# Air Quality Modeling Technical Support Document for the 2008 Ozone NAAQS Cross

- State Air Pollution Rule Proposal

Office of Air Quality Planning and Standards United States Environmental Protection Agency November 201 51 

### 1. Introduction In this technical support document (TSD) we describe the air quality modeling performed

to support the proposed Cross

- Stat e Air Pollution Rule for the 2008 ozone National Ambient Air Quality Standards ( NAAQS ) 1 

### . In this document, air quality modeling is used to project ozone concentrations at individual monitoring sites to 201 7 2 

### and to estimate state

- by

- state contributions
to those 201 7 concentrations. The projected 201 7 ozone concentrations are used to identify ozone monitoring sites that are projected to be nonattainment or have maintenance problems for
the 2008 ozone NA AQS in 2017 . Ozone contribution information is then used to quantify projected interstate contributions from emissions in each upwind state to ozone concentrations at projected 2017 nonattainment and maintenance sites in other states (i.e., in downwind states). The remaining sections of this TSD are as follows. Section 2 describes the air quality modeling platform and the evaluation of model predictions using measured concentrations. Section 3 defines the procedures for project ing ozone design value concentrati ons to 2017 and the approach for identifying monitoring sites with projected nonattainment and/or maintenance problems. Section 4 describes (1) the source contribution (i.e., apportionment) modeling and (2) the procedures for quantifying contributions to individual monitoring sites including
nonattainment and/or maintenance sites . For questions about the information in this TSD please
contact Norm Possiel at possiel.norm@epa.gov or (919) 541

- 5692. An electronic c opy of the
2009

– 2013 base period and projected 2017 ozone design values and 2017 ozone contributions
based on the proposal modeling can be obtained from docket EPA

- HQ

- OAR

- 2015

- 0500. The

ozone design values for individual monitoring sites are in docket i tem EPA

- HQ

- OAR

- 2015

- 0500

- 0006 and the ozone contributions are in docket item EPA

- HQ

- OAR

- 2015

- 0500

- 0007

. E lectronic copies of the ozone design values and contributions can also be obtained at www.epa.gov/airtr ansport

. 3

1 The EPA revised the levels of the primary and secondary 8

- hour ozone standards to 0.075 parts per million (ppm). 40 CFR 50.15. 73 FR 16436 (March 27, 2008 ).

2 2017 was selected as the future year analytic base case because 2017 corresponds to the attainment date for ozone nonattainment areas classified as Moderate.

3 Note that the air quality modeling used for the proposed rule was made available for public review as part of the August 4, 2015 Notice of Data Availability (80 FR 46271).2 

### 2. Air Quality Modeling Platform
EPA has developed a 2011

- based air quality modeling platform which includes emissions, meteorology and o ther inputs for 2011. The 2011 base year emissions were projected

t o a future year base case scenario, 2017 . The 2011 mode ling platform and projected 2017
emissions were used to drive the 2011 base year and 2017 base case air quality model
simulations. 4 

### The base year 2011 platform was chosen in part because it represents the most recent, complete set of base year emissions information currently available for national

- scale air quality modeling. In addition, the meteorological conditions during the summer of 2011 were generally conducive for ozone formation across much of the U.S., particularly the eastern U.S. 2.1 Air Quality Model Configuration The photochemical model simulations performed for this ozone transport assessment

use d the Comprehensive Air Quality Model with Extensions (CAMx version 6.1 1 ) (Environ,
2014 )

. CAMx is a three

- dimensional g rid

- 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. CAMx wa s applied with the carbon

- bond 6 revision 2 (CB6r2) gas

- phase chemistry mechanism 5 

### (Ruiz and Yarwood, 2013) and the Zhang dry deposition scheme (Zhang, et al., 2003)

. Figure 2

- 1 shows the geographic extent of the modeling domain that was used for air quality modeling in this analysis. The domain covers the 48 contiguous states along with the southern portions of Canada and the northern portions of Mexico. This modeling domain
contains 25 vertical layers with a top at about 17, 55 0 meters, or 50 millibars (mb), and horizontal

4 EPA also used the 2011

- based air quality modeling platform to perform a 2017 “illustrative” control case air quality model simulation to inform (1) the analysis to quantify upwind state emissions that significantly contribute to nonattainment or interfere with maintenance of the NAAQS in downwind states and (2) the analysis of the costs and benefits of this proposed rule. The 2017 illustrative control case emissions and air quality modeling results are described in the Ozone Transport Policy Analysis Proposed Rule TSD and in the Regulatory Impact Assessment for the proposed rule.

5 The “chemparam.2_CF” chemical parameter file was used in the CAMx model simulations.3 

### grid resolution of 12 km x 12 km. The model simulations produce hourly air quality concentrations for each 12 km grid cell across the modeling domain. CAMx requires a variety of input files that contain information pertaining to the modeling domain and simulation period. These include gridded, hourly emissions estimates and meteorological data, and initial and boundary concentration s. Separate emissions inventories

w ere prepared for the 2011 base year and the 2017 base case

. All other inputs (i.e. meteorological fields, initial concentration s, and boundary concentration s) were specified for the 2011 base year model application and remained unchanged for the future

- yea r model
simulation s 6



### .

Figure 2

- 1. M ap of the CAMx modeling d omain used for transport m odeling.

2.2 Characterization of 2011 Summer Meteorology
Meteorological conditions including temperature, humidity, winds, solar radiation, and vertical mixing affect the formation and transport of ambient ozone concentrations. Ozone is more readily formed on warm, sunny days when the air is stagnant. Conversely, ozone production is more limited on days that are cloudy, cool, rainy, and windy

6 The CAMx annual simulations for 2011 and 2017 were each performed using two time segments (January 1 through April 30, 2011 with a 10

- day ramp

- up period at the end of December 2010 and May 1 through December 31, 2011 with a 10

- day ramp

- up period at the end of Apri l 2011). The CAMx 2017 contribution modeling was performed for the period May 1 through September 30, 2011 with a 10

- day ramp

- up period at the end of April 2011.4 

### ( http://www.epa.gov/airtre nds/weather.html ). Statistical modeling analyses have shown that temperature and certain other meteorological variables are highly correlated with the magnitude of ozone concentrations (Camalier, et al., 2007). In selecting a year for air quality modeling it is important to simulate a variety of meteorological conditions that are generally associated with elevated air quality ( US EPA,
2014a ). Specifically for ozone, modeled time periods should reflect meteorological conditions that frequently correspond with observed 8

- hour daily maximum concentrations greater than the NAAQS at monitoring sites in nonattainment areas ( US EPA, 2014a ). However, because of
inter

- annual variability in weather patterns it is not possible to identify a single year that will be representative of “typical” meteorological conditions within each region of the U.S. As part of the development of the 2011 modeling platform we have examined the temperature and precipitation regimes across the U.S. in 2011 compared to long

- term, climat ological normal (i.e., average) conditions. Table 2

- 1 describes the observed 2011 surface temperature anomalies (i.e., departure from normal) for individual months from May through September relative to the period 1895

- 2011 for each of the nine National O ceanic and Atmospheric Administration (NOAA) climate regions shown in Figure 2

- 2. The aggregate temperature and precipitation anomalies by state for the core summer months, June through August, are shown in Figures 2

- 3 and 2

- 4, respectively. Overall, tempe ratures were warmer than normal during the summer of 2011 in nearly all regions, except for the West and Northwest. Record warmth occurred in portions of the South and Southwest regions. The summer months experienced below average precipitation for much o f the southern and southeastern US, whereas wetter conditions than average were experienced in California and in several northern tier states. Extensive drought conditions occurred in portions of the southern Great Plains states. The warmer and dryer cond itions were associated with a strong upper air ridge over the central U.S during the summer of 2011. While warmer than the long

- term normal, 2011 summer temperatures in the U.S. were comparable to those in several other recent years. For example, 2006, 200 7, 2010, 2012 were also all among the ten warmest on record. Analysis of meteorological

- adjusted trends in seasonal mean ozone for the period 2000 through 2012 indicates that, on a regional basis, the summer of 2011 was typical of high ozone years in the
E a stern U.S. in terms of being conducive to the formation of ozone

. In Figure 2

- 5, those years with downward adjustments (compared to the “unadjusted for weather” line) were generally5 

### more conducive to ozone formation than years that were adjusted upward. For example, in the Northeast region, the years 2002, 2005, 2007, 2010, 2011, and 2012 were more conducive to ozone formation than 2000, 2003, 2004, 2006, and 2009. In 2011, the South and Southeast regions had the largest adjustment (most ozone conducive) , whereas the Northeast and Central regions were slightly above normal. The Western regions experienced generally near normal ozone, with the Northwest somewhat below normal.
Table 2

- 1. Surface tempe rature anomalies in 2011 by month and geographic region.*

* Unshaded boxes with the “N” marker represent near

- normal temperatures that fall within the interquartile range. Blue colors indicate cooler than normal conditions, with the number of “C”s indicating the degree of the anomaly. CCC = coolest on record, CC = coolest 10 th percentile, C = coolest 25 th percentile. Red colors indicate warmer than normal conditions, with the number of “W”s indicating the degree of the anomaly. WWW = warmest on record, WW = warmest 10th percentile, W = warm est 25th percentile.



### Figure 2

- 2

. U.S. climate regions. ( http://www.ncdc.noaa.gov/monitoring

- references/maps/us

- climate

- regions.php ) 

## 2011 May Jun Jul Aug Sep Northeast W W WW N WW Southeast N WW WW WW N Ohio Valley N W WW W C Upper Midwest N N WW W N South N WW WWW WWW N Northern Rockies C N W W W Southwest C W WW WWW W Northwest CC C C W WW West C C N W WW6 

### Figure 2

- 3. Statewide rank of temperatures in 2011 relative to 1895

- 2011 period. ( http://www.ncdc.noaa.gov/temp

- and

- precip/maps.php )

Figure 2

- 4. Statewide rank of precipitation in 2011 relative to 1895

- 2011 period. ( http://www.ncdc.noaa.gov/temp

- and

- precip/maps.php )7 

### Figure 2

- 5a. Meteorological

- adjusted ozone trends by climate region: Northeast, Central (Ohio Valley), East North Central (Upper Midwest), Southeast, South, and Southwest. ( http://www.epa.gov/airtrends/weather.html )8 

### Figure 2

- 5b. Meteorological

- adjusted ozone trends by climate region: West North Central (Northern Rockies and Plains), West, and Northwest. ( http://www.epa.gov/airtrends/weather.html )

2.3 Meteorological Data for 2011 The meteorological data for air quality modeling of 2011 were derived from running Version 3.4 of the Weather Research Forecasting Model (WRF) (Skamarock , et al., 2008) . The meteorological outputs from WRF include hourly

- varyin g 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 simulation include
Pleim

- Xiu land surface mode l (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 do uble moment microphysics (Morrison, et al., 2005; Morrison and Gettelman, 2008) , and RRTMG longwave and shortwave radiation schemes ( Iacono, et.al., 2008 ).9 

### The WRF model simulation was initialized using the 12 km North American Model (12NAM) analysis produc t provided by the National Climatic Data Center ( NCDC ) . Where
12NAM data were unavailable, the 40km Eta Data Assimilation System (EDAS) analysis (ds609.2) from the National Center for Atmospheric Research ( NCA R) was used. Analysis nudging for t emperature, wind, and moisture was applied above the boundary layer only. The model simulations were conducted in 5.5 day block s with soil moisture and temperature carried
from one block to the next via the “ ipxwrf ” program (Gilliam and Pleim, 2010). Landuse and
land cover data were based on the 2006 National Land Cover Database (NLCD2006) data. 7 

### Sea surface temperatures at 1 km resolution were obtained from the Group for High Resolution Sea Surface Temperatures (GHRSST) (Stammer, et al., 2003). As shown in Table 2

- 2, t he WRF simulations were performed with 35 vertical layers up to 50 mb, with the thinnest layers being
nearest the surface to better resolve the planetary boundary layer (PBL). The WRF 35

- layer structure was collapsed to 25 layers for the CAMx air quality model simulations, as shown in
Table 2

- 2.
Table 2

- 2

. WRF and CAMx layers and their approximate height above ground level.

