&EPA
Air Quality Modeling Final Rule
Technical Support Document
2015 Ozone NAAQS Good Neighbor Plan
Office of Air Quality Planning and Standards
United States Environmental Protection Agency
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1. Introduction
In this technical support document (TSD) we describe the air quality modeling performed to
support the EPA's final Good Neighbor Plan for the 2015 Ozone National Ambient Air Quality
Standards (NAAQS) (i.e., final rule). For this final rule, air quality modeling is used to project ozone
design values1 at individual monitoring sites to the 2023 and 2026 analytic years2 and to estimate
state-by-state contributions to ozone design values at individual monitoring sites in these future years.
The projected ozone design values are evaluated to identify ozone monitoring sites that are expected
to have nonattainment or maintenance problems for the 2015 ozone National Ambient Air Quality
Standards (NAAQS) in the future (i.e., nonattainment and maintenance receptors).3 Ozone
contribution data for 2023 and 2026 are used to quantify projected interstate contributions from
emissions in each upwind state to ozone design values at projected nonattainment and maintenance
receptors in other states (i.e., in downwind states). The contributions from individual states to
nonattainment and maintenance receptors in other states (i.e., upwind states and downwind states,
respectively) are evaluated to identify upwind states that contribute greater than or equal to 1 percent
of the 2015 ozone NAAQS (i.e., 1 percent of 70 ppb which is 0.70 ppb) to one or more downwind
receptors. Upwind states that contribute at or above this threshold to particular receptors are referred
to as being "linked" to these receptors. In this final rule the EPA is making a finding that interstate
transport of ozone precursor emissions from 23 upwind states (Alabama, Arkansas, California,
Illinois, Indiana, Kentucky, Louisiana, Maryland, Michigan, Minnesota, Mississippi, Missouri,
Nevada, New Jersey, New York, Ohio, Oklahoma, Pennsylvania, Texas, Utah, Virginia, West
Virginia, and Wisconsin) are significantly contributing to nonattainment or interfering with
maintenance of the 2015 ozone NAAQS in downwind states, based on projected ozone precursor
emissions in the 2023 ozone season.
As described in this TSD, the EPA performed air quality modeling for the 2016 base year and
2023 and 2026 future years to project 2016-centered base period design values to each of these future
years. Ozone source apportionment modeling was performed using emissions projected to 2023 and
2026 to determine the contributions of total anthropogenic emissions from each state to projected
1 The ozone design value for a monitoring site is the 3-year average of the annual fourth-highest daily maximum 8-hour
average ozone concentrations at the site.
2 The rationale for using 2023 and 2026 as applicable future analytic years for this transport assessment is described in
the preamble for this final rule.
3 As described in section 3, the EPA also identified an additional type of maintenance-only receptors based on recent
measured data that exceed the NAAQS.
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ozone design values at individual monitoring sites nationwide for each of these years. The modeling
for 2023 and 2026 was used to identify receptors and upwind/downwind linkages to inform Step 1
and Step 2 of the 4-step interstate transport framework, respectively.4 In addition, modeling was
performed for the 2026 EGU plus non-EGU final rule control case that is analyzed in the Regulatory
Impact Analysis (RIA).
The remaining sections of this TSD are as follows. Section 2 describes the air quality
modeling platform and the evaluation of model predictions of maximum daily averge 8-hour (MDA8)
ozone concentrations using measured (i.e., observed) data. Section 3 describes the procedures for
projecting ozone design value concentrations and the approach for identifying monitoring sites
projected to have nonattainment and/or maintenance problems in 2023 and 2026. Section 4 describes
(1) the source apportionment modeling, (2) the procedures for quantifying contributions to individual
monitoring sites including nonattainment and/or maintenance receptors, and (3) the evaluation of
upwind state contributions to individual receptors in downwind states. In Section 5 we present the
results of the 2026 control case model run. Section 6 describes a back-trajectory analysis for selected
receptors in 2023. Appendix A describes the results of a sensitivity analysis designed to investigate
the possible causes of ozone under prediction in the 2016v2 modeling for the purpose of improving
model performance in the 2016v3 modeling for this final rule. Appendix B provides model
performance statistics and graphics for maximum daily averge 8-hour (MDA8) ozone concentrations
based on the 2016v3 base year modeling. Appendix C includes tables containing the ozone
contributions to each receptor from each state and other individual source "tags" tracked in the source
apportionment modeling for 2023 and for 2026. Appendix D contains tables which provide the total
upwind state contribution as a percent of the 2023 ozone average design value and as a percent of
ozone formed from total U.S. anthropogenic emissions at each receptor. Appendix E contains tables
which identify which upwind states are linked to individual downwind receptors in 2023 and 2026.
Appendix F contains graphics which display the spatial distribution of top 10-day average
contributions from individual states coved by this final rule based on modeling for 2023.
4 See the preamble for a detailed description of the 4-step interstate transport framework. In summary, for Step 1: identify
monitoring sites that are projected to have problems attaining and/or maintaining the NAAQS (i.e., nonattainment and/or
maintenance receptors); Step 2: identify states that impact those air quality problems in other (i.e., downwind) states
sufficiently such that the states are considered "linked" and therefore warrant further review and analysis; Step 3: identify
the emissions reductions necessary (if any), applying a multifactor analysis, to eliminate each linked upwind state's
significant contribution to nonattainment or interference with maintenance of the NAAQS at the locations identified in
Step 1; and Step 4: adopt permanent and enforceable measures needed to achieve those emissions reductions.
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The input and output modeling files for the 2016, 2023 and 2026 modeling to support this
final rule are available on data drives in the EPA docket office.5 A copy of the air quality model input
and/or output data can also be obtained by contacting Norm Possiel at possiel.norm@epa.gov.
2. Air Quality Modeling Platform
The EPA used version 3 of the 2016-based air quality modeling platform (i.e., 2016v3) to
provide the foundational model-input data sets for 2016, 2023, and 2026. In addition to emissions
data for 2016, 2023, and 2026, this platform includes meteorology, initial and boundary condition
concentrations, and other inputs representative of the 2016 base year. In response to public
comments on the 2016v2 base year and projected emissions inventories, the 2016v3 emissions
platform includes numerous updates to both anthropogenic and biogenic emissions and the addition
of NOx emissions from lightning strikes. These updates are described in the document Preparation
of Emissions Inventories for the 2016v3 North American Emissions Modeling Platform available in
the docket for this final rule.
2.1 Air Quality Model Configuration and Model Simulations
The photochemical model simulations performed for this final rule used the Comprehensive
Air Quality Model with Extensions (CAMx version 7.10, Ramboll, 2021). CAMx is a three-
dimensional grid-based Eulerian air quality model designed to simulate the formation and fate of
oxidant precursors, primary and secondary particulate matter concentrations, and deposition over
regional and urban spatial scales (e.g., the contiguous U.S.). Consideration of the different processes
(e.g., transport and deposition) that affect primary (directly emitted) and secondary (formed by
atmospheric chemical processes) pollutants at the regional scale in different locations is fundamental
to understanding and assessing the effects of emissions on air quality concentrations. For this final
rule, as in the CSAPR Update, Revised CSAPR Update, and the proposed disapprovals, the EPA used
the CAMx Ozone Source Apportionment Technology/Anthropogenic Precursor Culpability Analysis
(OSAT/APCA) technique6 to model ozone contributions, as described below in section 4.
The geographic extent of the modeling domains that were used for air quality modeling in this
analysis are shown in Figure 2-1. The large outer domain covers the 48 contiguous states along with
most of Canada and all of Mexico with a horizontal resolution of 36 x 36 km (i.e., 36 km domain).
5 A list of available model input and output data is provided in the file "Air Quality Modeling Files_2016v3 Platform"
which can be found in the docket for this final rule.
6 As part of this technique, ozone formed from reactions between biogenic VOC and NOx with anthropogenic
NOx and VOC are assigned to the source of anthropogenic emissions.
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The inner domain covers the 48 contiguous states along with adjacent portions of Canada and Mexico
at 12 x 12 km resolution (i.e., 12 km domain).
CAMx requires a variety of input files that contain information pertaining to the modeling
domain and simulation period. These include gridded, hourly emissions estimates and meteorological
data, and initial and boundary concentrations. Separate emissions inventories were prepared for the
2016 base year and the 2023 and 2026 projection years. All other inputs (i.e., meteorological fields,
initial concentrations, ozone column, photolysis rates, and boundary concentrations) were specified
for the 2016 base year model application and remained unchanged for the projection-year model
simulation.7
The 12 km CAMx model simulations performed for this final rule are listed in Table 2-1. The
simulation period for each run was preceded by a 15-day ramp-up period.
Table 2-1. Model run name, case name and simulation period for each model run.8
Analytic
Year
Model Run
Case Name
Simulation Period
2016
2016 baseline
2016gf
Annual
2023
2023 baseline
2023gf
Annual
7 The EPA used the CAMx7.1chemparam.CB6r5_CF2E chemical parameter file for all the CAMx model runs described
in this TSD.
8 Because the model simulations run in Greenwich Mean Time (GMT), the actual simulation period included October 1 in
order to obtain MDA8 ozone concentrations based on local time for September 30.
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Analytic
Year
Model Run
Case Name
Simulation Period
2023 state total anthropogenic contributions
2023gf_ussa
May - September
2026
2026 baseline
2026gf
Annual
2026 control case
2026gf cntl
Annual
2026 state total anthropogenic contributions
2026gf ussa
May - September
2.2 Meteorological Data for 2016
This section describes the meteorological modeling that was performed to provide
meteorological data for 2016 for input to air quality modeling. Note that the EPA used the same
meteorological data for the 2016v3 air quality modeling as was used for the 2016v2 air quality
modeling.
The 2016 meteorological data were derived from running Version 3.8 of the Weather
Research Forecasting Model (WRF) (Skamarock, et al., 2008). The meteorological outputs from
WRF include hourly-varying horizontal wind components (i.e., speed and direction), temperature,
moisture, vertical diffusion rates, and rainfall rates for each grid cell in each vertical layer. Selected
physics options used in the WRF simulations include Pleim-Xiu land surface model (Xiu and Pleim,
2001; Pleim and Xiu, 2003), Asymmetric Convective Model version 2 planetary boundary layer
scheme (Pleim 2007a,b), Kain-Fritsch cumulus parameterization (Kain, 2004) utilizing the moisture-
advection trigger (Ma and Tan, 2009), Morrison double moment microphysics (Morrison et al., 2005;
Morrison and Gettelman, 2008), and RRTMG longwave and shortwave radiation schemes (Iacono
et.al., 2008).
Both the 36 km and 12 km WRF model simulations utilize a Lambert conformal projection
centered at (-97,40) with true latitudes of 33 and 45 degrees north. The 36 km domain contains 184
cells in the X direction and 160 cells in the Y direction. The 12 km domain contains 412 cells in the
X direction and 372 cells in the Y direction. The atmosphere is resolved with 35 vertical layers up to
50 mb (see Table 2-2), with the thinnest layers being nearest the surface to better resolve the
planetary boundary layer (PBL).
The 36 km WRF model simulation was initialized using the 0.25-degree Global Forecast
System (GFS) analysis and 3-hour forecast from the 00 GMT, 06 GMT, 12 GMT, and 18 GMT
simulations. The 12 km model was initialized using the 12 km North American Model (12NAM)
analysis product provided by National Climatic Data Center (NCDC).9 The 40 km Eta Data
9 https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/north-american-mesoscale-forecast-SYStem-nam
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Assimilation System (EDAS) analysis (ds609.2) from the National Center for Atmospheric Research
(NCAR) was used where 12NAM data was unavailable.10 Analysis nudging for temperature, wind,
and moisture was applied above the boundary layer only. The model simulations were conducted
continuously. The 'ipxwrf program was used to initialize deep soil moisture at the start of the run
using a 10-day spin-up period (Gilliam and Pleim, 2010). Land use and land cover data were based on
the USGS for the 36NOAM simulation and the 2011 National Land Cover Database (NLCD 2011)
for the 12US simulation. Sea surface temperatures (SST) were ingested from the Group for High
Resolution Sea Surface Temperatures (GHRSST) (Stammer et al., 2003) 1 km resolution SST data.
Additionally, lightning data assimilation was utilized to suppress (force) deep convection where
lightning is absent (present) in observational data. This method is described by Heath et al. (2016)
and was employed to help improve precipitation estimates generated by the model.
Table 2-2. Vertical layers and their approximate height above ground level.
WRF Layer
Height (m)
Pressure (mb)
Sigma
17.556
14.780
12.822
1 1.282
10.002
8.901
7,932
7.064
6,275
5,553
4.885
4.264
3.683
3.136
2.619
2,226
1.941
1.665
1.485
1.308
1.134
964
797
714
632
551
470
390
5000
9750
14500
19250
24000
28750
33500
38250
43000
47750
52500
57250
62000
66750
71500
75300
78150
81000
82900
84800
86700
88600
90500
91450
92400
93350
94300
95250
0.00C
0.05C
0.10(
0.15(
0.20C
0.25C
0.30C
0.35C
0.40(
0.45(
0.50C
0.55C
0.60(
0.65(
0.70C
0.74(
0.77C
0.80C
0.82C
0.84(
0.86(
0.88C
0.90C
0.91(
0.92C
0.93C
0.94(
0.95C
10 https ://www .ready. noaa. gov/edas40 .php.
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WRF Layer
7
6
Height (m)
Pressure (mb)
Sigma
4
2
Surface
311
232
154
1 15
77
38
19
0
96200
97150
98100
98575
99050
99525
99763
100000
0.960
0.970
0.980
0.985
0.990
0.995
0.9975
1.000
Details of the annual 2016 meteorological model simulation and evaluation are provided in a separate
technical support document, which can be found in the docket for this final rule.11
The meteorological data generated by the WRF simulations were processed using wrfcamx
v4.7 (Ramboll 2021) meteorological data processing program to create 35-layer gridded model-ready
meteorological inputs to CAMx. In running wrfcamx, vertical eddy diffusivities (Kv) were calculated
using the Yonsei University (YSU) (Hong and Dudhia, 2006) mixing scheme. We used a minimum
Kv of 0.1 m2/sec except for urban grid cells where the minimum Kv was reset to 1.0 m2/sec within
the lowest 200 m of the surface in order to enhance mixing associated with the nighttime "urban heat
island" effect. In addition, we invoked the subgrid convection and subgrid stratoform cloud options in
our wrfcamx run for 2016.
2.3 Initial and Boundary Concentrations
The lateral boundary and initial species concentrations for the 36 km simulations were derived
from outputs of a three-dimensional global atmospheric chemistry model, GEOS-Chem global model
(I. Bey, et al., 2001) which was run for 2016. The GEOS-Chem predictions were used to provide
one-way dynamic boundary concentrations at one-hour intervals and an initial concentration field for the
36 km CAMx simulations for 2016, 2023 and 2026. In the 2016v2 modeling for the proposed rule, the
EPA used the hemispheric version of the Community Multi-scale Air Quality Model (H-CMAQ).12
The basis for selecting GEOS-Chem for the 2016v3 modeling is discussed in Appendix A.
Air quality modeling for the 36 km domain was used to provide initial and boundary
conditions for the nested 12 km domain model simulations. Both the 36 km and 12 km modeling
domains have 35 vertical layers with a top at about 17,550 meters, or 50 millibars (mb). The model
11 Meteorological Modeling for 2016.docx.
12 More information about the H-CMAQ model and other applications using this tool is available at:
https://www.epa.gov/cmaq/hemispheric-scale-applications. Note that the EPA used the same initial and boundary
conditions for the 2016v2 air quality modeling as was used for the 2016vl air quality modeling.
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simulations produce hourly air quality concentrations for each grid cell across each modeling domain.
Modeling for the 36 km domain was performed for 2016 and 2023. Outputs from the 2016 36 km
simulation were used to provide initial and boundary conditions for the 2016 12 km model
simulation. Outputs from the 2023 36 km simulation were used to provide initial and boundary
conditions for the 2023 and 2026 12 km simulations.
2.5 Air Quality Model Evaluation
An operational model performance evaluation for ozone was conducted to examine the ability
of the 2016v3 CAMx modeling system to simulate 2016 measured MDA8 ozone concentrations. This
evaluation focused on graphical analyses and statistical metrics of model predictions versus
observations. Details on the evaluation methodology, the calculation of performance statistics, and
results are provided in Appendix B. Overall, the ozone model performance statistics for the CAMx
2016v3 simulation are within the ranges found in other recent peer-reviewed applications (e.g.,
Simon et al, 2012 and Emory et al, 2017). As described in Appendix B, model performance for the
2016v3 platform is notably improved compared to model performance for the 2016v2 platform. The
model performance results demonstrate the scientific credibility of our 2016v3 modeling platform.
These results provide confidence in the ability of the modeling platform to provide a reasonable
projection of expected future year ozone concentrations and contributions. Model performance
statistics for individual monitoring sites for the period May through September are provided in a
spreadsheet file in the docket for this final rule.13
3. Identification of Future Nonattainment and Maintenance Receptors in 2023
3.1 Definition of Nonattainment and Maintenance Receptors
The ozone predictions from the 2016 base year and future case CAMx model simulations
were used to calculate average and maximum ozone design values for the 2023 and 2026 analytic
years using the approach described in this section. Following the approach used for the proposed
rule, we evaluated projected average and maximum design values in conjunction with the most
recent measured ozone design values (i.e., 2021)14 to identify nonattainment or maintenance sites in
each of the three future years. Those monitoring sites with future year average design values that
13 CAMx 2016v3 MDA8 03 Model Performance Stats by Site.
14 The 2021 design values are the most current official design values available for use in this final rule. The 2021 ozone
design values, by monitoring site, can be found in the following file in the docket: Final GNP 03 DVs Contributions.
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exceed the NAAQS (i.e., average design values of 71 ppb or greater)15 and that are currently
measuring nonattainment are considered to be nonattainment receptors. Similarly, monitoring sites
with a projected maximum design value that exceeds the NAAQS are projected to be maintenance
receptors. Maintenance-only receptors include those monitoring sites where the projected average
design value is below the NAAQS, but the maximum design value is above the NAAQS, and
monitoring sites with projected average design values that exceed the NAAQS, but for which
current design values based on measured data do not exceed the NAAQS.16 In addition, as
described in the preamble for this final rule, the EPA received comments stating that our
methodology to identify receptors in 2023 appears overly optimistic in light of current measured
data. These commenters suggest that the EPA give greater weight to current measured data as part
of the method for identifying projected receptors. In response to these comments the EPA has
developed an additional maintenance-only receptor category, which includes what we refer to as
"violating monitor" receptors. Specifically, the EPA has identified "violating monitor" receptors as
those monitoring sites with measured 2021 and preliminary 2022 design values and 4th high
maximum daily MDA8 ozone concentrations in both 2021 and 2022 (preliminary data) that exceed
the NAAQS, although model-projected design values for 2023 are below the NAAQS.17
The procedures for calculating projected average and maximum design values are described
below. The monitoring sites that are projected to be nonattainment and/or maintenance-only receptors
for the ozone NAAQS in 2023 and 2026 are used for assessing the contribution of emissions in
upwind states to downwind nonattainment and maintenance of the 2015 ozone NAAQS as part of this
final rule.
3.2 Approach for Projecting Ozone Design Values
The ozone predictions from the 2016, 2023, and 2026 model simulations were used to project
ambient (i.e., measured) ozone design values (DVs) to 2023 and 2026 based on an approach that
15 In determining compliance with the NAAQS, ozone design values are truncated to integer values. For example, a
design value of 70.9 parts per billion (ppb) is truncated to 70 ppb which is attainment. In this manner, design values at or
above 71.0 ppb are considered to be violations of the NAAQS.
16 The EPA's modeling guidance notes that projecting the highest (i.e., maximum) design value from the base period
provides an approach for evaluating attainment in periods with meteorological conditions especially conducive to high
ozone concentrations.
17 2021 4th high MDA8 ozone concentrations and preliminary 2022 design values and 4th high MDA8 ozone
concentrations for violating monitor receptors are provided in Table 3-3. Daily MDA8 ozone concentrations which can be
used to identify the 4th values in 2021 and the preliminary 4th high values in 2022 for other monitoring sites can be in the
file Final GNP 03 DVs Contributions.xls in the docket for this rule.
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follows from the EPA's guidance for attainment demonstration modeling (US EPA, 2018),18 as
summarized here. The modeling guidance recommends using 5-year weighted average ambient
design values centered on the base modeling year as the starting point for projecting average design
values to the future. Because 2016 is the base emissions year, we used the average ambient 8-hour
ozone design values for the period 2014 through 2018 (i.e., the average of design values for 2014-
2016, 2015-2017 and 2016-2018) to calculate the 5-year weighted average design values (i.e., 2016-
centered design values). The 5-year weighted average ambient design value at each site was projected
to 2023 and 2026 using the Software for Model Attainment Test Software - Community Edition
(SMAT-CE)19. This program calculates the 5-year weighted average design value based on observed
data and projects future year values using the relative response predicted by the model. Equation (3-1)
describes the recommended model attainment test in its simplest form, as applied for monitoring site
z:
(DVF)i = (RRF)i * (DVB)t Equation 3-1
DVFi is the estimated design value for the future year at monitoring site z; RRF^ is the relative
response factor for monitoring site z; and DVBX is the base period design value monitored at site z. The
relative response factor for each monitoring site (RRFi) is the fractional change in MDA8 ozone
between the base and future year. The RRF is based on the average ozone on model-predicted "high"
ozone days in grid cells in the vicinity of the monitoring site. The modeling guidance recommends
calculating RRFs based on the highest 10 modeled ozone days in the base year simulation at each
monitoring site. Specifically, the RRF for an individual monitoring site is the ratio of the average
MDA8 ozone concentration in the future year to the average MDA8 concentration in the 2016 base
year. The average values are calculated using MDA8 model predictions in the future year and in 2016
for the 10 highest days in the 2016 base year modeling. For cases in which the base year model
simulation does not have 10 days with ozone values >= 60 ppb at a site, we use all days with ozone
18 The EPA's ozone attainment demonstration modeling guidance is referred to as "the modeling guidance" in the
remainder of this document.
19 Software download information and documentation are available at https://www. epa. gov/scram/photochemical-
modeling-tools
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>= 60 ppb, as long as there were at least 5 days that meet this criterion. At monitor locations with less
than 5 days with modeled 2016 base year ozone >= 60 ppb, no RRF or DVF is calculated for the site
and the monitor in question was not included in this analysis.
The modeling guidance recommends calculating the RRF using the base year and future year
model predictions from the cells immediately surrounding the monitoring site along with the grid cell
in which the monitor is located. In this approach the RRF is based on a 3 x 3 array of 12 km grid cells
centered on the location of the grid cell containing the monitor.
As in the proposal, the EPA also projects design values based on a modified version of the
"3 x 3" approach for those monitoring sites located in coastal areas. In this alternative approach, the
EPA eliminated from the RRF calculations the modeling data in those grid cells that are dominated
by water (i.e., more than 50 percent of the area in the grid cell is water) and that do not contain a
monitoring site (i.e., if a grid cell is more than 50 percent water but contains an air quality monitor,
the data from that cell would remain in the calculation). The choice of more than 50 percent of the
grid cell area as water as the criteria for identifying overwater grid cells is based on the treatment of
land use in the Weather Research and Forecasting model (WRF).20 Specifically, in the WRF
meteorological model those grid cells that are greater than 50 percent overwater are treated as being
100 percent overwater. In such cases the meteorological conditions in the entire grid cell reflect the
vertical mixing and winds over water, even if part of the grid cell also happens to be over land with
land-based emissions, as can often be the case for coastal areas. Overlaying land-based emissions
with overwater meteorology may be representative of conditions at coastal monitors during times of
on-shore flow associated with synoptic conditions and/or sea-breeze or lake-breeze wind flows. But
there may be other times, particularly with off-shore wind flow when vertical mixing of land-based
emissions may be too limited due to the presence of overwater meteorology. Thus, for this modeling
projected average and maximum design values were calculated for individual monitoring sites based
on both the "3 x 3" approach as well as the alternative approach that eliminates overwater cells in
the RRF calculation for near-coastal areas (i.e., "no water" approach).
For both the "3 x 3" approach and the "no water" approach, the grid cell with the highest base
year MDA8 ozone concentration on each day in the applicable array of grid cells surrounding the
20 https://www.mmm.ucar.edu/weather-research-and-forecasting-model.
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location of the monitoring site21 is used for both the base and future components of the RRF
calculation. That is, the base and future year data are paired in space for the grid cell that has the
highest MDA8 concentration on the given day.
The approach for calculating projected maximum design values is similar to the approach for
calculating the projected average design values. To calculate projected maximum design values we
start with the highest (i.e., maximum) ambient design value from the 2016-centered 5-year period
(i.e., the maximum of design values from 2014-2016, 2014-2017, and 2016-2018). The base period
maximum design value at each site is projected to 2023 and 2026 using the site-specific RRFs, as
determined using the procedures for calculating RRFs described above.
For this final rule, the EPA is relying upon design values based on the "no water" approach
for identifying nonattainment and maintenance receptors and for calculating contributions, as
described in section 4, below.
Consistent with the truncation and rounding procedures for the 8-hour ozone NAAQS, the
projected design values are truncated to integers in units of ppb.22 Therefore, projected design values
that are greater than or equal to 71 ppb are considered to be violating the 2015 ozone NAAQS. For
those sites that are projected to be violating the NAAQS based on the projected average design
values, we examined the design values for 2021, which are the most recent concurred measured
design values at the time of this final action.23 As noted above, we identify nonattainment receptors as
those sites that are violating the NAAQS based on current measured air quality and also have
projected average design values of 71 ppb or greater. Maintenance-only receptors identified based on
monitoring plus modeling include both (1) those sites with projected average design values above the
NAAQS that are currently measuring clean data and (2) those sites with projected average design
values below the level of the NAAQS, but with projected maximum design values of 71 ppb or
greater.24 As noted above, the EPA also identified as maintenance-only receptors those monitoring
sites with measured 2021 and preliminary 2022 design values and 4th high maximum daily MDA8
21 For the "3 x 3" approach the applicable array contains the 9 grid cells that surround and include the grid cell containing
the monitoring site. The applicable array for the "no water" approach includes the grid cell containing the monitoring site
along with the subset of the "3 x 3" grid cells that are not classified as "water" grid cells using the criteria described in this
TSD.
22 40 CFR Part 50, Appendix U to Part 50 - Interpretation of the Primary and Secondary National Ambient Air Quality
Standards for Ozone.
