Air Quality Modeling Technical Support Document 

for the 

2008 Ozone NAAQS Cross-State Air Pollution Rule

Proposal

Office of Air Quality Planning and Standards

United States Environmental Protection Agency

November 2015
1

1. Introduction

In this technical support document (TSD) we describe the air quality modeling performed 

to support the proposed Cross-State Air Pollution Rule for the 2008 ozone National Ambient Air 

Quality Standards (NAAQS)

1

. In this document, air quality modeling is used to project ozone 

concentrations at individual monitoring sites to 20172 and to estimate state-by-state contributions 

to those 2017 concentrations. The projected 2017 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 states (i.e., in downwind states). 

The remaining sections of this TSD are as follows. Section 2 describes the air quality 

modeling platform and the evaluation of model predictions using measured concentrations. 

Section 3 defines the procedures for projecting ozone design value concentrations to 2017 and 

the approach for identifying monitoring sites with projected nonattainment and/or maintenance 

problems. Section 4 describes (1) the source contribution (i.e., apportionment) modeling and (2) 

the procedures for quantifying contributions to individual monitoring sites including 

nonattainment and/or maintenance sites. For questions about the information in this TSD please 

contact Norm Possiel at possiel.norm@epa.gov or (919) 541-5692. An electronic copy of the 

2009 – 2013 base period and projected 2017 ozone design values and 2017 ozone contributions 

based on the proposal modeling can be obtained from docket EPA-HQ-OAR-2015-0500. The 

ozone design values for individual monitoring sites are in docket item EPA-HQ-OAR-2015-

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

Electronic copies of the ozone design values and contributions can also be obtained at

www.epa.gov/airtransport.

3

 1 The EPA revised the levels of the primary and secondary 8-hour ozone standards to 0.075 parts per million (ppm). 

40 CFR 50.15. 73 FR 16436 (March 27, 2008).

2 2017 was selected as the future year analytic base case because 2017 corresponds to the attainment date for ozone 

nonattainment areas classified as Moderate.

3 Note that the air quality modeling used for the proposed rule was made available for public review as part of the 

August 4, 2015 Notice of Data Availability (80 FR 46271).
2

2. Air Quality Modeling Platform

EPA has developed a 2011-based air quality modeling platform which includes

emissions, meteorology and other inputs for 2011. The 2011 base year emissions were projected 

to a future year base case scenario, 2017. The 2011 modeling platform and projected 2017

emissions were used to drive the 2011 base year and 2017 base case air quality model 

simulations.4 The base year 2011 platform was chosen in part because it represents the most 

recent, complete set of base year emissions information currently available for national-scale air 

quality modeling. In addition, the meteorological conditions during the summer of 2011 were 

generally conducive for ozone formation across much of the U.S., particularly the eastern U.S. 

2.1 Air Quality Model Configuration

The photochemical model simulations performed for this ozone transport assessment

used the Comprehensive Air Quality Model with Extensions (CAMx version 6.11) (Environ, 

2014). CAMx is a three-dimensional grid-based Eulerian air quality model designed to simulate 

the formation and fate of oxidant precursors, primary and secondary particulate matter 

concentrations, and deposition over regional and urban spatial scales (e.g., the contiguous U.S.). 

Consideration of the different processes (e.g., transport and deposition) that affect primary 

(directly emitted) and secondary (formed by atmospheric processes) pollutants at the regional 

scale in different locations is fundamental to understanding and assessing the effects of 

emissions on air quality concentrations. CAMx was applied with the carbon-bond 6 revision 2 

(CB6r2) gas-phase chemistry mechanism5 (Ruiz and Yarwood, 2013) and the Zhang dry 

deposition scheme (Zhang, et al., 2003).

Figure 2-1 shows the geographic extent of the modeling domain that was used for air 

quality modeling in this analysis. The domain covers the 48 contiguous states along with the 

southern portions of Canada and the northern portions of Mexico. This modeling domain 

contains 25 vertical layers with a top at about 17,550 meters, or 50 millibars (mb), and horizontal 

 4 EPA also used the 2011-based air quality modeling platform to perform a 2017 “illustrative” control case air 

quality model simulation to inform (1) the analysis to quantify upwind state emissions that significantly contribute 

to nonattainment or interfere with maintenance of the NAAQS in downwind states and (2) the analysis of the costs 

and benefits of this proposed rule. The 2017 illustrative control case emissions and air quality modeling results are 

described in the Ozone Transport Policy Analysis Proposed Rule TSD and in the Regulatory Impact Assessment for 

the proposed rule.

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

grid resolution of 12 km x 12 km. The model simulations produce hourly air quality 

concentrations for each 12 km grid cell across the modeling domain. 

CAMx requires a variety of input files that contain information pertaining to the 

modeling domain and simulation period. These include gridded, hourly emissions estimates and 

meteorological data, and initial and boundary concentrations. Separate emissions inventories 

were prepared for the 2011 base year and the 2017 base case. All other inputs (i.e. 

meteorological fields, initial concentrations, and boundary concentrations) were specified for the 

2011 base year model application and remained unchanged for the future-year model

simulations6

.

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

2.2 Characterization of 2011 Summer Meteorology

Meteorological conditions including temperature, humidity, winds, solar radiation, and 

vertical mixing affect the formation and transport of ambient ozone concentrations. Ozone is 

more readily formed on warm, sunny days when the air is stagnant. Conversely, ozone 

production is more limited on days that are cloudy, cool, rainy, and windy 

 6 The CAMx annual simulations for 2011 and 2017 were each performed using two time segments (January 1 

through April 30, 2011 with a 10-day ramp-up period at the end of December 2010 and May 1 through December 

31, 2011 with a 10-day ramp-up period at the end of 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/weather.html). Statistical modeling analyses have shown that 

temperature and certain other meteorological variables are highly correlated with the magnitude 

of ozone concentrations (Camalier, et al., 2007).

In selecting a year for air quality modeling it is important to simulate a variety of 

meteorological conditions that are generally associated with elevated air quality (US EPA, 

2014a). Specifically for ozone, modeled time periods should reflect meteorological conditions 

that frequently correspond with observed 8-hour daily maximum concentrations greater than the 

NAAQS at monitoring sites in nonattainment areas (US EPA, 2014a). However, because of 

inter-annual variability in weather patterns it is not possible to identify a single year that will be 

representative of “typical” meteorological conditions within each region of the U.S. 

As part of the development of the 2011 modeling platform we have examined the 

temperature and precipitation regimes across the U.S. in 2011 compared to long-term, 

climatological normal (i.e., average) conditions. Table 2-1 describes the observed 2011 surface 

temperature anomalies (i.e., departure from normal) for individual months from May through 

September relative to the period 1895-2011 for each of the nine National Oceanic and 

Atmospheric Administration (NOAA) climate regions shown in Figure 2-2. The aggregate 

temperature and precipitation anomalies by state for the core summer months, June through 

August, are shown in Figures 2-3 and 2-4, respectively. 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 South and Southwest regions. The summer months 

experienced below average precipitation for much of the southern and southeastern US, whereas 

wetter conditions than average were experienced in California and in several northern tier states. 

Extensive drought conditions occurred in portions of the southern Great Plains states. The 

warmer and dryer conditions were associated with a strong upper air ridge over the central U.S 

during the summer of 2011. While warmer than the long-term normal, 2011 summer 

temperatures in the U.S. were comparable to those in several other recent years. For example, 

2006, 2007, 2010, 2012 were also all among the ten warmest on record. Analysis of 

meteorological-adjusted trends in seasonal mean ozone for the period 2000 through 2012 

indicates that, on a regional basis, the summer of 2011 was typical of high ozone years in the 

Eastern U.S. in terms of being conducive to the formation of ozone. In Figure 2-5, those years 

with downward adjustments (compared to the “unadjusted for weather” line) were generally 
5

more conducive to ozone formation than years that were adjusted upward. For example, in the 

Northeast region, the years 2002, 2005, 2007, 2010, 2011, and 2012 were more conducive to 

ozone formation than 2000, 2003, 2004, 2006, and 2009. In 2011, the South and Southeast 

regions had the largest adjustment (most ozone conducive), whereas the Northeast and Central 

regions were slightly above normal. The Western regions experienced generally near normal 

ozone, with the Northwest somewhat below normal. 

Table 2-1. Surface temperature anomalies in 2011 by month and geographic region.*

*Unshaded boxes with the “N” marker represent near-normal temperatures that fall within the 

interquartile range. Blue colors indicate cooler than normal conditions, with the number of “C”s 

indicating the degree of the anomaly. CCC = coolest on record, CC = coolest 10th percentile, C = 

coolest 25th percentile. Red colors indicate warmer than normal conditions, with the number of “W”s 

indicating the degree of the anomaly. WWW = warmest on record, WW = warmest 10th percentile, 

W = warmest 25th percentile.

Figure 2-2. U.S. climate regions.

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

2011 May Jun Jul Aug Sep

Northeast W W WW N WW

Southeast N WW WW WW N

Ohio Valley N W WW W C

Upper Midwest N N WW W N

South N WW WWW WWW N

Northern Rockies C N W W W

Southwest C W WW WWW W

Northwest CC C C W WW

West C C N W WW
6

Figure 2-3. Statewide rank of temperatures in 2011 relative to 1895-2011 period. 

(http://www.ncdc.noaa.gov/temp-and-precip/maps.php)

Figure 2-4. Statewide rank of precipitation in 2011 relative to 1895-2011 period. 

(http://www.ncdc.noaa.gov/temp-and-precip/maps.php)
7

 

 

 

Figure 2-5a. Meteorological-adjusted ozone trends by climate region: 

Northeast, Central (Ohio Valley), East North Central (Upper Midwest), Southeast, South, 

and Southwest. (http://www.epa.gov/airtrends/weather.html)
8

 

 

Figure 2-5b. Meteorological-adjusted ozone trends by climate region: 

West North Central (Northern Rockies and Plains), West, and Northwest.