CAMx Layers WRF Layers Sigma P Pressure (mb) Approxi mate Height (m AGL)
25 35 0.00 50.00 17,556 34 0.05 97.50 14,780 24 33 0.10 145.00 12,822 32 0.15 192.50 11,282 23 31 0.20 240.00 10,002 30 0.25 287.50 8,901 22 29 0.30 335.00 7,932 28 0.35 382.50 7,064 21 27 0.40 430.00 6,275 26 0.45 477.50 5,553 20 25 0.50 525.00 4,885 24 0.55 572.50 4,264 19 23 0.60 620.00 3,683 18 22 0.65 667.50 3,136 17 21 0.70 715.00 2,619

7 The 2006 NLCD data are available at http://www.mrlc.gov/nlcd06_data.php10 CAMx Layers WRF Layers Sigma P Pressure (mb) Approxi mate Height (m AGL)
16 20 0.74 753.00 2,226 15 19 0.77 781.50 1,941 14 18 0.80 810.00 1,665 13 17 0.82 829.00 1,485 12 16 0.84 848.00 1,308 11 15 0.86 867.00 1,134 10 14 0.88 886.00 964 9 13 0.90 905.00 797 12 0.91 914.50 714 8 11 0.92 924.00 632 10 0.93 933.50 551 7 9 0.94 943.00 470 8 0.95 952.50 390 6 7 0.96 962.00 311 5 6 0.97 971.50 232 4 5 0.98 981.00 154 4 0.99 985.75 115 3 3 0.99 990.50 77 2 2 1.00 995.25 38 1 1 1.00 997.63 19



### Details of the annual 2011 meteorological model simulation and evaluation are provided in a separate technical support document (US EPA, 2014b ) which can be obtained at http://www.epa.gov/ttn/scram/reports/MET_TSD_2011_final_11

- 26

- 14.pdf

The meteorological data generated by the WRF simulations were processed using a
modified version of the wrfcamx v4.0 (Environ, 2013) meteorological data processing program
to create model

- ready meteorological inputs to CAMx. The modification to wrfcamx included reactivating the wrfcamx option to analyze sub

- grid stratiform clouds from the WRF output in
order to more fully account for the effects of clouds on photolysis rates. In running wrfcamx, vertical eddy diffusivities (Kv) were calculated using the Y onsei University (Y SU ) (Hong and Dudhia, 2006) mixing scheme. We used a minimum Kv of 0.1 m 2 

### /sec except fo r urban grid cells11 

### where the minimum Kv was reset to 1.0 m 2 

### /sec within the lowest 200 m of the surface in order to enhance mixing associated with the nighttime “urban heat island” effect.

2. 4 Initial and Boundary Concentrations The lateral boundary and ini tial species concentrations are provided by a three

dimensional global atmospheric chemistry model, GEOS

- Chem (Yantosca, 2004) standard
version 8

- 03

- 02 with 8

- 02

- 01 chemistry. The global GEOS

- Chem model simulates atmospheric chemical and physical processes driven by assimilated meteorological observations from the NASA’s Goddard Earth Observing System (GEOS

- 5; additional information available at: http://gmao.gsfc.nasa.gov/GEOS/ and http://wiki.seas.harvard.edu/geos

- chem/index.php/GEOS

5). This model was run for 2011 with a grid resolution of 2.0 degrees x 2.5 degrees (latitude

longitude). The predictions were used to provide one

- way dynamic boundary concentration s at

one

- hour intervals and an initial concentration field for the CAMx simulations. The 2011
bou ndary concentrations from GEOS

- Chem were used for the 2011 and 201 7 model simulations. The procedures for translating GEOS

- Chem predictions to initial and boundary concentrations are described elsewhere (Henderson, 2014). More information about the GEOS

- Ch em model and other applications using this tool is available at: http://www

- as.harvard.edu/chemistry/trop/geos.

2.5 Emissions Inventories
CAMx requires detailed emissions inventories containing temporally allocated (i.e., hourly) emissions for each grid

- ce ll in the modeling domain for a large number of chemical species that act as primary pollutants and precursor s to secondary pollutants. A nnual emission
invento ries for 2011 and 2017 were preprocessed into CAMx

- ready inputs using the Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system (Houyoux et al., 2000)

. 8


### Information on the emissions inventories used as input to the CAMx model simulations can be
found in the following emissions inventory technical support document s : Preparation of Emissions Inventories for the Version 6.2, 2011 Emissions Modeling Platform (US EPA, 2015a )
and 2011 National Emissions Inventory, version 2 (US EPA, 2015b).

8 The SMOKE output e missions case name for the 2011 base year is “2011eh_cb6v2_v6_11g” and the emissions case name for the 2017 base case is “2017eh_cb6v2_v6_11g”.12 

### 2.6 Air Quality Model Evaluation An operational model performance evaluation for ozone was conducted to examine the ability of the CAMx v6.11 modeling system to replicate 2011 measured concentrations. This evaluation focused on statistical assessments of model predictions versus observati ons paired in time and space depending on the sampling period of measured data. Details on the evaluation methodology and the calculation of performance statis tics are provided in Appendix A. Overall,
the ozone model performance statistics for the CAMx 201 1 simulation are within or close to the ranges found in other recent peer

- reviewed applications (Simon et al, 2012). The model performance results , as described in Appendix A, demonstrate that the predictions from the 2011 modeling platform closely replica te the corresponding observed concentrations in terms of the magnitude, temporal fluctuations, and geographic differences for 8

- hour daily maximum ozone.
These results provide confidence that our application of CAMx using this 2011 modeling platform provid es a scientifically credible approach for assessing ozone concentrations relevant for the transport assessment described in this TSD

.

3. Identification of Future Nonattainment and Maintenance Receptors

3.1 Definition of Nonattainment and Maintenance Receptors
The approach in the propose rule for identifying the 2017 nonattainment and maintenance receptors is described in Section V.C of the preamble. I n brief, we are proposing to identify nonattainment receptors in this rulemaking as those sites that are violating the NAAQS based on current measured air quality (i.e., 2012

- 2014 design values) and that also have projected 2017 average design values that exceed the NAAQS (i.e., 2017 average design values of 76 ppb or greater ). 9 

### We followed the approach i n the CSAPR to identify sites that would have difficulty maintaining the 2008 ozone NAAQS in a scenario that takes into account historic variability in air quality at the monitoring site. In the CSAPR approach, monitoring sites with a 2017 maximum design value that exceeds the NAAQS, even if the 2017 average design value is below the NAAQS, are projected to have a maintenance problem in 2017. Monitoring sites with a 2017 average design value below the NAAQS, but with a maximum design value that exceeds the

9 In determining compliance with the NAAQS, ozone design values are truncated to integer values. For example, a design value of 75.9 ppb is truncated to 75 ppb which is attainment. In this manner, design values at or above 76.0 ppb are considered to be violations of the NAAQS.13 

### NAAQS, are considered maintenance

- only sites. In addition, those sites that have projected 2017 average design values that exceed the NAAQS, but are currently measuring clean data based on
2012

- 2014 design values are also defined as maintenance

- only recep tors

. Maintenance

- only receptors therefore 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 76 ppb or greater. In addition to the maintenance

- only receptors, the 2017 ozone nonattainment receptors are also maintenance receptors because the maximum design values for each of these sites is always greater than or eq ual to the average design value. The procedures for calculating projected 2017 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 2017 base case are used for assessing the contribution of emissions in upwind states to downwind nonattainment and maintenance of ozone NAAQS as p art of this proposal.

3.2 Approach for Projecting 2017 Ozone Design Values

The ozone pr edictions from the 2011 and 2017 CAMx model simulations were used to project ambient (i.e., measu red) ozone design values to 2017 following the approach described in
EPA’s current guidance for attainment de monstration modeling ( US EPA, 2014a ), 10 

### as summarized here . The modeling guidance recommends using 5

- year weighted average ambient design values 11 

### centered on the base modeling year as the starting point for projecting average design values to the future. Because 2011 is the base emissions year, we used the average
ambient 8

- hour ozone design values for the period 2009 through 2013 (i.e., the average of design
values for 2009

- 2011, 2010

- 2012 and 2011

- 2013) to calculate the 5

- year weighted average design values . The 5

- year weighted average ambient design value at each site was projected to
2017 using the Model Attainment Test Software program (Abt Associates, 2014 ) . This program
calculates the 5

- year weighted average DV based on observed data and projects future year

10 EPA’s ozone attainment demonstration modeling guidance is referred to as “the modeling g uidance” in the remainder of this document.

11 The air quality design value for a site is the 3

- year average of the annual fourth

- highest daily maximum 8

- hour average ozone concentration

.14 

### values using the relative response predicted by the model. Equation ( 3

- 1 ) describes the recommended model attainment test in its simplest form, as appl ied for monitoring site i : ( DVF ) i 

### = ( RRRRRR ) ii 

### ∗ ( DDDDDD ) ii 

### Equation 3

- 1

DVF i 

### is the estimated design value for the future year at monitoring site i ; RRF i 

### is the relative response factor for monitoring site i ; and DVB i 

### is the base period design value monito red at site i

. The relative response factor for each monitoring site ( RRRRRR ) ii 

### is the fractional change in 8

- hour daily maximum 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 was calculated based on the 10 highest days in t he 2011 base year modeling in the vicinity of each monitor location

. As recommended by the modeling guidance, we considered model response in grid cells immediately surrounding the monitoring site along with the grid cell in which the monitor is
located

. The RRF was based on a 3 x 3 array of 12 km grid cells centered on the location of the gri d cell containing the monitor. On each high ozone day, t he grid cell with the highest base year ozone value in the 3 x 3 array surrounding the location of the monitor ing site was used for both the base and future components of the RRF calculation (paired in space)

. In cases for which the
base year model simulation did not have 10 days with ozone values greater than or equal to 60 ppb at a site, we used all days with oz one >= 60 ppb, as long as there were at least 5 days that
meet that criteria. At monitor locations with less than 5 days with modeled 2011 base year ozone >= 60 ppb, no RRF or DVF was calculated for the site and the monitor in question was not
included in this analysis. The approach for calculating 2017 maximum design values is similar to the approach for calculating 2017 average design values. To calculate the 2017 maximum design value we start
with the highest (i.e., maximum) ambient design value from the 2011

- centered 5

- year period (i.e., the maximum of design values from 2009

- 2011, 2010

- 2012, and 2011

- 2013) . The base period maximum design value at each site was projected to 2017 using the site

- specific RRFs , as determined using the procedures for cal culating RRFs described above.
Table 3

- 1 contains the 2009

- 2013 base period average and maximum 8

- hour ozone design values, the 2017 base case average and maximum design values, and the 2012

- 2014 design values for the 8 sites in the eastern U.S. projected to be 2017 nonattainment receptors.15 

### Table 3

- 2 contains this same information for the 6 maintenance

- only sites in the eastern U.S. that are projected nonattainment but currently measuring clean data. Table 3

- 3 contains this same
information for the 23 maintenance

- only sites in the eastern U.S. that are projected to have average design values below the NAAQS, but maximum design values above the NAAQS. The design values for all monitoring sites in the U.S. are pr ovided in docket item EPA

- HQ

- OAR

2015

- 0500

- 0006. 12



### Table 3

- 1. Average and maximum 2009

- 2013 and 2017 base case 8

- hour ozone design values and 2012

- 2014 design values (ppb) at p roje cted n onattainment sites in the eastern U.S. (nonattainment r eceptors)

.