23 Official, concurred ozone design values for 2021 are provided in the file Final GNP 03 DVs Contributions which can
be found in the docket for this final rule.
24 In addition to the maintenance-only receptors, the projected nonattainment receptors are also maintenance receptors
because the maximum design values for each of these sites is always greater than or equal to the average design value.
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ozone concentrations in both 2021 and 2022 (preliminary data) that exceed the NAAQS, although
model-projected design values for 2023 are below the NAAQS.
The 2016-centered base period average and maximum design values, the projected average
and maximum design values for 2023 and the 2021 design values for monitoring sites that are
projected to be nonattainment or maintenance-only receptors in 2023 based on monitoring and
modeling using the "no water" approach are provided in Tables 3-1 and 3-2, respectively.25 In total,
in the 2023 base case there are a total of 33 projected modeling-based receptors nationwide including
14 nonattainment receptors in 9 different counties and 19 maintenance-only receptors in 13 additional
counties (Harris County, TX has both nonattainment and maintenance-only receptors).26
The projected average and maximum design values for 2023 and the 2021 and preliminary 2022
design values and the 2021 and preliminary 2022 4th high values for monitoring sites that are
identified as "violating monitor" maintenance-only receptors in 2023 are provided in Tables 3-3.
There are 49 monitoring sites that are identified as "violating-monitor" maintenance-only receptors in
2023.27 As noted earlier in this section, the EPA uses the approach of considering "violating-monitor"
maintenance-only receptors as confirmatory of the proposal's identification of receptors and does not
implicate additional linked states in this final rule. Rather, using this approach serves to strengthen
the analytical basis for our Step 2 findings by establishing that many upwind states covered in this
rule are also projected to contribute above 1 percent of the NAAQS to these additional "violating
monitor" maintenance-only receptors.
25 The "3 x 3" approach and the "no water" approach result in the same set of receptors. That is, the receptors identified
based on the "3 x 3" approach are also the receptors identified based on the "no water" approach. However, using design
values from the "3 x 3" approach, the maintenance-only receptor at site 550590019 in Kenosha County, WI would
become a nonattainment receptor because the average design value with the "3 x 3" approach is 72.0 ppb versus 70.8 ppb
with the "no water" approach. In addition, the maintenance-only receptor at site 090099002 in New Haven County, CT
would become a nonattainment receptor using the "no water" approach because the average design value with the "3 x 3"
approach is 71.2 ppb versus 70.5 ppb with the "no water" approach.
Projected design values for 2023 based on both the "3 x 3" and "no water" approaches for individual monitoring sites
nationwide are provided in the file "2016v3_Final FIPDVsstatecontributions" which can be found in the docket for
this final rule.
26 The EPA's modeling also projects that three monitoring sites in the Uinta Basin (i.e., monitor 490472003 in Uintah
County, Utah and monitors 490130002 and 490137011 in Duchesne County, Utah) will have average design values above
the NAAQS in 2023. However, as noted in the proposed rule, the Uinta Basin nonattainment area was designated as
nonattainment for the 2015 ozone NAAQS not because of an ongoing problem with summertime ozone (as is usually the
case in other parts of the country), but instead because it violates the ozone NAAQS in winter. The main causes of the
Uinta Basin's wintertime ozone are sources located at low elevations within the Basin, the Basin's unique topography,
and the influence of the wintertime meteorologic inversions that keep ozone and ozone precursors near the Basin floor and
restrict air flow in the Basin. Because of the localized nature of the ozone problem at these sites the EPA has not
identified these three monitors as receptors in Step 1 of this proposed rule.
27 The list of violating monitors in Table 3-3 does not include two such monitors in California (i.e., monitor site
060430006 in Mariposa County and monitoring site 061112002 in Ventura County).
13
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The average and maximum design values for nonattainment and maintenance receptors in 2026
are provided in Tables 3-5 and 3-6, respectively. There are 7 nonattainment receptors and 12
monitored plus modeled maintenance-only receptors in 2026.
Table 3-1. Average and maximum 2016-centered and 2023 base case 8-hour ozone design values and
2021 design values (ppb) at projected nonattainment receptors in 2023.
Monitor
ID
State
County
2016
Centered
Average
2016
Centered
Maximum
2023
Average
2023
Maximum
2021
060650016
CA
Riverside
79.0
80
72.2
73.1
78
060651016
CA
Riverside
99.7
101
91.0
92.2
95
080350004
CO
Douglas
77.3
78
71.3
71.9
83
080590006
CO
Jefferson
77.3
78
72.8
73.5
81
080590011
CO
Jefferson
79.3
80
73.5
74.1
83
090010017
CT
Fairfield
79.3
80
71.6
72.2
79
090013007
CT
Fairfield
82.0
83
72.9
73.8
81
090019003
CT
Fairfield
82.7
83
73.3
73.6
80
481671034
TX
Galveston
75.7
77
71.5
72.8
72
482010024
TX
Harris
79.3
81
75.1
76.7
74
490110004
UT
Davis
75.7
78
72.0
74.2
78
490353006
UT
Salt Lake
76.3
78
72.6
74.2
76
490353013
UT
Salt Lake
76.5
77
73.3
73.8
76
551170006
WI
Sheboygan
80.0
81
72.7
73.6
72
Table 3-2. Average and maximum 2016-centered and 2023 base case 8-hour ozone design values and
2021 design values (ppb) at projected maintenance-only receptors.
Monitor
ID
State
County
2016
Centered
Average
2016
Centered
Maximum
2023
Average
2023
Maximum
2021
40278011
AZ
Yuma
72.3
74
70.4
72.1
67
80690011
CO
Larimer
75.7
77
70.9
72.1
77
90099002
CT
New Haven
79.7
82
70.5
72.6
82
170310001
IL
Cook
73.0
77
68.2
71.9
71
170314201
IL
Cook
73.3
77
68.0
71.5
74
170317002
IL
Cook
74.0
77
68.5
71.3
73
350130021
NM
Dona Ana
72.7
74
70.8
72.1
80
350130022
NM
Dona Ana
71.3
74
69.7
72.4
75
350151005
NM
Eddv
69.7
74
69.7
74.1
77
350250008
NM
Lea
67.7
70
69.8
72.2
66
480391004
TX
Brazoria
74.7
77
70.4
72.5
75
14
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Monitor
ID
State
County
2016
Centered
Average
2016
Centered
Maximum
2023
Average
2023
Maximum
2021
481210034
TX
Denton
78.0
80
69.8
71.6
74
481410037
TX
El Paso
71.3
73
69.8
71.4
75
482010055
TX
Harris
76.0
77
70.9
71.9
77
482011034
TX
Harris
73.7
75
70.1
71.3
71
482011035
TX
Harris
71.3
75
67.8
71.3
71
530330023
WA
King
73.3
77
67.6
71.0
64
550590019
WI
Kenosha
78.0
79
70.8
71.7
74
551010020
WI
Racine
76.0
78
69.7
71.5
73
Table 3-3. Average and maximum 2023 design values, and 2021 and preliminary 2022 design values
and 4th high values at violating monitors (ppb).*
Monitor
ID
State
County
2023
Average
2023
Maximum
2021
2022 P*
2021
4th High
2022 P*
4th High
040070010
AZ
Gila
67.9
69.5
77
76
75
74
040130019
AZ
Maricopa
69.8
70.0
75
77
78
76
040131003
AZ
Maricopa
70.1
70.7
80
80
83
78
040131004
AZ
Maricopa
70.2
70.8
80
81
81
77
040131010
AZ
Maricopa
68.3
69.2
79
80
80
78
040132001
AZ
Maricopa
63.8
64.1
74
78
79
81
040132005
AZ
Maricopa
69.6
70.5
78
79
79
77
040133002
AZ
Maricopa
65.8
65.8
75
75
81
72
040134004
AZ
Maricopa
65.7
66.6
73
73
73
71
040134005
AZ
Maricopa
62.3
62.3
73
75
79
73
040134008
AZ
Maricopa
65.6
66.5
74
74
74
71
040134010
AZ
Maricopa
63.8
66.9
74
76
77
75
040137020
AZ
Maricopa
67.0
67.0
76
77
77
75
040137021
AZ
Maricopa
69.8
70.1
77
77
78
75
040137022
AZ
Maricopa
68.2
69.1
76
78
76
79
040137024
AZ
Maricopa
67.0
67.9
74
76
74
77
040139702
AZ
Maricopa
66.9
68.1
75
77
72
77
040139704
AZ
Maricopa
65.3
66.2
74
77
76
76
040139997
AZ
Maricopa
70.5
70.5
76
79
82
76
040218001
AZ
Pinal
67.8
69.0
75
76
73
77
080013001
CO
Adams
63.0
63.0
72
77
79
75
080050002
CO
Arapahoe
68.0
68.0
80
80
84
73
080310002
CO
Denver
63.6
64.8
72
74
77
71
080310026
CO
Denver
64.5
64.8
75
77
83
72
15
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Monitor
ID
State
County
2023
Average
2023
Maximum
2021
2022 P*
2021
4th High
2022 P*
4th High
090079007
CT
Middlesex
68.7
69.0
74
73
78
73
090110124
CT
New London
65.5
67.0
73
72
75
71
170310032
IL
Cook
67.3
69.8
75
75
77
72
170311601
IL
Cook
63.8
64.5
72
73
72
71
181270024
IN
Porter
63.4
64.6
72
73
72
73
260050003
MI
Allegan
66.2
67.4
75
75
78
73
261210039
MI
Muskegon
67.5
68.4
74
79
75
82
320030043
NV
Clark
68.4
69.4
73
75
74
74
350011012
NM
Bernalillo
63.8
66.0
72
73
76
74
350130008
NM
Dona Ana
65.6
66.3
72
76
79
78
361030002
NY
Suffolk
66.2
68.0
73
74
79
74
390850003
OH
Lake
64.3
64.6
72
74
72
76
480290052
TX
Bexar
67.1
67.8
73
74
78
72
480850005
TX
Collin
65.4
66.0
75
74
81
73
481130075
TX
Dallas
65.3
66.5
71
71
73
72
481211032
TX
Denton
65.9
67.7
76
77
85
77
482010051
TX
Harris
65.3
66.3
74
73
83
72
482010416
TX
Harris
68.8
70.4
73
73
78
71
484390075
TX
Tarrant
63.8
64.7
75
76
76
77
484391002
TX
Tarrant
64.1
65.7
72
77
76
80
484392003
TX
Tarrant
65.2
65.9
72
72
74
72
484393009
TX
Tarrant
67.5
68.1
74
75
75
75
490571003
UT
Weber
69.3
70.3
71
74
77
71
550590025
WI
Kenosha
67.6
70.7
72
73
72
71
550890008
WI
Ozaukee
65.2
65.8
71
72
72
72
* 2022 preliminary design values are based on 2022 measured MDA8 concentrations provided by state air
agencies to the EPA's Air Quality System (AQS), as of January 3, 2023.
Table 3-4. Average and maximum 2016-centered and 2026 base case 8-hour ozone design values and
2021 design values (ppb) at projected nonattainment receptors in 2026.
Monitor
ID
State
County
2016
Centered
Average
2016
Centered
Maximum
2026
Average
2026
Maximum
060650016
CA
Riverside
79.0
80
71.4
72.4
060651016
CA
Riverside
99.7
101
90.0
91.2
080590006
CO
Jefferson
77.3
78
72.0
72.6
080590011
CO
Jefferson
79.3
80
72.4
73.0
090019003
CT
Fairfield
82.7
83
71.3
71.5
482010024
TX
Harris
79.3
81
73.9
75.5
490353013
UT
Salt Lake
76.5
77
71.9
72.3
16
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Table 3-5. Average and maximum 2016-centered and 2026 base case 8-hour ozone design values and
2021 design values (ppb) at projected maintenance-only receptors in 2026.
Monitor
ID
State
County
2016
Centered
Average
2016
Centered
Maximum
2026
Average
2026
Maximum
040278011
AZ
Yuma
72.3
74
69.9
71.5
080690011
CO
Larimer
75.7
77
70.0
71.2
090013007
CT
Fairfield
82.0
83
70.9
71.7
350130021
NM
Dona Ana
72.7
74
69.9
71.2
350130022
NM
Dona Ana
71.3
74
69.0
71.6
350151005
NM
Eddy
69.7
74
69.1
73.4
350250008
NM
Lea
67.7
70
69.2
71.6
480391004
TX
Brazoria
74.7
77
69.1
71.2
481671034
TX
Galveston
75.7
77
70.2
71.4
490110004
UT
Davis
75.7
78
69.9
72.1
490353006
UT
Salt Lake
76.3
78
70.5
72.1
551170006
WI
Sheboygan
80.0
81
70.8
71.7
4. Ozone Contribution Modeling
As noted above, the EPA performed nationwide, state-level ozone source
apportionment modeling using the CAMx OSAT/APCA technique to provide data on the
contribution of projected 2023 NOx and VOC emissions from anthropogenic source sectors in
each state. The state-by-state anthropogenic source apportionment modeling is described in
section 4.1. In section 4.2 we describe the method for calculating the average contribution
metric for each source apportionment model run and in section 4.3 we present the results of
the state-by-state all anthropogenic modeling.
4.1 State-by-State All Anthropogenic Modeling
In the state-by-state source apportionment model run, we tracked the ozone formed
from each of the following contribution categories (i.e., "tags"):
• States - anthropogenic NOx and VOC emissions from each of the contiguous 48 states
and the District of Columbia tracked individually (emissions from all anthropogenic
sectors in a given state were combined);
• Biogenics - biogenic NOx and VOC emissions domain-wide;
• Initial and Boundary Concentrations - air quality concentrations used to initialize the 12 km
17
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model simulation and air quality concentrations transported into the 12 km modeling domain
from the lateral boundaries;
• Tribes - the collective emissions from those tribal lands for which we have point source inventory
data in the 2016v3 emissions platform (we did not model the contributions from individual
tribes);
• Canada and Mexico - collective anthropogenic emissions from sources in the portions of Canada
and Mexico that are within the 12 km modeling domain (contributions from Canada and Mexico
were not modeled separately);
• Fires - combined emissions from wild and prescribed fires domain-wide within the 12 km
modeling domain;
• Offshore - total emissions from offshore marine vessels and offshore drilling platforms; and
• NOx emissions from lightning strikes.
The above-listed tagged sources account for all ozone sources simulated by the model such that the
sum of tagged ozone contributions adds to the total modeled ozone at each hour and grid cell. The
source apportionment modeling provides hourly contributions to ozone from anthropogenic NOx
and VOC emissions in each state, individually, to ozone concentrations in each model grid cell.
The contributions to ozone from chemical reactions between biogenic NOx and biogenic VOC
emissions were modeled and assigned to the "biogenic" category. The contributions from wildfire
and prescribed fire NOx and VOC emissions were modeled and assigned to the "fires" category.
The contributions from the "biogenic", "offshore", and "fires" categories are not assigned to
individual states nor are they included in the state contributions.
4.2 Methodfor Calculating the Contribution Metric
As noted above, CAMx state-by-state source apportionment model runs for 2023 and 2026
were performed to obtain contributions for the period May through September using the projected
2023 and 2026 emissions and 2016 meteorology. The resulting hourly contributions28 from each tag
were processed to calculate an 8-hour average contribution metric value for each tag at each
monitoring site. The contribution metric values at each individual monitoring site are calculated using
model predictions for the grid cell containing the monitoring site. The process for calculating the
average contribution metric uses the source apportionment outputs in a "relative sense" to apportion
28 Contributions from anthropogenic emissions under "NOx-limited" and "VOC-limited" chemical regimes were
combined to obtain the net contribution from NOx and VOC anthropogenic emissions in each state.
18
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the projected average design value at each monitoring location into contributions from each
individual tag. This process is similar in concept to the approach described above for using model
predictions to calculate future year ozone design values.
The basic approach used to calculate the average contribution metric values for 2023 is
described by the following steps:
(1) For the model grid cells containing an ozone monitoring site, calculate the 8-hour average
contribution from each source tag to each monitoring site for the time period of the 2023 modeled
MDA8 ozone concentration on each day:
(2) Average the MDA8 concentrations for the top 10 modeled ozone concentration days in 2023 and
average the 8-hour contributions for each of these same days for each tag;
(3) Divide the 10-day average contribution for each tag by the corresponding 10-day average
concentration to obtain a Relative Contribution Factor (RCF) for each tag at each monitor; and
(4) Multiply the 2023 average design values by the corresponding RCF to produce the average
contribution metric value for each tag at each monitoring site in 2023.
These steps are written out mathematically in Equation 4-1 where Cti represents the
contribution metric value from tag, t, to monitor, DVF2023i represents the projected 2023 future
year average DV at monitor, /', O3Ct topl0 i is the average ozone contribution to MDA8 ozone from
tag, t, across the top-10 future year MDA8 modeled days at monitor, and, O3topl0 i is the average
ozone concentration across the top-10 future year MDA8 modeled days at monitor,
Ct i = DVF2023 i X 03Ct'toP10'1 Equation 4-1
U6topio,i
The contribution metric values calculated from step 4 are truncated to two digits to the right of
the decimal (e.g., a calculated contribution of 0.78963... is truncated to 0.78 ppb). As a result of
truncation, the tabulated contributions may not always sum to the future year average design value at
individual monitoring sites. In addition, when calculating the contribution metric values we applied a
criteria that 5 or more of the top 10 model-predicted concentration days must have MDA8
concentrations >= 60 ppb in the future year in order to calculate a valid contribution metric. The
criterion of having at least 5 days with MDA8 ozone concentrations >= 60 ppb was chosen to avoid
including contributions on days that are well below the NAAQS in the calculation of the contribution
metric. Using a minimum of 5 days up to a maximum of 10 days with MDA8 ozone >= 60 ppb aligns
with recommendations in the EPA's air quality modeling guidance for projecting future year design
values, as described above.
19
-------
To calculate contribution metric values from the 2026 source apportionment model runs, we
followed the same approach as described above for 2023, except that we calculated the average
contribution metric values for 2026 using the 2026 MDA8 concentrations and 2026 8-hour average
contributions for the same dates that were used to calculate the contribution metric values in 2023.
Even though 2026 is only 3 years beyond 2023, it is possible that changes in projected emissions
between 2023 and 2026 could potentially result in a change in the ranking of days based on model
predicted MDA8 ozone concentrations in 2026 compared to 2023 at some monitoring sites. Using
modeled contribution data from the same set of dates when calculating contribution metric values for
2023 and 2026 provides consistency in terms of the meteorology associated with the contributions
that are used to calculate contribution metric values in both years at individual monitoring sites. The
contribution metric values for monitoring sites nationwide for the 2023 and 2026 state-by-state source
apportionment model runs are provided in the file "Final GNP 03 DVs Contributions" which can be
found in the docket of this final rule. Note that this file contains data for monitoring sites that meet the
criteria for calculating valid contribution metric values, as described above.29
4.3 Results of State-by-State All Anthropogenic Modeling
The largest contribution from each state to monitoring plus modeled downwind receptors in
2023 is provided in Table 4-1.30 The largest contribution from each state to "violating monitor"
receptors in 2023 is provided in Table 4-2. The largest contribution from each state to receptors in
2026 is provided in Table 4-3.
The contribution metric values from each state and the other source tags at individual
nonattainment and maintenance-only sites in the 2023 and 2026 are provided in Appendix C. A table
with the total upwind state collective contribution expressed as the percent of the 2023 ozone design
value and as a percent of total U.S. anthropogenic ozone is provided in Appendix D. The upwind
states linked to each downwind receptor in 2023 and in 2026 are identified in Appendix E. The
spatial fields of top 10-day average contributions from individual states covered by this final rule are
provided in Appendix F.
29 Contribution metric values were not calculated for the receptor in King County, Washington because there were fewer
than 5 days with future year MDA8 ozone concentrations >= 60 ppb at this receptor.
30 For California the largest contribution to a downwind receptor in 2023 is the contribution to monitoring site 060651016,
which is a nonattainment receptor located on the Morongo Band of Mission Indians reservation in Riverside County,
California. See the preamble for information on how the EPA considers transport to receptors on tribal lands in this final
rule.
20
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Table 4-1. Largest contribution from each state to downwind nonattainment and maintenance-only
receptors in 2023 (ppb).
Upwind State
Largest
Contribution to
Downwind
Nonattainment
Receptors
Largest Contribution
to Downwind
Maintenance-Only
Receptors
Alabama
0.75
0.65
Arizona
0.54
1.69
Arkansas
0.94
1.21
California
35.27
6.31
Colorado
0.14
0.18
Connecticut
0.01
0.01
Delaware
0.44
0.56
District of Columbia
0.03
0.04
Florida
0.50
0.54
Georgia
0.18
0.17
Idaho
0.42
0.41
Illinois
13.89
19.09
Indiana
8.90
10.03
Iowa
0.67
0.90
Kansas
0.46
0.52
Kentucky
0.84
0.79
Louisiana
9.51
5.62
Maine
0.02
0.01
Maryland
1.13
1.28
Massachusetts
0.33
0.15
Michigan
1.59
1.56
Minnesota
0.36
0.85
Mississippi
1.32
0.91
Missouri
1.87
1.39
Montana
0.08
0.10
Nebraska
0.20
0.36
Nevada
1.11
1.13
New Hampshire
0.10
0.02
New Jersey
8.38
5.79
New Mexico
0.36
1.59
New York
16.10
11.29
North Carolina
0.45
0.66
North Dakota
0.18
0.45
21
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Largest
Contribution to
Largest Contribution
Downwind
to Downwind
Nonattainment
Maintenance-Only
Upwind State
Receptors
Receptors
Ohio
2.05
1.98
Oklahoma
0.79
1.01
Oregon
0.46
0.31
Pennsylvania
6.00
4.36
Rhode Island
0.04
0.01
South Carolina
0.16
0.18
South Dakota
0.05
0.08
Tennessee
0.60
0.68
Texas
1.03
4.74
Utah
1.29
0.98
Vermont
0.02
0.01
Virginia
1.16
1.76
Washington
0.16
0.09
West Virginia
1.37
1.49
Wisconsin
0.21
2.86
Wyoming
0.68
0.67
Table 4-2. Largest contribution to downwind 8-hour ozone "violating monitor" maintenance-only
receptors in 2023 (ppb).
Largest Contribution to
Downwind Violating
Monitor Maintenance-
Upwind State
Only Receptors
Alabama
0.79
Arizona
1.62
Arkansas
1.16
California
6.97
Colorado
0.39
Connecticut
0.17
Delaware
0.42
District of Columbia
0.03
Florida
0.50
Georgia
0.31
Idaho
0.46
Illinois
16.53
Indiana
9.39
22
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Largest Contribution to
Downwind Violating
Monitor Maintenance-
Upwind State
Only Receptors
Iowa
1.13
Kansas
0.82
Kentucky
1.57
Louisiana
5.06
Maine
0.02
Maryland
1.14
Massachusetts
0.39
Michigan
3.47
Minnesota
0.64
Mississippi
1.02
Missouri
2.95
Montana
0.12
Nebraska
0.43
Nevada
1.11
New Hampshire
0.10
New Jersey
8.00
New Mexico
0.34
New York
12.08
North Carolina
0.65
North Dakota
0.35
Ohio
2.25
Oklahoma
1.57
Oregon
0.36
Pennsylvania
5.20
Rhode Island
0.08
South Carolina
0.23
South Dakota
0.12
Tennessee
0.86
Texas
3.83
Utah
1.46
Vermont
0.03
Virginia
1.39
Washington
0.11
West Virginia
1.79
Wisconsin
5.10
Wyoming
0.42
23
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Table 4-3. Largest contribution from each state to downwind nonattainment and maintenance-only
receptors in 2026 (ppb).
Upwind State
Largest Contribution
to Downwind
Nonattainment
Receptors
Largest Contribution
to Downwind
Maintenance- Only
Receptors
Alabama
0.20
0.69
Arizona
0.44
1.34
Arkansas
0.53
1.16
California
34.03
6.16
Colorado
0.04
0.17
Connecticut
0.00
0.01
Delaware
0.43
0.41
District of Columbia
0.03
0.02
Florida
0.46
0.17
Georgia
0.13
0.16
Idaho
0.27
0.36
Illinois
0.63
13.57
Indiana
1.06
8.53
Iowa
0.14
0.62
Kansas
0.14
0.42
Kentucky
0.79
0.76
Louisiana
4.57
9.37
Maine
0.00
0.01
Maryland
1.06
0.92
Massachusetts
0.06
0.31
Michigan
1.39
1.47
Minnesota
0.15
0.32
Mississippi
0.29
1.15
Missouri
0.29
1.68
Montana
0.06
0.07
Nebraska
0.09
0.19
Nevada
0.67
0.90
New Hampshire
0.01
0.09
New Jersey
8.10
7.04
New Mexico
0.35
0.46
New York
12.65
12.34
North Carolina
0.40
0.42
North Dakota
0.09
0.17
Ohio
1.95
1.93
Oklahoma
0.19
0.74
24
-------
Upwind State
Largest Contribution
to Downwind
Nonattainment
Receptors
Largest Contribution
to Downwind
Maintenance- Only
Receptors
Oregon
0.26
0.41
Pennsylvania
5.47
4.94
Rhode Island
0.00
0.03
South Carolina
0.14
0.15
South Dakota
0.03
0.04
Tennessee
0.24
0.54
Texas
0.48
4.34
Utah
1.05
0.81
Vermont
0.01
0.02
Virginia
1.09
1.10
Washington
0.10
0.14
West Virginia
1.36
1.34
Wisconsin
0.17
0.18
Wyoming
0.40
0.59
In CSAPR, the CSAPR Update, and the Revised CSAPR Update, and in the proposal for this
final rule the EPA used a contribution screening threshold of 1 percent of the NAAQS to identify
upwind states that may significantly contribute to downwind nonattainment and/or maintenance
problems and which warrant further analysis to determine if emissions reductions might be required
from each state to address the downwind air quality problem. The EPA determined that 1 percent was
an appropriate threshold to use in Step 2 because there were important, even if relatively small,
contributions to identified nonattainment and maintenance receptors from multiple upwind states. The
EPA has historically found that the 1 percent threshold is appropriate for identifying interstate
transport linkages for states collectively contributing to downwind ozone nonattainment or
maintenance problems because that threshold captures a high percentage of the total pollution
transport affecting downwind receptors. The EPA received numerous comments on the use of the 1
percent screening threshold. Responses to these comments can be found in the preamble and in the
Response to Comments (RTC) document for this final rule.
