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# Air Quality Modeling Technical Support Document

for the 2008 Ozone NAAQS Proposed Transport Rule

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

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1 **1. Introduction**  In this technical support document (TSD) we describe the air quality modeling performed to support the proposed Transport Rule for the 2008 ozone National Ambient Air Quality Standards (NAAQS ) 1 . In this document, air quality modeling is used t o 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 n onattainment or have maintenance problems for the 2008 ozone NAAQS 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 nonattainm ent 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 projecting ozone design value concentrations 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., apportionm ent) 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 copy of the 2009

– 2013 base period and projected 2017 ozone design values and 2017 ozone contributions based on the updated modeling can be obtained from docket EPA - HQ - OAQ - 2015 - 0500. Th e ozone design values for individual monitoring sites are in docket item EPA - HQ - OAQ - 2015 - 0500 - 0006 and the ozone contributions are in docket item EPA - HQ - OAQ - 2015 - 0500 - 0007 . E lectronic copies of the ozone design values and contributions can also be obtained at www.epa.gov/airtransport .

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.*** E.O. 12866 Review

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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 ye ar emissions were projected to a future ye ar scenario, 2017 . The 2011 mode ling platform and projected 2017 emissions were used to drive the 2011 and 2017 air quality model simulations. The base year 2011 platform was chosen in part because it represents the most recent, complete set of base ye ar 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., particularl y 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 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 conti guous U.S.). Consideration of the different processes (e.g., transport and deposition) that affect primary (directl y emitted) and secondary (formed b y 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 3 (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 grid resolution of 12 km x 12 km. The model simulations produce hourl y 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, hourl y emissions estimates and

3 The “chemparam.2_CF” chemical parameter file was used in the CAMx model simu lations.*** E.O. 12866 Review

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3 meteorological data, and initial and boundary concentration s. Separate emissions inv entories were 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 - year model simulation s 4

.

Figure 2 - 1. M ap of the CAMx modeling domain 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 (http://www.epa. gov/airtrends/weather.html ). Statistical modeling anal ys es have shown that temperature and certain other meteorological variables are highl y correlated with the magnitude of ozone concentrations (Camalier, et al., 2007). In selecting a ye ar for air quality modeling it is important to simulate a variety of meteorological conditions that are generall y associated with elevated air quality (US EPA,

4 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

ram p - up period at the end of April 2011). The CAMx 2017 contribution modeling was performed for the period May 1 through September 30, 2011 with a 10 - day ram p - up period at the end of April 2011.*** E.O. 12866 Review

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4 2014a). Specifically

for ozone, modeled time periods should reflect meteorological conditions that frequentl y cor

respond 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 ye ar that will be representative of “ty

pical” 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, climatological 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 th e nine National Oceanic 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, respectivel y. Overall, temperatures were warmer than normal during the summer of 2011 in nearl y 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 of 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 conditions 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, 2007, 2010, 2012 were also all among the ten warmest on record. Analy sis 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 ty pical of high ozone ye ars in the E a stern U.S. in terms of being conducive to the formation of ozone . InFigure 2 - 5, those ye ars with downward adjustments (compared to the “unadjusted for weather” line) were generall y more conducive to ozone formation than years that were adjusted upward. For example, in the Northeast region, the ye ars 2002, 2005, 2007, 2010, 2011, and 2012 were more conducive to ozone formation than 2000, 2003, 2004, 2006, and 2009. In2011, the South and Southeast regions had the largest adjustment (most ozone conducive), whereas the Northeast and Central*** E.O. 12866 Review

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5 regions were slightl y 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 m onth 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 = warmest 25th percentile.