(http://www.epa.gov/airtrends/weather.html)

2.3 Meteorological Data for 2011

The meteorological data for air quality modeling of 2011 were derived from running 

Version 3.4 of the Weather Research Forecasting Model (WRF) (Skamarock, et al., 2008). The

meteorological outputs from WRF include hourly-varying horizontal wind components (i.e., 

speed and direction), temperature, moisture, vertical diffusion rates, and rainfall rates for each 

grid cell in each vertical layer. Selected physics options used in the WRF simulation include 

Pleim-Xiu land surface model (Xiu and Pleim, 2001; Pleim and Xiu, 2003), Asymmetric 

Convective Model version 2 planetary boundary layer scheme (Pleim 2007a,b), Kain-Fritsch 

cumulus parameterization (Kain, 2004) utilizing the moisture-advection trigger (Ma and Tan, 

2009), Morrison double moment microphysics (Morrison, et al., 2005; Morrison and Gettelman, 

2008), and RRTMG longwave and shortwave radiation schemes (Iacono, et.al., 2008).
9

The WRF model simulation was initialized using the 12km North American Model 

(12NAM) analysis product provided by the National Climatic Data Center (NCDC). Where 

12NAM data were unavailable, the 40km Eta Data Assimilation System (EDAS) analysis 

(ds609.2) from the National Center for Atmospheric Research (NCAR) was used. Analysis 

nudging for temperature, wind, and moisture was applied above the boundary layer only. The 

model simulations were conducted in 5.5 day blocks with soil moisture and temperature carried 

from one block to the next via the “ipxwrf” program (Gilliam and Pleim, 2010). Landuse and 

land cover data were based on the 2006 National Land Cover Database (NLCD2006) data.7 Sea 

surface temperatures at 1 km resolution were obtained from the Group for High Resolution Sea 

Surface Temperatures (GHRSST) (Stammer, et al., 2003). As shown in Table 2-2, the WRF 

simulations were performed with 35 vertical layers up to 50 mb, with the thinnest layers being 

nearest the surface to better resolve the planetary boundary layer (PBL). The WRF 35-layer 

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

Table 2-2.

Table 2-2. WRF and CAMx layers and their approximate height above ground level.

CAMx 

Layers

WRF 

Layers Sigma P Pressure (mb)

Approximate 

Height

(m AGL)

25 35 0.00 50.00 17,556

34 0.05 97.50 14,780

24 33 0.10 145.00 12,822

32 0.15 192.50 11,282

23 31 0.20 240.00 10,002

30 0.25 287.50 8,901

22 29 0.30 335.00 7,932

28 0.35 382.50 7,064

21 27 0.40 430.00 6,275

26 0.45 477.50 5,553

20 25 0.50 525.00 4,885

24 0.55 572.50 4,264

19 23 0.60 620.00 3,683

18 22 0.65 667.50 3,136

17 21 0.70 715.00 2,619

 7 The 2006 NLCD data are available at http://www.mrlc.gov/nlcd06_data.php
10

CAMx 

Layers

WRF 

Layers Sigma P Pressure (mb)

Approximate 

Height

(m AGL)

16 20 0.74 753.00 2,226

15 19 0.77 781.50 1,941

14 18 0.80 810.00 1,665

13 17 0.82 829.00 1,485

12 16 0.84 848.00 1,308

11 15 0.86 867.00 1,134

10 14 0.88 886.00 964

9 13 0.90 905.00 797

12 0.91 914.50 714

8 11 0.92 924.00 632

10 0.93 933.50 551

7 9 0.94 943.00 470

8 0.95 952.50 390

6 7 0.96 962.00 311

5 6 0.97 971.50 232

4 5 0.98 981.00 154

4 0.99 985.75 115

3 3 0.99 990.50 77

2 2 1.00 995.25 38

1 1 1.00 997.63 19

Details of the annual 2011 meteorological model simulation and evaluation are provided in a 

separate technical support document (US EPA, 2014b) which can be obtained at 

http://www.epa.gov/ttn/scram/reports/MET_TSD_2011_final_11-26-14.pdf

The meteorological data generated by the WRF simulations were processed using a 

modified version of the wrfcamx v4.0 (Environ, 2013) meteorological data processing program 

to create model-ready meteorological inputs to CAMx. The modification to wrfcamx included 

reactivating the wrfcamx option to analyze sub-grid stratiform clouds from the WRF output in 

order to more fully account for the effects of clouds on photolysis rates. In running wrfcamx, 

vertical eddy diffusivities (Kv) were calculated using the Yonsei University (YSU) (Hong and 

Dudhia, 2006) mixing scheme. We used a minimum Kv of 0.1 m2

/sec except for urban grid cells 
11

where the minimum Kv was reset to 1.0 m2/sec within the lowest 200 m of the surface in order to 

enhance mixing associated with the nighttime “urban heat island” effect.

2.4 Initial and Boundary Concentrations

The lateral boundary and initial species concentrations are provided by a threedimensional global atmospheric chemistry model, GEOS-Chem (Yantosca, 2004) standard 

version 8-03-02 with 8-02-01 chemistry. The global GEOS-Chem model simulates atmospheric 

chemical and physical processes driven by assimilated meteorological observations from the 

NASA’s Goddard Earth Observing System (GEOS-5; additional information available at: 

http://gmao.gsfc.nasa.gov/GEOS/ and http://wiki.seas.harvard.edu/geos-chem/index.php/GEOS5). This model was run for 2011 with a grid resolution of 2.0 degrees x 2.5 degrees (latitudelongitude). The predictions were used to provide one-way dynamic boundary concentrations at 

one-hour intervals and an initial concentration field for the CAMx simulations. The 2011 

boundary concentrations from GEOS-Chem were used for the 2011 and 2017 model simulations. 

The procedures for translating GEOS-Chem predictions to initial and boundary concentrations 

are described elsewhere (Henderson, 2014). More information about the GEOS-Chem model and 

other applications using this tool is available at: http://www-as.harvard.edu/chemistry/trop/geos.

2.5 Emissions Inventories

CAMx requires detailed emissions inventories containing temporally allocated (i.e., 

hourly) emissions for each grid-cell in the modeling domain for a large number of chemical 

species that act as primary pollutants and precursors to secondary pollutants. Annual emission 

inventories for 2011 and 2017 were preprocessed into CAMx-ready inputs using the Sparse 

Matrix Operator Kernel Emissions (SMOKE) modeling system (Houyoux et al., 2000).

8

Information on the emissions inventories used as input to the CAMx model simulations can be 

found in the following emissions inventory technical support documents: Preparation of 

Emissions Inventories for the Version 6.2, 2011 Emissions Modeling Platform (US EPA, 2015a)

and 2011 National Emissions Inventory, version 2 (US EPA, 2015b).

 8 The SMOKE output 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 evaluation for ozone was conducted to examine the 

ability of the CAMx v6.11 modeling system to replicate 2011 measured concentrations. This 

evaluation focused on statistical assessments of model predictions versus observations paired in 

time and space depending on the sampling period of measured data. Details on the evaluation 

methodology and the calculation of performance statistics are provided in Appendix A. Overall, 

the ozone model performance statistics for the CAMx 2011 simulation are within or close to the 

ranges found in other recent peer-reviewed applications (Simon et al, 2012). The model 

performance results, as described in Appendix A, demonstrate that the predictions from the 2011 

modeling platform closely replicate the corresponding observed concentrations in terms of the 

magnitude, temporal fluctuations, and geographic differences for 8-hour daily maximum ozone. 

These results provide confidence that our application of CAMx using this 2011 modeling 

platform provides a scientifically credible approach for assessing ozone concentrations relevant 

for the transport assessment described in this TSD.

3. Identification of Future Nonattainment and Maintenance Receptors

3.1 Definition of Nonattainment and Maintenance Receptors

The approach in the propose rule for identifying the 2017 nonattainment and maintenance 

receptors is described in Section V.C of the preamble. In brief, we are proposing to identify 

nonattainment receptors in this rulemaking as those sites that are violating the NAAQS based on 

current measured air quality (i.e., 2012-2014 design values) and that also have projected 2017 

average design values that exceed the NAAQS (i.e., 2017 average design values of 76 ppb or 

greater).9 We followed the approach in the CSAPR to identify sites that would have difficulty 

maintaining the 2008 ozone NAAQS in a scenario that takes into account historic variability in 

air quality at the monitoring site. In the CSAPR approach, monitoring sites with a 2017 

maximum design value that exceeds the NAAQS, even if the 2017 average design value is below 

the NAAQS, are projected to have a maintenance problem in 2017. Monitoring sites with a 2017 

average design value below the NAAQS, but with a maximum design value that exceeds the

 9 In determining compliance with the NAAQS, ozone design values are truncated to integer values. For example, a 

design value of 75.9 ppb is truncated to 75 ppb which is attainment. In this manner, design values at or above 76.0 

ppb are considered to be violations of the NAAQS.
13

NAAQS, are considered maintenance-only sites. In addition, those sites that have projected 2017 

average design values that exceed the NAAQS, but are currently measuring clean data based on 

2012-2014 design values are also defined as maintenance-only receptors. Maintenance-only 

receptors therefore include both (1) those sites with projected average design values above the 

NAAQS that are currently measuring clean data and (2) those sites with projected average design 

values below the level of the NAAQS, but with projected maximum design values of 76 ppb or 

greater. In addition to the maintenance-only receptors, the 2017 ozone nonattainment receptors 

are also maintenance receptors because the maximum design values for each of these sites is 

always greater than or equal to the average design value. The procedures for calculating 

projected 2017 average and maximum design values are described below. The monitoring sites 

that we project to be nonattainment and maintenance receptors for the ozone NAAQS in the 

2017 base case are used for assessing the contribution of emissions in upwind states to 

downwind nonattainment and maintenance of ozone NAAQS as part of this proposal. 

3.2 Approach for Projecting 2017 Ozone Design Values

The ozone predictions from the 2011 and 2017 CAMx model simulations were used to 

project ambient (i.e., measured) ozone design values to 2017 following the approach described in 

EPA’s current guidance for attainment demonstration modeling (US EPA, 2014a),10 as 

summarized here. The modeling guidance recommends using 5-year weighted average ambient 

design values11 centered on the base modeling year as the starting point for projecting average 

design values to the future. Because 2011 is the base emissions year, we used the average 

ambient 8-hour ozone design values for the period 2009 through 2013 (i.e., the average of design 

values for 2009-2011, 2010-2012 and 2011-2013) to calculate the 5-year weighted average 

design values. The 5-year weighted average ambient design value at each site was projected to 

2017 using the Model Attainment Test Software program (Abt Associates, 2014). This program 

calculates the 5-year weighted average DV based on observed data and projects future year 

 10 EPA’s ozone attainment demonstration modeling guidance is referred to as “the modeling guidance” in the 

remainder of this document.

11 The air quality design value for a site is the 3-year average of the annual fourth-highest daily maximum 8-hour 

average ozone concentration.
14

values using the relative response predicted by the model. Equation (3-1) describes the 

recommended model attainment test in its simplest form, as applied for monitoring site i:

(DVF)i = (RRRRRR)ii ∗ (DDDDDD)ii Equation 3-1

DVFi is the estimated design value for the future year at monitoring site i; RRFi is the relative 

response factor for monitoring site i; and DVBi is the base period design value monitored at site i. 

The relative response factor for each monitoring site (RRRRRR)ii is the fractional change in 8-hour 

daily maximum ozone between the base and future year. The RRF is based on the average ozone 

on model-predicted “high” ozone days in grid cells in the vicinity of the monitoring site. The 

modeling guidance recommends calculating RRFs based on the highest 10 modeled ozone days 

in the base year simulation at each monitoring site. Specifically, the RRF was calculated based 

on the 10 highest days in the 2011 base year modeling in the vicinity of each monitor location. 