Based on the maximum downwind contribution data in Table 4-1, the following 21 states
contribute at or above the 0.70 ppb threshold to downwind nonattainment receptors in 2023:
Alabama, Arkansas, California, Illinois, Indiana, Kentucky, Louisiana, Maryland, Michigan,
Mississippi, Missouri, Nevada, New Jersey, New York, Ohio, Oklahoma, Pennsylvania, Texas,
Utah, Virginia, and West Virginia. Based on the maximum downwind contribution data also in
25
-------
Table 4-1, the following 23 states contribute at or above the 0.70 ppb threshold to downwind
monitoring plus modeled maintenance-only receptors in 2023: Arizona, Arkansas, California,
Illinois, Indiana, Iowa, Kentucky, Louisiana, Maryland, Michigan, Minnesota, Mississippi,
Missouri, Nevada, New Jersey, New Mexico, New York, Ohio, Oklahoma, Texas, Virginia, West
Virginia, and Wisconsin. Finally, based on the maximum downwind contribution in Table 4-2, the
following additional states contribute at or above the 0.70 ppb threshold to downwind violating
monitor maintenance-only receptors in 2023: Kansas and Tennessee. Note that Arizona, Iowa,
Kansas, New Mexico, Tennessee, and Wyoming fall into one of two categories: (a) not linked to
downwind receptors based on the 2016v2 modeling for the proposal, but that are now linked based
on the 2016v3 modeling or are linked to a violating monitor; or (b) were linked to downwind
receptors based on 2016v2 modeling for the proposal, but that are now not linked to any receptors
based on the 2016v3 modeling or under the definition of receptors used at proposal.
Based on the maximum downwind contribution data in Table 4-3, the following 13 states
contribute at or above the 0.70 ppb threshold to downwind nonattainment receptors in 2026:
California, Indiana, Kentucky, Louisiana, Maryland, New Jersey, New York, Ohio, Pennsylvania,
Utah, Virginia, West Virginia. Based on the maximum downwind contribution data also in Table 4-3,
the following 8 states contribute at or above the 0.70 ppb threshold to downwind monitoring plus
modeled maintenance-only receptors in 2026: Arizona, Arkansas, Illinois, Mississippi, Missouri,
Nevada, Oklahoma, and Texas.
The contributions for individual upwind/downwind linkages for each upwind state are provided in
Appendix C. The upwind states linked to each receptor are provided in Appendix E.
5. 2026 Control Case Modeling
In this section we provide the results of the 2026 control case modeling. The control case
reflects the estimated emissions reductions expected to result from this final rule. The components of
the final rule control case are described in the RIA. The change in NOx emissions between the 2026
base case and the 2026 control case are provided, by state in Table 5-1. Note that negative values in
this table denote a reduction in emissions whereas positive values denote an increase in emissions.31
The impacts on emissions are rank ordered by the amount of emissions reduction (i.e., negative
31 The imposition of the final rule results in changes in regional electricity flows, resulting in changes in net imports. As a
result, some states (even those not subject to the rule) may see changes in emissions as a result of generation shifting.
26
-------
values are at the top). That is, in Table 5-1 the states with the largest NOx emissions reductions in the
final rule case are at the top of the list.
The "ppb" impacts on ozone design values at individual monitoring sites are shown in Figure
5-1. The spati al field of impacts on the modeled top 10-day average MDA8 ozone concentrations in
2026 is shown in Figure 5-2. Both figures indicate that the NOx emissions reductions in the control
case are expected to reduce ozone concentrations across broad regions of the eastern and western U S
within and downwind of the states covered by this final rule.
Expected Reductions in Ozone Design Values
Resulting from GNP NOx Emissions Reductions in 2026
50- y* i m m
45-B Q = 2.0
40* a. eo, *- & & * >=1'0,o2°
<8
30-
25-
X
o >= 0.50 to 1.0
o >= 0.25 to 0.50
o >=0.10 to 0.25
o <0.10
-120 -110 -100 -90 -80 -70
Figure 5-1. Reduction in design value concentrations at individual monitoring sites.
27
-------
Impact on Highest 10 Day Average Ozone Concentrations Expected
from EGU + Non-EGU NOx Emissions Reductions in 2026
I
-0.25
Figure 5-2. Impact of the control case NOx emissions reductions on the top 10-day average
MDA8 ozone concentrations (ppb) in 2026.
The estimated "ppb" impacts on projected 2026 ozone design values for those monitoring
sites that are identified as monitored plus modeled receptors in 2026 and/or in 2023 are provided in
Table 5-2. Table 5-3 provides the same information for the violating monitor-based maintenance-only
receptors in 20 23.32 Examining the emissions data in Table 5-1 together with the ppb impacts in
Table 5-2 and 5-3 indicates that the largest reductions in receptor ozone design values are predicted to
occur in the Houston-Galveston-Brazoria, Texas area. In this area the reductions from the final rule
case range from 0.7 to 0.9 ppb. At most of the receptors in both the Dallas/Ft Worth and the New
York/Coastal Connecticut areas the reductions in ozone design values range from 0.4 to 0.5 ppb. At
receptors in Indiana, Michigan, and Wisconsin near the shoreline of Lake Michigan, ozone design
values are projected to decline by 0.3 to 0.4 ppb, but by as much as 0.5 ppb at the receptor in
Muskegon, MI. Lesser reductions of 0.1 ppb are predicted in the urban and near-urban receptors in
Chicago. In the West, reductions in ozone design values just under 0.2 ppb are predicted at receptors
32 The approaches for identifying receptors are described in Section 3.
28
-------
in Denver with slightly greater reductions, just above 0.2 ppb, at receptors in Salt Lake City. At
receptors in Phoenix, California, El Paso/Las Cruces, and southeast New Mexico the reductions in
ozone design values are predicted to be less than 0.1 ppb.
Table 5-1. Impact on EGU and Non-EGU Ozone Season NOx Emissions by State in the 2026
Modeled Control Case (1,000 tons).
State
Final - Baseline
Louisiana
-12.6
Oklahoma
-9.9
Texas
-7.7
Arkansas
-7.3
Missouri
-6.9
Michigan
-5.3
Kentucky
-5.3
Utah
-5.2
Ohio
-4.9
West Virginia
-3.7
Indiana
-3.1
Mississippi
-3.0
Pennsylvania
-2.1
Illinois
-2.1
California
-1.7
Virginia
-1.6
Tribal
-1.3
Minnesota
-1.2
New York
-1.2
New lersey
-0.3
Arizona
-0.3
Alabama
-0.2
Maryland
-0.1
Nevada
0.0
Rhode Island
0.0
Florida
0.0
Maine
0.0
Oregon
0.0
Vermont
0.0
District of Columbia
0.0
Washington
0.0
Montana
0.0
Delaware
0.0
29
-------
State
Final - Baseline
Massachusetts
0.0
New Hampshire
0.0
New Mexico
0.0
Connecticut
0.0
Tennessee
0.0
South Dakota
0.0
Georgia
0.0
Nebraska
0.1
Idaho
0.1
Colorado
0.1
North Dakota
0.1
Wisconsin
0.1
South Carolina
0.2
Iowa
0.3
North Carolina
0.4
Kansas
0.4
Wyoming
0.5
Table 5-2. Ozone Impacts at Projected 2023 Nonattainment and Maintenance-Only Receptors (ppb)
for the Final Rule Modeled Control Case in 2026.
Site ID
State
County
Final Rule Case
40278011
Arizona
Yuma
-0.06
60650016
California
Riverside
-0.06
60651016
California
Riverside
-0.08
80350004
Colorado
Douglas
-0.17
80590006
Colorado
lefferson
-0.14
80590011
Colorado
lefferson
-0.11
80690011
Colorado
Larimer
-0.24
90010017
Connecticut
Fairfield
-0.38
90013007
Connecticut
Fairfield
-0.45
90019003
Connecticut
Fairfield
-0.46
90099002
Connecticut
New Haven
-0.43
170310001
Illinois
Cook
-0.08
170314201
Illinois
Cook
-0.09
170317002
Illinois
Cook
-0.11
350130021
New Mexico
Dona Ana
-0.02
350130022
New Mexico
Dona Ana
-0.03
350151005
New Mexico
Eddy
-0.02
30
-------
Site ID
State
County
Final Rule Case
350250008
New Mexico
Lea
-0.02
480391004
Texas
Brazoria
-0.82
481210034
Texas
Denton
-0.42
481410037
Texas
El Paso
-0.03
481671034
Texas
Galveston
-0.92
482010024
Texas
Harris
-0.68
482010055
Texas
Harris
-0.75
482011034
Texas
Harris
-0.72
482011035
Texas
Harris
-0.70
490110004
Utah
Davis
-0.22
490353006
Utah
Salt Lake
-0.22
490353013
Utah
Salt Lake
-0.15
550590019
Wisconsin
Kenosha
-0.21
551010020
Wisconsin
Racine
-0.22
551170006
Wisconsin
Sheboygan
-0.30
Table 5-3. Ozone Impacts at Violating-Monitor Maintenance-Only Receptors (ppb) in 2023 for the
Final Rule Modeled Control Case in 2026.
Site ID
State
County
Final Rule Case
40070010
Arizona
Gila
-0.07
40130019
Arizona
Maricopa
-0.04
40131003
Arizona
Maricopa
-0.05
40131004
Arizona
Maricopa
-0.05
40131010
Arizona
Maricopa
-0.05
40132001
Arizona
Maricopa
-0.04
40132005
Arizona
Maricopa
-0.06
40133002
Arizona
Maricopa
-0.04
40134004
Arizona
Maricopa
-0.05
40134005
Arizona
Maricopa
-0.04
40134008
Arizona
Maricopa
-0.05
40134010
Arizona
Maricopa
-0.06
40137020
Arizona
Maricopa
-0.04
40137021
Arizona
Maricopa
-0.06
40137022
Arizona
Maricopa
-0.05
40137024
Arizona
Maricopa
-0.04
40139702
Arizona
Maricopa
-0.05
40139704
Arizona
Maricopa
-0.06
40139997
Arizona
Maricopa
-0.04
31
-------
Site ID
State
County
Final Rule Case
40218001
Arizona
Pinal
-0.03
80013001
Colorado
Adams
-0.13
80050002
Colorado
Arapahoe
-0.18
80310002
Colorado
Denver
-0.13
80310026
Colorado
Denver
-0.13
90079007
Connecticut
Middlesex
-0.49
90110124
Connecticut
New London
-0.41
170310032
Illinois
Cook
-0.10
170311601
Illinois
Cook
-0.10
181270024
Indiana
Porter
-0.23
260050003
Michigan
Allegan
-0.39
261210039
Michigan
Muskegon
-0.50
320030043
Nevada
Clark
-0.15
350011012
New Mexico
Bernalillo
-0.04
350130008
New Mexico
Dona Ana
-0.02
361030002
New York
Suffolk
-0.39
390850003
Ohio
Lake
-0.70
480290052
Texas
Bexar
-0.28
480850005
Texas
Collin
-0.48
481130075
Texas
Dallas
-0.45
481211032
Texas
Denton
-0.41
482010051
Texas
Harris
-0.69
482010416
Texas
Harris
-0.73
484390075
Texas
Tarrant
-0.30
484391002
Texas
Tarrant
-0.38
484392003
Texas
Tarrant
-0.38
484393009
Texas
Tarrant
-0.32
490571003
Utah
Weber
-0.27
550590025
Wisconsin
Kenosha
-0.22
550890008
Wisconsin
Ozaukee
-0.24
6. Back Trajectory Analysis
As part of the assessment of interstate transport, the EPA used the HYSPLIT model (Stein, et al.,
2017) to provide a "transport climatology" for individual nonattainment and maintenance-only
receptors in 2023. This "transport climatology" provides a semi-quantitative approach to identify the
predominant transport patterns and interannual variability in transport for days with measured
exceedances. In this regard, the information from this analysis provides a qualitative method to
corroborate upwind-downwind linkages derived from the air quality contribution modeling. In this
-------
analysis the EPA ran the HYSPLIT model to construct 96-hour back trajectories for days with
measured exceedances during 2010 through 2021 at individual receptors. Back trajectories were
created for three start times on each exceedance day (8:00 am, 12:00 pm, and 3:00 pm Local Time for
each of six vertical elevations (100 m, 250 m, 500 m, 750 m, 1000 m, and 1500 m. Meteorological
data from the North American Mesoscale meteorological model (NAM) with 12 km horizontal
resolution were used in the HYSPLIT runs. The HYSPLIT outputs were processed to determine the
number of trajectory segments that crossed over a particular area.33 Each HYSPLIT hourly segment
was counted individually based on segment intersections. For visualization, the trajectory segment
counts were further processed using a 7-element Hanning Function.
Plots showing the back trajectories from each receptor are provided in the following documents
which can be found in the docket of this final rule: "Exceedance Day Trajectory Analysis_2010-
2021" This file contains (1) composite multi-start time, multi-elevation plots of exceedance day back
trajectories for each year individually for each receptor and (2) composite multi-start time, multi-year
composite plots of 500 m and 750 m exceedance day back trajectories for each receptor. These two
elevations were selected as generally representative of summertime mid-boundary layer transport.
33 The HYSPLIT segments were processed for a 0.25 degree (approximately 27 km) grid cell. Each grid cell constitutes a
particular area.
33
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7. References
Emery, C., Z. Liu, A. Russell, M. T. Odom, G. Yarwood, and N. Kumar, 2017. Recommendations on
Statistics and Benchmarks to Assess Photochemical Model Performance. J. Air and Waste
Management Association, 67, 582-598.
Gilliam, R.C. and J.E. Pleim, 2010. Performance Assessment of New Land Surface and Planetary
Boundary Layer Physics in the WRF-ARW. J. Appl. Meteor. Climatol., 49, 760-774.
Henderson, B.H., F. Akhtar, H.O.T. Pye, S.L. Napelenok, W.T. Hutzell, 2014. A Database and Tool
for Boundary Conditions for Regional Air Quality Modeling: Description and Evaluations,
Geoscientific Model Development, 7, 339-360.
Hong, S-Y, Y. Noh, and J. Dudhia, 2006. A New Vertical Diffusion Package with an Explicit
Treatment of Entrainment Processes. Mon. Wea. Rev., 134, 2318-2341.
Houyoux, M.R., Vukovich, J.M., Coats, C.J., Wheeler, N.J.M., Kasibhatla, P.S.,2000. Emissions
Inventory Development and Processing for the Seasonal Model for Regional Air Quality (SMRAQ)
project, Journal of Geophysical Research - Atmospheres, 105(D7), 9079-9090.
Iacono, M.J., J.S. Delamere, E.J. Mlawer, M.W. Shephard, S.A Clough, and W.D. Collins, 2008.
Radiative Forcing by Long-Lived Greenhouse Gases: Calculations with the AER Radiative
Transfer Models, J. Geophys. Res., 113,D13103.
I. Bey, D.J. Jacob, R.M. Yantosca, J.A. Logan, B.D. Field, A.M. Fiore, Q. Li, H.Y. Liu, L.J. Mickley,
M.G. Schultz. Global modeling of tropospheric chemistry with assimilated meteorology: model
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10.1029/200 ljd000807.
Kain, J.S., 2004. The Kain-Fritsch Convective Parameterization: An Update, J. Appl. Meteor., 43,
170-181.
Lyman, S, Trang, T. Inversion structure and winter ozone distribution in the Uintah Basin, Utah,
U.S.A. Atmospheric Environment. 123 (2015) 156-165.
Ma, L-M. and Tan Z-M, 2009. Improving the Behavior of Cumulus Parameterization for Tropical
Cyclone Prediction: Convective Trigger, Atmospheric Research, 92, 190-211.
Morrison, H.J., A. Curry, and V.I. Khvorostyanov, 2005. A New Double-Moment Microphysics
Parameterization for Application in Cloud and Climate Models. Part I: Description, J. Atmos.
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Morrison, H. and A. Gettelman, 2008. A New Two-Moment Bulk Stratiform Cloud Microphysics
Scheme in the Community Atmosphere Model, version 3 (CAM3). Part I: Description and
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Pleim, J.E. and A. Xiu, 2003. Development of a Land-Surface Model. Part II: Data Assimilation, J.
Appl. Meteor., 42, 1811-1822
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Pleim, J.E., 2007a. A Combined Local and Nonlocal Closure Model for the Atmospheric Boundary
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2
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Appendix A
Model Performance Sensitivity Analysis
-------
As noted in the preamble for this final action, in response to comments the EPA
examined the temporal and spatial characteristics of model under prediction in the 2016v2
modeling to investigate the possible causes of under prediction of MDA8 ozone concentrations
in different regions of the U.S. The EPA's analysis indicates that the under prediction was most
extensive during May and June with less bias during July and August in most regions of the U.S.
For example, in the Upper Midwest region model under prediction was larger in May and June
compared to July through September. Specifically, the normalized mean bias for days with
measured concentrations > 60 ppb improved from a 21.4 percent under prediction for May and
June to a 12.6 percent under prediction in the period July through September. As highest by the
presentation materials in this Appendix, the seasonal pattern in bias in the Upper Midwest region
improves somewhat gradually with time from the middle of May to the latter part of June. In
view of the seasonal pattern in bias in the Upper Midwest and in other regions of the U.S., EPA
focused its investigation of model performance on model inputs that, by their nature, have the
largest temporal variation within the ozone season. These inputs include emissions from biogenic
sources and lightning NOx, and contributions from transport of international anthropogenic
emissions and natural sources into the U.S. Both biogenic and lightning NOx emissions in the
U.S. dramatically increase from spring to summer.1'2 In contrast, ozone transported into the U.S.
from international anthropogenic and natural sources peaks during the period March through
1 Guenther, A.B., 1997. Seasonal and spatial variations in natural volatile organic compound
emissions. Ecol. Appl. 7, 34-45. http://dx.doi.org/10.1890/1051- 0761(1997)
007[0034:SASVIN]2.0.CO;2. Guenther, A., Hewitt, C.N., Erickson, D., Fall, R
2 Kang D, Mathur R, Pouliot GA, Gilliam RC, Wong DC. Significant ground-level ozone
attributed to lightning-induced nitrogen oxides during summertime over the Mountain West
States. NPJ Clim Atmos Sci. 2020 Jan 30;3:6. doi: 10.1038/s41612-020-0108-2. PMTD:
32181370; PMCID: PMC7075249.
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June, with lower contributions during July through September.3'4 To investigate the impacts of
the sources, EPA conducted sensitivity model runs which focused on the effects on model
performance of adding NOx emissions from lightning strikes, updating updated biogenic
emissions, and using an alternative approach for quantifying transport of ozone and precursor
pollutants into the U.S. from international anthropogenic and natural sources. In the 2016v2
modeling the amount of transport from international anthropogenic and natural sources was
based on a simulation of the hemispheric version of the Community Multi-scale Air Quality
Model (H-CMAQ) for 2016.5 The outputs from this hemispheric modeling were then used to
provide boundary conditions for national scale air quality modeling at proposal.6 Overall, H-
CMAQ tends to under-predict daytime ozone concentrations at rural and remote monitoring sites
across the U.S. during the spring of 2016 whereas the predictions from the GEOS-Chem global
3 Jaffe DA, Cooper OR, Fiore AM, Henderson BH, Tonnesen GS, Russell AG, Henze DK,
Langford AO, Lin M, Moore T. Scientific assessment of background ozone over the U.S.:
Implications for air quality management. Elernenta (Wash D C). 2018;6( 1 ):56. doi:
10.1525/elementa.309. PMID: 30364819; PMCID: PMC6198683.
4 Henderson, B.H., P. Dolwick, C. Jang, A., Eyth, J. Vukovich, R. Mathur, C. Hogrefe, N.
Possiel, G. Pouliot, B. Timin, K.W. Appel, 2019. Global Sources of North American Ozone.
Presented at the 18th Annual Conference of the UNC Institute for the Environment Community
Modeling and Analysis System (CMAS) Center, October 21-23, 2019.
5 Mathur, R., Gilliam, R., Bullock, O.R., Roselle, S., Pleim, J., Wong, D., Binkowski, F., and 1
Streets, D.: Extending the applicability of the community multiscale air quality model to 2
hemispheric scales: motivation, challenges, and progress. In: Steyn DG, Trini S (eds) Air 3
pollution modeling and its applications, XXI. Springer, Dordrecht, pp 175-179, 2012.
6 Boundary conditions are the concentrations of pollutants along the north, east, south, and west
boundaries of the air quality modeling domain. Boundary conditions vary in space and time and
are typically obtained from predictions of global or hemispheric models. Information on how
boundary conditions were developed for the final rule modeling can be found in the AQM TSD.
-------
model7 were generally less biased.8 During the summer of 2016 both models showed varying
degrees of over prediction with GEOS-Chem showing somewhat greater over-prediction,
compared to H-CMAQ. In view of those results, EPA examined the impacts of using
GEOSChem as an alternative to H-CMAQ for providing boundary conditions for the final rule
modeling.
For the lightning NOx, biogenics, and GEOSChem sensitivity runs, the EPA reran
2016v2 using each of these alternative inputs, individually. Results from these sensitivity runs
indicate that each of the three updates provides an improvement in model performance.
However, by far the greatest improvement in modeling performance is attributable to the use of
GEOSChem. In view of these results the EPA has included lightning NOx emissions, updated
biogenic emissions, and international transport from GEOSChem in the 2016v3 modeling used
for this final action. As described in Appendix B, the model has less bias and error regionally
and at individual nonattainment/maintenance receptors on high ozone days using these updates.
7 I. Bey, D.J. Jacob, R.M. Yantosca, J.A. Logan, B.D. Field, A.M. Fiore, Q. Li, H.Y. Liu, L.J.
Mickley, M.G. Schultz. Global modeling of tropospheric chemistry with assimilated
meteorology: model description and evaluation. J. Geophys. Res. Atmos., 106 (2001), pp. 23073-
23095, 10.1029/200 ljd000807.
8 Henderson, B.H., P. Dolwick, C. Jang, A., Eyth, J. Vukovich, R. Mathur, C. Hogrefe, G.
Pouliot, N. Possiel, B. Timin, K.W. Appel, 2022. Meteorological and Emission Sensitivity of
Hemispheric Ozone and PM2.5. Presented at the 21st Annual Conference of the UNC Institute
for the Environment Community Modeling and Analysis System (CMAS) Center, October 17-
19, 2022.
-------
2016 Model Performance Sensitivity
Analysis
i
-------
Bias (ppb) in MDA8 Ozone by Day -
2016v2 modeling generally under predicted measured MDA8 ozone concentrations, particularly in May and June
20
10
0
-10
-20
Upper Midwest
# of Sites: 110
_ Over prediction
a AaAwW r\ *
f\ A o
Underprediction
,
May 01 May 10 May 19 May 28 Jun 06 Jun14 Jun 22 Jun30 Jul 08 Jul 16 Jul 24 Aug 01 Aug 10 Aug 19 Aug 28 Sep 06 Sep 15 Sep 24
May 1 i Sept 30
20
10
0
10
¦20
Southwest
# of Sites: 138
May 01 May 10 May 19 May 28 Jun 06 Jun 14 Jun 22 Jun 30 Jul 08 Jul 16 Jul 24 Aug 01 Aug 10 Aug 19 Aug 28 Sep 06 Sep 15 Sep 24
North err
Northwest Rockies
West
Southwest
South
I
PJ Northeast
Southeast
-------
Modeling Platform Up
Address Mode! Performance Concerns
Initial & Boundary Concentrations: Use GEOSChem rather than Hemispheric
CMAQ (H-CMAQ) to provide initial and boundary concentrations for the
outer (36 km) modeling domain. (GEOSChem => 36US3 => 12US2 domain)
Biogenic Emissions: Use a recently released updated version of BEIS + BELD
land use (BEIS4/BELD6) which includes state-of-science emissions factors
and improved land use data
Lightning NOx: Include 3-D NOx emissions from lightning strikes using a soon-to-be published
method developed by ORD.
3
-------
Sensitivity Mode! Runs to Explore impacts of Potential Updates
• Baseline for sensitivities is the 2016fj (i.e., 2016v2 platform) model run
• Model Sensitivity Runs
1) BEIS4/BELD6 Biogenics
2) Include emissions of Lightning NOx
3) GEOS-Chem + BEIS4/BELD6 + Lightning NOx
• Results
o Model performance for MDA8 ozone improved with each of the three updates. The most
notable improvements resulted from using GEOS-Chem to provide initial and boundary
concentrations
-------
Time Series of Daily Bias in MDA8 Ozone & Bias - May Through September
75
70
65
n 60
Q.
§ 45
C in
O 40
35
30
25
2016fj BEIS4/BELD6
May 01 May 10 May 19 May 28 Jun 06 Jun14 Jun 22 Jun 30 Jul 08 Jul 16 Jul 24 Aug 01 Aug 10 Aug 19 Aug 28 Sep 06 Sep 15 Sep 24
# of Sites: 110
Midwest Avg MDAS Ozone
AQS Daily
CAMx_2016fj_v710_AQShJun8_12US2
CAMx_2016fj v710_CB6r5_GEOSChem
CAMx_2016fj beis4
CAMx
2016 Obs
20161 Base Model
2016fj GEOSChem
Date
15
10
JD
Q_
a.
CO
nj
in
CO
O
-5
-10
2016fj Lightning NOx
May 01 May 10 May 19 May 28 Jun 06 Jun 14 Jun 22 Jun 30 Jul 08 Jul 16 Jul 24 Aug 01 Aug 10 Aug 19 Aug 28 Sep 06 Sep 15 Sep 24
CAMx_2016fj_v710_AQShJun8_12US2
CAMx_2016fj_v710_CB6r5_GEOSChem_12US2
CAMx_2016fj_beis4_v710_CB6r5_12US2
CAMx_2016fjJnox_v710_CB6r5_12US2
# of Sites: 110
Midwest Avg MDA8 Bias
-------
75
70
65
2 60
Q.
t 55
X
£ 50
I 45
W a n
O 40
35
30
25
Time Series of Daily Bias in MDA8 Ozone
CAMx_20a6fj_beis4_v710_CB6rSJ2US2
Q^x_2Q&8fj_lnox
2016 Obs
201631 Base Model
2016fj GEOSChem
2016fj BEIS4/BELD6
May 01 May 10 May 19 May 28 Jun 06 Jun 14 Jun 22 Jun30 Jul 08 Jul 16 Jul 24 Aug 01 Aug 10 Aug 19 Aug 28 Sep 06 Sep 15 Sep 24
AQS Daily
CAMx_2016f j v710_AQShJun8_12US2
CAMx_201£fj_v710_CB6r5 GEOSChem_12U?