Figure 2 - 2 . U.S. climate regions. (http://www.ncdc.noaa.gov/monitoring - references/maps/us - climate - regions .php ) 

### 2011 **May Ju**  **n Ju**  **l Aug**  **Sep Northe**  **as t**  W W WW N WW **Southe as** **t** N WW WW WW N **Ohio V alley**  N W WW W C **Up pe**  **r M id**  **we st**  N N WW W N **South** N WW WWW WWW N **Northe rn R**  **ockies** C N W W W **Southw est**  C W WW WWW W **Northw est**  CC C C W WW **We st**  C C N W WW*** E.O. 12866 Review

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6 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 )*** E.O. 12866 Review

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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 )*** E.O. 12866 Review

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8 Figure 2 - 5b. Meteorological - adjusted ozone trends by climate region: West North Central (Northern Rockies and Plains), West, and Northwest. (http://www.epa.go v/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- 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 simulation include Pleim - Xiu land surface model (Xiu and Pleim, 2001; Pleim and Xiu, 2003) , Asy mmetric Convective Model version 2 planetary boundary

layer scheme (Pleim 2007a,b) , Kain - Fritsch cumulus parameterization (Kain, 2004) utilizing the mois ture - 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 ).*** E.O. 12866 Review

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9 The WRF model simulation was initialized using the 12 km North American Model (12NAM) anal ys is product provided by the National Climatic Data Center (NCDC ) . Where 12NAM data were unavailable, the 40km Eta Data Assimilation System (EDAS) anal ys is (ds609.2) from the National Center for Atmospheric Research (NCAR) was used. Anal ys is nudging for t emperature, wind, and moisture was applied above the boundary layer onl y. 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. 5 Sea surface temperatures at 1 km resolution were obtained from the Group for High Resolution Sea Surface Temperatures (GH RSST) (Stammer, et al., 2003). As shown in Table 2 - 2, the 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) Approximate

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

5 The 2006 NLCD data are available at http://www.mrlc.gov/nlcd06_data.php*** E.O. 12866 Review

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10 CAMx Layers WRF

Layers Sigma P Pressure

(mb) Approximate

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 b y 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 analy ze 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 m2/sec except for urban grid cells*** E.O. 12866 Review

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11 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. 2. 4 Initial and Boundary Concentrations The lateral boundary

and initial 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 atmosph eric chemical and phys ical processes driven by assimilated meteorological observations from the NASA’s Goddard Earth Observing Sy stem (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 dy namic boundary concentration s at one - hour intervals and an initial concentration field fo r the CAMx simulations. The 2011 boundary 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 - Chem 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 temporall y allocated (i.e. , hourly) emissions for each grid - cell in the modeling domain for a large number of chemical species that act as primary pollutants and precursors to secondary pollutants. Annual emission invento ries for 2011 and 2017 were preprocessed into CAMx - ready inputs using the Sparse Matrix Operator Kernel Emissions (SMOKE) modeling s ys tem (Houyoux et al., 2000) . 6 In formation 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).

6 The SMOKE output emissions 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”.*** E.O. 12866 Review

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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 s ys tem 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 replicate 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 provides 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. In 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). 7 We followed the approach in 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

7 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 viol ations of the NAAQS.*** E.O. 12866 Review

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13 NAAQS, are considered maintenance - only sites. In addition, those sites that have projected 2017 average design values that exceed the NAAQS, but are currentl y measuring clean data based on 2012 - 2014 design values are also defined as maintenance - onl y recep tors . Maintenance - onl y 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 equal 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 predictions from the 2011 and 2017 CAMx model simulations were used to project ambient (i.e., measured) ozone design values to 2017 following the approach described in EPA’s current guidance for attainment demonstration modeling (US EPA, 2014a), 8 as summarized here . The modeling guidance recommends using 5 - ye ar weighted average ambient design values 9 centered on the base modeling year as the starting point for projecting average design values to the future. Because 2011 is the base emissions ye ar, 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 - ye ar 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 b ased on observed data and projects future ye ar values using the relative response predicted b y the model. Equation ( 3 - 1 ) describes the recommended model attainment test in its simplest form, as applied for monitoring site i : 8 EPA’s ozone attainment demonstration modeling guidance is referred to as “the modeling guidance” in the remainder of this document. 9 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 .*** E.O. 12866 Review

