### 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 Protec tion 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 locat ions in the U.S., sources other than domestic
manmade emissions of ozone (O 3 

### ) precursors can contribute appreciably to monitored O 3 

### concentrations. The EPA is seeking input from states, tribes, and interested stakeholders on aspects of background O 3 

### that a re relevant to attaining the 2015 O 3 

### NAAQS in a manner consistent with the provisions of the Clean Air Act (CAA). This white paper clarifies the specific definition of background O 3 

### that EPA has used and will continue to use in addressing implementation of the O 3 

### NAAQS

, describes the sources and processes that l ead to background O 3 

### across the U.S., summarizes estimates of background

O 3 

### levels across the U.S., and describes policy tools that are available, or have been suggested, to
address implementation ch allenges that result from background O 3


### . The EPA intend s 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 O 3 

### . W e will evaluate the need for further guidance and/or rules to address background O 3 

### after receiving feedback on this white paper and after conducting the workshop.

The EPA revised the primary O 3 

### NAAQS to a level of 0.070 ppm (70 ppb) on October 1, 2015

. 1 

### This
level was dete rmined 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 i ssue of proximity to background O3 concentrations in

some areas. However

, the EPA considered the extent and import ance of background O 3 

### through out the NAAQS review process

. This began with t he integrated science assessm ent (ISA)

, which summarized the

state of knowledge regarding background O 3 

### in the peer

- reviewed literature. 3 

### Th e ISA was followed by
th e policy a ssessment (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 determin ed 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 O 3 

### levels across the U.S. 4 

### The n otice of proposed rulemaking (NPR) 5 

### for the

O 3 

### NAAQS identified CAA implementation provisions that air agencies can use to address background O 3


### . The regulatory impact analysis (RIA) that accompanied the proposed rule presented O 3 

### 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 O 3


### . As part of the communications material
associated with the final rule , the EPA provided information on tools for addressing background O 3


### .

With regard to the remainder of this white paper, Section 2 discusses how the EPA defines background O 3 

### and provides information on how background O 3 

### is formed and estimated. Section 3

summarizes estimates of current background O 3 

### levels over the U.S.

, and Section 4 discusses how these
l evels 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 O 3 

### and NAAQS implementat ion that warrant additional discussions with stakeholder groups

. The Appendix provides more information related to modeling estimates of background O 3 

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

2. Basics of background O 3 

### : definitio ns, formation

, and estimation techniques :

For the purposes of this white paper and the continuing discussion of background O 3 

### issues in the NAAQS implementation context, the EPA considers background O 3 

### to be any O 3 

### formed from sources or

processes other th an U.S. manmade emissions of nitrogen oxides (NOx), volatile organic compounds (VOC), methane (CH 4 

### ), and carbon monoxide (CO)

. 7 

### This definition of background is specifically referred to as U.S. background (USB). 8 

### It is important to recognize that USB does not include intra state or interstate transport of manmade O 3 

### , which can also influence O 3 

### 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 o f background O 3


### . From the highly local perspective, s ome 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, s ome 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 O 3
.

### 3

that all manmade emissions are controllable and

, therefore

, background O 3 

### is only generated from non

manmade sources.

Away from the earth’s surface, O 3 

### can have an atmospheric lifetime on the order of weeks. As a result, background O 3 

### , a nd to a lesser extent background O 3 

### 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 O 3 

### originates from production of O 3 

### in the stra tosphere through interactions between ultraviolet light and molecular oxygen

. O 3 

### exists in large quantities in the stratosphere and natural atmospheric exchange processes can transport stratospheric air into the troposphere

. During certain meteo rological conditions, discrete plumes of stratospheric air can be displaced far into the troposphere and impact ground

- level O 3 

### concentrations

. These events are called stratospheric in trusions and can result in relatively high USB levels of O 3 

### at the surfa ce, especially at higher

- elevation sites

. 9 

### Other n atural sources of O 3 

### precursor emissions include wildfires, lightning, and vegetation . B iogenic emissions of methane, which can be chemically converted to O 3 

### over relatively long time scales, can also contr ibute to USB O 3 

### levels. Finally, manmade precursor emissions from other

countries can contribute to the global burden of O 3 

### in the troposphere and to increased USB O 3 

### levels

.