Southwest Avg MDA8 Ozone
2016fj Lightning NOx
-15 H
May 01 May 10 May 19 May 28 Jun 06 Jun 14 Jun 22 Jun 30 Jul 08 Jul 16 Jul 24 Aug 01 Aug 10 Aug 19 Aug 28 Sep 06 Sep 15 Sep 24
10 -
5 -
0
-5 -
-10 -
CAMx_2016fj_v710_AQShJun8_12US2
CAMx_2016fj_v710_CB6r5_GEOSChem_12US2
CAMx_2016fj_beis4_v710_CB6r5_12US2
CAMx_2016fj_lnox_v710_CB6r5_12US2
Southwest Avg MDA8 Bias
# of Sites: 138
Date
-------
Bias (ppb) in MDA8
2016v2 vs GEOSChem
# of Sites: 110
Upper Midwest
May 01 May 10 May 19 May 28 Jun 06 Jun 14 Jun 22 Jun30 Jul 08 Jul 16 Jul 24 Aug 01 Aug 10 Aug 19 Aug 28 Sep 06 Sep 15 Sep 24
May 1 i Sept 30
May 01 May 10 May 19 May 28 Jun 06 Jun 14 Jun 22 Jun 30 Jul 08 Jul 16 Jul 24 Aug 01 Aug 10 Aug 19 Aug 28 Sep 06 Sep 15 Sep 24
# of Sites: 138
Southwest
12US2
Northeast
Southeast
-------
Sheboygan
CAMx_2016fj_lnox_beis4_GEOS_Chem_12US2 03_8hrmax for AQS_Daily_03 Site: 551170006 in Wl
100 -
90 -
80 -
70 -
60 -
50 -
40 -
20 -
Illlllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll—lllllllllllllllllllllllllllllllllllllllllllllllllll llllllllllllllllllllllllllllll I
Apr 14 Apr23 May 02 May 12 May 22 Jun 01 Jun 10 Jun 19 Jun 28 Jul 09 Jul 17 Jul 25 Aug 03 Aug 12 Aug 21 Aug 30 Sep 08 Sep 18 Sep 28
AQS Daily
CAMx_2016fj_lnox_beis4_GEOS_Chem_12US2
CAMx_2016fj_v710_AQShJun8_1
# of Sites: 1
Site: 551170006
Date
# of Sites: 1
CAMx_2016fj_lnox_beis4_GEOS_Chem_12US2
CAMx_2016fj_v710_AQShJun8_12US2 site: 551170006
Bias for CAMx_2016fjJnox_beis4_GEOS_Chem_12US2 Q3_8hrmax for AQS_Daily_03 for 20150501 to 20160930
20 -
10 -
-10 -
-20 -
-30 -
1111111II111111II11111111II III111111111II111II IIIII1111111111 11II1111111II1111111111—11111111111111111111111111111111111111 1111111111111 111111111111111II1111111111111 I
Apr 14 Apr23 May 02 May 12 May 22 Jun 01 Jun 10 Jun 19 Jun 28 Jul 09 Jul 17 Jul 25 Aug 03 Aug 12 Aug 21 Aug 30 Sep 08 Sep 18 Sep 28
Date
-------
Chicago-Alsip
CAMx_2016fj_lnox_beis4_GEOS_Chem_12US2 03_8hrmax for AQS_Daily_03 Site: 170310001 in IL
-O
Q_
Q_
CO
O
100
90
80
70
60
50
40
30
20
AQS Daily
CAMx_2016fj_lnox_beis4 GEOS_Chern_12US2
CAMx_201 6fj_v71 0_AQShJun8_12US2
# of Sites: 1
Site: 170310001
i(IM111111111111111111111111111111111m11ii11111iimii11m1111m11111111111m111111111ii11iiim11111m111u11111m11it1111111111111111111111m11M11111 rnnnnnnnnnnnm iiiiini
Jan 01 Jan 15 Jan 29 Feb 13 Feb 28 Mar 15 Mar 30 Apr 14 Apr 28 May 13 May 28 Jun12 Jun 26 Jul 10 Jul 23 Aug 06 Aug 21 Sep 05 Sep 23
Date
Bias for CAMx_2016fj_lnox_beis4_GEOS_Chem_12US2 Q3_8hrmax for AQS_Daily_Q3 for 20150501 to 20160930
# of Sites: 1
Site: 170310001
CAMx_2016fj_Inox_beis4_GEOS_Chem_12US2
CAMx_2016fj_v71 0_AQShJun8_12US2
Jan 01 Jan 15 Jan 29 Feb 13 Feb 28 Mar 15 Mar 30 Apr 14 Apr 28 May 13 May 28 Jun 12 Jun 26 Jul 10 Jul 23 Aug 06 Aug 21 Sep 05 Sep 23
Date
-------
May & June
July thru September
ads naiiu
Change in Bias for MDA8 03 >
60 using GEOS-Chem iC/BCs
Using GEOS-Chem
less bias at most monitoring
sites; the exceptions are
mainly at monitors in parts of
the Southeast in May and
June and in the Southeast,
Ohio Valley, and parts of the
Northeast in July thru
September
10
-------
Regional Performance Statistics for Days with Obs MDA8 03 > 60 ppb
Northeast
# Sites
Mean
Bias
(PPb)
Mean
Error
(PPb)
NMB
(%)
NME
(%)
South
# Sites
Mean
Bias
(PPb)
Mean
Error
(PPb)
NMB
(%)
NME
(%)
2016fj
187
-4.2
7.2
-6.2
10.7
2016fj
138
-7.8
9.2
-12.0
14.1
2016fj Update
187
1.5
6.9
2.3
10.3
2016fj Update
138
-1.5
6.6
-2.3
10.1
Ohio Valley
# Sites
Mean
Bias
(PPb)
Mean
Error
(PPb)
NMB
(%)
NME
(%)
Southwest
# Sites
Mean
Bias
(PPb)
Mean
Error
(PPb)
NMB
(%)
NME
(%)
2016fj
229
-7.1
8.8
-10.8
13.3
2016fj
137
-8.9
9.8
-13.8
15.2
2016fj Update
229
1.1
6.3
1.6
9.5
2016fj Update
137
-4.4
6.5
-6.9
10.0
Midwest
# Sites
Mean
Bias
(PPb)
Mean
Error
(PPb)
NMB
(%)
NME
(%)
I/l/est
# Sites
Mean
Bias
(PPb)
Mean
Error
(PPb)
NMB
(%)
NME
(%)
2016fj
108
-12.7
13.0
-19.1
19.5
2016fj
187
-9.7
11.4
-13.8
16.2
2016fj Update
108
-4.9
6.9
-7.4
10.3
2016fj Update
187
-8.1
9.7
-11.6
13.7
Southeast
# Sites
Mean
Bias
(PPb)
Mean
Error
(PPb)
NMB
(%)
NME
(%)
Northern
Rockies
# Sites
Mean
Bias
(PPb)
Mean
Error
(PPb)
NMB
(%)
NME
(%)
2016fj
182
-2.9
6.1
-4.5
9.4
2016fj
41
-11.9
12.4
-19.0
19.8
2016fj Update
182
2.8
6.4
4.3
9.8
2016fj Update
41
-7.6
7.9
-12.1
12.6
-------
Regional Performance Statistics Including All Days
Mean
Mean
Mean
Mean
Bias
Error
NMB
NME
Bias
Error
NMB
NME
Northeast
# Sites
(PPb)
(PPb)
(%)
(%)
South
# Sites
(PPb)
(PPb)
(%)
(%)
2016fj
192
0.2
6.3
0.6
14.3
2016fj
149
0.0
6.6
0.0
16.5
2016fj_Update
192
3.8
6.9
8.7
15.7
2016fj_Update
149
4.8
7.8
12.1
19.5
Mean
Mean
Mean
Mean
Bias
Error
NMB
NME
Bias
Error
NMB
NME
Ohio Valley
# Sites
(PPb)
(PPb)
(%)
(%)
Southwest
# Sites
(PPb)
(PPb)
(%)
(%)
2016fj
241
0.4
6.3
1.0
14.1
2016fj
138
-3.7
6.8
-7.1
13.1
2016fj_Update
241
6.0
7.9
13.4
17.6
2016fj_Update
138
0.5
5.6
1.0
10.7
Mean
Mean
Mean
Mean
Bias
Error
NMB
NME
Bias
Error
NMB
NME
Midwest
# Sites
(PPb)
(PPb)
(%)
(%)
West
# Sites
(PPb)
(PPb)
(%)
(%)
2016fj
110
-2.9
6.3
-6.9
15.2
2016fj
206
-3.0
7.4
-5.9
14.5
2016fj_Update
110
1.6
5.8
3.9
14.1
2016fj_Update
206
-0.7
6.8
-1.3
13.4
Mean
Mean
Mean
Mean
Bias
Error
NMB
NME
Northern
Bias
Error
NMB
NME
Southeast
# Sites
(PPb)
(PPb)
(%)
(%)
Rockies
# Sites
(PPb)
(PPb)
(%)
(%)
2016fj
186
1.9
6.1
4.7
14.9
2016fj
58
-3.8
6.1
-8.6
13.7
2016fj_Update
186
5.6
7.7
13.7
18.8
2016fj_Update
58
-0.8
4.9
-1.7
11.1
12
-------
i 9
Appendix
13
-------
Bias (ppb) in MDA8 Ozone by Day - 2
20 -
15 -
2 10"
a
a
* 5-
? 0-
ro
E
h -5 -
CO
0 -10 -
-15 -
-20 -
# of Sites: 110
Upper Midwest
May 01 May 10 May 19 May 28 Jun06 Jun 14 Jun22 Jun30 Jul 08 Jul 16 Jul 24 Aug 01 Aug 10 Aug 19 Aug 28 Sep 06 Sep 15 Sep 24
-20 ¦
May 01 May 10 May 19 May 28 Jun 06 Jun 14 Jun 22 Jun 30 Jul 08 Jul 16 Jul 24 Aug 01 Aug 10 Aug 19 Aug 28 Sep 06 Sep 15 Sep 24
20 -| # of Sites: 192
Northeast
10
5
0
-5
-10
-15 -
Date
Date
Date Date
-------
Avg Bias (ppb) on Days with Measured MDA8 > 60 ppb at 2016v2 Receptor Sites (Part 1)
May
- June Bias (ppb)
July-
Sept Bias (ppb)
Site ID
State
County
Site Name
2016fj
Base Case
2016fj
GEOSChem
BEIS4_LNOx
Bias Diff
2016fj
Base Case
2016fj
GEOSChem
BEIS4_LNOx
Bias Diff
40278011
AZ
Yuma
Supersite
-12.6
-7.3
-5.3
-9.3
-11.7
2.4
80350004
CO
Douglas
Chatfield
-5.7
0.1
-5.6
-7.2
-1.9
-5.4
80590006
CO
Jefferson
Rocky Flats
-6.9
-1.7
-5.2
-7.2
-2.5
-4.7
80590011
CO
Jefferson
NREL
-8.3
-3.1
-5.2
-7.7
-2.8
-4.9
80690011
CO
Larimer
Fort Collins
-10.0
-4.5
-5.5
-11.5
-7.2
-4.3
90010017
CT
Fairfield
Greenwich
1.2
-3.9
2.6
-3.8
-7.4
3.6
90013007
CT
Fairfield
Stratford
-6.1
-1.0
-5.0
-4.0
-0.2
-3.8
90019003
CT
Fairfield
Westport
-7.7
-2.3
-5.4
-3.9
-0.6
-3.3
90099002
CT
New Haven
Madison
-7.0
-2.2
-4.8
-4.5
-0.6
-3.9
170310001
IL
Cook
Alsip
-14.5
-5.5
-9.0
0.2
6.7
6.5
170310032
IL
Cook
South Water Plant
-17.7
-9.3
-8.4
-9.6
-0.7
-8.8
170310076
IL
Cook
Com Ed
-13.6
-5.1
-8.6
-2.4
5.3
2.9
170314201
IL
Cook
Northbrook
-15.2
-6.2
-9.0
-2.5
5.9
3.3
170317002
IL
Cook
Water Plant
-11.4
-2.0
-9.4
-7.7
1.0
-6.6
350130021
NM
Dona Ana
Desert View
-11.5
-6.0
-5.6
-13.4
-7.3
-6.1
350130022
NM
Dona Ana
Santa Teresa
-10.8
-5.0
-5.8
-12.1
-5.6
-6.5
420170012
PA
Bucks
Bristol
-6.4
1.7
-4.7
2.0
10.5
8.5
-------
Avg Bias (ppb) on Days with Measured MDA8 > 60 ppb at 2016v2 Receptor Sites (Part 2)
May
- June Bias (ppb)
July
Sept Bias (ppb)
Site ID
State
County
Site Name
2016fj
Base Case
2016fj
GEOSChem
BEIS4_LNOx
Bias Diff
2016fj
Base Case
2016fj
GEOSChem
BEIS4_LNOx
Bias Diff
480391004
TX
Brazoria
Manvel Croix Park
-6.0
0.2
-5.8
-11.3
-6.3
-5.0
481210034
TX
Denton
Denton
-7.2
0.1
-7.1
-9.4
-3.2
-6.2
481410037
TX
El Paso
UTEP
-12.7
-7.3
-5.4
-18.5
-11.8
-6.8
481671034
TX
Galveston
Galveston
-16.4
-10.3
-6.1
-16.0
-12.0
-4.0
482010024
TX
Harris
Houston Aldine
-1.5
5.1
3.6
-6.7
-0.4
-6.3
482010047
TX
Harris
Lang
2.8
9.7
6.9
-6.1
-0.3
-5.8
482010055
TX
Harris
Houston Bayland Park
-8.0
-2.3
-5.7
-1.2
3.9
2.7
482010416
TX
Harris
Park Place
-2.4
3.9
1.5
-1.6
3.6
2.0
482011034
TX
Harris
Houston East
-2.7
4.4
1.7
-16.9
-10.2
-6.7
482011035
TX
Harris
Clinton
1.3
9.0
7.7
2.6
9.0
6.4
490110004
UT
Davis
Bountiful Viewmont
-13.7
-9.3
-4.4
-8.3
-5.3
-3.0
490353006
UT
Salt Lake
Hawthorne
-18.7
-15.7
-3.0
-8.6
-6.0
-2.5
490353013
UT
Salt Lake
Herriman
-14.1
-8.9
-5.2
-9.8
-6.9
-2.8
490450004
UT
Tooele
Erda
-12.5
-7.4
-5.1
-8.9
-6.3
-2.6
490571003
UT
Weber
Harrisville
-12.8
-8.0
-4.9
-10.0
-7.3
-2.7
550590019
Wl
Kenosha
Chiwaukee
-21.3
-12.5
-8.8
-11.3
-4.1
-7.3
550590025
Wl
Kenosha
Kenosha - WaterTower
-15.6
-6.3
-9.3
-8.8
-0.3
-8.6
551010020
Wl
Racine
Racine - Payne And Dolan
-18.5
-9.3
-9.3
-13.4
-4.8
-8.6
551170006
Wl
Sheboygan
Sheboygan - Kohler Andrae
-22.9
-13.2
-9.8
-14.0
-5.5
-8.5
-------
Appendix B
Model Performance Evaluation for
2016v3 Base Year CAMx Simulation
-------
I. Introduction
An operational model evaluation was conducted for the 2016v3 base year CAMx v7.10
simulation performed for the 12 km U.S. modeling domain. The purpose of this evaluation is to
examine the ability of the 2016 air quality modeling platform to represent the magnitude and
spatial and temporal variability of measured (i.e., observed) maximum daily average (i.e.,
MDA8) ozone concentrations within the modeling domain. The evaluation presented here is
based on model simulations using the 2016v3 emissions platform (i.e., scenario name 2016gf).
As part of this evaluation, we compare model performance for the 2016v3 platform to model
performance for the 2016v2 platform that used for the proposed disapproval actions.
The model evaluation for ozone focuses on comparing 8-hour daily maximum (i.e.,
MDA8) ozone concentrations to the corresponding observed data at monitoring sites in the EPA
Air Quality System (AQS). The locations of the ozone monitoring sites in this network are
shown in Figure B-l.
This evaluation includes statistical measures and graphical displays of model
performance based upon model-predicted versus observed concentrations. The evaluation
focusses on model predicted and observed MDA8 ozone concentrations that were paired in space
and time. Model performance statistics were calculated for several spatial scales and temporal
periods. Statistics were calculated for individual monitoring sites and in aggregate for monitoring
sites within each of the nine climate regions of the 12 km U.S. modeling domain. The regions
include the Northeast, Ohio Valley, (Upper) Midwest, Southeast, South, Southwest, Northern
Rockies, Northwest and West1'2, which are defined based upon the states contained within the
1 The nine climate regions are defined by States where: Northeast includes CT, DE, ME, MA, MD, NH, NJ, NY,
PA, RI, and VT; Ohio Valley includes IL, IN, KY, MO, OH, TN, and WV; Midwest includes IA, MI, MN, and WI;
Southeast includes AL, FL, GA, NC, SC, and VA; South includes AR, KS, LA, MS, OK, and TX; Southwest
includes AZ, CO, NM, and UT; Northern Rockies includes MT, NE, ND, SD, WY; Northwest includes ID, OR, and
WA; and West includes CA and NV.
2 Note most monitoring sites in the West region are located in California (see Figures B-l and B-2), therefore the
statistics for the West region will be mostly representative of model performance in California ozone.
B-2
-------
National Oceanic and Atmospheric Administration (NOAA) climate regions (Figure B-2)3 as
defined in Karl and Koss (1984).
II. Methodology
Model performance statistics were created for the period May through September (i.e.,
seasonal) and for individual months during this time period. Statistics were created using data on
all days with valid observed data during this period as well as for the subset of days with
observed MDA8 concentrations > 60 ppb.4 The aggregate statistics by climate region and for the
monitor plus modeled 2023 receptors are presented in this appendix. Model performance
statistics for MDA8 ozone at individual monitoring sites nationwide based on days with
observed values > 60 ppb can be found in the docket in the file named "2016v3 CAMx Ozone
Model Performance Statistics by Site".
In addition to the above performance statistics, we prepared several graphical
presentations of model performance for MDA8 ozone. These graphical presentations include:
(1) maps that show the mean bias and error as well as normalized mean bias and error calculated
for MDA8 > 60 ppb for May through September at individual monitoring sites;
(2) maps that show the change in bias and error in the 2016v3 modeling compared to the 2016v2
modeling;
(2) bar and whisker plots that show the distribution of the predicted and observed MDA8 ozone
concentrations by month (May through September) and by region; and
(3) time series plots (May through September) of observed and predicted MDA8 ozone
concentrations for each region and for a selected set of monitoring sites that are projected to be
nonattainment or maintenance-only receptors in 2023.
The Atmospheric Model Evaluation Tool (AMET) was used to calculate the model
performance statistics used in this document (Gilliam et al., 2005). For this evaluation we have
selected the mean bias, mean error, normalized mean bias, and normalized mean error to
3 NOAA, National Centers for Environmental Information scientists have identified nine climatically consistent
regions within the contiguous U.S., http://www.ncdc.noaa.gov/monitoring-references/maps/us-climate-regions.php.
4 We limited the data to those observed and predicted pairs with observations that are > 60 ppb in order to focus on
concentrations at the upper portion of the distribution of values.
B-3
-------
characterize model performance, statistics which are consistent with the recommendations in
Simon et al. (2012) and EPA's photochemical modeling guidance (U.S. EPA, 2018).
Mean bias (MB) is the average of the difference (predicted - observed) divided by the
total number of replicates (n). Mean bias is given in units of ppb and is defined as:
MB = ~Hi(P ~ 0) , where P = predicted and O = observed concentrations
Mean error (ME) calculates the absolute value of the difference (predicted - observed)
divided by the total number of replicates (n). Mean error is given in units of ppb and is defined
as:
ME = i£I|P-0|
Normalized mean bias (NMB) is the average the difference (predicted - observed) over
the sum of observed values. NMB is a useful model performance indicator because it avoids over
inflating the observed range of values, especially at low concentrations. Normalized mean bias is
given in percentage units and is defined as:
NMB=lii2*100
Normalized mean error (NME) is the absolute value of the difference (predicted -
observed) over the sum of observed values. Normalized mean error is given in percentage units
and is defined as:
™E=w*100
III. Overview of Findings
As described in more detail below, the model performance statistics indicate that the
MDA8 ozone concentrations predicted by the 2016v2 CAMx modeling platform closely reflect
the corresponding MDA8 observed ozone concentrations in each region of the 12 km U.S.
modeling domain. The acceptability of model performance was judged by considering the
2016v2 CAMx performance results in light of the range of performance found in recent regional
ozone model applications (Emery et al., 2017; NRC, 2002; Phillips et al., 2007; Simon et al.,
B-4
-------
2012; U.S. EPA, 2005; U.S. EPA, 2009; U.S. EPA, 2010).5 These other modeling studies
represent a wide range of modeling analyses that cover various models, model configurations,
domains, years and/or episodes, chemical mechanisms, and aerosol modules. In particular,
Emery et.al. extend the results of Simon et.al., to include performance results from a few more
recent photochemical model applications. The results in the former paper indicate that about a
third of the top performing past applications have normalized mean bias and a normalized mean
error statistics for MDA8 ozone of less than +5 percent and less 15 percent, respectively. In
addition, two-thirds of past applications have normalized mean bias less than +15 percent and
normalized mean error less than 25 percent. These "benchmarks" are not intended to represent
"rigid pass/fail tests" but rather as "simple references to the range of recent historical
performance" that can be used to understand where the performance results of a particular
application "fall in the spectrum of past published results."
Overall, the ozone model performance results for the 2016v2 CAMx simulation are in
large part within the range found in other recent peer-reviewed and regulatory applications. The
model performance results, as described in this document, demonstrate that the predictions from
the 2016v2 modeling platform correspond closely to observed concentrations in terms of the
magnitude, temporal fluctuations, and geographic differences for MDA8 ozone concentrations.
5 Christopher Emery, Zhen Liu, Armistead G. Russell, M. Talat Odman, Greg Yarwood & Naresh Kumar (2017)
Recommendations on statistics and benchmarks to assess photochemical model performance, Journal of the Air &
Waste Management Association, 67:5, 582-598, DOI: 10.1080/10962247.2016.1265027
National Research Council (NRC), 2002. Estimating the Public Health Benefits of Proposed Air Pollution
Regulations, Washington, DC: National Academies Press.
U.S. Environmental Protection Agency; Technical Support Document for the Final Clean Air Interstate Rule: Air
Quality Modeling; Office of Air Quality Planning and Standards; RTP, NC; March 2005 (CAIR Docket OAR-2005-
0053-2149).
U.S. Environmental Protection Agency, Proposal to Designate an Emissions Control Area for Nitrogen Oxides,
Sulfur Oxides, and Particulate Matter: Technical Support Document. EPA-420-R-007, 329pp., 2009.
(http://www.epa.gov/otaa/regs/nonroad/marine/ci/420r09007.pdf)
Phillips, S., K. Wang, C. Jang, N. Possiel, M. Strum, T. Fox, 2007. Evaluation of 2002 Multi-pollutant
Platform: Air Toxics, Ozone, and Particulate Matter, 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8,
2008. (http://www.cmascenter.org/conference/2008/agenda.cfm').
U.S. Environmental Protection Agency, 2010, Renewable Fuel Standard Program (RFS2) Regulatory Impact
Analysis. EPA-420-R-10-006. February 2010. Sections 3.4.2.1.2 and 3.4.3.3. Docket EPA-HQ-0AR-2009-0472-
11332. (http://www.epa.gov/oms/renewablefuels/420r10006.pdD
Simon, H., Baker, K.R., and Phillips, S. (2012) Compilation and interpretation of photochemical model performance
statistics published between 2006 and 2012. Atmospheric Environment 61, 124-139.
B-5
-------
IV. Analysis of Model Performance Statistics and Graphics
The MDA8 ozone model performance bias and error statistics for the period May-
September for each climate region are provided in Table B-l. The statistics shown in this table
were calculated using all data pairs for May-September. Seasonal statistics for each region based
on the subset of days with observed MDA8 ozone > 60 ppb are presented in Table B-2. Seasonal
statistics at each receptor on days with observed MDA8 ozone > 60 ppb are presented in Table
B-3 for the 2016v3 and 2016v2 modeling.
Spatial plots of the mean bias and error as well as the normalized mean bias and error at
individual monitors nationwide are shown in Figures B-3 through B-8. Time series plots of
observed and predicted MDA8 ozone during the period May through September for each region
and for selected 2023 nonattainment and/or maintenance sites in 2023 are provided in Figures B-
9 and B-10, respectively.
CIRCLE=AQS_Daily;
Figure B-l. Location of ozone monitoring sites.
B-6
-------
U.S. Climate Regions
Figure B-2. NOAA climate regions (source: http://www.ncdc.noaa.gov/monitoring-references/maps/us-
climate-regions.php#references)
A. Seasonal and Monthly Performance
1. Model Performance Statistics by Region
The model performance statistics provided in Table B-l show that regional mean tends to
overpredict in most regions on average for all days during the period May through September in
each region. Normalized mean bias is less than + 5 percent in the Midwest, Southwest, Northern
Rockies, Northwest, and West. In the other four regions, normalized mean bias is between 5 and
15 percent. Normalized mean error is less than 15 percent in the Midwest, Southwest, Northern
Rockies, and West and between 15 and 20 percent in the other regions.
The model performance statistics for days with observed MDA8 ozone >60 ppb provided
in Table B-2 indicates that, on average, the model is relatively unbiased in all regions. The
seasonal mean bias for MDA8 ozone > 60 ppb is within + 5 ppb in six of the regions and within
+ 10 ppb in the other three regions. The mean error is less than 10 ppb in all regions. Normalized
mean bias is within 10 percent for all regions, except for the West where the normalized mean
bias is -11.5 percent. The normalized mean error is less than 15 percent in each region.
The model performance statistic for individual receptors in Table B-3 indicates improved
performance in the 2016v3 modeling compared to the 2016v2 modeling. The 2016v3 modeling
has significantly less bias and error on days with observed MDA8 ozone > 60 compared to the
B-7
-------
2016v2 modeling at all receptors, except for the Greenwich and Houston/Clinton. The
normalized mean bias and normalized mean error statistics are within the performance
benchmarks identified above.
