0.004
NOx
cus
500
L
25
48187
Texas
Guadalupe
0.005
NOx
cus
1000
H
1
18127
Indiana
Porter
0.008
NOx
cus
1000
H
2
18037
Indiana
Dubois
0.007
NOx
cus
1000
H
3
47055
Tennessee
Giles
0.006
NOx
cus
1000
H
4
1001
Alabama
Autauga
0.005
NOx
cus
1000
H
6
17155
Illinois
Putnam
0.005
NOx
cus
1000
H
7
17145
Illinois
Perry
0.006
NOx
cus
1000
H
8
47157
Tennessee
Shelby
0.003
NOx
cus
1000
H
9
28129
Mississippi
Smith
0.005
NOx
cus
1000
H
10
22071
Louisiana
Orleans
0.006
NOx
cus
1000
H
11
19095
Iowa
Iowa
0.008
NOx
cus
1000
H
12
29029
Missouri
Camden
0.006
NOx
cus
1000
H
13
5119
Arkansas
Pulaski
0.005
NOx
cus
1000
H
14
22061
Louisiana
Lincoln
0.003
NOx
cus
1000
H
15
22001
Louisiana
Acadia
0.005
NOx
cus
1000
H
16
31055
Nebraska
Douglas
0.006
NOx
cus
1000
H
17
20091
Kansas
Johnson
0.004
NOx
cus
1000
H
18
40101
Oklahoma
Muskogee
0.005
NOx
cus
1000
H
19
48213
Texas
Henderson
0.003
NOx
cus
1000
H
20
48201
Texas
Harris
0.004
NOx
cus
1000
H
21
31001
Nebraska
Adams
0.008
NOx
cus
1000
H
22
20155
Kansas
Reno
0.004
NOx
cus
1000
H
23
40017
Oklahoma
Canadian
0.003
NOx
cus
1000
H
24
48367
Texas
Parker
0.003
63
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Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
NOx
CUS
1000
H
25
48187
Texas
Guadalupe
0.003
NOx
CUS
1000
L
1
18127
Indiana
Porter
0.022
NOx
CUS
1000
L
2
18037
Indiana
Dubois
0.024
NOx
CUS
1000
L
3
47055
Tennessee
Giles
0.027
NOx
CUS
1000
L
4
1001
Alabama
Autauga
0.026
NOx
CUS
1000
L
6
17155
Illinois
Putnam
0.015
NOx
CUS
1000
L
7
17145
Illinois
Perry
0.024
NOx
CUS
1000
L
8
47157
Tennessee
Shelby
0.007
NOx
CUS
1000
L
9
28129
Mississippi
Smith
0.025
NOx
CUS
1000
L
10
22071
Louisiana
Orleans
0.020
NOx
CUS
1000
L
11
19095
Iowa
Iowa
0.019
NOx
CUS
1000
L
12
29029
Missouri
Camden
0.016
NOx
CUS
1000
L
13
5119
Arkansas
Pulaski
0.011
NOx
CUS
1000
L
14
22061
Louisiana
Lincoln
0.013
NOx
CUS
1000
L
15
22001
Louisiana
Acadia
0.027
NOx
CUS
1000
L
16
31055
Nebraska
Douglas
0.013
NOx
CUS
1000
L
17
20091
Kansas
Johnson
0.011
NOx
CUS
1000
L
18
40101
Oklahoma
Muskogee
0.016
NOx
CUS
1000
L
19
48213
Texas
Henderson
0.012
NOx
CUS
1000
L
20
48201
Texas
Harris
0.020
NOx
CUS
1000
L
21
31001
Nebraska
Adams
0.021
NOx
CUS
1000
L
22
20155
Kansas
Reno
0.010
NOx
CUS
1000
L
23
40017
Oklahoma
Canadian
0.008
NOx
CUS
1000
L
24
48367
Texas
Parker
0.010
NOx
CUS
1000
L
25
48187
Texas
Guadalupe
0.012
NOx
CUS
3000
H
1
18127
Indiana
Porter
0.025
NOx
CUS
3000
H
2
18037
Indiana
Dubois
0.026
NOx
CUS
3000
H
3
47055
Tennessee
Giles
0.024
NOx
CUS
3000
H
4
1001
Alabama
Autauga
0.023
NOx
CUS
3000
H
6
17155
Illinois
Putnam
0.017
NOx
CUS
3000
H
7
17145
Illinois
Perry
0.024
NOx
CUS
3000
H
8
47157
Tennessee
Shelby
0.012
NOx
CUS
3000
H
9
28129
Mississippi
Smith
0.021
NOx
CUS
3000
H
10
22071
Louisiana
Orleans
0.024
NOx
CUS
3000
H
11
19095
Iowa
Iowa
0.023
NOx
CUS
3000
H
12
29029
Missouri
Camden
0.019
NOx
CUS
3000
H
13
5119
Arkansas
Pulaski
0.017
NOx
CUS
3000
H
14
22061
Louisiana
Lincoln
0.014
NOx
CUS
3000
H
15
22001
Louisiana
Acadia
0.019
NOx
CUS
3000
H
16
31055
Nebraska
Douglas
0.018
NOx
CUS
3000
H
17
20091
Kansas
Johnson
0.013
NOx
CUS
3000
H
18
40101
Oklahoma
