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APPENDIX 3: ADDITIONAL AIR QUALITY ANALYSIS AND RESULTS

 

3A.1 2011 Model Evaluation for Ozone

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

model simulation performed for the 12-km U.S. modeling domain.1 The purpose of this 

evaluation was to examine the ability of the Ozone NAAQS RIA air quality modeling platform 

to replicate the magnitude and spatial and temporal variability of measured (i.e., observed) ozone 

concentrations within the modeling domain. The model evaluation for ozone was based upon 

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). Included in the evaluation are statistical measures of model 

performance based upon model-predicted versus observed concentrations that were paired in 

space and time on an hourly basis. 

Model performance statistics were calculated for several spatial scales and temporal 

periods. Statistics were calculated for individual monitoring sites and for each of five regions of 

the 12-km U.S. modeling domain. The regions include the Northeast, Midwest, Southeast, and 

Central and Western states which are defined based upon the states contained within the 

Regional Planning Organizations (RPOs)2. For maximum daily average 8-hour (MDA8) ozone, 

the statistics for each site and region were calculated for the May through September ozone 

season.3In addition to the performance statistics, we prepared several graphical presentations of 

model performance for MDA8 ozone which is the key pollutant for the Ozone NAAQS Rule. 

These graphical presentations include:

 

1 See Chapter 3, section 3.1.2 of the RIA document for a description of the 12-km U.S. modeling domain.

2 The subregions are defined by States where: Midwest is IL, IN, MI, OH, and WI; Northeast is CT, DE,

MA, MD, ME, NH, NJ, NY, PA, RI, and VT; Southeast is AL, FL, GA, KY, MS, NC, SC, TN, VA, and

WV; Central is AR, IA, KS, LA, MN, MO, NE, OK, and TX; West is AK, CA, OR, WA, AZ, NM, CO, UT, WY,

SD, ND, MT, ID, and NV.

3

In calculating the ozone season statistics we limited the data to those observed and predicted pairs with

observations that exceeded 60 ppb in order to focus on concentrations at the upper portion of the distribution of

values.
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(1) regional maps which show the mean bias and error as well as normalized mean bias and 

error calculated for MDA8 ≥ 60 ppb for May through September at individual monitoring sites,

(2) bar and whisker plots which show the distribution of the predicted and observed data 

by month (May through September) and by region, and

(3) time series plots (May through September) of observed and predicted concentrations 

for 13 representative high ozone sites in the urban areas with the highest projected ozone levels 

in each region from the 2025 base case CAMx simulation. 

The Atmospheric Model Evaluation Tool (AMET) was used to calculate the model 

performance statistics used in this document (Gilliam et al., 2005). For this analysis and 

summary of the 2011 model evaluation for ozone, we have selected the mean bias, mean error, 

normalized mean bias, and normalized mean error to characterize model performance which are 

consistent with the recommendations in Simon et al. (2012) and the draft SIP modeling guidance 

(US EPA 2014). As noted above, we calculated the performance statistics by the May through 

September ozone season.

Mean bias (MB) is used as 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 = 1

n

∑ (P − O)

n

1

, 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 = 1

n

∑ |P − O|

n

1

Normalized mean bias (NMB) is used as a normalization to facilitate a range of 

concentration magnitudes. This statistic averages the difference (predicted - observed) over the 

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

inflating the observed range of values, especially at low concentrations. Normalized mean bias is 

given in units of % and is defined as:
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NMB = ∑ (P−O)

n

1

∑ (O)

n

1

∗ 100

Normalized mean error (NME) is also similar to NMB, where the performance statistic is 

used as a normalization of the mean error. NME calculates the absolute value of the difference 

(predicted - observed) over the sum of observed values. Normalized mean error is given in units 

of % and is defined as:

NME = ∑ |P−O|

n

1

∑ (O)

n

1

∗ 100

In general, 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., 2011; US EPA, 2005; US 

EPA, 2009; US EPA, 2011). These other modeling studies represent a wide range of modeling 

analyses which 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 performed for the Ozone NAAQS are within the range found in 

other recent applications. The model performance results, as described in this document, 

demonstrate that the predictions from the Ozone NAAQS modeling platform closely replicate the 

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

spatial differences for 8-hour daily maximum ozone. 

Consistent with EPA’s guidance for attainment demonstration modeling, we have applied 

the model predictions performed as part of the Ozone NAAQS in a relative manner for 

projecting future concentrations of ozone. The National Research Council (NRC, 2002)states 

that using air quality modeling in a relative manner “may help reduce the bias introduced by 

modeling errors and, therefore, may be more accurate than using model results directly (absolute 

values) to estimate future pollutant levels”. Thus, the results of this evaluation together with the 

manner in which we are applying model predictions gives us confidence that our air quality 
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model applications using the CAMx 2011 modeling platform provides a scientifically credible 

approach for assessing ozone for the Ozone NAAQS Rule.

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 3A-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 5-month ozone season for 

each region are shown in Figures 3A-1 through 3A-5. Spatial plots of the mean bias and error as 

well as the normalized mean bias and error for individual monitors are shown in Figures 3A-6 

and 3A-9. The statistics shown in these two figures were calculated over the ozone season using 

data pairs on days with observed 8-hour ozone of ≥ 60 ppb. Time series plots of observed and 

predicted 8-hour ozone during the ozone season at the 13 representative high ozone monitoring 

sites are provided in Figure 3A-10a-m. These sites are listed in Table 3A-2.

As indicated by the statistics in Table 3A-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 in the Western region and at rural (CASTNET) sites in the 

central region 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 3A-1 through 3A-5. The 

predicted concentrations tend to be close to the observed 25th percentile, median and 75th 

percentile values for each region, although there is a small persistent overestimation bias for 

these metrics. The CAMx model also has a tendency to under-predict the highest observational 

concentrations at both the AQS and CASTNet network sites.

Figures 3A-6 through 3A-9 show the spatial variability in bias and error at monitor 

locations. Mean bias, as seen from Figure 3A-6, is less than 6 ppb at most of the sites across the 

modeling domain. Figure 3A-7 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 over-predict from the Southeast into the Mid-Atlantic 
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States and generally under predict in the Central and Western U.S. Model performance in the 

Midwest states shows both under and over predictions. 

Model error, as seen from Figure 3A-8, is 10 ppb or less at most of the sites across the 

modeling domain. Figure 3A-9 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 is evident at 

sites in several areas most notably along portions of the Northeast Corridor 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 at 13 high ozone monitoring sites. For this site specific analysis we present the 

time series of observed and predicted 8-hour daily maximum concentrations by site over the 

ozone season, May through September. These monitors were chosen as representative high 

ozone sites in urban areas with the highest projected ozone levels in the 2025 base case 

simulation. The results, as shown in Figures A-10a through m, indicate that the modeling 

platform replicates the day-to-day variability in ozone during this time period. For example, 

several of the sites not only have minimal bias but also accurately capture both the seasonal and 

day-to-day variability in the observations: Alleghany County, PA; Frederick County, MD; 

Wayne County, MI; Jefferson County, KY. Many additional sites generally track well and 

capture day-to-day variability but underestimate some of the peak ozone days: Tarrant County, 

TX; Brazoria County, TX; Harford County, MD; Queens County, NY; Suffolk County, NY; 

Sheboygan County, WI; Douglas County, CO. Finally, the daily modeled ozone at the two 

California sites evaluated correlates well with observations but has a persistent low bias. 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 ozone concentrations. 
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Table 3A-1. Daily maximum 8-hour ozone performance statistics ≥ 60 ppb by region, by 

network.

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

AQS

Northeast 3,746 0.6 7.3 0.9 10.7

Mid-West 4,240 -0.7 7.8 -1.0 11.5

Central 6,087 -4.4 8.2 -6.4 11.9

South 6,736 2.2 7.1 3.3 10.6

West 13,568 -6.6 9.2 -9.6 13.4

CASTNet

Northeast 264 1.1 5.9 1.7 8.7

Mid-West 240 -4.2 6.6 -6.3 9.8

Central 216 -8.2 8.7 -12.4 13.1

South 443 -0.7 5.7 -1.1 8.8

West 905 -11.0 11.5 -16.0 16.7

Figure 3A-1. Distribution of observed and predicted MDA8 ozone by month for the period 

May through September for the Northeast subregion, (a) AQS network and (b) 

CASTNet network. [symbol = median; top/bottom of box = 75th/25th 

percentiles; top/bottom line = max/min values]

a b
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Figure 3A-2. Distribution of observed and predicted MDA8 ozone by month for the period 

May through September for the Southeast subregion, (a) AQS network and (b) 

CASTNet network. 

Figure 3A-3. Distribution of observed and predicted MDA8 ozone by month for the period 

May through September for the Midwest subregion, (a) AQS network and (b) 

CASTNet network. 

a b

a b
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Figure 3A-4. Distribution of observed and predicted MDA8 ozone by month for the period 

May through September for the Central states, (a) AQS network and (b) 

CASTNet network. 

Figure 3A-5. Distribution of observed and predicted MDA8 ozone by month for the period 

May through September for the West, (a) AQS network and (b) CASTNet 

network. 

b

a

a b
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Figure 3A-6. Mean Bias (ppb) of MDA8 ozone greater than 60 ppb over the period MaySeptember 2011 at AQS and CASTNet monitoring sites in 12-km U.S. modeling 

domain. 

Figure 3A-7. Normalized Mean Bias (%) of MDA8 ozone greater than 60 ppb over the 

period May-September 2011 at AQS and CASTNet monitoring sites in 12-km 

U.S. modeling domain. 
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Figure 3A-8. Mean Error (ppb) of MDA8 ozone greater than 60 ppb over the period MaySeptember 2011 at AQS and CASTNet monitoring sites in 12-km U.S. modeling 

domain. 

Figure 3A-9. Normalized Mean Error (%) of MDA8 ozone greater than 60 ppb over the 

period May-September 2011 at AQS and CASTNet monitoring sites in 12-km 

U.S. modeling domain. 
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Table 3A-2. Key monitoring sites used for the ozone time series analysis.

County State Monitoring

Site ID

Fresno California 60195001

San Bernardino California 60710005

Tarrant Texas 484392003

Brazoria Texas 480391004

Allegheny Pennsylvania 420031005

Frederick Maryland 240210037

Harford Maryland 240251001

Queens New York 360810124

Suffolk New York 361030002

Sheboygan Wisconsin 551170006

Wayne Michigan 261630019

Jefferson Kentucky 211110067

Douglas Colorado 80350004

Figure 3A-10a. Time series of observed (black) and predicted (red) MDA8 ozone for 

May through September 2011 at site 60195001 in Fresno Co., California.
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Figure 3A-10b. Time series of observed (black) and predicted (red) MDA8 ozone for 

May through September 2011 at site 60710005 in San Bernardino Co., 

California.

Figure 3A-10c. Time series of observed (black) and predicted (red) MDA8 ozone for 

May through September 2011 at site 80350004 in Douglas Co., Colorado
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Figure 3A-10d. Time series of observed (black) and predicted (red) MDA8 ozone for 

May through September 2011 at site 484392003 in Tarrant Co., Texas.

Figure 3A-10e. Time series of observed (black) and predicted (red) MDA8 ozone for 

May through September 2011 at site 480391004 in Brazoria Co., Texas.
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Figure 3A-10f. Time series of observed (black) and predicted (red) MDA8 ozone for 

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

Figure 3A-10g. Time series of observed (black) and predicted (red) MDA8 ozone for 

May through September 2011 at site 261630019 in Wayne Co., Michigan.
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Figure 3A-10h. Time series of observed (black) and predicted (red) MDA8 ozone for 

May through September 2011 at site 211110067 in Jefferson Co., Kentucky.

Figure 3A-10i. Time series of observed (black) and predicted (red) MDA8 ozone for 

May through September 2011 at site 420031005 in Allegheny Co., Pennsylvania.
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Figure 3A-10j. Time series of observed (black) and predicted (red) MDA8 ozone for 

May through September 2011 at site 240210037 in Frederick Co., Maryland.

Figure 3A-10k. Time series of observed (black) and predicted (red) MDA8 ozone for 

May through September 2011 at site 240251001 in Harford Co., Maryland.
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Figure 3A-10l. Time series of observed (black) and predicted (red) MDA8 ozone for 

May through September 2011 at site 360810124 in Queens, New York.

Figure 3A-10m. Time series of observed (black) and predicted (red) MDA8 ozone for 

May through September 2011 at site 361030002 in Suffolk County, New York.

3A.2 California Sub-regions and Areas of Influence

As discussed in chapter 3 of the ozone RIA, we performed air quality modeling to gauge 

the sensitivity of ozone to 50% and 90% cuts in NOx emissions statewide in California. When 

applying the results of these model simulations to estimate emissions reductions to attain the 

current and alternative NAAQS, we made a simplifying assumption that the model-predicted 

ozone response at locations in the San Joaquin Valley and areas of California further north are 
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solely due to emissions changes in these areas. That is, we associated the predicted ozone 

changes in northern California to emissions changes in this portion of the state even though the 

air quality model simulation included reductions statewide. We made a similar assumption about 

the response of ozone to emissions changes for the southern portion of California. The northern 

and southern source regions were identified for the purpose of determining which emissions 

reductions would be considered to impact design values at specific monitors. In calculating the 

impact of emissions changes on design values only monitors within each source regions were 

assumed to be impacted from emissions from within that source region. For VOC emissions 

reductions from available known controls, the source regions were defined to include only those 

portions of the sub-regions that also fell within the NOx buffer area for which known NOx 

controls were applied (see figure 3-4 from chapter 3 of the RIA). Therefore, when determining 

the tons of emissions reductions needed to meet various standard levels, the impacts of emissions 

reductions within each source region were applied to just those monitors located within that 

regions. In addition, we determined the areas outside of California that are expected to be most 

affected by emissions reductions from each of the two California sub-regions (i.e., downwind 

impact areas) (Figure 3A-14). When creating the BenMap and FASOMGHG surfaces described 

in section 3.4 of the main chapter, we determined the impact on ozone in these downwind areas 

due to emissions reductions from within each of the two California sub-regions.

Several considerations were considered when delineating these two California sub-regions 

for the various steps in this analysis. The spatial extent of the two California NOx source regions 

were based on the geographic boundaries of California air basins as defined by the California Air 

Resources Board (CARB). CARB designates Air Basins for the “purpose of managing air 

resources” in areas with “generally . . . similar meteorology and geographic conditions 

throughout” (http://www.arb.ca.gov/ei/maps/statemap/abmap.htm). The various Californian Air 

Basins were then combined into two larger sub-regions for this analysis. The geographic 

groupings were based on general air flow patterns which are governed by mountain topography 

and onshore/offshore wind flows. The Air Basins, sub-regions, and predominant California wind 

patterns are shown in Figure 3A-11. One county, Kern County, was split between Air Basins 

that were assigned to different sub-regions: San Joaquin Valley Air Basin (Northern sub-region) 

and Mojave Desert Air Basin (Southern sub-region). Since emissions inventories are categorized 
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A.3-19

by county, we assigned all Kern County emissions to the Northern sub-region because 

Bakersfield, the most populated area of Kern County, is located in the Northern sub-region.

Figure 3A-11.a) Depiction of governing wind patterns and topography. b) California Air 

Basins and sub-regions used for this analysis. Northern sub-region is outlined in 

pink and southern sub-region is outlined in blue. 

