&EPA
Air Quality Modeling Technical Support Document
Federal Implementation Plan Addressing Regional Ozone
Transport for the 2015 Ozone National Ambient Air Quality
Standards Proposed Rulemaking
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 EPA's Federal Implementation Plan Addressing Regional Ozone Transport for the 2015
Ozone National Ambient Air Quality Standards proposed rulemaking.1 For this proposed rule, air
quality modeling is used to project ozone design values2 at individual monitoring sites to three future
analytic years: 2023 and20263 and to estimate state-by-state contributions to ozone design values at
individual monitoring sites in these two future years.4 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). Ozone contribution data for 2023 and 2026 is then 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.
As described in this TSD, EPA performed air quality modeling for a 2016 base year and 2023,
2026, and 2032 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
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.5
1 The information in this TSD is the same as the information provided in the Air Quality Modeling TSD for the proposed
SIP disapprovals, with one exception. This TSD also contains information on additional model runs that were used to
support the ozone transport policy analysis and the RIA for this proposed rule.
2 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.
3 The rationale for using 2023, and2026as the applicable future analytic years for this transport assessment is described in
the preamble for this proposed rule.
4 The EPA also performed air quality modeling for 2032 which is the analytic year that corresponds to the 2033
attainment date for nonattainment areas with a Serious area classification. The results of the 2032 modeling were not used
to support this proposed rule.
5 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
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In addition to the state-by-state modeling to quantify the downwind contributions from all
anthropogenic emissions in each upwind state, we performed state-sector source apportionment
modeling for 2026 to quantify the contributions from the EGU and non-EGU point source sectors.
This state-sector modeling was used to develop spatial fields of April through September average
maximum daily 8-hour average (MDA8) ozone concentrations to support the health benefits analysis
in the regulatory impact analysis (RIA) for this proposed rule.6 Finally, EPA performed a state-by-
state source apportionment model run in which we reduced 2026 base case EGU and non-EGU point
source NOx emissions by 30 percent. The results of this sensitivity model run were used to develop
ozone "calibration factors" to inform the EGU and non-EGU control analysis in Step 3 of the 4-step
transport framework.7
The remaining sections of this TSD are as follows. Section 2 describes the air quality
modeling platform and the evaluation of model predictions of 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 the future analytic years. 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. To request a copy of the available air
quality model input and/or output data please contact Norm Possiel at possiel.norm@epa.gov.8
2. Air Quality Modeling Platform
The EPA used a 2016-based air quality modeling platform to provide the foundational
model-input data sets for 2016 and the future analytic years. These inputs include emissions for
2016, 2023, 2026, and 2032 developed for the 2016v2 emissions modeling platform as well as
meteorology, initial and boundary condition concentrations, and other inputs representative of the
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.
6 The method EPA used to create the spatial fields of the April through September average MDA8 ozone concentrations is
described in Chapter 3 of the proposed rule RIA.
7 The calibration factors are "ppb/ton" values that are used to account, in part, for the non-l-to-1 proportional response of
ozone to NOx emissions reductions. See the Ozone Transport Policy Analysis Proposed Rule TSD for a description of the
method used to calculate the calibration factors and how these factors were applied for this proposed rule.
8 A list of available model input and output data is provided in the file "Air Quality Modeling Files_2016v2 Platform"
which can be found in the docket for this proposed rule.
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2016 base year. The 2016 v2 emissions modeling platform is described in the document
Preparation of Emissions Inventories for the 2016v2 North American Emissions Modeling Platform
available at https://www.epa.gov/air-emissions-modeling/2016v2-platform. The meteorological and
initial and boundary condition data used as input to the air quality modeling and the model
performance evaluation for MDA8 ozone are described below.
2.1 Air Quality Model Configuration and Model Simulations
The photochemical model simulations performed for this proposed 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 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 proposed
rulemaking, as in the CSAPR Update and Revised CSAPR Update, EPA used the CAMx Ozone
Source Apportionment Technology/Anthropogenic Precursor Culpability Analysis (OSAT/APCA)
technique9 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).
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).
9 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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Figure 2-1. Air quality modeling domains.
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, 2026, and 2032 projections. 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
simulations.10
The 12 km CAMx model simulations performed for this proposed rule are listed in Table 2-1.
The simulation period for each run was preceded by a 15-day ramp-up period. Note that the 2026ij
case was run for January through April and October through December and the model concentration
output were then combined with those from the May through September 2026fj_ussa case to create
outputs for an annual time period. Also, the 2026fj EGU and non-EGU state-sector source
apportionment model simulations were performed for April through September in order to provide
sector contribution data that aligns with the April through September average MDA8 concentration,
which is the primary health-based metric used to inform the ozone benefits analysis in the RIA.
® EPA used the CAMx7. lchemparam.CB6r5_CF2E chemical parameter file for all the CAMx model runs described in
this TSD.
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Table 2-1. Model run name, case name and simulation period for each model run.11
Analytic
Year
Model Run
Case Name
Simulation Period
2016
2016 baseline
20161]
Annual
2023
2023 baseline
20231]
Annual
2023 state total anthropogenic contributions
20231]_ussa
May - September
2026
2026 baseline
20261]
Annual
2026 state total anthropogenic contributions
20261]_ussa
May - September
2026 state EGU contributions
20261]_egusa
April - September
2026 state Non-EGU contributions
2026fh_nonegusa
April - September
2026 state EGU + Non-EGU 30% NOx cut
20261]_30nox
May - September
2032
2032 baseline
20321]
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 EPA used the same
meteorological data for the 2016v2 air quality modeling as was used for the 2016vl 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
11 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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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 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).12 The 40 km Eta Data Assimilation System (EDAS) analysis
(ds609.2) from the National Center for Atmospheric Research (NCAR) was used where 12NAM data
was unavailable.13 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 were ingested from the Group for High Resolution Sea Surface Temperatures
(GHRSST) (Stammer et al., 2003) 1 km 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.
Layer
Height (m)
Pressure (mb)
Sigma
35
17.556
5000
0.000
34
14.780
9750
0.050
55
12.822
14500
0.100
32
1 1.282
19250
0.150
31
10.002
24000
0.200
30
8.901
28750 f
0.250
29
7,932
33500 T
0.300
28
7.064
38250
0.350
27
6,275 '"|
43000
0.400
26
5.553 "i
47750 1
0.450
25
4.885
52500 1
0.500
24
4.264
57250 1
0.550
23
3.683
62000 !
0.600
22
3.136
66750
0.650
12 https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/north-american-mesoscale-forecast-SYStem-nam
13 https://www.ready.noaa.gov/edas40.php.
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Layer
Height (m)
Pressure (mb)
Sigma
21
2.619
71500
0.700
20
2,226
75300 f
0.740
19
1.941
78150
0.770
18
1.665
81000
0.800
17
1.485
82900
0.820
16
1.308
84800
0.840
15
1.134
86700
0.860
14
964
88600
0.880
13
797
90500 j
0.900
12
714
91450
0.910
1 1
632
92400
0.920
10
551
93350
0.930
9
470
94300
0.940
8
390
95250 ]
0.950
7
311
96200
0.960
6
232""
97150
0.970
5
154
98100
0.980
4
1 15
98575 '"j
0.985
->
77
99050 !
0.990
2
38
99525 1
0.995
1
19
99763
0.9975
Surface
0
100000 :
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 proposed rule.14
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 hemispheric atmospheric chemistry model, the Hemispheric
version of the Community Multi-scale Air Quality Model (H-CMAQ) version 3.1.1 which was run
14 Meteorological Modeling for 2016.docx.
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for 201615. The H-CMAQ 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 and 2023.
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
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, 2026, and 2032 12 km simulations.
2.5 Air Quality Model Evaluation
An operational model performance evaluation for ozone was conducted to examine the ability
of the 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 A. Overall, the ozone model performance statistics for the CAMx
20161] 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 Appendix A, 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. Thus, the model performance
results demonstrate the scientific credibility of our 2016v2 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 proposed rule.16
15 More information about the H-CMAQ model and other applications using this tool is available at:
https://www.epa.gov/cmaa/hemispheric-scale-applications. Note that EPA used the same initial and boundary conditions
for the 2016v2 air quality modeling as was used for the 2016vl air quality modeling.
16 CAMx 2016v2 MDA8 03 Model Performance Stats by Site.
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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, 2026, and 2032
analytic years using the approach described in this section. Following the general approach in the
Revised CSAPR Update, we evaluated projected average and maximum design values in
conjunction with the most recent measured ozone design values (i.e., 2020)17 to identify potential
nonattainment or maintenance sites in each of the three future years. Those monitoring sites with
future year average design values that exceed the NAAQS (i.e., average design values of 71 ppb or
greater)18 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. As described in the preamble for this proposed
rule, maintenance-only receptors include both 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.19
The procedures for calculating projected average and maximum design values are described
below. The monitoring sites that we project to be nonattainment and maintenance receptors for the
ozone NAAQS in the 2023 and 2026 base case scenarios 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 proposed rule.
3.2 Approach for Projecting Ozone Design Values
The ozone predictions from the CAMx model simulations were used to project ambient (i.e.,
measured) ozone design values (DVs) to 2023, 2026, and 2032 based on an approach that follows
from EPA's guidance for attainment demonstration modeling (US EPA, 2018),20 as summarized here.
17 The 2020 design values are the most current official design values available for use in this proposed rule. The 2020
ozone design values, by monitoring site, can be found in the following file in the docket: 2010 thru 2020 Ozone Design
Values.
18 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.
19 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.
20 EPA's ozone attainment demonstration modeling guidance is referred to as "the modeling guidance" in the remainder
of this document.
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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, 2026, and 2032
using the Software for Model Attainment Test Software - Community Edition (SMAT-CE)21. 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)t * (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 (RRF)t 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
>= 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 was based on a 3 x 3 array of 12 km grid
cells centered on the location of the grid cell containing the monitor.
21 Software download and documentation available at https://www.epa.gov/scram/photochemical-modeling-tools
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As in the Revised CSAPR Update, EPA also projected 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, 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, 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).22 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 our modeling
EPA calculated projected average and maximum design values at 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
location of the monitoring site23 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
22 https://www.mmm.ucar.edu/weather-research-and-forecasting-model.
23 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.
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maximum design value at each site was projected to 2023, 2026, and 2032 using the site-specific
RRFs, as determined using the procedures for calculating RRFs described above.
For this proposed rule, 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.24 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 2020, which are the most recent concurred measured
design values at the time of this proposed rule.25 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 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.26
The 2016-centered base period average and maximum design values, the projected average
and maximum design values for 2023 and the 2020 design values for monitoring sites that are
projected to be nonattainment or maintenance-only receptors in 2023 are provided in Tables 3-1 and
3-2 respectively.27 The average and maximum design values for nonattainment and maintenance
receptors in 2026 are provided in Tables 3-3 and 3-4, respectively, and 2032 are provide in Appendix
B for those monitoring sites that are receptors in 2023. Projected design values for 2023, 2026, and
2032 based on both the "3 x 3" and "no water" methods for individual monitoring sites nationwide
24 40 CFR Part 50, Appendix U to Part 50 - Interpretation of the Primary and Secondary National Ambient Air Quality
Standards for Ozone.
25 Official, concurred ozone design values for 2010 through 2020 are provided in the file: 2020 2020 Design Values.
Preliminary, un-concurred, 2021 design values are provided in the file Preliminary_2021 Design Values. Both of these
files can be found in the docket for this proposed rule.
26 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.
27 Using the 2023 design values from the "3x3" approach, the maintenance-only receptor at site 170317002 in Cook
County, IL would become a nonattainment receptor because the average design value with the "3 x 3" approach is 71.1
ppb versus 70.1 ppb with the "no water" approach. In addition, the monitor at site 170971007 in Lake County, IL which
was not projected to be a receptor using the "no water" approach would be a maintenance-only receptor with the "3 x 3"
approach because the maximum design value with the "no water" approach was 69.9 ppb versus a maximum design value
of 71.2 ppb with the "3 x 3" approach. However, including this Lake County, Illinois site as a receptor would not affect
which states are covered by this proposed rule.
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are provided in the file "2016v2_DVs_state_contributions" which can be found in the docket for this
proposed rule.
Table 3-1. Average and maximum 2016-centered and 2023 base case 8-hour ozone design values and
2020 design values (ppb) at projected nonattainment receptors in 2023.28
Monitor ID
State
County
2016
Centered
Average
2016
Centered
Maximum
2023
Average
2023
Maximum
2020
060170010
CA
El Dorado
85.3
88
76.3
78.7
84
060170020
CA
El Dorado
82.0
84
74.3
76.2
80
060190007
CA
Fresno
87.0
89
80.4
82.2
80
060190011
CA
Fresno
90.0
91
82.9
83.8
84
060190242
CA
Fresno
84.3
86
79.5
81.1
79
060194001
CA
Fresno
90.3
92
82.8
84.4
81
060195001
CA
Fresno
91.0
94
83.7
86.4
84
060250005
CA
Imperial
76.7
77
76.3
76.6
78
060251003
CA
Imperial
76.0
76
75.4
75.4
68
060290007
CA
Kern
87.7
89
82.8
84.0
93
060290008
CA
Kern
83.0
85
79.1
81.0
85
060290011
CA
Kern
83.3
85
78.8
80.4
86
060290014
CA
Kern
86.0
88
81.3
83.2
85
060290232
CA
Kern
79.3
82
74.9
77.5
83
060292012
CA
Kern
89.3
90
84.1
84.7
85
060295002
CA
Kern
87.3
89
82.4
84.0
89
060296001
CA
Kern
80.7
81
77.1
77.4
82
060311004
CA
Kings
83.3
84
76.9
77.6
80
060370002
CA
Los Angeles
94.3
99
88.0
92.4
97
060370016
CA
Los Angeles
100.0
103
93.4
96.2
107
060371201
CA
Los Angeles
88.3
91
82.7
85.3
92
060371602
CA
Los Angeles
75.7
76
73.6
73.9
78
060371701
CA
Los Angeles
92.0
95
85.6
88.4
88
060372005
CA
Los Angeles
84.7
86
80.7
81.9
93
060376012
CA
Los Angeles
98.0
100
91.6
93.4
101
060379033
CA
Los Angeles
87.3
89
80.7
82.2
80
060390004
CA
Madera
80.3
83
75.7
78.3
76
060392010
CA
Madera
82.7
84
77.0
78.2
78
060430003
CA
Mariposa
76.0
79
74.2
77.1
79
060470003
CA
Merced
80.7
82
74.7
75.9
76
060570005
CA
Nevada
86.3
90
78.1
81.5
82
060592022
CA
Orange
77.7
78
72.5
72.8
82
28 "N/A" is used to denote that there is no valid design value.
13
-------
Monitor ID
State
County
2016
Centered
Average
2016
Centered
Maximum
2023
Average
2023
Maximum
2020
060595001
CA
Orange
75.3
76
72.3
73.0
77
060610003
CA
Placer
85.0
88
77.1
79.8
N/A
060610004
CA
Placer
79.3
85
71.9
77.0
N/A
060610006
CA
Placer
80.0
81
72.8
73.7
72
060650008
CA
Riverside
76.5
79
71.0
73.3
N/A
060650012
CA
Riverside
95.3
98
85.9
88.3
99
060650016
CA
Riverside
79.0
80
72.0
72.9
78
060651016
CA
Riverside
99.7
101
89.8
90.9
99
060652002
CA
Riverside
82.7
85
76.4
78.5
84
060655001
CA
Riverside
88.7
91
80.5
82.6
88
060656001
CA
Riverside
92.3
93
83.5
84.1
94
060658001
CA
Riverside
96.7
98
89.5
90.7
96
060658005
CA
Riverside
95.0
98
87.9
90.7
98
060659001
CA
Riverside
88.7
91
80.8
82.9
87
060670002
CA
Sacramento
77.7
78
71.4
71.7
72
060670012
CA
Sacramento
82.3
83
74.8
75.4
N/A
060710001
CA
San Bernardino
79.0
80
74.5
75.4
81
060710005
CA
San Bernardino
110.3
112
100.3
101.8
109
060710012
CA
San Bernardino
95.0
98
87.3
90.1
90
060710306
CA
San Bernardino
84.0
86
76.8
78.6
83
060711004
CA
San Bernardino
105.7
109
97.2
100.2
106
060712002
CA
San Bernardino
97.7
99
90.1
91.3
102
060714001
CA
San Bernardino
90.3
91
82.6
83.3
87
060714003
CA
San Bernardino
104.0
107
95.2
98.0
114
060719002
CA
San Bernardino
87.3
89
80.1
81.6
86
060719004
CA
San Bernardino
108.7
111
99.5
101.6
110
060731006
CA
San Diego
83.0
84
76.9
77.9
79
060773005
CA
San Joaquin
77.3
79
71.3
72.8
70
060990005
CA
Stanislaus
81.0
82
75.4
76.3
79
060990006
CA
Stanislaus
83.7
84
77.5
77.8
80
061030004
CA
Tehama
79.7
81
72.3
73.4
74
061070006
CA
Tulare
84.7
86
79.1
80.3
83
061070009
CA
Tulare
89.0
89
82.6
82.6
88
061072002
CA
Tulare
82.7
85
75.5
77.6
83
061072010
CA
Tulare
84.0
86
77.0
78.8
80
061090005
CA
Tuolumne
80.7
83
75.6
77.8
77
080350004
CO
Douglas
77.3
78
71.7
72.3
81
080590006
CO
Jefferson
77.3
78
72.6
73.3
79
080590011
CO
Jefferson
79.3
80
73.8
74.4
80
080690011
CO
Larimer
75.7
77
71.3
72.6
75
14
-------
Monitor ID
State
County
2016
Centered
Average
2016
Centered
Maximum
2023
Average
2023
Maximum
2020
090010017
CT
Fairfield
79.3
80
73.0
73.7
82
090013007
CT
Fairfield
82.0
83
74.2
75.1
80
090019003
CT
Fairfield
82.7
83
76.1
76.4
79
090099002
CT
New Haven
79.7
82
71.8
73.9
80
481671034
TX
Galveston
75.7
77
71.1
72.3
74
482010024
TX
Harris
79.3
81
75.2
76.8
79
482010055
TX
Harris
76.0
77
71.0
72.0
76
490110004
UT
Davis
75.7
78
72.9
75.1
77
490353006
UT
Salt Lake
76.3
78
73.6
75.3
74
490353013
UT
Salt Lake
76.5
77
74.4
74.9
73
550590019
WI
Kenosha
78.0
79
72.8
73.7
74
551010020
WI
Racine
76.0
78
71.3
73.2
73
551170006
WI
Sheboygan
80.0
81
73.6
74.5
75
Table 3-2. Average and maximum 2016-centered and 2023 base case 8-hour ozone design values and
2020 design values (ppb) at projected maintenance-only receptors.