Muskogee
0.018
NOx
CUS
3000
H
19
48213
Texas
Henderson
0.012
NOx
CUS
3000
H
20
48201
Texas
Harris
0.015
NOx
CUS
3000
H
21
31001
Nebraska
Adams
0.025
NOx
CUS
3000
H
22
20155
Kansas
Reno
0.014
NOx
CUS
3000
H
23
40017
Oklahoma
Canadian
0.010
NOx
CUS
3000
H
24
48367
Texas
Parker
0.013
NOx
CUS
3000
H
25
48187
Texas
Guadalupe
0.014
NOx
EUS
500
H
1
23003
Maine
Aroostook
0.002
NOx
EUS
500
H
2
23031
Maine
York
0.002
NOx
EUS
500
H
3
25021
Massachusetts
Norfolk
0.001
NOx
EUS
500
H
4
25011
Massachusetts
Franklin
0.002
NOx
EUS
500
H
6
0
NONE
NONE
0.002
64
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Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
NOx
EUS
500
H
7
36051
New York
Livingston
0.003
NOx
EUS
500
H
8
42001
Pennsylvania
Adams
0.003
NOx
EUS
500
H
9
51053
Virginia
Dinwiddie
0.002
NOx
EUS
500
H
10
45051
South Carolina
Horry
0.002
NOx
EUS
500
H
11
26099
Michigan
Macomb
0.002
NOx
EUS
500
H
12
39157
Ohio
Tuscarawas
0.002
NOx
EUS
500
H
13
37009
North Carolina
Ashe
0.002
NOx
EUS
500
H
14
45005
South Carolina
Allendale
0.001
NOx
EUS
500
H
15
26103
Michigan
Marquette
0.001
NOx
EUS
500
H
16
26117
Michigan
Montcalm
0.003
NOx
EUS
500
H
17
18053
Indiana
Grant
0.004
NOx
EUS
500
H
18
21009
Kentucky
Barren
0.002
NOx
EUS
500
H
19
1123
Alabama
Tallapoosa
0.001
NOx
EUS
500
L
1
23003
Maine
Aroostook
0.007
NOx
EUS
500
L
2
23031
Maine
York
0.006
NOx
EUS
500
L
3
25021
Massachusetts
Norfolk
0.004
NOx
EUS
500
L
4
25011
Massachusetts
Franklin
0.007
NOx
EUS
500
L
6
0
NONE
NONE
0.006
NOx
EUS
500
L
7
36051
New York
Livingston
0.007
NOx
EUS
500
L
8
42001
Pennsylvania
Adams
0.010
NOx
EUS
500
L
9
51053
Virginia
Dinwiddie
0.005
NOx
EUS
500
L
10
45051
South Carolina
Horry
0.010
NOx
EUS
500
L
11
26099
Michigan
Macomb
0.007
NOx
EUS
500
L
12
39157
Ohio
Tuscarawas
0.006
NOx
EUS
500
L
13
37009
North Carolina
Ashe
0.004
NOx
EUS
500
L
14
45005
South Carolina
Allendale
0.006
NOx
EUS
500
L
15
26103
Michigan
Marquette
0.003
NOx
EUS
500
L
16
26117
Michigan
Montcalm
0.010
NOx
EUS
500
L
17
18053
Indiana
Grant
0.010
NOx
EUS
500
L
18
21009
Kentucky
Barren
0.007
NOx
EUS
500
L
19
1123
Alabama
Tallapoosa
0.003
NOx
EUS
1000
H
1
23003
Maine
Aroostook
0.004
NOx
EUS
1000
H
2
23031
Maine
York
0.004
NOx
EUS
1000
H
3
25021
Massachusetts
Norfolk
0.003
NOx
EUS
1000
H
4
25011
Massachusetts
Franklin
0.004
NOx
EUS
1000
H
6
0
NONE
NONE
0.003
NOx
EUS
1000
H
7
36051
New York
Livingston
0.006
NOx
EUS
1000
H
8
42001
Pennsylvania
Adams
0.006
NOx
EUS
1000
H
9
51053
Virginia
Dinwiddie
0.003
NOx
EUS
1000
H
10
45051
South Carolina
Horry
0.005
NOx
EUS
1000
H
11
26099
Michigan
Macomb
0.004
NOx
EUS
1000
H
12
39157
Ohio
Tuscarawas
0.003
NOx
EUS
1000
H
13
37009
North Carolina
Ashe
0.004
NOx
EUS
1000
H
14
45005
South Carolina
Allendale
0.003
NOx
EUS
1000
H
15
26103
Michigan
Marquette
0.002
NOx
EUS
1000
H
16
26117
Michigan
Montcalm
0.006
NOx
EUS
1000
H
17
18053
Indiana
Grant
0.007
NOx
EUS
1000
H
18
21009
Kentucky
Barren
0.004
NOx
EUS
1000
H
19
1123
Alabama
Tallapoosa
0.002
NOx
EUS
3000
H
1
23003
Maine
Aroostook
0.012
NOx
EUS
3000
H
2
23031
Maine
York
0.011
NOx
EUS
3000
H
3
25021
Massachusetts
Norfolk