The downwind receptor regions outside of California were determined by examining the 

spatial patterns of ozone impacted by the the 50% cut California in NOx. Ozone changes due to 

the state-wide emissions reductions appear to follow fairly distinct widespread plumes. From 

this analysis it was determined that impacts of emissions reductions from the Northern subregion were generally limited to Oregon, Washington, and a few Nevada counties near Carson 

City. Conversely, emissions reductions from the Southern sub-region appear to have widespread 

impacts across much of the Southwestern and Central US. One caveat is that the geographic 

extent of these downwind regions are based on general transport patterns, as determined by 

examining model outputs from a single year of meteorology (i.e., 2011) and are not intended to 

fully represent the downwind transport of ozone from California emissions on all days and at all 

times. However, this approach was necessary in order to match the Northern and Southern 

California sub-region emissions reductions to ozone impacts in downwind areas outside this 

state. Figure 3A-12 shows examples of the impact on 8-hr daily maximum ozone from 50% 

California NOx cuts on a few representative days. Figure 3A-13 shows the downwind receptor 

regions that were applied in this analysis. Note that VOC impacts were only applied within the 
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A.3-20

source regions consistent with how VOC reductions were treated for other areas of the country. 

Figure 3A-14 shows that ozone impacts from a 50% cut in US anthropogenic VOC emissions 

were localized within the two California sub-regions and do not appear to impact downwind 

states.

Figure 3A-12.Impact of 50% anthropogenic California NOx cuts (ppb) on 8-hr daily 

average ozone concentrations on three days in 2011
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Figure 3A-13.Downwind California receptor regions for Northern California (green) and 

Southern California (purple)
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Figure 3A-14.Impact of 50% US anthropogenic VOC cuts (ppb) on 8-hr daily average 

ozone concentrations on three days in 2011

3A.3 VOC impact areas

As described in chapter 3, we defined VOC impact regions for the following urban areas: 

New York City, Pittsburgh, Baltimore, Detroit, Chicago, Louisville, Houston, Dallas, Denver, 

Northern California and Southern California. Not only did these areas have the highest design 

values in each region, but ozone in these areas was also sensitive to VOC emissions reductions in 

our modeling. Figure 3A-15 shows the impact of 50% US anthropogenic VOC cuts on July 

monthly average 8-hour daily maximum ozone concentrations across the US. Ozone in each of 
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A.3-23

the areas listed above is shown to have at least 0.2 ppb response to VOC emissions cuts.

Figure 3A-15.Change in July average of 8-hr daily maximum ozone concentration (ppb) 

due to 50% cut in US anthropogenic VOC emissions

3A.4 Numeric examples of calculation methodology for changes in design values

In this section we use the data for two monitoring sites to demonstrate how changes in 

design values were calculated, as described in section 3.3. For each monitor, numerical 

examples are given for calculating the emissions reductions necessary to attain the current 75 

ppb NAAQS (i.e., the baseline scenario) as well as the 65 ppb scenario, which is incremental to 

the baseline. Note that design values are truncated when they are compared to a standard level, 

so a calculated design value of 75.9 is truncated to 75 ppb and, therefore, meets the current 75 

ppb standard. Similarly, a design value of 65.9 would meet an alternative standard level of 65. 

For each monitor, we start with the base case design value, then account for ozone changes 

simulated in the 111(d) sensitivity simulation and then apply equation 3-5 from chapter 3. 

DVj = DV2025,j + (R1,j × ∆E1) + (R2,j × ∆E2) + (R3,j × ∆E3) + ⋯ Eq 3-5

Example 1. Fresno California monitor 60195001 (baseline):
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DV60195001,baseline

= 83⏟.5

DV60195001,2025

+ −⏟0.7

∆DV60195001,111d

⏞ 

NOx+VOC

+ ( −9.9 × 10 ⏟ − 5

R60195001,CAcontrl,NCA

× 15⏟ ,000 

∆ECAcontrl,NCA

)

⏞ 

NOx

+ ( −1.3 × 10 ⏟ − 4

R60195001,CAcontrol+50NOx,NCA

× 8100 ⏟ + 32 , 000 

∆Emobile,2030+∆E

)

⏞ 

NOx

+ ( −9.9 × 10 ⏟ − 6

R60195001,VOC_50,NCA

× 3200 ⏟ + 22 ,000 

∆Emobile,2030+∆E

)

⏞ 

VOC

= 75.9 ppb

Example 2. Fresno California monitor 60195001 (65 ppb scenario):

DVj,65 = 75⏟.9

DVj,baseline

+ ( −1.3 × 10 ⏟ − 4

R60195001,CAcontrol+50NOx,NCA

× 61⏟ , 000 

∆E

)

⏞ 

NOx up to 50% of CA modeled control sensitivity

+ ( −2.0 × 10 ⏟ − 4

R60195001,CAcontrol50−90NOx,NCA

× 12⏟ ,000 

∆E

)

⏞ 

NOx beyond 50% of CA modeled control sensitivity

= 65.6 ppb

Example 3. Dallas monitor 484392003 (baseline):

DV484392003,baseline = 77⏟.1

DV484392003,2025

+ −⏟1.0

∆DV484392003,111d

⏞ 

NOx+VOC

+ ( −1.6 × 10 ⏟ − 5

R484392003,TXcontrol

× 44⏟ ,000 

∆E

)

⏞ 

NOx

= 75.4 ppb

Example 4. Dallas monitor 484392003 (65 ppb scenario):
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DV484392003,65 = 75⏟.4

DV484392003,baseline

+ ( −1.6 × 10 ⏟ − 5

R484392003,TXcontrol

× 56⏟ ,000 

∆E

)

⏞ 

NOx

+ ( −1.1 × 10 ⏟ − 5

R484392003,TXcontrol+50centralNOx

× 770 ⏟ ,000 

∆E

)

⏞ 

NOx

+ ( −9.2 × 10 ⏟ − 6

R484392003,VOC_50,Dallas

× 17⏟ ,000 

∆E

)

⏞ 

VOC

= 65.9 ppb

3A.5 Emissions reductions applied to create baseline and alternative standard level 

scenarios

The following tables present emissions reductions applied in each region to create the 

baseline and alternative standard level scenarios. These emissions reductions were determined 

using the methodology described in section 3.3.2 of the main RIA and demonstrated in section 

3A.4 of this appendix. Sector-specific controls used for these reductions are discussed in more 

detail in chapter 4 of the RIA. These emissions reductions were used to create the ozone 

surfaces described in section 3.4 of the RIA.

Table 3A-3. Emissions reductions applied to create the baseline scenario*

Emissions reductions (thousand tons) applied from

2025-2030 

California mobile 

source changes

NOx reductions 

from one of the 

explicit control

cases

VOC reductions 

identified from 

maxcontrol CoST 

run

Additional NOx 

reductions

Northeast N/A N/A N/A N/A

Midwest N/A N/A N/A N/A

Central N/A

45 (TX explicit 

emissions control 

case)

N/A N/A

Southwest N/A N/A N/A N/A

California 14 (NOx) 

6 (VOC)

29 (CA explicit 

emissions control 

case)

51 (in N and S CA 

buffer region)

32 (N California); 

130 (S California)

*These emission are in addition to changes modeled in the simulation representing option 

1(state) of the proposed carbon pollution guidelines under section 111(d) of the CAA.
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Table 3A-4. Emissions reductions applied beyond the baseline scenario to create the 70 

ppb scenario

Emissions reductions (thousand tons) applied from

NOx reductions from 

one of the explicit 

control cases*

VOC reductions identified 

from maxcontrol CoST 

run

Additional NOx reductions

Northeast 110 31 (NY area); 

6 (Baltimore area)

130 (98 within NE buffer; 31 

outside the NE buffer)

Midwest N/A N/A N/A

Central 58 (TX explicit emissions 

control case) 18 (Houston area) 350 (95 within TX buffer; 260 outside of TX buffer)

Southwest N/A N/A N/A

California Exhausted in baseline 

scenario

Exhausted in baseline 

scenario

38 (N California); 

15 (S California)

Table 3A-5. Emissions reductions applied beyond the baseline scenario to create the 65 

ppb scenario

Emissions reductions (thousand tons) applied from

NOx reductions from one of 

the explicit control cases*

VOC reductions 

identified from 

maxcontrol CoST run

Additional NOx reductions

Northeast 110 36 (NY area) 400 (300 within NE buffer;

97outside the NE buffer)

Midwest 250 28 (Chicago area) 180

Central 58 (TX explicit emissions 

control case)

18 (Houston area); 

17 (Dallas area)

770 (210 within TX buffer; 

560 outside of TX buffer)

Southwest 77 7 (Denver area) 36

California Exhausted in baseline scenario Exhausted in baseline 

scenario

73 (N California); 

32 (S California)

*In regions without a modeled explicit control case (Southwest and Midwest) this represents 

equivalent emissions reductions that would have been identified in an explicit control case that 

covered the entire region

Table 3A-6. Emissions reductions applied beyond the baseline scenario to create the 60 

ppb scenario

Emissions reductions (thousand tons) applied from

NOx reductions from one 

of the explicit control 

cases*

VOC reductions 

identified from 

maxcontrol CoST run

Additional NOx reductions

Northeast 110 36 (NY area); 

6 (Baltimore area)

610 (470 within NE buffer; 150 

outside the NE buffer)

Midwest 250

32 (Chicago area);

41 (additional Chicago 

area VOC control)

620
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

A.3-27

Central 58 (TX explicit emissions 

control case)

18 (Houston area);

18 (Dallas area)

1,200 (340 within TX buffer; 

910 outside of TX buffer)

Southwest 77 7 (Denver area) 240

California Exhausted in baseline 

scenario

Exhausted in baseline 

scenario

97 (N California); 

48 (S California)

*In regions without a modeled explicit control case (Southwest and Midwest) this represents 

equivalent emissions reductions that would have been identified in an explicit control case that 

covered the entire region

3A.6 Design values for all monitors included in the quantitative analysis 

Table 3A-7. Design Values for California Region monitors

Site ID lat long State County Base Case Baseline 70 65 60

60010007 37.69 -121.78 California Alameda 66 61 57 53 48

60010009 37.74 -122.17 California Alameda 47 44 42 40 36

60010011 37.81 -122.28 California Alameda 46 43 41 39 35

60012001 37.65 -122.03 California Alameda 54 51 48 45 41

60050002 38.34 -120.76 California Amador 60 54 50 46 43

60070007 39.71 -121.62 California Butte 63 58 54 50 47

60070008 39.76 -121.84 California Butte 53 49 46 43 40

60090001 38.20 -120.68 California Calaveras 63 57 53 49 46

60111002 39.20 -122.02 California Colusa 53 49 46 43 41

60130002 37.94 -122.03 California Contra Costa 65 60 57 53 48

60131002 38.01 -121.64 California Contra Costa 65 59 56 51 47

60131004 37.96 -122.36 California Contra Costa 51 47 45 42 38

60170010 38.73 -120.82 California El Dorado 67 60 55 50 46

60170012 38.81 -120.03 California El Dorado 61 59 57 56 55

60170020 38.89 -121.00 California El Dorado 69 62 57 52 47

60190007 36.71 -119.74 California Fresno 82 74 69 64 59

60190011 36.79 -119.77 California Fresno 81 73 68 63 58

60190242 36.84 -119.87 California Fresno 81 74 70 65 60

60192009 36.63 -120.38 California Fresno 64 59 55 52 49

60194001 36.60 -119.50 California Fresno 76 69 65 60 56

60195001 36.82 -119.72 California Fresno 83 75 70 65 60

60210003 39.53 -122.19 California Glenn 56 52 49 46 44

60250005 32.68 -115.48 California Imperial 69 62 61 60 59

60254003 33.03 -115.62 California Imperial 64 54 53 51 50

60254004 33.21 -115.55 California Imperial 63 53 52 50 48

60290007 35.35 -118.85 California Kern 81 74 70 65 60

60290008 35.05 -119.40 California Kern 72 67 63 59 55

60290011 35.05 -118.15 California Kern 71 58 57 55 53

60290014 35.36 -119.04 California Kern 77 71 66 61 56
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

A.3-28

Site ID lat long State County Base Case Baseline 70 65 60

60290232 35.44 -119.02 California Kern 77 71 66 61 57

60295002 35.24 -118.79 California Kern 74 68 64 59 55

60296001 35.50 -119.27 California Kern 73 67 63 59 55

60311004 36.31 -119.64 California Kings 74 67 63 59 55

60333001 39.03 -122.92 California Lake 48 45 43 41 38

60370002 34.14 -117.92 California Los Angeles 75 55 52 48 43

60370016 34.14 -117.85 California Los Angeles 88 65 61 56 51

60370113 34.05 -118.46 California Los Angeles 62 49 46 43 39

60371002 34.18 -118.32 California Los Angeles 73 53 49 46 42

60371103 34.07 -118.23 California Los Angeles 63 48 45 41 37

60371201 34.20 -118.53 California Los Angeles 83 62 59 55 51

60371302 33.90 -118.21 California Los Angeles 57 52 51 49 47

60371602 34.01 -118.07 California Los Angeles 66 55 52 48 44

60371701 34.07 -117.75 California Los Angeles 80 62 58 54 50

60372005 34.13 -118.13 California Los Angeles 74 55 51 47 43

60374002 33.82 -118.19 California Los Angeles 55 50 49 47 45

60376012 34.38 -118.53 California Los Angeles 88 64 60 56 51

60379033 34.67 -118.13 California Los Angeles 79 60 57 54 51

60390004 36.87 -120.01 California Madera 70 64 60 56 52

60392010 36.95 -120.03 California Madera 73 67 63 59 55

60410001 37.97 -122.52 California Marin 47 44 41 39 36

60430006 37.55 -119.84 California Mariposa 65 61 58 55 53

60470003 37.28 -120.43 California Merced 71 65 61 57 53

60530002 36.50 -121.73 California Monterey 50 40 39 37 36

60530008 36.21 -121.13 California Monterey 51 41 40 39 37

60531003 36.70 -121.64 California Monterey 46 36 35 33 32

60550003 38.31 -122.30 California Napa 53 49 46 43 40

60570005 39.23 -121.06 California Nevada 63 58 54 50 47

60570007 39.32 -120.85 California Nevada 61 56 52 48 45

60590007 33.83 -117.94 California Orange 60 49 47 44 42

60591003 33.67 -117.93 California Orange 58 48 46 44 41

60592022 33.63 -117.68 California Orange 60 45 42 40 37

60595001 33.93 -117.95 California Orange 66 54 51 48 45

60610003 38.94 -121.10 California Placer 69 62 57 52 47

60610004 39.10 -120.95 California Placer 61 55 51 47 44

60610006 38.75 -121.27 California Placer 70 63 58 53 48

60650004 34.01 -117.52 California Riverside 77 60 57 53 50

60650008 33.74 -115.82 California Riverside 56 47 46 44 43

60650009 33.45 -117.09 California Riverside 60 46 43 41 39

60650012 33.92 -116.86 California Riverside 85 64 60 56 52
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