As recommended by the modeling guidance, we considered model response in grid cells 

immediately surrounding the monitoring site along with the grid cell in which the monitor is 

located. The RRF was based on a 3 x 3 array of 12 km grid cells centered on the location of the 

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 calculation (paired in space). In cases for which the 

base year model simulation did not have 10 days with ozone values greater than or equal to 60 

ppb at a site, we used all days with ozone >= 60 ppb, as long as there were at least 5 days that 

meet that criteria. At monitor locations with less than 5 days with modeled 2011 base year ozone

>= 60 ppb, no RRF or DVF was calculated for the site and the monitor in question was not 

included in this analysis. 

The approach for calculating 2017 maximum design values is similar to the approach for 

calculating 2017 average design values. To calculate the 2017 maximum design value we start 

with the highest (i.e., maximum) ambient design value from the 2011-centered 5-year period 

(i.e., the maximum of design values from 2009-2011, 2010-2012, and 2011-2013). The base 

period maximum design value at each site was projected to 2017 using the site-specific RRFs, as 

determined using the procedures for calculating RRFs described above. 

Table 3-1 contains the 2009-2013 base period average and maximum 8-hour ozone 

design values, the 2017 base case average and maximum design values, and the 2012-2014 

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

Table 3-2 contains this same information for the 6 maintenance-only sites in the eastern U.S. that 

are projected nonattainment but currently measuring clean data. Table 3-3 contains this same 

information for the 23 maintenance-only sites in the eastern U.S. that are projected to have 

average design values below the NAAQS, but maximum design values above the NAAQS. The 

design values for all monitoring sites in the U.S. are provided in docket item EPA-HQ-OAR2015-0500-0006.12

Table 3-1. Average and maximum 2009-2013 and 2017 base case 8-hour ozone design values 

and 2012-2014 design values (ppb) at projected nonattainment sites in the eastern U.S. 

(nonattainment receptors).

Monitor ID State County

Average 

Design 

Value 

2009-2013

Maximum 

Design 

Value 

2009-2013

Average 

Design 

Value 

2017

Maximum 

Design 

Value 

2017

2012-2014 

Design 

Value

90013007 Connecticut Fairfield 84.3 89.0 77.1 81.4 84.0

90019003 Connecticut Fairfield 83.7 87.0 78.0 81.1 85.0

90099002 Connecticut New Haven 85.7 89.0 77.2 80.2 81.0

480391004 Texas Brazoria 88.0 89.0 81.4 82.3 80.0

481210034 Texas Denton 84.3 87.0 76.9 79.4 81.0

484392003 Texas Tarrant 87.3 90.0 79.6 82.1 77.0

484393009 Texas Tarrant 86.0 86.0 78.6 78.6 80.0

551170006 Wisconsin Sheboygan 84.3 87.0 77.0 79.4 81.0

 12 There are 7 sites in 3 counties in the West that were excluded from this listing because the ambient design values 

at these sites were dominated by wintertime ozone episodes and not summer season conditions that are the focus of 

this transport assessment. High winter ozone concentrations that have been observed in certain parts of the Western 

U.S. are believed to result from the combination of strong wintertime inversions, large NOx and VOC emissions 

from nearby oil and gas operations, increased UV intensity due to reflection off of snow surfaces and potentially 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)
16

Table 3-2. Average and maximum 2009-2013 and 2017 base case 8-hour ozone design values 

and 2012-2014 design values (ppb) at sites in the eastern U.S. that are projected nonattainment 

but currently measuring clean data (maintenance-only receptors).

Monitor ID State County

Average 

Design 

Value 

2009-2013

Maximum 

Design 

Value 

2009-2013

Average 

Design 

Value 

2017

Maximum 

Design 

Value 

2017

2012-2014 

Design 

Value

240251001 Maryland Harford 90.0 93.0 81.3 84.0 75.0

360850067 New York Richmond 81.3 83.0 76.3 77.8 73.0

361030002 New York Suffolk 83.3 85.0 79.2 80.8 73.0

390610006 Ohio Hamilton 82.0 85.0 76.3 79.1 75.0

482011034 Texas Harris 81.0 82.0 76.8 77.8 72.0

482011039 Texas Harris 82.0 84.0 78.2 80.2 72.0
17

Table 3-3. Average and maximum 2009-2013 and 2017 base case 8-hour ozone design values 

and 2012-2014 design values (ppb) at projected maintenance sites in the eastern U.S. Based on 

the CSAPR methodology (maintenance-only receptors).

Monitor ID State County

Average 

Design 

Value 

2009-2013

Maximum 

Design 

Value 

2009-2013

Average 

Design 

Value 

2017

Maximum 

Design 

Value 

2017

2012-2014 

Design 

Value

90010017 Connecticut Fairfield 80.3 83.0 75.8 78.4 82.0

211110067 Kentucky Jefferson 82.0 85.0 75.8 78.6

Incomplete 

Data

211850004 Kentucky Oldham 82.0 86.0 73.7 77.3 74.0

240053001 Maryland Baltimore 80.7 84.0 73.2 76.2 72.0

260050003 Michigan Allegan 82.7 86.0 75.5 78.5 83.0

261630019 Michigan Wayne 78.7 81.0 74.0 76.2 74.0

340071001 New Jersey Camden 82.7 87.0 74.2 78.1 76.0

340150002 New Jersey Gloucester 84.3 87.0 75.1 77.5 76.0

340230011 New Jersey Middlesex 81.3 85.0 73.0 76.3 74.0

340290006 New Jersey Ocean 82.0 85.0 73.9 76.6 75.0

360810124 New York Queens 78.0 80.0 75.7 77.6 72.0

420031005 Pennsylvania Allegheny 80.7 82.0 75.3 76.5 77.0

421010024 Pennsylvania Philadelphia 83.3 87.0 75.1 78.4 75.0

480850005 Texas Collin 82.7 84.0 74.9 76.0 78.0

481130069 Texas Dallas 79.7 84.0 74.0 78.0 78.0

481130075 Texas Dallas 82.0 83.0 75.8 76.7 77.0

481211032 Texas Denton 82.7 84.0 75.1 76.3 79.0

482010024 Texas Harris 80.3 83.0 75.9 78.5 72.0

482010026 Texas Harris 77.3 80.0 73.5 76.1 67.0

482010055 Texas Harris 81.3 83.0 75.4 77.0 75.0

482011050 Texas Harris 78.3 80.0 74.6 76.2 72.0

484390075 Texas Tarrant 82.0 83.0 75.5 76.4 79.0

484393011 Texas Tarrant 80.7 83.0 74.5 76.6 75.0
18

4. Ozone Contribution Modeling

4.1 Methodology

The EPA performed nationwide,13 state-level ozone source apportionment modeling 

using the CAMx OSAT/APCA technique14 (ENVIRON, 2014) to quantify the contribution of 

2017 base case NOX and VOC emissions from all sources in each state to projected 2017 ozone 

concentrations at ozone monitoring sites. In the source apportionment model run, we tracked the 

ozone formed from each of the following contribution categories (i.e., “tags”):

• States – anthropogenic NOX and VOC emissions from each state tracked individually 

(emissions from all anthropogenic sectors in a given state were combined);

• Biogenics – biogenic NOX and VOC emissions domain-wide (i.e., not by state)15;

• Boundary Concentrations – concentrations transported into the modeling domain;

• Tribes – the emissions from those tribal lands for which we have point source inventory 

data in the 2011 NEI (we did not model the contributions from individual tribes); 

• Canada and Mexico – anthropogenic emissions from sources in the portions of Canada 

and Mexico included in the modeling domain (we did not model the contributions from 

Canada and Mexico separately); 

• Fires – combined emissions from wild and prescribed fires domain-wide (i.e., not by 

state); and

• Offshore – combined emissions from offshore marine vessels and offshore drilling 

platforms.

The contribution modeling provided contributions to ozone from anthropogenic NOX and VOC 

emissions in each state, individually. The contributions to ozone from chemical reactions 

between biogenic NOX and VOC emissions were modeled and assigned to the “biogenic” 

category. The contributions from wild fire and prescribed fire NOX and VOC emissions were 

modeled and assigned to the “fires” category. That is, the contributions from the “biogenic” and 

 13 As shown in Figure 2-1, the EPA’s nationwide modeling includes the 48 contiguous states.

14 As part of this technique, ozone formed from reactions between biogenic VOC and NOx with anthropogenic NOx 

and VOC are assigned to the anthropogenic emissions.

15 Biogenic emissions and emissions from wild fires and prescribed fires were held constant between 2011 and 2017 

since (1) these emissions are tied to the 2011 meteorological conditions and (2) the focus of this rule is on the 

contribution from anthropogenic emissions to projected ozone nonattainment and maintenance.
19

“fires” categories are not assigned to individual states nor are they included in the state 

contributions. 

CAMx OSAT/APCA model run was performed for the period May 1 through September 

30 using the projected 2017 base case emissions and 2011 meteorology for this time period. The 

hourly contributions16 from each tag were processed to calculate an 8-hour average contribution 

metric. The process for calculating the contribution metric uses the contribution modeling 

outputs in a “relative sense” to apportion the projected 2017 average design value at each 

monitoring location into contributions from each individual tag. This process is similar in 

concept to the approach described above for using model predictions to calculate 2017 ozone 

design values. The approach used to calculate the contribution metric is described by the 

following steps:

Step 1. Modeled hourly ozone concentrations are used to calculate 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 total ozone concentrations to create a “pseudo” hourly ozone value 

for each tag for each hour in each grid cell.

Step 3. The hourly “pseudo” concentrations from Step 2 are used to calculate 8-hour average 

“pseudo” concentrations for each tag for the time period that corresponds to the MDA8

concentration from 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 exceedance days at a particular 

monitoring site then the data from the top five 2017 MDA8 concentration days are extracted and 

used in the calculations.17

 16 Contributions from anthropogenic emissions under “NOX-limited” and “VOC-limited” chemical regimes were 

combined to obtain the net contribution from NOX and VOC anthropogenic emissions in each state.

17 If there were fewer than 5 days with a modeled 2017 MDA8 concentration ≥ 60 ppb for the location of a particular 

monitoring site, then contributions were not calculated at that monitor.
20

Step 5. For each monitoring site and each tag, the 8-hour “pseudo” concentrations are then 

averaged across the days selected in Step 4 to create a multi-day average “pseudo” concentration 

for tag at each site. Similarly, the MDA8 concentrations were average across the days selected 

in Step 4.

Step 6. The multi-day average “pseudo” concentration and the corresponding multi-day average 

MDA8 concentration are used to create a Relative Contribution Factor (RCF) for each tag at 

each monitoring site. The RCF is the difference between the MDA8 concentration and the 

corresponding “pseudo” concentration, normalized by the MDA8 concentration.

Step 7. The 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. 

Step 8. The contributions calculated from Step 7 are truncated to two digits to the right of the 

decimal (e.g., a calculated contribution of 0.78963... is truncated to 0.78 ppb).