Monitor ID
State
County
2016
Centered
Average
2016
Centered
Maximum
2023
Average
2023
Maximum
2020
040278011
AZ
Yuma
72.3
74
70.5
72.2
68
060070007
CA
Butte
76.7
79
68.9
71.0
73
060090001
CA
Calaveras
77.0
78
70.9
71.9
72
060371103
CA
Los Angeles
73.0
74
70.5
71.5
76
060430006
CA
Mariposa
75.0
76
70.1
71.0
79
060675003
CA
Sacramento
77.3
79
70.2
71.7
70
060711234
CA
San Bernardino
72.3
76
70.6
74.2
76
061112002
CA
Ventura
77.3
78
70.9
71.6
77
170310001
IL
Cook
73.0
77
69.6
73.4
75
170310032
IL
Cook
72.3
75
69.8
72.4
74
170310076
IL
Cook
72.0
75
69.3
72.1
69
170314201
IL
Cook
73.3
77
69.9
73.4
77
170317002
IL
Cook
74.0
77
70.1
73.0
75
320030075
NV
Clark
75.0
76
70.0
71.0
74
350130021
NM
Dona Ana
72.7
74
70.9
72.2
78
350130022
NM
Dona Ana
71.3
74
69.5
72.1
74
420170012
PA
Bucks
79.3
81
70.7
72.2
74
480391004
TX
Brazoria
74.7
77
70.1
72.3
73
481210034
TX
Denton
78.0
80
70.4
72.2
72
481410037
TX
El Paso
71.3
73
69.6
71.3
76
15
-------
Monitor ID
State
County
2016
Centered
Average
2016
Centered
Maximum
2023
Average
2023
Maximum
2020
482011034
TX
Harris
73.7
75
70.3
71.6
73
482011035
TX
Harris
71.3
75
68.0
71.6
70
490450004
UT
Tooele
73.5
74
70.8
71.3
69
490570002
UT
Weber
73.0
75
70.6
72.5
N/A
490571003
UT
Weber
73.0
74
70.5
71.5
71
550590025
WI
Kenosha
73.7
77
69.2
72.3
74
Table 3-3. Average and maximum 2016-centered and 2026 base case 8-hour ozone design values
(ppb) at projected nonattainment receptors in 2023.
Monitor ID
State
County
2016
Centered
Average
2016
Centered
Maximum
2026
Average
2026
Maximum
60170010
CA
El Dorado
85.3
88
75.0
77.4
60170020
CA
El Dorado
82.0
84
73.2
75.0
60190007
CA
Fresno
87.0
89
79.5
81.3
60190011
CA
Fresno
90.0
91
81.9
82.8
60190242
CA
Fresno
84.3
86
78.7
80.3
60194001
CA
Fresno
90.3
92
81.8
83.3
60195001
CA
Fresno
91.0
94
82.7
85.4
60250005
CA
Imperial
76.7
77
76.2
76.5
60251003
CA
Imperial
76.0
76
75.3
75.3
60290007
CA
Kern
87.7
89
82.2
83.4
60290008
CA
Kern
83.0
85
78.6
80.5
60290011
CA
Kern
83.3
85
78.3
79.9
60290014
CA
Kern
86.0
88
80.7
82.6
60290232
CA
Kern
79.3
82
74.4
76.9
60292012
CA
Kern
89.3
90
83.4
84.1
60295002
CA
Kern
87.3
89
81.7
83.3
60296001
CA
Kern
80.7
81
76.5
76.8
60311004
CA
Kings
83.3
84
76.0
76.6
60370002
CA
Los Angeles
94.3
99
87.1
91.5
60370016
CA
Los Angeles
100.0
103
92.4
95.2
60371201
CA
Los Angeles
88.3
91
81.8
84.3
60371602
CA
Los Angeles
75.7
76
73.0
73.3
60371701
CA
Los Angeles
92.0
95
84.6
87.4
60372005
CA
Los Angeles
84.7
86
79.9
81.1
60376012
CA
Los Angeles
98.0
100
90.6
92.4
60379033
CA
Los Angeles
87.3
89
79.8
81.4
60390004
CA
Madera
80.3
83
75.0
77.5
16
-------
Monitor ID
State
County
2016
Centered
Average
2016
Centered
Maximum
2026
Average
2026
Maximum
60392010
CA
Madera
82.7
84
76.1
77.3
60430003
CA
Mariposa
76.0
79
74.0
76.9
60470003
CA
Merced
80.7
82
73.9
75.1
60570005
CA
Nevada
86.3
90
77.2
80.5
60592022
CA
Orange
77.7
78
71.8
72.1
60595001
CA
Orange
75.3
76
71.7
72.4
60610003
CA
Placer
85.0
88
75.9
78.6
60610006
CA
Placer
80.0
81
71.7
72.6
60650012
CA
Riverside
95.3
98
84.9
87.3
60650016
CA
Riverside
79.0
80
71.1
72.0
60651016
CA
Riverside
99.7
101
88.8
89.9
60652002
CA
Riverside
82.7
85
75.7
77.8
60655001
CA
Riverside
88.7
91
79.6
81.7
60656001
CA
Riverside
92.3
93
82.5
83.1
60658001
CA
Riverside
96.7
98
88.6
89.7
60658005
CA
Riverside
95.0
98
87.0
89.7
60659001
CA
Riverside
88.7
91
79.9
82.0
60670012
CA
Sacramento
82.3
83
73.6
74.3
60710001
CA
San Bernardino
79.0
80
74.0
74.9
60710005
CA
San Bernardino
110.3
112
99.2
100.7
60710012
CA
San Bernardino
95.0
98
86.4
89.2
60710306
CA
San Bernardino
84.0
86
76.0
77.8
60711004
CA
San Bernardino
105.7
109
96.1
99.1
60712002
CA
San Bernardino
97.7
99
89.2
90.4
60714001
CA
San Bernardino
90.3
91
81.7
82.4
60714003
CA
San Bernardino
104.0
107
94.2
97.0
60719002
CA
San Bernardino
87.3
89
79.3
80.9
60719004
CA
San Bernardino
108.7
111
98.5
100.6
60731006
CA
San Diego
83.0
84
76.1
77.0
60990005
CA
Stanislaus
81.0
82
74.7
75.6
60990006
CA
Stanislaus
83.7
84
76.7
77.0
61030004
CA
Tehama
79.7
81
71.5
72.6
61070006
CA
Tulare
84.7
86
78.2
79.4
61070009
CA
Tulare
89.0
89
81.6
81.6
61072002
CA
Tulare
82.7
85
74.3
76.4
61072010
CA
Tulare
84.0
86
75.9
77.7
61090005
CA
Tuolumne
80.7
83
75.0
77.1
80590006
CO
Jefferson
77.3
78
71.7
72.3
80590011
CO
Jefferson
79.3
80
72.6
73.3
90010017
CT
Fairfield
79.3
80
71.5
72.2
17
-------
Monitor ID
State
County
2016
Centered
Average
2016
Centered
Maximum
2026
Average
2026
Maximum
90013007
CT
Fairfield
82.0
83
72.8
73.7
90019003
CT
Fairfield
82.7
83
74.6
74.8
482010024
TX
Harris
79.3
81
74.2
75.7
490110004
UT
Davis
75.7
78
71.7
73.9
490353006
UT
Salt Lake
76.3
78
72.5
74.1
490353013
UT
Salt Lake
76.5
77
73.5
74.0
550590019
WI
Kenosha
78.0
79
71.7
72.6
551170006
WI
Sheboygan
80.0
81
72.3
73.2
Table 3-4. Average and maximum 2016-centered and 2026 base case 8-hour ozone design values
(ppb) at projected maintenance-only receptors.
Monitor ID
State
County
2016-
Centered
Average
2016-
Centered
Maximum
2026
Average
2026
Maximum
40278011
AZ
Yuma
72.3
74
70.1
71.8
60090001
CA
Calaveras
77.0
78
70.2
71.1
60610004
CA
Placer
79.3
85
70.9
76.0
60650008
CA
Riverside
76.5
79
70.4
72.7
60711234
CA
San Bernardino
72.3
76
70.3
74.0
60773005
CA
San Joaquin
77.3
79
70.8
72.4
80350004
CO
Douglas
77.3
78
70.5
71.1
80690011
CO
Larimer
75.7
77
70.6
71.8
90099002
CT
New Haven
79.7
82
70.4
72.4
170310001
IL
Cook
73.0
77
68.7
72.5
170310032
IL
Cook
72.3
75
69.1
71.7
170310076
IL
Cook
72.0
75
68.5
71.3
170314201
IL
Cook
73.3
77
68.9
72.4
170317002
IL
Cook
74.0
77
69.1
72.0
350130021
NM
Dona Ana
72.7
74
70.4
71.7
350130022
NM
Dona Ana
71.3
74
69.0
71.6
480391004
TX
Brazoria
74.7
77
69.1
71.2
481671034
TX
Galveston
75.7
77
70.2
71.4
490570002
UT
Weber
73.0
75
69.8
71.7
550590025
WI
Kenosha
73.7
77
68.1
71.1
551010020
WI
Racine
76.0
78
70.2
72.1
18
-------
In total, in the 2023 base case there are 111 receptors nationwide including 85
nonattainment receptors and 26 maintenance-only receptors.29 Of the 85 nonattainment
receptors in 2023, 75 remain nonattainment receptors while 8 are projected to become a
maintenance-only receptors and 2 are projected to be in attainment in 2026. Of the 26
maintenance-only receptors in 2023, 13 are projected to remain maintenance-only receptors and
13 are projected to be in attainment in 2026. In total, in the 2026 base case there are 96 receptors
nationwide including 75 nonattainment receptors and 21 maintenance-only receptors.
In 2023 we also project that three monitoring sites in the Uintah 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. However, these monitors are within the
Uinta Basin nonattainment area which 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 we have not identified these three monitors as receptors in
Step 1 of this proposed rule. Studies have found that meteorological inversion conditions and the
unique topographic features of the Uinta Basin combined with local ground-level emissions of VOC
and NOx result in winter conditions that aid in the formation of ozone in the Uinta Basin.30 The Uinta
Basin is a bowl surrounded by much higher mountain ranges with varying heights from over 7,500 to
13,000 feet. In environments such as this one, cooler, denser air becomes trapped in the Basin when
warmer air overrides the area during high pressure events, creating an inversion. Normal atmospheric
mixing does not occur in this type of environment; instead, pollutants settle due to high-pressure
ridges and low surface winds within the Basin. Only when there are cooler temperatures aloft, with
high winds, or when surface warming occurs, can a temperature inversion break down and allow
pollutants to mix out of the Basin.
29 As explained in section V.F. of the preamble, EPA is proposing to determine that certain monitoring sites in California
should not be treated as downwind receptors for the purpose of evaluating interstate transport for the 2015 ozone
NAAQS.
30 Final Report: 2014 Uinta Basin Winter Ozone Study, https://documents.deq.utah.gov/air-quality/planning/air-quality-
policy/D AQ-2015-021002.pdf
19
-------
Snow cover also plays an essential role in winter ozone episodes in the Uinta Basin. Snow
acts as a highly reflective surface for both visible and UV radiation, so the sun's rays cannot reach the
ground covered by snow to warm the surface, thereby enhancing the stability of the inversion layer.
In addition, because the UV radiation passes through the air column again as it is reflected away from
the ground, the total UV flux is nearly doubled compared to conditions without snow cover, and the
increased UV flux enhances the photochemical reactions of VOC and NOx that form ozone. At night,
cold down-sloping winds from the surrounding mountains can strengthen the inversion. The super-
stable atmosphere allows emissions to accumulate, and the sunny conditions during the daytime let
photochemical reactions take place. Only emissions with enough heat, plume velocity, or stack height
can escape the inversion, depending on the boundary layer height, and enter the unstable atmosphere
above the inversion.
As noted above, under wintertime temperature inversion conditions, cold air pools form at the
lower elevations in the Basin, and pollutants are trapped in the pooled air under the temperature
inversion. Providing that snow cover is present, inversions can persist for periods longer than a week,
until energetic weather systems break the temperature inversion and sweep out trapped pollutants.
While trapping locally emitted pollutants under an inversion layer within the Uinta Basin, the
inversion layer also limits transported pollutants from outside the Basin from entering and
contributing to ozone formation, as warmer air, carrying upwind emissions, tends to pass above and
across the colder air trapped below. Once a temperature inversion is present, there is limited, if any,
transport of pollutants horizontally or vertically that could impact local photochemistry (Lyman,
et.al., 2015). In fact, even different areas of the Basin are "relatively isolated from each other,
allowing spatial factors like elevation and proximity to sources to strongly influence ozone
concentrations at individual sites." Id.
4. Ozone Contribution Modeling
As noted above, EPA performed nationwide, state-level ozone source apportionment
modeling using the CAMx OSAT/APCA technique to provide data on the contribution of
projected 2023 and 2026 base case NOx and VOC emissions from all anthropogenic source
sectors in each state. In addition, EPA also performed source apportionment modeling for
2026 to quantify the contributions from EGUs and from non-EGU point sources in each state
as well as the contributions from each state for a scenario in which NOx emissions from both
EGUs and non-EGU point sources are reduced by 30 percent from projected 2026 levels in
each state. The state-by-state all anthropogenic source apportionment modeling is described in
20
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section 4.1. In section 4.2 we describe the method for calculating the average contribution
metric for each source apportionment model run. In section 4.3 we present the results of the
state-by-state all anthropogenic modeling and in section 4.4 we describe the state-sector
source apportionment 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
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 2016 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 included in 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; and
• Offshore - total emissions from offshore marine vessels and offshore drilling platforms.
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 provided 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.
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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 base case emissions and 2016 meteorology. The resulting hourly contributions31 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
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
31 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.
22
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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 5 days with MDA8 ozone >= 60 ppb aligns with recommendations in EPA's air quality
modeling guidance for projecting future year design values, as described above.
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 "2016v2_DVs_state_contributions" which can be
found in the docket of this proposed rule. Note that this file contains data for monitoring sites that meet
the criteria for calculating valid contribution metric values, as described above.32
4.3 Results of State-by-State All Anthropogenic Modeling
The contribution metric values from each state and the other source tags at individual
nonattainment and maintenance-only sites in the 2023 and 2026 state-by-state all anthropogenic
model runs are provided in Appendix C. In this appendix we also provide tables with the sum of
32 Contribution metric values were not calculated for the following receptors because there were fewer than 5 days with
future year MDA8 ozone concentrations >= 60 ppb at each receptor: Kern County, California (Monitor ID: 06029001),
Mariposa County, California (Monitor ID: 060430006), Tehama County, California (Monitor ID: 061030004), Larimer
County, Colorado (Monitor ID: 080690011), Dona Ana County, New Mexico (Monitor IDs: 350130021 and 350130022),
El Paso County, Texas (Monitor ID: 481410037), Galveston County, Texas (Monitor ID: 481671034), Tooele County,
Utah (Monitor ID: 490450004), and Sheboygan County, Wisconsin (Monitor ID: 551170006).
23
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upwind contributions (ppb) and the percent of ozone concentrations attributed to emissions in upwind
states. The largest contribution values from each state to downwind receptors in 2023 and in 2026 are
provided in Tables 4-1 and 4-2, respectively.33
Table 4-1. Largest contribution from each state to downwind nonattainment and maintenance-only
receptors in 2023 (units are ppb).
Upwind State
Largest Contribution
to Downwind
Nonattainment
Receptors
Largest Contribution
to Downwind
Maintenance-Only
Receptors
Alabama
0.88
0.71
Arizona
0.40
0.21
Arkansas
1.00
1.39
California
34.24
7.44
Colorado
0.07
0.20
Connecticut
0.01
0.21
Delaware
0.53
1.36
District of Columbia
0.04
0.07
Florida
0.16
0.15
Georgia
0.16
0.17
Idaho
0.55
0.57
Illinois
18.13
18.55
Indiana
6.60
7.10
Iowa
0.64
0.58
Kansas
0.42
0.59
Kentucky
0.83
0.88
Louisiana
5.39
7.03
Maine
0.01
0.01
Maryland
1.29
2.40
Massachusetts
0.30
0.30
Michigan
1.27
1.67
Minnesota
0.50
0.97
Mississippi
1.04
1.14
Missouri
1.08
1.66
Montana
0.08
0.11
33 For California the largest contributions to downwind nonattainment in 2023 and 2026 are the contributions 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 EPA considers transport to
receptors on tribal lands in this proposed rule.
24
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Upwind State
Largest Contribution
to Downwind
Nonattainment
Receptors
Largest Contribution
to Downwind
Maintenance-Only
Receptors
Nebraska
0.26
0.36
Nevada
0.89
0.58
New Hampshire
0.10
0.06
New Jersey
8.85
5.79
New Mexico
0.30
0.13
New York
16.81
1.80
North Carolina
0.61
0.33
North Dakota
0.12
0.37
Ohio
1.94
1.88
Oklahoma
0.57
1.19
Oregon
1.10
1.31
Pennsylvania
6.90
0.51
Rhode Island
0.04
0.04
South Carolina
0.19
0.07
South Dakota
0.05
0.09
Tennessee
0.60
0.94
Texas
1.72
1.81
Utah
1.37
0.10
Vermont
0.02
0.02
Virginia
1.77
1.63
Washington
0.34
0.40
West Virginia
1.45
1.44
Wisconsin
0.19
2.61
Wyoming
0.81
0.19
Table 4-2. Largest contribution from each state to downwind nonattainment and maintenance-only
receptors in 2026 (units are ppb).
Upwind State
Largest Contribution
to Downwind
Nonattainment
Receptors
Largest Contribution
to Downwind
Maintenance-Only
Receptors
Alabama
0.17
0.48
Arizona
0.35
0.23
Arkansas
0.62
1.30
California
33.45
4.85
Colorado
0.05
0.08
Connecticut
0.01
0.01
25
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Upwind State
Largest Contribution
to Downwind
Nonattainment
Receptors
Largest Contribution
to Downwind
Maintenance-Only
Receptors
Delaware
0.42
0.52
District of Columbia
0.03
0.04
Florida
0.10
0.09
Georgia
0.14
0.16
Idaho
0.48
0.48
Illinois
17.81
18.14
Indiana
6.43
6.99
Iowa
0.57
0.57
Kansas
0.40
0.57
Kentucky
0.80
0.80
Louisiana
4.25
6.97
Maine
0.01
0.01
Maryland
1.11
1.23
Massachusetts
0.29
0.14
Michigan
1.03
1.58
Minnesota
0.36
0.91
Mississippi
0.36
0.90
Missouri
0.98
1.53
Montana
0.07
0.08
Nebraska
0.11
0.23
Nevada
0.81
0.51
New Hampshire
0.09
0.02
New Jersey
8.54
5.47
New Mexico
0.29
0.23
New York
16.58
11.29
North Carolina
0.38
0.54
North Dakota
0.11
0.34
Ohio
1.78
1.83
Oklahoma
0.54
0.72
Oregon
0.98
0.88
Pennsylvania
6.82
4.74
Rhode Island
0.04
0.01
South Carolina
0.15
0.17
South Dakota
0.03
0.06
Tennessee
0.25
0.34
Texas
1.61
1.70
Utah
0.95
1.18
26
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Upwind State
Largest Contribution
to Downwind
Nonattainment
Receptors
Largest Contribution
to Downwind
Maintenance-Only
Receptors
Vermont
0.02
0.01
Virginia
1.14
1.68
Washington
0.31
0.28
West Virginia
1.23
1.35
Wisconsin
0.15
2.44
Wyoming
0.46
0.80
In CSAPR, the CSAPR Update, and the Revised CSAPR Update 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 the analysis for those rulemakings because there were important, even if relatively small,
contributions to identified nonattainment and maintenance receptors from multiple upwind states
mainly in the eastern U.S. The Agency 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.