A.3-29

Site ID lat long State County Base Case Baseline 70 65 60

60650016 33.58 -117.08 California Riverside 64 48 45 43 40

60651016 33.95 -116.83 California Riverside 87 65 61 58 53

60652002 33.71 -116.22 California Riverside 74 59 57 55 52

60655001 33.85 -116.54 California Riverside 81 63 60 57 54

60656001 33.79 -117.23 California Riverside 78 58 54 51 47

60658001 34.00 -117.42 California Riverside 89 68 64 60 55

60658005 34.00 -117.49 California Riverside 85 65 61 57 53

60659001 33.68 -117.33 California Riverside 74 55 52 49 45

60659003 33.61 -114.60 California Riverside 60 54 53 52 51

60670002 38.71 -121.38 California Sacramento 65 59 54 49 45

60670006 38.61 -121.37 California Sacramento 66 60 55 50 45

60670010 38.56 -121.49 California Sacramento 61 55 51 47 42

60670011 38.30 -121.42 California Sacramento 62 56 52 48 44

60670012 38.68 -121.16 California Sacramento 77 69 64 58 52

60670014 38.65 -121.51 California Sacramento 60 54 50 46 42

60675003 38.49 -121.21 California Sacramento 72 65 60 54 49

60690002 36.84 -121.36 California San Benito 54 42 40 38 36

60690003 36.49 -121.16 California San Benito 61 48 46 45 43

60710001 34.90 -117.02 California San Bernardino 69 57 55 53 51

60710005 34.24 -117.27 California San Bernardino 99 75 70 65 60

60710012 34.43 -117.56 California San Bernardino 85 65 62 58 54

60710306 34.51 -117.33 California San Bernardino 77 60 57 54 50

60711004 34.10 -117.63 California San Bernardino 91 71 66 62 56

60711234 35.76 -117.40 California San Bernardino 65 60 59 59 58

60712002 34.10 -117.49 California San Bernardino 96 74 69 64 59

60714001 34.42 -117.29 California San Bernardino 88 68 64 60 55

60714003 34.06 -117.15 California San Bernardino 97 73 68 64 59

60719002 34.07 -116.39 California San Bernardino 82 66 63 61 58

60719004 34.11 -117.27 California San Bernardino 91 69 64 60 55

60730001 32.63 -117.06 California San Diego 60 54 53 52 51

60730003 32.79 -116.94 California San Diego 61 49 47 45 43

60730006 32.84 -117.13 California San Diego 62 50 49 47 45

60731001 32.95 -117.26 California San Diego 57 48 46 45 44

60731002 33.13 -117.08 California San Diego 58 44 42 40 38

60731006 32.84 -116.77 California San Diego 69 55 52 50 47

60731008 33.22 -117.40 California San Diego 56 44 43 41 39

60731010 32.70 -117.15 California San Diego 55 49 48 47 46

60731016 32.85 -117.12 California San Diego 59 47 46 44 42

60731201 33.36 -117.09 California San Diego 58 45 43 41 39

60732007 32.55 -116.94 California San Diego 54 48 47 46 45
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

A.3-30

Site ID lat long State County Base Case Baseline 70 65 60

60771002 37.95 -121.27 California San Joaquin 59 54 50 46 42

60773005 37.68 -121.44 California San Joaquin 70 65 61 56 52

60790005 35.63 -120.69 California San Luis Obispo 55 46 44 43 42

60792006 35.26 -120.67 California San Luis Obispo 47 38 37 36 35

60793001 35.37 -120.84 California San Luis Obispo 46 39 38 38 37

60794002 35.03 -120.50 California San Luis Obispo 50 41 40 39 37

60798001 35.49 -120.67 California San Luis Obispo 53 44 43 42 40

60798005 35.64 -120.23 California San Luis Obispo 67 55 53 52 50

60798006 35.35 -120.04 California San Luis Obispo 65 53 51 49 47

60811001 37.48 -122.20 California San Mateo 53 50 49 46 42

60830008 34.46 -120.03 California Santa Barbara 51 44 43 42 41

60830011 34.43 -119.69 California Santa Barbara 49 42 41 40 39

60831008 34.95 -120.44 California Santa Barbara 43 35 34 33 32

60831013 34.73 -120.43 California Santa Barbara 54 45 44 42 41

60831014 34.54 -119.79 California Santa Barbara 58 49 48 47 45

60831018 34.53 -120.20 California Santa Barbara 49 43 43 42 41

60831021 34.40 -119.46 California Santa Barbara 58 49 48 47 46

60831025 34.49 -120.05 California Santa Barbara 60 52 50 49 48

60832004 34.64 -120.46 California Santa Barbara 47 40 39 38 37

60832011 34.45 -119.83 California Santa Barbara 49 42 41 41 40

60833001 34.61 -120.08 California Santa Barbara 52 44 43 41 40

60834003 34.60 -120.63 California Santa Barbara 54 47 46 45 44

60850002 37.00 -121.57 California Santa Clara 58 53 50 46 42

60850005 37.35 -121.89 California Santa Clara 56 52 49 45 41

60851001 37.23 -121.98 California Santa Clara 59 54 51 47 43

60852006 37.08 -121.60 California Santa Clara 62 57 53 49 45

60852009 37.32 -122.07 California Santa Clara 56 52 49 45 41

60870007 36.98 -121.99 California Santa Cruz 47 36 34 32 30

60890004 40.55 -122.38 California Shasta 52 47 44 41 38

60890007 40.45 -122.30 California Shasta 58 53 49 46 43

60890009 40.69 -122.40 California Shasta 60 54 51 47 44

60893003 40.54 -121.57 California Shasta 58 55 53 51 50

60950004 38.10 -122.24 California Solano 53 49 46 43 39

60950005 38.23 -122.08 California Solano 58 53 50 46 43

60953003 38.36 -121.95 California Solano 58 53 50 46 43

60970003 38.44 -122.71 California Sonoma 39 36 34 32 31

60990005 37.64 -120.99 California Stanislaus 66 60 56 52 48

60990006 37.49 -120.84 California Stanislaus 76 69 64 59 55

61010003 39.14 -121.62 California Sutter 55 50 47 43 41

61010004 39.21 -121.82 California Sutter 64 59 55 52 49
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

A.3-31

Site ID lat long State County Base Case Baseline 70 65 60

61030004 40.26 -122.09 California Tehama 65 60 56 53 50

61030005 40.18 -122.24 California Tehama 63 58 55 51 49

61070009 36.49 -118.83 California Tulare 79 72 68 64 60

61072002 36.33 -119.29 California Tulare 71 64 60 56 52

61072010 36.03 -119.06 California Tulare 74 68 64 60 56

61090005 37.98 -120.38 California Tuolumne 62 58 54 51 48

61110007 34.21 -118.87 California Ventura 64 49 47 44 42

61110009 34.40 -118.81 California Ventura 66 51 49 46 44

61111004 34.45 -119.23 California Ventura 67 56 55 54 52

61112002 34.28 -118.68 California Ventura 72 55 52 50 46

61113001 34.25 -119.14 California Ventura 55 44 43 42 40

61130004 38.53 -121.77 California Yolo 57 52 49 45 42

61131003 38.66 -121.73 California Yolo 60 54 51 47 43

*The design value from the monitor(s) with the highest projected ozone in each scenario is 

shown in bold blue text

Table 3A-8. Design Values for Southwest monitors

Site ID lat long State County Base Case Baseline 70 65 60

40051008 35.21 -111.65 Arizona Coconino 63 63 63 62 60

40070010 33.65 -111.11 Arizona Gila 62 62 62 60 52

40130019 33.48 -112.14 Arizona Maricopa 65 65 65 62 51

40131004 33.56 -112.07 Arizona Maricopa 66 66 66 62 51

40131010 33.45 -111.73 Arizona Maricopa 58 58 58 55 46

40132001 33.57 -112.19 Arizona Maricopa 62 62 62 58 48

40132005 33.71 -111.86 Arizona Maricopa 62 62 62 59 50

40133002 33.46 -112.05 Arizona Maricopa 62 62 62 59 48

40133003 33.48 -111.92 Arizona Maricopa 63 63 63 60 50

40134003 33.40 -112.08 Arizona Maricopa 64 64 64 60 50

40134004 33.30 -111.88 Arizona Maricopa 61 61 61 57 48

40134005 33.41 -111.93 Arizona Maricopa 59 59 59 55 46

40134008 33.82 -112.02 Arizona Maricopa 62 62 62 59 49

40134010 33.64 -112.34 Arizona Maricopa 58 58 58 55 46

40134011 33.37 -112.62 Arizona Maricopa 56 56 56 54 47

40137003 33.29 -112.16 Arizona Maricopa 60 60 60 57 49

40137020 33.49 -111.86 Arizona Maricopa 62 62 62 58 48

40137021 33.51 -111.76 Arizona Maricopa 63 63 63 60 50

40137022 33.47 -111.81 Arizona Maricopa 60 60 60 57 48

40137024 33.51 -111.84 Arizona Maricopa 61 61 61 58 48

40139508 33.98 -111.80 Arizona Maricopa 59 59 59 56 48

40139702 33.55 -111.61 Arizona Maricopa 62 62 62 59 49
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

A.3-32

Site ID lat long State County Base Case Baseline 70 65 60

40139704 33.61 -111.73 Arizona Maricopa 62 62 62 59 49

40139706 33.72 -111.67 Arizona Maricopa 61 61 61 58 49

40139997 33.50 -112.10 Arizona Maricopa 64 64 64 61 50

40170119 34.82 -109.89 Arizona Navajo 61 61 61 58 55

40190021 32.17 -110.74 Arizona Pima 62 62 62 57 51

40191011 32.20 -110.88 Arizona Pima 57 57 57 53 47

40191018 32.43 -111.06 Arizona Pima 58 58 58 56 50

40191020 32.05 -110.77 Arizona Pima 60 60 60 55 48

40191028 32.30 -110.98 Arizona Pima 57 57 57 54 48

40191030 31.88 -111.00 Arizona Pima 60 60 60 55 50

40191032 32.17 -110.98 Arizona Pima 56 56 56 52 46

40191034 32.38 -111.13 Arizona Pima 56 56 56 53 48

40213001 33.42 -111.54 Arizona Pinal 61 61 61 58 49

40213003 32.95 -111.76 Arizona Pinal 59 59 59 57 51

40213007 32.51 -111.31 Arizona Pinal 61 61 61 59 55

40217001 33.08 -111.74 Arizona Pinal 60 60 60 58 51

40218001 33.29 -111.29 Arizona Pinal 64 64 64 61 52

40258033 34.55 -112.48 Arizona Yavapai 63 63 63 62 60

80013001 39.84 -104.95 Colorado Adams 61 61 61 58 48

80050002 39.57 -104.96 Colorado Arapahoe 66 66 66 63 53

80050006 39.64 -104.57 Colorado Arapahoe 62 62 62 58 50

80130011 39.96 -105.24 Colorado Boulder 61 61 61 58 48

80310014 39.75 -105.03 Colorado Denver 58 58 58 55 46

80310025 39.70 -105.00 Colorado Denver 58 58 58 55 46

80350004 39.53 -105.07 Colorado Douglas 68 68 68 65 55

80410013 38.96 -104.82 Colorado El Paso 65 65 65 63 58

80410016 38.85 -104.90 Colorado El Paso 67 67 67 65 60

80450012 39.54 -107.78 Colorado Garfield 62 62 62 60 55

80590002 39.80 -105.10 Colorado Jefferson 58 58 58 55 46

80590005 39.64 -105.14 Colorado Jefferson 64 64 64 61 51

80590006 39.91 -105.19 Colorado Jefferson 67 67 67 63 53

80590011 39.74 -105.18 Colorado Jefferson 66 66 66 63 52

80590013 39.54 -105.30 Colorado Jefferson 62 62 62 59 49

80677001 37.14 -107.63 Colorado La Plata 64 64 64 63 60

80677003 37.10 -107.87 Colorado La Plata 63 63 63 62 59

80690007 40.28 -105.55 Colorado Larimer 64 64 64 61 53

80690011 40.59 -105.14 Colorado Larimer 68 68 68 64 54

80690012 40.64 -105.28 Colorado Larimer 61 61 61 58 49

80691004 40.58 -105.08 Colorado Larimer 60 60 60 57 47

80770020 39.13 -108.31 Colorado Mesa 63 63 63 62 57
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

A.3-33

Site ID lat long State County Base Case Baseline 70 65 60

80810002 40.51 -107.89 Colorado Moffat 61 61 61 59 55

80830006 37.35 -108.59 Colorado Montezuma 61 61 61 60 57

80830101 37.20 -108.49 Colorado Montezuma 60 60 60 59 55

81030005 40.04 -107.85 Colorado Rio Blanco 60 60 60 58 55

81230009 40.39 -104.74 Colorado Weld 67 67 67 64 54

320010002 39.47 -118.78 Nevada Churchill 53 53 53 53 52

320030022 36.39 -114.91 Nevada Clark 61 61 61 59 55

320030023 36.81 -114.06 Nevada Clark 57 57 57 56 55

320030043 36.11 -115.25 Nevada Clark 67 67 67 65 57

320030071 36.17 -115.26 Nevada Clark 66 66 66 64 57

320030073 36.17 -115.33 Nevada Clark 66 66 66 64 57

320030075 36.27 -115.24 Nevada Clark 65 65 65 63 56

320030538 36.14 -115.06 Nevada Clark 61 61 61 60 53

320030540 36.14 -115.08 Nevada Clark 61 61 61 60 53

320030601 35.98 -114.85 Nevada Clark 65 65 65 64 60

320031019 35.79 -115.36 Nevada Clark 66 66 66 64 60

320032002 36.19 -115.12 Nevada Clark 61 61 61 60 53

320190006 39.60 -119.25 Nevada Lyon 61 61 61 60 59

320310016 39.53 -119.81 Nevada Washoe 60 60 60 59 58

320310020 39.47 -119.78 Nevada Washoe 61 61 61 60 59

320310025 39.40 -119.74 Nevada Washoe 60 60 60 60 58

320311005 39.54 -119.75 Nevada Washoe 61 61 61 60 59

320312002 39.25 -119.96 Nevada Washoe 55 55 55 55 55

320312009 39.65 -119.84 Nevada Washoe 61 61 61 60 58

325100002 39.17 -119.73 Nevada Carson City 61 61 61 60 60

350010023 35.13 -106.59 New Mexico Bernalillo 60 60 60 58 54

350010024 35.06 -106.58 New Mexico Bernalillo 61 61 61 59 55

350010027 35.15 -106.70 New Mexico Bernalillo 64 64 64 62 59

350010029 35.02 -106.66 New Mexico Bernalillo 61 61 61 60 55

350010032 35.06 -106.76 New Mexico Bernalillo 58 58 58 57 52

350011012 35.19 -106.51 New Mexico Bernalillo 64 64 64 63 59

350011013 35.19 -106.61 New Mexico Bernalillo 61 61 61 59 55

350130008 31.93 -106.63 New Mexico Dona Ana 61 61 61 60 59

350130023 32.32 -106.77 New Mexico Dona Ana 60 60 60 59 58

350171003 32.69 -108.12 New Mexico Grant 61 61 61 61 59

350250008 32.73 -103.12 New Mexico Lea 60 60 60 60 58

350290003 32.26 -107.72 New Mexico Luna 59 59 59 58 57

350431001 35.30 -106.55 New Mexico Sandoval 56 56 56 55 52

350439004 35.62 -106.72 New Mexico Sandoval 59 59 59 59 57

350450009 36.74 -107.98 New Mexico San Juan 58 58 58 57 52
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

