COMMENTS OF THE ELECTRIC POWER RESEARCH INSTITUTE ON ENVIRONMENTAL
PROTECTION AGENCY
40 CFR Parts 50, 51, 52, 53, and 58
[EPA-HQ-OAR-2008-0699; FRL-9918-43-OAR]
National Ambient Air Quality Standards for Ozone
March 5th, 2015
The Electric Power Research Institute, Inc. (EPRI) respectfully submits the enclosed comments on the
U.S. Environmental Protection Agency’s (EPA’s) proposed rule titled National Ambient Air Quality
Standards for Ozone. EPRI thanks the EPA for the opportunity to comment on this proposed rule.
EPRI is a nonprofit corporation organized under the laws of the District of Columbia Nonprofit
Corporation Act and recognized as a tax exempt organization under Section 501(c)(3) of the U.S. Internal
Revenue Code of 1986, as amended, and acts in furtherance of its public benefit mission. EPRI was
established in 1972 and has principal offices and laboratories located in Palo Alto, Calif.; Charlotte, N.C.;
Knoxville, Tenn.; and Lenox, Mass. EPRI conducts research and development relating to the generation,
delivery, and use of electricity for the benefit of the public. An independent, nonprofit organization, EPRI
brings together its scientists and engineers as well as experts from academia and industry to help address
challenges in electricity, including reliability, efficiency, health, safety, and the environment. EPRI also
provides technology, policy and economic analyses to inform long-range research and development
planning, as well as supports research in emerging technologies.
More specifically related to this proposed rule, EPRI has been involved in air quality related research for
more than 40 years, with air quality health, atmospheric modeling, measurements, and risk assessment
studies. Air quality characterization and health and risk assessment have been central to EPRI’s activities
since its inception. These comments on the proposed rule reflect EPRI’s research activities in that they are
technical rather than legal in nature. The comments contained in this letter reflect only EPRI’s opinion
and expertise and do not necessarily reflect the opinions of those supporting and working with EPRI to
conduct collaborative research and development.
EPRI comments address two major issues related to the proposed rule for the ozone NAAQS. The
first focuses on a need for an integrated uncertainty analysis (IUA), using long-term respiratory
mortality from ozone as a case study, which, also informs use of a threshold impact. The second is
focused on how background ozone is treated when projecting future year ozone concentrations.
EPRI hopes its comments and technical feedback will be valuable to EPA.
Sincerely,
Anda Ray
Vice President, Environment
And Chief Sustainability Officer
EPRI Comments on National Ambient Air
Quality Standards for Ozone
March 5, 2015
COMMENTS ON NATIONAL AMBIENT AIR QUALITY STANDARDS FOR
OZONE
Docket ID: EPA-HQ-OAR-2008-0699
SUMMARY OF COMMENTS
EPRI comments deal with two major issues related to the proposed rule for the ozone
NAAQS (79 Fed. Reg. 75233). The first focuses on a need for an integrated uncertainty
analysis (IUA), using long-term respiratory mortality from ozone as a case study. The
second is focused on how background ozone is treated when projecting future year
ozone concentrations. The application of IUA in our case study illustrates how this
analysis can increase insight about the nature of the uncertainties in risk estimates that are
not captured in EPA’s current approach. Moreover, our case study indicates that the zerothreshold model used as the basis for the ozone NAAQS can overstate risk. Our analysis
of the background ozone suggests that the US background ozone concentrations have
steadily increased from 1970 to 2005 in the western U.S. and will continue to increase
from 2005 to 2020. The increasing background ozone concentrations could make it
difficult to meet the lower level of the range of the proposed ozone standard in cities in
the western and southwest U.S.
Integrated Uncertainty Analysis
Integrated uncertainty analysis incorporates probabilities over a number of key variables,
instead of a deterministic approach which uses finite values for each variable. The
proposed rule is predicated on risk estimates in the Health Risk and Exposure Assessment
for Ozone (HREA; EPA, 2014a). Thus, given the high level of policy relevance of HREA
risk estimates, it is important to provide a clear understanding about the degree of
uncertainty that is associated with them.
The National Academy of Sciences (NAS) has called on the Agency to integrate
sensitivity analyses into the main body of the document, and not rely on a single core risk
estimate (NAS, 2002). Furthermore, they recommended that IUA be adopted as a way of
integrating the various estimates into a summary of what can be understood about
ambient pollution risks. Such an analysis, however, has not yet been conducted by EPA.
IUA is a method for combining many different sources of uncertainty together into a
probability distribution on a predicted outcome, i.e., placing a distribution of possible
values on a given variable. It is important to perform an IUA when there are multiple
highly sensitive assumptions in an analysis. By defining a probability range over each
uncertain assumption, and then assessing the probabilities of the combined effects of all
those assumptions, a probability distribution on the overall risk estimate can be assessed.
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EPRI Comments on National Ambient Air
Quality Standards for Ozone
March 5, 2015
We describe here a methodology that can be used to conduct an IUA of respiratory
mortality risk from long-term ozone exposure1. The ozone HREA reports estimates of
long-term respiratory mortality risk based on an epidemiological association reported in
one paper (Jerrett et al., 2009). That paper finds a statistically significant association, but
also reports evidence that the underlying relationship, if causal, is markedly non-linear,
with an apparent threshold at 56 ppb. However, a zero-threshold model was used by EPA
in the ozone HREA.
We used long-term mortality due to ozone exposure as a case study to demonstrate the
IUA methodology. This work involved the development of a separate R-based
computational tool that can replicate BenMAP results when run deterministically, but
which does so with much greater computational efficiency. Leveraging the greater
computational efficiency, the tool allows users to specify probability distributions over
input assumptions to the risk formula, and then produces probability distributions of the
risk estimates that better reflect the overall uncertainties. Three input assumptions to the
long-term mortality risk calculation were treated as uncertain in this case study and for
which we specified probability distributions:



The level of a potential threshold in the concentration-response function
The slope of the concentration-response function
The change in ozone concentrations
For the level of a potential threshold, given the evidence in the study by Jerrett et al.
(2009), a probability distribution was assigned over the range of 40 ppb to 58 ppb. A
three-fourths probability was applied that the true threshold lies above 53 ppb; a twothirds probability was applied that the true threshold lies in the range of 55 ppb to 57 ppb.
A 1 in 10 chance was assigned that the true threshold lies between 40 and 50 ppb, and a 1
in 100 chance that it is as high as 58 ppb. This is not a symmetric distribution, the details
and rationale of which can be seen in the detailed comments. For the slope of the
concentration-response function, values were adopted from Sasser (2014) contingent on
the threshold assumption, with their standard errors used to define the probability
distributions on each respective slope. Finally, a 6% standard error was applied on the
predicted ozone in each county in the analysis (which implies a 95% confidence bound
around the projected level of ± 12%).
For our case study, we have focused on the national scale results from as-is ozone, found
in Chapter 8 of the HREA. The calculations were performed for each county of the U.S.,
which were aggregated to the national total. Our results indicate that:

1 Under the zero-threshold/deterministic model applied in the HREA, the 95%
range of estimated premature respiratory deaths due to ambient ozone in 2007 is
13,000 – 53,000, with a 50% probability that deaths are greater than 34,000. The
IUA calculation performed here shows results that are markedly different, with a
The development of the IUA tool and subsequent case study of long-term ozone mortality was conducted
by Dr. Anne Smith, NERA Economic Consulting, with funding from EPRI.
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March 5, 2015
95% range of 600 – 8,000 deaths and a probability of deaths greater than 34,000
of 0%. Thus, the zero-threshold model used in the HREA can overstate risk.