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14 ( DVF ) = ( ) ∗ ( ) Equation 3 - 1 DVF is the estimated design value for the future ye ar at monitoring site i ; RRF is the relative response factor for monitoring site i ; and DVB is the base period design value monitored at site i . The relative response factor for each monitoring site ( ) 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 . Specificall y, the RRF was calculated based on the 10 highest days in the 2011 base ye ar modeling in the vicinity of each monitor location . As recommended b y 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 , the grid cell with the highest base ye ar ozone value in the 3 x 3 array surrounding the location of the monitoring 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 ozone >= 60 ppb, as long as there were at least 5 days th at 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 maxi mum 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 - ye ar 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 calculating 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. Table 3 - 2 contains this same information for the 6 maintenance - only sites in the eastern U.S. that are projected nonattainment but currentl y measuring clean data. Table 3 - 3 contains this same*** E.O. 12866 Review

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15 information for the 23 maintenance - onl y 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 provided in docket item EPA - HQ - OAR2015 - 0500 - 0006. 10 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 receptors) .

Monitor ID State County Average Design Value 2009 - 2013 Maximum

Design Value 2009 - 2013 Average

Des ign 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

Table 3 - 2. Average and maximum 2009 - 2013 and 2017 base case 8 - hour ozone design values and 2012 - 2014 de sign values (ppb) at sites in the eastern U.S. that are projected nonattainment but currentl y measuring clean data (maintenance - only receptors) .

10 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 o il and gas operations, increased UV intensity due to reflection off of snow surfaces and potentially still 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 - Level Ozone (EPA, 2014d) (http://www.epa.gov/ttn/ecas/ria.html )*** E.O. 12866 Review

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16 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 72.0

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 - onl y receptors) .*** E.O. 12866 Review

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17 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 75.0

**4. Ozone Contribution Modeling**  4.1 Methodology*** E.O. 12866 Review

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18 The EPA performed nationwide, 11 state - level ozone source apportionment modeling using the CAMx OSAT/APCA technique 12 (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 monitoring sites. In the source apportionment model run, we tracked the ozone formed from e ach 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 b y state);  Boundary

Concentrations

– concentrations transported 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 sources in the portions of Canada and Mexico included in the modeling domain (we did not model the contributions from Canada and Mexico separatel y);  Fires

– co mbined emissions from wild and prescribed fires; and  Offshore

– combined emissions from offshore marine vessels and offshore drilling platforms. CAMx OSAT/APCA model run was performed for the period May 1 through September 30 using the projected 2017 base case emissions and 2011 meteorology for this time period. The hourly contributions 13 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 sense” to apportion the projected 2017 average design value at each monitoring location into contributions from each individual tag. This process is similar in concept to the approach describe d above for using model predictions to calculate 2017 ozone design values. The approach used to calculate the contribution metric is described by the following steps :

11 As shown in Figure 2 - 1, the EPA’s nationwide modeling includes the 48 contiguous states. 12 As part of this technique, ozone formed from reactions between biogenic VOC and NOx with anthropogenic NOx and VOC are assigned to the anthropogenic emissions. 13 Co ntributions 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.*** E.O. 12866 Review

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19 **Step 1.** Modeled h ourl y ozone concentrations are used to calculate the 8 - hour daily maximum ozone (MDA8) concentration in each grid c ell on each day . **Step 2.** The gridded 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 hourl y “pseudo” concentrations 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 grid 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 concentrations >=76 ppb (i.e., projected 2017 exceedance day s) 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 day s are extracted and used in the calculations. 14 **Step 5.** For each monitoring site and each tag, the 8 - hour “pseudo” concentrations are then averaged across the day s selected in Step 4 to create a multi - day average “pseudo” concentration for tag at each site. S imilarly , 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 concentration are used to create a Relative Contribution Factor (RC 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 RC F for each tag i s multiplied by the 2017 average ozone design valu e 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).