A.3-34

Site ID lat long State County Base Case Baseline 70 65 60

350450018 36.81 -107.65 New Mexico San Juan 64 64 64 62 57

350451005 36.80 -108.47 New Mexico San Juan 56 56 56 54 50

350451233 36.81 -108.70 New Mexico San Juan 55 55 55 53 48

350490021 35.62 -106.08 New Mexico Santa Fe 60 60 60 60 58

350610008 34.81 -106.74 New Mexico Valencia 58 58 58 57 52

490030003 41.49 -112.02 Utah Box Elder 59 59 59 57 49

490037001 41.95 -112.23 Utah Box Elder 60 60 60 59 55

490050004 41.73 -111.84 Utah Cache 59 59 59 57 54

490071003 39.61 -110.80 Utah Carbon 64 64 64 60 56

490110004 40.90 -111.88 Utah Davis 61 61 61 58 52

490131001 40.21 -110.84 Utah Duchesne 63 63 63 62 59

490352004 40.74 -112.21 Utah Salt Lake 65 65 65 62 54

490353006 40.74 -111.87 Utah Salt Lake 65 65 65 61 53

490450003 40.54 -112.30 Utah Tooele 64 64 64 61 53

490490002 40.25 -111.66 Utah Utah 64 64 64 62 57

490495008 40.43 -111.80 Utah Utah 59 59 59 57 53

490495010 40.14 -111.66 Utah Utah 63 63 63 61 57

490570002 41.21 -111.98 Utah Weber 64 64 64 61 54

490571003 41.30 -111.99 Utah Weber 64 64 64 61 53

560050123 44.65 -105.29 Wyoming Campbell 60 60 60 58 53

560050456 44.15 -105.53 Wyoming Campbell 60 60 60 58 53

560070100 41.39 -107.62 Wyoming Carbon 60 60 60 58 56

560130232 43.08 -107.55 Wyoming Fremont 61 61 61 60 58

560210100 41.18 -104.78 Wyoming Laramie 61 61 61 59 54

560350700 42.49 -110.10 Wyoming Sublette 60 60 60 59 58

560370077 41.16 -108.62 Wyoming Sweetwater 60 60 60 58 55

560370200 41.68 -108.02 Wyoming Sweetwater 60 60 60 57 53

560370300 41.75 -109.79 Wyoming Sweetwater 61 61 61 60 56

560410101 41.37 -111.04 Wyoming Uinta 58 58 58 57 54

*The design value from the monitor(s) with the highest projected ozone in each scenario is 

shown in bold blue text

Table 3A-9. Design Values for Central region monitors

Site ID lat long State County Base Case Baseline 70 65 60

50199991 34.18 -93.10 Arkansas Clark 56 54 52 48 44

50350005 35.20 -90.19 Arkansas Crittenden 64 63 63 61 60

51010002 35.83 -93.21 Arkansas Newton 57 55 54 49 46

51130003 34.45 -94.14 Arkansas Polk 65 63 60 56 53

51190007 34.76 -92.28 Arkansas Pulaski 55 52 51 44 39
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

A.3-35

Site ID lat long State County Base Case Baseline 70 65 60

51191002 34.84 -92.26 Arkansas Pulaski 58 55 53 47 42

51191008 34.68 -92.33 Arkansas Pulaski 57 54 52 46 42

51430005 36.18 -94.12 Arkansas Washington 62 60 58 54 50

200910010 38.84 -94.75 Kansas Johnson 60 59 55 51 46

201030003 39.33 -94.95 Kansas Leavenworth 59 58 54 50 44

201070002 38.14 -94.73 Kansas Linn 60 58 55 51 47

201619991 39.10 -96.61 Kansas Riley 63 62 60 57 55

201730001 37.78 -97.34 Kansas Sedgwick 55 54 52 49 46

201730010 37.70 -97.31 Kansas Sedgwick 64 63 61 57 53

201730018 37.90 -97.49 Kansas Sedgwick 63 62 59 55 51

201770013 39.02 -95.71 Kansas Shawnee 63 62 59 57 53

201910002 37.48 -97.37 Kansas Sumner 66 65 63 59 55

201950001 38.77 -99.76 Kansas Trego 67 67 65 62 60

202090021 39.12 -94.64 Kansas Wyandotte 56 55 51 47 42

220050004 30.23 -90.97 Louisiana Ascension 63 62 58 54 49

220150008 32.54 -93.75 Louisiana Bossier 69 67 61 55 49

220170001 32.68 -93.86 Louisiana Caddo 67 64 59 53 47

220190002 30.14 -93.37 Louisiana Calcasieu 67 67 64 61 57

220190008 30.26 -93.28 Louisiana Calcasieu 61 61 57 55 51

220190009 30.23 -93.58 Louisiana Calcasieu 66 64 61 57 53

220330003 30.42 -91.18 Louisiana East Baton Rouge 67 67 63 58 53

220330009 30.46 -91.18 Louisiana East Baton Rouge 64 63 59 56 51

220330013 30.70 -91.06 Louisiana East Baton Rouge 59 59 55 52 48

220470009 30.22 -91.32 Louisiana Iberville 63 62 58 54 49

220470012 30.21 -91.13 Louisiana Iberville 65 65 61 57 52

220511001 30.04 -90.28 Louisiana Jefferson 63 62 59 55 50

220550007 30.22 -92.05 Louisiana Lafayette 60 59 57 54 50

220570004 29.76 -90.77 Louisiana Lafourche 61 61 57 52 47

220630002 30.31 -90.81 Louisiana Livingston 62 61 57 53 49

220710012 29.99 -90.10 Louisiana Orleans 60 58 55 51 47

220730004 32.51 -92.05 Louisiana Ouachita 59 59 54 51 46

220770001 30.68 -91.37 Louisiana Pointe Coupee 62 61 58 54 50

220870004 29.94 -89.92 Louisiana St. Bernard 61 59 57 53 50

220890003 29.98 -90.41 Louisiana St. Charles 59 58 55 51 47

220930002 29.99 -90.82 Louisiana St. James 58 57 53 49 45

220950002 30.06 -90.61 Louisiana St. John the Baptist 62 61 57 53 48

221030002 30.43 -90.20 Louisiana St. Tammany 63 62 59 56 52

221210001 30.50 -91.21 Louisiana West Baton Rouge 60 59 55 52 47

280010004 31.56 -91.39 Mississippi Adams 56 55 53 51 49

280110001 33.75 -90.72 Mississippi Bolivar 63 62 60 57 53
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

A.3-36

Site ID lat long State County Base Case Baseline 70 65 60

280330002 34.82 -89.99 Mississippi DeSoto 58 57 57 54 52

280450003 30.30 -89.40 Mississippi Hancock 54 51 51 47 44

280470008 30.39 -89.05 Mississippi Harrison 58 53 55 47 44

280490010 32.39 -90.14 Mississippi Hinds 50 49 47 44 41

280590006 30.38 -88.53 Mississippi Jackson 57 55 55 51 48

280750003 32.36 -88.73 Mississippi Lauderdale 52 51 51 48 47

280810005 34.26 -88.77 Mississippi Lee 51 51 50 49 48

281619991 34.00 -89.80 Mississippi Yalobusha 53 52 51 49 48

290030001 39.95 -94.85 Missouri Andrew 60 59 55 51 46

290190011 39.08 -92.32 Missouri Boone 57 56 53 50 46

290270002 38.71 -92.09 Missouri Callaway 55 55 52 49 46

290370003 38.76 -94.58 Missouri Cass 58 57 53 50 45

290390001 37.69 -94.04 Missouri Cedar 63 61 58 54 50

290470003 39.41 -94.27 Missouri Clay 63 62 58 53 48

290470005 39.30 -94.38 Missouri Clay 62 61 57 52 47

290470006 39.33 -94.58 Missouri Clay 64 63 58 54 48

290490001 39.53 -94.56 Missouri Clinton 64 63 58 54 48

290770036 37.26 -93.30 Missouri Greene 57 56 53 49 46

290770042 37.32 -93.20 Missouri Greene 59 58 55 51 47

290970004 37.24 -94.42 Missouri Jasper 66 62 60 54 49

290990019 38.45 -90.40 Missouri Jefferson 64 64 60 57 52

291130003 39.04 -90.86 Missouri Lincoln 63 62 59 56 53

291370001 39.48 -91.79 Missouri Monroe 58 58 55 53 50

291570001 37.70 -89.70 Missouri Perry 61 62 59 58 56

291831002 38.87 -90.23 Missouri Saint Charles 68 67 63 58 53

291831004 38.90 -90.45 Missouri Saint Charles 65 64 61 57 53

291860005 37.90 -90.42 Missouri Sainte Genevieve 60 60 57 54 50

291890005 38.49 -90.71 Missouri Saint Louis 59 58 55 51 47

291890014 38.71 -90.48 Missouri Saint Louis 66 65 61 58 53

292130004 36.71 -93.22 Missouri Taney 58 56 54 50 46

295100085 38.66 -90.20 Missouri St. Louis City 64 63 59 55 49

400019009 35.75 -94.67 Oklahoma Adair 66 62 61 55 51

400159008 35.11 -98.25 Oklahoma Caddo 64 62 60 55 50

400170101 35.48 -97.75 Oklahoma Canadian 62 62 58 54 49

400219002 35.85 -94.99 Oklahoma Cherokee 66 61 61 54 49

400270049 35.32 -97.48 Oklahoma Cleveland 64 62 59 55 50

400310651 34.63 -98.43 Oklahoma Comanche 66 65 62 59 55

400370144 36.11 -96.36 Oklahoma Creek 65 62 60 54 48

400430860 36.16 -98.93 Oklahoma Dewey 66 65 63 60 56

400719010 36.96 -97.03 Oklahoma Kay 64 62 60 56 53
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

A.3-37

Site ID lat long State County Base Case Baseline 70 65 60

400871073 35.16 -97.47 Oklahoma McClain 63 61 58 54 50

400892001 34.48 -94.66 Oklahoma McCurtain 62 60 59 55 52

400979014 36.23 -95.25 Oklahoma Mayes 68 63 63 55 50

401090033 35.48 -97.49 Oklahoma Oklahoma 66 65 61 58 53

401090096 35.48 -97.30 Oklahoma Oklahoma 65 64 60 57 52

401091037 35.61 -97.48 Oklahoma Oklahoma 67 66 62 59 54

401159004 36.92 -94.84 Oklahoma Ottawa 64 61 60 54 50

401210415 34.90 -95.78 Oklahoma Pittsburg 65 63 60 55 50

401359021 35.41 -94.52 Oklahoma Sequoyah 63 61 59 54 50

401430137 36.36 -96.00 Oklahoma Tulsa 67 64 62 55 50

401430174 35.95 -96.00 Oklahoma Tulsa 65 61 60 52 46

401430178 36.13 -95.76 Oklahoma Tulsa 66 63 60 54 48

401431127 36.20 -95.98 Oklahoma Tulsa 67 64 62 55 49

480271047 31.09 -97.68 Texas Bell 64 62 60 57 54

480290032 29.52 -98.62 Texas Bexar 69 68 65 62 58

480290052 29.63 -98.56 Texas Bexar 71 69 66 63 59

480290059 29.28 -98.31 Texas Bexar 62 60 58 54 51

480391004 29.52 -95.39 Texas Brazoria 77 75 70 65 60

480391016 29.04 -95.47 Texas Brazoria 65 63 61 57 54

480610006 25.89 -97.49 Texas Cameron 58 57 56 54 52

480850005 33.13 -96.79 Texas Collin 72 70 65 61 56

481130069 32.82 -96.86 Texas Dallas 71 70 65 61 56

481130075 32.92 -96.81 Texas Dallas 72 71 66 62 57

481130087 32.68 -96.87 Texas Dallas 71 69 65 60 55

481210034 33.22 -97.20 Texas Denton 74 72 68 63 58

481211032 33.41 -96.94 Texas Denton 72 70 66 61 56

481390016 32.48 -97.03 Texas Ellis 67 66 62 58 54

481391044 32.18 -96.87 Texas Ellis 63 60 57 53 50

481410029 31.79 -106.32 Texas El Paso 58 58 58 57 56

481410055 31.75 -106.40 Texas El Paso 63 63 62 61 60

481410057 31.67 -106.29 Texas El Paso 63 62 62 61 60

481671034 29.25 -94.86 Texas Galveston 70 69 66 62 59

481830001 32.38 -94.71 Texas Gregg 73 67 62 54 48

482010024 29.90 -95.33 Texas Harris 74 73 68 64 59

482010026 29.80 -95.13 Texas Harris 71 69 65 61 56

482010029 30.04 -95.67 Texas Harris 72 71 67 63 59

482010046 29.83 -95.28 Texas Harris 70 69 64 60 56

482010047 29.83 -95.49 Texas Harris 70 69 64 60 55

482010051 29.62 -95.47 Texas Harris 71 70 65 60 55

482010055 29.70 -95.50 Texas Harris 72 71 66 61 56
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

A.3-38

Site ID lat long State County Base Case Baseline 70 65 60

482010062 29.63 -95.27 Texas Harris 70 69 64 59 54

482010066 29.72 -95.50 Texas Harris 69 68 63 59 54

482010070 29.74 -95.32 Texas Harris 69 68 63 59 54

482010075 29.75 -95.35 Texas Harris 71 69 64 60 55

482010416 29.69 -95.29 Texas Harris 70 69 64 59 54

482011015 29.76 -95.08 Texas Harris 68 67 63 59 54

482011034 29.77 -95.22 Texas Harris 75 73 68 63 58

482011035 29.73 -95.26 Texas Harris 72 71 66 61 56

482011039 29.67 -95.13 Texas Harris 76 74 70 65 60

482011050 29.58 -95.02 Texas Harris 72 71 67 63 59

482030002 32.67 -94.17 Texas Harrison 67 63 58 52 47

482150043 26.23 -98.29 Texas Hidalgo 56 55 54 52 51

482151048 26.13 -97.94 Texas Hidalgo 55 55 53 52 50

482210001 32.44 -97.80 Texas Hood 67 66 62 58 54

482311006 33.15 -96.12 Texas Hunt 62 61 57 54 50

482450009 30.04 -94.07 Texas Jefferson 66 64 61 56 52

482450011 29.90 -93.99 Texas Jefferson 66 65 61 57 53

482450022 29.86 -94.32 Texas Jefferson 64 62 59 55 51

482450101 29.73 -93.89 Texas Jefferson 70 69 66 62 58

482450102 29.94 -94.00 Texas Jefferson 63 62 58 54 50

482450628 29.87 -93.96 Texas Jefferson 65 63 60 56 52

482451035 29.98 -94.01 Texas Jefferson 65 63 59 55 51

482510003 32.35 -97.44 Texas Johnson 70 68 64 60 56

482570005 32.56 -96.32 Texas Kaufman 64 61 58 53 50

483091037 31.65 -97.07 Texas McLennan 65 63 60 55 52

483390078 30.35 -95.43 Texas Montgomery 68 67 63 59 55

483491051 32.03 -96.40 Texas Navarro 64 61 58 53 50

483550025 27.77 -97.43 Texas Nueces 66 64 62 59 56

483550026 27.83 -97.56 Texas Nueces 66 64 62 58 55

483611001 30.09 -93.76 Texas Orange 66 64 61 57 52

483611100 30.19 -93.87 Texas Orange 63 60 58 53 49

483670081 32.87 -97.91 Texas Parker 70 68 64 61 57

483739991 30.70 -94.67 Texas Polk 62 61 59 56 54

483970001 32.94 -96.46 Texas Rockwall 68 66 62 59 54

484230007 32.34 -95.42 Texas Smith 67 64 61 56 52

484390075 32.99 -97.48 Texas Tarrant 73 71 66 62 57

484391002 32.81 -97.36 Texas Tarrant 71 70 65 61 56

484392003 32.92 -97.28 Texas Tarrant 77 75 70 65 60

484393009 32.98 -97.06 Texas Tarrant 75 73 69 64 59

484393011 32.66 -97.09 Texas Tarrant 72 70 66 61 57
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