When examining risks by geographic region, in most areas of the country the
HREA approach indicates that between 12% and 18% (median range) of all
respiratory deaths are attributable to ozone exposure. In contrast, the IUA
indicates the median risk estimate is between 0% and 6% in some areas, and in
many other areas the median risk is 0%. Thus, in all cases, the HREA indicates
higher median risk than the IUA approach implies, but the degree of overstatement varies by location. IUA in this case has identified significant
probability of no risk at all in certain locations across the United States, and/or of
no risk reduction from a tightening of the ozone NAAQS. Specifically, our
results show that there is a significant possibility that there will be no benefits at
all in the majority of twelve urban areas considered in the HREA when tightening
the standard from 75 ppb to 70 ppb.
The IUA approach should provide decision-makers and other readers of an HREA with
much more insight and understanding about the nature of the uncertainties in the risk
estimates than the current approach used in the ozone HREA of emphasizing core
deterministic estimates followed by many separate sensitivity analyses that do not capture
the true uncertainty. This is the value of an IUA that the NAS committee was calling for.
This enhanced method for representing important uncertainties associated with making
quantitative risk estimates should be given close consideration by policymakers as a part
of the evidence informing the decision on the ozone NAAQS. However, regardless of
how IUA will alter the estimate of risk in each case in future applications, the method of
IUA should become the primary approach provided in HREAs and Regulatory Impact
Analyses (RIAs), using techniques illustrated in this set of comments. EPRI would be
pleased to share this computational tool with the Agency, if desired.
Background Ozone
The proposed rule has a section on how background ozone may be addressed in
implementing the ozone standard; the Agency recognizes that background ozone can be
significant in some areas on some days and can thus pose challenges to state agencies
when preparing implementation plans. Background ozone is comprised of ozone and
ozone-forming pollutants from natural as well as international sources. The Agency states
in the proposed rule (Page 536) that background ozone “could prevent ambient levels
from reaching attainment levels in locations where the impacts of such sources are large
relative to the impact of controllable man-made sources of NOx and VOC emissions
within the U.S., especially in locations with few remaining untapped opportunities for
local emission reductions.”
It has been reported (Park et al., 2004) that emissions from international sources that can
lead to formation of ozone have been increasing, and that North American Background
(NAB) and U.S. Background (USB) ozone may also be increasing due to these increasing
emissions (see Section 2.0 of detailed comments for definitions of these terms). However,
EPA uses the same background ozone levels (determined for 2011) in its modeling to
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project future ozone concentrations to 2025 from current ozone levels (modeled year
2011). Thus, EPA assumes that background ozone will remain constant from 2011 to
2025, when the evidence would suggest otherwise because of rising emissions from
international sources. We have performed air quality modeling simulations2 from 1970 to
2020 to show how background ozone concentrations in the U.S. may have changed and
may change in the future (Section 2.0 of our comments). EPRI would be pleased to share
the results of these modeling simulations with the Agency, if desired. The main results
from our modeling exercise are as follows:
2

USB ozone concentrations have steadily increased from 1970 to 2005 in the
western U.S. and will continue to increase from 2005 to 2020. In the eastern U.S.,
USB ozone concentrations appear to be flattening after 2000, except in the
northeast where they are declining because of decrease in Canadian emissions. Of
the major cities examined, Denver had the largest USB ozone concentrations,
with the fourth-highest daily maximum 8-hour average USB ozone concentration
predicted to be 60 ppb in 2020, although there are locations where those
concentrations were as high as 65 ppb.

NAB ozone concentrations are also higher in the western U.S. than the eastern
U.S. and have shown a steady increase from 1970 to 2005 and projected to
continue to increase from 2005 to 2020. Rising emissions from Asia and Mexico
have contributed to the increasing trend in the NAB and the USB ozone
concentrations, respectively.

Fourth-highest daily maximum 8-hour average USB ozone concentrations are
predicted to increase from 2005 to 2020 in the western U.S. by 1 to 3 ppb, decline
in the northeast by as much as 9 ppb (due to decreasing emissions in Canada), and
remain within 1 ppb in rest of the country.

By assuming the same background ozone in 2025 as calculated for 2011, EPA
may have underestimated the emissions reductions needed to reach attainment for
locations in the western U.S., and overestimated the emissions reductions needed
to reach attainment in the northeast. A more accurate approach would have been
to calculate future background concentrations separately using the estimated
international emissions for 2025.

These results also suggest how difficult it would be to meet the lower level of the
range of the proposed ozone standard in cities in the western and southwest U.S.,
given that fourth-highest daily maximum 8-hour average USB concentrations in
some of those locations are predicted to be close to 65 ppb in 2020.
The actual modeling was conducted by ENVIRON International, Inc. as part of a contract with EPRI.
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Quality Standards for Ozone
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DETAILED COMMENTS
1.0 INTEGRATED UNCERTAINTY ANALYSIS (IUA)
1.1 Background
The proposed rule for the ozone NAAQS (79 Fed. Reg. 75233) is predicated on risk
estimates in the Health Risk and Exposure Assessment for Ozone (HREA; EPA, 2014a).
Thus, given the high level of policy relevance of HREA risk estimates, it would be
important that they would provide a clear understanding about the degree of uncertainty
that is associated with them.
Currently, EPA’s approach for producing risk estimates based on epidemiological
evidence is to choose a single concentration-response function from the epidemiological
literature as its core assumption, and to make quantitative estimates of national and cityspecific risk using that model as the correct model. In this approach, the only quantitative
range provided around the core risk estimate is based on the variance of the statisticallyestimated parameters of that one epidemiological model. EPA’s approach then provides a
few sensitivity analyses that use a few of the other available epidemiologically-estimated
models, but these are treated as if they have less validity or likelihood. For example, the
HREA describes the core model as the one in which EPA has “greater overall
confidence”.3
EPA’s risk analysis approach was reviewed by a committee of the National Academy of
Sciences (NAS, 2002) which concluded that the above method of addressing
uncertainties that are “rooted in incomplete scientific knowledge” is one of the reasons
why EPA’s risk estimates are controversial and not widely accepted by others in the
policy community: There are several major barriers to broad acceptance of recent EPA health
benefits analyses. One barrier is the large amount of uncertainty inherent in these
analyses, and another is the manner in which the agency deals with this
uncertainty.4
3
HREA, p. 7-4. The full statement in the HREA is: “As with previous NAAQS risk assessments, for this
analysis we have generated two categories of risk estimates, including a set of core (or primary) estimates
and an additional set of sensitivity analyses. The core risk estimates utilize C-R functions based on
epidemiological studies for which we have relatively greater overall confidence and which provide the best
coverage for the broader O3 monitoring period (rather than focusing only on the summer season).
Although it is not strictly possible to assign quantitative levels of confidence to these core risk estimates
due to data limitations, they are generally based on inputs having higher overall levels of confidence
relative to risk estimates that are generated using other C-R functions. Therefore, emphasis is placed on
the core risk estimates in making observations regarding total risk and risk reductions associated with
recent conditions and after just meeting the existing and alternative standard levels.”
4
NAS (2002), p. 126.
