# Updated Air Quality Modeling Technical Support Document for the 2008 Ozone NAAQS Transport Assessment

Office of Air Quality Pl anning and Standards United States Environmental Protection Agency August 20151 1. Introduction In this technical support document (TSD) we describe the updated air quality modeling performed to help states address the requirements of Clean Air Act (CAA) Section 110(a)(2)(D)(i)(I), or “Good Neighbor SIPs,” fo r the 2008 ozone National Ambient Air Quality Standards (NAAQS) 1 . In this document, air quality modeling is used to project ozone

concentrations at individu al monitoring sites to 2017 2 and to estimate state- by-state contributions to those 2017 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 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 nonattainment and maintenance sites in other st ates (i.e., in downwind states). The approach for identifying nonattainment and maintenance sites and the methods for calculating contributions ar e consistent with the approach an d methods used in the Cross-State Air Pollution Rule (CSAPR, 76 FR 48208). The remaining sections of this TSD are as follows. Section 2 describes the air quality modeling platform and the evalua tion of model predic tions using measured concentrations. Section 3 defines the procedures for projecting ozone design va lue concentrations to 2017 and the approach for identifying monitoring sites wi th 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 monito ring sites including nonattainment and/or maintenance sites. For quest ions 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 de sign values and 2017 ozone contributions based on the updated modeling can be obtai ned from docket EPA-HQ-OAQ-2015-0500. The ozone design values for individual monito ring sites are in docke t item EPA-HQ-OAQ-2015-

0500-0006 and the ozone contributions are in docket item EPA-HQ-OAQ-2015-0500-0007.

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.2 Electronic copies of the ozone design values and contributions can also be obtained at www.epa.gov/airtransport

.

2. Air Quality Modeling Platform

EPA has developed a 2011-based air quali ty modeling platform which includes emissions, meteorology and other inputs for 2011. Th e 2011 base year emissions were projected to a future year scenario, 2017. The 2011 modelin g platform and projected 2017 emissions were

used to drive the 2011 and 2017 ai r quality model simulations. 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 mode ling. 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 performe d for this ozone transport assessment used the Comprehensive Air Quality Model w ith Extensions (CAMx version 6.11) (Environ, 2014). CAMx is a three-dimensional grid-based Eu lerian 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 depos ition) that affect primary (directly emitted) and secondary (formed by atmo spheric processes) pollu tants at the regional

scale in different locations is fundamental to understanding and assessing the effects of emissions on air quality concen trations. CAMx was applied w ith 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 analys is. The domain covers the 48 c ontiguous states along with the southern portions of Canada and the norther n portions of Mexico. This modeling domain contains 25 vertical layers with a top at about 17,550 meters, or 50 millibars (mb), and horizontal

3 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 simulati ons produce hourly air quality concentrations for each 12 km grid cell across the modeling domain. CAMx requires a variety of input files th at contain information pertaining to the modeling domain and simulation period. These in clude gridded, hourly emissions estimates and meteorological data, and initial and boundary co ncentrations. Separate emissions inventories were prepared for the 2011 base year and the 2017 base case. A ll other inputs (i.e. meteorological fields, initial concentrations, a nd boundary concentrations) were specified for the 2011 base year model application and rema ined unchanged for the future-year model

simulations 4

.

Figure 2-1. Map of the CAMx modeling domain used for transport modeling.

2.2 Characterization of 2011 Summer Meteorology
Meteorological conditions including temperat ure, humidity, winds, solar radiation, and vertical mixing affect the formation and transpor t of ambient ozone con centrations. Ozone is more readily formed on warm, sunny days wh en the air is stagnant. Conversely, ozone production is more limited on days that are cloudy, cool, rainy, and windy

4 The CAMx annual simulations for 2011 and 2017 were each performed using two time segments (January 1 through April 30, 2 011 with a 10-day ramp-up peri od at the end of December 2 010 and May 1 through December 31, 2011 with a 10-day ramp-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 ramp-up period at the end of April 2011.4 (http://www.epa.gov/airtrends/weath er.html). Statistical modeli ng analyses have shown that temperature and certain other mete orological 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 asso ciated with elevated air quality (US EPA, 2014a). Specifically for ozone, modeled time pe riods should reflect meteorological conditions that frequently correspond with observed 8-hour daily maximum concentrations greater than the NAAQS at monitoring sites in nonattainment ar eas (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 acro ss the U.S. in 2011 compared to long-term, climatological normal (i.e., averag e) conditions. Table 2-1 descri bes the observed 2011 surface temperature anomalies (i.e., departure from nor mal) for individual months from May through September relative to the period 1895-2011 fo r each of the nine National Oceanic and Atmospheric Administration (NOAA) climate regions shown in Figure 2-2. The aggregate temperature and precipitation anomalies by st ate for the core summer months, June through August, are shown in Figures 2-3 and 2-4, respectiv ely. Overall, temperatures 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 Sout h 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 wi th a strong upper air ri dge 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 t hose in several other recen t years. For example, 2006, 2007, 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 basi s, the summer of 2011 was typical of high ozone years in the Eastern U.S. in terms of being conducive to the formation of oz one. In Figure 2-5, those years with downward adjustments (compared to the “u nadjusted 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 West ern regions experienced generally near normal ozone, with the Northwest somewhat below normal. Table 2-1. Surface temperature anomalies in 2011 by month and geographic region.*

* Unshaded boxes with the “N” marker represent n ear-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-re ferences/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 NWWW Southwest C W WW WWW W Northwest CC C C W WW West C C N W WW6 Figure 2-3. Statewide rank of temperat ures 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. Meteorologi cal-adjusted ozone trends by climate region: Northeast, Central (Ohio Valley), East Nort h Central (Upper Midwes t), Southeast, South, and Southwest. ( http://www.epa.gov/airtrends/weather.html )8 Figure 2-5b. Meteorologi cal-adjusted ozone trends by climate region: West North Central (Northern Rockies and Plains), West, and Northwest. ( http://www.epa.gov/ai rtrends/weather.html )

2.3 Meteorological Data for 2011 The meteorological data for air quality m odeling of 2011 were derived from running

Version 3.4 of the Weather Research Forecasti ng Model (WRF) (Skamarock, et al., 2008). The meteorological outputs from WRF include hourly -varying horizontal wind components (i.e., speed and direction), temperature, moisture, ver tical diffusion rates, and rainfall rates for each grid cell in each vertical laye r. Selected physics options used in the WRF simulation include Pleim-Xiu land surface model (Xiu and Plei m, 2001; Pleim and Xiu, 2003), Asymmetric Convective Model version 2 planetary boundary la yer scheme (Pleim 2007a,b), Kain-Fritsch cumulus parameterization (Kain, 2004) utilizing the moisture-a dvection trigger (Ma and Tan, 2009), Morrison double moment microphysics (Morri son, 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 12km North American Model (12NAM) analysis product provided by the Na tional Climatic Data Center (NCDC). Where 12NAM data were unavailable, the 40km Eta Data Assimilation System (EDAS) analysis (ds609.2) from the National Center for Atmosphe ric Research (NCAR) was used. Analysis nudging for temperature, wind, and moisture wa s applied above the boundary layer only. The

model simulations were conducted in 5.5 day blocks with soil mois ture 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 Nati onal Land Cover Database (NLCD2006) data. 5 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, the WRF simulations were performed with 35 vertical layers up to 50 mb, w ith the thinnest layers being nearest the surface to better resolve the plan etary boundary layer (PBL). The WRF 35-layer structure was collapsed to 25 layers for the CA Mx air quality model si mulations, as shown in

Table 2-2.

Table 2-2. WRF and CAMx layers and their appr oximate 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.php10 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 mode l simulation and evaluatio n 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 wrfc amx v4.0 (Environ, 2013) meteorologi cal data processing program to create model-ready meteor ological inputs to CAMx. The m odification 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 rate s. In running wrfcamx, vertical eddy diffusivities (K v) were calculated using the Yo nsei University (YSU) (Hong and Dudhia, 2006) mixing scheme. We used a minimum Kv of 0.1 m 2 /sec except for 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 ni ghttime “urban heat island” effect.