USB O 3 

### levels can vary considerably in space and time

. When assessing USB O 3 

### con centrations, it is important to clari fy the averaging time being considered

. From a broad characterization perspective, it can be useful to identify annual or seasonal mean concentrations by location. However, f rom an air quality management perspective, it is more important to consider background concentrations on specific high O 3 

### days when concentrations may approach or exceed the NAAQS

. Section 3 of the white

paper s ummarize s the estimates of USB O 3 

### over both categories of averaging times.

While some sur face monitoring locations in certain rural areas in the inter

- mountain west ern U.S. 10



### can be substantially affected by USB O 3 

### , multiple analyses have shown that even the most remote O 3 

### 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 monitore d O 3 

### levels at a remote surface

site (e.g., Grand Canyon or Yellowstone National Parks) can be used as a proxy for USB O 3


### . This conclusion is supported by recent data analyses of rural O 3 

### observations in Nevada 12 

### and Utah 13 

### in which

it was demonstrated that natural sources, international O 3 

### transport, O 3 

### transported from upwind states, and O 3 

### transported from urban areas within the state all contributed to mo nitored O 3 

### levels at
rural sites in these two states

. Measurements of O 3 

### above the surface (e.g., from sondes, profilers, or aircraft ) can provide useful information about the influx of O 3 

### 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 d ocument, 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 En vironmental Quality (2013).

### 4

valu able toward informing USB conc entrations

. However

, vertical profile measurements of O 3 

### 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 oz one) , photochemical grid models have been widely used as a means to estimate the contribution of background sources to observed surface O 3 

### concentrations

. 14 

### Several modeling studies have attempted to estimate background O 3 

### levels by assessing the remaining O 3 

### 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 O 3 

### level s

. Another modeling technique , referred to as “source apportionment” modeling, can also be used to estimate the sources that contribute to modeled O 3 

### concentrations. This approach estimates the contribution of certain

source categories (e.g., natural sourc es, non

- U.S. manmade sources) to modeled O 3 

### at each model grid
cell on an hourly basis

. More information about the modeling estimates of USB O 3 

### is provided in the Appendix. Section 3 of the white paper summarizes the key findings from the EPA analyses of
b ackground O 3 

### 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 si mulations.

3. What are the current best estimates of U.S. background O 3 

### levels nationally ?

A. Summary of previous exercises to estimate background O 3 

### levels:

Over the past 10

- 15 years, multiple photochemical modeling analyses have been conducted to

estimate t he contribution of background sources on U.S. O 3 

### levels. The EPA summarized in the ISA for the 2015 NAAQS review t he modeling s tudies that were published before 2012

. 15 

### The main points from this summary we re : 1) seasonal mean bac kground concentrations are h ighest 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 an other in terms of seasonal mean O 3 

### background

estimates

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

. 16 

### Table 1 p rovides

summary information from the ISA regarding a modeling stud y 17 

### of USB O 3 

### 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).

### 5

selected locations from the CASTNET 18 

### monitoring network

. Model estimates of seasonal mean USB, daily 8

- hour ozone maxima ( MDA8 ) O 3 

### range from as high as 42 ppb in the spring at high elevation sites
in the w estern U.S. (non

- California) to as low as 24 ppb in the summer at sites in the n ortheast U.S.

Subsequent to the publication of the ISA, additional model

- based estimates of background O 3 

### have
beco me avail able that show greater variability in model estimates of background

. 19 

### T he global

G eophysical Fluid Dynamics Laboratory AM3 m odel was used to estimate springtime North American background ( NAB ) levels at high elevation western U.S. sites

. 20 

### (NAB is similar t o 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 O 3 

### values could be as high as 50 ppb

at many of thes e sites. An additional ana lysis used a coupled global

- regional modeling system that
included the Comprehensive Air Quality Model with Extensions ( CAMx ) O 3 

### sour ce apportionment technique to track the contribution of background sources to total O 3 

### within the simulation. 21 

### This analysi s concluded that “emissi ons

- influenced background

, ” a metric intended to represent the
combined influence of natural sources and sources of O 3 

### from outside the modeling domain on total
modeled O 3 

### , 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 O 3 

### at high elevation sites in the w estern U.S. Additionally, the EPA summarized the results of

zero

- out and source app ortionment

- based estimates of 2007 background levels in the PA for the 2015

O 3 

### NAAQS review

. These EPA estimates of background O 3 

### 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 O 3


### .

B. Recent estimates of USB concentrations from the EPA

The EPA estimated 2007 seasonal (i.e., April through October) mean USB MDA8 O 3 

### concentrations using a combination of the GEOS

- Chem global model and the Community Multi

- scale Air Quality ( CMAQ ) (zero out) and CAMx (source apportionment) regiona l models

. The two separate model approaches

estimate d similar background impacts over the rura l portions of the western U.S. 22 

### The greatest
difference between the two model estimation approaches occur red 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 C lean 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 si tes 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 previ ous estimation exercises summarized in
Section 3.A . The 2007 CMAQ and CAMx simulations estimated that seasonal mean USB MDA8 O 3 