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Quality Standards for Ozone
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The Committee described its reasons for why the types of sensitivity analyses in an
HREA are insufficient for communicating about uncertainty in a risk analysis:
The alternative calculations and sensitivity analyses conducted by EPA help to
describe the uncertainty in the analyses, but they are not sufficient. The major
problems with them are that EPA consigns them to an ancillary status and not to
the primary analysis, that the various sources of uncertainty are considered one
at a time, and that EPA explicitly offers no judgment as to the relative plausibility
of the alternative scenarios considered in these analyses. Without a combined,
simultaneous assessment of multiple uncertainty sources, it is impossible to gain
an appreciation of the overall magnitude of the uncertainty in the analysis. The
committee does not agree with the agency’s decision to have the reader determine
the plausibility and relative weighting of alternative assumptions and data
sources and integrate these assessments across uncertainty sources.5
The NAS advisors called for the Agency to integrate the sensitivity analyses into the
main body of the document, and not rely on a single core risk estimates. They went on to
recommend that IUA be adopted as a way of integrating the various estimates into a
summary of what can be understood about ambient pollution risks and benefits:
EPA should move the assessment of uncertainty from its ancillary analyses into its
primary analyses to provide a more realistic depiction of the overall degree of
uncertainty. This shift will entail the development of probabilistic, multiple-source
uncertainty models based not only on available data but also on expert judgment.
EPA should continue to use sensitivity analyses but should attempt to include
more than one source of uncertainty at a time. EPA also should strengthen its
efforts to identify the uncertainty sources that have the greatest influence on the
final results.6
The current HREA (EPA, 2014a) – and the Regulatory Impact Analysis (RIA) for the
proposed rule (EPA, 2014b) – continue the approach of relying on a single core model,
and using only the statistical error associated with that one model to indicate uncertainty
in the risk estimates. BenMAP, a computational tool used by the Agency for RIAs (and
starting in 2011, in HREAs) for NAAQS reviews, reflects EPA’s emphasis on core risk
estimates from single epidemiological models, combined with representation of
“uncertainty” that is based solely on the statistical variance around the core
epidemiological model’s parameter estimate.
In a review of BenMAP, Smith and Gans (2015) note that BenMAP’s design is focused
mainly on producing extremely detailed and disaggregated estimates of risks based on a
single model, which endows a sense of precision to BenMAP outputs that is not
consistent with what is actually known about the true underlying risk relationship. Smith
and Gans identify a need for BenMAP to be enhanced to emphasize performing
5
Ibid., p. 135.
6
Ibid., p. 11.
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Quality Standards for Ozone
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sensitivity analyses on gaps in knowledge about the true health-risk relationships that
may lie beneath purely statistical associations from epidemiological studies. Such lack of
knowledge is known by risk assessment professionals as “epistemic uncertainty.” It is
different conceptually from the statistical, or “aleatory” uncertainty that BenMAP
produces; it is also the larger form of uncertainty in ambient pollutant health risk
estimates. The NAS committee’s recommendations were also focused on the need to
better incorporate epistemic uncertainty in EPA’s risk analyses.
1.2 Development of Integrated Uncertainty Analysis (IUA) Approach
A core principle of risk analysis practice is that sensitivity analyses should be performed
to identify which uncertain assumptions could be material to a decision, and then
uncertainty analysis should be performed to integrate the multiple decision-relevant
uncertainties into an overall probability distribution on the risk estimates (Smith, 2015).
This principle was also reflected in the recommendations of NAS (2002). Integrated
uncertainty analysis (IUA) is a method for combining many different sources of
uncertainty together into a probability distribution on a predicted outcome. It is important
to perform when there are multiple highly sensitive assumptions in an analysis. This is
because any attempt to create a range that varies from an estimate that combines the most
pessimistic of all the possible assumptions down to an estimate that combines the most
optimistic of all the possible assumptions will be far too broad. That is, the probabilities
of each of those two extremes will be too small to have a likely relevance for decision
making purposes. By defining a probability range over each uncertain assumption, and
then assessing the probabilities of the combined effects of all those assumptions, one can
assess a probability distribution over the risk estimate. If some uncertain variables are
expected to be correlated with each other, these interactions can be directly accounted for
in the probability distribution on total risk.7 Accounting for such correlation may cause
the risk estimate’s confidence range to be narrowed if one of the assumptions drives the
risk estimate upwards while the other one moves in the direction of reducing the risk
estimate.
Another insight that often comes from an IUA is that the distribution of probability over a
risk estimate may not be symmetric. That is, the expected value of the risk estimate may
not lie near the middle of the ranges of values that are seen in a set of sensitivity analyses.
A majority of the probability may be associated with values that lie far to one side of the
middle of an overall confidence range (known as “skewness” in the distribution). In fact,
there may be a large probability that the risk or the change in risk would be zero, even if
the confidence range on those risk estimates is wide. Understanding this overall pattern of
likelihood across a confidence range can be very important when making a policy
decision to manage the risk. When there are multiple health endpoints, each with its own
sources of epistemic uncertainty, an IUA may show that each type of endpoint has a
different degree of skewness and/or range of uncertainty. Knowing the breadth and
skewness of uncertainty for different health endpoints that carry different levels of
7
An example is where an estimate of the slope of a concentration-response function would tend to be
higher if the estimated value of a threshold is higher.
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Quality Standards for Ozone
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societal concern can also provide valuable insight for the decision maker. Most
importantly, the probability distribution on a risk estimate for a particular health endpoint
may differ in very important ways from the apparent distribution that comes from risk
estimates that reflect only a single core concentration-response function and its statistical
variance.
1.3 Long-Term Ozone Exposure and Mortality
The ozone HREA reports estimates of two types of mortality risk: acute risks from daily
variations in ozone exposures (called “short-term” risk), and annual mortality rate
changes from chronic (e.g., multi-year average) ozone exposures (called “long-term”
risk). If the two types of ozone-mortality associations are causal, estimates of short-term
and long-term premature mortality risks probably have some relationship to each other
and are therefore considered not additive; however, evidence of such associations comes
from different types of epidemiological studies. EPRI’s comments focus solely on the
estimates of long-term mortality risk in the HREA as a case study, which are based on an
epidemiological association reported in one paper (Jerrett et al., 2009). That paper finds a
statistically significant association between ozone and mortality due to respiratory causes,
but also reports evidence that the underlying relationship, if causal, is markedly nonlinear. In Jerrett et al. (2009), the observed ozone data ranged from 33 ppb to 104 ppb. Onequarter of the cities had ozone in the range of 33 ppb to 53.1 ppb,8 and across that quartile
range, no indication of increasing risk with increasing average ozone was observed. After
the first quartile of cities, a positive slope emerged that is non-linear as well, i.e., the
slope becomes steeper at higher ozone levels. This is clear evidence of a threshold
relationship, with a threshold seeming to be at or above 50 ppb. Note that these ozone
levels are stated as the seasonal average of daily 1-hour maximum ozone, where the
season is April 1 to September 30. The NAAQS standard is stated in a different metric:
the 3-year average of the 4th highest daily maximum 8-hour average. They are not
comparable; the NAAQS level, being tied to worst case days, will always be higher than
the values used in the Jerrett et al. study. Thus, a threshold of 56 ppb in this
epidemiological study may be above even the current NAAQS standard of 75 ppb in
many parts of the country.
Jerrett et al. (2009) also tested for the best-fitting choice of threshold level in
supplemental materials made available online. The evidence indicates the most likely
threshold is 56 ppb. Figure 1 plots the log-likelihood values for each of the modeled
thresholds reported in the supplemental materials (note that lower log-likelihood values
indicate a better fit to the data). Figure 1 shows that every threshold assumption that was
tested (from 40 ppb to 59 ppb) fits the data better than the no-threshold model (which is
seen as the dot on the far left, at a zero assumed threshold). These outputs of the
epidemiological models show that the fit consistently improves in the range from 50 ppb
to 56 ppb, and quickly deteriorates immediately after that. The rate of improvement in fit
8
Ibid., Table 1, p. 1089.