2.4 Initial and Boundar y Concentrations The lateral boundary and init ial species concentrations are provided by a threedimensional global atmospheric chemistry m odel, GEOS-Chem (Yantosca, 2004) standard version 8-03-02 with 8-02-01 chemistry. The gl obal GEOS-Chem model simulates atmospheric chemical and physical processes driven by assi milated meteorological observations from the NASA’s Goddard Earth Observing System (GEO S-5; additional information available at: http://gmao.gsfc.nasa.gov/GEOS/ and http://wik i.seas.harvard.edu/geos-chem/index.php/GEOS5). This model was run for 2011 with a grid reso lution of 2.0 degrees x 2.5 degrees (latitudelongitude). The predictions were used to prov ide one-way dynamic boundary concentrations at

one-hour intervals and an init ial concentration field for th e CAMx simulations. The 2011 boundary concentrations from GEOS-Chem were used for th e 2011 and 2017 model simulations. The procedures for translating GEOS-Chem predic tions to initial and boun dary concentrations are described elsewhere (Henderson, 2014). More information about the GEOS-Chem model and other applications using this tool is availabl e at: http://www-as.harvard.e du/chemistry/trop/geos.

2.5 Emissions Inventories CAMx requires detailed emissions inventor ies containing temporally allocated (i.e., hourly) emissions for each grid-cell in the mode ling domain for a large number of chemical species that act as primary po llutants and precursors to second ary pollutants. Annual emission inventories for 2011 and 2017 were preprocessed into CAMx-ready inputs using the Sparse Matrix Operator Kernel Emissions (SM OKE) modeling system (Houyoux et al., 2000). 6

Information on the emissions inventories used as input to the CAMx mo del simulations can be found in the following emissions inventory t echnical support documents: Preparation of Emissions Inventories for the Version 6.2, 2011 Emissions Modeling Platform (US EPA, 2015a)

and 2011 National Emissions Inventor y, 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”.12 2.6 Air Quality Model Evaluation An operational model performance evaluati on 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 as sessments of model predictions versus observations paired in time and space depending on the sampling period of measured data. Details on the evaluation methodology and the calculation of performance stat istics are provided in Appendix A. Overall, the ozone model performance sta tistics for the CAMx 2011 simulati on are within or close to the ranges found in other recent peer-reviewed a pplications (Simon et al, 2012). The model performance results, as described in Appendix A, demonstrate that the predictions from the 2011 modeling platform closely replic ate the corresponding observed con centrations in terms of the magnitude, temporal fluctuations, and geographi c differences for 8-hour daily maximum ozone. These results provide confidence that our ap plication of CAMx using this 2011 modeling platform provides a scientifically credible appr oach for assessing ozone concentrations relevant for the transport assessment described in this TSD.

3. Identification of Future Nonattainment and Maintenance Sites The ozone predictions from the 2011 and 2017 CA Mx model simulations were used to project ambient (i.e., measured) ozone design valu es to 2017 following the approach described in EPA’s current guidance for attainment demonstration modeling (US EPA, 2014a), 7 as summarized below. The modeling guidance recomme nds using 5-year weighted average ambient design values 8 centered on the base modeling year as the starting point for projecting 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 thro ugh 2013 (i.e., the average of design values for 2009-2011, 2010-2012 and 2011-2013) to calculate the 5-ye ar 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 values using the

7 EPA’s ozone attainment demonstration modeling guidan ce is referred to as “the modeling guidance” in the remainder of this document.

8 The air quality design value for a site is the 3-year av erage of the annual fourth-h ighest daily maximum 8-hour average ozone concentration.13 relative response predicted by the model. E quation (3-1) describes the recommended model attainment test in its simplest form, as applied for monitoring site i : ሺDVFሻ ୧ ൌ ሺ ܴܴܨ ሻ ௜ ∗ ሺܦܸܤሻ ௜ Equation 3-1

DVF ୧ is the estimated design value for the future year at monitoring site i ; RRF ୧ is the relative response factor for monitoring site i ; and DVB ୧ is the base period design value monitored at site i

. The relative response factor for each monitoring site ሺ ܴܴܨ ሻ ௜ 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 ce lls in the vicinity of the monitoring site. The modeling guidance recommends calculating RRFs ba sed 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 the 2011 base year modeli ng 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 grid cell containing the monitor. On each high ozone day, the grid cell with the highest base year 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 calcula tion (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 p pb, as long as there were at least 5 days that

meet that criteria. At monitor lo cations 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 2017 projected average ozone design values were evaluated to identify those sites with design values that exceed the 2008 ozone NAAQS. 9 Consistent with the approach used in CSAPR, those sites with 2017 average design values that exceed the NAAQS are projected to be nonattainment in 2017 (i.e., nonattainment sites). As noted above, we followed the approach in the CSAPR to identify si tes that would have difficulty maintaining the 2008 ozone NAAQS in a

scenario that takes into account hi storic variability in air quality at the monitoring site. As part of

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 nonattainment.14 this approach, the highest (i.e., maximum) am bient design value from the 2011-centered 5-year period (i.e., the maximum of design va lues from 2009-2011, 2010-2012, and 2011-2013) was projected to 2017 for each site using the site -specific RRFs. Following the CSAPR approach, monitoring sites with a maximum design value that exceeds the NAAQS, even if the average design value is below the NAAQS, are projected to have a maintenance problem in 2017. In this regard, nonattainment sites are also maintena nce sites because the maximum design value at nonattainment sites is always greater than or eq ual to the 5-year weighted average. Monitoring sites with a 2017 average design value below the NAAQS, but with a maximum design value that exceeds the NAAQS, are considered maintenan ce-only sites. These sites are projected to have a maintenance problem, but not a nonattainment problem in 2017. The 2009-2013 base period ambient and projec ted 2017 base case average and maximum design values at individual monitoring site s are provided in dock et item EPA-HQ-OAQ-2015- 0500-0006, as noted above 10 . The base period ambient and projected 2017 average and maximum design values for nonattainment sites in the East 11 and West 12 are provided in Table 3- 1. The base period ambient and projected 2017 average and maximum design values for maintenance-only sites in the East a nd West are provided in Table 3-2.

10 There are 7 sites in 3 counties in the West that were ex cluded from this analysis because the ambient design values at these sites were dominated by wintertime ozon e episodes and not summer s eason conditions that are the focus of this transport assessment. High winter ozone concen trations 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 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 )

11 For the purposes of this document we include the 37 states and the District of Columbia in the region from Texas northward to North Dakota and eastward to the East Coast as comprising the Eastern U.S.

12 For the purposes of this document we include the follo wing 11 states as comprisi ng the Western U.S.: Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming.15 Table 3-1. 2009-2013 and 2017 average and maxi mum ozone design values at projected nonattainment sites in the East (top) and West (bottom) (units are ppb).