Based on the approach used in prior interstate ozone transport rules, upwind states that
contribute ozone in amounts at or above the 1 percent of the NAAQS threshold to a particular
downwind nonattainment or maintenance receptor are considered to be "linked" to that receptor in
Step 2 of the 4-step interstate transport framework. Each of the linked states is further analyzed in Step
3 to determine whether and what emissions from the upwind state contribute significantly to
downwind nonattainment and interfere with maintenance of the NAAQS. For the 2015 ozone NAAQS
the value of a 1 percent threshold is 0.70 ppb.
The maximum downwind contributions in Table 4-1 indicates that the following 22 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, Oregon, Pennsylvania, Texas, Utah,
Virginia, West Virginia, and Wyoming. Also based on the maximum downwind contributions in
Table 4-1, the following 23 states contribute at or above the 0.70 ppb threshold to downwind
27
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maintenance-only receptors in 2023: Alabama, Arkansas, California, Delaware, Illinois, Indiana,
Kentucky, Louisiana, Maryland, Michigan, Minnesota, Mississippi, Missouri, New Jersey, New
York, Ohio, Oklahoma, Oregon, Tennessee, Texas, Virginia, West Virginia, and Wisconsin.
In aggregate, the following 27 states contribute at or above 0.70 ppb to a downwind receptor
in 2023: Alabama, Arkansas, California, Delaware, Illinois, Indiana, Kentucky, Louisiana, Maryland,
Michigan, Minnesota, Mississippi, Missouri, Nevada, New Jersey, New York, Ohio, Oklahoma,
Oregon, Pennsylvania, Tennessee, Texas, Utah, Virginia, West Virginia, Wisconsin, and Wyoming.
Based on the maximum downwind contributions in Table 4-2, the following 24 states
contribute at or above 0.70 ppb to a downwind nonattainment and/or maintenance-only receptor in
2026: Arkansas, California, Illinois, Indiana, Kentucky, Louisiana, Maryland, Michigan, Minnesota,
Mississippi, Missouri, Nevada, New Jersey, New York, Ohio, Oklahoma, Oregon, Pennsylvania,
Texas, Utah, Virginia, West Virginia, Wisconsin, and Wyoming. Three states, Alabama, Delaware,
and Tennessee, that were linked in 2023 are not linked in 2026 because the receptor(s) to which each
state was linked in 2023 are projected to attain by 2026.
In Appendix D we provide the contributions for individual upwind/downwind linkages for
each upwind state. In Appendix E we identify the upwind state(s) that are linked to each receptor in
2023 and in 2026.
As noted above, when applying the 4-step interstate transport framework, an upwind state's
linkage to a downwind receptor alone does not determine whether the state significantly contributes
to nonattainment or interferes with maintenance of a NAAQS to a downwind state. The determination
of significant contribution is made in Step 3 as part of a multi-factor analysis, as described in the
Ozone Transport Policy Analysis Proposed Rule Technical Support Document for this proposed rule.
4.4 State-Sector Modeling
State-sector source apportionment modeling was performed for 2026 to obtain contributions
from EGUs and from non-EGU point sources in each state at each monitoring site, nationwide. In the
EGU run we tagged emissions from the "ptegu" emissions source sector in each state individually as
well as EGUs on tribal lands in the "tribal" tag. In addition to the state and tribal tags, there were
separate tags for initial and boundary conditions, biogenics, as well as a tag the included the total of
all other emissions within the modeling domain. For the non-EGU run we tagged the emissions from
sources in the "ptnonipm" sector plus emissions from Pipeline Transportation of Natural Gas (NAICS
28
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code 4862)34 and Basic Chemical Manufacturing (NAICS 3251) that are in the pt_oilgas sector.
Similar to the state-sector model run for EGUs, in the non-EGU run there were separate tags for non-
EGU point sources on tribal lands, initial and boundary conditions, biogenics, and the total of all
other emissions within the modeling domain. As described in Chapter 3 of the RIA for this proposed
rule, the outputs from the state-sector modeling were used to construct spatial fields of April through
September mean MDA8 concentrations to support the cost-benefit analysis of alternative EGU and
non-EGU control cases as part of the RIA.
In addition to the 2026 base case state-sector modeling, we conducted a state-by-state source
apportionment model run for 2026 in which NOx emissions from EGUs and non-EGUs (including
Pipeline Transportation of Natural Gas and Basic Chemical Manufacturing) were cut by 30 percent.
The outputs from this run were used to inform the development of "calibration" factors for use in the
ozone policy assessment for this proposed rule. The calibration factors were developed to account for
the non-linear response of ozone to changes in NOx emissions as part of the process for calculating
the estimated impacts of EGU and non-EGU emissions reductions on ozone design values. More
information on the development and application of calibration factors can be found in the Ozone
Transport Policy Analysis Proposed Rule TSD. In addition, we calculated contribution metric values
at individual monitoring sites from the tags in the state-EGU and state-non-EGU source
apportionment runs as well as the 30 percent EGU+Non-EGU NOx cut run. The contributions from
these three runs are provided in a file named: 2026_EGU_nonEGU_contributions, which can be
found in the docket for this proposed rule.
34 North American Industry Classification System.
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5. References
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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,
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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
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Radiative Forcing by Long-Lived Greenhouse Gases: Calculations with the AER Radiative
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U.S.A. Atmospheric Environment. 123 (2015) 156-165.
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Cyclone Prediction: Convective Trigger, Atmospheric Research, 92, 190-211.
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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
Numerical Tests, J. Climate, 21, 3642-3659.
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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Appendix A
Model Performance Evaluation for
2016v2 Base Year CAMx Simulation
-------
I. Introduction
An operational model evaluation was conducted for the 2016 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 2016v2 emissions platform (i.e., scenario name 2016fj).
The model evaluation for ozone focuses on comparisons of 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 A-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 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
National Oceanic and Atmospheric Administration (NOAA) climate regions (Figure A-2)3 as
defined in Karl and Koss (1984).
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 A-l and A-2), therefore the
statistics for the West region will be mostly representative of model performance in California ozone.
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.
A-2
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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 are
presented in this appendix. Model performance statistics for MDA8 ozone at individual
monitoring sites based on days with observed values > 60 ppb can be found in the docket in the
file named "2016v2 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) 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
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 ~ O) , where P = predicted and O = observed concentrations
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.
A-3
-------
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 = £;(np~0) * 100
Z?(o)
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:
NME = * 100
Z?(o)
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.,
2012; U.S. EPA, 2005; U.S. EPA, 2009; U.S. EPA, 2010).5 These other modeling studies
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).
A-4
-------
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 206 lv2 modeling platform correspond closely to observed concentrations in terms of the
magnitude, temporal fluctuations, and geographic differences for MDA8 ozone concentrations.
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 A-l. Performance statistics by climate
region, by month are provided in Table A-2. The statistics shown in these two tables were
calculated using all data pairs for May-September. Seasonal and monthly statistics based on
those days with observed MDA8 ozone > 60 ppb are presented in Table A-3 and A-4,
respectively.
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/420r090Q7.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.pdf)
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.
A-5
-------
The monthly distributions of observed and predi cted MDA8 ozone for each region are
shown in Figures A-3 through A-l 1. 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 A-12
through A-l 5. 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
are provided in Figures A-16 and A-17, respectively.
CIRCLE=AQS_Daily;
Figure A-l. Location of ozone monitoring sites.
U.S. Climate Regions
Figure A-2. NOAA climate regions (source: http://www.ncdc.noaa.gov/monitoring-references/maps/us-
climate-regions.php#references)
A-6
-------
A. Seasonal and Monthly Performance
1. Model Performance Statistics by Region
The model performance statistics provided in Table A-l show that regional mean bias is
less than +5 ppb6 and the mean error is between 6 and 7 ppb on average for all days during the
period May through September in each region. Normalized mean bias is less than + 5 percent in
the Northeast, Ohio Valley, Southeast, South, and Northwest. In the Midwest, Southwest,
Northern Rockies, and West, normalized mean bias is between 7 and 8 percent. Normalized
mean error is less than 15 percent in the Northeast, Ohio Valley, Southeast, Southwest, Northern
Rockies, and West and less than 20 percent in the other regions.
The monthly average statistics in Table A-2 show that the mean bias is generally +5 ppb
and the mean error is between 5 and 8 ppb during most months in each region. The main
exceptions are in the Northeast, Southwest, and Northern Rockies in May and during both May
and June in the Midwest where the mean bias is between 5 and 10 ppb and/or the mean error is
>8 ppb. The normalized mean bias values are less than +10 percent and the normalized mean
error is in the range of 10 to 20 percent in most months in each region. The main exception is the
Midwest region in May and June when the normalized mean biases are -14.7 percent and -20.6
percent, respectively, and the normalized mean errors are 21.5 percent and 16.8 percent,
respectively.
The model performance statistics for days with observed MDA8 ozone >60 ppb provided
in Table A-3 indicates that, on average, the model under predicts concentrations above this
threshold, although the bias and error are relatively low in most regions. In general, the seasonal
mean bias for MDA8 ozone > 60 ppb is close to or within +10 ppb in nearly all of the regions.
The exceptions are the Midwest and Northern Rockies where the mean bias shows under
prediction of approximately 12 ppb. The mean error is less than 10 ppb in the Northeast, Ohio
Valley, Southeast, South, and Southwest and between 10 and 15 ppb in other regions.
Normalized mean bias is within 10 percent for the Northeast, Southeast, and Northwest with
somewhat larger under prediction in Ohio Valley, South, Southwest, and West where the
normalized mean bias is between -10 and -15 percent. In the Midwest and the Northern Rockies
6 Note that "within +5 ppb" includes values that are greater than or equal to -5 ppb and less than or equal to 5 ppb.
A-7
-------
the normalized mean bias is -19 percent. The normalized mean error is less than approximately
15 percent for the Northeast, Ohio Valley, Southeast, South, and Southwest and less that 20
percent in the other regions. The model performance statistics by month, as provided in Table A-
4 indicate that under prediction of days >60 ppb is evident in most months in each region.
Although the amount of under prediction varies by month and by region, under prediction is
most notable in May in the Northeast, Ohio Valley, Midwest, and Southeast.
2. Distribution of Observed and Predicted MDA8 Ozone by Month
The monthly distributions of MDA8 model-predicted ozone for each region are provided
in Figures A-3 through A-l 1. In this section we focus on comparing the median and interquartile
range in the observed and predicted concentrations. In the Northeast and Ohio Valley the model
under predicts in May and June followed by over prediction in the remainder of the ozone
season. In the Midwest, the distribution of observed concentrations is under predicted in May
and June, but the median and 75th percentile values of the model generally align with the
corresponding observed data in July, August, and September. Observed peak values in this
region are notably under predicted in May, June, and August. In the Southeast, the distribution of
predictions generally corresponds well with that of the observed concentrations in May and June
with over prediction during the remainder of the ozone season. In the South, the distribution of
predicted concentrations tends to be close to that of the observed data at the 25th percentile,
median and 75th percentile values in most months with a tendency for under-prediction of peak
values in May and June. In the Southwest, the modeled values align with the median and
interquartile range of the observed values in May and June, but the decline in observed
concentrations after June is not as notable in the model predictions. In the Northern Rockies, the
model under predicts in May, June, and July, but closely captures the distribution of observed
concentrations in August and September. In the Northwest modeled MDA8 ozone under predicts
the observed values in May and June, but then more closely tracks the observed values in July,
August, and September. In the West region, the median and interquartile range of observed
ozone is under predicted in May through September with a tendency to under predict peak
values.
A-8
-------
3. Spatial Variability in Model Performance
Figures A-12 through A-15 show the spatial variability in bias and error for MDA8 ozone
on days with observed concentrations > 60 ppb. Mean bias, as seen in Figure A-12, is within + 5
ppb at many sites from portions of Texas northeastward to the Northeast Corridor. At monitors in
this area the normalized mean bias is generally within +10 percent, the mean error is mainly less
than 10 ppb and the normalized mean error is between 5 to 15 percent. At most monitoring sites
across the remainder of the East the model under predicts by 5 to 10 ppb, the normalized mean
bias is between -10 and -20 percent, the mean error is in the range of 10 to 15 ppb, with
normalized mean error of 10 to 15 percent. The exceptions are at some monitoring sites mainly
in most of Michigan, Wisconsin, the northern portions of Indiana and Illinois, and Upstate New
York where the magnitude of the low bias is 10 to 15 ppb, the normalized mean bias is -10 to -30
percent, the mean error is 10 to 15 ppb, and the normalized mean error is 15 to 25 percent.
Elsewhere in the U.S., the model generally under predicts MDA8 ozone > 60 ppb by
approximately 10 ppb, on average. In Arizona, Colorado, New Mexico, and Utah, there is
notable spatial heterogeneity in mean bias. For example, in Denver there are some sites with
mean bias within + 5 ppb while at relatively near-by monitors the model is low-biased by 5 to 10
ppb. In California, mean bias is within + 5 ppb with even some small overprediction at
monitoring sites near the coast. In and near Los Angeles and San Francisco the model under
predicts in the range of 5 to 10 ppb and 10 to 15 at monitors in these areas. In central California,
however, the model under prediction is as high as 20 ppb. For most monitoring sites in the West,
the normalized mean bias is -20 percent or less, except for central California where the
normalized mean bias is between -20 and -30 percent. Broadly, the mean error and normalized
mean error in the West are similar to monitors in the East, with mean error generally in the range
of 5 to 10 and 10 to 15 ppb and normalized mean error in the range of 10 to 15 and 15 to 20
percent at individual sites. Again, the notable exceptions are monitors in central California where
the mean error is 15 to 20 ppb and the normalized mean error is 20 to 25 percent.
A-9
-------
B. Observed and Predicted Temporal Patterns
In addition to the above analysis of overall model performance, we also examine how
well the 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 are provided in Figure A-
16. The plots in this figure 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 plots for the nine regions reveals that there are large differences in the day-to-day
variability among the regions. For example, the degree of temporal variability in MDA8 ozone
concentrations in the Northeast, Midwest, and Ohio Valley is much greater than in the Southeast
and South. In addition, the temporal variability in the Northern Rockies and Southwest is much
less than in other regions. As is evident from Figure A-16, 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, as shown in Figure A-17, indicate that, again, the
modeling platform generally replicates the day-to-day variability in ozone during this time period
at these sites.7 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 under
prediction, 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.
7 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 A-16 is representative of other receptors within the same
areas.
A-10
-------
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. The same is true at the Philadelphia-Bristol receptor, except that
the model over predicts the observed values from mid-July to mid-August. At the Chicago-Alsip
and Chicago-Evanston receptors, the model closely tracks the day-to-day variability during
nearly the entire period. At both receptors, the modeled concentrations are similar to the
corresponding measured values except for May and June when the model under predicts the
observed values at Alsip and to a lesser extent at Evanston. At the Kenosha-Water Tower
monitor the model tends to under predicts the observed values on most of the measured high
ozone days, most notably in May. Model predictions are lower than the corresponding observed
values on nearly all days at the Sheboygan monitor. The under prediction at the monitors in the
Chicago area as well as Kenosha and Sheboygan may be due in part to an underestimate of the
amount of ozone formed in the marine layer over Lake Michigan and the advection of high
ozone over the lake onshore as part of the lake breeze circulation near the land-water interface.
At Houston-Aldine receptor the modeled values closely track the variability and magnitude of
the corresponding observed values throughout the period May through September. The most
notable exceptions are on two episode-days, one in late June and another in late July, when the
model notably overpredicts (June) and underpredicts (July) the observed peak values on these
days. At other receptors in Texas (Dallas-Denton, Galveston, and Brazoria) the model also
closely replicates the intra-seasonal variation in ozone episodes in receptors. At the Denton and
Brazoria receptors, the model predictions are similar in magnitude to the observations, but the
model under predicts during May episodes at Galveston.
The day-to-day variability in observed MDA8 is significantly greater at receptors in the
East compared to receptors in Denver, Salt Lake City, and Las Vegas (see, for example, the plots
for Denver-Chatfield compared to the plot for Stratford). It is important to note that the model
accurately captures this substantial geographical difference in the temporal nature of ozone
concentrations at the receptors in Denver, Salt Lake City, and Las Vegas compared to receptors
in the East. At the receptors in each of these three western areas, the model tends to under predict
the corresponding observed MDA8 ozone concentrations.
A-ll
-------
C. Conclusions
In summary, the ozone model performance statistics for the CAMx 20161] (2016v2)
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 2016v2 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
2016v2 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 A-l. Performance statistics for MDA8 ozone for the period May through September (all
days).
Climate Region
Number of
Site-Days
MB
(ppb)
ME
(ppb)
NMB
(%)
NME
(%)
Northeast
27,724
0.2
6.3
0.6
14.3
Ohio Valley
33,784
0.4
6.3
1.0
14.1
Midwest
16,279
-2.9
6.3
-6.9
15.2
Southeast
26,500
1.9
6.1
4.7
14.9
South
21,427
0.0
6.6
0.0
16.5
Southwest
17,469
-3.9
6.9
-7.4
13.4
Northern Rockies
8,608
-3.5
6.2
-8.0
14.0
Northwest
4,012
0.1
6.5
0.2
17.5
West
29,789
-3.1
7.4
-6.0
14.5
A-12
-------
Table A-2. Performance statistics for each month in the period May through September (all days).
Midwest
Northeast
Number of
MB
ME
NMB
NME
Number of
MB
ME
NMB
NME
Month
Site-Days
(ppb)
(ppb)
m
l°u 1
Month
Site-Days
(ppb)
(ppb)
(%)
(%)
May
3332
-9.7
10.1
-20.6
21.5
May
5661
-6.1
7.7
-13.6
17.2
June
3210
-7.1
8.1
-14.7
16.8
June
5443
-2.1
5.9
-4.5
12.7
Julv
3236
0.4
4.8
1.1
11.8
July
5647
3.6
6.4
7.6
13.6
Auau^t
3296
1.1
4.8
3.0
12.4
August
5596
4.0
6.2
9.3
14.6
September
3205
1.0
3.7
3.0
11.1
September
5377
2.0
5.1
5.1
13.3
Ohio Valley
Southeast
Number of
MB
ME
NMB
NME
Number of
MB
ME
NMB
NME
Month
Site-Days
(ppb)
(ppb)
(%)
m
Month
Site-Dav s
(ppb)
(ppb)
(%)
(°oi
May
6543
-4.8
6.9
-10.4
14.7
May
5488
-1.8
5.3
-3.9
11.4
June
6319
-3.1
6.9
-5.8
13.1
June
5192
0.5
5.4
1.1
12.0
July
7128
4.0
6.6
9.2
15.1
July
5325
5.1
7.0
13.1
18.2
August
7054
3.1
6.3
7.7
15.7
Auaust
5338
3.2
6.5
9.2
18.6
September
6740
2.3
4.8
5.5
11.8
September
5157
2.8
6.2
7.0
15.9
South
Number of
MB
ME
NMB
NME
Month
Site-Days
(ppb)
(ppb)
(%)
(%)
May
4372
-1.2
7.1
-2.7
15.9
June
4231
-0.3
6.5
-0.7
15.4
July
4260
0.6
6.5
1.7
17.6
August
4328
-0.1
7.3
-0.2
20.2
September
4236
1.0
5.5
2.6
13.9
A-13
-------
Table A-2. Performance statistics for each month in the period May through September (continued).