### levels

range d from 25

- 50 ppb across the U.S., as shown in Figure 1

. Locations with seasonal mean contributions greater than 40 p pb 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 percent age ( e.g., 6 0

- 80%) of the seasonal mean total MDA8 O 3 

### at locations within the

inter

- mountain western U.S. and along the U.S. border s with Mexico and Canada

. A few locations outside of these areas (such as locations in Florida) also had relatively high fraction a l contributions of USB to seasonal means, but absolute O 3 

### concentrations modeled in these areas are lower and do not approach the level of the standard. In locations where O 3 

### levels are generally higher, for example urban

areas in California and the easter n U.S., the seasonal mean background fractions are relatively smaller (e.g., 40

- 60%).

From an implementation perspective, the values of USB O 3 

### on possible O 3 

### NAAQS exceedance days are a more meaningful consideration than seasonal mean levels . The first dr aft policy assessment
document considered thi s issue in detail, via a re

- analysis of zero

- out modelin g reviewed as part of the
ISA, and concluded that “results suggest that background concentrations on the days with the highest
total O 3 

### 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 O 3 

### [MDA8] concent rations.” 24



### This re

- analysis examined modeling results at the national level and by region

. Although absolute USB O 3 

### concentrations were generally higher in the w estern U.S. at high elevation sites than at other locations in the U.S., this analysis showed t hat the general pattern of background O 3 

### on days with high v ersu s low

O 3 

### levels was also seen in the inter

- mountain w est ern U.S., making the conclusions relevant even in locations with the highest seasonal mean background concentration s

.

The more recent modeling from the EPA using a 2007 base year

, and the two distinct modeling
metho dologies described above , corroborated the finding from the previous modeling analyses. Again, the highest modeled O 3 

### site

- days (i.e., days of more interest from an implementa tion perspective) tend
to have smaller fractional contributions from USB O 3 

### and conversely greater contributions from U.S.
manmade emissions. Figure s 2a and 2b show the distribution of daily USB MDA8 levels (absolute magnitudes and relative fractions, resp ectively) from the source apportionment simulation. The 2007 modeling shows that the days with highest O 3 

### levels have similar distributions (i.e., means, inter

- quartile ranges) of USB O 3 

### levels as days with lower values, down to approximately 40 ppb. As a result, when considered from a national perspective, the proportion of total O 3 

### that has USB origins is smaller on high

O 3 

### days (e.g., days > 60 ppb) than on the more common lower O 3 

### days that tend to drive seasonal means. Figure 2b also indicates that the re 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 pro ne 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 O 3 

### on certain observed high O 3 

### 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 DISCOVER

AQ field studies ) , and we anticipate that this work will result in increasingly improved estimat es of the
contributions of USB on high O 3 

### d ays. 26



### Based on previous modeling ex ercises and the more recent EPA analyses summarized in the policy
assessment document

, the EPA believes the following t hree conc lu sions summar ize the role of background O 3 

### in relationship to the O 3 

### NAAQS

.

i. USB O 3 

### can c omprise a considerable fraction of the total MDA8 O 3 

### 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 indica te that U.S. manmade emission sources are generally the
dominant contribu tor to the modeled exceedances of the 2015 O 3 

### NAAQS

, nationally and within individual regions across the country . Higher O 3 

### days generally have smaller fractional
contributions from U SB 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 O 3 

### concentrations above 70 ppb is less than 35 percent

. As with any other modeling exercise,
these simu lations have uncertaint ies 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 O 3 

### days.

iii. Analyses suggest that there can be infrequent events whe re MDA8 O 3 

### concentrations approach or exceed 70 ppb largely due to the influence of USB sources like a wildfire or stratospheric intrusion. A s discu ssed in more detail in Section 6 of this white paper , the CAA and EPA implementation policy allow for the ex clusion 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 in dicate that there may be also be a limited number of rural areas
where USB O 3 

### is appreciable, but not the sole contributor to an exceedance of the NAAQS. Even in these areas, there is no indication that USB O 3 

### concentrations will prevent
attainment of the 2015 O 3 

### NAAQS.

25 California Air Resources Board (2011), State of Wyoming Department of En vironmental Quality (2013), Langford et al. (2015).