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is slower between 42 ppb and 50 ppb, but this appears to be a local phenomenon because
it occurs in a range where there are very few observations at all.9
Figure 1. Plot of Goodness of Fit Indicators Across All Alternative Assumptions
About Location of a Threshold in Jerrett et al. (-2*Log-Likelihood Values)
Another indication that the most likely threshold is at 56 ppb can be found in the trend in
the coefficient of variation of the slope estimates for each threshold.10 Coefficients of
variation for each of the threshold assumptions modeled can be computed from additional
results of those runs provided by Jerrett and Burnett (see Sasser, 2014, Attachment 3).
There also one can find a consistently decreasing trend over threshold assumptions from
zero to 56 ppb, with a global minimum at a threshold assumption of 56 ppb.
The strong evidence of a threshold in the long-term ozone-mortality association raises the
question of whether long-term risk estimates might be sensitive to that input assumption.
Although no such sensitivity analysis was provided in the drafts of the HREA, a
sensitivity analysis was presented in a set of public comments, which found that the longterm respiratory mortality risk estimates from alternative models with differing threshold
assumptions varied significantly (Smith, 2014). A sensitivity analysis on the threshold
assumption was then added to the final HREA, but that document continues to use a zerothreshold model as its “core” estimate, while the sensitivity analyses are de-emphasized.
The following quote from the HREA’s overview chapter is one example of how attention
9
When there are no additional observations within a range, it is impossible for models assuming alternative
threshold levels within that range to indicate any change in the quality of their fit.
10
The coefficient of variation is the standard error of the estimate divided by the mean of the estimate. The
smaller the coefficient of variation, the better the slope fits the data over which it is estimated.
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to this finding of risk estimate sensitivity is de-emphasized rather than highlighted in the
manner that NAS (2002) advised:
Regarding long-term exposures to O3 and mortality changes at lower
concentrations, Jerrett et al. (2009) evaluated a number of C-R functions with
varying threshold levels. Statistical tests indicated little discernable improvement
in overall model fit when evaluating models that included thresholds, however
there remained uncertainty about the specific location of the threshold, if one did
exist (Jerrett et al., 2009; Sasser et al., 2014). In the absence of substantial
information in the scientific literature on alternative forms of C-R functions at low
O3 concentrations, the best estimate of the C-R function is a linear, no-threshold
function. The scientific literature does not provide sufficient information with
which to quantitatively characterize any potential additional uncertainty in the CR functions at lower O3 concentrations for use in the quantitative risk
assessment.11
In Chapter 8, the HREA provides a national estimate of long-term respiratory mortality
risk. Results reported here provide minimal indication of the uncertainty due to the
threshold assumption. To summarize results, it says: “For the application of Jerrett et al.
(2009) national average effect estimate for April-September, we estimate 45,000 (95%
CI, 17,000-70,000) premature O3-related respiratory deaths among adults age 30 and
older.”12 This statement is made in reference to a summary table (HREA Table 8-1) that
also provides no evidence that these results are sensitive to a key modeling assumption.
Later, results of the sensitivity analysis are provided, which show that if the threshold is
set at the best-fit level found in Jerrett et al. (2009), that core risk estimate of 45,000
becomes 1,600 (95% CI, 710 – 2,400).13 Before presenting its sensitivity results,
however, the HREA states that “None of the threshold models produce better predictions
than the linear model when a more stringent statistical test was used.”14
Similarly, in Chapter 7 on city-specific epidemiological risk estimates, the HREA
summarizes its estimates of long-term respiratory mortality for twelve cities in Table 712. The implied range of “uncertainty” is provided in parentheses in the table. This
summary table reflects only the zero threshold model in Jerrett et al. (2009), and the
ranges reflect only the variance on the statistical estimate of the slope of the
concentration-response function from that zero-threshold model. The HREA, when
referring to this summary table, makes no mention of sensitivity associated with these
estimates:
Estimates presented in Table 7-12 reflect respiratory mortality and include 95th
percentile confidence intervals representing uncertainty associated with the
11
HREA, p. 2-14.
12
HREA, p. 8-6.
13
HREA, p. 8-19.
14
HREA, p. 8-18. 10
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statistical fit of the effect estimates used. Estimates presented in these tables allow
for consideration for the magnitude of risk associated with just meeting the
existing standard and the pattern of risk reduction in meeting alternative
standards relative to the existing standard.15
After discussing policy-relevant inferences based on those core results only, that chapter
reports that a sensitivity analysis to several possible threshold levels ranging from 40 ppb
to 60 ppb “suggests that compared to the estimates generating by using a linear (nothreshold) model, these models can result in substantially lower estimates of O3attributable mortality across all of the standard levels considered.”16
The HREA’s conclusion on its city-specific risk estimates recaps the situation thus:
We have a reasonable degree of confidence in short-term O3-attributable
mortality and morbidity estimates for ten of the twelve study areas. … We have
somewhat lower confidence in our estimates of mortality risk attributable to longterm O3 exposures, primarily because there is only a single well designed study,
and because of the large impact of uncertainty around the existence and potential
location of a threshold in the C-R function for this endpoint.17 [emphasis added]
1.4 Case Study: Integrated Uncertainty Analysis of Respiratory Mortality Risk from
Long-Term Ozone Exposure
It is problematic for a risk analysis to provide uncertainty ranges that are based only on
statistical errors from a single model, particularly when it is not the best fitting model in
the paper from which it was extracted. It is also problematic for a risk analysis not to
highlight when it finds that a very important risk category is highly sensitive. When the
latter situation arises, the standard practice in risk analysis is to provide an IUA that
incorporates uncertainty on each sensitive input assumption, and brings results of that
analysis to the forefront of the report. The HREA and RIA do not do this. In an effort to
address this limitation, an IUA of respiratory mortality risk from long-term ozone
exposure was conducted; this section of our comments details the methods and results of
these analyses. This IUA was conducted on the same data used by the HREA. The results
are compared to those presented in the HREA, as summarized in the prior section of our
comments. Note that this work involved the development of a separate computational
tool that can replicate BenMAP results for any single core or sensitivity case when run
deterministically, but which does so with much greater computational efficiency18.
Leveraging the greater computational efficiency, this tool allows users to specify
probability distributions over input assumptions to the risk formula, and then produces
15
HREA, p. 7-52.
16
HREA, p. 7-79.
17
18
HREA, p. 7-87.
BenMAP’s code is not efficient enough to be able to be adapted to conduct the much more
computationally intensive calculations that are required of an IUA.
11
EPRI Comments on National Ambient Air
Quality Standards for Ozone
March 5, 2015
probability distributions of the risk estimates that reflect a probabilistic integration of
those uncertainties.
Three input assumptions to the long-term respiratory mortality risk calculation were
treated as uncertain:
 The level of a potential threshold in the concentration-response function
 The slope of the concentration-response function
 The change in ozone concentrations
For the first two of these, evidence about the relative likelihood of alternative threshold
values was derived from careful review of results of Jerrett et al. (2009) and its
supplemental materials. Setting a probability distribution over the level of the threshold
and the associated concentration-response slopes involves subjective judgment. In this
case, those judgments can be based on available evidence in the original epidemiological
study combined with additional information provided to EPA by two of the paper’s
authors (Sasser, 2014).19
First, a probability distribution was set on the level of the threshold. It is easy to make the
case that if one were to continue to apply the “core” model approach, it should use the
results of the long-term epidemiological model in which the threshold is fixed at 56 ppb.