0.007
NOx
EUS
3000
H
4
25011
Massachusetts
Franklin
0.010
NOx
EUS
3000
H
6
0
NONE
NONE
0.008
65
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Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
NOx
EUS
3000
H
7
36051
New York
Livingston
0.016
NOx
EUS
3000
H
8
42001
Pennsylvania
Adams
0.015
NOx
EUS
3000
H
9
51053
Virginia
Dinwiddie
0.009
NOx
EUS
3000
H
10
45051
South Carolina
Horry
0.012
NOx
EUS
3000
H
11
26099
Michigan
Macomb
0.011
NOx
EUS
3000
H
12
39157
Ohio
Tuscarawas
0.010
NOx
EUS
3000
H
13
37009
North Carolina
Ashe
0.010
NOx
EUS
3000
H
14
45005
South Carolina
Allendale
0.007
NOx
EUS
3000
H
15
26103
Michigan
Marquette
0.004
NOx
EUS
3000
H
16
26117
Michigan
Montcalm
0.015
NOx
EUS
3000
H
17
18053
Indiana
Grant
0.018
NOx
EUS
3000
H
18
21009
Kentucky
Barren
0.010
NOx
EUS
3000
H
19
1123
Alabama
Tallapoosa
0.004
NOx
WUS
500
H
1
38057
North Dakota
Mercer
0.003
NOx
WUS
500
H
2
38059
North Dakota
Morton
0.003
NOx
WUS
500
H
3
8123
Colorado
Weld
0.004
NOx
WUS
500
H
4
8011
Colorado
Bent
0.001
NOx
WUS
500
H
6
30083
Montana
Richland
Powder
0.002
NOx
WUS
500
H
7
30075
Montana
River
0.001
NOx
WUS
500
H
8
8069
Colorado
Larimer
0.001
NOx
WUS
500
H
9
8109
Colorado
Saguache
0.001
NOx
WUS
500
H
10
35035
New Mexico
Otero
0.000
NOx
WUS
500
H
11
30111
Montana
Yellowstone
0.002
NOx
WUS
500
H
12
49013
Utah
Duchesne
0.001
NOx
WUS
500
H
13
49037
Utah
San Juan
0.000
NOx
WUS
500
H
14
4007
Arizona
Gila
0.001
NOx
WUS
500
H
15
49049
Utah
Utah
0.005
NOx
WUS
500
H
16
49021
Utah
Iron
0.001
NOx
WUS
500
H
17
4012
Arizona
La Paz
0.000
NOx
WUS
500
H
18
41049
Oregon
Morrow
0.008
NOx
WUS
500
H
19
32001
Nevada
Churchill
0.001
NOx
WUS
500
H
20
6107
California
Tulare
0.023
NOx
WUS
500
H
21
6037
California
Los Angeles
0.001
NOx
WUS
500
H
22
53057
Washington
Skagit
0.005
NOx
WUS
500
H
23
53039
Washington
Klickitat
0.001
NOx
WUS
500
H
24
6063
California
Plumas
0.001
NOx
WUS
500
H
25
6047
California
Merced
0.015
NOx
WUS
500
H
26
6029
California
Kern
0.014
NOx
WUS
500
L
1
38057
North Dakota
Mercer
0.008
NOx
WUS
500
L
2
38059
North Dakota
Morton
0.007
NOx
WUS
500
L
3
8123
Colorado
Weld
0.008
NOx
WUS
500
L
4
8011
Colorado
Bent
0.002
NOx
WUS
500
L
6
30083
Montana
Richland
Powder
0.005
NOx
WUS
500
L
7
30075
Montana
River
0.003
NOx
WUS
500
L
8
8069
Colorado
Larimer
0.001
NOx
WUS
500
L
9
8109
Colorado
Saguache
0.002
NOx
WUS
500
L
10
35035
New Mexico
Otero
0.000
NOx
WUS
500
L
11
30111
Montana
Yellowstone
0.004
NOx
WUS
500
L
12
49013
Utah
Duchesne
0.001
NOx
WUS
500
L
13
49037
Utah
San Juan
0.000
NOx
WUS
500
L
14
4007
Arizona
Gila
0.001
NOx
WUS
500
L
15
49049
Utah
Utah
0.006
66
-------
Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
NOx
WUS
500
L
16
49021
Utah
Iron
0.001
NOx
WUS
500
L
17
4012
Arizona
La Paz
0.000
NOx
WUS
500
L
18
41049
Oregon
Morrow
0.013
NOx
WUS
500
L
19
32001
Nevada
Churchill
0.002
NOx
WUS
500
L
20
6107
California
Tulare
0.031
NOx
WUS
500
L
21
6037
California
Los Angeles
0.002
NOx
WUS
500
L
22
53057
Washington
Skagit
0.007
NOx
WUS
500
L
23
53039
Washington
Klickitat
0.002
NOx
WUS
500
L
24
6063
California
Plumas
0.002
NOx
WUS
500
L
25
6047
California
Merced
