1

Implementation of the 2015 Primary Ozone NAAQS: Issues Associated with Background Ozone

White Paper for Discussion

This paper discusses the issue of background ozone as part of the implementation of the 2015 ozone 

standards. The U.S. Environmental Protection Agency (EPA) is using this white paper to establish a 

common understanding and foundation for additional conversations on background ozone and to inform 

any further action by the Agency.

1. Overview:

The EPA recognizes that, periodically, in some locations in the U.S., sources other than domestic

manmade emissions of ozone (O3) precursors can contribute appreciably to monitored O3

concentrations. The EPA is seeking input from states, tribes, and interested stakeholders on aspects of

background O3 that are relevant to attaining the 2015 O3 NAAQS in a manner consistent with the 

provisions of the Clean Air Act (CAA). This white paper clarifies the specific definition of background O3

that EPA has used and will continue to use in addressing implementation of the O3 NAAQS, describes the 

sources and processes that lead to background O3 across the U.S., summarizes estimates of background 

O3 levels across the U.S., and describes policy tools that are available, or have been suggested, to 

address implementation challenges that result from background O3. The EPA intends to hold a workshop 

in early 2016 to discuss the information in this white paper and to further advance our collective 

understanding of the technical and policy issues associated with background O3. We will evaluate the 

need for further guidance and/or rules to address background O3 after receiving feedback on this white 

paper and after conducting the workshop.

The EPA revised the primary O3 NAAQS to a level of 0.070 ppm (70 ppb) on October 1, 2015.

1

This 

level was determined from health evidence to be requisite to protect public health with an adequate 

margin of safety.

2

The Administrator selected the final level of the NAAQS from the upper end of the 

range of proposed levels without considering the issue of proximity to background O3 concentrations in 

some areas. However, the EPA considered the extent and importance of background O3 throughout the 

NAAQS review process. This began with the integrated science assessment (ISA), which summarized the 

state of knowledge regarding background O3 in the peer-reviewed literature.3The ISA was followed by 

the policy assessment (PA), which described a pair of new air quality modeling analyses designed to 

 

1

“National Ambient Air Quality Standards for Ozone; Final Rule,” 80 Federal Register 65292 (Oct. 26, 2015; 

hereinafter “Final Ozone NAAQS”).

2 The Administrator also determined that a standard level of 0.070 ppm would provide a requisite level of 

protection to public welfare.

3 U.S. EPA (2013).
2

estimate current background O3 levels across the U.S.4The notice of proposed rulemaking (NPR)5for the 

O3 NAAQS identified CAA implementation provisions that air agencies can use to address background O3. 

The regulatory impact analysis (RIA) that accompanied the proposed rule presented O3 design value 

projections for 2025 and identified several locations in the western U.S. that had relatively small 

modeled responses to large regional NOx and VOC reductions.

6 Also, at the time of the proposal, the

EPA released a fact sheet and a summary document designed to address possible air agency and 

stakeholder implementation questions about background O3. As part of the communications material 

associated with the final rule, the EPA provided information on tools for addressing background O3. 

With regard to the remainder of this white paper, Section 2 discusses how the EPA defines 

background O3 and provides information on how background O3 is formed and estimated. Section 3 

summarizes estimates of current background O3 levels over the U.S., and Section 4 discusses how these 

levels may change in the future. Sections 5 and 6 provide preliminary conceptual models for attainment 

planning and a discussion of policy tools, respectively. Section 7 provides a preliminary list of questions 

related to background O3 and NAAQS implementation that warrant additional discussions with 

stakeholder groups. The Appendix provides more information related to modeling estimates of 

background O3, including the tables and figures referred to in this white paper. 

2. Basics of background O3: definitions, formation, and estimation techniques:

For the purposes of this white paper and the continuing discussion of background O3 issues in the 

NAAQS implementation context, the EPA considers background O3 to be any O3 formed from sources or 

processes other than U.S. manmade emissions of nitrogen oxides (NOx), volatile organic compounds 

(VOC), methane (CH4), and carbon monoxide (CO).

7

This definition of background is specifically referred 

to as U.S. background (USB).8It is important to recognize that USB does not include intrastate or 

interstate transport of manmade O3, which can also influence O3 concentrations in downwind areas, but 

which can be addressed by certain provisions of the CAA. The EPA acknowledges that stakeholders may

have their own definitions of background O3. From the highly local perspective, some may conclude that 

all emissions outside the specific locality are outside jurisdictional control and are, therefore,

background. At the other end of the spectrum, from an international perspective, some may conclude 

 

4 U.S. EPA (2014).

5

“National Ambient Air Quality Standards for Ozone; Proposed Rule”, 79 Federal Register 75234 (Dec. 17, 2014).

6 U.S. EPA (2015).

7

See Final Ozone NAAQS, 80 Federal Register at 65436.

8 Unless otherwise specified, any use of the term background from this point forward in the white paper refers 

specifically to U.S. background (USB). As part of the USB definition, one should note that determining which 

emissions are manmade, or from the U.S., can be difficult. There can be debate as to how to assign source 

categories such as international shipping or international aviation. Additionally, there is often debate as to 

whether certain types of fires (e.g., prescribed fires) should be considered manmade for the purpose of defining 

background O3.
3

that all manmade emissions are controllable and, therefore, background O3 is only generated from nonmanmade sources. 

Away from the earth’s surface, O3 can have an atmospheric lifetime on the order of weeks. As a 

result, background O3, and to a lesser extent background O3 precursors, can be transported long 

distances in the upper troposphere and be available to mix down to the surface when conditions are 

favorable. One of the largest natural sources of O3 originates from production of O3 in the stratosphere 

through interactions between ultraviolet light and molecular oxygen. O3 exists in large quantities in the 

stratosphere and natural atmospheric exchange processes can transport stratospheric air into the 

troposphere. During certain meteorological conditions, discrete plumes of stratospheric air can be 

displaced far into the troposphere and impact ground-level O3 concentrations. These events are called 

stratospheric intrusions and can result in relatively high USB levels of O3 at the surface, especially at 

higher-elevation sites.

9 Other natural sources of O3 precursor emissions include wildfires, lightning, and 

vegetation. Biogenic emissions of methane, which can be chemically converted to O3 over relatively long 

time scales, can also contribute to USB O3 levels. Finally, manmade precursor emissions from other 

countries can contribute to the global burden of O3 in the troposphere and to increased USB O3 levels.

USB O3 levels can vary considerably in space and time. When assessing USB O3 concentrations, it is 

important to clarify the averaging time being considered. From a broad characterization perspective, it 

can be useful to identify annual or seasonal mean concentrations by location. However, from an air 

quality management perspective, it is more important to consider background concentrations on 

specific high O3 days when concentrations may approach or exceed the NAAQS. Section 3 of the white 

paper summarizes the estimates of USB O3 over both categories of averaging times.

While some surface monitoring locations in certain rural areas in the inter-mountain western U.S.10

can be substantially affected by USB O3, multiple analyses have shown that even the most remote O3

monitoring locations in the U.S. are at least periodically affected by U.S. manmade emissions.

11 As a 

result, the EPA believes that it is inappropriate to assume that monitored O3 levels at a remote surface 

site (e.g., Grand Canyon or Yellowstone National Parks) can be used as a proxy for USB O3. This 

conclusion is supported by recent data analyses of rural O3 observations in Nevada12 and Utah13 in which 

it was demonstrated that natural sources, international O3 transport, O3 transported from upwind 

states, and O3 transported from urban areas within the state all contributed to monitored O3 levels at 

rural sites in these two states. Measurements of O3 above the surface (e.g., from sondes, profilers, or 

aircraft) can provide useful information about the influx of O3 from upwind locations and can be 

 

9

Langford et al. (2015); State of Wyoming Department of Environmental Quality (2013); Langford et al. (2009).