26 Crawford and Pickering (2014).

### 8

4. What are the expected O 3 

### and background O 3 

### levels across the U.S. in the near future?

A. Summary of ambient trends in USB O 3



### Ambient data analyses have shown that mid

- tropospheric O 3 

### concentrations in remote areas , within the U.S. and glob ally, 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 d ependent upon global changes in emissions of methane, as well as changes in other manmade

O 3 

### precursor emissions outside of the U.S., which are highly uncertain. 28 

### Additionally, climate change has the potential to affect global background O 3 

### levels via changes in temperatures, wildfire emissions, synoptic weather pat terns and other factors that influence O 3


### . 29



### While projecting future trends in emissions is highly uncertain, NO x 

### emissions are expected to continue to decline in North America and Europe out to 2030 and then stabilize. NO x 

### 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 NO x 

### emissions. Implementation of an aggressive climate change mitigation policy might halt the growth of N Ox 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. T her e 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 o ur understanding of the sources and impacts of background O 3 

### 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 Pollut ion Convention, we are leading an international scientific effort to improve the databases and modeling tools that enable us to characterize the intercontinental transport of O 3 

### and assess potential control strategies. We are also working with Mexico th rou gh the Border 2020 Program 31



### , Canada under the US

Canada Air Quality Agreement 32 

### , and China through agreements on cooperation with their environment
and science ministries 33 

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

emission s in these countries. W e 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=E n&n=1E841873

- 1

.

33 http://www2.epa.gov/international

- cooperation/epa

- collaboration

- china

.

### 9

Methane Initiative and the Climate and Clean Air Coalition to Reduce S hort Lived Climate Pollutants, to engage governments and the private sector to achieve decrease s in methane emissions which contribute to background O 3 

### . Ultimately, these efforts will benefit air quality in the U.S. by decreasing international contributions to background air pollution.

B. E stimates of future O 3 

### levels in 2025

As part of the final RI A that accompanied the 2015 revised O 3 

### NAAQS, the EPA conducted modeling for a future year of 2025 to project future O 3 

### design values as part of an illustrative analysis to estimate the costs and benefits of achieving the revised O 3 

### standards. Emissions in ventories 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 i n NOx and VOC
emissions. The EPA concluded that the O 3 

### levels at these 12 sites we re strongly influenced by USB (e.g., international emissions, stratospheric O 3 

### , wildfire emissions) or by interstate O 3 

### transport from domestic

man made sources located outside the region. Despite the small response to regional emissions reductions, the RIA modeling pr ojected enough O 3 

### reduction to yield design values less than the 70 ppb standard by 2025 at these 12 sites.

The RIA modeling also indicate d 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 th at additional reductions in U.S. man made NOx and VOC emissions could
result in attaining O 3 

### 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 O 3 

### areas in Southern California have persistent high O 3 

### (i.e., > 70 ppb) desp ite expected improvements. The RIA modeling predicts levels above 70 ppb in the Denver area, but the remainder of the inter

- mountain west ern U.S. is predicted to be at levels below 70 ppb by 2025

.

5. Preliminary conceptual model of O 3 

### attainment planning over the U.S. for the revised NAAQS

Under the 2

- year s chedule 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 O 3 

### 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 a n understanding of contributions to O 3 

### 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 Admini strator has insufficient information to promulgate the designations.

### 10

O 3 

### design values

, 35 

### recent emissions estimates by county

, 36 

### and more recent CAMx modeled source attribution estimates

. 37 

### The CAMx sour ce a pportionment data summarize the fraction of the near

- future ( 2017 ) O 3 

### design value prediction that is due to U.S. manmade s ources, as well as the fraction that is due

to in

- s tate anthropogenic emissions.

Tables 2a, 2b, and 2 c show the 2012

- 2014 O 3 

### design values, model source apportionment data, and

201 1 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 O 3


### . 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 O 3 

### attainment planning . While t he 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 O 3 

### values in this region are caused predominantly by U.S. manmade sources, eithe r from local i n

- state emissions or from interstate transport of manmade O 3 

### 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 emissio ns to O 3 

### design values was estimated to be

6 4 percent , ranging from a low of 39 percent (Bell County

, TX) to a hi gh of 7 5 percent ( Washington County, RI )

. Only three counties had an estimated U.S. manmade contribution of less than 50 percent.
The informati on suggests the preliminary conceptual model of O 3 

### 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 O 3 

### levels in the eastern U.S. over the past several
decades

. 38



### California

- A slightly different conceptual model of O 3 

### attainment planning is seen within California (Table 2b). At most locations across California, there is nearly equal contribut ion from manmade
emissions in Cal ifornia and USB sources, wit h generally small impacts from manmade transport from
outside the s tate

. 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 documenta tion.