0.023
NOx
WUS
500
L
26
6029
California
Kern
0.017
NOx
WUS
1000
H
1
38057
North Dakota
Mercer
0.006
NOx
WUS
1000
H
2
38059
North Dakota
Morton
0.005
NOx
WUS
1000
H
3
8123
Colorado
Weld
0.008
NOx
WUS
1000
H
4
8011
Colorado
Bent
0.002
NOx
WUS
1000
H
6
30083
Montana
Richland
Powder
0.004
NOx
WUS
1000
H
7
30075
Montana
River
0.002
NOx
WUS
1000
H
8
8069
Colorado
Larimer
0.002
NOx
WUS
1000
H
9
8109
Colorado
Saguache
0.002
NOx
WUS
1000
H
10
35035
New Mexico
Otero
0.000
NOx
WUS
1000
H
11
30111
Montana
Yellowstone
0.004
NOx
WUS
1000
H
12
49013
Utah
Duchesne
0.002
NOx
WUS
1000
H
13
49037
Utah
San Juan
0.000
NOx
WUS
1000
H
14
4007
Arizona
Gila
0.001
NOx
WUS
1000
H
15
49049
Utah
Utah
0.010
NOx
WUS
1000
H
16
49021
Utah
Iron
0.001
NOx
WUS
1000
H
17
4012
Arizona
La Paz
0.000
NOx
WUS
1000
H
18
41049
Oregon
Morrow
0.016
NOx
WUS
1000
H
19
32001
Nevada
Churchill
0.002
NOx
WUS
1000
H
20
6107
California
Tulare
0.045
NOx
WUS
1000
H
21
6037
California
Los Angeles
0.003
NOx
WUS
1000
H
22
53057
Washington
Skagit
0.009
NOx
WUS
1000
H
23
53039
Washington
Klickitat
0.003
NOx
WUS
1000
H
24
6063
California
Plumas
0.002
NOx
WUS
1000
H
25
6047
California
Merced
0.030
NOx
WUS
1000
H
26
6029
California
Kern
0.028
NOx
WUS
3000
H
1
38057
North Dakota
Mercer
0.016
NOx
WUS
3000
H
2
38059
North Dakota
Morton
0.013
NOx
WUS
3000
H
3
8123
Colorado
Weld
0.019
NOx
WUS
3000
H
4
8011
Colorado
Bent
0.007
NOx
WUS
3000
H
6
30083
Montana
Richland
Powder
0.009
NOx
WUS
3000
H
7
30075
Montana
River
0.006
NOx
WUS
3000
H
8
8069
Colorado
Larimer
0.005
NOx
WUS
3000
H
9
8109
Colorado
Saguache
0.007
NOx
WUS
3000
H
10
35035
New Mexico
Otero
0.001
NOx
WUS
3000
H
11
30111
Montana
Yellowstone
0.009
NOx
WUS
3000
H
12
49013
Utah
Duchesne
0.005
NOx
WUS
3000
H
13
49037
Utah
San Juan
0.001
NOx
WUS
3000
H
14
4007
Arizona
Gila
0.003
NOx
WUS
3000
H
15
49049
Utah
Utah
0.028
NOx
WUS
3000
H
16
49021
Utah
Iron
0.004
NOx
WUS
3000
H
17
4012
Arizona
La Paz
0.001
67
-------
Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
NOx
WUS
3000
H
18
41049
Oregon
Morrow
0.042
NOx
wus
3000
H
19
32001
Nevada
Churchill
0.006
NOx
WUS
3000
H
20
6107
California
Tulare
0.128
NOx
wus
3000
H
21
6037
California
Los Angeles
0.008
NOx
wus
3000
H
22
53057
Washington
Skagit
0.026
NOx
wus
3000
H
23
53039
Washington
Klickitat
0.008
NOx
wus
3000
H
24
6063
California
Plumas
0.007
NOx
wus
3000
H
25
6047
California
Merced
0.082
NOx
wus
3000
H
26
6029
California
Kern
0.080
S02
cus
500
L
1
18127
Indiana
Porter
0.026
S02
cus
500
L
2
18037
Indiana
Dubois
0.005
S02
cus
500
L
3
47055
Tennessee
Giles
0.009
S02
cus
500
L
4
1001
Alabama
Autauga
0.031
S02
cus
500
L
6
17155
Illinois
Putnam
0.008
S02
cus
500
L
7
17145
Illinois
Perry
0.010
S02
cus
500
L
8
47157
Tennessee
Shelby
0.009
S02
cus
500
L
9
28129
Mississippi
Smith
0.035
S02
cus
500
L
10
22071
Louisiana
Orleans
0.023
S02
cus
500
L
11
19095
Iowa
Iowa
0.029
S02
cus
500
L
12
29029
Missouri
Camden
0.011
S02
cus
500
L
13
5119
Arkansas
Pulaski
0.016
S02
cus
500
L
14
22061
Louisiana
Lincoln
0.009
S02
cus
500
L
15
22001
Louisiana
Acadia
0.041
S02
cus
500
L
16
31055
Nebraska
Douglas
0.016
S02
cus
500
L