10 In this document, the term “inter-mountain western U.S.” generally refers to locations in AZ, CO, NM, NV, UT, 

WY, and the high-elevation portions of eastern CA.

11 Parrish et al. (2009); Wigder et al. (2013).

12 Fine et al. (2015).

13 State of Utah Department of Environmental Quality (2013).
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valuable toward informing USB concentrations. However, vertical profile measurements of O3 tend to be 

infrequent and spatially sparse. 

Because of the limitations in quantifying USB contributions solely from monitoring data (i.e., 

monitors cannot distinguish the origins of the measured ozone), photochemical grid models have been 

widely used as a means to estimate the contribution of background sources to observed surface O3

concentrations.

14 Several modeling studies have attempted to estimate background O3 levels by 

assessing the remaining O3 in a model simulation in which certain emissions were removed. This basic 

approach, which is often referred to as “zero-out” modeling (i.e., U.S. manmade emissions are removed)

or “emissions perturbation” modeling, has been used to estimate USB O3 levels. Another modeling 

technique, referred to as “source apportionment” modeling, can also be used to estimate the sources 

that contribute to modeled O3 concentrations. This approach estimates the contribution of certain 

source categories (e.g., natural sources, non-U.S. manmade sources) to modeled O3 at each model grid 

cell on an hourly basis. More information about the modeling estimates of USB O3 is provided in the

Appendix. Section 3 of the white paper summarizes the key findings from the EPA analyses of 

background O3 levels using both the zero-out and source apportionment techniques. As discussed 

further below, it is important to remember that model estimates of USB are limited by the biases, 

errors, and uncertainties inherently associated with modeling simulations.

3. What are the current best estimates of U.S. background O3 levels nationally?

A. Summary of previous exercises to estimate background O3 levels:

Over the past 10-15 years, multiple photochemical modeling analyses have been conducted to 

estimate the contribution of background sources on U.S. O3 levels. The EPA summarized in the ISA for 

the 2015 NAAQS review the modeling studies that were published before 2012.

15 The main points from 

this summary were: 1) seasonal mean background concentrations are highest in the inter-mountain 

western U.S., 2) seasonal mean background concentrations are generally highest in the spring and early 

summer, 3) background impacts can occur on episodic and non-episodic scales with the highest 

concentrations associated with discrete events such as stratospheric intrusions or wildfires, and 4) air 

quality models compare reasonably with one another in terms of seasonal mean O3 background 

estimates, but are not capable of precise background estimates on a daily level.

16 Table 1 provides

summary information from the ISA regarding a modeling study

17 of USB O3 by region and season at 

 

14 Fiore et al. (2003); Wang et al. (2009); Zhang et al. (2011), Emery et al. (2012), Lin et al. (2012), EPA (2014); 

Lefohn et al. (2014); Dolwick et al. (2015).

15 U.S. EPA (2013).

16 EPA (2013).

17 Zhang et al (2011).
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selected locations from the CASTNET18 monitoring network. Model estimates of seasonal mean USB, 

daily 8-hour ozone maxima (MDA8) O3 range from as high as 42 ppb in the spring at high elevation sites 

in the western U.S. (non-California) to as low as 24 ppb in the summer at sites in the northeast U.S.

Subsequent to the publication of the ISA, additional model-based estimates of background O3 have 

become available that show greater variability in model estimates of background.

19 The global 

Geophysical Fluid Dynamics Laboratory AM3 model was used to estimate springtime North American 

background (NAB) levels at high elevation western U.S. sites.

20 (NAB is similar to USB except that NAB

does not include the contribution from manmade sources of emissions in Canada and Mexico as 

background.) This study concluded that April-June mean NAB MDA8 O3 values could be as high as 50 ppb 

at many of these sites. An additional analysis used a coupled global-regional modeling system that 

included the Comprehensive Air Quality Model with Extensions (CAMx) O3 source apportionment 

technique to track the contribution of background sources to total O3 within the simulation.21 This 

analysis concluded that “emissions-influenced background,” a metric intended to represent the 

combined influence of natural sources and sources of O3 from outside the modeling domain on total 

modeled O3, as well as combined chemical interactions between the U.S. manmade and background 

sources, could comprise a substantial fraction (e.g., greater than 70 percent) of the annual-average,

total hourly O3 at high elevation sites in the western U.S. Additionally, the EPA summarized the results of 

zero-out and source apportionment-based estimates of 2007 background levels in the PA for the 2015 

O3 NAAQS review. These EPA estimates of background O3 are summarized in more detail in the next 

section, first in terms of seasonal means, then in terms of USB levels on days with high modeled O3.

B. Recent estimates of USB concentrations from the EPA

The EPA estimated 2007 seasonal (i.e., April through October) mean USB MDA8 O3 concentrations 

using a combination of the GEOS-Chem global model and the Community Multi-scale Air Quality (CMAQ)

(zero out) and CAMx (source apportionment) regional models. The two separate model approaches 

estimated similar background impacts over the rural portions of the western U.S.22 The greatest 

difference between the two model estimation approaches occurred in urban areas, where the CAMx 

source apportionment technique predicted lower USB concentrations. The general consistency between 

the two approaches increased confidence in the model findings.

 

18 The Clean Air Status and Trends Network is a national monitoring network established to assess trends in 

pollutant concentrations, atmospheric deposition and ecological effects due to changes in air pollutant emissions. 

More information on CASTNET monitoring sites is available at http://www2.epa.gov/castnet.

19 Fiore et al. (2014).

20 For this analysis, we considered a site to be high-elevation if it was located at an altitude above 1 km mean sea 

level.

21 Lefohn et al. (2014).

22 Dolwick et al. (2015).
6

The EPA modeling was also roughly consistent with the previous estimation exercises summarized in 

Section 3.A. The 2007 CMAQ and CAMx simulations estimated that seasonal mean USB MDA8 O3 levels 

ranged from 25-50 ppb across the U.S., as shown in Figure 1. Locations with seasonal mean 

contributions greater than 40 ppb are confined to the inter-mountain western U.S., with substantially 

lower values in the eastern U.S. and along the Pacific Coast. 

From a seasonal mean, fractional contribution perspective, USB was estimated to represent a 

relatively larger percentage (e.g., 60-80%) of the seasonal mean total MDA8 O3 at locations within the 

inter-mountain western U.S. and along the U.S. borders with Mexico and Canada. A few locations 

outside of these areas (such as locations in Florida) also had relatively high fractional contributions of 

USB to seasonal means, but absolute O3 concentrations modeled in these areas are lower and do not 

approach the level of the standard. In locations where O3 levels are generally higher, for example urban 

areas in California and the eastern U.S., the seasonal mean background fractions are relatively smaller 

(e.g., 40-60%). 

From an implementation perspective, the values of USB O3 on possible O3 NAAQS exceedance days 

are a more meaningful consideration than seasonal mean levels. The first draft policy assessment 

document considered this issue in detail, via a re-analysis of zero-out modeling reviewed as part of the 

ISA, and concluded that “results suggest that background concentrations on the days with the highest 

total O3 concentrations are not dramatically higher than typical seasonal average background 

concentrations.”23 Based on this finding, the EPA concluded that “anthropogenic sources within the U.S. 

are largely responsible for 4th highest 8-hour [average] daily maximum O3 [MDA8] concentrations.”24

This re-analysis examined modeling results at the national level and by region. Although absolute USB O3

concentrations were generally higher in the western U.S. at high elevation sites than at other locations 

in the U.S., this analysis showed that the general pattern of background O3 on days with high versus low 

O3 levels was also seen in the inter-mountain western U.S., making the conclusions relevant even in 

locations with the highest seasonal mean background concentrations.