Monitor ID State County 2009-2013 Average Design Value 2009-2013 Maximum

Design Value 2017 Average Design Value 2017 Maximum

Design Value Sites in the East 90013007 Connecticut Fairfield 84.3 89.0 77.1 81.4 90019003 Connecticut Fairfield 83.7 87.0 78.0 81.1 90099002 Connecticut New Haven 85.7 89.0 77.2 80.2 240251001 Maryland Harford 90.0 93.0 81.3 84.0 360850067 New York Richmond 81.3 83.0 76.3 77.8 361030002 New York Suffolk 83.3 85.0 79.2 80.8 390610006 Ohio Hamilton 82.0 85.0 76.3 79.1 480391004 Texas Brazoria 88.0 89.0 81.4 82.3 481210034 Texas Denton 84.3 87.0 76.9 79.4 482011034 Texas Harris 81.0 82.0 76.8 77.8 482011039 Texas Harris 82.0 84.0 78.2 80.2 484392003 Texas Tarrant 87.3 90.0 79.6 82.1 484393009 Texas Tarrant 86.0 86.0 78.6 78.6 551170006 Wisconsin Sheboygan 84.3 87.0 77.0 79.4 Sites in the West 60190007 California Fresno 94.7 95.0 89.0 89.3 60190011 California Fresno 93.0 96.0 87.6 90.4 60190242 California Fresno 91.7 95.0 87.1 90.3 60194001 California Fresno 90.7 92.0 84.2 85.4 60195001 California Fresno 97.0 99.0 90.6 92.5 60251003 California Imperial 81.0 82.0 79.3 80.3 60290007 California Kern 91.7 96.0 86.2 90.2 60290008 California Kern 86.3 88.0 80.6 82.2 60290011 California Kern 80.0 81.0 76.2 77.1 60290014 California Kern 87.7 89.0 82.8 84.0 60290232 California Kern 87.3 89.0 82.2 83.8 60295002 California Kern 90.0 91.0 84.5 85.5 60296001 California Kern 84.3 86.0 79.7 81.3 60311004 California Kings 87.0 90.0 81.1 83.9 60370002 California Los Angeles 80.0 82.0 79.0 81.0 60370016 California Los Angeles 94.0 97.0 92.8 95.8 60371002 California Los Angeles 80.0 81.0 77.1 78.1 60371201 California Los Angeles 90.0 90.0 87.9 87.9 60371701 California Los Angeles 84.0 85.0 82.2 83.2 60372005 California Los Angeles 79.5 82.0 78.1 80.616 Monitor ID State County 2009-2013 Average Design Value 2009-2013 Maximum

Design Value 2017 Average Design Value 2017 Maximum

Design Value 60376012 California Los Angeles 97.3 99.0 94.5 96.2 60379033 California Los Angeles 90.0 91.0 86.0 86.9 60392010 California Madera 85.0 86.0 79.8 80.8 60470003 California Merced 82.7 84.0 78.1 79.3 60610006 California Placer 84.0 86.0 78.2 80.0 60650004 California Riverside 85.0 85.0 82.3 82.3 60650012 California Riverside 97.3 99.0 93.5 95.1 60651016 California Riverside 100.7 101.0 95.7 96.0 60652002 California Riverside 84.3 85.0 79.8 80.5 60655001 California Riverside 92.3 93.0 87.6 88.2 60656001 California Riverside 94.0 98.0 88.1 91.9 60658001 California Riverside 97.0 98.0 93.3 94.3 60658005 California Riverside 92.7 94.0 89.2 90.4 60659001 California Riverside 88.3 91.0 82.7 85.2 60670012 California Sacramento 93.3 95.0 85.7 87.3 60675003 California Sacramento 86.3 88.0 80.5 82.0 60710005 California San Bernardino 105.0 107.0 103.6 105.6 60710012 California San Bernardino 95.0 97.0 91.8 93.8 60710306 California San Bernardino 83.7 85.0 81.2 82.4 60711004 California San Bernardino 96.7 98.0 94.3 95.6 60712002 California San Bernardino 101.0 103.0 99.5 101.5 60714001 California San Bernardino 94.3 97.0 92.3 95.0 60714003 California San Bernardino 105.0 107.0 101.8 103.8 60719002 California San Bernardino 92.3 94.0 88.0 89.6 60719004 California San Bernardino 98.7 99.0 95.7 96.0 60731006 California San Diego 81.0 82.0 76.6 77.6 60990006 California Stanislaus 87.0 88.0 83.0 83.9 61070006 California Tulare 81.7 85.0 77.0 80.1 61070009 California Tulare 94.7 96.0 87.3 88.5 61072002 California Tulare 85.0 88.0 78.6 81.4 61072010 California Tulare 89.0 90.0 82.7 83.6 61112002 California Ventura 81.0 83.0 78.3 80.2 80350004 Colorado Douglas 80.7 83.0 76.0 78.1 80590006 Colorado Jefferson 80.3 83.0 76.3 78.817 Table 3-2. 2009-2013 and 2017 average and maxi mum ozone design values at projected maintenance-only sites in the East (top) and West (bottom) (units are ppb).

Monitor ID State County 2009-2013 Average Design Value 2009-2013 Maximum

Design Value 2017 Average Design Value 2017 Maximum

Design Value Sites in the East 90010017 Connecticut Fairfield 80.3 83.0 75.8 78.4 211110067 Kentucky Jefferson 82.0 85.0 75.8 78.6 211850004 Kentucky Oldham 82.0 86.0 73.7 77.3 240053001 Maryland Baltimore 80.7 84.0 73.2 76.2 260050003 Michigan Allegan 82.7 86.0 75.5 78.5 261630019 Michigan Wayne 78.7 81.0 74.0 76.2 340071001 New Jersey Camden 82.7 87.0 74.2 78.1 340150002 New Jersey Gloucester 84.3 87.0 75.1 77.5 340230011 New Jersey Middlesex 81.3 85.0 73.0 76.3 340290006 New Jersey Ocean 82.0 85.0 73.9 76.6 360810124 New York Queens 78.0 80.0 75.7 77.6 420031005 Pennsylvania Allegheny 80.7 82.0 75.3 76.5 421010024 Pennsylvania Philadelphia 83.3 87.0 75.1 78.4 480850005 Texas Collin 82.7 84.0 74.9 76.0 481130069 Texas Dallas 79.7 84.0 74.0 78.0 481130075 Texas Dallas 82.0 83.0 75.8 76.7 481211032 Texas Denton 82.7 84.0 75.1 76.3 482010024 Texas Harris 80.3 83.0 75.9 78.5 482010026 Texas Harris 77.3 80.0 73.5 76.1 482010055 Texas Harris 81.3 83.0 75.4 77.0 482011050 Texas Harris 78.3 80.0 74.6 76.2 484390075 Texas Tarrant 82.0 83.0 75.5 76.4 484393011 Texas Tarrant 80.7 83.0 74.5 76.6 Sites in the West 40131004 Arizona Maricopa 79.7 81.0 75.0 76.2 60170020 California El Dorado 82.7 84.0 75.1 76.3 60390004 California Madera 79.3 81.0 75.3 76.9 60610003 California Placer 83.0 85.0 75.4 77.2 60670006 California Sacramento 78.7 81.0 74.0 76.1 60773005 California San Joaquin 79.0 80.0 75.9 76.8 80050002 Colorado Arapahoe 76.7 79.0 74.4 76.6 80590011 Colorado Jefferson 78.7 82.0 75.8 78.918 4. Ozone Contribution Modeling The EPA performed nationwide, 13 state-level ozone source apportionment modeling using the CAMx OSAT/APCA technique 14 (ENVIRON, 2014) to quan tify the contribution of

2017 base case NO X and VOC emissions from all sources in each state to projected 2017 ozone

concentrations at ozone monitori ng sites. In the sour ce 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 sector s in a given state were combined);

 Biogenics – biogenic NO X and VOC emissions domain-w ide (i.e., not by state);

 Boundary Concentrations – concentrations transported into the modeling domain;

 Tribes – the emissions from those tribal land s for which we have poi nt source inventory data in the 2011 NEI (we did not model the contributions from i ndividual 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 separately);

 Fires – combined emissions from wild and prescribed fires; and

 Offshore – combined emissions from offs hore marine vessels and offshore drilling platforms.

CAMx OSAT/APCA model run wa s 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 15 from each tag were processed to ca lculate an 8-hour average contribution metric. The process for calculating the contri bution metric uses the contribution modeling outputs in a “relative sense” to apportion the projected 2017 av erage design value at each monitoring location into contributions from each individual tag. This process is similar in concept to the approach described above for us ing model predictions to calculate 2017 ozone

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 reactions between biogenic VOC and NOx with anthropogenic NOx and VOC are assigned to the anthropogenic emissions.