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 O 3 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 desi gn values that exceeded 70 ppb based on the 2012

- 2014 data is 50 percent, ranging from a low
of 3 1 percent (Imperial County ) to a high of 6 3 percent ( Orange County). Th is suggests th e conceptual model of attainment planning in California will be to continue to seek in

- state NOx and VOC emissi on reduction s, while assess i ng the impact of event

- driven USB sources like fires and stratospheric

intrusion s

. 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 Sa n Diego County)

.

Non

- California Portions of Western U.S

. - As noted earlier in th is white paper, the effects of USB O 3 

### are most notable at a relatively small number of sites in the inter

- mountain western U.S. As shown in Table 2c, there are 2 6 counties wi th at least one site where the 2012

- 2014 design value exceeds 70 ppb.

Across these 2 6 counties , there is a wide range of the extent to which USB influence s O 3 

### 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. T he 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

O 3 

### design value. Overall, this inform ation suggests that it will be important to assess and account for the
contributions from USB sources to O 3 

### nonattainment in this region, particularly in the rural portions

.

It should be noted that any conclusions from this initial conceptual model of att ainment planning for
the 2015 O 3 

### NAAQS are subject to change pending additional information, such as updated design value data, updated emissions data , updated O 3 

### trends, and any updated attribution modeling. For instance,
the currently available 2015 O 3 

### d ata (through the end of Sep t ember 2015) suggest that O 3 

### 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:

S ome state s and other stakeholders have expressed concern about the fairness and pra cticality 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 O 3 

### 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 da ta 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

.

P olicy tools are available, or have been recommended by commenters, to apply to area s experiencing exceedances of the O 3 

### NAAQS that are appreciably impacted by USB O 3 

### . The tool(s)
available for each affected location will depend on the specific nature of background O 3 

### in each area.

Some tools would provide relief from a nonattainment desi gnation ; 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 p ublic process steps are legally necessary to use the relief provisions. 41



### Exceptional Events Exclusions (CAA section 319) : Air monitoring data that would other wise indicate

a n exceedance of the O 3 

### standards and lead to a nonattainment designation may be exc luded 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 na tural 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 O 3 

### meet these criteria (e.g., routine biogenic VOC emissions, international manmade emissions)

. Other sou rces that contribute to background O 3 

### (e.g., wildfire s, stratospheric intrusions) may be eligible for treatment as exceptional

events

. A s tate 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 propos ed revisions to the 2007 Exceptional
Events Rule in November 2015 to further facilitate review and ap proval of O 3 

### - produ cing events

, such as stratospheric intrusions and wildfires

. The EPA intends to issue a f inal 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 exceed s 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 exclusio n 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 t he area not meeting the NAAQS. At monitor locations exceeding the 70 ppb standard where there are no or few nearby permanent sources of O 3 

### precursors, or where nearby sources are shown to be unlikely contributors on days with high O 3 

### , 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 inc lude: a) revising data handling procedures to exclude exceedances attributable to background O 3 , b) deferring designations in locations impacted by background O 3 , and /or c) designating areas influenced by background O 3 as unclassifiable. These additional

m echanisms were not included here due to legal or other deficiencies

.

42 “ Treatment of Data Influenced by Exceptional Events; Proposed Rule”, 80 Federal Register 2 2 4 (20 November
2015 ), pp. 7 2840

- 7 2897

.

### 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 certai n elevation for high elevation sites with no
local sources

, or other appropriate indicator s 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 d esignated nonattainment for the 2008 O 3 

### 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 area s (RTA s ) (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 qu alify, 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 sta te 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

. F our O 3 

### 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 O 3 

### standard. The EPA will work cooperatively with states to develop the request for a n RTA determination, and also provide assistance with meet ing other CAA

- required implementation program provisions for Marginal nonattainment areas (e.g., emissions statement rules, periodic emissions inventory, nonattainment NSR p rogram). 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 se ction 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 influen ce of international emissions. When applicable, t his CAA provision relieves states from imposing control measures on emissions sources in t he 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 inter national contribution on high O 3 

### days.

7. Questions for further discussion :

As noted earlier, t he 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 technic al 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 O 3 

### ? 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
characteriz ation of background O 3 

### 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 O 3 

### attainment planning align with stakeholder perspectives on the O 3 

### planning process?

E. H as the EPA identified all of the CAA mechanisms available to address areas influenced by background O 3 

### ?

F. What other app roaches (consistent with CAA provisions) should be considered to deal with background O 3 

### in implementing the 2015 O 3 

### NAAQS?

G. Are sufficient technical tools, data , and EPA guidance available to make the dem onstrations
necessary to invoke relevant CAA provis ions?

H. Do states want or need additional assistance from the EPA to develop the demo nstrations
necessary to invoke relevant CAA provisions?