However, an IUA approach further recognizes that the true threshold may not be exactly
56 ppb, even though, given the evidence summarized above, it is almost certainly in the
range of 40 ppb to 58 ppb, and most likely to be above 53 ppb.
To reflect subjective judgment, given the evidence in the epidemiological study described
above, a probability distribution was assigned over the range of 40 ppb to 58 ppb. A
three-fourths probability was applied that the true threshold lies above 53 ppb and a onefourth probability that it lies below 53 ppb; a two-thirds probability was applied that the
true threshold lies in the range of 55 ppb to 57 ppb. A 1 in 10 chance was assigned that
the true threshold lies between 40 and 50 ppb, and a 1 in 100 chance that it is as high as
58 ppb. This is not a symmetric distribution, reflecting the fact that the goodness of fit
falls off much more rapidly for thresholds above 56 ppb than it declines for thresholds
lower than 56 ppb, but its mode (greatest concentration of probability) is set at 56 ppb.
The full subjective probability distribution for this input is provided in Figure 2. The
rationale for this subjective distribution has been provided; it should be noted that other
professionals familiar with uncertainty analysis and subjective judgment might draw
different conclusions from a review of the same information, or may bring in additional
information that has not been considered. It is reasonable that they do so, but in doing so,
they should provide their own reasoning for their choice of range and where they would
concentrate the probabilities. Regardless of one’s subjective probability distribution, the
key point is that an IUA is needed, following the methods that are described and
illustrated in the rest of this section.
19
Attachment 3 of Sasser (2014) provides the estimated slope of the long-term respiratory mortality C-R
function above each modeled threshold and its standard error.
12
Probability that True Threshold is Less than Assumed Level
EPRI Comments on National Ambient Air
Quality Standards for Ozone
March 5, 2015
Figure 2. Subjective Probabilities for Level of Threshold Used in IUA
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60
Alternative Assumed Levels for Threshold
Second, the IUA calculation tool described here allows the user to make the slope of
concentration-response function be probabilistically dependent on the level of the
threshold, and probability distributions were defined for each slope. In general, one
would expect that the slope estimate would tend to increase as the threshold assumed is
increased, but the degree of this dependency may also require subjective judgment.
However, in this case, the original epidemiological study actually provides a different
slope coefficient estimate (with standard error) for each alternative threshold assumption
(Sasser, 2014, Attachment 3). Figure 3 shows how the slope varies as a function of the
assumed threshold level, and that the slope estimates follow the expected pattern. Those
values have been adopted as contingent on the threshold assumption, and their standard
errors were used to define the probability distributions on each respective slope.20
20
As noted in Smith and Gans (2015), even the slope of the C-R functions may be more uncertain than
what a single study, which relies on a single sample of people and air quality data, reports. However,
lacking any additional studies to provide us with more evidence on the inter-study variation for C-R
functions, this IUA case study was focused on the epistemic uncertainty associated with the threshold,
and only aleatory uncertainty was accounted for on the threshold-dependent slope input assumption. 13
EPRI Comments on National Ambient Air
Quality Standards for Ozone
March 5, 2015
Figure 3. Relative Risk Coefficient for Long-Term Respiratory Mortality as a
Function of Assumed Threshold Level (Source: Attachment 3 of Sasser, 2014)
Finally, the IUA tool allowed uncertainties on the true ozone level relative to the
projected ozone in the air quality simulation used in the HREA to be specified. Based on
information on the performance of models such as CMAQ, which produced the ozone air
quality grid used in the HREA, a 6% standard error was applied on the predicted ozone in
each county in the analysis (which implies a 95% confidence bound around the projected
level of ± 12%).
Having established the three probability distributions on inputs, the IUA tool generated
probability distributions on the risk for long-term respiratory mortality attributable to
ozone, inclusive of several key forms of epistemic uncertainty. The focus here is on the
national scale results from as-is ozone, found in Chapter 8 of the HREA. The calculations
were performed for each county of the US, which were aggregated to the national total.21
These are summarized in tabular form in Table 1 and shown in full detail with their
respective cumulative probability functions in Figure 4.22 They are contrasted to the
21
In doing the aggregation, the uncertainty in the predicted ozone concentration was assumed to be
independent across all counties. A useful extension of this tool would allow those prediction errors to
be spatially correlated.
22
A cumulative probability distribution shows the range of possible values of risk on the X-axis, and for
each possible risk value, the Y-axis reports the probability that the true risk is less than or equal to that X-axis value. The maximum feasible value over any set of possible inputs is at the point where the
curve reaches 100%. A confidence range, such as a 90% confidence range, can be determined by
reading the X-axis values at the 5% and 95% points on the cumulative curve. The median of the
probability distribution is the value associated with the 50% point.
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EPRI Comments on National Ambient Air
Quality Standards for Ozone
March 5, 2015
implied distribution from a single core model with a zero-threshold assumption, as used
in the HREA23.
Table 1. National Long-Term Respiratory Deaths per Year Due to 2006-2008
Ozone Levels: Integrated Uncertainty Analysis Estimates Compared to ZeroThreshold Model Estimates
23 Mean
Median
95% Range
Probability
Premature Deaths
Are > 34,000
Zero Threshold/
Deterministic (1P)
34,000
34,000
13,000-53,000
50%
Integrated
Uncertainty
Analysis (IUA)
2,400
1,800
600 – 8,000
0%
All of the models that were estimated using an assumed alternative threshold were performed with ozone
as the only pollutant in the model, called “1-P” models. The core results in the HREA had both ozone
and fine particulates in the model, called a “2-P” model. To provide a fair comparison between core
model results and results that include threshold models, the IUA results are compared to estimates
based on a 1-P zero-threshold model from the paper. This 1-P model produces a national risk estimate
of 34,000 deaths, compare to the 45,000 deaths that are predicted from the zero-threshold 2-P model. 15
EPRI Comments on National Ambient Air
Quality Standards for Ozone
March 5, 2015
Figure 4. National Long-Term Respiratory Deaths per Year Due to 2006-2008
Ozone Levels: Integrated Uncertainty Analysis Estimates Compared to ZeroThreshold Model Estimates (probabilities of the estimates).
Table 1 and Figure 4 represent the IUA results aggregated to the national level. However,
the IUA tool actually performed those computations for each county of the US.24 Figure 5
compares, for each county, the IUA’s median (50th percentile) estimate of the percent
increase in long-term respiratory mortality to the median estimate that is produced by the
single, zero-threshold assumption that is used for the core result of the HREA.25 The
difference observable at the national aggregate level becomes even more pronounced in
individual areas of the country. In most areas of the country, the zero-threshold model
indicates that between 12% and 18% of all respiratory deaths are attributable to ozone
exposure, while in contrast the IUA indicates the median risk estimate is between 0% and
6% in some areas (those shaded yellow in the top panel)26, and in many other areas the
24
The 12 km by 12 km grid was used for the national estimate in the HREA was converted to the country
level to perform the IUA, as the more precise locational detail is nearly irrelevant given these
uncertainties.
25
Percent increase in baseline mortality risk is a more informative way to present spatial differences in the
effect of ozone. Counts of total deaths will tend to be dominated by county population, and look more
like a map of population than reveal where the relative risk is most increased.
26
For those areas shaded in yellow (0% to 6%) in the top panel of Figure 5, the average risk elevation is
about 2%.
16
EPRI Comments on National Ambient Air
Quality Standards for Ozone
March 5, 2015
median risk is literally 0%. Thus, in all cases, the HREA indicates higher median risk
than the IUA approach does, but the degree of overstatement varies by location.