17
20091
Kansas
Johnson
0.019
S02
cus
500
L
18
40101
Oklahoma
Muskogee
0.014
S02
cus
500
L
19
48213
Texas
Henderson
0.006
S02
cus
500
L
20
48201
Texas
Harris
0.040
S02
cus
500
L
21
31001
Nebraska
Adams
0.037
S02
cus
500
L
22
20155
Kansas
Reno
0.007
S02
cus
500
L
23
40017
Oklahoma
Canadian
0.011
S02
cus
500
L
24
48367
Texas
Parker
0.008
S02
cus
500
L
25
48187
Texas
Guadalupe
0.013
S02
cus
1000
H
1
18127
Indiana
Porter
0.019
S02
cus
1000
H
2
18037
Indiana
Dubois
0.008
S02
cus
1000
H
3
47055
Tennessee
Giles
0.010
S02
cus
1000
H
4
1001
Alabama
Autauga
0.021
S02
cus
1000
H
6
17155
Illinois
Putnam
0.009
S02
cus
1000
H
7
17145
Illinois
Perry
0.009
S02
cus
1000
H
8
47157
Tennessee
Shelby
0.008
S02
cus
1000
H
9
28129
Mississippi
Smith
0.018
S02
cus
1000
H
10
22071
Louisiana
Orleans
0.023
S02
cus
1000
H
11
19095
Iowa
Iowa
0.020
S02
cus
1000
H
12
29029
Missouri
Camden
0.012
S02
cus
1000
H
13
5119
Arkansas
Pulaski
0.013
S02
cus
1000
H
14
22061
Louisiana
Lincoln
0.012
S02
cus
1000
H
15
22001
Louisiana
Acadia
0.027
S02
cus
1000
H
16
31055
Nebraska
Douglas
0.013
S02
cus
1000
H
17
20091
Kansas
Johnson
0.010
S02
cus
1000
H
18
40101
Oklahoma
Muskogee
0.008
S02
cus
1000
H
19
48213
Texas
Henderson
0.007
S02
cus
1000
H
20
48201
Texas
Harris
0.022
S02
cus
1000
H
21
31001
Nebraska
Adams
0.022
S02
cus
1000
H
22
20155
Kansas
Reno
0.009
68
-------
Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
S02
CUS
1000
H
23
40017
Oklahoma
Canadian
0.007
S02
CUS
1000
H
24
48367
Texas
Parker
0.009
S02
CUS
1000
H
25
48187
Texas
Guadalupe
0.014
S02
CUS
1000
L
1
18127
Indiana
Porter
0.064
S02
CUS
1000
L
2
18037
Indiana
Dubois
0.015
S02
CUS
1000
L
3
47055
Tennessee
Giles
0.033
S02
CUS
1000
L
4
1001
Alabama
Autauga
0.094
S02
CUS
1000
L
6
17155
Illinois
Putnam
0.025
S02
CUS
1000
L
7
17145
Illinois
Perry
0.029
S02
CUS
1000
L
8
47157
Tennessee
Shelby
0.021
S02
CUS
1000
L
9
28129
Mississippi
Smith
0.095
S02
CUS
1000
L
10
22071
Louisiana
Orleans
0.075
S02
CUS
1000
L
11
19095
Iowa
Iowa
0.067
S02
CUS
1000
L
12
29029
Missouri
Camden
0.035
S02
CUS
1000
L
13
5119
Arkansas
Pulaski
0.046
S02
CUS
1000
L
14
22061
Louisiana
Lincoln
0.030
S02
CUS
1000
L
15
22001
Louisiana
Acadia
0.111
S02
CUS
1000
L
16
31055
Nebraska
Douglas
0.043
S02
CUS
1000
L
17
20091
Kansas
Johnson
0.051
S02
CUS
1000
L
18
40101
Oklahoma
Muskogee
0.040
S02
CUS
1000
L
19
48213
Texas
Henderson
0.019
S02
CUS
1000
L
20
48201
Texas
Harris
0.111
S02
CUS
1000
L
21
31001
Nebraska
Adams
0.087
S02
CUS
1000
L
22
20155
Kansas
Reno
0.019
S02
CUS
1000
L
23
40017
Oklahoma
Canadian
0.030
S02
CUS
1000
L
24
48367
Texas
Parker
0.026
S02
CUS
1000
L
25
48187
Texas
Guadalupe
0.040
S02
CUS
3000
H
1
18127
Indiana
Porter
0.088
S02
CUS
3000
H
2
18037
Indiana
Dubois
0.047
S02
CUS
3000
H
3
47055
Tennessee
Giles
0.060
S02
CUS
3000
H
4
1001
Alabama
Autauga
0.114
S02
CUS
3000
H
6
17155
Illinois
Putnam
0.047
S02
CUS
3000
H
7
17145
Illinois
Perry
0.046
S02
CUS
3000
H
8
47157
Tennessee
Shelby
0.036
S02
CUS
3000
H
9
28129
Mississippi
Smith
0.094
S02
CUS
3000
H
10
22071
Louisiana
Orleans
0.138
S02
CUS
3000
H
11
19095
Iowa
Iowa
0.077
S02
CUS
3000
H
12
29029