Northwest
Northern Rockies
Number of
MB
ME
NMB
NME
Number of
MB
ME
NMB
NME
Month
Site-Days
(ppb)
(ppb)
(96)
(96)
Month
Site-Days
(ppb)
(ppb)
(96)
06)
May
832
-4.5
7.2
-11.0
17.7
May
1781
-8.0
8.9
-17.2
19.1
Jiuie
816
-2.7
7.2
-6.7
18.2
June
1691
-5.0
7.2
-10.4
14.9
July
827
1.7
5.6
4.9
16.2
Julv
1694
-2.7
5.6
-6.0
12.1
August
801
3.4
6.6
8.6
16.9
Auaust
1749
-1.6
5.1
-3.7
11.4
September
736
2.8
6.0
9.0
18.8
September
1693
-0.1
4.1
-0.4
11.7
West
Southwest
Number of
MB
ME
NMB
NME
Number of
MB
ME
NMB
NME
Month
Site-Days
(ppb)
(ppb)
(96)
(96)
Month
Site-Days
(ppb)
(ppb)
(96)
(96)
May
6027
-4.8
6.6
-10.1
14.1
May
3556
-7.0
8.0
-13.0
14.7
June
5841
-3.2
8.0
-6.1
15.1
June
3404
-2.5
6.1
-4.5
11.1
July
6095
-3.3
7.8
-6.2
14.7
Jul}'
3505
-5.5
7.8
-10.2
14.4
August
6110
-2.1
8.1
-3.9
15.1
August
3552
-4.5
7.5
-8.5
14.2
September
5716
-2.0
6.4
-4.0
13.1
September
3452
0.4
5.3
1.0
12.2
A-14
-------
Table A-3. Performance statistics for days with MDA8 ozone > 60 ppb for the period May
through September.
Climate Region
Number of Site-
Days > 60 ppb
MB
(ppb)
ME
(ppb)
NMB
(%)
NME
(%)
Northeast
2997
-4.1
7.1
-6.2
10.7
Ohio Valley
3211
-7.1
8.7
-10.9
13.3
Midwest
1134
-12.7
13.0
-19.1
19.5
Southeast
1477
-2.9
6.1
-4.5
9.4
South
993
-7.8
9.1
-12.0
14.1
Southwest
3054
-8.8
9.7
-13.6
15.1
Northern Rockies
215
-11.9
12.4
-19.0
19.8
Northwest
84
-5.8
10.8
-9.0
16.6
West
8279
-9.7
11.4
-13.8
16.2
A-15
-------
Table A-4. Performance statistics for days with MDA8 ozone > 60 ppb by month.
Midwest
Northeast
Number of
MB
ME
NMB
NME
Number of
MB
ME
NMB
NME
Month
Site-Days
(ppb)
(ppb)
(96)
("a )
Month
Site-Days
(ppb)
(ppb)
(96)
f°c )
May
369
-15.1
15.1
-23.0
23.1
May
620
-9.1
10.0
-13.0
14.2
Jiuie
473
-13.4
13.5
-20.1
20.1
June
787
-5.4
7.2
-8.2
11.0
Julv
125
-7.9
9.0
-12.0
13.7
My
898
-1.0
6.4
-1.5
9.5
Augiist
146
-9.3
10.1
-13.7
14.9
August
372
-0.7
5.5
-1.1
8.5
September
21
-5.5
5.6
-8.7
8.9
September
320
-4.6
6.1
-6.8
9.2
Ohio Valley
Southeast
Number of
MB
ME
NMB
NME
Number of
MB
ME
NMB
NME
Month
Site-Days
(ppb)
(ppb)
(96)
CJo I
Month
Site-Days
(ppb)
(ppb)
(96)
(96)
May
601
-10.9
10.9
-16.7
16.8
May
554
-5.9
6.8
-9.1
10.4
June
1552
-8.5
9.6
-12.9
14.4
June
492
-2.0
5.7
-3.0
8.6
July
348
-1.9
6.5
-2.9
10.1
July
14S
1.3
6.6
2.1
10.1
August
283
-3.3
6.9
-5.0
10.4
August
S
0.5
6.7
0.8
10.5
September
427
-3.6
5.9
-5.6
9.0
September
171
-1.4
4.8
-2.2
7.5
South
Month
Number of
Site-Days
MB
(ppb)
ME
(ppb)
NMB
(96)
NME
l°6 i
May
295
-9.1
9.5
-14.0
14.7
June
381
-6.4
8.1
-9.7
12.3
Julv
111
-9.0
11.0
-13.8
16.9
Auaust
58
-14.3
14.6
-22.0
22.4
September
148
-5.7
8.0
-8.9
12.5
A-16
-------
Table A-4. Performance statistics for days with MDA8 ozone > 60 ppb by month (continued).
Northwest
Northern Rockies
Number of
MB
ME
NMB
NME
Number of
MB
ME
NMB
NME
Month
Site-Days
(ppb)
(ppb)
(%)
(%)
Month
Site-Days
(ppb)
(PPb)
(%)
(%)
May
6
-12.8
12.8
-20.8
20.8
May
57
-17.6
17.6
-28.2
28.2
June
25
-8.1
13.0
-12.4
19.8
June
70
-12.6
12.6
-19.7
19.7
July
6
-8.6
8.6
-13.5
13.5
July
28
-10.9
10.9
-17.8
17.8
August
45
-2.8
9.3
-4.3
14.3
August
60
-6.3
8.1
-10.1
13.0
September
2
-17.9
17.9
-28.7
28.7
September
-
-
-
-
-
West
Southwest
Number of
MB
ME
NMB
NME
Number of
MB
ME
NMB
NME
Month
Site-Days
(ppb)
(ppb)
(%)
(%)
Month
Site-Days
(ppb)
(ppb)
(%)
i°fi)
May
80S
-11.4
11.6
-17.4
17.7
May
823
-10.2
10.4
-15.9
16.3
June
1869
-8.5
11.1
-12.0
15.5
June
827
-5.5
7.7
-8.4
11.8
Julv
2226
-10.1
11.4
-14.1
16.0
July
824
-11.2
11.7
-17.2
18.0
August
2268
-9.5
11.5
-13.4
16.2
August
556
-8.1
9.0
-12.5
14.0
September
1108
-10.2
11.6
-14.9
17.0
September
24
-10.1
10.1
-16.0
16.0
A-17
-------
AQS Daily, CAMx_2016fj_v710_CB6r5_12US2, 03_8hrmax, 20160501 to 20160930, Northeast
^3 AQS Daily
CAMx_2016fj_v710_CB6r5_12US2
II
II31 ^
Figure A-3. Distribution of observed and predicted VIDAS ozone by month for the period May
through September for the Northeast region, [line within box = median; top/bottom
of box = 75th/25th percentiles; top/bottom dots = peak/minimum values]
AQS Daily, CAMx_2016fj_v710_CB6r5_12US2, 03_8hrmax, 20160501 to 20160930, Ohio Valley
120- gig aqs Daily
CAMx_2016fj_v710_CB6r5_12US2
| 60-
° 50-
I
Figure A-4. Distribution of observed and predicted MDA8 ozone by month for the period May
through September for the Ohio Valley region.
A-18
-------
AQS Daily, CAMx_2016fj_v710_CB6r5_12US2, 03_8hrmax, 20160501 to 20160930, Upper Midwest
EjElAQS Daily
CAMx_2016fj_v710_CB6r5_12US2
Figure A-5. Distribution of observed and predicted MDA8 ozone by month for the period May
through September for the Midwest region.
AQS Daily, CAMx_2016fj_v710_CB6r5_12US2, 03_8hrmax, 20160501 to 20160930, Southeast
^3 AQS Daily
CAMx_2016fj_v710_CB6r5_12US2
^ m i«
Figure A-6. Distribution of observed and predicted MDA8 ozone by month for the period May
through September for the Southeast region.
A-19
-------
AQS Daily, CAMx_2016fj_v710_CB6r5_12US2, 03_8hrmax, 20160501 to 20160930, South
^3 AQS Daily
CAMx_2016fj_v710_CB6r5_12US2
H
¦ ¦ II
Figure A-7. Distribution of observed and predicted MDA8 ozone by month for the period May
through September for the South region.
AQS Daily, CAMx_2016fj_v710_CB6r5_12US2, 03_8hrmax, 20160501 to 20160930, Southeast
^3 AQS Daily
CAMx_2016fj_v710_CB6r5_12US2
^ m i«
Figure A-8. Distribution of observed and predicted MDA8 ozone by month for the period May
through September for the Southwest region.
A-20
-------
AQS Daily, CAMx_2016fj_v710_CB6r5_12US2, 03_8hrmax, 20160501 to 20160930, No. Rockies
^3 AQS Daily
CAMx_2016fj_v710_CB6r5_12US2
+
Figure A-9. Distribution of observed and predicted MDA8 ozone by month for the period May
through September for the Northern Rockies region.
AQS Daily, CAMx_2016fj_v710_CB6r5_12US2, 03_8hrmax, 20160501 to 20160930, Northwest
^3 AQS Daily
CAMx_2016fj_v710_CB6r5_12US2
II
Figure A-10. Distribution of observed and predicted MDA8 ozone by month for the period May
through September for the Northwest region.
A-21
-------
AQS Daily, CAMx_2016fj_v710_CB6r5_12US2, 03_8hrmax, 20160501 to 20160930, West
E-EIAQS Daily ;
^iCAMx_2016fj_v710_fcB6r5_f2US2 '
hi Oil 0ii pi ai
Figure A-l 1. Distribution of observed and predicted MDA8 ozone by month for the period May
through September for the West region.
units - ppb
coverage limit = 75%
>25
20
15
10
5
0
-5
-10
-15
-20
<-25
AQS Daily
Figure A-12. Mean Bias (ppb) of MDA8 ozone > 60 ppb over the period May-September, paired
in time and space.
Q3_8hrmax MB (ppb) for run CAMx_2016fi_v710_CB6r5_12US2 for 20160501 to 20160930
A-22
-------
units - %
coverage limit = 75%
>100
90
80
70
60
50
40
30
20
10
0
-10
-20
-30
-40
-50
-60
-70
-80
-90
<-100
for 20160501 to 20160930
• AQS Daily
Figure A-13. Normalized Mean Bias (%) of MDA8 ozone > 60 ppb over the period May-
September 2016, paired in space and time.
units - ppb
coverage limit = 75%
R
>50
45
40
35
30
25
20
15
10
5
0
AQS Daily
Figure A-14. Mean Error (ppb) of MDA8 ozone > 60 ppb over the period May-September 2016,
paired in time and space.
Q3_8hrmax ME (ppb) for run CAMx_2016fj_v710_CB6r5_12US2 for 20160501 to 20160930
A-23
-------
03_8hrmax NME (%) for run CAMx_2016fj_v710_CB6r5_12US2 for 20160501 to 20160930
• AQS Daily
Figure A-15. Normalized Mean Error (%) of MDA8 ozone > 60 ppb over the period May-
September 2016, paired in time and space.
A-24
-------
Figure A-16. Time series of observed and predicted regional average MDA8 ozone concentrations for the period
May through September 2016.
AOS Daly
C AMx _2016fj_v710_CB6f5_ 12US2
# ot Sites: 110
Midwest
— AQS Daily
— CAMx_2Q16l|_v710_CB6r5_t 2US2
# oJ Sites: 192
Northeast
May 01 MaylO May 19 May 28 Jun06 Jun 14 Jun22 Jun30 Jul OS Jul 16 Jul 24 Aug 01 Aug to Aug 19 Aug28 SepOC SepIS Sep24 May 01 May 10 May 19 May 28 Jun
-------
AOS Daify
CAMj 2016!j_v710_C86f5_ 12US2
I or Sim: 21
Northwest
1111II1M T M1IIIII111 (II11111 IT 11 Tl 31IIIII r MT! I r M11111ITI3 r 111 II n 31M M11II111T M1II1131M111IM r M n ITU Ml r II r M113111IM MI El I til 111 CM n I r M111 Cll
Ujy01 Mjry 10 May 19 Mjy28 Jun06 Jun 14 ±r>22 Jun30 Jul08 Xt 16 Jul24 AugOt Aug 10 Aug19 Aug 28 Sep06 S«plS Sep24
nresr5r
- cam*_2qiefj_v7io_cb&5_isuss Northern Rockies
—iii in 1111 rn 1111 ru i in 1111 in rin 111 mi mi in in i in 1111 in i in mi i [iiiui in i in in i in 11111111 in 1111 rm i! i mi mi in mi in mi nil in i in mi in
MjyOT Mjyio W^19 Mjy28 JunQ& Jun14 Jun22 Jun30 Jul OS JJ16 Ju<24 Aug 01 Aug 10 AugV9 Aug 28 S«p06 S«p15 Sec 24
"— AOS Daily
" — CAMx_20t6»i_v710_CB6f5J2US2
i of Sites 2-35
West
— AOS Daily
CAMjh_2016Ilv7I0_C86(5_12US2
I o« S'tes RO
Southwest
1111111111111111111II11111111111111111111T11111111111111II11111111111II1111111111111II11II11U111111111111II1111111T111111111111M11111111 tl 111111II11111
Way01 Mjy 10 M«y 19 Uay28 Jun06 Jun 14 Jir22 Jun30 Jul08 JJ16 Jul24 Aug01 Aug 10 Aug 19 Aug28 S4p06 SeplS S«p24
—m i r ii mm iii mi i linn iii rni iiinii iii mi i ii mi nil iii rn iii ii ii i ii 11 in ii 11 in rn mi in i ii i r n i in mi i iii tiiriMin rim m i imim Minin immi i ii
UjyOl UjylO Mayl9 Mjy28 Jun OS Jun 14 Jiri22 Jun 30 Jut 08 JuM6 Jul 24 AugQl AuglO Aug 19 Aug 28 S«>06 SepIS Sep24
A-26
-------
Figure A-l 7. Time series of observed and predicted MDA8 ozone concentrations for the period
May 1 through September 2016 for selected ozone receptor sites.