The more recent modeling from the EPA using a 2007 base year, and the two distinct modeling 

methodologies described above, corroborated the finding from the previous modeling analyses. Again, 

the highest modeled O3 site-days (i.e., days of more interest from an implementation perspective) tend 

to have smaller fractional contributions from USB O3 and conversely greater contributions from U.S. 

manmade emissions. Figures 2a and 2b show the distribution of daily USB MDA8 levels (absolute 

magnitudes and relative fractions, respectively) from the source apportionment simulation. The 2007 

modeling shows that the days with highest O3 levels have similar distributions (i.e., means, inter-quartile 

ranges) of USB O3 levels as days with lower values, down to approximately 40 ppb. As a result, when 

considered from a national perspective, the proportion of total O3 that has USB origins is smaller on high 

O3 days (e.g., days > 60 ppb) than on the more common lower O3 days that tend to drive seasonal 

means. Figure 2b also indicates that there are cases in which the model predicts much larger USB

proportions, as shown by the upper outliers in the figure. These infrequent episodes usually occur in 

relation to a specific event, and occur more often in specific geographical locations, such as at high 

 

23 U.S. EPA (2014) page 2-20, based on results from Zhang et al. (2011), Emery et al. (2012), and U.S. EPA (2012).

24 U.S. EPA (2014) page 2-20.
7

elevations (e.g., due to stratospheric intrusions) or areas prone to influences from wildfires. As noted in 

the ISA, the ability of the model to capture influences from discrete events is uncertain. There are 

multiple monitor-oriented assessments (i.e., non-modeling) that have also shown substantial influence 

of sources of USB O3 on certain observed high O3 days.

25 As in the modeling, these days generally occur 

in relation to a specific event (e.g., stratospheric intrusions, wildfires). EPA is working with states and 

other researchers to develop improved models (e.g., incorporating data collected during the DISCOVERAQ field studies), and we anticipate that this work will result in increasingly improved estimates of the 

contributions of USB on high O3 days.26

Based on previous modeling exercises and the more recent EPA analyses summarized in the policy 

assessment document, the EPA believes the following three conclusions summarize the role of 

background O3 in relationship to the O3 NAAQS.

i. USB O3 can comprise a considerable fraction of the total MDA8 O3 across the U.S., with the 

largest relative contributions at higher-elevation, rural locations in the inter-mountain 

western U.S. in the spring and early summer seasons.

ii. Existing modeling analyses indicate that U.S. manmade emission sources are generally the 

dominant contributor to the modeled exceedances of the 2015 O3 NAAQS, nationally and 

within individual regions across the country. Higher O3 days generally have smaller fractional 

contributions from USB across all regions. When averaged over the entire U.S., the models 

estimate that the mean USB fractional contribution to daily maximum 8-hour average O3

concentrations above 70 ppb is less than 35 percent. As with any other modeling exercise, 

these simulations have uncertainties and potential biases/errors and the EPA plans to work 

with states on monitoring and modeling studies to further improve our estimates of USB 

contributions on high O3 days.

iii. Analyses suggest that there can be infrequent events where MDA8 O3 concentrations 

approach or exceed 70 ppb largely due to the influence of USB sources like a wildfire or 

stratospheric intrusion. As discussed in more detail in Section 6 of this white paper, the CAA 

and EPA implementation policy allow for the exclusion of air quality monitoring data from 

design value calculations when there are exceedances caused by certain event-related USB 

influences. As a result, these “exceptional events” will not factor into attainability concerns. 

The EPA analyses also indicate that there may be also be a limited number of rural areas 

where USB O3 is appreciable, but not the sole contributor to an exceedance of the NAAQS. 

Even in these areas, there is no indication that USB O3 concentrations will prevent 

attainment of the 2015 O3 NAAQS. 

 

25 California Air Resources Board (2011), State of Wyoming Department of Environmental Quality (2013), Langford 

et al. (2015).

26 Crawford and Pickering (2014).
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4. What are the expected O3 and background O3 levels across the U.S. in the near future?

A. Summary of ambient trends in USB O3

Ambient data analyses have shown that mid-tropospheric O3 concentrations in remote areas, within 

the U.S. and globally, have been increasing over the past two decades at a rate of approximately 0.4 

ppb/year within an overall uncertainty range of 0.1 to 0.7 ppb/year.

27 Whether this trend continues is 

largely dependent upon global changes in emissions of methane, as well as changes in other manmade 

O3 precursor emissions outside of the U.S., which are highly uncertain.28 Additionally, climate change has 

the potential to affect global background O3 levels via changes in temperatures, wildfire emissions, 

synoptic weather patterns and other factors that influence O3.

29

While projecting future trends in emissions is highly uncertain, NOx emissions are expected to 

continue to decline in North America and Europe out to 2030 and then stabilize. NOx emissions in East 

and South Asia, however, are expected to continue to increase. Technologies and policies do exist that, 

if implemented, could lead to an overall decrease in global NOx emissions. Implementation of an 

aggressive climate change mitigation policy might halt the growth of NOx emissions globally, due to 

changes in fuels and efficiency. Total emissions of methane are expected to continue to increase globally 

into the future, albeit at a slower rate with the implementation of an aggressive climate change 

mitigation policy. There are known emissions control technologies and policies that could significantly 

decrease methane emissions globally.30

The EPA continues to work with other federal agencies, our counterparts in other countries, and the 

international community to improve our understanding of the sources and impacts of background O3 in 

order to enable and motivate control of pollution sources in other countries that affect the U.S. Working 

with the European Commission in the context of the Long-range Transboundary Air Pollution 

Convention, we are leading an international scientific effort to improve the databases and modeling 

tools that enable us to characterize the intercontinental transport of O3 and assess potential control 

strategies. We are also working with Mexico through the Border 2020 Program31, Canada under the USCanada Air Quality Agreement32, and China through agreements on cooperation with their environment 

and science ministries33 to improve air quality management and address key sources of ozone precursor 

emissions in these countries. We are also working through multilateral efforts, such as the Global 

 

27 Cooper et al. (2012); Lin et al. (2015).

28 Task Force on Hemispheric Transport of Air Pollution (2010). 

29 Jacob and Winner (2009).

30 Amann et al. (2013); Klimont et al. (2015).

31 http://www2.epa.gov/border2020/border-2020-partners.

32 https://www.ec.gc.ca/Air/default.asp?lang=En&n=1E841873-1.

33 http://www2.epa.gov/international-cooperation/epa-collaboration-china. 
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Methane Initiative and the Climate and Clean Air Coalition to Reduce Short Lived Climate Pollutants, to 

engage governments and the private sector to achieve decreases in methane emissions which 

contribute to background O3. Ultimately, these efforts will benefit air quality in the U.S. by decreasing 

international contributions to background air pollution.

B. Estimates of future O3 levels in 2025

As part of the final RIA that accompanied the 2015 revised O3 NAAQS, the EPA conducted modeling 

for a future year of 2025 to project future O3 design values as part of an illustrative analysis to estimate 

the costs and benefits of achieving the revised O3 standards. Emissions inventories were prepared for a 

2011 base year, a 2025 base case, and several 2025 emissions sensitivity scenarios. This modeling 

assumed no change in boundary conditions or meteorology between the base and future years. The 

final RIA modeling identified 12 sites (out of 1,165 sites analyzed) in rural portions of the inter-mountain 

western U.S. that had relatively small modeled response to large regional reductions in NOx and VOC 

emissions. The EPA concluded that the O3 levels at these 12 sites were strongly influenced by USB (e.g., 

international emissions, stratospheric O3, wildfire emissions) or by interstate O3 transport from domestic 

manmade sources located outside the region. Despite the small response to regional emissions 

reductions, the RIA modeling projected enough O3 reduction to yield design values less than the 70 ppb 

standard by 2025 at these 12 sites. 

