COMMENTS OF THE ELECTRIC POWER RESEARCH INSTITUTE ON ENVIRONMENTAL PROTECTION AGENCY. 40 CFR Parts 50, 51, 52, 53, and 58

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1 COMMENTS OF THE ELECTRIC POWER RESEARCH INSTITUTE ON ENVIRONMENTAL PROTECTION AGENCY 40 CFR Parts 50, 51, 52, 53, and 58 [EPA-HQ-OAR ; FRL OAR] National Ambient Air March 5 th, 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.

2 Sincerely, Anda Ray Vice President, Environment And Chief Sustainability Officer

3 COMMENTS ON NATIONAL AMBIENT AIR QUALITY STANDARDS FOR OZONE Docket ID: EPA-HQ-OAR SUMMARY OF COMMENTS EPRI comments deal with two major issues related to the proposed rule for the ozone NAAQS (79 Fed. Reg ). 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 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. 1

4 We describe here a methodology that can be used to conduct an IUA of respiratory mortality risk from long-term ozone exposure 1. 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: 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 1 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. 2

5 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 3

6 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 simulations 2 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: USB ozone concentrations have steadily increased from 1970 to 2005 in the western U.S. and will continue to increase from 2005 to 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 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 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 The actual modeling was conducted by ENVIRON International, Inc. as part of a contract with EPRI. 4

7 DETAILED COMMENTS EPRI Comments on National Ambient Air 1.0 INTEGRATED UNCERTAINTY ANALYSIS (IUA) 1.1 Background The proposed rule for the ozone NAAQS (79 Fed. Reg ) 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 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 O 3 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

8 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 Ibid., p

9 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. 7

10 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

11 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. 9

12 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 C- R 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 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 HREA, p HREA, p HREA, p

13 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 O3- attributable 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 efficiency 18. 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 HREA, p HREA, p 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

14 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

15 Figure 2. Subjective Probabilities for Level of Threshold Used in IUA Probability that True Threshold is Less than Assumed Level 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 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

16 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 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. 14

17 implied distribution from a single core model with a zero-threshold assumption, as used in the HREA 23. Table 1. National Long-Term Respiratory Deaths per Year Due to Ozone Levels: Integrated Uncertainty Analysis Estimates Compared to Zero- Threshold Model Estimates Mean Median 95% Range Probability Premature Deaths Are > 34,000 Zero Threshold/ Deterministic (1P) Integrated Uncertainty Analysis (IUA) 34,000 34,000 13,000-53,000 50% 2,400 1, ,000 0% 23 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

18 Figure 4. National Long-Term Respiratory Deaths per Year Due to Ozone Levels: Integrated Uncertainty Analysis Estimates Compared to Zero- Threshold 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 (50 th 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

19 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 10 th and 90 th 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 90 th 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 90 th 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 4 th 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. 17

20 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 18

21 ( EPRI Comments on National Ambient Air Figure 6. Range of IUA s Projected Percent Increase in Long-Term Respiratory Mortality Risk by County: (A) 10 th Percentile Estimate and (B) 90 th Percentile Estimate. (A) 10 th Percentile Risk from IUA (B) 90 th Percentile Risk from IUA 19

22 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: 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 27 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 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

23 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). City Means (95% Range) IUA Results Probability of no risk reduction HREA Results (from in Table 1) Means (95% Range) Probability of no risk reduction Atlanta, GA 5 (0 25) 75% 35 (12-59) 0% Baltimore, MD 4 (0 21) 75% 17 (6 29) 0% Boston, MA 0.8 (0 8) 95% 20 (7 33) 0% Cleveland, OH 0.6 (0 6) 95% 16 (6-27) 0% Denver, CO 13 (6 21) 0% 13 (4 21) 0% Detroit, MI 4 (0 22) 75% 28 (10 46) 0% Houston, TX 0.3 (0 3) 95% 8 (3 13) 0% Los Angeles, CA 82 (33 128) 0% 82 (28 140) 0% New York, NY 5 (0 52) 95% 140 (47 230) 0% Philadelphia, PA 6 (0 33) 75% 42 (14 69) 0% Sacramento, CA 0.6 (0 5) 95% 14 (5 22) 0% St. Louis, MO 5 (0 31) 75% 27 (9 45) 0% 21

24 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) City Means (95% Range) IUA Results Probability of no risk reduction HREA Results (difference between and in Table 1) Means (95% Range) Probability of no risk reduction Atlanta, GA 1 (0 5) 95% 29 (10-51) 0% Baltimore, MD 1 (0 7) 75% 18 (6 28) 0% Boston, MA 1 (0 13) 95% 33 (11 55) 0% Cleveland, OH 0.8 (0 7) 95% 19 (6-31) 0% Denver, CO 14 (5 21) 0% 13 (5 23) 0% Detroit, MI 2 (0.9 3) 95% 22 (7 36) 0% Houston, TX 0.3 (0 3) 95% 8 (2 13) 0% Los Angeles, CA 40 (0 88) 5% 58 (26 120) 0% New York, NY 5 (0 48) 95% 410 (143 0% 670) Philadelphia, PA 2 (0 4) 95% 45 (16 71) 0% Sacramento, CA.5 (0 5) 95% 12 (4 21) 0% St. Louis, MO 1 (0 3) 95% 29 (10 47) 0% 22

25 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). City Means (95% Range) IUA Results Probability of no risk reduction HREA Results (difference between and in Table 1) Means (95% Range) Probability of no risk reduction Atlanta, GA 1 (0 4) 95% 36 (12-50) 0% Baltimore, MD 0.9 (0 0.8) 95% 22 (7 36) 0% Boston, MA 0.5 (0 5) 95% 29 (10 52) 0% Cleveland, OH 0.9 (0 9) 95% 29 (10-44) 0% Denver, CO 7 (0 16) 35% 17 (6 27) 0% Detroit, MI 1 (0 5) 95% 28 (10 48) 0% Houston, TX 0.4 (0 4) 95% 11 (4 18) 0% Los Angeles, CA 17 (0 73) 75% 80 (29 140) 0% New York, NY Not attainable Philadelphia, PA 2 (0 14) 95% 43 (14 70) 0% Sacramento, CA 0.8 (0 7) 95% 18 (6 30) 0% St. Louis, MO 1 (0 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

26 2.0 BACKGROUND OZONE EPRI Comments on National Ambient Air 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 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 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 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

27 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 data 28 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 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 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 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 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 4 th highest daily maximum 8-hour USB ozone concentrations in those locations are predicted to be close to 65 ppb in 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 Again, the western U.S. is predicted to have higher NAB ozone concentrations than the eastern

28 U.S., and some locations are predicted to have 4 th 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 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 Figure 7. 4 th Highest Daily Maximum 8-hour Ozone Concentrations at Five Major U.S. Cities ppb US background H4MDA8 ozone concentration at 5 major US cities Year Denver Los Angeles Phoenix Philadelphia Atlanta 26

29 Figure 8. 4 th Highest Daily Maximum 8-hour USB Ozone Predicted by CAMx for

30 Figure 9. Change in 4 th Highest Daily Maximum 8-hour Ozone USB Predicted by CAMx from 2005 to

31 Figure 10. Total Anthropogenic NOx Emissions from 1970 to Total Anthropogenic NOx Emissions Emissions (1000 tons per year) ASIA LAM MAF OECD90 REF US 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). 29

32 Figure th Highest Daily Maximum 8-hour Ozone NAB Concentrations Predicted by GEOS-Chem for