Table 4-1 provides an example of the calculation of contributions from two states (state A 

and state B) to a particular nonattainment site starting with Step 4, above. The table includes the 

daily “pseudo” concentrations for state A and state B and corresponding MDA8 ozone 

concentrations on those days with 2017 model-predicted exceedances at this site. The MDA8 

ozone concentrations on these days are ranked-ordered in the table. The 2017 average design 

value for this example is 77.5 ppb. Using the data in Table 4-1, the RCF for state A and state B 

are calculated as:

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

(90.372 – 90.163) / 90.372 = 0.00231 for state B

The contributions from state A and state B to the 2017 average design value at this site are 

calculated as:

77.5 x 0.09422 = 7.3020 which is truncated to 7.30 ppb for state A, and

77.5 x 0.00231 = 0.1790 which is truncated to 0.17 ppb for state B
21

Table 4-1. Example calculation of ozone contributions (units are ppb).

The average contribution metric calculated in this manner is intended to provide a 

reasonable representation of the contribution from individual states to the projected 2017 design 

value, based on modeled transport patterns and other meteorological conditions generally 

associated with modeled high ozone concentrations in the vicinity of the monitoring site. This

average contribution metric is beneficial since the magnitude of the contributions is directly 

related to the magnitude of the design value at each site. 

4.2 Contribution Modeling Results

The contributions from each tag to individual nonattainment and maintenance-only sites 

are provided in Appendix B. The largest contributions from each state to 2017 downwind 

nonattainment sites and to downwind maintenance-only sites are provided in Table 4-2. The 

2017 contributions from each tag to each monitoring site are provided in docket item EPA-HQOAR-2015-0500-0007.

Month Day

Predicted MDA8 O3 on 

2017 Modeled 

Exceedance Days

"Pseudo"

8-Hr O3 for 

State A

"Pseudo"

8-Hr O3 for 

State B

7 11 110.832 98.741 110.817

7 6 102.098 89.017 102.081

7 21 100.739 87.983 100.560

6 9 94.793 87.976 93.179

6 8 92.255 84.707 92.207

7 18 84.768 72.196 84.635

8 1 81.719 81.065 81.718

7 17 81.453 73.034 81.443

7 22 78.377 74.500 78.303

6 16 76.695 69.357 76.695

90.372 81.857 90.163

Relative Contribution 

Factors => 0.09422 0.00231

Contributions => 7.3020 0.1790

Truncated 

Contributions => 7.30 0.17

2017 Average 

Design Value

is 77.5 ppb

Multi-Day

Average =>
22

Table 4-2. Largest ozone contributions from each state to downwind 2017 projected 

nonattainment and 2017 projected maintenance-only sites in the eastern U.S. (units are 

ppb).

Upwind 

State

Largest Downwind 

Contribution to 

Nonattainment

Receptors for 

Ozone (ppb)

Largest Downwind 

Contribution to 

Maintenance

Receptors for 

Ozone (ppb

AL 0.79 1.28

AR 0.98 2.15

CT 0.00 0.46

DE 0.37 2.23

DC 0.06 0.73

FL 0.54 0.72

GA 0.47 0.58

IL 17.48 23.17

IN 6.24 14.95

IA 0.61 0.85

KS 0.80 1.03

KY 0.75 11.17

LA 3.09 4.23

ME 0.00 0.08

MD 2.07 7.11

MA 0.10 0.37

MI 2.69 1.79

MN 0.40 0.47

MS 0.78 1.48

MO 1.63 3.69

NE 0.24 0.36

NH 0.02 0.07

NJ 8.84 12.38

NY 16.96 17.21

NC 0.55 0.93

ND 0.11 0.28

OH 2.18 7.92

OK 1.70 2.46

PA 9.39 15.93

RI 0.02 0.08

SC 0.16 0.21

SD 0.08 0.12

TN 0.51 1.67
23

Upwind 

State

Largest Downwind 

Contribution to 

Nonattainment

Receptors for 

Ozone (ppb)

Largest Downwind 

Contribution to 

Maintenance

Receptors for 

Ozone (ppb

TX 2.44 2.95

VT 0.01 0.05

VA 1.87 5.29

WV 0.95 3.11

WI 0.34 2.59

As discussed in the preamble, the EPA is proposing to establish an air quality screening 

threshold calculated as one percent of the NAAQS. For this rule, we propose an 8-hour ozone 

threshold of 0.75 ppb as the quantification of one percent of the 2008 ozone NAAQS. This 

threshold is used to identify upwind states that contribute to downwind ozone concentrations in 

amounts sufficient to “link” them to these to downwind nonattainment and maintenance 

receptors.

States in the East whose contributions to a specific receptor meet or exceed the screening 

threshold are considered linked to that receptor; those states’ ozone contributions and emissions 

(and available emission reductions) are analyzed further, as described in the premable, to 

determine whether and what emissions reductions might be required from each state. 

Based on the maximum downwind contributions in Table 4-2, the following states 

contribute at or above the 0.75 ppb threshold to downwind nonattainment receptors: Alabama, 

Arkansas, Illinois, Indiana, Kansas, Kentucky, Louisiana, Maryland, Michigan, Mississippi, 

Missouri, New Jersey, New York, Ohio, Oklahoma, Pennsylvania, Texas, Virginia, and West 

Virginia. Based on the maximum downwind contributions in Table 4-2, the following states 

contribute at or above the 0.75 ppb threshold to downwind maintenance-only receptors: 

Alabama, Arkansas, Delaware, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maryland, 

Michigan, Mississippi, Missouri, New Jersey, New York, North Carolina, Ohio, Oklahoma, 

Pennsylvania, Tennessee, Texas, Virginia, West Virginia, and Wisconsin. The linkages between 

each upwind state and downwind nonattainment receptors and maintenance-only receptors in the 

eastern U.S. are provided in Table 4-3 and Table 4-4, respectively.
24

Table 4-3. Linkages between each upwind state and downwind nonattainment receptors in the 

eastern U.S.

Upwind 

State

Downwind Nonattainment Receptors

AL Tarrant Co, TX 

(484392003)

AR Brazoria Co, TX 

(480391004)

Tarrant Co, TX 

(484392003)

Tarrant Co, TX 

(484393009)

IL Brazoria Co, TX 

(480391004)

Sheboygan Co, WI 

(551170006)

IN Fairfield Co, CT 

(90013007)

Fairfield Co, CT 

(90019003)

Sheboygan Co, WI 

(551170006)

KS Sheboygan Co, WI 

(551170006)

KY Sheboygan Co, WI 

(551170006)

LA

Brazoria Co, TX 

(480391004)

Denton Co, TX 

(481210034)

Tarrant Co, TX 

(484392003)

Tarrant Co, TX 

(484393009)

Sheboygan Co, WI 

(551170006)

MD Fairfield Co, CT 

(90013007)

Fairfield Co, CT 

(90019003)

New Haven Co, CT 

(90099002)

MI

Fairfield Co, CT 

(90013007)

Fairfield Co, CT 

(90019003)

New Haven Co, CT 

(90099002)

Sheboygan Co, WI 

(551170006)

MS Brazoria Co, TX 

(480391004)

MO Brazoria Co, TX 

(480391004)

Sheboygan Co, WI 

(551170006)

NJ Fairfield Co, CT 

(90013007)

Fairfield Co, CT 

(90019003)

New Haven Co, CT 

(90099002)

NY Fairfield Co, CT 

(90013007)

Fairfield Co, CT 

(90019003)

New Haven Co, CT 

(90099002)

OH

Fairfield Co, CT 

(90013007)

Fairfield Co, CT 

(90019003)

New Haven Co, CT 

(90099002)

Sheboygan Co, WI 

(551170006)

OK Denton Co, TX 

(481210034)

Tarrant Co, TX 

(484392003)

Tarrant Co, TX 

(484393009)
25

Upwind 

State

Downwind Nonattainment Receptors

Sheboygan Co, WI 

(551170006)

PA Fairfield Co, CT 

(90013007)

Fairfield Co, CT 

(90019003)

New Haven Co, CT 

(90099002)

TX Sheboygan Co, WI 

(551170006)

VA Fairfield Co, CT 

(90013007)

Fairfield Co, CT 

(90019003)

New Haven Co, CT 

(90099002)

WV Fairfield Co, CT 

(90013007)

Fairfield Co, CT 

(90019003)

Table 4-4. Linkages between each upwind states and downwind maintenance-only receptors in 

the eastern U.S.

Upwind 

State Downwind Nonattainment Receptors

AL Hamilton Co, OH 

(390610006)

Harris Co, TX 

(482010055)

AR

Oldham Co, KY 

(211850004)

Allegan Co, MI 

(260050003)

Dallas Co, TX 

(481130069)

Dallas Co, TX 

(481130075)

Harris Co, TX 

(482010026)

Harris Co, TX 

(482010055)

Harris Co, TX 

(482011039)

Harris Co, TX 

(482011050)

Tarrant Co, TX 

(484390075)

Tarrant Co, TX 

(484393011)

DE

Camden Co, NJ

(340071001)

Gloucester Co, NJ 

(340150002)

Ocean Co, NJ 

(340290006)

Philadelphia Co, PA 

(421010024)

IL

Jefferson Co, KY 

(211110067)

Oldham Co, KY 

(211850004)

Allegan Co, MI 

(260050003)

Wayne Co, MI 

(261630019)

Camden Co, NJ 

(340071001)

Gloucester Co, NJ 

(340150002)

Ocean Co, NJ 

(340290006)

Queens Co, NY 

(360810124)

Suffolk Co, NY 

(361030002)

Hamilton Co, OH 

(390610006)

Allegheny Co, PA 

(420031005)

Harris Co, TX 

(482010026)
26

Upwind 

State Downwind Nonattainment Receptors

Harris Co, TX 

(482011039)

IN

Jefferson Co, KY 

(211110067)

Oldham Co, KY 

(211850004)

Baltimore Co, MD 

(240053001)

Harford Co, MD 

(240251001)

Allegan Co, MI 

(260050003)

Wayne Co, MI 

(261630019)

Camden Co, NJ 

(340071001)

Gloucester Co, NJ 

(340150002)

Middlesex Co, NJ 

(340230011)

Ocean Co, NJ 

(340290006)

Queens Co, NY

(360810124)

Richmond Co, NY 

(360850067)

Suffolk Co, NY 

(361030002)

Hamilton Co, OH 

(390610006)

Allegheny Co, PA 

(420031005)

Philadelphia Co, PA 

(421010024)

IA Allegan Co, MI 

(260050003)

KS Allegan Co, MI 

(260050003)

Tarrant Co, TX 

(484390075)

Tarrant Co, TX 

(484393011)

KY

Baltimore Co, MD 

(240053001)

Harford Co, MD 

(240251001)

Camden Co, NJ 

(340071001)

Gloucester Co, NJ 

(340150002)

Middlesex Co, NJ 

(340230011)