2. Spatial Variability in Model Performance
Figures B-3 through B-6 show the spatial variability in bias and error for MDA8 ozone
on days with observed concentrations > 60 ppb. Mean bias, as seen in Figure B-3, is within + 5
ppb at many sites nationwide. The 2016v3 modeling tends to overpredict MDA8 concentrations
on days > 60 ppb in parts of the Southeast, Ohio Valley, and mid-Atlantic states. Biases within
the range of + 5 ppb or between -5 and -10 ppb are noted at monitoring sites elsewhere across the
U.S. Figure B-4 provides a comparison of the mean bias in the 2016v3 modeling to the mean
bias in the 2016v2 modeling. The figure indicates that model performance for days > 60 ppb is
improved at most monitoring sites nationwide, the exceptions are some of the monitoring sites in
the Southeast and mid-Atlantic states. As is evident from Figure B-5, the mean error is mainly
less than 10 ppb nationwide. The comparison of mean error between the 2016v3 modeling and
the 2016v2 modeling, as shown in Figure B-6, shows improved performance in terms of model
error with the 2016v3 modeling.
The normalized mean error on days > 60 ppb, as shown in Figure B-7, indicates that
model performance using this statistic is within the range of the performance benchmark offered
by Emery et al (i.e., + 15 %) at nearly all monitoring sites. The normalized mean bias and mean
bias statistics suggest a tendency for some over prediction by the model in portions of the South,
Southeast, and Northeast regions. As indicated in Figure B-8, normalized mean error is less than
the 25 percent benchmark at nearly all monitoring sites nationwide.
B. Observed and Predicted Temporal Patterns
In addition to the above analysis of overall model performance, we also examine how
well the 2016v3 modeling platform replicates day to day fluctuations in observed MDA8 ozone
concentrations for the period May through September in each region and for selected 2023
nonattainment and/or maintenance receptors.
Time series of regional average MDA8 ozone concentrations for the Northeast, Midwest,
Ohio Valley, South, Southeast, Southwest, and West regions are provided in Figure B-9. The
plots show that the modeled concentrations closely track the corresponding observed values in
terms of day-to-day fluctuations and the general magnitude of concentrations. Comparing the
B-8
-------
plots for these seven regions reveals that there are large differences in the day-to-day variability
among the regions in both observations and predictions. For example, the degree of temporal
variability in MDA8 ozone concentrations in the Northeast, Midwest, and Ohio Valley is much
greater than in the Southwest and West. As is evident from Figure B-9, the modeling platform
captures regional differences in the degree of temporal variability in MDA8 ozone
concentrations. The model performs equally as well in eastern and western regions in terms of
replicating the relative magnitude of concentrations and day-to-day variability that are
characteristic of observed MDA8 ozone concentrations in each region.
The time series for selected receptors indicate that, again, the modeling platform
generally replicates the day-to-day variability in ozone during this time period at these sites.6
That is, days with high modeled concentrations are generally also days with high measured
concentrations and, conversely, days with low modeled concentrations are also days with low
measured concentrations in most cases. Although there is a tendency for over prediction,
particularly on days with low measured ozone concentrations, the model predictions, as
illustrated by these receptors, captures the day-to-day variability in the observations, and also
generally the timing and relative magnitude of multi-day high ozone episodes. In this regard, the
model captures the geographic differences in the timing, duration, and general magnitude of
ozone episodes in different parts of the U.S.
At the Stratford and Madison receptors in Coastal Connecticut, the model closely
replicates both the day-to-day variability and magnitude of the observed MDA8 ozone
concentrations on most days. At the Chicago-Northbrook and Chicago-Evanston receptors, the
model closely tracks the day-to-day variability during the entire 5-month period. At both
receptors, the modeled concentrations are similar to the corresponding measured. At the
Kenosha-Chiwaukee and Sheboygan monitors the model captures the high ozone episodes, but
under predicts peak MDA8 ozone concentrations of several of the days with the highest
concentrations, particularly at the Sheboygan receptor. At the Dallas/Denton, Houston, and
Brazoria receptors the temporal pattern in the observed values appears to be rather "chaotic",
compared to the temporal pattern at other receptors in the eastern U.S. The model predictions
have a temporal pattern that is similar to the observed concentrations, but with a tendency for
6 The extent to which the day-to-day variability in model-predicted MDA8 ozone matches the corresponding
observations values at the receptors selected for Figure B-9 is generally representative of most other receptors within
the same areas.
B-9
-------
overprediction. In contrast to receptors in the East, the observed concentrations at receptors in
the West have much less temporal variability. This difference between the observed
concentrations in the East versus the West is well simulated by the model. Although the model
appears to capture most of the days with high ozone at the receptors in Denver, there is a
tendency for the model to under predict on the peak days at receptors in Salt Lake City.
C. Conclusions
In summary, the ozone model performance statistics for the CAMx 2016v3 (2016gf)
simulation are within or close to the ranges found in other recent peer-reviewed applications
(e.g., Simon et al, 2012 and Emory et al, 2017). As described in this appendix, the predictions
from the 2016v3 modeling platform generally correspond closely to observed concentrations in
terms of the magnitude, temporal fluctuations, and geographic differences for MDA8 ozone
concentrations. Thus, the model performance results demonstrate the scientific credibility of our
2016v3 modeling platform. These results provide confidence in the ability of the modeling
platform to provide a reasonable projection of expected future year ozone concentrations and
contributions.
Table B-l. Performance statistics for MDA8 ozone by Region for the period May through
September (all days) based on 2016v3 modeling.
Climate Region
Number
of Site-
Days
MB
(ppb)
ME
(ppb)
NMB
(%)
NME
(%)
Northeast
27,724
4.0
7.0
9.0
15.9
Ohio Valley
33,762
6.4
8.2
14.3
18.4
Midwest
16,279
1.9
6.0
4.5
14.6
Southeast
26,490
6.1
8.1
15.0
19.7
South
21,437
5.5
8.2
13.8
20.5
Southwest
19,926
2.0
5.9
3.8
11.4
Northern Rockies
8,471
-0.3
5.0
-0.6
11.3
Northwest
4,012
1.1
5.9
3.0
15.9
West
29,930
-0.5
6.9
-0.9
13.5
B-10
-------
Table B-2. Performance statistics for MDA8 ozone > 60 ppb by Region for the period May
through September based on 2016v3 modeling.
Climate Region
Number
of Site-
Days
MB
(ppb)
ME
(ppb)
NMB
(%)
NME
(%)
Northeast
2,997
1.7
7.0
2.5
10.4
Ohio Valley
3,211
1.5
6.4
2.3
9.8
Midwest
1,134
-4.6
6.8
-7.0
10.2
Southeast
1,447
3.3
6.6
5.1
10.2
South
993
-0.9
6.6
-1.3
10.1
Southwest
3,359
-3.2
5.9
-4.9
9.1
Northern Rockies
215
-6.0
7.1
-9.6
11.3
Northwest
84
-6.7
9.7
-10.3
14.9
West
8,279
-8.1
9.6
-11.5
13.6
Table B-3. Performance statistics for MDA8 ozone > 60 ppb for monitor plus modeled receptors
for the period May through September for the 2016v2 and 2016v3 modeling.
Mean
Mean
Normalized
Normalized
Bias
Error
Mean Bias
Mean Error
201ft
2016
2016
2016
2016
2016
2016
2016
Site ID
State
Receptor
\ 2
v3
\ 2
v3
\ 2
\ 2
v3
40278011
AZ
Yuma Supersite
-12
-8
13
9
-1 1
20
13
80350004
CO
Denver Chatfield
-7
0
S
5
-1"
i)
i:
7
80590006
CO
Denver Rocky Flats
-7
-1
S
5
-1 1
i:
8
80590011
CO
Denver NREL
-S
-2
5
-i:
13
8
80690011
CO
Fort Collins
-1 1
-5
1 1
6
-17
-S
17
9
90010017
CT
Greenwich
-7
(j
9
-4
-1 1
i:
12
90013007
CT
Stratford
_5
-1
9
-h
-1
12
12
90019003
CT
Westport
_5
-1
i)
8
-7
i:
11
90099002
CT
Madison
_5
-1
7
6
-S
m
9
170310001
IL
Chicago Alsip
-s
0
1 1
7
-i:
0
ifi
11
170314201
IL
Chicago Northbrook
-12
-3
13
7
-17
-4
IS
11
170317002
IL
Chicago Water Plant
-In
-1
1 ()
7
-15
-1
15
10
350130021
NM
Las Cruces Desert View
-i:
-5
13
7
-ly
-S
2(i
11
350130022
NM
Las Cruces Santa Teresa
-1 1
-4
i:
6
-IS
-h
|w
10
350151005
NM
Carlsbad
-i:
-5
13
7
-IW
-S
2o
11
350250008
NM
Hobbs
-13
-7
13
7
-:i
-1 1
21
11
480391004
TX
Brazoria
-s
-3
i)
5
-13
-4
13
8
B-ll
-------
Mean
Mean
Normalized
Normalized
Bias
Error
Mean Bias
Mean Error
2016
2016
2016
2016
2016
2016
2016
2016
Site ID
State
Receptor
v2
v3
v2
v3
v2
v3
v2
v3
481210034
TX
Denton
-8
-1
9
5
-12
-1
13
7
481410037
TX
El Paso UTEP
-15
-8
16
9
-23
-11
24
14
481671034
TX
Galveston
-16
-10
16
11
-24
-15
24
17
482010024
TX
Houston Aldme
-4
3
9
10
-7
4
13
15
482010055
TX
Houston Bayland Park
-6
0
7
5
-9
0
11
8
482011034
TX
Houston East
-7
1
10
7
-10
1
16
11
482011035
TX
Houston Clinton
2
9
6
9
3
15
10
15
490110004
UT
SLC Bountiful
Viewmont
-10
-5
10
6
-15
-8
15
10
490353006
UT
SLC Hawthorne
-10
-5
11
8
-14
-8
16
11
490353013
UT
SLC Herriman
-11
-6
11
7
-17
-9
17
11
550590019
WI
Kenosha Chiwaukee
-17
-9
18
12
-24
-13
25
17
551010020
WI
Racine
-16
-8
17
11
-23
-11
24
15
551170006
WI
Sheboygan Kohler Andre
-18
-8
18
10
-25
-12
25
13
03_8hrmax Bias for Run CAMx_2016gf_v710_CB6r5_12US2 for 20160501 to 20160930
• AQS Daily
Figure B-3. Mean Bias (ppb) of MDA8 ozone for days with observed values > 60 ppb over
the period May-September.
B-12
-------
CAMx_2016gt_v710_CB6r5_12US2 - CAM>„2016li_v710.AQShJun8_12US2 03_8hrma» bias difference (or 20160501 lo 20160930
Figure B-4. Difference in Mean Bias (ppb) (2016v3 Bias - 2016v2 Bias) of VIDAS ozone for
days with observed values > 60 ppb over the period May-September.
03_8hrmax Error for Run CAMx_2016gf_v710_CB6r5_12US2 for 20160501 to 20160930
AQS Daily
Figure B-5. Mean Error (ppb) of MDA8 ozone > 60 ppb over the period May-September 2016,
paired in time and space.
B-13
-------
CAMx_2016gf_v710_CB6r5_12US2 - CAMx_2Q16fJ_v71Q_AQShJun8_12US2 03_8hrmax Error Difference for 20160501 to 20160930
• AQS Daily
Figure B-6. Difference in Mean Error (2016v3 Error - 2016v2 Error) of VIDAS ozone (ppb)
for days with observed values > 60 ppb over the period May-September.
03_8hrmax NMB (%) for run CAMx_2016gf_v710_CB6r5_12US2 for 20160501 to 20160930
• AQS Daily
Figure B-7. Normalized Mean Bias (ppb) of MDA8 ozone for days with observed ozone > 60
ppb over the period May-September.
B-14
-------
03_8hrmax NME {%) for run CAMx_2016gf_v710_CB6r5_12US2 for 20160501 to 20160930
• AOS Daily
Figure B-8. Normalized Mean Error (ppb) of MDA8 ozone for days with observed ozone > 60
ppb over the period May-September.
B-15
-------
Figure B-9. Time series of observed and predicted regional average MDA8 ozone concentrations for the period
May through September 2016.
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May 01 May 10 May 19 May 28 Jun 06 Jun 14 Jun 22 Jun 30 Jul 08 Jul 16 Jul 24 AugOl Aug 10 Aug 19 Aug 28 Sep 06 Sep 15 Sep 24
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H of Sites: 110
Midwest
— AOS Daily
— CAMx_2016gl_v710_CB6»5J2US2
1 of Sites: 241
Ohio Valley
B-16
-------
Figure B-10. Observed and model predicted MDA8 ozone concentrations by day for the period May 1 through September at
selected nonattainment/maintenance receptors in 2023 (ppb).
Stratford, Connecticut
Date
Madison, Connecticut
May 01 MaylO Mayl9 May28 Jun07 Jur» 15 Jun23 Jul01 Jul09 Jul 18 Jul26 Aug 04 Aug 13 Aug22 Aug 31 Sep09 S*p 18 Sep27
Dale
Chicago Northbrook, Illinois
May 01 MaylO May20 May 29 Jun07 Jun 15 Jun23 JulOI Jul 09 Jul 17 Jul 25 Aug02 Aug 11 Aug 20 Aug 29 Sep 07 Sep 16 Sep2S
B-17
-------
Chicago Evanston, Illinois
Dale
Kenosha Chiwaukee Prairie, Wisconsin
Dale
Sheboygan, Wisconsin
Dale
B-18
-------
Dallas Denton Airport, Texas
AGS Daily
CAMx 2016g1_v710_CB6r5_12US2
# of Sites: 1
Site: 480391004
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Houston Aldine, Texas
AGS Daily
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# of Sites: f
Site: 482010024
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Houston Bayland Park, Texas
B-19
-------
Brazoria, Texas
Date
Denver Chatfield, Colorado
Date
Denver NREL, Colorado
May 01 May 10 May 19 May 28 Jun06 Jun 14 Jun22 Jun30 Jul 08 Jul 16 Jul 24 Aug 01 Aug 10 Aug 19 Aug 28 Sep 06 Sep 15 Sep 24
B-20
-------
Las Cruces Desert View, New Mexico
100 -
1111 in 11111 mi mi 111111111111 mi 111 mi 111111111111 mi i ii 11 ii i mi 11111111111111111 ii 11111 mi mi i ii i ui mi 111 mi i mi mi 1111 ii nun 1111111 mi
May 01 May 10 May 19 May 28 Jun06 Jun 14 Jun 22 Jun 30 Jul OS Jul 16 Jul 24 Aug 01 Aug 10 Aug 19 Aug 28 S«p06 Sep 15 Sep 24
——; 8 of Sites: i
AOS Daily
CAMx_2016gf_v710_CB6r5_12US2 Site: 350130021
Eddy County, New Mexico
May 01 May 10 May 19 May 28 Jun 06 Jun 14 Jun 22 Jun 30 Jul 08 Jul 16 Jul 24 Aug 01 Aug 10 Aug 19 Aug 28 Sep 06 Sep 15 Sep 24
Salt Lake City Bountiful Viewmont, Utah
100 -
~T77T~~~ iolShes 1
AOS Daity
CAMx_2016gl_v710_CB6r5_12US2 Ste: 490110004
rnnnnMriiim!mininTiinMiTMiiiMMiniMTniiiinium»TiuiiMMMnMiniiiMiiMiMiTiiinMm»ininninimMiuTinTiin!Mnniii i
May 01 May 10 May 19 May 28 Jun 06 Jun 14 Jun 22 Jun 30 Jul 08 Jul 16 Jul 24 Aug 01 Aug 10 Aug 19 Aug 28 Sep 06 Sep 15 Sep 24
B-21
-------
Salt Lake City Herriman, Utah
B-22
-------
Appendix C
Ozone Contributions to
Nonattainment & Maintenance-Only
Receptors in 2023 & 2026
-------
The tables in this appendix provide projected design values and contribution metric data
from each state and the other source tags to nonattainment and maintenance-only in 2023 and
2026. Highlighted values denote contributions greater than or equal to the 1 percent of the
NAAQS screening threshold. The contributions and design values are in units of ppb.
Contributions to individual monitoring sites is provided in the file: "Final GNP 03
DVs Contributions" which can be found in the docket for this final rule.
C-l
-------
Design Values and Contributions for Monitoring plus Modeled Receptors in 2023 - Part 1
Contributions
Site ID
ST
County
2023
Avg
2023
Max
AL
AZ
AR
CA
CO
cr
DE
DC
FL
GA
ID
IL
IN
IA
KS
KY
LA
ME
MD
MA
Ml
MN
MS
MO
MT
NE
NV
NH
NJ
40278011
AZ
Yuma
70.4
72.1
0.00
2.97
0.00
6.31
0.02
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.16
0.00
0.00
60650016
CA
Riverside
72.2
73.1
0.00
0.13
0.00
27.46
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.17
0.00
0.00
60651016
CA
Riverside
91.0
92.2
0.00
0.40
0.00
35.27
0.02
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.11
0.00
0.00
80350004
CO
Douglas
71.3
71.9
0.00
0.48
0.00
1.60
15.68
0.00
0.00
0.00
0.00
0.00
0.18
0.00
0.00
0.00
0.07
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.02
0.16
0.45
0.00
0.00
80590006
CO
Jefferson
72.8
73.5
0.00
0.54
0.00
1.44
16.82
0.00
0.00
0.00
0.00
0.00
0.11
0.00
0.00
0.00
0.05
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.05
0.45
0.00
0.00
80590011
CO
Jefferson
73.5
74.1
0.00
0.49
0.00
1.31
17.54
0.00
0.00
0.00
0.00
0.00
0.13
0.00
0.00
0.00
0.03
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.02
0.07
0.47
0.00
0.00
80690011
CO
Larimer
70.9
72.1
0.00
0.86
0.00
0.90
13.99
0.00
0.00
0.00
0.00
0.00
0.13
0.00
0.00
0.00
0.16
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.02
0.10
0.31
0.00
0.00
90010017
CT
Fairfield
71.6
72.2
0.02
0.01
0.10
0.02
0.04
4.59
0.35
0.01
0.01
0.03
0.02
0.51
0.89
0.13
0.06
0.60
0.12
0.00
0.78
0.06
1.25
0.15
0.05
0.23
0.05
0.06
0.01
0.01
8.17
90013007
CT
Fairfield
72.9
73.8
0.09
0.01
0.16
0.03
0.05
3.94
0.41
0.02
0.05
0.15
0.03
0.72
1.18
0.16
0.10
0.80
0.24
0.02
0.96
0.33
1.38
0.18
0.09
0.34
0.08
0.07
0.01
0.10
7.22
90019003
CT
Fairfield
73.3
73.6
0.09
0.01
0.15
0.03
0.05
2.52
0.44
0.03
0.05
0.15
0.02
0.67
1.16
0.15
0.10
0.84
0.24
0.00
1.13
0.06
1.44
0.17
0.09
0.32
0.07
0.07
0.01
0.01
8.38
90099002
CT
New Haven
70.5
72.6
0.09
0.01
0.14
0.02
0.04
3.85
0.56
0.04
0.05
0.17
0.03
0.71
1.05
0.21
0.09
0.79
0.17
0.01
1.28
0.15
1.31
0.23
0.08
0.32
0.08
0.09
0.01
0.02
5.79
170310001
IL
Cook
68.2
71.9
0.00
0.01
0.03
0.02
0.04
0.00
0.00
0.00
0.04
0.00
0.02
18.80
7.11
0.90
0.48
0.04
0.05
0.00
0.00
0.00
1.16
0.85
0.00
0.37
0.08
0.29
0.01
0.00
0.00
170314201
IL
Cook
68.0
71.5
0.00
0.01
0.06
0.05
0.05
0.00
0.00
0.00
0.01
0.00
0.03
23.46
5.42
0.42
0.18
0.06
0.02
0.00
0.00
0.00
1.56
0.50
0.00
0.54
0.07
0.10
0.01
0.00
0.00
170317002
IL
Cook
68.5
71.3
0.01
0.04
0.19
0.07
0.09
0.00
0.00
0.00
0.04
0.05
0.04
20.58
6.55
0.69
0.52
0.18
0.10
0.00
0.00
0.01
1.00
0.38
0.00
1.39
0.07
0.19
0.02
0.00
0.00