Mo nitor ID State County Average Design Value 2009

- 2013 Maximum Design Value 2009

- 2013 Average Design Value 2017 Maximum Design Value 2017 2012

- 2014 Design Value 90013007 Connecticut Fairfield 84.3 89.0 77.1 81.4 84.0 90019003 Connecticut Fairfield 83.7 87.0 78.0 81.1 85.0 90099002 Connecticut New Haven 85.7 89.0 77.2 80.2 81.0 480391004 Texas Brazoria 88.0 89.0 81.4 82.3 80.0 481210034 Texas Denton 84.3 87.0 76.9 79.4 81.0 484392003 Texas Tarrant 87.3 90.0 79.6 82.1 77.0 484393009 Texas Tarrant 86.0 86.0 78.6 78.6 80.0 551170006 Wisconsin Sheboygan 84.3 87.0 77.0 79.4 81.0

12 There are 7 sites in 3 counties in the West that were excluded from this listing because the ambient design values at these sites were dominated by wintertime ozone episodes and not summer season conditions that are the focus of this transport assessment . High winter ozone concentrations that have been observed in certain parts of the Western U.S. are believed to result from the combination of strong wintertime inversions, large NOx and VOC emissions from nearby oil and gas operations, increased UV intensity due to reflection off of snow surfaces and potentially sti ll uncharacterized sources of free radicals. The 7 sites excluded from this analysis are in Rio Blanco County, CO (site ID 081030006), Fremont County, WY (site ID 560130099), and Sublette County, WY (site IDs 560350097, 560350099, 560350100, 560350101, and 560351002). Information on the analysis to identify these sites as influenced by wintertime ozone episodes can be found in Appendix 3A of the Regulatory Impact Analysis of the Proposed Revisions to the National Ambient Air Quality Standards for Ground

- Lev el Ozone (EPA, 2014d) ( http://www.epa.gov/ttn/ecas/ria.html )16 

### Table 3

- 2. Average and maximum 2009

- 2013 and 2017 base case 8

- hour ozone design values and 2012

- 2014 d e sign values (ppb) at sites in the eastern U.S. that are projected nonattainment but currently measuring clean data (maintenance

- only r eceptors)

.

Monitor ID State County Average Design Value 2009

- 2013 Maximum Design Value 2009

- 2013 Average Design Value 2017 Maximum Design Value 2017 2012

- 2014 Design Value 240251001 Maryland Harford 90.0 93.0 81.3 84.0 75.0 360850067 New York Richmond 81.3 83.0 76.3 77.8 73.0 361030002 New York Suffolk 83.3 85.0 79.2 80.8 73.0 390610006 Ohio Hamilton 82.0 85.0 76.3 79.1 75.0 482011034 Texas Harris 81.0 82.0 76.8 77.8 72.0 482011039 Texas Harris 82.0 84.0 78.2 80.2 72.017 

### Table 3

- 3. Average and maximum 2009

- 2013 and 2017 base case 8

- hour ozone design values and 2012

- 2014 design v alues (ppb) at projected maintenance sites in the eastern U.S. Based on the CSAPR methodology (maintenance

- only r eceptors)

.

Monitor ID State County Average Design Value 2009

- 2013 Maximum Design Value 2009

- 2013 Average Design Value 2017 Maximum Design Value 2017 2012

- 2014 Design Value 90010017 Connecticut Fairfield 80.3 83.0 75.8 78.4 82.0

211110067 Kentucky Jefferson 82.0 85.0 75.8 78.6 Incomplete Data 211850004 Kentucky Oldham 82.0 86.0 73.7 77.3 74.0 240053001 Maryland Baltimore 80.7 84.0 73.2 76.2 72.0 260050003 Michigan Allegan 82.7 86.0 75.5 78.5 83.0 261630019 Michigan Wayne 78.7 81.0 74.0 76.2 74.0 340071001 New Jersey Camden 82.7 87.0 74.2 78.1 76.0 340150002 New Jersey Gloucester 84.3 87.0 75.1 77.5 76.0 340230011 New Jersey Middlesex 81.3 85.0 73.0 76.3 74.0 340290006 New Jersey Ocean 82.0 85.0 73.9 76.6 75.0 360810124 New York Queens 78.0 80.0 75.7 77.6 72.0 420031005 Pennsylvania Allegheny 80.7 82.0 75.3 76.5 77.0 421010024 Pennsylvania Philadelphia 83.3 87.0 75.1 78.4 75.0 480850005 Texas Collin 82.7 84.0 74.9 76.0 78.0 481130069 Texas Dallas 79.7 84.0 74.0 78.0 78.0 481130075 Texas Dallas 82.0 83.0 75.8 76.7 77.0 481211032 Texas Denton 82.7 84.0 75.1 76.3 79.0 482010024 Texas Harris 80.3 83.0 75.9 78.5 72.0 482010026 Texas Harris 77.3 80.0 73.5 76.1 67.0 482010055 Texas Harris 81.3 83.0 75.4 77.0 75.0 482011050 Texas Harris 78.3 80.0 74.6 76.2 72.0 484390075 Texas Tarrant 82.0 83.0 75.5 76.4 79.0 484393011 Texas Tarrant 80.7 83.0 74.5 76.6 75.018 

### 4. Ozone Contribution Modeling 4.1 Methodology The EPA performed nationwide, 13 

### state

- level ozone source apportionment modeling using the CAMx OSAT/APCA technique 14 

### (ENVIRON, 2014) to quantify the contribution of
2017 base case NO X 

### and VOC emissions from all sources in each state to projected 2017 ozone concentrations at ozone monitor ing sites. In the source apportionment model run, we tracked the ozone formed from each of the following contribution categories (i.e., “tags”):

• States

– anthropogenic NO X 

### and VOC emissions from each state tracked individually (emissions from all anthropogenic sectors in a given state were combined);

• Biogenics

– biogenic NO X 

### and VOC emissions domain

- wide (i.e., not by state) 15 

### ;

• Boundary Concentrations

– concentrations transp orted into the modeling domain;

• Tribes

– the emissions from those tribal lands for which we have point source inventory data in the 2011 NEI (we did not model the contributions from individual tribes);

• Canada and Mexico

– anthropogenic emissions from sour ces in the portions of Canada and Mexico included in the modeling domain (we did not model the contributions from Canada and Mexico separately);

• Fires

– combined emissions from wild and prescribed fires domain

- wide (i.e., not by state) ; and

• Offshore

– combined emissions from offshore marine vessels and offshore drilling platforms. The contribution modeling provided contributions to ozone from anthropogenic NO X 

### and VOC emissions in each state, individually. The contributions to ozone from chemical reacti ons between biogenic NO X 

### and VOC emissions were modeled and assigned to the “biogenic” category. The contributions from wild fire and prescribed fire NO X 

### and VOC emissions were modeled and assigned to the “fires” category. That is, the contributions from t he “biogenic” and

13 As shown in Figure 2

- 1, the EPA’s nationwide modeling includes the 48 contiguous states.

14 As part of this technique, ozone formed from rea ctions between biogenic VOC and NOx with anthropogenic NOx and VOC are assigned to the anthropogenic emissions.

15 Biogenic emissions and emissions from wild fires and prescribed fires were held constant between 2011 and 2017 since (1) these emissions are t ied to the 2011 meteorological conditions and (2) the focus of this rule is on the contribution from anthropogenic emissions to projected ozone nonattainment and maintenance.19 

### “fires” categories are not assigned to individual states nor are they included in the state
contributions.
CA Mx OSAT/APCA model run was performed for the period May 1 through Septe mber 30 using the projected 2017 base case emissions and 2011 meteorology for this time period. The hourly contributions 16 

### from each tag were processed to calculate an 8

- h our average contribution
metric. Th e process for calculating the contribution metric uses the contribution modeling outputs in a “relative sen se” to apportion the projected 2017 average design value at each monitoring location into contribut ions from each individual tag. This process is similar in concept to the approach describe d above for using model predictions to calcu late 2017 ozone design values. The approach used to calculate the contribution metric is described by the following steps : Step 1. Modeled h ourly ozone concentrations are used to calculate the 8

- hour daily maximum ozone (MDA8) concentration in each grid cell on each day. Step 2. The g ridded hourly ozone contributions from each tag are subtracted from the corresponding gridded hourly total ozone concentrations to create a “pseudo” hourly ozone value for each tag for each hour in each grid cell. Step 3

. The hourly “pseudo” concentr ations from Step 2 are used to calculate 8

- hour average “pseudo” concentrations for each tag for the time period that corresponds to the MDA8
concentration from S tep 1. S tep 2 results in spatial fields of 8

- hour average “pseudo”
concentrations for each gr id cell for each tag on each day. Step 4. The 8

- hour average “pseudo” concentrations for each tag and the MDA8 concentrations

are extracted for those grid cells containing ozone monitoring sites . We used the data for all days
with 2017 MDA8 concentratio ns >=76 ppb (i.e., projected 2017 exceedance days) in the
downstream calculations

. If there were fewer than five 2017 exceedance days at a particular monitoring site then the data from the top five 2017 MDA8 concentration days are extracted and
used in the calculations. 17

16 Contributions from anthropogenic emissions under “NO X - limited” and “VOC

- limited ” chemical regimes were combined to obtain the net contribution from NO X and VOC anthropogenic emissions in each state.

17 If there were fewer than 5 days with a modeled 2017 MDA8 concentration ≥ 60 ppb for the location of a particular monitoring site, then contributions were not calculated at that monitor.20 

### Step 5. For each monitoring site and each tag, the 8

- hour “pseudo” concentrations are then averaged across the days selected in Step 4 to create a multi

- day average “pseudo” concentration for tag at each site. Similarly, the MDA8 concentrations were average across the days selected in Step 4. Step 6. The multi

- day average “pseudo” concentration and the corresponding multi

- day average
MDA8 conce ntration are used to create a R elative Contribution Factor (R C F) for each tag at each monitoring site. The RCF is the difference between the MDA8 concentration and the corresponding “pseudo” concentration , normalized by the MDA8 concentration. Step 7. The R C F for each tag i s multiplied by the 2017 average ozone design value to create the ozone contribution metrics for each tag at each site. Note that the sum of the contributions from each tag equals th e 2017 average design value for that site. Step 8. The contributions calculated from Step 7 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).
Table 4

- 1 provides an example of the calculation of contributions from two states (state A and state B) to a particular nonattainment site starting with Step 4, above . The table includes the daily “pseudo” concentrations for state A and state B and corresponding MDA8 ozone
concentrations on th os e d ays with 2017 model

- predicted exceedances at this site. The MDA8

ozone concen trations on these days are ranked

- ordered in the table. The 2017 average design
value for this example is 77.5 ppb. Using the data in Table 4

- 1, the RCF for state A and state B

are calculated as: (90.372

– 81.857 ) / 90.372 = 0.09422 for state A , and (90.372

– 90.163 ) / 90.372 = 0.00231 for state B The contributions from state A and state B to the 2017 average design value at this site are
calculated as:
77.5 x 0.09422 = 7.3020 which is truncated to 7.30 ppb for state A, and
77.5 x 0.00231 = 0.1790 which is truncated to 0.17 ppb for state B21 

### Table 4

- 1. Example calculation of ozone contributions (units are ppb)

.