14 If there were fewer than 5 days with a modeled 2017 MDA8 concentration ≥ 60 ppb for the location of a particular mo nitoring site, then contributions were not calculated at that monitor.*** E.O. 12866 Review

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20 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 days with 2017 model -predicted exceedances at this site. The MDA8 ozone concentrations on these day s 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 B 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

Month Day Predi

cted MDA8 O3 on 2017 Model

ed Ex ceedance Days "P seudo"

8-Hr O3 for State A "P seudo"

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 Relati ve Contri bution Factors => 0.09422 0. 00231 Contri butions => 7.3020 0.1790 Tr uncated Contri butions => 7.30 0. 17 2017 A verag e DesignV al ue is7 7. 5 ppb Mul ti-Day Av erag e =>*** E.O. 12866 Review

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21 value, based on modeled transport patterns and other meteorological conditions generall y associated with modeled high ozone concentrations in the vicinity of the monitoring site. This average contribution metric 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 - onl y 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 OAQ - 2015 - 0500 - 0007 . 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) Larg est 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*** E.O. 12866 Review

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22 Upwind State Largest Downwind

Contribution to Nonattainment Receptors for Ozone (ppb) Larg est Downwind

Contribution to Maintenance Receptors for Ozone (ppb 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 1.67 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 contribute 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 anal yz ed 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,*** E.O. 12866 Review

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23 Arkansas, Illinois, Indiana, Kansas, Kentucky, Louisiana, Maryland, Michigan, Mississippi, Missouri, New Jersey , New York, Ohio, Oklahoma, Pennsylvania, Texas, Virgini a, 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, Il linois, Indiana, 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 downwind nonattainment receptors and maintenance - only receptors in the eastern U.S. are provided in Table 4 - 3 and Table 4 - 4 , respectively . Table 4 - 3. Linkages between each upwind state and downwind nonattainment receptors in the e astern U.S. Upwind State Downwind 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)*** E.O. 12866 Review

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24 Upwind State Downwind Nonattainment Receptors 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) 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 - onl y 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)*** E.O. 12866 Review

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25 Upwind State Downwind Nonattainment Receptors 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) 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)*** E.O. 12866 Review

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26 Upwind State Downwind Nonattainment Receptors 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)

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)*** E.O. 12866 Review

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27 Upwind State Downwind Nonattainment Receptors 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) 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)*** E.O. 12866 Review

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28 Upwind State Downwind Nonattainment Receptors 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)

**5 .**  **References** Abt Associates, 2014. User’s Guide: Modeled Attainment Test Software. http://www.epa.gov/scram001/modelingapps_mats.htm*** E.O. 12866 Review

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29 Camalier, L., W. Cox, and P. Dolwick , 2007. The Effects of Meteorology on Ozone in Urban Areas and Their Use in Assessing Ozone Trends. Atmospheric Environment, **41** , 7127 - 7137. ENVIRON, 2014. User's Guide Comprehensive Air Quality Model with Extensions version 6.1 1 , www.camx.com . ENVIRON International Corporation, Novato, CA. ENVIRON, 2013 . wrfcamx version 4.0 Release Notes. May 06, 2013. www.camx.com . ENVIRON International Corporation, Novato, CA. Gilliam , R.C. and J .E. P leim, 2010. Performance Assessment of New Land Surface and Planetary Boundary

L ayer Physics in the WRF - ARW. J. Appl. Meteor. Climatol. , **49** , 760

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Conditions for Regional Air Quality Modeling: Description and Evaluations, Geoscientific Model Development, **7** , 339 - 360. H ong , S - Y , Y . Noh , and J. Dudhia, 2006. A New Vertical Diffusion Package with an Explicit Treatment of Entrainment Processes. M on. Wea. Rev. , **134** , 2318

–2341. Houyoux, M.R., Vukovich, J.M., Coats, C.J., Whe eler, N.J.M., Kasibhatla, P.S.,2000. Emissions Inventory Development and P rocessing for the Seasonal Model for Regional Air Quality (SMRAQ) project, Journal of Geophysical Resea rch