Ocean Co, NJ 

(340290006)

Richmond Co, NY 

(360850067)

Hamilton Co, OH

(390610006)

Allegheny Co, PA 

(420031005)

Philadelphia Co, PA 

(421010024)

LA

Collin Co, TX 

(480850005)

Dallas Co, TX 

(481130069)

Dallas Co, TX 

(481130075)

Denton Co, TX 

(481211032)

Harris Co, TX 

(482010024)

Harris Co, TX 

(482010026)

Harris Co, TX 

(482010055)

Harris Co, TX 

(482011034)

Harris Co, TX 

(482011039)

Harris Co, TX 

(482011050)

Tarrant Co, TX 

(484390075)

Tarrant Co, TX 

(484393011)

MD

Fairfield Co, CT 

(90010017)

Gloucester Co, NJ 

(340150002)

Middlesex Co, NJ 

(340230011)

Ocean Co, NJ 

(340290006)

Queens Co, NY 

(360810124)

Richmond Co, NY 

(360850067)

Suffolk Co, NY 

(361030002)

Philadelphia Co, PA 

(421010024)
27

Upwind 

State Downwind Nonattainment Receptors

MI

Fairfield Co, CT 

(90010017)

Jefferson Co, KY 

(211110067)

Oldham Co, KY 

(211850004)

Harford Co, MD 

(240251001)

Camden Co, NJ 

(340071001)

Gloucester Co, NJ 

(340150002)

Ocean Co, NJ 

(340290006)

Queens Co, NY 

(360810124)

Richmond Co, NY 

(360850067)

Suffolk Co, NY 

(361030002)

Hamilton Co, OH 

(390610006)

Allegheny Co, PA 

(420031005)

MS Harris Co, TX 

(482010055)

Harris Co, TX 

(482011039)

MO

Oldham Co, KY 

(211850004)

Allegan Co, MI 

(260050003)

Camden Co, NJ 

(340071001)

Hamilton Co, OH 

(390610006)

Harris Co, TX 

(482010026)

Harris Co, TX 

(482010055)

Harris Co, TX 

(482011039)

Harris Co, TX 

(482011050)

NJ

Fairfield Co, CT 

(90010017)

Queens Co, NY 

(360810124)

Richmond Co, NY 

(360850067)

Suffolk Co, NY 

(361030002)

Philadelphia Co, PA 

(421010024)

NY

Fairfield Co, CT 

(90010017)

Camden Co, NJ 

(340071001)

Gloucester Co, NJ 

(340150002)

Middlesex Co, NJ 

(340230011)

Ocean Co, NJ 

(340290006)

NC Baltimore Co, MD 

(240053001)

OH

Fairfield Co, CT 

(90010017)

Jefferson Co, KY 

(211110067)

Oldham Co, KY 

(211850004)

Baltimore Co, MD 

(240053001)

Harford Co, MD 

(240251001)

Wayne Co, MI 

(261630019)

Camden Co, NJ 

(340071001)

Gloucester Co, NJ 

(340150002)

Middlesex Co, NJ 

(340230011)

Ocean Co, NJ 

(340290006)

Queens Co, NY 

(360810124)

Richmond Co, NY 

(360850067)

Suffolk Co, NY 

(361030002)

Allegheny Co, PA 

(420031005)

Philadelphia Co, PA 

(421010024)

OK

Allegan Co, MI 

(260050003)

Hamilton Co, OH 

(390610006)

Dallas Co, TX 

(481130069)

Dallas Co, TX 

(481130075)

Denton Co, TX 

(481211032)

Harris Co, TX 

(482010026)
28

Upwind 

State Downwind Nonattainment Receptors

Harris Co, TX 

(482011034)

Harris Co, TX 

(482011039)

Tarrant Co, TX 

(484390075)

Tarrant Co, TX 

(484393011)

PA

Fairfield Co, CT 

(90010017)

Baltimore Co, MD 

(240053001)

Harford Co, MD 

(240251001)

Camden Co, NJ 

(340071001)

Gloucester Co, NJ 

(340150002)

Middlesex Co, NJ 

(340230011)

Ocean Co, NJ 

(340290006)

Queens Co, NY 

(360810124)

Richmond Co, NY 

(360850067)

Suffolk Co, NY 

(361030002)

TN Hamilton Co, OH 

(390610006)

Philadelphia Co, PA 

(421010024)

TX

Baltimore Co, MD 

(240053001)

Harford Co, MD 

(240251001)

Allegan Co, MI 

(260050003)

Camden Co, NJ 

(340071001)

Gloucester Co, NJ 

(340150002)

Ocean Co, NJ 

(340290006)

Queens Co, NY 

(360810124)

Richmond Co, NY 

(360850067)

Suffolk Co, NY 

(361030002)

Hamilton Co, OH 

(390610006)

Allegheny Co, PA 

(420031005)

Philadelphia Co, PA 

(421010024)

VA

Fairfield Co, CT 

(90010017)

Baltimore Co, MD 

(240053001)

Harford Co, MD 

(240251001)

Gloucester Co, NJ 

(340150002)

Middlesex Co, NJ 

(340230011)

Ocean Co, NJ 

(340290006)

Queens Co, NY 

(360810124)

Richmond Co, NY 

(360850067)

Suffolk Co, NY 

(361030002)

Philadelphia Co, PA 

(421010024)

WV

Baltimore Co, MD 

(240053001)

Harford Co, MD 

(240251001)

Camden Co, NJ 

(340071001)

Gloucester Co, NJ 

(340150002)

Middlesex Co, NJ 

(340230011)

Ocean Co, NJ 

(340290006)

Queens Co, NY 

(360810124)

Richmond Co, NY 

(360850067)

Suffolk Co, NY 

(361030002)

Hamilton Co, OH 

(390610006)

Allegheny Co, PA 

(420031005)

Philadelphia Co, PA 

(421010024)

WI Allegan Co, MI 

(260050003)

Wayne Co, MI 

(261630019)
29

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31

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A-1 

Appendix A 

2011 Model Performance Evaluation 
A-2 

An operational model evaluation was conducted for the 2011 base year CAMx annual 

model simulation performed for the 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 presented 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 comparisons 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 Trends Network (CASTNet) (Figures A-1a and A1b). 

Included in the evaluation are statistical measures of model performance based upon 

model-predicted versus observed concentrations that were paired in space and time. Model 

performance statistics were calculated for several spatial scales and temporal periods. Statistics 

were calculated for individual monitoring sites and for each of nine climate regions of the 12-km 

U.S. modeling domain. The regions include the Northeast, Ohio Valley, Upper Midwest, 

Southeast, South, Southwest, Northern Rockies, Northwest and West1,2, which are defined based 

upon the states contained within the National Oceanic and Atmospheric Administration (NOAA) 

climate regions (Figure A-2)3 as defined in Karl and Koss (1984). 

For maximum daily average 8-hour (MDA8) ozone, model performance statistics were 

created for each climate region for the period May through September.4 In addition to the 

performance statistics, we prepared several graphical presentations of model performance for 

MDA8 ozone. These graphical presentations include: 

 

1

 The nine climate regions are defined by States where: Northeast includes CT, DE, ME, MA, MD, NH, NJ, NY, 

PA, RI, and VT; Ohio Valley includes IL, IN, KY, MO, OH, TN, and WV; Upper Midwest includes IA, MI, MN, 

and WI; Southeast includes AL, FL, GA, NC, SC, and VA; South includes AR, KS, LA, MS, OK, and TX; 

Southwest includes AZ, CO, NM, and UT; Northern Rockies includes MT, NE, ND, SD, WY; Northwest includes 

ID, OR, and WA; and West includes CA and NV. 

2

 Note most monitoring sites in the West region are located in California (see Figures 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 scientists have identified nine climatically consistent 

regions within the contiguous U.S., http://www.ncdc.noaa.gov/monitoring-references/maps/us-climate-regions.php. 4

 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 and 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 distribution of the predicted and observed MDA8 

ozone concentrations by month (May through September) and by region and by network; 

and 

(4) time series plots (May through September) 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 (AMET) 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 mean error to characterize model performance, 

statistics which are consistent with the recommendations in Simon et al. (2012) and the draft 

photochemical modeling guidance (US EPA, 2014c). As noted above, we calculated the 

performance statistics by climate region for the period May through September. 

Mean bias (MB) is the average of the difference (predicted – observed) divided by the 

total number of replicates (n). Mean bias is given in units of ppb and is defined as: 

MB = ଵ

௡ ∑ ሺܲെܱሻ ௡

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

Mean error (ME) calculates the absolute value of the difference (predicted - observed) 

divided by the total number of replicates (n). Mean error is given in units of ppb and is defined 

as: 

ME = ଵ

௡ ∑ |ܲ െ ܱ| ௡

ଵ

Normalized mean bias (NMB) is the average the difference (predicted - observed) over 

the sum of observed values. NMB is a useful model performance indicator because it avoids over 
A-4 

inflating the observed range of values, especially at low concentrations. 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 absolute value of the difference (predicted - 

observed) over the sum of observed values. Normalized mean error is given in percentage units 

and is defined as: 

NME = ∑ |௉ିை| ೙

భ

∑ ሺைሻ ೙

భ

∗ 100

As described in more detail below, the model performance statistics indicate that the 8-

hour daily maximum ozone concentrations predicted by the 2011 CAMx modeling platform 

closely reflect the corresponding 8-hour observed ozone 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 various 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 model performance results, as described in this 

document, demonstrate that the predictions from the 2011 modeling platform closely replicate 

the corresponding observed concentrations in terms of the magnitude, temporal fluctuations, and 

spatial differences for 8-hour daily maximum ozone. 

The density scatter plots of MDA8 ozone are provided Figure A-3. The 8-hour ozone 

model performance bias and error statistics by network for the ozone season (May-September 

average) for each region are provided in Table A-1. The statistics shown were calculated using 

data pairs on days with observed 8-hour ozone of ≥ 60 ppb. The distributions of observed and 

predicted 8-hour ozone by month in the period May through September for each region are 

shown in Figures A-4 through A-12. Spatial plots of the mean bias and error as well as the 
A-5 

normalized mean bias and error for individual monitors are shown in Figures A-13 through A16. Time series plots of observed and predicted MDA 8-hour ozone during the period May 

through September at 13 sites are provided in Figure A-17, (a) through (m). These sites are listed 

in Table A-2. 

The density scatter plots in Figure A-3 provide a qualitative comparison of modelpredicted and observed MDA8 ozone concentrations. In these plots the intensity of the colors 

indicates the density of individual observed/predicted paired values. The greatest number of 

individual paired values is denoted by the core area in white. The plots indicate that the 

predictions correspond closely to the observations in that a large 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 Southeast regions. In the West, high concentrations are 

under-predicted and low and mid-range concentrations are over-predicted. Observed and 

predicted values are in close agreement in the Southwest 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 region. 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 indicated 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 25th percentile, median and 75th percentile 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 Southwest5

. The 

CAMx model, as applied here, also has a tendency 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 variability 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 sites from the Southeast into the Northeast. 