350130021
NM
Dona Ana
70.8
72.1
0.01
1.04
0.00
0.31
0.15
0.00
0.00
0.00
0.05
0.01
0.02
0.00
0.00
0.00
0.06
0.00
0.02
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.01
0.05
0.00
0.00
350130022
NM
Dona Ana
69.7
72.4
0.00
1.06
0.00
0.31
0.18
0.00
0.00
0.00
0.04
0.01
0.02
0.00
0.00
0.00
0.05
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.01
0.05
0.00
0.00
350151005
NM
Eddy
69.7
74.1
0.00
1.34
0.02
0.63
0.18
0.00
0.00
0.00
0.00
0.00
0.04
0.00
0.00
0.00
0.10
0.00
0.03
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.07
0.08
0.08
0.00
0.00
350250008
NM
Lea
69.8
72.2
0.00
1.66
0.00
0.71
0.08
0.00
0.00
0.00
0.00
0.00
0.02
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.01
0.10
0.00
0.00
480391004
TX
Brazoria
70.4
72.5
0.27
0.01
1.21
0.01
0.05
0.00
0.00
0.00
0.10
0.10
0.01
0.07
0.07
0.25
0.37
0.09
5.21
0.00
0.00
0.00
0.00
0.14
0.53
0.64
0.05
0.21
0.00
0.00
0.00
481210034
TX
Denton
69.8
71.6
0.45
0.06
0.92
0.06
0.17
0.00
0.00
0.00
0.03
0.05
0.04
0.26
0.31
0.20
0.46
0.41
2.87
0.00
0.00
0.00
0.01
0.10
0.91
0.56
0.10
0.36
0.02
0.00
0.00
481410037
TX
El Paso
69.8
71.4
0.00
1.69
0.00
0.58
0.05
0.00
0.00
0.00
0.04
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.05
0.00
0.00
481671034
TX
Galveston
71.5
72.8
0.75
0.05
0.94
0.04
0.14
0.00
0.00
0.00
0.18
0.18
0.01
0.15
0.37
0.21
0.46
0.40
9.51
0.00
0.00
0.00
0.18
0.21
1.32
0.46
0.04
0.20
0.01
0.00
0.00
482010024
TX
Harris
75.1
76.7
0.23
0.00
0.57
0.00
0.01
0.00
0.00
0.00
0.50
0.05
0.00
0.01
0.01
0.16
0.16
0.01
4.75
0.00
0.00
0.00
0.00
0.10
0.35
0.25
0.01
0.10
0.00
0.00
0.00
482010055
TX
Harris
70.9
71.9
0.65
0.02
0.73
0.02
0.05
0.00
0.00
0.00
0.54
0.10
0.01
0.10
0.17
0.19
0.22
0.22
5.49
0.00
0.00
0.00
0.00
0.10
0.91
0.35
0.03
0.15
0.00
0.00
0.00
482011034
TX
Harris
70.1
71.3
0.33
0.01
0.93
0.01
0.02
0.00
0.00
0.00
0.18
0.10
0.00
0.05
0.06
0.23
0.22
0.05
5.62
0.00
0.00
0.00
0.00
0.11
0.47
0.45
0.03
0.15
0.00
0.00
0.00
482011035
TX
Harris
67.8
71.3
0.32
0.01
0.90
0.01
0.02
0.00
0.00
0.00
0.18
0.10
0.00
0.05
0.06
0.22
0.21
0.05
5.44
0.00
0.00
0.00
0.00
0.11
0.46
0.44
0.02
0.14
0.00
0.00
0.00
490110004
UT
Davis
72.0
74.2
0.00
0.28
0.00
2.46
0.03
0.00
0.00
0.00
0.00
0.00
0.42
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
1.00
0.00
0.00
490353006
UT
Salt Lake
72.6
74.2
0.00
0.26
0.00
2.75
0.03
0.00
0.00
0.00
0.00
0.00
0.37
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
1.11
0.00
0.00
490353013
UT
Salt Lake
73.3
73.8
0.00
0.28
0.00
2.18
0.04
0.00
0.00
0.00
0.00
0.00
0.31
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.02
0.00
0.00
0.00
0.00
0.77
0.00
0.00
550590019
Wl
Kenosha
70.8
71.7
0.00
0.02
0.18
0.05
0.05
0.01
0.00
0.00
0.03
0.00
0.03
19.09
8.06
0.70
0.40
0.25
0.11
0.00
0.05
0.01
1.02
0.40
0.01
1.01
0.05
0.14
0.01
0.00
0.03
551010020
Wl
Racine
69.7
71.5
0.03
0.02
0.34
0.05
0.04
0.01
0.00
0.00
0.03
0.01
0.02
14.15
10.03
0.62
0.35
0.32
0.34
0.00
0.05
0.02
0.95
0.41
0.15
1.19
0.04
0.12
0.01
0.00
0.03
551170006
Wl
Sheboygan
72.7
73.6
0.02
0.03
0.62
0.04
0.06
0.01
0.00
0.00
0.01
0.01
0.02
13.89
8.90
0.67
0.40
0.44
0.34
0.00
0.05
0.01
1.59
0.36
0.10
1.87
0.07
0.18
0.01
0.00
0.04
C-2
-------
Design Values and Contributions for Monitoring plus Modeled Receptors in 2023 - Part 2
Contributions
Site ID
ST
County
2023
Avg
2023
Max
NM
NY
NC
ND
OH
OK
OR
PA
Rl
SC
SD
TN
TX
UT
VT
VA
WA
WV
Wl
WY
TRIBAL
Canada
&
Mexico
Offshore
Fires
Initial &
Boundary
Biogenic
Lightning
NOx
40278011
AZ
Yuma
70.4
72.1
0.11
0.00
0.00
0.00
0.00
0.00
0.09
0.00
0.00
0.00
0.00
0.00
0.03
0.16
0.00
0.00
0.03
0.00
0.00
0.00
0.01
9.43
0.54
3.10
44.52
2.26
0.55
60650016
CA
Riverside
72.2
73.1
0.04
0.00
0.00
0.00
0.00
0.00
0.17
0.00
0.00
0.00
0.00
0.00
0.01
0.06
0.00
0.00
0.08
0.00
0.00
0.00
0.00
2.32
2.10
2.93
33.31
3.13
0.19
60651016
CA
Riverside
91.0
92.2
0.11
0.00
0.00
0.00
0.00
0.00
0.16
0.00
0.00
0.00
0.00
0.00
0.04
0.06
0.00
0.00
0.05
0.00
0.00
0.00
0.01
2.43
1.65
2.91
43.70
3.39
0.60
80350004
CO
Douglas
71.3
71.9
0.28
0.00
0.00
0.01
0.00
0.03
0.10
0.00
0.00
0.00
0.01
0.00
0.16
1.29
0.00
0.00
0.06
0.00
0.00
0.68
0.17
0.85
0.09
1.39
42.66
3.44
1.30
80590006
CO
Jefferson
72.8
73.5
0.36
0.00
0.00
0.00
0.00
0.03
0.10
0.00
0.00
0.00
0.00
0.00
0.21
1.17
0.00
0.00
0.04
0.00
0.00
0.46
0.13
0.61
0.06
1.25
44.08
3.38
1.33
80590011
CO
Jefferson
73.5
74.1
0.29
0.00
0.00
0.00
0.00
0.02
0.11
0.00
0.00
0.00
0.00
0.00
0.11
1.27
0.00
0.00
0.05
0.00
0.00
0.46
0.14
0.56
0.06
1.30
44.68
3.23
1.04
80690011
CO
Larimer
70.9
72.1
0.47
0.00
0.00
0.01
0.00
0.05
0.09
0.00
0.00
0.00
0.00
0.00
0.34
0.98
0.00
0.00
0.05
0.00
0.00
0.67
0.18
0.94
0.08
1.25
41.93
4.15
3.12
90010017
CT
Fairfield
71.6
72.2
0.02
16.10
0.18
0.08
1.34
0.09
0.02
5.83
0.00
0.04
0.03
0.17
0.33
0.02
0.01
0.59
0.03
0.79
0.16
0.06
0.00
2.86
0.50
0.32
18.24
4.66
0.52
90013007
CT
Fairfield
72.9
73.8
0.05
12.70
0.45
0.11
2.04
0.13
0.03
5.43
0.04
0.16
0.04
0.27
0.52
0.03
0.02
1.15
0.05
1.35
0.21
0.08
0.00
2.29
0.68
0.31
19.33
5.53
0.72
90019003
CT
Fairfield
73.3
73.6
0.04
12.96
0.44
0.09
2.05
0.14
0.02
6.00
0.00
0.15
0.04
0.28
0.52
0.03
0.01
1.16
0.04
1.37
0.20
0.07
0.00
2.27
0.64
0.34
19.42
5.54
0.73
90099002
CT
New Haven
70.5
72.6
0.03
11.29
0.66
0.14
1.98
0.10
0.02
4.36
0.01
0.18
0.05
0.26
0.36
0.02
0.01
1.76
0.05
1.49
0.21
0.07
0.00
2.58
1.22
0.30
19.39
5.74
0.54
170310001
IL
Cook
68.2
71.9
0.05
0.14
0.00
0.45
0.68
0.62
0.02
0.25
0.00
0.00
0.08
0.00
1.09
0.02
0.00
0.02
0.06
0.08
2.34
0.06
0.00
1.19
0.12
0.15
20.95
8.44
0.84
170314201
IL
Cook
68.0
71.5
0.06
0.28
0.00
0.22
1.21
0.32
0.04
0.25
0.00
0.00
0.04
0.00
1.05
0.02
0.01
0.00
0.06
0.07
2.86
0.08
0.00
1.42
0.04
0.17
18.18
7.87
0.94
170317002
IL
Cook
68.5
71.3
0.10
0.21
0.00
0.16
1.04
0.65
0.04
0.20
0.00
0.00
0.04
0.05
1.95
0.06
0.01
0.01
0.06
0.09
2.24
0.11
0.00
1.11
0.14
0.22
15.80
10.54
1.21
350130021
NM
Dona Ana
70.8
72.1
2.87
0.00
0.00
0.00
0.00
0.16
0.02
0.00
0.00
0.00
0.00
0.00
4.74
0.16
0.00
0.00
0.01
0.00
0.00
0.06
0.04
13.31
0.12
0.90
39.47
2.59
4.49
350130022
NM
Dona Ana
69.7
72.4
2.89
0.00
0.00
0.00
0.00
0.14
0.01
0.00
0.00
0.00
0.00
0.00
3.59
0.15
0.00
0.00
0.01
0.00
0.00
0.05
0.05
12.87
0.12
0.84
40.19
2.63
4.24
350151005
NM
Eddy
69.7
74.1
6.52
0.00
0.00
0.01
0.00
0.25
0.06
0.00
0.00
0.00
0.02
0.00
1.91
0.06
0.00
0.00
0.05
0.00
0.00
0.15
0.03
3.43
0.17
1.06
50.12
2.29
0.84
350250008
NM
Lea
69.8
72.2
10.23
0.00
0.00
0.01
0.00
0.01
0.02
0.00
0.00
0.00
0.00
0.00
2.17
0.10
0.00
0.00
0.01
0.00
0.00
0.05
0.02
3.81
0.15
0.60
45.73
2.23
1.96
480391004
TX
Brazoria
70.4
72.5
0.03
0.00
0.02
0.11
0.02
0.62
0.00
0.00
0.00
0.01
0.05
0.20
29.21
0.01
0.00
0.01
0.02
0.01
0.01
0.05
0.00
0.33
1.40
0.65
21.70
5.73
0.54
481210034
TX
Denton
69.8
71.6
0.11
0.00
0.00
0.09
0.09
1.01
0.02
0.01
0.00
0.00
0.08
0.68
28.72
0.07
0.00
0.00
0.04
0.00
0.02
0.23
0.01
0.33
0.37
0.85
20.40
7.34
0.75
481410037
TX
El Paso
69.8
71.4
1.59
0.00
0.00
0.00
0.00
0.01
0.01
0.00
0.00
0.00
0.00
0.00
3.17
0.04
0.00
0.00
0.00
0.00
0.00
0.01
0.02
13.73
0.16
1.41
40.91
2.38
3.76
481671034
TX
Galveston
71.5
72.8
0.13
0.01
0.03
0.16
0.31
0.79
0.00
0.03
0.00
0.07
0.04
0.60
19.31
0.04
0.00
0.01
0.01
0.03
0.08
0.10
0.00
0.41
5.74
0.78
19.49
6.64
0.62
482010024
TX
Harris
75.1
76.7
0.03
0.00
0.01
0.03
0.00
0.20
0.00
0.00
0.00
0.00
0.02
0.05
31.24
0.00
0.00
0.00
0.00
0.00
0.01
0.01
0.00
0.14
2.94
0.91
27.10
3.96
0.96
482010055
TX
Harris
70.9
71.9
0.05
0.00
0.01
0.05
0.03
0.23
0.00
0.00
0.00
0.01
0.02
0.47
28.74
0.02
0.00
0.00
0.01
0.00
0.01
0.04
0.00
0.30
2.42
0.81
21.47
5.10
0.79
482011034
TX
Harris
70.1
71.3
0.02
0.00
0.02
0.08
0.01
0.28
0.00
0.00
0.00
0.01
0.04
0.17
28.33
0.01
0.00
0.01
0.01
0.00
0.01
0.02
0.00
0.16
2.97
1.31
22.05
4.69
0.64
482011035
TX
Harris
67.8
71.3
0.02
0.00
0.02
0.07
0.01
0.27
0.00
0.00
0.00
0.01
0.03
0.16
27.40
0.01
0.00
0.01
0.01
0.00
0.01
0.02
0.00
0.15
2.88
1.26
21.32
4.54
0.62
490110004
UT
Davis
72.0
74.2
0.07
0.00
0.00
0.00
0.00
0.00
0.46
0.00
0.00
0.00
0.00
0.00
0.04
8.72
0.00
0.00
0.16
0.00
0.00
0.08
0.01
0.63
0.11
2.91
50.76
3.37
0.36
490353006
UT
Salt Lake
72.6
74.2
0.09
0.00
0.00
0.00
0.00
0.00
0.45
0.00
0.00
0.00
0.00
0.00
0.07
9.15
0.00
0.00
0.16
0.00
0.00
0.06
0.01
0.46
0.11
3.36
49.89
3.56
0.59
490353013
UT
Salt Lake
73.3
73.8
0.07
0.00
0.00
0.02
0.00
0.01
0.29
0.00
0.00
0.00
0.00
0.00
0.05
7.49
0.00
0.00
0.11
0.00
0.00
0.27
0.01
0.67
0.11
2.53
54.71
2.85
0.33
550590019
Wl
Kenosha
70.8
71.7
0.06
0.21
0.02
0.13
1.61
0.49
0.03
0.40
0.00
0.00
0.03
0.02
1.54
0.03
0.00
0.09
0.05
0.21
5.51
0.06
0.00
1.02
0.14
0.19
14.88
11.28
0.83
551010020
Wl
Racine
69.7
71.5
0.06
0.21
0.02
0.13
1.24
0.44
0.03
0.33
0.00
0.00
0.03
0.09
1.57
0.03
0.00
0.09
0.04
0.16
7.98
0.06
0.00
0.99
0.18
0.24
14.02
11.36
0.80
551170006
Wl
Sheboygan
72.7
73.6
0.05
0.27
0.02
0.18
1.55
0.63
0.01
0.46
0.00
0.00
0.05
0.15
1.03
0.03
0.00
0.08
0.04
0.16
7.22
0.08
0.00
1.35
0.09
0.27
17.35
10.79
0.83
C-3
-------
Design Values and Contributions for Violating Monitor Receptors in 2023 - Part 1
Contributions
Site ID
ST
County
2023
Avg
2023
Max
AL
AZ
AR
CA
CO
CT
DE
DC
FL
GA
ID
IL
IN
IA
KS
KY
LA
ME
MD
MA
Ml
MN
MS
MO
MT
NE
NV
NH
NJ
40070010
AZ
Gila
67.9
69.5
0.00
7.65
0.00
1.55
0.08
0.00
0.00
0.00
0.00
0.00
0.06
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.02
0.00
0.47
0.00
0.00
40130019
AZ
Maricopa
69.8
70.0
0.00
15.32
0.00
1.68
0.08
0.00
0.00
0.00
0.00
0.00
0.04
0.00
0.00
0.00
0.02
0.00
0.00
0.00
0.00
0.00
0.01
0.01
0.00
0.00
0.01
0.01
0.30
0.00
0.00
40131003
AZ
Maricopa
70.1
70.7
0.00
13.83
0.00
2.69
0.06
0.00
0.00
0.00
0.00
0.00
0.05
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.46
0.00
0.00
40131004
AZ
Maricopa
70.2
70.8
0.00
14.56
0.00
1.54
0.07
0.00
0.00
0.00
0.00
0.00
0.03
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.32
0.00
0.00
40131010
AZ
Maricopa
68.3
69.2
0.00
13.90
0.00
2.65
0.07
0.00
0.00
0.00
0.00
0.00
0.06
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.49
0.00
0.00
40132001
AZ
Maricopa
63.8
64.1
0.00
12.84
0.00
1.44
0.07
0.00
0.00
0.00
0.00
0.00
0.03
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.01
0.01
0.00
0.00
0.01
0.01
0.27
0.00
0.00
40132005
AZ
Maricopa
69.6
70.5
0.00
13.81
0.00
1.56
0.08
0.00
0.00
0.00
0.00
0.00
0.04
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.38
0.00
0.00
40133002
AZ
Maricopa
65.8
65.8
0.00
13.59
0.00
1.52
0.07
0.00
0.00
0.00
0.00
0.00
0.03
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.29
0.00
0.00
40134004
AZ
Maricopa
65.7
66.6
0.00
11.01
0.00
2.72
0.07
0.00
0.00
0.00
0.00
0.00
0.06
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.48
0.00
0.00
40134005
AZ
Maricopa
62.3
62.3
0.00
12.29
0.00
2.39
0.05
0.00
0.00
0.00
0.00
0.00
0.04
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.41
0.00
0.00
40134008
AZ
Maricopa
65.6
66.5
0.00
13.06
0.00
1.44
0.06
0.00
0.00
0.00
0.00
0.00
0.03
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.34
0.00
0.00
40134010
AZ
Maricopa
63.8
66.9
0.00
12.31
0.00
1.47
0.07
0.00
0.00
0.00
0.00
0.00
0.04
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.01
0.01
0.30
0.00
0.00
40137020
AZ
Maricopa
67.0
67.0
0.00
14.42
0.00
1.51
0.07
0.00
0.00
0.00
0.00
0.00
0.03
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.29
0.00
0.00
40137021
AZ
Maricopa
69.8
70.1
0.00
14.25
0.00
2.14
0.06
0.00
0.00
0.00
0.00
0.00
0.04
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.41
0.00
0.00
40137022
AZ
Maricopa
68.2
69.1
0.00
13.92
0.00
2.09
0.06
0.00
0.00
0.00
0.00
0.00
0.04
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.40
0.00
0.00
40137024
AZ
Maricopa
67.0
67.9
0.00
14.42
0.00
1.51
0.07
0.00
0.00
0.00
0.00
0.00
0.03
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.29
0.00
0.00
40139702
AZ
Maricopa
66.9
68.1
0.00
12.53
0.00
2.20
0.07
0.00
0.00
0.00
0.00
0.00
0.05
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.34
0.00
0.00
40139704
AZ
Maricopa
65.3
66.2
0.00
12.20
0.00
1.83
0.06
0.00
0.00
0.00
0.00
0.00
0.04
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.40
0.00
0.00
40139997
AZ
Maricopa
70.5
70.5
0.00
14.56
0.00
1.63
0.07
0.00
0.00
0.00
0.00
0.00
0.03
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.31
0.00
0.00
40218001
AZ
Pinal
67.8
69.0
0.00
9.81
0.00
2.05
0.08
0.00
0.00
0.00
0.00
0.00
0.06
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.02
0.00
0.36
0.00
0.00
80013001
CO
Adams
63.0
63.0
0.00
0.64
0.00
1.05
13.94
0.00
0.00
0.00
0.00
0.00
0.07
0.00
0.00
0.00
0.03
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.06
0.32
0.00
0.00
80050002
CO
Arapahoe
68.0
68.0
0.00
0.47
0.00
1.81
14.72
0.00
0.00
0.00
0.00
0.00
0.11
0.00
0.00
0.00
0.03
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.06
0.58
0.00
0.00
80310002
CO
Denver
63.6
64.8
0.00
0.65
0.00
1.06
14.08
0.00
0.00
0.00
0.00
0.00
0.07
0.00
0.00
0.00
0.03
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.06
0.32
0.00
0.00
80310026
CO
Denver
64.5
64.8
0.00
0.66
0.00
1.07
14.27
0.00
0.00
0.00
0.00
0.00
0.07
0.00
0.00
0.00
0.03
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.06
0.32
0.00
0.00
90079007
CT
Middlesex
68.7
69.0
0.10
0.01
0.17
0.03
0.04
5.39
0.36
0.03
0.09
0.31
0.03
0.55
0.95
0.13
0.09
0.88
0.26
0.02
1.04
0.39
0.85
0.13
0.10
0.33
0.07
0.06
0.01
0.10
5.29
90110124
CT
New London
65.5
67.0
0.16
0.01
0.20
0.02
0.02
6.76
0.29
0.03
0.07
0.19
0.01
0.55
0.84
0.15
0.10
0.53
0.21
0.00
0.97
0.15
1.36
0.15
0.12
0.38
0.03
0.05
0.00
0.00
3.93
170310032
IL
Cook
67.3
69.8
0.00
0.04
0.08
0.04
0.07
0.00
0.00
0.00
0.04
0.00
0.03
17.27
8.22
0.79
0.62
0.05
0.07
0.00
0.00
0.01
1.15
0.60
0.00
0.62
0.08
0.26
0.01
0.00
0.00
170311601
IL
Cook
63.8
64.5
0.00
0.00
0.04
0.02
0.04
0.00
0.00
0.00
0.02
0.00
0.03
17.08
5.85
0.61
0.27
0.06
0.02
0.00
0.00
0.01
2.03
0.59
0.00
0.44
0.08
0.16
0.01
0.00
0.01
181270024
IN
Porter
63.4
64.6
0.00
0.03
0.08
0.04
0.05
0.00
0.00
0.00
0.04
0.00
0.02
9.11
15.38
0.58
0.55
0.04
0.08
0.00
0.00
0.00
1.21
0.57
0.00
0.56
0.07
0.19
0.01
0.00
0.00
260050003
Ml
Allegan
66.2
67.4
0.01
0.04
0.61
0.10
0.20
0.00
0.00
0.00
0.03
0.01
0.06
10.66
6.47
1.13
0.82
0.60
0.36
0.00
0.00
0.00
2.02
0.64
0.08
2.17
0.10
0.26
0.03
0.00
0.00
261210039
Ml
Muskegon
67.5
68.4
0.04
0.04
1.08
0.06
0.17
0.00
0.00
0.00
0.03
0.09
0.04
14.29
9.39
0.37
0.58
0.74
0.57
0.00
0.00
0.00
1.98
0.22
0.14
2.95
0.08
0.19
0.02
0.00
0.00
320030043
NV
Clark
68.4
69.4
0.00
0.77
0.00
6.97
0.05
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
9.05
0.00
0.00
350011012
NM
Bernalillo
63.8
66.0
0.00
1.62
0.00
0.60
0.39
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.12
0.00
0.00
350130008
NM
Dona Ana
65.6
66.3
0.00
1.13
0.01
0.44
0.12
0.00
0.00
0.00
0.04
0.01
0.02
0.00
0.00
0.00
0.05
0.00
0.02
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.02
0.02
0.06
0.00
0.00
361030002
NY
Suffolk
66.2
68.0
0.05
0.01
0.16
0.03
0.05
0.17
0.42
0.03
0.02
0.09
0.02
0.50
0.96
0.19
0.13
0.74
0.15
0.00
1.14
0.02
1.12
0.14
0.06
0.35
0.06
0.07
0.01
0.00
8.00
390850003
OH
Lake
64.3
64.6
0.03
0.02
0.19
0.08
0.10
0.03
0.01
0.00
0.01
0.00
0.06
1.33
1.81
0.39
0.25
1.57
0.16
0.01
0.05
0.07
3.47
0.24
0.14
0.59
0.08
0.13
0.02
0.02
0.10
480290052
TX
Bexar
67.1
67.8
0.05
0.09
0.09
0.03
0.10
0.00
0.00
0.00
0.04
0.02
0.02
0.01
0.01
0.02
0.24
0.02
1.02
0.00
0.00
0.00
0.00
0.03
0.15
0.05
0.02
0.13
0.00
0.00
0.00
480850005
TX
Collin
65.4
66.0
0.51
0.11
0.64
0.08
0.13
0.00
0.00
0.00
0.08
0.13
0.03
0.29
0.31
0.21
0.24
0.43
2.96
0.00
0.00
0.00
0.01
0.08
0.84
0.53
0.08
0.18
0.02
0.00
0.00
481130075
TX
Dallas
65.3
66.5
0.47
0.05
0.93
0.05
0.14
0.00
0.00
0.00
0.03
0.07
0.04
0.35
0.39
0.32
0.61
0.51
2.85
0.00
0.00
0.00
0.00
0.19
0.87
0.66
0.10
0.43
0.01
0.00
0.00
481211032
TX
Denton
65.9
67.7
0.79
0.05
0.95
0.05
0.16
0.00
0.00
0.00
0.09
0.09
0.04
0.27
0.29
0.23
0.41
0.40
3.54
0.00
0.00
0.00
0.00
0.11
1.02
0.70
0.10
0.25
0.01
0.00
0.00
482010051
TX
Harris
65.3
66.3
0.59
0.02
0.68
0.02
0.05
0.00
0.00
0.00
0.50
0.09
0.01
0.09
0.16
0.18
0.20
0.20
5.06
0.00
0.00
0.00
0.00
0.10
0.84
0.32
0.02
0.14
0.00
0.00
0.00
482010416
TX
Harris
68.8
70.4
0.60
0.03
0.93
0.02
0.06
0.00
0.00
0.00
0.14
0.11
0.01
0.08
0.15
0.21
0.23
0.18
4.87
0.00
0.00
0.00
0.00
0.10
0.77
0.46
0.03
0.16
0.00
0.00
0.00
484390075
TX
Tarrant
63.8
64.7
0.41
0.05
0.81
0.05
0.14
0.00
0.00
0.00
0.02
0.04
0.04
0.31
0.41
0.17
0.39
0.46
1.76
0.00
0.00
0.00
0.01
0.09
0.84
0.42
0.09
0.31
0.01
0.00
0.00
484391002
TX
Tarrant
64.1
65.7
0.51
0.06
1.16
0.05
0.15
0.00
0.00
0.00
0.05
0.29
0.05
0.16
0.20
0.15
0.53
0.31
1.83
0.00
0.01
0.01
0.01
0.10
0.69
0.45
0.09
0.35
0.01
0.00
0.02
484392003
TX
Tarrant
65.2
65.9
0.44
0.05
0.85
0.04
0.15
0.00
0.00
0.00
0.02
0.04
0.05
0.30
0.41
0.21
0.53
0.47
1.84
0.00
0.00
0.00
0.00
0.10
0.88
0.51
0.12
0.37
0.01
0.00
0.00
484393009
TX
Tarrant
67.5
68.1
0.40
0.05
0.65
0.05
0.14
0.00
0.00
0.00
0.02
0.04
0.04
0.24
0.28
0.21
0.55
0.38
2.10
0.00
0.00
0.00
0.01
0.10
0.75
0.54
0.11
0.37
0.01
0.00
0.00
490571003
UT
Weber
69.3
70.3
0.00
0.36