I. What are stakeholders ’ perspectives on existing programs and cooperative agreements to reduce levels of background O 3 

### entering the U.S.?

### 15

Appendix: Additional detail on modeling estimates of background O 3 

### :

The photochemical grid models used by the EPA and air agencies for O 3 

### planning are regional scale models, covering domains ranging from metropolitan areas to the c ontinental U

. S

. with grid sizes of 4
km to 36 km. An important consideration in the use of these models to estimate background O 3 

### is how
to set the O 3 

### concentrati ons 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 O 3 

### 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. Alt hough 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 b etwe en 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

O 3


### .

Although the EPA anal yses 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 backg round 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, biog enic 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 agencie s to incorporate any new findings into the O 3 

### 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 Lo ng

- 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 an d 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 O 3 

### observations, seasonal mean model concentrations from the GEOS

- Chem global model, and GEOS

- Chem estimates of seasonal mean U SB O 3 

### at selected CASTNET sites by region

. 48

Region Spring mean

observed
MDA8 O 3 (ppb) Spring mean

base model
MDA8 O 3 (ppb) Spring mean

model USB
MDA8 O 3 (ppb) Summer mean

observed
MDA8 O 3 (ppb) Summer mean

base model
MDA8 O 3 (ppb) Summer mean

model USB
MDA 8 O 3 (ppb)
California 58 (+/

- 12) 52 (+/

- 11 ) 38 (+/

- 7 ) 69 (+/

- 14) 66 ( +/

- 18 ) 37 (+/

- 9)

West 5 4 (+/

- 9 ) 5 3 (+/

- 7) 42 (+/

- 6 ) 55 (+/

- 1 1 ) 55 (+/

- 1 1 ) 40 (+/

- 9)
North
Central 47 ( +/

- 10 ) 47 (+/

- 8) 33 (+/

- 6 ) 50 ( +/

- 12 ) 51 ( +/

- 14 ) 2 7 ( +/

- 7 )

Nort heast 4 8 ( +/

- 10 ) 45 (+/

- 7 ) 33 (+/

- 7 ) 45 (+/

- 14) 45 (+/

- 13) 24 (+/

- 7 )
Southeast 52 (+/

- 1 1 ) 5 1 (+/

- 7 ) 32 (+/

- 7 ) 52 ( +/

- 16 ) 54 (+/

- 9 ) 29 ( +/

- 10 )



### Figure 1

. Map of 2007 CMAQ

- estimated seasonal mean USB O 3 

### concentrations (ppb) from zero out
model ing

. 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 “nort heast” 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 O 3 

### , from 2007 CAMx source apportionment modeling, binned by model MDA8 O 3


### . Same as Figure 2

- 14 in the EPA Policy Assessment.

Figure 2b

. Dist ributions of the relative proportion of USB O 3 

### to total O 3 

### , from 2007 CAMx source apportionment modeling, binned by model MDA8 O 3


### . Same as Figure 2

- 15 in the EPA Policy Assessment.

### 18

Table 2a

. List of counties in the eastern U.S. with 2012

- 2014 O 3 

### 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 con tribution (%) 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

Geor gia 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

Il linois 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% 2 1

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 7 2 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 26 1610008 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 Jef ferson 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

Ne w 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 3610 30004 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 Cla rk 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 39061 0006 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 5 2% 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

Pennsyl vania 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 4206 30004 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 7 2 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

Te xas 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 4 82510003 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 5101 30020 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 Ma nitowoc 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 O 3 

### 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 desig n 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, an d 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

O 3 

### design values greater than 70 ppb. For counties with m ultiple 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

References :

Amann, M., Kilmont, Z., Wagner, F., 2013. Regional and Global Emissions of Air Pollutants: Recent
Trends and Future Scenarios . Ann. Rev. Environ. Res., http://dx.doi.org/10.1146/annurev

- environ

052912

- 173 303

. Association of Air Pollution Control Agencies, 2015. State Environmental Agency Perspectives on Background Ozone and Regulatory Relief. http://www.csg.org/aapca_site/documents/AAPCASurvey

StateEnvironmentalAgencyPerspectivesonBackgroundOzoneandRegulatoryRelief

- June201.pdf

. California Air Resources Board, 2011. Exceptional Events Demonstration for 1

- Hour Ozone Exceedances in the Sacramento Regional Nonattainment Air Due to 2008 Wildfires. http://www.epa.gov/air

- quality

analysis/exceptional

- events

- s ubmissions

- table#Ozone

. Cooper, O.R., Gao, R.S., Tarasick, D., Leblanc, T., Sweeney, C., 2012. Long

- term ozone trends at rural ozone monitoring sites across the United States, 1990

- 2010. J. Geophys. Res., http://dx.doi.org/10.1029/2012JD018261

. Cooper, O.R., Langford, A.O., Parrish, D.D., Fahey, D.W., 2015. Challenges of a Lowered U.S. Ozone Standard. Science, http://dx.doi.org/10.1126/science .aaa5748

.