Elsewhere, mean bias is generally 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 monitoring sites across the modeling 

domain. There are regional differences in model performance, where the model tends to overpredict from the Southeast into the Northeast and generally under predict in the Southwest, 

Northern Rockies, Northwest and West. Model 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 indicates that the normalized mean error 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 monitoring sites across the modeling domain. Somewhat greater error (i.e., greater 

than 15 percent) is evident at sites in several areas most notably along portions of the Northeast 

and in portions of Florida, North Dakota, Illinois, 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 day fluctuations in observed 8-hour daily maximum 

concentrations using data for 13 of the projected 2017 nonattainment and maintenance-only sites. 

For this site-specific analysis we present the time 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 the modeling platform generally replicates the dayto-day variability in ozone during this time period. For example, several 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 Philadelphia, PA. Other sites generally track 

well and capture day-to-day variability but underestimate 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 extended period from mid-July to 

mid-August with very low observed 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, Harford County, MD, Allegan, 

MI, and Hamilton County, OH closely track the day-to-day variability in the observed MDA8 

values, but some days are over predicted while other days are under predicted to some extent. 

Finally, the site in Fairfield County, CT tracks closely with the observations, but there is a 

tendency to over predict on several of the observed high ozone days. Looking across all 13 sites 

indicates that the modeling platform is able to capture the site to site differences in the short-term 

variability of the observed 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 statistics for MDA8 ozone > 60 ppb for May through September by 

climate region, by network. 

Network Climate region 

No. of 

Obs MB ME 

NMB 

(%) 

NME 

(%) 

AQS 

Northeast 3,998 2.2 7.4 3.2 10.8 

Ohio Valley 6,325 0.3 7.6 0.4 11.3 

Upper Midwest 1,162 -3.0 7.5 -4.4 11.0 

Southeast 37,280 -2.5 8.1 -3.6 11.9 

South 5,694 -3.7 8.1 -5.4 11.7 

Southwest 6,033 -5.2 7.9 -7.8 12.0 

Northern Rockies 380 -5.9 7.4 -9.4 11.7 

Northwest 79 -5.4 8.1 -8.5 12.6 

West 8,665 -7.3 9.5 -10.3 13.5 

CASTNet 

Northeast 264 2.3 6.1 3.4 9.1 

Ohio Valley 107 -2.3 6.2 -3.4 9.4 

Upper Midwest 38 -3.9 5.9 -5.8 8.8 

Southeast 2,068 -5.0 8.2 -7.5 12.1 

South 215 -7.1 8.0 -10.7 12.0 

Southwest 382 -7.7 8.6 -11.7 13.1 

Northern Rockies 110 -7.8 8.1 -12.2 12.8 

Northwest - - - - - 

West 425 -12.1 12.5 -16.6 17.1 
A-10 

Figure A-3. Density scatter plots of observed vs predicted MDA8 ozone for the Northeast, Ohio 

River Valley, Upper Midwest, Southeast, South, Southwest, Northern Rockies, 

Northwest, and West Regions. 
A-11 

Figure A-4. Distribution of observed and predicted MDA8 ozone by month for the period May 

through September for the Northeast region, (a) AQS Network and (b) CASTNet 

Network. [symbol = median; top/bottom of box = 75th/25th percentiles; top/bottom 

line = max/min values] 

Figure A-5. Distribution of observed and predicted MDA8 ozone by month for the period May 

through September for the Ohio Valley region, (a) AQS Network and (b) CASTNet 

Network. 

a b 

a b 
A-12 

Figure A-6. Distribution of observed 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 observed and predicted MDA8 ozone by month for the period May 

through September for the Southeast region, (a) AQS Network and (b) CASTNet 

Network. 

a b 

a b 
A-13 

 

Figure A-8. Distribution of observed 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 observed and predicted MDA8 ozone by month for the period May 

through September for the Southwest region, (a) AQS Network and (b) CASTNet 

Network. 

a b 

a b 
A-14 

Figure A-10. Distribution of observed and predicted 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 predicted MDA8 ozone by month for the period May 

through September for the Northwest region, (a) AQS Network and (b) CASTNet 

Network. 

a b 

a b 
A-15 

Figure A-12. Distribution of observed and predicted 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 the period May-September 2011 at 

AQS and CASTNet monitoring sites. 

a b 
A-16 

Figure A-14. Normalized Mean Bias (%) of MDA8 ozone > 60 ppb over the period MaySeptember 2011 at AQS and CASTNet monitoring sites. 

Figure A-15. Mean Error (ppb) of MDA8 ozone > 60 ppb over the period May-September 2011 

at AQS and CASTNet monitoring sites. 
A-17 

Figure A-16. Normalized Mean Error (%) of MDA8 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 New York 361030002 

Philadelphia Pennsylvania 421010024 

Harford Maryland 240251001 

Allegheny Pennsylvania 420031005 

Hamilton Ohio 390610006 

Jefferson Kentucky 211110067 

Allegan Michigan 261630019 

Sheboygan Wisconsin 551170006 

Tarrant Texas 484392003 

Brazoria Texas 480391004 

Douglas Colorado 080350004 

Imperial California 060251003 
A-18 

Figure A-17a. Time series of observed (black) 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 (black) 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 (black) 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 (black) and predicted (red) MDA8 ozone for May 

through September 2011 at site 551170006 in Sheboygan Co., Wisconsin.

Figure A-17j. Time series of observed (black) 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 (black) and predicted (red) MDA8 ozone for May 

through September 2011 at site 080350004 in Douglas Co., Colorado. 
A-24 

Figure A-17m. Time series of observed (black) and predicted (red) MDA8 ozone for May 

through September 2011 at site 060251003 in Imperial Co., California.
Appendix B

Contributions to 2017 8-Hour Ozone Design Values

at Projected 2017 Nonattainment and Maintenance-Only Sites 
This appendix contains tables with the projected ozone contributions from 2017

anthropogenic NOx and VOC emissions in each state to each projected 2017 nonattainment

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

maintenance-only receptors are defined in section 3 of this TSD. In addition to the state 

contributions, we have included the contributions from each of the other categories tracked in the 

contribution modeling including point source emissions on Tribal lands, anthropogenic 

emissions in Canada and Mexico, emissions from Offshore sources, Fires, Initial and Boundary 

concentrations, and Biogenics. 

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 provide the contributions from each state 

and the District of Columbia, individually. Lastly, we provide 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”. Note that the 

contributions presented in these tables may not sum exactly to the 2017 average design value due 

to truncation of the contributions to two places to the right of the decimal.
Contributions to 2017 Nonattainment and Maintenance-Only Sites in the East (Part 1)

Monitor ID State County

2017

Average 

DV

2017

Maximum 

DV AL AZ AR CA CO CT DE DC FL GA ID IL IN IA KS KY LA ME MD MA MI MN MS MO MT

90010017 Connecticut Fairfield 75.8 78.4 0.10 0.04 0.11 0.05 0.10 9.04 0.22 0.04 0.03 0.11 0.03 0.50 0.62 0.21 0.18 0.35 0.07 0.01 1.34 0.06 0.98 0.25 0.07 0.34 0.06

90013007 Connecticut Fairfield 77.1 81.4 0.15 0.04 0.16 0.04 0.11 5.22 0.34 0.06 0.02 0.20 0.01 0.49 0.76 0.10 0.16 0.48 0.06 0.00 2.07 0.05 0.89 0.11 0.09 0.27 0.03

90019003 Connecticut Fairfield 78.0 81.1 0.14 0.06 0.15 0.06 0.14 4.75 0.30 0.06 0.04 0.19 0.02 0.70 0.89 0.17 0.20 0.57 0.08 0.00 1.83 0.03 0.89 0.13 0.09 0.38 0.04

90099002 Connecticut New Haven 77.2 80.2 0.09 0.05 0.12 0.06 0.11 8.29 0.37 0.04 0.04 0.10 0.04 0.57 0.68 0.21 0.20 0.43 0.09 0.00 1.55 0.10 0.91 0.15 0.06 0.39 0.05

211110067 Kentucky Jefferson 75.8 78.6 0.04 0.03 0.06 0.07 0.13 0.00 0.00 0.00 0.03 0.04 0.05 1.15 11.33 0.23 0.21 22.87 0.10 0.00 0.00 0.00 1.20 0.35 0.04 0.27 0.17

211850004 Kentucky Oldham 73.7 77.3 0.06 0.03 0.85 0.05 0.14 0.00 0.00 0.00 0.03 0.04 0.04 0.89 14.95 0.14 0.17 20.93 0.33 0.00 0.00 0.00 0.91 0.25 0.10 0.82 0.15

240053001 Maryland Baltimore 73.2 76.2 0.34 0.07 0.20 0.08 0.14 0.01 0.12 0.64 0.10 0.27 0.02 0.64 1.53 0.19 0.22 1.77 0.20 0.00 23.15 0.01 0.69 0.11 0.13 0.47 0.05

240251001 Maryland Harford 81.3 84.0 0.40 0.09 0.22 0.11 0.19 0.01 0.15 0.73 0.11 0.30 0.03 0.67 1.88 0.19 0.28 1.97 0.25 0.00 24.59 0.00 0.83 0.11 0.15 0.52 0.06

260050003 Michigan Allegan 75.5 78.5 0.33 0.08 2.15 0.06 0.23 0.00 0.00 0.00 0.13 0.20 0.01 23.17 8.02 0.85 1.03 0.53 0.57 0.00 0.01 0.00 2.74 0.06 0.42 3.69 0.01

261630019 Michigan Wayne 74.0 76.2 0.02 0.03 0.09 0.10 0.10 0.00 0.00 0.00 0.03 0.03 0.05 0.90 2.64 0.30 0.23 0.67 0.11 0.00 0.03 0.00 20.77 0.47 0.04 0.28 0.13

340071001 New Jersey Camden 74.2 78.1 0.32 0.11 0.37 0.11 0.20 0.17 1.36 0.00 0.07 0.15 0.04 1.33 1.71 0.37 0.42 0.93 0.58 0.02 0.57 0.17 1.43 0.15 0.26 0.76 0.07

340150002 New Jersey Gloucester 75.1 77.5 0.34 0.10 0.15 0.11 0.16 0.10 2.23 0.30 0.14 0.33 0.03 0.87 1.02 0.27 0.17 1.23 0.26 0.00 7.11 0.09 1.01 0.15 0.13 0.48 0.06

340230011 New Jersey Middlesex 73.0 76.3 0.28 0.08 0.12 0.08 0.14 0.33 0.73 0.07 0.09 0.31 0.03 0.58 0.85 0.18 0.17 1.08 0.14 0.08 2.35 0.33 0.62 0.12 0.10 0.38 0.04