The average contribution metric calculated in this manner is intended to provide a reasonable representation of the contribution from individual states to the projected 2017 design value, based on modeled transport patterns and other meteorological conditions generally associated with modeled high ozone concentrations in the vicinity of the monitoring site. This average contribution metr ic is beneficial since the magnitude of the contributions is directly related to the magnitude of the design value at each site. 4.2 Contribution Modeling Results The contributions from each tag to individual nonattainment and maintenance

- only sites

are provided in Appendix B. The largest contributions from each state to 2017 downwind
nonattainment sites and to downwind maintenance

- only sites are provided in Table 4

- 2

. The
2017 contributions from each tag to each monitoring site are provided in docket item EPA

- HQ

OAR

- 2015

- 0500

- 0007

. Month Day Predicted MDA8 O3 on 2017 Modeled Exceedance Days "Pseudo" 8-Hr O3 for

State A "Pseudo" 8-Hr O3 for

State B 7 11 110.832 98.741 110.817 7 6 102.098 89.017 102.081 7 21 100.739 87.983 100.560 6 9 94.793 87.976 93.179 6 8 92.255 84.707 92.207 7 18 84.768 72.196 84.635 8 1 81.719 81.065 81.718 7 17 81.453 73.034 81.443 7 22 78.377 74.500 78.303 6 16 76.695 69.357 76.695

90.372 81.857 90.163
Relative Contribution Factors => 0.09422 0.00231
Contributions => 7.3020 0.1790
Truncated Contributions => 7.30 0.17 2017 Average Design Value is 77.5 ppb Multi-Day Average =>22 

### Table 4

- 2 . Largest ozone contributions from each state to downwind 201 7 projected nonattainment and 2017 projected maintenance

- only sites in the eastern U.S. (units are ppb).

Upwind State Largest Downwind Contribution to Nonattainment Receptors for Ozone (ppb) Largest Downwind Contribution to

Maintenance Receptors for

Ozone (ppb AL 0.79 1.28 AR 0.98 2.15 CT 0.00 0.46 DE 0.37 2.23 DC 0.06 0.73 FL 0.54 0.72 GA 0.47 0.58 IL 17.48 23.17 IN 6.24 14.95 IA 0.61 0.85 KS 0.80 1.03 KY 0.75 11.17 LA 3.09 4.23 ME 0.00 0.08 MD 2.07 7.11 MA 0.10 0.37 MI 2.69 1.79 MN 0.40 0.47 MS 0.78 1.48 MO 1.63 3.69 NE 0.24 0.36 NH 0.02 0.07 NJ 8.84 12.38 NY 16.96 17.21 NC 0.55 0.93 ND 0.11 0.28 OH 2.18 7.92 OK 1.70 2.46 PA 9.39 15.93 RI 0.02 0.08 SC 0.16 0.21 SD 0.08 0.12 TN 0.51 1.6723 Upwind State Largest Downwind Contribution to Nonattainment Receptors for Ozone (ppb) Largest Downwind Contribution to

Maintenance Receptors for

Ozone (ppb TX 2.44 2.95 VT 0.01 0.05 VA 1.87 5.29 WV 0.95 3.11 WI 0.34 2.59



### As discussed in the preamble , the E PA is proposing to establish an air quality screening threshold calculated as one percent of the NAAQS. F or this rule, we propose an 8

- hour ozone threshold of 0.75 ppb as the quantification of one percent of the 2008 ozone NAAQS. This
threshold is used to identify upwind states that con tribute to downwind ozone concentrations in
amounts sufficient to “link” them to these to downwind nonattainment and maintenance receptors. States in the East whose contributions to a specific receptor meet or exceed the screening
threshold are considered linked to that receptor; those states’ ozone contributions and emissions (and available emission reductions) are analyzed further, as described in the premable , to determine whether and what emissions reductions might be required from each state.
Based on the maximum downwind contributions in Table 4

- 2 , the following states contribute at or above the 0.75 ppb threshold to downwind nonattainment receptors: Alabama, Arkansas, Illinois, Indiana, Kansas, Kentucky, Louisiana, Maryland, Michigan, Mississippi, Missouri, New Jersey, New York, Ohio, Oklahoma, Pe nnsylvania, Texas, Virginia, and West Virginia. Based on the maximum downwind contributions in Table 4

- 2 , the following states contribute at or above the 0.75 ppb threshold to downwind maintenance

- only receptors: Alabama, Arkansas, Delaware, Illinois, Indi ana, Iowa, Kansas, Kentucky, Louisiana, Maryland, Michigan, Mississippi, Missouri, New Jersey, New York, North Carolina, Ohio, Oklahoma, Pennsylvania, Tennessee, Texas, Virginia, West Virginia, and Wisconsin. The linkages between each upwind state and down wind nonattainment receptors and maintenance

- only receptors in the eastern U.S. are provided in Table 4

- 3 and Table 4

- 4 , respectively.24 

### Table 4

- 3. Linkages between each upwind state and downwind nonattainment receptors in the

e astern U.S.

Upwind

State Dow nwind Nonattainment Receptors

AL Tarrant Co, TX (484392003)

AR Brazoria Co, TX (480391004) Tarrant Co, TX (484392003) Tarrant Co, TX (484393009)

IL Brazoria Co, TX (480391004) Sheboygan Co, WI (551170006)

IN Fairfield Co, CT (90013007) Fairfield Co, CT (90019003) Sheboygan Co, WI (551170006)

KS Sheboygan Co, WI (551170006)

KY Sheboygan Co, WI (551170006)

LA Brazoria Co, TX (480391004) Denton Co, TX (481210034) Tarrant Co, TX (484392003) Tarrant Co, TX (484393009) Sheboygan Co, WI (551170006)

MD Fairfield Co, CT (90013007) Fairfield Co, CT (90019003) New Haven Co, CT (90099002)

MI Fairfield Co, CT (90013007) Fairfield Co, CT (90019003) New Haven Co, CT (90099002) Sheboygan Co, WI (551170006)

MS Brazoria Co, TX (480391004)

MO Brazoria Co, TX (480391004) Sheboygan Co, WI (551170006)

NJ Fairfield Co, CT (90013007) Fairfield Co, CT (90019003) New Haven Co, CT (90099002)

NY Fairfield Co, CT (90013007) Fairfield Co, CT (90019003) New Haven Co, CT (90099002)

OH Fairfield Co, CT (90013007) Fairfield Co, CT (90019003) New Haven Co, CT (90099002) Sheboygan Co, WI (551170006)

OK Denton Co, TX (481210034) Tarrant Co, TX (484392003) Tarrant Co, TX (484393009)25 Upwind

State Dow nwind Nonattainment Receptors
Sheboygan Co, WI (551170006)

PA Fairfield Co, CT (90013007) Fairfield Co, CT (90019003) New Haven Co, CT (90099002)

TX Sheboygan Co, WI (551170006)

VA Fairfield Co, CT (90013007) Fairfield Co, CT (90019003) New Haven Co, CT (90099002)

WV Fairfield Co, CT (90013007) Fairfield Co, CT (90019003)



### Table 4

- 4. Linkages between each upwind states and downwind maintenance

- only r eceptors in the e astern U.S.

Upwind

State Downwind Nonattainment Receptors

AL Hamilton Co, OH (390610006) Harris Co, TX (482010055)

AR Oldham Co, KY (211850004) Allegan Co, MI (260050003) Dallas Co, TX (481130069) Dallas Co, TX (481130075) Harris Co, TX (482010026) Harris Co, TX (482010055) Harris Co, TX (482011039) Harris Co, TX (482011050) Tarrant Co, TX (484390075) Tarrant Co, TX (484393011)

DE Camden Co, NJ (340071001) Gloucester Co, NJ (340150002) Ocean Co, NJ (340290006) Philadelphia Co, PA (421010024)

IL Jefferson Co, KY (211110067) Oldham Co, KY (211850004) Allegan Co, MI (260050003) Wayne Co, MI (261630019) Camden Co, NJ (340071001) Gloucester Co, NJ (340150002) Ocean Co, NJ (340290006) Queens Co, NY (360810124) Suffolk Co, NY (361030002) Hamilton Co, OH (390610006) Allegheny Co, PA (420031005) Harris Co, TX (482010026)26 Upwind

State Downwind Nonattainment Receptors

Harris Co, TX (482011039)

IN Jefferson Co, KY (211110067) Oldham Co, KY (211850004) Baltimore Co, MD (240053001) Harford Co, MD (240251001) Allegan Co, MI (260050003) Wayne Co, MI (261630019) Camden Co, NJ (340071001) Gloucester Co, NJ (340150002) Middlesex Co, NJ (340230011) Ocean Co, NJ (340290006) Queens Co, NY (360810124) Richmond Co, NY (360850067) Suffolk Co, NY (361030002) Hamilton Co, OH (390610006) Allegheny Co, PA (420031005) Philadelphia Co, PA (421010024)

IA Allegan Co, MI (260050003)

KS Allegan Co, MI (260050003) Tarrant Co, TX (484390075) Tarrant Co, TX (484393011)

KY Baltimore Co, MD (240053001) Harford Co, MD (240251001) Camden Co, NJ (340071001) Gloucester Co, NJ (340150002) Middlesex Co, NJ (340230011) Ocean Co, NJ (340290006) Richmond Co, NY (360850067) Hamilton Co, OH (390610006) Allegheny Co, PA (420031005) Philadelphia Co, PA (421010024)

LA Collin Co, TX (480850005) Dallas Co, TX (481130069) Dallas Co, TX (481130075) Denton Co, TX (481211032) Harris Co, TX (482010024) Harris Co, TX (482010026) Harris Co, TX (482010055) Harris Co, TX (482011034) Harris Co, TX (482011039) Harris Co, TX (482011050) Tarrant Co, TX (484390075) Tarrant Co, TX (484393011)

MD Fairfield Co, CT (90010017) Gloucester Co, NJ (340150002) Middlesex Co, NJ (340230011) Ocean Co, NJ (340290006) Queens Co, NY (360810124) Richmond Co, NY (360850067) Suffolk Co, NY (361030002) Philadelphia Co, PA (421010024)27 Upwind

State Downwind Nonattainment Receptors

MI Fairfield Co, CT (90010017) Jefferson Co, KY (211110067) Oldham Co, KY (211850004) Harford Co, MD (240251001) Camden Co, NJ (340071001) Gloucester Co, NJ (340150002) Ocean Co, NJ (340290006) Queens Co, NY (360810124) Richmond Co, NY (360850067) Suffolk Co, NY (361030002) Hamilton Co, OH (390610006) Allegheny Co, PA (420031005)

MS Harris Co, TX (482010055) Harris Co, TX (482011039)

MO Oldham Co, KY (211850004) Allegan Co, MI (260050003) Camden Co, NJ (340071001) Hamilton Co, OH (390610006) Harris Co, TX (482010026) Harris Co, TX (482010055) Harris Co, TX (482011039) Harris Co, TX (482011050)

NJ Fairfield Co, CT (90010017) Queens Co, NY (360810124) Richmond Co, NY (360850067) Suffolk Co, NY (361030002) Philadelphia Co, PA (421010024)

NY Fairfield Co, CT (90010017) Camden Co, NJ (340071001) Gloucester Co, NJ (340150002) Middlesex Co, NJ (340230011) Ocean Co, NJ (340290006)

NC Baltimore Co, MD (240053001)

OH Fairfield Co, CT (90010017) Jefferson Co, KY (211110067) Oldham Co, KY (211850004) Baltimore Co, MD (240053001) Harford Co, MD (240251001) Wayne Co, MI (261630019) Camden Co, NJ (340071001) Gloucester Co, NJ (340150002) Middlesex Co, NJ (340230011) Ocean Co, NJ (340290006) Queens Co, NY (360810124) Richmond Co, NY (360850067) Suffolk Co, NY (361030002) Allegheny Co, PA (420031005) Philadelphia Co, PA (421010024)

OK Allegan Co, MI (260050003) Hamilton Co, OH (390610006) Dallas Co, TX (481130069) Dallas Co, TX (481130075) Denton Co, TX (481211032) Harris Co, TX (482010026)28 Upwind

State Downwind Nonattainment Receptors

Harris Co, TX (482011034) Harris Co, TX (482011039) Tarrant Co, TX (484390075) Tarrant Co, TX (484393011)

PA Fairfield Co, CT (90010017) Baltimore Co, MD (240053001) Harford Co, MD (240251001) Camden Co, NJ (340071001) Gloucester Co, NJ (340150002) Middlesex Co, NJ (340230011) Ocean Co, NJ (340290006) Queens Co, NY (360810124) Richmond Co, NY (360850067) Suffolk Co, NY (361030002)