Stratford, CT
100 -
80 -
70 -
60
50 -
40
30 -
20 H
Way 01 May 10 May 19 Way 28 Jun06 Jun14 Jur*22 Jun30 Jul 11 Jul 19 Jul 27 Aug 04 Aug 13 Aug 23 Sep 01 Sep 10 Sep 19 Sep 28
a of Sites: 1
Site: 090013007
AOS Daily
C AMx_2016fj_v710
Dale
Madison, CT
100 -
^ 80 -
1 70
| 60 -
®, 50 -
8
40 -
11II
Way 01 May 10 May 19 Way 28
HI M I 11 I 111 I 11 M I i 11 1111 111 I 111 I 111 I 1 111 I m IIII I 11 I 111 11II 1111 III I \i 11 111 I 1111 111 111 I I. I I 111 I 111 I 111 I III I II I
Jun 07 Jurvl5 Jun 23 Jul 01 Jul 09 Jul 18 Jul 26 Aug 04 Aug 13 Aug 22 Aug 31 Sep09 Sep 18 Sep 27
ol Sites: 1
Site: 090099002
AOS Daily
CAMx_2D1
12US2
Dale
A-27
-------
Philadelphia - Bristol
May 01 May 10 May 19 May 28 Jun 06 Jun14 Jon 28 Jul 06 Jul 14 Jul 22 Jul 30 Aug 09 Aug 18 Aug 2" Sep 05 Sep 14 Sep 23
Date
100 -
90
^ 80 -
JO
& 70 -
| 60 -
®, 50 -
3
40 -
30 -
20
"TTT"; a! of Sites: 1
AOS Daily
CAMx_2016fj_v7lO_CB6rS_12US2 Site: 420170012
ITT r Tin
Sep 23
100 -
90 -
80 -
70 -
60 -
50 -
40 -
30 -
20 -
"TTTT" # or Sites: I
AOS Daily
CAMx_2016fj_v710_CB6rS_12US2 Site: 17031Q001
Chicago - Aisip
May 01 May 10 May 19 May 28 Jun 07 Jun 15 Jur>23 Jul 01 JuJOS JuU7 Jul 25 Aug 02 Aug 11 Aug 20 Aug 29 Sep 07
Dane
A-28
-------
Chicago - Evanston
May 01 May 10 May 19 May 28 Jun 06 Jurv 15 Jun 23 Jul 01 Jul 09 Jul 17 Jut 25 Aug 02 Aug 12 Aug 21 Aug 30 Sep 10 Sep 19 Sep 28
Dale
Kenosha - Water Tower
May 01 May 10 May 19 May 28 Jun 06 Jun 14 Jun22 Jun30 Jul 08 Jul 16 Jul 24 Aug 01 Aug 10 Aug 19 Aug 28 Sep 06 Sep 15 Sep 24
100 -
90 -
_ 80 -
.q
& 70-
| 60
®| 50 -|
CO
° 40
30 -
20
AQS Daily
CAMx_2016fj_v710_CB6r5_12US2
# of Sites: 1
Site: 550590019
Date
A-29
-------
Sheboygan
Date
Houston - Aldine
AQS Daily
CAMx_2016fj_v710_CB6r5_12US2
# of Sites: 1
Site: 482010024
I 11 I III
mm I III mil
May 01 May 10 May 19 May 28 Jun 05 Jun 13 Jun 21 Jun29 Jul 07 Jul 15 Jul 23 Jul 31 Aug 08 Aug 16 Aug 25 Sep 03 Sep 12 Sep 21
Date
A-30
-------
Galveston
Date
Brazoria, TX
100 -
90 -
AQS Daily
CAMx_2016fj_v710_CB6r5_12US2
# of Sites: 1
Site: 480391004
80 -
70 -
60 -
50 -
40 -
30 -
20 -
inmiirnrmm11iiim111iitmmi 111111nin111n11111111111n111111111nn11
Ylf
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 07 Sep 16 Sep 25
Date
A-31
-------
Dallas - Denton
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
Date
100 -
90 -
_ 80 -
-O
a 70 -
I so
50
o
40 -
30 -
20
„ , # of Sites: 1
AQS Daily
CAMx_2016fj_v710_CB6r5_12US2 sile: 481210034
Denver - Chatfield
100 -
# ot sites: l
AQS Daily
CAMx_2016fj_v710_CB6r5_12US2 Site: 080350004
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
Date
A-32
-------
Denver - NREL
100 -
20 -
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_2016fLv710_CB6r5J2US2
# of Sites: 1
Site: 080590011
Date
Salt Lake City - Bountiful
100 -
20 -
111111111111II111II11II111II11111111 \ 111111111111111111111111111111111111111111111II111II111II111111111111111111111111111111111111111111111111111111111 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
"TTTT7 # of Sites: 1
AQS Daily
CAMx_2016fj_v710_CB6r5_12US2 site: 490110004
A-33
-------
Salt Lake City - Harrisonville
100 -
20 -
May 03 May 12 May 21 May 30 Jun 07 Jun 15 Jun 23 Jul 01 Jul 08 Jul 15 Jul 22 Jul 29 Aug 06 Aug 14 Aug 22 Aug 30 Sep 07 Sep 16 Sep 25
AQS Daily
CAMx_2016fLv710_CB6r5J2US2
# of Sites: 1
Site: 490571003
Las Vegas - Northwest
100 -
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
AQS Daily
CAMx 2016fj_v710_CB6r5_12US2
# of Sites: 1
Site: 320030075
A-34
-------
Appendix B
Projected 2023, 2026, and 2032 Average and
Maximum Design Values and
2020 Measured Design Values at Monitoring Sites that are
Receptors in 2023
-------
Site ID
ST
County
2016
Centered
Avg
2016
Centered
Max
2023
Avg
2023
Max
2026
Avg
2026
Max
2032
Avg
2032
Max
2020
40278011
AZ
Yuma
72.3
74
70.5
72.2
70.1
71.8
69.6
71.2
68
60070007
CA
Butte
76.7
79
68.9
71.0
68.1
70.1
65.7
67.7
73
60090001
CA
Calaveras
77.0
78
70.9
71.9
70.2
71.1
68.7
69.6
72
60170010
CA
El Dorado
85.3
88
76.3
78.7
75.0
77.4
72.9
75.2
84
60170020
CA
El Dorado
82.0
84
74.3
76.2
73.2
75.0
71.4
73.1
80
60190007
CA
Fresno
87.0
89
80.4
82.2
79.5
81.3
78.0
79.8
80
60190011
CA
Fresno
90.0
91
82.9
83.8
81.9
82.8
80.4
81.3
84
60190242
CA
Fresno
84.3
86
79.5
81.1
78.7
80.3
77.4
78.9
79
60194001
CA
Fresno
90.3
92
82.8
84.4
81.8
83.3
80.1
81.6
81
60195001
CA
Fresno
91.0
94
83.7
86.4
82.7
85.4
81.1
83.8
84
60250005
CA
Imperial
76.7
77
76.3
76.6
76.2
76.5
75.9
76.2
78
60251003
CA
Imperial
76.0
76
75.4
75.4
75.3
75.3
74.9
74.9
68
60290007
CA
Kern
87.7
89
82.8
84.0
82.2
83.4
80.6
81.8
93
60290008
CA
Kern
83.0
85
79.1
81.0
78.6
80.5
77.2
79.1
85
60290011
CA
Kern
83.3
85
78.8
80.4
78.3
79.9
77.4
79.0
86
60290014
CA
Kern
86.0
88
81.3
83.2
80.7
82.6
79.1
80.9
85
60290232
CA
Kern
79.3
82
74.9
77.5
74.4
76.9
72.9
75.4
83
60292012
CA
Kern
89.3
90
84.1
84.7
83.4
84.1
81.9
82.6
85
60295002
CA
Kern
87.3
89
82.4
84.0
81.7
83.3
80.2
81.7
89
60296001
CA
Kern
80.7
81
77.1
77.4
76.5
76.8
75.4
75.7
82
60311004
CA
Kings
83.3
84
76.9
77.6
76.0
76.6
74.5
75.1
80
60370002
CA
Los
Angeles
94.3
99
88.0
92.4
87.1
91.5
85.9
90.2
97
60370016
CA
Los
Angeles
100.0
103
93.4
96.2
92.4
95.2
91.1
93.8
107
60371103
CA
Los
Angeles
73.0
74
70.5
71.5
69.9
70.9
69.0
70.0
76
60371201
CA
Los
Angeles
88.3
91
82.7
85.3
81.8
84.3
80.5
83.0
92
60371602
CA
Los
Angeles
75.7
76
73.6
73.9
73.0
73.3
72.1
72.4
78
60371701
CA
Los
Angeles
92.0
95
85.6
88.4
84.6
87.4
83.4
86.1
88
60372005
CA
Los
Angeles
84.7
86
80.7
81.9
79.9
81.1
78.9
80.1
93
60376012
CA
Los
Angeles
98.0
100
91.6
93.4
90.6
92.4
89.2
91.1
101
60379033
CA
Los
Angeles
87.3
89
80.7
82.2
79.8
81.4
78.8
80.3
80
60390004
CA
Madera
80.3
83
75.7
78.3
75.0
77.5
73.7
76.2
76
B-l
-------
Site ID
ST
County
2016
Centered
Avg
2016
Centered
Max
2023
Avg
2023
Max
2026
Avg
2026
Max
2032
Avg
2032
Max
2020
60392010
CA
Madera
82.7
84
77.0
78.2
76.1
77.3
74.7
75.9
78
60430003
CA
Mariposa
76.0
79
74.2
77.1
74.0
76.9
73.6
76.5
79
60430006
CA
Mariposa
75.0
76
70.1
71.0
69.5
70.4
68.4
69.3
79
60470003
CA
Merced
80.7
82
74.7
75.9
73.9
75.1
72.3
73.5
76
60570005
CA
Nevada
86.3
90
78.1
81.5
77.2
80.5
75.2
78.4
82
60592022
CA
Orange
77.7
78
72.5
72.8
71.8
72.1
70.7
71.0
82
60595001
CA
Orange
75.3
76
72.3
73.0
71.7
72.4
70.8
71.4
77
60610003
CA
Placer
85.0
88
77.1
79.8
75.9
78.6
74.0
76.6
N/A1
60610004
CA
Placer
79.3
85
71.9
77.0
70.9
76.0
69.2
74.1
N/A
60610006
CA
Placer
80.0
81
72.8
73.7
71.7
72.6
69.9
70.7
72
60650008
CA
Riverside
76.5
79
71.0
73.3
70.4
72.7
69.7
72.0
N/A
60650012
CA
Riverside
95.3
98
85.9
88.3
84.9
87.3
83.6
86.0
99
60650016
CA
Riverside
79.0
80
72.0
72.9
71.1
72.0
69.9
70.8
78
60651016
CA
Riverside
99.7
101
89.8
90.9
88.8
89.9
87.5
88.6
99
60652002
CA
Riverside
82.7
85
76.4
78.5
75.7
77.8
74.9
77.0
84
60655001
CA
Riverside
88.7
91
80.5
82.6
79.6
81.7
78.7
80.7
88
60656001
CA
Riverside
92.3
93
83.5
84.1
82.5
83.1
81.3
81.9
94
60658001
CA
Riverside
96.7
98
89.5
90.7
88.6
89.7
87.4
88.6
96
60658005
CA
Riverside
95.0
98
87.9
90.7
87.0
89.7
85.9
88.6
98
60659001
CA
Riverside
88.7
91
80.8
82.9
79.9
82.0
78.7
80.7
87
60670002
CA
Sacramento
77.7
78
71.4
71.7
70.5
70.8
68.8
69.1
72
60670012
CA
Sacramento
82.3
83
74.8
75.4
73.6
74.3
71.8
72.4
N/A
60675003
CA
Sacramento
77.3
79
70.2
71.7
69.1
70.7
67.4
68.8
70
60710001
CA
San
Bernardino
79.0
80
74.5
75.4
74.0
74.9
73.4
74.3
81
60710005
CA
San
Bernardino
110.3
112
100.3
101.8
99.2
100.7
97.8
99.3
109
60710012
CA
San
Bernardino
95.0
98
87.3
90.1
86.4
89.2
85.4
88.1
90
60710306
CA
San
Bernardino
84.0
86
76.8
78.6
76.0
77.8
75.0
76.8
83
60711004
CA
San
Bernardino
105.7
109
97.2
100.2
96.1
99.1
94.6
97.5
106
60711234
CA
San
Bernardino
72.3
76
70.6
74.2
70.3
74.0
70.0
73.6
76
60712002
CA
San
Bernardino
97.7
99
90.1
91.3
89.2
90.4
88.0
89.2
102
60714001
CA
San
Bernardino
90.3
91
82.6
83.3
81.7
82.4
80.7
81.3
87
1 No valid official 2020 design value for this monitoring site at the time this table was prepared.
B-2
-------
Site ID
ST
County
2016
Centered
Avg
2016
Centered
Max
2023
Avg
2023
Max
2026
Avg
2026
Max
2032
Avg
2032
Max
2020
60714003
CA
San
Bernardino
104.0
107
95.2
98.0
94.2
97.0
93.0
95.7
114
60719002
CA
San
Bernardino
87.3
89
80.1
81.6
79.3
80.9
78.5
80.0
86
60719004
CA
San
Bernardino
108.7
111
99.5
101.6
98.5
100.6
97.2
99.3
110
60731006
CA
San Diego
83.0
84
76.9
77.9
76.1
77.0
74.9
75.8
79
60773005
CA
San
Joaquin
77.3
79
71.3
72.8
70.8
72.4
70.0
71.6
70
60990005
CA
Stanislaus
81.0
82
75.4
76.3
74.7
75.6
73.3
74.2
79
60990006
CA
Stanislaus
83.7
84
77.5
77.8
76.7
77.0
75.2
75.5
80
61030004
CA
Tehama
79.7
81
72.3
73.4
71.5
72.6
69.4
70.5
74
61070006
CA
Tulare
84.7
86
79.1
80.3
78.2
79.4
76.8
77.9
83
61070009
CA
Tulare
89.0
89
82.6
82.6
81.6
81.6
80.0
80.0
88
61072002
CA
Tulare
82.7
85
75.5
77.6
74.3
76.4
72.6
74.6
83
61072010
CA
Tulare
84.0
86
77.0
78.8
75.9
77.7
74.0
75.7
80
61090005
CA
Tuolumne
80.7
83
75.6
77.8
75.0
77.1
73.7
75.8
77
61112002
CA
Ventura
77.3
78
70.9
71.6
69.9
70.5
68.5
69.2
77
80350004
CO
Douglas
77.3
78
71.7
72.3
70.5
71.1
69.3
69.9
81
80590006
CO
Jefferson
77.3
78
72.6
73.3
71.7
72.3
70.6
71.3
79
80590011
CO
Jefferson
79.3
80
73.8
74.4
72.6
73.3
71.4
72.0
80
80690011
CO
Larimer
75.7
77
71.3
72.6
70.6
71.8
69.9
71.1
75
90010017
CT
Fairfield
79.3
80
73.0
73.7
71.5
72.2
69.9
70.5
82
90013007
CT
Fairfield
82.0
83
74.2
75.1
72.8
73.7
71.2
72.1
80
90019003
CT
Fairfield
82.7
83
76.1
76.4
74.6
74.8
72.9
73.1
79
90099002
CT
New Haven
79.7
82
71.8
73.9
70.4
72.4
68.7
70.7
80
170310001
IL
Cook
73.0
77
69.6
73.4
68.7
72.5
67.6
71.3
75
170310032
IL
Cook
72.3
75
69.8
72.4
69.1
71.7
68.1
70.6
74
170310076
IL
Cook
72.0
75
69.3
72.1
68.5
71.3
67.4
70.2
69
170314201
IL
Cook
73.3
77
69.9
73.4
68.9
72.4
67.8
71.2
77
170317002
IL
Cook
74.0
77
70.1
73.0
69.1
72.0
67.9
70.7
75
320030075
NV
Clark
75.0
76
70.0
71.0
69.0
69.9
67.8
68.7
74
350130021
NM
Dona Ana
72.7
74
70.9
72.2
70.4
71.7
69.6
70.9
78
350130022
NM
Dona Ana
71.3
74
69.5
72.1
69.0
71.6
68.1
70.7
74
420170012
PA
Bucks
79.3
81
70.7
72.2
69.2
70.7
67.7
69.1
74
480391004
TX
Brazoria
74.7
77
70.1
72.3
69.1
71.2
67.7
69.8
73
481210034
TX
Denton
78.0
80
70.4
72.2
69.0
70.8
67.9
69.6
72
481410037
TX
El Paso
71.3
73
69.6
71.3
69.2
70.9
68.6
70.2
76
481671034
TX
Galveston
75.7
77
71.1
72.3
70.2
71.4
69.1
70.3
74
482010024
TX
Harris
79.3
81
75.2
76.8
74.2
75.7
72.8
74.3
79
B-3
-------
Site ID
ST
County
2016
Centered
Avg
2016
Centered
Max
2023
Avg
2023
Max
2026
Avg
2026
Max
2032
Avg
2032
Max
2020
482010055
TX
Harris
76.0
77
71.0
72.0
69.8
70.8
68.3
69.1
76
482011034
TX
Harris
73.7
75
70.3
71.6
69.5
70.7
68.1
69.3
73
482011035
TX
Harris
71.3
75
68.0
71.6
67.2
70.7
65.9
69.3
70
490110004
UT
Davis
75.7
78
72.9
75.1
71.7
73.9
71.1
73.3
77
490353006
UT
Salt Lake
76.3
78
73.6
75.3
72.5
74.1
71.9
73.5
74
490353013
UT
Salt Lake
76.5
77
74.4
74.9
73.5
74.0
73.0
73.5
73
490450004
UT
Tooele
73.5
74
70.8
71.3
69.8
70.3
69.3
69.7
69
490570002
UT
Weber
73.0
75
70.6
72.5
69.8
71.7
69.2
71.1
N/A
490571003
UT
Weber
73.0
74
70.5
71.5
69.7
70.6
69.2
70.1
71
550590019
WI
Kenosha
78.0
79
72.8
73.7
71.7
72.6
70.4
71.3
74
550590025
WI
Kenosha
73.7
77
69.2
72.3
68.1
71.1
66.9
69.9
74
551010020
WI
Racine
76.0
78
71.3
73.2
70.2
72.1
69.1
70.9
73
551170006
WI
Sheboygan
80.0
81
73.6
74.5
72.3
73.2
71.0
71.8
75
B-4
-------
Appendix C
Ozone Contributions to
Nonattainment & Maintenance-Only Receptors
(Outside of California) in 2023 and 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 receptors
outside of California in 2023 and 2026. The contributions and design values are in units of ppb.
Contributions to individual monitoring sites is provided in the file:
"2016v2_DVs_state_contributions" which can be found in the docket for this proposed rule.