A.3-39

Site ID lat long State County Base Case Baseline 70 65 60

484530014 30.35 -97.76 Texas Travis 65 64 61 57 54

484530020 30.48 -97.87 Texas Travis 63 61 59 55 52

484690003 28.84 -97.01 Texas Victoria 63 60 57 53 50

484790016 27.51 -99.52 Texas Webb 60 59 57 56 54

*The design value from the monitor(s) with the highest projected ozone in each scenario is 

shown in bold blue text

Table 3A-10. Design Values for Midwest monitors

Site ID lat long State County Base Case Baseline 70 65 60

170010007 39.92 -91.34 Illinois Adams 57 56 56 55 54

170190007 40.24 -88.19 Illinois Champaign 58 58 58 55 53

170191001 40.05 -88.37 Illinois Champaign 60 59 59 57 54

170230001 39.21 -87.67 Illinois Clark 58 58 58 54 50

170310001 41.67 -87.73 Illinois Cook 63 62 62 58 54

170310032 41.76 -87.55 Illinois Cook 60 59 59 60 60

170310064 41.79 -87.60 Illinois Cook 55 54 54 55 55

170310076 41.75 -87.71 Illinois Cook 63 62 62 58 54

170311003 41.98 -87.79 Illinois Cook 52 51 51 53 54

170311601 41.67 -87.99 Illinois Cook 63 63 63 59 54

170314002 41.86 -87.75 Illinois Cook 57 56 56 56 56

170314007 42.06 -87.86 Illinois Cook 49 49 49 50 51

170314201 42.14 -87.80 Illinois Cook 56 56 56 58 59

170317002 42.06 -87.67 Illinois Cook 54 54 54 57 59

170436001 41.81 -88.07 Illinois DuPage 59 58 58 54 50

170491001 39.07 -88.55 Illinois Effingham 57 57 57 54 51

170650002 38.08 -88.62 Illinois Hamilton 62 63 63 60 56

170831001 39.11 -90.32 Illinois Jersey 62 62 62 61 60

170859991 42.29 -90.00 Illinois Jo Daviess 58 57 57 56 55

170890005 42.05 -88.27 Illinois Kane 61 60 60 56 52

170971007 42.47 -87.81 Illinois Lake 58 58 58 59 60

171110001 42.22 -88.24 Illinois McHenry 60 60 60 55 51

171132003 40.52 -89.00 Illinois McLean 58 56 56 53 51

171150013 39.87 -88.93 Illinois Macon 59 58 58 56 53

171170002 39.40 -89.81 Illinois Macoupin 57 56 56 55 54

171190008 38.89 -90.15 Illinois Madison 64 63 63 62 49

171191009 38.73 -89.96 Illinois Madison 62 61 61 60 58

171193007 38.86 -90.11 Illinois Madison 63 62 62 61 49

171199991 38.87 -89.62 Illinois Madison 60 59 59 58 57

171430024 40.69 -89.61 Illinois Peoria 54 51 51 48 45
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

A.3-40

Site ID lat long State County Base Case Baseline 70 65 60

171431001 40.75 -89.59 Illinois Peoria 61 58 58 55 52

171570001 38.18 -89.79 Illinois Randolph 58 57 57 55 53

171613002 41.51 -90.52 Illinois Rock Island 49 48 48 47 45

171630010 38.61 -90.16 Illinois Saint Clair 62 61 61 60 59

171670014 39.83 -89.64 Illinois Sangamon 58 57 57 56 55

171971011 41.22 -88.19 Illinois Will 55 54 54 50 46

172012001 42.33 -89.04 Illinois Winnebago 57 56 56 53 50

180030002 41.22 -85.02 Indiana Allen 56 56 56 53 49

180030004 41.09 -85.10 Indiana Allen 57 56 56 53 50

180110001 40.00 -86.40 Indiana Boone 60 60 60 56 51

180150002 40.54 -86.55 Indiana Carroll 58 57 57 54 50

180190008 38.39 -85.66 Indiana Clark 65 65 65 59 53

180350010 40.30 -85.25 Indiana Delaware 55 54 54 51 47

180390007 41.72 -85.83 Indiana Elkhart 56 55 55 51 48

180431004 38.31 -85.83 Indiana Floyd 63 63 63 58 52

180550001 38.99 -86.99 Indiana Greene 68 67 67 62 57

180570006 40.07 -85.99 Indiana Hamilton 57 57 57 53 49

180590003 39.94 -85.84 Indiana Hancock 53 53 53 49 45

180630004 39.76 -86.40 Indiana Hendricks 56 55 55 52 48

180690002 40.96 -85.38 Indiana Huntington 54 54 54 51 48

180710001 38.92 -86.08 Indiana Jackson 57 57 57 52 47

180810002 39.42 -86.15 Indiana Johnson 57 57 57 53 48

180839991 38.74 -87.49 Indiana Knox 65 65 65 60 55

180890022 41.61 -87.30 Indiana Lake 54 54 54 52 50

180890030 41.68 -87.49 Indiana Lake 58 58 58 56 54

180892008 41.64 -87.49 Indiana Lake 58 58 58 56 54

180910005 41.72 -86.91 Indiana LaPorte 66 66 66 62 59

180910010 41.63 -86.68 Indiana LaPorte 59 59 59 56 52

180950010 40.00 -85.66 Indiana Madison 54 54 54 50 46

180970050 39.86 -86.02 Indiana Marion 60 60 60 56 51

180970057 39.75 -86.19 Indiana Marion 59 58 58 54 50

180970073 39.79 -86.06 Indiana Marion 59 59 59 55 50

180970078 39.81 -86.11 Indiana Marion 59 59 59 55 50

181090005 39.58 -86.48 Indiana Morgan 56 56 56 52 47

181230009 38.11 -86.60 Indiana Perry 65 65 65 60 54

181270024 41.62 -87.20 Indiana Porter 56 56 56 54 52

181270026 41.51 -87.04 Indiana Porter 54 54 54 51 47

181290003 38.01 -87.72 Indiana Posey 62 62 62 58 53

181410010 41.55 -86.37 Indiana St. Joseph 52 51 51 48 45

181410015 41.70 -86.21 Indiana St. Joseph 58 57 57 53 49
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

A.3-41

Site ID lat long State County Base Case Baseline 70 65 60

181411007 41.74 -86.11 Indiana St. Joseph 53 53 53 49 45

181450001 39.61 -85.87 Indiana Shelby 60 60 60 56 51

181630013 38.11 -87.54 Indiana Vanderburgh 64 63 63 59 54

181630021 38.01 -87.58 Indiana Vanderburgh 63 63 63 59 54

181670018 39.49 -87.40 Indiana Vigo 55 55 55 51 47

181670024 39.56 -87.31 Indiana Vigo 55 55 55 51 47

181699991 40.82 -85.66 Indiana Wabash 61 60 60 57 53

181730008 38.05 -87.28 Indiana Warrick 63 63 63 59 54

181730009 38.19 -87.34 Indiana Warrick 63 62 62 58 53

181730011 37.95 -87.32 Indiana Warrick 64 64 64 59 54

210130002 36.61 -83.74 Kentucky Bell 52 52 52 49 46

210150003 38.92 -84.85 Kentucky Boone 56 56 56 51 46

210190017 38.46 -82.64 Kentucky Boyd 60 60 60 54 49

210290006 37.99 -85.71 Kentucky Bullitt 60 60 60 56 51

210373002 39.02 -84.47 Kentucky Campbell 64 64 64 58 52

210430500 38.24 -82.99 Kentucky Carter 57 56 56 52 47

210470006 36.91 -87.32 Kentucky Christian 62 61 61 58 55

210590005 37.78 -87.08 Kentucky Daviess 69 68 68 64 58

210610501 37.13 -86.15 Kentucky Edmonson 57 57 57 54 50

210670012 38.07 -84.50 Kentucky Fayette 58 58 58 54 50

210890007 38.55 -82.73 Kentucky Greenup 60 60 60 54 49

210910012 37.94 -86.90 Kentucky Hancock 65 65 65 60 54

210930006 37.71 -85.85 Kentucky Hardin 58 58 58 53 49

211010014 37.87 -87.46 Kentucky Henderson 68 68 68 63 58

211110027 38.14 -85.58 Kentucky Jefferson 65 65 65 60 54

211110051 38.06 -85.90 Kentucky Jefferson 68 68 68 62 57

211110067 38.23 -85.65 Kentucky Jefferson 70 70 70 64 58

211130001 37.89 -84.59 Kentucky Jessamine 57 59 59 55 51

211390003 37.16 -88.39 Kentucky Livingston 58 62 62 58 54

211451024 37.06 -88.57 Kentucky McCracken 58 63 63 60 57

211759991 37.92 -83.07 Kentucky Morgan 60 60 60 55 50

211850004 38.40 -85.44 Kentucky Oldham 66 66 66 61 55

211930003 37.28 -83.21 Kentucky Perry 62 62 62 57 52

211950002 37.48 -82.54 Kentucky Pike 63 63 63 57 52

211990003 37.10 -84.61 Kentucky Pulaski 53 53 53 49 45

212130004 36.71 -86.57 Kentucky Simpson 54 53 53 50 46

212218001 36.78 -87.85 Kentucky Trigg 57 57 57 53 49

212219991 36.78 -87.85 Kentucky Trigg 58 58 58 54 50

212270008 37.04 -86.25 Kentucky Warren 50 50 50 47 44

212299991 37.70 -85.05 Kentucky Washington 56 57 57 53 48
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

A.3-42

Site ID lat long State County Base Case Baseline 70 65 60

260050003 42.77 -86.15 Michigan Allegan 69 69 69 64 58

260190003 44.62 -86.11 Michigan Benzie 61 61 61 56 52

260210014 42.20 -86.31 Michigan Berrien 68 68 68 63 58

260270003 41.90 -86.00 Michigan Cass 63 62 62 58 54

260370001 42.80 -84.39 Michigan Clinton 57 56 56 52 49

260490021 43.05 -83.67 Michigan Genesee 61 60 60 57 53

260492001 43.17 -83.46 Michigan Genesee 60 59 59 55 52

260630007 43.84 -82.64 Michigan Huron 61 61 61 57 54

260650012 42.74 -84.53 Michigan Ingham 57 56 56 53 49

260770008 42.28 -85.54 Michigan Kalamazoo 60 59 59 56 52

260810020 42.98 -85.67 Michigan Kent 60 60 60 56 51

260810022 43.18 -85.42 Michigan Kent 59 58 58 54 50

260910007 42.00 -83.95 Michigan Lenawee 61 60 60 57 53

260990009 42.73 -82.79 Michigan Macomb 66 65 65 61 57

260991003 42.51 -83.01 Michigan Macomb 68 68 68 64 60

261010922 44.31 -86.24 Michigan Manistee 60 60 60 55 51

261050007 43.95 -86.29 Michigan Mason 61 60 60 56 52

261130001 44.31 -84.89 Michigan Missaukee 58 57 57 54 51

261210039 43.28 -86.31 Michigan Muskegon 66 65 65 60 55

261250001 42.46 -83.18 Michigan Oakland 66 66 66 62 57

261390005 42.89 -85.85 Michigan Ottawa 63 62 62 58 53

261470005 42.95 -82.46 Michigan St. Clair 64 63 63 60 56

261530001 46.29 -85.95 Michigan Schoolcraft 60 60 60 56 52

261579991 43.61 -83.36 Michigan Tuscola 58 57 57 54 50

261610008 42.24 -83.60 Michigan Washtenaw 62 62 62 58 54

261619991 42.42 -83.90 Michigan Washtenaw 61 60 60 56 53

261630001 42.23 -83.21 Michigan Wayne 61 61 61 58 55

261630019 42.43 -83.00 Michigan Wayne 69 68 68 64 60

261659991 44.18 -85.74 Michigan Wexford 56 55 55 52 49

390030009 40.77 -84.05 Ohio Allen 61 61 61 57 53

390071001 41.96 -80.57 Ohio Ashtabula 62 62 62 57 52

390090004 39.31 -82.12 Ohio Athens 57 57 57 53 48

390170004 39.38 -84.54 Ohio Butler 66 65 65 60 55

390170018 39.53 -84.39 Ohio Butler 66 65 65 60 54

390179991 39.53 -84.73 Ohio Butler 64 63 63 59 54

390230001 40.00 -83.80 Ohio Clark 61 60 60 56 51

390230003 39.86 -84.00 Ohio Clark 60 60 60 55 50

390250022 39.08 -84.14 Ohio Clermont 62 62 62 57 51

390271002 39.43 -83.79 Ohio Clinton 62 61 61 56 51

390350034 41.56 -81.58 Ohio Cuyahoga 59 58 58 58 57
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

A.3-43

Site ID lat long State County Base Case Baseline 70 65 60

390350060 41.49 -81.68 Ohio Cuyahoga 52 51 51 51 51

390350064 41.36 -81.86 Ohio Cuyahoga 56 56 56 55 54

390355002 41.54 -81.46 Ohio Cuyahoga 58 58 58 57 57

390410002 40.36 -83.06 Ohio Delaware 59 59 59 55 51

390479991 39.64 -83.26 Ohio Fayette 57 57 57 52 48

390490029 40.08 -82.82 Ohio Franklin 66 66 66 61 56

390490037 39.97 -82.96 Ohio Franklin 61 61 61 56 52

390490081 40.09 -82.96 Ohio Franklin 58 58 58 54 49

390550004 41.52 -81.25 Ohio Geauga 60 60 60 56 52

390570006 39.67 -83.94 Ohio Greene 58 57 57 53 48

390610006 39.28 -84.37 Ohio Hamilton 68 68 68 62 56

390610010 39.21 -84.69 Ohio Hamilton 64 64 64 58 53

390610040 39.13 -84.50 Ohio Hamilton 66 66 66 60 54

390810017 40.37 -80.62 Ohio Jefferson 61 60 60 57 53

390830002 40.31 -82.69 Ohio Knox 59 59 59 55 51

390850003 41.67 -81.42 Ohio Lake 59 59 59 59 58

390850007 41.73 -81.24 Ohio Lake 53 53 53 52 52

390870011 38.63 -82.46 Ohio Lawrence 55 55 55 50 45

390870012 38.51 -82.66 Ohio Lawrence 60 60 60 54 49

390890005 40.03 -82.43 Ohio Licking 59 58 58 54 49

390930018 41.42 -82.10 Ohio Lorain 54 53 53 53 53

390950024 41.64 -83.55 Ohio Lucas 56 55 55 54 53

390950027 41.49 -83.72 Ohio Lucas 58 58 58 54 51

390950034 41.68 -83.31 Ohio Lucas 61 61 61 59 56

390970007 39.79 -83.48 Ohio Madison 59 58 58 54 49

390990013 41.10 -80.66 Ohio Mahoning 57 57 57 53 50

391030004 41.06 -81.92 Ohio Medina 57 57 57 53 49

391090005 40.08 -84.11 Ohio Miami 59 59 59 54 50

391130037 39.79 -84.13 Ohio Montgomery 62 62 62 57 52

391219991 39.94 -81.34 Ohio Noble 52 51 51 48 44

391331001 41.18 -81.33 Ohio Portage 56 56 56 52 48

391351001 39.84 -84.72 Ohio Preble 59 58 58 55 51

391510016 40.83 -81.38 Ohio Stark 61 61 61 57 52

391510022 40.71 -81.60 Ohio Stark 58 58 58 53 49

391514005 40.93 -81.12 Ohio Stark 59 58 58 54 50

391530020 41.11 -81.50 Ohio Summit 59 59 59 55 50

391550009 41.45 -80.59 Ohio Trumbull 57 56 56 53 49

391550011 41.24 -80.66 Ohio Trumbull 61 61 61 57 53

391650007 39.43 -84.20 Ohio Warren 62 62 62 57 51

391670004 39.43 -81.46 Ohio Washington 60 59 59 55 50
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