TN Hamilton Co, OH (390610006) Philadelphia Co, PA (421010024)

TX Baltimore Co, MD (240053001) Harford Co, MD (240251001) Allegan Co, MI (260050003) Camden Co, NJ (340071001) Gloucester Co, NJ (340150002) Ocean Co, NJ (340290006) Queens Co, NY (360810124) Richmond Co, NY (360850067) Suffolk Co, NY (361030002) Hamilton Co, OH (390610006) Allegheny Co, PA (420031005) Philadelphia Co, PA (421010024)

VA Fairfield Co, CT (90010017) Baltimore Co, MD (240053001) Harford Co, MD (240251001) Gloucester Co, NJ (340150002) Middlesex Co, NJ (340230011) Ocean Co, NJ (340290006) Queens Co, NY (360810124) Richmond Co, NY (360850067) Suffolk Co, NY (361030002) Philadelphia Co, PA (421010024)

WV Baltimore Co, MD (240053001) Harford Co, MD (240251001) Camden Co, NJ (340071001) Gloucester Co, NJ (340150002) Middlesex Co, NJ (340230011) Ocean Co, NJ (340290006) Queens Co, NY (360810124) Richmond Co, NY (360850067) Suffolk Co, NY (361030002) Hamilton Co, OH (390610006) Allegheny Co, PA (420031005) Philadelphia Co, PA (421010024)

WI Allegan Co, MI (260050003) Wayne Co, MI (261630019)29 

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### A-1 

## Appendix A

2011 Model Perfor mance Evaluation

### A-2 An operational model evaluation was conducted for the 2011 base year CAMx annual model simulation performed for th e 12-km U.S. modeling domain. The purpose of this evaluation is to examine the ability of the 2011 air quality modeling platform to represent the magnitude and spatial and temporal variability of measured (i.e., observed) ozone concentrations within the modeling domain. The evaluation presen ted here is based on model simulations using the v2 version of the 2011 emissions platform (i.e., case name 2011eh_cb6v2_v6_11g). The model evaluation for ozone focuses on comparis ons of model predicted 8-hour daily maximum concentrations to the corresponding observed data at monitoring sites in the EPA Air Quality System (AQS) and the Clean Air Status and Tr ends Network (CASTNet) (Figures A-1a and A1b).
Included in the evaluation ar e statistical measures of model performance based upon model-predicted versus observed concentrations that were paired in space and time. Model performance statistics were calcul ated for several spatial scales and temporal periods. Statistics were calculated for individual monitoring sites and for each of nine climate regions of the 12-km U.S. modeling domain. The regi ons include the Northeast, Ohio Valley, Upper Midwest, Southeast, South, Southwest, Nort hern Rockies, Northwest and West 1,2 

### , 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).

For maximum daily average 8-hour (MDA8) oz one, model performance statistics were created for each climate region fo r the period May through September. 4 

### In addition to the performance statistics, we prepared several gr aphical presentations of model performance for MDA8 ozone. These graphical presentations include:

1 The nine climate regions are defined by States wher e: 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; Upper Midwest includes IA, MI, MN, and WI; Southeast includes AL, FL, GA, NC, SC, an d VA; South includes AR, KS, LA, MS, OK, and TX; Southwest includes AZ, CO, NM, and UT; Northern Rock ies 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 lo cated in California (see Figure s 2A-2a and 2A-2b), therefore statistics for the West will be mostly representative of California ozone air quality.
3 NOAA, National Centers for Environmental Information scie ntists have identified nine climatically consistent regions within the contiguous U.S., http://www.ncdc.noaa. gov/monitoring-references/maps/us-climate-regions.php.
4 In calculating the ozone season statistics we limited the data to those observed and predicted pairs with observations that are greater than or equal 60 ppb in order to focus on concentrations at the upper portion of the distribution of values.

### A-3 (1) density scatter plots of observed AQS data and predicted MDA8 ozone concentrations for May through September; (2) regional maps that show the mean bias a nd error as well as normalized mean bias and
error calculated for MDA8 ≥ 60 ppb for May through September at individual AQS and CASTNet monitoring sites; (3) bar and whisker plots that show the dist ribution of the predicted and observed MDA8 ozone concentrations by month (May thr ough September) and by region and by network;
and (4) time series plots (May through Septem ber) of observed and predicted MDA8 ozone concentrations for 13 of the projected 2017 nonattainment and maintenance-only sites as
identified below.

The Atmospheric Model Evaluation Tool (A MET) was used to calculate the model performance statistics used in this document (Gilliam et al., 2005). For this evaluation of the ozone predictions in the 2011 CAMx modeling platform, we have selected the mean bias, mean error, normalized mean bias, and normalized m ean error to characterize model performance,
statistics which are consistent with the recomm endations in Simon et al. (2012) and the draft photochemical modeling guidance (US EPA, 20 14c). As noted above, we calculated the performance statistics by climate regi on for the period May through September.

Mean bias (MB) is the average of the diffe rence (predicted – observed) divided by the total number of replicates ( n ). Mean bias is given in un its of ppb and is defined as:

MB = ଵ

௡ 

### ∑ ሺ ܲെܱ ሻ ௡ ଵ 

### , where P = predicted and O = observed concentrations.

Mean error (ME) calculates the absolute valu e of the difference (predicted - observed) divided by the total num ber of replicates ( n ). Mean error is given in units of ppb and is defined

as:

ME = ଵ

௡ 

### ∑ |ܲ െ ܱ| ௡ ଵ


### Normalized mean bias (NMB) is the average the difference (predicted - observed) over
the sum of observed values. NMB is a useful mo del performance indicator because it avoids over

### A-4 inflating the observed range of valu es, especially at low concentra tions. Normalized mean bias is given in percentage units and is defined as:

NMB = ∑ ሺ௉ିைሻ ೙ భ ∑ ሺைሻ

೙ భ 

### ∗ 100

Normalized mean error (NME) is the absolu te value of the difference (predicted - observed) over the sum of observed values. Norma lized mean error is given in percentage units
and is defined as:

NME = ∑ |௉ିை| ೙ భ ∑ ሺைሻ

೙ భ 

### ∗ 100

As described in more detail below, the mode l performance statistics indicate that the 8- hour daily maximum ozone concentrations pr edicted by the 2011 CAMx modeling platform closely reflect the corresponding 8-hour observed oz one concentrations in space and time in each region of the 12-km U.S. modeling domain. The acceptability of model performance was judged by considering the 2011 CAMx performance results in light of the range of performance found in recent regional ozone model applications (NRC, 2002; Phillips et al., 2007; Simon et al., 2012; US EPA, 2005; US EPA, 2009; US EPA, 2011). These other modeling studies represent a wide range of modeling analyses that cover variou s models, model configurations, domains, years and/or episodes, chemical mechanisms, and aerosol modules. Overall, the ozone model performance results for the 2011 CAMx simulations are within the range found in other recent peer-reviewed and regulatory applications. The m odel performance results, as described in this document, demonstrate that the predictions fr om the 2011 modeling platform closely replicate the corresponding observed concentrations in term s of the magnitude, tem poral fluctuations, and spatial differences for 8-hour daily maximum ozone.

The density scatter plots of MDA8 ozone are provided Figure A-3. The 8-hour ozone model performance bias and error statistics by network for the ozone season (May-September average) for each region are provided in Table A-1. The statistics shown were calculated using data pairs on days with observed 8-hour ozone of ≥ 60 ppb. The distributions of observed and predicted 8-hour ozone by month in the period May through September for each region are shown in Figures A-4 through A-12. Spatial plots of the mean bias and error as well as the

### A-5 normalized mean bias and error for individual monitors are shown in Figures A-13 through A16. Time series plots of observed and predic ted MDA 8-hour ozone during the period May through September at 13 sites are provided in Figu re A-17, (a) through (m). These sites are listed
in Table A-2.

The density scatter plots in Figure A-3 pr ovide a qualitative comparison of modelpredicted and observed MDA8 ozone concentrations. In these plots the intensity of the colors indicates the density of individua l observed/predicted paired valu es. The greatest number of individual paired values is denoted by the co re area in white. The plots indicate that the predictions correspond closely to the observations in that a larg e number of observed/predicted paired values lie along or close to the 1:1 line shown on each plot. Overall, performance is best
for observed values ≥ 60. The model tends to over-predict the observed values to some extent particularly at low and mid-range concentrations generally < 60 ppb in each of the regions. This
feature is most evident in the South and Southe ast regions. In the West, high concentrations are under-predicted and low and mid-range concentr ations are over-predicted. Observed and predicted values are in cl ose agreement in the Southw est and Northwest regions.

As indicated by the statistics in Table A- 1, bias and error for 8-hour daily maximum ozone are relatively low in each re gion. Generally, MB for 8-hour ozone ≥ 60 ppb during the ozone season is less than 5 ppb except at AQS sites in the West region and at rural CASTNet sites in the South, Southwest, and West for which ozone is somewhat under-predicted. The monthly distribution of 8-hour daily maximum ozone during the ozone season generally corresponds well with that of the observed concentrations, as i ndicated by the graphics in Figures A-4 through A-12. The distribution of predicted concentrations tends to be close to that of the
observed data at the 25 th 

### percentile, median and 75th percen tile values for each region, although there is a small persistent overestimation bias for these metrics in the Northeast, Southeast, and Ohio Valley regions, and under-prediction at CASTNet sites in the West and Southwest 5 

### . The CAMx model, as applied here, also has a tend ency to under-predict the highest observational concentrations at both the AQS and CASTNet network sites.

5 The over-prediction at CASTNet sites in the Northwest may not be representative of performance in rural areas of this region because there are so few observed and predicted data pairs in this region.

### A-6 Figures A-13 through A-16 show the spatial va riability in bias and error at monitor locations. Mean bias, as seen from Figure A-13, is less than 5 ppb at many sites across the East with over-prediction of 5 to 10 ppb at some s ites from the Southeast into the Northeast. Elsewhere, mean bias is genera lly in the range of -5 to -10 ppb. Figure A-14 indicates that the normalized mean bias for days with observed 8- hour daily maximum ozone greater than or equal to 60 ppb is within ± 10 percent at the vast majority of mon itoring sites across the modeling domain. There are regional differences in mode l performance, where the model tends to overpredict from the Southeast into the Northeast and generally under pred ict in the Southwest, Northern Rockies, Northwest and West. Mode l performance in the Ohio Valley and Upper
Midwest states shows both under and over predictions.

Model error, as seen from Figure A-15, is 10 ppb or less at most of the sites across the modeling domain. Figure A-16 indica tes that the normalized mean error for days with observed 8-hour daily maximum ozone greate r than or equal to 60 ppb is w ithin 10 percent at the vast majority of monitoring sites acr oss the modeling domain. Somewhat greater error (i.e., greater than 15 percent) is evident at s ites in several areas mo st notably along portions of the Northeast and in portions of Florida, North Dakota, Illinoi s, Ohio, North Carolina, and the western most part of the modeling domain.

In addition to the above analysis of overall model performance, we also examine how well the modeling platform replicates day to da y fluctuations in obser ved 8-hour daily maximum concentrations using data for 13 of the projec ted 2017 nonattainment and ma intenance-only sites. For this site-specific analysis we present the tim e series of observed and predicted 8-hour daily maximum concentrations by site over the period May through September. The results, as shown in Figures A-17 (a) through (m), indicate that th e modeling platform generally replicates the dayto-day variability in ozone during this time peri od. For example, severa l of the sites not only have minimal bias but also accurately capture the day-to-day variability in the observations: Jefferson County, KY; Alleghany County, PA; and Phila delphia, PA. Other sites generally track well and capture day-to-day variability but underes timate some of the peak ozone days: Imperial County, CA; Suffolk, NY; Tarrant County, TX ; Brazoria County, TX; and Sheboygan County,
WI. Note that at the site in Brazoria County, TX there is an ex tended period from mid-July to mid-August with very low observe d ozone concentrations, mostly in the range of 30 to 40 ppb.