15 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 em issions in each state.19 design values. The approach used to calculate the contribution metric is described by the following steps: Step 1. Modeled hourly ozone concentr ations are used to calculat e the 8-hour daily maximum ozone (MDA8) concentration in each grid cell on each day. Step 2. The gridded hourly ozone contributions from each tag are subtracted from the corresponding gridded hourly tota l ozone concentrations to creat e a “pseudo” hourly ozone value for each tag for each hour in each grid cell. Step 3. The hourly “pseudo” concentrations from St ep 2 are used to calculate 8-hour average “pseudo” concentrations for each tag for th e time period that corresponds to the MDA8 concentration from Step 1. Step 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 days) in the

downstream calculations. If there were fewer than five 2017 exceed ance days at a particular monitoring site then the data from the top five 2017 MDA8 concentration days are extracted and

used in the calculations. 16 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 averag e “pseudo” concentration for tag at each site. Similarly, the MDA8 concen trations 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 Rela tive Contribution Factor (RCF) for each tag at each monitoring site. The RCF is the difference between the MDA8 concentration and the corresponding “pseudo” concentration, nor malized by the MDA8 concentration. Step 7. The RCF for each tag is 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 the 2017 average design value for that site.

16 If there were fewer than 5 days w ith 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 8. The contributions calculated from Step 7 are truncated to two digits to the right of the decimal (e.g., a calculated contributi on of 0.78963... is truncated to 0.78 ppb). Table 4-1 provides an example of the calculati on of contributions from two states (state A and state B) to a particular nona ttainment site starting with Step 4, above. The table includes the daily “pseudo” concentrati ons for state A and state B and corresponding MDA8 ozone concentrations on those days w ith 2017 model-predicted exceedan ces at this site. The MDA8 ozone concentrations on these days are ranked-o rdered 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 oz one contributions (units are ppb).

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 =>21 The average contribution metric calculated in this manner is intended to provide a reasonable representation of the contribution from individual stat es to the projected 2017 design value, based on modeled transport patterns a nd other meteorological conditions generally 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. The contributions from each tag to individua l nonattainment and maintenance-only sites are provided in Appendix B. The largest co ntributions from each state to 2017 downwind

nonattainment sites and to downwind maintenanc e-only sites are provid ed in Table 4-2. The 2017 contributions from each tag to each monito ring site are provided in docket item EPA-HQOAQ-2015-0500-0007.

Table 4-2. Largest ozone contributions from each state to downwind 2017 projected nonattainment and 2017 projected maintena nce-only sites (units are ppb).

Upwind State Largest Contribution to a 2017 Nonattainment Site in Downwind States Largest Contribution to a 2017 Maintenance-Only Site in Downwind States Alabama 0.79 1.28 Arizona 1.78 0.41 Arkansas 1.24 2.15 California 1.75 3.44 Colorado 0.36 0.34 Connecticut 0.46 0.41 Delaware 0.68 2.23

District of Columbia 0.73 0.64 Florida 0.57 0.72 Georgia 0.58 0.56 Idaho 0.23 0.35 Illinois 17.48 23.17 Indiana 7.15 14.95 Iowa 0.61 0.85 Kansas 0.80 1.03 Kentucky 11.17 2.1422 Upwind State Largest Contribution to a 2017 Nonattainment Site in Downwind States Largest Contribution to a 2017 Maintenance-Only Site in Downwind States Louisiana 3.81 4.23 Maine 0.00 0.08 Maryland 2.39 7.11 Massachusetts 0.10 0.37 Michigan 2.69 1.79 Minnesota 0.40 0.47 Mississippi 0.78 1.48 Missouri 1.63 3.69 Montana 0.15 0.17 Nebraska 0.51 0.36 Nevada 0.84 0.73 New Hampshire 0.02 0.07 New Jersey 12.38 11.48 New Mexico 1.05 0.54 New York 16.96 17.21 North Carolina 0.55 0.93 North Dakota 0.14 0.28 Ohio 3.99 7.92 Oklahoma 1.70 2.46 Oregon 0.65 0.65 Pennsylvania 13.51 15.93 Rhode Island 0.02 0.08 South Carolina 0.19 0.21 South Dakota 0.08 0.12 Tennessee 1.67 0.90 Texas 2.44 2.95 Utah 1.59 1.66 Vermont 0.01 0.05 Virginia 5.29 4.70 Washington 0.22 0.09 West Virginia 2.99 3.11 Wisconsin 0.56 2.59 Wyoming 1.22 1.2223 5. References

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## Appendix A

2011 Model Perfor mance EvaluationA-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 overA-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 theA-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 17.1A-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 aA-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 aA-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 aA-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 aA-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 aA-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 060251003A-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-Ho ur Ozone Design Values at Projected 2017 Nonattainment and Maintenance-Only SitesThis appendix contains tables with th e projected ozone contributions from 2017 anthropogenic NOx and VOC emissions in each stat e to each projected 2017 nonattainment site and each maintenance-only site 1 . In addition, we have included the contributions from each of the other categories tr acked in the contribu tion modeling including po int source emissions on Tribal lands, anthropogenic emissi ons in Canada and Mexico, emi ssions from Offshore sources, Fires, Initial and Boundary concentrations, and Biogenics. The contributio n tables are presented below for the following groupings of monitoring sites:

- Contributions to 2017 nonattainment and maintenance-only sites in the East

- Contributions to 2017 nonattainment and mainte nance-only sites in the West (data for sites in Arizona and Colorado are provided first, followed by the data for sites in California) 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 containing the projected 2017 average and maximum design values. Next we provi de the contributions fr om each state and the District of Columbia, individually. Lastly, we pr ovide the contributions from the Tribal, Canada and Mexico, Offshore, Fires, Initial and Boundary concentrations, and Biogenics categories. The units of the 2017 design values and contributions are “ppb”. No te 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.