Missouri
Camden
0.063
S02
CUS
3000
H
13
5119
Arkansas
Pulaski
0.064
S02
CUS
3000
H
14
22061
Louisiana
Lincoln
0.065
S02
CUS
3000
H
15
22001
Louisiana
Acadia
0.115
S02
CUS
3000
H
16
31055
Nebraska
Douglas
0.059
S02
CUS
3000
H
17
20091
Kansas
Johnson
0.050
S02
CUS
3000
H
18
40101
Oklahoma
Muskogee
0.047
S02
CUS
3000
H
19
48213
Texas
Henderson
0.039
S02
CUS
3000
H
20
48201
Texas
Harris
0.100
S02
CUS
3000
H
21
31001
Nebraska
Adams
0.080
S02
CUS
3000
H
22
20155
Kansas
Reno
0.039
S02
CUS
3000
H
23
40017
Oklahoma
Canadian
0.030
S02
CUS
3000
H
24
48367
Texas
Parker
0.043
S02
CUS
3000
H
25
48187
Texas
Guadalupe
0.067
S02
EUS
500
H
1
23003
Maine
Aroostook
0.009
S02
EUS
500
H
2
23031
Maine
York
0.014
S02
EUS
500
H
3
25021
Massachusetts
Norfolk
0.006
69
-------
Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
S02
EUS
500
H
4
25011
Massachusetts
Franklin
0.005
S02
EUS
500
H
6
0
NONE
NONE
0.004
S02
EUS
500
H
7
36051
New York
Livingston
0.003
S02
EUS
500
H
8
42001
Pennsylvania
Adams
0.003
S02
EUS
500
H
9
51053
Virginia
Dinwiddie
0.007
S02
EUS
500
H
10
45051
South Carolina
Horry
0.006
S02
EUS
500
H
11
26099
Michigan
Macomb
0.004
S02
EUS
500
H
12
39157
Ohio
Tuscarawas
0.004
S02
EUS
500
H
13
37009
North Carolina
Ashe
0.007
S02
EUS
500
H
14
45005
South Carolina
Allendale
0.006
S02
EUS
500
H
15
26103
Michigan
Marquette
0.005
S02
EUS
500
H
16
26117
Michigan
Montcalm
0.004
S02
EUS
500
H
17
18053
Indiana
Grant
0.005
S02
EUS
500
H
18
21009
Kentucky
Barren
0.002
S02
EUS
500
H
19
1123
Alabama
Tallapoosa
0.005
S02
EUS
500
L
1
23003
Maine
Aroostook
0.021
S02
EUS
500
L
2
23031
Maine
York
0.025
S02
EUS
500
L
3
25021
Massachusetts
Norfolk
0.010
S02
EUS
500
L
4
25011
Massachusetts
Franklin
0.009
S02
EUS
500
L
6
0
NONE
NONE
0.013
S02
EUS
500
L
7
36051
New York
Livingston
0.006
S02
EUS
500
L
8
42001
Pennsylvania
Adams
0.009
S02
EUS
500
L
9
51053
Virginia
Dinwiddie
0.014
S02
EUS
500
L
10
45051
South Carolina
Horry
0.023
S02
EUS
500
L
11
26099
Michigan
Macomb
0.008
S02
EUS
500
L
12
39157
Ohio
Tuscarawas
0.009
S02
EUS
500
L
13
37009
North Carolina
Ashe
0.010
S02
EUS
500
L
14
45005
South Carolina
Allendale
0.016
S02
EUS
500
L
15
26103
Michigan
Marquette
0.010
S02
EUS
500
L
16
26117
Michigan
Montcalm
0.011
S02
EUS
500
L
17
18053
Indiana
Grant
0.011
S02
EUS
500
L
18
21009
Kentucky
Barren
0.004
S02
EUS
500
L
19
1123
Alabama
Tallapoosa
0.010
S02
EUS
1000
H
1
23003
Maine
Aroostook
0.016
S02
EUS
1000
H
2
23031
Maine
York
0.025
S02
EUS
1000
H
3
25021
Massachusetts
Norfolk
0.011
S02
EUS
1000
H
4
25011
Massachusetts
Franklin
0.009
S02
EUS
1000
H
6
0
NONE
NONE
0.008
S02
EUS
1000
H
7
36051
New York
Livingston
0.006
S02
EUS
1000
H
8
42001
Pennsylvania
Adams
0.006
S02
EUS
1000
H
9
51053
Virginia
Dinwiddie
0.013
S02
EUS
1000
H
10
45051
South Carolina
Horry
0.012
S02
EUS
1000
H
11
26099
Michigan
Macomb
0.007
S02
EUS
1000
H
12
39157
Ohio
Tuscarawas
0.008
S02
EUS
1000
H
13
37009
North Carolina
Ashe
0.013
S02
EUS
1000
H
14
45005
South Carolina
Allendale
0.011
S02
EUS
1000
H
15
26103
Michigan
Marquette
0.010
S02
EUS
1000
H
16
26117
Michigan
Montcalm
0.008
S02
EUS
1000
H
17
18053
Indiana