0.00
2.66
0.04
0.00
0.00
0.00
0.00
0.00
0.46
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
1.11
0.00
0.00
550590025
Wl
Kenosha
67.6
70.7
0.02
0.04
0.39
0.09
0.10
0.00
0.00
0.00
0.01
0.00
0.04
16.53
5.51
0.71
0.41
0.16
0.34
0.00
0.00
0.00
0.66
0.51
0.11
1.53
0.08
0.21
0.03
0.00
0.00
550890008
Wl
Ozaukee
65.2
65.8
0.03
0.04
0.39
0.07
0.09
0.01
0.00
0.00
0.01
0.00
0.04
11.46
5.75
0.69
0.43
0.22
0.25
0.00
0.04
0.01
0.92
0.36
0.14
1.64
0.08
0.21
0.02
0.00
0.03
C-4
-------
Design Values and Contributions for Violating Monitor Receptors in 2023 - Part 2
Contributions
Site ID
ST
County
2023
Avg
2023
Max
NM
NY
NC
ND
OH
OK
OR
PA
Rl
SC
SD
TN
TX
UT
VT
VA
WA
WV
Wl
WY
TRIBAL
Canada
&
Mexico
Offshore
Fires
Initial &
Boundary
Biogenic
Lightning
NOx
40070010
AZ
Gila
67.9
69.5
0.16
0.00
0.00
0.01
0.00
0.01
0.10
0.00
0.00
0.00
0.00
0.00
0.09
0.46
0.00
0.00
0.04
0.00
0.00
0.06
0.04
1.83
0.22
1.97
50.21
2.25
0.50
40130019
AZ
Maricopa
69.8
70.0
0.33
0.00
0.00
0.01
0.00
0.04
0.07
0.00
0.00
0.00
0.00
0.00
0.33
0.27
0.00
0.00
0.04
0.00
0.01
0.04
0.07
2.83
0.21
2.18
41.74
2.66
1.32
40131003
AZ
Maricopa
70.1
70.7
0.17
0.00
0.00
0.01
0.00
0.01
0.10
0.00
0.00
0.00
0.00
0.00
0.09
0.37
0.00
0.00
0.04
0.00
0.00
0.03
0.06
3.17
0.27
1.42
42.78
2.69
1.68
40131004
AZ
Maricopa
70.2
70.8
0.19
0.00
0.00
0.01
0.00
0.01
0.07
0.00
0.00
0.00
0.00
0.00
0.12
0.30
0.00
0.00
0.03
0.00
0.00
0.04
0.06
2.91
0.22
1.85
43.33
2.49
1.92
40131010
AZ
Maricopa
68.3
69.2
0.15
0.00
0.00
0.01
0.00
0.01
0.10
0.00
0.00
0.00
0.00
0.00
0.09
0.40
0.00
0.00
0.04
0.00
0.00
0.05
0.06
2.83
0.28
1.40
41.87
2.61
1.07
40132001
AZ
Maricopa
63.8
64.1
0.25
0.00
0.00
0.01
0.00
0.03
0.07
0.00
0.00
0.00
0.00
0.00
0.28
0.25
0.00
0.00
0.04
0.00
0.01
0.04
0.05
2.45
0.19
2.13
39.89
2.23
1.02
40132005
AZ
Maricopa
69.6
70.5
0.18
0.00
0.00
0.01
0.00
0.01
0.08
0.00
0.00
0.00
0.00
0.00
0.11
0.36
0.00
0.00
0.03
0.00
0.00
0.05
0.07
2.38
0.23
1.62
44.56
2.49
1.37
40133002
AZ
Maricopa
65.8
65.8
0.17
0.00
0.00
0.01
0.00
0.01
0.06
0.00
0.00
0.00
0.00
0.00
0.11
0.26
0.00
0.00
0.02
0.00
0.00
0.03
0.06
2.85
0.21
1.66
40.56
2.33
1.84
40134004
AZ
Maricopa
65.7
66.6
0.15
0.00
0.00
0.01
0.00
0.00
0.10
0.00
0.00
0.00
0.00
0.00
0.08
0.38
0.00
0.00
0.04
0.00
0.00
0.05
0.04
2.78
0.28
1.07
42.30
2.61
1.32
40134005
AZ
Maricopa
62.3
62.3
0.15
0.00
0.00
0.00
0.00
0.00
0.08
0.00
0.00
0.00
0.00
0.00
0.08
0.33
0.00
0.00
0.03
0.00
0.00
0.03
0.05
2.82
0.24
1.26
38.02
2.39
1.49
40134008
AZ
Maricopa
65.6
66.5
0.21
0.00
0.00
0.00
0.00
0.01
0.06
0.00
0.00
0.00
0.00
0.00
0.12
0.34
0.00
0.00
0.03
0.00
0.00
0.03
0.06
2.45
0.18
1.91
41.47
2.52
1.15
40134010
AZ
Maricopa
63.8
66.9
0.25
0.00
0.00
0.01
0.00
0.03
0.08
0.00
0.00
0.00
0.00
0.00
0.26
0.28
0.00
0.00
0.04
0.00
0.01
0.04
0.04
2.56
0.19
2.09
40.08
2.39
1.03
40137020
AZ
Maricopa
67.0
67.0
0.19
0.00
0.00
0.01
0.00
0.01
0.05
0.00
0.00
0.00
0.00
0.00
0.12
0.27
0.00
0.00
0.02
0.00
0.00
0.03
0.14
3.14
0.20
1.42
40.07
2.50
2.39
40137021
AZ
Maricopa
69.8
70.1
0.14
0.00
0.00
0.01
0.00
0.01
0.09
0.00
0.00
0.00
0.00
0.00
0.08
0.34
0.00
0.00
0.03
0.00
0.00
0.03
0.12
2.98
0.26
1.44
43.64
2.54
1.06
40137022
AZ
Maricopa
68.2
69.1
0.14
0.00
0.00
0.01
0.00
0.01
0.08
0.00
0.00
0.00
0.00
0.00
0.08
0.34
0.00
0.00
0.03
0.00
0.00
0.03
0.12
2.91
0.26
1.41
42.64
2.48
1.03
40137024
AZ
Maricopa
67.0
67.9
0.19
0.00
0.00
0.01
0.00
0.01
0.05
0.00
0.00
0.00
0.00
0.00
0.12
0.27
0.00
0.00
0.02
0.00
0.00
0.03
0.14
3.14
0.20
1.42
40.07
2.50
2.39
40139702
AZ
Maricopa
66.9
68.1
0.14
0.00
0.00
0.01
0.00
0.01
0.09
0.00
0.00
0.00
0.00
0.00
0.09
0.28
0.00
0.00
0.04
0.00
0.00
0.05
0.08
2.84
0.28
0.89
43.39
2.26
1.10
40139704
AZ
Maricopa
65.3
66.2
0.14
0.00
0.00
0.01
0.00
0.01
0.08
0.00
0.00
0.00
0.00
0.00
0.09
0.35
0.00
0.00
0.03
0.00
0.00
0.03
0.07
2.62
0.22
1.30
42.20
2.33
1.16
40139997
AZ
Maricopa
70.5
70.5
0.18
0.00
0.00
0.01
0.00
0.01
0.07
0.00
0.00
0.00
0.00
0.00
0.12
0.28
0.00
0.00
0.03
0.00
0.00
0.04
0.06
3.05
0.23
1.78
43.46
2.49
1.97
40218001
AZ
Pinal
67.8
69.0
0.15
0.00
0.00
0.01
0.00
0.01
0.08
0.00
0.00
0.00
0.00
0.00
0.09
0.34
0.00
0.00
0.04
0.00
0.00
0.06
0.04
2.70
0.28
0.90
47.09
2.31
1.20
80013001
CO
Adams
63.0
63.0
0.24
0.00
0.00
0.00
0.00
0.02
0.06
0.00
0.00
0.00
0.00
0.00
0.11
0.91
0.00
0.00
0.02
0.00
0.00
0.36
0.11
0.76
0.07
1.09
38.82
2.53
1.63
80050002
CO
Arapahoe
68.0
68.0
0.34
0.00
0.00
0.00
0.00
0.01
0.08
0.00
0.00
0.00
0.01
0.00
0.12
1.46
0.00
0.00
0.03
0.00
0.00
0.42
0.19
0.81
0.10
1.24
40.78
3.08
1.40
80310002
CO
Denver
63.6
64.8
0.24
0.00
0.00
0.00
0.00
0.02
0.06
0.00
0.00
0.00
0.00
0.00
0.11
0.92
0.00
0.00
0.02
0.00
0.00
0.36
0.11
0.76
0.07
1.10
39.19
2.55
1.65
80310026
CO
Denver
64.5
64.8
0.25
0.00
0.00
0.00
0.00
0.02
0.06
0.00
0.00
0.00
0.00
0.00
0.12
0.93
0.00
0.00
0.02
0.00
0.00
0.37
0.12
0.77
0.08
1.11
39.75
2.59
1.67
90079007
CT
Middlesex
68.7
69.0
0.05
10.22
0.65
0.11
2.25
0.14
0.03
5.11
0.04
0.23
0.04
0.42
0.56
0.03
0.03
1.39
0.06
1.63
0.17
0.07
0.00
1.95
0.88
0.20
17.97
5.63
0.77
90110124
CT
New London
65.5
67.0
0.02
12.08
0.43
0.06
1.70
0.14
0.01
3.81
0.08
0.16
0.02
0.27
0.55
0.01
0.00
1.02
0.04
0.82
0.18
0.03
0.00
2.86
0.94
0.19
16.83
5.13
0.56
170310032
IL
Cook
67.3
69.8
0.09
0.22
0.00
0.30
1.39
0.72
0.04
0.25
0.00
0.00
0.07
0.00
1.40
0.05
0.01
0.01
0.07
0.07
2.21
0.08
0.00
1.15
0.14
0.21
17.74
9.65
1.12
170311601
IL
Cook
63.8
64.5
0.05
0.28
0.00
0.35
1.49
0.47
0.03
0.34
0.00
0.00
0.06
0.00
0.78
0.02
0.01
0.02
0.06
0.09
1.63
0.06
0.00
1.97
0.06
0.15
18.33
9.07
0.83
181270024
IN
Porter
63.4
64.6
0.08
0.10
0.00
0.24
0.90
0.63
0.03
0.16
0.00
0.00
0.05
0.00
1.32
0.03
0.00
0.01
0.06
0.07
2.25
0.05
0.00
0.85
0.13
0.33
17.02
9.39
0.86
260050003
Ml
Allegan
66.2
67.4
0.09
0.00
0.00
0.26
0.93
1.14
0.04
0.10
0.00
0.00
0.06
0.09
1.68
0.07
0.00
0.00
0.07
0.11
5.10
0.20
0.01
0.45
0.11
0.30
16.97
10.93
0.85
261210039
Ml
Muskegon
67.5
68.4
0.10
0.00
0.01
0.12
1.20
1.20
0.03
0.12
0.00
0.01
0.03
0.21
1.52
0.06
0.00
0.02
0.05
0.17
2.62
0.16
0.00
0.38
0.17
0.38
14.54
10.31
0.78
320030043
NV
Clark
68.4
69.4
0.14
0.00
0.00
0.00
0.00
0.02
0.08
0.00
0.00
0.00
0.00
0.00
0.13
0.23
0.00
0.00
0.02
0.00
0.00
0.01
0.00
1.94
0.35
2.13
42.30
2.26
1.78
350011012
NM
Bernalillo
63.8
66.0
6.58
0.00
0.00
0.00
0.00
0.02
0.01
0.00
0.00
0.00
0.00
0.00
0.78
0.18
0.00
0.00
0.00
0.00
0.00
0.01
0.19
1.66
0.09
1.26
45.54
2.12
2.46
350130008
NM
Dona Ana
65.6
66.3
1.69
0.00
0.00
0.00
0.00
0.12
0.03
0.00
0.00
0.00
0.00
0.00
3.83
0.08
0.00
0.00
0.02
0.00
0.00
0.06
0.02
10.77
0.14
0.84
39.98
2.39
3.52
361030002
NY
Suffolk
66.2
68.0
0.03
12.55
0.45
0.09
1.98
0.19
0.02
5.20
0.00
0.09
0.04
0.23
0.59
0.02
0.00
1.19
0.04
1.18
0.13
0.06
0.00
2.33
0.83
0.29
17.82
5.28
0.63
390850003
OH
Lake
64.3
64.6
0.05
0.30
0.03
0.12
18.66
0.30
0.06
1.63
0.00
0.00
0.04
0.26
0.78
0.05
0.01
0.14
0.08
1.79
0.66
0.14
0.00
1.55
0.07
0.20
17.01
8.36
0.78
480290052
TX
Bexar
67.1
67.8
0.19
0.00
0.00
0.12
0.00
0.32
0.00
0.00
0.00
0.00
0.01
0.07
18.42
0.04
0.00
0.00
0.00
0.00
0.00
0.07
0.00
0.42
0.50
0.36
39.80
3.56
0.89
480850005
TX
Collin
65.4
66.0
0.18
0.00
0.00
0.08
0.08
0.29
0.02
0.00
0.00
0.04
0.04
0.74
27.06
0.05
0.00
0.00
0.03
0.00
0.02
0.15
0.01
0.53
0.47
0.73
19.31
6.79
0.72
481130075
TX
Dallas
65.3
66.5
0.15
0.00
0.00
0.18
0.11
1.57
0.02
0.00
0.00
0.01
0.12
0.86
21.71
0.05
0.00
0.00
0.04
0.00
0.02
0.17
0.00
0.35
0.29
0.91
21.85
7.05
0.55
481211032
TX
Denton
65.9
67.7
0.09
0.00
0.00
0.11
0.06
0.47
0.02
0.00
0.00
0.00
0.06
0.77
23.85
0.06
0.00
0.00
0.04
0.00
0.02
0.21
0.00
0.28
0.41
0.68
21.27
7.16
0.55
482010051
TX
Harris
65.3
66.3
0.04
0.00
0.01
0.04
0.03
0.22
0.00
0.00
0.00
0.01
0.02
0.43
26.47
0.02
0.00
0.00
0.01
0.00
0.01
0.04
0.00
0.28
2.23
0.75
19.77
4.70
0.73
482010416
TX
Harris
68.8
70.4
0.05
0.00
0.02
0.08
0.02
0.28
0.00
0.00
0.00
0.01
0.04
0.41
28.63
0.02
0.00
0.01
0.01
0.00
0.01
0.05
0.00
0.34
2.25
0.75
20.23
5.29
0.89
484390075
TX
Tarrant
63.8
64.7
0.10
0.00
0.00
0.14
0.09
1.34
0.02
0.00
0.00
0.00
0.08
0.70
24.97
0.06
0.00
0.00
0.04
0.00
0.02
0.16
0.01
0.27
0.25
0.70
20.26
6.75
0.78
484391002
TX
Tarrant
64.1
65.7
0.15
0.04
0.20
0.15
0.06
1.57
0.02
0.05
0.00
0.15
0.08
0.59
24.06
0.07
0.00
0.06
0.05
0.01
0.02
0.19
0.01
0.40
0.27
0.89
20.50
6.15
0.86
484392003
TX
Tarrant
65.2
65.9
0.10
0.00
0.00
0.16
0.09
1.55
0.02
0.00
0.00
0.00
0.10
0.74
24.84
0.05
0.00
0.00
0.07
0.00
0.02
0.20
0.01
0.32
0.17
0.79
20.99
6.67
0.71
484393009
TX
Tarrant
67.5
68.1
0.09
0.00
0.00
0.15
0.07
1.45
0.02
0.00
0.00
0.00
0.10
0.64
27.69
0.05
0.00
0.00
0.06
0.00
0.02
0.19
0.00
0.32
0.25
0.88
21.28
6.32
0.64
490571003
UT
Weber
69.3
70.3
0.06
0.00
0.00
0.00
0.00
0.00
0.36
0.00
0.00
0.00
0.00
0.00
0.03
8.27
0.00
0.00
0.11
0.00
0.00
0.30
0.04
0.66
0.11
1.73
48.73
3.70
0.43
550590025
Wl
Kenosha
67.6
70.7
0.10
0.12
0.00
0.20
0.67
0.56
0.05
0.10
0.00
0.00
0.05
0.11
1.83
0.06
0.00
0.00
0.08
0.02
5.37
0.12
0.00
0.95
0.17
0.21
16.06
11.81
1.20
550890008
Wl
Ozaukee
65.2
65.8
0.07
0.19
0.02
0.21
0.95
0.54
0.04
0.38
0.00
0.00
0.04
0.11
1.24
0.05
0.00
0.06
0.08
0.14
8.21
0.10
0.00
1.02
0.11
0.21
15.86
11.31
1.05
C-5
-------
Design Values and Contributions for Monitoring plus Modeled Receptors in 2026 - Part 1
Contributions
Site ID
ST
County
2026
Avg
2026
Max
AL
AZ
AR
CA
CO
a
DE
DC
FL
GA
ID
IL
IN
IA
KS
KY
LA
ME
MD
MA
Ml
MN
MS
MO
MT
NE
NV
NH
NJ
40278011
AZ
Yuma
69.9
71.5
0.00
2.67
0.00
6.16
0.02
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.14
0.00
0.00
60650016
CA
Riverside
71.4
72.4
0.00
0.10
0.00
26.78
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.14
0.00
0.00
60651016
CA
Riverside
90.0
91.2
0.00
0.34
0.00
34.03
0.02
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.09
0.00
0.00
80590006
CO
Jefferson
72.0
72.6
0.00
0.44
0.00
1.40
16.51
0.00
0.00
0.00
0.00
0.00
0.10
0.00
0.00
0.00
0.04
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.04
0.37
0.00
0.00
80590011
CO
Jefferson
72.4
73.0
0.00
0.40
0.00
1.27
17.15
0.00
0.00
0.00
0.00
0.00
0.11
0.00
0.00
0.00
0.03
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.06
0.38
0.00
0.00
80690011
CO
Larimer
70.0
71.2
0.00
0.71
0.00
0.88
13.53
0.00
0.00
0.00
0.00
0.00
0.12
0.00
0.00
0.00
0.15
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.02
0.09
0.25
0.00
0.00
90013007
CT
Fairfield
70.9
71.7
0.09
0.01
0.15
0.03
0.05
3.71
0.41
0.02
0.05
0.13
0.02
0.67
1.08
0.14
0.09
0.76
0.23
0.01
0.92
0.31
1.32
0.16
0.09
0.31
0.07
0.07
0.01
0.09
7.04
90019003
CT
Fairfield
71.3
71.5
0.08
0.01
0.14
0.03
0.04
2.42
0.43
0.03
0.05
0.13
0.02
0.63
1.06
0.14
0.09
0.79
0.23
0.00
1.06
0.06
1.39
0.15
0.08
0.29
0.06
0.06
0.01
0.01
8.10
350130021
NM
Dona Ana
69.9
71.2
0.01
0.82
0.00
0.30
0.14
0.00
0.00
0.00
0.04
0.01
0.02
0.00
0.00
0.00
0.06
0.00
0.02
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.01
0.04
0.00
0.00
350130022
NM
Dona Ana
69.0
71.6
0.00
0.82
0.00
0.31
0.17
0.00
0.00
0.00
0.04
0.01
0.02
0.00
0.00
0.00
0.05
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.01
0.04
0.00
0.00
350151005
NM
Eddy
69.1
73.4
0.00
1.06
0.02
0.62
0.17
0.00
0.00
0.00
0.00
0.00
0.03
0.00
0.00
0.00
0.09
0.00
0.03
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.06
0.07
0.07
0.00
0.00
350250008
NM
Lea
69.2
71.6
0.00
1.34
0.00
0.70
0.08
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.09
0.00
0.00
480391004
TX
Brazoria
69.1
71.2
0.25
0.00
1.16
0.01
0.05
0.00
0.00
0.00
0.09
0.09
0.01
0.06
0.06
0.23
0.34
0.08
5.03
0.00
0.00
0.00
0.00
0.13
0.48
0.58
0.04
0.19
0.00
0.00
0.00
481671034
TX
Galveston
70.2
71.4
0.69
0.04
0.88
0.04
0.13
0.00
0.00
0.00
0.17
0.16
0.01
0.14
0.34
0.19
0.42
0.37
9.37
0.00
0.00
0.00
0.17
0.19
1.15
0.42
0.04
0.18
0.01
0.00
0.00
482010024
TX
Harris
73.9
75.5
0.20
0.00
0.53
0.00
0.01
0.00
0.00
0.00
0.46
0.04
0.00
0.01
0.01
0.14
0.14
0.01
4.57
0.00
0.00
0.00
0.00
0.09
0.29
0.22
0.01
0.09
0.00
0.00
0.00
490110004
UT
Davis
69.9
72.1
0.00
0.24
0.00
2.42
0.03
0.00
0.00
0.00
0.00
0.00
0.36
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.83
0.00
0.00
490353006
UT
Salt Lake
70.5
72.1
0.00
0.21
0.00
2.63
0.03
0.00
0.00
0.00
0.00
0.00
0.33
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.90
0.00
0.00
490353013
UT
Salt Lake
71.9
72.3
0.00
0.21
0.00
2.16
0.04
0.00
0.00
0.00
0.00
0.00
0.27
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.02
0.00
0.00
0.00
0.00
0.67
0.00
0.00
551170006
Wl
Sheboygan
70.8
71.7
0.01
0.02
0.58
0.04
0.05
0.01
0.00
0.00
0.01
0.00
0.02
13.57
8.53
0.62
0.36
0.42
0.33
0.00
0.05
0.01
1.47
0.32
0.09
1.68
0.06
0.16
0.01
0.00
0.04
C-6
-------
Design Values and Contributions for Monitoring plus Modeled Receptors in 2026 - Part 2
Contributions
Site ID
ST
County
2026
Avg
2026
Max
NM
NY
NC
ND
OH
OK
OR
PA
Rl
sc
SD
TN
TX
UT
VT
VA
WA
WV
Wl
WY
TRIBAL
Canada
&
Mexico
Offshore
Fires
Initial &
Boundary
Biogenic
Lightning
NOx
40278011
AZ
Yuma
69.9
71.5
0.11
0.00
0.00
0.00
0.00
0.00
0.08
0.00
0.00
0.00
0.00
0.00
0.02
0.12
0.00
0.00
0.02
0.00
0.00
0.00
0.01
9.46
0.53
3.12
44.47
2.31
0.55
60650016
CA
Riverside
71.4
72.4
0.04
0.00
0.00
0.00
0.00
0.00
0.16
0.00
0.00
0.00
0.00
0.00
0.01
0.05
0.00
0.00
0.07
0.00
0.00
0.00
0.00
2.33
2.03
2.93
33.29
3.19
0.19
60651016
CA
Riverside
90.0
91.2
0.11
0.00
0.00
0.00
0.00
0.00
0.14
0.00
0.00
0.00
0.00
0.00
0.04
0.04
0.00
0.00
0.04
0.00
0.00
0.00
0.01
2.46
1.57
2.88
44.08
3.41
0.62
80590006
CO
Jefferson
72.0
72.6
0.35
0.00
0.00
0.00
0.00
0.03
0.09
0.00
0.00
0.00
0.00
0.00
0.20
0.97
0.00
0.00
0.04
0.00
0.00
0.40
0.13
0.60
0.06
1.22
44.08
3.46
1.34
80590011
CO
Jefferson
72.4
73.0
0.28
0.00
0.00
0.00
0.00
0.02
0.10
0.00
0.00
0.00
0.00
0.00
0.10
1.05
0.00
0.00
0.04
0.00
0.00
0.40
0.14
0.55
0.06
1.25
44.52
3.29
1.05
80690011
CO
Larimer
70.0
71.2
0.46
0.00
0.00
0.01
0.00
0.05
0.08
0.00
0.00
0.00
0.00
0.00
0.31
0.81
0.00
0.00
0.04
0.00
0.00
0.59
0.17
0.93
0.08
1.25
41.95
4.25
3.16
90013007
CT
Fairfield
70.9
71.7
0.04
12.34
0.42
0.11
1.93
0.12
0.02
4.94
0.03
0.15
0.04
0.23
0.48
0.02
0.02
1.10
0.04
1.34
0.18
0.07
0.00
2.20
0.69
0.31
19.31
5.73
0.73
90019003
CT
Fairfield
71.3
71.5
0.04
12.65
0.40
0.09
1.95
0.13
0.02
5.47
0.00
0.14
0.03
0.24
0.48
0.02
0.01
1.09
0.04
1.36
0.17
0.07
0.00
2.18
0.65
0.34
19.41
5.73
0.74
350130021
NM
Dona Ana
69.9
71.2
2.70
0.00
0.00
0.00
0.00
0.14
0.01
0.00
0.00
0.00
0.00
0.00
4.34
0.14
0.00
0.00
0.01
0.00
0.00
0.05
0.04
13.43
0.12
0.86
39.30
2.63
4.50
350130022
NM
Dona Ana
69.0
71.6
2.68
0.00
0.00
0.00
0.00
0.13
0.01
0.00
0.00
0.00
0.00
0.00
3.23
0.13
0.00
0.00
0.00
0.00
0.00
0.05
0.05
12.98
0.12
0.82
40.21
2.68
4.27
350151005
NM
Eddy
69.1
73.4
6.32
0.00
0.00
0.01
0.00
0.24
0.05
0.00
0.00
0.00
0.01
0.00
1.81
0.04
0.00
0.00
0.05
0.00
0.00
0.13
0.02
3.42
0.18
1.08
50.16
2.33
0.85
350250008
NM
Lea
69.2
71.6
9.95
0.00
0.00
0.01
0.00
0.01
0.02
0.00
0.00
0.00
0.00
0.00
2.10
0.07
0.00
0.00
0.01
0.00
0.00
0.04
0.02
3.80
0.15
0.62
45.82
2.25
1.97
480391004
TX
Brazoria
69.1
71.2
0.03
0.00
0.02
0.11
0.02
0.57
0.00
0.00
0.00
0.01
0.04
0.18
28.35
0.01
0.00
0.01
0.02
0.00
0.01
0.04
0.00
0.33
1.39
0.65
21.73
5.90
0.54
481671034
TX
Galveston
70.2
71.4
0.12
0.01
0.03
0.15
0.29
0.74
0.00
0.02
0.00
0.07
0.04
0.54
18.71
0.04
0.00
0.01
0.01
0.03
0.07
0.09
0.00
0.40
5.72
0.78
19.47
6.85
0.63
482010024
TX
Harris
73.9
75.5
0.03
0.00
0.01
0.03
0.00
0.19
0.00
0.00
0.00
0.00
0.02
0.05
30.50
0.00
0.00
0.00
0.00
0.00
0.01
0.01
0.00
0.13
2.93
0.88
27.03
4.08
0.97
490110004
UT
Davis
69.9
72.1
0.07
0.00
0.00
0.00
0.00
0.00
0.41
0.00
0.00
0.00
0.00
0.00
0.04
7.21
0.00
0.00
0.14
0.00
0.00
0.06
0.01
0.63
0.11
2.86
50.51
3.48
0.36
490353006
UT
Salt Lake
70.5
72.1
0.08
0.00
0.00
0.00
0.00
0.00
0.41
0.00
0.00
0.00
0.00
0.00
0.06
7.54
0.00
0.00
0.14
0.00
0.00
0.05
0.01
0.45
0.11
3.30
49.84
3.71
0.59
490353013
UT
Salt Lake
71.9
72.3
0.07
0.00
0.00
0.01
0.00
0.01
0.26
0.00
0.00
0.00
0.00
0.00
0.04
6.43
0.00
0.00
0.10
0.00
0.00
0.23
0.01
0.63
0.11
2.52
54.67
2.95
0.32
551170006
Wl
Sheboygan
70.8
71.7
0.05
0.25
0.02
0.17
1.46
0.58
0.01
0.42
0.00
0.00
0.04
0.14
0.95
0.02
0.00
0.07
0.03
0.16
6.48
0.07
0.00
1.29
0.09
0.27
17.37
11.25
0.84
C-7
-------
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C-8
-------
Appendix D
Upwind State Collective Contribution
in 2023
D-l
-------
This appendix provides the 2023 average and maximum design values, the "home state"
contribution, the total "collective" contribution from all upwind states, the total upwind contribution
expressed as a percent of total ozone (i.e., the 2023 average design value), and the total upwind
contribution expressed as a percent of the total U.S. anthropogenic contribution at each monitoring
site with projected 2023 average design values that exceed the 2015 NAAQS and each violating
monitor maintenance-only receptor. The design value and contribution data are in units of ppb.
Table D-l. Upwind contribution summary data at monitoring sites that are projected to have
maximum design values that exceed the NAAQS in 2015, based on air quality modeling.