Figure 6 provides maps of the 10th and 90th percentile risks in long-term respiratory
mortality across the US from the IUA analysis only, i.e, it shows the range of uncertainty
around the median impacts in panel (A) of Figure 5. Even the 90th percentile risk
estimates from the IUA are far smaller and less widespread across the nation than the
mean from a zero-threshold analysis such as in the HREA (e.g., as in panel (B) of Figure
5 using the 1-P zero-threshold model).
Figure 6 shows that the degree of difference of the IUA result from the core model (zerothreshold) result still differ by location even when considering the relative extremes of
the IUA projection. Even at the 90th percentile level, the IUA continues to indicate zero
risk in many parts of the U.S. where the HREA’s core analysis reports up to an 18%
increase in the probability of dying from respiratory illness due to ozone. The degree of
overstatement visible in these national maps is associated with areas of the U.S. with
relatively low ozone. Another question is whether this overstatement remains even in
areas with relatively high ozone. For its city-specific risk analyses, the HREA performed
risk estimates for twelve cities that can be characterized as having relatively high current
ozone (e.g., not attaining the current standard of 75 ppb for the 4th highest daily
maximum 8-hour average). We now consider how the IUA risk estimates for long-term
respiratory mortality compare to the HREA estimates at the city-specific level.
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EPRI Comments on National Ambient Air
Quality Standards for Ozone
March 5, 2015
Figure 5. Median Estimated Percent Increase in Long-Term Respiratory Mortality
Risk by County: (A) IUA Compared to (B) HREA No-Threshold Assumption.
(A) Median Estimates from IUA
(B) Median Estimates under HREA No-Threshold Assumption
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EPRI Comments on National Ambient Air
Quality Standards for Ozone
March 5, 2015
Figure 6. Range of IUA’s Projected Percent Increase in Long-Term Respiratory
Mortality Risk by County: (A) 10th Percentile Estimate and (B) 90th Percentile
Estimate.
(A) 10th Percentile Risk from IUA
(B) 90th Percentile Risk from IUA
(
19
EPRI Comments on National Ambient Air
Quality Standards for Ozone
March 5, 2015
Chapter 7 of the HREA also provides estimates of long-term respiratory mortality risks,
in this case calculated for twelve specific cities, or “urban study areas.”27 In this chapter,
the city-specific ozone levels for each simulation year are adjusted to reflect exact
attainment with alternative NAAQS levels from 60 to 70 ppb, as well as the current
standard of 75 ppb (which the cities do not yet attain). Thus, the focus of this chapter is
more on the changes in projected risks under alternative standards, rather than the total
risk. Although Chapter 7 considers both types of mortality risk and some morbidity
endpoints, EPRI’s comments address only the long-term respiratory mortality estimates
in that chapter. Application of the IUA for long-term respiratory mortality risk to the
twelve cities featured in Chapter 7 of the HREA finds that the overstatement of estimates
for that particular risk endpoint implicitly caused by the HREA’s core approach is
exceedingly large for most of these high ozone areas as well. Tables 2, 3, and 4 contrast the city-specific risk improvements projected by the IUA with
the HREA’s primary results summary for those cities for long-term respiratory mortality
risk. These three tables report the change in estimated premature respiratory mortality
when reducing long-term ambient ozone, respectively, from levels attaining the 75 ppb
NAAQS to levels just attaining a 70 ppb alternative NAAQS, then for the further
incremental change in premature mortality projected by moving from a 70 ppb alternative
NAAQS to the next tighter alternative NAAQS of 65 ppb, and finally the further change
if going from a 65 ppb alternative to the 60 ppb alternative. Changes in risks are
presented in this incremental manner because it reveals a number of points:
27

First, one can see that the HREA’s projected long-term mortality risk changes are
effectively identical for every extra 5 ppb of tightening of the NAAQS – a result
that comes from the simple linear, no-threshold assumption producing the HREA
primary results. There is no apparent stopping point in public health risks if one
treats the zero-threshold assumption as the single true risk relationship. In
contrast, the IUA results show a decreasing probability of further risk reduction
(i.e., declining mean risk reductions) for each incremental tightening of the
NAAQS. This captures the growing probability that the ozone levels at ever lower
alternative NAAQS levels will have fallen below an effects threshold that is
apparent in the epidemiological evidence. 
Additionally, it can be seen that there is a significant possibility that there will be
no benefits at all in the majority of the twelve cities even when tightening from 75
ppb to 70 ppb. The total probability that there would be zero risk reduction is
shown in the right subcolumn: it is between 75% and 95% for all but two cities
even for tightening the current standard to 70 ppb only, and it increases rapidly
for the tighter standards. In contrast, the HREA primary results imply 0% chance
These urban study areas are defined by the boundaries of the core based statistical areas (CBSAs) of
twelve U.S. cities. For brevity, we refer to them as cities hereafter. The risk estimates in Chapter 7 are
based on, separately, 2007 and 2009 ozone levels, whereas the national results discussed above are
based on average ozone over the years 2006-2008. For brevity while maintaining maximal
comparability to the national results above, the IUA estimates presented here for city-specific results
are from the HREA’s 2007-ozone simulations only.
20
EPRI Comments on National Ambient Air
Quality Standards for Ozone
March 5, 2015
of no effect even at the tightest standards (because the zero-threshold model
implies no threshold can ever be crossed no matter how low ozone actually falls).

Finally, the mean expected reduction in risk projected by the IUA is much smaller
than the HREA primary analysis indicates, except for Denver and Los Angeles for
the option to reduce the standard to 70 ppb. This is because the seasonal 1-hour
average ozone in those locations is high relative to the worst-case 8-hour peaks
that the NAAQS controls.
Summary information in these formats, which an IUA approach makes possible, should
provide decision-makers and other readers of an HREA with much more insight and
understanding about the nature of the uncertainties in the risk estimates than the current
HREA’s approach of emphasizing core estimates followed by many separate (nonintegrated) sensitivity analyses that end up being communicated in complicated figures.
Also important is that the IUA brings the sensitivity results directly into the primary
analysis, so that the HREA does not have to first present “core” findings that do not
reflect any of the epistemic uncertainties, and then at some later point attempt to
summarize a complex set of individual sensitivity analyses. This is the value of an IUA
that the NAS committee was calling for.
Table 2. Comparison of Results from IUA and HREA for Twelve City-Specific
Estimates of the Reduction (Deaths Per Year), and the Probability of Zero
Reduction, in Long-Term Respiratory Mortality Risk when Attaining a 70 ppb
NAAQS Relative to the 75 ppb NAAQS (2007 simulation year).