A.3-44

Site ID lat long State County Base Case Baseline 70 65 60

391730003 41.38 -83.61 Ohio Wood 60 60 60 56 52

470010101 35.97 -84.22 Tennessee Anderson 56 56 56 52 47

470090101 35.63 -83.94 Tennessee Blount 61 61 61 56 51

470090102 35.60 -83.78 Tennessee Blount 53 53 53 49 44

470259991 36.47 -83.83 Tennessee Claiborne 49 48 48 45 42

470370011 36.21 -86.74 Tennessee Davidson 51 51 51 47 43

470370026 36.15 -86.62 Tennessee Davidson 54 54 54 50 46

470419991 36.04 -85.73 Tennessee DeKalb 55 55 55 51 48

470651011 35.23 -85.18 Tennessee Hamilton 57 56 56 53 49

470654003 35.10 -85.16 Tennessee Hamilton 57 56 56 53 49

470890002 36.11 -83.60 Tennessee Jefferson 59 59 59 54 50

470930021 36.09 -83.76 Tennessee Knox 54 54 54 50 45

470931020 36.02 -83.87 Tennessee Knox 56 56 56 52 47

471050109 35.72 -84.34 Tennessee Loudon 59 58 58 54 49

471210104 35.29 -84.95 Tennessee Meigs 56 56 56 53 50

471490101 35.73 -86.60 Tennessee Rutherford 52 52 52 48 44

471550101 35.70 -83.61 Tennessee Sevier 59 58 58 55 51

471550102 35.56 -83.50 Tennessee Sevier 58 58 58 56 53

471570021 35.22 -90.02 Tennessee Shelby 63 62 62 57 53

471570075 35.15 -89.85 Tennessee Shelby 63 63 63 58 54

471571004 35.38 -89.83 Tennessee Shelby 61 59 59 56 52

471632002 36.54 -82.42 Tennessee Sullivan 62 62 62 57 52

471632003 36.58 -82.49 Tennessee Sullivan 61 61 61 56 52

471650007 36.30 -86.65 Tennessee Sumner 59 58 58 54 50

471650101 36.45 -86.56 Tennessee Sumner 55 54 54 51 47

471870106 35.95 -87.14 Tennessee Williamson 54 54 54 49 45

471890103 36.06 -86.29 Tennessee Wilson 55 55 55 51 47

540030003 39.45 -77.96 West Virginia Berkeley 56 55 55 53 50

540110006 38.42 -82.43 West Virginia Cabell 60 59 59 54 49

540219991 38.88 -80.85 West Virginia Gilmer 53 53 53 48 43

540250003 37.91 -80.63 West Virginia Greenbrier 55 55 55 51 47

540291004 40.42 -80.58 West Virginia Hancock 63 63 63 59 56

540390010 38.35 -81.63 West Virginia Kanawha 63 63 63 57 51

540610003 39.65 -79.92 West Virginia Monongalia 63 62 62 57 52

540690010 40.11 -80.70 West Virginia Ohio 61 60 60 56 52

540939991 39.09 -79.66 West Virginia Tucker 56 56 56 52 48

541071002 39.32 -81.55 West Virginia Wood 57 56 56 52 47

550090026 44.53 -87.91 Wisconsin Brown 59 58 58 55 51

550210015 43.32 -89.11 Wisconsin Columbia 56 55 55 52 50

550250041 43.10 -89.36 Wisconsin Dane 55 54 54 52 49
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

A.3-45

Site ID lat long State County Base Case Baseline 70 65 60

550270001 43.47 -88.62 Wisconsin Dodge 62 61 61 58 54

550290004 45.24 -86.99 Wisconsin Door 63 63 63 59 54

550350014 44.76 -91.14 Wisconsin Eau Claire 52 51 51 50 49

550390006 43.69 -88.42 Wisconsin Fond du Lac 61 60 60 57 53

550410007 45.56 -88.81 Wisconsin Forest 54 53 53 51 48

550550002 43.00 -88.82 Wisconsin Jefferson 58 57 57 55 52

550590019 42.50 -87.81 Wisconsin Kenosha 60 59 59 60 60

550610002 44.44 -87.51 Wisconsin Kewaunee 63 63 63 59 54

550630012 43.78 -91.23 Wisconsin La Crosse 54 53 53 52 51

550710007 44.14 -87.62 Wisconsin Manitowoc 66 65 65 60 55

550730012 44.71 -89.77 Wisconsin Marathon 54 53 53 51 49

550790010 43.02 -87.93 Wisconsin Milwaukee 56 55 55 53 51

550790026 43.06 -87.91 Wisconsin Milwaukee 60 60 60 57 54

550790085 43.18 -87.90 Wisconsin Milwaukee 65 64 64 61 57

550870009 44.31 -88.40 Wisconsin Outagamie 59 58 58 56 53

550890008 43.34 -87.92 Wisconsin Ozaukee 66 66 66 62 58

550890009 43.50 -87.81 Wisconsin Ozaukee 62 62 62 58 53

551010017 42.71 -87.80 Wisconsin Racine 57 57 57 57 56

551050024 42.51 -89.06 Wisconsin Rock 59 58 58 55 52

551110007 43.44 -89.68 Wisconsin Sauk 54 53 53 51 49

551170006 43.68 -87.72 Wisconsin Sheboygan 71 70 70 65 60

551199991 45.21 -90.60 Wisconsin Taylor 54 53 53 52 50

551270005 42.58 -88.50 Wisconsin Walworth 59 58 58 55 52

551330027 43.02 -88.22 Wisconsin Waukesha 57 57 57 53 50

*The design value from the monitor(s) with the highest projected ozone in each scenario is 

shown in bold blue text

Table 3A-11. Design Values for Northeast monitors

Site ID lat long State County Base Case Baseline 70 65 60

90010017 41.00 -73.59 Connecticut Fairfield 70 70 66 61 56

90011123 41.40 -73.44 Connecticut Fairfield 67 67 63 58 54

90013007 41.15 -73.10 Connecticut Fairfield 73 73 68 63 58

90019003 41.12 -73.34 Connecticut Fairfield 74 74 69 65 59

90031003 41.78 -72.63 Connecticut Hartford 63 63 59 54 50

90050005 41.82 -73.30 Connecticut Litchfield 58 58 54 50 46

90070007 41.55 -72.63 Connecticut Middlesex 65 65 61 56 52

90090027 41.30 -72.90 Connecticut New Haven 64 64 60 56 51

90099002 41.26 -72.55 Connecticut New Haven 72 72 67 63 57

90110124 41.35 -72.08 Connecticut New London 67 67 63 59 54

90131001 41.98 -72.39 Connecticut Tolland 63 63 59 55 50
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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Site ID lat long State County Base Case Baseline 70 65 60

90159991 41.84 -72.01 Connecticut Windham 58 58 55 51 47

100010002 38.98 -75.56 Delaware Kent 59 59 55 51 48

100031007 39.55 -75.73 Delaware New Castle 59 59 55 51 48

100031010 39.82 -75.56 Delaware New Castle 61 61 56 52 48

100031013 39.77 -75.50 Delaware New Castle 62 62 57 53 49

100032004 39.74 -75.56 Delaware New Castle 60 60 55 51 48

100051002 38.64 -75.61 Delaware Sussex 61 61 58 54 51

100051003 38.78 -75.16 Delaware Sussex 64 64 60 57 53

110010041 38.90 -76.95 D.C D.C. 59 59 55 50 46

110010043 38.92 -77.01 D.C. D.C. 62 62 58 53 49

230010014 43.97 -70.12 Maine Androscoggin 51 51 48 44 40

230052003 43.56 -70.21 Maine Cumberland 58 58 55 50 46

230090102 44.35 -68.23 Maine Hancock 59 59 56 53 50

230090103 44.38 -68.26 Maine Hancock 56 56 53 49 46

230112005 44.23 -69.79 Maine Kennebec 52 52 49 45 42

230130004 43.92 -69.26 Maine Knox 56 56 53 49 46

230173001 44.25 -70.86 Maine Oxford 46 46 45 43 42

230194008 44.74 -68.67 Maine Penobscot 48 48 45 42 39

230230006 44.01 -69.83 Maine Sagadahoc 50 50 47 44 40

230290019 44.53 -67.60 Maine Washington 50 50 48 45 43

230290032 44.96 -67.06 Maine Washington 47 47 45 43 41

230310038 43.66 -70.63 Maine York 50 50 47 44 41

230310040 43.59 -70.88 Maine York 53 53 50 47 44

230312002 43.34 -70.47 Maine York 60 60 57 52 48

240030014 38.90 -76.65 Maryland Anne Arundel 64 64 60 55 51

240051007 39.46 -76.63 Maryland Baltimore 65 65 60 55 51

240053001 39.31 -76.47 Maryland Baltimore 66 66 61 56 51

240090011 38.54 -76.62 Maryland Calvert 63 63 59 54 50

240130001 39.44 -77.04 Maryland Carroll 60 60 57 54 51

240150003 39.70 -75.86 Maryland Cecil 65 65 61 57 53

240170010 38.50 -76.81 Maryland Charles 62 62 58 54 51

240199991 38.45 -76.11 Maryland Dorchester 60 60 56 51 47

240210037 39.42 -77.38 Maryland Frederick 63 63 59 57 54

240230002 39.71 -79.01 Maryland Garrett 60 60 59 58 58

240251001 39.41 -76.30 Maryland Harford 73 73 68 62 57

240259001 39.56 -76.20 Maryland Harford 63 63 58 53 49

240290002 39.31 -75.80 Maryland Kent 62 62 57 53 49

240313001 39.11 -77.11 Maryland Montgomery 61 61 56 52 48

240330030 39.06 -76.88 Maryland

Prince 

George's 61 61 57 52 48
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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Site ID lat long State County Base Case Baseline 70 65 60

240338003 38.81 -76.74 Maryland

Prince 

George's 64 64 59 55 50

240339991 39.03 -76.82 Maryland

Prince 

George's 62 62 58 53 49

240430009 39.57 -77.72 Maryland Washington 59 59 57 55 54

245100054 39.33 -76.55 Maryland

Baltimore 

(City) 62 62 57 52 48

250010002 41.98 -70.02 Massachusetts Barnstable 60 60 57 53 48

250034002 42.64 -73.17 Massachusetts Berkshire 58 58 55 52 49

250051002 41.63 -70.88 Massachusetts Bristol 60 60 57 53 49

250070001 41.33 -70.79 Massachusetts Dukes 65 65 61 57 53

250092006 42.47 -70.97 Massachusetts Essex 58 58 56 53 50

250094005 42.81 -70.82 Massachusetts Essex 57 57 54 51 47

250095005 42.77 -71.10 Massachusetts Essex 57 57 54 50 46

250130008 42.19 -72.56 Massachusetts Hampden 60 60 57 52 48

250150103 42.40 -72.52 Massachusetts Hampshire 53 53 50 46 43

250154002 42.30 -72.33 Massachusetts Hampshire 58 58 54 50 47

250170009 42.63 -71.36 Massachusetts Middlesex 55 55 52 49 45

250171102 42.41 -71.48 Massachusetts Middlesex 55 55 52 48 45

250213003 42.21 -71.11 Massachusetts Norfolk 59 59 56 53 50

250250041 42.32 -70.97 Massachusetts Suffolk 57 57 55 52 48

250250042 42.33 -71.08 Massachusetts Suffolk 50 50 48 46 43

250270015 42.27 -71.88 Massachusetts Worcester 56 56 53 50 46

250270024 42.10 -71.62 Massachusetts Worcester 56 56 53 49 46

330012004 43.57 -71.50 New Hampshire Belknap 52 52 50 47 45

330050007 42.93 -72.27 New Hampshire Cheshire 51 51 48 46 43

330074001 44.27 -71.30 New Hampshire Coos 58 58 57 56 55

330074002 44.31 -71.22 New Hampshire Coos 51 51 50 49 48

330090010 43.63 -72.31 New Hampshire Grafton 50 50 48 46 44

330111011 42.72 -71.52 New Hampshire Hillsborough 54 54 51 48 45

330115001 42.86 -71.88 New Hampshire Hillsborough 58 58 55 51 49

330131007 43.22 -71.51 New Hampshire Merrimack 53 53 50 47 45

330150014 43.08 -70.75 New Hampshire Rockingham 55 55 52 48 44

330150016 43.05 -70.71 New Hampshire Rockingham 55 55 52 48 45

330150018 42.86 -71.38 New Hampshire Rockingham 56 56 53 49 46

340010006 39.46 -74.45 New Jersey Atlantic 61 61 57 54 50

340030006 40.87 -73.99 New Jersey Bergen 64 64 59 56 52

340071001 39.68 -74.86 New Jersey Camden 67 67 62 58 53

340110007 39.42 -75.03 New Jersey Cumberland 58 58 54 50 46

340130003 40.72 -74.19 New Jersey Essex 64 64 60 55 52
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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Site ID lat long State County Base Case Baseline 70 65 60

340150002 39.80 -75.21 New Jersey Gloucester 68 68 63 58 54

340170006 40.67 -74.13 New Jersey Hudson 64 64 59 55 51

340190001 40.52 -74.81 New Jersey Hunterdon 63 63 58 55 51

340210005 40.28 -74.74 New Jersey Mercer 65 65 60 56 52

340219991 40.31 -74.87 New Jersey Mercer 62 62 58 54 50

340230011 40.46 -74.43 New Jersey Middlesex 66 66 61 56 52

340250005 40.28 -74.01 New Jersey Monmouth 66 66 61 56 51

340273001 40.79 -74.68 New Jersey Morris 60 60 56 53 50

340290006 40.06 -74.44 New Jersey Ocean 67 67 62 57 53

340315001 41.06 -74.26 New Jersey Passaic 61 61 56 53 49

340410007 40.92 -75.07 New Jersey Warren 52 52 48 45 43

360010012 42.68 -73.76 New York Albany 57 57 52 49 46

360050133 40.87 -73.88 New York Bronx 64 64 60 56 52

360130006 42.50 -79.32 New York Chautauqua 62 62 61 59 58

360130011 42.29 -79.59 New York Chautauqua 62 62 60 59 57

360150003 42.11 -76.80 New York Chemung 57 57 55 53 52

360270007 41.79 -73.74 New York Dutchess 58 58 54 50 46

360290002 42.99 -78.77 New York Erie 61 61 60 60 60

360310002 44.37 -73.90 New York Essex 57 57 56 55 54

360310003 44.39 -73.86 New York Essex 57 57 55 55 54

360410005 43.45 -74.52 New York Hamilton 57 57 55 54 52

360430005 43.69 -74.99 New York Herkimer 55 55 54 53 52

360450002 44.09 -75.97 New York Jefferson 62 62 60 59 58

360530006 42.73 -75.78 New York Madison 55 55 53 51 49

360551007 43.15 -77.55 New York Monroe 59 59 58 57 56

360610135 40.82 -73.95 New York New York 64 64 61 58 55

360631006 43.22 -78.48 New York Niagara 64 64 63 62 58

360650004 43.30 -75.72 New York Oneida 53 53 52 50 49

360671015 43.05 -76.06 New York Onondaga 60 60 58 56 54

360715001 41.52 -74.22 New York Orange 56 56 52 48 44

360750003 43.28 -76.46 New York Oswego 58 58 56 55 53

360790005 41.46 -73.71 New York Putnam 58 58 54 50 46

360810124 40.74 -73.82 New York Queens 71 71 67 65 60

360830004 42.78 -73.46 New York Rensselaer 57 57 53 50 47

360850067 40.60 -74.13 New York Richmond 71 71 67 63 58

360870005 41.18 -74.03 New York Rockland 62 62 58 53 49

360910004 43.01 -73.65 New York Saratoga 56 56 52 49 47

360930003 42.80 -73.94 New York Schenectady 54 54 51 48 45

361010003 42.09 -77.21 New York Steuben 57 57 55 54 53

361030002 40.75 -73.42 New York Suffolk 75 75 70 65 60
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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Site ID lat long State County Base Case Baseline 70 65 60