Site ID
State
County
2023gf
AvgDV
2023gf
Max DV
Home
State
Upwind
Total
Anthro
Total
Upwind %
of Avg DV
Upwind % of
Total Anthro
40278011
AZ
Yuma
70.4
72.1
2.98
6.97
9.95
9.9%
70.1%
60170010
CA
El Dorado
75.3
77.7
27.04
1.17
28.21
1.6%
4.2%
60170020
CA
El Dorado
73.3
75.0
30.95
1.61
32.55
2.2%
4.9%
60190007
CA
Fresno
80.4
82.2
31.79
1.60
33.39
2.0%
4.8%
60190011
CA
Fresno
83.3
84.2
33.02
1.64
34.66
2.0%
4.7%
60190242
CA
Fresno
78.4
80.0
28.40
1.57
29.97
2.0%
5.2%
60194001
CA
Fresno
83.2
84.7
31.75
1.39
33.14
1.7%
4.2%
60195001
CA
Fresno
83.8
86.6
33.35
1.52
34.86
1.8%
4.4%
60250005
CA
Imperial
76.4
76.7
7.05
0.62
7.67
0.8%
8.1%
60251003
CA
Imperial
74.8
74.8
9.54
0.77
10.31
1.0%
7.5%
60290007
CA
Kern
82.2
83.4
28.80
1.22
30.02
1.5%
4.1%
60290008
CA
Kern
77.8
79.7
22.47
1.07
23.55
1.4%
4.6%
60290011
CA
Kern
77.9
79.5
15.58
0.93
16.50
1.2%
5.6%
60290014
CA
Kern
81.0
82.9
27.84
1.24
29.08
1.5%
4.3%
60290232
CA
Kern
74.8
77.3
26.63
0.94
27.57
1.3%
3.4%
60292012
CA
Kern
83.5
84.2
30.20
1.17
31.38
1.4%
3.7%
60295002
CA
Kern
81.7
83.3
27.19
1.25
28.44
1.5%
4.4%
60296001
CA
Kern
75.9
76.2
23.76
1.23
24.99
1.6%
4.9%
60311004
CA
Kings
76.7
77.4
25.40
1.00
26.40
1.3%
3.8%
60370002
CA
Los Angeles
91.2
95.7
43.25
0.71
43.97
0.8%
1.6%
60370016
CA
Los Angeles
96.7
99.6
45.86
0.75
46.62
0.8%
1.6%
60371103
CA
Los Angeles
70.5
71.5
32.83
0.78
33.62
1.1%
2.3%
60371201
CA
Los Angeles
83.1
85.6
34.27
1.07
35.34
1.3%
3.0%
60371602
CA
Los Angeles
75.9
76.2
36.50
0.84
37.34
1.1%
2.2%
60371701
CA
Los Angeles
88.1
91.0
43.25
0.55
43.80
0.6%
1.3%
60372005
CA
Los Angeles
81.7
83.0
38.01
0.88
38.88
1.1%
2.3%
60376012
CA
Los Angeles
91.3
93.2
40.12
1.04
41.15
1.1%
2.5%
60379033
CA
Los Angeles
79.4
81.0
24.98
1.02
26.00
1.3%
3.9%
D-2
-------
Site ID
State
County
2023gf
AvgDV
2023gf
Max DV
Home
State
Upwind
Total
Anthro
Total
Upwind %
of Avg DV
Upwind % of
Total Anthro
60390004
CA
Madera
74.7
77.2
27.06
1.49
28.55
2.0%
5.2%
60392010
CA
Madera
76.4
77.6
27.77
1.60
29.38
2.1%
5.5%
60430003
CA
Mariposa
72.1
75.0
9.74
0.87
10.61
1.2%
8.2%
60470003
CA
Merced
73.9
75.1
26.82
1.39
28.20
1.9%
4.9%
60570005
CA
Nevada
76.5
79.8
25.61
1.68
27.30
2.2%
6.2%
60592022
CA
Orange
72.5
72.7
30.36
0.51
30.87
0.7%
1.6%
60595001
CA
Orange
74.7
75.4
34.58
0.76
35.34
1.0%
2.1%
60610003
CA
Placer
75.9
78.6
32.04
1.67
33.71
2.2%
4.9%
60610004
CA
Placer
70.8
75.9
24.11
1.66
25.76
2.3%
6.4%
60610006
CA
Placer
72.5
73.4
34.24
1.61
35.85
2.2%
4.5%
60650008
CA
Riverside
71.4
73.8
14.66
0.92
15.58
1.3%
5.9%
60650012
CA
Riverside
87.1
89.6
35.52
1.20
36.72
1.4%
3.3%
60650016
CA
Riverside
72.2
73.1
27.46
0.73
28.19
1.0%
2.6%
60651016
CA
Riverside
91.0
92.2
35.27
1.01
36.28
1.1%
2.8%
60652002
CA
Riverside
76.1
78.3
14.23
0.87
15.10
1.1%
5.7%
60655001
CA
Riverside
80.5
82.6
24.52
1.14
25.66
1.4%
4.4%
60656001
CA
Riverside
84.6
85.2
37.50
0.79
38.30
0.9%
2.1%
60658001
CA
Riverside
91.3
92.6
46.25
0.73
46.98
0.8%
1.6%
60658005
CA
Riverside
89.7
92.6
45.44
0.72
46.16
0.8%
1.6%
60659001
CA
Riverside
81.4
83.5
34.10
0.82
34.92
1.0%
2.3%
60670002
CA
Sacramento
71.6
71.9
33.03
1.61
34.64
2.3%
4.7%
60670012
CA
Sacramento
73.4
74.0
31.62
1.23
32.86
1.7%
3.7%
60675003
CA
Sacramento
70.0
71.5
29.31
1.24
30.55
1.8%
4.0%
60710001
CA
San Bernardino
74.8
75.8
15.04
0.90
15.94
1.2%
5.6%
60710005
CA
San Bernardino
103.1
104.6
42.93
1.45
44.38
1.4%
3.3%
60710012
CA
San Bernardino
88.0
90.8
28.36
0.79
29.15
0.9%
2.7%
60710306
CA
San Bernardino
78.6
80.5
27.89
1.27
29.17
1.6%
4.4%
60711004
CA
San Bernardino
100.2
103.4
48.82
0.82
49.64
0.8%
1.7%
60711234
CA
San Bernardino
70.0
73.5
9.53
0.82
10.35
1.2%
7.9%
60712002
CA
San Bernardino
92.2
93.5
45.54
1.14
46.68
1.2%
2.4%
60714001
CA
San Bernardino
83.9
84.6
35.59
1.43
37.02
1.7%
3.8%
60714003
CA
San Bernardino
97.4
100.3
48.34
1.00
49.34
1.0%
2.0%
60719002
CA
San Bernardino
78.9
80.5
18.89
1.11
20.00
1.4%
5.6%
60719004
CA
San Bernardino
101.8
104.0
50.52
1.05
51.57
1.0%
2.0%
60731006
CA
San Diego
77.7
78.7
26.32
0.88
27.20
1.1%
3.2%
60773005
CA
San Joaquin
69.6
71.1
28.18
1.32
29.50
1.9%
4.5%
60990005
CA
Stanislaus
74.7
75.6
30.91
1.62
32.53
2.2%
5.0%
60990006
CA
Stanislaus
76.5
76.8
30.35
1.45
31.80
1.9%
4.5%
61070006
CA
Tulare
78.6
79.8
18.15
1.16
19.31
1.5%
6.0%
61070009
CA
Tulare
82.2
82.2
26.25
1.50
27.75
1.8%
5.4%
D-3
-------
Site ID
State
County
2023gf
AvgDV
2023gf
Max DV
Home
State
Upwind
Total
Anthro
Total
Upwind %
of Avg DV
Upwind % of
Total Anthro
61072002
CA
Tulare
75.6
77.7
28.93
1.22
30.15
1.6%
4.0%
61072010
CA
Tulare
77.0
78.8
27.49
1.11
28.60
1.4%
3.9%
61090005
CA
Tuolumne
73.4
75.5
19.84
1.45
21.29
2.0%
6.8%
80350004
CO
Douglas
71.3
71.9
15.68
5.68
21.36
8.0%
26.6%
80590006
CO
Jefferson
72.8
73.5
16.83
5.11
21.93
7.0%
23.3%
80590011
CO
Jefferson
73.5
74.1
17.55
4.90
22.45
6.7%
21.8%
80690011
CO
Larimer
70.9
72.1
14.00
5.22
19.22
7.4%
27.2%
90010017
CT
Fairfield
71.6
72.2
4.60
39.87
44.47
55.7%
89.7%
90013007
CT
Fairfield
72.9
73.8
3.94
40.05
44.00
54.9%
91.0%
90019003
CT
Fairfield
73.3
73.6
2.52
41.80
44.33
57.0%
94.3%
90099002
CT
New Haven
70.5
72.6
3.85
36.84
40.69
52.2%
90.5%
170310001
IL
Cook
68.2
71.9
18.81
17.66
36.47
25.9%
48.4%
170314201
IL
Cook
68.0
71.5
23.47
15.88
39.35
23.4%
40.4%
170317002
IL
Cook
68.5
71.3
20.58
18.85
39.43
27.5%
47.8%
350130021
NM
Dona Ana
70.8
72.1
2.88
6.98
9.86
9.9%
70.8%
350130022
NM
Dona Ana
69.7
72.4
2.89
5.83
8.72
8.4%
66.8%
350151005
NM
Eddy
69.7
74.1
6.52
5.20
11.72
7.5%
44.3%
350250008
NM
Lea
69.8
72.2
10.23
5.05
15.28
7.2%
33.0%
480391004
TX
Brazoria
70.4
72.5
29.22
10.79
40.01
15.3%
27.0%
481210034
TX
Denton
69.8
71.6
28.72
11.00
39.72
15.8%
27.7%
481410037
TX
El Paso
69.8
71.4
3.17
4.23
7.40
6.1%
57.1%
481671034
TX
Galveston
71.5
72.8
19.31
18.47
37.79
25.8%
48.9%
482010024
TX
Harris
75.1
76.7
31.24
7.82
39.06
10.4%
20.0%
482010055
TX
Harris
70.9
71.9
28.74
11.24
39.98
15.9%
28.1%
482011034
TX
Harris
70.1
71.3
28.34
9.91
38.25
14.1%
25.9%
482011035
TX
Harris
67.8
71.3
27.41
9.59
36.99
14.1%
25.9%
490110004
UT
Davis
72.0
74.2
8.73
5.08
13.81
7.1%
36.8%
490353006
UT
Salt Lake
72.6
74.2
9.15
5.42
14.58
7.5%
37.2%
490353013
UT
Salt Lake
73.3
73.8
7.50
4.55
12.05
6.2%
37.8%
550590019
Wl
Kenosha
70.8
71.7
5.51
36.92
42.43
52.1%
87.0%
551010020
Wl
Racine
69.7
71.5
7.99
34.08
42.07
48.9%
81.0%
551170006
Wl
Sheboygan
72.7
73.6
7.23
34.76
41.99
47.8%
82.8%
D-4
-------
Table D-2. Upwind contribution summary data at violating monitor receptors.
Site ID
State
County
2023gf
AvgDV
2023gf
Max DV
Home
State
Upwind
Total
Anthro
Total
Upwind %
of Avg DV
Upwind % of
Total Anthro
40070010
AZ
Gila
67.9
69.5
7.66
3.19
10.85
4.7%
29.4%
40130019
AZ
Maricopa
69.8
70.0
15.33
3.43
18.76
4.9%
18.3%
40131003
AZ
Maricopa
70.1
70.7
13.83
4.16
17.99
5.9%
23.1%
40131004
AZ
Maricopa
70.2
70.8
14.57
2.81
17.38
4.0%
16.2%
40131010
AZ
Maricopa
68.3
69.2
13.90
4.25
18.15
6.2%
23.4%
40132001
AZ
Maricopa
63.8
64.1
12.84
2.96
15.80
4.6%
18.7%
40132005
AZ
Maricopa
69.6
70.5
13.82
3.02
16.84
4.3%
17.9%
40133002
AZ
Maricopa
65.8
65.8
13.60
2.67
16.27
4.1%
16.4%
40134004
AZ
Maricopa
65.7
66.6
11.02
4.26
15.28
6.5%
27.9%
40134005
AZ
Maricopa
62.3
62.3
12.29
3.70
15.99
5.9%
23.1%
40134008
AZ
Maricopa
65.6
66.5
13.07
2.76
15.83
4.2%
17.5%
40134010
AZ
Maricopa
63.8
66.9
12.32
3.06
15.38
4.8%
19.9%
40137020
AZ
Maricopa
67.0
67.0
14.42
2.68
17.10
4.0%
15.7%
40137021
AZ
Maricopa
69.8
70.1
14.25
3.47
17.73
5.0%
19.6%
40137022
AZ
Maricopa
68.2
69.1
13.92
3.39
17.32
5.0%
19.6%
40137024
AZ
Maricopa
67.0
67.9
14.42
2.68
17.10
4.0%
15.7%
40139702
AZ
Maricopa
66.9
68.1
12.53
3.48
16.01
5.2%
21.7%
40139704
AZ
Maricopa
65.3
66.2
12.21
3.15
15.36
4.8%
20.5%
40139997
AZ
Maricopa
70.5
70.5
14.57
2.86
17.43
4.1%
16.4%
40218001
AZ
Pinal
67.8
69.0
9.81
3.44
13.25
5.1%
25.9%
60430006
CA
Mariposa
69.2
70.1
17.92
1.39
19.31
2.0%
7.2%
61112002
CA
Ventura
69.6
70.2
27.04
0.86
27.90
1.2%
3.1%
80013001
CO
Adams
63.0
63.0
13.95
4.00
17.95
6.4%
22.3%
80050002
CO
Arapahoe
68.0
68.0
14.73
5.63
20.36
8.3%
27.7%
80310002
CO
Denver
63.6
64.8
14.08
4.04
18.12
6.4%
22.3%
80310026
CO
Denver
64.5
64.8
14.28
4.10
18.38
6.4%
22.3%
90079007
CT
Middlesex
68.7
69.0
5.39
35.86
41.26
52.2%
86.9%
90110124
CT
New London
65.5
67.0
6.76
32.20
38.96
49.2%
82.6%
170310032
IL
Cook
67.3
69.8
17.28
19.99
37.27
29.7%
53.6%
170311601
IL
Cook
63.8
64.5
17.08
16.26
33.35
25.5%
48.8%
181270024
IN
Porter
63.4
64.6
15.38
19.39
34.77
30.6%
55.8%
260050003
Ml
Allegan
66.2
67.4
2.03
34.52
36.55
52.1%
94.4%
261210039
Ml
Muskegon
67.5
68.4
1.98
38.91
40.89
57.6%
95.2%
320030043
NV
Clark
68.4
69.4
9.06
8.54
17.60
12.5%
48.5%
350011012
NM
Bernalillo
63.8
66.0
6.58
3.86
10.44
6.1%
37.0%
350130008
NM
Dona Ana
65.6
66.3
1.69
6.22
7.91
9.5%
78.6%
361030002
NY
Suffolk
66.2
68.0
12.56
26.44
39.00
39.9%
67.8%
390850003
OH
Lake
64.3
64.6
18.67
17.65
36.31
27.4%
48.6%
480290052
TX
Bexar
67.1
67.8
18.42
3.11
21.53
4.6%
14.4%
D-5
-------
Site ID
State
County
2023gf
AvgDV
2023gf
Max DV
Home
State
Upwind
Total
Anthro
Total
Upwind %
of Avg DV
Upwind % of
Total Anthro
480850005
TX
Collin
65.4
66.0
27.06
9.75
36.81
14.9%
26.5%
481130075
TX
Dallas
65.3
66.5
21.71
12.54
34.26
19.2%
36.6%
481211032
TX
Denton
65.9
67.7
23.86
11.65
35.51
17.7%
32.8%
482010051
TX
Harris
65.3
66.3
26.47
10.35
36.82
15.9%
28.1%
482010416
TX
Harris
68.8
70.4
28.64
10.38
39.02
15.1%
26.6%
484390075
TX
Tarrant
63.8
64.7
24.97
9.78
34.75
15.3%
28.1%
484391002
TX
Tarrant
64.1
65.7
24.06
10.92
34.98
17.0%
31.2%
484392003
TX
Tarrant
65.2
65.9
24.84
10.67
35.51
16.4%
30.0%
484393009
TX
Tarrant
67.5
68.1
27.70
10.08
37.78
14.9%
26.7%
490571003
UT
Weber
69.3
70.3
8.27
5.58
13.85
8.1%
40.3%
550590025
Wl
Kenosha
67.6
70.7
5.38
31.77
37.15
47.0%
85.5%
550890008
Wl
Ozaukee
65.2
65.8
8.22
27.37
35.59
42.0%
76.9%
D-6
-------
Appendix E
Upwind Linkages for
Individual Receptors in 2023 & 2026
-------
Upwind states linked to monitored plus modeled receptors in 2023.
Site ID
State
County
Receptor
Upwind States Linked to Individual Monitor Plus Modeled
Receptors in 2023
40278011
AZ
Yuma
Yuma
CA
60650016
CA
Riverside
Temecula
CA
60651016
CA
Riverside
Morongo
CA
80350004
CO
Douglas
Chatfield
CA
UT
80590006
CO
Jefferson
Rocky Flats
CA
UT
80590011
CO
Jefferson
NREL
CA
UT
80690011
CO
Larimer
Fort Collins
AZ
CA
UT
90010017
CT
Fairfield
Greenwich
IN
MD
Ml
NJ
NY
OH
PA
WV
90013007
CT
Fairfield
Stratford
IL
IN
KY
MD
Ml
NJ
NY
OH
PA
VA
WV
90019003
CT
Fairfield
Westport
IN
KY
MD
Ml
NJ
NY
OH
PA
VA
WV
90099002
CT
New Haven
Madison
IL
IN
KY
MD
Ml
NJ
NY
OH
PA
VA
WV
170310001
IL
Cook
Alsip
IN
IA
Ml
MN
TX
Wl
170314201
IL
Cook
Northbrook
IN
Ml
OH
TX
Wl
170317002
IL
Cook
Evanston
IN
Ml
MO
OH
TX
Wl
350130021
NM
Dona Ana
Las Cruces Desert View
AZ
TX
350130022
NM
Dona Ana
Las Cruces Santa
AZ
TX
350151005
NM
Eddy
BLM
AZ
TX
350250008
NM
Lea
Hobbs
AZ
CA
TX
480391004
TX
Brazoria
Manvel Croix Park
AR
LA
481210034
TX
Denton
Denton Airport
AR
LA
MS
OK
481410037
TX
El Paso
UTEP
AZ
NM
481671034
TX
Galveston
Galveston
AL
AR
LA
MS
OK
482010024
TX
Harris
Houston Aldine
LA
482010055
TX
Harris
Houston Bayland Park
AR
LA
MS
482011034
TX
Harris
Houston East
AR
LA
482011035
TX
Harris
Houston Clinton
AR
LA
490110004
UT
Davis
Bountiful Viewmont
CA
NV
490353006
UT
Salt Lake
Hawthorne
CA
NV
490353013
UT
Salt Lake
Herriman
CA
NV
550590019
Wl
Kenosha
Chiwaukee Prairie
IA
IL
IN
Ml
MO
OH
TX
551010020
Wl
Racine
Racine
IL
IN
Ml
MO
OH
TX
551170006
Wl
Sheboygan
Sheboygan Kohler
IL
IN
Ml
MO
OH
TX
E-2
-------
Upwind states linked to violating monitor receptors in 2023.
Site ID
State
County
Receptor
Upwind States Linked to Individual Violating Monitor
Receptors in 2023
40070010
AZ
Gila
Tonto National Monument
CA
40130019
AZ
Mar
copa
West Phoenix
CA
40131003
AZ
Mar
copa
Mesa
CA
40131004
AZ
Mar
copa
North Phoenix
CA
40131010
AZ
Mar
copa
Falcon Field
CA
40132001
AZ
Mar
copa
Glendale
CA
40132005
AZ
Mar
copa
Pinnacle Peak
CA
40133002
AZ
Mar
copa
Central Phoenix
CA
40134004
AZ
Mar
copa
West Chandler
CA
40134005
AZ
Mar
copa
Tempe
CA
40134008
AZ
Mar
copa
Cave Creek
CA
40134010
AZ
Mar
copa
Dysart
CA
40137020
AZ
Mar
copa
Senior Center
CA
40137021
AZ
Mar
copa
Red Mountain
CA
40137022
AZ
Mar
copa
Lehi
CA
40137024
AZ
Mar
copa
High School
CA
40139702
AZ
Mar
copa
Tonto National Forest
CA
40139704
AZ
Mar
copa
Fountain Hills
CA
40139997
AZ
Mar
copa
JLG Supersite
CA
40218001
AZ
Pinal
Queen Valley
CA
80013001
CO
Adams
Welby
CA
UT
80050002
CO
Arapahoe
Highland Reservoir
CA
UT
80310002
CO
Denver
Denver Camp
CA
UT
80310026
CO
Denver
La Casa
CA
UT
90079007
CT
Middlesex
Middlesex
IN
KY
MD
Ml
NJ
NY
OH
PA
VA
WV
E-3
-------
Site ID
State
County
Receptor
Upwind States Linked to Individual Violating Monitor
Receptors in 2023
90110124
CT
New
London
Fort Griswold Park
IN
MD
Ml
NJ
NY
OH
PA
VA
WV
170310032
IL
Cook
South Water Plant
IN
IA
Ml
OH
OK
TX
Wl
170311601
IL
Cook
Cook County Trailer
IN
Ml
OH
TX
Wl
181270024
IN
Porter
Ogden Dunes
IL
Ml
OH
TX
Wl
260050003
Ml
Allegan
Holland
IL
IN
IA
KS
MO
OH
OK
TX
Wl
261210039
Ml
Muskegon
Muskegon
AR
IL
IN
KY
MO
OH
OK
TX
Wl
320030043
NV
Clark
Paul Meyer
AZ
CA
350011012
NM
Bernalillo
Foothills
AZ
TX
350130008
NM
Dona Ana
La Union
AZ
TX
361030002
NY
Suffolk
Babylon
IN
KY
MD
Ml
NJ
OH
PA
VA
WV
390850003
OH
Lake
Eastlake
IL
IN
KY
Ml
PA
TX
WV
480290052
TX
Bexar
Camp Bullis
LA
480850005
TX
Collin
Frisco
LA
MS
TN
481130075
TX
Dallas
Dallas North
AR
LA
MS
OK
TN
481211032
TX
Denton
Pilot Point
AL
AR
LA
MS
MO
TN
482010051
TX
Harris
Houston Croquet
LA
MS
482010416
TX
Harris
Park Place
AR
LA
MS
484390075
TX
Tarrant
Eagle Mountain Lake
AR
LA
MS
OK
TN
484391002
TX
Tarrant
Fort Worth Northwest
AR
LA
OK
484392003
TX
Tarrant
Keller
AR
LA
MS
OK
TN
484393009
TX
Tarrant
Grapevine
LA
MS
OK
490571003
UT
Weber
Harrisville
CA
NV
550590025
Wl
Kenosha
Kenosha Water Tower
IL
IN
IA
MO
TX
550890008
Wl
Ozaukee
Grafton
IL
IN
Ml
MO
OH
TX
E-4
-------
Upwind states linked to monitored plus modeled receptors in 2026.
Site ID
State
County
Receptor
Upwind States Linked to Individual Receptors in 2026
40278011
AZ
Yuma
Yuma
CA
60650016
CA
Riverside
Temecula (Pechanga)
CA
60651016
CA
Riverside
Morongo
CA
80590006
CO
Jefferson
Rocky Flats
CA
UT
80590011
CO
Jefferson
NREL
CA
UT
80690011
CO
Larimer
Ft Collins
AZ
CA
UT
90013007
CT
Fairfield
Stratford
IN
KY
MD
Ml
NJ
NY
OH
PA
VA
WV
90019003
CT
Fairfield
Westport
IN
KY
MD
Ml
NJ
NY
OH
PA
VA
WV
350130021
NM
Dona Ana
Las Cruces Desert View
AZ
TX
350130022
NM
Dona Ana
Las Cruces Santa Teresa
AZ
TX
350151005
NM
Eddy
Carlsbad BLM
AZ
TX
350250008
NM
Lea
Hobbs
AZ
CA
TX
480391004
TX
Brazoria
Manvel Croix Park
AR
LA
481671034
TX
Galveston
Galveston
AR
LA
MS
OK
482010024
TX
Harris
Houston Aldine
LA
490110004
UT
Davis
Bountiful Viewmont
CA
NV
490353006
UT
Salt Lake
Hawthorne
CA
NV
490353013
UT
Salt Lake
Herriman
CA
551170006
Wl
Sheboygan
Sheboygan Kohler Andrae
IL
IN
Ml
MO
OH
TX
E-5
-------
Appendix F
Spatial Fields of Top 10-Day Average Contributions from
Emissions in Upwind States in 2023
-------
This appendix contains maps showing the spatial distribution of top 10-day average contributions
in 2023 from individual upwind states covered by this final rule. The maps illustrate the transport
patterns that carry ozone formed from emissions in each state to both nearby and far distant
downwind areas. Note that the average contribution data shown on the maps do not conform to
the average contribution metric values calculated in Step 2 of the four-step transport framework.
The data shown on the maps are based on averaging the model-predicted contributions without
applying the criterion of a minimum of five days with model-predicted ozone concentrations
greater than or equal to 60 ppb and without applying the average modeled contributions in a
relative manner via the Relative Contribution Factor (RCF) to projected design values. In this
regard, the data on these maps should not be viewed as equivalent to the average contribution
metric.
-------
Alabama
data = [ll]2023gf_ussa_apca.tagged.03NV.12US2.camx.03_8hrm ax_LST.5-9.topl0avg.ioa pi
-------
Arkansas
data = [ll]2023gf_ussa_apca.tagged.03NV.12US2.camx.03_8hrm ax_LST.5-9.topl0avg.ioa pi
-------
California
246 r
1§€
data = [ll]2023gf_ussa_apca.tagged.03NV.12US2.camx.03_8hrm aK_LST.5-9.topl0avg.ioa pi
-------
Illinois
data = [ll]2023gf_ussa_apca.tagged.O3NV.12US2.cam>{.O3_8hrmax_LST.5-9.topl0avg.ioapi
-------
Indiana
-------
Kentucky
data = [ll]2023gf_ujsa_apca.tagged.O3NV.12US2.camx.O3_8hrmax_LST.5-9.topl0avg.ioapi
-------
Louisiana
396
data = [ll]2023gf_ussa_ .apca .tagged.03 NV.12US2 .camx.03_ihrm ax_LST.E-9.topl0avg.ioa pi
-------
Maryland
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7 n
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AaX
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jf
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u.
396
data = [ll]2Q23gf_ussa_apca.tagged.03NV.12US2.camx.03_8hrm ax_LST.5-9.topl0avg.ioa pi
-------
Michigan
vWS>
S /
data = [ll]2023gf_ussa_apca.tagged.03NV.12US2.camx.03_8hrm aK_LST.5-9.topl0avg.ioa pi
-------
Mississippi
'i,
M J1
I > &\
[X
y
^-i \ vf
r\ \ \/
V
>
—4 J
,/
1 k
r
"W
J
|
,«,-^T/'~Wv* (\ \ N
/ '/
j «3
Y
V/^\
3§e
data = [ll]2023gf_ujsa_apca.tagged.O3NV.12US2.camx.O3_8hrmax_LST.5-9.topl0avg.ioapi
-------
Minnesota
3i€
data = [ll]2023gf_ussa_apca.tagged.03NV.12US2.camx.03_8hrm ax_LST.5-9.topl0avg.ioa pi
-------
Missouri
data = [ll]2023gf:_ussa_apca.tagged.03NV.12US2.camx.03_8hrm ax_LST.5-9.topl0avg.ioa pi
-------
Nevada
data = [ll]2023gf_ussa_apca.tagged.03NV.12US2.camx.03_8hrm ax_LST.5-9.topl0avg.ioa pi
-------
New Jersey
ii
I 396
data = [ll]2023gf_ussa_apca.tagged.03NV.12US2.camx.03_8hrm aK_LST.5-9.topl0avg.ioa pi
-------
New York
ii
1 396
data = [ll]2023gf_ussa_ .apca .tagged.03 NV.12US2 .camx.03_ihrm ax_LST.E-9.topl0avg.ioa pi
-------
Ohio
data = [ll]2023gf_ussa_apca.tagged.O3NV.12US2.camx.O3_Shi*m ax_LST.5-9.topl0avg.ioapi
-------
Oklahoma
-------
Pennsylvania
Min = O.OOE+O at (1,1), Max = 21.560 at (319,150)
data = [ll]2023gf_ussa_apca.tagged.O3NV.12US2.camx.O3_8hrmax_LST.5-9.topl0avg.ioapi
-------
Texas
data = [ll]2023gf_ussa_apca.tagged.O3NV.12US2.camx.O3_8hrmax_LST.5-9.topl0avg.ioapi
-------
Utah
ii
1 396
data = [ll]2023gf_ussa_apca.tagged.O3NV.12US2.camx.O3_8hrmax_LST.5-9.topl0avg.ioapi
-------
Virginia
data = [ll]2023gf_ussa_apca.tagged.03NV.12US2.camx.O3_8hrmax_LST.5-9.topl0avg.ioapi
-------
West Virginia
data = [ll]2023gf_ussa_apca.tagged.O3NV.12US2.camx.O3_8hrmax_LST.5-9.topl0avg.ioapi
-------
Wisconsin
data = [ll]2023gf_ussa_apca.tagged.O3NV.12US2.camx.O3_8hrm ax_LST.5-9,topl0avg.ioapi
------- |