1 Nonattainment sites have projected 2017 average design values > 76.0 ppb. Maintenance-only sites have projected 2017 average design values < 76.0 ppb, put projected 2017 maximum design values > 76.0 ppb.Contributions to 2017 Nonattainment and Maintenance-Only Sites in the East (Part 1)MonitorID State County2017AverageDV2017MaximumDV AL AZ AR CA CO CT DE DC FL GA ID IL IN IA KS KY LA ME MD MA MI MN MS MO MT90010017 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 0.0690013007 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 0.0390019003 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 0.0490099002 Connecticut NewHaven 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 0.05211110067 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 0.17211850004 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 0.15240053001 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 0.130.47 0.05240251001 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 0.06260050003 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 0.01261630019 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 0.280.13340071001 NewJersey 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 0.07340150002 NewJersey 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 0.06340230011 NewJersey 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 0.04340290006 NewJersey 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 0.07360810124 NewYork 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 0.09360850067 NewYork 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 0.06361030002 NewYork 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 0.07390610006 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 0.10420031005 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 0.13421010024 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 0.04480391004 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 0.15480850005 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 0.03481130069 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 0.03481130075 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 0.03481210034 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 0.09481211032 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 0.12482010024 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 0.05482010026 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 0.11482010055 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 0.14482011034 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 0.10482011039 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 0.11482011050 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 0.03484390075 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 0.12484392003 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 0.10484393009 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 0.08484393011 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 0.13551170006 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 0.03Contributions to 2017 Nonattainment and Maintenance-Only Sites in the East (Part 2)MonitorID State County2017AverageDV2017MaximumDV NE NV NH NJ NM NY NC ND OH OK OR PA RI SC SD TN TX UT VT VA WA WV WI WY90010017 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 0.1690013007 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 0.1290019003 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 0.2090099002 Connecticut NewHaven 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 0.19211110067 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 0.27211850004 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 0.24240053001 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 0.17240251001 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 0.20260050003 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 0.12261630019 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 0.20340071001 NewJersey 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 0.24340150002 NewJersey 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 0.19340230011 NewJersey 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 0.17340290006 NewJersey 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 0.22360810124 NewYork 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 0.24360850067 NewYork 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 0.22361030002 NewYork 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 0.26390610006 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 0.22420031005 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 2.470.33 0.21421010024 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 0.16480391004 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 0.30480850005 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 0.16481130069 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 0.14481130075 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 0.16481210034 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 0.28481211032 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 0.33482010024 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 0.14482010026 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 0.23482010055 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 0.28482011034 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 0.21482011039 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 0.21482011050 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 0.09484390075 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 0.36484392003 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 0.36484393009 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 0.26484393011 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 0.30551170006 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 0.11Contributions to 2017 Nonattainment and Maintenance-Only Sites in the East (Part 3)MonitorID State County2017AverageDV2017MaximumDV TribalCanada&Mexico Offshore FiresInitial&BoundaryBiogenics90010017 Connecticut Fairfield 75.8 78.4 0.02 1.66 0.61 0.18 15.66 4.0890013007 Connecticut Fairfield 77.1 81.4 0.02 1.61 1.30 0.20 16.90 4.1790019003 Connecticut Fairfield 78.0 81.1 0.03 1.48 1.00 0.22 16.73 4.4990099002 Connecticut NewHaven 77.2 80.2 0.02 1.67 2.33 0.21 16.52 4.33211110067 Kentucky Jefferson 75.8 78.6 0.01 0.52 0.05 0.20 21.86 6.71211850004 Kentucky Oldham 73.7 77.3 0.02 0.41 0.14 0.22 18.81 7.04240053001 Maryland Baltimore 73.2 76.2 0.03 0.68 0.51 0.29 15.67 5.04240251001 Maryland Harford 81.3 84.0 0.04 0.73 0.44 0.30 16.63 6.08260050003 Michigan Allegan 75.5 78.5 0.04 0.28 0.34 0.73 11.71 9.01261630019 Michigan Wayne 74.0 76.2 0.01 4.89 0.08 0.17 24.10 5.57340071001 NewJersey Camden 74.2 78.1 0.04 1.73 0.55 0.46 14.07 6.14340150002 NewJersey Gloucester 75.1 77.5 0.03 1.05 0.62 0.30 15.90 5.75340230011 NewJersey Middlesex 73.0 76.3 0.03 1.95 0.64 0.25 16.30 5.01340290006 NewJersey Ocean 73.9 76.6 0.03 2.28 0.75 0.31 16.60 5.04360810124 NewYork Queens 75.7 77.6 0.02 2.00 1.54 0.21 17.29 5.14360850067 NewYork Richmond 76.3 77.8 0.03 1.74 1.21 0.29 16.47 5.51361030002 NewYork Suffolk 79.2 80.8 0.03 1.67 1.31 0.31 16.51 5.12390610006 Ohio Hamilton 76.3 79.1 0.03 0.76 0.21 0.58 17.48 7.45420031005 Pennsylvania Allegheny 75.3 76.5 0.02 1.38 0.11 0.38 21.40 5.03421010024 Pennsylvania Philadelphia 75.1 78.4 0.03 0.48 0.71 0.37 16.52 5.62480391004 Texas Brazoria 81.4 82.3 0.03 0.35 0.99 1.62 19.53 6.57480850005 Texas Collin 74.9 76.0 0.02 0.25 2.23 0.90 19.19 5.62481130069 Texas Dallas 74.0 78.0 0.02 0.20 1.93 1.20 22.27 5.78481130075 Texas Dallas 75.8 76.7 0.02 0.30 1.76 1.14 22.41 6.02481210034 Texas Denton 76.9 79.4 0.03 0.40 1.57 0.95 24.23 6.50481211032 Texas Denton 75.1 76.3 0.02 0.35 0.87 0.93 23.75 6.52482010024 Texas Harris 75.9 78.5 0.02 0.18 4.14 0.73 27.62 2.68482010026 Texas Harris 73.5 76.1 0.01 0.24 2.95 2.36 21.17 4.44482010055 Texas Harris 75.4 77.0 0.02 0.31 1.46 1.55 18.26 4.81482011034 Texas