The RIA modeling also indicated that the vast majority of counties throughout the eastern U.S. with 

2014 design values above 70 ppb would be below 70 ppb by 2025 as the result of anticipated reductions 

in U.S. manmade NOx and VOC emissions in the coming years due to existing federal regulations. The 

RIA modeling also shows that additional reductions in U.S. manmade NOx and VOC emissions could 

result in attaining O3 air quality in many parts of California that currently have design values above 70 

ppb. However, areas in the southern Central Valley and other historically high O3 areas in Southern 

California have persistent high O3 (i.e., > 70 ppb) despite expected improvements. The RIA modeling 

predicts levels above 70 ppb in the Denver area, but the remainder of the inter-mountain western U.S. is 

predicted to be at levels below 70 ppb by 2025. 

5. Preliminary conceptual model of O3 attainment planning over the U.S. for the revised NAAQS

Under the 2-year schedule required by CAA 107(d)(1) for initial area designations following the 

promulgation of a new or revised NAAQS, the EPA is required to make designation decisions for the 

2015 O3 NAAQS by October 2017, and generally, EPA would rely on monitoring data for the most recent 

3-year period in making such designations, which would mean using 2014-2016 data in making a 2017 

designation determination.

34 In order to build an understanding of contributions to O3 levels above 70 

ppb and a conceptual model of attainment planning, the EPA has compiled the most recent site-specific 

 

34 Such period may be extended for up to one year in the event the EPA Administrator has insufficient information 

to promulgate the designations.
10

O3 design values,

35 recent emissions estimates by county,36 and more recent CAMx modeled source 

attribution estimates.

37 The CAMx source apportionment data summarize the fraction of the near-future 

(2017) O3 design value prediction that is due to U.S. manmade sources, as well as the fraction that is due 

to in-state anthropogenic emissions.

Tables 2a, 2b, and 2c show the 2012-2014 O3 design values, model source apportionment data, and 

2011 NOx emissions data for all counties with at least one monitoring site that exceeded 70 ppb during 

the 2012-2014 period (i.e., the most recent period of official data), for three regions of the country: the

eastern U.S., California, and non-California portions of the western U.S. For sites with multiple monitors 

above the 70 ppb threshold, data are only provided for the location with the highest O3. The purpose of 

Tables 2a, 2b, and 2c is to combine several existing data sets (i.e., design values, emissions, source 

apportionment modeling) to examine the variability in these data in counties with 2012-2014 design 

values above 70 ppb and to inform preliminary conceptual models of O3 attainment planning. While the 

existing emissions, design values, and source apportionment data all represent different years (2011, 

2014, and 2017, respectively), the EPA believes the data can inform the conceptual models described 

below.

Eastern U.S. - As shown in Table 2a, there were 178 counties in the eastern U.S. with a monitor for 

which the design value exceeded 70 ppb for 2012-2014. The CAMx source apportionment modeling 

suggests that the highest O3 values in this region are caused predominantly by U.S. manmade sources, 

either from local in-state emissions or from interstate transport of manmade O3 from other states.

Across the 178 eastern U.S. counties with design values that exceeded 70 ppb for the 2012-2014 period, 

the average fractional contribution of U.S. manmade emissions to O3 design values was estimated to be 

64 percent, ranging from a low of 39 percent (Bell County, TX) to a high of 75 percent (Washington 

County, RI). Only three counties had an estimated U.S. manmade contribution of less than 50 percent. 

The information suggests the preliminary conceptual model of O3 attainment planning in the eastern 

U.S. would be to continue to employ measures that would achieve local and regional NOx and VOC 

reductions, which have been successful in lowering O3 levels in the eastern U.S. over the past several 

decades.

38

California - A slightly different conceptual model of O3 attainment planning is seen within California 

(Table 2b). At most locations across California, there is nearly equal contribution from manmade 

emissions in California and USB sources, with generally small impacts from manmade transport from 

outside the state. The average contribution of U.S. manmade emissions in the 27 California counties 

 

35 See design value information available at http://www3.epa.gov/airtrends/values.html.

36 County level NOx emissions were pulled from version 2 of the 2011 NEI. Provide link to documentation.

37 Based on 2017 CAMx source apportionment modeling that was released publically on January 22, 2015 as part of 

the memo: Information on the Interstate Transport “Good Neighbor” Provisions for the 2008 O3 National Ambient 

Air Quality Standards under Clean Air Act Section 110(a)(2)(D)(i)(I). A copy of this memo and related documents 

can be found at the following website: http://www3.epa.gov/airtransport/ozonetransportNAAQS.html

38 Cooper et al. (2012); Simon et al. (2014).
11

with design values that exceeded 70 ppb based on the 2012-2014 data is 50 percent, ranging from a low 

of 31 percent (Imperial County) to a high of 63 percent (Orange County). This suggests the conceptual 

model of attainment planning in California will be to continue to seek in-state NOx and VOC emission

reductions, while assessing the impact of event-driven USB sources like fires and stratospheric 

intrusions. The USB impacts of international emissions may also need to be assessed in California 

locations close to the Mexican border (e.g., Imperial County, and to a lesser degree San Diego County).

Non-California Portions of Western U.S. - As noted earlier in this white paper, the effects of USB O3

are most notable at a relatively small number of sites in the inter-mountain western U.S. As shown in 

Table 2c, there are 26 counties with at least one site where the 2012-2014 design value exceeds 70 ppb.

Across these 26 counties, there is a wide range of the extent to which USB influences O3 design values.

In certain highly urban locations in this region, such as Denver (Adams, Douglas, and Jefferson Counties,

CO) and Phoenix (Maricopa County, AZ), the modeling suggests a sizeable contribution to the ozone 

design values from U.S. manmade sources, ranging from 45 to 50 percent. In other urban locations, such 

as Las Vegas (Clark County, NV) or Salt Lake City (Salt Lake County, UT), the contribution from U.S. 

manmade emissions is smaller, with values around 30 percent. At rural sites within this region, the 

contribution from U.S. manmade emissions is still smaller. The CAMx modeling indicated that the county 

with the lowest influence from U.S. manmade emissions (i.e., the highest contribution from USB) is El 

Paso County, CO with only a 10 percent contribution from U.S. manmade sources to the projected 2017 

O3 design value. Overall, this information suggests that it will be important to assess and account for the 

contributions from USB sources to O3 nonattainment in this region, particularly in the rural portions.

It should be noted that any conclusions from this initial conceptual model of attainment planning for 

the 2015 O3 NAAQS are subject to change pending additional information, such as updated design value 

data, updated emissions data, updated O3 trends, and any updated attribution modeling. For instance, 

the currently available 2015 O3 data (through the end of September 2015) suggest that O3 levels were 

lower in 2015 than in 2012 at almost all of the sites in the inter-mountain western U.S. Thus, the 3-year 

design values for 2015 (and beyond) may be lower than the 2014 design values shown here, and fewer 

monitors may be above 70 ppb at the time that the EPA would complete initial area designations.

39

6. Overview of policy tools and issues for consideration:

Some states and other stakeholders have expressed concern about the fairness and practicality of 

applying the CAA’s regulatory relief mechanisms in locations where it can be argued that nearby 

manmade emissions are not largely responsible for elevated O3 levels.40 They argue that the CAA’s relief 

mechanisms provide insufficient relief, or they express skepticism that state and federal air 

 

39 Prior to the EPA making final designation decisions, we expect quality-assured, certified air quality monitoring 

data from 2016 will be available, and the EPA’s final designation decisions will be based on data from 2014 to 

2016.