361030004 40.96 -72.71 New York Suffolk 67 67 63 58 52

361030009 40.83 -73.06 New York Suffolk 72 72 67 62 57

361099991 42.40 -76.65 New York Tompkins 58 58 56 55 54

361111005 42.14 -74.49 New York Ulster 59 59 56 53 50

361173001 43.23 -77.17 New York Wayne 56 56 55 53 52

361192004 41.05 -73.76 New York Westchester 63 63 58 54 50

420010002 39.93 -77.25 Pennsylvania Adams 57 57 53 51 49

420019991 39.92 -77.31 Pennsylvania Adams 59 59 55 52 50

420030008 40.47 -79.96 Pennsylvania Allegheny 67 67 65 62 60

420030010 40.45 -80.02 Pennsylvania Allegheny 65 65 63 60 58

420030067 40.38 -80.17 Pennsylvania Allegheny 65 65 63 62 60

420031005 40.61 -79.73 Pennsylvania Allegheny 71 71 68 65 58

420050001 40.81 -79.56 Pennsylvania Armstrong 64 64 61 59 57

420070002 40.56 -80.50 Pennsylvania Beaver 63 63 62 62 56

420070005 40.68 -80.36 Pennsylvania Beaver 67 67 65 63 57

420070014 40.75 -80.32 Pennsylvania Beaver 64 64 63 61 60

420110006 40.51 -75.79 Pennsylvania Berks 59 59 54 51 48

420110011 40.38 -75.97 Pennsylvania Berks 62 62 57 54 51

420130801 40.54 -78.37 Pennsylvania Blair 66 66 61 58 55

420170012 40.11 -74.88 Pennsylvania Bucks 66 66 61 57 53

420210011 40.31 -78.92 Pennsylvania Cambria 62 62 58 55 52

420270100 40.81 -77.88 Pennsylvania Centre 64 64 60 57 55

420279991 40.72 -77.93 Pennsylvania Centre 65 65 61 58 55

420290100 39.83 -75.77 Pennsylvania Chester 61 61 55 51 47

420334000 41.12 -78.53 Pennsylvania Clearfield 65 65 61 58 56

420430401 40.25 -76.85 Pennsylvania Dauphin 59 59 54 51 48

420431100 40.27 -76.68 Pennsylvania Dauphin 63 63 57 53 50

420450002 39.84 -75.37 Pennsylvania Delaware 61 61 57 52 49

420479991 41.60 -78.77 Pennsylvania Elk 56 56 54 52 51

420490003 42.14 -80.04 Pennsylvania Erie 60 60 60 59 59

420550001 39.96 -77.48 Pennsylvania Franklin 56 56 53 51 49

420590002 39.81 -80.27 Pennsylvania Greene 59 59 58 57 57

420630004 40.56 -78.92 Pennsylvania Indiana 67 67 63 60 57

420690101 41.48 -75.58 Pennsylvania Lackawanna 60 60 56 53 50

420692006 41.44 -75.62 Pennsylvania Lackawanna 58 58 54 51 49

420710007 40.05 -76.28 Pennsylvania Lancaster 66 66 57 53 50

420710012 40.04 -76.11 Pennsylvania Lancaster 64 64 57 53 50

420730015 41.00 -80.35 Pennsylvania Lawrence 61 61 59 57 55

420750100 40.34 -76.38 Pennsylvania Lebanon 63 63 58 54 51

420770004 40.61 -75.43 Pennsylvania Lehigh 62 62 57 53 50
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

A.3-50

Site ID lat long State County Base Case Baseline 70 65 60

420791100 41.21 -76.00 Pennsylvania Luzerne 55 55 51 47 44

420791101 41.27 -75.85 Pennsylvania Luzerne 54 54 51 47 44

420810100 41.25 -76.92 Pennsylvania Lycoming 57 57 53 51 48

420850100 41.22 -80.48 Pennsylvania Mercer 62 62 61 60 59

420859991 41.43 -80.15 Pennsylvania Mercer 55 55 54 53 52

420890002 41.08 -75.32 Pennsylvania Monroe 54 54 50 47 45

420910013 40.11 -75.31 Pennsylvania Montgomery 63 63 58 55 51

420950025 40.63 -75.34 Pennsylvania Northampton 61 61 56 53 49

420958000 40.69 -75.24 Pennsylvania Northampton 57 57 52 49 46

420990301 40.46 -77.17 Pennsylvania Perry 59 59 55 53 51

421010004 40.01 -75.10 Pennsylvania Philadelphia 55 55 51 48 44

421010024 40.08 -75.01 Pennsylvania Philadelphia 69 69 65 60 56

421011002 40.04 -75.00 Pennsylvania Philadelphia 67 67 62 58 54

421119991 39.99 -79.25 Pennsylvania Somerset 55 55 53 51 50

421174000 41.64 -76.94 Pennsylvania Tioga 60 60 57 54 52

421250005 40.15 -79.90 Pennsylvania Washington 61 61 59 57 56

421250200 40.17 -80.26 Pennsylvania Washington 60 60 59 57 56

421255001 40.45 -80.42 Pennsylvania Washington 61 61 60 59 58

421290006 40.43 -79.69 Pennsylvania Westmoreland 63 63 60 57 55

421290008 40.30 -79.51 Pennsylvania Westmoreland 61 61 58 55 53

421330008 39.97 -76.70 Pennsylvania York 61 61 53 50 46

421330011 39.86 -76.46 Pennsylvania York 62 62 55 51 47

440030002 41.62 -71.72 Rhode Island Kent 61 61 57 52 48

440071010 41.84 -71.36 Rhode Island Providence 61 61 58 54 50

440090007 41.50 -71.42 Rhode Island Washington 64 64 60 56 51

500030004 42.89 -73.25 Vermont Bennington 54 54 51 48 46

500070007 44.53 -72.87 Vermont Chittenden 52 52 51 50 49

510030001 38.08 -78.50 Virginia Albemarle 54 54 52 50 49

510130020 38.86 -77.06 Virginia Arlington 65 65 60 55 51

510330001 38.20 -77.38 Virginia Caroline 57 57 53 49 45

510360002 37.34 -77.26 Virginia Charles 61 61 56 51 46

510410004 37.36 -77.59 Virginia Chesterfield 58 58 52 48 44

510590030 38.77 -77.10 Virginia Fairfax 65 65 60 55 51

510610002 38.47 -77.77 Virginia Fauquier 50 50 48 45 43

510690010 39.28 -78.08 Virginia Frederick 54 54 52 51 50

510719991 37.33 -80.56 Virginia Giles 51 51 50 49 49

510850003 37.61 -77.22 Virginia Hanover 58 58 53 49 45

510870014 37.56 -77.40 Virginia Henrico 61 61 55 50 46

511071005 39.02 -77.49 Virginia Loudoun 60 60 56 53 49

511130003 38.52 -78.44 Virginia Madison 59 59 57 56 56
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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Site ID lat long State County Base Case Baseline 70 65 60

511390004 38.66 -78.50 Virginia Page 55 55 54 53 52

511479991 37.17 -78.31 Virginia Prince Edward 52 52 48 46 44

511530009 38.85 -77.63 Virginia Prince William 58 58 55 52 49

511611004 37.28 -79.88 Virginia Roanoke 53 53 52 49 47

511630003 37.63 -79.51 Virginia Rockbridge 51 51 50 48 47

511650003 38.48 -78.82 Virginia Rockingham 55 55 53 53 52

511790001 38.48 -77.37 Virginia Stafford 57 57 53 48 44

511970002 36.89 -81.25 Virginia Wythe 57 57 56 55 55

515100009 38.81 -77.04 Virginia

Alexandria 

City 63 63 59 54 49

516500008 37.10 -76.39 Virginia Hampton City 58 58 55 51 47

518000004 36.90 -76.44 Virginia Suffolk City 59 59 57 53 49

518000005 36.67 -76.73 Virginia Suffolk City 56 56 54 52 50

*The design value from the monitor(s) with the highest projected ozone in each scenario is 

shown in bold blue text

3A.7 Monitors excluded from the quantitative analysis

There were 1219 ozone monitors with complete ozone data for at least one DV period

covering the years 2009-2013. Of those sites, we quantitatively analyzed 1150 in this analysis. 

As discussed in chapter 3, 69 sites were excluded from the quantitative analysis of emissions 

reductions needed to reach alternative standard levels. These sites fall into one of three 

categories, as discussed in more detail in the following three subsections.

3A.7.1 Sites without projections due insufficient days

Some monitors were excluded from the analysis because no future design value could be 

projected at the site. This occurred when there were not enough modeled high ozone days (4 or 

fewer) at the site to compute a design value according to EPA SIP modeling guidance. A list of 

the 36 sites falling into this category is given in Table 3A-12.

Table 3A-12. Monitors without projections due to insufficient high modeling days to meet 

EPA guidance for projecting design values

Site ID lat long State County

60231004 40.78 -124.18 California Humboldt

60450008 39.15 -123.20 California Mendocino

60750005 37.77 -122.40 California San Francisco

60932001 41.73 -122.63 California Siskiyou
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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Site ID lat long State County

160230101 43.46 -113.56 Idaho Butte

230031100 46.70 -68.03 Maine Aroostook

260330901 46.49 -84.36 Michigan Chippewa

270052013 46.85 -95.85 Minnesota Becker

270177416 46.71 -92.52 Minnesota Carlton

270750005 47.95 -91.50 Minnesota Lake

270834210 44.44 -95.82 Minnesota Lyon

271370034 48.41 -92.83 Minnesota Saint Louis

300298001 48.51 -114.00 Montana Flathead

300490004 46.85 -111.99 Montana Lewis and Clark

311079991 42.83 -97.85 Nebraska Knox

380070002 46.89 -103.38 North Dakota Billings

380130004 48.64 -102.40 North Dakota Burke

380150003 46.83 -100.77 North Dakota Burleigh

380171004 46.93 -96.86 North Dakota Cass

380250003 47.31 -102.53 North Dakota Dunn

380530002 47.58 -103.30 North Dakota McKenzie

380570004 47.30 -101.77 North Dakota Mercer

380650002 47.19 -101.43 North Dakota Oliver

410170122 44.02 -121.26 Oregon Deschutes

410290201 42.23 -122.79 Oregon Jackson

410591003 45.83 -119.26 Oregon Umatilla

460110003 44.35 -96.81 South Dakota Brookings

530090013 48.30 -124.62 Washington Clallam

530330080 47.57 -122.31 Washington King

530530012 46.78 -121.74 Washington Pierce

530570020 48.40 -122.50 Washington Skagit

530730005 48.95 -122.55 Washington Whatcom

550030010 46.60 -90.66 Wisconsin Ashland

551250001 46.05 -89.65 Wisconsin Vilas

560390008 43.67 -110.60 Wyoming Teton

560391011 44.56 -110.40 Wyoming Teton

3A.7.2 Winter ozone

As discussed in Chapter 2 of the RIA, high winter ozone concentrations that have been 

observed in mountain valleys in 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, 
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A.3-53

increased UV intensity due to reflection off of snow surfaces and potentially still uncharacterized 

sources of free radicals. Current modeling tools are not sufficient to properly characterize ozone 

formation for these winter ozone episodes due to 1) the challenging task of capturing complex 

local “cold pool” meteorology using a model resolution that is optimized to capture regional and 

synoptic scale process, 2) uncertainties in quantifying the local emissions from oil and gas 

operations and 3) uncertainties in the chemistry that occurs both in the atmosphere and on snow 

surfaces during these episodes. Therefore, it was not appropriate to project ozone design values 

at monitors impacted by winter events. To identify sites impacted by winter events, we examined 

the ambient data that went into creating the 2009-2013 5-year weighted design value in locations 

known to have conditions favorable for winter ozone formation (i.e. all sites in Wyoming, Utah, 

and Colorado). At these sites, we evaluated the four highest 8-hr daily maximum ozone values in 

each year from 2009-2013 to identify wintertime ozone episodes. A site was categorized as 

having a design value impacted by wintertime ozone if at least 20% of the days examined (4 out 

20) had ozone values greater than or equal to 75 ppb and occurred during a “winter” month 

(November-March). The seven sites identified as being affected by wintertime ozone events are 

listed in Table 3A-13.

Table 3A-13. Monitors determined to have design values affected by winter ozone events

Site ID lat long State County

# of 

summer

DV days*

>= 75

# of 

winter 

DV days*

>= 75

highest 

winter 8-

hr daily 

max

2009-

2013 DV

081030006 40.09 -108.76 Colorado Rio Blanco 0 7 106 71

560130099 42.53 -108.72 Wyoming Fremont 1 4 93 67

560350097 42.98 -110.35 Wyoming Sublette 0 3 83 64

560350099 42.72 -109.75 Wyoming Sublette 0 4 123 77

560350100 42.79 -110.06 Wyoming Sublette 0 4 84 67

560350101 42.87 -109.87 Wyoming Sublette 0 4 89 66

560351002 42.37 -109.56 Wyoming Sublette 0 4 94 68

*DV days defined here are the days with the 4 highest 8-hr daily maximum ozone values in each year from 2009-

2013 (20 days)

3A.7.3 Monitoring sites in rural/remote areas of the West and Southwest

As mentioned in chapter 3 of the RIA, model-predicted ozone concentrations at 26 sites in 

rural/remote areas in the West and Southwest were excluded from the quantitative analysis (see 

list of sites in Table 3A-14). All of these 26 monitoring sites have 2025 baseline concentrations 
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

A.3-54

below 70 ppb, which is the upper end of the NAAQS range being proposed by the EPA. 

Therefore, no emissions reductions would be required for these sites for a primary standard of 70 

ppb. Furthermore, only 15 of these sites would exceed a standard of 65 ppb. The remaining 11 

would only exceed a standard of 60 ppb, which is not within the EPA’s proposed range. 

These 26 sites have two common characteristics. First, they have small modeled response 

to large regional NOx and VOC reductions in 2025 compared to other sites in the region. 