Harris 76.8 77.8 0.01 0.22 4.08 1.58 22.78 4.22482011039 Texas Harris 78.2 80.2 0.01 0.29 2.50 2.70 21.03 5.31482011050 Texas Harris 74.6 76.2 0.01 0.31 3.95 1.84 17.62 4.39484390075 Texas Tarrant 75.5 76.4 0.03 0.58 0.99 2.06 22.71 6.49484392003 Texas Tarrant 79.6 82.1 0.03 0.44 1.62 1.33 23.94 6.28484393009 Texas Tarrant 78.6 78.6 0.02 0.34 1.62 1.40 23.61 5.78484393011 Texas Tarrant 74.5 76.6 0.03 0.69 0.65 1.54 22.51 6.25551170006 Wisconsin Sheboygan 77.0 79.4 0.03 0.47 0.67 0.56 14.91 7.79Contributions to 2017 Nonattainment and Maintenance-Only Sites inArizona and ColoradoMonitorID State County2017AverageDV2017MaximumDV AL AZ AR CA CO CT DE DC FL GA ID IL IN IA KS KY LA ME MD MA MI MN MS MO MT40131004 Arizona Maricopa 75.0 76.2 0.00 31.57 0.00 3.44 0.02 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0080050002 Colorado Arapahoe 74.4 76.6 0.00 0.32 0.00 1.16 25.84 0.00 0.00 0.00 0.00 0.00 0.35 0.00 0.00 0.00 0.23 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.0280350004 Colorado Douglas 76.0 78.1 0.00 0.39 0.00 1.23 26.56 0.00 0.00 0.00 0.00 0.00 0.23 0.00 0.00 0.00 0.39 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.0380590006 Colorado Jefferson 76.3 78.8 0.02 0.48 0.03 1.75 26.97 0.00 0.00 0.00 0.01 0.01 0.06 0.00 0.00 0.00 0.05 0.00 0.07 0.00 0.00 0.00 0.00 0.00 0.02 0.01 0.0480590011 Colorado Jefferson 75.8 78.9 0.01 0.41 0.03 1.93 27.77 0.00 0.00 0.00 0.01 0.01 0.13 0.00 0.00 0.00 0.16 0.00 0.05 0.00 0.00 0.00 0.00 0.00 0.01 0.02 0.01MonitorID State County2017AverageDV2017MaximumDV NE NV NH NJ NM NY NC ND OH OK OR PA RI SC SD TN TX UT VT VA WA WV WI WY40131004 Arizona Maricopa 75.0 76.2 0.00 0.27 0.00 0.00 0.06 0.00 0.00 0.00 0.00 0.02 0.07 0.00 0.00 0.00 0.00 0.00 0.16 0.11 0.00 0.00 0.03 0.00 0.00 0.0180050002 Colorado Arapahoe 74.4 76.6 0.32 0.46 0.00 0.00 0.09 0.00 0.00 0.00 0.00 0.11 0.17 0.00 0.00 0.00 0.01 0.00 0.16 1.66 0.00 0.00 0.05 0.00 0.00 1.2280350004 Colorado Douglas 76.0 78.1 0.51 0.49 0.00 0.00 0.13 0.00 0.00 0.05 0.00 0.15 0.12 0.00 0.00 0.00 0.05 0.00 0.35 1.59 0.00 0.00 0.04 0.00 0.00 1.2280590006 Colorado Jefferson 76.3 78.8 0.21 0.38 0.00 0.00 1.05 0.00 0.00 0.00 0.00 0.14 0.04 0.00 0.00 0.00 0.01 0.01 1.58 0.87 0.00 0.00 0.03 0.00 0.00 0.6780590011 Colorado Jefferson 75.8 78.9 0.23 0.50 0.00 0.00 0.54 0.00 0.00 0.00 0.00 0.17 0.10 0.00 0.00 0.00 0.01 0.01 1.15 1.34 0.00 0.00 0.02 0.00 0.00 0.73MonitorID State County2017AverageDV2017MaximumDV TribalCanada&Mexico Offshore FiresInitial&BoundaryBiogenics40131004 Arizona Maricopa 75.0 76.2 0.09 1.95 0.27 0.19 33.90 2.6880050002 Colorado Arapahoe 74.4 76.6 0.24 0.23 0.07 0.32 37.16 4.0680350004 Colorado Douglas 76.0 78.1 0.22 0.34 0.07 0.55 36.46 4.6280590006 Colorado Jefferson 76.3 78.8 0.33 0.78 0.17 0.74 34.04 5.5580590011 Colorado Jefferson 75.8 78.9 0.32 0.51 0.14 0.41 33.79 5.09Contributions to 2017 Nonattainment and Maintenance-Only Sites in the California (Part 1)MonitorID State County2017AverageDV2017MaximumDV AL AZAR CA CO CT DE DC FL GAID IL IN IAKS KY LAME MD MA MI MN MS MO MT60170020 California ElDorado 75.1 76.3 0.00 0.02 0.00 39.47 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060190007 California Fresno 89.0 89.3 0.00 0.04 0.00 42.10 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060190011 California Fresno 87.6 90.4 0.00 0.05 0.00 41.75 0.00 0.00 0.00 0.00 0.00 0.00 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060190242 California Fresno 87.1 90.3 0.00 0.17 0.00 37.42 0.00 0.00 0.00 0.00 0.00 0.00 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060194001 California Fresno 84.2 85.4 0.00 0.08 0.00 41.49 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060195001 California Fresno 90.6 92.5 0.00 0.08 0.00 44.66 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060251003 California Imperial 79.3 80.3 0.01 1.78 0.00 20.02 0.14 0.00 0.00 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.02 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.010.00 0.0160290007 California Kern 86.2 90.2 0.00 0.08 0.00 39.94 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.000.0060290008 California Kern 80.6 82.2 0.00 0.35 0.00 32.63 0.03 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.000.0060290011 California Kern 76.2 77.1 0.00 0.56 0.00 22.62 0.04 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.000.0060290014 California Kern 82.8 84.0 0.00 0.02 0.00 39.12 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.000.0060290232 California Kern 82.2 83.8 0.00 0.04 0.00 38.83 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.000.0060295002 California Kern 84.5 85.5 0.00 0.14 0.00 38.93 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.000.0060296001 California Kern 79.7 81.3 0.00 0.11 0.00 36.74 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.000.0060311004 California Kings 81.1 83.9 0.00 0.24 0.00 36.59 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060370002 California LosAngeles 79.0 81.0 0.00 0.47 0.00 42.47 0.04 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060370016 California LosAngeles 92.8 95.8 0.00 0.56 0.00 49.89 0.05 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060371002 California LosAngeles 77.1 78.1 0.00 0.21 0.00 43.28 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060371201 California LosAngeles 87.9 87.9 0.00 0.42 0.00 45.64 0.04 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060371701 California LosAngeles 82.2 83.2 0.00 0.43 0.00 44.50 0.04 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060372005 California LosAngeles 78.1 80.6 0.00 0.42 0.00 41.87 0.04 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060376012 California LosAngeles 94.5 96.2 0.00 0.55 0.00 47.48 0.06 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060379033 California LosAngeles 86.0 86.9 0.00 0.86 0.00 36.18 0.07 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.01 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060390004 California Madera 75.3 76.9 0.00 0.15 0.00 32.35 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060392010 California Madera 79.8 80.8 0.00 0.14 0.00 33.99 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060470003 California Merced 78.1 79.3 0.00 0.08 0.00 36.14 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060610003 California Placer 75.4 77.2 0.00 0.02 0.00 39.63 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060610006 California Placer 78.2 80.0 0.00 0.01 0.00 42.51 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060650004 California Riverside 82.3 82.3 0.00 0.47 0.00 44.40 0.05 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060650012 California Riverside 93.5 95.1 0.00 0.35 0.00 47.30 0.02 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060651016 California Riverside 95.7 96.0 0.00 0.40 0.00 46.77 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060652002 California Riverside 79.8 80.5 0.01 0.74 0.00 30.50 0.07 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.01 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.0060655001 California Riverside 87.6 88.2 0.01 0.46 0.01 33.10 0.08 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.02 0.00 0.05 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.0060656001 California Riverside 88.1 91.9 0.00 0.38 0.00 47.41 0.02 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060658001 California Riverside 93.3 94.3 0.00 0.45 0.00 50.64 0.04 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060658005 California Riverside 89.2 90.4 0.00 0.43 0.00 48.41 0.04 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060659001 California Riverside 82.7 85.2 0.00 0.30 0.00 44.36 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060670006 California Sacramento 74.0 76.1 0.00 0.00 0.00 41.18 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060670012 California Sacramento 85.7 87.3 0.00 0.01 0.00 47.44 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060675003 California Sacramento 80.5 82.0 0.00 0.01 0.00 44.43 