40 Association of Air Pollution Control Agencies (2015).
12

management agencies will be able to efficiently and successfully apply the CAA’s provisions without 

significant burden. 

Policy tools are available, or have been recommended by commenters, to apply to areas 

experiencing exceedances of the O3 NAAQS that are appreciably impacted by USB O3. The tool(s) 

available for each affected location will depend on the specific nature of background O3 in each area. 

Some tools would provide relief from a nonattainment designation; others would only provide relief 

from some of the CAA-prescribed nonattainment area requirements. To employ any of the available

tools, states would need to work cooperatively with the EPA to develop supporting documentation and 

to take whatever public process steps are legally necessary to use the relief provisions.41

Exceptional Events Exclusions (CAA section 319): Air monitoring data that would otherwise indicate 

an exceedance of the O3 standards and lead to a nonattainment designation may be excluded from 

designation determinations, if the data are determined to be affected by exceptional events. From an air 

quality perspective, an exceptional event is one that affects air quality, is not reasonably controllable or 

preventable, and is either a natural event or one caused by human activity that is unlikely to recur at a 

particular location.

42 It should be noted that not all sources of background O3 meet these criteria (e.g., 

routine biogenic VOC emissions, international manmade emissions). Other sources that contribute to 

background O3 (e.g., wildfires, stratospheric intrusions) may be eligible for treatment as exceptional 

events. A state may request that the EPA exclude data showing one or more exceedances of the NAAQS 

from design value calculations, which could be used in regulatory determinations, if it can demonstrate 

that an exceptional event caused the exceedance. The EPA proposed revisions to the 2007 Exceptional 

Events Rule in November 2015 to further facilitate review and approval of O3-producing events, such as 

stratospheric intrusions and wildfires. The EPA intends to issue a final rule in the summer of 2016. In 

some locations, the exclusion of data influenced by exceptional events may affect whether the design 

value for the location exceeds the 70 ppb standard. In other words, exclusion of one or more 

exceedances may mean that an area that would otherwise violate the standard is instead meeting it and

thus would be designated “unclassifiable/attainment.” Also, in some locations, the exclusion of data 

influenced by exceptional events may not result in a design value that meets the standard, but may 

lower the design value such that the area qualifies for a lower nonattainment classification and thus the 

area would be subject to fewer mandatory CAA requirements.

Small nonattainment area boundaries for sites minimally impacted by nearby sources (CAA section 

107(d)): The CAA requires a nonattainment area to be comprised of the area not meeting the NAAQS 

and the nearby area that is contributing to the area not meeting the NAAQS. At monitor locations 

exceeding the 70 ppb standard where there are no or few nearby permanent sources of O3 precursors, 

or where nearby sources are shown to be unlikely contributors on days with high O3, states can 

recommend, and the EPA may be able to finalize, a nonattainment area boundary that includes a limited 

 

41 Beyond the four policy tools discussed below, three other mechanisms for accounting for background ozone in 

the implementation of the new NAAQS have been suggested. These include: a) revising data handling procedures 

to exclude exceedances attributable to background O3, b) deferring designations in locations impacted by 

background O3, and/or c) designating areas influenced by background O3 as unclassifiable. These additional 

mechanisms were not included here due to legal or other deficiencies.

42

“Treatment of Data Influenced by Exceptional Events; Proposed Rule”, 80 Federal Register 224 (20 November

2015), pp. 72840-72897.
13

area associated with a reasonable jurisdictional boundary, for example, a park boundary for a monitor 

located in a national park. Additionally, land above a certain elevation for high elevation sites with no 

local sources, or other appropriate indicators may also be well-suited for a small nonattainment area 

boundary (see, for example, Tehama County, CA where portions of the area above 1,800 feet in 

elevation were designated nonattainment for the 2008 O3 standard). In some instances, these relatively 

small nonattainment areas may also help support a state’s request that an area be identified as a Rural 

Transport Area, a determination that provides relief from certain otherwise applicable requirements. A 

relatively small nonattainment boundary also limits the area subject to nonattainment New Source 

Review (NSR) permitting and federal conformity.

Rural transport areas (RTAs) (CAA section 182(h)): The RTA provisions of the CAA allow the EPA 

Administrator to determine that a nonattainment area can be treated as if it were a Marginal 

nonattainment area regardless of the area’s design value and regardless of whether the area attains the 

standard by any given deadline. To qualify, a nonattainment area must not be adjacent to, or include 

any part of, a Metropolitan Statistical Area (MSA) and must not have sources of NOx and VOC that 

significantly contribute to the violation in the area or to violations in other areas. If a state demonstrates 

to the satisfaction of the EPA Administrator that these conditions are met for an area, the state would 

not be required to develop an attainment plan and demonstration for the area. Four O3 nonattainment 

areas have previously been approved for RTA status: Door County Area, WI; Edmonson County Area, KY; 

Essex County Area (Whiteface Mountain), NY; and Smyth County Area (White Top Mountain), VA. These 

RTAs were approved for the 1-hour O3 standard. The EPA will work cooperatively with states to develop 

the request for an RTA determination, and also provide assistance with meeting other CAA-required 

implementation program provisions for Marginal nonattainment areas (e.g., emissions statement rules, 

periodic emissions inventory, nonattainment NSR program). The EPA is currently planning to include 

more specific guidance on how to demonstrate eligibility for a RTA determination in the forthcoming 

area designations guidance scheduled for release in early 2016.

International transport provisions (CAA section 179B): In nonattainment areas appreciably affected 

by international transport, the CAA provides that under certain circumstances the state’s attainment 

plan may be approved even if it does not demonstrate attainment. To receive such an approval, the 

state would need to show that its plan would achieve attainment by the relevant attainment date “but 

for” the influence of international emissions. When applicable, this CAA provision relieves states from 

imposing control measures on emissions sources in the state’s jurisdiction beyond those necessary to 

address reasonably controllable emissions from within the U.S. The EPA will assist states with 

conducting the analyses necessary to demonstrate “but for” attainment, including estimating the extent 

of international contribution on high O3 days.

7. Questions for further discussion:

As noted earlier, the EPA intends to hold a workshop in early 2016 to discuss the information in this 

white paper and to further advance our collective understanding of the technical and policy issues 

associated with background O3. The EPA plans to evaluate the need for further guidance to address 

background O3 after receiving feedback on this white paper and after conducting the workshop. Here is 

a list of questions we would like to stakeholders to consider for discussion at the workshop.
14

A. Has the EPA properly characterized the current best estimates of background O3? Are there 

additional existing data analyses or modeling simulations that need to be folded into the 

assessment? 

B. What additional data elements and/or model improvements are needed to improve

characterization of background O3 levels across the U.S.?

C. EPA has focused on USB in this white paper. Are there other definitions of background ozone 

that concern stakeholders?

D. Does the EPA preliminary conceptual model of O3 attainment planning align with stakeholder 

perspectives on the O3 planning process?

E. Has the EPA identified all of the CAA mechanisms available to address areas influenced by 

background O3?

F. What other approaches (consistent with CAA provisions) should be considered to deal with 

background O3 in implementing the 2015 O3 NAAQS?

G. Are sufficient technical tools, data, and EPA guidance available to make the demonstrations 

necessary to invoke relevant CAA provisions?

H. Do states want or need additional assistance from the EPA to develop the demonstrations 

necessary to invoke relevant CAA provisions?