### A-7 The model predicted values during this period in the range of 40 to 60 ppb which is not quite as
low as the observed values. The sites in Douglas County, CO, Harf ord County, MD, Allegan, MI, and Hamilton County, OH closely track the da y-to-day variability in the observed MDA8 values, but some days are over predicted while other days are under pred icted to some extent. Finally, the site in Fairfield County, CT tracks closely with th e observations, but there is a tendency to over predict on seve ral of the observed high ozone days. Looking across all 13 sites indicates that the modeling platform is able to capt ure the site to site differences in the short-term variability of the observe d ozone concentrations.

Figure A-1a. AQS ozone monitoring sites.

### A-8 Figure A-1b. CASTNet ozone monitoring sites.

Figure A-2. NOAA climate regions (source: http://www.ncdc.noaa.gov/monitoring-references/maps/usclimate-regions.php#references )

### A-9 Table A-1. Performance statis tics for MDA8 ozone > 60 ppb for May through September by climate region, by network.

Network Climate region No. of Obs MB ME NMB (%) NME (%)

AQS Northeast 3,998 2.2 7.4 3.2 10.8 Ohio Valley 6,325 0.3 7.6 0.4 11.3 Upper Midwest 1,162 -3.0 7.5 -4.4 11.0 Southeast 37,280 -2.5 8.1 -3.6 11.9 South 5,694 -3.7 8.1 -5.4 11.7 Southwest 6,033 -5.2 7.9 -7.8 12.0 Northern Rockies 380 -5.9 7.4 -9.4 11.7 Northwest 79 -5.4 8.1 -8.5 12.6 West 8,665 -7.3 9.5 -10.3 13.5

CASTNet Northeast 264 2.3 6.1 3.4 9.1 Ohio Valley 107 -2.3 6.2 -3.4 9.4 Upper Midwest 38 -3.9 5.9 -5.8 8.8 Southeast 2,068 -5.0 8.2 -7.5 12.1 South 215 -7.1 8.0 -10.7 12.0 Southwest 382 -7.7 8.6 -11.7 13.1 Northern Rockies 110 -7.8 8.1 -12.2 12.8 Northwest - - - - - West 425 -12.1 12.5 -16.6 17.1

### A-10 Figure A-3. Density scatter plots of observed vs predicted MDA8 ozone for the Northeast, Ohio River Valley, Upper Midwest, Southeast, South, Southwest, Northern Rockies, Northwest, and West Regions.

### A-11 Figure A-4. Distribution of obser ved and predicted MDA8 ozone by month for the period May through September for the Northeast re gion, (a) AQS Network and (b) CASTNet Network. [symbol = median; top/bottom of box = 75th/25th perc entiles; top/bottom line = max/min values]

Figure A-5. Distribution of obser ved and predicted MDA8 ozone by month for the period May through September for the Ohio Valley region, (a) AQS Network and (b) CASTNet Network. b a

b a

### A-12 Figure A-6. Distribution of obser ved and predicted MDA8 ozone by month for the period May through September for the Upper Midwest region, (a) AQS Network and (b) CASTNet Network.

Figure A-7. Distribution of obser ved and predicted MDA8 ozone by month for the period May through September for the Southeast re gion, (a) AQS Network and (b) CASTNet Network. b a

b a

### A-13 Figure A-8. Distribution of obser ved and predicted MDA8 ozone by month for the period May through September for the South region, (a) AQS Network and (b) CASTNet Network.

Figure A-9. Distribution of obser ved and predicted MDA8 ozone by month for the period May through September for the Southwest re gion, (a) AQS Network and (b) CASTNet Network. b a

b a

### A-14 Figure A-10. Distribution of observed and pred icted MDA8 ozone by month for the period May through September for the Northern Rockies region, (a) AQS Network and (b) CASTNet Network.

Figure A-11. Distribution of observed and pred icted MDA8 ozone by month for the period May through September for the Northwest re gion, (a) AQS Network and (b) CASTNet Network. b a b a

### A-15 Figure A-12. Distribution of observed and pred icted MDA8 ozone by month for the period May through September for the West region, (a) AQS Network and (b) CASTNet Network.

Figure A-13. Mean Bias (ppb) of MDA8 ozone > 60 ppb over th e period May-September 2011 at AQS and CASTNet monitoring sites. b a

### A-16 Figure A-14. Normalized Mean Bias (%) ofMDA8 ozone > 60 ppb over the period MaySeptember 2011 at AQS and CASTNet monitoring sites.

Figure A-15. Mean Error (ppb) of MDA8 oz one > 60 ppb over the period May-September 2011 at AQS and CASTNet monitoring sites.

### A-17 Figure A-16. Normalized Mean Error (%) ofMDA8 ozone > 60 ppb over the period MaySeptember 2011 at AQS and CASTNet monitoring sites.

Table A-2. Monitoring sites used for the ozone time series analysis.
County State Monitoring Site ID Fairfield Connecticut 090013007
Suffolk N ew Yor k

361030002 Philadelphia Pennsylvania 421010024 Harford Maryland 240251001 Allegheny Pennsylvania 420031005
Hamilton Ohio 390610006 Jefferson Kentucky 211110067 Allegan Michigan 261630019 Sheboygan Wisconsin 551170006
Tarrant Texas 484392003
Brazoria Texas 480391004 Douglas Colorado 080350004 Imperial California 060251003

### A-18 Figure A-17a. Time series of observed (bl ack) and predicted (red) MDA8 ozone for May through September 2011 at site 090013007 in Fairfield Co., Connecticut.

Figure A-17b. Time series of observed (black ) and predicted (red) MDA8 ozone for May through September 2011 at site 361030002 in Suffolk Co., New York.

### A-19 Figure A-17c. Time series of observed (bl ack) and predicted (red) MDA8 ozone for May through September 2011 at site 421010024 in Philadelphia Co., Pennsylvania.

Figure A-17d. Time series of observed (black ) and predicted (red) MDA8 ozone for May through September 2011 at site 240251001 in Harford Co., Maryland.

### A-20 Figure A-17e. Time series of observed (bl ack) and predicted (red) MDA8 ozone for May through September 2011 at site 420031005 in Allegheny Co., Pennsylvania.

Figure A-17f. Time series of observed (black) and predicted (red) MDA8 ozone for May through September 2011 at site 390610006 in Hamilton Co., Ohio.

### A-21 Figure A-17g. Time series of observed (black ) and predicted (red) MDA8 ozone for May through September 2011 at site 211110067 in Jefferson Co., Kentucky.

Figure A-17h. Time series of observed (black ) and predicted (red) MDA8 ozone for May through September 2011 at site 26005003 in Allegan Co., Michigan.

### A-22 Figure A-17i. Time series of observed (bl ack) and predicted (red) MDA8 ozone for May through September 2011 at site 551170006 in Sheboygan Co., Wisconsin.

Figure A-17j. Time series of observed (bl ack) and predicted (red) MDA8 ozone for May through September 2011 at site 484392003 in Tarrant Co., Texas.

### A-23 Figure A-17k. Time series of observed (black ) and predicted (red) MDA8 ozone for May through September 2011 at site 480391004 in Brazoria Co., Texas.

Figure A-17l. Time series of observed (bl ack) and predicted (red) MDA8 ozone for May through September 2011 at site 080350004 in Douglas Co., Colorado.

### A-24 Figure A-17m. Time series of observed (black) and predicted (red) MDA8 ozone for May through September 2011 at site 060251003 in Imperial Co., California.

## Appendix B
Contributions to 2017 8

- Hour Ozone Design Values at Projected 2017 Nonattainment and Maintenance

- Only Sites

### This appendix contains tables with the p rojected ozone contributions from 2017 anthropogenic NOx and VOC emissions in each state to each projected 2017 nonattainment receptor and each maintenance

- only receptor in the eastern U.S. Nonattainment and
maintenance

- only receptors are defined in section 3 of this TSD. In addition to the s tate contributions , we have included the contributions from each of the other categories tracked in the contribution modeling including point s ource emissions on T ribal lands, anthropogenic
emissions in Can ada and Mexico, emissions from Offshore sources, Fires, Initial and Boundary
concentrations
, and B iogenics.
For each monitoring site we provide the site ID, state name, and county name in the first three columns of the table. This information is followed by columns c ontaining the projected
2017 aver age and maximum design values. Next we provide the contributions from each state and the District of Columbia, individually. Lastly, we provide the contributions from the T ribal
, Canada and Mexico
, Offshore, Fires, Initial and Boundary concentrations, an d Biogenics categories. The units of the 2017 design values and contributions are “ppb ” . Note that the contributions presented in these tables may not sum exactly to the 2017 average design value due to truncation of the contributions to two places to the right of the decimal.

## Contributions to 2017 Nonattainment and Maintenance

- Only Sites in the East (Part 1)

Monitor ID State County 2017 Average

DV 2017 Maximum DV AL AZ AR CA CO CT DE DC FL GA ID IL IN IA KS KY LA ME MD MA MI MN MS MO MT 90010017 Connecticut Fairfield 75.8 78.4 0.10 0.04 0.11 0.05 0.10 9.04 0.22 0.04 0.03 0.11 0.03 0.50 0.62 0.21 0.18 0.35 0.07 0.01 1.34 0.06 0.98 0.25 0.07 0.34 0.06 90013007 Connecticut Fairfield 77.1 81.4 0.15 0.04 0.16 0.04 0.11 5.22 0.34 0.06 0.02 0.20 0.01 0.49 0.76 0.10 0.16 0.48 0.06 0.00 2.07 0.05 0.89 0.11 0.09 0.27 0.03 90019003 Connecticut Fairfield 78.0 81.1 0.14 0.06 0.15 0.06 0.14 4.75 0.30 0.06 0.04 0.19 0.02 0.70 0.89 0.17 0.20 0.57 0.08 0.00 1.83 0.03 0.89 0.13 0.09 0.38 0.04 90099002 Connecticut New Haven 77.2 80.2 0.09 0.05 0.12 0.06 0.11 8.29 0.37 0.04 0.04 0.10 0.04 0.57 0.68 0.21 0.20 0.43 0.09 0.00 1.55 0.10 0.91 0.15 0.06 0.39 0.05

211110067 Kentucky Jefferson 75.8 78.6 0.04 0.03 0.06 0.07 0.13 0.00 0.00 0.00 0.03 0.04 0.05 1.15 11.33 0.23 0.21 22.87 0.10 0.00 0.00 0.00 1.20 0.35 0.04 0.27 0.17 211850004 Kentucky Oldham 73.7 77.3 0.06 0.03 0.85 0.05 0.14 0.00 0.00 0.00 0.03 0.04 0.04 0.89 14.95 0.14 0.17 20.93 0.33 0.00 0.00 0.00 0.91 0.25 0.10 0.82 0.15

240053001 Maryland Baltimore 73.2 76.2 0.34 0.07 0.20 0.08 0.14 0.01 0.12 0.64 0.10 0.27 0.02 0.64 1.53 0.19 0.22 1.77 0.20 0.00 23.15 0.01 0.69 0.11 0.13 0.47 0.05 240251001 Maryland Harford 81.3 84.0 0.40 0.09 0.22 0.11 0.19 0.01 0.15 0.73 0.11 0.30 0.03 0.67 1.88 0.19 0.28 1.97 0.25 0.00 24.59 0.00 0.83 0.11 0.15 0.52 0.06