Crawford J.H

. , Pickering K.E., 2014, DISCOVER

- AQ: Advancing strategies for air quality observations in
the next decade. EM, September: 4

- 7.

Dolwick, P., Akhtar, F., Baker, K.R., Possiel, N., Simon, H., Tonnesen, G., 2015. Comparison of backgr ound ozone estimates over the western United States based on two separate model methodologies. Atmos. Environ., http://dx.doi.org/10.1016/j.atmosenv.2015.01.005

.
Emery, C., Jung, J., Downey , N., Johnson, J., Jimenez, M., Yarwood, G., Morris, R., 2012. Regional and global modeling estimates of policy relevant background ozone over the United States. Atmos. Environ., http://dx.doi .org/10.1016/j.atmosenv.2011.11.012

.
Fiore, A.M., Jacob, D.L., Liu, H., Yantosca, R.M., Fairlie, T.D., Li, Q., 2003. Variability in surface ozone background over the United States: Implications for air quality policy . J. Geophys. Res., http://dx.doi.org/10.1029/2003JD003855

.
Fiore, A. M.

, et al. , 2009. Multimodel estimates of intercontinental source

- receptor relationships for

oz one pollution. J. Geophys. Res., http://dx.doi.org/10.1029/2008JD010816

. Fiore, A.M., Oberman, J.T., Lin, M.Y., Zhang, L., Clifton, O.E., Jacob, D.J., Naik, V., Horowitz, L.W., Pinto, J.P., Milly, G.P., 2014. Estimating North American background ozone in U.S. surface air with two independent global models: Variability, uncertainties, and recommendations . Atmos. Environ., http://dx.doi.org/10.1016/j.atmosenv.2014.07.045

.
Fine, R

., Miller, M

. B

., Burley, J

., Ja ffe, D.A., Pierce, R.B., Lin, M., Gustin, M ., 20 15

. Variability and sources of surface ozone at rural sites in Nevada, USA: Results from two years of the Nevada Rural Ozone

Initiative

. Sci. Total Environ

. , http://dx.doi.org/10.1016/j.scitotenv.2014.12.027

.

### 26

Jacob, D.L

., Winner, D.A., 2009

. Effect of climate change on air quality. Atmos. Environ., http://dx.doi.org/10.1016/j.atmo senv.2008.09.051

.

Klimont

, Z

., Hoglund

, L

., Heyes

, C

., Rafaj

, P

., Schoepp, W., Cofala, J., Borken

- Kleefeld J., Purohit, P., Kupianen, K., Winiwarter, W., Amann, M., Zhao, B., Wang, S.X., Bertok, I., Sander, R., Kiesewetter, G.,

2015

. ECLIPSE Emissions Scenarios: Key Characteristics

. Presentation at Task Force on Hemispheric Transport of Air Pollution meeting

, 11

- 13 February, 2015, http://www.hta p.org/meetings/2015/2015_Feb11

- 13/presentations/4.Klimont

- HTAP

- IIASA

Feb2015.pdf

. Langford, A.O., Aikin, K.C., Eubank, C.S., Williams, E.J., 2009. Stratospheric contribution to high surface ozone in Colorado during springtime. Geophys. Res. Lett., http://dx.doi.org/10.1029/2009GL038367

. Langford, A.O., Senff, C.J., Alvarez, R.J., Brioude, J., Cooper, O.R., Holloway, J.S., Lin, M.Y., Marchbanks, R.D., Pierce, R.B., Sandberg, S.P., Weickmann, A.M., Wil liams, E.J., 2015. An overview of the 2013 Las Vegas Ozone Study (LVOS): Impact of stratospheric intrusions and long

- range transport on surface air quality . Atmos. Env iron.