I. What are stakeholders’ perspectives on existing programs and cooperative agreements to 

reduce levels of background O3 entering the U.S.?
15

Appendix: Additional detail on modeling estimates of background O3:

The photochemical grid models used by the EPA and air agencies for O3 planning are regional scale 

models, covering domains ranging from metropolitan areas to the continental U.S. with grid sizes of 4 

km to 36 km. An important consideration in the use of these models to estimate background O3 is how 

to set the O3 concentrations at the edges of the domain (i.e., the top and lateral boundary conditions).

Regional model boundary conditions can be informed by observations at surface sites near the 

boundary or from satellites, but they are typically determined using a global-scale photochemical grid 

model that covers the entire globe at a grid resolution between 50 km and 200 km. Regional models are 

developed to estimate O3 concentrations on an hourly basis, whereas global models are typically run 

with temporal resolutions of 3 or 6 hours and are often evaluated by comparison to monthly or seasonal 

average observations. Although global models can often reproduce the relative patterns of observations 

over large areas and time scales of synoptic meteorology (e.g., passing of a frontal system), the absolute 

values estimated by these coarser models can differ significantly between models and often have biases 

in comparison to observations.43 Any global model biases can be carried forward in the boundary 

conditions into the regional model, adding to the uncertainty in the regional modeled estimates of USB

O3.

Although the EPA analyses summarized in Section 3 utilized state-of-the-science modeling tools and 

best practice techniques for model input development and model evaluation, these estimates may 

contain biases and errors on specific days at specific sites.44 Comparisons of background estimates from 

these global model applications have been found to differ in magnitude. These differences are thought 

to result from differences in the treatment of stratospheric-tropospheric exchange, wildfire emissions, 

lightning NOx emissions, biogenic VOC emissions, and isoprene oxidation chemistry between the 

modeling systems.45 The EPA anticipates that improvements in ambient data collection and modeling 

capabilities will continue in the coming years, and we will work collectively with air agencies to 

incorporate any new findings into the O3 NAAQS implementation process.46 The EPA is also working with 

the international research community through such bodies as the Task Force on Hemispheric Transport 

of Air Pollution (HTAP) under the Convention on Long-range Transboundary Air Pollution (LRTAP) to 

improve our understanding of the intercontinental transport of air pollutants and the ability of global 

and regional models to estimate the influence of extra-regional sources of pollutants on air quality in the 

U.S.47

 

43 Fiore et al. (2009); Fiore et al. (2014).

44 Bias and error in air quality modeling simulations typically occur due to both uncertain inputs (e.g., emissions 

and meteorology) as well as from incomplete model treatment of the full physiochemical elements of the 

atmosphere. 

45 Fiore et al. (2014).

46 Cooper et al. (2015).

47 See http://www.htap.org.
16

Table 1. Subset of information from Table 3-1 of ISA. Summary of Zhang et al. (2011) estimates of 

seasonal mean MDA8 O3 observations, seasonal mean model concentrations from the GEOS-Chem 

global model, and GEOS-Chem estimates of seasonal mean USB O3 at selected CASTNET sites by 

region.

48

Region Spring mean 

observed 

MDA8 O3

(ppb)

Spring mean 

base model 

MDA8 O3

(ppb)

Spring mean 

model USB 

MDA8 O3

(ppb)

Summer mean 

observed 

MDA8 O3

(ppb)

Summer mean 

base model 

MDA8 O3

(ppb)

Summer mean 

model USB 

MDA8 O3

(ppb)

California 58 (+/- 12) 52 (+/- 11) 38 (+/- 7) 69 (+/- 14) 66 (+/- 18) 37 (+/- 9)

West 54 (+/- 9) 53 (+/- 7) 42 (+/- 6) 55 (+/- 11) 55 (+/- 11) 40 (+/- 9)

North 

Central

47 (+/- 10) 47 (+/- 8) 33 (+/- 6) 50 (+/- 12) 51 (+/- 14) 27 (+/- 7)

Northeast 48 (+/- 10) 45 (+/- 7) 33 (+/- 7) 45 (+/- 14) 45 (+/- 13) 24 (+/- 7)

Southeast 52 (+/- 11) 51 (+/- 7) 32 (+/- 7) 52 (+/- 16) 54 (+/- 9) 29 (+/- 10)

Figure 1. Map of 2007 CMAQ-estimated seasonal mean USB O3 concentrations (ppb) from zero out 

modeling. Same as Figure 2-11 in the EPA Policy Assessment.

 

48 The “west” region includes: AZ, CO, ID, MT, NM, NV, OR, UT, WA, and WY. The “north central” region includes: 

IA, IL, IN, KS, KY, MI, MN, MO, ND, NE, OH, SD, and WI. The “northeast” region includes: CT, DE, MA, MD, ME, NH, 

NJ, NY, PA, RI, VA, VT, and WV. The “southeast” region includes: AL, AR, FL, GA, LA, NC, MS, OK, SC, TN and TX.
17

Figure 2a. Distributions of absolute estimates of USB O3, from 2007 CAMx source apportionment 

modeling, binned by model MDA8 O3. Same as Figure 2-14 in the EPA Policy Assessment.

Figure 2b. Distributions of the relative proportion of USB O3 to total O3, from 2007 CAMx source 

apportionment modeling, binned by model MDA8 O3. Same as Figure 2-15 in the EPA Policy Assessment.
18

Table 2a. List of counties in the eastern U.S. with 2012-2014 O3 design values greater than 70 ppb. For 

counties with multiple sites greater than 70 ppb, only the site with the highest 2012-2014 DV is shown. 

The table lists the 2012-2014 O3 design values (ppb), the 4th high ozone value from 2014 (ppb), the 

model-estimated contribution (%) of U.S. sources to the projected 2017 design value in the county, the 

model-estimated contribution (%) of in-State sources to the projected 2017 design value, and the total 

NOx emissions in the county.

State County AIRS ID

2012-

2014

DV (ppb)

2014

4th High 

(ppb)

2017 DV from 

manmade

US sources (%)

2017 DV from 

manmade 

State sources 

(%)

County NOx 

emissions

2011 NEI v2 

(kTPY)

Arkansas Crittenden 50350005 71 67 61% 11% 8

Arkansas Pulaski 51191002 71 65 59% 41% 18

Connecticut Fairfield 90019003 85 81 71% 6% 18

Connecticut Hartford 90031003 77 77 70% 11% 18

Connecticut Middlesex 90070007 81 80 70% 10% 5

Connecticut New Haven 90099002 81 69 71% 11% 16

Connecticut New London 90110124 79 65 75% 14% 8

Connecticut Tolland 90131001 80 77 68% 13% 3

Delaware Kent 100010002 72 66 70% 2% 5

Delaware New Castle 100031007 71 71 69% 1% 14

Delaware Sussex 100051003 74 67 69% 9% 11

D.C D.C 110010043 73 68 70% 6% 9

Georgia DeKalb 130890002 72 70 68% 45% 15

Georgia Fulton 131210055 76 73 70% 47% 24

Georgia Gwinnett 131350002 72 68 67% 45% 17

Georgia Henry 131510002 77 75 67% 42% 7

Georgia Rockdale 132470001 77 79 69% 43% 2

Illinois Cook 170317002 78 72 69% 37% 113

Illinois Jersey 170831001 74 65 62% 18% 1

Illinois Lake 170971007 79 73 70% 38% 21

Illinois Madison 171190008 76 72 61% 16% 17

Illinois McLean 171132003 71 66 51% 22% 7

Illinois Randolph 171570001 72 71 62% 20% 7

Illinois Saint Clair 171630010 72 67 68% 17% 9

Indiana Boone 180110001 71 66 57% 27% 4

Indiana Clark 180190008 72 66 65% 20% 5

Indiana Floyd 180431004 73 66 65% 16% 4

Indiana Greene 180550001 71 64 65% 33% 2

Indiana LaPorte 180910005 79 70 66% 15% 9

Indiana Marion 180970073 71 65 61% 32% 39

Indiana Porter 181270024 73 71 64% 8% 18
19

State County AIRS ID

2012-

2014

DV (ppb)