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060710005 California SanBernardino 103.6 105.6 0.00 0.65 0.00 52.27 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060710012 California SanBernardino 91.8 93.8 0.00 0.73 0.00 33.10 0.05 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060710306 California SanBernardino 81.2 82.4 0.00 0.27 0.00 37.84 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060711004 California SanBernardino 94.3 95.6 0.00 0.62 0.00 48.53 0.05 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.0060712002 California SanBernardino 99.5 101.5 0.00 0.50 0.00 52.75 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060714001 California SanBernardino 92.3 95.0 0.00 0.47 0.00 44.68 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.0060714003 California SanBernardino 101.8 103.8 0.00 0.49 0.00 55.33 0.04 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060719002 California SanBernardino 88.0 89.6 0.01 0.51 0.00 35.03 0.06 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.01 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.0060719004 California SanBernardino 95.7 96.0 0.00 0.46 0.00 52.01 0.03 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060731006 California SanDiego 76.6 77.6 0.00 0.42 0.00 30.56 0.04 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060773005 California SanJoaquin 75.9 76.8 0.00 0.01 0.00 38.28 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060990006 California Stanislaus 83.0 83.9 0.00 0.06 0.00 44.21 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0061070006 California Tulare 77.0 80.1 0.00 0.06 0.00 19.96 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0061070009 California Tulare 87.3 88.5 0.00 0.06 0.00 33.13 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0061072002 California Tulare 78.6 81.4 0.00 0.03 0.00 39.42 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0061072010 California Tulare 82.7 83.6 0.00 0.04 0.00 38.19 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0061112002 California Ventura 78.3 80.2 0.00 0.63 0.00 37.54 0.06 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Contributions to 2017 Nonattainment and Maintenance-Only Sites in the California (Part 2)MonitorID State County2017AverageDV2017MaximumDV NE NV NH NJ NM NY NC ND OH OK OR PA RI SC SD TN TX UT VT VA WA WV WI WY60170020 California ElDorado 75.1 76.3 0.00 0.12 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.64 0.00 0.00 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.09 0.00 0.00 0.0060190007 California Fresno 89.0 89.3 0.00 0.75 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.44 0.00 0.00 0.00 0.00 0.00 0.02 0.28 0.00 0.00 0.07 0.00 0.00 0.0260190011 California Fresno 87.6 90.4 0.00 0.78 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.40 0.00 0.00 0.00 0.00 0.00 0.02 0.30 0.00 0.00 0.07 0.00 0.00 0.0260190242 California Fresno 87.1 90.3 0.00 0.84 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.32 0.00 0.00 0.00 0.00 0.00 0.02 0.33 0.00 0.00 0.06 0.00 0.00 0.0260194001 California Fresno 84.2 85.4 0.00 0.42 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.22 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.0060195001 California Fresno 90.6 92.5 0.00 0.60 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.28 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.00 0.00 0.0060251003 California Imperial 79.3 80.3 0.01 0.22 0.00 0.00 0.29 0.00 0.00 0.00 0.00 0.07 0.05 0.00 0.00 0.00 0.00 0.00 0.41 0.15 0.00 0.00 0.03 0.00 0.00 0.0860290007 California Kern 86.2 90.2 0.00 0.49 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.36 0.00 0.00 0.00 0.00 0.00 0.01 0.10 0.00 0.00 0.12 0.00 0.00 0.0060290008 California Kern 80.6 82.2 0.00 0.48 0.00 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.18 0.00 0.00 0.00 0.00 0.00 0.02 0.05 0.00 0.00 0.02 0.00 0.00 0.0160290011 California Kern 76.2 77.1 0.00 0.35 0.00 0.00 0.09 0.00 0.00 0.00 0.00 0.01 0.16 0.00 0.00 0.00 0.00 0.00 0.13 0.06 0.00 0.00 0.04 0.00 0.00 0.0260290014 California Kern 82.8 84.0 0.00 0.36 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.27 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.0060290232 California Kern 82.2 83.8 0.00 0.46 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.27 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.0060295002 California Kern 84.5 85.5 0.00 0.45 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.14 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060296001 California Kern 79.7 81.3 0.00 0.44 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.20 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0060311004 California Kings 81.1 83.9 0.00 0.53 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.22 0.00 0.00 0.00 0.00 0.00 0.02 0.01 0.00 0.00 0.01 0.00 0.00 0.0060370002 California LosAngeles 79.0 81.0 0.00 0.12 0.00 0.00 0.08 0.00 0.00 0.00 0.00 0.02 0.15 0.00 0.00 0.00 0.00 0.00 0.20 0.06 0.00 0.00 0.03 0.00 0.00 0.0260370016 California LosAngeles 92.8 95.8 0.00 0.14 0.00 0.00 0.09 0.00 0.00 0.00 0.00 0.03 0.18 0.00 0.00 0.00 0.00 0.00 0.24 0.07 0.00 0.00 0.04 0.00 0.00 0.0360371002 California LosAngeles 77.1 78.1 0.00 0.11 0.00 0.00 0.05 0.00 0.00 0.00 0.00 0.01 0.18 0.00 0.00 0.00 0.00 0.00 0.16 0.03 0.00 0.00 0.05 0.00 0.00 0.0160371201 California LosAngeles 87.9 87.9 0.00 0.20 0.00 0.00 0.06 0.00 0.00 0.00 0.00 0.02 0.16 0.00 0.00 0.00 0.00 0.00 0.16 0.09 0.00 0.00 0.04 0.00 0.00 0.0260371701 California LosAngeles 82.2 83.2 0.00 0.12 0.00 0.00 0.07 0.00 0.00 0.00 0.00 0.02 0.18 0.00 0.00 0.00 0.00 0.00 0.18 0.06 0.00 0.00 0.04 0.00 0.00 0.0260372005 California LosAngeles 78.1 80.6 0.00 0.16 0.00 0.00 0.06 0.00 0.00 0.00 0.00 0.02 0.10 0.00 0.00 0.00 0.00 0.00 0.16 0.09 0.00 0.00 0.02 0.00 0.00 0.0260376012 California LosAngeles 94.5 96.2 0.00 0.26 0.00 0.00 0.09 0.00 0.00 0.00 0.00 0.03 0.18 0.00 0.00 0.00 0.00 0.00 0.23 0.09 0.00 0.00 0.05 0.00 0.00 0.0360379033 California LosAngeles 86.0 86.9 0.00 0.30 0.00 0.00 0.12 0.00 0.00 0.00 0.00 0.03 0.17 0.00 0.00 0.00 0.00 0.00 0.22 0.09 0.00 0.00 0.05 0.00 0.00 0.0460390004 California Madera 75.3 76.9 0.00 0.73 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.28 0.00 0.00 0.00 0.00 0.00 0.02 0.28 0.00 0.00 0.05 0.00 0.00 0.0260392010 California Madera 79.8 80.8 0.00 0.76 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.35 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.04 0.00 0.00 0.0060470003 California Merced 78.1 79.3 0.00 0.36 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.35 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.04 0.00 0.00 0.0060610003 California Placer 75.4 77.2 0.00 0.12 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.65 0.00 0.00 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.09 0.00 0.00 0.0060610006 California Placer 78.2 80.0 0.00 0.45 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.65 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.22 0.00 0.00 0.0060650004 California Riverside 82.3 82.3 0.00 0.19 0.00 0.00 0.07 0.00 0.00 0.00 0.00 0.02 0.18 0.00 0.00 0.00 0.00 0.00 0.18 0.08 0.00 0.00 0.04 0.00 0.000.0360650012 California Riverside 93.5 95.1 0.00 0.26 0.00 0.00 0.04 0.00 0.00 0.00 0.00 0.02 0.15 0.00 0.00 0.00 0.00 0.00 0.14 0.09 0.00 0.00 0.05 0.00 0.000.0260651016 California Riverside 95.7 96.0 0.00 0.33 0.00 0.00 0.04 0.00 0.00 0.00 0.00 0.02 0.18 0.00 0.00 0.00 0.00 0.00 0.16 0.03 0.00 0.00 0.06 0.00 0.000.0160652002 California Riverside 79.8 80.5 0.00 0.44 0.00 0.00 0.11 0.00 0.00 0.00 0.00 0.05 0.08 0.00 0.00 0.00 0.00 0.00 0.33 0.08 0.00 0.00 0.02 0.00 0.000.0460655001 California Riverside 87.6 88.2 0.01 0.28 0.00 0.00 0.10 0.00 0.00 0.00 0.00 0.09 0.10 0.00 0.00 0.00 0.00 0.00 0.45 0.07 0.00 0.00 0.03 0.00 0.000.0560656001 California Riverside 88.1 91.9 0.00 0.14 0.00 0.00 0.04 0.00 0.00 0.00 0.00 0.01 0.18 0.00 0.00 0.00 0.00 0.00 0.07 0.06 0.00 0.00 0.06 0.00 0.000.0160658001 California Riverside 93.3 94.3 0.00 0.15 0.00 0.00 0.07 0.00 