Grant
0.009
S02
EUS
1000
H
18
21009
Kentucky
Barren
0.004
S02
EUS
1000
H
19
1123
Alabama
Tallapoosa
0.009
S02
EUS
3000
H
1
23003
Maine
Aroostook
0.037
S02
EUS
3000
H
2
23031
Maine
York
0.056
S02
EUS
3000
H
3
25021
Massachusetts
Norfolk
0.028
70
-------
Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
S02
EUS
3000
H
4
25011
Massachusetts
Franklin
0.023
S02
EUS
3000
H
6
0
NONE
NONE
0.020
S02
EUS
3000
H
7
36051
New York
Livingston
0.016
S02
EUS
3000
H
8
42001
Pennsylvania
Adams
0.016
S02
EUS
3000
H
9
51053
Virginia
Dinwiddie
0.031
S02
EUS
3000
H
10
45051
South Carolina
Horry
0.030
S02
EUS
3000
H
11
26099
Michigan
Macomb
0.019
S02
EUS
3000
H
12
39157
Ohio
Tuscarawas
0.022
S02
EUS
3000
H
13
37009
North Carolina
Ashe
0.033
S02
EUS
3000
H
14
45005
South Carolina
Allendale
0.029
S02
EUS
3000
H
15
26103
Michigan
Marquette
0.025
S02
EUS
3000
H
16
26117
Michigan
Montcalm
0.020
S02
EUS
3000
H
17
18053
Indiana
Grant
0.025
S02
EUS
3000
H
18
21009
Kentucky
Barren
0.014
S02
EUS
3000
H
19
1123
Alabama
Tallapoosa
0.024
S02
WUS
500
H
1
38057
North Dakota
Mercer
0.013
S02
WUS
500
H
2
38059
North Dakota
Morton
0.007
S02
WUS
500
H
3
8123
Colorado
Weld
0.005
S02
WUS
500
H
4
8011
Colorado
Bent
0.003
S02
WUS
500
H
6
30083
Montana
Richland
Powder
0.004
S02
WUS
500
H
7
30075
Montana
River
0.003
S02
WUS
500
H
8
8069
Colorado
Larimer
0.002
S02
WUS
500
H
9
8109
Colorado
Saguache
0.003
S02
WUS
500
H
10
35035
New Mexico
Otero
0.001
S02
WUS
500
H
11
30111
Montana
Yellowstone
0.004
S02
WUS
500
H
12
49013
Utah
Duchesne
0.003
S02
WUS
500
H
13
49037
Utah
San Juan
0.002
S02
WUS
500
H
14
4007
Arizona
Gila
0.001
S02
WUS
500
H
15
49049
Utah
Utah
0.005
S02
WUS
500
H
16
49021
Utah
Iron
0.003
S02
WUS
500
H
17
4012
Arizona
La Paz
0.002
S02
WUS
500
H
18
41049
Oregon
Morrow
0.007
S02
WUS
500
H
19
32001
Nevada
Churchill
0.007
S02
WUS
500
H
20
6107
California
Tulare
0.019
S02
WUS
500
H
21
6037
California
Los Angeles
0.002
S02
WUS
500
H
22
53057
Washington
Skagit
0.006
S02
WUS
500
H
23
53039
Washington
Klickitat
0.009
S02
WUS
500
H
24
6063
California
Plumas
0.007
S02
WUS
500
H
25
6047
California
Merced
0.009
S02
WUS
500
H
26
6029
California
Kern
0.009
S02
WUS
500
L
1
38057
North Dakota
Mercer
0.044
S02
WUS
500
L
2
38059
North Dakota
Morton
0.018
S02
WUS
500
L
3
8123
Colorado
Weld
0.009
S02
WUS
500
L
4
8011
Colorado
Bent
0.004
S02
WUS
500
L
6
30083
Montana
Richland
Powder
0.008
S02
WUS
500
L
7
30075
Montana
River
0.006
S02
WUS
500
L
8
8069
Colorado
Larimer
0.002
S02
WUS
500
L
9
8109
Colorado
Saguache
0.004
S02
WUS
500
L
10
35035
New Mexico
Otero
0.002
S02
WUS
500
L
11
30111
Montana
Yellowstone
0.005
S02
WUS
500
L
12
49013
Utah
Duchesne
0.004
S02
WUS
500
L
13
49037
Utah
San Juan
0.002
71
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Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
S02
WUS
500
L
14
4007
Arizona
Gila
0.002
S02
wus
500
L
15
49049
Utah
Utah
0.006
S02
WUS
500
L
16
49021
Utah
Iron
0.004
S02
wus
500
L
17
4012
Arizona
La Paz
0.003
S02
wus
500
L
18
41049
Oregon
Morrow
0.008
S02
wus
500
L
19
32001
Nevada
Churchill
0.009
S02
wus
500
L
20
6107
California
Tulare