2014

4th High 

(ppb)

2017 DV from 

manmade

US sources (%)

2017 DV from 

manmade 

State sources 

(%)

County NOx 

emissions

2011 NEI v2 

(kTPY)

Indiana St. Joseph 181410015 71 67 64% 13% 10

Indiana Warrick 181730011 71 66 61% 24% 15

Kansas Leavenworth 201030003 71 68 65% 22% 4

Kansas Sedgwick 201730010 73 69 52% 20% 18

Kansas Sumner 201910002 73 67 51% 11% 6

Kentucky Campbell 210373002 75 71 65% 20% 3

Kentucky Daviess 210590005 72 64 64% 30% 8

Kentucky Henderson 211010014 74 69 61% 21% 4

Kentucky Jefferson 211110027 71 65 63% 34% 38

Kentucky Livingston 211390003 72 65 60% 30% 2

Kentucky McCracken 211451024 72 65 57% 30% 19

Kentucky Oldham 211850004 74 68 64% 28% 2

Louisiana East Baton Rouge 220330003 72 75 65% 49% 22

Louisiana Livingston 220630002 71 73 66% 45% 4

Louisiana Pointe Coupee 220770001 71 71 64% 42% 16

Louisiana St. Tammany 221030002 71 74 66% 43% 8

Maine York 230312002 73 66 67% 2% 7

Maryland Anne Arundel 240030014 74 66 68% 21% 21

Maryland Baltimore 240053001 72 68 70% 32% 22

Maryland Calvert 240090011 73 70 69% 19% 3

Maryland Cecil 240150003 77 74 67% 18% 4

Maryland Charles 240170010 71 67 69% 18% 4

Maryland Harford 240251001 75 67 71% 30% 6

Maryland Kent 240290002 74 68 70% 28% 1

Maryland Prince George's 240338003 76 69 68% 18% 21

Massachusetts Hampshire 250154002 71 68 65% 5% 3

Michigan Allegan 260050003 83 77 71% 4% 5

Michigan Benzie 260190003 73 69 65% 3% 1

Michigan Berrien 260210014 79 73 69% 2% 7

Michigan Cass 260270003 73 66 66% 2% 2

Michigan Genesee 260490021 72 68 53% 24% 12

Michigan Huron 260630007 71 66 55% 21% 3

Michigan Kalamazoo 260770008 73 67 63% 7% 8

Michigan Kent 260810020 71 66 64% 14% 17

Michigan Lenawee 260910007 73 68 58% 20% 4

Michigan Macomb 260990009 74 71 62% 28% 21

Michigan Manistee 261010922 72 66 66% 3% 4

Michigan Mason 261050007 74 70 67% 2% 1

Michigan Muskegon 261210039 79 75 71% 7% 7
20

State County AIRS ID

2012-

2014

DV (ppb)

2014

4th High 

(ppb)

2017 DV from 

manmade

US sources (%)

2017 DV from 

manmade 

State sources 

(%)

County NOx 

emissions

2011 NEI v2 

(kTPY)

Michigan Ottawa 261390005 75 71 67% 6% 17

Michigan St. Clair 261470005 74 71 59% 24% 25

Michigan Washtenaw 261610008 73 70 59% 30% 12

Michigan Wayne 261630019 74 73 53% 28% 62

Mississippi Jackson 280590006 71 75 72% 29% 16

Missouri Clay 290470006 74 66 64% 23% 9

Missouri Clinton 290490001 73 64 63% 24% 1

Missouri Jasper 290970004 72 65 53% 3% 6

Missouri Jefferson 290990019 75 72 67% 42% 12

Missouri Lincoln 291130003 75 67 57% 28% 3

Missouri Perry 291570001 71 67 59% 9% 2

Missouri Saint Charles 291831002 78 72 64% 30% 18

Missouri Saint Louis 291890014 77 72 63% 34% 39

Missouri Sainte Genevieve 291860005 72 69 59% 18% 9

Missouri St. Louis City 295100085 73 66 67% 36% 11

New Jersey Camden 340071001 76 68 70% 15% 9

New Jersey Essex 340130003 73 70 68% 15% 14

New Jersey Gloucester 340150002 76 70 69% 6% 8

New Jersey Hunterdon 340190001 72 65 68% 7% 4

New Jersey Mercer 340210005 73 71 68% 8% 8

New Jersey Middlesex 340230011 74 71 68% 15% 16

New Jersey Monmouth 340250005 72 64 69% 19% 11

New Jersey Morris 340273001 72 68 67% 18% 9

New Jersey Ocean 340290006 75 72 67% 15% 8

New York Bronx 360050133 71 70 66% 7% 10

New York Chautauqua 360130006 71 66 61% 2% 8

New York Erie 360290002 71 63 58% 11% 21

New York Queens 360810124 72 63 67% 14% 29

New York Richmond 360850067 73 72 69% 6% 8

New York Rockland 360870005 72 68 69% 20% 5

New York Suffolk 361030004 75 64 74% 24% 39

New York Westchester 361192004 75 74 70% 12% 16

North Carolina Mecklenburg 371191009 73 68 63% 38% 29

Ohio Allen 390030009 71 66 57% 20% 8

Ohio Ashtabula 390071001 72 69 60% 27% 7

Ohio Butler 390179991 74 69 64% 17% 15

Ohio Clark 390230001 71 65 66% 26% 6

Ohio Clermont 390250022 75 68 66% 24% 22

Ohio Clinton 390271002 73 70 66% 21% 2
21

State County AIRS ID

2012-

2014

DV (ppb)

2014

4th High 

(ppb)

2017 DV from 

manmade

US sources (%)

2017 DV from 

manmade 

State sources 

(%)

County NOx 

emissions

2011 NEI v2 

(kTPY)

Ohio Cuyahoga 390350034 75 71 68% 32% 35

Ohio Delaware 390410002 71 66 59% 25% 7

Ohio Franklin 390490029 75 70 65% 34% 37

Ohio Hamilton 390610006 75 70 66% 22% 36

Ohio Lake 390850003 78 75 69% 38% 18

Ohio Lucas 390950024 71 70 61% 24% 24

Ohio Madison 390970007 71 69 64% 26% 3

Ohio Miami 391090005 71 66 56% 28% 5

Ohio Montgomery 391130037 72 69 65% 31% 20

Ohio Trumbull 391550011 72 65 58% 28% 10

Ohio Warren 391650007 72 71 67% 27% 7

Oklahoma Canadian 400170101 71 68 55% 38% 11

Oklahoma Cleveland 400270049 71 67 52% 32% 7

Oklahoma Comanche 400310651 73 69 46% 25% 7

Oklahoma Creek 400370144 72 66 62% 45% 7

Oklahoma Kay 400719010 73 69 52% 22% 7

Oklahoma Oklahoma 401091037 74 70 52% 30% 29

Oklahoma Tulsa 401431127 74 65 62% 45% 28

Pennsylvania Allegheny 420030008 73 65 67% 25% 35

Pennsylvania Armstrong 420050001 74 68 61% 30% 30

Pennsylvania Beaver 420070005 75 70 64% 18% 21

Pennsylvania Berks 420110011 71 68 62% 33% 14

Pennsylvania Bucks 420170012 75 71 68% 25% 13

Pennsylvania Chester 420290100 73 71 69% 23% 12

Pennsylvania Delaware 420450002 74 73 68% 20% 17

Pennsylvania Erie 420490003 71 65 64% 3% 11

Pennsylvania Indiana 420630004 74 68 63% 35% 36

Pennsylvania Lancaster 420710007 71 66 68% 37% 14

Pennsylvania Lawrence 420730015 72 68 68% 33% 4

Pennsylvania Lebanon 420750100 71 67 61% 32% 5

Pennsylvania Mercer 420850100 75 71 58% 8% 6

Pennsylvania Montgomery 420910013 72 72 68% 31% 17

Pennsylvania Philadelphia 421010024 75 72 69% 29% 21

Rhode Island Providence 440071010 73 64 62% 5% 12

Rhode Island Washington 440090007 74 63 75% 6% 3

Tennessee Jefferson 470890002 71 67 56% 30% 4

Tennessee Shelby 471570021 73 67 62% 37% 32

Tennessee Sumner 471650007 72 66 61% 34% 11

Texas Bell 480271047 72 69 48% 29% 11
22

State County AIRS ID

2012-

2014

DV (ppb)