C-l
-------
Design Values and Contributions for 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
40278011
AZ
Yuma
70.5
72.2
0.00
3.01
0.00
5.09
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
0.00
0.00
0.00
0.00
0.14
0.00
0.00
80350004
CO
Douglas
71.7
72.3
0.00
0.27
0.00
0.91
16.24
0.00
0.00
0.00
0.00
0.00
0.28
0.00
0.00
0.00
0.10
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.03
0.26
0.35
0.00
0.00
80590006
CO
Jefferson
72.6
73.3
0.00
0.37
0.00
1.03
17.69
0.00
0.00
0.00
0.00
0.00
0.13
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
0.02
0.05
0.37
0.00
0.00
80590011
CO
Jefferson
73.8
74.4
0.00
0.40
0.00
1.17
18.09
0.00
0.00
0.00
0.00
0.00
0.16
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
0.08
0.38
0.00
0.00
90010017
CT
Fairfield
73.0
73.7
0.02
0.00
0.08
0.02
0.03
9.53
0.27
0.01
0.01
0.03
0.01
0.46
0.69
0.10
0.05
0.54
0.11
0.00
0.63
0.05
1.07
0.13
0.05
0.20
0.05
0.04
0.00
0.01
6.90
90013007
CT
Fairfield
74.2
75.1
0.10
0.01
0.14
0.03
0.05
4.33
0.41
0.03
0.06
0.15
0.02
0.53
0.75
0.12
0.09
0.77
0.25
0.01
1.10
0.30
0.94
0.14
0.10
0.31
0.08
0.06
0.01
0.10
7.43
90019003
CT
Fairfield
76.1
76.4
0.11
0.01
0.14
0.03
0.05
2.95
0.43
0.03
0.06
0.15
0.02
0.53
0.76
0.12
0.09
0.82
0.25
0.01
1.13
0.30
0.92
0.14
0.09
0.31
0.08
0.06
0.01
0.10
8.85
90099002
CT
New Haven
71.8
73.9
0.11
0.01
0.13
0.02
0.04
4.05
0.53
0.04
0.06
0.16
0.02
0.66
0.87
0.18
0.08
0.83
0.18
0.01
1.29
0.15
1.27
0.23
0.08
0.29
0.07
0.08
0.00
0.02
5.67
170310001
IL
Cook
69.6
73.4
0.00
0.01
0.03
0.02
0.05
0.00
0.00
0.00
0.03
0.00
0.03
19.54
5.44
0.56
0.32
0.06
0.02
0.00
0.00
0.00
0.93
0.97
0.00
0.28
0.09
0.18
0.00
0.00
0.00
170310032
IL
Cook
69.8
72.4
0.00
0.03
0.07
0.04
0.08
0.00
0.00
0.00
0.06
0.00
0.03
16.71
7.03
0.58
0.59
0.05
0.08
0.00
0.00
0.01
1.21
0.62
0.00
0.56
0.07
0.23
0.01
0.00
0.01
170310076
IL
Cook
69.3
72.1
0.00
0.00
0.02
0.02
0.05
0.00
0.00
0.00
0.02
0.00
0.02
18.82
6.21
0.38
0.22
0.05
0.02
0.00
0.00
0.01
1.54
0.79
0.00
0.20
0.08
0.14
0.00
0.00
0.00
170314201
IL
Cook
69.9
73.4
0.00
0.01
0.06
0.05
0.06
0.00
0.00
0.00
0.01
0.00
0.03
21.58
4.65
0.36
0.17
0.05
0.02
0.00
0.00
0.01
1.67
0.47
0.00
0.56
0.07
0.09
0.01
0.00
0.00
170317002
IL
Cook
70.1
73.0
0.00
0.04
0.15
0.06
0.08
0.01
0.01
0.00
0.02
0.00
0.04
19.16
6.33
0.44
0.31
0.25
0.07
0.00
0.06
0.01
1.26
0.34
0.00
0.94
0.07
0.14
0.01
0.00
0.04
320030075
NV
Clark
70.0
71.0
0.00
0.21
0.00
7.44
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
0.00
8.46
0.00
0.00
420170012
PA
Bucks
70.7
72.2
0.07
0.01
0.07
0.02
0.04
0.21
1.36
0.07
0.02
0.08
0.02
0.42
0.73
0.09
0.07
0.88
0.08
0.01
2.40
0.30
0.75
0.15
0.03
0.18
0.06
0.05
0.00
0.06
5.79
480391004
TX
Brazoria
70.1
72.3
0.50
0.00
1.39
0.01
0.03
0.00
0.00
0.00
0.10
0.17
0.01
0.10
0.11
0.10
0.12
0.16
7.03
0.00
0.00
0.00
0.00
0.08
0.92
0.55
0.04
0.09
0.00
0.00
0.00
481210034
TX
Denton
70.4
72.2
0.71
0.05
0.76
0.06
0.20
0.00
0.00
0.00
0.04
0.07
0.05
0.36
0.36
0.22
0.43
0.54
3.22
0.00
0.00
0.00
0.00
0.11
1.14
0.53
0.11
0.36
0.01
0.00
0.00
482010024
TX
Harris
75.2
76.8
0.18
0.00
0.67
0.00
0.01
0.00
0.00
0.00
0.11
0.03
0.00
0.01
0.01
0.18
0.18
0.01
4.31
0.00
0.00
0.00
0.00
0.12
0.37
0.30
0.01
0.10
0.00
0.00
0.00
482010055
TX
Harris
71.0
72.0
0.88
0.03
1.00
0.02
0.07
0.00
0.00
0.00
0.16
0.09
0.01
0.13
0.19
0.26
0.28
0.26
5.39
0.00
0.00
0.00
0.00
0.16
1.04
0.50
0.04
0.18
0.00
0.00
0.00
482011034
TX
Harris
70.3
71.6
0.13
0.01
1.38
0.01
0.02
0.00
0.00
0.00
0.15
0.07
0.01
0.01
0.00
0.07
0.17
0.00
4.93
0.00
0.00
0.00
0.00
0.07
0.31
0.57
0.03
0.09
0.00
0.00
0.00
482011035
TX
Harris
68.0
71.6
0.12
0.01
1.34
0.01
0.02
0.00
0.00
0.00
0.14
0.07
0.01
0.01
0.00
0.06
0.16
0.00
4.77
0.00
0.00
0.00
0.00
0.07
0.30
0.55
0.03
0.09
0.00
0.00
0.00
490110004
UT
Davis
72.9
75.1
0.00
0.22
0.00
2.25
0.02
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
0.86
0.00
0.00
490353006
UT
Salt Lake
73.6
75.3
0.00
0.22
0.00
2.46
0.02
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
0.89
0.00
0.00
490353013
UT
Salt Lake
74.4
74.9
0.00
0.13
0.00
1.42
0.02
0.00
0.00
0.00
0.00
0.00
0.55
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
0.63
0.00
0.00
490570002
UT
Weber
70.6
72.5
0.00
0.13
0.00
2.24
0.01
0.00
0.00
0.00
0.00
0.00
0.53
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
0.58
0.00
0.00
490571003
UT
Weber
70.5
71.5
0.00
0.12
0.00
2.16
0.01
0.00
0.00
0.00
0.00
0.00
0.57
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
0.56
0.00
0.00
550590019
Wl
Kenosha
72.8
73.7
0.01
0.02
0.19
0.05
0.06
0.01
0.00
0.00
0.04
0.00
0.02
18.13
6.60
0.64
0.41
0.27
0.13
0.00
0.05
0.02
1.07
0.39
0.01
1.08
0.05
0.13
0.01
0.00
0.04
550590025
Wl
Kenosha
69.2
72.3
0.05
0.01
0.47
0.03
0.05
0.00
0.00
0.00
0.00
0.00
0.03
18.55
7.10
0.35
0.12
0.10
0.56
0.00
0.00
0.01
1.17
0.43
0.24
1.66
0.05
0.06
0.01
0.00
0.01
551010020
Wl
Racine
71.3
73.2
0.01
0.02
0.21
0.05
0.05
0.01
0.01
0.00
0.06
0.00
0.02
13.86
6.60
0.63
0.42
0.38
0.18
0.00
0.07
0.03
1.02
0.50
0.01
0.92
0.05
0.14
0.01
0.01
0.05
C-2
-------
Design Values and Contributions for 2023 - Part 2
Contributions
Site ID
ST
County
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
40278011
AZ
Yuma
0.02
0.00
0.00
0.00
0.00
0.00
0.09
0.00
0.00
0.00
0.00
0.00
0.00
0.10
0.00
0.00
0.02
0.00
0.00
0.00
0.00
7.61
0.38
3.45
48.69
1.81
80350004
CO
Douglas
0.25
0.00
0.00
0.01
0.00
0.06
0.20
0.00
0.00
0.00
0.02
0.00
0.16
1.37
0.00
0.00
0.09
0.00
0.00
0.81
0.05
0.40
0.04
1.39
44.50
3.78
80590006
CO
Jefferson
0.30
0.00
0.00
0.00
0.00
0.04
0.11
0.00
0.00
0.00
0.00
0.00
0.16
1.10
0.00
0.00
0.05
0.00
0.00
0.46
0.03
0.48
0.05
1.12
45.63
3.25
80590011
CO
Jefferson
0.25
0.00
0.00
0.00
0.00
0.03
0.15
0.00
0.00
0.00
0.00
0.00
0.08
1.06
0.00
0.00
0.07
0.00
0.00
0.46
0.04
0.45
0.05
1.34
46.16
3.21
90010017
CT
Fairfield
0.02
16.81
0.15
0.06
1.18
0.08
0.01
5.44
0.00
0.03
0.03
0.13
0.29
0.02
0.01
0.50
0.03
0.66
0.13
0.04
0.00
2.48
0.40
0.30
18.94
3.91
90013007
CT
Fairfield
0.04
13.56
0.43
0.10
1.87
0.14
0.03
6.37
0.04
0.16
0.04
0.26
0.51
0.03
0.02
1.19
0.05
1.30
0.16
0.06
0.00
2.02
0.69
0.24
21.23
4.97
90019003
CT
Fairfield
0.05
14.36
0.43
0.10
1.90
0.15
0.03
6.90
0.04
0.16
0.04
0.27
0.53
0.03
0.02
1.19
0.05
1.34
0.16
0.07
0.00
1.93
0.66
0.26
21.58
5.04
90099002
CT
New Haven
0.03
11.54
0.61
0.12
1.94
0.10
0.02
4.74
0.01
0.19
0.05
0.26
0.35
0.02
0.01
1.77
0.05
1.45
0.19
0.05
0.00
2.52
1.16
0.32
21.60
5.30
170310001
IL
Cook
0.05
0.09
0.00
0.37
0.82
0.55
0.03
0.20
0.00
0.00
0.07
0.00
0.86
0.02
0.00
0.02
0.07
0.11
2.41
0.05
0.00
0.87
0.07
0.17
26.83
7.16
170310032
IL
Cook
0.09
0.24
0.00
0.23
1.26
0.75
0.03
0.26
0.00
0.00
0.06
0.00
1.46
0.05
0.01
0.01
0.06
0.08
2.61
0.06
0.00
1.16
0.14
0.25
24.77
7.91
170310076
IL
Cook
0.04
0.30
0.00
0.30
1.23
0.35
0.03
0.30
0.00
0.00
0.06
0.00
0.59
0.01
0.01
0.01
0.06
0.09
2.47
0.05
0.00
1.37
0.05
0.18
26.32
6.64
170314201
IL
Cook
0.06
0.31
0.00
0.18
1.23
0.36
0.04
0.28
0.00
0.00
0.04
0.00
1.15
0.02
0.01
0.00
0.07
0.07
2.55
0.07
0.00
1.51
0.04
0.19
24.32
7.20
170317002
IL
Cook
0.09
0.30
0.02
0.14
1.69
0.52
0.04
0.51
0.00
0.00
0.04
0.02
1.58
0.06
0.01
0.11
0.07
0.26
1.47
0.08
0.00
1.31
0.09
0.25
22.49
8.83
320030075
NV
Clark
0.02
0.00
0.00
0.00
0.00
0.00
0.06
0.00
0.00
0.00
0.00
0.00
0.01
0.07
0.00
0.00
0.02
0.00
0.00
0.00
0.00
1.28
0.34
0.67
49.59
1.75
420170012
PA
Bucks
0.03
1.80
0.33
0.06
1.88
0.10
0.02
18.09
0.04
0.07
0.03
0.25
0.27
0.02
0.02
1.63
0.03
1.44
0.13
0.05
0.00
1.38
0.20
0.23
23.24
5.09
480391004
TX
Brazoria
0.01
0.00
0.03
0.11
0.04
0.11
0.01
0.01
0.00
0.02
0.04
0.36
29.89
0.01
0.00
0.02
0.02
0.01
0.02
0.03
0.00
0.17
1.74
0.43
19.46
5.81
481210034
TX
Denton
0.13
0.00
0.00
0.10
0.11
1.19
0.02
0.00
0.00
0.01
0.09
0.94
27.30
0.07
0.00
0.00
0.05
0.00
0.01
0.19
0.00
0.29
0.18
1.21
21.94
7.03
482010024
TX
Harris
0.02
0.00
0.00
0.03
0.00
0.25
0.00
0.00
0.00
0.00
0.02
0.06
30.28
0.00
0.00
0.00
0.01
0.00
0.01
0.01
0.00
0.16
2.29
1.21
30.06
4.00
482010055
TX
Harris
0.07
0.00
0.02
0.06
0.03
0.30
0.01
0.00
0.00
0.01
0.03
0.60
28.25
0.02
0.00
0.01
0.02
0.00
0.01
0.05
0.00
0.43
1.99
0.89
21.60
5.67
482011034
TX
Harris
0.03
0.00
0.02
0.10
0.00
0.21
0.00
0.00
0.00
0.01
0.04
0.07
30.82
0.01
0.00
0.01
0.02
0.00
0.01
0.02
0.00
0.25
1.60
1.51
22.31
5.00
482011035
TX
Harris
0.03
0.00
0.02
0.09
0.00
0.20
0.00
0.00
0.00
0.01
0.04
0.07
29.81
0.01
0.00
0.01
0.02
0.00
0.01
0.02
0.00
0.24
1.55
1.46
21.58
4.84
490110004
UT
Davis
0.05
0.00
0.00
0.00
0.00
0.00
0.44
0.00
0.00
0.00
0.00
0.00
0.03
8.82
0.00
0.00
0.16
0.00
0.00
0.05
0.00
0.49
0.09
2.90
52.77
3.26
490353006
UT
Salt Lake
0.05
0.00
0.00
0.00
0.00
0.00
0.42
0.00
0.00
0.00
0.00
0.00
0.03
9.00
0.00
0.00
0.15
0.00
0.00
0.04
0.00
0.48
0.09
3.00
53.12
3.15
490353013
UT
Salt Lake
0.06
0.00
0.00
0.00
0.00
0.00
0.58
0.00
0.00
0.00
0.00
0.00
0.03
9.26
0.00
0.00
0.21
0.00
0.00
0.05
0.00
0.25
0.05
3.15
54.94
2.96
490570002
UT
Weber
0.01
0.00
0.00
0.00
0.00
0.00
0.41
0.00
0.00
0.00
0.00
0.00
0.01
6.85
0.00
0.00
0.13
0.00
0.00
0.07
0.00
0.57
0.10
2.50
53.33
3.01
490571003
UT
Weber
0.01
0.00
0.00
0.00
0.00
0.00
0.40
0.00
0.00
0.00
0.00
0.00
0.01
6.00
0.00
0.00
0.13
0.00
0.00
0.07
0.00
0.52
0.10
2.50
54.28
2.97
550590019
Wl
Kenosha
0.06
0.24
0.02
0.11
1.67
0.57
0.03
0.46
0.00
0.00
0.03
0.02
1.72
0.03
0.01
0.10
0.05
0.23
6.06
0.05
0.00
1.10
0.15
0.25
19.42
10.70
550590025
Wl
Kenosha
0.07
0.27
0.00
0.14
1.33
0.41
0.03
0.24
0.00
0.00
0.04
0.13
1.81
0.02
0.01
0.01
0.05
0.04
2.82
0.06
0.00
1.50
0.16
0.22
19.53
8.98
551010020
Wl
Racine
0.06
0.24
0.03
0.12
1.00
0.49
0.03
0.42
0.00
0.01
0.03
0.04
1.34
0.03
0.01
0.14
0.05
0.23
11.13
0.04
0.00
0.82
0.19
0.31
18.26
10.71
C-3
-------
Design Values and Contributions for 2026 - Part 1
Contributions
Site ID
ST
County
2026
Avg
2026
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
40278011
AZ
Yuma
70.1
71.8
0.00
2.83
0.00
4.85
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
0.00
0.00
0.00
0.00
0.13
0.00
0.00
80350004
CO
Douglas
70.5
71.1
0.00
0.23
0.00
0.88
15.63
0.00
0.00
0.00
0.00
0.00
0.25
0.00
0.00
0.00
0.10
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.03
0.23
0.31
0.00
0.00
80590006
CO
Jefferson
71.7
72.3
0.00
0.32
0.00
0.99
17.27
0.00
0.00
0.00
0.00
0.00
0.12
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
0.02
0.05
0.33
0.00
0.00
80590011
CO
Jefferson
72.6
73.3
0.00
0.35
0.00
1.12
17.61
0.00
0.00
0.00
0.00
0.00
0.15
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
0.34
0.00
0.00
90010017
CT
Fairfield
71.5
72.2
0.02
0.00
0.08
0.01
0.03
9.34
0.26
0.01
0.00
0.02
0.01
0.44
0.64
0.09
0.05
0.51
0.11
0.00
0.60
0.05
1.02
0.12
0.05
0.18
0.04
0.04
0.00
0.01
6.60
90013007
CT
Fairfield
72.8
73.7
0.10
0.01
0.13
0.03
0.05
4.11
0.42
0.03
0.06
0.13
0.02
0.52
0.71
0.10
0.09
0.75
0.24
0.01
1.11
0.29
0.89
0.13
0.09
0.28
0.07
0.05
0.01
0.09
7.24
90019003
CT
Fairfield
74.6
74.8
0.10
0.01
0.13
0.03
0.05
2.86
0.42
0.03
0.06
0.14
0.02
0.51
0.71
0.10
0.09
0.80
0.25
0.01
1.08
0.29
0.88
0.13
0.09
0.28
0.07
0.05
0.01
0.09
8.54
90099002
CT
New Haven
70.4
72.4
0.10
0.01
0.12
0.01
0.04
3.88
0.52
0.04
0.05
0.15
0.02
0.64
0.82
0.17
0.08
0.80
0.18
0.01
1.23
0.14
1.21
0.21
0.08
0.27
0.07
0.07
0.00
0.02
5.47
170310001
IL
Cook
68.7
72.5
0.00
0.00
0.03
0.02
0.05
0.00
0.00
0.00
0.03
0.00
0.02
19.36
5.41
0.50
0.31
0.06
0.02
0.00
0.00
0.00
0.88
0.91
0.00
0.26
0.08
0.16
0.00
0.00
0.00
170310032
IL
Cook
69.1
71.7
0.00
0.03
0.07
0.04
0.07
0.00
0.00
0.00
0.05
0.00
0.02
16.57
6.99
0.51
0.57
0.05
0.08
0.00
0.00
0.01
1.15
0.59
0.00
0.52
0.07
0.20
0.01
0.00
0.01
170310076
IL
Cook
68.5
71.3
0.00
0.00
0.02
0.02
0.04
0.00
0.00
0.00
0.02
0.00
0.02
18.68
6.08
0.34
0.21
0.05
0.02
0.00
0.00
0.01
1.46
0.75
0.00
0.19
0.07
0.12
0.00
0.00
0.00
170314201
IL
Cook
68.9
72.4
0.00
0.01
0.06
0.05
0.05
0.00
0.00
0.00
0.01
0.00
0.03
21.38
4.54
0.32
0.17
0.05
0.02
0.00
0.00
0.01
1.58
0.44
0.00
0.51
0.07
0.08
0.01
0.00
0.00
170317002
IL
Cook
69.1
72.0
0.00
0.03
0.14
0.06
0.08
0.01
0.01
0.00
0.02
0.00
0.03
18.89
6.18
0.39
0.30
0.25
0.07
0.00
0.06
0.01
1.19
0.32
0.00
0.85
0.07
0.13
0.01
0.00
0.03
4S0391004
TX
Brazoria
69.1
71.2
0.48
0.00
1.30
0.01
0.03
0.00
0.00
0.00
0.09
0.16
0.01
0.10
0.10
0.09
0.11
0.15
6.97
0.00
0.00
0.00
0.00
0.08
0.90
0.50
0.04
0.08
0.00
0.00
0.00
482010024
TX
Harris
74.2
75.7
0.17
0.00
0.62
0.00
0.01
0.00
0.00
0.00
0.10
0.03
0.00
0.01
0.01
0.16
0.17
0.01
4.25
0.00
0.00
0.00
0.00
0.11
0.36
0.28
0.01
0.09
0.00
0.00
0.00
490110004
UT
Davis
71.7
73.9
0.00
0.19
0.00
2.18
0.02
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
0.78
0.00
0.00
490353006
UT
Salt Lake
72.5
74.1
0.00
0.20
0.00
2.38
0.02
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
0.00
0.00
0.00
0.00
0.00
0.81
0.00
0.00
490353013
UT
Salt Lake
73.5
74.0
0.00
0.11
0.00
1.36
0.02
0.00
0.00
0.00
0.00
0.00
0.48
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
0.56
0.00
0.00
490570002
UT
Weber
69.8
71.7
0.00
0.11
0.00
2.13
0.01
0.00
0.00
0.00
0.00
0.00
0.4S
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
0.51
0.00
0.00
550590019
Wl
Kenosha
71.7
72.6
0.00
0.02
0.18
0.05
0.05
0.01
0.00
0.00
0.04
0.00
0.02
17.81
6.43
0.57
0.40
0.26
0.13
0.00
0.05
0.02
1.03
0.36
0.01
0.98
0.04
0.11
0.01
0.00
0.03
550590025
Wl
Kenosha
68.1
71.1
0.05
0.01
0.44
0.03
0.05
0.00
0.00
0.00
0.00
0.00
0.03
18.14
6.98
0.30
0.12
0.09
0.55
0.00
0.00
0.01
1.11
0.40
0.23
1.53
0.05
0.05
0.00
0.00
0.01
551010020
Wl
Racine
70.2
72.1
0.01
0.02
0.19
0.05
0.05
0.01
0.01
0.00
0.05
0.00
0.02
13.54
6.52
0.57
0.41
0.36
0.17
0.00
0.07
0.03
0.96
0.46
0.01
0.84
0.04
0.12
0.01
0.01
0.05
C-4
-------
Design Values and Contributions for 2026 - Part 2
Contributions
Site ID
ST
County
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
40278011
AZ
Yuma
0.02
0.00
0.00
0.00
0.00
0.00
0.08
0.00
0.00
0.00
0.00
0.00
0.00
0.08
0.00
0.00
0.02
0.00
0.00
0.00
0.00
7.89
0.35
3.61
48.29
1.85
80350004
CO
Douglas
0.23
0.00
0.00
0.01
0.00
0.06
0.17
0.00
0.00
0.00
0.01
0.00
0.15
1.18
0.00
0.00
0.08
0.00
0.00
0.80
0.05
0.39
0.04
1.32
44.39
3.82
80590006
CO
Jefferson
0.29
0.00
0.00
0.00
0.00
0.04
0.10
0.00
0.00
0.00
0.00
0.00
0.15
0.95
0.00
0.00
0.04
0.00
0.00
0.46
0.04
0.47
0.04
1.08
45.48
3.31
80590011
CO
Jefferson
0.24
0.00
0.00
0.00
0.00
0.03
0.13
0.00
0.00
0.00
0.00
0.00
0.08
0.90
0.00
0.00
0.06
0.00
0.00
0.45
0.04
0.44
0.05
1.32
45.81
3.23
90010017
CT
Fairfield
0.02
16.58
0.13
0.05
1.10
0.07
0.01
5.32
0.00
0.03
0.02
0.12
0.26
0.01
0.00
0.48
0.03
0.61
0.12
0.04
0.00
2.37
0.40
0.30
18.80
4.00
90013007
CT
Fairfield
0.04
13.28
0.38
0.09
1.76
0.13
0.02
6.36
0.04
0.15
0.03
0.24
0.47
0.02
0.02
1.14
0.04
1.19
0.15
0.06
0.00
1.93
0.72
0.24
21.14
5.12
90019003
CT
Fairfield
0.04
14.18
0.38
0.09
1.78
0.14
0.02
6.82
0.04
0.15
0.03
0.25
0.49
0.02
0.02
1.13
0.04
1.23
0.15
0.06
0.00
1.85
0.67
0.25
21.47
5.18
90099002
CT
New Haven
0.03
11.29
0.54
0.11
1.83
0.10
0.02
4.74
0.01
0.17
0.04
0.24
0.33
0.02
0.01
1.68
0.04
1.35
0.18
0.05
0.00
2.42
1.17
0.32
21.58
5.46
170310001
IL
Cook
0.05
0.09
0.00
0.34
0.78
0.52
0.03
0.20
0.00
0.00
0.06
0.00
0.82
0.01
0.00
0.02
0.06
0.09
2.23
0.05
0.00
0.83
0.07
0.17
26.61
7.32
170310032
IL
Cook
0.09
0.23
0.00
0.22
1.22
0.72
0.02
0.27
0.00
0.00
0.05
0.00
1.39
0.04
0.01
0.01
0.05
0.07
2.44
0.06
0.00
1.12
0.14
0.25
24.75
8.12
170310076
IL
Cook
0.04
0.29
0.00
0.28
1.18
0.33
0.02
0.31
0.00
0.00
0.05
0.00
0.57
0.01
0.01
0.01
0.06
0.08
2.29
0.04
0.00
1.32
0.05
0.18
26.25
6.79
170314201
IL
Cook
0.06
0.29
0.00
0.17
1.19
0.34
0.03
0.29
0.00
0.00
0.03
0.00
1.09
0.02
0.01
0.00
0.06
0.07
2.36
0.06
0.00
1.45
0.04
0.19
24.13
7.38
170317002
IL
Cook
0.09
0.29
0.02
0.13
1.62
0.49
0.04
0.51
0.00
0.00
0.03
0.01
1.49
0.05
0.01
0.10
0.06
0.24
1.36
0.08
0.00
1.26
0.09
0.25
22.41
9.06
480391004
TX
Brazoria
0.01
0.00
0.03
0.10
0.04
0.11
0.01
0.01
0.00
0.02
0.04
0.34
29.28
0.01
0.00
0.01
0.02
0.01
0.01
0.02
0.00
0.16
1.72
0.42
19.33
5.97
482010024
TX
Harris
0.02
0.00
0.00
0.03
0.00
0.24
0.00
0.00
0.00
0.00
0.02
0.06
29.71
0.00
0.00
0.00
0.01
0.00
0.01
0.01
0.00
0.15
2.27
1.17
29.84
4.09
490110004
UT
Davis
0.05
0.00
0.00
0.00
0.00
0.00
0.39
0.00
0.00
0.00
0.00
0.00
0.03
8.03
0.00
0.00
0.15
0.00
0.00
0.05
0.00
0.48
0.08
2.87
52.64
3.32
490353006
UT
Salt Lake
0.05
0.00
0.00
0.00
0.00
0.00
0.37
0.00
0.00
0.00
0.00
0.00
0.03
8.29
0.00
0.00
0.14
0.00
0.00
0.04
0.00
0.48
0.09
2.98
52.97
3.23
490353013
UT
Salt Lake
0.06
0.00
0.00
0.00
0.00
0.00
0.52
0.00
0.00
0.00
0.00
0.00
0.03
8.90
0.00
0.00
0.20
0.00
0.00
0.05
0.00
0.24
0.05
3.14
54.68
3.01
490570002
UT
Weber
0.01
0.00
0.00
0.00
0.00
0.00
0.35
0.00
0.00
0.00
0.00
0.00
0.01
6.05
0.00
0.00
0.12
0.00
0.00
0.08
0.00
0.56
0.10
2.41
53.77
3.02
550590019
Wl
Kenosha
0.06
0.23
0.02
0.11
1.59
0.54
0.03
0.45
0.00
0.00
0.02
0.02
1.61
0.03
0.00
0.10
0.04
0.21
5.84
0.05
0.00
1.06
0.15
0.24
19.42
10.98
550590025
Wl
Kenosha
0.06
0.26
0.00
0.13
1.30
0.38
0.03
0.24
0.00
0.00
0.03
0.12
1.70
0.01
0.01
0.01
0.04
0.04
2.64
0.06
0.00
1.45
0.16
0.22
19.49
9.25
551010020
Wl
Racine
0.06
0.23
0.03
0.11
0.97
0.47
0.02
0.41
0.00
0.00
0.02
0.03
1.25
0.03
0.01
0.13
0.04
0.22
10.77
0.04
0.00
0.79
0.19
0.31
18.16
11.04
C-5
-------
2023 average design values, "home state" contributions, total contributions from all upwind
states, and the total upwind contribution expresses as a percent of total ozone (i.e., the 2023
average design value), (units are ppb).