0.042
S02
wus
500
L
21
6037
California
Los Angeles
0.002
S02
wus
500
L
22
53057
Washington
Skagit
0.006
S02
wus
500
L
23
53039
Washington
Klickitat
0.009
S02
wus
500
L
24
6063
California
Plumas
0.011
S02
wus
500
L
25
6047
California
Merced
0.018
S02
wus
500
L
26
6029
California
Kern
0.009
S02
wus
1000
H
1
38057
North Dakota
Mercer
0.026
S02
wus
1000
H
2
38059
North Dakota
Morton
0.013
S02
wus
1000
H
3
8123
Colorado
Weld
0.010
S02
wus
1000
H
4
8011
Colorado
Bent
0.006
S02
wus
1000
H
6
30083
Montana
Richland
Powder
0.008
S02
wus
1000
H
7
30075
Montana
River
0.006
S02
wus
1000
H
8
8069
Colorado
Larimer
0.003
S02
wus
1000
H
9
8109
Colorado
Saguache
0.005
S02
wus
1000
H
10
35035
New Mexico
Otero
0.003
S02
wus
1000
H
11
30111
Montana
Yellowstone
0.007
S02
wus
1000
H
12
49013
Utah
Duchesne
0.006
S02
wus
1000
H
13
49037
Utah
San Juan
0.003
S02
wus
1000
H
14
4007
Arizona
Gila
0.003
S02
wus
1000
H
15
49049
Utah
Utah
0.010
S02
wus
1000
H
16
49021
Utah
Iron
0.005
S02
wus
1000
H
17
4012
Arizona
La Paz
0.005
S02
wus
1000
H
18
41049
Oregon
Morrow
0.013
S02
wus
1000
H
19
32001
Nevada
Churchill
0.012
S02
wus
1000
H
20
6107
California
Tulare
0.036
S02
wus
1000
H
21
6037
California
Los Angeles
0.003
S02
wus
1000
H
22
53057
Washington
Skagit
0.012
S02
wus
1000
H
23
53039
Washington
Klickitat
0.016
S02
wus
1000
H
24
6063
California
Plumas
0.012
S02
wus
1000
H
25
6047
California
Merced
0.018
S02
wus
1000
H
26
6029
California
Kern
0.017
S02
wus
3000
H
1
38057
North Dakota
Mercer
0.075
S02
wus
3000
H
2
38059
North Dakota
Morton
0.031
S02
wus
3000
H
3
8123
Colorado
Weld
0.024
S02
wus
3000
H
4
8011
Colorado
Bent
0.016
S02
wus
3000
H
6
30083
Montana
Richland
Powder
0.018
S02
wus
3000
H
7
30075
Montana
River
0.017
S02
wus
3000
H
8
8069
Colorado
Larimer
0.008
S02
wus
3000
H
9
8109
Colorado
Saguache
0.015
S02
wus
3000
H
10
35035
New Mexico
Otero
0.009
S02
wus
3000
H
11
30111
Montana
Yellowstone
0.019
S02
wus
3000
H
12
49013
Utah
Duchesne
0.016
S02
wus
3000
H
13
49037
Utah
San Juan
0.010
S02
wus
3000
H
14
4007
Arizona
Gila
0.009
S02
wus
3000
H
15
49049
Utah
Utah
0.027
72
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Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
S02
WUS
3000
H
16
49021
Utah
Iron
0.013
S02
wus
3000
H
17
4012
Arizona
La Paz
0.014
S02
WUS
3000
H
18
41049
Oregon
Morrow
0.034
S02
wus
3000
H
19
32001
Nevada
Churchill
0.030
S02
wus
3000
H
20
6107
California
Tulare
0.096
S02
wus
3000
H
21
6037
California
Los Angeles
0.009
S02
wus
3000
H
22
53057
Washington
Skagit
0.033
S02
wus
3000
H
23
53039
Washington
Klickitat
0.039
S02
wus
3000
H
24
6063
California
Plumas
0.032
S02
wus
3000
H
25
6047
California
Merced
0.048
S02
wus
3000
H
26
6029
California
Kern
0.049
73
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Does not represent final agency action; Draft for Public Review and Comment, 12/01/2016
United States Office of Air Quality Planning and Standards Publication No. EPA-454/R-16-006
Environ mental Protection Air Quality Assessment Division December 2016
Agency Research Triangle Park, NC
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