0.00 0.00 0.00 0.02 0.21 0.00 0.00 0.00 0.00 0.00 0.18 0.06 0.00 0.00 0.05 0.00 0.000.0260658005 California Riverside 89.2 90.4 0.00 0.15 0.00 0.00 0.07 0.00 0.00 0.00 0.00 0.02 0.20 0.00 0.00 0.00 0.00 0.00 0.17 0.05 0.00 0.00 0.04 0.00 0.000.0260659001 California Riverside 82.7 85.2 0.00 0.10 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.16 0.00 0.00 0.00 0.00 0.00 0.05 0.05 0.00 0.00 0.04 0.00 0.000.0160670006 California Sacramento 74.0 76.1 0.00 0.43 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.47 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.06 0.00 0.00 0.0060670012 California Sacramento 85.7 87.3 0.00 0.22 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.64 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.16 0.00 0.00 0.0060675003 California Sacramento 80.5 82.0 0.00 0.56 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.44 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.00 0.00 0.0060710005 California SanBernardino 103.6 105.6 0.00 0.25 0.00 0.00 0.10 0.00 0.00 0.00 0.00 0.03 0.17 0.00 0.00 0.00 0.00 0.00 0.23 0.04 0.00 0.00 0.04 0.00 0.00 0.0260710012 California SanBernardino 91.8 93.8 0.00 0.17 0.00 0.00 0.09 0.00 0.00 0.00 0.00 0.01 0.10 0.00 0.00 0.00 0.00 0.00 0.09 0.06 0.00 0.00 0.01 0.00 0.00 0.0360710306 California SanBernardino 81.2 82.4 0.00 0.10 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.03 0.19 0.00 0.00 0.00 0.00 0.00 0.18 0.02 0.00 0.00 0.05 0.00 0.00 0.0260711004 California SanBernardino 94.3 95.6 0.00 0.16 0.00 0.00 0.10 0.00 0.00 0.00 0.00 0.02 0.19 0.00 0.00 0.00 0.00 0.00 0.22 0.07 0.00 0.00 0.04 0.00 0.00 0.0360712002 California SanBernardino 99.5 101.5 0.00 0.20 0.00 0.00 0.08 0.00 0.00 0.00 0.00 0.02 0.22 0.00 0.00 0.00 0.00 0.00 0.19 0.03 0.00 0.00 0.05 0.00 0.00 0.0260714001 California SanBernardino 92.3 95.0 0.00 0.26 0.00 0.00 0.09 0.00 0.00 0.00 0.00 0.04 0.15 0.00 0.00 0.00 0.00 0.00 0.26 0.03 0.00 0.00 0.04 0.00 0.00 0.0260714003 California SanBernardino 101.8 103.8 0.00 0.19 0.00 0.00 0.07 0.00 0.00 0.00 0.00 0.02 0.22 0.00 0.00 0.00 0.00 0.00 0.17 0.09 0.00 0.00 0.07 0.00 0.00 0.0260719002 California SanBernardino 88.0 89.6 0.00 0.12 0.00 0.00 0.10 0.00 0.00 0.00 0.00 0.06 0.15 0.00 0.00 0.00 0.00 0.00 0.37 0.04 0.00 0.00 0.05 0.00 0.00 0.0460719004 California SanBernardino 95.7 96.0 0.00 0.18 0.00 0.00 0.07 0.00 0.00 0.00 0.00 0.02 0.21 0.00 0.00 0.00 0.00 0.00 0.16 0.09 0.00 0.00 0.06 0.00 0.00 0.0260731006 California SanDiego 76.6 77.6 0.00 0.09 0.00 0.00 0.07 0.00 0.00 0.00 0.00 0.01 0.20 0.00 0.00 0.00 0.00 0.00 0.10 0.05 0.00 0.00 0.05 0.00 0.00 0.0260773005 California SanJoaquin 75.9 76.8 0.00 0.23 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.48 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.00 0.00 0.0060990006 California Stanislaus 83.0 83.9 0.00 0.24 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.35 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.04 0.00 0.00 0.0061070006 California Tulare 77.0 80.1 0.00 0.08 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.14 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.02 0.00 0.00 0.0061070009 California Tulare 87.3 88.5 0.00 0.21 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.22 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.04 0.00 0.00 0.0061072002 California Tulare 78.6 81.4 0.00 0.16 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.39 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.19 0.00 0.00 0.0061072010 California Tulare 82.7 83.6 0.00 0.35 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.44 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.20 0.00 0.00 0.0061112002 California Ventura 78.3 80.2 0.00 0.23 0.00 0.00 0.07 0.00 0.00 0.00 0.00 0.01 0.09 0.00 0.00 0.00 0.00 0.00 0.12 0.12 0.00 0.00 0.03 0.00 0.00 0.03Contributions to 2017 Nonattainment and Maintenance-Only Sites in the California (Part 3)MonitorID State County2017AverageDV2017MaximumDV TribalCanada&Mexico Offshore FiresInitial&BoundaryBiogenics60170020 California ElDorado 75.1 76.3 0.00 0.16 1.00 0.36 26.27 6.8260190007 California Fresno 89.0 89.3 0.00 0.09 0.98 1.02 30.52 12.5160190011 California Fresno 87.6 90.4 0.00 0.09 0.88 0.99 30.02 12.0860190242 California Fresno 87.1 90.3 0.01 0.16 0.97 1.75 31.05 13.8060194001 California Fresno 84.2 85.4 0.00 0.11 1.20 0.97 24.84 14.7760195001 California Fresno 90.6 92.5 0.00 0.09 0.88 0.88 29.45 13.5660251003 California Imperial 79.3 80.3 0.07 9.32 0.87 0.30 41.65 3.6060290007 California Kern 86.2 90.2 0.00 0.13 1.03 1.82 29.36 12.6560290008 California Kern 80.6 82.2 0.02 0.63 1.35 1.46 31.52 11.7060290011 California Kern 76.2 77.1 0.02 1.28 1.19 0.28 44.91 4.3060290014 California Kern 82.8 84.0 0.00 0.04 1.36 1.08 27.21 13.2660290232 California Kern 82.2 83.8 0.00 0.04 1.24 1.49 27.31 12.4360295002 California Kern 84.5 85.5 0.00 0.13 0.68 1.46 28.38 14.1160296001 California Kern 79.7 81.3 0.00 0.09 1.18 0.84 25.10 14.9560311004 California Kings 81.1 83.9 0.00 0.18 1.05 1.13 27.30 13.7160370002 California LosAngeles 79.0 81.0 0.02 1.50 3.10 0.75 27.14 2.6560370016 California LosAngeles 92.8 95.8 0.02 1.76 3.64 0.88 31.88 3.1160371002 California LosAngeles 77.1 78.1 0.01 0.78 3.79 0.83 24.22 3.2260371201 California LosAngeles 87.9 87.9 0.02 1.15 4.82 1.22 29.37 4.2860371701 California LosAngeles 82.2 83.2 0.02 1.50 3.49 0.77 27.88 2.6960372005 California LosAngeles 78.1 80.6 0.02 1.03 3.65 0.99 26.31 3.0060376012 California LosAngeles 94.5 96.2 0.03 1.57 3.94 1.06 33.83 4.8160379033 California LosAngeles 86.0 86.9 0.03 1.91 2.50 0.45 38.07 4.7360390004 California Madera 75.3 76.9 0.00 0.14 0.83 1.51 26.85 11.9360392010 California Madera 79.8 80.8 0.00 0.09 0.87 1.49 29.74 12.2360470003 California Merced 78.1 79.3 0.00 0.40 1.69 1.62 28.83 8.5260610003 California Placer 75.4 77.2 0.00 0.16 1.01 0.36 26.37 6.8560610006 California Placer 78.2 80.0 0.00 0.15 0.90 0.84 25.25 7.1060650004 California Riverside 82.3 82.3 0.02 1.73 3.78 0.81 27.29 2.7860650012 California Riverside 93.5 95.1 0.02 1.43 2.66 0.52 36.58 3.7060651016 California Riverside 95.7 96.0 0.01 1.70 2.81 0.40 39.00 3.6060652002 California Riverside 79.8 80.5 0.02 2.02 1.40 0.72 39.12 3.8060655001 California Riverside 87.6 88.2 0.02 1.94 1.99 0.65 44.61 3.3160656001 California Riverside 88.1 91.9 0.01 1.39 4.14 0.75 30.18 3.1360658001 California Riverside 93.3 94.3 0.02 1.77 4.08 0.82 31.54 3.0160658005 California Riverside 89.2 90.4 0.02 1.69 3.90 0.78 30.16 2.8860659001 California Riverside 82.7 85.2 0.01 1.17 4.36 0.56 28.73 2.6560670006 California Sacramento 74.0 76.1 0.00 0.04 0.70 0.39 23.30 7.3760670012 California Sacramento 85.7 87.3 0.00 0.12 0.87 1.04 27.77 7.3460675003 California Sacramento 80.5 82.0 0.00 0.03 0.41 1.28 25.88 7.3860710005 California SanBernardino 103.6 105.6 0.02 1.56 2.69 0.59 40.98 3.7560710012 California SanBernardino 91.8 93.8 0.03 0.70 1.01 0.33 52.48 2.6760710306 California SanBernardino 81.2 82.4 0.00 0.96 1.79 0.34 35.93 3.2660711004 California SanBernardino 94.3 95.6 0.03 1.86 3.43 0.80 34.85 3.1060712002 California SanBernardino 99.5 101.5 0.01 1.71 3.64 0.81 35.77 3.2960714001 California SanBernardino 92.3 95.0 0.01 1.50 2.19 0.49 38.42 3.4260714003 California SanBernardino 101.8 103.8 0.02 1.76 3.65 0.94 34.87 3.6660719002 California SanBernardino 88.0 89.6 0.02 1.99 2.20 0.43 43.14 3.4560719004 California SanBernardino 95.7 96.0 0.02 1.66 3.43 0.88 32.78 3.4460731006 California SanDiego 76.6 77.6 0.02 10.95 4.45 0.45 26.93 2.0560773005 California SanJoaquin 75.9 76.8 0.00 0.06 1.40 1.03 29.22 5.0960990006 California Stanislaus 83.0 83.9 0.00 0.38 1.95 2.00 25.58 8.1361070006 California Tulare 77.0 80.1 0.00 0.24 0.64 4.65 42.60 8.5061070009 California Tulare 87.3 88.5 0.00 0.22 0.93 2.69 35.19 14.5061072002 California Tulare 78.6 81.4 0.00 0.12 1.21 1.08 21.40 14.5361072010 California Tulare 82.7 83.6 0.00 0.07 1.23 1.19 24.73 16.2261112002 California Ventura 78.3 80.2 0.03 1.43 3.74 1.24 28.80 3.97