260050003 Michigan Allegan 75.5 78.5 0.33 0.08 2.15 0.06 0.23 0.00 0.00 0.00 0.13 0.20 0.01 23.17 8.02 0.85 1.03 0.53 0.57 0.00 0.01 0.00 2.74 0.06 0.42 3.69 0.01 261630019 Michigan Wayne 74.0 76.2 0.02 0.03 0.09 0.10 0.10 0.00 0.00 0.00 0.03 0.03 0.05 0.90 2.64 0.30 0.23 0.67 0.11 0.00 0.03 0.00 20.77 0.47 0.04 0.28 0.13

340071001 New Jersey Camden 74.2 78.1 0.32 0.11 0.37 0.11 0.20 0.17 1.36 0.00 0.07 0.15 0.04 1.33 1.71 0.37 0.42 0.93 0.58 0.02 0.57 0.17 1.43 0.15 0.26 0.76 0.07 340150002 New Jersey Gloucester 75.1 77.5 0.34 0.10 0.15 0.11 0.16 0.10 2.23 0.30 0.14 0.33 0.03 0.87 1.02 0.27 0.17 1.23 0.26 0.00 7.11 0.09 1.01 0.15 0.13 0.48 0.06 340230011 New Jersey Middlesex 73.0 76.3 0.28 0.08 0.12 0.08 0.14 0.33 0.73 0.07 0.09 0.31 0.03 0.58 0.85 0.18 0.17 1.08 0.14 0.08 2.35 0.33 0.62 0.12 0.10 0.38 0.04 340290006 New Jersey Ocean 73.9 76.6 0.21 0.06 0.23 0.08 0.15 0.36 0.76 0.06 0.05 0.17 0.04 0.80 1.06 0.34 0.26 0.88 0.19 0.07 2.01 0.37 1.31 0.17 0.12 0.52 0.07

360810124 New York Queens 75.7 77.6 0.13 0.07 0.12 0.09 0.13 0.41 0.60 0.07 0.08 0.20 0.05 0.84 0.80 0.34 0.24 0.49 0.14 0.00 2.15 0.06 1.79 0.21 0.06 0.49 0.09 360850067 New York Richmond 76.3 77.8 0.25 0.08 0.16 0.09 0.15 0.30 0.68 0.08 0.09 0.30 0.04 0.74 1.02 0.27 0.27 0.96 0.15 0.00 2.39 0.04 1.20 0.16 0.11 0.54 0.06 361030002 New York Suffolk 79.2 80.8 0.19 0.07 0.17 0.08 0.16 0.46 0.32 0.04 0.06 0.19 0.05 0.79 1.03 0.30 0.30 0.71 0.13 0.00 1.47 0.01 1.34 0.18 0.11 0.58 0.07

390610006 Ohio Hamilton 76.3 79.1 0.79 0.06 0.62 0.09 0.18 0.00 0.00 0.00 0.11 0.58 0.04 1.28 7.15 0.29 0.34 11.17 0.26 0.00 0.01 0.00 0.99 0.23 0.30 0.77 0.10

420031005 Pennsylvania Allegheny 75.3 76.5 0.16 0.05 0.22 0.11 0.13 0.00 0.00 0.00 0.06 0.17 0.07 1.35 1.86 0.17 0.32 1.64 0.17 0.00 0.05 0.00 1.51 0.15 0.13 0.55 0.13 421010024 Pennsylvania Philadelphia 75.1 78.4 0.51 0.09 0.23 0.09 0.16 0.03 1.33 0.16 0.14 0.54 0.02 0.65 1.71 0.17 0.21 2.14 0.18 0.00 5.10 0.03 0.33 0.07 0.17 0.52 0.04

480391004 Texas Brazoria 81.4 82.3 0.56 0.08 0.98 0.23 0.25 0.00 0.00 0.00 0.19 0.30 0.09 0.86 0.24 0.42 0.46 0.18 3.09 0.00 0.01 0.00 0.05 0.40 0.78 1.00 0.15 480850005 Texas Collin 74.9 76.0 0.41 0.07 0.35 0.10 0.19 0.00 0.00 0.00 0.61 0.24 0.02 0.03 0.03 0.02 0.20 0.05 1.40 0.00 0.02 0.00 0.01 0.00 0.24 0.12 0.03 481130069 Texas Dallas 74.0 78.0 0.41 0.05 0.83 0.09 0.15 0.00 0.00 0.00 0.72 0.33 0.02 0.13 0.06 0.04 0.19 0.10 1.44 0.00 0.02 0.00 0.01 0.04 0.23 0.19 0.03 481130075 Texas Dallas 75.8 76.7 0.34 0.05 0.92 0.08 0.18 0.00 0.00 0.00 0.65 0.24 0.02 0.14 0.06 0.04 0.23 0.09 1.22 0.00 0.02 0.00 0.01 0.04 0.18 0.21 0.03 481210034 Texas Denton 76.9 79.4 0.67 0.10 0.51 0.14 0.25 0.00 0.00 0.00 0.30 0.39 0.06 0.11 0.12 0.08 0.43 0.14 1.99 0.00 0.00 0.00 0.11 0.04 0.51 0.23 0.09 481211032 Texas Denton 75.1 76.3 0.29 0.07 0.68 0.11 0.29 0.00 0.00 0.00 0.27 0.21 0.07 0.05 0.04 0.05 0.39 0.05 1.77 0.00 0.02 0.00 0.06 0.03 0.47 0.15 0.12 482010024 Texas Harris 75.9 78.5 0.54 0.04 0.28 0.09 0.16 0.00 0.00 0.00 0.62 0.36 0.03 0.16 0.06 0.09 0.13 0.09 2.82 0.00 0.00 0.00 0.02 0.03 0.56 0.31 0.05 482010026 Texas Harris 73.5 76.1 0.09 0.02 1.21 0.08 0.23 0.00 0.00 0.00 0.07 0.05 0.05 0.76 0.11 0.35 0.42 0.05 3.66 0.00 0.00 0.00 0.24 0.26 0.49 1.13 0.11 482010055 Texas Harris 75.4 77.0 1.28 0.07 0.79 0.22 0.20 0.00 0.00 0.00 0.64 0.56 0.08 0.60 0.21 0.18 0.33 0.19 4.23 0.00 0.02 0.00 0.02 0.07 1.48 0.78 0.14 482011034 Texas Harris 76.8 77.8 0.46 0.04 0.52 0.12 0.18 0.00 0.00 0.00 0.57 0.27 0.05 0.40 0.07 0.30 0.39 0.06 3.81 0.00 0.00 0.00 0.10 0.27 0.65 0.69 0.10 482011039 Texas Harris 78.2 80.2 0.41 0.03 1.24 0.11 0.16 0.00 0.00 0.00 0.15 0.21 0.05 0.87 0.15 0.33 0.38 0.09 3.33 0.00 0.01 0.00 0.28 0.23 0.77 1.15 0.11 482011050 Texas Harris 74.6 76.2 0.32 0.03 0.92 0.06 0.11 0.00 0.00 0.00 0.18 0.17 0.01 0.74 0.14 0.24 0.21 0.06 2.76 0.00 0.00 0.00 0.34 0.26 0.61 0.75 0.03 484390075 Texas Tarrant 75.5 76.4 0.66 0.09 0.88 0.13 0.34 0.00 0.00 0.00 0.17 0.35 0.07 0.36 0.26 0.21 0.81 0.21 2.20 0.00 0.00 0.00 0.21 0.13 0.46 0.42 0.12 484392003 Texas Tarrant 79.6 82.1 0.79 0.10 0.87 0.14 0.36 0.00 0.00 0.00 0.34 0.47 0.07 0.35 0.27 0.09 0.37 0.22 2.64 0.00 0.00 0.00 0.12 0.07 0.54 0.34 0.10 484393009 Texas Tarrant 78.6 78.6 0.73 0.08 0.80 0.14 0.24 0.00 0.00 0.00 0.54 0.43 0.06 0.29 0.22 0.07 0.33 0.18 2.21 0.00 0.01 0.00 0.09 0.05 0.47 0.30 0.08 484393011 Texas Tarrant 74.5 76.6 0.15 0.08 1.19 0.11 0.26 0.00 0.00 0.00 0.13 0.16 0.07 0.32 0.10 0.21 0.77 0.09 1.47 0.00 0.01 0.00 0.18 0.13 0.17 0.44 0.13

551170006 Wisconsin Sheboygan 77.0 79.4 0.25 0.08 0.57 0.09 0.18 0.00 0.00 0.00 0.11 0.12 0.02 17.48 6.24 0.61 0.80 0.75 1.11 0.00 0.02 0.00 2.69 0.07 0.56 1.63 0.03

## Contributions to 2017 Nonattainment and Maintenance

- Only Sites in the East (Part 2)

Monitor ID State County 2017 Average

DV 2017 Maximum DV NE NV NH NJ NM NY NC ND OH OK OR PA RI SC SD TN TX UT VT VA WA WV WI WY 90010017 Connecticut Fairfield 75.8 78.4 0.13 0.01 0.01 7.98 0.05 17.21 0.33 0.13 1.57 0.30 0.01 7.14 0.01 0.07 0.06 0.17 0.47 0.06 0.01 1.51 0.02 0.67 0.35 0.16 90013007 Connecticut Fairfield 77.1 81.4 0.08 0.01 0.01 8.45 0.08 15.98 0.55 0.04 1.89 0.24 0.00 9.29 0.01 0.16 0.02 0.31 0.46 0.05 0.00 1.87 0.01 0.92 0.17 0.12 90019003 Connecticut Fairfield 78.0 81.1 0.12 0.02 0.00 8.84 0.08 16.16 0.47 0.06 2.18 0.34 0.01 9.39 0.00 0.14 0.03 0.28 0.59 0.07 0.00 1.75 0.01 0.95 0.22 0.20 90099002 Connecticut New Haven 77.2 80.2 0.13 0.02 0.02 7.04 0.06 16.96 0.38 0.11 1.68 0.36 0.01 7.16 0.02 0.07 0.05 0.18 0.56 0.07 0.01 1.22 0.02 0.66 0.21 0.19

211110067 Kentucky Jefferson 75.8 78.6 0.16 0.02 0.00 0.00 0.08 0.00 0.01 0.24 4.05 0.35 0.02 0.46 0.00 0.01 0.12 0.11 0.66 0.05 0.00 0.01 0.07 0.61 0.55 0.27 211850004 Kentucky Oldham 73.7 77.3 0.13 0.01 0.00 0.00 0.06 0.00 0.02 0.20 3.13 0.22 0.02 0.35 0.00 0.02 0.09 0.11 0.39 0.05 0.00 0.00 0.06 0.40 0.41 0.24

240053001 Maryland Baltimore 73.2 76.2 0.13 0.02 0.00 0.37 0.12 0.35 0.93 0.07 2.98 0.35 0.01 4.80 0.00 0.14 0.05 0.67 0.77 0.07 0.00 4.70 0.02 2.65 0.19 0.17 240251001 Maryland Harford 81.3 84.0 0.15 0.02 0.00 0.43 0.16 0.40 0.46 0.08 3.99 0.46 0.02 6.07 0.00 0.10 0.05 0.70 1.05 0.09 0.00 5.29 0.03 2.99 0.21 0.20

260050003 Michigan Allegan 75.5 78.5 0.17 0.01 0.00 0.00 0.21 0.00 0.05 0.01 0.10 1.78 0.00 0.02 0.00 0.05 0.02 0.68 2.95 0.06 0.00 0.03 0.00 0.03 2.59 0.12 261630019 Michigan Wayne 74.0 76.2 0.09 0.03 0.00 0.01 0.04 0.10 0.32 0.28 7.92 0.22 0.06 0.27 0.00 0.04 0.04 0.12 0.52 0.07 0.00 0.24 0.09 0.27 1.00 0.20