IUA Results
City
Means
(95% Range)
Atlanta, GA
Baltimore, MD
Boston, MA
Cleveland, OH
Denver, CO
Detroit, MI
Houston, TX
Los Angeles, CA
New York, NY
Philadelphia, PA
Sacramento, CA
St. Louis, MO
5 (0 – 25)
4 (0 – 21)
0.8 (0 – 8)
0.6 (0 – 6)
13 (6 – 21)
4 (0 – 22)
0.3 (0 – 3)
82 (33 – 128)
5 (0 – 52)
6 (0 – 33)
0.6 (0 – 5)
5 (0 – 31)
Probability of
no risk
reduction
75%
75%
95%
95%
0%
75%
95%
0%
95%
75%
95%
75%
21
HREA Results
(from “75-70” in Table 1)
Means
Probability of
(95% Range)
no risk
reduction
35 (12-59)
0%
17 (6 – 29)
0%
20 (7 – 33)
0%
16 (6-27)
0%
13 (4 – 21)
0%
28 (10 – 46)
0%
8 (3 – 13)
0%
82 (28 – 140)
0%
140 (47 – 230)
0%
42 (14 – 69)
0%
14 (5 – 22)
0%
27 (9 – 45)
0%
EPRI Comments on National Ambient Air
Quality Standards for Ozone
March 5, 2015
Table 3. Comparison of Results from IUA and HREA for Twelve City-Specific
Estimates of the Reduction (Deaths Per Year), and the Probability of Zero
Reduction, in Long-Term Respiratory Mortality Risk when Attaining a 65 ppb
NAAQS Relative to a 70 ppb NAAQS (2007 simulation year)
IUA Results
City
Means
(95% Range)
Atlanta, GA
Baltimore, MD
Boston, MA
Cleveland, OH
Denver, CO
Detroit, MI
Houston, TX
Los Angeles, CA
New York, NY
1 (0 – 5)
1 (0 – 7)
1 (0 – 13)
0.8 (0 – 7)
14 (5 – 21)
2 (0.9 – 3)
0.3 (0 – 3)
40 (0 – 88)
5 (0 – 48)
Probability of
no risk
reduction
95%
75%
95%
95%
0%
95%
95%
5%
95%
Philadelphia, PA
Sacramento, CA
St. Louis, MO
2 (0 – 4)
.5 (0 – 5)
1 (0 – 3)
95%
95%
95%
22
HREA Results
(difference between “75-70” and
“75-65” in Table 1)
Means
Probability of
(95% Range)
no risk
reduction
29 (10 - 51)
0%
18 (6 – 28)
0%
33 (11 – 55)
0%
19 (6 - 31)
0%
13 (5 – 23)
0%
22 (7 – 36)
0%
8 (2 – 13)
0%
58 (26 – 120)
0%
410 (143 –
0%
670)
45 (16 – 71)
0%
12 (4 – 21)
0%
29 (10 – 47)
0%
EPRI Comments on National Ambient Air
Quality Standards for Ozone
March 5, 2015
Table 4. Comparison of Results from IUA and HREA for Twelve City-Specific
Estimates of the Reduction (Deaths Per Year), and the Probability of Zero
Reduction, in Long-Term Respiratory Mortality Risk when Attaining a 60 ppb
NAAQS Relative to a 65 ppb NAAQS (2007 simulation year).
IUA Results
City
Means
(95% Range)
Atlanta, GA
Baltimore, MD
Boston, MA
Cleveland, OH
Denver, CO
Detroit, MI
Houston, TX
Los Angeles, CA
New York, NY
Philadelphia, PA
Sacramento, CA
St. Louis, MO
1 (0 – 4)
0.9 (0 – 0.8)
0.5 (0 – 5)
0.9 (0 – 9)
7 (0 – 16)
1 (0 – 5)
0.4 (0 – 4)
17 (0 – 73)
2 (0 – 14)
0.8 (0 – 7)
1 (0 – 0)
HREA Results
(difference between “75-65” and
“75-60” in Table 1)
Probability of
Means
Probability of
no risk
(95% Range)
no risk
reduction
reduction
95%
36 (12-50)
0%
95%
22 (7 – 36)
0%
95%
29 (10 – 52)
0%
95%
29 (10 - 44)
0%
35%
17 (6 – 27)
0%
95%
28 (10 – 48)
0%
95%
11 (4 – 18)
0%
75%
80 (29 – 140)
0%
Not attainable
95%
43 (14 – 70)
0%
95%
18 (6 – 30)
0%
95%
28 (10 – 48)
0%
1.5 Conclusions for IUA
The analyses above illustrate how an IUA can produce much different information about
the nature of the risks associated with ambient pollution. Often it is assumed that the
incorporation of multiple uncertainties will only increase the apparent degree of
uncertainty. This has not been the case in the analysis for long-term respiratory mortality
risk from ozone. In this case, assigning probabilities to alternative assumptions about the
shape and slope of the concentration-response function has greatly narrowed the
distribution. It has also shifted expected risks downwards, and shown a pronounced
skewness, with significant amounts of probability on the possibility of no risk at all in
certain locations across the U.S., and/or of no risk reduction from a tightening of the
ozone NAAQS.
Regardless of how an IUA will alter the information about uncertainty in each case in
future applications, the method of IUA should become the primary approach provided in
HREAs and RIAs, using techniques illustrated in this paper. EPRI would be pleased to
share the computational tool developed with the Agency, if desired.
23
EPRI Comments on National Ambient Air
Quality Standards for Ozone
March 5, 2015
2.0 BACKGROUND OZONE
In the proposed rule, the Agency recognizes that background ozone can be significant in
some areas and thus pose challenges to state agencies when preparing implementation
plans. Background ozone is comprised of ozone and ozone-forming pollutants from
natural as well as international sources. The Agency states in the proposed rule (Page
536) that background ozone “could prevent ambient levels from reaching attainment
levels in locations where the impacts of such sources are large relative to the impact of
controllable man-made sources of NOx and VOC emissions within the U.S., especially in
locations with few remaining untapped opportunities for local emission reductions.”
In the Policy Assessment (PA) document, EPA has provided three specific definitions of
background ozone: natural background, North American background, and United States
background: “Natural background (NB) is defined as the ozone that would exist in the
absence of any manmade ozone precursor emissions. North American background (NAB)
is defined as that ozone that would exist in the absence of any manmade ozone precursor
emissions from North America. U.S. background (USB) is defined as that ozone that
would exist in the absence of any manmade emissions inside the U.S.” It has been
reported (Park et al., 2004) that emissions from international sources that can lead to
formation of ozone have been increasing, and that NAB and USB ozone may also be
increasing due to those increasing emissions. However, EPA uses the same background
ozone levels (determined for 2011) in its modeling to project future ozone concentrations
to 2025 from current ozone levels (modeled year 2011). Thus, EPA assumes that
background ozone will remain constant from 2011 to 2025 without showing any
justification, when the evidence would suggest otherwise because of rising emissions
from international sources. We have performed air quality modeling simulations from
1970 to 2020 (annual simulations for 1970, 1980, 1990, 2000, 2005, and 2020) to show
how background ozone in the U.S. may have changed during that time period. 2005 was
the base year for our simulations as the input data were readily available for that year
from EPA.
First, we used a global Chemical Transport Model (CTM), GEOS-Chem, to simulate
global ozone concentrations for a 2x2.5 degree grid for several years between 1970 and
2020. Meteorology for 2005 was used from the Goddard Earth Observing System
Model, Version 5 (GEOS5). We developed year-specific anthropogenic emissions for all
source categories and conducted GEOS-Chem simulations (both base case and with
North American anthropogenic emissions set to zero) for the years 1970, 1980, 1990,
2000, 2005 and 2020. The zero-out North America simulations were conducted to obtain
the NAB in each year.
GEOS-Chem has separate emission inputs for the continental U.S. (CONUS) and the rest
of the world. The CONUS emissions were based on the 2005 National Emissions
Inventory (2005 NEI) available in GEOS-Chem and adjusted from 2005 to other
modeling years using projection factors based on EPA’s NEI Trends data. Most
anthropogenic emissions for the rest of the world are available in GEOS-Chem between
1970 and 2005 based on the EDGAR global inventory. For the emission components that
24
EPRI Comments on National Ambient Air
Quality Standards for Ozone
March 5, 2015
are not available in GEOS-Chem for the entire modeling period, we developed factors
based on available global inventories that are available over extended time periods.