Second, these monitors would have DVs that remain above the standard after applying 

reductions needed to bring large urban areas in the region into attainment. Figure 3A-16 shows 

the response of design values at all sites in the Southwest region to a 75% NOx reduction in this 

region. Although design values at many urban sites drop by more than 10 ppb beyond the 2025 

base case values, the response at the more remote and rural sites to modeled NOx reductions is 

relatively small. Many of these sites do show response between 2009-2013 DVs and base case 

2025 DVs (up to 15 ppb decreases) suggesting that national on-the-books controls and proposed 

EPA rules could lower ozone DVs in these areas. However, modeling of additional NOx 

reductions within the region provide little incremental benefit suggesting that most of the 

regional anthropogenic sources impacting ozone at these locations have already been accounted 

for in the 2025 base case scenario.
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

A.3-55

Figure 3A-16.Projected change in 2025 ozone design values with an additional 75% 

regional NOx control (Southwest region; stars represent sites identified in Table 

3A-14)

A variety of influences including transport from California and other regional transport, 

cross-border pollution from Mexico, and exceptional events (e.g., wildfires and stratospheric 

intrusions) could contribute to ozone concentrations at the 26 sites. Each of these contributors is 

described further below, along with the Clean Air Act provisions that offer varying degrees of 

regulatory relief. 

We have qualitatively characterized the predominant ozone influence for each site in Table 

3A-14. These qualitative characterizations are based on the modeled response to large regional 

NOx reductions in 2025, proximity to the Mexican border (i.e., potential influence from transborder pollution) and altitude (e.g., potential influence of ozone transported from the free 

troposphere: stratospheric intrusions or long range transport of international anthropogenic 

ozone). Figure 3A-17 shows the location of all sites listed in Table 3A-14 and for demonstrative 

purposes assigns each site to a category based on the predominant source of ozone in that 

location. As the table and figure indicate, all 26 sites have 2025 baseline design values below 70 
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

A.3-56

ppb, 15 sites have design values between 65-70 ppb, and 11 sites have design values between 60-

65 ppb. Of the 26 sites, 12 sites are characterized as border sites, 8 sites are characterized as 

being strongly influenced by California emissions, and 6 sites are influenced by other ozone 

sources. 

Table 3A-14. Monitors with limited response to regional NOx and national VOC emissions 

reductions in the 2025 baseline

Name Site ID State County Altitude 

(m)

Monitor 

Type

Predominant 

O3 Sources

2009-

2013 

DV

Baseline

DV

Chiricahua NM 40038001 Arizona Cochise 1570 CASTNET Mexican border 72 67

Grand Canyon NP 40058001 Arizona Coconino 2152 CASTNET California +

Other sources 71 66

Alamo Lake 40128000 Arizona La Paz 376 SLAMS California 71 65

Yuma Supersite 40278011 Arizona Yuma 51 SLAMS Mexican border 

+ California 75 67

El Centro-9

th st 60251003 California Imperial - SLAMS California + 

Mexican Border 81 66

Death Valley NM 60270101 California Inyo 125

Non-EPA 

Federal 

(NPS)

California + 

Other sources 71 66

Yosemite NP 60430003 California Mariposa 5265 CASTNET California + 

Other sources 77 68

Sequoia and Kings 

Canyon NP 61070006 California Tulare 1890

Non-EPA 

Federal 

(NPS)

California + 

Other sources 81 67

Gothic 80519991 Colorado Gunnison 2926 CASTNET Other sources 66 64

Weminuche 

Wilderness Area 80671004 Colorado La Plata 2367

Non-EPA 

Federal 

(USFS)

Southwest 

region + 

Other sources

72 68

Great Basin NP 320330101 Nevada White Pine 2060 CASTNET California + 

Other sources 72 66

Sunland Park City 

Yard 350130017 New Mexico Dona Ana - SLAMS Central region + Mexican border 66 63

3 Miles N of El 

Paso 350130020 New Mexico Dona Ana 1250 SLAMS

Central region + 

Mexican border 

+ Other sources

67 62
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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Name Site ID State County Altitude 

(m)

Monitor 

Type

Predominant 

O3 Sources

2009-

2013 

DV

Baseline

DV

2MI from MT 

Cristo Rey 350130021 New Mexico Dona Ana 1219 SLAMS Central region + Mexican border 71 67

US-Mexico Border 

Crossing 350130022 New Mexico Dona Ana 1280 SLAMS Central region + Mexican border 70 66

BLM land near 

Carlsbad 350151005 New Mexico Eddy 780 SLAMS

Central region + 

Southwest 

region + 

Mexican border

70 66

Big Bend NP 480430101 Texas Brewster 1052 CASTNET Mexican border 70 69

El Paso UTEP 481410037 Texas El Paso 1158 SLAMS Central region + 

Mexican border 71 67

Skyline Park 481410044 Texas El Paso 1158 SLAMS Central region + 

Mexican border 69 65

El Paso Chamizal 481410058 Texas El Paso 1201 SLAMS Central region + 

Mexican border 69 65

BLM 

Land/Carlsbad 483819991 Texas Randall 780 SLAMS

Central region + 

Mexican border 

+ Other sources

73 66

Canyonlands NP 490370101 Utah San Juan 1814 CASTNET Other sources 68 64

North Lava Flow 

Dr 490530006 Utah Washington 846 SLAMS California 68 62

Zion NP 490530130 Utah Washington 1213

Non-EPA 

Federal 

(NPS)

California + 

Other sources 71 65

Centennial 560019991 Wyoming Albany 3178 CASTNET Other sources 69 65

Pinedale 560359991 Wyoming Sublette 2388 CASTNET

Southwest 

region + Other 

sources

65 62
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

A.3-58

Figure 3A-17.Location of sites identified in Table 3A-14. Colored dots categorize sites by 

the predominant source of ozone. Many sites may be influenced by more than 

one source but are placed in a single category for illustrative purposes in this 

figure. All ozone monitoring sites categorized as not substantially affected by 

natural or transported influences in Table 3A-14 are shown as small diamonds. 

Gray diamonds represent sites that had DVs less than or equal to 60 ppb in the 

2025 baseline (or post-2025 baseline for California sites). Black diamonds 

represent sites that had DVs greater than 60 ppb in the 2025 baseline (or post2025 baseline for California sites).

In this section we look at examples of how these sites might leverage various Clean Air 

Act provisions to comply with requirements for lower alternative standard levels including: 

interstate transport provisions, exceptional events demonstrations, rural transport designations, 

and requirements for nonattainment areas in international border areas (i.e., section 179B of the 

Clean Air Act).
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Clean Air Act sections 110 and 126 have provisions designed to reduce significant 

transport contributions from upwind areas to downwind nonattainment areas. Although the RIA 

accounted for the impacts of regional NOx reductions it was not able to account for the impacts 

of emissions reductions in California on ozone at downwind sites in other states since California 

is likely to have an attainment date that is later than that of other western states. As discussed in 

the main RIA, many areas of California will not be required to meet the current ozone standard 

until after 2025 (i.e., 2027 or later) and may not be required to meet a new ozone standard until 

sometime between 2032 and 2038.4 Although California will likely implement some of the 

emissions reductions necessary to meet these standards prior to their attainment date, there is a 

considerable uncertainty about how to quantify the portion of the emissions reductions that may 

occur by 2025. Therefore, we did not account for the benefits of California emissions reductions 

on design values in downwind states.

 However, it is very likely that reductions in California emissions would lead to 

substantial reductions in DVs at some monitoring locations in downwind states. For example, as 

shown in Figure 3A-18, a 90% NOx reduction in California has the potential to substantially 

improve ozone concentrations at downwind receptors in Nevada, Utah, and Arizona. Given the 

number of monitors in California projected to violate a revised ozone standard in 2025, it is quite 

likely that the state will adopt substantial local and regional controls to reach attainment. These 

controls would benefit areas outside of California as well. In addition, California and other 

states may have obligations to reduce emissions if those emissions are contributing substantially 

to interstate transport, as required under sections 110 and 126 of the Clean Air Act. Although no 

assessment of state-receptor linkages has been completed for alternative levels of the NAAQS 

(70, 65, and 60 ppb) this figure indicates that it is possible that California (and other Western 

states) could potentially have significant benefit toward attainment at downwind receptors which 

might then be subject to interstate transport provisions of the Clean Air Act.

 

4 The EPA will likely finalize designations for a revised ozone NAAQS in late 2017. Depending on the precise 

timing of the effective date of those designations, nonattainment areas classified as Severe 15 will likely have to 

attain sometime between late 2032 and early 2033 and nonattainment areas classified as Extreme will likely have to 

attain sometime between late 2037 and early 2038. 
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Figure 3A-18.Projected change in 2025 ozone design values with an additional 90% 

California NOx control (Southwest region; stars represent sites identified in 

Table 3A-14)

An air agency can request and the EPA can agree to exclude data associated with eventinfluenced exceedances or violations of a NAAQS provided the event meets the statutory 

requirements in section 319 of the CAA: 

 The event “affects air quality.”

 The event “is not reasonably controllable or preventable.”

 The event is “caused by human activity that is unlikely to recur at a particular location or 

[is] a natural event.”5

The EPA’s implementing regulations, the 2007 Exceptional Events Rule, further specify that 

states must provide evidence that: 6

 

5 A natural event is further described in 40 CFR 50.1(k) as “an event in which human activity 

plays little or no direct causal role.”

6 See 72 Federal Register 13560 (March 22, 2007), 40 CFR Part 50.1, 40 CFR Part 50.14 and 40 

CFR Part 51.930.
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 “There is a clear causal relationship between the measurement under consideration and 

the event that is claimed to have affected the air quality in the area;”

 “The event is associated with a measured concentration in excess of normal historical 

fluctuations, including background;” and

 “There would have been no exceedance or violation but for the event.”

Once an air agency requests data exclusion by flagging the subject data and submitting 

supporting documentation showing that the data have been affected by exceptional events (e.g., 

stratospheric intrusions or wildfires) and the EPA concurs with this request, the event-influenced 

data would be excluded from the data set used in regulatory decisions, including determining 

whether or not an area is attaining or violating a NAAQS. As an example, Figure 3A-18 shows 

five years of daily ozone values at Weminuche Wilderness area in La Plata County, CO. This 

figure shows evidence of both episodic and persistent high ozone at this monitoring site between 

2009 and 2013. Several short periods of elevated ozone in springtime (March and April) could 

potentially be due to stratospheric intrusion, although more analysis would be required to 

definitively determine the source(s) contributing to these high concentrations. This figure also 

shows more prolonged periods of ozone values above 65 ppb which are unlikely to qualify for 

exclusion as exceptional events. In cases where design values are only 1-2 ppb above an 

alternative NAAQS level, excluding data from one or two days may be enough to show 

compliance with the standard even if there are still some periods with high ozone values that do 

not qualify. This is especially true because the standard is based on a three-year average. As can 

be seen in Figure 3A-19, some years (2009, 2012 and 2013) have substantially fewer days above 

65 ppb at this site than others (2010 and 2011). Eliminating a few high ozone days in 2010 and 

2011 could potentially bring this site’s projected design value below a threshold level when 

averaged with ozone concentrations from one or more low-ozone years.
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Figure 3A-19.Daily 8-hr maximum ozone values at ozone monitor in Weminuche 

Wilderness area in La Plata County Colorado from 2009-2013. Horizontal line 

provided at 65 ppb.

Other CAA provisions that could provide regulatory relief for air agencies and potential 

regulated entities include designation as a rural transport area (182(h)) or a determination that the 

area would have attained but for the contribution of international emissions (179B). Rural 

transport areas must show that the area does not contain emissions sources that substantially 

impact monitored ozone concentrations in the area or in other areas and that they are not in or 

adjacent to a Metropolitan Statistical Area (MSA). Section 179B demonstrations are used for 

areas that can demonstrate they would have attained the standard but for emissions emanating 

from outside the U.S. The EPA has used section 179B authority previously to approve attainment 

plans for Mexican border areas in El Paso, TX (O3, PM10, and CO plans); Nogales, AZ (PM10 

plan); and Imperial Valley, CA (PM10 plan). The 1-hour O3 attainment plan for El Paso, TX was 

approved by EPA as sufficient to demonstrate attainment of the NAAQS by the Moderate 

classification deadline of November 15, 1996, taking into account “but for” international 

emissions sources in Ciudad Juárez, Mexico (69 FR 32450, June 10, 2004). The state’s 
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demonstration included airshed modeling using only the U.S. emissions data because emissions 

data from Ciudad Juárez were not available.

3A.8 Calculation methodology for W126 metric

Calculation of the W126 metric occurs in several steps. The first step is to sum the 

weighted hourly ozone concentrations within each calendar month, resulting in monthly index 

values. Since plant and tree species are not photosynthetically active during nighttime hours, 

only ozone concentrations observed during daytime hours (defined as 8:00 AM to 8:00 PM local 

time) are included in the summations. The monthly W126 index values are calculated from the 

hourly ozone concentration data as follows:

Monthly W126 = ∑ ∑

Cdh

1+4403∗exp(−126∗Cdh)

19

h=8

N

d=1 Equation (3A-1)

where N is the number of days in the month, d is the day of the month (d = 1, 2, ..., N), h is the 

hour of the day (h = 0, 1, ..., 23), and Cdh is the hourly ozone concentration observed on day d, 

hour h, in parts per million.

Next, the monthly W126 index values are adjusted for missing data. If Nm is defined as the 

number of daytime ozone concentrations observed during month m (i.e. the number of terms in 

the monthly index summation), then the monthly data completeness rate is Vm = Nm / 12 * N. 

The monthly index values are adjusted by dividing them by their respective Vm. Monthly index 

values are not computed if the monthly data completeness rate is less than 75 percent (Vm < 

0.75). 

Finally, the annual W126 index values are computed as the maximum sum of their 

respective adjusted monthly index values occurring in three consecutive months (i.e., January–

March, February–April, etc.). Three-month periods spanning across two years (i.e., November–

January, December–February) are not considered, because the seasonal nature of ozone makes it 

unlikely for the maximum values to occur at that time of year. The annual W126 concentrations 

are considered valid if the data meet the annual data completeness requirements for the existing 
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standard. Three-year W126 index values are calculated by taking the average of annual W126 

index values in the same three-month period in three consecutive years.

3A.9 References

Gilliam, R. C., W. Appel, and S. Phillips. The Atmospheric Model Evaluation Tool (AMET): Meteorology Module. 

Presented at 4th Annual CMAS Models-3 Users Conference, Chapel Hill, NC, September 26 - 28, 2005. 

(http://www.cmascenter.org/)

National Research Council (NRC), 2002. Estimating the Public Health Benefits of Proposed Air Pollution 

Regulations, Washington, DC: National Academies Press.

Phillips, S., K. Wang, C. Jang, N. Possiel, M. Strum, T. Fox, 2007: Evaluation of 2002 Multi-pollutant Platform: Air 

Toxics, Ozone, and Particulate Matter, 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008.

Simon, H., Baker, K., Phillips, S., 2012: Compilation and interpretation of photochemical model performance 

statistics published between 2006 and 2012. Atmospheric Environment 61, 124-139. 

U.S. Environmental Protection Agency (US EPA), 2005; Technical Support Document for the Final Clean Air 

Interstate Rule: Air Quality Modeling; Office of Air Quality Planning and Standards; RTP, NC; (CAIR 

Docket OAR-2005-0053-2149).

U.S. Environmental Protection Agency (US EPA) 2009; Technical Support Document for the Proposal to Designate 

an Emissions Control Area for Nitrogen Oxides, Sulfur Oxides, and Particulate Matter: EPA-420-R-007, 

329pp. (http://www.epa.gov/otaq/regs/nonroad/marine/ci/420r09007.pdf)

U.S. Environmental Protection Agency (US EPA) 2014; Draft Modeling Guidance for demonstrating attainment of 

air quality goals for ozone, PM2.5, and regional haze. September 2014, U.S. Environmental Protection 

Agency, Research Triangle Park, NC, 27711.