– Atmospheres , **105(D7)** , 9079 - 9090. Iacono, M.J., J.S. Delamere, E.J. Mlawer, M.W. Shephard, S.A Clough, and W.D. Collins, 2008. Radiative Forcing b y Lo ng - Lived Greenhouse Gases: Calculations with the AER Radiative Transfer Models, J. Geophys. Res., **11 3**  , D13103 . Kain, J.S., 2004. The Kain - Fritsch Convective Parameterization: An Update, J. Appl. Meteor., **43** , 170 - 181. Ma, L - M. and Tan Z - M, 2009. Improving the B ehavior of Cumulus P arameterization for Tropical C yc lone Prediction: Convective Trigger, Atmospheric Research , **92** , 190 - 211. Morrison, H.J., A. Curry , and V. I . Khvorostyanov, 2005. A New Double - Moment Microphy sics Parameterization for Application in Cloud and Clim ate Models. Part I: Description, J. Atmos. Sci. , **62** , 1665

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30 Pleim, J.E., 2007b. A Combined Local and Nonlocal Closure M odel f or the Atmospheric Boundary L ayer. Part II: Application and Evaluation in a Mesoscale Meteorological M odel , J. Appl. Meteor. Climatol. , **46,** 1396

– 1409. Ruiz, L .H. and Yarwood, G., 2013. Interactions between Organic Aerosol and NOy : Influence on Oxidant Production. Prepared for the Texas AQRP (Project 12 - 012), b y the University of Texas at Austin, and ENVIRON International Corporation, Novato, CA. http://aqrp.ceer.utexas.edu/viewprojectsFY12 - 13.cfm?Prop_Num=12 - 012 Skamarock, W.C., J.B. Klemp, J. Dudhia, et al. 2008. A Description of the Advanced Research WRF Version 3. NCAR Tech. Note NCAR/TN - 475+STR. http://wwww.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf Simon, H., K.R. Baker, and S.B. Phillips, 2012. Compilation and Interpretation of Photochemical Model Performance Statistics P ublished between 2006 and 2012, Atmospheric Environment, **61** , 124 - 139. Stammer, D., F.J. Wentz, and C.L . Gentemann **,** 2003. Validation of Microwave Sea Surface Temperature Measurements for Climate Purposes , J. of Climate , **16(1)** , 73 - 87 . U.S. Enviro nmental Protection Agency , 2014a. Modeling Guidance for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze , Research Triangle Park, NC. (http://www.epa.gov/ttn/scram/guidance/guide/Draft_O3 - PM - RH_Modeling_Guidance 2014.pdf ) U.S. Environmental Protection Agency , 2014b . Meteorological Mo del Performance for Annual 2011 Simulati on WRF v3.4 , Research Triangle Park, NC . (http://www.epa.gov/scram001/) U.S. Environm ental Protection Agency , 2015a . Preparation of Emissions Inventories for the Version 6 .2 , 2011 Emissions Modeling Platform , Research Triangle Park, NC . (http://www.epa.gov/ttn/chief/emch) U.S. Environm ental Protection Agency , 2015b. 2011 National Emissions Inventory , version 2, Research Triangle Park, NC . (http://www.epa.gov/ttn/chief/emch) Xiu, A., and J.E. Pleim, 2001, Development of a Land Surface Model. Part I : Application in a Meso scale Meteorological Model, J. Appl. Meteor., **40** , 192 - 209 . Yantosca, B. 2004. GEOS - CHEMv7 - 01 - 02 User’s Guide, Atmospheric Chemistry Modeling Group, Harvard University , Cambridge, MA . Zhang, L., J. R. Brook, and R. Vet. 2003. A Revised Parameterization for Gaseous Dry D eposition in Air- Quality Models, Atmos. Chem. Phys ., **3** , 2067

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