2014

4th High 

(ppb)

2017 DV from 

manmade

US sources (%)

2017 DV from 

manmade 

State sources 

(%)

County NOx 

emissions

2011 NEI v2 

(kTPY)

Texas Bexar 480290052 80 72 49% 36% 48

Texas Brazoria 480391004 80 71 66% 48% 15

Texas Collin 480850005 78 74 65% 54% 12

Texas Dallas 481130069 78 66 60% 48% 51

Texas Denton 481210034 81 77 58% 44% 14

Texas Ellis 481390016 71 62 55% 44% 12

Texas Galveston 481671034 72 71 65% 36% 12

Texas Gregg 481830001 71 66 58% 32% 7

Texas Harris 482010066 76 70 65% 46% 99

Texas Hood 482210001 76 73 54% 37% 4

Texas Johnson 482510003 76 71 52% 41% 9

Texas Montgomery 483390078 76 72 53% 39% 9

Texas Parker 483670081 74 72 54% 36% 6

Texas Rockwall 483970001 73 66 56% 46% 2

Texas Smith 484230007 71 66 51% 29% 8

Texas Tarrant 484391002 80 79 59% 44% 45

Virginia Arlington 510130020 74 71 71% 21% 4

Virginia Fairfax 510590030 72 65 70% 26% 15

Wisconsin Dodge 550270001 72 71 56% 11% 4

Wisconsin Door 550290004 73 65 67% 11% 2

Wisconsin Kenosha 550590019 81 76 72% 11% 7

Wisconsin Kewaunee 550610002 73 65 68% 11% 1

Wisconsin Manitowoc 550710007 75 66 70% 12% 4

Wisconsin Milwaukee 550790085 77 69 71% 14% 27

Wisconsin Outagamie 550870009 71 70 53% 20% 8

Wisconsin Ozaukee 550890008 77 74 66% 11% 4

Wisconsin Sheboygan 551170006 81 72 69% 16% 7

Wisconsin Walworth 551270005 72 73 56% 5% 4
23

Table 2b. List of counties in California with 2012-2014 O3 design values greater than 70 ppb. For 

counties with multiple sites greater than 70 ppb, only the site with the highest 2012-2014 DV is shown. 

The table lists the 2012-2014 O3 design values (ppb), the 4th high ozone value from 2014 (ppb), the 

model-estimated contribution (%) of U.S. sources to the projected 2017 design value in the county, the 

model-estimated contribution (%) of in-State sources to the projected 2017 design value, and the total 

NOx emissions in the county.

State County AIRS ID

2012-

2014

DV (ppb)

2014

4th High 

(ppb)

2017 DV from 

manmade

US sources (%)

2017 DV from 

manmade 

State sources 

(%)

County NOx 

emissions

2011 NEI v2 

(kTPY)

California Alameda 60010007 72 76 59% 53% 28

California Amador 60050002 72 74 51% 49% 2

California Butte 60070007 74 74 47% 45% 8

California Calaveras 60090001 71 71 50% 48% 2

California El Dorado 60170010 84 82 56% 54% 4

California Fresno 60195001 95 97 51% 49% 29

California Imperial 60251003 80 78 31% 25% 11

California Kern 60295002 88 88 48% 46% 47

California Kings 60311004 84 86 48% 45% 8

California Los Angeles 60376012 97 97 56% 50% 136

California Madera 60392010 84 82 45% 43% 9

California Mariposa 60430006 78 77 41% 37% 1

California Merced 60470003 81 82 50% 46% 13

California Nevada 60570005 79 82 51% 47% 3

California Orange 60592022 74 78 63% 55% 32

California Placer 60610006 81 83 57% 54% 9

California Riverside 60651016 99 98 53% 49% 37

California Sacramento 60670012 85 81 58% 55% 20

California San Bernardino 60714003 102 99 59% 54% 69

California San Diego 60731006 79 80 47% 40% 43

California San Joaquin 60773005 79 80 53% 50% 23

California San Luis Obispo 60798005 76 73 39% 35% 8

California Stanislaus 60990006 84 81 57% 53% 13

California Tehama 61030004 75 76 41% 38% 6

California Tulare 61070009 91 89 40% 38% 16

California Tuolumne 61090005 73 75 47% 43% 3

California Ventura 61112002 79 81 55% 48% 12
24

Table 2c. List of counties in the inter-mountain western U.S., but outside of California, with 2012-2014 

O3 design values greater than 70 ppb. For counties with multiple sites greater than 70 ppb, only the site 

with the highest 2012-2014 DV is shown. The table lists the 2012-2014 O3 design values (ppb), the 4th 

high ozone value from 2014 (ppb), the model-estimated contribution (%) of U.S. sources to the 

projected 2017 design value in the county, the model-estimated contribution (%) of in-State sources to 

the projected 2017 design value, and the total NOx emissions in the county.

State County AIRS ID

2012-

2014

DV (ppb)

2014

4th High 

(ppb)

2017 DV from 

manmade

US sources (%)

2017 DV from 

manmade 

State sources 

(%)

County NOx 

emissions

2011 NEI v2 

(kTPY)

Arizona Cochise 40038001 71 68 14% 11% 20

Arizona Coconino 40051008 71 73 14% 9% 17

Arizona Gila 40070010 74 72 36% 30% 3

Arizona La Paz 40128000 72 71 24% 10% 6

Arizona Maricopa 40131004 80 78 48% 42% 88

Arizona Pima 40190021 71 69 32% 27% 27

Arizona Pinal 40218001 73 68 33% 28% 15

Arizona Yavapai 40258033 71 77 18% 11% 13

Arizona Yuma 40278011 77 78 27% 6% 8

Colorado Adams 80013001 73 67 45% 36% 25

Colorado Arapahoe 80050006 71 67 28% 20% 13

Colorado Boulder 80130011 75 70 39% 31% 10

Colorado Douglas 80350004 81 74 45% 35% 8

Colorado El Paso 80410013 71 64 10% 6% 22

Colorado Jefferson 80590006 82 77 46% 35% 14

Colorado Larimer 80690011 78 74 43% 32% 12

Colorado Weld 81230009 74 70 44% 36% 33

Nevada Clark 320030075 78 79 31% 17% 52

Nevada White Pine 320330101 71 64 15% 2% 1

New Mexico Dona Ana 350130022 74 66 26% 4% 12

New Mexico Eddy 350151005 71 72 30% 8% 12

Texas El Paso 481410037 72 70 20% 11% 19

Utah Salt Lake 490353006 75 72 30% 20% 29

Utah Tooele 490450003 71 69 27% 14% 6

Utah Utah 490495010 74 76 22% 9% 13

Utah Weber 490571003 73 70 28% 17% 6
25

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