Site ID
State
County
2023
Average
DV
Home State
Contribution
Upwind State
Contribution
Upwind
Contribution as a
Percent of 2023
Average DV
40278011
AZ
Yuma
70.5
3.01
5.51
8%
60070007
CA
Butte
68.9
23.90
1.92
3%
60090001
CA
Calaveras
70.9
21.86
0.95
1%
60170010
CA
El Dorado
76.3
27.81
1.22
2%
60170020
CA
El Dorado
74.3
29.76
1.41
2%
60190007
CA
Fresno
80.4
29.81
1.42
2%
60190011
CA
Fresno
82.9
31.77
1.54
2%
60190242
CA
Fresno
79.5
27.81
1.48
2%
60194001
CA
Fresno
82.8
28.00
1.08
1%
60195001
CA
Fresno
83.7
31.01
1.37
2%
60250005
CA
Imperial
76.3
6.24
0.38
0%
60251003
CA
Imperial
75.4
7.44
0.36
0%
60290007
CA
Kern
82.8
26.88
1.08
1%
60290008
CA
Kern
79.1
21.41
0.78
1%
60290011
CA
Kern
78.8
12.82
0.51
1%
60290014
CA
Kern
81.3
27.26
0.89
1%
60290232
CA
Kern
74.9
24.97
0.89
1%
60292012
CA
Kern
84.1
29.52
0.99
1%
60295002
CA
Kern
82.4
25.65
1.10
1%
60311004
CA
Kings
76.9
24.20
0.97
1%
60370002
CA
Los Angeles
88.0
44.96
0.57
1%
60370016
CA
Los Angeles
93.4
47.72
0.60
1%
60371103
CA
Los Angeles
70.5
33.04
0.58
1%
60371201
CA
Los Angeles
82.7
29.22
1.49
2%
60371602
CA
Los Angeles
73.6
36.80
0.64
1%
60371701
CA
Los Angeles
85.6
44.86
0.56
1%
60372005
CA
Los Angeles
80.7
39.95
0.65
1%
60376012
CA
Los Angeles
91.6
36.57
0.88
1%
60379033
CA
Los Angeles
80.7
22.84
0.49
1%
60390004
CA
Madera
75.7
26.48
1.41
2%
60392010
CA
Madera
77.0
25.17
1.20
2%
60430003
CA
Mariposa
74.2
5.92
0.47
1%
60470003
CA
Merced
74.7
24.52
0.88
1%
60570005
CA
Nevada
78.1
26.15
1.61
2%
60592022
CA
Orange
72.5
30.83
0.30
0%
C-6
-------
Site ID
State
County
2023
Average
DV
Home State
Contribution
Upwind State
Contribution
Upwind
Contribution as a
Percent of 2023
Average DV
60595001
CA
Orange
72.3
36.16
0.58
1%
60610003
CA
Placer
77.1
30.88
1.46
2%
60610004
CA
Placer
71.9
24.73
1.52
2%
60610006
CA
Placer
72.8
33.62
1.10
2%
60650008
CA
Riverside
71.0
15.25
0.29
0%
60650012
CA
Riverside
85.9
35.87
1.01
1%
60650016
CA
Riverside
72.0
26.32
0.48
1%
60651016
CA
Riverside
89.8
34.25
0.99
1%
60652002
CA
Riverside
76.4
14.29
0.66
1%
60655001
CA
Riverside
80.5
22.77
0.85
1%
60656001
CA
Riverside
83.5
36.25
0.71
1%
60658001
CA
Riverside
89.5
46.52
0.59
1%
60658005
CA
Riverside
87.9
45.69
0.58
1%
60659001
CA
Riverside
80.8
34.30
0.68
1%
60670002
CA
Sacramento
71.4
31.12
1.03
1%
60670012
CA
Sacramento
74.8
31.82
1.18
2%
60675003
CA
Sacramento
70.2
28.63
1.02
1%
60710001
CA
San Bernardino
74.5
13.49
0.47
1%
60710005
CA
San Bernardino
100.3
45.37
1.13
1%
60710012
CA
San Bernardino
87.3
26.10
0.54
1%
60710306
CA
San Bernardino
76.8
27.43
0.97
1%
60711004
CA
San Bernardino
97.2
49.90
0.65
1%
60711234
CA
San Bernardino
70.6
7.53
0.61
1%
60712002
CA
San Bernardino
90.1
45.97
0.79
1%
60714001
CA
San Bernardino
82.6
37.01
1.27
2%
60714003
CA
San Bernardino
95.2
48.10
0.91
1%
60719002
CA
San Bernardino
80.1
19.20
0.29
0%
60719004
CA
San Bernardino
99.5
50.28
0.95
1%
60731006
CA
San Diego
76.9
28.76
0.85
1%
60773005
CA
San Joaquin
71.3
26.61
1.07
2%
60990005
CA
Stanislaus
75.4
29.73
1.35
2%
60990006
CA
Stanislaus
77.5
27.86
1.09
1%
61070006
CA
Tulare
79.1
12.17
0.80
1%
61070009
CA
Tulare
82.6
23.16
1.10
1%
61072002
CA
Tulare
75.5
27.37
1.10
1%
61072010
CA
Tulare
77.0
25.97
1.09
1%
61090005
CA
Tuolumne
75.6
17.96
1.03
1%
61112002
CA
Ventura
70.9
25.16
0.68
1%
80350004
CO
Douglas
71.7
16.25
5.27
7%
C-7
-------
Site ID
State
County
2023
Average
DV
Home State
Contribution
Upwind State
Contribution
Upwind
Contribution as a
Percent of 2023
Average DV
80590006
CO
Jefferson
72.6
17.70
4.32
6%
80590011
CO
Jefferson
73.8
18.09
4.43
6%
90010017
CT
Fairfield
73.0
9.53
37.41
51%
90013007
CT
Fairfield
74.2
4.33
40.68
55%
90019003
CT
Fairfield
76.1
2.96
43.65
57%
90099002
CT
New Haven
71.8
4.05
36.83
51%
170310001
IL
Cook
69.6
19.54
14.92
21%
170310032
IL
Cook
69.8
16.71
18.82
27%
170310076
IL
Cook
69.3
18.82
15.89
23%
170314201
IL
Cook
69.9
21.59
15.02
21%
170317002
IL
Cook
70.1
19.16
17.94
26%
320030075
NV
Clark
70.0
8.47
7.87
11%
420170012
PA
Bucks
70.7
18.10
22.43
32%
480391004
TX
Brazoria
70.1
29.89
12.57
18%
481210034
TX
Denton
70.4
27.31
12.41
18%
482010024
TX
Harris
75.2
30.29
7.17
10%
482010055
TX
Harris
71.0
28.25
12.14
17%
482011034
TX
Harris
70.3
30.83
8.77
12%
482011035
TX
Harris
68.0
29.82
8.49
12%
490110004
UT
Davis
72.9
8.83
4.54
6%
490353006
UT
Salt Lake
73.6
9.00
4.72
6%
490353013
UT
Salt Lake
74.4
9.27
3.75
5%
490570002
UT
Weber
70.6
6.86
4.19
6%
490571003
UT
Weber
70.5
6.01
4.09
6%
550590019
WI
Kenosha
72.8
6.06
35.10
48%
550590025
WI
Kenosha
69.2
2.82
35.95
52%
551010020
WI
Racine
71.3
11.13
29.85
42%
C-8
-------
Appendix D
Upwind/Downwind Linkages
By Upwind State
-------
Ozone design values and contribution metric values in this appendix are in units of "ppb".
Highlighted design values exceed the 2015 NAAQS.
Upwind State: Alabama
2023
2023
2026
2026
Downwind
Avg
Max
2023
Avg
Max
2026
States
Receptors
DV
DV
Receptor Status
Contribution
DV
DV
Receptor Status
Contribution
TX
Houston-Bayland Park
71.0
72.0
Nonattainment
0.88
69.8
70.8
No Longer a Receptor
0.84
TX
Dallas-Denton
70.4
72.2
Maintenance-Only
0.71
69.0
70.8
No Longer a Receptor
0.66
Upwind State: Arkansas
2023
2023
2026
2026
Downwind
Avg
Max
2023
Avg
Max
2026
States
Receptors
DV
DV
Receptor Status
Contribution
DV
DV
Receptor Status
Contribution
TX
Houston-Bayland Park
71.0
72.0
Nonattainment
1.00
69.8
70.8
No Longer a Receptor
0.93
TX
Dallas-Denton
70.4
72.2
Maintenance-Only
0.76
69.0
70.8
No Longer a Receptor
0.72
TX
Houston-East
70.3
71.6
Maintenance-Only
1.38
69.0
70.8
No Longer a Receptor
1.29
TX
Houston-Brazoria
70.1
72.3
Maintenance-Only
1.39
69.1
71.2
Maintenance-Only
1.30
TX
Houston-Clinton
68.0
71.6
Maintenance-Only
1.34
67.2
70.7
No Longer a Receptor
1.25
D-l
-------
Upwind State: California
Downwind
States
Receptors
2023
Avg
DV
2023
Max
DV
Receptor Status
2023
Contribution
2026
Avg
DV
2026
Max
DV
Receptor Status
2026
Contribution
AZ
Yuma
70.5
72.2
Maintenance-Only
5.09
70.1
71.8
Maintenance-Only
4.85
CA
Morongo Tribe
89.8
90.9
Nonattainment
34.24
88.8
89.9
Nonattainment
33.45
CO
Denver-NREL
73.8
74.4
Nonattainment
1.17
72.6
73.3
Nonattainment
1.12
CO
Rocky Flats
72.6
73.3
Nonattainment
1.03
71.7
72.3
Nonattainment
0.99
CO
Denver-Chatfield
71.7
72.3
Nonattainment
0.91
70.5
71.1
Maintenance-Only
0.88
NV
Las Vegas-
Northwest
70.0
71.0
Maintenance-Only
7.44
69.0
69.9
No Longer a Receptor
7.15
UT
Salt Lake City-
Herriman
74.4
74.9
Nonattainment
1.42
73.5
74.0
Nonattainment
1.36
UT
Salt Lake City-
Hawthorne
73.6
75.3
Nonattainment
2.46
72.5
74.1
Nonattainment
2.38
UT
Salt Lake City-
Bountiful
72.9
75.1
Nonattainment
2.25
71.7
73.9
Nonattainment
2.18
UT
Salt Lake City-
Ogden
70.6
72.5
Maintenance-Only
2.24
69.8
71.7
Maintenance-Only
2.13
UT
Salt Lake City-
Harrisonville
70.5
71.5
Maintenance-Only
2.16
69.7
70.6
No Longer a Receptor
2.06
Upwind State: Delaware
2023
2023
2026
2026
Downwind
Avg
Max
2023
Avg
Max
2026
States
Receptors
DV
DV
Receptor Status
Contribution
DV
DV
Receptor Status
Contribution
PA
Philadelphia-Bristol
70.7
72.2
Maintenance -Only
1.36
69.2
70.7
No Longer a Receptor
1.32
D-2
-------
Upwind State: Illinois
Downwind
States
Receptors
2023
Avg
DV
2023
Max
DV
Receptor Status
2023
Contribution
2026
Avg
DV
2026
Max
DV
Receptor Status
2026
Contribution
WI
Kenosha-Water Tower
72.8
73.7
Nonattainment
18.13
71.7
72.6
Nonattainment
17.81
WI
Racine
71.3
73.2
Nonattainment
13.86
70.2
72.1
Maintenance-Only
13.54
WI
Kenosha-Chiwaukee
69.2
72.3
Maintenance -Only
18.55
68.1
71.1
Maintenance-Only
18.14
Upwind State: Indiana
Downwind
States
Receptors
2023
Avg
DV
2023
Max
DV
Receptor Status
2023
Contribution
2026
Avg
DV
2026
Max
DV
Receptor Status
2026
Contribution
CT
Westport
76.1
76.4
Nonattainment
0.76
74.6
74.8
Nonattainment
0.71
CT
Stratford
74.2
75.1
Nonattainment
0.75
72.8
73.7
Nonattainment
0.71
CT
Madison
71.8
73.9
Nonattainment
0.87
70.4
72.4
Maintenance-Only
0.82
IL
Chicago-Evanston
70.1
73.0
Maintenance-Only
6.33
69.1
72.0
Maintenance-Only
6.18
IL
Chicago-Northbrook
69.9
73.4
Maintenance-Only
4.65
68.9
72.4
Maintenance-Only
4.54
IL
Chicago-South
69.8
72.4
Maintenance-Only
7.03
69.1
71.7
Maintenance-Only
6.99
IL
Chicago-Alsip
69.6
73.4
Maintenance-Only
5.44
68.7
72.5
Maintenance-Only
5.41
IL
Chicago-ComEd
69.3
72.1
Maintenance-Only
6.21
68.5
71.3
Maintenance-Only
6.08
PA
Philadelphia-Bristol
70.7
72.2
Maintenance-Only
0.73
69.2
70.7
No Longer a Receptor
0.69
WI
Kenosha-Chiwaukee
72.8
73.7
Nonattainment
6.60
71.7
72.6
Nonattainment
6.43
WI
Racine
71.3
73.2
Nonattainment
6.60
70.2
72.1
Maintenance-Only
6.52
WI
Kenosha-Water Tower
69.2
72.3
Maintenance-Only
7.10
68.1
71.1
Maintenance-Only
6.98
D-3
-------
Upwind State: Kentucky
Downwind
States
Receptors
2023
Avg
DV
2023
Max
DV
Receptor Status
2023
Contribution
2026
Avg
DV
2026
Max
DV
Receptor Status
2026
Contribution
CT
Madison
71.8
73.9
Nonattainment
0.83
70.4
72.4
Maintenance-Only
0.80
CT
Westport
76.1
76.4
Nonattainment
0.82
74.6
74.8
Nonattainment
0.80
CT
Stratford
74.2
75.1
Nonattainment
0.77
72.8
73.7
Nonattainment
0.75
PA
Philadelphia-Bristol
70.7
72.2
Maintenance-Only
0.88
69.2
70.7
No Longer a Receptor
0.85
Upwind State: Louisiana
Downwind
States
Receptors
2023
Avg
DV
2023
Max
DV
Receptor Status
2023
Contribution
2026
Avg
DV
2026
Max
DV
Receptor Status
2026
Contribution
TX
Houston-Brazoria
70.1
72.3
Maintenance-Only
7.03
69.1
71.2
Maintenance -Only
6.97
TX
Dallas-Denton
70.4
72.2
Maintenance-Only
3.22
69.0
70.8
No Longer a Receptor
3.17
TX
Houston-Aldine
75.2
76.8
Nonattainment
4.31
74.2
75.7
Nonattainment
4.25
TX
Houston-Bayland
Park
71.0
72.0
Nonattainment
5.39
69.8
70.8
No Longer a Receptor
5.33
TX
Houston-East
70.3
71.6
Maintenance-Only
4.93
69.5
70.7
No Longer a Receptor
4.89
TX
Houston-Clinton
68.0
71.6
Maintenance-Only
4.77
67.2
70.7
No Longer a Receptor
4.73
D-4
-------
Upwind State: Maryland
Downwind
States
Receptors
2023
Avg
DV
2023
Max
DV
Receptor Status
2023
Contribution
2026
Avg
DV
2026
Max
DV
Receptor Status
2026
Contribution
CT
Stratford
74.2
75.1
Nonattainment
1.10
72.8
73.7
Nonattainment
1.11
CT
Westport
76.1
76.4
Nonattainment
1.13
74.6
74.8
Nonattainment
1.08
CT
Madison
71.8
73.9
Nonattainment
1.29
70.4
72.4
Maintenance-Only
1.23
PA
Philadelphia-Bristol
70.7
72.2
Maintenance-Only
2.40
69.2
70.7
No Longer a Receptor
2.28
Upwind State: Michigan
Downwind
States
Receptors
2023
Avg
DV
2023
Max
DV
Receptor Status
2023
Contribution
2026
Avg
DV
2026
Max
DV
Receptor Status
2026
Contribution
CT
Greenwich
73.0
73.7
Nonattainment
1.07
71.5
72.2
Nonattainment
1.02
CT
Stratford
74.2
75.1
Nonattainment
0.94
72.8
73.7
Nonattainment
0.89
CT
Westport
76.1
76.4
Nonattainment
0.92
74.6
74.8
Nonattainment
0.88
CT
Madison
71.8
73.9
Nonattainment
1.27
70.4
72.4
Maintenance-Only
1.21
IL
Chicago-Alsip
69.6
73.4
Maintenance-Only
0.93
68.7
72.5
Maintenance-Only
0.88
IL
Chicago-South
69.8
72.4
Maintenance -Only
1.21
69.1
71.7
Maintenance-Only
1.15
IL
Chicago-ComEd
69.3
72.1
Maintenance-Only
1.54
68.5
71.3
Maintenance-Only
1.46
IL
Chicago-
Northbrook
69.9
73.4
Maintenance-Only
1.67
68.9
72.4
Maintenance-Only
1.58
IL
Chicago-Evanston
70.1
73.0
Maintenance-Only
1.26
69.1
72.0
Maintenance-Only