, http://dx.doi.org/10 .1016/atmosenv.2014.08.040

. Lefohn, A.S., Emery, C., Shadwick, D., Wernli, H., Jung, J., Oltmans, S.J., 2014. Estimates of background
surface ozone concentrations in the United States based on model

- derived source apportionment. Atmos. Environ., http://dx.doi.org/10.1016/j.atmosenv.2013.11.033

. Lin, M., Fiore, A.M., Horowitz, L.W., Cooper, O.R., Naik, V., Holloway, J., Johnson, B.J., Oltmans, S.J., Middlebrook, A.M., Pollack, I.B., Ryerso n, T.B., Warner, J.X., Wiedinmyer, C., Wilson, J., Wyman, B., 2012. Transport of Asian ozone pollution into surface air over the western United States in spring. J. Geophys. Res., http://dx.doi.org/10.1 029/2011JD016961

.
Lin, M., Horowitz , L.W. , Cooper, O.R., Tarasick, D., Conley, S., Iraci, L.T., Johnson, B., Leblanc, T., Petropavlovskikh, I., Yates , E.L., 2015

. Revisiting the evidence of increasing springtime ozone mixing ratios in the free troposphere over western North America, Geophys. Res. Lett., 42, http://dx.doi.org/10.1002/2015GL065311

. Parrish, D.D., Millet, D.B., Goldstein, A.H, 2009. Increasing ozone in marine boundary layer inflow at the west coasts of North America and Europe. Atmos. Chem. Phys., http://dx.doi.org/10.5194/acp

- 9

- 1303

- 2009

.

Simon

, H.

, Reff

, A.

, Wells

, B., Xing, J., Frank, N., 2015

. Ozone Trends a cross the United St ates over a Period of Decreasing NOx and VOC Emissions

. Environ

. Sci. Technol

., http://dx.doi.org/10.1021/es504514z

. State of Utah Department of Environmental Quality, 2013. 2012 Utah Ozone Study. Divis ion of Air Quality Division, http://www.deq.utah.gov/Pollutants/O/ozone/docs/2013/05May/2012_Utah_Ozone_Study.pdf

. State of Wyoming Department of Environm ental Quality, 2013. Exceptional Event Package for the Environmental Protection Agency: Big Piney and Boulder, Wyoming Ozone Standard Exceedances June 14, 2012. Air Quality Division, http://www.epa.gov/sites/production/files/2015

- 05/documents/june_14_2012_bigpiney_boulder_si_package.pdf

.

### 27

Task Force on Hemisphe ric Transport of Air Pollution, 2010. Hemispheric Transport of Air Pollution 2 010,
Part A: O3 and Particulate Matter. Air Pollution Studies No. 17. Geneva: United Nations Economic Commission for Europe, ECE/EB.AIR/100, http://www.htap.org

. U.S. Environmental Protection Agency, 2012. Regional and S easonal Analysis of North American Background Ozone Estimates from Two Studies. Memo to Ozone NAAQS Review Docket EPA

- HQ

- OAR

2012

- 0699, Office of Air Quality Planning and Standards, http://www3.epa.gov/ttn/naaqs/standards/ozone/data/20120814BackgroundOzone.pdf

.

U.S. En vironmental Protection Agency, 2013 . Integrated Science Assessment for O 3 

### and Related Photochemical Oxidants, Research Triangle Park, NC. EPA/600/R

- 10/076

, http://www3.epa.gov/ttn/naaqs/standards/ozone/s_o3_2008_isa.html

.

U.S. Environmental Protection Agency, 2014. Policy Assessment for the Review of the O 3 

### National
Amb ient Air Quality Standards, Research Triangle Park, NC, EPA

- 452/R

- 14

- 006

, http://www3.epa.gov/ttn/naaqs/standards/ozone/s_o3_2008_pa.html

.

U.S. En vironmental Protection Agency

, 2015. Regulatory Impact Analysis of the Final Revisions to the National Ambient Air Quality Standards for Ground

- Level O 3 

### , EPA

- 452/R

- 15

- 007

, http://www3.epa.gov/airquality/ ozonepollution/pdfs/20151001ria.pdf

. Wang, H., Jacob, D.J., Le Sager, P., Streets, D.G., Park, R.J., Gilliland, A.B., van Donkelaar, A., 2009. Surface ozone background in the United States: Canadian and Mexican pollution influences , Atmos. Environ., http://dx.doi.org/10.1016/j.atmosenv.2008.11.036

. Wigder, N.L., Jaffe, D.A., Saketa, F.A., 2013. Ozone and particulate matter enhancements from regional
wildfires observed at Mount Bachelor duri ng 2004

– 2011 . Atmos. Env iron

., http://dx.doi.org/10.1016/j.atmosenv.2013.04.026

. Zhang, L., Jacob, D.J., Downey, N.V., Wood, D.A., Blewitt, D., Carouge, C.C., van Donkelaar, A., Jones,

D.B.A ., Murray, L.T., Wang, Y. 2011. Improved estimate of the policy

- relevant background ozone in the United States using the GEOS

- Chem global model with 1/2° × 2/3° horizontal resolution over North

America , Atmos. Environ., http://dx.doi.org/10.1016/j.atmosenv.2011.07.054

.