340290006 New Jersey Ocean 73.9 76.6 0.21 0.06 0.23 0.08 0.15 0.36 0.76 0.06 0.05 0.17 0.04 0.80 1.06 0.34 0.26 0.88 0.19 0.07 2.01 0.37 1.31 0.17 0.12 0.52 0.07

360810124 New York Queens 75.7 77.6 0.13 0.07 0.12 0.09 0.13 0.41 0.60 0.07 0.08 0.20 0.05 0.84 0.80 0.34 0.24 0.49 0.14 0.00 2.15 0.06 1.79 0.21 0.06 0.49 0.09

360850067 New York Richmond 76.3 77.8 0.25 0.08 0.16 0.09 0.15 0.30 0.68 0.08 0.09 0.30 0.04 0.74 1.02 0.27 0.27 0.96 0.15 0.00 2.39 0.04 1.20 0.16 0.11 0.54 0.06

361030002 New York Suffolk 79.2 80.8 0.19 0.07 0.17 0.08 0.16 0.46 0.32 0.04 0.06 0.19 0.05 0.79 1.03 0.30 0.30 0.71 0.13 0.00 1.47 0.01 1.34 0.18 0.11 0.58 0.07

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

420031005 Pennsylvania Allegheny 75.3 76.5 0.16 0.05 0.22 0.11 0.13 0.00 0.00 0.00 0.06 0.17 0.07 1.35 1.86 0.17 0.32 1.64 0.17 0.00 0.05 0.00 1.51 0.15 0.13 0.55 0.13

421010024 Pennsylvania Philadelphia 75.1 78.4 0.51 0.09 0.23 0.09 0.16 0.03 1.33 0.16 0.14 0.54 0.02 0.65 1.71 0.17 0.21 2.14 0.18 0.00 5.10 0.03 0.33 0.07 0.17 0.52 0.04

480391004 Texas Brazoria 81.4 82.3 0.56 0.08 0.98 0.23 0.25 0.00 0.00 0.00 0.19 0.30 0.09 0.86 0.24 0.42 0.46 0.18 3.09 0.00 0.01 0.00 0.05 0.40 0.78 1.00 0.15

480850005 Texas Collin 74.9 76.0 0.41 0.07 0.35 0.10 0.19 0.00 0.00 0.00 0.61 0.24 0.02 0.03 0.03 0.02 0.20 0.05 1.40 0.00 0.02 0.00 0.01 0.00 0.24 0.12 0.03

481130069 Texas Dallas 74.0 78.0 0.41 0.05 0.83 0.09 0.15 0.00 0.00 0.00 0.72 0.33 0.02 0.13 0.06 0.04 0.19 0.10 1.44 0.00 0.02 0.00 0.01 0.04 0.23 0.19 0.03

481130075 Texas Dallas 75.8 76.7 0.34 0.05 0.92 0.08 0.18 0.00 0.00 0.00 0.65 0.24 0.02 0.14 0.06 0.04 0.23 0.09 1.22 0.00 0.02 0.00 0.01 0.04 0.18 0.21 0.03

481210034 Texas Denton 76.9 79.4 0.67 0.10 0.51 0.14 0.25 0.00 0.00 0.00 0.30 0.39 0.06 0.11 0.12 0.08 0.43 0.14 1.99 0.00 0.00 0.00 0.11 0.04 0.51 0.23 0.09

481211032 Texas Denton 75.1 76.3 0.29 0.07 0.68 0.11 0.29 0.00 0.00 0.00 0.27 0.21 0.07 0.05 0.04 0.05 0.39 0.05 1.77 0.00 0.02 0.00 0.06 0.03 0.47 0.15 0.12

482010024 Texas Harris 75.9 78.5 0.54 0.04 0.28 0.09 0.16 0.00 0.00 0.00 0.62 0.36 0.03 0.16 0.06 0.09 0.13 0.09 2.82 0.00 0.00 0.00 0.02 0.03 0.56 0.31 0.05

482010026 Texas Harris 73.5 76.1 0.09 0.02 1.21 0.08 0.23 0.00 0.00 0.00 0.07 0.05 0.05 0.76 0.11 0.35 0.42 0.05 3.66 0.00 0.00 0.00 0.24 0.26 0.49 1.13 0.11

482010055 Texas Harris 75.4 77.0 1.28 0.07 0.79 0.22 0.20 0.00 0.00 0.00 0.64 0.56 0.08 0.60 0.21 0.18 0.33 0.19 4.23 0.00 0.02 0.00 0.02 0.07 1.48 0.78 0.14

482011034 Texas Harris 76.8 77.8 0.46 0.04 0.52 0.12 0.18 0.00 0.00 0.00 0.57 0.27 0.05 0.40 0.07 0.30 0.39 0.06 3.81 0.00 0.00 0.00 0.10 0.27 0.65 0.69 0.10

482011039 Texas Harris 78.2 80.2 0.41 0.03 1.24 0.11 0.16 0.00 0.00 0.00 0.15 0.21 0.05 0.87 0.15 0.33 0.38 0.09 3.33 0.00 0.01 0.00 0.28 0.23 0.77 1.15 0.11

482011050 Texas Harris 74.6 76.2 0.32 0.03 0.92 0.06 0.11 0.00 0.00 0.00 0.18 0.17 0.01 0.74 0.14 0.24 0.21 0.06 2.76 0.00 0.00 0.00 0.34 0.26 0.61 0.75 0.03

484390075 Texas Tarrant 75.5 76.4 0.66 0.09 0.88 0.13 0.34 0.00 0.00 0.00 0.17 0.35 0.07 0.36 0.26 0.21 0.81 0.21 2.20 0.00 0.00 0.00 0.21 0.13 0.46 0.42 0.12

484392003 Texas Tarrant 79.6 82.1 0.79 0.10 0.87 0.14 0.36 0.00 0.00 0.00 0.34 0.47 0.07 0.35 0.27 0.09 0.37 0.22 2.64 0.00 0.00 0.00 0.12 0.07 0.54 0.34 0.10

484393009 Texas Tarrant 78.6 78.6 0.73 0.08 0.80 0.14 0.24 0.00 0.00 0.00 0.54 0.43 0.06 0.29 0.22 0.07 0.33 0.18 2.21 0.00 0.01 0.00 0.09 0.05 0.47 0.30 0.08

484393011 Texas Tarrant 74.5 76.6 0.15 0.08 1.19 0.11 0.26 0.00 0.00 0.00 0.13 0.16 0.07 0.32 0.10 0.21 0.77 0.09 1.47 0.00 0.01 0.00 0.18 0.13 0.17 0.44 0.13

551170006 Wisconsin Sheboygan 77.0 79.4 0.25 0.08 0.57 0.09 0.18 0.00 0.00 0.00 0.11 0.12 0.02 17.48 6.24 0.61 0.80 0.75 1.11 0.00 0.02 0.00 2.69 0.07 0.56 1.63 0.03
Contributions to 2017 Nonattainment and Maintenance-Only Sites in the East (Part 2)

Monitor ID State County

2017

Average 

DV

2017

Maximum 

DV NE NV NH NJ NM NY NC ND OH OK OR PA RI SC SD TN TX UT VT VA WA WV WI WY

90010017 Connecticut Fairfield 75.8 78.4 0.13 0.01 0.01 7.98 0.05 17.21 0.33 0.13 1.57 0.30 0.01 7.14 0.01 0.07 0.06 0.17 0.47 0.06 0.01 1.51 0.02 0.67 0.35 0.16

90013007 Connecticut Fairfield 77.1 81.4 0.08 0.01 0.01 8.45 0.08 15.98 0.55 0.04 1.89 0.24 0.00 9.29 0.01 0.16 0.02 0.31 0.46 0.05 0.00 1.87 0.01 0.92 0.17 0.12

90019003 Connecticut Fairfield 78.0 81.1 0.12 0.02 0.00 8.84 0.08 16.16 0.47 0.06 2.18 0.34 0.01 9.39 0.00 0.14 0.03 0.28 0.59 0.07 0.00 1.75 0.01 0.95 0.22 0.20

90099002 Connecticut New Haven 77.2 80.2 0.13 0.02 0.02 7.04 0.06 16.96 0.38 0.11 1.68 0.36 0.01 7.16 0.02 0.07 0.05 0.18 0.56 0.07 0.01 1.22 0.02 0.66 0.21 0.19

211110067 Kentucky Jefferson 75.8 78.6 0.16 0.02 0.00 0.00 0.08 0.00 0.01 0.24 4.05 0.35 0.02 0.46 0.00 0.01 0.12 0.11 0.66 0.05 0.00 0.01 0.07 0.61 0.55 0.27

211850004 Kentucky Oldham 73.7 77.3 0.13 0.01 0.00 0.00 0.06 0.00 0.02 0.20 3.13 0.22 0.02 0.35 0.00 0.02 0.09 0.11 0.39 0.05 0.00 0.00 0.06 0.40 0.41 0.24

240053001 Maryland Baltimore 73.2 76.2 0.13 0.02 0.00 0.37 0.12 0.35 0.93 0.07 2.98 0.35 0.01 4.80 0.00 0.14 0.05 0.67 0.77 0.07 0.00 4.70 0.02 2.65 0.19 0.17

240251001 Maryland Harford 81.3 84.0 0.15 0.02 0.00 0.43 0.16 0.40 0.46 0.08 3.99 0.46 0.02 6.07 0.00 0.10 0.05 0.70 1.05 0.09 0.00 5.29 0.03 2.99 0.21 0.20

260050003 Michigan Allegan 75.5 78.5 0.17 0.01 0.00 0.00 0.21 0.00 0.05 0.01 0.10 1.78 0.00 0.02 0.00 0.05 0.02 0.68 2.95 0.06 0.00 0.03 0.00 0.03 2.59 0.12

261630019 Michigan Wayne 74.0 76.2 0.09 0.03 0.00 0.01 0.04 0.10 0.32 0.28 7.92 0.22 0.06 0.27 0.00 0.04 0.04 0.12 0.52 0.07 0.00 0.24 0.09 0.27 1.00 0.20

340071001 New Jersey Camden 74.2 78.1 0.23 0.02 0.04 10.93 0.22 2.70 0.07 0.10 4.16 0.69 0.02 15.93 0.03 0.04 0.09 0.25 1.52 0.10 0.04 0.32 0.03 1.07 0.44 0.24

340150002 New Jersey Gloucester 75.1 77.5 0.11 0.02 0.01 4.41 0.19 1.22 0.33 0.07 3.85 0.35 0.01 15.73 0.02 0.07 0.04 0.38 1.07 0.08 0.02 3.33 0.02 2.35 0.44 0.19

340230011 New Jersey Middlesex 73.0 76.3 0.10 0.02 0.07 10.78 0.12 3.71 0.50 0.05 2.42 0.32 0.01 15.16 0.07 0.12 0.03 0.48 0.73 0.08 0.05 2.10 0.01 1.91 0.30 0.17