340071001 New Jersey Camden 74.2 78.1 0.23 0.02 0.04 10.93 0.22 2.70 0.07 0.10 4.16 0.69 0.02 15.93 0.03 0.04 0.09 0.25 1.52 0.10 0.04 0.32 0.03 1.07 0.44 0.24 340150002 New Jersey Gloucester 75.1 77.5 0.11 0.02 0.01 4.41 0.19 1.22 0.33 0.07 3.85 0.35 0.01 15.73 0.02 0.07 0.04 0.38 1.07 0.08 0.02 3.33 0.02 2.35 0.44 0.19 340230011 New Jersey Middlesex 73.0 76.3 0.10 0.02 0.07 10.78 0.12 3.71 0.50 0.05 2.42 0.32 0.01 15.16 0.07 0.12 0.03 0.48 0.73 0.08 0.05 2.10 0.01 1.91 0.30 0.17 340290006 New Jersey Ocean 73.9 76.6 0.17 0.02 0.07 11.44 0.10 5.10 0.33 0.11 2.54 0.44 0.02 13.30 0.08 0.09 0.07 0.40 0.95 0.08 0.05 1.07 0.03 1.08 0.47 0.22

360810124 New York Queens 75.7 77.6 0.18 0.02 0.01 11.48 0.09 10.61 0.39 0.17 2.36 0.43 0.02 8.83 0.01 0.11 0.08 0.16 0.76 0.09 0.00 2.10 0.03 0.92 0.49 0.24 360850067 New York Richmond 76.3 77.8 0.18 0.02 0.00 12.38 0.12 4.57 0.55 0.11 2.70 0.44 0.01 13.51 0.00 0.14 0.07 0.42 0.83 0.10 0.00 2.27 0.02 1.64 0.33 0.22 361030002 New York Suffolk 79.2 80.8 0.21 0.02 0.00 11.37 0.09 15.95 0.38 0.14 2.49 0.52 0.01 9.39 0.00 0.08 0.08 0.35 0.82 0.10 0.00 1.62 0.02 0.98 0.27 0.26

390610006 Ohio Hamilton 76.3 79.1 0.19 0.03 0.00 0.00 0.14 0.11 0.13 0.12 16.72 0.76 0.03 0.37 0.00 0.14 0.06 1.67 1.74 0.08 0.00 0.11 0.04 0.97 0.56 0.22

420031005 Pennsylvania Allegheny 75.3 76.5 0.21 0.03 0.00 0.00 0.09 0.24 0.07 0.11 7.89 0.48 0.04 23.66 0.00 0.05 0.10 0.62 0.84 0.07 0.00 0.16 0.05 2.47 0.33 0.21 421010024 Pennsylvania Philadelphia 75.1 78.4 0.11 0.02 0.00 1.66 0.16 0.20 0.72 0.04 3.55 0.38 0.01 21.46 0.00 0.20 0.03 0.90 0.94 0.07 0.00 2.63 0.00 3.11 0.14 0.16

480391004 Texas Brazoria 81.4 82.3 0.24 0.07 0.00 0.00 0.11 0.00 0.08 0.06 0.04 0.72 0.05 0.03 0.00 0.08 0.08 0.37 38.93 0.14 0.00 0.04 0.05 0.03 0.34 0.30 480850005 Texas Collin 74.9 76.0 0.05 0.03 0.00 0.00 0.13 0.01 0.14 0.00 0.04 0.73 0.01 0.04 0.00 0.11 0.01 0.10 40.58 0.08 0.00 0.07 0.01 0.04 0.00 0.16 481130069 Texas Dallas 74.0 78.0 0.05 0.03 0.00 0.00 0.08 0.01 0.22 0.01 0.05 0.77 0.01 0.05 0.00 0.21 0.01 0.12 35.26 0.07 0.00 0.09 0.01 0.06 0.01 0.14 481130075 Texas Dallas 75.8 76.7 0.06 0.03 0.00 0.00 0.09 0.01 0.16 0.01 0.04 0.86 0.01 0.04 0.00 0.12 0.01 0.10 37.23 0.08 0.00 0.07 0.01 0.05 0.01 0.16 481210034 Texas Denton 76.9 79.4 0.17 0.06 0.00 0.00 0.13 0.01 0.08 0.02 0.10 1.33 0.02 0.03 0.00 0.09 0.03 0.19 33.85 0.15 0.00 0.03 0.02 0.04 0.06 0.28 481211032 Texas Denton 75.1 76.3 0.16 0.04 0.00 0.00 0.11 0.01 0.11 0.02 0.06 1.61 0.02 0.06 0.00 0.11 0.03 0.15 34.09 0.11 0.00 0.07 0.04 0.05 0.03 0.33 482010024 Texas Harris 75.9 78.5 0.07 0.03 0.00 0.00 0.08 0.00 0.14 0.02 0.03 0.24 0.01 0.02 0.00 0.17 0.02 0.19 32.55 0.07 0.00 0.05 0.01 0.04 0.02 0.14 482010026 Texas Harris 73.5 76.1 0.26 0.03 0.00 0.00 0.06 0.00 0.00 0.06 0.02 1.05 0.02 0.00 0.00 0.01 0.06 0.12 30.45 0.07 0.00 0.00 0.03 0.00 0.28 0.23 482010055 Texas Harris 75.4 77.0 0.13 0.07 0.00 0.00 0.11 0.00 0.16 0.04 0.06 0.20 0.04 0.03 0.00 0.15 0.04 0.41 34.58 0.12 0.00 0.06 0.04 0.05 0.08 0.28 482011034 Texas Harris 76.8 77.8 0.21 0.04 0.00 0.00 0.07 0.00 0.16 0.05 0.04 0.94 0.02 0.02 0.00 0.19 0.05 0.10 32.18 0.09 0.00 0.05 0.02 0.04 0.18 0.21 482011039 Texas Harris 78.2 80.2 0.21 0.04 0.00 0.00 0.05 0.01 0.06 0.05 0.03 0.83 0.02 0.02 0.00 0.05 0.05 0.31 33.69 0.07 0.00 0.03 0.03 0.02 0.28 0.21 482011050 Texas Harris 74.6 76.2 0.15 0.01 0.00 0.00 0.05 0.01 0.05 0.03 0.03 0.67 0.00 0.02 0.00 0.05 0.04 0.21 36.47 0.03 0.00 0.02 0.01 0.01 0.35 0.09 484390075 Texas Tarrant 75.5 76.4 0.36 0.05 0.00 0.00 0.15 0.02 0.05 0.05 0.15 2.46 0.04 0.07 0.00 0.04 0.07 0.20 29.82 0.16 0.00 0.03 0.04 0.05 0.15 0.36 484392003 Texas Tarrant 79.6 82.1 0.18 0.06 0.00 0.00 0.14 0.01 0.09 0.03 0.12 1.23 0.03 0.03 0.00 0.10 0.03 0.21 34.52 0.18 0.00 0.03 0.03 0.04 0.09 0.36 484393009 Texas Tarrant 78.6 78.6 0.14 0.05 0.00 0.00 0.11 0.01 0.13 0.02 0.11 1.03 0.02 0.05 0.00 0.11 0.03 0.20 35.64 0.13 0.00 0.06 0.02 0.05 0.07 0.26 484393011 Texas Tarrant 74.5 76.6 0.34 0.04 0.00 0.00 0.15 0.02 0.10 0.04 0.10 2.41 0.03 0.09 0.00 0.12 0.08 0.10 32.06 0.14 0.00 0.05 0.04 0.07 0.17 0.30

551170006 Wisconsin Sheboygan 77.0 79.4 0.09 0.02 0.00 0.00 0.21 0.02 0.06 0.04 0.78 1.70 0.01 0.20 0.00 0.04 0.02 0.51 2.44 0.07 0.00 0.08 0.01 0.29 12.22 0.11

## Contributions to 2017 Nonattainment and Maintenance

- Only Sites in the East (Part 3)

Monitor ID State County 2017 Average

DV 2017 Maximum DV Tribal Canada & Mexico Offshore Fires Initial & Boundary Biogenics 90010017 Connecticut Fairfield 75.8 78.4 0.02 1.66 0.61 0.18 15.66 4.08 90013007 Connecticut Fairfield 77.1 81.4 0.02 1.61 1.30 0.20 16.90 4.17 90019003 Connecticut Fairfield 78.0 81.1 0.03 1.48 1.00 0.22 16.73 4.49 90099002 Connecticut New Haven 77.2 80.2 0.02 1.67 2.33 0.21 16.52 4.33

211110067 Kentucky Jefferson 75.8 78.6 0.01 0.52 0.05 0.20 21.86 6.71 211850004 Kentucky Oldham 73.7 77.3 0.02 0.41 0.14 0.22 18.81 7.04

240053001 Maryland Baltimore 73.2 76.2 0.03 0.68 0.51 0.29 15.67 5.04 240251001 Maryland Harford 81.3 84.0 0.04 0.73 0.44 0.30 16.63 6.08

260050003 Michigan Allegan 75.5 78.5 0.04 0.28 0.34 0.73 11.71 9.01 261630019 Michigan Wayne 74.0 76.2 0.01 4.89 0.08 0.17 24.10 5.57

340071001 New Jersey Camden 74.2 78.1 0.04 1.73 0.55 0.46 14.07 6.14 340150002 New Jersey Gloucester 75.1 77.5 0.03 1.05 0.62 0.30 15.90 5.75 340230011 New Jersey Middlesex 73.0 76.3 0.03 1.95 0.64 0.25 16.30 5.01 340290006 New Jersey Ocean 73.9 76.6 0.03 2.28 0.75 0.31 16.60 5.04

360810124 New York Queens 75.7 77.6 0.02 2.00 1.54 0.21 17.29 5.14 360850067 New York Richmond 76.3 77.8 0.03 1.74 1.21 0.29 16.47 5.51 361030002 New York Suffolk 79.2 80.8 0.03 1.67 1.31 0.31 16.51 5.12

390610006 Ohio Hamilton 76.3 79.1 0.03 0.76 0.21 0.58 17.48 7.45

420031005 Pennsylvania Allegheny 75.3 76.5 0.02 1.38 0.11 0.38 21.40 5.03 421010024 Pennsylvania Philadelphia 75.1 78.4 0.03 0.48 0.71 0.37 16.52 5.62

480391004 Texas Brazoria 81.4 82.3 0.03 0.35 0.99 1.62 19.53 6.57 480850005 Texas Collin 74.9 76.0 0.02 0.25 2.23 0.90 19.19 5.62 481130069 Texas Dallas 74.0 78.0 0.02 0.20 1.93 1.20 22.27 5.78 481130075 Texas Dallas 75.8 76.7 0.02 0.30 1.76 1.14 22.41 6.02 481210034 Texas Denton 76.9 79.4 0.03 0.40 1.57 0.95 24.23 6.50 481211032 Texas Denton 75.1 76.3 0.02 0.35 0.87 0.93 23.75 6.52 482010024 Texas Harris 75.9 78.5 0.02 0.18 4.14 0.73 27.62 2.68 482010026 Texas Harris 73.5 76.1 0.01 0.24 2.95 2.36 21.17 4.44 482010055 Texas Harris 75.4 77.0 0.02 0.31 1.46 1.55 18.26 4.81 482011034 Texas Harris 76.8 77.8 0.01 0.22 4.08 1.58 22.78 4.22 482011039 Texas Harris 78.2 80.2 0.01 0.29 2.50 2.70 21.03 5.31 482011050 Texas Harris 74.6 76.2 0.01 0.31 3.95 1.84 17.62 4.39 484390075 Texas Tarrant 75.5 76.4 0.03 0.58 0.99 2.06 22.71 6.49 484392003 Texas Tarrant 79.6 82.1 0.03 0.44 1.62 1.33 23.94 6.28 484393009 Texas Tarrant 78.6 78.6 0.02 0.34 1.62 1.40 23.61 5.78 484393011 Texas Tarrant 74.5 76.6 0.03 0.69 0.65 1.54 22.51 6.25

551170006 Wisconsin Sheboygan 77.0 79.4 0.03 0.47 0.67 0.56 14.91 7.79