For 2020 emissions projection in GEOS-Chem, the RCP data28 that serve as input for
climate and atmospheric chemistry modeling as part of the global modeling studies were
used. There are four RCP scenarios available and the RCP8.5 scenario (Riahi et al., 2007)
has the least aggressive emission reductions over the period 2000-2100 and therefore is
most likely to represent actual emissions for the 2020 time horizon. Anthropogenic
emissions in this database are available at a regional level (5 regions total) covering the
entire World, and gridded with 0.5x0.5 degree resolution. The RCP8.5 scenario was used
to develop projections from the year 2005 to year 2020.
We then used a regional CTM, CAMx, to simulate regional ozone within the continental
United States for a 36-km horizontal grid for the same years as the GEOS-Chem
simulations, the results of which were used to provide boundary conditions to the CAMx
model. For the CAMx simulations, we zeroed out U.S. anthropogenic emissions to obtain
USB concentrations of ozone.
The results of predicted USB ozone are shown in Figure 7 for five representative cities.
Background ozone concentrations in the western U.S. cities (Denver, Los Angeles, and
Phoenix) have been rising from 1970 and are predicted to continue to rise from 2005 to
2020, whereas Philadelphia shows a decline after 2000 and Atlanta shows flattening of
background ozone from 2005 to 2020.
Figure 8 shows the spatial distribution of USB ozone for the continental U.S. in 2020,
and Figure 9 shows the predicted change in USB ozone from 2005 to 2020. It is evident
that USB ozone varies significantly from location to location, with the fourth-highest
daily maximum 8-hour concentrations above 60 ppb in some locations. Generally, USB
ozone concentrations in 2020 are higher in the western and southwest U.S. indicating
influence from rising pollutant emissions in Asia and Mexico (Figure 10 illustrates rising
NOx emissions from Asia).
From 2005 to 2020, USB ozone concentrations are predicted to increase in the western
U.S. and decrease in the northeast (due to declining emissions in Canada). By assuming
the same background ozone in modeling the 2011 and 2025 cases, EPA may be
underestimating the emissions reductions needed to reach attainment in locations where
USB is predicted to increase, and overestimating the emissions reductions needed in
locations where USB is predicted to decrease. These results also suggest how difficult it
would be to meet the lower level of the range of ozone standards proposed in cities in the
western and southwest U.S., given that 4th highest daily maximum 8-hour USB ozone
concentrations in those locations are predicted to be close to 65 ppb in 2020.
It is also instructive to see the relationship between USB and NAB ozone. Figure 11
shows NAB ozone concentrations predicted by the GEOS-Chem model in 2020. Again,
the western U.S. is predicted to have higher NAB ozone concentrations than the eastern
28
https://tntcat.iiasa.ac.at:8743/RcpDb/dsd?Action=htmlpage&page=welcome 25
EPRI Comments on National Ambient Air
Quality Standards for Ozone
March 5, 2015
th
U.S., and some locations are predicted to have 4 highest daily maximum 8-hour NAB
ozone concentrations close to 60 ppb.
In summary, U.S. background ozone concentrations have been steadily increasing in the
western U.S., and is predicted to continue to increase in the future due to rising emissions
from Asia and Mexico. This has implications not only for increased difficulty of attaining
the proposed ozone standards, but also calls into question the 2025 ozone projections
modeled by EPA that assumed background concentrations to remain the same as in 2011.
A more accurate approach would be to estimate 2025 background ozone concentrations
separately, and then use those as boundary conditions to project future ozone
concentrations in 2025.
Figure 7. 4th Highest Daily Maximum 8-hour Ozone Concentrations at Five Major
U.S. Cities
US‐background H4MDA8 ozone concentration at 5 major US cities
65
60
ppb
55
Denver
50
Los Angeles
45
Phoenix
40
Philadelphia
35
Atlanta
30
1970
1980
1990
2000
2005
Year
26
2020
EPRI Comments on National Ambient Air
Quality Standards for Ozone
March 5, 2015
th
Figure 8. 4 Highest Daily Maximum 8-hour USB Ozone Predicted by CAMx for
2020
27
EPRI Comments on National Ambient Air
Quality Standards for Ozone
March 5, 2015
th
Figure 9. Change in 4 Highest Daily Maximum 8-hour Ozone USB Predicted by
CAMx from 2005 to 2020 28
EPRI Comments on National Ambient Air
Quality Standards for Ozone
March 5, 2015
Figure 10. Total Anthropogenic NOx Emissions from 1970 to 2020
Total Anthropogenic NOx Emissions
60000
Emissions (1000 tons per year)
50000
40000
ASIA
LAM
30000
MAF
OECD90
20000
REF
US
10000
0
1970
1980
1990
2000
2005
2020
Year
OECD90 = Includes the OECD 90 countries, therefore encompassing the countries included in the regions Western Europe (Austria,
Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, Netherlands, Norway, Portugal, Spain,
Sweden, Switzerland, Turkey, United Kingdom), Northern America (Canada, United States of America) and Pacific OECD
(Australia, Fiji, French Polynesia, Guam, Japan, New Caledonia, New Zealand, Samoa, Solomon Islands, Vanuatu);
REF = Countries from the Reforming Economies region (Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria,
Croatia, Cyprus, Czech Republic, Estonia, Georgia, Hungary, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Malta, Poland, Republic of
Moldova, Romania, Russian Federation, Slovakia, Slovenia, Tajikistan, TFYR Macedonia, Turkmenistan, Ukraine, Uzbekistan,
Yugoslavia);
ASIA = The countries included in the regions China + (China, China Hong Kong SAR, China Macao SAR, Mongolia, Taiwan) ,
India + (Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, Sri Lanka) and Rest of Asia (Brunei Darussalam,
Cambodia, Democratic People's Republic of Korea, East Timor, Indonesia, Lao People's Democratic Republic, Malaysia, Myanmar,
Papua New Guinea, Philippines, Republic of Korea, Singapore, Thailand, Viet Nam) are aggregated into this region;
MAF = This region includes the Middle East (Bahrain, Iran (Islamic Republic of), Iraq, Israel, Jordan, Kuwait, Lebanon, Oman,
Qatar, Saudi Arabia, Syrian Arab Republic, United Arab Emirates, Yemen) and African (Algeria, Angola, Benin, Botswana, Burkina
Faso, Burundi, Cote d'Ivoire, Cameroon, Cape Verde, Central African Republic, Chad, Comoros, Congo, Democratic Republic of the
Congo, Djibouti, Egypt, Equatorial Guinea, Eritrea, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho,
Liberia, Libyan Arab Jamahiriya, Madagascar, Malawi, Mali, Mauritania, Mauritius, Morocco, Mozambique, Namibia, Niger,
Nigeria, Reunion, Rwanda, Senegal, Sierra Leone, Somalia, South Africa, Sudan, Swaziland, Togo, Tunisia, Uganda, United Republic
of Tanzania, Western Sahara, Zambia, Zimbabwe) countries;
LAM = This region includes the Latin American countries (Argentina, Bahamas, Barbados, Belize, Bolivia, Brazil, Chile, Colombia,
Costa Rica, Cuba, Dominican Republic, Ecuador, El Salvador, Guadeloupe, Guatemala, Guyana, Haiti, Honduras, Jamaica,
Martinique, Mexico, Netherlands Antilles, Nicaragua, Panama, Paraguay, Peru, Puerto Rico, Suriname, Trinidad and Tobago,
Uruguay, Venezuela).
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Quality Standards for Ozone
March 5, 2015
th
Figure 11. 4 Highest Daily Maximum 8-hour Ozone NAB Concentrations
Predicted by GEOS-Chem for 2020
30
EPRI Comments on National Ambient Air
Quality Standards for Ozone
March 5, 2015
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