340290006 New Jersey Ocean 73.9 76.6 0.17 0.02 0.07 11.44 0.10 5.10 0.33 0.11 2.54 0.44 0.02 13.30 0.08 0.09 0.07 0.40 0.95 0.08 0.05 1.07 0.03 1.08 0.47 0.22

360810124 New York Queens 75.7 77.6 0.18 0.02 0.01 11.48 0.09 10.61 0.39 0.17 2.36 0.43 0.02 8.83 0.01 0.11 0.08 0.16 0.76 0.09 0.00 2.10 0.03 0.92 0.49 0.24

360850067 New York Richmond 76.3 77.8 0.18 0.02 0.00 12.38 0.12 4.57 0.55 0.11 2.70 0.44 0.01 13.51 0.00 0.14 0.07 0.42 0.83 0.10 0.00 2.27 0.02 1.64 0.33 0.22

361030002 New York Suffolk 79.2 80.8 0.21 0.02 0.00 11.37 0.09 15.95 0.38 0.14 2.49 0.52 0.01 9.39 0.00 0.08 0.08 0.35 0.82 0.10 0.00 1.62 0.02 0.98 0.27 0.26

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

420031005 Pennsylvania Allegheny 75.3 76.5 0.21 0.03 0.00 0.00 0.09 0.24 0.07 0.11 7.89 0.48 0.04 23.66 0.00 0.05 0.10 0.62 0.84 0.07 0.00 0.16 0.05 2.47 0.33 0.21

421010024 Pennsylvania Philadelphia 75.1 78.4 0.11 0.02 0.00 1.66 0.16 0.20 0.72 0.04 3.55 0.38 0.01 21.46 0.00 0.20 0.03 0.90 0.94 0.07 0.00 2.63 0.00 3.11 0.14 0.16

480391004 Texas Brazoria 81.4 82.3 0.24 0.07 0.00 0.00 0.11 0.00 0.08 0.06 0.04 0.72 0.05 0.03 0.00 0.08 0.08 0.37 38.93 0.14 0.00 0.04 0.05 0.03 0.34 0.30

480850005 Texas Collin 74.9 76.0 0.05 0.03 0.00 0.00 0.13 0.01 0.14 0.00 0.04 0.73 0.01 0.04 0.00 0.11 0.01 0.10 40.58 0.08 0.00 0.07 0.01 0.04 0.00 0.16

481130069 Texas Dallas 74.0 78.0 0.05 0.03 0.00 0.00 0.08 0.01 0.22 0.01 0.05 0.77 0.01 0.05 0.00 0.21 0.01 0.12 35.26 0.07 0.00 0.09 0.01 0.06 0.01 0.14

481130075 Texas Dallas 75.8 76.7 0.06 0.03 0.00 0.00 0.09 0.01 0.16 0.01 0.04 0.86 0.01 0.04 0.00 0.12 0.01 0.10 37.23 0.08 0.00 0.07 0.01 0.05 0.01 0.16

481210034 Texas Denton 76.9 79.4 0.17 0.06 0.00 0.00 0.13 0.01 0.08 0.02 0.10 1.33 0.02 0.03 0.00 0.09 0.03 0.19 33.85 0.15 0.00 0.03 0.02 0.04 0.06 0.28

481211032 Texas Denton 75.1 76.3 0.16 0.04 0.00 0.00 0.11 0.01 0.11 0.02 0.06 1.61 0.02 0.06 0.00 0.11 0.03 0.15 34.09 0.11 0.00 0.07 0.04 0.05 0.03 0.33

482010024 Texas Harris 75.9 78.5 0.07 0.03 0.00 0.00 0.08 0.00 0.14 0.02 0.03 0.24 0.01 0.02 0.00 0.17 0.02 0.19 32.55 0.07 0.00 0.05 0.01 0.04 0.02 0.14

482010026 Texas Harris 73.5 76.1 0.26 0.03 0.00 0.00 0.06 0.00 0.00 0.06 0.02 1.05 0.02 0.00 0.00 0.01 0.06 0.12 30.45 0.07 0.00 0.00 0.03 0.00 0.28 0.23

482010055 Texas Harris 75.4 77.0 0.13 0.07 0.00 0.00 0.11 0.00 0.16 0.04 0.06 0.20 0.04 0.03 0.00 0.15 0.04 0.41 34.58 0.12 0.00 0.06 0.04 0.05 0.08 0.28

482011034 Texas Harris 76.8 77.8 0.21 0.04 0.00 0.00 0.07 0.00 0.16 0.05 0.04 0.94 0.02 0.02 0.00 0.19 0.05 0.10 32.18 0.09 0.00 0.05 0.02 0.04 0.18 0.21

482011039 Texas Harris 78.2 80.2 0.21 0.04 0.00 0.00 0.05 0.01 0.06 0.05 0.03 0.83 0.02 0.02 0.00 0.05 0.05 0.31 33.69 0.07 0.00 0.03 0.03 0.02 0.28 0.21

482011050 Texas Harris 74.6 76.2 0.15 0.01 0.00 0.00 0.05 0.01 0.05 0.03 0.03 0.67 0.00 0.02 0.00 0.05 0.04 0.21 36.47 0.03 0.00 0.02 0.01 0.01 0.35 0.09

484390075 Texas Tarrant 75.5 76.4 0.36 0.05 0.00 0.00 0.15 0.02 0.05 0.05 0.15 2.46 0.04 0.07 0.00 0.04 0.07 0.20 29.82 0.16 0.00 0.03 0.04 0.05 0.15 0.36

484392003 Texas Tarrant 79.6 82.1 0.18 0.06 0.00 0.00 0.14 0.01 0.09 0.03 0.12 1.23 0.03 0.03 0.00 0.10 0.03 0.21 34.52 0.18 0.00 0.03 0.03 0.04 0.09 0.36

484393009 Texas Tarrant 78.6 78.6 0.14 0.05 0.00 0.00 0.11 0.01 0.13 0.02 0.11 1.03 0.02 0.05 0.00 0.11 0.03 0.20 35.64 0.13 0.00 0.06 0.02 0.05 0.07 0.26

484393011 Texas Tarrant 74.5 76.6 0.34 0.04 0.00 0.00 0.15 0.02 0.10 0.04 0.10 2.41 0.03 0.09 0.00 0.12 0.08 0.10 32.06 0.14 0.00 0.05 0.04 0.07 0.17 0.30

551170006 Wisconsin Sheboygan 77.0 79.4 0.09 0.02 0.00 0.00 0.21 0.02 0.06 0.04 0.78 1.70 0.01 0.20 0.00 0.04 0.02 0.51 2.44 0.07 0.00 0.08 0.01 0.29 12.22 0.11
Contributions to 2017 Nonattainment and Maintenance-Only Sites in the East (Part 3)

Monitor ID State County

2017

Average 

DV

2017

Maximum 

DV Tribal

Canada 

& 

Mexico Offshore Fires

Initial & 

Boundary Biogenics

90010017 Connecticut Fairfield 75.8 78.4 0.02 1.66 0.61 0.18 15.66 4.08

90013007 Connecticut Fairfield 77.1 81.4 0.02 1.61 1.30 0.20 16.90 4.17

90019003 Connecticut Fairfield 78.0 81.1 0.03 1.48 1.00 0.22 16.73 4.49

90099002 Connecticut New Haven 77.2 80.2 0.02 1.67 2.33 0.21 16.52 4.33

211110067 Kentucky Jefferson 75.8 78.6 0.01 0.52 0.05 0.20 21.86 6.71

211850004 Kentucky Oldham 73.7 77.3 0.02 0.41 0.14 0.22 18.81 7.04

240053001 Maryland Baltimore 73.2 76.2 0.03 0.68 0.51 0.29 15.67 5.04

240251001 Maryland Harford 81.3 84.0 0.04 0.73 0.44 0.30 16.63 6.08

260050003 Michigan Allegan 75.5 78.5 0.04 0.28 0.34 0.73 11.71 9.01

261630019 Michigan Wayne 74.0 76.2 0.01 4.89 0.08 0.17 24.10 5.57

340071001 New Jersey Camden 74.2 78.1 0.04 1.73 0.55 0.46 14.07 6.14

340150002 New Jersey Gloucester 75.1 77.5 0.03 1.05 0.62 0.30 15.90 5.75

340230011 New Jersey Middlesex 73.0 76.3 0.03 1.95 0.64 0.25 16.30 5.01

340290006 New Jersey Ocean 73.9 76.6 0.03 2.28 0.75 0.31 16.60 5.04

360810124 New York Queens 75.7 77.6 0.02 2.00 1.54 0.21 17.29 5.14

360850067 New York Richmond 76.3 77.8 0.03 1.74 1.21 0.29 16.47 5.51

361030002 New York Suffolk 79.2 80.8 0.03 1.67 1.31 0.31 16.51 5.12

390610006 Ohio Hamilton 76.3 79.1 0.03 0.76 0.21 0.58 17.48 7.45

420031005 Pennsylvania Allegheny 75.3 76.5 0.02 1.38 0.11 0.38 21.40 5.03

421010024 Pennsylvania Philadelphia 75.1 78.4 0.03 0.48 0.71 0.37 16.52 5.62

480391004 Texas Brazoria 81.4 82.3 0.03 0.35 0.99 1.62 19.53 6.57

480850005 Texas Collin 74.9 76.0 0.02 0.25 2.23 0.90 19.19 5.62

481130069 Texas Dallas 74.0 78.0 0.02 0.20 1.93 1.20 22.27 5.78

481130075 Texas Dallas 75.8 76.7 0.02 0.30 1.76 1.14 22.41 6.02

481210034 Texas Denton 76.9 79.4 0.03 0.40 1.57 0.95 24.23 6.50

481211032 Texas Denton 75.1 76.3 0.02 0.35 0.87 0.93 23.75 6.52

482010024 Texas Harris 75.9 78.5 0.02 0.18 4.14 0.73 27.62 2.68

482010026 Texas Harris 73.5 76.1 0.01 0.24 2.95 2.36 21.17 4.44

482010055 Texas Harris 75.4 77.0 0.02 0.31 1.46 1.55 18.26 4.81

482011034 Texas Harris 76.8 77.8 0.01 0.22 4.08 1.58 22.78 4.22

482011039 Texas Harris 78.2 80.2 0.01 0.29 2.50 2.70 21.03 5.31

482011050 Texas Harris 74.6 76.2 0.01 0.31 3.95 1.84 17.62 4.39

484390075 Texas Tarrant 75.5 76.4 0.03 0.58 0.99 2.06 22.71 6.49

484392003 Texas Tarrant 79.6 82.1 0.03 0.44 1.62 1.33 23.94 6.28

484393009 Texas Tarrant 78.6 78.6 0.02 0.34 1.62 1.40 23.61 5.78

484393011 Texas Tarrant 74.5 76.6 0.03 0.69 0.65 1.54 22.51 6.25

551170006 Wisconsin Sheboygan 77.0 79.4 0.03 0.47 0.67 0.56 14.91 7.79