1.19
PA
Philadelphia-Bristol
70.7
72.2
Maintenance-Only
0.75
69.2
70.7
No Longer a Receptor
0.71
WI
Kenosha-Water
Tower
72.8
73.7
Nonattainment
1.07
71.7
72.6
Nonattainment
1.03
WI
Kenosha-
Chiwaukee
69.2
72.3
Maintenance-Only
1.17
68.1
71.1
Maintenance-Only
1.11
WI
Racine
71.3
73.2
Nonattainment
1.02
70.2
72.1
Maintenance-Only
0.96
D-5
-------
Upwind State: Minnesota
2023
2023
2026
2026
Downwind
Avg
Max
2023
Avg
Max
2026
States
Receptors
DV
DV
Receptor Status
Contribution
DV
DV
Receptor Status
Contribution
IL
Chicago-Alsip
69.6
73.4
Maintenance-Only
0.97
68.7
72.5
Maintenance-Only
0.91
IL
Chicago-ComEd
69.3
72.1
Maintenance-Only
0.79
68.5
71.3
Maintenance-Only
0.75
Upwind State: Mississippi
Downwind
States
Receptors
2023
Avg
DV
2023
Max
DV
Receptor Status
2023
Contribution
2026
Avg
DV
2026
Max
DV
Receptor Status
2026
Contribution
TX
Houston-Brazoria
70.1
72.3
Maintenance-Only
0.92
69.1
71.2
Maintenance -Only
0.90
TX
Dallas-Denton
70.4
72.2
Maintenance-Only
1.14
69.0
70.8
No Longer a Receptor
1.09
TX
Houston-Bayland
Park
71.0
72.0
Nonattainment
1.04
69.8
70.8
No Longer a Receptor
1.02
Upwind State: Missouri
Downwind
States
Receptors
2023
Avg
DV
2023
Max
DV
Receptor Status
2023
Contribution
2026
Avg
DV
2026
Max
DV
Receptor Status
2026
Contribution
IL
Chicago-Evanston
70.1
73.0
Maintenance-Only
0.94
69.1
72.0
Maintenance -Only
0.85
WI
Kenosha-Water
Tower
72.8
73.7
Nonattainment
1.08
71.7
72.6
Nonattainment
0.98
WI
Kenosha-
Chiwaukee
69.2
72.3
Maintenance-Only
1.66
68.1
71.1
Maintenance -Only
1.53
WI
Racine
71.3
73.2
Nonattainment
0.92
70.2
72.1
Maintenance -Only
0.84
D-6
-------
Upwind State: Nevada
Downwind
States
Receptors
2023
Avg
DV
2023
Max
DV
Receptor Status
2023
Contribution
2026
Avg
DV
2026
Max
DV
Receptor Status
2026
Contribution
UT
Salt Lake City-
Bountiful
72.9
75.1
Nonattainment
0.86
71.7
73.9
Nonattainment
0.78
UT
Salt Lake City-
Hawthorne
73.6
75.3
Nonattainment
0.89
72.5
74.1
Nonattainment
0.81
Upwind State: New Jersey
Downwind
States
Receptors
2023
Avg
DV
2023
Max
DV
Receptor Status
2023
Contribution
2026
Avg
DV
2026
Max
DV
Receptor Status
2026
Contribution
CT
Greenwich
73.0
73.7
Nonattainment
6.90
71.5
72.2
Nonattainment
6.60
CT
Stratford
74.2
75.1
Nonattainment
7.43
72.8
73.7
Nonattainment
7.24
CT
Westport
76.1
76.4
Nonattainment
8.85
74.6
74.8
Nonattainment
8.54
CT
Madison
71.8
73.9
Nonattainment
5.67
70.4
72.4
Maintenance-Only
5.47
PA
Philadelphia-Bristol
70.7
72.2
Maintenance-Only
5.79
69.2
70.7
No Longer a Receptor
5.51
Upwind State: New York
Downwind
States
Receptors
2023
Avg
DV
2023
Max
DV
Receptor Status
2023
Contribution
2026
Avg
DV
2026
Max
DV
Receptor Status
2026
Contribution
CT
Greenwich
73.0
73.7
Nonattainment
16.81
71.5
72.2
Nonattainment
16.58
CT
Stratford
74.2
75.1
Nonattainment
13.56
72.8
73.7
Nonattainment
13.28
CT
Westport
76.1
76.4
Nonattainment
14.36
74.6
74.8
Nonattainment
14.18
CT
Madison
71.8
73.9
Nonattainment
11.54
70.4
72.4
Maintenance-Only
11.29
PA
Philadelphia-Bristol
70.7
72.2
Maintenance-Only
1.80
69.2
70.7
No Longer a Receptor
1.73
D-7
-------
Upwind State: Ohio
Downwind
States
Receptors
2023
Avg
DV
2023
Max
DV
Receptor Status
2023
Contribution
2026
Avg
DV
2026
Max
DV
Receptor Status
2026
Contribution
CT
Greenwich
73.0
73.7
Nonattainment
1.18
71.5
72.2
Nonattainment
1.10
CT
Stratford
74.2
75.1
Nonattainment
1.87
72.8
73.7
Nonattainment
1.76
CT
Westport
76.1
76.4
Nonattainment
1.90
74.6
74.8
Nonattainment
1.78
CT
Madison
71.8
73.9
Nonattainment
1.94
70.4
72.4
Maintenance-Only
1.83
IL
Chicago-Alsip
69.6
73.4
Maintenance-Only
0.82
68.7
72.5
Maintenance-Only
0.78
IL
Chicago-South
69.8
72.4
Maintenance-Only
1.26
69.1
71.7
Maintenance-Only
1.22
IL
Chicago-ComEd
69.3
72.1
Maintenance-Only
1.23
68.5
71.3
Maintenance-Only
1.18
IL
Chicago-Northbrook
69.9
73.4
Maintenance-Only
1.23
68.9
72.4
Maintenance-Only
1.19
IL
Chicago-Evanston
70.1
73.0
Maintenance-Only
1.69
69.1
72.0
Maintenance-Only
1.62
PA
Philadelphia-Bristol
70.7
72.2
Maintenance-Only
1.88
69.2
70.7
No Longer a Receptor
1.76
WI
Kenosha-Water
Tower
72.8
73.7
Nonattainment
1.67
71.7
72.6
Nonattainment
1.59
WI
Kenosha-Chiwaukee
69.2
72.3
Maintenance-Only
1.33
68.1
71.1
Maintenance-Only
1.30
WI
Racine
71.3
73.2
Nonattainment
1.00
70.2
72.1
Maintenance-Only
0.97
Upwind State: Oklahoma
2023
2023
2026
2026
Downwind
Avg
Max
2023
Avg
Max
2026
States
Receptors
DV
DV
Receptor Status
Contribution
DV
DV
Receptor Status
Contribution
IL
Chicago-South
69.8
72.4
Maintenance-Only
0.75
69.1
71.7
Maintenance-Only
0.72
TX
Dallas-Denton
70.4
72.2
Maintenance-Only
1.19
69.0
70.8
No Longer a Receptor
1.12
D-8
-------
Upwind State: Pennsylvania
2023
2023
2026
2026
Downwind
Avg
Max
2023
Avg
Max
2026
States
Receptors
DV
DV
Receptor Status
Contribution
DV
DV
Receptor Status
Contribution
CT
Greenwich
73.0
73.7
Nonattainment
5.44
71.5
72.2
Nonattainment
5.32
CT
Stratford
74.2
75.1
Nonattainment
6.37
72.8
73.7
Nonattainment
6.36
CT
Westport
76.1
76.4
Nonattainment
6.90
74.6
74.8
Nonattainment
6.82
CT
Madison
71.8
73.9
Nonattainment
4.74
70.4
72.4
Maintenance-Only
4.74
Upwind State: Tennessee
2023
2023
2026
2026
Downwind
Avg
Max
2023
Avg
Max
2026
States
Receptors
DV
DV
Receptor Status
Contribution
DV
DV
Receptor Status
Contribution
TX
Dallas-Denton
70.4
72.2
Maintenance -Only
0.94
69.0
70.8
No Longer a
Receptor
0.87
Upwind State: Texas
Downwind
States
Receptors
2023
Avg
DV
2023
Max
DV
Receptor Status
2023
Contribution
2026
Avg
DV
2026
Max
DV
Receptor Status
2026
Contribution
IL
Chicago-Alsip
69.6
73.4
Maintenance -Only
0.86
68.7
72.5
Maintenance -Only
0.82
IL
Chicago-South
69.8
72.4
Maintenance -Only
1.46
69.1
71.7
Maintenance -Only
1.39
IL
Chicago-Northbrook
69.9
73.4
Maintenance -Only
1.15
68.9
72.4
Maintenance -Only
1.09
IL
Chicago-Evanston
70.1
73.0
Maintenance -Only
1.58
69.1
72.0
Maintenance -Only
1.49
WI
Kenosha-Water Tower
72.8
73.7
Nonattainment
1.72
71.7
72.6
Nonattainment
1.61
WI
Kenosha-Chiwaukee
69.2
72.3
Maintenance -Only
1.81
68.1
71.1
Maintenance -Only
1.70
WI
Racine
71.3
73.2
Nonattainment
1.34
70.2
72.1
Maintenance -Only
1.25
D-9
-------
Upwind State: Utah
2023
2023
2026
2026
Downwind
Avg
Max
2023
Avg
Max
2026
States
Receptors
DV
DV
Receptor Status
Contribution
DV
DV
Receptor Status
Contribution
CO
Denver-Chatfield
71.7
72.3
Nonattainment
1.37
70.5
71.1
Maintenance -Only
1.18
CO
Rocky Flats
72.6
73.3
Nonattainment
1.10
71.7
72.3
Nonattainment
0.95
CO
Denver-NREL
73.8
74.4
Nonattainment
1.06
72.6
73.3
Nonattainment
0.90
Upwind State: Virginia
Downwind
States
Receptors
2023
Avg
DV
2023
Max
DV
Receptor Status
2023
Contribution
2026
Avg
DV
2026
Max
DV
Receptor Status
2026
Contribution
CT
Stratford
74.2
75.1
Nonattainment
1.19
72.8
73.7
Nonattainment
1.14
CT
Westport
76.1
76.4
Nonattainment
1.19
74.6
74.8
Nonattainment
1.13
CT
Madison
71.8
73.9
Nonattainment
1.77
70.4
72.4
Maintenance -Only
1.68
PA
Philadelphia-Bristol
70.7
72.2
Maintenance -Only
1.63
69.2
70.7
No Longer a
Receptor
1.52
Upwind State: West Virginia
2023
2023
2026
2026
Downwind
Avg
Max
2023
Avg
Max
2026
States
Receptors
DV
DV
Receptor Status
Contribution
DV
DV
Receptor Status
Contribution
CT
Stratford
74.2
75.1
Nonattainment
1.30
72.8
73.7
Nonattainment
1.19
CT
Westport
76.1
76.4
Nonattainment
1.34
74.6
74.8
Nonattainment
1.23
CT
Madison
71.8
73.9
Nonattainment
1.45
70.4
72.4
Maintenance -Only
1.35
PA
Philadelphia-Bristol
70.7
72.2
Maintenance -Only
1.44
69.2
70.7
No Longer a Receptor
1.34
D-10
-------
Upwind State: Wisconsin
2023
2023
2026
2026
Downwind
Avg
Max
2023
Avg
Max
2026
States
Receptors
DV
DV
Receptor Status
Contribution
DV
DV
Receptor Status
Contribution
IL
Chicago/Alsip
69.6
73.4
Maintenance -Only
2.41
68.7
72.5
Maintenance -Only
2.23
IL
Chicago/South
69.8
72.4
Maintenance -Only
2.61
69.1
71.7
Maintenance -Only
2.44
IL
Chicago/ComEd
69.3
72.1
Maintenance -Only
2.47
68.5
71.3
Maintenance -Only
2.29
IL
Chicago/Northbrook
69.9
73.4
Maintenance -Only
2.55
68.9
72.4
Maintenance -Only
2.36
IL
Chicago/Evanston
70.1
73.0
Maintenance -Only
1.47
69.1
72.0
Maintenance -Only
1.36
Upwind State: Wyoming
2023
2023
2026
2026
Downwind
Avg
Max
2023
Avg
Max
2026
States
Receptors
DV
DV
Receptor Status
Contribution
DV
DV
Receptor Status
Contribution
CO
Denver-Chatfield
71.7
72.3
Nonattainment
0.81
70.5
71.1
Maintenance -Only
0.80
D-ll
-------
Appendix E
Upwind Linkages for
Individual Receptors in 2023 and 2026
-------
Site ID
State
County
Receptor Name
Upwind States Linked to Individual Receptors in 2023
40278011
AZ
Yuma
Yuma
CA
80350004
CO
Douglas
Denver-Chatfield
CA
UT
WY
80590006
CO
Jefferson
Rocky Flats
CA
UT
80590011
CO
Jefferson
Denver-NREL
CA
UT
90010017
CT
Fairfield
Greenwich
MI
NJ
NY
OH
PA
90013007
CT
Fairfield
Stratford
IN
KY
MD
MI
NJ
NY
OH
PA
VA
wv
90019003
CT
Fairfield
Westport
IN
KY
MD
MI
NJ
NY
OH
PA
VA
wv
90099002
CT
New Haven
Madison
IN
KY
MD
MI
NJ
NY
OH
PA
VA
wv
170310001
IL
Cook
Chicago-Alsip
IN
MI
MN
OH
TX
WI
170310032
IL
Cook
Chicago-South
IN
MI
OH
OK
TX
WI
170310076
IL
Cook
Chicago-ComEd
IN
MI
MN
OH
WI
170314201
IL
Cook
Chicago-Northbrook
IN
MI
OH
TX
WI
170317002
IL
Cook
Chicago-Evanston
IN
MI
MO
OH
TX
WI
320030075
NV
Clark
Las Vega-Northwest
CA
420170012
PA
Bucks
Philadelphia-Bristol
DE
IN
KY
MD
MI
NJ
NY
OH
VA
wv
480391004
TX
Brazoria
Houston-Brazoria
AR
LA
MS
481210034
TX
Denton
Dallas-Denton
AL
AR
LA
MS
OK
TN
482010024
TX
Harris
Houston-Aldine
LA
482010055
TX
Harris
Houston-Bayland Park
AL
AR
LA
MS
482011034
TX
Harris
Houston-East
AR
LA
482011035
TX
Harris
Houston-Clinton
AR
LA
490110004
UT
Davis
Salt Lake City-Bountiful
CA
NV
490353006
UT
Salt Lake
Salt Lake City-Hawthorne
CA
NV
490353013
UT
Salt Lake
Salt Lake City-Herriman
CA
490570002
UT
Weber
Salt Lake City-Ogden
CA
490571003
UT
Weber
Salt Lake City-Harrisonville
CA
550590019
WI
Kenosha
Kenosha-Water Tower
IL
IN
MI
MO
TX
550590025
WI
Kenosha
Kenosha-Chiwaukee
IL
IN
MI
MO
TX
551010020
WI
Racine
Racine
IL
IN
MI
MO
TX
E-l
-------
Site ID
State
County
Receptor Name
Upwind States Linked to Individual Receptors in 2026
40278011
AZ
Yuma
Yuma
CA
80350004
CO
Douglas
Denver-Chatfield
CA
UT
WY
80590006
CO
Jefferson
Rocky Flats
CA
UT
80590011
CO
Jefferson
Denver-NREL
CA
UT
90010017
CT
Fairfield
Greenwich
MI
NJ
NY
OH
PA
90013007
CT
Fairfield
Stratford
IN
KY
MD
MI
NJ
NY
OH
PA
VA
wv
90019003
CT
Fairfield
Westport
IN
KY
MD
MI
NJ
NY
OH
PA
VA
wv
90099002
CT
New Haven
Madison
IN
KY
MD
MI
NJ
NY
OH
PA
VA
wv
170310001
IL
Cook
Chicago-Alsip
IN
MI
MN
OH
TX
WI
170310032
IL
Cook
Chicago-South
IN
MI
OH
OK
TX
WI
170310076
IL
Cook
Chicago-ComEd
IN
MI
MN
OH
WI
170314201
IL
Cook
Chicago-Northbrook
IN
MI
OH
TX
WI
170317002
IL
Cook
Chicago-Evanston
IN
MI
MO
OH
TX
WI
480391004
TX
Brazoria
Houston-Brazoria
AR
LA
MS
482010024
TX
Harris
Houston-Aldine
LA
490110004
UT
Davis
Salt Lake City-Bountiful
CA
NV
490353006
UT
Salt Lake
Salt Lake City-Hawthorne
CA
NV
490353013
UT
Salt Lake
Salt Lake City-Herriman
CA
490570002
UT
Weber
Salt Lake City-Ogden
CA
550590019
WI
Kenosha
Kenosha-Water Tower
IL
IN
MI
MO
OH
TX
550590025
WI
Kenosha
Kenosha-Chiwaukee
IL
IN
MI
MO
OH
TX
551010020
WI
Racine
Racine
IL
IN
MI
MO
OH
TX
E-2
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